path
stringlengths 8
399
| content_id
stringlengths 40
40
| detected_licenses
sequence | license_type
stringclasses 2
values | repo_name
stringlengths 6
109
| repo_url
stringlengths 25
128
| star_events_count
int64 0
52.9k
| fork_events_count
int64 0
7.07k
| gha_license_id
stringclasses 9
values | gha_event_created_at
timestamp[us] | gha_updated_at
timestamp[us] | gha_language
stringclasses 28
values | language
stringclasses 1
value | is_generated
bool 1
class | is_vendor
bool 1
class | conversion_extension
stringclasses 17
values | size
int64 317
10.5M
| script
stringlengths 245
9.7M
| script_size
int64 245
9.7M
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
/TransferNet.ipynb | 298b13fbe203aaccefdfcbd04151391487d7f482 | [] | no_license | JinSuJinSu/jupyter-notebook | https://github.com/JinSuJinSu/jupyter-notebook | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 69,952 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import Input, models, layers, optimizers, metrics
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.applications import VGG16
# -
b_size = 5
train_datagen = ImageDataGenerator(rescale=1./255, horizontal_flip = True, # 수평 대칭 이미지를 50%확률로 만듬
width_shift_range = 0.1, # 전체 크기의 10% 범위에서 좌우로 이동
height_shift_range = 0.1,
fill_mode = 'nearest')
train_generator = train_datagen.flow_from_directory('train',target_size=(150,150),
batch_size=b_size, class_mode='binary')
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory('test',target_size=(150,150),
batch_size=b_size, class_mode='binary')
transfer_model = VGG16(weights='imagenet', include_top=False, input_shape=(150,150,3))
transfer_model.trainable = False
transfer_model.summary()
finetune_model = Sequential()
finetune_model.add(transfer_model)
finetune_model.add(Flatten())
finetune_model.add(Dense(64, activation='relu'))
finetune_model.add(Dense(2, activation='softmax'))
finetune_model.summary()
# +
finetune_model.compile(loss='sparse_categorical_crossentropy',optimizer=optimizers.Adam(learning_rate=0.0002),\
metrics=['accuracy'])
steps_train = len(train_generator)
steps_test = len(test_generator)
# +
history = finetune_model.fit(
train_generator,
steps_per_epoch=steps_train,
epochs=20,
validation_data=test_generator,
validation_steps=steps_test )
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
y_vloss = history.history['val_loss']
y_loss = history.history['loss']
x_len = np.arange(len(y_loss))
plt.plot(x_len, acc, marker='.', c='cornflowerblue', label='Trainset_acc')
plt.plot(x_len, val_acc, marker='.', c='blue', label='Testset_acc')
plt.plot(x_len, y_vloss, marker='.', c='red', label='Testset_loss')
plt.plot(x_len, y_loss, marker='.', c='lightcoral', label='Trainset_loss')
plt.legend(loc='upper left')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss/acc')
plt.show()
| 2,688 |
/workshop/nipype_tutorial/notebooks/resources_python_cheat_sheet.ipynb | 4e9fab148d469d6fb48f2a4de3e1fb231770749f | [
"BSD-3-Clause"
] | permissive | miykael/workshop_pybrain | https://github.com/miykael/workshop_pybrain | 41 | 28 | BSD-3-Clause | 2020-11-07T19:11:49 | 2020-11-06T22:35:56 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 19,190 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Python Cheat Sheet
#
# The following content is taken from http://www.ias.u-psud.fr/pperso/aboucaud/python/cheatsheet.html
#
# This cheat sheet should serve as a short refresher to everybody who hasn't used Python for some time.
# ## Pure Python
# ### Types
a = 2 # integer
b = 5.0 # float
c = 8.3e5 # exponential
d = 1.5 + 0.5j # complex
e = 4 > 5 # boolean
f = 'word' # string
# ### Lists
a = ['red', 'blue', 'green'] # manually initialization
b = list(range(5)) # initialization through a function
c = [nu**2 for nu in b] # initialize through list comprehension
d = [nu**2 for nu in b if nu < 3] # list comprehension with condition
e = c[0] # access element
f = c[1:2] # access a slice of the list
g = ['re', 'bl'] + ['gr'] # list concatenation
h = ['re'] * 5 # repeat a list
['re', 'bl'].index('re') # returns index of 're'
're' in ['re', 'bl'] # true if 're' in list
sorted([3, 2, 1]) # returns sorted list
z = ['red'] + ['green', 'blue'] # list concatenation
# ### Dictionaries
a = {'red': 'rouge', 'blue': 'bleu', 'green': 'vert'} # dictionary
b = a['red'] # translate item
c = [value for key, value in a.items()] # loop through contents
d = a.get('yellow', 'no translation found') # return default
# ### Strings
a = 'red' # assignment
char = a[2] # access individual characters
'red ' + 'blue' # string concatenation
'1, 2, three'.split(',') # split string into list
'.'.join(['1', '2', 'three']) # concatenate list into string
# ### Operators
a = 2 # assignment
b = [2,3] # assign a list
a += 1 # change and assign, try also `*=` and `/=`
3 + 2 # addition
3 / 2 # integer division (python2) or float division (python3)
3 // 2 # integer division
3 * 2 # multiplication
3 ** 2 # exponent
3 % 2 # remainder
abs(-3) # absolute value
1 == 1 # equal
2 > 1 # larger
2 < 1 # smaller
1 != 2 # not equal
1 != 2 and 2 < 3 # logical AND
1 != 2 or 2 < 3 # logical OR
not 1 == 2 # logical NOT
a in b # test if a is in b
a is b # test if objects point to the same memory (id)
# ### Control Flow
# +
# if/elif/else
a, b = 1, 2
if a + b == 3:
print ('True')
elif a + b == 1:
print ('False')
else:
print ('?')
# for
a = ['red', 'blue', 'green']
for color in a:
print (color)
# while
number = 1
while number < 10:
print (number)
number += 1
# break
number = 1
while True:
print (number)
number += 1
if number > 10:
break
# continue
for i in range(20):
if i % 2 == 0:
continue
print (i)
# -
# ### Functions, Classes, Generators, Decorators
# +
# Function
def myfunc(a1, a2):
return a1 * a2
a1, a2 = 4, 5
x = myfunc(a1, a2)
# Class
class Point(object):
def __init__(self, x):
self.x = x
def __call__(self):
print (self.x)
x = Point(3)
# Generators
def firstn(n):
num = 0
while num < n:
yield num
num += 1
# consume the generator with list comprehension
x = [i for i in firstn(10)]
# Decorators
class myDecorator(object):
def __init__(self, f):
self.f = f
def __call__(self):
print ("call")
self.f()
@myDecorator
def my_funct():
print ('func')
my_funct()
# -
# ## IPython
# ### Python console
# +
<object>? # Information about the object
<object>.<TAB> # tab completion
# measure runtime of a function:
# %timeit range(1000)
100000 loops, best of 3: 7.76 us per loop
# run scripts and debug
# %run
# %run -d # run in debug mode
# %run -t # measures execution time
# %run -p # runs a profiler
# %debug # jumps to the debugger after an exception
# %pdb # run debugger automatically on exception
# examine history
# %history
# %history ~1/1-5 # lines 1-5 of last session
# run shell commands
# !make # prefix command with "!"
# clean namespace
# %reset
# -
# ### Debugger commands
n # execute next line
# ## NumPy
import numpy as np
# ### array initialization
np.array([2, 3, 4]) # direct initialization
np.empty(20, dtype=np.float32) # single precision array with 20 entries
np.zeros(200) # initialize 200 zeros
np.ones((3,3), dtype=np.int32) # 3 x 3 integer matrix with ones
np.eye(200) # ones on the diagonal
np.zeros_like(a) # returns array with zeros and the shape of a
np.linspace(0., 10., 100) # 100 points from 0 to 10
np.arange(0, 100, 2) # points from 0 to <100 with step width 2
np.logspace(-5, 2, 100) # 100 log-spaced points between 1e-5 and 1e2
a = np.array([[2, 3], [4, 5]])
np.copy(a) # copy array to new memory
# ### reading/ writing files
np.fromfile(fname/object, dtype=np.float32, count=5) # read binary data from file
np.loadtxt(fname/object, skiprows=2, delimiter=',') # read ascii data from file
# ### array properties and operations
a.shape # a tuple with the lengths of each axis
len(a) # length of axis 0
a.ndim # number of dimensions (axes)
a.sort(axis=1) # sort array along axis
a.flatten() # collapse array to one dimension
a.conj() # return complex conjugate
a.astype(np.int16) # cast to integer
np.argmax(a, axis=0) # return index of maximum along a given axis
np.cumsum(a) # return cumulative sum
np.any(a) # True if any element is True
np.all(a) # True if all elements are True
np.argsort(a, axis=1) # return sorted index array along axis
# ### indexing
a = np.arange(100) # initialization with 0 - 99
a[: 3] = 0 # set the first three indices to zero
a[1: 5] = 1 # set indices 1-4 to 1
start, stop, step = 10, 20, 2
a[start:stop:step] # general form of indexing/slicing
a[None, :] # transform to column vector
a[[1, 1, 3, 8]] # return array with values of the indices
a = a.reshape(10, 10) # transform to 10 x 10 matrix
a.T # return transposed view
np.transpose(a, (1, 0)) # transpose array to new axis order
a[a < 2] # returns array that fulfills element-wise condition
# ### boolean arrays
a, b = np.arange(100), 6 * np.arange(1, 101)
a < 2 # returns array with boolean values
np.logical_and(a < 2, b > 10) # element-wise logical and
np.logical_or(a < 2, b > 10) # element-wise logical or
~a # invert boolean array
np.invert(a) # invert boolean array
# ### element-wise operations and math functions
y, x = np.arange(10), np.arange(1, 11)
a * 5 # multiplication with scalar
a + 5 # addition with scalar
a + b # addition with array b
a / b # division with b (np.NaN for division by zero)
np.exp(a) # exponential (complex and real)
np.power(a,b) # a to the power b
np.sin(a) # sine
np.cos(a) # cosine
np.arctan2(y, x) # arctan(y/x)
np.arcsin(x) # arcsin
np.radians(a) # degrees to radians
np.degrees(a) # radians to degrees
np.var(a) # variance of array
np.std(a, axis=0) # standard deviation
# ### inner / outer products
a, b = np.array([[2, 3], [4, 5]]), np.array([[20, 30], [40, 50]])
np.dot(a, b) # inner matrix product: a_mi b_in
np.einsum('ik,kl->il', a, b) # einstein summation convention
np.sum(a, axis=1) # sum over axis 1
np.abs(a) # return array with absolute values
a[None, :] + b[:, None] # outer sum
a[None, :] * b[:, None] # outer product
np.outer(a, b) # outer product
np.sum(a * a.T) # matrix norm
# ### interpolation, integration
np.trapz(y, x=None, dx=1.0, axis=0) # integrate along axis 0
np.interp(x=2.5, xp=[1, 2, 3], fp=[3, 2, 0]) # interpolate function xp, yp at points x
# ### fft
np.fft.fft(y) # complex fourier transform of y
freqs = np.fft.fftfreq(len(y)) # fft frequencies for a given length
np.fft.fftshift(freqs) # shifts zero frequency to the middle
np.fft.rfft(y) # real fourier transform of y
np.fft.rfftfreq(len(y)) # real fft frequencies for a given length
# ### rounding
a=3.56
np.ceil(a) # rounds to nearest upper int
np.floor(a) # rounds to nearest lower int
np.round(a) # rounds to neares int
# ### random variables
np.random.normal(loc=0, scale=2, size=100) # 100 normal distributed random numbers
np.random.seed(23032) # resets the seed value
np.random.rand(200) # 200 random numbers in [0, 1)
np.random.uniform(1, 30, 200) # 200 random numbers in [1, 30)
np.random.randint(1, 15, 300) # 300 random integers between [1, 15]
# ## Matplotlib
import matplotlib.pyplot as plt
# ### figures and axes
fig = plt.figure(figsize=(5, 2), facecolor='black') # initialize figure
ax = fig.add_subplot(3, 2, 2) # add second subplot in a 3 x 2 grid
fig, axes = plt.subplots(5, 2, figsize=(5, 5)) # return fig and array of axes in a 5 x 2 grid
ax = fig.add_axes(left=.3, bottom=.1, width=.6, height=.8) # manually add axes at a certain position
# ### figures and axes properties
fig.suptitle('title') # big figure title
fig.subplots_adjust(bottom=0.1,
right=0.8,
top=0.9,
wspace=0.2,
hspace=0.5) # adjust subplot positions
fig.tight_layout(pad=0.1,
h_pad=0.5,
w_pad=0.5,
rect=None) # adjust subplots to fit perfectly into fig
ax.set_xlabel() # set xlabel
ax.set_ylabel() # set ylabel
ax.set_xlim(1, 2) # sets x limits
ax.set_ylim(3, 4) # sets y limits
ax.set_title('blabla') # sets the axis title
ax.set(xlabel='bla') # set multiple parameters at once
ax.legend(loc='upper center') # activate legend
ax.grid(True, which='both') # activate grid
bbox = ax.get_position() # returns the axes bounding box
bbox.x0 + bbox.width # bounding box parameters
# ### plotting routines
ax.plot(x,y, '-o', c='red', lw=2, label='bla') # plots a line
ax.scatter(x,y, s=20, c=color) # scatter plot
ax.pcolormesh(xx,yy,zz, shading='gouraud') # fast colormesh function
ax.colormesh(xx,yy,zz, norm=norm) # slower colormesh function
ax.contour(xx,yy,zz, cmap='jet') # contour line plot
ax.contourf(xx,yy,zz, vmin=2, vmax=4) # filled contours plot
n, bins, patch = ax.hist(x, 50) # histogram
ax.imshow(matrix, origin='lower', extent=(x1, x2, y1, y2)) # show image
ax.specgram(y, FS=0.1, noverlap=128, scale='linear') # plot a spectrogram
| 11,593 |
/Mini_Project_Clustering - Preston, Tom.ipynb | da795f0ec43b28e4303ddbb9b4f0d06398577b0e | [] | no_license | tom1presto/springboard_assignments | https://github.com/tom1presto/springboard_assignments | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 618,905 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Customer Segmentation using Clustering
# ***
# This mini-project is based on [this blog post](http://blog.yhat.com/posts/customer-segmentation-using-python.html) by yhat. Please feel free to refer to the post for additional information, and solutions.
# +
# %matplotlib inline
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
# Setup Seaborn
sns.set_style("whitegrid")
sns.set_context("poster")
# -
# ## Data
#
# The dataset contains information on marketing newsletters/e-mail campaigns (e-mail offers sent to customers) and transaction level data from customers. The transactional data shows which offer customers responded to, and what the customer ended up buying. The data is presented as an Excel workbook containing two worksheets. Each worksheet contains a different dataset.
df_offers = pd.read_excel("./WineKMC.xlsx", sheet_name=0)
df_offers.columns = ["offer_id", "campaign", "varietal", "min_qty", "discount", "origin", "past_peak"]
df_offers.head()
# We see that the first dataset contains information about each offer such as the month it is in effect and several attributes about the wine that the offer refers to: the variety, minimum quantity, discount, country of origin and whether or not it is past peak. The second dataset in the second worksheet contains transactional data -- which offer each customer responded to.
df_transactions = pd.read_excel("./WineKMC.xlsx", sheet_name=1)
df_transactions.columns = ["customer_name", "offer_id"]
df_transactions['n'] = 1
df_transactions.head()
# ## Data wrangling
# We're trying to learn more about how our customers behave, so we can use their behavior (whether or not they purchased something based on an offer) as a way to group similar minded customers together. We can then study those groups to look for patterns and trends which can help us formulate future offers.
#
# The first thing we need is a way to compare customers. To do this, we're going to create a matrix that contains each customer and a 0/1 indicator for whether or not they responded to a given offer.
# <div class="span5 alert alert-info">
# <h3>Checkup Exercise Set I</h3>
#
# <p><b>Exercise:</b> Create a data frame where each row has the following columns (Use the pandas [`merge`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html) and [`pivot_table`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html) functions for this purpose):
# <ul>
# <li> customer_name
# <li> One column for each offer, with a 1 if the customer responded to the offer
# </ul>
# <p>Make sure you also deal with any weird values such as `NaN`. Read the documentation to develop your solution.</p>
# </div>
#your turn
# join the offers and transaction tables
df = pd.merge(df_offers, df_transactions)
# create a pivot table providing us the number of times each customer responded to a given offer
df_pivot = df.pivot_table(index=['customer_name'], columns=['offer_id'], values = 'n')
# fill NA values with 0
df_pivot = df_pivot.fillna(0).reset_index()
# save list of 0/1 columns for use later
x_cols = df_pivot.columns[1:]
print(df_pivot.head(20))
# ## K-Means Clustering
#
# Recall that in K-Means Clustering we want to *maximize* the distance between centroids and *minimize* the distance between data points and the respective centroid for the cluster they are in. True evaluation for unsupervised learning would require labeled data; however, we can use a variety of intuitive metrics to try to pick the number of clusters K. We will introduce two methods: the Elbow method, the Silhouette method and the gap statistic.
# ### Choosing K: The Elbow Sum-of-Squares Method
#
# The first method looks at the sum-of-squares error in each cluster against $K$. We compute the distance from each data point to the center of the cluster (centroid) to which the data point was assigned.
#
# $$SS = \sum_k \sum_{x_i \in C_k} \sum_{x_j \in C_k} \left( x_i - x_j \right)^2 = \sum_k \sum_{x_i \in C_k} \left( x_i - \mu_k \right)^2$$
#
# where $x_i$ is a point, $C_k$ represents cluster $k$ and $\mu_k$ is the centroid for cluster $k$. We can plot SS vs. $K$ and choose the *elbow point* in the plot as the best value for $K$. The elbow point is the point at which the plot starts descending much more slowly.
# <div class="span5 alert alert-info">
# <h3>Checkup Exercise Set II</h3>
#
# <p><b>Exercise:</b></p>
# <ul>
# <li> What values of $SS$ do you believe represent better clusterings? Why?
# <li> Create a numpy matrix `x_cols` with only the columns representing the offers (i.e. the 0/1 colums)
# <li> Write code that applies the [`KMeans`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) clustering method from scikit-learn to this matrix.
# <li> Construct a plot showing $SS$ for each $K$ and pick $K$ using this plot. For simplicity, test $2 \le K \le 10$.
# <li> Make a bar chart showing the number of points in each cluster for k-means under the best $K$.
# <li> What challenges did you experience using the Elbow method to pick $K$?
# </ul>
# </div>
# # your turn
# The SS (sum of squares) is focused on mimimizing the distance from the centroids. The lower the value, the better. As always, the challenge is finding a suitable (typically fewer) number of centroids
# and minimizing the SS for them. Having too many centroids leads to overfitting the data.
# create numpy matrix
import numpy as np
x_cols_matrix = np.matrix(df_pivot.iloc[:,x_cols])
x_cols_matrix
# +
from sklearn.cluster import KMeans
# test 2 <= k <= 10
k_range = range(2,11)
# print a list that has the SS values for each k values
kmeans_ss = [KMeans(n_clusters = k, random_state=5).fit(x_cols_matrix).inertia_ for k in k_range]
# plot the graphs
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(k_range, kmeans_ss, 'b*-', linewidth= 1.0)
ax.set_ylim((150,275))
plt.xlabel('Number of Clusters (k)')
plt.ylabel('Sum of Squares Error (SS)')
plt.title("Sum of Squares Errors - 2 to 10 clusters")
# +
# There is no clear "elbow" in the clusters above however K = 8 seems to be the closest thing to the 'elbow' for this exercise
# show the number of members in each cluster
best_k = 8
best_k_clusters = KMeans(n_clusters = best_k, random_state = 5)
df_pivot['cluster'] = best_k_clusters.fit_predict(np.matrix(df_pivot.iloc[:,2:33]))
print(df_pivot.head(20))
counts = pd.DataFrame(df_pivot.cluster.value_counts())
counts.columns = ['count']
counts['cluster'] = counts.index
# plot
plt.bar(list(counts['cluster']),list(counts['count']))
plt.xlabel('Clusters')
plt.ylabel('Number of Members')
plt.title('Membership Counts for Each Cluster (8 Total Clusters)')
# -
# With eight clusters and a small sample size, there does not seem to be be value in all 8 clusters. A lower number of clusters is probably better given
# the suspected overfitting with the small cluster membership for cluster 4 and 6
# ### Choosing K: The Silhouette Method
#
# There exists another method that measures how well each datapoint $x_i$ "fits" its assigned cluster *and also* how poorly it fits into other clusters. This is a different way of looking at the same objective. Denote $a_{x_i}$ as the *average* distance from $x_i$ to all other points within its own cluster $k$. The lower the value, the better. On the other hand $b_{x_i}$ is the minimum average distance from $x_i$ to points in a different cluster, minimized over clusters. That is, compute separately for each cluster the average distance from $x_i$ to the points within that cluster, and then take the minimum. The silhouette $s(x_i)$ is defined as
#
# $$s(x_i) = \frac{b_{x_i} - a_{x_i}}{\max{\left( a_{x_i}, b_{x_i}\right)}}$$
#
# The silhouette score is computed on *every datapoint in every cluster*. The silhouette score ranges from -1 (a poor clustering) to +1 (a very dense clustering) with 0 denoting the situation where clusters overlap. Some criteria for the silhouette coefficient is provided in the table below.
# <pre>
#
# | Range | Interpretation |
# |-------------|-----------------------------------------------|
# | 0.71 - 1.0 | A strong structure has been found. |
# | 0.51 - 0.7 | A reasonable structure has been found. |
# | 0.26 - 0.5 | The structure is weak and could be artificial.|
# | < 0.25 | No substantial structure has been found. |
#
# </pre>
# Source: http://www.stat.berkeley.edu/~spector/s133/Clus.html
# Fortunately, scikit-learn provides a function to compute this for us (phew!) called [`sklearn.metrics.silhouette_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html). Take a look at [this article](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html) on picking $K$ in scikit-learn, as it will help you in the next exercise set.
# <div class="span5 alert alert-info">
# <h3>Checkup Exercise Set III</h3>
#
# <p><b>Exercise:</b> Using the documentation for the `silhouette_score` function above, construct a series of silhouette plots like the ones in the article linked above.</p>
#
# <p><b>Exercise:</b> Compute the average silhouette score for each $K$ and plot it. What $K$ does the plot suggest we should choose? Does it differ from what we found using the Elbow method?</p>
# </div>
# +
# Your turn.
# Your turn.
from sklearn.metrics import silhouette_score, silhouette_samples
import matplotlib.cm as cm
# looping approach based off linked article approach
#fig = plt.figure()
for k in k_range:
##
# build the KMeans model and obtain cluster labels
##
# a random state of 5 is used for consistency with the above results
clusterer = KMeans( n_clusters = k, random_state = 5 )
cluster_labels = clusterer.fit_predict(df_pivot.iloc[:,2:33])
# get silhouette score and print result
slht_avg = silhouette_score(df_pivot.iloc[:,2:33], cluster_labels)
print("For k = %.0f, the average silhouette score is %.4f" % (k, slht_avg))
# get silhouette scores for each observation
obs_slht_vals = silhouette_samples(df_pivot.iloc[:,2:33], cluster_labels)
##
# build graphs for the distances in each cluster
##
# build framework for graphs
fig,ax = plt.subplots(1,1)
#ax = fig.add_subplot(3,3,k-1)
ax.set_xlim([-0.25, 0.6])
ax.set_ylim([0, len(x_cols_matrix) + (k + 1) * 2.5])
# build graphs by building data
y_lower = 2.5 # starting lower bound; will be updated as the graph is built
for i in range(k):
# obtain and sort data for graphing
cluster_slht_vals = obs_slht_vals[cluster_labels == i] # cluster-specific silhouette values for graphing
cluster_slht_vals.sort() # sort in default (ascending) order
# set graph space for the cluster and a color
cluster_size = cluster_slht_vals.shape[0] # find out how many obs in the cluster
y_upper = y_lower + cluster_size # set an upper bound based for the individual cluster
color = cm.nipy_spectral(float(i) / k) # set a color for the plot of distances in this cluster
# fill graph with data
ax.fill_betweenx(y = np.arange(y_lower, y_upper), x1 = 0, x2 = cluster_slht_vals,
facecolor = color, edgecolor = color, alpha = 0.7)
# label axes and set for next cluster
ax.text(-0.05, y_lower + 0.5 * cluster_size, str(i)) # label plot areas for cluster
y_lower = y_upper + 2.5 # provide space between plots for each cluster
# layout griding system for the plot
ax.grid(False) # remove grid lines
ax.axvline(x = slht_avg, color = "red", linestyle = "--") # add a line for silhouette average
ax.set_yticks([]) # remove y ticks
# add figure labels
ax.set_title("Silhouette Plot for Customer Data with %.0f Clusters" % k)
ax.set_ylabel("Cluster label")
ax.set_xlabel("Silhouette Coefficient Values")
# -
# ### Choosing $K$: The Gap Statistic
#
# There is one last method worth covering for picking $K$, the so-called Gap statistic. The computation for the gap statistic builds on the sum-of-squares established in the Elbow method discussion, and compares it to the sum-of-squares of a "null distribution," that is, a random set of points with no clustering. The estimate for the optimal number of clusters $K$ is the value for which $\log{SS}$ falls the farthest below that of the reference distribution:
#
# $$G_k = E_n^*\{\log SS_k\} - \log SS_k$$
#
# In other words a good clustering yields a much larger difference between the reference distribution and the clustered data. The reference distribution is a Monte Carlo (randomization) procedure that constructs $B$ random distributions of points within the bounding box (limits) of the original data and then applies K-means to this synthetic distribution of data points.. $E_n^*\{\log SS_k\}$ is just the average $SS_k$ over all $B$ replicates. We then compute the standard deviation $\sigma_{SS}$ of the values of $SS_k$ computed from the $B$ replicates of the reference distribution and compute
#
# $$s_k = \sqrt{1+1/B}\sigma_{SS}$$
#
# Finally, we choose $K=k$ such that $G_k \geq G_{k+1} - s_{k+1}$.
# ### Aside: Choosing $K$ when we Have Labels
#
# Unsupervised learning expects that we do not have the labels. In some situations, we may wish to cluster data that is labeled. Computing the optimal number of clusters is much easier if we have access to labels. There are several methods available. We will not go into the math or details since it is rare to have access to the labels, but we provide the names and references of these measures.
#
# * Adjusted Rand Index
# * Mutual Information
# * V-Measure
# * Fowlkes–Mallows index
#
# See [this article](http://scikit-learn.org/stable/modules/clustering.html) for more information about these metrics.
# ## Visualizing Clusters using PCA
#
# How do we visualize clusters? If we only had two features, we could likely plot the data as is. But we have 100 data points each containing 32 features (dimensions). Principal Component Analysis (PCA) will help us reduce the dimensionality of our data from 32 to something lower. For a visualization on the coordinate plane, we will use 2 dimensions. In this exercise, we're going to use it to transform our multi-dimensional dataset into a 2 dimensional dataset.
#
# This is only one use of PCA for dimension reduction. We can also use PCA when we want to perform regression but we have a set of highly correlated variables. PCA untangles these correlations into a smaller number of features/predictors all of which are orthogonal (not correlated). PCA is also used to reduce a large set of variables into a much smaller one.
# <div class="span5 alert alert-info">
# <h3>Checkup Exercise Set IV</h3>
#
# <p><b>Exercise:</b> Use PCA to plot your clusters:</p>
#
# <ul>
# <li> Use scikit-learn's [`PCA`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) function to reduce the dimensionality of your clustering data to 2 components
# <li> Create a data frame with the following fields:
# <ul>
# <li> customer name
# <li> cluster id the customer belongs to
# <li> the two PCA components (label them `x` and `y`)
# </ul>
# <li> Plot a scatterplot of the `x` vs `y` columns
# <li> Color-code points differently based on cluster ID
# <li> How do the clusters look?
# <li> Based on what you see, what seems to be the best value for $K$? Moreover, which method of choosing $K$ seems to have produced the optimal result visually?
# </ul>
#
# <p><b>Exercise:</b> Now look at both the original raw data about the offers and transactions and look at the fitted clusters. Tell a story about the clusters in context of the original data. For example, do the clusters correspond to wine variants or something else interesting?</p>
# </div>
# +
#your turn
from sklearn.decomposition import PCA
# +
# run PCA with 2 components
pca = PCA(n_components=2)
wine_factors = np.matrix(pca.fit_transform(x_cols_matrix))
wine_factors_df = pd.DataFrame(wine_factors)
wine_factors_df.columns = ['x','y']
# dataset build
# initialize the dataset with names and factors
wine_clusters = pd.DataFrame(df_pivot.iloc[:,0])
wine_clusters['x'], wine_clusters['y'] = wine_factors_df['x'], wine_factors_df['y']
# add cluster labels
for k in k_range:
# calc cluster lables
clusterer = KMeans(n_clusters = k, random_state=5)
cluster_labels = clusterer.fit_predict(wine_factors)
# addpend labels to dataframe
wine_clusters[str('clusters_' + str(k))] = cluster_labels
wine_clusters.head()
# +
# Build Scatterplots
x_range = np.ptp(wine_clusters['x'])
x_min, x_max = np.min(wine_clusters['x']) - 0.05 * x_range, np.max(wine_clusters['x']) - 0.05 * x_range
y_range = np.ptp(wine_clusters['y'])
y_min, y_max = np.min(wine_clusters['y']) - 0.05 * y_range, np.max(wine_clusters['y']) - 0.05 * y_range
# setup figure and fill with data
fig = plt.figure(figsize=(12,12))
for k in k_range:
# setup plots and axes
#ax = plt.figure(figsize=(3,3))
ax = fig.add_subplot(3,3,k-1)
ax.set_xlim([x_min, x_max])
ax.set_ylim([y_min, y_max])
ax.set_xticks([])
ax.set_yticks([])
# fill data in the scatterplots
plt.scatter(x = wine_clusters['x'], y = wine_clusters['y'], s = 40,
c = wine_clusters.iloc[:,k + 1], cmap = 'Set1')
ax.set_title('Clusters for k = ' + str(k))
# -
# What we've done is we've taken those columns of 0/1 indicator variables, and we've transformed them into a 2-D dataset. We took one column and arbitrarily called it `x` and then called the other `y`. Now we can throw each point into a scatterplot. We color coded each point based on it's cluster so it's easier to see them.
# <div class="span5 alert alert-info">
# <h3>Exercise Set V</h3>
#
# <p>As we saw earlier, PCA has a lot of other uses. Since we wanted to visualize our data in 2 dimensions, restricted the number of dimensions to 2 in PCA. But what is the true optimal number of dimensions?</p>
#
# <p><b>Exercise:</b> Using a new PCA object shown in the next cell, plot the `explained_variance_` field and look for the elbow point, the point where the curve's rate of descent seems to slow sharply. This value is one possible value for the optimal number of dimensions. What is it?</p>
# </div>
# +
#your turn
# Initialize a new PCA model with a default number of components.
#import sklearn.decomposition
#pca = sklearn.decomposition.PCA()
#pca.fit(X)
# Do the rest on your own :)
pca = PCA()
factors = pca.fit(x_cols_matrix)
# elbow curve
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(111)
ax.plot(factors.explained_variance_, 'b*-')
plt.grid(True)
plt.xlabel('Number of Principal Components')
plt.ylabel('Percentage of variance explained')
plt.title('Variance Explained vs. Components')
# -
# ## Other Clustering Algorithms
#
# k-means is only one of a ton of clustering algorithms. Below is a brief description of several clustering algorithms, and the table provides references to the other clustering algorithms in scikit-learn.
#
# * **Affinity Propagation** does not require the number of clusters $K$ to be known in advance! AP uses a "message passing" paradigm to cluster points based on their similarity.
#
# * **Spectral Clustering** uses the eigenvalues of a similarity matrix to reduce the dimensionality of the data before clustering in a lower dimensional space. This is tangentially similar to what we did to visualize k-means clusters using PCA. The number of clusters must be known a priori.
#
# * **Ward's Method** applies to hierarchical clustering. Hierarchical clustering algorithms take a set of data and successively divide the observations into more and more clusters at each layer of the hierarchy. Ward's method is used to determine when two clusters in the hierarchy should be combined into one. It is basically an extension of hierarchical clustering. Hierarchical clustering is *divisive*, that is, all observations are part of the same cluster at first, and at each successive iteration, the clusters are made smaller and smaller. With hierarchical clustering, a hierarchy is constructed, and there is not really the concept of "number of clusters." The number of clusters simply determines how low or how high in the hierarchy we reference and can be determined empirically or by looking at the [dendogram](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.cluster.hierarchy.dendrogram.html).
#
# * **Agglomerative Clustering** is similar to hierarchical clustering but but is not divisive, it is *agglomerative*. That is, every observation is placed into its own cluster and at each iteration or level or the hierarchy, observations are merged into fewer and fewer clusters until convergence. Similar to hierarchical clustering, the constructed hierarchy contains all possible numbers of clusters and it is up to the analyst to pick the number by reviewing statistics or the dendogram.
#
# * **DBSCAN** is based on point density rather than distance. It groups together points with many nearby neighbors. DBSCAN is one of the most cited algorithms in the literature. It does not require knowing the number of clusters a priori, but does require specifying the neighborhood size.
# ### Clustering Algorithms in Scikit-learn
# <table border="1">
# <colgroup>
# <col width="15%" />
# <col width="16%" />
# <col width="20%" />
# <col width="27%" />
# <col width="22%" />
# </colgroup>
# <thead valign="bottom">
# <tr><th>Method name</th>
# <th>Parameters</th>
# <th>Scalability</th>
# <th>Use Case</th>
# <th>Geometry (metric used)</th>
# </tr>
# </thead>
# <tbody valign="top">
# <tr><td>K-Means</span></a></td>
# <td>number of clusters</td>
# <td>Very large<span class="pre">n_samples</span>, medium <span class="pre">n_clusters</span> with
# MiniBatch code</td>
# <td>General-purpose, even cluster size, flat geometry, not too many clusters</td>
# <td>Distances between points</td>
# </tr>
# <tr><td>Affinity propagation</td>
# <td>damping, sample preference</td>
# <td>Not scalable with n_samples</td>
# <td>Many clusters, uneven cluster size, non-flat geometry</td>
# <td>Graph distance (e.g. nearest-neighbor graph)</td>
# </tr>
# <tr><td>Mean-shift</td>
# <td>bandwidth</td>
# <td>Not scalable with <span class="pre">n_samples</span></td>
# <td>Many clusters, uneven cluster size, non-flat geometry</td>
# <td>Distances between points</td>
# </tr>
# <tr><td>Spectral clustering</td>
# <td>number of clusters</td>
# <td>Medium <span class="pre">n_samples</span>, small <span class="pre">n_clusters</span></td>
# <td>Few clusters, even cluster size, non-flat geometry</td>
# <td>Graph distance (e.g. nearest-neighbor graph)</td>
# </tr>
# <tr><td>Ward hierarchical clustering</td>
# <td>number of clusters</td>
# <td>Large <span class="pre">n_samples</span> and <span class="pre">n_clusters</span></td>
# <td>Many clusters, possibly connectivity constraints</td>
# <td>Distances between points</td>
# </tr>
# <tr><td>Agglomerative clustering</td>
# <td>number of clusters, linkage type, distance</td>
# <td>Large <span class="pre">n_samples</span> and <span class="pre">n_clusters</span></td>
# <td>Many clusters, possibly connectivity constraints, non Euclidean
# distances</td>
# <td>Any pairwise distance</td>
# </tr>
# <tr><td>DBSCAN</td>
# <td>neighborhood size</td>
# <td>Very large <span class="pre">n_samples</span>, medium <span class="pre">n_clusters</span></td>
# <td>Non-flat geometry, uneven cluster sizes</td>
# <td>Distances between nearest points</td>
# </tr>
# <tr><td>Gaussian mixtures</td>
# <td>many</td>
# <td>Not scalable</td>
# <td>Flat geometry, good for density estimation</td>
# <td>Mahalanobis distances to centers</td>
# </tr>
# <tr><td>Birch</td>
# <td>branching factor, threshold, optional global clusterer.</td>
# <td>Large <span class="pre">n_clusters</span> and <span class="pre">n_samples</span></td>
# <td>Large dataset, outlier removal, data reduction.</td>
# <td>Euclidean distance between points</td>
# </tr>
# </tbody>
# </table>
# Source: http://scikit-learn.org/stable/modules/clustering.html
# <div class="span5 alert alert-info">
# <h3>Exercise Set VI</h3>
#
# <p><b>Exercise:</b> Try clustering using the following algorithms. </p>
# <ol>
# <li>Affinity propagation
# <li>Spectral clustering
# <li>Agglomerative clustering
# <li>DBSCAN
# </ol>
# <p>How do their results compare? Which performs the best? Tell a story why you think it performs the best.</p>
# </div>
#
# +
# Affinity Propagation
# Affinity propagation allows damping setting - this will test a range of dampening settings
damp_range = np.arange(0.5, 1, 0.05)
print(f'Affinity Proagation Results:')
for d in damp_range:
# run clustering and predict labels
clusterer = sklearn.cluster.AffinityPropagation(damping= d)
cluster_labels = clusterer.fit_predict(x_cols_matrix)
# obtain and report silhoutette scores
slht_avg = silhouette_score(x_cols_matrix, cluster_labels)
print(f' For d = {d:.2f}, the average silhoutte score is {slht_avg:.2f}')
# +
# Spectral clustering
# cycle through the previously established k-range
print(f'Spectral Clustering Results:')
for k in k_range:
clusterer = sklearn.cluster.SpectralClustering(n_clusters = k, random_state = 5)
cluster_labels = clusterer.fit_predict(x_cols_matrix)
# obtain and report silhoutette scores
slht_avg = silhouette_score(x_cols_matrix, cluster_labels)
print(f' For k = {k}, the average silhoutte score is {slht_avg:.2f}')
# +
# Agglomerative Clustering
print(f'Agglomerative Clustering Results:')
for k in k_range:
clusterer = sklearn.cluster.AgglomerativeClustering(n_clusters = k)
cluster_labels = clusterer.fit_predict(x_cols_matrix)
# obtain and report silhoutette scores
slht_avg = silhouette_score(x_cols_matrix, cluster_labels)
print(f' For k = {k}, the average silhoutte score is {slht_avg:.2f}')
# +
# DBSCAN
print(f'DBSCAN Clustering Results:')
clusterer = sklearn.cluster.DBSCAN()
cluster_labels = clusterer.fit_predict( x_cols_matrix )
print("Cluster labels under default setting or when min_samples >= 4: \n")
print(cluster_labels)
print("\n")
# eps did not produce any clustering result, but rather, shows the lack of clustering structure by assigning all
# data points to the same cluster
min_s_range = [1,2,3]
for s in min_s_range:
clusterer = sklearn.cluster.DBSCAN( min_samples = s )
cluster_labels = clusterer.fit_predict( x_cols_matrix )
slht_avg = silhouette_score(x_cols_matrix, cluster_labels)
print(f' For min_samples = {s}, the average silhoutte score is {slht_avg:.2f}')
# -
# **Results:**
# **Affinity Propagation:**
#
# The resulting narrow silhoutte score of either .12 or .09 based on a testing damper values of .5 to .95 shows little meaningful clusters in the data
#
# **Spectral Clustering**
#
# The spectral cluster values should work well with small datasets however the results also showed no real pattern in the data
#
# **Agglomerative Clustering**
#
# Agglomerative clustering is typically for larger datasets so it was not expected to work well on this small dataset. These results showed no real pattern
#
# **DBSCAN**
#
# When minimum samples are >= 4, every data point was put in the same cluster. For minimum samples 1, the value is .19 and is not significant. The minimum
# sample setting of 2 and 3 provide negative results further showing the last of clustering in the data.
#
# This exercise did highlight the value of the different algorithims being able to be run interchangably with minimum coding changes.
#
#
n_iter=10, cv=5, iid=False, n_jobs=25)
hs_lSVM.fit(X_train,y_train)
# Projection matrix from CKA
W_cka_lSVM += [hs_lSVM.best_estimator_.named_steps['Projection'].Wcka]
# ---------------------------------------------------------------------------------------------------------------------------
# Validation
# Linear
y_pred_L = hs_Lineal.best_estimator_.predict(X_test)
accuracy_L[fold-1] = accuracy_score(y_test,y_pred_L)
cm_temp = confusion_matrix(y_test,y_pred_L)
cm_L[fold-1,:,:] = 100*cm_temp.astype('float') / cm_temp.sum(axis=1)[:, np.newaxis]
plot_confusion_matrix(y_test, y_pred_L, classes=np.unique(y),normalize=True,title='ACC = %.1f %% Fold %d' % (100*accuracy_L[fold-1],fold) + '_'+ label_models[0])
plt.autoscale()
save_fig(img_path,label_models[0]+'_Fold'+str(fold))
plt.show()
cr_L += [classification_report(y_test,y_pred_L)]
print(cr_L[-1])
# Best model storage
# best_mod_L += [hs_Lineal.best_estimator_, accuracy_L,cm_L,cr_L, sel_fts_L]
# best_mod_L += [hs_Lineal.best_estimator_]
best_pms_L += [hs_Lineal.best_params_]
joblib.dump(best_pms_L, filename + "LinealCKA" + ".pkl")
# Logistic Regression
y_pred_LogR = hs_LogR.best_estimator_.predict(X_test)
accuracy_LogR[fold-1]= accuracy_score(y_test,y_pred_LogR)
cm_temp = confusion_matrix(y_test,y_pred_LogR)
cm_LogR[fold-1,:,:] = 100*cm_temp.astype('float') / cm_temp.sum(axis=1)[:, np.newaxis]
plot_confusion_matrix(y_test, y_pred_LogR, classes=np.unique(y),normalize=True,title='ACC = %.1f %% Fold %d' % (100*accuracy_LogR[fold-1],fold) + '_'+ label_models[1])
plt.autoscale()
save_fig(img_path,label_models[1]+'_Fold'+str(fold))
plt.show()
cr_LogR += [classification_report(y_test,y_pred_LogR)]
print(cr_LogR[-1])
# Best model storage
# best_mod_LogR += [hs_LogR.best_estimator_, accuracy_LogR,cm_LogR,cr_LogR, sel_fts_LogR]
# best_mod_LogR += [hs_LogR.best_estimator_]
best_pms_LogR += [hs_LogR.best_params_]
joblib.dump(best_pms_LogR, filename + "LogRCKA" + ".pkl")
# Linear SVM
y_pred_lSVM = hs_lSVM.best_estimator_.predict(X_test)
accuracy_lSVM[fold-1]= accuracy_score(y_test,y_pred_lSVM)
cm_temp = confusion_matrix(y_test,y_pred_lSVM)
cm_LogR[fold-1,:,:] = 100*cm_temp.astype('float') / cm_temp.sum(axis=1)[:, np.newaxis]
plot_confusion_matrix(y_test, y_pred_lSVM, classes=np.unique(y),normalize=True,title='ACC = %.1f %% Fold %d' % (100*accuracy_lSVM[fold-1],fold) + '_'+ label_models[2])
plt.autoscale()
save_fig(img_path,label_models[2]+'_Fold'+str(fold))
plt.show()
cr_lSVM += [classification_report(y_test,y_pred_lSVM)]
print(cr_lSVM[-1])
# Best model storage
# best_mod_lSVM += [hs_lSVM.best_estimator_, accuracy_lSVM,cm_lSVM,cr_lSVM, sel_fts_lSVM]
# best_mod_lSVM += [hs_lSVM.best_estimator_]
best_pms_lSVM += [hs_lSVM.best_params_]
joblib.dump(best_pms_lSVM, filename + "lSVMCKA" + ".pkl")
# Results dictionary creation
L_dict = {'accuracy_L': accuracy_L,
'cm_L': cm_L,
'cr_L': cr_L,
'W_cka_L': W_cka_L,
'X_train_cka': X_train_cka,
'X_test_cka': X_test_cka,
'y_train_cka':y_train_cka,
'y_test_cka':y_test_cka}
LogR_dict = {'accuracy_LogR': accuracy_LogR,
'cm_LogR': cm_LogR,
'cr_LogR': cr_LogR,
'W_cka_LogR': W_cka_LogR,
'X_train_cka': X_train_cka,
'X_test_cka': X_test_cka,
'y_train_cka':y_train_cka,
'y_test_cka':y_test_cka}
lSVM_dict = {'accuracy_lSVM': accuracy_lSVM,
'cm_lSVM': cm_lSVM,
'cr_lSVM': cr_lSVM,
'W_cka_lSVM': W_cka_lSVM,
'X_train_cka': X_train_cka,
'X_test_cka': X_test_cka,
'y_train_cka':y_train_cka,
'y_test_cka':y_test_cka}
Results = [L_dict, LogR_dict, lSVM_dict]
joblib.dump(Results, rslt_dir + "Fold" + str(fold) + ".pkl")
# + [markdown] id="wK_XCEUEbXuK" colab_type="text"
# Average result printing
# + id="Zy8zArEbbXuL" colab_type="code" colab={} outputId="0da9312a-0d6e-44f1-d338-9c25184da7f4"
print('Linear Classifier')
print(str(np.mean(np.array(Results[0]['accuracy_L']))*100) + '+/-' + str(np.std(np.array(Results[0]['accuracy_L']))*100))
print('Logistic Regression Classifier')
print(str(np.mean(np.array(Results[1]['accuracy_LogR']))*100) + '+/-' + str(np.std(np.array(Results[1]['accuracy_LogR']))*100))
print('Linear SVM Classifier')
print(str(np.mean(np.array(Results[2]['accuracy_lSVM']))*100) + '+/-' + str(np.std(np.array(Results[2]['accuracy_lSVM']))*100))
# + [markdown] id="0HWLNso-bXuN" colab_type="text"
# Projection matrix plotting
# + id="m33vHkGPbXuN" colab_type="code" colab={} outputId="e328ed1d-03ac-4061-cb3d-40c451c0005b"
W = Results[1]['W_cka_LogR'][0]
Xp = X_train_cka[0].dot(W)
# Plotting the projection matrix
plt.scatter(Xp[:,1],Xp[:,2],c = y_train_cka[0])
# + [markdown] id="uFovnQxgbXuQ" colab_type="text"
# # **Step 5: Region Selection**
# + [markdown] id="HYBSdOpIbXuQ" colab_type="text"
# The algorithm now runs over the regions to know whose are more informative than others
# + id="-DCo6cL6bXuQ" colab_type="code" colab={} outputId="f0423e3a-d752-4396-8180-f05c37a7aea3"
# Declaracion de variables
n_partitions = 10
test_per = 0.695
n_classes = len(np.unique(y))
f_step = 1500
ftr_vec = np.arange(f_step,int((X.shape[1]))+f_step,f_step).astype(int)
fold = 0
# Arrays and lists to store at each fold
train_idx = []
test_idx = []
accuracy_L = np.zeros((n_partitions,len(ftr_vec)))
accuracy_LogR= np.zeros((n_partitions,len(ftr_vec)))
accuracy_lSVM= np.zeros((n_partitions,len(ftr_vec)))
cm_L = np.zeros((n_partitions,n_classes,n_classes))
cm_LogR = np.zeros((n_partitions,n_classes,n_classes))
cm_lSVM = np.zeros((n_partitions,n_classes,n_classes))
cr_L = []
cr_LogR = []
cr_lSVM = []
best_mod_L = []
best_mod_LogR= []
best_mod_lSVM= []
best_pms_L = []
best_pms_LogR= []
best_pms_lSVM= []
# + id="mMbxY_JibXuS" colab_type="code" colab={}
# Setting the data partition scheme to work like HoldOut validation
sss = StratifiedShuffleSplit(n_splits = n_partitions, test_size = test_per, random_state=42)
# + id="oNmIYcKubXuU" colab_type="code" colab={}
# Step declaration
steps = [
[('Preprocessing', StandardScaler()),
('Classification',SGDClassifier())], # Clasificador Lineal
]
# Grid declaration
parameters = [
{'Classification__penalty': ['l1', 'l2', 'elasticnet'] },
]
# Model labels
label_models = ['Linear']
# + id="5rIdLT3ybXuW" colab_type="code" colab={}
# Directory to save results and plots
rslt_dir = img_dir + '/RegionSelection/RS_BoCF/Results_RS_BoCF_Py'
sys.path.append(rslt_dir)
img_path = img_dir + '/RegionSelection/RS_BoCF/'
sys.path.append(img_path)
# + id="4TedGMHJbXuY" colab_type="code" colab={} outputId="3f217e5c-70b1-47ec-a7e1-d4f3ddc0a7c1"
# Traininig/Testing loop
for feature in range(0,len(ftr_vec)): # For loop over regions
print("Region = ", str(feature+1) +'/'+ str(21))
fold = 0
# Initializa variables
train_idx = []
test_idx = []
cm_L = np.zeros((n_partitions,n_classes,n_classes))
cr_L = []
best_mod_L = []
best_pms_L = []
for train_index, test_index in tqdm(sss.split(X,y)):
# Training/testing index storage
train_idx += [train_index]
test_idx += [test_index]
# Number of partitions flag
fold = fold + 1
print("Iteration = ", str(fold) +'/'+ str(n_partitions))
# Iteration file name
filename = img_path + "/Fold" + str(fold) + "Region" + str(feature+1)
# Train/Test partition and matrix storing to apply CKA over them for
# visualization
X_train, X_test = X[train_index,0:ftr_vec[feature]], X[test_index,0:ftr_vec[feature]]
y_train, y_test = y[train_index], y[test_index]
# ---------------------------------------------------------------------------------------------------------------------------
# Training
# Linear
print('Linear Model')
# Using GridSearchCV
hs_Lineal = GridSearchCV(Pipeline(steps[0]), parameters[0], n_jobs = 25, cv = 5, scoring = 'balanced_accuracy', verbose = 50)
# Using RandomizedSearchCV
# hs_Lineal = RandomizedSearchCV(Pipeline(steps[0]), param_distributions=parameters[0],n_iter=10, cv=5, iid=False, n_jobs=2)
hs_Lineal.fit(X_train,y_train)
# Projection matrix from CKA
# W_cka_L += [hs_Lineal.best_estimator_.named_steps['Projection'].Wcka]
# ---------------------------------------------------------------------------------------------------------------------------
# Validation
# Linear
y_pred_L = hs_Lineal.best_estimator_.predict(X_test)
accuracy_L[fold-1,feature] = accuracy_score(y_test,y_pred_L)
cm_temp = confusion_matrix(y_test,y_pred_L)
cm_L[fold-1,:,:] = 100*cm_temp.astype('float') / cm_temp.sum(axis=1)[:, np.newaxis]
plot_confusion_matrix(y_test, y_pred_L, classes=np.unique(y),normalize=True,title='ACC = %.1f %% Fold %d' % (100*accuracy_L[fold-1,feature],fold) + '_'+ label_models[0])
plt.autoscale()
save_fig(img_path,label_models[0]+'_Fold'+str(fold)+ "Region" + str(feature+1))
plt.show()
cr_L += [classification_report(y_test,y_pred_L)]
print(cr_L[-1])
# Best model storage
# best_mod_L += [hs_Lineal.best_estimator_, accuracy_L,cm_L,cr_L, sel_fts_L]
# best_mod_L += [hs_Lineal.best_estimator_]
# best_pms_L += [hs_Lineal.best_params_,accuracy_L,cm_L,cr_L,W_cka_L]
best_pms_L += [hs_Lineal.best_params_]
# joblib.dump(best_pms_L, filename + "LinealCKA" + ".pkl")
# Results dictionary creation
L_dict = {'accuracy_L': accuracy_L,
'cm_L': cm_L,
'cr_L': cr_L,
'best_pms_L':best_pms_L}
Results = [L_dict]
joblib.dump(Results, rslt_dir + "Region" + str(feature+1) +".pkl")
# + id="x-_yFDgdbXub" colab_type="code" colab={} outputId="4d4735ff-991c-432f-a729-be9398aab4ca"
# Get mean and standard deviation vectors
mean_vec_l = np.mean(Results[0]['accuracy_L'], axis = 0)
std_vec_l = np.std(Results[0]['accuracy_L'], axis = 0)
# mean_vec_lr = np.mean(Results[1]['accuracy_LogR'], axis = 0)
# std_vec_lr = np.std(Results[1]['accuracy_LogR'], axis = 0)
# mean_vec_lsvm = np.mean(Results[2]['accuracy_lSVM'], axis = 0)
# std_vec_lsvm = np.std(Results[2]['accuracy_lSVM'], axis = 0)
reg_vec = np.arange(1,22,1)
# Plotting
plt.figure()
plt.plot(reg_vec,mean_vec_l)
plt.fill_between(reg_vec, mean_vec_l-std_vec_l, mean_vec_l+std_vec_l, alpha=0.2)
# plt.ylim(0.7,0.9)
# # plt.plot(reg_vec,mean_vec_lr)
# # plt.fill_between(reg_vec, mean_vec_lr-std_vec_lr, mean_vec_lr+std_vec_lr, alpha=0.2)
# # plt.plot(reg_vec,mean_vec_lsvm)
# # plt.fill_between(reg_vec, mean_vec_lsvm-std_vec_lsvm, mean_vec_lsvm+std_vec_lsvm, alpha=0.2)
plt.xticks(reg_vec, reg_vec)
# + id="6EkXi_qIbXud" colab_type="code" colab={}
# Storing the data
Data = np.c_[reg_vec.reshape(1,-1).T,mean_vec_l.T,std_vec_l.T]
np.savetxt(img_path + '/ETH80DBRSL.dat', Data, delimiter=' ')
# + id="EnZBDfXlbXuf" colab_type="code" colab={} outputId="b8061073-8bf4-4eb4-c530-a11ee68bf9c4"
# Computing optimal number of regiones acording to target
Results = np.loadtxt(img_path + 'ETH80DBRSL.dat')
# Normalizing the number of regions
Results[:,0] = Results[:,0] - min(Results[:,0])
Results[:,0] = Results[:,0]/max(Results[:,0])
# Normalizing the accuracy
Results[:,1] = Results[:,1] - min(Results[:,1])
Results[:,1] = Results[:,1]/max(Results[:,1])
# Normalizing the standard deviation
Results[:,2] = Results[:,2] - min(Results[:,2])
Results[:,2] = Results[:,2]/max(Results[:,2])
# Ideal result
Target = np.array((0,1,0))
# Computing the minimum distance between the ideal result and our results
dist = cdist(Target.reshape(1,-1),Results, 'euclidean')
# Showing the optimum number of regions
print('The ideal number of regions is: ' + str((np.argmin(dist)+1)))
# + [markdown] id="ZRdOIobcbXui" colab_type="text"
# # **Step 6: Projecting the selected regions using CKA**
# + id="jhCwPgmmbXui" colab_type="code" colab={}
# Selecting the regions to project
X = X[:,0:7500]
# + id="-M_DQLWibXuk" colab_type="code" colab={}
# Variable declaration
n_partitions = 80
test_per = 0.67
n_classes = len(np.unique(y))
fold = 0
train_idx = []
test_idx = []
alpha_L = []
alpha_LogR = []
alpha_lSVM = []
sel_fts_L = []
sel_fts_LogR = []
sel_fts_lSVM = []
thld_L = []
thld_LogR = []
thld_lSVM = []
sel_fts_Lt = []
sel_fts_LogRt= []
sel_fts_lSVMt= []
nfeats_L = []
nfeats_LogR = []
nfeats_lSVM = []
accuracy_L = np.zeros((n_partitions))
accuracy_LogR= np.zeros((n_partitions))
accuracy_lSVM= np.zeros((n_partitions))
cm_L = np.zeros((n_partitions,n_classes,n_classes))
cm_LogR = np.zeros((n_partitions,n_classes,n_classes))
cm_lSVM = np.zeros((n_partitions,n_classes,n_classes))
cr_L = []
cr_LogR = []
cr_lSVM = []
best_mod_L = []
best_mod_LogR= []
best_mod_lSVM= []
best_pms_L = []
best_pms_LogR= []
best_pms_lSVM= []
# + id="L333hz5ZbXum" colab_type="code" colab={}
# Matrix declaration to store train/test matrices and their labels, and projection matrices from CKA
X_train_cka = []
X_test_cka = []
y_train_cka = []
y_test_cka = []
W_cka_L = []
W_cka_LogR = []
W_cka_lSVM = []
# Step declaration
steps = [
[('Preprocessing', StandardScaler()),
('Projection', MiniBatchCKA(Q = 0.95, batch=41)),
('Preprocessing2',StandardScaler()),
('Classification',SGDClassifier())], # Clasificador Lineal
[('Preprocessing', StandardScaler()),
('Projection', MiniBatchCKA(Q = 0.95, batch=41)),
('Preprocessing2',StandardScaler()),
('Classification',LogisticRegression())], # Regresion Logistica
[('Preprocessing', StandardScaler()),
('Projection', MiniBatchCKA(Q = 0.95, batch=41)),
('Preprocessing2',StandardScaler()),
('Classification',LinearSVC())], # Maquina de Vectores de Soporte
]
# Grid declaration
parameters = [
{'Classification__penalty': ['l1', 'l2', 'elasticnet'] },
{'Classification__C': [0.01,0.1,1,10]},
{'Classification__C': [0.1,1,10,100,1000]}
]
# Model labels
label_models = ['LinearCKA','LogisticRegressionCKA','LinearSVCKA']
# + id="LhX47qskbXuo" colab_type="code" colab={}
# Directory to save results and plots
rslt_dir = img_dir + '/RegionSelection/ReliefF_CKA_RS_BoCF/Results_ReliefF_CKA_RS_BoCF_Py'
sys.path.append(rslt_dir)
img_path = img_dir + '/RegionSelection/ReliefF_CKA_RS_BoCF/'
sys.path.append(img_path)
# + [markdown] id="DkPhd5hXbXuq" colab_type="text"
# Loop to optimize CKA projection
# + id="k2yqalQybXur" colab_type="code" colab={} outputId="d8ffa3e8-7536-40f8-ea26-1aa37b4c89a5"
# Traininig/Testing loop implementing leave one objet out as Wand et. al. (2014) suggests it
fold = 0
for i in tqdm(range(len(np.unique(lobj)))):
# Number of partitions flag
fold = fold + 1
print("Iteration = ", str(fold) +'/'+ str(n_partitions))
# Iteration file name
filename = img_path + "/Fold" + str(fold)
# Train/Test partition and matrix storing to apply CKA over them for
# visualization
X_train, X_test = X[lobj!=i+1], X[lobj==i+1]
y_train, y_test = y[lobj!=i+1], y[lobj==i+1]
X_train_cka = X_train
X_test_cka = X_test
y_train_cka = y_train
y_test_cka = y_test
# ---------------------------------------------------------------------------------------------------------------------------
# Training
# Linear
print('Linear Model')
# Using GridSearchCV
# hs_Lineal = GridSearchCV(Pipeline(steps[0]), parameters[0], n_jobs = 6, cv = 5, scoring = 'balanced_accuracy', verbose = 50)
# Using RandomizedSearchCV
hs_Lineal = RandomizedSearchCV(Pipeline(steps[0]), param_distributions=parameters[0],n_iter=10, cv=5, iid=False, n_jobs=20)
hs_Lineal.fit(X_train,y_train)
# Projection matrix from CKA
W_cka_L = hs_Lineal.best_estimator_.named_steps['Projection'].Wcka
# Logistic Regression
print('Logistic Regression Model')
# Usaing GridSearchCV
# hs_LogR = GridSearchCV(Pipeline(steps[1]), parameters[1], n_jobs = 6, cv = 5, scoring = 'balanced_accuracy', verbose = 50)
# Using RandomizedSearchCV
hs_LogR = RandomizedSearchCV(Pipeline(steps[1]), param_distributions=parameters[1],n_iter=10, cv=5, iid=False,n_jobs=20)
hs_LogR.fit(X_train,y_train)
# Projection matrix from CKA
W_cka_LogR = hs_LogR.best_estimator_.named_steps['Projection'].Wcka
# Linear SVM
print('Linear SVM Model')
# Using GridSearchCV
#hs_lSVM = GridSearchCV(Pipeline(steps[2]), parameters[2], n_jobs = 6, cv = 5, scoring = 'balanced_accuracy', verbose = 50)
# Using RandomizedSearchCV
hs_lSVM = RandomizedSearchCV(Pipeline(steps[2]), param_distributions=parameters[2],n_iter=10, cv=5, iid=False, n_jobs=20)
hs_lSVM.fit(X_train,y_train)
# Projection matrix from CKA
W_cka_lSVM = hs_lSVM.best_estimator_.named_steps['Projection'].Wcka
# ---------------------------------------------------------------------------------------------------------------------------
# Validation
# Linear
y_pred_L = hs_Lineal.best_estimator_.predict(X_test)
accuracy_L[fold-1] = accuracy_score(y_test,y_pred_L)
# cm_temp = confusion_matrix(y_test,y_pred_L)
# cm_L[fold-1,:,:] = 100*cm_temp.astype('float') / cm_temp.sum(axis=1)[:, np.newaxis]
# plot_confusion_matrix(y_test, y_pred_L, classes=np.unique(y),normalize=True,title='ACC = %.1f %% Fold %d' % (100*accuracy_L[fold-1],fold) + '_'+ label_models[0])
# plt.autoscale()
# save_fig(img_path,label_models[0]+'_Fold'+str(fold))
# plt.show()
cr_L += [classification_report(y_test,y_pred_L)]
print(cr_L[-1])
# Best model storage
# best_mod_L += [hs_Lineal.best_estimator_, accuracy_L,cm_L,cr_L, sel_fts_L]
# best_mod_L += [hs_Lineal.best_estimator_]
best_pms_L += [hs_Lineal.best_params_]
joblib.dump(best_pms_L, filename + "LinealCKA" + ".pkl")
# Logistic Regression
y_pred_LogR = hs_LogR.best_estimator_.predict(X_test)
accuracy_LogR[fold-1]= accuracy_score(y_test,y_pred_LogR)
# cm_temp = confusion_matrix(y_test,y_pred_LogR)
# cm_LogR[fold-1,:,:] = 100*cm_temp.astype('float') / cm_temp.sum(axis=1)[:, np.newaxis]
# plot_confusion_matrix(y_test, y_pred_LogR, classes=np.unique(y),normalize=True,title='ACC = %.1f %% Fold %d' % (100*accuracy_LogR[fold-1],fold) + '_'+ label_models[1])
# plt.autoscale()
# save_fig(img_path,label_models[1]+'_Fold'+str(fold))
# plt.show()
cr_LogR += [classification_report(y_test,y_pred_LogR)]
print(cr_LogR[-1])
# Best model storage
# best_mod_LogR += [hs_LogR.best_estimator_, accuracy_LogR,cm_LogR,cr_LogR, sel_fts_LogR]
# best_mod_LogR += [hs_LogR.best_estimator_]
best_pms_LogR += [hs_LogR.best_params_]
joblib.dump(best_pms_LogR, filename + "LogRCKA" + ".pkl")
# Linear SVM
y_pred_lSVM = hs_lSVM.best_estimator_.predict(X_test)
accuracy_lSVM[fold-1]= accuracy_score(y_test,y_pred_lSVM)
# cm_temp = confusion_matrix(y_test,y_pred_lSVM)
# cm_LogR[fold-1,:,:] = 100*cm_temp.astype('float') / cm_temp.sum(axis=1)[:, np.newaxis]
# plot_confusion_matrix(y_test, y_pred_lSVM, classes=np.unique(y),normalize=True,title='ACC = %.1f %% Fold %d' % (100*accuracy_lSVM[fold-1],fold) + '_'+ label_models[2])
# plt.autoscale()
# save_fig(img_path,label_models[2]+'_Fold'+str(fold))
# plt.show()
cr_lSVM += [classification_report(y_test,y_pred_lSVM)]
print(cr_lSVM[-1])
# Best model storage
# best_mod_lSVM += [hs_lSVM.best_estimator_, accuracy_lSVM,cm_lSVM,cr_lSVM, sel_fts_lSVM]
# best_mod_lSVM += [hs_lSVM.best_estimator_]
best_pms_lSVM += [hs_lSVM.best_params_]
joblib.dump(best_pms_lSVM, filename + "lSVMCKA" + ".pkl")
# Results dictionary creation
L_dict = {'accuracy_L': accuracy_L,
# 'cm_L': cm_L,
'cr_L': cr_L,
'W_cka_L': W_cka_L,
'X_train_cka': X_train_cka,
'X_test_cka': X_test_cka,
'y_train_cka':y_train_cka,
'y_test_cka':y_test_cka}
LogR_dict = {'accuracy_LogR': accuracy_LogR,
# 'cm_LogR': cm_LogR,
'cr_LogR': cr_LogR,
'W_cka_LogR': W_cka_LogR,
'X_train_cka': X_train_cka,
'X_test_cka': X_test_cka,
'y_train_cka':y_train_cka,
'y_test_cka':y_test_cka}
lSVM_dict = {'accuracy_lSVM': accuracy_lSVM,
# 'cm_lSVM': cm_lSVM,
'cr_lSVM': cr_lSVM,
'W_cka_lSVM': W_cka_lSVM,
'X_train_cka': X_train_cka,
'X_test_cka': X_test_cka,
'y_train_cka':y_train_cka,
'y_test_cka':y_test_cka}
Results = [L_dict, LogR_dict, lSVM_dict]
joblib.dump(Results, rslt_dir + ".pkl")
# + [markdown] id="pmwznt52bXuv" colab_type="text"
# Average result printing
# + id="qpy3I8V3bXuv" colab_type="code" colab={} outputId="5cd1487e-0023-4c0a-fb88-117d40c2926e"
print('Linear Classifier')
print(str(np.mean(np.array(Results[0]['accuracy_L']))*100) + '+/-' + str(np.std(np.array(Results[0]['accuracy_L']))*100))
print('Logistic Regression Classifier')
print(str(np.mean(np.array(Results[1]['accuracy_LogR']))*100) + '+/-' + str(np.std(np.array(Results[1]['accuracy_LogR']))*100))
print('Linear SVM Classifier')
print(str(np.mean(np.array(Results[2]['accuracy_lSVM']))*100) + '+/-' + str(np.std(np.array(Results[2]['accuracy_lSVM']))*100))
# + [markdown] id="7y-rz7w4bXux" colab_type="text"
# Projection matrix plotting
# + id="Fs0ZO57zbXuy" colab_type="code" colab={} outputId="3f381de2-a905-4606-c1b9-016e00001b5d"
W = Results[1]['W_cka_LogR']
Xp = X_train_cka.dot(W)
# Plotting the projection matrix
plt.scatter(Xp[:,1],Xp[:,2],c = y_train_cka)
# + id="Z985KU9_bXu0" colab_type="code" colab={}
| 51,166 |
/code/error-handling.ipynb | 0f4431ae240c8921ef7a12e9e67e0a30c05880ee | [
"MIT"
] | permissive | vicb1/python-reference | https://github.com/vicb1/python-reference | 1 | 0 | MIT | 2022-06-21T23:43:38 | 2022-02-22T01:06:50 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 2,394 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # write errors to file
# +
import datetime
import logging
logger = logging.getLogger()
logging.basicConfig(filename='errors_log.log', level=logging.DEBUG)
logging.debug('Started run, time: ' + str(datetime.datetime.now()))
logging.info('this is an info message')
logging.error('test error message')
logging.debug('This is a debug message')
logging.warning('tbllalfhldfhd, warning.')
logging.exception('Got exception on main handler, time')
try:
print('start running program')
variable = error_variable # will error out
except:
logging.exception('Got exception on main handler, time: ' + str(datetime.datetime.now()))
# raise # use "raise" to exit program right away, without finishing
logging.debug('Finished run, time: ' + str(datetime.datetime.now()))
print('rest of program')
# -
# # try - except - finally
try:
print('t')
sd = 6 + 'fsdf'
except:
print('e')
finally:
print('f')
| 1,192 |
/solutions/rank-4/model2/as-meter2-no-1099-xgb-meter0-fold0.ipynb | 64e382bd2ba425407ae95ee8f97b6c60e5b9f4fe | [
"MIT"
] | permissive | mattmotoki/ashrae-great-energy-predictor-3-solution-analysis | https://github.com/mattmotoki/ashrae-great-energy-predictor-3-solution-analysis | 0 | 0 | MIT | 2020-05-17T09:40:58 | 2020-05-16T12:53:38 | null | Jupyter Notebook | false | false | .py | 663,500 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + _cell_guid="b1076dfc-b9ad-4769-8c92-a6c4dae69d19" _kg_hide-input=true _kg_hide-output=true _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
import gc
import os
from pathlib import Path
import random
import sys
from os.path import join as pjoin
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm_notebook as tqdm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.core.display import display, HTML
# --- plotly ---
from plotly import tools, subplots
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
import plotly.figure_factory as ff
# --- models ---
from sklearn import preprocessing
from sklearn.model_selection import KFold
import lightgbm as lgb
import xgboost as xgb
import catboost as cb
# + _cell_guid="79c7e3d0-c299-4dcb-8224-4455121ee9b0" _kg_hide-input=true _kg_hide-output=true _uuid="d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
RAW_DATA_DIR = os.path.join('..', 'input', 'ashrae-energy-prediction')
weather_dtypes = {
'site_id': np.uint8,
'air_temperature': np.float32,
'cloud_coverage': np.float32,
'dew_temperature': np.float32,
'precip_depth_1_hr': np.float32,
'sea_level_pressure': np.float32,
'wind_direction': np.float32,
'wind_speed': np.float32,
}
weather_train = pd.read_csv(pjoin(RAW_DATA_DIR, 'weather_train.csv'),dtype=weather_dtypes,
parse_dates=['timestamp'])
weather_test = pd.read_csv(pjoin(RAW_DATA_DIR, 'weather_test.csv'),dtype=weather_dtypes,
parse_dates=['timestamp'])
weather = pd.concat([weather_train,weather_test],ignore_index=True)
del weather_train, weather_test
weather_key = ['site_id', 'timestamp']
temp_skeleton = weather[weather_key + ['air_temperature']].drop_duplicates(subset=weather_key).sort_values(by=weather_key).copy()
del weather
# -
data_to_plot = temp_skeleton.copy()
data_to_plot["hour"] = data_to_plot["timestamp"].dt.hour
count = 1
plt.figure(figsize=(25, 15))
for site_id, data_by_site in data_to_plot.groupby('site_id'):
by_site_by_hour = data_by_site.groupby('hour').mean()
ax = plt.subplot(4, 4, count)
plt.plot(by_site_by_hour.index,by_site_by_hour['air_temperature'],'xb-')
ax.set_title('site: '+str(site_id))
count += 1
plt.tight_layout()
plt.show()
del data_to_plot
# +
# calculate ranks of hourly temperatures within date/site_id chunks
temp_skeleton['temp_rank'] = temp_skeleton.groupby(['site_id', temp_skeleton.timestamp.dt.date])['air_temperature'].rank('average')
# create a dataframe of site_ids (0-16) x mean hour rank of temperature within day (0-23)
df_2d = temp_skeleton.groupby(['site_id', temp_skeleton.timestamp.dt.hour])['temp_rank'].mean().unstack(level=1)
# Subtract the columnID of temperature peak by 14, getting the timestamp alignment gap.
site_ids_offsets = pd.Series(df_2d.values.argmax(axis=1) - 14)
site_ids_offsets.index.name = 'site_id'
def timestamp_align(df):
df['offset'] = df.site_id.map(site_ids_offsets)
df['timestamp_aligned'] = (df.timestamp - pd.to_timedelta(df.offset, unit='H'))
df['timestamp'] = df['timestamp_aligned']
del df['timestamp_aligned']
return df
# + _kg_hide-input=true _kg_hide-output=true
# Original code from https://www.kaggle.com/gemartin/load-data-reduce-memory-usage by @gemartin
# Modified to support timestamp type, categorical type
# Modified to add option to use float16 or not. feather format does not support float16.
from pandas.api.types import is_datetime64_any_dtype as is_datetime
from pandas.api.types import is_categorical_dtype
def reduce_mem_usage(df, use_float16=False):
""" iterate through all the columns of a dataframe and modify the data type
to reduce memory usage.
"""
start_mem = df.memory_usage().sum() / 1024**2
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
for col in df.columns:
if is_datetime(df[col]) or is_categorical_dtype(df[col]):
# skip datetime type or categorical type
continue
col_type = df[col].dtype
if col_type != object:
c_min = df[col].min()
c_max = df[col].max()
if str(col_type)[:3] == 'int':
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
df[col] = df[col].astype(np.int8)
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
df[col] = df[col].astype(np.int16)
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
else:
if use_float16 and c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
df[col] = df[col].astype(np.float16)
elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
df[col] = df[col].astype(np.float32)
else:
df[col] = df[col].astype(np.float64)
else:
df[col] = df[col].astype('category')
end_mem = df.memory_usage().sum() / 1024**2
print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))
return df
# + _kg_hide-input=true _kg_hide-output=true
# !ls ../input
# + _kg_hide-input=true _kg_hide-output=true
# %%time
#root = Path('../input/ashrae-feather-format-for-fast-loading')
root = pjoin('..', 'output', 'ashrae-feather-format-for-fast-loading')
train_df = pd.read_feather(pjoin(root, 'train.feather'))
weather_train_df = pd.read_feather(pjoin(root, 'weather_train.feather'))
building_meta_df = pd.read_feather(pjoin(root, 'building_metadata.feather'))
print('loading...')
test_df = pd.read_feather(pjoin(root, 'test.feather'))
weather_test_df = pd.read_feather(pjoin(root, 'weather_test.feather'))
# + _kg_hide-input=true _kg_hide-output=true
building_site_dict = dict(zip(building_meta_df['building_id'], building_meta_df['site_id']))
site_meter_raw = train_df[['building_id', 'meter', 'timestamp', 'meter_reading']].copy()
site_meter_raw['site_id'] = site_meter_raw.building_id.map(building_site_dict)
del site_meter_raw['building_id']
site_meter_to_plot = site_meter_raw.copy()
site_meter_to_plot["hour"] = site_meter_to_plot["timestamp"].dt.hour
elec_to_plot = site_meter_to_plot[site_meter_to_plot.meter == 0]
# + _kg_hide-input=true _kg_hide-output=false
count = 1
plt.figure(figsize=(25, 50))
for site_id, data_by_site in elec_to_plot.groupby('site_id'):
by_site_by_hour = data_by_site.groupby('hour').mean()
ax = plt.subplot(15, 4, count)
plt.plot(by_site_by_hour.index,by_site_by_hour['meter_reading'],'xb-')
ax.set_title('site: '+str(site_id))
count += 1
plt.tight_layout()
plt.show()
del elec_to_plot, site_meter_to_plot, building_site_dict, site_meter_raw
# -
train_exception = pd.read_pickle(pjoin('..', 'output', 'fork-of-ashrae-eda-exception-label5', 'train_exception.pkl'))
train_df['exception'] = train_exception.exception.values
del train_exception
gc.collect()
# +
########################### Building DF merge through concat
#################################################################################
# Benefits of concat:
## Faster for huge datasets (columns number)
## No dtype change for dataset
## Consume less memmory
temp_df = train_df[['building_id']]
temp_df = temp_df.merge(building_meta_df, on=['building_id'], how='left')
del temp_df['building_id']
train_df = pd.concat([train_df, temp_df], axis=1)
temp_df = test_df[['building_id']]
temp_df = temp_df.merge(building_meta_df, on=['building_id'], how='left')
del temp_df['building_id']
test_df = pd.concat([test_df, temp_df], axis=1)
del temp_df
# +
########################### Weather DF merge over concat (to not lose type)
#################################################################################
# Benefits of concat:
## Faster for huge datasets (columns number)
## No dtype change for dataset
## Consume less memmory
train_weather_df = timestamp_align(weather_train_df)
temp_df = train_df[['site_id','timestamp']]
temp_df = temp_df.merge(train_weather_df, on=['site_id','timestamp'], how='left')
del temp_df['site_id'], temp_df['timestamp']
train_df = pd.concat([train_df, temp_df], axis=1)
del train_weather_df, temp_df
gc.collect()
test_weather_df = timestamp_align(weather_test_df)
test_temp_df = test_df[['site_id','timestamp']]
test_temp_df = test_temp_df.merge(test_weather_df, on=['site_id','timestamp'], how='left')
del test_temp_df['site_id'], test_temp_df['timestamp']
test_df = pd.concat([test_df, test_temp_df], axis=1)
del test_weather_df, test_temp_df
gc.collect()
# +
# # 添加simplefe特征
# # 最后使用，验证强一点点，测试弱一点点，可忽略不计
# ########################### Smooth readings 单用，验证强点，测试弱一点
# #################################################################################
# TARGET = 'meter_reading'
# train_df['month'] = train_df["timestamp"].dt.month
# test_df['month'] = test_df["timestamp"].dt.month
# train_df['s_uid'] = train_df['site_id'].astype(str) +'_'+\
# train_df['month'].astype(str) +'_'+\
# train_df['meter'].astype(str) +'_'+\
# train_df['primary_use'].astype(str)
# temp_df = train_df.groupby(['s_uid'])[TARGET].apply(lambda x: int(np.percentile(x,99)))
# temp_df = temp_df.to_dict()
# train_df['s_uid'] = train_df['s_uid'].map(temp_df)
# train_df[TARGET] = np.where(train_df[TARGET]>train_df['s_uid'], train_df['s_uid'], train_df[TARGET])
# del train_df['s_uid'], temp_df
# +
# Building and site id
for enc_col in ['building_id', 'site_id']:
# 该操作有重复
temp_df = train_df.groupby([enc_col])['meter'].agg(['unique'])
temp_df['unique'] = temp_df['unique'].apply(lambda x: '_'.join(str(x))).astype(str)
le = LabelEncoder()
temp_df['unique'] = le.fit_transform(temp_df['unique']).astype(np.int8)
temp_df = temp_df['unique'].to_dict()
train_df[enc_col+'_uid_enc'] = train_df[enc_col].map(temp_df)
test_df[enc_col+'_uid_enc'] = test_df[enc_col].map(temp_df)
# Nunique
temp_dict = train_df.groupby([enc_col])['meter'].agg(['nunique'])['nunique'].to_dict()
train_df[enc_col+'-m_nunique'] = train_df[enc_col].map(temp_dict).astype(np.int8)
test_df[enc_col+'-m_nunique'] = test_df[enc_col].map(temp_dict).astype(np.int8)
del temp_df, temp_dict
# +
train_df["hour"] = train_df["timestamp"].dt.hour
test_df["hour"] = test_df["timestamp"].dt.hour
train_df['DT_W'] = train_df['timestamp'].dt.weekofyear.astype(np.int8)
test_df['DT_W'] = test_df['timestamp'].dt.weekofyear.astype(np.int8)
for df in [train_df, test_df]:
# for df in [train_df]:
df['DT_w_hour'] = np.where((df['hour']>5)&(df['hour']<13),1,0)
df['DT_w_hour'] = np.where((df['hour']>12)&(df['hour']<19),2,df['DT_w_hour'])
df['DT_w_hour'] = np.where((df['hour']>18),3,df['DT_w_hour'])
df['DT_w_temp'] = df.groupby(['site_id','DT_W','DT_w_hour'])['air_temperature'].transform('mean')
df['DT_w_dew_temp'] = df.groupby(['site_id','DT_W','DT_w_hour'])['dew_temperature'].transform('mean')
i_cols = [
'DT_w_hour',
]
for col in i_cols:
# del train_df[col]
del train_df[col], test_df[col]
# +
# new_feature = ['building_id_uid_enc', 'building_id-m_nunique',
# 'site_id_uid_enc', 'site_id-m_nunique', 'DT_w_temp',
# 'DT_w_dew_temp']
new_feature = ['building_id_uid_enc', 'building_id-m_nunique',
'site_id_uid_enc','DT_w_dew_temp']
# -
# cut_target = np.copy(train_df.meter_reading.values)
train_nf = train_df[new_feature]
test_nf = test_df[new_feature]
del train_df, test_df, weather_test_df, weather_train_df, building_meta_df
gc.collect()
# +
# %%time
# root = Path('../input/ashrae-feather-format-for-fast-loading')
# train_df = pd.read_feather(root/'train.feather')
# weather_train_df = pd.read_feather(root/'weather_train.feather')
# building_meta_df = pd.read_feather(root/'building_metadata.feather')
root = os.path.join('..', 'output', 'ashrae-feather-format-for-fast-loading')
train_df = pd.read_feather(pjoin(root, 'train.feather'))
weather_train_df = pd.read_feather(pjoin(root, 'weather_train.feather'))
building_meta_df = pd.read_feather(pjoin(root, 'building_metadata.feather'))
# -
train_exception = pd.read_pickle(os.path.join('..', 'output', 'fork-of-ashrae-eda-exception-label5', 'train_exception.pkl'))
train_df['exception'] = train_exception.exception.values
del train_exception
gc.collect()
train_df.exception.value_counts(dropna=False)
# 验证强一点，测试弱一点
train_df.loc[(train_df.building_id == 1099) & (train_df.meter == 2) & (train_df.meter_reading > 30000), 'exception'] = 4
train_df.exception.value_counts(dropna=False)
train_df = pd.concat([train_df, train_nf], axis=1)
# test_df = pd.concat([test_df, test_nf], axis=1)
del train_nf
gc.collect()
# + _kg_hide-output=true
def preprocess(df):
df["hour"] = df["timestamp"].dt.hour
df["weekend"] = df["timestamp"].dt.weekday
# df["month"] = df["timestamp"].dt.month
# df["dayofweek"] = df["timestamp"].dt.dayofweek
# df['DT_day_month'] = df['timestamp'].dt.day.astype(np.int8)
# df['DT_W'] = df['timestamp'].dt.weekofyear.astype(np.int8)
def add_lag_feature(weather_df, window=3):
group_df = weather_df.groupby('site_id')
# cols = ['air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure', 'wind_direction', 'wind_speed']
cols = ['air_temperature','dew_temperature']
rolled = group_df[cols].rolling(window=window, min_periods=0)
lag_mean = rolled.mean().reset_index().astype(np.float16)
lag_max = rolled.max().reset_index().astype(np.float16)
lag_min = rolled.min().reset_index().astype(np.float16)
# lag_std = rolled.std().reset_index().astype(np.float16)
for col in cols:
weather_df[f'{col}_mean_lag{window}'] = lag_mean[col]
weather_df[f'{col}_max_lag{window}'] = lag_max[col]
weather_df[f'{col}_min_lag{window}'] = lag_min[col]
# weather_df[f'{col}_std_lag{window}'] = lag_std[col]
train_df['date'] = train_df['timestamp'].dt.date
train_df['meter_reading_log1p'] = np.log1p(train_df['meter_reading'])
debug = False
preprocess(train_df)
# https://www.kaggle.com/ryches/simple-lgbm-solution
df_group = train_df.groupby('building_id')['meter_reading_log1p']
# building_mean = df_group.mean().astype(np.float16)
building_median = df_group.median().astype(np.float16)
# building_min = df_group.min().astype(np.float16)
# building_max = df_group.max().astype(np.float16)
# building_std = df_group.std().astype(np.float16)
# train_df['building_mean'] = train_df['building_id'].map(building_mean)
train_df['building_median'] = train_df['building_id'].map(building_median)
# train_df['building_min'] = train_df['building_id'].map(building_min)
# train_df['building_max'] = train_df['building_id'].map(building_max)
# train_df['building_std'] = train_df['building_id'].map(building_std)
weather_train_df = timestamp_align(weather_train_df)
weather_train_df = weather_train_df.groupby('site_id').apply(lambda group: group.interpolate(limit_direction='both'))
add_lag_feature(weather_train_df, window=3)
add_lag_feature(weather_train_df, window=72)
# weather_col = ['site_id', 'timestamp', 'air_temperature', 'cloud_coverage',
# 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',
# 'wind_direction', 'wind_speed', 'offset']
weather_col = ['site_id', 'timestamp', 'air_temperature','precip_depth_1_hr']
weather_col += ['air_temperature_mean_lag72','dew_temperature_mean_lag72','air_temperature_max_lag3','air_temperature_min_lag3','dew_temperature_mean_lag3']
weather_col += ['air_temperature_mean_lag3']
weather_train_df = weather_train_df[weather_col]
gc.collect()
primary_use_list = building_meta_df['primary_use'].unique()
primary_use_dict = {key: value for value, key in enumerate(primary_use_list)}
print('primary_use_dict: ', primary_use_dict)
building_meta_df['primary_use'] = building_meta_df['primary_use'].map(primary_use_dict)
gc.collect()
reduce_mem_usage(train_df, use_float16=True)
reduce_mem_usage(building_meta_df, use_float16=True)
reduce_mem_usage(weather_train_df, use_float16=True)
# -
building_meta_df.primary_use = building_meta_df.primary_use.astype(np.int8)
train_df.columns
# +
category_cols = ['building_id', 'site_id', 'primary_use'] # , 'meter'
feature_cols = ['square_feet', 'year_built', 'floor_count','hour','weekend','building_median','air_temperature']
# 强一点
feature_cols += ['precip_depth_1_hr']
# 有进步
feature_cols += ['air_temperature_mean_lag72','dew_temperature_mean_lag72','air_temperature_max_lag3','air_temperature_min_lag3','dew_temperature_mean_lag3']
# 添加验证弱，测试强, 这样的话，验证和测试的提升整体上就比较相关
# feature_cols += ['air_temperature_max_lag72','air_temperature_min_lag72','air_temperature_std_lag72',
# 'cloud_coverage_mean_lag72','wind_speed_mean_lag3']
# 再加验证强点，测试弱点， 添加后，变为验证弱点，测试强点了
feature_cols += ['air_temperature_mean_lag3']
# 都弱一点
feature_cols += ['building_id_uid_enc']
# 验证强点,测试强多
feature_cols += ['building_id-m_nunique']
# 都弱点
feature_cols += ['site_id_uid_enc']
# 都强
feature_cols += ['DT_w_dew_temp']
# -
T_RESULTS = train_df[['meter_reading']]
T_RESULTS['kfold'] = 0
# +
# def create_X_y(train_df, target_meter):
# target_train_df = train_df[train_df['meter'] == target_meter]
# target_train_df = target_train_df.merge(building_meta_df, on='building_id', how='left')
# target_train_df = target_train_df.merge(weather_train_df, on=['site_id', 'timestamp'], how='left')
# X_train = target_train_df[feature_cols + category_cols + ['exception']]
# y_train = target_train_df['meter_reading_log1p'].values
# del target_train_df
# return X_train, y_train
def create_X_y(train_df, target_meter):
target_train_df = train_df[train_df['meter'] == target_meter]
target_train_df = target_train_df.merge(building_meta_df, on='building_id', how='left')
target_train_df = target_train_df.merge(weather_train_df, on=['site_id', 'timestamp'], how='left')
target_train_df.index = train_df[train_df['meter'] == target_meter].index
X_train = target_train_df[feature_cols + category_cols + ['exception']]
y_train = target_train_df['meter_reading_log1p']
del target_train_df
return X_train, y_train
def fit_lgbm(train, val, devices=(-1,), seed=None, cat_features=None, num_rounds=1500, lr=0.1, bf=0.1):
"""Train Light GBM model"""
X_train, y_train = train
X_valid, y_valid = val
metric = 'l2'
params = {'num_leaves': 31,
'objective': 'regression',
# 'max_depth': -1,
'learning_rate': lr,
"boosting": "gbdt",
"bagging_freq": 5,
"bagging_fraction": bf,
"feature_fraction": 0.9,
"metric": metric,
# "verbosity": -1,
# 'reg_alpha': 0.1,
# 'reg_lambda': 0.3
}
device = devices[0]
if device == -1:
# use cpu
pass
else:
# use gpu
print(f'using gpu device_id {device}...')
params.update({'device': 'gpu', 'gpu_device_id': device})
params['seed'] = seed
early_stop = 20
verbose_eval = 20
d_train = lgb.Dataset(X_train, label=y_train, categorical_feature=cat_features)
d_valid = lgb.Dataset(X_valid, label=y_valid, categorical_feature=cat_features)
watchlist = [d_train, d_valid]
print('training LGB:')
model = lgb.train(params,
train_set=d_train,
num_boost_round=num_rounds,
valid_sets=watchlist,
verbose_eval=verbose_eval,
early_stopping_rounds=early_stop)
# predictions
y_pred_valid = model.predict(X_valid, num_iteration=model.best_iteration)
print('best_score', model.best_score)
log = {'train/mae': model.best_score['training']['l2'],
'valid/mae': model.best_score['valid_1']['l2']}
return model, y_pred_valid, log
folds = 5
seed = 666
shuffle = False
kf = KFold(n_splits=folds, shuffle=shuffle, random_state=seed)
# +
def fit_xgb(train, val, devices=(-1,), seed=None, cat_features=None, num_rounds=1500, lr=0.1, bf=0.1):
"""Train Light GBM model"""
X_train, y_train = train
X_valid, y_valid = val
# metric = 'l2'
# params = {'num_leaves': 31,
# 'objective': 'regression',
# # 'max_depth': -1,
# 'learning_rate': lr,
# "boosting": "gbdt",
# "bagging_freq": 5,
# "bagging_fraction": bf,
# "feature_fraction": 0.9,
# "metric": metric,
# # "verbosity": -1,
# # 'reg_alpha': 0.1,
# # 'reg_lambda': 0.3
# }
# device = devices[0]
# if device == -1:
# # use cpu
# pass
# else:
# # use gpu
# print(f'using gpu device_id {device}...')
# params.update({'device': 'gpu', 'gpu_device_id': device})
# params['seed'] = seed
model = xgb.XGBRegressor(
n_estimators=6000,
max_depth=8,
# num_boost_round=500,
learning_rate=lr,
subsample=0.8,
colsample_bytree=0.4,
# missing=np.nan,
objective ='reg:squarederror',
tree_method='hist',
seed=seed
)
print('training XGB:')
model.fit(X_train, y_train,
eval_set=[(X_train, y_train),(X_valid, y_valid)],
verbose=20, early_stopping_rounds=50)
# model.fit(X_train, y_train,
# eval_set=[train,val],
# verbose=20, early_stopping_rounds=50)
# early_stop = 20
# verbose_eval = 20
# d_train = lgb.Dataset(X_train, label=y_train, categorical_feature=cat_features)
# d_valid = lgb.Dataset(X_valid, label=y_valid, categorical_feature=cat_features)
# watchlist = [d_train, d_valid]
# print('training LGB:')
# model = lgb.train(params,
# train_set=d_train,
# num_boost_round=num_rounds,
# valid_sets=watchlist,
# verbose_eval=verbose_eval,
# early_stopping_rounds=early_stop)
# predictions
y_pred_valid = model.predict(X_valid)
# print('best_score', model.best_score)
# log = {'train/mae': model.best_score['training']['l2'],
# 'valid/mae': model.best_score['valid_1']['l2']}
return model, y_pred_valid
# +
target_meter = 0
X_train, y_train = create_X_y(train_df, target_meter=target_meter)
del train_df, weather_train_df
gc.collect()
# +
########################### Check memory usage
#################################################################################
import psutil
def get_memory_usage():
return np.round(psutil.Process(os.getpid()).memory_info()[0]/2.**30, 2)
def sizeof_fmt(num, suffix='B'):
for unit in ['','Ki','Mi','Gi','Ti','Pi','Ei','Zi']:
if abs(num) < 1024.0:
return "%3.1f%s%s" % (num, unit, suffix)
num /= 1024.0
return "%.1f%s%s" % (num, 'Yi', suffix)
for name, size in sorted(((name, sys.getsizeof(value)) for name,value in locals().items()),
key= lambda x: -x[1])[:10]:
print("{:>30}: {:>8}".format(name,sizeof_fmt(size)))
print('Memory in Gb', get_memory_usage())
# -
del df
gc.collect()
# + _kg_hide-output=true
# target_meter = 0
# X_train, y_train = create_X_y(train_df, target_meter=target_meter)
# del train_df, weather_train_df
# gc.collect()
# y_valid_pred_total = np.zeros(X_train.shape[0])
gc.collect()
print('target_meter', target_meter, X_train.shape)
cat_features = [X_train.columns.get_loc(cat_col) for cat_col in category_cols]
print('cat_features', cat_features)
models0 = []
for fold_, (train_idx, valid_idx) in enumerate(kf.split(X_train, y_train)):
if fold_ == 0:
print(f'train_{fold_}')
# tr_x = X_train.iloc[train_idx,:]
# vl_x = X_train.iloc[valid_idx,:]
# tr_y = y_train[tr_x[(tr_x.exception != 3) & (tr_x.exception != 1)].index.values]
# v_y = y_train[vl_x[(vl_x.exception != 3) & (vl_x.exception != 1)].index.values]
# tr_x = tr_x[(tr_x.exception != 3) & (tr_x.exception != 1)][feature_cols + category_cols]
# vl_x = vl_x[(vl_x.exception != 3) & (vl_x.exception != 1)][feature_cols + category_cols]
tr_x = X_train.iloc[train_idx,:]
vl_x = X_train.iloc[valid_idx,:]
tr_y = y_train[tr_x[(tr_x.exception != 3) & (tr_x.exception != 1) & (tr_x.exception != 4)].index.values]
v_y = y_train[vl_x[(vl_x.exception != 3) & (vl_x.exception != 1) & (vl_x.exception != 4)].index.values]
tr_x = tr_x[(tr_x.exception != 3) & (tr_x.exception != 1) & (tr_x.exception != 4)][feature_cols + category_cols]
vl_x = vl_x[(vl_x.exception != 3) & (vl_x.exception != 1) & (vl_x.exception != 4)][feature_cols + category_cols]
train_data = tr_x, tr_y
valid_data = vl_x, v_y
del tr_x, tr_y, vl_x, v_y
gc.collect()
# train_data = X_train.iloc[train_idx,:], y_train[train_idx]
# valid_data = X_train.iloc[valid_idx,:], y_train[valid_idx]
print('train', len(train_idx), 'valid', len(valid_idx))
# model, y_pred_valid, log = fit_cb(train_data, valid_data, cat_features=cat_features, devices=[0,])
# model, y_pred_valid, log = fit_lgbm(train_data, valid_data, cat_features=category_cols,
# num_rounds=1000, lr=0.05, bf=0.7)
# y_valid_pred_total[valid_idx] = y_pred_valid
model, y_pred_valid = fit_xgb(train_data, valid_data, cat_features=category_cols,
num_rounds=1000, lr=0.05, bf=0.7)
del train_data, valid_data
gc.collect()
t_prediction = model.predict(X_train.iloc[valid_idx,:][feature_cols + category_cols])
T_RESULTS.iloc[X_train.iloc[valid_idx,:].index, 1] = np.expm1(t_prediction)
models0.append(model)
del model
gc.collect()
if debug:
break
# sns.distplot(y_train)
del X_train, y_train
gc.collect()
# + _kg_hide-output=false
# target_meter = 1
# X_train, y_train = create_X_y(train_df, target_meter=target_meter)
# # y_valid_pred_total = np.zeros(X_train.shape[0])
# gc.collect()
# print('target_meter', target_meter, X_train.shape)
# cat_features = [X_train.columns.get_loc(cat_col) for cat_col in category_cols]
# print('cat_features', cat_features)
# models1 = []
# for train_idx, valid_idx in kf.split(X_train, y_train):
# # tr_x = X_train.iloc[train_idx,:]
# # vl_x = X_train.iloc[valid_idx,:]
# # tr_y = y_train[tr_x[(tr_x.exception != 3) & (tr_x.exception != 1)].index.values]
# # v_y = y_train[vl_x[(vl_x.exception != 3) & (vl_x.exception != 1)].index.values]
# # tr_x = tr_x[(tr_x.exception != 3) & (tr_x.exception != 1)][feature_cols + category_cols]
# # vl_x = vl_x[(vl_x.exception != 3) & (vl_x.exception != 1)][feature_cols + category_cols]
# tr_x = X_train.iloc[train_idx,:]
# vl_x = X_train.iloc[valid_idx,:]
# tr_y = y_train[tr_x[(tr_x.exception != 3) & (tr_x.exception != 1) & (tr_x.exception != 4)].index.values]
# v_y = y_train[vl_x[(vl_x.exception != 3) & (vl_x.exception != 1) & (vl_x.exception != 4)].index.values]
# tr_x = tr_x[(tr_x.exception != 3) & (tr_x.exception != 1) & (tr_x.exception != 4)][feature_cols + category_cols]
# vl_x = vl_x[(vl_x.exception != 3) & (vl_x.exception != 1) & (vl_x.exception != 4)][feature_cols + category_cols]
# train_data = tr_x, tr_y
# valid_data = vl_x, v_y
# del tr_x, tr_y, vl_x, v_y
# gc.collect()
# # train_data = X_train.iloc[train_idx,:], y_train[train_idx]
# # valid_data = X_train.iloc[valid_idx,:], y_train[valid_idx]
# print('train', len(train_idx), 'valid', len(valid_idx))
# # model, y_pred_valid, log = fit_cb(train_data, valid_data, cat_features=cat_features, devices=[0,])
# # model, y_pred_valid, log = fit_lgbm(train_data, valid_data, cat_features=category_cols, num_rounds=1000,
# # lr=0.05, bf=0.5)
# # y_valid_pred_total[valid_idx] = y_pred_valid
# model, y_pred_valid = fit_xgb(train_data, valid_data, cat_features=category_cols,
# num_rounds=1000, lr=0.05, bf=0.7)
# del train_data, valid_data
# gc.collect()
# t_prediction = model.predict(X_train.iloc[valid_idx,:][feature_cols + category_cols])
# T_RESULTS.iloc[X_train.iloc[valid_idx,:].index, 1] = np.expm1(t_prediction)
# models1.append(model)
# gc.collect()
# if debug:
# break
# sns.distplot(y_train)
# del X_train, y_train
# gc.collect()
# + _kg_hide-output=true
# target_meter = 2
# X_train, y_train = create_X_y(train_df, target_meter=target_meter)
# # y_valid_pred_total = np.zeros(X_train.shape[0])
# gc.collect()
# print('target_meter', target_meter, X_train.shape)
# cat_features = [X_train.columns.get_loc(cat_col) for cat_col in category_cols]
# print('cat_features', cat_features)
# models2 = []
# for train_idx, valid_idx in kf.split(X_train, y_train):
# # tr_x = X_train.iloc[train_idx,:]
# # vl_x = X_train.iloc[valid_idx,:]
# # tr_y = y_train[tr_x[(tr_x.exception != 3) & (tr_x.exception != 1)].index.values]
# # v_y = y_train[vl_x[(vl_x.exception != 3) & (vl_x.exception != 1)].index.values]
# # tr_x = tr_x[(tr_x.exception != 3) & (tr_x.exception != 1)][feature_cols + category_cols]
# # vl_x = vl_x[(vl_x.exception != 3) & (vl_x.exception != 1)][feature_cols + category_cols]
# tr_x = X_train.iloc[train_idx,:]
# vl_x = X_train.iloc[valid_idx,:]
# tr_y = y_train[tr_x[(tr_x.exception != 3) & (tr_x.exception != 1) & (tr_x.exception != 4)].index.values]
# v_y = y_train[vl_x[(vl_x.exception != 3) & (vl_x.exception != 1) & (vl_x.exception != 4)].index.values]
# tr_x = tr_x[(tr_x.exception != 3) & (tr_x.exception != 1) & (tr_x.exception != 4)][feature_cols + category_cols]
# vl_x = vl_x[(vl_x.exception != 3) & (vl_x.exception != 1) & (vl_x.exception != 4)][feature_cols + category_cols]
# train_data = tr_x, tr_y
# valid_data = vl_x, v_y
# del tr_x, tr_y, vl_x, v_y
# gc.collect()
# # train_data = X_train.iloc[train_idx,:], y_train[train_idx]
# # valid_data = X_train.iloc[valid_idx,:], y_train[valid_idx]
# print('train', len(train_idx), 'valid', len(valid_idx))
# # model, y_pred_valid, log = fit_cb(train_data, valid_data, cat_features=cat_features, devices=[0,])
# # model, y_pred_valid, log = fit_lgbm(train_data, valid_data, cat_features=category_cols,
# # num_rounds=1000, lr=0.05, bf=0.8)
# # y_valid_pred_total[valid_idx] = y_pred_valid
# model, y_pred_valid = fit_xgb(train_data, valid_data, cat_features=category_cols,
# num_rounds=1000, lr=0.05, bf=0.7)
# del train_data, valid_data
# gc.collect()
# t_prediction = model.predict(X_train.iloc[valid_idx,:][feature_cols + category_cols])
# T_RESULTS.iloc[X_train.iloc[valid_idx,:].index, 1] = np.expm1(t_prediction)
# models2.append(model)
# gc.collect()
# if debug:
# break
# sns.distplot(y_train)
# del X_train, y_train
# gc.collect()
# + _kg_hide-output=true
# target_meter = 3
# X_train, y_train = create_X_y(train_df, target_meter=target_meter)
# # y_valid_pred_total = np.zeros(X_train.shape[0])
# gc.collect()
# print('target_meter', target_meter, X_train.shape)
# cat_features = [X_train.columns.get_loc(cat_col) for cat_col in category_cols]
# print('cat_features', cat_features)
# models3 = []
# for train_idx, valid_idx in kf.split(X_train, y_train):
# # tr_x = X_train.iloc[train_idx,:]
# # vl_x = X_train.iloc[valid_idx,:]
# # tr_y = y_train[tr_x[(tr_x.exception != 3) & (tr_x.exception != 1)].index.values]
# # v_y = y_train[vl_x[(vl_x.exception != 3) & (vl_x.exception != 1)].index.values]
# # tr_x = tr_x[(tr_x.exception != 3) & (tr_x.exception != 1)][feature_cols + category_cols]
# # vl_x = vl_x[(vl_x.exception != 3) & (vl_x.exception != 1)][feature_cols + category_cols]
# tr_x = X_train.iloc[train_idx,:]
# vl_x = X_train.iloc[valid_idx,:]
# tr_y = y_train[tr_x[(tr_x.exception != 3) & (tr_x.exception != 1) & (tr_x.exception != 4)].index.values]
# v_y = y_train[vl_x[(vl_x.exception != 3) & (vl_x.exception != 1) & (vl_x.exception != 4)].index.values]
# tr_x = tr_x[(tr_x.exception != 3) & (tr_x.exception != 1) & (tr_x.exception != 4)][feature_cols + category_cols]
# vl_x = vl_x[(vl_x.exception != 3) & (vl_x.exception != 1) & (vl_x.exception != 4)][feature_cols + category_cols]
# train_data = tr_x, tr_y
# valid_data = vl_x, v_y
# del tr_x, tr_y, vl_x, v_y
# gc.collect()
# # train_data = X_train.iloc[train_idx,:], y_train[train_idx]
# # valid_data = X_train.iloc[valid_idx,:], y_train[valid_idx]
# print('train', len(train_idx), 'valid', len(valid_idx))
# # model, y_pred_valid, log = fit_cb(train_data, valid_data, cat_features=cat_features, devices=[0,])
# # model, y_pred_valid, log = fit_lgbm(train_data, valid_data, cat_features=category_cols, num_rounds=1000,
# # lr=0.03, bf=0.9)
# # y_valid_pred_total[valid_idx] = y_pred_valid
# model, y_pred_valid = fit_xgb(train_data, valid_data, cat_features=category_cols,
# num_rounds=1000, lr=0.03, bf=0.9)
# del train_data, valid_data
# gc.collect()
# t_prediction = model.predict(X_train.iloc[valid_idx,:][feature_cols + category_cols])
# T_RESULTS.iloc[X_train.iloc[valid_idx,:].index, 1] = np.expm1(t_prediction)
# models3.append(model)
# gc.collect()
# if debug:
# break
# sns.distplot(y_train)
# del X_train, y_train
# gc.collect()
# -
from sklearn.metrics import mean_squared_error, mean_squared_log_error
TARGET = 'meter_reading'
T_RESULTS['kfold'] = T_RESULTS['kfold'].clip(0,None)
print('rmse score', np.sqrt(mean_squared_log_error(T_RESULTS[TARGET], T_RESULTS['kfold'])))
print('#'*20)
output_path = os.path.join('..', 'output', 'as-meter2-no-1099-xgb-meter0-fold0')
T_RESULTS.to_pickle(os.path.join(output_path, 'T_RESULTS.pkl'))
# + _kg_hide-input=true
# print('rmse score', np.sqrt(mean_squared_log_error(T_RESULTS[TARGET], T_RESULTS['kfold'])))
# print('------------------------------------')
# cv_score_idx = train_df[train_df.exception != 1].index.values
# print('1全体非异常rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[(train_df.exception == -1) | (train_df.exception == 1)].index.values
# print('1全体异常建筑rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[train_df.exception == -1].index.values
# print('1全体异常建筑正常部分rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[train_df.exception == 1].index.values
# print('1全体异常建筑异常部分rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[(train_df.exception != 1) & (train_df.exception != -1)].index.values
# print('1全体正常建筑rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# print('------------------------------------')
# cv_score_idx = train_df[train_df.exception != 3].index.values
# print('3全体非异常rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[(train_df.exception == -3) | (train_df.exception == 3)].index.values
# print('3全体异常建筑rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[train_df.exception == -3].index.values
# print('3全体异常建筑正常部分rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[train_df.exception == 3].index.values
# print('3全体异常建筑异常部分rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[(train_df.exception != 3) & (train_df.exception != -3)].index.values
# print('3全体正常建筑rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# print('------------------------------------')
# cv_score_idx = train_df[(train_df.exception != 3) & (train_df.exception != 1)].index.values
# print('13全体非异常rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[(train_df.exception == -3) | (train_df.exception == 3) | (train_df.exception == -1) | (train_df.exception == 1)].index.values
# print('13全体异常建筑rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[(train_df.exception == -3) | (train_df.exception == -1)].index.values
# print('13全体异常建筑正常部分rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[(train_df.exception == 3) | (train_df.exception == 1)].index.values
# print('13全体异常建筑异常部分rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# cv_score_idx = train_df[(train_df.exception != 3) & (train_df.exception != -3) & (train_df.exception != 1) & (train_df.exception != -1)].index.values
# print('13全体正常建筑rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[cv_score_idx, TARGET], T_RESULTS.loc[cv_score_idx, 'kfold'])))
# print('------------------------------------')
# + _kg_hide-input=true
weather_nan_id = [20204274, 20204275, 20204276, 20204277, 20204278, 20204279, 20204280, 20204281, 20204282, 20204283, 20204284, 20204285, 20204286, 20204287, 20204288, 20204289, 20204290, 20204291, 20204292, 20204293, 20204294, 20204295, 20204296, 20204297, 20204298, 20204299, 20204300, 20204301, 20204302, 20204303, 20204304, 20204305, 20204306, 20204307, 20204308, 20204309, 20204310, 20204311, 20204312, 20204313, 20204314, 20204315, 20204316, 20204317, 20204318, 20204319, 20204320, 20204321, 20204322, 20204323, 20204324, 20204325, 20204326, 20204327, 20204328, 20204329, 20204330, 20204331, 20204332, 20204333, 20204334, 20204335, 20204336, 20204337, 20204338, 20204339, 20204340, 20204341, 20204342, 20204343, 20204344, 20204345, 20204346, 20204347, 20204348, 20204349, 20204350, 20204351, 20204352, 20204353, 20204354, 20204355, 20204356, 20204357, 20204358, 20204359, 20204360, 20204361, 20204362, 20204363, 20204364, 20204365, 20204366, 20204367, 20204368, 20204369, 20204370, 20204371, 20204372, 20204373, 20204374, 20204375, 20204376, 20204377, 20204378, 20204379, 20204380, 20204381, 20204382, 20204383, 20204384, 20204385, 20204386, 20204387, 20204388, 20204389, 20204390, 20204391, 20204392, 20204393, 20204394, 20204395, 20204396, 20204397, 20204398, 20204399, 20204400, 20204401, 20206637, 20206638, 20206639, 20206640, 20206641, 20206642, 20206643, 20206644, 20206645, 20206646, 20206647, 20206648, 20206649, 20206650, 20206651, 20206652, 20206653, 20206654, 20206655, 20206656, 20206657, 20206658, 20206659, 20206660, 20206661, 20206662, 20206663, 20206664, 20206665, 20206666, 20206667, 20206668, 20206669, 20206670, 20206671, 20206672, 20206673, 20206674, 20206675, 20206676, 20206677, 20206678, 20206679, 20206680, 20206681, 20206682, 20206683, 20206684, 20206685, 20206686, 20206687, 20206688, 20206689, 20206690, 20206691, 20206692, 20206693, 20206694, 20206695, 20206696, 20206697, 20206698, 20206699, 20206700, 20206701, 20206702, 20206703, 20206704, 20206705, 20206706, 20206707, 20206708, 20206709, 20206710, 20206711, 20206712, 20206713, 20206714, 20206715, 20206716, 20206717, 20206718, 20206719, 20206720, 20206721, 20206722, 20206723, 20206724, 20206725, 20206726, 20206727, 20206728, 20206729, 20206730, 20206731, 20206732, 20206733, 20206734, 20206735, 20206736, 20206737, 20206738, 20206739, 20206740, 20206741, 20206742, 20206743, 20206744, 20206745, 20206746, 20206747, 20206748, 20206749, 20206750, 20206751, 20206752, 20206753, 20206754, 20206755, 20206756, 20206757, 20206758, 20206759, 20206760, 20206761, 20206762, 20206763, 20206764, 20209003, 20209004, 20209005, 20209006, 20209007, 20209008, 20209009, 20209010, 20209011, 20209012, 20209013, 20209014, 20209015, 20209016, 20209017, 20209018, 20209019, 20209020, 20209021, 20209022, 20209023, 20209024, 20209025, 20209026, 20209027, 20209028, 20209029, 20209030, 20209031, 20209032, 20209033, 20209034, 20209035, 20209036, 20209037, 20209038, 20209039, 20209040, 20209041, 20209042, 20209043, 20209044, 20209045, 20209046, 20209047, 20209048, 20209049, 20209050, 20209051, 20209052, 20209053, 20209054, 20209055, 20209056, 20209057, 20209058, 20209059, 20209060, 20209061, 20209062, 20209063, 20209064, 20209065, 20209066, 20209067, 20209068, 20209069, 20209070, 20209071, 20209072, 20209073, 20209074, 20209075, 20209076, 20209077, 20209078, 20209079, 20209080, 20209081, 20209082, 20209083, 20209084, 20209085, 20209086, 20209087, 20209088, 20209089, 20209090, 20209091, 20209092, 20209093, 20209094, 20209095, 20209096, 20209097, 20209098, 20209099, 20209100, 20209101, 20209102, 20209103, 20209104, 20209105, 20209106, 20209107, 20209108, 20209109, 20209110, 20209111, 20209112, 20209113, 20209114, 20209115, 20209116, 20209117, 20209118, 20209119, 20209120, 20209121, 20209122, 20209123, 20209124, 20209125, 20209126, 20209127, 20209128, 20209129, 20209130, 20211368, 20211369, 20211370, 20211371, 20211372, 20211373, 20211374, 20211375, 20211376, 20211377, 20211378, 20211379, 20211380, 20211381, 20211382, 20211383, 20211384, 20211385, 20211386, 20211387, 20211388, 20211389, 20211390, 20211391, 20211392, 20211393, 20211394, 20211395, 20211396, 20211397, 20211398, 20211399, 20211400, 20211401, 20211402, 20211403, 20211404, 20211405, 20211406, 20211407, 20211408, 20211409, 20211410, 20211411, 20211412, 20211413, 20211414, 20211415, 20211416, 20211417, 20211418, 20211419, 20211420, 20211421, 20211422, 20211423, 20211424, 20211425, 20211426, 20211427, 20211428, 20211429, 20211430, 20211431, 20211432, 20211433, 20211434, 20211435, 20211436, 20211437, 20211438, 20211439, 20211440, 20211441, 20211442, 20211443, 20211444, 20211445, 20211446, 20211447, 20211448, 20211449, 20211450, 20211451, 20211452, 20211453, 20211454, 20211455, 20211456, 20211457, 20211458, 20211459, 20211460, 20211461, 20211462, 20211463, 20211464, 20211465, 20211466, 20211467, 20211468, 20211469, 20211470, 20211471, 20211472, 20211473, 20211474, 20211475, 20211476, 20211477, 20211478, 20211479, 20211480, 20211481, 20211482, 20211483, 20211484, 20211485, 20211486, 20211487, 20211488, 20211489, 20211490, 20211491, 20211492, 20211493, 20211494, 20211495, 20213734, 20213735, 20213736, 20213737, 20213738, 20213739, 20213740, 20213741, 20213742, 20213743, 20213744, 20213745, 20213746, 20213747, 20213748, 20213749, 20213750, 20213751, 20213752, 20213753, 20213754, 20213755, 20213756, 20213757, 20213758, 20213759, 20213760, 20213761, 20213762, 20213763, 20213764, 20213765, 20213766, 20213767, 20213768, 20213769, 20213770, 20213771, 20213772, 20213773, 20213774, 20213775, 20213776, 20213777, 20213778, 20213779, 20213780, 20213781, 20213782, 20213783, 20213784, 20213785, 20213786, 20213787, 20213788, 20213789, 20213790, 20213791, 20213792, 20213793, 20213794, 20213795, 20213796, 20213797, 20213798, 20213799, 20213800, 20213801, 20213802, 20213803, 20213804, 20213805, 20213806, 20213807, 20213808, 20213809, 20213810, 20213811, 20213812, 20213813, 20213814, 20213815, 20213816, 20213817, 20213818, 20213819, 20213820, 20213821, 20213822, 20213823, 20213824, 20213825, 20213826, 20213827, 20213828, 20213829, 20213830, 20213831, 20213832, 20213833, 20213834, 20213835, 20213836, 20213837, 20213838, 20213839, 20213840, 20213841, 20213842, 20213843, 20213844, 20213845, 20213846, 20213847, 20213848, 20213849, 20213850, 20213851, 20213852, 20213853, 20213854, 20213855, 20213856, 20213857, 20213858, 20213859, 20213860, 20213861, 20195011, 20195012, 20195013, 20195014, 20195015, 20195016, 20195017, 20195018, 20195019, 20195020, 20195021, 20195022, 20195023, 20195024, 20195025, 20195026, 20195027, 20195028, 20195029, 20195030, 20195031, 20195032, 20195033, 20195034, 20195035, 20195036, 20195037, 20195038, 20195039, 20195040, 20195041, 20195042, 20195043, 20195044, 20195045, 20195046, 20195047, 20195048, 20195049, 20195050, 20195051, 20195052, 20195053, 20195054, 20195055, 20195056, 20195057, 20195058, 20195059, 20195060, 20195061, 20195062, 20195063, 20195064, 20195065, 20195066, 20195067, 20195068, 20195069, 20195070, 20195071, 20195072, 20195073, 20195074, 20195075, 20195076, 20195077, 20195078, 20195079, 20195080, 20195081, 20195082, 20195083, 20195084, 20195085, 20195086, 20195087, 20195088, 20195089, 20195090, 20195091, 20195092, 20195093, 20195094, 20195095, 20195096, 20195097, 20195098, 20195099, 20195100, 20195101, 20195102, 20195103, 20195104, 20195105, 20195106, 20195107, 20195108, 20195109, 20195110, 20195111, 20195112, 20195113, 20195114, 20195115, 20195116, 20195117, 20195118, 20195119, 20195120, 20195121, 20195122, 20195123, 20195124, 20195125, 20195126, 20195127, 20195128, 20195129, 20195130, 20195131, 20195132, 20195133, 20195134, 20195135, 20195136, 20195137, 20195138, 20195139, 20195140, 20195141, 20195142, 20195143, 20195144, 20195145, 20195146, 20195147, 20195148, 20195149, 20195150, 20195151, 20195152, 20195153, 20195154, 20195155, 20195156, 20195157, 20195158, 20195159, 20195160, 20195161, 20195162, 20195163, 20195164, 20195165, 20195166, 20195167, 20195168, 20195169, 20195170, 20195171, 20195172, 20195173, 20195174, 20195175, 20195176, 20195177, 20195178, 20195179, 20195180, 20195181, 20195182, 20195183, 20195184, 20195185, 20195186, 20195187, 20195188, 20195189, 20195190, 20195191, 20195192, 20195193, 20195194, 20195195, 20195196, 20195197, 20195198, 20195199, 20195200, 20195201, 20195202, 20195203, 20195204, 20195205, 20195206, 20195207, 20195208, 20195209, 20195210, 20195211, 20195212, 20195213, 20195214, 20195215, 20195216, 20195217, 20195218, 20195219, 20195220, 20195221, 20195222, 20195223, 20195224, 20195225, 20195226, 20195227, 20195228, 20195229, 20195230, 20195231, 20195232, 20195233, 20195234, 20195235, 20195236, 20195237, 20195238, 20195239, 20195240, 20195241, 20195242, 20195243, 20195244, 20195245, 20195246, 20195247, 20195248, 20195249, 20195250, 20195251, 20195252, 20195253, 20195254, 20195255, 20195256, 20195257, 20195258, 20195259, 20195260, 20195261, 20195262, 20195263, 20195264, 20195265, 20195266, 20195267, 20195268, 20195269, 20195270, 20195271, 20195272, 20195273, 20195274, 20195275, 20195276, 20195277, 20195278, 20195279, 20195280, 20195281, 20195282, 20195283, 20195284, 20195285, 20195286, 20195287, 20195288, 20195289, 20195290, 20195291, 20195292, 20195293, 20195294, 20195295, 20195296, 20195297, 20195298, 20197376, 20197377, 20197378, 20197379, 20197380, 20197381, 20197382, 20197383, 20197384, 20197385, 20197386, 20197387, 20197388, 20197389, 20197390, 20197391, 20197392, 20197393, 20197394, 20197395, 20197396, 20197397, 20197398, 20197399, 20197400, 20197401, 20197402, 20197403, 20197404, 20197405, 20197406, 20197407, 20197408, 20197409, 20197410, 20197411, 20197412, 20197413, 20197414, 20197415, 20197416, 20197417, 20197418, 20197419, 20197420, 20197421, 20197422, 20197423, 20197424, 20197425, 20197426, 20197427, 20197428, 20197429, 20197430, 20197431, 20197432, 20197433, 20197434, 20197435, 20197436, 20197437, 20197438, 20197439, 20197440, 20197441, 20197442, 20197443, 20197444, 20197445, 20197446, 20197447, 20197448, 20197449, 20197450, 20197451, 20197452, 20197453, 20197454, 20197455, 20197456, 20197457, 20197458, 20197459, 20197460, 20197461, 20197462, 20197463, 20197464, 20197465, 20197466, 20197467, 20197468, 20197469, 20197470, 20197471, 20197472, 20197473, 20197474, 20197475, 20197476, 20197477, 20197478, 20197479, 20197480, 20197481, 20197482, 20197483, 20197484, 20197485, 20197486, 20197487, 20197488, 20197489, 20197490, 20197491, 20197492, 20197493, 20197494, 20197495, 20197496, 20197497, 20197498, 20197499, 20197500, 20197501, 20197502, 20197503, 20197504, 20197505, 20197506, 20197507, 20197508, 20197509, 20197510, 20197511, 20197512, 20197513, 20197514, 20197515, 20197516, 20197517, 20197518, 20197519, 20197520, 20197521, 20197522, 20197523, 20197524, 20197525, 20197526, 20197527, 20197528, 20197529, 20197530, 20197531, 20197532, 20197533, 20197534, 20197535, 20197536, 20197537, 20197538, 20197539, 20197540, 20197541, 20197542, 20197543, 20197544, 20197545, 20197546, 20197547, 20197548, 20197549, 20197550, 20197551, 20197552, 20197553, 20197554, 20197555, 20197556, 20197557, 20197558, 20197559, 20197560, 20197561, 20197562, 20197563, 20197564, 20197565, 20197566, 20197567, 20197568, 20197569, 20197570, 20197571, 20197572, 20197573, 20197574, 20197575, 20197576, 20197577, 20197578, 20197579, 20197580, 20197581, 20197582, 20197583, 20197584, 20197585, 20197586, 20197587, 20197588, 20197589, 20197590, 20197591, 20197592, 20197593, 20197594, 20197595, 20197596, 20197597, 20197598, 20197599, 20197600, 20197601, 20197602, 20197603, 20197604, 20197605, 20197606, 20197607, 20197608, 20197609, 20197610, 20197611, 20197612, 20197613, 20197614, 20197615, 20197616, 20197617, 20197618, 20197619, 20197620, 20197621, 20197622, 20197623, 20197624, 20197625, 20197626, 20197627, 20197628, 20197629, 20197630, 20197631, 20197632, 20197633, 20197634, 20197635, 20197636, 20197637, 20197638, 20197639, 20197640, 20197641, 20197642, 20197643, 20197644, 20197645, 20197646, 20197647, 20197648, 20197649, 20197650, 20197651, 20197652, 20197653, 20197654, 20197655, 20197656, 20197657, 20197658, 20197659, 20197660, 20197661, 20197662, 20197663, 20199740, 20199741, 20199742, 20199743, 20199744, 20199745, 20199746, 20199747, 20199748, 20199749, 20199750, 20199751, 20199752, 20199753, 20199754, 20199755, 20199756, 20199757, 20199758, 20199759, 20199760, 20199761, 20199762, 20199763, 20199764, 20199765, 20199766, 20199767, 20199768, 20199769, 20199770, 20199771, 20199772, 20199773, 20199774, 20199775, 20199776, 20199777, 20199778, 20199779, 20199780, 20199781, 20199782, 20199783, 20199784, 20199785, 20199786, 20199787, 20199788, 20199789, 20199790, 20199791, 20199792, 20199793, 20199794, 20199795, 20199796, 20199797, 20199798, 20199799, 20199800, 20199801, 20199802, 20199803, 20199804, 20199805, 20199806, 20199807, 20199808, 20199809, 20199810, 20199811, 20199812, 20199813, 20199814, 20199815, 20199816, 20199817, 20199818, 20199819, 20199820, 20199821, 20199822, 20199823, 20199824, 20199825, 20199826, 20199827, 20199828, 20199829, 20199830, 20199831, 20199832, 20199833, 20199834, 20199835, 20199836, 20199837, 20199838, 20199839, 20199840, 20199841, 20199842, 20199843, 20199844, 20199845, 20199846, 20199847, 20199848, 20199849, 20199850, 20199851, 20199852, 20199853, 20199854, 20199855, 20199856, 20199857, 20199858, 20199859, 20199860, 20199861, 20199862, 20199863, 20199864, 20199865, 20199866, 20199867, 20199868, 20199869, 20199870, 20199871, 20199872, 20199873, 20199874, 20199875, 20199876, 20199877, 20199878, 20199879, 20199880, 20199881, 20199882, 20199883, 20199884, 20199885, 20199886, 20199887, 20199888, 20199889, 20199890, 20199891, 20199892, 20199893, 20199894, 20199895, 20199896, 20199897, 20199898, 20199899, 20199900, 20199901, 20199902, 20199903, 20199904, 20199905, 20199906, 20199907, 20199908, 20199909, 20199910, 20199911, 20199912, 20199913, 20199914, 20199915, 20199916, 20199917, 20199918, 20199919, 20199920, 20199921, 20199922, 20199923, 20199924, 20199925, 20199926, 20199927, 20199928, 20199929, 20199930, 20199931, 20199932, 20199933, 20199934, 20199935, 20199936, 20199937, 20199938, 20199939, 20199940, 20199941, 20199942, 20199943, 20199944, 20199945, 20199946, 20199947, 20199948, 20199949, 20199950, 20199951, 20199952, 20199953, 20199954, 20199955, 20199956, 20199957, 20199958, 20199959, 20199960, 20199961, 20199962, 20199963, 20199964, 20199965, 20199966, 20199967, 20199968, 20199969, 20199970, 20199971, 20199972, 20199973, 20199974, 20199975, 20199976, 20199977, 20199978, 20199979, 20199980, 20199981, 20199982, 20199983, 20199984, 20199985, 20199986, 20199987, 20199988, 20199989, 20199990, 20199991, 20199992, 20199993, 20199994, 20199995, 20199996, 20199997, 20199998, 20199999, 20200000, 20200001, 20200002, 20200003, 20200004, 20200005, 20200006, 20200007, 20200008, 20200009, 20200010, 20200011, 20200012, 20200013, 20200014, 20200015, 20200016, 20200017, 20200018, 20200019, 20200020, 20200021, 20200022, 20200023, 20200024, 20200025, 20200026, 20200027, 20202102, 20202103, 20202104, 20202105, 20202106, 20202107, 20202108, 20202109, 20202110, 20202111, 20202112, 20202113, 20202114, 20202115, 20202116, 20202117, 20202118, 20202119, 20202120, 20202121, 20202122, 20202123, 20202124, 20202125, 20202126, 20202127, 20202128, 20202129, 20202130, 20202131, 20202132, 20202133, 20202134, 20202135, 20202136, 20202137, 20202138, 20202139, 20202140, 20202141, 20202142, 20202143, 20202144, 20202145, 20202146, 20202147, 20202148, 20202149, 20202150, 20202151, 20202152, 20202153, 20202154, 20202155, 20202156, 20202157, 20202158, 20202159, 20202160, 20202161, 20202162, 20202163, 20202164, 20202165, 20202166, 20202167, 20202168, 20202169, 20202170, 20202171, 20202172, 20202173, 20202174, 20202175, 20202176, 20202177, 20202178, 20202179, 20202180, 20202181, 20202182, 20202183, 20202184, 20202185, 20202186, 20202187, 20202188, 20202189, 20202190, 20202191, 20202192, 20202193, 20202194, 20202195, 20202196, 20202197, 20202198, 20202199, 20202200, 20202201, 20202202, 20202203, 20202204, 20202205, 20202206, 20202207, 20202208, 20202209, 20202210, 20202211, 20202212, 20202213, 20202214, 20202215, 20202216, 20202217, 20202218, 20202219, 20202220, 20202221, 20202222, 20202223, 20202224, 20202225, 20202226, 20202227, 20202228, 20202229, 20202230, 20202231, 20202232, 20202233, 20202234, 20202235, 20202236, 20202237, 20202238, 20202239, 20202240, 20202241, 20202242, 20202243, 20202244, 20202245, 20202246, 20202247, 20202248, 20202249, 20202250, 20202251, 20202252, 20202253, 20202254, 20202255, 20202256, 20202257, 20202258, 20202259, 20202260, 20202261, 20202262, 20202263, 20202264, 20202265, 20202266, 20202267, 20202268, 20202269, 20202270, 20202271, 20202272, 20202273, 20202274, 20202275, 20202276, 20202277, 20202278, 20202279, 20202280, 20202281, 20202282, 20202283, 20202284, 20202285, 20202286, 20202287, 20202288, 20202289, 20202290, 20202291, 20202292, 20202293, 20202294, 20202295, 20202296, 20202297, 20202298, 20202299, 20202300, 20202301, 20202302, 20202303, 20202304, 20202305, 20202306, 20202307, 20202308, 20202309, 20202310, 20202311, 20202312, 20202313, 20202314, 20202315, 20202316, 20202317, 20202318, 20202319, 20202320, 20202321, 20202322, 20202323, 20202324, 20202325, 20202326, 20202327, 20202328, 20202329, 20202330, 20202331, 20202332, 20202333, 20202334, 20202335, 20202336, 20202337, 20202338, 20202339, 20202340, 20202341, 20202342, 20202343, 20202344, 20202345, 20202346, 20202347, 20202348, 20202349, 20202350, 20202351, 20202352, 20202353, 20202354, 20202355, 20202356, 20202357, 20202358, 20202359, 20202360, 20202361, 20202362, 20202363, 20202364, 20202365, 20202366, 20202367, 20202368, 20202369, 20202370, 20202371, 20202372, 20202373, 20202374, 20202375, 20202376, 20202377, 20202378, 20202379, 20202380, 20202381, 20202382, 20202383, 20202384, 20202385, 20202386, 20202387, 20202388, 20202389, 20204465, 20204466, 20204467, 20204468, 20204469, 20204470, 20204471, 20204472, 20204473, 20204474, 20204475, 20204476, 20204477, 20204478, 20204479, 20204480, 20204481, 20204482, 20204483, 20204484, 20204485, 20204486, 20204487, 20204488, 20204489, 20204490, 20204491, 20204492, 20204493, 20204494, 20204495, 20204496, 20204497, 20204498, 20204499, 20204500, 20204501, 20204502, 20204503, 20204504, 20204505, 20204506, 20204507, 20204508, 20204509, 20204510, 20204511, 20204512, 20204513, 20204514, 20204515, 20204516, 20204517, 20204518, 20204519, 20204520, 20204521, 20204522, 20204523, 20204524, 20204525, 20204526, 20204527, 20204528, 20204529, 20204530, 20204531, 20204532, 20204533, 20204534, 20204535, 20204536, 20204537, 20204538, 20204539, 20204540, 20204541, 20204542, 20204543, 20204544, 20204545, 20204546, 20204547, 20204548, 20204549, 20204550, 20204551, 20204552, 20204553, 20204554, 20204555, 20204556, 20204557, 20204558, 20204559, 20204560, 20204561, 20204562, 20204563, 20204564, 20204565, 20204566, 20204567, 20204568, 20204569, 20204570, 20204571, 20204572, 20204573, 20204574, 20204575, 20204576, 20204577, 20204578, 20204579, 20204580, 20204581, 20204582, 20204583, 20204584, 20204585, 20204586, 20204587, 20204588, 20204589, 20204590, 20204591, 20204592, 20204593, 20204594, 20204595, 20204596, 20204597, 20204598, 20204599, 20204600, 20204601, 20204602, 20204603, 20204604, 20204605, 20204606, 20204607, 20204608, 20204609, 20204610, 20204611, 20204612, 20204613, 20204614, 20204615, 20204616, 20204617, 20204618, 20204619, 20204620, 20204621, 20204622, 20204623, 20204624, 20204625, 20204626, 20204627, 20204628, 20204629, 20204630, 20204631, 20204632, 20204633, 20204634, 20204635, 20204636, 20204637, 20204638, 20204639, 20204640, 20204641, 20204642, 20204643, 20204644, 20204645, 20204646, 20204647, 20204648, 20204649, 20204650, 20204651, 20204652, 20204653, 20204654, 20204655, 20204656, 20204657, 20204658, 20204659, 20204660, 20204661, 20204662, 20204663, 20204664, 20204665, 20204666, 20204667, 20204668, 20204669, 20204670, 20204671, 20204672, 20204673, 20204674, 20204675, 20204676, 20204677, 20204678, 20204679, 20204680, 20204681, 20204682, 20204683, 20204684, 20204685, 20204686, 20204687, 20204688, 20204689, 20204690, 20204691, 20204692, 20204693, 20204694, 20204695, 20204696, 20204697, 20204698, 20204699, 20204700, 20204701, 20204702, 20204703, 20204704, 20204705, 20204706, 20204707, 20204708, 20204709, 20204710, 20204711, 20204712, 20204713, 20204714, 20204715, 20204716, 20204717, 20204718, 20204719, 20204720, 20204721, 20204722, 20204723, 20204724, 20204725, 20204726, 20204727, 20204728, 20204729, 20204730, 20204731, 20204732, 20204733, 20204734, 20204735, 20204736, 20204737, 20204738, 20204739, 20204740, 20204741, 20204742, 20204743, 20204744, 20204745, 20204746, 20204747, 20204748, 20204749, 20204750, 20204751, 20204752, 20206828, 20206829, 20206830, 20206831, 20206832, 20206833, 20206834, 20206835, 20206836, 20206837, 20206838, 20206839, 20206840, 20206841, 20206842, 20206843, 20206844, 20206845, 20206846, 20206847, 20206848, 20206849, 20206850, 20206851, 20206852, 20206853, 20206854, 20206855, 20206856, 20206857, 20206858, 20206859, 20206860, 20206861, 20206862, 20206863, 20206864, 20206865, 20206866, 20206867, 20206868, 20206869, 20206870, 20206871, 20206872, 20206873, 20206874, 20206875, 20206876, 20206877, 20206878, 20206879, 20206880, 20206881, 20206882, 20206883, 20206884, 20206885, 20206886, 20206887, 20206888, 20206889, 20206890, 20206891, 20206892, 20206893, 20206894, 20206895, 20206896, 20206897, 20206898, 20206899, 20206900, 20206901, 20206902, 20206903, 20206904, 20206905, 20206906, 20206907, 20206908, 20206909, 20206910, 20206911, 20206912, 20206913, 20206914, 20206915, 20206916, 20206917, 20206918, 20206919, 20206920, 20206921, 20206922, 20206923, 20206924, 20206925, 20206926, 20206927, 20206928, 20206929, 20206930, 20206931, 20206932, 20206933, 20206934, 20206935, 20206936, 20206937, 20206938, 20206939, 20206940, 20206941, 20206942, 20206943, 20206944, 20206945, 20206946, 20206947, 20206948, 20206949, 20206950, 20206951, 20206952, 20206953, 20206954, 20206955, 20206956, 20206957, 20206958, 20206959, 20206960, 20206961, 20206962, 20206963, 20206964, 20206965, 20206966, 20206967, 20206968, 20206969, 20206970, 20206971, 20206972, 20206973, 20206974, 20206975, 20206976, 20206977, 20206978, 20206979, 20206980, 20206981, 20206982, 20206983, 20206984, 20206985, 20206986, 20206987, 20206988, 20206989, 20206990, 20206991, 20206992, 20206993, 20206994, 20206995, 20206996, 20206997, 20206998, 20206999, 20207000, 20207001, 20207002, 20207003, 20207004, 20207005, 20207006, 20207007, 20207008, 20207009, 20207010, 20207011, 20207012, 20207013, 20207014, 20207015, 20207016, 20207017, 20207018, 20207019, 20207020, 20207021, 20207022, 20207023, 20207024, 20207025, 20207026, 20207027, 20207028, 20207029, 20207030, 20207031, 20207032, 20207033, 20207034, 20207035, 20207036, 20207037, 20207038, 20207039, 20207040, 20207041, 20207042, 20207043, 20207044, 20207045, 20207046, 20207047, 20207048, 20207049, 20207050, 20207051, 20207052, 20207053, 20207054, 20207055, 20207056, 20207057, 20207058, 20207059, 20207060, 20207061, 20207062, 20207063, 20207064, 20207065, 20207066, 20207067, 20207068, 20207069, 20207070, 20207071, 20207072, 20207073, 20207074, 20207075, 20207076, 20207077, 20207078, 20207079, 20207080, 20207081, 20207082, 20207083, 20207084, 20207085, 20207086, 20207087, 20207088, 20207089, 20207090, 20207091, 20207092, 20207093, 20207094, 20207095, 20207096, 20207097, 20207098, 20207099, 20207100, 20207101, 20207102, 20207103, 20207104, 20207105, 20207106, 20207107, 20207108, 20207109, 20207110, 20207111, 20207112, 20207113, 20207114, 20207115, 20209194, 20209195, 20209196, 20209197, 20209198, 20209199, 20209200, 20209201, 20209202, 20209203, 20209204, 20209205, 20209206, 20209207, 20209208, 20209209, 20209210, 20209211, 20209212, 20209213, 20209214, 20209215, 20209216, 20209217, 20209218, 20209219, 20209220, 20209221, 20209222, 20209223, 20209224, 20209225, 20209226, 20209227, 20209228, 20209229, 20209230, 20209231, 20209232, 20209233, 20209234, 20209235, 20209236, 20209237, 20209238, 20209239, 20209240, 20209241, 20209242, 20209243, 20209244, 20209245, 20209246, 20209247, 20209248, 20209249, 20209250, 20209251, 20209252, 20209253, 20209254, 20209255, 20209256, 20209257, 20209258, 20209259, 20209260, 20209261, 20209262, 20209263, 20209264, 20209265, 20209266, 20209267, 20209268, 20209269, 20209270, 20209271, 20209272, 20209273, 20209274, 20209275, 20209276, 20209277, 20209278, 20209279, 20209280, 20209281, 20209282, 20209283, 20209284, 20209285, 20209286, 20209287, 20209288, 20209289, 20209290, 20209291, 20209292, 20209293, 20209294, 20209295, 20209296, 20209297, 20209298, 20209299, 20209300, 20209301, 20209302, 20209303, 20209304, 20209305, 20209306, 20209307, 20209308, 20209309, 20209310, 20209311, 20209312, 20209313, 20209314, 20209315, 20209316, 20209317, 20209318, 20209319, 20209320, 20209321, 20209322, 20209323, 20209324, 20209325, 20209326, 20209327, 20209328, 20209329, 20209330, 20209331, 20209332, 20209333, 20209334, 20209335, 20209336, 20209337, 20209338, 20209339, 20209340, 20209341, 20209342, 20209343, 20209344, 20209345, 20209346, 20209347, 20209348, 20209349, 20209350, 20209351, 20209352, 20209353, 20209354, 20209355, 20209356, 20209357, 20209358, 20209359, 20209360, 20209361, 20209362, 20209363, 20209364, 20209365, 20209366, 20209367, 20209368, 20209369, 20209370, 20209371, 20209372, 20209373, 20209374, 20209375, 20209376, 20209377, 20209378, 20209379, 20209380, 20209381, 20209382, 20209383, 20209384, 20209385, 20209386, 20209387, 20209388, 20209389, 20209390, 20209391, 20209392, 20209393, 20209394, 20209395, 20209396, 20209397, 20209398, 20209399, 20209400, 20209401, 20209402, 20209403, 20209404, 20209405, 20209406, 20209407, 20209408, 20209409, 20209410, 20209411, 20209412, 20209413, 20209414, 20209415, 20209416, 20209417, 20209418, 20209419, 20209420, 20209421, 20209422, 20209423, 20209424, 20209425, 20209426, 20209427, 20209428, 20209429, 20209430, 20209431, 20209432, 20209433, 20209434, 20209435, 20209436, 20209437, 20209438, 20209439, 20209440, 20209441, 20209442, 20209443, 20209444, 20209445, 20209446, 20209447, 20209448, 20209449, 20209450, 20209451, 20209452, 20209453, 20209454, 20209455, 20209456, 20209457, 20209458, 20209459, 20209460, 20209461, 20209462, 20209463, 20209464, 20209465, 20209466, 20209467, 20209468, 20209469, 20209470, 20209471, 20209472, 20209473, 20209474, 20209475, 20209476, 20209477, 20209478, 20209479, 20209480, 20209481, 20211559, 20211560, 20211561, 20211562, 20211563, 20211564, 20211565, 20211566, 20211567, 20211568, 20211569, 20211570, 20211571, 20211572, 20211573, 20211574, 20211575, 20211576, 20211577, 20211578, 20211579, 20211580, 20211581, 20211582, 20211583, 20211584, 20211585, 20211586, 20211587, 20211588, 20211589, 20211590, 20211591, 20211592, 20211593, 20211594, 20211595, 20211596, 20211597, 20211598, 20211599, 20211600, 20211601, 20211602, 20211603, 20211604, 20211605, 20211606, 20211607, 20211608, 20211609, 20211610, 20211611, 20211612, 20211613, 20211614, 20211615, 20211616, 20211617, 20211618, 20211619, 20211620, 20211621, 20211622, 20211623, 20211624, 20211625, 20211626, 20211627, 20211628, 20211629, 20211630, 20211631, 20211632, 20211633, 20211634, 20211635, 20211636, 20211637, 20211638, 20211639, 20211640, 20211641, 20211642, 20211643, 20211644, 20211645, 20211646, 20211647, 20211648, 20211649, 20211650, 20211651, 20211652, 20211653, 20211654, 20211655, 20211656, 20211657, 20211658, 20211659, 20211660, 20211661, 20211662, 20211663, 20211664, 20211665, 20211666, 20211667, 20211668, 20211669, 20211670, 20211671, 20211672, 20211673, 20211674, 20211675, 20211676, 20211677, 20211678, 20211679, 20211680, 20211681, 20211682, 20211683, 20211684, 20211685, 20211686, 20211687, 20211688, 20211689, 20211690, 20211691, 20211692, 20211693, 20211694, 20211695, 20211696, 20211697, 20211698, 20211699, 20211700, 20211701, 20211702, 20211703, 20211704, 20211705, 20211706, 20211707, 20211708, 20211709, 20211710, 20211711, 20211712, 20211713, 20211714, 20211715, 20211716, 20211717, 20211718, 20211719, 20211720, 20211721, 20211722, 20211723, 20211724, 20211725, 20211726, 20211727, 20211728, 20211729, 20211730, 20211731, 20211732, 20211733, 20211734, 20211735, 20211736, 20211737, 20211738, 20211739, 20211740, 20211741, 20211742, 20211743, 20211744, 20211745, 20211746, 20211747, 20211748, 20211749, 20211750, 20211751, 20211752, 20211753, 20211754, 20211755, 20211756, 20211757, 20211758, 20211759, 20211760, 20211761, 20211762, 20211763, 20211764, 20211765, 20211766, 20211767, 20211768, 20211769, 20211770, 20211771, 20211772, 20211773, 20211774, 20211775, 20211776, 20211777, 20211778, 20211779, 20211780, 20211781, 20211782, 20211783, 20211784, 20211785, 20211786, 20211787, 20211788, 20211789, 20211790, 20211791, 20211792, 20211793, 20211794, 20211795, 20211796, 20211797, 20211798, 20211799, 20211800, 20211801, 20211802, 20211803, 20211804, 20211805, 20211806, 20211807, 20211808, 20211809, 20211810, 20211811, 20211812, 20211813, 20211814, 20211815, 20211816, 20211817, 20211818, 20211819, 20211820, 20211821, 20211822, 20211823, 20211824, 20211825, 20211826, 20211827, 20211828, 20211829, 20211830, 20211831, 20211832, 20211833, 20211834, 20211835, 20211836, 20211837, 20211838, 20211839, 20211840, 20211841, 20211842, 20211843, 20211844, 20211845, 20211846, 20213925, 20213926, 20213927, 20213928, 20213929, 20213930, 20213931, 20213932, 20213933, 20213934, 20213935, 20213936, 20213937, 20213938, 20213939, 20213940, 20213941, 20213942, 20213943, 20213944, 20213945, 20213946, 20213947, 20213948, 20213949, 20213950, 20213951, 20213952, 20213953, 20213954, 20213955, 20213956, 20213957, 20213958, 20213959, 20213960, 20213961, 20213962, 20213963, 20213964, 20213965, 20213966, 20213967, 20213968, 20213969, 20213970, 20213971, 20213972, 20213973, 20213974, 20213975, 20213976, 20213977, 20213978, 20213979, 20213980, 20213981, 20213982, 20213983, 20213984, 20213985, 20213986, 20213987, 20213988, 20213989, 20213990, 20213991, 20213992, 20213993, 20213994, 20213995, 20213996, 20213997, 20213998, 20213999, 20214000, 20214001, 20214002, 20214003, 20214004, 20214005, 20214006, 20214007, 20214008, 20214009, 20214010, 20214011, 20214012, 20214013, 20214014, 20214015, 20214016, 20214017, 20214018, 20214019, 20214020, 20214021, 20214022, 20214023, 20214024, 20214025, 20214026, 20214027, 20214028, 20214029, 20214030, 20214031, 20214032, 20214033, 20214034, 20214035, 20214036, 20214037, 20214038, 20214039, 20214040, 20214041, 20214042, 20214043, 20214044, 20214045, 20214046, 20214047, 20214048, 20214049, 20214050, 20214051, 20214052, 20214053, 20214054, 20214055, 20214056, 20214057, 20214058, 20214059, 20214060, 20214061, 20214062, 20214063, 20214064, 20214065, 20214066, 20214067, 20214068, 20214069, 20214070, 20214071, 20214072, 20214073, 20214074, 20214075, 20214076, 20214077, 20214078, 20214079, 20214080, 20214081, 20214082, 20214083, 20214084, 20214085, 20214086, 20214087, 20214088, 20214089, 20214090, 20214091, 20214092, 20214093, 20214094, 20214095, 20214096, 20214097, 20214098, 20214099, 20214100, 20214101, 20214102, 20214103, 20214104, 20214105, 20214106, 20214107, 20214108, 20214109, 20214110, 20214111, 20214112, 20214113, 20214114, 20214115, 20214116, 20214117, 20214118, 20214119, 20214120, 20214121, 20214122, 20214123, 20214124, 20214125, 20214126, 20214127, 20214128, 20214129, 20214130, 20214131, 20214132, 20214133, 20214134, 20214135, 20214136, 20214137, 20214138, 20214139, 20214140, 20214141, 20214142, 20214143, 20214144, 20214145, 20214146, 20214147, 20214148, 20214149, 20214150, 20214151, 20214152, 20214153, 20214154, 20214155, 20214156, 20214157, 20214158, 20214159, 20214160, 20214161, 20214162, 20214163, 20214164, 20214165, 20214166, 20214167, 20214168, 20214169, 20214170, 20214171, 20214172, 20214173, 20214174, 20214175, 20214176, 20214177, 20214178, 20214179, 20214180, 20214181, 20214182, 20214183, 20214184, 20214185, 20214186, 20214187, 20214188, 20214189, 20214190, 20214191, 20214192, 20214193, 20214194, 20214195, 20214196, 20214197, 20214198, 20214199, 20214200, 20214201, 20214202, 20214203, 20214204, 20214205, 20214206, 20214207, 20214208, 20214209, 20214210, 20214211, 20214212, 20202390, 20202391, 20202392, 20202393, 20202394, 20202395, 20202396, 20202397, 20202398, 20202399, 20202400, 20202401, 20202402, 20202403, 20202404, 20202405, 20202406, 20202407, 20202408, 20202409, 20202410, 20202411, 20202412, 20202413, 20202414, 20202415, 20202416, 20202417, 20202418, 20202419, 20202420, 20202421, 20202422, 20202423, 20202424, 20202425, 20202426, 20202427, 20202428, 20202429, 20202430, 20202431, 20202432, 20202433, 20202434, 20202435, 20202436, 20202437, 20202438, 20202439, 20202440, 20202441, 20202442, 20202443, 20202444, 20202445, 20202446, 20202447, 20202448, 20202449, 20202450, 20202451, 20202452, 20202453, 20202454, 20202455, 20202456, 20202457, 20202458, 20202459, 20202460, 20202461, 20202462, 20202463, 20202464, 20202465, 20202466, 20202467, 20202468, 20202469, 20202470, 20202471, 20202472, 20202473, 20202474, 20202475, 20202476, 20202477, 20202478, 20202479, 20202480, 20202481, 20202482, 20202483, 20202484, 20202485, 20202486, 20202487, 20202488, 20202489, 20202490, 20202491, 20202492, 20202493, 20202494, 20202495, 20202496, 20202497, 20202498, 20202499, 20202500, 20202501, 20202502, 20202503, 20202504, 20202505, 20202506, 20202507, 20202508, 20202509, 20202510, 20202511, 20202512, 20202513, 20202514, 20202515, 20202516, 20202517, 20202518, 20202519, 20202520, 20202521, 20202522, 20202523, 20202524, 20202525, 20202526, 20202527, 20202528, 20202529, 20202530, 20202531, 20202532, 20202533, 20202534, 20202535, 20202536, 20202537, 20202538, 20202539, 20202540, 20202541, 20202542, 20202543, 20202544, 20202545, 20202546, 20202547, 20202548, 20202549, 20202550, 20202551, 20202552, 20202553, 20202554, 20202555, 20202556, 20202557, 20202558, 20202559, 20202560, 20202561, 20202562, 20202563, 20202564, 20202565, 20202566, 20202567, 20202568, 20202569, 20202570, 20202571, 20202572, 20202573, 20202574, 20202575, 20202576, 20202577, 20202578, 20202579, 20202580, 20202581, 20202582, 20202583, 20202584, 20202585, 20202586, 20202587, 20202588, 20202589, 20202590, 20202591, 20202592, 20202593, 20202594, 20202595, 20202596, 20202597, 20202598, 20202599, 20202600, 20202601, 20202602, 20202603, 20202604, 20202605, 20202606, 20202607, 20202608, 20202609, 20202610, 20202611, 20202612, 20202613, 20202614, 20202615, 20202616, 20202617, 20202618, 20202619, 20202620, 20202621, 20202622, 20202623, 20202624, 20202625, 20202626, 20202627, 20202628, 20202629, 20202630, 20202631, 20202632, 20202633, 20202634, 20202635, 20202636, 20202637, 20202638, 20202639, 20202640, 20202641, 20202642, 20202643, 20202644, 20202645, 20202646, 20202647, 20202648, 20202649, 20202650, 20202651, 20202652, 20202653, 20202654, 20202655, 20202656, 20202657, 20202658, 20202659, 20202660, 20204753, 20204754, 20204755, 20204756, 20204757, 20204758, 20204759, 20204760, 20204761, 20204762, 20204763, 20204764, 20204765, 20204766, 20204767, 20204768, 20204769, 20204770, 20204771, 20204772, 20204773, 20204774, 20204775, 20204776, 20204777, 20204778, 20204779, 20204780, 20204781, 20204782, 20204783, 20204784, 20204785, 20204786, 20204787, 20204788, 20204789, 20204790, 20204791, 20204792, 20204793, 20204794, 20204795, 20204796, 20204797, 20204798, 20204799, 20204800, 20204801, 20204802, 20204803, 20204804, 20204805, 20204806, 20204807, 20204808, 20204809, 20204810, 20204811, 20204812, 20204813, 20204814, 20204815, 20204816, 20204817, 20204818, 20204819, 20204820, 20204821, 20204822, 20204823, 20204824, 20204825, 20204826, 20204827, 20204828, 20204829, 20204830, 20204831, 20204832, 20204833, 20204834, 20204835, 20204836, 20204837, 20204838, 20204839, 20204840, 20204841, 20204842, 20204843, 20204844, 20204845, 20204846, 20204847, 20204848, 20204849, 20204850, 20204851, 20204852, 20204853, 20204854, 20204855, 20204856, 20204857, 20204858, 20204859, 20204860, 20204861, 20204862, 20204863, 20204864, 20204865, 20204866, 20204867, 20204868, 20204869, 20204870, 20204871, 20204872, 20204873, 20204874, 20204875, 20204876, 20204877, 20204878, 20204879, 20204880, 20204881, 20204882, 20204883, 20204884, 20204885, 20204886, 20204887, 20204888, 20204889, 20204890, 20204891, 20204892, 20204893, 20204894, 20204895, 20204896, 20204897, 20204898, 20204899, 20204900, 20204901, 20204902, 20204903, 20204904, 20204905, 20204906, 20204907, 20204908, 20204909, 20204910, 20204911, 20204912, 20204913, 20204914, 20204915, 20204916, 20204917, 20204918, 20204919, 20204920, 20204921, 20204922, 20204923, 20204924, 20204925, 20204926, 20204927, 20204928, 20204929, 20204930, 20204931, 20204932, 20204933, 20204934, 20204935, 20204936, 20204937, 20204938, 20204939, 20204940, 20204941, 20204942, 20204943, 20204944, 20204945, 20204946, 20204947, 20204948, 20204949, 20204950, 20204951, 20204952, 20204953, 20204954, 20204955, 20204956, 20204957, 20204958, 20204959, 20204960, 20204961, 20204962, 20204963, 20204964, 20204965, 20204966, 20204967, 20204968, 20204969, 20204970, 20204971, 20204972, 20204973, 20204974, 20204975, 20204976, 20204977, 20204978, 20204979, 20204980, 20204981, 20204982, 20204983, 20204984, 20204985, 20204986, 20204987, 20204988, 20204989, 20204990, 20204991, 20204992, 20204993, 20204994, 20204995, 20204996, 20204997, 20204998, 20204999, 20205000, 20205001, 20205002, 20205003, 20205004, 20205005, 20205006, 20205007, 20205008, 20205009, 20205010, 20205011, 20205012, 20205013, 20205014, 20205015, 20205016, 20205017, 20205018, 20205019, 20205020, 20205021, 20205022, 20205023, 20207116, 20207117, 20207118, 20207119, 20207120, 20207121, 20207122, 20207123, 20207124, 20207125, 20207126, 20207127, 20207128, 20207129, 20207130, 20207131, 20207132, 20207133, 20207134, 20207135, 20207136, 20207137, 20207138, 20207139, 20207140, 20207141, 20207142, 20207143, 20207144, 20207145, 20207146, 20207147, 20207148, 20207149, 20207150, 20207151, 20207152, 20207153, 20207154, 20207155, 20207156, 20207157, 20207158, 20207159, 20207160, 20207161, 20207162, 20207163, 20207164, 20207165, 20207166, 20207167, 20207168, 20207169, 20207170, 20207171, 20207172, 20207173, 20207174, 20207175, 20207176, 20207177, 20207178, 20207179, 20207180, 20207181, 20207182, 20207183, 20207184, 20207185, 20207186, 20207187, 20207188, 20207189, 20207190, 20207191, 20207192, 20207193, 20207194, 20207195, 20207196, 20207197, 20207198, 20207199, 20207200, 20207201, 20207202, 20207203, 20207204, 20207205, 20207206, 20207207, 20207208, 20207209, 20207210, 20207211, 20207212, 20207213, 20207214, 20207215, 20207216, 20207217, 20207218, 20207219, 20207220, 20207221, 20207222, 20207223, 20207224, 20207225, 20207226, 20207227, 20207228, 20207229, 20207230, 20207231, 20207232, 20207233, 20207234, 20207235, 20207236, 20207237, 20207238, 20207239, 20207240, 20207241, 20207242, 20207243, 20207244, 20207245, 20207246, 20207247, 20207248, 20207249, 20207250, 20207251, 20207252, 20207253, 20207254, 20207255, 20207256, 20207257, 20207258, 20207259, 20207260, 20207261, 20207262, 20207263, 20207264, 20207265, 20207266, 20207267, 20207268, 20207269, 20207270, 20207271, 20207272, 20207273, 20207274, 20207275, 20207276, 20207277, 20207278, 20207279, 20207280, 20207281, 20207282, 20207283, 20207284, 20207285, 20207286, 20207287, 20207288, 20207289, 20207290, 20207291, 20207292, 20207293, 20207294, 20207295, 20207296, 20207297, 20207298, 20207299, 20207300, 20207301, 20207302, 20207303, 20207304, 20207305, 20207306, 20207307, 20207308, 20207309, 20207310, 20207311, 20207312, 20207313, 20207314, 20207315, 20207316, 20207317, 20207318, 20207319, 20207320, 20207321, 20207322, 20207323, 20207324, 20207325, 20207326, 20207327, 20207328, 20207329, 20207330, 20207331, 20207332, 20207333, 20207334, 20207335, 20207336, 20207337, 20207338, 20207339, 20207340, 20207341, 20207342, 20207343, 20207344, 20207345, 20207346, 20207347, 20207348, 20207349, 20207350, 20207351, 20207352, 20207353, 20207354, 20207355, 20207356, 20207357, 20207358, 20207359, 20207360, 20207361, 20207362, 20207363, 20207364, 20207365, 20207366, 20207367, 20207368, 20207369, 20207370, 20207371, 20207372, 20207373, 20207374, 20207375, 20207376, 20207377, 20207378, 20207379, 20207380, 20207381, 20207382, 20207383, 20207384, 20207385, 20207386, 20209482, 20209483, 20209484, 20209485, 20209486, 20209487, 20209488, 20209489, 20209490, 20209491, 20209492, 20209493, 20209494, 20209495, 20209496, 20209497, 20209498, 20209499, 20209500, 20209501, 20209502, 20209503, 20209504, 20209505, 20209506, 20209507, 20209508, 20209509, 20209510, 20209511, 20209512, 20209513, 20209514, 20209515, 20209516, 20209517, 20209518, 20209519, 20209520, 20209521, 20209522, 20209523, 20209524, 20209525, 20209526, 20209527, 20209528, 20209529, 20209530, 20209531, 20209532, 20209533, 20209534, 20209535, 20209536, 20209537, 20209538, 20209539, 20209540, 20209541, 20209542, 20209543, 20209544, 20209545, 20209546, 20209547, 20209548, 20209549, 20209550, 20209551, 20209552, 20209553, 20209554, 20209555, 20209556, 20209557, 20209558, 20209559, 20209560, 20209561, 20209562, 20209563, 20209564, 20209565, 20209566, 20209567, 20209568, 20209569, 20209570, 20209571, 20209572, 20209573, 20209574, 20209575, 20209576, 20209577, 20209578, 20209579, 20209580, 20209581, 20209582, 20209583, 20209584, 20209585, 20209586, 20209587, 20209588, 20209589, 20209590, 20209591, 20209592, 20209593, 20209594, 20209595, 20209596, 20209597, 20209598, 20209599, 20209600, 20209601, 20209602, 20209603, 20209604, 20209605, 20209606, 20209607, 20209608, 20209609, 20209610, 20209611, 20209612, 20209613, 20209614, 20209615, 20209616, 20209617, 20209618, 20209619, 20209620, 20209621, 20209622, 20209623, 20209624, 20209625, 20209626, 20209627, 20209628, 20209629, 20209630, 20209631, 20209632, 20209633, 20209634, 20209635, 20209636, 20209637, 20209638, 20209639, 20209640, 20209641, 20209642, 20209643, 20209644, 20209645, 20209646, 20209647, 20209648, 20209649, 20209650, 20209651, 20209652, 20209653, 20209654, 20209655, 20209656, 20209657, 20209658, 20209659, 20209660, 20209661, 20209662, 20209663, 20209664, 20209665, 20209666, 20209667, 20209668, 20209669, 20209670, 20209671, 20209672, 20209673, 20209674, 20209675, 20209676, 20209677, 20209678, 20209679, 20209680, 20209681, 20209682, 20209683, 20209684, 20209685, 20209686, 20209687, 20209688, 20209689, 20209690, 20209691, 20209692, 20209693, 20209694, 20209695, 20209696, 20209697, 20209698, 20209699, 20209700, 20209701, 20209702, 20209703, 20209704, 20209705, 20209706, 20209707, 20209708, 20209709, 20209710, 20209711, 20209712, 20209713, 20209714, 20209715, 20209716, 20209717, 20209718, 20209719, 20209720, 20209721, 20209722, 20209723, 20209724, 20209725, 20209726, 20209727, 20209728, 20209729, 20209730, 20209731, 20209732, 20209733, 20209734, 20209735, 20209736, 20209737, 20209738, 20209739, 20209740, 20209741, 20209742, 20209743, 20209744, 20209745, 20209746, 20209747, 20209748, 20209749, 20209750, 20209751, 20209752, 20211847, 20211848, 20211849, 20211850, 20211851, 20211852, 20211853, 20211854, 20211855, 20211856, 20211857, 20211858, 20211859, 20211860, 20211861, 20211862, 20211863, 20211864, 20211865, 20211866, 20211867, 20211868, 20211869, 20211870, 20211871, 20211872, 20211873, 20211874, 20211875, 20211876, 20211877, 20211878, 20211879, 20211880, 20211881, 20211882, 20211883, 20211884, 20211885, 20211886, 20211887, 20211888, 20211889, 20211890, 20211891, 20211892, 20211893, 20211894, 20211895, 20211896, 20211897, 20211898, 20211899, 20211900, 20211901, 20211902, 20211903, 20211904, 20211905, 20211906, 20211907, 20211908, 20211909, 20211910, 20211911, 20211912, 20211913, 20211914, 20211915, 20211916, 20211917, 20211918, 20211919, 20211920, 20211921, 20211922, 20211923, 20211924, 20211925, 20211926, 20211927, 20211928, 20211929, 20211930, 20211931, 20211932, 20211933, 20211934, 20211935, 20211936, 20211937, 20211938, 20211939, 20211940, 20211941, 20211942, 20211943, 20211944, 20211945, 20211946, 20211947, 20211948, 20211949, 20211950, 20211951, 20211952, 20211953, 20211954, 20211955, 20211956, 20211957, 20211958, 20211959, 20211960, 20211961, 20211962, 20211963, 20211964, 20211965, 20211966, 20211967, 20211968, 20211969, 20211970, 20211971, 20211972, 20211973, 20211974, 20211975, 20211976, 20211977, 20211978, 20211979, 20211980, 20211981, 20211982, 20211983, 20211984, 20211985, 20211986, 20211987, 20211988, 20211989, 20211990, 20211991, 20211992, 20211993, 20211994, 20211995, 20211996, 20211997, 20211998, 20211999, 20212000, 20212001, 20212002, 20212003, 20212004, 20212005, 20212006, 20212007, 20212008, 20212009, 20212010, 20212011, 20212012, 20212013, 20212014, 20212015, 20212016, 20212017, 20212018, 20212019, 20212020, 20212021, 20212022, 20212023, 20212024, 20212025, 20212026, 20212027, 20212028, 20212029, 20212030, 20212031, 20212032, 20212033, 20212034, 20212035, 20212036, 20212037, 20212038, 20212039, 20212040, 20212041, 20212042, 20212043, 20212044, 20212045, 20212046, 20212047, 20212048, 20212049, 20212050, 20212051, 20212052, 20212053, 20212054, 20212055, 20212056, 20212057, 20212058, 20212059, 20212060, 20212061, 20212062, 20212063, 20212064, 20212065, 20212066, 20212067, 20212068, 20212069, 20212070, 20212071, 20212072, 20212073, 20212074, 20212075, 20212076, 20212077, 20212078, 20212079, 20212080, 20212081, 20212082, 20212083, 20212084, 20212085, 20212086, 20212087, 20212088, 20212089, 20212090, 20212091, 20212092, 20212093, 20212094, 20212095, 20212096, 20212097, 20212098, 20212099, 20212100, 20212101, 20212102, 20212103, 20212104, 20212105, 20212106, 20212107, 20212108, 20212109, 20212110, 20212111, 20212112, 20212113, 20212114, 20212115, 20212116, 20212117, 20214213, 20214214, 20214215, 20214216, 20214217, 20214218, 20214219, 20214220, 20214221, 20214222, 20214223, 20214224, 20214225, 20214226, 20214227, 20214228, 20214229, 20214230, 20214231, 20214232, 20214233, 20214234, 20214235, 20214236, 20214237, 20214238, 20214239, 20214240, 20214241, 20214242, 20214243, 20214244, 20214245, 20214246, 20214247, 20214248, 20214249, 20214250, 20214251, 20214252, 20214253, 20214254, 20214255, 20214256, 20214257, 20214258, 20214259, 20214260, 20214261, 20214262, 20214263, 20214264, 20214265, 20214266, 20214267, 20214268, 20214269, 20214270, 20214271, 20214272, 20214273, 20214274, 20214275, 20214276, 20214277, 20214278, 20214279, 20214280, 20214281, 20214282, 20214283, 20214284, 20214285, 20214286, 20214287, 20214288, 20214289, 20214290, 20214291, 20214292, 20214293, 20214294, 20214295, 20214296, 20214297, 20214298, 20214299, 20214300, 20214301, 20214302, 20214303, 20214304, 20214305, 20214306, 20214307, 20214308, 20214309, 20214310, 20214311, 20214312, 20214313, 20214314, 20214315, 20214316, 20214317, 20214318, 20214319, 20214320, 20214321, 20214322, 20214323, 20214324, 20214325, 20214326, 20214327, 20214328, 20214329, 20214330, 20214331, 20214332, 20214333, 20214334, 20214335, 20214336, 20214337, 20214338, 20214339, 20214340, 20214341, 20214342, 20214343, 20214344, 20214345, 20214346, 20214347, 20214348, 20214349, 20214350, 20214351, 20214352, 20214353, 20214354, 20214355, 20214356, 20214357, 20214358, 20214359, 20214360, 20214361, 20214362, 20214363, 20214364, 20214365, 20214366, 20214367, 20214368, 20214369, 20214370, 20214371, 20214372, 20214373, 20214374, 20214375, 20214376, 20214377, 20214378, 20214379, 20214380, 20214381, 20214382, 20214383, 20214384, 20214385, 20214386, 20214387, 20214388, 20214389, 20214390, 20214391, 20214392, 20214393, 20214394, 20214395, 20214396, 20214397, 20214398, 20214399, 20214400, 20214401, 20214402, 20214403, 20214404, 20214405, 20214406, 20214407, 20214408, 20214409, 20214410, 20214411, 20214412, 20214413, 20214414, 20214415, 20214416, 20214417, 20214418, 20214419, 20214420, 20214421, 20214422, 20214423, 20214424, 20214425, 20214426, 20214427, 20214428, 20214429, 20214430, 20214431, 20214432, 20214433, 20214434, 20214435, 20214436, 20214437, 20214438, 20214439, 20214440, 20214441, 20214442, 20214443, 20214444, 20214445, 20214446, 20214447, 20214448, 20214449, 20214450, 20214451, 20214452, 20214453, 20214454, 20214455, 20214456, 20214457, 20214458, 20214459, 20214460, 20214461, 20214462, 20214463, 20214464, 20214465, 20214466, 20214467, 20214468, 20214469, 20214470, 20214471, 20214472, 20214473, 20214474, 20214475, 20214476, 20214477, 20214478, 20214479, 20214480, 20214481, 20214482, 20214483, 20197935, 20197936, 20197937, 20197938, 20197939, 20197940, 20197941, 20197942, 20197943, 20197944, 20197945, 20197946, 20197947, 20197948, 20197949, 20197950, 20197951, 20197952, 20197953, 20197954, 20197955, 20197956, 20197957, 20197958, 20197959, 20197960, 20197961, 20197962, 20197963, 20197964, 20197965, 20197966, 20197967, 20197968, 20197969, 20197970, 20197971, 20197972, 20197973, 20197974, 20197975, 20197976, 20197977, 20197978, 20197979, 20197980, 20197981, 20197982, 20197983, 20197984, 20197985, 20197986, 20197987, 20197988, 20197989, 20197990, 20197991, 20197992, 20197993, 20197994, 20197995, 20197996, 20197997, 20197998, 20197999, 20198000, 20198001, 20198002, 20198003, 20198004, 20198005, 20198006, 20198007, 20198008, 20198009, 20198010, 20198011, 20198012, 20198013, 20198014, 20198015, 20198016, 20198017, 20198018, 20198019, 20198020, 20198021, 20198022, 20198023, 20198024, 20198025, 20200299, 20200300, 20200301, 20200302, 20200303, 20200304, 20200305, 20200306, 20200307, 20200308, 20200309, 20200310, 20200311, 20200312, 20200313, 20200314, 20200315, 20200316, 20200317, 20200318, 20200319, 20200320, 20200321, 20200322, 20200323, 20200324, 20200325, 20200326, 20200327, 20200328, 20200329, 20200330, 20200331, 20200332, 20200333, 20200334, 20200335, 20200336, 20200337, 20200338, 20200339, 20200340, 20200341, 20200342, 20200343, 20200344, 20200345, 20200346, 20200347, 20200348, 20200349, 20200350, 20200351, 20200352, 20200353, 20200354, 20200355, 20200356, 20200357, 20200358, 20200359, 20200360, 20200361, 20200362, 20200363, 20200364, 20200365, 20200366, 20200367, 20200368, 20200369, 20200370, 20200371, 20200372, 20200373, 20200374, 20200375, 20200376, 20200377, 20200378, 20200379, 20200380, 20200381, 20200382, 20200383, 20200384, 20200385, 20200386, 20200387, 20200388, 20200389, 20202661, 20202662, 20202663, 20202664, 20202665, 20202666, 20202667, 20202668, 20202669, 20202670, 20202671, 20202672, 20202673, 20202674, 20202675, 20202676, 20202677, 20202678, 20202679, 20202680, 20202681, 20202682, 20202683, 20202684, 20202685, 20202686, 20202687, 20202688, 20202689, 20202690, 20202691, 20202692, 20202693, 20202694, 20202695, 20202696, 20202697, 20202698, 20202699, 20202700, 20202701, 20202702, 20202703, 20202704, 20202705, 20202706, 20202707, 20202708, 20202709, 20202710, 20202711, 20202712, 20202713, 20202714, 20202715, 20202716, 20202717, 20202718, 20202719, 20202720, 20202721, 20202722, 20202723, 20202724, 20202725, 20202726, 20202727, 20202728, 20202729, 20202730, 20202731, 20202732, 20202733, 20202734, 20202735, 20202736, 20202737, 20202738, 20202739, 20202740, 20202741, 20202742, 20202743, 20202744, 20202745, 20202746, 20202747, 20202748, 20202749, 20202750, 20202751, 20205024, 20205025, 20205026, 20205027, 20205028, 20205029, 20205030, 20205031, 20205032, 20205033, 20205034, 20205035, 20205036, 20205037, 20205038, 20205039, 20205040, 20205041, 20205042, 20205043, 20205044, 20205045, 20205046, 20205047, 20205048, 20205049, 20205050, 20205051, 20205052, 20205053, 20205054, 20205055, 20205056, 20205057, 20205058, 20205059, 20205060, 20205061, 20205062, 20205063, 20205064, 20205065, 20205066, 20205067, 20205068, 20205069, 20205070, 20205071, 20205072, 20205073, 20205074, 20205075, 20205076, 20205077, 20205078, 20205079, 20205080, 20205081, 20205082, 20205083, 20205084, 20205085, 20205086, 20205087, 20205088, 20205089, 20205090, 20205091, 20205092, 20205093, 20205094, 20205095, 20205096, 20205097, 20205098, 20205099, 20205100, 20205101, 20205102, 20205103, 20205104, 20205105, 20205106, 20205107, 20205108, 20205109, 20205110, 20205111, 20205112, 20205113, 20205114, 20207387, 20207388, 20207389, 20207390, 20207391, 20207392, 20207393, 20207394, 20207395, 20207396, 20207397, 20207398, 20207399, 20207400, 20207401, 20207402, 20207403, 20207404, 20207405, 20207406, 20207407, 20207408, 20207409, 20207410, 20207411, 20207412, 20207413, 20207414, 20207415, 20207416, 20207417, 20207418, 20207419, 20207420, 20207421, 20207422, 20207423, 20207424, 20207425, 20207426, 20207427, 20207428, 20207429, 20207430, 20207431, 20207432, 20207433, 20207434, 20207435, 20207436, 20207437, 20207438, 20207439, 20207440, 20207441, 20207442, 20207443, 20207444, 20207445, 20207446, 20207447, 20207448, 20207449, 20207450, 20207451, 20207452, 20207453, 20207454, 20207455, 20207456, 20207457, 20207458, 20207459, 20207460, 20207461, 20207462, 20207463, 20207464, 20207465, 20207466, 20207467, 20207468, 20207469, 20207470, 20207471, 20207472, 20207473, 20207474, 20207475, 20207476, 20207477, 20209753, 20209754, 20209755, 20209756, 20209757, 20209758, 20209759, 20209760, 20209761, 20209762, 20209763, 20209764, 20209765, 20209766, 20209767, 20209768, 20209769, 20209770, 20209771, 20209772, 20209773, 20209774, 20209775, 20209776, 20209777, 20209778, 20209779, 20209780, 20209781, 20209782, 20209783, 20209784, 20209785, 20209786, 20209787, 20209788, 20209789, 20209790, 20209791, 20209792, 20209793, 20209794, 20209795, 20209796, 20209797, 20209798, 20209799, 20209800, 20209801, 20209802, 20209803, 20209804, 20209805, 20209806, 20209807, 20209808, 20209809, 20209810, 20209811, 20209812, 20209813, 20209814, 20209815, 20209816, 20209817, 20209818, 20209819, 20209820, 20209821, 20209822, 20209823, 20209824, 20209825, 20209826, 20209827, 20209828, 20209829, 20209830, 20209831, 20209832, 20209833, 20209834, 20209835, 20209836, 20209837, 20209838, 20209839, 20209840, 20209841, 20209842, 20212118, 20212119, 20212120, 20212121, 20212122, 20212123, 20212124, 20212125, 20212126, 20212127, 20212128, 20212129, 20212130, 20212131, 20212132, 20212133, 20212134, 20212135, 20212136, 20212137, 20212138, 20212139, 20212140, 20212141, 20212142, 20212143, 20212144, 20212145, 20212146, 20212147, 20212148, 20212149, 20212150, 20212151, 20212152, 20212153, 20212154, 20212155, 20212156, 20212157, 20212158, 20212159, 20212160, 20212161, 20212162, 20212163, 20212164, 20212165, 20212166, 20212167, 20212168, 20212169, 20212170, 20212171, 20212172, 20212173, 20212174, 20212175, 20212176, 20212177, 20212178, 20212179, 20212180, 20212181, 20212182, 20212183, 20212184, 20212185, 20212186, 20212187, 20212188, 20212189, 20212190, 20212191, 20212192, 20212193, 20212194, 20212195, 20212196, 20212197, 20212198, 20212199, 20212200, 20212201, 20212202, 20212203, 20212204, 20212205, 20212206, 20212207, 20212208, 20214484, 20214485, 20214486, 20214487, 20214488, 20214489, 20214490, 20214491, 20214492, 20214493, 20214494, 20214495, 20214496, 20214497, 20214498, 20214499, 20214500, 20214501, 20214502, 20214503, 20214504, 20214505, 20214506, 20214507, 20214508, 20214509, 20214510, 20214511, 20214512, 20214513, 20214514, 20214515, 20214516, 20214517, 20214518, 20214519, 20214520, 20214521, 20214522, 20214523, 20214524, 20214525, 20214526, 20214527, 20214528, 20214529, 20214530, 20214531, 20214532, 20214533, 20214534, 20214535, 20214536, 20214537, 20214538, 20214539, 20214540, 20214541, 20214542, 20214543, 20214544, 20214545, 20214546, 20214547, 20214548, 20214549, 20214550, 20214551, 20214552, 20214553, 20214554, 20214555, 20214556, 20214557, 20214558, 20214559, 20214560, 20214561, 20214562, 20214563, 20214564, 20214565, 20214566, 20214567, 20214568, 20214569, 20214570, 20214571, 20214572, 20214573, 20214574, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 20202841, 20202842, 20202843, 20202844, 20202845, 20202846, 20202847, 20202848, 20202849, 20202850, 20202851, 20202852, 20202853, 20202854, 20202855, 20202856, 20202857, 20202858, 20202859, 20202860, 20202861, 20202862, 20202863, 20202864, 20202865, 20202866, 20202867, 20202868, 20202869, 20202870, 20202871, 20202872, 20202873, 20202874, 20202875, 20202876, 20202877, 20202878, 20202879, 20202880, 20202881, 20202882, 20202883, 20202884, 20202885, 20202886, 20202887, 20202888, 20202889, 20202890, 20202891, 20202892, 20202893, 20202894, 20202895, 20202896, 20202897, 20202898, 20202899, 20202900, 20202901, 20202902, 20202903, 20202904, 20202905, 20202906, 20202907, 20202908, 20202909, 20202910, 20202911, 20202912, 20202913, 20202914, 20202915, 20202916, 20202917, 20202918, 20205204, 20205205, 20205206, 20205207, 20205208, 20205209, 20205210, 20205211, 20205212, 20205213, 20205214, 20205215, 20205216, 20205217, 20205218, 20205219, 20205220, 20205221, 20205222, 20205223, 20205224, 20205225, 20205226, 20205227, 20205228, 20205229, 20205230, 20205231, 20205232, 20205233, 20205234, 20205235, 20205236, 20205237, 20205238, 20205239, 20205240, 20205241, 20205242, 20205243, 20205244, 20205245, 20205246, 20205247, 20205248, 20205249, 20205250, 20205251, 20205252, 20205253, 20205254, 20205255, 20205256, 20205257, 20205258, 20205259, 20205260, 20205261, 20205262, 20205263, 20205264, 20205265, 20205266, 20205267, 20205268, 20205269, 20205270, 20205271, 20205272, 20205273, 20205274, 20205275, 20205276, 20205277, 20205278, 20205279, 20205280, 20205281, 20205282, 20207567, 20207568, 20207569, 20207570, 20207571, 20207572, 20207573, 20207574, 20207575, 20207576, 20207577, 20207578, 20207579, 20207580, 20207581, 20207582, 20207583, 20207584, 20207585, 20207586, 20207587, 20207588, 20207589, 20207590, 20207591, 20207592, 20207593, 20207594, 20207595, 20207596, 20207597, 20207598, 20207599, 20207600, 20207601, 20207602, 20207603, 20207604, 20207605, 20207606, 20207607, 20207608, 20207609, 20207610, 20207611, 20207612, 20207613, 20207614, 20207615, 20207616, 20207617, 20207618, 20207619, 20207620, 20207621, 20207622, 20207623, 20207624, 20207625, 20207626, 20207627, 20207628, 20207629, 20207630, 20207631, 20207632, 20207633, 20207634, 20207635, 20207636, 20207637, 20207638, 20207639, 20207640, 20207641, 20207642, 20207643, 20207644, 20209932, 20209933, 20209934, 20209935, 20209936, 20209937, 20209938, 20209939, 20209940, 20209941, 20209942, 20209943, 20209944, 20209945, 20209946, 20209947, 20209948, 20209949, 20209950, 20209951, 20209952, 20209953, 20209954, 20209955, 20209956, 20209957, 20209958, 20209959, 20209960, 20209961, 20209962, 20209963, 20209964, 20209965, 20209966, 20209967, 20209968, 20209969, 20209970, 20209971, 20209972, 20209973, 20209974, 20209975, 20209976, 20209977, 20209978, 20209979, 20209980, 20209981, 20209982, 20209983, 20209984, 20209985, 20209986, 20209987, 20209988, 20209989, 20209990, 20209991, 20209992, 20209993, 20209994, 20209995, 20209996, 20209997, 20209998, 20209999, 20210000, 20210001, 20210002, 20210003, 20210004, 20210005, 20210006, 20210007, 20210008, 20210009, 20210010, 20212298, 20212299, 20212300, 20212301, 20212302, 20212303, 20212304, 20212305, 20212306, 20212307, 20212308, 20212309, 20212310, 20212311, 20212312, 20212313, 20212314, 20212315, 20212316, 20212317, 20212318, 20212319, 20212320, 20212321, 20212322, 20212323, 20212324, 20212325, 20212326, 20212327, 20212328, 20212329, 20212330, 20212331, 20212332, 20212333, 20212334, 20212335, 20212336, 20212337, 20212338, 20212339, 20212340, 20212341, 20212342, 20212343, 20212344, 20212345, 20212346, 20212347, 20212348, 20212349, 20212350, 20212351, 20212352, 20212353, 20212354, 20212355, 20212356, 20212357, 20212358, 20212359, 20212360, 20212361, 20212362, 20212363, 20212364, 20212365, 20212366, 20212367, 20212368, 20212369, 20212370, 20212371, 20212372, 20212373, 20212374, 20212375, 20212376, 20214664, 20214665, 20214666, 20214667, 20214668, 20214669, 20214670, 20214671, 20214672, 20214673, 20214674, 20214675, 20214676, 20214677, 20214678, 20214679, 20214680, 20214681, 20214682, 20214683, 20214684, 20214685, 20214686, 20214687, 20214688, 20214689, 20214690, 20214691, 20214692, 20214693, 20214694, 20214695, 20214696, 20214697, 20214698, 20214699, 20214700, 20214701, 20214702, 20214703, 20214704, 20214705, 20214706, 20214707, 20214708, 20214709, 20214710, 20214711, 20214712, 20214713, 20214714, 20214715, 20214716, 20214717, 20214718, 20214719, 20214720, 20214721, 20214722, 20214723, 20214724, 20214725, 20214726, 20214727, 20214728, 20214729, 20214730, 20214731, 20214732, 20214733, 20214734, 20214735, 20214736, 20214737, 20214738, 20214739, 20214740, 20214741, 20214742, 20202919, 20202920, 20202921, 20202922, 20202923, 20202924, 20202925, 20202926, 20202927, 20202928, 20202929, 20202930, 20202931, 20202932, 20202933, 20202934, 20202935, 20202936, 20202937, 20202938, 20202939, 20202940, 20202941, 20202942, 20202943, 20202944, 20202945, 20202946, 20202947, 20202948, 20202949, 20202950, 20202951, 20202952, 20202953, 20202954, 20202955, 20202956, 20202957, 20202958, 20202959, 20202960, 20205283, 20205284, 20205285, 20205286, 20205287, 20205288, 20205289, 20205290, 20205291, 20205292, 20205293, 20205294, 20205295, 20205296, 20205297, 20205298, 20205299, 20205300, 20205301, 20205302, 20205303, 20205304, 20205305, 20205306, 20205307, 20205308, 20205309, 20205310, 20205311, 20205312, 20205313, 20205314, 20205315, 20205316, 20205317, 20205318, 20205319, 20205320, 20205321, 20205322, 20205323, 20205324, 20207645, 20207646, 20207647, 20207648, 20207649, 20207650, 20207651, 20207652, 20207653, 20207654, 20207655, 20207656, 20207657, 20207658, 20207659, 20207660, 20207661, 20207662, 20207663, 20207664, 20207665, 20207666, 20207667, 20207668, 20207669, 20207670, 20207671, 20207672, 20207673, 20207674, 20207675, 20207676, 20207677, 20207678, 20207679, 20207680, 20207681, 20207682, 20207683, 20207684, 20207685, 20207686, 20210011, 20210012, 20210013, 20210014, 20210015, 20210016, 20210017, 20210018, 20210019, 20210020, 20210021, 20210022, 20210023, 20210024, 20210025, 20210026, 20210027, 20210028, 20210029, 20210030, 20210031, 20210032, 20210033, 20210034, 20210035, 20210036, 20210037, 20210038, 20210039, 20210040, 20210041, 20210042, 20210043, 20210044, 20210045, 20210046, 20210047, 20210048, 20210049, 20210050, 20210051, 20210052, 20212377, 20212378, 20212379, 20212380, 20212381, 20212382, 20212383, 20212384, 20212385, 20212386, 20212387, 20212388, 20212389, 20212390, 20212391, 20212392, 20212393, 20212394, 20212395, 20212396, 20212397, 20212398, 20212399, 20212400, 20212401, 20212402, 20212403, 20212404, 20212405, 20212406, 20212407, 20212408, 20212409, 20212410, 20212411, 20212412, 20212413, 20212414, 20212415, 20212416, 20212417, 20212418, 20214743, 20214744, 20214745, 20214746, 20214747, 20214748, 20214749, 20214750, 20214751, 20214752, 20214753, 20214754, 20214755, 20214756, 20214757, 20214758, 20214759, 20214760, 20214761, 20214762, 20214763, 20214764, 20214765, 20214766, 20214767, 20214768, 20214769, 20214770, 20214771, 20214772, 20214773, 20214774, 20214775, 20214776, 20214777, 20214778, 20214779, 20214780, 20214781, 20214782, 20214783, 20214784, 20205325, 20205326, 20205327, 20205328, 20205329, 20205330, 20205331, 20205332, 20205333, 20205334, 20205335, 20205336, 20205337, 20205338, 20205339, 20205340, 20205341, 20205342, 20205343, 20205344, 20205345, 20205346, 20205347, 20205348, 20205349, 20205350, 20205351, 20205352, 20205353, 20205354, 20205355, 20205356, 20205357, 20205358, 20205359, 20205360, 20205361, 20205362, 20205363, 20205364, 20205365, 20205366, 20205367, 20205368, 20205369, 20205370, 20205371, 20205372, 20205373, 20205374, 20205375, 20205376, 20205377, 20205378, 20205379, 20205380, 20205381, 20205382, 20205383, 20205384, 20205385, 20205386, 20205387, 20205388, 20205389, 20205390, 20205391, 20205392, 20205393, 20205394, 20207687, 20207688, 20207689, 20207690, 20207691, 20207692, 20207693, 20207694, 20207695, 20207696, 20207697, 20207698, 20207699, 20207700, 20207701, 20207702, 20207703, 20207704, 20207705, 20207706, 20207707, 20207708, 20207709, 20207710, 20207711, 20207712, 20207713, 20207714, 20207715, 20207716, 20207717, 20207718, 20207719, 20207720, 20207721, 20207722, 20207723, 20207724, 20207725, 20207726, 20207727, 20207728, 20207729, 20207730, 20207731, 20207732, 20207733, 20207734, 20207735, 20207736, 20207737, 20207738, 20207739, 20207740, 20207741, 20207742, 20207743, 20207744, 20207745, 20207746, 20207747, 20207748, 20207749, 20207750, 20207751, 20207752, 20207753, 20207754, 20207755, 20207756, 20210053, 20210054, 20210055, 20210056, 20210057, 20210058, 20210059, 20210060, 20210061, 20210062, 20210063, 20210064, 20210065, 20210066, 20210067, 20210068, 20210069, 20210070, 20210071, 20210072, 20210073, 20210074, 20210075, 20210076, 20210077, 20210078, 20210079, 20210080, 20210081, 20210082, 20210083, 20210084, 20210085, 20210086, 20210087, 20210088, 20210089, 20210090, 20210091, 20210092, 20210093, 20210094, 20210095, 20210096, 20210097, 20210098, 20210099, 20210100, 20210101, 20210102, 20210103, 20210104, 20210105, 20210106, 20210107, 20210108, 20210109, 20210110, 20210111, 20210112, 20210113, 20210114, 20210115, 20210116, 20210117, 20210118, 20210119, 20210120, 20210121, 20210122, 20212419, 20212420, 20212421, 20212422, 20212423, 20212424, 20212425, 20212426, 20212427, 20212428, 20212429, 20212430, 20212431, 20212432, 20212433, 20212434, 20212435, 20212436, 20212437, 20212438, 20212439, 20212440, 20212441, 20212442, 20212443, 20212444, 20212445, 20212446, 20212447, 20212448, 20212449, 20212450, 20212451, 20212452, 20212453, 20212454, 20212455, 20212456, 20212457, 20212458, 20212459, 20212460, 20212461, 20212462, 20212463, 20212464, 20212465, 20212466, 20212467, 20212468, 20212469, 20212470, 20212471, 20212472, 20212473, 20212474, 20212475, 20212476, 20212477, 20212478, 20212479, 20212480, 20212481, 20212482, 20212483, 20212484, 20212485, 20212486, 20212487, 20212488, 20214785, 20214786, 20214787, 20214788, 20214789, 20214790, 20214791, 20214792, 20214793, 20214794, 20214795, 20214796, 20214797, 20214798, 20214799, 20214800, 20214801, 20214802, 20214803, 20214804, 20214805, 20214806, 20214807, 20214808, 20214809, 20214810, 20214811, 20214812, 20214813, 20214814, 20214815, 20214816, 20214817, 20214818, 20214819, 20214820, 20214821, 20214822, 20214823, 20214824, 20214825, 20214826, 20214827, 20214828, 20214829, 20214830, 20214831, 20214832, 20214833, 20214834, 20214835, 20214836, 20214837, 20214838, 20214839, 20214840, 20214841, 20214842, 20214843, 20214844, 20214845, 20214846, 20214847, 20214848, 20214849, 20214850, 20214851, 20214852, 20214853, 20214854, 20200669, 20200670, 20200671, 20200672, 20200673, 20200674, 20200675, 20200676, 20200677, 20200678, 20200679, 20200680, 20200681, 20200682, 20200683, 20200684, 20200685, 20200686, 20200687, 20200688, 20200689, 20200690, 20200691, 20200692, 20200693, 20200694, 20200695, 20200696, 20200697, 20200698, 20200699, 20200700, 20200701, 20200702, 20200703, 20200704, 20200705, 20200706, 20200707, 20200708, 20200709, 20200710, 20200711, 20200712, 20200713, 20200714, 20200715, 20200716, 20200717, 20200718, 20200719, 20200720, 20200721, 20200722, 20200723, 20200724, 20200725, 20200726, 20200727, 20200728, 20200729, 20200730, 20200731, 20200732, 20200733, 20200734, 20200735, 20200736, 20200737, 20200738, 20200739, 20200740, 20200741, 20200742, 20200743, 20200744, 20200745, 20200746, 20200747, 20200748, 20200749, 20200750, 20200751, 20200752, 20200753, 20200754, 20200755, 20200756, 20200757, 20200758, 20200759, 20200760, 20200761, 20200762, 20200763, 20200764, 20200765, 20200766, 20200767, 20200768, 20200769, 20200770, 20200771, 20200772, 20200773, 20200774, 20200775, 20200776, 20200777, 20200778, 20200779, 20200780, 20200781, 20200782, 20200783, 20200784, 20200785, 20200786, 20200787, 20200788, 20200789, 20200790, 20200791, 20200792, 20200793, 20200794, 20200795, 20200796, 20200797, 20200798, 20200799, 20200800, 20200801, 20200802, 20200803, 20200804, 20200805, 20200806, 20200807, 20200808, 20200809, 20200810, 20200811, 20200812, 20200813, 20200814, 20200815, 20200816, 20200817, 20200818, 20200819, 20200820, 20200821, 20200822, 20200823, 20200824, 20200825, 20200826, 20200827, 20200828, 20200829, 20200830, 20200831, 20200832, 20200833, 20200834, 20200835, 20200836, 20200837, 20200838, 20200839, 20200840, 20200841, 20200842, 20200843, 20200844, 20200845, 20200846, 20200847, 20200848, 20200849, 20200850, 20200851, 20200852, 20200853, 20200854, 20200855, 20200856, 20200857, 20200858, 20200859, 20200860, 20200861, 20200862, 20200863, 20200864, 20200865, 20200866, 20200867, 20200868, 20200869, 20200870, 20200871, 20200872, 20200873, 20200874, 20200875, 20200876, 20200877, 20200878, 20200879, 20200880, 20200881, 20200882, 20200883, 20200884, 20200885, 20200886, 20200887, 20200888, 20200889, 20200890, 20200891, 20200892, 20200893, 20200894, 20200895, 20200896, 20200897, 20200898, 20200899, 20200900, 20200901, 20200902, 20200903, 20200904, 20200905, 20200906, 20200907, 20200908, 20200909, 20200910, 20200911, 20200912, 20200913, 20200914, 20200915, 20200916, 20200917, 20200918, 20200919, 20200920, 20200921, 20200922, 20200923, 20200924, 20200925, 20200926, 20200927, 20200928, 20200929, 20200930, 20200931, 20200932, 20200933, 20200934, 20200935, 20200936, 20200937, 20200938, 20200939, 20200940, 20200941, 20200942, 20200943, 20200944, 20200945, 20200946, 20200947, 20200948, 20200949, 20200950, 20200951, 20200952, 20200953, 20200954, 20200955, 20200956, 20200957, 20200958, 20200959, 20200960, 20200961, 20200962, 20200963, 20200964, 20200965, 20200966, 20200967, 20200968, 20200969, 20200970, 20200971, 20200972, 20200973, 20203031, 20203032, 20203033, 20203034, 20203035, 20203036, 20203037, 20203038, 20203039, 20203040, 20203041, 20203042, 20203043, 20203044, 20203045, 20203046, 20203047, 20203048, 20203049, 20203050, 20203051, 20203052, 20203053, 20203054, 20203055, 20203056, 20203057, 20203058, 20203059, 20203060, 20203061, 20203062, 20203063, 20203064, 20203065, 20203066, 20203067, 20203068, 20203069, 20203070, 20203071, 20203072, 20203073, 20203074, 20203075, 20203076, 20203077, 20203078, 20203079, 20203080, 20203081, 20203082, 20203083, 20203084, 20203085, 20203086, 20203087, 20203088, 20203089, 20203090, 20203091, 20203092, 20203093, 20203094, 20203095, 20203096, 20203097, 20203098, 20203099, 20203100, 20203101, 20203102, 20203103, 20203104, 20203105, 20203106, 20203107, 20203108, 20203109, 20203110, 20203111, 20203112, 20203113, 20203114, 20203115, 20203116, 20203117, 20203118, 20203119, 20203120, 20203121, 20203122, 20203123, 20203124, 20203125, 20203126, 20203127, 20203128, 20203129, 20203130, 20203131, 20203132, 20203133, 20203134, 20203135, 20203136, 20203137, 20203138, 20203139, 20203140, 20203141, 20203142, 20203143, 20203144, 20203145, 20203146, 20203147, 20203148, 20203149, 20203150, 20203151, 20203152, 20203153, 20203154, 20203155, 20203156, 20203157, 20203158, 20203159, 20203160, 20203161, 20203162, 20203163, 20203164, 20203165, 20203166, 20203167, 20203168, 20203169, 20203170, 20203171, 20203172, 20203173, 20203174, 20203175, 20203176, 20203177, 20203178, 20203179, 20203180, 20203181, 20203182, 20203183, 20203184, 20203185, 20203186, 20203187, 20203188, 20203189, 20203190, 20203191, 20203192, 20203193, 20203194, 20203195, 20203196, 20203197, 20203198, 20203199, 20203200, 20203201, 20203202, 20203203, 20203204, 20203205, 20203206, 20203207, 20203208, 20203209, 20203210, 20203211, 20203212, 20203213, 20203214, 20203215, 20203216, 20203217, 20203218, 20203219, 20203220, 20203221, 20203222, 20203223, 20203224, 20203225, 20203226, 20203227, 20203228, 20203229, 20203230, 20203231, 20203232, 20203233, 20203234, 20203235, 20203236, 20203237, 20203238, 20203239, 20203240, 20203241, 20203242, 20203243, 20203244, 20203245, 20203246, 20203247, 20203248, 20203249, 20203250, 20203251, 20203252, 20203253, 20203254, 20203255, 20203256, 20203257, 20203258, 20203259, 20203260, 20203261, 20203262, 20203263, 20203264, 20203265, 20203266, 20203267, 20203268, 20203269, 20203270, 20203271, 20203272, 20203273, 20203274, 20203275, 20203276, 20203277, 20203278, 20203279, 20203280, 20203281, 20203282, 20203283, 20203284, 20203285, 20203286, 20203287, 20203288, 20203289, 20203290, 20203291, 20203292, 20203293, 20203294, 20203295, 20203296, 20203297, 20203298, 20203299, 20203300, 20203301, 20203302, 20203303, 20203304, 20203305, 20203306, 20203307, 20203308, 20203309, 20203310, 20203311, 20203312, 20203313, 20203314, 20203315, 20203316, 20203317, 20203318, 20203319, 20203320, 20203321, 20203322, 20203323, 20203324, 20203325, 20203326, 20203327, 20203328, 20203329, 20203330, 20203331, 20203332, 20203333, 20203334, 20203335, 20203336, 20205395, 20205396, 20205397, 20205398, 20205399, 20205400, 20205401, 20205402, 20205403, 20205404, 20205405, 20205406, 20205407, 20205408, 20205409, 20205410, 20205411, 20205412, 20205413, 20205414, 20205415, 20205416, 20205417, 20205418, 20205419, 20205420, 20205421, 20205422, 20205423, 20205424, 20205425, 20205426, 20205427, 20205428, 20205429, 20205430, 20205431, 20205432, 20205433, 20205434, 20205435, 20205436, 20205437, 20205438, 20205439, 20205440, 20205441, 20205442, 20205443, 20205444, 20205445, 20205446, 20205447, 20205448, 20205449, 20205450, 20205451, 20205452, 20205453, 20205454, 20205455, 20205456, 20205457, 20205458, 20205459, 20205460, 20205461, 20205462, 20205463, 20205464, 20205465, 20205466, 20205467, 20205468, 20205469, 20205470, 20205471, 20205472, 20205473, 20205474, 20205475, 20205476, 20205477, 20205478, 20205479, 20205480, 20205481, 20205482, 20205483, 20205484, 20205485, 20205486, 20205487, 20205488, 20205489, 20205490, 20205491, 20205492, 20205493, 20205494, 20205495, 20205496, 20205497, 20205498, 20205499, 20205500, 20205501, 20205502, 20205503, 20205504, 20205505, 20205506, 20205507, 20205508, 20205509, 20205510, 20205511, 20205512, 20205513, 20205514, 20205515, 20205516, 20205517, 20205518, 20205519, 20205520, 20205521, 20205522, 20205523, 20205524, 20205525, 20205526, 20205527, 20205528, 20205529, 20205530, 20205531, 20205532, 20205533, 20205534, 20205535, 20205536, 20205537, 20205538, 20205539, 20205540, 20205541, 20205542, 20205543, 20205544, 20205545, 20205546, 20205547, 20205548, 20205549, 20205550, 20205551, 20205552, 20205553, 20205554, 20205555, 20205556, 20205557, 20205558, 20205559, 20205560, 20205561, 20205562, 20205563, 20205564, 20205565, 20205566, 20205567, 20205568, 20205569, 20205570, 20205571, 20205572, 20205573, 20205574, 20205575, 20205576, 20205577, 20205578, 20205579, 20205580, 20205581, 20205582, 20205583, 20205584, 20205585, 20205586, 20205587, 20205588, 20205589, 20205590, 20205591, 20205592, 20205593, 20205594, 20205595, 20205596, 20205597, 20205598, 20205599, 20205600, 20205601, 20205602, 20205603, 20205604, 20205605, 20205606, 20205607, 20205608, 20205609, 20205610, 20205611, 20205612, 20205613, 20205614, 20205615, 20205616, 20205617, 20205618, 20205619, 20205620, 20205621, 20205622, 20205623, 20205624, 20205625, 20205626, 20205627, 20205628, 20205629, 20205630, 20205631, 20205632, 20205633, 20205634, 20205635, 20205636, 20205637, 20205638, 20205639, 20205640, 20205641, 20205642, 20205643, 20205644, 20205645, 20205646, 20205647, 20205648, 20205649, 20205650, 20205651, 20205652, 20205653, 20205654, 20205655, 20205656, 20205657, 20205658, 20205659, 20205660, 20205661, 20205662, 20205663, 20205664, 20205665, 20205666, 20205667, 20205668, 20205669, 20205670, 20205671, 20205672, 20205673, 20205674, 20205675, 20205676, 20205677, 20205678, 20205679, 20205680, 20205681, 20205682, 20205683, 20205684, 20205685, 20205686, 20205687, 20205688, 20205689, 20205690, 20205691, 20205692, 20205693, 20205694, 20205695, 20205696, 20205697, 20205698, 20205699, 20207757, 20207758, 20207759, 20207760, 20207761, 20207762, 20207763, 20207764, 20207765, 20207766, 20207767, 20207768, 20207769, 20207770, 20207771, 20207772, 20207773, 20207774, 20207775, 20207776, 20207777, 20207778, 20207779, 20207780, 20207781, 20207782, 20207783, 20207784, 20207785, 20207786, 20207787, 20207788, 20207789, 20207790, 20207791, 20207792, 20207793, 20207794, 20207795, 20207796, 20207797, 20207798, 20207799, 20207800, 20207801, 20207802, 20207803, 20207804, 20207805, 20207806, 20207807, 20207808, 20207809, 20207810, 20207811, 20207812, 20207813, 20207814, 20207815, 20207816, 20207817, 20207818, 20207819, 20207820, 20207821, 20207822, 20207823, 20207824, 20207825, 20207826, 20207827, 20207828, 20207829, 20207830, 20207831, 20207832, 20207833, 20207834, 20207835, 20207836, 20207837, 20207838, 20207839, 20207840, 20207841, 20207842, 20207843, 20207844, 20207845, 20207846, 20207847, 20207848, 20207849, 20207850, 20207851, 20207852, 20207853, 20207854, 20207855, 20207856, 20207857, 20207858, 20207859, 20207860, 20207861, 20207862, 20207863, 20207864, 20207865, 20207866, 20207867, 20207868, 20207869, 20207870, 20207871, 20207872, 20207873, 20207874, 20207875, 20207876, 20207877, 20207878, 20207879, 20207880, 20207881, 20207882, 20207883, 20207884, 20207885, 20207886, 20207887, 20207888, 20207889, 20207890, 20207891, 20207892, 20207893, 20207894, 20207895, 20207896, 20207897, 20207898, 20207899, 20207900, 20207901, 20207902, 20207903, 20207904, 20207905, 20207906, 20207907, 20207908, 20207909, 20207910, 20207911, 20207912, 20207913, 20207914, 20207915, 20207916, 20207917, 20207918, 20207919, 20207920, 20207921, 20207922, 20207923, 20207924, 20207925, 20207926, 20207927, 20207928, 20207929, 20207930, 20207931, 20207932, 20207933, 20207934, 20207935, 20207936, 20207937, 20207938, 20207939, 20207940, 20207941, 20207942, 20207943, 20207944, 20207945, 20207946, 20207947, 20207948, 20207949, 20207950, 20207951, 20207952, 20207953, 20207954, 20207955, 20207956, 20207957, 20207958, 20207959, 20207960, 20207961, 20207962, 20207963, 20207964, 20207965, 20207966, 20207967, 20207968, 20207969, 20207970, 20207971, 20207972, 20207973, 20207974, 20207975, 20207976, 20207977, 20207978, 20207979, 20207980, 20207981, 20207982, 20207983, 20207984, 20207985, 20207986, 20207987, 20207988, 20207989, 20207990, 20207991, 20207992, 20207993, 20207994, 20207995, 20207996, 20207997, 20207998, 20207999, 20208000, 20208001, 20208002, 20208003, 20208004, 20208005, 20208006, 20208007, 20208008, 20208009, 20208010, 20208011, 20208012, 20208013, 20208014, 20208015, 20208016, 20208017, 20208018, 20208019, 20208020, 20208021, 20208022, 20208023, 20208024, 20208025, 20208026, 20208027, 20208028, 20208029, 20208030, 20208031, 20208032, 20208033, 20208034, 20208035, 20208036, 20208037, 20208038, 20208039, 20208040, 20208041, 20208042, 20208043, 20208044, 20208045, 20208046, 20208047, 20208048, 20208049, 20208050, 20208051, 20208052, 20208053, 20208054, 20208055, 20208056, 20208057, 20208058, 20208059, 20208060, 20208061, 20208062, 20210123, 20210124, 20210125, 20210126, 20210127, 20210128, 20210129, 20210130, 20210131, 20210132, 20210133, 20210134, 20210135, 20210136, 20210137, 20210138, 20210139, 20210140, 20210141, 20210142, 20210143, 20210144, 20210145, 20210146, 20210147, 20210148, 20210149, 20210150, 20210151, 20210152, 20210153, 20210154, 20210155, 20210156, 20210157, 20210158, 20210159, 20210160, 20210161, 20210162, 20210163, 20210164, 20210165, 20210166, 20210167, 20210168, 20210169, 20210170, 20210171, 20210172, 20210173, 20210174, 20210175, 20210176, 20210177, 20210178, 20210179, 20210180, 20210181, 20210182, 20210183, 20210184, 20210185, 20210186, 20210187, 20210188, 20210189, 20210190, 20210191, 20210192, 20210193, 20210194, 20210195, 20210196, 20210197, 20210198, 20210199, 20210200, 20210201, 20210202, 20210203, 20210204, 20210205, 20210206, 20210207, 20210208, 20210209, 20210210, 20210211, 20210212, 20210213, 20210214, 20210215, 20210216, 20210217, 20210218, 20210219, 20210220, 20210221, 20210222, 20210223, 20210224, 20210225, 20210226, 20210227, 20210228, 20210229, 20210230, 20210231, 20210232, 20210233, 20210234, 20210235, 20210236, 20210237, 20210238, 20210239, 20210240, 20210241, 20210242, 20210243, 20210244, 20210245, 20210246, 20210247, 20210248, 20210249, 20210250, 20210251, 20210252, 20210253, 20210254, 20210255, 20210256, 20210257, 20210258, 20210259, 20210260, 20210261, 20210262, 20210263, 20210264, 20210265, 20210266, 20210267, 20210268, 20210269, 20210270, 20210271, 20210272, 20210273, 20210274, 20210275, 20210276, 20210277, 20210278, 20210279, 20210280, 20210281, 20210282, 20210283, 20210284, 20210285, 20210286, 20210287, 20210288, 20210289, 20210290, 20210291, 20210292, 20210293, 20210294, 20210295, 20210296, 20210297, 20210298, 20210299, 20210300, 20210301, 20210302, 20210303, 20210304, 20210305, 20210306, 20210307, 20210308, 20210309, 20210310, 20210311, 20210312, 20210313, 20210314, 20210315, 20210316, 20210317, 20210318, 20210319, 20210320, 20210321, 20210322, 20210323, 20210324, 20210325, 20210326, 20210327, 20210328, 20210329, 20210330, 20210331, 20210332, 20210333, 20210334, 20210335, 20210336, 20210337, 20210338, 20210339, 20210340, 20210341, 20210342, 20210343, 20210344, 20210345, 20210346, 20210347, 20210348, 20210349, 20210350, 20210351, 20210352, 20210353, 20210354, 20210355, 20210356, 20210357, 20210358, 20210359, 20210360, 20210361, 20210362, 20210363, 20210364, 20210365, 20210366, 20210367, 20210368, 20210369, 20210370, 20210371, 20210372, 20210373, 20210374, 20210375, 20210376, 20210377, 20210378, 20210379, 20210380, 20210381, 20210382, 20210383, 20210384, 20210385, 20210386, 20210387, 20210388, 20210389, 20210390, 20210391, 20210392, 20210393, 20210394, 20210395, 20210396, 20210397, 20210398, 20210399, 20210400, 20210401, 20210402, 20210403, 20210404, 20210405, 20210406, 20210407, 20210408, 20210409, 20210410, 20210411, 20210412, 20210413, 20210414, 20210415, 20210416, 20210417, 20210418, 20210419, 20210420, 20210421, 20210422, 20210423, 20210424, 20210425, 20210426, 20210427, 20212489, 20212490, 20212491, 20212492, 20212493, 20212494, 20212495, 20212496, 20212497, 20212498, 20212499, 20212500, 20212501, 20212502, 20212503, 20212504, 20212505, 20212506, 20212507, 20212508, 20212509, 20212510, 20212511, 20212512, 20212513, 20212514, 20212515, 20212516, 20212517, 20212518, 20212519, 20212520, 20212521, 20212522, 20212523, 20212524, 20212525, 20212526, 20212527, 20212528, 20212529, 20212530, 20212531, 20212532, 20212533, 20212534, 20212535, 20212536, 20212537, 20212538, 20212539, 20212540, 20212541, 20212542, 20212543, 20212544, 20212545, 20212546, 20212547, 20212548, 20212549, 20212550, 20212551, 20212552, 20212553, 20212554, 20212555, 20212556, 20212557, 20212558, 20212559, 20212560, 20212561, 20212562, 20212563, 20212564, 20212565, 20212566, 20212567, 20212568, 20212569, 20212570, 20212571, 20212572, 20212573, 20212574, 20212575, 20212576, 20212577, 20212578, 20212579, 20212580, 20212581, 20212582, 20212583, 20212584, 20212585, 20212586, 20212587, 20212588, 20212589, 20212590, 20212591, 20212592, 20212593, 20212594, 20212595, 20212596, 20212597, 20212598, 20212599, 20212600, 20212601, 20212602, 20212603, 20212604, 20212605, 20212606, 20212607, 20212608, 20212609, 20212610, 20212611, 20212612, 20212613, 20212614, 20212615, 20212616, 20212617, 20212618, 20212619, 20212620, 20212621, 20212622, 20212623, 20212624, 20212625, 20212626, 20212627, 20212628, 20212629, 20212630, 20212631, 20212632, 20212633, 20212634, 20212635, 20212636, 20212637, 20212638, 20212639, 20212640, 20212641, 20212642, 20212643, 20212644, 20212645, 20212646, 20212647, 20212648, 20212649, 20212650, 20212651, 20212652, 20212653, 20212654, 20212655, 20212656, 20212657, 20212658, 20212659, 20212660, 20212661, 20212662, 20212663, 20212664, 20212665, 20212666, 20212667, 20212668, 20212669, 20212670, 20212671, 20212672, 20212673, 20212674, 20212675, 20212676, 20212677, 20212678, 20212679, 20212680, 20212681, 20212682, 20212683, 20212684, 20212685, 20212686, 20212687, 20212688, 20212689, 20212690, 20212691, 20212692, 20212693, 20212694, 20212695, 20212696, 20212697, 20212698, 20212699, 20212700, 20212701, 20212702, 20212703, 20212704, 20212705, 20212706, 20212707, 20212708, 20212709, 20212710, 20212711, 20212712, 20212713, 20212714, 20212715, 20212716, 20212717, 20212718, 20212719, 20212720, 20212721, 20212722, 20212723, 20212724, 20212725, 20212726, 20212727, 20212728, 20212729, 20212730, 20212731, 20212732, 20212733, 20212734, 20212735, 20212736, 20212737, 20212738, 20212739, 20212740, 20212741, 20212742, 20212743, 20212744, 20212745, 20212746, 20212747, 20212748, 20212749, 20212750, 20212751, 20212752, 20212753, 20212754, 20212755, 20212756, 20212757, 20212758, 20212759, 20212760, 20212761, 20212762, 20212763, 20212764, 20212765, 20212766, 20212767, 20212768, 20212769, 20212770, 20212771, 20212772, 20212773, 20212774, 20212775, 20212776, 20212777, 20212778, 20212779, 20212780, 20212781, 20212782, 20212783, 20212784, 20212785, 20212786, 20212787, 20212788, 20212789, 20212790, 20212791, 20212792, 20212793, 20212794, 20214855, 20214856, 20214857, 20214858, 20214859, 20214860, 20214861, 20214862, 20214863, 20214864, 20214865, 20214866, 20214867, 20214868, 20214869, 20214870, 20214871, 20214872, 20214873, 20214874, 20214875, 20214876, 20214877, 20214878, 20214879, 20214880, 20214881, 20214882, 20214883, 20214884, 20214885, 20214886, 20214887, 20214888, 20214889, 20214890, 20214891, 20214892, 20214893, 20214894, 20214895, 20214896, 20214897, 20214898, 20214899, 20214900, 20214901, 20214902, 20214903, 20214904, 20214905, 20214906, 20214907, 20214908, 20214909, 20214910, 20214911, 20214912, 20214913, 20214914, 20214915, 20214916, 20214917, 20214918, 20214919, 20214920, 20214921, 20214922, 20214923, 20214924, 20214925, 20214926, 20214927, 20214928, 20214929, 20214930, 20214931, 20214932, 20214933, 20214934, 20214935, 20214936, 20214937, 20214938, 20214939, 20214940, 20214941, 20214942, 20214943, 20214944, 20214945, 20214946, 20214947, 20214948, 20214949, 20214950, 20214951, 20214952, 20214953, 20214954, 20214955, 20214956, 20214957, 20214958, 20214959, 20214960, 20214961, 20214962, 20214963, 20214964, 20214965, 20214966, 20214967, 20214968, 20214969, 20214970, 20214971, 20214972, 20214973, 20214974, 20214975, 20214976, 20214977, 20214978, 20214979, 20214980, 20214981, 20214982, 20214983, 20214984, 20214985, 20214986, 20214987, 20214988, 20214989, 20214990, 20214991, 20214992, 20214993, 20214994, 20214995, 20214996, 20214997, 20214998, 20214999, 20215000, 20215001, 20215002, 20215003, 20215004, 20215005, 20215006, 20215007, 20215008, 20215009, 20215010, 20215011, 20215012, 20215013, 20215014, 20215015, 20215016, 20215017, 20215018, 20215019, 20215020, 20215021, 20215022, 20215023, 20215024, 20215025, 20215026, 20215027, 20215028, 20215029, 20215030, 20215031, 20215032, 20215033, 20215034, 20215035, 20215036, 20215037, 20215038, 20215039, 20215040, 20215041, 20215042, 20215043, 20215044, 20215045, 20215046, 20215047, 20215048, 20215049, 20215050, 20215051, 20215052, 20215053, 20215054, 20215055, 20215056, 20215057, 20215058, 20215059, 20215060, 20215061, 20215062, 20215063, 20215064, 20215065, 20215066, 20215067, 20215068, 20215069, 20215070, 20215071, 20215072, 20215073, 20215074, 20215075, 20215076, 20215077, 20215078, 20215079, 20215080, 20215081, 20215082, 20215083, 20215084, 20215085, 20215086, 20215087, 20215088, 20215089, 20215090, 20215091, 20215092, 20215093, 20215094, 20215095, 20215096, 20215097, 20215098, 20215099, 20215100, 20215101, 20215102, 20215103, 20215104, 20215105, 20215106, 20215107, 20215108, 20215109, 20215110, 20215111, 20215112, 20215113, 20215114, 20215115, 20215116, 20215117, 20215118, 20215119, 20215120, 20215121, 20215122, 20215123, 20215124, 20215125, 20215126, 20215127, 20215128, 20215129, 20215130, 20215131, 20215132, 20215133, 20215134, 20215135, 20215136, 20215137, 20215138, 20215139, 20215140, 20215141, 20215142, 20215143, 20215144, 20215145, 20215146, 20215147, 20215148, 20215149, 20215150, 20215151, 20215152, 20215153, 20215154, 20215155, 20215156, 20215157, 20215158, 20215159, 20198610, 20198611, 20198612, 20198613, 20198614, 20198615, 20198616, 20198617, 20198618, 20198619, 20198620, 20198621, 20198622, 20198623, 20198624, 20198625, 20198626, 20198627, 20198628, 20198629, 20198630, 20198631, 20198632, 20198633, 20198634, 20198635, 20198636, 20198637, 20198638, 20198639, 20198640, 20198641, 20198642, 20198643, 20198644, 20198645, 20198646, 20198647, 20198648, 20198649, 20198650, 20198651, 20198652, 20198653, 20198654, 20200974, 20200975, 20200976, 20200977, 20200978, 20200979, 20200980, 20200981, 20200982, 20200983, 20200984, 20200985, 20200986, 20200987, 20200988, 20200989, 20200990, 20200991, 20200992, 20200993, 20200994, 20200995, 20200996, 20200997, 20200998, 20200999, 20201000, 20201001, 20201002, 20201003, 20201004, 20201005, 20201006, 20201007, 20201008, 20201009, 20201010, 20201011, 20201012, 20201013, 20201014, 20201015, 20201016, 20201017, 20201018, 20201019, 20203337, 20203338, 20203339, 20203340, 20203341, 20203342, 20203343, 20203344, 20203345, 20203346, 20203347, 20203348, 20203349, 20203350, 20203351, 20203352, 20203353, 20203354, 20203355, 20203356, 20203357, 20203358, 20203359, 20203360, 20203361, 20203362, 20203363, 20203364, 20203365, 20203366, 20203367, 20203368, 20203369, 20203370, 20203371, 20203372, 20203373, 20203374, 20203375, 20203376, 20203377, 20203378, 20203379, 20203380, 20203381, 20203382, 20205700, 20205701, 20205702, 20205703, 20205704, 20205705, 20205706, 20205707, 20205708, 20205709, 20205710, 20205711, 20205712, 20205713, 20205714, 20205715, 20205716, 20205717, 20205718, 20205719, 20205720, 20205721, 20205722, 20205723, 20205724, 20205725, 20205726, 20205727, 20205728, 20205729, 20205730, 20205731, 20205732, 20205733, 20205734, 20205735, 20205736, 20205737, 20205738, 20205739, 20205740, 20205741, 20205742, 20205743, 20205744, 20205745, 20208063, 20208064, 20208065, 20208066, 20208067, 20208068, 20208069, 20208070, 20208071, 20208072, 20208073, 20208074, 20208075, 20208076, 20208077, 20208078, 20208079, 20208080, 20208081, 20208082, 20208083, 20208084, 20208085, 20208086, 20208087, 20208088, 20208089, 20208090, 20208091, 20208092, 20208093, 20208094, 20208095, 20208096, 20208097, 20208098, 20208099, 20208100, 20208101, 20208102, 20208103, 20208104, 20208105, 20208106, 20208107, 20208108, 20210428, 20210429, 20210430, 20210431, 20210432, 20210433, 20210434, 20210435, 20210436, 20210437, 20210438, 20210439, 20210440, 20210441, 20210442, 20210443, 20210444, 20210445, 20210446, 20210447, 20210448, 20210449, 20210450, 20210451, 20210452, 20210453, 20210454, 20210455, 20210456, 20210457, 20210458, 20210459, 20210460, 20210461, 20210462, 20210463, 20210464, 20210465, 20210466, 20210467, 20210468, 20210469, 20210470, 20210471, 20210472, 20210473, 20212795, 20212796, 20212797, 20212798, 20212799, 20212800, 20212801, 20212802, 20212803, 20212804, 20212805, 20212806, 20212807, 20212808, 20212809, 20212810, 20212811, 20212812, 20212813, 20212814, 20212815, 20212816, 20212817, 20212818, 20212819, 20212820, 20212821, 20212822, 20212823, 20212824, 20212825, 20212826, 20212827, 20212828, 20212829, 20212830, 20212831, 20212832, 20212833, 20212834, 20212835, 20212836, 20212837, 20212838, 20212839, 20212840, 20215160, 20215161, 20215162, 20215163, 20215164, 20215165, 20215166, 20215167, 20215168, 20215169, 20215170, 20215171, 20215172, 20215173, 20215174, 20215175, 20215176, 20215177, 20215178, 20215179, 20215180, 20215181, 20215182, 20215183, 20215184, 20215185, 20215186, 20215187, 20215188, 20215189, 20215190, 20215191, 20215192, 20215193, 20215194, 20215195, 20215196, 20215197, 20215198, 20215199, 20215200, 20215201, 20215202, 20215203, 20215204, 20215205, 20203383, 20203384, 20203385, 20203386, 20203387, 20203388, 20203389, 20203390, 20203391, 20203392, 20203393, 20203394, 20203395, 20203396, 20205746, 20205747, 20205748, 20205749, 20205750, 20205751, 20205752, 20205753, 20205754, 20205755, 20205756, 20205757, 20205758, 20205759, 20208109, 20208110, 20208111, 20208112, 20208113, 20208114, 20208115, 20208116, 20208117, 20208118, 20208119, 20208120, 20208121, 20208122, 20210474, 20210475, 20210476, 20210477, 20210478, 20210479, 20210480, 20210481, 20210482, 20210483, 20210484, 20210485, 20210486, 20210487, 20212841, 20212842, 20212843, 20212844, 20212845, 20212846, 20212847, 20212848, 20212849, 20212850, 20212851, 20212852, 20212853, 20212854, 20215206, 20215207, 20215208, 20215209, 20215210, 20215211, 20215212, 20215213, 20215214, 20215215, 20215216, 20215217, 20215218, 20215219, 20201070, 20201071, 20201072, 20201073, 20201074, 20201075, 20201076, 20201077, 20201078, 20201079, 20201080, 20201081, 20201082, 20201083, 20201084, 20201085, 20201086, 20201087, 20201088, 20201089, 20201090, 20201091, 20201092, 20201093, 20201094, 20201095, 20201096, 20201097, 20201098, 20201099, 20201100, 20201101, 20201102, 20201103, 20201104, 20201105, 20201106, 20201107, 20201108, 20201109, 20201110, 20201111, 20201112, 20201113, 20201114, 20201115, 20201116, 20201117, 20201118, 20201119, 20201120, 20201121, 20201122, 20201123, 20201124, 20201125, 20201126, 20201127, 20201128, 20201129, 20201130, 20201131, 20201132, 20201133, 20201134, 20201135, 20201136, 20201137, 20201138, 20201139, 20201140, 20201141, 20201142, 20201143, 20201144, 20201145, 20201146, 20201147, 20201148, 20201149, 20201150, 20201151, 20201152, 20201153, 20201154, 20201155, 20201156, 20201157, 20201158, 20201159, 20201160, 20201161, 20201162, 20201163, 20201164, 20201165, 20201166, 20201167, 20201168, 20201169, 20201170, 20201171, 20201172, 20201173, 20201174, 20201175, 20201176, 20201177, 20201178, 20201179, 20201180, 20201181, 20201182, 20201183, 20201184, 20201185, 20201186, 20201187, 20201188, 20201189, 20201190, 20201191, 20201192, 20201193, 20201194, 20201195, 20201196, 20201197, 20201198, 20201199, 20201200, 20201201, 20201202, 20201203, 20201204, 20201205, 20201206, 20201207, 20201208, 20201209, 20201210, 20201211, 20201212, 20201213, 20201214, 20201215, 20201216, 20201217, 20201218, 20201219, 20201220, 20201221, 20201222, 20201223, 20201224, 20201225, 20201226, 20201227, 20201228, 20201229, 20201230, 20201231, 20201232, 20201233, 20201234, 20201235, 20201236, 20201237, 20201238, 20201239, 20201240, 20201241, 20201242, 20201243, 20201244, 20201245, 20201246, 20201247, 20201248, 20201249, 20201250, 20201251, 20201252, 20201253, 20201254, 20201255, 20201256, 20201257, 20201258, 20201259, 20201260, 20201261, 20201262, 20201263, 20201264, 20201265, 20201266, 20201267, 20201268, 20201269, 20201270, 20201271, 20201272, 20201273, 20201274, 20201275, 20201276, 20201277, 20201278, 20201279, 20201280, 20201281, 20201282, 20201283, 20201284, 20201285, 20201286, 20201287, 20201288, 20201289, 20201290, 20201291, 20201292, 20201293, 20201294, 20201295, 20201296, 20201297, 20201298, 20201299, 20201300, 20201301, 20201302, 20201303, 20201304, 20201305, 20201306, 20201307, 20201308, 20201309, 20201310, 20201311, 20201312, 20201313, 20201314, 20201315, 20201316, 20201317, 20201318, 20201319, 20201320, 20201321, 20201322, 20201323, 20201324, 20201325, 20201326, 20201327, 20201328, 20201329, 20201330, 20201331, 20201332, 20201333, 20201334, 20201335, 20201336, 20201337, 20201338, 20201339, 20201340, 20201341, 20201342, 20201343, 20201344, 20201345, 20201346, 20201347, 20201348, 20201349, 20201350, 20201351, 20201352, 20201353, 20201354, 20201355, 20201356, 20201357, 20201358, 20201359, 20201360, 20201361, 20201362, 20201363, 20201364, 20201365, 20201366, 20201367, 20201368, 20201369, 20201370, 20201371, 20201372, 20201373, 20201374, 20201375, 20201376, 20201377, 20201378, 20203433, 20203434, 20203435, 20203436, 20203437, 20203438, 20203439, 20203440, 20203441, 20203442, 20203443, 20203444, 20203445, 20203446, 20203447, 20203448, 20203449, 20203450, 20203451, 20203452, 20203453, 20203454, 20203455, 20203456, 20203457, 20203458, 20203459, 20203460, 20203461, 20203462, 20203463, 20203464, 20203465, 20203466, 20203467, 20203468, 20203469, 20203470, 20203471, 20203472, 20203473, 20203474, 20203475, 20203476, 20203477, 20203478, 20203479, 20203480, 20203481, 20203482, 20203483, 20203484, 20203485, 20203486, 20203487, 20203488, 20203489, 20203490, 20203491, 20203492, 20203493, 20203494, 20203495, 20203496, 20203497, 20203498, 20203499, 20203500, 20203501, 20203502, 20203503, 20203504, 20203505, 20203506, 20203507, 20203508, 20203509, 20203510, 20203511, 20203512, 20203513, 20203514, 20203515, 20203516, 20203517, 20203518, 20203519, 20203520, 20203521, 20203522, 20203523, 20203524, 20203525, 20203526, 20203527, 20203528, 20203529, 20203530, 20203531, 20203532, 20203533, 20203534, 20203535, 20203536, 20203537, 20203538, 20203539, 20203540, 20203541, 20203542, 20203543, 20203544, 20203545, 20203546, 20203547, 20203548, 20203549, 20203550, 20203551, 20203552, 20203553, 20203554, 20203555, 20203556, 20203557, 20203558, 20203559, 20203560, 20203561, 20203562, 20203563, 20203564, 20203565, 20203566, 20203567, 20203568, 20203569, 20203570, 20203571, 20203572, 20203573, 20203574, 20203575, 20203576, 20203577, 20203578, 20203579, 20203580, 20203581, 20203582, 20203583, 20203584, 20203585, 20203586, 20203587, 20203588, 20203589, 20203590, 20203591, 20203592, 20203593, 20203594, 20203595, 20203596, 20203597, 20203598, 20203599, 20203600, 20203601, 20203602, 20203603, 20203604, 20203605, 20203606, 20203607, 20203608, 20203609, 20203610, 20203611, 20203612, 20203613, 20203614, 20203615, 20203616, 20203617, 20203618, 20203619, 20203620, 20203621, 20203622, 20203623, 20203624, 20203625, 20203626, 20203627, 20203628, 20203629, 20203630, 20203631, 20203632, 20203633, 20203634, 20203635, 20203636, 20203637, 20203638, 20203639, 20203640, 20203641, 20203642, 20203643, 20203644, 20203645, 20203646, 20203647, 20203648, 20203649, 20203650, 20203651, 20203652, 20203653, 20203654, 20203655, 20203656, 20203657, 20203658, 20203659, 20203660, 20203661, 20203662, 20203663, 20203664, 20203665, 20203666, 20203667, 20203668, 20203669, 20203670, 20203671, 20203672, 20203673, 20203674, 20203675, 20203676, 20203677, 20203678, 20203679, 20203680, 20203681, 20203682, 20203683, 20203684, 20203685, 20203686, 20203687, 20203688, 20203689, 20203690, 20203691, 20203692, 20203693, 20203694, 20203695, 20203696, 20203697, 20203698, 20203699, 20203700, 20203701, 20203702, 20203703, 20203704, 20203705, 20203706, 20203707, 20203708, 20203709, 20203710, 20203711, 20203712, 20203713, 20203714, 20203715, 20203716, 20203717, 20203718, 20203719, 20203720, 20203721, 20203722, 20203723, 20203724, 20203725, 20203726, 20203727, 20203728, 20203729, 20203730, 20203731, 20203732, 20203733, 20203734, 20203735, 20203736, 20203737, 20203738, 20203739, 20203740, 20203741, 20205796, 20205797, 20205798, 20205799, 20205800, 20205801, 20205802, 20205803, 20205804, 20205805, 20205806, 20205807, 20205808, 20205809, 20205810, 20205811, 20205812, 20205813, 20205814, 20205815, 20205816, 20205817, 20205818, 20205819, 20205820, 20205821, 20205822, 20205823, 20205824, 20205825, 20205826, 20205827, 20205828, 20205829, 20205830, 20205831, 20205832, 20205833, 20205834, 20205835, 20205836, 20205837, 20205838, 20205839, 20205840, 20205841, 20205842, 20205843, 20205844, 20205845, 20205846, 20205847, 20205848, 20205849, 20205850, 20205851, 20205852, 20205853, 20205854, 20205855, 20205856, 20205857, 20205858, 20205859, 20205860, 20205861, 20205862, 20205863, 20205864, 20205865, 20205866, 20205867, 20205868, 20205869, 20205870, 20205871, 20205872, 20205873, 20205874, 20205875, 20205876, 20205877, 20205878, 20205879, 20205880, 20205881, 20205882, 20205883, 20205884, 20205885, 20205886, 20205887, 20205888, 20205889, 20205890, 20205891, 20205892, 20205893, 20205894, 20205895, 20205896, 20205897, 20205898, 20205899, 20205900, 20205901, 20205902, 20205903, 20205904, 20205905, 20205906, 20205907, 20205908, 20205909, 20205910, 20205911, 20205912, 20205913, 20205914, 20205915, 20205916, 20205917, 20205918, 20205919, 20205920, 20205921, 20205922, 20205923, 20205924, 20205925, 20205926, 20205927, 20205928, 20205929, 20205930, 20205931, 20205932, 20205933, 20205934, 20205935, 20205936, 20205937, 20205938, 20205939, 20205940, 20205941, 20205942, 20205943, 20205944, 20205945, 20205946, 20205947, 20205948, 20205949, 20205950, 20205951, 20205952, 20205953, 20205954, 20205955, 20205956, 20205957, 20205958, 20205959, 20205960, 20205961, 20205962, 20205963, 20205964, 20205965, 20205966, 20205967, 20205968, 20205969, 20205970, 20205971, 20205972, 20205973, 20205974, 20205975, 20205976, 20205977, 20205978, 20205979, 20205980, 20205981, 20205982, 20205983, 20205984, 20205985, 20205986, 20205987, 20205988, 20205989, 20205990, 20205991, 20205992, 20205993, 20205994, 20205995, 20205996, 20205997, 20205998, 20205999, 20206000, 20206001, 20206002, 20206003, 20206004, 20206005, 20206006, 20206007, 20206008, 20206009, 20206010, 20206011, 20206012, 20206013, 20206014, 20206015, 20206016, 20206017, 20206018, 20206019, 20206020, 20206021, 20206022, 20206023, 20206024, 20206025, 20206026, 20206027, 20206028, 20206029, 20206030, 20206031, 20206032, 20206033, 20206034, 20206035, 20206036, 20206037, 20206038, 20206039, 20206040, 20206041, 20206042, 20206043, 20206044, 20206045, 20206046, 20206047, 20206048, 20206049, 20206050, 20206051, 20206052, 20206053, 20206054, 20206055, 20206056, 20206057, 20206058, 20206059, 20206060, 20206061, 20206062, 20206063, 20206064, 20206065, 20206066, 20206067, 20206068, 20206069, 20206070, 20206071, 20206072, 20206073, 20206074, 20206075, 20206076, 20206077, 20206078, 20206079, 20206080, 20206081, 20206082, 20206083, 20206084, 20206085, 20206086, 20206087, 20206088, 20206089, 20206090, 20206091, 20206092, 20206093, 20206094, 20206095, 20206096, 20206097, 20206098, 20206099, 20206100, 20206101, 20206102, 20206103, 20206104, 20208159, 20208160, 20208161, 20208162, 20208163, 20208164, 20208165, 20208166, 20208167, 20208168, 20208169, 20208170, 20208171, 20208172, 20208173, 20208174, 20208175, 20208176, 20208177, 20208178, 20208179, 20208180, 20208181, 20208182, 20208183, 20208184, 20208185, 20208186, 20208187, 20208188, 20208189, 20208190, 20208191, 20208192, 20208193, 20208194, 20208195, 20208196, 20208197, 20208198, 20208199, 20208200, 20208201, 20208202, 20208203, 20208204, 20208205, 20208206, 20208207, 20208208, 20208209, 20208210, 20208211, 20208212, 20208213, 20208214, 20208215, 20208216, 20208217, 20208218, 20208219, 20208220, 20208221, 20208222, 20208223, 20208224, 20208225, 20208226, 20208227, 20208228, 20208229, 20208230, 20208231, 20208232, 20208233, 20208234, 20208235, 20208236, 20208237, 20208238, 20208239, 20208240, 20208241, 20208242, 20208243, 20208244, 20208245, 20208246, 20208247, 20208248, 20208249, 20208250, 20208251, 20208252, 20208253, 20208254, 20208255, 20208256, 20208257, 20208258, 20208259, 20208260, 20208261, 20208262, 20208263, 20208264, 20208265, 20208266, 20208267, 20208268, 20208269, 20208270, 20208271, 20208272, 20208273, 20208274, 20208275, 20208276, 20208277, 20208278, 20208279, 20208280, 20208281, 20208282, 20208283, 20208284, 20208285, 20208286, 20208287, 20208288, 20208289, 20208290, 20208291, 20208292, 20208293, 20208294, 20208295, 20208296, 20208297, 20208298, 20208299, 20208300, 20208301, 20208302, 20208303, 20208304, 20208305, 20208306, 20208307, 20208308, 20208309, 20208310, 20208311, 20208312, 20208313, 20208314, 20208315, 20208316, 20208317, 20208318, 20208319, 20208320, 20208321, 20208322, 20208323, 20208324, 20208325, 20208326, 20208327, 20208328, 20208329, 20208330, 20208331, 20208332, 20208333, 20208334, 20208335, 20208336, 20208337, 20208338, 20208339, 20208340, 20208341, 20208342, 20208343, 20208344, 20208345, 20208346, 20208347, 20208348, 20208349, 20208350, 20208351, 20208352, 20208353, 20208354, 20208355, 20208356, 20208357, 20208358, 20208359, 20208360, 20208361, 20208362, 20208363, 20208364, 20208365, 20208366, 20208367, 20208368, 20208369, 20208370, 20208371, 20208372, 20208373, 20208374, 20208375, 20208376, 20208377, 20208378, 20208379, 20208380, 20208381, 20208382, 20208383, 20208384, 20208385, 20208386, 20208387, 20208388, 20208389, 20208390, 20208391, 20208392, 20208393, 20208394, 20208395, 20208396, 20208397, 20208398, 20208399, 20208400, 20208401, 20208402, 20208403, 20208404, 20208405, 20208406, 20208407, 20208408, 20208409, 20208410, 20208411, 20208412, 20208413, 20208414, 20208415, 20208416, 20208417, 20208418, 20208419, 20208420, 20208421, 20208422, 20208423, 20208424, 20208425, 20208426, 20208427, 20208428, 20208429, 20208430, 20208431, 20208432, 20208433, 20208434, 20208435, 20208436, 20208437, 20208438, 20208439, 20208440, 20208441, 20208442, 20208443, 20208444, 20208445, 20208446, 20208447, 20208448, 20208449, 20208450, 20208451, 20208452, 20208453, 20208454, 20208455, 20208456, 20208457, 20208458, 20208459, 20208460, 20208461, 20208462, 20208463, 20208464, 20208465, 20208466, 20208467, 20210524, 20210525, 20210526, 20210527, 20210528, 20210529, 20210530, 20210531, 20210532, 20210533, 20210534, 20210535, 20210536, 20210537, 20210538, 20210539, 20210540, 20210541, 20210542, 20210543, 20210544, 20210545, 20210546, 20210547, 20210548, 20210549, 20210550, 20210551, 20210552, 20210553, 20210554, 20210555, 20210556, 20210557, 20210558, 20210559, 20210560, 20210561, 20210562, 20210563, 20210564, 20210565, 20210566, 20210567, 20210568, 20210569, 20210570, 20210571, 20210572, 20210573, 20210574, 20210575, 20210576, 20210577, 20210578, 20210579, 20210580, 20210581, 20210582, 20210583, 20210584, 20210585, 20210586, 20210587, 20210588, 20210589, 20210590, 20210591, 20210592, 20210593, 20210594, 20210595, 20210596, 20210597, 20210598, 20210599, 20210600, 20210601, 20210602, 20210603, 20210604, 20210605, 20210606, 20210607, 20210608, 20210609, 20210610, 20210611, 20210612, 20210613, 20210614, 20210615, 20210616, 20210617, 20210618, 20210619, 20210620, 20210621, 20210622, 20210623, 20210624, 20210625, 20210626, 20210627, 20210628, 20210629, 20210630, 20210631, 20210632, 20210633, 20210634, 20210635, 20210636, 20210637, 20210638, 20210639, 20210640, 20210641, 20210642, 20210643, 20210644, 20210645, 20210646, 20210647, 20210648, 20210649, 20210650, 20210651, 20210652, 20210653, 20210654, 20210655, 20210656, 20210657, 20210658, 20210659, 20210660, 20210661, 20210662, 20210663, 20210664, 20210665, 20210666, 20210667, 20210668, 20210669, 20210670, 20210671, 20210672, 20210673, 20210674, 20210675, 20210676, 20210677, 20210678, 20210679, 20210680, 20210681, 20210682, 20210683, 20210684, 20210685, 20210686, 20210687, 20210688, 20210689, 20210690, 20210691, 20210692, 20210693, 20210694, 20210695, 20210696, 20210697, 20210698, 20210699, 20210700, 20210701, 20210702, 20210703, 20210704, 20210705, 20210706, 20210707, 20210708, 20210709, 20210710, 20210711, 20210712, 20210713, 20210714, 20210715, 20210716, 20210717, 20210718, 20210719, 20210720, 20210721, 20210722, 20210723, 20210724, 20210725, 20210726, 20210727, 20210728, 20210729, 20210730, 20210731, 20210732, 20210733, 20210734, 20210735, 20210736, 20210737, 20210738, 20210739, 20210740, 20210741, 20210742, 20210743, 20210744, 20210745, 20210746, 20210747, 20210748, 20210749, 20210750, 20210751, 20210752, 20210753, 20210754, 20210755, 20210756, 20210757, 20210758, 20210759, 20210760, 20210761, 20210762, 20210763, 20210764, 20210765, 20210766, 20210767, 20210768, 20210769, 20210770, 20210771, 20210772, 20210773, 20210774, 20210775, 20210776, 20210777, 20210778, 20210779, 20210780, 20210781, 20210782, 20210783, 20210784, 20210785, 20210786, 20210787, 20210788, 20210789, 20210790, 20210791, 20210792, 20210793, 20210794, 20210795, 20210796, 20210797, 20210798, 20210799, 20210800, 20210801, 20210802, 20210803, 20210804, 20210805, 20210806, 20210807, 20210808, 20210809, 20210810, 20210811, 20210812, 20210813, 20210814, 20210815, 20210816, 20210817, 20210818, 20210819, 20210820, 20210821, 20210822, 20210823, 20210824, 20210825, 20210826, 20210827, 20210828, 20210829, 20210830, 20210831, 20210832, 20212891, 20212892, 20212893, 20212894, 20212895, 20212896, 20212897, 20212898, 20212899, 20212900, 20212901, 20212902, 20212903, 20212904, 20212905, 20212906, 20212907, 20212908, 20212909, 20212910, 20212911, 20212912, 20212913, 20212914, 20212915, 20212916, 20212917, 20212918, 20212919, 20212920, 20212921, 20212922, 20212923, 20212924, 20212925, 20212926, 20212927, 20212928, 20212929, 20212930, 20212931, 20212932, 20212933, 20212934, 20212935, 20212936, 20212937, 20212938, 20212939, 20212940, 20212941, 20212942, 20212943, 20212944, 20212945, 20212946, 20212947, 20212948, 20212949, 20212950, 20212951, 20212952, 20212953, 20212954, 20212955, 20212956, 20212957, 20212958, 20212959, 20212960, 20212961, 20212962, 20212963, 20212964, 20212965, 20212966, 20212967, 20212968, 20212969, 20212970, 20212971, 20212972, 20212973, 20212974, 20212975, 20212976, 20212977, 20212978, 20212979, 20212980, 20212981, 20212982, 20212983, 20212984, 20212985, 20212986, 20212987, 20212988, 20212989, 20212990, 20212991, 20212992, 20212993, 20212994, 20212995, 20212996, 20212997, 20212998, 20212999, 20213000, 20213001, 20213002, 20213003, 20213004, 20213005, 20213006, 20213007, 20213008, 20213009, 20213010, 20213011, 20213012, 20213013, 20213014, 20213015, 20213016, 20213017, 20213018, 20213019, 20213020, 20213021, 20213022, 20213023, 20213024, 20213025, 20213026, 20213027, 20213028, 20213029, 20213030, 20213031, 20213032, 20213033, 20213034, 20213035, 20213036, 20213037, 20213038, 20213039, 20213040, 20213041, 20213042, 20213043, 20213044, 20213045, 20213046, 20213047, 20213048, 20213049, 20213050, 20213051, 20213052, 20213053, 20213054, 20213055, 20213056, 20213057, 20213058, 20213059, 20213060, 20213061, 20213062, 20213063, 20213064, 20213065, 20213066, 20213067, 20213068, 20213069, 20213070, 20213071, 20213072, 20213073, 20213074, 20213075, 20213076, 20213077, 20213078, 20213079, 20213080, 20213081, 20213082, 20213083, 20213084, 20213085, 20213086, 20213087, 20213088, 20213089, 20213090, 20213091, 20213092, 20213093, 20213094, 20213095, 20213096, 20213097, 20213098, 20213099, 20213100, 20213101, 20213102, 20213103, 20213104, 20213105, 20213106, 20213107, 20213108, 20213109, 20213110, 20213111, 20213112, 20213113, 20213114, 20213115, 20213116, 20213117, 20213118, 20213119, 20213120, 20213121, 20213122, 20213123, 20213124, 20213125, 20213126, 20213127, 20213128, 20213129, 20213130, 20213131, 20213132, 20213133, 20213134, 20213135, 20213136, 20213137, 20213138, 20213139, 20213140, 20213141, 20213142, 20213143, 20213144, 20213145, 20213146, 20213147, 20213148, 20213149, 20213150, 20213151, 20213152, 20213153, 20213154, 20213155, 20213156, 20213157, 20213158, 20213159, 20213160, 20213161, 20213162, 20213163, 20213164, 20213165, 20213166, 20213167, 20213168, 20213169, 20213170, 20213171, 20213172, 20213173, 20213174, 20213175, 20213176, 20213177, 20213178, 20213179, 20213180, 20213181, 20213182, 20213183, 20213184, 20213185, 20213186, 20213187, 20213188, 20213189, 20213190, 20213191, 20213192, 20213193, 20213194, 20213195, 20213196, 20213197, 20213198, 20213199, 20215256, 20215257, 20215258, 20215259, 20215260, 20215261, 20215262, 20215263, 20215264, 20215265, 20215266, 20215267, 20215268, 20215269, 20215270, 20215271, 20215272, 20215273, 20215274, 20215275, 20215276, 20215277, 20215278, 20215279, 20215280, 20215281, 20215282, 20215283, 20215284, 20215285, 20215286, 20215287, 20215288, 20215289, 20215290, 20215291, 20215292, 20215293, 20215294, 20215295, 20215296, 20215297, 20215298, 20215299, 20215300, 20215301, 20215302, 20215303, 20215304, 20215305, 20215306, 20215307, 20215308, 20215309, 20215310, 20215311, 20215312, 20215313, 20215314, 20215315, 20215316, 20215317, 20215318, 20215319, 20215320, 20215321, 20215322, 20215323, 20215324, 20215325, 20215326, 20215327, 20215328, 20215329, 20215330, 20215331, 20215332, 20215333, 20215334, 20215335, 20215336, 20215337, 20215338, 20215339, 20215340, 20215341, 20215342, 20215343, 20215344, 20215345, 20215346, 20215347, 20215348, 20215349, 20215350, 20215351, 20215352, 20215353, 20215354, 20215355, 20215356, 20215357, 20215358, 20215359, 20215360, 20215361, 20215362, 20215363, 20215364, 20215365, 20215366, 20215367, 20215368, 20215369, 20215370, 20215371, 20215372, 20215373, 20215374, 20215375, 20215376, 20215377, 20215378, 20215379, 20215380, 20215381, 20215382, 20215383, 20215384, 20215385, 20215386, 20215387, 20215388, 20215389, 20215390, 20215391, 20215392, 20215393, 20215394, 20215395, 20215396, 20215397, 20215398, 20215399, 20215400, 20215401, 20215402, 20215403, 20215404, 20215405, 20215406, 20215407, 20215408, 20215409, 20215410, 20215411, 20215412, 20215413, 20215414, 20215415, 20215416, 20215417, 20215418, 20215419, 20215420, 20215421, 20215422, 20215423, 20215424, 20215425, 20215426, 20215427, 20215428, 20215429, 20215430, 20215431, 20215432, 20215433, 20215434, 20215435, 20215436, 20215437, 20215438, 20215439, 20215440, 20215441, 20215442, 20215443, 20215444, 20215445, 20215446, 20215447, 20215448, 20215449, 20215450, 20215451, 20215452, 20215453, 20215454, 20215455, 20215456, 20215457, 20215458, 20215459, 20215460, 20215461, 20215462, 20215463, 20215464, 20215465, 20215466, 20215467, 20215468, 20215469, 20215470, 20215471, 20215472, 20215473, 20215474, 20215475, 20215476, 20215477, 20215478, 20215479, 20215480, 20215481, 20215482, 20215483, 20215484, 20215485, 20215486, 20215487, 20215488, 20215489, 20215490, 20215491, 20215492, 20215493, 20215494, 20215495, 20215496, 20215497, 20215498, 20215499, 20215500, 20215501, 20215502, 20215503, 20215504, 20215505, 20215506, 20215507, 20215508, 20215509, 20215510, 20215511, 20215512, 20215513, 20215514, 20215515, 20215516, 20215517, 20215518, 20215519, 20215520, 20215521, 20215522, 20215523, 20215524, 20215525, 20215526, 20215527, 20215528, 20215529, 20215530, 20215531, 20215532, 20215533, 20215534, 20215535, 20215536, 20215537, 20215538, 20215539, 20215540, 20215541, 20215542, 20215543, 20215544, 20215545, 20215546, 20215547, 20215548, 20215549, 20215550, 20215551, 20215552, 20215553, 20215554, 20215555, 20215556, 20215557, 20215558, 20215559, 20215560, 20215561, 20215562, 20215563, 20215564, 20203742, 20203743, 20203744, 20203745, 20203746, 20203747, 20203748, 20203749, 20203750, 20203751, 20203752, 20203753, 20203754, 20203755, 20203756, 20203757, 20203758, 20203759, 20203760, 20203761, 20203762, 20203763, 20203764, 20203765, 20203766, 20203767, 20203768, 20203769, 20203770, 20203771, 20203772, 20203773, 20203774, 20203775, 20203776, 20203777, 20203778, 20203779, 20203780, 20203781, 20203782, 20203783, 20203784, 20203785, 20203786, 20203787, 20203788, 20203789, 20203790, 20203791, 20203792, 20203793, 20203794, 20203795, 20203796, 20203797, 20203798, 20203799, 20203800, 20203801, 20203802, 20203803, 20203804, 20203805, 20203806, 20203807, 20203808, 20203809, 20203810, 20203811, 20203812, 20203813, 20203814, 20203815, 20203816, 20203817, 20203818, 20203819, 20203820, 20203821, 20203822, 20203823, 20203824, 20203825, 20203826, 20203827, 20203828, 20203829, 20203830, 20203831, 20203832, 20203833, 20203834, 20203835, 20203836, 20203837, 20203838, 20203839, 20203840, 20203841, 20203842, 20203843, 20203844, 20203845, 20203846, 20203847, 20203848, 20203849, 20203850, 20203851, 20203852, 20203853, 20203854, 20203855, 20203856, 20203857, 20203858, 20203859, 20203860, 20203861, 20203862, 20203863, 20203864, 20203865, 20203866, 20203867, 20203868, 20203869, 20203870, 20203871, 20203872, 20203873, 20203874, 20203875, 20203876, 20203877, 20203878, 20203879, 20203880, 20203881, 20203882, 20203883, 20203884, 20203885, 20203886, 20203887, 20203888, 20203889, 20203890, 20203891, 20203892, 20203893, 20203894, 20203895, 20203896, 20203897, 20203898, 20203899, 20203900, 20203901, 20203902, 20203903, 20203904, 20203905, 20203906, 20203907, 20203908, 20203909, 20203910, 20203911, 20203912, 20203913, 20203914, 20203915, 20203916, 20203917, 20203918, 20203919, 20203920, 20203921, 20203922, 20203923, 20203924, 20203925, 20203926, 20203927, 20203928, 20203929, 20203930, 20203931, 20203932, 20203933, 20203934, 20203935, 20203936, 20203937, 20203938, 20203939, 20203940, 20203941, 20203942, 20203943, 20203944, 20203945, 20203946, 20203947, 20203948, 20203949, 20203950, 20203951, 20203952, 20203953, 20203954, 20203955, 20203956, 20203957, 20203958, 20203959, 20203960, 20203961, 20203962, 20203963, 20203964, 20203965, 20203966, 20203967, 20203968, 20203969, 20203970, 20203971, 20203972, 20203973, 20203974, 20203975, 20203976, 20203977, 20203978, 20203979, 20203980, 20203981, 20203982, 20203983, 20203984, 20203985, 20203986, 20203987, 20203988, 20203989, 20203990, 20203991, 20203992, 20203993, 20203994, 20203995, 20203996, 20203997, 20203998, 20203999, 20204000, 20204001, 20204002, 20204003, 20204004, 20204005, 20204006, 20204007, 20204008, 20204009, 20204010, 20204011, 20204012, 20204013, 20204014, 20204015, 20204016, 20204017, 20204018, 20204019, 20204020, 20204021, 20204022, 20204023, 20204024, 20206105, 20206106, 20206107, 20206108, 20206109, 20206110, 20206111, 20206112, 20206113, 20206114, 20206115, 20206116, 20206117, 20206118, 20206119, 20206120, 20206121, 20206122, 20206123, 20206124, 20206125, 20206126, 20206127, 20206128, 20206129, 20206130, 20206131, 20206132, 20206133, 20206134, 20206135, 20206136, 20206137, 20206138, 20206139, 20206140, 20206141, 20206142, 20206143, 20206144, 20206145, 20206146, 20206147, 20206148, 20206149, 20206150, 20206151, 20206152, 20206153, 20206154, 20206155, 20206156, 20206157, 20206158, 20206159, 20206160, 20206161, 20206162, 20206163, 20206164, 20206165, 20206166, 20206167, 20206168, 20206169, 20206170, 20206171, 20206172, 20206173, 20206174, 20206175, 20206176, 20206177, 20206178, 20206179, 20206180, 20206181, 20206182, 20206183, 20206184, 20206185, 20206186, 20206187, 20206188, 20206189, 20206190, 20206191, 20206192, 20206193, 20206194, 20206195, 20206196, 20206197, 20206198, 20206199, 20206200, 20206201, 20206202, 20206203, 20206204, 20206205, 20206206, 20206207, 20206208, 20206209, 20206210, 20206211, 20206212, 20206213, 20206214, 20206215, 20206216, 20206217, 20206218, 20206219, 20206220, 20206221, 20206222, 20206223, 20206224, 20206225, 20206226, 20206227, 20206228, 20206229, 20206230, 20206231, 20206232, 20206233, 20206234, 20206235, 20206236, 20206237, 20206238, 20206239, 20206240, 20206241, 20206242, 20206243, 20206244, 20206245, 20206246, 20206247, 20206248, 20206249, 20206250, 20206251, 20206252, 20206253, 20206254, 20206255, 20206256, 20206257, 20206258, 20206259, 20206260, 20206261, 20206262, 20206263, 20206264, 20206265, 20206266, 20206267, 20206268, 20206269, 20206270, 20206271, 20206272, 20206273, 20206274, 20206275, 20206276, 20206277, 20206278, 20206279, 20206280, 20206281, 20206282, 20206283, 20206284, 20206285, 20206286, 20206287, 20206288, 20206289, 20206290, 20206291, 20206292, 20206293, 20206294, 20206295, 20206296, 20206297, 20206298, 20206299, 20206300, 20206301, 20206302, 20206303, 20206304, 20206305, 20206306, 20206307, 20206308, 20206309, 20206310, 20206311, 20206312, 20206313, 20206314, 20206315, 20206316, 20206317, 20206318, 20206319, 20206320, 20206321, 20206322, 20206323, 20206324, 20206325, 20206326, 20206327, 20206328, 20206329, 20206330, 20206331, 20206332, 20206333, 20206334, 20206335, 20206336, 20206337, 20206338, 20206339, 20206340, 20206341, 20206342, 20206343, 20206344, 20206345, 20206346, 20206347, 20206348, 20206349, 20206350, 20206351, 20206352, 20206353, 20206354, 20206355, 20206356, 20206357, 20206358, 20206359, 20206360, 20206361, 20206362, 20206363, 20206364, 20206365, 20206366, 20206367, 20206368, 20206369, 20206370, 20206371, 20206372, 20206373, 20206374, 20206375, 20206376, 20206377, 20206378, 20206379, 20206380, 20206381, 20206382, 20206383, 20206384, 20206385, 20206386, 20206387, 20208468, 20208469, 20208470, 20208471, 20208472, 20208473, 20208474, 20208475, 20208476, 20208477, 20208478, 20208479, 20208480, 20208481, 20208482, 20208483, 20208484, 20208485, 20208486, 20208487, 20208488, 20208489, 20208490, 20208491, 20208492, 20208493, 20208494, 20208495, 20208496, 20208497, 20208498, 20208499, 20208500, 20208501, 20208502, 20208503, 20208504, 20208505, 20208506, 20208507, 20208508, 20208509, 20208510, 20208511, 20208512, 20208513, 20208514, 20208515, 20208516, 20208517, 20208518, 20208519, 20208520, 20208521, 20208522, 20208523, 20208524, 20208525, 20208526, 20208527, 20208528, 20208529, 20208530, 20208531, 20208532, 20208533, 20208534, 20208535, 20208536, 20208537, 20208538, 20208539, 20208540, 20208541, 20208542, 20208543, 20208544, 20208545, 20208546, 20208547, 20208548, 20208549, 20208550, 20208551, 20208552, 20208553, 20208554, 20208555, 20208556, 20208557, 20208558, 20208559, 20208560, 20208561, 20208562, 20208563, 20208564, 20208565, 20208566, 20208567, 20208568, 20208569, 20208570, 20208571, 20208572, 20208573, 20208574, 20208575, 20208576, 20208577, 20208578, 20208579, 20208580, 20208581, 20208582, 20208583, 20208584, 20208585, 20208586, 20208587, 20208588, 20208589, 20208590, 20208591, 20208592, 20208593, 20208594, 20208595, 20208596, 20208597, 20208598, 20208599, 20208600, 20208601, 20208602, 20208603, 20208604, 20208605, 20208606, 20208607, 20208608, 20208609, 20208610, 20208611, 20208612, 20208613, 20208614, 20208615, 20208616, 20208617, 20208618, 20208619, 20208620, 20208621, 20208622, 20208623, 20208624, 20208625, 20208626, 20208627, 20208628, 20208629, 20208630, 20208631, 20208632, 20208633, 20208634, 20208635, 20208636, 20208637, 20208638, 20208639, 20208640, 20208641, 20208642, 20208643, 20208644, 20208645, 20208646, 20208647, 20208648, 20208649, 20208650, 20208651, 20208652, 20208653, 20208654, 20208655, 20208656, 20208657, 20208658, 20208659, 20208660, 20208661, 20208662, 20208663, 20208664, 20208665, 20208666, 20208667, 20208668, 20208669, 20208670, 20208671, 20208672, 20208673, 20208674, 20208675, 20208676, 20208677, 20208678, 20208679, 20208680, 20208681, 20208682, 20208683, 20208684, 20208685, 20208686, 20208687, 20208688, 20208689, 20208690, 20208691, 20208692, 20208693, 20208694, 20208695, 20208696, 20208697, 20208698, 20208699, 20208700, 20208701, 20208702, 20208703, 20208704, 20208705, 20208706, 20208707, 20208708, 20208709, 20208710, 20208711, 20208712, 20208713, 20208714, 20208715, 20208716, 20208717, 20208718, 20208719, 20208720, 20208721, 20208722, 20208723, 20208724, 20208725, 20208726, 20208727, 20208728, 20208729, 20208730, 20208731, 20208732, 20208733, 20208734, 20208735, 20208736, 20208737, 20208738, 20208739, 20208740, 20208741, 20208742, 20208743, 20208744, 20208745, 20208746, 20208747, 20208748, 20208749, 20208750, 20208751, 20208752, 20208753, 20210833, 20210834, 20210835, 20210836, 20210837, 20210838, 20210839, 20210840, 20210841, 20210842, 20210843, 20210844, 20210845, 20210846, 20210847, 20210848, 20210849, 20210850, 20210851, 20210852, 20210853, 20210854, 20210855, 20210856, 20210857, 20210858, 20210859, 20210860, 20210861, 20210862, 20210863, 20210864, 20210865, 20210866, 20210867, 20210868, 20210869, 20210870, 20210871, 20210872, 20210873, 20210874, 20210875, 20210876, 20210877, 20210878, 20210879, 20210880, 20210881, 20210882, 20210883, 20210884, 20210885, 20210886, 20210887, 20210888, 20210889, 20210890, 20210891, 20210892, 20210893, 20210894, 20210895, 20210896, 20210897, 20210898, 20210899, 20210900, 20210901, 20210902, 20210903, 20210904, 20210905, 20210906, 20210907, 20210908, 20210909, 20210910, 20210911, 20210912, 20210913, 20210914, 20210915, 20210916, 20210917, 20210918, 20210919, 20210920, 20210921, 20210922, 20210923, 20210924, 20210925, 20210926, 20210927, 20210928, 20210929, 20210930, 20210931, 20210932, 20210933, 20210934, 20210935, 20210936, 20210937, 20210938, 20210939, 20210940, 20210941, 20210942, 20210943, 20210944, 20210945, 20210946, 20210947, 20210948, 20210949, 20210950, 20210951, 20210952, 20210953, 20210954, 20210955, 20210956, 20210957, 20210958, 20210959, 20210960, 20210961, 20210962, 20210963, 20210964, 20210965, 20210966, 20210967, 20210968, 20210969, 20210970, 20210971, 20210972, 20210973, 20210974, 20210975, 20210976, 20210977, 20210978, 20210979, 20210980, 20210981, 20210982, 20210983, 20210984, 20210985, 20210986, 20210987, 20210988, 20210989, 20210990, 20210991, 20210992, 20210993, 20210994, 20210995, 20210996, 20210997, 20210998, 20210999, 20211000, 20211001, 20211002, 20211003, 20211004, 20211005, 20211006, 20211007, 20211008, 20211009, 20211010, 20211011, 20211012, 20211013, 20211014, 20211015, 20211016, 20211017, 20211018, 20211019, 20211020, 20211021, 20211022, 20211023, 20211024, 20211025, 20211026, 20211027, 20211028, 20211029, 20211030, 20211031, 20211032, 20211033, 20211034, 20211035, 20211036, 20211037, 20211038, 20211039, 20211040, 20211041, 20211042, 20211043, 20211044, 20211045, 20211046, 20211047, 20211048, 20211049, 20211050, 20211051, 20211052, 20211053, 20211054, 20211055, 20211056, 20211057, 20211058, 20211059, 20211060, 20211061, 20211062, 20211063, 20211064, 20211065, 20211066, 20211067, 20211068, 20211069, 20211070, 20211071, 20211072, 20211073, 20211074, 20211075, 20211076, 20211077, 20211078, 20211079, 20211080, 20211081, 20211082, 20211083, 20211084, 20211085, 20211086, 20211087, 20211088, 20211089, 20211090, 20211091, 20211092, 20211093, 20211094, 20211095, 20211096, 20211097, 20211098, 20211099, 20211100, 20211101, 20211102, 20211103, 20211104, 20211105, 20211106, 20211107, 20211108, 20211109, 20211110, 20211111, 20211112, 20211113, 20211114, 20211115, 20211116, 20211117, 20211118, 20213200, 20213201, 20213202, 20213203, 20213204, 20213205, 20213206, 20213207, 20213208, 20213209, 20213210, 20213211, 20213212, 20213213, 20213214, 20213215, 20213216, 20213217, 20213218, 20213219, 20213220, 20213221, 20213222, 20213223, 20213224, 20213225, 20213226, 20213227, 20213228, 20213229, 20213230, 20213231, 20213232, 20213233, 20213234, 20213235, 20213236, 20213237, 20213238, 20213239, 20213240, 20213241, 20213242, 20213243, 20213244, 20213245, 20213246, 20213247, 20213248, 20213249, 20213250, 20213251, 20213252, 20213253, 20213254, 20213255, 20213256, 20213257, 20213258, 20213259, 20213260, 20213261, 20213262, 20213263, 20213264, 20213265, 20213266, 20213267, 20213268, 20213269, 20213270, 20213271, 20213272, 20213273, 20213274, 20213275, 20213276, 20213277, 20213278, 20213279, 20213280, 20213281, 20213282, 20213283, 20213284, 20213285, 20213286, 20213287, 20213288, 20213289, 20213290, 20213291, 20213292, 20213293, 20213294, 20213295, 20213296, 20213297, 20213298, 20213299, 20213300, 20213301, 20213302, 20213303, 20213304, 20213305, 20213306, 20213307, 20213308, 20213309, 20213310, 20213311, 20213312, 20213313, 20213314, 20213315, 20213316, 20213317, 20213318, 20213319, 20213320, 20213321, 20213322, 20213323, 20213324, 20213325, 20213326, 20213327, 20213328, 20213329, 20213330, 20213331, 20213332, 20213333, 20213334, 20213335, 20213336, 20213337, 20213338, 20213339, 20213340, 20213341, 20213342, 20213343, 20213344, 20213345, 20213346, 20213347, 20213348, 20213349, 20213350, 20213351, 20213352, 20213353, 20213354, 20213355, 20213356, 20213357, 20213358, 20213359, 20213360, 20213361, 20213362, 20213363, 20213364, 20213365, 20213366, 20213367, 20213368, 20213369, 20213370, 20213371, 20213372, 20213373, 20213374, 20213375, 20213376, 20213377, 20213378, 20213379, 20213380, 20213381, 20213382, 20213383, 20213384, 20213385, 20213386, 20213387, 20213388, 20213389, 20213390, 20213391, 20213392, 20213393, 20213394, 20213395, 20213396, 20213397, 20213398, 20213399, 20213400, 20213401, 20213402, 20213403, 20213404, 20213405, 20213406, 20213407, 20213408, 20213409, 20213410, 20213411, 20213412, 20213413, 20213414, 20213415, 20213416, 20213417, 20213418, 20213419, 20213420, 20213421, 20213422, 20213423, 20213424, 20213425, 20213426, 20213427, 20213428, 20213429, 20213430, 20213431, 20213432, 20213433, 20213434, 20213435, 20213436, 20213437, 20213438, 20213439, 20213440, 20213441, 20213442, 20213443, 20213444, 20213445, 20213446, 20213447, 20213448, 20213449, 20213450, 20213451, 20213452, 20213453, 20213454, 20213455, 20213456, 20213457, 20213458, 20213459, 20213460, 20213461, 20213462, 20213463, 20213464, 20213465, 20213466, 20213467, 20213468, 20213469, 20213470, 20213471, 20213472, 20213473, 20213474, 20213475, 20213476, 20213477, 20213478, 20213479, 20213480, 20213481, 20213482, 20213483, 20213484, 20215565, 20215566, 20215567, 20215568, 20215569, 20215570, 20215571, 20215572, 20215573, 20215574, 20215575, 20215576, 20215577, 20215578, 20215579, 20215580, 20215581, 20215582, 20215583, 20215584, 20215585, 20215586, 20215587, 20215588, 20215589, 20215590, 20215591, 20215592, 20215593, 20215594, 20215595, 20215596, 20215597, 20215598, 20215599, 20215600, 20215601, 20215602, 20215603, 20215604, 20215605, 20215606, 20215607, 20215608, 20215609, 20215610, 20215611, 20215612, 20215613, 20215614, 20215615, 20215616, 20215617, 20215618, 20215619, 20215620, 20215621, 20215622, 20215623, 20215624, 20215625, 20215626, 20215627, 20215628, 20215629, 20215630, 20215631, 20215632, 20215633, 20215634, 20215635, 20215636, 20215637, 20215638, 20215639, 20215640, 20215641, 20215642, 20215643, 20215644, 20215645, 20215646, 20215647, 20215648, 20215649, 20215650, 20215651, 20215652, 20215653, 20215654, 20215655, 20215656, 20215657, 20215658, 20215659, 20215660, 20215661, 20215662, 20215663, 20215664, 20215665, 20215666, 20215667, 20215668, 20215669, 20215670, 20215671, 20215672, 20215673, 20215674, 20215675, 20215676, 20215677, 20215678, 20215679, 20215680, 20215681, 20215682, 20215683, 20215684, 20215685, 20215686, 20215687, 20215688, 20215689, 20215690, 20215691, 20215692, 20215693, 20215694, 20215695, 20215696, 20215697, 20215698, 20215699, 20215700, 20215701, 20215702, 20215703, 20215704, 20215705, 20215706, 20215707, 20215708, 20215709, 20215710, 20215711, 20215712, 20215713, 20215714, 20215715, 20215716, 20215717, 20215718, 20215719, 20215720, 20215721, 20215722, 20215723, 20215724, 20215725, 20215726, 20215727, 20215728, 20215729, 20215730, 20215731, 20215732, 20215733, 20215734, 20215735, 20215736, 20215737, 20215738, 20215739, 20215740, 20215741, 20215742, 20215743, 20215744, 20215745, 20215746, 20215747, 20215748, 20215749, 20215750, 20215751, 20215752, 20215753, 20215754, 20215755, 20215756, 20215757, 20215758, 20215759, 20215760, 20215761, 20215762, 20215763, 20215764, 20215765, 20215766, 20215767, 20215768, 20215769, 20215770, 20215771, 20215772, 20215773, 20215774, 20215775, 20215776, 20215777, 20215778, 20215779, 20215780, 20215781, 20215782, 20215783, 20215784, 20215785, 20215786, 20215787, 20215788, 20215789, 20215790, 20215791, 20215792, 20215793, 20215794, 20215795, 20215796, 20215797, 20215798, 20215799, 20215800, 20215801, 20215802, 20215803, 20215804, 20215805, 20215806, 20215807, 20215808, 20215809, 20215810, 20215811, 20215812, 20215813, 20215814, 20215815, 20215816, 20215817, 20215818, 20215819, 20215820, 20215821, 20215822, 20215823, 20215824, 20215825, 20215826, 20215827, 20215828, 20215829, 20215830, 20215831, 20215832, 20215833, 20215834, 20215835, 20215836, 20215837, 20215838, 20215839, 20215840, 20215841, 20215842, 20215843, 20215844, 20215845, 20215846, 20215847, 20215848, 20215849, 20215850, 20204025, 20204026, 20204027, 20204028, 20204029, 20204030, 20204031, 20204032, 20204033, 20204034, 20204035, 20204036, 20204037, 20204038, 20204039, 20204040, 20204041, 20204042, 20204043, 20204044, 20204045, 20204046, 20204047, 20204048, 20204049, 20204050, 20204051, 20204052, 20204053, 20204054, 20204055, 20204056, 20204057, 20204058, 20204059, 20204060, 20204061, 20204062, 20204063, 20204064, 20204065, 20204066, 20204067, 20204068, 20204069, 20204070, 20204071, 20204072, 20204073, 20204074, 20204075, 20204076, 20204077, 20204078, 20204079, 20204080, 20204081, 20204082, 20204083, 20204084, 20204085, 20204086, 20204087, 20204088, 20204089, 20204090, 20204091, 20204092, 20204093, 20204094, 20204095, 20204096, 20204097, 20204098, 20204099, 20204100, 20204101, 20204102, 20204103, 20204104, 20204105, 20204106, 20204107, 20204108, 20204109, 20204110, 20204111, 20204112, 20204113, 20204114, 20204115, 20204116, 20204117, 20204118, 20204119, 20204120, 20204121, 20204122, 20204123, 20204124, 20204125, 20204126, 20204127, 20204128, 20204129, 20204130, 20204131, 20204132, 20204133, 20204134, 20204135, 20204136, 20204137, 20204138, 20204139, 20204140, 20204141, 20204142, 20204143, 20204144, 20204145, 20204146, 20204147, 20204148, 20204149, 20204150, 20204151, 20204152, 20204153, 20204154, 20204155, 20204156, 20204157, 20204158, 20204159, 20204160, 20204161, 20204162, 20204163, 20204164, 20204165, 20204166, 20204167, 20204168, 20204169, 20204170, 20204171, 20204172, 20204173, 20204174, 20204175, 20204176, 20204177, 20204178, 20204179, 20204180, 20204181, 20204182, 20204183, 20204184, 20204185, 20204186, 20204187, 20204188, 20204189, 20204190, 20204191, 20204192, 20204193, 20204194, 20204195, 20204196, 20204197, 20204198, 20204199, 20204200, 20204201, 20204202, 20204203, 20204204, 20204205, 20204206, 20204207, 20204208, 20204209, 20204210, 20204211, 20204212, 20204213, 20204214, 20204215, 20204216, 20204217, 20204218, 20204219, 20204220, 20204221, 20204222, 20204223, 20204224, 20204225, 20204226, 20204227, 20204228, 20204229, 20204230, 20204231, 20204232, 20204233, 20204234, 20204235, 20204236, 20204237, 20204238, 20204239, 20204240, 20204241, 20204242, 20204243, 20204244, 20204245, 20204246, 20204247, 20204248, 20204249, 20204250, 20204251, 20204252, 20204253, 20204254, 20204255, 20204256, 20204257, 20204258, 20204259, 20204260, 20204261, 20204262, 20204263, 20204264, 20204265, 20204266, 20204267, 20204268, 20204269, 20204270, 20204271, 20204272, 20204273, 20206388, 20206389, 20206390, 20206391, 20206392, 20206393, 20206394, 20206395, 20206396, 20206397, 20206398, 20206399, 20206400, 20206401, 20206402, 20206403, 20206404, 20206405, 20206406, 20206407, 20206408, 20206409, 20206410, 20206411, 20206412, 20206413, 20206414, 20206415, 20206416, 20206417, 20206418, 20206419, 20206420, 20206421, 20206422, 20206423, 20206424, 20206425, 20206426, 20206427, 20206428, 20206429, 20206430, 20206431, 20206432, 20206433, 20206434, 20206435, 20206436, 20206437, 20206438, 20206439, 20206440, 20206441, 20206442, 20206443, 20206444, 20206445, 20206446, 20206447, 20206448, 20206449, 20206450, 20206451, 20206452, 20206453, 20206454, 20206455, 20206456, 20206457, 20206458, 20206459, 20206460, 20206461, 20206462, 20206463, 20206464, 20206465, 20206466, 20206467, 20206468, 20206469, 20206470, 20206471, 20206472, 20206473, 20206474, 20206475, 20206476, 20206477, 20206478, 20206479, 20206480, 20206481, 20206482, 20206483, 20206484, 20206485, 20206486, 20206487, 20206488, 20206489, 20206490, 20206491, 20206492, 20206493, 20206494, 20206495, 20206496, 20206497, 20206498, 20206499, 20206500, 20206501, 20206502, 20206503, 20206504, 20206505, 20206506, 20206507, 20206508, 20206509, 20206510, 20206511, 20206512, 20206513, 20206514, 20206515, 20206516, 20206517, 20206518, 20206519, 20206520, 20206521, 20206522, 20206523, 20206524, 20206525, 20206526, 20206527, 20206528, 20206529, 20206530, 20206531, 20206532, 20206533, 20206534, 20206535, 20206536, 20206537, 20206538, 20206539, 20206540, 20206541, 20206542, 20206543, 20206544, 20206545, 20206546, 20206547, 20206548, 20206549, 20206550, 20206551, 20206552, 20206553, 20206554, 20206555, 20206556, 20206557, 20206558, 20206559, 20206560, 20206561, 20206562, 20206563, 20206564, 20206565, 20206566, 20206567, 20206568, 20206569, 20206570, 20206571, 20206572, 20206573, 20206574, 20206575, 20206576, 20206577, 20206578, 20206579, 20206580, 20206581, 20206582, 20206583, 20206584, 20206585, 20206586, 20206587, 20206588, 20206589, 20206590, 20206591, 20206592, 20206593, 20206594, 20206595, 20206596, 20206597, 20206598, 20206599, 20206600, 20206601, 20206602, 20206603, 20206604, 20206605, 20206606, 20206607, 20206608, 20206609, 20206610, 20206611, 20206612, 20206613, 20206614, 20206615, 20206616, 20206617, 20206618, 20206619, 20206620, 20206621, 20206622, 20206623, 20206624, 20206625, 20206626, 20206627, 20206628, 20206629, 20206630, 20206631, 20206632, 20206633, 20206634, 20206635, 20206636, 20208754, 20208755, 20208756, 20208757, 20208758, 20208759, 20208760, 20208761, 20208762, 20208763, 20208764, 20208765, 20208766, 20208767, 20208768, 20208769, 20208770, 20208771, 20208772, 20208773, 20208774, 20208775, 20208776, 20208777, 20208778, 20208779, 20208780, 20208781, 20208782, 20208783, 20208784, 20208785, 20208786, 20208787, 20208788, 20208789, 20208790, 20208791, 20208792, 20208793, 20208794, 20208795, 20208796, 20208797, 20208798, 20208799, 20208800, 20208801, 20208802, 20208803, 20208804, 20208805, 20208806, 20208807, 20208808, 20208809, 20208810, 20208811, 20208812, 20208813, 20208814, 20208815, 20208816, 20208817, 20208818, 20208819, 20208820, 20208821, 20208822, 20208823, 20208824, 20208825, 20208826, 20208827, 20208828, 20208829, 20208830, 20208831, 20208832, 20208833, 20208834, 20208835, 20208836, 20208837, 20208838, 20208839, 20208840, 20208841, 20208842, 20208843, 20208844, 20208845, 20208846, 20208847, 20208848, 20208849, 20208850, 20208851, 20208852, 20208853, 20208854, 20208855, 20208856, 20208857, 20208858, 20208859, 20208860, 20208861, 20208862, 20208863, 20208864, 20208865, 20208866, 20208867, 20208868, 20208869, 20208870, 20208871, 20208872, 20208873, 20208874, 20208875, 20208876, 20208877, 20208878, 20208879, 20208880, 20208881, 20208882, 20208883, 20208884, 20208885, 20208886, 20208887, 20208888, 20208889, 20208890, 20208891, 20208892, 20208893, 20208894, 20208895, 20208896, 20208897, 20208898, 20208899, 20208900, 20208901, 20208902, 20208903, 20208904, 20208905, 20208906, 20208907, 20208908, 20208909, 20208910, 20208911, 20208912, 20208913, 20208914, 20208915, 20208916, 20208917, 20208918, 20208919, 20208920, 20208921, 20208922, 20208923, 20208924, 20208925, 20208926, 20208927, 20208928, 20208929, 20208930, 20208931, 20208932, 20208933, 20208934, 20208935, 20208936, 20208937, 20208938, 20208939, 20208940, 20208941, 20208942, 20208943, 20208944, 20208945, 20208946, 20208947, 20208948, 20208949, 20208950, 20208951, 20208952, 20208953, 20208954, 20208955, 20208956, 20208957, 20208958, 20208959, 20208960, 20208961, 20208962, 20208963, 20208964, 20208965, 20208966, 20208967, 20208968, 20208969, 20208970, 20208971, 20208972, 20208973, 20208974, 20208975, 20208976, 20208977, 20208978, 20208979, 20208980, 20208981, 20208982, 20208983, 20208984, 20208985, 20208986, 20208987, 20208988, 20208989, 20208990, 20208991, 20208992, 20208993, 20208994, 20208995, 20208996, 20208997, 20208998, 20208999, 20209000, 20209001, 20209002, 20211119, 20211120, 20211121, 20211122, 20211123, 20211124, 20211125, 20211126, 20211127, 20211128, 20211129, 20211130, 20211131, 20211132, 20211133, 20211134, 20211135, 20211136, 20211137, 20211138, 20211139, 20211140, 20211141, 20211142, 20211143, 20211144, 20211145, 20211146, 20211147, 20211148, 20211149, 20211150, 20211151, 20211152, 20211153, 20211154, 20211155, 20211156, 20211157, 20211158, 20211159, 20211160, 20211161, 20211162, 20211163, 20211164, 20211165, 20211166, 20211167, 20211168, 20211169, 20211170, 20211171, 20211172, 20211173, 20211174, 20211175, 20211176, 20211177, 20211178, 20211179, 20211180, 20211181, 20211182, 20211183, 20211184, 20211185, 20211186, 20211187, 20211188, 20211189, 20211190, 20211191, 20211192, 20211193, 20211194, 20211195, 20211196, 20211197, 20211198, 20211199, 20211200, 20211201, 20211202, 20211203, 20211204, 20211205, 20211206, 20211207, 20211208, 20211209, 20211210, 20211211, 20211212, 20211213, 20211214, 20211215, 20211216, 20211217, 20211218, 20211219, 20211220, 20211221, 20211222, 20211223, 20211224, 20211225, 20211226, 20211227, 20211228, 20211229, 20211230, 20211231, 20211232, 20211233, 20211234, 20211235, 20211236, 20211237, 20211238, 20211239, 20211240, 20211241, 20211242, 20211243, 20211244, 20211245, 20211246, 20211247, 20211248, 20211249, 20211250, 20211251, 20211252, 20211253, 20211254, 20211255, 20211256, 20211257, 20211258, 20211259, 20211260, 20211261, 20211262, 20211263, 20211264, 20211265, 20211266, 20211267, 20211268, 20211269, 20211270, 20211271, 20211272, 20211273, 20211274, 20211275, 20211276, 20211277, 20211278, 20211279, 20211280, 20211281, 20211282, 20211283, 20211284, 20211285, 20211286, 20211287, 20211288, 20211289, 20211290, 20211291, 20211292, 20211293, 20211294, 20211295, 20211296, 20211297, 20211298, 20211299, 20211300, 20211301, 20211302, 20211303, 20211304, 20211305, 20211306, 20211307, 20211308, 20211309, 20211310, 20211311, 20211312, 20211313, 20211314, 20211315, 20211316, 20211317, 20211318, 20211319, 20211320, 20211321, 20211322, 20211323, 20211324, 20211325, 20211326, 20211327, 20211328, 20211329, 20211330, 20211331, 20211332, 20211333, 20211334, 20211335, 20211336, 20211337, 20211338, 20211339, 20211340, 20211341, 20211342, 20211343, 20211344, 20211345, 20211346, 20211347, 20211348, 20211349, 20211350, 20211351, 20211352, 20211353, 20211354, 20211355, 20211356, 20211357, 20211358, 20211359, 20211360, 20211361, 20211362, 20211363, 20211364, 20211365, 20211366, 20211367, 20213485, 20213486, 20213487, 20213488, 20213489, 20213490, 20213491, 20213492, 20213493, 20213494, 20213495, 20213496, 20213497, 20213498, 20213499, 20213500, 20213501, 20213502, 20213503, 20213504, 20213505, 20213506, 20213507, 20213508, 20213509, 20213510, 20213511, 20213512, 20213513, 20213514, 20213515, 20213516, 20213517, 20213518, 20213519, 20213520, 20213521, 20213522, 20213523, 20213524, 20213525, 20213526, 20213527, 20213528, 20213529, 20213530, 20213531, 20213532, 20213533, 20213534, 20213535, 20213536, 20213537, 20213538, 20213539, 20213540, 20213541, 20213542, 20213543, 20213544, 20213545, 20213546, 20213547, 20213548, 20213549, 20213550, 20213551, 20213552, 20213553, 20213554, 20213555, 20213556, 20213557, 20213558, 20213559, 20213560, 20213561, 20213562, 20213563, 20213564, 20213565, 20213566, 20213567, 20213568, 20213569, 20213570, 20213571, 20213572, 20213573, 20213574, 20213575, 20213576, 20213577, 20213578, 20213579, 20213580, 20213581, 20213582, 20213583, 20213584, 20213585, 20213586, 20213587, 20213588, 20213589, 20213590, 20213591, 20213592, 20213593, 20213594, 20213595, 20213596, 20213597, 20213598, 20213599, 20213600, 20213601, 20213602, 20213603, 20213604, 20213605, 20213606, 20213607, 20213608, 20213609, 20213610, 20213611, 20213612, 20213613, 20213614, 20213615, 20213616, 20213617, 20213618, 20213619, 20213620, 20213621, 20213622, 20213623, 20213624, 20213625, 20213626, 20213627, 20213628, 20213629, 20213630, 20213631, 20213632, 20213633, 20213634, 20213635, 20213636, 20213637, 20213638, 20213639, 20213640, 20213641, 20213642, 20213643, 20213644, 20213645, 20213646, 20213647, 20213648, 20213649, 20213650, 20213651, 20213652, 20213653, 20213654, 20213655, 20213656, 20213657, 20213658, 20213659, 20213660, 20213661, 20213662, 20213663, 20213664, 20213665, 20213666, 20213667, 20213668, 20213669, 20213670, 20213671, 20213672, 20213673, 20213674, 20213675, 20213676, 20213677, 20213678, 20213679, 20213680, 20213681, 20213682, 20213683, 20213684, 20213685, 20213686, 20213687, 20213688, 20213689, 20213690, 20213691, 20213692, 20213693, 20213694, 20213695, 20213696, 20213697, 20213698, 20213699, 20213700, 20213701, 20213702, 20213703, 20213704, 20213705, 20213706, 20213707, 20213708, 20213709, 20213710, 20213711, 20213712, 20213713, 20213714, 20213715, 20213716, 20213717, 20213718, 20213719, 20213720, 20213721, 20213722, 20213723, 20213724, 20213725, 20213726, 20213727, 20213728, 20213729, 20213730, 20213731, 20213732, 20213733, 20215851, 20215852, 20215853, 20215854, 20215855, 20215856, 20215857, 20215858, 20215859, 20215860, 20215861, 20215862, 20215863, 20215864, 20215865, 20215866, 20215867, 20215868, 20215869, 20215870, 20215871, 20215872, 20215873, 20215874, 20215875, 20215876, 20215877, 20215878, 20215879, 20215880, 20215881, 20215882, 20215883, 20215884, 20215885, 20215886, 20215887, 20215888, 20215889, 20215890, 20215891, 20215892, 20215893, 20215894, 20215895, 20215896, 20215897, 20215898, 20215899, 20215900, 20215901, 20215902, 20215903, 20215904, 20215905, 20215906, 20215907, 20215908, 20215909, 20215910, 20215911, 20215912, 20215913, 20215914, 20215915, 20215916, 20215917, 20215918, 20215919, 20215920, 20215921, 20215922, 20215923, 20215924, 20215925, 20215926, 20215927, 20215928, 20215929, 20215930, 20215931, 20215932, 20215933, 20215934, 20215935, 20215936, 20215937, 20215938, 20215939, 20215940, 20215941, 20215942, 20215943, 20215944, 20215945, 20215946, 20215947, 20215948, 20215949, 20215950, 20215951, 20215952, 20215953, 20215954, 20215955, 20215956, 20215957, 20215958, 20215959, 20215960, 20215961, 20215962, 20215963, 20215964, 20215965, 20215966, 20215967, 20215968, 20215969, 20215970, 20215971, 20215972, 20215973, 20215974, 20215975, 20215976, 20215977, 20215978, 20215979, 20215980, 20215981, 20215982, 20215983, 20215984, 20215985, 20215986, 20215987, 20215988, 20215989, 20215990, 20215991, 20215992, 20215993, 20215994, 20215995, 20215996, 20215997, 20215998, 20215999, 20216000, 20216001, 20216002, 20216003, 20216004, 20216005, 20216006, 20216007, 20216008, 20216009, 20216010, 20216011, 20216012, 20216013, 20216014, 20216015, 20216016, 20216017, 20216018, 20216019, 20216020, 20216021, 20216022, 20216023, 20216024, 20216025, 20216026, 20216027, 20216028, 20216029, 20216030, 20216031, 20216032, 20216033, 20216034, 20216035, 20216036, 20216037, 20216038, 20216039, 20216040, 20216041, 20216042, 20216043, 20216044, 20216045, 20216046, 20216047, 20216048, 20216049, 20216050, 20216051, 20216052, 20216053, 20216054, 20216055, 20216056, 20216057, 20216058, 20216059, 20216060, 20216061, 20216062, 20216063, 20216064, 20216065, 20216066, 20216067, 20216068, 20216069, 20216070, 20216071, 20216072, 20216073, 20216074, 20216075, 20216076, 20216077, 20216078, 20216079, 20216080, 20216081, 20216082, 20216083, 20216084, 20216085, 20216086, 20216087, 20216088, 20216089, 20216090, 20216091, 20216092, 20216093, 20216094, 20216095, 20216096, 20216097, 20216098, 20216099]
# + _kg_hide-input=true
# (train_df[(train_df.timestamp > '2016-12-31 18:00:00') & (train_df.site_id == 0)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 14:00:00') & (train_df.site_id == 2)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 17:00:00') & (train_df.site_id == 3)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 15:00:00') & (train_df.site_id == 4)].index.tolist() +
# train_df[(train_df.timestamp < '2016-01-01 01:00:00') & (train_df.site_id == 5)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 17:00:00') & (train_df.site_id == 6)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 17:00:00') & (train_df.site_id == 7)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 18:00:00') & (train_df.site_id == 8)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 16:00:00') & (train_df.site_id == 9)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 15:00:00') & (train_df.site_id == 10)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 17:00:00') & (train_df.site_id == 11)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 16:00:00') & (train_df.site_id == 13)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 17:00:00') & (train_df.site_id == 14)].index.tolist() +
# train_df[(train_df.timestamp > '2016-12-31 17:00:00') & (train_df.site_id == 15)].index.tolist()
# )
print('经过时间调整变为nan的rmse', np.sqrt(mean_squared_log_error(T_RESULTS.loc[weather_nan_id, TARGET], T_RESULTS.loc[weather_nan_id, 'kfold'])))
# -
del T_RESULTS
gc.collect()
# +
# del train_df, weather_train_df
# gc.collect()
# train_df = pd.read_feather(root/'train.feather')
print('loading...')
test_df = pd.read_feather(pjoin(root, 'test.feather'))
weather_test_df = pd.read_feather(pjoin(root, 'weather_test.feather'))
# -
# train_df = pd.concat([train_df, train_nf], axis=1)
test_df = pd.concat([test_df, test_nf], axis=1)
del test_nf
gc.collect()
# +
print('preprocessing building...')
test_df['date'] = test_df['timestamp'].dt.date
preprocess(test_df)
# test_df['building_mean'] = test_df['building_id'].map(building_mean)
test_df['building_median'] = test_df['building_id'].map(building_median)
# test_df['building_min'] = test_df['building_id'].map(building_min)
# test_df['building_max'] = test_df['building_id'].map(building_max)
# test_df['building_std'] = test_df['building_id'].map(building_std)
print('preprocessing weather...')
weather_test_df = timestamp_align(weather_test_df)
weather_test_df = weather_test_df.groupby('site_id').apply(lambda group: group.interpolate(limit_direction='both'))
weather_test_df.groupby('site_id').apply(lambda group: group.isna().sum())
add_lag_feature(weather_test_df, window=3)
add_lag_feature(weather_test_df, window=72)
weather_test_df = weather_test_df[weather_col]
gc.collect()
print('reduce mem usage...')
reduce_mem_usage(test_df, use_float16=True)
reduce_mem_usage(weather_test_df, use_float16=True)
gc.collect()
# -
test_df.columns
sample_submission = pd.read_feather(pjoin(root, 'sample_submission.feather'))
reduce_mem_usage(sample_submission)
def create_X(test_df, target_meter):
target_test_df = test_df[test_df['meter'] == target_meter]
target_test_df = target_test_df.merge(building_meta_df, on='building_id', how='left')
target_test_df = target_test_df.merge(weather_test_df, on=['site_id', 'timestamp'], how='left')
X_test = target_test_df[feature_cols + category_cols]
gc.collect()
return X_test
def pred(X_test, models, batch_size=1000000):
iterations = (X_test.shape[0] + batch_size -1) // batch_size
print('iterations', iterations)
y_test_pred_total = np.zeros(X_test.shape[0])
for i, model in enumerate(models):
print(f'predicting {i}-th model')
for k in tqdm(range(iterations)):
# y_pred_test = model.predict(X_test[k*batch_size:(k+1)*batch_size], num_iteration=model.best_iteration)
y_pred_test = model.predict(X_test[k*batch_size:(k+1)*batch_size])
y_test_pred_total[k*batch_size:(k+1)*batch_size] += y_pred_test
y_test_pred_total /= len(models)
return y_test_pred_total
X_test = create_X(test_df, target_meter=0)
del test_df
gc.collect()
for name, size in sorted(((name, sys.getsizeof(value)) for name,value in locals().items()),
key= lambda x: -x[1])[:10]:
print("{:>30}: {:>8}".format(name,sizeof_fmt(size)))
print('Memory in Gb', get_memory_usage())
# +
# %%time
# X_test = create_X(test_df, target_meter=0)
# gc.collect()
print('开始')
y_test0 = pred(X_test, models0, batch_size=500000)
sns.distplot(y_test0)
del X_test
gc.collect()
# +
# # %%time
# X_test = create_X(test_df, target_meter=1)
# gc.collect()
# y_test1 = pred(X_test, models1)
# sns.distplot(y_test1)
# del X_test
# gc.collect()
# +
# # %%time
# X_test = create_X(test_df, target_meter=2)
# gc.collect()
# y_test2 = pred(X_test, models2)
# sns.distplot(y_test2)
# del X_test
# gc.collect()
# +
# X_test = create_X(test_df, target_meter=3)
# gc.collect()
# y_test3 = pred(X_test, models3)
# sns.distplot(y_test3)
# del X_test
# gc.collect()
# -
test_df = pd.read_feather(os.path.join(root, 'test.feather'))
# +
# sample_submission.loc[test_df['meter'] == 0, 'meter_reading'] = np.expm1(y_test0)
sample_submission.loc[test_df['meter'] == 0, 'meter_reading'] = y_test0
# sample_submission.loc[test_df['meter'] == 1, 'meter_reading'] = np.expm1(y_test1)
# sample_submission.loc[test_df['meter'] == 2, 'meter_reading'] = np.expm1(y_test2)
# sample_submission.loc[test_df['meter'] == 3, 'meter_reading'] = np.expm1(y_test3)
# +
# sample_submission['meter_reading'] = sample_submission['meter_reading'].clip(0,None)
# -
sample_submission
# sample_submission.to_csv('submission.csv', index=False, float_format='%.4f')
output_path = os.path.join('..', 'output', 'as-meter2-no-1099-xgb-meter0-fold0')
sample_submission.to_csv(os.path.join(output_path, 'submission.csv'), index=False)
sample_submission.head()
# +
# np.log1p(sample_submission['meter_reading']).hist()
| 191,815 |
/Analyses/alex-genetics-data/.ipynb_checkpoints/Alex_data-checkpoint.ipynb | 962a604df0aa09e48593a14b3d86e4fc5f7ffb66 | [] | no_license | loftusa/General-Projects-And-Scripts | https://github.com/loftusa/General-Projects-And-Scripts | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 91,233 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: py3
# language: python
# name: py3
# ---
# # Messing with alex mccoy's genetics data
# +
# TODO
# -
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 1000)
# ## Get data, open as dataframe
# - short list of genes that changed 3-fold higher or lower
# - organize by treatment group
df = pd.read_csv('Lodge-rna-seq-24hr-after-ketJ-L6.csv').iloc[:, [0, 1, 2, 4, 5]]
df.head()
# ## Filter dataframe
# ### The first five ketamine vs vehicle fold change greater than 2 or less than 2 (49 total)
len(df[(df.iloc[:, 1] > 2) | (df.iloc[:, 1] < -2)])
df[(df.iloc[:, 1] > 2) | (df.iloc[:, 1] < -2)]
# ### all ketamine vs vehicle fold change greater than 3
df[(df.iloc[:, 1] > 3) | (df.iloc[:, 1] < -3)]
# ### the first five l655 fold change greater than 2 (there are 114 total)
len(df[(df.iloc[:, 3] > 2) | (df.iloc[:, 3] < -2)])
df[(df.iloc[:, 3] > 2) | (df.iloc[:, 3] < -2)]
# ### all l655 fold change greater than 3
df[(df.iloc[:, 3] > 3) | (df.iloc[:, 3] < -3)]
| 1,253 |
/2_2012136111.ipynb | 2696219c25457ad5ad9c860e1eb8c2d5a517fea9 | [] | no_license | Wonjuny0804/18FW-Python | https://github.com/Wonjuny0804/18FW-Python | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 11,362 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# 문제1번.(미래의 요일 맞추기) 사용자가 오늘의 요일을 정수로 입력하는 프로그램을 작성하시오.
# 문제에 대한 설명. 숫자를 0~6까지 사용한다면 0-일요일, 1-월요일 ....6-토요일 이라고하자. 일주일은 총 7일이다. 그러므로 오늘이 예를 들어 오늘이 1-월요일이면 일주일 뒤 1+7=8 이므로 8은 월요일이다. 이때 8은 7로 나눴을 때 나머지가 1이므로 나머지로 요일을 확인할 수 있다. 이렇게하면 일요일은 나머지가 0인 날이고 즉 7의 배수가 되는 날들 그리고 월요일은 나머지가 1, 화요일은 2, 수요일은 3...토요일은 6이된다.
#
# 경과일수를 요일에 더하고 7로 나눠서 나온 나머지로 경과한 날의 요일을 맞출 수 있다.
# +
print("****요일별 날짜****")
print("0-일요일 1-월요일 2-화요일 3-수요일 4-목요일 5-금요일 6-토요일")
day = input("오늘의 요일을 입력하세요:") #오늘의 요일을 day에 저장
day = eval(day) #오늘의 요일 값을 숫자로 바꿔준다.
daypass = input("오늘부터 경과한 일수를 입력하세요:") #경과한 일수를 daypass에 저장
daypass = eval(daypass) #daypass의 값을 숫자로 저장
future = day + daypass #future에 미래의 요일의 숫자값을 저장
date = future % 7 #date에 7로 나눈 나머지 값을 저장
if date==0:
future = '일요일'
elif date==1:
future = '월요일'
elif date==2:
future = '화요일'
elif date==3:
future = '수요일'
elif date==4:
future = '목요일'
elif date==5:
future = '금요일'
else:
future = '토요일' #나머지를 가지고 날짜를 future에 저장하는 조건문
if day==0:
today = '일요일'
elif day==1:
today = '월요일'
elif daty==2:
today = '화요일'
elif day==3:
today = '수요일'
elif day==4:
today = '목요일'
elif day==5:
today = '금요일'
else:
today = '토요일' #오늘의 요일을 today에 문자로 저장하는 조건문
print("오늘은 ",today,"이고 미래의 요일은 ",future,"입니다.") #최종 출력문
# -
# 문제 2번.(숫자 검사하기) 사용자로부터 하나의 정수를 입력받고, 그 정수가 5와 6 모두 나누어지는지, 5 또는 6으로 나누어지는지, 혹은 두 정수 모두로는 나누어지지 않지만 둘 중에 하나로만 나누어지는지를 검사하는 프로그램을 작성하시오
# 문제에 대한 설명. 5와 6으로 모두 나누어 떨어지면 입력 받은 수는 5와6의 공배수이다. 즉 30의 배수. 5또는 6으로 나누어 떨어지면 30의 배수에 5와 6의 배수가 포함된 것이다. 전자의 경우에는 30으로 나눠서 나머지가 0인지만 확인해 주면 되는데 후자의 경우에는 5와 6으로 나눠서 각각 나머지가 0인지 확인해주는 조건문이 필요하다. 그리고 그 나머지 값이 0이되는지 0이 아닌지에 따라서 질문 세 가지에 답할수 있는 조건문을 작성해줘야한다.
# +
number = eval(input("하나의 정수를 입력하세요:")) #number에 입력받은 수를 숫자값으로 저장
mod5 = number % 5 #mod5에는 5로 나눠떨어지는 확인하기 위한 나머지 값을 저장
mod6 = number % 6 #mod6에는 6으로 나눠떨어지는 확인하기 위한 나머지 값을 저장
if(mod5==0)and(mod6==0): #5와 6의 공통된 배수인지 확인하는 조건문
print(number,"은/는 5와 6으로 나누어집니까? True")
else:
print(number,"은/는 5와 6으로 나누어집니까? False")
if(mod5==0)or(mod6==0): #5또는 6의 공통된 배수인지 확인하는 조건문
print(number,"은/는 5 혹은 6으로 나누어집니까? True")
else:
print(number,"은/는 5 혹은 6으로 나누어집니까? False")
if(mod5==0)and(mod6==0): #5또는 6의 배수이지만 30의 배수가 아닌것
print(number,"은/는 5와 6으로 나누어지지만, 둘 모두로는 나누어지지 않습니까? False") #을 확인하는 조건문
elif(mod5==0)or(mod6==0):
print(number,"은/는 5와 6으로 나누어지지만, 둘 모두로는 나누어지지 않습니까? True")
else:
print(number,"은/는 5와 6으로 나누어지지만, 둘 모두로는 나누어지지 않습니까? False")
# -
# 문제3번. 도시의 인구가 현재 30,000명이고 매년 3%비율로 늘어나고 있다. 100,000명의 인구가 될 때까지 얼마나 많은 해가 소요되는지를 계산하는 프로그램을 작성하시오.
# 문제 설명.
#
# 1. 현재 인구는 30000명이다.
# 2. 매년 증가하는 비율은 3%이므로 1.03씩 증가하고 있다. ex) 1년뒤-> 3만x1.06 = 31800
# 3. 10만명이 될때까지 얼마나 많은 해가 소요되는지 계산한다면 먼저 계속적으로 증가되는 값을 따로 저장한다.ex)future
# 4. 반복문을 통해서 1.06을 곱해주는데 곱해준 수 만큼 날짜가 지난 것이므로 반복해서 돌린 i가 소요된 해가 된다.
# +
present = eval(input("현재의 인구수를 입력하세요:")) #현재의 인구수를 present에 저장한다.
print("매년 인구 증가율은 3%입니다.")
future = 1 #미래의 인구 값을 저장할 변수
count = 1 #얼마나 많은 해가 지났는지 count해줄 변수
while present < 100000:
present *= 1.06
count = count+1
print(count,"년 후에 인구 10만명을 넘어섭니다.")
# -
# 문제4번. 양수와 음수 개수 세기 및 평균 계산하기. 불특정 개수의 정수를 읽은 후, 양수와 음수가 몇 개씩 읽혔는지 겨정하고, 입력값의 개수와 평균을 계산하는 프로그램을 작성하시오(0은 세지 않는다).프로그램은 입력값 0으로 종료된다. 평균갑을 부동소수점 숫자로 출력한다.
# 문제 설명. 양수와 음수를 입력하면 그것들을 계속 읽어들이고 다른 변수에 저장해야한다. 반복문으로 계속 읽어들이면 된다. 반복문을 통해서 계속 입력을 받아들이면 그것들을 각각 양수와 음수로 구분하는 조건문이 있고 그것으로 양수와 음수의 개수를 각각 저장한다. 그리고 모든 수를 각각 더하면 총합이 나오고 받아들인 수의 개수를 총합으로 나누면 평균값이 나온다.
# +
num = 1 #종료키가 된는 변수
positive = 0 #양수의 개수를 저장할 변수
negative = 0 #음수의 개수를 저장할 변수
count = 0 #입력된 숫자의 개수를 저장하는 변수
total = 0 #총 입력된 숫자를 계속적으로 더해서 저장하는 변수
while num!=0:
num = eval(input("정수를 입력하세요. 입력값이 0이면 종료됩니다:"))
if num>0:
positive += 1
count += 1
elif num<0:
negative += 1
count += 1
else:
break
total += num #while문이 돌아가는 동안 계속적으로 저장한다.
print("양수의 개수는 ",positive,"개 입니다.")
print("음수의 개수는 ",negative,"개 입니다.")
print("총합은",total,"입니다.")
print("평균은",total/count,"입니다.")
# -
# 문제 5번. (최대공약수 계산하기) 예제 코드 lec05/GreatestCommonDivisor.py 에서 최대공약수를 구하는 방법을 알아보았다. 두 정수 n1 과 n2의 최대공약 수를 찾는 또 다른 해결방법은 다음과 같다. 우선, n1 과 n2 중 작은 수를 d 라고 한 후, d, d-1, d-2, …, 2, 1 의 순서로 각각 d 가 n1 과 n2 의 공약수 인지 검사한다. 첫 번째로 나타난 공약수가 두 수 n1 과 n2 의 최대공약수이 다. 이 방법으로 최대공약수를 구하는 프로그램을 작성하시오
# 문제설명.
# 1. 먼저 두개의 정수를 입력 받는다
# 2. 두 개의 정수를 입력 받으면 n1, n2에 저장한다.
# 3. 두 개의 정수 중에 작은 수를 d라는 변수에 저장한다.
# 4. d를 n1 또는 n2로 나누어서 나누어떨어지면 d가 최대 공약수가 되겠지만 그렇지 않다면 즉 %로 나눠서 나머지가 0이 아니면 d-1,d-2..로 계속 나눈다.
# 5. 나누다가 나머지가 0이 되는 값이 두 수 n1, n2의 최대공약수이다.
# +
n1 = eval(input("첫 번째 정수를 입력하세요:")) #n1에 첫번쨰 정수를 저장
n2 = eval(input("두 번째 정수를 입력하세요:")) #n2에 두번째 정수를 저장
d=0 #n1,n2중 작은 값을 저장할 변수
b=0 #n1,n2중 큰 값을 저장할 변수
mod=1 #mod에는 나머지 값을 저장한다.
if n1 > n2:
d = n2
b = n1
else:
d = n1
b = n2
while mod!= 0: #mod가 0일때까지는 계속 돌아간다.
mod = b % d #나눈 나머지를 mod에 저장한다.
d = d-1 #나눠떨어지지 않으면 d를 계속 1씩 줄여나간다.
print("최대공약수는",d+1,"입니다.")
# -
# 문제6번. (윤년 출력하기) 21 세기(2001 년부터 2100 년까지)의 모든 윤년 을 한 행에 10 개씩 출력하는 프로그램을 작성하시오. 연도는 단 공백 한 개 로 구분된다.
# 윤년은 윤달이나 윤날이 드는 해라고 해서 2월 29일이 있는 날이다. 4년마다 한 번씩 찾아온다. 우리나라에서는 2000년이 윤년이였다. 2000->2004->2008...->2020...>2100이다.
#
# 1. 2000년부터 시작이므로 2000에 계속 4를 더한다.
# 2. 한 행에 10개씩 출력해야되므로 count를 0부터 9까지 돌린다
# 3. count가 9가 되면 행을 바꾸고 count를 9로 초기화한다
# 4. leap(윤년 변수)가 2100을 넘어서면 더 이상 출력하지 않고 break한다.
# +
count = 0 #10개 씩 출력하기 위한 변수
leap = 2000 #leap는 윤년의 정보를 저장하는 변수
while leap < 2100:
leap += 4
print(leap,end=' ') #줄바꾸지 않게하는 end=' '
if leap > 2100:
break
else:
count += 1
if count == 10:
print(" ") #줄바꿔주는print(" ")
count = 0 #이중 조건문을 사용해서 count를 10가 되면 0으로 초기화한다.
# -
| 6,660 |
/programming/.ipynb_checkpoints/Uso de Variáveis-checkpoint.ipynb | 0bce7b887a22db4cb4ffadf9d52326a8e2cdb0ac | [] | no_license | ThiagoVsky/courses | https://github.com/ThiagoVsky/courses | 0 | 0 | null | 2018-03-15T13:11:04 | 2018-03-14T15:32:11 | null | Jupyter Notebook | false | false | .py | 14,974 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Variáveis
# ___
# ## Introdução
# Considere a seguinte tabela de salários:
#
# | Funcionário | Salário Atual |
# |:---:|:---:|
# | A | 1000 |
# | B | 1200 |
# | C | 1500 |
#
# Foi solicitado ao analista que seja desenvolvido um programa para cálculo dos novos salários. Supondo que os salários tiveram um aumento de 20% no último mês, considere o seguinte código para cálculo dos novos salários:
# +
# Salário 1
print("O novo salário do funcionário 1 é:",(1000 * 1.20))
# Salário 2
print("O novo salário do funcionário 2 é:",(1200 * 1.20))
# Salário 3
print("O novo salário do funcionário 3 é:",(1500 * 1.20))
# -
# No ano seguinte, a alíquota de aumento dos salários foi de 12%. Qual seria o procedimento a ser adotado para refletir essa alteração? No caso do código acima, cada linha deveria ser alterada:
# +
# Salário 1
print("O novo salário do funcionário 1 é:",(1000 * 1.12))
# Salário 2
print("O novo salário do funcionário 2 é:",(1200 * 1.12))
# Salário 3
print("O novo salário do funcionário 3 é:",(1500 * 1.12))
# -
# O mais correto seria usar um elemento que armazenasse o percentual de aumento e usá-lo no código:
# +
# elemento que armazenará o percentual de aumento (12% somado a 1.00)
aumento = 1.12
# Salário 1
print("O novo salário do funcionário 1 é:",(1000 * aumento))
# Salário 2
print("O novo salário do funcionário 2 é:",(1200 * aumento))
# Salário 3
print("O novo salário do funcionário 3 é:",(1500 * aumento))
# -
# Note que, caso haja uma alíquota diferente no outro ano, basta alterar uma linha de código e não três, como nas células anteriores. Esse elemento, que armazena valores para serem usados ao longo do código, é conhecido como **variável**.
# ## Definições
# Uma variável nada mais é que uma espécie de *container* que armazenará um valor em memória. Algumas características importantes sobre variáveis:
#
# - Uma variável contém o nome e o valor que armazena em determinado momento
# - O valor pode ser alterado
# - Variáveis podem ser de diferentes tipos (texto, número, data, etc)
# - Uma variável pode ser usada em diferentes partes do código
# - O valor de uma variável pode ser copiado para outra, sobrescrevendo o valor anterior
# ## Nomes de Variáveis
# | Exemplo | Pode? | Por que? |
# |:---:|:---:|:---:|
# | nome do professor | NÃO | Não se usa espaço |
# | nome_do_professor | SIM | Pode usar underscore |
# | nome-do-professor | NÃO | Não pode usar hífen |
# | 1nome | NÃO | Não pode usar número no início do nome da variável |
# | nome1 | SIM | Com exceção do início, pode-se usar números no meio ou fim |
# | \$nome | NÃO | O único caracter possível é _
#
# Experimente na célula abaixo criar uma variável para cada exemplo acima e verifique as mensagens de erro:
#
# +
# teste os exemplos de nomes de variáveis aqui
# -
# Python, assim como Java, é *case sensitive*, ou seja, diferencia letras maiúsculas de minúsculas:
# +
nome = "Fernando"
Nome = "Python"
# O que será impresso? Fernando ou Python?
print(Nome)
# -
# ## Atribuição
# A operação de armazenar um valor em uma variável é chamada de **atribuição** e é feita com o operador de igual (=). Atenção para o fato de que boa parte das linguagens de programação a operação de atribuição é feita com = e a operação de comparação de igualdade é feita com ==.
# +
i = 3 # lê-se: a variável i recebe o valor 3
# verificando o valor de i
print(i)
# armazena na variável comparação (i==3), que no caso seria verdadeiro
comparacao = (i==3)
# verificando o valor de comparacao
print(comparacao)
# -
# Implemente na célula abaixo uma variável que receba a sua idade. Na linha seguinte faça a impressão:
# +
# crie uma variável chamada minhaIdade e atribua a sua idade a essa variável
# implemente abaixo a instrução para imprimir o valor dessa variável
# -
# Em Python, podem ser feitas atribuições em lote, ou seja, uma atribuição para três variáveis diferentes:
# +
i = j = k = 1
print(i)
print(j)
print(k)
# -
# ## Exemplos
# Considere o código abaixo:
i = 3
j = 5
print(i+j)
# Teria outra forma de melhorar o código acima? E se a soma de i e j fosse ser usada em outro lugar no código. Não seria o caso de se criar uma outra variável apenas para o resultado?
i = 3
j = 5
soma = i + j
print(soma)
# Note que, embora o código tenha aumentado em uma linha, o valor da soma de i e j pode ser usado em outras partes do código. Agora considere o código abaixo:
# +
i = 3
j = 5
soma = i + j
print("O resultado é:", soma)
produto = i * j
print("O resultado é:", produto)
# -
# Note que os valores de i e j foram usados em diferentes partes do código, para as operações de soma e multiplicação. No entanto, ainda há possibilidades de melhorar esse código com o uso de variáveis. Como você faria? Implemente na célula abaixo:
# +
# implemente aqui
# -
# Agora considere a seguinte sequência de instruções:
# +
i = 4
j = 5
soma = i + j
k = 3
soma = k
print(soma)
# -
# Qual será o valor final da variável soma? Execute o código acima para responder a questão.
# A instrução
# ```python
# print(soma)
# ```
# irá imprimir o valor 3 pois, embora a variável soma tenha recebido o valor da soma de i e j, logo após dessa instrução a variável soma recebe uma cópia do valor que está na variável k, através da instrução:
#
# ```python
# soma = k
# ```
# A instrução acima basicamente executa o seguinte *copie o valor da variável k dentro da variável soma*. Isso quer dizer que tanto a variável *soma* quanto a variável *k* terão o valor *3* após o final da execução desse trecho de código. Para entender melhor, verifique o estado de cada uma ao longo da execução do código:
# | | Instrução | i | j | soma | k |
# |:---:| :---:| :---: | :---: | :---: | :---: |
# | 1 | i = 4| 4 | - | - | - |
# | 2 | j = 5| 4 | 5 | - | - |
# | 3 | soma = i + j| 4 | 5 | 9 | - |
# | 4 | k = 3| 4 | 5 | 9 | 3 |
# | 5 | soma = k| 4 | 9 | 3 | 3 |
#
# Note que, ao final da execução da linha 5, o valor da variável soma muda de 9 para 3. Além disso, o fato do valor da variável k ser copiado para a variável soma, não implica que o valor k perderá o seu valor, continuando com o valor 3 ao final da execução da linha.
# ## Tipos
# +
i = 3
j = 6
soma = i + j
print("A soma é:", soma)
i = "Python"
print("Você está programando em:", i)
# -
# O que aconteceu com a variável i? Por que ela armazenou os valores 3 e "Python" sem dar erro, já que 3 é do tipo int e "Python" é do tipo String?
# Ao contrário de linguagens como Java, não há necessidade de definir o tipo da variável de forma antecipada. Python irá inferir o tipo de acordo com o valor que está dentro dela:
i = 3 # int
i = "Python" # string
i = False # boolean
i = 3.0 # float
i = 0XF # hexadecimal
i = 0B10 # binário
# ## Exercícios
# ** Ex 1: Crie um programa que some dois números, usando uma variável para cada valor manipulado no cálculo. **
# +
# implemente aqui
# -
# ** Ex 2: Implemente um programa que, dado o nome do usuário, imprima uma mensagem ("Bom dia"/"Boa noite"). Use tantas variáveis quanto forem necessárias.**
# +
# implemente aqui
# -
# ** Ex 3: Modifique o programa abaixo, de modo que se troque os números por variáveis.**
# +
media = (6 + 8)/2
if (media>=6):
print("Aluno aprovado")
else:
print("Aluno reprovado")
# -
# ** Ex 4: Qual o valor final da variável z?**
x = 1
y = x + 1
z = y + x
print(z)
# ** Ex 5: Use a função type() para descobrir o tipo de cada uma das variáveis abaixo. **
i = 3.0
j = 4
k = 0xAF
l = "Python"
m = (4 == 6)
print("i ",type(i))
print("j ",type(j))
n = False
print('k ',type(k),k)
print('l ',type(l))
print('m ',type(m))
print('n ',type(n))
print('t ',type(type(k)))
| 7,921 |
/PythonLab_2/code/Linear regression.ipynb | 79a60126cfa0ddae8d5b27d2935cc1fb532d1f2f | [] | no_license | Jakkula134/PythonICPs | https://github.com/Jakkula134/PythonICPs | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 5,324,210 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + id="HQAUhoXIUNYp" colab_type="code" outputId="c144fd79-7bf2-4e0a-80f9-c381a25f0289" executionInfo={"status": "ok", "timestamp": 1589095484869, "user_tz": 300, "elapsed": 9407, "user": {"displayName": "Sai Tejaswi K", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjazYsScRsH1gybaeda9DLBjRoDYSbZRSrtb_hL=s64", "userId": "03908401170557800418"}} colab={"base_uri": "https://localhost:8080/", "height": 1000}
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import pandas as pd
from keras.optimizers import SGD, Adam, Adamax
from sklearn.model_selection import train_test_split
from keras.callbacks import TensorBoard
from sklearn.preprocessing import LabelEncoder
from keras import metrics
import matplotlib.pyplot as plt
dataset=pd.read_csv('/content/drive/My Drive/Colab Notebooks/insurance.csv')
le = LabelEncoder()
dataset['region'] = le.fit_transform(dataset['region'].astype('str'))
dataset['sex'] = le.fit_transform(dataset['sex'].astype('str'))
dataset['smoker'] = le.fit_transform(dataset['smoker'].astype('str'))
print(dataset.head())
# dataset = dataset.values
# split into input (X) and output (Y) variables
X = dataset.iloc[:,0:6]
Y = dataset.iloc[:,6]
X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
test_size=0.25, random_state=100)
# HyperParameters2
activation_function="tanh"
learning_rate=0.1
epochs=50
b_size=32
decay_rate= learning_rate / epochs
adam= Adam(lr=learning_rate, decay=decay_rate)
#Define the model
model = Sequential()
model.add(Dense(50, input_dim = 6, activation=activation_function))
model.add(Dropout(0.1))
model.add(Dense(20, activation=activation_function))
model.add(Dense(10, activation=activation_function))
model.add(Dense(1,input_dim = 6, activation=activation_function))
model.compile(optimizer = "Adamax", loss = 'mean_squared_error', metrics = [metrics.mae])
tbCallBack = TensorBoard(log_dir='./Graph1', histogram_freq=0, write_graph=True, write_images=True)
hist = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=epochs, batch_size=b_size,callbacks=[tbCallBack])
# Final evaluation of the model
mae, loss= model.evaluate(X_test, Y_test, verbose=0)
print(mae, loss)
# accuracy history
plt.plot(hist.history['mean_absolute_error'])
plt.plot(hist.history['val_mean_absolute_error'])
plt.title('model mae')
plt.ylabel('mae')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# loss
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# + id="jXFQGAMuVDAj" colab_type="code" outputId="a329f9b4-5f4e-45ae-dc37-50b7fdab892f" executionInfo={"status": "ok", "timestamp": 1589009265366, "user_tz": 300, "elapsed": 3859, "user": {"displayName": "Sai Tejaswi K", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjazYsScRsH1gybaeda9DLBjRoDYSbZRSrtb_hL=s64", "userId": "03908401170557800418"}} colab={"resources": {"https://localhost:6006/": {"data": "<!doctype html><!--
@license
Copyright 2016 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
--><html lang="en"><meta charset="utf-8">
  <title>TensorBoard</title>
  <link rel="shortcut icon" href="data:image/png;base64,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">
  <link rel="apple-touch-icon" href="data:image/png;base64,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">

  







































































































































































































<style>
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/uYECMKoHcO9x1wdmbyHIm3-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/sTdaA6j0Psb920Vjv-mrzH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/_VYFx-s824kXq_Ul2BHqYH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/tnj4SB6DNbdaQnsM8CFqBX-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/Ks_cVxiCiwUWVsFWFA3Bjn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/NJ4vxlgWwWbEsv18dAhqnn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/isZ-wbCXNKAbnjo6_TwHToX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/77FXFjRbGzN4aCrSFhlh3oX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/jSN2CGVDbcVyCnfJfjSdfIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/UX6i4JxQDm3fVTc1CPuwqoX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/97uahxiqZRoncBaCEI3aW4X0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/PwZc-YbIL414wB9rB1IAPYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC14sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC_ZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcCwt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC1BW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC4gp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC6E8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC9DiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/OpXUqTo0UgQQhGj_SFdLWBkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/WxrXJa0C3KdtC7lMafG4dRkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/cDKhRaXnQTOVbaoxwdOr9xkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/1hZf02POANh32k2VkgEoUBkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/vPcynSL0qHq_6dX7lKVByXYhjbSpvc47ee6xR_80Hnw.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/vSzulfKSK0LLjjfeaxcREhkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/K23cxWVTrIFD6DJsEVi07RkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/Fl4y0QdOxyyTHEGMXX8kcYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/0eC6fl06luXEYWpBSJvXCIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/I3S1wsgSg9YCurV6PUkTOYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/-L14Jk06m6pUHB-5mXQQnYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/Hgo13k-tfSpn0qi1SFdUfZBw1xU1rKptJj_0jans920.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/Pru33qjShpZSmG3z6VYwnYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/NYDWBdD4gIq26G5XYbHsFIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at14sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at_ZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0atwt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at1BW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at4gp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at6E8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at9DiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/oHi30kwQWvpCWqAhzHcCSIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/ZLqKeelYbATG60EpZBSDy4X0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/mx9Uck6uB63VIKFYnEMXrYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/rGvHdJnr2l75qb0YND9NyIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/RxZJdnzeo3R5zSexge8UUZBw1xU1rKptJj_0jans920.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/oOeFwZNlrTefzLYmlVV1UIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/mbmhprMH69Zi6eEPBYVFhYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0V4sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0fZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0Qt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0VBW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0Ygp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0aE8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0dDiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY14sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY_ZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpYwt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY1BW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY4gp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY6E8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY9DiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz1x-M1I1w5OMiqnVF8xBLhU.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59FzwXaAXup5mZlfK6xRLrhsco.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fzwn6Wqxo-xwxilDXPU8chVU.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz1T7aJLK6nKpn36IMwTcMMc.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz_79_ZuUxCigM2DespTnFaw.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz4gd9OEPUCN3AdYW0e8tat4.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz8bIQSYZnWLaWC9QNCpTK_U.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
</style>


<style>
  html,
  body {
    margin: 0;
    padding: 0;
    height: 100%;
    font-family: Roboto, sans-serif;
  }
</style>






<custom-style>
  <style is="custom-style">
    [hidden] {
      display: none !important;
    }
  </style>
</custom-style>

<custom-style>
  <style is="custom-style">
    html {

      --layout: {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      };

      --layout-inline: {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      };

      --layout-horizontal: {
        @apply --layout;

        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      };

      --layout-horizontal-reverse: {
        @apply --layout;

        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      };

      --layout-vertical: {
        @apply --layout;

        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      };

      --layout-vertical-reverse: {
        @apply --layout;

        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      };

      --layout-wrap: {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      };

      --layout-wrap-reverse: {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      };

      --layout-flex-auto: {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      };

      --layout-flex-none: {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      };

      --layout-flex: {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      };

      --layout-flex-2: {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      };

      --layout-flex-3: {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      };

      --layout-flex-4: {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      };

      --layout-flex-5: {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      };

      --layout-flex-6: {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      };

      --layout-flex-7: {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      };

      --layout-flex-8: {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      };

      --layout-flex-9: {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      };

      --layout-flex-10: {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      };

      --layout-flex-11: {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      };

      --layout-flex-12: {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      };

      /* alignment in cross axis */

      --layout-start: {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      };

      --layout-center: {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      };

      --layout-end: {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      };

      --layout-baseline: {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      };

      /* alignment in main axis */

      --layout-start-justified: {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      };

      --layout-center-justified: {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      };

      --layout-end-justified: {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      };

      --layout-around-justified: {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      };

      --layout-justified: {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      };

      --layout-center-center: {
        @apply --layout-center;
        @apply --layout-center-justified;
      };

      /* self alignment */

      --layout-self-start: {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      };

      --layout-self-center: {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      };

      --layout-self-end: {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      };

      --layout-self-stretch: {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      };

      --layout-self-baseline: {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      };

      /* multi-line alignment in main axis */

      --layout-start-aligned: {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      };

      --layout-end-aligned: {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      };

      --layout-center-aligned: {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      };

      --layout-between-aligned: {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      };

      --layout-around-aligned: {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      };

      /*******************************
                Other Layout
      *******************************/

      --layout-block: {
        display: block;
      };

      --layout-invisible: {
        visibility: hidden !important;
      };

      --layout-relative: {
        position: relative;
      };

      --layout-fit: {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-scroll: {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      };

      --layout-fullbleed: {
        margin: 0;
        height: 100vh;
      };

      /* fixed position */

      --layout-fixed-top: {
        position: fixed;
        top: 0;
        left: 0;
        right: 0;
      };

      --layout-fixed-right: {
        position: fixed;
        top: 0;
        right: 0;
        bottom: 0;
      };

      --layout-fixed-bottom: {
        position: fixed;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-fixed-left: {
        position: fixed;
        top: 0;
        bottom: 0;
        left: 0;
      };

    }
  </style>
</custom-style>





















<dom-module id="paper-ripple">

  <template>
    <style>
      :host {
        display: block;
        position: absolute;
        border-radius: inherit;
        overflow: hidden;
        top: 0;
        left: 0;
        right: 0;
        bottom: 0;

        /* See PolymerElements/paper-behaviors/issues/34. On non-Chrome browsers,
         * creating a node (with a position:absolute) in the middle of an event
         * handler "interrupts" that event handler (which happens when the
         * ripple is created on demand) */
        pointer-events: none;
      }

      :host([animating]) {
        /* This resolves a rendering issue in Chrome (as of 40) where the
           ripple is not properly clipped by its parent (which may have
           rounded corners). See: http://jsbin.com/temexa/4

           Note: We only apply this style conditionally. Otherwise, the browser
           will create a new compositing layer for every ripple element on the
           page, and that would be bad. */
        -webkit-transform: translate(0, 0);
        transform: translate3d(0, 0, 0);
      }

      #background,
      #waves,
      .wave-container,
      .wave {
        pointer-events: none;
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
      }

      #background,
      .wave {
        opacity: 0;
      }

      #waves,
      .wave {
        overflow: hidden;
      }

      .wave-container,
      .wave {
        border-radius: 50%;
      }

      :host(.circle) #background,
      :host(.circle) #waves {
        border-radius: 50%;
      }

      :host(.circle) .wave-container {
        overflow: hidden;
      }
    </style>

    <div id="background"></div>
    <div id="waves"></div>
  </template>
</dom-module>











<custom-style>
  <style is="custom-style">
    html {

      --shadow-transition: {
        transition: box-shadow 0.28s cubic-bezier(0.4, 0, 0.2, 1);
      };

      --shadow-none: {
        box-shadow: none;
      };

      /* from http://codepen.io/shyndman/pen/c5394ddf2e8b2a5c9185904b57421cdb */

      --shadow-elevation-2dp: {
        box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.14),
                    0 1px 5px 0 rgba(0, 0, 0, 0.12),
                    0 3px 1px -2px rgba(0, 0, 0, 0.2);
      };

      --shadow-elevation-3dp: {
        box-shadow: 0 3px 4px 0 rgba(0, 0, 0, 0.14),
                    0 1px 8px 0 rgba(0, 0, 0, 0.12),
                    0 3px 3px -2px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-4dp: {
        box-shadow: 0 4px 5px 0 rgba(0, 0, 0, 0.14),
                    0 1px 10px 0 rgba(0, 0, 0, 0.12),
                    0 2px 4px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-6dp: {
        box-shadow: 0 6px 10px 0 rgba(0, 0, 0, 0.14),
                    0 1px 18px 0 rgba(0, 0, 0, 0.12),
                    0 3px 5px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-8dp: {
        box-shadow: 0 8px 10px 1px rgba(0, 0, 0, 0.14),
                    0 3px 14px 2px rgba(0, 0, 0, 0.12),
                    0 5px 5px -3px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-12dp: {
        box-shadow: 0 12px 16px 1px rgba(0, 0, 0, 0.14),
                    0 4px 22px 3px rgba(0, 0, 0, 0.12),
                    0 6px 7px -4px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-16dp: {
        box-shadow: 0 16px 24px 2px rgba(0, 0, 0, 0.14),
                    0  6px 30px 5px rgba(0, 0, 0, 0.12),
                    0  8px 10px -5px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-24dp: {
        box-shadow: 0 24px 38px 3px rgba(0, 0, 0, 0.14),
                    0 9px 46px 8px rgba(0, 0, 0, 0.12),
                    0 11px 15px -7px rgba(0, 0, 0, 0.4);
      };
    }
  </style>
</custom-style>




<dom-module id="paper-material-styles">
  <template>
    <style>
      :host, html {
        --paper-material: {
          display: block;
          position: relative;
        };
        --paper-material-elevation-1: {
          @apply --shadow-elevation-2dp;
        };
        --paper-material-elevation-2: {
          @apply --shadow-elevation-4dp;
        };
        --paper-material-elevation-3: {
          @apply --shadow-elevation-6dp;
        };
        --paper-material-elevation-4: {
          @apply --shadow-elevation-8dp;
        };
        --paper-material-elevation-5: {
          @apply --shadow-elevation-16dp;
        };
      }
      :host(.paper-material), .paper-material {
        @apply --paper-material;
      }
      :host(.paper-material[elevation="1"]), .paper-material[elevation="1"] {
        @apply --paper-material-elevation-1;
      }
      :host(.paper-material[elevation="2"]), .paper-material[elevation="2"] {
        @apply --paper-material-elevation-2;
      }
      :host(.paper-material[elevation="3"]), .paper-material[elevation="3"] {
        @apply --paper-material-elevation-3;
      }
      :host(.paper-material[elevation="4"]), .paper-material[elevation="4"] {
        @apply --paper-material-elevation-4;
      }
      :host(.paper-material[elevation="5"]), .paper-material[elevation="5"] {
        @apply --paper-material-elevation-5;
      }
    </style>
  </template>
</dom-module>




<dom-module id="paper-button">
  <template strip-whitespace>
    <style include="paper-material-styles">
      /* Need to specify the same specificity as the styles imported from paper-material. */
      :host {
        @apply --layout-inline;
        @apply --layout-center-center;
        position: relative;
        box-sizing: border-box;
        min-width: 5.14em;
        margin: 0 0.29em;
        background: transparent;
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;
        font: inherit;
        text-transform: uppercase;
        outline-width: 0;
        border-radius: 3px;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;
        cursor: pointer;
        z-index: 0;
        padding: 0.7em 0.57em;

        @apply --paper-font-common-base;
        @apply --paper-button;
      }

      :host([elevation="1"]) {
        @apply --paper-material-elevation-1;
      }

      :host([elevation="2"]) {
        @apply --paper-material-elevation-2;
      }

      :host([elevation="3"]) {
        @apply --paper-material-elevation-3;
      }

      :host([elevation="4"]) {
        @apply --paper-material-elevation-4;
      }

      :host([elevation="5"]) {
        @apply --paper-material-elevation-5;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host([raised].keyboard-focus) {
        font-weight: bold;
        @apply --paper-button-raised-keyboard-focus;
      }

      :host(:not([raised]).keyboard-focus) {
        font-weight: bold;
        @apply --paper-button-flat-keyboard-focus;
      }

      :host([disabled]) {
        background: #eaeaea;
        color: #a8a8a8;
        cursor: auto;
        pointer-events: none;

        @apply --paper-button-disabled;
      }

      :host([animated]) {
        @apply --shadow-transition;
      }

      paper-ripple {
        color: var(--paper-button-ink-color);
      }
    </style>

    <slot></slot>
  </template>

  
</dom-module>





<custom-style>
  <style is="custom-style">
    html {

      /* Material Design color palette for Google products */

      --google-red-100: #f4c7c3;
      --google-red-300: #e67c73;
      --google-red-500: #db4437;
      --google-red-700: #c53929;

      --google-blue-100: #c6dafc;
      --google-blue-300: #7baaf7;
      --google-blue-500: #4285f4;
      --google-blue-700: #3367d6;

      --google-green-100: #b7e1cd;
      --google-green-300: #57bb8a;
      --google-green-500: #0f9d58;
      --google-green-700: #0b8043;

      --google-yellow-100: #fce8b2;
      --google-yellow-300: #f7cb4d;
      --google-yellow-500: #f4b400;
      --google-yellow-700: #f09300;

      --google-grey-100: #f5f5f5;
      --google-grey-300: #e0e0e0;
      --google-grey-500: #9e9e9e;
      --google-grey-700: #616161;

      /* Material Design color palette from online spec document */

      --paper-red-50: #ffebee;
      --paper-red-100: #ffcdd2;
      --paper-red-200: #ef9a9a;
      --paper-red-300: #e57373;
      --paper-red-400: #ef5350;
      --paper-red-500: #f44336;
      --paper-red-600: #e53935;
      --paper-red-700: #d32f2f;
      --paper-red-800: #c62828;
      --paper-red-900: #b71c1c;
      --paper-red-a100: #ff8a80;
      --paper-red-a200: #ff5252;
      --paper-red-a400: #ff1744;
      --paper-red-a700: #d50000;

      --paper-pink-50: #fce4ec;
      --paper-pink-100: #f8bbd0;
      --paper-pink-200: #f48fb1;
      --paper-pink-300: #f06292;
      --paper-pink-400: #ec407a;
      --paper-pink-500: #e91e63;
      --paper-pink-600: #d81b60;
      --paper-pink-700: #c2185b;
      --paper-pink-800: #ad1457;
      --paper-pink-900: #880e4f;
      --paper-pink-a100: #ff80ab;
      --paper-pink-a200: #ff4081;
      --paper-pink-a400: #f50057;
      --paper-pink-a700: #c51162;

      --paper-purple-50: #f3e5f5;
      --paper-purple-100: #e1bee7;
      --paper-purple-200: #ce93d8;
      --paper-purple-300: #ba68c8;
      --paper-purple-400: #ab47bc;
      --paper-purple-500: #9c27b0;
      --paper-purple-600: #8e24aa;
      --paper-purple-700: #7b1fa2;
      --paper-purple-800: #6a1b9a;
      --paper-purple-900: #4a148c;
      --paper-purple-a100: #ea80fc;
      --paper-purple-a200: #e040fb;
      --paper-purple-a400: #d500f9;
      --paper-purple-a700: #aa00ff;

      --paper-deep-purple-50: #ede7f6;
      --paper-deep-purple-100: #d1c4e9;
      --paper-deep-purple-200: #b39ddb;
      --paper-deep-purple-300: #9575cd;
      --paper-deep-purple-400: #7e57c2;
      --paper-deep-purple-500: #673ab7;
      --paper-deep-purple-600: #5e35b1;
      --paper-deep-purple-700: #512da8;
      --paper-deep-purple-800: #4527a0;
      --paper-deep-purple-900: #311b92;
      --paper-deep-purple-a100: #b388ff;
      --paper-deep-purple-a200: #7c4dff;
      --paper-deep-purple-a400: #651fff;
      --paper-deep-purple-a700: #6200ea;

      --paper-indigo-50: #e8eaf6;
      --paper-indigo-100: #c5cae9;
      --paper-indigo-200: #9fa8da;
      --paper-indigo-300: #7986cb;
      --paper-indigo-400: #5c6bc0;
      --paper-indigo-500: #3f51b5;
      --paper-indigo-600: #3949ab;
      --paper-indigo-700: #303f9f;
      --paper-indigo-800: #283593;
      --paper-indigo-900: #1a237e;
      --paper-indigo-a100: #8c9eff;
      --paper-indigo-a200: #536dfe;
      --paper-indigo-a400: #3d5afe;
      --paper-indigo-a700: #304ffe;

      --paper-blue-50: #e3f2fd;
      --paper-blue-100: #bbdefb;
      --paper-blue-200: #90caf9;
      --paper-blue-300: #64b5f6;
      --paper-blue-400: #42a5f5;
      --paper-blue-500: #2196f3;
      --paper-blue-600: #1e88e5;
      --paper-blue-700: #1976d2;
      --paper-blue-800: #1565c0;
      --paper-blue-900: #0d47a1;
      --paper-blue-a100: #82b1ff;
      --paper-blue-a200: #448aff;
      --paper-blue-a400: #2979ff;
      --paper-blue-a700: #2962ff;

      --paper-light-blue-50: #e1f5fe;
      --paper-light-blue-100: #b3e5fc;
      --paper-light-blue-200: #81d4fa;
      --paper-light-blue-300: #4fc3f7;
      --paper-light-blue-400: #29b6f6;
      --paper-light-blue-500: #03a9f4;
      --paper-light-blue-600: #039be5;
      --paper-light-blue-700: #0288d1;
      --paper-light-blue-800: #0277bd;
      --paper-light-blue-900: #01579b;
      --paper-light-blue-a100: #80d8ff;
      --paper-light-blue-a200: #40c4ff;
      --paper-light-blue-a400: #00b0ff;
      --paper-light-blue-a700: #0091ea;

      --paper-cyan-50: #e0f7fa;
      --paper-cyan-100: #b2ebf2;
      --paper-cyan-200: #80deea;
      --paper-cyan-300: #4dd0e1;
      --paper-cyan-400: #26c6da;
      --paper-cyan-500: #00bcd4;
      --paper-cyan-600: #00acc1;
      --paper-cyan-700: #0097a7;
      --paper-cyan-800: #00838f;
      --paper-cyan-900: #006064;
      --paper-cyan-a100: #84ffff;
      --paper-cyan-a200: #18ffff;
      --paper-cyan-a400: #00e5ff;
      --paper-cyan-a700: #00b8d4;

      --paper-teal-50: #e0f2f1;
      --paper-teal-100: #b2dfdb;
      --paper-teal-200: #80cbc4;
      --paper-teal-300: #4db6ac;
      --paper-teal-400: #26a69a;
      --paper-teal-500: #009688;
      --paper-teal-600: #00897b;
      --paper-teal-700: #00796b;
      --paper-teal-800: #00695c;
      --paper-teal-900: #004d40;
      --paper-teal-a100: #a7ffeb;
      --paper-teal-a200: #64ffda;
      --paper-teal-a400: #1de9b6;
      --paper-teal-a700: #00bfa5;

      --paper-green-50: #e8f5e9;
      --paper-green-100: #c8e6c9;
      --paper-green-200: #a5d6a7;
      --paper-green-300: #81c784;
      --paper-green-400: #66bb6a;
      --paper-green-500: #4caf50;
      --paper-green-600: #43a047;
      --paper-green-700: #388e3c;
      --paper-green-800: #2e7d32;
      --paper-green-900: #1b5e20;
      --paper-green-a100: #b9f6ca;
      --paper-green-a200: #69f0ae;
      --paper-green-a400: #00e676;
      --paper-green-a700: #00c853;

      --paper-light-green-50: #f1f8e9;
      --paper-light-green-100: #dcedc8;
      --paper-light-green-200: #c5e1a5;
      --paper-light-green-300: #aed581;
      --paper-light-green-400: #9ccc65;
      --paper-light-green-500: #8bc34a;
      --paper-light-green-600: #7cb342;
      --paper-light-green-700: #689f38;
      --paper-light-green-800: #558b2f;
      --paper-light-green-900: #33691e;
      --paper-light-green-a100: #ccff90;
      --paper-light-green-a200: #b2ff59;
      --paper-light-green-a400: #76ff03;
      --paper-light-green-a700: #64dd17;

      --paper-lime-50: #f9fbe7;
      --paper-lime-100: #f0f4c3;
      --paper-lime-200: #e6ee9c;
      --paper-lime-300: #dce775;
      --paper-lime-400: #d4e157;
      --paper-lime-500: #cddc39;
      --paper-lime-600: #c0ca33;
      --paper-lime-700: #afb42b;
      --paper-lime-800: #9e9d24;
      --paper-lime-900: #827717;
      --paper-lime-a100: #f4ff81;
      --paper-lime-a200: #eeff41;
      --paper-lime-a400: #c6ff00;
      --paper-lime-a700: #aeea00;

      --paper-yellow-50: #fffde7;
      --paper-yellow-100: #fff9c4;
      --paper-yellow-200: #fff59d;
      --paper-yellow-300: #fff176;
      --paper-yellow-400: #ffee58;
      --paper-yellow-500: #ffeb3b;
      --paper-yellow-600: #fdd835;
      --paper-yellow-700: #fbc02d;
      --paper-yellow-800: #f9a825;
      --paper-yellow-900: #f57f17;
      --paper-yellow-a100: #ffff8d;
      --paper-yellow-a200: #ffff00;
      --paper-yellow-a400: #ffea00;
      --paper-yellow-a700: #ffd600;

      --paper-amber-50: #fff8e1;
      --paper-amber-100: #ffecb3;
      --paper-amber-200: #ffe082;
      --paper-amber-300: #ffd54f;
      --paper-amber-400: #ffca28;
      --paper-amber-500: #ffc107;
      --paper-amber-600: #ffb300;
      --paper-amber-700: #ffa000;
      --paper-amber-800: #ff8f00;
      --paper-amber-900: #ff6f00;
      --paper-amber-a100: #ffe57f;
      --paper-amber-a200: #ffd740;
      --paper-amber-a400: #ffc400;
      --paper-amber-a700: #ffab00;

      --paper-orange-50: #fff3e0;
      --paper-orange-100: #ffe0b2;
      --paper-orange-200: #ffcc80;
      --paper-orange-300: #ffb74d;
      --paper-orange-400: #ffa726;
      --paper-orange-500: #ff9800;
      --paper-orange-600: #fb8c00;
      --paper-orange-700: #f57c00;
      --paper-orange-800: #ef6c00;
      --paper-orange-900: #e65100;
      --paper-orange-a100: #ffd180;
      --paper-orange-a200: #ffab40;
      --paper-orange-a400: #ff9100;
      --paper-orange-a700: #ff6500;

      --paper-deep-orange-50: #fbe9e7;
      --paper-deep-orange-100: #ffccbc;
      --paper-deep-orange-200: #ffab91;
      --paper-deep-orange-300: #ff8a65;
      --paper-deep-orange-400: #ff7043;
      --paper-deep-orange-500: #ff5722;
      --paper-deep-orange-600: #f4511e;
      --paper-deep-orange-700: #e64a19;
      --paper-deep-orange-800: #d84315;
      --paper-deep-orange-900: #bf360c;
      --paper-deep-orange-a100: #ff9e80;
      --paper-deep-orange-a200: #ff6e40;
      --paper-deep-orange-a400: #ff3d00;
      --paper-deep-orange-a700: #dd2c00;

      --paper-brown-50: #efebe9;
      --paper-brown-100: #d7ccc8;
      --paper-brown-200: #bcaaa4;
      --paper-brown-300: #a1887f;
      --paper-brown-400: #8d6e63;
      --paper-brown-500: #795548;
      --paper-brown-600: #6d4c41;
      --paper-brown-700: #5d4037;
      --paper-brown-800: #4e342e;
      --paper-brown-900: #3e2723;

      --paper-grey-50: #fafafa;
      --paper-grey-100: #f5f5f5;
      --paper-grey-200: #eeeeee;
      --paper-grey-300: #e0e0e0;
      --paper-grey-400: #bdbdbd;
      --paper-grey-500: #9e9e9e;
      --paper-grey-600: #757575;
      --paper-grey-700: #616161;
      --paper-grey-800: #424242;
      --paper-grey-900: #212121;

      --paper-blue-grey-50: #eceff1;
      --paper-blue-grey-100: #cfd8dc;
      --paper-blue-grey-200: #b0bec5;
      --paper-blue-grey-300: #90a4ae;
      --paper-blue-grey-400: #78909c;
      --paper-blue-grey-500: #607d8b;
      --paper-blue-grey-600: #546e7a;
      --paper-blue-grey-700: #455a64;
      --paper-blue-grey-800: #37474f;
      --paper-blue-grey-900: #263238;

      /* opacity for dark text on a light background */
      --dark-divider-opacity: 0.12;
      --dark-disabled-opacity: 0.38; /* or hint text or icon */
      --dark-secondary-opacity: 0.54;
      --dark-primary-opacity: 0.87;

      /* opacity for light text on a dark background */
      --light-divider-opacity: 0.12;
      --light-disabled-opacity: 0.3; /* or hint text or icon */
      --light-secondary-opacity: 0.7;
      --light-primary-opacity: 1.0;

    }

  </style>
</custom-style>




<custom-style>
  <style is="custom-style">
    html {
      /*
       * You can use these generic variables in your elements for easy theming.
       * For example, if all your elements use `--primary-text-color` as its main
       * color, then switching from a light to a dark theme is just a matter of
       * changing the value of `--primary-text-color` in your application.
       */
      --primary-text-color: var(--light-theme-text-color);
      --primary-background-color: var(--light-theme-background-color);
      --secondary-text-color: var(--light-theme-secondary-color);
      --disabled-text-color: var(--light-theme-disabled-color);
      --divider-color: var(--light-theme-divider-color);
      --error-color: var(--paper-deep-orange-a700);

      /*
       * Primary and accent colors. Also see color.html for more colors.
       */
      --primary-color: var(--paper-indigo-500);
      --light-primary-color: var(--paper-indigo-100);
      --dark-primary-color: var(--paper-indigo-700);

      --accent-color: var(--paper-pink-a200);
      --light-accent-color: var(--paper-pink-a100);
      --dark-accent-color: var(--paper-pink-a400);


      /*
       * Material Design Light background theme
       */
      --light-theme-background-color: #ffffff;
      --light-theme-base-color: #000000;
      --light-theme-text-color: var(--paper-grey-900);
      --light-theme-secondary-color: #737373;  /* for secondary text and icons */
      --light-theme-disabled-color: #9b9b9b;  /* disabled/hint text */
      --light-theme-divider-color: #dbdbdb;

      /*
       * Material Design Dark background theme
       */
      --dark-theme-background-color: var(--paper-grey-900);
      --dark-theme-base-color: #ffffff;
      --dark-theme-text-color: #ffffff;
      --dark-theme-secondary-color: #bcbcbc;  /* for secondary text and icons */
      --dark-theme-disabled-color: #646464;  /* disabled/hint text */
      --dark-theme-divider-color: #3c3c3c;

      /*
       * Deprecated values because of their confusing names.
       */
      --text-primary-color: var(--dark-theme-text-color);
      --default-primary-color: var(--primary-color);
    }
  </style>
</custom-style>
































<dom-module id="paper-checkbox">
  <template strip-whitespace>
    <style>
      :host {
        display: inline-block;
        white-space: nowrap;
        cursor: pointer;
        --calculated-paper-checkbox-size: var(--paper-checkbox-size, 18px);
        /* -1px is a sentinel for the default and is replaced in `attached`. */
        --calculated-paper-checkbox-ink-size: var(--paper-checkbox-ink-size, -1px);
        @apply --paper-font-common-base;
        line-height: 0;
        -webkit-tap-highlight-color: transparent;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:focus) {
        outline: none;
      }

      .hidden {
        display: none;
      }

      #checkboxContainer {
        display: inline-block;
        position: relative;
        width: var(--calculated-paper-checkbox-size);
        height: var(--calculated-paper-checkbox-size);
        min-width: var(--calculated-paper-checkbox-size);
        margin: var(--paper-checkbox-margin, initial);
        vertical-align: var(--paper-checkbox-vertical-align, middle);
        background-color: var(--paper-checkbox-unchecked-background-color, transparent);
      }

      #ink {
        position: absolute;

        /* Center the ripple in the checkbox by negative offsetting it by
         * (inkWidth - rippleWidth) / 2 */
        top: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
        left: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
        width: var(--calculated-paper-checkbox-ink-size);
        height: var(--calculated-paper-checkbox-ink-size);
        color: var(--paper-checkbox-unchecked-ink-color, var(--primary-text-color));
        opacity: 0.6;
        pointer-events: none;
      }

      #ink:dir(rtl) {
        right: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
        left: auto;
      }

      #ink[checked] {
        color: var(--paper-checkbox-checked-ink-color, var(--primary-color));
      }

      #checkbox {
        position: relative;
        box-sizing: border-box;
        height: 100%;
        border: solid 2px;
        border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
        border-radius: 2px;
        pointer-events: none;
        -webkit-transition: background-color 140ms, border-color 140ms;
        transition: background-color 140ms, border-color 140ms;
      }

      /* checkbox checked animations */
      #checkbox.checked #checkmark {
        -webkit-animation: checkmark-expand 140ms ease-out forwards;
        animation: checkmark-expand 140ms ease-out forwards;
      }

      @-webkit-keyframes checkmark-expand {
        0% {
          -webkit-transform: scale(0, 0) rotate(45deg);
        }
        100% {
          -webkit-transform: scale(1, 1) rotate(45deg);
        }
      }

      @keyframes checkmark-expand {
        0% {
          transform: scale(0, 0) rotate(45deg);
        }
        100% {
          transform: scale(1, 1) rotate(45deg);
        }
      }

      #checkbox.checked {
        background-color: var(--paper-checkbox-checked-color, var(--primary-color));
        border-color: var(--paper-checkbox-checked-color, var(--primary-color));
      }

      #checkmark {
        position: absolute;
        width: 36%;
        height: 70%;
        border-style: solid;
        border-top: none;
        border-left: none;
        border-right-width: calc(2/15 * var(--calculated-paper-checkbox-size));
        border-bottom-width: calc(2/15 * var(--calculated-paper-checkbox-size));
        border-color: var(--paper-checkbox-checkmark-color, white);
        -webkit-transform-origin: 97% 86%;
        transform-origin: 97% 86%;
        box-sizing: content-box; /* protect against page-level box-sizing */
      }

      #checkmark:dir(rtl) {
        -webkit-transform-origin: 50% 14%;
        transform-origin: 50% 14%;
      }

      /* label */
      #checkboxLabel {
        position: relative;
        display: inline-block;
        vertical-align: middle;
        padding-left: var(--paper-checkbox-label-spacing, 8px);
        white-space: normal;
        line-height: normal;
        color: var(--paper-checkbox-label-color, var(--primary-text-color));
        @apply --paper-checkbox-label;
      }

      :host([checked]) #checkboxLabel {
        color: var(--paper-checkbox-label-checked-color, var(--paper-checkbox-label-color, var(--primary-text-color)));
        @apply --paper-checkbox-label-checked;
      }

      #checkboxLabel:dir(rtl) {
        padding-right: var(--paper-checkbox-label-spacing, 8px);
        padding-left: 0;
      }

      #checkboxLabel[hidden] {
        display: none;
      }

      /* disabled state */

      :host([disabled]) #checkbox {
        opacity: 0.5;
        border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
      }

      :host([disabled][checked]) #checkbox {
        background-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
        opacity: 0.5;
      }

      :host([disabled]) #checkboxLabel  {
        opacity: 0.65;
      }

      /* invalid state */
      #checkbox.invalid:not(.checked) {
        border-color: var(--paper-checkbox-error-color, var(--error-color));
      }
    </style>

    <div id="checkboxContainer">
      <div id="checkbox" class$="[[_computeCheckboxClass(checked, invalid)]]">
        <div id="checkmark" class$="[[_computeCheckmarkClass(checked)]]"></div>
      </div>
    </div>

    <div id="checkboxLabel"><slot></slot></div>
  </template>

  
</dom-module>












<dom-module id="iron-icon">
  <template>
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center-center;
        position: relative;

        vertical-align: middle;

        fill: var(--iron-icon-fill-color, currentcolor);
        stroke: var(--iron-icon-stroke-color, none);

        width: var(--iron-icon-width, 24px);
        height: var(--iron-icon-height, 24px);
        @apply --iron-icon;
      }

      :host([hidden]) {
        display: none;
      }
    </style>
  </template>

  

</dom-module>









<dom-module id="iron-a11y-announcer">
  <template>
    <style>
      :host {
        display: inline-block;
        position: fixed;
        clip: rect(0px,0px,0px,0px);
      }
    </style>
    <div aria-live$="[[mode]]">[[_text]]</div>
  </template>

  
</dom-module>





<dom-module id="iron-input">
  <template>
    <style>
      :host {
        display: inline-block;
      }
    </style>
    <slot id="content"></slot>
  </template>
  
</dom-module>












<custom-style>
  <style is="custom-style">
    html {

      /* Shared Styles */
      --paper-font-common-base: {
        font-family: 'Roboto', 'Noto', sans-serif;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-code: {
        font-family: 'Roboto Mono', 'Consolas', 'Menlo', monospace;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-expensive-kerning: {
        text-rendering: optimizeLegibility;
      };

      --paper-font-common-nowrap: {
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
      };

      /* Material Font Styles */

      --paper-font-display4: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 112px;
        font-weight: 300;
        letter-spacing: -.044em;
        line-height: 120px;
      };

      --paper-font-display3: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 56px;
        font-weight: 400;
        letter-spacing: -.026em;
        line-height: 60px;
      };

      --paper-font-display2: {
        @apply --paper-font-common-base;

        font-size: 45px;
        font-weight: 400;
        letter-spacing: -.018em;
        line-height: 48px;
      };

      --paper-font-display1: {
        @apply --paper-font-common-base;

        font-size: 34px;
        font-weight: 400;
        letter-spacing: -.01em;
        line-height: 40px;
      };

      --paper-font-headline: {
        @apply --paper-font-common-base;

        font-size: 24px;
        font-weight: 400;
        letter-spacing: -.012em;
        line-height: 32px;
      };

      --paper-font-title: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 20px;
        font-weight: 500;
        line-height: 28px;
      };

      --paper-font-subhead: {
        @apply --paper-font-common-base;

        font-size: 16px;
        font-weight: 400;
        line-height: 24px;
      };

      --paper-font-body2: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-body1: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 400;
        line-height: 20px;
      };

      --paper-font-caption: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 12px;
        font-weight: 400;
        letter-spacing: 0.011em;
        line-height: 20px;
      };

      --paper-font-menu: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 13px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-button: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 14px;
        font-weight: 500;
        letter-spacing: 0.018em;
        line-height: 24px;
        text-transform: uppercase;
      };

      --paper-font-code2: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 700;
        line-height: 20px;
      };

      --paper-font-code1: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 500;
        line-height: 20px;
      };

    }

  </style>
</custom-style>








<dom-module id="paper-input-char-counter">
  <template>
    <style>
      :host {
        display: inline-block;
        float: right;

        @apply --paper-font-caption;
        @apply --paper-input-char-counter;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:dir(rtl)) {
        float: left;
      }
    </style>

    <span>[[_charCounterStr]]</span>
  </template>
</dom-module>










<custom-style>
  <style is="custom-style">
    html {
      --paper-input-container-shared-input-style: {
        position: relative; /* to make a stacking context */
        outline: none;
        box-shadow: none;
        padding: 0;
        margin: 0;
        width: 100%;
        max-width: 100%;
        background: transparent;
        border: none;
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        -webkit-appearance: none;
        text-align: inherit;
        vertical-align: bottom;

        @apply --paper-font-subhead;
      };
    }
  </style>
</custom-style>

<dom-module id="paper-input-container">
  <template>
    <style>
      :host {
        display: block;
        padding: 8px 0;
        @apply --paper-input-container;
      }

      :host([inline]) {
        display: inline-block;
      }

      :host([disabled]) {
        pointer-events: none;
        opacity: 0.33;

        @apply --paper-input-container-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      [hidden] {
        display: none !important;
      }

      .floated-label-placeholder {
        @apply --paper-font-caption;
      }

      .underline {
        height: 2px;
        position: relative;
      }

      .focused-line {
        @apply --layout-fit;
        border-bottom: 2px solid var(--paper-input-container-focus-color, var(--primary-color));

        -webkit-transform-origin: center center;
        transform-origin: center center;
        -webkit-transform: scale3d(0,1,1);
        transform: scale3d(0,1,1);

        @apply --paper-input-container-underline-focus;
      }

      .underline.is-highlighted .focused-line {
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .underline.is-invalid .focused-line {
        border-color: var(--paper-input-container-invalid-color, var(--error-color));
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .unfocused-line {
        @apply --layout-fit;
        border-bottom: 1px solid var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline;
      }

      :host([disabled]) .unfocused-line {
        border-bottom: 1px dashed;
        border-color: var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline-disabled;
      }

      .input-wrapper {
        @apply --layout-horizontal;
        @apply --layout-center;
        position: relative;
      }

      .input-content {
        @apply --layout-flex-auto;
        @apply --layout-relative;
        max-width: 100%;
      }

      .input-content ::slotted(label),
      .input-content ::slotted(.paper-input-label) {
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        font: inherit;
        color: var(--paper-input-container-color, var(--secondary-text-color));
        -webkit-transition: -webkit-transform 0.25s, width 0.25s;
        transition: transform 0.25s, width 0.25s;
        -webkit-transform-origin: left top;
        transform-origin: left top;
        /* Fix for safari not focusing 0-height date/time inputs with -webkit-apperance: none; */
        min-height: 1px;

        @apply --paper-font-common-nowrap;
        @apply --paper-font-subhead;
        @apply --paper-input-container-label;
        @apply --paper-transition-easing;
      }

      .input-content.label-is-floating ::slotted(label),
      .input-content.label-is-floating ::slotted(.paper-input-label) {
        -webkit-transform: translateY(-75%) scale(0.75);
        transform: translateY(-75%) scale(0.75);

        /* Since we scale to 75/100 of the size, we actually have 100/75 of the
        original space now available */
        width: 133%;

        @apply --paper-input-container-label-floating;
      }

      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(label),
      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(.paper-input-label) {
        right: 0;
        left: auto;
        -webkit-transform-origin: right top;
        transform-origin: right top;
      }

      .input-content.label-is-highlighted ::slotted(label),
      .input-content.label-is-highlighted ::slotted(.paper-input-label) {
        color: var(--paper-input-container-focus-color, var(--primary-color));

        @apply --paper-input-container-label-focus;
      }

      .input-content.is-invalid ::slotted(label),
      .input-content.is-invalid ::slotted(.paper-input-label) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .input-content.label-is-hidden ::slotted(label),
      .input-content.label-is-hidden ::slotted(.paper-input-label) {
        visibility: hidden;
      }

      .input-content ::slotted(input),
      .input-content ::slotted(iron-input),
      .input-content ::slotted(textarea),
      .input-content ::slotted(iron-autogrow-textarea),
      .input-content ::slotted(.paper-input-input) {
        @apply --paper-input-container-shared-input-style;
        /* The apply shim doesn't apply the nested color custom property,
          so we have to re-apply it here. */
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        @apply --paper-input-container-input;
      }

      .input-content ::slotted(input)::-webkit-outer-spin-button,
      .input-content ::slotted(input)::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      .input-content.focused ::slotted(input),
      .input-content.focused ::slotted(iron-input),
      .input-content.focused ::slotted(textarea),
      .input-content.focused ::slotted(iron-autogrow-textarea),
      .input-content.focused ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-focus;
      }

      .input-content.is-invalid ::slotted(input),
      .input-content.is-invalid ::slotted(iron-input),
      .input-content.is-invalid ::slotted(textarea),
      .input-content.is-invalid ::slotted(iron-autogrow-textarea),
      .input-content.is-invalid ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-invalid;
      }

      .prefix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;
        @apply --paper-input-prefix;
      }

      .suffix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;

        @apply --paper-input-suffix;
      }

      /* Firefox sets a min-width on the input, which can cause layout issues */
      .input-content ::slotted(input) {
        min-width: 0;
      }

      .input-content ::slotted(textarea) {
        resize: none;
      }

      .add-on-content {
        position: relative;
      }

      .add-on-content.is-invalid ::slotted(*) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .add-on-content.is-highlighted ::slotted(*) {
        color: var(--paper-input-container-focus-color, var(--primary-color));
      }
    </style>

    <div class="floated-label-placeholder" aria-hidden="true" hidden="[[noLabelFloat]]">&nbsp;</div>

    <div class="input-wrapper">
      <span class="prefix"><slot name="prefix"></slot></span>

      <div class$="[[_computeInputContentClass(noLabelFloat,alwaysFloatLabel,focused,invalid,_inputHasContent)]]" id="labelAndInputContainer">
        <slot name="label"></slot>
        <slot name="input"></slot>
      </div>

      <span class="suffix"><slot name="suffix"></slot></span>
    </div>

    <div class$="[[_computeUnderlineClass(focused,invalid)]]">
      <div class="unfocused-line"></div>
      <div class="focused-line"></div>
    </div>

    <div class$="[[_computeAddOnContentClass(focused,invalid)]]">
      <slot name="add-on"></slot>
    </div>
  </template>
</dom-module>










<dom-module id="paper-input-error">
  <template>
    <style>
      :host {
        display: inline-block;
        visibility: hidden;

        color: var(--paper-input-container-invalid-color, var(--error-color));

        @apply --paper-font-caption;
        @apply --paper-input-error;
        position: absolute;
        left:0;
        right:0;
      }

      :host([invalid]) {
        visibility: visible;
      };
    </style>

    <slot></slot>
  </template>
</dom-module>






<dom-module id="paper-input">
  <template>
    <style>
      :host {
        display: block;
      }

      :host([focused]) {
        outline: none;
      }

      :host([hidden]) {
        display: none !important;
      }

      input {
        /* Firefox sets a min-width on the input, which can cause layout issues */
        min-width: 0;
      }

      /* In 1.x, the <input> is distributed to paper-input-container, which styles it.
      In 2.x the <iron-input> is distributed to paper-input-container, which styles
      it, but in order for this to work correctly, we need to reset some
      of the native input's properties to inherit (from the iron-input) */
      iron-input > input {
        @apply --paper-input-container-shared-input-style;
        font-family: inherit;
        font-weight: inherit;
        font-size: inherit;
        letter-spacing: inherit;
        word-spacing: inherit;
        line-height: inherit;
        text-shadow: inherit;
        color: inherit;
        cursor: inherit;
      }

      input:disabled {
        @apply --paper-input-container-input-disabled;
      }

      input::-webkit-outer-spin-button,
      input::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      input::-webkit-clear-button {
        @apply --paper-input-container-input-webkit-clear;
      }

      input::-webkit-calendar-picker-indicator {
        @apply --paper-input-container-input-webkit-calendar-picker-indicator;
      }

      input::-webkit-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input:-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-ms-clear {
        @apply --paper-input-container-ms-clear;
      }

      input::-ms-reveal {
        @apply --paper-input-container-ms-reveal;
      }

      input:-ms-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container id="container" no-label-float="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <slot name="prefix" slot="prefix"></slot>

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <span id="template-placeholder"></span>

      <slot name="suffix" slot="suffix"></slot>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
  </template>

  
  <template id="v0">
    <input is="iron-input" slot="input" class="input-element" id$="[[_inputId]]" aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" disabled$="[[disabled]]" title$="[[title]]" bind-value="{{value}}" invalid="{{invalid}}" prevent-invalid-input="[[preventInvalidInput]]" allowed-pattern="[[allowedPattern]]" validator="[[validator]]" type$="[[type]]" pattern$="[[pattern]]" required$="[[required]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" min$="[[min]]" max$="[[max]]" step$="[[step]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" list$="[[list]]" size$="[[size]]" autocapitalize$="[[autocapitalize]]" autocorrect$="[[autocorrect]]" on-change="_onChange" tabindex$="[[tabIndex]]" autosave$="[[autosave]]" results$="[[results]]" accept$="[[accept]]" multiple$="[[multiple]]">
  </template>

  <template id="v1">
    
    <iron-input bind-value="{{value}}" slot="input" class="input-element" id$="[[_inputId]]" maxlength$="[[maxlength]]" allowed-pattern="[[allowedPattern]]" invalid="{{invalid}}" validator="[[validator]]">
      <input aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" disabled$="[[disabled]]" title$="[[title]]" type$="[[type]]" pattern$="[[pattern]]" required$="[[required]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" min$="[[min]]" max$="[[max]]" step$="[[step]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" list$="[[list]]" size$="[[size]]" autocapitalize$="[[autocapitalize]]" autocorrect$="[[autocorrect]]" on-change="_onChange" tabindex$="[[tabIndex]]" autosave$="[[autosave]]" results$="[[results]]" accept$="[[accept]]" multiple$="[[multiple]]">
    </iron-input>
  </template>

</dom-module>























<dom-module id="iron-overlay-backdrop">

  <template>
    <style>
      :host {
        position: fixed;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        background-color: var(--iron-overlay-backdrop-background-color, #000);
        opacity: 0;
        transition: opacity 0.2s;
        pointer-events: none;
        @apply --iron-overlay-backdrop;
      }

      :host(.opened) {
        opacity: var(--iron-overlay-backdrop-opacity, 0.6);
        pointer-events: auto;
        @apply --iron-overlay-backdrop-opened;
      }
    </style>

    <slot></slot>
  </template>

</dom-module>
































<dom-module id="iron-dropdown">
  <template>
    <style>
      :host {
        position: fixed;
      }

      #contentWrapper ::slotted(*) {
        overflow: auto;
      }

      #contentWrapper.animating ::slotted(*) {
        overflow: hidden;
        pointer-events: none;
      }
    </style>

    <div id="contentWrapper">
      <slot id="content" name="dropdown-content"></slot>
    </div>
  </template>

  
</dom-module>





























<dom-module id="paper-menu-button">
  <template>
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;

        @apply --paper-menu-button;
      }

      :host([disabled]) {
        cursor: auto;
        color: var(--disabled-text-color);

        @apply --paper-menu-button-disabled;
      }

      iron-dropdown {
        @apply --paper-menu-button-dropdown;
      }

      .dropdown-content {
        @apply --shadow-elevation-2dp;

        position: relative;
        border-radius: 2px;
        background-color: var(--paper-menu-button-dropdown-background, var(--primary-background-color));

        @apply --paper-menu-button-content;
      }

      :host([vertical-align="top"]) .dropdown-content {
        margin-bottom: 20px;
        margin-top: -10px;
        top: 10px;
      }

      :host([vertical-align="bottom"]) .dropdown-content {
        bottom: 10px;
        margin-bottom: -10px;
        margin-top: 20px;
      }

      #trigger {
        cursor: pointer;
      }
    </style>

    <div id="trigger" on-tap="toggle">
      <slot name="dropdown-trigger"></slot>
    </div>

    <iron-dropdown id="dropdown" opened="{{opened}}" horizontal-align="[[horizontalAlign]]" vertical-align="[[verticalAlign]]" dynamic-align="[[dynamicAlign]]" horizontal-offset="[[horizontalOffset]]" vertical-offset="[[verticalOffset]]" no-overlap="[[noOverlap]]" open-animation-config="[[openAnimationConfig]]" close-animation-config="[[closeAnimationConfig]]" no-animations="[[noAnimations]]" focus-target="[[_dropdownContent]]" allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]" on-iron-overlay-canceled="__onIronOverlayCanceled">
      <div slot="dropdown-content" class="dropdown-content">
        <slot id="content" name="dropdown-content"></slot>
      </div>
    </iron-dropdown>
  </template>

  
</dom-module>










<iron-iconset-svg name="paper-dropdown-menu" size="24">
<svg><defs>
<g id="arrow-drop-down"><path d="M7 10l5 5 5-5z"></path></g>
</defs></svg>
</iron-iconset-svg>



<dom-module id="paper-dropdown-menu-shared-styles">
  <template>
    <style>
      :host {
        display: inline-block;
        position: relative;
        text-align: left;

        /* NOTE(cdata): Both values are needed, since some phones require the
         * value to be `transparent`.
         */
        -webkit-tap-highlight-color: rgba(0,0,0,0);
        -webkit-tap-highlight-color: transparent;

        --paper-input-container-input: {
          overflow: hidden;
          white-space: nowrap;
          text-overflow: ellipsis;
          max-width: 100%;
          box-sizing: border-box;
          cursor: pointer;
        };

        @apply --paper-dropdown-menu;
      }

      :host([disabled]) {
        @apply --paper-dropdown-menu-disabled;
      }

      :host([noink]) paper-ripple {
        display: none;
      }

      :host([no-label-float]) paper-ripple {
        top: 8px;
      }

      paper-ripple {
        top: 12px;
        left: 0px;
        bottom: 8px;
        right: 0px;

        @apply --paper-dropdown-menu-ripple;
      }

      paper-menu-button {
        display: block;
        padding: 0;

        @apply --paper-dropdown-menu-button;
      }

      paper-input {
        @apply --paper-dropdown-menu-input;
      }

      iron-icon {
        color: var(--disabled-text-color);

        @apply --paper-dropdown-menu-icon;
      }
    </style>
  </template>
</dom-module>




<dom-module id="paper-dropdown-menu">
  <template>
    <style include="paper-dropdown-menu-shared-styles"></style>

    
    <span role="button"></span>
    <paper-menu-button id="menuButton" vertical-align="[[verticalAlign]]" horizontal-align="[[horizontalAlign]]" dynamic-align="[[dynamicAlign]]" vertical-offset="[[_computeMenuVerticalOffset(noLabelFloat, verticalOffset)]]" disabled="[[disabled]]" no-animations="[[noAnimations]]" on-iron-select="_onIronSelect" on-iron-deselect="_onIronDeselect" opened="{{opened}}" close-on-activate allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]">
      
      <div class="dropdown-trigger" slot="dropdown-trigger">
        <paper-ripple></paper-ripple>
        
        <paper-input type="text" invalid="[[invalid]]" readonly disabled="[[disabled]]" value="[[value]]" placeholder="[[placeholder]]" error-message="[[errorMessage]]" always-float-label="[[alwaysFloatLabel]]" no-label-float="[[noLabelFloat]]" label="[[label]]">
          
          <iron-icon icon="paper-dropdown-menu:arrow-drop-down" suffix slot="suffix"></iron-icon>
        </paper-input>
      </div>
      <slot id="content" name="dropdown-content" slot="dropdown-content"></slot>
    </paper-menu-button>
  </template>

  
</dom-module>























<dom-module id="paper-listbox">
  <template>
    <style>
      :host {
        display: block;
        padding: 8px 0;

        background: var(--paper-listbox-background-color, var(--primary-background-color));
        color: var(--paper-listbox-color, var(--primary-text-color));

        @apply --paper-listbox;
      }
    </style>

    <slot></slot>
  </template>

  
</dom-module>

















<dom-module id="paper-item-shared-styles">
  <template>
    <style>
      :host, .paper-item {
        display: block;
        position: relative;
        min-height: var(--paper-item-min-height, 48px);
        padding: 0px 16px;
      }

      .paper-item {
        @apply --paper-font-subhead;
        border:none;
        outline: none;
        background: white;
        width: 100%;
        text-align: left;
      }

      :host([hidden]), .paper-item[hidden] {
        display: none !important;
      }

      :host(.iron-selected), .paper-item.iron-selected {
        font-weight: var(--paper-item-selected-weight, bold);

        @apply --paper-item-selected;
      }

      :host([disabled]), .paper-item[disabled] {
        color: var(--paper-item-disabled-color, var(--disabled-text-color));

        @apply --paper-item-disabled;
      }

      :host(:focus), .paper-item:focus {
        position: relative;
        outline: 0;

        @apply --paper-item-focused;
      }

      :host(:focus):before, .paper-item:focus:before {
        @apply --layout-fit;

        background: currentColor;
        content: '';
        opacity: var(--dark-divider-opacity);
        pointer-events: none;

        @apply --paper-item-focused-before;
      }
    </style>
  </template>
</dom-module>




<dom-module id="paper-item">
  <template>
    <style include="paper-item-shared-styles">
      :host {
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-subhead;

        @apply --paper-item;
      }
    </style>
    <slot></slot>
  </template>

  
</dom-module>


















<dom-module id="tf-backend">
  
</dom-module>




















<dom-module id="tf-storage">
  
</dom-module>



<dom-module id="tf-tag-filterer">
  <template>
    <paper-input no-label-float label="Filter tags (regular expressions supported)" value="{{_tagFilter}}" class="search-input">
      <iron-icon prefix icon="search" slot="prefix"></iron-icon>
    </paper-input>
    <style>
      :host {
        display: block;
        margin: 10px 5px 10px 10px;
      }
    </style>
  </template>
  
</dom-module>






<dom-module id="iron-flex">
  <template>
    <style>
      .layout.horizontal,
      .layout.vertical {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.inline {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      }

      .layout.horizontal {
        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      }

      .layout.vertical {
        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      }

      .layout.wrap {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      }

      .layout.no-wrap {
        -ms-flex-wrap: nowrap;
        -webkit-flex-wrap: nowrap;
        flex-wrap: nowrap;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .flex {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-auto {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      }

      .flex-none {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      }
    </style>
  </template>
</dom-module>


<dom-module id="iron-flex-reverse">
  <template>
    <style>
      .layout.horizontal-reverse,
      .layout.vertical-reverse {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.horizontal-reverse {
        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      }

      .layout.vertical-reverse {
        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      }

      .layout.wrap-reverse {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      }
    </style>
  </template>
</dom-module>


<dom-module id="iron-flex-alignment">
  <template>
    <style>
      /**
       * Alignment in cross axis.
       */
      .layout.start {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.end {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      }

      .layout.baseline {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      }

      /**
       * Alignment in main axis.
       */
      .layout.start-justified {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .layout.end-justified {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      }

      .layout.around-justified {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      }

      .layout.justified {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      }

      /**
       * Self alignment.
       */
      .self-start {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      }

      .self-center {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      }

      .self-end {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      }

      .self-stretch {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      }

      .self-baseline {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      }

      /**
       * multi-line alignment in main axis.
       */
      .layout.start-aligned {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      }

      .layout.end-aligned {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      }

      .layout.center-aligned {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      }

      .layout.between-aligned {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      }

      .layout.around-aligned {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      }
    </style>
  </template>
</dom-module>

<dom-module id="iron-flex-factors">
  <template>
    <style>
      .flex,
      .flex-1 {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-2 {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      }

      .flex-3 {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      }

      .flex-4 {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      }

      .flex-5 {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      }

      .flex-6 {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      }

      .flex-7 {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      }

      .flex-8 {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      }

      .flex-9 {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      }

      .flex-10 {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      }

      .flex-11 {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      }

      .flex-12 {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      }
    </style>
  </template>
</dom-module>


<dom-module id="iron-positioning">
  <template>
    <style>
      .block {
        display: block;
      }

      [hidden] {
        display: none !important;
      }

      .invisible {
        visibility: hidden !important;
      }

      .relative {
        position: relative;
      }

      .fit {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      body.fullbleed {
        margin: 0;
        height: 100vh;
      }

      .scroll {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      }

      /* fixed position */
      .fixed-bottom,
      .fixed-left,
      .fixed-right,
      .fixed-top {
        position: fixed;
      }

      .fixed-top {
        top: 0;
        left: 0;
        right: 0;
      }

      .fixed-right {
        top: 0;
        right: 0;
        bottom: 0;
      }

      .fixed-bottom {
        right: 0;
        bottom: 0;
        left: 0;
      }

      .fixed-left {
        top: 0;
        bottom: 0;
        left: 0;
      }
    </style>
  </template>
</dom-module>









<style is="custom-style">
  :root {
    --tb-orange-weak: #ffa726;
    --tb-orange-strong: #f57c00;
    --tb-orange-dark: #dc7320;
    --tb-grey-darker: #e2e2e2;
    --tb-grey-lighter: #f3f3f3;
    --tb-ui-dark-accent: #757575;
    --tb-ui-light-accent: #e0e0e0;
    --tb-graph-faded: #e0d4b3;
  }
</style>


<dom-module id="dashboard-style">
  <template>
    <style include="iron-flex"></style>
    <style>
      :host {
        --sidebar-vertical-padding: 15px;
        --sidebar-left-padding: 30px;
      }

      [slot='sidebar'] {
        box-sizing: border-box;
        display: flex;
        flex-direction: column;
        height: 100%;
        margin-right: 20px;
        overflow-x: hidden;
        padding: 5px 0;
        text-overflow: ellipsis;
      }

      tf-runs-selector {
        flex-grow: 1;
        flex-shrink: 1;
        left: var(--sidebar-left-padding);
        max-height: calc(100% - var(--sidebar-vertical-padding) * 2);
        overflow: hidden;
        position: absolute;
        right: 0;
      }

      .search-input {
        margin: 10px 5px 0 10px;
      }

      .sidebar-section {
        border-top: solid 1px rgba(0, 0, 0, 0.12);
        padding: var(--sidebar-vertical-padding) 0
          var(--sidebar-vertical-padding) var(--sidebar-left-padding);
        position: relative;
      }

      .sidebar-section:first-of-type {
        border: none;
      }

      .sidebar-section:last-of-type {
        flex-grow: 1;
        display: flex;
      }

      .sidebar-section paper-button {
        margin: 5px;
      }

      .sidebar-section paper-button:first-of-type {
        margin-left: 0 !important;
      }

      .sidebar-section paper-button:last-of-type {
        margin-right: 0 !important;
      }

      .sidebar-section > :first-child {
        margin-top: 0;
        padding-top: 0;
      }

      .sidebar-section > :last-child {
        margin-bottom: 0;
        padding-bottom: 0;
      }

      .sidebar-section h3 {
        color: var(--paper-grey-800);
        display: block;
        font-size: 14px;
        font-weight: normal;
        margin: 10px 0 5px;
        pointer-events: none;
      }

      paper-checkbox {
        --paper-checkbox-checked-color: var(--tb-ui-dark-accent);
        --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);
        font-size: 15px;
        margin-top: 5px;
      }
    </style>
  </template>
</dom-module>





<dom-module id="scrollbar-style">
  <template>
    <style>
      .scrollbar::-webkit-scrollbar-track {
        visibility: hidden;
      }

      .scrollbar::-webkit-scrollbar {
        width: 10px;
      }

      .scrollbar::-webkit-scrollbar-thumb {
        border-radius: 10px;
        -webkit-box-shadow: inset 0 0 2px rgba(0, 0, 0, 0.3);
        background-color: var(--paper-grey-500);
        color: var(--paper-grey-900);
      }
      .scrollbar {
        box-sizing: border-box;
      }
    </style>
  </template>
</dom-module>




<dom-module id="tf-dashboard-layout">
  <template>
    <div id="sidebar">
      <slot name="sidebar"></slot>
    </div>

    <div id="center">
      <slot name="center" class="scollbar"></slot>
    </div>
    <style include="scrollbar-style"></style>
    <style>
      :host {
        display: flex;
        flex-direction: row;
        height: 100%;
      }

      #sidebar {
        flex: 0 0 var(--tf-dashboard-layout-sidebar-basis, 25%);
        height: 100%;
        max-width: var(--tf-dashboard-layout-sidebar-max-width, 350px);
        min-width: var(--tf-dashboard-layout-sidebar-min-width, 270px);
        overflow-y: auto;
        text-overflow: ellipsis;
      }

      #center {
        flex-grow: 1;
        flex-shrink: 1;
        height: 100%;
        overflow: hidden;
      }

      ::slotted([slot='center']) {
        height: 100%;
        overflow-x: hidden;
        overflow-y: auto;
        width: 100%;
        will-change: transform;
      }

      .tf-graph-dashboard #center {
        background: #fff;
      }
    </style>
  </template>
  
</dom-module>





<dom-module id="tf-option-selector">
  <template>
    <div id="wrap">
      <h3>[[name]]</h3>
      <div class="content-wrapper"><slot></slot></div>
    </div>
    <style>
      .content-wrapper ::slotted(*) {
        background: none;
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin-top: 10px;
      }

      .content-wrapper ::slotted(*) {
        background: none;
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin-top: 10px;
      }

      .content-wrapper ::slotted(.selected) {
        background-color: var(--tb-ui-dark-accent);
        color: white !important;
      }

      h3 {
        color: var(--paper-grey-800);
        display: block;
        font-size: 14px;
        font-weight: normal;
        margin: 0 0 5px;
        pointer-events: none;
      }
    </style>
  </template>
  
</dom-module>









<dom-module id="iron-collapse">

  <template>

    <style>
      :host {
        display: block;
        transition-duration: var(--iron-collapse-transition-duration, 300ms);
        /* Safari 10 needs this property prefixed to correctly apply the custom property */
        -webkit-transition-duration: var(--iron-collapse-transition-duration, 300ms);
        overflow: visible;
      }

      :host(.iron-collapse-closed) {
        display: none;
      }

      :host(:not(.iron-collapse-opened)) {
        overflow: hidden;
      }
    </style>

    <slot></slot>

  </template>

</dom-module>



















<dom-module id="tf-category-paginated-view">
  <template>
    <template is="dom-if" if="[[_paneRendered]]" id="ifRendered">
      <button class="heading" on-tap="_togglePane" open-button$="[[opened]]">
        <span class="name">
          <template is="dom-if" if="[[_isSearchResults]]">
            <template is="dom-if" if="[[_isCompositeSearch(category)]]">
              <span>Tags matching multiple experiments</span>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span>&nbsp;<strong>(malformed regular expression)</strong></span>
              </template>
            </template>
            <template is="dom-if" if="[[!_isCompositeSearch(category)]]">
              <span class="light">Tags matching /</span>
              <span class="category-name" title$="[[category.name]]">[[category.name]]</span>
              <span class="light">/</span>
              <template is="dom-if" if="[[_isUniversalSearchQuery]]">
                <span> (all tags)</span>
              </template>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span> <strong>(malformed regular expression)</strong></span>
              </template>
            </template>
          </template>
          <template is="dom-if" if="[[!_isSearchResults]]">
            <span class="category-name" title$="[[category.name]]">[[category.name]]</span>
          </template>
        </span>
        <span class="count">
          <template is="dom-if" if="[[_hasMultiple]]">
            <span>[[_count]]</span>
          </template>
          <iron-icon icon="expand-more" class="expand-arrow"></iron-icon>
        </span>
      </button>
      
      <iron-collapse opened="[[opened]]" no-animation>
        <div class="content">
          <span id="top-of-container"></span>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div class="big-page-buttons" style="margin-bottom: 10px;">
              <paper-button on-tap="_performPreviousPage" disabled$="[[!_hasPreviousPage]]">Previous page</paper-button>
              <paper-button on-tap="_performNextPage" disabled$="[[!_hasNextPage]]">Next page</paper-button>
            </div>
          </template>

          <div id="items">
            <slot name="items"></slot>
          </div>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div id="controls-container">
              <div style="display: inline-block; padding: 0 5px">
                Page
                <paper-input id="page-input" type="number" no-label-float min="1" max="[[_pageCount]]" value="[[_pageInputValue]]" on-input="_handlePageInputEvent" on-change="_handlePageChangeEvent" on-focus="_handlePageFocusEvent" on-blur="_handlePageBlurEvent"></paper-input>
                of [[_pageCount]]
              </div>
            </div>

            <div class="big-page-buttons" style="margin-top: 10px;">
              <paper-button on-tap="_performPreviousPage" disabled$="[[!_hasPreviousPage]]">Previous page</paper-button>
              <paper-button on-tap="_performNextPage" disabled$="[[!_hasNextPage]]">Next page</paper-button>
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>
    <style>
      :host {
        display: block;
        margin: 0 5px 1px 10px;
      }

      :host(:first-of-type) {
        margin-top: 10px;
      }

      :host(:last-of-type) {
        margin-bottom: 20px;
      }

      .heading {
        background-color: white;
        border: none;
        cursor: pointer;
        width: 100%;
        font-size: 15px;
        line-height: 1;
        box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2);
        padding: 10px 15px;
        display: flex;
        align-items: center;
        justify-content: space-between;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      [open-button] {
        border-bottom-left-radius: 0 !important;
        border-bottom-right-radius: 0 !important;
      }

      [open-button] .expand-arrow {
        transform: rotateZ(180deg);
      }

      .name {
        display: inline-flex;
        overflow: hidden;
      }

      .light {
        color: var(--paper-grey-500);
      }

      .category-name {
        white-space: pre;
        overflow: hidden;
        text-overflow: ellipsis;
        padding: 2px 0;
      }

      .count {
        margin: 0 5px;
        font-size: 12px;
        color: var(--paper-grey-500);
        display: flex;
        align-items: center;
        flex: none;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      .content {
        display: flex;
        flex-direction: column;
        background: white;
        border-bottom-left-radius: 2px;
        border-bottom-right-radius: 2px;
        border-top: none;
        border: 1px solid #dedede;
        padding: 15px;
      }

      .light {
        color: var(--paper-grey-500);
      }

      #controls-container {
        justify-content: center;
        display: flex;
        flex-direction: row;
        flex-grow: 0;
        flex-shrink: 0;
        width: 100%;
      }

      #controls-container paper-button {
        display: inline-block;
      }

      .big-page-buttons {
        display: flex;
      }

      .big-page-buttons paper-button {
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
        display: inline-block;
        flex-basis: 0;
        flex-grow: 1;
        flex-shrink: 1;
        font-size: 13px;
      }

      .big-page-buttons paper-button[disabled] {
        background: none;
      }

      slot {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }

      #page-input {
        display: inline-block;
        width: var(--tf-category-paginated-view-page-input-width, 100%);
      }
    </style>
  </template>
  
</dom-module>

















<dom-module id="paper-dialog-shared-styles">
  <template>
    <style>
      :host {
        display: block;
        margin: 24px 40px;

        background: var(--paper-dialog-background-color, var(--primary-background-color));
        color: var(--paper-dialog-color, var(--primary-text-color));

        @apply --paper-font-body1;
        @apply --shadow-elevation-16dp;
        @apply --paper-dialog;
      }

      :host > ::slotted(*) {
        margin-top: 20px;
        padding: 0 24px;
      }

      :host > ::slotted(.no-padding) {
        padding: 0;
      }

      
      :host > ::slotted(*:first-child) {
        margin-top: 24px;
      }

      :host > ::slotted(*:last-child) {
        margin-bottom: 24px;
      }

      /* In 1.x, this selector was `:host > ::content h2`. In 2.x <slot> allows
      to select direct children only, which increases the weight of this
      selector, so we have to re-define first-child/last-child margins below. */
      :host > ::slotted(h2) {
        position: relative;
        margin: 0;

        @apply --paper-font-title;
        @apply --paper-dialog-title;
      }

      /* Apply mixin again, in case it sets margin-top. */
      :host > ::slotted(h2:first-child) {
        margin-top: 24px;
        @apply --paper-dialog-title;
      }

      /* Apply mixin again, in case it sets margin-bottom. */
      :host > ::slotted(h2:last-child) {
        margin-bottom: 24px;
        @apply --paper-dialog-title;
      }

      :host > ::slotted(.paper-dialog-buttons),
      :host > ::slotted(.buttons) {
        position: relative;
        padding: 8px 8px 8px 24px;
        margin: 0;

        color: var(--paper-dialog-button-color, var(--primary-color));

        @apply --layout-horizontal;
        @apply --layout-end-justified;
      }
    </style>
  </template>
</dom-module>



<dom-module id="paper-dialog">
  <template>
    <style include="paper-dialog-shared-styles"></style>
    <slot></slot>
  </template>
</dom-module>











<dom-module id="tf-color-scale">
  
  
</dom-module>




<iron-iconset-svg name="icons" size="24">
<svg><defs>
<g id="3d-rotation"><path d="M7.52 21.48C4.25 19.94 1.91 16.76 1.55 13H.05C.56 19.16 5.71 24 12 24l.66-.03-3.81-3.81-1.33 1.32zm.89-6.52c-.19 0-.37-.03-.52-.08-.16-.06-.29-.13-.4-.24-.11-.1-.2-.22-.26-.37-.06-.14-.09-.3-.09-.47h-1.3c0 .36.07.68.21.95.14.27.33.5.56.69.24.18.51.32.82.41.3.1.62.15.96.15.37 0 .72-.05 1.03-.15.32-.1.6-.25.83-.44s.42-.43.55-.72c.13-.29.2-.61.2-.97 0-.19-.02-.38-.07-.56-.05-.18-.12-.35-.23-.51-.1-.16-.24-.3-.4-.43-.17-.13-.37-.23-.61-.31.2-.09.37-.2.52-.33.15-.13.27-.27.37-.42.1-.15.17-.3.22-.46.05-.16.07-.32.07-.48 0-.36-.06-.68-.18-.96-.12-.28-.29-.51-.51-.69-.2-.19-.47-.33-.77-.43C9.1 8.05 8.76 8 8.39 8c-.36 0-.69.05-1 .16-.3.11-.57.26-.79.45-.21.19-.38.41-.51.67-.12.26-.18.54-.18.85h1.3c0-.17.03-.32.09-.45s.14-.25.25-.34c.11-.09.23-.17.38-.22.15-.05.3-.08.48-.08.4 0 .7.1.89.31.19.2.29.49.29.86 0 .18-.03.34-.08.49-.05.15-.14.27-.25.37-.11.1-.25.18-.41.24-.16.06-.36.09-.58.09H7.5v1.03h.77c.22 0 .42.02.6.07s.33.13.45.23c.12.11.22.24.29.4.07.16.1.35.1.57 0 .41-.12.72-.35.93-.23.23-.55.33-.95.33zm8.55-5.92c-.32-.33-.7-.59-1.14-.77-.43-.18-.92-.27-1.46-.27H12v8h2.3c.55 0 1.06-.09 1.51-.27.45-.18.84-.43 1.16-.76.32-.33.57-.73.74-1.19.17-.47.26-.99.26-1.57v-.4c0-.58-.09-1.1-.26-1.57-.18-.47-.43-.87-.75-1.2zm-.39 3.16c0 .42-.05.79-.14 1.13-.1.33-.24.62-.43.85-.19.23-.43.41-.71.53-.29.12-.62.18-.99.18h-.91V9.12h.97c.72 0 1.27.23 1.64.69.38.46.57 1.12.57 1.99v.4zM12 0l-.66.03 3.81 3.81 1.33-1.33c3.27 1.55 5.61 4.72 5.96 8.48h1.5C23.44 4.84 18.29 0 12 0z" /></g>
<g id="accessibility"><path d="M12 2c1.1 0 2 .9 2 2s-.9 2-2 2-2-.9-2-2 .9-2 2-2zm9 7h-6v13h-2v-6h-2v6H9V9H3V7h18v2z" /></g>
<g id="accessible"><circle cx="12" cy="4" r="2" /><path d="M19 13v-2c-1.54.02-3.09-.75-4.07-1.83l-1.29-1.43c-.17-.19-.38-.34-.61-.45-.01 0-.01-.01-.02-.01H13c-.35-.2-.75-.3-1.19-.26C10.76 7.11 10 8.04 10 9.09V15c0 1.1.9 2 2 2h5v5h2v-5.5c0-1.1-.9-2-2-2h-3v-3.45c1.29 1.07 3.25 1.94 5 1.95zm-6.17 5c-.41 1.16-1.52 2-2.83 2-1.66 0-3-1.34-3-3 0-1.31.84-2.41 2-2.83V12.1c-2.28.46-4 2.48-4 4.9 0 2.76 2.24 5 5 5 2.42 0 4.44-1.72 4.9-4h-2.07z" /></g>
<g id="account-balance"><path d="M4 10v7h3v-7H4zm6 0v7h3v-7h-3zM2 22h19v-3H2v3zm14-12v7h3v-7h-3zm-4.5-9L2 6v2h19V6l-9.5-5z" /></g>
<g id="account-balance-wallet"><path d="M21 18v1c0 1.1-.9 2-2 2H5c-1.11 0-2-.9-2-2V5c0-1.1.89-2 2-2h14c1.1 0 2 .9 2 2v1h-9c-1.11 0-2 .9-2 2v8c0 1.1.89 2 2 2h9zm-9-2h10V8H12v8zm4-2.5c-.83 0-1.5-.67-1.5-1.5s.67-1.5 1.5-1.5 1.5.67 1.5 1.5-.67 1.5-1.5 1.5z" /></g>
<g id="account-box"><path d="M3 5v14c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2H5c-1.11 0-2 .9-2 2zm12 4c0 1.66-1.34 3-3 3s-3-1.34-3-3 1.34-3 3-3 3 1.34 3 3zm-9 8c0-2 4-3.1 6-3.1s6 1.1 6 3.1v1H6v-1z" /></g>
<g id="account-circle"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 3c1.66 0 3 1.34 3 3s-1.34 3-3 3-3-1.34-3-3 1.34-3 3-3zm0 14.2c-2.5 0-4.71-1.28-6-3.22.03-1.99 4-3.08 6-3.08 1.99 0 5.97 1.09 6 3.08-1.29 1.94-3.5 3.22-6 3.22z" /></g>
<g id="add"><path d="M19 13h-6v6h-2v-6H5v-2h6V5h2v6h6v2z" /></g>
<g id="add-alert"><path d="M10.01 21.01c0 1.1.89 1.99 1.99 1.99s1.99-.89 1.99-1.99h-3.98zm8.87-4.19V11c0-3.25-2.25-5.97-5.29-6.69v-.72C13.59 2.71 12.88 2 12 2s-1.59.71-1.59 1.59v.72C7.37 5.03 5.12 7.75 5.12 11v5.82L3 18.94V20h18v-1.06l-2.12-2.12zM16 13.01h-3v3h-2v-3H8V11h3V8h2v3h3v2.01z" /></g>
<g id="add-box"><path d="M19 3H5c-1.11 0-2 .9-2 2v14c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-2 10h-4v4h-2v-4H7v-2h4V7h2v4h4v2z" /></g>
<g id="add-circle"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm5 11h-4v4h-2v-4H7v-2h4V7h2v4h4v2z" /></g>
<g id="add-circle-outline"><path d="M13 7h-2v4H7v2h4v4h2v-4h4v-2h-4V7zm-1-5C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 18c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8z" /></g>
<g id="add-shopping-cart"><path d="M11 9h2V6h3V4h-3V1h-2v3H8v2h3v3zm-4 9c-1.1 0-1.99.9-1.99 2S5.9 22 7 22s2-.9 2-2-.9-2-2-2zm10 0c-1.1 0-1.99.9-1.99 2s.89 2 1.99 2 2-.9 2-2-.9-2-2-2zm-9.83-3.25l.03-.12.9-1.63h7.45c.75 0 1.41-.41 1.75-1.03l3.86-7.01L19.42 4h-.01l-1.1 2-2.76 5H8.53l-.13-.27L6.16 6l-.95-2-.94-2H1v2h2l3.6 7.59-1.35 2.45c-.16.28-.25.61-.25.96 0 1.1.9 2 2 2h12v-2H7.42c-.13 0-.25-.11-.25-.25z" /></g>
<g id="alarm"><path d="M22 5.72l-4.6-3.86-1.29 1.53 4.6 3.86L22 5.72zM7.88 3.39L6.6 1.86 2 5.71l1.29 1.53 4.59-3.85zM12.5 8H11v6l4.75 2.85.75-1.23-4-2.37V8zM12 4c-4.97 0-9 4.03-9 9s4.02 9 9 9c4.97 0 9-4.03 9-9s-4.03-9-9-9zm0 16c-3.87 0-7-3.13-7-7s3.13-7 7-7 7 3.13 7 7-3.13 7-7 7z" /></g>
<g id="alarm-add"><path d="M7.88 3.39L6.6 1.86 2 5.71l1.29 1.53 4.59-3.85zM22 5.72l-4.6-3.86-1.29 1.53 4.6 3.86L22 5.72zM12 4c-4.97 0-9 4.03-9 9s4.02 9 9 9c4.97 0 9-4.03 9-9s-4.03-9-9-9zm0 16c-3.87 0-7-3.13-7-7s3.13-7 7-7 7 3.13 7 7-3.13 7-7 7zm1-11h-2v3H8v2h3v3h2v-3h3v-2h-3V9z" /></g>
<g id="alarm-off"><path d="M12 6c3.87 0 7 3.13 7 7 0 .84-.16 1.65-.43 2.4l1.52 1.52c.58-1.19.91-2.51.91-3.92 0-4.97-4.03-9-9-9-1.41 0-2.73.33-3.92.91L9.6 6.43C10.35 6.16 11.16 6 12 6zm10-.28l-4.6-3.86-1.29 1.53 4.6 3.86L22 5.72zM2.92 2.29L1.65 3.57 2.98 4.9l-1.11.93 1.42 1.42 1.11-.94.8.8C3.83 8.69 3 10.75 3 13c0 4.97 4.02 9 9 9 2.25 0 4.31-.83 5.89-2.2l2.2 2.2 1.27-1.27L3.89 3.27l-.97-.98zm13.55 16.1C15.26 19.39 13.7 20 12 20c-3.87 0-7-3.13-7-7 0-1.7.61-3.26 1.61-4.47l9.86 9.86zM8.02 3.28L6.6 1.86l-.86.71 1.42 1.42.86-.71z" /></g>
<g id="alarm-on"><path d="M22 5.72l-4.6-3.86-1.29 1.53 4.6 3.86L22 5.72zM7.88 3.39L6.6 1.86 2 5.71l1.29 1.53 4.59-3.85zM12 4c-4.97 0-9 4.03-9 9s4.02 9 9 9c4.97 0 9-4.03 9-9s-4.03-9-9-9zm0 16c-3.87 0-7-3.13-7-7s3.13-7 7-7 7 3.13 7 7-3.13 7-7 7zm-1.46-5.47L8.41 12.4l-1.06 1.06 3.18 3.18 6-6-1.06-1.06-4.93 4.95z" /></g>
<g id="all-out"><path d="M16.21 4.16l4 4v-4zm4 12l-4 4h4zm-12 4l-4-4v4zm-4-12l4-4h-4zm12.95-.95c-2.73-2.73-7.17-2.73-9.9 0s-2.73 7.17 0 9.9 7.17 2.73 9.9 0 2.73-7.16 0-9.9zm-1.1 8.8c-2.13 2.13-5.57 2.13-7.7 0s-2.13-5.57 0-7.7 5.57-2.13 7.7 0 2.13 5.57 0 7.7z" /></g>
<g id="android"><path d="M6 18c0 .55.45 1 1 1h1v3.5c0 .83.67 1.5 1.5 1.5s1.5-.67 1.5-1.5V19h2v3.5c0 .83.67 1.5 1.5 1.5s1.5-.67 1.5-1.5V19h1c.55 0 1-.45 1-1V8H6v10zM3.5 8C2.67 8 2 8.67 2 9.5v7c0 .83.67 1.5 1.5 1.5S5 17.33 5 16.5v-7C5 8.67 4.33 8 3.5 8zm17 0c-.83 0-1.5.67-1.5 1.5v7c0 .83.67 1.5 1.5 1.5s1.5-.67 1.5-1.5v-7c0-.83-.67-1.5-1.5-1.5zm-4.97-5.84l1.3-1.3c.2-.2.2-.51 0-.71-.2-.2-.51-.2-.71 0l-1.48 1.48C13.85 1.23 12.95 1 12 1c-.96 0-1.86.23-2.66.63L7.85.15c-.2-.2-.51-.2-.71 0-.2.2-.2.51 0 .71l1.31 1.31C6.97 3.26 6 5.01 6 7h12c0-1.99-.97-3.75-2.47-4.84zM10 5H9V4h1v1zm5 0h-1V4h1v1z" /></g>
<g id="announcement"><path d="M20 2H4c-1.1 0-1.99.9-1.99 2L2 22l4-4h14c1.1 0 2-.9 2-2V4c0-1.1-.9-2-2-2zm-7 9h-2V5h2v6zm0 4h-2v-2h2v2z" /></g>
<g id="apps"><path d="M4 8h4V4H4v4zm6 12h4v-4h-4v4zm-6 0h4v-4H4v4zm0-6h4v-4H4v4zm6 0h4v-4h-4v4zm6-10v4h4V4h-4zm-6 4h4V4h-4v4zm6 6h4v-4h-4v4zm0 6h4v-4h-4v4z" /></g>
<g id="archive"><path d="M20.54 5.23l-1.39-1.68C18.88 3.21 18.47 3 18 3H6c-.47 0-.88.21-1.16.55L3.46 5.23C3.17 5.57 3 6.02 3 6.5V19c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V6.5c0-.48-.17-.93-.46-1.27zM12 17.5L6.5 12H10v-2h4v2h3.5L12 17.5zM5.12 5l.81-1h12l.94 1H5.12z" /></g>
<g id="arrow-back"><path d="M20 11H7.83l5.59-5.59L12 4l-8 8 8 8 1.41-1.41L7.83 13H20v-2z" /></g>
<g id="arrow-downward"><path d="M20 12l-1.41-1.41L13 16.17V4h-2v12.17l-5.58-5.59L4 12l8 8 8-8z" /></g>
<g id="arrow-drop-down"><path d="M7 10l5 5 5-5z" /></g>
<g id="arrow-drop-down-circle"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 12l-4-4h8l-4 4z" /></g>
<g id="arrow-drop-up"><path d="M7 14l5-5 5 5z" /></g>
<g id="arrow-forward"><path d="M12 4l-1.41 1.41L16.17 11H4v2h12.17l-5.58 5.59L12 20l8-8z" /></g>
<g id="arrow-upward"><path d="M4 12l1.41 1.41L11 7.83V20h2V7.83l5.58 5.59L20 12l-8-8-8 8z" /></g>
<g id="aspect-ratio"><path d="M19 12h-2v3h-3v2h5v-5zM7 9h3V7H5v5h2V9zm14-6H3c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h18c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm0 16.01H3V4.99h18v14.02z" /></g>
<g id="assessment"><path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z" /></g>
<g id="assignment"><path d="M19 3h-4.18C14.4 1.84 13.3 1 12 1c-1.3 0-2.4.84-2.82 2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-7 0c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zm2 14H7v-2h7v2zm3-4H7v-2h10v2zm0-4H7V7h10v2z" /></g>
<g id="assignment-ind"><path d="M19 3h-4.18C14.4 1.84 13.3 1 12 1c-1.3 0-2.4.84-2.82 2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-7 0c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zm0 4c1.66 0 3 1.34 3 3s-1.34 3-3 3-3-1.34-3-3 1.34-3 3-3zm6 12H6v-1.4c0-2 4-3.1 6-3.1s6 1.1 6 3.1V19z" /></g>
<g id="assignment-late"><path d="M19 3h-4.18C14.4 1.84 13.3 1 12 1c-1.3 0-2.4.84-2.82 2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-6 15h-2v-2h2v2zm0-4h-2V8h2v6zm-1-9c-.55 0-1-.45-1-1s.45-1 1-1 1 .45 1 1-.45 1-1 1z" /></g>
<g id="assignment-return"><path d="M19 3h-4.18C14.4 1.84 13.3 1 12 1c-1.3 0-2.4.84-2.82 2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-7 0c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zm4 12h-4v3l-5-5 5-5v3h4v4z" /></g>
<g id="assignment-returned"><path d="M19 3h-4.18C14.4 1.84 13.3 1 12 1c-1.3 0-2.4.84-2.82 2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-7 0c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zm0 15l-5-5h3V9h4v4h3l-5 5z" /></g>
<g id="assignment-turned-in"><path d="M19 3h-4.18C14.4 1.84 13.3 1 12 1c-1.3 0-2.4.84-2.82 2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-7 0c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zm-2 14l-4-4 1.41-1.41L10 14.17l6.59-6.59L18 9l-8 8z" /></g>
<g id="attachment"><path d="M2 12.5C2 9.46 4.46 7 7.5 7H18c2.21 0 4 1.79 4 4s-1.79 4-4 4H9.5C8.12 15 7 13.88 7 12.5S8.12 10 9.5 10H17v2H9.41c-.55 0-.55 1 0 1H18c1.1 0 2-.9 2-2s-.9-2-2-2H7.5C5.57 9 4 10.57 4 12.5S5.57 16 7.5 16H17v2H7.5C4.46 18 2 15.54 2 12.5z" /></g>
<g id="autorenew"><path d="M12 6v3l4-4-4-4v3c-4.42 0-8 3.58-8 8 0 1.57.46 3.03 1.24 4.26L6.7 14.8c-.45-.83-.7-1.79-.7-2.8 0-3.31 2.69-6 6-6zm6.76 1.74L17.3 9.2c.44.84.7 1.79.7 2.8 0 3.31-2.69 6-6 6v-3l-4 4 4 4v-3c4.42 0 8-3.58 8-8 0-1.57-.46-3.03-1.24-4.26z" /></g>
<g id="backspace"><path d="M22 3H7c-.69 0-1.23.35-1.59.88L0 12l5.41 8.11c.36.53.9.89 1.59.89h15c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-3 12.59L17.59 17 14 13.41 10.41 17 9 15.59 12.59 12 9 8.41 10.41 7 14 10.59 17.59 7 19 8.41 15.41 12 19 15.59z" /></g>
<g id="backup"><path d="M19.35 10.04C18.67 6.59 15.64 4 12 4 9.11 4 6.6 5.64 5.35 8.04 2.34 8.36 0 10.91 0 14c0 3.31 2.69 6 6 6h13c2.76 0 5-2.24 5-5 0-2.64-2.05-4.78-4.65-4.96zM14 13v4h-4v-4H7l5-5 5 5h-3z" /></g>
<g id="block"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zM4 12c0-4.42 3.58-8 8-8 1.85 0 3.55.63 4.9 1.69L5.69 16.9C4.63 15.55 4 13.85 4 12zm8 8c-1.85 0-3.55-.63-4.9-1.69L18.31 7.1C19.37 8.45 20 10.15 20 12c0 4.42-3.58 8-8 8z" /></g>
<g id="book"><path d="M18 2H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h12c1.1 0 2-.9 2-2V4c0-1.1-.9-2-2-2zM6 4h5v8l-2.5-1.5L6 12V4z" /></g>
<g id="bookmark"><path d="M17 3H7c-1.1 0-1.99.9-1.99 2L5 21l7-3 7 3V5c0-1.1-.9-2-2-2z" /></g>
<g id="bookmark-border"><path d="M17 3H7c-1.1 0-1.99.9-1.99 2L5 21l7-3 7 3V5c0-1.1-.9-2-2-2zm0 15l-5-2.18L7 18V5h10v13z" /></g>
<g id="bug-report"><path d="M20 8h-2.81c-.45-.78-1.07-1.45-1.82-1.96L17 4.41 15.59 3l-2.17 2.17C12.96 5.06 12.49 5 12 5c-.49 0-.96.06-1.41.17L8.41 3 7 4.41l1.62 1.63C7.88 6.55 7.26 7.22 6.81 8H4v2h2.09c-.05.33-.09.66-.09 1v1H4v2h2v1c0 .34.04.67.09 1H4v2h2.81c1.04 1.79 2.97 3 5.19 3s4.15-1.21 5.19-3H20v-2h-2.09c.05-.33.09-.66.09-1v-1h2v-2h-2v-1c0-.34-.04-.67-.09-1H20V8zm-6 8h-4v-2h4v2zm0-4h-4v-2h4v2z" /></g>
<g id="build"><path d="M22.7 19l-9.1-9.1c.9-2.3.4-5-1.5-6.9-2-2-5-2.4-7.4-1.3L9 6 6 9 1.6 4.7C.4 7.1.9 10.1 2.9 12.1c1.9 1.9 4.6 2.4 6.9 1.5l9.1 9.1c.4.4 1 .4 1.4 0l2.3-2.3c.5-.4.5-1.1.1-1.4z" /></g>
<g id="cached"><path d="M19 8l-4 4h3c0 3.31-2.69 6-6 6-1.01 0-1.97-.25-2.8-.7l-1.46 1.46C8.97 19.54 10.43 20 12 20c4.42 0 8-3.58 8-8h3l-4-4zM6 12c0-3.31 2.69-6 6-6 1.01 0 1.97.25 2.8.7l1.46-1.46C15.03 4.46 13.57 4 12 4c-4.42 0-8 3.58-8 8H1l4 4 4-4H6z" /></g>
<g id="camera-enhance"><path d="M9 3L7.17 5H4c-1.1 0-2 .9-2 2v12c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2h-3.17L15 3H9zm3 15c-2.76 0-5-2.24-5-5s2.24-5 5-5 5 2.24 5 5-2.24 5-5 5zm0-1l1.25-2.75L16 13l-2.75-1.25L12 9l-1.25 2.75L8 13l2.75 1.25z" /></g>
<g id="cancel"><path d="M12 2C6.47 2 2 6.47 2 12s4.47 10 10 10 10-4.47 10-10S17.53 2 12 2zm5 13.59L15.59 17 12 13.41 8.41 17 7 15.59 10.59 12 7 8.41 8.41 7 12 10.59 15.59 7 17 8.41 13.41 12 17 15.59z" /></g>
<g id="card-giftcard"><path d="M20 6h-2.18c.11-.31.18-.65.18-1 0-1.66-1.34-3-3-3-1.05 0-1.96.54-2.5 1.35l-.5.67-.5-.68C10.96 2.54 10.05 2 9 2 7.34 2 6 3.34 6 5c0 .35.07.69.18 1H4c-1.11 0-1.99.89-1.99 2L2 19c0 1.11.89 2 2 2h16c1.11 0 2-.89 2-2V8c0-1.11-.89-2-2-2zm-5-2c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zM9 4c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zm11 15H4v-2h16v2zm0-5H4V8h5.08L7 10.83 8.62 12 11 8.76l1-1.36 1 1.36L15.38 12 17 10.83 14.92 8H20v6z" /></g>
<g id="card-membership"><path d="M20 2H4c-1.11 0-2 .89-2 2v11c0 1.11.89 2 2 2h4v5l4-2 4 2v-5h4c1.11 0 2-.89 2-2V4c0-1.11-.89-2-2-2zm0 13H4v-2h16v2zm0-5H4V4h16v6z" /></g>
<g id="card-travel"><path d="M20 6h-3V4c0-1.11-.89-2-2-2H9c-1.11 0-2 .89-2 2v2H4c-1.11 0-2 .89-2 2v11c0 1.11.89 2 2 2h16c1.11 0 2-.89 2-2V8c0-1.11-.89-2-2-2zM9 4h6v2H9V4zm11 15H4v-2h16v2zm0-5H4V8h3v2h2V8h6v2h2V8h3v6z" /></g>
<g id="change-history"><path d="M12 7.77L18.39 18H5.61L12 7.77M12 4L2 20h20L12 4z" /></g>
<g id="check"><path d="M9 16.17L4.83 12l-1.42 1.41L9 19 21 7l-1.41-1.41z" /></g>
<g id="check-box"><path d="M19 3H5c-1.11 0-2 .9-2 2v14c0 1.1.89 2 2 2h14c1.11 0 2-.9 2-2V5c0-1.1-.89-2-2-2zm-9 14l-5-5 1.41-1.41L10 14.17l7.59-7.59L19 8l-9 9z" /></g>
<g id="check-box-outline-blank"><path d="M19 5v14H5V5h14m0-2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2z" /></g>
<g id="check-circle"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-2 15l-5-5 1.41-1.41L10 14.17l7.59-7.59L19 8l-9 9z" /></g>
<g id="chevron-left"><path d="M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z" /></g>
<g id="chevron-right"><path d="M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z" /></g>
<g id="chrome-reader-mode"><path d="M13 12h7v1.5h-7zm0-2.5h7V11h-7zm0 5h7V16h-7zM21 4H3c-1.1 0-2 .9-2 2v13c0 1.1.9 2 2 2h18c1.1 0 2-.9 2-2V6c0-1.1-.9-2-2-2zm0 15h-9V6h9v13z" /></g>
<g id="class"><path d="M18 2H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h12c1.1 0 2-.9 2-2V4c0-1.1-.9-2-2-2zM6 4h5v8l-2.5-1.5L6 12V4z" /></g>
<g id="clear"><path d="M19 6.41L17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z" /></g>
<g id="close"><path d="M19 6.41L17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z" /></g>
<g id="cloud"><path d="M19.35 10.04C18.67 6.59 15.64 4 12 4 9.11 4 6.6 5.64 5.35 8.04 2.34 8.36 0 10.91 0 14c0 3.31 2.69 6 6 6h13c2.76 0 5-2.24 5-5 0-2.64-2.05-4.78-4.65-4.96z" /></g>
<g id="cloud-circle"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm4.5 14H8c-1.66 0-3-1.34-3-3s1.34-3 3-3l.14.01C8.58 8.28 10.13 7 12 7c2.21 0 4 1.79 4 4h.5c1.38 0 2.5 1.12 2.5 2.5S17.88 16 16.5 16z" /></g>
<g id="cloud-done"><path d="M19.35 10.04C18.67 6.59 15.64 4 12 4 9.11 4 6.6 5.64 5.35 8.04 2.34 8.36 0 10.91 0 14c0 3.31 2.69 6 6 6h13c2.76 0 5-2.24 5-5 0-2.64-2.05-4.78-4.65-4.96zM10 17l-3.5-3.5 1.41-1.41L10 14.17 15.18 9l1.41 1.41L10 17z" /></g>
<g id="cloud-download"><path d="M19.35 10.04C18.67 6.59 15.64 4 12 4 9.11 4 6.6 5.64 5.35 8.04 2.34 8.36 0 10.91 0 14c0 3.31 2.69 6 6 6h13c2.76 0 5-2.24 5-5 0-2.64-2.05-4.78-4.65-4.96zM17 13l-5 5-5-5h3V9h4v4h3z" /></g>
<g id="cloud-off"><path d="M19.35 10.04C18.67 6.59 15.64 4 12 4c-1.48 0-2.85.43-4.01 1.17l1.46 1.46C10.21 6.23 11.08 6 12 6c3.04 0 5.5 2.46 5.5 5.5v.5H19c1.66 0 3 1.34 3 3 0 1.13-.64 2.11-1.56 2.62l1.45 1.45C23.16 18.16 24 16.68 24 15c0-2.64-2.05-4.78-4.65-4.96zM3 5.27l2.75 2.74C2.56 8.15 0 10.77 0 14c0 3.31 2.69 6 6 6h11.73l2 2L21 20.73 4.27 4 3 5.27zM7.73 10l8 8H6c-2.21 0-4-1.79-4-4s1.79-4 4-4h1.73z" /></g>
<g id="cloud-queue"><path d="M19.35 10.04C18.67 6.59 15.64 4 12 4 9.11 4 6.6 5.64 5.35 8.04 2.34 8.36 0 10.91 0 14c0 3.31 2.69 6 6 6h13c2.76 0 5-2.24 5-5 0-2.64-2.05-4.78-4.65-4.96zM19 18H6c-2.21 0-4-1.79-4-4s1.79-4 4-4h.71C7.37 7.69 9.48 6 12 6c3.04 0 5.5 2.46 5.5 5.5v.5H19c1.66 0 3 1.34 3 3s-1.34 3-3 3z" /></g>
<g id="cloud-upload"><path d="M19.35 10.04C18.67 6.59 15.64 4 12 4 9.11 4 6.6 5.64 5.35 8.04 2.34 8.36 0 10.91 0 14c0 3.31 2.69 6 6 6h13c2.76 0 5-2.24 5-5 0-2.64-2.05-4.78-4.65-4.96zM14 13v4h-4v-4H7l5-5 5 5h-3z" /></g>
<g id="code"><path d="M9.4 16.6L4.8 12l4.6-4.6L8 6l-6 6 6 6 1.4-1.4zm5.2 0l4.6-4.6-4.6-4.6L16 6l6 6-6 6-1.4-1.4z" /></g>
<g id="compare-arrows"><path d="M9.01 14H2v2h7.01v3L13 15l-3.99-4v3zm5.98-1v-3H22V8h-7.01V5L11 9l3.99 4z" /></g>
<g id="content-copy"><path d="M16 1H4c-1.1 0-2 .9-2 2v14h2V3h12V1zm3 4H8c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h11c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm0 16H8V7h11v14z" /></g>
<g id="content-cut"><path d="M9.64 7.64c.23-.5.36-1.05.36-1.64 0-2.21-1.79-4-4-4S2 3.79 2 6s1.79 4 4 4c.59 0 1.14-.13 1.64-.36L10 12l-2.36 2.36C7.14 14.13 6.59 14 6 14c-2.21 0-4 1.79-4 4s1.79 4 4 4 4-1.79 4-4c0-.59-.13-1.14-.36-1.64L12 14l7 7h3v-1L9.64 7.64zM6 8c-1.1 0-2-.89-2-2s.9-2 2-2 2 .89 2 2-.9 2-2 2zm0 12c-1.1 0-2-.89-2-2s.9-2 2-2 2 .89 2 2-.9 2-2 2zm6-7.5c-.28 0-.5-.22-.5-.5s.22-.5.5-.5.5.22.5.5-.22.5-.5.5zM19 3l-6 6 2 2 7-7V3z" /></g>
<g id="content-paste"><path d="M19 2h-4.18C14.4.84 13.3 0 12 0c-1.3 0-2.4.84-2.82 2H5c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V4c0-1.1-.9-2-2-2zm-7 0c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zm7 18H5V4h2v3h10V4h2v16z" /></g>
<g id="copyright"><path d="M10.08 10.86c.05-.33.16-.62.3-.87s.34-.46.59-.62c.24-.15.54-.22.91-.23.23.01.44.05.63.13.2.09.38.21.52.36s.25.33.34.53.13.42.14.64h1.79c-.02-.47-.11-.9-.28-1.29s-.4-.73-.7-1.01-.66-.5-1.08-.66-.88-.23-1.39-.23c-.65 0-1.22.11-1.7.34s-.88.53-1.2.92-.56.84-.71 1.36S8 11.29 8 11.87v.27c0 .58.08 1.12.23 1.64s.39.97.71 1.35.72.69 1.2.91 1.05.34 1.7.34c.47 0 .91-.08 1.32-.23s.77-.36 1.08-.63.56-.58.74-.94.29-.74.3-1.15h-1.79c-.01.21-.06.4-.15.58s-.21.33-.36.46-.32.23-.52.3c-.19.07-.39.09-.6.1-.36-.01-.66-.08-.89-.23-.25-.16-.45-.37-.59-.62s-.25-.55-.3-.88-.08-.67-.08-1v-.27c0-.35.03-.68.08-1.01zM12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 18c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8z" /></g>
<g id="create"><path d="M3 17.25V21h3.75L17.81 9.94l-3.75-3.75L3 17.25zM20.71 7.04c.39-.39.39-1.02 0-1.41l-2.34-2.34c-.39-.39-1.02-.39-1.41 0l-1.83 1.83 3.75 3.75 1.83-1.83z" /></g>
<g id="create-new-folder"><path d="M20 6h-8l-2-2H4c-1.11 0-1.99.89-1.99 2L2 18c0 1.11.89 2 2 2h16c1.11 0 2-.89 2-2V8c0-1.11-.89-2-2-2zm-1 8h-3v3h-2v-3h-3v-2h3V9h2v3h3v2z" /></g>
<g id="credit-card"><path d="M20 4H4c-1.11 0-1.99.89-1.99 2L2 18c0 1.11.89 2 2 2h16c1.11 0 2-.89 2-2V6c0-1.11-.89-2-2-2zm0 14H4v-6h16v6zm0-10H4V6h16v2z" /></g>
<g id="dashboard"><path d="M3 13h8V3H3v10zm0 8h8v-6H3v6zm10 0h8V11h-8v10zm0-18v6h8V3h-8z" /></g>
<g id="date-range"><path d="M9 11H7v2h2v-2zm4 0h-2v2h2v-2zm4 0h-2v2h2v-2zm2-7h-1V2h-2v2H8V2H6v2H5c-1.11 0-1.99.9-1.99 2L3 20c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V6c0-1.1-.9-2-2-2zm0 16H5V9h14v11z" /></g>
<g id="delete"><path d="M6 19c0 1.1.9 2 2 2h8c1.1 0 2-.9 2-2V7H6v12zM19 4h-3.5l-1-1h-5l-1 1H5v2h14V4z" /></g>
<g id="delete-forever"><path d="M6 19c0 1.1.9 2 2 2h8c1.1 0 2-.9 2-2V7H6v12zm2.46-7.12l1.41-1.41L12 12.59l2.12-2.12 1.41 1.41L13.41 14l2.12 2.12-1.41 1.41L12 15.41l-2.12 2.12-1.41-1.41L10.59 14l-2.13-2.12zM15.5 4l-1-1h-5l-1 1H5v2h14V4z" /></g>
<g id="delete-sweep"><path d="M15 16h4v2h-4zm0-8h7v2h-7zm0 4h6v2h-6zM3 18c0 1.1.9 2 2 2h6c1.1 0 2-.9 2-2V8H3v10zM14 5h-3l-1-1H6L5 5H2v2h12z" /></g>
<g id="description"><path d="M14 2H6c-1.1 0-1.99.9-1.99 2L4 20c0 1.1.89 2 1.99 2H18c1.1 0 2-.9 2-2V8l-6-6zm2 16H8v-2h8v2zm0-4H8v-2h8v2zm-3-5V3.5L18.5 9H13z" /></g>
<g id="dns"><path d="M20 13H4c-.55 0-1 .45-1 1v6c0 .55.45 1 1 1h16c.55 0 1-.45 1-1v-6c0-.55-.45-1-1-1zM7 19c-1.1 0-2-.9-2-2s.9-2 2-2 2 .9 2 2-.9 2-2 2zM20 3H4c-.55 0-1 .45-1 1v6c0 .55.45 1 1 1h16c.55 0 1-.45 1-1V4c0-.55-.45-1-1-1zM7 9c-1.1 0-2-.9-2-2s.9-2 2-2 2 .9 2 2-.9 2-2 2z" /></g>
<g id="done"><path d="M9 16.2L4.8 12l-1.4 1.4L9 19 21 7l-1.4-1.4L9 16.2z" /></g>
<g id="done-all"><path d="M18 7l-1.41-1.41-6.34 6.34 1.41 1.41L18 7zm4.24-1.41L11.66 16.17 7.48 12l-1.41 1.41L11.66 19l12-12-1.42-1.41zM.41 13.41L6 19l1.41-1.41L1.83 12 .41 13.41z" /></g>
<g id="donut-large"><path d="M11 5.08V2c-5 .5-9 4.81-9 10s4 9.5 9 10v-3.08c-3-.48-6-3.4-6-6.92s3-6.44 6-6.92zM18.97 11H22c-.47-5-4-8.53-9-9v3.08C16 5.51 18.54 8 18.97 11zM13 18.92V22c5-.47 8.53-4 9-9h-3.03c-.43 3-2.97 5.49-5.97 5.92z" /></g>
<g id="donut-small"><path d="M11 9.16V2c-5 .5-9 4.79-9 10s4 9.5 9 10v-7.16c-1-.41-2-1.52-2-2.84s1-2.43 2-2.84zM14.86 11H22c-.48-4.75-4-8.53-9-9v7.16c1 .3 1.52.98 1.86 1.84zM13 14.84V22c5-.47 8.52-4.25 9-9h-7.14c-.34.86-.86 1.54-1.86 1.84z" /></g>
<g id="drafts"><path d="M21.99 8c0-.72-.37-1.35-.94-1.7L12 1 2.95 6.3C2.38 6.65 2 7.28 2 8v10c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2l-.01-10zM12 13L3.74 7.84 12 3l8.26 4.84L12 13z" /></g>
<g id="eject"><path d="M5 17h14v2H5zm7-12L5.33 15h13.34z" /></g>
<g id="error"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm1 15h-2v-2h2v2zm0-4h-2V7h2v6z" /></g>
<g id="error-outline"><path d="M11 15h2v2h-2zm0-8h2v6h-2zm.99-5C6.47 2 2 6.48 2 12s4.47 10 9.99 10C17.52 22 22 17.52 22 12S17.52 2 11.99 2zM12 20c-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8-3.58 8-8 8z" /></g>
<g id="euro-symbol"><path d="M15 18.5c-2.51 0-4.68-1.42-5.76-3.5H15v-2H8.58c-.05-.33-.08-.66-.08-1s.03-.67.08-1H15V9H9.24C10.32 6.92 12.5 5.5 15 5.5c1.61 0 3.09.59 4.23 1.57L21 5.3C19.41 3.87 17.3 3 15 3c-3.92 0-7.24 2.51-8.48 6H3v2h3.06c-.04.33-.06.66-.06 1 0 .34.02.67.06 1H3v2h3.52c1.24 3.49 4.56 6 8.48 6 2.31 0 4.41-.87 6-2.3l-1.78-1.77c-1.13.98-2.6 1.57-4.22 1.57z" /></g>
<g id="event"><path d="M17 12h-5v5h5v-5zM16 1v2H8V1H6v2H5c-1.11 0-1.99.9-1.99 2L3 19c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2h-1V1h-2zm3 18H5V8h14v11z" /></g>
<g id="event-seat"><path d="M4 18v3h3v-3h10v3h3v-6H4zm15-8h3v3h-3zM2 10h3v3H2zm15 3H7V5c0-1.1.9-2 2-2h6c1.1 0 2 .9 2 2v8z" /></g>
<g id="exit-to-app"><path d="M10.09 15.59L11.5 17l5-5-5-5-1.41 1.41L12.67 11H3v2h9.67l-2.58 2.59zM19 3H5c-1.11 0-2 .9-2 2v4h2V5h14v14H5v-4H3v4c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2z" /></g>
<g id="expand-less"><path d="M12 8l-6 6 1.41 1.41L12 10.83l4.59 4.58L18 14z" /></g>
<g id="expand-more"><path d="M16.59 8.59L12 13.17 7.41 8.59 6 10l6 6 6-6z" /></g>
<g id="explore"><path d="M12 10.9c-.61 0-1.1.49-1.1 1.1s.49 1.1 1.1 1.1c.61 0 1.1-.49 1.1-1.1s-.49-1.1-1.1-1.1zM12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm2.19 12.19L6 18l3.81-8.19L18 6l-3.81 8.19z" /></g>
<g id="extension"><path d="M20.5 11H19V7c0-1.1-.9-2-2-2h-4V3.5C13 2.12 11.88 1 10.5 1S8 2.12 8 3.5V5H4c-1.1 0-1.99.9-1.99 2v3.8H3.5c1.49 0 2.7 1.21 2.7 2.7s-1.21 2.7-2.7 2.7H2V20c0 1.1.9 2 2 2h3.8v-1.5c0-1.49 1.21-2.7 2.7-2.7 1.49 0 2.7 1.21 2.7 2.7V22H17c1.1 0 2-.9 2-2v-4h1.5c1.38 0 2.5-1.12 2.5-2.5S21.88 11 20.5 11z" /></g>
<g id="face"><path d="M9 11.75c-.69 0-1.25.56-1.25 1.25s.56 1.25 1.25 1.25 1.25-.56 1.25-1.25-.56-1.25-1.25-1.25zm6 0c-.69 0-1.25.56-1.25 1.25s.56 1.25 1.25 1.25 1.25-.56 1.25-1.25-.56-1.25-1.25-1.25zM12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 18c-4.41 0-8-3.59-8-8 0-.29.02-.58.05-.86 2.36-1.05 4.23-2.98 5.21-5.37C11.07 8.33 14.05 10 17.42 10c.78 0 1.53-.09 2.25-.26.21.71.33 1.47.33 2.26 0 4.41-3.59 8-8 8z" /></g>
<g id="favorite"><path d="M12 21.35l-1.45-1.32C5.4 15.36 2 12.28 2 8.5 2 5.42 4.42 3 7.5 3c1.74 0 3.41.81 4.5 2.09C13.09 3.81 14.76 3 16.5 3 19.58 3 22 5.42 22 8.5c0 3.78-3.4 6.86-8.55 11.54L12 21.35z" /></g>
<g id="favorite-border"><path d="M16.5 3c-1.74 0-3.41.81-4.5 2.09C10.91 3.81 9.24 3 7.5 3 4.42 3 2 5.42 2 8.5c0 3.78 3.4 6.86 8.55 11.54L12 21.35l1.45-1.32C18.6 15.36 22 12.28 22 8.5 22 5.42 19.58 3 16.5 3zm-4.4 15.55l-.1.1-.1-.1C7.14 14.24 4 11.39 4 8.5 4 6.5 5.5 5 7.5 5c1.54 0 3.04.99 3.57 2.36h1.87C13.46 5.99 14.96 5 16.5 5c2 0 3.5 1.5 3.5 3.5 0 2.89-3.14 5.74-7.9 10.05z" /></g>
<g id="feedback"><path d="M20 2H4c-1.1 0-1.99.9-1.99 2L2 22l4-4h14c1.1 0 2-.9 2-2V4c0-1.1-.9-2-2-2zm-7 12h-2v-2h2v2zm0-4h-2V6h2v4z" /></g>
<g id="file-download"><path d="M19 9h-4V3H9v6H5l7 7 7-7zM5 18v2h14v-2H5z" /></g>
<g id="file-upload"><path d="M9 16h6v-6h4l-7-7-7 7h4zm-4 2h14v2H5z" /></g>
<g id="filter-list"><path d="M10 18h4v-2h-4v2zM3 6v2h18V6H3zm3 7h12v-2H6v2z" /></g>
<g id="find-in-page"><path d="M20 19.59V8l-6-6H6c-1.1 0-1.99.9-1.99 2L4 20c0 1.1.89 2 1.99 2H18c.45 0 .85-.15 1.19-.4l-4.43-4.43c-.8.52-1.74.83-2.76.83-2.76 0-5-2.24-5-5s2.24-5 5-5 5 2.24 5 5c0 1.02-.31 1.96-.83 2.75L20 19.59zM9 13c0 1.66 1.34 3 3 3s3-1.34 3-3-1.34-3-3-3-3 1.34-3 3z" /></g>
<g id="find-replace"><path d="M11 6c1.38 0 2.63.56 3.54 1.46L12 10h6V4l-2.05 2.05C14.68 4.78 12.93 4 11 4c-3.53 0-6.43 2.61-6.92 6H6.1c.46-2.28 2.48-4 4.9-4zm5.64 9.14c.66-.9 1.12-1.97 1.28-3.14H15.9c-.46 2.28-2.48 4-4.9 4-1.38 0-2.63-.56-3.54-1.46L10 12H4v6l2.05-2.05C7.32 17.22 9.07 18 11 18c1.55 0 2.98-.51 4.14-1.36L20 21.49 21.49 20l-4.85-4.86z" /></g>
<g id="fingerprint"><path d="M17.81 4.47c-.08 0-.16-.02-.23-.06C15.66 3.42 14 3 12.01 3c-1.98 0-3.86.47-5.57 1.41-.24.13-.54.04-.68-.2-.13-.24-.04-.55.2-.68C7.82 2.52 9.86 2 12.01 2c2.13 0 3.99.47 6.03 1.52.25.13.34.43.21.67-.09.18-.26.28-.44.28zM3.5 9.72c-.1 0-.2-.03-.29-.09-.23-.16-.28-.47-.12-.7.99-1.4 2.25-2.5 3.75-3.27C9.98 4.04 14 4.03 17.15 5.65c1.5.77 2.76 1.86 3.75 3.25.16.22.11.54-.12.7-.23.16-.54.11-.7-.12-.9-1.26-2.04-2.25-3.39-2.94-2.87-1.47-6.54-1.47-9.4.01-1.36.7-2.5 1.7-3.4 2.96-.08.14-.23.21-.39.21zm6.25 12.07c-.13 0-.26-.05-.35-.15-.87-.87-1.34-1.43-2.01-2.64-.69-1.23-1.05-2.73-1.05-4.34 0-2.97 2.54-5.39 5.66-5.39s5.66 2.42 5.66 5.39c0 .28-.22.5-.5.5s-.5-.22-.5-.5c0-2.42-2.09-4.39-4.66-4.39-2.57 0-4.66 1.97-4.66 4.39 0 1.44.32 2.77.93 3.85.64 1.15 1.08 1.64 1.85 2.42.19.2.19.51 0 .71-.11.1-.24.15-.37.15zm7.17-1.85c-1.19 0-2.24-.3-3.1-.89-1.49-1.01-2.38-2.65-2.38-4.39 0-.28.22-.5.5-.5s.5.22.5.5c0 1.41.72 2.74 1.94 3.56.71.48 1.54.71 2.54.71.24 0 .64-.03 1.04-.1.27-.05.53.13.58.41.05.27-.13.53-.41.58-.57.11-1.07.12-1.21.12zM14.91 22c-.04 0-.09-.01-.13-.02-1.59-.44-2.63-1.03-3.72-2.1-1.4-1.39-2.17-3.24-2.17-5.22 0-1.62 1.38-2.94 3.08-2.94 1.7 0 3.08 1.32 3.08 2.94 0 1.07.93 1.94 2.08 1.94s2.08-.87 2.08-1.94c0-3.77-3.25-6.83-7.25-6.83-2.84 0-5.44 1.58-6.61 4.03-.39.81-.59 1.76-.59 2.8 0 .78.07 2.01.67 3.61.1.26-.03.55-.29.64-.26.1-.55-.04-.64-.29-.49-1.31-.73-2.61-.73-3.96 0-1.2.23-2.29.68-3.24 1.33-2.79 4.28-4.6 7.51-4.6 4.55 0 8.25 3.51 8.25 7.83 0 1.62-1.38 2.94-3.08 2.94s-3.08-1.32-3.08-2.94c0-1.07-.93-1.94-2.08-1.94s-2.08.87-2.08 1.94c0 1.71.66 3.31 1.87 4.51.95.94 1.86 1.46 3.27 1.85.27.07.42.35.35.61-.05.23-.26.38-.47.38z" /></g>
<g id="first-page"><path d="M18.41 16.59L13.82 12l4.59-4.59L17 6l-6 6 6 6zM6 6h2v12H6z" /></g>
<g id="flag"><path d="M14.4 6L14 4H5v17h2v-7h5.6l.4 2h7V6z" /></g>
<g id="flight-land"><path d="M2.5 19h19v2h-19zm7.18-5.73l4.35 1.16 5.31 1.42c.8.21 1.62-.26 1.84-1.06.21-.8-.26-1.62-1.06-1.84l-5.31-1.42-2.76-9.02L10.12 2v8.28L5.15 8.95l-.93-2.32-1.45-.39v5.17l1.6.43 5.31 1.43z" /></g>
<g id="flight-takeoff"><path d="M2.5 19h19v2h-19zm19.57-9.36c-.21-.8-1.04-1.28-1.84-1.06L14.92 10l-6.9-6.43-1.93.51 4.14 7.17-4.97 1.33-1.97-1.54-1.45.39 1.82 3.16.77 1.33 1.6-.43 5.31-1.42 4.35-1.16L21 11.49c.81-.23 1.28-1.05 1.07-1.85z" /></g>
<g id="flip-to-back"><path d="M9 7H7v2h2V7zm0 4H7v2h2v-2zm0-8c-1.11 0-2 .9-2 2h2V3zm4 12h-2v2h2v-2zm6-12v2h2c0-1.1-.9-2-2-2zm-6 0h-2v2h2V3zM9 17v-2H7c0 1.1.89 2 2 2zm10-4h2v-2h-2v2zm0-4h2V7h-2v2zm0 8c1.1 0 2-.9 2-2h-2v2zM5 7H3v12c0 1.1.89 2 2 2h12v-2H5V7zm10-2h2V3h-2v2zm0 12h2v-2h-2v2z" /></g>
<g id="flip-to-front"><path d="M3 13h2v-2H3v2zm0 4h2v-2H3v2zm2 4v-2H3c0 1.1.89 2 2 2zM3 9h2V7H3v2zm12 12h2v-2h-2v2zm4-18H9c-1.11 0-2 .9-2 2v10c0 1.1.89 2 2 2h10c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm0 12H9V5h10v10zm-8 6h2v-2h-2v2zm-4 0h2v-2H7v2z" /></g>
<g id="folder"><path d="M10 4H4c-1.1 0-1.99.9-1.99 2L2 18c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V8c0-1.1-.9-2-2-2h-8l-2-2z" /></g>
<g id="folder-open"><path d="M20 6h-8l-2-2H4c-1.1 0-1.99.9-1.99 2L2 18c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V8c0-1.1-.9-2-2-2zm0 12H4V8h16v10z" /></g>
<g id="folder-shared"><path d="M20 6h-8l-2-2H4c-1.1 0-1.99.9-1.99 2L2 18c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V8c0-1.1-.9-2-2-2zm-5 3c1.1 0 2 .9 2 2s-.9 2-2 2-2-.9-2-2 .9-2 2-2zm4 8h-8v-1c0-1.33 2.67-2 4-2s4 .67 4 2v1z" /></g>
<g id="font-download"><path d="M9.93 13.5h4.14L12 7.98zM20 2H4c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V4c0-1.1-.9-2-2-2zm-4.05 16.5l-1.14-3H9.17l-1.12 3H5.96l5.11-13h1.86l5.11 13h-2.09z" /></g>
<g id="forward"><path d="M12 8V4l8 8-8 8v-4H4V8z" /></g>
<g id="fullscreen"><path d="M7 14H5v5h5v-2H7v-3zm-2-4h2V7h3V5H5v5zm12 7h-3v2h5v-5h-2v3zM14 5v2h3v3h2V5h-5z" /></g>
<g id="fullscreen-exit"><path d="M5 16h3v3h2v-5H5v2zm3-8H5v2h5V5H8v3zm6 11h2v-3h3v-2h-5v5zm2-11V5h-2v5h5V8h-3z" /></g>
<g id="g-translate"><path d="M20 5h-9.12L10 2H4c-1.1 0-2 .9-2 2v13c0 1.1.9 2 2 2h7l1 3h8c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zM7.17 14.59c-2.25 0-4.09-1.83-4.09-4.09s1.83-4.09 4.09-4.09c1.04 0 1.99.37 2.74 1.07l.07.06-1.23 1.18-.06-.05c-.29-.27-.78-.59-1.52-.59-1.31 0-2.38 1.09-2.38 2.42s1.07 2.42 2.38 2.42c1.37 0 1.96-.87 2.12-1.46H7.08V9.91h3.95l.01.07c.04.21.05.4.05.61 0 2.35-1.61 4-3.92 4zm6.03-1.71c.33.6.74 1.18 1.19 1.7l-.54.53-.65-2.23zm.77-.76h-.99l-.31-1.04h3.99s-.34 1.31-1.56 2.74c-.52-.62-.89-1.23-1.13-1.7zM21 20c0 .55-.45 1-1 1h-7l2-2-.81-2.77.92-.92L17.79 18l.73-.73-2.71-2.68c.9-1.03 1.6-2.25 1.92-3.51H19v-1.04h-3.64V9h-1.04v1.04h-1.96L11.18 6H20c.55 0 1 .45 1 1v13z" /></g>
<g id="gavel"><path d="M1 21h12v2H1zM5.245 8.07l2.83-2.827 14.14 14.142-2.828 2.828zM12.317 1l5.657 5.656-2.83 2.83-5.654-5.66zM3.825 9.485l5.657 5.657-2.828 2.828-5.657-5.657z" /></g>
<g id="gesture"><path d="M4.59 6.89c.7-.71 1.4-1.35 1.71-1.22.5.2 0 1.03-.3 1.52-.25.42-2.86 3.89-2.86 6.31 0 1.28.48 2.34 1.34 2.98.75.56 1.74.73 2.64.46 1.07-.31 1.95-1.4 3.06-2.77 1.21-1.49 2.83-3.44 4.08-3.44 1.63 0 1.65 1.01 1.76 1.79-3.78.64-5.38 3.67-5.38 5.37 0 1.7 1.44 3.09 3.21 3.09 1.63 0 4.29-1.33 4.69-6.1H21v-2.5h-2.47c-.15-1.65-1.09-4.2-4.03-4.2-2.25 0-4.18 1.91-4.94 2.84-.58.73-2.06 2.48-2.29 2.72-.25.3-.68.84-1.11.84-.45 0-.72-.83-.36-1.92.35-1.09 1.4-2.86 1.85-3.52.78-1.14 1.3-1.92 1.3-3.28C8.95 3.69 7.31 3 6.44 3 5.12 3 3.97 4 3.72 4.25c-.36.36-.66.66-.88.93l1.75 1.71zm9.29 11.66c-.31 0-.74-.26-.74-.72 0-.6.73-2.2 2.87-2.76-.3 2.69-1.43 3.48-2.13 3.48z" /></g>
<g id="get-app"><path d="M19 9h-4V3H9v6H5l7 7 7-7zM5 18v2h14v-2H5z" /></g>
<g id="gif"><path d="M11.5 9H13v6h-1.5zM9 9H6c-.6 0-1 .5-1 1v4c0 .5.4 1 1 1h3c.6 0 1-.5 1-1v-2H8.5v1.5h-2v-3H10V10c0-.5-.4-1-1-1zm10 1.5V9h-4.5v6H16v-2h2v-1.5h-2v-1z" /></g>
<g id="grade"><path d="M12 17.27L18.18 21l-1.64-7.03L22 9.24l-7.19-.61L12 2 9.19 8.63 2 9.24l5.46 4.73L5.82 21z" /></g>
<g id="group-work"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zM8 17.5c-1.38 0-2.5-1.12-2.5-2.5s1.12-2.5 2.5-2.5 2.5 1.12 2.5 2.5-1.12 2.5-2.5 2.5zM9.5 8c0-1.38 1.12-2.5 2.5-2.5s2.5 1.12 2.5 2.5-1.12 2.5-2.5 2.5S9.5 9.38 9.5 8zm6.5 9.5c-1.38 0-2.5-1.12-2.5-2.5s1.12-2.5 2.5-2.5 2.5 1.12 2.5 2.5-1.12 2.5-2.5 2.5z" /></g>
<g id="help"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm1 17h-2v-2h2v2zm2.07-7.75l-.9.92C13.45 12.9 13 13.5 13 15h-2v-.5c0-1.1.45-2.1 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41 0-1.1-.9-2-2-2s-2 .9-2 2H8c0-2.21 1.79-4 4-4s4 1.79 4 4c0 .88-.36 1.68-.93 2.25z" /></g>
<g id="help-outline"><path d="M11 18h2v-2h-2v2zm1-16C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 18c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8zm0-14c-2.21 0-4 1.79-4 4h2c0-1.1.9-2 2-2s2 .9 2 2c0 2-3 1.75-3 5h2c0-2.25 3-2.5 3-5 0-2.21-1.79-4-4-4z" /></g>
<g id="highlight-off"><path d="M14.59 8L12 10.59 9.41 8 8 9.41 10.59 12 8 14.59 9.41 16 12 13.41 14.59 16 16 14.59 13.41 12 16 9.41 14.59 8zM12 2C6.47 2 2 6.47 2 12s4.47 10 10 10 10-4.47 10-10S17.53 2 12 2zm0 18c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8z" /></g>
<g id="history"><path d="M13 3c-4.97 0-9 4.03-9 9H1l3.89 3.89.07.14L9 12H6c0-3.87 3.13-7 7-7s7 3.13 7 7-3.13 7-7 7c-1.93 0-3.68-.79-4.94-2.06l-1.42 1.42C8.27 19.99 10.51 21 13 21c4.97 0 9-4.03 9-9s-4.03-9-9-9zm-1 5v5l4.28 2.54.72-1.21-3.5-2.08V8H12z" /></g>
<g id="home"><path d="M10 20v-6h4v6h5v-8h3L12 3 2 12h3v8z" /></g>
<g id="hourglass-empty"><path d="M6 2v6h.01L6 8.01 10 12l-4 4 .01.01H6V22h12v-5.99h-.01L18 16l-4-4 4-3.99-.01-.01H18V2H6zm10 14.5V20H8v-3.5l4-4 4 4zm-4-5l-4-4V4h8v3.5l-4 4z" /></g>
<g id="hourglass-full"><path d="M6 2v6h.01L6 8.01 10 12l-4 4 .01.01H6V22h12v-5.99h-.01L18 16l-4-4 4-3.99-.01-.01H18V2H6z" /></g>
<g id="http"><path d="M4.5 11h-2V9H1v6h1.5v-2.5h2V15H6V9H4.5v2zm2.5-.5h1.5V15H10v-4.5h1.5V9H7v1.5zm5.5 0H14V15h1.5v-4.5H17V9h-4.5v1.5zm9-1.5H18v6h1.5v-2h2c.8 0 1.5-.7 1.5-1.5v-1c0-.8-.7-1.5-1.5-1.5zm0 2.5h-2v-1h2v1z" /></g>
<g id="https"><path d="M18 8h-1V6c0-2.76-2.24-5-5-5S7 3.24 7 6v2H6c-1.1 0-2 .9-2 2v10c0 1.1.9 2 2 2h12c1.1 0 2-.9 2-2V10c0-1.1-.9-2-2-2zm-6 9c-1.1 0-2-.9-2-2s.9-2 2-2 2 .9 2 2-.9 2-2 2zm3.1-9H8.9V6c0-1.71 1.39-3.1 3.1-3.1 1.71 0 3.1 1.39 3.1 3.1v2z" /></g>
<g id="important-devices"><path d="M23 11.01L18 11c-.55 0-1 .45-1 1v9c0 .55.45 1 1 1h5c.55 0 1-.45 1-1v-9c0-.55-.45-.99-1-.99zM23 20h-5v-7h5v7zM20 2H2C.89 2 0 2.89 0 4v12c0 1.1.89 2 2 2h7v2H7v2h8v-2h-2v-2h2v-2H2V4h18v5h2V4c0-1.11-.9-2-2-2zm-8.03 7L11 6l-.97 3H7l2.47 1.76-.94 2.91 2.47-1.8 2.47 1.8-.94-2.91L15 9h-3.03z" /></g>
<g id="inbox"><path d="M19 3H4.99c-1.11 0-1.98.89-1.98 2L3 19c0 1.1.88 2 1.99 2H19c1.1 0 2-.9 2-2V5c0-1.11-.9-2-2-2zm0 12h-4c0 1.66-1.35 3-3 3s-3-1.34-3-3H4.99V5H19v10z" /></g>
<g id="indeterminate-check-box"><path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-2 10H7v-2h10v2z" /></g>
<g id="info"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm1 15h-2v-6h2v6zm0-8h-2V7h2v2z" /></g>
<g id="info-outline"><path d="M11 17h2v-6h-2v6zm1-15C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 18c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8zM11 9h2V7h-2v2z" /></g>
<g id="input"><path d="M21 3.01H3c-1.1 0-2 .9-2 2V9h2V4.99h18v14.03H3V15H1v4.01c0 1.1.9 1.98 2 1.98h18c1.1 0 2-.88 2-1.98v-14c0-1.11-.9-2-2-2zM11 16l4-4-4-4v3H1v2h10v3z" /></g>
<g id="invert-colors"><path d="M17.66 7.93L12 2.27 6.34 7.93c-3.12 3.12-3.12 8.19 0 11.31C7.9 20.8 9.95 21.58 12 21.58c2.05 0 4.1-.78 5.66-2.34 3.12-3.12 3.12-8.19 0-11.31zM12 19.59c-1.6 0-3.11-.62-4.24-1.76C6.62 16.69 6 15.19 6 13.59s.62-3.11 1.76-4.24L12 5.1v14.49z" /></g>
<g id="label"><path d="M17.63 5.84C17.27 5.33 16.67 5 16 5L5 5.01C3.9 5.01 3 5.9 3 7v10c0 1.1.9 1.99 2 1.99L16 19c.67 0 1.27-.33 1.63-.84L22 12l-4.37-6.16z" /></g>
<g id="label-outline"><path d="M17.63 5.84C17.27 5.33 16.67 5 16 5L5 5.01C3.9 5.01 3 5.9 3 7v10c0 1.1.9 1.99 2 1.99L16 19c.67 0 1.27-.33 1.63-.84L22 12l-4.37-6.16zM16 17H5V7h11l3.55 5L16 17z" /></g>
<g id="language"><path d="M11.99 2C6.47 2 2 6.48 2 12s4.47 10 9.99 10C17.52 22 22 17.52 22 12S17.52 2 11.99 2zm6.93 6h-2.95c-.32-1.25-.78-2.45-1.38-3.56 1.84.63 3.37 1.91 4.33 3.56zM12 4.04c.83 1.2 1.48 2.53 1.91 3.96h-3.82c.43-1.43 1.08-2.76 1.91-3.96zM4.26 14C4.1 13.36 4 12.69 4 12s.1-1.36.26-2h3.38c-.08.66-.14 1.32-.14 2 0 .68.06 1.34.14 2H4.26zm.82 2h2.95c.32 1.25.78 2.45 1.38 3.56-1.84-.63-3.37-1.9-4.33-3.56zm2.95-8H5.08c.96-1.66 2.49-2.93 4.33-3.56C8.81 5.55 8.35 6.75 8.03 8zM12 19.96c-.83-1.2-1.48-2.53-1.91-3.96h3.82c-.43 1.43-1.08 2.76-1.91 3.96zM14.34 14H9.66c-.09-.66-.16-1.32-.16-2 0-.68.07-1.35.16-2h4.68c.09.65.16 1.32.16 2 0 .68-.07 1.34-.16 2zm.25 5.56c.6-1.11 1.06-2.31 1.38-3.56h2.95c-.96 1.65-2.49 2.93-4.33 3.56zM16.36 14c.08-.66.14-1.32.14-2 0-.68-.06-1.34-.14-2h3.38c.16.64.26 1.31.26 2s-.1 1.36-.26 2h-3.38z" /></g>
<g id="last-page"><path d="M5.59 7.41L10.18 12l-4.59 4.59L7 18l6-6-6-6zM16 6h2v12h-2z" /></g>
<g id="launch"><path d="M19 19H5V5h7V3H5c-1.11 0-2 .9-2 2v14c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2v-7h-2v7zM14 3v2h3.59l-9.83 9.83 1.41 1.41L19 6.41V10h2V3h-7z" /></g>
<g id="lightbulb-outline"><path d="M9 21c0 .55.45 1 1 1h4c.55 0 1-.45 1-1v-1H9v1zm3-19C8.14 2 5 5.14 5 9c0 2.38 1.19 4.47 3 5.74V17c0 .55.45 1 1 1h6c.55 0 1-.45 1-1v-2.26c1.81-1.27 3-3.36 3-5.74 0-3.86-3.14-7-7-7zm2.85 11.1l-.85.6V16h-4v-2.3l-.85-.6C7.8 12.16 7 10.63 7 9c0-2.76 2.24-5 5-5s5 2.24 5 5c0 1.63-.8 3.16-2.15 4.1z" /></g>
<g id="line-style"><path d="M3 16h5v-2H3v2zm6.5 0h5v-2h-5v2zm6.5 0h5v-2h-5v2zM3 20h2v-2H3v2zm4 0h2v-2H7v2zm4 0h2v-2h-2v2zm4 0h2v-2h-2v2zm4 0h2v-2h-2v2zM3 12h8v-2H3v2zm10 0h8v-2h-8v2zM3 4v4h18V4H3z" /></g>
<g id="line-weight"><path d="M3 17h18v-2H3v2zm0 3h18v-1H3v1zm0-7h18v-3H3v3zm0-9v4h18V4H3z" /></g>
<g id="link"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z" /></g>
<g id="list"><path d="M3 13h2v-2H3v2zm0 4h2v-2H3v2zm0-8h2V7H3v2zm4 4h14v-2H7v2zm0 4h14v-2H7v2zM7 7v2h14V7H7z" /></g>
<g id="lock"><path d="M18 8h-1V6c0-2.76-2.24-5-5-5S7 3.24 7 6v2H6c-1.1 0-2 .9-2 2v10c0 1.1.9 2 2 2h12c1.1 0 2-.9 2-2V10c0-1.1-.9-2-2-2zm-6 9c-1.1 0-2-.9-2-2s.9-2 2-2 2 .9 2 2-.9 2-2 2zm3.1-9H8.9V6c0-1.71 1.39-3.1 3.1-3.1 1.71 0 3.1 1.39 3.1 3.1v2z" /></g>
<g id="lock-open"><path d="M12 17c1.1 0 2-.9 2-2s-.9-2-2-2-2 .9-2 2 .9 2 2 2zm6-9h-1V6c0-2.76-2.24-5-5-5S7 3.24 7 6h1.9c0-1.71 1.39-3.1 3.1-3.1 1.71 0 3.1 1.39 3.1 3.1v2H6c-1.1 0-2 .9-2 2v10c0 1.1.9 2 2 2h12c1.1 0 2-.9 2-2V10c0-1.1-.9-2-2-2zm0 12H6V10h12v10z" /></g>
<g id="lock-outline"><path d="M12 17c1.1 0 2-.9 2-2s-.9-2-2-2-2 .9-2 2 .9 2 2 2zm6-9h-1V6c0-2.76-2.24-5-5-5S7 3.24 7 6v2H6c-1.1 0-2 .9-2 2v10c0 1.1.9 2 2 2h12c1.1 0 2-.9 2-2V10c0-1.1-.9-2-2-2zM8.9 6c0-1.71 1.39-3.1 3.1-3.1s3.1 1.39 3.1 3.1v2H8.9V6zM18 20H6V10h12v10z" /></g>
<g id="low-priority"><path d="M14 5h8v2h-8zm0 5.5h8v2h-8zm0 5.5h8v2h-8zM2 11.5C2 15.08 4.92 18 8.5 18H9v2l3-3-3-3v2h-.5C6.02 16 4 13.98 4 11.5S6.02 7 8.5 7H12V5H8.5C4.92 5 2 7.92 2 11.5z" /></g>
<g id="loyalty"><path d="M21.41 11.58l-9-9C12.05 2.22 11.55 2 11 2H4c-1.1 0-2 .9-2 2v7c0 .55.22 1.05.59 1.42l9 9c.36.36.86.58 1.41.58.55 0 1.05-.22 1.41-.59l7-7c.37-.36.59-.86.59-1.41 0-.55-.23-1.06-.59-1.42zM5.5 7C4.67 7 4 6.33 4 5.5S4.67 4 5.5 4 7 4.67 7 5.5 6.33 7 5.5 7zm11.77 8.27L13 19.54l-4.27-4.27C8.28 14.81 8 14.19 8 13.5c0-1.38 1.12-2.5 2.5-2.5.69 0 1.32.28 1.77.74l.73.72.73-.73c.45-.45 1.08-.73 1.77-.73 1.38 0 2.5 1.12 2.5 2.5 0 .69-.28 1.32-.73 1.77z" /></g>
<g id="mail"><path d="M20 4H4c-1.1 0-1.99.9-1.99 2L2 18c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V6c0-1.1-.9-2-2-2zm0 4l-8 5-8-5V6l8 5 8-5v2z" /></g>
<g id="markunread"><path d="M20 4H4c-1.1 0-1.99.9-1.99 2L2 18c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V6c0-1.1-.9-2-2-2zm0 4l-8 5-8-5V6l8 5 8-5v2z" /></g>
<g id="markunread-mailbox"><path d="M20 6H10v6H8V4h6V0H6v6H4c-1.1 0-2 .9-2 2v12c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V8c0-1.1-.9-2-2-2z" /></g>
<g id="menu"><path d="M3 18h18v-2H3v2zm0-5h18v-2H3v2zm0-7v2h18V6H3z" /></g>
<g id="more-horiz"><path d="M6 10c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm12 0c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm-6 0c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2z" /></g>
<g id="more-vert"><path d="M12 8c1.1 0 2-.9 2-2s-.9-2-2-2-2 .9-2 2 .9 2 2 2zm0 2c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm0 6c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2z" /></g>
<g id="motorcycle"><path d="M19.44 9.03L15.41 5H11v2h3.59l2 2H5c-2.8 0-5 2.2-5 5s2.2 5 5 5c2.46 0 4.45-1.69 4.9-4h1.65l2.77-2.77c-.21.54-.32 1.14-.32 1.77 0 2.8 2.2 5 5 5s5-2.2 5-5c0-2.65-1.97-4.77-4.56-4.97zM7.82 15C7.4 16.15 6.28 17 5 17c-1.63 0-3-1.37-3-3s1.37-3 3-3c1.28 0 2.4.85 2.82 2H5v2h2.82zM19 17c-1.66 0-3-1.34-3-3s1.34-3 3-3 3 1.34 3 3-1.34 3-3 3z" /></g>
<g id="move-to-inbox"><path d="M19 3H4.99c-1.11 0-1.98.9-1.98 2L3 19c0 1.1.88 2 1.99 2H19c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm0 12h-4c0 1.66-1.35 3-3 3s-3-1.34-3-3H4.99V5H19v10zm-3-5h-2V7h-4v3H8l4 4 4-4z" /></g>
<g id="next-week"><path d="M20 7h-4V5c0-.55-.22-1.05-.59-1.41C15.05 3.22 14.55 3 14 3h-4c-1.1 0-2 .9-2 2v2H4c-1.1 0-2 .9-2 2v11c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V9c0-1.1-.9-2-2-2zM10 5h4v2h-4V5zm1 13.5l-1-1 3-3-3-3 1-1 4 4-4 4z" /></g>
<g id="note-add"><path d="M14 2H6c-1.1 0-1.99.9-1.99 2L4 20c0 1.1.89 2 1.99 2H18c1.1 0 2-.9 2-2V8l-6-6zm2 14h-3v3h-2v-3H8v-2h3v-3h2v3h3v2zm-3-7V3.5L18.5 9H13z" /></g>
<g id="offline-pin"><path d="M12 2C6.5 2 2 6.5 2 12s4.5 10 10 10 10-4.5 10-10S17.5 2 12 2zm5 16H7v-2h10v2zm-6.7-4L7 10.7l1.4-1.4 1.9 1.9 5.3-5.3L17 7.3 10.3 14z" /></g>
<g id="opacity"><path d="M17.66 8L12 2.35 6.34 8C4.78 9.56 4 11.64 4 13.64s.78 4.11 2.34 5.67 3.61 2.35 5.66 2.35 4.1-.79 5.66-2.35S20 15.64 20 13.64 19.22 9.56 17.66 8zM6 14c.01-2 .62-3.27 1.76-4.4L12 5.27l4.24 4.38C17.38 10.77 17.99 12 18 14H6z" /></g>
<g id="open-in-browser"><path d="M19 4H5c-1.11 0-2 .9-2 2v12c0 1.1.89 2 2 2h4v-2H5V8h14v10h-4v2h4c1.1 0 2-.9 2-2V6c0-1.1-.89-2-2-2zm-7 6l-4 4h3v6h2v-6h3l-4-4z" /></g>
<g id="open-in-new"><path d="M19 19H5V5h7V3H5c-1.11 0-2 .9-2 2v14c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2v-7h-2v7zM14 3v2h3.59l-9.83 9.83 1.41 1.41L19 6.41V10h2V3h-7z" /></g>
<g id="open-with"><path d="M10 9h4V6h3l-5-5-5 5h3v3zm-1 1H6V7l-5 5 5 5v-3h3v-4zm14 2l-5-5v3h-3v4h3v3l5-5zm-9 3h-4v3H7l5 5 5-5h-3v-3z" /></g>
<g id="pageview"><path d="M11.5 9C10.12 9 9 10.12 9 11.5s1.12 2.5 2.5 2.5 2.5-1.12 2.5-2.5S12.88 9 11.5 9zM20 4H4c-1.1 0-2 .9-2 2v12c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V6c0-1.1-.9-2-2-2zm-3.21 14.21l-2.91-2.91c-.69.44-1.51.7-2.39.7C9.01 16 7 13.99 7 11.5S9.01 7 11.5 7 16 9.01 16 11.5c0 .88-.26 1.69-.7 2.39l2.91 2.9-1.42 1.42z" /></g>
<g id="pan-tool"><path d="M23 5.5V20c0 2.2-1.8 4-4 4h-7.3c-1.08 0-2.1-.43-2.85-1.19L1 14.83s1.26-1.23 1.3-1.25c.22-.19.49-.29.79-.29.22 0 .42.06.6.16.04.01 4.31 2.46 4.31 2.46V4c0-.83.67-1.5 1.5-1.5S11 3.17 11 4v7h1V1.5c0-.83.67-1.5 1.5-1.5S15 .67 15 1.5V11h1V2.5c0-.83.67-1.5 1.5-1.5s1.5.67 1.5 1.5V11h1V5.5c0-.83.67-1.5 1.5-1.5s1.5.67 1.5 1.5z" /></g>
<g id="payment"><path d="M20 4H4c-1.11 0-1.99.89-1.99 2L2 18c0 1.11.89 2 2 2h16c1.11 0 2-.89 2-2V6c0-1.11-.89-2-2-2zm0 14H4v-6h16v6zm0-10H4V6h16v2z" /></g>
<g id="perm-camera-mic"><path d="M20 5h-3.17L15 3H9L7.17 5H4c-1.1 0-2 .9-2 2v12c0 1.1.9 2 2 2h7v-2.09c-2.83-.48-5-2.94-5-5.91h2c0 2.21 1.79 4 4 4s4-1.79 4-4h2c0 2.97-2.17 5.43-5 5.91V21h7c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm-6 8c0 1.1-.9 2-2 2s-2-.9-2-2V9c0-1.1.9-2 2-2s2 .9 2 2v4z" /></g>
<g id="perm-contact-calendar"><path d="M19 3h-1V1h-2v2H8V1H6v2H5c-1.11 0-2 .9-2 2v14c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm-7 3c1.66 0 3 1.34 3 3s-1.34 3-3 3-3-1.34-3-3 1.34-3 3-3zm6 12H6v-1c0-2 4-3.1 6-3.1s6 1.1 6 3.1v1z" /></g>
<g id="perm-data-setting"><path d="M18.99 11.5c.34 0 .67.03 1 .07L20 0 0 20h11.56c-.04-.33-.07-.66-.07-1 0-4.14 3.36-7.5 7.5-7.5zm3.71 7.99c.02-.16.04-.32.04-.49 0-.17-.01-.33-.04-.49l1.06-.83c.09-.08.12-.21.06-.32l-1-1.73c-.06-.11-.19-.15-.31-.11l-1.24.5c-.26-.2-.54-.37-.85-.49l-.19-1.32c-.01-.12-.12-.21-.24-.21h-2c-.12 0-.23.09-.25.21l-.19 1.32c-.3.13-.59.29-.85.49l-1.24-.5c-.11-.04-.24 0-.31.11l-1 1.73c-.06.11-.04.24.06.32l1.06.83c-.02.16-.03.32-.03.49 0 .17.01.33.03.49l-1.06.83c-.09.08-.12.21-.06.32l1 1.73c.06.11.19.15.31.11l1.24-.5c.26.2.54.37.85.49l.19 1.32c.02.12.12.21.25.21h2c.12 0 .23-.09.25-.21l.19-1.32c.3-.13.59-.29.84-.49l1.25.5c.11.04.24 0 .31-.11l1-1.73c.06-.11.03-.24-.06-.32l-1.07-.83zm-3.71 1.01c-.83 0-1.5-.67-1.5-1.5s.67-1.5 1.5-1.5 1.5.67 1.5 1.5-.67 1.5-1.5 1.5z" /></g>
<g id="perm-device-information"><path d="M13 7h-2v2h2V7zm0 4h-2v6h2v-6zm4-9.99L7 1c-1.1 0-2 .9-2 2v18c0 1.1.9 2 2 2h10c1.1 0 2-.9 2-2V3c0-1.1-.9-1.99-2-1.99zM17 19H7V5h10v14z" /></g>
<g id="perm-identity"><path d="M12 5.9c1.16 0 2.1.94 2.1 2.1s-.94 2.1-2.1 2.1S9.9 9.16 9.9 8s.94-2.1 2.1-2.1m0 9c2.97 0 6.1 1.46 6.1 2.1v1.1H5.9V17c0-.64 3.13-2.1 6.1-2.1M12 4C9.79 4 8 5.79 8 8s1.79 4 4 4 4-1.79 4-4-1.79-4-4-4zm0 9c-2.67 0-8 1.34-8 4v3h16v-3c0-2.66-5.33-4-8-4z" /></g>
<g id="perm-media"><path d="M2 6H0v5h.01L0 20c0 1.1.9 2 2 2h18v-2H2V6zm20-2h-8l-2-2H6c-1.1 0-1.99.9-1.99 2L4 16c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V6c0-1.1-.9-2-2-2zM7 15l4.5-6 3.5 4.51 2.5-3.01L21 15H7z" /></g>
<g id="perm-phone-msg"><path d="M20 15.5c-1.25 0-2.45-.2-3.57-.57-.35-.11-.74-.03-1.02.24l-2.2 2.2c-2.83-1.44-5.15-3.75-6.59-6.58l2.2-2.21c.28-.27.36-.66.25-1.01C8.7 6.45 8.5 5.25 8.5 4c0-.55-.45-1-1-1H4c-.55 0-1 .45-1 1 0 9.39 7.61 17 17 17 .55 0 1-.45 1-1v-3.5c0-.55-.45-1-1-1zM12 3v10l3-3h6V3h-9z" /></g>
<g id="perm-scan-wifi"><path d="M12 3C6.95 3 3.15 4.85 0 7.23L12 22 24 7.25C20.85 4.87 17.05 3 12 3zm1 13h-2v-6h2v6zm-2-8V6h2v2h-2z" /></g>
<g id="pets"><circle cx="4.5" cy="9.5" r="2.5" /><circle cx="9" cy="5.5" r="2.5" /><circle cx="15" cy="5.5" r="2.5" /><circle cx="19.5" cy="9.5" r="2.5" /><path d="M17.34 14.86c-.87-1.02-1.6-1.89-2.48-2.91-.46-.54-1.05-1.08-1.75-1.32-.11-.04-.22-.07-.33-.09-.25-.04-.52-.04-.78-.04s-.53 0-.79.05c-.11.02-.22.05-.33.09-.7.24-1.28.78-1.75 1.32-.87 1.02-1.6 1.89-2.48 2.91-1.31 1.31-2.92 2.76-2.62 4.79.29 1.02 1.02 2.03 2.33 2.32.73.15 3.06-.44 5.54-.44h.18c2.48 0 4.81.58 5.54.44 1.31-.29 2.04-1.31 2.33-2.32.31-2.04-1.3-3.49-2.61-4.8z" /></g>
<g id="picture-in-picture"><path d="M19 7h-8v6h8V7zm2-4H3c-1.1 0-2 .9-2 2v14c0 1.1.9 1.98 2 1.98h18c1.1 0 2-.88 2-1.98V5c0-1.1-.9-2-2-2zm0 16.01H3V4.98h18v14.03z" /></g>
<g id="picture-in-picture-alt"><path d="M19 11h-8v6h8v-6zm4 8V4.98C23 3.88 22.1 3 21 3H3c-1.1 0-2 .88-2 1.98V19c0 1.1.9 2 2 2h18c1.1 0 2-.9 2-2zm-2 .02H3V4.97h18v14.05z" /></g>
<g id="play-for-work"><path d="M11 5v5.59H7.5l4.5 4.5 4.5-4.5H13V5h-2zm-5 9c0 3.31 2.69 6 6 6s6-2.69 6-6h-2c0 2.21-1.79 4-4 4s-4-1.79-4-4H6z" /></g>
<g id="polymer"><path d="M19 4h-4L7.11 16.63 4.5 12 9 4H5L.5 12 5 20h4l7.89-12.63L19.5 12 15 20h4l4.5-8z" /></g>
<g id="power-settings-new"><path d="M13 3h-2v10h2V3zm4.83 2.17l-1.42 1.42C17.99 7.86 19 9.81 19 12c0 3.87-3.13 7-7 7s-7-3.13-7-7c0-2.19 1.01-4.14 2.58-5.42L6.17 5.17C4.23 6.82 3 9.26 3 12c0 4.97 4.03 9 9 9s9-4.03 9-9c0-2.74-1.23-5.18-3.17-6.83z" /></g>
<g id="pregnant-woman"><path d="M9 4c0-1.11.89-2 2-2s2 .89 2 2-.89 2-2 2-2-.89-2-2zm7 9c-.01-1.34-.83-2.51-2-3 0-1.66-1.34-3-3-3s-3 1.34-3 3v7h2v5h3v-5h3v-4z" /></g>
<g id="print"><path d="M19 8H5c-1.66 0-3 1.34-3 3v6h4v4h12v-4h4v-6c0-1.66-1.34-3-3-3zm-3 11H8v-5h8v5zm3-7c-.55 0-1-.45-1-1s.45-1 1-1 1 .45 1 1-.45 1-1 1zm-1-9H6v4h12V3z" /></g>
<g id="query-builder"><path d="M11.99 2C6.47 2 2 6.48 2 12s4.47 10 9.99 10C17.52 22 22 17.52 22 12S17.52 2 11.99 2zM12 20c-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8-3.58 8-8 8zm.5-13H11v6l5.25 3.15.75-1.23-4.5-2.67z" /></g>
<g id="question-answer"><path d="M21 6h-2v9H6v2c0 .55.45 1 1 1h11l4 4V7c0-.55-.45-1-1-1zm-4 6V3c0-.55-.45-1-1-1H3c-.55 0-1 .45-1 1v14l4-4h10c.55 0 1-.45 1-1z" /></g>
<g id="radio-button-checked"><path d="M12 7c-2.76 0-5 2.24-5 5s2.24 5 5 5 5-2.24 5-5-2.24-5-5-5zm0-5C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 18c-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8-3.58 8-8 8z" /></g>
<g id="radio-button-unchecked"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 18c-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8-3.58 8-8 8z" /></g>
<g id="receipt"><path d="M18 17H6v-2h12v2zm0-4H6v-2h12v2zm0-4H6V7h12v2zM3 22l1.5-1.5L6 22l1.5-1.5L9 22l1.5-1.5L12 22l1.5-1.5L15 22l1.5-1.5L18 22l1.5-1.5L21 22V2l-1.5 1.5L18 2l-1.5 1.5L15 2l-1.5 1.5L12 2l-1.5 1.5L9 2 7.5 3.5 6 2 4.5 3.5 3 2v20z" /></g>
<g id="record-voice-over"><circle cx="9" cy="9" r="4" /><path d="M9 15c-2.67 0-8 1.34-8 4v2h16v-2c0-2.66-5.33-4-8-4zm7.76-9.64l-1.68 1.69c.84 1.18.84 2.71 0 3.89l1.68 1.69c2.02-2.02 2.02-5.07 0-7.27zM20.07 2l-1.63 1.63c2.77 3.02 2.77 7.56 0 10.74L20.07 16c3.9-3.89 3.91-9.95 0-14z" /></g>
<g id="redeem"><path d="M20 6h-2.18c.11-.31.18-.65.18-1 0-1.66-1.34-3-3-3-1.05 0-1.96.54-2.5 1.35l-.5.67-.5-.68C10.96 2.54 10.05 2 9 2 7.34 2 6 3.34 6 5c0 .35.07.69.18 1H4c-1.11 0-1.99.89-1.99 2L2 19c0 1.11.89 2 2 2h16c1.11 0 2-.89 2-2V8c0-1.11-.89-2-2-2zm-5-2c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zM9 4c.55 0 1 .45 1 1s-.45 1-1 1-1-.45-1-1 .45-1 1-1zm11 15H4v-2h16v2zm0-5H4V8h5.08L7 10.83 8.62 12 11 8.76l1-1.36 1 1.36L15.38 12 17 10.83 14.92 8H20v6z" /></g>
<g id="redo"><path d="M18.4 10.6C16.55 8.99 14.15 8 11.5 8c-4.65 0-8.58 3.03-9.96 7.22L3.9 16c1.05-3.19 4.05-5.5 7.6-5.5 1.95 0 3.73.72 5.12 1.88L13 16h9V7l-3.6 3.6z" /></g>
<g id="refresh"><path d="M17.65 6.35C16.2 4.9 14.21 4 12 4c-4.42 0-7.99 3.58-7.99 8s3.57 8 7.99 8c3.73 0 6.84-2.55 7.73-6h-2.08c-.82 2.33-3.04 4-5.65 4-3.31 0-6-2.69-6-6s2.69-6 6-6c1.66 0 3.14.69 4.22 1.78L13 11h7V4l-2.35 2.35z" /></g>
<g id="remove"><path d="M19 13H5v-2h14v2z" /></g>
<g id="remove-circle"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm5 11H7v-2h10v2z" /></g>
<g id="remove-circle-outline"><path d="M7 11v2h10v-2H7zm5-9C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm0 18c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8z" /></g>
<g id="remove-shopping-cart"><path d="M22.73 22.73L2.77 2.77 2 2l-.73-.73L0 2.54l4.39 4.39 2.21 4.66-1.35 2.45c-.16.28-.25.61-.25.96 0 1.1.9 2 2 2h7.46l1.38 1.38c-.5.36-.83.95-.83 1.62 0 1.1.89 2 1.99 2 .67 0 1.26-.33 1.62-.84L21.46 24l1.27-1.27zM7.42 15c-.14 0-.25-.11-.25-.25l.03-.12.9-1.63h2.36l2 2H7.42zm8.13-2c.75 0 1.41-.41 1.75-1.03l3.58-6.49c.08-.14.12-.31.12-.48 0-.55-.45-1-1-1H6.54l9.01 9zM7 18c-1.1 0-1.99.9-1.99 2S5.9 22 7 22s2-.9 2-2-.9-2-2-2z" /></g>
<g id="reorder"><path d="M3 15h18v-2H3v2zm0 4h18v-2H3v2zm0-8h18V9H3v2zm0-6v2h18V5H3z" /></g>
<g id="reply"><path d="M10 9V5l-7 7 7 7v-4.1c5 0 8.5 1.6 11 5.1-1-5-4-10-11-11z" /></g>
<g id="reply-all"><path d="M7 8V5l-7 7 7 7v-3l-4-4 4-4zm6 1V5l-7 7 7 7v-4.1c5 0 8.5 1.6 11 5.1-1-5-4-10-11-11z" /></g>
<g id="report"><path d="M15.73 3H8.27L3 8.27v7.46L8.27 21h7.46L21 15.73V8.27L15.73 3zM12 17.3c-.72 0-1.3-.58-1.3-1.3 0-.72.58-1.3 1.3-1.3.72 0 1.3.58 1.3 1.3 0 .72-.58 1.3-1.3 1.3zm1-4.3h-2V7h2v6z" /></g>
<g id="report-problem"><path d="M1 21h22L12 2 1 21zm12-3h-2v-2h2v2zm0-4h-2v-4h2v4z" /></g>
<g id="restore"><path d="M13 3c-4.97 0-9 4.03-9 9H1l3.89 3.89.07.14L9 12H6c0-3.87 3.13-7 7-7s7 3.13 7 7-3.13 7-7 7c-1.93 0-3.68-.79-4.94-2.06l-1.42 1.42C8.27 19.99 10.51 21 13 21c4.97 0 9-4.03 9-9s-4.03-9-9-9zm-1 5v5l4.28 2.54.72-1.21-3.5-2.08V8H12z" /></g>
<g id="restore-page"><path d="M14 2H6c-1.1 0-1.99.9-1.99 2L4 20c0 1.1.89 2 1.99 2H18c1.1 0 2-.9 2-2V8l-6-6zm-2 16c-2.05 0-3.81-1.24-4.58-3h1.71c.63.9 1.68 1.5 2.87 1.5 1.93 0 3.5-1.57 3.5-3.5S13.93 9.5 12 9.5c-1.35 0-2.52.78-3.1 1.9l1.6 1.6h-4V9l1.3 1.3C8.69 8.92 10.23 8 12 8c2.76 0 5 2.24 5 5s-2.24 5-5 5z" /></g>
<g id="room"><path d="M12 2C8.13 2 5 5.13 5 9c0 5.25 7 13 7 13s7-7.75 7-13c0-3.87-3.13-7-7-7zm0 9.5c-1.38 0-2.5-1.12-2.5-2.5s1.12-2.5 2.5-2.5 2.5 1.12 2.5 2.5-1.12 2.5-2.5 2.5z" /></g>
<g id="rounded-corner"><path d="M19 19h2v2h-2v-2zm0-2h2v-2h-2v2zM3 13h2v-2H3v2zm0 4h2v-2H3v2zm0-8h2V7H3v2zm0-4h2V3H3v2zm4 0h2V3H7v2zm8 16h2v-2h-2v2zm-4 0h2v-2h-2v2zm4 0h2v-2h-2v2zm-8 0h2v-2H7v2zm-4 0h2v-2H3v2zM21 8c0-2.76-2.24-5-5-5h-5v2h5c1.65 0 3 1.35 3 3v5h2V8z" /></g>
<g id="rowing"><path d="M8.5 14.5L4 19l1.5 1.5L9 17h2l-2.5-2.5zM15 1c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm6 20.01L18 24l-2.99-3.01V19.5l-7.1-7.09c-.31.05-.61.07-.91.07v-2.16c1.66.03 3.61-.87 4.67-2.04l1.4-1.55c.19-.21.43-.38.69-.5.29-.14.62-.23.96-.23h.03C15.99 6.01 17 7.02 17 8.26v5.75c0 .84-.35 1.61-.92 2.16l-3.58-3.58v-2.27c-.63.52-1.43 1.02-2.29 1.39L16.5 18H18l3 3.01z" /></g>
<g id="save"><path d="M17 3H5c-1.11 0-2 .9-2 2v14c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V7l-4-4zm-5 16c-1.66 0-3-1.34-3-3s1.34-3 3-3 3 1.34 3 3-1.34 3-3 3zm3-10H5V5h10v4z" /></g>
<g id="schedule"><path d="M11.99 2C6.47 2 2 6.48 2 12s4.47 10 9.99 10C17.52 22 22 17.52 22 12S17.52 2 11.99 2zM12 20c-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8-3.58 8-8 8zm.5-13H11v6l5.25 3.15.75-1.23-4.5-2.67z" /></g>
<g id="search"><path d="M15.5 14h-.79l-.28-.27C15.41 12.59 16 11.11 16 9.5 16 5.91 13.09 3 9.5 3S3 5.91 3 9.5 5.91 16 9.5 16c1.61 0 3.09-.59 4.23-1.57l.27.28v.79l5 4.99L20.49 19l-4.99-5zm-6 0C7.01 14 5 11.99 5 9.5S7.01 5 9.5 5 14 7.01 14 9.5 11.99 14 9.5 14z" /></g>
<g id="select-all"><path d="M3 5h2V3c-1.1 0-2 .9-2 2zm0 8h2v-2H3v2zm4 8h2v-2H7v2zM3 9h2V7H3v2zm10-6h-2v2h2V3zm6 0v2h2c0-1.1-.9-2-2-2zM5 21v-2H3c0 1.1.9 2 2 2zm-2-4h2v-2H3v2zM9 3H7v2h2V3zm2 18h2v-2h-2v2zm8-8h2v-2h-2v2zm0 8c1.1 0 2-.9 2-2h-2v2zm0-12h2V7h-2v2zm0 8h2v-2h-2v2zm-4 4h2v-2h-2v2zm0-16h2V3h-2v2zM7 17h10V7H7v10zm2-8h6v6H9V9z" /></g>
<g id="send"><path d="M2.01 21L23 12 2.01 3 2 10l15 2-15 2z" /></g>
<g id="settings"><path d="M19.43 12.98c.04-.32.07-.64.07-.98s-.03-.66-.07-.98l2.11-1.65c.19-.15.24-.42.12-.64l-2-3.46c-.12-.22-.39-.3-.61-.22l-2.49 1c-.52-.4-1.08-.73-1.69-.98l-.38-2.65C14.46 2.18 14.25 2 14 2h-4c-.25 0-.46.18-.49.42l-.38 2.65c-.61.25-1.17.59-1.69.98l-2.49-1c-.23-.09-.49 0-.61.22l-2 3.46c-.13.22-.07.49.12.64l2.11 1.65c-.04.32-.07.65-.07.98s.03.66.07.98l-2.11 1.65c-.19.15-.24.42-.12.64l2 3.46c.12.22.39.3.61.22l2.49-1c.52.4 1.08.73 1.69.98l.38 2.65c.03.24.24.42.49.42h4c.25 0 .46-.18.49-.42l.38-2.65c.61-.25 1.17-.59 1.69-.98l2.49 1c.23.09.49 0 .61-.22l2-3.46c.12-.22.07-.49-.12-.64l-2.11-1.65zM12 15.5c-1.93 0-3.5-1.57-3.5-3.5s1.57-3.5 3.5-3.5 3.5 1.57 3.5 3.5-1.57 3.5-3.5 3.5z" /></g>
<g id="settings-applications"><path d="M12 10c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm7-7H5c-1.11 0-2 .9-2 2v14c0 1.1.89 2 2 2h14c1.11 0 2-.9 2-2V5c0-1.1-.89-2-2-2zm-1.75 9c0 .23-.02.46-.05.68l1.48 1.16c.13.11.17.3.08.45l-1.4 2.42c-.09.15-.27.21-.43.15l-1.74-.7c-.36.28-.76.51-1.18.69l-.26 1.85c-.03.17-.18.3-.35.3h-2.8c-.17 0-.32-.13-.35-.29l-.26-1.85c-.43-.18-.82-.41-1.18-.69l-1.74.7c-.16.06-.34 0-.43-.15l-1.4-2.42c-.09-.15-.05-.34.08-.45l1.48-1.16c-.03-.23-.05-.46-.05-.69 0-.23.02-.46.05-.68l-1.48-1.16c-.13-.11-.17-.3-.08-.45l1.4-2.42c.09-.15.27-.21.43-.15l1.74.7c.36-.28.76-.51 1.18-.69l.26-1.85c.03-.17.18-.3.35-.3h2.8c.17 0 .32.13.35.29l.26 1.85c.43.18.82.41 1.18.69l1.74-.7c.16-.06.34 0 .43.15l1.4 2.42c.09.15.05.34-.08.45l-1.48 1.16c.03.23.05.46.05.69z" /></g>
<g id="settings-backup-restore"><path d="M14 12c0-1.1-.9-2-2-2s-2 .9-2 2 .9 2 2 2 2-.9 2-2zm-2-9c-4.97 0-9 4.03-9 9H0l4 4 4-4H5c0-3.87 3.13-7 7-7s7 3.13 7 7-3.13 7-7 7c-1.51 0-2.91-.49-4.06-1.3l-1.42 1.44C8.04 20.3 9.94 21 12 21c4.97 0 9-4.03 9-9s-4.03-9-9-9z" /></g>
<g id="settings-bluetooth"><path d="M11 24h2v-2h-2v2zm-4 0h2v-2H7v2zm8 0h2v-2h-2v2zm2.71-18.29L12 0h-1v7.59L6.41 3 5 4.41 10.59 10 5 15.59 6.41 17 11 12.41V20h1l5.71-5.71-4.3-4.29 4.3-4.29zM13 3.83l1.88 1.88L13 7.59V3.83zm1.88 10.46L13 16.17v-3.76l1.88 1.88z" /></g>
<g id="settings-brightness"><path d="M21 3H3c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h18c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm0 16.01H3V4.99h18v14.02zM8 16h2.5l1.5 1.5 1.5-1.5H16v-2.5l1.5-1.5-1.5-1.5V8h-2.5L12 6.5 10.5 8H8v2.5L6.5 12 8 13.5V16zm4-7c1.66 0 3 1.34 3 3s-1.34 3-3 3V9z" /></g>
<g id="settings-cell"><path d="M7 24h2v-2H7v2zm4 0h2v-2h-2v2zm4 0h2v-2h-2v2zM16 .01L8 0C6.9 0 6 .9 6 2v16c0 1.1.9 2 2 2h8c1.1 0 2-.9 2-2V2c0-1.1-.9-1.99-2-1.99zM16 16H8V4h8v12z" /></g>
<g id="settings-ethernet"><path d="M7.77 6.76L6.23 5.48.82 12l5.41 6.52 1.54-1.28L3.42 12l4.35-5.24zM7 13h2v-2H7v2zm10-2h-2v2h2v-2zm-6 2h2v-2h-2v2zm6.77-7.52l-1.54 1.28L20.58 12l-4.35 5.24 1.54 1.28L23.18 12l-5.41-6.52z" /></g>
<g id="settings-input-antenna"><path d="M12 5c-3.87 0-7 3.13-7 7h2c0-2.76 2.24-5 5-5s5 2.24 5 5h2c0-3.87-3.13-7-7-7zm1 9.29c.88-.39 1.5-1.26 1.5-2.29 0-1.38-1.12-2.5-2.5-2.5S9.5 10.62 9.5 12c0 1.02.62 1.9 1.5 2.29v3.3L7.59 21 9 22.41l3-3 3 3L16.41 21 13 17.59v-3.3zM12 1C5.93 1 1 5.93 1 12h2c0-4.97 4.03-9 9-9s9 4.03 9 9h2c0-6.07-4.93-11-11-11z" /></g>
<g id="settings-input-component"><path d="M5 2c0-.55-.45-1-1-1s-1 .45-1 1v4H1v6h6V6H5V2zm4 14c0 1.3.84 2.4 2 2.82V23h2v-4.18c1.16-.41 2-1.51 2-2.82v-2H9v2zm-8 0c0 1.3.84 2.4 2 2.82V23h2v-4.18C6.16 18.4 7 17.3 7 16v-2H1v2zM21 6V2c0-.55-.45-1-1-1s-1 .45-1 1v4h-2v6h6V6h-2zm-8-4c0-.55-.45-1-1-1s-1 .45-1 1v4H9v6h6V6h-2V2zm4 14c0 1.3.84 2.4 2 2.82V23h2v-4.18c1.16-.41 2-1.51 2-2.82v-2h-6v2z" /></g>
<g id="settings-input-composite"><path d="M5 2c0-.55-.45-1-1-1s-1 .45-1 1v4H1v6h6V6H5V2zm4 14c0 1.3.84 2.4 2 2.82V23h2v-4.18c1.16-.41 2-1.51 2-2.82v-2H9v2zm-8 0c0 1.3.84 2.4 2 2.82V23h2v-4.18C6.16 18.4 7 17.3 7 16v-2H1v2zM21 6V2c0-.55-.45-1-1-1s-1 .45-1 1v4h-2v6h6V6h-2zm-8-4c0-.55-.45-1-1-1s-1 .45-1 1v4H9v6h6V6h-2V2zm4 14c0 1.3.84 2.4 2 2.82V23h2v-4.18c1.16-.41 2-1.51 2-2.82v-2h-6v2z" /></g>
<g id="settings-input-hdmi"><path d="M18 7V4c0-1.1-.9-2-2-2H8c-1.1 0-2 .9-2 2v3H5v6l3 6v3h8v-3l3-6V7h-1zM8 4h8v3h-2V5h-1v2h-2V5h-1v2H8V4z" /></g>
<g id="settings-input-svideo"><path d="M8 11.5c0-.83-.67-1.5-1.5-1.5S5 10.67 5 11.5 5.67 13 6.5 13 8 12.33 8 11.5zm7-5c0-.83-.67-1.5-1.5-1.5h-3C9.67 5 9 5.67 9 6.5S9.67 8 10.5 8h3c.83 0 1.5-.67 1.5-1.5zM8.5 15c-.83 0-1.5.67-1.5 1.5S7.67 18 8.5 18s1.5-.67 1.5-1.5S9.33 15 8.5 15zM12 1C5.93 1 1 5.93 1 12s4.93 11 11 11 11-4.93 11-11S18.07 1 12 1zm0 20c-4.96 0-9-4.04-9-9s4.04-9 9-9 9 4.04 9 9-4.04 9-9 9zm5.5-11c-.83 0-1.5.67-1.5 1.5s.67 1.5 1.5 1.5 1.5-.67 1.5-1.5-.67-1.5-1.5-1.5zm-2 5c-.83 0-1.5.67-1.5 1.5s.67 1.5 1.5 1.5 1.5-.67 1.5-1.5-.67-1.5-1.5-1.5z" /></g>
<g id="settings-overscan"><path d="M12.01 5.5L10 8h4l-1.99-2.5zM18 10v4l2.5-1.99L18 10zM6 10l-2.5 2.01L6 14v-4zm8 6h-4l2.01 2.5L14 16zm7-13H3c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h18c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm0 16.01H3V4.99h18v14.02z" /></g>
<g id="settings-phone"><path d="M13 9h-2v2h2V9zm4 0h-2v2h2V9zm3 6.5c-1.25 0-2.45-.2-3.57-.57-.35-.11-.74-.03-1.02.24l-2.2 2.2c-2.83-1.44-5.15-3.75-6.59-6.58l2.2-2.21c.28-.27.36-.66.25-1.01C8.7 6.45 8.5 5.25 8.5 4c0-.55-.45-1-1-1H4c-.55 0-1 .45-1 1 0 9.39 7.61 17 17 17 .55 0 1-.45 1-1v-3.5c0-.55-.45-1-1-1zM19 9v2h2V9h-2z" /></g>
<g id="settings-power"><path d="M7 24h2v-2H7v2zm4 0h2v-2h-2v2zm2-22h-2v10h2V2zm3.56 2.44l-1.45 1.45C16.84 6.94 18 8.83 18 11c0 3.31-2.69 6-6 6s-6-2.69-6-6c0-2.17 1.16-4.06 2.88-5.12L7.44 4.44C5.36 5.88 4 8.28 4 11c0 4.42 3.58 8 8 8s8-3.58 8-8c0-2.72-1.36-5.12-3.44-6.56zM15 24h2v-2h-2v2z" /></g>
<g id="settings-remote"><path d="M15 9H9c-.55 0-1 .45-1 1v12c0 .55.45 1 1 1h6c.55 0 1-.45 1-1V10c0-.55-.45-1-1-1zm-3 6c-1.1 0-2-.9-2-2s.9-2 2-2 2 .9 2 2-.9 2-2 2zM7.05 6.05l1.41 1.41C9.37 6.56 10.62 6 12 6s2.63.56 3.54 1.46l1.41-1.41C15.68 4.78 13.93 4 12 4s-3.68.78-4.95 2.05zM12 0C8.96 0 6.21 1.23 4.22 3.22l1.41 1.41C7.26 3.01 9.51 2 12 2s4.74 1.01 6.36 2.64l1.41-1.41C17.79 1.23 15.04 0 12 0z" /></g>
<g id="settings-voice"><path d="M7 24h2v-2H7v2zm5-11c1.66 0 2.99-1.34 2.99-3L15 4c0-1.66-1.34-3-3-3S9 2.34 9 4v6c0 1.66 1.34 3 3 3zm-1 11h2v-2h-2v2zm4 0h2v-2h-2v2zm4-14h-1.7c0 3-2.54 5.1-5.3 5.1S6.7 13 6.7 10H5c0 3.41 2.72 6.23 6 6.72V20h2v-3.28c3.28-.49 6-3.31 6-6.72z" /></g>
<g id="shop"><path d="M16 6V4c0-1.11-.89-2-2-2h-4c-1.11 0-2 .89-2 2v2H2v13c0 1.11.89 2 2 2h16c1.11 0 2-.89 2-2V6h-6zm-6-2h4v2h-4V4zM9 18V9l7.5 4L9 18z" /></g>
<g id="shop-two"><path d="M3 9H1v11c0 1.11.89 2 2 2h14c1.11 0 2-.89 2-2H3V9zm15-4V3c0-1.11-.89-2-2-2h-4c-1.11 0-2 .89-2 2v2H5v11c0 1.11.89 2 2 2h14c1.11 0 2-.89 2-2V5h-5zm-6-2h4v2h-4V3zm0 12V8l5.5 3-5.5 4z" /></g>
<g id="shopping-basket"><path d="M17.21 9l-4.38-6.56c-.19-.28-.51-.42-.83-.42-.32 0-.64.14-.83.43L6.79 9H2c-.55 0-1 .45-1 1 0 .09.01.18.04.27l2.54 9.27c.23.84 1 1.46 1.92 1.46h13c.92 0 1.69-.62 1.93-1.46l2.54-9.27L23 10c0-.55-.45-1-1-1h-4.79zM9 9l3-4.4L15 9H9zm3 8c-1.1 0-2-.9-2-2s.9-2 2-2 2 .9 2 2-.9 2-2 2z" /></g>
<g id="shopping-cart"><path d="M7 18c-1.1 0-1.99.9-1.99 2S5.9 22 7 22s2-.9 2-2-.9-2-2-2zM1 2v2h2l3.6 7.59-1.35 2.45c-.16.28-.25.61-.25.96 0 1.1.9 2 2 2h12v-2H7.42c-.14 0-.25-.11-.25-.25l.03-.12.9-1.63h7.45c.75 0 1.41-.41 1.75-1.03l3.58-6.49c.08-.14.12-.31.12-.48 0-.55-.45-1-1-1H5.21l-.94-2H1zm16 16c-1.1 0-1.99.9-1.99 2s.89 2 1.99 2 2-.9 2-2-.9-2-2-2z" /></g>
<g id="sort"><path d="M3 18h6v-2H3v2zM3 6v2h18V6H3zm0 7h12v-2H3v2z" /></g>
<g id="speaker-notes"><path d="M20 2H4c-1.1 0-1.99.9-1.99 2L2 22l4-4h14c1.1 0 2-.9 2-2V4c0-1.1-.9-2-2-2zM8 14H6v-2h2v2zm0-3H6V9h2v2zm0-3H6V6h2v2zm7 6h-5v-2h5v2zm3-3h-8V9h8v2zm0-3h-8V6h8v2z" /></g>
<g id="speaker-notes-off"><path d="M10.54 11l-.54-.54L7.54 8 6 6.46 2.38 2.84 1.27 1.73 0 3l2.01 2.01L2 22l4-4h9l5.73 5.73L22 22.46 17.54 18l-7-7zM8 14H6v-2h2v2zm-2-3V9l2 2H6zm14-9H4.08L10 7.92V6h8v2h-7.92l1 1H18v2h-4.92l6.99 6.99C21.14 17.95 22 17.08 22 16V4c0-1.1-.9-2-2-2z" /></g>
<g id="spellcheck"><path d="M12.45 16h2.09L9.43 3H7.57L2.46 16h2.09l1.12-3h5.64l1.14 3zm-6.02-5L8.5 5.48 10.57 11H6.43zm15.16.59l-8.09 8.09L9.83 16l-1.41 1.41 5.09 5.09L23 13l-1.41-1.41z" /></g>
<g id="star"><path d="M12 17.27L18.18 21l-1.64-7.03L22 9.24l-7.19-.61L12 2 9.19 8.63 2 9.24l5.46 4.73L5.82 21z" /></g>
<g id="star-border"><path d="M22 9.24l-7.19-.62L12 2 9.19 8.63 2 9.24l5.46 4.73L5.82 21 12 17.27 18.18 21l-1.63-7.03L22 9.24zM12 15.4l-3.76 2.27 1-4.28-3.32-2.88 4.38-.38L12 6.1l1.71 4.04 4.38.38-3.32 2.88 1 4.28L12 15.4z" /></g>
<g id="star-half"><path d="M22 9.24l-7.19-.62L12 2 9.19 8.63 2 9.24l5.46 4.73L5.82 21 12 17.27 18.18 21l-1.63-7.03L22 9.24zM12 15.4V6.1l1.71 4.04 4.38.38-3.32 2.88 1 4.28L12 15.4z" /></g>
<g id="stars"><path d="M11.99 2C6.47 2 2 6.48 2 12s4.47 10 9.99 10C17.52 22 22 17.52 22 12S17.52 2 11.99 2zm4.24 16L12 15.45 7.77 18l1.12-4.81-3.73-3.23 4.92-.42L12 5l1.92 4.53 4.92.42-3.73 3.23L16.23 18z" /></g>
<g id="store"><path d="M20 4H4v2h16V4zm1 10v-2l-1-5H4l-1 5v2h1v6h10v-6h4v6h2v-6h1zm-9 4H6v-4h6v4z" /></g>
<g id="subdirectory-arrow-left"><path d="M11 9l1.42 1.42L8.83 14H18V4h2v12H8.83l3.59 3.58L11 21l-6-6 6-6z" /></g>
<g id="subdirectory-arrow-right"><path d="M19 15l-6 6-1.42-1.42L15.17 16H4V4h2v10h9.17l-3.59-3.58L13 9l6 6z" /></g>
<g id="subject"><path d="M14 17H4v2h10v-2zm6-8H4v2h16V9zM4 15h16v-2H4v2zM4 5v2h16V5H4z" /></g>
<g id="supervisor-account"><path d="M16.5 12c1.38 0 2.49-1.12 2.49-2.5S17.88 7 16.5 7C15.12 7 14 8.12 14 9.5s1.12 2.5 2.5 2.5zM9 11c1.66 0 2.99-1.34 2.99-3S10.66 5 9 5C7.34 5 6 6.34 6 8s1.34 3 3 3zm7.5 3c-1.83 0-5.5.92-5.5 2.75V19h11v-2.25c0-1.83-3.67-2.75-5.5-2.75zM9 13c-2.33 0-7 1.17-7 3.5V19h7v-2.25c0-.85.33-2.34 2.37-3.47C10.5 13.1 9.66 13 9 13z" /></g>
<g id="swap-horiz"><path d="M6.99 11L3 15l3.99 4v-3H14v-2H6.99v-3zM21 9l-3.99-4v3H10v2h7.01v3L21 9z" /></g>
<g id="swap-vert"><path d="M16 17.01V10h-2v7.01h-3L15 21l4-3.99h-3zM9 3L5 6.99h3V14h2V6.99h3L9 3z" /></g>
<g id="swap-vertical-circle"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zM6.5 9L10 5.5 13.5 9H11v4H9V9H6.5zm11 6L14 18.5 10.5 15H13v-4h2v4h2.5z" /></g>
<g id="system-update-alt"><path d="M12 16.5l4-4h-3v-9h-2v9H8l4 4zm9-13h-6v1.99h6v14.03H3V5.49h6V3.5H3c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h18c1.1 0 2-.9 2-2v-14c0-1.1-.9-2-2-2z" /></g>
<g id="tab"><path d="M21 3H3c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h18c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm0 16H3V5h10v4h8v10z" /></g>
<g id="tab-unselected"><path d="M1 9h2V7H1v2zm0 4h2v-2H1v2zm0-8h2V3c-1.1 0-2 .9-2 2zm8 16h2v-2H9v2zm-8-4h2v-2H1v2zm2 4v-2H1c0 1.1.9 2 2 2zM21 3h-8v6h10V5c0-1.1-.9-2-2-2zm0 14h2v-2h-2v2zM9 5h2V3H9v2zM5 21h2v-2H5v2zM5 5h2V3H5v2zm16 16c1.1 0 2-.9 2-2h-2v2zm0-8h2v-2h-2v2zm-8 8h2v-2h-2v2zm4 0h2v-2h-2v2z" /></g>
<g id="text-format"><path d="M5 17v2h14v-2H5zm4.5-4.2h5l.9 2.2h2.1L12.75 4h-1.5L6.5 15h2.1l.9-2.2zM12 5.98L13.87 11h-3.74L12 5.98z" /></g>
<g id="theaters"><path d="M18 3v2h-2V3H8v2H6V3H4v18h2v-2h2v2h8v-2h2v2h2V3h-2zM8 17H6v-2h2v2zm0-4H6v-2h2v2zm0-4H6V7h2v2zm10 8h-2v-2h2v2zm0-4h-2v-2h2v2zm0-4h-2V7h2v2z" /></g>
<g id="thumb-down"><path d="M15 3H6c-.83 0-1.54.5-1.84 1.22l-3.02 7.05c-.09.23-.14.47-.14.73v1.91l.01.01L1 14c0 1.1.9 2 2 2h6.31l-.95 4.57-.03.32c0 .41.17.79.44 1.06L9.83 23l6.59-6.59c.36-.36.58-.86.58-1.41V5c0-1.1-.9-2-2-2zm4 0v12h4V3h-4z" /></g>
<g id="thumb-up"><path d="M1 21h4V9H1v12zm22-11c0-1.1-.9-2-2-2h-6.31l.95-4.57.03-.32c0-.41-.17-.79-.44-1.06L14.17 1 7.59 7.59C7.22 7.95 7 8.45 7 9v10c0 1.1.9 2 2 2h9c.83 0 1.54-.5 1.84-1.22l3.02-7.05c.09-.23.14-.47.14-.73v-1.91l-.01-.01L23 10z" /></g>
<g id="thumbs-up-down"><path d="M12 6c0-.55-.45-1-1-1H5.82l.66-3.18.02-.23c0-.31-.13-.59-.33-.8L5.38 0 .44 4.94C.17 5.21 0 5.59 0 6v6.5c0 .83.67 1.5 1.5 1.5h6.75c.62 0 1.15-.38 1.38-.91l2.26-5.29c.07-.17.11-.36.11-.55V6zm10.5 4h-6.75c-.62 0-1.15.38-1.38.91l-2.26 5.29c-.07.17-.11.36-.11.55V18c0 .55.45 1 1 1h5.18l-.66 3.18-.02.24c0 .31.13.59.33.8l.79.78 4.94-4.94c.27-.27.44-.65.44-1.06v-6.5c0-.83-.67-1.5-1.5-1.5z" /></g>
<g id="timeline"><path d="M23 8c0 1.1-.9 2-2 2-.18 0-.35-.02-.51-.07l-3.56 3.55c.05.16.07.34.07.52 0 1.1-.9 2-2 2s-2-.9-2-2c0-.18.02-.36.07-.52l-2.55-2.55c-.16.05-.34.07-.52.07s-.36-.02-.52-.07l-4.55 4.56c.05.16.07.33.07.51 0 1.1-.9 2-2 2s-2-.9-2-2 .9-2 2-2c.18 0 .35.02.51.07l4.56-4.55C8.02 9.36 8 9.18 8 9c0-1.1.9-2 2-2s2 .9 2 2c0 .18-.02.36-.07.52l2.55 2.55c.16-.05.34-.07.52-.07s.36.02.52.07l3.55-3.56C19.02 8.35 19 8.18 19 8c0-1.1.9-2 2-2s2 .9 2 2z" /></g>
<g id="toc"><path d="M3 9h14V7H3v2zm0 4h14v-2H3v2zm0 4h14v-2H3v2zm16 0h2v-2h-2v2zm0-10v2h2V7h-2zm0 6h2v-2h-2v2z" /></g>
<g id="today"><path d="M19 3h-1V1h-2v2H8V1H6v2H5c-1.11 0-1.99.9-1.99 2L3 19c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm0 16H5V8h14v11zM7 10h5v5H7z" /></g>
<g id="toll"><path d="M15 4c-4.42 0-8 3.58-8 8s3.58 8 8 8 8-3.58 8-8-3.58-8-8-8zm0 14c-3.31 0-6-2.69-6-6s2.69-6 6-6 6 2.69 6 6-2.69 6-6 6zM3 12c0-2.61 1.67-4.83 4-5.65V4.26C3.55 5.15 1 8.27 1 12s2.55 6.85 6 7.74v-2.09c-2.33-.82-4-3.04-4-5.65z" /></g>
<g id="touch-app"><path d="M9 11.24V7.5C9 6.12 10.12 5 11.5 5S14 6.12 14 7.5v3.74c1.21-.81 2-2.18 2-3.74C16 5.01 13.99 3 11.5 3S7 5.01 7 7.5c0 1.56.79 2.93 2 3.74zm9.84 4.63l-4.54-2.26c-.17-.07-.35-.11-.54-.11H13v-6c0-.83-.67-1.5-1.5-1.5S10 6.67 10 7.5v10.74l-3.43-.72c-.08-.01-.15-.03-.24-.03-.31 0-.59.13-.79.33l-.79.8 4.94 4.94c.27.27.65.44 1.06.44h6.79c.75 0 1.33-.55 1.44-1.28l.75-5.27c.01-.07.02-.14.02-.2 0-.62-.38-1.16-.91-1.38z" /></g>
<g id="track-changes"><path d="M19.07 4.93l-1.41 1.41C19.1 7.79 20 9.79 20 12c0 4.42-3.58 8-8 8s-8-3.58-8-8c0-4.08 3.05-7.44 7-7.93v2.02C8.16 6.57 6 9.03 6 12c0 3.31 2.69 6 6 6s6-2.69 6-6c0-1.66-.67-3.16-1.76-4.24l-1.41 1.41C15.55 9.9 16 10.9 16 12c0 2.21-1.79 4-4 4s-4-1.79-4-4c0-1.86 1.28-3.41 3-3.86v2.14c-.6.35-1 .98-1 1.72 0 1.1.9 2 2 2s2-.9 2-2c0-.74-.4-1.38-1-1.72V2h-1C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10c0-2.76-1.12-5.26-2.93-7.07z" /></g>
<g id="translate"><path d="M12.87 15.07l-2.54-2.51.03-.03c1.74-1.94 2.98-4.17 3.71-6.53H17V4h-7V2H8v2H1v1.99h11.17C11.5 7.92 10.44 9.75 9 11.35 8.07 10.32 7.3 9.19 6.69 8h-2c.73 1.63 1.73 3.17 2.98 4.56l-5.09 5.02L4 19l5-5 3.11 3.11.76-2.04zM18.5 10h-2L12 22h2l1.12-3h4.75L21 22h2l-4.5-12zm-2.62 7l1.62-4.33L19.12 17h-3.24z" /></g>
<g id="trending-down"><path d="M16 18l2.29-2.29-4.88-4.88-4 4L2 7.41 3.41 6l6 6 4-4 6.3 6.29L22 12v6z" /></g>
<g id="trending-flat"><path d="M22 12l-4-4v3H3v2h15v3z" /></g>
<g id="trending-up"><path d="M16 6l2.29 2.29-4.88 4.88-4-4L2 16.59 3.41 18l6-6 4 4 6.3-6.29L22 12V6z" /></g>
<g id="turned-in"><path d="M17 3H7c-1.1 0-1.99.9-1.99 2L5 21l7-3 7 3V5c0-1.1-.9-2-2-2z" /></g>
<g id="turned-in-not"><path d="M17 3H7c-1.1 0-1.99.9-1.99 2L5 21l7-3 7 3V5c0-1.1-.9-2-2-2zm0 15l-5-2.18L7 18V5h10v13z" /></g>
<g id="unarchive"><path d="M20.55 5.22l-1.39-1.68C18.88 3.21 18.47 3 18 3H6c-.47 0-.88.21-1.15.55L3.46 5.22C3.17 5.57 3 6.01 3 6.5V19c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V6.5c0-.49-.17-.93-.45-1.28zM12 9.5l5.5 5.5H14v2h-4v-2H6.5L12 9.5zM5.12 5l.82-1h12l.93 1H5.12z" /></g>
<g id="undo"><path d="M12.5 8c-2.65 0-5.05.99-6.9 2.6L2 7v9h9l-3.62-3.62c1.39-1.16 3.16-1.88 5.12-1.88 3.54 0 6.55 2.31 7.6 5.5l2.37-.78C21.08 11.03 17.15 8 12.5 8z" /></g>
<g id="unfold-less"><path d="M7.41 18.59L8.83 20 12 16.83 15.17 20l1.41-1.41L12 14l-4.59 4.59zm9.18-13.18L15.17 4 12 7.17 8.83 4 7.41 5.41 12 10l4.59-4.59z" /></g>
<g id="unfold-more"><path d="M12 5.83L15.17 9l1.41-1.41L12 3 7.41 7.59 8.83 9 12 5.83zm0 12.34L8.83 15l-1.41 1.41L12 21l4.59-4.59L15.17 15 12 18.17z" /></g>
<g id="update"><path d="M21 10.12h-6.78l2.74-2.82c-2.73-2.7-7.15-2.8-9.88-.1-2.73 2.71-2.73 7.08 0 9.79 2.73 2.71 7.15 2.71 9.88 0C18.32 15.65 19 14.08 19 12.1h2c0 1.98-.88 4.55-2.64 6.29-3.51 3.48-9.21 3.48-12.72 0-3.5-3.47-3.53-9.11-.02-12.58 3.51-3.47 9.14-3.47 12.65 0L21 3v7.12zM12.5 8v4.25l3.5 2.08-.72 1.21L11 13V8h1.5z" /></g>
<g id="verified-user"><path d="M12 1L3 5v6c0 5.55 3.84 10.74 9 12 5.16-1.26 9-6.45 9-12V5l-9-4zm-2 16l-4-4 1.41-1.41L10 14.17l6.59-6.59L18 9l-8 8z" /></g>
<g id="view-agenda"><path d="M20 13H3c-.55 0-1 .45-1 1v6c0 .55.45 1 1 1h17c.55 0 1-.45 1-1v-6c0-.55-.45-1-1-1zm0-10H3c-.55 0-1 .45-1 1v6c0 .55.45 1 1 1h17c.55 0 1-.45 1-1V4c0-.55-.45-1-1-1z" /></g>
<g id="view-array"><path d="M4 18h3V5H4v13zM18 5v13h3V5h-3zM8 18h9V5H8v13z" /></g>
<g id="view-carousel"><path d="M7 19h10V4H7v15zm-5-2h4V6H2v11zM18 6v11h4V6h-4z" /></g>
<g id="view-column"><path d="M10 18h5V5h-5v13zm-6 0h5V5H4v13zM16 5v13h5V5h-5z" /></g>
<g id="view-day"><path d="M2 21h19v-3H2v3zM20 8H3c-.55 0-1 .45-1 1v6c0 .55.45 1 1 1h17c.55 0 1-.45 1-1V9c0-.55-.45-1-1-1zM2 3v3h19V3H2z" /></g>
<g id="view-headline"><path d="M4 15h16v-2H4v2zm0 4h16v-2H4v2zm0-8h16V9H4v2zm0-6v2h16V5H4z" /></g>
<g id="view-list"><path d="M4 14h4v-4H4v4zm0 5h4v-4H4v4zM4 9h4V5H4v4zm5 5h12v-4H9v4zm0 5h12v-4H9v4zM9 5v4h12V5H9z" /></g>
<g id="view-module"><path d="M4 11h5V5H4v6zm0 7h5v-6H4v6zm6 0h5v-6h-5v6zm6 0h5v-6h-5v6zm-6-7h5V5h-5v6zm6-6v6h5V5h-5z" /></g>
<g id="view-quilt"><path d="M10 18h5v-6h-5v6zm-6 0h5V5H4v13zm12 0h5v-6h-5v6zM10 5v6h11V5H10z" /></g>
<g id="view-stream"><path d="M4 18h17v-6H4v6zM4 5v6h17V5H4z" /></g>
<g id="view-week"><path d="M6 5H3c-.55 0-1 .45-1 1v12c0 .55.45 1 1 1h3c.55 0 1-.45 1-1V6c0-.55-.45-1-1-1zm14 0h-3c-.55 0-1 .45-1 1v12c0 .55.45 1 1 1h3c.55 0 1-.45 1-1V6c0-.55-.45-1-1-1zm-7 0h-3c-.55 0-1 .45-1 1v12c0 .55.45 1 1 1h3c.55 0 1-.45 1-1V6c0-.55-.45-1-1-1z" /></g>
<g id="visibility"><path d="M12 4.5C7 4.5 2.73 7.61 1 12c1.73 4.39 6 7.5 11 7.5s9.27-3.11 11-7.5c-1.73-4.39-6-7.5-11-7.5zM12 17c-2.76 0-5-2.24-5-5s2.24-5 5-5 5 2.24 5 5-2.24 5-5 5zm0-8c-1.66 0-3 1.34-3 3s1.34 3 3 3 3-1.34 3-3-1.34-3-3-3z" /></g>
<g id="visibility-off"><path d="M12 7c2.76 0 5 2.24 5 5 0 .65-.13 1.26-.36 1.83l2.92 2.92c1.51-1.26 2.7-2.89 3.43-4.75-1.73-4.39-6-7.5-11-7.5-1.4 0-2.74.25-3.98.7l2.16 2.16C10.74 7.13 11.35 7 12 7zM2 4.27l2.28 2.28.46.46C3.08 8.3 1.78 10.02 1 12c1.73 4.39 6 7.5 11 7.5 1.55 0 3.03-.3 4.38-.84l.42.42L19.73 22 21 20.73 3.27 3 2 4.27zM7.53 9.8l1.55 1.55c-.05.21-.08.43-.08.65 0 1.66 1.34 3 3 3 .22 0 .44-.03.65-.08l1.55 1.55c-.67.33-1.41.53-2.2.53-2.76 0-5-2.24-5-5 0-.79.2-1.53.53-2.2zm4.31-.78l3.15 3.15.02-.16c0-1.66-1.34-3-3-3l-.17.01z" /></g>
<g id="warning"><path d="M1 21h22L12 2 1 21zm12-3h-2v-2h2v2zm0-4h-2v-4h2v4z" /></g>
<g id="watch-later"><path d="M12 2C6.5 2 2 6.5 2 12s4.5 10 10 10 10-4.5 10-10S17.5 2 12 2zm4.2 14.2L11 13V7h1.5v5.2l4.5 2.7-.8 1.3z" /></g>
<g id="weekend"><path d="M21 10c-1.1 0-2 .9-2 2v3H5v-3c0-1.1-.9-2-2-2s-2 .9-2 2v5c0 1.1.9 2 2 2h18c1.1 0 2-.9 2-2v-5c0-1.1-.9-2-2-2zm-3-5H6c-1.1 0-2 .9-2 2v2.15c1.16.41 2 1.51 2 2.82V14h12v-2.03c0-1.3.84-2.4 2-2.82V7c0-1.1-.9-2-2-2z" /></g>
<g id="work"><path d="M20 6h-4V4c0-1.11-.89-2-2-2h-4c-1.11 0-2 .89-2 2v2H4c-1.11 0-1.99.89-1.99 2L2 19c0 1.11.89 2 2 2h16c1.11 0 2-.89 2-2V8c0-1.11-.89-2-2-2zm-6 0h-4V4h4v2z" /></g>
<g id="youtube-searched-for"><path d="M17.01 14h-.8l-.27-.27c.98-1.14 1.57-2.61 1.57-4.23 0-3.59-2.91-6.5-6.5-6.5s-6.5 3-6.5 6.5H2l3.84 4 4.16-4H6.51C6.51 7 8.53 5 11.01 5s4.5 2.01 4.5 4.5c0 2.48-2.02 4.5-4.5 4.5-.65 0-1.26-.14-1.82-.38L7.71 15.1c.97.57 2.09.9 3.3.9 1.61 0 3.08-.59 4.22-1.57l.27.27v.79l5.01 4.99L22 19l-4.99-5z" /></g>
<g id="zoom-in"><path d="M15.5 14h-.79l-.28-.27C15.41 12.59 16 11.11 16 9.5 16 5.91 13.09 3 9.5 3S3 5.91 3 9.5 5.91 16 9.5 16c1.61 0 3.09-.59 4.23-1.57l.27.28v.79l5 4.99L20.49 19l-4.99-5zm-6 0C7.01 14 5 11.99 5 9.5S7.01 5 9.5 5 14 7.01 14 9.5 11.99 14 9.5 14zm2.5-4h-2v2H9v-2H7V9h2V7h1v2h2v1z" /></g>
<g id="zoom-out"><path d="M15.5 14h-.79l-.28-.27C15.41 12.59 16 11.11 16 9.5 16 5.91 13.09 3 9.5 3S3 5.91 3 9.5 5.91 16 9.5 16c1.61 0 3.09-.59 4.23-1.57l.27.28v.79l5 4.99L20.49 19l-4.99-5zm-6 0C7.01 14 5 11.99 5 9.5S7.01 5 9.5 5 14 7.01 14 9.5 11.99 14 9.5 14zM7 9h5v1H7z" /></g>
</defs></svg>
</iron-iconset-svg>









<dom-module id="paper-icon-button">
  <template strip-whitespace>
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;
        -webkit-user-select: none;
        -moz-user-select: none;
        -ms-user-select: none;
        user-select: none;
        cursor: pointer;
        z-index: 0;
        line-height: 1;

        width: 40px;
        height: 40px;

        /* NOTE: Both values are needed, since some phones require the value to be `transparent`. */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        /* Because of polymer/2558, this style has lower specificity than * */
        box-sizing: border-box !important;

        @apply --paper-icon-button;
      }

      :host #ink {
        color: var(--paper-icon-button-ink-color, var(--primary-text-color));
        opacity: 0.6;
      }

      :host([disabled]) {
        color: var(--paper-icon-button-disabled-text, var(--disabled-text-color));
        pointer-events: none;
        cursor: auto;

        @apply --paper-icon-button-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:hover) {
        @apply --paper-icon-button-hover;
      }

      iron-icon {
        --iron-icon-width: 100%;
        --iron-icon-height: 100%;
      }
    </style>

    <iron-icon id="icon" src="[[src]]" icon="[[icon]]" alt$="[[alt]]"></iron-icon>
  </template>

  
</dom-module>








<dom-module id="run-color-style">
  <template>
    <style>
      [color-class='light-blue'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-light-blue-500);
        --paper-checkbox-checked-ink-color: var(--paper-light-blue-500);
        --paper-checkbox-unchecked-color: var(--paper-light-blue-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-light-blue-900);
      }
      [color-class='red'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-red-500);
        --paper-checkbox-checked-ink-color: var(--paper-red-500);
        --paper-checkbox-unchecked-color: var(--paper-red-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-red-900);
      }
      [color-class='green'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-green-500);
        --paper-checkbox-checked-ink-color: var(--paper-green-500);
        --paper-checkbox-unchecked-color: var(--paper-green-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-green-900);
      }
      [color-class='purple'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-purple-500);
        --paper-checkbox-checked-ink-color: var(--paper-purple-500);
        --paper-checkbox-unchecked-color: var(--paper-purple-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-purple-900);
      }
      [color-class='teal'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-teal-500);
        --paper-checkbox-checked-ink-color: var(--paper-teal-500);
        --paper-checkbox-unchecked-color: var(--paper-teal-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-teal-900);
      }
      [color-class='pink'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-pink-500);
        --paper-checkbox-checked-ink-color: var(--paper-pink-500);
        --paper-checkbox-unchecked-color: var(--paper-pink-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-pink-900);
      }
      [color-class='orange'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-orange-500);
        --paper-checkbox-checked-ink-color: var(--paper-orange-500);
        --paper-checkbox-unchecked-color: var(--paper-orange-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-orange-900);
      }
      [color-class='brown'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-brown-500);
        --paper-checkbox-checked-ink-color: var(--paper-brown-500);
        --paper-checkbox-unchecked-color: var(--paper-brown-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-brown-900);
      }
      [color-class='indigo'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-indigo-500);
        --paper-checkbox-checked-ink-color: var(--paper-indigo-500);
        --paper-checkbox-unchecked-color: var(--paper-indigo-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-indigo-900);
      }
    </style>
  </template>
</dom-module>




<dom-module id="tf-multi-checkbox">
  <template>
    <style include="scrollbar-style"></style>
    <style include="run-color-style"></style>

    <paper-input id="names-regex" no-label-float label="Write a regex to filter runs" value="[[regex]]" on-bind-value-changed="_debouncedRegexChange"></paper-input>
    <div id="outer-container" class="scrollbar">
      <template is="dom-repeat" items="[[namesMatchingRegex]]" on-dom-change="synchronizeColors">
        <div class="name-row">
          <div class="icon-container checkbox-container vertical-align-container">
            <paper-checkbox class="checkbox vertical-align-center" id$="checkbox-[[item]]" name="[[item]]" checked$="[[_isChecked(item, selectionState.*)]]" on-change="_checkboxChange"></paper-checkbox>
          </div>
          <div class="icon-container isolator-container vertical-align-container">
            <paper-icon-button icon="radio-button-unchecked" class="isolator vertical-align-center" on-tap="_isolateName" name="[[item]]"></paper-icon-button>
          </div>
          <div class="item-label-container">
            <span>[[item]]</span>
          </div>
        </div>
      </template>
    </div>
    <style>
      paper-input {
        --paper-input-container-focus-color: var(--tb-orange-strong);
        --paper-input-container-input: {
          font-size: 14px;
        }
        --paper-input-container-label: {
          font-size: 14px;
        }
      }
      :host {
        display: flex;
        flex-direction: column;
        height: 100%;
        overflow: hidden;
      }
      #outer-container {
        overflow-y: auto;
        overflow-x: hidden;
        width: 100%;
        flex-grow: 1;
        flex-shrink: 1;
        word-wrap: break-word;
      }
      .name-row {
        padding-top: 5px;
        padding-bottom: 5px;
        display: flex;
        flex-direction: row;
        font-size: 13px;
        word-break: break-all; /* makes wrapping of hyperparam strings better */
      }
      .icon-container {
        flex-grow: 0;
        flex-shrink: 0;
        padding-left: 2px;
      }
      .checkbox {
        padding-left: 2px;
        width: 18px;
        height: 18px;
      }
      .isolator {
        width: 18px;
        height: 18px;
        padding: 0px;
      }
      .isolator-container {
        padding-left: 6px;
        padding-right: 3px;
      }
      .checkbox-container {
        padding-left: 2px;
      }
      .item-label-container {
        padding-left: 5px;
        flex-grow: 1;
        flex-shrink: 1;
        width: 0px; /* hack to get the flex-grow to work properly */
      }
      .tooltip-value-container {
        display: flex;
        justify-content: center;
        flex-grow: 0;
        flex-shrink: 0;
        text-align: right;
        padding-left: 2px;
      }
      .vertical-align-container {
        display: flex;
        justify-content: center;
      }
      .vertical-align-container .vertical-align-center {
        align-self: center;
      }
      .vertical-align-container .vertical-align-top {
        align-self: start;
      }
    </style>
  </template>
  
</dom-module>




<dom-module id="tf-wbr-string">
  <template>
    
    <template is="dom-repeat" items="[[_parts]]" as="part">[[part]]<wbr></template>
  </template>
  
</dom-module>



<dom-module id="tf-runs-selector">
  <template>
    <paper-dialog with-backdrop id="data-location-dialog">
      <h2>Data Location</h2>
      <tf-wbr-string value="[[dataLocation]]" />
    </paper-dialog>
    <div id="top-text">
      <h3 id="tooltip-help" class="tooltip-container">Runs</h3>
    </div>
    <tf-multi-checkbox id="multiCheckbox" names="[[runs]]" selection-state="{{runSelectionState}}" out-selected="{{selectedRuns}}" regex="{{regexInput}}" coloring="[[coloring]]"></tf-multi-checkbox>
    <paper-button class="x-button" id="toggle-all" on-tap="_toggleAll">
      Toggle All Runs
    </paper-button>
    <template is="dom-if" if="[[dataLocation]]">
      <div id="data-location">
        <tf-wbr-string value="[[_clippedDataLocation]]" /><template is="dom-if" if="[[_shouldShowExpandDataLocationButton(dataLocation, _dataLocationClipLength)]]"><a href="" on-click="_openDataLocationDialog">…</a>
        </template>
      </div>
    </template>
    <style>
      :host {
        box-sizing: border-box;
        display: flex;
        flex-direction: column;
        padding-bottom: 10px;
      }
      #top-text {
        width: 100%;
        flex-grow: 0;
        flex-shrink: 0;
        padding-right: 16px;
        box-sizing: border-box;
        color: var(--paper-grey-800);
      }
      tf-multi-checkbox {
        display: flex;
        flex-grow: 1;
        flex-shrink: 1;
        overflow: hidden;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      #tooltip-help {
        color: var(--paper-grey-800);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }
      paper-button {
        margin-left: 0;
      }
      #data-location {
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin: 5px 0 0 0;
        max-width: 288px;
      }
    </style>
  </template>
  
</dom-module>




















<dom-module id="paper-spinner-styles">
  <template>
    <style>
      /*
      /**************************/
      /* STYLES FOR THE SPINNER */
      /**************************/

      /*
       * Constants:
       *      ARCSIZE     = 270 degrees (amount of circle the arc takes up)
       *      ARCTIME     = 1333ms (time it takes to expand and contract arc)
       *      ARCSTARTROT = 216 degrees (how much the start location of the arc
       *                                should rotate each time, 216 gives us a
       *                                5 pointed star shape (it's 360/5 * 3).
       *                                For a 7 pointed star, we might do
       *                                360/7 * 3 = 154.286)
       *      SHRINK_TIME = 400ms
       */

      :host {
        display: inline-block;
        position: relative;
        width: 28px;
        height: 28px;

        /* 360 * ARCTIME / (ARCSTARTROT + (360-ARCSIZE)) */
        --paper-spinner-container-rotation-duration: 1568ms;

        /* ARCTIME */
        --paper-spinner-expand-contract-duration: 1333ms;

        /* 4 * ARCTIME */
        --paper-spinner-full-cycle-duration: 5332ms;

        /* SHRINK_TIME */
        --paper-spinner-cooldown-duration: 400ms;
      }

      #spinnerContainer {
        width: 100%;
        height: 100%;

        /* The spinner does not have any contents that would have to be
         * flipped if the direction changes. Always use ltr so that the
         * style works out correctly in both cases. */
        direction: ltr;
      }

      #spinnerContainer.active {
        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;
        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;
      }

      @-webkit-keyframes container-rotate {
        to { -webkit-transform: rotate(360deg) }
      }

      @keyframes container-rotate {
        to { transform: rotate(360deg) }
      }

      .spinner-layer {
        position: absolute;
        width: 100%;
        height: 100%;
        opacity: 0;
        white-space: nowrap;
        color: var(--paper-spinner-color, var(--google-blue-500));
      }

      .layer-1 {
        color: var(--paper-spinner-layer-1-color, var(--google-blue-500));
      }

      .layer-2 {
        color: var(--paper-spinner-layer-2-color, var(--google-red-500));
      }

      .layer-3 {
        color: var(--paper-spinner-layer-3-color, var(--google-yellow-500));
      }

      .layer-4 {
        color: var(--paper-spinner-layer-4-color, var(--google-green-500));
      }

      /**
       * IMPORTANT NOTE ABOUT CSS ANIMATION PROPERTIES (keanulee):
       *
       * iOS Safari (tested on iOS 8.1) does not handle animation-delay very well - it doesn't
       * guarantee that the animation will start _exactly_ after that value. So we avoid using
       * animation-delay and instead set custom keyframes for each color (as layer-2undant as it
       * seems).
       */
      .active .spinner-layer {
        -webkit-animation-name: fill-unfill-rotate;
        -webkit-animation-duration: var(--paper-spinner-full-cycle-duration);
        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);
        -webkit-animation-iteration-count: infinite;
        animation-name: fill-unfill-rotate;
        animation-duration: var(--paper-spinner-full-cycle-duration);
        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);
        animation-iteration-count: infinite;
        opacity: 1;
      }

      .active .spinner-layer.layer-1 {
        -webkit-animation-name: fill-unfill-rotate, layer-1-fade-in-out;
        animation-name: fill-unfill-rotate, layer-1-fade-in-out;
      }

      .active .spinner-layer.layer-2 {
        -webkit-animation-name: fill-unfill-rotate, layer-2-fade-in-out;
        animation-name: fill-unfill-rotate, layer-2-fade-in-out;
      }

      .active .spinner-layer.layer-3 {
        -webkit-animation-name: fill-unfill-rotate, layer-3-fade-in-out;
        animation-name: fill-unfill-rotate, layer-3-fade-in-out;
      }

      .active .spinner-layer.layer-4 {
        -webkit-animation-name: fill-unfill-rotate, layer-4-fade-in-out;
        animation-name: fill-unfill-rotate, layer-4-fade-in-out;
      }

      @-webkit-keyframes fill-unfill-rotate {
        12.5% { -webkit-transform: rotate(135deg) } /* 0.5 * ARCSIZE */
        25%   { -webkit-transform: rotate(270deg) } /* 1   * ARCSIZE */
        37.5% { -webkit-transform: rotate(405deg) } /* 1.5 * ARCSIZE */
        50%   { -webkit-transform: rotate(540deg) } /* 2   * ARCSIZE */
        62.5% { -webkit-transform: rotate(675deg) } /* 2.5 * ARCSIZE */
        75%   { -webkit-transform: rotate(810deg) } /* 3   * ARCSIZE */
        87.5% { -webkit-transform: rotate(945deg) } /* 3.5 * ARCSIZE */
        to    { -webkit-transform: rotate(1080deg) } /* 4   * ARCSIZE */
      }

      @keyframes fill-unfill-rotate {
        12.5% { transform: rotate(135deg) } /* 0.5 * ARCSIZE */
        25%   { transform: rotate(270deg) } /* 1   * ARCSIZE */
        37.5% { transform: rotate(405deg) } /* 1.5 * ARCSIZE */
        50%   { transform: rotate(540deg) } /* 2   * ARCSIZE */
        62.5% { transform: rotate(675deg) } /* 2.5 * ARCSIZE */
        75%   { transform: rotate(810deg) } /* 3   * ARCSIZE */
        87.5% { transform: rotate(945deg) } /* 3.5 * ARCSIZE */
        to    { transform: rotate(1080deg) } /* 4   * ARCSIZE */
      }

      @-webkit-keyframes layer-1-fade-in-out {
        0% { opacity: 1 }
        25% { opacity: 1 }
        26% { opacity: 0 }
        89% { opacity: 0 }
        90% { opacity: 1 }
        to { opacity: 1 }
      }

      @keyframes layer-1-fade-in-out {
        0% { opacity: 1 }
        25% { opacity: 1 }
        26% { opacity: 0 }
        89% { opacity: 0 }
        90% { opacity: 1 }
        to { opacity: 1 }
      }

      @-webkit-keyframes layer-2-fade-in-out {
        0% { opacity: 0 }
        15% { opacity: 0 }
        25% { opacity: 1 }
        50% { opacity: 1 }
        51% { opacity: 0 }
        to { opacity: 0 }
      }

      @keyframes layer-2-fade-in-out {
        0% { opacity: 0 }
        15% { opacity: 0 }
        25% { opacity: 1 }
        50% { opacity: 1 }
        51% { opacity: 0 }
        to { opacity: 0 }
      }

      @-webkit-keyframes layer-3-fade-in-out {
        0% { opacity: 0 }
        40% { opacity: 0 }
        50% { opacity: 1 }
        75% { opacity: 1 }
        76% { opacity: 0 }
        to { opacity: 0 }
      }

      @keyframes layer-3-fade-in-out {
        0% { opacity: 0 }
        40% { opacity: 0 }
        50% { opacity: 1 }
        75% { opacity: 1 }
        76% { opacity: 0 }
        to { opacity: 0 }
      }

      @-webkit-keyframes layer-4-fade-in-out {
        0% { opacity: 0 }
        65% { opacity: 0 }
        75% { opacity: 1 }
        90% { opacity: 1 }
        to { opacity: 0 }
      }

      @keyframes layer-4-fade-in-out {
        0% { opacity: 0 }
        65% { opacity: 0 }
        75% { opacity: 1 }
        90% { opacity: 1 }
        to { opacity: 0 }
      }

      .circle-clipper {
        display: inline-block;
        position: relative;
        width: 50%;
        height: 100%;
        overflow: hidden;
      }

      /**
       * Patch the gap that appear between the two adjacent div.circle-clipper while the
       * spinner is rotating (appears on Chrome 50, Safari 9.1.1, and Edge).
       */
      .spinner-layer::after {
        left: 45%;
        width: 10%;
        border-top-style: solid;
      }

      .spinner-layer::after,
      .circle-clipper::after {
        content: '';
        box-sizing: border-box;
        position: absolute;
        top: 0;
        border-width: var(--paper-spinner-stroke-width, 3px);
        border-radius: 50%;
      }

      .circle-clipper::after {
        bottom: 0;
        width: 200%;
        border-style: solid;
        border-bottom-color: transparent !important;
      }

      .circle-clipper.left::after {
        left: 0;
        border-right-color: transparent !important;
        -webkit-transform: rotate(129deg);
        transform: rotate(129deg);
      }

      .circle-clipper.right::after {
        left: -100%;
        border-left-color: transparent !important;
        -webkit-transform: rotate(-129deg);
        transform: rotate(-129deg);
      }

      .active .gap-patch::after,
      .active .circle-clipper::after {
        -webkit-animation-duration: var(--paper-spinner-expand-contract-duration);
        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);
        -webkit-animation-iteration-count: infinite;
        animation-duration: var(--paper-spinner-expand-contract-duration);
        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);
        animation-iteration-count: infinite;
      }

      .active .circle-clipper.left::after {
        -webkit-animation-name: left-spin;
        animation-name: left-spin;
      }

      .active .circle-clipper.right::after {
        -webkit-animation-name: right-spin;
        animation-name: right-spin;
      }

      @-webkit-keyframes left-spin {
        0% { -webkit-transform: rotate(130deg) }
        50% { -webkit-transform: rotate(-5deg) }
        to { -webkit-transform: rotate(130deg) }
      }

      @keyframes left-spin {
        0% { transform: rotate(130deg) }
        50% { transform: rotate(-5deg) }
        to { transform: rotate(130deg) }
      }

      @-webkit-keyframes right-spin {
        0% { -webkit-transform: rotate(-130deg) }
        50% { -webkit-transform: rotate(5deg) }
        to { -webkit-transform: rotate(-130deg) }
      }

      @keyframes right-spin {
        0% { transform: rotate(-130deg) }
        50% { transform: rotate(5deg) }
        to { transform: rotate(-130deg) }
      }

      #spinnerContainer.cooldown {
        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);
        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);
      }

      @-webkit-keyframes fade-out {
        0% { opacity: 1 }
        to { opacity: 0 }
      }

      @keyframes fade-out {
        0% { opacity: 1 }
        to { opacity: 0 }
      }
    </style>
  </template>
</dom-module>




<dom-module id="paper-spinner-lite">
  <template strip-whitespace>
    <style include="paper-spinner-styles"></style>

    <div id="spinnerContainer" class-name="[[__computeContainerClasses(active, __coolingDown)]]" on-animationend="__reset" on-webkit-animation-end="__reset">
      <div class="spinner-layer">
        <div class="circle-clipper left"></div>
        <div class="circle-clipper right"></div>
      </div>
    </div>
  </template>

  
</dom-module>











<style>
.plottable-colors-0 {
  background-color: #5279c7; /* INDIGO */
}

.plottable-colors-1 {
  background-color: #fd373e; /* CORAL_RED */
}

.plottable-colors-2 {
  background-color: #63c261; /* FERN */
}

.plottable-colors-3 {
  background-color: #fad419; /* BRIGHT_SUN */
}

.plottable-colors-4 {
  background-color: #2c2b6f; /* JACARTA */
}

.plottable-colors-5 {
  background-color: #ff7939; /* BURNING_ORANGE */
}

.plottable-colors-6 {
  background-color: #db2e65; /* CERISE_RED */
}

.plottable-colors-7 {
  background-color: #99ce50; /* CONIFER */
}

.plottable-colors-8 {
  background-color: #962565; /* ROYAL_HEATH */
}

.plottable-colors-9 {
  background-color: #06cccc; /* ROBINS_EGG_BLUE */
}

/**
 * User-supplied renderTo element.
 */
.plottable {
  display: block; /* must be block elements for width/height calculations to work in Firefox. */
  pointer-events: visibleFill;
  position: relative;
  /**
   * Pre 3.0, users could set the dimension of the root element in two ways: either using CSS
   * (inline or through a stylesheet), or using the SVG width/height attributes. By default, we
   * set the SVG width/height attributes to 100%.
   *
   * Post 3.0 the root element is always a normal div and the only way to set the dimensions is
   * to use CSS. To replicate the "100%-by-default" behavior, we apply width/height 100%.
   */
  width: 100%;
  height: 100%;
}

/**
 * The _element that roots each Component's DOM.
 */
.plottable .component {
  /* Allow components to be positioned with explicit left/top/width/height styles */
  position: absolute;
}

.plottable .background-container,
.plottable .content,
.plottable .foreground-container {
  position: absolute;
  width: 100%;
  height: 100%;
}

/**
 * Don't allow svg elements above the content to steal events
 */
.plottable .foreground-container {
  pointer-events: none;
}

.plottable .component-overflow-hidden {
  overflow: hidden;
}

.plottable .component-overflow-visible {
  overflow: visible;
}

.plottable .plot-canvas-container {
  width: 100%;
  height: 100%;
  overflow: hidden;
}

.plottable .plot-canvas {
  width: 100%;
  height: 100%;
  /**
   * Play well with deferred rendering.
   */
  transform-origin: 0px 0px 0px;
}

.plottable text {
  text-rendering: geometricPrecision;
}

.plottable .label text {
  font-family: "Helvetica Neue", sans-serif;
  fill: #32313F;
}

.plottable .bar-label-text-area text {
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
}

.plottable .label-area text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 14px;
}

.plottable .light-label text {
  fill: white;
}

.plottable .dark-label text {
  fill: #32313F;
}

.plottable .off-bar-label text {
  fill: #32313F;
}

.plottable .stacked-bar-label text {
  fill: #32313F;
  font-style: normal;
}

.plottable .stacked-bar-plot .off-bar-label {
  /* HACKHACK #2795: correct off-bar label logic to be implemented on StackedBar */
  visibility: hidden !important;
}

.plottable .axis-label text {
  font-size: 10px;
  font-weight: bold;
  letter-spacing: 1px;
  line-height: normal;
  text-transform: uppercase;
}

.plottable .title-label text {
  font-size: 20px;
  font-weight: bold;
}

.plottable .axis line.baseline {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis line.tick-mark {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
  font-weight: 200;
  line-height: normal;
}

.plottable .axis .annotation-circle {
  fill: white;
  stroke-width: 1px;
  stroke: #CCC;
}

.plottable .axis .annotation-line {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis .annotation-rect {
  stroke: #CCC;
  stroke-width: 1px;
  fill: white;
}

.plottable .bar-plot .baseline {
  stroke: #999;
}

.plottable .gridlines line {
  stroke: #3C3C3C; /* hackhack: gridlines should be solid; see #820 */
  opacity: 0.25;
  stroke-width: 1px;
}

.plottable .selection-box-layer .selection-area {
  fill: black;
  fill-opacity: 0.03;
  stroke: #CCC;
}
/* DragBoxLayer */
.plottable .drag-box-layer.x-resizable .drag-edge-lr {
  cursor: ew-resize;
}
.plottable .drag-box-layer.y-resizable .drag-edge-tb {
  cursor: ns-resize;
}

.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tl {
  cursor: nwse-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tr {
  cursor: nesw-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-bl {
  cursor: nesw-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-br {
  cursor: nwse-resize;
}

.plottable .drag-box-layer.movable .selection-area {
  cursor: move; /* IE fallback */
  cursor: -moz-grab;
  cursor: -webkit-grab;
  cursor: grab;
}

.plottable .drag-box-layer.movable .selection-area:active {
  cursor: -moz-grabbing;
  cursor: -webkit-grabbing;
  cursor: grabbing;
}
/* /DragBoxLayer */

.plottable .guide-line-layer line.guide-line {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .drag-line-layer.enabled.vertical line.drag-edge {
  cursor: ew-resize;
}

.plottable .drag-line-layer.enabled.horizontal line.drag-edge {
  cursor: ns-resize;
}

.plottable .legend text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
  font-weight: bold;
  line-height: normal;
}

.plottable .interpolated-color-legend rect.swatch-bounding-box {
  fill: none;
  stroke: #CCC;
  stroke-width: 1px;
  pointer-events: none;
}

.plottable .waterfall-plot line.connector {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .pie-plot .arc.outline {
  stroke-linejoin: round;
}
</style>

<dom-module id="plottable-style">
  <template>
    <style>
.plottable-colors-0 {
  background-color: #5279c7; /* INDIGO */
}

.plottable-colors-1 {
  background-color: #fd373e; /* CORAL_RED */
}

.plottable-colors-2 {
  background-color: #63c261; /* FERN */
}

.plottable-colors-3 {
  background-color: #fad419; /* BRIGHT_SUN */
}

.plottable-colors-4 {
  background-color: #2c2b6f; /* JACARTA */
}

.plottable-colors-5 {
  background-color: #ff7939; /* BURNING_ORANGE */
}

.plottable-colors-6 {
  background-color: #db2e65; /* CERISE_RED */
}

.plottable-colors-7 {
  background-color: #99ce50; /* CONIFER */
}

.plottable-colors-8 {
  background-color: #962565; /* ROYAL_HEATH */
}

.plottable-colors-9 {
  background-color: #06cccc; /* ROBINS_EGG_BLUE */
}

/**
 * User-supplied renderTo element.
 */
.plottable {
  display: block; /* must be block elements for width/height calculations to work in Firefox. */
  pointer-events: visibleFill;
  position: relative;
  /**
   * Pre 3.0, users could set the dimension of the root element in two ways: either using CSS
   * (inline or through a stylesheet), or using the SVG width/height attributes. By default, we
   * set the SVG width/height attributes to 100%.
   *
   * Post 3.0 the root element is always a normal div and the only way to set the dimensions is
   * to use CSS. To replicate the "100%-by-default" behavior, we apply width/height 100%.
   */
  width: 100%;
  height: 100%;
}

/**
 * The _element that roots each Component's DOM.
 */
.plottable .component {
  /* Allow components to be positioned with explicit left/top/width/height styles */
  position: absolute;
}

.plottable .background-container,
.plottable .content,
.plottable .foreground-container {
  position: absolute;
  width: 100%;
  height: 100%;
}

/**
 * Don't allow svg elements above the content to steal events
 */
.plottable .foreground-container {
  pointer-events: none;
}

.plottable .component-overflow-hidden {
  overflow: hidden;
}

.plottable .component-overflow-visible {
  overflow: visible;
}

.plottable .plot-canvas-container {
  width: 100%;
  height: 100%;
  overflow: hidden;
}

.plottable .plot-canvas {
  width: 100%;
  height: 100%;
  /**
   * Play well with deferred rendering.
   */
  transform-origin: 0px 0px 0px;
}

.plottable text {
  text-rendering: geometricPrecision;
}

.plottable .label text {
  font-family: "Helvetica Neue", sans-serif;
  fill: #32313F;
}

.plottable .bar-label-text-area text {
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
}

.plottable .label-area text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 14px;
}

.plottable .light-label text {
  fill: white;
}

.plottable .dark-label text {
  fill: #32313F;
}

.plottable .off-bar-label text {
  fill: #32313F;
}

.plottable .stacked-bar-label text {
  fill: #32313F;
  font-style: normal;
}

.plottable .stacked-bar-plot .off-bar-label {
  /* HACKHACK #2795: correct off-bar label logic to be implemented on StackedBar */
  visibility: hidden !important;
}

.plottable .axis-label text {
  font-size: 10px;
  font-weight: bold;
  letter-spacing: 1px;
  line-height: normal;
  text-transform: uppercase;
}

.plottable .title-label text {
  font-size: 20px;
  font-weight: bold;
}

.plottable .axis line.baseline {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis line.tick-mark {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
  font-weight: 200;
  line-height: normal;
}

.plottable .axis .annotation-circle {
  fill: white;
  stroke-width: 1px;
  stroke: #CCC;
}

.plottable .axis .annotation-line {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis .annotation-rect {
  stroke: #CCC;
  stroke-width: 1px;
  fill: white;
}

.plottable .bar-plot .baseline {
  stroke: #999;
}

.plottable .gridlines line {
  stroke: #3C3C3C; /* hackhack: gridlines should be solid; see #820 */
  opacity: 0.25;
  stroke-width: 1px;
}

.plottable .selection-box-layer .selection-area {
  fill: black;
  fill-opacity: 0.03;
  stroke: #CCC;
}
/* DragBoxLayer */
.plottable .drag-box-layer.x-resizable .drag-edge-lr {
  cursor: ew-resize;
}
.plottable .drag-box-layer.y-resizable .drag-edge-tb {
  cursor: ns-resize;
}

.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tl {
  cursor: nwse-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tr {
  cursor: nesw-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-bl {
  cursor: nesw-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-br {
  cursor: nwse-resize;
}

.plottable .drag-box-layer.movable .selection-area {
  cursor: move; /* IE fallback */
  cursor: -moz-grab;
  cursor: -webkit-grab;
  cursor: grab;
}

.plottable .drag-box-layer.movable .selection-area:active {
  cursor: -moz-grabbing;
  cursor: -webkit-grabbing;
  cursor: grabbing;
}
/* /DragBoxLayer */

.plottable .guide-line-layer line.guide-line {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .drag-line-layer.enabled.vertical line.drag-edge {
  cursor: ew-resize;
}

.plottable .drag-line-layer.enabled.horizontal line.drag-edge {
  cursor: ns-resize;
}

.plottable .legend text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
  font-weight: bold;
  line-height: normal;
}

.plottable .interpolated-color-legend rect.swatch-bounding-box {
  fill: none;
  stroke: #CCC;
  stroke-width: 1px;
  pointer-events: none;
}

.plottable .waterfall-plot line.connector {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .pie-plot .arc.outline {
  stroke-linejoin: round;
}
</style>
  </template>
</dom-module>













<dom-module id="vz-chart-tooltip">
  
</dom-module>











<dom-module id="vz-pan-zoom-style">
  <template>
    <style>
      .help {
        align-items: center;
        animation-delay: 1s;
        animation-duration: 1s;
        animation-name: fade-out;
        background: rgba(30, 30, 30, 0.6);
        bottom: 0;
        color: #fff;
        display: flex;
        justify-content: center;
        left: 0;
        opacity: 1;
        padding: 20px;
        pointer-events: none;
        position: absolute;
        right: 0;
        top: 0;
      }

      .help > span {
        white-space: normal;
      }

      @keyframes fade-out {
        0% {
          opacity: 1;
        }

        100% {
          opacity: 0;
        }
      }
    </style>
  </template>
</dom-module>



<dom-module id="vz-line-chart2">
  <template>
    <div id="chartdiv"></div>
    <vz-chart-tooltip id="tooltip" position="[[tooltipPosition]]" content-component-name="vz-line-chart-tooltip"></vz-chart-tooltip>
    <style include="plottable-style"></style>
    <style include="vz-pan-zoom-style"></style>
    <style>
      :host {
        -moz-user-select: none;
        -webkit-user-select: none;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        outline: none;
        position: relative;
        white-space: nowrap;
      }
      div {
        -webkit-user-select: none;
        -moz-user-select: none;
        flex-grow: 1;
        flex-shrink: 1;
      }

      #chartdiv .main {
        cursor: crosshair;
      }

      :host(.pankey) #chartdiv :not(.drag-zooming) .main {
        cursor: -webkit-grab;
        cursor: grab;
      }

      :host(.mousedown) #chartdiv .panning .main {
        cursor: -webkit-grabbing;
        cursor: grabbing;
      }

      #chartdiv line.guide-line {
        stroke: #999;
        stroke-width: 1.5px;
      }
      #chartdiv:hover {
        will-change: transform;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }
    </style>
  </template>
  
  
  
  
  
</dom-module>

<dom-module id="vz-line-chart-tooltip">
  <template>
    <div class="content">
      <table>
        <thead></thead>
        <tbody></tbody>
      </table>
    </div>
    <style>
      :host {
        pointer-events: none;
      }

      .content {
        background: rgba(0, 0, 0, 0.8);
        border-radius: 4px;
        color: #fff;
        overflow: hidden;
        pointer-events: none;
      }

      table {
        font-size: 13px;
        line-height: 1.4em;
        margin-top: 10px;
        padding: 8px;
      }

      thead {
        font-size: 14px;
      }

      tbody {
        font-size: 13px;
        line-height: 21px;
        white-space: nowrap;
      }

      td {
        padding: 0 5px;
      }

      .swatch {
        border-radius: 50%;
        display: block;
        height: 18px;
        width: 18px;
      }

      .closest .swatch {
        box-shadow: inset 0 0 0 2px #fff;
      }

      th {
        padding: 0 5px;
        text-align: left;
      }

      .distant td:not(.swatch) {
        opacity: 0.8;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-line-chart-data-loader">
  <template>
    <div id="chart-and-spinner-container">
      <vz-line-chart2 id="chart" data-loading$="[[dataLoading]]" color-scale="[[colorScale]]" default-x-range="[[defaultXRange]]" default-y-range="[[defaultYRange]]" fill-area="[[fillArea]]" ignore-y-outliers="[[ignoreYOutliers]]" on-chart-attached="_onChartAttached" smoothing-enabled="[[smoothingEnabled]]" smoothing-weight="[[smoothingWeight]]" symbol-function="[[symbolFunction]]" tooltip-columns="[[tooltipColumns]]" tooltip-position="[[tooltipPosition]]" tooltip-sorting-method="[[tooltipSortingMethod]]" x-components-creation-method="[[xComponentsCreationMethod]]" x-type="[[xType]]" y-value-accessor="[[yValueAccessor]]"></vz-line-chart2>
      <template is="dom-if" if="[[dataLoading]]">
        <div id="loading-spinner-container">
          <paper-spinner-lite active></paper-spinner-lite>
        </div>
      </template>
    </div>
    <style>
      :host {
        height: 100%;
        width: 100%;
        display: flex;
        flex-direction: column;
      }

      :host([_maybe-rendered-in-bad-state]) vz-line-chart {
        visibility: hidden;
      }

      #chart-and-spinner-container {
        display: flex;
        flex-grow: 1;
        position: relative;
      }

      #loading-spinner-container {
        align-items: center;
        bottom: 0;
        display: flex;
        display: flex;
        justify-content: center;
        left: 0;
        pointer-events: none;
        position: absolute;
        right: 0;
        top: 0;
      }

      vz-line-chart2 {
        -webkit-user-select: none;
        -moz-user-select: none;
      }

      vz-line-chart2[data-loading] {
        opacity: 0.3;
      }
    </style>
  </template>
  
  
</dom-module>










<dom-module id="paper-dialog-scrollable">

  <template>
    <style>

      :host {
        display: block;
        @apply --layout-relative;
      }

      :host(.is-scrolled:not(:first-child))::before {
        content: '';
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      :host(.can-scroll:not(.scrolled-to-bottom):not(:last-child))::after {
        content: '';
        position: absolute;
        bottom: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      .scrollable {
        padding: 0 24px;

        @apply --layout-scroll;
        @apply --paper-dialog-scrollable;
      }

      .fit {
        @apply --layout-fit;
      }
    </style>

    <div id="scrollable" class="scrollable" on-scroll="updateScrollState">
      <slot></slot>
    </div>
  </template>

</dom-module>







<dom-module id="tf-markdown-view">
  <template>
    <div id="markdown" inner-h-t-m-l="[[html]]"></div>
    <style>
      /*
       * Reduce topmost and bottommost margins from 16px to 0.3em (renders
       * at about 4.8px) to keep the layout compact. This improves the
       * appearance when there is only one line of text; standard Markdown
       * renderers will still include a `<p>` element.
       *
       * By targeting only the top-level, extremal elements, we preserve any
       * actual paragraph breaks and only change the padding against the
       * component edges.
       */
      #markdown > p:first-child {
        margin-top: 0.3em;
      }
      #markdown > p:last-child {
        margin-bottom: 0.3em;
      }

      /* Pleasant styles for Markdown tables. */
      #markdown table {
        border-collapse: collapse;
      }
      #markdown table th {
        font-weight: 600;
      }
      #markdown table th,
      #markdown table td {
        padding: 6px 13px;
        border: 1px solid #dfe2e5;
      }
      #markdown table tr {
        background-color: #fff;
        border-top: 1px solid #c6cbd1;
      }
    </style>
  </template>
  
</dom-module>

<dom-module id="tf-card-heading-style">
  <template>
    <style>
      figcaption {
        width: 100%;
      }

      /** Horizontal line of labels. */
      .heading-row {
        margin-top: -4px;
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }

      /** Piece of text in the figure caption. */
      .heading-label {
        flex-grow: 1;
        margin-top: 4px;
        max-width: 100%;
        word-wrap: break-word;
      }

      /** Makes label show on the right. */
      .heading-right {
        flex-grow: 0;
      }
    </style>
  </template>
</dom-module>





<dom-module id="tf-card-heading">
  <template>
    <div class="container">
      <figcaption class="content">
        <div class="heading-row">
          <template is="dom-if" if="[[_nameLabel]]">
            <div itemprop="name" class="heading-label name">
              [[_nameLabel]]
            </div>
          </template>
          <template is="dom-if" if="[[run]]">
            
            
            <span>
              <span itemprop="run" id="heading-run" class="heading-label heading-right run">[[run]]</span>
            </span>
          </template>
        </div>
        <template is="dom-if" if="[[_tagLabel]]">
          <div class="heading-row">
            <div class="heading-label">
              tag: <span itemprop="tag">[[_tagLabel]]</span>
            </div>
          </div>
        </template>
        <slot></slot>
      </figcaption>
      <template is="dom-if" if="[[description]]">
        <paper-icon-button icon="info" on-tap="_toggleDescriptionDialog" title="Show summary description"></paper-icon-button>
      </template>
      <paper-dialog id="descriptionDialog" no-overlap horizontal-align="auto" vertical-align="auto">
        <paper-dialog-scrollable>
          <tf-markdown-view html="[[description]]"></tf-markdown-view>
        </paper-dialog-scrollable>
      </paper-dialog>
    </div>
    <style include="tf-card-heading-style">
      .container {
        display: flex;
      }
      .content {
        font-size: 12px;
        flex-grow: 1;
      }
      .name {
        font-size: 14px;
      }
      .run {
        font-size: 11px;
        width: auto;
        border-radius: 3px;
        font-weight: bold;
        padding: 1px 4px 2px;
      }
      paper-icon-button {
        flex-grow: 0;
      }
      paper-dialog-scrollable {
        max-width: 640px;
      }
      #heading-run {
        background: var(--tf-card-heading-background-color);
        color: var(--tf-card-heading-color);
      }
    </style>
  </template>
  
</dom-module>






<dom-module id="tf-downloader">
  <template>
    <paper-dropdown-menu no-label-float="true" label="run to download" selected-item-label="{{_run}}">
      <paper-listbox slot="dropdown-content">
        <template is="dom-repeat" items="[[runs]]">
          <paper-item no-label-float="true">[[item]]</paper-item>
        </template>
      </paper-listbox>
    </paper-dropdown-menu>
    <template is="dom-if" if="[[_run]]">
      <a download="[[_csvName(tag, _run)]]" href="[[_csvUrl(tag, _run, urlFn)]]">CSV</a><a download="[[_jsonName(tag, _run)]]" href="[[_jsonUrl(tag, _run, urlFn)]]">JSON</a>
    </template>
    <style>
      :host {
        display: flex;
        align-items: center;
        height: 32px;
      }
      paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }
      a {
        font-size: 10px;
        margin: 0 0.2em;
      }
      paper-input {
        font-size: 22px;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-scalar-card">
  <template>
    <tf-card-heading tag="[[tag]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader active="[[active]]" color-scale="[[_getColorScale(colorScale)]]" data-series="[[_getDataSeries(dataToLoad.*)]]" data-to-load="[[dataToLoad]]" get-data-load-name="[[_getDataLoadName]]" get-data-load-url="[[getDataLoadUrl]]" request-data="[[requestData]]" ignore-y-outliers="[[ignoreYOutliers]]" load-data-callback="[[_loadDataCallback]]" load-key="[[tag]]" log-scale-active="[[_logScaleActive]]" request-manager="[[requestManager]]" smoothing-enabled="[[smoothingEnabled]]" smoothing-weight="[[smoothingWeight]]" tag-metadata="[[tagMetadata]]" tooltip-columns="[[_tooltipColumns]]" tooltip-position="auto" tooltip-sorting-method="[[tooltipSortingMethod]]" x-type="[[xType]]">
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
      <paper-icon-button selected$="[[_logScaleActive]]" icon="line-weight" on-tap="_toggleLogScale" title="Toggle y-axis log scale"></paper-icon-button>
      <paper-icon-button icon="settings-overscan" on-tap="_resetDomain" title="Fit domain to data"></paper-icon-button>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <paper-menu-button on-paper-dropdown-open="_updateDownloadLink">
          <paper-icon-button class="dropdown-trigger" slot="dropdown-trigger" icon="file-download"></paper-icon-button>
          <paper-listbox class="dropdown-content" slot="dropdown-content">
            <paper-item>
              <a id="svgLink" download="[[tag]].svg">
                Download Current Chart as SVG
              </a>
            </paper-item>
          </paper-listbox>
        </paper-menu-button>
      </template>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <tf-downloader runs="[[_runsFromData(dataToLoad)]]" tag="[[tag]]" url-fn="[[_downloadUrlFn]]"></tf-downloader>
        </div>
      </template>
    </div>
    <style>
      :host {
        margin: 5px;
        display: block;
        width: 330px;
      }

      :host([_expanded]) {
        width: 100%;
      }

      :host([_expanded]) #tf-line-chart-data-loader-container {
        height: 400px;
      }

      #tf-line-chart-data-loader-container {
        height: 200px;
        width: 100%;
      }

      tf-card-heading {
        display: block;
        margin-bottom: 10px;
      }

      #buttons {
        display: flex;
        flex-direction: row;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      .download-links {
        display: flex;
        height: 32px;
      }

      .download-links a {
        align-self: center;
        font-size: 10px;
        margin: 2px;
      }

      .download-links paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }

      paper-menu-button {
        padding: 0;
      }
      paper-item a {
        color: inherit;
        text-decoration: none;
        white-space: nowrap;
      }
    </style>
  </template>
  
</dom-module>




















<dom-module id="paper-progress">
  <template>
    <style>
      :host {
        display: block;
        width: 200px;
        position: relative;
        overflow: hidden;
      }

      :host([hidden]), [hidden] {
        display: none !important;
      }

      #progressContainer {
        @apply --paper-progress-container;
        position: relative;
      }

      #progressContainer,
      /* the stripe for the indeterminate animation*/
      .indeterminate::after {
        height: var(--paper-progress-height, 4px);
      }

      #primaryProgress,
      #secondaryProgress,
      .indeterminate::after {
        @apply --layout-fit;
      }

      #progressContainer,
      .indeterminate::after {
        background: var(--paper-progress-container-color, var(--google-grey-300));
      }

      :host(.transiting) #primaryProgress,
      :host(.transiting) #secondaryProgress {
        -webkit-transition-property: -webkit-transform;
        transition-property: transform;

        /* Duration */
        -webkit-transition-duration: var(--paper-progress-transition-duration, 0.08s);
        transition-duration: var(--paper-progress-transition-duration, 0.08s);

        /* Timing function */
        -webkit-transition-timing-function: var(--paper-progress-transition-timing-function, ease);
        transition-timing-function: var(--paper-progress-transition-timing-function, ease);

        /* Delay */
        -webkit-transition-delay: var(--paper-progress-transition-delay, 0s);
        transition-delay: var(--paper-progress-transition-delay, 0s);
      }

      #primaryProgress,
      #secondaryProgress {
        @apply --layout-fit;
        -webkit-transform-origin: left center;
        transform-origin: left center;
        -webkit-transform: scaleX(0);
        transform: scaleX(0);
        will-change: transform;
      }

      #primaryProgress {
        background: var(--paper-progress-active-color, var(--google-green-500));
      }

      #secondaryProgress {
        background: var(--paper-progress-secondary-color, var(--google-green-100));
      }

      :host([disabled]) #primaryProgress {
        background: var(--paper-progress-disabled-active-color, var(--google-grey-500));
      }

      :host([disabled]) #secondaryProgress {
        background: var(--paper-progress-disabled-secondary-color, var(--google-grey-300));
      }

      :host(:not([disabled])) #primaryProgress.indeterminate {
        -webkit-transform-origin: right center;
        transform-origin: right center;
        -webkit-animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      :host(:not([disabled])) #primaryProgress.indeterminate::after {
        content: "";
        -webkit-transform-origin: center center;
        transform-origin: center center;

        -webkit-animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      @-webkit-keyframes indeterminate-bar {
        0% {
          -webkit-transform: scaleX(1) translateX(-100%);
        }
        50% {
          -webkit-transform: scaleX(1) translateX(0%);
        }
        75% {
          -webkit-transform: scaleX(1) translateX(0%);
          -webkit-animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          -webkit-transform: scaleX(0) translateX(0%);
        }
      }

      @-webkit-keyframes indeterminate-splitter {
        0% {
          -webkit-transform: scaleX(.75) translateX(-125%);
        }
        30% {
          -webkit-transform: scaleX(.75) translateX(-125%);
          -webkit-animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
        100% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
      }

      @keyframes indeterminate-bar {
        0% {
          transform: scaleX(1) translateX(-100%);
        }
        50% {
          transform: scaleX(1) translateX(0%);
        }
        75% {
          transform: scaleX(1) translateX(0%);
          animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          transform: scaleX(0) translateX(0%);
        }
      }

      @keyframes indeterminate-splitter {
        0% {
          transform: scaleX(.75) translateX(-125%);
        }
        30% {
          transform: scaleX(.75) translateX(-125%);
          animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          transform: scaleX(.75) translateX(125%);
        }
        100% {
          transform: scaleX(.75) translateX(125%);
        }
      }
    </style>

    <div id="progressContainer">
      <div id="secondaryProgress" hidden$="[[_hideSecondaryProgress(secondaryRatio)]]"></div>
      <div id="primaryProgress"></div>
    </div>
  </template>
</dom-module>







<dom-module id="paper-slider">
  <template strip-whitespace>
    <style>
      :host {
        @apply --layout;
        @apply --layout-justified;
        @apply --layout-center;
        width: 200px;
        cursor: default;
        -webkit-user-select: none;
        -moz-user-select: none;
        -ms-user-select: none;
        user-select: none;
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        --paper-progress-active-color: var(--paper-slider-active-color, var(--google-blue-700));
        --paper-progress-secondary-color: var(--paper-slider-secondary-color, var(--google-blue-300));
        --paper-progress-disabled-active-color: var(--paper-slider-disabled-active-color, var(--paper-grey-400));
        --paper-progress-disabled-secondary-color: var(--paper-slider-disabled-secondary-color, var(--paper-grey-400));
        --calculated-paper-slider-height: var(--paper-slider-height, 2px);
      }

      /* focus shows the ripple */
      :host(:focus) {
        outline: none;
      }

      /**
       * NOTE(keanulee): Though :host-context is not universally supported, some pages
       * still rely on paper-slider being flipped when dir="rtl" is set on body. For full
       * compatability, dir="rtl" must be explicitly set on paper-slider.
       */
      :dir(rtl) #sliderContainer {
        -webkit-transform: scaleX(-1);
        transform: scaleX(-1);
      }

      /**
       * NOTE(keanulee): This is separate from the rule above because :host-context may
       * not be recognized.
       */
      :host([dir="rtl"]) #sliderContainer {
        -webkit-transform: scaleX(-1);
        transform: scaleX(-1);
      }

      /**
       * NOTE(keanulee): Needed to override the :host-context rule (where supported)
       * to support LTR sliders in RTL pages.
       */
      :host([dir="ltr"]) #sliderContainer {
        -webkit-transform: scaleX(1);
        transform: scaleX(1);
      }

      #sliderContainer {
        position: relative;
        width: 100%;
        height: calc(30px + var(--calculated-paper-slider-height));
        margin-left: calc(15px + var(--calculated-paper-slider-height)/2);
        margin-right: calc(15px + var(--calculated-paper-slider-height)/2);
      }

      #sliderContainer:focus {
        outline: 0;
      }

      #sliderContainer.editable {
        margin-top: 12px;
        margin-bottom: 12px;
      }

      .bar-container {
        position: absolute;
        top: 0;
        bottom: 0;
        left: 0;
        right: 0;
        overflow: hidden;
      }

      .ring > .bar-container {
        left: calc(5px + var(--calculated-paper-slider-height)/2);
        transition: left 0.18s ease;
      }

      .ring.expand.dragging > .bar-container {
        transition: none;
      }

      .ring.expand:not(.pin) > .bar-container {
        left: calc(8px + var(--calculated-paper-slider-height)/2);
      }

      #sliderBar {
        padding: 15px 0;
        width: 100%;
        background-color: var(--paper-slider-bar-color, transparent);
        --paper-progress-container-color: var(--paper-slider-container-color, var(--paper-grey-400));
        --paper-progress-height: var(--calculated-paper-slider-height);
      }

      .slider-markers {
        position: absolute;
        top: calc(14px + var(--paper-slider-height,2px)/2);
        height: var(--calculated-paper-slider-height);
        left: 0;
        right: -1px;
        box-sizing: border-box;
        pointer-events: none;
        @apply --layout-horizontal;
      }

      .slider-marker {
        @apply --layout-flex;
      }
      .slider-markers::after,
      .slider-marker::after {
        content: "";
        display: block;
        margin-left: -1px;
        width: 2px;
        height: var(--calculated-paper-slider-height);
        border-radius: 50%;
        background-color: var(--paper-slider-markers-color, #000);
      }

      .slider-knob {
        position: absolute;
        left: 0;
        top: 0;
        margin-left: calc(-15px - var(--calculated-paper-slider-height)/2);
        width: calc(30px + var(--calculated-paper-slider-height));
        height: calc(30px + var(--calculated-paper-slider-height));
      }

      .transiting > .slider-knob {
        transition: left 0.08s ease;
      }

      .slider-knob:focus {
        outline: none;
      }

      .slider-knob.dragging {
        transition: none;
      }

      .snaps > .slider-knob.dragging {
        transition: -webkit-transform 0.08s ease;
        transition: transform 0.08s ease;
      }

      .slider-knob-inner {
        margin: 10px;
        width: calc(100% - 20px);
        height: calc(100% - 20px);
        background-color: var(--paper-slider-knob-color, var(--google-blue-700));
        border: 2px solid var(--paper-slider-knob-color, var(--google-blue-700));
        border-radius: 50%;

        -moz-box-sizing: border-box;
        box-sizing: border-box;

        transition-property: -webkit-transform, background-color, border;
        transition-property: transform, background-color, border;
        transition-duration: 0.18s;
        transition-timing-function: ease;
      }

      .expand:not(.pin) > .slider-knob > .slider-knob-inner {
        -webkit-transform: scale(1.5);
        transform: scale(1.5);
      }

      .ring > .slider-knob > .slider-knob-inner {
        background-color: var(--paper-slider-knob-start-color, transparent);
        border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
      }

      .slider-knob-inner::before {
        background-color: var(--paper-slider-pin-color, var(--google-blue-700));
      }

      .pin > .slider-knob > .slider-knob-inner::before {
        content: "";
        position: absolute;
        top: 0;
        left: 50%;
        margin-left: -13px;
        width: 26px;
        height: 26px;
        border-radius: 50% 50% 50% 0;

        -webkit-transform: rotate(-45deg) scale(0) translate(0);
        transform: rotate(-45deg) scale(0) translate(0);
      }

      .slider-knob-inner::before,
      .slider-knob-inner::after {
        transition: -webkit-transform .18s ease, background-color .18s ease;
        transition: transform .18s ease, background-color .18s ease;
      }

      .pin.ring > .slider-knob > .slider-knob-inner::before {
        background-color: var(--paper-slider-pin-start-color, var(--paper-grey-400));
      }

      .pin.expand > .slider-knob > .slider-knob-inner::before {
        -webkit-transform: rotate(-45deg) scale(1) translate(17px, -17px);
        transform: rotate(-45deg) scale(1) translate(17px, -17px);
      }

      .pin > .slider-knob > .slider-knob-inner::after {
        content: attr(value);
        position: absolute;
        top: 0;
        left: 50%;
        margin-left: -16px;
        width: 32px;
        height: 26px;
        text-align: center;
        color: var(--paper-slider-font-color, #fff);
        font-size: 10px;

        -webkit-transform: scale(0) translate(0);
        transform: scale(0) translate(0);
      }

      .pin.expand > .slider-knob > .slider-knob-inner::after {
        -webkit-transform: scale(1) translate(0, -17px);
        transform: scale(1) translate(0, -17px);
      }

      /* paper-input */
      .slider-input {
        width: 50px;
        overflow: hidden;
        --paper-input-container-input: {
          text-align: center;
          @apply --paper-slider-input-container-input;
        };
        @apply --paper-slider-input;
      }

      /* disabled state */
      #sliderContainer.disabled {
        pointer-events: none;
      }

      .disabled > .slider-knob > .slider-knob-inner {
        background-color: var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
        border: 2px solid var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
        -webkit-transform: scale3d(0.75, 0.75, 1);
        transform: scale3d(0.75, 0.75, 1);
      }

      .disabled.ring > .slider-knob > .slider-knob-inner {
        background-color: var(--paper-slider-knob-start-color, transparent);
        border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
      }

      paper-ripple {
        color: var(--paper-slider-knob-color, var(--google-blue-700));
      }
    </style>

    <div id="sliderContainer" class$="[[_getClassNames(disabled, pin, snaps, immediateValue, min, expand, dragging, transiting, editable)]]">
      <div class="bar-container">
        <paper-progress disabled$="[[disabled]]" id="sliderBar" aria-hidden="true" min="[[min]]" max="[[max]]" step="[[step]]" value="[[immediateValue]]" secondary-progress="[[secondaryProgress]]" on-down="_bardown" on-up="_resetKnob" on-track="_bartrack" on-tap="_barclick">
        </paper-progress>
      </div>

      <template is="dom-if" if="[[snaps]]">
        <div class="slider-markers">
          <template is="dom-repeat" items="[[markers]]">
            <div class="slider-marker"></div>
          </template>
        </div>
      </template>

      <div id="sliderKnob" class="slider-knob" on-down="_knobdown" on-up="_resetKnob" on-track="_onTrack" on-transitionend="_knobTransitionEnd">
          <div class="slider-knob-inner" value$="[[immediateValue]]"></div>
      </div>
    </div>

    <template is="dom-if" if="[[editable]]">
      <paper-input id="input" type="number" step="[[step]]" min="[[min]]" max="[[max]]" class="slider-input" disabled$="[[disabled]]" value="[[immediateValue]]" on-change="_changeValue" on-keydown="_inputKeyDown" no-label-float>
      </paper-input>
    </template>
  </template>

  
</dom-module>





<dom-module id="tf-smoothing-input">
  <template>
    <h3 class="title">Smoothing</h3>
    <div class="smoothing-block">
      <paper-slider id="slider" immediate-value="{{_immediateWeightNumberForPaperSlider}}" max="[[max]]" min="[[min]]" pin step="[[step]]" type="number" value="{{weight}}"></paper-slider>
      <paper-input id="input" label="weight" no-label-float value="{{_inputWeightStringForPaperInput}}" type="number" step="[[step]]" min="[[min]]" max="[[max]]"></paper-input>
    </div>
    <style>
      .title {
        color: var(--paper-grey-800);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }

      .smoothing-block {
        display: flex;
      }

      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
        flex-grow: 2;
      }

      paper-input {
        --paper-input-container-focus-color: var(--tb-orange-strong);
        --paper-input-container-input: {
          font-size: 14px;
        }
        --paper-input-container-label: {
          font-size: 14px;
        }
        width: 60px;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-scalar-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <div class="line-item">
            <paper-checkbox id="show-download-links" checked="{{_showDownloadLinks}}">Show data download links</paper-checkbox>
          </div>
          <div class="line-item">
            <paper-checkbox id="ignore-y-outlier" checked="{{_ignoreYOutliers}}">Ignore outliers in chart scaling</paper-checkbox>
          </div>
          <div id="tooltip-sorting">
            <div>Tooltip sorting method:</div>
            <paper-dropdown-menu no-label-float selected-item-label="{{_tooltipSortingMethod}}">
              <paper-listbox class="dropdown-content" selected="0" slot="dropdown-content">
                <paper-item>default</paper-item>
                <paper-item>descending</paper-item>
                <paper-item>ascending</paper-item>
                <paper-item>nearest</paper-item>
              </paper-listbox>
            </paper-dropdown-menu>
          </div>
        </div>
        <div class="sidebar-section">
          <tf-smoothing-input weight="{{_smoothingWeight}}" step="0.001" min="0" max="0.999"></tf-smoothing-input>
        </div>
        <div class="sidebar-section">
          <tf-option-selector id="x-type-selector" name="Horizontal Axis" selected-id="{{_xType}}">
            <paper-button id="step">step</paper-button><paper-button id="relative">relative</paper-button><paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No scalar data was found.</h3>
            <p>Probable causes:
            <ul>
              <li>You haven’t written any scalar data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]" get-category-item-key="[[_getCategoryItemKey]]">
              <template>
                <tf-scalar-card active="[[active]]" data-to-load="[[item.series]]" ignore-y-outliers="[[_ignoreYOutliers]]" multi-experiments="[[_getMultiExperiments(dataSelection)]]" request-manager="[[_requestManager]]" show-download-links="[[_showDownloadLinks]]" smoothing-enabled="[[_smoothingEnabled]]" smoothing-weight="[[_smoothingWeight]]" tag-metadata="[[_tagMetadata(category, _runToTagInfo, item)]]" tag="[[item.tag]]" tooltip-sorting-method="[[_tooltipSortingMethod]]" x-type="[[_xType]]"></tf-scalar-card>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      #tooltip-sorting {
        align-items: center;
        display: flex;
        font-size: 14px;
        margin-top: 15px;
      }
      #tooltip-sorting paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }
      .line-item {
        display: block;
        padding-top: 5px;
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      .center {
        overflow-x: hidden;
      }
    </style>
  </template>

  
</dom-module>
































<dom-module id="tf-custom-scalar-card-style">
  <template>
    <style>
      :host {
        margin: 5px 10px;
        display: inline-block;
        width: 330px;
        vertical-align: text-top;
      }

      :host([_expanded]) {
        width: 100%;
      }

      :host([_expanded]) #tf-line-chart-data-loader-container {
        height: 400px;
      }

      h1 {
        font-size: 19px;
        font-weight: normal;
      }

      #tf-line-chart-data-loader-container {
        height: 200px;
        width: 100%;
      }

      #buttons {
        display: flex;
        flex-direction: row;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      .download-links {
        display: flex;
        height: 32px;
      }

      .download-links a {
        font-size: 10px;
        align-self: center;
        margin: 2px;
      }

      .download-links paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }
    </style>
  </template>
</dom-module>





<dom-module id="tf-custom-scalar-margin-chart-card">
  <template>
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader id="loader" active="[[active]]" color-scale="[[_colorScale]]" data-series="[[_seriesNames]]" get-data-load-url="[[_dataUrl]]" fill-area="[[_fillArea]]" ignore-y-outliers="[[ignoreYOutliers]]" load-key="[[_tagFilter]]" data-to-load="[[runs]]" log-scale-active="[[_logScaleActive]]" load-data-callback="[[_createProcessDataFunction(marginChartSeries)]]" request-manager="[[requestManager]]" symbol-function="[[_createSymbolFunction()]]" tooltip-columns="[[_tooltipColumns]]" tooltip-sorting-method="[[tooltipSortingMethod]]" x-type="[[xType]]">
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
      <paper-icon-button selected$="[[_logScaleActive]]" icon="line-weight" on-tap="_toggleLogScale" title="Toggle y-axis log scale"></paper-icon-button>
      <paper-icon-button icon="settings-overscan" on-tap="_resetDomain" title="Fit domain to data"></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu no-label-float="true" label="series to download" selected-item-label="{{_dataSeriesNameToDownload}}">
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template is="dom-repeat" items="[[_seriesNames]]" as="dataSeriesName">
                <paper-item no-label-float="true">[[dataSeriesName]]</paper-item>
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a download="[[_dataSeriesNameToDownload]].csv" href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]">CSV</a>
          <a download="[[_dataSeriesNameToDownload]].json" href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]">JSON</a>
        </div>
      </template>
    </div>

    
    <template is="dom-if" if="[[_missingTags.length]]">
      <div class="collapsible-list-title">
        <paper-icon-button icon="[[_getToggleCollapsibleIcon(_missingTagsCollapsibleOpened)]]" on-click="_toggleMissingTagsCollapsibleOpen" class="toggle-collapsible-button">
        </paper-icon-button>
        <span class="collapsible-title-text">
          <iron-icon icon="icons:error"></iron-icon> Missing Tags
        </span>
      </div>
      <iron-collapse opened="[[_missingTagsCollapsibleOpened]]">
        <div class="error-content">
          <iron-icon class="error-icon" icon="icons:error"></iron-icon>
          <template is="dom-repeat" items="[[_missingTags]]" as="missingEntry">
            <div class="missing-tags-for-run-container">
              Run "[[missingEntry.run]]" lacks data for tags
              <ul>
                <template is="dom-repeat" items="[[missingEntry.tags]]" as="tag">
                  <li>[[tag]]</li>
                </template>
              </ul>
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>

    <template is="dom-if" if="[[_tagFilterInvalid]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        This regular expresion is invalid:<br>
        <span class="invalid-regex">[[_tagFilter]]</span>
      </div>
    </template>

    <template is="dom-if" if="[[_stepsMismatch]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        The steps for value, lower, and upper tags do not match:
        <ul>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.value]]</span>:
            [[_separateWithCommas(_stepsMismatch.valueSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.lower]]</span>:
            [[_separateWithCommas(_stepsMismatch.lowerSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.upper]]</span>:
            [[_separateWithCommas(_stepsMismatch.upperSteps)]]
          </li>
        </ul>
      </div>
    </template>

    <div id="matches-container">
      <div class="collapsible-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button icon="[[_getToggleCollapsibleIcon(_matchesListOpened)]]" on-click="_toggleMatchesOpen" class="toggle-matches-button">
          </paper-icon-button>
        </template>

        <span class="collapsible-title-text">
          Matches ([[_seriesNames.length]])
        </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template is="dom-repeat" items="[[_seriesNames]]" as="seriesName" id="match-list-repeat" on-dom-change="_matchListEntryColorUpdated">
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      .error-content {
        background: #f00;
        border-radius: 5px;
        color: #fff;
        margin: 10px 0 0 0;
        padding: 10px;
      }

      .error-icon {
        display: block;
        fill: #fff;
        margin: 0 auto 5px auto;
      }

      .invalid-regex {
        font-weight: bold;
      }

      .error-content ul {
        margin: 1px 0 0 0;
        padding: 0 0 0 19px;
      }

      .tag-name {
        font-weight: bold;
      }

      .collapsible-list-title {
        margin: 10px 0 5px 0;
      }

      .collapsible-title-text {
        vertical-align: middle;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .missing-tags-for-run-container {
        margin: 8px 0 0 0;
      }
    </style>
  </template>
  
</dom-module>


















<dom-module id="tf-custom-scalar-multi-line-chart-card">
  <template>
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader id="loader" active="[[active]]" color-scale="[[_colorScale]]" data-series="[[_seriesNames]]" get-data-load-url="[[_dataUrl]]" ignore-y-outliers="[[ignoreYOutliers]]" load-key="[[_tagFilter]]" data-to-load="[[runs]]" log-scale-active="[[_logScaleActive]]" load-data-callback="[[_createProcessDataFunction()]]" request-manager="[[requestManager]]" smoothing-enabled="[[smoothingEnabled]]" smoothing-weight="[[smoothingWeight]]" symbol-function="[[_createSymbolFunction()]]" tooltip-sorting-method="[[tooltipSortingMethod]]" x-type="[[xType]]">
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
      <paper-icon-button selected$="[[_logScaleActive]]" icon="line-weight" on-tap="_toggleLogScale" title="Toggle y-axis log scale"></paper-icon-button>
      <paper-icon-button icon="settings-overscan" on-tap="_resetDomain" title="Fit domain to data"></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu no-label-float="true" label="series to download" selected-item-label="{{_dataSeriesNameToDownload}}">
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template is="dom-repeat" items="[[_seriesNames]]" as="dataSeriesName">
                <paper-item no-label-float="true">[[dataSeriesName]]</paper-item>
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a download="[[_dataSeriesNameToDownload]].csv" href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]">CSV</a>
          <a download="[[_dataSeriesNameToDownload]].json" href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]">JSON</a>
        </div>
      </template>
    </div>
    <div id="matches-container">
      <div id="matches-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button icon="[[_getToggleMatchesIcon(_matchesListOpened)]]" on-click="_toggleMatchesOpen" class="toggle-matches-button">
          </paper-icon-button>
        </template>

        <span class="matches-text">
          Matches ([[_seriesNames.length]])
        </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template is="dom-repeat" items="[[_seriesNames]]" as="seriesName" id="match-list-repeat" on-dom-change="_matchListEntryColorUpdated">
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      #matches-list-title {
        margin: 10px 0 5px 0;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .matches-text {
        vertical-align: middle;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-custom-scalar-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <div class="line-item">
            <paper-checkbox checked="{{_showDownloadLinks}}">Show data download links</paper-checkbox>
          </div>
          <div class="line-item">
            <paper-checkbox checked="{{_ignoreYOutliers}}">Ignore outliers in chart scaling</paper-checkbox>
          </div>
          <div id="tooltip-sorting">
            <div id="tooltip-sorting-label">Tooltip sorting method:</div>
            <paper-dropdown-menu no-label-float selected-item-label="{{_tooltipSortingMethod}}">
              <paper-listbox class="dropdown-content" selected="0" slot="dropdown-content">
                <paper-item>default</paper-item>
                <paper-item>descending</paper-item>
                <paper-item>ascending</paper-item>
                <paper-item>nearest</paper-item>
              </paper-listbox>
            </paper-dropdown-menu>
          </div>
        </div>
        <div class="sidebar-section">
          <tf-smoothing-input weight="{{_smoothingWeight}}" step="0.001" min="0" max="1"></tf-smoothing-input>
        </div>
        <div class="sidebar-section">
          <tf-option-selector id="x-type-selector" name="Horizontal Axis" selected-id="{{_xType}}">
            <paper-button id="step">step</paper-button><paper-button id="relative">relative</paper-button><paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center" id="categories-container">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>The custom scalars dashboard is inactive.</h3>
            <p>Probable causes:
            <ol>
              <li>You haven't laid out the dashboard.</li>
              <li>You haven’t written any scalar data to your event files.</li>
            </ol>

            <p>
              To lay out the dashboard, pass a <code>Layout</code> protobuffer
              to the <code>set_layout</code> method. For example,
            
            <pre>from tensorboard import summary
from tensorboard.plugins.custom_scalar import layout_pb2
...
# This action does not have to be performed at every step, so the action is not
# taken care of by an op in the graph. We only need to specify the layout once
# (instead of per step).
layout_summary = summary_lib.custom_scalar_pb(layout_pb2.Layout(
  category=[
    layout_pb2.Category(
      title='losses',
      chart=[
          layout_pb2.Chart(
              title='losses',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'loss.*'],
              )),
          layout_pb2.Chart(
              title='baz',
              margin=layout_pb2.MarginChartContent(
                series=[
                  layout_pb2.MarginChartContent.Series(
                    value='loss/baz/scalar_summary',
                    lower='baz_lower/baz/scalar_summary',
                    upper='baz_upper/baz/scalar_summary'),
                ],
              )),
      ]),
    layout_pb2.Category(
      title='trig functions',
      chart=[
          layout_pb2.Chart(
              title='wave trig functions',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/cosine', r'trigFunctions/sine'],
              )),
          # The range of tangent is different. Let's give it its own chart.
          layout_pb2.Chart(
              title='tan',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/tangent'],
              )),
      ],
      # This category we care less about. Let's make it initially closed.
      closed=True),
  ]))
writer.add_summary(layout_summary)
</pre>
            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view as="chart" category="[[category]]" disable-pagination initial-opened="[[category.metadata.opened]]">
              <template>
                <div>
                  <template is="dom-if" if="[[chart.multiline]]">
                    <tf-custom-scalar-multi-line-chart-card active="[[active]]" request-manager="[[_requestManager]]" runs="[[_selectedRuns]]" title="[[chart.title]]" x-type="[[_xType]]" smoothing-enabled="[[_smoothingEnabled]]" smoothing-weight="[[_smoothingWeight]]" tooltip-sorting-method="[[tooltipSortingMethod]]" ignore-y-outliers="[[_ignoreYOutliers]]" show-download-links="[[_showDownloadLinks]]" tag-regexes="[[chart.multiline.tag]]"></tf-custom-scalar-multi-line-chart-card>
                  </template>
                  <template is="dom-if" if="[[chart.margin]]">
                    <tf-custom-scalar-margin-chart-card active="[[active]]" request-manager="[[_requestManager]]" runs="[[_selectedRuns]]" title="[[chart.title]]" x-type="[[_xType]]" tooltip-sorting-method="[[tooltipSortingMethod]]" ignore-y-outliers="[[_ignoreYOutliers]]" show-download-links="[[_showDownloadLinks]]" margin-chart-series="[[chart.margin.series]]"></tf-custom-scalar-margin-chart-card>
                  </template>
                </div>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      #tooltip-sorting {
        align-items: center;
        display: flex;
        font-size: 14px;
        margin-top: 15px;
      }
      #tooltip-sorting paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }
      .line-item {
        display: block;
        padding-top: 5px;
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>

  
</dom-module>





























<dom-module id="tf-image-loader">
  <template>
    <tf-card-heading tag="[[tag]]" run="[[run]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]" color="[[_runColor]]">
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div>sample: [[_sampleText]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold">[[_toLocaleString(_stepValue)]]</span>
          </div>
          <div class="heading-label heading-right datetime">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isImageLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div>
          <paper-slider id="steps" immediate-value="{{_stepIndex}}" max="[[_maxStepIndex]]" max-markers="[[_maxStepIndex]]" snaps step="1" value="{{_stepIndex}}"></paper-slider>
        </div>
      </template>
    </tf-card-heading>

    
    <a id="main-image-container" role="button" aria-label="Toggle actual size" aria-expanded$="[[_getAriaExpanded(actualSize)]]" on-tap="_handleTap"></a>

    <style include="tf-card-heading-style">
      /** Make button a div. */
      button {
        width: 100%;
        display: block;
        background: none;
        border: 0;
        padding: 0;
      }

      /** Firefox: Get rid of dotted line inside button. */
      button::-moz-focus-inner {
        border: 0;
        padding: 0;
      }

      /** Firefox: Simulate Chrome's outer glow on button when focused. */
      button:-moz-focusring {
        outline: none;
        box-shadow: 0px 0px 1px 2px Highlight;
      }

      :host {
        display: block;
        width: 350px;
        height: auto;
        position: relative;
        margin: 0 15px 40px 0;
        overflow-x: auto;
      }

      /** When actual size shown is on, use the actual image width. */
      :host([actual-size]) {
        max-width: 100%;
        width: auto;
      }

      :host([actual-size]) #main-image-container {
        max-height: none;
        width: auto;
      }

      :host([actual-size]) #main-image-container img {
        width: auto;
      }

      paper-spinner-lite {
        width: 14px;
        height: 14px;
        vertical-align: text-bottom;
        --paper-spinner-color: var(--tb-orange-strong);
      }

      #steps {
        height: 15px;
        margin: 0 0 0 -15px;
        /*
         * 31 comes from adding a padding of 15px from both sides of the
         * paper-slider, subtracting 1px so that the slider width aligns
         * with the image (the last slider marker takes up 1px), and
         * adding 2px to account for a border of 1px on both sides of
         * the image. 30 - 1 + 2.
         */
        width: calc(100% + 31px);
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }

      #main-image-container {
        max-height: 1024px;
        overflow: auto;
      }

      #main-image-container img {
        cursor: pointer;
        display: block;
        image-rendering: -moz-crisp-edges;
        image-rendering: pixelated;
        width: 100%;
        height: auto;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      [hidden] {
        display: none;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-image-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <div class="line-item">
            <paper-checkbox checked="{{_actualSize}}">Show actual image size</paper-checkbox>
          </div>
        </div>
        <div class="sidebar-section">
          <h3 class="tooltip-container">Brightness adjustment</h3>
          <div class="resettable-slider-container">
            <paper-slider min="0" max="2" snaps pin step="0.01" value="{{_brightnessAdjustment}}" immediate-value="{{_brightnessAdjustment}}"></paper-slider>
            <paper-button class="x-button" on-tap="_resetBrightness" disabled="[[_brightnessIsDefault]]">Reset</paper-button>
          </div>
        </div>
        <div class="sidebar-section">
          <h3 class="tooltip-container">Contrast adjustment</h3>
          <div class="resettable-slider-container">
            <paper-slider min="0" max="500" snaps pin step="1" value="{{_contrastPercentage}}" immediate-value="{{_contrastPercentage}}"></paper-slider>
            <paper-button class="x-button" on-tap="_resetContrast" disabled="[[_contrastIsDefault]]">Reset</paper-button>
          </div>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector id="runs-selector" selected-runs="{{_selectedRuns}}"></tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No image data was found.</h3>
            <p>Probable causes:
            <ul>
              <li>You haven’t written any image data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-image-loader active="[[active]]" run="[[item.run]]" tag="[[item.tag]]" sample="[[item.sample]]" of-samples="[[item.ofSamples]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]" request-manager="[[_requestManager]]" actual-size="[[_actualSize]]" brightness-adjustment="[[_brightnessAdjustment]]" contrast-percentage="[[_contrastPercentage]]"></tf-image-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .resettable-slider-container {
        display: flex;
      }
      .resettable-slider-container paper-slider {
        flex-grow: 1;
      }
      .resettable-slider-container paper-button {
        flex-grow: 0;
      }
      .resettable-slider-container paper-button[disabled] {
        background-color: unset;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }
    </style>
  </template>
  
</dom-module>























<dom-module id="tf-audio-loader">
  <template>
    <tf-card-heading tag="[[tag]]" run="[[run]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]" color="[[_runColor]]">
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div class="heading-row">
          <div class="heading-label">
            sample: [[_sampleText]] of [[totalSamples]]
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step <strong>[[_currentDatum.step]]</strong>
          </div>
          <template is="dom-if" if="[[_currentDatum.wall_time]]">
            <div class="heading-label heading-right">
              [[_currentDatum.wall_time]]
            </div>
          </template>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div class="heading-row">
          <paper-slider id="steps" immediate-value="{{_stepIndex}}" max="[[_maxStepIndex]]" max-markers="[[_maxStepIndex]]" snaps step="1" value="{{_stepIndex}}"></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <template is="dom-if" if="[[_hasAtLeastOneStep]]">
      <audio controls src$="[[_currentDatum.url]]" type$="[[_currentDatum.contentType]]"></audio>
      <tf-markdown-view html="[[_currentDatum.label]]"></tf-markdown-view>
    </template>
    <div id="main-audio-container"></div>

    <style include="tf-card-heading-style">
      :host {
        display: block;
        width: 350px;
        height: auto;
        position: relative;
        --step-slider-knob-color: #424242;
        margin-right: 15px;
        margin-bottom: 15px;
      }

      #steps {
        height: 15px;
        margin: 0 0 0 -15px;
        width: 100%;
        box-sizing: border-box;
        padding: 0 5px; /* so the slider knob doesn't butt out */
        margin-top: 5px;
        --paper-slider-active-color: var(--step-slider-knob-color);
        --paper-slider-knob-color: var(--step-slider-knob-color);
        --paper-slider-pin-color: var(--step-slider-knob-color);
        --paper-slider-knob-start-color: var(--step-slider-knob-color);
        --paper-slider-knob-start-border-color: var(--step-slider-knob-color);
        --paper-slider-pin-start-color: var(--step-slider-knob-color);
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-audio-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <tf-runs-selector id="runs-selector" selected-runs="{{_selectedRuns}}"></tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No audio data was found.</h3>
            <p>Probable causes:
            <ul>
              <li>You haven’t written any audio data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-audio-loader active="[[active]]" run="[[item.run]]" tag="[[item.tag]]" sample="[[item.sample]]" total-samples="[[item.totalSamples]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]" request-manager="[[_requestManager]]"></tf-audio-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>
  
</dom-module>













<dom-module id="iron-autogrow-textarea">
  <template>
    <style>
      :host {
        display: inline-block;
        position: relative;
        width: 400px;
        border: 1px solid;
        padding: 2px;
        -moz-appearance: textarea;
        -webkit-appearance: textarea;
        overflow: hidden;
      }

      .mirror-text {
        visibility: hidden;
        word-wrap: break-word;
        @apply --iron-autogrow-textarea;
      }

      .fit {
        @apply --layout-fit;
      }

      textarea {
        position: relative;
        outline: none;
        border: none;
        resize: none;
        background: inherit;
        color: inherit;
        /* see comments in template */
        width: 100%;
        height: 100%;
        font-size: inherit;
        font-family: inherit;
        line-height: inherit;
        text-align: inherit;
        @apply --iron-autogrow-textarea;
      }

      textarea::-webkit-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea::-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-ms-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }
    </style>

    
    
    <div id="mirror" class="mirror-text" aria-hidden="true">&nbsp;</div>

    
    <div class="textarea-container fit">
      <textarea id="textarea" name$="[[name]]" aria-label$="[[label]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" disabled$="[[disabled]]" rows$="[[rows]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]"></textarea>
    </div>
  </template>
</dom-module>











<dom-module id="paper-textarea">
  <template>
    <style>
      :host {
        display: block;
      }

      :host([hidden]) {
        display: none !important;
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container no-label-float$="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <iron-autogrow-textarea class="paper-input-input" slot="input" id$="[[_inputId]]" aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" bind-value="{{value}}" invalid="{{invalid}}" validator$="[[validator]]" disabled$="[[disabled]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" autocapitalize$="[[autocapitalize]]" rows$="[[rows]]" max-rows$="[[maxRows]]" on-change="_onChange"></iron-autogrow-textarea>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
  </template>
</dom-module>













<dom-module id="paper-toast">
  <template>
    <style>
      :host {
        display: block;
        position: fixed;
        background-color: var(--paper-toast-background-color, #323232);
        color: var(--paper-toast-color, #f1f1f1);
        min-height: 48px;
        min-width: 288px;
        padding: 16px 24px;
        box-sizing: border-box;
        box-shadow: 0 2px 5px 0 rgba(0, 0, 0, 0.26);
        border-radius: 2px;
        margin: 12px;
        font-size: 14px;
        cursor: default;
        -webkit-transition: -webkit-transform 0.3s, opacity 0.3s;
        transition: transform 0.3s, opacity 0.3s;
        opacity: 0;
        -webkit-transform: translateY(100px);
        transform: translateY(100px);
        @apply --paper-font-common-base;
      }

      :host(.capsule) {
        border-radius: 24px;
      }

      :host(.fit-bottom) {
        width: 100%;
        min-width: 0;
        border-radius: 0;
        margin: 0;
      }

      :host(.paper-toast-open) {
        opacity: 1;
        -webkit-transform: translateY(0px);
        transform: translateY(0px);
      }
    </style>

    <span id="label">{{text}}</span>
    <slot></slot>
  </template>

  
</dom-module>

















<dom-module id="paper-toggle-button">
  <template strip-whitespace>

    <style>
      :host {
        display: inline-block;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-common-base;
      }

      :host([disabled]) {
        pointer-events: none;
      }

      :host(:focus) {
        outline:none;
      }

      .toggle-bar {
        position: absolute;
        height: 100%;
        width: 100%;
        border-radius: 8px;
        pointer-events: none;
        opacity: 0.4;
        transition: background-color linear .08s;
        background-color: var(--paper-toggle-button-unchecked-bar-color, #000000);

        @apply --paper-toggle-button-unchecked-bar;
      }

      .toggle-button {
        position: absolute;
        top: -3px;
        left: 0;
        height: 20px;
        width: 20px;
        border-radius: 50%;
        box-shadow: 0 1px 5px 0 rgba(0, 0, 0, 0.6);
        transition: -webkit-transform linear .08s, background-color linear .08s;
        transition: transform linear .08s, background-color linear .08s;
        will-change: transform;
        background-color: var(--paper-toggle-button-unchecked-button-color, var(--paper-grey-50));

        @apply --paper-toggle-button-unchecked-button;
      }

      .toggle-button.dragging {
        -webkit-transition: none;
        transition: none;
      }

      :host([checked]:not([disabled])) .toggle-bar {
        opacity: 0.5;
        background-color: var(--paper-toggle-button-checked-bar-color, var(--primary-color));

        @apply --paper-toggle-button-checked-bar;
      }

      :host([disabled]) .toggle-bar {
        background-color: #000;
        opacity: 0.12;
      }

      :host([checked]) .toggle-button {
        -webkit-transform: translate(16px, 0);
        transform: translate(16px, 0);
      }

      :host([checked]:not([disabled])) .toggle-button {
        background-color: var(--paper-toggle-button-checked-button-color, var(--primary-color));

        @apply --paper-toggle-button-checked-button;
      }

      :host([disabled]) .toggle-button {
        background-color: #bdbdbd;
        opacity: 1;
      }

      .toggle-ink {
        position: absolute;
        top: -14px;
        left: -14px;
        right: auto;
        bottom: auto;
        width: 48px;
        height: 48px;
        opacity: 0.5;
        pointer-events: none;
        color: var(--paper-toggle-button-unchecked-ink-color, var(--primary-text-color));

        @apply --paper-toggle-button-unchecked-ink;
      }

      :host([checked]) .toggle-ink {
        color: var(--paper-toggle-button-checked-ink-color, var(--primary-color));

        @apply --paper-toggle-button-checked-ink;
      }

      .toggle-container {
        display: inline-block;
        position: relative;
        width: 36px;
        height: 14px;
        /* The toggle button has an absolute position of -3px; The extra 1px
        /* accounts for the toggle button shadow box. */
        margin: 4px 1px;
      }

      .toggle-label {
        position: relative;
        display: inline-block;
        vertical-align: middle;
        padding-left: var(--paper-toggle-button-label-spacing, 8px);
        pointer-events: none;
        color: var(--paper-toggle-button-label-color, var(--primary-text-color));
      }

      /* invalid state */
      :host([invalid]) .toggle-bar {
        background-color: var(--paper-toggle-button-invalid-bar-color, var(--error-color));
      }

      :host([invalid]) .toggle-button {
        background-color: var(--paper-toggle-button-invalid-button-color, var(--error-color));
      }

      :host([invalid]) .toggle-ink {
        color: var(--paper-toggle-button-invalid-ink-color, var(--error-color));
      }
    </style>

    <div class="toggle-container">
      <div id="toggleBar" class="toggle-bar"></div>
      <div id="toggleButton" class="toggle-button"></div>
    </div>

    <div class="toggle-label"><slot></slot></div>

  </template>

  
</dom-module>








































<dom-module id="tf-graph-minimap">
  <template>
    <style>
      :host {
        background-color: white;
        transition: opacity 0.3s linear;
        pointer-events: auto;
      }

      :host(.hidden) {
        opacity: 0;
        pointer-events: none;
      }

      canvas {
        border: 1px solid #999;
      }

      rect {
        fill: white;
        stroke: #111111;
        stroke-width: 1px;
        fill-opacity: 0;
        filter: url(#minimapDropShadow);
        cursor: move;
      }

      svg {
        position: absolute;
      }
    </style>
    <svg>
      <defs>
        <filter id="minimapDropShadow" x="-20%" y="-20%" width="150%" height="150%">
          <feoffset result="offOut" in="SourceGraphic" dx="1" dy="1"></feoffset>
          <fecolormatrix result="matrixOut" in="offOut" type="matrix" values="0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.5 0"></fecolormatrix>
          <fegaussianblur result="blurOut" in="matrixOut" stddeviation="2"></fegaussianblur>
          <feblend in="SourceGraphic" in2="blurOut" mode="normal"></feblend>
        </filter>
      </defs>
      <rect></rect>
    </svg>
    <canvas class="first"></canvas>
    
    <canvas class="second"></canvas>
    <canvas class="download"></canvas>
  </template>
  
</dom-module>



<dom-module id="tf-graph-scene">
  <template>
    <style>
      :host {
        display: flex;
        font-size: 20px;
        height: 100%;
        width: 100%;
      }

      #svg {
        flex: 1;
        font-family: Roboto, sans-serif;
        height: 100%;
        overflow: hidden;
        width: 100%;
      }

      #hidden {
        position: fixed;
        top: 0px;
        visibility: hidden;
      }

      /* --- Node and annotation-node for Metanode --- */

      .meta > .nodeshape > rect,
      .meta > .annotation-node > rect {
        cursor: pointer;
        fill: hsl(0, 0%, 70%);
      }
      .node.meta.highlighted > .nodeshape > rect,
      .node.meta.highlighted > .annotation-node > rect {
        stroke-width: 2;
      }
      .annotation.meta.highlighted > .nodeshape > rect,
      .annotation.meta.highlighted > .annotation-node > rect {
        stroke-width: 1;
      }
      .meta.selected > .nodeshape > rect,
      .meta.selected > .annotation-node > rect {
        stroke: red;
        stroke-width: 2;
      }
      .node.meta.selected.expanded > .nodeshape > rect,
      .node.meta.selected.expanded > .annotation-node > rect {
        stroke: red;
        stroke-width: 3;
      }
      .annotation.meta.selected > .nodeshape > rect,
      .annotation.meta.selected > .annotation-node > rect {
        stroke: red;
        stroke-width: 2;
      }
      .node.meta.selected.expanded.highlighted > .nodeshape > rect,
      .node.meta.selected.expanded.highlighted > .annotation-node > rect {
        stroke: red;
        stroke-width: 4;
      }

      .faded,
      .faded rect,
      .faded ellipse,
      .faded path,
      .faded use,
      #rectHatch line,
      #ellipseHatch line {
        color: #e0d4b3 !important;
        fill: white;
        stroke: #e0d4b3 !important;
      }

      .faded path {
        stroke-width: 1px !important;
      }

      .faded rect {
        fill: url(#rectHatch) !important;
      }

      .faded ellipse,
      .faded use {
        fill: url(#ellipseHatch) !important;
      }

      .faded text {
        opacity: 0;
      }

      /* Rules used for input-tracing. */
      .input-highlight > * > rect,
      .input-highlight > * > ellipse,
      .input-highlight > * > use {
        fill: white;
        stroke: #ff9800 !important;
      }

      /*  - Faded non-input styling */
      .non-input > * > rect,
.non-input > * > ellipse,
.non-input > * > use,
/* For Const nodes. */
.non-input > * > .constant:not([class*="input-highlight"]) >
  .annotation-node > ellipse,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
        stroke: #e0d4b3 !important;
        stroke-width: inherit;
        stroke-dasharray: inherit;
      }

      .non-input path {
        visibility: hidden;
      }

      .non-input > .nodeshape > rect,
.non-input > .annotation-node > rect,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
        fill: url(#rectHatch) !important;
      }

      .non-input ellipse,
      .non-input use {
        fill: url(#ellipseHatch) !important;
      }

      .non-input > text {
        opacity: 0;
      }

      .non-input .annotation > .annotation-edge {
        marker-end: url(#annotation-arrowhead-faded);
      }

      .non-input .annotation > .annotation-edge.refline {
        marker-start: url(#ref-annotation-arrowhead-faded);
      }

      /* Input edges. */
      .input-edge-highlight > text {
        fill: black !important;
      }
      .input-highlight > .in-annotations > .annotation > .annotation-edge,
      .input-highlight-selected
        > .in-annotations
        > .annotation
        > .annotation-edge {
        stroke: #999 !important;
      }

      /* Non-input edges. */
      .non-input-edge-highlight,
.non-input > g > .annotation > path,
/* Annotation styles (label and edges respectively). */
.non-input > g >
.annotation:not(.input-highlight):not(.input-highlight-selected) >
.annotation-label
/*.annotation-edge*/
 {
        visibility: hidden;
      }

      /* --- Op Node --- */

      .op > .nodeshape > .nodecolortarget,
      .op > .annotation-node > .nodecolortarget {
        cursor: pointer;
        fill: #fff;
        stroke: #ccc;
      }

      .op.selected > .nodeshape > .nodecolortarget,
      .op.selected > .annotation-node > .nodecolortarget {
        stroke: red;
        stroke-width: 2;
      }

      .op.highlighted > .nodeshape > .nodecolortarget,
      .op.highlighted > .annotation-node > .nodecolortarget {
        stroke-width: 2;
      }

      /* --- Series Node --- */

      /* By default, don't show the series background <rect>. */
      .series > .nodeshape > rect {
        fill: hsl(0, 0%, 70%);
        fill-opacity: 0;
        stroke-dasharray: 5, 5;
        stroke-opacity: 0;
        cursor: pointer;
      }

      /* Once expanded, show the series background <rect> and hide the <use>. */
      .series.expanded > .nodeshape > rect {
        fill-opacity: 0.15;
        stroke: hsl(0, 0%, 70%);
        stroke-opacity: 1;
      }
      .series.expanded > .nodeshape > use {
        visibility: hidden;
      }

      /**
 * TODO: Simplify this by applying a stable class name to all <g>
 * elements that currently have either the nodeshape or annotation-node classes.
 */
      .series > .nodeshape > use,
      .series > .annotation-node > use {
        stroke: #ccc;
      }
      .series.highlighted > .nodeshape > use,
      .series.highlighted > .annotation-node > use {
        stroke-width: 2;
      }
      .series.selected > .nodeshape > use,
      .series.selected > .annotation-node > use {
        stroke: red;
        stroke-width: 2;
      }

      .series.selected > .nodeshape > rect {
        stroke: red;
        stroke-width: 2;
      }

      .annotation.series.selected > .annotation-node > use {
        stroke: red;
        stroke-width: 2;
      }

      /* --- Bridge Node --- */
      .bridge > .nodeshape > rect {
        stroke: #f0f;
        opacity: 0.2;
        display: none;
      }

      /* --- Structural Elements --- */
      .edge > path.edgeline.structural {
        stroke: #f0f;
        opacity: 0.2;
        display: none;
      }

      /* Reference Edge */
      .edge > path.edgeline.referenceedge {
        stroke: #ffb74d;
        opacity: 1;
      }

      /* --- Series Nodes --- */

      /* Hide the rect for a series' annotation. */
      .series > .annotation-node > rect {
        display: none;
      }

      /* --- Node label --- */

      .node > text.nodelabel {
        cursor: pointer;
        fill: #444;
      }

      .meta.expanded > text.nodelabel {
        font-size: 9px;
      }

      .series > text.nodelabel {
        font-size: 8px;
      }

      .op > text.nodelabel {
        font-size: 6px;
      }

      .bridge > text.nodelabel {
        display: none;
      }

      .node.meta.expanded > text.nodelabel {
        cursor: normal;
      }

      .annotation.meta.highlighted > text.annotation-label {
        fill: #50a3f7;
      }

      .annotation.meta.selected > text.annotation-label {
        fill: #4285f4;
      }

      /* --- Annotation --- */

      /* only applied for annotations that are not summary or constant.
(.summary, .constant gets overridden below) */
      .annotation > .annotation-node > * {
        stroke-width: 0.5;
        stroke-dasharray: 1, 1;
      }

      .annotation.summary > .annotation-node > *,
      .annotation.constant > .annotation-node > * {
        stroke-width: 1;
        stroke-dasharray: none;
      }

      .annotation > .annotation-edge {
        fill: none;
        stroke: #aaa;
        stroke-width: 0.5;
        marker-end: url(#annotation-arrowhead);
      }

      .faded .annotation > .annotation-edge {
        marker-end: url(#annotation-arrowhead-faded);
      }

      .annotation > .annotation-edge.refline {
        marker-start: url(#ref-annotation-arrowhead);
      }

      .faded .annotation > .annotation-edge.refline {
        marker-start: url(#ref-annotation-arrowhead-faded);
      }

      .annotation > .annotation-control-edge {
        stroke-dasharray: 1, 1;
      }

      #annotation-arrowhead {
        fill: #aaa;
      }

      #annotation-arrowhead-faded {
        fill: #e0d4b3;
      }

      #ref-annotation-arrowhead {
        fill: #aaa;
      }

      #ref-annotation-arrowhead-faded {
        fill: #e0d4b3;
      }

      .annotation > .annotation-label {
        font-size: 5px;
        cursor: pointer;
      }
      .annotation > .annotation-label.annotation-ellipsis {
        cursor: default;
      }

      /* Hide annotations on expanded meta nodes since they're redundant. */
      .expanded > .in-annotations,
      .expanded > .out-annotations {
        display: none;
      }

      /* --- Annotation: Constant --- */

      .constant > .annotation-node > ellipse {
        cursor: pointer;
        fill: white;
        stroke: #848484;
      }

      .constant.selected > .annotation-node > ellipse {
        fill: white;
        stroke: red;
      }

      .constant.highlighted > .annotation-node > ellipse {
        stroke-width: 1.5;
      }

      /* --- Annotation: Summary --- */

      .summary > .annotation-node > ellipse {
        cursor: pointer;
        fill: #db4437;
        stroke: #db4437;
      }

      .summary.selected > .annotation-node > ellipse {
        fill: #a52714;
        stroke: #a52714;
      }

      .summary.highlighted > .annotation-node > ellipse {
        stroke-width: 1.5;
      }

      /* --- Edge --- */

      .edge > path.edgeline {
        fill: none;
        stroke: #bbb;
        stroke-linecap: round;
        stroke-width: 0.75;
      }

      .edge .selectableedge {
        cursor: pointer;
      }

      .selectededge > path.edgeline {
        cursor: default;
        stroke: #f00;
      }

      .edge.selectededge text {
        fill: #000;
      }

      /* Labels showing tensor shapes on edges */
      .edge > text {
        font-size: 3.5px;
        fill: #666;
      }

      .dataflow-arrowhead {
        fill: #bbb;
      }

      .reference-arrowhead {
        fill: #ffb74d;
      }

      .selected-arrowhead {
        fill: #f00;
      }

      .edge .control-dep {
        stroke-dasharray: 2, 2;
      }

      /* --- Group node expand/collapse button --- */

      /* Hides expand/collapse buttons when a node isn't expanded or highlighted. Using
   incredibly small opacity so that the bounding box of the <g> parent still takes
   this container into account even when it isn't visible */
      .node:not(.highlighted):not(.expanded) > .nodeshape > .buttoncontainer {
        opacity: 0.01;
      }
      .node.highlighted > .nodeshape > .buttoncontainer {
        cursor: pointer;
      }
      .buttoncircle {
        fill: #e7811d;
      }
      .buttoncircle:hover {
        fill: #b96717;
      }
      .expandbutton,
      .collapsebutton {
        stroke: white;
      }
      /* Do not let the path elements in the button take pointer focus */
      .node > .nodeshape > .buttoncontainer > .expandbutton,
      .node > .nodeshape > .buttoncontainer > .collapsebutton {
        pointer-events: none;
      }
      /* Only show the expand button when a node is collapsed and only show the
   collapse button when a node is expanded. */
      .node.expanded > .nodeshape > .buttoncontainer > .expandbutton {
        display: none;
      }
      .node:not(.expanded) > .nodeshape > .buttoncontainer > .collapsebutton {
        display: none;
      }

      .health-pill-stats {
        font-size: 4px;
        text-anchor: middle;
      }

      .health-pill rect {
        filter: url(#health-pill-shadow);
        rx: 3;
        ry: 3;
      }

      .titleContainer {
        position: relative;
        top: 20px;
      }

      .title,
      .auxTitle,
      .functionLibraryTitle {
        position: absolute;
      }

      #minimap {
        position: absolute;
        right: 20px;
        bottom: 20px;
      }

      .context-menu {
        position: absolute;
        display: none;
        background-color: #e2e2e2;
        border-radius: 2px;
        font-size: 14px;
        min-width: 150px;
        border: 1px solid #d4d4d4;
      }

      .context-menu ul {
        list-style-type: none;
        margin: 0;
        padding: 0;
        cursor: default;
      }

      .context-menu ul li {
        padding: 4px 16px;
      }

      .context-menu ul li:hover {
        background-color: #f3913e;
        color: white;
      }
    </style>
    <div class="titleContainer">
      <div id="title" class="title">Main Graph</div>
      <div id="auxTitle" class="auxTitle">Auxiliary Nodes</div>
      <div id="functionLibraryTitle" class="functionLibraryTitle">
        Functions
      </div>
    </div>
    <svg id="svg">
      <defs>
        
        <path id="reference-arrowhead-path" d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0" />
        <marker class="reference-arrowhead" id="reference-arrowhead-small" viewbox="0 0 10 10" markerwidth="5" markerheight="5" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#reference-arrowhead-path" />
        </marker>
        <marker class="reference-arrowhead" id="reference-arrowhead-medium" viewbox="0 0 10 10" markerwidth="13" markerheight="13" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#reference-arrowhead-path" />
        </marker>
        <marker class="reference-arrowhead" id="reference-arrowhead-large" viewbox="0 0 10 10" markerwidth="16" markerheight="16" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#reference-arrowhead-path" />
        </marker>
        <marker class="reference-arrowhead" id="reference-arrowhead-xlarge" viewbox="0 0 10 10" markerwidth="20" markerheight="20" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#reference-arrowhead-path" />
        </marker>

        
        <path id="dataflow-arrowhead-path" d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0" />
        <marker class="dataflow-arrowhead" id="dataflow-arrowhead-small" viewbox="0 0 10 10" markerwidth="5" markerheight="5" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#dataflow-arrowhead-path" />
        </marker>
        <marker class="dataflow-arrowhead" id="dataflow-arrowhead-medium" viewbox="0 0 10 10" markerwidth="13" markerheight="13" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#dataflow-arrowhead-path" />
        </marker>
        <marker class="dataflow-arrowhead" id="dataflow-arrowhead-large" viewbox="0 0 10 10" markerwidth="16" markerheight="16" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#dataflow-arrowhead-path" />
        </marker>
        <marker class="dataflow-arrowhead" id="dataflow-arrowhead-xlarge" viewbox="0 0 10 10" markerwidth="20" markerheight="20" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#dataflow-arrowhead-path" />
        </marker>

        
        <marker id="annotation-arrowhead" markerwidth="5" markerheight="5" refx="5" refy="2.5" orient="auto">
          <path d="M 0,0 L 5,2.5 L 0,5 L 0,0" />
        </marker>
        <marker id="annotation-arrowhead-faded" markerwidth="5" markerheight="5" refx="5" refy="2.5" orient="auto">
          <path d="M 0,0 L 5,2.5 L 0,5 L 0,0" />
        </marker>
        <marker id="ref-annotation-arrowhead" markerwidth="5" markerheight="5" refx="0" refy="2.5" orient="auto">
          <path d="M 5,0 L 0,2.5 L 5,5 L 5,0" />
        </marker>
        <marker id="ref-annotation-arrowhead-faded" markerwidth="5" markerheight="5" refx="0" refy="2.5" orient="auto">
          <path d="M 5,0 L 0,2.5 L 5,5 L 5,0" />
        </marker>
        
        <ellipse id="op-node-stamp" rx="7.5" ry="3" stroke="inherit" fill="inherit" />
        
        <ellipse id="op-node-annotation-stamp" rx="5" ry="2" stroke="inherit" fill="inherit" />
        
        <g id="op-series-vertical-stamp">
          <use xlink:href="#op-node-stamp" x="8" y="9" />
          <use xlink:href="#op-node-stamp" x="8" y="6" />
          <use xlink:href="#op-node-stamp" x="8" y="3" />
        </g>
        
        <g id="op-series-horizontal-stamp">
          <use xlink:href="#op-node-stamp" x="16" y="4" />
          <use xlink:href="#op-node-stamp" x="12" y="4" />
          <use xlink:href="#op-node-stamp" x="8" y="4" />
        </g>
        
        <g id="op-series-annotation-stamp">
          <use xlink:href="#op-node-annotation-stamp" x="9" y="2" />
          <use xlink:href="#op-node-annotation-stamp" x="7" y="2" />
          <use xlink:href="#op-node-annotation-stamp" x="5" y="2" />
        </g>
        <svg id="summary-icon" fill="#848484" height="12" viewbox="0 0 24 24" width="12">
          <path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z" />
        </svg>

        
        <pattern id="rectHatch" patterntransform="rotate(45 0 0)" width="5" height="5" patternunits="userSpaceOnUse">
          <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1" />
        </pattern>
        <pattern id="ellipseHatch" patterntransform="rotate(45 0 0)" width="2" height="2" patternunits="userSpaceOnUse">
          <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1" />
        </pattern>

        
        <filter id="health-pill-shadow" x="-40%" y="-40%" width="180%" height="180%">
          <fegaussianblur in="SourceAlpha" stdDeviation="0.8" />
          <feoffset dx="0" dy="0" result="offsetblur" />
          <feflood flood-color="#000000" />
          <fecomposite in2="offsetblur" operator="in" />
          <femerge>
            <femergenode />
            <femergenode in="SourceGraphic" />
          </femerge>
        </filter>
      </defs>
      
      <rect fill="white" width="10000" height="10000"></rect>
      <g id="root"></g>
    </svg>
    <tf-graph-minimap id="minimap"></tf-graph-minimap>
    <div id="contextMenu" class="context-menu"></div>
  </template>
  
</dom-module>


<dom-module id="tf-graph">
  <template>
    <style>
      .container {
        width: 100%;
        height: 100%;
        background: white;
        box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2);
      }

      .vertical {
        width: 100%;
        height: 100%;
        @apply --layout-vertical;
      }

      .auto {
        @apply --layout-flex-auto;
        @apply --layout-vertical;
      }

      h2 {
        text-align: center;
      }

      paper-button {
        text-transform: none;
      }
    </style>
    <div class="container">
      <div class="vertical">
        <template is="dom-if" if="[[title]]">
          <h2>[[title]]</h2>
        </template>
        <tf-graph-scene id="scene" class="auto" render-hierarchy="[[renderHierarchy]]" highlighted-node="[[_getVisible(highlightedNode)]]" selected-node="{{selectedNode}}" selected-edge="{{selectedEdge}}" color-by="[[colorBy]]" progress="[[progress]]" node-context-menu-items="[[nodeContextMenuItems]]" node-names-to-health-pills="[[nodeNamesToHealthPills]]" health-pill-step-index="{{healthPillStepIndex}}" handle-edge-selected="[[handleEdgeSelected]]" trace-inputs="[[traceInputs]]"></tf-graph-scene>
      </div>
    </div>
  </template>
</dom-module>




















<dom-module id="tf-debugger-continue-dialog">
  <template>
    <paper-button raised class="continue-button" on-click="_continueButtonCallback">
      <span>[[_continueButtonText]]</span>
    </paper-button>
    <paper-dialog with-backdrop id="continueDialog">
      <h2>Continue...</h2>
      <div class="continue-to-type">
        <div class="continue-to-type-name">
          Over Session Runs:
        </div>
        <paper-input id="continueNum" class="input-box" label="Number of Session Runs (including the current one):" always-float-label type="number" min="1" step="1" value="{{continueNum}}"></paper-input>
        <paper-icon-button class="go-button" icon="arrow-forward" title="Session Runs Go" on-tap="_sessionRunGoButtonCallback">
        </paper-icon-button>
      </div>
      <div class="continue-to-type">
        <div class="continue-to-type-name">
          Till Condition Met by Watched Tensor
        </div>
        <paper-dropdown-menu id="tensorConditionDropdown" class="input-box" no-label-float="true" label="Tensor Condition" selected-item-label="{{_selectedTensorCondition}}">
          
          <paper-listbox id="tensorConditionMenu" class="dropdown-content" slot="dropdown-content">
            <paper-item no-label-float="true">Contains +/-∞ or NaN</paper-item>
            <paper-item no-label-float="true">Contains +/-∞</paper-item>
            <paper-item no-label-float="true">Contains NaN</paper-item>
            <paper-item no-label-float="true">Max &gt;</paper-item>
            <paper-item no-label-float="true">Max &lt;</paper-item>
            <paper-item no-label-float="true">Min &gt;</paper-item>
            <paper-item no-label-float="true">Min &lt;</paper-item>
            <paper-item no-label-float="true">Max - Min &gt;</paper-item>
            <paper-item no-label-float="true">Max - Min &lt;</paper-item>
            <paper-item no-label-float="true">Mean &gt;</paper-item>
            <paper-item no-label-float="true">Mean &lt;</paper-item>
            <paper-item no-label-float="true">Standard deviation &gt;</paper-item>
            <paper-item no-label-float="true">Standard deviation &lt;</paper-item>
          </paper-listbox>
        </paper-dropdown-menu>
        <paper-icon-button class="go-button" icon="arrow-forward" title="Tensor Condition Go" on-tap="_tensorContinueGoButtonCallback">
        </paper-icon-button>
        <paper-input id="ref-value" class="input-box" label="Reference value to compare to" type="number" value="{{_tensorConditionRefValue}}" hidden="[[_isRefValueInputHidden]]">
        </paper-input>
      </div>
    </paper-dialog>
    <style include="dashboard-style"></style>
    <style>
      :host .continue-to-type-name {
        font-weight: bold;
      }
      :host paper-dialog {
        width: 36vw;
      }
      :host .input-box {
        display: inline-block;
        position: relative;
        width: 80%;
        font-size: 110%;
      }
      :host .go-button {
        position: relative;
        width: 15%;
        display: inline-block;
      }
    </style>
  </template>
  
</dom-module>




<dom-module id="tf-debugger-initial-dialog">
  <template>
    
    <template is="dom-if" if="[[_open]]">
      <div id="dashboard-backdrop"></div>
    </template>
    <paper-dialog id="dialog" no-cancel-on-outside-click no-cancel-on-esc-key opened="{{_open}}">
      <h2 id="dialog-title">[[_title]]</h2>
      <template is="dom-if" if="[[_hasCustomMessage]]">
        <div class="custom-message">[[_customMessage]]</div>
      </template>
      <template is="dom-if" if="[[!_hasCustomMessage]]">
        <div class="code-example">
          <div class="code-example-section">
            <div class="code-example-section-title">
              <a href="https://www.tensorflow.org/api_docs/python/tf/Session" target="_blank" rel="noopener noreferrer">tf.Session</a>:
            </div>
            <pre class="code-snippet">import tensorflow as tf
from tensorflow.python import debug as tf_debug

sess = tf.Session()
sess = tf_debug.TensorBoardDebugWrapperSession(sess, "[[_host]]:[[_port]]")
sess.run(my_fetches)
          </pre>
          </div>
          <div class="code-example-section">
            <div class="code-example-section-title">
              <a href="https://www.tensorflow.org/programmers_guide/estimators" target="_blank" rel="noopener noreferrer">Estimator</a>
              |
              <a href="https://www.tensorflow.org/api_docs/python/tf/train/MonitoredSession" target="_blank" rel="noopener noreferrer">MonitoredSession</a>:
            </div>
            <pre class="code-snippet">import tensorflow as tf
from tensorflow.python import debug as tf_debug

hook = tf_debug.TensorBoardDebugHook("[[_host]]:[[_port]]")
my_estimator.fit(x=x_data, y=y_data, steps=1000, monitors=[hook])
            </pre>
          </div>
          <div class="code-example-section">
            <div class="code-example-section-title">
              <a href="https://keras.io/models/model/" target="_blank" rel="noopener noreferrer">Keras Model</a>:
            </div>
            <pre class="code-snippet">import tensorflow as tf
from tensorflow.python import debug as tf_debug
import keras

keras.backend.set_session(
    tf_debug.TensorBoardDebugWrapperSession(tf.Session(), "[[_host]]:[[_port]]"))
# Define your keras model, called "model".
model.fit(...)
            </pre>
          </div>
        </div>
      </template>
    </paper-dialog>
    <style>
      /** We rely on a separate `_hidden` property instead of directly making use
          of the `_open` attribute because this CSS specification may strangely
          affect other elements throughout TensorBoard. See #899. */
      :host([_hidden]) {
        display: none;
      }
      :host,
      #dashboard-backdrop {
        position: absolute;
        top: 0;
        bottom: 0;
        left: 0;
        right: 0;
      }

      #dashboard-backdrop {
        background: rgba(0, 0, 0, 0.6);
      }

      .code-example {
        margin: 10px;
        font-family: monospace;
      }
      .code-example-section {
        padding-bottom: 15px;
      }
      .code-example-section-title {
        font-weight: bold;
      }
      .code-snippet {
        padding-left: 1em;
      }

      #dialog-title {
        padding-bottom: 15px;
      }

      .custom-message {
        margin-top: 0;
        margin-bottom: 15px;
      }
    </style>
  </template>
  
</dom-module>





<dom-module id="tf-debugger-resizer">
  <template>
    <div class="bars">
      <div class="bars-rotator">
        <span class="bars-text">| |</span>
      </div>
    </div>
    <style>
      :host([_resizer-identifier]) {
        position: absolute;
        background: #ccc;
        user-select: none;
      }

      :host([is-horizontal]) {
        cursor: row-resize;
        height: 10px;
        left: 0;
        right: 0;
      }

      :host([_is-vertical]) {
        cursor: col-resize;
        right: -15px;
        top: 0;
        bottom: 0;
        width: 10px;
      }

      .bars {
        width: 80%;
        text-align: center;
        position: absolute;
        top: 50%;
        left: 50%;
        font-size: 5px;
        transform: translate(-50%, -50%);
      }

      /** This block prevents the bars rotator from having a height that is
          the entire viewport, thus occluding it and giving it an undesired cursor
          value. */
      .bars-rotator {
        display: inline-block;
      }

      :host([is-horizontal]) .bars-rotator {
        transform: rotate(90deg);
      }

      .bars-text {
        transform: scaleY(15);
        white-space: nowrap;
        display: block;
        font-weight: 400;
      }
    </style>
  </template>
  
</dom-module>












<dom-module id="tf-op-selector">
  <template>
    <div>
      <paper-dropdown-menu id="filter-mode" no-label-float="true" label="Filter Mode" selected-item-label="{{_filterMode}}">
        <paper-listbox class="dropdown-content" slot="dropdown-content">
          <paper-item no-label-float="true">Node Name</paper-item>
          <paper-item no-label-float="true">Op Type</paper-item>
        </paper-listbox>
      </paper-dropdown-menu>
      <paper-input id="filter-input" label="Filter Regex" always-float-label value="{{_filterInput}}"></paper-input>
    </div>
    <paper-spinner-lite active class="spinner" id="loading-spinner" hidden="[[!_isLoading]]">
    </paper-spinner-lite>
    <div id="selector-hierarchy"></div>
    <style>
      .indented-level-container .content-container {
        margin: 0 0 0 20px;
      }

      .level-container iron-collapse {
        padding: 0 0 0 20px;
      }

      paper-checkbox {
        display: inline-block;
        width: 18px;
        height: 18px;
        margin: 0 8px 0 0;
      }

      .op-type {
        padding-right: 10px;
        color: #444;
      }

      .op-title-leaf {
        text-decoration: underline;
        cursor: pointer;
      }

      .op-title-leaf:hover {
        color: blue;
      }

      .partial-checkbox {
        background: #f57c00;
      }

      .node-expand-button {
        margin: 0 0 0 -13px;
      }

      .level-title-text {
        display: inline-block;
        font-weight: 800;
        margin: 0 0 0 -1px;
      }

      .op-description {
        font-weight: 300;
        margin: 0 0 0 27px;
        padding: 10px 0;
      }

      .spinner {
        width: 20px;
        height: 20px;
        vertical-align: middle;
      }

      #filter-mode {
        width: 150px;
        display: inline-block;
      }

      #filter-input {
        width: 250px;
        display: inline-block;
      }

      .highlighted {
        color: red;
      }
      .highlighted > .op-type {
        color: red;
      }

      #selector-hierarchy {
        width: 100%;
      }

      [hidden] {
        display: none;
      }
    </style>
  </template>
  
  
</dom-module>





<dom-module id="tf-session-runs-view">
  <template>
    <div class="session-runs-div">
      <div class="section-title">Session Runs</div>
      <table id="session-runs-table" align="left" class="session-runs-table">
        <tr align="left">
          <th>Feeds</th>
          <th>Fetches</th>
          <th>Targets</th>
          <th>#(Devices)</th>
          <th>Count</th>
        </tr>
      </table>
    </div>
    <style>
      :host {
        display: block;
        padding: 20px 0;
      }

      .section-title {
        font-size: 110%;
        font-weight: bold;
      }
      :host .indented-level-container .content-container {
        margin: 0 0 0 10px;
      }

      /* TODO(cais): This needs work: the table shouldn't get too wide when
         there are many feeds/fetches/targte names. */
      .session-runs-table {
        align-content: left;
        align-items: left;
        text-align: left;
        font-size: 90%;
        border-style: solid 1px black;
        table-layout: fixed;
        width: 100%;
        word-break: break-all;
        padding-top: 3px;
        padding-left: 3px;
        padding-right: 3px;
        box-shadow: 3px 3px #ddd;
      }
      .active-session-run {
        background-color: #ffffe0;
        font-weight: bold;
      }
      .sole-active-session-run {
        background-color: rgb(172, 232, 188);
        font-weight: bold;
      }

      .node-or-tensor-element {
        text-decoration: underline;
        cursor: pointer;
      }

      .node-or-tensor-element:hover {
        color: blue;
      }
    </style>
  </template>
  
</dom-module>





<dom-module id="tf-source-code-view">
  <template>
    <div id="fullStackDialog" hidden$="[[!_fullStackShown]]">
      <div id="full-stack-title">
        <paper-icon-button icon="filter-list" disabled="true">
        </paper-icon-button>
        Full Stack Trace of Node:
        <div id="full-stack-node-name">"[[_fullStackNodeName]]"</div>
        <paper-icon-button icon="close" id="close-full-stack-button" title="Close Full Stack" on-tap="_closeFullStackDialog">
        </paper-icon-button>
      </div>
      <ul id="full-stack-content"></ul>
    </div>
    <paper-tabs id="source-files-tabs" selected="{{_filePathSelected}}">
      <template is="dom-repeat" items="[[_shortFilePaths]]">
        <paper-tab id="[[item.id]]">[[item.name]]</paper-tab>
      </template>
    </paper-tabs>
    <div id="source-file-content" class="source-content">
      <template is="dom-repeat" items="[[_fileLines]]">
        <div class$="{{item.sourceClass}}" id="source-line-[[item.lineno]]">
          <span class="source-line-number" id="source-lineno-[[item.lineno]]">
            [[item.lineno]]
          </span>
          <span class="source-line-node-toggle" id="source-line-node-toggle-[[item.lineno]]">
            [[item.numNodes]]
          </span>
          <span class="source-line-text" id="source-line-text-[[item.lineno]]">
            [[item.text]]
          </span>
          <div class="source-line-nodes" id="source-line-nodes-[[item.lineno]]"></div>
        </div>
      </template>
    </div>
    <style>
      #source-files-tabs {
        position: relative;
        height: 8%;
      }
      .source-content {
        position: relative;
        height: 90%;
        font-family: monospace;
        font-size: 90%;
        overflow-x: scroll;
        overflow-y: scroll;
      }
      .source-content :hover {
        background-color: #ffff00;
      }
      .highlighted-source-line {
        background-color: #ffffe0;
      }
      .source-line-number {
        display: inline-block;
        color: lightblue;
        width: 2em;
        text-align: right;
        padding-right: 1em;
      }
      .source-line-node-toggle {
        display: inline-block;
        color: blue;
        width: 5em;
        text-align: right;
        padding-right: 1em;
        text-decoration: underline;
        cursor: pointer;
      }
      .source-line-nodes {
        padding-left: 4em;
        text-decoration: underline;
        cursor: pointer;
        color: blue;
        margin-top: 0em;
        margin-bottom: 0em;
        margin-right: 1em;
      }
      .source-line-node-entry {
        margin-right: 1em;
        background-color: yellow;
      }
      .source-line-nodes span {
        text-decoration: none;
        background-color: yellow;
      }
      .source-line-text {
        display: inline;
        word-wrap: break-word;
      }
      #fullStackDialog {
        z-index: 1000;
        position: absolute;
        top: 10%;
        left: 50%;
        width: 45%;
        height: 85%;
        background-color: white;
        border: 1px solid gray;
        font-family: monospace;
        box-shadow: 3px 3px #ddd;
        overflow-y: auto;
      }
      #full-stack-title {
        font-size: 110%;
        position: relative;
        width: 100%;
        background-color: #eee;
        text-align: center;
        font-weight: bold;
      }
      #full-stack-node-name {
        color: blue;
      }
      :host #full-stack-content {
        padding-top: 1em;
        padding-right: 0.5em;
        margin-top: 0.5em;
        font-size: 90%;
        word-wrap: break-word;
        overflow: auto;
      }
      .stack-frame-clickable {
        color: blue;
        text-decoration: underline;
        cursor: pointer;
      }
      .stack-frame-nonclickable {
        color: #555;
      }
      #close-full-stack-button {
        float: right;
      }
    </style>
  </template>
  
</dom-module>





<dom-module id="tf-tensor-data-summary">
  <template>
    <span class="section-title">Tensor Value Overview</span>
    <div id="tensor-data-div" class="tensor-data-div">
      <table id="tensor-data-table" align="left" class="tensor-data-table">
        <thead>
          <tr align="left">
            <th>Tensor</th>
            <th>Count</th>
            <th>DType</th>
            <th>Shape</th>
            <th width="25%">Value</th>
            <th width="25%">
              Health Pill
              <paper-toggle-button id="show-health-pills" checked="{{_healthPillsEnabled}}">
              </paper-toggle-button>
              <paper-card>
                <div class="health-pill-legend" id="health-pill-legend"></div>
              </paper-card>
            </th>
            <th width="5%"></th>
          </tr>
        </thead>
        <tbody></tbody>
      </table>
    </div>
    <style>
      :host #tensor-data-div {
        height: 100%;
        overflow-y: auto;
      }
      .section-title {
        font-size: 110%;
        font-weight: bold;
      }
      :host .indented-level-container .content-container {
        margin: 0 0 0 10px;
      }
      :host .tensor-data-table {
        align-content: left;
        align-items: left;
        display: block;
        text-align: left;
        vertical-align: middle;
        width: 100%;
        padding-top: 3px;
        padding-left: 3px;
        padding-right: 3px;
        box-shadow: 3px 3px #ddd;
      }
      :host #tensor-data-table th {
        vertical-align: top;
      }
      :host .active-tensor {
        background-color: #ffffe0;
        font-weight: bold;
        border: solid 1px #888;
      }
      :host .highlighted {
        color: red;
      }
      :host .health-pill-legend {
        float: right;
        font-weight: normal;
      }
      :host #show-health-pills {
        display: inline-block;
      }
      .value-expansion-link {
        text-decoration: underline;
        cursor: pointer;
      }
      .value-expansion-link :hover {
        color: blue;
      }
      .health-pill :hover {
        cursor: pointer;
      }
      .tensor-name {
        text-decoration: underline;
        cursor: pointer;
      }
      .tensor-name :hover {
        color: blue;
      }
    </style>
  </template>
  
</dom-module>



<style>/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
 Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
     http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

.tensor-widget {
  font-family: monospace;
  font-size: 14px;
  overflow-x: hidden;
  overflow-y: hidden;
  position: relative;
}

.tensor-widget-dim {
  border: 1px solid rgb(160, 160, 160);
  display: inline-block;
  font-size: 12px;
  height: 14px;
  line-height: 14px;
  margin-left: 15px;
  margin-right: 15px;
  padding: 2px;
}

.tensor-widget-dim-comma {
  color: rgb(128, 128, 128);
  display: inline-block;
  font-size: 12px;
  height: 14px;
  line-height: 14px;
}

.tensor-widget-dim-highlighted {
  border: 1px solid rgb(100, 180, 255);
  font-weight: bold;
}

.tensor-widget-dim-brackets {
  color: rgb(128, 128, 128);
  display: inline-block;
  font-size: 8pt;
}

.tensor-widget-dim-dropdown {
  background-color: rgb(255, 255, 255);
  border: 1px solid rgb(128, 128, 128);
  box-shadow: 2px 2px 2px #b0b0b0;
  cursor: pointer;
  width: 180px;
  z-index: 1000;
}

.tensor-widget-dim-dropdown-menu-item {
  border-bottom: 1px solid rgb(180, 180, 180);
  font-size: 12px;
  padding: 3px;
  user-select: none;
}

.tensor-widget-dim-dropdown-menu-item-active {
  background-color: rgb(100, 180, 255);
}

.tensor-widget-dim-dropdown-menu-item-disabled {
  color: rgb(128, 128, 128);
}

.tensor-widget-dtype {
  align-content: center;
  color: rgb(60, 60, 60);
  display: inline-block;
  font-size: 8pt;
  height: 48px;
  line-height: 22px;
  max-height: 22px;
  padding-left: 14px;
  padding-right: 10px;
  position: relative;
  vertical-align: middle;
}

.tensor-widget-dtype-label {
  color: rgb(128, 128, 128);
}

.tensor-widget-header {
  background-color: rgb(252, 252, 252);
  box-shadow: 2px 2px 2px #b0b0b0;
  height: 40px;
  line-height: 40px;
  max-height: 40px;
  position: relative;
  vertical-align: middle;
  width: 100%;
}

.tensor-widget-info {
  align-content: center;
  color: rgb(0, 0, 255);
  display: inline-block;
  font-size: 8pt;
  height: 22px;
  line-height: 22px;
  margin-left: 8px;
  max-height: 22px;
  position: relative;
  vertical-align: middle;
}

.tensor-widget-menu-thumb {
  color: rgb(32, 33, 36);
  cursor: pointer;
  display: inline-block;
  font-weight: bold;
  font-size: 16px;
  margin-left: 10px;
  margin-right: 5px;
  position: relative;
  user-select: none;
}

.tensor-widget-menu-thumb:hover {
  color: rgb(227, 116, 0);
}

.tensor-widget-shape {
  color: rgb(60, 60, 60);
  display: inline-block;
  margin-left: 12px;
}

.tensor-widget-shape-label {
  color: rgb(128, 128, 128);
  display: inline-block;
}

.tensor-widget-shape-value {
  display: inline-block;
}

.tensor-widget-slicing-group {
  background-color: rgb(250, 250, 250);
  border-bottom: 1px solid rgb(190, 190, 190);
  display: block;
  height: 18px;
  text-align: center;
  padding-bottom: 5px;
  padding-top: 5px;
}

.tensor-widget-tensor-name {
  color: black;
  display: inline-block;
  font-weight: bold;
}

.tensor-widget-left-ruler-tick {
  background-color: var(--ruler-background-color);
  border-bottom: var(--border-style);
  border-top: var(--border-style);
  box-shadow: var(--border-style);
  color: rgb(110, 110, 110);
  cursor: pointer;
  display: inline-block;
  font-size: 12px;
  height: 29px;
  line-height: 29px;
  margin-left: 0px;
  max-width: 45px;
  text-align: center;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-top-ruler {
  height: 24px;
  white-space: nowrap;
}

.tensor-widget-value-tooltip {
  background-color: rgb(240, 240, 240);
  border: 1px solid rgb(160, 160, 160);
  box-shadow: 1px 1px 1px #b0b0b0;
  display: none;
  font-size: 13px;
  padding: 5px;
  position: absolute;
  user-select: none;
  width: 240px;
}

.tensor-widget-value-tooltip-colorbar {
  height: 24px;
  width: 95%;
}

.tensor-widget-value-tooltip-indices {
  font-weight: bold;
}

.tensor-widget-value-tooltip-value {
  margin-top: 20px;
}

.tensor-widget-top-ruler-tick {
  background-color: var(--ruler-background-color);
  border-bottom: var(--border-style);
  border-right: var(--border-style);
  color: rgb(110, 110, 110);
  cursor: pointer;
  display: inline-block;
  font-size: 12px;
  height: 24px;
  line-height: 24px;
  padding-right: 2px;
  text-align: center;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-value-div {
  border-bottom: var(--border-style);
  border-right: var(--border-style);
  cursor: pointer;
  display: inline-block;
  font-size: 80%;
  height: 24px;
  line-height: 24px;
  max-width: 45px;
  padding-right: 2px;
  text-align: right;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-value-div-selection {
  font-weight: bold;
}

.tensor-widget-value-div-selection-bottom {
  border-bottom: 0.5px solid blue;
}

.tensor-widget-value-div-selection-left {
  border-left: 0.5px solid blue;
}

.tensor-widget-value-div-selection-right {
  border-right: 0.5px solid blue;
}

.tensor-widget-value-div-selection-top {
  border-top: 0.5px solid blue;
}

.tensor-widget-value-section {
  --border-style: 1px solid rgb(140, 140, 140);
  --ruler-background-color: rgb(210, 210, 210);
  -moz-user-select: none;
  -ms-user-select: none;
  -khtml-user-select: none;
  -webkit-touch-callout: none;
  -webkit-user-select: none;
}

.tensor-widget-value-row {
  height: 25px;
  line-height: 25px;
  white-space: nowrap;
}
</style>

<dom-module id="tensor-widget-style">
  <template>
    <style>/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
 Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
     http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

.tensor-widget {
  font-family: monospace;
  font-size: 14px;
  overflow-x: hidden;
  overflow-y: hidden;
  position: relative;
}

.tensor-widget-dim {
  border: 1px solid rgb(160, 160, 160);
  display: inline-block;
  font-size: 12px;
  height: 14px;
  line-height: 14px;
  margin-left: 15px;
  margin-right: 15px;
  padding: 2px;
}

.tensor-widget-dim-comma {
  color: rgb(128, 128, 128);
  display: inline-block;
  font-size: 12px;
  height: 14px;
  line-height: 14px;
}

.tensor-widget-dim-highlighted {
  border: 1px solid rgb(100, 180, 255);
  font-weight: bold;
}

.tensor-widget-dim-brackets {
  color: rgb(128, 128, 128);
  display: inline-block;
  font-size: 8pt;
}

.tensor-widget-dim-dropdown {
  background-color: rgb(255, 255, 255);
  border: 1px solid rgb(128, 128, 128);
  box-shadow: 2px 2px 2px #b0b0b0;
  cursor: pointer;
  width: 180px;
  z-index: 1000;
}

.tensor-widget-dim-dropdown-menu-item {
  border-bottom: 1px solid rgb(180, 180, 180);
  font-size: 12px;
  padding: 3px;
  user-select: none;
}

.tensor-widget-dim-dropdown-menu-item-active {
  background-color: rgb(100, 180, 255);
}

.tensor-widget-dim-dropdown-menu-item-disabled {
  color: rgb(128, 128, 128);
}

.tensor-widget-dtype {
  align-content: center;
  color: rgb(60, 60, 60);
  display: inline-block;
  font-size: 8pt;
  height: 48px;
  line-height: 22px;
  max-height: 22px;
  padding-left: 14px;
  padding-right: 10px;
  position: relative;
  vertical-align: middle;
}

.tensor-widget-dtype-label {
  color: rgb(128, 128, 128);
}

.tensor-widget-header {
  background-color: rgb(252, 252, 252);
  box-shadow: 2px 2px 2px #b0b0b0;
  height: 40px;
  line-height: 40px;
  max-height: 40px;
  position: relative;
  vertical-align: middle;
  width: 100%;
}

.tensor-widget-info {
  align-content: center;
  color: rgb(0, 0, 255);
  display: inline-block;
  font-size: 8pt;
  height: 22px;
  line-height: 22px;
  margin-left: 8px;
  max-height: 22px;
  position: relative;
  vertical-align: middle;
}

.tensor-widget-menu-thumb {
  color: rgb(32, 33, 36);
  cursor: pointer;
  display: inline-block;
  font-weight: bold;
  font-size: 16px;
  margin-left: 10px;
  margin-right: 5px;
  position: relative;
  user-select: none;
}

.tensor-widget-menu-thumb:hover {
  color: rgb(227, 116, 0);
}

.tensor-widget-shape {
  color: rgb(60, 60, 60);
  display: inline-block;
  margin-left: 12px;
}

.tensor-widget-shape-label {
  color: rgb(128, 128, 128);
  display: inline-block;
}

.tensor-widget-shape-value {
  display: inline-block;
}

.tensor-widget-slicing-group {
  background-color: rgb(250, 250, 250);
  border-bottom: 1px solid rgb(190, 190, 190);
  display: block;
  height: 18px;
  text-align: center;
  padding-bottom: 5px;
  padding-top: 5px;
}

.tensor-widget-tensor-name {
  color: black;
  display: inline-block;
  font-weight: bold;
}

.tensor-widget-left-ruler-tick {
  background-color: var(--ruler-background-color);
  border-bottom: var(--border-style);
  border-top: var(--border-style);
  box-shadow: var(--border-style);
  color: rgb(110, 110, 110);
  cursor: pointer;
  display: inline-block;
  font-size: 12px;
  height: 29px;
  line-height: 29px;
  margin-left: 0px;
  max-width: 45px;
  text-align: center;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-top-ruler {
  height: 24px;
  white-space: nowrap;
}

.tensor-widget-value-tooltip {
  background-color: rgb(240, 240, 240);
  border: 1px solid rgb(160, 160, 160);
  box-shadow: 1px 1px 1px #b0b0b0;
  display: none;
  font-size: 13px;
  padding: 5px;
  position: absolute;
  user-select: none;
  width: 240px;
}

.tensor-widget-value-tooltip-colorbar {
  height: 24px;
  width: 95%;
}

.tensor-widget-value-tooltip-indices {
  font-weight: bold;
}

.tensor-widget-value-tooltip-value {
  margin-top: 20px;
}

.tensor-widget-top-ruler-tick {
  background-color: var(--ruler-background-color);
  border-bottom: var(--border-style);
  border-right: var(--border-style);
  color: rgb(110, 110, 110);
  cursor: pointer;
  display: inline-block;
  font-size: 12px;
  height: 24px;
  line-height: 24px;
  padding-right: 2px;
  text-align: center;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-value-div {
  border-bottom: var(--border-style);
  border-right: var(--border-style);
  cursor: pointer;
  display: inline-block;
  font-size: 80%;
  height: 24px;
  line-height: 24px;
  max-width: 45px;
  padding-right: 2px;
  text-align: right;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-value-div-selection {
  font-weight: bold;
}

.tensor-widget-value-div-selection-bottom {
  border-bottom: 0.5px solid blue;
}

.tensor-widget-value-div-selection-left {
  border-left: 0.5px solid blue;
}

.tensor-widget-value-div-selection-right {
  border-right: 0.5px solid blue;
}

.tensor-widget-value-div-selection-top {
  border-top: 0.5px solid blue;
}

.tensor-widget-value-section {
  --border-style: 1px solid rgb(140, 140, 140);
  --ruler-background-color: rgb(210, 210, 210);
  -moz-user-select: none;
  -ms-user-select: none;
  -khtml-user-select: none;
  -webkit-touch-callout: none;
  -webkit-user-select: none;
}

.tensor-widget-value-row {
  height: 25px;
  line-height: 25px;
  white-space: nowrap;
}
</style>
  </template>
</dom-module>








<dom-module id="tf-debugger-line-chart">
  <template>
    <vz-line-chart2 x-components-creation-method="[[_lineChartXComponentsCreationMethod]]" y-value-accessor="[[_lineChartYValueAccessor]]" tooltip-columns="[[_lineChartTooltipColumns]]" smoothing-enabled="[[_lineChartSmoothingEnabled]]"></vz-line-chart2>
    <style>
      vz-line-chart2 {
        height: 300px;
        position: relative;
      }
    </style>
  </template>

  
</dom-module>



<dom-module id="tf-tensor-value-view">
  <template>
    <paper-toast id="tensorValueToast" text="" always-on-top></paper-toast>
    <table class="tensor-value-view-table">
      <tr>
        <td colspan="2">
          <div>
            <paper-item id="tensor-name" on-tap="tensorNameCallback">
              <span class="tensor-name-text">[[tensorName]]</span>
            </paper-item>
            <paper-icon-button icon="close" class="value-view-icon-button" id="value-view-icon-button" title="Close" on-tap="closeButtonCallback"></paper-icon-button>
            <paper-icon-button icon="forward" class="value-view-icon-button" id="value-view-icon-button" title="Continue to" on-tap="continueToButtonCallback"></paper-icon-button>
          </div>
        </td>
      </tr>
      <tr class="tensor-value-value-tr">
        <td>
          <template is="dom-if" if="[[_useTensorWidget]]">
            <div id="tensor-widget"></div>
          </template>

          <template is="dom-if" if="[[!_useTensorWidget]]">
            <paper-item id="debug-op"></paper-item>
            <div>
              <paper-input class="inline value-card-input" label="Slicing" id="slicing" value="{{slicing}}" on-change="refresh">
              </paper-input>
              <div>
                <paper-input class="inline value-card-input" label="Time Indices" id="time-indices" value="{{timeIndices}}" on-change="refresh">
                </paper-input>
                <paper-button raised id="time-indices-toggle-button" class="tensor-value-buttons" on-click="_timeIndicesToggleButtonCallback">Full History</paper-button>
              </div>

              </div></template></td><td class="tensor-value-view-td">
                <template is="dom-if" if="[[_isValueScalar]]">
                  <paper-input class="inline" label="Scalar Value" id="value-scalar" value="[[_dataScalar]]">
                  </paper-input>
                </template>
                <template is="dom-if" if="[[_isValueLineChart]]">
                  <tf-debugger-line-chart data="[[_lineChartData]]"></tf-debugger-line-chart>
                </template>
                <template is="dom-if" if="[[_isValueImage]]">
                  <img class="value-image" height="250px" width="250px" src$="[[_dataImageSrc]]">
                </template>
              </td>
            
          
        
      </tr>
    </table>

    <style include="tensor-widget-style"></style>
    <style>
      .tensor-value-buttons {
        height: 75%;
        font-size: 10px;
      }
      .tensor-value-view-table {
        width: 500px;
        display: inline-table;
        border-spacing: 5px;
        padding-top: 3px;
        padding-bottom: 3px;
        padding-left: 3px;
        padding-right: 3px;
        background-color: #f8f8f8;
        box-shadow: 3px 3px 1px 1px #d8d8d8;
      }
      .tensor-value-view-td {
        width: 350px;
      }
      .value-card-input {
        width: 150px;
      }
      #tensor-name {
        display: inline-block;
        position: relative;
        width: 50%;
        cursor: pointer;
      }
      .tensor-name-text {
        color: blue;
        text-decoration: underline;
      }
      #debug-op {
        font-size: 90%;
      }
      .value-image {
        image-rendering: pixelated;
      }
      .value-view-icon-button {
        display: inline-block;
        float: right;
        text-align: right;
        width: 20%;
        text-decoration: underline;
        cursor: pointer;
        font-size: 90%;
        color: blue;
      }
      #tensor-widget {
        border: 1px solid rgb(160, 160, 160);
        /* box-sizing: content-box;
        -moz-box-sizing: content-box;
        -webkit-box-sizing: content-box; */
        height: 280px;
        width: 484px;
      }
      #slicing,
      #time-indices {
        --paper-input-container-input: {
          font-family: monospace;
        }
      }
    </style>
  </template>
  
</dom-module>


<dom-module id="tf-tensor-value-multi-view">
  <template>
    <div id="multiView">
      <div class="section-title">Tensor Values</div>
      <div id="multi-tensor-view-container"></div>
    </div>
    <style>
      .section-title {
        font-size: 110%;
        font-weight: bold;
      }
      #multiView {
        background-color: #fff;
        padding-top: 3px;
        padding-left: 3px;
        padding-right: 3px;
        box-shadow: 3px 3px #eee;
      }
    </style>
  </template>
  
</dom-module>


<dom-module id="tf-debugger-dashboard">
  <template>
    <paper-toast id="toast" text="" always-on-top></paper-toast>
    <tf-debugger-initial-dialog id="initialDialog"></tf-debugger-initial-dialog>
    
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar" id="left-pane">
        <div id="node-entries" class="node-entries">
          <div class="debugger-section-title">Runtime Node List</div>
          <div class="toggle-source-code">
            Show Code:
            <paper-toggle-button class="toggle-source-code" id="show-source-code" checked="{{_sourceCodeShown}}"></paper-toggle-button>
          </div>
          <tf-op-selector debug-watches="[[_debugWatches]]" debug-watch-change="[[_createDebugWatchChangeHandler()]]" node-clicked="[[_createNodeClickedHandler()]]" force-expand-and-check-node-name="[[_forceExpandAndCheckNodeName]]" force-expand-node-name="[[_forceExpandNodeName]]">
          </tf-op-selector>
        </div>
        <div id="source-code-view-div" class="source-code-view-div" hidden$="{{!_sourceCodeShown}}">
          <div class="debugger-section-title">Source Code</div>
          <tf-source-code-view id="sourceCodeView" request-manager="[[_requestManager]]" debug-watches="[[_debugWatches]]" focus-node-name="[[_sourceFocusNodeName]]" node-clicked="[[_createNodeClickedHandler()]]" continue-to-node="[[_createContinueToNodeHandler()]]"></tf-source-code-view>
        </div>
        <tf-debugger-resizer current-length="{{_leftPaneWidth}}" min-length="[[_minleftPaneWidth]]" max-length="[[_maxleftPaneWidth]]">
        </tf-debugger-resizer>
        <div>
          <tf-session-runs-view id="sessionRunsView" latest-session-run="[[_latestSessionRun]]" session-run-key-to-device-names="[[_sessionRunKey2DeviceNames]]" sole-active="[[_sessionRunSoleActive]]" node-or-tensor-clicked="[[_createFeedFetchTargetClickedHandler()]]">
          </tf-session-runs-view>
        </div>
        <div class="buttons-container">
          <paper-button raised class="continue-button" on-click="_step">
            <span>[[_stepButtonText]]</span>
          </paper-button>
          <tf-debugger-continue-dialog id="continueDialog" session-run-go="[[_createSessionRunGo()]]" tensor-condition-go="[[_createTensorConditionGo()]]" force-continuation-stop="[[_createForceContinuationStop()]]">
          </tf-debugger-continue-dialog>
        </div>
        <div class="container">
          <tf-graph-loader id="loader" out-graph-hierarchy="{{graphHierarchy}}" out-graph="{{graph}}" out-stats="{{stats}}" progress="{{_graphProgress}}"></tf-graph-loader>
        </div>
      </div>
      <div class="center" slot="center" id="center-content">
        <div id="top-right-quadrant">
          <paper-tabs selected="{{_topRightSelected}}">
            <template is="dom-repeat" items="[[_topRightTabs]]">
              <paper-tab id="[[item.id]]">[[item.name]]</paper-tab>
            </template>
          </paper-tabs>
          <div class="runtime-graph-device">
            <span id="runtime-graph-device-name"> </span>
            <paper-dropdown-menu id="active-runtime-graph-device-name" no-label-float="true" label="Device name" selected-item-label="{{_activeRuntimeGraphDeviceName}}">
              <paper-listbox class="dropdown-content" slot="dropdown-content">
                <template is="dom-repeat" items="[[_activeSessionRunDevices]]">
                  <paper-item no-label-float="true">[[item]]</paper-item>
                </template>
              </paper-listbox>
            </paper-dropdown-menu>
            <paper-spinner-lite class="spinner" id="top-right-spinner" hidden="[[!_busy]]" active="[[_busy]]">
            </paper-spinner-lite>
          </div>
          <paper-progress id="top-right-progress-bar" value="0"></paper-progress>
          <template is="dom-if" if="[[_isTopRightRuntimeGraphsActive]]">
            <div id="graph-container">
              <tf-graph id="graph" graph-hierarchy="[[graphHierarchy]]" basic-graph="[[graph]]" stats="[[stats]]" progress="{{_graphProgress}}" color-by="structure" color-by-params="{{colorByParams}}" render-hierarchy="{{_renderHierarchy}}" node-context-menu-items="[[_createNodeContextMenuItems()]]"></tf-graph>
              <div class="context-menu"></div>
            </div>
          </template>
          <template is="dom-if" if="[[_isTopRightTensorValuesActive]]">
            <tf-tensor-value-multi-view id="tensorValueMultiView" continue-to-callback="[[_createContinueToCallback()]]" tensor-name-clicked="[[_createNodeClickedHandler()]]" get-health-pill="[[_createGetHealthPill()]]">
            </tf-tensor-value-multi-view>
          </template>
        </div>

        <tf-debugger-resizer is-horizontal="true" current-length="{{_topRightQuadrantHeight}}" min-length="[[_minTopRightQuadrantHeight]]" max-length="[[_maxTopRightQuadrantHeight]]">
        </tf-debugger-resizer>

        <div id="tensor-data" class="tensor-data">
          <tf-tensor-data-summary id="tensorDataSummary" latest-tensor-data="[[_latestTensorData]]" expand-handler="[[_createTensorDataExpandHandler()]]" continue-to-callback="[[_createContinueToCallback()]]" highlighted-node-name="[[_highlightNodeName]]" tensor-name-clicked="[[_createNodeClickedHandler()]]" get-health-pill="[[_createGetHealthPill()]]">
          </tf-tensor-data-summary>
        </div>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      :host {
        display: block;
        position: absolute;
        left: 0;
        right: 0;
        top: 0;
        bottom: 0;
        overflow: hidden;
      }
      paper-toast {
        text-align: center;
        font-size: 110%;
        width: 40vw;
        margin-left: 30vw;
      }
      tf-dashboard-layout {
        --tf-dashboard-layout-sidebar-basis: auto;
        --tf-dashboard-layout-sidebar-max-width: none;
        --tf-dashboard-layout-sidebar-min-width: none;
      }
      .debugger-section-title {
        font-size: 110%;
        font-weight: bold;
      }
      paper-tabs {
        color: #555;
        font-weight: normal;
      }
      paper-tab.iron-selected {
        color: black;
        font-weight: bold;
      }
      #initialDialog {
        /** This matches the default z-index of paper-dialog backdrops. */
        z-index: 102;
      }
      /** Resize the region for the graph as the user resizes the region. */
      #graph-container {
        height: calc(100% - 120px);
        /** Clip the minimap if the height of the graph container is small. */
        overflow: hidden;
        position: relative;
      }
      #graph {
        position: relative;
        display: block;
        width: 100%;
        height: 100%;
      }
      #tooltip-sorting {
        display: flex;
        font-size: 14px;
        margin-top: 5px;
      }
      #tooltip-sorting-label {
        margin-top: 13px;
      }
      #tooltip-sorting paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }
      #x-type-selector paper-button {
        margin: 5px 3px;
      }
      .runtime-graph-device {
        align-items: center;
        display: flex;
        flex-wrap: wrap;
      }
      #runtime-graph-device-name {
        font-size: 85%;
        word-break: break-all;
        display: inline-block;
      }
      #active-runtime-graph-device-name {
        font-size: 85%;
        width: 350px;
        display: inline-block;
      }
      #top-right-progress-bar {
        width: 100%;
        display: inline-block;
        vertical-align: middle;
      }
      .line-item {
        display: block;
        padding-top: 5px;
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      .sidebar {
        height: 100%;
        overflow-x: visible;
        position: relative;
      }
      .center {
        position: relative;
        height: 100%;
      }
      tf-debugger-resizer {
        right: -10px;
      }
      #center-content {
        position: absolute;
        right: 0;
      }
      /** The resizer should have no space to the left of it. */
      #center-content tf-debugger-resizer[is-horizontal] {
        margin-left: -23px;
      }
      .context-menu {
        position: absolute;
        display: none;
        background-color: #e2e2e2;
        border-radius: 2px;
        font-size: 14px;
        min-width: 150px;
        border: 1px solid #d4d4d4;
      }
      .spinner {
        width: 20px;
        height: 20px;
        vertical-align: middle;
      }
      .node-entries {
        box-shadow: 3px 3px #ddd;
        box-sizing: border-box;
        height: 80%;
        overflow: auto;
        padding-left: 3px;
        padding-right: 3px;
        padding-top: 3px;
        position: relative;
        vertical-align: top;
        width: 100%;
      }
      .source-code-view-div {
        position: relative;
        height: 40%;
        width: 100%;
        vertical-align: top;
        overflow: hidden;
        padding-top: 3px;
        padding-left: 3px;
        padding-right: 3px;
        box-shadow: 3px 3px #ddd;
      }
      #sessionRunsView {
        position: relative;
        width: 100%;
        overflow: auto;
        max-height: 25vh;
      }
      .buttons-container {
        padding: 20px 0;
      }
      #tensor-data {
        position: absolute;
        bottom: 0;
        left: 0;
        right: 0;
        padding: 20px 0;
        margin: 0 0 20px 0;
      }
      #tensorDataSummary {
        position: absolute;
        bottom: 0;
        left: 0;
        right: 0;
        top: 0;
      }
      #top-right-quadrant {
        height: 66%;
        overflow: auto;
      }
      .toggle-source-code {
        margin-right: 1em;
        font-size: 80%;
        float: right;
      }
      .context-menu ul {
        list-style-type: none;
        margin: 0;
        padding: 0;
        cursor: default;
      }
      .context-menu ul li {
        padding: 4px 16px;
      }
      .context-menu ul li:hover {
        background-color: #f3913e;
        color: white;
      }

      paper-input {
        width: 200px;
      }
      .inline,
      paper-item {
        display: inline;
      }

      vz-line-chart {
        height: 300px;
        position: relative;
      }
      [hidden] {
        display: none;
      }
    </style>
  </template>
  
  
</dom-module>











<dom-module id="paper-material-shared-styles">
  <template>
    <style>
      :host {
        display: block;
        position: relative;
      }

      :host([elevation="1"]) {
        @apply --shadow-elevation-2dp;
      }

      :host([elevation="2"]) {
        @apply --shadow-elevation-4dp;
      }

      :host([elevation="3"]) {
        @apply --shadow-elevation-6dp;
      }

      :host([elevation="4"]) {
        @apply --shadow-elevation-8dp;
      }

      :host([elevation="5"]) {
        @apply --shadow-elevation-16dp;
      }
    </style>
  </template>
</dom-module>




<dom-module id="paper-material">
  <template>
    <style include="paper-material-shared-styles"></style>
    <style>
      :host([animated]) {
        @apply --shadow-transition;
      }
      :host {
        @apply --paper-material;
      }
    </style>

    <slot></slot>
  </template>
</dom-module>













<dom-module id="tf-graph-debugger-data-card">
  <template>
    <style>
      :host {
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }

      .health-pill-legend {
        padding: 15px;
      }

      .health-pill-legend h2 {
        text-align: left;
      }

      .health-pill-entry {
        margin: 10px 10px 10px 0;
      }

      .health-pill-entry .color-preview {
        width: 26px;
        height: 26px;
        border-radius: 3px;
        display: inline-block;
        margin: 0 10px 0 0;
      }

      .health-pill-entry .color-label,
      .health-pill-entry .tensor-count {
        color: #777;
        display: inline-block;
        height: 26px;
        font-size: 22px;
        line-height: 26px;
        vertical-align: top;
      }

      .health-pill-entry .tensor-count {
        float: right;
      }

      #health-pill-step-slider {
        width: 100%;
        margin: 0 0 0 -15px;
        /* 31 comes from adding a padding of 15px from both sides of the paper-slider, subtracting
   * 1px so that the slider width aligns with the image (the last slider marker takes up 1px),
   * and adding 2px to account for a border of 1px on both sides of the image. 30 - 1 + 2.
   * Apparently, the paper-slider lacks a mixin for those padding values. */
        width: calc(100% + 31px);
      }

      #health-pills-loading-spinner {
        width: 20px;
        height: 20px;
        vertical-align: top;
      }

      #health-pill-step-number-input {
        text-align: center;
        vertical-align: top;
      }

      #numeric-alerts-table-container {
        max-height: 400px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      #numeric-alerts-table {
        text-align: left;
      }

      #numeric-alerts-table td {
        vertical-align: top;
      }

      #numeric-alerts-table .first-offense-td {
        display: inline-block;
      }

      .first-offense-td {
        width: 80px;
      }

      .tensor-device-td {
        max-width: 140px;
        word-wrap: break-word;
      }

      .tensor-section-within-table {
        color: #266236;
        cursor: pointer;
        opacity: 0.8;
        text-decoration: underline;
      }

      .tensor-section-within-table:hover {
        opacity: 1;
      }

      .device-section-within-table {
        color: #666;
      }

      .mini-health-pill {
        width: 130px;
      }

      .mini-health-pill > div {
        height: 100%;
        width: 60px;
        border-radius: 3px;
      }

      #event-counts-th {
        padding: 0 0 0 10px;
      }

      .negative-inf-mini-health-pill-section {
        background: rgb(255, 141, 0);
        width: 20px;
      }

      .positive-inf-mini-health-pill-section {
        background: rgb(0, 62, 212);
        width: 20px;
      }

      .nan-mini-health-pill-section {
        background: rgb(204, 47, 44);
        width: 20px;
      }

      .negative-inf-mini-health-pill-section,
      .positive-inf-mini-health-pill-section,
      .nan-mini-health-pill-section {
        color: #fff;
        display: inline-block;
        height: 100%;
        line-height: 20px;
        margin: 0 0 0 10px;
        text-align: center;
      }

      .no-numeric-alerts-notification {
        margin: 0;
      }
    </style>
    <paper-material elevation="1" class="card health-pill-legend">
      <div class="title">
        Enable all (not just sampled) steps. Requires slow disk read.
      </div>
      <paper-toggle-button id="enableAllStepsModeToggle" checked="{{allStepsModeEnabled}}">
      </paper-toggle-button>
      <h2>
        Step of Health Pills:
        <template is="dom-if" if="[[allStepsModeEnabled]]">
          <input type="number" id="health-pill-step-number-input" min="0" max="[[_biggestStepEverSeen]]" value="{{specificHealthPillStep::input}}">
        </template>
        <template is="dom-if" if="[[!allStepsModeEnabled]]">
          [[_currentStepDisplayValue]]
        </template>
        <paper-spinner-lite active hidden$="[[!areHealthPillsLoading]]" id="health-pills-loading-spinner"></paper-spinner-lite>
      </h2>
      <template is="dom-if" if="[[allStepsModeEnabled]]">
        <paper-slider id="health-pill-step-slider" immediate-value="{{specificHealthPillStep}}" max="[[_biggestStepEverSeen]]" snaps step="1" value="{{specificHealthPillStep}}"></paper-slider>
      </template>
      <template is="dom-if" if="[[!allStepsModeEnabled]]">
        <template is="dom-if" if="[[_maxStepIndex]]">
          <paper-slider id="health-pill-step-slider" immediate-value="{{healthPillStepIndex}}" max="[[_maxStepIndex]]" snaps step="1" value="{{healthPillStepIndex}}"></paper-slider>
        </template>
      </template>
      <h2>
        Health Pill
        <template is="dom-if" if="[[healthPillValuesForSelectedNode]]">
          Counts for Selected Node
        </template>
        <template is="dom-if" if="[[!healthPillValuesForSelectedNode]]">
          Legend
        </template>
      </h2>
      <template is="dom-repeat" items="[[healthPillEntries]]">
        <div class="health-pill-entry">
          <div class="color-preview" style="background:[[item.background_color]]"></div>
          <div class="color-label">[[item.label]]</div>
          <div class="tensor-count">
            [[_computeTensorCountString(healthPillValuesForSelectedNode,
            index)]]
          </div>
        </div>
      </template>
      <div hidden$="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]">
        <h2 id="numeric-alerts-header">Numeric Alerts</h2>
        <p>
          Alerts are sorted from top to bottom by increasing timestamp.
        
        <div id="numeric-alerts-table-container">
          <table id="numeric-alerts-table">
            <thead>
              <tr>
                <th>First Offense</th>
                <th>Tensor (Device)</th>
                <th id="event-counts-th">Event Counts</th>
              </tr>
            </thead>
            <tbody id="numeric-alerts-body"></tbody>
          </table>
        </div>
      </div>
      <template is="dom-if" if="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]">
        <p class="no-numeric-alerts-notification">
          No numeric alerts so far. That is likely good. Alerts indicate the
          presence of NaN or (+/-) Infinity values, which may be concerning.
        
      
    
  
  













<dom-module id="iron-list">
  <template>
    <style>
      :host {
        display: block;
      }

      @media only screen and (-webkit-max-device-pixel-ratio: 1) {
        :host {
          will-change: transform;
        }
      }

      #items {
        @apply --iron-list-items-container;
        position: relative;
      }

      :host(:not([grid])) #items > ::slotted(*) {
        width: 100%;
      }

      #items > ::slotted(*) {
        box-sizing: border-box;
        margin: 0;
        position: absolute;
        top: 0;
        will-change: transform;
      }
    </style>

    <array-selector id="selector" items="{{items}}" selected="{{selectedItems}}" selected-item="{{selectedItem}}"></array-selector>

    </template></dom-module><div id="items">
      <slot></slot>
    </div>

  </template>
</paper-material></template></dom-module>













<dom-module id="paper-item-body">
  <template>
    <style>
      :host {
        overflow: hidden; /* needed for text-overflow: ellipsis to work on ff */
        @apply --layout-vertical;
        @apply --layout-center-justified;
        @apply --layout-flex;
      }

      :host([two-line]) {
        min-height: var(--paper-item-body-two-line-min-height, 72px);
      }

      :host([three-line]) {
        min-height: var(--paper-item-body-three-line-min-height, 88px);
      }

      :host > ::slotted(*) {
        overflow: hidden;
        text-overflow: ellipsis;
        white-space: nowrap;
      }

      :host > ::slotted([secondary]) {
        @apply --paper-font-body1;

        color: var(--paper-item-body-secondary-color, var(--secondary-text-color));

        @apply --paper-item-body-secondary;
      }
    </style>

    <slot></slot>
  </template>

  
</dom-module>








<dom-module id="tf-graph-icon">
  <template>
    <style>
      :host {
        font-size: 0;
      }

      .faded-rect {
        fill: url(#rectHatch);
      }

      .faded-ellipse {
        fill: url(#ellipseHatch);
      }

      .faded-rect,
      .faded-ellipse,
      .faded-series {
        stroke: var(--tb-graph-faded) !important;
      }
      #rectHatch line,
      #ellipseHatch line {
        color: #e0d4b3 !important;
        fill: white;
        stroke: #e0d4b3 !important;
      }
    </style>
    
    <svg height="0" width="0" id="svgDefs">
      <defs>
        
        <pattern id="rectHatch" patterntransform="rotate(45 0 0)" width="5" height="5" patternunits="userSpaceOnUse">
          <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1" />
        </pattern>
        <pattern id="ellipseHatch" patterntransform="rotate(45 0 0)" width="2" height="2" patternunits="userSpaceOnUse">
          <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1" />
        </pattern>
        
        <ellipse id="op-node-stamp" rx="7.5" ry="3" stroke="inherit" fill="inherit" />
        
        <ellipse id="op-node-annotation-stamp" rx="5" ry="2" stroke="inherit" fill="inherit" />
        
        <g id="op-series-vertical-stamp">
          <use xlink:href="#op-node-stamp" x="8" y="9" />
          <use xlink:href="#op-node-stamp" x="8" y="6" />
          <use xlink:href="#op-node-stamp" x="8" y="3" />
        </g>
        <g id="op-series-horizontal-stamp">
          <use xlink:href="#op-node-stamp" x="16" y="4" />
          <use xlink:href="#op-node-stamp" x="12" y="4" />
          <use xlink:href="#op-node-stamp" x="8" y="4" />
        </g>
        <g id="summary-icon" fill="#848484" height="12" viewbox="0 0 24 24" width="12">
          <path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z" />
        </g>
      </defs>
    </svg>
    <template is="dom-if" if="[[_isType(type, 'CONST')]]">
      <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 10 10">
        <circle cx="5" cy="5" r="3" fill$="[[_fill]]" stroke$="[[_stroke]]" />
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SUMMARY')]]">
      <svg width$="[[height]]" height$="[[height]]" viewbox="0 0 24 24" fill="#848484">
        <path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z" />
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'OP')]]">
      <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 16 8">
        <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#op-node-stamp" fill$="[[_fill]]" stroke$="[[_stroke]]" class$="{{_fadedClass(faded, 'ellipse')}}" x="8" y="4" />
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'META')]]">
      <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 37 16">
        <rect x="1" y="1" fill$="[[_fill]]" stroke$="[[_stroke]]" class$="{{_fadedClass(faded, 'rect')}}" stroke-width="2px" height="14" width="35" rx="5" ry="5" />
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SERIES')]]">
      <template is="dom-if" if="[[vertical]]">
        <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 16 15">
          <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#op-series-vertical-stamp" fill$="[[_fill]]" stroke$="[[_stroke]]" class$="{{_fadedClass(faded, 'series')}}" x="0" y="2" />
        </svg>
      </template>
      <template is="dom-if" if="[[!vertical]]">
        <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 24 10">
          <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#op-series-horizontal-stamp" fill$="[[_fill]]" stroke$="[[_stroke]]" class$="{{_fadedClass(faded, 'series')}}" x="0" y="1" />
        </svg>
      </template>
    </template>
  </template>

  
</dom-module>


<dom-module id="tf-node-icon">
  <template>
    <style>
      tf-graph-icon {
        --tb-graph-faded: var(--tb-graph-faded);
      }
    </style>
    <tf-graph-icon id="icon" type="[[_getType(node, summary, const, type)]]" height="[[height]]" fill-override="[[_fillOverride]]" stroke-override="[[_getStrokeOverride(_fillOverride)]]" faded="[[_getFaded(renderInfo)]]" vertical="[[_isVertical(node, vertical)]]"></tf-graph-icon>
  </template>

  
</dom-module>


<dom-module id="tf-graph-op-compat-list-item">
  <template>
    <style>
      #list-item {
        width: 100%;
        color: #565656;
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>

    <div id="list-item" on-mouseover="_nodeListener" on-mouseout="_nodeListener" on-click="_nodeListener">
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon class="node-icon" height="12" color-by="[[colorBy]]" color-by-params="[[colorByParams]]" node="[[itemNode]]" render-info="[[itemRenderInfo]]" template-index="[[templateIndex]]">
        </tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
      </div>
    </div>
  </template>

  
</dom-module>


<dom-module id="tf-graph-op-compat-card">
  <template>
    <style>
      :host {
        max-height: 500px;
      }

      .incompatible-ops-list {
        height: 350px;
        max-height: 400px;
        overflow-y: scroll;
        display: flex;
        flex-direction: column;
      }

      iron-list {
        flex: 1 1 auto;
      }

      paper-item {
        padding: 0;
        background: #e9e9e9;
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
        font-weight: 500;
        font-size: 12pt;
        width: 100%;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .subtitle {
        font-size: 12pt;
        color: #5e5e5e;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .non-control-list-item {
        padding-left: 10px;
      }

      div.op-compat-display {
        margin-top: 10px;
        display: inline-block;
      }

      svg.op-compat {
        width: 250px;
        height: 25px;
        float: left;
      }

      div.op-compat-value {
        float: right;
        height: 100%;
        font-size: 14px;
        color: black;
        margin-left: 10px;
      }
    </style>

    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button icon="{{_getToggleIcon(_expanded)}}" on-click="_toggleExpanded" class="toggle-button">
          </paper-icon-button>
          <div class="node-name" id="nodetitle">[[nodeTitle]]</div>
        </div>
        <div secondary>
          <div class="subtitle">
            <div class="op-compat-display">
              <svg class="op-compat" preserveaspectratio="xMinYMid meet" viewbox="0 0 250 25">
                <defs>
                  <lineargradient id="op-compat-fill">
                    <stop offset="0" stop-color$="[[_opCompatColor]]"></stop>
                    <stop offset$="[[_opCompatScore]]" stop-color$="[[_opCompatColor]]"></stop>
                    <stop offset$="[[_opCompatScore]]" stop-color$="[[_opIncompatColor]]"></stop>
                    <stop offset="1" stop-color$="[[_opIncompatColor ]]"></stop>
                  </lineargradient>
                </defs>
                <rect height="25" width="250" rx="5" ry="5" style="fill: url('#op-compat-fill');" />
              </svg>
              <div class="op-compat-value">[[_opCompatScoreLabel]]</div>
            </div>
          </div>
        </div>
      </paper-item-body>
    </paper-item>

    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          Incompatible Operations: (<span>[[_totalIncompatOps]]</span>)
          <iron-list class="incompatible-ops-list" id="incompatibleOpsList" items="[[_incompatibleOpNodes]]">
            <template>
              <tf-graph-op-compat-list-item class="non-control-list-item" item-node="[[item]]" item-render-info="[[_getRenderInfo(item.name, renderHierarchy)]]" name="[[item.name]]" template-index="[[_templateIndex]]" color-by="[[colorBy]]" item-type="incompatible-ops">
              </tf-graph-op-compat-list-item>
            </template>
          </iron-list>
        </div>
      </template>
    </iron-collapse>
  </template>

  
</dom-module>














<dom-module id="tf-node-list-item">
  <template>
    <style>
      #list-item {
        width: 100%;
        color: #565656;
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>
    <div id="list-item" on-mouseover="_nodeListener" on-mouseout="_nodeListener" on-click="_nodeListener">
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon class="node-icon" height="12" color-by="[[colorBy]]" color-by-params="[[colorByParams]]" node="[[itemNode]]" render-info="[[itemRenderInfo]]" template-index="[[templateIndex]]"></tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
        <span class="edge-label">[[edgeLabel]]</span>
      </div>
    </div>
  </template>

  
</dom-module>


<dom-module id="tf-node-info">
  <template>
    <style>
      .sub-list-group {
        font-weight: 500;
        font-size: 12pt;
        padding-bottom: 8px;
        width: 100%;
      }

      .sub-list {
        max-height: 300px;
        overflow-y: scroll;
      }

      .attr-left {
        float: left;
        width: 30%;
        word-wrap: break-word;
        color: #565656;
        font-size: 11pt;
        font-weight: 400;
      }

      .attr-right {
        margin-left: 30%;
        word-wrap: break-word;
        color: #565656;
        font-weight: 400;
      }

      .sub-list-table {
        display: table;
        width: 100%;
      }

      .sub-list-table-row {
        display: table-row;
      }

      .sub-list-table-row .sub-list-table-cell:last-child {
        text-align: right;
      }

      .sub-list-table-cell {
        color: #565656;
        display: table-cell;
        font-size: 11pt;
        font-weight: 400;
        max-width: 200px;
        padding: 0 4px;
      }

      paper-item {
        padding: 0;
        background: #e9e9e9;
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
      }

      .controlDeps {
        padding: 0 0 0 8px;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .node-icon {
        float: right;
      }

      .subtitle {
        font-size: 12pt;
        color: #5e5e5e;
      }

      .controlLine {
        font-size: 11pt;
        font-weight: 400;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .control-toggle-button {
        float: left;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-include-group {
        padding-top: 4px;
      }

      .toggle-include {
        margin: 5px 6px;
        text-transform: none;
        padding: 4px 6px;
        font-size: 10pt;
        background-color: #fafafa;
        color: #666;
      }

      .toggle-include:hover {
        background-color: var(--google-yellow-100);
      }

      .non-control-list-item {
        padding-left: 10px;
      }
    </style>
    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button icon="{{_getToggleIcon(_expanded)}}" on-click="_toggleExpanded" class="toggle-button">
          </paper-icon-button>
          <div class="node-name" id="nodetitle"></div>
        </div>
        <div secondary>
          <tf-node-icon class="node-icon" node="[[_node]]" render-info="[[_getRenderInfo(graphNodeName, renderHierarchy)]]" color-by="[[colorBy]]" template-index="[[_templateIndex]]"></tf-node-icon>
          <template is="dom-if" if="{{_node.op}}">
            <div class="subtitle">
              Operation:
              <span>[[_node.op]]</span>
            </div>
          </template>
          <template is="dom-if" if="{{_node.metagraph}}">
            <div class="subtitle">
              Subgraph:
              <span>[[_node.cardinality]]</span> nodes
            </div>
          </template>
        </div>
      </paper-item-body>
    </paper-item>
    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          <div class="sub-list-group attributes">
            Attributes (<span>[[_attributes.length]]</span>)
            <iron-list class="sub-list" id="attributesList" items="[[_attributes]]">
              <template>
                <div>
                  <div class="attr-left">[[item.key]]</div>
                  <div class="attr-right">[[item.value]]</div>
                </div>
              </template>
            </iron-list>
          </div>

          <template is="dom-if" if="{{_device}}">
            <div class="sub-list-group device">
              <div class="attr-left">Device</div>
              <div class="attr-right">[[_device]]</div>
            </div>
          </template>

          <div class="sub-list-group predecessors">
            Inputs (<span>[[_totalPredecessors]]</span>)
            <iron-list class="sub-list" id="inputsList" items="[[_predecessors.regular]]">
              <template>
                <tf-node-list-item class="non-control-list-item" card-node="[[_node]]" item-node="[[item.node]]" edge-label="[[item.edgeLabel]]" item-render-info="[[item.renderInfo]]" name="[[item.name]]" item-type="predecessors" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_predecessors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button icon="{{_getToggleIcon(_openedControlPred)}}" on-click="_toggleControlPred" class="control-toggle-button">
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlPred}}" no-animation>
                  <template is="dom-if" if="{{_openedControlPred}}" restamp="true">
                    <iron-list class="sub-list" items="[[_predecessors.control]]">
                      <template>
                        <tf-node-list-item card-node="[[_node]]" item-node="[[item.node]]" item-render-info="[[item.renderInfo]]" name="[[item.name]]" item-type="predecessors" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>

          <div class="sub-list-group successors">
            Outputs (<span>[[_totalSuccessors]]</span>)
            <iron-list class="sub-list" id="outputsList" items="[[_successors.regular]]">
              <template>
                <tf-node-list-item class="non-control-list-item" card-node="[[_node]]" item-node="[[item.node]]" edge-label="[[item.edgeLabel]]" item-render-info="[[item.renderInfo]]" name="[[item.name]]" item-type="successor" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_successors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button icon="{{_getToggleIcon(_openedControlSucc)}}" on-click="_toggleControlSucc" class="control-toggle-button">
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlSucc}}" no-animation>
                  <template is="dom-if" if="{{_openedControlSucc}}" restamp="true">
                    <iron-list class="sub-list" items="[[_successors.control]]">
                      <template>
                        <tf-node-list-item card-node="[[_node]]" item-node="[[item.node]]" item-render-info="[[item.renderInfo]]" name="[[item.name]]" item-type="successors" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>
          <template is="dom-if" if="{{_hasDisplayableNodeStats}}">
            <div class="sub-list-group node-stats">
              Node Stats
              <div class="sub-list-table">
                <template is="dom-if" if="{{_nodeStats.totalBytes}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Memory</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedBytes]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_getTotalMicros(_nodeStats)}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Compute Time</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedComputeTime]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_nodeStats.outputSize}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Tensor Output Sizes</div>
                    <div class="sub-list-table-cell">
                      <template is="dom-repeat" items="{{_nodeStatsFormattedOutputSizes}}">
                        [[item]] <br>
                      </template>
                    </div>
                  </div>
                </template>
              </div>
            </div>
          </template>

          <template is="dom-if" if="[[_functionUsages.length]]">
            <div class="sub-list-group predecessors">
              Usages of the Function (<span>[[_functionUsages.length]]</span>)
              <iron-list class="sub-list" id="functionUsagesList" items="[[_functionUsages]]">
                <template>
                  <tf-node-list-item class="non-control-list-item" card-node="[[_node]]" item-node="[[item]]" name="[[item.name]]" item-type="functionUsages" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                  </tf-node-list-item>
                </template>
              </iron-list>
            </div>
          </template>

          <template is="dom-if" if="[[!_isLibraryFunction(_node)]]">
            <div class="toggle-include-group">
              <paper-button raised class="toggle-include" on-click="_toggleInclude">
                <span>[[_auxButtonText]]</span>
              </paper-button>
            </div>
          </template>

          <template is="dom-if" if="{{_isInSeries(_node)}}">
            <div class="toggle-include-group">
              <paper-button raised class="toggle-include" on-click="_toggleGroup">
                <span>[[_groupButtonText]]</span>
              </paper-button>
            </div>
          </template>
        </div>
      </template>
    </iron-collapse>
  </template>

  
</dom-module>


<dom-module id="tf-graph-info">
  <template>
    <style>
      :host {
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
        max-height: 650px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }
    </style>
    <template is="dom-if" if="{{selectedNode}}">
      <paper-material elevation="1" class="card">
        <tf-node-info graph-hierarchy="[[graphHierarchy]]" render-hierarchy="[[renderHierarchy]]" flat-graph="[[graph]]" graph-node-name="[[selectedNode]]" node-include="[[selectedNodeInclude]]" highlighted-node="{{highlightedNode}}" color-by="[[colorBy]]">
        </tf-node-info>
      </paper-material>
    </template>
    <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
      <tf-graph-op-compat-card graph-hierarchy="[[graphHierarchy]]" hierarchy-params="[[hierarchyParams]]" render-hierarchy="[[renderHierarchy]]" color-by="[[colorBy]]" node-title="[[compatNodeTitle]]">
      </tf-graph-op-compat-card>
    </template>
    <template is="dom-if" if="[[_healthPillsAvailable(debuggerDataEnabled, nodeNamesToHealthPills)]]">
      <tf-graph-debugger-data-card render-hierarchy="[[renderHierarchy]]" debugger-numeric-alerts="[[debuggerNumericAlerts]]" node-names-to-health-pills="[[nodeNamesToHealthPills]]" selected-node="{{selectedNode}}" highlighted-node="{{highlightedNode}}" are-health-pills-loading="[[areHealthPillsLoading]]" all-steps-mode-enabled="{{allStepsModeEnabled}}" specific-health-pill-step="{{specificHealthPillStep}}" health-pill-step-index="{{healthPillStepIndex}}">
      </tf-graph-debugger-data-card>
    </template>
  </template>
  
</dom-module>




<dom-module id="tf-graph-board">
  <template>
    <style>
      ::host {
        display: block;
      }

      /deep/ .close {
        position: absolute;
        cursor: pointer;
        left: 15px;
        bottom: 15px;
      }

      .container {
        width: 100%;
        height: 100%;
        opacity: 1;
      }

      .container.loading {
        cursor: progress;
        opacity: 0.1;
      }

      .container.loading.error {
        cursor: auto;
      }

      #info {
        position: absolute;
        right: 5px;
        top: 5px;
        padding: 0px;
        max-width: 380px;
        min-width: 320px;
        background-color: rgba(255, 255, 255, 0.9);
        @apply --shadow-elevation-2dp;
      }

      #main {
        width: 100%;
        height: 100%;
      }

      #progress-bar {
        display: flex;
        flex-direction: column;
        align-items: center;
        justify-content: center;
        width: 100%;
        position: absolute;
        top: 40px;
        left: 0;
        font-size: 13px;
      }

      #progress-msg {
        margin-bottom: 5px;
        white-space: pre-wrap;
        width: 400px;
      }

      paper-progress {
        width: 400px;
        --paper-progress-height: 6px;
        --paper-progress-active-color: #f3913e;
      }

      .context-menu {
        position: absolute;
        display: none;
        background-color: #e2e2e2;
        border-radius: 2px;
        font-size: 14px;
        min-width: 150px;
        border: 1px solid #d4d4d4;
      }

      /deep/ .context-menu ul {
        list-style-type: none;
        margin: 0;
        padding: 0;
        cursor: default;
      }

      /deep/ .context-menu ul li {
        padding: 4px 16px;
      }

      /deep/ .context-menu ul li:hover {
        background-color: #f3913e;
        color: white;
      }
    </style>
    <template is="dom-if" if="[[_isNotComplete(progress)]]">
      <div id="progress-bar">
        <div id="progress-msg">[[progress.msg]]</div>
        <paper-progress value="[[progress.value]]"></paper-progress>
      </div>
    </template>
    <div class$="[[_getContainerClass(progress)]]">
      <div id="main">
        <tf-graph id="graph" graph-hierarchy="{{graphHierarchy}}" basic-graph="[[graph]]" hierarchy-params="[[hierarchyParams]]" render-hierarchy="{{renderHierarchy}}" devices-for-stats="[[devicesForStats]]" stats="[[stats]]" selected-node="{{selectedNode}}" highlighted-node="{{_highlightedNode}}" color-by="[[colorBy]]" color-by-params="{{colorByParams}}" progress="{{progress}}" edge-label-function="[[edgeLabelFunction]]" edge-width-function="[[edgeWidthFunction]]" node-names-to-health-pills="[[nodeNamesToHealthPills]]" health-pill-step-index="[[healthPillStepIndex]]" handle-node-selected="[[handleNodeSelected]]" handle-edge-selected="[[handleEdgeSelected]]" trace-inputs="[[traceInputs]]"></tf-graph>
      </div>
      <div id="info">
        <tf-graph-info id="graph-info" title="selected" graph-hierarchy="[[graphHierarchy]]" hierarchy-params="[[hierarchyParams]]" render-hierarchy="[[renderHierarchy]]" graph="[[graph]]" selected-node="{{selectedNode}}" selected-node-include="{{_selectedNodeInclude}}" highlighted-node="{{_highlightedNode}}" color-by="[[colorBy]]" color-by-params="[[colorByParams]]" debugger-data-enabled="[[debuggerDataEnabled]]" are-health-pills-loading="[[areHealthPillsLoading]]" debugger-numeric-alerts="[[debuggerNumericAlerts]]" node-names-to-health-pills="[[nodeNamesToHealthPills]]" all-steps-mode-enabled="{{allStepsModeEnabled}}" specific-health-pill-step="{{specificHealthPillStep}}" health-pill-step-index="{{healthPillStepIndex}}" compat-node-title="[[compatNodeTitle]]" on-node-toggle-inclusion="_onNodeInclusionToggled" on-node-toggle-seriesgroup="_onNodeSeriesGroupToggled"></tf-graph-info>
      </div>
    </div>
  </template>
</dom-module>























<dom-module id="paper-radio-button">
  <template strip-whitespace>
    <style>
      :host {
        display: inline-block;
        line-height: 0;
        white-space: nowrap;
        cursor: pointer;
        @apply --paper-font-common-base;
        --calculated-paper-radio-button-size: var(--paper-radio-button-size, 16px);
        /* -1px is a sentinel for the default and is replace in `attached`. */
        --calculated-paper-radio-button-ink-size: var(--paper-radio-button-ink-size, -1px);
      }

      :host(:focus) {
        outline: none;
      }

      #radioContainer {
        @apply --layout-inline;
        @apply --layout-center-center;
        position: relative;
        width: var(--calculated-paper-radio-button-size);
        height: var(--calculated-paper-radio-button-size);
        vertical-align: middle;

        @apply --paper-radio-button-radio-container;
      }

      #ink {
        position: absolute;
        top: 50%;
        left: 50%;
        right: auto;
        width: var(--calculated-paper-radio-button-ink-size);
        height: var(--calculated-paper-radio-button-ink-size);
        color: var(--paper-radio-button-unchecked-ink-color, var(--primary-text-color));
        opacity: 0.6;
        pointer-events: none;
        -webkit-transform: translate(-50%, -50%);
        transform: translate(-50%, -50%);
      }

      #ink[checked] {
        color: var(--paper-radio-button-checked-ink-color, var(--primary-color));
      }

      #offRadio, #onRadio {
        position: absolute;
        box-sizing: border-box;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        border-radius: 50%;
      }

      #offRadio {
        border: 2px solid var(--paper-radio-button-unchecked-color, var(--primary-text-color));
        background-color: var(--paper-radio-button-unchecked-background-color, transparent);
        transition: border-color 0.28s;
      }

      #onRadio {
        background-color: var(--paper-radio-button-checked-color, var(--primary-color));
        -webkit-transform: scale(0);
        transform: scale(0);
        transition: -webkit-transform ease 0.28s;
        transition: transform ease 0.28s;
        will-change: transform;
      }

      :host([checked]) #offRadio {
        border-color: var(--paper-radio-button-checked-color, var(--primary-color));
      }

      :host([checked]) #onRadio {
        -webkit-transform: scale(0.5);
        transform: scale(0.5);
      }

      #radioLabel {
        line-height: normal;
        position: relative;
        display: inline-block;
        vertical-align: middle;
        margin-left: var(--paper-radio-button-label-spacing, 10px);
        white-space: normal;
        color: var(--paper-radio-button-label-color, var(--primary-text-color));

        @apply --paper-radio-button-label;
      }

      :host([checked]) #radioLabel {
        @apply --paper-radio-button-label-checked;
      }

      #radioLabel:dir(rtl) {
        margin-left: 0;
        margin-right: var(--paper-radio-button-label-spacing, 10px);
      }

      #radioLabel[hidden] {
        display: none;
      }

      /* disabled state */

      :host([disabled]) #offRadio {
        border-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
        opacity: 0.5;
      }

      :host([disabled][checked]) #onRadio {
        background-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
        opacity: 0.5;
      }

      :host([disabled]) #radioLabel {
        /* slightly darker than the button, so that it's readable */
        opacity: 0.65;
      }
    </style>

    <div id="radioContainer">
      <div id="offRadio"></div>
      <div id="onRadio"></div>
    </div>

    <div id="radioLabel"><slot></slot></div>
  </template>

  
</dom-module>




<dom-module id="paper-radio-group">
  <template>
    <style>
      :host {
        display: inline-block;
      }

      :host ::slotted(*) {
        padding: var(--paper-radio-group-item-padding, 12px);
      }
    </style>

    <slot></slot>
  </template>
</dom-module>









<dom-module id="paper-tooltip">
  <template>
    <style>
      :host {
        display: block;
        position: absolute;
        outline: none;
        z-index: 1002;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;
        cursor: default;
      }

      #tooltip {
        display: block;
        outline: none;
        @apply --paper-font-common-base;
        font-size: 10px;
        line-height: 1;
        background-color: var(--paper-tooltip-background, #616161);
        color: var(--paper-tooltip-text-color, white);
        padding: 8px;
        border-radius: 2px;
        @apply --paper-tooltip;
      }

      @keyframes keyFrameScaleUp {
        0% {
          transform: scale(0.0);
        }
        100% {
          transform: scale(1.0);
        }
      }

      @keyframes keyFrameScaleDown {
        0% {
          transform: scale(1.0);
        }
        100% {
          transform: scale(0.0);
        }
      }

      @keyframes keyFrameFadeInOpacity {
        0% {
          opacity: 0;
        }
        100% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameFadeOutOpacity {
        0% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        100% {
          opacity: 0;
        }
      }

      @keyframes keyFrameSlideDownIn {
        0% {
          transform: translateY(-2000px);
          opacity: 0;
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameSlideDownOut {
        0% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(-2000px);
          opacity: 0;
        }
      }

      .fade-in-animation {
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameFadeInOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .fade-out-animation {
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 0ms);
        animation-name: keyFrameFadeOutOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-up-animation {
        transform: scale(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameScaleUp;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-down-animation {
        transform: scale(1);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameScaleDown;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation {
        transform: translateY(-2000px);
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownIn;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation-out {
        transform: translateY(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownOut;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .cancel-animation {
        animation-delay: -30s !important;
      }

      /* Thanks IE 10. */

      .hidden {
        display: none !important;
      }
    </style>

    <div id="tooltip" class="hidden">
      <slot></slot>
    </div>
  </template>

  
</dom-module>










<dom-module id="tf-graph-node-search">
  <template>
    <div id="search-container">
      <paper-input id="runs-regex" label="Search nodes. Regexes supported." value="{{_rawRegexInput}}">
      </paper-input>
      <div id="search-results-anchor">
        <div id="search-results">
          <template is="dom-repeat" items="[[_regexMatches]]">
            <div id="search-match" on-click="_matchClicked">[[item]]</div>
          </template>
        </div>
      </div>
    </div>
    <style>
      #search-container {
        width: 100%;
        overflow: visible;
      }

      #runs-regex {
        width: 100%;
      }

      #search-results-anchor {
        position: relative;
      }

      #search-results {
        color: #fff;
        position: absolute;
        max-height: 200px;
        overflow-x: hidden;
        overflow-y: auto;
        text-align: right;
        max-width: 100%;
        box-sizing: border-box;
      }

      #search-match {
        background: var(--tb-orange-strong);
        padding: 3px;
        float: right;
        width: 100%;
        box-sizing: border-box;
        direction: rtl;
      }

      #search-match:hover {
        background: var(--tb-orange-weak);
        cursor: pointer;
      }
    </style>
  </template>
  
</dom-module>


<dom-module id="tf-graph-controls">
  <template>
    <style>
      :host {
        color: gray;
        display: flex;
        flex-direction: column;
        font-size: 12px;
        width: 100%;
      }

      paper-dropdown-menu {
        --paper-dropdown-menu-input: {
          padding: 0;
          color: gray;
        }
        --iron-icon-width: 15px;
        --iron-icon-height: 15px;
        --primary-text-color: gray;
        --paper-item-min-height: 30px;
      }

      paper-button[raised].keyboard-focus {
        font-weight: normal;
      }

      .run-dropdown {
        --paper-input-container: {
          padding: 8px 0 8px 10px;
        }
      }

      .color-dropdown {
        --paper-input-container: {
          padding: 9px 0 0 13px;
        }
      }

      table {
        border-collapse: collapse;
        border-spacing: 0;
      }

      table td {
        padding: 0;
        margin: 0;
      }

      .allcontrols {
        padding: 0 20px 20px;
        flex-grow: 1;
        overflow-y: auto;
      }

      .legend-holder {
        background: #e9e9e9;
        border-top: 1px solid #ccc;
        box-sizing: border-box;
        color: #555;
        padding: 15px 20px;
        width: 100%;
      }

      .toggle-legend-button {
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-legend-text {
        vertical-align: middle;
      }

      paper-radio-button {
        display: block;
        padding: 5px;
      }
      svg.icon,
      tf-graph-icon {
        width: 60px;
        height: 18px;
      }
      .domainValues {
        margin-bottom: 10px;
        width: 165px;
      }
      .domainStart {
        float: left;
      }
      .domainEnd {
        float: right;
      }
      .colorBox {
        width: 20px;
      }

      .image-icon {
        width: 24px;
        height: 24px;
      }

      .help-icon {
        height: 15px;
        margin: 0;
        padding: 0;
      }

      .gray {
        color: #666;
      }

      .title {
        font-size: 16px;
        margin: 8px 5px 8px 0;
        color: black;
      }
      .title small {
        font-weight: normal;
      }
      .deviceList,
      .xlaClusterList {
        max-height: 200px;
        overflow-y: auto;
      }

      #file {
        padding: 8px 0;
      }

      .color-legend-row {
        align-items: center;
        clear: both;
        display: flex;
        height: 20px;
        margin-top: 5px;
      }

      .color-legend-row .label,
      .color-legend-row svg,
      .color-legend-row tf-graph-icon {
        flex: 0 0 40px;
        margin-right: 20px;
      }

      .devices-checkbox input {
        text-align: left;
        vertical-align: middle;
      }

      .control-holder .icon-button {
        font-size: 14px;
        margin: 0 -5px;
        padding: 5px;
      }

      .button-text {
        padding-left: 20px;
        text-transform: none;
      }

      .upload-button {
        width: 165px;
        height: 25px;
        text-transform: none;
        margin-top: 4px;
      }

      .button-icon {
        width: 26px;
        height: 26px;
        color: var(--paper-orange-500);
      }

      .hidden-input {
        height: 0px;
        width: 0px;
        overflow: hidden;
      }

      .allcontrols .control-holder {
        clear: both;
        display: flex;
        justify-content: space-between;
      }

      .allcontrols .control-holder paper-radio-group {
        margin-top: 5px;
      }

      span.counter {
        font-size: 13px;
        color: gray;
      }

      .runs paper-item {
        --paper-item: {
          white-space: nowrap;
        }
      }

      table.control-holder {
        border: 0;
        border-collapse: collapse;
      }

      table.tf-graph-controls td.input-element-table-data {
        padding: 0 0 0 20px;
      }

      .spacer {
        flex-grow: 1;
      }

      .color-text {
        overflow: hidden;
      }

      /** Override inline styles that suppress pointer events for disabled buttons. Otherwise, the */
      /*  tooltips do not appear. */
      paper-radio-group paper-radio-button {
        pointer-events: auto !important;
      }

      .legend-clarifier {
        color: #266236;
        cursor: help;
        display: inline-block;
        text-decoration: underline;
      }

      .legend-clarifier paper-tooltip {
        width: 150px;
      }

      /** Otherwise, polymer UI controls appear atop node search. */
      tf-graph-node-search {
        z-index: 1;
        width: 100%;
      }

      paper-dropdown-menu {
        flex-grow: 1;
      }
    </style>

    <div class="allcontrols">
      <div class="control-holder">
        <tf-graph-node-search selected-node="{{selectedNode}}" render-hierarchy="[[renderHierarchy]]"></tf-graph-node-search>
      </div>
      <div class="control-holder">
        <paper-button class="icon-button" on-tap="_fit" alt="Fit to screen">
          <iron-icon icon="aspect-ratio" class="button-icon"></iron-icon>
          <span class="button-text">Fit to Screen</span>
        </paper-button>
      </div>
      <div class="control-holder">
        <paper-button class="icon-button" on-click="download" alt="Download PNG">
          <iron-icon icon="file-download" class="button-icon"></iron-icon>
          <span class="button-text">Download PNG</span>
        </paper-button>
        <a href="#" id="graphdownload" class="title" download="graph.png"></a>
      </div>
      <div class="control-holder runs">
        <div class="title">
          Run <span class="counter">([[datasets.length]])</span>
        </div>
        <paper-dropdown-menu no-label-float no-animations noink horizontal-align="left" class="run-dropdown">
          <paper-listbox class="dropdown-content" selected="{{_selectedRunIndex}}" slot="dropdown-content">
            <template is="dom-repeat" items="[[datasets]]">
              <paper-item>[[item.name]]</paper-item>
            </template>
          </paper-listbox>
        </paper-dropdown-menu>
      </div>
      <template is="dom-if" if="[[showSessionRunsDropdown]]">
        <div class="control-holder">
          <div class="title">
            Tag
            <span class="counter">([[_numTags(datasets, _selectedRunIndex)]])</span>
          </div>
          <paper-dropdown-menu no-label-float no-animations horizontal-align="left" noink class="run-dropdown">
            <paper-listbox class="dropdown-content" selected="{{_selectedTagIndex}}" slot="dropdown-content">
              <template is="dom-repeat" items="[[_getTags(datasets, _selectedRunIndex)]]">
                <paper-item>[[item.displayName]]</paper-item>
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
        </div>
      </template>
      <template is="dom-if" if="[[showUploadButton]]">
        <div class="control-holder">
          <div class="title">Upload</div>
          <paper-button raised class="upload-button" on-click="_getFile" title="Upload a graph pbtxt file to view the graph">
            Choose File
          </paper-button>
          <div class="hidden-input">
            <input type="file" id="file" name="file" on-change="_updateFileInput" accept=".pbtxt">
          </div>
        </div>
      </template>
      <div class="control-holder">
        <paper-radio-group selected="{{_selectedGraphType}}">
          
          <paper-radio-button name="op_graph" disabled="[[_getSelectionOpGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]">Graph</paper-radio-button>
          <paper-radio-button name="conceptual_graph" disabled="[[_getSelectionConceptualGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]">Conceptual Graph</paper-radio-button>
          <paper-radio-button name="profile" disabled="[[_getSelectionProfileDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]">Profile</paper-radio-button>
        </paper-radio-group>
      </div>
      <div class="control-holder">
        <div>
          <paper-toggle-button checked="{{traceInputs}}" class="title">
            Trace inputs
          </paper-toggle-button>
        </div>
      </div>
      <template is="dom-if" if="[[healthPillsFeatureEnabled]]">
        <div class="control-holder">
          <paper-toggle-button checked="{{healthPillsToggledOn}}" class="title">Show health pills</paper-toggle-button>
        </div>
      </template>
      <div class="control-holder">
        <div class="title">Color</div>
        <paper-radio-group selected="{{colorBy}}">
          <paper-radio-button name="structure">Structure</paper-radio-button>

          <paper-radio-button name="device">Device</paper-radio-button>

          <paper-radio-button id="xla-cluster-radio-button" name="xla_cluster" disabled="[[!_xlaClustersProvided(renderHierarchy)]]">
            XLA Cluster
          </paper-radio-button>
          <paper-tooltip animation-delay="0" for="xla-cluster-radio-button" position="right" offset="0">
            Coloring by XLA cluster is only enabled if at least 1 op specifies
            an XLA cluster.
          </paper-tooltip>

          <paper-radio-button id="compute-time-radio-button" name="compute_time" disabled="[[!stats]]">
            Compute time
          </paper-radio-button>
          <paper-tooltip animation-delay="0" for="compute-time-radio-button" position="right" offset="0">
            Coloring by compute time is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button id="memory-radio-button" name="memory" disabled="[[!stats]]">
            Memory
          </paper-radio-button>
          <paper-tooltip animation-delay="0" for="memory-radio-button" position="right" offset="0">
            Coloring by memory is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button id="tpu-compatibility-radio-button" name="op_compatibility">
            TPU Compatibility
          </paper-radio-button>
          <paper-tooltip animation-delay="0" for="tpu-compatibility-radio-button" position="right" offset="0">
            Coloring by whether an operation is compatible for the TPU device.
          </paper-tooltip>
        </paper-radio-group>
        <span class="spacer"></span>
      </div>
      <div>
        <template is="dom-if" if="[[_isGradientColoring(stats, colorBy)]]">
          <svg width="140" height="20" style="margin: 0 5px" class="color-text">
            <defs>
              <lineargradient id="linearGradient" x1="0%" y1="0%" x2="100%" y2="0%">
                <stop class="start" offset="0%" stop-color$="[[_currentGradientParams.startColor]]" />
                <stop class="end" offset="100%" stop-color$="[[_currentGradientParams.endColor]]" />
              </lineargradient>
            </defs>
            <rect x="0" y="0" width="135" height="20" fill="url(#linearGradient)" stroke="black" />
          </svg>
          <div class="domainValues color-text">
            <div class="domainStart">[[_currentGradientParams.minValue]]</div>
            <div class="domainEnd">[[_currentGradientParams.maxValue]]</div>
          </div>
          <br style="clear: both">
          <div>Devices included in stats:</div>
          <div class="deviceList">
            <template is="dom-repeat" items="[[_currentDevices]]">
              <div class="color-legend-row devices-checkbox">
                <span><input type="checkbox" value$="[[item.device]]" checked$="[[item.used]]" on-click="_deviceCheckboxClicked"></span>
                <span>[[item.suffix]]</span>
                <template is="dom-if" if="[[item.ignoredMsg]]">
                  <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
                  <paper-tooltip position="right" offset="0" animation-delay="0">[[item.ignoredMsg]]</paper-tooltip>
                </template>
              </div>
            </template>
          </div>
        </template>
        <template is="dom-if" if="[[_equals(colorBy, 'structure')]]">
          <div class="color-text">
            <div class="color-legend-row">
              <span class="label">
                colors
              </span>
              <span class="color-legend-value">same substructure</span>
            </div>
            <div class="color-legend-row">
              <tf-graph-icon type="META" height="16" fill-override="#eee" stroke-override="#a6a6a6"></tf-graph-icon>
              <span class="color-legend-value">unique substructure</span>
            </div>
          </div>
        </template>
        <template is="dom-if" if="[[_equals(colorBy, 'device')]]">
          <div>
            <template is="dom-repeat" items="[[_currentDeviceParams]]">
              <div class="color-legend-row">
                <tf-graph-icon type="META" height="16" fill-override="[[item.color]]" stroke-override="#a6a6a6"></tf-graph-icon>
                <span class="color-legend-value">[[item.device]]</span>
              </div>
            </template>
            <div class="color-legend-row">
              <tf-graph-icon type="META" height="16" fill-override="#eee" stroke-override="#a6a6a6"></tf-graph-icon>
              <span class="color-legend-value">unknown device</span>
            </div>
          </div>
        </template>
        <template is="dom-if" if="[[_equals(colorBy, 'xla_cluster')]]">
          <div>
            <template is="dom-repeat" items="[[_currentXlaClusterParams]]">
              <div class="color-legend-row">
                <svg>
                  <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#unfilled-rect" x="0" y="0" style="fill:[[item.color]]" />
                </svg>
                <span class="color-legend-value">[[item.xla_cluster]]</span>
              </div>
            </template>
            <div class="color-legend-row">
              <svg>
                <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#grey-rect" x="0" y="0" />
              </svg>
              <span class="color-legend-value">unknown XLA cluster</span>
            </div>
          </div>
        </template>
        <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
          <div class="color-text">
            <div class="color-legend-row">
              <tf-graph-icon type="OP" height="16" fill-override="#0f9d58" stroke-override="#ccc"></tf-graph-icon>
              <span class="color-legend-value">Valid Op</span>
            </div>
            <div class="color-legend-row">
              <tf-graph-icon type="OP" height="16" fill-override="#db4437" stroke-override="#ccc"></tf-graph-icon>
              <span class="color-legend-value">Invalid Op</span>
            </div>
          </div>
        </template>
        <template is="dom-if" if="[[_statsNotNull(stats)]]">
          <div class="color-legend-row">
            <tf-graph-icon type="META" height="16" faded></tf-graph-icon>
            <span class="color-legend-value">unused substructure</span>
          </div>
        </template>
      </div>
    </div>
    <div class="legend-holder">
      <paper-icon-button icon="[[_getToggleLegendIcon(_legendOpened)]]" on-click="_toggleLegendOpen" class="toggle-legend-button">
      </paper-icon-button>
      <span class="toggle-legend-text">
        [[_getToggleText(_legendOpened)]]
      </span>
      <iron-collapse opened="[[_legendOpened]]">
        <div>
          <table>
            <tr>
              <td><div class="title">Graph</div></td>
              <td>(* = expandable)</td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="META" height="16" fill-override="#d9d9d9" stroke-override="#ccc"></tf-graph-icon>
              </td>
              <td>
                Namespace<span class="gray">*</span>
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Encapsulates a set of nodes. Namespace is hierarchical and
                    based on scope.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="OP" height="16"></tf-graph-icon>
              </td>
              <td>
                OpNode
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Node that performs an operation. These nodes cannot expand.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="SERIES" height="16"></tf-graph-icon>
              </td>
              <td>
                Unconnected series<span class="gray">*</span>
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Sequence of numbered nodes that are not connected to each
                    other.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="SERIES" height="16" vertical></tf-graph-icon>
              </td>
              <td>
                Connected series<span class="gray">*</span>
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Sequence of numbered nodes that are connected to each other.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <svg class="icon">
                  <circle fill="white" stroke="#848484" cx="10" cy="10" r="5" />
                </svg>
              </td>
              <td>
                Constant
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Node that outputs a constant value.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="SUMMARY" height="20"></tf-graph-icon>
              </td>
              <td>
                Summary
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Node that collects data for visualization within
                    TensorBoard.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <svg class="icon" height="15px" preserveaspectratio="xMinYMid meet" viewbox="0 0 15 15">
                  <defs>
                    <marker id="dataflow-arrowhead-legend" fill="#bbb" markerwidth="10" markerheight="10" refx="9" refy="5" orient="auto-start-reverse">
                      <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0" />
                    </marker>
                  </defs>
                  <path marker-end="url(#dataflow-arrowhead-legend)" stroke="#bbb" d="M2 9 l 29 0" stroke-linecap="round" />
                </svg>
              </td>
              <td>
                Dataflow edge
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Edge showing the data flow between operations. Edges flow
                    upwards unless arrowheads specify otherwise.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <svg class="icon" height="15px" preserveaspectratio="xMinYMid meet" viewbox="0 0 15 15">
                  <path stroke="#bbb" d="M2 9 l 29 0" stroke-linecap="round" stroke-dasharray="2, 2" />
                </svg>
              </td>
              <td>
                Control dependency edge
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Edge showing the control dependency between operations.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <svg class="icon" height="15px" preserveaspectratio="xMinYMid meet" viewbox="0 0 15 15">
                  <defs>
                    <marker id="reference-arrowhead-legend" fill="#FFB74D" markerwidth="10" markerheight="10" refx="9" refy="5" orient="auto-start-reverse">
                      <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0" />
                    </marker>
                  </defs>
                  <path marker-end="url(#reference-arrowhead-legend)" stroke="#FFB74D" d="M2 9 l 29 0" stroke-linecap="round" />
                </svg>
              </td>
              <td>
                Reference edge
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Edge showing that the outgoing operation node can mutate the
                    incoming tensor.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
          </table>
        </div>
      </iron-collapse>
    </div>
  </template>
</dom-module>















<dom-module id="tf-graph-dashboard">
  <template>
    <paper-dialog id="error-dialog" with-backdrop></paper-dialog>
    <template is="dom-if" if="[[_datasetsState(_datasetsFetched, _datasets, 'EMPTY')]]">
      <div style="max-width: 540px; margin: 80px auto 0 auto;">
        <h3>No graph definition files were found.</h3>
        <p>
          To store a graph, create a
          <code>tf.summary.FileWriter</code>
          and pass the graph either via the constructor, or by calling its
          <code>add_graph()</code> method. You may want to check out the
          <a href="https://www.tensorflow.org/get_started/graph_viz">graph visualizer tutorial</a>.
        

        <p>
          If you’re new to using TensorBoard, and want to find out how to add
          data and set up your event files, check out the
          <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
          and perhaps the
          <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
        

        <p>
          If you think TensorBoard is configured properly, please see
          <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
          and consider filing an issue on GitHub.
        
      </div>
    </template>
    <template is="dom-if" if="[[_datasetsState(_datasetsFetched, _datasets, 'PRESENT')]]">
      <tf-dashboard-layout>
        <tf-graph-controls id="controls" class="sidebar" slot="sidebar" devices-for-stats="{{_devicesForStats}}" color-by-params="[[_colorByParams]]" stats="[[_stats]]" color-by="{{_colorBy}}" datasets="[[_datasets]]" render-hierarchy="[[_renderHierarchy]]" selection="{{_selection}}" selected-file="{{_selectedFile}}" selected-node="{{_selectedNode}}" health-pills-feature-enabled="[[_debuggerDataEnabled]]" health-pills-toggled-on="{{healthPillsToggledOn}}" on-fit-tap="_fit" trace-inputs="{{_traceInputs}}"></tf-graph-controls>
        <div class="center" slot="center">
          <tf-graph-dashboard-loader id="loader" datasets="[[_datasets]]" selection="[[_selection]]" selected-file="[[_selectedFile]]" out-graph-hierarchy="{{_graphHierarchy}}" out-graph="{{_graph}}" out-stats="{{_stats}}" progress="{{_progress}}" hierarchy-params="[[_hierarchyParams]]" compatibility-provider="[[_compatibilityProvider]]"></tf-graph-dashboard-loader>
          <tf-graph-board id="graphboard" devices-for-stats="[[_devicesForStats]]" color-by="[[_colorBy]]" color-by-params="{{_colorByParams}}" graph-hierarchy="[[_graphHierarchy]]" graph="[[_graph]]" hierarchy-params="[[_hierarchyParams]]" progress="[[_progress]]" debugger-data-enabled="[[_debuggerDataEnabled]]" are-health-pills-loading="[[_areHealthPillsLoading]]" debugger-numeric-alerts="[[_debuggerNumericAlerts]]" node-names-to-health-pills="[[_nodeNamesToHealthPills]]" all-steps-mode-enabled="{{allStepsModeEnabled}}" specific-health-pill-step="{{specificHealthPillStep}}" health-pill-step-index="[[_healthPillStepIndex]]" render-hierarchy="{{_renderHierarchy}}" selected-node="{{_selectedNode}}" stats="[[_stats]]" trace-inputs="[[_traceInputs]]"></tf-graph-board>
        </div>
      </tf-dashboard-layout>
    </template>
    <style>
      :host /deep/ {
        font-family: 'Roboto', sans-serif;
      }

      .sidebar {
        display: flex;
        height: 100%;
      }

      .center {
        position: relative;
        height: 100%;
      }

      paper-dialog {
        padding: 20px;
      }
    </style>
  </template>
</dom-module>



























<dom-module id="vz-distribution-chart">
  <template>
    <style include="plottable-style"></style>
    <div id="chartdiv"></div>
    <style>
      :host {
        -webkit-user-select: none;
        -moz-user-select: none;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        position: relative;
      }
      #chartdiv {
        -webkit-user-select: none;
        -moz-user-select: none;
        flex-grow: 1;
        flex-shrink: 1;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-distribution-loader">
  <template>
    <tf-card-heading tag="[[tag]]" run="[[run]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]" color="[[_runColor]]"></tf-card-heading>
    
    <vz-distribution-chart id="chart" x-type="[[xType]]" color-scale="[[_colorScale]]"></vz-distribution-chart>
    <div style="display: flex; flex-direction: row;">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
    </div>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 330px;
        height: 235px;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      :host([_expanded]) {
        width: 700px;
        height: 500px;
      }

      vz-histogram-timeseries {
        -moz-user-select: none;
        -webkit-user-select: none;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      tf-card-heading {
        margin-bottom: 10px;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-distribution-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <tf-option-selector id="xTypeSelector" name="Horizontal axis" selected-id="{{_xType}}">
            <paper-button id="step">step</paper-button>
            <paper-button id="relative">relative</paper-button>
            <paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>

      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No distribution data was found.</h3>
            <p>Probable causes:
            <ul>
              <li>
                You haven’t written any histogram data to your event files.
                (Histograms and distributions both use the histogram summary
                operation.)
              </li>

              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-distribution-loader active="[[active]]" run="[[item.run]]" tag="[[item.tag]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]" x-type="[[_xType]]" request-manager="[[_requestManager]]"></tf-distribution-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>

  
</dom-module>

























<dom-module id="vz-histogram-timeseries">
  <template>
    <div id="tooltip"><span></span></div>
    <svg id="svg">
      <g>
        <g class="axis x"></g>
        <g class="axis y"></g>
        <g class="axis y slice"></g>
        <g class="stage">
          <rect class="background"></rect>
        </g>
        <g class="x-axis-hover"></g>
        <g class="y-axis-hover"></g>
        <g class="y-slice-axis-hover"></g>
      </g>
    </svg>

    <style>
      :host {
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        position: relative;
      }

      svg {
        font-family: roboto, sans-serif;
        overflow: visible;
        display: block;
        width: 100%;
        flex-grow: 1;
        flex-shrink: 1;
      }

      #tooltip {
        position: absolute;
        display: block;
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
      }

      .background {
        fill-opacity: 0;
        fill: red;
      }

      .histogram {
        pointer-events: none;
      }

      .hover {
        font-size: 9px;
        dominant-baseline: middle;
        opacity: 0;
      }

      .hover circle {
        stroke: white;
        stroke-opacity: 0.5;
        stroke-width: 1px;
      }

      .hover text {
        fill: black;
        opacity: 0;
      }

      .hover.hover-closest circle {
        fill: black !important;
      }

      .hover.hover-closest text {
        opacity: 1;
      }

      .baseline {
        stroke: black;
        stroke-opacity: 0.1;
      }

      .outline {
        fill: none;
        stroke: white;
        stroke-opacity: 0.5;
      }

      .outline.outline-hover {
        stroke: black !important;
        stroke-opacity: 1;
      }

      .x-axis-hover,
      .y-axis-hover,
      .y-slice-axis-hover {
        pointer-events: none;
      }

      .x-axis-hover .label,
      .y-axis-hover .label,
      .y-slice-axis-hover .label {
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
        text-anchor: end;
      }

      .x-axis-hover text {
        text-anchor: middle;
      }

      .y-axis-hover text,
      .y-slice-axis-hover text {
        text-anchor: start;
      }

      .x-axis-hover line,
      .y-axis-hover line,
      .y-slice-axis-hover line {
        stroke: black;
      }

      .x-axis-hover rect,
      .y-axis-hover rect,
      .y-slice-axis-hover rect {
        fill: white;
      }

      .axis {
        font-size: 11px;
      }

      .axis path.domain {
        fill: none;
      }

      .axis .tick line {
        stroke: #ddd;
      }

      .axis.slice {
        opacity: 0;
      }

      .axis.slice .tick line {
        stroke-dasharray: 2;
      }

      .small .axis text {
        display: none;
      }
      .small .axis .tick:first-of-type text {
        display: block;
      }
      .small .axis .tick:last-of-type text {
        display: block;
      }
      .medium .axis text {
        display: none;
      }
      .medium .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
      .large .axis text {
        display: none;
      }
      .large .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
    </style>
  </template>

  
</dom-module>







<dom-module id="tf-histogram-loader">
  <template>
    <tf-card-heading tag="[[tag]]" run="[[run]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]" color="[[_runColor]]"></tf-card-heading>
    
    <vz-histogram-timeseries id="chart" time-property="[[timeProperty]]" mode="[[histogramMode]]" color-scale="[[_colorScaleFunction]]"></vz-histogram-timeseries>
    <div style="display: flex; flex-direction: row;">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
    </div>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 330px;
        height: 235px;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      :host([_expanded]) {
        width: 700px;
        height: 500px;
      }

      vz-histogram-timeseries {
        -moz-user-select: none;
        -webkit-user-select: none;
        will-change: transform;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      tf-card-heading {
        margin-bottom: 10px;
        width: 90%;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-histogram-dashboard">
  <template>
    <tf-dashboard-layout>
      <div slot="sidebar">
        <div class="sidebar-section">
          <tf-option-selector id="histogramModeSelector" name="Histogram mode" selected-id="{{_histogramMode}}">
            <paper-button id="overlay">overlay</paper-button>
            <paper-button id="offset">offset</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-option-selector id="timePropertySelector" name="Offset time axis" selected-id="{{_timeProperty}}">
            <paper-button id="step">step</paper-button>
            <paper-button id="relative">relative</paper-button>
            <paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No histogram data was found.</h3>
            <p>Probable causes:
            <ul>
              <li>
                You haven’t written any histogram data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-histogram-loader run="[[item.run]]" tag="[[item.tag]]" active="[[active]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]" time-property="[[_timeProperty]]" histogram-mode="[[_histogramMode]]" request-manager="[[_requestManager]]"></tf-histogram-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>

  
</dom-module>























<dom-module id="tf-text-loader">
  <template>
    <tf-card-heading run="[[run]]" tag="[[tag]]" color="[[_runColor]]">
    </tf-card-heading>
    <paper-material elevation="1" id="steps-container" class="container scrollbar" style="border-color: [[_runColor]]">
      <template is="dom-repeat" items="[[_texts]]">
        <paper-material elevation="1" class="step-container">
          step <span class="step-value">[[_formatStep(item.step)]]</span>
        </paper-material>
        <paper-material elevation="1" class="text">
          <tf-markdown-view html="[[item.text]]"></tf-markdown-view>
        </paper-material>
      </template>
    </paper-material>
    <style include="scrollbar-style"></style>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 100%;
        height: auto;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      .scrollbar {
        will-change: transform;
      }
      #steps-container {
        border-radius: 3px;
        border: 2px solid /* color computed and set as inline style */;
        display: block;
        max-height: 500px;
        overflow: auto;
        padding: 10px;
      }
      .text {
        background-color: white;
        border-radius: 0 3px 3px 3px;
        padding: 5px;
        word-break: break-word;
      }
      .step-container {
        background-color: var(--tb-ui-light-accent);
        border-bottom: none;
        border-radius: 3px 3px 0 0;
        border: 1px solid #ccc;
        display: inline-block;
        font-size: 12px;
        font-style: italic;
        margin-left: -1px; /* to correct for border */
        padding: 3px;
      }
      .step-container:not(:first-child) {
        margin-top: 15px;
      }

      tf-card-heading {
        margin-bottom: 10px;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-text-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No text data was found.</h3>
            <p>Probable causes:
            <ul>
              <li>You haven’t written any text data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-text-loader active="[[active]]" tag="[[item.tag]]" run="[[item.run]]" request-manager="[[_requestManager]]"></tf-text-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>
  
</dom-module>

























<dom-module id="tf-pr-curve-card">
  <template>
    <tf-card-heading tag="[[tag]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]"></tf-card-heading>

    <tf-line-chart-data-loader x-components-creation-method="[[_xComponentsCreationMethod]]" y-value-accessor="[[_yValueAccessor]]" tooltip-columns="[[_tooltipColumns]]" color-scale="[[_colorScaleFunction]]" default-x-range="[[_defaultXRange]]" default-y-range="[[_defaultYRange]]" smoothing-enabled="[[_smoothingEnabled]]" request-manager="[[requestManager]]" data-to-load="[[runs]]" data-series="[[runs]]" load-key="[[tag]]" get-data-load-url="[[_dataUrl]]" load-data-callback="[[_createProcessDataFunction()]]" active="[[active]]"></tf-line-chart-data-loader>

    <div id="buttons-row">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
      <paper-icon-button icon="settings-overscan" on-tap="_resetDomain" title="Reset axes to [0, 1]."></paper-icon-button>
    </div>

    <div id="step-legend">
      <template is="dom-repeat" items="[[_runsWithStepAvailable]]" as="run">
        <div class="legend-row">
          <div class="color-box" style="background: [[_computeRunColor(run)]];"></div>
          [[run]] is at
          <span class="step-label-text">
            step [[_computeCurrentStepForRun(_runToPrCurveEntry, run)]] </span><br>
          <span class="wall-time-label-text">
            ([[_computeCurrentWallTimeForRun(_runToPrCurveEntry, run)]])
          </span>
        </div>
      </template>
    </div>

    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 500px;
        margin-right: 10px;
        margin-bottom: 25px;
      }
      :host([_expanded]) {
        width: 100%;
      }
      tf-line-chart-data-loader {
        height: 300px;
        position: relative;
      }
      :host([_expanded]) tf-line-chart-data-loader {
        height: 600px;
      }
      #buttons-row {
        display: flex;
        flex-direction: row;
      }
      #buttons-row paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      #buttons-row paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      #step-legend {
        box-sizing: border-box;
        font-size: 0.8em;
        max-height: 200px;
        overflow-y: auto;
        padding: 0 0 0 10px;
        width: 100%;
      }
      .legend-row {
        margin: 5px 0 5px 0;
        width: 100%;
      }
      .color-box {
        display: inline-block;
        border-radius: 1px;
        width: 10px;
        height: 10px;
      }
      .step-label-text {
        font-weight: bold;
      }
      .wall-time-label-text {
        color: #888;
        font-size: 0.8em;
      }
    </style>
  </template>
  
</dom-module>








<dom-module id="tf-pr-curve-steps-selector">
  <template>
    <template is="dom-repeat" items="[[_runsWithSliders]]" as="run">
      <div class="run-widget">
        <div class="run-display-container">
          <div class="run-color-box" style="background:[[_computeColorForRun(run)]];"></div>
          <div class="run-text">
            [[run]]
          </div>
        </div>
        <div class="step-display-container">
          [[_computeTimeTextForRun(runToAvailableTimeEntries, _runToStepIndex,
          run, timeDisplayType)]]
        </div>
        <paper-slider data-run$="[[run]]" step="1" type="number" min="0" max="[[_computeMaxStepIndexForRun(runToAvailableTimeEntries, run)]]" value="[[_getStep(_runToStepIndex, run)]]" on-immediate-value-changed="_sliderValueChanged"></paper-slider>
      </div>
    </template>
    <style>
      .run-widget {
        margin: 10px 0 0 0;
      }
      paper-slider {
        margin: -8px 0 0 -15px;
        width: 100%;
      }
      .step-display-container {
        font-size: 0.9em;
        margin: 0 15px 0 0;
      }
      .run-text {
        display: inline-block;
      }
      .run-color-box {
        width: 12px;
        height: 12px;
        border-radius: 3px;
        display: inline-block;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-pr-curve-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <tf-option-selector id="time-type-selector" name="Time Display Type" selected-id="{{_timeDisplayType}}">
            <paper-button id="step">step</paper-button><paper-button id="relative">relative</paper-button><paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <template is="dom-if" if="[[_runToAvailableTimeEntries]]">
          <div class="sidebar-section" id="steps-selector-container">
            <tf-pr-curve-steps-selector runs="[[_relevantSelectedRuns]]" run-to-step="{{_runToStep}}" run-to-available-time-entries="[[_runToAvailableTimeEntries]]" time-display-type="[[_timeDisplayType]]"></tf-pr-curve-steps-selector>
          </div>
        </template>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No precision–recall curve data was found.</h3>
            <p>Probable causes:
            <ul>
              <li>
                You haven’t written any precision–recall data to your event
                files.
              </li>
              <li>
                TensorBoard can’t find your event files.
              </li>
            </ul>
            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]" get-category-item-key="[[_getCategoryItemKey]]">
              <template>
                <tf-pr-curve-card active="[[active]]" runs="[[item.runs]]" tag="[[item.tag]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.runs, item.tag)]]" request-manager="[[_requestManager]]" run-to-step-cap="[[_runToStep]]"></tf-pr-curve-card>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      /** Do not let the steps selector occlude the run selector. */
      #steps-selector-container {
        max-height: 40%;
        overflow-y: auto;
      }
    </style>
  </template>

  
</dom-module>





<dom-module id="tf-profile-redirect-dashboard">
  <template>
    <div class="message">
      <h3>The profile plugin has moved.</h3>
      <p>
        Please install the new version of the profile plugin from PyPI by
        running the following command from the machine running TensorBoard:
      
      <textarea id="commandTextarea" readonly rows="1" on-blur="_removeCopiedMessage">
[[_installCommand]]</textarea>
      <div id="copyContainer">
        <span id="copiedMessage"></span>
        <paper-button raised on-tap="_copyInstallCommand">Copy to clipboard</paper-button>
      </div>
    </div>

    <style>
      .message {
        margin: 80px auto 0 auto;
        max-width: 540px;
      }
      #commandTextarea {
        margin-top: 1ex;
        padding: 1ex 1em;
        resize: vertical;
        width: 100%;
      }
      #copyContainer {
        display: flex;
      }
      #copiedMessage {
        align-self: center;
        flex-grow: 1;
        font-style: italic;
        padding-right: 1em;
        text-align: right;
      }
    </style>
  </template>

  
</dom-module>













<dom-module id="tf-plugin-dialog">
  <template>
    
    <template is="dom-if" if="[[_open]]">
      <div id="dashboard-backdrop"></div>
    </template>
    <paper-dialog id="dialog" modal opened="{{_open}}" with-backdrop="[[_useNativeBackdrop]]">
      <h2 id="dialog-title">[[_title]]</h2>
      <div class="custom-message">[[_customMessage]]</div>
    </paper-dialog>
    <style>
      /** We rely on a separate `_hidden` property instead of directly making use
          of the `_open` attribute because this CSS specification may strangely
          affect other elements throughout TensorBoard. See #899. */
      #dashboard-backdrop {
        background: rgba(0, 0, 0, 0.6);
        width: 100%;
        height: 100%;
      }

      #dialog-title {
        padding-bottom: 15px;
      }

      .custom-message {
        margin-top: 0;
        margin-bottom: 15px;
      }
    </style>
  </template>
  
</dom-module>





<dom-module id="tf-beholder-video">
  <template>
    <div id="container">
      <img id="video" src$="[[_imageURL]]">
    </div>

    <style>
      img {
        image-rendering: pixelated;
        margin-right: 10px;
      }
    </style>
  </template>

  
</dom-module>




<dom-module id="tf-beholder-info">
  <template>
    <template is="dom-repeat" items="[[_items]]">
      <div class="section-info" style$="height: [[item.height]]px">
        <ul>
          <li>[[item.name]]</li>
          <li>shape: [[item.shape]]</li>
          <li>range: [ [[item.min]], [[item.max]] ]</li>
          <li>mean: [[item.mean]]</li>
        </ul>
      </div>
    </template>

    <style>
      .section-info {
        margin: 0 0 5px 0;
      }
      .section-info ul {
        list-style-type: none;
        margin: 0;
        padding-left: 10px;
      }
    </style>
  </template>

  
</dom-module>


<dom-module id="tf-beholder-dashboard">
  <template>
    <tf-plugin-dialog id="initialDialog"></tf-plugin-dialog>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <template is="dom-if" if="[[_controls_disabled]]">
          <div class="sidebar-section">
            <p class="controls-disabled-message">
              Controls disabled: directory is not writeable.
            
            <p class="disclaimer">
              Beholder requires write access to the log directory in order to
              communicate visualization changes to the <code>Beholder</code>
              instance in your model.
            
          </div>
        </template>
        <div class="sidebar-section">
          <h3>Values</h3>
          <paper-radio-group id="valuesSelector" selected="{{_values}}">
            <paper-radio-button name="trainable_variables" disabled="[[_controls_disabled]]">
              <pre>tf.trainable_variables()</pre>
            </paper-radio-button>
            <paper-radio-button id="option-arrays" name="arrays" disabled="[[_controls_disabled]]">
              <pre>b.update(arrays=[NP_ARRAYS])</pre>
            </paper-radio-button>
            <paper-radio-button id="option-frames" name="frames" disabled="[[_controls_disabled]]">
              <pre>b.update(frame=NP_ARRAY)</pre>
            </paper-radio-button>
          </paper-radio-group>

          <template is="dom-if" if="[[_valuesNotFrame(_values)]]">
            <paper-checkbox checked="{{_showAll}}" disabled="[[_controls_disabled]]">Show all data <i>(can be resource intensive)</i></paper-checkbox>
          </template>
        </div>

        <template is="dom-if" if="[[_valuesNotFrame(_values)]]">
          <div class="sidebar-section">
            <h3>Mode</h3>
            <paper-radio-group id="modeSelector" selected="{{_mode}}">
              <paper-radio-button name="current" disabled="[[_controls_disabled]]">
                current values
              </paper-radio-button>
              <paper-radio-button name="variance" disabled="[[_controls_disabled]]">
                variance over train steps
              </paper-radio-button>
            </paper-radio-group>
            <template is="dom-if" if="[[_varianceSelected(_mode)]]">
              <h4>Variance timesteps: {{_windowSize}}</h4>
              <paper-slider id="windowSlider" value="{{_windowSize}}" type="number" step="1" min="2" max="20" pin="true" disabled="[[_controls_disabled]]">
              </paper-slider>
            </template>
          </div>

          <div class="sidebar-section">
            <h3>Image scaling</h3>
            <paper-radio-group id="scalingSelector" selected="{{_scaling}}">
              <paper-radio-button id="option-layer" name="layer" disabled="[[_controls_disabled]]">
                per section
              </paper-radio-button>
              <paper-tooltip for="option-layer" position="right">
                Black is the lowest value in that section, white is that largest
                value in that section.
              </paper-tooltip>

              <paper-radio-button id="option-network" name="network" disabled="[[_controls_disabled]]">
                all sections
              </paper-radio-button>
              <paper-tooltip for="option-network" position="right">
                Black is the smallest value in all sections, white is the
                largest value in all sections.
              </paper-tooltip>
            </paper-radio-group>

            <div id="colormap-selection">
              <div id="colormap-selection-label">Colormap:</div>
              <paper-dropdown-menu no-label-float selected-item-label="{{_colormap}}" disabled="[[_controls_disabled]]">
                <paper-listbox slot="dropdown-content" selected="0">
                  <paper-item>magma</paper-item>
                  <paper-item>inferno</paper-item>
                  <paper-item>plasma</paper-item>
                  <paper-item>viridis</paper-item>
                  <paper-item>grayscale</paper-item>
                </paper-listbox>
              </paper-dropdown-menu>
            </div>
          </div>
        </template>

        <div class="sidebar-section">
          <h3>Updates per second: {{_FPS}}</h3>
          <paper-slider id="FPSSlider" value="{{_FPS}}" type="number" step="1" min="0" max="30" pin="true" disabled="[[_controls_disabled]]">
          </paper-slider>
        </div>

        <div class="sidebar-section">
          <div>
            <paper-button class="x-button" id="record_button" on-tap="_toggleRecord" disabled="[[_controls_disabled]]">
              [[_recordText]]
            </paper-button>
          </div>
        </div>

        <div class="sidebar-section">
          <p class="disclaimer">
            Note: Beholder currently only works well on local file systems.
          
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[!_is_active]]">
          <div class="no-data-warning">
            <h3>No Beholder data was found.</h3>

            <p>Probable causes:
            <ul>
              <li>Your script isn't running.</li>
              <li>You aren't calling <code>beholder.update()</code>.</li>
            </ul>

            <p>
              To use Beholder, import and instantiate the
              <code>Beholder</code> class, and call its
              <code>update</code> method with a <code>Session</code> argument
              after every train step:
            

            <pre>from tensorboard.plugins.beholder import Beholder
beholder = Beholder(LOG_DIRECTORY)

# inside train loop
beholder.update(
  session=sess,
  arrays=list_of_np_arrays,  # optional argument
  frame=two_dimensional_np_array,  # optional argument
)</pre>
            <p>
              If using <code>tf.train.MonitoredSession</code>, you can use
              <code>BeholderHook</code>:
            

            <pre>from tensorboard.plugins.beholder import BeholderHook
beholder_hook = BeholderHook(LOG_DIRECTORY)
with MonitoredSession(..., hooks=[beholder_hook]) as sess:
  sess.run(train_op)</pre>

            <p>
              If you think everything is set up properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/beholder/README.md">the README</a>
              for more information and consider filing an issue on GitHub.
            

            <p class="disclaimer">
              Note: Beholder currently only works well on local file systems.
            
          </div>
        </template>

        <template is="dom-if" if="[[_is_active]]">
          <tf-beholder-video id="video" fps="[[_FPS]]"></tf-beholder-video>

          <template is="dom-if" if="[[_valuesNotFrame(_values)]]">
            <tf-beholder-info id="info" fps="[[_FPS]]"> </tf-beholder-info>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .center {
        height: 100%;
        display: flex;
        padding: 0;
      }

      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0;
      }

      paper-checkbox {
        display: block;
        padding: 4px;
      }

      paper-radio-button {
        display: flex;
        padding: 5px;

        --paper-radio-button-radio-container: {
          flex-grow: 0;
          flex-shrink: 0;
        }

        --paper-radio-button-label: {
          font-size: 13px;
          overflow: hidden;
          text-overflow: ellipsis;
        }
      }

      paper-radio-group {
        margin-top: 5px;
        width: 100%;
      }

      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
        flex-grow: 2;
      }

      pre {
        display: inline;
      }

      paper-button#record_button {
        color: #d32f2f;
      }

      paper-button#record_button.is-recording {
        background: #d32f2f;
        color: white;
      }

      .sidebar-section.beholder-dashboard:last-child {
        flex-grow: 0;
      }

      #colormap-selection {
        display: flex;
        margin-top: 5px;
      }

      #colormap-selection-label {
        margin-top: 13px;
      }

      #colormap-selection paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }

      h4 {
        font-size: 14px;
        font-weight: normal;
        margin: 5px 0;
      }

      p.disclaimer {
        color: #999;
        font-style: italic;
      }

      p.controls-disabled-message {
        color: #c00;
        font-weight: bold;
      }

      .sidebar {
        font-size: 14px;
      }
    </style>
  </template>
  
</dom-module>




















<dom-module id="vaadin-split-layout">
  <template>
    <style>
      :host {
        display: flex;
        overflow: hidden !important;
        transform: translateZ(0);
      }

      :host([vertical]) {
        flex-direction: column;
      }

      :host ::slotted(*) {
        flex: 1 1 auto;
        overflow: auto;
      }

      :host > #splitter {
        flex: none;
        position: relative;
        z-index: 1;
        overflow: visible;
        min-width: 8px;
        min-height: 8px;
        background: var(--divider-color, #ccc);
        fill: var(--primary-background-color, #fff);
        @apply --vaadin-split-layout-splitter;
      }

      :host(:not([vertical])) > #splitter {
        cursor: ew-resize;
      }

      :host([vertical]) > #splitter {
        cursor: ns-resize;
      }

      #handle,
      #splitter ::slotted([slot=handle]) {
        position: absolute;
        top: 50%;
        left: 50%;
        transform: translate(-50%, -50%);
      }

      :host([vertical]) > #splitter #handle {
        transform: translate(-50%, -50%) rotate(90deg);
      }
    </style>
    <slot id="primary" name="primary"></slot>
    <div id="splitter" on-track="_onHandleTrack" on-down="_preventDefault">
      <slot name="handle">
        <svg id="handle" width="40" height="40">
          <rect x="19" y="8" width="2" height="24"></rect>
        </svg>
      </slot>
    </div>
    <slot id="secondary" name="secondary"></slot>
  </template>

  
</dom-module>




<dom-module id="tf-hparams-query-pane">
  <template>
    <div class="pane">
      <vaadin-split-layout vertical>
        <vaadin-split-layout vertical id="hyperparameters-metrics-statuses">
          <vaadin-split-layout vertical id="hyperparameters-metrics">
            <div class="section hyperparameters">
              <div class="section-title">Hyperparameters</div>
              <template is="dom-repeat" items="{{_hparams}}" as="hparam">
                <div class="hparam">
                  <paper-checkbox checked="{{hparam.displayed}}" class="hparam-checkbox">
                    [[_hparamName(hparam.info)]]
                  </paper-checkbox>
                  
                  
                  <template is="dom-if" if="[[hparam.filter.domainDiscrete]]">
                    <template is="dom-repeat" items="[[hparam.filter.domainDiscrete]]">
                      <paper-checkbox checked="{{item.checked}}" class="discrete-value-checkbox" on-change="_queryServer">
                        [[_prettyPrint(item.value)]]
                      </paper-checkbox>
                    </template>
                  </template>
                  
                  <template is="dom-if" if="[[hparam.filter.interval]]">
                    <paper-input label="Min" value="{{hparam.filter.interval.min.value}}" allowed_pattern="[0-9.e\-]" on-value-changed="_queryServer" error-message="Invalid input" invalid="[[hparam.filter.interval.min.invalid]]" placeholder="-infinity">
                    </paper-input>
                    <paper-input label="Max" value="{{hparam.filter.interval.max.value}}" allowed_pattern="[0-9.e\-]" on-value-changed="_queryServer" error-message="Invalid input" invalid="[[hparam.filter.interval.max.invalid]]" placeholder="+infinity">
                    </paper-input>
                  </template>
                  
                  <template is="dom-if" if="[[hparam.filter.regexp]]">
                    <paper-input label="Regular expression" value="{{hparam.filter.regexp}}" on-value-changed="_queryServer">
                    </paper-input>
                  </template>
                </div>
              </template>
            </div>
            <div class="section metrics">
              <div class="section-title">Metrics</div>
              <template is="dom-repeat" items="{{_metrics}}" as="metric">
                <div class="metric">
                  
                  <paper-checkbox checked="{{metric.displayed}}" class="metric-checkbox">
                    [[_metricName(metric.info)]]
                  </paper-checkbox>
                  <div class="inline-element">
                    <paper-input label="Min" value="{{metric.filter.interval.min.value}}" allowed-pattern="[0-9.e\-]" on-value-changed="_queryServer" error-message="Invalid input" invalid="{{metric.filter.interval.min.invalid}}" placeholder="-infinity">
                    </paper-input>
                  </div>
                  <div class="inline-element">
                    <paper-input label="Max" allowed-pattern="[0-9.e\-]" value="{{metric.filter.interval.max.value}}" on-value-changed="_queryServer" error-message="Invalid input" invalid="{{metric.filter.interval.max.invalid}}" placeholder="+infinity">
                    </paper-input>
                  </div>
                </div>
              </template>
            </div>
          </vaadin-split-layout>
          <div class="section status">
            <div class="section-title">Status</div>
            <template is="dom-repeat" items="[[_statuses]]" as="status">
              <paper-checkbox checked="{{status.allowed}}" on-change="_queryServer">
                [[status.displayName]]
              </paper-checkbox>
            </template>
          </div>
        </vaadin-split-layout>
        <vaadin-split-layout vertical id="sorting-paging">
          <div class="section sorting">
            <div class="section-title">Sorting</div>
            <paper-dropdown-menu label="Sort by" on-selected-item-changed="_queryServer" horizontal-align="left">
              <paper-listbox class="dropdown-content" slot="dropdown-content" selected="{{_sortByIndex}}" on-selected-item-changed="_queryServer">
                <template is="dom-repeat" items="[[_hparams]]" as="hparam">
                  <paper-item>
                    [[_hparamName(hparam.info)]]
                  </paper-item>
                </template>
                <template is="dom-repeat" items="[[_metrics]]" as="metric">
                  <paper-item>
                    [[_metricName(metric.info)]]
                  </paper-item>
                </template>
              </paper-listbox>
            </paper-dropdown-menu>
            <paper-dropdown-menu label="Direction" on-selected-item-changed="_queryServer" horizontal-align="left">
              <paper-listbox class="dropdown-content" slot="dropdown-content" selected="{{_sortDirection}}">
                <paper-item>Ascending</paper-item>
                <paper-item>Descending</paper-item>
              </paper-listbox>
            </paper-dropdown-menu>
          </div>
          <vaadin-split-layout vertical id="paging-download">
            <div class="section paging">
              <div class="section-title">Paging</div>
              <div>
                Number of matching session groups:
                [[_totalSessionGroupsCountStr]]
              </div>
              <div class="inline-element page-number-input">
                <paper-input label="Page #" value="{{_pageNumberInput.value}}" allowed-pattern="[0-9]" error-message="Invalid input" invalid="[[_pageNumberInput.invalid]]" on-value-changed="_queryServer">
                  <div slot="suffix" class="page-suffix">
                    / [[_pageCountStr]]
                  </div>
                </paper-input>
              </div>
              <div class="inline-element page-size-input">
                <paper-input label="Max # of session groups per page:" value="{{_pageSizeInput.value}}" allowed-pattern="[0-9]" error-message="Invalid input" invalid="[[_pageSizeInput.invalid]]" on-value-changed="_queryServer">
                </paper-input>
              </div>
            </div>
            <div class="section download">
              <template is="dom-if" if="[[_sessionGroupsRequest]]">
                Download data as
                <span>
                  <a id="csvLink" download="hparams_table.csv" href="[[_csvUrl(_sessionGroupsRequest, configuration)]]">CSV</a>
                  <a id="jsonLink" download="hparams_table.json" href="[[_jsonUrl(_sessionGroupsRequest, configuration)]]">JSON</a>
                  <a id="latexLink" download="hparams_table.tex" href="[[_latexUrl(_sessionGroupsRequest, configuration)]]">LaTeX</a>
                </span>
              </template>
            </div>
          </vaadin-split-layout>
        </vaadin-split-layout>
      </vaadin-split-layout>
    </div>
    <style>
      .pane {
        display: flex;
        flex-direction: column;
        height: 100%;
      }
      .section {
        margin: 5px 10px 5px 10px;
        overflow-y: auto;
      }
      .section-title {
        display: block;
        font-weight: bold;
        text-decoration: underline;
        margin-bottom: 7px;
      }
      #hyperparameters-metrics-statuses {
        flex-basis: 70%;
        flex-shrink: 1;
        flex-grow: 1;
      }
      #hyperparameters-metrics {
        flex-basis: 90%;
        flex-shrink: 1;
        flex-grow: 1;
      }
      .hyperparameters {
        flex-basis: auto;
        flex-shrink: 1;
        flex-grow: 1;
      }
      .metrics {
        flex-basis: auto;
        flex-shrink: 1;
        flex-grow: 1;
      }
      .statuses {
        flex-basis: auto;
        flex-shrink: 0;
        flex-grow: 0;
      }
      #sorting-paging {
        flex-basis: 30%;
        flex-shrink: 0;
        flex-grow: 0;
      }
      #paging-download {
        flex-basis: 90%;
        flex-shrink: 1;
        flex-grow: 1;
      }
      .sorting {
        flex-basis: auto;
        flex-shrink: 0;
        flex-grow: 0;
      }
      .paging {
        flex-basis: auto;
        flex-shrink: 0;
        flex-grow: 0;
      }
      .download {
        flex-basis: auto;
        flex-shrink: 0;
        flex-grow: 0;
      }
      .discrete-value-checkbox,
      .metric-checkbox,
      .hparam-checkbox {
        display: block;
      }
      .discrete-value-checkbox {
        margin-left: 20px;
      }
      .hparam,
      .metric {
        display: block;
      }
      .inline-element {
        display: inline-block;
        width: 40%;
        margin-left: 10px;
      }
      .page-number-input {
        width: 20%;
      }
      .page-size-input {
        width: 60%;
      }
      vaadin-split-layout {
        height: 100%;
      }
      paper-listbox {
        max-height: 15em;
      }
      .page-suffix {
        white-space: nowrap;
      }
    </style>
  </template>
  
</dom-module>







<dom-module id="iron-pages">

  <template>
    <style>
      :host {
        display: block;
      }

      :host > ::slotted(:not(slot):not(.iron-selected)) {
        display: none !important;
      }
    </style>

    <slot></slot>
  </template>

  
</dom-module>







<dom-module id="paper-header-panel">
  <template>
    <style>
      :host {
        @apply --layout-vertical;
        position: relative;
        height: 100%;
        @apply --paper-header-panel;
      }

      #mainContainer {
        @apply --layout-flex;
        position: relative;
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      #mainPanel {
        @apply --layout-vertical;
        @apply --layout-flex;
        position: relative;
        min-height: 0;
        @apply --paper-header-panel-body;
      }

      #mainContainer {
        @apply --paper-header-panel-container;
      }

      /*
       * mode: scroll
       */
      :host([mode=scroll]) #mainContainer {
        @apply --paper-header-panel-scroll-container;
        overflow: visible;
      }

      :host([mode=scroll]) {
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      /*
       * mode: cover
       */
      :host([mode=cover]) #mainContainer {
        @apply --paper-header-panel-cover-container;
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      :host([mode=cover]) #mainPanel {
        position: static;
      }

      /*
       * mode: standard
       */
      :host([mode=standard]) #mainContainer {
        @apply --paper-header-panel-standard-container;
      }

      /*
       * mode: seamed
       */
      :host([mode=seamed]) #mainContainer {
        @apply --paper-header-panel-seamed-container;
      }


      /*
       * mode: waterfall
       */
      :host([mode=waterfall]) #mainContainer {
        @apply --paper-header-panel-waterfall-container;
      }

      /*
       * mode: waterfall-tall
       */
      :host([mode=waterfall-tall]) #mainContainer {
        @apply --paper-header-panel-waterfall-tall-container;
      }

      #dropShadow {
        transition: opacity 0.5s;
        height: 6px;
        box-shadow: inset 0px 5px 6px -3px rgba(0, 0, 0, 0.4);
        @apply --paper-header-panel-shadow;
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        opacity: 0;
        pointer-events: none;
      }

      #dropShadow.has-shadow {
        opacity: 1;
      }

      #mainContainer > ::slotted(.fit) {
        @apply --layout-fit;
      }

    </style>

    <slot id="headerSlot" name="header"></slot>

    <div id="mainPanel">
      <div id="mainContainer" class$="[[_computeMainContainerClass(mode)]]">
        <slot></slot>
      </div>
      <div id="dropShadow"></div>
    </div>
  </template>

  
</dom-module>










<dom-module id="paper-tab">
  <template>
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center;
        @apply --layout-center-justified;
        @apply --layout-flex-auto;

        position: relative;
        padding: 0 12px;
        overflow: hidden;
        cursor: pointer;
        vertical-align: middle;

        @apply --paper-font-common-base;
        @apply --paper-tab;
      }

      :host(:focus) {
        outline: none;
      }

      :host([link]) {
        padding: 0;
      }

      .tab-content {
        height: 100%;
        transform: translateZ(0);
          -webkit-transform: translateZ(0);
        transition: opacity 0.1s cubic-bezier(0.4, 0.0, 1, 1);
        @apply --layout-horizontal;
        @apply --layout-center-center;
        @apply --layout-flex-auto;
        @apply --paper-tab-content;
      }

      :host(:not(.iron-selected)) > .tab-content {
        opacity: 0.8;

        @apply --paper-tab-content-unselected;
      }

      :host(:focus) .tab-content {
        opacity: 1;
        font-weight: 700;
      }

      paper-ripple {
        color: var(--paper-tab-ink, var(--paper-yellow-a100));
      }

      .tab-content > ::slotted(a) {
        @apply --layout-flex-auto;

        height: 100%;
      }
    </style>

    <div class="tab-content">
      <slot></slot>
    </div>
  </template>

  
</dom-module>










<iron-iconset-svg name="paper-tabs" size="24">
<svg><defs>
<g id="chevron-left"><path d="M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z" /></g>
<g id="chevron-right"><path d="M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z" /></g>
</defs></svg>
</iron-iconset-svg>





<dom-module id="paper-tabs">
  <template>
    <style>
      :host {
        @apply --layout;
        @apply --layout-center;

        height: 48px;
        font-size: 14px;
        font-weight: 500;
        overflow: hidden;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;

        /* NOTE: Both values are needed, since some phones require the value to be `transparent`. */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        @apply --paper-tabs;
      }

      :host(:dir(rtl)) {
        @apply --layout-horizontal-reverse;
      }

      #tabsContainer {
        position: relative;
        height: 100%;
        white-space: nowrap;
        overflow: hidden;
        @apply --layout-flex-auto;
        @apply --paper-tabs-container;
      }

      #tabsContent {
        height: 100%;
        -moz-flex-basis: auto;
        -ms-flex-basis: auto;
        flex-basis: auto;
        @apply --paper-tabs-content;
      }

      #tabsContent.scrollable {
        position: absolute;
        white-space: nowrap;
      }

      #tabsContent:not(.scrollable),
      #tabsContent.scrollable.fit-container {
        @apply --layout-horizontal;
      }

      #tabsContent.scrollable.fit-container {
        min-width: 100%;
      }

      #tabsContent.scrollable.fit-container > ::slotted(*) {
        /* IE - prevent tabs from compressing when they should scroll. */
        -ms-flex: 1 0 auto;
        -webkit-flex: 1 0 auto;
        flex: 1 0 auto;
      }

      .hidden {
        display: none;
      }

      .not-visible {
        opacity: 0;
        cursor: default;
      }

      paper-icon-button {
        width: 48px;
        height: 48px;
        padding: 12px;
        margin: 0 4px;
      }

      #selectionBar {
        position: absolute;
        height: 0;
        bottom: 0;
        left: 0;
        right: 0;
        border-bottom: 2px solid var(--paper-tabs-selection-bar-color, var(--paper-yellow-a100));
          -webkit-transform: scale(0);
        transform: scale(0);
          -webkit-transform-origin: left center;
        transform-origin: left center;
          transition: -webkit-transform;
        transition: transform;

        @apply --paper-tabs-selection-bar;
      }

      #selectionBar.align-bottom {
        top: 0;
        bottom: auto;
      }

      #selectionBar.expand {
        transition-duration: 0.15s;
        transition-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
      }

      #selectionBar.contract {
        transition-duration: 0.18s;
        transition-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
      }

      #tabsContent > ::slotted(:not(#selectionBar)) {
        height: 100%;
      }
    </style>

    <paper-icon-button icon="paper-tabs:chevron-left" class$="[[_computeScrollButtonClass(_leftHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onLeftScrollButtonDown" tabindex="-1"></paper-icon-button>

    <div id="tabsContainer" on-track="_scroll" on-down="_down">
      <div id="tabsContent" class$="[[_computeTabsContentClass(scrollable, fitContainer)]]">
        <div id="selectionBar" class$="[[_computeSelectionBarClass(noBar, alignBottom)]]" on-transitionend="_onBarTransitionEnd"></div>
        <slot></slot>
      </div>
    </div>

    <paper-icon-button icon="paper-tabs:chevron-right" class$="[[_computeScrollButtonClass(_rightHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onRightScrollButtonDown" tabindex="-1"></paper-icon-button>

  </template>

  
</dom-module>








<dom-module id="paper-toolbar">
  <template>
    <style>
      :host {
        --calculated-paper-toolbar-height: var(--paper-toolbar-height, 64px);
        --calculated-paper-toolbar-sm-height: var(--paper-toolbar-sm-height, 56px);
        display: block;
        position: relative;
        box-sizing: border-box;
        -moz-box-sizing: border-box;
        height: var(--calculated-paper-toolbar-height);
        background: var(--paper-toolbar-background, var(--primary-color));
        color: var(--paper-toolbar-color, var(--dark-theme-text-color));
        @apply --paper-toolbar;
      }

      :host(.animate) {
        transition: var(--paper-toolbar-transition, height 0.18s ease-in);
      }

      :host(.medium-tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 2);
        @apply --paper-toolbar-medium;
      }

      :host(.tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 3);
        @apply --paper-toolbar-tall;
      }

      .toolbar-tools {
        position: relative;
        height: var(--calculated-paper-toolbar-height);
        padding: 0 16px;
        pointer-events: none;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-toolbar-content;
      }

      /*
       * TODO: Where should media query breakpoints live so they can be shared between elements?
       */

      @media (max-width: 600px) {
        :host {
          height: var(--calculated-paper-toolbar-sm-height);
        }

        :host(.medium-tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 2);
        }

        :host(.tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 3);
        }

        .toolbar-tools {
          height: var(--calculated-paper-toolbar-sm-height);
        }
      }

      #topBar {
        position: relative;
      }

      /* middle bar */
      #middleBar {
        position: absolute;
        top: 0;
        right: 0;
        left: 0;
      }

      :host(.tall) #middleBar,
      :host(.medium-tall) #middleBar {
        -webkit-transform: translateY(100%);
        transform: translateY(100%);
      }

      /* bottom bar */
      #bottomBar {
        position: absolute;
        right: 0;
        bottom: 0;
        left: 0;
      }

      /*
       * make elements (e.g. buttons) respond to mouse/touch events
       *
       * `.toolbar-tools` disables touch events so multiple toolbars can stack and not
       * absorb events. All children must have pointer events re-enabled to work as
       * expected.
       */
      .toolbar-tools > ::slotted(*:not([disabled])) {
        pointer-events: auto;
      }

      .toolbar-tools > ::slotted(.title) {
        @apply --paper-font-common-base;
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
        font-size: 20px;
        font-weight: 400;
        line-height: 1;
        pointer-events: none;
        @apply --layout-flex;
      }

      .toolbar-tools > ::slotted(.title) {
        margin-left: 56px;
      }

      .toolbar-tools > ::slotted(paper-icon-button + .title) {
        margin-left: 0;
      }

      /**
       * The --paper-toolbar-title mixin is applied here instead of above to
       * fix the issue with margin-left being ignored due to css ordering.
       */
      .toolbar-tools > ::slotted(.title) {
        @apply --paper-toolbar-title;
      }

      .toolbar-tools > ::slotted(paper-icon-button[icon=menu]) {
        margin-right: 24px;
      }

      .toolbar-tools > ::slotted(.fit) {
        position: absolute;
        top: auto;
        right: 0;
        bottom: 0;
        left: 0;
        width: auto;
        margin: 0;
      }

      /* TODO(noms): Until we have a better solution for classes that don't use
       * /deep/ create our own.
       */
      .start-justified {
        @apply --layout-start-justified;
      }

      .center-justified {
        @apply --layout-center-justified;
      }

      .end-justified {
        @apply --layout-end-justified;
      }

      .around-justified {
        @apply --layout-around-justified;
      }

      .justified {
        @apply --layout-justified;
      }
    </style>

    <div id="topBar" class$="toolbar-tools [[_computeBarExtraClasses(justify)]]">
      <slot name="top"></slot>
    </div>

    <div id="middleBar" class$="toolbar-tools [[_computeBarExtraClasses(middleJustify)]]">
      <slot name="middle"></slot>
    </div>

    <div id="bottomBar" class$="toolbar-tools [[_computeBarExtraClasses(bottomJustify)]]">
      <slot name="bottom"></slot>
    </div>
  </template>

  
</dom-module>











<dom-module id="tf-hparams-scale-and-color-controls">
  <template>
    <div class="control-panel">
      
      <paper-dropdown-menu label="Color by" id="colorByDropDownMenu" horizontal-align="left">
        <paper-listbox class="dropdown-content" slot="dropdown-content" selected="{{options.colorByColumnIndex}}" id="colorByListBox">
          <template is="dom-repeat" items="[[options.columns]]" as="column" id="colorByColumnTemplate">
            <paper-item disabled="[[!_isNumericColumn(column.index)]]">
              [[column.name]]
            </paper-item>
          </template>
        </paper-listbox>
      </paper-dropdown-menu>

      
      <div class="columns-container">
        
        <template is="dom-repeat" items="{{options.columns}}" as="column">
          <template is="dom-if" if="[[_isNumericColumn(column.index)]]">
            <div class="column">
              <div class="column-title">
                [[column.name]]
              </div>
              <div>
                <paper-radio-group class="scale-radio-group" selected="{{column.scale}}">
                  <paper-radio-button name="LINEAR">
                    Linear
                  </paper-radio-button>
                  
                  <paper-radio-button id="logScaleButton_[[column.name]]" name="LOG" disabled="[[!_allowLogScale(column, sessionGroups.*)]]">
                    Logarithmic
                  </paper-radio-button>
                  <paper-radio-button name="QUANTILE">
                    Quantile
                  </paper-radio-button>
                </paper-radio-group>
              </div>
            </div>
          </template>
        </template>
      </div>
    </div>

    <style>
      :host {
        display: block;
      }
      .control-panel {
        overflow: auto;
      }
      .column {
        flex-grow: 1;
        flex-shrink: 1;
        margin-right: 5px;
        border: solid 1px darkgray;
        padding: 3px;
      }
      .column-title {
        /* Fit every title in one line so the radio boxes align vertically. */
        white-space: nowrap;
        text-decoration: underline;
      }
      .columns-container {
        display: flex;
        flex-direction: row;
      }
      .scale-radio-group paper-radio-button {
        padding: 2px;
        display: block;
      }
      paper-listbox {
        max-height: 15em;
      }
    </style>
  </template>

  
</dom-module>












<dom-module id="vaadin-grid-active-item-themability-styles">
  <template>
    <style>
      vaadin-grid-table .vaadin-grid-row[active] .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-body-row-active-cell);
      }
    </style>
  </template>
</dom-module>





<dom-module id="vaadin-grid-table-table-scroll-styles">
  <template>
    <style>
      #table {
        position: relative;
        overflow: auto;
        -webkit-overflow-scrolling: touch;
        z-index: -2;
      }

      vaadin-grid-table[ios] #table {
        transform: none;
      }

      vaadin-grid-table[fixed-sections] #table {
        transform: none;
      }
    </style>
  </template>
</dom-module>

<dom-module id="vaadin-grid-table-scroll-styles">
  <template>
    <style>
      vaadin-grid-table {
        transform: translateZ(0);
      }

      vaadin-grid-table-header {
        position: absolute;
        top: 0;
        width: 100%;
      }

      vaadin-grid-table-footer {
        position: absolute;
        bottom: 0;
        width: 100%;
      }

      vaadin-grid-table-body {
        z-index: -1;
      }

      vaadin-grid-table[fixed-sections] {
        /* Any value other than ‘none’ for the transform results in the creation of both a stacking context and
        a containing block. The object acts as a containing block for fixed positioned descendants. */
        transform: translateZ(0);
        overflow: hidden;
      }

      vaadin-grid-table[fixed-sections] vaadin-grid-table-header,
      vaadin-grid-table[fixed-sections] vaadin-grid-table-footer {
        position: fixed;
      }

      vaadin-grid-table[fixed-sections] vaadin-grid-table-body#items {
        position: fixed;
        width: 100%;
        will-change: transform;
      }
    </style>
  </template>
</dom-module>









<dom-module id="vaadin-grid-table-cell"></dom-module>
<dom-module id="vaadin-grid-table-header-cell"></dom-module>
<dom-module id="vaadin-grid-table-footer-cell"></dom-module>
<dom-module id="vaadin-grid-sizer-cell"></dom-module>




<dom-module id="vaadin-grid-sizer">
  <template>
    <style>
      :host {
        display: flex;
        visibility: hidden;
      }

      .cell {
        display: block;
        flex-shrink: 0;
        line-height: 0;
        font-size: 1px;
        margin-top: -1em;
      }

      .cell[hidden] {
        display: none;
      }
    </style>

    <template is="dom-repeat" items="[[_columns]]" as="column">
      <vaadin-grid-sizer-cell class="cell" column="[[column]]">&nbsp;</vaadin-grid-sizer-cell>
    </template>

  </template>
  
</dom-module>


<dom-module id="vaadin-grid-table-outer-scroller">
  <template>
    <style>
      :host {
        display: block;
        height: 100%;
        width: 100%;
        position: absolute;
        top: 0;
        box-sizing: border-box;
        overflow: auto;
      }

      :host([passthrough]) {
        pointer-events: none;
      }

      :host([ios]) {
        pointer-events: all;
        z-index: -3;
      }

      :host([ios][scrolling]) {
        z-index: 0;
      }
    </style>

    <slot></slot>

  </template>
  
</dom-module>









<dom-module id="vaadin-grid-table-focus-trap">
  <template>
    <style>
     :host {
       position: absolute;
       z-index: -3;
       height: 0;
       overflow: hidden;
     }

     :host(:focus),
     .primary:focus,
     ::slotted(.primary:focus),
     .secondary:focus,
     ::slotted(.secondary:focus) {
       outline: none;
     }
    </style>

    
    <div class="primary" tabindex="0" role="gridcell" on-focus="_onBaitFocus" on-blur="_onBaitBlur"><div aria-hidden="true">&nbsp;</div></div>
    <div class="secondary" tabindex="-1" role="gridcell" on-focus="_onBaitFocus" on-blur="_onBaitBlur"><div aria-hidden="true">&nbsp;</div></div>

    <slot></slot>
  </template>
  
</dom-module>



<dom-module id="vaadin-grid-table-row"></dom-module>
<dom-module id="vaadin-grid-table-header-row"></dom-module>








<dom-module id="vaadin-grid-row-details-styles">
  <template>
    <style>
      [detailscell] {
        position: absolute;
        bottom: 0;
        left: 0;
        width: 100%;
      }
    </style>
  </template>
</dom-module>
<dom-module id="vaadin-grid-row-details-themability-styles">
  <template>
    <style>
      .vaadin-grid-cell[detailscell] ::slotted(vaadin-grid-cell-content) {
        background: #fff;
        @apply(--vaadin-grid-body-row-details-cell);
      }
    </style>
  </template>
</dom-module>




<dom-module id="vaadin-grid-data-provider-themability-styles">
  <template>
    <style>

      /* Anim */
      @keyframes vaadin-grid-spin-360 {
        100% {
          transform: rotate(360deg);
        }
      }
      @-webkit-keyframes vaadin-grid-spin-360 {
        100% {
          -webkit-transform: rotate(360deg);
          transform: rotate(360deg);
        }
      }

      #spinner {
        border: 2px solid var(--primary-color, #03A9F4);
        border-radius: 50%;
        border-right-color: transparent;
        border-top-color: transparent;
        content: "";
        height: 16px;
        left: 50%;
        margin-left: -8px;
        margin-top: -8px;
        position: absolute;
        top: 50%;
        width: 16px;
        pointer-events: none;
        opacity: 0;
        @apply(--vaadin-grid-loading-spinner);
      }

      :host([loading]) #spinner {
        opacity: 1;
        -webkit-animation: vaadin-grid-spin-360 400ms linear infinite;
        animation: vaadin-grid-spin-360 400ms linear infinite;
      }

      :host([loading]) #items {
        opacity: 0.5;
      }

    </style>
  </template>
</dom-module>






<dom-module id="vaadin-grid-selection-themability-styles">
  <template>
    <style>
      vaadin-grid-table .vaadin-grid-row[selected] .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        background-color: var(--paper-grey-100, rgb(243, 243, 243));
        @apply(--vaadin-grid-body-row-selected-cell);
      }
    </style>
  </template>
</dom-module>




<dom-module id="vaadin-grid-navigation-themability-styles">
  <template>
    <style>
      :host(:focus),
      #table:focus {
        outline: none;
      }

      :host([navigating]:not([interacting])) [focused] > .vaadin-grid-row[focused] > [focused] ::slotted(vaadin-grid-cell-content) {
        box-shadow: inset 0 0 0 3px rgba(0, 0, 0, 0.3);
        @apply(--vaadin-grid-focused-cell);
      }
    </style>
  </template>
</dom-module>



<dom-module id="vaadin-grid-column-reordering-themability-styles">
  <template>
    <style>
      vaadin-grid-table[reordering] .vaadin-grid-cell {
        background: #000;
      }

      :host([reordering]) .vaadin-grid-cell[reorder-status="dragging"] {
        background: var(--primary-color, #000);
      }

      vaadin-grid-table[reordering] .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        transition: opacity 300ms;
        transform: translateZ(0);
        opacity: 0.8;
      }

      #scroller .vaadin-grid-cell[reorder-status="allowed"] ::slotted(vaadin-grid-cell-content) {
        opacity: 1;
      }

      #scroller .vaadin-grid-cell[reorder-status="dragging"] {
        background: var(--primary-color, #000);
      }

      #scroller .vaadin-grid-cell[reorder-status="dragging"] ::slotted(vaadin-grid-cell-content) {
        opacity: 0.95;
      }
    </style>
  </template>
</dom-module>









<dom-module id="vaadin-grid-table-styles">
  <template>
    <style>

      @keyframes appear {
        to {
          opacity: 1;
        }
      }

      vaadin-grid-table {
        display: block;
        position: relative;
        animation: 1ms appear;
      }

      @media only screen and (-webkit-max-device-pixel-ratio: 1) {
        :host {
          will-change: transform;
        }
      }

      #items {
        position: relative;
      }

      #items {
        border-top: 0 solid transparent;
        border-bottom: 0 solid transparent;
      }

      #items > .vaadin-grid-row {
        box-sizing: border-box;
        margin: 0;
        position: absolute;
      }

      vaadin-grid-table-body {
        display: block;
      }

      vaadin-grid-table-header .vaadin-grid-cell,
      vaadin-grid-table-footer .vaadin-grid-cell {
        top: 0;
      }

      .vaadin-grid-cell {
        padding: 0;
        flex-shrink: 0;
        flex-grow: 1;
        box-sizing: border-box;
        display: flex;
      }

      .vaadin-grid-cell:not([detailscell]) {
        position: relative;
      }

      .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
         width: 100%;
         display: inline-flex;
         justify-content: center;
         flex-direction: column;
         white-space: nowrap;
         overflow: hidden;
      }

      .vaadin-grid-column-resize-handle {
        position: absolute;
        right: 0;
        height: 100%;
        cursor: col-resize;
        z-index: 1;
      }

      .vaadin-grid-column-resize-handle::before {
        position: absolute;
        content: "";
        height: 100%;
        width: 35px;
        transform: translateX(-50%);
      }

      [lastcolumn] .vaadin-grid-column-resize-handle::before,
      [last-frozen] .vaadin-grid-column-resize-handle::before {
        width: 18px;
        transform: translateX(-100%);
      }

      vaadin-grid-table[column-reordering-allowed] #header,
      vaadin-grid-table[column-resizing] {
        -ms-user-select: none;
        -moz-user-select: none;
        -webkit-user-select: none;
        user-select: none;
      }

      vaadin-grid-table[column-resizing] {
        cursor: col-resize;
      }

      .vaadin-grid-row:not([hidden]) {
        display: flex;
        width: 100%;
      }

      [frozen] {
        z-index: 2;
      }

      [hidden] {
        display: none;
      }

      vaadin-grid-table[no-content-pointer-events] .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        pointer-events: none;
      }
    </style>
  </template>
</dom-module>

<dom-module id="vaadin-grid-table-table-styles">
  <template>
    <style>
      :host([ios][column-resizing]) #outerscroller {
        overflow: hidden;
      }

      #fixedsizer,
      #outersizer {
        border-top: 0 solid transparent;
        border-bottom: 0 solid transparent;
      }

      #table {
        height: 100%;
        width: 100%;
        display: block;
        overflow: auto;
        box-sizing: border-box;
      }

      #table[overflow-hidden],
      #outerscroller[overflow-hidden] {
        overflow: hidden;
      }

      vaadin-grid-sizer {
        position: relative;
        width: 100%;
      }

      #sizerwrapper {
        position: absolute;
        width: 100%;
        z-index: -100;
      }

      #reorderghost {
        visibility: hidden;
        position: fixed;
        opacity: 0.5;
        pointer-events: none;
      }

      ::slotted(vaadin-grid-column),
      ::slotted(vaadin-grid-selection-column),
      ::slotted(vaadin-grid-column-group) {
        display: none;
      }

    </style>
  </template>
</dom-module>

<dom-module id="vaadin-grid-table-themability-styles">
  <template>
    <style>

      /* Default borders */
      vaadin-grid-table-header .vaadin-grid-row:last-child .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        border-bottom: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      vaadin-grid-table-footer .vaadin-grid-row:first-child .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        border-top: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      vaadin-grid-table-body .vaadin-grid-row:not([lastrow]) .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        border-bottom: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      [last-frozen] ::slotted(vaadin-grid-cell-content) {
        border-right: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      /* Column resize handle */

      .vaadin-grid-column-resize-handle {
        border-right: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
        @apply(--vaadin-grid-column-resize-handle);
      }

      /* Cells */
      vaadin-grid-table .vaadin-grid-row .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        background: #fff;
        text-align: left;
        padding: 8px;
        box-sizing: border-box;
        @apply(--vaadin-grid-cell);
      }

      vaadin-grid-table-header .vaadin-grid-row .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        font-weight: 500;
        @apply(--vaadin-grid-header-cell);
      }

      vaadin-grid-table-footer .vaadin-grid-row .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        font-weight: 500;
        @apply(--vaadin-grid-footer-cell);
      }

      vaadin-grid-table-body .vaadin-grid-row .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-body-cell);
      }

      vaadin-grid-table-body [odd] .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-body-row-odd-cell);
      }

      vaadin-grid-table .vaadin-grid-row .vaadin-grid-cell:not([detailscell])[last-frozen] ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-cell-last-frozen);
      }

      vaadin-grid-table:not([scrolling]) vaadin-grid-table-body .vaadin-grid-row:hover .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-body-row-hover-cell);
      }

      vaadin-grid-table .vaadin-grid-row .vaadin-grid-cell.vaadin-grid-cell[lastcolumn] ::slotted(vaadin-grid-cell-content) {
        border-right: none;
      }

    </style>
  </template>
</dom-module>

<dom-module id="vaadin-grid-table">
  <template>
      <style include="vaadin-grid-table-table-scroll-styles"></style>
      <style include="vaadin-grid-table-table-styles"></style>

      <style include="vaadin-grid-data-provider-themability-styles"></style>

      <div id="spinner"></div>
      <div id="table" tabindex="-1" overflow-hidden$="[[_hideTableOverflow(scrollbarWidth, safari)]]">
        <div id="sizerwrapper">
          <vaadin-grid-sizer id="fixedsizer" top="[[_estScrollHeight]]" column-tree="[[columnTree]]"></vaadin-grid-sizer>
        </div>
        <slot name="header"></slot>
        <slot name="items"></slot>
        <slot name="footer"></slot>
      </div>

      <div id="reorderghost"></div>
      <vaadin-grid-table-outer-scroller id="outerscroller" scroll-target="[[scrollTarget]]" overflow-hidden$="[[_hideOuterScroller(scrollbarWidth, safari)]]" ios$="[[ios]]" scrolling$="[[scrolling]]">
      <vaadin-grid-sizer id="outersizer" top="[[_estScrollHeight]]" column-tree="[[columnTree]]"></vaadin-grid-sizer>
    </vaadin-grid-table-outer-scroller>
    <slot></slot>
    <slot name="footerFocusTrap"></slot>
  </template>
</dom-module>








<dom-module id="vaadin-grid-column">
  
</dom-module>















<dom-module id="vaadin-grid">
  <template>
    <style>
      :host {
        display: block;
        height: 400px;
        background: var(--primary-background-color, #fff);
        box-sizing: border-box;
        border: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      :host(:focus) {
        -webkit-tap-highlight-color: transparent;
      }

      :host(:focus) {
        outline: none;
      }

      #scroller {
        height: 100%;
        width: 100%;
      }
    </style>

    <style include="vaadin-grid-table-scroll-styles"></style>
    <style include="vaadin-grid-row-details-styles"></style>
    <style include="vaadin-grid-table-styles"></style>

    <style include="vaadin-grid-table-themability-styles"></style>
    <style include="vaadin-grid-selection-themability-styles"></style>
    <style include="vaadin-grid-navigation-themability-styles"></style>
    <style include="vaadin-grid-active-item-themability-styles"></style>
    <style include="vaadin-grid-row-details-themability-styles"></style>
    <style include="vaadin-grid-column-reordering-themability-styles"></style>

    <vaadin-grid-table id="scroller" loading$="[[_loading]]" bind-data="[[_bindData]]" size="[[size]]" column-tree="[[_columnTree]]" row-details-template="[[_rowDetailsTemplate]]" column-reordering-allowed="[[columnReorderingAllowed]]">
      <vaadin-grid-table-header id="header" slot="header" target="[[_getContentTarget()]]" column-tree="[[_columnTree]]"></vaadin-grid-table-header>
      <vaadin-grid-table-body id="items" slot="items"></vaadin-grid-table-body>
      <vaadin-grid-table-footer id="footer" slot="footer" target="[[_getContentTarget()]]" column-tree="[[_columnTree]]"></vaadin-grid-table-footer>

      
      <slot name="footerFocusTrap"></slot>

      
      
      <slot></slot>

      <vaadin-grid-table-focus-trap id="footerFocusTrap" slot="footerFocusTrap" on-focus-gained="_onFooterFocus" on-focus-lost="_onFocusout">
      </vaadin-grid-table-focus-trap>
    </vaadin-grid-table>
  </template>
</dom-module>












<dom-module id="tf-hparams-session-group-details">
  <template>
    <template is="dom-if" if="[[!sessionGroup]]">
      <div>
        <h3>No session group selected</h3>
        <p>Please select a session group to see its metric-graphs here.
      </div>
    </template>
    <template is="dom-if" if="[[!_haveMetrics(visibleSchema.*)]]">
      <div>
        <h3>No metrics are enabled</h3>
        <p>Please enable some metrics to see content here.
      </div>
    </template>
    <div class="layout horizontal wrap session-group-details">
      <template is="dom-if" if="[[_haveMetricsAndSessionGroup(visibleSchema.*,
                                                  sessionGroup)]]">
        <template is="dom-repeat" items="[[visibleSchema.metricInfos]]" as="metricInfo">
          
          <tf-scalar-card class="scalar-card" color-scale="[[_colorScale]]" data-to-load="[[_computeSeriesForSessionGroupMetric(sessionGroup,
                          metricInfo)]]" tag="[[metricInfo.name.tag]]" tag-metadata="[[_computeTagMetadata(metricInfo)]]" x-type="[[_xType]]" multi-experiments="[[_noMultiExperiments]]" request-data="[[_requestData]]" active>
          </tf-scalar-card>
        </template>
      </template>
    </div>
    
    <style include="iron-flex">
      :host {
        display: block;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="tf-hparams-table-view">
  <template>
    <vaadin-grid class="session-group-table" id="sessionGroupsTable" column-reordering-allowed items="[[sessionGroups]]">
      <vaadin-grid-column flex-grow="0" width="10em" resizable>
        <template class="header">
          <div class="table-header table-cell">Trial ID</div>
        </template>
        <template>
          <div class="table-cell">[[item.name]]</div>
        </template>
      </vaadin-grid-column>
      <template is="dom-if" if="[[enableShowMetrics]]">
        <vaadin-grid-column flex-grow="0" width="5em" resizable>
          <template class="header">
            <div class="table-header table-cell">Show Metrics</div>
          </template>
          <template>
            <paper-checkbox class="table-cell" checked="{{expanded}}">
            </paper-checkbox>
          </template>
        </vaadin-grid-column>
      </template>
      <template is="dom-repeat" items="[[visibleSchema.hparamInfos]]" as="hparamInfo" index-as="hparamIndex">
        <vaadin-grid-column flex-grow="2" width="10em" resizable>
          <template class="header">
            <div class="table-header table-cell">
              [[_hparamName(hparamInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupHParam(item, hparamInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template is="dom-repeat" items="{{visibleSchema.metricInfos}}" as="metricInfo" index-as="metricIndex">
        <vaadin-grid-column flex-grow="2" width="10em" resizable>
          <template class="header">
            <div class="table-header table-cell">
              [[_metricName(metricInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupMetric(item, metricInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template class="row-details">
        <tf-hparams-session-group-details backend="[[backend]]" experiment-name="[[experimentName]]" session-group="[[item]]" visible-schema="[[visibleSchema]]" class="session-group-details">
        </tf-hparams-session-group-details>
      </template>
    </vaadin-grid>

    <style>
      :host {
        display: block;
      }
      .table-cell {
        white-space: nowrap;
        text-overflow: ellipsis;
        overflow: hidden;
      }
      .table-header {
        /* line-break overflowing column headers */
        white-space: normal;
        overflow-wrap: break-word;
      }
      .session-group-table {
        height: 100%;
      }
      .session-group-details {
        height: 360px;
        overflow-y: auto;
      }
    </style>
  </template>

  
</dom-module>


<dom-module id="tf-hparams-session-group-values">
  <template>
    
    <template is="dom-if" if="[[_propertiesArePopulated(visibleSchema, sessionGroup)]]">
      
      <tf-hparams-table-view visible-schema="[[visibleSchema]]" session-groups="[[_singletonSessionGroups(sessionGroup)]]">
      </tf-hparams-table-view>
    </template>
    <template is="dom-if" if="[[!_propertiesArePopulated(visibleSchema, sessionGroup)]]">
      <div>
        Click or hover over a session group to display its values here.
      </div>
    </template>

    <style>
      :host {
        display: block;
      }
    </style>
  </template>
  
</dom-module>







<dom-module id="tf-hparams-parallel-coords-plot">
  <template>
    <div id="container">
      <svg id="svg"></svg>
    </div>
    <style>
      :host {
        display: block;
      }
      svg {
        font: 10px sans-serif;
      }

      .background path {
        fill: none;
        stroke: #ddd;
        shape-rendering: crispEdges;
      }

      .foreground path {
        fill: none;
        stroke-opacity: 0.7;
        stroke-width: 1;
      }

      /* Will be set on foreground paths that are not "contained" in the current
         axes brushes. If no brushes are set, no path will have this class. */
      .foreground .invisible-path {
        display: none;
      }

      /* Style for the path closest to the mouse pointer (typically will become
      the selected path when the user clicks). */
      .foreground .peaked-path {
        stroke-width: 3;
      }

      /* The currently selected path class. We use !important to override the
         inline style that sets the regular color of a path. */
      .foreground .selected-path {
        stroke-width: 3 !important;
        stroke: #0f0 !important;
      }

      #container {
        height: 100%;
        width: 100%;
      }

      svg {
        width: 100%;
        height: 100%;
      }

      .axis text {
        text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
        fill: #000;
        cursor: move;
      }
    </style>
  </template>

  
  
  
  
</dom-module>





<dom-module id="tf-hparams-parallel-coords-view">
  <template>
    
    <div class="pane">
      <vaadin-split-layout vertical>
        
        <tf-hparams-scale-and-color-controls id="controls" class="section" configuration="[[configuration]]" session-groups="[[sessionGroups]]" options="{{_options}}">
        </tf-hparams-scale-and-color-controls>
        <vaadin-split-layout vertical>
          
          <tf-hparams-parallel-coords-plot id="plot" class="section" session-groups="[[sessionGroups]]" selected-session-group="{{_selectedGroup}}" closest-session-group="{{_closestGroup}}" options="[[_options]]">
          </tf-hparams-parallel-coords-plot>
          <vaadin-split-layout vertical>
            <tf-hparams-session-group-values id="values" class="section" visible-schema="[[configuration.visibleSchema]]" session-group="[[_closestOrSelected(
                             _closestGroup, _selectedGroup)]]">
            </tf-hparams-session-group-values>
            <tf-hparams-session-group-details id="details" class="section" backend="[[backend]]" experiment-name="[[experimentName]]" session-group="[[_selectedGroup]]" visible-schema="[[configuration.visibleSchema]]">
            </tf-hparams-session-group-details>
          </vaadin-split-layout>
        </vaadin-split-layout>
      </vaadin-split-layout>
    </div>

    <style>
      .pane {
        display: flex;
        flex-direction: column;
        height: 100%;
      }
      .section {
        margin: 10px;
      }
      #controls {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #plot {
        flex-grow: 1;
        flex-shrink: 1;
        flex-basis: auto;
        height: 100%;
        overflow-y: auto;
      }
      #values {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: 95px;
        overflow-y: auto;
        max-height: fit-content;
      }
      #details {
        flex-grow: 0;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      vaadin-split-layout {
        height: 100%;
      }
    </style>
  </template>

  
</dom-module>








<dom-module id="tf-hparams-scatter-plot-matrix-plot">
  <template>
    <div id="container">
      <svg id="svg"></svg>
    </div>

    <style>
      :host {
        display: block;
      }
      svg {
        font: 10px sans-serif;
      }

      /* The closest data point marker to the mouse pointer. We use !important
         to override the inline style that sets the regular style of a marker.
      */
      .closest-marker {
        r: 6 !important;
      }

      /* The currently selected data point marker. We use !important to
         override the inline style that sets the regular style of a marker. */
      .selected-marker {
        r: 6 !important;
        fill: #0f0 !important;
      }
    </style>
  </template>

  
</dom-module>





<dom-module id="tf-hparams-scatter-plot-matrix-view">
  <template>
    <div class="pane">
      <vaadin-split-layout vertical>
        
        <tf-hparams-scale-and-color-controls class="section" id="controls" configuration="[[configuration]]" session-groups="[[sessionGroups]]" options="{{_options}}">
        </tf-hparams-scale-and-color-controls>
        <vaadin-split-layout vertical>
          
          <tf-hparams-scatter-plot-matrix-plot class="section" id="plot" visible-schema="[[configuration.visibleSchema]]" session-groups="[[sessionGroups]]" selected-session-group="{{_selectedGroup}}" closest-session-group="{{_closestGroup}}" options="[[_options]]">
          </tf-hparams-scatter-plot-matrix-plot>
          <vaadin-split-layout vertical>
            <tf-hparams-session-group-values class="section" id="values" visible-schema="[[configuration.visibleSchema]]" session-group="[[_closestOrSelected(
                                 _closestGroup, _selectedGroup)]]">
            </tf-hparams-session-group-values>
            
            <tf-hparams-session-group-details class="section" id="details" backend="[[backend]]" experiment-name="[[experimentName]]" session-group="[[_selectedGroup]]" visible-schema="[[configuration.visibleSchema]]">
            </tf-hparams-session-group-details>
          </vaadin-split-layout>
        </vaadin-split-layout>
      </vaadin-split-layout>
    </div>
    <style>
      .pane {
        display: flex;
        flex-direction: column;
        height: 100%;
      }
      .section {
        margin: 10px;
      }
      #controls {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #plot {
        flex-grow: 1;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #values {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: 95px;
        overflow-y: auto;
        max-height: fit-content;
      }
      #details {
        flex-grow: 0;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      vaadin-split-layout {
        height: 100%;
      }
    </style>
  </template>

  
</dom-module>




<dom-module id="tf-hparams-sessions-pane">
  <template>
    <paper-header-panel>
      <paper-toolbar slot="header" class="tab-bar">
        <paper-tabs selected="{{_selectedTab}}" slot="top">
          
          <paper-tab view-id="table-view">
            TABLE VIEW
          </paper-tab>
          <paper-tab view-id="parallel-coords-view">
            PARALLEL COORDINATES VIEW
          </paper-tab>
          <paper-tab view-id="scatter-plot-matrix-view">
            SCATTER PLOT MATRIX VIEW
          </paper-tab>
          <div class="help-and-feedback">
            <template is="dom-if" if="[[bugReportUrl]]">
              <a href$="[[bugReportUrl]]" target="_blank" rel="noopener noreferrer">
                <paper-button id="bug-report" raised title="Send a bug report or feature request">
                  Bug Report / Feature Request
                </paper-button>
              </a>
            </template>
            <template is="dom-if" if="[[helpUrl]]">
              <a href$="[[helpUrl]]" target="_blank" rel="noopener noreferrer">
                <paper-icon-button icon="help-outline" title="View documentation">
                </paper-icon-button>
              </a>
            </template>
          </div>
        </paper-tabs>
      </paper-toolbar>
      <iron-pages selected="[[_selectedTab]]" class="fit tab-view">
        <div id="0" class="tab">
          <tf-hparams-table-view backend="[[backend]]" experiment-name="[[experimentName]]" visible-schema="[[configuration.visibleSchema]]" session-groups="[[sessionGroups]]" enable-show-metrics>
          </tf-hparams-table-view>
        </div>
        <div id="1" class="tab">
          <tf-hparams-parallel-coords-view backend="[[backend]]" experiment-name="[[experimentName]]" configuration="[[configuration]]" session-groups="[[sessionGroups]]">
          </tf-hparams-parallel-coords-view>
        </div>
        <div id="2" class="tab">
          <tf-hparams-scatter-plot-matrix-view backend="[[backend]]" experiment-name="[[experimentName]]" configuration="[[configuration]]" session-groups="[[sessionGroups]]">
          </tf-hparams-scatter-plot-matrix-view>
        </div>
      </iron-pages>
    </paper-header-panel>

    <style>
      .tab-view {
        height: 100%;
      }
      .tab-bar {
        overflow-y: auto;
        color: white;
        background-color: var(
          --tb-toolbar-background-color,
          var(--tb-orange-strong)
        );
      }
      .tab {
        height: 100%;
      }
      paper-tabs {
        flex-grow: 1;
        width: 100%;
        height: 100%;
        --paper-tabs-selection-bar-color: white;
        --paper-tabs-content: {
          -webkit-font-smoothing: antialiased;
        }
      }
      tf-hparams-table-view {
        width: 100%;
        height: 100%;
      }
      .help-and-feedback {
        display: inline-flex; /* Ensure that icons stay aligned */
        justify-content: flex-end;
        align-items: center;
        text-align: right;
        color: white;
      }
      #bug-report {
        border: solid black;
        background: red;
        white-space: normal;
        word-break: break-words;
        font-size: 12px;
        max-width: 150px;
        text-align: left;
      }
      .help-and-feedback a {
        color: white;
        text-decoration: none;
      }
    </style>
  </template>

  
</dom-module>










<dom-module id="tf-hparams-main">
  <template>
    <vaadin-split-layout>
      <div class="sidebar" slot="sidebar">
        <tf-hparams-query-pane id="query-pane" backend="[[backend]]" experiment-name="[[experimentName]]" configuration="{{_configuration}}" session-groups="{{_sessionGroups}}">
        </tf-hparams-query-pane>
      </div>
      <div class="center" slot="center">
        <tf-hparams-sessions-pane id="sessions-pane" backend="[[backend]]" help-url="[[helpUrl]]" bug-report-url="[[bugReportUrl]]" experiment-name="[[experimentName]]" configuration="[[_configuration]]" session-groups="[[_sessionGroups]]">
        </tf-hparams-sessions-pane>
      </div>
    </vaadin-split-layout>
    <tf-hparams-google-analytics-tracker id="tracker" tracking-id="[[trackingId]]" name="tf_hparams">
    </tf-hparams-google-analytics-tracker>

    <style>
      vaadin-split-layout {
        width: 100%;
      }

      .sidebar {
        width: 20%;
        height: 100%;
        overflow: auto;
        flex-grow: 0;
        flex-shrink: 0;
        min-width: 10%;
      }

      .center {
        height: 100%;
        overflow-y: auto;
        flex-grow: 1;
        flex-shrink: 1;
        width: 80%;
      }

      :host {
        display: flex;
        flex-direction: row;
        height: 100%;
        width: 100%;
      }
    </style>
  </template>

  
</dom-module>







<dom-module id="tf-hparams-dashboard">
  <template>
    
    <tf-hparams-main id="hparams-main" backend="[[_backend]]" experiment-name="">
    </tf-hparams-main>
  </template>
  
</dom-module>

























<dom-module id="tf-mesh-loader">
  <template>
    <tf-card-heading color="[[_runColor]]" class="tf-mesh-loader-header">
      <template is="dom-if" if="[[_hasMultipleSamples(ofSamples)]]">
        <div>sample: [[_getSampleText(sample)]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep(_steps)]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold">[[toLocaleString_(_stepValue)]]</span>
          </div>
          <div class="heading-label heading-right">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isMeshLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps(_steps)]]">
        <div>
          <paper-slider id="steps" immediate-value="{{_stepIndex}}" max="[[_getMaxStepIndex(_steps)]]" max-markers="[[_getMaxStepIndex(_steps)]]" snaps step="1" value="{{_stepIndex}}"></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <style>
      paper-slider {
        width: 100%;
        margin-left: 1px;
        margin-right: 1px;
      }
      .tf-mesh-loader-header {
        display: block;
        height: 105px;
      }
      [hidden] {
        display: none;
      }
    </style>
  </template>
  
</dom-module>



<dom-module id="mesh-dashboard">
  <template>
    <tf-dashboard-layout>
      <div slot="sidebar" class="all-controls">
        <div class="sidebar-section view-control">
          <h3 class="title">Point of view</h3>
          <div>
            <paper-radio-group id="view-radio-group" selected="{{_selectedView}}">
              <paper-radio-button id="all-radio-button" name="all">
                Display all points
              </paper-radio-button>
              <paper-tooltip animation-delay="0" for="all-radio-button" position="right" offset="0">
                Zoom and center camera to display all points at once. Note, that
                some points could be too far (i.e. too small) to be visible.
              </paper-tooltip>
              <paper-radio-button id="user-radio-button" name="user">
                Current view
              </paper-radio-button>
              <paper-tooltip animation-delay="0" for="user-radio-button" position="right" offset="0">
                Keep current camera position and zoom level.
              </paper-tooltip>
              <paper-radio-button id="share-radio-button" name="share">
                Share viewpoint
              </paper-radio-button>
              <paper-tooltip animation-delay="0" for="share-radio-button" position="right" offset="0">
                Share viewpoint among all cameras.
              </paper-tooltip>
            </paper-radio-group>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No point cloud data was found.</h3>
            <p>Probable causes:
            <ul>
              <li>
                You haven’t written any point cloud data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-mesh-loader active="[[active]]" selected-view="[[_selectedView]]" run="[[item.run]]" tag="[[item.tag]]" sample="[[item.sample]]" of-samples="[[item.ofSamples]]" request-manager="[[_requestManager]]" class="tf-mesh-loader-container" on-camera-position-change="_onCameraPositionChanged">
                </tf-mesh-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-radio-button {
        display: block;
        padding: 5px;
      }
      .sidebar-section h3.title {
        color: var(--paper-grey-800);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }

      .runs-selector {
        flex-grow: 1;
      }

      tf-runs-selector {
        display: flex;
      }

      .view-control {
        display: block !important;
      }

      .view-control h3.title {
        padding-top: 16px;
        padding-bottom: 16px;
      }

      .allcontrols .view-control paper-radio-group {
        margin-top: 5px;
      }
      /* Layout must be horizontal, i.e. items arranged in a row. If items cannot fit in a row,
       * they should be moved to next line. All items must be square at all times. Minimum size of
       * the item is 480px. This means that maximum size of the item must be 480px + 479px = 959px.
       * */
      .horizontal {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }
      tf-mesh-loader {
        width: 480px;
        flex-basis: 480px;
        flex-grow: 1;
        display: block;
      }
    </style>
  </template>

  
</dom-module>































<dom-module id="tf-tensorboard">
  <template>
    <paper-dialog with-backdrop id="settings">
      <h2>Settings</h2>
      <paper-checkbox id="auto-reload-checkbox" checked="{{autoReloadEnabled}}">
        Reload data every <span>[[autoReloadIntervalSecs]]</span>s.
      </paper-checkbox>
      <paper-input id="paginationLimitInput" label="Pagination limit" always-float-label type="number" min="1" step="1" on-change="_paginationLimitChanged" on-value-changed="_paginationLimitValidate"></paper-input>
    </paper-dialog>
    <paper-header-panel>
      <paper-toolbar id="toolbar" slot="header" class="header">
        <div id="toolbar-content" slot="top">
          <template is="dom-if" if="[[!_homePath]]">
            <div class="toolbar-title">[[brand]]</div>
          </template>
          <template is="dom-if" if="[[_homePath]]">
            <a href="[[_homePath]]" rel="noopener noreferrer" class="toolbar-title">[[brand]]</a>
          </template>
          <template is="dom-if" if="[[_activeDashboardsNotLoaded]]">
            <span class="toolbar-message">
              Loading active dashboards…
            </span>
          </template>
          <template is="dom-if" if="[[_activeDashboardsLoaded]]">
            <paper-tabs noink scrollable selected="{{_selectedDashboard}}" attr-for-selected="data-dashboard">
              <template is="dom-repeat" items="[[_dashboardData]]" as="dashboardDatum">
                <template is="dom-if" if="[[_isDashboardActive(disabledDashboards, _activeDashboards, dashboardDatum)]]">
                  <paper-tab data-dashboard$="[[dashboardDatum.plugin]]" title="[[dashboardDatum.tabName]]">
                    [[dashboardDatum.tabName]]
                  </paper-tab>
                </template>
              </template>
            </paper-tabs>
            <template is="dom-if" if="[[_inactiveDashboardsExist(_dashboardData, disabledDashboards, _activeDashboards)]]">
              <paper-dropdown-menu label="INACTIVE" no-label-float noink style="margin-left: 12px">
                <paper-listbox id="inactive-dashboards-menu" slot="dropdown-content" selected="{{_selectedDashboard}}" attr-for-selected="data-dashboard">
                  <template is="dom-repeat" items="[[_dashboardData]]" as="dashboardDatum">
                    <template is="dom-if" if="[[_isDashboardInactive(disabledDashboards, _activeDashboards, dashboardDatum)]]" restamp>
                      <paper-item data-dashboard$="[[dashboardDatum.plugin]]">[[dashboardDatum.tabName]]</paper-item>
                    </template>
                  </template>
                </paper-listbox>
              </paper-dropdown-menu>
            </template>
          </template>
          <div class="global-actions">
            <slot name="injected-header-items"></slot>
            <paper-icon-button id="reload-button" class$="[[_getDataRefreshingClass(_refreshing)]]" disabled$="[[_isReloadDisabled]]" icon="refresh" on-tap="reload" title$="Last updated: [[_lastReloadTimeShort]]"></paper-icon-button>
            <paper-icon-button icon="settings" on-tap="openSettings" id="settings-button"></paper-icon-button>
            <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md" rel="noopener noreferrer" tabindex="-1" target="_blank">
              <paper-icon-button icon="help-outline"></paper-icon-button>
            </a>
          </div>
        </div>
      </paper-toolbar>

      <div id="content-pane" class="fit">
        <slot name="injected-overview"></slot>
        <div id="content">
          <template is="dom-if" if="[[_activeDashboardsFailedToLoad]]">
            <div class="warning-message">
              <h3>Failed to load the set of active dashboards.</h3>
              <p>
                This can occur if the TensorBoard backend is no longer running.
                Perhaps this page is cached?
              

              <p>
                If you think that you’ve fixed the problem, click the reload
                button in the top-right.
                <template is="dom-if" if="[[autoReloadEnabled]]">
                  We’ll try to reload every
                  [[autoReloadIntervalSecs]]&nbsp;seconds as well.
                </template>
              

              <p>
                <i>Last reload: [[_lastReloadTime]]</i>
                <template is="dom-if" if="[[_dataLocation]]">
                  </template><p>
                    <i>Log directory:
                      <span id="data_location">[[_dataLocation]]</span></i>
                  
                
              <p>
            </div>
          </template>
          <template is="dom-if" if="[[_showNoDashboardsMessage]]">
            <div class="warning-message">
              <h3>No dashboards are active for the current data set.</h3>
              <p>Probable causes:
              <ul>
                <li>You haven’t written any data to your event files.</li>
                <li>TensorBoard can’t find your event files.</li>
              </ul>

              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
              <p>
                If you think TensorBoard is configured properly, please see
                <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
                and consider filing an issue on GitHub.
              

              <p>
                <i>Last reload: [[_lastReloadTime]]</i>
                <template is="dom-if" if="[[_dataLocation]]">
                  </template><p>
                    <i>Data location:
                      <span id="data_location">[[_dataLocation]]</span></i>
                  
                
              <p>
            </div>
          </template>
          <template is="dom-if" if="[[_showNoSuchDashboardMessage]]">
            <div class="warning-message">
              <h3>
                There’s no dashboard by the name of
                “<tt>[[_selectedDashboard]]</tt>.”
              </h3>
              <template is="dom-if" if="[[_activeDashboardsLoaded]]">
                <p>You can select a dashboard from the list above.

              <p>
                <i>Last reload: [[_lastReloadTime]]</i>
                <template is="dom-if" if="[[_dataLocation]]">
                  </template><p>
                    <i>Data location:
                      <span id="data_location">[[_dataLocation]]</span></i>
                  
                
              <p>
            </template></div>
          </template>
          <template is="dom-repeat" id="dashboards-template" items="[[_dashboardData]]" as="dashboardDatum" on-dom-change="_onTemplateChanged">
            <div class="dashboard-container" data-dashboard$="[[dashboardDatum.plugin]]" data-selected$="[[_selectedStatus(_selectedDashboard, dashboardDatum.plugin)]]">
              
            </div>
          </template>
        </div>
      </div>
    </paper-header-panel>

    <style>
      :host {
        height: 100%;
        display: block;
        background-color: var(--paper-grey-100);
      }

      #toolbar {
        background-color: var(
          --tb-toolbar-background-color,
          var(--tb-orange-strong)
        );
        -webkit-font-smoothing: antialiased;
      }

      .toolbar-title {
        font-size: 20px;
        margin-left: 6px;
        /* Increase clickable area for case where title is an anchor. */
        padding: 4px;
        text-rendering: optimizeLegibility;
        letter-spacing: -0.025em;
        font-weight: 500;
        display: var(--tb-toolbar-title-display, block);
      }

      a.toolbar-title {
        /* Override default anchor color. */
        color: inherit;
        /* Override default anchor text-decoration. */
        text-decoration: none;
      }

      .toolbar-message {
        opacity: 0.7;
        -webkit-font-smoothing: antialiased;
        font-size: 14px;
        font-weight: 500;
      }

      paper-tabs {
        flex-grow: 1;
        width: 100%;
        height: 100%;
        --paper-tabs-selection-bar-color: white;
        --paper-tabs-content: {
          -webkit-font-smoothing: antialiased;
          text-transform: uppercase;
        }
      }

      paper-dropdown-menu {
        --paper-input-container-color: rgba(255, 255, 255, 0.8);
        --paper-input-container-focus-color: white;
        --paper-input-container-input-color: white;
        --paper-dropdown-menu-icon: {
          color: white;
        }
        --paper-dropdown-menu-input: {
          -webkit-font-smoothing: antialiased;
          font-size: 14px;
          font-weight: 500;
        }
        --paper-input-container-label: {
          -webkit-font-smoothing: antialiased;
          font-size: 14px;
          font-weight: 500;
        }
      }

      paper-dropdown-menu paper-item {
        -webkit-font-smoothing: antialiased;
        font-size: 14px;
        font-weight: 500;
        text-transform: uppercase;
      }

      #inactive-dashboards-menu {
        --paper-listbox-background-color: var(
          --tb-toolbar-background-color,
          var(--tb-orange-strong)
        );
        --paper-listbox-color: white;
      }

      .global-actions {
        display: inline-flex; /* Ensure that icons stay aligned */
        justify-content: flex-end;
        align-items: center;
        text-align: right;
        color: white;
      }

      .global-actions a {
        color: white;
      }

      #toolbar-content {
        width: 100%;
        height: 100%;
        display: flex;
        flex-direction: row;
        justify-content: space-between;
        align-items: center;
      }

      #content-pane {
        align-items: stretch;
        display: flex;
        flex-direction: column;
        height: 100%;
        justify-content: stretch;
        width: 100%;
      }

      #content {
        flex: 1 1;
        overflow: hidden;
      }

      .dashboard-container {
        height: 100%;
      }

      /* Hide unselected dashboards. We still display them within a container
         of height 0 since Plottable produces degenerate charts when charts are
         reloaded while not displayed. */
      .dashboard-container:not([data-selected]) {
        max-height: 0;
        overflow: hidden;
        position: relative;
        /** We further make containers invisible. Some elements may anchor to
            the viewport instead of the container, in which case setting the max
            height here to 0 will not hide them. */
        visibility: hidden;
      }

      .dashboard-container iframe {
        border: none;
        height: 100%;
        width: 100%;
      }

      .warning-message {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }

      [disabled] {
        opacity: 0.2;
        color: white;
      }

      #reload-button.refreshing {
        animation: rotate 2s linear infinite;
      }

      @keyframes rotate {
        0% {
          transform: rotate(0deg);
        }
        50% {
          transform: rotate(180deg);
        }
        100% {
          transform: rotate(360deg);
        }
      }
    </style>
  </template>
  
  
</dom-module>


  <body>
    <tf-tensorboard use-hash brand="TensorBoard"></tf-tensorboard>
  

<script src="index.js"></script>", "ok": true, "headers": [["content-type", "text/html; charset=utf-8"]], "status": 200, "status_text": ""}, "https://localhost:6006/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2": {"data": "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", "ok": true, "headers": [["content-type", "font/woff2"]], "status": 200, "status_text": ""}, "https://localhost:6006/index.js": {"data": "var CLOSURE_NO_DEPS = true;
// Copyright 2006 The Closure Library Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS-IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

/**
 * @fileoverview Bootstrap for the Google JS Library (Closure).
 *
 * In uncompiled mode base.js will attempt to load Closure's deps file, unless
 * the global <code>CLOSURE_NO_DEPS</code> is set to true.  This allows projects
 * to include their own deps file(s) from different locations.
 *
 * Avoid including base.js more than once. This is strictly discouraged and not
 * supported. goog.require(...) won't work properly in that case.
 *
 * @provideGoog
 */


/**
 * @define {boolean} Overridden to true by the compiler.
 */
var COMPILED = false;


/**
 * Base namespace for the Closure library.  Checks to see goog is already
 * defined in the current scope before assigning to prevent clobbering if
 * base.js is loaded more than once.
 *
 * @const
 */
var goog = goog || {};

/**
 * Reference to the global object.
 * https://www.ecma-international.org/ecma-262/9.0/index.html#sec-global-object
 *
 * More info on this implementation here:
 * https://docs.google.com/document/d/1NAeW4Wk7I7FV0Y2tcUFvQdGMc89k2vdgSXInw8_nvCI/edit
 *
 * @const
 * @suppress {undefinedVars} self won't be referenced unless `this` is falsy.
 * @type {!Global}
 */
goog.global =
    // Check `this` first for backwards compatibility.
    // Valid unless running as an ES module or in a function wrapper called
    //   without setting `this` properly.
    // Note that base.js can't usefully be imported as an ES module, but it may
    // be compiled into bundles that are loadable as ES modules.
    this ||
    // https://developer.mozilla.org/en-US/docs/Web/API/Window/self
    // For in-page browser environments and workers.
    self;


/**
 * A hook for overriding the define values in uncompiled mode.
 *
 * In uncompiled mode, `CLOSURE_UNCOMPILED_DEFINES` may be defined before
 * loading base.js.  If a key is defined in `CLOSURE_UNCOMPILED_DEFINES`,
 * `goog.define` will use the value instead of the default value.  This
 * allows flags to be overwritten without compilation (this is normally
 * accomplished with the compiler's "define" flag).
 *
 * Example:
 * <pre>
 *   var CLOSURE_UNCOMPILED_DEFINES = {'goog.DEBUG': false};
 * </pre>
 *
 * @type {Object<string, (string|number|boolean)>|undefined}
 */
goog.global.CLOSURE_UNCOMPILED_DEFINES;


/**
 * A hook for overriding the define values in uncompiled or compiled mode,
 * like CLOSURE_UNCOMPILED_DEFINES but effective in compiled code.  In
 * uncompiled code CLOSURE_UNCOMPILED_DEFINES takes precedence.
 *
 * Also unlike CLOSURE_UNCOMPILED_DEFINES the values must be number, boolean or
 * string literals or the compiler will emit an error.
 *
 * While any @define value may be set, only those set with goog.define will be
 * effective for uncompiled code.
 *
 * Example:
 * <pre>
 *   var CLOSURE_DEFINES = {'goog.DEBUG': false} ;
 * </pre>
 *
 * @type {Object<string, (string|number|boolean)>|undefined}
 */
goog.global.CLOSURE_DEFINES;


/**
 * Returns true if the specified value is not undefined.
 *
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is defined.
 * @deprecated Use `val !== undefined` instead.
 */
goog.isDef = function(val) {
  // void 0 always evaluates to undefined and hence we do not need to depend on
  // the definition of the global variable named 'undefined'.
  return val !== void 0;
};

/**
 * Returns true if the specified value is a string.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is a string.
 * @deprecated Use `typeof val === 'string'` instead.
 */
goog.isString = function(val) {
  return typeof val == 'string';
};


/**
 * Returns true if the specified value is a boolean.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is boolean.
 * @deprecated Use `typeof val === 'boolean'` instead.
 */
goog.isBoolean = function(val) {
  return typeof val == 'boolean';
};


/**
 * Returns true if the specified value is a number.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is a number.
 * @deprecated Use `typeof val === 'number'` instead.
 */
goog.isNumber = function(val) {
  return typeof val == 'number';
};


/**
 * Builds an object structure for the provided namespace path, ensuring that
 * names that already exist are not overwritten. For example:
 * "a.b.c" -> a = {};a.b={};a.b.c={};
 * Used by goog.provide and goog.exportSymbol.
 * @param {string} name name of the object that this file defines.
 * @param {*=} opt_object the object to expose at the end of the path.
 * @param {Object=} opt_objectToExportTo The object to add the path to; default
 *     is `goog.global`.
 * @private
 */
goog.exportPath_ = function(name, opt_object, opt_objectToExportTo) {
  var parts = name.split('.');
  var cur = opt_objectToExportTo || goog.global;

  // Internet Explorer exhibits strange behavior when throwing errors from
  // methods externed in this manner.  See the testExportSymbolExceptions in
  // base_test.html for an example.
  if (!(parts[0] in cur) && typeof cur.execScript != 'undefined') {
    cur.execScript('var ' + parts[0]);
  }

  for (var part; parts.length && (part = parts.shift());) {
    if (!parts.length && opt_object !== undefined) {
      // last part and we have an object; use it
      cur[part] = opt_object;
    } else if (cur[part] && cur[part] !== Object.prototype[part]) {
      cur = cur[part];
    } else {
      cur = cur[part] = {};
    }
  }
};


/**
 * Defines a named value. In uncompiled mode, the value is retrieved from
 * CLOSURE_DEFINES or CLOSURE_UNCOMPILED_DEFINES if the object is defined and
 * has the property specified, and otherwise used the defined defaultValue.
 * When compiled the default can be overridden using the compiler options or the
 * value set in the CLOSURE_DEFINES object. Returns the defined value so that it
 * can be used safely in modules. Note that the value type MUST be either
 * boolean, number, or string.
 *
 * @param {string} name The distinguished name to provide.
 * @param {T} defaultValue
 * @return {T} The defined value.
 * @template T
 */
goog.define = function(name, defaultValue) {
  var value = defaultValue;
  if (!COMPILED) {
    var uncompiledDefines = goog.global.CLOSURE_UNCOMPILED_DEFINES;
    var defines = goog.global.CLOSURE_DEFINES;
    if (uncompiledDefines &&
        // Anti DOM-clobbering runtime check (b/37736576).
        /** @type {?} */ (uncompiledDefines).nodeType === undefined &&
        Object.prototype.hasOwnProperty.call(uncompiledDefines, name)) {
      value = uncompiledDefines[name];
    } else if (
        defines &&
        // Anti DOM-clobbering runtime check (b/37736576).
        /** @type {?} */ (defines).nodeType === undefined &&
        Object.prototype.hasOwnProperty.call(defines, name)) {
      value = defines[name];
    }
  }
  return value;
};


/**
 * @define {number} Integer year indicating the set of browser features that are
 * guaranteed to be present.  This is defined to include exactly features that
 * work correctly on all "modern" browsers that are stable on January 1 of the
 * specified year.  For example,
 * ```js
 * if (goog.FEATURESET_YEAR >= 2019) {
 *   // use APIs known to be available on all major stable browsers Jan 1, 2019
 * } else {
 *   // polyfill for older browsers
 * }
 * ```
 * This is intended to be the primary define for removing
 * unnecessary browser compatibility code (such as ponyfills and workarounds),
 * and should inform the default value for most other defines:
 * ```js
 * const ASSUME_NATIVE_PROMISE =
 *     goog.define('ASSUME_NATIVE_PROMISE', goog.FEATURESET_YEAR >= 2016);
 * ```
 *
 * The default assumption is that IE9 is the lowest supported browser, which was
 * first available Jan 1, 2012.
 *
 * TODO(user): Reference more thorough documentation when it's available.
 */
goog.FEATURESET_YEAR = goog.define('goog.FEATURESET_YEAR', 2012);


/**
 * @define {boolean} DEBUG is provided as a convenience so that debugging code
 * that should not be included in a production. It can be easily stripped
 * by specifying --define goog.DEBUG=false to the Closure Compiler aka
 * JSCompiler. For example, most toString() methods should be declared inside an
 * "if (goog.DEBUG)" conditional because they are generally used for debugging
 * purposes and it is difficult for the JSCompiler to statically determine
 * whether they are used.
 */
goog.DEBUG = goog.define('goog.DEBUG', true);


/**
 * @define {string} LOCALE defines the locale being used for compilation. It is
 * used to select locale specific data to be compiled in js binary. BUILD rule
 * can specify this value by "--define goog.LOCALE=<locale_name>" as a compiler
 * option.
 *
 * Take into account that the locale code format is important. You should use
 * the canonical Unicode format with hyphen as a delimiter. Language must be
 * lowercase, Language Script - Capitalized, Region - UPPERCASE.
 * There are few examples: pt-BR, en, en-US, sr-Latin-BO, zh-Hans-CN.
 *
 * See more info about locale codes here:
 * http://www.unicode.org/reports/tr35/#Unicode_Language_and_Locale_Identifiers
 *
 * For language codes you should use values defined by ISO 693-1. See it here
 * http://www.w3.org/WAI/ER/IG/ert/iso639.htm. There is only one exception from
 * this rule: the Hebrew language. For legacy reasons the old code (iw) should
 * be used instead of the new code (he).
 *
 */
goog.LOCALE = goog.define('goog.LOCALE', 'en');  // default to en


/**
 * @define {boolean} Whether this code is running on trusted sites.
 *
 * On untrusted sites, several native functions can be defined or overridden by
 * external libraries like Prototype, Datejs, and JQuery and setting this flag
 * to false forces closure to use its own implementations when possible.
 *
 * If your JavaScript can be loaded by a third party site and you are wary about
 * relying on non-standard implementations, specify
 * "--define goog.TRUSTED_SITE=false" to the compiler.
 */
goog.TRUSTED_SITE = goog.define('goog.TRUSTED_SITE', true);


/**
 * @define {boolean} Whether a project is expected to be running in strict mode.
 *
 * This define can be used to trigger alternate implementations compatible with
 * running in EcmaScript Strict mode or warn about unavailable functionality.
 * @see https://goo.gl/PudQ4y
 *
 */
goog.STRICT_MODE_COMPATIBLE = goog.define('goog.STRICT_MODE_COMPATIBLE', false);


/**
 * @define {boolean} Whether code that calls {@link goog.setTestOnly} should
 *     be disallowed in the compilation unit.
 */
goog.DISALLOW_TEST_ONLY_CODE =
    goog.define('goog.DISALLOW_TEST_ONLY_CODE', COMPILED && !goog.DEBUG);


/**
 * @define {boolean} Whether to use a Chrome app CSP-compliant method for
 *     loading scripts via goog.require. @see appendScriptSrcNode_.
 */
goog.ENABLE_CHROME_APP_SAFE_SCRIPT_LOADING =
    goog.define('goog.ENABLE_CHROME_APP_SAFE_SCRIPT_LOADING', false);


/**
 * Defines a namespace in Closure.
 *
 * A namespace may only be defined once in a codebase. It may be defined using
 * goog.provide() or goog.module().
 *
 * The presence of one or more goog.provide() calls in a file indicates
 * that the file defines the given objects/namespaces.
 * Provided symbols must not be null or undefined.
 *
 * In addition, goog.provide() creates the object stubs for a namespace
 * (for example, goog.provide("goog.foo.bar") will create the object
 * goog.foo.bar if it does not already exist).
 *
 * Build tools also scan for provide/require/module statements
 * to discern dependencies, build dependency files (see deps.js), etc.
 *
 * @see goog.require
 * @see goog.module
 * @param {string} name Namespace provided by this file in the form
 *     "goog.package.part".
 */
goog.provide = function(name) {
  if (goog.isInModuleLoader_()) {
    throw new Error('goog.provide cannot be used within a module.');
  }
  if (!COMPILED) {
    // Ensure that the same namespace isn't provided twice.
    // A goog.module/goog.provide maps a goog.require to a specific file
    if (goog.isProvided_(name)) {
      throw new Error('Namespace "' + name + '" already declared.');
    }
  }

  goog.constructNamespace_(name);
};


/**
 * @param {string} name Namespace provided by this file in the form
 *     "goog.package.part".
 * @param {Object=} opt_obj The object to embed in the namespace.
 * @private
 */
goog.constructNamespace_ = function(name, opt_obj) {
  if (!COMPILED) {
    delete goog.implicitNamespaces_[name];

    var namespace = name;
    while ((namespace = namespace.substring(0, namespace.lastIndexOf('.')))) {
      if (goog.getObjectByName(namespace)) {
        break;
      }
      goog.implicitNamespaces_[namespace] = true;
    }
  }

  goog.exportPath_(name, opt_obj);
};


/**
 * Returns CSP nonce, if set for any script tag.
 * @param {?Window=} opt_window The window context used to retrieve the nonce.
 *     Defaults to global context.
 * @return {string} CSP nonce or empty string if no nonce is present.
 */
goog.getScriptNonce = function(opt_window) {
  if (opt_window && opt_window != goog.global) {
    return goog.getScriptNonce_(opt_window.document);
  }
  if (goog.cspNonce_ === null) {
    goog.cspNonce_ = goog.getScriptNonce_(goog.global.document);
  }
  return goog.cspNonce_;
};


/**
 * According to the CSP3 spec a nonce must be a valid base64 string.
 * @see https://www.w3.org/TR/CSP3/#grammardef-base64-value
 * @private @const
 */
goog.NONCE_PATTERN_ = /^[\w+/_-]+[=]{0,2}$/;


/**
 * @private {?string}
 */
goog.cspNonce_ = null;


/**
 * Returns CSP nonce, if set for any script tag.
 * @param {!Document} doc
 * @return {string} CSP nonce or empty string if no nonce is present.
 * @private
 */
goog.getScriptNonce_ = function(doc) {
  var script = doc.querySelector && doc.querySelector('script[nonce]');
  if (script) {
    // Try to get the nonce from the IDL property first, because browsers that
    // implement additional nonce protection features (currently only Chrome) to
    // prevent nonce stealing via CSS do not expose the nonce via attributes.
    // See https://github.com/whatwg/html/issues/2369
    var nonce = script['nonce'] || script.getAttribute('nonce');
    if (nonce && goog.NONCE_PATTERN_.test(nonce)) {
      return nonce;
    }
  }
  return '';
};


/**
 * Module identifier validation regexp.
 * Note: This is a conservative check, it is very possible to be more lenient,
 *   the primary exclusion here is "/" and "\" and a leading ".", these
 *   restrictions are intended to leave the door open for using goog.require
 *   with relative file paths rather than module identifiers.
 * @private
 */
goog.VALID_MODULE_RE_ = /^[a-zA-Z_$][a-zA-Z0-9._$]*$/;


/**
 * Defines a module in Closure.
 *
 * Marks that this file must be loaded as a module and claims the namespace.
 *
 * A namespace may only be defined once in a codebase. It may be defined using
 * goog.provide() or goog.module().
 *
 * goog.module() has three requirements:
 * - goog.module may not be used in the same file as goog.provide.
 * - goog.module must be the first statement in the file.
 * - only one goog.module is allowed per file.
 *
 * When a goog.module annotated file is loaded, it is enclosed in
 * a strict function closure. This means that:
 * - any variables declared in a goog.module file are private to the file
 * (not global), though the compiler is expected to inline the module.
 * - The code must obey all the rules of "strict" JavaScript.
 * - the file will be marked as "use strict"
 *
 * NOTE: unlike goog.provide, goog.module does not declare any symbols by
 * itself. If declared symbols are desired, use
 * goog.module.declareLegacyNamespace().
 *
 *
 * See the public goog.module proposal: http://goo.gl/Va1hin
 *
 * @param {string} name Namespace provided by this file in the form
 *     "goog.package.part", is expected but not required.
 * @return {void}
 */
goog.module = function(name) {
  if (typeof name !== 'string' || !name ||
      name.search(goog.VALID_MODULE_RE_) == -1) {
    throw new Error('Invalid module identifier');
  }
  if (!goog.isInGoogModuleLoader_()) {
    throw new Error(
        'Module ' + name + ' has been loaded incorrectly. Note, ' +
        'modules cannot be loaded as normal scripts. They require some kind of ' +
        'pre-processing step. You\'re likely trying to load a module via a ' +
        'script tag or as a part of a concatenated bundle without rewriting the ' +
        'module. For more info see: ' +
        'https://github.com/google/closure-library/wiki/goog.module:-an-ES6-module-like-alternative-to-goog.provide.');
  }
  if (goog.moduleLoaderState_.moduleName) {
    throw new Error('goog.module may only be called once per module.');
  }

  // Store the module name for the loader.
  goog.moduleLoaderState_.moduleName = name;
  if (!COMPILED) {
    // Ensure that the same namespace isn't provided twice.
    // A goog.module/goog.provide maps a goog.require to a specific file
    if (goog.isProvided_(name)) {
      throw new Error('Namespace "' + name + '" already declared.');
    }
    delete goog.implicitNamespaces_[name];
  }
};


/**
 * @param {string} name The module identifier.
 * @return {?} The module exports for an already loaded module or null.
 *
 * Note: This is not an alternative to goog.require, it does not
 * indicate a hard dependency, instead it is used to indicate
 * an optional dependency or to access the exports of a module
 * that has already been loaded.
 * @suppress {missingProvide}
 */
goog.module.get = function(name) {
  return goog.module.getInternal_(name);
};


/**
 * @param {string} name The module identifier.
 * @return {?} The module exports for an already loaded module or null.
 * @private
 */
goog.module.getInternal_ = function(name) {
  if (!COMPILED) {
    if (name in goog.loadedModules_) {
      return goog.loadedModules_[name].exports;
    } else if (!goog.implicitNamespaces_[name]) {
      var ns = goog.getObjectByName(name);
      return ns != null ? ns : null;
    }
  }
  return null;
};


/**
 * Types of modules the debug loader can load.
 * @enum {string}
 */
goog.ModuleType = {
  ES6: 'es6',
  GOOG: 'goog'
};


/**
 * @private {?{
 *   moduleName: (string|undefined),
 *   declareLegacyNamespace:boolean,
 *   type: ?goog.ModuleType
 * }}
 */
goog.moduleLoaderState_ = null;


/**
 * @private
 * @return {boolean} Whether a goog.module or an es6 module is currently being
 *     initialized.
 */
goog.isInModuleLoader_ = function() {
  return goog.isInGoogModuleLoader_() || goog.isInEs6ModuleLoader_();
};


/**
 * @private
 * @return {boolean} Whether a goog.module is currently being initialized.
 */
goog.isInGoogModuleLoader_ = function() {
  return !!goog.moduleLoaderState_ &&
      goog.moduleLoaderState_.type == goog.ModuleType.GOOG;
};


/**
 * @private
 * @return {boolean} Whether an es6 module is currently being initialized.
 */
goog.isInEs6ModuleLoader_ = function() {
  var inLoader = !!goog.moduleLoaderState_ &&
      goog.moduleLoaderState_.type == goog.ModuleType.ES6;

  if (inLoader) {
    return true;
  }

  var jscomp = goog.global['$jscomp'];

  if (jscomp) {
    // jscomp may not have getCurrentModulePath if this is a compiled bundle
    // that has some of the runtime, but not all of it. This can happen if
    // optimizations are turned on so the unused runtime is removed but renaming
    // and Closure pass are off (so $jscomp is still named $jscomp and the
    // goog.provide/require calls still exist).
    if (typeof jscomp.getCurrentModulePath != 'function') {
      return false;
    }

    // Bundled ES6 module.
    return !!jscomp.getCurrentModulePath();
  }

  return false;
};


/**
 * Provide the module's exports as a globally accessible object under the
 * module's declared name.  This is intended to ease migration to goog.module
 * for files that have existing usages.
 * @suppress {missingProvide}
 */
goog.module.declareLegacyNamespace = function() {
  if (!COMPILED && !goog.isInGoogModuleLoader_()) {
    throw new Error(
        'goog.module.declareLegacyNamespace must be called from ' +
        'within a goog.module');
  }
  if (!COMPILED && !goog.moduleLoaderState_.moduleName) {
    throw new Error(
        'goog.module must be called prior to ' +
        'goog.module.declareLegacyNamespace.');
  }
  goog.moduleLoaderState_.declareLegacyNamespace = true;
};


/**
 * Associates an ES6 module with a Closure module ID so that is available via
 * goog.require. The associated ID  acts like a goog.module ID - it does not
 * create any global names, it is merely available via goog.require /
 * goog.module.get / goog.forwardDeclare / goog.requireType. goog.require and
 * goog.module.get will return the entire module as if it was import *'d. This
 * allows Closure files to reference ES6 modules for the sake of migration.
 *
 * @param {string} namespace
 * @suppress {missingProvide}
 */
goog.declareModuleId = function(namespace) {
  if (!COMPILED) {
    if (!goog.isInEs6ModuleLoader_()) {
      throw new Error(
          'goog.declareModuleId may only be called from ' +
          'within an ES6 module');
    }
    if (goog.moduleLoaderState_ && goog.moduleLoaderState_.moduleName) {
      throw new Error(
          'goog.declareModuleId may only be called once per module.');
    }
    if (namespace in goog.loadedModules_) {
      throw new Error(
          'Module with namespace "' + namespace + '" already exists.');
    }
  }
  if (goog.moduleLoaderState_) {
    // Not bundled - debug loading.
    goog.moduleLoaderState_.moduleName = namespace;
  } else {
    // Bundled - not debug loading, no module loader state.
    var jscomp = goog.global['$jscomp'];
    if (!jscomp || typeof jscomp.getCurrentModulePath != 'function') {
      throw new Error(
          'Module with namespace "' + namespace +
          '" has been loaded incorrectly.');
    }
    var exports = jscomp.require(jscomp.getCurrentModulePath());
    goog.loadedModules_[namespace] = {
      exports: exports,
      type: goog.ModuleType.ES6,
      moduleId: namespace
    };
  }
};


/**
 * Marks that the current file should only be used for testing, and never for
 * live code in production.
 *
 * In the case of unit tests, the message may optionally be an exact namespace
 * for the test (e.g. 'goog.stringTest'). The linter will then ignore the extra
 * provide (if not explicitly defined in the code).
 *
 * @param {string=} opt_message Optional message to add to the error that's
 *     raised when used in production code.
 */
goog.setTestOnly = function(opt_message) {
  if (goog.DISALLOW_TEST_ONLY_CODE) {
    opt_message = opt_message || '';
    throw new Error(
        'Importing test-only code into non-debug environment' +
        (opt_message ? ': ' + opt_message : '.'));
  }
};


/**
 * Forward declares a symbol. This is an indication to the compiler that the
 * symbol may be used in the source yet is not required and may not be provided
 * in compilation.
 *
 * The most common usage of forward declaration is code that takes a type as a
 * function parameter but does not need to require it. By forward declaring
 * instead of requiring, no hard dependency is made, and (if not required
 * elsewhere) the namespace may never be required and thus, not be pulled
 * into the JavaScript binary. If it is required elsewhere, it will be type
 * checked as normal.
 *
 * Before using goog.forwardDeclare, please read the documentation at
 * https://github.com/google/closure-compiler/wiki/Bad-Type-Annotation to
 * understand the options and tradeoffs when working with forward declarations.
 *
 * @param {string} name The namespace to forward declare in the form of
 *     "goog.package.part".
 */
goog.forwardDeclare = function(name) {};


/**
 * Forward declare type information. Used to assign types to goog.global
 * referenced object that would otherwise result in unknown type references
 * and thus block property disambiguation.
 */
goog.forwardDeclare('Document');
goog.forwardDeclare('HTMLScriptElement');
goog.forwardDeclare('XMLHttpRequest');


if (!COMPILED) {
  /**
   * Check if the given name has been goog.provided. This will return false for
   * names that are available only as implicit namespaces.
   * @param {string} name name of the object to look for.
   * @return {boolean} Whether the name has been provided.
   * @private
   */
  goog.isProvided_ = function(name) {
    return (name in goog.loadedModules_) ||
        (!goog.implicitNamespaces_[name] && goog.getObjectByName(name) != null);
  };

  /**
   * Namespaces implicitly defined by goog.provide. For example,
   * goog.provide('goog.events.Event') implicitly declares that 'goog' and
   * 'goog.events' must be namespaces.
   *
   * @type {!Object<string, (boolean|undefined)>}
   * @private
   */
  goog.implicitNamespaces_ = {'goog.module': true};

  // NOTE: We add goog.module as an implicit namespace as goog.module is defined
  // here and because the existing module package has not been moved yet out of
  // the goog.module namespace. This satisifies both the debug loader and
  // ahead-of-time dependency management.
}


/**
 * Returns an object based on its fully qualified external name.  The object
 * is not found if null or undefined.  If you are using a compilation pass that
 * renames property names beware that using this function will not find renamed
 * properties.
 *
 * @param {string} name The fully qualified name.
 * @param {Object=} opt_obj The object within which to look; default is
 *     |goog.global|.
 * @return {?} The value (object or primitive) or, if not found, null.
 */
goog.getObjectByName = function(name, opt_obj) {
  var parts = name.split('.');
  var cur = opt_obj || goog.global;
  for (var i = 0; i < parts.length; i++) {
    cur = cur[parts[i]];
    if (cur == null) {
      return null;
    }
  }
  return cur;
};


/**
 * Globalizes a whole namespace, such as goog or goog.lang.
 *
 * @param {!Object} obj The namespace to globalize.
 * @param {Object=} opt_global The object to add the properties to.
 * @deprecated Properties may be explicitly exported to the global scope, but
 *     this should no longer be done in bulk.
 */
goog.globalize = function(obj, opt_global) {
  var global = opt_global || goog.global;
  for (var x in obj) {
    global[x] = obj[x];
  }
};


/**
 * Adds a dependency from a file to the files it requires.
 * @param {string} relPath The path to the js file.
 * @param {!Array<string>} provides An array of strings with
 *     the names of the objects this file provides.
 * @param {!Array<string>} requires An array of strings with
 *     the names of the objects this file requires.
 * @param {boolean|!Object<string>=} opt_loadFlags Parameters indicating
 *     how the file must be loaded.  The boolean 'true' is equivalent
 *     to {'module': 'goog'} for backwards-compatibility.  Valid properties
 *     and values include {'module': 'goog'} and {'lang': 'es6'}.
 */
goog.addDependency = function(relPath, provides, requires, opt_loadFlags) {
  if (!COMPILED && goog.DEPENDENCIES_ENABLED) {
    goog.debugLoader_.addDependency(relPath, provides, requires, opt_loadFlags);
  }
};




// NOTE(nnaze): The debug DOM loader was included in base.js as an original way
// to do "debug-mode" development.  The dependency system can sometimes be
// confusing, as can the debug DOM loader's asynchronous nature.
//
// With the DOM loader, a call to goog.require() is not blocking -- the script
// will not load until some point after the current script.  If a namespace is
// needed at runtime, it needs to be defined in a previous script, or loaded via
// require() with its registered dependencies.
//
// User-defined namespaces may need their own deps file. For a reference on
// creating a deps file, see:
// Externally: https://developers.google.com/closure/library/docs/depswriter
//
// Because of legacy clients, the DOM loader can't be easily removed from
// base.js.  Work was done to make it disableable or replaceable for
// different environments (DOM-less JavaScript interpreters like Rhino or V8,
// for example). See bootstrap/ for more information.


/**
 * @define {boolean} Whether to enable the debug loader.
 *
 * If enabled, a call to goog.require() will attempt to load the namespace by
 * appending a script tag to the DOM (if the namespace has been registered).
 *
 * If disabled, goog.require() will simply assert that the namespace has been
 * provided (and depend on the fact that some outside tool correctly ordered
 * the script).
 */
goog.ENABLE_DEBUG_LOADER = goog.define('goog.ENABLE_DEBUG_LOADER', true);


/**
 * @param {string} msg
 * @private
 */
goog.logToConsole_ = function(msg) {
  if (goog.global.console) {
    goog.global.console['error'](msg);
  }
};


/**
 * Implements a system for the dynamic resolution of dependencies that works in
 * parallel with the BUILD system.
 *
 * Note that all calls to goog.require will be stripped by the compiler.
 *
 * @see goog.provide
 * @param {string} namespace Namespace (as was given in goog.provide,
 *     goog.module, or goog.declareModuleId) in the form
 *     "goog.package.part".
 * @return {?} If called within a goog.module or ES6 module file, the associated
 *     namespace or module otherwise null.
 */
goog.require = function(namespace) {
  if (!COMPILED) {
    // Might need to lazy load on old IE.
    if (goog.ENABLE_DEBUG_LOADER) {
      goog.debugLoader_.requested(namespace);
    }

    // If the object already exists we do not need to do anything.
    if (goog.isProvided_(namespace)) {
      if (goog.isInModuleLoader_()) {
        return goog.module.getInternal_(namespace);
      }
    } else if (goog.ENABLE_DEBUG_LOADER) {
      var moduleLoaderState = goog.moduleLoaderState_;
      goog.moduleLoaderState_ = null;
      try {
        goog.debugLoader_.load_(namespace);
      } finally {
        goog.moduleLoaderState_ = moduleLoaderState;
      }
    }

    return null;
  }
};


/**
 * Requires a symbol for its type information. This is an indication to the
 * compiler that the symbol may appear in type annotations, yet it is not
 * referenced at runtime.
 *
 * When called within a goog.module or ES6 module file, the return value may be
 * assigned to or destructured into a variable, but it may not be otherwise used
 * in code outside of a type annotation.
 *
 * Note that all calls to goog.requireType will be stripped by the compiler.
 *
 * @param {string} namespace Namespace (as was given in goog.provide,
 *     goog.module, or goog.declareModuleId) in the form
 *     "goog.package.part".
 * @return {?}
 */
goog.requireType = function(namespace) {
  // Return an empty object so that single-level destructuring of the return
  // value doesn't crash at runtime when using the debug loader. Multi-level
  // destructuring isn't supported.
  return {};
};


/**
 * Path for included scripts.
 * @type {string}
 */
goog.basePath = '';


/**
 * A hook for overriding the base path.
 * @type {string|undefined}
 */
goog.global.CLOSURE_BASE_PATH;


/**
 * Whether to attempt to load Closure's deps file. By default, when uncompiled,
 * deps files will attempt to be loaded.
 * @type {boolean|undefined}
 */
goog.global.CLOSURE_NO_DEPS;


/**
 * A function to import a single script. This is meant to be overridden when
 * Closure is being run in non-HTML contexts, such as web workers. It's defined
 * in the global scope so that it can be set before base.js is loaded, which
 * allows deps.js to be imported properly.
 *
 * The first parameter the script source, which is a relative URI. The second,
 * optional parameter is the script contents, in the event the script needed
 * transformation. It should return true if the script was imported, false
 * otherwise.
 * @type {(function(string, string=): boolean)|undefined}
 */
goog.global.CLOSURE_IMPORT_SCRIPT;


/**
 * Null function used for default values of callbacks, etc.
 * @return {void} Nothing.
 */
goog.nullFunction = function() {};


/**
 * When defining a class Foo with an abstract method bar(), you can do:
 * Foo.prototype.bar = goog.abstractMethod
 *
 * Now if a subclass of Foo fails to override bar(), an error will be thrown
 * when bar() is invoked.
 *
 * @type {!Function}
 * @throws {Error} when invoked to indicate the method should be overridden.
 * @deprecated Use "@abstract" annotation instead of goog.abstractMethod in new
 *     code. See
 *     https://github.com/google/closure-compiler/wiki/@abstract-classes-and-methods
 */
goog.abstractMethod = function() {
  throw new Error('unimplemented abstract method');
};


/**
 * Adds a `getInstance` static method that always returns the same
 * instance object.
 * @param {!Function} ctor The constructor for the class to add the static
 *     method to.
 * @suppress {missingProperties} 'instance_' isn't a property on 'Function'
 *     but we don't have a better type to use here.
 */
goog.addSingletonGetter = function(ctor) {
  // instance_ is immediately set to prevent issues with sealed constructors
  // such as are encountered when a constructor is returned as the export object
  // of a goog.module in unoptimized code.
  // Delcare type to avoid conformance violations that ctor.instance_ is unknown
  /** @type {undefined|!Object} @suppress {underscore} */
  ctor.instance_ = undefined;
  ctor.getInstance = function() {
    if (ctor.instance_) {
      return ctor.instance_;
    }
    if (goog.DEBUG) {
      // NOTE: JSCompiler can't optimize away Array#push.
      goog.instantiatedSingletons_[goog.instantiatedSingletons_.length] = ctor;
    }
    // Cast to avoid conformance violations that ctor.instance_ is unknown
    return /** @type {!Object|undefined} */ (ctor.instance_) = new ctor;
  };
};


/**
 * All singleton classes that have been instantiated, for testing. Don't read
 * it directly, use the `goog.testing.singleton` module. The compiler
 * removes this variable if unused.
 * @type {!Array<!Function>}
 * @private
 */
goog.instantiatedSingletons_ = [];


/**
 * @define {boolean} Whether to load goog.modules using `eval` when using
 * the debug loader.  This provides a better debugging experience as the
 * source is unmodified and can be edited using Chrome Workspaces or similar.
 * However in some environments the use of `eval` is banned
 * so we provide an alternative.
 */
goog.LOAD_MODULE_USING_EVAL = goog.define('goog.LOAD_MODULE_USING_EVAL', true);


/**
 * @define {boolean} Whether the exports of goog.modules should be sealed when
 * possible.
 */
goog.SEAL_MODULE_EXPORTS = goog.define('goog.SEAL_MODULE_EXPORTS', goog.DEBUG);


/**
 * The registry of initialized modules:
 * The module identifier or path to module exports map.
 * @private @const {!Object<string, {exports:?,type:string,moduleId:string}>}
 */
goog.loadedModules_ = {};


/**
 * True if the debug loader enabled and used.
 * @const {boolean}
 */
goog.DEPENDENCIES_ENABLED = !COMPILED && goog.ENABLE_DEBUG_LOADER;


/**
 * @define {string} How to decide whether to transpile.  Valid values
 * are 'always', 'never', and 'detect'.  The default ('detect') is to
 * use feature detection to determine which language levels need
 * transpilation.
 */
// NOTE(sdh): we could expand this to accept a language level to bypass
// detection: e.g. goog.TRANSPILE == 'es5' would transpile ES6 files but
// would leave ES3 and ES5 files alone.
goog.TRANSPILE = goog.define('goog.TRANSPILE', 'detect');

/**
 * @define {boolean} If true assume that ES modules have already been
 * transpiled by the jscompiler (in the same way that transpile.js would
 * transpile them - to jscomp modules). Useful only for servers that wish to use
 * the debug loader and transpile server side. Thus this is only respected if
 * goog.TRANSPILE is "never".
 */
goog.ASSUME_ES_MODULES_TRANSPILED =
    goog.define('goog.ASSUME_ES_MODULES_TRANSPILED', false);


/**
 * @define {string} If a file needs to be transpiled what the output language
 * should be. By default this is the highest language level this file detects
 * the current environment supports. Generally this flag should not be set, but
 * it could be useful to override. Example: If the current environment supports
 * ES6 then by default ES7+ files will be transpiled to ES6, unless this is
 * overridden.
 *
 * Valid values include: es3, es5, es6, es7, and es8. Anything not recognized
 * is treated as es3.
 *
 * Note that setting this value does not force transpilation. Just if
 * transpilation occurs this will be the output. So this is most useful when
 * goog.TRANSPILE is set to 'always' and then forcing the language level to be
 * something lower than what the environment detects.
 */
goog.TRANSPILE_TO_LANGUAGE = goog.define('goog.TRANSPILE_TO_LANGUAGE', '');


/**
 * @define {string} Path to the transpiler.  Executing the script at this
 * path (relative to base.js) should define a function $jscomp.transpile.
 */
goog.TRANSPILER = goog.define('goog.TRANSPILER', 'transpile.js');


/**
 * @package {?boolean}
 * Visible for testing.
 */
goog.hasBadLetScoping = null;


/**
 * @return {boolean}
 * @package Visible for testing.
 */
goog.useSafari10Workaround = function() {
  if (goog.hasBadLetScoping == null) {
    var hasBadLetScoping;
    try {
      hasBadLetScoping = !eval(
          '"use strict";' +
          'let x = 1; function f() { return typeof x; };' +
          'f() == "number";');
    } catch (e) {
      // Assume that ES6 syntax isn't supported.
      hasBadLetScoping = false;
    }
    goog.hasBadLetScoping = hasBadLetScoping;
  }
  return goog.hasBadLetScoping;
};


/**
 * @param {string} moduleDef
 * @return {string}
 * @package Visible for testing.
 */
goog.workaroundSafari10EvalBug = function(moduleDef) {
  return '(function(){' + moduleDef +
      '\n' +  // Terminate any trailing single line comment.
      ';' +   // Terminate any trailing expression.
      '})();\n';
};


/**
 * @param {function(?):?|string} moduleDef The module definition.
 */
goog.loadModule = function(moduleDef) {
  // NOTE: we allow function definitions to be either in the from
  // of a string to eval (which keeps the original source intact) or
  // in a eval forbidden environment (CSP) we allow a function definition
  // which in its body must call `goog.module`, and return the exports
  // of the module.
  var previousState = goog.moduleLoaderState_;
  try {
    goog.moduleLoaderState_ = {
      moduleName: '',
      declareLegacyNamespace: false,
      type: goog.ModuleType.GOOG
    };
    var exports;
    if (goog.isFunction(moduleDef)) {
      exports = moduleDef.call(undefined, {});
    } else if (typeof moduleDef === 'string') {
      if (goog.useSafari10Workaround()) {
        moduleDef = goog.workaroundSafari10EvalBug(moduleDef);
      }

      exports = goog.loadModuleFromSource_.call(undefined, moduleDef);
    } else {
      throw new Error('Invalid module definition');
    }

    var moduleName = goog.moduleLoaderState_.moduleName;
    if (typeof moduleName === 'string' && moduleName) {
      // Don't seal legacy namespaces as they may be used as a parent of
      // another namespace
      if (goog.moduleLoaderState_.declareLegacyNamespace) {
        goog.constructNamespace_(moduleName, exports);
      } else if (
          goog.SEAL_MODULE_EXPORTS && Object.seal &&
          typeof exports == 'object' && exports != null) {
        Object.seal(exports);
      }

      var data = {
        exports: exports,
        type: goog.ModuleType.GOOG,
        moduleId: goog.moduleLoaderState_.moduleName
      };
      goog.loadedModules_[moduleName] = data;
    } else {
      throw new Error('Invalid module name \"' + moduleName + '\"');
    }
  } finally {
    goog.moduleLoaderState_ = previousState;
  }
};


/**
 * @private @const
 */
goog.loadModuleFromSource_ = /** @type {function(string):?} */ (function() {
  // NOTE: we avoid declaring parameters or local variables here to avoid
  // masking globals or leaking values into the module definition.
  'use strict';
  var exports = {};
  eval(arguments[0]);
  return exports;
});


/**
 * Normalize a file path by removing redundant ".." and extraneous "." file
 * path components.
 * @param {string} path
 * @return {string}
 * @private
 */
goog.normalizePath_ = function(path) {
  var components = path.split('/');
  var i = 0;
  while (i < components.length) {
    if (components[i] == '.') {
      components.splice(i, 1);
    } else if (
        i && components[i] == '..' && components[i - 1] &&
        components[i - 1] != '..') {
      components.splice(--i, 2);
    } else {
      i++;
    }
  }
  return components.join('/');
};


/**
 * Provides a hook for loading a file when using Closure's goog.require() API
 * with goog.modules.  In particular this hook is provided to support Node.js.
 *
 * @type {(function(string):string)|undefined}
 */
goog.global.CLOSURE_LOAD_FILE_SYNC;


/**
 * Loads file by synchronous XHR. Should not be used in production environments.
 * @param {string} src Source URL.
 * @return {?string} File contents, or null if load failed.
 * @private
 */
goog.loadFileSync_ = function(src) {
  if (goog.global.CLOSURE_LOAD_FILE_SYNC) {
    return goog.global.CLOSURE_LOAD_FILE_SYNC(src);
  } else {
    try {
      /** @type {XMLHttpRequest} */
      var xhr = new goog.global['XMLHttpRequest']();
      xhr.open('get', src, false);
      xhr.send();
      // NOTE: Successful http: requests have a status of 200, but successful
      // file: requests may have a status of zero.  Any other status, or a
      // thrown exception (particularly in case of file: requests) indicates
      // some sort of error, which we treat as a missing or unavailable file.
      return xhr.status == 0 || xhr.status == 200 ? xhr.responseText : null;
    } catch (err) {
      // No need to rethrow or log, since errors should show up on their own.
      return null;
    }
  }
};


/**
 * Lazily retrieves the transpiler and applies it to the source.
 * @param {string} code JS code.
 * @param {string} path Path to the code.
 * @param {string} target Language level output.
 * @return {string} The transpiled code.
 * @private
 */
goog.transpile_ = function(code, path, target) {
  var jscomp = goog.global['$jscomp'];
  if (!jscomp) {
    goog.global['$jscomp'] = jscomp = {};
  }
  var transpile = jscomp.transpile;
  if (!transpile) {
    var transpilerPath = goog.basePath + goog.TRANSPILER;
    var transpilerCode = goog.loadFileSync_(transpilerPath);
    if (transpilerCode) {
      // This must be executed synchronously, since by the time we know we
      // need it, we're about to load and write the ES6 code synchronously,
      // so a normal script-tag load will be too slow. Wrapped in a function
      // so that code is eval'd in the global scope.
      (function() {
        (0, eval)(transpilerCode + '\n//# sourceURL=' + transpilerPath);
      }).call(goog.global);
      // Even though the transpiler is optional, if $gwtExport is found, it's
      // a sign the transpiler was loaded and the $jscomp.transpile *should*
      // be there.
      if (goog.global['$gwtExport'] && goog.global['$gwtExport']['$jscomp'] &&
          !goog.global['$gwtExport']['$jscomp']['transpile']) {
        throw new Error(
            'The transpiler did not properly export the "transpile" ' +
            'method. $gwtExport: ' + JSON.stringify(goog.global['$gwtExport']));
      }
      // transpile.js only exports a single $jscomp function, transpile. We
      // grab just that and add it to the existing definition of $jscomp which
      // contains the polyfills.
      goog.global['$jscomp'].transpile =
          goog.global['$gwtExport']['$jscomp']['transpile'];
      jscomp = goog.global['$jscomp'];
      transpile = jscomp.transpile;
    }
  }
  if (!transpile) {
    // The transpiler is an optional component.  If it's not available then
    // replace it with a pass-through function that simply logs.
    var suffix = ' requires transpilation but no transpiler was found.';
    transpile = jscomp.transpile = function(code, path) {
      // TODO(sdh): figure out some way to get this error to show up
      // in test results, noting that the failure may occur in many
      // different ways, including in loadModule() before the test
      // runner even comes up.
      goog.logToConsole_(path + suffix);
      return code;
    };
  }
  // Note: any transpilation errors/warnings will be logged to the console.
  return transpile(code, path, target);
};

//==============================================================================
// Language Enhancements
//==============================================================================


/**
 * This is a "fixed" version of the typeof operator.  It differs from the typeof
 * operator in such a way that null returns 'null' and arrays return 'array'.
 * @param {?} value The value to get the type of.
 * @return {string} The name of the type.
 */
goog.typeOf = function(value) {
  var s = typeof value;
  if (s == 'object') {
    if (value) {
      // Check these first, so we can avoid calling Object.prototype.toString if
      // possible.
      //
      // IE improperly marshals typeof across execution contexts, but a
      // cross-context object will still return false for "instanceof Object".
      if (value instanceof Array) {
        return 'array';
      } else if (value instanceof Object) {
        return s;
      }

      // HACK: In order to use an Object prototype method on the arbitrary
      //   value, the compiler requires the value be cast to type Object,
      //   even though the ECMA spec explicitly allows it.
      var className = Object.prototype.toString.call(
          /** @type {!Object} */ (value));
      // In Firefox 3.6, attempting to access iframe window objects' length
      // property throws an NS_ERROR_FAILURE, so we need to special-case it
      // here.
      if (className == '[object Window]') {
        return 'object';
      }

      // We cannot always use constructor == Array or instanceof Array because
      // different frames have different Array objects. In IE6, if the iframe
      // where the array was created is destroyed, the array loses its
      // prototype. Then dereferencing val.splice here throws an exception, so
      // we can't use goog.isFunction. Calling typeof directly returns 'unknown'
      // so that will work. In this case, this function will return false and
      // most array functions will still work because the array is still
      // array-like (supports length and []) even though it has lost its
      // prototype.
      // Mark Miller noticed that Object.prototype.toString
      // allows access to the unforgeable [[Class]] property.
      //  15.2.4.2 Object.prototype.toString ( )
      //  When the toString method is called, the following steps are taken:
      //      1. Get the [[Class]] property of this object.
      //      2. Compute a string value by concatenating the three strings
      //         "[object ", Result(1), and "]".
      //      3. Return Result(2).
      // and this behavior survives the destruction of the execution context.
      if ((className == '[object Array]' ||
           // In IE all non value types are wrapped as objects across window
           // boundaries (not iframe though) so we have to do object detection
           // for this edge case.
           typeof value.length == 'number' &&
               typeof value.splice != 'undefined' &&
               typeof value.propertyIsEnumerable != 'undefined' &&
               !value.propertyIsEnumerable('splice')

               )) {
        return 'array';
      }
      // HACK: There is still an array case that fails.
      //     function ArrayImpostor() {}
      //     ArrayImpostor.prototype = [];
      //     var impostor = new ArrayImpostor;
      // this can be fixed by getting rid of the fast path
      // (value instanceof Array) and solely relying on
      // (value && Object.prototype.toString.vall(value) === '[object Array]')
      // but that would require many more function calls and is not warranted
      // unless closure code is receiving objects from untrusted sources.

      // IE in cross-window calls does not correctly marshal the function type
      // (it appears just as an object) so we cannot use just typeof val ==
      // 'function'. However, if the object has a call property, it is a
      // function.
      if ((className == '[object Function]' ||
           typeof value.call != 'undefined' &&
               typeof value.propertyIsEnumerable != 'undefined' &&
               !value.propertyIsEnumerable('call'))) {
        return 'function';
      }

    } else {
      return 'null';
    }

  } else if (s == 'function' && typeof value.call == 'undefined') {
    // In Safari typeof nodeList returns 'function', and on Firefox typeof
    // behaves similarly for HTML{Applet,Embed,Object}, Elements and RegExps. We
    // would like to return object for those and we can detect an invalid
    // function by making sure that the function object has a call method.
    return 'object';
  }
  return s;
};


/**
 * Returns true if the specified value is null.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is null.
 * @deprecated Use `val === null` instead.
 */
goog.isNull = function(val) {
  return val === null;
};


/**
 * Returns true if the specified value is defined and not null.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is defined and not null.
 * @deprecated Use `val != null` instead.
 */
goog.isDefAndNotNull = function(val) {
  // Note that undefined == null.
  return val != null;
};


/**
 * Returns true if the specified value is an array.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is an array.
 */
goog.isArray = function(val) {
  return goog.typeOf(val) == 'array';
};


/**
 * Returns true if the object looks like an array. To qualify as array like
 * the value needs to be either a NodeList or an object with a Number length
 * property. Note that for this function neither strings nor functions are
 * considered "array-like".
 *
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is an array.
 */
goog.isArrayLike = function(val) {
  var type = goog.typeOf(val);
  // We do not use goog.isObject here in order to exclude function values.
  return type == 'array' || type == 'object' && typeof val.length == 'number';
};


/**
 * Returns true if the object looks like a Date. To qualify as Date-like the
 * value needs to be an object and have a getFullYear() function.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is a like a Date.
 */
goog.isDateLike = function(val) {
  return goog.isObject(val) && typeof val.getFullYear == 'function';
};


/**
 * Returns true if the specified value is a function.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is a function.
 */
goog.isFunction = function(val) {
  return goog.typeOf(val) == 'function';
};


/**
 * Returns true if the specified value is an object.  This includes arrays and
 * functions.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is an object.
 */
goog.isObject = function(val) {
  var type = typeof val;
  return type == 'object' && val != null || type == 'function';
  // return Object(val) === val also works, but is slower, especially if val is
  // not an object.
};


/**
 * Gets a unique ID for an object. This mutates the object so that further calls
 * with the same object as a parameter returns the same value. The unique ID is
 * guaranteed to be unique across the current session amongst objects that are
 * passed into `getUid`. There is no guarantee that the ID is unique or
 * consistent across sessions. It is unsafe to generate unique ID for function
 * prototypes.
 *
 * @param {Object} obj The object to get the unique ID for.
 * @return {number} The unique ID for the object.
 */
goog.getUid = function(obj) {
  // TODO(arv): Make the type stricter, do not accept null.

  // In Opera window.hasOwnProperty exists but always returns false so we avoid
  // using it. As a consequence the unique ID generated for BaseClass.prototype
  // and SubClass.prototype will be the same.
  // TODO(b/141512323): UUIDs are broken for ctors with class-side inheritance.
  return obj[goog.UID_PROPERTY_] ||
      (obj[goog.UID_PROPERTY_] = ++goog.uidCounter_);
};


/**
 * Whether the given object is already assigned a unique ID.
 *
 * This does not modify the object.
 *
 * @param {!Object} obj The object to check.
 * @return {boolean} Whether there is an assigned unique id for the object.
 */
goog.hasUid = function(obj) {
  return !!obj[goog.UID_PROPERTY_];
};


/**
 * Removes the unique ID from an object. This is useful if the object was
 * previously mutated using `goog.getUid` in which case the mutation is
 * undone.
 * @param {Object} obj The object to remove the unique ID field from.
 */
goog.removeUid = function(obj) {
  // TODO(arv): Make the type stricter, do not accept null.

  // In IE, DOM nodes are not instances of Object and throw an exception if we
  // try to delete.  Instead we try to use removeAttribute.
  if (obj !== null && 'removeAttribute' in obj) {
    obj.removeAttribute(goog.UID_PROPERTY_);
  }

  try {
    delete obj[goog.UID_PROPERTY_];
  } catch (ex) {
  }
};


/**
 * Name for unique ID property. Initialized in a way to help avoid collisions
 * with other closure JavaScript on the same page.
 * @type {string}
 * @private
 */
goog.UID_PROPERTY_ = 'closure_uid_' + ((Math.random() * 1e9) >>> 0);


/**
 * Counter for UID.
 * @type {number}
 * @private
 */
goog.uidCounter_ = 0;


/**
 * Adds a hash code field to an object. The hash code is unique for the
 * given object.
 * @param {Object} obj The object to get the hash code for.
 * @return {number} The hash code for the object.
 * @deprecated Use goog.getUid instead.
 */
goog.getHashCode = goog.getUid;


/**
 * Removes the hash code field from an object.
 * @param {Object} obj The object to remove the field from.
 * @deprecated Use goog.removeUid instead.
 */
goog.removeHashCode = goog.removeUid;


/**
 * Clones a value. The input may be an Object, Array, or basic type. Objects and
 * arrays will be cloned recursively.
 *
 * WARNINGS:
 * <code>goog.cloneObject</code> does not detect reference loops. Objects that
 * refer to themselves will cause infinite recursion.
 *
 * <code>goog.cloneObject</code> is unaware of unique identifiers, and copies
 * UIDs created by <code>getUid</code> into cloned results.
 *
 * @param {*} obj The value to clone.
 * @return {*} A clone of the input value.
 * @deprecated goog.cloneObject is unsafe. Prefer the goog.object methods.
 */
goog.cloneObject = function(obj) {
  var type = goog.typeOf(obj);
  if (type == 'object' || type == 'array') {
    if (typeof obj.clone === 'function') {
      return obj.clone();
    }
    var clone = type == 'array' ? [] : {};
    for (var key in obj) {
      clone[key] = goog.cloneObject(obj[key]);
    }
    return clone;
  }

  return obj;
};


/**
 * A native implementation of goog.bind.
 * @param {?function(this:T, ...)} fn A function to partially apply.
 * @param {T} selfObj Specifies the object which this should point to when the
 *     function is run.
 * @param {...*} var_args Additional arguments that are partially applied to the
 *     function.
 * @return {!Function} A partially-applied form of the function goog.bind() was
 *     invoked as a method of.
 * @template T
 * @private
 */
goog.bindNative_ = function(fn, selfObj, var_args) {
  return /** @type {!Function} */ (fn.call.apply(fn.bind, arguments));
};


/**
 * A pure-JS implementation of goog.bind.
 * @param {?function(this:T, ...)} fn A function to partially apply.
 * @param {T} selfObj Specifies the object which this should point to when the
 *     function is run.
 * @param {...*} var_args Additional arguments that are partially applied to the
 *     function.
 * @return {!Function} A partially-applied form of the function goog.bind() was
 *     invoked as a method of.
 * @template T
 * @private
 */
goog.bindJs_ = function(fn, selfObj, var_args) {
  if (!fn) {
    throw new Error();
  }

  if (arguments.length > 2) {
    var boundArgs = Array.prototype.slice.call(arguments, 2);
    return function() {
      // Prepend the bound arguments to the current arguments.
      var newArgs = Array.prototype.slice.call(arguments);
      Array.prototype.unshift.apply(newArgs, boundArgs);
      return fn.apply(selfObj, newArgs);
    };

  } else {
    return function() {
      return fn.apply(selfObj, arguments);
    };
  }
};


/**
 * Partially applies this function to a particular 'this object' and zero or
 * more arguments. The result is a new function with some arguments of the first
 * function pre-filled and the value of this 'pre-specified'.
 *
 * Remaining arguments specified at call-time are appended to the pre-specified
 * ones.
 *
 * Also see: {@link #partial}.
 *
 * Usage:
 * <pre>var barMethBound = goog.bind(myFunction, myObj, 'arg1', 'arg2');
 * barMethBound('arg3', 'arg4');</pre>
 *
 * @param {?function(this:T, ...)} fn A function to partially apply.
 * @param {T} selfObj Specifies the object which this should point to when the
 *     function is run.
 * @param {...*} var_args Additional arguments that are partially applied to the
 *     function.
 * @return {!Function} A partially-applied form of the function goog.bind() was
 *     invoked as a method of.
 * @template T
 * @suppress {deprecated} See above.
 */
goog.bind = function(fn, selfObj, var_args) {
  // TODO(nicksantos): narrow the type signature.
  if (Function.prototype.bind &&
      // NOTE(nicksantos): Somebody pulled base.js into the default Chrome
      // extension environment. This means that for Chrome extensions, they get
      // the implementation of Function.prototype.bind that calls goog.bind
      // instead of the native one. Even worse, we don't want to introduce a
      // circular dependency between goog.bind and Function.prototype.bind, so
      // we have to hack this to make sure it works correctly.
      Function.prototype.bind.toString().indexOf('native code') != -1) {
    goog.bind = goog.bindNative_;
  } else {
    goog.bind = goog.bindJs_;
  }
  return goog.bind.apply(null, arguments);
};


/**
 * Like goog.bind(), except that a 'this object' is not required. Useful when
 * the target function is already bound.
 *
 * Usage:
 * var g = goog.partial(f, arg1, arg2);
 * g(arg3, arg4);
 *
 * @param {Function} fn A function to partially apply.
 * @param {...*} var_args Additional arguments that are partially applied to fn.
 * @return {!Function} A partially-applied form of the function goog.partial()
 *     was invoked as a method of.
 */
goog.partial = function(fn, var_args) {
  var args = Array.prototype.slice.call(arguments, 1);
  return function() {
    // Clone the array (with slice()) and append additional arguments
    // to the existing arguments.
    var newArgs = args.slice();
    newArgs.push.apply(newArgs, arguments);
    return fn.apply(/** @type {?} */ (this), newArgs);
  };
};


/**
 * Copies all the members of a source object to a target object. This method
 * does not work on all browsers for all objects that contain keys such as
 * toString or hasOwnProperty. Use goog.object.extend for this purpose.
 *
 * NOTE: Some have advocated for the use of goog.mixin to setup classes
 * with multiple inheritence (traits, mixins, etc).  However, as it simply
 * uses "for in", this is not compatible with ES6 classes whose methods are
 * non-enumerable.  Changing this, would break cases where non-enumerable
 * properties are not expected.
 *
 * @param {Object} target Target.
 * @param {Object} source Source.
 * @deprecated Prefer Object.assign
 */
goog.mixin = function(target, source) {
  for (var x in source) {
    target[x] = source[x];
  }

  // For IE7 or lower, the for-in-loop does not contain any properties that are
  // not enumerable on the prototype object (for example, isPrototypeOf from
  // Object.prototype) but also it will not include 'replace' on objects that
  // extend String and change 'replace' (not that it is common for anyone to
  // extend anything except Object).
};


/**
 * @return {number} An integer value representing the number of milliseconds
 *     between midnight, January 1, 1970 and the current time.
 * @deprecated Use Date.now
 */
goog.now = (goog.TRUSTED_SITE && Date.now) || (function() {
             // Unary plus operator converts its operand to a number which in
             // the case of
             // a date is done by calling getTime().
             return +new Date();
           });


/**
 * Evals JavaScript in the global scope.  In IE this uses execScript, other
 * browsers use goog.global.eval. If goog.global.eval does not evaluate in the
 * global scope (for example, in Safari), appends a script tag instead.
 * Throws an exception if neither execScript or eval is defined.
 * @param {string} script JavaScript string.
 */
goog.globalEval = function(script) {
  if (goog.global.execScript) {
    goog.global.execScript(script, 'JavaScript');
  } else if (goog.global.eval) {
    // Test to see if eval works
    if (goog.evalWorksForGlobals_ == null) {
      try {
        goog.global.eval('var _evalTest_ = 1;');
      } catch (ignore) {
      }
      if (typeof goog.global['_evalTest_'] != 'undefined') {
        try {
          delete goog.global['_evalTest_'];
        } catch (ignore) {
          // Microsoft edge fails the deletion above in strict mode.
        }
        goog.evalWorksForGlobals_ = true;
      } else {
        goog.evalWorksForGlobals_ = false;
      }
    }

    if (goog.evalWorksForGlobals_) {
      goog.global.eval(script);
    } else {
      /** @type {!Document} */
      var doc = goog.global.document;
      var scriptElt =
          /** @type {!HTMLScriptElement} */ (doc.createElement('script'));
      scriptElt.type = 'text/javascript';
      scriptElt.defer = false;
      // Note(user): can't use .innerHTML since "t('<test>')" will fail and
      // .text doesn't work in Safari 2.  Therefore we append a text node.
      scriptElt.appendChild(doc.createTextNode(script));
      doc.head.appendChild(scriptElt);
      doc.head.removeChild(scriptElt);
    }
  } else {
    throw new Error('goog.globalEval not available');
  }
};


/**
 * Indicates whether or not we can call 'eval' directly to eval code in the
 * global scope. Set to a Boolean by the first call to goog.globalEval (which
 * empirically tests whether eval works for globals). @see goog.globalEval
 * @type {?boolean}
 * @private
 */
goog.evalWorksForGlobals_ = null;


/**
 * Optional map of CSS class names to obfuscated names used with
 * goog.getCssName().
 * @private {!Object<string, string>|undefined}
 * @see goog.setCssNameMapping
 */
goog.cssNameMapping_;


/**
 * Optional obfuscation style for CSS class names. Should be set to either
 * 'BY_WHOLE' or 'BY_PART' if defined.
 * @type {string|undefined}
 * @private
 * @see goog.setCssNameMapping
 */
goog.cssNameMappingStyle_;



/**
 * A hook for modifying the default behavior goog.getCssName. The function
 * if present, will receive the standard output of the goog.getCssName as
 * its input.
 *
 * @type {(function(string):string)|undefined}
 */
goog.global.CLOSURE_CSS_NAME_MAP_FN;


/**
 * Handles strings that are intended to be used as CSS class names.
 *
 * This function works in tandem with @see goog.setCssNameMapping.
 *
 * Without any mapping set, the arguments are simple joined with a hyphen and
 * passed through unaltered.
 *
 * When there is a mapping, there are two possible styles in which these
 * mappings are used. In the BY_PART style, each part (i.e. in between hyphens)
 * of the passed in css name is rewritten according to the map. In the BY_WHOLE
 * style, the full css name is looked up in the map directly. If a rewrite is
 * not specified by the map, the compiler will output a warning.
 *
 * When the mapping is passed to the compiler, it will replace calls to
 * goog.getCssName with the strings from the mapping, e.g.
 *     var x = goog.getCssName('foo');
 *     var y = goog.getCssName(this.baseClass, 'active');
 *  becomes:
 *     var x = 'foo';
 *     var y = this.baseClass + '-active';
 *
 * If one argument is passed it will be processed, if two are passed only the
 * modifier will be processed, as it is assumed the first argument was generated
 * as a result of calling goog.getCssName.
 *
 * @param {string} className The class name.
 * @param {string=} opt_modifier A modifier to be appended to the class name.
 * @return {string} The class name or the concatenation of the class name and
 *     the modifier.
 */
goog.getCssName = function(className, opt_modifier) {
  // String() is used for compatibility with compiled soy where the passed
  // className can be non-string objects.
  if (String(className).charAt(0) == '.') {
    throw new Error(
        'className passed in goog.getCssName must not start with ".".' +
        ' You passed: ' + className);
  }

  var getMapping = function(cssName) {
    return goog.cssNameMapping_[cssName] || cssName;
  };

  var renameByParts = function(cssName) {
    // Remap all the parts individually.
    var parts = cssName.split('-');
    var mapped = [];
    for (var i = 0; i < parts.length; i++) {
      mapped.push(getMapping(parts[i]));
    }
    return mapped.join('-');
  };

  var rename;
  if (goog.cssNameMapping_) {
    rename =
        goog.cssNameMappingStyle_ == 'BY_WHOLE' ? getMapping : renameByParts;
  } else {
    rename = function(a) {
      return a;
    };
  }

  var result =
      opt_modifier ? className + '-' + rename(opt_modifier) : rename(className);

  // The special CLOSURE_CSS_NAME_MAP_FN allows users to specify further
  // processing of the class name.
  if (goog.global.CLOSURE_CSS_NAME_MAP_FN) {
    return goog.global.CLOSURE_CSS_NAME_MAP_FN(result);
  }

  return result;
};


/**
 * Sets the map to check when returning a value from goog.getCssName(). Example:
 * <pre>
 * goog.setCssNameMapping({
 *   "goog": "a",
 *   "disabled": "b",
 * });
 *
 * var x = goog.getCssName('goog');
 * // The following evaluates to: "a a-b".
 * goog.getCssName('goog') + ' ' + goog.getCssName(x, 'disabled')
 * </pre>
 * When declared as a map of string literals to string literals, the JSCompiler
 * will replace all calls to goog.getCssName() using the supplied map if the
 * --process_closure_primitives flag is set.
 *
 * @param {!Object} mapping A map of strings to strings where keys are possible
 *     arguments to goog.getCssName() and values are the corresponding values
 *     that should be returned.
 * @param {string=} opt_style The style of css name mapping. There are two valid
 *     options: 'BY_PART', and 'BY_WHOLE'.
 * @see goog.getCssName for a description.
 */
goog.setCssNameMapping = function(mapping, opt_style) {
  goog.cssNameMapping_ = mapping;
  goog.cssNameMappingStyle_ = opt_style;
};


/**
 * To use CSS renaming in compiled mode, one of the input files should have a
 * call to goog.setCssNameMapping() with an object literal that the JSCompiler
 * can extract and use to replace all calls to goog.getCssName(). In uncompiled
 * mode, JavaScript code should be loaded before this base.js file that declares
 * a global variable, CLOSURE_CSS_NAME_MAPPING, which is used below. This is
 * to ensure that the mapping is loaded before any calls to goog.getCssName()
 * are made in uncompiled mode.
 *
 * A hook for overriding the CSS name mapping.
 * @type {!Object<string, string>|undefined}
 */
goog.global.CLOSURE_CSS_NAME_MAPPING;


if (!COMPILED && goog.global.CLOSURE_CSS_NAME_MAPPING) {
  // This does not call goog.setCssNameMapping() because the JSCompiler
  // requires that goog.setCssNameMapping() be called with an object literal.
  goog.cssNameMapping_ = goog.global.CLOSURE_CSS_NAME_MAPPING;
}


/**
 * Gets a localized message.
 *
 * This function is a compiler primitive. If you give the compiler a localized
 * message bundle, it will replace the string at compile-time with a localized
 * version, and expand goog.getMsg call to a concatenated string.
 *
 * Messages must be initialized in the form:
 * <code>
 * var MSG_NAME = goog.getMsg('Hello {$placeholder}', {'placeholder': 'world'});
 * </code>
 *
 * This function produces a string which should be treated as plain text. Use
 * {@link goog.html.SafeHtmlFormatter} in conjunction with goog.getMsg to
 * produce SafeHtml.
 *
 * @param {string} str Translatable string, places holders in the form {$foo}.
 * @param {Object<string, string>=} opt_values Maps place holder name to value.
 * @param {{html: boolean}=} opt_options Options:
 *     html: Escape '<' in str to '&lt;'. Used by Closure Templates where the
 *     generated code size and performance is critical which is why {@link
 *     goog.html.SafeHtmlFormatter} is not used. The value must be literal true
 *     or false.
 * @return {string} message with placeholders filled.
 */
goog.getMsg = function(str, opt_values, opt_options) {
  if (opt_options && opt_options.html) {
    // Note that '&' is not replaced because the translation can contain HTML
    // entities.
    str = str.replace(/</g, '&lt;');
  }
  if (opt_values) {
    str = str.replace(/\{\$([^}]+)}/g, function(match, key) {
      return (opt_values != null && key in opt_values) ? opt_values[key] :
                                                         match;
    });
  }
  return str;
};


/**
 * Gets a localized message. If the message does not have a translation, gives a
 * fallback message.
 *
 * This is useful when introducing a new message that has not yet been
 * translated into all languages.
 *
 * This function is a compiler primitive. Must be used in the form:
 * <code>var x = goog.getMsgWithFallback(MSG_A, MSG_B);</code>
 * where MSG_A and MSG_B were initialized with goog.getMsg.
 *
 * @param {string} a The preferred message.
 * @param {string} b The fallback message.
 * @return {string} The best translated message.
 */
goog.getMsgWithFallback = function(a, b) {
  return a;
};


/**
 * Exposes an unobfuscated global namespace path for the given object.
 * Note that fields of the exported object *will* be obfuscated, unless they are
 * exported in turn via this function or goog.exportProperty.
 *
 * Also handy for making public items that are defined in anonymous closures.
 *
 * ex. goog.exportSymbol('public.path.Foo', Foo);
 *
 * ex. goog.exportSymbol('public.path.Foo.staticFunction', Foo.staticFunction);
 *     public.path.Foo.staticFunction();
 *
 * ex. goog.exportSymbol('public.path.Foo.prototype.myMethod',
 *                       Foo.prototype.myMethod);
 *     new public.path.Foo().myMethod();
 *
 * @param {string} publicPath Unobfuscated name to export.
 * @param {*} object Object the name should point to.
 * @param {Object=} opt_objectToExportTo The object to add the path to; default
 *     is goog.global.
 */
goog.exportSymbol = function(publicPath, object, opt_objectToExportTo) {
  goog.exportPath_(publicPath, object, opt_objectToExportTo);
};


/**
 * Exports a property unobfuscated into the object's namespace.
 * ex. goog.exportProperty(Foo, 'staticFunction', Foo.staticFunction);
 * ex. goog.exportProperty(Foo.prototype, 'myMethod', Foo.prototype.myMethod);
 * @param {Object} object Object whose static property is being exported.
 * @param {string} publicName Unobfuscated name to export.
 * @param {*} symbol Object the name should point to.
 */
goog.exportProperty = function(object, publicName, symbol) {
  object[publicName] = symbol;
};


/**
 * Inherit the prototype methods from one constructor into another.
 *
 * Usage:
 * <pre>
 * function ParentClass(a, b) { }
 * ParentClass.prototype.foo = function(a) { };
 *
 * function ChildClass(a, b, c) {
 *   ChildClass.base(this, 'constructor', a, b);
 * }
 * goog.inherits(ChildClass, ParentClass);
 *
 * var child = new ChildClass('a', 'b', 'see');
 * child.foo(); // This works.
 * </pre>
 *
 * @param {!Function} childCtor Child class.
 * @param {!Function} parentCtor Parent class.
 * @suppress {strictMissingProperties} superClass_ and base is not defined on
 *    Function.
 */
goog.inherits = function(childCtor, parentCtor) {
  /** @constructor */
  function tempCtor() {}
  tempCtor.prototype = parentCtor.prototype;
  childCtor.superClass_ = parentCtor.prototype;
  childCtor.prototype = new tempCtor();
  /** @override */
  childCtor.prototype.constructor = childCtor;

  /**
   * Calls superclass constructor/method.
   *
   * This function is only available if you use goog.inherits to
   * express inheritance relationships between classes.
   *
   * NOTE: This is a replacement for goog.base and for superClass_
   * property defined in childCtor.
   *
   * @param {!Object} me Should always be "this".
   * @param {string} methodName The method name to call. Calling
   *     superclass constructor can be done with the special string
   *     'constructor'.
   * @param {...*} var_args The arguments to pass to superclass
   *     method/constructor.
   * @return {*} The return value of the superclass method/constructor.
   */
  childCtor.base = function(me, methodName, var_args) {
    // Copying using loop to avoid deop due to passing arguments object to
    // function. This is faster in many JS engines as of late 2014.
    var args = new Array(arguments.length - 2);
    for (var i = 2; i < arguments.length; i++) {
      args[i - 2] = arguments[i];
    }
    return parentCtor.prototype[methodName].apply(me, args);
  };
};


/**
 * Call up to the superclass.
 *
 * If this is called from a constructor, then this calls the superclass
 * constructor with arguments 1-N.
 *
 * If this is called from a prototype method, then you must pass the name of the
 * method as the second argument to this function. If you do not, you will get a
 * runtime error. This calls the superclass' method with arguments 2-N.
 *
 * This function only works if you use goog.inherits to express inheritance
 * relationships between your classes.
 *
 * This function is a compiler primitive. At compile-time, the compiler will do
 * macro expansion to remove a lot of the extra overhead that this function
 * introduces. The compiler will also enforce a lot of the assumptions that this
 * function makes, and treat it as a compiler error if you break them.
 *
 * @param {!Object} me Should always be "this".
 * @param {*=} opt_methodName The method name if calling a super method.
 * @param {...*} var_args The rest of the arguments.
 * @return {*} The return value of the superclass method.
 * @suppress {es5Strict} This method can not be used in strict mode, but
 *     all Closure Library consumers must depend on this file.
 * @deprecated goog.base is not strict mode compatible.  Prefer the static
 *     "base" method added to the constructor by goog.inherits
 *     or ES6 classes and the "super" keyword.
 */
goog.base = function(me, opt_methodName, var_args) {
  var caller = arguments.callee.caller;

  if (goog.STRICT_MODE_COMPATIBLE || (goog.DEBUG && !caller)) {
    throw new Error(
        'arguments.caller not defined.  goog.base() cannot be used ' +
        'with strict mode code. See ' +
        'http://www.ecma-international.org/ecma-262/5.1/#sec-C');
  }

  if (typeof caller.superClass_ !== 'undefined') {
    // Copying using loop to avoid deop due to passing arguments object to
    // function. This is faster in many JS engines as of late 2014.
    var ctorArgs = new Array(arguments.length - 1);
    for (var i = 1; i < arguments.length; i++) {
      ctorArgs[i - 1] = arguments[i];
    }
    // This is a constructor. Call the superclass constructor.
    return /** @type {!Function} */ (caller.superClass_)
        .constructor.apply(me, ctorArgs);
  }

  if (typeof opt_methodName != 'string' && typeof opt_methodName != 'symbol') {
    throw new Error(
        'method names provided to goog.base must be a string or a symbol');
  }

  // Copying using loop to avoid deop due to passing arguments object to
  // function. This is faster in many JS engines as of late 2014.
  var args = new Array(arguments.length - 2);
  for (var i = 2; i < arguments.length; i++) {
    args[i - 2] = arguments[i];
  }
  var foundCaller = false;
  for (var proto = me.constructor.prototype; proto;
       proto = Object.getPrototypeOf(proto)) {
    if (proto[opt_methodName] === caller) {
      foundCaller = true;
    } else if (foundCaller) {
      return proto[opt_methodName].apply(me, args);
    }
  }

  // If we did not find the caller in the prototype chain, then one of two
  // things happened:
  // 1) The caller is an instance method.
  // 2) This method was not called by the right caller.
  if (me[opt_methodName] === caller) {
    return me.constructor.prototype[opt_methodName].apply(me, args);
  } else {
    throw new Error(
        'goog.base called from a method of one name ' +
        'to a method of a different name');
  }
};


/**
 * Allow for aliasing within scope functions.  This function exists for
 * uncompiled code - in compiled code the calls will be inlined and the aliases
 * applied.  In uncompiled code the function is simply run since the aliases as
 * written are valid JavaScript.
 *
 *
 * @param {function()} fn Function to call.  This function can contain aliases
 *     to namespaces (e.g. "var dom = goog.dom") or classes
 *     (e.g. "var Timer = goog.Timer").
 */
goog.scope = function(fn) {
  if (goog.isInModuleLoader_()) {
    throw new Error('goog.scope is not supported within a module.');
  }
  fn.call(goog.global);
};


/*
 * To support uncompiled, strict mode bundles that use eval to divide source
 * like so:
 *    eval('someSource;//# sourceUrl sourcefile.js');
 * We need to export the globally defined symbols "goog" and "COMPILED".
 * Exporting "goog" breaks the compiler optimizations, so we required that
 * be defined externally.
 * NOTE: We don't use goog.exportSymbol here because we don't want to trigger
 * extern generation when that compiler option is enabled.
 */
if (!COMPILED) {
  goog.global['COMPILED'] = COMPILED;
}


//==============================================================================
// goog.defineClass implementation
//==============================================================================


/**
 * Creates a restricted form of a Closure "class":
 *   - from the compiler's perspective, the instance returned from the
 *     constructor is sealed (no new properties may be added).  This enables
 *     better checks.
 *   - the compiler will rewrite this definition to a form that is optimal
 *     for type checking and optimization (initially this will be a more
 *     traditional form).
 *
 * @param {Function} superClass The superclass, Object or null.
 * @param {goog.defineClass.ClassDescriptor} def
 *     An object literal describing
 *     the class.  It may have the following properties:
 *     "constructor": the constructor function
 *     "statics": an object literal containing methods to add to the constructor
 *        as "static" methods or a function that will receive the constructor
 *        function as its only parameter to which static properties can
 *        be added.
 *     all other properties are added to the prototype.
 * @return {!Function} The class constructor.
 * @deprecated Use ES6 class syntax instead.
 */
goog.defineClass = function(superClass, def) {
  // TODO(johnlenz): consider making the superClass an optional parameter.
  var constructor = def.constructor;
  var statics = def.statics;
  // Wrap the constructor prior to setting up the prototype and static methods.
  if (!constructor || constructor == Object.prototype.constructor) {
    constructor = function() {
      throw new Error(
          'cannot instantiate an interface (no constructor defined).');
    };
  }

  var cls = goog.defineClass.createSealingConstructor_(constructor, superClass);
  if (superClass) {
    goog.inherits(cls, superClass);
  }

  // Remove all the properties that should not be copied to the prototype.
  delete def.constructor;
  delete def.statics;

  goog.defineClass.applyProperties_(cls.prototype, def);
  if (statics != null) {
    if (statics instanceof Function) {
      statics(cls);
    } else {
      goog.defineClass.applyProperties_(cls, statics);
    }
  }

  return cls;
};


/**
 * @typedef {{
 *   constructor: (!Function|undefined),
 *   statics: (Object|undefined|function(Function):void)
 * }}
 */
goog.defineClass.ClassDescriptor;


/**
 * @define {boolean} Whether the instances returned by goog.defineClass should
 *     be sealed when possible.
 *
 * When sealing is disabled the constructor function will not be wrapped by
 * goog.defineClass, making it incompatible with ES6 class methods.
 */
goog.defineClass.SEAL_CLASS_INSTANCES =
    goog.define('goog.defineClass.SEAL_CLASS_INSTANCES', goog.DEBUG);


/**
 * If goog.defineClass.SEAL_CLASS_INSTANCES is enabled and Object.seal is
 * defined, this function will wrap the constructor in a function that seals the
 * results of the provided constructor function.
 *
 * @param {!Function} ctr The constructor whose results maybe be sealed.
 * @param {Function} superClass The superclass constructor.
 * @return {!Function} The replacement constructor.
 * @private
 */
goog.defineClass.createSealingConstructor_ = function(ctr, superClass) {
  if (!goog.defineClass.SEAL_CLASS_INSTANCES) {
    // Do now wrap the constructor when sealing is disabled. Angular code
    // depends on this for injection to work properly.
    return ctr;
  }

  // Compute whether the constructor is sealable at definition time, rather
  // than when the instance is being constructed.
  var superclassSealable = !goog.defineClass.isUnsealable_(superClass);

  /**
   * @this {Object}
   * @return {?}
   */
  var wrappedCtr = function() {
    // Don't seal an instance of a subclass when it calls the constructor of
    // its super class as there is most likely still setup to do.
    var instance = ctr.apply(this, arguments) || this;
    instance[goog.UID_PROPERTY_] = instance[goog.UID_PROPERTY_];

    if (this.constructor === wrappedCtr && superclassSealable &&
        Object.seal instanceof Function) {
      Object.seal(instance);
    }
    return instance;
  };

  return wrappedCtr;
};


/**
 * @param {Function} ctr The constructor to test.
 * @return {boolean} Whether the constructor has been tagged as unsealable
 *     using goog.tagUnsealableClass.
 * @private
 */
goog.defineClass.isUnsealable_ = function(ctr) {
  return ctr && ctr.prototype &&
      ctr.prototype[goog.UNSEALABLE_CONSTRUCTOR_PROPERTY_];
};


// TODO(johnlenz): share these values with the goog.object
/**
 * The names of the fields that are defined on Object.prototype.
 * @type {!Array<string>}
 * @private
 * @const
 */
goog.defineClass.OBJECT_PROTOTYPE_FIELDS_ = [
  'constructor', 'hasOwnProperty', 'isPrototypeOf', 'propertyIsEnumerable',
  'toLocaleString', 'toString', 'valueOf'
];


// TODO(johnlenz): share this function with the goog.object
/**
 * @param {!Object} target The object to add properties to.
 * @param {!Object} source The object to copy properties from.
 * @private
 */
goog.defineClass.applyProperties_ = function(target, source) {
  // TODO(johnlenz): update this to support ES5 getters/setters

  var key;
  for (key in source) {
    if (Object.prototype.hasOwnProperty.call(source, key)) {
      target[key] = source[key];
    }
  }

  // For IE the for-in-loop does not contain any properties that are not
  // enumerable on the prototype object (for example isPrototypeOf from
  // Object.prototype) and it will also not include 'replace' on objects that
  // extend String and change 'replace' (not that it is common for anyone to
  // extend anything except Object).
  for (var i = 0; i < goog.defineClass.OBJECT_PROTOTYPE_FIELDS_.length; i++) {
    key = goog.defineClass.OBJECT_PROTOTYPE_FIELDS_[i];
    if (Object.prototype.hasOwnProperty.call(source, key)) {
      target[key] = source[key];
    }
  }
};


/**
 * Sealing classes breaks the older idiom of assigning properties on the
 * prototype rather than in the constructor. As such, goog.defineClass
 * must not seal subclasses of these old-style classes until they are fixed.
 * Until then, this marks a class as "broken", instructing defineClass
 * not to seal subclasses.
 * @param {!Function} ctr The legacy constructor to tag as unsealable.
 */
goog.tagUnsealableClass = function(ctr) {
  if (!COMPILED && goog.defineClass.SEAL_CLASS_INSTANCES) {
    ctr.prototype[goog.UNSEALABLE_CONSTRUCTOR_PROPERTY_] = true;
  }
};


/**
 * Name for unsealable tag property.
 * @const @private {string}
 */
goog.UNSEALABLE_CONSTRUCTOR_PROPERTY_ = 'goog_defineClass_legacy_unsealable';


// There's a bug in the compiler where without collapse properties the
// Closure namespace defines do not guard code correctly. To help reduce code
// size also check for !COMPILED even though it redundant until this is fixed.
if (!COMPILED && goog.DEPENDENCIES_ENABLED) {

  /**
   * Tries to detect whether is in the context of an HTML document.
   * @return {boolean} True if it looks like HTML document.
   * @private
   */
  goog.inHtmlDocument_ = function() {
    /** @type {!Document} */
    var doc = goog.global.document;
    return doc != null && 'write' in doc;  // XULDocument misses write.
  };


  /**
   * We'd like to check for if the document readyState is 'loading'; however
   * there are bugs on IE 10 and below where the readyState being anything other
   * than 'complete' is not reliable.
   * @return {boolean}
   * @private
   */
  goog.isDocumentLoading_ = function() {
    // attachEvent is available on IE 6 thru 10 only, and thus can be used to
    // detect those browsers.
    /** @type {!HTMLDocument} */
    var doc = goog.global.document;
    return doc.attachEvent ? doc.readyState != 'complete' :
                             doc.readyState == 'loading';
  };


  /**
   * Tries to detect the base path of base.js script that bootstraps Closure.
   * @private
   */
  goog.findBasePath_ = function() {
    if (goog.global.CLOSURE_BASE_PATH != undefined &&
        // Anti DOM-clobbering runtime check (b/37736576).
        typeof goog.global.CLOSURE_BASE_PATH === 'string') {
      goog.basePath = goog.global.CLOSURE_BASE_PATH;
      return;
    } else if (!goog.inHtmlDocument_()) {
      return;
    }
    /** @type {!Document} */
    var doc = goog.global.document;
    // If we have a currentScript available, use it exclusively.
    var currentScript = doc.currentScript;
    if (currentScript) {
      var scripts = [currentScript];
    } else {
      var scripts = doc.getElementsByTagName('SCRIPT');
    }
    // Search backwards since the current script is in almost all cases the one
    // that has base.js.
    for (var i = scripts.length - 1; i >= 0; --i) {
      var script = /** @type {!HTMLScriptElement} */ (scripts[i]);
      var src = script.src;
      var qmark = src.lastIndexOf('?');
      var l = qmark == -1 ? src.length : qmark;
      if (src.substr(l - 7, 7) == 'base.js') {
        goog.basePath = src.substr(0, l - 7);
        return;
      }
    }
  };

  goog.findBasePath_();

  /** @struct @constructor @final */
  goog.Transpiler = function() {
    /** @private {?Object<string, boolean>} */
    this.requiresTranspilation_ = null;
    /** @private {string} */
    this.transpilationTarget_ = goog.TRANSPILE_TO_LANGUAGE;
  };


  /**
   * Returns a newly created map from language mode string to a boolean
   * indicating whether transpilation should be done for that mode as well as
   * the highest level language that this environment supports.
   *
   * Guaranteed invariant:
   * For any two modes, l1 and l2 where l2 is a newer mode than l1,
   * `map[l1] == true` implies that `map[l2] == true`.
   *
   * Note this method is extracted and used elsewhere, so it cannot rely on
   * anything external (it should easily be able to be transformed into a
   * standalone, top level function).
   *
   * @private
   * @return {{
   *   target: string,
   *   map: !Object<string, boolean>
   * }}
   */
  goog.Transpiler.prototype.createRequiresTranspilation_ = function() {
    var transpilationTarget = 'es3';
    var /** !Object<string, boolean> */ requiresTranspilation = {'es3': false};
    var transpilationRequiredForAllLaterModes = false;

    /**
     * Adds an entry to requiresTranspliation for the given language mode.
     *
     * IMPORTANT: Calls must be made in order from oldest to newest language
     * mode.
     * @param {string} modeName
     * @param {function(): boolean} isSupported Returns true if the JS engine
     *     supports the given mode.
     */
    function addNewerLanguageTranspilationCheck(modeName, isSupported) {
      if (transpilationRequiredForAllLaterModes) {
        requiresTranspilation[modeName] = true;
      } else if (isSupported()) {
        transpilationTarget = modeName;
        requiresTranspilation[modeName] = false;
      } else {
        requiresTranspilation[modeName] = true;
        transpilationRequiredForAllLaterModes = true;
      }
    }

    /**
     * Does the given code evaluate without syntax errors and return a truthy
     * result?
     */
    function /** boolean */ evalCheck(/** string */ code) {
      try {
        return !!eval(code);
      } catch (ignored) {
        return false;
      }
    }

    var userAgent = goog.global.navigator && goog.global.navigator.userAgent ?
        goog.global.navigator.userAgent :
        '';

    // Identify ES3-only browsers by their incorrect treatment of commas.
    addNewerLanguageTranspilationCheck('es5', function() {
      return evalCheck('[1,].length==1');
    });
    addNewerLanguageTranspilationCheck('es6', function() {
      // Edge has a non-deterministic (i.e., not reproducible) bug with ES6:
      // https://github.com/Microsoft/ChakraCore/issues/1496.
      var re = /Edge\/(\d+)(\.\d)*/i;
      var edgeUserAgent = userAgent.match(re);
      if (edgeUserAgent) {
        // The Reflect.construct test below is flaky on Edge. It can sometimes
        // pass or fail on 40 15.15063, so just exit early for Edge and treat
        // it as ES5. Until we're on a more up to date version just always use
        // ES5. See https://github.com/Microsoft/ChakraCore/issues/3217.
        return false;
      }
      // Test es6: [FF50 (?), Edge 14 (?), Chrome 50]
      //   (a) default params (specifically shadowing locals),
      //   (b) destructuring, (c) block-scoped functions,
      //   (d) for-of (const), (e) new.target/Reflect.construct
      var es6fullTest =
          'class X{constructor(){if(new.target!=String)throw 1;this.x=42}}' +
          'let q=Reflect.construct(X,[],String);if(q.x!=42||!(q instanceof ' +
          'String))throw 1;for(const a of[2,3]){if(a==2)continue;function ' +
          'f(z={a}){let a=0;return z.a}{function f(){return 0;}}return f()' +
          '==3}';

      return evalCheck('(()=>{"use strict";' + es6fullTest + '})()');
    });
    // ** and **= are the only new features in 'es7'
    addNewerLanguageTranspilationCheck('es7', function() {
      return evalCheck('2 ** 2 == 4');
    });
    // async functions are the only new features in 'es8'
    addNewerLanguageTranspilationCheck('es8', function() {
      return evalCheck('async () => 1, true');
    });
    addNewerLanguageTranspilationCheck('es9', function() {
      return evalCheck('({...rest} = {}), true');
    });
    addNewerLanguageTranspilationCheck('es_next', function() {
      return false;  // assume it always need to transpile
    });
    return {target: transpilationTarget, map: requiresTranspilation};
  };


  /**
   * Determines whether the given language needs to be transpiled.
   * @param {string} lang
   * @param {string|undefined} module
   * @return {boolean}
   */
  goog.Transpiler.prototype.needsTranspile = function(lang, module) {
    if (goog.TRANSPILE == 'always') {
      return true;
    } else if (goog.TRANSPILE == 'never') {
      return false;
    } else if (!this.requiresTranspilation_) {
      var obj = this.createRequiresTranspilation_();
      this.requiresTranspilation_ = obj.map;
      this.transpilationTarget_ = this.transpilationTarget_ || obj.target;
    }
    if (lang in this.requiresTranspilation_) {
      if (this.requiresTranspilation_[lang]) {
        return true;
      } else if (
          goog.inHtmlDocument_() && module == 'es6' &&
          !('noModule' in goog.global.document.createElement('script'))) {
        return true;
      } else {
        return false;
      }
    } else {
      throw new Error('Unknown language mode: ' + lang);
    }
  };


  /**
   * Lazily retrieves the transpiler and applies it to the source.
   * @param {string} code JS code.
   * @param {string} path Path to the code.
   * @return {string} The transpiled code.
   */
  goog.Transpiler.prototype.transpile = function(code, path) {
    // TODO(johnplaisted): We should delete goog.transpile_ and just have this
    // function. But there's some compile error atm where goog.global is being
    // stripped incorrectly without this.
    return goog.transpile_(code, path, this.transpilationTarget_);
  };


  /** @private @final {!goog.Transpiler} */
  goog.transpiler_ = new goog.Transpiler();

  /**
   * Rewrites closing script tags in input to avoid ending an enclosing script
   * tag.
   *
   * @param {string} str
   * @return {string}
   * @private
   */
  goog.protectScriptTag_ = function(str) {
    return str.replace(/<\/(SCRIPT)/ig, '\\x3c/$1');
  };


  /**
   * A debug loader is responsible for downloading and executing javascript
   * files in an unbundled, uncompiled environment.
   *
   * This can be custimized via the setDependencyFactory method, or by
   * CLOSURE_IMPORT_SCRIPT/CLOSURE_LOAD_FILE_SYNC.
   *
   * @struct @constructor @final @private
   */
  goog.DebugLoader_ = function() {
    /** @private @const {!Object<string, !goog.Dependency>} */
    this.dependencies_ = {};
    /** @private @const {!Object<string, string>} */
    this.idToPath_ = {};
    /** @private @const {!Object<string, boolean>} */
    this.written_ = {};
    /** @private @const {!Array<!goog.Dependency>} */
    this.loadingDeps_ = [];
    /** @private {!Array<!goog.Dependency>} */
    this.depsToLoad_ = [];
    /** @private {boolean} */
    this.paused_ = false;
    /** @private {!goog.DependencyFactory} */
    this.factory_ = new goog.DependencyFactory(goog.transpiler_);
    /** @private @const {!Object<string, !Function>} */
    this.deferredCallbacks_ = {};
    /** @private @const {!Array<string>} */
    this.deferredQueue_ = [];
  };

  /**
   * @param {!Array<string>} namespaces
   * @param {function(): undefined} callback Function to call once all the
   *     namespaces have loaded.
   */
  goog.DebugLoader_.prototype.bootstrap = function(namespaces, callback) {
    var cb = callback;
    function resolve() {
      if (cb) {
        goog.global.setTimeout(cb, 0);
        cb = null;
      }
    }

    if (!namespaces.length) {
      resolve();
      return;
    }

    var deps = [];
    for (var i = 0; i < namespaces.length; i++) {
      var path = this.getPathFromDeps_(namespaces[i]);
      if (!path) {
        throw new Error('Unregonized namespace: ' + namespaces[i]);
      }
      deps.push(this.dependencies_[path]);
    }

    var require = goog.require;
    var loaded = 0;
    for (var i = 0; i < namespaces.length; i++) {
      require(namespaces[i]);
      deps[i].onLoad(function() {
        if (++loaded == namespaces.length) {
          resolve();
        }
      });
    }
  };


  /**
   * Loads the Closure Dependency file.
   *
   * Exposed a public function so CLOSURE_NO_DEPS can be set to false, base
   * loaded, setDependencyFactory called, and then this called. i.e. allows
   * custom loading of the deps file.
   */
  goog.DebugLoader_.prototype.loadClosureDeps = function() {
    // Circumvent addDependency, which would try to transpile deps.js if
    // transpile is set to always.
    var relPath = 'deps.js';
    this.depsToLoad_.push(this.factory_.createDependency(
        goog.normalizePath_(goog.basePath + relPath), relPath, [], [], {},
        false));
    this.loadDeps_();
  };


  /**
   * Notifies the debug loader when a dependency has been requested.
   *
   * @param {string} absPathOrId Path of the dependency or goog id.
   * @param {boolean=} opt_force
   */
  goog.DebugLoader_.prototype.requested = function(absPathOrId, opt_force) {
    var path = this.getPathFromDeps_(absPathOrId);
    if (path &&
        (opt_force || this.areDepsLoaded_(this.dependencies_[path].requires))) {
      var callback = this.deferredCallbacks_[path];
      if (callback) {
        delete this.deferredCallbacks_[path];
        callback();
      }
    }
  };


  /**
   * Sets the dependency factory, which can be used to create custom
   * goog.Dependency implementations to control how dependencies are loaded.
   *
   * @param {!goog.DependencyFactory} factory
   */
  goog.DebugLoader_.prototype.setDependencyFactory = function(factory) {
    this.factory_ = factory;
  };


  /**
   * Travserses the dependency graph and queues the given dependency, and all of
   * its transitive dependencies, for loading and then starts loading if not
   * paused.
   *
   * @param {string} namespace
   * @private
   */
  goog.DebugLoader_.prototype.load_ = function(namespace) {
    if (!this.getPathFromDeps_(namespace)) {
      var errorMessage = 'goog.require could not find: ' + namespace;

      goog.logToConsole_(errorMessage);
      throw Error(errorMessage);
    } else {
      var loader = this;

      var deps = [];

      /** @param {string} namespace */
      var visit = function(namespace) {
        var path = loader.getPathFromDeps_(namespace);

        if (!path) {
          throw new Error('Bad dependency path or symbol: ' + namespace);
        }

        if (loader.written_[path]) {
          return;
        }

        loader.written_[path] = true;

        var dep = loader.dependencies_[path];
        for (var i = 0; i < dep.requires.length; i++) {
          if (!goog.isProvided_(dep.requires[i])) {
            visit(dep.requires[i]);
          }
        }

        deps.push(dep);
      };

      visit(namespace);

      var wasLoading = !!this.depsToLoad_.length;
      this.depsToLoad_ = this.depsToLoad_.concat(deps);

      if (!this.paused_ && !wasLoading) {
        this.loadDeps_();
      }
    }
  };


  /**
   * Loads any queued dependencies until they are all loaded or paused.
   *
   * @private
   */
  goog.DebugLoader_.prototype.loadDeps_ = function() {
    var loader = this;
    var paused = this.paused_;

    while (this.depsToLoad_.length && !paused) {
      (function() {
        var loadCallDone = false;
        var dep = loader.depsToLoad_.shift();

        var loaded = false;
        loader.loading_(dep);

        var controller = {
          pause: function() {
            if (loadCallDone) {
              throw new Error('Cannot call pause after the call to load.');
            } else {
              paused = true;
            }
          },
          resume: function() {
            if (loadCallDone) {
              loader.resume_();
            } else {
              // Some dep called pause and then resume in the same load call.
              // Just keep running this same loop.
              paused = false;
            }
          },
          loaded: function() {
            if (loaded) {
              throw new Error('Double call to loaded.');
            }

            loaded = true;
            loader.loaded_(dep);
          },
          pending: function() {
            // Defensive copy.
            var pending = [];
            for (var i = 0; i < loader.loadingDeps_.length; i++) {
              pending.push(loader.loadingDeps_[i]);
            }
            return pending;
          },
          /**
           * @param {goog.ModuleType} type
           */
          setModuleState: function(type) {
            goog.moduleLoaderState_ = {
              type: type,
              moduleName: '',
              declareLegacyNamespace: false
            };
          },
          /** @type {function(string, string, string=)} */
          registerEs6ModuleExports: function(
              path, exports, opt_closureNamespace) {
            if (opt_closureNamespace) {
              goog.loadedModules_[opt_closureNamespace] = {
                exports: exports,
                type: goog.ModuleType.ES6,
                moduleId: opt_closureNamespace || ''
              };
            }
          },
          /** @type {function(string, ?)} */
          registerGoogModuleExports: function(moduleId, exports) {
            goog.loadedModules_[moduleId] = {
              exports: exports,
              type: goog.ModuleType.GOOG,
              moduleId: moduleId
            };
          },
          clearModuleState: function() {
            goog.moduleLoaderState_ = null;
          },
          defer: function(callback) {
            if (loadCallDone) {
              throw new Error(
                  'Cannot register with defer after the call to load.');
            }
            loader.defer_(dep, callback);
          },
          areDepsLoaded: function() {
            return loader.areDepsLoaded_(dep.requires);
          }
        };

        try {
          dep.load(controller);
        } finally {
          loadCallDone = true;
        }
      })();
    }

    if (paused) {
      this.pause_();
    }
  };


  /** @private */
  goog.DebugLoader_.prototype.pause_ = function() {
    this.paused_ = true;
  };


  /** @private */
  goog.DebugLoader_.prototype.resume_ = function() {
    if (this.paused_) {
      this.paused_ = false;
      this.loadDeps_();
    }
  };


  /**
   * Marks the given dependency as loading (load has been called but it has not
   * yet marked itself as finished). Useful for dependencies that want to know
   * what else is loading. Example: goog.modules cannot eval if there are
   * loading dependencies.
   *
   * @param {!goog.Dependency} dep
   * @private
   */
  goog.DebugLoader_.prototype.loading_ = function(dep) {
    this.loadingDeps_.push(dep);
  };


  /**
   * Marks the given dependency as having finished loading and being available
   * for require.
   *
   * @param {!goog.Dependency} dep
   * @private
   */
  goog.DebugLoader_.prototype.loaded_ = function(dep) {
    for (var i = 0; i < this.loadingDeps_.length; i++) {
      if (this.loadingDeps_[i] == dep) {
        this.loadingDeps_.splice(i, 1);
        break;
      }
    }

    for (var i = 0; i < this.deferredQueue_.length; i++) {
      if (this.deferredQueue_[i] == dep.path) {
        this.deferredQueue_.splice(i, 1);
        break;
      }
    }

    if (this.loadingDeps_.length == this.deferredQueue_.length &&
        !this.depsToLoad_.length) {
      // Something has asked to load these, but they may not be directly
      // required again later, so load them now that we know we're done loading
      // everything else. e.g. a goog module entry point.
      while (this.deferredQueue_.length) {
        this.requested(this.deferredQueue_.shift(), true);
      }
    }

    dep.loaded();
  };


  /**
   * @param {!Array<string>} pathsOrIds
   * @return {boolean}
   * @private
   */
  goog.DebugLoader_.prototype.areDepsLoaded_ = function(pathsOrIds) {
    for (var i = 0; i < pathsOrIds.length; i++) {
      var path = this.getPathFromDeps_(pathsOrIds[i]);
      if (!path ||
          (!(path in this.deferredCallbacks_) &&
           !goog.isProvided_(pathsOrIds[i]))) {
        return false;
      }
    }

    return true;
  };


  /**
   * @param {string} absPathOrId
   * @return {?string}
   * @private
   */
  goog.DebugLoader_.prototype.getPathFromDeps_ = function(absPathOrId) {
    if (absPathOrId in this.idToPath_) {
      return this.idToPath_[absPathOrId];
    } else if (absPathOrId in this.dependencies_) {
      return absPathOrId;
    } else {
      return null;
    }
  };


  /**
   * @param {!goog.Dependency} dependency
   * @param {!Function} callback
   * @private
   */
  goog.DebugLoader_.prototype.defer_ = function(dependency, callback) {
    this.deferredCallbacks_[dependency.path] = callback;
    this.deferredQueue_.push(dependency.path);
  };


  /**
   * Interface for goog.Dependency implementations to have some control over
   * loading of dependencies.
   *
   * @record
   */
  goog.LoadController = function() {};


  /**
   * Tells the controller to halt loading of more dependencies.
   */
  goog.LoadController.prototype.pause = function() {};


  /**
   * Tells the controller to resume loading of more dependencies if paused.
   */
  goog.LoadController.prototype.resume = function() {};


  /**
   * Tells the controller that this dependency has finished loading.
   *
   * This causes this to be removed from pending() and any load callbacks to
   * fire.
   */
  goog.LoadController.prototype.loaded = function() {};


  /**
   * List of dependencies on which load has been called but which have not
   * called loaded on their controller. This includes the current dependency.
   *
   * @return {!Array<!goog.Dependency>}
   */
  goog.LoadController.prototype.pending = function() {};


  /**
   * Registers an object as an ES6 module's exports so that goog.modules may
   * require it by path.
   *
   * @param {string} path Full path of the module.
   * @param {?} exports
   * @param {string=} opt_closureNamespace Closure namespace to associate with
   *     this module.
   */
  goog.LoadController.prototype.registerEs6ModuleExports = function(
      path, exports, opt_closureNamespace) {};


  /**
   * Sets the current module state.
   *
   * @param {goog.ModuleType} type Type of module.
   */
  goog.LoadController.prototype.setModuleState = function(type) {};


  /**
   * Clears the current module state.
   */
  goog.LoadController.prototype.clearModuleState = function() {};


  /**
   * Registers a callback to call once the dependency is actually requested
   * via goog.require + all of the immediate dependencies have been loaded or
   * all other files have been loaded. Allows for lazy loading until
   * require'd without pausing dependency loading, which is needed on old IE.
   *
   * @param {!Function} callback
   */
  goog.LoadController.prototype.defer = function(callback) {};


  /**
   * @return {boolean}
   */
  goog.LoadController.prototype.areDepsLoaded = function() {};


  /**
   * Basic super class for all dependencies Closure Library can load.
   *
   * This default implementation is designed to load untranspiled, non-module
   * scripts in a web broswer.
   *
   * For transpiled non-goog.module files {@see goog.TranspiledDependency}.
   * For goog.modules see {@see goog.GoogModuleDependency}.
   * For untranspiled ES6 modules {@see goog.Es6ModuleDependency}.
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @struct @constructor
   */
  goog.Dependency = function(
      path, relativePath, provides, requires, loadFlags) {
    /** @const */
    this.path = path;
    /** @const */
    this.relativePath = relativePath;
    /** @const */
    this.provides = provides;
    /** @const */
    this.requires = requires;
    /** @const */
    this.loadFlags = loadFlags;
    /** @private {boolean} */
    this.loaded_ = false;
    /** @private {!Array<function()>} */
    this.loadCallbacks_ = [];
  };


  /**
   * @return {string} The pathname part of this dependency's path if it is a
   *     URI.
   */
  goog.Dependency.prototype.getPathName = function() {
    var pathName = this.path;
    var protocolIndex = pathName.indexOf('://');
    if (protocolIndex >= 0) {
      pathName = pathName.substring(protocolIndex + 3);
      var slashIndex = pathName.indexOf('/');
      if (slashIndex >= 0) {
        pathName = pathName.substring(slashIndex + 1);
      }
    }
    return pathName;
  };


  /**
   * @param {function()} callback Callback to fire as soon as this has loaded.
   * @final
   */
  goog.Dependency.prototype.onLoad = function(callback) {
    if (this.loaded_) {
      callback();
    } else {
      this.loadCallbacks_.push(callback);
    }
  };


  /**
   * Marks this dependency as loaded and fires any callbacks registered with
   * onLoad.
   * @final
   */
  goog.Dependency.prototype.loaded = function() {
    this.loaded_ = true;
    var callbacks = this.loadCallbacks_;
    this.loadCallbacks_ = [];
    for (var i = 0; i < callbacks.length; i++) {
      callbacks[i]();
    }
  };


  /**
   * Whether or not document.written / appended script tags should be deferred.
   *
   * @private {boolean}
   */
  goog.Dependency.defer_ = false;


  /**
   * Map of script ready / state change callbacks. Old IE cannot handle putting
   * these properties on goog.global.
   *
   * @private @const {!Object<string, function(?):undefined>}
   */
  goog.Dependency.callbackMap_ = {};


  /**
   * @param {function(...?):?} callback
   * @return {string}
   * @private
   */
  goog.Dependency.registerCallback_ = function(callback) {
    var key = Math.random().toString(32);
    goog.Dependency.callbackMap_[key] = callback;
    return key;
  };


  /**
   * @param {string} key
   * @private
   */
  goog.Dependency.unregisterCallback_ = function(key) {
    delete goog.Dependency.callbackMap_[key];
  };


  /**
   * @param {string} key
   * @param {...?} var_args
   * @private
   * @suppress {unusedPrivateMembers}
   */
  goog.Dependency.callback_ = function(key, var_args) {
    if (key in goog.Dependency.callbackMap_) {
      var callback = goog.Dependency.callbackMap_[key];
      var args = [];
      for (var i = 1; i < arguments.length; i++) {
        args.push(arguments[i]);
      }
      callback.apply(undefined, args);
    } else {
      var errorMessage = 'Callback key ' + key +
          ' does not exist (was base.js loaded more than once?).';
      throw Error(errorMessage);
    }
  };


  /**
   * Starts loading this dependency. This dependency can pause loading if it
   * needs to and resume it later via the controller interface.
   *
   * When this is loaded it should call controller.loaded(). Note that this will
   * end up calling the loaded method of this dependency; there is no need to
   * call it explicitly.
   *
   * @param {!goog.LoadController} controller
   */
  goog.Dependency.prototype.load = function(controller) {
    if (goog.global.CLOSURE_IMPORT_SCRIPT) {
      if (goog.global.CLOSURE_IMPORT_SCRIPT(this.path)) {
        controller.loaded();
      } else {
        controller.pause();
      }
      return;
    }

    if (!goog.inHtmlDocument_()) {
      goog.logToConsole_(
          'Cannot use default debug loader outside of HTML documents.');
      if (this.relativePath == 'deps.js') {
        // Some old code is relying on base.js auto loading deps.js failing with
        // no error before later setting CLOSURE_IMPORT_SCRIPT.
        // CLOSURE_IMPORT_SCRIPT should be set *before* base.js is loaded, or
        // CLOSURE_NO_DEPS set to true.
        goog.logToConsole_(
            'Consider setting CLOSURE_IMPORT_SCRIPT before loading base.js, ' +
            'or setting CLOSURE_NO_DEPS to true.');
        controller.loaded();
      } else {
        controller.pause();
      }
      return;
    }

    /** @type {!HTMLDocument} */
    var doc = goog.global.document;

    // If the user tries to require a new symbol after document load,
    // something has gone terribly wrong. Doing a document.write would
    // wipe out the page. This does not apply to the CSP-compliant method
    // of writing script tags.
    if (doc.readyState == 'complete' &&
        !goog.ENABLE_CHROME_APP_SAFE_SCRIPT_LOADING) {
      // Certain test frameworks load base.js multiple times, which tries
      // to write deps.js each time. If that happens, just fail silently.
      // These frameworks wipe the page between each load of base.js, so this
      // is OK.
      var isDeps = /\bdeps.js$/.test(this.path);
      if (isDeps) {
        controller.loaded();
        return;
      } else {
        throw Error('Cannot write "' + this.path + '" after document load');
      }
    }

    if (!goog.ENABLE_CHROME_APP_SAFE_SCRIPT_LOADING &&
        goog.isDocumentLoading_()) {
      var key = goog.Dependency.registerCallback_(function(script) {
        if (!goog.DebugLoader_.IS_OLD_IE_ || script.readyState == 'complete') {
          goog.Dependency.unregisterCallback_(key);
          controller.loaded();
        }
      });
      var nonceAttr = !goog.DebugLoader_.IS_OLD_IE_ && goog.getScriptNonce() ?
          ' nonce="' + goog.getScriptNonce() + '"' :
          '';
      var event =
          goog.DebugLoader_.IS_OLD_IE_ ? 'onreadystatechange' : 'onload';
      var defer = goog.Dependency.defer_ ? 'defer' : '';
      var script = '<script src="' + this.path + '" ' + event +
          '="goog.Dependency.callback_(\'' + key +
          '\', this)" type="text/javascript" ' + defer + nonceAttr + '><' +
          '/script>';
      doc.write(
          goog.TRUSTED_TYPES_POLICY_ ?
              goog.TRUSTED_TYPES_POLICY_.createHTML(script) :
              script);
    } else {
      var scriptEl =
          /** @type {!HTMLScriptElement} */ (doc.createElement('script'));
      scriptEl.defer = goog.Dependency.defer_;
      scriptEl.async = false;
      scriptEl.type = 'text/javascript';

      // If CSP nonces are used, propagate them to dynamically created scripts.
      // This is necessary to allow nonce-based CSPs without 'strict-dynamic'.
      var nonce = goog.getScriptNonce();
      if (nonce) {
        scriptEl.setAttribute('nonce', nonce);
      }

      if (goog.DebugLoader_.IS_OLD_IE_) {
        // Execution order is not guaranteed on old IE, halt loading and write
        // these scripts one at a time, after each loads.
        controller.pause();
        scriptEl.onreadystatechange = function() {
          if (scriptEl.readyState == 'loaded' ||
              scriptEl.readyState == 'complete') {
            controller.loaded();
            controller.resume();
          }
        };
      } else {
        scriptEl.onload = function() {
          scriptEl.onload = null;
          controller.loaded();
        };
      }

      scriptEl.src = goog.TRUSTED_TYPES_POLICY_ ?
          goog.TRUSTED_TYPES_POLICY_.createScriptURL(this.path) :
          this.path;
      doc.head.appendChild(scriptEl);
    }
  };


  /**
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides Should be an empty array.
   *     TODO(johnplaisted) add support for adding closure namespaces to ES6
   *     modules for interop purposes.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @struct @constructor
   * @extends {goog.Dependency}
   */
  goog.Es6ModuleDependency = function(
      path, relativePath, provides, requires, loadFlags) {
    goog.Es6ModuleDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
  };
  goog.inherits(goog.Es6ModuleDependency, goog.Dependency);


  /** @override */
  goog.Es6ModuleDependency.prototype.load = function(controller) {
    if (goog.global.CLOSURE_IMPORT_SCRIPT) {
      if (goog.global.CLOSURE_IMPORT_SCRIPT(this.path)) {
        controller.loaded();
      } else {
        controller.pause();
      }
      return;
    }

    if (!goog.inHtmlDocument_()) {
      goog.logToConsole_(
          'Cannot use default debug loader outside of HTML documents.');
      controller.pause();
      return;
    }

    /** @type {!HTMLDocument} */
    var doc = goog.global.document;

    var dep = this;

    // TODO(johnplaisted): Does document.writing really speed up anything? Any
    // difference between this and just waiting for interactive mode and then
    // appending?
    function write(src, contents) {
      if (contents) {
        var script = '<script type="module" crossorigin>' + contents + '</' +
            'script>';
        doc.write(
            goog.TRUSTED_TYPES_POLICY_ ?
                goog.TRUSTED_TYPES_POLICY_.createHTML(script) :
                script);
      } else {
        var script = '<script type="module" crossorigin src="' + src + '"></' +
            'script>';
        doc.write(
            goog.TRUSTED_TYPES_POLICY_ ?
                goog.TRUSTED_TYPES_POLICY_.createHTML(script) :
                script);
      }
    }

    function append(src, contents) {
      var scriptEl =
          /** @type {!HTMLScriptElement} */ (doc.createElement('script'));
      scriptEl.defer = true;
      scriptEl.async = false;
      scriptEl.type = 'module';
      scriptEl.setAttribute('crossorigin', true);

      // If CSP nonces are used, propagate them to dynamically created scripts.
      // This is necessary to allow nonce-based CSPs without 'strict-dynamic'.
      var nonce = goog.getScriptNonce();
      if (nonce) {
        scriptEl.setAttribute('nonce', nonce);
      }

      if (contents) {
        scriptEl.textContent = goog.TRUSTED_TYPES_POLICY_ ?
            goog.TRUSTED_TYPES_POLICY_.createScript(contents) :
            contents;
      } else {
        scriptEl.src = goog.TRUSTED_TYPES_POLICY_ ?
            goog.TRUSTED_TYPES_POLICY_.createScriptURL(src) :
            src;
      }

      doc.head.appendChild(scriptEl);
    }

    var create;

    if (goog.isDocumentLoading_()) {
      create = write;
      // We can ONLY call document.write if we are guaranteed that any
      // non-module script tags document.written after this are deferred.
      // Small optimization, in theory document.writing is faster.
      goog.Dependency.defer_ = true;
    } else {
      create = append;
    }

    // Write 4 separate tags here:
    // 1) Sets the module state at the correct time (just before execution).
    // 2) A src node for this, which just hopefully lets the browser load it a
    //    little early (no need to parse #3).
    // 3) Import the module and register it.
    // 4) Clear the module state at the correct time. Guaranteed to run even
    //    if there is an error in the module (#3 will not run if there is an
    //    error in the module).
    var beforeKey = goog.Dependency.registerCallback_(function() {
      goog.Dependency.unregisterCallback_(beforeKey);
      controller.setModuleState(goog.ModuleType.ES6);
    });
    create(undefined, 'goog.Dependency.callback_("' + beforeKey + '")');

    // TODO(johnplaisted): Does this really speed up anything?
    create(this.path, undefined);

    var registerKey = goog.Dependency.registerCallback_(function(exports) {
      goog.Dependency.unregisterCallback_(registerKey);
      controller.registerEs6ModuleExports(
          dep.path, exports, goog.moduleLoaderState_.moduleName);
    });
    create(
        undefined,
        'import * as m from "' + this.path + '"; goog.Dependency.callback_("' +
            registerKey + '", m)');

    var afterKey = goog.Dependency.registerCallback_(function() {
      goog.Dependency.unregisterCallback_(afterKey);
      controller.clearModuleState();
      controller.loaded();
    });
    create(undefined, 'goog.Dependency.callback_("' + afterKey + '")');
  };


  /**
   * Superclass of any dependency that needs to be loaded into memory,
   * transformed, and then eval'd (goog.modules and transpiled files).
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @struct @constructor @abstract
   * @extends {goog.Dependency}
   */
  goog.TransformedDependency = function(
      path, relativePath, provides, requires, loadFlags) {
    goog.TransformedDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
    /** @private {?string} */
    this.contents_ = null;

    /**
     * Whether to lazily make the synchronous XHR (when goog.require'd) or make
     * the synchronous XHR when initially loading. On FireFox 61 there is a bug
     * where an ES6 module cannot make a synchronous XHR (rather, it can, but if
     * it does then no other ES6 modules will load after).
     *
     * tl;dr we lazy load due to bugs on older browsers and eager load due to
     * bugs on newer ones.
     *
     * https://bugzilla.mozilla.org/show_bug.cgi?id=1477090
     *
     * @private @const {boolean}
     */
    this.lazyFetch_ = !goog.inHtmlDocument_() ||
        !('noModule' in goog.global.document.createElement('script'));
  };
  goog.inherits(goog.TransformedDependency, goog.Dependency);


  /** @override */
  goog.TransformedDependency.prototype.load = function(controller) {
    var dep = this;

    function fetch() {
      dep.contents_ = goog.loadFileSync_(dep.path);

      if (dep.contents_) {
        dep.contents_ = dep.transform(dep.contents_);
        if (dep.contents_) {
          dep.contents_ += '\n//# sourceURL=' + dep.path;
        }
      }
    }

    if (goog.global.CLOSURE_IMPORT_SCRIPT) {
      fetch();
      if (this.contents_ &&
          goog.global.CLOSURE_IMPORT_SCRIPT('', this.contents_)) {
        this.contents_ = null;
        controller.loaded();
      } else {
        controller.pause();
      }
      return;
    }


    var isEs6 = this.loadFlags['module'] == goog.ModuleType.ES6;

    if (!this.lazyFetch_) {
      fetch();
    }

    function load() {
      if (dep.lazyFetch_) {
        fetch();
      }

      if (!dep.contents_) {
        // loadFileSync_ or transform are responsible. Assume they logged an
        // error.
        return;
      }

      if (isEs6) {
        controller.setModuleState(goog.ModuleType.ES6);
      }

      var namespace;

      try {
        var contents = dep.contents_;
        dep.contents_ = null;
        goog.globalEval(contents);
        if (isEs6) {
          namespace = goog.moduleLoaderState_.moduleName;
        }
      } finally {
        if (isEs6) {
          controller.clearModuleState();
        }
      }

      if (isEs6) {
        // Due to circular dependencies this may not be available for require
        // right now.
        goog.global['$jscomp']['require']['ensure'](
            [dep.getPathName()], function() {
              controller.registerEs6ModuleExports(
                  dep.path,
                  goog.global['$jscomp']['require'](dep.getPathName()),
                  namespace);
            });
      }

      controller.loaded();
    }

    // Do not fetch now; in FireFox 47 the synchronous XHR doesn't block all
    // events. If we fetched now and then document.write'd the contents the
    // document.write would be an eval and would execute too soon! Instead write
    // a script tag to fetch and eval synchronously at the correct time.
    function fetchInOwnScriptThenLoad() {
      /** @type {!HTMLDocument} */
      var doc = goog.global.document;

      var key = goog.Dependency.registerCallback_(function() {
        goog.Dependency.unregisterCallback_(key);
        load();
      });

      var script = '<script type="text/javascript">' +
          goog.protectScriptTag_('goog.Dependency.callback_("' + key + '");') +
          '</' +
          'script>';
      doc.write(
          goog.TRUSTED_TYPES_POLICY_ ?
              goog.TRUSTED_TYPES_POLICY_.createHTML(script) :
              script);
    }

    // If one thing is pending it is this.
    var anythingElsePending = controller.pending().length > 1;

    // If anything else is loading we need to lazy load due to bugs in old IE.
    // Specifically script tags with src and script tags with contents could
    // execute out of order if document.write is used, so we cannot use
    // document.write. Do not pause here; it breaks old IE as well.
    var useOldIeWorkAround =
        anythingElsePending && goog.DebugLoader_.IS_OLD_IE_;

    // Additionally if we are meant to defer scripts but the page is still
    // loading (e.g. an ES6 module is loading) then also defer. Or if we are
    // meant to defer and anything else is pending then defer (those may be
    // scripts that did not need transformation and are just script tags with
    // defer set to true, and we need to evaluate after that deferred script).
    var needsAsyncLoading = goog.Dependency.defer_ &&
        (anythingElsePending || goog.isDocumentLoading_());

    if (useOldIeWorkAround || needsAsyncLoading) {
      // Note that we only defer when we have to rather than 100% of the time.
      // Always defering would work, but then in theory the order of
      // goog.require calls would then matter. We want to enforce that most of
      // the time the order of the require calls does not matter.
      controller.defer(function() {
        load();
      });
      return;
    }
    // TODO(johnplaisted): Externs are missing onreadystatechange for
    // HTMLDocument.
    /** @type {?} */
    var doc = goog.global.document;

    var isInternetExplorer =
        goog.inHtmlDocument_() && 'ActiveXObject' in goog.global;

    // Don't delay in any version of IE. There's bug around this that will
    // cause out of order script execution. This means that on older IE ES6
    // modules will load too early (while the document is still loading + the
    // dom is not available). The other option is to load too late (when the
    // document is complete and the onload even will never fire). This seems
    // to be the lesser of two evils as scripts already act like the former.
    if (isEs6 && goog.inHtmlDocument_() && goog.isDocumentLoading_() &&
        !isInternetExplorer) {
      goog.Dependency.defer_ = true;
      // Transpiled ES6 modules still need to load like regular ES6 modules,
      // aka only after the document is interactive.
      controller.pause();
      var oldCallback = doc.onreadystatechange;
      doc.onreadystatechange = function() {
        if (doc.readyState == 'interactive') {
          doc.onreadystatechange = oldCallback;
          load();
          controller.resume();
        }
        if (goog.isFunction(oldCallback)) {
          oldCallback.apply(undefined, arguments);
        }
      };
    } else {
      // Always eval on old IE.
      if (goog.DebugLoader_.IS_OLD_IE_ || !goog.inHtmlDocument_() ||
          !goog.isDocumentLoading_()) {
        load();
      } else {
        fetchInOwnScriptThenLoad();
      }
    }
  };


  /**
   * @param {string} contents
   * @return {string}
   * @abstract
   */
  goog.TransformedDependency.prototype.transform = function(contents) {};


  /**
   * Any non-goog.module dependency which needs to be transpiled before eval.
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @param {!goog.Transpiler} transpiler
   * @struct @constructor
   * @extends {goog.TransformedDependency}
   */
  goog.TranspiledDependency = function(
      path, relativePath, provides, requires, loadFlags, transpiler) {
    goog.TranspiledDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
    /** @protected @const*/
    this.transpiler = transpiler;
  };
  goog.inherits(goog.TranspiledDependency, goog.TransformedDependency);


  /** @override */
  goog.TranspiledDependency.prototype.transform = function(contents) {
    // Transpile with the pathname so that ES6 modules are domain agnostic.
    return this.transpiler.transpile(contents, this.getPathName());
  };


  /**
   * An ES6 module dependency that was transpiled to a jscomp module outside
   * of the debug loader, e.g. server side.
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @struct @constructor
   * @extends {goog.TransformedDependency}
   */
  goog.PreTranspiledEs6ModuleDependency = function(
      path, relativePath, provides, requires, loadFlags) {
    goog.PreTranspiledEs6ModuleDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
  };
  goog.inherits(
      goog.PreTranspiledEs6ModuleDependency, goog.TransformedDependency);


  /** @override */
  goog.PreTranspiledEs6ModuleDependency.prototype.transform = function(
      contents) {
    return contents;
  };


  /**
   * A goog.module, transpiled or not. Will always perform some minimal
   * transformation even when not transpiled to wrap in a goog.loadModule
   * statement.
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @param {boolean} needsTranspile
   * @param {!goog.Transpiler} transpiler
   * @struct @constructor
   * @extends {goog.TransformedDependency}
   */
  goog.GoogModuleDependency = function(
      path, relativePath, provides, requires, loadFlags, needsTranspile,
      transpiler) {
    goog.GoogModuleDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
    /** @private @const */
    this.needsTranspile_ = needsTranspile;
    /** @private @const */
    this.transpiler_ = transpiler;
  };
  goog.inherits(goog.GoogModuleDependency, goog.TransformedDependency);


  /** @override */
  goog.GoogModuleDependency.prototype.transform = function(contents) {
    if (this.needsTranspile_) {
      contents = this.transpiler_.transpile(contents, this.getPathName());
    }

    if (!goog.LOAD_MODULE_USING_EVAL || goog.global.JSON === undefined) {
      return '' +
          'goog.loadModule(function(exports) {' +
          '"use strict";' + contents +
          '\n' +  // terminate any trailing single line comment.
          ';return exports' +
          '});' +
          '\n//# sourceURL=' + this.path + '\n';
    } else {
      return '' +
          'goog.loadModule(' +
          goog.global.JSON.stringify(
              contents + '\n//# sourceURL=' + this.path + '\n') +
          ');';
    }
  };


  /**
   * Whether the browser is IE9 or earlier, which needs special handling
   * for deferred modules.
   * @const @private {boolean}
   */
  goog.DebugLoader_.IS_OLD_IE_ = !!(
      !goog.global.atob && goog.global.document && goog.global.document['all']);


  /**
   * @param {string} relPath
   * @param {!Array<string>|undefined} provides
   * @param {!Array<string>} requires
   * @param {boolean|!Object<string>=} opt_loadFlags
   * @see goog.addDependency
   */
  goog.DebugLoader_.prototype.addDependency = function(
      relPath, provides, requires, opt_loadFlags) {
    provides = provides || [];
    relPath = relPath.replace(/\\/g, '/');
    var path = goog.normalizePath_(goog.basePath + relPath);
    if (!opt_loadFlags || typeof opt_loadFlags === 'boolean') {
      opt_loadFlags = opt_loadFlags ? {'module': goog.ModuleType.GOOG} : {};
    }
    var dep = this.factory_.createDependency(
        path, relPath, provides, requires, opt_loadFlags,
        goog.transpiler_.needsTranspile(
            opt_loadFlags['lang'] || 'es3', opt_loadFlags['module']));
    this.dependencies_[path] = dep;
    for (var i = 0; i < provides.length; i++) {
      this.idToPath_[provides[i]] = path;
    }
    this.idToPath_[relPath] = path;
  };


  /**
   * Creates goog.Dependency instances for the debug loader to load.
   *
   * Should be overridden to have the debug loader use custom subclasses of
   * goog.Dependency.
   *
   * @param {!goog.Transpiler} transpiler
   * @struct @constructor
   */
  goog.DependencyFactory = function(transpiler) {
    /** @protected @const */
    this.transpiler = transpiler;
  };


  /**
   * @param {string} path Absolute path of the file.
   * @param {string} relativePath Path relative to closure’s base.js.
   * @param {!Array<string>} provides Array of provided goog.provide/module ids.
   * @param {!Array<string>} requires Array of required goog.provide/module /
   *     relative ES6 module paths.
   * @param {!Object<string, string>} loadFlags
   * @param {boolean} needsTranspile True if the file needs to be transpiled
   *     per the goog.Transpiler.
   * @return {!goog.Dependency}
   */
  goog.DependencyFactory.prototype.createDependency = function(
      path, relativePath, provides, requires, loadFlags, needsTranspile) {

    if (loadFlags['module'] == goog.ModuleType.GOOG) {
      return new goog.GoogModuleDependency(
          path, relativePath, provides, requires, loadFlags, needsTranspile,
          this.transpiler);
    } else if (needsTranspile) {
      return new goog.TranspiledDependency(
          path, relativePath, provides, requires, loadFlags, this.transpiler);
    } else {
      if (loadFlags['module'] == goog.ModuleType.ES6) {
        if (goog.TRANSPILE == 'never' && goog.ASSUME_ES_MODULES_TRANSPILED) {
          return new goog.PreTranspiledEs6ModuleDependency(
              path, relativePath, provides, requires, loadFlags);
        } else {
          return new goog.Es6ModuleDependency(
              path, relativePath, provides, requires, loadFlags);
        }
      } else {
        return new goog.Dependency(
            path, relativePath, provides, requires, loadFlags);
      }
    }
  };


  /** @private @const */
  goog.debugLoader_ = new goog.DebugLoader_();


  /**
   * Loads the Closure Dependency file.
   *
   * Exposed a public function so CLOSURE_NO_DEPS can be set to false, base
   * loaded, setDependencyFactory called, and then this called. i.e. allows
   * custom loading of the deps file.
   */
  goog.loadClosureDeps = function() {
    goog.debugLoader_.loadClosureDeps();
  };


  /**
   * Sets the dependency factory, which can be used to create custom
   * goog.Dependency implementations to control how dependencies are loaded.
   *
   * Note: if you wish to call this function and provide your own implemnetation
   * it is a wise idea to set CLOSURE_NO_DEPS to true, otherwise the dependency
   * file and all of its goog.addDependency calls will use the default factory.
   * You can call goog.loadClosureDeps to load the Closure dependency file
   * later, after your factory is injected.
   *
   * @param {!goog.DependencyFactory} factory
   */
  goog.setDependencyFactory = function(factory) {
    goog.debugLoader_.setDependencyFactory(factory);
  };


  if (!goog.global.CLOSURE_NO_DEPS) {
    goog.debugLoader_.loadClosureDeps();
  }


  /**
   * Bootstraps the given namespaces and calls the callback once they are
   * available either via goog.require. This is a replacement for using
   * `goog.require` to bootstrap Closure JavaScript. Previously a `goog.require`
   * in an HTML file would guarantee that the require'd namespace was available
   * in the next immediate script tag. With ES6 modules this no longer a
   * guarantee.
   *
   * @param {!Array<string>} namespaces
   * @param {function(): ?} callback Function to call once all the namespaces
   *     have loaded. Always called asynchronously.
   */
  goog.bootstrap = function(namespaces, callback) {
    goog.debugLoader_.bootstrap(namespaces, callback);
  };
}


/**
 * @define {string} Trusted Types policy name. If non-empty then Closure will
 * use Trusted Types.
 */
goog.TRUSTED_TYPES_POLICY_NAME =
    goog.define('goog.TRUSTED_TYPES_POLICY_NAME', '');


/**
 * Returns the parameter.
 * @param {string} s
 * @return {string}
 * @private
 */
goog.identity_ = function(s) {
  return s;
};


/**
 * Creates Trusted Types policy if Trusted Types are supported by the browser.
 * The policy just blesses any string as a Trusted Type. It is not visibility
 * restricted because anyone can also call TrustedTypes.createPolicy directly.
 * However, the allowed names should be restricted by a HTTP header and the
 * reference to the created policy should be visibility restricted.
 * @param {string} name
 * @return {?TrustedTypePolicy}
 */
goog.createTrustedTypesPolicy = function(name) {
  var policy = null;
  // TODO(koto): Remove window.TrustedTypes variant when the newer API ships.
  var policyFactory = goog.global.trustedTypes || goog.global.TrustedTypes;
  if (!policyFactory || !policyFactory.createPolicy) {
    return policy;
  }
  // TrustedTypes.createPolicy throws if called with a name that is already
  // registered, even in report-only mode. Until the API changes, catch the
  // error not to break the applications functionally. In such case, the code
  // will fall back to using regular Safe Types.
  // TODO(koto): Remove catching once createPolicy API stops throwing.
  try {
    policy = policyFactory.createPolicy(name, {
      createHTML: goog.identity_,
      createScript: goog.identity_,
      createScriptURL: goog.identity_,
      createURL: goog.identity_
    });
  } catch (e) {
    goog.logToConsole_(e.message);
  }
  return policy;
};


/** @private @const {?TrustedTypePolicy} */
goog.TRUSTED_TYPES_POLICY_ = goog.TRUSTED_TYPES_POLICY_NAME ?
    goog.createTrustedTypesPolicy(goog.TRUSTED_TYPES_POLICY_NAME + '#base') :
    null;

// Copyright 2019 The Closure Library Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS-IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// This file has been auto-generated by GenJsDeps, please do not edit.

// Disable Clang formatter for this file.
// See http://goo.gl/SdiwZH
// clang-format off

goog.addDependency('collections/sets.js', ['goog.collections.sets'], ['goog.labs.collections.iterables'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('collections/sets_test.js', ['goog.collections.setsTest'], ['goog.collections.sets', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferred.js', ['goog.async.Deferred', 'goog.async.Deferred.AlreadyCalledError', 'goog.async.Deferred.CanceledError'], ['goog.Promise', 'goog.Thenable', 'goog.array', 'goog.asserts', 'goog.debug.Error'], {});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferred_async_test.js', ['goog.async.deferredAsyncTest'], ['goog.async.Deferred', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferred_test.js', ['goog.async.deferredTest'], ['goog.Promise', 'goog.Thenable', 'goog.async.Deferred', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferredlist.js', ['goog.async.DeferredList'], ['goog.async.Deferred'], {});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferredlist_test.js', ['goog.async.deferredListTest'], ['goog.array', 'goog.async.Deferred', 'goog.async.DeferredList', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto/proto.js', ['goog.proto'], ['goog.proto.Serializer'], {});
goog.addDependency('proto/serializer.js', ['goog.proto.Serializer'], ['goog.json.Serializer', 'goog.string'], {});
goog.addDependency('proto/serializer_test.js', ['goog.protoTest'], ['goog.proto', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('a11y/aria/announcer.js', ['goog.a11y.aria.Announcer'], ['goog.Disposable', 'goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.LivePriority', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.object'], {});
goog.addDependency('a11y/aria/announcer_test.js', ['goog.a11y.aria.AnnouncerTest'], ['goog.a11y.aria', 'goog.a11y.aria.Announcer', 'goog.a11y.aria.LivePriority', 'goog.a11y.aria.State', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.iframe', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('a11y/aria/aria.js', ['goog.a11y.aria'], ['goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.a11y.aria.datatables', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.object', 'goog.string'], {});
goog.addDependency('a11y/aria/aria_test.js', ['goog.a11y.ariaTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('a11y/aria/attributes.js', ['goog.a11y.aria.AutoCompleteValues', 'goog.a11y.aria.CheckedValues', 'goog.a11y.aria.DropEffectValues', 'goog.a11y.aria.ExpandedValues', 'goog.a11y.aria.GrabbedValues', 'goog.a11y.aria.InvalidValues', 'goog.a11y.aria.LivePriority', 'goog.a11y.aria.OrientationValues', 'goog.a11y.aria.PressedValues', 'goog.a11y.aria.RelevantValues', 'goog.a11y.aria.SelectedValues', 'goog.a11y.aria.SortValues', 'goog.a11y.aria.State'], [], {});
goog.addDependency('a11y/aria/datatables.js', ['goog.a11y.aria.datatables'], ['goog.a11y.aria.State', 'goog.object'], {});
goog.addDependency('a11y/aria/roles.js', ['goog.a11y.aria.Role'], [], {});
goog.addDependency('array/array.js', ['goog.array'], ['goog.asserts'], {});
goog.addDependency('array/array_test.js', ['goog.arrayTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es7', 'module': 'goog'});
goog.addDependency('asserts/asserts.js', ['goog.asserts', 'goog.asserts.AssertionError'], ['goog.debug.Error', 'goog.dom.NodeType'], {});
goog.addDependency('asserts/asserts_test.js', ['goog.assertsTest'], ['goog.asserts', 'goog.asserts.AssertionError', 'goog.dom', 'goog.dom.TagName', 'goog.reflect', 'goog.string', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/animationdelay.js', ['goog.async.AnimationDelay'], ['goog.Disposable', 'goog.events', 'goog.functions'], {});
goog.addDependency('async/animationdelay_test.js', ['goog.async.AnimationDelayTest'], ['goog.Promise', 'goog.Timer', 'goog.async.AnimationDelay', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/conditionaldelay.js', ['goog.async.ConditionalDelay'], ['goog.Disposable', 'goog.async.Delay'], {});
goog.addDependency('async/conditionaldelay_test.js', ['goog.async.ConditionalDelayTest'], ['goog.async.ConditionalDelay', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/debouncer.js', ['goog.async.Debouncer'], ['goog.Disposable', 'goog.Timer'], {});
goog.addDependency('async/debouncer_test.js', ['goog.async.DebouncerTest'], ['goog.array', 'goog.async.Debouncer', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/delay.js', ['goog.Delay', 'goog.async.Delay'], ['goog.Disposable', 'goog.Timer'], {});
goog.addDependency('async/delay_test.js', ['goog.async.DelayTest'], ['goog.async.Delay', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/freelist.js', ['goog.async.FreeList'], [], {'lang': 'es6'});
goog.addDependency('async/freelist_test.js', ['goog.async.FreeListTest'], ['goog.async.FreeList', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/nexttick.js', ['goog.async.nextTick', 'goog.async.throwException'], ['goog.debug.entryPointRegistry', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.labs.userAgent.browser', 'goog.labs.userAgent.engine', 'goog.string.Const'], {});
goog.addDependency('async/nexttick_test.js', ['goog.async.nextTickTest'], ['goog.Promise', 'goog.async.nextTick', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.dom', 'goog.dom.TagName', 'goog.labs.userAgent.browser', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/run.js', ['goog.async.run'], ['goog.async.WorkQueue', 'goog.async.nextTick', 'goog.async.throwException'], {});
goog.addDependency('async/run_next_tick_test.js', ['goog.async.runNextTickTest'], ['goog.async.run', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/run_test.js', ['goog.async.runTest'], ['goog.async.run', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/throttle.js', ['goog.Throttle', 'goog.async.Throttle'], ['goog.Disposable', 'goog.Timer'], {});
goog.addDependency('async/throttle_test.js', ['goog.async.ThrottleTest'], ['goog.async.Throttle', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/workqueue.js', ['goog.async.WorkItem', 'goog.async.WorkQueue'], ['goog.asserts', 'goog.async.FreeList'], {});
goog.addDependency('async/workqueue_test.js', ['goog.async.WorkQueueTest'], ['goog.async.WorkQueue', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('base.js', ['goog'], [], {});
goog.addDependency('base_module_test.js', ['goog.baseModuleTest'], ['goog.Timer', 'goog.test_module', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('base_test.js', ['goog.baseTest'], ['goog.Promise', 'goog.Timer', 'goog.Uri', 'goog.dom', 'goog.dom.TagName', 'goog.object', 'goog.test_module', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit', 'goog.testing.recordFunction', 'goog.userAgent'], {'lang': 'es6'});
goog.addDependency('color/alpha.js', ['goog.color.alpha'], ['goog.color'], {});
goog.addDependency('color/alpha_test.js', ['goog.color.alphaTest'], ['goog.array', 'goog.color.alpha', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('color/color.js', ['goog.color', 'goog.color.Hsl', 'goog.color.Hsv', 'goog.color.Rgb'], ['goog.color.names', 'goog.math'], {});
goog.addDependency('color/color_test.js', ['goog.colorTest'], ['goog.array', 'goog.color', 'goog.color.names', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('color/names.js', ['goog.color.names'], [], {});
goog.addDependency('crypt/aes.js', ['goog.crypt.Aes'], ['goog.asserts', 'goog.crypt.BlockCipher'], {});
goog.addDependency('crypt/aes_test.js', ['goog.crypt.AesTest'], ['goog.crypt', 'goog.crypt.Aes', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/arc4.js', ['goog.crypt.Arc4'], ['goog.asserts'], {});
goog.addDependency('crypt/arc4_test.js', ['goog.crypt.Arc4Test'], ['goog.array', 'goog.crypt.Arc4', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/base64.js', ['goog.crypt.base64'], ['goog.asserts', 'goog.crypt', 'goog.string', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es5'});
goog.addDependency('crypt/base64_test.js', ['goog.crypt.base64Test'], ['goog.crypt', 'goog.crypt.base64', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/basen.js', ['goog.crypt.baseN'], [], {'lang': 'es6'});
goog.addDependency('crypt/basen_test.js', ['goog.crypt.baseNTest'], ['goog.crypt.baseN', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/blobhasher.js', ['goog.crypt.BlobHasher', 'goog.crypt.BlobHasher.EventType'], ['goog.asserts', 'goog.events.EventTarget', 'goog.fs', 'goog.log'], {});
goog.addDependency('crypt/blobhasher_test.js', ['goog.crypt.BlobHasherTest'], ['goog.crypt', 'goog.crypt.BlobHasher', 'goog.crypt.Md5', 'goog.events', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/blockcipher.js', ['goog.crypt.BlockCipher'], [], {});
goog.addDependency('crypt/bytestring_perf.js', ['goog.crypt.byteArrayToStringPerf'], ['goog.array', 'goog.dom', 'goog.testing.PerformanceTable'], {});
goog.addDependency('crypt/cbc.js', ['goog.crypt.Cbc'], ['goog.array', 'goog.asserts', 'goog.crypt', 'goog.crypt.BlockCipher'], {});
goog.addDependency('crypt/cbc_test.js', ['goog.crypt.CbcTest'], ['goog.crypt', 'goog.crypt.Aes', 'goog.crypt.Cbc', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/crypt.js', ['goog.crypt'], ['goog.array', 'goog.asserts'], {});
goog.addDependency('crypt/crypt_test.js', ['goog.cryptTest'], ['goog.crypt', 'goog.string', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/ctr.js', ['goog.crypt.Ctr'], ['goog.array', 'goog.asserts', 'goog.crypt'], {});
goog.addDependency('crypt/ctr_test.js', ['goog.crypt.CtrTest'], ['goog.crypt', 'goog.crypt.Aes', 'goog.crypt.Ctr', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('crypt/hash.js', ['goog.crypt.Hash'], [], {});
goog.addDependency('crypt/hash32.js', ['goog.crypt.hash32'], ['goog.crypt'], {});
goog.addDependency('crypt/hash32_test.js', ['goog.crypt.hash32Test'], ['goog.crypt.hash32', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/hashtester.js', ['goog.crypt.hashTester'], ['goog.array', 'goog.crypt', 'goog.dom', 'goog.dom.TagName', 'goog.reflect', 'goog.testing.PerformanceTable', 'goog.testing.PseudoRandom', 'goog.testing.asserts'], {});
goog.addDependency('crypt/hmac.js', ['goog.crypt.Hmac'], ['goog.crypt.Hash'], {});
goog.addDependency('crypt/hmac_test.js', ['goog.crypt.HmacTest'], ['goog.crypt.Hmac', 'goog.crypt.Sha1', 'goog.crypt.hashTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/md5.js', ['goog.crypt.Md5'], ['goog.crypt.Hash'], {});
goog.addDependency('crypt/md5_test.js', ['goog.crypt.Md5Test'], ['goog.crypt', 'goog.crypt.Md5', 'goog.crypt.hashTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/pbkdf2.js', ['goog.crypt.pbkdf2'], ['goog.array', 'goog.asserts', 'goog.crypt', 'goog.crypt.Hmac', 'goog.crypt.Sha1'], {});
goog.addDependency('crypt/pbkdf2_test.js', ['goog.crypt.pbkdf2Test'], ['goog.crypt', 'goog.crypt.pbkdf2', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha1.js', ['goog.crypt.Sha1'], ['goog.crypt.Hash'], {});
goog.addDependency('crypt/sha1_test.js', ['goog.crypt.Sha1Test'], ['goog.crypt', 'goog.crypt.Sha1', 'goog.crypt.hashTester', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha2.js', ['goog.crypt.Sha2'], ['goog.array', 'goog.asserts', 'goog.crypt.Hash'], {});
goog.addDependency('crypt/sha224.js', ['goog.crypt.Sha224'], ['goog.crypt.Sha2'], {});
goog.addDependency('crypt/sha224_test.js', ['goog.crypt.Sha224Test'], ['goog.crypt', 'goog.crypt.Sha224', 'goog.crypt.hashTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha256.js', ['goog.crypt.Sha256'], ['goog.crypt.Sha2'], {});
goog.addDependency('crypt/sha256_test.js', ['goog.crypt.Sha256Test'], ['goog.crypt', 'goog.crypt.Sha256', 'goog.crypt.hashTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha2_64bit.js', ['goog.crypt.Sha2_64bit'], ['goog.array', 'goog.asserts', 'goog.crypt.Hash', 'goog.math.Long'], {});
goog.addDependency('crypt/sha2_64bit_test.js', ['goog.crypt.Sha2_64bit_test'], ['goog.array', 'goog.crypt', 'goog.crypt.Sha384', 'goog.crypt.Sha512', 'goog.crypt.Sha512_256', 'goog.crypt.hashTester', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha384.js', ['goog.crypt.Sha384'], ['goog.crypt.Sha2_64bit'], {});
goog.addDependency('crypt/sha512.js', ['goog.crypt.Sha512'], ['goog.crypt.Sha2_64bit'], {});
goog.addDependency('crypt/sha512_256.js', ['goog.crypt.Sha512_256'], ['goog.crypt.Sha2_64bit'], {});
goog.addDependency('cssom/cssom.js', ['goog.cssom', 'goog.cssom.CssRuleType'], ['goog.array', 'goog.dom', 'goog.dom.TagName'], {});
goog.addDependency('cssom/cssom_test.js', ['goog.cssomTest'], ['goog.array', 'goog.cssom', 'goog.cssom.CssRuleType', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('cssom/iframe/style.js', ['goog.cssom.iframe.style'], ['goog.asserts', 'goog.cssom', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.string', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('cssom/iframe/style_test.js', ['goog.cssom.iframe.styleTest'], ['goog.cssom', 'goog.cssom.iframe.style', 'goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/datamanager.js', ['goog.ds.DataManager'], ['goog.ds.BasicNodeList', 'goog.ds.DataNode', 'goog.ds.Expr', 'goog.object', 'goog.string', 'goog.structs', 'goog.structs.Map'], {});
goog.addDependency('datasource/datasource.js', ['goog.ds.BaseDataNode', 'goog.ds.BasicNodeList', 'goog.ds.DataNode', 'goog.ds.DataNodeList', 'goog.ds.EmptyNodeList', 'goog.ds.LoadState', 'goog.ds.SortedNodeList', 'goog.ds.Util', 'goog.ds.logger'], ['goog.array', 'goog.log'], {});
goog.addDependency('datasource/datasource_test.js', ['goog.ds.JsDataSourceTest'], ['goog.dom.xml', 'goog.ds.DataManager', 'goog.ds.JsDataSource', 'goog.ds.SortedNodeList', 'goog.ds.XmlDataSource', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/expr.js', ['goog.ds.Expr'], ['goog.ds.BasicNodeList', 'goog.ds.EmptyNodeList', 'goog.string'], {});
goog.addDependency('datasource/expr_test.js', ['goog.ds.ExprTest'], ['goog.ds.DataManager', 'goog.ds.Expr', 'goog.ds.JsDataSource', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/fastdatanode.js', ['goog.ds.AbstractFastDataNode', 'goog.ds.FastDataNode', 'goog.ds.FastListNode', 'goog.ds.PrimitiveFastDataNode'], ['goog.ds.DataManager', 'goog.ds.DataNodeList', 'goog.ds.EmptyNodeList', 'goog.string'], {});
goog.addDependency('datasource/fastdatanode_test.js', ['goog.ds.FastDataNodeTest'], ['goog.array', 'goog.ds.DataManager', 'goog.ds.Expr', 'goog.ds.FastDataNode', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/jsdatasource.js', ['goog.ds.JsDataSource', 'goog.ds.JsPropertyDataSource'], ['goog.ds.BaseDataNode', 'goog.ds.BasicNodeList', 'goog.ds.DataManager', 'goog.ds.DataNode', 'goog.ds.EmptyNodeList', 'goog.ds.LoadState'], {});
goog.addDependency('datasource/jsondatasource.js', ['goog.ds.JsonDataSource'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.ds.DataManager', 'goog.ds.JsDataSource', 'goog.ds.LoadState', 'goog.ds.logger', 'goog.log'], {});
goog.addDependency('datasource/jsxmlhttpdatasource.js', ['goog.ds.JsXmlHttpDataSource'], ['goog.Uri', 'goog.ds.DataManager', 'goog.ds.FastDataNode', 'goog.ds.LoadState', 'goog.ds.logger', 'goog.events', 'goog.log', 'goog.net.EventType', 'goog.net.XhrIo'], {});
goog.addDependency('datasource/jsxmlhttpdatasource_test.js', ['goog.ds.JsXmlHttpDataSourceTest'], ['goog.ds.JsXmlHttpDataSource', 'goog.testing.TestQueue', 'goog.testing.net.XhrIo', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/xmldatasource.js', ['goog.ds.XmlDataSource', 'goog.ds.XmlHttpDataSource'], ['goog.Uri', 'goog.dom.NodeType', 'goog.dom.xml', 'goog.ds.BasicNodeList', 'goog.ds.DataManager', 'goog.ds.DataNode', 'goog.ds.LoadState', 'goog.ds.logger', 'goog.log', 'goog.net.XhrIo', 'goog.string'], {});
goog.addDependency('date/date.js', ['goog.date', 'goog.date.Date', 'goog.date.DateTime', 'goog.date.Interval', 'goog.date.month', 'goog.date.weekDay'], ['goog.asserts', 'goog.date.DateLike', 'goog.i18n.DateTimeSymbols', 'goog.string'], {});
goog.addDependency('date/date_test.js', ['goog.dateTest'], ['goog.array', 'goog.date', 'goog.date.Date', 'goog.date.DateTime', 'goog.date.Interval', 'goog.date.month', 'goog.date.weekDay', 'goog.i18n.DateTimeSymbols', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.platform', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('date/datelike.js', ['goog.date.DateLike'], [], {});
goog.addDependency('date/daterange.js', ['goog.date.DateRange', 'goog.date.DateRange.Iterator', 'goog.date.DateRange.StandardDateRangeKeys'], ['goog.date.Date', 'goog.date.Interval', 'goog.iter.Iterator', 'goog.iter.StopIteration'], {});
goog.addDependency('date/daterange_test.js', ['goog.date.DateRangeTest'], ['goog.date.Date', 'goog.date.DateRange', 'goog.date.Interval', 'goog.i18n.DateTimeSymbols', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('date/duration.js', ['goog.date.duration'], ['goog.i18n.DateTimeFormat', 'goog.i18n.MessageFormat'], {});
goog.addDependency('date/duration_test.js', ['goog.date.durationTest'], ['goog.date.duration', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbols_bn', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_fa', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('date/relative.js', ['goog.date.relative', 'goog.date.relative.TimeDeltaFormatter', 'goog.date.relative.Unit'], ['goog.i18n.DateTimeFormat', 'goog.i18n.DateTimePatterns', 'goog.i18n.RelativeDateTimeFormat'], {});
goog.addDependency('date/relative_test.js', ['goog.date.relativeTest'], ['goog.date.relativeCommonTests'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('date/relativecommontests.js', ['goog.date.relativeCommonTests'], ['goog.date.DateTime', 'goog.date.relative', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimePatterns_ar', 'goog.i18n.DateTimePatterns_bn', 'goog.i18n.DateTimePatterns_es', 'goog.i18n.DateTimePatterns_fa', 'goog.i18n.DateTimePatterns_fr', 'goog.i18n.DateTimePatterns_no', 'goog.i18n.DateTimeSymbols_ar', 'goog.i18n.DateTimeSymbols_bn', 'goog.i18n.DateTimeSymbols_es', 'goog.i18n.DateTimeSymbols_fa', 'goog.i18n.DateTimeSymbols_fr', 'goog.i18n.DateTimeSymbols_no', 'goog.i18n.NumberFormatSymbols_bn', 'goog.i18n.NumberFormatSymbols_en', 'goog.i18n.NumberFormatSymbols_fa', 'goog.i18n.NumberFormatSymbols_no', 'goog.i18n.relativeDateTimeSymbols', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('date/utcdatetime.js', ['goog.date.UtcDateTime'], ['goog.date', 'goog.date.Date', 'goog.date.DateTime', 'goog.date.Interval'], {});
goog.addDependency('date/utcdatetime_test.js', ['goog.date.UtcDateTimeTest'], ['goog.date.Interval', 'goog.date.UtcDateTime', 'goog.date.month', 'goog.date.weekDay', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('db/cursor.js', ['goog.db.Cursor'], ['goog.async.Deferred', 'goog.db.Error', 'goog.db.KeyRange', 'goog.debug', 'goog.events.EventTarget'], {});
goog.addDependency('db/db.js', ['goog.db', 'goog.db.BlockedCallback', 'goog.db.UpgradeNeededCallback'], ['goog.asserts', 'goog.async.Deferred', 'goog.db.Error', 'goog.db.IndexedDb', 'goog.db.Transaction'], {});
goog.addDependency('db/db_test.js', ['goog.dbTest'], ['goog.Promise', 'goog.array', 'goog.db', 'goog.db.Cursor', 'goog.db.Error', 'goog.db.IndexedDb', 'goog.db.KeyRange', 'goog.db.Transaction', 'goog.events', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('db/error.js', ['goog.db.DomErrorLike', 'goog.db.Error', 'goog.db.Error.ErrorCode', 'goog.db.Error.ErrorName', 'goog.db.Error.VersionChangeBlockedError'], ['goog.asserts', 'goog.debug.Error'], {});
goog.addDependency('db/index.js', ['goog.db.Index'], ['goog.async.Deferred', 'goog.db.Cursor', 'goog.db.Error', 'goog.db.KeyRange', 'goog.debug'], {});
goog.addDependency('db/indexeddb.js', ['goog.db.IndexedDb'], ['goog.db.Error', 'goog.db.ObjectStore', 'goog.db.Transaction', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget'], {'lang': 'es6'});
goog.addDependency('db/keyrange.js', ['goog.db.KeyRange'], [], {});
goog.addDependency('db/objectstore.js', ['goog.db.ObjectStore'], ['goog.async.Deferred', 'goog.db.Cursor', 'goog.db.Error', 'goog.db.Index', 'goog.db.KeyRange', 'goog.debug'], {});
goog.addDependency('db/transaction.js', ['goog.db.Transaction', 'goog.db.Transaction.TransactionMode'], ['goog.async.Deferred', 'goog.db.Error', 'goog.db.ObjectStore', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventTarget'], {});
goog.addDependency('debug/console.js', ['goog.debug.Console'], ['goog.debug.LogManager', 'goog.debug.Logger', 'goog.debug.TextFormatter'], {});
goog.addDependency('debug/console_test.js', ['goog.debug.ConsoleTest'], ['goog.debug.Console', 'goog.debug.LogRecord', 'goog.debug.Logger', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/debug.js', ['goog.debug'], ['goog.array', 'goog.debug.errorcontext', 'goog.userAgent'], {});
goog.addDependency('debug/debug_test.js', ['goog.debugTest'], ['goog.debug', 'goog.debug.errorcontext', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/debugwindow.js', ['goog.debug.DebugWindow'], ['goog.debug.HtmlFormatter', 'goog.debug.LogManager', 'goog.debug.Logger', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.string.Const', 'goog.structs.CircularBuffer', 'goog.userAgent'], {});
goog.addDependency('debug/debugwindow_test.js', ['goog.debug.DebugWindowTest'], ['goog.debug.DebugWindow', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/devcss/devcss.js', ['goog.debug.DevCss', 'goog.debug.DevCss.UserAgent'], ['goog.asserts', 'goog.cssom', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('debug/devcss/devcss_test.js', ['goog.debug.DevCssTest'], ['goog.debug.DevCss', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/devcss/devcssrunner.js', ['goog.debug.devCssRunner'], ['goog.debug.DevCss'], {});
goog.addDependency('debug/divconsole.js', ['goog.debug.DivConsole'], ['goog.debug.HtmlFormatter', 'goog.debug.LogManager', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.string.Const', 'goog.style'], {});
goog.addDependency('debug/enhanceerror_test.js', ['goog.debugEnhanceErrorTest'], ['goog.debug', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/entrypointregistry.js', ['goog.debug.EntryPointMonitor', 'goog.debug.entryPointRegistry'], ['goog.asserts'], {});
goog.addDependency('debug/entrypointregistry_test.js', ['goog.debug.entryPointRegistryTest'], ['goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/error.js', ['goog.debug.Error'], [], {'lang': 'es6'});
goog.addDependency('debug/error_test.js', ['goog.debug.ErrorTest'], ['goog.debug.Error', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/errorcontext.js', ['goog.debug.errorcontext'], [], {});
goog.addDependency('debug/errorcontext_test.js', ['goog.debug.errorcontextTest'], ['goog.debug.errorcontext', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/errorhandler.js', ['goog.debug.ErrorHandler', 'goog.debug.ErrorHandler.ProtectedFunctionError'], ['goog.Disposable', 'goog.asserts', 'goog.debug', 'goog.debug.EntryPointMonitor', 'goog.debug.Error', 'goog.debug.Trace'], {'lang': 'es6'});
goog.addDependency('debug/errorhandler_async_test.js', ['goog.debug.ErrorHandlerAsyncTest'], ['goog.Promise', 'goog.debug.ErrorHandler', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('debug/errorhandler_test.js', ['goog.debug.ErrorHandlerTest'], ['goog.debug.ErrorHandler', 'goog.testing.MockControl', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/errorhandlerweakdep.js', ['goog.debug.errorHandlerWeakDep'], [], {});
goog.addDependency('debug/errorreporter.js', ['goog.debug.ErrorReporter', 'goog.debug.ErrorReporter.ExceptionEvent'], ['goog.asserts', 'goog.debug', 'goog.debug.Error', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.debug.errorcontext', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.log', 'goog.net.XhrIo', 'goog.object', 'goog.string', 'goog.uri.utils', 'goog.userAgent'], {});
goog.addDependency('debug/errorreporter_test.js', ['goog.debug.ErrorReporterTest'], ['goog.debug.Error', 'goog.debug.ErrorReporter', 'goog.debug.errorcontext', 'goog.events', 'goog.functions', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/fancywindow.js', ['goog.debug.FancyWindow'], ['goog.array', 'goog.asserts', 'goog.debug.DebugWindow', 'goog.debug.LogManager', 'goog.debug.Logger', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.object', 'goog.string', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('debug/formatter.js', ['goog.debug.Formatter', 'goog.debug.HtmlFormatter', 'goog.debug.TextFormatter'], ['goog.debug', 'goog.debug.Logger', 'goog.debug.RelativeTimeProvider', 'goog.html.SafeHtml', 'goog.html.SafeUrl', 'goog.html.uncheckedconversions', 'goog.string.Const'], {});
goog.addDependency('debug/formatter_test.js', ['goog.debug.FormatterTest'], ['goog.debug.HtmlFormatter', 'goog.debug.LogRecord', 'goog.debug.Logger', 'goog.html.SafeHtml', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/fpsdisplay.js', ['goog.debug.FpsDisplay'], ['goog.asserts', 'goog.async.AnimationDelay', 'goog.dom', 'goog.dom.TagName', 'goog.ui.Component'], {});
goog.addDependency('debug/fpsdisplay_test.js', ['goog.debug.FpsDisplayTest'], ['goog.Timer', 'goog.debug.FpsDisplay', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/logbuffer.js', ['goog.debug.LogBuffer'], ['goog.asserts', 'goog.debug.LogRecord'], {});
goog.addDependency('debug/logbuffer_test.js', ['goog.debug.LogBufferTest'], ['goog.debug.LogBuffer', 'goog.debug.Logger', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/logger.js', ['goog.debug.LogManager', 'goog.debug.Loggable', 'goog.debug.Logger', 'goog.debug.Logger.Level'], ['goog.array', 'goog.asserts', 'goog.debug', 'goog.debug.LogBuffer', 'goog.debug.LogRecord'], {});
goog.addDependency('debug/logger_test.js', ['goog.debug.LoggerTest'], ['goog.debug.LogManager', 'goog.debug.Logger', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/logrecord.js', ['goog.debug.LogRecord'], [], {});
goog.addDependency('debug/logrecordserializer.js', ['goog.debug.logRecordSerializer'], ['goog.debug.LogRecord', 'goog.debug.Logger', 'goog.json', 'goog.object'], {});
goog.addDependency('debug/logrecordserializer_test.js', ['goog.debug.logRecordSerializerTest'], ['goog.debug.LogRecord', 'goog.debug.Logger', 'goog.debug.logRecordSerializer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/relativetimeprovider.js', ['goog.debug.RelativeTimeProvider'], [], {});
goog.addDependency('debug/tracer.js', ['goog.debug.StopTraceDetail', 'goog.debug.Trace'], ['goog.array', 'goog.asserts', 'goog.debug.Logger', 'goog.iter', 'goog.log', 'goog.structs.Map', 'goog.structs.SimplePool'], {});
goog.addDependency('debug/tracer_test.js', ['goog.debug.TraceTest'], ['goog.array', 'goog.debug.Trace', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('defineclass_test.js', ['goog.defineClassTest'], ['goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('delegate/delegateregistry.js', ['goog.delegate.DelegateRegistry'], ['goog.array', 'goog.asserts', 'goog.debug'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('delegate/delegateregistry_test.js', ['goog.delegate.DelegateRegistryTest'], ['goog.array', 'goog.delegate.DelegateRegistry', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('delegate/delegates.js', ['goog.delegate.delegates'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('delegate/delegates_test.js', ['goog.delegate.delegatesTest'], ['goog.delegate.delegates', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('disposable/disposable.js', ['goog.Disposable', 'goog.dispose', 'goog.disposeAll'], ['goog.disposable.IDisposable'], {});
goog.addDependency('disposable/disposable_test.js', ['goog.DisposableTest'], ['goog.Disposable', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('disposable/idisposable.js', ['goog.disposable.IDisposable'], [], {});
goog.addDependency('dom/abstractmultirange.js', ['goog.dom.AbstractMultiRange'], ['goog.array', 'goog.dom', 'goog.dom.AbstractRange', 'goog.dom.TextRange'], {});
goog.addDependency('dom/abstractrange.js', ['goog.dom.AbstractRange', 'goog.dom.RangeIterator', 'goog.dom.RangeType'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.SavedCaretRange', 'goog.dom.TagIterator', 'goog.userAgent'], {});
goog.addDependency('dom/abstractrange_test.js', ['goog.dom.AbstractRangeTest'], ['goog.dom', 'goog.dom.AbstractRange', 'goog.dom.Range', 'goog.dom.TagName', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/animationframe/animationframe.js', ['goog.dom.animationFrame', 'goog.dom.animationFrame.Spec', 'goog.dom.animationFrame.State'], ['goog.dom.animationFrame.polyfill'], {});
goog.addDependency('dom/animationframe/animationframe_test.js', ['goog.dom.AnimationFrameTest'], ['goog.dom.animationFrame', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/animationframe/polyfill.js', ['goog.dom.animationFrame.polyfill'], [], {'lang': 'es6'});
goog.addDependency('dom/annotate.js', ['goog.dom.annotate', 'goog.dom.annotate.AnnotateFn'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.object'], {});
goog.addDependency('dom/annotate_test.js', ['goog.dom.annotateTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.annotate', 'goog.html.SafeHtml', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/asserts.js', ['goog.dom.asserts'], ['goog.asserts'], {});
goog.addDependency('dom/asserts_test.js', ['goog.dom.assertsTest'], ['goog.dom.asserts', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/attr.js', ['goog.dom.Attr'], [], {});
goog.addDependency('dom/browserfeature.js', ['goog.dom.BrowserFeature'], ['goog.userAgent'], {});
goog.addDependency('dom/browserfeature_test.js', ['goog.dom.BrowserFeatureTest'], ['goog.dom', 'goog.dom.BrowserFeature', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/browserrange/abstractrange.js', ['goog.dom.browserrange.AbstractRange'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.RangeEndpoint', 'goog.dom.TagName', 'goog.dom.TextRangeIterator', 'goog.iter', 'goog.math.Coordinate', 'goog.string', 'goog.string.StringBuffer', 'goog.userAgent'], {});
goog.addDependency('dom/browserrange/browserrange.js', ['goog.dom.browserrange', 'goog.dom.browserrange.Error'], ['goog.dom', 'goog.dom.BrowserFeature', 'goog.dom.NodeType', 'goog.dom.browserrange.GeckoRange', 'goog.dom.browserrange.IeRange', 'goog.dom.browserrange.OperaRange', 'goog.dom.browserrange.W3cRange', 'goog.dom.browserrange.WebKitRange', 'goog.userAgent'], {});
goog.addDependency('dom/browserrange/browserrange_test.js', ['goog.dom.browserrangeTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.RangeEndpoint', 'goog.dom.TagName', 'goog.dom.browserrange', 'goog.html.testing', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/browserrange/geckorange.js', ['goog.dom.browserrange.GeckoRange'], ['goog.dom.browserrange.W3cRange'], {});
goog.addDependency('dom/browserrange/ierange.js', ['goog.dom.browserrange.IeRange'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.RangeEndpoint', 'goog.dom.TagName', 'goog.dom.browserrange.AbstractRange', 'goog.dom.safe', 'goog.html.uncheckedconversions', 'goog.log', 'goog.string'], {});
goog.addDependency('dom/browserrange/operarange.js', ['goog.dom.browserrange.OperaRange'], ['goog.dom.browserrange.W3cRange'], {});
goog.addDependency('dom/browserrange/w3crange.js', ['goog.dom.browserrange.W3cRange'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.RangeEndpoint', 'goog.dom.TagName', 'goog.dom.browserrange.AbstractRange', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('dom/browserrange/webkitrange.js', ['goog.dom.browserrange.WebKitRange'], ['goog.dom.RangeEndpoint', 'goog.dom.browserrange.W3cRange', 'goog.userAgent'], {});
goog.addDependency('dom/bufferedviewportsizemonitor.js', ['goog.dom.BufferedViewportSizeMonitor'], ['goog.asserts', 'goog.async.Delay', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType'], {});
goog.addDependency('dom/bufferedviewportsizemonitor_test.js', ['goog.dom.BufferedViewportSizeMonitorTest'], ['goog.dom.BufferedViewportSizeMonitor', 'goog.dom.ViewportSizeMonitor', 'goog.events', 'goog.events.EventType', 'goog.math.Size', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/classes.js', ['goog.dom.classes'], ['goog.array'], {});
goog.addDependency('dom/classes_test.js', ['goog.dom.classes_test'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classes', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/classlist.js', ['goog.dom.classlist'], ['goog.array'], {});
goog.addDependency('dom/classlist_test.js', ['goog.dom.classlist_test'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/controlrange.js', ['goog.dom.ControlRange', 'goog.dom.ControlRangeIterator'], ['goog.array', 'goog.dom', 'goog.dom.AbstractMultiRange', 'goog.dom.AbstractRange', 'goog.dom.RangeIterator', 'goog.dom.RangeType', 'goog.dom.SavedRange', 'goog.dom.TagWalkType', 'goog.dom.TextRange', 'goog.iter.StopIteration', 'goog.userAgent'], {});
goog.addDependency('dom/controlrange_test.js', ['goog.dom.ControlRangeTest'], ['goog.dom', 'goog.dom.ControlRange', 'goog.dom.RangeType', 'goog.dom.TagName', 'goog.dom.TextRange', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/dataset.js', ['goog.dom.dataset'], ['goog.labs.userAgent.browser', 'goog.string', 'goog.userAgent.product'], {});
goog.addDependency('dom/dataset_test.js', ['goog.dom.datasetTest'], ['goog.dom', 'goog.dom.dataset', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/dom.js', ['goog.dom', 'goog.dom.Appendable', 'goog.dom.DomHelper'], ['goog.array', 'goog.asserts', 'goog.dom.BrowserFeature', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.uncheckedconversions', 'goog.math.Coordinate', 'goog.math.Size', 'goog.object', 'goog.string', 'goog.string.Unicode', 'goog.userAgent'], {});
goog.addDependency('dom/dom_compile_test.js', ['goog.dom.DomCompileTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/dom_test.js', ['goog.dom.dom_test'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.BrowserFeature', 'goog.dom.DomHelper', 'goog.dom.InputType', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.functions', 'goog.html.SafeUrl', 'goog.html.testing', 'goog.object', 'goog.string.Const', 'goog.string.Unicode', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/fontsizemonitor.js', ['goog.dom.FontSizeMonitor', 'goog.dom.FontSizeMonitor.EventType'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.userAgent'], {});
goog.addDependency('dom/fontsizemonitor_test.js', ['goog.dom.FontSizeMonitorTest'], ['goog.dom', 'goog.dom.FontSizeMonitor', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/forms.js', ['goog.dom.forms'], ['goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.structs.Map', 'goog.window'], {});
goog.addDependency('dom/forms_test.js', ['goog.dom.formsTest'], ['goog.dom', 'goog.dom.forms', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/fullscreen.js', ['goog.dom.fullscreen', 'goog.dom.fullscreen.EventType'], ['goog.dom'], {});
goog.addDependency('dom/fullscreen_test.js', ['goog.dom.fullscreen_test'], ['goog.dom.DomHelper', 'goog.dom.fullscreen', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/htmlelement.js', ['goog.dom.HtmlElement'], [], {});
goog.addDependency('dom/iframe.js', ['goog.dom.iframe'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyle', 'goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('dom/iframe_test.js', ['goog.dom.iframeTest'], ['goog.dom', 'goog.dom.iframe', 'goog.html.SafeHtml', 'goog.html.SafeStyle', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/inputtype.js', ['goog.dom.InputType'], [], {});
goog.addDependency('dom/inputtype_test.js', ['goog.dom.InputTypeTest'], ['goog.dom.InputType', 'goog.object', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/iter.js', ['goog.dom.iter.AncestorIterator', 'goog.dom.iter.ChildIterator', 'goog.dom.iter.SiblingIterator'], ['goog.iter.Iterator', 'goog.iter.StopIteration'], {});
goog.addDependency('dom/iter_test.js', ['goog.dom.iterTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.iter.AncestorIterator', 'goog.dom.iter.ChildIterator', 'goog.dom.iter.SiblingIterator', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/multirange.js', ['goog.dom.MultiRange', 'goog.dom.MultiRangeIterator'], ['goog.array', 'goog.dom', 'goog.dom.AbstractMultiRange', 'goog.dom.AbstractRange', 'goog.dom.RangeIterator', 'goog.dom.RangeType', 'goog.dom.SavedRange', 'goog.dom.TextRange', 'goog.iter', 'goog.iter.StopIteration', 'goog.log'], {});
goog.addDependency('dom/multirange_test.js', ['goog.dom.MultiRangeTest'], ['goog.dom', 'goog.dom.MultiRange', 'goog.dom.Range', 'goog.iter', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/nodeiterator.js', ['goog.dom.NodeIterator'], ['goog.dom.TagIterator'], {});
goog.addDependency('dom/nodeiterator_test.js', ['goog.dom.NodeIteratorTest'], ['goog.dom', 'goog.dom.NodeIterator', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/nodeoffset.js', ['goog.dom.NodeOffset'], ['goog.Disposable', 'goog.dom.TagName'], {});
goog.addDependency('dom/nodeoffset_test.js', ['goog.dom.NodeOffsetTest'], ['goog.dom', 'goog.dom.NodeOffset', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/nodetype.js', ['goog.dom.NodeType'], [], {});
goog.addDependency('dom/pattern/abstractpattern.js', ['goog.dom.pattern.AbstractPattern'], ['goog.dom.TagWalkType', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/allchildren.js', ['goog.dom.pattern.AllChildren'], ['goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/callback/callback.js', ['goog.dom.pattern.callback'], ['goog.dom', 'goog.dom.TagWalkType', 'goog.iter'], {});
goog.addDependency('dom/pattern/callback/counter.js', ['goog.dom.pattern.callback.Counter'], [], {});
goog.addDependency('dom/pattern/callback/test.js', ['goog.dom.pattern.callback.Test'], ['goog.iter.StopIteration'], {});
goog.addDependency('dom/pattern/childmatches.js', ['goog.dom.pattern.ChildMatches'], ['goog.dom.pattern.AllChildren', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/endtag.js', ['goog.dom.pattern.EndTag'], ['goog.dom.TagWalkType', 'goog.dom.pattern.Tag'], {});
goog.addDependency('dom/pattern/fulltag.js', ['goog.dom.pattern.FullTag'], ['goog.dom.pattern.MatchType', 'goog.dom.pattern.StartTag', 'goog.dom.pattern.Tag'], {});
goog.addDependency('dom/pattern/matcher.js', ['goog.dom.pattern.Matcher'], ['goog.dom.TagIterator', 'goog.dom.pattern.MatchType', 'goog.iter'], {});
goog.addDependency('dom/pattern/matcher_test.js', ['goog.dom.pattern.matcherTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.pattern.EndTag', 'goog.dom.pattern.FullTag', 'goog.dom.pattern.Matcher', 'goog.dom.pattern.Repeat', 'goog.dom.pattern.Sequence', 'goog.dom.pattern.StartTag', 'goog.dom.pattern.callback.Counter', 'goog.dom.pattern.callback.Test', 'goog.iter.StopIteration', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/pattern/nodetype.js', ['goog.dom.pattern.NodeType'], ['goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/pattern.js', ['goog.dom.pattern', 'goog.dom.pattern.MatchType'], [], {});
goog.addDependency('dom/pattern/pattern_test.js', ['goog.dom.patternTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagWalkType', 'goog.dom.pattern.AllChildren', 'goog.dom.pattern.ChildMatches', 'goog.dom.pattern.EndTag', 'goog.dom.pattern.FullTag', 'goog.dom.pattern.MatchType', 'goog.dom.pattern.NodeType', 'goog.dom.pattern.Repeat', 'goog.dom.pattern.Sequence', 'goog.dom.pattern.StartTag', 'goog.dom.pattern.Text', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/pattern/repeat.js', ['goog.dom.pattern.Repeat'], ['goog.dom.NodeType', 'goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/sequence.js', ['goog.dom.pattern.Sequence'], ['goog.dom.NodeType', 'goog.dom.pattern', 'goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/starttag.js', ['goog.dom.pattern.StartTag'], ['goog.dom.TagWalkType', 'goog.dom.pattern.Tag'], {});
goog.addDependency('dom/pattern/tag.js', ['goog.dom.pattern.Tag'], ['goog.dom.pattern', 'goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType', 'goog.object'], {});
goog.addDependency('dom/pattern/text.js', ['goog.dom.pattern.Text'], ['goog.dom.NodeType', 'goog.dom.pattern', 'goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/range.js', ['goog.dom.Range'], ['goog.dom', 'goog.dom.AbstractRange', 'goog.dom.BrowserFeature', 'goog.dom.ControlRange', 'goog.dom.MultiRange', 'goog.dom.NodeType', 'goog.dom.TextRange'], {});
goog.addDependency('dom/range_test.js', ['goog.dom.RangeTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.RangeType', 'goog.dom.TagName', 'goog.dom.TextRange', 'goog.dom.browserrange', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/rangeendpoint.js', ['goog.dom.RangeEndpoint'], [], {});
goog.addDependency('dom/safe.js', ['goog.dom.safe', 'goog.dom.safe.InsertAdjacentHtmlPosition'], ['goog.asserts', 'goog.dom.asserts', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.uncheckedconversions', 'goog.string.Const', 'goog.string.internal'], {});
goog.addDependency('dom/safe_test.js', ['goog.dom.safeTest'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.dom.safe.InsertAdjacentHtmlPosition', 'goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.testing', 'goog.string', 'goog.string.Const', 'goog.testing', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/savedcaretrange.js', ['goog.dom.SavedCaretRange'], ['goog.array', 'goog.dom', 'goog.dom.SavedRange', 'goog.dom.TagName', 'goog.string'], {});
goog.addDependency('dom/savedcaretrange_test.js', ['goog.dom.SavedCaretRangeTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.SavedCaretRange', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/savedrange.js', ['goog.dom.SavedRange'], ['goog.Disposable', 'goog.log'], {});
goog.addDependency('dom/savedrange_test.js', ['goog.dom.SavedRangeTest'], ['goog.dom', 'goog.dom.Range', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/selection.js', ['goog.dom.selection'], ['goog.dom.InputType', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('dom/selection_test.js', ['goog.dom.selectionTest'], ['goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.selection', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/tagiterator.js', ['goog.dom.TagIterator', 'goog.dom.TagWalkType'], ['goog.dom', 'goog.dom.NodeType', 'goog.iter.Iterator', 'goog.iter.StopIteration'], {});
goog.addDependency('dom/tagiterator_test.js', ['goog.dom.TagIteratorTest'], ['goog.dom', 'goog.dom.TagIterator', 'goog.dom.TagName', 'goog.dom.TagWalkType', 'goog.iter', 'goog.iter.StopIteration', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/tagname.js', ['goog.dom.TagName'], ['goog.dom.HtmlElement'], {});
goog.addDependency('dom/tagname_test.js', ['goog.dom.TagNameTest'], ['goog.dom.TagName', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/tags.js', ['goog.dom.tags'], ['goog.object'], {});
goog.addDependency('dom/tags_test.js', ['goog.dom.tagsTest'], ['goog.dom.tags', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/textassert.js', ['goog.dom.textAssert'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName'], {});
goog.addDependency('dom/textassert_test.js', ['goog.dom.textassert_test'], ['goog.dom.textAssert', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/textrange.js', ['goog.dom.TextRange'], ['goog.array', 'goog.dom', 'goog.dom.AbstractRange', 'goog.dom.RangeType', 'goog.dom.SavedRange', 'goog.dom.TagName', 'goog.dom.TextRangeIterator', 'goog.dom.browserrange', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('dom/textrange_test.js', ['goog.dom.TextRangeTest'], ['goog.dom', 'goog.dom.ControlRange', 'goog.dom.Range', 'goog.dom.TextRange', 'goog.math.Coordinate', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/textrangeiterator.js', ['goog.dom.TextRangeIterator'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.RangeIterator', 'goog.dom.TagName', 'goog.iter.StopIteration'], {});
goog.addDependency('dom/textrangeiterator_test.js', ['goog.dom.TextRangeIteratorTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.TextRangeIterator', 'goog.iter.StopIteration', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/uri.js', ['goog.dom.uri'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.uncheckedconversions', 'goog.string.Const'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/uri_test.js', ['goog.dom.uriTest'], ['goog.dom.uri', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/vendor.js', ['goog.dom.vendor'], ['goog.string', 'goog.userAgent'], {});
goog.addDependency('dom/vendor_test.js', ['goog.dom.vendorTest'], ['goog.array', 'goog.dom.vendor', 'goog.labs.userAgent.util', 'goog.testing.MockUserAgent', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/viewportsizemonitor.js', ['goog.dom.ViewportSizeMonitor'], ['goog.dom', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.math.Size'], {});
goog.addDependency('dom/viewportsizemonitor_test.js', ['goog.dom.ViewportSizeMonitorTest'], ['goog.dom.ViewportSizeMonitor', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.math.Size', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/xml.js', ['goog.dom.xml'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.safe', 'goog.html.legacyconversions', 'goog.userAgent'], {});
goog.addDependency('dom/xml_test.js', ['goog.dom.xmlTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.xml', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/browserfeature.js', ['goog.editor.BrowserFeature'], ['goog.editor.defines', 'goog.labs.userAgent.browser', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('editor/browserfeature_test.js', ['goog.editor.BrowserFeatureTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/clicktoeditwrapper.js', ['goog.editor.ClickToEditWrapper'], ['goog.Disposable', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.range', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventType'], {});
goog.addDependency('editor/clicktoeditwrapper_test.js', ['goog.editor.ClickToEditWrapperTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.ClickToEditWrapper', 'goog.editor.SeamlessField', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/command.js', ['goog.editor.Command'], [], {});
goog.addDependency('editor/contenteditablefield.js', ['goog.editor.ContentEditableField'], ['goog.asserts', 'goog.editor.Field', 'goog.log'], {});
goog.addDependency('editor/contenteditablefield_test.js', ['goog.editor.ContentEditableFieldTest'], ['goog.dom', 'goog.editor.ContentEditableField', 'goog.html.SafeHtml', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/defines.js', ['goog.editor.defines'], [], {});
goog.addDependency('editor/field.js', ['goog.editor.Field', 'goog.editor.Field.EventType'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.array', 'goog.asserts', 'goog.async.Delay', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.safe', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.PluginImpl', 'goog.editor.icontent', 'goog.editor.icontent.FieldFormatInfo', 'goog.editor.icontent.FieldStyleInfo', 'goog.editor.node', 'goog.editor.range', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.log', 'goog.log.Level', 'goog.string', 'goog.string.Unicode', 'goog.style', 'goog.userAgent', 'goog.userAgent.product'], {});
goog.addDependency('editor/field_test.js', ['goog.editor.field_test'], ['goog.array', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.range', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.functions', 'goog.html.SafeHtml', 'goog.testing.LooseMock', 'goog.testing.MockClock', 'goog.testing.dom', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/focus.js', ['goog.editor.focus'], ['goog.dom.selection'], {});
goog.addDependency('editor/focus_test.js', ['goog.editor.focusTest'], ['goog.dom.selection', 'goog.editor.BrowserFeature', 'goog.editor.focus', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/icontent.js', ['goog.editor.icontent', 'goog.editor.icontent.FieldFormatInfo', 'goog.editor.icontent.FieldStyleInfo'], ['goog.dom', 'goog.editor.BrowserFeature', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('editor/icontent_test.js', ['goog.editor.icontentTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.icontent', 'goog.editor.icontent.FieldFormatInfo', 'goog.editor.icontent.FieldStyleInfo', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/link.js', ['goog.editor.Link'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.node', 'goog.editor.range', 'goog.string', 'goog.string.Unicode', 'goog.uri.utils', 'goog.uri.utils.ComponentIndex'], {});
goog.addDependency('editor/link_test.js', ['goog.editor.LinkTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Link', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/node.js', ['goog.editor.node'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.iter.ChildIterator', 'goog.dom.iter.SiblingIterator', 'goog.iter', 'goog.object', 'goog.string', 'goog.string.Unicode', 'goog.userAgent'], {});
goog.addDependency('editor/node_test.js', ['goog.editor.nodeTest'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.editor.node', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugin.js', ['goog.editor.Plugin'], ['goog.editor.Field', 'goog.editor.PluginImpl'], {});
goog.addDependency('editor/plugin_impl.js', ['goog.editor.PluginImpl'], ['goog.events.EventTarget', 'goog.functions', 'goog.log', 'goog.object', 'goog.reflect', 'goog.userAgent'], {});
goog.addDependency('editor/plugin_test.js', ['goog.editor.PluginTest'], ['goog.editor.Field', 'goog.editor.Plugin', 'goog.functions', 'goog.testing.StrictMock', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/abstractbubbleplugin.js', ['goog.editor.plugins.AbstractBubblePlugin'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.editor.Plugin', 'goog.editor.style', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.actionEventWrapper', 'goog.functions', 'goog.string.Unicode', 'goog.ui.Component', 'goog.ui.editor.Bubble', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/abstractbubbleplugin_test.js', ['goog.editor.plugins.AbstractBubblePluginTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.plugins.AbstractBubblePlugin', 'goog.events.BrowserEvent', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.functions', 'goog.style', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.editor.Bubble', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/abstractdialogplugin.js', ['goog.editor.plugins.AbstractDialogPlugin', 'goog.editor.plugins.AbstractDialogPlugin.EventType'], ['goog.dom', 'goog.dom.Range', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.range', 'goog.events', 'goog.ui.editor.AbstractDialog'], {});
goog.addDependency('editor/plugins/abstractdialogplugin_test.js', ['goog.editor.plugins.AbstractDialogPluginTest'], ['goog.dom', 'goog.dom.SavedRange', 'goog.dom.TagName', 'goog.editor.Field', 'goog.editor.plugins.AbstractDialogPlugin', 'goog.events.Event', 'goog.events.EventHandler', 'goog.functions', 'goog.html.SafeHtml', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.testSuite', 'goog.ui.editor.AbstractDialog', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/abstracttabhandler.js', ['goog.editor.plugins.AbstractTabHandler'], ['goog.editor.Plugin', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/abstracttabhandler_test.js', ['goog.editor.plugins.AbstractTabHandlerTest'], ['goog.editor.Field', 'goog.editor.plugins.AbstractTabHandler', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.testing.StrictMock', 'goog.testing.editor.FieldMock', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/basictextformatter.js', ['goog.editor.plugins.BasicTextFormatter', 'goog.editor.plugins.BasicTextFormatter.COMMAND'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Link', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.range', 'goog.editor.style', 'goog.iter', 'goog.iter.StopIteration', 'goog.log', 'goog.object', 'goog.string', 'goog.string.Unicode', 'goog.style', 'goog.ui.editor.messages', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/basictextformatter_test.js', ['goog.editor.plugins.BasicTextFormatterTest'], ['goog.array', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.plugins.BasicTextFormatter', 'goog.html.SafeHtml', 'goog.object', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.LooseMock', 'goog.testing.PropertyReplacer', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/blockquote.js', ['goog.editor.plugins.Blockquote'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Plugin', 'goog.editor.node', 'goog.functions', 'goog.log'], {});
goog.addDependency('editor/plugins/blockquote_test.js', ['goog.editor.plugins.BlockquoteTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.plugins.Blockquote', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/emoticons.js', ['goog.editor.plugins.Emoticons'], ['goog.dom.TagName', 'goog.editor.Plugin', 'goog.editor.range', 'goog.functions', 'goog.ui.emoji.Emoji', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/emoticons_test.js', ['goog.editor.plugins.EmoticonsTest'], ['goog.Uri', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.editor.Field', 'goog.editor.plugins.Emoticons', 'goog.testing.testSuite', 'goog.ui.emoji.Emoji', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/enterhandler.js', ['goog.editor.plugins.EnterHandler'], ['goog.dom', 'goog.dom.NodeOffset', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.plugins.Blockquote', 'goog.editor.range', 'goog.editor.style', 'goog.events.KeyCodes', 'goog.functions', 'goog.object', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/enterhandler_test.js', ['goog.editor.plugins.EnterHandlerTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.plugins.Blockquote', 'goog.editor.plugins.EnterHandler', 'goog.editor.range', 'goog.events', 'goog.events.KeyCodes', 'goog.html.testing', 'goog.testing.ExpectedFailures', 'goog.testing.MockClock', 'goog.testing.dom', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/firststrong.js', ['goog.editor.plugins.FirstStrong'], ['goog.dom.NodeType', 'goog.dom.TagIterator', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.range', 'goog.i18n.bidi', 'goog.i18n.uChar', 'goog.iter', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/firststrong_test.js', ['goog.editor.plugins.FirstStrongTest'], ['goog.dom.Range', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.plugins.FirstStrong', 'goog.editor.range', 'goog.events.KeyCodes', 'goog.html.testing', 'goog.testing.MockClock', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/headerformatter.js', ['goog.editor.plugins.HeaderFormatter'], ['goog.editor.Command', 'goog.editor.Plugin', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/headerformatter_test.js', ['goog.editor.plugins.HeaderFormatterTest'], ['goog.dom', 'goog.editor.Command', 'goog.editor.plugins.BasicTextFormatter', 'goog.editor.plugins.HeaderFormatter', 'goog.events.BrowserEvent', 'goog.testing.LooseMock', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/linkbubble.js', ['goog.editor.plugins.LinkBubble', 'goog.editor.plugins.LinkBubble.Action'], ['goog.array', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.Link', 'goog.editor.plugins.AbstractBubblePlugin', 'goog.functions', 'goog.string', 'goog.style', 'goog.ui.editor.messages', 'goog.uri.utils', 'goog.window'], {});
goog.addDependency('editor/plugins/linkbubble_test.js', ['goog.editor.plugins.LinkBubbleTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.Link', 'goog.editor.plugins.LinkBubble', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.string', 'goog.style', 'goog.testing.FunctionMock', 'goog.testing.PropertyReplacer', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/linkdialogplugin.js', ['goog.editor.plugins.LinkDialogPlugin'], ['goog.array', 'goog.dom', 'goog.editor.Command', 'goog.editor.plugins.AbstractDialogPlugin', 'goog.events.EventHandler', 'goog.functions', 'goog.ui.editor.AbstractDialog', 'goog.ui.editor.LinkDialog', 'goog.uri.utils'], {});
goog.addDependency('editor/plugins/linkdialogplugin_test.js', ['goog.ui.editor.plugins.LinkDialogTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Link', 'goog.editor.plugins.LinkDialogPlugin', 'goog.html.SafeHtml', 'goog.string', 'goog.string.Unicode', 'goog.testing.MockControl', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.editor.dom', 'goog.testing.events', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.ui.editor.AbstractDialog', 'goog.ui.editor.LinkDialog', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/linkshortcutplugin.js', ['goog.editor.plugins.LinkShortcutPlugin'], ['goog.editor.Command', 'goog.editor.Plugin'], {});
goog.addDependency('editor/plugins/linkshortcutplugin_test.js', ['goog.editor.plugins.LinkShortcutPluginTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.Field', 'goog.editor.plugins.BasicTextFormatter', 'goog.editor.plugins.LinkBubble', 'goog.editor.plugins.LinkShortcutPlugin', 'goog.events.KeyCodes', 'goog.testing.PropertyReplacer', 'goog.testing.dom', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/listtabhandler.js', ['goog.editor.plugins.ListTabHandler'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.plugins.AbstractTabHandler', 'goog.iter'], {});
goog.addDependency('editor/plugins/listtabhandler_test.js', ['goog.editor.plugins.ListTabHandlerTest'], ['goog.dom', 'goog.editor.Command', 'goog.editor.plugins.ListTabHandler', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.functions', 'goog.testing.StrictMock', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/loremipsum.js', ['goog.editor.plugins.LoremIpsum'], ['goog.asserts', 'goog.dom', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.node', 'goog.functions', 'goog.html.SafeHtml', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/loremipsum_test.js', ['goog.editor.plugins.LoremIpsumTest'], ['goog.dom', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.plugins.LoremIpsum', 'goog.html.SafeHtml', 'goog.string.Unicode', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/removeformatting.js', ['goog.editor.plugins.RemoveFormatting'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.range', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/removeformatting_test.js', ['goog.editor.plugins.RemoveFormattingTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.plugins.RemoveFormatting', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.dom', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/spacestabhandler.js', ['goog.editor.plugins.SpacesTabHandler'], ['goog.dom.TagName', 'goog.editor.plugins.AbstractTabHandler', 'goog.editor.range'], {});
goog.addDependency('editor/plugins/spacestabhandler_test.js', ['goog.editor.plugins.SpacesTabHandlerTest'], ['goog.dom', 'goog.dom.Range', 'goog.editor.plugins.SpacesTabHandler', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.functions', 'goog.testing.StrictMock', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/tableeditor.js', ['goog.editor.plugins.TableEditor'], ['goog.array', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Plugin', 'goog.editor.Table', 'goog.editor.node', 'goog.editor.range', 'goog.object', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/tableeditor_test.js', ['goog.editor.plugins.TableEditorTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.plugins.TableEditor', 'goog.object', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.TestCase', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/tagonenterhandler.js', ['goog.editor.plugins.TagOnEnterHandler'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.node', 'goog.editor.plugins.EnterHandler', 'goog.editor.range', 'goog.editor.style', 'goog.events.KeyCodes', 'goog.functions', 'goog.string.Unicode', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/tagonenterhandler_test.js', ['goog.editor.plugins.TagOnEnterHandlerTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.plugins.TagOnEnterHandler', 'goog.events.KeyCodes', 'goog.html.SafeHtml', 'goog.string.Unicode', 'goog.testing.dom', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/undoredo.js', ['goog.editor.plugins.UndoRedo'], ['goog.dom', 'goog.dom.NodeOffset', 'goog.dom.Range', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.plugins.UndoRedoManager', 'goog.editor.plugins.UndoRedoState', 'goog.events', 'goog.events.EventHandler', 'goog.log', 'goog.object'], {});
goog.addDependency('editor/plugins/undoredo_test.js', ['goog.editor.plugins.UndoRedoTest'], ['goog.array', 'goog.dom', 'goog.dom.browserrange', 'goog.editor.Field', 'goog.editor.plugins.LoremIpsum', 'goog.editor.plugins.UndoRedo', 'goog.events', 'goog.functions', 'goog.html.SafeHtml', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/undoredomanager.js', ['goog.editor.plugins.UndoRedoManager', 'goog.editor.plugins.UndoRedoManager.EventType'], ['goog.editor.plugins.UndoRedoState', 'goog.events', 'goog.events.EventTarget'], {});
goog.addDependency('editor/plugins/undoredomanager_test.js', ['goog.editor.plugins.UndoRedoManagerTest'], ['goog.editor.plugins.UndoRedoManager', 'goog.editor.plugins.UndoRedoState', 'goog.events', 'goog.testing.StrictMock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/undoredostate.js', ['goog.editor.plugins.UndoRedoState'], ['goog.events.EventTarget'], {});
goog.addDependency('editor/plugins/undoredostate_test.js', ['goog.editor.plugins.UndoRedoStateTest'], ['goog.editor.plugins.UndoRedoState', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/range.js', ['goog.editor.range', 'goog.editor.range.Point'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.RangeEndpoint', 'goog.dom.SavedCaretRange', 'goog.editor.node', 'goog.editor.style', 'goog.iter', 'goog.userAgent'], {});
goog.addDependency('editor/range_test.js', ['goog.editor.rangeTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.range', 'goog.editor.range.Point', 'goog.string', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/seamlessfield.js', ['goog.editor.SeamlessField'], ['goog.cssom.iframe.style', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.dom.safe', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.icontent', 'goog.editor.icontent.FieldFormatInfo', 'goog.editor.icontent.FieldStyleInfo', 'goog.editor.node', 'goog.events', 'goog.events.EventType', 'goog.html.SafeHtml', 'goog.log', 'goog.style'], {});
goog.addDependency('editor/seamlessfield_test.js', ['goog.editor.seamlessfield_test'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.SeamlessField', 'goog.events', 'goog.functions', 'goog.html.SafeHtml', 'goog.style', 'goog.testing.MockClock', 'goog.testing.MockRange', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/style.js', ['goog.editor.style'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.object', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('editor/style_test.js', ['goog.editor.styleTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.style', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.style', 'goog.testing.LooseMock', 'goog.testing.mockmatchers', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/table.js', ['goog.editor.Table', 'goog.editor.TableCell', 'goog.editor.TableRow'], ['goog.asserts', 'goog.dom', 'goog.dom.DomHelper', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.log', 'goog.string.Unicode', 'goog.style'], {});
goog.addDependency('editor/table_test.js', ['goog.editor.TableTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.Table', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/actioneventwrapper.js', ['goog.events.actionEventWrapper'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.dom', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.EventWrapper', 'goog.events.KeyCodes'], {});
goog.addDependency('events/actioneventwrapper_test.js', ['goog.events.actionEventWrapperTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.events', 'goog.events.EventHandler', 'goog.events.KeyCodes', 'goog.events.actionEventWrapper', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/actionhandler.js', ['goog.events.ActionEvent', 'goog.events.ActionHandler', 'goog.events.ActionHandler.EventType', 'goog.events.BeforeActionEvent'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('events/actionhandler_test.js', ['goog.events.ActionHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.ActionHandler', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/browserevent.js', ['goog.events.BrowserEvent', 'goog.events.BrowserEvent.MouseButton', 'goog.events.BrowserEvent.PointerType'], ['goog.debug', 'goog.events.BrowserFeature', 'goog.events.Event', 'goog.events.EventType', 'goog.reflect', 'goog.userAgent'], {});
goog.addDependency('events/browserevent_test.js', ['goog.events.BrowserEventTest'], ['goog.events.BrowserEvent', 'goog.events.BrowserFeature', 'goog.math.Coordinate', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/browserfeature.js', ['goog.events.BrowserFeature'], ['goog.userAgent'], {});
goog.addDependency('events/event.js', ['goog.events.Event', 'goog.events.EventLike'], ['goog.Disposable', 'goog.events.EventId'], {});
goog.addDependency('events/event_test.js', ['goog.events.EventTest'], ['goog.events.Event', 'goog.events.EventId', 'goog.events.EventTarget', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventhandler.js', ['goog.events.EventHandler'], ['goog.Disposable', 'goog.events', 'goog.object'], {});
goog.addDependency('events/eventhandler_test.js', ['goog.events.EventHandlerTest'], ['goog.events', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventid.js', ['goog.events.EventId'], [], {});
goog.addDependency('events/events.js', ['goog.events', 'goog.events.CaptureSimulationMode', 'goog.events.Key', 'goog.events.ListenableType'], ['goog.asserts', 'goog.debug.entryPointRegistry', 'goog.events.BrowserEvent', 'goog.events.BrowserFeature', 'goog.events.Listenable', 'goog.events.ListenerMap'], {});
goog.addDependency('events/events_test.js', ['goog.eventsTest'], ['goog.asserts.AssertionError', 'goog.debug.EntryPointMonitor', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.BrowserFeature', 'goog.events.CaptureSimulationMode', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.Listener', 'goog.functions', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtarget.js', ['goog.events.EventTarget'], ['goog.Disposable', 'goog.asserts', 'goog.events', 'goog.events.Event', 'goog.events.Listenable', 'goog.events.ListenerMap', 'goog.object'], {});
goog.addDependency('events/eventtarget_test.js', ['goog.events.EventTargetTest'], ['goog.events.EventTarget', 'goog.events.Listenable', 'goog.events.eventTargetTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtarget_via_googevents_test.js', ['goog.events.EventTargetGoogEventsTest'], ['goog.events', 'goog.events.EventTarget', 'goog.events.eventTargetTester', 'goog.testing', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtarget_via_w3cinterface_test.js', ['goog.events.EventTargetW3CTest'], ['goog.events.EventTarget', 'goog.events.eventTargetTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtargettester.js', ['goog.events.eventTargetTester'], ['goog.array', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.Listenable', 'goog.testing.asserts', 'goog.testing.recordFunction'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtype.js', ['goog.events.EventType', 'goog.events.MouseAsMouseEventType', 'goog.events.MouseEvents', 'goog.events.PointerAsMouseEventType', 'goog.events.PointerAsTouchEventType', 'goog.events.PointerFallbackEventType', 'goog.events.PointerTouchFallbackEventType'], ['goog.events.BrowserFeature', 'goog.userAgent'], {});
goog.addDependency('events/eventtype_test.js', ['goog.events.EventTypeTest'], ['goog.events.BrowserFeature', 'goog.events.EventType', 'goog.events.PointerFallbackEventType', 'goog.events.PointerTouchFallbackEventType', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventwrapper.js', ['goog.events.EventWrapper'], [], {});
goog.addDependency('events/filedrophandler.js', ['goog.events.FileDropHandler', 'goog.events.FileDropHandler.EventType'], ['goog.array', 'goog.dom', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.log', 'goog.log.Level'], {});
goog.addDependency('events/filedrophandler_test.js', ['goog.events.FileDropHandlerTest'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.FileDropHandler', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/focushandler.js', ['goog.events.FocusHandler', 'goog.events.FocusHandler.EventType'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.userAgent'], {});
goog.addDependency('events/imehandler.js', ['goog.events.ImeHandler', 'goog.events.ImeHandler.Event', 'goog.events.ImeHandler.EventType'], ['goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('events/imehandler_test.js', ['goog.events.ImeHandlerTest'], ['goog.array', 'goog.dom', 'goog.events', 'goog.events.ImeHandler', 'goog.events.KeyCodes', 'goog.object', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/inputhandler.js', ['goog.events.InputHandler', 'goog.events.InputHandler.EventType'], ['goog.Timer', 'goog.dom.TagName', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('events/inputhandler_test.js', ['goog.events.InputHandlerTest'], ['goog.dom', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/keycodes.js', ['goog.events.KeyCodes'], ['goog.userAgent'], {});
goog.addDependency('events/keycodes_test.js', ['goog.events.KeyCodesTest'], ['goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.object', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/keyhandler.js', ['goog.events.KeyEvent', 'goog.events.KeyHandler', 'goog.events.KeyHandler.EventType'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('events/keyhandler_test.js', ['goog.events.KeyEventTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/keynames.js', ['goog.events.KeyNames'], [], {});
goog.addDependency('events/keys.js', ['goog.events.Keys'], [], {'lang': 'es5'});
goog.addDependency('events/listenable.js', ['goog.events.Listenable', 'goog.events.ListenableKey'], ['goog.events.EventId'], {});
goog.addDependency('events/listenable_test.js', ['goog.events.ListenableTest'], ['goog.events.Listenable', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/listener.js', ['goog.events.Listener'], ['goog.events.ListenableKey'], {});
goog.addDependency('events/listenermap.js', ['goog.events.ListenerMap'], ['goog.array', 'goog.events.Listener', 'goog.object'], {});
goog.addDependency('events/listenermap_test.js', ['goog.events.ListenerMapTest'], ['goog.dispose', 'goog.events', 'goog.events.EventId', 'goog.events.EventTarget', 'goog.events.ListenerMap', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/mousewheelhandler.js', ['goog.events.MouseWheelEvent', 'goog.events.MouseWheelHandler', 'goog.events.MouseWheelHandler.EventType'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.math', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('events/mousewheelhandler_test.js', ['goog.events.MouseWheelHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.MouseWheelEvent', 'goog.events.MouseWheelHandler', 'goog.functions', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/onlinehandler.js', ['goog.events.OnlineHandler', 'goog.events.OnlineHandler.EventType'], ['goog.Timer', 'goog.events.BrowserFeature', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.net.NetworkStatusMonitor'], {});
goog.addDependency('events/onlinelistener_test.js', ['goog.events.OnlineHandlerTest'], ['goog.events', 'goog.events.BrowserFeature', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.OnlineHandler', 'goog.net.NetworkStatusMonitor', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/pastehandler.js', ['goog.events.PasteHandler', 'goog.events.PasteHandler.EventType', 'goog.events.PasteHandler.State'], ['goog.Timer', 'goog.async.ConditionalDelay', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.log', 'goog.userAgent'], {});
goog.addDependency('events/pastehandler_test.js', ['goog.events.PasteHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.PasteHandler', 'goog.testing.MockClock', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/wheelevent.js', ['goog.events.WheelEvent'], ['goog.asserts', 'goog.events.BrowserEvent'], {});
goog.addDependency('events/wheelhandler.js', ['goog.events.WheelHandler'], ['goog.dom', 'goog.events', 'goog.events.EventTarget', 'goog.events.WheelEvent', 'goog.style', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('events/wheelhandler_test.js', ['goog.events.WheelHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.WheelEvent', 'goog.events.WheelHandler', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/emailaddress.js', ['goog.format.EmailAddress'], ['goog.string'], {});
goog.addDependency('format/emailaddress_test.js', ['goog.format.EmailAddressTest'], ['goog.array', 'goog.format.EmailAddress', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/format.js', ['goog.format'], ['goog.i18n.GraphemeBreak', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('format/format_test.js', ['goog.formatTest'], ['goog.dom', 'goog.dom.TagName', 'goog.format', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/htmlprettyprinter.js', ['goog.format.HtmlPrettyPrinter', 'goog.format.HtmlPrettyPrinter.Buffer'], ['goog.dom.TagName', 'goog.object', 'goog.string.StringBuffer'], {});
goog.addDependency('format/htmlprettyprinter_test.js', ['goog.format.HtmlPrettyPrinterTest'], ['goog.format.HtmlPrettyPrinter', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/internationalizedemailaddress.js', ['goog.format.InternationalizedEmailAddress'], ['goog.format.EmailAddress', 'goog.string'], {});
goog.addDependency('format/internationalizedemailaddress_test.js', ['goog.format.InternationalizedEmailAddressTest'], ['goog.array', 'goog.format.InternationalizedEmailAddress', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/jsonprettyprinter.js', ['goog.format.JsonPrettyPrinter', 'goog.format.JsonPrettyPrinter.SafeHtmlDelimiters', 'goog.format.JsonPrettyPrinter.TextDelimiters'], ['goog.html.SafeHtml', 'goog.json', 'goog.json.Serializer', 'goog.string', 'goog.string.format'], {});
goog.addDependency('format/jsonprettyprinter_test.js', ['goog.format.JsonPrettyPrinterTest'], ['goog.format.JsonPrettyPrinter', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fs/entry.js', ['goog.fs.DirectoryEntry', 'goog.fs.DirectoryEntry.Behavior', 'goog.fs.Entry', 'goog.fs.FileEntry'], [], {});
goog.addDependency('fs/entryimpl.js', ['goog.fs.DirectoryEntryImpl', 'goog.fs.EntryImpl', 'goog.fs.FileEntryImpl'], ['goog.array', 'goog.async.Deferred', 'goog.fs.DirectoryEntry', 'goog.fs.Entry', 'goog.fs.Error', 'goog.fs.FileEntry', 'goog.fs.FileWriter', 'goog.functions', 'goog.string'], {});
goog.addDependency('fs/error.js', ['goog.fs.DOMErrorLike', 'goog.fs.Error', 'goog.fs.Error.ErrorCode'], ['goog.asserts', 'goog.debug.Error', 'goog.object', 'goog.string'], {});
goog.addDependency('fs/filereader.js', ['goog.fs.FileReader', 'goog.fs.FileReader.EventType', 'goog.fs.FileReader.ReadyState'], ['goog.async.Deferred', 'goog.events.EventTarget', 'goog.fs.Error', 'goog.fs.ProgressEvent'], {});
goog.addDependency('fs/filesaver.js', ['goog.fs.FileSaver', 'goog.fs.FileSaver.EventType', 'goog.fs.FileSaver.ReadyState'], ['goog.events.EventTarget', 'goog.fs.Error', 'goog.fs.ProgressEvent'], {});
goog.addDependency('fs/filesystem.js', ['goog.fs.FileSystem'], [], {});
goog.addDependency('fs/filesystemimpl.js', ['goog.fs.FileSystemImpl'], ['goog.fs.DirectoryEntryImpl', 'goog.fs.FileSystem'], {});
goog.addDependency('fs/filewriter.js', ['goog.fs.FileWriter'], ['goog.fs.Error', 'goog.fs.FileSaver'], {});
goog.addDependency('fs/fs.js', ['goog.fs'], ['goog.array', 'goog.async.Deferred', 'goog.fs.Error', 'goog.fs.FileReader', 'goog.fs.FileSystemImpl', 'goog.fs.url', 'goog.userAgent'], {});
goog.addDependency('fs/fs_test.js', ['goog.fsTest'], ['goog.Promise', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.fs', 'goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.fs.FileReader', 'goog.fs.FileSaver', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fs/progressevent.js', ['goog.fs.ProgressEvent'], ['goog.events.Event'], {});
goog.addDependency('fs/url.js', ['goog.fs.url'], [], {'lang': 'es6'});
goog.addDependency('fs/url_test.js', ['goog.urlTest'], ['goog.fs.url', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('functions/functions.js', ['goog.functions'], [], {'lang': 'es6'});
goog.addDependency('functions/functions_test.js', ['goog.functionsTest'], ['goog.array', 'goog.functions', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/abstractdragdrop.js', ['goog.fx.AbstractDragDrop', 'goog.fx.AbstractDragDrop.EventType', 'goog.fx.DragDropEvent', 'goog.fx.DragDropItem'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.fx.Dragger', 'goog.math.Box', 'goog.math.Coordinate', 'goog.style'], {});
goog.addDependency('fx/abstractdragdrop_test.js', ['goog.fx.AbstractDragDropTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.functions', 'goog.fx.AbstractDragDrop', 'goog.fx.DragDropItem', 'goog.math.Box', 'goog.math.Coordinate', 'goog.style', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('fx/anim/anim.js', ['goog.fx.anim', 'goog.fx.anim.Animated'], ['goog.async.AnimationDelay', 'goog.async.Delay', 'goog.object'], {});
goog.addDependency('fx/anim/anim_test.js', ['goog.fx.animTest'], ['goog.async.AnimationDelay', 'goog.async.Delay', 'goog.events', 'goog.functions', 'goog.fx.Animation', 'goog.fx.anim', 'goog.object', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/animation.js', ['goog.fx.Animation', 'goog.fx.Animation.EventType', 'goog.fx.Animation.State', 'goog.fx.AnimationEvent'], ['goog.array', 'goog.asserts', 'goog.events.Event', 'goog.fx.Transition', 'goog.fx.TransitionBase', 'goog.fx.anim', 'goog.fx.anim.Animated'], {});
goog.addDependency('fx/animation_test.js', ['goog.fx.AnimationTest'], ['goog.events', 'goog.fx.Animation', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/animationqueue.js', ['goog.fx.AnimationParallelQueue', 'goog.fx.AnimationQueue', 'goog.fx.AnimationSerialQueue'], ['goog.array', 'goog.asserts', 'goog.events', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.fx.TransitionBase'], {});
goog.addDependency('fx/animationqueue_test.js', ['goog.fx.AnimationQueueTest'], ['goog.events', 'goog.fx.Animation', 'goog.fx.AnimationParallelQueue', 'goog.fx.AnimationSerialQueue', 'goog.fx.Transition', 'goog.fx.anim', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/css3/fx.js', ['goog.fx.css3'], ['goog.fx.css3.Transition'], {});
goog.addDependency('fx/css3/transition.js', ['goog.fx.css3.Transition'], ['goog.Timer', 'goog.asserts', 'goog.fx.TransitionBase', 'goog.style', 'goog.style.transition'], {});
goog.addDependency('fx/css3/transition_test.js', ['goog.fx.css3.TransitionTest'], ['goog.dispose', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.fx.Transition', 'goog.fx.css3.Transition', 'goog.style.transition', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/cssspriteanimation.js', ['goog.fx.CssSpriteAnimation'], ['goog.fx.Animation'], {});
goog.addDependency('fx/cssspriteanimation_test.js', ['goog.fx.CssSpriteAnimationTest'], ['goog.fx.CssSpriteAnimation', 'goog.math.Box', 'goog.math.Size', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/dom.js', ['goog.fx.dom', 'goog.fx.dom.BgColorTransform', 'goog.fx.dom.ColorTransform', 'goog.fx.dom.Fade', 'goog.fx.dom.FadeIn', 'goog.fx.dom.FadeInAndShow', 'goog.fx.dom.FadeOut', 'goog.fx.dom.FadeOutAndHide', 'goog.fx.dom.PredefinedEffect', 'goog.fx.dom.Resize', 'goog.fx.dom.ResizeHeight', 'goog.fx.dom.ResizeWidth', 'goog.fx.dom.Scroll', 'goog.fx.dom.Slide', 'goog.fx.dom.SlideFrom', 'goog.fx.dom.Swipe'], ['goog.color', 'goog.events', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.style', 'goog.style.bidi'], {});
goog.addDependency('fx/dragdrop.js', ['goog.fx.DragDrop'], ['goog.fx.AbstractDragDrop', 'goog.fx.DragDropItem'], {});
goog.addDependency('fx/dragdropgroup.js', ['goog.fx.DragDropGroup'], ['goog.dom', 'goog.fx.AbstractDragDrop', 'goog.fx.DragDropItem'], {});
goog.addDependency('fx/dragdropgroup_test.js', ['goog.fx.DragDropGroupTest'], ['goog.events', 'goog.fx.DragDropGroup', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/dragger.js', ['goog.fx.DragEvent', 'goog.fx.Dragger', 'goog.fx.Dragger.EventType'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.style', 'goog.style.bidi', 'goog.userAgent'], {});
goog.addDependency('fx/dragger_test.js', ['goog.fx.DraggerTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventType', 'goog.fx.Dragger', 'goog.math.Rect', 'goog.style.bidi', 'goog.testing.StrictMock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/draglistgroup.js', ['goog.fx.DragListDirection', 'goog.fx.DragListGroup', 'goog.fx.DragListGroup.EventType', 'goog.fx.DragListGroupEvent', 'goog.fx.DragListPermission'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventId', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.fx.Dragger', 'goog.math.Coordinate', 'goog.string', 'goog.style'], {});
goog.addDependency('fx/draglistgroup_test.js', ['goog.fx.DragListGroupTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.BrowserFeature', 'goog.events.Event', 'goog.events.EventType', 'goog.fx.DragEvent', 'goog.fx.DragListDirection', 'goog.fx.DragListGroup', 'goog.fx.DragListPermission', 'goog.fx.Dragger', 'goog.math.Coordinate', 'goog.object', 'goog.style', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/dragscrollsupport.js', ['goog.fx.DragScrollSupport'], ['goog.Disposable', 'goog.Timer', 'goog.dom', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.math.Coordinate', 'goog.style'], {});
goog.addDependency('fx/dragscrollsupport_test.js', ['goog.fx.DragScrollSupportTest'], ['goog.fx.DragScrollSupport', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/easing.js', ['goog.fx.easing'], [], {});
goog.addDependency('fx/easing_test.js', ['goog.fx.easingTest'], ['goog.fx.easing', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/fx.js', ['goog.fx'], ['goog.asserts', 'goog.fx.Animation', 'goog.fx.Animation.EventType', 'goog.fx.Animation.State', 'goog.fx.AnimationEvent', 'goog.fx.Transition.EventType', 'goog.fx.easing'], {});
goog.addDependency('fx/fx_test.js', ['goog.fxTest'], ['goog.fx.Animation', 'goog.object', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/transition.js', ['goog.fx.Transition', 'goog.fx.Transition.EventType'], [], {});
goog.addDependency('fx/transitionbase.js', ['goog.fx.TransitionBase', 'goog.fx.TransitionBase.State'], ['goog.events.EventTarget', 'goog.fx.Transition'], {});
goog.addDependency('goog.js', [], [], {'lang': 'es6', 'module': 'es6'});
goog.addDependency('graphics/abstractgraphics.js', ['goog.graphics.AbstractGraphics'], ['goog.dom', 'goog.graphics.AffineTransform', 'goog.graphics.Element', 'goog.graphics.EllipseElement', 'goog.graphics.Fill', 'goog.graphics.Font', 'goog.graphics.GroupElement', 'goog.graphics.Path', 'goog.graphics.PathElement', 'goog.graphics.RectElement', 'goog.graphics.Stroke', 'goog.graphics.StrokeAndFillElement', 'goog.graphics.TextElement', 'goog.math.Coordinate', 'goog.math.Size', 'goog.style', 'goog.ui.Component'], {});
goog.addDependency('graphics/affinetransform.js', ['goog.graphics.AffineTransform'], [], {'lang': 'es6'});
goog.addDependency('graphics/affinetransform_test.js', ['goog.graphics.AffineTransformTest'], ['goog.array', 'goog.graphics', 'goog.graphics.AffineTransform', 'goog.math', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/canvaselement.js', ['goog.graphics.CanvasEllipseElement', 'goog.graphics.CanvasGroupElement', 'goog.graphics.CanvasImageElement', 'goog.graphics.CanvasPathElement', 'goog.graphics.CanvasRectElement', 'goog.graphics.CanvasTextElement'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.graphics.EllipseElement', 'goog.graphics.Font', 'goog.graphics.GroupElement', 'goog.graphics.ImageElement', 'goog.graphics.Path', 'goog.graphics.PathElement', 'goog.graphics.RectElement', 'goog.graphics.TextElement', 'goog.html.SafeHtml', 'goog.html.uncheckedconversions', 'goog.math', 'goog.string', 'goog.string.Const'], {});
goog.addDependency('graphics/canvasgraphics.js', ['goog.graphics.CanvasGraphics'], ['goog.dom.TagName', 'goog.events.EventType', 'goog.graphics.AbstractGraphics', 'goog.graphics.CanvasEllipseElement', 'goog.graphics.CanvasGroupElement', 'goog.graphics.CanvasImageElement', 'goog.graphics.CanvasPathElement', 'goog.graphics.CanvasRectElement', 'goog.graphics.CanvasTextElement', 'goog.graphics.Font', 'goog.graphics.SolidFill', 'goog.math.Size', 'goog.style'], {});
goog.addDependency('graphics/canvasgraphics_test.js', ['goog.graphics.CanvasGraphicsTest'], ['goog.dom', 'goog.graphics.CanvasGraphics', 'goog.graphics.SolidFill', 'goog.graphics.Stroke', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/element.js', ['goog.graphics.Element'], ['goog.asserts', 'goog.events', 'goog.events.EventTarget', 'goog.events.Listenable', 'goog.graphics.AffineTransform', 'goog.math'], {});
goog.addDependency('graphics/ellipseelement.js', ['goog.graphics.EllipseElement'], ['goog.graphics.StrokeAndFillElement'], {});
goog.addDependency('graphics/ext/coordinates.js', ['goog.graphics.ext.coordinates'], ['goog.string'], {});
goog.addDependency('graphics/ext/coordinates_test.js', ['goog.graphics.ext.coordinatesTest'], ['goog.graphics', 'goog.graphics.ext.coordinates', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/ext/element.js', ['goog.graphics.ext.Element'], ['goog.events.EventTarget', 'goog.functions', 'goog.graphics.ext.coordinates'], {});
goog.addDependency('graphics/ext/element_test.js', ['goog.graphics.ext.ElementTest'], ['goog.graphics', 'goog.graphics.ext', 'goog.testing.StrictMock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/ext/ellipse.js', ['goog.graphics.ext.Ellipse'], ['goog.graphics.ext.StrokeAndFillElement'], {});
goog.addDependency('graphics/ext/ext.js', ['goog.graphics.ext'], ['goog.graphics.ext.Ellipse', 'goog.graphics.ext.Graphics', 'goog.graphics.ext.Group', 'goog.graphics.ext.Image', 'goog.graphics.ext.Rectangle', 'goog.graphics.ext.Shape', 'goog.graphics.ext.coordinates'], {});
goog.addDependency('graphics/ext/graphics.js', ['goog.graphics.ext.Graphics'], ['goog.events', 'goog.events.EventType', 'goog.graphics', 'goog.graphics.ext.Group'], {});
goog.addDependency('graphics/ext/group.js', ['goog.graphics.ext.Group'], ['goog.array', 'goog.graphics.ext.Element'], {});
goog.addDependency('graphics/ext/image.js', ['goog.graphics.ext.Image'], ['goog.graphics.ext.Element'], {});
goog.addDependency('graphics/ext/path.js', ['goog.graphics.ext.Path'], ['goog.graphics.AffineTransform', 'goog.graphics.Path', 'goog.math.Rect'], {});
goog.addDependency('graphics/ext/path_test.js', ['goog.graphics.ext.PathTest'], ['goog.graphics', 'goog.graphics.ext.Path', 'goog.math.Rect', 'goog.testing.graphics', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/ext/rectangle.js', ['goog.graphics.ext.Rectangle'], ['goog.graphics.ext.StrokeAndFillElement'], {});
goog.addDependency('graphics/ext/shape.js', ['goog.graphics.ext.Shape'], ['goog.graphics.ext.StrokeAndFillElement'], {});
goog.addDependency('graphics/ext/strokeandfillelement.js', ['goog.graphics.ext.StrokeAndFillElement'], ['goog.graphics.ext.Element'], {});
goog.addDependency('graphics/fill.js', ['goog.graphics.Fill'], [], {});
goog.addDependency('graphics/font.js', ['goog.graphics.Font'], [], {});
goog.addDependency('graphics/graphics.js', ['goog.graphics'], ['goog.dom', 'goog.graphics.CanvasGraphics', 'goog.graphics.SvgGraphics', 'goog.graphics.VmlGraphics', 'goog.userAgent'], {});
goog.addDependency('graphics/groupelement.js', ['goog.graphics.GroupElement'], ['goog.graphics.Element'], {});
goog.addDependency('graphics/imageelement.js', ['goog.graphics.ImageElement'], ['goog.graphics.Element'], {});
goog.addDependency('graphics/lineargradient.js', ['goog.graphics.LinearGradient'], ['goog.asserts', 'goog.graphics.Fill'], {});
goog.addDependency('graphics/path.js', ['goog.graphics.Path', 'goog.graphics.Path.Segment'], ['goog.array', 'goog.graphics.AffineTransform', 'goog.math'], {});
goog.addDependency('graphics/path_test.js', ['goog.graphics.PathTest'], ['goog.array', 'goog.graphics.AffineTransform', 'goog.graphics.Path', 'goog.testing.graphics', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/pathelement.js', ['goog.graphics.PathElement'], ['goog.graphics.StrokeAndFillElement'], {});
goog.addDependency('graphics/paths.js', ['goog.graphics.paths'], ['goog.graphics.Path', 'goog.math.Coordinate'], {});
goog.addDependency('graphics/paths_test.js', ['goog.graphics.pathsTest'], ['goog.dom', 'goog.graphics', 'goog.graphics.paths', 'goog.math.Coordinate', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/rectelement.js', ['goog.graphics.RectElement'], ['goog.graphics.StrokeAndFillElement'], {});
goog.addDependency('graphics/solidfill.js', ['goog.graphics.SolidFill'], ['goog.graphics.Fill'], {});
goog.addDependency('graphics/solidfill_test.js', ['goog.graphics.SolidFillTest'], ['goog.graphics.SolidFill', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/stroke.js', ['goog.graphics.Stroke'], [], {});
goog.addDependency('graphics/strokeandfillelement.js', ['goog.graphics.StrokeAndFillElement'], ['goog.graphics.Element'], {});
goog.addDependency('graphics/svgelement.js', ['goog.graphics.SvgEllipseElement', 'goog.graphics.SvgGroupElement', 'goog.graphics.SvgImageElement', 'goog.graphics.SvgPathElement', 'goog.graphics.SvgRectElement', 'goog.graphics.SvgTextElement'], ['goog.dom', 'goog.graphics.EllipseElement', 'goog.graphics.GroupElement', 'goog.graphics.ImageElement', 'goog.graphics.PathElement', 'goog.graphics.RectElement', 'goog.graphics.TextElement'], {});
goog.addDependency('graphics/svggraphics.js', ['goog.graphics.SvgGraphics'], ['goog.Timer', 'goog.dom', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.graphics.AbstractGraphics', 'goog.graphics.Font', 'goog.graphics.LinearGradient', 'goog.graphics.Path', 'goog.graphics.SolidFill', 'goog.graphics.Stroke', 'goog.graphics.SvgEllipseElement', 'goog.graphics.SvgGroupElement', 'goog.graphics.SvgImageElement', 'goog.graphics.SvgPathElement', 'goog.graphics.SvgRectElement', 'goog.graphics.SvgTextElement', 'goog.math', 'goog.math.Size', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('graphics/svggraphics_test.js', ['goog.graphics.SvgGraphicsTest'], ['goog.dom', 'goog.graphics.AffineTransform', 'goog.graphics.SolidFill', 'goog.graphics.SvgGraphics', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/textelement.js', ['goog.graphics.TextElement'], ['goog.graphics.StrokeAndFillElement'], {});
goog.addDependency('graphics/vmlelement.js', ['goog.graphics.VmlEllipseElement', 'goog.graphics.VmlGroupElement', 'goog.graphics.VmlImageElement', 'goog.graphics.VmlPathElement', 'goog.graphics.VmlRectElement', 'goog.graphics.VmlTextElement'], ['goog.dom', 'goog.graphics.EllipseElement', 'goog.graphics.GroupElement', 'goog.graphics.ImageElement', 'goog.graphics.PathElement', 'goog.graphics.RectElement', 'goog.graphics.TextElement'], {});
goog.addDependency('graphics/vmlgraphics.js', ['goog.graphics.VmlGraphics'], ['goog.array', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.graphics.AbstractGraphics', 'goog.graphics.Font', 'goog.graphics.LinearGradient', 'goog.graphics.Path', 'goog.graphics.SolidFill', 'goog.graphics.VmlEllipseElement', 'goog.graphics.VmlGroupElement', 'goog.graphics.VmlImageElement', 'goog.graphics.VmlPathElement', 'goog.graphics.VmlRectElement', 'goog.graphics.VmlTextElement', 'goog.html.uncheckedconversions', 'goog.math', 'goog.math.Size', 'goog.reflect', 'goog.string', 'goog.string.Const', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('history/event.js', ['goog.history.Event'], ['goog.events.Event', 'goog.history.EventType'], {});
goog.addDependency('history/eventtype.js', ['goog.history.EventType'], [], {});
goog.addDependency('history/history.js', ['goog.History', 'goog.History.Event', 'goog.History.EventType'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.safe', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.history.Event', 'goog.history.EventType', 'goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.html.uncheckedconversions', 'goog.labs.userAgent.device', 'goog.memoize', 'goog.string', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('history/history_test.js', ['goog.HistoryTest'], ['goog.History', 'goog.dispose', 'goog.dom', 'goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('history/html5history.js', ['goog.history.Html5History', 'goog.history.Html5History.TokenTransformer'], ['goog.asserts', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.history.Event'], {});
goog.addDependency('history/html5history_test.js', ['goog.history.Html5HistoryTest'], ['goog.Timer', 'goog.events', 'goog.events.EventType', 'goog.history.EventType', 'goog.history.Html5History', 'goog.testing.MockControl', 'goog.testing.mockmatchers', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/cssspecificity.js', ['goog.html.CssSpecificity'], ['goog.userAgent', 'goog.userAgent.product'], {'module': 'goog'});
goog.addDependency('html/cssspecificity_test.js', ['goog.html.CssSpecificityTest'], ['goog.html.CssSpecificity', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/flash.js', ['goog.html.flash'], ['goog.asserts', 'goog.html.SafeHtml'], {});
goog.addDependency('html/flash_test.js', ['goog.html.flashTest'], ['goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.html.flash', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/legacyconversions.js', ['goog.html.legacyconversions'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl'], {});
goog.addDependency('html/legacyconversions_test.js', ['goog.html.legacyconversionsTest'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.legacyconversions', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safehtml.js', ['goog.html.SafeHtml'], ['goog.array', 'goog.asserts', 'goog.dom.TagName', 'goog.dom.tags', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.trustedtypes', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.DirectionalString', 'goog.labs.userAgent.browser', 'goog.object', 'goog.string.Const', 'goog.string.TypedString', 'goog.string.internal'], {});
goog.addDependency('html/safehtml_test.js', ['goog.html.safeHtmlTest'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.testing', 'goog.html.trustedtypes', 'goog.i18n.bidi.Dir', 'goog.labs.userAgent.browser', 'goog.object', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safehtmlformatter.js', ['goog.html.SafeHtmlFormatter'], ['goog.asserts', 'goog.dom.tags', 'goog.html.SafeHtml', 'goog.string'], {});
goog.addDependency('html/safehtmlformatter_test.js', ['goog.html.safeHtmlFormatterTest'], ['goog.html.SafeHtml', 'goog.html.SafeHtmlFormatter', 'goog.html.SafeUrl', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safescript.js', ['goog.html.SafeScript'], ['goog.asserts', 'goog.html.trustedtypes', 'goog.string.Const', 'goog.string.TypedString'], {});
goog.addDependency('html/safescript_test.js', ['goog.html.safeScriptTest'], ['goog.html.SafeScript', 'goog.html.trustedtypes', 'goog.object', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safestyle.js', ['goog.html.SafeStyle'], ['goog.array', 'goog.asserts', 'goog.html.SafeUrl', 'goog.string.Const', 'goog.string.TypedString', 'goog.string.internal'], {'lang': 'es5'});
goog.addDependency('html/safestyle_test.js', ['goog.html.safeStyleTest'], ['goog.html.SafeStyle', 'goog.html.SafeUrl', 'goog.object', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safestylesheet.js', ['goog.html.SafeStyleSheet'], ['goog.array', 'goog.asserts', 'goog.html.SafeStyle', 'goog.object', 'goog.string.Const', 'goog.string.TypedString', 'goog.string.internal'], {});
goog.addDependency('html/safestylesheet_test.js', ['goog.html.safeStyleSheetTest'], ['goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.object', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safeurl.js', ['goog.html.SafeUrl'], ['goog.asserts', 'goog.fs.url', 'goog.html.TrustedResourceUrl', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.DirectionalString', 'goog.string.Const', 'goog.string.TypedString', 'goog.string.internal'], {});
goog.addDependency('html/safeurl_test.js', ['goog.html.safeUrlTest'], ['goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.safeUrlTestVectors', 'goog.i18n.bidi.Dir', 'goog.object', 'goog.string.Const', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safeurl_test_vectors.js', ['goog.html.safeUrlTestVectors'], [], {});
goog.addDependency('html/sanitizer/attributewhitelist.js', ['goog.html.sanitizer.AttributeSanitizedWhitelist', 'goog.html.sanitizer.AttributeWhitelist'], [], {});
goog.addDependency('html/sanitizer/csspropertysanitizer.js', ['goog.html.sanitizer.CssPropertySanitizer'], ['goog.asserts', 'goog.html.SafeUrl', 'goog.object', 'goog.string'], {'module': 'goog'});
goog.addDependency('html/sanitizer/csspropertysanitizer_test.js', ['goog.html.sanitizer.CssPropertySanitizerTest'], ['goog.functions', 'goog.html.SafeUrl', 'goog.html.sanitizer.CssPropertySanitizer', 'goog.html.sanitizer.noclobber', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/csssanitizer.js', ['goog.html.sanitizer.CssSanitizer'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.CssSpecificity', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.sanitizer.CssPropertySanitizer', 'goog.html.sanitizer.noclobber', 'goog.html.uncheckedconversions', 'goog.object', 'goog.string', 'goog.string.Const', 'goog.userAgent', 'goog.userAgent.product'], {});
goog.addDependency('html/sanitizer/csssanitizer_test.js', ['goog.html.CssSanitizerTest'], ['goog.array', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.sanitizer.CssSanitizer', 'goog.html.testing', 'goog.string', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/elementweakmap.js', ['goog.html.sanitizer.ElementWeakMap'], ['goog.html.sanitizer.noclobber'], {'module': 'goog'});
goog.addDependency('html/sanitizer/elementweakmap_test.js', ['goog.html.sanitizer.ElementWeakMapTest'], ['goog.html.sanitizer.ElementWeakMap', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/html_test_vectors.js', ['goog.html.htmlTestVectors'], [], {'lang': 'es5'});
goog.addDependency('html/sanitizer/htmlsanitizer.js', ['goog.html.sanitizer.HtmlSanitizer', 'goog.html.sanitizer.HtmlSanitizer.Builder', 'goog.html.sanitizer.HtmlSanitizerAttributePolicy', 'goog.html.sanitizer.HtmlSanitizerPolicy', 'goog.html.sanitizer.HtmlSanitizerPolicyContext', 'goog.html.sanitizer.HtmlSanitizerPolicyHints', 'goog.html.sanitizer.HtmlSanitizerUrlPolicy'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.sanitizer.AttributeSanitizedWhitelist', 'goog.html.sanitizer.AttributeWhitelist', 'goog.html.sanitizer.CssSanitizer', 'goog.html.sanitizer.SafeDomTreeProcessor', 'goog.html.sanitizer.TagBlacklist', 'goog.html.sanitizer.TagWhitelist', 'goog.html.sanitizer.noclobber', 'goog.html.uncheckedconversions', 'goog.object', 'goog.string', 'goog.string.Const'], {'lang': 'es5'});
goog.addDependency('html/sanitizer/htmlsanitizer_test.js', ['goog.html.HtmlSanitizerTest'], ['goog.array', 'goog.dom', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.SafeUrl', 'goog.html.sanitizer.HtmlSanitizer', 'goog.html.sanitizer.HtmlSanitizer.Builder', 'goog.html.sanitizer.TagWhitelist', 'goog.html.testing', 'goog.object', 'goog.string.Const', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/htmlsanitizer_unified_test.js', ['goog.html.HtmlSanitizerUnifiedTest'], ['goog.html.SafeHtml', 'goog.html.htmlTestVectors', 'goog.html.sanitizer.HtmlSanitizer.Builder', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/noclobber.js', ['goog.html.sanitizer.noclobber'], ['goog.asserts', 'goog.dom.NodeType', 'goog.userAgent.product'], {'lang': 'es5', 'module': 'goog'});
goog.addDependency('html/sanitizer/noclobber_test.js', ['goog.html.sanitizer.noclobberTest'], ['goog.dom.NodeType', 'goog.html.sanitizer.noclobber', 'goog.testing.PropertyReplacer', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/safedomtreeprocessor.js', ['goog.html.sanitizer.SafeDomTreeProcessor'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.sanitizer.ElementWeakMap', 'goog.html.sanitizer.noclobber', 'goog.html.uncheckedconversions', 'goog.log', 'goog.string.Const', 'goog.userAgent'], {'module': 'goog'});
goog.addDependency('html/sanitizer/safedomtreeprocessor_test.js', ['goog.html.sanitizer.SafeDomTreeProcessorTest'], ['goog.html.sanitizer.SafeDomTreeProcessor', 'goog.html.sanitizer.noclobber', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/tagblacklist.js', ['goog.html.sanitizer.TagBlacklist'], [], {});
goog.addDependency('html/sanitizer/tagwhitelist.js', ['goog.html.sanitizer.TagWhitelist'], [], {});
goog.addDependency('html/sanitizer/unsafe.js', ['goog.html.sanitizer.unsafe'], ['goog.asserts', 'goog.html.sanitizer.HtmlSanitizer.Builder', 'goog.string', 'goog.string.Const'], {});
goog.addDependency('html/sanitizer/unsafe_test.js', ['goog.html.UnsafeTest'], ['goog.html.SafeHtml', 'goog.html.sanitizer.AttributeWhitelist', 'goog.html.sanitizer.HtmlSanitizer', 'goog.html.sanitizer.TagWhitelist', 'goog.html.sanitizer.unsafe', 'goog.string.Const', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/silverlight.js', ['goog.html.silverlight'], ['goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.html.flash', 'goog.string.Const'], {});
goog.addDependency('html/silverlight_test.js', ['goog.html.silverlightTest'], ['goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.html.silverlight', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/testing.js', ['goog.html.testing'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.testing.mockmatchers.ArgumentMatcher'], {});
goog.addDependency('html/textextractor.js', ['goog.html.textExtractor'], ['goog.array', 'goog.dom.TagName', 'goog.html.sanitizer.HtmlSanitizer', 'goog.object', 'goog.userAgent'], {});
goog.addDependency('html/textextractor_test.js', ['goog.html.textExtractorTest'], ['goog.html.textExtractor', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/trustedresourceurl.js', ['goog.html.TrustedResourceUrl'], ['goog.asserts', 'goog.html.trustedtypes', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.DirectionalString', 'goog.string.Const', 'goog.string.TypedString'], {});
goog.addDependency('html/trustedresourceurl_test.js', ['goog.html.trustedResourceUrlTest'], ['goog.html.TrustedResourceUrl', 'goog.html.trustedtypes', 'goog.i18n.bidi.Dir', 'goog.object', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/trustedtypes.js', ['goog.html.trustedtypes'], [], {});
goog.addDependency('html/uncheckedconversions.js', ['goog.html.uncheckedconversions'], ['goog.asserts', 'goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.string.internal'], {});
goog.addDependency('html/uncheckedconversions_test.js', ['goog.html.uncheckedconversionsTest'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.uncheckedconversions', 'goog.i18n.bidi.Dir', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/utils.js', ['goog.html.utils'], ['goog.string'], {});
goog.addDependency('html/utils_test.js', ['goog.html.UtilsTest'], ['goog.array', 'goog.dom.TagName', 'goog.html.utils', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/bidi.js', ['goog.i18n.bidi', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.DirectionalString', 'goog.i18n.bidi.Format'], [], {'lang': 'es6'});
goog.addDependency('i18n/bidi_test.js', ['goog.i18n.bidiTest'], ['goog.i18n.bidi', 'goog.i18n.bidi.Dir', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/bidiformatter.js', ['goog.i18n.BidiFormatter'], ['goog.html.SafeHtml', 'goog.i18n.bidi', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.Format'], {});
goog.addDependency('i18n/bidiformatter_test.js', ['goog.i18n.BidiFormatterTest'], ['goog.html.SafeHtml', 'goog.html.testing', 'goog.i18n.BidiFormatter', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.Format', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/charlistdecompressor.js', ['goog.i18n.CharListDecompressor'], ['goog.array', 'goog.i18n.uChar'], {});
goog.addDependency('i18n/charlistdecompressor_test.js', ['goog.i18n.CharListDecompressorTest'], ['goog.i18n.CharListDecompressor', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/charpickerdata.js', ['goog.i18n.CharPickerData'], [], {});
goog.addDependency('i18n/collation.js', ['goog.i18n.collation'], [], {'lang': 'es6'});
goog.addDependency('i18n/collation_test.js', ['goog.i18n.collationTest'], ['goog.i18n.collation', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/compactnumberformatsymbols.js', ['goog.i18n.CompactNumberFormatSymbols', 'goog.i18n.CompactNumberFormatSymbols_af', 'goog.i18n.CompactNumberFormatSymbols_am', 'goog.i18n.CompactNumberFormatSymbols_ar', 'goog.i18n.CompactNumberFormatSymbols_ar_DZ', 'goog.i18n.CompactNumberFormatSymbols_ar_EG', 'goog.i18n.CompactNumberFormatSymbols_az', 'goog.i18n.CompactNumberFormatSymbols_be', 'goog.i18n.CompactNumberFormatSymbols_bg', 'goog.i18n.CompactNumberFormatSymbols_bn', 'goog.i18n.CompactNumberFormatSymbols_br', 'goog.i18n.CompactNumberFormatSymbols_bs', 'goog.i18n.CompactNumberFormatSymbols_ca', 'goog.i18n.CompactNumberFormatSymbols_chr', 'goog.i18n.CompactNumberFormatSymbols_cs', 'goog.i18n.CompactNumberFormatSymbols_cy', 'goog.i18n.CompactNumberFormatSymbols_da', 'goog.i18n.CompactNumberFormatSymbols_de', 'goog.i18n.CompactNumberFormatSymbols_de_AT', 'goog.i18n.CompactNumberFormatSymbols_de_CH', 'goog.i18n.CompactNumberFormatSymbols_el', 'goog.i18n.CompactNumberFormatSymbols_en', 'goog.i18n.CompactNumberFormatSymbols_en_AU', 'goog.i18n.CompactNumberFormatSymbols_en_CA', 'goog.i18n.CompactNumberFormatSymbols_en_GB', 'goog.i18n.CompactNumberFormatSymbols_en_IE', 'goog.i18n.CompactNumberFormatSymbols_en_IN', 'goog.i18n.CompactNumberFormatSymbols_en_SG', 'goog.i18n.CompactNumberFormatSymbols_en_US', 'goog.i18n.CompactNumberFormatSymbols_en_ZA', 'goog.i18n.CompactNumberFormatSymbols_es', 'goog.i18n.CompactNumberFormatSymbols_es_419', 'goog.i18n.CompactNumberFormatSymbols_es_ES', 'goog.i18n.CompactNumberFormatSymbols_es_MX', 'goog.i18n.CompactNumberFormatSymbols_es_US', 'goog.i18n.CompactNumberFormatSymbols_et', 'goog.i18n.CompactNumberFormatSymbols_eu', 'goog.i18n.CompactNumberFormatSymbols_fa', 'goog.i18n.CompactNumberFormatSymbols_fi', 'goog.i18n.CompactNumberFormatSymbols_fil', 'goog.i18n.CompactNumberFormatSymbols_fr', 'goog.i18n.CompactNumberFormatSymbols_fr_CA', 'goog.i18n.CompactNumberFormatSymbols_ga', 'goog.i18n.CompactNumberFormatSymbols_gl', 'goog.i18n.CompactNumberFormatSymbols_gsw', 'goog.i18n.CompactNumberFormatSymbols_gu', 'goog.i18n.CompactNumberFormatSymbols_haw', 'goog.i18n.CompactNumberFormatSymbols_he', 'goog.i18n.CompactNumberFormatSymbols_hi', 'goog.i18n.CompactNumberFormatSymbols_hr', 'goog.i18n.CompactNumberFormatSymbols_hu', 'goog.i18n.CompactNumberFormatSymbols_hy', 'goog.i18n.CompactNumberFormatSymbols_id', 'goog.i18n.CompactNumberFormatSymbols_in', 'goog.i18n.CompactNumberFormatSymbols_is', 'goog.i18n.CompactNumberFormatSymbols_it', 'goog.i18n.CompactNumberFormatSymbols_iw', 'goog.i18n.CompactNumberFormatSymbols_ja', 'goog.i18n.CompactNumberFormatSymbols_ka', 'goog.i18n.CompactNumberFormatSymbols_kk', 'goog.i18n.CompactNumberFormatSymbols_km', 'goog.i18n.CompactNumberFormatSymbols_kn', 'goog.i18n.CompactNumberFormatSymbols_ko', 'goog.i18n.CompactNumberFormatSymbols_ky', 'goog.i18n.CompactNumberFormatSymbols_ln', 'goog.i18n.CompactNumberFormatSymbols_lo', 'goog.i18n.CompactNumberFormatSymbols_lt', 'goog.i18n.CompactNumberFormatSymbols_lv', 'goog.i18n.CompactNumberFormatSymbols_mk', 'goog.i18n.CompactNumberFormatSymbols_ml', 'goog.i18n.CompactNumberFormatSymbols_mn', 'goog.i18n.CompactNumberFormatSymbols_mo', 'goog.i18n.CompactNumberFormatSymbols_mr', 'goog.i18n.CompactNumberFormatSymbols_ms', 'goog.i18n.CompactNumberFormatSymbols_mt', 'goog.i18n.CompactNumberFormatSymbols_my', 'goog.i18n.CompactNumberFormatSymbols_nb', 'goog.i18n.CompactNumberFormatSymbols_ne', 'goog.i18n.CompactNumberFormatSymbols_nl', 'goog.i18n.CompactNumberFormatSymbols_no', 'goog.i18n.CompactNumberFormatSymbols_no_NO', 'goog.i18n.CompactNumberFormatSymbols_or', 'goog.i18n.CompactNumberFormatSymbols_pa', 'goog.i18n.CompactNumberFormatSymbols_pl', 'goog.i18n.CompactNumberFormatSymbols_pt', 'goog.i18n.CompactNumberFormatSymbols_pt_BR', 'goog.i18n.CompactNumberFormatSymbols_pt_PT', 'goog.i18n.CompactNumberFormatSymbols_ro', 'goog.i18n.CompactNumberFormatSymbols_ru', 'goog.i18n.CompactNumberFormatSymbols_sh', 'goog.i18n.CompactNumberFormatSymbols_si', 'goog.i18n.CompactNumberFormatSymbols_sk', 'goog.i18n.CompactNumberFormatSymbols_sl', 'goog.i18n.CompactNumberFormatSymbols_sq', 'goog.i18n.CompactNumberFormatSymbols_sr', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn', 'goog.i18n.CompactNumberFormatSymbols_sv', 'goog.i18n.CompactNumberFormatSymbols_sw', 'goog.i18n.CompactNumberFormatSymbols_ta', 'goog.i18n.CompactNumberFormatSymbols_te', 'goog.i18n.CompactNumberFormatSymbols_th', 'goog.i18n.CompactNumberFormatSymbols_tl', 'goog.i18n.CompactNumberFormatSymbols_tr', 'goog.i18n.CompactNumberFormatSymbols_uk', 'goog.i18n.CompactNumberFormatSymbols_ur', 'goog.i18n.CompactNumberFormatSymbols_uz', 'goog.i18n.CompactNumberFormatSymbols_vi', 'goog.i18n.CompactNumberFormatSymbols_zh', 'goog.i18n.CompactNumberFormatSymbols_zh_CN', 'goog.i18n.CompactNumberFormatSymbols_zh_HK', 'goog.i18n.CompactNumberFormatSymbols_zh_TW', 'goog.i18n.CompactNumberFormatSymbols_zu'], [], {});
goog.addDependency('i18n/compactnumberformatsymbolsext.js', ['goog.i18n.CompactNumberFormatSymbolsExt', 'goog.i18n.CompactNumberFormatSymbols_af_NA', 'goog.i18n.CompactNumberFormatSymbols_af_ZA', 'goog.i18n.CompactNumberFormatSymbols_agq', 'goog.i18n.CompactNumberFormatSymbols_agq_CM', 'goog.i18n.CompactNumberFormatSymbols_ak', 'goog.i18n.CompactNumberFormatSymbols_ak_GH', 'goog.i18n.CompactNumberFormatSymbols_am_ET', 'goog.i18n.CompactNumberFormatSymbols_ar_001', 'goog.i18n.CompactNumberFormatSymbols_ar_AE', 'goog.i18n.CompactNumberFormatSymbols_ar_BH', 'goog.i18n.CompactNumberFormatSymbols_ar_DJ', 'goog.i18n.CompactNumberFormatSymbols_ar_EH', 'goog.i18n.CompactNumberFormatSymbols_ar_ER', 'goog.i18n.CompactNumberFormatSymbols_ar_IL', 'goog.i18n.CompactNumberFormatSymbols_ar_IQ', 'goog.i18n.CompactNumberFormatSymbols_ar_JO', 'goog.i18n.CompactNumberFormatSymbols_ar_KM', 'goog.i18n.CompactNumberFormatSymbols_ar_KW', 'goog.i18n.CompactNumberFormatSymbols_ar_LB', 'goog.i18n.CompactNumberFormatSymbols_ar_LY', 'goog.i18n.CompactNumberFormatSymbols_ar_MA', 'goog.i18n.CompactNumberFormatSymbols_ar_MR', 'goog.i18n.CompactNumberFormatSymbols_ar_OM', 'goog.i18n.CompactNumberFormatSymbols_ar_PS', 'goog.i18n.CompactNumberFormatSymbols_ar_QA', 'goog.i18n.CompactNumberFormatSymbols_ar_SA', 'goog.i18n.CompactNumberFormatSymbols_ar_SD', 'goog.i18n.CompactNumberFormatSymbols_ar_SO', 'goog.i18n.CompactNumberFormatSymbols_ar_SS', 'goog.i18n.CompactNumberFormatSymbols_ar_SY', 'goog.i18n.CompactNumberFormatSymbols_ar_TD', 'goog.i18n.CompactNumberFormatSymbols_ar_TN', 'goog.i18n.CompactNumberFormatSymbols_ar_XB', 'goog.i18n.CompactNumberFormatSymbols_ar_YE', 'goog.i18n.CompactNumberFormatSymbols_as', 'goog.i18n.CompactNumberFormatSymbols_as_IN', 'goog.i18n.CompactNumberFormatSymbols_asa', 'goog.i18n.CompactNumberFormatSymbols_asa_TZ', 'goog.i18n.CompactNumberFormatSymbols_ast', 'goog.i18n.CompactNumberFormatSymbols_ast_ES', 'goog.i18n.CompactNumberFormatSymbols_az_Cyrl', 'goog.i18n.CompactNumberFormatSymbols_az_Cyrl_AZ', 'goog.i18n.CompactNumberFormatSymbols_az_Latn', 'goog.i18n.CompactNumberFormatSymbols_az_Latn_AZ', 'goog.i18n.CompactNumberFormatSymbols_bas', 'goog.i18n.CompactNumberFormatSymbols_bas_CM', 'goog.i18n.CompactNumberFormatSymbols_be_BY', 'goog.i18n.CompactNumberFormatSymbols_bem', 'goog.i18n.CompactNumberFormatSymbols_bem_ZM', 'goog.i18n.CompactNumberFormatSymbols_bez', 'goog.i18n.CompactNumberFormatSymbols_bez_TZ', 'goog.i18n.CompactNumberFormatSymbols_bg_BG', 'goog.i18n.CompactNumberFormatSymbols_bm', 'goog.i18n.CompactNumberFormatSymbols_bm_ML', 'goog.i18n.CompactNumberFormatSymbols_bn_BD', 'goog.i18n.CompactNumberFormatSymbols_bn_IN', 'goog.i18n.CompactNumberFormatSymbols_bo', 'goog.i18n.CompactNumberFormatSymbols_bo_CN', 'goog.i18n.CompactNumberFormatSymbols_bo_IN', 'goog.i18n.CompactNumberFormatSymbols_br_FR', 'goog.i18n.CompactNumberFormatSymbols_brx', 'goog.i18n.CompactNumberFormatSymbols_brx_IN', 'goog.i18n.CompactNumberFormatSymbols_bs_Cyrl', 'goog.i18n.CompactNumberFormatSymbols_bs_Cyrl_BA', 'goog.i18n.CompactNumberFormatSymbols_bs_Latn', 'goog.i18n.CompactNumberFormatSymbols_bs_Latn_BA', 'goog.i18n.CompactNumberFormatSymbols_ca_AD', 'goog.i18n.CompactNumberFormatSymbols_ca_ES', 'goog.i18n.CompactNumberFormatSymbols_ca_FR', 'goog.i18n.CompactNumberFormatSymbols_ca_IT', 'goog.i18n.CompactNumberFormatSymbols_ccp', 'goog.i18n.CompactNumberFormatSymbols_ccp_BD', 'goog.i18n.CompactNumberFormatSymbols_ccp_IN', 'goog.i18n.CompactNumberFormatSymbols_ce', 'goog.i18n.CompactNumberFormatSymbols_ce_RU', 'goog.i18n.CompactNumberFormatSymbols_ceb', 'goog.i18n.CompactNumberFormatSymbols_ceb_PH', 'goog.i18n.CompactNumberFormatSymbols_cgg', 'goog.i18n.CompactNumberFormatSymbols_cgg_UG', 'goog.i18n.CompactNumberFormatSymbols_chr_US', 'goog.i18n.CompactNumberFormatSymbols_ckb', 'goog.i18n.CompactNumberFormatSymbols_ckb_IQ', 'goog.i18n.CompactNumberFormatSymbols_ckb_IR', 'goog.i18n.CompactNumberFormatSymbols_cs_CZ', 'goog.i18n.CompactNumberFormatSymbols_cy_GB', 'goog.i18n.CompactNumberFormatSymbols_da_DK', 'goog.i18n.CompactNumberFormatSymbols_da_GL', 'goog.i18n.CompactNumberFormatSymbols_dav', 'goog.i18n.CompactNumberFormatSymbols_dav_KE', 'goog.i18n.CompactNumberFormatSymbols_de_BE', 'goog.i18n.CompactNumberFormatSymbols_de_DE', 'goog.i18n.CompactNumberFormatSymbols_de_IT', 'goog.i18n.CompactNumberFormatSymbols_de_LI', 'goog.i18n.CompactNumberFormatSymbols_de_LU', 'goog.i18n.CompactNumberFormatSymbols_dje', 'goog.i18n.CompactNumberFormatSymbols_dje_NE', 'goog.i18n.CompactNumberFormatSymbols_dsb', 'goog.i18n.CompactNumberFormatSymbols_dsb_DE', 'goog.i18n.CompactNumberFormatSymbols_dua', 'goog.i18n.CompactNumberFormatSymbols_dua_CM', 'goog.i18n.CompactNumberFormatSymbols_dyo', 'goog.i18n.CompactNumberFormatSymbols_dyo_SN', 'goog.i18n.CompactNumberFormatSymbols_dz', 'goog.i18n.CompactNumberFormatSymbols_dz_BT', 'goog.i18n.CompactNumberFormatSymbols_ebu', 'goog.i18n.CompactNumberFormatSymbols_ebu_KE', 'goog.i18n.CompactNumberFormatSymbols_ee', 'goog.i18n.CompactNumberFormatSymbols_ee_GH', 'goog.i18n.CompactNumberFormatSymbols_ee_TG', 'goog.i18n.CompactNumberFormatSymbols_el_CY', 'goog.i18n.CompactNumberFormatSymbols_el_GR', 'goog.i18n.CompactNumberFormatSymbols_en_001', 'goog.i18n.CompactNumberFormatSymbols_en_150', 'goog.i18n.CompactNumberFormatSymbols_en_AE', 'goog.i18n.CompactNumberFormatSymbols_en_AG', 'goog.i18n.CompactNumberFormatSymbols_en_AI', 'goog.i18n.CompactNumberFormatSymbols_en_AS', 'goog.i18n.CompactNumberFormatSymbols_en_AT', 'goog.i18n.CompactNumberFormatSymbols_en_BB', 'goog.i18n.CompactNumberFormatSymbols_en_BE', 'goog.i18n.CompactNumberFormatSymbols_en_BI', 'goog.i18n.CompactNumberFormatSymbols_en_BM', 'goog.i18n.CompactNumberFormatSymbols_en_BS', 'goog.i18n.CompactNumberFormatSymbols_en_BW', 'goog.i18n.CompactNumberFormatSymbols_en_BZ', 'goog.i18n.CompactNumberFormatSymbols_en_CC', 'goog.i18n.CompactNumberFormatSymbols_en_CH', 'goog.i18n.CompactNumberFormatSymbols_en_CK', 'goog.i18n.CompactNumberFormatSymbols_en_CM', 'goog.i18n.CompactNumberFormatSymbols_en_CX', 'goog.i18n.CompactNumberFormatSymbols_en_CY', 'goog.i18n.CompactNumberFormatSymbols_en_DE', 'goog.i18n.CompactNumberFormatSymbols_en_DG', 'goog.i18n.CompactNumberFormatSymbols_en_DK', 'goog.i18n.CompactNumberFormatSymbols_en_DM', 'goog.i18n.CompactNumberFormatSymbols_en_ER', 'goog.i18n.CompactNumberFormatSymbols_en_FI', 'goog.i18n.CompactNumberFormatSymbols_en_FJ', 'goog.i18n.CompactNumberFormatSymbols_en_FK', 'goog.i18n.CompactNumberFormatSymbols_en_FM', 'goog.i18n.CompactNumberFormatSymbols_en_GD', 'goog.i18n.CompactNumberFormatSymbols_en_GG', 'goog.i18n.CompactNumberFormatSymbols_en_GH', 'goog.i18n.CompactNumberFormatSymbols_en_GI', 'goog.i18n.CompactNumberFormatSymbols_en_GM', 'goog.i18n.CompactNumberFormatSymbols_en_GU', 'goog.i18n.CompactNumberFormatSymbols_en_GY', 'goog.i18n.CompactNumberFormatSymbols_en_HK', 'goog.i18n.CompactNumberFormatSymbols_en_IL', 'goog.i18n.CompactNumberFormatSymbols_en_IM', 'goog.i18n.CompactNumberFormatSymbols_en_IO', 'goog.i18n.CompactNumberFormatSymbols_en_JE', 'goog.i18n.CompactNumberFormatSymbols_en_JM', 'goog.i18n.CompactNumberFormatSymbols_en_KE', 'goog.i18n.CompactNumberFormatSymbols_en_KI', 'goog.i18n.CompactNumberFormatSymbols_en_KN', 'goog.i18n.CompactNumberFormatSymbols_en_KY', 'goog.i18n.CompactNumberFormatSymbols_en_LC', 'goog.i18n.CompactNumberFormatSymbols_en_LR', 'goog.i18n.CompactNumberFormatSymbols_en_LS', 'goog.i18n.CompactNumberFormatSymbols_en_MG', 'goog.i18n.CompactNumberFormatSymbols_en_MH', 'goog.i18n.CompactNumberFormatSymbols_en_MO', 'goog.i18n.CompactNumberFormatSymbols_en_MP', 'goog.i18n.CompactNumberFormatSymbols_en_MS', 'goog.i18n.CompactNumberFormatSymbols_en_MT', 'goog.i18n.CompactNumberFormatSymbols_en_MU', 'goog.i18n.CompactNumberFormatSymbols_en_MW', 'goog.i18n.CompactNumberFormatSymbols_en_MY', 'goog.i18n.CompactNumberFormatSymbols_en_NA', 'goog.i18n.CompactNumberFormatSymbols_en_NF', 'goog.i18n.CompactNumberFormatSymbols_en_NG', 'goog.i18n.CompactNumberFormatSymbols_en_NL', 'goog.i18n.CompactNumberFormatSymbols_en_NR', 'goog.i18n.CompactNumberFormatSymbols_en_NU', 'goog.i18n.CompactNumberFormatSymbols_en_NZ', 'goog.i18n.CompactNumberFormatSymbols_en_PG', 'goog.i18n.CompactNumberFormatSymbols_en_PH', 'goog.i18n.CompactNumberFormatSymbols_en_PK', 'goog.i18n.CompactNumberFormatSymbols_en_PN', 'goog.i18n.CompactNumberFormatSymbols_en_PR', 'goog.i18n.CompactNumberFormatSymbols_en_PW', 'goog.i18n.CompactNumberFormatSymbols_en_RW', 'goog.i18n.CompactNumberFormatSymbols_en_SB', 'goog.i18n.CompactNumberFormatSymbols_en_SC', 'goog.i18n.CompactNumberFormatSymbols_en_SD', 'goog.i18n.CompactNumberFormatSymbols_en_SE', 'goog.i18n.CompactNumberFormatSymbols_en_SH', 'goog.i18n.CompactNumberFormatSymbols_en_SI', 'goog.i18n.CompactNumberFormatSymbols_en_SL', 'goog.i18n.CompactNumberFormatSymbols_en_SS', 'goog.i18n.CompactNumberFormatSymbols_en_SX', 'goog.i18n.CompactNumberFormatSymbols_en_SZ', 'goog.i18n.CompactNumberFormatSymbols_en_TC', 'goog.i18n.CompactNumberFormatSymbols_en_TK', 'goog.i18n.CompactNumberFormatSymbols_en_TO', 'goog.i18n.CompactNumberFormatSymbols_en_TT', 'goog.i18n.CompactNumberFormatSymbols_en_TV', 'goog.i18n.CompactNumberFormatSymbols_en_TZ', 'goog.i18n.CompactNumberFormatSymbols_en_UG', 'goog.i18n.CompactNumberFormatSymbols_en_UM', 'goog.i18n.CompactNumberFormatSymbols_en_US_POSIX', 'goog.i18n.CompactNumberFormatSymbols_en_VC', 'goog.i18n.CompactNumberFormatSymbols_en_VG', 'goog.i18n.CompactNumberFormatSymbols_en_VI', 'goog.i18n.CompactNumberFormatSymbols_en_VU', 'goog.i18n.CompactNumberFormatSymbols_en_WS', 'goog.i18n.CompactNumberFormatSymbols_en_XA', 'goog.i18n.CompactNumberFormatSymbols_en_ZM', 'goog.i18n.CompactNumberFormatSymbols_en_ZW', 'goog.i18n.CompactNumberFormatSymbols_eo', 'goog.i18n.CompactNumberFormatSymbols_eo_001', 'goog.i18n.CompactNumberFormatSymbols_es_AR', 'goog.i18n.CompactNumberFormatSymbols_es_BO', 'goog.i18n.CompactNumberFormatSymbols_es_BR', 'goog.i18n.CompactNumberFormatSymbols_es_BZ', 'goog.i18n.CompactNumberFormatSymbols_es_CL', 'goog.i18n.CompactNumberFormatSymbols_es_CO', 'goog.i18n.CompactNumberFormatSymbols_es_CR', 'goog.i18n.CompactNumberFormatSymbols_es_CU', 'goog.i18n.CompactNumberFormatSymbols_es_DO', 'goog.i18n.CompactNumberFormatSymbols_es_EA', 'goog.i18n.CompactNumberFormatSymbols_es_EC', 'goog.i18n.CompactNumberFormatSymbols_es_GQ', 'goog.i18n.CompactNumberFormatSymbols_es_GT', 'goog.i18n.CompactNumberFormatSymbols_es_HN', 'goog.i18n.CompactNumberFormatSymbols_es_IC', 'goog.i18n.CompactNumberFormatSymbols_es_NI', 'goog.i18n.CompactNumberFormatSymbols_es_PA', 'goog.i18n.CompactNumberFormatSymbols_es_PE', 'goog.i18n.CompactNumberFormatSymbols_es_PH', 'goog.i18n.CompactNumberFormatSymbols_es_PR', 'goog.i18n.CompactNumberFormatSymbols_es_PY', 'goog.i18n.CompactNumberFormatSymbols_es_SV', 'goog.i18n.CompactNumberFormatSymbols_es_UY', 'goog.i18n.CompactNumberFormatSymbols_es_VE', 'goog.i18n.CompactNumberFormatSymbols_et_EE', 'goog.i18n.CompactNumberFormatSymbols_eu_ES', 'goog.i18n.CompactNumberFormatSymbols_ewo', 'goog.i18n.CompactNumberFormatSymbols_ewo_CM', 'goog.i18n.CompactNumberFormatSymbols_fa_AF', 'goog.i18n.CompactNumberFormatSymbols_fa_IR', 'goog.i18n.CompactNumberFormatSymbols_ff', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_BF', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_CM', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_GH', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_GM', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_GN', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_GW', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_LR', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_MR', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_NE', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_NG', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_SL', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_SN', 'goog.i18n.CompactNumberFormatSymbols_fi_FI', 'goog.i18n.CompactNumberFormatSymbols_fil_PH', 'goog.i18n.CompactNumberFormatSymbols_fo', 'goog.i18n.CompactNumberFormatSymbols_fo_DK', 'goog.i18n.CompactNumberFormatSymbols_fo_FO', 'goog.i18n.CompactNumberFormatSymbols_fr_BE', 'goog.i18n.CompactNumberFormatSymbols_fr_BF', 'goog.i18n.CompactNumberFormatSymbols_fr_BI', 'goog.i18n.CompactNumberFormatSymbols_fr_BJ', 'goog.i18n.CompactNumberFormatSymbols_fr_BL', 'goog.i18n.CompactNumberFormatSymbols_fr_CD', 'goog.i18n.CompactNumberFormatSymbols_fr_CF', 'goog.i18n.CompactNumberFormatSymbols_fr_CG', 'goog.i18n.CompactNumberFormatSymbols_fr_CH', 'goog.i18n.CompactNumberFormatSymbols_fr_CI', 'goog.i18n.CompactNumberFormatSymbols_fr_CM', 'goog.i18n.CompactNumberFormatSymbols_fr_DJ', 'goog.i18n.CompactNumberFormatSymbols_fr_DZ', 'goog.i18n.CompactNumberFormatSymbols_fr_FR', 'goog.i18n.CompactNumberFormatSymbols_fr_GA', 'goog.i18n.CompactNumberFormatSymbols_fr_GF', 'goog.i18n.CompactNumberFormatSymbols_fr_GN', 'goog.i18n.CompactNumberFormatSymbols_fr_GP', 'goog.i18n.CompactNumberFormatSymbols_fr_GQ', 'goog.i18n.CompactNumberFormatSymbols_fr_HT', 'goog.i18n.CompactNumberFormatSymbols_fr_KM', 'goog.i18n.CompactNumberFormatSymbols_fr_LU', 'goog.i18n.CompactNumberFormatSymbols_fr_MA', 'goog.i18n.CompactNumberFormatSymbols_fr_MC', 'goog.i18n.CompactNumberFormatSymbols_fr_MF', 'goog.i18n.CompactNumberFormatSymbols_fr_MG', 'goog.i18n.CompactNumberFormatSymbols_fr_ML', 'goog.i18n.CompactNumberFormatSymbols_fr_MQ', 'goog.i18n.CompactNumberFormatSymbols_fr_MR', 'goog.i18n.CompactNumberFormatSymbols_fr_MU', 'goog.i18n.CompactNumberFormatSymbols_fr_NC', 'goog.i18n.CompactNumberFormatSymbols_fr_NE', 'goog.i18n.CompactNumberFormatSymbols_fr_PF', 'goog.i18n.CompactNumberFormatSymbols_fr_PM', 'goog.i18n.CompactNumberFormatSymbols_fr_RE', 'goog.i18n.CompactNumberFormatSymbols_fr_RW', 'goog.i18n.CompactNumberFormatSymbols_fr_SC', 'goog.i18n.CompactNumberFormatSymbols_fr_SN', 'goog.i18n.CompactNumberFormatSymbols_fr_SY', 'goog.i18n.CompactNumberFormatSymbols_fr_TD', 'goog.i18n.CompactNumberFormatSymbols_fr_TG', 'goog.i18n.CompactNumberFormatSymbols_fr_TN', 'goog.i18n.CompactNumberFormatSymbols_fr_VU', 'goog.i18n.CompactNumberFormatSymbols_fr_WF', 'goog.i18n.CompactNumberFormatSymbols_fr_YT', 'goog.i18n.CompactNumberFormatSymbols_fur', 'goog.i18n.CompactNumberFormatSymbols_fur_IT', 'goog.i18n.CompactNumberFormatSymbols_fy', 'goog.i18n.CompactNumberFormatSymbols_fy_NL', 'goog.i18n.CompactNumberFormatSymbols_ga_IE', 'goog.i18n.CompactNumberFormatSymbols_gd', 'goog.i18n.CompactNumberFormatSymbols_gd_GB', 'goog.i18n.CompactNumberFormatSymbols_gl_ES', 'goog.i18n.CompactNumberFormatSymbols_gsw_CH', 'goog.i18n.CompactNumberFormatSymbols_gsw_FR', 'goog.i18n.CompactNumberFormatSymbols_gsw_LI', 'goog.i18n.CompactNumberFormatSymbols_gu_IN', 'goog.i18n.CompactNumberFormatSymbols_guz', 'goog.i18n.CompactNumberFormatSymbols_guz_KE', 'goog.i18n.CompactNumberFormatSymbols_gv', 'goog.i18n.CompactNumberFormatSymbols_gv_IM', 'goog.i18n.CompactNumberFormatSymbols_ha', 'goog.i18n.CompactNumberFormatSymbols_ha_GH', 'goog.i18n.CompactNumberFormatSymbols_ha_NE', 'goog.i18n.CompactNumberFormatSymbols_ha_NG', 'goog.i18n.CompactNumberFormatSymbols_haw_US', 'goog.i18n.CompactNumberFormatSymbols_he_IL', 'goog.i18n.CompactNumberFormatSymbols_hi_IN', 'goog.i18n.CompactNumberFormatSymbols_hr_BA', 'goog.i18n.CompactNumberFormatSymbols_hr_HR', 'goog.i18n.CompactNumberFormatSymbols_hsb', 'goog.i18n.CompactNumberFormatSymbols_hsb_DE', 'goog.i18n.CompactNumberFormatSymbols_hu_HU', 'goog.i18n.CompactNumberFormatSymbols_hy_AM', 'goog.i18n.CompactNumberFormatSymbols_ia', 'goog.i18n.CompactNumberFormatSymbols_ia_001', 'goog.i18n.CompactNumberFormatSymbols_id_ID', 'goog.i18n.CompactNumberFormatSymbols_ig', 'goog.i18n.CompactNumberFormatSymbols_ig_NG', 'goog.i18n.CompactNumberFormatSymbols_ii', 'goog.i18n.CompactNumberFormatSymbols_ii_CN', 'goog.i18n.CompactNumberFormatSymbols_is_IS', 'goog.i18n.CompactNumberFormatSymbols_it_CH', 'goog.i18n.CompactNumberFormatSymbols_it_IT', 'goog.i18n.CompactNumberFormatSymbols_it_SM', 'goog.i18n.CompactNumberFormatSymbols_it_VA', 'goog.i18n.CompactNumberFormatSymbols_ja_JP', 'goog.i18n.CompactNumberFormatSymbols_jgo', 'goog.i18n.CompactNumberFormatSymbols_jgo_CM', 'goog.i18n.CompactNumberFormatSymbols_jmc', 'goog.i18n.CompactNumberFormatSymbols_jmc_TZ', 'goog.i18n.CompactNumberFormatSymbols_jv', 'goog.i18n.CompactNumberFormatSymbols_jv_ID', 'goog.i18n.CompactNumberFormatSymbols_ka_GE', 'goog.i18n.CompactNumberFormatSymbols_kab', 'goog.i18n.CompactNumberFormatSymbols_kab_DZ', 'goog.i18n.CompactNumberFormatSymbols_kam', 'goog.i18n.CompactNumberFormatSymbols_kam_KE', 'goog.i18n.CompactNumberFormatSymbols_kde', 'goog.i18n.CompactNumberFormatSymbols_kde_TZ', 'goog.i18n.CompactNumberFormatSymbols_kea', 'goog.i18n.CompactNumberFormatSymbols_kea_CV', 'goog.i18n.CompactNumberFormatSymbols_khq', 'goog.i18n.CompactNumberFormatSymbols_khq_ML', 'goog.i18n.CompactNumberFormatSymbols_ki', 'goog.i18n.CompactNumberFormatSymbols_ki_KE', 'goog.i18n.CompactNumberFormatSymbols_kk_KZ', 'goog.i18n.CompactNumberFormatSymbols_kkj', 'goog.i18n.CompactNumberFormatSymbols_kkj_CM', 'goog.i18n.CompactNumberFormatSymbols_kl', 'goog.i18n.CompactNumberFormatSymbols_kl_GL', 'goog.i18n.CompactNumberFormatSymbols_kln', 'goog.i18n.CompactNumberFormatSymbols_kln_KE', 'goog.i18n.CompactNumberFormatSymbols_km_KH', 'goog.i18n.CompactNumberFormatSymbols_kn_IN', 'goog.i18n.CompactNumberFormatSymbols_ko_KP', 'goog.i18n.CompactNumberFormatSymbols_ko_KR', 'goog.i18n.CompactNumberFormatSymbols_kok', 'goog.i18n.CompactNumberFormatSymbols_kok_IN', 'goog.i18n.CompactNumberFormatSymbols_ks', 'goog.i18n.CompactNumberFormatSymbols_ks_IN', 'goog.i18n.CompactNumberFormatSymbols_ksb', 'goog.i18n.CompactNumberFormatSymbols_ksb_TZ', 'goog.i18n.CompactNumberFormatSymbols_ksf', 'goog.i18n.CompactNumberFormatSymbols_ksf_CM', 'goog.i18n.CompactNumberFormatSymbols_ksh', 'goog.i18n.CompactNumberFormatSymbols_ksh_DE', 'goog.i18n.CompactNumberFormatSymbols_ku', 'goog.i18n.CompactNumberFormatSymbols_ku_TR', 'goog.i18n.CompactNumberFormatSymbols_kw', 'goog.i18n.CompactNumberFormatSymbols_kw_GB', 'goog.i18n.CompactNumberFormatSymbols_ky_KG', 'goog.i18n.CompactNumberFormatSymbols_lag', 'goog.i18n.CompactNumberFormatSymbols_lag_TZ', 'goog.i18n.CompactNumberFormatSymbols_lb', 'goog.i18n.CompactNumberFormatSymbols_lb_LU', 'goog.i18n.CompactNumberFormatSymbols_lg', 'goog.i18n.CompactNumberFormatSymbols_lg_UG', 'goog.i18n.CompactNumberFormatSymbols_lkt', 'goog.i18n.CompactNumberFormatSymbols_lkt_US', 'goog.i18n.CompactNumberFormatSymbols_ln_AO', 'goog.i18n.CompactNumberFormatSymbols_ln_CD', 'goog.i18n.CompactNumberFormatSymbols_ln_CF', 'goog.i18n.CompactNumberFormatSymbols_ln_CG', 'goog.i18n.CompactNumberFormatSymbols_lo_LA', 'goog.i18n.CompactNumberFormatSymbols_lrc', 'goog.i18n.CompactNumberFormatSymbols_lrc_IQ', 'goog.i18n.CompactNumberFormatSymbols_lrc_IR', 'goog.i18n.CompactNumberFormatSymbols_lt_LT', 'goog.i18n.CompactNumberFormatSymbols_lu', 'goog.i18n.CompactNumberFormatSymbols_lu_CD', 'goog.i18n.CompactNumberFormatSymbols_luo', 'goog.i18n.CompactNumberFormatSymbols_luo_KE', 'goog.i18n.CompactNumberFormatSymbols_luy', 'goog.i18n.CompactNumberFormatSymbols_luy_KE', 'goog.i18n.CompactNumberFormatSymbols_lv_LV', 'goog.i18n.CompactNumberFormatSymbols_mas', 'goog.i18n.CompactNumberFormatSymbols_mas_KE', 'goog.i18n.CompactNumberFormatSymbols_mas_TZ', 'goog.i18n.CompactNumberFormatSymbols_mer', 'goog.i18n.CompactNumberFormatSymbols_mer_KE', 'goog.i18n.CompactNumberFormatSymbols_mfe', 'goog.i18n.CompactNumberFormatSymbols_mfe_MU', 'goog.i18n.CompactNumberFormatSymbols_mg', 'goog.i18n.CompactNumberFormatSymbols_mg_MG', 'goog.i18n.CompactNumberFormatSymbols_mgh', 'goog.i18n.CompactNumberFormatSymbols_mgh_MZ', 'goog.i18n.CompactNumberFormatSymbols_mgo', 'goog.i18n.CompactNumberFormatSymbols_mgo_CM', 'goog.i18n.CompactNumberFormatSymbols_mi', 'goog.i18n.CompactNumberFormatSymbols_mi_NZ', 'goog.i18n.CompactNumberFormatSymbols_mk_MK', 'goog.i18n.CompactNumberFormatSymbols_ml_IN', 'goog.i18n.CompactNumberFormatSymbols_mn_MN', 'goog.i18n.CompactNumberFormatSymbols_mr_IN', 'goog.i18n.CompactNumberFormatSymbols_ms_BN', 'goog.i18n.CompactNumberFormatSymbols_ms_MY', 'goog.i18n.CompactNumberFormatSymbols_ms_SG', 'goog.i18n.CompactNumberFormatSymbols_mt_MT', 'goog.i18n.CompactNumberFormatSymbols_mua', 'goog.i18n.CompactNumberFormatSymbols_mua_CM', 'goog.i18n.CompactNumberFormatSymbols_my_MM', 'goog.i18n.CompactNumberFormatSymbols_mzn', 'goog.i18n.CompactNumberFormatSymbols_mzn_IR', 'goog.i18n.CompactNumberFormatSymbols_naq', 'goog.i18n.CompactNumberFormatSymbols_naq_NA', 'goog.i18n.CompactNumberFormatSymbols_nb_NO', 'goog.i18n.CompactNumberFormatSymbols_nb_SJ', 'goog.i18n.CompactNumberFormatSymbols_nd', 'goog.i18n.CompactNumberFormatSymbols_nd_ZW', 'goog.i18n.CompactNumberFormatSymbols_nds', 'goog.i18n.CompactNumberFormatSymbols_nds_DE', 'goog.i18n.CompactNumberFormatSymbols_nds_NL', 'goog.i18n.CompactNumberFormatSymbols_ne_IN', 'goog.i18n.CompactNumberFormatSymbols_ne_NP', 'goog.i18n.CompactNumberFormatSymbols_nl_AW', 'goog.i18n.CompactNumberFormatSymbols_nl_BE', 'goog.i18n.CompactNumberFormatSymbols_nl_BQ', 'goog.i18n.CompactNumberFormatSymbols_nl_CW', 'goog.i18n.CompactNumberFormatSymbols_nl_NL', 'goog.i18n.CompactNumberFormatSymbols_nl_SR', 'goog.i18n.CompactNumberFormatSymbols_nl_SX', 'goog.i18n.CompactNumberFormatSymbols_nmg', 'goog.i18n.CompactNumberFormatSymbols_nmg_CM', 'goog.i18n.CompactNumberFormatSymbols_nn', 'goog.i18n.CompactNumberFormatSymbols_nn_NO', 'goog.i18n.CompactNumberFormatSymbols_nnh', 'goog.i18n.CompactNumberFormatSymbols_nnh_CM', 'goog.i18n.CompactNumberFormatSymbols_nus', 'goog.i18n.CompactNumberFormatSymbols_nus_SS', 'goog.i18n.CompactNumberFormatSymbols_nyn', 'goog.i18n.CompactNumberFormatSymbols_nyn_UG', 'goog.i18n.CompactNumberFormatSymbols_om', 'goog.i18n.CompactNumberFormatSymbols_om_ET', 'goog.i18n.CompactNumberFormatSymbols_om_KE', 'goog.i18n.CompactNumberFormatSymbols_or_IN', 'goog.i18n.CompactNumberFormatSymbols_os', 'goog.i18n.CompactNumberFormatSymbols_os_GE', 'goog.i18n.CompactNumberFormatSymbols_os_RU', 'goog.i18n.CompactNumberFormatSymbols_pa_Arab', 'goog.i18n.CompactNumberFormatSymbols_pa_Arab_PK', 'goog.i18n.CompactNumberFormatSymbols_pa_Guru', 'goog.i18n.CompactNumberFormatSymbols_pa_Guru_IN', 'goog.i18n.CompactNumberFormatSymbols_pl_PL', 'goog.i18n.CompactNumberFormatSymbols_ps', 'goog.i18n.CompactNumberFormatSymbols_ps_AF', 'goog.i18n.CompactNumberFormatSymbols_ps_PK', 'goog.i18n.CompactNumberFormatSymbols_pt_AO', 'goog.i18n.CompactNumberFormatSymbols_pt_CH', 'goog.i18n.CompactNumberFormatSymbols_pt_CV', 'goog.i18n.CompactNumberFormatSymbols_pt_GQ', 'goog.i18n.CompactNumberFormatSymbols_pt_GW', 'goog.i18n.CompactNumberFormatSymbols_pt_LU', 'goog.i18n.CompactNumberFormatSymbols_pt_MO', 'goog.i18n.CompactNumberFormatSymbols_pt_MZ', 'goog.i18n.CompactNumberFormatSymbols_pt_ST', 'goog.i18n.CompactNumberFormatSymbols_pt_TL', 'goog.i18n.CompactNumberFormatSymbols_qu', 'goog.i18n.CompactNumberFormatSymbols_qu_BO', 'goog.i18n.CompactNumberFormatSymbols_qu_EC', 'goog.i18n.CompactNumberFormatSymbols_qu_PE', 'goog.i18n.CompactNumberFormatSymbols_rm', 'goog.i18n.CompactNumberFormatSymbols_rm_CH', 'goog.i18n.CompactNumberFormatSymbols_rn', 'goog.i18n.CompactNumberFormatSymbols_rn_BI', 'goog.i18n.CompactNumberFormatSymbols_ro_MD', 'goog.i18n.CompactNumberFormatSymbols_ro_RO', 'goog.i18n.CompactNumberFormatSymbols_rof', 'goog.i18n.CompactNumberFormatSymbols_rof_TZ', 'goog.i18n.CompactNumberFormatSymbols_ru_BY', 'goog.i18n.CompactNumberFormatSymbols_ru_KG', 'goog.i18n.CompactNumberFormatSymbols_ru_KZ', 'goog.i18n.CompactNumberFormatSymbols_ru_MD', 'goog.i18n.CompactNumberFormatSymbols_ru_RU', 'goog.i18n.CompactNumberFormatSymbols_ru_UA', 'goog.i18n.CompactNumberFormatSymbols_rw', 'goog.i18n.CompactNumberFormatSymbols_rw_RW', 'goog.i18n.CompactNumberFormatSymbols_rwk', 'goog.i18n.CompactNumberFormatSymbols_rwk_TZ', 'goog.i18n.CompactNumberFormatSymbols_sah', 'goog.i18n.CompactNumberFormatSymbols_sah_RU', 'goog.i18n.CompactNumberFormatSymbols_saq', 'goog.i18n.CompactNumberFormatSymbols_saq_KE', 'goog.i18n.CompactNumberFormatSymbols_sbp', 'goog.i18n.CompactNumberFormatSymbols_sbp_TZ', 'goog.i18n.CompactNumberFormatSymbols_sd', 'goog.i18n.CompactNumberFormatSymbols_sd_PK', 'goog.i18n.CompactNumberFormatSymbols_se', 'goog.i18n.CompactNumberFormatSymbols_se_FI', 'goog.i18n.CompactNumberFormatSymbols_se_NO', 'goog.i18n.CompactNumberFormatSymbols_se_SE', 'goog.i18n.CompactNumberFormatSymbols_seh', 'goog.i18n.CompactNumberFormatSymbols_seh_MZ', 'goog.i18n.CompactNumberFormatSymbols_ses', 'goog.i18n.CompactNumberFormatSymbols_ses_ML', 'goog.i18n.CompactNumberFormatSymbols_sg', 'goog.i18n.CompactNumberFormatSymbols_sg_CF', 'goog.i18n.CompactNumberFormatSymbols_shi', 'goog.i18n.CompactNumberFormatSymbols_shi_Latn', 'goog.i18n.CompactNumberFormatSymbols_shi_Latn_MA', 'goog.i18n.CompactNumberFormatSymbols_shi_Tfng', 'goog.i18n.CompactNumberFormatSymbols_shi_Tfng_MA', 'goog.i18n.CompactNumberFormatSymbols_si_LK', 'goog.i18n.CompactNumberFormatSymbols_sk_SK', 'goog.i18n.CompactNumberFormatSymbols_sl_SI', 'goog.i18n.CompactNumberFormatSymbols_smn', 'goog.i18n.CompactNumberFormatSymbols_smn_FI', 'goog.i18n.CompactNumberFormatSymbols_sn', 'goog.i18n.CompactNumberFormatSymbols_sn_ZW', 'goog.i18n.CompactNumberFormatSymbols_so', 'goog.i18n.CompactNumberFormatSymbols_so_DJ', 'goog.i18n.CompactNumberFormatSymbols_so_ET', 'goog.i18n.CompactNumberFormatSymbols_so_KE', 'goog.i18n.CompactNumberFormatSymbols_so_SO', 'goog.i18n.CompactNumberFormatSymbols_sq_AL', 'goog.i18n.CompactNumberFormatSymbols_sq_MK', 'goog.i18n.CompactNumberFormatSymbols_sq_XK', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl_BA', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl_ME', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl_RS', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl_XK', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn_BA', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn_ME', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn_RS', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn_XK', 'goog.i18n.CompactNumberFormatSymbols_sv_AX', 'goog.i18n.CompactNumberFormatSymbols_sv_FI', 'goog.i18n.CompactNumberFormatSymbols_sv_SE', 'goog.i18n.CompactNumberFormatSymbols_sw_CD', 'goog.i18n.CompactNumberFormatSymbols_sw_KE', 'goog.i18n.CompactNumberFormatSymbols_sw_TZ', 'goog.i18n.CompactNumberFormatSymbols_sw_UG', 'goog.i18n.CompactNumberFormatSymbols_ta_IN', 'goog.i18n.CompactNumberFormatSymbols_ta_LK', 'goog.i18n.CompactNumberFormatSymbols_ta_MY', 'goog.i18n.CompactNumberFormatSymbols_ta_SG', 'goog.i18n.CompactNumberFormatSymbols_te_IN', 'goog.i18n.CompactNumberFormatSymbols_teo', 'goog.i18n.CompactNumberFormatSymbols_teo_KE', 'goog.i18n.CompactNumberFormatSymbols_teo_UG', 'goog.i18n.CompactNumberFormatSymbols_tg', 'goog.i18n.CompactNumberFormatSymbols_tg_TJ', 'goog.i18n.CompactNumberFormatSymbols_th_TH', 'goog.i18n.CompactNumberFormatSymbols_ti', 'goog.i18n.CompactNumberFormatSymbols_ti_ER', 'goog.i18n.CompactNumberFormatSymbols_ti_ET', 'goog.i18n.CompactNumberFormatSymbols_tk', 'goog.i18n.CompactNumberFormatSymbols_tk_TM', 'goog.i18n.CompactNumberFormatSymbols_to', 'goog.i18n.CompactNumberFormatSymbols_to_TO', 'goog.i18n.CompactNumberFormatSymbols_tr_CY', 'goog.i18n.CompactNumberFormatSymbols_tr_TR', 'goog.i18n.CompactNumberFormatSymbols_tt', 'goog.i18n.CompactNumberFormatSymbols_tt_RU', 'goog.i18n.CompactNumberFormatSymbols_twq', 'goog.i18n.CompactNumberFormatSymbols_twq_NE', 'goog.i18n.CompactNumberFormatSymbols_tzm', 'goog.i18n.CompactNumberFormatSymbols_tzm_MA', 'goog.i18n.CompactNumberFormatSymbols_ug', 'goog.i18n.CompactNumberFormatSymbols_ug_CN', 'goog.i18n.CompactNumberFormatSymbols_uk_UA', 'goog.i18n.CompactNumberFormatSymbols_ur_IN', 'goog.i18n.CompactNumberFormatSymbols_ur_PK', 'goog.i18n.CompactNumberFormatSymbols_uz_Arab', 'goog.i18n.CompactNumberFormatSymbols_uz_Arab_AF', 'goog.i18n.CompactNumberFormatSymbols_uz_Cyrl', 'goog.i18n.CompactNumberFormatSymbols_uz_Cyrl_UZ', 'goog.i18n.CompactNumberFormatSymbols_uz_Latn', 'goog.i18n.CompactNumberFormatSymbols_uz_Latn_UZ', 'goog.i18n.CompactNumberFormatSymbols_vai', 'goog.i18n.CompactNumberFormatSymbols_vai_Latn', 'goog.i18n.CompactNumberFormatSymbols_vai_Latn_LR', 'goog.i18n.CompactNumberFormatSymbols_vai_Vaii', 'goog.i18n.CompactNumberFormatSymbols_vai_Vaii_LR', 'goog.i18n.CompactNumberFormatSymbols_vi_VN', 'goog.i18n.CompactNumberFormatSymbols_vun', 'goog.i18n.CompactNumberFormatSymbols_vun_TZ', 'goog.i18n.CompactNumberFormatSymbols_wae', 'goog.i18n.CompactNumberFormatSymbols_wae_CH', 'goog.i18n.CompactNumberFormatSymbols_wo', 'goog.i18n.CompactNumberFormatSymbols_wo_SN', 'goog.i18n.CompactNumberFormatSymbols_xh', 'goog.i18n.CompactNumberFormatSymbols_xh_ZA', 'goog.i18n.CompactNumberFormatSymbols_xog', 'goog.i18n.CompactNumberFormatSymbols_xog_UG', 'goog.i18n.CompactNumberFormatSymbols_yav', 'goog.i18n.CompactNumberFormatSymbols_yav_CM', 'goog.i18n.CompactNumberFormatSymbols_yi', 'goog.i18n.CompactNumberFormatSymbols_yi_001', 'goog.i18n.CompactNumberFormatSymbols_yo', 'goog.i18n.CompactNumberFormatSymbols_yo_BJ', 'goog.i18n.CompactNumberFormatSymbols_yo_NG', 'goog.i18n.CompactNumberFormatSymbols_yue', 'goog.i18n.CompactNumberFormatSymbols_yue_Hans', 'goog.i18n.CompactNumberFormatSymbols_yue_Hans_CN', 'goog.i18n.CompactNumberFormatSymbols_yue_Hant', 'goog.i18n.CompactNumberFormatSymbols_yue_Hant_HK', 'goog.i18n.CompactNumberFormatSymbols_zgh', 'goog.i18n.CompactNumberFormatSymbols_zgh_MA', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans_CN', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans_HK', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans_MO', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans_SG', 'goog.i18n.CompactNumberFormatSymbols_zh_Hant', 'goog.i18n.CompactNumberFormatSymbols_zh_Hant_HK', 'goog.i18n.CompactNumberFormatSymbols_zh_Hant_MO', 'goog.i18n.CompactNumberFormatSymbols_zh_Hant_TW', 'goog.i18n.CompactNumberFormatSymbols_zu_ZA'], ['goog.i18n.CompactNumberFormatSymbols'], {});
goog.addDependency('i18n/currency.js', ['goog.i18n.currency', 'goog.i18n.currency.CurrencyInfo', 'goog.i18n.currency.CurrencyInfoTier2'], [], {'lang': 'es6'});
goog.addDependency('i18n/currency_test.js', ['goog.i18n.currencyTest'], ['goog.i18n.NumberFormat', 'goog.i18n.currency', 'goog.i18n.currency.CurrencyInfo', 'goog.object', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/currencycodemap.js', ['goog.i18n.currencyCodeMap', 'goog.i18n.currencyCodeMapTier2'], [], {});
goog.addDependency('i18n/dateintervalformat.js', ['goog.i18n.DateIntervalFormat'], ['goog.array', 'goog.asserts', 'goog.date.DateLike', 'goog.date.DateRange', 'goog.date.DateTime', 'goog.date.Interval', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbolsType', 'goog.i18n.TimeZone', 'goog.i18n.dateIntervalSymbols', 'goog.object'], {'lang': 'es5', 'module': 'goog'});
goog.addDependency('i18n/dateintervalformat_test.js', ['goog.i18n.DateIntervalFormatTest'], ['goog.date.Date', 'goog.date.DateRange', 'goog.date.DateTime', 'goog.date.Interval', 'goog.i18n.DateIntervalFormat', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeSymbols_ar_EG', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_fr_CA', 'goog.i18n.DateTimeSymbols_gl', 'goog.i18n.DateTimeSymbols_hi', 'goog.i18n.DateTimeSymbols_zh', 'goog.i18n.TimeZone', 'goog.i18n.dateIntervalPatterns', 'goog.i18n.dateIntervalSymbols', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/dateintervalpatterns.js', ['goog.i18n.dateIntervalPatterns'], ['goog.i18n.dateIntervalSymbols'], {'module': 'goog'});
goog.addDependency('i18n/dateintervalpatternsext.js', ['goog.i18n.dateIntervalPatternsExt'], ['goog.i18n.dateIntervalPatterns'], {'module': 'goog'});
goog.addDependency('i18n/dateintervalsymbols.js', ['goog.i18n.dateIntervalSymbols'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/dateintervalsymbolsext.js', ['goog.i18n.dateIntervalSymbolsExt'], ['goog.i18n.dateIntervalSymbols'], {'module': 'goog'});
goog.addDependency('i18n/datetimeformat.js', ['goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeFormat.Format'], ['goog.asserts', 'goog.date', 'goog.i18n.DateTimeSymbols', 'goog.i18n.TimeZone', 'goog.string'], {});
goog.addDependency('i18n/datetimeformat_test.js', ['goog.i18n.DateTimeFormatTest'], ['goog.date.Date', 'goog.date.DateTime', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimePatterns', 'goog.i18n.DateTimePatterns_ar_EG', 'goog.i18n.DateTimePatterns_bg', 'goog.i18n.DateTimePatterns_de', 'goog.i18n.DateTimePatterns_en', 'goog.i18n.DateTimePatterns_en_XA', 'goog.i18n.DateTimePatterns_fa', 'goog.i18n.DateTimePatterns_fr', 'goog.i18n.DateTimePatterns_ja', 'goog.i18n.DateTimePatterns_sv', 'goog.i18n.DateTimePatterns_zh_HK', 'goog.i18n.DateTimePatterns_zh_Hant_TW', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbols_ar_AE', 'goog.i18n.DateTimeSymbols_ar_EG', 'goog.i18n.DateTimeSymbols_ar_SA', 'goog.i18n.DateTimeSymbols_bn_BD', 'goog.i18n.DateTimeSymbols_de', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_en_GB', 'goog.i18n.DateTimeSymbols_en_IE', 'goog.i18n.DateTimeSymbols_en_IN', 'goog.i18n.DateTimeSymbols_en_US', 'goog.i18n.DateTimeSymbols_fa', 'goog.i18n.DateTimeSymbols_fr', 'goog.i18n.DateTimeSymbols_fr_DJ', 'goog.i18n.DateTimeSymbols_he_IL', 'goog.i18n.DateTimeSymbols_ja', 'goog.i18n.DateTimeSymbols_ro_RO', 'goog.i18n.DateTimeSymbols_sv', 'goog.i18n.TimeZone', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/datetimeparse.js', ['goog.i18n.DateTimeParse'], ['goog.asserts', 'goog.date', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeSymbols'], {});
goog.addDependency('i18n/datetimeparse_test.js', ['goog.i18n.DateTimeParseTest'], ['goog.date.Date', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeParse', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_fa', 'goog.i18n.DateTimeSymbols_fr', 'goog.i18n.DateTimeSymbols_pl', 'goog.i18n.DateTimeSymbols_zh', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/datetimepatterns.js', ['goog.i18n.DateTimePatterns', 'goog.i18n.DateTimePatterns_af', 'goog.i18n.DateTimePatterns_am', 'goog.i18n.DateTimePatterns_ar', 'goog.i18n.DateTimePatterns_ar_DZ', 'goog.i18n.DateTimePatterns_ar_EG', 'goog.i18n.DateTimePatterns_az', 'goog.i18n.DateTimePatterns_be', 'goog.i18n.DateTimePatterns_bg', 'goog.i18n.DateTimePatterns_bn', 'goog.i18n.DateTimePatterns_br', 'goog.i18n.DateTimePatterns_bs', 'goog.i18n.DateTimePatterns_ca', 'goog.i18n.DateTimePatterns_chr', 'goog.i18n.DateTimePatterns_cs', 'goog.i18n.DateTimePatterns_cy', 'goog.i18n.DateTimePatterns_da', 'goog.i18n.DateTimePatterns_de', 'goog.i18n.DateTimePatterns_de_AT', 'goog.i18n.DateTimePatterns_de_CH', 'goog.i18n.DateTimePatterns_el', 'goog.i18n.DateTimePatterns_en', 'goog.i18n.DateTimePatterns_en_AU', 'goog.i18n.DateTimePatterns_en_CA', 'goog.i18n.DateTimePatterns_en_GB', 'goog.i18n.DateTimePatterns_en_IE', 'goog.i18n.DateTimePatterns_en_IN', 'goog.i18n.DateTimePatterns_en_SG', 'goog.i18n.DateTimePatterns_en_US', 'goog.i18n.DateTimePatterns_en_ZA', 'goog.i18n.DateTimePatterns_es', 'goog.i18n.DateTimePatterns_es_419', 'goog.i18n.DateTimePatterns_es_ES', 'goog.i18n.DateTimePatterns_es_MX', 'goog.i18n.DateTimePatterns_es_US', 'goog.i18n.DateTimePatterns_et', 'goog.i18n.DateTimePatterns_eu', 'goog.i18n.DateTimePatterns_fa', 'goog.i18n.DateTimePatterns_fi', 'goog.i18n.DateTimePatterns_fil', 'goog.i18n.DateTimePatterns_fr', 'goog.i18n.DateTimePatterns_fr_CA', 'goog.i18n.DateTimePatterns_ga', 'goog.i18n.DateTimePatterns_gl', 'goog.i18n.DateTimePatterns_gsw', 'goog.i18n.DateTimePatterns_gu', 'goog.i18n.DateTimePatterns_haw', 'goog.i18n.DateTimePatterns_he', 'goog.i18n.DateTimePatterns_hi', 'goog.i18n.DateTimePatterns_hr', 'goog.i18n.DateTimePatterns_hu', 'goog.i18n.DateTimePatterns_hy', 'goog.i18n.DateTimePatterns_id', 'goog.i18n.DateTimePatterns_in', 'goog.i18n.DateTimePatterns_is', 'goog.i18n.DateTimePatterns_it', 'goog.i18n.DateTimePatterns_iw', 'goog.i18n.DateTimePatterns_ja', 'goog.i18n.DateTimePatterns_ka', 'goog.i18n.DateTimePatterns_kk', 'goog.i18n.DateTimePatterns_km', 'goog.i18n.DateTimePatterns_kn', 'goog.i18n.DateTimePatterns_ko', 'goog.i18n.DateTimePatterns_ky', 'goog.i18n.DateTimePatterns_ln', 'goog.i18n.DateTimePatterns_lo', 'goog.i18n.DateTimePatterns_lt', 'goog.i18n.DateTimePatterns_lv', 'goog.i18n.DateTimePatterns_mk', 'goog.i18n.DateTimePatterns_ml', 'goog.i18n.DateTimePatterns_mn', 'goog.i18n.DateTimePatterns_mo', 'goog.i18n.DateTimePatterns_mr', 'goog.i18n.DateTimePatterns_ms', 'goog.i18n.DateTimePatterns_mt', 'goog.i18n.DateTimePatterns_my', 'goog.i18n.DateTimePatterns_nb', 'goog.i18n.DateTimePatterns_ne', 'goog.i18n.DateTimePatterns_nl', 'goog.i18n.DateTimePatterns_no', 'goog.i18n.DateTimePatterns_no_NO', 'goog.i18n.DateTimePatterns_or', 'goog.i18n.DateTimePatterns_pa', 'goog.i18n.DateTimePatterns_pl', 'goog.i18n.DateTimePatterns_pt', 'goog.i18n.DateTimePatterns_pt_BR', 'goog.i18n.DateTimePatterns_pt_PT', 'goog.i18n.DateTimePatterns_ro', 'goog.i18n.DateTimePatterns_ru', 'goog.i18n.DateTimePatterns_sh', 'goog.i18n.DateTimePatterns_si', 'goog.i18n.DateTimePatterns_sk', 'goog.i18n.DateTimePatterns_sl', 'goog.i18n.DateTimePatterns_sq', 'goog.i18n.DateTimePatterns_sr', 'goog.i18n.DateTimePatterns_sr_Latn', 'goog.i18n.DateTimePatterns_sv', 'goog.i18n.DateTimePatterns_sw', 'goog.i18n.DateTimePatterns_ta', 'goog.i18n.DateTimePatterns_te', 'goog.i18n.DateTimePatterns_th', 'goog.i18n.DateTimePatterns_tl', 'goog.i18n.DateTimePatterns_tr', 'goog.i18n.DateTimePatterns_uk', 'goog.i18n.DateTimePatterns_ur', 'goog.i18n.DateTimePatterns_uz', 'goog.i18n.DateTimePatterns_vi', 'goog.i18n.DateTimePatterns_zh', 'goog.i18n.DateTimePatterns_zh_CN', 'goog.i18n.DateTimePatterns_zh_HK', 'goog.i18n.DateTimePatterns_zh_TW', 'goog.i18n.DateTimePatterns_zu'], [], {});
goog.addDependency('i18n/datetimepatternsext.js', ['goog.i18n.DateTimePatternsExt', 'goog.i18n.DateTimePatterns_af_NA', 'goog.i18n.DateTimePatterns_af_ZA', 'goog.i18n.DateTimePatterns_agq', 'goog.i18n.DateTimePatterns_agq_CM', 'goog.i18n.DateTimePatterns_ak', 'goog.i18n.DateTimePatterns_ak_GH', 'goog.i18n.DateTimePatterns_am_ET', 'goog.i18n.DateTimePatterns_ar_001', 'goog.i18n.DateTimePatterns_ar_AE', 'goog.i18n.DateTimePatterns_ar_BH', 'goog.i18n.DateTimePatterns_ar_DJ', 'goog.i18n.DateTimePatterns_ar_EH', 'goog.i18n.DateTimePatterns_ar_ER', 'goog.i18n.DateTimePatterns_ar_IL', 'goog.i18n.DateTimePatterns_ar_IQ', 'goog.i18n.DateTimePatterns_ar_JO', 'goog.i18n.DateTimePatterns_ar_KM', 'goog.i18n.DateTimePatterns_ar_KW', 'goog.i18n.DateTimePatterns_ar_LB', 'goog.i18n.DateTimePatterns_ar_LY', 'goog.i18n.DateTimePatterns_ar_MA', 'goog.i18n.DateTimePatterns_ar_MR', 'goog.i18n.DateTimePatterns_ar_OM', 'goog.i18n.DateTimePatterns_ar_PS', 'goog.i18n.DateTimePatterns_ar_QA', 'goog.i18n.DateTimePatterns_ar_SA', 'goog.i18n.DateTimePatterns_ar_SD', 'goog.i18n.DateTimePatterns_ar_SO', 'goog.i18n.DateTimePatterns_ar_SS', 'goog.i18n.DateTimePatterns_ar_SY', 'goog.i18n.DateTimePatterns_ar_TD', 'goog.i18n.DateTimePatterns_ar_TN', 'goog.i18n.DateTimePatterns_ar_XB', 'goog.i18n.DateTimePatterns_ar_YE', 'goog.i18n.DateTimePatterns_as', 'goog.i18n.DateTimePatterns_as_IN', 'goog.i18n.DateTimePatterns_asa', 'goog.i18n.DateTimePatterns_asa_TZ', 'goog.i18n.DateTimePatterns_ast', 'goog.i18n.DateTimePatterns_ast_ES', 'goog.i18n.DateTimePatterns_az_Cyrl', 'goog.i18n.DateTimePatterns_az_Cyrl_AZ', 'goog.i18n.DateTimePatterns_az_Latn', 'goog.i18n.DateTimePatterns_az_Latn_AZ', 'goog.i18n.DateTimePatterns_bas', 'goog.i18n.DateTimePatterns_bas_CM', 'goog.i18n.DateTimePatterns_be_BY', 'goog.i18n.DateTimePatterns_bem', 'goog.i18n.DateTimePatterns_bem_ZM', 'goog.i18n.DateTimePatterns_bez', 'goog.i18n.DateTimePatterns_bez_TZ', 'goog.i18n.DateTimePatterns_bg_BG', 'goog.i18n.DateTimePatterns_bm', 'goog.i18n.DateTimePatterns_bm_ML', 'goog.i18n.DateTimePatterns_bn_BD', 'goog.i18n.DateTimePatterns_bn_IN', 'goog.i18n.DateTimePatterns_bo', 'goog.i18n.DateTimePatterns_bo_CN', 'goog.i18n.DateTimePatterns_bo_IN', 'goog.i18n.DateTimePatterns_br_FR', 'goog.i18n.DateTimePatterns_brx', 'goog.i18n.DateTimePatterns_brx_IN', 'goog.i18n.DateTimePatterns_bs_Cyrl', 'goog.i18n.DateTimePatterns_bs_Cyrl_BA', 'goog.i18n.DateTimePatterns_bs_Latn', 'goog.i18n.DateTimePatterns_bs_Latn_BA', 'goog.i18n.DateTimePatterns_ca_AD', 'goog.i18n.DateTimePatterns_ca_ES', 'goog.i18n.DateTimePatterns_ca_FR', 'goog.i18n.DateTimePatterns_ca_IT', 'goog.i18n.DateTimePatterns_ccp', 'goog.i18n.DateTimePatterns_ccp_BD', 'goog.i18n.DateTimePatterns_ccp_IN', 'goog.i18n.DateTimePatterns_ce', 'goog.i18n.DateTimePatterns_ce_RU', 'goog.i18n.DateTimePatterns_ceb', 'goog.i18n.DateTimePatterns_ceb_PH', 'goog.i18n.DateTimePatterns_cgg', 'goog.i18n.DateTimePatterns_cgg_UG', 'goog.i18n.DateTimePatterns_chr_US', 'goog.i18n.DateTimePatterns_ckb', 'goog.i18n.DateTimePatterns_ckb_IQ', 'goog.i18n.DateTimePatterns_ckb_IR', 'goog.i18n.DateTimePatterns_cs_CZ', 'goog.i18n.DateTimePatterns_cy_GB', 'goog.i18n.DateTimePatterns_da_DK', 'goog.i18n.DateTimePatterns_da_GL', 'goog.i18n.DateTimePatterns_dav', 'goog.i18n.DateTimePatterns_dav_KE', 'goog.i18n.DateTimePatterns_de_BE', 'goog.i18n.DateTimePatterns_de_DE', 'goog.i18n.DateTimePatterns_de_IT', 'goog.i18n.DateTimePatterns_de_LI', 'goog.i18n.DateTimePatterns_de_LU', 'goog.i18n.DateTimePatterns_dje', 'goog.i18n.DateTimePatterns_dje_NE', 'goog.i18n.DateTimePatterns_dsb', 'goog.i18n.DateTimePatterns_dsb_DE', 'goog.i18n.DateTimePatterns_dua', 'goog.i18n.DateTimePatterns_dua_CM', 'goog.i18n.DateTimePatterns_dyo', 'goog.i18n.DateTimePatterns_dyo_SN', 'goog.i18n.DateTimePatterns_dz', 'goog.i18n.DateTimePatterns_dz_BT', 'goog.i18n.DateTimePatterns_ebu', 'goog.i18n.DateTimePatterns_ebu_KE', 'goog.i18n.DateTimePatterns_ee', 'goog.i18n.DateTimePatterns_ee_GH', 'goog.i18n.DateTimePatterns_ee_TG', 'goog.i18n.DateTimePatterns_el_CY', 'goog.i18n.DateTimePatterns_el_GR', 'goog.i18n.DateTimePatterns_en_001', 'goog.i18n.DateTimePatterns_en_150', 'goog.i18n.DateTimePatterns_en_AE', 'goog.i18n.DateTimePatterns_en_AG', 'goog.i18n.DateTimePatterns_en_AI', 'goog.i18n.DateTimePatterns_en_AS', 'goog.i18n.DateTimePatterns_en_AT', 'goog.i18n.DateTimePatterns_en_BB', 'goog.i18n.DateTimePatterns_en_BE', 'goog.i18n.DateTimePatterns_en_BI', 'goog.i18n.DateTimePatterns_en_BM', 'goog.i18n.DateTimePatterns_en_BS', 'goog.i18n.DateTimePatterns_en_BW', 'goog.i18n.DateTimePatterns_en_BZ', 'goog.i18n.DateTimePatterns_en_CC', 'goog.i18n.DateTimePatterns_en_CH', 'goog.i18n.DateTimePatterns_en_CK', 'goog.i18n.DateTimePatterns_en_CM', 'goog.i18n.DateTimePatterns_en_CX', 'goog.i18n.DateTimePatterns_en_CY', 'goog.i18n.DateTimePatterns_en_DE', 'goog.i18n.DateTimePatterns_en_DG', 'goog.i18n.DateTimePatterns_en_DK', 'goog.i18n.DateTimePatterns_en_DM', 'goog.i18n.DateTimePatterns_en_ER', 'goog.i18n.DateTimePatterns_en_FI', 'goog.i18n.DateTimePatterns_en_FJ', 'goog.i18n.DateTimePatterns_en_FK', 'goog.i18n.DateTimePatterns_en_FM', 'goog.i18n.DateTimePatterns_en_GD', 'goog.i18n.DateTimePatterns_en_GG', 'goog.i18n.DateTimePatterns_en_GH', 'goog.i18n.DateTimePatterns_en_GI', 'goog.i18n.DateTimePatterns_en_GM', 'goog.i18n.DateTimePatterns_en_GU', 'goog.i18n.DateTimePatterns_en_GY', 'goog.i18n.DateTimePatterns_en_HK', 'goog.i18n.DateTimePatterns_en_IL', 'goog.i18n.DateTimePatterns_en_IM', 'goog.i18n.DateTimePatterns_en_IO', 'goog.i18n.DateTimePatterns_en_JE', 'goog.i18n.DateTimePatterns_en_JM', 'goog.i18n.DateTimePatterns_en_KE', 'goog.i18n.DateTimePatterns_en_KI', 'goog.i18n.DateTimePatterns_en_KN', 'goog.i18n.DateTimePatterns_en_KY', 'goog.i18n.DateTimePatterns_en_LC', 'goog.i18n.DateTimePatterns_en_LR', 'goog.i18n.DateTimePatterns_en_LS', 'goog.i18n.DateTimePatterns_en_MG', 'goog.i18n.DateTimePatterns_en_MH', 'goog.i18n.DateTimePatterns_en_MO', 'goog.i18n.DateTimePatterns_en_MP', 'goog.i18n.DateTimePatterns_en_MS', 'goog.i18n.DateTimePatterns_en_MT', 'goog.i18n.DateTimePatterns_en_MU', 'goog.i18n.DateTimePatterns_en_MW', 'goog.i18n.DateTimePatterns_en_MY', 'goog.i18n.DateTimePatterns_en_NA', 'goog.i18n.DateTimePatterns_en_NF', 'goog.i18n.DateTimePatterns_en_NG', 'goog.i18n.DateTimePatterns_en_NL', 'goog.i18n.DateTimePatterns_en_NR', 'goog.i18n.DateTimePatterns_en_NU', 'goog.i18n.DateTimePatterns_en_NZ', 'goog.i18n.DateTimePatterns_en_PG', 'goog.i18n.DateTimePatterns_en_PH', 'goog.i18n.DateTimePatterns_en_PK', 'goog.i18n.DateTimePatterns_en_PN', 'goog.i18n.DateTimePatterns_en_PR', 'goog.i18n.DateTimePatterns_en_PW', 'goog.i18n.DateTimePatterns_en_RW', 'goog.i18n.DateTimePatterns_en_SB', 'goog.i18n.DateTimePatterns_en_SC', 'goog.i18n.DateTimePatterns_en_SD', 'goog.i18n.DateTimePatterns_en_SE', 'goog.i18n.DateTimePatterns_en_SH', 'goog.i18n.DateTimePatterns_en_SI', 'goog.i18n.DateTimePatterns_en_SL', 'goog.i18n.DateTimePatterns_en_SS', 'goog.i18n.DateTimePatterns_en_SX', 'goog.i18n.DateTimePatterns_en_SZ', 'goog.i18n.DateTimePatterns_en_TC', 'goog.i18n.DateTimePatterns_en_TK', 'goog.i18n.DateTimePatterns_en_TO', 'goog.i18n.DateTimePatterns_en_TT', 'goog.i18n.DateTimePatterns_en_TV', 'goog.i18n.DateTimePatterns_en_TZ', 'goog.i18n.DateTimePatterns_en_UG', 'goog.i18n.DateTimePatterns_en_UM', 'goog.i18n.DateTimePatterns_en_US_POSIX', 'goog.i18n.DateTimePatterns_en_VC', 'goog.i18n.DateTimePatterns_en_VG', 'goog.i18n.DateTimePatterns_en_VI', 'goog.i18n.DateTimePatterns_en_VU', 'goog.i18n.DateTimePatterns_en_WS', 'goog.i18n.DateTimePatterns_en_XA', 'goog.i18n.DateTimePatterns_en_ZM', 'goog.i18n.DateTimePatterns_en_ZW', 'goog.i18n.DateTimePatterns_eo', 'goog.i18n.DateTimePatterns_eo_001', 'goog.i18n.DateTimePatterns_es_AR', 'goog.i18n.DateTimePatterns_es_BO', 'goog.i18n.DateTimePatterns_es_BR', 'goog.i18n.DateTimePatterns_es_BZ', 'goog.i18n.DateTimePatterns_es_CL', 'goog.i18n.DateTimePatterns_es_CO', 'goog.i18n.DateTimePatterns_es_CR', 'goog.i18n.DateTimePatterns_es_CU', 'goog.i18n.DateTimePatterns_es_DO', 'goog.i18n.DateTimePatterns_es_EA', 'goog.i18n.DateTimePatterns_es_EC', 'goog.i18n.DateTimePatterns_es_GQ', 'goog.i18n.DateTimePatterns_es_GT', 'goog.i18n.DateTimePatterns_es_HN', 'goog.i18n.DateTimePatterns_es_IC', 'goog.i18n.DateTimePatterns_es_NI', 'goog.i18n.DateTimePatterns_es_PA', 'goog.i18n.DateTimePatterns_es_PE', 'goog.i18n.DateTimePatterns_es_PH', 'goog.i18n.DateTimePatterns_es_PR', 'goog.i18n.DateTimePatterns_es_PY', 'goog.i18n.DateTimePatterns_es_SV', 'goog.i18n.DateTimePatterns_es_UY', 'goog.i18n.DateTimePatterns_es_VE', 'goog.i18n.DateTimePatterns_et_EE', 'goog.i18n.DateTimePatterns_eu_ES', 'goog.i18n.DateTimePatterns_ewo', 'goog.i18n.DateTimePatterns_ewo_CM', 'goog.i18n.DateTimePatterns_fa_AF', 'goog.i18n.DateTimePatterns_fa_IR', 'goog.i18n.DateTimePatterns_ff', 'goog.i18n.DateTimePatterns_ff_Latn', 'goog.i18n.DateTimePatterns_ff_Latn_BF', 'goog.i18n.DateTimePatterns_ff_Latn_CM', 'goog.i18n.DateTimePatterns_ff_Latn_GH', 'goog.i18n.DateTimePatterns_ff_Latn_GM', 'goog.i18n.DateTimePatterns_ff_Latn_GN', 'goog.i18n.DateTimePatterns_ff_Latn_GW', 'goog.i18n.DateTimePatterns_ff_Latn_LR', 'goog.i18n.DateTimePatterns_ff_Latn_MR', 'goog.i18n.DateTimePatterns_ff_Latn_NE', 'goog.i18n.DateTimePatterns_ff_Latn_NG', 'goog.i18n.DateTimePatterns_ff_Latn_SL', 'goog.i18n.DateTimePatterns_ff_Latn_SN', 'goog.i18n.DateTimePatterns_fi_FI', 'goog.i18n.DateTimePatterns_fil_PH', 'goog.i18n.DateTimePatterns_fo', 'goog.i18n.DateTimePatterns_fo_DK', 'goog.i18n.DateTimePatterns_fo_FO', 'goog.i18n.DateTimePatterns_fr_BE', 'goog.i18n.DateTimePatterns_fr_BF', 'goog.i18n.DateTimePatterns_fr_BI', 'goog.i18n.DateTimePatterns_fr_BJ', 'goog.i18n.DateTimePatterns_fr_BL', 'goog.i18n.DateTimePatterns_fr_CD', 'goog.i18n.DateTimePatterns_fr_CF', 'goog.i18n.DateTimePatterns_fr_CG', 'goog.i18n.DateTimePatterns_fr_CH', 'goog.i18n.DateTimePatterns_fr_CI', 'goog.i18n.DateTimePatterns_fr_CM', 'goog.i18n.DateTimePatterns_fr_DJ', 'goog.i18n.DateTimePatterns_fr_DZ', 'goog.i18n.DateTimePatterns_fr_FR', 'goog.i18n.DateTimePatterns_fr_GA', 'goog.i18n.DateTimePatterns_fr_GF', 'goog.i18n.DateTimePatterns_fr_GN', 'goog.i18n.DateTimePatterns_fr_GP', 'goog.i18n.DateTimePatterns_fr_GQ', 'goog.i18n.DateTimePatterns_fr_HT', 'goog.i18n.DateTimePatterns_fr_KM', 'goog.i18n.DateTimePatterns_fr_LU', 'goog.i18n.DateTimePatterns_fr_MA', 'goog.i18n.DateTimePatterns_fr_MC', 'goog.i18n.DateTimePatterns_fr_MF', 'goog.i18n.DateTimePatterns_fr_MG', 'goog.i18n.DateTimePatterns_fr_ML', 'goog.i18n.DateTimePatterns_fr_MQ', 'goog.i18n.DateTimePatterns_fr_MR', 'goog.i18n.DateTimePatterns_fr_MU', 'goog.i18n.DateTimePatterns_fr_NC', 'goog.i18n.DateTimePatterns_fr_NE', 'goog.i18n.DateTimePatterns_fr_PF', 'goog.i18n.DateTimePatterns_fr_PM', 'goog.i18n.DateTimePatterns_fr_RE', 'goog.i18n.DateTimePatterns_fr_RW', 'goog.i18n.DateTimePatterns_fr_SC', 'goog.i18n.DateTimePatterns_fr_SN', 'goog.i18n.DateTimePatterns_fr_SY', 'goog.i18n.DateTimePatterns_fr_TD', 'goog.i18n.DateTimePatterns_fr_TG', 'goog.i18n.DateTimePatterns_fr_TN', 'goog.i18n.DateTimePatterns_fr_VU', 'goog.i18n.DateTimePatterns_fr_WF', 'goog.i18n.DateTimePatterns_fr_YT', 'goog.i18n.DateTimePatterns_fur', 'goog.i18n.DateTimePatterns_fur_IT', 'goog.i18n.DateTimePatterns_fy', 'goog.i18n.DateTimePatterns_fy_NL', 'goog.i18n.DateTimePatterns_ga_IE', 'goog.i18n.DateTimePatterns_gd', 'goog.i18n.DateTimePatterns_gd_GB', 'goog.i18n.DateTimePatterns_gl_ES', 'goog.i18n.DateTimePatterns_gsw_CH', 'goog.i18n.DateTimePatterns_gsw_FR', 'goog.i18n.DateTimePatterns_gsw_LI', 'goog.i18n.DateTimePatterns_gu_IN', 'goog.i18n.DateTimePatterns_guz', 'goog.i18n.DateTimePatterns_guz_KE', 'goog.i18n.DateTimePatterns_gv', 'goog.i18n.DateTimePatterns_gv_IM', 'goog.i18n.DateTimePatterns_ha', 'goog.i18n.DateTimePatterns_ha_GH', 'goog.i18n.DateTimePatterns_ha_NE', 'goog.i18n.DateTimePatterns_ha_NG', 'goog.i18n.DateTimePatterns_haw_US', 'goog.i18n.DateTimePatterns_he_IL', 'goog.i18n.DateTimePatterns_hi_IN', 'goog.i18n.DateTimePatterns_hr_BA', 'goog.i18n.DateTimePatterns_hr_HR', 'goog.i18n.DateTimePatterns_hsb', 'goog.i18n.DateTimePatterns_hsb_DE', 'goog.i18n.DateTimePatterns_hu_HU', 'goog.i18n.DateTimePatterns_hy_AM', 'goog.i18n.DateTimePatterns_ia', 'goog.i18n.DateTimePatterns_ia_001', 'goog.i18n.DateTimePatterns_id_ID', 'goog.i18n.DateTimePatterns_ig', 'goog.i18n.DateTimePatterns_ig_NG', 'goog.i18n.DateTimePatterns_ii', 'goog.i18n.DateTimePatterns_ii_CN', 'goog.i18n.DateTimePatterns_is_IS', 'goog.i18n.DateTimePatterns_it_CH', 'goog.i18n.DateTimePatterns_it_IT', 'goog.i18n.DateTimePatterns_it_SM', 'goog.i18n.DateTimePatterns_it_VA', 'goog.i18n.DateTimePatterns_ja_JP', 'goog.i18n.DateTimePatterns_jgo', 'goog.i18n.DateTimePatterns_jgo_CM', 'goog.i18n.DateTimePatterns_jmc', 'goog.i18n.DateTimePatterns_jmc_TZ', 'goog.i18n.DateTimePatterns_jv', 'goog.i18n.DateTimePatterns_jv_ID', 'goog.i18n.DateTimePatterns_ka_GE', 'goog.i18n.DateTimePatterns_kab', 'goog.i18n.DateTimePatterns_kab_DZ', 'goog.i18n.DateTimePatterns_kam', 'goog.i18n.DateTimePatterns_kam_KE', 'goog.i18n.DateTimePatterns_kde', 'goog.i18n.DateTimePatterns_kde_TZ', 'goog.i18n.DateTimePatterns_kea', 'goog.i18n.DateTimePatterns_kea_CV', 'goog.i18n.DateTimePatterns_khq', 'goog.i18n.DateTimePatterns_khq_ML', 'goog.i18n.DateTimePatterns_ki', 'goog.i18n.DateTimePatterns_ki_KE', 'goog.i18n.DateTimePatterns_kk_KZ', 'goog.i18n.DateTimePatterns_kkj', 'goog.i18n.DateTimePatterns_kkj_CM', 'goog.i18n.DateTimePatterns_kl', 'goog.i18n.DateTimePatterns_kl_GL', 'goog.i18n.DateTimePatterns_kln', 'goog.i18n.DateTimePatterns_kln_KE', 'goog.i18n.DateTimePatterns_km_KH', 'goog.i18n.DateTimePatterns_kn_IN', 'goog.i18n.DateTimePatterns_ko_KP', 'goog.i18n.DateTimePatterns_ko_KR', 'goog.i18n.DateTimePatterns_kok', 'goog.i18n.DateTimePatterns_kok_IN', 'goog.i18n.DateTimePatterns_ks', 'goog.i18n.DateTimePatterns_ks_IN', 'goog.i18n.DateTimePatterns_ksb', 'goog.i18n.DateTimePatterns_ksb_TZ', 'goog.i18n.DateTimePatterns_ksf', 'goog.i18n.DateTimePatterns_ksf_CM', 'goog.i18n.DateTimePatterns_ksh', 'goog.i18n.DateTimePatterns_ksh_DE', 'goog.i18n.DateTimePatterns_ku', 'goog.i18n.DateTimePatterns_ku_TR', 'goog.i18n.DateTimePatterns_kw', 'goog.i18n.DateTimePatterns_kw_GB', 'goog.i18n.DateTimePatterns_ky_KG', 'goog.i18n.DateTimePatterns_lag', 'goog.i18n.DateTimePatterns_lag_TZ', 'goog.i18n.DateTimePatterns_lb', 'goog.i18n.DateTimePatterns_lb_LU', 'goog.i18n.DateTimePatterns_lg', 'goog.i18n.DateTimePatterns_lg_UG', 'goog.i18n.DateTimePatterns_lkt', 'goog.i18n.DateTimePatterns_lkt_US', 'goog.i18n.DateTimePatterns_ln_AO', 'goog.i18n.DateTimePatterns_ln_CD', 'goog.i18n.DateTimePatterns_ln_CF', 'goog.i18n.DateTimePatterns_ln_CG', 'goog.i18n.DateTimePatterns_lo_LA', 'goog.i18n.DateTimePatterns_lrc', 'goog.i18n.DateTimePatterns_lrc_IQ', 'goog.i18n.DateTimePatterns_lrc_IR', 'goog.i18n.DateTimePatterns_lt_LT', 'goog.i18n.DateTimePatterns_lu', 'goog.i18n.DateTimePatterns_lu_CD', 'goog.i18n.DateTimePatterns_luo', 'goog.i18n.DateTimePatterns_luo_KE', 'goog.i18n.DateTimePatterns_luy', 'goog.i18n.DateTimePatterns_luy_KE', 'goog.i18n.DateTimePatterns_lv_LV', 'goog.i18n.DateTimePatterns_mas', 'goog.i18n.DateTimePatterns_mas_KE', 'goog.i18n.DateTimePatterns_mas_TZ', 'goog.i18n.DateTimePatterns_mer', 'goog.i18n.DateTimePatterns_mer_KE', 'goog.i18n.DateTimePatterns_mfe', 'goog.i18n.DateTimePatterns_mfe_MU', 'goog.i18n.DateTimePatterns_mg', 'goog.i18n.DateTimePatterns_mg_MG', 'goog.i18n.DateTimePatterns_mgh', 'goog.i18n.DateTimePatterns_mgh_MZ', 'goog.i18n.DateTimePatterns_mgo', 'goog.i18n.DateTimePatterns_mgo_CM', 'goog.i18n.DateTimePatterns_mi', 'goog.i18n.DateTimePatterns_mi_NZ', 'goog.i18n.DateTimePatterns_mk_MK', 'goog.i18n.DateTimePatterns_ml_IN', 'goog.i18n.DateTimePatterns_mn_MN', 'goog.i18n.DateTimePatterns_mr_IN', 'goog.i18n.DateTimePatterns_ms_BN', 'goog.i18n.DateTimePatterns_ms_MY', 'goog.i18n.DateTimePatterns_ms_SG', 'goog.i18n.DateTimePatterns_mt_MT', 'goog.i18n.DateTimePatterns_mua', 'goog.i18n.DateTimePatterns_mua_CM', 'goog.i18n.DateTimePatterns_my_MM', 'goog.i18n.DateTimePatterns_mzn', 'goog.i18n.DateTimePatterns_mzn_IR', 'goog.i18n.DateTimePatterns_naq', 'goog.i18n.DateTimePatterns_naq_NA', 'goog.i18n.DateTimePatterns_nb_NO', 'goog.i18n.DateTimePatterns_nb_SJ', 'goog.i18n.DateTimePatterns_nd', 'goog.i18n.DateTimePatterns_nd_ZW', 'goog.i18n.DateTimePatterns_nds', 'goog.i18n.DateTimePatterns_nds_DE', 'goog.i18n.DateTimePatterns_nds_NL', 'goog.i18n.DateTimePatterns_ne_IN', 'goog.i18n.DateTimePatterns_ne_NP', 'goog.i18n.DateTimePatterns_nl_AW', 'goog.i18n.DateTimePatterns_nl_BE', 'goog.i18n.DateTimePatterns_nl_BQ', 'goog.i18n.DateTimePatterns_nl_CW', 'goog.i18n.DateTimePatterns_nl_NL', 'goog.i18n.DateTimePatterns_nl_SR', 'goog.i18n.DateTimePatterns_nl_SX', 'goog.i18n.DateTimePatterns_nmg', 'goog.i18n.DateTimePatterns_nmg_CM', 'goog.i18n.DateTimePatterns_nn', 'goog.i18n.DateTimePatterns_nn_NO', 'goog.i18n.DateTimePatterns_nnh', 'goog.i18n.DateTimePatterns_nnh_CM', 'goog.i18n.DateTimePatterns_nus', 'goog.i18n.DateTimePatterns_nus_SS', 'goog.i18n.DateTimePatterns_nyn', 'goog.i18n.DateTimePatterns_nyn_UG', 'goog.i18n.DateTimePatterns_om', 'goog.i18n.DateTimePatterns_om_ET', 'goog.i18n.DateTimePatterns_om_KE', 'goog.i18n.DateTimePatterns_or_IN', 'goog.i18n.DateTimePatterns_os', 'goog.i18n.DateTimePatterns_os_GE', 'goog.i18n.DateTimePatterns_os_RU', 'goog.i18n.DateTimePatterns_pa_Arab', 'goog.i18n.DateTimePatterns_pa_Arab_PK', 'goog.i18n.DateTimePatterns_pa_Guru', 'goog.i18n.DateTimePatterns_pa_Guru_IN', 'goog.i18n.DateTimePatterns_pl_PL', 'goog.i18n.DateTimePatterns_ps', 'goog.i18n.DateTimePatterns_ps_AF', 'goog.i18n.DateTimePatterns_ps_PK', 'goog.i18n.DateTimePatterns_pt_AO', 'goog.i18n.DateTimePatterns_pt_CH', 'goog.i18n.DateTimePatterns_pt_CV', 'goog.i18n.DateTimePatterns_pt_GQ', 'goog.i18n.DateTimePatterns_pt_GW', 'goog.i18n.DateTimePatterns_pt_LU', 'goog.i18n.DateTimePatterns_pt_MO', 'goog.i18n.DateTimePatterns_pt_MZ', 'goog.i18n.DateTimePatterns_pt_ST', 'goog.i18n.DateTimePatterns_pt_TL', 'goog.i18n.DateTimePatterns_qu', 'goog.i18n.DateTimePatterns_qu_BO', 'goog.i18n.DateTimePatterns_qu_EC', 'goog.i18n.DateTimePatterns_qu_PE', 'goog.i18n.DateTimePatterns_rm', 'goog.i18n.DateTimePatterns_rm_CH', 'goog.i18n.DateTimePatterns_rn', 'goog.i18n.DateTimePatterns_rn_BI', 'goog.i18n.DateTimePatterns_ro_MD', 'goog.i18n.DateTimePatterns_ro_RO', 'goog.i18n.DateTimePatterns_rof', 'goog.i18n.DateTimePatterns_rof_TZ', 'goog.i18n.DateTimePatterns_ru_BY', 'goog.i18n.DateTimePatterns_ru_KG', 'goog.i18n.DateTimePatterns_ru_KZ', 'goog.i18n.DateTimePatterns_ru_MD', 'goog.i18n.DateTimePatterns_ru_RU', 'goog.i18n.DateTimePatterns_ru_UA', 'goog.i18n.DateTimePatterns_rw', 'goog.i18n.DateTimePatterns_rw_RW', 'goog.i18n.DateTimePatterns_rwk', 'goog.i18n.DateTimePatterns_rwk_TZ', 'goog.i18n.DateTimePatterns_sah', 'goog.i18n.DateTimePatterns_sah_RU', 'goog.i18n.DateTimePatterns_saq', 'goog.i18n.DateTimePatterns_saq_KE', 'goog.i18n.DateTimePatterns_sbp', 'goog.i18n.DateTimePatterns_sbp_TZ', 'goog.i18n.DateTimePatterns_sd', 'goog.i18n.DateTimePatterns_sd_PK', 'goog.i18n.DateTimePatterns_se', 'goog.i18n.DateTimePatterns_se_FI', 'goog.i18n.DateTimePatterns_se_NO', 'goog.i18n.DateTimePatterns_se_SE', 'goog.i18n.DateTimePatterns_seh', 'goog.i18n.DateTimePatterns_seh_MZ', 'goog.i18n.DateTimePatterns_ses', 'goog.i18n.DateTimePatterns_ses_ML', 'goog.i18n.DateTimePatterns_sg', 'goog.i18n.DateTimePatterns_sg_CF', 'goog.i18n.DateTimePatterns_shi', 'goog.i18n.DateTimePatterns_shi_Latn', 'goog.i18n.DateTimePatterns_shi_Latn_MA', 'goog.i18n.DateTimePatterns_shi_Tfng', 'goog.i18n.DateTimePatterns_shi_Tfng_MA', 'goog.i18n.DateTimePatterns_si_LK', 'goog.i18n.DateTimePatterns_sk_SK', 'goog.i18n.DateTimePatterns_sl_SI', 'goog.i18n.DateTimePatterns_smn', 'goog.i18n.DateTimePatterns_smn_FI', 'goog.i18n.DateTimePatterns_sn', 'goog.i18n.DateTimePatterns_sn_ZW', 'goog.i18n.DateTimePatterns_so', 'goog.i18n.DateTimePatterns_so_DJ', 'goog.i18n.DateTimePatterns_so_ET', 'goog.i18n.DateTimePatterns_so_KE', 'goog.i18n.DateTimePatterns_so_SO', 'goog.i18n.DateTimePatterns_sq_AL', 'goog.i18n.DateTimePatterns_sq_MK', 'goog.i18n.DateTimePatterns_sq_XK', 'goog.i18n.DateTimePatterns_sr_Cyrl', 'goog.i18n.DateTimePatterns_sr_Cyrl_BA', 'goog.i18n.DateTimePatterns_sr_Cyrl_ME', 'goog.i18n.DateTimePatterns_sr_Cyrl_RS', 'goog.i18n.DateTimePatterns_sr_Cyrl_XK', 'goog.i18n.DateTimePatterns_sr_Latn_BA', 'goog.i18n.DateTimePatterns_sr_Latn_ME', 'goog.i18n.DateTimePatterns_sr_Latn_RS', 'goog.i18n.DateTimePatterns_sr_Latn_XK', 'goog.i18n.DateTimePatterns_sv_AX', 'goog.i18n.DateTimePatterns_sv_FI', 'goog.i18n.DateTimePatterns_sv_SE', 'goog.i18n.DateTimePatterns_sw_CD', 'goog.i18n.DateTimePatterns_sw_KE', 'goog.i18n.DateTimePatterns_sw_TZ', 'goog.i18n.DateTimePatterns_sw_UG', 'goog.i18n.DateTimePatterns_ta_IN', 'goog.i18n.DateTimePatterns_ta_LK', 'goog.i18n.DateTimePatterns_ta_MY', 'goog.i18n.DateTimePatterns_ta_SG', 'goog.i18n.DateTimePatterns_te_IN', 'goog.i18n.DateTimePatterns_teo', 'goog.i18n.DateTimePatterns_teo_KE', 'goog.i18n.DateTimePatterns_teo_UG', 'goog.i18n.DateTimePatterns_tg', 'goog.i18n.DateTimePatterns_tg_TJ', 'goog.i18n.DateTimePatterns_th_TH', 'goog.i18n.DateTimePatterns_ti', 'goog.i18n.DateTimePatterns_ti_ER', 'goog.i18n.DateTimePatterns_ti_ET', 'goog.i18n.DateTimePatterns_tk', 'goog.i18n.DateTimePatterns_tk_TM', 'goog.i18n.DateTimePatterns_to', 'goog.i18n.DateTimePatterns_to_TO', 'goog.i18n.DateTimePatterns_tr_CY', 'goog.i18n.DateTimePatterns_tr_TR', 'goog.i18n.DateTimePatterns_tt', 'goog.i18n.DateTimePatterns_tt_RU', 'goog.i18n.DateTimePatterns_twq', 'goog.i18n.DateTimePatterns_twq_NE', 'goog.i18n.DateTimePatterns_tzm', 'goog.i18n.DateTimePatterns_tzm_MA', 'goog.i18n.DateTimePatterns_ug', 'goog.i18n.DateTimePatterns_ug_CN', 'goog.i18n.DateTimePatterns_uk_UA', 'goog.i18n.DateTimePatterns_ur_IN', 'goog.i18n.DateTimePatterns_ur_PK', 'goog.i18n.DateTimePatterns_uz_Arab', 'goog.i18n.DateTimePatterns_uz_Arab_AF', 'goog.i18n.DateTimePatterns_uz_Cyrl', 'goog.i18n.DateTimePatterns_uz_Cyrl_UZ', 'goog.i18n.DateTimePatterns_uz_Latn', 'goog.i18n.DateTimePatterns_uz_Latn_UZ', 'goog.i18n.DateTimePatterns_vai', 'goog.i18n.DateTimePatterns_vai_Latn', 'goog.i18n.DateTimePatterns_vai_Latn_LR', 'goog.i18n.DateTimePatterns_vai_Vaii', 'goog.i18n.DateTimePatterns_vai_Vaii_LR', 'goog.i18n.DateTimePatterns_vi_VN', 'goog.i18n.DateTimePatterns_vun', 'goog.i18n.DateTimePatterns_vun_TZ', 'goog.i18n.DateTimePatterns_wae', 'goog.i18n.DateTimePatterns_wae_CH', 'goog.i18n.DateTimePatterns_wo', 'goog.i18n.DateTimePatterns_wo_SN', 'goog.i18n.DateTimePatterns_xh', 'goog.i18n.DateTimePatterns_xh_ZA', 'goog.i18n.DateTimePatterns_xog', 'goog.i18n.DateTimePatterns_xog_UG', 'goog.i18n.DateTimePatterns_yav', 'goog.i18n.DateTimePatterns_yav_CM', 'goog.i18n.DateTimePatterns_yi', 'goog.i18n.DateTimePatterns_yi_001', 'goog.i18n.DateTimePatterns_yo', 'goog.i18n.DateTimePatterns_yo_BJ', 'goog.i18n.DateTimePatterns_yo_NG', 'goog.i18n.DateTimePatterns_yue', 'goog.i18n.DateTimePatterns_yue_Hans', 'goog.i18n.DateTimePatterns_yue_Hans_CN', 'goog.i18n.DateTimePatterns_yue_Hant', 'goog.i18n.DateTimePatterns_yue_Hant_HK', 'goog.i18n.DateTimePatterns_zgh', 'goog.i18n.DateTimePatterns_zgh_MA', 'goog.i18n.DateTimePatterns_zh_Hans', 'goog.i18n.DateTimePatterns_zh_Hans_CN', 'goog.i18n.DateTimePatterns_zh_Hans_HK', 'goog.i18n.DateTimePatterns_zh_Hans_MO', 'goog.i18n.DateTimePatterns_zh_Hans_SG', 'goog.i18n.DateTimePatterns_zh_Hant', 'goog.i18n.DateTimePatterns_zh_Hant_HK', 'goog.i18n.DateTimePatterns_zh_Hant_MO', 'goog.i18n.DateTimePatterns_zh_Hant_TW', 'goog.i18n.DateTimePatterns_zu_ZA'], ['goog.i18n.DateTimePatterns'], {});
goog.addDependency('i18n/datetimesymbols.js', ['goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbolsType', 'goog.i18n.DateTimeSymbols_af', 'goog.i18n.DateTimeSymbols_am', 'goog.i18n.DateTimeSymbols_ar', 'goog.i18n.DateTimeSymbols_ar_DZ', 'goog.i18n.DateTimeSymbols_ar_EG', 'goog.i18n.DateTimeSymbols_az', 'goog.i18n.DateTimeSymbols_be', 'goog.i18n.DateTimeSymbols_bg', 'goog.i18n.DateTimeSymbols_bn', 'goog.i18n.DateTimeSymbols_br', 'goog.i18n.DateTimeSymbols_bs', 'goog.i18n.DateTimeSymbols_ca', 'goog.i18n.DateTimeSymbols_chr', 'goog.i18n.DateTimeSymbols_cs', 'goog.i18n.DateTimeSymbols_cy', 'goog.i18n.DateTimeSymbols_da', 'goog.i18n.DateTimeSymbols_de', 'goog.i18n.DateTimeSymbols_de_AT', 'goog.i18n.DateTimeSymbols_de_CH', 'goog.i18n.DateTimeSymbols_el', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_en_AU', 'goog.i18n.DateTimeSymbols_en_CA', 'goog.i18n.DateTimeSymbols_en_GB', 'goog.i18n.DateTimeSymbols_en_IE', 'goog.i18n.DateTimeSymbols_en_IN', 'goog.i18n.DateTimeSymbols_en_ISO', 'goog.i18n.DateTimeSymbols_en_SG', 'goog.i18n.DateTimeSymbols_en_US', 'goog.i18n.DateTimeSymbols_en_ZA', 'goog.i18n.DateTimeSymbols_es', 'goog.i18n.DateTimeSymbols_es_419', 'goog.i18n.DateTimeSymbols_es_ES', 'goog.i18n.DateTimeSymbols_es_MX', 'goog.i18n.DateTimeSymbols_es_US', 'goog.i18n.DateTimeSymbols_et', 'goog.i18n.DateTimeSymbols_eu', 'goog.i18n.DateTimeSymbols_fa', 'goog.i18n.DateTimeSymbols_fi', 'goog.i18n.DateTimeSymbols_fil', 'goog.i18n.DateTimeSymbols_fr', 'goog.i18n.DateTimeSymbols_fr_CA', 'goog.i18n.DateTimeSymbols_ga', 'goog.i18n.DateTimeSymbols_gl', 'goog.i18n.DateTimeSymbols_gsw', 'goog.i18n.DateTimeSymbols_gu', 'goog.i18n.DateTimeSymbols_haw', 'goog.i18n.DateTimeSymbols_he', 'goog.i18n.DateTimeSymbols_hi', 'goog.i18n.DateTimeSymbols_hr', 'goog.i18n.DateTimeSymbols_hu', 'goog.i18n.DateTimeSymbols_hy', 'goog.i18n.DateTimeSymbols_id', 'goog.i18n.DateTimeSymbols_in', 'goog.i18n.DateTimeSymbols_is', 'goog.i18n.DateTimeSymbols_it', 'goog.i18n.DateTimeSymbols_iw', 'goog.i18n.DateTimeSymbols_ja', 'goog.i18n.DateTimeSymbols_ka', 'goog.i18n.DateTimeSymbols_kk', 'goog.i18n.DateTimeSymbols_km', 'goog.i18n.DateTimeSymbols_kn', 'goog.i18n.DateTimeSymbols_ko', 'goog.i18n.DateTimeSymbols_ky', 'goog.i18n.DateTimeSymbols_ln', 'goog.i18n.DateTimeSymbols_lo', 'goog.i18n.DateTimeSymbols_lt', 'goog.i18n.DateTimeSymbols_lv', 'goog.i18n.DateTimeSymbols_mk', 'goog.i18n.DateTimeSymbols_ml', 'goog.i18n.DateTimeSymbols_mn', 'goog.i18n.DateTimeSymbols_mo', 'goog.i18n.DateTimeSymbols_mr', 'goog.i18n.DateTimeSymbols_ms', 'goog.i18n.DateTimeSymbols_mt', 'goog.i18n.DateTimeSymbols_my', 'goog.i18n.DateTimeSymbols_nb', 'goog.i18n.DateTimeSymbols_ne', 'goog.i18n.DateTimeSymbols_nl', 'goog.i18n.DateTimeSymbols_no', 'goog.i18n.DateTimeSymbols_no_NO', 'goog.i18n.DateTimeSymbols_or', 'goog.i18n.DateTimeSymbols_pa', 'goog.i18n.DateTimeSymbols_pl', 'goog.i18n.DateTimeSymbols_pt', 'goog.i18n.DateTimeSymbols_pt_BR', 'goog.i18n.DateTimeSymbols_pt_PT', 'goog.i18n.DateTimeSymbols_ro', 'goog.i18n.DateTimeSymbols_ru', 'goog.i18n.DateTimeSymbols_sh', 'goog.i18n.DateTimeSymbols_si', 'goog.i18n.DateTimeSymbols_sk', 'goog.i18n.DateTimeSymbols_sl', 'goog.i18n.DateTimeSymbols_sq', 'goog.i18n.DateTimeSymbols_sr', 'goog.i18n.DateTimeSymbols_sr_Latn', 'goog.i18n.DateTimeSymbols_sv', 'goog.i18n.DateTimeSymbols_sw', 'goog.i18n.DateTimeSymbols_ta', 'goog.i18n.DateTimeSymbols_te', 'goog.i18n.DateTimeSymbols_th', 'goog.i18n.DateTimeSymbols_tl', 'goog.i18n.DateTimeSymbols_tr', 'goog.i18n.DateTimeSymbols_uk', 'goog.i18n.DateTimeSymbols_ur', 'goog.i18n.DateTimeSymbols_uz', 'goog.i18n.DateTimeSymbols_vi', 'goog.i18n.DateTimeSymbols_zh', 'goog.i18n.DateTimeSymbols_zh_CN', 'goog.i18n.DateTimeSymbols_zh_HK', 'goog.i18n.DateTimeSymbols_zh_TW', 'goog.i18n.DateTimeSymbols_zu'], [], {});
goog.addDependency('i18n/datetimesymbolsext.js', ['goog.i18n.DateTimeSymbolsExt', 'goog.i18n.DateTimeSymbols_af_NA', 'goog.i18n.DateTimeSymbols_af_ZA', 'goog.i18n.DateTimeSymbols_agq', 'goog.i18n.DateTimeSymbols_agq_CM', 'goog.i18n.DateTimeSymbols_ak', 'goog.i18n.DateTimeSymbols_ak_GH', 'goog.i18n.DateTimeSymbols_am_ET', 'goog.i18n.DateTimeSymbols_ar_001', 'goog.i18n.DateTimeSymbols_ar_AE', 'goog.i18n.DateTimeSymbols_ar_BH', 'goog.i18n.DateTimeSymbols_ar_DJ', 'goog.i18n.DateTimeSymbols_ar_EH', 'goog.i18n.DateTimeSymbols_ar_ER', 'goog.i18n.DateTimeSymbols_ar_IL', 'goog.i18n.DateTimeSymbols_ar_IQ', 'goog.i18n.DateTimeSymbols_ar_JO', 'goog.i18n.DateTimeSymbols_ar_KM', 'goog.i18n.DateTimeSymbols_ar_KW', 'goog.i18n.DateTimeSymbols_ar_LB', 'goog.i18n.DateTimeSymbols_ar_LY', 'goog.i18n.DateTimeSymbols_ar_MA', 'goog.i18n.DateTimeSymbols_ar_MR', 'goog.i18n.DateTimeSymbols_ar_OM', 'goog.i18n.DateTimeSymbols_ar_PS', 'goog.i18n.DateTimeSymbols_ar_QA', 'goog.i18n.DateTimeSymbols_ar_SA', 'goog.i18n.DateTimeSymbols_ar_SD', 'goog.i18n.DateTimeSymbols_ar_SO', 'goog.i18n.DateTimeSymbols_ar_SS', 'goog.i18n.DateTimeSymbols_ar_SY', 'goog.i18n.DateTimeSymbols_ar_TD', 'goog.i18n.DateTimeSymbols_ar_TN', 'goog.i18n.DateTimeSymbols_ar_XB', 'goog.i18n.DateTimeSymbols_ar_YE', 'goog.i18n.DateTimeSymbols_as', 'goog.i18n.DateTimeSymbols_as_IN', 'goog.i18n.DateTimeSymbols_asa', 'goog.i18n.DateTimeSymbols_asa_TZ', 'goog.i18n.DateTimeSymbols_ast', 'goog.i18n.DateTimeSymbols_ast_ES', 'goog.i18n.DateTimeSymbols_az_Cyrl', 'goog.i18n.DateTimeSymbols_az_Cyrl_AZ', 'goog.i18n.DateTimeSymbols_az_Latn', 'goog.i18n.DateTimeSymbols_az_Latn_AZ', 'goog.i18n.DateTimeSymbols_bas', 'goog.i18n.DateTimeSymbols_bas_CM', 'goog.i18n.DateTimeSymbols_be_BY', 'goog.i18n.DateTimeSymbols_bem', 'goog.i18n.DateTimeSymbols_bem_ZM', 'goog.i18n.DateTimeSymbols_bez', 'goog.i18n.DateTimeSymbols_bez_TZ', 'goog.i18n.DateTimeSymbols_bg_BG', 'goog.i18n.DateTimeSymbols_bm', 'goog.i18n.DateTimeSymbols_bm_ML', 'goog.i18n.DateTimeSymbols_bn_BD', 'goog.i18n.DateTimeSymbols_bn_IN', 'goog.i18n.DateTimeSymbols_bo', 'goog.i18n.DateTimeSymbols_bo_CN', 'goog.i18n.DateTimeSymbols_bo_IN', 'goog.i18n.DateTimeSymbols_br_FR', 'goog.i18n.DateTimeSymbols_brx', 'goog.i18n.DateTimeSymbols_brx_IN', 'goog.i18n.DateTimeSymbols_bs_Cyrl', 'goog.i18n.DateTimeSymbols_bs_Cyrl_BA', 'goog.i18n.DateTimeSymbols_bs_Latn', 'goog.i18n.DateTimeSymbols_bs_Latn_BA', 'goog.i18n.DateTimeSymbols_ca_AD', 'goog.i18n.DateTimeSymbols_ca_ES', 'goog.i18n.DateTimeSymbols_ca_FR', 'goog.i18n.DateTimeSymbols_ca_IT', 'goog.i18n.DateTimeSymbols_ccp', 'goog.i18n.DateTimeSymbols_ccp_BD', 'goog.i18n.DateTimeSymbols_ccp_IN', 'goog.i18n.DateTimeSymbols_ce', 'goog.i18n.DateTimeSymbols_ce_RU', 'goog.i18n.DateTimeSymbols_ceb', 'goog.i18n.DateTimeSymbols_ceb_PH', 'goog.i18n.DateTimeSymbols_cgg', 'goog.i18n.DateTimeSymbols_cgg_UG', 'goog.i18n.DateTimeSymbols_chr_US', 'goog.i18n.DateTimeSymbols_ckb', 'goog.i18n.DateTimeSymbols_ckb_IQ', 'goog.i18n.DateTimeSymbols_ckb_IR', 'goog.i18n.DateTimeSymbols_cs_CZ', 'goog.i18n.DateTimeSymbols_cy_GB', 'goog.i18n.DateTimeSymbols_da_DK', 'goog.i18n.DateTimeSymbols_da_GL', 'goog.i18n.DateTimeSymbols_dav', 'goog.i18n.DateTimeSymbols_dav_KE', 'goog.i18n.DateTimeSymbols_de_BE', 'goog.i18n.DateTimeSymbols_de_DE', 'goog.i18n.DateTimeSymbols_de_IT', 'goog.i18n.DateTimeSymbols_de_LI', 'goog.i18n.DateTimeSymbols_de_LU', 'goog.i18n.DateTimeSymbols_dje', 'goog.i18n.DateTimeSymbols_dje_NE', 'goog.i18n.DateTimeSymbols_dsb', 'goog.i18n.DateTimeSymbols_dsb_DE', 'goog.i18n.DateTimeSymbols_dua', 'goog.i18n.DateTimeSymbols_dua_CM', 'goog.i18n.DateTimeSymbols_dyo', 'goog.i18n.DateTimeSymbols_dyo_SN', 'goog.i18n.DateTimeSymbols_dz', 'goog.i18n.DateTimeSymbols_dz_BT', 'goog.i18n.DateTimeSymbols_ebu', 'goog.i18n.DateTimeSymbols_ebu_KE', 'goog.i18n.DateTimeSymbols_ee', 'goog.i18n.DateTimeSymbols_ee_GH', 'goog.i18n.DateTimeSymbols_ee_TG', 'goog.i18n.DateTimeSymbols_el_CY', 'goog.i18n.DateTimeSymbols_el_GR', 'goog.i18n.DateTimeSymbols_en_001', 'goog.i18n.DateTimeSymbols_en_150', 'goog.i18n.DateTimeSymbols_en_AE', 'goog.i18n.DateTimeSymbols_en_AG', 'goog.i18n.DateTimeSymbols_en_AI', 'goog.i18n.DateTimeSymbols_en_AS', 'goog.i18n.DateTimeSymbols_en_AT', 'goog.i18n.DateTimeSymbols_en_BB', 'goog.i18n.DateTimeSymbols_en_BE', 'goog.i18n.DateTimeSymbols_en_BI', 'goog.i18n.DateTimeSymbols_en_BM', 'goog.i18n.DateTimeSymbols_en_BS', 'goog.i18n.DateTimeSymbols_en_BW', 'goog.i18n.DateTimeSymbols_en_BZ', 'goog.i18n.DateTimeSymbols_en_CC', 'goog.i18n.DateTimeSymbols_en_CH', 'goog.i18n.DateTimeSymbols_en_CK', 'goog.i18n.DateTimeSymbols_en_CM', 'goog.i18n.DateTimeSymbols_en_CX', 'goog.i18n.DateTimeSymbols_en_CY', 'goog.i18n.DateTimeSymbols_en_DE', 'goog.i18n.DateTimeSymbols_en_DG', 'goog.i18n.DateTimeSymbols_en_DK', 'goog.i18n.DateTimeSymbols_en_DM', 'goog.i18n.DateTimeSymbols_en_ER', 'goog.i18n.DateTimeSymbols_en_FI', 'goog.i18n.DateTimeSymbols_en_FJ', 'goog.i18n.DateTimeSymbols_en_FK', 'goog.i18n.DateTimeSymbols_en_FM', 'goog.i18n.DateTimeSymbols_en_GD', 'goog.i18n.DateTimeSymbols_en_GG', 'goog.i18n.DateTimeSymbols_en_GH', 'goog.i18n.DateTimeSymbols_en_GI', 'goog.i18n.DateTimeSymbols_en_GM', 'goog.i18n.DateTimeSymbols_en_GU', 'goog.i18n.DateTimeSymbols_en_GY', 'goog.i18n.DateTimeSymbols_en_HK', 'goog.i18n.DateTimeSymbols_en_IL', 'goog.i18n.DateTimeSymbols_en_IM', 'goog.i18n.DateTimeSymbols_en_IO', 'goog.i18n.DateTimeSymbols_en_JE', 'goog.i18n.DateTimeSymbols_en_JM', 'goog.i18n.DateTimeSymbols_en_KE', 'goog.i18n.DateTimeSymbols_en_KI', 'goog.i18n.DateTimeSymbols_en_KN', 'goog.i18n.DateTimeSymbols_en_KY', 'goog.i18n.DateTimeSymbols_en_LC', 'goog.i18n.DateTimeSymbols_en_LR', 'goog.i18n.DateTimeSymbols_en_LS', 'goog.i18n.DateTimeSymbols_en_MG', 'goog.i18n.DateTimeSymbols_en_MH', 'goog.i18n.DateTimeSymbols_en_MO', 'goog.i18n.DateTimeSymbols_en_MP', 'goog.i18n.DateTimeSymbols_en_MS', 'goog.i18n.DateTimeSymbols_en_MT', 'goog.i18n.DateTimeSymbols_en_MU', 'goog.i18n.DateTimeSymbols_en_MW', 'goog.i18n.DateTimeSymbols_en_MY', 'goog.i18n.DateTimeSymbols_en_NA', 'goog.i18n.DateTimeSymbols_en_NF', 'goog.i18n.DateTimeSymbols_en_NG', 'goog.i18n.DateTimeSymbols_en_NL', 'goog.i18n.DateTimeSymbols_en_NR', 'goog.i18n.DateTimeSymbols_en_NU', 'goog.i18n.DateTimeSymbols_en_NZ', 'goog.i18n.DateTimeSymbols_en_PG', 'goog.i18n.DateTimeSymbols_en_PH', 'goog.i18n.DateTimeSymbols_en_PK', 'goog.i18n.DateTimeSymbols_en_PN', 'goog.i18n.DateTimeSymbols_en_PR', 'goog.i18n.DateTimeSymbols_en_PW', 'goog.i18n.DateTimeSymbols_en_RW', 'goog.i18n.DateTimeSymbols_en_SB', 'goog.i18n.DateTimeSymbols_en_SC', 'goog.i18n.DateTimeSymbols_en_SD', 'goog.i18n.DateTimeSymbols_en_SE', 'goog.i18n.DateTimeSymbols_en_SH', 'goog.i18n.DateTimeSymbols_en_SI', 'goog.i18n.DateTimeSymbols_en_SL', 'goog.i18n.DateTimeSymbols_en_SS', 'goog.i18n.DateTimeSymbols_en_SX', 'goog.i18n.DateTimeSymbols_en_SZ', 'goog.i18n.DateTimeSymbols_en_TC', 'goog.i18n.DateTimeSymbols_en_TK', 'goog.i18n.DateTimeSymbols_en_TO', 'goog.i18n.DateTimeSymbols_en_TT', 'goog.i18n.DateTimeSymbols_en_TV', 'goog.i18n.DateTimeSymbols_en_TZ', 'goog.i18n.DateTimeSymbols_en_UG', 'goog.i18n.DateTimeSymbols_en_UM', 'goog.i18n.DateTimeSymbols_en_US_POSIX', 'goog.i18n.DateTimeSymbols_en_VC', 'goog.i18n.DateTimeSymbols_en_VG', 'goog.i18n.DateTimeSymbols_en_VI', 'goog.i18n.DateTimeSymbols_en_VU', 'goog.i18n.DateTimeSymbols_en_WS', 'goog.i18n.DateTimeSymbols_en_XA', 'goog.i18n.DateTimeSymbols_en_ZM', 'goog.i18n.DateTimeSymbols_en_ZW', 'goog.i18n.DateTimeSymbols_eo', 'goog.i18n.DateTimeSymbols_eo_001', 'goog.i18n.DateTimeSymbols_es_AR', 'goog.i18n.DateTimeSymbols_es_BO', 'goog.i18n.DateTimeSymbols_es_BR', 'goog.i18n.DateTimeSymbols_es_BZ', 'goog.i18n.DateTimeSymbols_es_CL', 'goog.i18n.DateTimeSymbols_es_CO', 'goog.i18n.DateTimeSymbols_es_CR', 'goog.i18n.DateTimeSymbols_es_CU', 'goog.i18n.DateTimeSymbols_es_DO', 'goog.i18n.DateTimeSymbols_es_EA', 'goog.i18n.DateTimeSymbols_es_EC', 'goog.i18n.DateTimeSymbols_es_GQ', 'goog.i18n.DateTimeSymbols_es_GT', 'goog.i18n.DateTimeSymbols_es_HN', 'goog.i18n.DateTimeSymbols_es_IC', 'goog.i18n.DateTimeSymbols_es_NI', 'goog.i18n.DateTimeSymbols_es_PA', 'goog.i18n.DateTimeSymbols_es_PE', 'goog.i18n.DateTimeSymbols_es_PH', 'goog.i18n.DateTimeSymbols_es_PR', 'goog.i18n.DateTimeSymbols_es_PY', 'goog.i18n.DateTimeSymbols_es_SV', 'goog.i18n.DateTimeSymbols_es_UY', 'goog.i18n.DateTimeSymbols_es_VE', 'goog.i18n.DateTimeSymbols_et_EE', 'goog.i18n.DateTimeSymbols_eu_ES', 'goog.i18n.DateTimeSymbols_ewo', 'goog.i18n.DateTimeSymbols_ewo_CM', 'goog.i18n.DateTimeSymbols_fa_AF', 'goog.i18n.DateTimeSymbols_fa_IR', 'goog.i18n.DateTimeSymbols_ff', 'goog.i18n.DateTimeSymbols_ff_Latn', 'goog.i18n.DateTimeSymbols_ff_Latn_BF', 'goog.i18n.DateTimeSymbols_ff_Latn_CM', 'goog.i18n.DateTimeSymbols_ff_Latn_GH', 'goog.i18n.DateTimeSymbols_ff_Latn_GM', 'goog.i18n.DateTimeSymbols_ff_Latn_GN', 'goog.i18n.DateTimeSymbols_ff_Latn_GW', 'goog.i18n.DateTimeSymbols_ff_Latn_LR', 'goog.i18n.DateTimeSymbols_ff_Latn_MR', 'goog.i18n.DateTimeSymbols_ff_Latn_NE', 'goog.i18n.DateTimeSymbols_ff_Latn_NG', 'goog.i18n.DateTimeSymbols_ff_Latn_SL', 'goog.i18n.DateTimeSymbols_ff_Latn_SN', 'goog.i18n.DateTimeSymbols_fi_FI', 'goog.i18n.DateTimeSymbols_fil_PH', 'goog.i18n.DateTimeSymbols_fo', 'goog.i18n.DateTimeSymbols_fo_DK', 'goog.i18n.DateTimeSymbols_fo_FO', 'goog.i18n.DateTimeSymbols_fr_BE', 'goog.i18n.DateTimeSymbols_fr_BF', 'goog.i18n.DateTimeSymbols_fr_BI', 'goog.i18n.DateTimeSymbols_fr_BJ', 'goog.i18n.DateTimeSymbols_fr_BL', 'goog.i18n.DateTimeSymbols_fr_CD', 'goog.i18n.DateTimeSymbols_fr_CF', 'goog.i18n.DateTimeSymbols_fr_CG', 'goog.i18n.DateTimeSymbols_fr_CH', 'goog.i18n.DateTimeSymbols_fr_CI', 'goog.i18n.DateTimeSymbols_fr_CM', 'goog.i18n.DateTimeSymbols_fr_DJ', 'goog.i18n.DateTimeSymbols_fr_DZ', 'goog.i18n.DateTimeSymbols_fr_FR', 'goog.i18n.DateTimeSymbols_fr_GA', 'goog.i18n.DateTimeSymbols_fr_GF', 'goog.i18n.DateTimeSymbols_fr_GN', 'goog.i18n.DateTimeSymbols_fr_GP', 'goog.i18n.DateTimeSymbols_fr_GQ', 'goog.i18n.DateTimeSymbols_fr_HT', 'goog.i18n.DateTimeSymbols_fr_KM', 'goog.i18n.DateTimeSymbols_fr_LU', 'goog.i18n.DateTimeSymbols_fr_MA', 'goog.i18n.DateTimeSymbols_fr_MC', 'goog.i18n.DateTimeSymbols_fr_MF', 'goog.i18n.DateTimeSymbols_fr_MG', 'goog.i18n.DateTimeSymbols_fr_ML', 'goog.i18n.DateTimeSymbols_fr_MQ', 'goog.i18n.DateTimeSymbols_fr_MR', 'goog.i18n.DateTimeSymbols_fr_MU', 'goog.i18n.DateTimeSymbols_fr_NC', 'goog.i18n.DateTimeSymbols_fr_NE', 'goog.i18n.DateTimeSymbols_fr_PF', 'goog.i18n.DateTimeSymbols_fr_PM', 'goog.i18n.DateTimeSymbols_fr_RE', 'goog.i18n.DateTimeSymbols_fr_RW', 'goog.i18n.DateTimeSymbols_fr_SC', 'goog.i18n.DateTimeSymbols_fr_SN', 'goog.i18n.DateTimeSymbols_fr_SY', 'goog.i18n.DateTimeSymbols_fr_TD', 'goog.i18n.DateTimeSymbols_fr_TG', 'goog.i18n.DateTimeSymbols_fr_TN', 'goog.i18n.DateTimeSymbols_fr_VU', 'goog.i18n.DateTimeSymbols_fr_WF', 'goog.i18n.DateTimeSymbols_fr_YT', 'goog.i18n.DateTimeSymbols_fur', 'goog.i18n.DateTimeSymbols_fur_IT', 'goog.i18n.DateTimeSymbols_fy', 'goog.i18n.DateTimeSymbols_fy_NL', 'goog.i18n.DateTimeSymbols_ga_IE', 'goog.i18n.DateTimeSymbols_gd', 'goog.i18n.DateTimeSymbols_gd_GB', 'goog.i18n.DateTimeSymbols_gl_ES', 'goog.i18n.DateTimeSymbols_gsw_CH', 'goog.i18n.DateTimeSymbols_gsw_FR', 'goog.i18n.DateTimeSymbols_gsw_LI', 'goog.i18n.DateTimeSymbols_gu_IN', 'goog.i18n.DateTimeSymbols_guz', 'goog.i18n.DateTimeSymbols_guz_KE', 'goog.i18n.DateTimeSymbols_gv', 'goog.i18n.DateTimeSymbols_gv_IM', 'goog.i18n.DateTimeSymbols_ha', 'goog.i18n.DateTimeSymbols_ha_GH', 'goog.i18n.DateTimeSymbols_ha_NE', 'goog.i18n.DateTimeSymbols_ha_NG', 'goog.i18n.DateTimeSymbols_haw_US', 'goog.i18n.DateTimeSymbols_he_IL', 'goog.i18n.DateTimeSymbols_hi_IN', 'goog.i18n.DateTimeSymbols_hr_BA', 'goog.i18n.DateTimeSymbols_hr_HR', 'goog.i18n.DateTimeSymbols_hsb', 'goog.i18n.DateTimeSymbols_hsb_DE', 'goog.i18n.DateTimeSymbols_hu_HU', 'goog.i18n.DateTimeSymbols_hy_AM', 'goog.i18n.DateTimeSymbols_ia', 'goog.i18n.DateTimeSymbols_ia_001', 'goog.i18n.DateTimeSymbols_id_ID', 'goog.i18n.DateTimeSymbols_ig', 'goog.i18n.DateTimeSymbols_ig_NG', 'goog.i18n.DateTimeSymbols_ii', 'goog.i18n.DateTimeSymbols_ii_CN', 'goog.i18n.DateTimeSymbols_is_IS', 'goog.i18n.DateTimeSymbols_it_CH', 'goog.i18n.DateTimeSymbols_it_IT', 'goog.i18n.DateTimeSymbols_it_SM', 'goog.i18n.DateTimeSymbols_it_VA', 'goog.i18n.DateTimeSymbols_ja_JP', 'goog.i18n.DateTimeSymbols_jgo', 'goog.i18n.DateTimeSymbols_jgo_CM', 'goog.i18n.DateTimeSymbols_jmc', 'goog.i18n.DateTimeSymbols_jmc_TZ', 'goog.i18n.DateTimeSymbols_jv', 'goog.i18n.DateTimeSymbols_jv_ID', 'goog.i18n.DateTimeSymbols_ka_GE', 'goog.i18n.DateTimeSymbols_kab', 'goog.i18n.DateTimeSymbols_kab_DZ', 'goog.i18n.DateTimeSymbols_kam', 'goog.i18n.DateTimeSymbols_kam_KE', 'goog.i18n.DateTimeSymbols_kde', 'goog.i18n.DateTimeSymbols_kde_TZ', 'goog.i18n.DateTimeSymbols_kea', 'goog.i18n.DateTimeSymbols_kea_CV', 'goog.i18n.DateTimeSymbols_khq', 'goog.i18n.DateTimeSymbols_khq_ML', 'goog.i18n.DateTimeSymbols_ki', 'goog.i18n.DateTimeSymbols_ki_KE', 'goog.i18n.DateTimeSymbols_kk_KZ', 'goog.i18n.DateTimeSymbols_kkj', 'goog.i18n.DateTimeSymbols_kkj_CM', 'goog.i18n.DateTimeSymbols_kl', 'goog.i18n.DateTimeSymbols_kl_GL', 'goog.i18n.DateTimeSymbols_kln', 'goog.i18n.DateTimeSymbols_kln_KE', 'goog.i18n.DateTimeSymbols_km_KH', 'goog.i18n.DateTimeSymbols_kn_IN', 'goog.i18n.DateTimeSymbols_ko_KP', 'goog.i18n.DateTimeSymbols_ko_KR', 'goog.i18n.DateTimeSymbols_kok', 'goog.i18n.DateTimeSymbols_kok_IN', 'goog.i18n.DateTimeSymbols_ks', 'goog.i18n.DateTimeSymbols_ks_IN', 'goog.i18n.DateTimeSymbols_ksb', 'goog.i18n.DateTimeSymbols_ksb_TZ', 'goog.i18n.DateTimeSymbols_ksf', 'goog.i18n.DateTimeSymbols_ksf_CM', 'goog.i18n.DateTimeSymbols_ksh', 'goog.i18n.DateTimeSymbols_ksh_DE', 'goog.i18n.DateTimeSymbols_ku', 'goog.i18n.DateTimeSymbols_ku_TR', 'goog.i18n.DateTimeSymbols_kw', 'goog.i18n.DateTimeSymbols_kw_GB', 'goog.i18n.DateTimeSymbols_ky_KG', 'goog.i18n.DateTimeSymbols_lag', 'goog.i18n.DateTimeSymbols_lag_TZ', 'goog.i18n.DateTimeSymbols_lb', 'goog.i18n.DateTimeSymbols_lb_LU', 'goog.i18n.DateTimeSymbols_lg', 'goog.i18n.DateTimeSymbols_lg_UG', 'goog.i18n.DateTimeSymbols_lkt', 'goog.i18n.DateTimeSymbols_lkt_US', 'goog.i18n.DateTimeSymbols_ln_AO', 'goog.i18n.DateTimeSymbols_ln_CD', 'goog.i18n.DateTimeSymbols_ln_CF', 'goog.i18n.DateTimeSymbols_ln_CG', 'goog.i18n.DateTimeSymbols_lo_LA', 'goog.i18n.DateTimeSymbols_lrc', 'goog.i18n.DateTimeSymbols_lrc_IQ', 'goog.i18n.DateTimeSymbols_lrc_IR', 'goog.i18n.DateTimeSymbols_lt_LT', 'goog.i18n.DateTimeSymbols_lu', 'goog.i18n.DateTimeSymbols_lu_CD', 'goog.i18n.DateTimeSymbols_luo', 'goog.i18n.DateTimeSymbols_luo_KE', 'goog.i18n.DateTimeSymbols_luy', 'goog.i18n.DateTimeSymbols_luy_KE', 'goog.i18n.DateTimeSymbols_lv_LV', 'goog.i18n.DateTimeSymbols_mas', 'goog.i18n.DateTimeSymbols_mas_KE', 'goog.i18n.DateTimeSymbols_mas_TZ', 'goog.i18n.DateTimeSymbols_mer', 'goog.i18n.DateTimeSymbols_mer_KE', 'goog.i18n.DateTimeSymbols_mfe', 'goog.i18n.DateTimeSymbols_mfe_MU', 'goog.i18n.DateTimeSymbols_mg', 'goog.i18n.DateTimeSymbols_mg_MG', 'goog.i18n.DateTimeSymbols_mgh', 'goog.i18n.DateTimeSymbols_mgh_MZ', 'goog.i18n.DateTimeSymbols_mgo', 'goog.i18n.DateTimeSymbols_mgo_CM', 'goog.i18n.DateTimeSymbols_mi', 'goog.i18n.DateTimeSymbols_mi_NZ', 'goog.i18n.DateTimeSymbols_mk_MK', 'goog.i18n.DateTimeSymbols_ml_IN', 'goog.i18n.DateTimeSymbols_mn_MN', 'goog.i18n.DateTimeSymbols_mr_IN', 'goog.i18n.DateTimeSymbols_ms_BN', 'goog.i18n.DateTimeSymbols_ms_MY', 'goog.i18n.DateTimeSymbols_ms_SG', 'goog.i18n.DateTimeSymbols_mt_MT', 'goog.i18n.DateTimeSymbols_mua', 'goog.i18n.DateTimeSymbols_mua_CM', 'goog.i18n.DateTimeSymbols_my_MM', 'goog.i18n.DateTimeSymbols_mzn', 'goog.i18n.DateTimeSymbols_mzn_IR', 'goog.i18n.DateTimeSymbols_naq', 'goog.i18n.DateTimeSymbols_naq_NA', 'goog.i18n.DateTimeSymbols_nb_NO', 'goog.i18n.DateTimeSymbols_nb_SJ', 'goog.i18n.DateTimeSymbols_nd', 'goog.i18n.DateTimeSymbols_nd_ZW', 'goog.i18n.DateTimeSymbols_nds', 'goog.i18n.DateTimeSymbols_nds_DE', 'goog.i18n.DateTimeSymbols_nds_NL', 'goog.i18n.DateTimeSymbols_ne_IN', 'goog.i18n.DateTimeSymbols_ne_NP', 'goog.i18n.DateTimeSymbols_nl_AW', 'goog.i18n.DateTimeSymbols_nl_BE', 'goog.i18n.DateTimeSymbols_nl_BQ', 'goog.i18n.DateTimeSymbols_nl_CW', 'goog.i18n.DateTimeSymbols_nl_NL', 'goog.i18n.DateTimeSymbols_nl_SR', 'goog.i18n.DateTimeSymbols_nl_SX', 'goog.i18n.DateTimeSymbols_nmg', 'goog.i18n.DateTimeSymbols_nmg_CM', 'goog.i18n.DateTimeSymbols_nn', 'goog.i18n.DateTimeSymbols_nn_NO', 'goog.i18n.DateTimeSymbols_nnh', 'goog.i18n.DateTimeSymbols_nnh_CM', 'goog.i18n.DateTimeSymbols_nus', 'goog.i18n.DateTimeSymbols_nus_SS', 'goog.i18n.DateTimeSymbols_nyn', 'goog.i18n.DateTimeSymbols_nyn_UG', 'goog.i18n.DateTimeSymbols_om', 'goog.i18n.DateTimeSymbols_om_ET', 'goog.i18n.DateTimeSymbols_om_KE', 'goog.i18n.DateTimeSymbols_or_IN', 'goog.i18n.DateTimeSymbols_os', 'goog.i18n.DateTimeSymbols_os_GE', 'goog.i18n.DateTimeSymbols_os_RU', 'goog.i18n.DateTimeSymbols_pa_Arab', 'goog.i18n.DateTimeSymbols_pa_Arab_PK', 'goog.i18n.DateTimeSymbols_pa_Guru', 'goog.i18n.DateTimeSymbols_pa_Guru_IN', 'goog.i18n.DateTimeSymbols_pl_PL', 'goog.i18n.DateTimeSymbols_ps', 'goog.i18n.DateTimeSymbols_ps_AF', 'goog.i18n.DateTimeSymbols_ps_PK', 'goog.i18n.DateTimeSymbols_pt_AO', 'goog.i18n.DateTimeSymbols_pt_CH', 'goog.i18n.DateTimeSymbols_pt_CV', 'goog.i18n.DateTimeSymbols_pt_GQ', 'goog.i18n.DateTimeSymbols_pt_GW', 'goog.i18n.DateTimeSymbols_pt_LU', 'goog.i18n.DateTimeSymbols_pt_MO', 'goog.i18n.DateTimeSymbols_pt_MZ', 'goog.i18n.DateTimeSymbols_pt_ST', 'goog.i18n.DateTimeSymbols_pt_TL', 'goog.i18n.DateTimeSymbols_qu', 'goog.i18n.DateTimeSymbols_qu_BO', 'goog.i18n.DateTimeSymbols_qu_EC', 'goog.i18n.DateTimeSymbols_qu_PE', 'goog.i18n.DateTimeSymbols_rm', 'goog.i18n.DateTimeSymbols_rm_CH', 'goog.i18n.DateTimeSymbols_rn', 'goog.i18n.DateTimeSymbols_rn_BI', 'goog.i18n.DateTimeSymbols_ro_MD', 'goog.i18n.DateTimeSymbols_ro_RO', 'goog.i18n.DateTimeSymbols_rof', 'goog.i18n.DateTimeSymbols_rof_TZ', 'goog.i18n.DateTimeSymbols_ru_BY', 'goog.i18n.DateTimeSymbols_ru_KG', 'goog.i18n.DateTimeSymbols_ru_KZ', 'goog.i18n.DateTimeSymbols_ru_MD', 'goog.i18n.DateTimeSymbols_ru_RU', 'goog.i18n.DateTimeSymbols_ru_UA', 'goog.i18n.DateTimeSymbols_rw', 'goog.i18n.DateTimeSymbols_rw_RW', 'goog.i18n.DateTimeSymbols_rwk', 'goog.i18n.DateTimeSymbols_rwk_TZ', 'goog.i18n.DateTimeSymbols_sah', 'goog.i18n.DateTimeSymbols_sah_RU', 'goog.i18n.DateTimeSymbols_saq', 'goog.i18n.DateTimeSymbols_saq_KE', 'goog.i18n.DateTimeSymbols_sbp', 'goog.i18n.DateTimeSymbols_sbp_TZ', 'goog.i18n.DateTimeSymbols_sd', 'goog.i18n.DateTimeSymbols_sd_PK', 'goog.i18n.DateTimeSymbols_se', 'goog.i18n.DateTimeSymbols_se_FI', 'goog.i18n.DateTimeSymbols_se_NO', 'goog.i18n.DateTimeSymbols_se_SE', 'goog.i18n.DateTimeSymbols_seh', 'goog.i18n.DateTimeSymbols_seh_MZ', 'goog.i18n.DateTimeSymbols_ses', 'goog.i18n.DateTimeSymbols_ses_ML', 'goog.i18n.DateTimeSymbols_sg', 'goog.i18n.DateTimeSymbols_sg_CF', 'goog.i18n.DateTimeSymbols_shi', 'goog.i18n.DateTimeSymbols_shi_Latn', 'goog.i18n.DateTimeSymbols_shi_Latn_MA', 'goog.i18n.DateTimeSymbols_shi_Tfng', 'goog.i18n.DateTimeSymbols_shi_Tfng_MA', 'goog.i18n.DateTimeSymbols_si_LK', 'goog.i18n.DateTimeSymbols_sk_SK', 'goog.i18n.DateTimeSymbols_sl_SI', 'goog.i18n.DateTimeSymbols_smn', 'goog.i18n.DateTimeSymbols_smn_FI', 'goog.i18n.DateTimeSymbols_sn', 'goog.i18n.DateTimeSymbols_sn_ZW', 'goog.i18n.DateTimeSymbols_so', 'goog.i18n.DateTimeSymbols_so_DJ', 'goog.i18n.DateTimeSymbols_so_ET', 'goog.i18n.DateTimeSymbols_so_KE', 'goog.i18n.DateTimeSymbols_so_SO', 'goog.i18n.DateTimeSymbols_sq_AL', 'goog.i18n.DateTimeSymbols_sq_MK', 'goog.i18n.DateTimeSymbols_sq_XK', 'goog.i18n.DateTimeSymbols_sr_Cyrl', 'goog.i18n.DateTimeSymbols_sr_Cyrl_BA', 'goog.i18n.DateTimeSymbols_sr_Cyrl_ME', 'goog.i18n.DateTimeSymbols_sr_Cyrl_RS', 'goog.i18n.DateTimeSymbols_sr_Cyrl_XK', 'goog.i18n.DateTimeSymbols_sr_Latn_BA', 'goog.i18n.DateTimeSymbols_sr_Latn_ME', 'goog.i18n.DateTimeSymbols_sr_Latn_RS', 'goog.i18n.DateTimeSymbols_sr_Latn_XK', 'goog.i18n.DateTimeSymbols_sv_AX', 'goog.i18n.DateTimeSymbols_sv_FI', 'goog.i18n.DateTimeSymbols_sv_SE', 'goog.i18n.DateTimeSymbols_sw_CD', 'goog.i18n.DateTimeSymbols_sw_KE', 'goog.i18n.DateTimeSymbols_sw_TZ', 'goog.i18n.DateTimeSymbols_sw_UG', 'goog.i18n.DateTimeSymbols_ta_IN', 'goog.i18n.DateTimeSymbols_ta_LK', 'goog.i18n.DateTimeSymbols_ta_MY', 'goog.i18n.DateTimeSymbols_ta_SG', 'goog.i18n.DateTimeSymbols_te_IN', 'goog.i18n.DateTimeSymbols_teo', 'goog.i18n.DateTimeSymbols_teo_KE', 'goog.i18n.DateTimeSymbols_teo_UG', 'goog.i18n.DateTimeSymbols_tg', 'goog.i18n.DateTimeSymbols_tg_TJ', 'goog.i18n.DateTimeSymbols_th_TH', 'goog.i18n.DateTimeSymbols_ti', 'goog.i18n.DateTimeSymbols_ti_ER', 'goog.i18n.DateTimeSymbols_ti_ET', 'goog.i18n.DateTimeSymbols_tk', 'goog.i18n.DateTimeSymbols_tk_TM', 'goog.i18n.DateTimeSymbols_to', 'goog.i18n.DateTimeSymbols_to_TO', 'goog.i18n.DateTimeSymbols_tr_CY', 'goog.i18n.DateTimeSymbols_tr_TR', 'goog.i18n.DateTimeSymbols_tt', 'goog.i18n.DateTimeSymbols_tt_RU', 'goog.i18n.DateTimeSymbols_twq', 'goog.i18n.DateTimeSymbols_twq_NE', 'goog.i18n.DateTimeSymbols_tzm', 'goog.i18n.DateTimeSymbols_tzm_MA', 'goog.i18n.DateTimeSymbols_ug', 'goog.i18n.DateTimeSymbols_ug_CN', 'goog.i18n.DateTimeSymbols_uk_UA', 'goog.i18n.DateTimeSymbols_ur_IN', 'goog.i18n.DateTimeSymbols_ur_PK', 'goog.i18n.DateTimeSymbols_uz_Arab', 'goog.i18n.DateTimeSymbols_uz_Arab_AF', 'goog.i18n.DateTimeSymbols_uz_Cyrl', 'goog.i18n.DateTimeSymbols_uz_Cyrl_UZ', 'goog.i18n.DateTimeSymbols_uz_Latn', 'goog.i18n.DateTimeSymbols_uz_Latn_UZ', 'goog.i18n.DateTimeSymbols_vai', 'goog.i18n.DateTimeSymbols_vai_Latn', 'goog.i18n.DateTimeSymbols_vai_Latn_LR', 'goog.i18n.DateTimeSymbols_vai_Vaii', 'goog.i18n.DateTimeSymbols_vai_Vaii_LR', 'goog.i18n.DateTimeSymbols_vi_VN', 'goog.i18n.DateTimeSymbols_vun', 'goog.i18n.DateTimeSymbols_vun_TZ', 'goog.i18n.DateTimeSymbols_wae', 'goog.i18n.DateTimeSymbols_wae_CH', 'goog.i18n.DateTimeSymbols_wo', 'goog.i18n.DateTimeSymbols_wo_SN', 'goog.i18n.DateTimeSymbols_xh', 'goog.i18n.DateTimeSymbols_xh_ZA', 'goog.i18n.DateTimeSymbols_xog', 'goog.i18n.DateTimeSymbols_xog_UG', 'goog.i18n.DateTimeSymbols_yav', 'goog.i18n.DateTimeSymbols_yav_CM', 'goog.i18n.DateTimeSymbols_yi', 'goog.i18n.DateTimeSymbols_yi_001', 'goog.i18n.DateTimeSymbols_yo', 'goog.i18n.DateTimeSymbols_yo_BJ', 'goog.i18n.DateTimeSymbols_yo_NG', 'goog.i18n.DateTimeSymbols_yue', 'goog.i18n.DateTimeSymbols_yue_Hans', 'goog.i18n.DateTimeSymbols_yue_Hans_CN', 'goog.i18n.DateTimeSymbols_yue_Hant', 'goog.i18n.DateTimeSymbols_yue_Hant_HK', 'goog.i18n.DateTimeSymbols_zgh', 'goog.i18n.DateTimeSymbols_zgh_MA', 'goog.i18n.DateTimeSymbols_zh_Hans', 'goog.i18n.DateTimeSymbols_zh_Hans_CN', 'goog.i18n.DateTimeSymbols_zh_Hans_HK', 'goog.i18n.DateTimeSymbols_zh_Hans_MO', 'goog.i18n.DateTimeSymbols_zh_Hans_SG', 'goog.i18n.DateTimeSymbols_zh_Hant', 'goog.i18n.DateTimeSymbols_zh_Hant_HK', 'goog.i18n.DateTimeSymbols_zh_Hant_MO', 'goog.i18n.DateTimeSymbols_zh_Hant_TW', 'goog.i18n.DateTimeSymbols_zu_ZA'], ['goog.i18n.DateTimeSymbols'], {});
goog.addDependency('i18n/graphemebreak.js', ['goog.i18n.GraphemeBreak'], ['goog.asserts', 'goog.i18n.uChar', 'goog.structs.InversionMap'], {});
goog.addDependency('i18n/graphemebreak_test.js', ['goog.i18n.GraphemeBreakTest'], ['goog.i18n.GraphemeBreak', 'goog.i18n.uChar', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/localefeature.js', ['goog.i18n.LocaleFeature'], [], {'module': 'goog'});
goog.addDependency('i18n/localefeature_test.js', ['goog.i18n.LocaleFeatureTest'], ['goog.i18n.LocaleFeature', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/messageformat.js', ['goog.i18n.MessageFormat'], ['goog.array', 'goog.asserts', 'goog.i18n.CompactNumberFormatSymbols', 'goog.i18n.NumberFormat', 'goog.i18n.NumberFormatSymbols', 'goog.i18n.ordinalRules', 'goog.i18n.pluralRules'], {});
goog.addDependency('i18n/messageformat_test.js', ['goog.i18n.MessageFormatTest'], ['goog.i18n.MessageFormat', 'goog.i18n.NumberFormatSymbols_hr', 'goog.i18n.pluralRules', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/mime.js', ['goog.i18n.mime', 'goog.i18n.mime.encode'], ['goog.array', 'goog.i18n.uChar'], {});
goog.addDependency('i18n/mime_test.js', ['goog.i18n.mime.encodeTest'], ['goog.i18n.mime.encode', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/numberformat.js', ['goog.i18n.NumberFormat', 'goog.i18n.NumberFormat.CurrencyStyle', 'goog.i18n.NumberFormat.Format'], ['goog.asserts', 'goog.i18n.CompactNumberFormatSymbols', 'goog.i18n.NumberFormatSymbols', 'goog.i18n.NumberFormatSymbols_u_nu_latn', 'goog.i18n.currency', 'goog.math', 'goog.string'], {});
goog.addDependency('i18n/numberformat_test.js', ['goog.i18n.NumberFormatTest'], ['goog.i18n.CompactNumberFormatSymbols', 'goog.i18n.CompactNumberFormatSymbols_de', 'goog.i18n.CompactNumberFormatSymbols_en', 'goog.i18n.CompactNumberFormatSymbols_fr', 'goog.i18n.NumberFormat', 'goog.i18n.NumberFormatSymbols', 'goog.i18n.NumberFormatSymbols_ar_EG', 'goog.i18n.NumberFormatSymbols_ar_EG_u_nu_latn', 'goog.i18n.NumberFormatSymbols_de', 'goog.i18n.NumberFormatSymbols_en', 'goog.i18n.NumberFormatSymbols_en_AU', 'goog.i18n.NumberFormatSymbols_en_US', 'goog.i18n.NumberFormatSymbols_fi', 'goog.i18n.NumberFormatSymbols_fr', 'goog.i18n.NumberFormatSymbols_pl', 'goog.i18n.NumberFormatSymbols_ro', 'goog.i18n.NumberFormatSymbols_u_nu_latn', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/numberformatsymbols.js', ['goog.i18n.NumberFormatSymbols', 'goog.i18n.NumberFormatSymbols_af', 'goog.i18n.NumberFormatSymbols_am', 'goog.i18n.NumberFormatSymbols_ar', 'goog.i18n.NumberFormatSymbols_ar_DZ', 'goog.i18n.NumberFormatSymbols_ar_EG', 'goog.i18n.NumberFormatSymbols_ar_EG_u_nu_latn', 'goog.i18n.NumberFormatSymbols_az', 'goog.i18n.NumberFormatSymbols_be', 'goog.i18n.NumberFormatSymbols_bg', 'goog.i18n.NumberFormatSymbols_bn', 'goog.i18n.NumberFormatSymbols_bn_u_nu_latn', 'goog.i18n.NumberFormatSymbols_br', 'goog.i18n.NumberFormatSymbols_bs', 'goog.i18n.NumberFormatSymbols_ca', 'goog.i18n.NumberFormatSymbols_chr', 'goog.i18n.NumberFormatSymbols_cs', 'goog.i18n.NumberFormatSymbols_cy', 'goog.i18n.NumberFormatSymbols_da', 'goog.i18n.NumberFormatSymbols_de', 'goog.i18n.NumberFormatSymbols_de_AT', 'goog.i18n.NumberFormatSymbols_de_CH', 'goog.i18n.NumberFormatSymbols_el', 'goog.i18n.NumberFormatSymbols_en', 'goog.i18n.NumberFormatSymbols_en_AU', 'goog.i18n.NumberFormatSymbols_en_CA', 'goog.i18n.NumberFormatSymbols_en_GB', 'goog.i18n.NumberFormatSymbols_en_IE', 'goog.i18n.NumberFormatSymbols_en_IN', 'goog.i18n.NumberFormatSymbols_en_SG', 'goog.i18n.NumberFormatSymbols_en_US', 'goog.i18n.NumberFormatSymbols_en_ZA', 'goog.i18n.NumberFormatSymbols_es', 'goog.i18n.NumberFormatSymbols_es_419', 'goog.i18n.NumberFormatSymbols_es_ES', 'goog.i18n.NumberFormatSymbols_es_MX', 'goog.i18n.NumberFormatSymbols_es_US', 'goog.i18n.NumberFormatSymbols_et', 'goog.i18n.NumberFormatSymbols_eu', 'goog.i18n.NumberFormatSymbols_fa', 'goog.i18n.NumberFormatSymbols_fa_u_nu_latn', 'goog.i18n.NumberFormatSymbols_fi', 'goog.i18n.NumberFormatSymbols_fil', 'goog.i18n.NumberFormatSymbols_fr', 'goog.i18n.NumberFormatSymbols_fr_CA', 'goog.i18n.NumberFormatSymbols_ga', 'goog.i18n.NumberFormatSymbols_gl', 'goog.i18n.NumberFormatSymbols_gsw', 'goog.i18n.NumberFormatSymbols_gu', 'goog.i18n.NumberFormatSymbols_haw', 'goog.i18n.NumberFormatSymbols_he', 'goog.i18n.NumberFormatSymbols_hi', 'goog.i18n.NumberFormatSymbols_hr', 'goog.i18n.NumberFormatSymbols_hu', 'goog.i18n.NumberFormatSymbols_hy', 'goog.i18n.NumberFormatSymbols_id', 'goog.i18n.NumberFormatSymbols_in', 'goog.i18n.NumberFormatSymbols_is', 'goog.i18n.NumberFormatSymbols_it', 'goog.i18n.NumberFormatSymbols_iw', 'goog.i18n.NumberFormatSymbols_ja', 'goog.i18n.NumberFormatSymbols_ka', 'goog.i18n.NumberFormatSymbols_kk', 'goog.i18n.NumberFormatSymbols_km', 'goog.i18n.NumberFormatSymbols_kn', 'goog.i18n.NumberFormatSymbols_ko', 'goog.i18n.NumberFormatSymbols_ky', 'goog.i18n.NumberFormatSymbols_ln', 'goog.i18n.NumberFormatSymbols_lo', 'goog.i18n.NumberFormatSymbols_lt', 'goog.i18n.NumberFormatSymbols_lv', 'goog.i18n.NumberFormatSymbols_mk', 'goog.i18n.NumberFormatSymbols_ml', 'goog.i18n.NumberFormatSymbols_mn', 'goog.i18n.NumberFormatSymbols_mo', 'goog.i18n.NumberFormatSymbols_mr', 'goog.i18n.NumberFormatSymbols_mr_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ms', 'goog.i18n.NumberFormatSymbols_mt', 'goog.i18n.NumberFormatSymbols_my', 'goog.i18n.NumberFormatSymbols_my_u_nu_latn', 'goog.i18n.NumberFormatSymbols_nb', 'goog.i18n.NumberFormatSymbols_ne', 'goog.i18n.NumberFormatSymbols_ne_u_nu_latn', 'goog.i18n.NumberFormatSymbols_nl', 'goog.i18n.NumberFormatSymbols_no', 'goog.i18n.NumberFormatSymbols_no_NO', 'goog.i18n.NumberFormatSymbols_or', 'goog.i18n.NumberFormatSymbols_pa', 'goog.i18n.NumberFormatSymbols_pl', 'goog.i18n.NumberFormatSymbols_pt', 'goog.i18n.NumberFormatSymbols_pt_BR', 'goog.i18n.NumberFormatSymbols_pt_PT', 'goog.i18n.NumberFormatSymbols_ro', 'goog.i18n.NumberFormatSymbols_ru', 'goog.i18n.NumberFormatSymbols_sh', 'goog.i18n.NumberFormatSymbols_si', 'goog.i18n.NumberFormatSymbols_sk', 'goog.i18n.NumberFormatSymbols_sl', 'goog.i18n.NumberFormatSymbols_sq', 'goog.i18n.NumberFormatSymbols_sr', 'goog.i18n.NumberFormatSymbols_sr_Latn', 'goog.i18n.NumberFormatSymbols_sv', 'goog.i18n.NumberFormatSymbols_sw', 'goog.i18n.NumberFormatSymbols_ta', 'goog.i18n.NumberFormatSymbols_te', 'goog.i18n.NumberFormatSymbols_th', 'goog.i18n.NumberFormatSymbols_tl', 'goog.i18n.NumberFormatSymbols_tr', 'goog.i18n.NumberFormatSymbols_u_nu_latn', 'goog.i18n.NumberFormatSymbols_uk', 'goog.i18n.NumberFormatSymbols_ur', 'goog.i18n.NumberFormatSymbols_uz', 'goog.i18n.NumberFormatSymbols_vi', 'goog.i18n.NumberFormatSymbols_zh', 'goog.i18n.NumberFormatSymbols_zh_CN', 'goog.i18n.NumberFormatSymbols_zh_HK', 'goog.i18n.NumberFormatSymbols_zh_TW', 'goog.i18n.NumberFormatSymbols_zu'], [], {});
goog.addDependency('i18n/numberformatsymbolsext.js', ['goog.i18n.NumberFormatSymbolsExt', 'goog.i18n.NumberFormatSymbols_af_NA', 'goog.i18n.NumberFormatSymbols_af_ZA', 'goog.i18n.NumberFormatSymbols_agq', 'goog.i18n.NumberFormatSymbols_agq_CM', 'goog.i18n.NumberFormatSymbols_ak', 'goog.i18n.NumberFormatSymbols_ak_GH', 'goog.i18n.NumberFormatSymbols_am_ET', 'goog.i18n.NumberFormatSymbols_ar_001', 'goog.i18n.NumberFormatSymbols_ar_AE', 'goog.i18n.NumberFormatSymbols_ar_AE_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_BH', 'goog.i18n.NumberFormatSymbols_ar_BH_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_DJ', 'goog.i18n.NumberFormatSymbols_ar_DJ_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_EH', 'goog.i18n.NumberFormatSymbols_ar_ER', 'goog.i18n.NumberFormatSymbols_ar_ER_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_IL', 'goog.i18n.NumberFormatSymbols_ar_IL_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_IQ', 'goog.i18n.NumberFormatSymbols_ar_IQ_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_JO', 'goog.i18n.NumberFormatSymbols_ar_JO_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_KM', 'goog.i18n.NumberFormatSymbols_ar_KM_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_KW', 'goog.i18n.NumberFormatSymbols_ar_KW_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_LB', 'goog.i18n.NumberFormatSymbols_ar_LB_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_LY', 'goog.i18n.NumberFormatSymbols_ar_MA', 'goog.i18n.NumberFormatSymbols_ar_MR', 'goog.i18n.NumberFormatSymbols_ar_MR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_OM', 'goog.i18n.NumberFormatSymbols_ar_OM_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_PS', 'goog.i18n.NumberFormatSymbols_ar_PS_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_QA', 'goog.i18n.NumberFormatSymbols_ar_QA_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SA', 'goog.i18n.NumberFormatSymbols_ar_SA_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SD', 'goog.i18n.NumberFormatSymbols_ar_SD_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SO', 'goog.i18n.NumberFormatSymbols_ar_SO_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SS', 'goog.i18n.NumberFormatSymbols_ar_SS_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SY', 'goog.i18n.NumberFormatSymbols_ar_SY_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_TD', 'goog.i18n.NumberFormatSymbols_ar_TD_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_TN', 'goog.i18n.NumberFormatSymbols_ar_XB', 'goog.i18n.NumberFormatSymbols_ar_YE', 'goog.i18n.NumberFormatSymbols_ar_YE_u_nu_latn', 'goog.i18n.NumberFormatSymbols_as', 'goog.i18n.NumberFormatSymbols_as_IN', 'goog.i18n.NumberFormatSymbols_as_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_as_u_nu_latn', 'goog.i18n.NumberFormatSymbols_asa', 'goog.i18n.NumberFormatSymbols_asa_TZ', 'goog.i18n.NumberFormatSymbols_ast', 'goog.i18n.NumberFormatSymbols_ast_ES', 'goog.i18n.NumberFormatSymbols_az_Cyrl', 'goog.i18n.NumberFormatSymbols_az_Cyrl_AZ', 'goog.i18n.NumberFormatSymbols_az_Latn', 'goog.i18n.NumberFormatSymbols_az_Latn_AZ', 'goog.i18n.NumberFormatSymbols_bas', 'goog.i18n.NumberFormatSymbols_bas_CM', 'goog.i18n.NumberFormatSymbols_be_BY', 'goog.i18n.NumberFormatSymbols_bem', 'goog.i18n.NumberFormatSymbols_bem_ZM', 'goog.i18n.NumberFormatSymbols_bez', 'goog.i18n.NumberFormatSymbols_bez_TZ', 'goog.i18n.NumberFormatSymbols_bg_BG', 'goog.i18n.NumberFormatSymbols_bm', 'goog.i18n.NumberFormatSymbols_bm_ML', 'goog.i18n.NumberFormatSymbols_bn_BD', 'goog.i18n.NumberFormatSymbols_bn_BD_u_nu_latn', 'goog.i18n.NumberFormatSymbols_bn_IN', 'goog.i18n.NumberFormatSymbols_bn_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_bo', 'goog.i18n.NumberFormatSymbols_bo_CN', 'goog.i18n.NumberFormatSymbols_bo_IN', 'goog.i18n.NumberFormatSymbols_br_FR', 'goog.i18n.NumberFormatSymbols_brx', 'goog.i18n.NumberFormatSymbols_brx_IN', 'goog.i18n.NumberFormatSymbols_bs_Cyrl', 'goog.i18n.NumberFormatSymbols_bs_Cyrl_BA', 'goog.i18n.NumberFormatSymbols_bs_Latn', 'goog.i18n.NumberFormatSymbols_bs_Latn_BA', 'goog.i18n.NumberFormatSymbols_ca_AD', 'goog.i18n.NumberFormatSymbols_ca_ES', 'goog.i18n.NumberFormatSymbols_ca_FR', 'goog.i18n.NumberFormatSymbols_ca_IT', 'goog.i18n.NumberFormatSymbols_ccp', 'goog.i18n.NumberFormatSymbols_ccp_BD', 'goog.i18n.NumberFormatSymbols_ccp_BD_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ccp_IN', 'goog.i18n.NumberFormatSymbols_ccp_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ccp_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ce', 'goog.i18n.NumberFormatSymbols_ce_RU', 'goog.i18n.NumberFormatSymbols_ceb', 'goog.i18n.NumberFormatSymbols_ceb_PH', 'goog.i18n.NumberFormatSymbols_cgg', 'goog.i18n.NumberFormatSymbols_cgg_UG', 'goog.i18n.NumberFormatSymbols_chr_US', 'goog.i18n.NumberFormatSymbols_ckb', 'goog.i18n.NumberFormatSymbols_ckb_IQ', 'goog.i18n.NumberFormatSymbols_ckb_IQ_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ckb_IR', 'goog.i18n.NumberFormatSymbols_ckb_IR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ckb_u_nu_latn', 'goog.i18n.NumberFormatSymbols_cs_CZ', 'goog.i18n.NumberFormatSymbols_cy_GB', 'goog.i18n.NumberFormatSymbols_da_DK', 'goog.i18n.NumberFormatSymbols_da_GL', 'goog.i18n.NumberFormatSymbols_dav', 'goog.i18n.NumberFormatSymbols_dav_KE', 'goog.i18n.NumberFormatSymbols_de_BE', 'goog.i18n.NumberFormatSymbols_de_DE', 'goog.i18n.NumberFormatSymbols_de_IT', 'goog.i18n.NumberFormatSymbols_de_LI', 'goog.i18n.NumberFormatSymbols_de_LU', 'goog.i18n.NumberFormatSymbols_dje', 'goog.i18n.NumberFormatSymbols_dje_NE', 'goog.i18n.NumberFormatSymbols_dsb', 'goog.i18n.NumberFormatSymbols_dsb_DE', 'goog.i18n.NumberFormatSymbols_dua', 'goog.i18n.NumberFormatSymbols_dua_CM', 'goog.i18n.NumberFormatSymbols_dyo', 'goog.i18n.NumberFormatSymbols_dyo_SN', 'goog.i18n.NumberFormatSymbols_dz', 'goog.i18n.NumberFormatSymbols_dz_BT', 'goog.i18n.NumberFormatSymbols_dz_BT_u_nu_latn', 'goog.i18n.NumberFormatSymbols_dz_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ebu', 'goog.i18n.NumberFormatSymbols_ebu_KE', 'goog.i18n.NumberFormatSymbols_ee', 'goog.i18n.NumberFormatSymbols_ee_GH', 'goog.i18n.NumberFormatSymbols_ee_TG', 'goog.i18n.NumberFormatSymbols_el_CY', 'goog.i18n.NumberFormatSymbols_el_GR', 'goog.i18n.NumberFormatSymbols_en_001', 'goog.i18n.NumberFormatSymbols_en_150', 'goog.i18n.NumberFormatSymbols_en_AE', 'goog.i18n.NumberFormatSymbols_en_AG', 'goog.i18n.NumberFormatSymbols_en_AI', 'goog.i18n.NumberFormatSymbols_en_AS', 'goog.i18n.NumberFormatSymbols_en_AT', 'goog.i18n.NumberFormatSymbols_en_BB', 'goog.i18n.NumberFormatSymbols_en_BE', 'goog.i18n.NumberFormatSymbols_en_BI', 'goog.i18n.NumberFormatSymbols_en_BM', 'goog.i18n.NumberFormatSymbols_en_BS', 'goog.i18n.NumberFormatSymbols_en_BW', 'goog.i18n.NumberFormatSymbols_en_BZ', 'goog.i18n.NumberFormatSymbols_en_CC', 'goog.i18n.NumberFormatSymbols_en_CH', 'goog.i18n.NumberFormatSymbols_en_CK', 'goog.i18n.NumberFormatSymbols_en_CM', 'goog.i18n.NumberFormatSymbols_en_CX', 'goog.i18n.NumberFormatSymbols_en_CY', 'goog.i18n.NumberFormatSymbols_en_DE', 'goog.i18n.NumberFormatSymbols_en_DG', 'goog.i18n.NumberFormatSymbols_en_DK', 'goog.i18n.NumberFormatSymbols_en_DM', 'goog.i18n.NumberFormatSymbols_en_ER', 'goog.i18n.NumberFormatSymbols_en_FI', 'goog.i18n.NumberFormatSymbols_en_FJ', 'goog.i18n.NumberFormatSymbols_en_FK', 'goog.i18n.NumberFormatSymbols_en_FM', 'goog.i18n.NumberFormatSymbols_en_GD', 'goog.i18n.NumberFormatSymbols_en_GG', 'goog.i18n.NumberFormatSymbols_en_GH', 'goog.i18n.NumberFormatSymbols_en_GI', 'goog.i18n.NumberFormatSymbols_en_GM', 'goog.i18n.NumberFormatSymbols_en_GU', 'goog.i18n.NumberFormatSymbols_en_GY', 'goog.i18n.NumberFormatSymbols_en_HK', 'goog.i18n.NumberFormatSymbols_en_IL', 'goog.i18n.NumberFormatSymbols_en_IM', 'goog.i18n.NumberFormatSymbols_en_IO', 'goog.i18n.NumberFormatSymbols_en_JE', 'goog.i18n.NumberFormatSymbols_en_JM', 'goog.i18n.NumberFormatSymbols_en_KE', 'goog.i18n.NumberFormatSymbols_en_KI', 'goog.i18n.NumberFormatSymbols_en_KN', 'goog.i18n.NumberFormatSymbols_en_KY', 'goog.i18n.NumberFormatSymbols_en_LC', 'goog.i18n.NumberFormatSymbols_en_LR', 'goog.i18n.NumberFormatSymbols_en_LS', 'goog.i18n.NumberFormatSymbols_en_MG', 'goog.i18n.NumberFormatSymbols_en_MH', 'goog.i18n.NumberFormatSymbols_en_MO', 'goog.i18n.NumberFormatSymbols_en_MP', 'goog.i18n.NumberFormatSymbols_en_MS', 'goog.i18n.NumberFormatSymbols_en_MT', 'goog.i18n.NumberFormatSymbols_en_MU', 'goog.i18n.NumberFormatSymbols_en_MW', 'goog.i18n.NumberFormatSymbols_en_MY', 'goog.i18n.NumberFormatSymbols_en_NA', 'goog.i18n.NumberFormatSymbols_en_NF', 'goog.i18n.NumberFormatSymbols_en_NG', 'goog.i18n.NumberFormatSymbols_en_NL', 'goog.i18n.NumberFormatSymbols_en_NR', 'goog.i18n.NumberFormatSymbols_en_NU', 'goog.i18n.NumberFormatSymbols_en_NZ', 'goog.i18n.NumberFormatSymbols_en_PG', 'goog.i18n.NumberFormatSymbols_en_PH', 'goog.i18n.NumberFormatSymbols_en_PK', 'goog.i18n.NumberFormatSymbols_en_PN', 'goog.i18n.NumberFormatSymbols_en_PR', 'goog.i18n.NumberFormatSymbols_en_PW', 'goog.i18n.NumberFormatSymbols_en_RW', 'goog.i18n.NumberFormatSymbols_en_SB', 'goog.i18n.NumberFormatSymbols_en_SC', 'goog.i18n.NumberFormatSymbols_en_SD', 'goog.i18n.NumberFormatSymbols_en_SE', 'goog.i18n.NumberFormatSymbols_en_SH', 'goog.i18n.NumberFormatSymbols_en_SI', 'goog.i18n.NumberFormatSymbols_en_SL', 'goog.i18n.NumberFormatSymbols_en_SS', 'goog.i18n.NumberFormatSymbols_en_SX', 'goog.i18n.NumberFormatSymbols_en_SZ', 'goog.i18n.NumberFormatSymbols_en_TC', 'goog.i18n.NumberFormatSymbols_en_TK', 'goog.i18n.NumberFormatSymbols_en_TO', 'goog.i18n.NumberFormatSymbols_en_TT', 'goog.i18n.NumberFormatSymbols_en_TV', 'goog.i18n.NumberFormatSymbols_en_TZ', 'goog.i18n.NumberFormatSymbols_en_UG', 'goog.i18n.NumberFormatSymbols_en_UM', 'goog.i18n.NumberFormatSymbols_en_US_POSIX', 'goog.i18n.NumberFormatSymbols_en_VC', 'goog.i18n.NumberFormatSymbols_en_VG', 'goog.i18n.NumberFormatSymbols_en_VI', 'goog.i18n.NumberFormatSymbols_en_VU', 'goog.i18n.NumberFormatSymbols_en_WS', 'goog.i18n.NumberFormatSymbols_en_XA', 'goog.i18n.NumberFormatSymbols_en_ZM', 'goog.i18n.NumberFormatSymbols_en_ZW', 'goog.i18n.NumberFormatSymbols_eo', 'goog.i18n.NumberFormatSymbols_eo_001', 'goog.i18n.NumberFormatSymbols_es_AR', 'goog.i18n.NumberFormatSymbols_es_BO', 'goog.i18n.NumberFormatSymbols_es_BR', 'goog.i18n.NumberFormatSymbols_es_BZ', 'goog.i18n.NumberFormatSymbols_es_CL', 'goog.i18n.NumberFormatSymbols_es_CO', 'goog.i18n.NumberFormatSymbols_es_CR', 'goog.i18n.NumberFormatSymbols_es_CU', 'goog.i18n.NumberFormatSymbols_es_DO', 'goog.i18n.NumberFormatSymbols_es_EA', 'goog.i18n.NumberFormatSymbols_es_EC', 'goog.i18n.NumberFormatSymbols_es_GQ', 'goog.i18n.NumberFormatSymbols_es_GT', 'goog.i18n.NumberFormatSymbols_es_HN', 'goog.i18n.NumberFormatSymbols_es_IC', 'goog.i18n.NumberFormatSymbols_es_NI', 'goog.i18n.NumberFormatSymbols_es_PA', 'goog.i18n.NumberFormatSymbols_es_PE', 'goog.i18n.NumberFormatSymbols_es_PH', 'goog.i18n.NumberFormatSymbols_es_PR', 'goog.i18n.NumberFormatSymbols_es_PY', 'goog.i18n.NumberFormatSymbols_es_SV', 'goog.i18n.NumberFormatSymbols_es_UY', 'goog.i18n.NumberFormatSymbols_es_VE', 'goog.i18n.NumberFormatSymbols_et_EE', 'goog.i18n.NumberFormatSymbols_eu_ES', 'goog.i18n.NumberFormatSymbols_ewo', 'goog.i18n.NumberFormatSymbols_ewo_CM', 'goog.i18n.NumberFormatSymbols_fa_AF', 'goog.i18n.NumberFormatSymbols_fa_AF_u_nu_latn', 'goog.i18n.NumberFormatSymbols_fa_IR', 'goog.i18n.NumberFormatSymbols_fa_IR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ff', 'goog.i18n.NumberFormatSymbols_ff_Latn', 'goog.i18n.NumberFormatSymbols_ff_Latn_BF', 'goog.i18n.NumberFormatSymbols_ff_Latn_CM', 'goog.i18n.NumberFormatSymbols_ff_Latn_GH', 'goog.i18n.NumberFormatSymbols_ff_Latn_GM', 'goog.i18n.NumberFormatSymbols_ff_Latn_GN', 'goog.i18n.NumberFormatSymbols_ff_Latn_GW', 'goog.i18n.NumberFormatSymbols_ff_Latn_LR', 'goog.i18n.NumberFormatSymbols_ff_Latn_MR', 'goog.i18n.NumberFormatSymbols_ff_Latn_NE', 'goog.i18n.NumberFormatSymbols_ff_Latn_NG', 'goog.i18n.NumberFormatSymbols_ff_Latn_SL', 'goog.i18n.NumberFormatSymbols_ff_Latn_SN', 'goog.i18n.NumberFormatSymbols_fi_FI', 'goog.i18n.NumberFormatSymbols_fil_PH', 'goog.i18n.NumberFormatSymbols_fo', 'goog.i18n.NumberFormatSymbols_fo_DK', 'goog.i18n.NumberFormatSymbols_fo_FO', 'goog.i18n.NumberFormatSymbols_fr_BE', 'goog.i18n.NumberFormatSymbols_fr_BF', 'goog.i18n.NumberFormatSymbols_fr_BI', 'goog.i18n.NumberFormatSymbols_fr_BJ', 'goog.i18n.NumberFormatSymbols_fr_BL', 'goog.i18n.NumberFormatSymbols_fr_CD', 'goog.i18n.NumberFormatSymbols_fr_CF', 'goog.i18n.NumberFormatSymbols_fr_CG', 'goog.i18n.NumberFormatSymbols_fr_CH', 'goog.i18n.NumberFormatSymbols_fr_CI', 'goog.i18n.NumberFormatSymbols_fr_CM', 'goog.i18n.NumberFormatSymbols_fr_DJ', 'goog.i18n.NumberFormatSymbols_fr_DZ', 'goog.i18n.NumberFormatSymbols_fr_FR', 'goog.i18n.NumberFormatSymbols_fr_GA', 'goog.i18n.NumberFormatSymbols_fr_GF', 'goog.i18n.NumberFormatSymbols_fr_GN', 'goog.i18n.NumberFormatSymbols_fr_GP', 'goog.i18n.NumberFormatSymbols_fr_GQ', 'goog.i18n.NumberFormatSymbols_fr_HT', 'goog.i18n.NumberFormatSymbols_fr_KM', 'goog.i18n.NumberFormatSymbols_fr_LU', 'goog.i18n.NumberFormatSymbols_fr_MA', 'goog.i18n.NumberFormatSymbols_fr_MC', 'goog.i18n.NumberFormatSymbols_fr_MF', 'goog.i18n.NumberFormatSymbols_fr_MG', 'goog.i18n.NumberFormatSymbols_fr_ML', 'goog.i18n.NumberFormatSymbols_fr_MQ', 'goog.i18n.NumberFormatSymbols_fr_MR', 'goog.i18n.NumberFormatSymbols_fr_MU', 'goog.i18n.NumberFormatSymbols_fr_NC', 'goog.i18n.NumberFormatSymbols_fr_NE', 'goog.i18n.NumberFormatSymbols_fr_PF', 'goog.i18n.NumberFormatSymbols_fr_PM', 'goog.i18n.NumberFormatSymbols_fr_RE', 'goog.i18n.NumberFormatSymbols_fr_RW', 'goog.i18n.NumberFormatSymbols_fr_SC', 'goog.i18n.NumberFormatSymbols_fr_SN', 'goog.i18n.NumberFormatSymbols_fr_SY', 'goog.i18n.NumberFormatSymbols_fr_TD', 'goog.i18n.NumberFormatSymbols_fr_TG', 'goog.i18n.NumberFormatSymbols_fr_TN', 'goog.i18n.NumberFormatSymbols_fr_VU', 'goog.i18n.NumberFormatSymbols_fr_WF', 'goog.i18n.NumberFormatSymbols_fr_YT', 'goog.i18n.NumberFormatSymbols_fur', 'goog.i18n.NumberFormatSymbols_fur_IT', 'goog.i18n.NumberFormatSymbols_fy', 'goog.i18n.NumberFormatSymbols_fy_NL', 'goog.i18n.NumberFormatSymbols_ga_IE', 'goog.i18n.NumberFormatSymbols_gd', 'goog.i18n.NumberFormatSymbols_gd_GB', 'goog.i18n.NumberFormatSymbols_gl_ES', 'goog.i18n.NumberFormatSymbols_gsw_CH', 'goog.i18n.NumberFormatSymbols_gsw_FR', 'goog.i18n.NumberFormatSymbols_gsw_LI', 'goog.i18n.NumberFormatSymbols_gu_IN', 'goog.i18n.NumberFormatSymbols_guz', 'goog.i18n.NumberFormatSymbols_guz_KE', 'goog.i18n.NumberFormatSymbols_gv', 'goog.i18n.NumberFormatSymbols_gv_IM', 'goog.i18n.NumberFormatSymbols_ha', 'goog.i18n.NumberFormatSymbols_ha_GH', 'goog.i18n.NumberFormatSymbols_ha_NE', 'goog.i18n.NumberFormatSymbols_ha_NG', 'goog.i18n.NumberFormatSymbols_haw_US', 'goog.i18n.NumberFormatSymbols_he_IL', 'goog.i18n.NumberFormatSymbols_hi_IN', 'goog.i18n.NumberFormatSymbols_hr_BA', 'goog.i18n.NumberFormatSymbols_hr_HR', 'goog.i18n.NumberFormatSymbols_hsb', 'goog.i18n.NumberFormatSymbols_hsb_DE', 'goog.i18n.NumberFormatSymbols_hu_HU', 'goog.i18n.NumberFormatSymbols_hy_AM', 'goog.i18n.NumberFormatSymbols_ia', 'goog.i18n.NumberFormatSymbols_ia_001', 'goog.i18n.NumberFormatSymbols_id_ID', 'goog.i18n.NumberFormatSymbols_ig', 'goog.i18n.NumberFormatSymbols_ig_NG', 'goog.i18n.NumberFormatSymbols_ii', 'goog.i18n.NumberFormatSymbols_ii_CN', 'goog.i18n.NumberFormatSymbols_is_IS', 'goog.i18n.NumberFormatSymbols_it_CH', 'goog.i18n.NumberFormatSymbols_it_IT', 'goog.i18n.NumberFormatSymbols_it_SM', 'goog.i18n.NumberFormatSymbols_it_VA', 'goog.i18n.NumberFormatSymbols_ja_JP', 'goog.i18n.NumberFormatSymbols_jgo', 'goog.i18n.NumberFormatSymbols_jgo_CM', 'goog.i18n.NumberFormatSymbols_jmc', 'goog.i18n.NumberFormatSymbols_jmc_TZ', 'goog.i18n.NumberFormatSymbols_jv', 'goog.i18n.NumberFormatSymbols_jv_ID', 'goog.i18n.NumberFormatSymbols_ka_GE', 'goog.i18n.NumberFormatSymbols_kab', 'goog.i18n.NumberFormatSymbols_kab_DZ', 'goog.i18n.NumberFormatSymbols_kam', 'goog.i18n.NumberFormatSymbols_kam_KE', 'goog.i18n.NumberFormatSymbols_kde', 'goog.i18n.NumberFormatSymbols_kde_TZ', 'goog.i18n.NumberFormatSymbols_kea', 'goog.i18n.NumberFormatSymbols_kea_CV', 'goog.i18n.NumberFormatSymbols_khq', 'goog.i18n.NumberFormatSymbols_khq_ML', 'goog.i18n.NumberFormatSymbols_ki', 'goog.i18n.NumberFormatSymbols_ki_KE', 'goog.i18n.NumberFormatSymbols_kk_KZ', 'goog.i18n.NumberFormatSymbols_kkj', 'goog.i18n.NumberFormatSymbols_kkj_CM', 'goog.i18n.NumberFormatSymbols_kl', 'goog.i18n.NumberFormatSymbols_kl_GL', 'goog.i18n.NumberFormatSymbols_kln', 'goog.i18n.NumberFormatSymbols_kln_KE', 'goog.i18n.NumberFormatSymbols_km_KH', 'goog.i18n.NumberFormatSymbols_kn_IN', 'goog.i18n.NumberFormatSymbols_ko_KP', 'goog.i18n.NumberFormatSymbols_ko_KR', 'goog.i18n.NumberFormatSymbols_kok', 'goog.i18n.NumberFormatSymbols_kok_IN', 'goog.i18n.NumberFormatSymbols_ks', 'goog.i18n.NumberFormatSymbols_ks_IN', 'goog.i18n.NumberFormatSymbols_ks_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ks_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ksb', 'goog.i18n.NumberFormatSymbols_ksb_TZ', 'goog.i18n.NumberFormatSymbols_ksf', 'goog.i18n.NumberFormatSymbols_ksf_CM', 'goog.i18n.NumberFormatSymbols_ksh', 'goog.i18n.NumberFormatSymbols_ksh_DE', 'goog.i18n.NumberFormatSymbols_ku', 'goog.i18n.NumberFormatSymbols_ku_TR', 'goog.i18n.NumberFormatSymbols_kw', 'goog.i18n.NumberFormatSymbols_kw_GB', 'goog.i18n.NumberFormatSymbols_ky_KG', 'goog.i18n.NumberFormatSymbols_lag', 'goog.i18n.NumberFormatSymbols_lag_TZ', 'goog.i18n.NumberFormatSymbols_lb', 'goog.i18n.NumberFormatSymbols_lb_LU', 'goog.i18n.NumberFormatSymbols_lg', 'goog.i18n.NumberFormatSymbols_lg_UG', 'goog.i18n.NumberFormatSymbols_lkt', 'goog.i18n.NumberFormatSymbols_lkt_US', 'goog.i18n.NumberFormatSymbols_ln_AO', 'goog.i18n.NumberFormatSymbols_ln_CD', 'goog.i18n.NumberFormatSymbols_ln_CF', 'goog.i18n.NumberFormatSymbols_ln_CG', 'goog.i18n.NumberFormatSymbols_lo_LA', 'goog.i18n.NumberFormatSymbols_lrc', 'goog.i18n.NumberFormatSymbols_lrc_IQ', 'goog.i18n.NumberFormatSymbols_lrc_IQ_u_nu_latn', 'goog.i18n.NumberFormatSymbols_lrc_IR', 'goog.i18n.NumberFormatSymbols_lrc_IR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_lrc_u_nu_latn', 'goog.i18n.NumberFormatSymbols_lt_LT', 'goog.i18n.NumberFormatSymbols_lu', 'goog.i18n.NumberFormatSymbols_lu_CD', 'goog.i18n.NumberFormatSymbols_luo', 'goog.i18n.NumberFormatSymbols_luo_KE', 'goog.i18n.NumberFormatSymbols_luy', 'goog.i18n.NumberFormatSymbols_luy_KE', 'goog.i18n.NumberFormatSymbols_lv_LV', 'goog.i18n.NumberFormatSymbols_mas', 'goog.i18n.NumberFormatSymbols_mas_KE', 'goog.i18n.NumberFormatSymbols_mas_TZ', 'goog.i18n.NumberFormatSymbols_mer', 'goog.i18n.NumberFormatSymbols_mer_KE', 'goog.i18n.NumberFormatSymbols_mfe', 'goog.i18n.NumberFormatSymbols_mfe_MU', 'goog.i18n.NumberFormatSymbols_mg', 'goog.i18n.NumberFormatSymbols_mg_MG', 'goog.i18n.NumberFormatSymbols_mgh', 'goog.i18n.NumberFormatSymbols_mgh_MZ', 'goog.i18n.NumberFormatSymbols_mgo', 'goog.i18n.NumberFormatSymbols_mgo_CM', 'goog.i18n.NumberFormatSymbols_mi', 'goog.i18n.NumberFormatSymbols_mi_NZ', 'goog.i18n.NumberFormatSymbols_mk_MK', 'goog.i18n.NumberFormatSymbols_ml_IN', 'goog.i18n.NumberFormatSymbols_mn_MN', 'goog.i18n.NumberFormatSymbols_mr_IN', 'goog.i18n.NumberFormatSymbols_mr_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ms_BN', 'goog.i18n.NumberFormatSymbols_ms_MY', 'goog.i18n.NumberFormatSymbols_ms_SG', 'goog.i18n.NumberFormatSymbols_mt_MT', 'goog.i18n.NumberFormatSymbols_mua', 'goog.i18n.NumberFormatSymbols_mua_CM', 'goog.i18n.NumberFormatSymbols_my_MM', 'goog.i18n.NumberFormatSymbols_my_MM_u_nu_latn', 'goog.i18n.NumberFormatSymbols_mzn', 'goog.i18n.NumberFormatSymbols_mzn_IR', 'goog.i18n.NumberFormatSymbols_mzn_IR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_mzn_u_nu_latn', 'goog.i18n.NumberFormatSymbols_naq', 'goog.i18n.NumberFormatSymbols_naq_NA', 'goog.i18n.NumberFormatSymbols_nb_NO', 'goog.i18n.NumberFormatSymbols_nb_SJ', 'goog.i18n.NumberFormatSymbols_nd', 'goog.i18n.NumberFormatSymbols_nd_ZW', 'goog.i18n.NumberFormatSymbols_nds', 'goog.i18n.NumberFormatSymbols_nds_DE', 'goog.i18n.NumberFormatSymbols_nds_NL', 'goog.i18n.NumberFormatSymbols_ne_IN', 'goog.i18n.NumberFormatSymbols_ne_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ne_NP', 'goog.i18n.NumberFormatSymbols_ne_NP_u_nu_latn', 'goog.i18n.NumberFormatSymbols_nl_AW', 'goog.i18n.NumberFormatSymbols_nl_BE', 'goog.i18n.NumberFormatSymbols_nl_BQ', 'goog.i18n.NumberFormatSymbols_nl_CW', 'goog.i18n.NumberFormatSymbols_nl_NL', 'goog.i18n.NumberFormatSymbols_nl_SR', 'goog.i18n.NumberFormatSymbols_nl_SX', 'goog.i18n.NumberFormatSymbols_nmg', 'goog.i18n.NumberFormatSymbols_nmg_CM', 'goog.i18n.NumberFormatSymbols_nn', 'goog.i18n.NumberFormatSymbols_nn_NO', 'goog.i18n.NumberFormatSymbols_nnh', 'goog.i18n.NumberFormatSymbols_nnh_CM', 'goog.i18n.NumberFormatSymbols_nus', 'goog.i18n.NumberFormatSymbols_nus_SS', 'goog.i18n.NumberFormatSymbols_nyn', 'goog.i18n.NumberFormatSymbols_nyn_UG', 'goog.i18n.NumberFormatSymbols_om', 'goog.i18n.NumberFormatSymbols_om_ET', 'goog.i18n.NumberFormatSymbols_om_KE', 'goog.i18n.NumberFormatSymbols_or_IN', 'goog.i18n.NumberFormatSymbols_os', 'goog.i18n.NumberFormatSymbols_os_GE', 'goog.i18n.NumberFormatSymbols_os_RU', 'goog.i18n.NumberFormatSymbols_pa_Arab', 'goog.i18n.NumberFormatSymbols_pa_Arab_PK', 'goog.i18n.NumberFormatSymbols_pa_Arab_PK_u_nu_latn', 'goog.i18n.NumberFormatSymbols_pa_Arab_u_nu_latn', 'goog.i18n.NumberFormatSymbols_pa_Guru', 'goog.i18n.NumberFormatSymbols_pa_Guru_IN', 'goog.i18n.NumberFormatSymbols_pl_PL', 'goog.i18n.NumberFormatSymbols_ps', 'goog.i18n.NumberFormatSymbols_ps_AF', 'goog.i18n.NumberFormatSymbols_ps_AF_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ps_PK', 'goog.i18n.NumberFormatSymbols_ps_PK_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ps_u_nu_latn', 'goog.i18n.NumberFormatSymbols_pt_AO', 'goog.i18n.NumberFormatSymbols_pt_CH', 'goog.i18n.NumberFormatSymbols_pt_CV', 'goog.i18n.NumberFormatSymbols_pt_GQ', 'goog.i18n.NumberFormatSymbols_pt_GW', 'goog.i18n.NumberFormatSymbols_pt_LU', 'goog.i18n.NumberFormatSymbols_pt_MO', 'goog.i18n.NumberFormatSymbols_pt_MZ', 'goog.i18n.NumberFormatSymbols_pt_ST', 'goog.i18n.NumberFormatSymbols_pt_TL', 'goog.i18n.NumberFormatSymbols_qu', 'goog.i18n.NumberFormatSymbols_qu_BO', 'goog.i18n.NumberFormatSymbols_qu_EC', 'goog.i18n.NumberFormatSymbols_qu_PE', 'goog.i18n.NumberFormatSymbols_rm', 'goog.i18n.NumberFormatSymbols_rm_CH', 'goog.i18n.NumberFormatSymbols_rn', 'goog.i18n.NumberFormatSymbols_rn_BI', 'goog.i18n.NumberFormatSymbols_ro_MD', 'goog.i18n.NumberFormatSymbols_ro_RO', 'goog.i18n.NumberFormatSymbols_rof', 'goog.i18n.NumberFormatSymbols_rof_TZ', 'goog.i18n.NumberFormatSymbols_ru_BY', 'goog.i18n.NumberFormatSymbols_ru_KG', 'goog.i18n.NumberFormatSymbols_ru_KZ', 'goog.i18n.NumberFormatSymbols_ru_MD', 'goog.i18n.NumberFormatSymbols_ru_RU', 'goog.i18n.NumberFormatSymbols_ru_UA', 'goog.i18n.NumberFormatSymbols_rw', 'goog.i18n.NumberFormatSymbols_rw_RW', 'goog.i18n.NumberFormatSymbols_rwk', 'goog.i18n.NumberFormatSymbols_rwk_TZ', 'goog.i18n.NumberFormatSymbols_sah', 'goog.i18n.NumberFormatSymbols_sah_RU', 'goog.i18n.NumberFormatSymbols_saq', 'goog.i18n.NumberFormatSymbols_saq_KE', 'goog.i18n.NumberFormatSymbols_sbp', 'goog.i18n.NumberFormatSymbols_sbp_TZ', 'goog.i18n.NumberFormatSymbols_sd', 'goog.i18n.NumberFormatSymbols_sd_PK', 'goog.i18n.NumberFormatSymbols_sd_PK_u_nu_latn', 'goog.i18n.NumberFormatSymbols_sd_u_nu_latn', 'goog.i18n.NumberFormatSymbols_se', 'goog.i18n.NumberFormatSymbols_se_FI', 'goog.i18n.NumberFormatSymbols_se_NO', 'goog.i18n.NumberFormatSymbols_se_SE', 'goog.i18n.NumberFormatSymbols_seh', 'goog.i18n.NumberFormatSymbols_seh_MZ', 'goog.i18n.NumberFormatSymbols_ses', 'goog.i18n.NumberFormatSymbols_ses_ML', 'goog.i18n.NumberFormatSymbols_sg', 'goog.i18n.NumberFormatSymbols_sg_CF', 'goog.i18n.NumberFormatSymbols_shi', 'goog.i18n.NumberFormatSymbols_shi_Latn', 'goog.i18n.NumberFormatSymbols_shi_Latn_MA', 'goog.i18n.NumberFormatSymbols_shi_Tfng', 'goog.i18n.NumberFormatSymbols_shi_Tfng_MA', 'goog.i18n.NumberFormatSymbols_si_LK', 'goog.i18n.NumberFormatSymbols_sk_SK', 'goog.i18n.NumberFormatSymbols_sl_SI', 'goog.i18n.NumberFormatSymbols_smn', 'goog.i18n.NumberFormatSymbols_smn_FI', 'goog.i18n.NumberFormatSymbols_sn', 'goog.i18n.NumberFormatSymbols_sn_ZW', 'goog.i18n.NumberFormatSymbols_so', 'goog.i18n.NumberFormatSymbols_so_DJ', 'goog.i18n.NumberFormatSymbols_so_ET', 'goog.i18n.NumberFormatSymbols_so_KE', 'goog.i18n.NumberFormatSymbols_so_SO', 'goog.i18n.NumberFormatSymbols_sq_AL', 'goog.i18n.NumberFormatSymbols_sq_MK', 'goog.i18n.NumberFormatSymbols_sq_XK', 'goog.i18n.NumberFormatSymbols_sr_Cyrl', 'goog.i18n.NumberFormatSymbols_sr_Cyrl_BA', 'goog.i18n.NumberFormatSymbols_sr_Cyrl_ME', 'goog.i18n.NumberFormatSymbols_sr_Cyrl_RS', 'goog.i18n.NumberFormatSymbols_sr_Cyrl_XK', 'goog.i18n.NumberFormatSymbols_sr_Latn_BA', 'goog.i18n.NumberFormatSymbols_sr_Latn_ME', 'goog.i18n.NumberFormatSymbols_sr_Latn_RS', 'goog.i18n.NumberFormatSymbols_sr_Latn_XK', 'goog.i18n.NumberFormatSymbols_sv_AX', 'goog.i18n.NumberFormatSymbols_sv_FI', 'goog.i18n.NumberFormatSymbols_sv_SE', 'goog.i18n.NumberFormatSymbols_sw_CD', 'goog.i18n.NumberFormatSymbols_sw_KE', 'goog.i18n.NumberFormatSymbols_sw_TZ', 'goog.i18n.NumberFormatSymbols_sw_UG', 'goog.i18n.NumberFormatSymbols_ta_IN', 'goog.i18n.NumberFormatSymbols_ta_LK', 'goog.i18n.NumberFormatSymbols_ta_MY', 'goog.i18n.NumberFormatSymbols_ta_SG', 'goog.i18n.NumberFormatSymbols_te_IN', 'goog.i18n.NumberFormatSymbols_teo', 'goog.i18n.NumberFormatSymbols_teo_KE', 'goog.i18n.NumberFormatSymbols_teo_UG', 'goog.i18n.NumberFormatSymbols_tg', 'goog.i18n.NumberFormatSymbols_tg_TJ', 'goog.i18n.NumberFormatSymbols_th_TH', 'goog.i18n.NumberFormatSymbols_ti', 'goog.i18n.NumberFormatSymbols_ti_ER', 'goog.i18n.NumberFormatSymbols_ti_ET', 'goog.i18n.NumberFormatSymbols_tk', 'goog.i18n.NumberFormatSymbols_tk_TM', 'goog.i18n.NumberFormatSymbols_to', 'goog.i18n.NumberFormatSymbols_to_TO', 'goog.i18n.NumberFormatSymbols_tr_CY', 'goog.i18n.NumberFormatSymbols_tr_TR', 'goog.i18n.NumberFormatSymbols_tt', 'goog.i18n.NumberFormatSymbols_tt_RU', 'goog.i18n.NumberFormatSymbols_twq', 'goog.i18n.NumberFormatSymbols_twq_NE', 'goog.i18n.NumberFormatSymbols_tzm', 'goog.i18n.NumberFormatSymbols_tzm_MA', 'goog.i18n.NumberFormatSymbols_ug', 'goog.i18n.NumberFormatSymbols_ug_CN', 'goog.i18n.NumberFormatSymbols_uk_UA', 'goog.i18n.NumberFormatSymbols_ur_IN', 'goog.i18n.NumberFormatSymbols_ur_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ur_PK', 'goog.i18n.NumberFormatSymbols_uz_Arab', 'goog.i18n.NumberFormatSymbols_uz_Arab_AF', 'goog.i18n.NumberFormatSymbols_uz_Arab_AF_u_nu_latn', 'goog.i18n.NumberFormatSymbols_uz_Arab_u_nu_latn', 'goog.i18n.NumberFormatSymbols_uz_Cyrl', 'goog.i18n.NumberFormatSymbols_uz_Cyrl_UZ', 'goog.i18n.NumberFormatSymbols_uz_Latn', 'goog.i18n.NumberFormatSymbols_uz_Latn_UZ', 'goog.i18n.NumberFormatSymbols_vai', 'goog.i18n.NumberFormatSymbols_vai_Latn', 'goog.i18n.NumberFormatSymbols_vai_Latn_LR', 'goog.i18n.NumberFormatSymbols_vai_Vaii', 'goog.i18n.NumberFormatSymbols_vai_Vaii_LR', 'goog.i18n.NumberFormatSymbols_vi_VN', 'goog.i18n.NumberFormatSymbols_vun', 'goog.i18n.NumberFormatSymbols_vun_TZ', 'goog.i18n.NumberFormatSymbols_wae', 'goog.i18n.NumberFormatSymbols_wae_CH', 'goog.i18n.NumberFormatSymbols_wo', 'goog.i18n.NumberFormatSymbols_wo_SN', 'goog.i18n.NumberFormatSymbols_xh', 'goog.i18n.NumberFormatSymbols_xh_ZA', 'goog.i18n.NumberFormatSymbols_xog', 'goog.i18n.NumberFormatSymbols_xog_UG', 'goog.i18n.NumberFormatSymbols_yav', 'goog.i18n.NumberFormatSymbols_yav_CM', 'goog.i18n.NumberFormatSymbols_yi', 'goog.i18n.NumberFormatSymbols_yi_001', 'goog.i18n.NumberFormatSymbols_yo', 'goog.i18n.NumberFormatSymbols_yo_BJ', 'goog.i18n.NumberFormatSymbols_yo_NG', 'goog.i18n.NumberFormatSymbols_yue', 'goog.i18n.NumberFormatSymbols_yue_Hans', 'goog.i18n.NumberFormatSymbols_yue_Hans_CN', 'goog.i18n.NumberFormatSymbols_yue_Hant', 'goog.i18n.NumberFormatSymbols_yue_Hant_HK', 'goog.i18n.NumberFormatSymbols_zgh', 'goog.i18n.NumberFormatSymbols_zgh_MA', 'goog.i18n.NumberFormatSymbols_zh_Hans', 'goog.i18n.NumberFormatSymbols_zh_Hans_CN', 'goog.i18n.NumberFormatSymbols_zh_Hans_HK', 'goog.i18n.NumberFormatSymbols_zh_Hans_MO', 'goog.i18n.NumberFormatSymbols_zh_Hans_SG', 'goog.i18n.NumberFormatSymbols_zh_Hant', 'goog.i18n.NumberFormatSymbols_zh_Hant_HK', 'goog.i18n.NumberFormatSymbols_zh_Hant_MO', 'goog.i18n.NumberFormatSymbols_zh_Hant_TW', 'goog.i18n.NumberFormatSymbols_zu_ZA'], ['goog.i18n.NumberFormatSymbols', 'goog.i18n.NumberFormatSymbols_u_nu_latn'], {});
goog.addDependency('i18n/ordinalrules.js', ['goog.i18n.ordinalRules'], [], {'lang': 'es6'});
goog.addDependency('i18n/pluralrules.js', ['goog.i18n.pluralRules'], [], {'lang': 'es6'});
goog.addDependency('i18n/pluralrules_test.js', ['goog.i18n.pluralRulesTest'], ['goog.i18n.pluralRules', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/relativedatetimeformat.js', ['goog.i18n.RelativeDateTimeFormat'], ['goog.asserts', 'goog.i18n.LocaleFeature', 'goog.i18n.MessageFormat', 'goog.i18n.relativeDateTimeSymbols'], {'lang': 'es5', 'module': 'goog'});
goog.addDependency('i18n/relativedatetimeformat_test.js', ['goog.i18n.RelativeDateTimeFormatTest'], ['goog.i18n.LocaleFeature', 'goog.i18n.NumberFormatSymbols_ar_EG', 'goog.i18n.NumberFormatSymbols_en', 'goog.i18n.NumberFormatSymbols_es', 'goog.i18n.NumberFormatSymbols_fa', 'goog.i18n.RelativeDateTimeFormat', 'goog.i18n.relativeDateTimeSymbols', 'goog.i18n.relativeDateTimeSymbolsExt', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/relativedatetimesymbols.js', ['goog.i18n.relativeDateTimeSymbols'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/relativedatetimesymbolsext.js', ['goog.i18n.relativeDateTimeSymbolsExt'], ['goog.i18n.relativeDateTimeSymbols'], {'lang': 'es5', 'module': 'goog'});
goog.addDependency('i18n/timezone.js', ['goog.i18n.TimeZone'], ['goog.array', 'goog.date.DateLike', 'goog.object', 'goog.string'], {});
goog.addDependency('i18n/timezone_test.js', ['goog.i18n.TimeZoneTest'], ['goog.i18n.TimeZone', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/uchar.js', ['goog.i18n.uChar'], [], {'lang': 'es6'});
goog.addDependency('i18n/uchar/localnamefetcher.js', ['goog.i18n.uChar.LocalNameFetcher'], ['goog.i18n.uChar.NameFetcher', 'goog.i18n.uCharNames', 'goog.log'], {});
goog.addDependency('i18n/uchar/localnamefetcher_test.js', ['goog.i18n.uChar.LocalNameFetcherTest'], ['goog.i18n.uChar.LocalNameFetcher', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/uchar/namefetcher.js', ['goog.i18n.uChar.NameFetcher'], [], {});
goog.addDependency('i18n/uchar/remotenamefetcher.js', ['goog.i18n.uChar.RemoteNameFetcher'], ['goog.Disposable', 'goog.Uri', 'goog.events', 'goog.i18n.uChar', 'goog.i18n.uChar.NameFetcher', 'goog.log', 'goog.net.EventType', 'goog.net.XhrIo'], {});
goog.addDependency('i18n/uchar/remotenamefetcher_test.js', ['goog.i18n.uChar.RemoteNameFetcherTest'], ['goog.i18n.uChar.RemoteNameFetcher', 'goog.net.XhrIo', 'goog.testing.net.XhrIo', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/uchar_test.js', ['goog.i18n.uCharTest'], ['goog.i18n.uChar', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/ucharnames.js', ['goog.i18n.uCharNames'], ['goog.i18n.uChar'], {});
goog.addDependency('i18n/ucharnames_test.js', ['goog.i18n.uCharNamesTest'], ['goog.i18n.uCharNames', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('iter/es6.js', ['goog.iter.es6'], ['goog.iter.Iterable', 'goog.iter.Iterator', 'goog.iter.StopIteration'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('iter/es6_test.js', ['goog.iter.es6Test'], ['goog.iter', 'goog.iter.es6', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('iter/iter.js', ['goog.iter', 'goog.iter.Iterable', 'goog.iter.Iterator', 'goog.iter.StopIteration'], ['goog.array', 'goog.asserts', 'goog.functions', 'goog.math'], {});
goog.addDependency('iter/iter_test.js', ['goog.iterTest'], ['goog.iter', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('json/hybrid.js', ['goog.json.hybrid'], ['goog.asserts', 'goog.json'], {});
goog.addDependency('json/hybrid_test.js', ['goog.json.hybridTest'], ['goog.json', 'goog.json.hybrid', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('json/json.js', ['goog.json', 'goog.json.Replacer', 'goog.json.Reviver', 'goog.json.Serializer'], [], {'lang': 'es6'});
goog.addDependency('json/json_perf.js', ['goog.jsonPerf'], ['goog.dom', 'goog.json', 'goog.math', 'goog.string', 'goog.testing.PerformanceTable', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit'], {});
goog.addDependency('json/json_test.js', ['goog.jsonTest'], ['goog.functions', 'goog.json', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('json/jsonable.js', ['goog.json.Jsonable'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('json/nativejsonprocessor.js', ['goog.json.NativeJsonProcessor'], ['goog.asserts', 'goog.json.Processor'], {});
goog.addDependency('json/processor.js', ['goog.json.Processor'], ['goog.string.Parser', 'goog.string.Stringifier'], {});
goog.addDependency('json/processor_test.js', ['goog.json.processorTest'], ['goog.json.NativeJsonProcessor', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/collections/iterables.js', ['goog.labs.collections.iterables'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/collections/iterables_test.js', ['goog.labs.iterableTest'], ['goog.labs.collections.iterables', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/dom/pagevisibilitymonitor.js', ['goog.labs.dom.PageVisibilityEvent', 'goog.labs.dom.PageVisibilityMonitor', 'goog.labs.dom.PageVisibilityState'], ['goog.dom', 'goog.dom.vendor', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.memoize'], {});
goog.addDependency('labs/dom/pagevisibilitymonitor_test.js', ['goog.labs.dom.PageVisibilityMonitorTest'], ['goog.events', 'goog.functions', 'goog.labs.dom.PageVisibilityMonitor', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/events/nondisposableeventtarget.js', ['goog.labs.events.NonDisposableEventTarget'], ['goog.array', 'goog.asserts', 'goog.events.Event', 'goog.events.Listenable', 'goog.events.ListenerMap', 'goog.object'], {});
goog.addDependency('labs/events/nondisposableeventtarget_test.js', ['goog.labs.events.NonDisposableEventTargetTest'], ['goog.events.Listenable', 'goog.events.eventTargetTester', 'goog.labs.events.NonDisposableEventTarget', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/events/nondisposableeventtarget_via_googevents_test.js', ['goog.labs.events.NonDisposableEventTargetGoogEventsTest'], ['goog.events', 'goog.events.eventTargetTester', 'goog.labs.events.NonDisposableEventTarget', 'goog.testing', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/events/touch.js', ['goog.labs.events.touch', 'goog.labs.events.touch.TouchData'], ['goog.array', 'goog.asserts', 'goog.events.EventType', 'goog.string'], {});
goog.addDependency('labs/events/touch_test.js', ['goog.labs.events.touchTest'], ['goog.labs.events.touch', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/format/csv.js', ['goog.labs.format.csv', 'goog.labs.format.csv.ParseError', 'goog.labs.format.csv.Token'], ['goog.array', 'goog.asserts', 'goog.debug.Error', 'goog.object', 'goog.string', 'goog.string.newlines'], {});
goog.addDependency('labs/format/csv_test.js', ['goog.labs.format.csvTest'], ['goog.labs.format.csv', 'goog.labs.format.csv.ParseError', 'goog.object', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/i18n/listformat.js', ['goog.labs.i18n.GenderInfo', 'goog.labs.i18n.GenderInfo.Gender', 'goog.labs.i18n.ListFormat'], ['goog.asserts', 'goog.labs.i18n.ListFormatSymbols'], {});
goog.addDependency('labs/i18n/listformat_test.js', ['goog.labs.i18n.ListFormatTest'], ['goog.labs.i18n.GenderInfo', 'goog.labs.i18n.ListFormat', 'goog.labs.i18n.ListFormatSymbols', 'goog.labs.i18n.ListFormatSymbols_el', 'goog.labs.i18n.ListFormatSymbols_en', 'goog.labs.i18n.ListFormatSymbols_fr', 'goog.labs.i18n.ListFormatSymbols_ml', 'goog.labs.i18n.ListFormatSymbols_zu', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/i18n/listsymbols.js', ['goog.labs.i18n.ListFormatSymbols', 'goog.labs.i18n.ListFormatSymbols_af', 'goog.labs.i18n.ListFormatSymbols_am', 'goog.labs.i18n.ListFormatSymbols_ar', 'goog.labs.i18n.ListFormatSymbols_ar_DZ', 'goog.labs.i18n.ListFormatSymbols_ar_EG', 'goog.labs.i18n.ListFormatSymbols_az', 'goog.labs.i18n.ListFormatSymbols_be', 'goog.labs.i18n.ListFormatSymbols_bg', 'goog.labs.i18n.ListFormatSymbols_bn', 'goog.labs.i18n.ListFormatSymbols_br', 'goog.labs.i18n.ListFormatSymbols_bs', 'goog.labs.i18n.ListFormatSymbols_ca', 'goog.labs.i18n.ListFormatSymbols_chr', 'goog.labs.i18n.ListFormatSymbols_cs', 'goog.labs.i18n.ListFormatSymbols_cy', 'goog.labs.i18n.ListFormatSymbols_da', 'goog.labs.i18n.ListFormatSymbols_de', 'goog.labs.i18n.ListFormatSymbols_de_AT', 'goog.labs.i18n.ListFormatSymbols_de_CH', 'goog.labs.i18n.ListFormatSymbols_el', 'goog.labs.i18n.ListFormatSymbols_en', 'goog.labs.i18n.ListFormatSymbols_en_AU', 'goog.labs.i18n.ListFormatSymbols_en_CA', 'goog.labs.i18n.ListFormatSymbols_en_GB', 'goog.labs.i18n.ListFormatSymbols_en_IE', 'goog.labs.i18n.ListFormatSymbols_en_IN', 'goog.labs.i18n.ListFormatSymbols_en_SG', 'goog.labs.i18n.ListFormatSymbols_en_US', 'goog.labs.i18n.ListFormatSymbols_en_ZA', 'goog.labs.i18n.ListFormatSymbols_es', 'goog.labs.i18n.ListFormatSymbols_es_419', 'goog.labs.i18n.ListFormatSymbols_es_ES', 'goog.labs.i18n.ListFormatSymbols_es_MX', 'goog.labs.i18n.ListFormatSymbols_es_US', 'goog.labs.i18n.ListFormatSymbols_et', 'goog.labs.i18n.ListFormatSymbols_eu', 'goog.labs.i18n.ListFormatSymbols_fa', 'goog.labs.i18n.ListFormatSymbols_fi', 'goog.labs.i18n.ListFormatSymbols_fil', 'goog.labs.i18n.ListFormatSymbols_fr', 'goog.labs.i18n.ListFormatSymbols_fr_CA', 'goog.labs.i18n.ListFormatSymbols_ga', 'goog.labs.i18n.ListFormatSymbols_gl', 'goog.labs.i18n.ListFormatSymbols_gsw', 'goog.labs.i18n.ListFormatSymbols_gu', 'goog.labs.i18n.ListFormatSymbols_haw', 'goog.labs.i18n.ListFormatSymbols_he', 'goog.labs.i18n.ListFormatSymbols_hi', 'goog.labs.i18n.ListFormatSymbols_hr', 'goog.labs.i18n.ListFormatSymbols_hu', 'goog.labs.i18n.ListFormatSymbols_hy', 'goog.labs.i18n.ListFormatSymbols_id', 'goog.labs.i18n.ListFormatSymbols_in', 'goog.labs.i18n.ListFormatSymbols_is', 'goog.labs.i18n.ListFormatSymbols_it', 'goog.labs.i18n.ListFormatSymbols_iw', 'goog.labs.i18n.ListFormatSymbols_ja', 'goog.labs.i18n.ListFormatSymbols_ka', 'goog.labs.i18n.ListFormatSymbols_kk', 'goog.labs.i18n.ListFormatSymbols_km', 'goog.labs.i18n.ListFormatSymbols_kn', 'goog.labs.i18n.ListFormatSymbols_ko', 'goog.labs.i18n.ListFormatSymbols_ky', 'goog.labs.i18n.ListFormatSymbols_ln', 'goog.labs.i18n.ListFormatSymbols_lo', 'goog.labs.i18n.ListFormatSymbols_lt', 'goog.labs.i18n.ListFormatSymbols_lv', 'goog.labs.i18n.ListFormatSymbols_mk', 'goog.labs.i18n.ListFormatSymbols_ml', 'goog.labs.i18n.ListFormatSymbols_mn', 'goog.labs.i18n.ListFormatSymbols_mo', 'goog.labs.i18n.ListFormatSymbols_mr', 'goog.labs.i18n.ListFormatSymbols_ms', 'goog.labs.i18n.ListFormatSymbols_mt', 'goog.labs.i18n.ListFormatSymbols_my', 'goog.labs.i18n.ListFormatSymbols_nb', 'goog.labs.i18n.ListFormatSymbols_ne', 'goog.labs.i18n.ListFormatSymbols_nl', 'goog.labs.i18n.ListFormatSymbols_no', 'goog.labs.i18n.ListFormatSymbols_no_NO', 'goog.labs.i18n.ListFormatSymbols_or', 'goog.labs.i18n.ListFormatSymbols_pa', 'goog.labs.i18n.ListFormatSymbols_pl', 'goog.labs.i18n.ListFormatSymbols_pt', 'goog.labs.i18n.ListFormatSymbols_pt_BR', 'goog.labs.i18n.ListFormatSymbols_pt_PT', 'goog.labs.i18n.ListFormatSymbols_ro', 'goog.labs.i18n.ListFormatSymbols_ru', 'goog.labs.i18n.ListFormatSymbols_sh', 'goog.labs.i18n.ListFormatSymbols_si', 'goog.labs.i18n.ListFormatSymbols_sk', 'goog.labs.i18n.ListFormatSymbols_sl', 'goog.labs.i18n.ListFormatSymbols_sq', 'goog.labs.i18n.ListFormatSymbols_sr', 'goog.labs.i18n.ListFormatSymbols_sr_Latn', 'goog.labs.i18n.ListFormatSymbols_sv', 'goog.labs.i18n.ListFormatSymbols_sw', 'goog.labs.i18n.ListFormatSymbols_ta', 'goog.labs.i18n.ListFormatSymbols_te', 'goog.labs.i18n.ListFormatSymbols_th', 'goog.labs.i18n.ListFormatSymbols_tl', 'goog.labs.i18n.ListFormatSymbols_tr', 'goog.labs.i18n.ListFormatSymbols_uk', 'goog.labs.i18n.ListFormatSymbols_ur', 'goog.labs.i18n.ListFormatSymbols_uz', 'goog.labs.i18n.ListFormatSymbols_vi', 'goog.labs.i18n.ListFormatSymbols_zh', 'goog.labs.i18n.ListFormatSymbols_zh_CN', 'goog.labs.i18n.ListFormatSymbols_zh_HK', 'goog.labs.i18n.ListFormatSymbols_zh_TW', 'goog.labs.i18n.ListFormatSymbols_zu'], [], {});
goog.addDependency('labs/i18n/listsymbolsext.js', ['goog.labs.i18n.ListFormatSymbolsExt', 'goog.labs.i18n.ListFormatSymbols_af_NA', 'goog.labs.i18n.ListFormatSymbols_af_ZA', 'goog.labs.i18n.ListFormatSymbols_agq', 'goog.labs.i18n.ListFormatSymbols_agq_CM', 'goog.labs.i18n.ListFormatSymbols_ak', 'goog.labs.i18n.ListFormatSymbols_ak_GH', 'goog.labs.i18n.ListFormatSymbols_am_ET', 'goog.labs.i18n.ListFormatSymbols_ar_001', 'goog.labs.i18n.ListFormatSymbols_ar_AE', 'goog.labs.i18n.ListFormatSymbols_ar_BH', 'goog.labs.i18n.ListFormatSymbols_ar_DJ', 'goog.labs.i18n.ListFormatSymbols_ar_EH', 'goog.labs.i18n.ListFormatSymbols_ar_ER', 'goog.labs.i18n.ListFormatSymbols_ar_IL', 'goog.labs.i18n.ListFormatSymbols_ar_IQ', 'goog.labs.i18n.ListFormatSymbols_ar_JO', 'goog.labs.i18n.ListFormatSymbols_ar_KM', 'goog.labs.i18n.ListFormatSymbols_ar_KW', 'goog.labs.i18n.ListFormatSymbols_ar_LB', 'goog.labs.i18n.ListFormatSymbols_ar_LY', 'goog.labs.i18n.ListFormatSymbols_ar_MA', 'goog.labs.i18n.ListFormatSymbols_ar_MR', 'goog.labs.i18n.ListFormatSymbols_ar_OM', 'goog.labs.i18n.ListFormatSymbols_ar_PS', 'goog.labs.i18n.ListFormatSymbols_ar_QA', 'goog.labs.i18n.ListFormatSymbols_ar_SA', 'goog.labs.i18n.ListFormatSymbols_ar_SD', 'goog.labs.i18n.ListFormatSymbols_ar_SO', 'goog.labs.i18n.ListFormatSymbols_ar_SS', 'goog.labs.i18n.ListFormatSymbols_ar_SY', 'goog.labs.i18n.ListFormatSymbols_ar_TD', 'goog.labs.i18n.ListFormatSymbols_ar_TN', 'goog.labs.i18n.ListFormatSymbols_ar_XB', 'goog.labs.i18n.ListFormatSymbols_ar_YE', 'goog.labs.i18n.ListFormatSymbols_as', 'goog.labs.i18n.ListFormatSymbols_as_IN', 'goog.labs.i18n.ListFormatSymbols_asa', 'goog.labs.i18n.ListFormatSymbols_asa_TZ', 'goog.labs.i18n.ListFormatSymbols_ast', 'goog.labs.i18n.ListFormatSymbols_ast_ES', 'goog.labs.i18n.ListFormatSymbols_az_Cyrl', 'goog.labs.i18n.ListFormatSymbols_az_Cyrl_AZ', 'goog.labs.i18n.ListFormatSymbols_az_Latn', 'goog.labs.i18n.ListFormatSymbols_az_Latn_AZ', 'goog.labs.i18n.ListFormatSymbols_bas', 'goog.labs.i18n.ListFormatSymbols_bas_CM', 'goog.labs.i18n.ListFormatSymbols_be_BY', 'goog.labs.i18n.ListFormatSymbols_bem', 'goog.labs.i18n.ListFormatSymbols_bem_ZM', 'goog.labs.i18n.ListFormatSymbols_bez', 'goog.labs.i18n.ListFormatSymbols_bez_TZ', 'goog.labs.i18n.ListFormatSymbols_bg_BG', 'goog.labs.i18n.ListFormatSymbols_bm', 'goog.labs.i18n.ListFormatSymbols_bm_ML', 'goog.labs.i18n.ListFormatSymbols_bn_BD', 'goog.labs.i18n.ListFormatSymbols_bn_IN', 'goog.labs.i18n.ListFormatSymbols_bo', 'goog.labs.i18n.ListFormatSymbols_bo_CN', 'goog.labs.i18n.ListFormatSymbols_bo_IN', 'goog.labs.i18n.ListFormatSymbols_br_FR', 'goog.labs.i18n.ListFormatSymbols_brx', 'goog.labs.i18n.ListFormatSymbols_brx_IN', 'goog.labs.i18n.ListFormatSymbols_bs_Cyrl', 'goog.labs.i18n.ListFormatSymbols_bs_Cyrl_BA', 'goog.labs.i18n.ListFormatSymbols_bs_Latn', 'goog.labs.i18n.ListFormatSymbols_bs_Latn_BA', 'goog.labs.i18n.ListFormatSymbols_ca_AD', 'goog.labs.i18n.ListFormatSymbols_ca_ES', 'goog.labs.i18n.ListFormatSymbols_ca_FR', 'goog.labs.i18n.ListFormatSymbols_ca_IT', 'goog.labs.i18n.ListFormatSymbols_ccp', 'goog.labs.i18n.ListFormatSymbols_ccp_BD', 'goog.labs.i18n.ListFormatSymbols_ccp_IN', 'goog.labs.i18n.ListFormatSymbols_ce', 'goog.labs.i18n.ListFormatSymbols_ce_RU', 'goog.labs.i18n.ListFormatSymbols_ceb', 'goog.labs.i18n.ListFormatSymbols_ceb_PH', 'goog.labs.i18n.ListFormatSymbols_cgg', 'goog.labs.i18n.ListFormatSymbols_cgg_UG', 'goog.labs.i18n.ListFormatSymbols_chr_US', 'goog.labs.i18n.ListFormatSymbols_ckb', 'goog.labs.i18n.ListFormatSymbols_ckb_IQ', 'goog.labs.i18n.ListFormatSymbols_ckb_IR', 'goog.labs.i18n.ListFormatSymbols_cs_CZ', 'goog.labs.i18n.ListFormatSymbols_cy_GB', 'goog.labs.i18n.ListFormatSymbols_da_DK', 'goog.labs.i18n.ListFormatSymbols_da_GL', 'goog.labs.i18n.ListFormatSymbols_dav', 'goog.labs.i18n.ListFormatSymbols_dav_KE', 'goog.labs.i18n.ListFormatSymbols_de_BE', 'goog.labs.i18n.ListFormatSymbols_de_DE', 'goog.labs.i18n.ListFormatSymbols_de_IT', 'goog.labs.i18n.ListFormatSymbols_de_LI', 'goog.labs.i18n.ListFormatSymbols_de_LU', 'goog.labs.i18n.ListFormatSymbols_dje', 'goog.labs.i18n.ListFormatSymbols_dje_NE', 'goog.labs.i18n.ListFormatSymbols_dsb', 'goog.labs.i18n.ListFormatSymbols_dsb_DE', 'goog.labs.i18n.ListFormatSymbols_dua', 'goog.labs.i18n.ListFormatSymbols_dua_CM', 'goog.labs.i18n.ListFormatSymbols_dyo', 'goog.labs.i18n.ListFormatSymbols_dyo_SN', 'goog.labs.i18n.ListFormatSymbols_dz', 'goog.labs.i18n.ListFormatSymbols_dz_BT', 'goog.labs.i18n.ListFormatSymbols_ebu', 'goog.labs.i18n.ListFormatSymbols_ebu_KE', 'goog.labs.i18n.ListFormatSymbols_ee', 'goog.labs.i18n.ListFormatSymbols_ee_GH', 'goog.labs.i18n.ListFormatSymbols_ee_TG', 'goog.labs.i18n.ListFormatSymbols_el_CY', 'goog.labs.i18n.ListFormatSymbols_el_GR', 'goog.labs.i18n.ListFormatSymbols_en_001', 'goog.labs.i18n.ListFormatSymbols_en_150', 'goog.labs.i18n.ListFormatSymbols_en_AE', 'goog.labs.i18n.ListFormatSymbols_en_AG', 'goog.labs.i18n.ListFormatSymbols_en_AI', 'goog.labs.i18n.ListFormatSymbols_en_AS', 'goog.labs.i18n.ListFormatSymbols_en_AT', 'goog.labs.i18n.ListFormatSymbols_en_BB', 'goog.labs.i18n.ListFormatSymbols_en_BE', 'goog.labs.i18n.ListFormatSymbols_en_BI', 'goog.labs.i18n.ListFormatSymbols_en_BM', 'goog.labs.i18n.ListFormatSymbols_en_BS', 'goog.labs.i18n.ListFormatSymbols_en_BW', 'goog.labs.i18n.ListFormatSymbols_en_BZ', 'goog.labs.i18n.ListFormatSymbols_en_CC', 'goog.labs.i18n.ListFormatSymbols_en_CH', 'goog.labs.i18n.ListFormatSymbols_en_CK', 'goog.labs.i18n.ListFormatSymbols_en_CM', 'goog.labs.i18n.ListFormatSymbols_en_CX', 'goog.labs.i18n.ListFormatSymbols_en_CY', 'goog.labs.i18n.ListFormatSymbols_en_DE', 'goog.labs.i18n.ListFormatSymbols_en_DG', 'goog.labs.i18n.ListFormatSymbols_en_DK', 'goog.labs.i18n.ListFormatSymbols_en_DM', 'goog.labs.i18n.ListFormatSymbols_en_ER', 'goog.labs.i18n.ListFormatSymbols_en_FI', 'goog.labs.i18n.ListFormatSymbols_en_FJ', 'goog.labs.i18n.ListFormatSymbols_en_FK', 'goog.labs.i18n.ListFormatSymbols_en_FM', 'goog.labs.i18n.ListFormatSymbols_en_GD', 'goog.labs.i18n.ListFormatSymbols_en_GG', 'goog.labs.i18n.ListFormatSymbols_en_GH', 'goog.labs.i18n.ListFormatSymbols_en_GI', 'goog.labs.i18n.ListFormatSymbols_en_GM', 'goog.labs.i18n.ListFormatSymbols_en_GU', 'goog.labs.i18n.ListFormatSymbols_en_GY', 'goog.labs.i18n.ListFormatSymbols_en_HK', 'goog.labs.i18n.ListFormatSymbols_en_IL', 'goog.labs.i18n.ListFormatSymbols_en_IM', 'goog.labs.i18n.ListFormatSymbols_en_IO', 'goog.labs.i18n.ListFormatSymbols_en_JE', 'goog.labs.i18n.ListFormatSymbols_en_JM', 'goog.labs.i18n.ListFormatSymbols_en_KE', 'goog.labs.i18n.ListFormatSymbols_en_KI', 'goog.labs.i18n.ListFormatSymbols_en_KN', 'goog.labs.i18n.ListFormatSymbols_en_KY', 'goog.labs.i18n.ListFormatSymbols_en_LC', 'goog.labs.i18n.ListFormatSymbols_en_LR', 'goog.labs.i18n.ListFormatSymbols_en_LS', 'goog.labs.i18n.ListFormatSymbols_en_MG', 'goog.labs.i18n.ListFormatSymbols_en_MH', 'goog.labs.i18n.ListFormatSymbols_en_MO', 'goog.labs.i18n.ListFormatSymbols_en_MP', 'goog.labs.i18n.ListFormatSymbols_en_MS', 'goog.labs.i18n.ListFormatSymbols_en_MT', 'goog.labs.i18n.ListFormatSymbols_en_MU', 'goog.labs.i18n.ListFormatSymbols_en_MW', 'goog.labs.i18n.ListFormatSymbols_en_MY', 'goog.labs.i18n.ListFormatSymbols_en_NA', 'goog.labs.i18n.ListFormatSymbols_en_NF', 'goog.labs.i18n.ListFormatSymbols_en_NG', 'goog.labs.i18n.ListFormatSymbols_en_NL', 'goog.labs.i18n.ListFormatSymbols_en_NR', 'goog.labs.i18n.ListFormatSymbols_en_NU', 'goog.labs.i18n.ListFormatSymbols_en_NZ', 'goog.labs.i18n.ListFormatSymbols_en_PG', 'goog.labs.i18n.ListFormatSymbols_en_PH', 'goog.labs.i18n.ListFormatSymbols_en_PK', 'goog.labs.i18n.ListFormatSymbols_en_PN', 'goog.labs.i18n.ListFormatSymbols_en_PR', 'goog.labs.i18n.ListFormatSymbols_en_PW', 'goog.labs.i18n.ListFormatSymbols_en_RW', 'goog.labs.i18n.ListFormatSymbols_en_SB', 'goog.labs.i18n.ListFormatSymbols_en_SC', 'goog.labs.i18n.ListFormatSymbols_en_SD', 'goog.labs.i18n.ListFormatSymbols_en_SE', 'goog.labs.i18n.ListFormatSymbols_en_SH', 'goog.labs.i18n.ListFormatSymbols_en_SI', 'goog.labs.i18n.ListFormatSymbols_en_SL', 'goog.labs.i18n.ListFormatSymbols_en_SS', 'goog.labs.i18n.ListFormatSymbols_en_SX', 'goog.labs.i18n.ListFormatSymbols_en_SZ', 'goog.labs.i18n.ListFormatSymbols_en_TC', 'goog.labs.i18n.ListFormatSymbols_en_TK', 'goog.labs.i18n.ListFormatSymbols_en_TO', 'goog.labs.i18n.ListFormatSymbols_en_TT', 'goog.labs.i18n.ListFormatSymbols_en_TV', 'goog.labs.i18n.ListFormatSymbols_en_TZ', 'goog.labs.i18n.ListFormatSymbols_en_UG', 'goog.labs.i18n.ListFormatSymbols_en_UM', 'goog.labs.i18n.ListFormatSymbols_en_US_POSIX', 'goog.labs.i18n.ListFormatSymbols_en_VC', 'goog.labs.i18n.ListFormatSymbols_en_VG', 'goog.labs.i18n.ListFormatSymbols_en_VI', 'goog.labs.i18n.ListFormatSymbols_en_VU', 'goog.labs.i18n.ListFormatSymbols_en_WS', 'goog.labs.i18n.ListFormatSymbols_en_XA', 'goog.labs.i18n.ListFormatSymbols_en_ZM', 'goog.labs.i18n.ListFormatSymbols_en_ZW', 'goog.labs.i18n.ListFormatSymbols_eo', 'goog.labs.i18n.ListFormatSymbols_eo_001', 'goog.labs.i18n.ListFormatSymbols_es_AR', 'goog.labs.i18n.ListFormatSymbols_es_BO', 'goog.labs.i18n.ListFormatSymbols_es_BR', 'goog.labs.i18n.ListFormatSymbols_es_BZ', 'goog.labs.i18n.ListFormatSymbols_es_CL', 'goog.labs.i18n.ListFormatSymbols_es_CO', 'goog.labs.i18n.ListFormatSymbols_es_CR', 'goog.labs.i18n.ListFormatSymbols_es_CU', 'goog.labs.i18n.ListFormatSymbols_es_DO', 'goog.labs.i18n.ListFormatSymbols_es_EA', 'goog.labs.i18n.ListFormatSymbols_es_EC', 'goog.labs.i18n.ListFormatSymbols_es_GQ', 'goog.labs.i18n.ListFormatSymbols_es_GT', 'goog.labs.i18n.ListFormatSymbols_es_HN', 'goog.labs.i18n.ListFormatSymbols_es_IC', 'goog.labs.i18n.ListFormatSymbols_es_NI', 'goog.labs.i18n.ListFormatSymbols_es_PA', 'goog.labs.i18n.ListFormatSymbols_es_PE', 'goog.labs.i18n.ListFormatSymbols_es_PH', 'goog.labs.i18n.ListFormatSymbols_es_PR', 'goog.labs.i18n.ListFormatSymbols_es_PY', 'goog.labs.i18n.ListFormatSymbols_es_SV', 'goog.labs.i18n.ListFormatSymbols_es_UY', 'goog.labs.i18n.ListFormatSymbols_es_VE', 'goog.labs.i18n.ListFormatSymbols_et_EE', 'goog.labs.i18n.ListFormatSymbols_eu_ES', 'goog.labs.i18n.ListFormatSymbols_ewo', 'goog.labs.i18n.ListFormatSymbols_ewo_CM', 'goog.labs.i18n.ListFormatSymbols_fa_AF', 'goog.labs.i18n.ListFormatSymbols_fa_IR', 'goog.labs.i18n.ListFormatSymbols_ff', 'goog.labs.i18n.ListFormatSymbols_ff_Latn', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_BF', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_CM', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_GH', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_GM', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_GN', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_GW', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_LR', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_MR', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_NE', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_NG', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_SL', 'goog.labs.i18n.ListFormatSymbols_ff_Latn_SN', 'goog.labs.i18n.ListFormatSymbols_fi_FI', 'goog.labs.i18n.ListFormatSymbols_fil_PH', 'goog.labs.i18n.ListFormatSymbols_fo', 'goog.labs.i18n.ListFormatSymbols_fo_DK', 'goog.labs.i18n.ListFormatSymbols_fo_FO', 'goog.labs.i18n.ListFormatSymbols_fr_BE', 'goog.labs.i18n.ListFormatSymbols_fr_BF', 'goog.labs.i18n.ListFormatSymbols_fr_BI', 'goog.labs.i18n.ListFormatSymbols_fr_BJ', 'goog.labs.i18n.ListFormatSymbols_fr_BL', 'goog.labs.i18n.ListFormatSymbols_fr_CD', 'goog.labs.i18n.ListFormatSymbols_fr_CF', 'goog.labs.i18n.ListFormatSymbols_fr_CG', 'goog.labs.i18n.ListFormatSymbols_fr_CH', 'goog.labs.i18n.ListFormatSymbols_fr_CI', 'goog.labs.i18n.ListFormatSymbols_fr_CM', 'goog.labs.i18n.ListFormatSymbols_fr_DJ', 'goog.labs.i18n.ListFormatSymbols_fr_DZ', 'goog.labs.i18n.ListFormatSymbols_fr_FR', 'goog.labs.i18n.ListFormatSymbols_fr_GA', 'goog.labs.i18n.ListFormatSymbols_fr_GF', 'goog.labs.i18n.ListFormatSymbols_fr_GN', 'goog.labs.i18n.ListFormatSymbols_fr_GP', 'goog.labs.i18n.ListFormatSymbols_fr_GQ', 'goog.labs.i18n.ListFormatSymbols_fr_HT', 'goog.labs.i18n.ListFormatSymbols_fr_KM', 'goog.labs.i18n.ListFormatSymbols_fr_LU', 'goog.labs.i18n.ListFormatSymbols_fr_MA', 'goog.labs.i18n.ListFormatSymbols_fr_MC', 'goog.labs.i18n.ListFormatSymbols_fr_MF', 'goog.labs.i18n.ListFormatSymbols_fr_MG', 'goog.labs.i18n.ListFormatSymbols_fr_ML', 'goog.labs.i18n.ListFormatSymbols_fr_MQ', 'goog.labs.i18n.ListFormatSymbols_fr_MR', 'goog.labs.i18n.ListFormatSymbols_fr_MU', 'goog.labs.i18n.ListFormatSymbols_fr_NC', 'goog.labs.i18n.ListFormatSymbols_fr_NE', 'goog.labs.i18n.ListFormatSymbols_fr_PF', 'goog.labs.i18n.ListFormatSymbols_fr_PM', 'goog.labs.i18n.ListFormatSymbols_fr_RE', 'goog.labs.i18n.ListFormatSymbols_fr_RW', 'goog.labs.i18n.ListFormatSymbols_fr_SC', 'goog.labs.i18n.ListFormatSymbols_fr_SN', 'goog.labs.i18n.ListFormatSymbols_fr_SY', 'goog.labs.i18n.ListFormatSymbols_fr_TD', 'goog.labs.i18n.ListFormatSymbols_fr_TG', 'goog.labs.i18n.ListFormatSymbols_fr_TN', 'goog.labs.i18n.ListFormatSymbols_fr_VU', 'goog.labs.i18n.ListFormatSymbols_fr_WF', 'goog.labs.i18n.ListFormatSymbols_fr_YT', 'goog.labs.i18n.ListFormatSymbols_fur', 'goog.labs.i18n.ListFormatSymbols_fur_IT', 'goog.labs.i18n.ListFormatSymbols_fy', 'goog.labs.i18n.ListFormatSymbols_fy_NL', 'goog.labs.i18n.ListFormatSymbols_ga_IE', 'goog.labs.i18n.ListFormatSymbols_gd', 'goog.labs.i18n.ListFormatSymbols_gd_GB', 'goog.labs.i18n.ListFormatSymbols_gl_ES', 'goog.labs.i18n.ListFormatSymbols_gsw_CH', 'goog.labs.i18n.ListFormatSymbols_gsw_FR', 'goog.labs.i18n.ListFormatSymbols_gsw_LI', 'goog.labs.i18n.ListFormatSymbols_gu_IN', 'goog.labs.i18n.ListFormatSymbols_guz', 'goog.labs.i18n.ListFormatSymbols_guz_KE', 'goog.labs.i18n.ListFormatSymbols_gv', 'goog.labs.i18n.ListFormatSymbols_gv_IM', 'goog.labs.i18n.ListFormatSymbols_ha', 'goog.labs.i18n.ListFormatSymbols_ha_GH', 'goog.labs.i18n.ListFormatSymbols_ha_NE', 'goog.labs.i18n.ListFormatSymbols_ha_NG', 'goog.labs.i18n.ListFormatSymbols_haw_US', 'goog.labs.i18n.ListFormatSymbols_he_IL', 'goog.labs.i18n.ListFormatSymbols_hi_IN', 'goog.labs.i18n.ListFormatSymbols_hr_BA', 'goog.labs.i18n.ListFormatSymbols_hr_HR', 'goog.labs.i18n.ListFormatSymbols_hsb', 'goog.labs.i18n.ListFormatSymbols_hsb_DE', 'goog.labs.i18n.ListFormatSymbols_hu_HU', 'goog.labs.i18n.ListFormatSymbols_hy_AM', 'goog.labs.i18n.ListFormatSymbols_ia', 'goog.labs.i18n.ListFormatSymbols_ia_001', 'goog.labs.i18n.ListFormatSymbols_id_ID', 'goog.labs.i18n.ListFormatSymbols_ig', 'goog.labs.i18n.ListFormatSymbols_ig_NG', 'goog.labs.i18n.ListFormatSymbols_ii', 'goog.labs.i18n.ListFormatSymbols_ii_CN', 'goog.labs.i18n.ListFormatSymbols_is_IS', 'goog.labs.i18n.ListFormatSymbols_it_CH', 'goog.labs.i18n.ListFormatSymbols_it_IT', 'goog.labs.i18n.ListFormatSymbols_it_SM', 'goog.labs.i18n.ListFormatSymbols_it_VA', 'goog.labs.i18n.ListFormatSymbols_ja_JP', 'goog.labs.i18n.ListFormatSymbols_jgo', 'goog.labs.i18n.ListFormatSymbols_jgo_CM', 'goog.labs.i18n.ListFormatSymbols_jmc', 'goog.labs.i18n.ListFormatSymbols_jmc_TZ', 'goog.labs.i18n.ListFormatSymbols_jv', 'goog.labs.i18n.ListFormatSymbols_jv_ID', 'goog.labs.i18n.ListFormatSymbols_ka_GE', 'goog.labs.i18n.ListFormatSymbols_kab', 'goog.labs.i18n.ListFormatSymbols_kab_DZ', 'goog.labs.i18n.ListFormatSymbols_kam', 'goog.labs.i18n.ListFormatSymbols_kam_KE', 'goog.labs.i18n.ListFormatSymbols_kde', 'goog.labs.i18n.ListFormatSymbols_kde_TZ', 'goog.labs.i18n.ListFormatSymbols_kea', 'goog.labs.i18n.ListFormatSymbols_kea_CV', 'goog.labs.i18n.ListFormatSymbols_khq', 'goog.labs.i18n.ListFormatSymbols_khq_ML', 'goog.labs.i18n.ListFormatSymbols_ki', 'goog.labs.i18n.ListFormatSymbols_ki_KE', 'goog.labs.i18n.ListFormatSymbols_kk_KZ', 'goog.labs.i18n.ListFormatSymbols_kkj', 'goog.labs.i18n.ListFormatSymbols_kkj_CM', 'goog.labs.i18n.ListFormatSymbols_kl', 'goog.labs.i18n.ListFormatSymbols_kl_GL', 'goog.labs.i18n.ListFormatSymbols_kln', 'goog.labs.i18n.ListFormatSymbols_kln_KE', 'goog.labs.i18n.ListFormatSymbols_km_KH', 'goog.labs.i18n.ListFormatSymbols_kn_IN', 'goog.labs.i18n.ListFormatSymbols_ko_KP', 'goog.labs.i18n.ListFormatSymbols_ko_KR', 'goog.labs.i18n.ListFormatSymbols_kok', 'goog.labs.i18n.ListFormatSymbols_kok_IN', 'goog.labs.i18n.ListFormatSymbols_ks', 'goog.labs.i18n.ListFormatSymbols_ks_IN', 'goog.labs.i18n.ListFormatSymbols_ksb', 'goog.labs.i18n.ListFormatSymbols_ksb_TZ', 'goog.labs.i18n.ListFormatSymbols_ksf', 'goog.labs.i18n.ListFormatSymbols_ksf_CM', 'goog.labs.i18n.ListFormatSymbols_ksh', 'goog.labs.i18n.ListFormatSymbols_ksh_DE', 'goog.labs.i18n.ListFormatSymbols_ku', 'goog.labs.i18n.ListFormatSymbols_ku_TR', 'goog.labs.i18n.ListFormatSymbols_kw', 'goog.labs.i18n.ListFormatSymbols_kw_GB', 'goog.labs.i18n.ListFormatSymbols_ky_KG', 'goog.labs.i18n.ListFormatSymbols_lag', 'goog.labs.i18n.ListFormatSymbols_lag_TZ', 'goog.labs.i18n.ListFormatSymbols_lb', 'goog.labs.i18n.ListFormatSymbols_lb_LU', 'goog.labs.i18n.ListFormatSymbols_lg', 'goog.labs.i18n.ListFormatSymbols_lg_UG', 'goog.labs.i18n.ListFormatSymbols_lkt', 'goog.labs.i18n.ListFormatSymbols_lkt_US', 'goog.labs.i18n.ListFormatSymbols_ln_AO', 'goog.labs.i18n.ListFormatSymbols_ln_CD', 'goog.labs.i18n.ListFormatSymbols_ln_CF', 'goog.labs.i18n.ListFormatSymbols_ln_CG', 'goog.labs.i18n.ListFormatSymbols_lo_LA', 'goog.labs.i18n.ListFormatSymbols_lrc', 'goog.labs.i18n.ListFormatSymbols_lrc_IQ', 'goog.labs.i18n.ListFormatSymbols_lrc_IR', 'goog.labs.i18n.ListFormatSymbols_lt_LT', 'goog.labs.i18n.ListFormatSymbols_lu', 'goog.labs.i18n.ListFormatSymbols_lu_CD', 'goog.labs.i18n.ListFormatSymbols_luo', 'goog.labs.i18n.ListFormatSymbols_luo_KE', 'goog.labs.i18n.ListFormatSymbols_luy', 'goog.labs.i18n.ListFormatSymbols_luy_KE', 'goog.labs.i18n.ListFormatSymbols_lv_LV', 'goog.labs.i18n.ListFormatSymbols_mas', 'goog.labs.i18n.ListFormatSymbols_mas_KE', 'goog.labs.i18n.ListFormatSymbols_mas_TZ', 'goog.labs.i18n.ListFormatSymbols_mer', 'goog.labs.i18n.ListFormatSymbols_mer_KE', 'goog.labs.i18n.ListFormatSymbols_mfe', 'goog.labs.i18n.ListFormatSymbols_mfe_MU', 'goog.labs.i18n.ListFormatSymbols_mg', 'goog.labs.i18n.ListFormatSymbols_mg_MG', 'goog.labs.i18n.ListFormatSymbols_mgh', 'goog.labs.i18n.ListFormatSymbols_mgh_MZ', 'goog.labs.i18n.ListFormatSymbols_mgo', 'goog.labs.i18n.ListFormatSymbols_mgo_CM', 'goog.labs.i18n.ListFormatSymbols_mi', 'goog.labs.i18n.ListFormatSymbols_mi_NZ', 'goog.labs.i18n.ListFormatSymbols_mk_MK', 'goog.labs.i18n.ListFormatSymbols_ml_IN', 'goog.labs.i18n.ListFormatSymbols_mn_MN', 'goog.labs.i18n.ListFormatSymbols_mr_IN', 'goog.labs.i18n.ListFormatSymbols_ms_BN', 'goog.labs.i18n.ListFormatSymbols_ms_MY', 'goog.labs.i18n.ListFormatSymbols_ms_SG', 'goog.labs.i18n.ListFormatSymbols_mt_MT', 'goog.labs.i18n.ListFormatSymbols_mua', 'goog.labs.i18n.ListFormatSymbols_mua_CM', 'goog.labs.i18n.ListFormatSymbols_my_MM', 'goog.labs.i18n.ListFormatSymbols_mzn', 'goog.labs.i18n.ListFormatSymbols_mzn_IR', 'goog.labs.i18n.ListFormatSymbols_naq', 'goog.labs.i18n.ListFormatSymbols_naq_NA', 'goog.labs.i18n.ListFormatSymbols_nb_NO', 'goog.labs.i18n.ListFormatSymbols_nb_SJ', 'goog.labs.i18n.ListFormatSymbols_nd', 'goog.labs.i18n.ListFormatSymbols_nd_ZW', 'goog.labs.i18n.ListFormatSymbols_nds', 'goog.labs.i18n.ListFormatSymbols_nds_DE', 'goog.labs.i18n.ListFormatSymbols_nds_NL', 'goog.labs.i18n.ListFormatSymbols_ne_IN', 'goog.labs.i18n.ListFormatSymbols_ne_NP', 'goog.labs.i18n.ListFormatSymbols_nl_AW', 'goog.labs.i18n.ListFormatSymbols_nl_BE', 'goog.labs.i18n.ListFormatSymbols_nl_BQ', 'goog.labs.i18n.ListFormatSymbols_nl_CW', 'goog.labs.i18n.ListFormatSymbols_nl_NL', 'goog.labs.i18n.ListFormatSymbols_nl_SR', 'goog.labs.i18n.ListFormatSymbols_nl_SX', 'goog.labs.i18n.ListFormatSymbols_nmg', 'goog.labs.i18n.ListFormatSymbols_nmg_CM', 'goog.labs.i18n.ListFormatSymbols_nn', 'goog.labs.i18n.ListFormatSymbols_nn_NO', 'goog.labs.i18n.ListFormatSymbols_nnh', 'goog.labs.i18n.ListFormatSymbols_nnh_CM', 'goog.labs.i18n.ListFormatSymbols_nus', 'goog.labs.i18n.ListFormatSymbols_nus_SS', 'goog.labs.i18n.ListFormatSymbols_nyn', 'goog.labs.i18n.ListFormatSymbols_nyn_UG', 'goog.labs.i18n.ListFormatSymbols_om', 'goog.labs.i18n.ListFormatSymbols_om_ET', 'goog.labs.i18n.ListFormatSymbols_om_KE', 'goog.labs.i18n.ListFormatSymbols_or_IN', 'goog.labs.i18n.ListFormatSymbols_os', 'goog.labs.i18n.ListFormatSymbols_os_GE', 'goog.labs.i18n.ListFormatSymbols_os_RU', 'goog.labs.i18n.ListFormatSymbols_pa_Arab', 'goog.labs.i18n.ListFormatSymbols_pa_Arab_PK', 'goog.labs.i18n.ListFormatSymbols_pa_Guru', 'goog.labs.i18n.ListFormatSymbols_pa_Guru_IN', 'goog.labs.i18n.ListFormatSymbols_pl_PL', 'goog.labs.i18n.ListFormatSymbols_ps', 'goog.labs.i18n.ListFormatSymbols_ps_AF', 'goog.labs.i18n.ListFormatSymbols_ps_PK', 'goog.labs.i18n.ListFormatSymbols_pt_AO', 'goog.labs.i18n.ListFormatSymbols_pt_CH', 'goog.labs.i18n.ListFormatSymbols_pt_CV', 'goog.labs.i18n.ListFormatSymbols_pt_GQ', 'goog.labs.i18n.ListFormatSymbols_pt_GW', 'goog.labs.i18n.ListFormatSymbols_pt_LU', 'goog.labs.i18n.ListFormatSymbols_pt_MO', 'goog.labs.i18n.ListFormatSymbols_pt_MZ', 'goog.labs.i18n.ListFormatSymbols_pt_ST', 'goog.labs.i18n.ListFormatSymbols_pt_TL', 'goog.labs.i18n.ListFormatSymbols_qu', 'goog.labs.i18n.ListFormatSymbols_qu_BO', 'goog.labs.i18n.ListFormatSymbols_qu_EC', 'goog.labs.i18n.ListFormatSymbols_qu_PE', 'goog.labs.i18n.ListFormatSymbols_rm', 'goog.labs.i18n.ListFormatSymbols_rm_CH', 'goog.labs.i18n.ListFormatSymbols_rn', 'goog.labs.i18n.ListFormatSymbols_rn_BI', 'goog.labs.i18n.ListFormatSymbols_ro_MD', 'goog.labs.i18n.ListFormatSymbols_ro_RO', 'goog.labs.i18n.ListFormatSymbols_rof', 'goog.labs.i18n.ListFormatSymbols_rof_TZ', 'goog.labs.i18n.ListFormatSymbols_ru_BY', 'goog.labs.i18n.ListFormatSymbols_ru_KG', 'goog.labs.i18n.ListFormatSymbols_ru_KZ', 'goog.labs.i18n.ListFormatSymbols_ru_MD', 'goog.labs.i18n.ListFormatSymbols_ru_RU', 'goog.labs.i18n.ListFormatSymbols_ru_UA', 'goog.labs.i18n.ListFormatSymbols_rw', 'goog.labs.i18n.ListFormatSymbols_rw_RW', 'goog.labs.i18n.ListFormatSymbols_rwk', 'goog.labs.i18n.ListFormatSymbols_rwk_TZ', 'goog.labs.i18n.ListFormatSymbols_sah', 'goog.labs.i18n.ListFormatSymbols_sah_RU', 'goog.labs.i18n.ListFormatSymbols_saq', 'goog.labs.i18n.ListFormatSymbols_saq_KE', 'goog.labs.i18n.ListFormatSymbols_sbp', 'goog.labs.i18n.ListFormatSymbols_sbp_TZ', 'goog.labs.i18n.ListFormatSymbols_sd', 'goog.labs.i18n.ListFormatSymbols_sd_PK', 'goog.labs.i18n.ListFormatSymbols_se', 'goog.labs.i18n.ListFormatSymbols_se_FI', 'goog.labs.i18n.ListFormatSymbols_se_NO', 'goog.labs.i18n.ListFormatSymbols_se_SE', 'goog.labs.i18n.ListFormatSymbols_seh', 'goog.labs.i18n.ListFormatSymbols_seh_MZ', 'goog.labs.i18n.ListFormatSymbols_ses', 'goog.labs.i18n.ListFormatSymbols_ses_ML', 'goog.labs.i18n.ListFormatSymbols_sg', 'goog.labs.i18n.ListFormatSymbols_sg_CF', 'goog.labs.i18n.ListFormatSymbols_shi', 'goog.labs.i18n.ListFormatSymbols_shi_Latn', 'goog.labs.i18n.ListFormatSymbols_shi_Latn_MA', 'goog.labs.i18n.ListFormatSymbols_shi_Tfng', 'goog.labs.i18n.ListFormatSymbols_shi_Tfng_MA', 'goog.labs.i18n.ListFormatSymbols_si_LK', 'goog.labs.i18n.ListFormatSymbols_sk_SK', 'goog.labs.i18n.ListFormatSymbols_sl_SI', 'goog.labs.i18n.ListFormatSymbols_smn', 'goog.labs.i18n.ListFormatSymbols_smn_FI', 'goog.labs.i18n.ListFormatSymbols_sn', 'goog.labs.i18n.ListFormatSymbols_sn_ZW', 'goog.labs.i18n.ListFormatSymbols_so', 'goog.labs.i18n.ListFormatSymbols_so_DJ', 'goog.labs.i18n.ListFormatSymbols_so_ET', 'goog.labs.i18n.ListFormatSymbols_so_KE', 'goog.labs.i18n.ListFormatSymbols_so_SO', 'goog.labs.i18n.ListFormatSymbols_sq_AL', 'goog.labs.i18n.ListFormatSymbols_sq_MK', 'goog.labs.i18n.ListFormatSymbols_sq_XK', 'goog.labs.i18n.ListFormatSymbols_sr_Cyrl', 'goog.labs.i18n.ListFormatSymbols_sr_Cyrl_BA', 'goog.labs.i18n.ListFormatSymbols_sr_Cyrl_ME', 'goog.labs.i18n.ListFormatSymbols_sr_Cyrl_RS', 'goog.labs.i18n.ListFormatSymbols_sr_Cyrl_XK', 'goog.labs.i18n.ListFormatSymbols_sr_Latn_BA', 'goog.labs.i18n.ListFormatSymbols_sr_Latn_ME', 'goog.labs.i18n.ListFormatSymbols_sr_Latn_RS', 'goog.labs.i18n.ListFormatSymbols_sr_Latn_XK', 'goog.labs.i18n.ListFormatSymbols_sv_AX', 'goog.labs.i18n.ListFormatSymbols_sv_FI', 'goog.labs.i18n.ListFormatSymbols_sv_SE', 'goog.labs.i18n.ListFormatSymbols_sw_CD', 'goog.labs.i18n.ListFormatSymbols_sw_KE', 'goog.labs.i18n.ListFormatSymbols_sw_TZ', 'goog.labs.i18n.ListFormatSymbols_sw_UG', 'goog.labs.i18n.ListFormatSymbols_ta_IN', 'goog.labs.i18n.ListFormatSymbols_ta_LK', 'goog.labs.i18n.ListFormatSymbols_ta_MY', 'goog.labs.i18n.ListFormatSymbols_ta_SG', 'goog.labs.i18n.ListFormatSymbols_te_IN', 'goog.labs.i18n.ListFormatSymbols_teo', 'goog.labs.i18n.ListFormatSymbols_teo_KE', 'goog.labs.i18n.ListFormatSymbols_teo_UG', 'goog.labs.i18n.ListFormatSymbols_tg', 'goog.labs.i18n.ListFormatSymbols_tg_TJ', 'goog.labs.i18n.ListFormatSymbols_th_TH', 'goog.labs.i18n.ListFormatSymbols_ti', 'goog.labs.i18n.ListFormatSymbols_ti_ER', 'goog.labs.i18n.ListFormatSymbols_ti_ET', 'goog.labs.i18n.ListFormatSymbols_tk', 'goog.labs.i18n.ListFormatSymbols_tk_TM', 'goog.labs.i18n.ListFormatSymbols_to', 'goog.labs.i18n.ListFormatSymbols_to_TO', 'goog.labs.i18n.ListFormatSymbols_tr_CY', 'goog.labs.i18n.ListFormatSymbols_tr_TR', 'goog.labs.i18n.ListFormatSymbols_tt', 'goog.labs.i18n.ListFormatSymbols_tt_RU', 'goog.labs.i18n.ListFormatSymbols_twq', 'goog.labs.i18n.ListFormatSymbols_twq_NE', 'goog.labs.i18n.ListFormatSymbols_tzm', 'goog.labs.i18n.ListFormatSymbols_tzm_MA', 'goog.labs.i18n.ListFormatSymbols_ug', 'goog.labs.i18n.ListFormatSymbols_ug_CN', 'goog.labs.i18n.ListFormatSymbols_uk_UA', 'goog.labs.i18n.ListFormatSymbols_ur_IN', 'goog.labs.i18n.ListFormatSymbols_ur_PK', 'goog.labs.i18n.ListFormatSymbols_uz_Arab', 'goog.labs.i18n.ListFormatSymbols_uz_Arab_AF', 'goog.labs.i18n.ListFormatSymbols_uz_Cyrl', 'goog.labs.i18n.ListFormatSymbols_uz_Cyrl_UZ', 'goog.labs.i18n.ListFormatSymbols_uz_Latn', 'goog.labs.i18n.ListFormatSymbols_uz_Latn_UZ', 'goog.labs.i18n.ListFormatSymbols_vai', 'goog.labs.i18n.ListFormatSymbols_vai_Latn', 'goog.labs.i18n.ListFormatSymbols_vai_Latn_LR', 'goog.labs.i18n.ListFormatSymbols_vai_Vaii', 'goog.labs.i18n.ListFormatSymbols_vai_Vaii_LR', 'goog.labs.i18n.ListFormatSymbols_vi_VN', 'goog.labs.i18n.ListFormatSymbols_vun', 'goog.labs.i18n.ListFormatSymbols_vun_TZ', 'goog.labs.i18n.ListFormatSymbols_wae', 'goog.labs.i18n.ListFormatSymbols_wae_CH', 'goog.labs.i18n.ListFormatSymbols_wo', 'goog.labs.i18n.ListFormatSymbols_wo_SN', 'goog.labs.i18n.ListFormatSymbols_xh', 'goog.labs.i18n.ListFormatSymbols_xh_ZA', 'goog.labs.i18n.ListFormatSymbols_xog', 'goog.labs.i18n.ListFormatSymbols_xog_UG', 'goog.labs.i18n.ListFormatSymbols_yav', 'goog.labs.i18n.ListFormatSymbols_yav_CM', 'goog.labs.i18n.ListFormatSymbols_yi', 'goog.labs.i18n.ListFormatSymbols_yi_001', 'goog.labs.i18n.ListFormatSymbols_yo', 'goog.labs.i18n.ListFormatSymbols_yo_BJ', 'goog.labs.i18n.ListFormatSymbols_yo_NG', 'goog.labs.i18n.ListFormatSymbols_yue', 'goog.labs.i18n.ListFormatSymbols_yue_Hans', 'goog.labs.i18n.ListFormatSymbols_yue_Hans_CN', 'goog.labs.i18n.ListFormatSymbols_yue_Hant', 'goog.labs.i18n.ListFormatSymbols_yue_Hant_HK', 'goog.labs.i18n.ListFormatSymbols_zgh', 'goog.labs.i18n.ListFormatSymbols_zgh_MA', 'goog.labs.i18n.ListFormatSymbols_zh_Hans', 'goog.labs.i18n.ListFormatSymbols_zh_Hans_CN', 'goog.labs.i18n.ListFormatSymbols_zh_Hans_HK', 'goog.labs.i18n.ListFormatSymbols_zh_Hans_MO', 'goog.labs.i18n.ListFormatSymbols_zh_Hans_SG', 'goog.labs.i18n.ListFormatSymbols_zh_Hant', 'goog.labs.i18n.ListFormatSymbols_zh_Hant_HK', 'goog.labs.i18n.ListFormatSymbols_zh_Hant_MO', 'goog.labs.i18n.ListFormatSymbols_zh_Hant_TW', 'goog.labs.i18n.ListFormatSymbols_zu_ZA'], ['goog.labs.i18n.ListFormatSymbols'], {});
goog.addDependency('labs/mock/mock.js', ['goog.labs.mock', 'goog.labs.mock.TimeoutError', 'goog.labs.mock.VerificationError'], ['goog.array', 'goog.asserts', 'goog.debug', 'goog.debug.Error', 'goog.functions', 'goog.labs.mock.timeout', 'goog.labs.mock.timeout.TimeoutMode', 'goog.labs.mock.verification', 'goog.labs.mock.verification.BaseVerificationMode', 'goog.labs.mock.verification.VerificationMode', 'goog.object'], {'lang': 'es6'});
goog.addDependency('labs/mock/mock_test.js', ['goog.labs.mockTest'], ['goog.array', 'goog.labs.mock', 'goog.labs.mock.TimeoutError', 'goog.labs.mock.VerificationError', 'goog.labs.mock.timeout', 'goog.labs.mock.verification', 'goog.labs.testing.AnythingMatcher', 'goog.labs.testing.GreaterThanMatcher', 'goog.string', 'goog.testing.jsunit'], {'lang': 'es8'});
goog.addDependency('labs/mock/timeoutmode.js', ['goog.labs.mock.timeout', 'goog.labs.mock.timeout.TimeoutMode'], [], {'lang': 'es6'});
goog.addDependency('labs/mock/verificationmode.js', ['goog.labs.mock.verification', 'goog.labs.mock.verification.BaseVerificationMode', 'goog.labs.mock.verification.VerificationMode'], [], {'lang': 'es6'});
goog.addDependency('labs/mock/verificationmode_test.js', ['goog.labs.mock.VerificationModeTest'], ['goog.labs.mock.verification', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/image.js', ['goog.labs.net.image'], ['goog.Promise', 'goog.dom.safe', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.html.SafeUrl', 'goog.net.EventType', 'goog.userAgent'], {});
goog.addDependency('labs/net/image_test.js', ['goog.labs.net.imageTest'], ['goog.labs.net.image', 'goog.string', 'goog.testing.TestCase', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel.js', ['goog.net.WebChannel'], ['goog.events', 'goog.events.Event', 'goog.events.Listenable', 'goog.net.XmlHttpFactory'], {});
goog.addDependency('labs/net/webchannel/basetestchannel.js', ['goog.labs.net.webChannel.BaseTestChannel'], ['goog.labs.net.webChannel.Channel', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.requestStats', 'goog.net.WebChannel'], {});
goog.addDependency('labs/net/webchannel/channel.js', ['goog.labs.net.webChannel.Channel'], [], {'lang': 'es6'});
goog.addDependency('labs/net/webchannel/channelrequest.js', ['goog.labs.net.webChannel.ChannelRequest'], ['goog.Timer', 'goog.async.Throttle', 'goog.events.EventHandler', 'goog.labs.net.webChannel.Channel', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.environment', 'goog.labs.net.webChannel.requestStats', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.WebChannel', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('labs/net/webchannel/channelrequest_test.js', ['goog.labs.net.webChannel.channelRequestTest'], ['goog.Uri', 'goog.functions', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.requestStats', 'goog.labs.net.webChannel.requestStats.ServerReachability', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.net.XhrIo', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/connectionstate.js', ['goog.labs.net.webChannel.ConnectionState'], [], {});
goog.addDependency('labs/net/webchannel/environment.js', ['goog.labs.net.webChannel.environment'], ['goog.userAgent'], {'module': 'goog'});
goog.addDependency('labs/net/webchannel/environment_test.js', ['goog.labs.net.webChannel.EnvironmentTest'], ['goog.labs.net.webChannel.environment', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/forwardchannelrequestpool.js', ['goog.labs.net.webChannel.ForwardChannelRequestPool'], ['goog.array', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.Wire', 'goog.string', 'goog.structs.Set'], {'module': 'goog'});
goog.addDependency('labs/net/webchannel/forwardchannelrequestpool_test.js', ['goog.labs.net.webChannel.ForwardChannelRequestPoolTest'], ['goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.ForwardChannelRequestPool', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/netutils.js', ['goog.labs.net.webChannel.netUtils'], ['goog.Uri', 'goog.labs.net.webChannel.WebChannelDebug'], {});
goog.addDependency('labs/net/webchannel/requeststats.js', ['goog.labs.net.webChannel.requestStats', 'goog.labs.net.webChannel.requestStats.Event', 'goog.labs.net.webChannel.requestStats.ServerReachability', 'goog.labs.net.webChannel.requestStats.ServerReachabilityEvent', 'goog.labs.net.webChannel.requestStats.Stat', 'goog.labs.net.webChannel.requestStats.StatEvent', 'goog.labs.net.webChannel.requestStats.TimingEvent'], ['goog.events.Event', 'goog.events.EventTarget'], {});
goog.addDependency('labs/net/webchannel/webchannelbase.js', ['goog.labs.net.webChannel.WebChannelBase'], ['goog.Uri', 'goog.array', 'goog.asserts', 'goog.async.run', 'goog.json', 'goog.labs.net.webChannel.BaseTestChannel', 'goog.labs.net.webChannel.Channel', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.ConnectionState', 'goog.labs.net.webChannel.ForwardChannelRequestPool', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.Wire', 'goog.labs.net.webChannel.WireV8', 'goog.labs.net.webChannel.netUtils', 'goog.labs.net.webChannel.requestStats', 'goog.net.WebChannel', 'goog.net.XhrIo', 'goog.net.XmlHttpFactory', 'goog.net.rpc.HttpCors', 'goog.object', 'goog.string', 'goog.structs'], {});
goog.addDependency('labs/net/webchannel/webchannelbase_test.js', ['goog.labs.net.webChannel.webChannelBaseTest'], ['goog.Timer', 'goog.array', 'goog.dom', 'goog.functions', 'goog.json', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.ForwardChannelRequestPool', 'goog.labs.net.webChannel.WebChannelBase', 'goog.labs.net.webChannel.WebChannelBaseTransport', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.Wire', 'goog.labs.net.webChannel.netUtils', 'goog.labs.net.webChannel.requestStats', 'goog.labs.net.webChannel.requestStats.Stat', 'goog.structs.Map', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/webchannelbasetransport.js', ['goog.labs.net.webChannel.WebChannelBaseTransport'], ['goog.asserts', 'goog.events.EventTarget', 'goog.json', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.WebChannelBase', 'goog.labs.net.webChannel.Wire', 'goog.log', 'goog.net.WebChannel', 'goog.net.WebChannelTransport', 'goog.object', 'goog.string', 'goog.string.path'], {});
goog.addDependency('labs/net/webchannel/webchannelbasetransport_test.js', ['goog.labs.net.webChannel.webChannelBaseTransportTest'], ['goog.events', 'goog.functions', 'goog.json', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.WebChannelBase', 'goog.labs.net.webChannel.WebChannelBaseTransport', 'goog.labs.net.webChannel.Wire', 'goog.net.WebChannel', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/webchanneldebug.js', ['goog.labs.net.webChannel.WebChannelDebug'], ['goog.json', 'goog.log'], {});
goog.addDependency('labs/net/webchannel/wire.js', ['goog.labs.net.webChannel.Wire'], [], {'lang': 'es6'});
goog.addDependency('labs/net/webchannel/wirev8.js', ['goog.labs.net.webChannel.WireV8'], ['goog.asserts', 'goog.json', 'goog.json.NativeJsonProcessor', 'goog.labs.net.webChannel.Wire', 'goog.structs'], {});
goog.addDependency('labs/net/webchannel/wirev8_test.js', ['goog.labs.net.webChannel.WireV8Test'], ['goog.labs.net.webChannel.WireV8', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchanneltransport.js', ['goog.net.WebChannelTransport'], [], {});
goog.addDependency('labs/net/webchanneltransportfactory.js', ['goog.net.createWebChannelTransport'], ['goog.functions', 'goog.labs.net.webChannel.WebChannelBaseTransport'], {});
goog.addDependency('labs/net/xhr.js', ['goog.labs.net.xhr', 'goog.labs.net.xhr.Error', 'goog.labs.net.xhr.HttpError', 'goog.labs.net.xhr.Options', 'goog.labs.net.xhr.PostData', 'goog.labs.net.xhr.ResponseType', 'goog.labs.net.xhr.TimeoutError'], ['goog.Promise', 'goog.asserts', 'goog.debug.Error', 'goog.net.HttpStatus', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.uri.utils', 'goog.userAgent'], {});
goog.addDependency('labs/net/xhr_test.js', ['goog.labs.net.xhrTest'], ['goog.Promise', 'goog.events', 'goog.events.EventType', 'goog.labs.net.xhr', 'goog.net.WrapperXmlHttpFactory', 'goog.net.XhrLike', 'goog.net.XmlHttp', 'goog.testing.MockClock', 'goog.testing.TestCase', 'goog.testing.jsunit', 'goog.userAgent'], {'lang': 'es6'});
goog.addDependency('labs/pubsub/broadcastpubsub.js', ['goog.labs.pubsub.BroadcastPubSub'], ['goog.Disposable', 'goog.Timer', 'goog.array', 'goog.async.run', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.log', 'goog.math', 'goog.pubsub.PubSub', 'goog.storage.Storage', 'goog.storage.mechanism.HTML5LocalStorage', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('labs/pubsub/broadcastpubsub_test.js', ['goog.labs.pubsub.BroadcastPubSubTest'], ['goog.array', 'goog.debug.Logger', 'goog.json', 'goog.labs.pubsub.BroadcastPubSub', 'goog.storage.Storage', 'goog.structs.Map', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/storage/boundedcollectablestorage.js', ['goog.labs.storage.BoundedCollectableStorage'], ['goog.array', 'goog.asserts', 'goog.iter', 'goog.storage.CollectableStorage', 'goog.storage.ErrorCode', 'goog.storage.ExpiringStorage'], {});
goog.addDependency('labs/storage/boundedcollectablestorage_test.js', ['goog.labs.storage.BoundedCollectableStorageTest'], ['goog.labs.storage.BoundedCollectableStorage', 'goog.storage.collectableStorageTester', 'goog.storage.storageTester', 'goog.testing.MockClock', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/structs/multimap.js', ['goog.labs.structs.Multimap'], ['goog.array', 'goog.object'], {'lang': 'es6'});
goog.addDependency('labs/structs/multimap_test.js', ['goog.labs.structs.MultimapTest'], ['goog.labs.structs.Multimap', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/style/pixeldensitymonitor.js', ['goog.labs.style.PixelDensityMonitor', 'goog.labs.style.PixelDensityMonitor.Density', 'goog.labs.style.PixelDensityMonitor.EventType'], ['goog.events', 'goog.events.EventTarget'], {});
goog.addDependency('labs/style/pixeldensitymonitor_test.js', ['goog.labs.style.PixelDensityMonitorTest'], ['goog.array', 'goog.dom.DomHelper', 'goog.events', 'goog.labs.style.PixelDensityMonitor', 'goog.testing.MockControl', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/assertthat.js', ['goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat'], ['goog.debug.Error'], {});
goog.addDependency('labs/testing/assertthat_test.js', ['goog.labs.testing.assertThatTest'], ['goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/decoratormatcher.js', ['goog.labs.testing.AnythingMatcher'], ['goog.labs.testing.Matcher'], {});
goog.addDependency('labs/testing/decoratormatcher_test.js', ['goog.labs.testing.decoratorMatcherTest'], ['goog.labs.testing.AnythingMatcher', 'goog.labs.testing.GreaterThanMatcher', 'goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/dictionarymatcher.js', ['goog.labs.testing.HasEntriesMatcher', 'goog.labs.testing.HasEntryMatcher', 'goog.labs.testing.HasKeyMatcher', 'goog.labs.testing.HasValueMatcher'], ['goog.asserts', 'goog.labs.testing.Matcher', 'goog.object'], {});
goog.addDependency('labs/testing/dictionarymatcher_test.js', ['goog.labs.testing.dictionaryMatcherTest'], ['goog.labs.testing.HasEntryMatcher', 'goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/environment.js', ['goog.labs.testing.Environment'], ['goog.Thenable', 'goog.array', 'goog.asserts', 'goog.debug.Console', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('labs/testing/environment_test.js', ['goog.labs.testing.environmentTest'], ['goog.asserts', 'goog.labs.testing.Environment', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('labs/testing/environment_usage_test.js', ['goog.labs.testing.environmentUsageTest'], ['goog.labs.testing.Environment', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/json_fuzzing.js', ['goog.labs.testing.JsonFuzzing'], ['goog.string', 'goog.testing.PseudoRandom'], {});
goog.addDependency('labs/testing/json_fuzzing_test.js', ['goog.labs.testing.JsonFuzzingTest'], ['goog.json', 'goog.labs.testing.JsonFuzzing', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/logicmatcher.js', ['goog.labs.testing.AllOfMatcher', 'goog.labs.testing.AnyOfMatcher', 'goog.labs.testing.IsNotMatcher', 'goog.labs.testing.logicMatchers'], ['goog.array', 'goog.labs.testing.Matcher'], {});
goog.addDependency('labs/testing/logicmatcher_test.js', ['goog.labs.testing.logicMatcherTest'], ['goog.labs.testing.AllOfMatcher', 'goog.labs.testing.GreaterThanMatcher', 'goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/matcher.js', ['goog.labs.testing.Matcher'], [], {'lang': 'es6'});
goog.addDependency('labs/testing/numbermatcher.js', ['goog.labs.testing.AnyNumberMatcher', 'goog.labs.testing.CloseToMatcher', 'goog.labs.testing.EqualToMatcher', 'goog.labs.testing.GreaterThanEqualToMatcher', 'goog.labs.testing.GreaterThanMatcher', 'goog.labs.testing.LessThanEqualToMatcher', 'goog.labs.testing.LessThanMatcher'], ['goog.asserts', 'goog.labs.testing.Matcher'], {});
goog.addDependency('labs/testing/numbermatcher_test.js', ['goog.labs.testing.numberMatcherTest'], ['goog.labs.testing.LessThanMatcher', 'goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/objectmatcher.js', ['goog.labs.testing.AnyObjectMatcher', 'goog.labs.testing.HasPropertyMatcher', 'goog.labs.testing.InstanceOfMatcher', 'goog.labs.testing.IsNullMatcher', 'goog.labs.testing.IsNullOrUndefinedMatcher', 'goog.labs.testing.IsUndefinedMatcher', 'goog.labs.testing.ObjectEqualsMatcher'], ['goog.labs.testing.Matcher'], {});
goog.addDependency('labs/testing/objectmatcher_test.js', ['goog.labs.testing.objectMatcherTest'], ['goog.labs.testing.MatcherError', 'goog.labs.testing.ObjectEqualsMatcher', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/stringmatcher.js', ['goog.labs.testing.AnyStringMatcher', 'goog.labs.testing.ContainsStringMatcher', 'goog.labs.testing.EndsWithMatcher', 'goog.labs.testing.EqualToIgnoringWhitespaceMatcher', 'goog.labs.testing.EqualsMatcher', 'goog.labs.testing.RegexMatcher', 'goog.labs.testing.StartsWithMatcher', 'goog.labs.testing.StringContainsInOrderMatcher'], ['goog.asserts', 'goog.labs.testing.Matcher', 'goog.string'], {});
goog.addDependency('labs/testing/stringmatcher_test.js', ['goog.labs.testing.stringMatcherTest'], ['goog.labs.testing.MatcherError', 'goog.labs.testing.StringContainsInOrderMatcher', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/browser.js', ['goog.labs.userAgent.browser'], ['goog.array', 'goog.labs.userAgent.util', 'goog.object', 'goog.string.internal'], {});
goog.addDependency('labs/useragent/browser_test.js', ['goog.labs.userAgent.browserTest'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/device.js', ['goog.labs.userAgent.device'], ['goog.labs.userAgent.util'], {});
goog.addDependency('labs/useragent/device_test.js', ['goog.labs.userAgent.deviceTest'], ['goog.labs.userAgent.device', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/engine.js', ['goog.labs.userAgent.engine'], ['goog.array', 'goog.labs.userAgent.util', 'goog.string'], {});
goog.addDependency('labs/useragent/engine_test.js', ['goog.labs.userAgent.engineTest'], ['goog.labs.userAgent.engine', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/extra.js', ['goog.labs.userAgent.extra'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.platform'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/extra_test.js', ['goog.labs.userAgent.extraTest'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.extra', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/platform.js', ['goog.labs.userAgent.platform'], ['goog.labs.userAgent.util', 'goog.string'], {});
goog.addDependency('labs/useragent/platform_test.js', ['goog.labs.userAgent.platformTest'], ['goog.labs.userAgent.platform', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/test_agents.js', ['goog.labs.userAgent.testAgents'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/util.js', ['goog.labs.userAgent.util'], ['goog.string.internal'], {});
goog.addDependency('labs/useragent/util_test.js', ['goog.labs.userAgent.utilTest'], ['goog.functions', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/verifier.js', ['goog.labs.useragent.verifier'], [], {'lang': 'es6'});
goog.addDependency('labs/useragent/verifier_test.js', ['goog.labs.useragent.verifierTest'], ['goog.labs.userAgent.browser', 'goog.labs.useragent.verifier', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('loader/abstractmodulemanager.js', ['goog.loader.AbstractModuleManager', 'goog.loader.AbstractModuleManager.CallbackType', 'goog.loader.AbstractModuleManager.FailureType'], ['goog.module.AbstractModuleLoader', 'goog.module.ModuleInfo', 'goog.module.ModuleLoadCallback'], {});
goog.addDependency('loader/activemodulemanager.js', ['goog.loader.activeModuleManager'], ['goog.asserts', 'goog.loader.AbstractModuleManager'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('locale/countries.js', ['goog.locale.countries'], [], {});
goog.addDependency('locale/countrylanguagenames_test.js', ['goog.locale.countryLanguageNamesTest'], ['goog.locale', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('locale/defaultlocalenameconstants.js', ['goog.locale.defaultLocaleNameConstants'], [], {});
goog.addDependency('locale/genericfontnames.js', ['goog.locale.genericFontNames'], [], {});
goog.addDependency('locale/genericfontnames_test.js', ['goog.locale.genericFontNamesTest'], ['goog.locale.genericFontNames', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('locale/genericfontnamesdata.js', ['goog.locale.genericFontNamesData'], [], {});
goog.addDependency('locale/locale.js', ['goog.locale'], ['goog.locale.nativeNameConstants'], {});
goog.addDependency('locale/nativenameconstants.js', ['goog.locale.nativeNameConstants'], [], {});
goog.addDependency('locale/scriptToLanguages.js', ['goog.locale.scriptToLanguages'], ['goog.locale'], {});
goog.addDependency('locale/timezonedetection.js', ['goog.locale.timeZoneDetection'], ['goog.locale.TimeZoneFingerprint'], {});
goog.addDependency('locale/timezonedetection_test.js', ['goog.locale.timeZoneDetectionTest'], ['goog.locale.timeZoneDetection', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('locale/timezonefingerprint.js', ['goog.locale.TimeZoneFingerprint'], [], {});
goog.addDependency('locale/timezonelist.js', ['goog.locale.TimeZoneList', 'goog.locale.getTimeZoneAllLongNames', 'goog.locale.getTimeZoneSelectedLongNames', 'goog.locale.getTimeZoneSelectedShortNames'], ['goog.locale'], {});
goog.addDependency('locale/timezonelist_test.js', ['goog.locale.TimeZoneListTest'], ['goog.locale', 'goog.locale.TimeZoneList', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('log/log.js', ['goog.log', 'goog.log.Level', 'goog.log.LogRecord', 'goog.log.Logger'], ['goog.debug', 'goog.debug.LogManager', 'goog.debug.LogRecord', 'goog.debug.Logger'], {});
goog.addDependency('log/log_test.js', ['goog.logTest'], ['goog.debug.LogManager', 'goog.log', 'goog.log.Level', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/affinetransform.js', ['goog.math.AffineTransform'], [], {'lang': 'es6'});
goog.addDependency('math/affinetransform_test.js', ['goog.math.AffineTransformTest'], ['goog.array', 'goog.math', 'goog.math.AffineTransform', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/bezier.js', ['goog.math.Bezier'], ['goog.math', 'goog.math.Coordinate'], {});
goog.addDependency('math/bezier_test.js', ['goog.math.BezierTest'], ['goog.math', 'goog.math.Bezier', 'goog.math.Coordinate', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/box.js', ['goog.math.Box'], ['goog.asserts', 'goog.math.Coordinate'], {});
goog.addDependency('math/box_test.js', ['goog.math.BoxTest'], ['goog.math.Box', 'goog.math.Coordinate', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/coordinate.js', ['goog.math.Coordinate'], ['goog.math'], {});
goog.addDependency('math/coordinate3.js', ['goog.math.Coordinate3'], [], {'lang': 'es6'});
goog.addDependency('math/coordinate3_test.js', ['goog.math.Coordinate3Test'], ['goog.math.Coordinate3', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/coordinate_test.js', ['goog.math.CoordinateTest'], ['goog.math.Coordinate', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/exponentialbackoff.js', ['goog.math.ExponentialBackoff'], ['goog.asserts'], {});
goog.addDependency('math/exponentialbackoff_test.js', ['goog.math.ExponentialBackoffTest'], ['goog.math.ExponentialBackoff', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/integer.js', ['goog.math.Integer'], ['goog.reflect'], {});
goog.addDependency('math/integer_test.js', ['goog.math.IntegerTest'], ['goog.math.Integer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/interpolator/interpolator1.js', ['goog.math.interpolator.Interpolator1'], [], {});
goog.addDependency('math/interpolator/linear1.js', ['goog.math.interpolator.Linear1'], ['goog.array', 'goog.asserts', 'goog.math', 'goog.math.interpolator.Interpolator1'], {});
goog.addDependency('math/interpolator/linear1_test.js', ['goog.math.interpolator.Linear1Test'], ['goog.math.interpolator.Linear1', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/interpolator/pchip1.js', ['goog.math.interpolator.Pchip1'], ['goog.math', 'goog.math.interpolator.Spline1'], {});
goog.addDependency('math/interpolator/pchip1_test.js', ['goog.math.interpolator.Pchip1Test'], ['goog.math.interpolator.Pchip1', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/interpolator/spline1.js', ['goog.math.interpolator.Spline1'], ['goog.array', 'goog.asserts', 'goog.math', 'goog.math.interpolator.Interpolator1', 'goog.math.tdma'], {});
goog.addDependency('math/interpolator/spline1_test.js', ['goog.math.interpolator.Spline1Test'], ['goog.math.interpolator.Spline1', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/irect.js', ['goog.math.IRect'], [], {});
goog.addDependency('math/line.js', ['goog.math.Line'], ['goog.math', 'goog.math.Coordinate'], {});
goog.addDependency('math/line_test.js', ['goog.math.LineTest'], ['goog.math.Coordinate', 'goog.math.Line', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/long.js', ['goog.math.Long'], ['goog.asserts', 'goog.reflect'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/long_test.js', ['goog.math.LongTest'], ['goog.asserts', 'goog.math.Long', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/math.js', ['goog.math'], ['goog.array', 'goog.asserts'], {});
goog.addDependency('math/math_test.js', ['goog.mathTest'], ['goog.math', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/matrix.js', ['goog.math.Matrix'], ['goog.array', 'goog.asserts', 'goog.math', 'goog.math.Size', 'goog.string'], {});
goog.addDependency('math/matrix_test.js', ['goog.math.MatrixTest'], ['goog.math.Matrix', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/path.js', ['goog.math.Path', 'goog.math.Path.Segment'], ['goog.array', 'goog.math', 'goog.math.AffineTransform'], {});
goog.addDependency('math/path_test.js', ['goog.math.PathTest'], ['goog.array', 'goog.math.AffineTransform', 'goog.math.Path', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/paths.js', ['goog.math.paths'], ['goog.math.Coordinate', 'goog.math.Path'], {});
goog.addDependency('math/paths_test.js', ['goog.math.pathsTest'], ['goog.math.Coordinate', 'goog.math.paths', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/range.js', ['goog.math.Range'], ['goog.asserts'], {});
goog.addDependency('math/range_test.js', ['goog.math.RangeTest'], ['goog.math.Range', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/rangeset.js', ['goog.math.RangeSet'], ['goog.array', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.math.Range'], {});
goog.addDependency('math/rangeset_test.js', ['goog.math.RangeSetTest'], ['goog.iter', 'goog.math.Range', 'goog.math.RangeSet', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/rect.js', ['goog.math.Rect'], ['goog.asserts', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.IRect', 'goog.math.Size'], {});
goog.addDependency('math/rect_test.js', ['goog.math.RectTest'], ['goog.math.Box', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.math.Size', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/size.js', ['goog.math.Size'], [], {'lang': 'es6'});
goog.addDependency('math/size_test.js', ['goog.math.SizeTest'], ['goog.math.Size', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/tdma.js', ['goog.math.tdma'], [], {'lang': 'es6'});
goog.addDependency('math/tdma_test.js', ['goog.math.tdmaTest'], ['goog.math.tdma', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/vec2.js', ['goog.math.Vec2'], ['goog.math', 'goog.math.Coordinate'], {'lang': 'es6'});
goog.addDependency('math/vec2_test.js', ['goog.math.Vec2Test'], ['goog.math.Vec2', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/vec3.js', ['goog.math.Vec3'], ['goog.math', 'goog.math.Coordinate3'], {});
goog.addDependency('math/vec3_test.js', ['goog.math.Vec3Test'], ['goog.math.Coordinate3', 'goog.math.Vec3', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('memoize/memoize.js', ['goog.memoize'], [], {'lang': 'es6'});
goog.addDependency('memoize/memoize_test.js', ['goog.memoizeTest'], ['goog.memoize', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/abstractchannel.js', ['goog.messaging.AbstractChannel'], ['goog.Disposable', 'goog.json', 'goog.log', 'goog.messaging.MessageChannel'], {});
goog.addDependency('messaging/abstractchannel_test.js', ['goog.messaging.AbstractChannelTest'], ['goog.messaging.AbstractChannel', 'goog.testing.MockControl', 'goog.testing.async.MockControl', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/bufferedchannel.js', ['goog.messaging.BufferedChannel'], ['goog.Disposable', 'goog.Timer', 'goog.events', 'goog.log', 'goog.messaging.MessageChannel', 'goog.messaging.MultiChannel'], {});
goog.addDependency('messaging/bufferedchannel_test.js', ['goog.messaging.BufferedChannelTest'], ['goog.debug.Console', 'goog.dom', 'goog.dom.TagName', 'goog.log', 'goog.log.Level', 'goog.messaging.BufferedChannel', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.async.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/deferredchannel.js', ['goog.messaging.DeferredChannel'], ['goog.Disposable', 'goog.messaging.MessageChannel'], {});
goog.addDependency('messaging/deferredchannel_test.js', ['goog.messaging.DeferredChannelTest'], ['goog.async.Deferred', 'goog.messaging.DeferredChannel', 'goog.testing.MockControl', 'goog.testing.async.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/loggerclient.js', ['goog.messaging.LoggerClient'], ['goog.Disposable', 'goog.debug', 'goog.debug.LogManager', 'goog.debug.Logger'], {});
goog.addDependency('messaging/loggerclient_test.js', ['goog.messaging.LoggerClientTest'], ['goog.debug', 'goog.debug.Logger', 'goog.messaging.LoggerClient', 'goog.testing.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/loggerserver.js', ['goog.messaging.LoggerServer'], ['goog.Disposable', 'goog.log', 'goog.log.Level'], {});
goog.addDependency('messaging/loggerserver_test.js', ['goog.messaging.LoggerServerTest'], ['goog.debug.LogManager', 'goog.debug.Logger', 'goog.log', 'goog.log.Level', 'goog.messaging.LoggerServer', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/messagechannel.js', ['goog.messaging.MessageChannel'], [], {});
goog.addDependency('messaging/messaging.js', ['goog.messaging'], [], {});
goog.addDependency('messaging/messaging_test.js', ['goog.testing.messaging.MockMessageChannelTest'], ['goog.messaging', 'goog.testing.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/multichannel.js', ['goog.messaging.MultiChannel', 'goog.messaging.MultiChannel.VirtualChannel'], ['goog.Disposable', 'goog.log', 'goog.messaging.MessageChannel', 'goog.object'], {});
goog.addDependency('messaging/multichannel_test.js', ['goog.messaging.MultiChannelTest'], ['goog.messaging.MultiChannel', 'goog.testing.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.mockmatchers.IgnoreArgument', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/portcaller.js', ['goog.messaging.PortCaller'], ['goog.Disposable', 'goog.async.Deferred', 'goog.messaging.DeferredChannel', 'goog.messaging.PortChannel', 'goog.messaging.PortNetwork', 'goog.object'], {});
goog.addDependency('messaging/portcaller_test.js', ['goog.messaging.PortCallerTest'], ['goog.events.EventTarget', 'goog.messaging.PortCaller', 'goog.messaging.PortNetwork', 'goog.testing.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/portchannel.js', ['goog.messaging.PortChannel'], ['goog.Timer', 'goog.array', 'goog.async.Deferred', 'goog.debug', 'goog.events', 'goog.events.EventType', 'goog.json', 'goog.log', 'goog.messaging.AbstractChannel', 'goog.messaging.DeferredChannel', 'goog.object', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('messaging/portchannel_test.js', ['goog.messaging.PortChannelTest'], ['goog.Promise', 'goog.Timer', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.json', 'goog.messaging.PortChannel', 'goog.testing.MockControl', 'goog.testing.TestCase', 'goog.testing.messaging.MockMessageEvent', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/portnetwork.js', ['goog.messaging.PortNetwork'], [], {});
goog.addDependency('messaging/portnetwork_test.js', ['goog.messaging.PortNetworkTest'], ['goog.Promise', 'goog.Timer', 'goog.labs.userAgent.browser', 'goog.messaging.PortChannel', 'goog.messaging.PortOperator', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/portoperator.js', ['goog.messaging.PortOperator'], ['goog.Disposable', 'goog.asserts', 'goog.log', 'goog.messaging.PortChannel', 'goog.messaging.PortNetwork', 'goog.object'], {});
goog.addDependency('messaging/portoperator_test.js', ['goog.messaging.PortOperatorTest'], ['goog.messaging.PortNetwork', 'goog.messaging.PortOperator', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.messaging.MockMessagePort', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/respondingchannel.js', ['goog.messaging.RespondingChannel'], ['goog.Disposable', 'goog.Promise', 'goog.log', 'goog.messaging.MultiChannel'], {});
goog.addDependency('messaging/respondingchannel_test.js', ['goog.messaging.RespondingChannelTest'], ['goog.Promise', 'goog.messaging.RespondingChannel', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/testdata/portchannel_worker.js', ['goog.messaging.testdata.portchannel_worker'], ['goog.messaging.PortChannel'], {});
goog.addDependency('messaging/testdata/portnetwork_worker1.js', ['goog.messaging.testdata.portnetwork_worker1'], ['goog.messaging.PortCaller', 'goog.messaging.PortChannel'], {});
goog.addDependency('messaging/testdata/portnetwork_worker2.js', ['goog.messaging.testdata.portnetwork_worker2'], ['goog.messaging.PortCaller', 'goog.messaging.PortChannel'], {});
goog.addDependency('module/abstractmoduleloader.js', ['goog.module.AbstractModuleLoader'], ['goog.module', 'goog.module.ModuleInfo'], {});
goog.addDependency('module/basemodule.js', ['goog.module.BaseModule'], ['goog.Disposable', 'goog.module'], {});
goog.addDependency('module/loader.js', ['goog.module.Loader'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.legacyconversions', 'goog.module', 'goog.object'], {});
goog.addDependency('module/module.js', ['goog.module'], [], {});
goog.addDependency('module/moduleinfo.js', ['goog.module.ModuleInfo'], ['goog.Disposable', 'goog.async.throwException', 'goog.functions', 'goog.html.TrustedResourceUrl', 'goog.module', 'goog.module.BaseModule', 'goog.module.ModuleLoadCallback'], {});
goog.addDependency('module/moduleinfo_test.js', ['goog.module.ModuleInfoTest'], ['goog.module.BaseModule', 'goog.module.ModuleInfo', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('module/moduleloadcallback.js', ['goog.module.ModuleLoadCallback'], ['goog.debug.entryPointRegistry', 'goog.module'], {});
goog.addDependency('module/moduleloadcallback_test.js', ['goog.module.ModuleLoadCallbackTest'], ['goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.functions', 'goog.module.ModuleLoadCallback', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('module/moduleloader.js', ['goog.module.ModuleLoader'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.safe', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventId', 'goog.events.EventTarget', 'goog.functions', 'goog.html.TrustedResourceUrl', 'goog.labs.userAgent.browser', 'goog.log', 'goog.module.AbstractModuleLoader', 'goog.net.BulkLoader', 'goog.net.EventType', 'goog.net.jsloader', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6'});
goog.addDependency('module/moduleloader_test.js', ['goog.module.ModuleLoaderTest'], ['goog.Promise', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.functions', 'goog.html.TrustedResourceUrl', 'goog.loader.activeModuleManager', 'goog.module.ModuleLoader', 'goog.module.ModuleManager', 'goog.net.BulkLoader', 'goog.net.XmlHttp', 'goog.object', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.events.EventObserver', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('module/modulemanager.js', ['goog.module.ModuleManager', 'goog.module.ModuleManager.CallbackType', 'goog.module.ModuleManager.FailureType'], ['goog.array', 'goog.asserts', 'goog.async.Deferred', 'goog.debug.Trace', 'goog.disposable.IDisposable', 'goog.disposeAll', 'goog.loader.AbstractModuleManager', 'goog.loader.activeModuleManager', 'goog.log', 'goog.module', 'goog.module.ModuleInfo', 'goog.module.ModuleLoadCallback', 'goog.object'], {'lang': 'es6'});
goog.addDependency('module/modulemanager_test.js', ['goog.module.ModuleManagerTest'], ['goog.array', 'goog.functions', 'goog.module.BaseModule', 'goog.module.ModuleManager', 'goog.testing', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('module/testdata/modA_1.js', ['goog.module.testdata.modA_1'], [], {});
goog.addDependency('module/testdata/modA_2.js', ['goog.module.testdata.modA_2'], ['goog.module.ModuleManager'], {});
goog.addDependency('module/testdata/modB_1.js', ['goog.module.testdata.modB_1'], ['goog.module.ModuleManager'], {});
goog.addDependency('net/browserchannel.js', ['goog.net.BrowserChannel', 'goog.net.BrowserChannel.Error', 'goog.net.BrowserChannel.Event', 'goog.net.BrowserChannel.Handler', 'goog.net.BrowserChannel.LogSaver', 'goog.net.BrowserChannel.QueuedMap', 'goog.net.BrowserChannel.ServerReachability', 'goog.net.BrowserChannel.ServerReachabilityEvent', 'goog.net.BrowserChannel.Stat', 'goog.net.BrowserChannel.StatEvent', 'goog.net.BrowserChannel.State', 'goog.net.BrowserChannel.TimingEvent'], ['goog.Uri', 'goog.array', 'goog.asserts', 'goog.debug.TextFormatter', 'goog.events.Event', 'goog.events.EventTarget', 'goog.json', 'goog.json.NativeJsonProcessor', 'goog.log', 'goog.net.BrowserTestChannel', 'goog.net.ChannelDebug', 'goog.net.ChannelRequest', 'goog.net.XhrIo', 'goog.net.tmpnetwork', 'goog.object', 'goog.string', 'goog.structs', 'goog.structs.CircularBuffer'], {});
goog.addDependency('net/browserchannel_test.js', ['goog.net.BrowserChannelTest'], ['goog.Timer', 'goog.array', 'goog.dom', 'goog.functions', 'goog.json', 'goog.net.BrowserChannel', 'goog.net.ChannelDebug', 'goog.net.ChannelRequest', 'goog.net.tmpnetwork', 'goog.structs.Map', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/browsertestchannel.js', ['goog.net.BrowserTestChannel'], ['goog.json.NativeJsonProcessor', 'goog.net.ChannelRequest', 'goog.net.ChannelRequest.Error', 'goog.net.tmpnetwork', 'goog.string.Parser'], {});
goog.addDependency('net/bulkloader.js', ['goog.net.BulkLoader'], ['goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.log', 'goog.net.BulkLoaderHelper', 'goog.net.EventType', 'goog.net.XhrIo'], {});
goog.addDependency('net/bulkloader_test.js', ['goog.net.BulkLoaderTest'], ['goog.events.Event', 'goog.events.EventHandler', 'goog.net.BulkLoader', 'goog.net.EventType', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/bulkloaderhelper.js', ['goog.net.BulkLoaderHelper'], ['goog.Disposable'], {});
goog.addDependency('net/channeldebug.js', ['goog.net.ChannelDebug'], ['goog.json', 'goog.log'], {'lang': 'es6'});
goog.addDependency('net/channelrequest.js', ['goog.net.ChannelRequest', 'goog.net.ChannelRequest.Error'], ['goog.Timer', 'goog.async.Throttle', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events.EventHandler', 'goog.html.SafeUrl', 'goog.html.uncheckedconversions', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('net/channelrequest_test.js', ['goog.net.ChannelRequestTest'], ['goog.Uri', 'goog.functions', 'goog.net.BrowserChannel', 'goog.net.ChannelDebug', 'goog.net.ChannelRequest', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.net.XhrIo', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/cookies.js', ['goog.net.Cookies', 'goog.net.cookies'], ['goog.asserts', 'goog.string'], {'lang': 'es5'});
goog.addDependency('net/cookies_test.js', ['goog.net.cookiesTest'], ['goog.array', 'goog.net.Cookies', 'goog.net.cookies', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/corsxmlhttpfactory.js', ['goog.net.CorsXmlHttpFactory', 'goog.net.IeCorsXhrAdapter'], ['goog.net.HttpStatus', 'goog.net.XhrLike', 'goog.net.XmlHttp', 'goog.net.XmlHttpFactory'], {});
goog.addDependency('net/corsxmlhttpfactory_test.js', ['goog.net.CorsXmlHttpFactoryTest'], ['goog.net.CorsXmlHttpFactory', 'goog.net.IeCorsXhrAdapter', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/crossdomainrpc.js', ['goog.net.CrossDomainRpc'], ['goog.Uri', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.html.SafeHtml', 'goog.log', 'goog.net.EventType', 'goog.net.HttpStatus', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('net/crossdomainrpc_test.js', ['goog.net.CrossDomainRpcTest'], ['goog.Promise', 'goog.log', 'goog.net.CrossDomainRpc', 'goog.testing.TestCase', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/errorcode.js', ['goog.net.ErrorCode'], [], {});
goog.addDependency('net/eventtype.js', ['goog.net.EventType'], [], {});
goog.addDependency('net/fetchxmlhttpfactory.js', ['goog.net.FetchXmlHttp', 'goog.net.FetchXmlHttpFactory'], ['goog.asserts', 'goog.events.EventTarget', 'goog.functions', 'goog.log', 'goog.net.XhrLike', 'goog.net.XmlHttpFactory'], {'lang': 'es5'});
goog.addDependency('net/fetchxmlhttpfactory_test.js', ['goog.net.FetchXmlHttpFactoryTest'], ['goog.net.FetchXmlHttp', 'goog.net.FetchXmlHttpFactory', 'goog.testing.MockControl', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/filedownloader.js', ['goog.net.FileDownloader', 'goog.net.FileDownloader.Error'], ['goog.Disposable', 'goog.asserts', 'goog.async.Deferred', 'goog.crypt.hash32', 'goog.debug.Error', 'goog.events', 'goog.events.EventHandler', 'goog.fs', 'goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.fs.FileSaver', 'goog.net.EventType', 'goog.net.XhrIo', 'goog.net.XhrIoPool', 'goog.object'], {});
goog.addDependency('net/filedownloader_test.js', ['goog.net.FileDownloaderTest'], ['goog.fs.Error', 'goog.net.ErrorCode', 'goog.net.FileDownloader', 'goog.net.XhrIo', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.fs', 'goog.testing.fs.FileSystem', 'goog.testing.net.XhrIoPool', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/httpstatus.js', ['goog.net.HttpStatus'], [], {});
goog.addDependency('net/httpstatusname.js', ['goog.net.HttpStatusName'], [], {});
goog.addDependency('net/iframeio.js', ['goog.net.IframeIo', 'goog.net.IframeIo.IncrementalDataEvent'], ['goog.Timer', 'goog.Uri', 'goog.array', 'goog.asserts', 'goog.debug.HtmlFormatter', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.html.SafeUrl', 'goog.html.legacyconversions', 'goog.html.uncheckedconversions', 'goog.json', 'goog.log', 'goog.log.Level', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.reflect', 'goog.string', 'goog.string.Const', 'goog.structs', 'goog.userAgent'], {});
goog.addDependency('net/iframeio_test.js', ['goog.net.IframeIoTest'], ['goog.debug', 'goog.debug.DivConsole', 'goog.debug.LogManager', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.log', 'goog.log.Level', 'goog.net.IframeIo', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.jsunit', 'goog.userAgent'], {'lang': 'es6'});
goog.addDependency('net/iframeloadmonitor.js', ['goog.net.IframeLoadMonitor'], ['goog.dom', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.userAgent'], {});
goog.addDependency('net/iframeloadmonitor_test.js', ['goog.net.IframeLoadMonitorTest'], ['goog.Promise', 'goog.Timer', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.net.IframeLoadMonitor', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/imageloader.js', ['goog.net.ImageLoader'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.net.EventType', 'goog.object', 'goog.userAgent'], {});
goog.addDependency('net/imageloader_test.js', ['goog.net.ImageLoaderTest'], ['goog.Promise', 'goog.Timer', 'goog.array', 'goog.dispose', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.net.EventType', 'goog.net.ImageLoader', 'goog.object', 'goog.string', 'goog.testing.TestCase', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/ipaddress.js', ['goog.net.IpAddress', 'goog.net.Ipv4Address', 'goog.net.Ipv6Address'], ['goog.array', 'goog.math.Integer', 'goog.object', 'goog.string'], {});
goog.addDependency('net/ipaddress_test.js', ['goog.net.IpAddressTest'], ['goog.array', 'goog.math.Integer', 'goog.net.IpAddress', 'goog.net.Ipv4Address', 'goog.net.Ipv6Address', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/jsloader.js', ['goog.net.jsloader', 'goog.net.jsloader.Error', 'goog.net.jsloader.ErrorCode', 'goog.net.jsloader.Options'], ['goog.array', 'goog.async.Deferred', 'goog.debug.Error', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.TrustedResourceUrl', 'goog.object'], {});
goog.addDependency('net/jsloader_test.js', ['goog.net.jsloaderTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.html.TrustedResourceUrl', 'goog.net.jsloader', 'goog.net.jsloader.ErrorCode', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/jsonp.js', ['goog.net.Jsonp'], ['goog.html.TrustedResourceUrl', 'goog.net.jsloader', 'goog.object'], {});
goog.addDependency('net/jsonp_test.js', ['goog.net.JsonpTest'], ['goog.html.TrustedResourceUrl', 'goog.net.Jsonp', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/mockiframeio.js', ['goog.net.MockIFrameIo'], ['goog.events.EventTarget', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.IframeIo'], {});
goog.addDependency('net/multiiframeloadmonitor.js', ['goog.net.MultiIframeLoadMonitor'], ['goog.events', 'goog.net.IframeLoadMonitor'], {});
goog.addDependency('net/multiiframeloadmonitor_test.js', ['goog.net.MultiIframeLoadMonitorTest'], ['goog.Promise', 'goog.Timer', 'goog.dom', 'goog.dom.TagName', 'goog.net.MultiIframeLoadMonitor', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/networkstatusmonitor.js', ['goog.net.NetworkStatusMonitor'], ['goog.events.Listenable'], {});
goog.addDependency('net/networktester.js', ['goog.net.NetworkTester'], ['goog.Timer', 'goog.Uri', 'goog.dom.safe', 'goog.log'], {});
goog.addDependency('net/networktester_test.js', ['goog.net.NetworkTesterTest'], ['goog.Uri', 'goog.net.NetworkTester', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/rpc/httpcors.js', ['goog.net.rpc.HttpCors'], ['goog.Uri', 'goog.object', 'goog.string', 'goog.uri.utils'], {'module': 'goog'});
goog.addDependency('net/rpc/httpcors_test.js', ['goog.net.rpc.HttpCorsTest'], ['goog.Uri', 'goog.net.rpc.HttpCors', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/base64pbstreamparser.js', ['goog.net.streams.Base64PbStreamParser'], ['goog.asserts', 'goog.net.streams.Base64StreamDecoder', 'goog.net.streams.PbStreamParser', 'goog.net.streams.StreamParser'], {'module': 'goog'});
goog.addDependency('net/streams/base64pbstreamparser_test.js', ['goog.net.streams.Base64PbStreamParserTest'], ['goog.crypt.base64', 'goog.net.streams.Base64PbStreamParser', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/base64streamdecoder.js', ['goog.net.streams.Base64StreamDecoder'], ['goog.asserts', 'goog.crypt.base64'], {});
goog.addDependency('net/streams/base64streamdecoder_test.js', ['goog.net.streams.Base64StreamDecoderTest'], ['goog.net.streams.Base64StreamDecoder', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/jsonstreamparser.js', ['goog.net.streams.JsonStreamParser', 'goog.net.streams.JsonStreamParser.Options'], ['goog.asserts', 'goog.net.streams.StreamParser', 'goog.net.streams.utils'], {});
goog.addDependency('net/streams/jsonstreamparser_test.js', ['goog.net.streams.JsonStreamParserTest'], ['goog.array', 'goog.json', 'goog.labs.testing.JsonFuzzing', 'goog.net.streams.JsonStreamParser', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.uri.utils'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/nodereadablestream.js', ['goog.net.streams.NodeReadableStream'], [], {});
goog.addDependency('net/streams/pbjsonstreamparser.js', ['goog.net.streams.PbJsonStreamParser'], ['goog.asserts', 'goog.net.streams.JsonStreamParser', 'goog.net.streams.StreamParser', 'goog.net.streams.utils'], {'module': 'goog'});
goog.addDependency('net/streams/pbjsonstreamparser_test.js', ['goog.net.streams.PbJsonStreamParserTest'], ['goog.net.streams.PbJsonStreamParser', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/pbstreamparser.js', ['goog.net.streams.PbStreamParser'], ['goog.asserts', 'goog.net.streams.StreamParser'], {});
goog.addDependency('net/streams/pbstreamparser_test.js', ['goog.net.streams.PbStreamParserTest'], ['goog.net.streams.PbStreamParser', 'goog.object', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/streamfactory.js', ['goog.net.streams.createXhrNodeReadableStream'], ['goog.asserts', 'goog.net.streams.XhrNodeReadableStream', 'goog.net.streams.XhrStreamReader'], {});
goog.addDependency('net/streams/streamparser.js', ['goog.net.streams.StreamParser'], [], {});
goog.addDependency('net/streams/utils.js', ['goog.net.streams.utils'], [], {'module': 'goog'});
goog.addDependency('net/streams/xhrnodereadablestream.js', ['goog.net.streams.XhrNodeReadableStream'], ['goog.array', 'goog.log', 'goog.net.streams.NodeReadableStream', 'goog.net.streams.XhrStreamReader'], {});
goog.addDependency('net/streams/xhrnodereadablestream_test.js', ['goog.net.streams.XhrNodeReadableStreamTest'], ['goog.net.streams.NodeReadableStream', 'goog.net.streams.XhrNodeReadableStream', 'goog.net.streams.XhrStreamReader', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/xhrstreamreader.js', ['goog.net.streams.XhrStreamReader'], ['goog.events.EventHandler', 'goog.log', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.HttpStatus', 'goog.net.XhrIo', 'goog.net.XmlHttp', 'goog.net.streams.Base64PbStreamParser', 'goog.net.streams.JsonStreamParser', 'goog.net.streams.PbJsonStreamParser', 'goog.net.streams.PbStreamParser', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('net/streams/xhrstreamreader_test.js', ['goog.net.streams.XhrStreamReaderTest'], ['goog.net.ErrorCode', 'goog.net.HttpStatus', 'goog.net.XhrIo', 'goog.net.XmlHttp', 'goog.net.streams.Base64PbStreamParser', 'goog.net.streams.JsonStreamParser', 'goog.net.streams.PbJsonStreamParser', 'goog.net.streams.PbStreamParser', 'goog.net.streams.XhrStreamReader', 'goog.object', 'goog.testing.net.XhrIo', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/testdata/jsloader_test1.js', ['goog.net.testdata.jsloader_test1'], [], {});
goog.addDependency('net/testdata/jsloader_test2.js', ['goog.net.testdata.jsloader_test2'], [], {});
goog.addDependency('net/testdata/jsloader_test3.js', ['goog.net.testdata.jsloader_test3'], [], {});
goog.addDependency('net/testdata/jsloader_test4.js', ['goog.net.testdata.jsloader_test4'], [], {});
goog.addDependency('net/tmpnetwork.js', ['goog.net.tmpnetwork'], ['goog.Uri', 'goog.dom.safe', 'goog.net.ChannelDebug'], {});
goog.addDependency('net/websocket.js', ['goog.net.WebSocket', 'goog.net.WebSocket.ErrorEvent', 'goog.net.WebSocket.EventType', 'goog.net.WebSocket.MessageEvent'], ['goog.Timer', 'goog.asserts', 'goog.debug.entryPointRegistry', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.log'], {'lang': 'es5'});
goog.addDependency('net/websocket_test.js', ['goog.net.WebSocketTest'], ['goog.debug.EntryPointMonitor', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.events', 'goog.functions', 'goog.net.WebSocket', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/wrapperxmlhttpfactory.js', ['goog.net.WrapperXmlHttpFactory'], ['goog.net.XhrLike', 'goog.net.XmlHttpFactory'], {});
goog.addDependency('net/xhrio.js', ['goog.net.XhrIo', 'goog.net.XhrIo.ResponseType'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.debug.entryPointRegistry', 'goog.events.EventTarget', 'goog.json.hybrid', 'goog.log', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.HttpStatus', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.structs', 'goog.structs.Map', 'goog.uri.utils', 'goog.userAgent'], {});
goog.addDependency('net/xhrio_test.js', ['goog.net.XhrIoTest'], ['goog.Uri', 'goog.debug.EntryPointMonitor', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.events', 'goog.functions', 'goog.net.EventType', 'goog.net.WrapperXmlHttpFactory', 'goog.net.XhrIo', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit', 'goog.testing.net.XhrIo', 'goog.testing.recordFunction', 'goog.userAgent.product'], {'lang': 'es6'});
goog.addDependency('net/xhriopool.js', ['goog.net.XhrIoPool'], ['goog.net.XhrIo', 'goog.structs.PriorityPool'], {});
goog.addDependency('net/xhriopool_test.js', ['goog.net.XhrIoPoolTest'], ['goog.net.XhrIoPool', 'goog.structs.Map', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xhrlike.js', ['goog.net.XhrLike'], [], {});
goog.addDependency('net/xhrmanager.js', ['goog.net.XhrManager', 'goog.net.XhrManager.Event', 'goog.net.XhrManager.Request'], ['goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.XhrIo', 'goog.net.XhrIoPool', 'goog.structs.Map'], {});
goog.addDependency('net/xhrmanager_test.js', ['goog.net.XhrManagerTest'], ['goog.events', 'goog.net.EventType', 'goog.net.XhrIo', 'goog.net.XhrManager', 'goog.testing.net.XhrIoPool', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xmlhttp.js', ['goog.net.DefaultXmlHttpFactory', 'goog.net.XmlHttp', 'goog.net.XmlHttp.OptionType', 'goog.net.XmlHttp.ReadyState', 'goog.net.XmlHttpDefines'], ['goog.asserts', 'goog.net.WrapperXmlHttpFactory', 'goog.net.XmlHttpFactory'], {});
goog.addDependency('net/xmlhttpfactory.js', ['goog.net.XmlHttpFactory'], ['goog.net.XhrLike'], {});
goog.addDependency('net/xpc/crosspagechannel.js', ['goog.net.xpc.CrossPageChannel'], ['goog.Uri', 'goog.async.Deferred', 'goog.async.Delay', 'goog.dispose', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.html.legacyconversions', 'goog.json', 'goog.log', 'goog.messaging.AbstractChannel', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.ChannelStates', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.DirectTransport', 'goog.net.xpc.NativeMessagingTransport', 'goog.net.xpc.TransportTypes', 'goog.net.xpc.UriCfgFields', 'goog.string', 'goog.uri.utils', 'goog.userAgent'], {});
goog.addDependency('net/xpc/crosspagechannel_test.js', ['goog.net.xpc.CrossPageChannelTest'], ['goog.Disposable', 'goog.Promise', 'goog.Timer', 'goog.Uri', 'goog.dom', 'goog.dom.TagName', 'goog.labs.userAgent.browser', 'goog.log', 'goog.log.Level', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannel', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.TransportTypes', 'goog.object', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.jsunit'], {'lang': 'es8'});
goog.addDependency('net/xpc/crosspagechannelrole.js', ['goog.net.xpc.CrossPageChannelRole'], [], {});
goog.addDependency('net/xpc/directtransport.js', ['goog.net.xpc.DirectTransport'], ['goog.Timer', 'goog.async.Deferred', 'goog.events.EventHandler', 'goog.log', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.Transport', 'goog.net.xpc.TransportTypes', 'goog.object'], {});
goog.addDependency('net/xpc/directtransport_test.js', ['goog.net.xpc.DirectTransportTest'], ['goog.Promise', 'goog.dom', 'goog.dom.TagName', 'goog.labs.userAgent.browser', 'goog.log', 'goog.log.Level', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannel', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.TransportTypes', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xpc/iframepollingtransport.js', ['goog.net.xpc.IframePollingTransport', 'goog.net.xpc.IframePollingTransport.Receiver', 'goog.net.xpc.IframePollingTransport.Sender'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.log', 'goog.log.Level', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.Transport', 'goog.net.xpc.TransportTypes', 'goog.userAgent'], {});
goog.addDependency('net/xpc/iframepollingtransport_test.js', ['goog.net.xpc.IframePollingTransportTest'], ['goog.Timer', 'goog.dom', 'goog.dom.TagName', 'goog.functions', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannel', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.IframePollingTransport', 'goog.object', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xpc/nativemessagingtransport.js', ['goog.net.xpc.NativeMessagingTransport'], ['goog.Timer', 'goog.asserts', 'goog.async.Deferred', 'goog.events', 'goog.events.EventHandler', 'goog.log', 'goog.net.xpc', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.Transport', 'goog.net.xpc.TransportTypes'], {});
goog.addDependency('net/xpc/nativemessagingtransport_test.js', ['goog.net.xpc.NativeMessagingTransportTest'], ['goog.dom', 'goog.events', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannel', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.NativeMessagingTransport', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xpc/relay.js', ['goog.net.xpc.relay'], [], {'lang': 'es6'});
goog.addDependency('net/xpc/transport.js', ['goog.net.xpc.Transport'], ['goog.Disposable', 'goog.dom', 'goog.net.xpc.TransportNames'], {});
goog.addDependency('net/xpc/xpc.js', ['goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.ChannelStates', 'goog.net.xpc.TransportNames', 'goog.net.xpc.TransportTypes', 'goog.net.xpc.UriCfgFields'], ['goog.log'], {});
goog.addDependency('object/object.js', ['goog.object'], [], {'lang': 'es6'});
goog.addDependency('object/object_test.js', ['goog.objectTest'], ['goog.array', 'goog.functions', 'goog.object', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/absoluteposition.js', ['goog.positioning.AbsolutePosition'], ['goog.math.Coordinate', 'goog.positioning', 'goog.positioning.AbstractPosition'], {});
goog.addDependency('positioning/abstractposition.js', ['goog.positioning.AbstractPosition'], [], {});
goog.addDependency('positioning/anchoredposition.js', ['goog.positioning.AnchoredPosition'], ['goog.positioning', 'goog.positioning.AbstractPosition'], {});
goog.addDependency('positioning/anchoredposition_test.js', ['goog.positioning.AnchoredPositionTest'], ['goog.dom', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/anchoredviewportposition.js', ['goog.positioning.AnchoredViewportPosition'], ['goog.positioning', 'goog.positioning.AnchoredPosition', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus'], {});
goog.addDependency('positioning/anchoredviewportposition_test.js', ['goog.positioning.AnchoredViewportPositionTest'], ['goog.dom', 'goog.math.Box', 'goog.positioning.AnchoredViewportPosition', 'goog.positioning.Corner', 'goog.positioning.OverflowStatus', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/clientposition.js', ['goog.positioning.ClientPosition'], ['goog.asserts', 'goog.dom', 'goog.math.Coordinate', 'goog.positioning', 'goog.positioning.AbstractPosition', 'goog.style'], {});
goog.addDependency('positioning/clientposition_test.js', ['goog.positioning.clientPositionTest'], ['goog.dom', 'goog.dom.TagName', 'goog.positioning.ClientPosition', 'goog.positioning.Corner', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/menuanchoredposition.js', ['goog.positioning.MenuAnchoredPosition'], ['goog.positioning.AnchoredViewportPosition', 'goog.positioning.Overflow'], {});
goog.addDependency('positioning/menuanchoredposition_test.js', ['goog.positioning.MenuAnchoredPositionTest'], ['goog.dom', 'goog.dom.TagName', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/positioning.js', ['goog.positioning', 'goog.positioning.Corner', 'goog.positioning.CornerBit', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.math.Size', 'goog.style', 'goog.style.bidi'], {});
goog.addDependency('positioning/positioning_test.js', ['goog.positioningTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.labs.userAgent.browser', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.Size', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/viewportclientposition.js', ['goog.positioning.ViewportClientPosition'], ['goog.dom', 'goog.math.Coordinate', 'goog.positioning', 'goog.positioning.ClientPosition', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus', 'goog.style'], {});
goog.addDependency('positioning/viewportclientposition_test.js', ['goog.positioning.ViewportClientPositionTest'], ['goog.dom', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.positioning.ViewportClientPosition', 'goog.style', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/viewportposition.js', ['goog.positioning.ViewportPosition'], ['goog.math.Coordinate', 'goog.positioning', 'goog.positioning.AbstractPosition', 'goog.positioning.Corner', 'goog.style'], {});
goog.addDependency('promise/nativeresolver.js', ['goog.promise.NativeResolver'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('promise/nativeresolver_test.js', ['goog.promise.nativeResolverTest'], ['goog.promise.NativeResolver', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('promise/promise.js', ['goog.Promise'], ['goog.Thenable', 'goog.asserts', 'goog.async.FreeList', 'goog.async.run', 'goog.async.throwException', 'goog.debug.Error', 'goog.promise.Resolver'], {});
goog.addDependency('promise/promise_test.js', ['goog.PromiseTest'], ['goog.Promise', 'goog.Thenable', 'goog.Timer', 'goog.functions', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('promise/resolver.js', ['goog.promise.Resolver'], [], {});
goog.addDependency('promise/testsuiteadapter.js', ['goog.promise.testSuiteAdapter'], ['goog.Promise'], {});
goog.addDependency('promise/thenable.js', ['goog.Thenable'], [], {});
goog.addDependency('proto2/descriptor.js', ['goog.proto2.Descriptor', 'goog.proto2.Metadata'], ['goog.array', 'goog.asserts', 'goog.object', 'goog.string'], {});
goog.addDependency('proto2/descriptor_test.js', ['goog.proto2.DescriptorTest'], ['goog.proto2.Descriptor', 'goog.proto2.Message', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/fielddescriptor.js', ['goog.proto2.FieldDescriptor'], ['goog.asserts', 'goog.string'], {});
goog.addDependency('proto2/fielddescriptor_test.js', ['goog.proto2.FieldDescriptorTest'], ['goog.proto2.FieldDescriptor', 'goog.proto2.Message', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/lazydeserializer.js', ['goog.proto2.LazyDeserializer'], ['goog.asserts', 'goog.proto2.Message', 'goog.proto2.Serializer'], {});
goog.addDependency('proto2/message.js', ['goog.proto2.Message'], ['goog.asserts', 'goog.proto2.Descriptor', 'goog.proto2.FieldDescriptor'], {});
goog.addDependency('proto2/message_test.js', ['goog.proto2.MessageTest'], ['goog.testing.testSuite', 'proto2.TestAllTypes', 'proto2.TestAllTypes.NestedEnum', 'proto2.TestAllTypes.NestedMessage', 'proto2.TestAllTypes.OptionalGroup', 'proto2.TestAllTypes.RepeatedGroup'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/objectserializer.js', ['goog.proto2.ObjectSerializer'], ['goog.asserts', 'goog.proto2.FieldDescriptor', 'goog.proto2.Serializer', 'goog.string'], {});
goog.addDependency('proto2/objectserializer_test.js', ['goog.proto2.ObjectSerializerTest'], ['goog.proto2.ObjectSerializer', 'goog.proto2.Serializer', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'proto2.TestAllTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/package_test.pb.js', ['someprotopackage.TestPackageTypes'], ['goog.proto2.Message', 'proto2.TestAllTypes'], {'lang': 'es6'});
goog.addDependency('proto2/pbliteserializer.js', ['goog.proto2.PbLiteSerializer'], ['goog.asserts', 'goog.proto2.FieldDescriptor', 'goog.proto2.LazyDeserializer', 'goog.proto2.Serializer'], {});
goog.addDependency('proto2/pbliteserializer_test.js', ['goog.proto2.PbLiteSerializerTest'], ['goog.proto2.PbLiteSerializer', 'goog.testing.testSuite', 'proto2.TestAllTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/proto_test.js', ['goog.proto2.messageTest'], ['goog.proto2.FieldDescriptor', 'goog.testing.testSuite', 'proto2.TestAllTypes', 'proto2.TestDefaultParent', 'someprotopackage.TestPackageTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/serializer.js', ['goog.proto2.Serializer'], ['goog.asserts', 'goog.proto2.FieldDescriptor', 'goog.proto2.Message'], {});
goog.addDependency('proto2/test.pb.js', ['proto2.TestAllTypes', 'proto2.TestAllTypes.NestedEnum', 'proto2.TestAllTypes.NestedMessage', 'proto2.TestAllTypes.OptionalGroup', 'proto2.TestAllTypes.RepeatedGroup', 'proto2.TestDefaultChild', 'proto2.TestDefaultParent'], ['goog.proto2.Message'], {});
goog.addDependency('proto2/textformatserializer.js', ['goog.proto2.TextFormatSerializer'], ['goog.array', 'goog.asserts', 'goog.math', 'goog.object', 'goog.proto2.FieldDescriptor', 'goog.proto2.Message', 'goog.proto2.Serializer', 'goog.string'], {});
goog.addDependency('proto2/textformatserializer_test.js', ['goog.proto2.TextFormatSerializerTest'], ['goog.proto2.ObjectSerializer', 'goog.proto2.TextFormatSerializer', 'goog.testing.testSuite', 'proto2.TestAllTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/util.js', ['goog.proto2.Util'], ['goog.asserts'], {});
goog.addDependency('pubsub/pubsub.js', ['goog.pubsub.PubSub'], ['goog.Disposable', 'goog.array', 'goog.async.run'], {});
goog.addDependency('pubsub/pubsub_test.js', ['goog.pubsub.PubSubTest'], ['goog.array', 'goog.pubsub.PubSub', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('pubsub/topicid.js', ['goog.pubsub.TopicId'], [], {});
goog.addDependency('pubsub/typedpubsub.js', ['goog.pubsub.TypedPubSub'], ['goog.Disposable', 'goog.pubsub.PubSub'], {});
goog.addDependency('pubsub/typedpubsub_test.js', ['goog.pubsub.TypedPubSubTest'], ['goog.array', 'goog.pubsub.TopicId', 'goog.pubsub.TypedPubSub', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('reflect/reflect.js', ['goog.reflect'], [], {'lang': 'es6'});
goog.addDependency('reflect/reflect_test.js', ['goog.reflectTest'], ['goog.object', 'goog.reflect', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/chain_test.js', ['goog.result.chainTest'], ['goog.Timer', 'goog.result', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/combine_test.js', ['goog.result.combineTest'], ['goog.Timer', 'goog.array', 'goog.result', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/deferredadaptor.js', ['goog.result.DeferredAdaptor'], ['goog.async.Deferred', 'goog.result', 'goog.result.Result'], {});
goog.addDependency('result/deferredadaptor_test.js', ['goog.result.DeferredAdaptorTest'], ['goog.async.Deferred', 'goog.result', 'goog.result.DeferredAdaptor', 'goog.result.SimpleResult', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/dependentresult.js', ['goog.result.DependentResult'], ['goog.result.Result'], {});
goog.addDependency('result/result_interface.js', ['goog.result.Result'], ['goog.Thenable'], {});
goog.addDependency('result/resultutil.js', ['goog.result'], ['goog.array', 'goog.result.DependentResult', 'goog.result.Result', 'goog.result.SimpleResult'], {});
goog.addDependency('result/resultutil_test.js', ['goog.resultTest'], ['goog.result', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/simpleresult.js', ['goog.result.SimpleResult', 'goog.result.SimpleResult.StateError'], ['goog.Promise', 'goog.Thenable', 'goog.debug.Error', 'goog.result.Result'], {});
goog.addDependency('result/simpleresult_test.js', ['goog.result.SimpleResultTest'], ['goog.Promise', 'goog.Thenable', 'goog.Timer', 'goog.result', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/transform_test.js', ['goog.result.transformTest'], ['goog.Timer', 'goog.result', 'goog.result.SimpleResult', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/wait_test.js', ['goog.result.waitTest'], ['goog.Timer', 'goog.result', 'goog.result.SimpleResult', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('soy/data.js', ['goog.soy.data.SanitizedContent', 'goog.soy.data.SanitizedContentKind', 'goog.soy.data.SanitizedCss', 'goog.soy.data.SanitizedHtml', 'goog.soy.data.SanitizedHtmlAttribute', 'goog.soy.data.SanitizedJs', 'goog.soy.data.SanitizedTrustedResourceUri', 'goog.soy.data.SanitizedUri'], ['goog.Uri', 'goog.asserts', 'goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.uncheckedconversions', 'goog.i18n.bidi.Dir', 'goog.string.Const'], {});
goog.addDependency('soy/data_test.js', ['goog.soy.dataTest'], ['goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.soy.testHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('soy/renderer.js', ['goog.soy.InjectedDataSupplier', 'goog.soy.Renderer'], ['goog.asserts', 'goog.dom', 'goog.soy', 'goog.soy.data.SanitizedContent', 'goog.soy.data.SanitizedContentKind'], {});
goog.addDependency('soy/renderer_test.js', ['goog.soy.RendererTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.html.SafeHtml', 'goog.i18n.bidi.Dir', 'goog.soy.Renderer', 'goog.soy.data.SanitizedContentKind', 'goog.soy.testHelper', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('soy/soy.js', ['goog.soy'], ['goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.soy.data.SanitizedContent'], {});
goog.addDependency('soy/soy_test.js', ['goog.soyTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.functions', 'goog.soy', 'goog.soy.testHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('soy/soy_testhelper.js', ['goog.soy.testHelper'], ['goog.dom', 'goog.dom.TagName', 'goog.i18n.bidi.Dir', 'goog.soy.data.SanitizedContent', 'goog.soy.data.SanitizedContentKind', 'goog.soy.data.SanitizedCss', 'goog.soy.data.SanitizedTrustedResourceUri', 'goog.string', 'goog.userAgent'], {'lang': 'es6'});
goog.addDependency('spell/spellcheck.js', ['goog.spell.SpellCheck', 'goog.spell.SpellCheck.WordChangedEvent'], ['goog.Timer', 'goog.events.Event', 'goog.events.EventTarget', 'goog.structs.Set'], {});
goog.addDependency('spell/spellcheck_test.js', ['goog.spell.SpellCheckTest'], ['goog.spell.SpellCheck', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('stats/basicstat.js', ['goog.stats.BasicStat'], ['goog.asserts', 'goog.log', 'goog.string.format', 'goog.structs.CircularBuffer'], {});
goog.addDependency('stats/basicstat_test.js', ['goog.stats.BasicStatTest'], ['goog.array', 'goog.stats.BasicStat', 'goog.string.format', 'goog.testing.PseudoRandom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/collectablestorage.js', ['goog.storage.CollectableStorage'], ['goog.array', 'goog.iter', 'goog.storage.ErrorCode', 'goog.storage.ExpiringStorage', 'goog.storage.RichStorage'], {});
goog.addDependency('storage/collectablestorage_test.js', ['goog.storage.CollectableStorageTest'], ['goog.storage.CollectableStorage', 'goog.storage.collectableStorageTester', 'goog.storage.storageTester', 'goog.testing.MockClock', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/collectablestoragetester.js', ['goog.storage.collectableStorageTester'], ['goog.testing.asserts'], {});
goog.addDependency('storage/encryptedstorage.js', ['goog.storage.EncryptedStorage'], ['goog.crypt', 'goog.crypt.Arc4', 'goog.crypt.Sha1', 'goog.crypt.base64', 'goog.json', 'goog.json.Serializer', 'goog.storage.CollectableStorage', 'goog.storage.ErrorCode', 'goog.storage.RichStorage'], {});
goog.addDependency('storage/encryptedstorage_test.js', ['goog.storage.EncryptedStorageTest'], ['goog.json', 'goog.storage.EncryptedStorage', 'goog.storage.ErrorCode', 'goog.storage.RichStorage', 'goog.storage.collectableStorageTester', 'goog.storage.storageTester', 'goog.testing.MockClock', 'goog.testing.PseudoRandom', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/errorcode.js', ['goog.storage.ErrorCode'], [], {});
goog.addDependency('storage/expiringstorage.js', ['goog.storage.ExpiringStorage'], ['goog.storage.RichStorage'], {});
goog.addDependency('storage/expiringstorage_test.js', ['goog.storage.ExpiringStorageTest'], ['goog.storage.ExpiringStorage', 'goog.storage.storageTester', 'goog.testing.MockClock', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/errorcode.js', ['goog.storage.mechanism.ErrorCode'], [], {});
goog.addDependency('storage/mechanism/errorhandlingmechanism.js', ['goog.storage.mechanism.ErrorHandlingMechanism'], ['goog.storage.mechanism.Mechanism'], {});
goog.addDependency('storage/mechanism/errorhandlingmechanism_test.js', ['goog.storage.mechanism.ErrorHandlingMechanismTest'], ['goog.storage.mechanism.ErrorHandlingMechanism', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/html5localstorage.js', ['goog.storage.mechanism.HTML5LocalStorage'], ['goog.storage.mechanism.HTML5WebStorage'], {});
goog.addDependency('storage/mechanism/html5localstorage_test.js', ['goog.storage.mechanism.HTML5LocalStorageTest'], ['goog.storage.mechanism.HTML5LocalStorage', 'goog.storage.mechanism.mechanismSeparationTester', 'goog.storage.mechanism.mechanismSharingTester', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/html5sessionstorage.js', ['goog.storage.mechanism.HTML5SessionStorage'], ['goog.storage.mechanism.HTML5WebStorage'], {});
goog.addDependency('storage/mechanism/html5sessionstorage_test.js', ['goog.storage.mechanism.HTML5SessionStorageTest'], ['goog.storage.mechanism.HTML5SessionStorage', 'goog.storage.mechanism.mechanismSeparationTester', 'goog.storage.mechanism.mechanismSharingTester', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/html5webstorage.js', ['goog.storage.mechanism.HTML5WebStorage'], ['goog.asserts', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.storage.mechanism.ErrorCode', 'goog.storage.mechanism.IterableMechanism'], {});
goog.addDependency('storage/mechanism/html5webstorage_test.js', ['goog.storage.mechanism.HTML5WebStorageTest'], ['goog.storage.mechanism.ErrorCode', 'goog.storage.mechanism.HTML5WebStorage', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/ieuserdata.js', ['goog.storage.mechanism.IEUserData'], ['goog.asserts', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.storage.mechanism.ErrorCode', 'goog.storage.mechanism.IterableMechanism', 'goog.structs.Map', 'goog.userAgent'], {});
goog.addDependency('storage/mechanism/ieuserdata_test.js', ['goog.storage.mechanism.IEUserDataTest'], ['goog.storage.mechanism.IEUserData', 'goog.storage.mechanism.mechanismSeparationTester', 'goog.storage.mechanism.mechanismSharingTester', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/iterablemechanism.js', ['goog.storage.mechanism.IterableMechanism'], ['goog.array', 'goog.asserts', 'goog.iter', 'goog.storage.mechanism.Mechanism'], {});
goog.addDependency('storage/mechanism/iterablemechanismtester.js', ['goog.storage.mechanism.iterableMechanismTester'], ['goog.iter', 'goog.iter.StopIteration', 'goog.testing.asserts'], {});
goog.addDependency('storage/mechanism/mechanism.js', ['goog.storage.mechanism.Mechanism'], [], {});
goog.addDependency('storage/mechanism/mechanismfactory.js', ['goog.storage.mechanism.mechanismfactory'], ['goog.storage.mechanism.HTML5LocalStorage', 'goog.storage.mechanism.HTML5SessionStorage', 'goog.storage.mechanism.IEUserData', 'goog.storage.mechanism.PrefixedMechanism'], {});
goog.addDependency('storage/mechanism/mechanismfactory_test.js', ['goog.storage.mechanism.mechanismfactoryTest'], ['goog.storage.mechanism.mechanismfactory', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/mechanismseparationtester.js', ['goog.storage.mechanism.mechanismSeparationTester'], ['goog.iter.StopIteration', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.asserts'], {});
goog.addDependency('storage/mechanism/mechanismsharingtester.js', ['goog.storage.mechanism.mechanismSharingTester'], ['goog.iter.StopIteration', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.asserts'], {});
goog.addDependency('storage/mechanism/mechanismtestdefinition.js', ['goog.storage.mechanism.mechanismTestDefinition'], [], {});
goog.addDependency('storage/mechanism/mechanismtester.js', ['goog.storage.mechanism.mechanismTester'], ['goog.storage.mechanism.ErrorCode', 'goog.testing.asserts', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('storage/mechanism/prefixedmechanism.js', ['goog.storage.mechanism.PrefixedMechanism'], ['goog.iter.Iterator', 'goog.storage.mechanism.IterableMechanism'], {});
goog.addDependency('storage/mechanism/prefixedmechanism_test.js', ['goog.storage.mechanism.PrefixedMechanismTest'], ['goog.storage.mechanism.HTML5LocalStorage', 'goog.storage.mechanism.PrefixedMechanism', 'goog.storage.mechanism.mechanismSeparationTester', 'goog.storage.mechanism.mechanismSharingTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/richstorage.js', ['goog.storage.RichStorage', 'goog.storage.RichStorage.Wrapper'], ['goog.storage.ErrorCode', 'goog.storage.Storage'], {});
goog.addDependency('storage/richstorage_test.js', ['goog.storage.RichStorageTest'], ['goog.storage.ErrorCode', 'goog.storage.RichStorage', 'goog.storage.storageTester', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/storage.js', ['goog.storage.Storage'], ['goog.json', 'goog.storage.ErrorCode'], {});
goog.addDependency('storage/storage_test.js', ['goog.storage.storage_test'], ['goog.functions', 'goog.storage.ErrorCode', 'goog.storage.Storage', 'goog.storage.storageTester', 'goog.testing.asserts', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/storagetester.js', ['goog.storage.storageTester'], ['goog.storage.Storage', 'goog.structs.Map', 'goog.testing.asserts'], {});
goog.addDependency('streams/defines.js', ['goog.streams.defines'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full.js', ['goog.streams.full'], ['goog.streams.defines', 'goog.streams.fullImpl', 'goog.streams.fullNativeImpl', 'goog.streams.fullTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_impl.js', ['goog.streams.fullImpl'], ['goog.asserts', 'goog.promise.NativeResolver', 'goog.streams.fullTypes', 'goog.streams.liteImpl'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_impl_test.js', ['goog.streams.fullImplTest'], ['goog.streams.fullImpl', 'goog.streams.fullTestCases', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_native_impl.js', ['goog.streams.fullNativeImpl'], ['goog.streams.fullTypes', 'goog.streams.liteNativeImpl'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_native_impl_test.js', ['goog.streams.fullNativeImplTest'], ['goog.streams.fullNativeImpl', 'goog.streams.fullTestCases', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_test_cases.js', ['goog.streams.fullTestCases'], ['goog.streams.fullTypes', 'goog.streams.liteTestCases', 'goog.testing.recordFunction'], {'lang': 'es9', 'module': 'goog'});
goog.addDependency('streams/full_types.js', ['goog.streams.fullTypes'], ['goog.streams.liteTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite.js', ['goog.streams.lite'], ['goog.streams.defines', 'goog.streams.liteImpl', 'goog.streams.liteNativeImpl', 'goog.streams.liteTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_impl.js', ['goog.streams.liteImpl'], ['goog.asserts', 'goog.promise.NativeResolver', 'goog.streams.liteTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_impl_test.js', ['goog.streams.liteImplTest'], ['goog.streams.liteImpl', 'goog.streams.liteTestCases', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_native_impl.js', ['goog.streams.liteNativeImpl'], ['goog.streams.liteTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_native_impl_test.js', ['goog.streams.liteNativeImplTest'], ['goog.streams.liteNativeImpl', 'goog.streams.liteTestCases', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_test_cases.js', ['goog.streams.liteTestCases'], ['goog.streams.liteTypes', 'goog.testing.jsunit'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('streams/lite_types.js', ['goog.streams.liteTypes'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/const.js', ['goog.string.Const'], ['goog.asserts', 'goog.string.TypedString'], {});
goog.addDependency('string/const_test.js', ['goog.string.constTest'], ['goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/internal.js', ['goog.string.internal'], [], {'lang': 'es6'});
goog.addDependency('string/linkify.js', ['goog.string.linkify'], ['goog.html.SafeHtml', 'goog.string'], {});
goog.addDependency('string/linkify_test.js', ['goog.string.linkifyTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.string', 'goog.string.linkify', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/newlines.js', ['goog.string.newlines', 'goog.string.newlines.Line'], ['goog.array'], {});
goog.addDependency('string/newlines_test.js', ['goog.string.newlinesTest'], ['goog.string.newlines', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/parser.js', ['goog.string.Parser'], [], {});
goog.addDependency('string/path.js', ['goog.string.path'], ['goog.array', 'goog.string'], {});
goog.addDependency('string/path_test.js', ['goog.string.pathTest'], ['goog.string.path', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/string.js', ['goog.string', 'goog.string.Unicode'], ['goog.dom.safe', 'goog.html.uncheckedconversions', 'goog.string.Const', 'goog.string.internal'], {});
goog.addDependency('string/string_test.js', ['goog.stringTest'], ['goog.dom', 'goog.dom.TagName', 'goog.functions', 'goog.object', 'goog.string', 'goog.string.Unicode', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/stringbuffer.js', ['goog.string.StringBuffer'], [], {'lang': 'es6'});
goog.addDependency('string/stringbuffer_test.js', ['goog.string.StringBufferTest'], ['goog.string.StringBuffer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/stringformat.js', ['goog.string.format'], ['goog.string'], {});
goog.addDependency('string/stringformat_test.js', ['goog.string.formatTest'], ['goog.string.format', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/stringifier.js', ['goog.string.Stringifier'], [], {});
goog.addDependency('string/typedstring.js', ['goog.string.TypedString'], [], {});
goog.addDependency('structs/avltree.js', ['goog.structs.AvlTree'], ['goog.asserts', 'goog.structs.Collection'], {'module': 'goog'});
goog.addDependency('structs/avltree_test.js', ['goog.structs.AvlTreeTest'], ['goog.array', 'goog.structs.AvlTree', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/circularbuffer.js', ['goog.structs.CircularBuffer'], [], {'lang': 'es6'});
goog.addDependency('structs/circularbuffer_test.js', ['goog.structs.CircularBufferTest'], ['goog.structs.CircularBuffer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/collection.js', ['goog.structs.Collection'], [], {});
goog.addDependency('structs/collection_test.js', ['goog.structs.CollectionTest'], ['goog.structs.AvlTree', 'goog.structs.Set', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/heap.js', ['goog.structs.Heap'], ['goog.array', 'goog.object', 'goog.structs.Node'], {});
goog.addDependency('structs/heap_test.js', ['goog.structs.HeapTest'], ['goog.structs', 'goog.structs.Heap', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/inversionmap.js', ['goog.structs.InversionMap'], ['goog.array', 'goog.asserts'], {});
goog.addDependency('structs/inversionmap_test.js', ['goog.structs.InversionMapTest'], ['goog.structs.InversionMap', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/linkedmap.js', ['goog.structs.LinkedMap'], ['goog.structs.Map'], {});
goog.addDependency('structs/linkedmap_test.js', ['goog.structs.LinkedMapTest'], ['goog.structs.LinkedMap', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/map.js', ['goog.structs.Map'], ['goog.iter.Iterator', 'goog.iter.StopIteration'], {});
goog.addDependency('structs/map_test.js', ['goog.structs.MapTest'], ['goog.iter', 'goog.structs', 'goog.structs.Map', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/node.js', ['goog.structs.Node'], [], {});
goog.addDependency('structs/pool.js', ['goog.structs.Pool'], ['goog.Disposable', 'goog.structs.Queue', 'goog.structs.Set'], {});
goog.addDependency('structs/pool_test.js', ['goog.structs.PoolTest'], ['goog.structs.Pool', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/prioritypool.js', ['goog.structs.PriorityPool'], ['goog.structs.Pool', 'goog.structs.PriorityQueue'], {});
goog.addDependency('structs/prioritypool_test.js', ['goog.structs.PriorityPoolTest'], ['goog.structs.PriorityPool', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/priorityqueue.js', ['goog.structs.PriorityQueue'], ['goog.structs.Heap'], {});
goog.addDependency('structs/priorityqueue_test.js', ['goog.structs.PriorityQueueTest'], ['goog.structs', 'goog.structs.PriorityQueue', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/quadtree.js', ['goog.structs.QuadTree', 'goog.structs.QuadTree.Node', 'goog.structs.QuadTree.Point'], ['goog.math.Coordinate'], {});
goog.addDependency('structs/quadtree_test.js', ['goog.structs.QuadTreeTest'], ['goog.structs', 'goog.structs.QuadTree', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/queue.js', ['goog.structs.Queue'], ['goog.array'], {});
goog.addDependency('structs/queue_test.js', ['goog.structs.QueueTest'], ['goog.structs.Queue', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/set.js', ['goog.structs.Set'], ['goog.structs', 'goog.structs.Collection', 'goog.structs.Map'], {});
goog.addDependency('structs/set_test.js', ['goog.structs.SetTest'], ['goog.iter', 'goog.structs', 'goog.structs.Set', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/simplepool.js', ['goog.structs.SimplePool'], ['goog.Disposable'], {});
goog.addDependency('structs/stringset.js', ['goog.structs.StringSet'], ['goog.asserts', 'goog.iter'], {});
goog.addDependency('structs/stringset_test.js', ['goog.structs.StringSetTest'], ['goog.array', 'goog.iter', 'goog.structs.StringSet', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/structs.js', ['goog.structs'], ['goog.array', 'goog.object'], {});
goog.addDependency('structs/structs_test.js', ['goog.structsTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.structs', 'goog.structs.Map', 'goog.structs.Set', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/treenode.js', ['goog.structs.TreeNode'], ['goog.array', 'goog.asserts', 'goog.structs.Node'], {});
goog.addDependency('structs/treenode_test.js', ['goog.structs.TreeNodeTest'], ['goog.structs.TreeNode', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/trie.js', ['goog.structs.Trie'], ['goog.object', 'goog.structs'], {});
goog.addDependency('structs/trie_test.js', ['goog.structs.TrieTest'], ['goog.object', 'goog.structs', 'goog.structs.Trie', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/bidi.js', ['goog.style.bidi'], ['goog.dom', 'goog.style', 'goog.userAgent', 'goog.userAgent.platform', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('style/bidi_test.js', ['goog.style.bidiTest'], ['goog.dom', 'goog.style', 'goog.style.bidi', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/cursor.js', ['goog.style.cursor'], ['goog.userAgent'], {});
goog.addDependency('style/cursor_test.js', ['goog.style.cursorTest'], ['goog.style.cursor', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/style.js', ['goog.style'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.vendor', 'goog.html.SafeStyleSheet', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.math.Size', 'goog.object', 'goog.reflect', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('style/style_document_scroll_test.js', ['goog.style.style_document_scroll_test'], ['goog.dom', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/style_test.js', ['goog.style_test'], ['goog.array', 'goog.color', 'goog.dom', 'goog.dom.TagName', 'goog.events.BrowserEvent', 'goog.html.testing', 'goog.labs.userAgent.util', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.math.Size', 'goog.object', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.MockUserAgent', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgentTestUtil', 'goog.userAgentTestUtil.UserAgents'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/style_webkit_scrollbars_test.js', ['goog.style.webkitScrollbarsTest'], ['goog.asserts', 'goog.style', 'goog.styleScrollbarTester', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/stylescrollbartester.js', ['goog.styleScrollbarTester'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.testing.asserts'], {});
goog.addDependency('style/transform.js', ['goog.style.transform'], ['goog.functions', 'goog.math.Coordinate', 'goog.math.Coordinate3', 'goog.style', 'goog.userAgent', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('style/transform_test.js', ['goog.style.transformTest'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.style.transform', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/transition.js', ['goog.style.transition', 'goog.style.transition.Css3Property'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.dom.vendor', 'goog.functions', 'goog.html.SafeHtml', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('style/transition_test.js', ['goog.style.transitionTest'], ['goog.style', 'goog.style.transition', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('test_module.js', ['goog.test_module'], ['goog.test_module_dep'], {'module': 'goog'});
goog.addDependency('test_module_dep.js', ['goog.test_module_dep'], [], {'module': 'goog'});
goog.addDependency('testing/assertionfailure.js', ['goog.testing.safe.assertionFailure'], ['goog.asserts', 'goog.testing.asserts'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/asserts.js', ['goog.testing.asserts'], ['goog.testing.JsUnitException'], {});
goog.addDependency('testing/asserts_test.js', ['goog.testing.assertsTest'], ['goog.Promise', 'goog.array', 'goog.async.Deferred', 'goog.dom', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.structs.Map', 'goog.structs.Set', 'goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/async/mockcontrol.js', ['goog.testing.async.MockControl'], ['goog.asserts', 'goog.async.Deferred', 'goog.debug', 'goog.testing.MockControl', 'goog.testing.asserts', 'goog.testing.mockmatchers.IgnoreArgument'], {});
goog.addDependency('testing/async/mockcontrol_test.js', ['goog.testing.async.MockControlTest'], ['goog.async.Deferred', 'goog.testing.MockControl', 'goog.testing.asserts', 'goog.testing.async.MockControl', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/asynctestcase.js', ['goog.testing.AsyncTestCase', 'goog.testing.AsyncTestCase.ControlBreakingException'], ['goog.asserts', 'goog.testing.TestCase', 'goog.testing.asserts'], {});
goog.addDependency('testing/asynctestcase_async_test.js', ['goog.testing.AsyncTestCaseAsyncTest'], ['goog.testing.AsyncTestCase', 'goog.testing.TestCase', 'goog.testing.jsunit'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/asynctestcase_noasync_test.js', ['goog.testing.AsyncTestCaseSyncTest'], ['goog.testing.AsyncTestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/asynctestcase_test.js', ['goog.testing.AsyncTestCaseTest'], ['goog.debug.Error', 'goog.testing.AsyncTestCase', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/benchmark.js', ['goog.testing.benchmark'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.PerformanceTable', 'goog.testing.PerformanceTimer', 'goog.testing.TestCase'], {});
goog.addDependency('testing/continuationtestcase.js', ['goog.testing.ContinuationTestCase', 'goog.testing.ContinuationTestCase.ContinuationTest', 'goog.testing.ContinuationTestCase.Step'], ['goog.array', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.testing.TestCase', 'goog.testing.asserts'], {});
goog.addDependency('testing/continuationtestcase_test.js', ['goog.testing.ContinuationTestCaseTest'], ['goog.events', 'goog.events.EventTarget', 'goog.testing.ContinuationTestCase', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.jsunit'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/deferredtestcase.js', ['goog.testing.DeferredTestCase'], ['goog.async.Deferred', 'goog.testing.AsyncTestCase', 'goog.testing.TestCase'], {});
goog.addDependency('testing/deferredtestcase_test.js', ['goog.testing.DeferredTestCaseTest'], ['goog.async.Deferred', 'goog.testing.DeferredTestCase', 'goog.testing.TestCase', 'goog.testing.TestRunner', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/dom.js', ['goog.testing.dom'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.AbstractRange', 'goog.dom.InputType', 'goog.dom.NodeIterator', 'goog.dom.NodeType', 'goog.dom.TagIterator', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.iter', 'goog.object', 'goog.string', 'goog.style', 'goog.testing.asserts', 'goog.userAgent'], {});
goog.addDependency('testing/dom_test.js', ['goog.testing.domTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/editor/dom.js', ['goog.testing.editor.dom'], ['goog.dom.AbstractRange', 'goog.dom.NodeType', 'goog.dom.TagIterator', 'goog.dom.TagWalkType', 'goog.iter', 'goog.string', 'goog.testing.asserts'], {});
goog.addDependency('testing/editor/dom_test.js', ['goog.testing.editor.domTest'], ['goog.dom', 'goog.dom.TagName', 'goog.functions', 'goog.testing.editor.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/editor/fieldmock.js', ['goog.testing.editor.FieldMock'], ['goog.dom', 'goog.dom.Range', 'goog.editor.Field', 'goog.testing.LooseMock', 'goog.testing.mockmatchers'], {});
goog.addDependency('testing/editor/testhelper.js', ['goog.testing.editor.TestHelper'], ['goog.Disposable', 'goog.dom', 'goog.dom.Range', 'goog.editor.BrowserFeature', 'goog.editor.node', 'goog.editor.plugins.AbstractBubblePlugin', 'goog.testing.dom'], {});
goog.addDependency('testing/editor/testhelper_test.js', ['goog.testing.editor.TestHelperTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.node', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/events/eventobserver.js', ['goog.testing.events.EventObserver'], ['goog.array', 'goog.events.Event'], {});
goog.addDependency('testing/events/eventobserver_test.js', ['goog.testing.events.EventObserverTest'], ['goog.array', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.testing.events.EventObserver', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/events/events.js', ['goog.testing.events', 'goog.testing.events.Event'], ['goog.Disposable', 'goog.asserts', 'goog.dom.NodeType', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.BrowserFeature', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.object', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('testing/events/events_test.js', ['goog.testing.eventsTest'], ['goog.array', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Coordinate', 'goog.string', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/events/matchers.js', ['goog.testing.events.EventMatcher'], ['goog.events.Event', 'goog.testing.mockmatchers.ArgumentMatcher'], {});
goog.addDependency('testing/events/matchers_test.js', ['goog.testing.events.EventMatcherTest'], ['goog.events.Event', 'goog.testing.events.EventMatcher', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/events/onlinehandler.js', ['goog.testing.events.OnlineHandler'], ['goog.events.EventTarget', 'goog.net.NetworkStatusMonitor'], {});
goog.addDependency('testing/events/onlinehandler_test.js', ['goog.testing.events.OnlineHandlerTest'], ['goog.events', 'goog.net.NetworkStatusMonitor', 'goog.testing.events.EventObserver', 'goog.testing.events.OnlineHandler', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/expectedfailures.js', ['goog.testing.ExpectedFailures'], ['goog.asserts', 'goog.debug.DivConsole', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.log', 'goog.style', 'goog.testing.JsUnitException', 'goog.testing.TestCase', 'goog.testing.asserts'], {});
goog.addDependency('testing/expectedfailures_test.js', ['goog.testing.ExpectedFailuresTest'], ['goog.debug.Logger', 'goog.testing.ExpectedFailures', 'goog.testing.JsUnitException', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/blob.js', ['goog.testing.fs.Blob'], ['goog.crypt', 'goog.crypt.base64'], {});
goog.addDependency('testing/fs/blob_test.js', ['goog.testing.fs.BlobTest'], ['goog.dom', 'goog.testing.fs.Blob', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/directoryentry_test.js', ['goog.testing.fs.DirectoryEntryTest'], ['goog.array', 'goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.testing.MockClock', 'goog.testing.TestCase', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/entry.js', ['goog.testing.fs.DirectoryEntry', 'goog.testing.fs.Entry', 'goog.testing.fs.FileEntry'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.async.Deferred', 'goog.fs.DirectoryEntry', 'goog.fs.DirectoryEntryImpl', 'goog.fs.Entry', 'goog.fs.Error', 'goog.fs.FileEntry', 'goog.functions', 'goog.object', 'goog.string', 'goog.testing.fs.File', 'goog.testing.fs.FileWriter'], {});
goog.addDependency('testing/fs/entry_test.js', ['goog.testing.fs.EntryTest'], ['goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.testing.MockClock', 'goog.testing.TestCase', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/file.js', ['goog.testing.fs.File'], ['goog.testing.fs.Blob'], {});
goog.addDependency('testing/fs/fileentry_test.js', ['goog.testing.fs.FileEntryTest'], ['goog.testing.MockClock', 'goog.testing.fs.FileEntry', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/filereader.js', ['goog.testing.fs.FileReader'], ['goog.Timer', 'goog.events.EventTarget', 'goog.fs.Error', 'goog.fs.FileReader', 'goog.testing.fs.Blob', 'goog.testing.fs.ProgressEvent'], {});
goog.addDependency('testing/fs/filereader_test.js', ['goog.testing.fs.FileReaderTest'], ['goog.Promise', 'goog.array', 'goog.events', 'goog.fs.Error', 'goog.fs.FileReader', 'goog.object', 'goog.testing.events.EventObserver', 'goog.testing.fs.FileReader', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/filesystem.js', ['goog.testing.fs.FileSystem'], ['goog.fs.FileSystem', 'goog.testing.fs.DirectoryEntry'], {});
goog.addDependency('testing/fs/filewriter.js', ['goog.testing.fs.FileWriter'], ['goog.Timer', 'goog.events.EventTarget', 'goog.fs.Error', 'goog.fs.FileSaver', 'goog.string', 'goog.testing.fs.Blob', 'goog.testing.fs.File', 'goog.testing.fs.ProgressEvent'], {});
goog.addDependency('testing/fs/filewriter_test.js', ['goog.testing.fs.FileWriterTest'], ['goog.Promise', 'goog.array', 'goog.events', 'goog.fs.Error', 'goog.fs.FileSaver', 'goog.object', 'goog.testing.MockClock', 'goog.testing.events.EventObserver', 'goog.testing.fs.Blob', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/fs.js', ['goog.testing.fs'], ['goog.Timer', 'goog.array', 'goog.async.Deferred', 'goog.fs', 'goog.testing.PropertyReplacer', 'goog.testing.fs.Blob', 'goog.testing.fs.FileSystem'], {});
goog.addDependency('testing/fs/fs_test.js', ['goog.testing.fsTest'], ['goog.testing.fs', 'goog.testing.fs.Blob', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/integration_test.js', ['goog.testing.fs.integrationTest'], ['goog.Promise', 'goog.events', 'goog.fs', 'goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.fs.FileSaver', 'goog.testing.PropertyReplacer', 'goog.testing.fs', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/progressevent.js', ['goog.testing.fs.ProgressEvent'], ['goog.events.Event'], {});
goog.addDependency('testing/functionmock.js', ['goog.testing', 'goog.testing.FunctionMock', 'goog.testing.GlobalFunctionMock', 'goog.testing.MethodMock'], ['goog.object', 'goog.testing.LooseMock', 'goog.testing.Mock', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock'], {});
goog.addDependency('testing/functionmock_test.js', ['goog.testing.FunctionMockTest'], ['goog.array', 'goog.string', 'goog.testing', 'goog.testing.FunctionMock', 'goog.testing.Mock', 'goog.testing.StrictMock', 'goog.testing.asserts', 'goog.testing.mockmatchers', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/graphics.js', ['goog.testing.graphics'], ['goog.graphics.Path', 'goog.testing.asserts'], {});
goog.addDependency('testing/i18n/asserts.js', ['goog.testing.i18n.asserts'], ['goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('testing/i18n/asserts_test.js', ['goog.testing.i18n.assertsTest'], ['goog.testing.ExpectedFailures', 'goog.testing.i18n.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/jstdasyncwrapper.js', ['goog.testing.JsTdAsyncWrapper'], ['goog.Promise'], {});
goog.addDependency('testing/jstdtestcaseadapter.js', ['goog.testing.JsTdTestCaseAdapter'], ['goog.async.run', 'goog.functions', 'goog.testing.JsTdAsyncWrapper', 'goog.testing.TestCase', 'goog.testing.jsunit'], {});
goog.addDependency('testing/jsunit.js', ['goog.testing.jsunit'], ['goog.dom.TagName', 'goog.testing.TestCase', 'goog.testing.TestRunner', 'goog.userAgent'], {});
goog.addDependency('testing/jsunitexception.js', ['goog.testing.JsUnitException'], ['goog.testing.stacktrace'], {});
goog.addDependency('testing/loosemock.js', ['goog.testing.LooseExpectationCollection', 'goog.testing.LooseMock'], ['goog.array', 'goog.asserts', 'goog.structs.Map', 'goog.structs.Set', 'goog.testing.Mock'], {});
goog.addDependency('testing/loosemock_test.js', ['goog.testing.LooseMockTest'], ['goog.testing.LooseMock', 'goog.testing.mockmatchers', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/messaging/mockmessagechannel.js', ['goog.testing.messaging.MockMessageChannel'], ['goog.messaging.AbstractChannel', 'goog.testing.MockControl', 'goog.testing.asserts'], {});
goog.addDependency('testing/messaging/mockmessageevent.js', ['goog.testing.messaging.MockMessageEvent'], ['goog.events.BrowserEvent', 'goog.events.EventType', 'goog.testing.events.Event'], {});
goog.addDependency('testing/messaging/mockmessageport.js', ['goog.testing.messaging.MockMessagePort'], ['goog.events.EventTarget', 'goog.testing.MockControl'], {});
goog.addDependency('testing/messaging/mockportnetwork.js', ['goog.testing.messaging.MockPortNetwork'], ['goog.messaging.PortNetwork', 'goog.testing.messaging.MockMessageChannel'], {});
goog.addDependency('testing/mock.js', ['goog.testing.Mock', 'goog.testing.MockExpectation'], ['goog.Promise', 'goog.array', 'goog.asserts', 'goog.object', 'goog.promise.Resolver', 'goog.testing.JsUnitException', 'goog.testing.MockInterface', 'goog.testing.mockmatchers'], {});
goog.addDependency('testing/mock_test.js', ['goog.testing.MockTest'], ['goog.array', 'goog.testing', 'goog.testing.Mock', 'goog.testing.MockControl', 'goog.testing.MockExpectation', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/mockclassfactory.js', ['goog.testing.MockClassFactory', 'goog.testing.MockClassRecord'], ['goog.array', 'goog.object', 'goog.testing.LooseMock', 'goog.testing.StrictMock', 'goog.testing.TestCase', 'goog.testing.mockmatchers'], {});
goog.addDependency('testing/mockclassfactory_test.js', ['fake.BaseClass', 'fake.ChildClass', 'goog.testing.MockClassFactoryTest'], ['goog.testing', 'goog.testing.MockClassFactory', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('testing/mockclock.js', ['goog.testing.MockClock'], ['goog.Disposable', 'goog.Promise', 'goog.Thenable', 'goog.async.run', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.events.Event'], {});
goog.addDependency('testing/mockclock_test.js', ['goog.testing.MockClockTest'], ['goog.Promise', 'goog.Timer', 'goog.events', 'goog.functions', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockcontrol.js', ['goog.testing.MockControl'], ['goog.Promise', 'goog.array', 'goog.testing', 'goog.testing.LooseMock', 'goog.testing.StrictMock'], {});
goog.addDependency('testing/mockcontrol_test.js', ['goog.testing.MockControlTest'], ['goog.testing.Mock', 'goog.testing.MockControl', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockinterface.js', ['goog.testing.MockInterface'], ['goog.Promise'], {});
goog.addDependency('testing/mockmatchers.js', ['goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.mockmatchers.IgnoreArgument', 'goog.testing.mockmatchers.InstanceOf', 'goog.testing.mockmatchers.ObjectEquals', 'goog.testing.mockmatchers.RegexpMatch', 'goog.testing.mockmatchers.SaveArgument', 'goog.testing.mockmatchers.TypeOf'], ['goog.array', 'goog.dom', 'goog.testing.asserts'], {});
goog.addDependency('testing/mockmatchers_test.js', ['goog.testing.mockmatchersTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.jsunit', 'goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher'], {'lang': 'es6'});
goog.addDependency('testing/mockrandom.js', ['goog.testing.MockRandom'], ['goog.Disposable'], {});
goog.addDependency('testing/mockrandom_test.js', ['goog.testing.MockRandomTest'], ['goog.testing.MockRandom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockrange.js', ['goog.testing.MockRange'], ['goog.dom.AbstractRange', 'goog.testing.LooseMock'], {});
goog.addDependency('testing/mockrange_test.js', ['goog.testing.MockRangeTest'], ['goog.testing.MockRange', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockstorage.js', ['goog.testing.MockStorage'], ['goog.structs.Map'], {});
goog.addDependency('testing/mockstorage_test.js', ['goog.testing.MockStorageTest'], ['goog.testing.MockStorage', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockuseragent.js', ['goog.testing.MockUserAgent'], ['goog.Disposable', 'goog.labs.userAgent.util', 'goog.testing.PropertyReplacer', 'goog.userAgent'], {});
goog.addDependency('testing/mockuseragent_test.js', ['goog.testing.MockUserAgentTest'], ['goog.dispose', 'goog.testing.MockUserAgent', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/multitestrunner.js', ['goog.testing.MultiTestRunner', 'goog.testing.MultiTestRunner.TestFrame'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventHandler', 'goog.functions', 'goog.object', 'goog.string', 'goog.testing.TestCase', 'goog.ui.Component', 'goog.ui.ServerChart', 'goog.ui.TableSorter'], {});
goog.addDependency('testing/multitestrunner_test.js', ['goog.testing.MultiTestRunnerTest'], ['goog.Promise', 'goog.array', 'goog.events', 'goog.testing.MockControl', 'goog.testing.MultiTestRunner', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/net/mockiframeio.js', ['goog.testing.net.MockIFrameIo'], ['goog.events.EventTarget', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.IframeIo', 'goog.testing.TestQueue'], {});
goog.addDependency('testing/net/xhrio.js', ['goog.testing.net.XhrIo'], ['goog.Uri', 'goog.array', 'goog.dom.xml', 'goog.events', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.HttpStatus', 'goog.net.XhrIo', 'goog.net.XmlHttp', 'goog.object', 'goog.structs', 'goog.structs.Map', 'goog.testing.TestQueue', 'goog.uri.utils'], {});
goog.addDependency('testing/net/xhrio_test.js', ['goog.testing.net.XhrIoTest'], ['goog.dom.xml', 'goog.events', 'goog.events.Event', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.XmlHttp', 'goog.object', 'goog.testing.MockControl', 'goog.testing.asserts', 'goog.testing.mockmatchers.InstanceOf', 'goog.testing.net.XhrIo', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/net/xhriopool.js', ['goog.testing.net.XhrIoPool'], ['goog.net.XhrIoPool', 'goog.testing.net.XhrIo'], {});
goog.addDependency('testing/objectpropertystring.js', ['goog.testing.ObjectPropertyString'], [], {});
goog.addDependency('testing/parallel_closure_test_suite.js', ['goog.testing.parallelClosureTestSuite'], ['goog.Promise', 'goog.asserts', 'goog.events', 'goog.json', 'goog.testing.MultiTestRunner', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/parallel_closure_test_suite_test.js', ['goog.testing.parallelClosureTestSuiteTest'], ['goog.dom', 'goog.testing.MockControl', 'goog.testing.MultiTestRunner', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.parallelClosureTestSuite', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/performancetable.js', ['goog.testing.PerformanceTable'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.testing.PerformanceTimer'], {});
goog.addDependency('testing/performancetimer.js', ['goog.testing.PerformanceTimer', 'goog.testing.PerformanceTimer.Task'], ['goog.array', 'goog.async.Deferred', 'goog.math'], {'lang': 'es6'});
goog.addDependency('testing/performancetimer_test.js', ['goog.testing.PerformanceTimerTest'], ['goog.async.Deferred', 'goog.dom', 'goog.math', 'goog.testing.MockClock', 'goog.testing.PerformanceTimer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/propertyreplacer.js', ['goog.testing.PropertyReplacer'], ['goog.asserts', 'goog.userAgent'], {});
goog.addDependency('testing/propertyreplacer_test.js', ['goog.testing.PropertyReplacerTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/proto2/proto2.js', ['goog.testing.proto2'], ['goog.proto2.Message', 'goog.proto2.ObjectSerializer', 'goog.testing.asserts'], {});
goog.addDependency('testing/proto2/proto2_test.js', ['goog.testing.proto2Test'], ['goog.testing.proto2', 'goog.testing.testSuite', 'proto2.TestAllTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/pseudorandom.js', ['goog.testing.PseudoRandom'], ['goog.Disposable'], {});
goog.addDependency('testing/pseudorandom_test.js', ['goog.testing.PseudoRandomTest'], ['goog.testing.PseudoRandom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/recordfunction.js', ['goog.testing.FunctionCall', 'goog.testing.recordConstructor', 'goog.testing.recordFunction'], ['goog.Promise', 'goog.promise.Resolver', 'goog.testing.asserts'], {});
goog.addDependency('testing/recordfunction_test.js', ['goog.testing.recordFunctionTest'], ['goog.functions', 'goog.testing.PropertyReplacer', 'goog.testing.recordConstructor', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/shardingtestcase.js', ['goog.testing.ShardingTestCase'], ['goog.asserts', 'goog.testing.TestCase'], {});
goog.addDependency('testing/shardingtestcase_test.js', ['goog.testing.ShardingTestCaseTest'], ['goog.testing.ShardingTestCase', 'goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.jsunit'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/singleton.js', ['goog.testing.singleton'], [], {'lang': 'es6'});
goog.addDependency('testing/singleton_test.js', ['goog.testing.singletonTest'], ['goog.testing.asserts', 'goog.testing.singleton', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/stacktrace.js', ['goog.testing.stacktrace', 'goog.testing.stacktrace.Frame'], [], {'lang': 'es6'});
goog.addDependency('testing/stacktrace_test.js', ['goog.testing.stacktraceTest'], ['goog.functions', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock', 'goog.testing.asserts', 'goog.testing.stacktrace', 'goog.testing.stacktrace.Frame', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/storage/fakemechanism.js', ['goog.testing.storage.FakeMechanism'], ['goog.storage.mechanism.IterableMechanism', 'goog.structs.Map'], {});
goog.addDependency('testing/strictmock.js', ['goog.testing.StrictMock'], ['goog.array', 'goog.asserts', 'goog.structs.Set', 'goog.testing.Mock'], {});
goog.addDependency('testing/strictmock_test.js', ['goog.testing.StrictMockTest'], ['goog.testing.StrictMock', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/style/layoutasserts.js', ['goog.testing.style.layoutasserts'], ['goog.style', 'goog.testing.asserts', 'goog.testing.style'], {});
goog.addDependency('testing/style/layoutasserts_test.js', ['goog.testing.style.layoutassertsTest'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.testing.style.layoutasserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/style/style.js', ['goog.testing.style'], ['goog.dom', 'goog.math.Rect', 'goog.style'], {});
goog.addDependency('testing/style/style_test.js', ['goog.testing.styleTest'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.testing.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/testcase.js', ['goog.testing.TestCase', 'goog.testing.TestCase.Error', 'goog.testing.TestCase.Order', 'goog.testing.TestCase.Result', 'goog.testing.TestCase.Test'], ['goog.Promise', 'goog.Thenable', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.object', 'goog.testing.JsUnitException', 'goog.testing.asserts'], {});
goog.addDependency('testing/testcase_test.js', ['goog.testing.TestCaseTest'], ['goog.Promise', 'goog.Timer', 'goog.functions', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.FunctionMock', 'goog.testing.JsUnitException', 'goog.testing.MethodMock', 'goog.testing.MockRandom', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/testqueue.js', ['goog.testing.TestQueue'], [], {});
goog.addDependency('testing/testrunner.js', ['goog.testing.TestRunner'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.json', 'goog.testing.TestCase', 'goog.userAgent'], {});
goog.addDependency('testing/testrunner_test.js', ['goog.testing.TestRunnerTest'], ['goog.testing.TestCase', 'goog.testing.TestRunner', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/testsuite.js', ['goog.testing.testSuite'], ['goog.labs.testing.Environment', 'goog.testing.TestCase'], {});
goog.addDependency('testing/testsuite_test.js', ['goog.testing.testSuiteTest'], ['goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/ui/rendererasserts.js', ['goog.testing.ui.rendererasserts'], ['goog.testing.asserts', 'goog.ui.ControlRenderer'], {});
goog.addDependency('testing/ui/rendererasserts_test.js', ['goog.testing.ui.rendererassertsTest'], ['goog.testing.asserts', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.ControlRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/ui/rendererharness.js', ['goog.testing.ui.RendererHarness'], ['goog.Disposable', 'goog.dom.NodeType', 'goog.testing.asserts', 'goog.testing.dom', 'goog.ui.Control', 'goog.ui.ControlRenderer'], {});
goog.addDependency('testing/ui/style.js', ['goog.testing.ui.style'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.testing.asserts'], {});
goog.addDependency('testing/ui/style_test.js', ['goog.testing.ui.styleTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.style'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('timer/timer.js', ['goog.Timer'], ['goog.Promise', 'goog.events.EventTarget'], {});
goog.addDependency('timer/timer_test.js', ['goog.TimerTest'], ['goog.Promise', 'goog.Timer', 'goog.events', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('tweak/entries.js', ['goog.tweak.BaseEntry', 'goog.tweak.BasePrimitiveSetting', 'goog.tweak.BaseSetting', 'goog.tweak.BooleanGroup', 'goog.tweak.BooleanInGroupSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.ButtonAction', 'goog.tweak.NumericSetting', 'goog.tweak.StringSetting'], ['goog.array', 'goog.asserts', 'goog.log', 'goog.object'], {});
goog.addDependency('tweak/entries_test.js', ['goog.tweak.BaseEntryTest'], ['goog.testing.MockControl', 'goog.testing.testSuite', 'goog.tweak.testhelpers'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('tweak/registry.js', ['goog.tweak.Registry'], ['goog.array', 'goog.asserts', 'goog.log', 'goog.string', 'goog.tweak.BasePrimitiveSetting', 'goog.tweak.BaseSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.NumericSetting', 'goog.tweak.StringSetting', 'goog.uri.utils'], {});
goog.addDependency('tweak/registry_test.js', ['goog.tweak.RegistryTest'], ['goog.asserts.AssertionError', 'goog.testing.testSuite', 'goog.tweak', 'goog.tweak.testhelpers'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('tweak/testhelpers.js', ['goog.tweak.testhelpers'], ['goog.tweak', 'goog.tweak.BooleanGroup', 'goog.tweak.BooleanInGroupSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.ButtonAction', 'goog.tweak.NumericSetting', 'goog.tweak.Registry', 'goog.tweak.StringSetting'], {});
goog.addDependency('tweak/tweak.js', ['goog.tweak', 'goog.tweak.ConfigParams'], ['goog.asserts', 'goog.tweak.BaseSetting', 'goog.tweak.BooleanGroup', 'goog.tweak.BooleanInGroupSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.ButtonAction', 'goog.tweak.NumericSetting', 'goog.tweak.Registry', 'goog.tweak.StringSetting'], {});
goog.addDependency('tweak/tweakui.js', ['goog.tweak.EntriesPanel', 'goog.tweak.TweakUi'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.object', 'goog.string.Const', 'goog.style', 'goog.tweak', 'goog.tweak.BaseEntry', 'goog.tweak.BooleanGroup', 'goog.tweak.BooleanInGroupSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.ButtonAction', 'goog.tweak.NumericSetting', 'goog.tweak.StringSetting', 'goog.ui.Zippy', 'goog.userAgent'], {});
goog.addDependency('tweak/tweakui_test.js', ['goog.tweak.TweakUiTest'], ['goog.dom', 'goog.dom.TagName', 'goog.string', 'goog.testing.testSuite', 'goog.tweak', 'goog.tweak.TweakUi', 'goog.tweak.testhelpers'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/abstractspellchecker.js', ['goog.ui.AbstractSpellChecker', 'goog.ui.AbstractSpellChecker.AsyncResult'], ['goog.a11y.aria', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.selection', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.math.Coordinate', 'goog.spell.SpellCheck', 'goog.structs.Set', 'goog.style', 'goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.MenuSeparator', 'goog.ui.PopupMenu'], {});
goog.addDependency('ui/ac/ac.js', ['goog.ui.ac'], ['goog.ui.ac.ArrayMatcher', 'goog.ui.ac.AutoComplete', 'goog.ui.ac.InputHandler', 'goog.ui.ac.Renderer'], {});
goog.addDependency('ui/ac/ac_test.js', ['goog.ui.acTest'], ['goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.classlist', 'goog.dom.selection', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.ac', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/arraymatcher.js', ['goog.ui.ac.ArrayMatcher'], ['goog.string'], {});
goog.addDependency('ui/ac/arraymatcher_test.js', ['goog.ui.ac.ArrayMatcherTest'], ['goog.testing.testSuite', 'goog.ui.ac.ArrayMatcher'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/autocomplete.js', ['goog.ui.ac.AutoComplete', 'goog.ui.ac.AutoComplete.EventType'], ['goog.array', 'goog.asserts', 'goog.events', 'goog.events.EventTarget', 'goog.object', 'goog.ui.ac.RenderOptions'], {});
goog.addDependency('ui/ac/autocomplete_test.js', ['goog.ui.ac.AutoCompleteTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.string', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.ui.ac.AutoComplete', 'goog.ui.ac.InputHandler', 'goog.ui.ac.RenderOptions', 'goog.ui.ac.Renderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/cachingmatcher.js', ['goog.ui.ac.CachingMatcher'], ['goog.array', 'goog.async.Throttle', 'goog.ui.ac.ArrayMatcher', 'goog.ui.ac.RenderOptions'], {});
goog.addDependency('ui/ac/cachingmatcher_test.js', ['goog.ui.ac.CachingMatcherTest'], ['goog.testing.MockControl', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.ui.ac.CachingMatcher'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/inputhandler.js', ['goog.ui.ac.InputHandler'], ['goog.Disposable', 'goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.selection', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.string', 'goog.userAgent', 'goog.userAgent.product'], {});
goog.addDependency('ui/ac/inputhandler_test.js', ['goog.ui.ac.InputHandlerTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.selection', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.functions', 'goog.object', 'goog.testing.MockClock', 'goog.testing.testSuite', 'goog.ui.ac.InputHandler', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/remote.js', ['goog.ui.ac.Remote'], ['goog.ui.ac.AutoComplete', 'goog.ui.ac.InputHandler', 'goog.ui.ac.RemoteArrayMatcher', 'goog.ui.ac.Renderer'], {});
goog.addDependency('ui/ac/remotearraymatcher.js', ['goog.ui.ac.RemoteArrayMatcher'], ['goog.Disposable', 'goog.Uri', 'goog.events', 'goog.net.EventType', 'goog.net.XhrIo'], {});
goog.addDependency('ui/ac/remotearraymatcher_test.js', ['goog.ui.ac.RemoteArrayMatcherTest'], ['goog.net.XhrIo', 'goog.testing.MockControl', 'goog.testing.net.XhrIo', 'goog.testing.testSuite', 'goog.ui.ac.RemoteArrayMatcher'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/renderer.js', ['goog.ui.ac.Renderer', 'goog.ui.ac.Renderer.CustomRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dispose', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.fx.dom.FadeInAndShow', 'goog.fx.dom.FadeOutAndHide', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.string', 'goog.style', 'goog.ui.IdGenerator', 'goog.ui.ac.AutoComplete'], {'lang': 'es6'});
goog.addDependency('ui/ac/renderer_test.js', ['goog.ui.ac.RendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.fx.dom.FadeInAndShow', 'goog.fx.dom.FadeOutAndHide', 'goog.string', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.ac.AutoComplete', 'goog.ui.ac.Renderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/renderoptions.js', ['goog.ui.ac.RenderOptions'], [], {});
goog.addDependency('ui/ac/richinputhandler.js', ['goog.ui.ac.RichInputHandler'], ['goog.ui.ac.InputHandler'], {});
goog.addDependency('ui/ac/richremote.js', ['goog.ui.ac.RichRemote'], ['goog.ui.ac.AutoComplete', 'goog.ui.ac.Remote', 'goog.ui.ac.Renderer', 'goog.ui.ac.RichInputHandler', 'goog.ui.ac.RichRemoteArrayMatcher'], {});
goog.addDependency('ui/ac/richremotearraymatcher.js', ['goog.ui.ac.RichRemoteArrayMatcher'], ['goog.dom', 'goog.ui.ac.RemoteArrayMatcher'], {});
goog.addDependency('ui/ac/richremotearraymatcher_test.js', ['goog.ui.ac.RichRemoteArrayMatcherTest'], ['goog.net.XhrIo', 'goog.testing.MockControl', 'goog.testing.net.XhrIo', 'goog.testing.testSuite', 'goog.ui.ac.RichRemoteArrayMatcher'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/activitymonitor.js', ['goog.ui.ActivityMonitor'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType'], {});
goog.addDependency('ui/activitymonitor_test.js', ['goog.ui.ActivityMonitorTest'], ['goog.dom', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.ActivityMonitor'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/advancedtooltip.js', ['goog.ui.AdvancedTooltip'], ['goog.events', 'goog.events.EventType', 'goog.math.Box', 'goog.math.Coordinate', 'goog.style', 'goog.ui.Tooltip', 'goog.userAgent'], {});
goog.addDependency('ui/advancedtooltip_test.js', ['goog.ui.AdvancedTooltipTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events.Event', 'goog.events.EventType', 'goog.math.Box', 'goog.math.Coordinate', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.AdvancedTooltip', 'goog.ui.Tooltip', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/animatedzippy.js', ['goog.ui.AnimatedZippy'], ['goog.a11y.aria.Role', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.fx.easing', 'goog.ui.Zippy', 'goog.ui.ZippyEvent'], {});
goog.addDependency('ui/animatedzippy_test.js', ['goog.ui.AnimatedZippyTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.events', 'goog.functions', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.ui.AnimatedZippy', 'goog.ui.Zippy'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/attachablemenu.js', ['goog.ui.AttachableMenu'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events.Event', 'goog.events.KeyCodes', 'goog.string', 'goog.style', 'goog.ui.ItemEvent', 'goog.ui.MenuBase', 'goog.ui.PopupBase', 'goog.userAgent'], {});
goog.addDependency('ui/bidiinput.js', ['goog.ui.BidiInput'], ['goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.InputHandler', 'goog.i18n.bidi', 'goog.ui.Component'], {});
goog.addDependency('ui/bidiinput_test.js', ['goog.ui.BidiInputTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.BidiInput'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/bubble.js', ['goog.ui.Bubble'], ['goog.Timer', 'goog.dom.safe', 'goog.events', 'goog.events.EventType', 'goog.html.SafeHtml', 'goog.math.Box', 'goog.positioning', 'goog.positioning.AbsolutePosition', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.positioning.CornerBit', 'goog.string.Const', 'goog.style', 'goog.ui.Component', 'goog.ui.Popup'], {});
goog.addDependency('ui/button.js', ['goog.ui.Button', 'goog.ui.Button.Side'], ['goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.ui.ButtonRenderer', 'goog.ui.ButtonSide', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.NativeButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/button_test.js', ['goog.ui.ButtonTest'], ['goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Button', 'goog.ui.ButtonRenderer', 'goog.ui.ButtonSide', 'goog.ui.Component', 'goog.ui.NativeButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/buttonrenderer.js', ['goog.ui.ButtonRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.ui.ButtonSide', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/buttonrenderer_test.js', ['goog.ui.ButtonRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Button', 'goog.ui.ButtonRenderer', 'goog.ui.ButtonSide', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/buttonside.js', ['goog.ui.ButtonSide'], [], {});
goog.addDependency('ui/charcounter.js', ['goog.ui.CharCounter', 'goog.ui.CharCounter.Display'], ['goog.dom', 'goog.events', 'goog.events.EventTarget', 'goog.events.InputHandler'], {});
goog.addDependency('ui/charcounter_test.js', ['goog.ui.CharCounterTest'], ['goog.dom', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.ui.CharCounter', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/charpicker.js', ['goog.ui.CharPicker'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.i18n.CharListDecompressor', 'goog.i18n.CharPickerData', 'goog.i18n.uChar', 'goog.i18n.uChar.NameFetcher', 'goog.structs.Set', 'goog.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.ContainerScroller', 'goog.ui.FlatButtonRenderer', 'goog.ui.HoverCard', 'goog.ui.LabelInput', 'goog.ui.Menu', 'goog.ui.MenuButton', 'goog.ui.MenuItem', 'goog.ui.Tooltip'], {});
goog.addDependency('ui/charpicker_test.js', ['goog.ui.CharPickerTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dispose', 'goog.dom', 'goog.events.Event', 'goog.events.EventType', 'goog.i18n.CharPickerData', 'goog.i18n.uChar.NameFetcher', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.ui.CharPicker', 'goog.ui.FlatButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/checkbox.js', ['goog.ui.Checkbox', 'goog.ui.Checkbox.State'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.string', 'goog.ui.CheckboxRenderer', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.registry'], {});
goog.addDependency('ui/checkbox_test.js', ['goog.ui.CheckboxTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.KeyCodes', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Checkbox', 'goog.ui.CheckboxRenderer', 'goog.ui.Component', 'goog.ui.ControlRenderer', 'goog.ui.decorate'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/checkboxmenuitem.js', ['goog.ui.CheckBoxMenuItem'], ['goog.ui.MenuItem', 'goog.ui.registry'], {});
goog.addDependency('ui/checkboxrenderer.js', ['goog.ui.CheckboxRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.object', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/colormenubutton.js', ['goog.ui.ColorMenuButton'], ['goog.array', 'goog.object', 'goog.ui.ColorMenuButtonRenderer', 'goog.ui.ColorPalette', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.MenuButton', 'goog.ui.registry'], {});
goog.addDependency('ui/colormenubuttonrenderer.js', ['goog.ui.ColorMenuButtonRenderer'], ['goog.asserts', 'goog.color', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.MenuButtonRenderer', 'goog.userAgent'], {});
goog.addDependency('ui/colormenubuttonrenderer_test.js', ['goog.ui.ColorMenuButtonTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.testing.ui.RendererHarness', 'goog.testing.ui.rendererasserts', 'goog.ui.ColorMenuButton', 'goog.ui.ColorMenuButtonRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/colorpalette.js', ['goog.ui.ColorPalette'], ['goog.array', 'goog.color', 'goog.dom.TagName', 'goog.style', 'goog.ui.Palette', 'goog.ui.PaletteRenderer'], {});
goog.addDependency('ui/colorpalette_test.js', ['goog.ui.ColorPaletteTest'], ['goog.color', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.ColorPalette'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/colorpicker.js', ['goog.ui.ColorPicker', 'goog.ui.ColorPicker.EventType'], ['goog.ui.ColorPalette', 'goog.ui.Component'], {});
goog.addDependency('ui/combobox.js', ['goog.ui.ComboBox', 'goog.ui.ComboBoxItem'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.log', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.ItemEvent', 'goog.ui.LabelInput', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.MenuSeparator', 'goog.ui.registry', 'goog.userAgent'], {});
goog.addDependency('ui/combobox_test.js', ['goog.ui.ComboBoxTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.KeyCodes', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.ComboBox', 'goog.ui.ComboBoxItem', 'goog.ui.Component', 'goog.ui.ControlRenderer', 'goog.ui.LabelInput', 'goog.ui.Menu', 'goog.ui.MenuItem'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/component.js', ['goog.ui.Component', 'goog.ui.Component.Error', 'goog.ui.Component.EventType', 'goog.ui.Component.State'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.object', 'goog.style', 'goog.ui.IdGenerator'], {});
goog.addDependency('ui/component_test.js', ['goog.ui.ComponentTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.events.EventTarget', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.Component'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/componentutil.js', ['goog.ui.ComponentUtil'], ['goog.events.MouseAsMouseEventType', 'goog.events.MouseEvents', 'goog.events.PointerAsMouseEventType'], {});
goog.addDependency('ui/componentutil_test.js', ['goog.ui.ComponentUtilTest'], ['goog.events.MouseAsMouseEventType', 'goog.events.PointerAsMouseEventType', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.ComponentUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/container.js', ['goog.ui.Container', 'goog.ui.Container.EventType', 'goog.ui.Container.Orientation'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.object', 'goog.style', 'goog.ui.Component', 'goog.ui.ComponentUtil', 'goog.ui.ContainerRenderer', 'goog.ui.Control'], {});
goog.addDependency('ui/container_test.js', ['goog.ui.ContainerTest'], ['goog.a11y.aria', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.KeyCodes', 'goog.events.KeyEvent', 'goog.events.PointerFallbackEventType', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Container', 'goog.ui.Control'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/containerrenderer.js', ['goog.ui.ContainerRenderer'], ['goog.a11y.aria', 'goog.array', 'goog.asserts', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.string', 'goog.style', 'goog.ui.registry', 'goog.userAgent'], {});
goog.addDependency('ui/containerrenderer_test.js', ['goog.ui.ContainerRendererTest'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Container', 'goog.ui.ContainerRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/containerscroller.js', ['goog.ui.ContainerScroller'], ['goog.Disposable', 'goog.Timer', 'goog.events.EventHandler', 'goog.style', 'goog.ui.Component', 'goog.ui.Container'], {});
goog.addDependency('ui/containerscroller_test.js', ['goog.ui.ContainerScrollerTest'], ['goog.dom', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Container', 'goog.ui.ContainerScroller'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/control.js', ['goog.ui.Control'], ['goog.Disposable', 'goog.array', 'goog.dom', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.string', 'goog.ui.Component', 'goog.ui.ComponentUtil', 'goog.ui.ControlContent', 'goog.ui.ControlRenderer', 'goog.ui.registry', 'goog.userAgent'], {});
goog.addDependency('ui/control_test.js', ['goog.ui.ControlTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.events.PointerFallbackEventType', 'goog.html.testing', 'goog.object', 'goog.string', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.ControlRenderer', 'goog.ui.registry', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/controlcontent.js', ['goog.ui.ControlContent'], [], {});
goog.addDependency('ui/controlrenderer.js', ['goog.ui.ControlRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.object', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.ControlContent', 'goog.userAgent'], {});
goog.addDependency('ui/controlrenderer_test.js', ['goog.ui.ControlRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.object', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.ControlRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/cookieeditor.js', ['goog.ui.CookieEditor'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.events.EventType', 'goog.net.cookies', 'goog.string', 'goog.style', 'goog.ui.Component'], {});
goog.addDependency('ui/cookieeditor_test.js', ['goog.ui.CookieEditorTest'], ['goog.dom', 'goog.events.Event', 'goog.events.EventType', 'goog.net.cookies', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.CookieEditor'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/css3buttonrenderer.js', ['goog.ui.Css3ButtonRenderer'], ['goog.asserts', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Button', 'goog.ui.ButtonRenderer', 'goog.ui.Component', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.registry'], {});
goog.addDependency('ui/css3menubuttonrenderer.js', ['goog.ui.Css3MenuButtonRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.MenuButton', 'goog.ui.MenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/cssnames.js', ['goog.ui.INLINE_BLOCK_CLASSNAME'], [], {});
goog.addDependency('ui/custombutton.js', ['goog.ui.CustomButton'], ['goog.ui.Button', 'goog.ui.CustomButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/custombuttonrenderer.js', ['goog.ui.CustomButtonRenderer'], ['goog.a11y.aria.Role', 'goog.asserts', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.string', 'goog.ui.ButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME'], {});
goog.addDependency('ui/customcolorpalette.js', ['goog.ui.CustomColorPalette'], ['goog.color', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.ColorPalette', 'goog.ui.Component'], {});
goog.addDependency('ui/customcolorpalette_test.js', ['goog.ui.CustomColorPaletteTest'], ['goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.ui.CustomColorPalette'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/datepicker.js', ['goog.ui.DatePicker', 'goog.ui.DatePicker.Events', 'goog.ui.DatePickerEvent'], ['goog.a11y.aria', 'goog.asserts', 'goog.date.Date', 'goog.date.DateRange', 'goog.date.Interval', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyHandler', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimePatterns', 'goog.i18n.DateTimeSymbols', 'goog.style', 'goog.ui.Component', 'goog.ui.DefaultDatePickerRenderer', 'goog.ui.IdGenerator'], {});
goog.addDependency('ui/datepicker_test.js', ['goog.ui.DatePickerTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.date.Date', 'goog.date.DateRange', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.KeyCodes', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbols_en_US', 'goog.i18n.DateTimeSymbols_zh_HK', 'goog.style', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.DatePicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/datepickerrenderer.js', ['goog.ui.DatePickerRenderer'], [], {});
goog.addDependency('ui/decorate.js', ['goog.ui.decorate'], ['goog.ui.registry'], {});
goog.addDependency('ui/decorate_test.js', ['goog.ui.decorateTest'], ['goog.testing.testSuite', 'goog.ui.decorate', 'goog.ui.registry'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/defaultdatepickerrenderer.js', ['goog.ui.DefaultDatePickerRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.ui.DatePickerRenderer'], {});
goog.addDependency('ui/dialog.js', ['goog.ui.Dialog', 'goog.ui.Dialog.ButtonSet', 'goog.ui.Dialog.ButtonSet.DefaultButtons', 'goog.ui.Dialog.DefaultButtonCaptions', 'goog.ui.Dialog.DefaultButtonKeys', 'goog.ui.Dialog.Event', 'goog.ui.Dialog.EventType'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.safe', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.Keys', 'goog.fx.Dragger', 'goog.html.SafeHtml', 'goog.math.Rect', 'goog.string', 'goog.structs.Map', 'goog.style', 'goog.ui.ModalPopup'], {});
goog.addDependency('ui/dialog_test.js', ['goog.ui.DialogTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.fx.css3', 'goog.html.SafeHtml', 'goog.html.testing', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Dialog', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/dimensionpicker.js', ['goog.ui.DimensionPicker'], ['goog.events.BrowserEvent.PointerType', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Size', 'goog.ui.Component', 'goog.ui.ComponentUtil', 'goog.ui.Control', 'goog.ui.DimensionPickerRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/dimensionpicker_test.js', ['goog.ui.DimensionPickerTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.math.Size', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.DimensionPicker', 'goog.ui.DimensionPickerRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/dimensionpickerrenderer.js', ['goog.ui.DimensionPickerRenderer'], ['goog.a11y.aria.Announcer', 'goog.a11y.aria.LivePriority', 'goog.dom', 'goog.dom.TagName', 'goog.i18n.bidi', 'goog.style', 'goog.ui.ControlRenderer', 'goog.userAgent'], {});
goog.addDependency('ui/dimensionpickerrenderer_test.js', ['goog.ui.DimensionPickerRendererTest'], ['goog.a11y.aria.LivePriority', 'goog.array', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.DimensionPicker', 'goog.ui.DimensionPickerRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/dragdropdetector.js', ['goog.ui.DragDropDetector', 'goog.ui.DragDropDetector.EventType', 'goog.ui.DragDropDetector.ImageDropEvent', 'goog.ui.DragDropDetector.LinkDropEvent'], ['goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.math.Coordinate', 'goog.string', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('ui/drilldownrow.js', ['goog.ui.DrilldownRow'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.string.Unicode', 'goog.ui.Component'], {});
goog.addDependency('ui/drilldownrow_test.js', ['goog.ui.DrilldownRowTest'], ['goog.dom', 'goog.dom.TagName', 'goog.html.SafeHtml', 'goog.testing.testSuite', 'goog.ui.DrilldownRow'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/editor/abstractdialog.js', ['goog.ui.editor.AbstractDialog', 'goog.ui.editor.AbstractDialog.Builder', 'goog.ui.editor.AbstractDialog.EventType'], ['goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events.EventTarget', 'goog.string', 'goog.ui.Dialog', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/editor/abstractdialog_test.js', ['goog.ui.editor.AbstractDialogTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.KeyCodes', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.testSuite', 'goog.ui.editor.AbstractDialog', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/editor/bubble.js', ['goog.ui.editor.Bubble'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.ViewportSizeMonitor', 'goog.dom.classlist', 'goog.editor.style', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.functions', 'goog.log', 'goog.math.Box', 'goog.object', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.PopupBase', 'goog.userAgent'], {});
goog.addDependency('ui/editor/bubble_test.js', ['goog.ui.editor.BubbleTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.positioning.Corner', 'goog.positioning.OverflowStatus', 'goog.string', 'goog.style', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.editor.Bubble', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/editor/defaulttoolbar.js', ['goog.ui.editor.ButtonDescriptor', 'goog.ui.editor.DefaultToolbar'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.editor.Command', 'goog.style', 'goog.ui.editor.ToolbarFactory', 'goog.ui.editor.messages', 'goog.userAgent'], {});
goog.addDependency('ui/editor/linkdialog.js', ['goog.ui.editor.LinkDialog', 'goog.ui.editor.LinkDialog.BeforeTestLinkEvent', 'goog.ui.editor.LinkDialog.EventType', 'goog.ui.editor.LinkDialog.OkEvent'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.editor.BrowserFeature', 'goog.editor.Link', 'goog.editor.focus', 'goog.editor.node', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.InputHandler', 'goog.html.SafeHtml', 'goog.html.SafeHtmlFormatter', 'goog.string', 'goog.string.Unicode', 'goog.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.LinkButtonRenderer', 'goog.ui.editor.AbstractDialog', 'goog.ui.editor.TabPane', 'goog.ui.editor.messages', 'goog.userAgent', 'goog.window'], {});
goog.addDependency('ui/editor/linkdialog_test.js', ['goog.ui.editor.LinkDialogTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Link', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.style', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.dom', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.testSuite', 'goog.ui.editor.AbstractDialog', 'goog.ui.editor.LinkDialog', 'goog.ui.editor.messages', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/editor/messages.js', ['goog.ui.editor.messages'], ['goog.html.SafeHtmlFormatter'], {});
goog.addDependency('ui/editor/tabpane.js', ['goog.ui.editor.TabPane'], ['goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.style', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.Tab', 'goog.ui.TabBar'], {});
goog.addDependency('ui/editor/toolbarcontroller.js', ['goog.ui.editor.ToolbarController'], ['goog.editor.Field', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.ui.Component'], {});
goog.addDependency('ui/editor/toolbarfactory.js', ['goog.ui.editor.ToolbarFactory'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.string', 'goog.string.Unicode', 'goog.style', 'goog.ui.Component', 'goog.ui.Container', 'goog.ui.Option', 'goog.ui.Toolbar', 'goog.ui.ToolbarButton', 'goog.ui.ToolbarColorMenuButton', 'goog.ui.ToolbarMenuButton', 'goog.ui.ToolbarRenderer', 'goog.ui.ToolbarSelect', 'goog.userAgent'], {});
goog.addDependency('ui/editor/toolbarfactory_test.js', ['goog.ui.editor.ToolbarFactoryTest'], ['goog.dom', 'goog.testing.ExpectedFailures', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.ui.editor.ToolbarFactory', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/emoji.js', ['goog.ui.emoji.Emoji'], [], {});
goog.addDependency('ui/emoji/emojipalette.js', ['goog.ui.emoji.EmojiPalette'], ['goog.events.EventType', 'goog.net.ImageLoader', 'goog.ui.Palette', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPaletteRenderer'], {});
goog.addDependency('ui/emoji/emojipaletterenderer.js', ['goog.ui.emoji.EmojiPaletteRenderer'], ['goog.a11y.aria', 'goog.asserts', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.style', 'goog.ui.PaletteRenderer', 'goog.ui.emoji.Emoji'], {});
goog.addDependency('ui/emoji/emojipicker.js', ['goog.ui.emoji.EmojiPicker'], ['goog.dom.TagName', 'goog.style', 'goog.ui.Component', 'goog.ui.TabPane', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPalette', 'goog.ui.emoji.EmojiPaletteRenderer', 'goog.ui.emoji.ProgressiveEmojiPaletteRenderer'], {});
goog.addDependency('ui/emoji/emojipicker_test.js', ['goog.ui.emoji.EmojiPickerTest'], ['goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventHandler', 'goog.style', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPicker', 'goog.ui.emoji.SpriteInfo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/fast_nonprogressive_emojipicker_test.js', ['goog.ui.emoji.FastNonProgressiveEmojiPickerTest'], ['goog.Promise', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.net.EventType', 'goog.style', 'goog.testing.TestCase', 'goog.testing.testSuite', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPicker', 'goog.ui.emoji.SpriteInfo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/fast_progressive_emojipicker_test.js', ['goog.ui.emoji.FastProgressiveEmojiPickerTest'], ['goog.Promise', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.net.EventType', 'goog.style', 'goog.testing.testSuite', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPicker', 'goog.ui.emoji.SpriteInfo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/popupemojipicker.js', ['goog.ui.emoji.PopupEmojiPicker'], ['goog.events.EventType', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.ui.Component', 'goog.ui.Popup', 'goog.ui.emoji.EmojiPicker'], {});
goog.addDependency('ui/emoji/popupemojipicker_test.js', ['goog.ui.emoji.PopupEmojiPickerTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.emoji.PopupEmojiPicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/progressiveemojipaletterenderer.js', ['goog.ui.emoji.ProgressiveEmojiPaletteRenderer'], ['goog.dom.TagName', 'goog.style', 'goog.ui.emoji.EmojiPaletteRenderer'], {});
goog.addDependency('ui/emoji/spriteinfo.js', ['goog.ui.emoji.SpriteInfo'], [], {'lang': 'es6'});
goog.addDependency('ui/emoji/spriteinfo_test.js', ['goog.ui.emoji.SpriteInfoTest'], ['goog.testing.testSuite', 'goog.ui.emoji.SpriteInfo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/filteredmenu.js', ['goog.ui.FilteredMenu'], ['goog.a11y.aria', 'goog.a11y.aria.AutoCompleteValues', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.FilterObservingMenuItem', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.userAgent'], {});
goog.addDependency('ui/filteredmenu_test.js', ['goog.ui.FilteredMenuTest'], ['goog.a11y.aria', 'goog.a11y.aria.AutoCompleteValues', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Rect', 'goog.style', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.FilteredMenu', 'goog.ui.MenuItem'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/filterobservingmenuitem.js', ['goog.ui.FilterObservingMenuItem'], ['goog.ui.FilterObservingMenuItemRenderer', 'goog.ui.MenuItem', 'goog.ui.registry'], {});
goog.addDependency('ui/filterobservingmenuitemrenderer.js', ['goog.ui.FilterObservingMenuItemRenderer'], ['goog.ui.MenuItemRenderer'], {});
goog.addDependency('ui/flatbuttonrenderer.js', ['goog.ui.FlatButtonRenderer'], ['goog.a11y.aria.Role', 'goog.asserts', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Button', 'goog.ui.ButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.registry'], {});
goog.addDependency('ui/flatmenubuttonrenderer.js', ['goog.ui.FlatMenuButtonRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.ui.FlatButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.Menu', 'goog.ui.MenuButton', 'goog.ui.MenuRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/formpost.js', ['goog.ui.FormPost'], ['goog.array', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.ui.Component'], {});
goog.addDependency('ui/formpost_test.js', ['goog.ui.FormPostTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.object', 'goog.testing.testSuite', 'goog.ui.FormPost', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/gauge.js', ['goog.ui.Gauge', 'goog.ui.GaugeColoredRange'], ['goog.a11y.aria', 'goog.asserts', 'goog.dom.TagName', 'goog.events', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.fx.easing', 'goog.graphics', 'goog.graphics.Font', 'goog.graphics.Path', 'goog.graphics.SolidFill', 'goog.math', 'goog.ui.Component', 'goog.ui.GaugeTheme'], {});
goog.addDependency('ui/gaugetheme.js', ['goog.ui.GaugeTheme'], ['goog.graphics.LinearGradient', 'goog.graphics.SolidFill', 'goog.graphics.Stroke'], {});
goog.addDependency('ui/hovercard.js', ['goog.ui.HoverCard', 'goog.ui.HoverCard.EventType', 'goog.ui.HoverCard.TriggerEvent'], ['goog.array', 'goog.dom', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.ui.AdvancedTooltip', 'goog.ui.PopupBase', 'goog.ui.Tooltip'], {});
goog.addDependency('ui/hovercard_test.js', ['goog.ui.HoverCardTest'], ['goog.dom', 'goog.events', 'goog.math.Coordinate', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.HoverCard'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/hsvapalette.js', ['goog.ui.HsvaPalette'], ['goog.array', 'goog.color.alpha', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.style', 'goog.ui.Component', 'goog.ui.HsvPalette'], {});
goog.addDependency('ui/hsvapalette_test.js', ['goog.ui.HsvaPaletteTest'], ['goog.color.alpha', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.Event', 'goog.math.Coordinate', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.HsvaPalette', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/hsvpalette.js', ['goog.ui.HsvPalette'], ['goog.color', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.style', 'goog.style.bidi', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/hsvpalette_test.js', ['goog.ui.HsvPaletteTest'], ['goog.color', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.math.Coordinate', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.HsvPalette', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/idgenerator.js', ['goog.ui.IdGenerator'], [], {});
goog.addDependency('ui/idletimer.js', ['goog.ui.IdleTimer'], ['goog.Timer', 'goog.events', 'goog.events.EventTarget', 'goog.structs.Set', 'goog.ui.ActivityMonitor'], {});
goog.addDependency('ui/idletimer_test.js', ['goog.ui.IdleTimerTest'], ['goog.events', 'goog.testing.MockClock', 'goog.testing.testSuite', 'goog.ui.IdleTimer', 'goog.ui.MockActivityMonitor'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/iframemask.js', ['goog.ui.IframeMask'], ['goog.Disposable', 'goog.Timer', 'goog.dom', 'goog.dom.iframe', 'goog.events.EventHandler', 'goog.structs.Pool', 'goog.style'], {});
goog.addDependency('ui/iframemask_test.js', ['goog.ui.IframeMaskTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.iframe', 'goog.structs.Pool', 'goog.style', 'goog.testing.MockClock', 'goog.testing.StrictMock', 'goog.testing.testSuite', 'goog.ui.IframeMask', 'goog.ui.Popup', 'goog.ui.PopupBase', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/imagelessbuttonrenderer.js', ['goog.ui.ImagelessButtonRenderer'], ['goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.CustomButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.registry'], {});
goog.addDependency('ui/imagelessmenubuttonrenderer.js', ['goog.ui.ImagelessMenuButtonRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.MenuButton', 'goog.ui.MenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/inputdatepicker.js', ['goog.ui.InputDatePicker'], ['goog.date.DateTime', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.i18n.DateTimeParse', 'goog.string', 'goog.ui.Component', 'goog.ui.DatePicker', 'goog.ui.LabelInput', 'goog.ui.PopupBase', 'goog.ui.PopupDatePicker'], {});
goog.addDependency('ui/inputdatepicker_test.js', ['goog.ui.InputDatePickerTest'], ['goog.dom', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeParse', 'goog.testing.testSuite', 'goog.ui.InputDatePicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/itemevent.js', ['goog.ui.ItemEvent'], ['goog.events.Event'], {});
goog.addDependency('ui/keyboardeventdata.js', ['goog.ui.KeyboardEventData'], ['goog.asserts', 'goog.events.BrowserEvent'], {'lang': 'es6'});
goog.addDependency('ui/keyboardshortcuthandler.js', ['goog.ui.KeyboardShortcutEvent', 'goog.ui.KeyboardShortcutHandler', 'goog.ui.KeyboardShortcutHandler.EventType', 'goog.ui.KeyboardShortcutHandler.Modifiers'], ['goog.array', 'goog.asserts', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyNames', 'goog.events.Keys', 'goog.object', 'goog.ui.KeyboardEventData', 'goog.ui.SyntheticKeyboardEvent', 'goog.userAgent'], {});
goog.addDependency('ui/keyboardshortcuthandler_test.js', ['goog.ui.KeyboardShortcutHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.KeyboardShortcutHandler', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/labelinput.js', ['goog.ui.LabelInput'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/labelinput_test.js', ['goog.ui.LabelInputTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.classlist', 'goog.events.EventType', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.LabelInput', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/linkbuttonrenderer.js', ['goog.ui.LinkButtonRenderer'], ['goog.ui.Button', 'goog.ui.FlatButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/media/flashobject.js', ['goog.ui.media.FlashObject', 'goog.ui.media.FlashObject.ScriptAccessLevel', 'goog.ui.media.FlashObject.Wmodes'], ['goog.asserts', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.html.TrustedResourceUrl', 'goog.html.flash', 'goog.log', 'goog.object', 'goog.string', 'goog.structs.Map', 'goog.style', 'goog.ui.Component', 'goog.userAgent', 'goog.userAgent.flash'], {});
goog.addDependency('ui/media/flashobject_test.js', ['goog.ui.media.FlashObjectTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.html.testing', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/flickr.js', ['goog.ui.media.FlickrSet', 'goog.ui.media.FlickrSetModel'], ['goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/flickr_test.js', ['goog.ui.media.FlickrSetTest'], ['goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.FlickrSet', 'goog.ui.media.FlickrSetModel', 'goog.ui.media.Media'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/googlevideo.js', ['goog.ui.media.GoogleVideo', 'goog.ui.media.GoogleVideoModel'], ['goog.html.TrustedResourceUrl', 'goog.string', 'goog.string.Const', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/googlevideo_test.js', ['goog.ui.media.GoogleVideoTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.GoogleVideo', 'goog.ui.media.GoogleVideoModel', 'goog.ui.media.Media'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/media.js', ['goog.ui.media.Media', 'goog.ui.media.MediaRenderer'], ['goog.asserts', 'goog.dom.TagName', 'goog.style', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/media/media_test.js', ['goog.ui.media.MediaTest'], ['goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.math.Size', 'goog.testing.testSuite', 'goog.ui.ControlRenderer', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/mediamodel.js', ['goog.ui.media.MediaModel', 'goog.ui.media.MediaModel.Category', 'goog.ui.media.MediaModel.Credit', 'goog.ui.media.MediaModel.Credit.Role', 'goog.ui.media.MediaModel.Credit.Scheme', 'goog.ui.media.MediaModel.Medium', 'goog.ui.media.MediaModel.MimeType', 'goog.ui.media.MediaModel.Player', 'goog.ui.media.MediaModel.SubTitle', 'goog.ui.media.MediaModel.Thumbnail'], ['goog.array', 'goog.html.TrustedResourceUrl'], {});
goog.addDependency('ui/media/mediamodel_test.js', ['goog.ui.media.MediaModelTest'], ['goog.testing.testSuite', 'goog.ui.media.MediaModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/mp3.js', ['goog.ui.media.Mp3'], ['goog.string', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/mp3_test.js', ['goog.ui.media.Mp3Test'], ['goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.Mp3'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/photo.js', ['goog.ui.media.Photo'], ['goog.dom.TagName', 'goog.ui.media.Media', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/photo_test.js', ['goog.ui.media.PhotoTest'], ['goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.media.MediaModel', 'goog.ui.media.Photo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/picasa.js', ['goog.ui.media.PicasaAlbum', 'goog.ui.media.PicasaAlbumModel'], ['goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/picasa_test.js', ['goog.ui.media.PicasaTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.PicasaAlbum', 'goog.ui.media.PicasaAlbumModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/vimeo.js', ['goog.ui.media.Vimeo', 'goog.ui.media.VimeoModel'], ['goog.html.TrustedResourceUrl', 'goog.string', 'goog.string.Const', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/vimeo_test.js', ['goog.ui.media.VimeoTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.Vimeo', 'goog.ui.media.VimeoModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/youtube.js', ['goog.ui.media.Youtube', 'goog.ui.media.YoutubeModel'], ['goog.dom.TagName', 'goog.html.TrustedResourceUrl', 'goog.string', 'goog.string.Const', 'goog.ui.Component', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/youtube_test.js', ['goog.ui.media.YoutubeTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.Youtube', 'goog.ui.media.YoutubeModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menu.js', ['goog.ui.Menu', 'goog.ui.Menu.EventType'], ['goog.dom.TagName', 'goog.math.Coordinate', 'goog.string', 'goog.style', 'goog.ui.Component.EventType', 'goog.ui.Component.State', 'goog.ui.Container', 'goog.ui.Container.Orientation', 'goog.ui.MenuHeader', 'goog.ui.MenuItem', 'goog.ui.MenuRenderer', 'goog.ui.MenuSeparator'], {});
goog.addDependency('ui/menu_test.js', ['goog.ui.MenuTest'], ['goog.dom', 'goog.events', 'goog.math.Coordinate', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Menu'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menubar.js', ['goog.ui.menuBar'], ['goog.ui.Container', 'goog.ui.MenuBarRenderer'], {});
goog.addDependency('ui/menubardecorator.js', ['goog.ui.menuBarDecorator'], ['goog.ui.MenuBarRenderer', 'goog.ui.menuBar', 'goog.ui.registry'], {});
goog.addDependency('ui/menubarrenderer.js', ['goog.ui.MenuBarRenderer'], ['goog.a11y.aria.Role', 'goog.ui.Container', 'goog.ui.ContainerRenderer'], {});
goog.addDependency('ui/menubase.js', ['goog.ui.MenuBase'], ['goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyHandler', 'goog.ui.Popup'], {});
goog.addDependency('ui/menubutton.js', ['goog.ui.MenuButton'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.positioning.Overflow', 'goog.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.IdGenerator', 'goog.ui.Menu', 'goog.ui.MenuButtonRenderer', 'goog.ui.MenuItem', 'goog.ui.MenuRenderer', 'goog.ui.SubMenu', 'goog.ui.registry', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6'});
goog.addDependency('ui/menubutton_test.js', ['goog.ui.MenuButtonTest'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.positioning.Overflow', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.MenuButton', 'goog.ui.MenuItem', 'goog.ui.SubMenu', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menubuttonrenderer.js', ['goog.ui.MenuButtonRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.ui.CustomButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.Menu', 'goog.ui.MenuRenderer'], {});
goog.addDependency('ui/menubuttonrenderer_test.js', ['goog.ui.MenuButtonRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.MenuButton', 'goog.ui.MenuButtonRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menuheader.js', ['goog.ui.MenuHeader'], ['goog.ui.Component', 'goog.ui.Control', 'goog.ui.MenuHeaderRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/menuheaderrenderer.js', ['goog.ui.MenuHeaderRenderer'], ['goog.ui.ControlRenderer'], {});
goog.addDependency('ui/menuitem.js', ['goog.ui.MenuItem'], ['goog.a11y.aria.Role', 'goog.array', 'goog.dom', 'goog.dom.classlist', 'goog.math.Coordinate', 'goog.string', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.MenuItemRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/menuitem_test.js', ['goog.ui.MenuItemTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.KeyCodes', 'goog.html.testing', 'goog.math.Coordinate', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.MenuItemRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menuitemrenderer.js', ['goog.ui.MenuItemRenderer'], ['goog.a11y.aria.Role', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/menuitemrenderer_test.js', ['goog.ui.MenuItemRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.MenuItemRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menurenderer.js', ['goog.ui.MenuRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.ui.ContainerRenderer', 'goog.ui.Separator'], {});
goog.addDependency('ui/menuseparator.js', ['goog.ui.MenuSeparator'], ['goog.ui.MenuSeparatorRenderer', 'goog.ui.Separator', 'goog.ui.registry'], {});
goog.addDependency('ui/menuseparatorrenderer.js', ['goog.ui.MenuSeparatorRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/menuseparatorrenderer_test.js', ['goog.ui.MenuSeparatorRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.MenuSeparator', 'goog.ui.MenuSeparatorRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/mockactivitymonitor.js', ['goog.ui.MockActivityMonitor'], ['goog.events.EventType', 'goog.ui.ActivityMonitor'], {});
goog.addDependency('ui/mockactivitymonitor_test.js', ['goog.ui.MockActivityMonitorTest'], ['goog.events', 'goog.functions', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.ActivityMonitor', 'goog.ui.MockActivityMonitor'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/modalariavisibilityhelper.js', ['goog.ui.ModalAriaVisibilityHelper'], ['goog.a11y.aria', 'goog.a11y.aria.State'], {});
goog.addDependency('ui/modalariavisibilityhelper_test.js', ['goog.ui.ModalAriaVisibilityHelperTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.string', 'goog.testing.testSuite', 'goog.ui.ModalAriaVisibilityHelper'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/modalpopup.js', ['goog.ui.ModalPopup'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.animationFrame', 'goog.dom.classlist', 'goog.dom.iframe', 'goog.events', 'goog.events.EventType', 'goog.events.FocusHandler', 'goog.fx.Transition', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.ModalAriaVisibilityHelper', 'goog.ui.PopupBase', 'goog.userAgent'], {});
goog.addDependency('ui/modalpopup_test.js', ['goog.ui.ModalPopupTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dispose', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.fx.Transition', 'goog.fx.css3', 'goog.string', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.ModalPopup', 'goog.ui.PopupBase'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/nativebuttonrenderer.js', ['goog.ui.NativeButtonRenderer'], ['goog.asserts', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.ui.ButtonRenderer', 'goog.ui.Component'], {});
goog.addDependency('ui/nativebuttonrenderer_test.js', ['goog.ui.NativeButtonRendererTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.testing.ExpectedFailures', 'goog.testing.events', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.NativeButtonRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/option.js', ['goog.ui.Option'], ['goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.registry'], {});
goog.addDependency('ui/palette.js', ['goog.ui.Palette'], ['goog.array', 'goog.dom', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Size', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.PaletteRenderer', 'goog.ui.SelectionModel'], {});
goog.addDependency('ui/palette_test.js', ['goog.ui.PaletteTest'], ['goog.a11y.aria', 'goog.dom', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyEvent', 'goog.testing.events.Event', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Container', 'goog.ui.Palette'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/paletterenderer.js', ['goog.ui.PaletteRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeIterator', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.dataset', 'goog.iter', 'goog.style', 'goog.ui.ControlRenderer', 'goog.userAgent'], {});
goog.addDependency('ui/paletterenderer_test.js', ['goog.ui.PaletteRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.Palette', 'goog.ui.PaletteRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/plaintextspellchecker.js', ['goog.ui.PlainTextSpellChecker'], ['goog.Timer', 'goog.a11y.aria', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.spell.SpellCheck', 'goog.style', 'goog.ui.AbstractSpellChecker', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/plaintextspellchecker_test.js', ['goog.ui.PlainTextSpellCheckerTest'], ['goog.Timer', 'goog.dom', 'goog.events.KeyCodes', 'goog.spell.SpellCheck', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.AbstractSpellChecker', 'goog.ui.PlainTextSpellChecker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popup.js', ['goog.ui.Popup'], ['goog.math.Box', 'goog.positioning.AbstractPosition', 'goog.positioning.Corner', 'goog.style', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/popup_test.js', ['goog.ui.PopupTest'], ['goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.style', 'goog.testing.testSuite', 'goog.ui.Popup', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popupbase.js', ['goog.ui.PopupBase', 'goog.ui.PopupBase.EventType', 'goog.ui.PopupBase.Type'], ['goog.Timer', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.fx.Transition', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('ui/popupbase_test.js', ['goog.ui.PopupBaseTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.fx.Transition', 'goog.fx.css3', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.PopupBase'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popupcolorpicker.js', ['goog.ui.PopupColorPicker'], ['goog.asserts', 'goog.dom.classlist', 'goog.events.EventType', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.ui.ColorPicker', 'goog.ui.Component', 'goog.ui.Popup'], {});
goog.addDependency('ui/popupcolorpicker_test.js', ['goog.ui.PopupColorPickerTest'], ['goog.dom', 'goog.events', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.ColorPicker', 'goog.ui.PopupColorPicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popupdatepicker.js', ['goog.ui.PopupDatePicker'], ['goog.events.EventType', 'goog.positioning.AnchoredViewportPosition', 'goog.positioning.Corner', 'goog.style', 'goog.ui.Component', 'goog.ui.DatePicker', 'goog.ui.Popup', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/popupdatepicker_test.js', ['goog.ui.PopupDatePickerTest'], ['goog.date.Date', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.style', 'goog.testing.MockControl', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.DatePicker', 'goog.ui.PopupBase', 'goog.ui.PopupDatePicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popupmenu.js', ['goog.ui.PopupMenu'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.BrowserEvent.MouseButton', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.positioning.AnchoredViewportPosition', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.positioning.Overflow', 'goog.positioning.ViewportClientPosition', 'goog.structs.Map', 'goog.style', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/popupmenu_test.js', ['goog.ui.PopupMenuTest'], ['goog.dom', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Box', 'goog.math.Coordinate', 'goog.positioning.Corner', 'goog.style', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.PopupMenu'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/progressbar.js', ['goog.ui.ProgressBar', 'goog.ui.ProgressBar.Orientation'], ['goog.a11y.aria', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.ui.Component', 'goog.ui.RangeModel', 'goog.userAgent'], {});
goog.addDependency('ui/prompt.js', ['goog.ui.Prompt'], ['goog.Timer', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.functions', 'goog.html.SafeHtml', 'goog.ui.Component', 'goog.ui.Dialog', 'goog.userAgent'], {});
goog.addDependency('ui/prompt_test.js', ['goog.ui.PromptTest'], ['goog.dom.selection', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.functions', 'goog.string', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.BidiInput', 'goog.ui.Dialog', 'goog.ui.Prompt', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/rangemodel.js', ['goog.ui.RangeModel'], ['goog.events.EventTarget', 'goog.ui.Component'], {});
goog.addDependency('ui/rangemodel_test.js', ['goog.ui.RangeModelTest'], ['goog.testing.testSuite', 'goog.ui.RangeModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ratings.js', ['goog.ui.Ratings', 'goog.ui.Ratings.EventType'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.ui.Component'], {});
goog.addDependency('ui/registry.js', ['goog.ui.registry'], ['goog.asserts', 'goog.dom.classlist'], {});
goog.addDependency('ui/registry_test.js', ['goog.ui.registryTest'], ['goog.object', 'goog.testing.testSuite', 'goog.ui.registry'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/richtextspellchecker.js', ['goog.ui.RichTextSpellChecker'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.math.Coordinate', 'goog.spell.SpellCheck', 'goog.string.StringBuffer', 'goog.style', 'goog.ui.AbstractSpellChecker', 'goog.ui.Component', 'goog.ui.PopupMenu'], {});
goog.addDependency('ui/richtextspellchecker_test.js', ['goog.ui.RichTextSpellCheckerTest'], ['goog.dom.Range', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.KeyCodes', 'goog.object', 'goog.spell.SpellCheck', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.RichTextSpellChecker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/roundedpanel.js', ['goog.ui.BaseRoundedPanel', 'goog.ui.CssRoundedPanel', 'goog.ui.GraphicsRoundedPanel', 'goog.ui.RoundedPanel', 'goog.ui.RoundedPanel.Corner'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.graphics', 'goog.graphics.Path', 'goog.graphics.SolidFill', 'goog.graphics.Stroke', 'goog.math', 'goog.math.Coordinate', 'goog.style', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/roundedpanel_test.js', ['goog.ui.RoundedPanelTest'], ['goog.testing.testSuite', 'goog.ui.CssRoundedPanel', 'goog.ui.GraphicsRoundedPanel', 'goog.ui.RoundedPanel', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/roundedtabrenderer.js', ['goog.ui.RoundedTabRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.ui.Tab', 'goog.ui.TabBar', 'goog.ui.TabRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/scrollfloater.js', ['goog.ui.ScrollFloater', 'goog.ui.ScrollFloater.EventType'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.style', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/scrollfloater_test.js', ['goog.ui.ScrollFloaterTest'], ['goog.dom', 'goog.events', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.ScrollFloater'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/select.js', ['goog.ui.Select'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.events.EventType', 'goog.ui.Component', 'goog.ui.IdGenerator', 'goog.ui.MenuButton', 'goog.ui.MenuItem', 'goog.ui.MenuRenderer', 'goog.ui.SelectionModel', 'goog.ui.registry'], {});
goog.addDependency('ui/select_test.js', ['goog.ui.SelectTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.CustomButtonRenderer', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.Select', 'goog.ui.Separator'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/selectionmenubutton.js', ['goog.ui.SelectionMenuButton', 'goog.ui.SelectionMenuButton.SelectionState'], ['goog.dom.InputType', 'goog.dom.TagName', 'goog.events.EventType', 'goog.style', 'goog.ui.Component', 'goog.ui.MenuButton', 'goog.ui.MenuItem', 'goog.ui.registry'], {});
goog.addDependency('ui/selectionmenubutton_test.js', ['goog.ui.SelectionMenuButtonTest'], ['goog.dom', 'goog.events', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.SelectionMenuButton'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/selectionmodel.js', ['goog.ui.SelectionModel'], ['goog.array', 'goog.events.EventTarget', 'goog.events.EventType'], {});
goog.addDependency('ui/selectionmodel_test.js', ['goog.ui.SelectionModelTest'], ['goog.array', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.SelectionModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/separator.js', ['goog.ui.Separator'], ['goog.a11y.aria', 'goog.asserts', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.MenuSeparatorRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/serverchart.js', ['goog.ui.ServerChart', 'goog.ui.ServerChart.AxisDisplayType', 'goog.ui.ServerChart.ChartType', 'goog.ui.ServerChart.EncodingType', 'goog.ui.ServerChart.Event', 'goog.ui.ServerChart.LegendPosition', 'goog.ui.ServerChart.MaximumValue', 'goog.ui.ServerChart.MultiAxisAlignment', 'goog.ui.ServerChart.MultiAxisType', 'goog.ui.ServerChart.UriParam', 'goog.ui.ServerChart.UriTooLongEvent'], ['goog.Uri', 'goog.array', 'goog.asserts', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events.Event', 'goog.string', 'goog.ui.Component'], {});
goog.addDependency('ui/serverchart_test.js', ['goog.ui.ServerChartTest'], ['goog.Uri', 'goog.events', 'goog.testing.testSuite', 'goog.ui.ServerChart'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/slider.js', ['goog.ui.Slider', 'goog.ui.Slider.Orientation'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.dom', 'goog.dom.TagName', 'goog.ui.SliderBase'], {});
goog.addDependency('ui/sliderbase.js', ['goog.ui.SliderBase', 'goog.ui.SliderBase.AnimationFactory', 'goog.ui.SliderBase.Orientation'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.events.MouseWheelHandler', 'goog.functions', 'goog.fx.AnimationParallelQueue', 'goog.fx.Dragger', 'goog.fx.Transition', 'goog.fx.dom.ResizeHeight', 'goog.fx.dom.ResizeWidth', 'goog.fx.dom.Slide', 'goog.math', 'goog.math.Coordinate', 'goog.style', 'goog.style.bidi', 'goog.ui.Component', 'goog.ui.RangeModel'], {});
goog.addDependency('ui/sliderbase_test.js', ['goog.ui.SliderBaseTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.fx.Animation', 'goog.math.Coordinate', 'goog.style', 'goog.style.bidi', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.mockmatchers', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.SliderBase', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/splitpane.js', ['goog.ui.SplitPane', 'goog.ui.SplitPane.Orientation'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.fx.Dragger', 'goog.math.Rect', 'goog.math.Size', 'goog.style', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/splitpane_test.js', ['goog.ui.SplitPaneTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.math.Size', 'goog.style', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.SplitPane'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/style/app/buttonrenderer.js', ['goog.ui.style.app.ButtonRenderer'], ['goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Button', 'goog.ui.CustomButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.registry'], {});
goog.addDependency('ui/style/app/buttonrenderer_test.js', ['goog.ui.style.app.ButtonRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.style.app.ButtonRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/style/app/menubuttonrenderer.js', ['goog.ui.style.app.MenuButtonRenderer'], ['goog.a11y.aria.Role', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.ui.Menu', 'goog.ui.MenuRenderer', 'goog.ui.style.app.ButtonRenderer'], {});
goog.addDependency('ui/style/app/menubuttonrenderer_test.js', ['goog.ui.style.app.MenuButtonRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.style', 'goog.ui.Component', 'goog.ui.MenuButton', 'goog.ui.style.app.MenuButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/style/app/primaryactionbuttonrenderer.js', ['goog.ui.style.app.PrimaryActionButtonRenderer'], ['goog.ui.Button', 'goog.ui.registry', 'goog.ui.style.app.ButtonRenderer'], {});
goog.addDependency('ui/style/app/primaryactionbuttonrenderer_test.js', ['goog.ui.style.app.PrimaryActionButtonRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.style.app.PrimaryActionButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/submenu.js', ['goog.ui.SubMenu'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events.KeyCodes', 'goog.positioning.AnchoredViewportPosition', 'goog.positioning.Corner', 'goog.style', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.SubMenuRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/submenu_test.js', ['goog.ui.SubMenuTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.functions', 'goog.positioning', 'goog.positioning.Overflow', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.SubMenu', 'goog.ui.SubMenuRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/submenurenderer.js', ['goog.ui.SubMenuRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.style', 'goog.ui.Menu', 'goog.ui.MenuItemRenderer'], {});
goog.addDependency('ui/synthetickeyboardevent.js', ['goog.ui.SyntheticKeyboardEvent'], ['goog.events.Event', 'goog.ui.KeyboardEventData'], {});
goog.addDependency('ui/tab.js', ['goog.ui.Tab'], ['goog.ui.Component', 'goog.ui.Control', 'goog.ui.TabRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/tab_test.js', ['goog.ui.TabTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Tab', 'goog.ui.TabRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tabbar.js', ['goog.ui.TabBar', 'goog.ui.TabBar.Location'], ['goog.ui.Component.EventType', 'goog.ui.Container', 'goog.ui.Container.Orientation', 'goog.ui.Tab', 'goog.ui.TabBarRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/tabbar_test.js', ['goog.ui.TabBarTest'], ['goog.dom', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Container', 'goog.ui.Tab', 'goog.ui.TabBar', 'goog.ui.TabBarRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tabbarrenderer.js', ['goog.ui.TabBarRenderer'], ['goog.a11y.aria.Role', 'goog.object', 'goog.ui.ContainerRenderer'], {});
goog.addDependency('ui/tabbarrenderer_test.js', ['goog.ui.TabBarRendererTest'], ['goog.a11y.aria.Role', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Container', 'goog.ui.TabBar', 'goog.ui.TabBarRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tablesorter.js', ['goog.ui.TableSorter', 'goog.ui.TableSorter.EventType'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.functions', 'goog.ui.Component'], {});
goog.addDependency('ui/tablesorter_test.js', ['goog.ui.TableSorterTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.TableSorter'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tabpane.js', ['goog.ui.TabPane', 'goog.ui.TabPane.Events', 'goog.ui.TabPane.TabLocation', 'goog.ui.TabPane.TabPage', 'goog.ui.TabPaneEvent'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.html.SafeStyleSheet', 'goog.style'], {});
goog.addDependency('ui/tabpane_test.js', ['goog.ui.TabPaneTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.TabPane'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tabrenderer.js', ['goog.ui.TabRenderer'], ['goog.a11y.aria.Role', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/tabrenderer_test.js', ['goog.ui.TabRendererTest'], ['goog.a11y.aria.Role', 'goog.dom', 'goog.dom.classlist', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Tab', 'goog.ui.TabRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/textarea.js', ['goog.ui.Textarea', 'goog.ui.Textarea.EventType'], ['goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events.EventType', 'goog.style', 'goog.ui.Control', 'goog.ui.TextareaRenderer', 'goog.userAgent'], {});
goog.addDependency('ui/textarea_test.js', ['goog.ui.TextareaTest'], ['goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.events.EventObserver', 'goog.testing.testSuite', 'goog.ui.Textarea', 'goog.ui.TextareaRenderer', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/textarearenderer.js', ['goog.ui.TextareaRenderer'], ['goog.dom.TagName', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/togglebutton.js', ['goog.ui.ToggleButton'], ['goog.ui.Button', 'goog.ui.Component', 'goog.ui.CustomButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbar.js', ['goog.ui.Toolbar'], ['goog.ui.Container', 'goog.ui.ToolbarRenderer'], {});
goog.addDependency('ui/toolbar_test.js', ['goog.ui.ToolbarTest'], ['goog.a11y.aria', 'goog.dom', 'goog.events.EventType', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.Toolbar', 'goog.ui.ToolbarMenuButton'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/toolbarbutton.js', ['goog.ui.ToolbarButton'], ['goog.ui.Button', 'goog.ui.ToolbarButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarbuttonrenderer.js', ['goog.ui.ToolbarButtonRenderer'], ['goog.ui.CustomButtonRenderer'], {});
goog.addDependency('ui/toolbarcolormenubutton.js', ['goog.ui.ToolbarColorMenuButton'], ['goog.ui.ColorMenuButton', 'goog.ui.ToolbarColorMenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarcolormenubuttonrenderer.js', ['goog.ui.ToolbarColorMenuButtonRenderer'], ['goog.asserts', 'goog.dom.classlist', 'goog.ui.ColorMenuButtonRenderer', 'goog.ui.MenuButtonRenderer', 'goog.ui.ToolbarMenuButtonRenderer'], {});
goog.addDependency('ui/toolbarcolormenubuttonrenderer_test.js', ['goog.ui.ToolbarColorMenuButtonRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.RendererHarness', 'goog.testing.ui.rendererasserts', 'goog.ui.ToolbarColorMenuButton', 'goog.ui.ToolbarColorMenuButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/toolbarmenubutton.js', ['goog.ui.ToolbarMenuButton'], ['goog.ui.MenuButton', 'goog.ui.ToolbarMenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarmenubuttonrenderer.js', ['goog.ui.ToolbarMenuButtonRenderer'], ['goog.ui.MenuButtonRenderer'], {});
goog.addDependency('ui/toolbarrenderer.js', ['goog.ui.ToolbarRenderer'], ['goog.a11y.aria.Role', 'goog.dom.TagName', 'goog.ui.Container', 'goog.ui.ContainerRenderer', 'goog.ui.Separator', 'goog.ui.ToolbarSeparatorRenderer'], {});
goog.addDependency('ui/toolbarselect.js', ['goog.ui.ToolbarSelect'], ['goog.ui.Select', 'goog.ui.ToolbarMenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarseparator.js', ['goog.ui.ToolbarSeparator'], ['goog.ui.Separator', 'goog.ui.ToolbarSeparatorRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarseparatorrenderer.js', ['goog.ui.ToolbarSeparatorRenderer'], ['goog.asserts', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.MenuSeparatorRenderer'], {});
goog.addDependency('ui/toolbarseparatorrenderer_test.js', ['goog.ui.ToolbarSeparatorRendererTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.ToolbarSeparator', 'goog.ui.ToolbarSeparatorRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/toolbartogglebutton.js', ['goog.ui.ToolbarToggleButton'], ['goog.ui.ToggleButton', 'goog.ui.ToolbarButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/tooltip.js', ['goog.ui.Tooltip', 'goog.ui.Tooltip.CursorTooltipPosition', 'goog.ui.Tooltip.ElementTooltipPosition', 'goog.ui.Tooltip.State'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.EventType', 'goog.events.FocusHandler', 'goog.math.Box', 'goog.math.Coordinate', 'goog.positioning', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus', 'goog.positioning.ViewportPosition', 'goog.structs.Set', 'goog.style', 'goog.ui.Popup', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/tooltip_test.js', ['goog.ui.TooltipTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.FocusHandler', 'goog.html.testing', 'goog.math.Coordinate', 'goog.positioning.AbsolutePosition', 'goog.style', 'goog.testing.MockClock', 'goog.testing.TestQueue', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.PopupBase', 'goog.ui.Tooltip', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tree/basenode.js', ['goog.ui.tree.BaseNode', 'goog.ui.tree.BaseNode.EventType'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom.safe', 'goog.events.Event', 'goog.events.KeyCodes', 'goog.html.SafeHtml', 'goog.html.SafeStyle', 'goog.string', 'goog.string.StringBuffer', 'goog.style', 'goog.ui.Component'], {});
goog.addDependency('ui/tree/basenode_test.js', ['goog.ui.tree.BaseNodeTest'], ['goog.a11y.aria', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.tree.BaseNode', 'goog.ui.tree.TreeControl', 'goog.ui.tree.TreeNode'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tree/treecontrol.js', ['goog.ui.tree.TreeControl'], ['goog.a11y.aria', 'goog.asserts', 'goog.dom.classlist', 'goog.events.EventType', 'goog.events.FocusHandler', 'goog.events.KeyHandler', 'goog.html.SafeHtml', 'goog.log', 'goog.ui.tree.BaseNode', 'goog.ui.tree.TreeNode', 'goog.ui.tree.TypeAhead', 'goog.userAgent'], {});
goog.addDependency('ui/tree/treecontrol_test.js', ['goog.ui.tree.TreeControlTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.tree.TreeControl'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tree/treenode.js', ['goog.ui.tree.TreeNode'], ['goog.ui.tree.BaseNode'], {});
goog.addDependency('ui/tree/typeahead.js', ['goog.ui.tree.TypeAhead', 'goog.ui.tree.TypeAhead.Offset'], ['goog.array', 'goog.events.KeyCodes', 'goog.string', 'goog.structs.Trie'], {});
goog.addDependency('ui/tree/typeahead_test.js', ['goog.ui.tree.TypeAheadTest'], ['goog.dom', 'goog.events.KeyCodes', 'goog.testing.testSuite', 'goog.ui.tree.TreeControl', 'goog.ui.tree.TypeAhead'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tristatemenuitem.js', ['goog.ui.TriStateMenuItem', 'goog.ui.TriStateMenuItem.State'], ['goog.dom.classlist', 'goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.TriStateMenuItemRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/tristatemenuitemrenderer.js', ['goog.ui.TriStateMenuItemRenderer'], ['goog.asserts', 'goog.dom.classlist', 'goog.ui.MenuItemRenderer'], {});
goog.addDependency('ui/twothumbslider.js', ['goog.ui.TwoThumbSlider'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.dom', 'goog.dom.TagName', 'goog.ui.SliderBase'], {});
goog.addDependency('ui/twothumbslider_test.js', ['goog.ui.TwoThumbSliderTest'], ['goog.testing.testSuite', 'goog.ui.SliderBase', 'goog.ui.TwoThumbSlider'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/zippy.js', ['goog.ui.Zippy', 'goog.ui.Zippy.Events', 'goog.ui.ZippyEvent'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.classlist', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.style'], {});
goog.addDependency('ui/zippy_test.js', ['goog.ui.ZippyTest'], ['goog.a11y.aria', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.KeyCodes', 'goog.object', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Zippy'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('uri/uri.js', ['goog.Uri', 'goog.Uri.QueryData'], ['goog.array', 'goog.asserts', 'goog.string', 'goog.structs', 'goog.structs.Map', 'goog.uri.utils', 'goog.uri.utils.ComponentIndex', 'goog.uri.utils.StandardQueryParam'], {});
goog.addDependency('uri/uri_test.js', ['goog.UriTest'], ['goog.Uri', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('uri/utils.js', ['goog.uri.utils', 'goog.uri.utils.ComponentIndex', 'goog.uri.utils.QueryArray', 'goog.uri.utils.QueryValue', 'goog.uri.utils.StandardQueryParam'], ['goog.array', 'goog.asserts', 'goog.string'], {});
goog.addDependency('uri/utils_test.js', ['goog.uri.utilsTest'], ['goog.functions', 'goog.string', 'goog.testing.testSuite', 'goog.uri.utils'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/adobereader.js', ['goog.userAgent.adobeReader'], ['goog.string', 'goog.userAgent'], {'module': 'goog'});
goog.addDependency('useragent/adobereader_test.js', ['goog.userAgent.adobeReaderTest'], ['goog.testing.testSuite', 'goog.userAgent.adobeReader'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/flash.js', ['goog.userAgent.flash'], ['goog.string'], {});
goog.addDependency('useragent/flash_test.js', ['goog.userAgent.flashTest'], ['goog.testing.testSuite', 'goog.userAgent.flash'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/iphoto.js', ['goog.userAgent.iphoto'], ['goog.string', 'goog.userAgent'], {});
goog.addDependency('useragent/jscript.js', ['goog.userAgent.jscript'], ['goog.string'], {});
goog.addDependency('useragent/jscript_test.js', ['goog.userAgent.jscriptTest'], ['goog.testing.testSuite', 'goog.userAgent.jscript'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/keyboard.js', ['goog.userAgent.keyboard'], ['goog.labs.userAgent.platform'], {});
goog.addDependency('useragent/keyboard_test.js', ['goog.userAgent.keyboardTest'], ['goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.MockUserAgent', 'goog.testing.testSuite', 'goog.userAgent.keyboard', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/platform.js', ['goog.userAgent.platform'], ['goog.string', 'goog.userAgent'], {});
goog.addDependency('useragent/platform_test.js', ['goog.userAgent.platformTest'], ['goog.testing.MockUserAgent', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.platform', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/product.js', ['goog.userAgent.product'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.platform', 'goog.userAgent'], {});
goog.addDependency('useragent/product_isversion.js', ['goog.userAgent.product.isVersion'], ['goog.labs.userAgent.platform', 'goog.string', 'goog.userAgent', 'goog.userAgent.product'], {});
goog.addDependency('useragent/product_test.js', ['goog.userAgent.productTest'], ['goog.array', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.MockUserAgent', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/useragent.js', ['goog.userAgent'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.engine', 'goog.labs.userAgent.platform', 'goog.labs.userAgent.util', 'goog.reflect', 'goog.string'], {});
goog.addDependency('useragent/useragent_quirks_test.js', ['goog.userAgentQuirksTest'], ['goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/useragent_test.js', ['goog.userAgentTest'], ['goog.array', 'goog.labs.userAgent.platform', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/useragenttestutil.js', ['goog.userAgentTestUtil', 'goog.userAgentTestUtil.UserAgents'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.engine', 'goog.labs.userAgent.platform', 'goog.object', 'goog.userAgent', 'goog.userAgent.keyboard', 'goog.userAgent.platform', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('vec/float32array.js', ['goog.vec.Float32Array'], [], {'lang': 'es6'});
goog.addDependency('vec/float32array_test.js', ['goog.vec.Float32ArrayTest'], ['goog.testing.testSuite', 'goog.vec.Float32Array'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/float64array.js', ['goog.vec.Float64Array'], [], {'lang': 'es6'});
goog.addDependency('vec/float64array_test.js', ['goog.vec.Float64ArrayTest'], ['goog.testing.testSuite', 'goog.vec.Float64Array'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat3.js', ['goog.vec.Mat3'], ['goog.vec'], {});
goog.addDependency('vec/mat3_test.js', ['goog.vec.Mat3Test'], ['goog.testing.testSuite', 'goog.vec.Mat3'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat3d.js', ['goog.vec.mat3d', 'goog.vec.mat3d.Type'], ['goog.vec', 'goog.vec.vec3d.Type'], {});
goog.addDependency('vec/mat3d_test.js', ['goog.vec.mat3dTest'], ['goog.testing.testSuite', 'goog.vec.mat3d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat3f.js', ['goog.vec.mat3f', 'goog.vec.mat3f.Type'], ['goog.vec', 'goog.vec.vec3f.Type'], {});
goog.addDependency('vec/mat3f_test.js', ['goog.vec.mat3fTest'], ['goog.testing.testSuite', 'goog.vec.mat3f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat4.js', ['goog.vec.Mat4'], ['goog.vec', 'goog.vec.Vec3', 'goog.vec.Vec4'], {});
goog.addDependency('vec/mat4_test.js', ['goog.vec.Mat4Test'], ['goog.testing.testSuite', 'goog.vec.Mat4', 'goog.vec.Vec3', 'goog.vec.Vec4'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat4d.js', ['goog.vec.mat4d', 'goog.vec.mat4d.Type'], ['goog.vec', 'goog.vec.Quaternion', 'goog.vec.vec3d', 'goog.vec.vec4d'], {});
goog.addDependency('vec/mat4d_test.js', ['goog.vec.mat4dTest'], ['goog.testing.testSuite', 'goog.vec.Quaternion', 'goog.vec.mat4d', 'goog.vec.vec3d', 'goog.vec.vec4d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat4f.js', ['goog.vec.mat4f', 'goog.vec.mat4f.Type'], ['goog.vec', 'goog.vec.Quaternion', 'goog.vec.vec3f', 'goog.vec.vec4f'], {});
goog.addDependency('vec/mat4f_test.js', ['goog.vec.mat4fTest'], ['goog.testing.testSuite', 'goog.vec.Quaternion', 'goog.vec.mat4f', 'goog.vec.vec3f', 'goog.vec.vec4f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/quaternion.js', ['goog.vec.Quaternion', 'goog.vec.Quaternion.AnyType'], ['goog.vec', 'goog.vec.Vec3', 'goog.vec.Vec4'], {});
goog.addDependency('vec/quaternion_test.js', ['goog.vec.QuaternionTest'], ['goog.testing.testSuite', 'goog.vec.Mat3', 'goog.vec.Mat4', 'goog.vec.Quaternion', 'goog.vec.Vec3', 'goog.vec.vec3f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/ray.js', ['goog.vec.Ray'], ['goog.vec.Vec3'], {});
goog.addDependency('vec/ray_test.js', ['goog.vec.RayTest'], ['goog.testing.testSuite', 'goog.vec.Ray'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec.js', ['goog.vec', 'goog.vec.AnyType', 'goog.vec.ArrayType', 'goog.vec.Float32', 'goog.vec.Float64', 'goog.vec.Number'], ['goog.vec.Float32Array', 'goog.vec.Float64Array'], {});
goog.addDependency('vec/vec2.js', ['goog.vec.Vec2'], ['goog.vec'], {});
goog.addDependency('vec/vec2_test.js', ['goog.vec.Vec2Test'], ['goog.testing.testSuite', 'goog.vec.Vec2'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec2d.js', ['goog.vec.vec2d', 'goog.vec.vec2d.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec2d_test.js', ['goog.vec.vec2dTest'], ['goog.testing.testSuite', 'goog.vec.vec2d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec2f.js', ['goog.vec.vec2f', 'goog.vec.vec2f.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec2f_test.js', ['goog.vec.vec2fTest'], ['goog.testing.testSuite', 'goog.vec.vec2f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec3.js', ['goog.vec.Vec3'], ['goog.vec'], {});
goog.addDependency('vec/vec3_test.js', ['goog.vec.Vec3Test'], ['goog.testing.testSuite', 'goog.vec.Vec3'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec3d.js', ['goog.vec.vec3d', 'goog.vec.vec3d.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec3d_test.js', ['goog.vec.vec3dTest'], ['goog.testing.testSuite', 'goog.vec.vec3d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec3f.js', ['goog.vec.vec3f', 'goog.vec.vec3f.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec3f_test.js', ['goog.vec.vec3fTest'], ['goog.testing.testSuite', 'goog.vec.vec3f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec4.js', ['goog.vec.Vec4'], ['goog.vec'], {});
goog.addDependency('vec/vec4_test.js', ['goog.vec.Vec4Test'], ['goog.testing.testSuite', 'goog.vec.Vec4'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec4d.js', ['goog.vec.vec4d', 'goog.vec.vec4d.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec4d_test.js', ['goog.vec.vec4dTest'], ['goog.testing.testSuite', 'goog.vec.vec4d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec4f.js', ['goog.vec.vec4f', 'goog.vec.vec4f.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec4f_test.js', ['goog.vec.vec4fTest'], ['goog.testing.testSuite', 'goog.vec.vec4f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('webgl/webgl.js', ['goog.webgl'], [], {});
goog.addDependency('window/window.js', ['goog.window'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeUrl', 'goog.html.uncheckedconversions', 'goog.labs.userAgent.platform', 'goog.string', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('window/window_test.js', ['goog.windowTest'], ['goog.Promise', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.functions', 'goog.html.SafeUrl', 'goog.labs.userAgent.browser', 'goog.labs.userAgent.engine', 'goog.labs.userAgent.platform', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.testSuite', 'goog.window'], {'lang': 'es6', 'module': 'goog'});

//# sourceURL=build:/external/com_google_javascript_closure_library/closure/goog/base.js
function wa(b){var d=0;return function(){return d<b.length?{done:!1,value:b[d++]}:{done:!0}}}function gb(b){return{next:wa(b)}}function lb(b){var d="undefined"!=typeof Symbol&&Symbol.iterator&&b[Symbol.iterator];return d?d.call(b):gb(b)}var wb=function(b){return"undefined"!=typeof window&&window===b?b:"undefined"!=typeof global&&null!=global?global:b}(this),Rb="function"==typeof Object.defineProperties?Object.defineProperty:function(b,d,f){b!=Array.prototype&&b!=Object.prototype&&(b[d]=f.value)};
function Ub(b,d){if(d){var f=wb;b=b.split(".");for(var h=0;h<b.length-1;h++){var k=b[h];k in f||(f[k]={});f=f[k]}b=b[b.length-1];h=f[b];d=d(h);d!=h&&null!=d&&Rb(f,b,{configurable:!0,writable:!0,value:d})}}
Ub("Promise",function(b){function d(p){this.state_=0;this.result_=void 0;this.onSettledCallbacks_=[];var m=this.createResolveAndReject_();try{p(m.resolve,m.reject)}catch(n){m.reject(n)}}function f(){this.batch_=null}function h(p){switch(typeof p){case "object":return null!=p;case "function":return!0;default:return!1}}function k(p){return p instanceof d?p:new d(function(m){m(p)})}if(b)return b;f.prototype.asyncExecute=function(p){if(null==this.batch_){this.batch_=[];var m=this;this.asyncExecuteFunction(function(){m.executeBatch_()})}this.batch_.push(p)};
var t=wb.setTimeout;f.prototype.asyncExecuteFunction=function(p){t(p,0)};f.prototype.executeBatch_=function(){for(;this.batch_&&this.batch_.length;){var p=this.batch_;this.batch_=[];for(var m=0;m<p.length;++m){var n=p[m];p[m]=null;try{n()}catch(q){this.asyncThrow_(q)}}}this.batch_=null};f.prototype.asyncThrow_=function(p){this.asyncExecuteFunction(function(){throw p;})};d.prototype.createResolveAndReject_=function(){function p(q){return function(u){n||(n=!0,q.call(m,u))}}var m=this,n=!1;return{resolve:p(this.resolveTo_),
reject:p(this.reject_)}};d.prototype.resolveTo_=function(p){p===this?this.reject_(new TypeError("A Promise cannot resolve to itself")):p instanceof d?this.settleSameAsPromise_(p):h(p)?this.resolveToNonPromiseObj_(p):this.fulfill_(p)};d.prototype.resolveToNonPromiseObj_=function(p){var m=void 0;try{m=p.then}catch(n){this.reject_(n);return}"function"==typeof m?this.settleSameAsThenable_(m,p):this.fulfill_(p)};d.prototype.reject_=function(p){this.settle_(2,p)};d.prototype.fulfill_=function(p){this.settle_(1,
p)};d.prototype.settle_=function(p,m){if(0!=this.state_)throw Error("Cannot settle("+p+", "+m+"): Promise already settled in state"+this.state_);this.state_=p;this.result_=m;this.executeOnSettledCallbacks_()};d.prototype.executeOnSettledCallbacks_=function(){if(null!=this.onSettledCallbacks_){for(var p=0;p<this.onSettledCallbacks_.length;++p)l.asyncExecute(this.onSettledCallbacks_[p]);this.onSettledCallbacks_=null}};var l=new f;d.prototype.settleSameAsPromise_=function(p){var m=this.createResolveAndReject_();
p.callWhenSettled_(m.resolve,m.reject)};d.prototype.settleSameAsThenable_=function(p,m){var n=this.createResolveAndReject_();try{p.call(m,n.resolve,n.reject)}catch(q){n.reject(q)}};d.prototype.then=function(p,m){function n(A,y){return"function"==typeof A?function(w){try{q(A(w))}catch(C){u(C)}}:y}var q,u,x=new d(function(A,y){q=A;u=y});this.callWhenSettled_(n(p,q),n(m,u));return x};d.prototype.catch=function(p){return this.then(void 0,p)};d.prototype.callWhenSettled_=function(p,m){function n(){switch(q.state_){case 1:p(q.result_);
break;case 2:m(q.result_);break;default:throw Error("Unexpected state: "+q.state_);}}var q=this;null==this.onSettledCallbacks_?l.asyncExecute(n):this.onSettledCallbacks_.push(n)};d.resolve=k;d.reject=function(p){return new d(function(m,n){n(p)})};d.race=function(p){return new d(function(m,n){for(var q=lb(p),u=q.next();!u.done;u=q.next())k(u.value).callWhenSettled_(m,n)})};d.all=function(p){var m=lb(p),n=m.next();return n.done?k([]):new d(function(q,u){function x(w){return function(C){A[w]=C;y--;0==
y&&q(A)}}var A=[],y=0;do A.push(void 0),y++,k(n.value).callWhenSettled_(x(A.length-1),u),n=m.next();while(!n.done)})};return d});function ac(b){function d(h){return b.next(h)}function f(h){return b.throw(h)}return new Promise(function(h,k){function t(l){l.done?h(l.value):Promise.resolve(l.value).then(d,f).then(t,k)}t(b.next())})}function hc(b){return ac(b())};
//# sourceURL=build:/external/com_google_javascript_closure_library/closure/goog/deps.js
//# sourceURL=build://analytics.html.js
window.ga=function(){};

// Copyright 2014 Google Inc. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
//     You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//     See the License for the specific language governing permissions and
// limitations under the License.

!function(a,b){var c={},d={},e={},f=null;!function(a,b){function c(a){if("number"==typeof a)return a;var b={};for(var c in a)b[c]=a[c];return b}function d(){this._delay=0,this._endDelay=0,this._fill="none",this._iterationStart=0,this._iterations=1,this._duration=0,this._playbackRate=1,this._direction="normal",this._easing="linear",this._easingFunction=w}function e(){return a.isDeprecated("Invalid timing inputs","2016-03-02","TypeError exceptions will be thrown instead.",!0)}function f(b,c,e){var f=new d;return c&&(f.fill="both",f.duration="auto"),"number"!=typeof b||isNaN(b)?void 0!==b&&Object.getOwnPropertyNames(b).forEach(function(c){if("auto"!=b[c]){if(("number"==typeof f[c]||"duration"==c)&&("number"!=typeof b[c]||isNaN(b[c])))return;if("fill"==c&&-1==u.indexOf(b[c]))return;if("direction"==c&&-1==v.indexOf(b[c]))return;if("playbackRate"==c&&1!==b[c]&&a.isDeprecated("AnimationEffectTiming.playbackRate","2014-11-28","Use Animation.playbackRate instead."))return;f[c]=b[c]}}):f.duration=b,f}function g(a){return"number"==typeof a&&(a=isNaN(a)?{duration:0}:{duration:a}),a}function h(b,c){return b=a.numericTimingToObject(b),f(b,c)}function i(a,b,c,d){return 0>a||a>1||0>c||c>1?w:function(e){function f(a,b,c){return 3*a*(1-c)*(1-c)*c+3*b*(1-c)*c*c+c*c*c}if(0==e||1==e)return e;for(var g=0,h=1;;){var i=(g+h)/2,j=f(a,c,i);if(Math.abs(e-j)<1e-4)return f(b,d,i);e>j?g=i:h=i}}}function j(a,b){return function(c){if(c>=1)return 1;var d=1/a;return c+=b*d,c-c%d}}function k(a){B||(B=document.createElement("div").style),B.animationTimingFunction="",B.animationTimingFunction=a;var b=B.animationTimingFunction;if(""==b&&e())throw new TypeError(a+" is not a valid value for easing");var c=D.exec(b);if(c)return i.apply(this,c.slice(1).map(Number));var d=E.exec(b);if(d)return j(Number(d[1]),{start:x,middle:y,end:z}[d[2]]);var f=A[b];return f?f:w}function l(a){return Math.abs(m(a)/a.playbackRate)}function m(a){return a.duration*a.iterations}function n(a,b,c){return null==b?F:b<c.delay?G:b>=c.delay+a?H:I}function o(a,b,c,d,e){switch(d){case G:return"backwards"==b||"both"==b?0:null;case I:return c-e;case H:return"forwards"==b||"both"==b?a:null;case F:return null}}function p(a,b,c,d){return(d.playbackRate<0?b-a:b)*d.playbackRate+c}function q(a,b,c,d,e){return c===1/0||c===-(1/0)||c-d==b&&e.iterations&&(e.iterations+e.iterationStart)%1==0?a:c%a}function r(a,b,c,d){return 0===c?0:b==a?d.iterationStart+d.iterations-1:Math.floor(c/a)}function s(a,b,c,d){var e=a%2>=1,f="normal"==d.direction||d.direction==(e?"alternate-reverse":"alternate"),g=f?c:b-c,h=g/b;return b*d._easingFunction(h)}function t(a,b,c){var d=n(a,b,c),e=o(a,c.fill,b,d,c.delay);if(null===e)return null;if(0===a)return d===G?0:1;var f=c.iterationStart*c.duration,g=p(a,e,f,c),h=q(c.duration,m(c),g,f,c),i=r(c.duration,h,g,c);return s(i,c.duration,h,c)/c.duration}var u="backwards|forwards|both|none".split("|"),v="reverse|alternate|alternate-reverse".split("|"),w=function(a){return a};d.prototype={_setMember:function(b,c){this["_"+b]=c,this._effect&&(this._effect._timingInput[b]=c,this._effect._timing=a.normalizeTimingInput(this._effect._timingInput),this._effect.activeDuration=a.calculateActiveDuration(this._effect._timing),this._effect._animation&&this._effect._animation._rebuildUnderlyingAnimation())},get playbackRate(){return this._playbackRate},set delay(a){this._setMember("delay",a)},get delay(){return this._delay},set endDelay(a){this._setMember("endDelay",a)},get endDelay(){return this._endDelay},set fill(a){this._setMember("fill",a)},get fill(){return this._fill},set iterationStart(a){if((isNaN(a)||0>a)&&e())throw new TypeError("iterationStart must be a non-negative number, received: "+timing.iterationStart);this._setMember("iterationStart",a)},get iterationStart(){return this._iterationStart},set duration(a){if("auto"!=a&&(isNaN(a)||0>a)&&e())throw new TypeError("duration must be non-negative or auto, received: "+a);this._setMember("duration",a)},get duration(){return this._duration},set direction(a){this._setMember("direction",a)},get direction(){return this._direction},set easing(a){this._easingFunction=k(a),this._setMember("easing",a)},get easing(){return this._easing},set iterations(a){if((isNaN(a)||0>a)&&e())throw new TypeError("iterations must be non-negative, received: "+a);this._setMember("iterations",a)},get iterations(){return this._iterations}};var x=1,y=.5,z=0,A={ease:i(.25,.1,.25,1),"ease-in":i(.42,0,1,1),"ease-out":i(0,0,.58,1),"ease-in-out":i(.42,0,.58,1),"step-start":j(1,x),"step-middle":j(1,y),"step-end":j(1,z)},B=null,C="\\s*(-?\\d+\\.?\\d*|-?\\.\\d+)\\s*",D=new RegExp("cubic-bezier\\("+C+","+C+","+C+","+C+"\\)"),E=/steps\(\s*(\d+)\s*,\s*(start|middle|end)\s*\)/,F=0,G=1,H=2,I=3;a.cloneTimingInput=c,a.makeTiming=f,a.numericTimingToObject=g,a.normalizeTimingInput=h,a.calculateActiveDuration=l,a.calculateTimeFraction=t,a.calculatePhase=n,a.toTimingFunction=k}(c,f),function(a,b){function c(a,b){return a in j?j[a][b]||b:b}function d(a,b,d){var e=g[a];if(e){h.style[a]=b;for(var f in e){var i=e[f],j=h.style[i];d[i]=c(i,j)}}else d[a]=c(a,b)}function e(a){var b=[];for(var c in a)if(!(c in["easing","offset","composite"])){var d=a[c];Array.isArray(d)||(d=[d]);for(var e,f=d.length,g=0;f>g;g++)e={},"offset"in a?e.offset=a.offset:1==f?e.offset=1:e.offset=g/(f-1),"easing"in a&&(e.easing=a.easing),"composite"in a&&(e.composite=a.composite),e[c]=d[g],b.push(e)}return b.sort(function(a,b){return a.offset-b.offset}),b}function f(a){function b(){var a=c.length;null==c[a-1].offset&&(c[a-1].offset=1),a>1&&null==c[0].offset&&(c[0].offset=0);for(var b=0,d=c[0].offset,e=1;a>e;e++){var f=c[e].offset;if(null!=f){for(var g=1;e-b>g;g++)c[b+g].offset=d+(f-d)*g/(e-b);b=e,d=f}}}if(null==a)return[];window.Symbol&&Symbol.iterator&&Array.prototype.from&&a[Symbol.iterator]&&(a=Array.from(a)),Array.isArray(a)||(a=e(a));for(var c=a.map(function(a){var b={};for(var c in a){var e=a[c];if("offset"==c){if(null!=e&&(e=Number(e),!isFinite(e)))throw new TypeError("keyframe offsets must be numbers.")}else{if("composite"==c)throw{type:DOMException.NOT_SUPPORTED_ERR,name:"NotSupportedError",message:"add compositing is not supported"};e=""+e}d(c,e,b)}return void 0==b.offset&&(b.offset=null),b}),f=!0,g=-(1/0),h=0;h<c.length;h++){var i=c[h].offset;if(null!=i){if(g>i)throw{code:DOMException.INVALID_MODIFICATION_ERR,name:"InvalidModificationError",message:"Keyframes are not loosely sorted by offset. Sort or specify offsets."};g=i}else f=!1}return c=c.filter(function(a){return a.offset>=0&&a.offset<=1}),f||b(),c}var g={background:["backgroundImage","backgroundPosition","backgroundSize","backgroundRepeat","backgroundAttachment","backgroundOrigin","backgroundClip","backgroundColor"],border:["borderTopColor","borderTopStyle","borderTopWidth","borderRightColor","borderRightStyle","borderRightWidth","borderBottomColor","borderBottomStyle","borderBottomWidth","borderLeftColor","borderLeftStyle","borderLeftWidth"],borderBottom:["borderBottomWidth","borderBottomStyle","borderBottomColor"],borderColor:["borderTopColor","borderRightColor","borderBottomColor","borderLeftColor"],borderLeft:["borderLeftWidth","borderLeftStyle","borderLeftColor"],borderRadius:["borderTopLeftRadius","borderTopRightRadius","borderBottomRightRadius","borderBottomLeftRadius"],borderRight:["borderRightWidth","borderRightStyle","borderRightColor"],borderTop:["borderTopWidth","borderTopStyle","borderTopColor"],borderWidth:["borderTopWidth","borderRightWidth","borderBottomWidth","borderLeftWidth"],flex:["flexGrow","flexShrink","flexBasis"],font:["fontFamily","fontSize","fontStyle","fontVariant","fontWeight","lineHeight"],margin:["marginTop","marginRight","marginBottom","marginLeft"],outline:["outlineColor","outlineStyle","outlineWidth"],padding:["paddingTop","paddingRight","paddingBottom","paddingLeft"]},h=document.createElementNS("http://www.w3.org/1999/xhtml","div"),i={thin:"1px",medium:"3px",thick:"5px"},j={borderBottomWidth:i,borderLeftWidth:i,borderRightWidth:i,borderTopWidth:i,fontSize:{"xx-small":"60%","x-small":"75%",small:"89%",medium:"100%",large:"120%","x-large":"150%","xx-large":"200%"},fontWeight:{normal:"400",bold:"700"},outlineWidth:i,textShadow:{none:"0px 0px 0px transparent"},boxShadow:{none:"0px 0px 0px 0px transparent"}};a.convertToArrayForm=e,a.normalizeKeyframes=f}(c,f),function(a){var b={};a.isDeprecated=function(a,c,d,e){var f=e?"are":"is",g=new Date,h=new Date(c);return h.setMonth(h.getMonth()+3),h>g?(a in b||console.warn("Web Animations: "+a+" "+f+" deprecated and will stop working on "+h.toDateString()+". "+d),b[a]=!0,!1):!0},a.deprecated=function(b,c,d,e){var f=e?"are":"is";if(a.isDeprecated(b,c,d,e))throw new Error(b+" "+f+" no longer supported. "+d)}}(c),function(){if(document.documentElement.animate){var a=document.documentElement.animate([],0),b=!0;if(a&&(b=!1,"play|currentTime|pause|reverse|playbackRate|cancel|finish|startTime|playState".split("|").forEach(function(c){void 0===a[c]&&(b=!0)})),!b)return}!function(a,b,c){function d(a){for(var b={},c=0;c<a.length;c++)for(var d in a[c])if("offset"!=d&&"easing"!=d&&"composite"!=d){var e={offset:a[c].offset,easing:a[c].easing,value:a[c][d]};b[d]=b[d]||[],b[d].push(e)}for(var f in b){var g=b[f];if(0!=g[0].offset||1!=g[g.length-1].offset)throw{type:DOMException.NOT_SUPPORTED_ERR,name:"NotSupportedError",message:"Partial keyframes are not supported"}}return b}function e(c){var d=[];for(var e in c)for(var f=c[e],g=0;g<f.length-1;g++){var h=f[g].offset,i=f[g+1].offset,j=f[g].value,k=f[g+1].value,l=f[g].easing;h==i&&(1==i?j=k:k=j),d.push({startTime:h,endTime:i,easing:a.toTimingFunction(l?l:"linear"),property:e,interpolation:b.propertyInterpolation(e,j,k)})}return d.sort(function(a,b){return a.startTime-b.startTime}),d}b.convertEffectInput=function(c){var f=a.normalizeKeyframes(c),g=d(f),h=e(g);return function(a,c){if(null!=c)h.filter(function(a){return 0>=c&&0==a.startTime||c>=1&&1==a.endTime||c>=a.startTime&&c<=a.endTime}).forEach(function(d){var e=c-d.startTime,f=d.endTime-d.startTime,g=0==f?0:d.easing(e/f);b.apply(a,d.property,d.interpolation(g))});else for(var d in g)"offset"!=d&&"easing"!=d&&"composite"!=d&&b.clear(a,d)}}}(c,d,f),function(a,b,c){function d(a){return a.replace(/-(.)/g,function(a,b){return b.toUpperCase()})}function e(a,b,c){h[c]=h[c]||[],h[c].push([a,b])}function f(a,b,c){for(var f=0;f<c.length;f++){var g=c[f];e(a,b,d(g))}}function g(c,e,f){var g=c;/-/.test(c)&&!a.isDeprecated("Hyphenated property names","2016-03-22","Use camelCase instead.",!0)&&(g=d(c)),"initial"!=e&&"initial"!=f||("initial"==e&&(e=i[g]),"initial"==f&&(f=i[g]));for(var j=e==f?[]:h[g],k=0;j&&k<j.length;k++){var l=j[k][0](e),m=j[k][0](f);if(void 0!==l&&void 0!==m){var n=j[k][1](l,m);if(n){var o=b.Interpolation.apply(null,n);return function(a){return 0==a?e:1==a?f:o(a)}}}}return b.Interpolation(!1,!0,function(a){return a?f:e})}var h={};b.addPropertiesHandler=f;var i={backgroundColor:"transparent",backgroundPosition:"0% 0%",borderBottomColor:"currentColor",borderBottomLeftRadius:"0px",borderBottomRightRadius:"0px",borderBottomWidth:"3px",borderLeftColor:"currentColor",borderLeftWidth:"3px",borderRightColor:"currentColor",borderRightWidth:"3px",borderSpacing:"2px",borderTopColor:"currentColor",borderTopLeftRadius:"0px",borderTopRightRadius:"0px",borderTopWidth:"3px",bottom:"auto",clip:"rect(0px, 0px, 0px, 0px)",color:"black",fontSize:"100%",fontWeight:"400",height:"auto",left:"auto",letterSpacing:"normal",lineHeight:"120%",marginBottom:"0px",marginLeft:"0px",marginRight:"0px",marginTop:"0px",maxHeight:"none",maxWidth:"none",minHeight:"0px",minWidth:"0px",opacity:"1.0",outlineColor:"invert",outlineOffset:"0px",outlineWidth:"3px",paddingBottom:"0px",paddingLeft:"0px",paddingRight:"0px",paddingTop:"0px",right:"auto",textIndent:"0px",textShadow:"0px 0px 0px transparent",top:"auto",transform:"",verticalAlign:"0px",visibility:"visible",width:"auto",wordSpacing:"normal",zIndex:"auto"};b.propertyInterpolation=g}(c,d,f),function(a,b,c){function d(b){var c=a.calculateActiveDuration(b),d=function(d){return a.calculateTimeFraction(c,d,b)};return d._totalDuration=b.delay+c+b.endDelay,d._isCurrent=function(d){var e=a.calculatePhase(c,d,b);return e===PhaseActive||e===PhaseBefore},d}b.KeyframeEffect=function(c,e,f,g){var h,i=d(a.normalizeTimingInput(f)),j=b.convertEffectInput(e),k=function(){j(c,h)};return k._update=function(a){return h=i(a),null!==h},k._clear=function(){j(c,null)},k._hasSameTarget=function(a){return c===a},k._isCurrent=i._isCurrent,k._totalDuration=i._totalDuration,k._id=g,k},b.NullEffect=function(a){var b=function(){a&&(a(),a=null)};return b._update=function(){return null},b._totalDuration=0,b._isCurrent=function(){return!1},b._hasSameTarget=function(){return!1},b}}(c,d,f),function(a,b){a.apply=function(b,c,d){b.style[a.propertyName(c)]=d},a.clear=function(b,c){b.style[a.propertyName(c)]=""}}(d,f),function(a){window.Element.prototype.animate=function(b,c){var d="";return c&&c.id&&(d=c.id),a.timeline._play(a.KeyframeEffect(this,b,c,d))}}(d),function(a,b){function c(a,b,d){if("number"==typeof a&&"number"==typeof b)return a*(1-d)+b*d;if("boolean"==typeof a&&"boolean"==typeof b)return.5>d?a:b;if(a.length==b.length){for(var e=[],f=0;f<a.length;f++)e.push(c(a[f],b[f],d));return e}throw"Mismatched interpolation arguments "+a+":"+b}a.Interpolation=function(a,b,d){return function(e){return d(c(a,b,e))}}}(d,f),function(a,b,c){a.sequenceNumber=0;var d=function(a,b,c){this.target=a,this.currentTime=b,this.timelineTime=c,this.type="finish",this.bubbles=!1,this.cancelable=!1,this.currentTarget=a,this.defaultPrevented=!1,this.eventPhase=Event.AT_TARGET,this.timeStamp=Date.now()};b.Animation=function(b){this.id="",b&&b._id&&(this.id=b._id),this._sequenceNumber=a.sequenceNumber++,this._currentTime=0,this._startTime=null,this._paused=!1,this._playbackRate=1,this._inTimeline=!0,this._finishedFlag=!0,this.onfinish=null,this._finishHandlers=[],this._effect=b,this._inEffect=this._effect._update(0),this._idle=!0,this._currentTimePending=!1},b.Animation.prototype={_ensureAlive:function(){this.playbackRate<0&&0===this.currentTime?this._inEffect=this._effect._update(-1):this._inEffect=this._effect._update(this.currentTime),this._inTimeline||!this._inEffect&&this._finishedFlag||(this._inTimeline=!0,b.timeline._animations.push(this))},_tickCurrentTime:function(a,b){a!=this._currentTime&&(this._currentTime=a,this._isFinished&&!b&&(this._currentTime=this._playbackRate>0?this._totalDuration:0),this._ensureAlive())},get currentTime(){return this._idle||this._currentTimePending?null:this._currentTime},set currentTime(a){a=+a,isNaN(a)||(b.restart(),this._paused||null==this._startTime||(this._startTime=this._timeline.currentTime-a/this._playbackRate),this._currentTimePending=!1,this._currentTime!=a&&(this._tickCurrentTime(a,!0),b.invalidateEffects()))},get startTime(){return this._startTime},set startTime(a){a=+a,isNaN(a)||this._paused||this._idle||(this._startTime=a,this._tickCurrentTime((this._timeline.currentTime-this._startTime)*this.playbackRate),b.invalidateEffects())},get playbackRate(){return this._playbackRate},set playbackRate(a){if(a!=this._playbackRate){var b=this.currentTime;this._playbackRate=a,this._startTime=null,"paused"!=this.playState&&"idle"!=this.playState&&this.play(),null!=b&&(this.currentTime=b)}},get _isFinished(){return!this._idle&&(this._playbackRate>0&&this._currentTime>=this._totalDuration||this._playbackRate<0&&this._currentTime<=0)},get _totalDuration(){return this._effect._totalDuration},get playState(){return this._idle?"idle":null==this._startTime&&!this._paused&&0!=this.playbackRate||this._currentTimePending?"pending":this._paused?"paused":this._isFinished?"finished":"running"},play:function(){this._paused=!1,(this._isFinished||this._idle)&&(this._currentTime=this._playbackRate>0?0:this._totalDuration,this._startTime=null),this._finishedFlag=!1,this._idle=!1,this._ensureAlive(),b.invalidateEffects()},pause:function(){this._isFinished||this._paused||this._idle||(this._currentTimePending=!0),this._startTime=null,this._paused=!0},finish:function(){this._idle||(this.currentTime=this._playbackRate>0?this._totalDuration:0,this._startTime=this._totalDuration-this.currentTime,this._currentTimePending=!1,b.invalidateEffects())},cancel:function(){this._inEffect&&(this._inEffect=!1,this._idle=!0,this._finishedFlag=!0,this.currentTime=0,this._startTime=null,this._effect._update(null),b.invalidateEffects())},reverse:function(){this.playbackRate*=-1,this.play()},addEventListener:function(a,b){"function"==typeof b&&"finish"==a&&this._finishHandlers.push(b)},removeEventListener:function(a,b){if("finish"==a){var c=this._finishHandlers.indexOf(b);c>=0&&this._finishHandlers.splice(c,1)}},_fireEvents:function(a){if(this._isFinished){if(!this._finishedFlag){var b=new d(this,this._currentTime,a),c=this._finishHandlers.concat(this.onfinish?[this.onfinish]:[]);setTimeout(function(){c.forEach(function(a){a.call(b.target,b)})},0),this._finishedFlag=!0}}else this._finishedFlag=!1},_tick:function(a,b){this._idle||this._paused||(null==this._startTime?b&&(this.startTime=a-this._currentTime/this.playbackRate):this._isFinished||this._tickCurrentTime((a-this._startTime)*this.playbackRate)),b&&(this._currentTimePending=!1,this._fireEvents(a))},get _needsTick(){return this.playState in{pending:1,running:1}||!this._finishedFlag}}}(c,d,f),function(a,b,c){function d(a){var b=j;j=[],a<p.currentTime&&(a=p.currentTime),h(a,!0),b.forEach(function(b){b[1](a)}),g(),l=void 0}function e(a,b){return a._sequenceNumber-b._sequenceNumber}function f(){this._animations=[],this.currentTime=window.performance&&performance.now?performance.now():0}function g(){o.forEach(function(a){a()}),o.length=0}function h(a,c){n=!1;var d=b.timeline;d.currentTime=a,d._animations.sort(e),m=!1;var f=d._animations;d._animations=[];var g=[],h=[];f=f.filter(function(b){b._tick(a,c),b._inEffect?h.push(b._effect):g.push(b._effect),b._needsTick&&(m=!0);var d=b._inEffect||b._needsTick;return b._inTimeline=d,d}),o.push.apply(o,g),o.push.apply(o,h),d._animations.push.apply(d._animations,f),m&&requestAnimationFrame(function(){})}var i=window.requestAnimationFrame,j=[],k=0;window.requestAnimationFrame=function(a){var b=k++;return 0==j.length&&i(d),j.push([b,a]),b},window.cancelAnimationFrame=function(a){j.forEach(function(b){b[0]==a&&(b[1]=function(){})})},f.prototype={_play:function(c){c._timing=a.normalizeTimingInput(c.timing);var d=new b.Animation(c);return d._idle=!1,d._timeline=this,this._animations.push(d),b.restart(),b.invalidateEffects(),d}};var l=void 0,m=!1,n=!1;b.restart=function(){return m||(m=!0,requestAnimationFrame(function(){}),n=!0),n},b.invalidateEffects=function(){h(b.timeline.currentTime,!1),g()};var o=[],p=new f;b.timeline=p}(c,d,f),function(a){function b(a,b){var c=a.exec(b);return c?(c=a.ignoreCase?c[0].toLowerCase():c[0],[c,b.substr(c.length)]):void 0}function c(a,b){b=b.replace(/^\s*/,"");var c=a(b);return c?[c[0],c[1].replace(/^\s*/,"")]:void 0}function d(a,d,e){a=c.bind(null,a);for(var f=[];;){var g=a(e);if(!g)return[f,e];if(f.push(g[0]),e=g[1],g=b(d,e),!g||""==g[1])return[f,e];e=g[1]}}function e(a,b){for(var c=0,d=0;d<b.length&&(!/\s|,/.test(b[d])||0!=c);d++)if("("==b[d])c++;else if(")"==b[d]&&(c--,0==c&&d++,0>=c))break;var e=a(b.substr(0,d));return void 0==e?void 0:[e,b.substr(d)]}function f(a,b){for(var c=a,d=b;c&&d;)c>d?c%=d:d%=c;return c=a*b/(c+d)}function g(a){return function(b){var c=a(b);return c&&(c[0]=void 0),c}}function h(a,b){return function(c){var d=a(c);return d?d:[b,c]}}function i(b,c){for(var d=[],e=0;e<b.length;e++){var f=a.consumeTrimmed(b[e],c);if(!f||""==f[0])return;void 0!==f[0]&&d.push(f[0]),c=f[1]}return""==c?d:void 0}function j(a,b,c,d,e){for(var g=[],h=[],i=[],j=f(d.length,e.length),k=0;j>k;k++){var l=b(d[k%d.length],e[k%e.length]);if(!l)return;g.push(l[0]),h.push(l[1]),i.push(l[2])}return[g,h,function(b){var d=b.map(function(a,b){return i[b](a)}).join(c);return a?a(d):d}]}function k(a,b,c){for(var d=[],e=[],f=[],g=0,h=0;h<c.length;h++)if("function"==typeof c[h]){var i=c[h](a[g],b[g++]);d.push(i[0]),e.push(i[1]),f.push(i[2])}else!function(a){d.push(!1),e.push(!1),f.push(function(){return c[a]})}(h);return[d,e,function(a){for(var b="",c=0;c<a.length;c++)b+=f[c](a[c]);return b}]}a.consumeToken=b,a.consumeTrimmed=c,a.consumeRepeated=d,a.consumeParenthesised=e,a.ignore=g,a.optional=h,a.consumeList=i,a.mergeNestedRepeated=j.bind(null,null),a.mergeWrappedNestedRepeated=j,a.mergeList=k}(d),function(a){function b(b){function c(b){var c=a.consumeToken(/^inset/i,b);if(c)return d.inset=!0,c;var c=a.consumeLengthOrPercent(b);if(c)return d.lengths.push(c[0]),c;var c=a.consumeColor(b);return c?(d.color=c[0],c):void 0}var d={inset:!1,lengths:[],color:null},e=a.consumeRepeated(c,/^/,b);return e&&e[0].length?[d,e[1]]:void 0}function c(c){var d=a.consumeRepeated(b,/^,/,c);return d&&""==d[1]?d[0]:void 0}function d(b,c){for(;b.lengths.length<Math.max(b.lengths.length,c.lengths.length);)b.lengths.push({px:0});for(;c.lengths.length<Math.max(b.lengths.length,c.lengths.length);)c.lengths.push({px:0});if(b.inset==c.inset&&!!b.color==!!c.color){for(var d,e=[],f=[[],0],g=[[],0],h=0;h<b.lengths.length;h++){var i=a.mergeDimensions(b.lengths[h],c.lengths[h],2==h);f[0].push(i[0]),g[0].push(i[1]),e.push(i[2])}if(b.color&&c.color){var j=a.mergeColors(b.color,c.color);f[1]=j[0],g[1]=j[1],d=j[2]}return[f,g,function(a){for(var c=b.inset?"inset ":" ",f=0;f<e.length;f++)c+=e[f](a[0][f])+" ";return d&&(c+=d(a[1])),c}]}}function e(b,c,d,e){function f(a){return{inset:a,color:[0,0,0,0],lengths:[{px:0},{px:0},{px:0},{px:0}]}}for(var g=[],h=[],i=0;i<d.length||i<e.length;i++){var j=d[i]||f(e[i].inset),k=e[i]||f(d[i].inset);g.push(j),h.push(k)}return a.mergeNestedRepeated(b,c,g,h)}var f=e.bind(null,d,", ");a.addPropertiesHandler(c,f,["box-shadow","text-shadow"])}(d),function(a,b){function c(a){return a.toFixed(3).replace(".000","")}function d(a,b,c){return Math.min(b,Math.max(a,c))}function e(a){return/^\s*[-+]?(\d*\.)?\d+\s*$/.test(a)?Number(a):void 0}function f(a,b){return[a,b,c]}function g(a,b){return 0!=a?i(0,1/0)(a,b):void 0}function h(a,b){return[a,b,function(a){return Math.round(d(1,1/0,a))}]}function i(a,b){return function(e,f){return[e,f,function(e){return c(d(a,b,e))}]}}function j(a,b){return[a,b,Math.round]}a.clamp=d,a.addPropertiesHandler(e,i(0,1/0),["border-image-width","line-height"]),a.addPropertiesHandler(e,i(0,1),["opacity","shape-image-threshold"]),a.addPropertiesHandler(e,g,["flex-grow","flex-shrink"]),a.addPropertiesHandler(e,h,["orphans","widows"]),a.addPropertiesHandler(e,j,["z-index"]),a.parseNumber=e,a.mergeNumbers=f,a.numberToString=c}(d,f),function(a,b){function c(a,b){return"visible"==a||"visible"==b?[0,1,function(c){return 0>=c?a:c>=1?b:"visible"}]:void 0}a.addPropertiesHandler(String,c,["visibility"])}(d),function(a,b){function c(a){a=a.trim(),f.fillStyle="#000",f.fillStyle=a;var b=f.fillStyle;if(f.fillStyle="#fff",f.fillStyle=a,b==f.fillStyle){f.fillRect(0,0,1,1);var c=f.getImageData(0,0,1,1).data;f.clearRect(0,0,1,1);var d=c[3]/255;return[c[0]*d,c[1]*d,c[2]*d,d]}}function d(b,c){return[b,c,function(b){function c(a){return Math.max(0,Math.min(255,a))}if(b[3])for(var d=0;3>d;d++)b[d]=Math.round(c(b[d]/b[3]));return b[3]=a.numberToString(a.clamp(0,1,b[3])),"rgba("+b.join(",")+")"}]}var e=document.createElementNS("http://www.w3.org/1999/xhtml","canvas");e.width=e.height=1;var f=e.getContext("2d");a.addPropertiesHandler(c,d,["background-color","border-bottom-color","border-left-color","border-right-color","border-top-color","color","outline-color","text-decoration-color"]),a.consumeColor=a.consumeParenthesised.bind(null,c),a.mergeColors=d}(d,f),function(a,b){function c(a,b){if(b=b.trim().toLowerCase(),"0"==b&&"px".search(a)>=0)return{px:0};if(/^[^(]*$|^calc/.test(b)){b=b.replace(/calc\(/g,"(");var c={};b=b.replace(a,function(a){return c[a]=null,"U"+a});for(var d="U("+a.source+")",e=b.replace(/[-+]?(\d*\.)?\d+/g,"N").replace(new RegExp("N"+d,"g"),"D").replace(/\s[+-]\s/g,"O").replace(/\s/g,""),f=[/N\*(D)/g,/(N|D)[*\/]N/g,/(N|D)O\1/g,/\((N|D)\)/g],g=0;g<f.length;)f[g].test(e)?(e=e.replace(f[g],"$1"),g=0):g++;if("D"==e){for(var h in c){var i=eval(b.replace(new RegExp("U"+h,"g"),"").replace(new RegExp(d,"g"),"*0"));if(!isFinite(i))return;c[h]=i}return c}}}function d(a,b){return e(a,b,!0)}function e(b,c,d){var e,f=[];for(e in b)f.push(e);for(e in c)f.indexOf(e)<0&&f.push(e);return b=f.map(function(a){return b[a]||0}),c=f.map(function(a){return c[a]||0}),[b,c,function(b){var c=b.map(function(c,e){return 1==b.length&&d&&(c=Math.max(c,0)),a.numberToString(c)+f[e]}).join(" + ");return b.length>1?"calc("+c+")":c}]}var f="px|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc",g=c.bind(null,new RegExp(f,"g")),h=c.bind(null,new RegExp(f+"|%","g")),i=c.bind(null,/deg|rad|grad|turn/g);a.parseLength=g,a.parseLengthOrPercent=h,a.consumeLengthOrPercent=a.consumeParenthesised.bind(null,h),a.parseAngle=i,a.mergeDimensions=e;var j=a.consumeParenthesised.bind(null,g),k=a.consumeRepeated.bind(void 0,j,/^/),l=a.consumeRepeated.bind(void 0,k,/^,/);a.consumeSizePairList=l;var m=function(a){var b=l(a);return b&&""==b[1]?b[0]:void 0},n=a.mergeNestedRepeated.bind(void 0,d," "),o=a.mergeNestedRepeated.bind(void 0,n,",");a.mergeNonNegativeSizePair=n,a.addPropertiesHandler(m,o,["background-size"]),a.addPropertiesHandler(h,d,["border-bottom-width","border-image-width","border-left-width","border-right-width","border-top-width","flex-basis","font-size","height","line-height","max-height","max-width","outline-width","width"]),a.addPropertiesHandler(h,e,["border-bottom-left-radius","border-bottom-right-radius","border-top-left-radius","border-top-right-radius","bottom","left","letter-spacing","margin-bottom","margin-left","margin-right","margin-top","min-height","min-width","outline-offset","padding-bottom","padding-left","padding-right","padding-top","perspective","right","shape-margin","text-indent","top","vertical-align","word-spacing"])}(d,f),function(a,b){function c(b){return a.consumeLengthOrPercent(b)||a.consumeToken(/^auto/,b)}function d(b){var d=a.consumeList([a.ignore(a.consumeToken.bind(null,/^rect/)),a.ignore(a.consumeToken.bind(null,/^\(/)),a.consumeRepeated.bind(null,c,/^,/),a.ignore(a.consumeToken.bind(null,/^\)/))],b);return d&&4==d[0].length?d[0]:void 0}function e(b,c){return"auto"==b||"auto"==c?[!0,!1,function(d){var e=d?b:c;if("auto"==e)return"auto";var f=a.mergeDimensions(e,e);return f[2](f[0])}]:a.mergeDimensions(b,c)}function f(a){return"rect("+a+")"}var g=a.mergeWrappedNestedRepeated.bind(null,f,e,", ");a.parseBox=d,a.mergeBoxes=g,a.addPropertiesHandler(d,g,["clip"])}(d,f),function(a,b){function c(a){return function(b){var c=0;return a.map(function(a){return a===k?b[c++]:a})}}function d(a){return a}function e(b){if(b=b.toLowerCase().trim(),"none"==b)return[];for(var c,d=/\s*(\w+)\(([^)]*)\)/g,e=[],f=0;c=d.exec(b);){if(c.index!=f)return;f=c.index+c[0].length;var g=c[1],h=n[g];if(!h)return;var i=c[2].split(","),j=h[0];if(j.length<i.length)return;for(var k=[],o=0;o<j.length;o++){var p,q=i[o],r=j[o];if(p=q?{A:function(b){return"0"==b.trim()?m:a.parseAngle(b)},N:a.parseNumber,T:a.parseLengthOrPercent,L:a.parseLength}[r.toUpperCase()](q):{a:m,n:k[0],t:l}[r],void 0===p)return;k.push(p)}if(e.push({t:g,d:k}),d.lastIndex==b.length)return e}}function f(a){return a.toFixed(6).replace(".000000","")}function g(b,c){if(b.decompositionPair!==c){b.decompositionPair=c;var d=a.makeMatrixDecomposition(b)}if(c.decompositionPair!==b){c.decompositionPair=b;var e=a.makeMatrixDecomposition(c)}return null==d[0]||null==e[0]?[[!1],[!0],function(a){return a?c[0].d:b[0].d}]:(d[0].push(0),e[0].push(1),[d,e,function(b){var c=a.quat(d[0][3],e[0][3],b[5]),g=a.composeMatrix(b[0],b[1],b[2],c,b[4]),h=g.map(f).join(",");return h}])}function h(a){return a.replace(/[xy]/,"")}function i(a){return a.replace(/(x|y|z|3d)?$/,"3d")}function j(b,c){var d=a.makeMatrixDecomposition&&!0,e=!1;if(!b.length||!c.length){b.length||(e=!0,b=c,c=[]);for(var f=0;f<b.length;f++){var j=b[f].t,k=b[f].d,l="scale"==j.substr(0,5)?1:0;c.push({t:j,d:k.map(function(a){if("number"==typeof a)return l;var b={};for(var c in a)b[c]=l;return b})})}}var m=function(a,b){return"perspective"==a&&"perspective"==b||("matrix"==a||"matrix3d"==a)&&("matrix"==b||"matrix3d"==b)},o=[],p=[],q=[];if(b.length!=c.length){if(!d)return;var r=g(b,c);o=[r[0]],p=[r[1]],q=[["matrix",[r[2]]]]}else for(var f=0;f<b.length;f++){var j,s=b[f].t,t=c[f].t,u=b[f].d,v=c[f].d,w=n[s],x=n[t];if(m(s,t)){if(!d)return;var r=g([b[f]],[c[f]]);o.push(r[0]),p.push(r[1]),q.push(["matrix",[r[2]]])}else{if(s==t)j=s;else if(w[2]&&x[2]&&h(s)==h(t))j=h(s),u=w[2](u),v=x[2](v);else{if(!w[1]||!x[1]||i(s)!=i(t)){if(!d)return;var r=g(b,c);o=[r[0]],p=[r[1]],q=[["matrix",[r[2]]]];break}j=i(s),u=w[1](u),v=x[1](v)}for(var y=[],z=[],A=[],B=0;B<u.length;B++){var C="number"==typeof u[B]?a.mergeNumbers:a.mergeDimensions,r=C(u[B],v[B]);y[B]=r[0],z[B]=r[1],A.push(r[2])}o.push(y),p.push(z),q.push([j,A])}}if(e){var D=o;o=p,p=D}return[o,p,function(a){return a.map(function(a,b){var c=a.map(function(a,c){return q[b][1][c](a)}).join(",");return"matrix"==q[b][0]&&16==c.split(",").length&&(q[b][0]="matrix3d"),q[b][0]+"("+c+")"}).join(" ")}]}var k=null,l={px:0},m={deg:0},n={matrix:["NNNNNN",[k,k,0,0,k,k,0,0,0,0,1,0,k,k,0,1],d],matrix3d:["NNNNNNNNNNNNNNNN",d],rotate:["A"],rotatex:["A"],rotatey:["A"],rotatez:["A"],rotate3d:["NNNA"],perspective:["L"],scale:["Nn",c([k,k,1]),d],scalex:["N",c([k,1,1]),c([k,1])],scaley:["N",c([1,k,1]),c([1,k])],scalez:["N",c([1,1,k])],scale3d:["NNN",d],skew:["Aa",null,d],skewx:["A",null,c([k,m])],skewy:["A",null,c([m,k])],translate:["Tt",c([k,k,l]),d],translatex:["T",c([k,l,l]),c([k,l])],translatey:["T",c([l,k,l]),c([l,k])],translatez:["L",c([l,l,k])],translate3d:["TTL",d]};a.addPropertiesHandler(e,j,["transform"])}(d,f),function(a,b){function c(a,b){b.concat([a]).forEach(function(b){b in document.documentElement.style&&(d[a]=b)})}var d={};c("transform",["webkitTransform","msTransform"]),c("transformOrigin",["webkitTransformOrigin"]),c("perspective",["webkitPerspective"]),c("perspectiveOrigin",["webkitPerspectiveOrigin"]),a.propertyName=function(a){return d[a]||a}}(d,f)}(),!function(){if(void 0===document.createElement("div").animate([]).oncancel){var a;if(window.performance&&performance.now)var a=function(){return performance.now()};else var a=function(){return Date.now()};var b=function(a,b,c){this.target=a,this.currentTime=b,this.timelineTime=c,this.type="cancel",this.bubbles=!1,this.cancelable=!1,this.currentTarget=a,this.defaultPrevented=!1,this.eventPhase=Event.AT_TARGET,this.timeStamp=Date.now()},c=window.Element.prototype.animate;window.Element.prototype.animate=function(d,e){var f=c.call(this,d,e);f._cancelHandlers=[],f.oncancel=null;var g=f.cancel;f.cancel=function(){g.call(this);var c=new b(this,null,a()),d=this._cancelHandlers.concat(this.oncancel?[this.oncancel]:[]);setTimeout(function(){d.forEach(function(a){a.call(c.target,c)})},0)};var h=f.addEventListener;f.addEventListener=function(a,b){"function"==typeof b&&"cancel"==a?this._cancelHandlers.push(b):h.call(this,a,b)};var i=f.removeEventListener;return f.removeEventListener=function(a,b){if("cancel"==a){var c=this._cancelHandlers.indexOf(b);c>=0&&this._cancelHandlers.splice(c,1)}else i.call(this,a,b)},f}}}(),function(a){var b=document.documentElement,c=null,d=!1;try{var e=getComputedStyle(b).getPropertyValue("opacity"),f="0"==e?"1":"0";c=b.animate({opacity:[f,f]},{duration:1}),c.currentTime=0,d=getComputedStyle(b).getPropertyValue("opacity")==f}catch(g){}finally{c&&c.cancel()}if(!d){var h=window.Element.prototype.animate;window.Element.prototype.animate=function(b,c){return window.Symbol&&Symbol.iterator&&Array.prototype.from&&b[Symbol.iterator]&&(b=Array.from(b)),Array.isArray(b)||null===b||(b=a.convertToArrayForm(b)),h.call(this,b,c)}}}(c),!function(a,b,c){function d(a){var b=window.document.timeline;b.currentTime=a,b._discardAnimations(),0==b._animations.length?f=!1:requestAnimationFrame(d);
}var e=window.requestAnimationFrame;window.requestAnimationFrame=function(a){return e(function(b){window.document.timeline._updateAnimationsPromises(),a(b),window.document.timeline._updateAnimationsPromises()})},b.AnimationTimeline=function(){this._animations=[],this.currentTime=void 0},b.AnimationTimeline.prototype={getAnimations:function(){return this._discardAnimations(),this._animations.slice()},_updateAnimationsPromises:function(){b.animationsWithPromises=b.animationsWithPromises.filter(function(a){return a._updatePromises()})},_discardAnimations:function(){this._updateAnimationsPromises(),this._animations=this._animations.filter(function(a){return"finished"!=a.playState&&"idle"!=a.playState})},_play:function(a){var c=new b.Animation(a,this);return this._animations.push(c),b.restartWebAnimationsNextTick(),c._updatePromises(),c._animation.play(),c._updatePromises(),c},play:function(a){return a&&a.remove(),this._play(a)}};var f=!1;b.restartWebAnimationsNextTick=function(){f||(f=!0,requestAnimationFrame(d))};var g=new b.AnimationTimeline;b.timeline=g;try{Object.defineProperty(window.document,"timeline",{configurable:!0,get:function(){return g}})}catch(h){}try{window.document.timeline=g}catch(h){}}(c,e,f),function(a,b,c){b.animationsWithPromises=[],b.Animation=function(b,c){if(this.id="",b&&b._id&&(this.id=b._id),this.effect=b,b&&(b._animation=this),!c)throw new Error("Animation with null timeline is not supported");this._timeline=c,this._sequenceNumber=a.sequenceNumber++,this._holdTime=0,this._paused=!1,this._isGroup=!1,this._animation=null,this._childAnimations=[],this._callback=null,this._oldPlayState="idle",this._rebuildUnderlyingAnimation(),this._animation.cancel(),this._updatePromises()},b.Animation.prototype={_updatePromises:function(){var a=this._oldPlayState,b=this.playState;return this._readyPromise&&b!==a&&("idle"==b?(this._rejectReadyPromise(),this._readyPromise=void 0):"pending"==a?this._resolveReadyPromise():"pending"==b&&(this._readyPromise=void 0)),this._finishedPromise&&b!==a&&("idle"==b?(this._rejectFinishedPromise(),this._finishedPromise=void 0):"finished"==b?this._resolveFinishedPromise():"finished"==a&&(this._finishedPromise=void 0)),this._oldPlayState=this.playState,this._readyPromise||this._finishedPromise},_rebuildUnderlyingAnimation:function(){this._updatePromises();var a,c,d,e,f=!!this._animation;f&&(a=this.playbackRate,c=this._paused,d=this.startTime,e=this.currentTime,this._animation.cancel(),this._animation._wrapper=null,this._animation=null),(!this.effect||this.effect instanceof window.KeyframeEffect)&&(this._animation=b.newUnderlyingAnimationForKeyframeEffect(this.effect),b.bindAnimationForKeyframeEffect(this)),(this.effect instanceof window.SequenceEffect||this.effect instanceof window.GroupEffect)&&(this._animation=b.newUnderlyingAnimationForGroup(this.effect),b.bindAnimationForGroup(this)),this.effect&&this.effect._onsample&&b.bindAnimationForCustomEffect(this),f&&(1!=a&&(this.playbackRate=a),null!==d?this.startTime=d:null!==e?this.currentTime=e:null!==this._holdTime&&(this.currentTime=this._holdTime),c&&this.pause()),this._updatePromises()},_updateChildren:function(){if(this.effect&&"idle"!=this.playState){var a=this.effect._timing.delay;this._childAnimations.forEach(function(c){this._arrangeChildren(c,a),this.effect instanceof window.SequenceEffect&&(a+=b.groupChildDuration(c.effect))}.bind(this))}},_setExternalAnimation:function(a){if(this.effect&&this._isGroup)for(var b=0;b<this.effect.children.length;b++)this.effect.children[b]._animation=a,this._childAnimations[b]._setExternalAnimation(a)},_constructChildAnimations:function(){if(this.effect&&this._isGroup){var a=this.effect._timing.delay;this._removeChildAnimations(),this.effect.children.forEach(function(c){var d=window.document.timeline._play(c);this._childAnimations.push(d),d.playbackRate=this.playbackRate,this._paused&&d.pause(),c._animation=this.effect._animation,this._arrangeChildren(d,a),this.effect instanceof window.SequenceEffect&&(a+=b.groupChildDuration(c))}.bind(this))}},_arrangeChildren:function(a,b){null===this.startTime?a.currentTime=this.currentTime-b/this.playbackRate:a.startTime!==this.startTime+b/this.playbackRate&&(a.startTime=this.startTime+b/this.playbackRate)},get timeline(){return this._timeline},get playState(){return this._animation?this._animation.playState:"idle"},get finished(){return window.Promise?(this._finishedPromise||(-1==b.animationsWithPromises.indexOf(this)&&b.animationsWithPromises.push(this),this._finishedPromise=new Promise(function(a,b){this._resolveFinishedPromise=function(){a(this)},this._rejectFinishedPromise=function(){b({type:DOMException.ABORT_ERR,name:"AbortError"})}}.bind(this)),"finished"==this.playState&&this._resolveFinishedPromise()),this._finishedPromise):(console.warn("Animation Promises require JavaScript Promise constructor"),null)},get ready(){return window.Promise?(this._readyPromise||(-1==b.animationsWithPromises.indexOf(this)&&b.animationsWithPromises.push(this),this._readyPromise=new Promise(function(a,b){this._resolveReadyPromise=function(){a(this)},this._rejectReadyPromise=function(){b({type:DOMException.ABORT_ERR,name:"AbortError"})}}.bind(this)),"pending"!==this.playState&&this._resolveReadyPromise()),this._readyPromise):(console.warn("Animation Promises require JavaScript Promise constructor"),null)},get onfinish(){return this._animation.onfinish},set onfinish(a){"function"==typeof a?this._animation.onfinish=function(b){b.target=this,a.call(this,b)}.bind(this):this._animation.onfinish=a},get oncancel(){return this._animation.oncancel},set oncancel(a){"function"==typeof a?this._animation.oncancel=function(b){b.target=this,a.call(this,b)}.bind(this):this._animation.oncancel=a},get currentTime(){this._updatePromises();var a=this._animation.currentTime;return this._updatePromises(),a},set currentTime(a){this._updatePromises(),this._animation.currentTime=isFinite(a)?a:Math.sign(a)*Number.MAX_VALUE,this._register(),this._forEachChild(function(b,c){b.currentTime=a-c}),this._updatePromises()},get startTime(){return this._animation.startTime},set startTime(a){this._updatePromises(),this._animation.startTime=isFinite(a)?a:Math.sign(a)*Number.MAX_VALUE,this._register(),this._forEachChild(function(b,c){b.startTime=a+c}),this._updatePromises()},get playbackRate(){return this._animation.playbackRate},set playbackRate(a){this._updatePromises();var b=this.currentTime;this._animation.playbackRate=a,this._forEachChild(function(b){b.playbackRate=a}),"paused"!=this.playState&&"idle"!=this.playState&&this.play(),null!==b&&(this.currentTime=b),this._updatePromises()},play:function(){this._updatePromises(),this._paused=!1,this._animation.play(),-1==this._timeline._animations.indexOf(this)&&this._timeline._animations.push(this),this._register(),b.awaitStartTime(this),this._forEachChild(function(a){var b=a.currentTime;a.play(),a.currentTime=b}),this._updatePromises()},pause:function(){this._updatePromises(),this.currentTime&&(this._holdTime=this.currentTime),this._animation.pause(),this._register(),this._forEachChild(function(a){a.pause()}),this._paused=!0,this._updatePromises()},finish:function(){this._updatePromises(),this._animation.finish(),this._register(),this._updatePromises()},cancel:function(){this._updatePromises(),this._animation.cancel(),this._register(),this._removeChildAnimations(),this._updatePromises()},reverse:function(){this._updatePromises();var a=this.currentTime;this._animation.reverse(),this._forEachChild(function(a){a.reverse()}),null!==a&&(this.currentTime=a),this._updatePromises()},addEventListener:function(a,b){var c=b;"function"==typeof b&&(c=function(a){a.target=this,b.call(this,a)}.bind(this),b._wrapper=c),this._animation.addEventListener(a,c)},removeEventListener:function(a,b){this._animation.removeEventListener(a,b&&b._wrapper||b)},_removeChildAnimations:function(){for(;this._childAnimations.length;)this._childAnimations.pop().cancel()},_forEachChild:function(b){var c=0;if(this.effect.children&&this._childAnimations.length<this.effect.children.length&&this._constructChildAnimations(),this._childAnimations.forEach(function(a){b.call(this,a,c),this.effect instanceof window.SequenceEffect&&(c+=a.effect.activeDuration)}.bind(this)),"pending"!=this.playState){var d=this.effect._timing,e=this.currentTime;null!==e&&(e=a.calculateTimeFraction(a.calculateActiveDuration(d),e,d)),(null==e||isNaN(e))&&this._removeChildAnimations()}}},window.Animation=b.Animation}(c,e,f),function(a,b,c){function d(b){this._frames=a.normalizeKeyframes(b)}function e(){for(var a=!1;i.length;){var b=i.shift();b._updateChildren(),a=!0}return a}var f=function(a){if(a._animation=void 0,a instanceof window.SequenceEffect||a instanceof window.GroupEffect)for(var b=0;b<a.children.length;b++)f(a.children[b])};b.removeMulti=function(a){for(var b=[],c=0;c<a.length;c++){var d=a[c];d._parent?(-1==b.indexOf(d._parent)&&b.push(d._parent),d._parent.children.splice(d._parent.children.indexOf(d),1),d._parent=null,f(d)):d._animation&&d._animation.effect==d&&(d._animation.cancel(),d._animation.effect=new KeyframeEffect(null,[]),d._animation._callback&&(d._animation._callback._animation=null),d._animation._rebuildUnderlyingAnimation(),f(d))}for(c=0;c<b.length;c++)b[c]._rebuild()},b.KeyframeEffect=function(b,c,e,f){return this.target=b,this._parent=null,e=a.numericTimingToObject(e),this._timingInput=a.cloneTimingInput(e),this._timing=a.normalizeTimingInput(e),this.timing=a.makeTiming(e,!1,this),this.timing._effect=this,"function"==typeof c?(a.deprecated("Custom KeyframeEffect","2015-06-22","Use KeyframeEffect.onsample instead."),this._normalizedKeyframes=c):this._normalizedKeyframes=new d(c),this._keyframes=c,this.activeDuration=a.calculateActiveDuration(this._timing),this._id=f,this},b.KeyframeEffect.prototype={getFrames:function(){return"function"==typeof this._normalizedKeyframes?this._normalizedKeyframes:this._normalizedKeyframes._frames},set onsample(a){if("function"==typeof this.getFrames())throw new Error("Setting onsample on custom effect KeyframeEffect is not supported.");this._onsample=a,this._animation&&this._animation._rebuildUnderlyingAnimation()},get parent(){return this._parent},clone:function(){if("function"==typeof this.getFrames())throw new Error("Cloning custom effects is not supported.");var b=new KeyframeEffect(this.target,[],a.cloneTimingInput(this._timingInput),this._id);return b._normalizedKeyframes=this._normalizedKeyframes,b._keyframes=this._keyframes,b},remove:function(){b.removeMulti([this])}};var g=Element.prototype.animate;Element.prototype.animate=function(a,c){var d="";return c&&c.id&&(d=c.id),b.timeline._play(new b.KeyframeEffect(this,a,c,d))};var h=document.createElementNS("http://www.w3.org/1999/xhtml","div");b.newUnderlyingAnimationForKeyframeEffect=function(a){if(a){var b=a.target||h,c=a._keyframes;"function"==typeof c&&(c=[]);var d=a._timingInput;d.id=a._id}else var b=h,c=[],d=0;return g.apply(b,[c,d])},b.bindAnimationForKeyframeEffect=function(a){a.effect&&"function"==typeof a.effect._normalizedKeyframes&&b.bindAnimationForCustomEffect(a)};var i=[];b.awaitStartTime=function(a){null===a.startTime&&a._isGroup&&(0==i.length&&requestAnimationFrame(e),i.push(a))};var j=window.getComputedStyle;Object.defineProperty(window,"getComputedStyle",{configurable:!0,enumerable:!0,value:function(){window.document.timeline._updateAnimationsPromises();var a=j.apply(this,arguments);return e()&&(a=j.apply(this,arguments)),window.document.timeline._updateAnimationsPromises(),a}}),window.KeyframeEffect=b.KeyframeEffect,window.Element.prototype.getAnimations=function(){return document.timeline.getAnimations().filter(function(a){return null!==a.effect&&a.effect.target==this}.bind(this))}}(c,e,f),function(a,b,c){function d(a){a._registered||(a._registered=!0,g.push(a),h||(h=!0,requestAnimationFrame(e)))}function e(a){var b=g;g=[],b.sort(function(a,b){return a._sequenceNumber-b._sequenceNumber}),b=b.filter(function(a){a();var b=a._animation?a._animation.playState:"idle";return"running"!=b&&"pending"!=b&&(a._registered=!1),a._registered}),g.push.apply(g,b),g.length?(h=!0,requestAnimationFrame(e)):h=!1}var f=(document.createElementNS("http://www.w3.org/1999/xhtml","div"),0);b.bindAnimationForCustomEffect=function(b){var c,e=b.effect.target,g="function"==typeof b.effect.getFrames();c=g?b.effect.getFrames():b.effect._onsample;var h=b.effect.timing,i=null;h=a.normalizeTimingInput(h);var j=function(){var d=j._animation?j._animation.currentTime:null;null!==d&&(d=a.calculateTimeFraction(a.calculateActiveDuration(h),d,h),isNaN(d)&&(d=null)),d!==i&&(g?c(d,e,b.effect):c(d,b.effect,b.effect._animation)),i=d};j._animation=b,j._registered=!1,j._sequenceNumber=f++,b._callback=j,d(j)};var g=[],h=!1;b.Animation.prototype._register=function(){this._callback&&d(this._callback)}}(c,e,f),function(a,b,c){function d(a){return a._timing.delay+a.activeDuration+a._timing.endDelay}function e(b,c,d){this._id=d,this._parent=null,this.children=b||[],this._reparent(this.children),c=a.numericTimingToObject(c),this._timingInput=a.cloneTimingInput(c),this._timing=a.normalizeTimingInput(c,!0),this.timing=a.makeTiming(c,!0,this),this.timing._effect=this,"auto"===this._timing.duration&&(this._timing.duration=this.activeDuration)}window.SequenceEffect=function(){e.apply(this,arguments)},window.GroupEffect=function(){e.apply(this,arguments)},e.prototype={_isAncestor:function(a){for(var b=this;null!==b;){if(b==a)return!0;b=b._parent}return!1},_rebuild:function(){for(var a=this;a;)"auto"===a.timing.duration&&(a._timing.duration=a.activeDuration),a=a._parent;this._animation&&this._animation._rebuildUnderlyingAnimation()},_reparent:function(a){b.removeMulti(a);for(var c=0;c<a.length;c++)a[c]._parent=this},_putChild:function(a,b){for(var c=b?"Cannot append an ancestor or self":"Cannot prepend an ancestor or self",d=0;d<a.length;d++)if(this._isAncestor(a[d]))throw{type:DOMException.HIERARCHY_REQUEST_ERR,name:"HierarchyRequestError",message:c};for(var d=0;d<a.length;d++)b?this.children.push(a[d]):this.children.unshift(a[d]);this._reparent(a),this._rebuild()},append:function(){this._putChild(arguments,!0)},prepend:function(){this._putChild(arguments,!1)},get parent(){return this._parent},get firstChild(){return this.children.length?this.children[0]:null},get lastChild(){return this.children.length?this.children[this.children.length-1]:null},clone:function(){for(var b=a.cloneTimingInput(this._timingInput),c=[],d=0;d<this.children.length;d++)c.push(this.children[d].clone());return this instanceof GroupEffect?new GroupEffect(c,b):new SequenceEffect(c,b)},remove:function(){b.removeMulti([this])}},window.SequenceEffect.prototype=Object.create(e.prototype),Object.defineProperty(window.SequenceEffect.prototype,"activeDuration",{get:function(){var a=0;return this.children.forEach(function(b){a+=d(b)}),Math.max(a,0)}}),window.GroupEffect.prototype=Object.create(e.prototype),Object.defineProperty(window.GroupEffect.prototype,"activeDuration",{get:function(){var a=0;return this.children.forEach(function(b){a=Math.max(a,d(b))}),a}}),b.newUnderlyingAnimationForGroup=function(c){var d,e=null,f=function(b){var c=d._wrapper;return c&&"pending"!=c.playState&&c.effect?null==b?void c._removeChildAnimations():0==b&&c.playbackRate<0&&(e||(e=a.normalizeTimingInput(c.effect.timing)),b=a.calculateTimeFraction(a.calculateActiveDuration(e),-1,e),isNaN(b)||null==b)?(c._forEachChild(function(a){a.currentTime=-1}),void c._removeChildAnimations()):void 0:void 0},g=new KeyframeEffect(null,[],c._timing,c._id);return g.onsample=f,d=b.timeline._play(g)},b.bindAnimationForGroup=function(a){a._animation._wrapper=a,a._isGroup=!0,b.awaitStartTime(a),a._constructChildAnimations(),a._setExternalAnimation(a)},b.groupChildDuration=d}(c,e,f),b["true"]=a}({},function(){return this}());

/**
@license @nocompile
Copyright (c) 2018 The Polymer Project Authors. All rights reserved.
This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
Code distributed by Google as part of the polymer project is also
subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
(function(){/*

 Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
 This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
 The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
 The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
 Code distributed by Google as part of the polymer project is also
 subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
'use strict';var n,p="undefined"!=typeof window&&window===this?this:"undefined"!=typeof global&&null!=global?global:this,aa="function"==typeof Object.defineProperties?Object.defineProperty:function(a,b,c){a!=Array.prototype&&a!=Object.prototype&&(a[b]=c.value)};function ba(){ba=function(){};p.Symbol||(p.Symbol=ca)}var ca=function(){var a=0;return function(b){return"jscomp_symbol_"+(b||"")+a++}}();
function da(){ba();var a=p.Symbol.iterator;a||(a=p.Symbol.iterator=p.Symbol("iterator"));"function"!=typeof Array.prototype[a]&&aa(Array.prototype,a,{configurable:!0,writable:!0,value:function(){return ea(this)}});da=function(){}}function ea(a){var b=0;return fa(function(){return b<a.length?{done:!1,value:a[b++]}:{done:!0}})}function fa(a){da();a={next:a};a[p.Symbol.iterator]=function(){return this};return a}function ia(a){da();var b=a[Symbol.iterator];return b?b.call(a):ea(a)}
function ja(a){for(var b,c=[];!(b=a.next()).done;)c.push(b.value);return c}
(function(){if(!function(){var a=document.createEvent("Event");a.initEvent("foo",!0,!0);a.preventDefault();return a.defaultPrevented}()){var a=Event.prototype.preventDefault;Event.prototype.preventDefault=function(){this.cancelable&&(a.call(this),Object.defineProperty(this,"defaultPrevented",{get:function(){return!0},configurable:!0}))}}var b=/Trident/.test(navigator.userAgent);if(!window.CustomEvent||b&&"function"!==typeof window.CustomEvent)window.CustomEvent=function(a,b){b=b||{};var c=document.createEvent("CustomEvent");
c.initCustomEvent(a,!!b.bubbles,!!b.cancelable,b.detail);return c},window.CustomEvent.prototype=window.Event.prototype;if(!window.Event||b&&"function"!==typeof window.Event){var c=window.Event;window.Event=function(a,b){b=b||{};var c=document.createEvent("Event");c.initEvent(a,!!b.bubbles,!!b.cancelable);return c};if(c)for(var d in c)window.Event[d]=c[d];window.Event.prototype=c.prototype}if(!window.MouseEvent||b&&"function"!==typeof window.MouseEvent){b=window.MouseEvent;window.MouseEvent=function(a,
b){b=b||{};var c=document.createEvent("MouseEvent");c.initMouseEvent(a,!!b.bubbles,!!b.cancelable,b.view||window,b.detail,b.screenX,b.screenY,b.clientX,b.clientY,b.ctrlKey,b.altKey,b.shiftKey,b.metaKey,b.button,b.relatedTarget);return c};if(b)for(d in b)window.MouseEvent[d]=b[d];window.MouseEvent.prototype=b.prototype}Array.from||(Array.from=function(a){return[].slice.call(a)});Object.assign||(Object.assign=function(a,b){for(var c=[].slice.call(arguments,1),d=0,e;d<c.length;d++)if(e=c[d])for(var f=
a,m=e,q=Object.getOwnPropertyNames(m),x=0;x<q.length;x++)e=q[x],f[e]=m[e];return a})})(window.WebComponents);(function(){function a(){}function b(a,b){if(!a.childNodes.length)return[];switch(a.nodeType){case Node.DOCUMENT_NODE:return ua.call(a,b);case Node.DOCUMENT_FRAGMENT_NODE:return lb.call(a,b);default:return U.call(a,b)}}var c="undefined"===typeof HTMLTemplateElement,d=!(document.createDocumentFragment().cloneNode()instanceof DocumentFragment),e=!1;/Trident/.test(navigator.userAgent)&&function(){function a(a,b){if(a instanceof DocumentFragment)for(var d;d=a.firstChild;)c.call(this,d,b);else c.call(this,
a,b);return a}e=!0;var b=Node.prototype.cloneNode;Node.prototype.cloneNode=function(a){a=b.call(this,a);this instanceof DocumentFragment&&(a.__proto__=DocumentFragment.prototype);return a};DocumentFragment.prototype.querySelectorAll=HTMLElement.prototype.querySelectorAll;DocumentFragment.prototype.querySelector=HTMLElement.prototype.querySelector;Object.defineProperties(DocumentFragment.prototype,{nodeType:{get:function(){return Node.DOCUMENT_FRAGMENT_NODE},configurable:!0},localName:{get:function(){},
configurable:!0},nodeName:{get:function(){return"#document-fragment"},configurable:!0}});var c=Node.prototype.insertBefore;Node.prototype.insertBefore=a;var d=Node.prototype.appendChild;Node.prototype.appendChild=function(b){b instanceof DocumentFragment?a.call(this,b,null):d.call(this,b);return b};var f=Node.prototype.removeChild,g=Node.prototype.replaceChild;Node.prototype.replaceChild=function(b,c){b instanceof DocumentFragment?(a.call(this,b,c),f.call(this,c)):g.call(this,b,c);return c};Document.prototype.createDocumentFragment=
function(){var a=this.createElement("df");a.__proto__=DocumentFragment.prototype;return a};var h=Document.prototype.importNode;Document.prototype.importNode=function(a,b){b=h.call(this,a,b||!1);a instanceof DocumentFragment&&(b.__proto__=DocumentFragment.prototype);return b}}();var f=Node.prototype.cloneNode,g=Document.prototype.createElement,h=Document.prototype.importNode,k=Node.prototype.removeChild,l=Node.prototype.appendChild,m=Node.prototype.replaceChild,q=DOMParser.prototype.parseFromString,
x=Object.getOwnPropertyDescriptor(window.HTMLElement.prototype,"innerHTML")||{get:function(){return this.innerHTML},set:function(a){this.innerHTML=a}},M=Object.getOwnPropertyDescriptor(window.Node.prototype,"childNodes")||{get:function(){return this.childNodes}},U=Element.prototype.querySelectorAll,ua=Document.prototype.querySelectorAll,lb=DocumentFragment.prototype.querySelectorAll,mb=function(){if(!c){var a=document.createElement("template"),b=document.createElement("template");b.content.appendChild(document.createElement("div"));
a.content.appendChild(b);a=a.cloneNode(!0);return 0===a.content.childNodes.length||0===a.content.firstChild.content.childNodes.length||d}}();if(c){var S=document.implementation.createHTMLDocument("template"),C=!0,V=document.createElement("style");V.textContent="template{display:none;}";var ha=document.head;ha.insertBefore(V,ha.firstElementChild);a.prototype=Object.create(HTMLElement.prototype);var va=!document.createElement("div").hasOwnProperty("innerHTML");a.G=function(b){if(!b.content&&b.namespaceURI===
document.documentElement.namespaceURI){b.content=S.createDocumentFragment();for(var c;c=b.firstChild;)l.call(b.content,c);if(va)b.__proto__=a.prototype;else if(b.cloneNode=function(b){return a.a(this,b)},C)try{P(b),W(b)}catch(Tg){C=!1}a.C(b.content)}};var X={option:["select"],thead:["table"],col:["colgroup","table"],tr:["tbody","table"],th:["tr","tbody","table"],td:["tr","tbody","table"]},P=function(b){Object.defineProperty(b,"innerHTML",{get:function(){return nb(this)},set:function(b){var c=X[(/<([a-z][^/\0>\x20\t\r\n\f]+)/i.exec(b)||
["",""])[1].toLowerCase()];if(c)for(var d=0;d<c.length;d++)b="<"+c[d]+">"+b+"</"+c[d]+">";S.body.innerHTML=b;for(a.C(S);this.content.firstChild;)k.call(this.content,this.content.firstChild);b=S.body;if(c)for(d=0;d<c.length;d++)b=b.lastChild;for(;b.firstChild;)l.call(this.content,b.firstChild)},configurable:!0})},W=function(a){Object.defineProperty(a,"outerHTML",{get:function(){return"<template>"+this.innerHTML+"</template>"},set:function(a){if(this.parentNode){S.body.innerHTML=a;for(a=this.ownerDocument.createDocumentFragment();S.body.firstChild;)l.call(a,
S.body.firstChild);m.call(this.parentNode,a,this)}else throw Error("Failed to set the 'outerHTML' property on 'Element': This element has no parent node.");},configurable:!0})};P(a.prototype);W(a.prototype);a.C=function(c){c=b(c,"template");for(var d=0,e=c.length,f;d<e&&(f=c[d]);d++)a.G(f)};document.addEventListener("DOMContentLoaded",function(){a.C(document)});Document.prototype.createElement=function(){var b=g.apply(this,arguments);"template"===b.localName&&a.G(b);return b};DOMParser.prototype.parseFromString=
function(){var b=q.apply(this,arguments);a.C(b);return b};Object.defineProperty(HTMLElement.prototype,"innerHTML",{get:function(){return nb(this)},set:function(b){x.set.call(this,b);a.C(this)},configurable:!0,enumerable:!0});var Ve=/[&\u00A0"]/g,yc=/[&\u00A0<>]/g,zc=function(a){switch(a){case "&":return"&amp;";case "<":return"&lt;";case ">":return"&gt;";case '"':return"&quot;";case "\u00a0":return"&nbsp;"}};V=function(a){for(var b={},c=0;c<a.length;c++)b[a[c]]=!0;return b};var We=V("area base br col command embed hr img input keygen link meta param source track wbr".split(" ")),
Xe=V("style script xmp iframe noembed noframes plaintext noscript".split(" ")),nb=function(a,b){"template"===a.localName&&(a=a.content);for(var c="",d=b?b(a):M.get.call(a),e=0,f=d.length,g;e<f&&(g=d[e]);e++){a:{var h=g;var k=a;var l=b;switch(h.nodeType){case Node.ELEMENT_NODE:for(var P=h.localName,m="<"+P,W=h.attributes,q=0;k=W[q];q++)m+=" "+k.name+'="'+k.value.replace(Ve,zc)+'"';m+=">";h=We[P]?m:m+nb(h,l)+"</"+P+">";break a;case Node.TEXT_NODE:h=h.data;h=k&&Xe[k.localName]?h:h.replace(yc,zc);break a;
case Node.COMMENT_NODE:h="\x3c!--"+h.data+"--\x3e";break a;default:throw window.console.error(h),Error("not implemented");}}c+=h}return c}}if(c||mb){a.a=function(a,b){var c=f.call(a,!1);this.G&&this.G(c);b&&(l.call(c.content,f.call(a.content,!0)),ob(c.content,a.content));return c};var ob=function(c,d){if(d.querySelectorAll&&(d=b(d,"template"),0!==d.length)){c=b(c,"template");for(var e=0,f=c.length,g,h;e<f;e++)h=d[e],g=c[e],a&&a.G&&a.G(h),m.call(g.parentNode,Ye.call(h,!0),g)}},Ye=Node.prototype.cloneNode=
function(b){if(!e&&d&&this instanceof DocumentFragment)if(b)var c=Ze.call(this.ownerDocument,this,!0);else return this.ownerDocument.createDocumentFragment();else this.nodeType===Node.ELEMENT_NODE&&"template"===this.localName&&this.namespaceURI==document.documentElement.namespaceURI?c=a.a(this,b):c=f.call(this,b);b&&ob(c,this);return c},Ze=Document.prototype.importNode=function(c,d){d=d||!1;if("template"===c.localName)return a.a(c,d);var e=h.call(this,c,d);if(d){ob(e,c);c=b(e,'script:not([type]),script[type="application/javascript"],script[type="text/javascript"]');
for(var f,k=0;k<c.length;k++){f=c[k];d=g.call(document,"script");d.textContent=f.textContent;for(var l=f.attributes,P=0,W;P<l.length;P++)W=l[P],d.setAttribute(W.name,W.value);m.call(f.parentNode,d,f)}}return e}}c&&(window.HTMLTemplateElement=a)})();var ka=setTimeout;function la(){}function ma(a,b){return function(){a.apply(b,arguments)}}function r(a){if(!(this instanceof r))throw new TypeError("Promises must be constructed via new");if("function"!==typeof a)throw new TypeError("not a function");this.u=0;this.ma=!1;this.h=void 0;this.I=[];na(a,this)}
function oa(a,b){for(;3===a.u;)a=a.h;0===a.u?a.I.push(b):(a.ma=!0,pa(function(){var c=1===a.u?b.Na:b.Oa;if(null===c)(1===a.u?qa:ra)(b.ga,a.h);else{try{var d=c(a.h)}catch(e){ra(b.ga,e);return}qa(b.ga,d)}}))}function qa(a,b){try{if(b===a)throw new TypeError("A promise cannot be resolved with itself.");if(b&&("object"===typeof b||"function"===typeof b)){var c=b.then;if(b instanceof r){a.u=3;a.h=b;sa(a);return}if("function"===typeof c){na(ma(c,b),a);return}}a.u=1;a.h=b;sa(a)}catch(d){ra(a,d)}}
function ra(a,b){a.u=2;a.h=b;sa(a)}function sa(a){2===a.u&&0===a.I.length&&pa(function(){a.ma||"undefined"!==typeof console&&console&&console.warn("Possible Unhandled Promise Rejection:",a.h)});for(var b=0,c=a.I.length;b<c;b++)oa(a,a.I[b]);a.I=null}function ta(a,b,c){this.Na="function"===typeof a?a:null;this.Oa="function"===typeof b?b:null;this.ga=c}function na(a,b){var c=!1;try{a(function(a){c||(c=!0,qa(b,a))},function(a){c||(c=!0,ra(b,a))})}catch(d){c||(c=!0,ra(b,d))}}
r.prototype["catch"]=function(a){return this.then(null,a)};r.prototype.then=function(a,b){var c=new this.constructor(la);oa(this,new ta(a,b,c));return c};r.prototype["finally"]=function(a){var b=this.constructor;return this.then(function(c){return b.resolve(a()).then(function(){return c})},function(c){return b.resolve(a()).then(function(){return b.reject(c)})})};
function wa(a){return new r(function(b,c){function d(a,g){try{if(g&&("object"===typeof g||"function"===typeof g)){var h=g.then;if("function"===typeof h){h.call(g,function(b){d(a,b)},c);return}}e[a]=g;0===--f&&b(e)}catch(m){c(m)}}if(!a||"undefined"===typeof a.length)throw new TypeError("Promise.all accepts an array");var e=Array.prototype.slice.call(a);if(0===e.length)return b([]);for(var f=e.length,g=0;g<e.length;g++)d(g,e[g])})}
function xa(a){return a&&"object"===typeof a&&a.constructor===r?a:new r(function(b){b(a)})}function ya(a){return new r(function(b,c){c(a)})}function za(a){return new r(function(b,c){for(var d=0,e=a.length;d<e;d++)a[d].then(b,c)})}var pa="function"===typeof setImmediate&&function(a){setImmediate(a)}||function(a){ka(a,0)};/*

Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
Code distributed by Google as part of the polymer project is also
subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
if(!window.Promise){window.Promise=r;r.prototype.then=r.prototype.then;r.all=wa;r.race=za;r.resolve=xa;r.reject=ya;var Aa=document.createTextNode(""),Ba=[];(new MutationObserver(function(){for(var a=Ba.length,b=0;b<a;b++)Ba[b]();Ba.splice(0,a)})).observe(Aa,{characterData:!0});pa=function(a){Ba.push(a);Aa.textContent=0<Aa.textContent.length?"":"a"}};(function(a){function b(a,b){if("function"===typeof window.CustomEvent)return new CustomEvent(a,b);var c=document.createEvent("CustomEvent");c.initCustomEvent(a,!!b.bubbles,!!b.cancelable,b.detail);return c}function c(a){if(M)return a.ownerDocument!==document?a.ownerDocument:null;var b=a.__importDoc;if(!b&&a.parentNode){b=a.parentNode;if("function"===typeof b.closest)b=b.closest("link[rel=import]");else for(;!h(b)&&(b=b.parentNode););a.__importDoc=b}return b}function d(a){var b=m(document,"link[rel=import]:not([import-dependency])"),
c=b.length;c?q(b,function(b){return g(b,function(){0===--c&&a()})}):a()}function e(a){function b(){"loading"!==document.readyState&&document.body&&(document.removeEventListener("readystatechange",b),a())}document.addEventListener("readystatechange",b);b()}function f(a){e(function(){return d(function(){return a&&a()})})}function g(a,b){if(a.__loaded)b&&b();else if("script"===a.localName&&!a.src||"style"===a.localName&&!a.firstChild)a.__loaded=!0,b&&b();else{var c=function(d){a.removeEventListener(d.type,
c);a.__loaded=!0;b&&b()};a.addEventListener("load",c);ha&&"style"===a.localName||a.addEventListener("error",c)}}function h(a){return a.nodeType===Node.ELEMENT_NODE&&"link"===a.localName&&"import"===a.rel}function k(){var a=this;this.a={};this.b=0;this.c=new MutationObserver(function(b){return a.Ja(b)});this.c.observe(document.head,{childList:!0,subtree:!0});this.loadImports(document)}function l(a){q(m(a,"template"),function(a){q(m(a.content,'script:not([type]),script[type="application/javascript"],script[type="text/javascript"],script[type="module"]'),
function(a){var b=document.createElement("script");q(a.attributes,function(a){return b.setAttribute(a.name,a.value)});b.textContent=a.textContent;a.parentNode.replaceChild(b,a)});l(a.content)})}function m(a,b){return a.childNodes.length?a.querySelectorAll(b):U}function q(a,b,c){var d=a?a.length:0,e=c?-1:1;for(c=c?d-1:0;c<d&&0<=c;c+=e)b(a[c],c)}var x=document.createElement("link"),M="import"in x,U=x.querySelectorAll("*"),ua=null;!1==="currentScript"in document&&Object.defineProperty(document,"currentScript",
{get:function(){return ua||("complete"!==document.readyState?document.scripts[document.scripts.length-1]:null)},configurable:!0});var lb=/(url\()([^)]*)(\))/g,mb=/(@import[\s]+(?!url\())([^;]*)(;)/g,S=/(<link[^>]*)(rel=['|"]?stylesheet['|"]?[^>]*>)/g,C={Ea:function(a,b){a.href&&a.setAttribute("href",C.X(a.getAttribute("href"),b));a.src&&a.setAttribute("src",C.X(a.getAttribute("src"),b));if("style"===a.localName){var c=C.qa(a.textContent,b,lb);a.textContent=C.qa(c,b,mb)}},qa:function(a,b,c){return a.replace(c,
function(a,c,d,e){a=d.replace(/["']/g,"");b&&(a=C.X(a,b));return c+"'"+a+"'"+e})},X:function(a,b){if(void 0===C.aa){C.aa=!1;try{var c=new URL("b","http://a");c.pathname="c%20d";C.aa="http://a/c%20d"===c.href}catch(yc){}}if(C.aa)return(new URL(a,b)).href;c=C.xa;c||(c=document.implementation.createHTMLDocument("temp"),C.xa=c,c.ja=c.createElement("base"),c.head.appendChild(c.ja),c.ia=c.createElement("a"));c.ja.href=b;c.ia.href=a;return c.ia.href||a}},V={async:!0,load:function(a,b,c){if(a)if(a.match(/^data:/)){a=
a.split(",");var d=a[1];d=-1<a[0].indexOf(";base64")?atob(d):decodeURIComponent(d);b(d)}else{var e=new XMLHttpRequest;e.open("GET",a,V.async);e.onload=function(){var a=e.responseURL||e.getResponseHeader("Location");a&&0===a.indexOf("/")&&(a=(location.origin||location.protocol+"//"+location.host)+a);var d=e.response||e.responseText;304===e.status||0===e.status||200<=e.status&&300>e.status?b(d,a):c(d)};e.send()}else c("error: href must be specified")}},ha=/Trident/.test(navigator.userAgent)||/Edge\/\d./i.test(navigator.userAgent);
k.prototype.loadImports=function(a){var b=this;a=m(a,"link[rel=import]");q(a,function(a){return b.g(a)})};k.prototype.g=function(a){var b=this,c=a.href;if(void 0!==this.a[c]){var d=this.a[c];d&&d.__loaded&&(a.__import=d,this.f(a))}else this.b++,this.a[c]="pending",V.load(c,function(a,d){a=b.Ka(a,d||c);b.a[c]=a;b.b--;b.loadImports(a);b.l()},function(){b.a[c]=null;b.b--;b.l()})};k.prototype.Ka=function(a,b){if(!a)return document.createDocumentFragment();ha&&(a=a.replace(S,function(a,b,c){return-1===
a.indexOf("type=")?b+" type=import-disable "+c:a}));var c=document.createElement("template");c.innerHTML=a;if(c.content)a=c.content,l(a);else for(a=document.createDocumentFragment();c.firstChild;)a.appendChild(c.firstChild);if(c=a.querySelector("base"))b=C.X(c.getAttribute("href"),b),c.removeAttribute("href");c=m(a,'link[rel=import],link[rel=stylesheet][href][type=import-disable],style:not([type]),link[rel=stylesheet][href]:not([type]),script:not([type]),script[type="application/javascript"],script[type="text/javascript"],script[type="module"]');
var d=0;q(c,function(a){g(a);C.Ea(a,b);a.setAttribute("import-dependency","");if("script"===a.localName&&!a.src&&a.textContent){if("module"===a.type)throw Error("Inline module scripts are not supported in HTML Imports.");a.setAttribute("src","data:text/javascript;charset=utf-8,"+encodeURIComponent(a.textContent+("\n//# sourceURL="+b+(d?"-"+d:"")+".js\n")));a.textContent="";d++}});return a};k.prototype.l=function(){var a=this;if(!this.b){this.c.disconnect();this.flatten(document);var b=!1,c=!1,d=function(){c&&
b&&(a.loadImports(document),a.b||(a.c.observe(document.head,{childList:!0,subtree:!0}),a.da()))};this.Ma(function(){c=!0;d()});this.La(function(){b=!0;d()})}};k.prototype.flatten=function(a){var b=this;a=m(a,"link[rel=import]");q(a,function(a){var c=b.a[a.href];(a.__import=c)&&c.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&(b.a[a.href]=a,a.readyState="loading",a.__import=a,b.flatten(c),a.appendChild(c))})};k.prototype.La=function(a){function b(e){if(e<d){var f=c[e],h=document.createElement("script");f.removeAttribute("import-dependency");
q(f.attributes,function(a){return h.setAttribute(a.name,a.value)});ua=h;f.parentNode.replaceChild(h,f);g(h,function(){ua=null;b(e+1)})}else a()}var c=m(document,"script[import-dependency]"),d=c.length;b(0)};k.prototype.Ma=function(a){var b=m(document,"style[import-dependency],link[rel=stylesheet][import-dependency]"),d=b.length;if(d){var e=ha&&!!document.querySelector("link[rel=stylesheet][href][type=import-disable]");q(b,function(b){g(b,function(){b.removeAttribute("import-dependency");0===--d&&
a()});if(e&&b.parentNode!==document.head){var f=document.createElement(b.localName);f.__appliedElement=b;f.setAttribute("type","import-placeholder");b.parentNode.insertBefore(f,b.nextSibling);for(f=c(b);f&&c(f);)f=c(f);f.parentNode!==document.head&&(f=null);document.head.insertBefore(b,f);b.removeAttribute("type")}})}else a()};k.prototype.da=function(){var a=this,b=m(document,"link[rel=import]");q(b,function(b){return a.f(b)},!0)};k.prototype.f=function(a){a.__loaded||(a.__loaded=!0,a.import&&(a.import.readyState=
"complete"),a.dispatchEvent(b(a.import?"load":"error",{bubbles:!1,cancelable:!1,detail:void 0})))};k.prototype.Ja=function(a){var b=this;q(a,function(a){return q(a.addedNodes,function(a){a&&a.nodeType===Node.ELEMENT_NODE&&(h(a)?b.g(a):b.loadImports(a))})})};var va=null;if(M)x=m(document,"link[rel=import]"),q(x,function(a){a.import&&"loading"===a.import.readyState||(a.__loaded=!0)}),x=function(a){a=a.target;h(a)&&(a.__loaded=!0)},document.addEventListener("load",x,!0),document.addEventListener("error",
x,!0);else{var X=Object.getOwnPropertyDescriptor(Node.prototype,"baseURI");Object.defineProperty((!X||X.configurable?Node:Element).prototype,"baseURI",{get:function(){var a=h(this)?this:c(this);return a?a.href:X&&X.get?X.get.call(this):(document.querySelector("base")||window.location).href},configurable:!0,enumerable:!0});Object.defineProperty(HTMLLinkElement.prototype,"import",{get:function(){return this.__import||null},configurable:!0,enumerable:!0});e(function(){va=new k})}f(function(){return document.dispatchEvent(b("HTMLImportsLoaded",
{cancelable:!0,bubbles:!0,detail:void 0}))});a.useNative=M;a.whenReady=f;a.importForElement=c;a.loadImports=function(a){va&&va.loadImports(a)}})(window.HTMLImports=window.HTMLImports||{});/*

 Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
 This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
 The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
 The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
 Code distributed by Google as part of the polymer project is also
 subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
window.WebComponents=window.WebComponents||{flags:{}};var Ca=document.querySelector('script[src*="webcomponents-lite.js"]'),Da=/wc-(.+)/,t={};if(!t.noOpts){location.search.slice(1).split("&").forEach(function(a){a=a.split("=");var b;a[0]&&(b=a[0].match(Da))&&(t[b[1]]=a[1]||!0)});if(Ca)for(var Ea=0,Fa=void 0;Fa=Ca.attributes[Ea];Ea++)"src"!==Fa.name&&(t[Fa.name]=Fa.value||!0);if(t.log&&t.log.split){var Ga=t.log.split(",");t.log={};Ga.forEach(function(a){t.log[a]=!0})}else t.log={}}
window.WebComponents.flags=t;var Ha=t.shadydom;Ha&&(window.ShadyDOM=window.ShadyDOM||{},window.ShadyDOM.force=Ha);var Ia=t.register||t.ce;Ia&&window.customElements&&(window.customElements.forcePolyfill=Ia);/*

Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
Code distributed by Google as part of the polymer project is also
subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
function Ja(){this.pa=this.root=null;this.T=!1;this.D=this.P=this.ca=this.assignedSlot=this.assignedNodes=this.H=null;this.childNodes=this.nextSibling=this.previousSibling=this.lastChild=this.firstChild=this.parentNode=this.K=void 0;this.ka=this.la=!1;this.O={}}Ja.prototype.toJSON=function(){return{}};function u(a){a.__shady||(a.__shady=new Ja);return a.__shady}function v(a){return a&&a.__shady};var w=window.ShadyDOM||{};w.Ga=!(!Element.prototype.attachShadow||!Node.prototype.getRootNode);var Ka=Object.getOwnPropertyDescriptor(Node.prototype,"firstChild");w.m=!!(Ka&&Ka.configurable&&Ka.get);w.ea=w.force||!w.Ga;w.J=w.noPatch||!1;w.oa=w.preferPerformance;function y(a){return(a=v(a))&&void 0!==a.firstChild}function z(a){return"ShadyRoot"===a.za}function La(a){return(a=(a=v(a))&&a.root)&&Ma(a)}
var Na=Element.prototype,Oa=Na.matches||Na.matchesSelector||Na.mozMatchesSelector||Na.msMatchesSelector||Na.oMatchesSelector||Na.webkitMatchesSelector,Pa=document.createTextNode(""),Qa=0,Ra=[];(new MutationObserver(function(){for(;Ra.length;)try{Ra.shift()()}catch(a){throw Pa.textContent=Qa++,a;}})).observe(Pa,{characterData:!0});function Sa(a){Ra.push(a);Pa.textContent=Qa++}var Ta=!!document.contains;function Ua(a,b){for(;b;){if(b==a)return!0;b=b.__shady_parentNode}return!1}
function Va(a){for(var b=a.length-1;0<=b;b--){var c=a[b],d=c.getAttribute("id")||c.getAttribute("name");d&&"length"!==d&&isNaN(d)&&(a[d]=c)}a.item=function(b){return a[b]};a.namedItem=function(b){if("length"!==b&&isNaN(b)&&a[b])return a[b];for(var c=ia(a),d=c.next();!d.done;d=c.next())if(d=d.value,(d.getAttribute("id")||d.getAttribute("name"))==b)return d;return null};return a}
function A(a,b,c,d){c=void 0===c?"":c;for(var e in b){var f=b[e];if(!(d&&0<=d.indexOf(e))){f.configurable=!0;var g=c+e;if(f.value)a[g]=f.value;else try{Object.defineProperty(a,g,f)}catch(h){}}}}function B(a){var b={};Object.getOwnPropertyNames(a).forEach(function(c){b[c]=Object.getOwnPropertyDescriptor(a,c)});return b};var Wa=[],Xa;function Ya(a){Xa||(Xa=!0,Sa(Za));Wa.push(a)}function Za(){Xa=!1;for(var a=!!Wa.length;Wa.length;)Wa.shift()();return a}Za.list=Wa;function $a(){this.a=!1;this.addedNodes=[];this.removedNodes=[];this.S=new Set}function ab(a){a.a||(a.a=!0,Sa(function(){a.flush()}))}$a.prototype.flush=function(){if(this.a){this.a=!1;var a=this.takeRecords();a.length&&this.S.forEach(function(b){b(a)})}};$a.prototype.takeRecords=function(){if(this.addedNodes.length||this.removedNodes.length){var a=[{addedNodes:this.addedNodes,removedNodes:this.removedNodes}];this.addedNodes=[];this.removedNodes=[];return a}return[]};
function bb(a,b){var c=u(a);c.H||(c.H=new $a);c.H.S.add(b);var d=c.H;return{ya:b,F:d,Aa:a,takeRecords:function(){return d.takeRecords()}}}function cb(a){var b=a&&a.F;b&&(b.S.delete(a.ya),b.S.size||(u(a.Aa).H=null))}
function db(a,b){var c=b.getRootNode();return a.map(function(a){var b=c===a.target.getRootNode();if(b&&a.addedNodes){if(b=Array.from(a.addedNodes).filter(function(a){return c===a.getRootNode()}),b.length)return a=Object.create(a),Object.defineProperty(a,"addedNodes",{value:b,configurable:!0}),a}else if(b)return a}).filter(function(a){return a})};var eb=/[&\u00A0"]/g,fb=/[&\u00A0<>]/g;function gb(a){switch(a){case "&":return"&amp;";case "<":return"&lt;";case ">":return"&gt;";case '"':return"&quot;";case "\u00a0":return"&nbsp;"}}function hb(a){for(var b={},c=0;c<a.length;c++)b[a[c]]=!0;return b}var ib=hb("area base br col command embed hr img input keygen link meta param source track wbr".split(" ")),jb=hb("style script xmp iframe noembed noframes plaintext noscript".split(" "));
function kb(a,b){"template"===a.localName&&(a=a.content);for(var c="",d=b?b(a):a.childNodes,e=0,f=d.length,g=void 0;e<f&&(g=d[e]);e++){a:{var h=g;var k=a,l=b;switch(h.nodeType){case Node.ELEMENT_NODE:k=h.localName;for(var m="<"+k,q=h.attributes,x=0,M;M=q[x];x++)m+=" "+M.name+'="'+M.value.replace(eb,gb)+'"';m+=">";h=ib[k]?m:m+kb(h,l)+"</"+k+">";break a;case Node.TEXT_NODE:h=h.data;h=k&&jb[k.localName]?h:h.replace(fb,gb);break a;case Node.COMMENT_NODE:h="\x3c!--"+h.data+"--\x3e";break a;default:throw window.console.error(h),
Error("not implemented");}}c+=h}return c};var pb=w.m,qb={querySelector:function(a){return this.__shady_native_querySelector(a)},querySelectorAll:function(a){return this.__shady_native_querySelectorAll(a)}},rb={};function sb(a){rb[a]=function(b){return b["__shady_native_"+a]}}function tb(a,b){A(a,b,"__shady_native_");for(var c in b)sb(c)}function D(a,b){b=void 0===b?[]:b;for(var c=0;c<b.length;c++){var d=b[c],e=Object.getOwnPropertyDescriptor(a,d);e&&(Object.defineProperty(a,"__shady_native_"+d,e),e.value?qb[d]||(qb[d]=e.value):sb(d))}}
var E=document.createTreeWalker(document,NodeFilter.SHOW_ALL,null,!1),F=document.createTreeWalker(document,NodeFilter.SHOW_ELEMENT,null,!1),ub=document.implementation.createHTMLDocument("inert");function vb(a){for(var b;b=a.__shady_native_firstChild;)a.__shady_native_removeChild(b)}var wb=["firstElementChild","lastElementChild","children","childElementCount"],xb=["querySelector","querySelectorAll"];
function yb(){var a=["dispatchEvent","addEventListener","removeEventListener"];window.EventTarget?D(window.EventTarget.prototype,a):(D(Node.prototype,a),D(Window.prototype,a));pb?D(Node.prototype,"parentNode firstChild lastChild previousSibling nextSibling childNodes parentElement textContent".split(" ")):tb(Node.prototype,{parentNode:{get:function(){E.currentNode=this;return E.parentNode()}},firstChild:{get:function(){E.currentNode=this;return E.firstChild()}},lastChild:{get:function(){E.currentNode=
this;return E.lastChild()}},previousSibling:{get:function(){E.currentNode=this;return E.previousSibling()}},nextSibling:{get:function(){E.currentNode=this;return E.nextSibling()}},childNodes:{get:function(){var a=[];E.currentNode=this;for(var c=E.firstChild();c;)a.push(c),c=E.nextSibling();return a}},parentElement:{get:function(){F.currentNode=this;return F.parentNode()}},textContent:{get:function(){switch(this.nodeType){case Node.ELEMENT_NODE:case Node.DOCUMENT_FRAGMENT_NODE:for(var a=document.createTreeWalker(this,
NodeFilter.SHOW_TEXT,null,!1),c="",d;d=a.nextNode();)c+=d.nodeValue;return c;default:return this.nodeValue}},set:function(a){if("undefined"===typeof a||null===a)a="";switch(this.nodeType){case Node.ELEMENT_NODE:case Node.DOCUMENT_FRAGMENT_NODE:vb(this);(0<a.length||this.nodeType===Node.ELEMENT_NODE)&&this.__shady_native_insertBefore(document.createTextNode(a),void 0);break;default:this.nodeValue=a}}}});D(Node.prototype,"appendChild insertBefore removeChild replaceChild cloneNode contains".split(" "));
a={firstElementChild:{get:function(){F.currentNode=this;return F.firstChild()}},lastElementChild:{get:function(){F.currentNode=this;return F.lastChild()}},children:{get:function(){var a=[];F.currentNode=this;for(var c=F.firstChild();c;)a.push(c),c=F.nextSibling();return Va(a)}},childElementCount:{get:function(){return this.children?this.children.length:0}}};pb?(D(Element.prototype,wb),D(Element.prototype,["previousElementSibling","nextElementSibling","innerHTML"]),Object.getOwnPropertyDescriptor(HTMLElement.prototype,
"children")&&D(HTMLElement.prototype,["children"]),Object.getOwnPropertyDescriptor(HTMLElement.prototype,"innerHTML")&&D(HTMLElement.prototype,["innerHTML"])):(tb(Element.prototype,a),tb(Element.prototype,{previousElementSibling:{get:function(){F.currentNode=this;return F.previousSibling()}},nextElementSibling:{get:function(){F.currentNode=this;return F.nextSibling()}},innerHTML:{get:function(){return kb(this,function(a){return a.__shady_native_childNodes})},set:function(a){var b="template"===this.localName?
this.content:this;vb(b);var d=this.localName||"div";d=this.namespaceURI&&this.namespaceURI!==ub.namespaceURI?ub.createElementNS(this.namespaceURI,d):ub.createElement(d);d.innerHTML=a;for(a="template"===this.localName?d.content:d;d=a.__shady_native_firstChild;)b.__shady_native_insertBefore(d,void 0)}}}));D(Element.prototype,"setAttribute getAttribute hasAttribute removeAttribute focus blur".split(" "));D(Element.prototype,xb);D(HTMLElement.prototype,["focus","blur","contains"]);pb&&D(HTMLElement.prototype,
["parentElement","children","innerHTML"]);window.HTMLTemplateElement&&D(window.HTMLTemplateElement.prototype,["innerHTML"]);pb?D(DocumentFragment.prototype,wb):tb(DocumentFragment.prototype,a);D(DocumentFragment.prototype,xb);pb?(D(Document.prototype,wb),D(Document.prototype,["activeElement"])):tb(Document.prototype,a);D(Document.prototype,["importNode","getElementById"]);D(Document.prototype,xb)};var zb=B({get childNodes(){return this.__shady_childNodes},get firstChild(){return this.__shady_firstChild},get lastChild(){return this.__shady_lastChild},get textContent(){return this.__shady_textContent},set textContent(a){this.__shady_textContent=a},get childElementCount(){return this.__shady_childElementCount},get children(){return this.__shady_children},get firstElementChild(){return this.__shady_firstElementChild},get lastElementChild(){return this.__shady_lastElementChild},get innerHTML(){return this.__shady_innerHTML},
set innerHTML(a){return this.__shady_innerHTML=a},get shadowRoot(){return this.__shady_shadowRoot}}),Ab=B({get parentElement(){return this.__shady_parentElement},get parentNode(){return this.__shady_parentNode},get nextSibling(){return this.__shady_nextSibling},get previousSibling(){return this.__shady_previousSibling},get nextElementSibling(){return this.__shady_nextElementSibling},get previousElementSibling(){return this.__shady_previousElementSibling},get className(){return this.__shady_className},
set className(a){return this.__shady_className=a}}),Bb;for(Bb in zb)zb[Bb].enumerable=!1;for(var Cb in Ab)Ab[Cb].enumerable=!1;var Db=w.m||w.J,Eb=Db?function(){}:function(a){var b=u(a);b.la||(b.la=!0,A(a,Ab))},Fb=Db?function(){}:function(a){var b=u(a);b.ka||(b.ka=!0,A(a,zb))};var Gb="__eventWrappers"+Date.now(),Hb=function(){var a=Object.getOwnPropertyDescriptor(Event.prototype,"composed");return a?function(b){return a.get.call(b)}:null}(),Ib={blur:!0,focus:!0,focusin:!0,focusout:!0,click:!0,dblclick:!0,mousedown:!0,mouseenter:!0,mouseleave:!0,mousemove:!0,mouseout:!0,mouseover:!0,mouseup:!0,wheel:!0,beforeinput:!0,input:!0,keydown:!0,keyup:!0,compositionstart:!0,compositionupdate:!0,compositionend:!0,touchstart:!0,touchend:!0,touchmove:!0,touchcancel:!0,pointerover:!0,
pointerenter:!0,pointerdown:!0,pointermove:!0,pointerup:!0,pointercancel:!0,pointerout:!0,pointerleave:!0,gotpointercapture:!0,lostpointercapture:!0,dragstart:!0,drag:!0,dragenter:!0,dragleave:!0,dragover:!0,drop:!0,dragend:!0,DOMActivate:!0,DOMFocusIn:!0,DOMFocusOut:!0,keypress:!0},Jb={DOMAttrModified:!0,DOMAttributeNameChanged:!0,DOMCharacterDataModified:!0,DOMElementNameChanged:!0,DOMNodeInserted:!0,DOMNodeInsertedIntoDocument:!0,DOMNodeRemoved:!0,DOMNodeRemovedFromDocument:!0,DOMSubtreeModified:!0};
function Kb(a){return a instanceof Node?a.__shady_getRootNode():a}function Lb(a,b){var c=[],d=a;for(a=Kb(a);d;)c.push(d),d.__shady_assignedSlot?d=d.__shady_assignedSlot:d.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&d.host&&(b||d!==a)?d=d.host:d=d.__shady_parentNode;c[c.length-1]===document&&c.push(window);return c}function Mb(a){a.__composedPath||(a.__composedPath=Lb(a.target,!0));return a.__composedPath}
function Nb(a,b){if(!z)return a;a=Lb(a,!0);for(var c=0,d,e=void 0,f,g=void 0;c<b.length;c++)if(d=b[c],f=Kb(d),f!==e&&(g=a.indexOf(f),e=f),!z(f)||-1<g)return d}function Ob(a){function b(b,d){b=new a(b,d);b.__composed=d&&!!d.composed;return b}b.__proto__=a;b.prototype=a.prototype;return b}var Pb={focus:!0,blur:!0};function Qb(a){return a.__target!==a.target||a.__relatedTarget!==a.relatedTarget}
function Rb(a,b,c){if(c=b.__handlers&&b.__handlers[a.type]&&b.__handlers[a.type][c])for(var d=0,e;(e=c[d])&&(!Qb(a)||a.target!==a.relatedTarget)&&(e.call(b,a),!a.__immediatePropagationStopped);d++);}
function Sb(a){var b=a.composedPath();Object.defineProperty(a,"currentTarget",{get:function(){return d},configurable:!0});for(var c=b.length-1;0<=c;c--){var d=b[c];Rb(a,d,"capture");if(a.Z)return}Object.defineProperty(a,"eventPhase",{get:function(){return Event.AT_TARGET}});var e;for(c=0;c<b.length;c++){d=b[c];var f=v(d);f=f&&f.root;if(0===c||f&&f===e)if(Rb(a,d,"bubble"),d!==window&&(e=d.__shady_getRootNode()),a.Z)break}}
function Tb(a,b,c,d,e,f){for(var g=0;g<a.length;g++){var h=a[g],k=h.type,l=h.capture,m=h.once,q=h.passive;if(b===h.node&&c===k&&d===l&&e===m&&f===q)return g}return-1}
function Ub(a,b,c){if(b){var d=typeof b;if("function"===d||"object"===d)if("object"!==d||b.handleEvent&&"function"===typeof b.handleEvent){if(Jb[a])return this.__shady_native_addEventListener(a,b,c);if(c&&"object"===typeof c){var e=!!c.capture;var f=!!c.once;var g=!!c.passive}else e=!!c,g=f=!1;var h=c&&c.$||this,k=b[Gb];if(k){if(-1<Tb(k,h,a,e,f,g))return}else b[Gb]=[];k=function(e){f&&this.__shady_removeEventListener(a,b,c);e.__target||Vb(e);if(h!==this){var g=Object.getOwnPropertyDescriptor(e,"currentTarget");
Object.defineProperty(e,"currentTarget",{get:function(){return h},configurable:!0})}e.__previousCurrentTarget=e.currentTarget;if(!z(h)||-1!=e.composedPath().indexOf(h))if(e.composed||-1<e.composedPath().indexOf(h))if(Qb(e)&&e.target===e.relatedTarget)e.eventPhase===Event.BUBBLING_PHASE&&e.stopImmediatePropagation();else if(e.eventPhase===Event.CAPTURING_PHASE||e.bubbles||e.target===h||h instanceof Window){var k="function"===d?b.call(h,e):b.handleEvent&&b.handleEvent(e);h!==this&&(g?(Object.defineProperty(e,
"currentTarget",g),g=null):delete e.currentTarget);return k}};b[Gb].push({node:h,type:a,capture:e,once:f,passive:g,Ya:k});Pb[a]?(this.__handlers=this.__handlers||{},this.__handlers[a]=this.__handlers[a]||{capture:[],bubble:[]},this.__handlers[a][e?"capture":"bubble"].push(k)):this.__shady_native_addEventListener(a,k,c)}}}
function Wb(a,b,c){if(b){if(Jb[a])return this.__shady_native_removeEventListener(a,b,c);if(c&&"object"===typeof c){var d=!!c.capture;var e=!!c.once;var f=!!c.passive}else d=!!c,f=e=!1;var g=c&&c.$||this,h=void 0;var k=null;try{k=b[Gb]}catch(l){}k&&(e=Tb(k,g,a,d,e,f),-1<e&&(h=k.splice(e,1)[0].Ya,k.length||(b[Gb]=void 0)));this.__shady_native_removeEventListener(a,h||b,c);h&&Pb[a]&&this.__handlers&&this.__handlers[a]&&(a=this.__handlers[a][d?"capture":"bubble"],h=a.indexOf(h),-1<h&&a.splice(h,1))}}
function Xb(){for(var a in Pb)window.__shady_native_addEventListener(a,function(a){a.__target||(Vb(a),Sb(a))},!0)}
var Yb=B({get composed(){void 0===this.__composed&&(Hb?this.__composed="focusin"===this.type||"focusout"===this.type||Hb(this):!1!==this.isTrusted&&(this.__composed=Ib[this.type]));return this.__composed||!1},composedPath:function(){this.__composedPath||(this.__composedPath=Lb(this.__target,this.composed));return this.__composedPath},get target(){return Nb(this.currentTarget||this.__previousCurrentTarget,this.composedPath())},get relatedTarget(){if(!this.__relatedTarget)return null;this.__relatedTargetComposedPath||
(this.__relatedTargetComposedPath=Lb(this.__relatedTarget,!0));return Nb(this.currentTarget||this.__previousCurrentTarget,this.__relatedTargetComposedPath)},stopPropagation:function(){Event.prototype.stopPropagation.call(this);this.Z=!0},stopImmediatePropagation:function(){Event.prototype.stopImmediatePropagation.call(this);this.Z=this.__immediatePropagationStopped=!0}});
function Vb(a){a.__target=a.target;a.__relatedTarget=a.relatedTarget;if(w.m){var b=Object.getPrototypeOf(a);if(!Object.hasOwnProperty(b,"__shady_patchedProto")){var c=Object.create(b);c.__shady_sourceProto=b;A(c,Yb);b.__shady_patchedProto=c}a.__proto__=b.__shady_patchedProto}else A(a,Yb)}var Zb=Ob(Event),$b=Ob(CustomEvent),ac=Ob(MouseEvent);
function bc(){if(!Hb&&Object.getOwnPropertyDescriptor(Event.prototype,"isTrusted")){var a=function(){var a=new MouseEvent("click",{bubbles:!0,cancelable:!0,composed:!0});this.__shady_dispatchEvent(a)};Element.prototype.click?Element.prototype.click=a:HTMLElement.prototype.click&&(HTMLElement.prototype.click=a)}}var cc=Object.getOwnPropertyNames(Document.prototype).filter(function(a){return"on"===a.substring(0,2)});function dc(a,b){return{index:a,L:[],R:b}}
function ec(a,b,c,d){var e=0,f=0,g=0,h=0,k=Math.min(b-e,d-f);if(0==e&&0==f)a:{for(g=0;g<k;g++)if(a[g]!==c[g])break a;g=k}if(b==a.length&&d==c.length){h=a.length;for(var l=c.length,m=0;m<k-g&&fc(a[--h],c[--l]);)m++;h=m}e+=g;f+=g;b-=h;d-=h;if(0==b-e&&0==d-f)return[];if(e==b){for(b=dc(e,0);f<d;)b.L.push(c[f++]);return[b]}if(f==d)return[dc(e,b-e)];k=e;g=f;d=d-g+1;h=b-k+1;b=Array(d);for(l=0;l<d;l++)b[l]=Array(h),b[l][0]=l;for(l=0;l<h;l++)b[0][l]=l;for(l=1;l<d;l++)for(m=1;m<h;m++)if(a[k+m-1]===c[g+l-1])b[l][m]=
b[l-1][m-1];else{var q=b[l-1][m]+1,x=b[l][m-1]+1;b[l][m]=q<x?q:x}k=b.length-1;g=b[0].length-1;d=b[k][g];for(a=[];0<k||0<g;)0==k?(a.push(2),g--):0==g?(a.push(3),k--):(h=b[k-1][g-1],l=b[k-1][g],m=b[k][g-1],q=l<m?l<h?l:h:m<h?m:h,q==h?(h==d?a.push(0):(a.push(1),d=h),k--,g--):q==l?(a.push(3),k--,d=l):(a.push(2),g--,d=m));a.reverse();b=void 0;k=[];for(g=0;g<a.length;g++)switch(a[g]){case 0:b&&(k.push(b),b=void 0);e++;f++;break;case 1:b||(b=dc(e,0));b.R++;e++;b.L.push(c[f]);f++;break;case 2:b||(b=dc(e,0));
b.R++;e++;break;case 3:b||(b=dc(e,0)),b.L.push(c[f]),f++}b&&k.push(b);return k}function fc(a,b){return a===b};function gc(a,b,c){Eb(a);c=c||null;var d=u(a),e=u(b),f=c?u(c):null;d.previousSibling=c?f.previousSibling:b.__shady_lastChild;if(f=v(d.previousSibling))f.nextSibling=a;if(f=v(d.nextSibling=c))f.previousSibling=a;d.parentNode=b;c?c===e.firstChild&&(e.firstChild=a):(e.lastChild=a,e.firstChild||(e.firstChild=a));e.childNodes=null}
function hc(a,b,c){Fb(b);var d=u(b);void 0!==d.firstChild&&(d.childNodes=null);if(a.nodeType===Node.DOCUMENT_FRAGMENT_NODE){d=a.__shady_childNodes;for(var e=0;e<d.length;e++)gc(d[e],b,c);a=u(a);b=void 0!==a.firstChild?null:void 0;a.firstChild=a.lastChild=b;a.childNodes=b}else gc(a,b,c)}
function ic(a,b){var c=u(a);b=u(b);a===b.firstChild&&(b.firstChild=c.nextSibling);a===b.lastChild&&(b.lastChild=c.previousSibling);a=c.previousSibling;var d=c.nextSibling;a&&(u(a).nextSibling=d);d&&(u(d).previousSibling=a);c.parentNode=c.previousSibling=c.nextSibling=void 0;void 0!==b.childNodes&&(b.childNodes=null)}
function jc(a){var b=u(a);if(void 0===b.firstChild){b.childNodes=null;var c=b.firstChild=a.__shady_native_firstChild||null;b.lastChild=a.__shady_native_lastChild||null;Fb(a);b=c;for(c=void 0;b;b=b.__shady_native_nextSibling){var d=u(b);d.parentNode=a;d.nextSibling=b.__shady_native_nextSibling||null;d.previousSibling=c||null;c=b;Eb(b)}}};var kc=null;function G(){kc||(kc=window.ShadyCSS&&window.ShadyCSS.ScopingShim);return kc||null}function lc(a,b){var c=G();c&&c.unscopeNode(a,b)}function mc(a,b){var c=G();if(!c)return!0;if(a.nodeType===Node.DOCUMENT_FRAGMENT_NODE){c=!0;a=a.__shady_childNodes;for(var d=0;c&&d<a.length;d++)c=c&&mc(a[d],b);return c}return a.nodeType!==Node.ELEMENT_NODE?!0:c.currentScopeForNode(a)===b}function nc(a){if(a.nodeType!==Node.ELEMENT_NODE)return"";var b=G();return b?b.currentScopeForNode(a):""}
function oc(a,b){if(a){a.nodeType===Node.ELEMENT_NODE&&b(a);a=a.__shady_childNodes;for(var c=0,d;c<a.length;c++)d=a[c],d.nodeType===Node.ELEMENT_NODE&&oc(d,b)}};var pc=window.document,qc=w.oa,rc=Object.getOwnPropertyDescriptor(Node.prototype,"isConnected"),sc=rc&&rc.get;function tc(a){for(var b;b=a.__shady_firstChild;)a.__shady_removeChild(b)}function uc(a){var b=v(a);if(b&&void 0!==b.K){b=a.__shady_childNodes;for(var c=0,d=b.length,e=void 0;c<d&&(e=b[c]);c++)uc(e)}if(a=v(a))a.K=void 0}function vc(a){var b=a;a&&"slot"===a.localName&&(b=(b=(b=v(a))&&b.D)&&b.length?b[0]:vc(a.__shady_nextSibling));return b}
function wc(a,b,c){if(a=(a=v(a))&&a.H)b&&a.addedNodes.push(b),c&&a.removedNodes.push(c),ab(a)}
var Cc=B({get parentNode(){var a=v(this);a=a&&a.parentNode;return void 0!==a?a:this.__shady_native_parentNode},get firstChild(){var a=v(this);a=a&&a.firstChild;return void 0!==a?a:this.__shady_native_firstChild},get lastChild(){var a=v(this);a=a&&a.lastChild;return void 0!==a?a:this.__shady_native_lastChild},get nextSibling(){var a=v(this);a=a&&a.nextSibling;return void 0!==a?a:this.__shady_native_nextSibling},get previousSibling(){var a=v(this);a=a&&a.previousSibling;return void 0!==a?a:this.__shady_native_previousSibling},
get childNodes(){if(y(this)){var a=v(this);if(!a.childNodes){a.childNodes=[];for(var b=this.__shady_firstChild;b;b=b.__shady_nextSibling)a.childNodes.push(b)}var c=a.childNodes}else c=this.__shady_native_childNodes;c.item=function(a){return c[a]};return c},get parentElement(){var a=v(this);(a=a&&a.parentNode)&&a.nodeType!==Node.ELEMENT_NODE&&(a=null);return void 0!==a?a:this.__shady_native_parentElement},get isConnected(){if(sc&&sc.call(this))return!0;if(this.nodeType==Node.DOCUMENT_FRAGMENT_NODE)return!1;
var a=this.ownerDocument;if(Ta){if(a.__shady_native_contains(this))return!0}else if(a.documentElement&&a.documentElement.__shady_native_contains(this))return!0;for(a=this;a&&!(a instanceof Document);)a=a.__shady_parentNode||(z(a)?a.host:void 0);return!!(a&&a instanceof Document)},get textContent(){if(y(this)){for(var a=[],b=0,c=this.__shady_childNodes,d;d=c[b];b++)d.nodeType!==Node.COMMENT_NODE&&a.push(d.__shady_textContent);return a.join("")}return this.__shady_native_textContent},set textContent(a){if("undefined"===
typeof a||null===a)a="";switch(this.nodeType){case Node.ELEMENT_NODE:case Node.DOCUMENT_FRAGMENT_NODE:if(!y(this)&&w.m){var b=this.__shady_firstChild;(b!=this.__shady_lastChild||b&&b.nodeType!=Node.TEXT_NODE)&&tc(this);this.__shady_native_textContent=a}else tc(this),(0<a.length||this.nodeType===Node.ELEMENT_NODE)&&this.__shady_insertBefore(document.createTextNode(a));break;default:this.nodeValue=a}},insertBefore:function(a,b){if(this.ownerDocument!==pc&&a.ownerDocument!==pc)return this.__shady_native_insertBefore(a,
b),a;if(a===this)throw Error("Failed to execute 'appendChild' on 'Node': The new child element contains the parent.");if(b){var c=v(b);c=c&&c.parentNode;if(void 0!==c&&c!==this||void 0===c&&b.__shady_native_parentNode!==this)throw Error("Failed to execute 'insertBefore' on 'Node': The node before which the new node is to be inserted is not a child of this node.");}if(b===a)return a;var d=[],e=(c=xc(this))?c.host.localName:nc(this),f=a.__shady_parentNode;if(f){var g=nc(a);f.__shady_removeChild(a,!!c||
!xc(a))}f=!0;var h=(!qc||void 0===a.__noInsertionPoint)&&!mc(a,e),k=c&&!a.__noInsertionPoint&&(!qc||a.nodeType===Node.DOCUMENT_FRAGMENT_NODE);if(k||h)h&&(g=g||nc(a)),oc(a,function(a){k&&"slot"===a.localName&&d.push(a);if(h){var b=g;G()&&(b&&lc(a,b),(b=G())&&b.scopeNode(a,e))}});if("slot"===this.localName||d.length)d.length&&(c.c=c.c||[],c.a=c.a||[],c.b=c.b||{},c.c.push.apply(c.c,d instanceof Array?d:ja(ia(d)))),c&&Ac(c);y(this)&&(hc(a,this,b),c=v(this),La(this)?(Ac(c.root),f=!1):c.root&&(f=!1));f?
(c=z(this)?this.host:this,b?(b=vc(b),c.__shady_native_insertBefore(a,b)):c.__shady_native_appendChild(a)):a.ownerDocument!==this.ownerDocument&&this.ownerDocument.adoptNode(a);wc(this,a);return a},appendChild:function(a){return this.__shady_insertBefore(a)},removeChild:function(a,b){b=void 0===b?!1:b;if(this.ownerDocument!==pc)return this.__shady_native_removeChild(a);if(a.__shady_parentNode!==this)throw Error("The node to be removed is not a child of this node: "+a);var c=xc(a),d=c&&Bc(c,a),e=v(this);
if(y(this)&&(ic(a,this),La(this))){Ac(e.root);var f=!0}if(G()&&!b&&c){var g=nc(a);oc(a,function(a){lc(a,g)})}uc(a);c&&((b=this&&"slot"===this.localName)&&(f=!0),(d||b)&&Ac(c));f||(f=z(this)?this.host:this,(!e.root&&"slot"!==a.localName||f===a.__shady_native_parentNode)&&f.__shady_native_removeChild(a));wc(this,null,a);return a},replaceChild:function(a,b){this.__shady_insertBefore(a,b);this.__shady_removeChild(b);return a},cloneNode:function(a){if("template"==this.localName)return this.__shady_native_cloneNode(a);
var b=this.__shady_native_cloneNode(!1);if(a&&b.nodeType!==Node.ATTRIBUTE_NODE){a=this.__shady_childNodes;for(var c=0,d;c<a.length;c++)d=a[c].__shady_cloneNode(!0),b.__shady_appendChild(d)}return b},getRootNode:function(a){if(this&&this.nodeType){var b=u(this),c=b.K;void 0===c&&(z(this)?(c=this,b.K=c):(c=(c=this.__shady_parentNode)?c.__shady_getRootNode(a):this,document.documentElement.__shady_native_contains(this)&&(b.K=c)));return c}},contains:function(a){return Ua(this,a)}});function Dc(a,b,c){var d=[];Ec(a.__shady_childNodes,b,c,d);return d}function Ec(a,b,c,d){for(var e=0,f=a.length,g=void 0;e<f&&(g=a[e]);e++){var h;if(h=g.nodeType===Node.ELEMENT_NODE){h=g;var k=b,l=c,m=d,q=k(h);q&&m.push(h);l&&l(q)?h=q:(Ec(h.__shady_childNodes,k,l,m),h=void 0)}if(h)break}}
var Fc=B({get firstElementChild(){var a=v(this);if(a&&void 0!==a.firstChild){for(a=this.__shady_firstChild;a&&a.nodeType!==Node.ELEMENT_NODE;)a=a.__shady_nextSibling;return a}return this.__shady_native_firstElementChild},get lastElementChild(){var a=v(this);if(a&&void 0!==a.lastChild){for(a=this.__shady_lastChild;a&&a.nodeType!==Node.ELEMENT_NODE;)a=a.__shady_previousSibling;return a}return this.__shady_native_lastElementChild},get children(){return y(this)?Va(Array.prototype.filter.call(this.__shady_childNodes,
function(a){return a.nodeType===Node.ELEMENT_NODE})):this.__shady_native_children},get childElementCount(){var a=this.__shady_children;return a?a.length:0}}),Gc=B({querySelector:function(a){return Dc(this,function(b){return Oa.call(b,a)},function(a){return!!a})[0]||null},querySelectorAll:function(a,b){if(b){b=Array.prototype.slice.call(this.__shady_native_querySelectorAll(a));var c=this.__shady_getRootNode();return b.filter(function(a){return a.__shady_getRootNode()==c})}return Dc(this,function(b){return Oa.call(b,
a)})}}),Hc=w.oa?Object.assign({},Fc):Fc;Object.assign(Fc,Gc);var Ic=B({getElementById:function(a){return""===a?null:Dc(this,function(b){return b.id==a},function(a){return!!a})[0]||null}});var Jc=B({get activeElement(){var a=w.m?document.__shady_native_activeElement:document.activeElement;if(!a||!a.nodeType)return null;var b=!!z(this);if(!(this===document||b&&this.host!==a&&this.host.__shady_native_contains(a)))return null;for(b=xc(a);b&&b!==this;)a=b.host,b=xc(a);return this===document?b?null:a:b===this?a:null}});var Kc=document.implementation.createHTMLDocument("inert"),Lc=B({get innerHTML(){return y(this)?kb("template"===this.localName?this.content:this,function(a){return a.__shady_childNodes}):this.__shady_native_innerHTML},set innerHTML(a){if("template"===this.localName)this.__shady_native_innerHTML=a;else{tc(this);var b=this.localName||"div";b=this.namespaceURI&&this.namespaceURI!==Kc.namespaceURI?Kc.createElementNS(this.namespaceURI,b):Kc.createElement(b);for(w.m?b.__shady_native_innerHTML=a:b.innerHTML=
a;a=b.__shady_firstChild;)this.__shady_insertBefore(a)}}});var Mc=B({addEventListener:function(a,b,c){"object"!==typeof c&&(c={capture:!!c});c.$=this;this.host.__shady_addEventListener(a,b,c)},removeEventListener:function(a,b,c){"object"!==typeof c&&(c={capture:!!c});c.$=this;this.host.__shady_removeEventListener(a,b,c)}});function Nc(a,b){A(a,Mc,b);A(a,Jc,b);A(a,Lc,b);A(a,Fc,b);w.J&&!b?(A(a,Cc,b),A(a,Ic,b)):w.m||(A(a,Ab),A(a,zb))};var Oc={},Pc=w.deferConnectionCallbacks&&"loading"===document.readyState,Qc;function Rc(a){var b=[];do b.unshift(a);while(a=a.__shady_parentNode);return b}
function Sc(a,b,c){if(a!==Oc)throw new TypeError("Illegal constructor");this.za="ShadyRoot";this.host=b;this.mode=c&&c.mode;jc(b);a=u(b);a.root=this;a.pa="closed"!==this.mode?this:null;a=u(this);a.firstChild=a.lastChild=a.parentNode=a.nextSibling=a.previousSibling=null;a.childNodes=[];this.ba=this.B=!1;this.c=this.b=this.a=null;if(w.preferPerformance)for(;a=b.__shady_native_firstChild;)b.__shady_native_removeChild(a);else Ac(this)}function Ac(a){a.B||(a.B=!0,Ya(function(){return Tc(a)}))}
function Tc(a){var b;if(b=a.B){for(var c;a;)a:{a.B&&(c=a),b=a;a=b.host.__shady_getRootNode();if(z(a)&&(b=v(b.host))&&0<b.N)break a;a=void 0}b=c}(c=b)&&c._renderSelf()}
Sc.prototype._renderSelf=function(){var a=Pc;Pc=!0;this.B=!1;if(this.a){Uc(this);for(var b=0,c;b<this.a.length;b++){c=this.a[b];var d=v(c),e=d.assignedNodes;d.assignedNodes=[];d.D=[];if(d.ca=e)for(d=0;d<e.length;d++){var f=v(e[d]);f.P=f.assignedSlot;f.assignedSlot===c&&(f.assignedSlot=null)}}for(b=this.host.__shady_firstChild;b;b=b.__shady_nextSibling)Vc(this,b);for(b=0;b<this.a.length;b++){c=this.a[b];e=v(c);if(!e.assignedNodes.length)for(d=c.__shady_firstChild;d;d=d.__shady_nextSibling)Vc(this,
d,c);(d=(d=v(c.__shady_parentNode))&&d.root)&&(Ma(d)||d.B)&&d._renderSelf();Wc(this,e.D,e.assignedNodes);if(d=e.ca){for(f=0;f<d.length;f++)v(d[f]).P=null;e.ca=null;d.length>e.assignedNodes.length&&(e.T=!0)}e.T&&(e.T=!1,Xc(this,c))}c=this.a;b=[];for(e=0;e<c.length;e++)d=c[e].__shady_parentNode,(f=v(d))&&f.root||!(0>b.indexOf(d))||b.push(d);for(c=0;c<b.length;c++){f=b[c];e=f===this?this.host:f;d=[];f=f.__shady_childNodes;for(var g=0;g<f.length;g++){var h=f[g];if("slot"==h.localName){h=v(h).D;for(var k=
0;k<h.length;k++)d.push(h[k])}else d.push(h)}f=Array.prototype.slice.call(e.__shady_native_childNodes);g=ec(d,d.length,f,f.length);k=h=0;for(var l=void 0;h<g.length&&(l=g[h]);h++){for(var m=0,q=void 0;m<l.L.length&&(q=l.L[m]);m++)q.__shady_native_parentNode===e&&e.__shady_native_removeChild(q),f.splice(l.index+k,1);k-=l.R}k=0;for(l=void 0;k<g.length&&(l=g[k]);k++)for(h=f[l.index],m=l.index;m<l.index+l.R;m++)q=d[m],e.__shady_native_insertBefore(q,h),f.splice(m,0,q)}}if(!w.preferPerformance&&!this.ba)for(b=
this.host.__shady_childNodes,c=0,e=b.length;c<e;c++)d=b[c],f=v(d),d.__shady_native_parentNode!==this.host||"slot"!==d.localName&&f.assignedSlot||this.host.__shady_native_removeChild(d);this.ba=!0;Pc=a;Qc&&Qc()};function Vc(a,b,c){var d=u(b),e=d.P;d.P=null;c||(c=(a=a.b[b.__shady_slot||"__catchall"])&&a[0]);c?(u(c).assignedNodes.push(b),d.assignedSlot=c):d.assignedSlot=void 0;e!==d.assignedSlot&&d.assignedSlot&&(u(d.assignedSlot).T=!0)}
function Wc(a,b,c){for(var d=0,e=void 0;d<c.length&&(e=c[d]);d++)if("slot"==e.localName){var f=v(e).assignedNodes;f&&f.length&&Wc(a,b,f)}else b.push(c[d])}function Xc(a,b){b.__shady_native_dispatchEvent(new Event("slotchange"));b=v(b);b.assignedSlot&&Xc(a,b.assignedSlot)}
function Uc(a){if(a.c&&a.c.length){for(var b=a.c,c,d=0;d<b.length;d++){var e=b[d];jc(e);var f=e.__shady_parentNode;jc(f);f=v(f);f.N=(f.N||0)+1;f=Yc(e);a.b[f]?(c=c||{},c[f]=!0,a.b[f].push(e)):a.b[f]=[e];a.a.push(e)}if(c)for(var g in c)a.b[g]=Zc(a.b[g]);a.c=[]}}function Yc(a){var b=a.name||a.getAttribute("name")||"__catchall";return a.wa=b}
function Zc(a){return a.sort(function(a,c){a=Rc(a);for(var b=Rc(c),e=0;e<a.length;e++){c=a[e];var f=b[e];if(c!==f)return a=Array.from(c.__shady_parentNode.__shady_childNodes),a.indexOf(c)-a.indexOf(f)}})}
function Bc(a,b){if(a.a){Uc(a);var c=a.b,d;for(d in c)for(var e=c[d],f=0;f<e.length;f++){var g=e[f];if(Ua(b,g)){e.splice(f,1);var h=a.a.indexOf(g);0<=h&&(a.a.splice(h,1),(h=v(g.__shady_parentNode))&&h.N&&h.N--);f--;g=v(g);if(h=g.D)for(var k=0;k<h.length;k++){var l=h[k],m=l.__shady_native_parentNode;m&&m.__shady_native_removeChild(l)}g.D=[];g.assignedNodes=[];h=!0}}return h}}function Ma(a){Uc(a);return!(!a.a||!a.a.length)}
(function(a){a.__proto__=DocumentFragment.prototype;Nc(a,"__shady_");Nc(a);Object.defineProperties(a,{nodeType:{value:Node.DOCUMENT_FRAGMENT_NODE,configurable:!0},nodeName:{value:"#document-fragment",configurable:!0},nodeValue:{value:null,configurable:!0}});["localName","namespaceURI","prefix"].forEach(function(b){Object.defineProperty(a,b,{value:void 0,configurable:!0})});["ownerDocument","baseURI","isConnected"].forEach(function(b){Object.defineProperty(a,b,{get:function(){return this.host[b]},
configurable:!0})})})(Sc.prototype);
if(window.customElements&&w.ea&&!w.preferPerformance){var $c=new Map;Qc=function(){var a=[];$c.forEach(function(b,c){a.push([c,b])});$c.clear();for(var b=0;b<a.length;b++){var c=a[b][0];a[b][1]?c.ua():c.va()}};Pc&&document.addEventListener("readystatechange",function(){Pc=!1;Qc()},{once:!0});var ad=function(a,b,c){var d=0,e="__isConnected"+d++;if(b||c)a.prototype.connectedCallback=a.prototype.ua=function(){Pc?$c.set(this,!0):this[e]||(this[e]=!0,b&&b.call(this))},a.prototype.disconnectedCallback=
a.prototype.va=function(){Pc?this.isConnected||$c.set(this,!1):this[e]&&(this[e]=!1,c&&c.call(this))};return a},bd=window.customElements.define;Object.defineProperty(window.CustomElementRegistry.prototype,"define",{value:function(a,b){var c=b.prototype.connectedCallback,d=b.prototype.disconnectedCallback;bd.call(window.customElements,a,ad(b,c,d));b.prototype.connectedCallback=c;b.prototype.disconnectedCallback=d}})}function xc(a){a=a.__shady_getRootNode();if(z(a))return a};function cd(a){this.node=a}n=cd.prototype;n.addEventListener=function(a,b,c){return this.node.__shady_addEventListener(a,b,c)};n.removeEventListener=function(a,b,c){return this.node.__shady_removeEventListener(a,b,c)};n.appendChild=function(a){return this.node.__shady_appendChild(a)};n.insertBefore=function(a,b){return this.node.__shady_insertBefore(a,b)};n.removeChild=function(a){return this.node.__shady_removeChild(a)};n.replaceChild=function(a,b){return this.node.__shady_replaceChild(a,b)};
n.cloneNode=function(a){return this.node.__shady_cloneNode(a)};n.getRootNode=function(a){return this.node.__shady_getRootNode(a)};n.contains=function(a){return this.node.__shady_contains(a)};n.dispatchEvent=function(a){return this.node.__shady_dispatchEvent(a)};n.setAttribute=function(a,b){this.node.__shady_setAttribute(a,b)};n.getAttribute=function(a){return this.node.__shady_native_getAttribute(a)};n.hasAttribute=function(a){return this.node.__shady_native_hasAttribute(a)};n.removeAttribute=function(a){this.node.__shady_removeAttribute(a)};
n.attachShadow=function(a){return this.node.__shady_attachShadow(a)};n.focus=function(){this.node.__shady_native_focus()};n.blur=function(){this.node.__shady_blur()};n.importNode=function(a,b){if(this.node.nodeType===Node.DOCUMENT_NODE)return this.node.__shady_importNode(a,b)};n.getElementById=function(a){if(this.node.nodeType===Node.DOCUMENT_NODE)return this.node.__shady_getElementById(a)};n.querySelector=function(a){return this.node.__shady_querySelector(a)};
n.querySelectorAll=function(a,b){return this.node.__shady_querySelectorAll(a,b)};n.assignedNodes=function(a){if("slot"===this.node.localName)return this.node.__shady_assignedNodes(a)};
p.Object.defineProperties(cd.prototype,{activeElement:{configurable:!0,enumerable:!0,get:function(){if(z(this.node)||this.node.nodeType===Node.DOCUMENT_NODE)return this.node.__shady_activeElement}},_activeElement:{configurable:!0,enumerable:!0,get:function(){return this.activeElement}},host:{configurable:!0,enumerable:!0,get:function(){if(z(this.node))return this.node.host}},parentNode:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_parentNode}},firstChild:{configurable:!0,
enumerable:!0,get:function(){return this.node.__shady_firstChild}},lastChild:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_lastChild}},nextSibling:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_nextSibling}},previousSibling:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_previousSibling}},childNodes:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_childNodes}},parentElement:{configurable:!0,enumerable:!0,
get:function(){return this.node.__shady_parentElement}},firstElementChild:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_firstElementChild}},lastElementChild:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_lastElementChild}},nextElementSibling:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_nextElementSibling}},previousElementSibling:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_previousElementSibling}},
children:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_children}},childElementCount:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_childElementCount}},shadowRoot:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_shadowRoot}},assignedSlot:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_assignedSlot}},isConnected:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_isConnected}},innerHTML:{configurable:!0,
enumerable:!0,get:function(){return this.node.__shady_innerHTML},set:function(a){this.node.__shady_innerHTML=a}},textContent:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_textContent},set:function(a){this.node.__shady_textContent=a}},slot:{configurable:!0,enumerable:!0,get:function(){return this.node.__shady_slot},set:function(a){this.node.__shady_slot=a}}});
cc.forEach(function(a){Object.defineProperty(cd.prototype,a,{get:function(){return this.node["__shady_"+a]},set:function(b){this.node["__shady_"+a]=b},configurable:!0})});var dd=new WeakMap;function ed(a){if(z(a)||a instanceof cd)return a;var b=dd.get(a);b||(b=new cd(a),dd.set(a,b));return b};var fd=B({dispatchEvent:function(a){Za();return this.__shady_native_dispatchEvent(a)},addEventListener:Ub,removeEventListener:Wb});var gd=B({get assignedSlot(){var a=this.__shady_parentNode;(a=a&&a.__shady_shadowRoot)&&Tc(a);return(a=v(this))&&a.assignedSlot||null}});var hd=window.document;function id(a,b){if("slot"===b)a=a.__shady_parentNode,La(a)&&Ac(v(a).root);else if("slot"===a.localName&&"name"===b&&(b=xc(a))){if(b.a){Uc(b);var c=a.wa,d=Yc(a);if(d!==c){c=b.b[c];var e=c.indexOf(a);0<=e&&c.splice(e,1);c=b.b[d]||(b.b[d]=[]);c.push(a);1<c.length&&(b.b[d]=Zc(c))}}Ac(b)}}
var jd=B({get previousElementSibling(){var a=v(this);if(a&&void 0!==a.previousSibling){for(a=this.__shady_previousSibling;a&&a.nodeType!==Node.ELEMENT_NODE;)a=a.__shady_previousSibling;return a}return this.__shady_native_previousElementSibling},get nextElementSibling(){var a=v(this);if(a&&void 0!==a.nextSibling){for(a=this.__shady_nextSibling;a&&a.nodeType!==Node.ELEMENT_NODE;)a=a.__shady_nextSibling;return a}return this.__shady_native_nextElementSibling},get slot(){return this.getAttribute("slot")},
set slot(a){this.__shady_setAttribute("slot",a)},get shadowRoot(){var a=v(this);return a&&a.pa||null},get className(){return this.getAttribute("class")||""},set className(a){this.__shady_setAttribute("class",a)},setAttribute:function(a,b){if(this.ownerDocument!==hd)this.__shady_native_setAttribute(a,b);else{var c;(c=G())&&"class"===a?(c.setElementClass(this,b),c=!0):c=!1;c||(this.__shady_native_setAttribute(a,b),id(this,a))}},removeAttribute:function(a){this.__shady_native_removeAttribute(a);id(this,
a)},attachShadow:function(a){if(!this)throw Error("Must provide a host.");if(!a)throw Error("Not enough arguments.");return new Sc(Oc,this,a)}});var kd=B({blur:function(){var a=v(this);(a=(a=a&&a.root)&&a.activeElement)?a.__shady_blur():this.__shady_native_blur()}});cc.forEach(function(a){kd[a]={set:function(b){var c=u(this),d=a.substring(2);c.O[a]&&this.removeEventListener(d,c.O[a]);this.__shady_addEventListener(d,b);c.O[a]=b},get:function(){var b=v(this);return b&&b.O[a]},configurable:!0}});var ld=B({assignedNodes:function(a){if("slot"===this.localName){var b=this.__shady_getRootNode();b&&z(b)&&Tc(b);return(b=v(this))?(a&&a.flatten?b.D:b.assignedNodes)||[]:[]}}});var md=window.document,nd=B({importNode:function(a,b){if(a.ownerDocument!==md||"template"===a.localName)return this.__shady_native_importNode(a,b);var c=this.__shady_native_importNode(a,!1);if(b){a=a.__shady_childNodes;b=0;for(var d;b<a.length;b++)d=this.__shady_importNode(a[b],!0),c.__shady_appendChild(d)}return c}});var od=B({addEventListener:Ub.bind(window),removeEventListener:Wb.bind(window)});var pd={};Object.getOwnPropertyDescriptor(HTMLElement.prototype,"parentElement")&&(pd.parentElement=Cc.parentElement);Object.getOwnPropertyDescriptor(HTMLElement.prototype,"contains")&&(pd.contains=Cc.contains);Object.getOwnPropertyDescriptor(HTMLElement.prototype,"children")&&(pd.children=Fc.children);Object.getOwnPropertyDescriptor(HTMLElement.prototype,"innerHTML")&&(pd.innerHTML=Lc.innerHTML);Object.getOwnPropertyDescriptor(HTMLElement.prototype,"className")&&(pd.className=jd.className);
var qd={EventTarget:[fd],Node:[Cc,window.EventTarget?null:fd],Text:[gd],Element:[jd,Fc,gd,!w.m||"innerHTML"in Element.prototype?Lc:null,window.HTMLSlotElement?null:ld],HTMLElement:[kd,pd],HTMLSlotElement:[ld],DocumentFragment:[Hc,Ic],Document:[nd,Hc,Ic,Jc],Window:[od]},rd=w.m?null:["innerHTML","textContent"];function sd(a){var b=a?null:rd,c={},d;for(d in qd)c.W=window[d]&&window[d].prototype,qd[d].forEach(function(c){return function(d){return c.W&&d&&A(c.W,d,a,b)}}(c)),c={W:c.W}};if(w.ea){var ShadyDOM={inUse:w.ea,patch:function(a){Fb(a);Eb(a);return a},isShadyRoot:z,enqueue:Ya,flush:Za,flushInitial:function(a){!a.ba&&a.B&&Tc(a)},settings:w,filterMutations:db,observeChildren:bb,unobserveChildren:cb,deferConnectionCallbacks:w.deferConnectionCallbacks,preferPerformance:w.preferPerformance,handlesDynamicScoping:!0,wrap:w.J?ed:function(a){return a},Wrapper:cd,composedPath:Mb,noPatch:w.J,nativeMethods:qb,nativeTree:rb};window.ShadyDOM=ShadyDOM;yb();sd("__shady_");Object.defineProperty(document,
"_activeElement",Jc.activeElement);A(Window.prototype,od,"__shady_");w.J||(sd(),bc());Xb();window.Event=Zb;window.CustomEvent=$b;window.MouseEvent=ac;window.ShadowRoot=Sc};var td=new Set("annotation-xml color-profile font-face font-face-src font-face-uri font-face-format font-face-name missing-glyph".split(" "));function ud(a){var b=td.has(a);a=/^[a-z][.0-9_a-z]*-[\-.0-9_a-z]*$/.test(a);return!b&&a}function H(a){var b=a.isConnected;if(void 0!==b)return b;for(;a&&!(a.__CE_isImportDocument||a instanceof Document);)a=a.parentNode||(window.ShadowRoot&&a instanceof ShadowRoot?a.host:void 0);return!(!a||!(a.__CE_isImportDocument||a instanceof Document))}
function vd(a,b){for(;b&&b!==a&&!b.nextSibling;)b=b.parentNode;return b&&b!==a?b.nextSibling:null}
function wd(a,b,c){c=void 0===c?new Set:c;for(var d=a;d;){if(d.nodeType===Node.ELEMENT_NODE){var e=d;b(e);var f=e.localName;if("link"===f&&"import"===e.getAttribute("rel")){d=e.import;if(d instanceof Node&&!c.has(d))for(c.add(d),d=d.firstChild;d;d=d.nextSibling)wd(d,b,c);d=vd(a,e);continue}else if("template"===f){d=vd(a,e);continue}if(e=e.__CE_shadowRoot)for(e=e.firstChild;e;e=e.nextSibling)wd(e,b,c)}d=d.firstChild?d.firstChild:vd(a,d)}}function I(a,b,c){a[b]=c};function xd(){this.a=new Map;this.g=new Map;this.f=[];this.c=!1}function yd(a,b,c){a.a.set(b,c);a.g.set(c.constructorFunction,c)}function zd(a,b){a.c=!0;a.f.push(b)}function Ad(a,b){a.c&&wd(b,function(b){return a.b(b)})}xd.prototype.b=function(a){if(this.c&&!a.__CE_patched){a.__CE_patched=!0;for(var b=0;b<this.f.length;b++)this.f[b](a)}};function J(a,b){var c=[];wd(b,function(a){return c.push(a)});for(b=0;b<c.length;b++){var d=c[b];1===d.__CE_state?a.connectedCallback(d):Bd(a,d)}}
function K(a,b){var c=[];wd(b,function(a){return c.push(a)});for(b=0;b<c.length;b++){var d=c[b];1===d.__CE_state&&a.disconnectedCallback(d)}}
function L(a,b,c){c=void 0===c?{}:c;var d=c.Xa||new Set,e=c.Y||function(b){return Bd(a,b)},f=[];wd(b,function(b){if("link"===b.localName&&"import"===b.getAttribute("rel")){var c=b.import;c instanceof Node&&(c.__CE_isImportDocument=!0,c.__CE_hasRegistry=!0);c&&"complete"===c.readyState?c.__CE_documentLoadHandled=!0:b.addEventListener("load",function(){var c=b.import;if(!c.__CE_documentLoadHandled){c.__CE_documentLoadHandled=!0;var f=new Set(d);f.delete(c);L(a,c,{Xa:f,Y:e})}})}else f.push(b)},d);if(a.c)for(b=
0;b<f.length;b++)a.b(f[b]);for(b=0;b<f.length;b++)e(f[b])}
function Bd(a,b){if(void 0===b.__CE_state){var c=b.ownerDocument;if(c.defaultView||c.__CE_isImportDocument&&c.__CE_hasRegistry)if(c=a.a.get(b.localName)){c.constructionStack.push(b);var d=c.constructorFunction;try{try{if(new d!==b)throw Error("The custom element constructor did not produce the element being upgraded.");}finally{c.constructionStack.pop()}}catch(g){throw b.__CE_state=2,g;}b.__CE_state=1;b.__CE_definition=c;if(c.attributeChangedCallback)for(c=c.observedAttributes,d=0;d<c.length;d++){var e=
c[d],f=b.getAttribute(e);null!==f&&a.attributeChangedCallback(b,e,null,f,null)}H(b)&&a.connectedCallback(b)}}}xd.prototype.connectedCallback=function(a){var b=a.__CE_definition;b.connectedCallback&&b.connectedCallback.call(a)};xd.prototype.disconnectedCallback=function(a){var b=a.__CE_definition;b.disconnectedCallback&&b.disconnectedCallback.call(a)};
xd.prototype.attributeChangedCallback=function(a,b,c,d,e){var f=a.__CE_definition;f.attributeChangedCallback&&-1<f.observedAttributes.indexOf(b)&&f.attributeChangedCallback.call(a,b,c,d,e)};function Cd(a){var b=document;this.b=a;this.a=b;this.F=void 0;L(this.b,this.a);"loading"===this.a.readyState&&(this.F=new MutationObserver(this.c.bind(this)),this.F.observe(this.a,{childList:!0,subtree:!0}))}function Dd(a){a.F&&a.F.disconnect()}Cd.prototype.c=function(a){var b=this.a.readyState;"interactive"!==b&&"complete"!==b||Dd(this);for(b=0;b<a.length;b++)for(var c=a[b].addedNodes,d=0;d<c.length;d++)L(this.b,c[d])};function Ed(){var a=this;this.a=this.h=void 0;this.b=new Promise(function(b){a.a=b;a.h&&b(a.h)})}Ed.prototype.resolve=function(a){if(this.h)throw Error("Already resolved.");this.h=a;this.a&&this.a(a)};function N(a){this.c=!1;this.a=a;this.l=new Map;this.f=function(a){return a()};this.b=!1;this.g=[];this.da=new Cd(a)}n=N.prototype;
n.sa=function(a,b){var c=this;if(!(b instanceof Function))throw new TypeError("Custom element constructors must be functions.");if(!ud(a))throw new SyntaxError("The element name '"+a+"' is not valid.");if(this.a.a.get(a))throw Error("A custom element with name '"+a+"' has already been defined.");if(this.c)throw Error("A custom element is already being defined.");this.c=!0;try{var d=function(a){var b=e[a];if(void 0!==b&&!(b instanceof Function))throw Error("The '"+a+"' callback must be a function.");
return b},e=b.prototype;if(!(e instanceof Object))throw new TypeError("The custom element constructor's prototype is not an object.");var f=d("connectedCallback");var g=d("disconnectedCallback");var h=d("adoptedCallback");var k=d("attributeChangedCallback");var l=b.observedAttributes||[]}catch(m){return}finally{this.c=!1}b={localName:a,constructorFunction:b,connectedCallback:f,disconnectedCallback:g,adoptedCallback:h,attributeChangedCallback:k,observedAttributes:l,constructionStack:[]};yd(this.a,
a,b);this.g.push(b);this.b||(this.b=!0,this.f(function(){return Fd(c)}))};n.Y=function(a){L(this.a,a)};
function Fd(a){if(!1!==a.b){a.b=!1;for(var b=a.g,c=[],d=new Map,e=0;e<b.length;e++)d.set(b[e].localName,[]);L(a.a,document,{Y:function(b){if(void 0===b.__CE_state){var e=b.localName,f=d.get(e);f?f.push(b):a.a.a.get(e)&&c.push(b)}}});for(e=0;e<c.length;e++)Bd(a.a,c[e]);for(;0<b.length;){var f=b.shift();e=f.localName;f=d.get(f.localName);for(var g=0;g<f.length;g++)Bd(a.a,f[g]);(e=a.l.get(e))&&e.resolve(void 0)}}}n.get=function(a){if(a=this.a.a.get(a))return a.constructorFunction};
n.ta=function(a){if(!ud(a))return Promise.reject(new SyntaxError("'"+a+"' is not a valid custom element name."));var b=this.l.get(a);if(b)return b.b;b=new Ed;this.l.set(a,b);this.a.a.get(a)&&!this.g.some(function(b){return b.localName===a})&&b.resolve(void 0);return b.b};n.Pa=function(a){Dd(this.da);var b=this.f;this.f=function(c){return a(function(){return b(c)})}};window.CustomElementRegistry=N;N.prototype.define=N.prototype.sa;N.prototype.upgrade=N.prototype.Y;N.prototype.get=N.prototype.get;
N.prototype.whenDefined=N.prototype.ta;N.prototype.polyfillWrapFlushCallback=N.prototype.Pa;var Gd=window.Document.prototype.createElement,Hd=window.Document.prototype.createElementNS,Id=window.Document.prototype.importNode,Jd=window.Document.prototype.prepend,Kd=window.Document.prototype.append,Ld=window.DocumentFragment.prototype.prepend,Md=window.DocumentFragment.prototype.append,Nd=window.Node.prototype.cloneNode,Od=window.Node.prototype.appendChild,Pd=window.Node.prototype.insertBefore,Qd=window.Node.prototype.removeChild,Rd=window.Node.prototype.replaceChild,Sd=Object.getOwnPropertyDescriptor(window.Node.prototype,
"textContent"),Td=window.Element.prototype.attachShadow,Ud=Object.getOwnPropertyDescriptor(window.Element.prototype,"innerHTML"),Vd=window.Element.prototype.getAttribute,Wd=window.Element.prototype.setAttribute,Xd=window.Element.prototype.removeAttribute,Yd=window.Element.prototype.getAttributeNS,Zd=window.Element.prototype.setAttributeNS,$d=window.Element.prototype.removeAttributeNS,ae=window.Element.prototype.insertAdjacentElement,be=window.Element.prototype.insertAdjacentHTML,ce=window.Element.prototype.prepend,
de=window.Element.prototype.append,ee=window.Element.prototype.before,fe=window.Element.prototype.after,ge=window.Element.prototype.replaceWith,he=window.Element.prototype.remove,ie=window.HTMLElement,je=Object.getOwnPropertyDescriptor(window.HTMLElement.prototype,"innerHTML"),ke=window.HTMLElement.prototype.insertAdjacentElement,le=window.HTMLElement.prototype.insertAdjacentHTML;var me=new function(){};function ne(){var a=oe;window.HTMLElement=function(){function b(){var b=this.constructor,d=a.g.get(b);if(!d)throw Error("The custom element being constructed was not registered with `customElements`.");var e=d.constructionStack;if(0===e.length)return e=Gd.call(document,d.localName),Object.setPrototypeOf(e,b.prototype),e.__CE_state=1,e.__CE_definition=d,a.b(e),e;d=e.length-1;var f=e[d];if(f===me)throw Error("The HTMLElement constructor was either called reentrantly for this constructor or called multiple times.");
e[d]=me;Object.setPrototypeOf(f,b.prototype);a.b(f);return f}b.prototype=ie.prototype;Object.defineProperty(b.prototype,"constructor",{writable:!0,configurable:!0,enumerable:!1,value:b});return b}()};function pe(a,b,c){function d(b){return function(c){for(var d=[],e=0;e<arguments.length;++e)d[e]=arguments[e];e=[];for(var f=[],l=0;l<d.length;l++){var m=d[l];m instanceof Element&&H(m)&&f.push(m);if(m instanceof DocumentFragment)for(m=m.firstChild;m;m=m.nextSibling)e.push(m);else e.push(m)}b.apply(this,d);for(d=0;d<f.length;d++)K(a,f[d]);if(H(this))for(d=0;d<e.length;d++)f=e[d],f instanceof Element&&J(a,f)}}void 0!==c.V&&(b.prepend=d(c.V));void 0!==c.append&&(b.append=d(c.append))};function qe(){var a=oe;I(Document.prototype,"createElement",function(b){if(this.__CE_hasRegistry){var c=a.a.get(b);if(c)return new c.constructorFunction}b=Gd.call(this,b);a.b(b);return b});I(Document.prototype,"importNode",function(b,c){b=Id.call(this,b,!!c);this.__CE_hasRegistry?L(a,b):Ad(a,b);return b});I(Document.prototype,"createElementNS",function(b,c){if(this.__CE_hasRegistry&&(null===b||"http://www.w3.org/1999/xhtml"===b)){var d=a.a.get(c);if(d)return new d.constructorFunction}b=Hd.call(this,
b,c);a.b(b);return b});pe(a,Document.prototype,{V:Jd,append:Kd})};function re(){function a(a,d){Object.defineProperty(a,"textContent",{enumerable:d.enumerable,configurable:!0,get:d.get,set:function(a){if(this.nodeType===Node.TEXT_NODE)d.set.call(this,a);else{var c=void 0;if(this.firstChild){var e=this.childNodes,h=e.length;if(0<h&&H(this)){c=Array(h);for(var k=0;k<h;k++)c[k]=e[k]}}d.set.call(this,a);if(c)for(a=0;a<c.length;a++)K(b,c[a])}}})}var b=oe;I(Node.prototype,"insertBefore",function(a,d){if(a instanceof DocumentFragment){var c=Array.prototype.slice.apply(a.childNodes);
a=Pd.call(this,a,d);if(H(this))for(d=0;d<c.length;d++)J(b,c[d]);return a}c=H(a);d=Pd.call(this,a,d);c&&K(b,a);H(this)&&J(b,a);return d});I(Node.prototype,"appendChild",function(a){if(a instanceof DocumentFragment){var c=Array.prototype.slice.apply(a.childNodes);a=Od.call(this,a);if(H(this))for(var e=0;e<c.length;e++)J(b,c[e]);return a}c=H(a);e=Od.call(this,a);c&&K(b,a);H(this)&&J(b,a);return e});I(Node.prototype,"cloneNode",function(a){a=Nd.call(this,!!a);this.ownerDocument.__CE_hasRegistry?L(b,a):
Ad(b,a);return a});I(Node.prototype,"removeChild",function(a){var c=H(a),e=Qd.call(this,a);c&&K(b,a);return e});I(Node.prototype,"replaceChild",function(a,d){if(a instanceof DocumentFragment){var c=Array.prototype.slice.apply(a.childNodes);a=Rd.call(this,a,d);if(H(this))for(K(b,d),d=0;d<c.length;d++)J(b,c[d]);return a}c=H(a);var f=Rd.call(this,a,d),g=H(this);g&&K(b,d);c&&K(b,a);g&&J(b,a);return f});Sd&&Sd.get?a(Node.prototype,Sd):zd(b,function(b){a(b,{enumerable:!0,configurable:!0,get:function(){for(var a=
[],b=0;b<this.childNodes.length;b++)a.push(this.childNodes[b].textContent);return a.join("")},set:function(a){for(;this.firstChild;)Qd.call(this,this.firstChild);Od.call(this,document.createTextNode(a))}})})};function se(a){function b(b){return function(c){for(var d=[],e=0;e<arguments.length;++e)d[e]=arguments[e];e=[];for(var h=[],k=0;k<d.length;k++){var l=d[k];l instanceof Element&&H(l)&&h.push(l);if(l instanceof DocumentFragment)for(l=l.firstChild;l;l=l.nextSibling)e.push(l);else e.push(l)}b.apply(this,d);for(d=0;d<h.length;d++)K(a,h[d]);if(H(this))for(d=0;d<e.length;d++)h=e[d],h instanceof Element&&J(a,h)}}var c=Element.prototype;void 0!==ee&&(c.before=b(ee));void 0!==ee&&(c.after=b(fe));void 0!==ge&&
I(c,"replaceWith",function(b){for(var c=[],d=0;d<arguments.length;++d)c[d]=arguments[d];d=[];for(var g=[],h=0;h<c.length;h++){var k=c[h];k instanceof Element&&H(k)&&g.push(k);if(k instanceof DocumentFragment)for(k=k.firstChild;k;k=k.nextSibling)d.push(k);else d.push(k)}h=H(this);ge.apply(this,c);for(c=0;c<g.length;c++)K(a,g[c]);if(h)for(K(a,this),c=0;c<d.length;c++)g=d[c],g instanceof Element&&J(a,g)});void 0!==he&&I(c,"remove",function(){var b=H(this);he.call(this);b&&K(a,this)})};function te(){function a(a,b){Object.defineProperty(a,"innerHTML",{enumerable:b.enumerable,configurable:!0,get:b.get,set:function(a){var c=this,e=void 0;H(this)&&(e=[],wd(this,function(a){a!==c&&e.push(a)}));b.set.call(this,a);if(e)for(var f=0;f<e.length;f++){var g=e[f];1===g.__CE_state&&d.disconnectedCallback(g)}this.ownerDocument.__CE_hasRegistry?L(d,this):Ad(d,this);return a}})}function b(a,b){I(a,"insertAdjacentElement",function(a,c){var e=H(c);a=b.call(this,a,c);e&&K(d,c);H(a)&&J(d,c);return a})}
function c(a,b){function c(a,b){for(var c=[];a!==b;a=a.nextSibling)c.push(a);for(b=0;b<c.length;b++)L(d,c[b])}I(a,"insertAdjacentHTML",function(a,d){a=a.toLowerCase();if("beforebegin"===a){var e=this.previousSibling;b.call(this,a,d);c(e||this.parentNode.firstChild,this)}else if("afterbegin"===a)e=this.firstChild,b.call(this,a,d),c(this.firstChild,e);else if("beforeend"===a)e=this.lastChild,b.call(this,a,d),c(e||this.firstChild,null);else if("afterend"===a)e=this.nextSibling,b.call(this,a,d),c(this.nextSibling,
e);else throw new SyntaxError("The value provided ("+String(a)+") is not one of 'beforebegin', 'afterbegin', 'beforeend', or 'afterend'.");})}var d=oe;Td&&I(Element.prototype,"attachShadow",function(a){return this.__CE_shadowRoot=a=Td.call(this,a)});Ud&&Ud.get?a(Element.prototype,Ud):je&&je.get?a(HTMLElement.prototype,je):zd(d,function(b){a(b,{enumerable:!0,configurable:!0,get:function(){return Nd.call(this,!0).innerHTML},set:function(a){var b="template"===this.localName,c=b?this.content:this,d=Hd.call(document,
this.namespaceURI,this.localName);for(d.innerHTML=a;0<c.childNodes.length;)Qd.call(c,c.childNodes[0]);for(a=b?d.content:d;0<a.childNodes.length;)Od.call(c,a.childNodes[0])}})});I(Element.prototype,"setAttribute",function(a,b){if(1!==this.__CE_state)return Wd.call(this,a,b);var c=Vd.call(this,a);Wd.call(this,a,b);b=Vd.call(this,a);d.attributeChangedCallback(this,a,c,b,null)});I(Element.prototype,"setAttributeNS",function(a,b,c){if(1!==this.__CE_state)return Zd.call(this,a,b,c);var e=Yd.call(this,a,
b);Zd.call(this,a,b,c);c=Yd.call(this,a,b);d.attributeChangedCallback(this,b,e,c,a)});I(Element.prototype,"removeAttribute",function(a){if(1!==this.__CE_state)return Xd.call(this,a);var b=Vd.call(this,a);Xd.call(this,a);null!==b&&d.attributeChangedCallback(this,a,b,null,null)});I(Element.prototype,"removeAttributeNS",function(a,b){if(1!==this.__CE_state)return $d.call(this,a,b);var c=Yd.call(this,a,b);$d.call(this,a,b);var e=Yd.call(this,a,b);c!==e&&d.attributeChangedCallback(this,b,c,e,a)});ke?b(HTMLElement.prototype,
ke):ae?b(Element.prototype,ae):console.warn("Custom Elements: `Element#insertAdjacentElement` was not patched.");le?c(HTMLElement.prototype,le):be?c(Element.prototype,be):console.warn("Custom Elements: `Element#insertAdjacentHTML` was not patched.");pe(d,Element.prototype,{V:ce,append:de});se(d)};var ue=window.customElements;if(!ue||ue.forcePolyfill||"function"!=typeof ue.define||"function"!=typeof ue.get){var oe=new xd;ne();qe();pe(oe,DocumentFragment.prototype,{V:Ld,append:Md});re();te();document.__CE_hasRegistry=!0;var customElements=new N(oe);Object.defineProperty(window,"customElements",{configurable:!0,enumerable:!0,value:customElements})};function ve(){this.end=this.start=0;this.rules=this.parent=this.previous=null;this.cssText=this.parsedCssText="";this.atRule=!1;this.type=0;this.parsedSelector=this.selector=this.keyframesName=""}
function we(a){a=a.replace(xe,"").replace(ye,"");var b=ze,c=a,d=new ve;d.start=0;d.end=c.length;for(var e=d,f=0,g=c.length;f<g;f++)if("{"===c[f]){e.rules||(e.rules=[]);var h=e,k=h.rules[h.rules.length-1]||null;e=new ve;e.start=f+1;e.parent=h;e.previous=k;h.rules.push(e)}else"}"===c[f]&&(e.end=f+1,e=e.parent||d);return b(d,a)}
function ze(a,b){var c=b.substring(a.start,a.end-1);a.parsedCssText=a.cssText=c.trim();a.parent&&(c=b.substring(a.previous?a.previous.end:a.parent.start,a.start-1),c=Ae(c),c=c.replace(Be," "),c=c.substring(c.lastIndexOf(";")+1),c=a.parsedSelector=a.selector=c.trim(),a.atRule=0===c.indexOf("@"),a.atRule?0===c.indexOf("@media")?a.type=Ce:c.match(De)&&(a.type=Ee,a.keyframesName=a.selector.split(Be).pop()):a.type=0===c.indexOf("--")?Fe:Ge);if(c=a.rules)for(var d=0,e=c.length,f=void 0;d<e&&(f=c[d]);d++)ze(f,
b);return a}function Ae(a){return a.replace(/\\([0-9a-f]{1,6})\s/gi,function(a,c){a=c;for(c=6-a.length;c--;)a="0"+a;return"\\"+a})}
function He(a,b,c){c=void 0===c?"":c;var d="";if(a.cssText||a.rules){var e=a.rules,f;if(f=e)f=e[0],f=!(f&&f.selector&&0===f.selector.indexOf("--"));if(f){f=0;for(var g=e.length,h=void 0;f<g&&(h=e[f]);f++)d=He(h,b,d)}else b?b=a.cssText:(b=a.cssText,b=b.replace(Ie,"").replace(Je,""),b=b.replace(Ke,"").replace(Le,"")),(d=b.trim())&&(d="  "+d+"\n")}d&&(a.selector&&(c+=a.selector+" {\n"),c+=d,a.selector&&(c+="}\n\n"));return c}
var Ge=1,Ee=7,Ce=4,Fe=1E3,xe=/\/\*[^*]*\*+([^/*][^*]*\*+)*\//gim,ye=/@import[^;]*;/gim,Ie=/(?:^[^;\-\s}]+)?--[^;{}]*?:[^{};]*?(?:[;\n]|$)/gim,Je=/(?:^[^;\-\s}]+)?--[^;{}]*?:[^{};]*?{[^}]*?}(?:[;\n]|$)?/gim,Ke=/@apply\s*\(?[^);]*\)?\s*(?:[;\n]|$)?/gim,Le=/[^;:]*?:[^;]*?var\([^;]*\)(?:[;\n]|$)?/gim,De=/^@[^\s]*keyframes/,Be=/\s+/g;var O=!(window.ShadyDOM&&window.ShadyDOM.inUse),Me;function Ne(a){Me=a&&a.shimcssproperties?!1:O||!(navigator.userAgent.match(/AppleWebKit\/601|Edge\/15/)||!window.CSS||!CSS.supports||!CSS.supports("box-shadow","0 0 0 var(--foo)"))}var Oe;window.ShadyCSS&&void 0!==window.ShadyCSS.cssBuild&&(Oe=window.ShadyCSS.cssBuild);var Pe=!(!window.ShadyCSS||!window.ShadyCSS.disableRuntime);
window.ShadyCSS&&void 0!==window.ShadyCSS.nativeCss?Me=window.ShadyCSS.nativeCss:window.ShadyCSS?(Ne(window.ShadyCSS),window.ShadyCSS=void 0):Ne(window.WebComponents&&window.WebComponents.flags);var Q=Me,Qe=Oe;var Re=/(?:^|[;\s{]\s*)(--[\w-]*?)\s*:\s*(?:((?:'(?:\\'|.)*?'|"(?:\\"|.)*?"|\([^)]*?\)|[^};{])+)|\{([^}]*)\}(?:(?=[;\s}])|$))/gi,Se=/(?:^|\W+)@apply\s*\(?([^);\n]*)\)?/gi,Te=/(--[\w-]+)\s*([:,;)]|$)/gi,Ue=/(animation\s*:)|(animation-name\s*:)/,$e=/@media\s(.*)/,af=/\{[^}]*\}/g;var bf=new Set;function cf(a,b){if(!a)return"";"string"===typeof a&&(a=we(a));b&&df(a,b);return He(a,Q)}function ef(a){!a.__cssRules&&a.textContent&&(a.__cssRules=we(a.textContent));return a.__cssRules||null}function ff(a){return!!a.parent&&a.parent.type===Ee}function df(a,b,c,d){if(a){var e=!1,f=a.type;if(d&&f===Ce){var g=a.selector.match($e);g&&(window.matchMedia(g[1]).matches||(e=!0))}f===Ge?b(a):c&&f===Ee?c(a):f===Fe&&(e=!0);if((a=a.rules)&&!e)for(e=0,f=a.length,g=void 0;e<f&&(g=a[e]);e++)df(g,b,c,d)}}
function gf(a,b,c,d){var e=document.createElement("style");b&&e.setAttribute("scope",b);e.textContent=a;hf(e,c,d);return e}var jf=null;function kf(a){a=document.createComment(" Shady DOM styles for "+a+" ");var b=document.head;b.insertBefore(a,(jf?jf.nextSibling:null)||b.firstChild);return jf=a}function hf(a,b,c){b=b||document.head;b.insertBefore(a,c&&c.nextSibling||b.firstChild);jf?a.compareDocumentPosition(jf)===Node.DOCUMENT_POSITION_PRECEDING&&(jf=a):jf=a}
function lf(a,b){for(var c=0,d=a.length;b<d;b++)if("("===a[b])c++;else if(")"===a[b]&&0===--c)return b;return-1}function mf(a,b){var c=a.indexOf("var(");if(-1===c)return b(a,"","","");var d=lf(a,c+3),e=a.substring(c+4,d);c=a.substring(0,c);a=mf(a.substring(d+1),b);d=e.indexOf(",");return-1===d?b(c,e.trim(),"",a):b(c,e.substring(0,d).trim(),e.substring(d+1).trim(),a)}function nf(a,b){O?a.setAttribute("class",b):window.ShadyDOM.nativeMethods.setAttribute.call(a,"class",b)}
var of=window.ShadyDOM&&window.ShadyDOM.wrap||function(a){return a};function pf(a){var b=a.localName,c="";b?-1<b.indexOf("-")||(c=b,b=a.getAttribute&&a.getAttribute("is")||""):(b=a.is,c=a.extends);return{is:b,M:c}}function qf(a){for(var b=[],c="",d=0;0<=d&&d<a.length;d++)if("("===a[d]){var e=lf(a,d);c+=a.slice(d,e+1);d=e}else","===a[d]?(b.push(c),c=""):c+=a[d];c&&b.push(c);return b}
function rf(a){if(void 0!==Qe)return Qe;if(void 0===a.__cssBuild){var b=a.getAttribute("css-build");if(b)a.__cssBuild=b;else{a:{b="template"===a.localName?a.content.firstChild:a.firstChild;if(b instanceof Comment&&(b=b.textContent.trim().split(":"),"css-build"===b[0])){b=b[1];break a}b=""}if(""!==b){var c="template"===a.localName?a.content.firstChild:a.firstChild;c.parentNode.removeChild(c)}a.__cssBuild=b}}return a.__cssBuild||""}
function sf(a){a=void 0===a?"":a;return""!==a&&Q?O?"shadow"===a:"shady"===a:!1};function tf(){}function uf(a,b){vf(R,a,function(a){wf(a,b||"")})}function vf(a,b,c){b.nodeType===Node.ELEMENT_NODE&&c(b);var d;"template"===b.localName?d=(b.content||b._content||b).childNodes:d=b.children||b.childNodes;if(d)for(b=0;b<d.length;b++)vf(a,d[b],c)}
function wf(a,b,c){if(b)if(a.classList)c?(a.classList.remove("style-scope"),a.classList.remove(b)):(a.classList.add("style-scope"),a.classList.add(b));else if(a.getAttribute){var d=a.getAttribute("class");c?d&&(b=d.replace("style-scope","").replace(b,""),nf(a,b)):nf(a,(d?d+" ":"")+"style-scope "+b)}}function xf(a,b,c){vf(R,a,function(a){wf(a,b,!0);wf(a,c)})}function yf(a,b){vf(R,a,function(a){wf(a,b||"",!0)})}
function zf(a,b,c,d,e){var f=R;e=void 0===e?"":e;""===e&&(O||"shady"===(void 0===d?"":d)?e=cf(b,c):(a=pf(a),e=Af(f,b,a.is,a.M,c)+"\n\n"));return e.trim()}function Af(a,b,c,d,e){var f=Bf(c,d);c=c?"."+c:"";return cf(b,function(b){b.c||(b.selector=b.j=Cf(a,b,a.b,c,f),b.c=!0);e&&e(b,c,f)})}function Bf(a,b){return b?"[is="+a+"]":a}
function Cf(a,b,c,d,e){var f=qf(b.selector);if(!ff(b)){b=0;for(var g=f.length,h=void 0;b<g&&(h=f[b]);b++)f[b]=c.call(a,h,d,e)}return f.filter(function(a){return!!a}).join(",")}function Df(a){return a.replace(Ef,function(a,c,d){-1<d.indexOf("+")?d=d.replace(/\+/g,"___"):-1<d.indexOf("___")&&(d=d.replace(/___/g,"+"));return":"+c+"("+d+")"})}
function Ff(a){for(var b=[],c;c=a.match(Gf);){var d=c.index,e=lf(a,d);if(-1===e)throw Error(c.input+" selector missing ')'");c=a.slice(d,e+1);a=a.replace(c,"\ue000");b.push(c)}return{ha:a,matches:b}}function Hf(a,b){var c=a.split("\ue000");return b.reduce(function(a,b,f){return a+b+c[f+1]},c[0])}
tf.prototype.b=function(a,b,c){var d=!1;a=a.trim();var e=Ef.test(a);e&&(a=a.replace(Ef,function(a,b,c){return":"+b+"("+c.replace(/\s/g,"")+")"}),a=Df(a));var f=Gf.test(a);if(f){var g=Ff(a);a=g.ha;g=g.matches}a=a.replace(If,":host $1");a=a.replace(Jf,function(a,e,f){d||(a=Kf(f,e,b,c),d=d||a.stop,e=a.Ca,f=a.value);return e+f});f&&(a=Hf(a,g));e&&(a=Df(a));return a};
function Kf(a,b,c,d){var e=a.indexOf("::slotted");0<=a.indexOf(":host")?a=Lf(a,d):0!==e&&(a=c?Mf(a,c):a);c=!1;0<=e&&(b="",c=!0);if(c){var f=!0;c&&(a=a.replace(Nf,function(a,b){return" > "+b}))}a=a.replace(Of,function(a,b,c){return'[dir="'+c+'"] '+b+", "+b+'[dir="'+c+'"]'});return{value:a,Ca:b,stop:f}}
function Mf(a,b){a=a.split(/(\[.+?\])/);for(var c=[],d=0;d<a.length;d++)if(1===d%2)c.push(a[d]);else{var e=a[d];if(""!==e||d!==a.length-1)e=e.split(":"),e[0]+=b,c.push(e.join(":"))}return c.join("")}function Lf(a,b){var c=a.match(Pf);return(c=c&&c[2].trim()||"")?c[0].match(Qf)?a.replace(Pf,function(a,c,f){return b+f}):c.split(Qf)[0]===b?c:"should_not_match":a.replace(":host",b)}function Rf(a){":root"===a.selector&&(a.selector="html")}
tf.prototype.c=function(a){return a.match(":host")?"":a.match("::slotted")?this.b(a,":not(.style-scope)"):Mf(a.trim(),":not(.style-scope)")};p.Object.defineProperties(tf.prototype,{a:{configurable:!0,enumerable:!0,get:function(){return"style-scope"}}});
var Ef=/:(nth[-\w]+)\(([^)]+)\)/,Jf=/(^|[\s>+~]+)((?:\[.+?\]|[^\s>+~=[])+)/g,Qf=/[[.:#*]/,If=/^(::slotted)/,Pf=/(:host)(?:\(((?:\([^)(]*\)|[^)(]*)+?)\))/,Nf=/(?:::slotted)(?:\(((?:\([^)(]*\)|[^)(]*)+?)\))/,Of=/(.*):dir\((?:(ltr|rtl))\)/,Gf=/:(?:matches|any|-(?:webkit|moz)-any)/,R=new tf;function Sf(a,b,c,d,e){this.A=a||null;this.b=b||null;this.fa=c||[];this.o=null;this.cssBuild=e||"";this.M=d||"";this.a=this.s=this.w=null}function T(a){return a?a.__styleInfo:null}function Tf(a,b){return a.__styleInfo=b}Sf.prototype.c=function(){return this.A};Sf.prototype._getStyleRules=Sf.prototype.c;function Uf(a){var b=this.matches||this.matchesSelector||this.mozMatchesSelector||this.msMatchesSelector||this.oMatchesSelector||this.webkitMatchesSelector;return b&&b.call(this,a)}var Vf=navigator.userAgent.match("Trident");function Wf(){}function Xf(a){var b={},c=[],d=0;df(a,function(a){Yf(a);a.index=d++;a=a.i.cssText;for(var c;c=Te.exec(a);){var e=c[1];":"!==c[2]&&(b[e]=!0)}},function(a){c.push(a)});a.b=c;a=[];for(var e in b)a.push(e);return a}
function Yf(a){if(!a.i){var b={},c={};Zf(a,c)&&(b.v=c,a.rules=null);b.cssText=a.parsedCssText.replace(af,"").replace(Re,"");a.i=b}}function Zf(a,b){var c=a.i;if(c){if(c.v)return Object.assign(b,c.v),!0}else{c=a.parsedCssText;for(var d;a=Re.exec(c);){d=(a[2]||a[3]).trim();if("inherit"!==d||"unset"!==d)b[a[1].trim()]=d;d=!0}return d}}
function $f(a,b,c){b&&(b=0<=b.indexOf(";")?ag(a,b,c):mf(b,function(b,e,f,g){if(!e)return b+g;(e=$f(a,c[e],c))&&"initial"!==e?"apply-shim-inherit"===e&&(e="inherit"):e=$f(a,c[f]||f,c)||f;return b+(e||"")+g}));return b&&b.trim()||""}
function ag(a,b,c){b=b.split(";");for(var d=0,e,f;d<b.length;d++)if(e=b[d]){Se.lastIndex=0;if(f=Se.exec(e))e=$f(a,c[f[1]],c);else if(f=e.indexOf(":"),-1!==f){var g=e.substring(f);g=g.trim();g=$f(a,g,c)||g;e=e.substring(0,f)+g}b[d]=e&&e.lastIndexOf(";")===e.length-1?e.slice(0,-1):e||""}return b.join(";")}
function bg(a,b){var c={},d=[];df(a,function(a){a.i||Yf(a);var e=a.j||a.parsedSelector;b&&a.i.v&&e&&Uf.call(b,e)&&(Zf(a,c),a=a.index,e=parseInt(a/32,10),d[e]=(d[e]||0)|1<<a%32)},null,!0);return{v:c,key:d}}
function cg(a,b,c,d){b.i||Yf(b);if(b.i.v){var e=pf(a);a=e.is;e=e.M;e=a?Bf(a,e):"html";var f=b.parsedSelector,g=":host > *"===f||"html"===f,h=0===f.indexOf(":host")&&!g;"shady"===c&&(g=f===e+" > *."+e||-1!==f.indexOf("html"),h=!g&&0===f.indexOf(e));if(g||h)c=e,h&&(b.j||(b.j=Cf(R,b,R.b,a?"."+a:"",e)),c=b.j||e),d({ha:c,Ia:h,Za:g})}}function dg(a,b,c){var d={},e={};df(b,function(b){cg(a,b,c,function(c){Uf.call(a._element||a,c.ha)&&(c.Ia?Zf(b,d):Zf(b,e))})},null,!0);return{Ra:e,Ha:d}}
function eg(a,b,c,d){var e=pf(b),f=Bf(e.is,e.M),g=new RegExp("(?:^|[^.#[:])"+(b.extends?"\\"+f.slice(0,-1)+"\\]":f)+"($|[.:[\\s>+~])"),h=T(b);e=h.A;h=h.cssBuild;var k=fg(e,d);return zf(b,e,function(b){var e="";b.i||Yf(b);b.i.cssText&&(e=ag(a,b.i.cssText,c));b.cssText=e;if(!O&&!ff(b)&&b.cssText){var h=e=b.cssText;null==b.na&&(b.na=Ue.test(e));if(b.na)if(null==b.U){b.U=[];for(var l in k)h=k[l],h=h(e),e!==h&&(e=h,b.U.push(l))}else{for(l=0;l<b.U.length;++l)h=k[b.U[l]],e=h(e);h=e}b.cssText=h;b.j=b.j||
b.selector;e="."+d;l=qf(b.j);h=0;for(var M=l.length,U=void 0;h<M&&(U=l[h]);h++)l[h]=U.match(g)?U.replace(f,e):e+" "+U;b.selector=l.join(",")}},h)}function fg(a,b){a=a.b;var c={};if(!O&&a)for(var d=0,e=a[d];d<a.length;e=a[++d]){var f=e,g=b;f.f=new RegExp("\\b"+f.keyframesName+"(?!\\B|-)","g");f.a=f.keyframesName+"-"+g;f.j=f.j||f.selector;f.selector=f.j.replace(f.keyframesName,f.a);c[e.keyframesName]=gg(e)}return c}function gg(a){return function(b){return b.replace(a.f,a.a)}}
function hg(a,b){var c=ig,d=ef(a);a.textContent=cf(d,function(a){var d=a.cssText=a.parsedCssText;a.i&&a.i.cssText&&(d=d.replace(Ie,"").replace(Je,""),a.cssText=ag(c,d,b))})}p.Object.defineProperties(Wf.prototype,{a:{configurable:!0,enumerable:!0,get:function(){return"x-scope"}}});var ig=new Wf;var jg={},kg=window.customElements;if(kg&&!O&&!Pe){var lg=kg.define;kg.define=function(a,b,c){jg[a]||(jg[a]=kf(a));lg.call(kg,a,b,c)}};function mg(){this.cache={}}mg.prototype.store=function(a,b,c,d){var e=this.cache[a]||[];e.push({v:b,styleElement:c,s:d});100<e.length&&e.shift();this.cache[a]=e};function ng(){}var og=new RegExp(R.a+"\\s*([^\\s]*)");function pg(a){return(a=(a.classList&&a.classList.value?a.classList.value:a.getAttribute("class")||"").match(og))?a[1]:""}function qg(a){var b=of(a).getRootNode();return b===a||b===a.ownerDocument?"":(a=b.host)?pf(a).is:""}
function rg(a){for(var b=0;b<a.length;b++){var c=a[b];if(c.target!==document.documentElement&&c.target!==document.head)for(var d=0;d<c.addedNodes.length;d++){var e=c.addedNodes[d];if(e.nodeType===Node.ELEMENT_NODE){var f=e.getRootNode(),g=pg(e);if(g&&f===e.ownerDocument&&("style"!==e.localName&&"template"!==e.localName||""===rf(e)))yf(e,g);else if(f instanceof ShadowRoot)for(f=qg(e),f!==g&&xf(e,g,f),e=window.ShadyDOM.nativeMethods.querySelectorAll.call(e,":not(."+R.a+")"),g=0;g<e.length;g++){f=e[g];
var h=qg(f);h&&wf(f,h)}}}}}
if(!(O||window.ShadyDOM&&window.ShadyDOM.handlesDynamicScoping)){var sg=new MutationObserver(rg),tg=function(a){sg.observe(a,{childList:!0,subtree:!0})};if(window.customElements&&!window.customElements.polyfillWrapFlushCallback)tg(document);else{var ug=function(){tg(document.body)};window.HTMLImports?window.HTMLImports.whenReady(ug):requestAnimationFrame(function(){if("loading"===document.readyState){var a=function(){ug();document.removeEventListener("readystatechange",a)};document.addEventListener("readystatechange",
a)}else ug()})}ng=function(){rg(sg.takeRecords())}}var vg=ng;var wg={};var xg=Promise.resolve();function yg(a){if(a=wg[a])a._applyShimCurrentVersion=a._applyShimCurrentVersion||0,a._applyShimValidatingVersion=a._applyShimValidatingVersion||0,a._applyShimNextVersion=(a._applyShimNextVersion||0)+1}function zg(a){return a._applyShimCurrentVersion===a._applyShimNextVersion}function Ag(a){a._applyShimValidatingVersion=a._applyShimNextVersion;a._validating||(a._validating=!0,xg.then(function(){a._applyShimCurrentVersion=a._applyShimNextVersion;a._validating=!1}))};var Bg={},Cg=new mg;function Y(){this.l={};this.c=document.documentElement;var a=new ve;a.rules=[];this.f=Tf(this.c,new Sf(a));this.g=!1;this.b=this.a=null}n=Y.prototype;n.flush=function(){vg()};n.Fa=function(a){return ef(a)};n.Va=function(a){return cf(a)};n.prepareTemplate=function(a,b,c){this.prepareTemplateDom(a,b);this.prepareTemplateStyles(a,b,c)};
n.prepareTemplateStyles=function(a,b,c){if(!a._prepared&&!Pe){O||jg[b]||(jg[b]=kf(b));a._prepared=!0;a.name=b;a.extends=c;wg[b]=a;var d=rf(a),e=sf(d);c={is:b,extends:c};for(var f=[],g=a.content.querySelectorAll("style"),h=0;h<g.length;h++){var k=g[h];if(k.hasAttribute("shady-unscoped")){if(!O){var l=k.textContent;bf.has(l)||(bf.add(l),l=k.cloneNode(!0),document.head.appendChild(l));k.parentNode.removeChild(k)}}else f.push(k.textContent),k.parentNode.removeChild(k)}f=f.join("").trim()+(Bg[b]||"");
Dg(this);if(!e){if(g=!d)g=Se.test(f)||Re.test(f),Se.lastIndex=0,Re.lastIndex=0;h=we(f);g&&Q&&this.a&&this.a.transformRules(h,b);a._styleAst=h}g=[];Q||(g=Xf(a._styleAst));if(!g.length||Q)h=O?a.content:null,b=jg[b]||null,d=zf(c,a._styleAst,null,d,e?f:""),d=d.length?gf(d,c.is,h,b):null,a._style=d;a.a=g}};n.Qa=function(a,b){Bg[b]=a.join(" ")};n.prepareTemplateDom=function(a,b){if(!Pe){var c=rf(a);O||"shady"===c||a._domPrepared||(a._domPrepared=!0,uf(a.content,b))}};
function Eg(a){var b=pf(a),c=b.is;b=b.M;var d=jg[c]||null,e=wg[c];if(e){c=e._styleAst;var f=e.a;e=rf(e);b=new Sf(c,d,f,b,e);Tf(a,b);return b}}function Fg(a){!a.b&&window.ShadyCSS&&window.ShadyCSS.CustomStyleInterface&&(a.b=window.ShadyCSS.CustomStyleInterface,a.b.transformCallback=function(b){a.ra(b)},a.b.validateCallback=function(){requestAnimationFrame(function(){(a.b.enqueued||a.g)&&a.flushCustomStyles()})})}
function Dg(a){!a.a&&window.ShadyCSS&&window.ShadyCSS.ApplyShim&&(a.a=window.ShadyCSS.ApplyShim,a.a.invalidCallback=yg);Fg(a)}
n.flushCustomStyles=function(){if(!Pe&&(Dg(this),this.b)){var a=this.b.processStyles();if(this.b.enqueued&&!sf(this.f.cssBuild)){if(Q){if(!this.f.cssBuild)for(var b=0;b<a.length;b++){var c=this.b.getStyleForCustomStyle(a[b]);if(c&&Q&&this.a){var d=ef(c);Dg(this);this.a.transformRules(d);c.textContent=cf(d)}}}else{Gg(this,this.c,this.f);for(b=0;b<a.length;b++)(c=this.b.getStyleForCustomStyle(a[b]))&&hg(c,this.f.w);this.g&&this.styleDocument()}this.b.enqueued=!1}}};
n.styleElement=function(a,b){if(Pe){if(b){T(a)||Tf(a,new Sf(null));var c=T(a);c.o=c.o||{};Object.assign(c.o,b);Hg(this,a,c)}}else if(c=T(a)||Eg(a))if(a!==this.c&&(this.g=!0),b&&(c.o=c.o||{},Object.assign(c.o,b)),Q)Hg(this,a,c);else if(this.flush(),Gg(this,a,c),c.fa&&c.fa.length){b=pf(a).is;var d;a:{if(d=Cg.cache[b])for(var e=d.length-1;0<=e;e--){var f=d[e];b:{var g=c.fa;for(var h=0;h<g.length;h++){var k=g[h];if(f.v[k]!==c.w[k]){g=!1;break b}}g=!0}if(g){d=f;break a}}d=void 0}g=d?d.styleElement:null;
e=c.s;(f=d&&d.s)||(f=this.l[b]=(this.l[b]||0)+1,f=b+"-"+f);c.s=f;f=c.s;h=ig;h=g?g.textContent||"":eg(h,a,c.w,f);k=T(a);var l=k.a;l&&!O&&l!==g&&(l._useCount--,0>=l._useCount&&l.parentNode&&l.parentNode.removeChild(l));O?k.a?(k.a.textContent=h,g=k.a):h&&(g=gf(h,f,a.shadowRoot,k.b)):g?g.parentNode||(Vf&&-1<h.indexOf("@media")&&(g.textContent=h),hf(g,null,k.b)):h&&(g=gf(h,f,null,k.b));g&&(g._useCount=g._useCount||0,k.a!=g&&g._useCount++,k.a=g);f=g;O||(g=c.s,k=h=a.getAttribute("class")||"",e&&(k=h.replace(new RegExp("\\s*x-scope\\s*"+
e+"\\s*","g")," ")),k+=(k?" ":"")+"x-scope "+g,h!==k&&nf(a,k));d||Cg.store(b,c.w,f,c.s)}};
function Hg(a,b,c){var d=pf(b).is;if(c.o){var e=c.o,f;for(f in e)null===f?b.style.removeProperty(f):b.style.setProperty(f,e[f])}e=wg[d];if(!(!e&&b!==a.c||e&&""!==rf(e))&&e&&e._style&&!zg(e)){if(zg(e)||e._applyShimValidatingVersion!==e._applyShimNextVersion)Dg(a),a.a&&a.a.transformRules(e._styleAst,d),e._style.textContent=zf(b,c.A),Ag(e);O&&(a=b.shadowRoot)&&(a=a.querySelector("style"))&&(a.textContent=zf(b,c.A));c.A=e._styleAst}}
function Ig(a,b){return(b=of(b).getRootNode().host)?T(b)||Eg(b)?b:Ig(a,b):a.c}function Gg(a,b,c){var d=Ig(a,b),e=T(d),f=e.w;d===a.c||f||(Gg(a,d,e),f=e.w);a=Object.create(f||null);d=dg(b,c.A,c.cssBuild);b=bg(e.A,b).v;Object.assign(a,d.Ha,b,d.Ra);b=c.o;for(var g in b)if((e=b[g])||0===e)a[g]=e;g=ig;b=Object.getOwnPropertyNames(a);for(e=0;e<b.length;e++)d=b[e],a[d]=$f(g,a[d],a);c.w=a}n.styleDocument=function(a){this.styleSubtree(this.c,a)};
n.styleSubtree=function(a,b){var c=of(a),d=c.shadowRoot;(d||a===this.c)&&this.styleElement(a,b);if(a=d&&(d.children||d.childNodes))for(c=0;c<a.length;c++)this.styleSubtree(a[c]);else if(c=c.children||c.childNodes)for(a=0;a<c.length;a++)this.styleSubtree(c[a])};
n.ra=function(a){var b=this,c=rf(a);c!==this.f.cssBuild&&(this.f.cssBuild=c);if(!sf(c)){var d=ef(a);df(d,function(a){if(O)Rf(a);else{var d=R;a.selector=a.parsedSelector;Rf(a);a.selector=a.j=Cf(d,a,d.c,void 0,void 0)}Q&&""===c&&(Dg(b),b.a&&b.a.transformRule(a))});Q?a.textContent=cf(d):this.f.A.rules.push(d)}};n.getComputedStyleValue=function(a,b){var c;Q||(c=(T(a)||T(Ig(this,a))).w[b]);return(c=c||window.getComputedStyle(a).getPropertyValue(b))?c.trim():""};
n.Ua=function(a,b){var c=of(a).getRootNode();b=b?b.split(/\s/):[];c=c.host&&c.host.localName;if(!c){var d=a.getAttribute("class");if(d){d=d.split(/\s/);for(var e=0;e<d.length;e++)if(d[e]===R.a){c=d[e+1];break}}}c&&b.push(R.a,c);Q||(c=T(a))&&c.s&&b.push(ig.a,c.s);nf(a,b.join(" "))};n.Ba=function(a){return T(a)};n.Ta=function(a,b){wf(a,b)};n.Wa=function(a,b){wf(a,b,!0)};n.Sa=function(a){return qg(a)};n.Da=function(a){return pg(a)};Y.prototype.flush=Y.prototype.flush;Y.prototype.prepareTemplate=Y.prototype.prepareTemplate;
Y.prototype.styleElement=Y.prototype.styleElement;Y.prototype.styleDocument=Y.prototype.styleDocument;Y.prototype.styleSubtree=Y.prototype.styleSubtree;Y.prototype.getComputedStyleValue=Y.prototype.getComputedStyleValue;Y.prototype.setElementClass=Y.prototype.Ua;Y.prototype._styleInfoForNode=Y.prototype.Ba;Y.prototype.transformCustomStyleForDocument=Y.prototype.ra;Y.prototype.getStyleAst=Y.prototype.Fa;Y.prototype.styleAstToString=Y.prototype.Va;Y.prototype.flushCustomStyles=Y.prototype.flushCustomStyles;
Y.prototype.scopeNode=Y.prototype.Ta;Y.prototype.unscopeNode=Y.prototype.Wa;Y.prototype.scopeForNode=Y.prototype.Sa;Y.prototype.currentScopeForNode=Y.prototype.Da;Y.prototype.prepareAdoptedCssText=Y.prototype.Qa;Object.defineProperties(Y.prototype,{nativeShadow:{get:function(){return O}},nativeCss:{get:function(){return Q}}});var Z=new Y,Jg,Kg;window.ShadyCSS&&(Jg=window.ShadyCSS.ApplyShim,Kg=window.ShadyCSS.CustomStyleInterface);
window.ShadyCSS={ScopingShim:Z,prepareTemplate:function(a,b,c){Z.flushCustomStyles();Z.prepareTemplate(a,b,c)},prepareTemplateDom:function(a,b){Z.prepareTemplateDom(a,b)},prepareTemplateStyles:function(a,b,c){Z.flushCustomStyles();Z.prepareTemplateStyles(a,b,c)},styleSubtree:function(a,b){Z.flushCustomStyles();Z.styleSubtree(a,b)},styleElement:function(a){Z.flushCustomStyles();Z.styleElement(a)},styleDocument:function(a){Z.flushCustomStyles();Z.styleDocument(a)},flushCustomStyles:function(){Z.flushCustomStyles()},
getComputedStyleValue:function(a,b){return Z.getComputedStyleValue(a,b)},nativeCss:Q,nativeShadow:O,cssBuild:Qe,disableRuntime:Pe};Jg&&(window.ShadyCSS.ApplyShim=Jg);Kg&&(window.ShadyCSS.CustomStyleInterface=Kg);var Lg=window.customElements,Mg=window.HTMLImports,Ng=window.HTMLTemplateElement;window.WebComponents=window.WebComponents||{};if(Lg&&Lg.polyfillWrapFlushCallback){var Og,Pg=function(){if(Og){Ng.C&&Ng.C(window.document);var a=Og;Og=null;a();return!0}},Qg=Mg.whenReady;Lg.polyfillWrapFlushCallback(function(a){Og=a;Qg(Pg)});Mg.whenReady=function(a){Qg(function(){Pg()?Mg.whenReady(a):a()})}}
Mg.whenReady(function(){requestAnimationFrame(function(){window.WebComponents.ready=!0;document.dispatchEvent(new CustomEvent("WebComponentsReady",{bubbles:!0}))})});var Rg=document.createElement("style");Rg.textContent="body {transition: opacity ease-in 0.2s; } \nbody[unresolved] {opacity: 0; display: block; overflow: hidden; position: relative; } \n";var Sg=document.querySelector("head");Sg.insertBefore(Rg,Sg.firstChild);}).call(this);



(function(){/*

Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
Code distributed by Google as part of the polymer project is also
subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
'use strict';var l=!(window.ShadyDOM&&window.ShadyDOM.inUse),p;function r(a){p=a&&a.shimcssproperties?!1:l||!(navigator.userAgent.match(/AppleWebKit\/601|Edge\/15/)||!window.CSS||!CSS.supports||!CSS.supports("box-shadow","0 0 0 var(--foo)"))}var t;window.ShadyCSS&&void 0!==window.ShadyCSS.cssBuild&&(t=window.ShadyCSS.cssBuild);var aa=!(!window.ShadyCSS||!window.ShadyCSS.disableRuntime);
window.ShadyCSS&&void 0!==window.ShadyCSS.nativeCss?p=window.ShadyCSS.nativeCss:window.ShadyCSS?(r(window.ShadyCSS),window.ShadyCSS=void 0):r(window.WebComponents&&window.WebComponents.flags);var u=p,v=t;function w(){this.end=this.start=0;this.rules=this.parent=this.previous=null;this.cssText=this.parsedCssText="";this.atRule=!1;this.type=0;this.parsedSelector=this.selector=this.keyframesName=""}
function x(a){a=a.replace(ba,"").replace(ca,"");var b=y,c=a,e=new w;e.start=0;e.end=c.length;for(var d=e,f=0,g=c.length;f<g;f++)if("{"===c[f]){d.rules||(d.rules=[]);var h=d,k=h.rules[h.rules.length-1]||null;d=new w;d.start=f+1;d.parent=h;d.previous=k;h.rules.push(d)}else"}"===c[f]&&(d.end=f+1,d=d.parent||e);return b(e,a)}
function y(a,b){var c=b.substring(a.start,a.end-1);a.parsedCssText=a.cssText=c.trim();a.parent&&(c=b.substring(a.previous?a.previous.end:a.parent.start,a.start-1),c=da(c),c=c.replace(z," "),c=c.substring(c.lastIndexOf(";")+1),c=a.parsedSelector=a.selector=c.trim(),a.atRule=0===c.indexOf("@"),a.atRule?0===c.indexOf("@media")?a.type=A:c.match(ea)&&(a.type=B,a.keyframesName=a.selector.split(z).pop()):a.type=0===c.indexOf("--")?C:D);if(c=a.rules)for(var e=0,d=c.length,f=void 0;e<d&&(f=c[e]);e++)y(f,b);
return a}function da(a){return a.replace(/\\([0-9a-f]{1,6})\s/gi,function(a,c){a=c;for(c=6-a.length;c--;)a="0"+a;return"\\"+a})}
function E(a,b,c){c=void 0===c?"":c;var e="";if(a.cssText||a.rules){var d=a.rules,f;if(f=d)f=d[0],f=!(f&&f.selector&&0===f.selector.indexOf("--"));if(f){f=0;for(var g=d.length,h=void 0;f<g&&(h=d[f]);f++)e=E(h,b,e)}else b?b=a.cssText:(b=a.cssText,b=b.replace(fa,"").replace(ha,""),b=b.replace(ia,"").replace(ja,"")),(e=b.trim())&&(e="  "+e+"\n")}e&&(a.selector&&(c+=a.selector+" {\n"),c+=e,a.selector&&(c+="}\n\n"));return c}
var D=1,B=7,A=4,C=1E3,ba=/\/\*[^*]*\*+([^/*][^*]*\*+)*\//gim,ca=/@import[^;]*;/gim,fa=/(?:^[^;\-\s}]+)?--[^;{}]*?:[^{};]*?(?:[;\n]|$)/gim,ha=/(?:^[^;\-\s}]+)?--[^;{}]*?:[^{};]*?{[^}]*?}(?:[;\n]|$)?/gim,ia=/@apply\s*\(?[^);]*\)?\s*(?:[;\n]|$)?/gim,ja=/[^;:]*?:[^;]*?var\([^;]*\)(?:[;\n]|$)?/gim,ea=/^@[^\s]*keyframes/,z=/\s+/g;var G=/(?:^|[;\s{]\s*)(--[\w-]*?)\s*:\s*(?:((?:'(?:\\'|.)*?'|"(?:\\"|.)*?"|\([^)]*?\)|[^};{])+)|\{([^}]*)\}(?:(?=[;\s}])|$))/gi,H=/(?:^|\W+)@apply\s*\(?([^);\n]*)\)?/gi,ka=/@media\s(.*)/;var I=new Set;function J(a){if(!a)return"";"string"===typeof a&&(a=x(a));return E(a,u)}function K(a){!a.__cssRules&&a.textContent&&(a.__cssRules=x(a.textContent));return a.__cssRules||null}function L(a,b,c,e){if(a){var d=!1,f=a.type;if(e&&f===A){var g=a.selector.match(ka);g&&(window.matchMedia(g[1]).matches||(d=!0))}f===D?b(a):c&&f===B?c(a):f===C&&(d=!0);if((a=a.rules)&&!d)for(d=0,f=a.length,g=void 0;d<f&&(g=a[d]);d++)L(g,b,c,e)}}
function M(a,b){var c=a.indexOf("var(");if(-1===c)return b(a,"","","");a:{var e=0;var d=c+3;for(var f=a.length;d<f;d++)if("("===a[d])e++;else if(")"===a[d]&&0===--e)break a;d=-1}e=a.substring(c+4,d);c=a.substring(0,c);a=M(a.substring(d+1),b);d=e.indexOf(",");return-1===d?b(c,e.trim(),"",a):b(c,e.substring(0,d).trim(),e.substring(d+1).trim(),a)}
function N(a){if(void 0!==v)return v;if(void 0===a.__cssBuild){var b=a.getAttribute("css-build");if(b)a.__cssBuild=b;else{a:{b="template"===a.localName?a.content.firstChild:a.firstChild;if(b instanceof Comment&&(b=b.textContent.trim().split(":"),"css-build"===b[0])){b=b[1];break a}b=""}if(""!==b){var c="template"===a.localName?a.content.firstChild:a.firstChild;c.parentNode.removeChild(c)}a.__cssBuild=b}}return a.__cssBuild||""};var la=/;\s*/m,ma=/^\s*(initial)|(inherit)\s*$/,O=/\s*!important/;function P(){this.a={}}P.prototype.set=function(a,b){a=a.trim();this.a[a]={h:b,i:{}}};P.prototype.get=function(a){a=a.trim();return this.a[a]||null};var Q=null;function R(){this.b=this.c=null;this.a=new P}R.prototype.o=function(a){a=H.test(a)||G.test(a);H.lastIndex=0;G.lastIndex=0;return a};
R.prototype.m=function(a,b){if(void 0===a._gatheredStyle){var c=[];for(var e=a.content.querySelectorAll("style"),d=0;d<e.length;d++){var f=e[d];if(f.hasAttribute("shady-unscoped")){if(!l){var g=f.textContent;I.has(g)||(I.add(g),g=f.cloneNode(!0),document.head.appendChild(g));f.parentNode.removeChild(f)}}else c.push(f.textContent),f.parentNode.removeChild(f)}(c=c.join("").trim())?(e=document.createElement("style"),e.textContent=c,a.content.insertBefore(e,a.content.firstChild),c=e):c=null;a._gatheredStyle=
c}return(a=a._gatheredStyle)?this.j(a,b):null};R.prototype.j=function(a,b){b=void 0===b?"":b;var c=K(a);this.l(c,b);a.textContent=J(c);return c};R.prototype.f=function(a){var b=this,c=K(a);L(c,function(a){":root"===a.selector&&(a.selector="html");b.g(a)});a.textContent=J(c);return c};R.prototype.l=function(a,b){var c=this;this.c=b;L(a,function(a){c.g(a)});this.c=null};R.prototype.g=function(a){a.cssText=na(this,a.parsedCssText,a);":root"===a.selector&&(a.selector=":host > *")};
function na(a,b,c){b=b.replace(G,function(b,d,f,g){return oa(a,b,d,f,g,c)});return S(a,b,c)}function pa(a,b){for(var c=b;c.parent;)c=c.parent;var e={},d=!1;L(c,function(c){(d=d||c===b)||c.selector===b.selector&&Object.assign(e,T(a,c.parsedCssText))});return e}
function S(a,b,c){for(var e;e=H.exec(b);){var d=e[0],f=e[1];e=e.index;var g=b.slice(0,e+d.indexOf("@apply"));b=b.slice(e+d.length);var h=c?pa(a,c):{};Object.assign(h,T(a,g));d=void 0;var k=a;f=f.replace(la,"");var n=[];var m=k.a.get(f);m||(k.a.set(f,{}),m=k.a.get(f));if(m){k.c&&(m.i[k.c]=!0);var q=m.h;for(d in q)k=h&&h[d],m=[d,": var(",f,"_-_",d],k&&m.push(",",k.replace(O,"")),m.push(")"),O.test(q[d])&&m.push(" !important"),n.push(m.join(""))}d=n.join("; ");b=g+d+b;H.lastIndex=e+d.length}return b}
function T(a,b,c){c=void 0===c?!1:c;b=b.split(";");for(var e,d,f={},g=0,h;g<b.length;g++)if(e=b[g])if(h=e.split(":"),1<h.length){e=h[0].trim();d=h.slice(1).join(":");if(c){var k=a;h=e;var n=ma.exec(d);n&&(n[1]?(k.b||(k.b=document.createElement("meta"),k.b.setAttribute("apply-shim-measure",""),k.b.style.all="initial",document.head.appendChild(k.b)),h=window.getComputedStyle(k.b).getPropertyValue(h)):h="apply-shim-inherit",d=h)}f[e]=d}return f}function qa(a,b){if(Q)for(var c in b.i)c!==a.c&&Q(c)}
function oa(a,b,c,e,d,f){e&&M(e,function(b,c){c&&a.a.get(c)&&(d="@apply "+c+";")});if(!d)return b;var g=S(a,""+d,f);f=b.slice(0,b.indexOf("--"));var h=g=T(a,g,!0),k=a.a.get(c),n=k&&k.h;n?h=Object.assign(Object.create(n),g):a.a.set(c,h);var m=[],q,Z=!1;for(q in h){var F=g[q];void 0===F&&(F="initial");!n||q in n||(Z=!0);m.push(c+"_-_"+q+": "+F)}Z&&qa(a,k);k&&(k.h=h);e&&(f=b+";"+f);return f+m.join("; ")+";"}R.prototype.detectMixin=R.prototype.o;R.prototype.transformStyle=R.prototype.j;
R.prototype.transformCustomStyle=R.prototype.f;R.prototype.transformRules=R.prototype.l;R.prototype.transformRule=R.prototype.g;R.prototype.transformTemplate=R.prototype.m;R.prototype._separator="_-_";Object.defineProperty(R.prototype,"invalidCallback",{get:function(){return Q},set:function(a){Q=a}});var U={};var ra=Promise.resolve();function sa(a){if(a=U[a])a._applyShimCurrentVersion=a._applyShimCurrentVersion||0,a._applyShimValidatingVersion=a._applyShimValidatingVersion||0,a._applyShimNextVersion=(a._applyShimNextVersion||0)+1}function ta(a){return a._applyShimCurrentVersion===a._applyShimNextVersion}function ua(a){a._applyShimValidatingVersion=a._applyShimNextVersion;a._validating||(a._validating=!0,ra.then(function(){a._applyShimCurrentVersion=a._applyShimNextVersion;a._validating=!1}))};var V=new R;function W(){this.a=null;V.invalidCallback=sa}function X(a){!a.a&&window.ShadyCSS.CustomStyleInterface&&(a.a=window.ShadyCSS.CustomStyleInterface,a.a.transformCallback=function(a){V.f(a)},a.a.validateCallback=function(){requestAnimationFrame(function(){a.a.enqueued&&a.flushCustomStyles()})})}W.prototype.prepareTemplate=function(a,b){X(this);""===N(a)&&(U[b]=a,b=V.m(a,b),a._styleAst=b)};
W.prototype.flushCustomStyles=function(){X(this);if(this.a){var a=this.a.processStyles();if(this.a.enqueued){for(var b=0;b<a.length;b++){var c=this.a.getStyleForCustomStyle(a[b]);c&&V.f(c)}this.a.enqueued=!1}}};
W.prototype.styleSubtree=function(a,b){X(this);if(b)for(var c in b)null===c?a.style.removeProperty(c):a.style.setProperty(c,b[c]);if(a.shadowRoot)for(this.styleElement(a),a=a.shadowRoot.children||a.shadowRoot.childNodes,b=0;b<a.length;b++)this.styleSubtree(a[b]);else for(a=a.children||a.childNodes,b=0;b<a.length;b++)this.styleSubtree(a[b])};
W.prototype.styleElement=function(a){X(this);var b=a.localName,c;b?-1<b.indexOf("-")?c=b:c=a.getAttribute&&a.getAttribute("is")||"":c=a.is;b=U[c];if(!(b&&""!==N(b)||!b||ta(b))){if(ta(b)||b._applyShimValidatingVersion!==b._applyShimNextVersion)this.prepareTemplate(b,c),ua(b);if(a=a.shadowRoot)if(a=a.querySelector("style"))a.__cssRules=b._styleAst,a.textContent=J(b._styleAst)}};W.prototype.styleDocument=function(a){X(this);this.styleSubtree(document.body,a)};
if(!window.ShadyCSS||!window.ShadyCSS.ScopingShim){var Y=new W,va=window.ShadyCSS&&window.ShadyCSS.CustomStyleInterface;window.ShadyCSS={prepareTemplate:function(a,b){Y.flushCustomStyles();Y.prepareTemplate(a,b)},prepareTemplateStyles:function(a,b,c){window.ShadyCSS.prepareTemplate(a,b,c)},prepareTemplateDom:function(){},styleSubtree:function(a,b){Y.flushCustomStyles();Y.styleSubtree(a,b)},styleElement:function(a){Y.flushCustomStyles();Y.styleElement(a)},styleDocument:function(a){Y.flushCustomStyles();
Y.styleDocument(a)},getComputedStyleValue:function(a,b){return(a=window.getComputedStyle(a).getPropertyValue(b))?a.trim():""},flushCustomStyles:function(){Y.flushCustomStyles()},nativeCss:u,nativeShadow:l,cssBuild:v,disableRuntime:aa};va&&(window.ShadyCSS.CustomStyleInterface=va)}window.ShadyCSS.ApplyShim=V;}).call(this);




(function() {
  'use strict';

  const userPolymer = window.Polymer;

  /**
   * @namespace Polymer
   * @summary Polymer is a lightweight library built on top of the web
   *   standards-based Web Components API's, and makes it easy to build your
   *   own custom HTML elements.
   * @param {!PolymerInit} info Prototype for the custom element. It must contain
   *   an `is` property to specify the element name. Other properties populate
   *   the element prototype. The `properties`, `observers`, `hostAttributes`,
   *   and `listeners` properties are processed to create element features.
   * @return {!Object} Returns a custom element class for the given provided
   *   prototype `info` object. The name of the element if given by `info.is`.
   */
  window.Polymer = function(info) {
    return window.Polymer._polymerFn(info);
  };

  // support user settings on the Polymer object
  if (userPolymer) {
    Object.assign(Polymer, userPolymer);
  }

  // To be plugged by legacy implementation if loaded
  /* eslint-disable valid-jsdoc */
  /**
   * @param {!PolymerInit} info Prototype for the custom element. It must contain
   *   an `is` property to specify the element name. Other properties populate
   *   the element prototype. The `properties`, `observers`, `hostAttributes`,
   *   and `listeners` properties are processed to create element features.
   * @return {!Object} Returns a custom element class for the given provided
   *   prototype `info` object. The name of the element if given by `info.is`.
   */
  window.Polymer._polymerFn = function(info) { // eslint-disable-line no-unused-vars
    throw new Error('Load polymer.html to use the Polymer() function.');
  };
  /* eslint-enable */

  window.Polymer.version = '2.7.0';

  /* eslint-disable no-unused-vars */
  /*
  When using Closure Compiler, JSCompiler_renameProperty(property, object) is replaced by the munged name for object[property]
  We cannot alias this function, so we have to use a small shim that has the same behavior when not compiling.
  */
  window.JSCompiler_renameProperty = function(prop, obj) {
    return prop;
  };
  /* eslint-enable */

})();



  (function() {
    'use strict';

    let CSS_URL_RX = /(url\()([^)]*)(\))/g;
    let ABS_URL = /(^\/)|(^#)|(^[\w-\d]*:)/;
    let workingURL;
    let resolveDoc;
    /**
     * Resolves the given URL against the provided `baseUri'.
     * 
     * Note that this function performs no resolution for URLs that start
     * with `/` (absolute URLs) or `#` (hash identifiers).  For general purpose
     * URL resolution, use `window.URL`.
     *
     * @memberof Polymer.ResolveUrl
     * @param {string} url Input URL to resolve
     * @param {?string=} baseURI Base URI to resolve the URL against
     * @return {string} resolved URL
     */
    function resolveUrl(url, baseURI) {
      if (url && ABS_URL.test(url)) {
        return url;
      }
      // Lazy feature detection.
      if (workingURL === undefined) {
        workingURL = false;
        try {
          const u = new URL('b', 'http://a');
          u.pathname = 'c%20d';
          workingURL = (u.href === 'http://a/c%20d');
        } catch (e) {
          // silently fail
        }
      }
      if (!baseURI) {
        baseURI = document.baseURI || window.location.href;
      }
      if (workingURL) {
        return (new URL(url, baseURI)).href;
      }
      // Fallback to creating an anchor into a disconnected document.
      if (!resolveDoc) {
        resolveDoc = document.implementation.createHTMLDocument('temp');
        resolveDoc.base = resolveDoc.createElement('base');
        resolveDoc.head.appendChild(resolveDoc.base);
        resolveDoc.anchor = resolveDoc.createElement('a');
        resolveDoc.body.appendChild(resolveDoc.anchor);
      }
      resolveDoc.base.href = baseURI;
      resolveDoc.anchor.href = url;
      return resolveDoc.anchor.href || url;

    }

    /**
     * Resolves any relative URL's in the given CSS text against the provided
     * `ownerDocument`'s `baseURI`.
     *
     * @memberof Polymer.ResolveUrl
     * @param {string} cssText CSS text to process
     * @param {string} baseURI Base URI to resolve the URL against
     * @return {string} Processed CSS text with resolved URL's
     */
    function resolveCss(cssText, baseURI) {
      return cssText.replace(CSS_URL_RX, function(m, pre, url, post) {
        return pre + '\'' +
          resolveUrl(url.replace(/["']/g, ''), baseURI) +
          '\'' + post;
      });
    }

    /**
     * Returns a path from a given `url`. The path includes the trailing
     * `/` from the url.
     *
     * @memberof Polymer.ResolveUrl
     * @param {string} url Input URL to transform
     * @return {string} resolved path
     */
    function pathFromUrl(url) {
      return url.substring(0, url.lastIndexOf('/') + 1);
    }

    /**
     * Module with utilities for resolving relative URL's.
     *
     * @namespace
     * @memberof Polymer
     * @summary Module with utilities for resolving relative URL's.
     */
    Polymer.ResolveUrl = {
      resolveCss: resolveCss,
      resolveUrl: resolveUrl,
      pathFromUrl: pathFromUrl
    };

  })();



/** @suppress {deprecated} */
(function() {
  'use strict';

  /**
   * Sets the global, legacy settings.
   *
   * @deprecated
   * @namespace
   * @memberof Polymer
   */
  Polymer.Settings = Polymer.Settings || {};

  Polymer.Settings.useShadow = !(window.ShadyDOM);
  Polymer.Settings.useNativeCSSProperties =
    Boolean(!window.ShadyCSS || window.ShadyCSS.nativeCss);
  Polymer.Settings.useNativeCustomElements =
    !(window.customElements.polyfillWrapFlushCallback);


  /**
   * Globally settable property that is automatically assigned to
   * `Polymer.ElementMixin` instances, useful for binding in templates to
   * make URL's relative to an application's root.  Defaults to the main
   * document URL, but can be overridden by users.  It may be useful to set
   * `Polymer.rootPath` to provide a stable application mount path when
   * using client side routing.
   *
   * @memberof Polymer
   */
  Polymer.rootPath = Polymer.rootPath ||
    Polymer.ResolveUrl.pathFromUrl(document.baseURI || window.location.href);

  /**
   * Sets the global rootPath property used by `Polymer.ElementMixin` and
   * available via `Polymer.rootPath`.
   *
   * @memberof Polymer
   * @param {string} path The new root path
   * @return {void}
   */
  Polymer.setRootPath = function(path) {
    Polymer.rootPath = path;
  };

  /**
   * A global callback used to sanitize any value before inserting it into the DOM. The callback signature is:
   *
   *     Polymer = {
   *       sanitizeDOMValue: function(value, name, type, node) { ... }
   *     }
   *
   * Where:
   *
   * `value` is the value to sanitize.
   * `name` is the name of an attribute or property (for example, href).
   * `type` indicates where the value is being inserted: one of property, attribute, or text.
   * `node` is the node where the value is being inserted.
   *
   * @type {(function(*,string,string,Node):*)|undefined}
   * @memberof Polymer
   */
  Polymer.sanitizeDOMValue = Polymer.sanitizeDOMValue || null;

  /**
   * Sets the global sanitizeDOMValue available via `Polymer.sanitizeDOMValue`.
   *
   * @memberof Polymer
   * @param {(function(*,string,string,Node):*)|undefined} newSanitizeDOMValue the global sanitizeDOMValue callback
   * @return {void}
   */
  Polymer.setSanitizeDOMValue = function(newSanitizeDOMValue) {
    Polymer.sanitizeDOMValue = newSanitizeDOMValue;
  };

  /**
   * Globally settable property to make Polymer Gestures use passive TouchEvent listeners when recognizing gestures.
   * When set to `true`, gestures made from touch will not be able to prevent scrolling, allowing for smoother
   * scrolling performance.
   * Defaults to `false` for backwards compatibility.
   *
   * @memberof Polymer
   */
  Polymer.passiveTouchGestures = Polymer.passiveTouchGestures || false;

  /**
   * Sets `passiveTouchGestures` globally for all elements using Polymer Gestures.
   *
   * @memberof Polymer
   * @param {boolean} usePassive enable or disable passive touch gestures globally
   * @return {void}
   */
  Polymer.setPassiveTouchGestures = function(usePassive) {
    Polymer.passiveTouchGestures = usePassive;
  };

  Polymer.legacyOptimizations = Polymer.legacyOptimizations ||
      window.PolymerSettings && window.PolymerSettings.legacyOptimizations || false;

  /**
   * Sets `legacyOptimizations` globally for all elements. Enables
   * optimizations when only legacy Polymer() style elements are used.
   *
   * @memberof Polymer
   * @param {boolean} useLegacyOptimizations enable or disable legacy optimizations globally.
   * @return {void}
   */
  Polymer.setLegacyOptimizations = function(useLegacyOptimizations) {
    Polymer.legacyOptimizations = useLegacyOptimizations;
  };
})();



(function() {

  'use strict';

  // unique global id for deduping mixins.
  let dedupeId = 0;

  /**
   * @constructor
   * @extends {Function}
   * @private
   */
  function MixinFunction(){}
  /** @type {(WeakMap | undefined)} */
  MixinFunction.prototype.__mixinApplications;
  /** @type {(Object | undefined)} */
  MixinFunction.prototype.__mixinSet;

  /* eslint-disable valid-jsdoc */
  /**
   * Wraps an ES6 class expression mixin such that the mixin is only applied
   * if it has not already been applied its base argument. Also memoizes mixin
   * applications.
   *
   * @memberof Polymer
   * @template T
   * @param {T} mixin ES6 class expression mixin to wrap
   * @return {T}
   * @suppress {invalidCasts}
   */
  Polymer.dedupingMixin = function(mixin) {
    let mixinApplications = /** @type {!MixinFunction} */(mixin).__mixinApplications;
    if (!mixinApplications) {
      mixinApplications = new WeakMap();
      /** @type {!MixinFunction} */(mixin).__mixinApplications = mixinApplications;
    }
    // maintain a unique id for each mixin
    let mixinDedupeId = dedupeId++;
    function dedupingMixin(base) {
      let baseSet = /** @type {!MixinFunction} */(base).__mixinSet;
      if (baseSet && baseSet[mixinDedupeId]) {
        return base;
      }
      let map = mixinApplications;
      let extended = map.get(base);
      if (!extended) {
        extended = /** @type {!Function} */(mixin)(base);
        map.set(base, extended);
      }
      // copy inherited mixin set from the extended class, or the base class
      // NOTE: we avoid use of Set here because some browser (IE11)
      // cannot extend a base Set via the constructor.
      let mixinSet = Object.create(/** @type {!MixinFunction} */(extended).__mixinSet || baseSet || null);
      mixinSet[mixinDedupeId] = true;
      /** @type {!MixinFunction} */(extended).__mixinSet = mixinSet;
      return extended;
    }

    return /** @type {T} */ (dedupingMixin);
  };
  /* eslint-enable valid-jsdoc */

})();



(function() {
  'use strict';

  const MODULE_STYLE_LINK_SELECTOR = 'link[rel=import][type~=css]';
  const INCLUDE_ATTR = 'include';
  const SHADY_UNSCOPED_ATTR = 'shady-unscoped';

  function importModule(moduleId) {
    const /** Polymer.DomModule */ PolymerDomModule = customElements.get('dom-module');
    if (!PolymerDomModule) {
      return null;
    }
    return PolymerDomModule.import(moduleId);
  }

  function styleForImport(importDoc) {
    // NOTE: polyfill affordance.
    // under the HTMLImports polyfill, there will be no 'body',
    // but the import pseudo-doc can be used directly.
    let container = importDoc.body ? importDoc.body : importDoc;
    const importCss = Polymer.ResolveUrl.resolveCss(container.textContent,
      importDoc.baseURI);
    const style = document.createElement('style');
    style.textContent = importCss;
    return style;
  }

  /** @typedef {{assetpath: string}} */
  let templateWithAssetPath; // eslint-disable-line no-unused-vars

  /**
   * Module with utilities for collection CSS text from `<templates>`, external
   * stylesheets, and `dom-module`s.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module with utilities for collection CSS text from various sources.
   */
  const StyleGather = {

    /**
     * Returns a list of <style> elements in a space-separated list of `dom-module`s.
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleIds List of dom-module id's within which to
     * search for css.
     * @return {!Array<!HTMLStyleElement>} Array of contained <style> elements
     * @this {StyleGather}
     */
     stylesFromModules(moduleIds) {
      const modules = moduleIds.trim().split(/\s+/);
      const styles = [];
      for (let i=0; i < modules.length; i++) {
        styles.push(...this.stylesFromModule(modules[i]));
      }
      return styles;
    },

    /**
     * Returns a list of <style> elements in a given `dom-module`.
     * Styles in a `dom-module` can come either from `<style>`s within the
     * first `<template>`, or else from one or more
     * `<link rel="import" type="css">` links outside the template.
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleId dom-module id to gather styles from
     * @return {!Array<!HTMLStyleElement>} Array of contained styles.
     * @this {StyleGather}
     */
    stylesFromModule(moduleId) {
      const m = importModule(moduleId);

      if (!m) {
        console.warn('Could not find style data in module named', moduleId);
        return [];
      }

      if (m._styles === undefined) {
        const styles = [];
        // module imports: <link rel="import" type="css">
        styles.push(...this._stylesFromModuleImports(m));
        // include css from the first template in the module
        const template = m.querySelector('template');
        if (template) {
          styles.push(...this.stylesFromTemplate(template,
            /** @type {templateWithAssetPath} */(m).assetpath));
        }

        m._styles = styles;
      }

      return m._styles;
    },

    /**
     * Returns the `<style>` elements within a given template.
     *
     * @memberof Polymer.StyleGather
     * @param {!HTMLTemplateElement} template Template to gather styles from
     * @param {string} baseURI baseURI for style content
     * @return {!Array<!HTMLStyleElement>} Array of styles
     * @this {StyleGather}
     */
    stylesFromTemplate(template, baseURI) {
      if (!template._styles) {
        const styles = [];
        // if element is a template, get content from its .content
        const e$ = template.content.querySelectorAll('style');
        for (let i=0; i < e$.length; i++) {
          let e = e$[i];
          // support style sharing by allowing styles to "include"
          // other dom-modules that contain styling
          let include = e.getAttribute(INCLUDE_ATTR);
          if (include) {
            styles.push(...this.stylesFromModules(include).filter(function(item, index, self) {
              return self.indexOf(item) === index;
            }));
          }
          if (baseURI) {
            e.textContent = Polymer.ResolveUrl.resolveCss(e.textContent, baseURI);
          }
          styles.push(e);
        }
        template._styles = styles;
      }
      return template._styles;
    },

    /**
     * Returns a list of <style> elements  from stylesheets loaded via `<link rel="import" type="css">` links within the specified `dom-module`.
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleId Id of `dom-module` to gather CSS from
     * @return {!Array<!HTMLStyleElement>} Array of contained styles.
     * @this {StyleGather}
     */
     stylesFromModuleImports(moduleId) {
      let m = importModule(moduleId);
      return m ? this._stylesFromModuleImports(m) : [];
    },

    /**
     * @memberof Polymer.StyleGather
     * @this {StyleGather}
     * @param {!HTMLElement} module dom-module element that could contain `<link rel="import" type="css">` styles
     * @return {!Array<!HTMLStyleElement>} Array of contained styles
     */
    _stylesFromModuleImports(module) {
      const styles = [];
      const p$ = module.querySelectorAll(MODULE_STYLE_LINK_SELECTOR);
      for (let i=0; i < p$.length; i++) {
        let p = p$[i];
        if (p.import) {
          const importDoc = p.import;
          const unscoped = p.hasAttribute(SHADY_UNSCOPED_ATTR);
          if (unscoped && !importDoc._unscopedStyle) {
            const style = styleForImport(importDoc);
            style.setAttribute(SHADY_UNSCOPED_ATTR, '');
            importDoc._unscopedStyle = style;
          } else if (!importDoc._style) {
            importDoc._style = styleForImport(importDoc);
          }
          styles.push(unscoped ? importDoc._unscopedStyle : importDoc._style);
        }
      }
      return styles;
    },

    /**
     *
     * Returns CSS text of styles in a space-separated list of `dom-module`s.
     * Note: This method is deprecated, use `stylesFromModules` instead.
     *
     * @deprecated
     * @memberof Polymer.StyleGather
     * @param {string} moduleIds List of dom-module id's within which to
     * search for css.
     * @return {string} Concatenated CSS content from specified `dom-module`s
     * @this {StyleGather}
     */
     cssFromModules(moduleIds) {
      let modules = moduleIds.trim().split(/\s+/);
      let cssText = '';
      for (let i=0; i < modules.length; i++) {
        cssText += this.cssFromModule(modules[i]);
      }
      return cssText;
    },

    /**
     * Returns CSS text of styles in a given `dom-module`.  CSS in a `dom-module`
     * can come either from `<style>`s within the first `<template>`, or else
     * from one or more `<link rel="import" type="css">` links outside the
     * template.
     *
     * Any `<styles>` processed are removed from their original location.
     * Note: This method is deprecated, use `styleFromModule` instead.
     *
     * @deprecated
     * @memberof Polymer.StyleGather
     * @param {string} moduleId dom-module id to gather styles from
     * @return {string} Concatenated CSS content from specified `dom-module`
     * @this {StyleGather}
     */
    cssFromModule(moduleId) {
      let m = importModule(moduleId);
      if (m && m._cssText === undefined) {
        // module imports: <link rel="import" type="css">
        let cssText = this._cssFromModuleImports(m);
        // include css from the first template in the module
        let t = m.querySelector('template');
        if (t) {
          cssText += this.cssFromTemplate(t,
            /** @type {templateWithAssetPath} */(m).assetpath);
        }
        m._cssText = cssText || null;
      }
      if (!m) {
        console.warn('Could not find style data in module named', moduleId);
      }
      return m && m._cssText || '';
    },

    /**
     * Returns CSS text of `<styles>` within a given template.
     *
     * Any `<styles>` processed are removed from their original location.
     * Note: This method is deprecated, use `styleFromTemplate` instead.
     *
     * @deprecated
     * @memberof Polymer.StyleGather
     * @param {!HTMLTemplateElement} template Template to gather styles from
     * @param {string} baseURI Base URI to resolve the URL against
     * @return {string} Concatenated CSS content from specified template
     * @this {StyleGather}
     */
    cssFromTemplate(template, baseURI) {
      let cssText = '';
      const e$ = this.stylesFromTemplate(template, baseURI);
      // if element is a template, get content from its .content
      for (let i=0; i < e$.length; i++) {
        let e = e$[i];
        if (e.parentNode) {
          e.parentNode.removeChild(e);
        }
        cssText += e.textContent;
      }
      return cssText;
    },

    /**
     * Returns CSS text from stylesheets loaded via `<link rel="import" type="css">`
     * links within the specified `dom-module`.
     *
     * Note: This method is deprecated, use `stylesFromModuleImports` instead.
     *
     * @deprecated
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleId Id of `dom-module` to gather CSS from
     * @return {string} Concatenated CSS content from links in specified `dom-module`
     * @this {StyleGather}
     */
    cssFromModuleImports(moduleId) {
      let m = importModule(moduleId);
      return m ? this._cssFromModuleImports(m) : '';
    },

    /**
     * @deprecated
     * @memberof Polymer.StyleGather
     * @this {StyleGather}
     * @param {!HTMLElement} module dom-module element that could contain `<link rel="import" type="css">` styles
     * @return {string} Concatenated CSS content from links in the dom-module
     */
     _cssFromModuleImports(module) {
      let cssText = '';
      let styles = this._stylesFromModuleImports(module);
      for (let i=0; i < styles.length; i++) {
        cssText += styles[i].textContent;
      }
      return cssText;
    }
  };

  Polymer.StyleGather = StyleGather;
})();


(function() {
  'use strict';

  let modules = {};
  let lcModules = {};
  function setModule(id, module) {
    // store id separate from lowercased id so that
    // in all cases mixedCase id will stored distinctly
    // and lowercase version is a fallback
    modules[id] = lcModules[id.toLowerCase()] = module;
  }
  function findModule(id) {
    return modules[id] || lcModules[id.toLowerCase()];
  }

  function styleOutsideTemplateCheck(inst) {
    if (inst.querySelector('style')) {
      console.warn('dom-module %s has style outside template', inst.id);
    }
  }

  /**
   * The `dom-module` element registers the dom it contains to the name given
   * by the module's id attribute. It provides a unified database of dom
   * accessible via its static `import` API.
   *
   * A key use case of `dom-module` is for providing custom element `<template>`s
   * via HTML imports that are parsed by the native HTML parser, that can be
   * relocated during a bundling pass and still looked up by `id`.
   *
   * Example:
   *
   *     <dom-module id="foo">
   *       <img src="stuff.png">
   *     </dom-module>
   *
   * Then in code in some other location that cannot access the dom-module above
   *
   *     let img = customElements.get('dom-module').import('foo', 'img');
   *
   * @customElement
   * @extends HTMLElement
   * @memberof Polymer
   * @summary Custom element that provides a registry of relocatable DOM content
   *   by `id` that is agnostic to bundling.
   * @unrestricted
   */
  class DomModule extends HTMLElement {

    static get observedAttributes() { return ['id']; }

    /**
     * Retrieves the element specified by the css `selector` in the module
     * registered by `id`. For example, this.import('foo', 'img');
     * @param {string} id The id of the dom-module in which to search.
     * @param {string=} selector The css selector by which to find the element.
     * @return {Element} Returns the element which matches `selector` in the
     * module registered at the specified `id`.
     */
    static import(id, selector) {
      if (id) {
        let m = findModule(id);
        if (m && selector) {
          return m.querySelector(selector);
        }
        return m;
      }
      return null;
    }

    /* eslint-disable no-unused-vars */
    /**
     * @param {string} name Name of attribute.
     * @param {?string} old Old value of attribute.
     * @param {?string} value Current value of attribute.
     * @param {?string} namespace Attribute namespace.
     * @return {void}
     */
    attributeChangedCallback(name, old, value, namespace) {
      if (old !== value) {
        this.register();
      }
    }
    /* eslint-enable no-unused-args */

    /**
     * The absolute URL of the original location of this `dom-module`.
     *
     * This value will differ from this element's `ownerDocument` in the
     * following ways:
     * - Takes into account any `assetpath` attribute added during bundling
     *   to indicate the original location relative to the bundled location
     * - Uses the HTMLImports polyfill's `importForElement` API to ensure
     *   the path is relative to the import document's location since
     *   `ownerDocument` is not currently polyfilled
     */
    get assetpath() {
      // Don't override existing assetpath.
      if (!this.__assetpath) {
        // note: assetpath set via an attribute must be relative to this
        // element's location; accomodate polyfilled HTMLImports
        const owner = window.HTMLImports && HTMLImports.importForElement ?
          HTMLImports.importForElement(this) || document : this.ownerDocument;
        const url = Polymer.ResolveUrl.resolveUrl(
          this.getAttribute('assetpath') || '', owner.baseURI);
        this.__assetpath = Polymer.ResolveUrl.pathFromUrl(url);
      }
      return this.__assetpath;
    }

    /**
     * Registers the dom-module at a given id. This method should only be called
     * when a dom-module is imperatively created. For
     * example, `document.createElement('dom-module').register('foo')`.
     * @param {string=} id The id at which to register the dom-module.
     * @return {void}
     */
    register(id) {
      id = id || this.id;
      if (id) {
        // Under strictTemplatePolicy, reject and null out any re-registered
        // dom-module since it is ambiguous whether first-in or last-in is trusted 
        if (Polymer.strictTemplatePolicy && findModule(id) !== undefined) {
          setModule(id, null);
          throw new Error(`strictTemplatePolicy: dom-module ${id} re-registered`);
        }
        this.id = id;
        setModule(id, this);
        styleOutsideTemplateCheck(this);
      }
    }
  }

  DomModule.prototype['modules'] = modules;

  customElements.define('dom-module', DomModule);

  /** @const */
  Polymer.DomModule = DomModule;

})();


(function() {
  'use strict';

  /**
   * Module with utilities for manipulating structured data path strings.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module with utilities for manipulating structured data path strings.
   */
  const Path = {

    /**
     * Returns true if the given string is a structured data path (has dots).
     *
     * Example:
     *
     * ```
     * Polymer.Path.isPath('foo.bar.baz') // true
     * Polymer.Path.isPath('foo')         // false
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} path Path string
     * @return {boolean} True if the string contained one or more dots
     */
    isPath: function(path) {
      return path.indexOf('.') >= 0;
    },

    /**
     * Returns the root property name for the given path.
     *
     * Example:
     *
     * ```
     * Polymer.Path.root('foo.bar.baz') // 'foo'
     * Polymer.Path.root('foo')         // 'foo'
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} path Path string
     * @return {string} Root property name
     */
    root: function(path) {
      let dotIndex = path.indexOf('.');
      if (dotIndex === -1) {
        return path;
      }
      return path.slice(0, dotIndex);
    },

    /**
     * Given `base` is `foo.bar`, `foo` is an ancestor, `foo.bar` is not
     * Returns true if the given path is an ancestor of the base path.
     *
     * Example:
     *
     * ```
     * Polymer.Path.isAncestor('foo.bar', 'foo')         // true
     * Polymer.Path.isAncestor('foo.bar', 'foo.bar')     // false
     * Polymer.Path.isAncestor('foo.bar', 'foo.bar.baz') // false
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} base Path string to test against.
     * @param {string} path Path string to test.
     * @return {boolean} True if `path` is an ancestor of `base`.
     */
    isAncestor: function(base, path) {
      //     base.startsWith(path + '.');
      return base.indexOf(path + '.') === 0;
    },

    /**
     * Given `base` is `foo.bar`, `foo.bar.baz` is an descendant
     *
     * Example:
     *
     * ```
     * Polymer.Path.isDescendant('foo.bar', 'foo.bar.baz') // true
     * Polymer.Path.isDescendant('foo.bar', 'foo.bar')     // false
     * Polymer.Path.isDescendant('foo.bar', 'foo')         // false
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} base Path string to test against.
     * @param {string} path Path string to test.
     * @return {boolean} True if `path` is a descendant of `base`.
     */
    isDescendant: function(base, path) {
      //     path.startsWith(base + '.');
      return path.indexOf(base + '.') === 0;
    },

    /**
     * Replaces a previous base path with a new base path, preserving the
     * remainder of the path.
     *
     * User must ensure `path` has a prefix of `base`.
     *
     * Example:
     *
     * ```
     * Polymer.Path.translate('foo.bar', 'zot', 'foo.bar.baz') // 'zot.baz'
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} base Current base string to remove
     * @param {string} newBase New base string to replace with
     * @param {string} path Path to translate
     * @return {string} Translated string
     */
    translate: function(base, newBase, path) {
      return newBase + path.slice(base.length);
    },

    /**
     * @param {string} base Path string to test against
     * @param {string} path Path string to test
     * @return {boolean} True if `path` is equal to `base`
     * @this {Path}
     */
    matches: function(base, path) {
      return (base === path) ||
             this.isAncestor(base, path) ||
             this.isDescendant(base, path);
    },

    /**
     * Converts array-based paths to flattened path.  String-based paths
     * are returned as-is.
     *
     * Example:
     *
     * ```
     * Polymer.Path.normalize(['foo.bar', 0, 'baz'])  // 'foo.bar.0.baz'
     * Polymer.Path.normalize('foo.bar.0.baz')        // 'foo.bar.0.baz'
     * ```
     *
     * @memberof Polymer.Path
     * @param {string | !Array<string|number>} path Input path
     * @return {string} Flattened path
     */
    normalize: function(path) {
      if (Array.isArray(path)) {
        let parts = [];
        for (let i=0; i<path.length; i++) {
          let args = path[i].toString().split('.');
          for (let j=0; j<args.length; j++) {
            parts.push(args[j]);
          }
        }
        return parts.join('.');
      } else {
        return path;
      }
    },

    /**
     * Splits a path into an array of property names. Accepts either arrays
     * of path parts or strings.
     *
     * Example:
     *
     * ```
     * Polymer.Path.split(['foo.bar', 0, 'baz'])  // ['foo', 'bar', '0', 'baz']
     * Polymer.Path.split('foo.bar.0.baz')        // ['foo', 'bar', '0', 'baz']
     * ```
     *
     * @memberof Polymer.Path
     * @param {string | !Array<string|number>} path Input path
     * @return {!Array<string>} Array of path parts
     * @this {Path}
     * @suppress {checkTypes}
     */
    split: function(path) {
      if (Array.isArray(path)) {
        return this.normalize(path).split('.');
      }
      return path.toString().split('.');
    },

    /**
     * Reads a value from a path.  If any sub-property in the path is `undefined`,
     * this method returns `undefined` (will never throw.
     *
     * @memberof Polymer.Path
     * @param {Object} root Object from which to dereference path from
     * @param {string | !Array<string|number>} path Path to read
     * @param {Object=} info If an object is provided to `info`, the normalized
     *  (flattened) path will be set to `info.path`.
     * @return {*} Value at path, or `undefined` if the path could not be
     *  fully dereferenced.
     * @this {Path}
     */
    get: function(root, path, info) {
      let prop = root;
      let parts = this.split(path);
      // Loop over path parts[0..n-1] and dereference
      for (let i=0; i<parts.length; i++) {
        if (!prop) {
          return;
        }
        let part = parts[i];
        prop = prop[part];
      }
      if (info) {
        info.path = parts.join('.');
      }
      return prop;
    },

    /**
     * Sets a value to a path.  If any sub-property in the path is `undefined`,
     * this method will no-op.
     *
     * @memberof Polymer.Path
     * @param {Object} root Object from which to dereference path from
     * @param {string | !Array<string|number>} path Path to set
     * @param {*} value Value to set to path
     * @return {string | undefined} The normalized version of the input path
     * @this {Path}
     */
    set: function(root, path, value) {
      let prop = root;
      let parts = this.split(path);
      let last = parts[parts.length-1];
      if (parts.length > 1) {
        // Loop over path parts[0..n-2] and dereference
        for (let i=0; i<parts.length-1; i++) {
          let part = parts[i];
          prop = prop[part];
          if (!prop) {
            return;
          }
        }
        // Set value to object at end of path
        prop[last] = value;
      } else {
        // Simple property set
        prop[path] = value;
      }
      return parts.join('.');
    }

  };

  /**
   * Returns true if the given string is a structured data path (has dots).
   *
   * This function is deprecated.  Use `Polymer.Path.isPath` instead.
   *
   * Example:
   *
   * ```
   * Polymer.Path.isDeep('foo.bar.baz') // true
   * Polymer.Path.isDeep('foo')         // false
   * ```
   *
   * @deprecated
   * @memberof Polymer.Path
   * @param {string} path Path string
   * @return {boolean} True if the string contained one or more dots
   */
  Path.isDeep = Path.isPath;

  Polymer.Path = Path;

})();


(function() {
  'use strict';

  const caseMap = {};
  const DASH_TO_CAMEL = /-[a-z]/g;
  const CAMEL_TO_DASH = /([A-Z])/g;

  /**
   * Module with utilities for converting between "dash-case" and "camelCase"
   * identifiers.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module that provides utilities for converting between "dash-case"
   *   and "camelCase".
   */
  const CaseMap = {

    /**
     * Converts "dash-case" identifier (e.g. `foo-bar-baz`) to "camelCase"
     * (e.g. `fooBarBaz`).
     *
     * @memberof Polymer.CaseMap
     * @param {string} dash Dash-case identifier
     * @return {string} Camel-case representation of the identifier
     */
    dashToCamelCase(dash) {
      return caseMap[dash] || (
        caseMap[dash] = dash.indexOf('-') < 0 ? dash : dash.replace(DASH_TO_CAMEL,
          (m) => m[1].toUpperCase()
        )
      );
    },

    /**
     * Converts "camelCase" identifier (e.g. `fooBarBaz`) to "dash-case"
     * (e.g. `foo-bar-baz`).
     *
     * @memberof Polymer.CaseMap
     * @param {string} camel Camel-case identifier
     * @return {string} Dash-case representation of the identifier
     */
    camelToDashCase(camel) {
      return caseMap[camel] || (
        caseMap[camel] = camel.replace(CAMEL_TO_DASH, '-$1').toLowerCase()
      );
    }

  };

  Polymer.CaseMap = CaseMap;
})();


(function() {

  'use strict';

  // Microtask implemented using Mutation Observer
  let microtaskCurrHandle = 0;
  let microtaskLastHandle = 0;
  let microtaskCallbacks = [];
  let microtaskNodeContent = 0;
  let microtaskNode = document.createTextNode('');
  new window.MutationObserver(microtaskFlush).observe(microtaskNode, {characterData: true});

  function microtaskFlush() {
    const len = microtaskCallbacks.length;
    for (let i = 0; i < len; i++) {
      let cb = microtaskCallbacks[i];
      if (cb) {
        try {
          cb();
        } catch (e) {
          setTimeout(() => { throw e; });
        }
      }
    }
    microtaskCallbacks.splice(0, len);
    microtaskLastHandle += len;
  }

  /**
   * Module that provides a number of strategies for enqueuing asynchronous
   * tasks.  Each sub-module provides a standard `run(fn)` interface that returns a
   * handle, and a `cancel(handle)` interface for canceling async tasks before
   * they run.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module that provides a number of strategies for enqueuing asynchronous
   * tasks.
   */
  Polymer.Async = {

    /**
     * Async interface wrapper around `setTimeout`.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface wrapper around `setTimeout`.
     */
    timeOut: {
      /**
       * Returns a sub-module with the async interface providing the provided
       * delay.
       *
       * @memberof Polymer.Async.timeOut
       * @param {number=} delay Time to wait before calling callbacks in ms
       * @return {!AsyncInterface} An async timeout interface
       */
      after(delay) {
        return {
          run(fn) { return window.setTimeout(fn, delay); },
          cancel(handle) {
            window.clearTimeout(handle);
          }
        };
      },
      /**
       * Enqueues a function called in the next task.
       *
       * @memberof Polymer.Async.timeOut
       * @param {!Function} fn Callback to run
       * @param {number=} delay Delay in milliseconds
       * @return {number} Handle used for canceling task
       */
      run(fn, delay) {
        return window.setTimeout(fn, delay);
      },
      /**
       * Cancels a previously enqueued `timeOut` callback.
       *
       * @memberof Polymer.Async.timeOut
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        window.clearTimeout(handle);
      }
    },

    /**
     * Async interface wrapper around `requestAnimationFrame`.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface wrapper around `requestAnimationFrame`.
     */
    animationFrame: {
      /**
       * Enqueues a function called at `requestAnimationFrame` timing.
       *
       * @memberof Polymer.Async.animationFrame
       * @param {function(number):void} fn Callback to run
       * @return {number} Handle used for canceling task
       */
      run(fn) {
        return window.requestAnimationFrame(fn);
      },
      /**
       * Cancels a previously enqueued `animationFrame` callback.
       *
       * @memberof Polymer.Async.animationFrame
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        window.cancelAnimationFrame(handle);
      }
    },

    /**
     * Async interface wrapper around `requestIdleCallback`.  Falls back to
     * `setTimeout` on browsers that do not support `requestIdleCallback`.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface wrapper around `requestIdleCallback`.
     */
    idlePeriod: {
      /**
       * Enqueues a function called at `requestIdleCallback` timing.
       *
       * @memberof Polymer.Async.idlePeriod
       * @param {function(!IdleDeadline):void} fn Callback to run
       * @return {number} Handle used for canceling task
       */
      run(fn) {
        return window.requestIdleCallback ?
          window.requestIdleCallback(fn) :
          window.setTimeout(fn, 16);
      },
      /**
       * Cancels a previously enqueued `idlePeriod` callback.
       *
       * @memberof Polymer.Async.idlePeriod
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        window.cancelIdleCallback ?
          window.cancelIdleCallback(handle) :
          window.clearTimeout(handle);
      }
    },

    /**
     * Async interface for enqueuing callbacks that run at microtask timing.
     *
     * Note that microtask timing is achieved via a single `MutationObserver`,
     * and thus callbacks enqueued with this API will all run in a single
     * batch, and not interleaved with other microtasks such as promises.
     * Promises are avoided as an implementation choice for the time being
     * due to Safari bugs that cause Promises to lack microtask guarantees.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface for enqueuing callbacks that run at microtask
     *   timing.
     */
    microTask: {

      /**
       * Enqueues a function called at microtask timing.
       *
       * @memberof Polymer.Async.microTask
       * @param {!Function=} callback Callback to run
       * @return {number} Handle used for canceling task
       */
      run(callback) {
        microtaskNode.textContent = microtaskNodeContent++;
        microtaskCallbacks.push(callback);
        return microtaskCurrHandle++;
      },

      /**
       * Cancels a previously enqueued `microTask` callback.
       *
       * @memberof Polymer.Async.microTask
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        const idx = handle - microtaskLastHandle;
        if (idx >= 0) {
          if (!microtaskCallbacks[idx]) {
            throw new Error('invalid async handle: ' + handle);
          }
          microtaskCallbacks[idx] = null;
        }
      }

    }
  };

})();


  (function () {

    'use strict';

    /** @const {!AsyncInterface} */
    const microtask = Polymer.Async.microTask;

    /**
     * Element class mixin that provides basic meta-programming for creating one
     * or more property accessors (getter/setter pair) that enqueue an async
     * (batched) `_propertiesChanged` callback.
     *
     * For basic usage of this mixin, call `MyClass.createProperties(props)`
     * once at class definition time to create property accessors for properties
     * named in props, implement `_propertiesChanged` to react as desired to
     * property changes, and implement `static get observedAttributes()` and
     * include lowercase versions of any property names that should be set from
     * attributes. Last, call `this._enableProperties()` in the element's
     * `connectedCallback` to enable the accessors.
     *
     * @mixinFunction
     * @polymer
     * @memberof Polymer
     * @summary Element class mixin for reacting to property changes from
     *   generated property accessors.
     */
    Polymer.PropertiesChanged = Polymer.dedupingMixin(superClass => {

      /**
       * @polymer
       * @mixinClass
       * @extends {superClass}
       * @implements {Polymer_PropertiesChanged}
       * @unrestricted
       */
      class PropertiesChanged extends superClass {

        /**
         * Creates property accessors for the given property names.
         * @param {!Object} props Object whose keys are names of accessors.
         * @return {void}
         * @protected
         */
        static createProperties(props) {
          const proto = this.prototype;
          for (let prop in props) {
            // don't stomp an existing accessor
            if (!(prop in proto)) {
              proto._createPropertyAccessor(prop);
            }
          }
        }

        /**
         * Returns an attribute name that corresponds to the given property.
         * The attribute name is the lowercased property name. Override to
         * customize this mapping.
         * @param {string} property Property to convert
         * @return {string} Attribute name corresponding to the given property.
         *
         * @protected
         */
        static attributeNameForProperty(property) {
          return property.toLowerCase();
        }

        /**
         * Override point to provide a type to which to deserialize a value to
         * a given property.
         * @param {string} name Name of property
         *
         * @protected
         */
        static typeForProperty(name) { } //eslint-disable-line no-unused-vars

        /**
         * Creates a setter/getter pair for the named property with its own
         * local storage.  The getter returns the value in the local storage,
         * and the setter calls `_setProperty`, which updates the local storage
         * for the property and enqueues a `_propertiesChanged` callback.
         *
         * This method may be called on a prototype or an instance.  Calling
         * this method may overwrite a property value that already exists on
         * the prototype/instance by creating the accessor.
         *
         * @param {string} property Name of the property
         * @param {boolean=} readOnly When true, no setter is created; the
         *   protected `_setProperty` function must be used to set the property
         * @return {void}
         * @protected
         */
        _createPropertyAccessor(property, readOnly) {
          this._addPropertyToAttributeMap(property);
          if (!this.hasOwnProperty('__dataHasAccessor')) {
            this.__dataHasAccessor = Object.assign({}, this.__dataHasAccessor);
          }
          if (!this.__dataHasAccessor[property]) {
            this.__dataHasAccessor[property] = true;
            this._definePropertyAccessor(property, readOnly);
          }
        }

        /**
         * Adds the given `property` to a map matching attribute names
         * to property names, using `attributeNameForProperty`. This map is
         * used when deserializing attribute values to properties.
         *
         * @param {string} property Name of the property
         */
        _addPropertyToAttributeMap(property) {
          if (!this.hasOwnProperty('__dataAttributes')) {
            this.__dataAttributes = Object.assign({}, this.__dataAttributes);
          }
          if (!this.__dataAttributes[property]) {
            const attr = this.constructor.attributeNameForProperty(property);
            this.__dataAttributes[attr] = property;
          }
        }

        /**
         * Defines a property accessor for the given property.
         * @param {string} property Name of the property
         * @param {boolean=} readOnly When true, no setter is created
         * @return {void}
         */
         _definePropertyAccessor(property, readOnly) {
          Object.defineProperty(this, property, {
            /* eslint-disable valid-jsdoc */
            /** @this {PropertiesChanged} */
            get() {
              return this._getProperty(property);
            },
            /** @this {PropertiesChanged} */
            set: readOnly ? function () {} : function (value) {
              this._setProperty(property, value);
            }
            /* eslint-enable */
          });
        }

        constructor() {
          super();
          this.__dataEnabled = false;
          this.__dataReady = false;
          this.__dataInvalid = false;
          this.__data = {};
          this.__dataPending = null;
          this.__dataOld = null;
          this.__dataInstanceProps = null;
          this.__serializing = false;
          this._initializeProperties();
        }

        /**
         * Lifecycle callback called when properties are enabled via
         * `_enableProperties`.
         *
         * Users may override this function to implement behavior that is
         * dependent on the element having its property data initialized, e.g.
         * from defaults (initialized from `constructor`, `_initializeProperties`),
         * `attributeChangedCallback`, or values propagated from host e.g. via
         * bindings.  `super.ready()` must be called to ensure the data system
         * becomes enabled.
         *
         * @return {void}
         * @public
         */
        ready() {
          this.__dataReady = true;
          this._flushProperties();
        }

        /**
         * Initializes the local storage for property accessors.
         *
         * Provided as an override point for performing any setup work prior
         * to initializing the property accessor system.
         *
         * @return {void}
         * @protected
         */
        _initializeProperties() {
          // Capture instance properties; these will be set into accessors
          // during first flush. Don't set them here, since we want
          // these to overwrite defaults/constructor assignments
          for (let p in this.__dataHasAccessor) {
            if (this.hasOwnProperty(p)) {
              this.__dataInstanceProps = this.__dataInstanceProps || {};
              this.__dataInstanceProps[p] = this[p];
              delete this[p];
            }
          }
        }

        /**
         * Called at ready time with bag of instance properties that overwrote
         * accessors when the element upgraded.
         *
         * The default implementation sets these properties back into the
         * setter at ready time.  This method is provided as an override
         * point for customizing or providing more efficient initialization.
         *
         * @param {Object} props Bag of property values that were overwritten
         *   when creating property accessors.
         * @return {void}
         * @protected
         */
        _initializeInstanceProperties(props) {
          Object.assign(this, props);
        }

        /**
         * Updates the local storage for a property (via `_setPendingProperty`)
         * and enqueues a `_proeprtiesChanged` callback.
         *
         * @param {string} property Name of the property
         * @param {*} value Value to set
         * @return {void}
         * @protected
         */
        _setProperty(property, value) {
          if (this._setPendingProperty(property, value)) {
            this._invalidateProperties();
          }
        }

        /**
         * Returns the value for the given property.
         * @param {string} property Name of property
         * @return {*} Value for the given property
         * @protected
         */
        _getProperty(property) {
          return this.__data[property];
        }

        /* eslint-disable no-unused-vars */
        /**
         * Updates the local storage for a property, records the previous value,
         * and adds it to the set of "pending changes" that will be passed to the
         * `_propertiesChanged` callback.  This method does not enqueue the
         * `_propertiesChanged` callback.
         *
         * @param {string} property Name of the property
         * @param {*} value Value to set
         * @param {boolean=} ext Not used here; affordance for closure
         * @return {boolean} Returns true if the property changed
         * @protected
         */
        _setPendingProperty(property, value, ext) {
          let old = this.__data[property];
          let changed = this._shouldPropertyChange(property, value, old);
          if (changed) {
            if (!this.__dataPending) {
              this.__dataPending = {};
              this.__dataOld = {};
            }
            // Ensure old is captured from the last turn
            if (this.__dataOld && !(property in this.__dataOld)) {
              this.__dataOld[property] = old;
            }
            this.__data[property] = value;
            this.__dataPending[property] = value;
          }
          return changed;
        }
        /* eslint-enable */

        /**
         * Marks the properties as invalid, and enqueues an async
         * `_propertiesChanged` callback.
         *
         * @return {void}
         * @protected
         */
        _invalidateProperties() {
          if (!this.__dataInvalid && this.__dataReady) {
            this.__dataInvalid = true;
            microtask.run(() => {
              if (this.__dataInvalid) {
                this.__dataInvalid = false;
                this._flushProperties();
              }
            });
          }
        }

        /**
         * Call to enable property accessor processing. Before this method is
         * called accessor values will be set but side effects are
         * queued. When called, any pending side effects occur immediately.
         * For elements, generally `connectedCallback` is a normal spot to do so.
         * It is safe to call this method multiple times as it only turns on
         * property accessors once.
         *
         * @return {void}
         * @protected
         */
        _enableProperties() {
          if (!this.__dataEnabled) {
            this.__dataEnabled = true;
            if (this.__dataInstanceProps) {
              this._initializeInstanceProperties(this.__dataInstanceProps);
              this.__dataInstanceProps = null;
            }
            this.ready();
          }
        }

        /**
         * Calls the `_propertiesChanged` callback with the current set of
         * pending changes (and old values recorded when pending changes were
         * set), and resets the pending set of changes. Generally, this method
         * should not be called in user code.
         *
         * @return {void}
         * @protected
         */
        _flushProperties() {
          const props = this.__data;
          const changedProps = this.__dataPending;
          const old = this.__dataOld;
          if (this._shouldPropertiesChange(props, changedProps, old)) {
            this.__dataPending = null;
            this.__dataOld = null;
            this._propertiesChanged(props, changedProps, old);
          }
        }

        /**
         * Called in `_flushProperties` to determine if `_propertiesChanged`
         * should be called. The default implementation returns true if
         * properties are pending. Override to customize when
         * `_propertiesChanged` is called.
         * @param {!Object} currentProps Bag of all current accessor values
         * @param {!Object} changedProps Bag of properties changed since the last
         *   call to `_propertiesChanged`
         * @param {!Object} oldProps Bag of previous values for each property
         *   in `changedProps`
         * @return {boolean} true if changedProps is truthy
         */
        _shouldPropertiesChange(currentProps, changedProps, oldProps) { // eslint-disable-line no-unused-vars
          return Boolean(changedProps);
        }

        /**
         * Callback called when any properties with accessors created via
         * `_createPropertyAccessor` have been set.
         *
         * @param {!Object} currentProps Bag of all current accessor values
         * @param {!Object} changedProps Bag of properties changed since the last
         *   call to `_propertiesChanged`
         * @param {!Object} oldProps Bag of previous values for each property
         *   in `changedProps`
         * @return {void}
         * @protected
         */
        _propertiesChanged(currentProps, changedProps, oldProps) { // eslint-disable-line no-unused-vars
        }

        /**
         * Method called to determine whether a property value should be
         * considered as a change and cause the `_propertiesChanged` callback
         * to be enqueued.
         *
         * The default implementation returns `true` if a strict equality
         * check fails. The method always returns false for `NaN`.
         *
         * Override this method to e.g. provide stricter checking for
         * Objects/Arrays when using immutable patterns.
         *
         * @param {string} property Property name
         * @param {*} value New property value
         * @param {*} old Previous property value
         * @return {boolean} Whether the property should be considered a change
         *   and enqueue a `_proeprtiesChanged` callback
         * @protected
         */
        _shouldPropertyChange(property, value, old) {
          return (
            // Strict equality check
            (old !== value &&
              // This ensures (old==NaN, value==NaN) always returns false
              (old === old || value === value))
          );
        }

        /**
         * Implements native Custom Elements `attributeChangedCallback` to
         * set an attribute value to a property via `_attributeToProperty`.
         *
         * @param {string} name Name of attribute that changed
         * @param {?string} old Old attribute value
         * @param {?string} value New attribute value
         * @param {?string} namespace Attribute namespace.
         * @return {void}
         * @suppress {missingProperties} Super may or may not implement the callback
         */
        attributeChangedCallback(name, old, value, namespace) {
          if (old !== value) {
            this._attributeToProperty(name, value);
          }
          if (super.attributeChangedCallback) {
            super.attributeChangedCallback(name, old, value, namespace);
          }
        }

        /**
         * Deserializes an attribute to its associated property.
         *
         * This method calls the `_deserializeValue` method to convert the string to
         * a typed value.
         *
         * @param {string} attribute Name of attribute to deserialize.
         * @param {?string} value of the attribute.
         * @param {*=} type type to deserialize to, defaults to the value
         * returned from `typeForProperty`
         * @return {void}
         */
        _attributeToProperty(attribute, value, type) {
          if (!this.__serializing) {
            const map = this.__dataAttributes;
            const property = map && map[attribute] || attribute;
            this[property] = this._deserializeValue(value, type ||
              this.constructor.typeForProperty(property));
          }
        }

        /**
         * Serializes a property to its associated attribute.
         *
         * @suppress {invalidCasts} Closure can't figure out `this` is an element.
         *
         * @param {string} property Property name to reflect.
         * @param {string=} attribute Attribute name to reflect to.
         * @param {*=} value Property value to refect.
         * @return {void}
         */
        _propertyToAttribute(property, attribute, value) {
          this.__serializing = true;
          value = (arguments.length < 3) ? this[property] : value;
          this._valueToNodeAttribute(/** @type {!HTMLElement} */(this), value,
            attribute || this.constructor.attributeNameForProperty(property));
          this.__serializing = false;
        }

        /**
         * Sets a typed value to an HTML attribute on a node.
         *
         * This method calls the `_serializeValue` method to convert the typed
         * value to a string.  If the `_serializeValue` method returns `undefined`,
         * the attribute will be removed (this is the default for boolean
         * type `false`).
         *
         * @param {Element} node Element to set attribute to.
         * @param {*} value Value to serialize.
         * @param {string} attribute Attribute name to serialize to.
         * @return {void}
         */
        _valueToNodeAttribute(node, value, attribute) {
          const str = this._serializeValue(value);
          if (str === undefined) {
            node.removeAttribute(attribute);
          } else {
            node.setAttribute(attribute, str);
          }
        }

        /**
         * Converts a typed JavaScript value to a string.
         *
         * This method is called when setting JS property values to
         * HTML attributes.  Users may override this method to provide
         * serialization for custom types.
         *
         * @param {*} value Property value to serialize.
         * @return {string | undefined} String serialized from the provided
         * property  value.
         */
        _serializeValue(value) {
          switch (typeof value) {
            case 'boolean':
              return value ? '' : undefined;
            default:
              return value != null ? value.toString() : undefined;
          }
        }

        /**
         * Converts a string to a typed JavaScript value.
         *
         * This method is called when reading HTML attribute values to
         * JS properties.  Users may override this method to provide
         * deserialization for custom `type`s. Types for `Boolean`, `String`,
         * and `Number` convert attributes to the expected types.
         *
         * @param {?string} value Value to deserialize.
         * @param {*=} type Type to deserialize the string to.
         * @return {*} Typed value deserialized from the provided string.
         */
        _deserializeValue(value, type) {
          switch (type) {
            case Boolean:
              return (value !== null);
            case Number:
              return Number(value);
            default:
              return value;
          }
        }

      }

      return PropertiesChanged;
    });


  })();



(function() {

  'use strict';

  let caseMap = Polymer.CaseMap;

  // Save map of native properties; this forms a blacklist or properties
  // that won't have their values "saved" by `saveAccessorValue`, since
  // reading from an HTMLElement accessor from the context of a prototype throws
  const nativeProperties = {};
  let proto = HTMLElement.prototype;
  while (proto) {
    let props = Object.getOwnPropertyNames(proto);
    for (let i=0; i<props.length; i++) {
      nativeProperties[props[i]] = true;
    }
    proto = Object.getPrototypeOf(proto);
  }

  /**
   * Used to save the value of a property that will be overridden with
   * an accessor. If the `model` is a prototype, the values will be saved
   * in `__dataProto`, and it's up to the user (or downstream mixin) to
   * decide how/when to set these values back into the accessors.
   * If `model` is already an instance (it has a `__data` property), then
   * the value will be set as a pending property, meaning the user should
   * call `_invalidateProperties` or `_flushProperties` to take effect
   *
   * @param {Object} model Prototype or instance
   * @param {string} property Name of property
   * @return {void}
   * @private
   */
  function saveAccessorValue(model, property) {
    // Don't read/store value for any native properties since they could throw
    if (!nativeProperties[property]) {
      let value = model[property];
      if (value !== undefined) {
        if (model.__data) {
          // Adding accessor to instance; update the property
          // It is the user's responsibility to call _flushProperties
          model._setPendingProperty(property, value);
        } else {
          // Adding accessor to proto; save proto's value for instance-time use
          if (!model.__dataProto) {
            model.__dataProto = {};
          } else if (!model.hasOwnProperty(JSCompiler_renameProperty('__dataProto', model))) {
            model.__dataProto = Object.create(model.__dataProto);
          }
          model.__dataProto[property] = value;
        }
      }
    }
  }

  /**
   * Element class mixin that provides basic meta-programming for creating one
   * or more property accessors (getter/setter pair) that enqueue an async
   * (batched) `_propertiesChanged` callback.
   *
   * For basic usage of this mixin:
   * 
   * -   Declare attributes to observe via the standard `static get observedAttributes()`. Use
   *     `dash-case` attribute names to represent `camelCase` property names. 
   * -   Implement the `_propertiesChanged` callback on the class.
   * -   Call `MyClass.createPropertiesForAttributes()` **once** on the class to generate 
   *     property accessors for each observed attribute. This must be called before the first 
   *     instance is created, for example, by calling it before calling `customElements.define`.
   *     It can also be called lazily from the element's `constructor`, as long as it's guarded so
   *     that the call is only made once, when the first instance is created.
   * -   Call `this._enableProperties()` in the element's `connectedCallback` to enable 
   *     the accessors.
   *
   * Any `observedAttributes` will automatically be
   * deserialized via `attributeChangedCallback` and set to the associated
   * property using `dash-case`-to-`camelCase` convention.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.PropertiesChanged
   * @memberof Polymer
   * @summary Element class mixin for reacting to property changes from
   *   generated property accessors.
   */
  Polymer.PropertyAccessors = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_PropertiesChanged}
     * @unrestricted
     * @private
     */
     const base = Polymer.PropertiesChanged(superClass);

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_PropertyAccessors}
     * @extends {base}
     * @unrestricted
     */
    class PropertyAccessors extends base {

      /**
       * Generates property accessors for all attributes in the standard
       * static `observedAttributes` array.
       *
       * Attribute names are mapped to property names using the `dash-case` to
       * `camelCase` convention
       *
       * @return {void}
       */
      static createPropertiesForAttributes() {
        let a$ = this.observedAttributes;
        for (let i=0; i < a$.length; i++) {
          this.prototype._createPropertyAccessor(caseMap.dashToCamelCase(a$[i]));
        }
      }

      /**
       * Returns an attribute name that corresponds to the given property.
       * By default, converts camel to dash case, e.g. `fooBar` to `foo-bar`.
       * @param {string} property Property to convert
       * @return {string} Attribute name corresponding to the given property.
       *
       * @protected
       */
      static attributeNameForProperty(property) {
        return caseMap.camelToDashCase(property);
      }

      /**
       * Overrides PropertiesChanged implementation to initialize values for
       * accessors created for values that already existed on the element
       * prototype.
       *
       * @return {void}
       * @protected
       */
      _initializeProperties() {
        if (this.__dataProto) {
          this._initializeProtoProperties(this.__dataProto);
          this.__dataProto = null;
        }
        super._initializeProperties();
      }

      /**
       * Called at instance time with bag of properties that were overwritten
       * by accessors on the prototype when accessors were created.
       *
       * The default implementation sets these properties back into the
       * setter at instance time.  This method is provided as an override
       * point for customizing or providing more efficient initialization.
       *
       * @param {Object} props Bag of property values that were overwritten
       *   when creating property accessors.
       * @return {void}
       * @protected
       */
      _initializeProtoProperties(props) {
        for (let p in props) {
          this._setProperty(p, props[p]);
        }
      }

      /**
       * Ensures the element has the given attribute. If it does not,
       * assigns the given value to the attribute.
       *
       * @suppress {invalidCasts} Closure can't figure out `this` is infact an element
       *
       * @param {string} attribute Name of attribute to ensure is set.
       * @param {string} value of the attribute.
       * @return {void}
       */
      _ensureAttribute(attribute, value) {
        const el = /** @type {!HTMLElement} */(this);
        if (!el.hasAttribute(attribute)) {
          this._valueToNodeAttribute(el, value, attribute);
        }
      }

      /**
       * Overrides PropertiesChanged implemention to serialize objects as JSON.
       *
       * @param {*} value Property value to serialize.
       * @return {string | undefined} String serialized from the provided property value.
       */
      _serializeValue(value) {
        /* eslint-disable no-fallthrough */
        switch (typeof value) {
          case 'object':
            if (value instanceof Date) {
              return value.toString();
            } else if (value) {
              try {
                return JSON.stringify(value);
              } catch(x) {
                return '';
              }
            }

          default:
            return super._serializeValue(value);
        }
      }

      /**
       * Converts a string to a typed JavaScript value.
       *
       * This method is called by Polymer when reading HTML attribute values to
       * JS properties.  Users may override this method on Polymer element
       * prototypes to provide deserialization for custom `type`s.  Note,
       * the `type` argument is the value of the `type` field provided in the
       * `properties` configuration object for a given property, and is
       * by convention the constructor for the type to deserialize.
       *
       *
       * @param {?string} value Attribute value to deserialize.
       * @param {*=} type Type to deserialize the string to.
       * @return {*} Typed value deserialized from the provided string.
       */
      _deserializeValue(value, type) {
        /**
         * @type {*}
         */
        let outValue;
        switch (type) {
          case Object:
            try {
              outValue = JSON.parse(/** @type {string} */(value));
            } catch(x) {
              // allow non-JSON literals like Strings and Numbers
              outValue = value;
            }
            break;
          case Array:
            try {
              outValue = JSON.parse(/** @type {string} */(value));
            } catch(x) {
              outValue = null;
              console.warn(`Polymer::Attributes: couldn't decode Array as JSON: ${value}`);
            }
            break;
          case Date:
            outValue = isNaN(value) ? String(value) : Number(value);
            outValue = new Date(outValue);
            break;
          default:
            outValue = super._deserializeValue(value, type);
            break;
        }
        return outValue;
      }
      /* eslint-enable no-fallthrough */

      /**
       * Overrides PropertiesChanged implementation to save existing prototype
       * property value so that it can be reset.
       * @param {string} property Name of the property
       * @param {boolean=} readOnly When true, no setter is created
       *
       * When calling on a prototype, any overwritten values are saved in
       * `__dataProto`, and it is up to the subclasser to decide how/when
       * to set those properties back into the accessor.  When calling on an
       * instance, the overwritten value is set via `_setPendingProperty`,
       * and the user should call `_invalidateProperties` or `_flushProperties`
       * for the values to take effect.
       * @protected
       * @return {void}
       */
      _definePropertyAccessor(property, readOnly) {
        saveAccessorValue(this, property);
        super._definePropertyAccessor(property, readOnly);
      }

      /**
       * Returns true if this library created an accessor for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if an accessor was created
       */
      _hasAccessor(property) {
        return this.__dataHasAccessor && this.__dataHasAccessor[property];
      }

      /**
       * Returns true if the specified property has a pending change.
       *
       * @param {string} prop Property name
       * @return {boolean} True if property has a pending change
       * @protected
       */
      _isPropertyPending(prop) {
        return Boolean(this.__dataPending && (prop in this.__dataPending));
      }

    }

    return PropertyAccessors;

  });

})();


(function() {

  'use strict';

  const walker = document.createTreeWalker(document, NodeFilter.SHOW_ALL,
      null, false);

  // 1.x backwards-compatible auto-wrapper for template type extensions
  // This is a clear layering violation and gives favored-nation status to
  // dom-if and dom-repeat templates.  This is a conceit we're choosing to keep
  // a.) to ease 1.x backwards-compatibility due to loss of `is`, and
  // b.) to maintain if/repeat capability in parser-constrained elements
  //     (e.g. table, select) in lieu of native CE type extensions without
  //     massive new invention in this space (e.g. directive system)
  const templateExtensions = {
    'dom-if': true,
    'dom-repeat': true
  };
  function wrapTemplateExtension(node) {
    let is = node.getAttribute('is');
    if (is && templateExtensions[is]) {
      let t = node;
      t.removeAttribute('is');
      node = t.ownerDocument.createElement(is);
      t.parentNode.replaceChild(node, t);
      node.appendChild(t);
      while(t.attributes.length) {
        node.setAttribute(t.attributes[0].name, t.attributes[0].value);
        t.removeAttribute(t.attributes[0].name);
      }
    }
    return node;
  }

  function findTemplateNode(root, nodeInfo) {
    // recursively ascend tree until we hit root
    let parent = nodeInfo.parentInfo && findTemplateNode(root, nodeInfo.parentInfo);
    // unwind the stack, returning the indexed node at each level
    if (parent) {
      // note: marginally faster than indexing via childNodes
      // (http://jsperf.com/childnodes-lookup)
      walker.currentNode = parent;
      for (let n=walker.firstChild(), i=0; n; n=walker.nextSibling()) {
        if (nodeInfo.parentIndex === i++) {
          return n;
        }
      }
    } else {
      return root;
    }
  }

  // construct `$` map (from id annotations)
  function applyIdToMap(inst, map, node, nodeInfo) {
    if (nodeInfo.id) {
      map[nodeInfo.id] = node;
    }
  }

  // install event listeners (from event annotations)
  function applyEventListener(inst, node, nodeInfo) {
    if (nodeInfo.events && nodeInfo.events.length) {
      for (let j=0, e$=nodeInfo.events, e; (j<e$.length) && (e=e$[j]); j++) {
        inst._addMethodEventListenerToNode(node, e.name, e.value, inst);
      }
    }
  }

  // push configuration references at configure time
  function applyTemplateContent(inst, node, nodeInfo) {
    if (nodeInfo.templateInfo) {
      node._templateInfo = nodeInfo.templateInfo;
    }
  }

  function createNodeEventHandler(context, eventName, methodName) {
    // Instances can optionally have a _methodHost which allows redirecting where
    // to find methods. Currently used by `templatize`.
    context = context._methodHost || context;
    let handler = function(e) {
      if (context[methodName]) {
        context[methodName](e, e.detail);
      } else {
        console.warn('listener method `' + methodName + '` not defined');
      }
    };
    return handler;
  }

  /**
   * Element mixin that provides basic template parsing and stamping, including
   * the following template-related features for stamped templates:
   *
   * - Declarative event listeners (`on-eventname="listener"`)
   * - Map of node id's to stamped node instances (`this.$.id`)
   * - Nested template content caching/removal and re-installation (performance
   *   optimization)
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin that provides basic template parsing and stamping
   */
  Polymer.TemplateStamp = Polymer.dedupingMixin(superClass => {

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_TemplateStamp}
     */
    class TemplateStamp extends superClass {

      /**
       * Scans a template to produce template metadata.
       *
       * Template-specific metadata are stored in the object returned, and node-
       * specific metadata are stored in objects in its flattened `nodeInfoList`
       * array.  Only nodes in the template that were parsed as nodes of
       * interest contain an object in `nodeInfoList`.  Each `nodeInfo` object
       * contains an `index` (`childNodes` index in parent) and optionally
       * `parent`, which points to node info of its parent (including its index).
       *
       * The template metadata object returned from this method has the following
       * structure (many fields optional):
       *
       * ```js
       *   {
       *     // Flattened list of node metadata (for nodes that generated metadata)
       *     nodeInfoList: [
       *       {
       *         // `id` attribute for any nodes with id's for generating `$` map
       *         id: {string},
       *         // `on-event="handler"` metadata
       *         events: [
       *           {
       *             name: {string},   // event name
       *             value: {string},  // handler method name
       *           }, ...
       *         ],
       *         // Notes when the template contained a `<slot>` for shady DOM
       *         // optimization purposes
       *         hasInsertionPoint: {boolean},
       *         // For nested `<template>`` nodes, nested template metadata
       *         templateInfo: {object}, // nested template metadata
       *         // Metadata to allow efficient retrieval of instanced node
       *         // corresponding to this metadata
       *         parentInfo: {number},   // reference to parent nodeInfo>
       *         parentIndex: {number},  // index in parent's `childNodes` collection
       *         infoIndex: {number},    // index of this `nodeInfo` in `templateInfo.nodeInfoList`
       *       },
       *       ...
       *     ],
       *     // When true, the template had the `strip-whitespace` attribute
       *     // or was nested in a template with that setting
       *     stripWhitespace: {boolean},
       *     // For nested templates, nested template content is moved into
       *     // a document fragment stored here; this is an optimization to
       *     // avoid the cost of nested template cloning
       *     content: {DocumentFragment}
       *   }
       * ```
       *
       * This method kicks off a recursive treewalk as follows:
       *
       * ```
       *    _parseTemplate <---------------------+
       *      _parseTemplateContent              |
       *        _parseTemplateNode  <------------|--+
       *          _parseTemplateNestedTemplate --+  |
       *          _parseTemplateChildNodes ---------+
       *          _parseTemplateNodeAttributes
       *            _parseTemplateNodeAttribute
       *
       * ```
       *
       * These methods may be overridden to add custom metadata about templates
       * to either `templateInfo` or `nodeInfo`.
       *
       * Note that this method may be destructive to the template, in that
       * e.g. event annotations may be removed after being noted in the
       * template metadata.
       *
       * @param {!HTMLTemplateElement} template Template to parse
       * @param {TemplateInfo=} outerTemplateInfo Template metadata from the outer
       *   template, for parsing nested templates
       * @return {!TemplateInfo} Parsed template metadata
       */
      static _parseTemplate(template, outerTemplateInfo) {
        // since a template may be re-used, memo-ize metadata
        if (!template._templateInfo) {
          let templateInfo = template._templateInfo = {};
          templateInfo.nodeInfoList = [];
          templateInfo.stripWhiteSpace = Polymer.legacyOptimizations ||
            (outerTemplateInfo && outerTemplateInfo.stripWhiteSpace) ||
            template.hasAttribute('strip-whitespace');
          this._parseTemplateContent(template, templateInfo, {parent: null});
        }
        return template._templateInfo;
      }

      static _parseTemplateContent(template, templateInfo, nodeInfo) {
        return this._parseTemplateNode(template.content, templateInfo, nodeInfo);
      }

      /**
       * Parses template node and adds template and node metadata based on
       * the current node, and its `childNodes` and `attributes`.
       *
       * This method may be overridden to add custom node or template specific
       * metadata based on this node.
       *
       * @param {Node} node Node to parse
       * @param {!TemplateInfo} templateInfo Template metadata for current template
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNode(node, templateInfo, nodeInfo) {
        let noted;
        let element = /** @type {Element} */(node);
        if (element.localName == 'template' && !element.hasAttribute('preserve-content')) {
          noted = this._parseTemplateNestedTemplate(element, templateInfo, nodeInfo) || noted;
        } else if (element.localName === 'slot') {
          // For ShadyDom optimization, indicating there is an insertion point
          templateInfo.hasInsertionPoint = true;
        }
        walker.currentNode = element;
        if (walker.firstChild()) {
          noted = this._parseTemplateChildNodes(element, templateInfo, nodeInfo) || noted;
        }
        if (element.hasAttributes && element.hasAttributes()) {
          noted = this._parseTemplateNodeAttributes(element, templateInfo, nodeInfo) || noted;
        }
        return noted;
      }

      /**
       * Parses template child nodes for the given root node.
       *
       * This method also wraps whitelisted legacy template extensions
       * (`is="dom-if"` and `is="dom-repeat"`) with their equivalent element
       * wrappers, collapses text nodes, and strips whitespace from the template
       * if the `templateInfo.stripWhitespace` setting was provided.
       *
       * @param {Node} root Root node whose `childNodes` will be parsed
       * @param {!TemplateInfo} templateInfo Template metadata for current template
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @return {void}
       */
      static _parseTemplateChildNodes(root, templateInfo, nodeInfo) {
        if (root.localName === 'script' || root.localName === 'style') {
          return;
        }
        walker.currentNode = root;
        for (let node=walker.firstChild(), parentIndex=0, next; node; node=next) {
          // Wrap templates
          if (node.localName == 'template') {
            node = wrapTemplateExtension(node);
          }
          // collapse adjacent textNodes: fixes an IE issue that can cause
          // text nodes to be inexplicably split =(
          // note that root.normalize() should work but does not so we do this
          // manually.
          walker.currentNode = node;
          next = walker.nextSibling();
          if (node.nodeType === Node.TEXT_NODE) {
            let /** Node */ n = next;
            while (n && (n.nodeType === Node.TEXT_NODE)) {
              node.textContent += n.textContent;
              next = walker.nextSibling();
              root.removeChild(n);
              n = next;
            }
            // optionally strip whitespace
            if (templateInfo.stripWhiteSpace && !node.textContent.trim()) {
              root.removeChild(node);
              continue;
            }
          }
          let childInfo = { parentIndex, parentInfo: nodeInfo };
          if (this._parseTemplateNode(node, templateInfo, childInfo)) {
            childInfo.infoIndex = templateInfo.nodeInfoList.push(/** @type {!NodeInfo} */(childInfo)) - 1;
          }
          // Increment if not removed
          walker.currentNode = node;
          if (walker.parentNode()) {
            parentIndex++;
          }
        }
      }

      /**
       * Parses template content for the given nested `<template>`.
       *
       * Nested template info is stored as `templateInfo` in the current node's
       * `nodeInfo`. `template.content` is removed and stored in `templateInfo`.
       * It will then be the responsibility of the host to set it back to the
       * template and for users stamping nested templates to use the
       * `_contentForTemplate` method to retrieve the content for this template
       * (an optimization to avoid the cost of cloning nested template content).
       *
       * @param {HTMLTemplateElement} node Node to parse (a <template>)
       * @param {TemplateInfo} outerTemplateInfo Template metadata for current template
       *   that includes the template `node`
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNestedTemplate(node, outerTemplateInfo, nodeInfo) {
        let templateInfo = this._parseTemplate(node, outerTemplateInfo);
        let content = templateInfo.content =
          node.content.ownerDocument.createDocumentFragment();
        content.appendChild(node.content);
        nodeInfo.templateInfo = templateInfo;
        return true;
      }

      /**
       * Parses template node attributes and adds node metadata to `nodeInfo`
       * for nodes of interest.
       *
       * @param {Element} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template.
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNodeAttributes(node, templateInfo, nodeInfo) {
        // Make copy of original attribute list, since the order may change
        // as attributes are added and removed
        let noted = false;
        let attrs = Array.from(node.attributes);
        for (let i=attrs.length-1, a; (a=attrs[i]); i--) {
          noted = this._parseTemplateNodeAttribute(node, templateInfo, nodeInfo, a.name, a.value) || noted;
        }
        return noted;
      }

      /**
       * Parses a single template node attribute and adds node metadata to
       * `nodeInfo` for attributes of interest.
       *
       * This implementation adds metadata for `on-event="handler"` attributes
       * and `id` attributes.
       *
       * @param {Element} node Node to parse
       * @param {!TemplateInfo} templateInfo Template metadata for current template
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @param {string} name Attribute name
       * @param {string} value Attribute value
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNodeAttribute(node, templateInfo, nodeInfo, name, value) {
        // events (on-*)
        if (name.slice(0, 3) === 'on-') {
          node.removeAttribute(name);
          nodeInfo.events = nodeInfo.events || [];
          nodeInfo.events.push({
            name: name.slice(3),
            value
          });
          return true;
        }
        // static id
        else if (name === 'id') {
          nodeInfo.id = value;
          return true;
        }
        return false;
      }

      /**
       * Returns the `content` document fragment for a given template.
       *
       * For nested templates, Polymer performs an optimization to cache nested
       * template content to avoid the cost of cloning deeply nested templates.
       * This method retrieves the cached content for a given template.
       *
       * @param {HTMLTemplateElement} template Template to retrieve `content` for
       * @return {DocumentFragment} Content fragment
       */
      static _contentForTemplate(template) {
        let templateInfo = /** @type {HTMLTemplateElementWithInfo} */ (template)._templateInfo;
        return (templateInfo && templateInfo.content) || template.content;
      }

      /**
       * Clones the provided template content and returns a document fragment
       * containing the cloned dom.
       *
       * The template is parsed (once and memoized) using this library's
       * template parsing features, and provides the following value-added
       * features:
       * * Adds declarative event listeners for `on-event="handler"` attributes
       * * Generates an "id map" for all nodes with id's under `$` on returned
       *   document fragment
       * * Passes template info including `content` back to templates as
       *   `_templateInfo` (a performance optimization to avoid deep template
       *   cloning)
       *
       * Note that the memoized template parsing process is destructive to the
       * template: attributes for bindings and declarative event listeners are
       * removed after being noted in notes, and any nested `<template>.content`
       * is removed and stored in notes as well.
       *
       * @param {!HTMLTemplateElement} template Template to stamp
       * @return {!StampedTemplate} Cloned template content
       */
      _stampTemplate(template) {
        // Polyfill support: bootstrap the template if it has not already been
        if (template && !template.content &&
            window.HTMLTemplateElement && HTMLTemplateElement.decorate) {
          HTMLTemplateElement.decorate(template);
        }
        let templateInfo = this.constructor._parseTemplate(template);
        let nodeInfo = templateInfo.nodeInfoList;
        let content = templateInfo.content || template.content;
        let dom = /** @type {DocumentFragment} */ (document.importNode(content, true));
        // NOTE: ShadyDom optimization indicating there is an insertion point
        dom.__noInsertionPoint = !templateInfo.hasInsertionPoint;
        let nodes = dom.nodeList = new Array(nodeInfo.length);
        dom.$ = {};
        for (let i=0, l=nodeInfo.length, info; (i<l) && (info=nodeInfo[i]); i++) {
          let node = nodes[i] = findTemplateNode(dom, info);
          applyIdToMap(this, dom.$, node, info);
          applyTemplateContent(this, node, info);
          applyEventListener(this, node, info);
        }
        dom = /** @type {!StampedTemplate} */(dom); // eslint-disable-line no-self-assign
        return dom;
      }

      /**
       * Adds an event listener by method name for the event provided.
       *
       * This method generates a handler function that looks up the method
       * name at handling time.
       *
       * @param {!Node} node Node to add listener on
       * @param {string} eventName Name of event
       * @param {string} methodName Name of method
       * @param {*=} context Context the method will be called on (defaults
       *   to `node`)
       * @return {Function} Generated handler function
       */
      _addMethodEventListenerToNode(node, eventName, methodName, context) {
        context = context || node;
        let handler = createNodeEventHandler(context, eventName, methodName);
        this._addEventListenerToNode(node, eventName, handler);
        return handler;
      }

      /**
       * Override point for adding custom or simulated event handling.
       *
       * @param {!Node} node Node to add event listener to
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to add
       * @return {void}
       */
      _addEventListenerToNode(node, eventName, handler) {
        node.addEventListener(eventName, handler);
      }

      /**
       * Override point for adding custom or simulated event handling.
       *
       * @param {!Node} node Node to remove event listener from
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to remove
       * @return {void}
       */
      _removeEventListenerFromNode(node, eventName, handler) {
        node.removeEventListener(eventName, handler);
      }

    }

    return TemplateStamp;

  });

})();


(function() {

  'use strict';

  /** @const {Object} */
  const CaseMap = Polymer.CaseMap;

  // Monotonically increasing unique ID used for de-duping effects triggered
  // from multiple properties in the same turn
  let dedupeId = 0;

  /**
   * Property effect types; effects are stored on the prototype using these keys
   * @enum {string}
   */
  const TYPES = {
    COMPUTE: '__computeEffects',
    REFLECT: '__reflectEffects',
    NOTIFY: '__notifyEffects',
    PROPAGATE: '__propagateEffects',
    OBSERVE: '__observeEffects',
    READ_ONLY: '__readOnly'
  };

  /** @const {RegExp} */
  const capitalAttributeRegex = /[A-Z]/;

  /**
   * @typedef {{
   * name: (string | undefined),
   * structured: (boolean | undefined),
   * wildcard: (boolean | undefined)
   * }}
   */
  let DataTrigger; //eslint-disable-line no-unused-vars

  /**
   * @typedef {{
   * info: ?,
   * trigger: (!DataTrigger | undefined),
   * fn: (!Function | undefined)
   * }}
   */
  let DataEffect; //eslint-disable-line no-unused-vars

  let PropertyEffectsType; //eslint-disable-line no-unused-vars

  /**
   * Ensures that the model has an own-property map of effects for the given type.
   * The model may be a prototype or an instance.
   *
   * Property effects are stored as arrays of effects by property in a map,
   * by named type on the model. e.g.
   *
   *   __computeEffects: {
   *     foo: [ ... ],
   *     bar: [ ... ]
   *   }
   *
   * If the model does not yet have an effect map for the type, one is created
   * and returned.  If it does, but it is not an own property (i.e. the
   * prototype had effects), the the map is deeply cloned and the copy is
   * set on the model and returned, ready for new effects to be added.
   *
   * @param {Object} model Prototype or instance
   * @param {string} type Property effect type
   * @return {Object} The own-property map of effects for the given type
   * @private
   */
  function ensureOwnEffectMap(model, type) {
    let effects = model[type];
    if (!effects) {
      effects = model[type] = {};
    } else if (!model.hasOwnProperty(type)) {
      effects = model[type] = Object.create(model[type]);
      for (let p in effects) {
        let protoFx = effects[p];
        let instFx = effects[p] = Array(protoFx.length);
        for (let i=0; i<protoFx.length; i++) {
          instFx[i] = protoFx[i];
        }
      }
    }
    return effects;
  }

  // -- effects ----------------------------------------------

  /**
   * Runs all effects of a given type for the given set of property changes
   * on an instance.
   *
   * @param {!PropertyEffectsType} inst The instance with effects to run
   * @param {Object} effects Object map of property-to-Array of effects
   * @param {Object} props Bag of current property changes
   * @param {Object=} oldProps Bag of previous values for changed properties
   * @param {boolean=} hasPaths True with `props` contains one or more paths
   * @param {*=} extraArgs Additional metadata to pass to effect function
   * @return {boolean} True if an effect ran for this property
   * @private
   */
  function runEffects(inst, effects, props, oldProps, hasPaths, extraArgs) {
    if (effects) {
      let ran = false;
      let id = dedupeId++;
      for (let prop in props) {
        if (runEffectsForProperty(inst, effects, id, prop, props, oldProps, hasPaths, extraArgs)) {
          ran = true;
        }
      }
      return ran;
    }
    return false;
  }

  /**
   * Runs a list of effects for a given property.
   *
   * @param {!PropertyEffectsType} inst The instance with effects to run
   * @param {Object} effects Object map of property-to-Array of effects
   * @param {number} dedupeId Counter used for de-duping effects
   * @param {string} prop Name of changed property
   * @param {*} props Changed properties
   * @param {*} oldProps Old properties
   * @param {boolean=} hasPaths True with `props` contains one or more paths
   * @param {*=} extraArgs Additional metadata to pass to effect function
   * @return {boolean} True if an effect ran for this property
   * @private
   */
  function runEffectsForProperty(inst, effects, dedupeId, prop, props, oldProps, hasPaths, extraArgs) {
    let ran = false;
    let rootProperty = hasPaths ? Polymer.Path.root(prop) : prop;
    let fxs = effects[rootProperty];
    if (fxs) {
      for (let i=0, l=fxs.length, fx; (i<l) && (fx=fxs[i]); i++) {
        if ((!fx.info || fx.info.lastRun !== dedupeId) &&
            (!hasPaths || pathMatchesTrigger(prop, fx.trigger))) {
          if (fx.info) {
            fx.info.lastRun = dedupeId;
          }
          fx.fn(inst, prop, props, oldProps, fx.info, hasPaths, extraArgs);
          ran = true;
        }
      }
    }
    return ran;
  }

  /**
   * Determines whether a property/path that has changed matches the trigger
   * criteria for an effect.  A trigger is a descriptor with the following
   * structure, which matches the descriptors returned from `parseArg`.
   * e.g. for `foo.bar.*`:
   * ```
   * trigger: {
   *   name: 'a.b',
   *   structured: true,
   *   wildcard: true
   * }
   * ```
   * If no trigger is given, the path is deemed to match.
   *
   * @param {string} path Path or property that changed
   * @param {DataTrigger} trigger Descriptor
   * @return {boolean} Whether the path matched the trigger
   */
  function pathMatchesTrigger(path, trigger) {
    if (trigger) {
      let triggerPath = trigger.name;
      return (triggerPath == path) ||
        (trigger.structured && Polymer.Path.isAncestor(triggerPath, path)) ||
        (trigger.wildcard && Polymer.Path.isDescendant(triggerPath, path));
    } else {
      return true;
    }
  }

  /**
   * Implements the "observer" effect.
   *
   * Calls the method with `info.methodName` on the instance, passing the
   * new and old values.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {void}
   * @private
   */
  function runObserverEffect(inst, property, props, oldProps, info) {
    let fn = typeof info.method === "string" ? inst[info.method] : info.method;
    let changedProp = info.property;
    if (fn) {
      fn.call(inst, inst.__data[changedProp], oldProps[changedProp]);
    } else if (!info.dynamicFn) {
      console.warn('observer method `' + info.method + '` not defined');
    }
  }

  /**
   * Runs "notify" effects for a set of changed properties.
   *
   * This method differs from the generic `runEffects` method in that it
   * will dispatch path notification events in the case that the property
   * changed was a path and the root property for that path didn't have a
   * "notify" effect.  This is to maintain 1.0 behavior that did not require
   * `notify: true` to ensure object sub-property notifications were
   * sent.
   *
   * @param {!PropertyEffectsType} inst The instance with effects to run
   * @param {Object} notifyProps Bag of properties to notify
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @return {void}
   * @private
   */
  function runNotifyEffects(inst, notifyProps, props, oldProps, hasPaths) {
    // Notify
    let fxs = inst[TYPES.NOTIFY];
    let notified;
    let id = dedupeId++;
    // Try normal notify effects; if none, fall back to try path notification
    for (let prop in notifyProps) {
      if (notifyProps[prop]) {
        if (fxs && runEffectsForProperty(inst, fxs, id, prop, props, oldProps, hasPaths)) {
          notified = true;
        } else if (hasPaths && notifyPath(inst, prop, props)) {
          notified = true;
        }
      }
    }
    // Flush host if we actually notified and host was batching
    // And the host has already initialized clients; this prevents
    // an issue with a host observing data changes before clients are ready.
    let host;
    if (notified && (host = inst.__dataHost) && host._invalidateProperties) {
      host._invalidateProperties();
    }
  }

  /**
   * Dispatches {property}-changed events with path information in the detail
   * object to indicate a sub-path of the property was changed.
   *
   * @param {!PropertyEffectsType} inst The element from which to fire the event
   * @param {string} path The path that was changed
   * @param {Object} props Bag of current property changes
   * @return {boolean} Returns true if the path was notified
   * @private
   */
  function notifyPath(inst, path, props) {
    let rootProperty = Polymer.Path.root(path);
    if (rootProperty !== path) {
      let eventName = Polymer.CaseMap.camelToDashCase(rootProperty) + '-changed';
      dispatchNotifyEvent(inst, eventName, props[path], path);
      return true;
    }
    return false;
  }

  /**
   * Dispatches {property}-changed events to indicate a property (or path)
   * changed.
   *
   * @param {!PropertyEffectsType} inst The element from which to fire the event
   * @param {string} eventName The name of the event to send ('{property}-changed')
   * @param {*} value The value of the changed property
   * @param {string | null | undefined} path If a sub-path of this property changed, the path
   *   that changed (optional).
   * @return {void}
   * @private
   * @suppress {invalidCasts}
   */
  function dispatchNotifyEvent(inst, eventName, value, path) {
    let detail = {
      value: value,
      queueProperty: true
    };
    if (path) {
      detail.path = path;
    }
    /** @type {!HTMLElement} */(inst).dispatchEvent(new CustomEvent(eventName, { detail }));
  }

  /**
   * Implements the "notify" effect.
   *
   * Dispatches a non-bubbling event named `info.eventName` on the instance
   * with a detail object containing the new `value`.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @return {void}
   * @private
   */
  function runNotifyEffect(inst, property, props, oldProps, info, hasPaths) {
    let rootProperty = hasPaths ? Polymer.Path.root(property) : property;
    let path = rootProperty != property ? property : null;
    let value = path ? Polymer.Path.get(inst, path) : inst.__data[property];
    if (path && value === undefined) {
      value = props[property];  // specifically for .splices
    }
    dispatchNotifyEvent(inst, info.eventName, value, path);
  }

  /**
   * Handler function for 2-way notification events. Receives context
   * information captured in the `addNotifyListener` closure from the
   * `__notifyListeners` metadata.
   *
   * Sets the value of the notified property to the host property or path.  If
   * the event contained path information, translate that path to the host
   * scope's name for that path first.
   *
   * @param {CustomEvent} event Notification event (e.g. '<property>-changed')
   * @param {!PropertyEffectsType} inst Host element instance handling the notification event
   * @param {string} fromProp Child element property that was bound
   * @param {string} toPath Host property/path that was bound
   * @param {boolean} negate Whether the binding was negated
   * @return {void}
   * @private
   */
  function handleNotification(event, inst, fromProp, toPath, negate) {
    let value;
    let detail = /** @type {Object} */(event.detail);
    let fromPath = detail && detail.path;
    if (fromPath) {
      toPath = Polymer.Path.translate(fromProp, toPath, fromPath);
      value = detail && detail.value;
    } else {
      value = event.currentTarget[fromProp];
    }
    value = negate ? !value : value;
    if (!inst[TYPES.READ_ONLY] || !inst[TYPES.READ_ONLY][toPath]) {
      if (inst._setPendingPropertyOrPath(toPath, value, true, Boolean(fromPath))
        && (!detail || !detail.queueProperty)) {
        inst._invalidateProperties();
      }
    }
  }

  /**
   * Implements the "reflect" effect.
   *
   * Sets the attribute named `info.attrName` to the given property value.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {void}
   * @private
   */
  function runReflectEffect(inst, property, props, oldProps, info) {
    let value = inst.__data[property];
    if (Polymer.sanitizeDOMValue) {
      value = Polymer.sanitizeDOMValue(value, info.attrName, 'attribute', /** @type {Node} */(inst));
    }
    inst._propertyToAttribute(property, info.attrName, value);
  }

  /**
   * Runs "computed" effects for a set of changed properties.
   *
   * This method differs from the generic `runEffects` method in that it
   * continues to run computed effects based on the output of each pass until
   * there are no more newly computed properties.  This ensures that all
   * properties that will be computed by the initial set of changes are
   * computed before other effects (binding propagation, observers, and notify)
   * run.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {!Object} changedProps Bag of changed properties
   * @param {!Object} oldProps Bag of previous values for changed properties
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @return {void}
   * @private
   */
  function runComputedEffects(inst, changedProps, oldProps, hasPaths) {
    let computeEffects = inst[TYPES.COMPUTE];
    if (computeEffects) {
      let inputProps = changedProps;
      while (runEffects(inst, computeEffects, inputProps, oldProps, hasPaths)) {
        Object.assign(oldProps, inst.__dataOld);
        Object.assign(changedProps, inst.__dataPending);
        inputProps = inst.__dataPending;
        inst.__dataPending = null;
      }
    }
  }

  /**
   * Implements the "computed property" effect by running the method with the
   * values of the arguments specified in the `info` object and setting the
   * return value to the computed property specified.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {void}
   * @private
   */
  function runComputedEffect(inst, property, props, oldProps, info) {
    let result = runMethodEffect(inst, property, props, oldProps, info);
    let computedProp = info.methodInfo;
    if (inst.__dataHasAccessor && inst.__dataHasAccessor[computedProp]) {
      inst._setPendingProperty(computedProp, result, true);
    } else {
      inst[computedProp] = result;
    }
  }

  /**
   * Computes path changes based on path links set up using the `linkPaths`
   * API.
   *
   * @param {!PropertyEffectsType} inst The instance whose props are changing
   * @param {string | !Array<(string|number)>} path Path that has changed
   * @param {*} value Value of changed path
   * @return {void}
   * @private
   */
  function computeLinkedPaths(inst, path, value) {
    let links = inst.__dataLinkedPaths;
    if (links) {
      let link;
      for (let a in links) {
        let b = links[a];
        if (Polymer.Path.isDescendant(a, path)) {
          link = Polymer.Path.translate(a, b, path);
          inst._setPendingPropertyOrPath(link, value, true, true);
        } else if (Polymer.Path.isDescendant(b, path)) {
          link = Polymer.Path.translate(b, a, path);
          inst._setPendingPropertyOrPath(link, value, true, true);
        }
      }
    }
  }

  // -- bindings ----------------------------------------------

  /**
   * Adds binding metadata to the current `nodeInfo`, and binding effects
   * for all part dependencies to `templateInfo`.
   *
   * @param {Function} constructor Class that `_parseTemplate` is currently
   *   running on
   * @param {TemplateInfo} templateInfo Template metadata for current template
   * @param {NodeInfo} nodeInfo Node metadata for current template node
   * @param {string} kind Binding kind, either 'property', 'attribute', or 'text'
   * @param {string} target Target property name
   * @param {!Array<!BindingPart>} parts Array of binding part metadata
   * @param {string=} literal Literal text surrounding binding parts (specified
   *   only for 'property' bindings, since these must be initialized as part
   *   of boot-up)
   * @return {void}
   * @private
   */
  function addBinding(constructor, templateInfo, nodeInfo, kind, target, parts, literal) {
    // Create binding metadata and add to nodeInfo
    nodeInfo.bindings = nodeInfo.bindings || [];
    let /** Binding */ binding = { kind, target, parts, literal, isCompound: (parts.length !== 1) };
    nodeInfo.bindings.push(binding);
    // Add listener info to binding metadata
    if (shouldAddListener(binding)) {
      let {event, negate} = binding.parts[0];
      binding.listenerEvent = event || (CaseMap.camelToDashCase(target) + '-changed');
      binding.listenerNegate = negate;
    }
    // Add "propagate" property effects to templateInfo
    let index = templateInfo.nodeInfoList.length;
    for (let i=0; i<binding.parts.length; i++) {
      let part = binding.parts[i];
      part.compoundIndex = i;
      addEffectForBindingPart(constructor, templateInfo, binding, part, index);
    }
  }

  /**
   * Adds property effects to the given `templateInfo` for the given binding
   * part.
   *
   * @param {Function} constructor Class that `_parseTemplate` is currently
   *   running on
   * @param {TemplateInfo} templateInfo Template metadata for current template
   * @param {!Binding} binding Binding metadata
   * @param {!BindingPart} part Binding part metadata
   * @param {number} index Index into `nodeInfoList` for this node
   * @return {void}
   */
  function addEffectForBindingPart(constructor, templateInfo, binding, part, index) {
    if (!part.literal) {
      if (binding.kind === 'attribute' && binding.target[0] === '-') {
        console.warn('Cannot set attribute ' + binding.target +
          ' because "-" is not a valid attribute starting character');
      } else {
        let dependencies = part.dependencies;
        let info = { index, binding, part, evaluator: constructor };
        for (let j=0; j<dependencies.length; j++) {
          let trigger = dependencies[j];
          if (typeof trigger == 'string') {
            trigger = parseArg(trigger);
            trigger.wildcard = true;
          }
          constructor._addTemplatePropertyEffect(templateInfo, trigger.rootProperty, {
            fn: runBindingEffect,
            info, trigger
          });
        }
      }
    }
  }

  /**
   * Implements the "binding" (property/path binding) effect.
   *
   * Note that binding syntax is overridable via `_parseBindings` and
   * `_evaluateBinding`.  This method will call `_evaluateBinding` for any
   * non-literal parts returned from `_parseBindings`.  However,
   * there is no support for _path_ bindings via custom binding parts,
   * as this is specific to Polymer's path binding syntax.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} path Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @param {Array} nodeList List of nodes associated with `nodeInfoList` template
   *   metadata
   * @return {void}
   * @private
   */
  function runBindingEffect(inst, path, props, oldProps, info, hasPaths, nodeList) {
    let node = nodeList[info.index];
    let binding = info.binding;
    let part = info.part;
    // Subpath notification: transform path and set to client
    // e.g.: foo="{{obj.sub}}", path: 'obj.sub.prop', set 'foo.prop'=obj.sub.prop
    if (hasPaths && part.source && (path.length > part.source.length) &&
        (binding.kind == 'property') && !binding.isCompound &&
        node.__isPropertyEffectsClient &&
        node.__dataHasAccessor && node.__dataHasAccessor[binding.target]) {
      let value = props[path];
      path = Polymer.Path.translate(part.source, binding.target, path);
      if (node._setPendingPropertyOrPath(path, value, false, true)) {
        inst._enqueueClient(node);
      }
    } else {
      let value = info.evaluator._evaluateBinding(inst, part, path, props, oldProps, hasPaths);
      // Propagate value to child
      applyBindingValue(inst, node, binding, part, value);
    }
  }

  /**
   * Sets the value for an "binding" (binding) effect to a node,
   * either as a property or attribute.
   *
   * @param {!PropertyEffectsType} inst The instance owning the binding effect
   * @param {Node} node Target node for binding
   * @param {!Binding} binding Binding metadata
   * @param {!BindingPart} part Binding part metadata
   * @param {*} value Value to set
   * @return {void}
   * @private
   */
  function applyBindingValue(inst, node, binding, part, value) {
    value = computeBindingValue(node, value, binding, part);
    if (Polymer.sanitizeDOMValue) {
      value = Polymer.sanitizeDOMValue(value, binding.target, binding.kind, node);
    }
    if (binding.kind == 'attribute') {
      // Attribute binding
      inst._valueToNodeAttribute(/** @type {Element} */(node), value, binding.target);
    } else {
      // Property binding
      let prop = binding.target;
      if (node.__isPropertyEffectsClient &&
          node.__dataHasAccessor && node.__dataHasAccessor[prop]) {
        if (!node[TYPES.READ_ONLY] || !node[TYPES.READ_ONLY][prop]) {
          if (node._setPendingProperty(prop, value)) {
            inst._enqueueClient(node);
          }
        }
      } else  {
        inst._setUnmanagedPropertyToNode(node, prop, value);
      }
    }
  }

  /**
   * Transforms an "binding" effect value based on compound & negation
   * effect metadata, as well as handling for special-case properties
   *
   * @param {Node} node Node the value will be set to
   * @param {*} value Value to set
   * @param {!Binding} binding Binding metadata
   * @param {!BindingPart} part Binding part metadata
   * @return {*} Transformed value to set
   * @private
   */
  function computeBindingValue(node, value, binding, part) {
    if (binding.isCompound) {
      let storage = node.__dataCompoundStorage[binding.target];
      storage[part.compoundIndex] = value;
      value = storage.join('');
    }
    if (binding.kind !== 'attribute') {
      // Some browsers serialize `undefined` to `"undefined"`
      if (binding.target === 'textContent' ||
          (binding.target === 'value' &&
            (node.localName === 'input' || node.localName === 'textarea'))) {
        value = value == undefined ? '' : value;
      }
    }
    return value;
  }

  /**
   * Returns true if a binding's metadata meets all the requirements to allow
   * 2-way binding, and therefore a `<property>-changed` event listener should be
   * added:
   * - used curly braces
   * - is a property (not attribute) binding
   * - is not a textContent binding
   * - is not compound
   *
   * @param {!Binding} binding Binding metadata
   * @return {boolean} True if 2-way listener should be added
   * @private
   */
  function shouldAddListener(binding) {
    return Boolean(binding.target) &&
           binding.kind != 'attribute' &&
           binding.kind != 'text' &&
           !binding.isCompound &&
           binding.parts[0].mode === '{';
  }

  /**
   * Setup compound binding storage structures, notify listeners, and dataHost
   * references onto the bound nodeList.
   *
   * @param {!PropertyEffectsType} inst Instance that bas been previously bound
   * @param {TemplateInfo} templateInfo Template metadata
   * @return {void}
   * @private
   */
  function setupBindings(inst, templateInfo) {
    // Setup compound storage, dataHost, and notify listeners
    let {nodeList, nodeInfoList} = templateInfo;
    if (nodeInfoList.length) {
      for (let i=0; i < nodeInfoList.length; i++) {
        let info = nodeInfoList[i];
        let node = nodeList[i];
        let bindings = info.bindings;
        if (bindings) {
          for (let i=0; i<bindings.length; i++) {
            let binding = bindings[i];
            setupCompoundStorage(node, binding);
            addNotifyListener(node, inst, binding);
          }
        }
        node.__dataHost = inst;
      }
    }
  }

  /**
   * Initializes `__dataCompoundStorage` local storage on a bound node with
   * initial literal data for compound bindings, and sets the joined
   * literal parts to the bound property.
   *
   * When changes to compound parts occur, they are first set into the compound
   * storage array for that property, and then the array is joined to result in
   * the final value set to the property/attribute.
   *
   * @param {Node} node Bound node to initialize
   * @param {Binding} binding Binding metadata
   * @return {void}
   * @private
   */
  function setupCompoundStorage(node, binding) {
    if (binding.isCompound) {
      // Create compound storage map
      let storage = node.__dataCompoundStorage ||
        (node.__dataCompoundStorage = {});
      let parts = binding.parts;
      // Copy literals from parts into storage for this binding
      let literals = new Array(parts.length);
      for (let j=0; j<parts.length; j++) {
        literals[j] = parts[j].literal;
      }
      let target = binding.target;
      storage[target] = literals;
      // Configure properties with their literal parts
      if (binding.literal && binding.kind == 'property') {
        node[target] = binding.literal;
      }
    }
  }

  /**
   * Adds a 2-way binding notification event listener to the node specified
   *
   * @param {Object} node Child element to add listener to
   * @param {!PropertyEffectsType} inst Host element instance to handle notification event
   * @param {Binding} binding Binding metadata
   * @return {void}
   * @private
   */
  function addNotifyListener(node, inst, binding) {
    if (binding.listenerEvent) {
      let part = binding.parts[0];
      node.addEventListener(binding.listenerEvent, function(e) {
        handleNotification(e, inst, binding.target, part.source, part.negate);
      });
    }
  }

  // -- for method-based effects (complexObserver & computed) --------------

  /**
   * Adds property effects for each argument in the method signature (and
   * optionally, for the method name if `dynamic` is true) that calls the
   * provided effect function.
   *
   * @param {Element | Object} model Prototype or instance
   * @param {!MethodSignature} sig Method signature metadata
   * @param {string} type Type of property effect to add
   * @param {Function} effectFn Function to run when arguments change
   * @param {*=} methodInfo Effect-specific information to be included in
   *   method effect metadata
   * @param {boolean|Object=} dynamicFn Boolean or object map indicating whether
   *   method names should be included as a dependency to the effect. Note,
   *   defaults to true if the signature is static (sig.static is true).
   * @return {void}
   * @private
   */
  function createMethodEffect(model, sig, type, effectFn, methodInfo, dynamicFn) {
    dynamicFn = sig.static || (dynamicFn &&
      (typeof dynamicFn !== 'object' || dynamicFn[sig.methodName]));
    let info = {
      methodName: sig.methodName,
      args: sig.args,
      methodInfo,
      dynamicFn
    };
    for (let i=0, arg; (i<sig.args.length) && (arg=sig.args[i]); i++) {
      if (!arg.literal) {
        model._addPropertyEffect(arg.rootProperty, type, {
          fn: effectFn, info: info, trigger: arg
        });
      }
    }
    if (dynamicFn) {
      model._addPropertyEffect(sig.methodName, type, {
        fn: effectFn, info: info
      });
    }
  }

  /**
   * Calls a method with arguments marshaled from properties on the instance
   * based on the method signature contained in the effect metadata.
   *
   * Multi-property observers, computed properties, and inline computing
   * functions call this function to invoke the method, then use the return
   * value accordingly.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {*} Returns the return value from the method invocation
   * @private
   */
  function runMethodEffect(inst, property, props, oldProps, info) {
    // Instances can optionally have a _methodHost which allows redirecting where
    // to find methods. Currently used by `templatize`.
    let context = inst._methodHost || inst;
    let fn = context[info.methodName];
    if (fn) {
      let args = inst._marshalArgs(info.args, property, props);
      return fn.apply(context, args);
    } else if (!info.dynamicFn) {
      console.warn('method `' + info.methodName + '` not defined');
    }
  }

  const emptyArray = [];

  // Regular expressions used for binding
  const IDENT  = '(?:' + '[a-zA-Z_$][\\w.:$\\-*]*' + ')';
  const NUMBER = '(?:' + '[-+]?[0-9]*\\.?[0-9]+(?:[eE][-+]?[0-9]+)?' + ')';
  const SQUOTE_STRING = '(?:' + '\'(?:[^\'\\\\]|\\\\.)*\'' + ')';
  const DQUOTE_STRING = '(?:' + '"(?:[^"\\\\]|\\\\.)*"' + ')';
  const STRING = '(?:' + SQUOTE_STRING + '|' + DQUOTE_STRING + ')';
  const ARGUMENT = '(?:(' + IDENT + '|' + NUMBER + '|' +  STRING + ')\\s*' + ')';
  const ARGUMENTS = '(?:' + ARGUMENT + '(?:,\\s*' + ARGUMENT + ')*' + ')';
  const ARGUMENT_LIST = '(?:' + '\\(\\s*' +
                                '(?:' + ARGUMENTS + '?' + ')' +
                              '\\)\\s*' + ')';
  const BINDING = '(' + IDENT + '\\s*' + ARGUMENT_LIST + '?' + ')'; // Group 3
  const OPEN_BRACKET = '(\\[\\[|{{)' + '\\s*';
  const CLOSE_BRACKET = '(?:]]|}})';
  const NEGATE = '(?:(!)\\s*)?'; // Group 2
  const EXPRESSION = OPEN_BRACKET + NEGATE + BINDING + CLOSE_BRACKET;
  const bindingRegex = new RegExp(EXPRESSION, "g");

  /**
   * Create a string from binding parts of all the literal parts
   *
   * @param {!Array<BindingPart>} parts All parts to stringify
   * @return {string} String made from the literal parts
   */
  function literalFromParts(parts) {
    let s = '';
    for (let i=0; i<parts.length; i++) {
      let literal = parts[i].literal;
      s += literal || '';
    }
    return s;
  }

  /**
   * Parses an expression string for a method signature, and returns a metadata
   * describing the method in terms of `methodName`, `static` (whether all the
   * arguments are literals), and an array of `args`
   *
   * @param {string} expression The expression to parse
   * @return {?MethodSignature} The method metadata object if a method expression was
   *   found, otherwise `undefined`
   * @private
   */
  function parseMethod(expression) {
    // tries to match valid javascript property names
    let m = expression.match(/([^\s]+?)\(([\s\S]*)\)/);
    if (m) {
      let methodName = m[1];
      let sig = { methodName, static: true, args: emptyArray };
      if (m[2].trim()) {
        // replace escaped commas with comma entity, split on un-escaped commas
        let args = m[2].replace(/\\,/g, '&comma;').split(',');
        return parseArgs(args, sig);
      } else {
        return sig;
      }
    }
    return null;
  }

  /**
   * Parses an array of arguments and sets the `args` property of the supplied
   * signature metadata object. Sets the `static` property to false if any
   * argument is a non-literal.
   *
   * @param {!Array<string>} argList Array of argument names
   * @param {!MethodSignature} sig Method signature metadata object
   * @return {!MethodSignature} The updated signature metadata object
   * @private
   */
  function parseArgs(argList, sig) {
    sig.args = argList.map(function(rawArg) {
      let arg = parseArg(rawArg);
      if (!arg.literal) {
        sig.static = false;
      }
      return arg;
    }, this);
    return sig;
  }

  /**
   * Parses an individual argument, and returns an argument metadata object
   * with the following fields:
   *
   *   {
   *     value: 'prop',        // property/path or literal value
   *     literal: false,       // whether argument is a literal
   *     structured: false,    // whether the property is a path
   *     rootProperty: 'prop', // the root property of the path
   *     wildcard: false       // whether the argument was a wildcard '.*' path
   *   }
   *
   * @param {string} rawArg The string value of the argument
   * @return {!MethodArg} Argument metadata object
   * @private
   */
  function parseArg(rawArg) {
    // clean up whitespace
    let arg = rawArg.trim()
      // replace comma entity with comma
      .replace(/&comma;/g, ',')
      // repair extra escape sequences; note only commas strictly need
      // escaping, but we allow any other char to be escaped since its
      // likely users will do this
      .replace(/\\(.)/g, '\$1')
      ;
    // basic argument descriptor
    let a = {
      name: arg,
      value: '',
      literal: false
    };
    // detect literal value (must be String or Number)
    let fc = arg[0];
    if (fc === '-') {
      fc = arg[1];
    }
    if (fc >= '0' && fc <= '9') {
      fc = '#';
    }
    switch(fc) {
      case "'":
      case '"':
        a.value = arg.slice(1, -1);
        a.literal = true;
        break;
      case '#':
        a.value = Number(arg);
        a.literal = true;
        break;
    }
    // if not literal, look for structured path
    if (!a.literal) {
      a.rootProperty = Polymer.Path.root(arg);
      // detect structured path (has dots)
      a.structured = Polymer.Path.isPath(arg);
      if (a.structured) {
        a.wildcard = (arg.slice(-2) == '.*');
        if (a.wildcard) {
          a.name = arg.slice(0, -2);
        }
      }
    }
    return a;
  }

  // data api

  /**
   * Sends array splice notifications (`.splices` and `.length`)
   *
   * Note: this implementation only accepts normalized paths
   *
   * @param {!PropertyEffectsType} inst Instance to send notifications to
   * @param {Array} array The array the mutations occurred on
   * @param {string} path The path to the array that was mutated
   * @param {Array} splices Array of splice records
   * @return {void}
   * @private
   */
  function notifySplices(inst, array, path, splices) {
    let splicesPath = path + '.splices';
    inst.notifyPath(splicesPath, { indexSplices: splices });
    inst.notifyPath(path + '.length', array.length);
    // Null here to allow potentially large splice records to be GC'ed.
    inst.__data[splicesPath] = {indexSplices: null};
  }

  /**
   * Creates a splice record and sends an array splice notification for
   * the described mutation
   *
   * Note: this implementation only accepts normalized paths
   *
   * @param {!PropertyEffectsType} inst Instance to send notifications to
   * @param {Array} array The array the mutations occurred on
   * @param {string} path The path to the array that was mutated
   * @param {number} index Index at which the array mutation occurred
   * @param {number} addedCount Number of added items
   * @param {Array} removed Array of removed items
   * @return {void}
   * @private
   */
  function notifySplice(inst, array, path, index, addedCount, removed) {
    notifySplices(inst, array, path, [{
      index: index,
      addedCount: addedCount,
      removed: removed,
      object: array,
      type: 'splice'
    }]);
  }

  /**
   * Returns an upper-cased version of the string.
   *
   * @param {string} name String to uppercase
   * @return {string} Uppercased string
   * @private
   */
  function upper(name) {
    return name[0].toUpperCase() + name.substring(1);
  }

  /**
   * Element class mixin that provides meta-programming for Polymer's template
   * binding and data observation (collectively, "property effects") system.
   *
   * This mixin uses provides the following key static methods for adding
   * property effects to an element class:
   * - `addPropertyEffect`
   * - `createPropertyObserver`
   * - `createMethodObserver`
   * - `createNotifyingProperty`
   * - `createReadOnlyProperty`
   * - `createReflectedProperty`
   * - `createComputedProperty`
   * - `bindTemplate`
   *
   * Each method creates one or more property accessors, along with metadata
   * used by this mixin's implementation of `_propertiesChanged` to perform
   * the property effects.
   *
   * Underscored versions of the above methods also exist on the element
   * prototype for adding property effects on instances at runtime.
   *
   * Note that this mixin overrides several `PropertyAccessors` methods, in
   * many cases to maintain guarantees provided by the Polymer 1.x features;
   * notably it changes property accessors to be synchronous by default
   * whereas the default when using `PropertyAccessors` standalone is to be
   * async by default.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.TemplateStamp
   * @appliesMixin Polymer.PropertyAccessors
   * @memberof Polymer
   * @summary Element class mixin that provides meta-programming for Polymer's
   * template binding and data observation system.
   */
  Polymer.PropertyEffects = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_PropertyAccessors}
     * @implements {Polymer_TemplateStamp}
     * @unrestricted
     * @private
     */
    const propertyEffectsBase = Polymer.TemplateStamp(Polymer.PropertyAccessors(superClass));

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_PropertyEffects}
     * @extends {propertyEffectsBase}
     * @unrestricted
     */
    class PropertyEffects extends propertyEffectsBase {

      constructor() {
        super();
        /** @type {boolean} */
        // Used to identify users of this mixin, ala instanceof
        this.__isPropertyEffectsClient = true;
        /** @type {number} */
        // NOTE: used to track re-entrant calls to `_flushProperties`
        // path changes dirty check against `__dataTemp` only during one "turn"
        // and are cleared when `__dataCounter` returns to 0.
        this.__dataCounter = 0;
        /** @type {boolean} */
        this.__dataClientsReady;
        /** @type {Array} */
        this.__dataPendingClients;
        /** @type {Object} */
        this.__dataToNotify;
        /** @type {Object} */
        this.__dataLinkedPaths;
        /** @type {boolean} */
        this.__dataHasPaths;
        /** @type {Object} */
        this.__dataCompoundStorage;
        /** @type {Polymer_PropertyEffects} */
        this.__dataHost;
        /** @type {!Object} */
        this.__dataTemp;
        /** @type {boolean} */
        this.__dataClientsInitialized;
        /** @type {!Object} */
        this.__data;
        /** @type {!Object} */
        this.__dataPending;
        /** @type {!Object} */
        this.__dataOld;
        /** @type {Object} */
        this.__computeEffects;
        /** @type {Object} */
        this.__reflectEffects;
        /** @type {Object} */
        this.__notifyEffects;
        /** @type {Object} */
        this.__propagateEffects;
        /** @type {Object} */
        this.__observeEffects;
        /** @type {Object} */
        this.__readOnly;
        /** @type {!TemplateInfo} */
        this.__templateInfo;
      }

      get PROPERTY_EFFECT_TYPES() {
        return TYPES;
      }

      /**
       * @return {void}
       */
      _initializeProperties() {
        super._initializeProperties();
        hostStack.registerHost(this);
        this.__dataClientsReady = false;
        this.__dataPendingClients = null;
        this.__dataToNotify = null;
        this.__dataLinkedPaths = null;
        this.__dataHasPaths = false;
        // May be set on instance prior to upgrade
        this.__dataCompoundStorage = this.__dataCompoundStorage || null;
        this.__dataHost = this.__dataHost || null;
        this.__dataTemp = {};
        this.__dataClientsInitialized = false;
      }

      /**
       * Overrides `Polymer.PropertyAccessors` implementation to provide a
       * more efficient implementation of initializing properties from
       * the prototype on the instance.
       *
       * @override
       * @param {Object} props Properties to initialize on the prototype
       * @return {void}
       */
      _initializeProtoProperties(props) {
        this.__data = Object.create(props);
        this.__dataPending = Object.create(props);
        this.__dataOld = {};
      }

      /**
       * Overrides `Polymer.PropertyAccessors` implementation to avoid setting
       * `_setProperty`'s `shouldNotify: true`.
       *
       * @override
       * @param {Object} props Properties to initialize on the instance
       * @return {void}
       */
      _initializeInstanceProperties(props) {
        let readOnly = this[TYPES.READ_ONLY];
        for (let prop in props) {
          if (!readOnly || !readOnly[prop]) {
            this.__dataPending = this.__dataPending || {};
            this.__dataOld = this.__dataOld || {};
            this.__data[prop] = this.__dataPending[prop] = props[prop];
          }
        }
      }

      // Prototype setup ----------------------------------------

      /**
       * Equivalent to static `addPropertyEffect` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property that should trigger the effect
       * @param {string} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @param {Object=} effect Effect metadata object
       * @return {void}
       * @protected
       */
      _addPropertyEffect(property, type, effect) {
        this._createPropertyAccessor(property, type == TYPES.READ_ONLY);
        // effects are accumulated into arrays per property based on type
        let effects = ensureOwnEffectMap(this, type)[property];
        if (!effects) {
          effects = this[type][property] = [];
        }
        effects.push(effect);
      }

      /**
       * Removes the given property effect.
       *
       * @param {string} property Property the effect was associated with
       * @param {string} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @param {Object=} effect Effect metadata object to remove
       * @return {void}
       */
      _removePropertyEffect(property, type, effect) {
        let effects = ensureOwnEffectMap(this, type)[property];
        let idx = effects.indexOf(effect);
        if (idx >= 0) {
          effects.splice(idx, 1);
        }
      }

      /**
       * Returns whether the current prototype/instance has a property effect
       * of a certain type.
       *
       * @param {string} property Property name
       * @param {string=} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasPropertyEffect(property, type) {
        let effects = this[type];
        return Boolean(effects && effects[property]);
      }

      /**
       * Returns whether the current prototype/instance has a "read only"
       * accessor for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasReadOnlyEffect(property) {
        return this._hasPropertyEffect(property, TYPES.READ_ONLY);
      }

      /**
       * Returns whether the current prototype/instance has a "notify"
       * property effect for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasNotifyEffect(property) {
        return this._hasPropertyEffect(property, TYPES.NOTIFY);
      }

      /**
       * Returns whether the current prototype/instance has a "reflect to attribute"
       * property effect for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasReflectEffect(property) {
        return this._hasPropertyEffect(property, TYPES.REFLECT);
      }

      /**
       * Returns whether the current prototype/instance has a "computed"
       * property effect for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasComputedEffect(property) {
        return this._hasPropertyEffect(property, TYPES.COMPUTE);
      }

      // Runtime ----------------------------------------

      /**
       * Sets a pending property or path.  If the root property of the path in
       * question had no accessor, the path is set, otherwise it is enqueued
       * via `_setPendingProperty`.
       *
       * This function isolates relatively expensive functionality necessary
       * for the public API (`set`, `setProperties`, `notifyPath`, and property
       * change listeners via {{...}} bindings), such that it is only done
       * when paths enter the system, and not at every propagation step.  It
       * also sets a `__dataHasPaths` flag on the instance which is used to
       * fast-path slower path-matching code in the property effects host paths.
       *
       * `path` can be a path string or array of path parts as accepted by the
       * public API.
       *
       * @param {string | !Array<number|string>} path Path to set
       * @param {*} value Value to set
       * @param {boolean=} shouldNotify Set to true if this change should
       *  cause a property notification event dispatch
       * @param {boolean=} isPathNotification If the path being set is a path
       *   notification of an already changed value, as opposed to a request
       *   to set and notify the change.  In the latter `false` case, a dirty
       *   check is performed and then the value is set to the path before
       *   enqueuing the pending property change.
       * @return {boolean} Returns true if the property/path was enqueued in
       *   the pending changes bag.
       * @protected
       */
      _setPendingPropertyOrPath(path, value, shouldNotify, isPathNotification) {
        if (isPathNotification ||
            Polymer.Path.root(Array.isArray(path) ? path[0] : path) !== path) {
          // Dirty check changes being set to a path against the actual object,
          // since this is the entry point for paths into the system; from here
          // the only dirty checks are against the `__dataTemp` cache to prevent
          // duplicate work in the same turn only. Note, if this was a notification
          // of a change already set to a path (isPathNotification: true),
          // we always let the change through and skip the `set` since it was
          // already dirty checked at the point of entry and the underlying
          // object has already been updated
          if (!isPathNotification) {
            let old = Polymer.Path.get(this, path);
            path = /** @type {string} */ (Polymer.Path.set(this, path, value));
            // Use property-accessor's simpler dirty check
            if (!path || !super._shouldPropertyChange(path, value, old)) {
              return false;
            }
          }
          this.__dataHasPaths = true;
          if (this._setPendingProperty(/**@type{string}*/(path), value, shouldNotify)) {
            computeLinkedPaths(this, path, value);
            return true;
          }
        } else {
          if (this.__dataHasAccessor && this.__dataHasAccessor[path]) {
            return this._setPendingProperty(/**@type{string}*/(path), value, shouldNotify);
          } else {
            this[path] = value;
          }
        }
        return false;
      }

      /**
       * Applies a value to a non-Polymer element/node's property.
       *
       * The implementation makes a best-effort at binding interop:
       * Some native element properties have side-effects when
       * re-setting the same value (e.g. setting `<input>.value` resets the
       * cursor position), so we do a dirty-check before setting the value.
       * However, for better interop with non-Polymer custom elements that
       * accept objects, we explicitly re-set object changes coming from the
       * Polymer world (which may include deep object changes without the
       * top reference changing), erring on the side of providing more
       * information.
       *
       * Users may override this method to provide alternate approaches.
       *
       * @param {!Node} node The node to set a property on
       * @param {string} prop The property to set
       * @param {*} value The value to set
       * @return {void}
       * @protected
       */
      _setUnmanagedPropertyToNode(node, prop, value) {
        // It is a judgment call that resetting primitives is
        // "bad" and resettings objects is also "good"; alternatively we could
        // implement a whitelist of tag & property values that should never
        // be reset (e.g. <input>.value && <select>.value)
        if (value !== node[prop] || typeof value == 'object') {
          node[prop] = value;
        }
      }

      /**
       * Overrides the `PropertiesChanged` implementation to introduce special
       * dirty check logic depending on the property & value being set:
       *
       * 1. Any value set to a path (e.g. 'obj.prop': 42 or 'obj.prop': {...})
       *    Stored in `__dataTemp`, dirty checked against `__dataTemp`
       * 2. Object set to simple property (e.g. 'prop': {...})
       *    Stored in `__dataTemp` and `__data`, dirty checked against
       *    `__dataTemp` by default implementation of `_shouldPropertyChange`
       * 3. Primitive value set to simple property (e.g. 'prop': 42)
       *    Stored in `__data`, dirty checked against `__data`
       *
       * The dirty-check is important to prevent cycles due to two-way
       * notification, but paths and objects are only dirty checked against any
       * previous value set during this turn via a "temporary cache" that is
       * cleared when the last `_propertiesChanged` exits. This is so:
       * a. any cached array paths (e.g. 'array.3.prop') may be invalidated
       *    due to array mutations like shift/unshift/splice; this is fine
       *    since path changes are dirty-checked at user entry points like `set`
       * b. dirty-checking for objects only lasts one turn to allow the user
       *    to mutate the object in-place and re-set it with the same identity
       *    and have all sub-properties re-propagated in a subsequent turn.
       *
       * The temp cache is not necessarily sufficient to prevent invalid array
       * paths, since a splice can happen during the same turn (with pathological
       * user code); we could introduce a "fixup" for temporarily cached array
       * paths if needed: https://github.com/Polymer/polymer/issues/4227
       *
       * @override
       * @param {string} property Name of the property
       * @param {*} value Value to set
       * @param {boolean=} shouldNotify True if property should fire notification
       *   event (applies only for `notify: true` properties)
       * @return {boolean} Returns true if the property changed
       */
      _setPendingProperty(property, value, shouldNotify) {
        let isPath = this.__dataHasPaths && Polymer.Path.isPath(property);
        let prevProps = isPath ? this.__dataTemp : this.__data;
        if (this._shouldPropertyChange(property, value, prevProps[property])) {
          if (!this.__dataPending) {
            this.__dataPending = {};
            this.__dataOld = {};
          }
          // Ensure old is captured from the last turn
          if (!(property in this.__dataOld)) {
            this.__dataOld[property] = this.__data[property];
          }
          // Paths are stored in temporary cache (cleared at end of turn),
          // which is used for dirty-checking, all others stored in __data
          if (isPath) {
            this.__dataTemp[property] = value;
          } else {
            this.__data[property] = value;
          }
          // All changes go into pending property bag, passed to _propertiesChanged
          this.__dataPending[property] = value;
          // Track properties that should notify separately
          if (isPath || (this[TYPES.NOTIFY] && this[TYPES.NOTIFY][property])) {
            this.__dataToNotify = this.__dataToNotify || {};
            this.__dataToNotify[property] = shouldNotify;
          }
          return true;
        }
        return false;
      }

      /**
       * Overrides base implementation to ensure all accessors set `shouldNotify`
       * to true, for per-property notification tracking.
       *
       * @override
       * @param {string} property Name of the property
       * @param {*} value Value to set
       * @return {void}
       */
      _setProperty(property, value) {
        if (this._setPendingProperty(property, value, true)) {
          this._invalidateProperties();
        }
      }

      /**
       * Overrides `PropertyAccessor`'s default async queuing of
       * `_propertiesChanged`: if `__dataReady` is false (has not yet been
       * manually flushed), the function no-ops; otherwise flushes
       * `_propertiesChanged` synchronously.
       *
       * @override
       * @return {void}
       */
      _invalidateProperties() {
        if (this.__dataReady) {
          this._flushProperties();
        }
      }

      /**
       * Enqueues the given client on a list of pending clients, whose
       * pending property changes can later be flushed via a call to
       * `_flushClients`.
       *
       * @param {Object} client PropertyEffects client to enqueue
       * @return {void}
       * @protected
       */
      _enqueueClient(client) {
        this.__dataPendingClients = this.__dataPendingClients || [];
        if (client !== this) {
          this.__dataPendingClients.push(client);
        }
      }

      /**
       * Overrides superclass implementation.
       *
       * @return {void}
       * @protected
       */
      _flushProperties() {
        this.__dataCounter++;
        super._flushProperties();
        this.__dataCounter--;
      }

      /**
       * Flushes any clients previously enqueued via `_enqueueClient`, causing
       * their `_flushProperties` method to run.
       *
       * @return {void}
       * @protected
       */
      _flushClients() {
        if (!this.__dataClientsReady) {
          this.__dataClientsReady = true;
          this._readyClients();
          // Override point where accessors are turned on; importantly,
          // this is after clients have fully readied, providing a guarantee
          // that any property effects occur only after all clients are ready.
          this.__dataReady = true;
        } else {
          this.__enableOrFlushClients();
        }
      }

      // NOTE: We ensure clients either enable or flush as appropriate. This
      // handles two corner cases:
      // (1) clients flush properly when connected/enabled before the host
      // enables; e.g.
      //   (a) Templatize stamps with no properties and does not flush and
      //   (b) the instance is inserted into dom and
      //   (c) then the instance flushes.
      // (2) clients enable properly when not connected/enabled when the host
      // flushes; e.g.
      //   (a) a template is runtime stamped and not yet connected/enabled
      //   (b) a host sets a property, causing stamped dom to flush
      //   (c) the stamped dom enables.
      __enableOrFlushClients() {
        let clients = this.__dataPendingClients;
        if (clients) {
          this.__dataPendingClients = null;
          for (let i=0; i < clients.length; i++) {
            let client = clients[i];
            if (!client.__dataEnabled) {
              client._enableProperties();
            } else if (client.__dataPending) {
              client._flushProperties();
            }
          }
        }
      }

      /**
       * Perform any initial setup on client dom. Called before the first
       * `_flushProperties` call on client dom and before any element
       * observers are called.
       *
       * @return {void}
       * @protected
       */
      _readyClients() {
        this.__enableOrFlushClients();
      }

      /**
       * Sets a bag of property changes to this instance, and
       * synchronously processes all effects of the properties as a batch.
       *
       * Property names must be simple properties, not paths.  Batched
       * path propagation is not supported.
       *
       * @param {Object} props Bag of one or more key-value pairs whose key is
       *   a property and value is the new value to set for that property.
       * @param {boolean=} setReadOnly When true, any private values set in
       *   `props` will be set. By default, `setProperties` will not set
       *   `readOnly: true` root properties.
       * @return {void}
       * @public
       */
      setProperties(props, setReadOnly) {
        for (let path in props) {
          if (setReadOnly || !this[TYPES.READ_ONLY] || !this[TYPES.READ_ONLY][path]) {
            //TODO(kschaaf): explicitly disallow paths in setProperty?
            // wildcard observers currently only pass the first changed path
            // in the `info` object, and you could do some odd things batching
            // paths, e.g. {'foo.bar': {...}, 'foo': null}
            this._setPendingPropertyOrPath(path, props[path], true);
          }
        }
        this._invalidateProperties();
      }

      /**
       * Overrides `PropertyAccessors` so that property accessor
       * side effects are not enabled until after client dom is fully ready.
       * Also calls `_flushClients` callback to ensure client dom is enabled
       * that was not enabled as a result of flushing properties.
       *
       * @override
       * @return {void}
       */
      ready() {
        // It is important that `super.ready()` is not called here as it
        // immediately turns on accessors. Instead, we wait until `readyClients`
        // to enable accessors to provide a guarantee that clients are ready
        // before processing any accessors side effects.
        this._flushProperties();
        // If no data was pending, `_flushProperties` will not `flushClients`
        // so ensure this is done.
        if (!this.__dataClientsReady) {
          this._flushClients();
        }
        // Before ready, client notifications do not trigger _flushProperties.
        // Therefore a flush is necessary here if data has been set.
        if (this.__dataPending) {
          this._flushProperties();
        }
      }

      /**
       * Implements `PropertyAccessors`'s properties changed callback.
       *
       * Runs each class of effects for the batch of changed properties in
       * a specific order (compute, propagate, reflect, observe, notify).
       *
       * @param {!Object} currentProps Bag of all current accessor values
       * @param {!Object} changedProps Bag of properties changed since the last
       *   call to `_propertiesChanged`
       * @param {!Object} oldProps Bag of previous values for each property
       *   in `changedProps`
       * @return {void}
       */
      _propertiesChanged(currentProps, changedProps, oldProps) {
        // ----------------------------
        // let c = Object.getOwnPropertyNames(changedProps || {});
        // window.debug && console.group(this.localName + '#' + this.id + ': ' + c);
        // if (window.debug) { debugger; }
        // ----------------------------
        let hasPaths = this.__dataHasPaths;
        this.__dataHasPaths = false;
        // Compute properties
        runComputedEffects(this, changedProps, oldProps, hasPaths);
        // Clear notify properties prior to possible reentry (propagate, observe),
        // but after computing effects have a chance to add to them
        let notifyProps = this.__dataToNotify;
        this.__dataToNotify = null;
        // Propagate properties to clients
        this._propagatePropertyChanges(changedProps, oldProps, hasPaths);
        // Flush clients
        this._flushClients();
        // Reflect properties
        runEffects(this, this[TYPES.REFLECT], changedProps, oldProps, hasPaths);
        // Observe properties
        runEffects(this, this[TYPES.OBSERVE], changedProps, oldProps, hasPaths);
        // Notify properties to host
        if (notifyProps) {
          runNotifyEffects(this, notifyProps, changedProps, oldProps, hasPaths);
        }
        // Clear temporary cache at end of turn
        if (this.__dataCounter == 1) {
          this.__dataTemp = {};
        }
        // ----------------------------
        // window.debug && console.groupEnd(this.localName + '#' + this.id + ': ' + c);
        // ----------------------------
      }

      /**
       * Called to propagate any property changes to stamped template nodes
       * managed by this element.
       *
       * @param {Object} changedProps Bag of changed properties
       * @param {Object} oldProps Bag of previous values for changed properties
       * @param {boolean} hasPaths True with `props` contains one or more paths
       * @return {void}
       * @protected
       */
      _propagatePropertyChanges(changedProps, oldProps, hasPaths) {
        if (this[TYPES.PROPAGATE]) {
          runEffects(this, this[TYPES.PROPAGATE], changedProps, oldProps, hasPaths);
        }
        let templateInfo = this.__templateInfo;
        while (templateInfo) {
          runEffects(this, templateInfo.propertyEffects, changedProps, oldProps,
            hasPaths, templateInfo.nodeList);
          templateInfo = templateInfo.nextTemplateInfo;
        }
      }

      /**
       * Aliases one data path as another, such that path notifications from one
       * are routed to the other.
       *
       * @param {string | !Array<string|number>} to Target path to link.
       * @param {string | !Array<string|number>} from Source path to link.
       * @return {void}
       * @public
       */
      linkPaths(to, from) {
        to = Polymer.Path.normalize(to);
        from = Polymer.Path.normalize(from);
        this.__dataLinkedPaths = this.__dataLinkedPaths || {};
        this.__dataLinkedPaths[to] = from;
      }

      /**
       * Removes a data path alias previously established with `_linkPaths`.
       *
       * Note, the path to unlink should be the target (`to`) used when
       * linking the paths.
       *
       * @param {string | !Array<string|number>} path Target path to unlink.
       * @return {void}
       * @public
       */
      unlinkPaths(path) {
        path = Polymer.Path.normalize(path);
        if (this.__dataLinkedPaths) {
          delete this.__dataLinkedPaths[path];
        }
      }

      /**
       * Notify that an array has changed.
       *
       * Example:
       *
       *     this.items = [ {name: 'Jim'}, {name: 'Todd'}, {name: 'Bill'} ];
       *     ...
       *     this.items.splice(1, 1, {name: 'Sam'});
       *     this.items.push({name: 'Bob'});
       *     this.notifySplices('items', [
       *       { index: 1, removed: [{name: 'Todd'}], addedCount: 1, object: this.items, type: 'splice' },
       *       { index: 3, removed: [], addedCount: 1, object: this.items, type: 'splice'}
       *     ]);
       *
       * @param {string} path Path that should be notified.
       * @param {Array} splices Array of splice records indicating ordered
       *   changes that occurred to the array. Each record should have the
       *   following fields:
       *    * index: index at which the change occurred
       *    * removed: array of items that were removed from this index
       *    * addedCount: number of new items added at this index
       *    * object: a reference to the array in question
       *    * type: the string literal 'splice'
       *
       *   Note that splice records _must_ be normalized such that they are
       *   reported in index order (raw results from `Object.observe` are not
       *   ordered and must be normalized/merged before notifying).
       * @return {void}
       * @public
      */
      notifySplices(path, splices) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        notifySplices(this, array, info.path, splices);
      }

      /**
       * Convenience method for reading a value from a path.
       *
       * Note, if any part in the path is undefined, this method returns
       * `undefined` (this method does not throw when dereferencing undefined
       * paths).
       *
       * @param {(string|!Array<(string|number)>)} path Path to the value
       *   to read.  The path may be specified as a string (e.g. `foo.bar.baz`)
       *   or an array of path parts (e.g. `['foo.bar', 'baz']`).  Note that
       *   bracketed expressions are not supported; string-based path parts
       *   *must* be separated by dots.  Note that when dereferencing array
       *   indices, the index may be used as a dotted part directly
       *   (e.g. `users.12.name` or `['users', 12, 'name']`).
       * @param {Object=} root Root object from which the path is evaluated.
       * @return {*} Value at the path, or `undefined` if any part of the path
       *   is undefined.
       * @public
       */
      get(path, root) {
        return Polymer.Path.get(root || this, path);
      }

      /**
       * Convenience method for setting a value to a path and notifying any
       * elements bound to the same path.
       *
       * Note, if any part in the path except for the last is undefined,
       * this method does nothing (this method does not throw when
       * dereferencing undefined paths).
       *
       * @param {(string|!Array<(string|number)>)} path Path to the value
       *   to write.  The path may be specified as a string (e.g. `'foo.bar.baz'`)
       *   or an array of path parts (e.g. `['foo.bar', 'baz']`).  Note that
       *   bracketed expressions are not supported; string-based path parts
       *   *must* be separated by dots.  Note that when dereferencing array
       *   indices, the index may be used as a dotted part directly
       *   (e.g. `'users.12.name'` or `['users', 12, 'name']`).
       * @param {*} value Value to set at the specified path.
       * @param {Object=} root Root object from which the path is evaluated.
       *   When specified, no notification will occur.
       * @return {void}
       * @public
      */
      set(path, value, root) {
        if (root) {
          Polymer.Path.set(root, path, value);
        } else {
          if (!this[TYPES.READ_ONLY] || !this[TYPES.READ_ONLY][/** @type {string} */(path)]) {
            if (this._setPendingPropertyOrPath(path, value, true)) {
              this._invalidateProperties();
            }
          }
        }
      }

      /**
       * Adds items onto the end of the array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.push`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @param {...*} items Items to push onto array
       * @return {number} New length of the array.
       * @public
       */
      push(path, ...items) {
        let info = {path: ''};
        let array = /** @type {Array}*/(Polymer.Path.get(this, path, info));
        let len = array.length;
        let ret = array.push(...items);
        if (items.length) {
          notifySplice(this, array, info.path, len, items.length, []);
        }
        return ret;
      }

      /**
       * Removes an item from the end of array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.pop`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @return {*} Item that was removed.
       * @public
       */
      pop(path) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        let hadLength = Boolean(array.length);
        let ret = array.pop();
        if (hadLength) {
          notifySplice(this, array, info.path, array.length, 0, [ret]);
        }
        return ret;
      }

      /**
       * Starting from the start index specified, removes 0 or more items
       * from the array and inserts 0 or more new items in their place.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.splice`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @param {number} start Index from which to start removing/inserting.
       * @param {number} deleteCount Number of items to remove.
       * @param {...*} items Items to insert into array.
       * @return {Array} Array of removed items.
       * @public
       */
      splice(path, start, deleteCount, ...items) {
        let info = {path : ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        // Normalize fancy native splice handling of crazy start values
        if (start < 0) {
          start = array.length - Math.floor(-start);
        } else if (start) {
          start = Math.floor(start);
        }
        // array.splice does different things based on the number of arguments
        // you pass in. Therefore, array.splice(0) and array.splice(0, undefined)
        // do different things. In the former, the whole array is cleared. In the
        // latter, no items are removed.
        // This means that we need to detect whether 1. one of the arguments
        // is actually passed in and then 2. determine how many arguments
        // we should pass on to the native array.splice
        //
        let ret;
        // Omit any additional arguments if they were not passed in
        if (arguments.length === 2) {
          ret = array.splice(start);
        // Either start was undefined and the others were defined, but in this
        // case we can safely pass on all arguments
        //
        // Note: this includes the case where none of the arguments were passed in,
        // e.g. this.splice('array'). However, if both start and deleteCount
        // are undefined, array.splice will not modify the array (as expected)
        } else {
          ret = array.splice(start, deleteCount, ...items);
        }
        // At the end, check whether any items were passed in (e.g. insertions)
        // or if the return array contains items (e.g. deletions).
        // Only notify if items were added or deleted.
        if (items.length || ret.length) {
          notifySplice(this, array, info.path, start, items.length, ret);
        }
        return ret;
      }

      /**
       * Removes an item from the beginning of array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.pop`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @return {*} Item that was removed.
       * @public
       */
      shift(path) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        let hadLength = Boolean(array.length);
        let ret = array.shift();
        if (hadLength) {
          notifySplice(this, array, info.path, 0, 0, [ret]);
        }
        return ret;
      }

      /**
       * Adds items onto the beginning of the array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.push`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @param {...*} items Items to insert info array
       * @return {number} New length of the array.
       * @public
       */
      unshift(path, ...items) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        let ret = array.unshift(...items);
        if (items.length) {
          notifySplice(this, array, info.path, 0, items.length, []);
        }
        return ret;
      }

      /**
       * Notify that a path has changed.
       *
       * Example:
       *
       *     this.item.user.name = 'Bob';
       *     this.notifyPath('item.user.name');
       *
       * @param {string} path Path that should be notified.
       * @param {*=} value Value at the path (optional).
       * @return {void}
       * @public
      */
      notifyPath(path, value) {
        /** @type {string} */
        let propPath;
        if (arguments.length == 1) {
          // Get value if not supplied
          let info = {path: ''};
          value = Polymer.Path.get(this, path, info);
          propPath = info.path;
        } else if (Array.isArray(path)) {
          // Normalize path if needed
          propPath = Polymer.Path.normalize(path);
        } else {
          propPath = /** @type{string} */(path);
        }
        if (this._setPendingPropertyOrPath(propPath, value, true, true)) {
          this._invalidateProperties();
        }
      }

      /**
       * Equivalent to static `createReadOnlyProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @param {boolean=} protectedSetter Creates a custom protected setter
       *   when `true`.
       * @return {void}
       * @protected
       */
      _createReadOnlyProperty(property, protectedSetter) {
        this._addPropertyEffect(property, TYPES.READ_ONLY);
        if (protectedSetter) {
          this['_set' + upper(property)] = /** @this {PropertyEffects} */function(value) {
            this._setProperty(property, value);
          };
        }
      }

      /**
       * Equivalent to static `createPropertyObserver` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @param {string|function(*,*)} method Function or name of observer method to call
       * @param {boolean=} dynamicFn Whether the method name should be included as
       *   a dependency to the effect.
       * @return {void}
       * @protected
       */
      _createPropertyObserver(property, method, dynamicFn) {
        let info = { property, method, dynamicFn: Boolean(dynamicFn) };
        this._addPropertyEffect(property, TYPES.OBSERVE, {
          fn: runObserverEffect, info, trigger: {name: property}
        });
        if (dynamicFn) {
          this._addPropertyEffect(/** @type {string} */(method), TYPES.OBSERVE, {
            fn: runObserverEffect, info, trigger: {name: method}
          });
        }
      }

      /**
       * Equivalent to static `createMethodObserver` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating
       *   whether method names should be included as a dependency to the effect.
       * @return {void}
       * @protected
       */
      _createMethodObserver(expression, dynamicFn) {
        let sig = parseMethod(expression);
        if (!sig) {
          throw new Error("Malformed observer expression '" + expression + "'");
        }
        createMethodEffect(this, sig, TYPES.OBSERVE, runMethodEffect, null, dynamicFn);
      }

      /**
       * Equivalent to static `createNotifyingProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      _createNotifyingProperty(property) {
        this._addPropertyEffect(property, TYPES.NOTIFY, {
          fn: runNotifyEffect,
          info: {
            eventName: CaseMap.camelToDashCase(property) + '-changed',
            property: property
          }
        });
      }

      /**
       * Equivalent to static `createReflectedProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      _createReflectedProperty(property) {
        let attr = this.constructor.attributeNameForProperty(property);
        if (attr[0] === '-') {
          console.warn('Property ' + property + ' cannot be reflected to attribute ' +
            attr + ' because "-" is not a valid starting attribute name. Use a lowercase first letter for the property instead.');
        } else {
          this._addPropertyEffect(property, TYPES.REFLECT, {
            fn: runReflectEffect,
            info: {
              attrName: attr
            }
          });
        }
      }

      /**
       * Equivalent to static `createComputedProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Name of computed property to set
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating
       *   whether method names should be included as a dependency to the effect.
       * @return {void}
       * @protected
       */
      _createComputedProperty(property, expression, dynamicFn) {
        let sig = parseMethod(expression);
        if (!sig) {
          throw new Error("Malformed computed expression '" + expression + "'");
        }
        createMethodEffect(this, sig, TYPES.COMPUTE, runComputedEffect, property, dynamicFn);
      }

      /**
       * Gather the argument values for a method specified in the provided array
       * of argument metadata.
       *
       * The `path` and `value` arguments are used to fill in wildcard descriptor
       * when the method is being called as a result of a path notification.
       *
       * @param {!Array<!MethodArg>} args Array of argument metadata
       * @param {string} path Property/path name that triggered the method effect
       * @param {Object} props Bag of current property changes
       * @return {Array<*>} Array of argument values
       * @private
       */
      _marshalArgs(args, path, props) {
        const data = this.__data;
        let values = [];
        for (let i=0, l=args.length; i<l; i++) {
          let arg = args[i];
          let name = arg.name;
          let v;
          if (arg.literal) {
            v = arg.value;
          } else {
            if (arg.structured) {
              v = Polymer.Path.get(data, name);
              // when data is not stored e.g. `splices`
              if (v === undefined) {
                v = props[name];
              }
            } else {
              v = data[name];
            }
          }
          if (arg.wildcard) {
            // Only send the actual path changed info if the change that
            // caused the observer to run matched the wildcard
            let baseChanged = (name.indexOf(path + '.') === 0);
            let matches = (path.indexOf(name) === 0 && !baseChanged);
            values[i] = {
              path: matches ? path : name,
              value: matches ? props[path] : v,
              base: v
            };
          } else {
            values[i] = v;
          }
        }
        return values;
      }

      // -- static class methods ------------

      /**
       * Ensures an accessor exists for the specified property, and adds
       * to a list of "property effects" that will run when the accessor for
       * the specified property is set.  Effects are grouped by "type", which
       * roughly corresponds to a phase in effect processing.  The effect
       * metadata should be in the following form:
       *
       *     {
       *       fn: effectFunction, // Reference to function to call to perform effect
       *       info: { ... }       // Effect metadata passed to function
       *       trigger: {          // Optional triggering metadata; if not provided
       *         name: string      // the property is treated as a wildcard
       *         structured: boolean
       *         wildcard: boolean
       *       }
       *     }
       *
       * Effects are called from `_propertiesChanged` in the following order by
       * type:
       *
       * 1. COMPUTE
       * 2. PROPAGATE
       * 3. REFLECT
       * 4. OBSERVE
       * 5. NOTIFY
       *
       * Effect functions are called with the following signature:
       *
       *     effectFunction(inst, path, props, oldProps, info, hasPaths)
       *
       * @param {string} property Property that should trigger the effect
       * @param {string} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @param {Object=} effect Effect metadata object
       * @return {void}
       * @protected
       */
      static addPropertyEffect(property, type, effect) {
        this.prototype._addPropertyEffect(property, type, effect);
      }

      /**
       * Creates a single-property observer for the given property.
       *
       * @param {string} property Property name
       * @param {string|function(*,*)} method Function or name of observer method to call
       * @param {boolean=} dynamicFn Whether the method name should be included as
       *   a dependency to the effect.
       * @return {void}
       * @protected
       */
      static createPropertyObserver(property, method, dynamicFn) {
        this.prototype._createPropertyObserver(property, method, dynamicFn);
      }

      /**
       * Creates a multi-property "method observer" based on the provided
       * expression, which should be a string in the form of a normal JavaScript
       * function signature: `'methodName(arg1, [..., argn])'`.  Each argument
       * should correspond to a property or path in the context of this
       * prototype (or instance), or may be a literal string or number.
       *
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating
       * @return {void}
       *   whether method names should be included as a dependency to the effect.
       * @protected
       */
      static createMethodObserver(expression, dynamicFn) {
        this.prototype._createMethodObserver(expression, dynamicFn);
      }

      /**
       * Causes the setter for the given property to dispatch `<property>-changed`
       * events to notify of changes to the property.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      static createNotifyingProperty(property) {
        this.prototype._createNotifyingProperty(property);
      }

      /**
       * Creates a read-only accessor for the given property.
       *
       * To set the property, use the protected `_setProperty` API.
       * To create a custom protected setter (e.g. `_setMyProp()` for
       * property `myProp`), pass `true` for `protectedSetter`.
       *
       * Note, if the property will have other property effects, this method
       * should be called first, before adding other effects.
       *
       * @param {string} property Property name
       * @param {boolean=} protectedSetter Creates a custom protected setter
       *   when `true`.
       * @return {void}
       * @protected
       */
      static createReadOnlyProperty(property, protectedSetter) {
        this.prototype._createReadOnlyProperty(property, protectedSetter);
      }

      /**
       * Causes the setter for the given property to reflect the property value
       * to a (dash-cased) attribute of the same name.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      static createReflectedProperty(property) {
        this.prototype._createReflectedProperty(property);
      }

      /**
       * Creates a computed property whose value is set to the result of the
       * method described by the given `expression` each time one or more
       * arguments to the method changes.  The expression should be a string
       * in the form of a normal JavaScript function signature:
       * `'methodName(arg1, [..., argn])'`
       *
       * @param {string} property Name of computed property to set
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating whether
       *   method names should be included as a dependency to the effect.
       * @return {void}
       * @protected
       */
      static createComputedProperty(property, expression, dynamicFn) {
        this.prototype._createComputedProperty(property, expression, dynamicFn);
      }

      /**
       * Parses the provided template to ensure binding effects are created
       * for them, and then ensures property accessors are created for any
       * dependent properties in the template.  Binding effects for bound
       * templates are stored in a linked list on the instance so that
       * templates can be efficiently stamped and unstamped.
       *
       * @param {!HTMLTemplateElement} template Template containing binding
       *   bindings
       * @return {!TemplateInfo} Template metadata object
       * @protected
       */
      static bindTemplate(template) {
        return this.prototype._bindTemplate(template);
      }

      // -- binding ----------------------------------------------

      /**
       * Equivalent to static `bindTemplate` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * This method may be called on the prototype (for prototypical template
       * binding, to avoid creating accessors every instance) once per prototype,
       * and will be called with `runtimeBinding: true` by `_stampTemplate` to
       * create and link an instance of the template metadata associated with a
       * particular stamping.
       *
       * @param {!HTMLTemplateElement} template Template containing binding
       *   bindings
       * @param {boolean=} instanceBinding When false (default), performs
       *   "prototypical" binding of the template and overwrites any previously
       *   bound template for the class. When true (as passed from
       *   `_stampTemplate`), the template info is instanced and linked into
       *   the list of bound templates.
       * @return {!TemplateInfo} Template metadata object; for `runtimeBinding`,
       *   this is an instance of the prototypical template info
       * @protected
       */
      _bindTemplate(template, instanceBinding) {
        let templateInfo = this.constructor._parseTemplate(template);
        let wasPreBound = this.__templateInfo == templateInfo;
        // Optimization: since this is called twice for proto-bound templates,
        // don't attempt to recreate accessors if this template was pre-bound
        if (!wasPreBound) {
          for (let prop in templateInfo.propertyEffects) {
            this._createPropertyAccessor(prop);
          }
        }
        if (instanceBinding) {
          // For instance-time binding, create instance of template metadata
          // and link into list of templates if necessary
          templateInfo = /** @type {!TemplateInfo} */(Object.create(templateInfo));
          templateInfo.wasPreBound = wasPreBound;
          if (!wasPreBound && this.__templateInfo) {
            let last = this.__templateInfoLast || this.__templateInfo;
            this.__templateInfoLast = last.nextTemplateInfo = templateInfo;
            templateInfo.previousTemplateInfo = last;
            return templateInfo;
          }
        }
        return this.__templateInfo = templateInfo;
      }

      /**
       * Adds a property effect to the given template metadata, which is run
       * at the "propagate" stage of `_propertiesChanged` when the template
       * has been bound to the element via `_bindTemplate`.
       *
       * The `effect` object should match the format in `_addPropertyEffect`.
       *
       * @param {Object} templateInfo Template metadata to add effect to
       * @param {string} prop Property that should trigger the effect
       * @param {Object=} effect Effect metadata object
       * @return {void}
       * @protected
       */
      static _addTemplatePropertyEffect(templateInfo, prop, effect) {
        let hostProps = templateInfo.hostProps = templateInfo.hostProps || {};
        hostProps[prop] = true;
        let effects = templateInfo.propertyEffects = templateInfo.propertyEffects || {};
        let propEffects = effects[prop] = effects[prop] || [];
        propEffects.push(effect);
      }

      /**
       * Stamps the provided template and performs instance-time setup for
       * Polymer template features, including data bindings, declarative event
       * listeners, and the `this.$` map of `id`'s to nodes.  A document fragment
       * is returned containing the stamped DOM, ready for insertion into the
       * DOM.
       *
       * This method may be called more than once; however note that due to
       * `shadycss` polyfill limitations, only styles from templates prepared
       * using `ShadyCSS.prepareTemplate` will be correctly polyfilled (scoped
       * to the shadow root and support CSS custom properties), and note that
       * `ShadyCSS.prepareTemplate` may only be called once per element. As such,
       * any styles required by in runtime-stamped templates must be included
       * in the main element template.
       *
       * @param {!HTMLTemplateElement} template Template to stamp
       * @return {!StampedTemplate} Cloned template content
       * @override
       * @protected
       */
      _stampTemplate(template) {
        // Ensures that created dom is `_enqueueClient`'d to this element so
        // that it can be flushed on next call to `_flushProperties`
        hostStack.beginHosting(this);
        let dom = super._stampTemplate(template);
        hostStack.endHosting(this);
        let templateInfo = /** @type {!TemplateInfo} */(this._bindTemplate(template, true));
        // Add template-instance-specific data to instanced templateInfo
        templateInfo.nodeList = dom.nodeList;
        // Capture child nodes to allow unstamping of non-prototypical templates
        if (!templateInfo.wasPreBound) {
          let nodes = templateInfo.childNodes = [];
          for (let n=dom.firstChild; n; n=n.nextSibling) {
            nodes.push(n);
          }
        }
        dom.templateInfo = templateInfo;
        // Setup compound storage, 2-way listeners, and dataHost for bindings
        setupBindings(this, templateInfo);
        // Flush properties into template nodes if already booted
        if (this.__dataReady) {
          runEffects(this, templateInfo.propertyEffects, this.__data, null,
            false, templateInfo.nodeList);
        }
        return dom;
      }

      /**
       * Removes and unbinds the nodes previously contained in the provided
       * DocumentFragment returned from `_stampTemplate`.
       *
       * @param {!StampedTemplate} dom DocumentFragment previously returned
       *   from `_stampTemplate` associated with the nodes to be removed
       * @return {void}
       * @protected
       */
      _removeBoundDom(dom) {
        // Unlink template info
        let templateInfo = dom.templateInfo;
        if (templateInfo.previousTemplateInfo) {
          templateInfo.previousTemplateInfo.nextTemplateInfo =
            templateInfo.nextTemplateInfo;
        }
        if (templateInfo.nextTemplateInfo) {
          templateInfo.nextTemplateInfo.previousTemplateInfo =
            templateInfo.previousTemplateInfo;
        }
        if (this.__templateInfoLast == templateInfo) {
          this.__templateInfoLast = templateInfo.previousTemplateInfo;
        }
        templateInfo.previousTemplateInfo = templateInfo.nextTemplateInfo = null;
        // Remove stamped nodes
        let nodes = templateInfo.childNodes;
        for (let i=0; i<nodes.length; i++) {
          let node = nodes[i];
          node.parentNode.removeChild(node);
        }
      }

      /**
       * Overrides default `TemplateStamp` implementation to add support for
       * parsing bindings from `TextNode`'s' `textContent`.  A `bindings`
       * array is added to `nodeInfo` and populated with binding metadata
       * with information capturing the binding target, and a `parts` array
       * with one or more metadata objects capturing the source(s) of the
       * binding.
       *
       * @override
       * @param {Node} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template node
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       * @protected
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateNode(node, templateInfo, nodeInfo) {
        let noted = super._parseTemplateNode(node, templateInfo, nodeInfo);
        if (node.nodeType === Node.TEXT_NODE) {
          let parts = this._parseBindings(node.textContent, templateInfo);
          if (parts) {
            // Initialize the textContent with any literal parts
            // NOTE: default to a space here so the textNode remains; some browsers
            // (IE) omit an empty textNode following cloneNode/importNode.
            node.textContent = literalFromParts(parts) || ' ';
            addBinding(this, templateInfo, nodeInfo, 'text', 'textContent', parts);
            noted = true;
          }
        }
        return noted;
      }

      /**
       * Overrides default `TemplateStamp` implementation to add support for
       * parsing bindings from attributes.  A `bindings`
       * array is added to `nodeInfo` and populated with binding metadata
       * with information capturing the binding target, and a `parts` array
       * with one or more metadata objects capturing the source(s) of the
       * binding.
       *
       * @override
       * @param {Element} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template node
       * @param {string} name Attribute name
       * @param {string} value Attribute value
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       * @protected
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateNodeAttribute(node, templateInfo, nodeInfo, name, value) {
        let parts = this._parseBindings(value, templateInfo);
        if (parts) {
          // Attribute or property
          let origName = name;
          let kind = 'property';
          // The only way we see a capital letter here is if the attr has
          // a capital letter in it per spec. In this case, to make sure
          // this binding works, we go ahead and make the binding to the attribute.
          if (capitalAttributeRegex.test(name)) {
            kind = 'attribute';
          } else if (name[name.length-1] == '$') {
            name = name.slice(0, -1);
            kind = 'attribute';
          }
          // Initialize attribute bindings with any literal parts
          let literal = literalFromParts(parts);
          if (literal && kind == 'attribute') {
            // Ensure a ShadyCSS template scoped style is not removed
            // when a class$ binding's initial literal value is set.
            if (name == 'class' && node.hasAttribute('class')) {
              literal += ' ' + node.getAttribute(name);
            }
            node.setAttribute(name, literal);
          }
          // Clear attribute before removing, since IE won't allow removing
          // `value` attribute if it previously had a value (can't
          // unconditionally set '' before removing since attributes with `$`
          // can't be set using setAttribute)
          if (node.localName === 'input' && origName === 'value') {
            node.setAttribute(origName, '');
          }
          // Remove annotation
          node.removeAttribute(origName);
          // Case hackery: attributes are lower-case, but bind targets
          // (properties) are case sensitive. Gambit is to map dash-case to
          // camel-case: `foo-bar` becomes `fooBar`.
          // Attribute bindings are excepted.
          if (kind === 'property') {
            name = Polymer.CaseMap.dashToCamelCase(name);
          }
          addBinding(this, templateInfo, nodeInfo, kind, name, parts, literal);
          return true;
        } else {
          return super._parseTemplateNodeAttribute(node, templateInfo, nodeInfo, name, value);
        }
      }

      /**
       * Overrides default `TemplateStamp` implementation to add support for
       * binding the properties that a nested template depends on to the template
       * as `_host_<property>`.
       *
       * @override
       * @param {Node} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template node
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       * @protected
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateNestedTemplate(node, templateInfo, nodeInfo) {
        let noted = super._parseTemplateNestedTemplate(node, templateInfo, nodeInfo);
        // Merge host props into outer template and add bindings
        let hostProps = nodeInfo.templateInfo.hostProps;
        let mode = '{';
        for (let source in hostProps) {
          let parts = [{ mode, source, dependencies: [source] }];
          addBinding(this, templateInfo, nodeInfo, 'property', '_host_' + source, parts);
        }
        return noted;
      }

      /**
       * Called to parse text in a template (either attribute values or
       * textContent) into binding metadata.
       *
       * Any overrides of this method should return an array of binding part
       * metadata  representing one or more bindings found in the provided text
       * and any "literal" text in between.  Any non-literal parts will be passed
       * to `_evaluateBinding` when any dependencies change.  The only required
       * fields of each "part" in the returned array are as follows:
       *
       * - `dependencies` - Array containing trigger metadata for each property
       *   that should trigger the binding to update
       * - `literal` - String containing text if the part represents a literal;
       *   in this case no `dependencies` are needed
       *
       * Additional metadata for use by `_evaluateBinding` may be provided in
       * each part object as needed.
       *
       * The default implementation handles the following types of bindings
       * (one or more may be intermixed with literal strings):
       * - Property binding: `[[prop]]`
       * - Path binding: `[[object.prop]]`
       * - Negated property or path bindings: `[[!prop]]` or `[[!object.prop]]`
       * - Two-way property or path bindings (supports negation):
       *   `{{prop}}`, `{{object.prop}}`, `{{!prop}}` or `{{!object.prop}}`
       * - Inline computed method (supports negation):
       *   `[[compute(a, 'literal', b)]]`, `[[!compute(a, 'literal', b)]]`
       *
       * The default implementation uses a regular expression for best
       * performance. However, the regular expression uses a white-list of
       * allowed characters in a data-binding, which causes problems for
       * data-bindings that do use characters not in this white-list.
       *
       * Instead of updating the white-list with all allowed characters,
       * there is a StrictBindingParser (see lib/mixins/strict-binding-parser)
       * that uses a state machine instead. This state machine is able to handle
       * all characters. However, it is slightly less performant, therefore we
       * extracted it into a separate optional mixin.
       *
       * @param {string} text Text to parse from attribute or textContent
       * @param {Object} templateInfo Current template metadata
       * @return {Array<!BindingPart>} Array of binding part metadata
       * @protected
       */
      static _parseBindings(text, templateInfo) {
        let parts = [];
        let lastIndex = 0;
        let m;
        // Example: "literal1{{prop}}literal2[[!compute(foo,bar)]]final"
        // Regex matches:
        //        Iteration 1:  Iteration 2:
        // m[1]: '{{'          '[['
        // m[2]: ''            '!'
        // m[3]: 'prop'        'compute(foo,bar)'
        while ((m = bindingRegex.exec(text)) !== null) {
          // Add literal part
          if (m.index > lastIndex) {
            parts.push({literal: text.slice(lastIndex, m.index)});
          }
          // Add binding part
          let mode = m[1][0];
          let negate = Boolean(m[2]);
          let source = m[3].trim();
          let customEvent = false, notifyEvent = '', colon = -1;
          if (mode == '{' && (colon = source.indexOf('::')) > 0) {
            notifyEvent = source.substring(colon + 2);
            source = source.substring(0, colon);
            customEvent = true;
          }
          let signature = parseMethod(source);
          let dependencies = [];
          if (signature) {
            // Inline computed function
            let {args, methodName} = signature;
            for (let i=0; i<args.length; i++) {
              let arg = args[i];
              if (!arg.literal) {
                dependencies.push(arg);
              }
            }
            let dynamicFns = templateInfo.dynamicFns;
            if (dynamicFns && dynamicFns[methodName] || signature.static) {
              dependencies.push(methodName);
              signature.dynamicFn = true;
            }
          } else {
            // Property or path
            dependencies.push(source);
          }
          parts.push({
            source, mode, negate, customEvent, signature, dependencies,
            event: notifyEvent
          });
          lastIndex = bindingRegex.lastIndex;
        }
        // Add a final literal part
        if (lastIndex && lastIndex < text.length) {
          let literal = text.substring(lastIndex);
          if (literal) {
            parts.push({
              literal: literal
            });
          }
        }
        if (parts.length) {
          return parts;
        } else {
          return null;
        }
      }

      /**
       * Called to evaluate a previously parsed binding part based on a set of
       * one or more changed dependencies.
       *
       * @param {this} inst Element that should be used as scope for
       *   binding dependencies
       * @param {BindingPart} part Binding part metadata
       * @param {string} path Property/path that triggered this effect
       * @param {Object} props Bag of current property changes
       * @param {Object} oldProps Bag of previous values for changed properties
       * @param {boolean} hasPaths True with `props` contains one or more paths
       * @return {*} Value the binding part evaluated to
       * @protected
       */
      static _evaluateBinding(inst, part, path, props, oldProps, hasPaths) {
        let value;
        if (part.signature) {
          value = runMethodEffect(inst, path, props, oldProps, part.signature);
        } else if (path != part.source) {
          value = Polymer.Path.get(inst, part.source);
        } else {
          if (hasPaths && Polymer.Path.isPath(path)) {
            value = Polymer.Path.get(inst, path);
          } else {
            value = inst.__data[path];
          }
        }
        if (part.negate) {
          value = !value;
        }
        return value;
      }

    }

    // make a typing for closure :P
    PropertyEffectsType = PropertyEffects;

    return PropertyEffects;
  });

  /**
   * Helper api for enqueuing client dom created by a host element.
   *
   * By default elements are flushed via `_flushProperties` when
   * `connectedCallback` is called. Elements attach their client dom to
   * themselves at `ready` time which results from this first flush.
   * This provides an ordering guarantee that the client dom an element
   * creates is flushed before the element itself (i.e. client `ready`
   * fires before host `ready`).
   *
   * However, if `_flushProperties` is called *before* an element is connected,
   * as for example `Templatize` does, this ordering guarantee cannot be
   * satisfied because no elements are connected. (Note: Bound elements that
   * receive data do become enqueued clients and are properly ordered but
   * unbound elements are not.)
   *
   * To maintain the desired "client before host" ordering guarantee for this
   * case we rely on the "host stack. Client nodes registers themselves with
   * the creating host element when created. This ensures that all client dom
   * is readied in the proper order, maintaining the desired guarantee.
   *
   * @private
   */
  let hostStack = {

    stack: [],

    /**
     * @param {*} inst Instance to add to hostStack
     * @return {void}
     * @this {hostStack}
     */
    registerHost(inst) {
      if (this.stack.length) {
        let host = this.stack[this.stack.length-1];
        host._enqueueClient(inst);
      }
    },

    /**
     * @param {*} inst Instance to begin hosting
     * @return {void}
     * @this {hostStack}
     */
    beginHosting(inst) {
      this.stack.push(inst);
    },

    /**
     * @param {*} inst Instance to end hosting
     * @return {void}
     * @this {hostStack}
     */
    endHosting(inst) {
      let stackLen = this.stack.length;
      if (stackLen && this.stack[stackLen-1] == inst) {
        this.stack.pop();
      }
    }

  };

})();


(function() {
  'use strict';

  /**
   * Provides basic tracking of element definitions (registrations) and
   * instance counts.
   *
   * @namespace
   * @summary Provides basic tracking of element definitions (registrations) and
   * instance counts.
   */
  Polymer.telemetry = {
    /**
     * Total number of Polymer element instances created.
     * @type {number}
     */
    instanceCount: 0,
    /**
     * Array of Polymer element classes that have been finalized.
     * @type {Array<Polymer.Element>}
     */
    registrations: [],
    /**
     * @param {!PolymerElementConstructor} prototype Element prototype to log
     * @this {this}
     * @private
     */
    _regLog: function(prototype) {
      console.log('[' + prototype.is + ']: registered');
    },
    /**
     * Registers a class prototype for telemetry purposes.
     * @param {HTMLElement} prototype Element prototype to register
     * @this {this}
     * @protected
     */
    register: function(prototype) {
      this.registrations.push(prototype);
      Polymer.log && this._regLog(prototype);
    },
    /**
     * Logs all elements registered with an `is` to the console.
     * @public
     * @this {this}
     */
    dumpRegistrations: function() {
      this.registrations.forEach(this._regLog);
    }
  };

})();


(function() {
  'use strict';

  /**
   * Creates a copy of `props` with each property normalized such that
   * upgraded it is an object with at least a type property { type: Type}.
   *
   * @param {Object} props Properties to normalize
   * @return {Object} Copy of input `props` with normalized properties that
   * are in the form {type: Type}
   * @private
   */
  function normalizeProperties(props) {
    const output = {};
    for (let p in props) {
      const o = props[p];
      output[p] = (typeof o === 'function') ? {type: o} : o;
    }
    return output;
  }

  /**
   * Mixin that provides a minimal starting point to using the PropertiesChanged
   * mixin by providing a mechanism to declare properties in a static
   * getter (e.g. static get properties() { return { foo: String } }). Changes
   * are reported via the `_propertiesChanged` method.
   *
   * This mixin provides no specific support for rendering. Users are expected
   * to create a ShadowRoot and put content into it and update it in whatever
   * way makes sense. This can be done in reaction to properties changing by
   * implementing `_propertiesChanged`.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.PropertiesChanged
   * @memberof Polymer
   * @summary Mixin that provides a minimal starting point for using
   * the PropertiesChanged mixin by providing a declarative `properties` object.
   */
   Polymer.PropertiesMixin = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_PropertiesChanged}
     * @private
     */
    const base = Polymer.PropertiesChanged(superClass);

    /**
     * Returns the super class constructor for the given class, if it is an
     * instance of the PropertiesMixin.
     *
     * @param {!PropertiesMixinConstructor} constructor PropertiesMixin constructor
     * @return {PropertiesMixinConstructor} Super class constructor
     */
    function superPropertiesClass(constructor) {
      const superCtor = Object.getPrototypeOf(constructor);

      // Note, the `PropertiesMixin` class below only refers to the class
      // generated by this call to the mixin; the instanceof test only works
      // because the mixin is deduped and guaranteed only to apply once, hence
      // all constructors in a proto chain will see the same `PropertiesMixin`
      return (superCtor.prototype instanceof PropertiesMixin) ?
        /** @type {PropertiesMixinConstructor} */ (superCtor) : null;
    }

    /**
     * Returns a memoized version of the `properties` object for the
     * given class. Properties not in object format are converted to at
     * least {type}.
     *
     * @param {PropertiesMixinConstructor} constructor PropertiesMixin constructor
     * @return {Object} Memoized properties object
     */
    function ownProperties(constructor) {
      if (!constructor.hasOwnProperty(JSCompiler_renameProperty('__ownProperties', constructor))) {
        let props = null;

        if (constructor.hasOwnProperty(JSCompiler_renameProperty('properties', constructor))) {
          const properties = constructor.properties;
          
          if (properties) {
            props = normalizeProperties(properties);
          }
        }

        constructor.__ownProperties = props;
      }
      return constructor.__ownProperties;
    }

    /**
     * @polymer
     * @mixinClass
     * @extends {base}
     * @implements {Polymer_PropertiesMixin}
     * @unrestricted
     */
    class PropertiesMixin extends base {

      /**
       * Implements standard custom elements getter to observes the attributes
       * listed in `properties`.
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static get observedAttributes() {
        if (!this.hasOwnProperty('__observedAttributes')) {
          Polymer.telemetry.register(this.prototype);
          const props = this._properties;
          this.__observedAttributes = props ? Object.keys(props).map(p => this.attributeNameForProperty(p)) : [];
        }
        return this.__observedAttributes;
      }

      /**
       * Finalizes an element definition, including ensuring any super classes
       * are also finalized. This includes ensuring property
       * accessors exist on the element prototype. This method calls
       * `_finalizeClass` to finalize each constructor in the prototype chain.
       * @return {void}
       */
      static finalize() {
        if (!this.hasOwnProperty(JSCompiler_renameProperty('__finalized', this))) {
          const superCtor = superPropertiesClass(/** @type {PropertiesMixinConstructor} */(this));
          if (superCtor) {
            superCtor.finalize();
          }
          this.__finalized = true;
          this._finalizeClass();
        }
      }

      /**
       * Finalize an element class. This includes ensuring property
       * accessors exist on the element prototype. This method is called by
       * `finalize` and finalizes the class constructor.
       *
       * @protected
       */
      static _finalizeClass() {
        const props = ownProperties(/** @type {PropertiesMixinConstructor} */(this));
        if (props) {
          this.createProperties(props);
        }
      }

      /**
       * Returns a memoized version of all properties, including those inherited
       * from super classes. Properties not in object format are converted to
       * at least {type}.
       *
       * @return {Object} Object containing properties for this class
       * @protected
       */
      static get _properties() {
        if (!this.hasOwnProperty(
          JSCompiler_renameProperty('__properties', this))) {
          const superCtor = superPropertiesClass(/** @type {PropertiesMixinConstructor} */(this));
          this.__properties = Object.assign({},
            superCtor && superCtor._properties,
            ownProperties(/** @type {PropertiesMixinConstructor} */(this)));
        }
        return this.__properties;
      }

      /**
       * Overrides `PropertiesChanged` method to return type specified in the
       * static `properties` object for the given property.
       * @param {string} name Name of property
       * @return {*} Type to which to deserialize attribute
       *
       * @protected
       */
      static typeForProperty(name) {
        const info = this._properties[name];
        return info && info.type;
      }

      /**
       * Overrides `PropertiesChanged` method and adds a call to
       * `finalize` which lazily configures the element's property accessors.
       * @override
       * @return {void}
       */
      _initializeProperties() {
        Polymer.telemetry.instanceCount++;
        this.constructor.finalize();
        super._initializeProperties();
      }

      /**
       * Called when the element is added to a document.
       * Calls `_enableProperties` to turn on property system from
       * `PropertiesChanged`.
       * @suppress {missingProperties} Super may or may not implement the callback
       * @return {void}
       */
      connectedCallback() {
        if (super.connectedCallback) {
          super.connectedCallback();
        }
        this._enableProperties();
      }

      /**
       * Called when the element is removed from a document
       * @suppress {missingProperties} Super may or may not implement the callback
       * @return {void}
       */
      disconnectedCallback() {
        if (super.disconnectedCallback) {
          super.disconnectedCallback();
        }
      }

    }

    return PropertiesMixin;

  });

})();



(function() {
  'use strict';

  const builtCSS = window.ShadyCSS && window.ShadyCSS['cssBuild'];

  /**
   * Element class mixin that provides the core API for Polymer's meta-programming
   * features including template stamping, data-binding, attribute deserialization,
   * and property change observation.
   *
   * Subclassers may provide the following static getters to return metadata
   * used to configure Polymer's features for the class:
   *
   * - `static get is()`: When the template is provided via a `dom-module`,
   *   users should return the `dom-module` id from a static `is` getter.  If
   *   no template is needed or the template is provided directly via the
   *   `template` getter, there is no need to define `is` for the element.
   *
   * - `static get template()`: Users may provide the template directly (as
   *   opposed to via `dom-module`) by implementing a static `template` getter.
   *   The getter may return an `HTMLTemplateElement` or a string, which will
   *   automatically be parsed into a template.
   *
   * - `static get properties()`: Should return an object describing
   *   property-related metadata used by Polymer features (key: property name
   *   value: object containing property metadata). Valid keys in per-property
   *   metadata include:
   *   - `type` (String|Number|Object|Array|...): Used by
   *     `attributeChangedCallback` to determine how string-based attributes
   *     are deserialized to JavaScript property values.
   *   - `notify` (boolean): Causes a change in the property to fire a
   *     non-bubbling event called `<property>-changed`. Elements that have
   *     enabled two-way binding to the property use this event to observe changes.
   *   - `readOnly` (boolean): Creates a getter for the property, but no setter.
   *     To set a read-only property, use the private setter method
   *     `_setProperty(property, value)`.
   *   - `observer` (string): Observer method name that will be called when
   *     the property changes. The arguments of the method are
   *     `(value, previousValue)`.
   *   - `computed` (string): String describing method and dependent properties
   *     for computing the value of this property (e.g. `'computeFoo(bar, zot)'`).
   *     Computed properties are read-only by default and can only be changed
   *     via the return value of the computing method.
   *
   * - `static get observers()`: Array of strings describing multi-property
   *   observer methods and their dependent properties (e.g.
   *   `'observeABC(a, b, c)'`).
   *
   * The base class provides default implementations for the following standard
   * custom element lifecycle callbacks; users may override these, but should
   * call the super method to ensure
   * - `constructor`: Run when the element is created or upgraded
   * - `connectedCallback`: Run each time the element is connected to the
   *   document
   * - `disconnectedCallback`: Run each time the element is disconnected from
   *   the document
   * - `attributeChangedCallback`: Run each time an attribute in
   *   `observedAttributes` is set or removed (note: this element's default
   *   `observedAttributes` implementation will automatically return an array
   *   of dash-cased attributes based on `properties`)
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.PropertyEffects
   * @appliesMixin Polymer.PropertiesMixin
   * @memberof Polymer
   * @property rootPath {string} Set to the value of `Polymer.rootPath`,
   *   which defaults to the main document path
   * @property importPath {string} Set to the value of the class's static
   *   `importPath` property, which defaults to the path of this element's
   *   `dom-module` (when `is` is used), but can be overridden for other
   *   import strategies.
   * @summary Element class mixin that provides the core API for Polymer's
   * meta-programming features.
   */
  Polymer.ElementMixin = Polymer.dedupingMixin(base => {

    /**
     * @constructor
     * @extends {base}
     * @implements {Polymer_PropertyEffects}
     * @implements {Polymer_PropertiesMixin}
     * @private
     */
    const polymerElementBase = Polymer.PropertiesMixin(Polymer.PropertyEffects(base));

    /**
     * Returns a list of properties with default values.
     * This list is created as an optimization since it is a subset of
     * the list returned from `_properties`.
     * This list is used in `_initializeProperties` to set property defaults.
     *
     * @param {PolymerElementConstructor} constructor Element class
     * @return {PolymerElementProperties} Flattened properties for this class
     *   that have default values
     * @private
     */
    function propertyDefaults(constructor) {
      if (!constructor.hasOwnProperty(
        JSCompiler_renameProperty('__propertyDefaults', constructor))) {
        constructor.__propertyDefaults = null;
        let props = constructor._properties;
        for (let p in props) {
          let info = props[p];
          if ('value' in info) {
            constructor.__propertyDefaults = constructor.__propertyDefaults || {};
            constructor.__propertyDefaults[p] = info;
          }
        }
      }
      return constructor.__propertyDefaults;
    }

    /**
     * Returns a memoized version of the `observers` array.
     * @param {PolymerElementConstructor} constructor Element class
     * @return {Array} Array containing own observers for the given class
     * @protected
     */
    function ownObservers(constructor) {
      if (!constructor.hasOwnProperty(
        JSCompiler_renameProperty('__ownObservers', constructor))) {
          constructor.__ownObservers =
          constructor.hasOwnProperty(JSCompiler_renameProperty('observers', constructor)) ?
          /** @type {PolymerElementConstructor} */ (constructor).observers : null;
      }
      return constructor.__ownObservers;
    }

    /**
     * Creates effects for a property.
     *
     * Note, once a property has been set to
     * `readOnly`, `computed`, `reflectToAttribute`, or `notify`
     * these values may not be changed. For example, a subclass cannot
     * alter these settings. However, additional `observers` may be added
     * by subclasses.
     *
     * The info object should contain property metadata as follows:
     *
     * * `type`: {function} type to which an attribute matching the property
     * is deserialized. Note the property is camel-cased from a dash-cased
     * attribute. For example, 'foo-bar' attribute is deserialized to a
     * property named 'fooBar'.
     *
     * * `readOnly`: {boolean} creates a readOnly property and
     * makes a private setter for the private of the form '_setFoo' for a
     * property 'foo',
     *
     * * `computed`: {string} creates a computed property. A computed property
     * is also automatically set to `readOnly: true`. The value is calculated
     * by running a method and arguments parsed from the given string. For
     * example 'compute(foo)' will compute a given property when the
     * 'foo' property changes by executing the 'compute' method. This method
     * must return the computed value.
     *
     * * `reflectToAttribute`: {boolean} If true, the property value is reflected
     * to an attribute of the same name. Note, the attribute is dash-cased
     * so a property named 'fooBar' is reflected as 'foo-bar'.
     *
     * * `notify`: {boolean} sends a non-bubbling notification event when
     * the property changes. For example, a property named 'foo' sends an
     * event named 'foo-changed' with `event.detail` set to the value of
     * the property.
     *
     * * observer: {string} name of a method that runs when the property
     * changes. The arguments of the method are (value, previousValue).
     *
     * Note: Users may want control over modifying property
     * effects via subclassing. For example, a user might want to make a
     * reflectToAttribute property not do so in a subclass. We've chosen to
     * disable this because it leads to additional complication.
     * For example, a readOnly effect generates a special setter. If a subclass
     * disables the effect, the setter would fail unexpectedly.
     * Based on feedback, we may want to try to make effects more malleable
     * and/or provide an advanced api for manipulating them.
     * Also consider adding warnings when an effect cannot be changed.
     *
     * @param {!PolymerElement} proto Element class prototype to add accessors
     *   and effects to
     * @param {string} name Name of the property.
     * @param {Object} info Info object from which to create property effects.
     * Supported keys:
     * @param {Object} allProps Flattened map of all properties defined in this
     *   element (including inherited properties)
     * @return {void}
     * @private
     */
    function createPropertyFromConfig(proto, name, info, allProps) {
      // computed forces readOnly...
      if (info.computed) {
        info.readOnly = true;
      }
      // Note, since all computed properties are readOnly, this prevents
      // adding additional computed property effects (which leads to a confusing
      // setup where multiple triggers for setting a property)
      // While we do have `hasComputedEffect` this is set on the property's
      // dependencies rather than itself.
      if (info.computed && !proto._hasReadOnlyEffect(name)) {
        proto._createComputedProperty(name, info.computed, allProps);
      }
      if (info.readOnly && !proto._hasReadOnlyEffect(name)) {
        proto._createReadOnlyProperty(name, !info.computed);
      }
      if (info.reflectToAttribute && !proto._hasReflectEffect(name)) {
        proto._createReflectedProperty(name);
      }
      if (info.notify && !proto._hasNotifyEffect(name)) {
        proto._createNotifyingProperty(name);
      }
      // always add observer
      if (info.observer) {
        proto._createPropertyObserver(name, info.observer, allProps[info.observer]);
      }
      // always create the mapping from attribute back to property for deserialization.
      proto._addPropertyToAttributeMap(name);
    }

    /**
     * Process all style elements in the element template. Styles with the
     * `include` attribute are processed such that any styles in
     * the associated "style modules" are included in the element template.
     * @param {PolymerElementConstructor} klass Element class
     * @param {!HTMLTemplateElement} template Template to process
     * @param {string} is Name of element
     * @param {string} baseURI Base URI for element
     * @private
     */
    function processElementStyles(klass, template, is, baseURI) {
      if (!builtCSS) {
        const templateStyles = template.content.querySelectorAll('style');
        const stylesWithImports = Polymer.StyleGather.stylesFromTemplate(template);
        // insert styles from <link rel="import" type="css"> at the top of the template
        const linkedStyles = Polymer.StyleGather.stylesFromModuleImports(is);
        const firstTemplateChild = template.content.firstElementChild;
        for (let idx = 0; idx < linkedStyles.length; idx++) {
          let s = linkedStyles[idx];
          s.textContent = klass._processStyleText(s.textContent, baseURI);
          template.content.insertBefore(s, firstTemplateChild);
        }
        // keep track of the last "concrete" style in the template we have encountered
        let templateStyleIndex = 0;
        // ensure all gathered styles are actually in this template.
        for (let i = 0; i < stylesWithImports.length; i++) {
          let s = stylesWithImports[i];
          let templateStyle = templateStyles[templateStyleIndex];
          // if the style is not in this template, it's been "included" and
          // we put a clone of it in the template before the style that included it
          if (templateStyle !== s) {
            s = s.cloneNode(true);
            templateStyle.parentNode.insertBefore(s, templateStyle);
          } else {
            templateStyleIndex++;
          }
          s.textContent = klass._processStyleText(s.textContent, baseURI);
        }
      }
      if (window.ShadyCSS) {
        window.ShadyCSS.prepareTemplate(template, is);
      }
    }

    /**
     * Look up template from dom-module for element
     *
     * @param {!string} is Element name to look up
     * @return {!HTMLTemplateElement} Template found in dom module, or
     *   undefined if not found
     * @protected
     */
    function getTemplateFromDomModule(is) {
      let template = null;
      if (is && Polymer.DomModule) {
        template = Polymer.DomModule.import(is, 'template');
        // Under strictTemplatePolicy, require any element with an `is`
        // specified to have a dom-module
        if (Polymer.strictTemplatePolicy && !template) {
          throw new Error(`strictTemplatePolicy: expecting dom-module or null template for ${is}`);
        }
      }
      return template;
    }

  /**
     * @polymer
     * @mixinClass
     * @unrestricted
     * @implements {Polymer_ElementMixin}
     */
    class PolymerElement extends polymerElementBase {

      /**
       * Override of PropertiesMixin _finalizeClass to create observers and
       * find the template.
       * @return {void}
       * @protected
       * @override
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _finalizeClass() {
        super._finalizeClass();
        const observers = ownObservers(this);
        if (observers) {
          this.createObservers(observers, this._properties);
        }
        this._prepareTemplate();
      }

      static _prepareTemplate() {
        // note: create "working" template that is finalized at instance time
        let template = /** @type {PolymerElementConstructor} */ (this).template;
        if (template) {
          if (typeof template === 'string') {
            let t = document.createElement('template');
            t.innerHTML = template;
            template = t;
          } else if (!Polymer.legacyOptimizations) {
             template = template.cloneNode(true);
          }
        }

        this.prototype._template = template;
      }

      /**
       * Override of PropertiesChanged createProperties to create accessors
       * and property effects for all of the properties.
       * @return {void}
       * @protected
       * @override
       */
      static createProperties(props) {
        for (let p in props) {
          createPropertyFromConfig(this.prototype, p, props[p], props);
        }
      }

      /**
       * Creates observers for the given `observers` array.
       * Leverages `PropertyEffects` to create observers.
       * @param {Object} observers Array of observer descriptors for
       *   this class
       * @param {Object} dynamicFns Object containing keys for any properties
       *   that are functions and should trigger the effect when the function
       *   reference is changed
       * @return {void}
       * @protected
       */
      static createObservers(observers, dynamicFns) {
        const proto = this.prototype;
        for (let i=0; i < observers.length; i++) {
          proto._createMethodObserver(observers[i], dynamicFns);
        }
      }

      /**
       * Returns the template that will be stamped into this element's shadow root.
       *
       * If a `static get is()` getter is defined, the default implementation
       * will return the first `<template>` in a `dom-module` whose `id`
       * matches this element's `is`.
       *
       * Users may override this getter to return an arbitrary template
       * (in which case the `is` getter is unnecessary). The template returned
       * may be either an `HTMLTemplateElement` or a string that will be
       * automatically parsed into a template.
       *
       * Note that when subclassing, if the super class overrode the default
       * implementation and the subclass would like to provide an alternate
       * template via a `dom-module`, it should override this getter and
       * return `Polymer.DomModule.import(this.is, 'template')`.
       *
       * If a subclass would like to modify the super class template, it should
       * clone it rather than modify it in place.  If the getter does expensive
       * work such as cloning/modifying a template, it should memoize the
       * template for maximum performance:
       *
       *   let memoizedTemplate;
       *   class MySubClass extends MySuperClass {
       *     static get template() {
       *       if (!memoizedTemplate) {
       *         memoizedTemplate = MySuperClass.template.cloneNode(true);
       *         let subContent = document.createElement('div');
       *         subContent.textContent = 'This came from MySubClass';
       *         memoizedTemplate.content.appendChild(subContent);
       *       }
       *       return memoizedTemplate;
       *     }
       *   }
       *
       * @return {HTMLTemplateElement|string} Template to be stamped
       */
      static get template() {
        // Explanation of template-related properties:
        // - constructor.template (this getter): the template for the class.
        //     This can come from the prototype (for legacy elements), from a
        //     dom-module, or from the super class's template (or can be overridden
        //     altogether by the user)
        // - constructor._template: memoized version of constructor.template
        // - prototype._template: working template for the element, which will be
        //     parsed and modified in place. It is a cloned version of
        //     constructor.template, saved in _finalizeClass(). Note that before
        //     this getter is called, for legacy elements this could be from a
        //     _template field on the info object passed to Polymer(), a behavior,
        //     or set in registered(); once the static getter runs, a clone of it
        //     will overwrite it on the prototype as the working template.
        if (!this.hasOwnProperty(JSCompiler_renameProperty('_template', this))) {
          this._template =
            // If user has put template on prototype (e.g. in legacy via registered
            // callback or info object), prefer that first
            this.prototype.hasOwnProperty(JSCompiler_renameProperty('_template', this.prototype)) ?
            this.prototype._template :
            // Look in dom-module associated with this element's is
            (getTemplateFromDomModule(/** @type {PolymerElementConstructor}*/ (this).is) ||
            // Next look for superclass template (call the super impl this
            // way so that `this` points to the superclass)
            Object.getPrototypeOf(/** @type {PolymerElementConstructor}*/ (this).prototype).constructor.template);
        }
        return this._template;
      }

      /**
       * Set the template.
       *
       * @param {!HTMLTemplateElement|string} value Template to set.
       */
      static set template(value) {
        this._template = value;
      }

      /**
       * Path matching the url from which the element was imported.
       *
       * This path is used to resolve url's in template style cssText.
       * The `importPath` property is also set on element instances and can be
       * used to create bindings relative to the import path.
       *
       * For elements defined in ES modules, users should implement
       * `static get importMeta() { return import.meta; }`, and the default
       * implementation of `importPath` will  return `import.meta.url`'s path.
       * For elements defined in HTML imports, this getter will return the path
       * to the document containing a `dom-module` element matching this
       * element's static `is` property.
       *
       * Note, this path should contain a trailing `/`.
       *
       * @return {string} The import path for this element class
       * @suppress {missingProperties}
       */
      static get importPath() {
        if (!this.hasOwnProperty(JSCompiler_renameProperty('_importPath', this))) {
          const meta = this.importMeta;
          if (meta) {
            this._importPath = Polymer.ResolveUrl.pathFromUrl(meta.url);
          } else {
            const module = Polymer.DomModule && Polymer.DomModule.import(/** @type {PolymerElementConstructor} */ (this).is);
            this._importPath = (module && module.assetpath) ||
              Object.getPrototypeOf(/** @type {PolymerElementConstructor}*/ (this).prototype).constructor.importPath;
          }
        }
        return this._importPath;
      }

      constructor() {
        super();
        /** @type {HTMLTemplateElement} */
        this._template;
        /** @type {string} */
        this._importPath;
        /** @type {string} */
        this.rootPath;
        /** @type {string} */
        this.importPath;
        /** @type {StampedTemplate | HTMLElement | ShadowRoot} */
        this.root;
        /** @type {!Object<string, !Element>} */
        this.$;
      }

      /**
       * Overrides the default `Polymer.PropertyAccessors` to ensure class
       * metaprogramming related to property accessors and effects has
       * completed (calls `finalize`).
       *
       * It also initializes any property defaults provided via `value` in
       * `properties` metadata.
       *
       * @return {void}
       * @override
       * @suppress {invalidCasts}
       */
      _initializeProperties() {
        this.constructor.finalize();
        // note: finalize template when we have access to `localName` to
        // avoid dependence on `is` for polyfilling styling.
        this.constructor._finalizeTemplate(/** @type {!HTMLElement} */(this).localName);
        super._initializeProperties();
        // set path defaults
        this.rootPath = Polymer.rootPath;
        this.importPath = this.constructor.importPath;
        // apply property defaults...
        let p$ = propertyDefaults(this.constructor);
        if (!p$) {
          return;
        }
        for (let p in p$) {
          let info = p$[p];
          // Don't set default value if there is already an own property, which
          // happens when a `properties` property with default but no effects had
          // a property set (e.g. bound) by its host before upgrade
          if (!this.hasOwnProperty(p)) {
            let value = typeof info.value == 'function' ?
              info.value.call(this) :
              info.value;
            // Set via `_setProperty` if there is an accessor, to enable
            // initializing readOnly property defaults
            if (this._hasAccessor(p)) {
              this._setPendingProperty(p, value, true);
            } else {
              this[p] = value;
            }
          }
        }
      }

      /**
       * Gather style text for a style element in the template.
       *
       * @param {string} cssText Text containing styling to process
       * @param {string} baseURI Base URI to rebase CSS paths against
       * @return {string} The processed CSS text
       * @protected
       */
      static _processStyleText(cssText, baseURI) {
        return Polymer.ResolveUrl.resolveCss(cssText, baseURI);
      }

      /**
      * Configures an element `proto` to function with a given `template`.
      * The element name `is` and extends `ext` must be specified for ShadyCSS
      * style scoping.
      *
      * @param {string} is Tag name (or type extension name) for this element
      * @return {void}
      * @protected
      */
      static _finalizeTemplate(is) {
        /** @const {HTMLTemplateElement} */
        const template = this.prototype._template;
        if (template && !template.__polymerFinalized) {
          template.__polymerFinalized = true;
          const importPath = this.importPath;
          const baseURI = importPath ? Polymer.ResolveUrl.resolveUrl(importPath) : '';
          // e.g. support `include="module-name"`, and ShadyCSS
          processElementStyles(this, template, is, baseURI);
          this.prototype._bindTemplate(template);
        }
      }

      /**
       * Provides a default implementation of the standard Custom Elements
       * `connectedCallback`.
       *
       * The default implementation enables the property effects system and
       * flushes any pending properties, and updates shimmed CSS properties
       * when using the ShadyCSS scoping/custom properties polyfill.
       *
       * @suppress {missingProperties, invalidCasts} Super may or may not implement the callback
       * @return {void}
       */
      connectedCallback() {
        if (window.ShadyCSS && this._template) {
          window.ShadyCSS.styleElement(/** @type {!HTMLElement} */(this));
        }
        super.connectedCallback();
      }

      /**
       * Stamps the element template.
       *
       * @return {void}
       * @override
       */
      ready() {
        if (this._template) {
          this.root = this._stampTemplate(this._template);
          this.$ = this.root.$;
        }
        super.ready();
      }

      /**
       * Implements `PropertyEffects`'s `_readyClients` call. Attaches
       * element dom by calling `_attachDom` with the dom stamped from the
       * element's template via `_stampTemplate`. Note that this allows
       * client dom to be attached to the element prior to any observers
       * running.
       *
       * @return {void}
       * @override
       */
      _readyClients() {
        if (this._template) {
          this.root = this._attachDom(/** @type {StampedTemplate} */(this.root));
        }
        // The super._readyClients here sets the clients initialized flag.
        // We must wait to do this until after client dom is created/attached
        // so that this flag can be checked to prevent notifications fired
        // during this process from being handled before clients are ready.
        super._readyClients();
      }


      /**
       * Attaches an element's stamped dom to itself. By default,
       * this method creates a `shadowRoot` and adds the dom to it.
       * However, this method may be overridden to allow an element
       * to put its dom in another location.
       *
       * @throws {Error}
       * @suppress {missingReturn}
       * @param {StampedTemplate} dom to attach to the element.
       * @return {ShadowRoot} node to which the dom has been attached.
       */
      _attachDom(dom) {
        if (this.attachShadow) {
          if (dom) {
            if (!this.shadowRoot) {
              this.attachShadow({mode: 'open'});
            }
            this.shadowRoot.appendChild(dom);
            return this.shadowRoot;
          }
          return null;
        } else {
          throw new Error('ShadowDOM not available. ' +
            // TODO(sorvell): move to compile-time conditional when supported
          'Polymer.Element can create dom as children instead of in ' +
          'ShadowDOM by setting `this.root = this;\` before \`ready\`.');
        }
      }

      /**
       * When using the ShadyCSS scoping and custom property shim, causes all
       * shimmed styles in this element (and its subtree) to be updated
       * based on current custom property values.
       *
       * The optional parameter overrides inline custom property styles with an
       * object of properties where the keys are CSS properties, and the values
       * are strings.
       *
       * Example: `this.updateStyles({'--color': 'blue'})`
       *
       * These properties are retained unless a value of `null` is set.
       *
       * Note: This function does not support updating CSS mixins.
       * You can not dynamically change the value of an `@apply`.
       *
       * @param {Object=} properties Bag of custom property key/values to
       *   apply to this element.
       * @return {void}
       * @suppress {invalidCasts}
       */
      updateStyles(properties) {
        if (window.ShadyCSS) {
          window.ShadyCSS.styleSubtree(/** @type {!HTMLElement} */(this), properties);
        }
      }

      /**
       * Rewrites a given URL relative to a base URL. The base URL defaults to
       * the original location of the document containing the `dom-module` for
       * this element. This method will return the same URL before and after
       * bundling.
       *
       * Note that this function performs no resolution for URLs that start
       * with `/` (absolute URLs) or `#` (hash identifiers).  For general purpose
       * URL resolution, use `window.URL`.
       *
       * @param {string} url URL to resolve.
       * @param {string=} base Optional base URL to resolve against, defaults
       * to the element's `importPath`
       * @return {string} Rewritten URL relative to base
       */
      resolveUrl(url, base) {
        if (!base && this.importPath) {
          base = Polymer.ResolveUrl.resolveUrl(this.importPath);
        }
        return Polymer.ResolveUrl.resolveUrl(url, base);
      }

      /**
       * Overrides `PropertyAccessors` to add map of dynamic functions on
       * template info, for consumption by `PropertyEffects` template binding
       * code. This map determines which method templates should have accessors
       * created for them.
       *
       * @override
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateContent(template, templateInfo, nodeInfo) {
        templateInfo.dynamicFns = templateInfo.dynamicFns || this._properties;
        return super._parseTemplateContent(template, templateInfo, nodeInfo);
      }

    }

    return PolymerElement;
  });

  /**
   * When using the ShadyCSS scoping and custom property shim, causes all
   * shimmed `styles` (via `custom-style`) in the document (and its subtree)
   * to be updated based on current custom property values.
   *
   * The optional parameter overrides inline custom property styles with an
   * object of properties where the keys are CSS properties, and the values
   * are strings.
   *
   * Example: `Polymer.updateStyles({'--color': 'blue'})`
   *
   * These properties are retained unless a value of `null` is set.
   *
   * @param {Object=} props Bag of custom property key/values to
   *   apply to the document.
   * @return {void}
   */
  Polymer.updateStyles = function(props) {
    if (window.ShadyCSS) {
      window.ShadyCSS.styleDocument(props);
    }
  };

})();


(function() {
  'use strict';

  /**
   * @summary Collapse multiple callbacks into one invocation after a timer.
   * @memberof Polymer
   */
  class Debouncer {
    constructor() {
      this._asyncModule = null;
      this._callback = null;
      this._timer = null;
    }
    /**
     * Sets the scheduler; that is, a module with the Async interface,
     * a callback and optional arguments to be passed to the run function
     * from the async module.
     *
     * @param {!AsyncInterface} asyncModule Object with Async interface.
     * @param {function()} callback Callback to run.
     * @return {void}
     */
    setConfig(asyncModule, callback) {
      this._asyncModule = asyncModule;
      this._callback = callback;
      this._timer = this._asyncModule.run(() => {
        this._timer = null;
        this._callback();
      });
    }
    /**
     * Cancels an active debouncer and returns a reference to itself.
     *
     * @return {void}
     */
    cancel() {
      if (this.isActive()) {
        this._asyncModule.cancel(this._timer);
        this._timer = null;
      }
    }
    /**
     * Flushes an active debouncer and returns a reference to itself.
     *
     * @return {void}
     */
    flush() {
      if (this.isActive()) {
        this.cancel();
        this._callback();
      }
    }
    /**
     * Returns true if the debouncer is active.
     *
     * @return {boolean} True if active.
     */
    isActive() {
      return this._timer != null;
    }
    /**
     * Creates a debouncer if no debouncer is passed as a parameter
     * or it cancels an active debouncer otherwise. The following
     * example shows how a debouncer can be called multiple times within a
     * microtask and "debounced" such that the provided callback function is
     * called once. Add this method to a custom element:
     *
     * _debounceWork() {
     *   this._debounceJob = Polymer.Debouncer.debounce(this._debounceJob,
     *       Polymer.Async.microTask, () => {
     *     this._doWork();
     *   });
     * }
     *
     * If the `_debounceWork` method is called multiple times within the same
     * microtask, the `_doWork` function will be called only once at the next
     * microtask checkpoint.
     *
     * Note: In testing it is often convenient to avoid asynchrony. To accomplish
     * this with a debouncer, you can use `Polymer.enqueueDebouncer` and
     * `Polymer.flush`. For example, extend the above example by adding
     * `Polymer.enqueueDebouncer(this._debounceJob)` at the end of the
     * `_debounceWork` method. Then in a test, call `Polymer.flush` to ensure
     * the debouncer has completed.
     *
     * @param {Debouncer?} debouncer Debouncer object.
     * @param {!AsyncInterface} asyncModule Object with Async interface
     * @param {function()} callback Callback to run.
     * @return {!Debouncer} Returns a debouncer object.
     */
    static debounce(debouncer, asyncModule, callback) {
      if (debouncer instanceof Debouncer) {
        debouncer.cancel();
      } else {
        debouncer = new Debouncer();
      }
      debouncer.setConfig(asyncModule, callback);
      return debouncer;
    }
  }

  /** @const */
  Polymer.Debouncer = Debouncer;
})();


(function() {

  'use strict';

  // detect native touch action support
  let HAS_NATIVE_TA = typeof document.head.style.touchAction === 'string';
  let GESTURE_KEY = '__polymerGestures';
  let HANDLED_OBJ = '__polymerGesturesHandled';
  let TOUCH_ACTION = '__polymerGesturesTouchAction';
  // radius for tap and track
  let TAP_DISTANCE = 25;
  let TRACK_DISTANCE = 5;
  // number of last N track positions to keep
  let TRACK_LENGTH = 2;

  // Disabling "mouse" handlers for 2500ms is enough
  let MOUSE_TIMEOUT = 2500;
  let MOUSE_EVENTS = ['mousedown', 'mousemove', 'mouseup', 'click'];
  // an array of bitmask values for mapping MouseEvent.which to MouseEvent.buttons
  let MOUSE_WHICH_TO_BUTTONS = [0, 1, 4, 2];
  let MOUSE_HAS_BUTTONS = (function() {
    try {
      return new MouseEvent('test', {buttons: 1}).buttons === 1;
    } catch (e) {
      return false;
    }
  })();

  /**
   * @param {string} name Possible mouse event name
   * @return {boolean} true if mouse event, false if not
   */
  function isMouseEvent(name) {
    return MOUSE_EVENTS.indexOf(name) > -1;
  }

  /* eslint no-empty: ["error", { "allowEmptyCatch": true }] */
  // check for passive event listeners
  let SUPPORTS_PASSIVE = false;
  (function() {
    try {
      let opts = Object.defineProperty({}, 'passive', {get() {SUPPORTS_PASSIVE = true;}});
      window.addEventListener('test', null, opts);
      window.removeEventListener('test', null, opts);
    } catch(e) {}
  })();

  /**
   * Generate settings for event listeners, dependant on `Polymer.passiveTouchGestures`
   *
   * @param {string} eventName Event name to determine if `{passive}` option is needed
   * @return {{passive: boolean} | undefined} Options to use for addEventListener and removeEventListener
   */
  function PASSIVE_TOUCH(eventName) {
    if (isMouseEvent(eventName) || eventName === 'touchend') {
      return;
    }
    if (HAS_NATIVE_TA && SUPPORTS_PASSIVE && Polymer.passiveTouchGestures) {
      return {passive: true};
    } else {
      return;
    }
  }

  // Check for touch-only devices
  let IS_TOUCH_ONLY = navigator.userAgent.match(/iP(?:[oa]d|hone)|Android/);

  let GestureRecognizer = function(){}; // eslint-disable-line no-unused-vars
  /** @type {function(): void} */
  GestureRecognizer.prototype.reset;
  /** @type {function(MouseEvent): void | undefined} */
  GestureRecognizer.prototype.mousedown;
  /** @type {(function(MouseEvent): void | undefined)} */
  GestureRecognizer.prototype.mousemove;
  /** @type {(function(MouseEvent): void | undefined)} */
  GestureRecognizer.prototype.mouseup;
  /** @type {(function(TouchEvent): void | undefined)} */
  GestureRecognizer.prototype.touchstart;
  /** @type {(function(TouchEvent): void | undefined)} */
  GestureRecognizer.prototype.touchmove;
  /** @type {(function(TouchEvent): void | undefined)} */
  GestureRecognizer.prototype.touchend;
  /** @type {(function(MouseEvent): void | undefined)} */
  GestureRecognizer.prototype.click;

  // keep track of any labels hit by the mouseCanceller
  /** @type {!Array<!HTMLLabelElement>} */
  const clickedLabels = [];

  /** @type {!Object<boolean>} */
  const labellable = {
    'button': true,
    'input': true,
    'keygen': true,
    'meter': true,
    'output': true,
    'textarea': true,
    'progress': true,
    'select': true
  };

  // Defined at https://html.spec.whatwg.org/multipage/form-control-infrastructure.html#enabling-and-disabling-form-controls:-the-disabled-attribute
  /** @type {!Object<boolean>} */
  const canBeDisabled = {
    'button': true,
    'command': true,
    'fieldset': true,
    'input': true,
    'keygen': true,
    'optgroup': true,
    'option': true,
    'select': true,
    'textarea': true
  };

  /**
   * @param {HTMLElement} el Element to check labelling status
   * @return {boolean} element can have labels
   */
  function canBeLabelled(el) {
    return labellable[el.localName] || false;
  }

  /**
   * @param {HTMLElement} el Element that may be labelled.
   * @return {!Array<!HTMLLabelElement>} Relevant label for `el`
   */
  function matchingLabels(el) {
    let labels = Array.from(/** @type {HTMLInputElement} */(el).labels || []);
    // IE doesn't have `labels` and Safari doesn't populate `labels`
    // if element is in a shadowroot.
    // In this instance, finding the non-ancestor labels is enough,
    // as the mouseCancellor code will handle ancstor labels
    if (!labels.length) {
      labels = [];
      let root = el.getRootNode();
      // if there is an id on `el`, check for all labels with a matching `for` attribute
      if (el.id) {
        let matching = root.querySelectorAll(`label[for = ${el.id}]`);
        for (let i = 0; i < matching.length; i++) {
          labels.push(/** @type {!HTMLLabelElement} */(matching[i]));
        }
      }
    }
    return labels;
  }

  // touch will make synthetic mouse events
  // `preventDefault` on touchend will cancel them,
  // but this breaks `<input>` focus and link clicks
  // disable mouse handlers for MOUSE_TIMEOUT ms after
  // a touchend to ignore synthetic mouse events
  let mouseCanceller = function(mouseEvent) {
    // Check for sourceCapabilities, used to distinguish synthetic events
    // if mouseEvent did not come from a device that fires touch events,
    // it was made by a real mouse and should be counted
    // http://wicg.github.io/InputDeviceCapabilities/#dom-inputdevicecapabilities-firestouchevents
    let sc = mouseEvent.sourceCapabilities;
    if (sc && !sc.firesTouchEvents) {
      return;
    }
    // skip synthetic mouse events
    mouseEvent[HANDLED_OBJ] = {skip: true};
    // disable "ghost clicks"
    if (mouseEvent.type === 'click') {
      let clickFromLabel = false;
      let path = mouseEvent.composedPath && mouseEvent.composedPath();
      if (path) {
        for (let i = 0; i < path.length; i++) {
          if (path[i].nodeType === Node.ELEMENT_NODE) {
            if (path[i].localName === 'label') {
              clickedLabels.push(path[i]);
            } else if (canBeLabelled(path[i])) {
              let ownerLabels = matchingLabels(path[i]);
              // check if one of the clicked labels is labelling this element
              for (let j = 0; j < ownerLabels.length; j++) {
                clickFromLabel = clickFromLabel || clickedLabels.indexOf(ownerLabels[j]) > -1;
              }
            }
          }
          if (path[i] === POINTERSTATE.mouse.target) {
            return;
          }
        }
      }
      // if one of the clicked labels was labelling the target element,
      // this is not a ghost click
      if (clickFromLabel) {
        return;
      }
      mouseEvent.preventDefault();
      mouseEvent.stopPropagation();
    }
  };

  /**
   * @param {boolean=} setup True to add, false to remove.
   * @return {void}
   */
  function setupTeardownMouseCanceller(setup) {
    let events = IS_TOUCH_ONLY ? ['click'] : MOUSE_EVENTS;
    for (let i = 0, en; i < events.length; i++) {
      en = events[i];
      if (setup) {
        // reset clickLabels array
        clickedLabels.length = 0;
        document.addEventListener(en, mouseCanceller, true);
      } else {
        document.removeEventListener(en, mouseCanceller, true);
      }
    }
  }

  function ignoreMouse(e) {
    if (!POINTERSTATE.mouse.mouseIgnoreJob) {
      setupTeardownMouseCanceller(true);
    }
    let unset = function() {
      setupTeardownMouseCanceller();
      POINTERSTATE.mouse.target = null;
      POINTERSTATE.mouse.mouseIgnoreJob = null;
    };
    POINTERSTATE.mouse.target = e.composedPath()[0];
    POINTERSTATE.mouse.mouseIgnoreJob = Polymer.Debouncer.debounce(
          POINTERSTATE.mouse.mouseIgnoreJob
        , Polymer.Async.timeOut.after(MOUSE_TIMEOUT)
        , unset);
  }

  /**
   * @param {MouseEvent} ev event to test for left mouse button down
   * @return {boolean} has left mouse button down
   */
  function hasLeftMouseButton(ev) {
    let type = ev.type;
    // exit early if the event is not a mouse event
    if (!isMouseEvent(type)) {
      return false;
    }
    // ev.button is not reliable for mousemove (0 is overloaded as both left button and no buttons)
    // instead we use ev.buttons (bitmask of buttons) or fall back to ev.which (deprecated, 0 for no buttons, 1 for left button)
    if (type === 'mousemove') {
      // allow undefined for testing events
      let buttons = ev.buttons === undefined ? 1 : ev.buttons;
      if ((ev instanceof window.MouseEvent) && !MOUSE_HAS_BUTTONS) {
        buttons = MOUSE_WHICH_TO_BUTTONS[ev.which] || 0;
      }
      // buttons is a bitmask, check that the left button bit is set (1)
      return Boolean(buttons & 1);
    } else {
      // allow undefined for testing events
      let button = ev.button === undefined ? 0 : ev.button;
      // ev.button is 0 in mousedown/mouseup/click for left button activation
      return button === 0;
    }
  }

  function isSyntheticClick(ev) {
    if (ev.type === 'click') {
      // ev.detail is 0 for HTMLElement.click in most browsers
      if (ev.detail === 0) {
        return true;
      }
      // in the worst case, check that the x/y position of the click is within
      // the bounding box of the target of the event
      // Thanks IE 10 >:(
      let t = Gestures._findOriginalTarget(ev);
      // make sure the target of the event is an element so we can use getBoundingClientRect,
      // if not, just assume it is a synthetic click
      if (!t.nodeType || /** @type {Element} */(t).nodeType !== Node.ELEMENT_NODE) {
        return true;
      }
      let bcr = /** @type {Element} */(t).getBoundingClientRect();
      // use page x/y to account for scrolling
      let x = ev.pageX, y = ev.pageY;
      // ev is a synthetic click if the position is outside the bounding box of the target
      return !((x >= bcr.left && x <= bcr.right) && (y >= bcr.top && y <= bcr.bottom));
    }
    return false;
  }

  let POINTERSTATE = {
    mouse: {
      target: null,
      mouseIgnoreJob: null
    },
    touch: {
      x: 0,
      y: 0,
      id: -1,
      scrollDecided: false
    }
  };

  function firstTouchAction(ev) {
    let ta = 'auto';
    let path = ev.composedPath && ev.composedPath();
    if (path) {
      for (let i = 0, n; i < path.length; i++) {
        n = path[i];
        if (n[TOUCH_ACTION]) {
          ta = n[TOUCH_ACTION];
          break;
        }
      }
    }
    return ta;
  }

  function trackDocument(stateObj, movefn, upfn) {
    stateObj.movefn = movefn;
    stateObj.upfn = upfn;
    document.addEventListener('mousemove', movefn);
    document.addEventListener('mouseup', upfn);
  }

  function untrackDocument(stateObj) {
    document.removeEventListener('mousemove', stateObj.movefn);
    document.removeEventListener('mouseup', stateObj.upfn);
    stateObj.movefn = null;
    stateObj.upfn = null;
  }

  // use a document-wide touchend listener to start the ghost-click prevention mechanism
  // Use passive event listeners, if supported, to not affect scrolling performance
  document.addEventListener('touchend', ignoreMouse, SUPPORTS_PASSIVE ? {passive: true} : false);

  /**
   * Module for adding listeners to a node for the following normalized
   * cross-platform "gesture" events:
   * - `down` - mouse or touch went down
   * - `up` - mouse or touch went up
   * - `tap` - mouse click or finger tap
   * - `track` - mouse drag or touch move
   *
   * @namespace
   * @memberof Polymer
   * @summary Module for adding cross-platform gesture event listeners.
   */
  const Gestures = {
    gestures: {},
    recognizers: [],

    /**
     * Finds the element rendered on the screen at the provided coordinates.
     *
     * Similar to `document.elementFromPoint`, but pierces through
     * shadow roots.
     *
     * @memberof Polymer.Gestures
     * @param {number} x Horizontal pixel coordinate
     * @param {number} y Vertical pixel coordinate
     * @return {Element} Returns the deepest shadowRoot inclusive element
     * found at the screen position given.
     */
    deepTargetFind: function(x, y) {
      let node = document.elementFromPoint(x, y);
      let next = node;
      // this code path is only taken when native ShadowDOM is used
      // if there is a shadowroot, it may have a node at x/y
      // if there is not a shadowroot, exit the loop
      while (next && next.shadowRoot && !window.ShadyDOM) {
        // if there is a node at x/y in the shadowroot, look deeper
        let oldNext = next;
        next = next.shadowRoot.elementFromPoint(x, y);
        // on Safari, elementFromPoint may return the shadowRoot host
        if (oldNext === next) {
          break;
        }
        if (next) {
          node = next;
        }
      }
      return node;
    },
    /**
     * a cheaper check than ev.composedPath()[0];
     *
     * @private
     * @param {Event} ev Event.
     * @return {EventTarget} Returns the event target.
     */
    _findOriginalTarget: function(ev) {
      // shadowdom
      if (ev.composedPath) {
        const targets = /** @type {!Array<!EventTarget>} */(ev.composedPath());
        // It shouldn't be, but sometimes targets is empty (window on Safari).
        return targets.length > 0 ? targets[0] : ev.target;
      }
      // shadydom
      return ev.target;
    },

    /**
     * @private
     * @param {Event} ev Event.
     * @return {void}
     */
    _handleNative: function(ev) {
      let handled;
      let type = ev.type;
      let node = ev.currentTarget;
      let gobj = node[GESTURE_KEY];
      if (!gobj) {
        return;
      }
      let gs = gobj[type];
      if (!gs) {
        return;
      }
      if (!ev[HANDLED_OBJ]) {
        ev[HANDLED_OBJ] = {};
        if (type.slice(0, 5) === 'touch') {
          ev = /** @type {TouchEvent} */(ev); // eslint-disable-line no-self-assign
          let t = ev.changedTouches[0];
          if (type === 'touchstart') {
            // only handle the first finger
            if (ev.touches.length === 1) {
              POINTERSTATE.touch.id = t.identifier;
            }
          }
          if (POINTERSTATE.touch.id !== t.identifier) {
            return;
          }
          if (!HAS_NATIVE_TA) {
            if (type === 'touchstart' || type === 'touchmove') {
              Gestures._handleTouchAction(ev);
            }
          }
        }
      }
      handled = ev[HANDLED_OBJ];
      // used to ignore synthetic mouse events
      if (handled.skip) {
        return;
      }
      // reset recognizer state
      for (let i = 0, r; i < Gestures.recognizers.length; i++) {
        r = Gestures.recognizers[i];
        if (gs[r.name] && !handled[r.name]) {
          if (r.flow && r.flow.start.indexOf(ev.type) > -1 && r.reset) {
            r.reset();
          }
        }
      }
      // enforce gesture recognizer order
      for (let i = 0, r; i < Gestures.recognizers.length; i++) {
        r = Gestures.recognizers[i];
        if (gs[r.name] && !handled[r.name]) {
          handled[r.name] = true;
          r[type](ev);
        }
      }
    },

    /**
     * @private
     * @param {TouchEvent} ev Event.
     * @return {void}
     */
    _handleTouchAction: function(ev) {
      let t = ev.changedTouches[0];
      let type = ev.type;
      if (type === 'touchstart') {
        POINTERSTATE.touch.x = t.clientX;
        POINTERSTATE.touch.y = t.clientY;
        POINTERSTATE.touch.scrollDecided = false;
      } else if (type === 'touchmove') {
        if (POINTERSTATE.touch.scrollDecided) {
          return;
        }
        POINTERSTATE.touch.scrollDecided = true;
        let ta = firstTouchAction(ev);
        let prevent = false;
        let dx = Math.abs(POINTERSTATE.touch.x - t.clientX);
        let dy = Math.abs(POINTERSTATE.touch.y - t.clientY);
        if (!ev.cancelable) {
          // scrolling is happening
        } else if (ta === 'none') {
          prevent = true;
        } else if (ta === 'pan-x') {
          prevent = dy > dx;
        } else if (ta === 'pan-y') {
          prevent = dx > dy;
        }
        if (prevent) {
          ev.preventDefault();
        } else {
          Gestures.prevent('track');
        }
      }
    },

    /**
     * Adds an event listener to a node for the given gesture type.
     *
     * @memberof Polymer.Gestures
     * @param {!Node} node Node to add listener on
     * @param {string} evType Gesture type: `down`, `up`, `track`, or `tap`
     * @param {!function(!Event):void} handler Event listener function to call
     * @return {boolean} Returns true if a gesture event listener was added.
     * @this {Gestures}
     */
    addListener: function(node, evType, handler) {
      if (this.gestures[evType]) {
        this._add(node, evType, handler);
        return true;
      }
      return false;
    },

    /**
     * Removes an event listener from a node for the given gesture type.
     *
     * @memberof Polymer.Gestures
     * @param {!Node} node Node to remove listener from
     * @param {string} evType Gesture type: `down`, `up`, `track`, or `tap`
     * @param {!function(!Event):void} handler Event listener function previously passed to
     *  `addListener`.
     * @return {boolean} Returns true if a gesture event listener was removed.
     * @this {Gestures}
     */
    removeListener: function(node, evType, handler) {
      if (this.gestures[evType]) {
        this._remove(node, evType, handler);
        return true;
      }
      return false;
    },

    /**
     * automate the event listeners for the native events
     *
     * @private
     * @param {!HTMLElement} node Node on which to add the event.
     * @param {string} evType Event type to add.
     * @param {function(!Event)} handler Event handler function.
     * @return {void}
     * @this {Gestures}
     */
    _add: function(node, evType, handler) {
      let recognizer = this.gestures[evType];
      let deps = recognizer.deps;
      let name = recognizer.name;
      let gobj = node[GESTURE_KEY];
      if (!gobj) {
        node[GESTURE_KEY] = gobj = {};
      }
      for (let i = 0, dep, gd; i < deps.length; i++) {
        dep = deps[i];
        // don't add mouse handlers on iOS because they cause gray selection overlays
        if (IS_TOUCH_ONLY && isMouseEvent(dep) && dep !== 'click') {
          continue;
        }
        gd = gobj[dep];
        if (!gd) {
          gobj[dep] = gd = {_count: 0};
        }
        if (gd._count === 0) {
          node.addEventListener(dep, this._handleNative, PASSIVE_TOUCH(dep));
        }
        gd[name] = (gd[name] || 0) + 1;
        gd._count = (gd._count || 0) + 1;
      }
      node.addEventListener(evType, handler);
      if (recognizer.touchAction) {
        this.setTouchAction(node, recognizer.touchAction);
      }
    },

    /**
     * automate event listener removal for native events
     *
     * @private
     * @param {!HTMLElement} node Node on which to remove the event.
     * @param {string} evType Event type to remove.
     * @param {function(Event?)} handler Event handler function.
     * @return {void}
     * @this {Gestures}
     */
    _remove: function(node, evType, handler) {
      let recognizer = this.gestures[evType];
      let deps = recognizer.deps;
      let name = recognizer.name;
      let gobj = node[GESTURE_KEY];
      if (gobj) {
        for (let i = 0, dep, gd; i < deps.length; i++) {
          dep = deps[i];
          gd = gobj[dep];
          if (gd && gd[name]) {
            gd[name] = (gd[name] || 1) - 1;
            gd._count = (gd._count || 1) - 1;
            if (gd._count === 0) {
              node.removeEventListener(dep, this._handleNative, PASSIVE_TOUCH(dep));
            }
          }
        }
      }
      node.removeEventListener(evType, handler);
    },

    /**
     * Registers a new gesture event recognizer for adding new custom
     * gesture event types.
     *
     * @memberof Polymer.Gestures
     * @param {!GestureRecognizer} recog Gesture recognizer descriptor
     * @return {void}
     * @this {Gestures}
     */
    register: function(recog) {
      this.recognizers.push(recog);
      for (let i = 0; i < recog.emits.length; i++) {
        this.gestures[recog.emits[i]] = recog;
      }
    },

    /**
     * @private
     * @param {string} evName Event name.
     * @return {Object} Returns the gesture for the given event name.
     * @this {Gestures}
     */
    _findRecognizerByEvent: function(evName) {
      for (let i = 0, r; i < this.recognizers.length; i++) {
        r = this.recognizers[i];
        for (let j = 0, n; j < r.emits.length; j++) {
          n = r.emits[j];
          if (n === evName) {
            return r;
          }
        }
      }
      return null;
    },

    /**
     * Sets scrolling direction on node.
     *
     * This value is checked on first move, thus it should be called prior to
     * adding event listeners.
     *
     * @memberof Polymer.Gestures
     * @param {!Element} node Node to set touch action setting on
     * @param {string} value Touch action value
     * @return {void}
     */
    setTouchAction: function(node, value) {
      if (HAS_NATIVE_TA) {
        // NOTE: add touchAction async so that events can be added in
        // custom element constructors. Otherwise we run afoul of custom
        // elements restriction against settings attributes (style) in the
        // constructor.
        Polymer.Async.microTask.run(() => {
          node.style.touchAction = value;
        });
      }
      node[TOUCH_ACTION] = value;
    },

    /**
     * Dispatches an event on the `target` element of `type` with the given
     * `detail`.
     * @private
     * @param {!EventTarget} target The element on which to fire an event.
     * @param {string} type The type of event to fire.
     * @param {!Object=} detail The detail object to populate on the event.
     * @return {void}
     */
    _fire: function(target, type, detail) {
      let ev = new Event(type, { bubbles: true, cancelable: true, composed: true });
      ev.detail = detail;
      target.dispatchEvent(ev);
      // forward `preventDefault` in a clean way
      if (ev.defaultPrevented) {
        let preventer = detail.preventer || detail.sourceEvent;
        if (preventer && preventer.preventDefault) {
          preventer.preventDefault();
        }
      }
    },

    /**
     * Prevents the dispatch and default action of the given event name.
     *
     * @memberof Polymer.Gestures
     * @param {string} evName Event name.
     * @return {void}
     * @this {Gestures}
     */
    prevent: function(evName) {
      let recognizer = this._findRecognizerByEvent(evName);
      if (recognizer.info) {
        recognizer.info.prevent = true;
      }
    },

    /**
     * Reset the 2500ms timeout on processing mouse input after detecting touch input.
     *
     * Touch inputs create synthesized mouse inputs anywhere from 0 to 2000ms after the touch.
     * This method should only be called during testing with simulated touch inputs.
     * Calling this method in production may cause duplicate taps or other Gestures.
     *
     * @memberof Polymer.Gestures
     * @return {void}
     */
    resetMouseCanceller: function() {
      if (POINTERSTATE.mouse.mouseIgnoreJob) {
        POINTERSTATE.mouse.mouseIgnoreJob.flush();
      }
    }
  };

  /* eslint-disable valid-jsdoc */

  Gestures.register({
    name: 'downup',
    deps: ['mousedown', 'touchstart', 'touchend'],
    flow: {
      start: ['mousedown', 'touchstart'],
      end: ['mouseup', 'touchend']
    },
    emits: ['down', 'up'],

    info: {
      movefn: null,
      upfn: null
    },

    /**
     * @this {GestureRecognizer}
     * @return {void}
     */
    reset: function() {
      untrackDocument(this.info);
    },

    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    mousedown: function(e) {
      if (!hasLeftMouseButton(e)) {
        return;
      }
      let t = Gestures._findOriginalTarget(e);
      let self = this;
      let movefn = function movefn(e) {
        if (!hasLeftMouseButton(e)) {
          self._fire('up', t, e);
          untrackDocument(self.info);
        }
      };
      let upfn = function upfn(e) {
        if (hasLeftMouseButton(e)) {
          self._fire('up', t, e);
        }
        untrackDocument(self.info);
      };
      trackDocument(this.info, movefn, upfn);
      this._fire('down', t, e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchstart: function(e) {
      this._fire('down', Gestures._findOriginalTarget(e), e.changedTouches[0], e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchend: function(e) {
      this._fire('up', Gestures._findOriginalTarget(e), e.changedTouches[0], e);
    },
    /**
     * @param {string} type
     * @param {!EventTarget} target
     * @param {Event} event
     * @param {Function} preventer
     * @return {void}
     */
    _fire: function(type, target, event, preventer) {
      Gestures._fire(target, type, {
        x: event.clientX,
        y: event.clientY,
        sourceEvent: event,
        preventer: preventer,
        prevent: function(e) {
          return Gestures.prevent(e);
        }
      });
    }
  });

  Gestures.register({
    name: 'track',
    touchAction: 'none',
    deps: ['mousedown', 'touchstart', 'touchmove', 'touchend'],
    flow: {
      start: ['mousedown', 'touchstart'],
      end: ['mouseup', 'touchend']
    },
    emits: ['track'],

    info: {
      x: 0,
      y: 0,
      state: 'start',
      started: false,
      moves: [],
      /** @this {GestureRecognizer} */
      addMove: function(move) {
        if (this.moves.length > TRACK_LENGTH) {
          this.moves.shift();
        }
        this.moves.push(move);
      },
      movefn: null,
      upfn: null,
      prevent: false
    },

    /**
     * @this {GestureRecognizer}
     * @return {void}
     */
    reset: function() {
      this.info.state = 'start';
      this.info.started = false;
      this.info.moves = [];
      this.info.x = 0;
      this.info.y = 0;
      this.info.prevent = false;
      untrackDocument(this.info);
    },

    /**
     * @this {GestureRecognizer}
     * @param {number} x
     * @param {number} y
     * @return {boolean}
     */
    hasMovedEnough: function(x, y) {
      if (this.info.prevent) {
        return false;
      }
      if (this.info.started) {
        return true;
      }
      let dx = Math.abs(this.info.x - x);
      let dy = Math.abs(this.info.y - y);
      return (dx >= TRACK_DISTANCE || dy >= TRACK_DISTANCE);
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    mousedown: function(e) {
      if (!hasLeftMouseButton(e)) {
        return;
      }
      let t = Gestures._findOriginalTarget(e);
      let self = this;
      let movefn = function movefn(e) {
        let x = e.clientX, y = e.clientY;
        if (self.hasMovedEnough(x, y)) {
          // first move is 'start', subsequent moves are 'move', mouseup is 'end'
          self.info.state = self.info.started ? (e.type === 'mouseup' ? 'end' : 'track') : 'start';
          if (self.info.state === 'start') {
            // if and only if tracking, always prevent tap
            Gestures.prevent('tap');
          }
          self.info.addMove({x: x, y: y});
          if (!hasLeftMouseButton(e)) {
            // always _fire "end"
            self.info.state = 'end';
            untrackDocument(self.info);
          }
          self._fire(t, e);
          self.info.started = true;
        }
      };
      let upfn = function upfn(e) {
        if (self.info.started) {
          movefn(e);
        }

        // remove the temporary listeners
        untrackDocument(self.info);
      };
      // add temporary document listeners as mouse retargets
      trackDocument(this.info, movefn, upfn);
      this.info.x = e.clientX;
      this.info.y = e.clientY;
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchstart: function(e) {
      let ct = e.changedTouches[0];
      this.info.x = ct.clientX;
      this.info.y = ct.clientY;
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchmove: function(e) {
      let t = Gestures._findOriginalTarget(e);
      let ct = e.changedTouches[0];
      let x = ct.clientX, y = ct.clientY;
      if (this.hasMovedEnough(x, y)) {
        if (this.info.state === 'start') {
          // if and only if tracking, always prevent tap
          Gestures.prevent('tap');
        }
        this.info.addMove({x: x, y: y});
        this._fire(t, ct);
        this.info.state = 'track';
        this.info.started = true;
      }
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchend: function(e) {
      let t = Gestures._findOriginalTarget(e);
      let ct = e.changedTouches[0];
      // only trackend if track was started and not aborted
      if (this.info.started) {
        // reset started state on up
        this.info.state = 'end';
        this.info.addMove({x: ct.clientX, y: ct.clientY});
        this._fire(t, ct, e);
      }
    },

    /**
     * @this {GestureRecognizer}
     * @param {!EventTarget} target
     * @param {Touch} touch
     * @return {void}
     */
    _fire: function(target, touch) {
      let secondlast = this.info.moves[this.info.moves.length - 2];
      let lastmove = this.info.moves[this.info.moves.length - 1];
      let dx = lastmove.x - this.info.x;
      let dy = lastmove.y - this.info.y;
      let ddx, ddy = 0;
      if (secondlast) {
        ddx = lastmove.x - secondlast.x;
        ddy = lastmove.y - secondlast.y;
      }
      Gestures._fire(target, 'track', {
        state: this.info.state,
        x: touch.clientX,
        y: touch.clientY,
        dx: dx,
        dy: dy,
        ddx: ddx,
        ddy: ddy,
        sourceEvent: touch,
        hover: function() {
          return Gestures.deepTargetFind(touch.clientX, touch.clientY);
        }
      });
    }

  });

  Gestures.register({
    name: 'tap',
    deps: ['mousedown', 'click', 'touchstart', 'touchend'],
    flow: {
      start: ['mousedown', 'touchstart'],
      end: ['click', 'touchend']
    },
    emits: ['tap'],
    info: {
      x: NaN,
      y: NaN,
      prevent: false
    },
    /**
     * @this {GestureRecognizer}
     * @return {void}
     */
    reset: function() {
      this.info.x = NaN;
      this.info.y = NaN;
      this.info.prevent = false;
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    save: function(e) {
      this.info.x = e.clientX;
      this.info.y = e.clientY;
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    mousedown: function(e) {
      if (hasLeftMouseButton(e)) {
        this.save(e);
      }
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    click: function(e) {
      if (hasLeftMouseButton(e)) {
        this.forward(e);
      }
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchstart: function(e) {
      this.save(e.changedTouches[0], e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchend: function(e) {
      this.forward(e.changedTouches[0], e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {Event | Touch} e
     * @param {Event=} preventer
     * @return {void}
     */
    forward: function(e, preventer) {
      let dx = Math.abs(e.clientX - this.info.x);
      let dy = Math.abs(e.clientY - this.info.y);
      // find original target from `preventer` for TouchEvents, or `e` for MouseEvents
      let t = Gestures._findOriginalTarget(/** @type {Event} */(preventer || e));
      if (!t || (canBeDisabled[/** @type {!HTMLElement} */(t).localName] && t.hasAttribute('disabled'))) {
        return;
      }
      // dx,dy can be NaN if `click` has been simulated and there was no `down` for `start`
      if (isNaN(dx) || isNaN(dy) || (dx <= TAP_DISTANCE && dy <= TAP_DISTANCE) || isSyntheticClick(e)) {
        // prevent taps from being generated if an event has canceled them
        if (!this.info.prevent) {
          Gestures._fire(t, 'tap', {
            x: e.clientX,
            y: e.clientY,
            sourceEvent: e,
            preventer: preventer
          });
        }
      }
    }
  });

  /* eslint-enable valid-jsdoc */

  /** @deprecated */
  Gestures.findOriginalTarget = Gestures._findOriginalTarget;

  /** @deprecated */
  Gestures.add = Gestures.addListener;

  /** @deprecated */
  Gestures.remove = Gestures.removeListener;

  Polymer.Gestures = Gestures;

})();


(function() {

  'use strict';

  /**
   * @const {Polymer.Gestures}
   */
  const gestures = Polymer.Gestures;

  /**
   * Element class mixin that provides API for adding Polymer's cross-platform
   * gesture events to nodes.
   *
   * The API is designed to be compatible with override points implemented
   * in `Polymer.TemplateStamp` such that declarative event listeners in
   * templates will support gesture events when this mixin is applied along with
   * `Polymer.TemplateStamp`.
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin that provides API for adding Polymer's cross-platform
   * gesture events to nodes
   */
  Polymer.GestureEventListeners = Polymer.dedupingMixin(superClass => {

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_GestureEventListeners}
     */
    class GestureEventListeners extends superClass {

      /**
       * Add the event listener to the node if it is a gestures event.
       *
       * @param {!Node} node Node to add event listener to
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to add
       * @return {void}
       */
      _addEventListenerToNode(node, eventName, handler) {
        if (!gestures.addListener(node, eventName, handler)) {
          super._addEventListenerToNode(node, eventName, handler);
        }
      }

      /**
       * Remove the event listener to the node if it is a gestures event.
       *
       * @param {!Node} node Node to remove event listener from
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to remove
       * @return {void}
       */
      _removeEventListenerFromNode(node, eventName, handler) {
        if (!gestures.removeListener(node, eventName, handler)) {
          super._removeEventListenerFromNode(node, eventName, handler);
        }
      }

    }

    return GestureEventListeners;

  });

})();


  (function() {
    'use strict';

    const HOST_DIR = /:host\(:dir\((ltr|rtl)\)\)/g;
    const HOST_DIR_REPLACMENT = ':host([dir="$1"])';

    const EL_DIR = /([\s\w-#\.\[\]\*]*):dir\((ltr|rtl)\)/g;
    const EL_DIR_REPLACMENT = ':host([dir="$2"]) $1';

    const DIR_CHECK = /:dir\((?:ltr|rtl)\)/;
    
    const SHIM_SHADOW = Boolean(window['ShadyDOM'] && window['ShadyDOM']['inUse']);

    /**
     * @type {!Array<!Polymer_DirMixin>}
     */
    const DIR_INSTANCES = [];

    /** @type {MutationObserver} */
    let observer = null;

    let DOCUMENT_DIR = '';

    function getRTL() {
      DOCUMENT_DIR = document.documentElement.getAttribute('dir');
    }

    /**
     * @param {!Polymer_DirMixin} instance Instance to set RTL status on
     */
    function setRTL(instance) {
      if (!instance.__autoDirOptOut) {
        const el = /** @type {!HTMLElement} */(instance);
        el.setAttribute('dir', DOCUMENT_DIR);
      }
    }

    function updateDirection() {
      getRTL();
      DOCUMENT_DIR = document.documentElement.getAttribute('dir');
      for (let i = 0; i < DIR_INSTANCES.length; i++) {
        setRTL(DIR_INSTANCES[i]);
      }
    }

    function takeRecords() {
      if (observer && observer.takeRecords().length) {
        updateDirection();
      }
    }

    /**
     * Element class mixin that allows elements to use the `:dir` CSS Selector to have
     * text direction specific styling.
     *
     * With this mixin, any stylesheet provided in the template will transform `:dir` into
     * `:host([dir])` and sync direction with the page via the element's `dir` attribute.
     *
     * Elements can opt out of the global page text direction by setting the `dir` attribute
     * directly in `ready()` or in HTML.
     *
     * Caveats:
     * - Applications must set `<html dir="ltr">` or `<html dir="rtl">` to sync direction
     * - Automatic left-to-right or right-to-left styling is sync'd with the `<html>` element only.
     * - Changing `dir` at runtime is supported.
     * - Opting out of the global direction styling is permanent
     *
     * @mixinFunction
     * @polymer
     * @appliesMixin Polymer.PropertyAccessors
     * @memberof Polymer
     */
    Polymer.DirMixin = Polymer.dedupingMixin((base) => {

      if (!SHIM_SHADOW) {
        if (!observer) {
          getRTL();
          observer = new MutationObserver(updateDirection);
          observer.observe(document.documentElement, {attributes: true, attributeFilter: ['dir']});
        }
      }

      /**
       * @constructor
       * @extends {base}
       * @implements {Polymer_PropertyAccessors}
       * @private
       */
      const elementBase = Polymer.PropertyAccessors(base);

      /**
       * @polymer
       * @mixinClass
       * @implements {Polymer_DirMixin}
       */
      class Dir extends elementBase {

        /**
         * @override
         * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
         */
        static _processStyleText(cssText, baseURI) {
          cssText = super._processStyleText(cssText, baseURI);
          if (!SHIM_SHADOW && DIR_CHECK.test(cssText)) {
            cssText = this._replaceDirInCssText(cssText);
            this.__activateDir = true;
          }
          return cssText;
        }

        /**
         * Replace `:dir` in the given CSS text
         *
         * @param {string} text CSS text to replace DIR
         * @return {string} Modified CSS
         */
        static _replaceDirInCssText(text) {
          let replacedText = text;
          replacedText = replacedText.replace(HOST_DIR, HOST_DIR_REPLACMENT);
          replacedText = replacedText.replace(EL_DIR, EL_DIR_REPLACMENT);
          return replacedText;
        }

        constructor() {
          super();
          /** @type {boolean} */
          this.__autoDirOptOut = false;
        }

        /**
         * @suppress {invalidCasts} Closure doesn't understand that `this` is an HTMLElement
         * @return {void}
         */
        ready() {
          super.ready();
          this.__autoDirOptOut = /** @type {!HTMLElement} */(this).hasAttribute('dir');
        }

        /**
         * @suppress {missingProperties} If it exists on elementBase, it can be super'd
         * @return {void}
         */
        connectedCallback() {
          if (elementBase.prototype.connectedCallback) {
            super.connectedCallback();
          }
          if (this.constructor.__activateDir) {
            takeRecords();
            DIR_INSTANCES.push(this);
            setRTL(this);
          }
        }

        /**
         * @suppress {missingProperties} If it exists on elementBase, it can be super'd
         * @return {void}
         */
        disconnectedCallback() {
          if (elementBase.prototype.disconnectedCallback) {
            super.disconnectedCallback();
          }
          if (this.constructor.__activateDir) {
            const idx = DIR_INSTANCES.indexOf(this);
            if (idx > -1) {
              DIR_INSTANCES.splice(idx, 1);
            }
          }
        }
      }

      Dir.__activateDir = false;

      return Dir;
    });
  })();



(function() {

  'use strict';

  // run a callback when HTMLImports are ready or immediately if
  // this api is not available.
  function whenImportsReady(cb) {
    if (window.HTMLImports) {
      HTMLImports.whenReady(cb);
    } else {
      cb();
    }
  }

  /**
   * Convenience method for importing an HTML document imperatively.
   *
   * This method creates a new `<link rel="import">` element with
   * the provided URL and appends it to the document to start loading.
   * In the `onload` callback, the `import` property of the `link`
   * element will contain the imported document contents.
   *
   * @memberof Polymer
   * @param {string} href URL to document to load.
   * @param {?function(!Event):void=} onload Callback to notify when an import successfully
   *   loaded.
   * @param {?function(!ErrorEvent):void=} onerror Callback to notify when an import
   *   unsuccessfully loaded.
   * @param {boolean=} optAsync True if the import should be loaded `async`.
   *   Defaults to `false`.
   * @return {!HTMLLinkElement} The link element for the URL to be loaded.
   */
  Polymer.importHref = function(href, onload, onerror, optAsync) {
    let link = /** @type {HTMLLinkElement} */
      (document.head.querySelector('link[href="' + href + '"][import-href]'));
    if (!link) {
      link = /** @type {HTMLLinkElement} */ (document.createElement('link'));
      link.rel = 'import';
      link.href = href;
      link.setAttribute('import-href', '');
    }
    // always ensure link has `async` attribute if user specified one,
    // even if it was previously not async. This is considered less confusing.
    if (optAsync) {
      link.setAttribute('async', '');
    }
    // NOTE: the link may now be in 3 states: (1) pending insertion,
    // (2) inflight, (3) already loaded. In each case, we need to add
    // event listeners to process callbacks.
    let cleanup = function() {
      link.removeEventListener('load', loadListener);
      link.removeEventListener('error', errorListener);
    };
    let loadListener = function(event) {
      cleanup();
      // In case of a successful load, cache the load event on the link so
      // that it can be used to short-circuit this method in the future when
      // it is called with the same href param.
      link.__dynamicImportLoaded = true;
      if (onload) {
        whenImportsReady(() => {
          onload(event);
        });
      }
    };
    let errorListener = function(event) {
      cleanup();
      // In case of an error, remove the link from the document so that it
      // will be automatically created again the next time `importHref` is
      // called.
      if (link.parentNode) {
        link.parentNode.removeChild(link);
      }
      if (onerror) {
        whenImportsReady(() => {
          onerror(event);
        });
      }
    };
    link.addEventListener('load', loadListener);
    link.addEventListener('error', errorListener);
    if (link.parentNode == null) {
      document.head.appendChild(link);
    // if the link already loaded, dispatch a fake load event
    // so that listeners are called and get a proper event argument.
    } else if (link.__dynamicImportLoaded) {
      link.dispatchEvent(new Event('load'));
    }
    return link;
  };

})();


(function() {

  'use strict';

  let scheduled = false;
  let beforeRenderQueue = [];
  let afterRenderQueue = [];

  function schedule() {
    scheduled = true;
    // before next render
    requestAnimationFrame(function() {
      scheduled = false;
      flushQueue(beforeRenderQueue);
      // after the render
      setTimeout(function() {
        runQueue(afterRenderQueue);
      });
    });
  }

  function flushQueue(queue) {
    while (queue.length) {
      callMethod(queue.shift());
    }
  }

  function runQueue(queue) {
    for (let i=0, l=queue.length; i < l; i++) {
      callMethod(queue.shift());
    }
  }

  function callMethod(info) {
    const context = info[0];
    const callback = info[1];
    const args = info[2];
    try {
      callback.apply(context, args);
    } catch(e) {
      setTimeout(() => {
        throw e;
      });
    }
  }

  function flush() {
    while (beforeRenderQueue.length || afterRenderQueue.length) {
      flushQueue(beforeRenderQueue);
      flushQueue(afterRenderQueue);
    }
    scheduled = false;
  }

  /**
   * Module for scheduling flushable pre-render and post-render tasks.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module for scheduling flushable pre-render and post-render tasks.
   */
  Polymer.RenderStatus = {

    /**
     * Enqueues a callback which will be run before the next render, at
     * `requestAnimationFrame` timing.
     *
     * This method is useful for enqueuing work that requires DOM measurement,
     * since measurement may not be reliable in custom element callbacks before
     * the first render, as well as for batching measurement tasks in general.
     *
     * Tasks in this queue may be flushed by calling `Polymer.RenderStatus.flush()`.
     *
     * @memberof Polymer.RenderStatus
     * @param {*} context Context object the callback function will be bound to
     * @param {function(...*):void} callback Callback function
     * @param {!Array=} args An array of arguments to call the callback function with
     * @return {void}
     */
    beforeNextRender: function(context, callback, args) {
      if (!scheduled) {
        schedule();
      }
      beforeRenderQueue.push([context, callback, args]);
    },

    /**
     * Enqueues a callback which will be run after the next render, equivalent
     * to one task (`setTimeout`) after the next `requestAnimationFrame`.
     *
     * This method is useful for tuning the first-render performance of an
     * element or application by deferring non-critical work until after the
     * first paint.  Typical non-render-critical work may include adding UI
     * event listeners and aria attributes.
     *
     * @memberof Polymer.RenderStatus
     * @param {*} context Context object the callback function will be bound to
     * @param {function(...*):void} callback Callback function
     * @param {!Array=} args An array of arguments to call the callback function with
     * @return {void}
     */
    afterNextRender: function(context, callback, args) {
      if (!scheduled) {
        schedule();
      }
      afterRenderQueue.push([context, callback, args]);
    },

    /**
     * Flushes all `beforeNextRender` tasks, followed by all `afterNextRender`
     * tasks.
     *
     * @memberof Polymer.RenderStatus
     * @return {void}
     */
    flush: flush

  };

})();


(function() {
  'use strict';

  // unresolved

  function resolve() {
    document.body.removeAttribute('unresolved');
  }

  if (window.WebComponents) {
    window.addEventListener('WebComponentsReady', resolve);
  } else {
    if (document.readyState === 'interactive' || document.readyState === 'complete') {
      resolve();
    } else {
      window.addEventListener('DOMContentLoaded', resolve);
    }
  }

})();


(function() {

  'use strict';

  function newSplice(index, removed, addedCount) {
    return {
      index: index,
      removed: removed,
      addedCount: addedCount
    };
  }

  const EDIT_LEAVE = 0;
  const EDIT_UPDATE = 1;
  const EDIT_ADD = 2;
  const EDIT_DELETE = 3;

  // Note: This function is *based* on the computation of the Levenshtein
  // "edit" distance. The one change is that "updates" are treated as two
  // edits - not one. With Array splices, an update is really a delete
  // followed by an add. By retaining this, we optimize for "keeping" the
  // maximum array items in the original array. For example:
  //
  //   'xxxx123' -> '123yyyy'
  //
  // With 1-edit updates, the shortest path would be just to update all seven
  // characters. With 2-edit updates, we delete 4, leave 3, and add 4. This
  // leaves the substring '123' intact.
  function calcEditDistances(current, currentStart, currentEnd,
                              old, oldStart, oldEnd) {
    // "Deletion" columns
    let rowCount = oldEnd - oldStart + 1;
    let columnCount = currentEnd - currentStart + 1;
    let distances = new Array(rowCount);

    // "Addition" rows. Initialize null column.
    for (let i = 0; i < rowCount; i++) {
      distances[i] = new Array(columnCount);
      distances[i][0] = i;
    }

    // Initialize null row
    for (let j = 0; j < columnCount; j++)
      distances[0][j] = j;

    for (let i = 1; i < rowCount; i++) {
      for (let j = 1; j < columnCount; j++) {
        if (equals(current[currentStart + j - 1], old[oldStart + i - 1]))
          distances[i][j] = distances[i - 1][j - 1];
        else {
          let north = distances[i - 1][j] + 1;
          let west = distances[i][j - 1] + 1;
          distances[i][j] = north < west ? north : west;
        }
      }
    }

    return distances;
  }

  // This starts at the final weight, and walks "backward" by finding
  // the minimum previous weight recursively until the origin of the weight
  // matrix.
  function spliceOperationsFromEditDistances(distances) {
    let i = distances.length - 1;
    let j = distances[0].length - 1;
    let current = distances[i][j];
    let edits = [];
    while (i > 0 || j > 0) {
      if (i == 0) {
        edits.push(EDIT_ADD);
        j--;
        continue;
      }
      if (j == 0) {
        edits.push(EDIT_DELETE);
        i--;
        continue;
      }
      let northWest = distances[i - 1][j - 1];
      let west = distances[i - 1][j];
      let north = distances[i][j - 1];

      let min;
      if (west < north)
        min = west < northWest ? west : northWest;
      else
        min = north < northWest ? north : northWest;

      if (min == northWest) {
        if (northWest == current) {
          edits.push(EDIT_LEAVE);
        } else {
          edits.push(EDIT_UPDATE);
          current = northWest;
        }
        i--;
        j--;
      } else if (min == west) {
        edits.push(EDIT_DELETE);
        i--;
        current = west;
      } else {
        edits.push(EDIT_ADD);
        j--;
        current = north;
      }
    }

    edits.reverse();
    return edits;
  }

  /**
   * Splice Projection functions:
   *
   * A splice map is a representation of how a previous array of items
   * was transformed into a new array of items. Conceptually it is a list of
   * tuples of
   *
   *   <index, removed, addedCount>
   *
   * which are kept in ascending index order of. The tuple represents that at
   * the |index|, |removed| sequence of items were removed, and counting forward
   * from |index|, |addedCount| items were added.
   */

  /**
   * Lacking individual splice mutation information, the minimal set of
   * splices can be synthesized given the previous state and final state of an
   * array. The basic approach is to calculate the edit distance matrix and
   * choose the shortest path through it.
   *
   * Complexity: O(l * p)
   *   l: The length of the current array
   *   p: The length of the old array
   *
   * @param {!Array} current The current "changed" array for which to
   * calculate splices.
   * @param {number} currentStart Starting index in the `current` array for
   * which splices are calculated.
   * @param {number} currentEnd Ending index in the `current` array for
   * which splices are calculated.
   * @param {!Array} old The original "unchanged" array to compare `current`
   * against to determine splices.
   * @param {number} oldStart Starting index in the `old` array for
   * which splices are calculated.
   * @param {number} oldEnd Ending index in the `old` array for
   * which splices are calculated.
   * @return {!Array} Returns an array of splice record objects. Each of these
   * contains: `index` the location where the splice occurred; `removed`
   * the array of removed items from this location; `addedCount` the number
   * of items added at this location.
   */
  function calcSplices(current, currentStart, currentEnd,
                        old, oldStart, oldEnd) {
    let prefixCount = 0;
    let suffixCount = 0;
    let splice;

    let minLength = Math.min(currentEnd - currentStart, oldEnd - oldStart);
    if (currentStart == 0 && oldStart == 0)
      prefixCount = sharedPrefix(current, old, minLength);

    if (currentEnd == current.length && oldEnd == old.length)
      suffixCount = sharedSuffix(current, old, minLength - prefixCount);

    currentStart += prefixCount;
    oldStart += prefixCount;
    currentEnd -= suffixCount;
    oldEnd -= suffixCount;

    if (currentEnd - currentStart == 0 && oldEnd - oldStart == 0)
      return [];

    if (currentStart == currentEnd) {
      splice = newSplice(currentStart, [], 0);
      while (oldStart < oldEnd)
        splice.removed.push(old[oldStart++]);

      return [ splice ];
    } else if (oldStart == oldEnd)
      return [ newSplice(currentStart, [], currentEnd - currentStart) ];

    let ops = spliceOperationsFromEditDistances(
        calcEditDistances(current, currentStart, currentEnd,
                               old, oldStart, oldEnd));

    splice = undefined;
    let splices = [];
    let index = currentStart;
    let oldIndex = oldStart;
    for (let i = 0; i < ops.length; i++) {
      switch(ops[i]) {
        case EDIT_LEAVE:
          if (splice) {
            splices.push(splice);
            splice = undefined;
          }

          index++;
          oldIndex++;
          break;
        case EDIT_UPDATE:
          if (!splice)
            splice = newSplice(index, [], 0);

          splice.addedCount++;
          index++;

          splice.removed.push(old[oldIndex]);
          oldIndex++;
          break;
        case EDIT_ADD:
          if (!splice)
            splice = newSplice(index, [], 0);

          splice.addedCount++;
          index++;
          break;
        case EDIT_DELETE:
          if (!splice)
            splice = newSplice(index, [], 0);

          splice.removed.push(old[oldIndex]);
          oldIndex++;
          break;
      }
    }

    if (splice) {
      splices.push(splice);
    }
    return splices;
  }

  function sharedPrefix(current, old, searchLength) {
    for (let i = 0; i < searchLength; i++)
      if (!equals(current[i], old[i]))
        return i;
    return searchLength;
  }

  function sharedSuffix(current, old, searchLength) {
    let index1 = current.length;
    let index2 = old.length;
    let count = 0;
    while (count < searchLength && equals(current[--index1], old[--index2]))
      count++;

    return count;
  }

  function calculateSplices(current, previous) {
    return calcSplices(current, 0, current.length, previous, 0,
                            previous.length);
  }

  function equals(currentValue, previousValue) {
    return currentValue === previousValue;
  }

  /**
   * @namespace
   * @memberof Polymer
   * @summary Module that provides utilities for diffing arrays.
   */
  Polymer.ArraySplice = {
    /**
     * Returns an array of splice records indicating the minimum edits required
     * to transform the `previous` array into the `current` array.
     *
     * Splice records are ordered by index and contain the following fields:
     * - `index`: index where edit started
     * - `removed`: array of removed items from this index
     * - `addedCount`: number of items added at this index
     *
     * This function is based on the Levenshtein "minimum edit distance"
     * algorithm. Note that updates are treated as removal followed by addition.
     *
     * The worst-case time complexity of this algorithm is `O(l * p)`
     *   l: The length of the current array
     *   p: The length of the previous array
     *
     * However, the worst-case complexity is reduced by an `O(n)` optimization
     * to detect any shared prefix & suffix between the two arrays and only
     * perform the more expensive minimum edit distance calculation over the
     * non-shared portions of the arrays.
     *
     * @function
     * @memberof Polymer.ArraySplice
     * @param {!Array} current The "changed" array for which splices will be
     * calculated.
     * @param {!Array} previous The "unchanged" original array to compare
     * `current` against to determine the splices.
     * @return {!Array} Returns an array of splice record objects. Each of these
     * contains: `index` the location where the splice occurred; `removed`
     * the array of removed items from this location; `addedCount` the number
     * of items added at this location.
     */
    calculateSplices
  };

})();


(function() {
  'use strict';

  /**
   * Returns true if `node` is a slot element
   * @param {Node} node Node to test.
   * @return {boolean} Returns true if the given `node` is a slot
   * @private
   */
  function isSlot(node) {
    return (node.localName === 'slot');
  }

  /**
   * Class that listens for changes (additions or removals) to
   * "flattened nodes" on a given `node`. The list of flattened nodes consists
   * of a node's children and, for any children that are `<slot>` elements,
   * the expanded flattened list of `assignedNodes`.
   * For example, if the observed node has children `<a></a><slot></slot><b></b>`
   * and the `<slot>` has one `<div>` assigned to it, then the flattened
   * nodes list is `<a></a><div></div><b></b>`. If the `<slot>` has other
   * `<slot>` elements assigned to it, these are flattened as well.
   *
   * The provided `callback` is called whenever any change to this list
   * of flattened nodes occurs, where an addition or removal of a node is
   * considered a change. The `callback` is called with one argument, an object
   * containing an array of any `addedNodes` and `removedNodes`.
   *
   * Note: the callback is called asynchronous to any changes
   * at a microtask checkpoint. This is because observation is performed using
   * `MutationObserver` and the `<slot>` element's `slotchange` event which
   * are asynchronous.
   *
   * An example:
   * ```js
   * class TestSelfObserve extends Polymer.Element {
   *   static get is() { return 'test-self-observe';}
   *   connectedCallback() {
   *     super.connectedCallback();
   *     this._observer = new Polymer.FlattenedNodesObserver(this, (info) => {
   *       this.info = info;
   *     });
   *   }
   *   disconnectedCallback() {
   *     super.disconnectedCallback();
   *     this._observer.disconnect();
   *   }
   * }
   * customElements.define(TestSelfObserve.is, TestSelfObserve);
   * ```
   *
   * @memberof Polymer
   * @summary Class that listens for changes (additions or removals) to
   * "flattened nodes" on a given `node`.
   */
  class FlattenedNodesObserver {

    /**
     * Returns the list of flattened nodes for the given `node`.
     * This list consists of a node's children and, for any children
     * that are `<slot>` elements, the expanded flattened list of `assignedNodes`.
     * For example, if the observed node has children `<a></a><slot></slot><b></b>`
     * and the `<slot>` has one `<div>` assigned to it, then the flattened
     * nodes list is `<a></a><div></div><b></b>`. If the `<slot>` has other
     * `<slot>` elements assigned to it, these are flattened as well.
     *
     * @param {HTMLElement|HTMLSlotElement} node The node for which to return the list of flattened nodes.
     * @return {Array} The list of flattened nodes for the given `node`.
    */
    static getFlattenedNodes(node) {
      if (isSlot(node)) {
        node = /** @type {HTMLSlotElement} */(node); // eslint-disable-line no-self-assign
        return node.assignedNodes({flatten: true});
      } else {
        return Array.from(node.childNodes).map((node) => {
          if (isSlot(node)) {
            node = /** @type {HTMLSlotElement} */(node); // eslint-disable-line no-self-assign
            return node.assignedNodes({flatten: true});
          } else {
            return [node];
          }
        }).reduce((a, b) => a.concat(b), []);
      }
    }

    /**
     * @param {Element} target Node on which to listen for changes.
     * @param {?function(!Element, { target: !Element, addedNodes: !Array<!Element>, removedNodes: !Array<!Element> }):void} callback Function called when there are additions
     * or removals from the target's list of flattened nodes.
    */
    constructor(target, callback) {
      /**
       * @type {MutationObserver}
       * @private
       */
      this._shadyChildrenObserver = null;
      /**
       * @type {MutationObserver}
       * @private
       */
      this._nativeChildrenObserver = null;
      this._connected = false;
      /**
       * @type {Element}
       * @private
       */
      this._target = target;
      this.callback = callback;
      this._effectiveNodes = [];
      this._observer = null;
      this._scheduled = false;
      /**
       * @type {function()}
       * @private
       */
      this._boundSchedule = () => {
        this._schedule();
      };
      this.connect();
      this._schedule();
    }

    /**
     * Activates an observer. This method is automatically called when
     * a `FlattenedNodesObserver` is created. It should only be called to
     * re-activate an observer that has been deactivated via the `disconnect` method.
     *
     * @return {void}
     */
    connect() {
      if (isSlot(this._target)) {
        this._listenSlots([this._target]);
      } else if (this._target.children) {
        this._listenSlots(this._target.children);
        if (window.ShadyDOM) {
          this._shadyChildrenObserver =
            ShadyDOM.observeChildren(this._target, (mutations) => {
              this._processMutations(mutations);
            });
        } else {
          this._nativeChildrenObserver =
            new MutationObserver((mutations) => {
              this._processMutations(mutations);
            });
          this._nativeChildrenObserver.observe(this._target, {childList: true});
        }
      }
      this._connected = true;
    }

    /**
     * Deactivates the flattened nodes observer. After calling this method
     * the observer callback will not be called when changes to flattened nodes
     * occur. The `connect` method may be subsequently called to reactivate
     * the observer.
     *
     * @return {void}
     */
    disconnect() {
      if (isSlot(this._target)) {
        this._unlistenSlots([this._target]);
      } else if (this._target.children) {
        this._unlistenSlots(this._target.children);
        if (window.ShadyDOM && this._shadyChildrenObserver) {
          ShadyDOM.unobserveChildren(this._shadyChildrenObserver);
          this._shadyChildrenObserver = null;
        } else if (this._nativeChildrenObserver) {
          this._nativeChildrenObserver.disconnect();
          this._nativeChildrenObserver = null;
        }
      }
      this._connected = false;
    }

    /**
     * @return {void}
     * @private
     */
    _schedule() {
      if (!this._scheduled) {
        this._scheduled = true;
        Polymer.Async.microTask.run(() => this.flush());
      }
    }

    /**
     * @param {Array<MutationRecord>} mutations Mutations signaled by the mutation observer
     * @return {void}
     * @private
     */
    _processMutations(mutations) {
      this._processSlotMutations(mutations);
      this.flush();
    }

    /**
     * @param {Array<MutationRecord>} mutations Mutations signaled by the mutation observer
     * @return {void}
     * @private
     */
    _processSlotMutations(mutations) {
      if (mutations) {
        for (let i=0; i < mutations.length; i++) {
          let mutation = mutations[i];
          if (mutation.addedNodes) {
            this._listenSlots(mutation.addedNodes);
          }
          if (mutation.removedNodes) {
            this._unlistenSlots(mutation.removedNodes);
          }
        }
      }
    }

    /**
     * Flushes the observer causing any pending changes to be immediately
     * delivered the observer callback. By default these changes are delivered
     * asynchronously at the next microtask checkpoint.
     *
     * @return {boolean} Returns true if any pending changes caused the observer
     * callback to run.
     */
    flush() {
      if (!this._connected) {
        return false;
      }
      if (window.ShadyDOM) {
        ShadyDOM.flush();
      }
      if (this._nativeChildrenObserver) {
        this._processSlotMutations(this._nativeChildrenObserver.takeRecords());
      } else if (this._shadyChildrenObserver) {
        this._processSlotMutations(this._shadyChildrenObserver.takeRecords());
      }
      this._scheduled = false;
      let info = {
        target: this._target,
        addedNodes: [],
        removedNodes: []
      };
      let newNodes = this.constructor.getFlattenedNodes(this._target);
      let splices = Polymer.ArraySplice.calculateSplices(newNodes,
        this._effectiveNodes);
      // process removals
      for (let i=0, s; (i<splices.length) && (s=splices[i]); i++) {
        for (let j=0, n; (j < s.removed.length) && (n=s.removed[j]); j++) {
          info.removedNodes.push(n);
        }
      }
      // process adds
      for (let i=0, s; (i<splices.length) && (s=splices[i]); i++) {
        for (let j=s.index; j < s.index + s.addedCount; j++) {
          info.addedNodes.push(newNodes[j]);
        }
      }
      // update cache
      this._effectiveNodes = newNodes;
      let didFlush = false;
      if (info.addedNodes.length || info.removedNodes.length) {
        didFlush = true;
        this.callback.call(this._target, info);
      }
      return didFlush;
    }

    /**
     * @param {!Array<Element|Node>|!NodeList<Node>} nodeList Nodes that could change
     * @return {void}
     * @private
     */
    _listenSlots(nodeList) {
      for (let i=0; i < nodeList.length; i++) {
        let n = nodeList[i];
        if (isSlot(n)) {
          n.addEventListener('slotchange', this._boundSchedule);
        }
      }
    }

    /**
     * @param {!Array<Element|Node>|!NodeList<Node>} nodeList Nodes that could change
     * @return {void}
     * @private
     */
    _unlistenSlots(nodeList) {
      for (let i=0; i < nodeList.length; i++) {
        let n = nodeList[i];
        if (isSlot(n)) {
          n.removeEventListener('slotchange', this._boundSchedule);
        }
      }
    }

  }

  Polymer.FlattenedNodesObserver = FlattenedNodesObserver;

})();


(function() {
  'use strict';

  let debouncerQueue = [];

  /**
   * Adds a `Polymer.Debouncer` to a list of globally flushable tasks.
   *
   * @memberof Polymer
   * @param {!Polymer.Debouncer} debouncer Debouncer to enqueue
   * @return {void}
   */
  Polymer.enqueueDebouncer = function(debouncer) {
    debouncerQueue.push(debouncer);
  };

  function flushDebouncers() {
    const didFlush = Boolean(debouncerQueue.length);
    while (debouncerQueue.length) {
      try {
        debouncerQueue.shift().flush();
      } catch(e) {
        setTimeout(() => {
          throw e;
        });
      }
    }
    return didFlush;
  }

  /**
   * Forces several classes of asynchronously queued tasks to flush:
   * - Debouncers added via `enqueueDebouncer`
   * - ShadyDOM distribution
   *
   * @memberof Polymer
   * @return {void}
   */
  Polymer.flush = function() {
    let shadyDOM, debouncers;
    do {
      shadyDOM = window.ShadyDOM && ShadyDOM.flush();
      if (window.ShadyCSS && window.ShadyCSS.ScopingShim) {
        window.ShadyCSS.ScopingShim.flush();
      }
      debouncers = flushDebouncers();
    } while (shadyDOM || debouncers);
  };

})();


(function() {
  'use strict';

  const p = Element.prototype;
  /**
   * @const {function(this:Node, string): boolean}
   */
  const normalizedMatchesSelector = p.matches || p.matchesSelector ||
    p.mozMatchesSelector || p.msMatchesSelector ||
    p.oMatchesSelector || p.webkitMatchesSelector;

  /**
   * Cross-platform `element.matches` shim.
   *
   * @function matchesSelector
   * @memberof Polymer.dom
   * @param {!Node} node Node to check selector against
   * @param {string} selector Selector to match
   * @return {boolean} True if node matched selector
   */
  const matchesSelector = function(node, selector) {
    return normalizedMatchesSelector.call(node, selector);
  };

  /**
   * Node API wrapper class returned from `Polymer.dom.(target)` when
   * `target` is a `Node`.
   *
   * @memberof Polymer
   */
  class DomApi {

    /**
     * @param {Node} node Node for which to create a Polymer.dom helper object.
     */
    constructor(node) {
      this.node = node;
    }

    /**
     * Returns an instance of `Polymer.FlattenedNodesObserver` that
     * listens for node changes on this element.
     *
     * @param {function(!Element, { target: !Element, addedNodes: !Array<!Element>, removedNodes: !Array<!Element> }):void} callback Called when direct or distributed children
     *   of this element changes
     * @return {!Polymer.FlattenedNodesObserver} Observer instance
     */
    observeNodes(callback) {
      return new Polymer.FlattenedNodesObserver(this.node, callback);
    }

    /**
     * Disconnects an observer previously created via `observeNodes`
     *
     * @param {!Polymer.FlattenedNodesObserver} observerHandle Observer instance
     *   to disconnect.
     * @return {void}
     */
    unobserveNodes(observerHandle) {
      observerHandle.disconnect();
    }

    /**
     * Provided as a backwards-compatible API only.  This method does nothing.
     * @return {void}
     */
    notifyObserver() {}

    /**
     * Returns true if the provided node is contained with this element's
     * light-DOM children or shadow root, including any nested shadow roots
     * of children therein.
     *
     * @param {Node} node Node to test
     * @return {boolean} Returns true if the given `node` is contained within
     *   this element's light or shadow DOM.
     */
    deepContains(node) {
      if (this.node.contains(node)) {
        return true;
      }
      let n = node;
      let doc = node.ownerDocument;
      // walk from node to `this` or `document`
      while (n && n !== doc && n !== this.node) {
        // use logical parentnode, or native ShadowRoot host
        n = n.parentNode || n.host;
      }
      return n === this.node;
    }

    /**
     * Returns the root node of this node.  Equivalent to `getRoodNode()`.
     *
     * @return {Node} Top most element in the dom tree in which the node
     * exists. If the node is connected to a document this is either a
     * shadowRoot or the document; otherwise, it may be the node
     * itself or a node or document fragment containing it.
     */
    getOwnerRoot() {
      return this.node.getRootNode();
    }

    /**
     * For slot elements, returns the nodes assigned to the slot; otherwise
     * an empty array. It is equivalent to `<slot>.addignedNodes({flatten:true})`.
     *
     * @return {!Array<!Node>} Array of assigned nodes
     */
    getDistributedNodes() {
      return (this.node.localName === 'slot') ?
        this.node.assignedNodes({flatten: true}) :
        [];
    }

    /**
     * Returns an array of all slots this element was distributed to.
     *
     * @return {!Array<!HTMLSlotElement>} Description
     */
    getDestinationInsertionPoints() {
      let ip$ = [];
      let n = this.node.assignedSlot;
      while (n) {
        ip$.push(n);
        n = n.assignedSlot;
      }
      return ip$;
    }

    /**
     * Calls `importNode` on the `ownerDocument` for this node.
     *
     * @param {!Node} node Node to import
     * @param {boolean} deep True if the node should be cloned deeply during
     *   import
     * @return {Node} Clone of given node imported to this owner document
     */
    importNode(node, deep) {
      let doc = this.node instanceof Document ? this.node :
        this.node.ownerDocument;
      return doc.importNode(node, deep);
    }

    /**
     * @return {!Array<!Node>} Returns a flattened list of all child nodes and
     * nodes assigned to child slots.
     */
    getEffectiveChildNodes() {
      return Polymer.FlattenedNodesObserver.getFlattenedNodes(this.node);
    }

    /**
     * Returns a filtered list of flattened child elements for this element based
     * on the given selector.
     *
     * @param {string} selector Selector to filter nodes against
     * @return {!Array<!HTMLElement>} List of flattened child elements
     */
    queryDistributedElements(selector) {
      let c$ = this.getEffectiveChildNodes();
      let list = [];
      for (let i=0, l=c$.length, c; (i<l) && (c=c$[i]); i++) {
        if ((c.nodeType === Node.ELEMENT_NODE) &&
            matchesSelector(c, selector)) {
          list.push(c);
        }
      }
      return list;
    }

    /**
     * For shadow roots, returns the currently focused element within this
     * shadow root.
     *
     * @return {Node|undefined} Currently focused element
     */
    get activeElement() {
      let node = this.node;
      return node._activeElement !== undefined ? node._activeElement : node.activeElement;
    }
  }

  function forwardMethods(proto, methods) {
    for (let i=0; i < methods.length; i++) {
      let method = methods[i];
      /* eslint-disable valid-jsdoc */
      proto[method] = /** @this {DomApi} */ function() {
        return this.node[method].apply(this.node, arguments);
      };
      /* eslint-enable */
    }
  }

  function forwardReadOnlyProperties(proto, properties) {
    for (let i=0; i < properties.length; i++) {
      let name = properties[i];
      Object.defineProperty(proto, name, {
        get: function() {
          const domApi = /** @type {DomApi} */(this);
          return domApi.node[name];
        },
        configurable: true
      });
    }
  }

  function forwardProperties(proto, properties) {
    for (let i=0; i < properties.length; i++) {
      let name = properties[i];
      Object.defineProperty(proto, name, {
        get: function() {
          const domApi = /** @type {DomApi} */(this);
          return domApi.node[name];
        },
        set: function(value) {
          /** @type {DomApi} */ (this).node[name] = value;
        },
        configurable: true
      });
    }
  }

  forwardMethods(DomApi.prototype, [
    'cloneNode', 'appendChild', 'insertBefore', 'removeChild',
    'replaceChild', 'setAttribute', 'removeAttribute',
    'querySelector', 'querySelectorAll'
  ]);

  forwardReadOnlyProperties(DomApi.prototype, [
    'parentNode', 'firstChild', 'lastChild',
    'nextSibling', 'previousSibling', 'firstElementChild',
    'lastElementChild', 'nextElementSibling', 'previousElementSibling',
    'childNodes', 'children', 'classList'
  ]);

  forwardProperties(DomApi.prototype, [
    'textContent', 'innerHTML'
  ]);


  /**
   * Event API wrapper class returned from `Polymer.dom.(target)` when
   * `target` is an `Event`.
   */
  class EventApi {
    constructor(event) {
      this.event = event;
    }

    /**
     * Returns the first node on the `composedPath` of this event.
     *
     * @return {!EventTarget} The node this event was dispatched to
     */
    get rootTarget() {
      return this.event.composedPath()[0];
    }

    /**
     * Returns the local (re-targeted) target for this event.
     *
     * @return {!EventTarget} The local (re-targeted) target for this event.
     */
    get localTarget() {
      return this.event.target;
    }

    /**
     * Returns the `composedPath` for this event.
     * @return {!Array<!EventTarget>} The nodes this event propagated through
     */
    get path() {
      return this.event.composedPath();
    }
  }

  Polymer.DomApi = DomApi;

  /**
   * @function
   * @param {boolean=} deep
   * @return {!Node}
   */
  Polymer.DomApi.prototype.cloneNode;
  /**
   * @function
   * @param {!Node} node
   * @return {!Node}
   */
  Polymer.DomApi.prototype.appendChild;
  /**
   * @function
   * @param {!Node} newChild
   * @param {Node} refChild
   * @return {!Node}
   */
  Polymer.DomApi.prototype.insertBefore;
  /**
   * @function
   * @param {!Node} node
   * @return {!Node}
   */
  Polymer.DomApi.prototype.removeChild;
  /**
   * @function
   * @param {!Node} oldChild
   * @param {!Node} newChild
   * @return {!Node}
   */
  Polymer.DomApi.prototype.replaceChild;
  /**
   * @function
   * @param {string} name
   * @param {string} value
   * @return {void}
   */
  Polymer.DomApi.prototype.setAttribute;
  /**
   * @function
   * @param {string} name
   * @return {void}
   */
  Polymer.DomApi.prototype.removeAttribute;
  /**
   * @function
   * @param {string} selector
   * @return {?Element}
   */
  Polymer.DomApi.prototype.querySelector;
  /**
   * @function
   * @param {string} selector
   * @return {!NodeList<!Element>}
   */
  Polymer.DomApi.prototype.querySelectorAll;

  /**
   * Legacy DOM and Event manipulation API wrapper factory used to abstract
   * differences between native Shadow DOM and "Shady DOM" when polyfilling on
   * older browsers.
   *
   * Note that in Polymer 2.x use of `Polymer.dom` is no longer required and
   * in the majority of cases simply facades directly to the standard native
   * API.
   *
   * @namespace
   * @summary Legacy DOM and Event manipulation API wrapper factory used to
   * abstract differences between native Shadow DOM and "Shady DOM."
   * @memberof Polymer
   * @param {(Node|Event)=} obj Node or event to operate on
   * @return {!DomApi|!EventApi} Wrapper providing either node API or event API
   */
  Polymer.dom = function(obj) {
    obj = obj || document;
    if (!obj.__domApi) {
      let helper;
      if (obj instanceof Event) {
        helper = new EventApi(obj);
      } else {
        helper = new DomApi(obj);
      }
      obj.__domApi = helper;
    }
    return obj.__domApi;
  };

  Polymer.dom.matchesSelector = matchesSelector;

  /**
   * Forces several classes of asynchronously queued tasks to flush:
   * - Debouncers added via `Polymer.enqueueDebouncer`
   * - ShadyDOM distribution
   *
   * This method facades to `Polymer.flush`.
   *
   * @memberof Polymer.dom
   */
  Polymer.dom.flush = Polymer.flush;

  /**
   * Adds a `Polymer.Debouncer` to a list of globally flushable tasks.
   *
   * This method facades to `Polymer.enqueueDebouncer`.
   *
   * @memberof Polymer.dom
   * @param {!Polymer.Debouncer} debouncer Debouncer to enqueue
   */
  Polymer.dom.addDebouncer = Polymer.enqueueDebouncer;
})();


(function() {

  'use strict';

  let styleInterface = window.ShadyCSS;

  /**
   * Element class mixin that provides Polymer's "legacy" API intended to be
   * backward-compatible to the greatest extent possible with the API
   * found on the Polymer 1.x `Polymer.Base` prototype applied to all elements
   * defined using the `Polymer({...})` function.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.ElementMixin
   * @appliesMixin Polymer.GestureEventListeners
   * @property isAttached {boolean} Set to `true` in this element's
   *   `connectedCallback` and `false` in `disconnectedCallback`
   * @memberof Polymer
   * @summary Element class mixin that provides Polymer's "legacy" API
   */
  Polymer.LegacyElementMixin = Polymer.dedupingMixin((base) => {

    /**
     * @constructor
     * @extends {base}
     * @implements {Polymer_ElementMixin}
     * @implements {Polymer_GestureEventListeners}
     * @implements {Polymer_DirMixin}
     * @private
     */
    const legacyElementBase = Polymer.DirMixin(Polymer.GestureEventListeners(Polymer.ElementMixin(base)));

    /**
     * Map of simple names to touch action names
     * @dict
     */
    const DIRECTION_MAP = {
      'x': 'pan-x',
      'y': 'pan-y',
      'none': 'none',
      'all': 'auto'
    };

    /**
     * @polymer
     * @mixinClass
     * @extends {legacyElementBase}
     * @implements {Polymer_LegacyElementMixin}
     * @unrestricted
     */
    class LegacyElement extends legacyElementBase {

      constructor() {
        super();
        /** @type {boolean} */
        this.isAttached;
        /** @type {WeakMap<!Element, !Object<string, !Function>>} */
        this.__boundListeners;
        /** @type {Object<string, Function>} */
        this._debouncers;
      }

      /**
       * Forwards `importMeta` from the prototype (i.e. from the info object
       * passed to `Polymer({...})`) to the static API.
       *
       * @return {!Object} The `import.meta` object set on the prototype
       * @suppress {missingProperties} `this` is always in the instance in
       *  closure for some reason even in a static method, rather than the class
       */
      static get importMeta() {
        return this.prototype.importMeta;
      }

      /**
       * Legacy callback called during the `constructor`, for overriding
       * by the user.
       * @return {void}
       */
      created() {}

      /**
       * Provides an implementation of `connectedCallback`
       * which adds Polymer legacy API's `attached` method.
       * @return {void}
       * @override
       */
      connectedCallback() {
        super.connectedCallback();
        this.isAttached = true;
        this.attached();
      }

      /**
       * Legacy callback called during `connectedCallback`, for overriding
       * by the user.
       * @return {void}
       */
      attached() {}

      /**
       * Provides an implementation of `disconnectedCallback`
       * which adds Polymer legacy API's `detached` method.
       * @return {void}
       * @override
       */
      disconnectedCallback() {
        super.disconnectedCallback();
        this.isAttached = false;
        this.detached();
      }

      /**
       * Legacy callback called during `disconnectedCallback`, for overriding
       * by the user.
       * @return {void}
       */
      detached() {}

      /**
       * Provides an override implementation of `attributeChangedCallback`
       * which adds the Polymer legacy API's `attributeChanged` method.
       * @param {string} name Name of attribute.
       * @param {?string} old Old value of attribute.
       * @param {?string} value Current value of attribute.
       * @param {?string} namespace Attribute namespace.
       * @return {void}
       * @override
       */
      attributeChangedCallback(name, old, value, namespace) {
        if (old !== value) {
          super.attributeChangedCallback(name, old, value, namespace);
          this.attributeChanged(name, old, value);
        }
      }

      /**
       * Legacy callback called during `attributeChangedChallback`, for overriding
       * by the user.
       * @param {string} name Name of attribute.
       * @param {?string} old Old value of attribute.
       * @param {?string} value Current value of attribute.
       * @return {void}
       */
      attributeChanged(name, old, value) {} // eslint-disable-line no-unused-vars

      /**
       * Overrides the default `Polymer.PropertyEffects` implementation to
       * add support for class initialization via the `_registered` callback.
       * This is called only when the first instance of the element is created.
       *
       * @return {void}
       * @override
       * @suppress {invalidCasts}
       */
      _initializeProperties() {
        let proto = Object.getPrototypeOf(this);
        if (!proto.hasOwnProperty('__hasRegisterFinished')) {
          this._registered();
          // backstop in case the `_registered` implementation does not set this
          proto.__hasRegisterFinished = true;
        }
        super._initializeProperties();
        this.root = /** @type {HTMLElement} */(this);
        this.created();
        // Ensure listeners are applied immediately so that they are
        // added before declarative event listeners. This allows an element to
        // decorate itself via an event prior to any declarative listeners
        // seeing the event. Note, this ensures compatibility with 1.x ordering.
        this._applyListeners();
      }

      /**
       * Called automatically when an element is initializing.
       * Users may override this method to perform class registration time
       * work. The implementation should ensure the work is performed
       * only once for the class.
       * @protected
       * @return {void}
       */
      _registered() {}

      /**
       * Overrides the default `Polymer.PropertyEffects` implementation to
       * add support for installing `hostAttributes` and `listeners`.
       *
       * @return {void}
       * @override
       */
      ready() {
        this._ensureAttributes();
        super.ready();
      }

      /**
       * Ensures an element has required attributes. Called when the element
       * is being readied via `ready`. Users should override to set the
       * element's required attributes. The implementation should be sure
       * to check and not override existing attributes added by
       * the user of the element. Typically, setting attributes should be left
       * to the element user and not done here; reasonable exceptions include
       * setting aria roles and focusability.
       * @protected
       * @return {void}
       */
      _ensureAttributes() {}

      /**
       * Adds element event listeners. Called when the element
       * is being readied via `ready`. Users should override to
       * add any required element event listeners.
       * In performance critical elements, the work done here should be kept
       * to a minimum since it is done before the element is rendered. In
       * these elements, consider adding listeners asynchronously so as not to
       * block render.
       * @protected
       * @return {void}
       */
      _applyListeners() {}

      /**
       * Converts a typed JavaScript value to a string.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features. To customize
       * how properties are serialized to attributes for attribute bindings and
       * `reflectToAttribute: true` properties as well as this method, override
       * the `_serializeValue` method provided by `Polymer.PropertyAccessors`.
       *
       * @param {*} value Value to deserialize
       * @return {string | undefined} Serialized value
       */
      serialize(value) {
        return this._serializeValue(value);
      }

      /**
       * Converts a string to a typed JavaScript value.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features.  To customize
       * how attributes are deserialized to properties for in
       * `attributeChangedCallback`, override `_deserializeValue` method
       * provided by `Polymer.PropertyAccessors`.
       *
       * @param {string} value String to deserialize
       * @param {*} type Type to deserialize the string to
       * @return {*} Returns the deserialized value in the `type` given.
       */
      deserialize(value, type) {
        return this._deserializeValue(value, type);
      }

      /**
       * Serializes a property to its associated attribute.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features.
       *
       * @param {string} property Property name to reflect.
       * @param {string=} attribute Attribute name to reflect.
       * @param {*=} value Property value to reflect.
       * @return {void}
       */
      reflectPropertyToAttribute(property, attribute, value) {
        this._propertyToAttribute(property, attribute, value);
      }

      /**
       * Sets a typed value to an HTML attribute on a node.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features.
       *
       * @param {*} value Value to serialize.
       * @param {string} attribute Attribute name to serialize to.
       * @param {Element} node Element to set attribute to.
       * @return {void}
       */
      serializeValueToAttribute(value, attribute, node) {
        this._valueToNodeAttribute(/** @type {Element} */ (node || this), value, attribute);
      }

      /**
       * Copies own properties (including accessor descriptors) from a source
       * object to a target object.
       *
       * @param {Object} prototype Target object to copy properties to.
       * @param {Object} api Source object to copy properties from.
       * @return {Object} prototype object that was passed as first argument.
       */
      extend(prototype, api) {
        if (!(prototype && api)) {
          return prototype || api;
        }
        let n$ = Object.getOwnPropertyNames(api);
        for (let i=0, n; (i<n$.length) && (n=n$[i]); i++) {
          let pd = Object.getOwnPropertyDescriptor(api, n);
          if (pd) {
            Object.defineProperty(prototype, n, pd);
          }
        }
        return prototype;
      }

      /**
       * Copies props from a source object to a target object.
       *
       * Note, this method uses a simple `for...in` strategy for enumerating
       * properties.  To ensure only `ownProperties` are copied from source
       * to target and that accessor implementations are copied, use `extend`.
       *
       * @param {!Object} target Target object to copy properties to.
       * @param {!Object} source Source object to copy properties from.
       * @return {!Object} Target object that was passed as first argument.
       */
      mixin(target, source) {
        for (let i in source) {
          target[i] = source[i];
        }
        return target;
      }

      /**
       * Sets the prototype of an object.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features.
       * @param {Object} object The object on which to set the prototype.
       * @param {Object} prototype The prototype that will be set on the given
       * `object`.
       * @return {Object} Returns the given `object` with its prototype set
       * to the given `prototype` object.
       */
      chainObject(object, prototype) {
        if (object && prototype && object !== prototype) {
          object.__proto__ = prototype;
        }
        return object;
      }

      /* **** Begin Template **** */

      /**
       * Calls `importNode` on the `content` of the `template` specified and
       * returns a document fragment containing the imported content.
       *
       * @param {HTMLTemplateElement} template HTML template element to instance.
       * @return {!DocumentFragment} Document fragment containing the imported
       *   template content.
      */
      instanceTemplate(template) {
        let content = this.constructor._contentForTemplate(template);
        let dom = /** @type {!DocumentFragment} */
          (document.importNode(content, true));
        return dom;
      }

      /* **** Begin Events **** */



      /**
       * Dispatches a custom event with an optional detail value.
       *
       * @param {string} type Name of event type.
       * @param {*=} detail Detail value containing event-specific
       *   payload.
       * @param {{ bubbles: (boolean|undefined), cancelable: (boolean|undefined), composed: (boolean|undefined) }=}
       *  options Object specifying options.  These may include:
       *  `bubbles` (boolean, defaults to `true`),
       *  `cancelable` (boolean, defaults to false), and
       *  `node` on which to fire the event (HTMLElement, defaults to `this`).
       * @return {!Event} The new event that was fired.
       */
      fire(type, detail, options) {
        options = options || {};
        detail = (detail === null || detail === undefined) ? {} : detail;
        let event = new Event(type, {
          bubbles: options.bubbles === undefined ? true : options.bubbles,
          cancelable: Boolean(options.cancelable),
          composed: options.composed === undefined ? true: options.composed
        });
        event.detail = detail;
        let node = options.node || this;
        node.dispatchEvent(event);
        return event;
      }

      /**
       * Convenience method to add an event listener on a given element,
       * late bound to a named method on this element.
       *
       * @param {Element} node Element to add event listener to.
       * @param {string} eventName Name of event to listen for.
       * @param {string} methodName Name of handler method on `this` to call.
       * @return {void}
       */
      listen(node, eventName, methodName) {
        node = /** @type {!Element} */ (node || this);
        let hbl = this.__boundListeners ||
          (this.__boundListeners = new WeakMap());
        let bl = hbl.get(node);
        if (!bl) {
          bl = {};
          hbl.set(node, bl);
        }
        let key = eventName + methodName;
        if (!bl[key]) {
          bl[key] = this._addMethodEventListenerToNode(
            node, eventName, methodName, this);
        }
      }

      /**
       * Convenience method to remove an event listener from a given element,
       * late bound to a named method on this element.
       *
       * @param {Element} node Element to remove event listener from.
       * @param {string} eventName Name of event to stop listening to.
       * @param {string} methodName Name of handler method on `this` to not call
       anymore.
       * @return {void}
       */
      unlisten(node, eventName, methodName) {
        node = /** @type {!Element} */ (node || this);
        let bl = this.__boundListeners && this.__boundListeners.get(node);
        let key = eventName + methodName;
        let handler = bl && bl[key];
        if (handler) {
          this._removeEventListenerFromNode(node, eventName, handler);
          bl[key] = null;
        }
      }

      /**
       * Override scrolling behavior to all direction, one direction, or none.
       *
       * Valid scroll directions:
       *   - 'all': scroll in any direction
       *   - 'x': scroll only in the 'x' direction
       *   - 'y': scroll only in the 'y' direction
       *   - 'none': disable scrolling for this node
       *
       * @param {string=} direction Direction to allow scrolling
       * Defaults to `all`.
       * @param {Element=} node Element to apply scroll direction setting.
       * Defaults to `this`.
       * @return {void}
       */
      setScrollDirection(direction, node) {
        Polymer.Gestures.setTouchAction(/** @type {Element} */ (node || this), DIRECTION_MAP[direction] || 'auto');
      }
      /* **** End Events **** */

      /**
       * Convenience method to run `querySelector` on this local DOM scope.
       *
       * This function calls `Polymer.dom(this.root).querySelector(slctr)`.
       *
       * @param {string} slctr Selector to run on this local DOM scope
       * @return {Element} Element found by the selector, or null if not found.
       */
      $$(slctr) {
        return this.root.querySelector(slctr);
      }

      /**
       * Return the element whose local dom within which this element
       * is contained. This is a shorthand for
       * `this.getRootNode().host`.
       * @this {Element}
       */
      get domHost() {
        let root = this.getRootNode();
        return (root instanceof DocumentFragment) ? /** @type {ShadowRoot} */ (root).host : root;
      }

      /**
       * Force this element to distribute its children to its local dom.
       * This should not be necessary as of Polymer 2.0.2 and is provided only
       * for backwards compatibility.
       * @return {void}
       */
      distributeContent() {
        if (window.ShadyDOM && this.shadowRoot) {
          ShadyDOM.flush();
        }
      }

      /**
       * Returns a list of nodes that are the effective childNodes. The effective
       * childNodes list is the same as the element's childNodes except that
       * any `<content>` elements are replaced with the list of nodes distributed
       * to the `<content>`, the result of its `getDistributedNodes` method.
       * @return {!Array<!Node>} List of effective child nodes.
       * @suppress {invalidCasts} LegacyElementMixin must be applied to an HTMLElement
       */
      getEffectiveChildNodes() {
        const thisEl = /** @type {Element} */ (this);
        const domApi = /** @type {Polymer.DomApi} */(Polymer.dom(thisEl));
        return domApi.getEffectiveChildNodes();
      }

      /**
       * Returns a list of nodes distributed within this element that match
       * `selector`. These can be dom children or elements distributed to
       * children that are insertion points.
       * @param {string} selector Selector to run.
       * @return {!Array<!Node>} List of distributed elements that match selector.
       * @suppress {invalidCasts} LegacyElementMixin must be applied to an HTMLElement
       */
      queryDistributedElements(selector) {
        const thisEl = /** @type {Element} */ (this);
        const domApi = /** @type {Polymer.DomApi} */(Polymer.dom(thisEl));
        return domApi.queryDistributedElements(selector);
      }

      /**
       * Returns a list of elements that are the effective children. The effective
       * children list is the same as the element's children except that
       * any `<content>` elements are replaced with the list of elements
       * distributed to the `<content>`.
       *
       * @return {!Array<!Node>} List of effective children.
       */
      getEffectiveChildren() {
        let list = this.getEffectiveChildNodes();
        return list.filter(function(/** @type {!Node} */ n) {
          return (n.nodeType === Node.ELEMENT_NODE);
        });
      }

      /**
       * Returns a string of text content that is the concatenation of the
       * text content's of the element's effective childNodes (the elements
       * returned by <a href="#getEffectiveChildNodes>getEffectiveChildNodes</a>.
       *
       * @return {string} List of effective children.
       */
      getEffectiveTextContent() {
        let cn = this.getEffectiveChildNodes();
        let tc = [];
        for (let i=0, c; (c = cn[i]); i++) {
          if (c.nodeType !== Node.COMMENT_NODE) {
            tc.push(c.textContent);
          }
        }
        return tc.join('');
      }

      /**
       * Returns the first effective childNode within this element that
       * match `selector`. These can be dom child nodes or elements distributed
       * to children that are insertion points.
       * @param {string} selector Selector to run.
       * @return {Node} First effective child node that matches selector.
       */
      queryEffectiveChildren(selector) {
        let e$ = this.queryDistributedElements(selector);
        return e$ && e$[0];
      }

      /**
       * Returns a list of effective childNodes within this element that
       * match `selector`. These can be dom child nodes or elements distributed
       * to children that are insertion points.
       * @param {string} selector Selector to run.
       * @return {!Array<!Node>} List of effective child nodes that match selector.
       */
      queryAllEffectiveChildren(selector) {
        return this.queryDistributedElements(selector);
      }

      /**
       * Returns a list of nodes distributed to this element's `<slot>`.
       *
       * If this element contains more than one `<slot>` in its local DOM,
       * an optional selector may be passed to choose the desired content.
       *
       * @param {string=} slctr CSS selector to choose the desired
       *   `<slot>`.  Defaults to `content`.
       * @return {!Array<!Node>} List of distributed nodes for the `<slot>`.
       */
      getContentChildNodes(slctr) {
        let content = this.root.querySelector(slctr || 'slot');
        return content ? /** @type {Polymer.DomApi} */(Polymer.dom(content)).getDistributedNodes() : [];
      }

      /**
       * Returns a list of element children distributed to this element's
       * `<slot>`.
       *
       * If this element contains more than one `<slot>` in its
       * local DOM, an optional selector may be passed to choose the desired
       * content.  This method differs from `getContentChildNodes` in that only
       * elements are returned.
       *
       * @param {string=} slctr CSS selector to choose the desired
       *   `<content>`.  Defaults to `content`.
       * @return {!Array<!HTMLElement>} List of distributed nodes for the
       *   `<slot>`.
       * @suppress {invalidCasts}
       */
      getContentChildren(slctr) {
        let children = /** @type {!Array<!HTMLElement>} */(this.getContentChildNodes(slctr).filter(function(n) {
          return (n.nodeType === Node.ELEMENT_NODE);
        }));
        return children;
      }

      /**
       * Checks whether an element is in this element's light DOM tree.
       *
       * @param {?Node} node The element to be checked.
       * @return {boolean} true if node is in this element's light DOM tree.
       * @suppress {invalidCasts} LegacyElementMixin must be applied to an HTMLElement
       */
      isLightDescendant(node) {
        const thisNode = /** @type {Node} */ (this);
        return thisNode !== node && thisNode.contains(node) &&
          thisNode.getRootNode() === node.getRootNode();
      }

      /**
       * Checks whether an element is in this element's local DOM tree.
       *
       * @param {!Element} node The element to be checked.
       * @return {boolean} true if node is in this element's local DOM tree.
       */
      isLocalDescendant(node) {
        return this.root === node.getRootNode();
      }

      /**
       * No-op for backwards compatibility. This should now be handled by
       * ShadyCss library.
       * @param  {*} container Unused
       * @param  {*} shouldObserve Unused
       * @return {void}
       */
      scopeSubtree(container, shouldObserve) { // eslint-disable-line no-unused-vars
      }

      /**
       * Returns the computed style value for the given property.
       * @param {string} property The css property name.
       * @return {string} Returns the computed css property value for the given
       * `property`.
       * @suppress {invalidCasts} LegacyElementMixin must be applied to an HTMLElement
       */
      getComputedStyleValue(property) {
        return styleInterface.getComputedStyleValue(/** @type {!Element} */(this), property);
      }

      // debounce

      /**
       * Call `debounce` to collapse multiple requests for a named task into
       * one invocation which is made after the wait time has elapsed with
       * no new request.  If no wait time is given, the callback will be called
       * at microtask timing (guaranteed before paint).
       *
       *     debouncedClickAction(e) {
       *       // will not call `processClick` more than once per 100ms
       *       this.debounce('click', function() {
       *        this.processClick();
       *       } 100);
       *     }
       *
       * @param {string} jobName String to identify the debounce job.
       * @param {function():void} callback Function that is called (with `this`
       *   context) when the wait time elapses.
       * @param {number} wait Optional wait time in milliseconds (ms) after the
       *   last signal that must elapse before invoking `callback`
       * @return {!Object} Returns a debouncer object on which exists the
       * following methods: `isActive()` returns true if the debouncer is
       * active; `cancel()` cancels the debouncer if it is active;
       * `flush()` immediately invokes the debounced callback if the debouncer
       * is active.
       */
      debounce(jobName, callback, wait) {
        this._debouncers = this._debouncers || {};
        return this._debouncers[jobName] = Polymer.Debouncer.debounce(
              this._debouncers[jobName]
            , wait > 0 ? Polymer.Async.timeOut.after(wait) : Polymer.Async.microTask
            , callback.bind(this));
      }

      /**
       * Returns whether a named debouncer is active.
       *
       * @param {string} jobName The name of the debouncer started with `debounce`
       * @return {boolean} Whether the debouncer is active (has not yet fired).
       */
      isDebouncerActive(jobName) {
        this._debouncers = this._debouncers || {};
        let debouncer = this._debouncers[jobName];
        return !!(debouncer && debouncer.isActive());
      }

      /**
       * Immediately calls the debouncer `callback` and inactivates it.
       *
       * @param {string} jobName The name of the debouncer started with `debounce`
       * @return {void}
       */
      flushDebouncer(jobName) {
        this._debouncers = this._debouncers || {};
        let debouncer = this._debouncers[jobName];
        if (debouncer) {
          debouncer.flush();
        }
      }

      /**
       * Cancels an active debouncer.  The `callback` will not be called.
       *
       * @param {string} jobName The name of the debouncer started with `debounce`
       * @return {void}
       */
      cancelDebouncer(jobName) {
        this._debouncers = this._debouncers || {};
        let debouncer = this._debouncers[jobName];
        if (debouncer) {
          debouncer.cancel();
        }
      }

      /**
       * Runs a callback function asynchronously.
       *
       * By default (if no waitTime is specified), async callbacks are run at
       * microtask timing, which will occur before paint.
       *
       * @param {!Function} callback The callback function to run, bound to `this`.
       * @param {number=} waitTime Time to wait before calling the
       *   `callback`.  If unspecified or 0, the callback will be run at microtask
       *   timing (before paint).
       * @return {number} Handle that may be used to cancel the async job.
       */
      async(callback, waitTime) {
        return waitTime > 0 ? Polymer.Async.timeOut.run(callback.bind(this), waitTime) :
            ~Polymer.Async.microTask.run(callback.bind(this));
      }

      /**
       * Cancels an async operation started with `async`.
       *
       * @param {number} handle Handle returned from original `async` call to
       *   cancel.
       * @return {void}
       */
      cancelAsync(handle) {
        handle < 0 ? Polymer.Async.microTask.cancel(~handle) :
            Polymer.Async.timeOut.cancel(handle);
      }

      // other

      /**
       * Convenience method for creating an element and configuring it.
       *
       * @param {string} tag HTML element tag to create.
       * @param {Object=} props Object of properties to configure on the
       *    instance.
       * @return {!Element} Newly created and configured element.
       */
      create(tag, props) {
        let elt = document.createElement(tag);
        if (props) {
          if (elt.setProperties) {
            elt.setProperties(props);
          } else {
            for (let n in props) {
              elt[n] = props[n];
            }
          }
        }
        return elt;
      }

      /**
       * Convenience method for importing an HTML document imperatively.
       *
       * This method creates a new `<link rel="import">` element with
       * the provided URL and appends it to the document to start loading.
       * In the `onload` callback, the `import` property of the `link`
       * element will contain the imported document contents.
       *
       * @param {string} href URL to document to load.
       * @param {?function(!Event):void=} onload Callback to notify when an import successfully
       *   loaded.
       * @param {?function(!ErrorEvent):void=} onerror Callback to notify when an import
       *   unsuccessfully loaded.
       * @param {boolean=} optAsync True if the import should be loaded `async`.
       *   Defaults to `false`.
       * @return {!HTMLLinkElement} The link element for the URL to be loaded.
       */
      importHref(href, onload, onerror, optAsync) { // eslint-disable-line no-unused-vars
        let loadFn = onload ? onload.bind(this) : null;
        let errorFn = onerror ? onerror.bind(this) : null;
        return Polymer.importHref(href, loadFn, errorFn, optAsync);
      }

      /**
       * Polyfill for Element.prototype.matches, which is sometimes still
       * prefixed.
       *
       * @param {string} selector Selector to test.
       * @param {!Element=} node Element to test the selector against.
       * @return {boolean} Whether the element matches the selector.
       */
      elementMatches(selector, node) {
        return Polymer.dom.matchesSelector(/** @type {!Element} */ (node || this), selector);
      }

      /**
       * Toggles an HTML attribute on or off.
       *
       * @param {string} name HTML attribute name
       * @param {boolean=} bool Boolean to force the attribute on or off.
       *    When unspecified, the state of the attribute will be reversed.
       * @param {Element=} node Node to target.  Defaults to `this`.
       * @return {void}
       */
      toggleAttribute(name, bool, node) {
        node = /** @type {Element} */ (node || this);
        if (arguments.length == 1) {
          bool = !node.hasAttribute(name);
        }
        if (bool) {
          node.setAttribute(name, '');
        } else {
          node.removeAttribute(name);
        }
      }


      /**
       * Toggles a CSS class on or off.
       *
       * @param {string} name CSS class name
       * @param {boolean=} bool Boolean to force the class on or off.
       *    When unspecified, the state of the class will be reversed.
       * @param {Element=} node Node to target.  Defaults to `this`.
       * @return {void}
       */
      toggleClass(name, bool, node) {
        node = /** @type {Element} */ (node || this);
        if (arguments.length == 1) {
          bool = !node.classList.contains(name);
        }
        if (bool) {
          node.classList.add(name);
        } else {
          node.classList.remove(name);
        }
      }

      /**
       * Cross-platform helper for setting an element's CSS `transform` property.
       *
       * @param {string} transformText Transform setting.
       * @param {Element=} node Element to apply the transform to.
       * Defaults to `this`
       * @return {void}
       */
      transform(transformText, node) {
        node = /** @type {Element} */ (node || this);
        node.style.webkitTransform = transformText;
        node.style.transform = transformText;
      }

      /**
       * Cross-platform helper for setting an element's CSS `translate3d`
       * property.
       *
       * @param {number} x X offset.
       * @param {number} y Y offset.
       * @param {number} z Z offset.
       * @param {Element=} node Element to apply the transform to.
       * Defaults to `this`.
       * @return {void}
       */
      translate3d(x, y, z, node) {
        node = /** @type {Element} */ (node || this);
        this.transform('translate3d(' + x + ',' + y + ',' + z + ')', node);
      }

      /**
       * Removes an item from an array, if it exists.
       *
       * If the array is specified by path, a change notification is
       * generated, so that observers, data bindings and computed
       * properties watching that path can update.
       *
       * If the array is passed directly, **no change
       * notification is generated**.
       *
       * @param {string | !Array<number|string>} arrayOrPath Path to array from which to remove the item
       *   (or the array itself).
       * @param {*} item Item to remove.
       * @return {Array} Array containing item removed.
       */
      arrayDelete(arrayOrPath, item) {
        let index;
        if (Array.isArray(arrayOrPath)) {
          index = arrayOrPath.indexOf(item);
          if (index >= 0) {
            return arrayOrPath.splice(index, 1);
          }
        } else {
          let arr = Polymer.Path.get(this, arrayOrPath);
          index = arr.indexOf(item);
          if (index >= 0) {
            return this.splice(arrayOrPath, index, 1);
          }
        }
        return null;
      }

      // logging

      /**
       * Facades `console.log`/`warn`/`error` as override point.
       *
       * @param {string} level One of 'log', 'warn', 'error'
       * @param {Array} args Array of strings or objects to log
       * @return {void}
       */
      _logger(level, args) {
        // accept ['foo', 'bar'] and [['foo', 'bar']]
        if (Array.isArray(args) && args.length === 1 && Array.isArray(args[0])) {
          args = args[0];
        }
        switch(level) {
          case 'log':
          case 'warn':
          case 'error':
            console[level](...args);
        }
      }

      /**
       * Facades `console.log` as an override point.
       *
       * @param {...*} args Array of strings or objects to log
       * @return {void}
       */
      _log(...args) {
        this._logger('log', args);
      }

      /**
       * Facades `console.warn` as an override point.
       *
       * @param {...*} args Array of strings or objects to log
       * @return {void}
       */
      _warn(...args) {
        this._logger('warn', args);
      }

      /**
       * Facades `console.error` as an override point.
       *
       * @param {...*} args Array of strings or objects to log
       * @return {void}
       */
      _error(...args) {
        this._logger('error', args);
      }

      /**
       * Formats a message using the element type an a method name.
       *
       * @param {string} methodName Method name to associate with message
       * @param {...*} args Array of strings or objects to log
       * @return {Array} Array with formatting information for `console`
       *   logging.
       */
      _logf(methodName, ...args) {
        return ['[%s::%s]', this.is, methodName, ...args];
      }

    }

    LegacyElement.prototype.is = '';

    return LegacyElement;

  });

})();



  (function() {

    'use strict';

    const lifecycleProps = {
      attached: true,
      detached: true,
      ready: true,
      created: true,
      beforeRegister: true,
      registered: true,
      attributeChanged: true,
      listeners: true,
      hostAttributes: true
    };

    const excludeOnInfo = {
      attached: true,
      detached: true,
      ready: true,
      created: true,
      beforeRegister: true,
      registered: true,
      attributeChanged: true,
      behaviors: true,
      _noAccessors: true
    };

    const excludeOnBehaviors = Object.assign({
      listeners: true,
      hostAttributes: true,
      properties: true,
      observers: true,
    }, excludeOnInfo);

    function copyProperties(source, target, excludeProps) {
      const noAccessors = source._noAccessors;
      for (let p in source) {
        if (!(p in excludeProps)) {
          if (noAccessors) {
            target[p] = source[p];
          } else {
            let pd = Object.getOwnPropertyDescriptor(source, p);
            if (pd) {
              // ensure property is configurable so that a later behavior can
              // re-configure it.
              pd.configurable = true;
              Object.defineProperty(target, p, pd);
            }
          }
        }
      }
    }

    /**
     * Applies a "legacy" behavior or array of behaviors to the provided class.
     *
     * Note: this method will automatically also apply the `Polymer.LegacyElementMixin`
     * to ensure that any legacy behaviors can rely on legacy Polymer API on
     * the underlying element.
     *
     * @template T
     * @param {!Object|!Array<!Object>} behaviors Behavior object or array of behaviors.
     * @param {function(new:T)} klass Element class.
     * @return {function(new:T)} Returns a new Element class extended by the
     * passed in `behaviors` and also by `Polymer.LegacyElementMixin`.
     * @memberof Polymer
     * @suppress {invalidCasts, checkTypes}
     */
    function mixinBehaviors(behaviors, klass) {
      return GenerateClassFromInfo({}, Polymer.LegacyElementMixin(klass), behaviors);
    }

    // NOTE:
    // 1.x
    // Behaviors were mixed in *in reverse order* and de-duped on the fly.
    // The rule was that behavior properties were copied onto the element
    // prototype if and only if the property did not already exist.
    // Given: Polymer{ behaviors: [A, B, C, A, B]}, property copy order was:
    // (1), B, (2), A, (3) C. This means prototype properties win over
    // B properties win over A win over C. This mirrors what would happen
    // with inheritance if element extended B extended A extended C.
    //
    // Again given, Polymer{ behaviors: [A, B, C, A, B]}, the resulting
    // `behaviors` array was [C, A, B].
    // Behavior lifecycle methods were called in behavior array order
    // followed by the element, e.g. (1) C.created, (2) A.created,
    // (3) B.created, (4) element.created. There was no support for
    // super, and "super-behavior" methods were callable only by name).
    //
    // 2.x
    // Behaviors are made into proper mixins which live in the
    // element's prototype chain. Behaviors are placed in the element prototype
    // eldest to youngest and de-duped youngest to oldest:
    // So, first [A, B, C, A, B] becomes [C, A, B] then,
    // the element prototype becomes (oldest) (1) Polymer.Element, (2) class(C),
    // (3) class(A), (4) class(B), (5) class(Polymer({...})).
    // Result:
    // This means element properties win over B properties win over A win
    // over C. (same as 1.x)
    // If lifecycle is called (super then me), order is
    // (1) C.created, (2) A.created, (3) B.created, (4) element.created
    // (again same as 1.x)
    function applyBehaviors(proto, behaviors, lifecycle) {
      for (let i=0; i<behaviors.length; i++) {
        applyInfo(proto, behaviors[i], lifecycle, excludeOnBehaviors);
      }
    }

    function applyInfo(proto, info, lifecycle, excludeProps) {
      copyProperties(info, proto, excludeProps);
      for (let p in lifecycleProps) {
        if (info[p]) {
          lifecycle[p] = lifecycle[p] || [];
          lifecycle[p].push(info[p]);
        }
      }
    }

    /**
     * @param {Array} behaviors List of behaviors to flatten.
     * @param {Array=} list Target list to flatten behaviors into.
     * @param {Array=} exclude List of behaviors to exclude from the list.
     * @return {!Array} Returns the list of flattened behaviors.
     */
    function flattenBehaviors(behaviors, list, exclude) {
      list = list || [];
      for (let i=behaviors.length-1; i >= 0; i--) {
        let b = behaviors[i];
        if (b) {
          if (Array.isArray(b)) {
            flattenBehaviors(b, list);
          } else {
            // dedup
            if (list.indexOf(b) < 0 && (!exclude || exclude.indexOf(b) < 0)) {
              list.unshift(b);
            }
          }
        } else {
          console.warn('behavior is null, check for missing or 404 import');
        }
      }
      return list;
    }

    /* Note about construction and extension of legacy classes.
      [Changed in Q4 2018 to optimize performance.]

      When calling `Polymer` or `mixinBehaviors`, the generated class below is
      made. The list of behaviors was previously made into one generated class per
      behavior, but this is no longer the case as behaviors are now called
      manually. Note, there may *still* be multiple generated classes in the
      element's prototype chain if extension is used with `mixinBehaviors`.

      The generated class is directly tied to the info object and behaviors
      used to create it. That list of behaviors is filtered so it's only the
      behaviors not active on the superclass. In order to call through to the
      entire list of lifecycle methods, it's important to call `super`.

      The element's `properties` and `observers` are controlled via the finalization
      mechanism provided by `PropertiesMixin`. `Properties` and `observers` are
      collected by manually traversing the prototype chain and merging.

      To limit changes, the `_registered` method is called via `_initializeProperties`
      and not `_finalizeClass`.
    */
    /**
     * @param {!PolymerInit} info Polymer info object
     * @param {function(new:HTMLElement)} Base base class to extend with info object
     * @param {Object} behaviors behaviors to copy into the element
     * @return {function(new:HTMLElement)} Generated class
     * @suppress {checkTypes}
     * @private
     */
    function GenerateClassFromInfo(info, Base, behaviors) {

      // manages behavior and lifecycle processing (filled in after class definition)
      let behaviorList;
      const lifecycle = {};

      /** @private */
      class PolymerGenerated extends Base {

        // explicitly not calling super._finalizeClass
        static _finalizeClass() {
          // if calling via a subclass that hasn't been generated, pass through to super
          if (!this.hasOwnProperty(window.JSCompiler_renameProperty('generatedFrom', this))) {
            super._finalizeClass();
          } else {
            // interleave properties and observers per behavior and `info`
            if (behaviorList) {
              for (let i=0, b; i < behaviorList.length; i++) {
                b = behaviorList[i];
                if (b.properties) {
                  this.createProperties(b.properties);
                }
                if (b.observers) {
                  this.createObservers(b.observers, b.properties);
                }
              }
            }
            if (info.properties) {
              this.createProperties(info.properties);
            }
            if (info.observers) {
              this.createObservers(info.observers, info.properties);
            }
            // make sure to prepare the element template
            this._prepareTemplate();
          }
        }

        static get properties() {
          const properties = {};
          if (behaviorList) {
            for (let i=0; i < behaviorList.length; i++) {
              Object.assign(properties, behaviorList[i].properties);
            }
          }
          Object.assign(properties, info.properties);
          return properties;
        }

        static get observers() {
          let observers = [];
          if (behaviorList) {
            for (let i=0, b; i < behaviorList.length; i++) {
              b = behaviorList[i];
              if (b.observers) {
                observers = observers.concat(b.observers);
              }
            }
          }
          if (info.observers) {
            observers = observers.concat(info.observers);
          }
          return observers;
        }

        /**
         * @return {void}
         */
        created() {
          super.created();
          const list = lifecycle.created;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this);
            }
          }
        }

        /**
         * @return {void}
         */
        _registered() {
          /* NOTE: `beforeRegister` is called here for bc, but the behavior
            is different than in 1.x. In 1.0, the method was called *after*
            mixing prototypes together but *before* processing of meta-objects.
            However, dynamic effects can still be set here and can be done either
            in `beforeRegister` or `registered`. It is no longer possible to set
            `is` in `beforeRegister` as you could in 1.x.
          */
          // only proceed if the generated class' prototype has not been registered.
          const generatedProto = PolymerGenerated.prototype;
          if (!generatedProto.hasOwnProperty('__hasRegisterFinished')) {
            generatedProto.__hasRegisterFinished = true;
            // ensure superclass is registered first.
            super._registered();
            // copy properties onto the generated class lazily if we're optimizing,
            if (Polymer.legacyOptimizations) {
              copyPropertiesToProto(generatedProto);
            }
            // make sure legacy lifecycle is called on the *element*'s prototype
            // and not the generated class prototype; if the element has been
            // extended, these are *not* the same.
            const proto = Object.getPrototypeOf(this);
            let list = lifecycle.beforeRegister;
            if (list) {
              for (let i=0; i < list.length; i++) {
                list[i].call(proto);
              }
            }
            list = lifecycle.registered;
            if (list) {
              for (let i=0; i < list.length; i++) {
                list[i].call(proto);
              }
            }
          }
        }

        /**
         * @return {void}
         */
        _applyListeners() {
          super._applyListeners();
          const list = lifecycle.listeners;
          if (list) {
            for (let i=0; i < list.length; i++) {
              const listeners = list[i];
              if (listeners) {
                for (let l in listeners) {
                  this._addMethodEventListenerToNode(this, l, listeners[l]);
                }
              }
            }
          }
        }

        // note: exception to "super then me" rule;
        // do work before calling super so that super attributes
        // only apply if not already set.
        /**
         * @return {void}
         */
        _ensureAttributes() {
          const list = lifecycle.hostAttributes;
          if (list) {
            for (let i=list.length-1; i >= 0; i--) {
              const hostAttributes = list[i];
              for (let a in hostAttributes) {
                  this._ensureAttribute(a, hostAttributes[a]);
                }
            }
          }
          super._ensureAttributes();
        }

        /**
         * @return {void}
         */
        ready() {
          super.ready();
          let list = lifecycle.ready;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this);
            }
          }
        }

        /**
         * @return {void}
         */
        attached() {
          super.attached();
          let list = lifecycle.attached;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this);
            }
          }
        }

        /**
         * @return {void}
         */
        detached() {
          super.detached();
          let list = lifecycle.detached;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this);
            }
          }
        }

        /**
         * Implements native Custom Elements `attributeChangedCallback` to
         * set an attribute value to a property via `_attributeToProperty`.
         *
         * @param {string} name Name of attribute that changed
         * @param {?string} old Old attribute value
         * @param {?string} value New attribute value
         * @return {void}
         */
        attributeChanged(name, old, value) {
          super.attributeChanged();
          let list = lifecycle.attributeChanged;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this, name, old, value);
            }
          }
        }
      }

      // apply behaviors, note actual copying is done lazily at first instance creation
      if (behaviors) {
        // NOTE: ensure the behavior is extending a class with
        // legacy element api. This is necessary since behaviors expect to be able
        // to access 1.x legacy api.
        if (!Array.isArray(behaviors)) {
          behaviors = [behaviors];
        }
        let superBehaviors = Base.prototype.behaviors;
        // get flattened, deduped list of behaviors *not* already on super class
        behaviorList = flattenBehaviors(behaviors, null, superBehaviors);
        PolymerGenerated.prototype.behaviors = superBehaviors ?
          superBehaviors.concat(behaviors) : behaviorList;
      }

      const copyPropertiesToProto = (proto) => {
        if (behaviorList) {
          applyBehaviors(proto, behaviorList, lifecycle);
        }
        applyInfo(proto, info, lifecycle, excludeOnInfo);
      };

      // copy properties if we're not optimizing
      if (!Polymer.legacyOptimizations) {
        copyPropertiesToProto(PolymerGenerated.prototype);
      }

      PolymerGenerated.generatedFrom = info;

      return PolymerGenerated;
    }

    /**
     * Generates a class that extends `Polymer.LegacyElement` based on the
     * provided info object.  Metadata objects on the `info` object
     * (`properties`, `observers`, `listeners`, `behaviors`, `is`) are used
     * for Polymer's meta-programming systems, and any functions are copied
     * to the generated class.
     *
     * Valid "metadata" values are as follows:
     *
     * `is`: String providing the tag name to register the element under. In
     * addition, if a `dom-module` with the same id exists, the first template
     * in that `dom-module` will be stamped into the shadow root of this element,
     * with support for declarative event listeners (`on-...`), Polymer data
     * bindings (`[[...]]` and `{{...}}`), and id-based node finding into
     * `this.$`.
     *
     * `properties`: Object describing property-related metadata used by Polymer
     * features (key: property names, value: object containing property metadata).
     * Valid keys in per-property metadata include:
     * - `type` (String|Number|Object|Array|...): Used by
     *   `attributeChangedCallback` to determine how string-based attributes
     *   are deserialized to JavaScript property values.
     * - `notify` (boolean): Causes a change in the property to fire a
     *   non-bubbling event called `<property>-changed`. Elements that have
     *   enabled two-way binding to the property use this event to observe changes.
     * - `readOnly` (boolean): Creates a getter for the property, but no setter.
     *   To set a read-only property, use the private setter method
     *   `_setProperty(property, value)`.
     * - `observer` (string): Observer method name that will be called when
     *   the property changes. The arguments of the method are
     *   `(value, previousValue)`.
     * - `computed` (string): String describing method and dependent properties
     *   for computing the value of this property (e.g. `'computeFoo(bar, zot)'`).
     *   Computed properties are read-only by default and can only be changed
     *   via the return value of the computing method.
     *
     * `observers`: Array of strings describing multi-property observer methods
     *  and their dependent properties (e.g. `'observeABC(a, b, c)'`).
     *
     * `listeners`: Object describing event listeners to be added to each
     *  instance of this element (key: event name, value: method name).
     *
     * `behaviors`: Array of additional `info` objects containing metadata
     * and callbacks in the same format as the `info` object here which are
     * merged into this element.
     *
     * `hostAttributes`: Object listing attributes to be applied to the host
     *  once created (key: attribute name, value: attribute value).  Values
     *  are serialized based on the type of the value.  Host attributes should
     *  generally be limited to attributes such as `tabIndex` and `aria-...`.
     *  Attributes in `hostAttributes` are only applied if a user-supplied
     *  attribute is not already present (attributes in markup override
     *  `hostAttributes`).
     *
     * In addition, the following Polymer-specific callbacks may be provided:
     * - `registered`: called after first instance of this element,
     * - `created`: called during `constructor`
     * - `attached`: called during `connectedCallback`
     * - `detached`: called during `disconnectedCallback`
     * - `ready`: called before first `attached`, after all properties of
     *   this element have been propagated to its template and all observers
     *   have run
     *
     * @param {!PolymerInit} info Object containing Polymer metadata and functions
     *   to become class methods.
     * @template T
     * @param {function(T):T} mixin Optional mixin to apply to legacy base class
     *   before extending with Polymer metaprogramming.
     * @return {function(new:HTMLElement)} Generated class
     * @memberof Polymer
     */
    Polymer.Class = function(info, mixin) {
      if (!info) {
        console.warn('Polymer.Class requires `info` argument');
      }
      let klass = mixin ? mixin(Polymer.LegacyElementMixin(HTMLElement)) :
          Polymer.LegacyElementMixin(HTMLElement);
      klass = GenerateClassFromInfo(info, klass, info.behaviors);
      if (info._enableDisableUpgrade) {
        klass = Polymer.DisableUpgradeMixin(klass);
      }
      // decorate klass with registration info
      klass.is = klass.prototype.is = info.is;
      return klass;
    };

    Polymer.mixinBehaviors = mixinBehaviors;

  })();




  (function() {
    'use strict';

    /**
     * Legacy class factory and registration helper for defining Polymer
     * elements.
     *
     * This method is equivalent to
     * `customElements.define(info.is, Polymer.Class(info));`
     *
     * See `Polymer.Class` for details on valid legacy metadata format for `info`.
     *
     * @global
     * @override
     * @function Polymer
     * @param {!PolymerInit} info Object containing Polymer metadata and functions
     *   to become class methods.
     * @return {function(new: HTMLElement)} Generated class
     * @suppress {duplicate, invalidCasts, checkTypes}
     */
    window.Polymer._polymerFn = function(info) {
      // if input is a `class` (aka a function with a prototype), use the prototype
      // remember that the `constructor` will never be called
      let klass;
      if (typeof info === 'function') {
        klass = info;
      } else {
        klass = Polymer.Class(info);
      }
      customElements.define(klass.is, /** @type {!HTMLElement} */(klass));
      return klass;
    };

  })();



(function() {
  'use strict';

  // Common implementation for mixin & behavior
  function mutablePropertyChange(inst, property, value, old, mutableData) {
    let isObject;
    if (mutableData) {
      isObject = (typeof value === 'object' && value !== null);
      // Pull `old` for Objects from temp cache, but treat `null` as a primitive
      if (isObject) {
        old = inst.__dataTemp[property];
      }
    }
    // Strict equality check, but return false for NaN===NaN
    let shouldChange = (old !== value && (old === old || value === value));
    // Objects are stored in temporary cache (cleared at end of
    // turn), which is used for dirty-checking
    if (isObject && shouldChange) {
      inst.__dataTemp[property] = value;
    }
    return shouldChange;
  }

  /**
   * Element class mixin to skip strict dirty-checking for objects and arrays
   * (always consider them to be "dirty"), for use on elements utilizing
   * `Polymer.PropertyEffects`
   *
   * By default, `Polymer.PropertyEffects` performs strict dirty checking on
   * objects, which means that any deep modifications to an object or array will
   * not be propagated unless "immutable" data patterns are used (i.e. all object
   * references from the root to the mutation were changed).
   *
   * Polymer also provides a proprietary data mutation and path notification API
   * (e.g. `notifyPath`, `set`, and array mutation API's) that allow efficient
   * mutation and notification of deep changes in an object graph to all elements
   * bound to the same object graph.
   *
   * In cases where neither immutable patterns nor the data mutation API can be
   * used, applying this mixin will cause Polymer to skip dirty checking for
   * objects and arrays (always consider them to be "dirty").  This allows a
   * user to make a deep modification to a bound object graph, and then either
   * simply re-set the object (e.g. `this.items = this.items`) or call `notifyPath`
   * (e.g. `this.notifyPath('items')`) to update the tree.  Note that all
   * elements that wish to be updated based on deep mutations must apply this
   * mixin or otherwise skip strict dirty checking for objects/arrays.
   * Specifically, any elements in the binding tree between the source of a
   * mutation and the consumption of it must apply this mixin or enable the
   * `Polymer.OptionalMutableData` mixin.
   *
   * In order to make the dirty check strategy configurable, see
   * `Polymer.OptionalMutableData`.
   *
   * Note, the performance characteristics of propagating large object graphs
   * will be worse as opposed to using strict dirty checking with immutable
   * patterns or Polymer's path notification API.
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin to skip strict dirty-checking for objects
   *   and arrays
   */
  Polymer.MutableData = Polymer.dedupingMixin(superClass => {

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_MutableData}
     */
    class MutableData extends superClass {
      /**
       * Overrides `Polymer.PropertyEffects` to provide option for skipping
       * strict equality checking for Objects and Arrays.
       *
       * This method pulls the value to dirty check against from the `__dataTemp`
       * cache (rather than the normal `__data` cache) for Objects.  Since the temp
       * cache is cleared at the end of a turn, this implementation allows
       * side-effects of deep object changes to be processed by re-setting the
       * same object (using the temp cache as an in-turn backstop to prevent
       * cycles due to 2-way notification).
       *
       * @param {string} property Property name
       * @param {*} value New property value
       * @param {*} old Previous property value
       * @return {boolean} Whether the property should be considered a change
       * @protected
       */
      _shouldPropertyChange(property, value, old) {
        return mutablePropertyChange(this, property, value, old, true);
      }

    }

    return MutableData;

  });


  /**
   * Element class mixin to add the optional ability to skip strict
   * dirty-checking for objects and arrays (always consider them to be
   * "dirty") by setting a `mutable-data` attribute on an element instance.
   *
   * By default, `Polymer.PropertyEffects` performs strict dirty checking on
   * objects, which means that any deep modifications to an object or array will
   * not be propagated unless "immutable" data patterns are used (i.e. all object
   * references from the root to the mutation were changed).
   *
   * Polymer also provides a proprietary data mutation and path notification API
   * (e.g. `notifyPath`, `set`, and array mutation API's) that allow efficient
   * mutation and notification of deep changes in an object graph to all elements
   * bound to the same object graph.
   *
   * In cases where neither immutable patterns nor the data mutation API can be
   * used, applying this mixin will allow Polymer to skip dirty checking for
   * objects and arrays (always consider them to be "dirty").  This allows a
   * user to make a deep modification to a bound object graph, and then either
   * simply re-set the object (e.g. `this.items = this.items`) or call `notifyPath`
   * (e.g. `this.notifyPath('items')`) to update the tree.  Note that all
   * elements that wish to be updated based on deep mutations must apply this
   * mixin or otherwise skip strict dirty checking for objects/arrays.
   * Specifically, any elements in the binding tree between the source of a
   * mutation and the consumption of it must enable this mixin or apply the
   * `Polymer.MutableData` mixin.
   *
   * While this mixin adds the ability to forgo Object/Array dirty checking,
   * the `mutableData` flag defaults to false and must be set on the instance.
   *
   * Note, the performance characteristics of propagating large object graphs
   * will be worse by relying on `mutableData: true` as opposed to using
   * strict dirty checking with immutable patterns or Polymer's path notification
   * API.
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin to optionally skip strict dirty-checking
   *   for objects and arrays
   */
  Polymer.OptionalMutableData = Polymer.dedupingMixin(superClass => {

    /**
     * @mixinClass
     * @polymer
     * @implements {Polymer_OptionalMutableData}
     */
    class OptionalMutableData extends superClass {

      static get properties() {
        return {
          /**
           * Instance-level flag for configuring the dirty-checking strategy
           * for this element.  When true, Objects and Arrays will skip dirty
           * checking, otherwise strict equality checking will be used.
           */
          mutableData: Boolean
        };
      }

      /**
       * Overrides `Polymer.PropertyEffects` to provide option for skipping
       * strict equality checking for Objects and Arrays.
       *
       * When `this.mutableData` is true on this instance, this method
       * pulls the value to dirty check against from the `__dataTemp` cache
       * (rather than the normal `__data` cache) for Objects.  Since the temp
       * cache is cleared at the end of a turn, this implementation allows
       * side-effects of deep object changes to be processed by re-setting the
       * same object (using the temp cache as an in-turn backstop to prevent
       * cycles due to 2-way notification).
       *
       * @param {string} property Property name
       * @param {*} value New property value
       * @param {*} old Previous property value
       * @return {boolean} Whether the property should be considered a change
       * @protected
       */
      _shouldPropertyChange(property, value, old) {
        return mutablePropertyChange(this, property, value, old, this.mutableData);
      }
    }

    return OptionalMutableData;

  });

  // Export for use by legacy behavior
  Polymer.MutableData._mutablePropertyChange = mutablePropertyChange;

})();


  (function() {
    'use strict';

    // Base class for HTMLTemplateElement extension that has property effects
    // machinery for propagating host properties to children. This is an ES5
    // class only because Babel (incorrectly) requires super() in the class
    // constructor even though no `this` is used and it returns an instance.
    let newInstance = null;

    /**
     * @constructor
     * @extends {HTMLTemplateElement}
     * @private
     */
    function HTMLTemplateElementExtension() { return newInstance; }
    HTMLTemplateElementExtension.prototype = Object.create(HTMLTemplateElement.prototype, {
      constructor: {
        value: HTMLTemplateElementExtension,
        writable: true
      }
    });

    /**
     * @constructor
     * @implements {Polymer_PropertyEffects}
     * @extends {HTMLTemplateElementExtension}
     * @private
     */
    const DataTemplate = Polymer.PropertyEffects(HTMLTemplateElementExtension);

    /**
     * @constructor
     * @implements {Polymer_MutableData}
     * @extends {DataTemplate}
     * @private
     */
    const MutableDataTemplate = Polymer.MutableData(DataTemplate);

    // Applies a DataTemplate subclass to a <template> instance
    function upgradeTemplate(template, constructor) {
      newInstance = template;
      Object.setPrototypeOf(template, constructor.prototype);
      new constructor();
      newInstance = null;
    }

    /**
     * Base class for TemplateInstance.
     * @constructor
     * @implements {Polymer_PropertyEffects}
     * @private
     */
    const base = Polymer.PropertyEffects(class {});

    /**
     * @polymer
     * @customElement
     * @appliesMixin Polymer.PropertyEffects
     * @unrestricted
     */
    class TemplateInstanceBase extends base {
      constructor(props) {
        super();
        this._configureProperties(props);
        this.root = this._stampTemplate(this.__dataHost);
        // Save list of stamped children
        let children = this.children = [];
        for (let n = this.root.firstChild; n; n=n.nextSibling) {
          children.push(n);
          n.__templatizeInstance = this;
        }
        if (this.__templatizeOwner &&
          this.__templatizeOwner.__hideTemplateChildren__) {
          this._showHideChildren(true);
        }
        // Flush props only when props are passed if instance props exist
        // or when there isn't instance props.
        let options = this.__templatizeOptions;
        if ((props && options.instanceProps) || !options.instanceProps) {
          this._enableProperties();
        }
      }
      /**
       * Configure the given `props` by calling `_setPendingProperty`. Also
       * sets any properties stored in `__hostProps`.
       * @private
       * @param {Object} props Object of property name-value pairs to set.
       * @return {void}
       */
      _configureProperties(props) {
        let options = this.__templatizeOptions;
        if (options.forwardHostProp) {
          for (let hprop in this.__hostProps) {
            this._setPendingProperty(hprop, this.__dataHost['_host_' + hprop]);
          }
        }
        // Any instance props passed in the constructor will overwrite host props;
        // normally this would be a user error but we don't specifically filter them
        for (let iprop in props) {
          this._setPendingProperty(iprop, props[iprop]);
        }
      }
      /**
       * Forwards a host property to this instance.  This method should be
       * called on instances from the `options.forwardHostProp` callback
       * to propagate changes of host properties to each instance.
       *
       * Note this method enqueues the change, which are flushed as a batch.
       *
       * @param {string} prop Property or path name
       * @param {*} value Value of the property to forward
       * @return {void}
       */
      forwardHostProp(prop, value) {
        if (this._setPendingPropertyOrPath(prop, value, false, true)) {
          this.__dataHost._enqueueClient(this);
        }
      }

      /**
       * Override point for adding custom or simulated event handling.
       *
       * @param {!Node} node Node to add event listener to
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to add
       * @return {void}
       */
      _addEventListenerToNode(node, eventName, handler) {
        if (this._methodHost && this.__templatizeOptions.parentModel) {
          // If this instance should be considered a parent model, decorate
          // events this template instance as `model`
          this._methodHost._addEventListenerToNode(node, eventName, (e) => {
            e.model = this;
            handler(e);
          });
        } else {
          // Otherwise delegate to the template's host (which could be)
          // another template instance
          let templateHost = this.__dataHost.__dataHost;
          if (templateHost) {
            templateHost._addEventListenerToNode(node, eventName, handler);
          }
        }
      }
      /**
       * Shows or hides the template instance top level child elements. For
       * text nodes, `textContent` is removed while "hidden" and replaced when
       * "shown."
       * @param {boolean} hide Set to true to hide the children;
       * set to false to show them.
       * @return {void}
       * @protected
       */
      _showHideChildren(hide) {
        let c = this.children;
        for (let i=0; i<c.length; i++) {
          let n = c[i];
          // Ignore non-changes
          if (Boolean(hide) != Boolean(n.__hideTemplateChildren__)) {
            if (n.nodeType === Node.TEXT_NODE) {
              if (hide) {
                n.__polymerTextContent__ = n.textContent;
                n.textContent = '';
              } else {
                n.textContent = n.__polymerTextContent__;
              }
            // remove and replace slot
            } else if (n.localName === 'slot') {
              if (hide) {
                n.__polymerReplaced__ = document.createComment('hidden-slot');
                n.parentNode.replaceChild(n.__polymerReplaced__, n);
              } else {
                const replace = n.__polymerReplaced__;
                if (replace) {
                  replace.parentNode.replaceChild(n, replace);
                }
              }
            }

            else if (n.style) {
              if (hide) {
                n.__polymerDisplay__ = n.style.display;
                n.style.display = 'none';
              } else {
                n.style.display = n.__polymerDisplay__;
              }
            }
          }
          n.__hideTemplateChildren__ = hide;
          if (n._showHideChildren) {
            n._showHideChildren(hide);
          }
        }
      }
      /**
       * Overrides default property-effects implementation to intercept
       * textContent bindings while children are "hidden" and cache in
       * private storage for later retrieval.
       *
       * @param {!Node} node The node to set a property on
       * @param {string} prop The property to set
       * @param {*} value The value to set
       * @return {void}
       * @protected
       */
      _setUnmanagedPropertyToNode(node, prop, value) {
        if (node.__hideTemplateChildren__ &&
            node.nodeType == Node.TEXT_NODE && prop == 'textContent') {
          node.__polymerTextContent__ = value;
        } else {
          super._setUnmanagedPropertyToNode(node, prop, value);
        }
      }
      /**
       * Find the parent model of this template instance.  The parent model
       * is either another templatize instance that had option `parentModel: true`,
       * or else the host element.
       *
       * @return {!Polymer_PropertyEffects} The parent model of this instance
       */
      get parentModel() {
        let model = this.__parentModel;
        if (!model) {
          let options;
          model = this;
          do {
            // A template instance's `__dataHost` is a <template>
            // `model.__dataHost.__dataHost` is the template's host
            model = model.__dataHost.__dataHost;
          } while ((options = model.__templatizeOptions) && !options.parentModel);
          this.__parentModel = model;
        }
        return model;
      }

      /**
       * Stub of HTMLElement's `dispatchEvent`, so that effects that may
       * dispatch events safely no-op.
       *
       * @param {Event} event Event to dispatch
       * @return {boolean} Always true.
       */
       dispatchEvent(event) { // eslint-disable-line no-unused-vars
         return true;
      }
    }

    /** @type {!DataTemplate} */
    TemplateInstanceBase.prototype.__dataHost;
    /** @type {!TemplatizeOptions} */
    TemplateInstanceBase.prototype.__templatizeOptions;
    /** @type {!Polymer_PropertyEffects} */
    TemplateInstanceBase.prototype._methodHost;
    /** @type {!Object} */
    TemplateInstanceBase.prototype.__templatizeOwner;
    /** @type {!Object} */
    TemplateInstanceBase.prototype.__hostProps;

    /**
     * @constructor
     * @extends {TemplateInstanceBase}
     * @implements {Polymer_MutableData}
     * @private
     */
    const MutableTemplateInstanceBase = Polymer.MutableData(TemplateInstanceBase);

    function findMethodHost(template) {
      // Technically this should be the owner of the outermost template.
      // In shadow dom, this is always getRootNode().host, but we can
      // approximate this via cooperation with our dataHost always setting
      // `_methodHost` as long as there were bindings (or id's) on this
      // instance causing it to get a dataHost.
      let templateHost = template.__dataHost;
      return templateHost && templateHost._methodHost || templateHost;
    }

    /* eslint-disable valid-jsdoc */
    /**
     * @suppress {missingProperties} class.prototype is not defined for some reason
     */
    function createTemplatizerClass(template, templateInfo, options) {
      // Anonymous class created by the templatize
      let base = options.mutableData ?
        MutableTemplateInstanceBase : TemplateInstanceBase;
      // Affordance for global mixins onto TemplatizeInstance
      if (Polymer.Templatize.mixin) {
        base = Polymer.Templatize.mixin(base);
      }
      /**
       * @constructor
       * @extends {base}
       * @private
       */
      let klass = class extends base { };
      klass.prototype.__templatizeOptions = options;
      klass.prototype._bindTemplate(template);
      addNotifyEffects(klass, template, templateInfo, options);
      return klass;
    }

    /**
     * @suppress {missingProperties} class.prototype is not defined for some reason
     */
    function addPropagateEffects(template, templateInfo, options) {
      let userForwardHostProp = options.forwardHostProp;
      if (userForwardHostProp) {
        // Provide data API and property effects on memoized template class
        let klass = templateInfo.templatizeTemplateClass;
        if (!klass) {
          let base = options.mutableData ? MutableDataTemplate : DataTemplate;
          /** @private */
          klass = templateInfo.templatizeTemplateClass =
            class TemplatizedTemplate extends base {};
          // Add template - >instances effects
          // and host <- template effects
          let hostProps = templateInfo.hostProps;
          for (let prop in hostProps) {
            klass.prototype._addPropertyEffect('_host_' + prop,
              klass.prototype.PROPERTY_EFFECT_TYPES.PROPAGATE,
              {fn: createForwardHostPropEffect(prop, userForwardHostProp)});
            klass.prototype._createNotifyingProperty('_host_' + prop);
          }
        }
        upgradeTemplate(template, klass);
        // Mix any pre-bound data into __data; no need to flush this to
        // instances since they pull from the template at instance-time
        if (template.__dataProto) {
          // Note, generally `__dataProto` could be chained, but it's guaranteed
          // to not be since this is a vanilla template we just added effects to
          Object.assign(template.__data, template.__dataProto);
        }
        // Clear any pending data for performance
        template.__dataTemp = {};
        template.__dataPending = null;
        template.__dataOld = null;
        template._enableProperties();
      }
    }
    /* eslint-enable valid-jsdoc */

    function createForwardHostPropEffect(hostProp, userForwardHostProp) {
      return function forwardHostProp(template, prop, props) {
        userForwardHostProp.call(template.__templatizeOwner,
          prop.substring('_host_'.length), props[prop]);
      };
    }

    function addNotifyEffects(klass, template, templateInfo, options) {
      let hostProps = templateInfo.hostProps || {};
      for (let iprop in options.instanceProps) {
        delete hostProps[iprop];
        let userNotifyInstanceProp = options.notifyInstanceProp;
        if (userNotifyInstanceProp) {
          klass.prototype._addPropertyEffect(iprop,
            klass.prototype.PROPERTY_EFFECT_TYPES.NOTIFY,
            {fn: createNotifyInstancePropEffect(iprop, userNotifyInstanceProp)});
        }
      }
      if (options.forwardHostProp && template.__dataHost) {
        for (let hprop in hostProps) {
          klass.prototype._addPropertyEffect(hprop,
            klass.prototype.PROPERTY_EFFECT_TYPES.NOTIFY,
            {fn: createNotifyHostPropEffect()});
        }
      }
    }

    function createNotifyInstancePropEffect(instProp, userNotifyInstanceProp) {
      return function notifyInstanceProp(inst, prop, props) {
        userNotifyInstanceProp.call(inst.__templatizeOwner,
          inst, prop, props[prop]);
      };
    }

    function createNotifyHostPropEffect() {
      return function notifyHostProp(inst, prop, props) {
        inst.__dataHost._setPendingPropertyOrPath('_host_' + prop, props[prop], true, true);
      };
    }

    /**
     * Module for preparing and stamping instances of templates that utilize
     * Polymer's data-binding and declarative event listener features.
     *
     * Example:
     *
     *     // Get a template from somewhere, e.g. light DOM
     *     let template = this.querySelector('template');
     *     // Prepare the template
     *     let TemplateClass = Polymer.Templatize.templatize(template);
     *     // Instance the template with an initial data model
     *     let instance = new TemplateClass({myProp: 'initial'});
     *     // Insert the instance's DOM somewhere, e.g. element's shadow DOM
     *     this.shadowRoot.appendChild(instance.root);
     *     // Changing a property on the instance will propagate to bindings
     *     // in the template
     *     instance.myProp = 'new value';
     *
     * The `options` dictionary passed to `templatize` allows for customizing
     * features of the generated template class, including how outer-scope host
     * properties should be forwarded into template instances, how any instance
     * properties added into the template's scope should be notified out to
     * the host, and whether the instance should be decorated as a "parent model"
     * of any event handlers.
     *
     *     // Customize property forwarding and event model decoration
     *     let TemplateClass = Polymer.Templatize.templatize(template, this, {
     *       parentModel: true,
     *       forwardHostProp(property, value) {...},
     *       instanceProps: {...},
     *       notifyInstanceProp(instance, property, value) {...},
     *     });
     *
     * @namespace
     * @memberof Polymer
     * @summary Module for preparing and stamping instances of templates
     *   utilizing Polymer templating features.
     */
    Polymer.Templatize = {

      /**
       * Returns an anonymous `Polymer.PropertyEffects` class bound to the
       * `<template>` provided.  Instancing the class will result in the
       * template being stamped into a document fragment stored as the instance's
       * `root` property, after which it can be appended to the DOM.
       *
       * Templates may utilize all Polymer data-binding features as well as
       * declarative event listeners.  Event listeners and inline computing
       * functions in the template will be called on the host of the template.
       *
       * The constructor returned takes a single argument dictionary of initial
       * property values to propagate into template bindings.  Additionally
       * host properties can be forwarded in, and instance properties can be
       * notified out by providing optional callbacks in the `options` dictionary.
       *
       * Valid configuration in `options` are as follows:
       *
       * - `forwardHostProp(property, value)`: Called when a property referenced
       *   in the template changed on the template's host. As this library does
       *   not retain references to templates instanced by the user, it is the
       *   templatize owner's responsibility to forward host property changes into
       *   user-stamped instances.  The `instance.forwardHostProp(property, value)`
       *    method on the generated class should be called to forward host
       *   properties into the template to prevent unnecessary property-changed
       *   notifications. Any properties referenced in the template that are not
       *   defined in `instanceProps` will be notified up to the template's host
       *   automatically.
       * - `instanceProps`: Dictionary of property names that will be added
       *   to the instance by the templatize owner.  These properties shadow any
       *   host properties, and changes within the template to these properties
       *   will result in `notifyInstanceProp` being called.
       * - `mutableData`: When `true`, the generated class will skip strict
       *   dirty-checking for objects and arrays (always consider them to be
       *   "dirty").
       * - `notifyInstanceProp(instance, property, value)`: Called when
       *   an instance property changes.  Users may choose to call `notifyPath`
       *   on e.g. the owner to notify the change.
       * - `parentModel`: When `true`, events handled by declarative event listeners
       *   (`on-event="handler"`) will be decorated with a `model` property pointing
       *   to the template instance that stamped it.  It will also be returned
       *   from `instance.parentModel` in cases where template instance nesting
       *   causes an inner model to shadow an outer model.
       *
       * All callbacks are called bound to the `owner`. Any context
       * needed for the callbacks (such as references to `instances` stamped)
       * should be stored on the `owner` such that they can be retrieved via
       * `this`.
       *
       * When `options.forwardHostProp` is declared as an option, any properties
       * referenced in the template will be automatically forwarded from the host of
       * the `<template>` to instances, with the exception of any properties listed in
       * the `options.instanceProps` object.  `instanceProps` are assumed to be
       * managed by the owner of the instances, either passed into the constructor
       * or set after the fact.  Note, any properties passed into the constructor will
       * always be set to the instance (regardless of whether they would normally
       * be forwarded from the host).
       *
       * Note that `templatize()` can be run only once for a given `<template>`.
       * Further calls will result in an error. Also, there is a special
       * behavior if the template was duplicated through a mechanism such as
       * `<dom-repeat>` or `<test-fixture>`. In this case, all calls to
       * `templatize()` return the same class for all duplicates of a template.
       * The class returned from `templatize()` is generated only once using
       * the `options` from the first call. This means that any `options`
       * provided to subsequent calls will be ignored. Therefore, it is very
       * important not to close over any variables inside the callbacks. Also,
       * arrow functions must be avoided because they bind the outer `this`.
       * Inside the callbacks, any contextual information can be accessed
       * through `this`, which points to the `owner`.
       *
       * @memberof Polymer.Templatize
       * @param {!HTMLTemplateElement} template Template to templatize
       * @param {Polymer_PropertyEffects=} owner Owner of the template instances;
       *   any optional callbacks will be bound to this owner.
       * @param {Object=} options Options dictionary (see summary for details)
       * @return {function(new:TemplateInstanceBase)} Generated class bound to the template
       *   provided
       * @suppress {invalidCasts}
       */
      templatize(template, owner, options) {
        // Under strictTemplatePolicy, the templatized element must be owned
        // by a (trusted) Polymer element, indicated by existence of _methodHost;
        // e.g. for dom-if & dom-repeat in main document, _methodHost is null
        if (Polymer.strictTemplatePolicy && !findMethodHost(template)) {
          throw new Error('strictTemplatePolicy: template owner not trusted');
        }
        options = /** @type {!TemplatizeOptions} */(options || {});
        if (template.__templatizeOwner) {
          throw new Error('A <template> can only be templatized once');
        }
        template.__templatizeOwner = owner;
        const ctor = owner ? owner.constructor : TemplateInstanceBase;
        let templateInfo = ctor._parseTemplate(template);
        // Get memoized base class for the prototypical template, which
        // includes property effects for binding template & forwarding
        let baseClass = templateInfo.templatizeInstanceClass;
        if (!baseClass) {
          baseClass = createTemplatizerClass(template, templateInfo, options);
          templateInfo.templatizeInstanceClass = baseClass;
        }
        // Host property forwarding must be installed onto template instance
        addPropagateEffects(template, templateInfo, options);
        // Subclass base class and add reference for this specific template
        /** @private */
        let klass = class TemplateInstance extends baseClass {};
        klass.prototype._methodHost = findMethodHost(template);
        klass.prototype.__dataHost = template;
        klass.prototype.__templatizeOwner = owner;
        klass.prototype.__hostProps = templateInfo.hostProps;
        klass = /** @type {function(new:TemplateInstanceBase)} */(klass); //eslint-disable-line no-self-assign
        return klass;
      },

      /**
       * Returns the template "model" associated with a given element, which
       * serves as the binding scope for the template instance the element is
       * contained in. A template model is an instance of
       * `TemplateInstanceBase`, and should be used to manipulate data
       * associated with this template instance.
       *
       * Example:
       *
       *   let model = modelForElement(el);
       *   if (model.index < 10) {
       *     model.set('item.checked', true);
       *   }
       *
       * @memberof Polymer.Templatize
       * @param {HTMLTemplateElement} template The model will be returned for
       *   elements stamped from this template
       * @param {Node=} node Node for which to return a template model.
       * @return {TemplateInstanceBase} Template instance representing the
       *   binding scope for the element
       */
      modelForElement(template, node) {
        let model;
        while (node) {
          // An element with a __templatizeInstance marks the top boundary
          // of a scope; walk up until we find one, and then ensure that
          // its __dataHost matches `this`, meaning this dom-repeat stamped it
          if ((model = node.__templatizeInstance)) {
            // Found an element stamped by another template; keep walking up
            // from its __dataHost
            if (model.__dataHost != template) {
              node = model.__dataHost;
            } else {
              return model;
            }
          } else {
            // Still in a template scope, keep going up until
            // a __templatizeInstance is found
            node = node.parentNode;
          }
        }
        return null;
      }
    };

    Polymer.TemplateInstanceBase = TemplateInstanceBase;

  })();



  (function() {
    'use strict';

    let TemplateInstanceBase = Polymer.TemplateInstanceBase; // eslint-disable-line

    /**
     * @typedef {{
     *   _templatizerTemplate: HTMLTemplateElement,
     *   _parentModel: boolean,
     *   _instanceProps: Object,
     *   _forwardHostPropV2: Function,
     *   _notifyInstancePropV2: Function,
     *   ctor: TemplateInstanceBase
     * }}
     */
    let TemplatizerUser; // eslint-disable-line

    /**
     * The `Polymer.Templatizer` behavior adds methods to generate instances of
     * templates that are each managed by an anonymous `Polymer.PropertyEffects`
     * instance where data-bindings in the stamped template content are bound to
     * accessors on itself.
     *
     * This behavior is provided in Polymer 2.x as a hybrid-element convenience
     * only.  For non-hybrid usage, the `Polymer.Templatize` library
     * should be used instead.
     *
     * Example:
     *
     *     // Get a template from somewhere, e.g. light DOM
     *     let template = this.querySelector('template');
     *     // Prepare the template
     *     this.templatize(template);
     *     // Instance the template with an initial data model
     *     let instance = this.stamp({myProp: 'initial'});
     *     // Insert the instance's DOM somewhere, e.g. light DOM
     *     Polymer.dom(this).appendChild(instance.root);
     *     // Changing a property on the instance will propagate to bindings
     *     // in the template
     *     instance.myProp = 'new value';
     *
     * Users of `Templatizer` may need to implement the following abstract
     * API's to determine how properties and paths from the host should be
     * forwarded into to instances:
     *
     *     _forwardHostPropV2: function(prop, value)
     *
     * Likewise, users may implement these additional abstract API's to determine
     * how instance-specific properties that change on the instance should be
     * forwarded out to the host, if necessary.
     *
     *     _notifyInstancePropV2: function(inst, prop, value)
     *
     * In order to determine which properties are instance-specific and require
     * custom notification via `_notifyInstanceProp`, define an `_instanceProps`
     * object containing keys for each instance prop, for example:
     *
     *     _instanceProps: {
     *       item: true,
     *       index: true
     *     }
     *
     * Any properties used in the template that are not defined in _instanceProp
     * will be forwarded out to the Templatize `owner` automatically.
     *
     * Users may also implement the following abstract function to show or
     * hide any DOM generated using `stamp`:
     *
     *     _showHideChildren: function(shouldHide)
     *
     * Note that some callbacks are suffixed with `V2` in the Polymer 2.x behavior
     * as the implementations will need to differ from the callbacks required
     * by the 1.x Templatizer API due to changes in the `TemplateInstance` API
     * between versions 1.x and 2.x.
     *
     * @polymerBehavior
     */
    Polymer.Templatizer = {

      /**
       * Generates an anonymous `TemplateInstance` class (stored as `this.ctor`)
       * for the provided template.  This method should be called once per
       * template to prepare an element for stamping the template, followed
       * by `stamp` to create new instances of the template.
       *
       * @param {!HTMLTemplateElement} template Template to prepare
       * @param {boolean=} mutableData When `true`, the generated class will skip
       *   strict dirty-checking for objects and arrays (always consider them to
       *   be "dirty"). Defaults to false.
       * @return {void}
       * @this {TemplatizerUser}
       */
      templatize(template, mutableData) {
        this._templatizerTemplate = template;
        this.ctor = Polymer.Templatize.templatize(template, this, {
          mutableData: Boolean(mutableData),
          parentModel: this._parentModel,
          instanceProps: this._instanceProps,
          forwardHostProp: this._forwardHostPropV2,
          notifyInstanceProp: this._notifyInstancePropV2
        });
      },

      /**
       * Creates an instance of the template prepared by `templatize`.  The object
       * returned is an instance of the anonymous class generated by `templatize`
       * whose `root` property is a document fragment containing newly cloned
       * template content, and which has property accessors corresponding to
       * properties referenced in template bindings.
       *
       * @param {Object=} model Object containing initial property values to
       *   populate into the template bindings.
       * @return {TemplateInstanceBase} Returns the created instance of
       * the template prepared by `templatize`.
       * @this {TemplatizerUser}
       */
      stamp(model) {
        return new this.ctor(model);
      },

      /**
       * Returns the template "model" (`TemplateInstance`) associated with
       * a given element, which serves as the binding scope for the template
       * instance the element is contained in.  A template model should be used
       * to manipulate data associated with this template instance.
       *
       * @param {HTMLElement} el Element for which to return a template model.
       * @return {TemplateInstanceBase} Model representing the binding scope for
       *   the element.
       * @this {TemplatizerUser}
       */
      modelForElement(el) {
        return Polymer.Templatize.modelForElement(this._templatizerTemplate, el);
      }
    };

  })();



  (function() {
    'use strict';

    /**
     * @constructor
     * @extends {HTMLElement}
     * @implements {Polymer_PropertyEffects}
     * @implements {Polymer_OptionalMutableData}
     * @implements {Polymer_GestureEventListeners}
     * @private
     */
    const domBindBase =
      Polymer.GestureEventListeners(
        Polymer.OptionalMutableData(
          Polymer.PropertyEffects(HTMLElement)));

    /**
     * Custom element to allow using Polymer's template features (data binding,
     * declarative event listeners, etc.) in the main document without defining
     * a new custom element.
     *
     * `<template>` tags utilizing bindings may be wrapped with the `<dom-bind>`
     * element, which will immediately stamp the wrapped template into the main
     * document and bind elements to the `dom-bind` element itself as the
     * binding scope.
     *
     * @polymer
     * @customElement
     * @appliesMixin Polymer.PropertyEffects
     * @appliesMixin Polymer.OptionalMutableData
     * @appliesMixin Polymer.GestureEventListeners
     * @extends {domBindBase}
     * @memberof Polymer
     * @summary Custom element to allow using Polymer's template features (data
     *   binding, declarative event listeners, etc.) in the main document.
     */
    class DomBind extends domBindBase {

      static get observedAttributes() { return ['mutable-data']; }

      constructor() {
        super();
        if (Polymer.strictTemplatePolicy) {
          throw new Error(`strictTemplatePolicy: dom-bind not allowed`);
        }
        this.root = null;
        this.$ = null;
        this.__children = null;
      }

      /** @return {void} */
      attributeChangedCallback() {
        // assumes only one observed attribute
        this.mutableData = true;
      }

      /** @return {void} */
      connectedCallback() {
        this.style.display = 'none';
        this.render();
      }

      /** @return {void} */
      disconnectedCallback() {
        this.__removeChildren();
      }

      __insertChildren() {
        this.parentNode.insertBefore(this.root, this);
      }

      __removeChildren() {
        if (this.__children) {
          for (let i=0; i<this.__children.length; i++) {
            this.root.appendChild(this.__children[i]);
          }
        }
      }

      /**
       * Forces the element to render its content. This is typically only
       * necessary to call if HTMLImports with the async attribute are used.
       * @return {void}
       */
      render() {
        let template;
        if (!this.__children) {
          template = /** @type {HTMLTemplateElement} */(template || this.querySelector('template'));
          if (!template) {
            // Wait until childList changes and template should be there by then
            let observer = new MutationObserver(() => {
              template = /** @type {HTMLTemplateElement} */(this.querySelector('template'));
              if (template) {
                observer.disconnect();
                this.render();
              } else {
                throw new Error('dom-bind requires a <template> child');
              }
            });
            observer.observe(this, {childList: true});
            return;
          }
          this.root = this._stampTemplate(template);
          this.$ = this.root.$;
          this.__children = [];
          for (let n=this.root.firstChild; n; n=n.nextSibling) {
            this.__children[this.__children.length] = n;
          }
          this._enableProperties();
        }
        this.__insertChildren();
        this.dispatchEvent(new CustomEvent('dom-change', {
          bubbles: true,
          composed: true
        }));
      }

    }

    customElements.define('dom-bind', DomBind);

    /** @const */
    Polymer.DomBind = DomBind;

  })();



  (function() {
    'use strict';

    /**
     * Class representing a static string value which can be used to filter
     * strings by asseting that they have been created via this class. The
     * `value` property returns the string passed to the constructor.
     */
    class LiteralString {
      constructor(string) {
        /** @type {string} */
        this.value = string.toString();
      }
      /**
       * @return {string} LiteralString string value
       */
      toString() {
        return this.value;
      }
    }

    /**
     * @param {*} value Object to stringify into HTML
     * @return {string} HTML stringified form of `obj`
     */
    function literalValue(value) {
      if (value instanceof LiteralString) {
        return /** @type {!LiteralString} */(value).value;
      } else {
        throw new Error(`non-literal value passed to Polymer.htmlLiteral: ${value}`);
      }
    }

    /**
     * @param {*} value Object to stringify into HTML
     * @return {string} HTML stringified form of `obj`
     */
    function htmlValue(value) {
      if (value instanceof HTMLTemplateElement) {
        return /** @type {!HTMLTemplateElement } */(value).innerHTML;
      } else if (value instanceof LiteralString) {
        return literalValue(value);
      } else {
        throw new Error(`non-template value passed to Polymer.html: ${value}`);
      }
    }

    /**
     * A template literal tag that creates an HTML <template> element from the
     * contents of the string.
     *
     * This allows you to write a Polymer Template in JavaScript.
     *
     * Templates can be composed by interpolating `HTMLTemplateElement`s in
     * expressions in the JavaScript template literal. The nested template's
     * `innerHTML` is included in the containing template.  The only other
     * values allowed in expressions are those returned from `Polymer.htmlLiteral`
     * which ensures only literal values from JS source ever reach the HTML, to
     * guard against XSS risks.
     *
     * All other values are disallowed in expressions to help prevent XSS
     * attacks; however, `Polymer.htmlLiteral` can be used to compose static
     * string values into templates. This is useful to compose strings into
     * places that do not accept html, like the css text of a `style`
     * element.
     *
     * Example:
     *
     *     static get template() {
     *       return Polymer.html`
     *         <style>:host{ content:"..." }</style>
     *         <div class="shadowed">${this.partialTemplate}</div>
     *         ${super.template}
     *       `;
     *     }
     *     static get partialTemplate() { return Polymer.html`<span>Partial!</span>`; }
     *
     * @memberof Polymer
     * @param {!ITemplateArray} strings Constant parts of tagged template literal
     * @param {...*} values Variable parts of tagged template literal
     * @return {!HTMLTemplateElement} Constructed HTMLTemplateElement
     */
    Polymer.html = function html(strings, ...values) {
      const template = /** @type {!HTMLTemplateElement} */(document.createElement('template'));
      template.innerHTML = values.reduce((acc, v, idx) =>
          acc + htmlValue(v) + strings[idx + 1], strings[0]);
      return template;
    };

    /**
     * An html literal tag that can be used with `Polymer.html` to compose.
     * a literal string.
     *
     * Example:
     *
     *     static get template() {
     *       return Polymer.html`
     *         <style>
     *           :host { display: block; }
     *           ${styleTemplate}
     *         </style>
     *         <div class="shadowed">${staticValue}</div>
     *         ${super.template}
     *       `;
     *     }
     *     static get styleTemplate() { return Polymer.htmlLiteral`.shadowed { background: gray; }`; }
     *
     * @memberof Polymer
     * @param {!ITemplateArray} strings Constant parts of tagged template literal
     * @param {...*} values Variable parts of tagged template literal
     * @return {!LiteralString} Constructed literal string
     */
    Polymer.htmlLiteral = function(strings, ...values) {
      return new LiteralString(values.reduce((acc, v, idx) =>
          acc + literalValue(v) + strings[idx + 1], strings[0]));
    };
  })();


(function() {
  'use strict';

  /**
   * Base class that provides the core API for Polymer's meta-programming
   * features including template stamping, data-binding, attribute deserialization,
   * and property change observation.
   *
   * @customElement
   * @memberof Polymer
   * @constructor
   * @implements {Polymer_ElementMixin}
   * @extends {HTMLElement}
   * @appliesMixin Polymer.ElementMixin
   * @summary Custom element base class that provides the core API for Polymer's
   *   key meta-programming features including template stamping, data-binding,
   *   attribute deserialization, and property change observation
   */
  Polymer.Element = Polymer.ElementMixin(HTMLElement);

  // NOTE: this is here for modulizer to export `html` for the module version of this file
  Polymer.html = Polymer.html;
})();


(function() {
  'use strict';

  let TemplateInstanceBase = Polymer.TemplateInstanceBase; // eslint-disable-line

  /**
   * @constructor
   * @implements {Polymer_OptionalMutableData}
   * @extends {Polymer.Element}
   * @private
   */
  const domRepeatBase = Polymer.OptionalMutableData(Polymer.Element);

  /**
   * The `<dom-repeat>` element will automatically stamp and binds one instance
   * of template content to each object in a user-provided array.
   * `dom-repeat` accepts an `items` property, and one instance of the template
   * is stamped for each item into the DOM at the location of the `dom-repeat`
   * element.  The `item` property will be set on each instance's binding
   * scope, thus templates should bind to sub-properties of `item`.
   *
   * Example:
   *
   * ```html
   * <dom-module id="employee-list">
   *
   *   <template>
   *
   *     <div> Employee list: </div>
   *     <dom-repeat items="{{employees}}">
   *       <template>
   *         <div>First name: <span>{{item.first}}</span></div>
   *         <div>Last name: <span>{{item.last}}</span></div>
   *       </template>
   *     </dom-repeat>
   *
   *   </template>
   *
   * </dom-module>
   * ```
   *
   * With the following custom element definition:
   *
   * ```js
   * class EmployeeList extends Polymer.Element {
   *   static get is() { return 'employee-list'; }
   *   static get properties() {
   *     return {
   *       employees: {
   *         value() {
   *           return [
   *             {first: 'Bob', last: 'Smith'},
   *             {first: 'Sally', last: 'Johnson'},
   *             ...
   *           ];
   *         }
   *       }
   *     };
   *   }
   * }
   * ```
   *
   * Notifications for changes to items sub-properties will be forwarded to template
   * instances, which will update via the normal structured data notification system.
   *
   * Mutations to the `items` array itself should be made using the Array
   * mutation API's on `Polymer.Base` (`push`, `pop`, `splice`, `shift`,
   * `unshift`), and template instances will be kept in sync with the data in the
   * array.
   *
   * Events caught by event handlers within the `dom-repeat` template will be
   * decorated with a `model` property, which represents the binding scope for
   * each template instance.  The model is an instance of Polymer.Base, and should
   * be used to manipulate data on the instance, for example
   * `event.model.set('item.checked', true);`.
   *
   * Alternatively, the model for a template instance for an element stamped by
   * a `dom-repeat` can be obtained using the `modelForElement` API on the
   * `dom-repeat` that stamped it, for example
   * `this.$.domRepeat.modelForElement(event.target).set('item.checked', true);`.
   * This may be useful for manipulating instance data of event targets obtained
   * by event handlers on parents of the `dom-repeat` (event delegation).
   *
   * A view-specific filter/sort may be applied to each `dom-repeat` by supplying a
   * `filter` and/or `sort` property.  This may be a string that names a function on
   * the host, or a function may be assigned to the property directly.  The functions
   * should implemented following the standard `Array` filter/sort API.
   *
   * In order to re-run the filter or sort functions based on changes to sub-fields
   * of `items`, the `observe` property may be set as a space-separated list of
   * `item` sub-fields that should cause a re-filter/sort when modified.  If
   * the filter or sort function depends on properties not contained in `items`,
   * the user should observe changes to those properties and call `render` to update
   * the view based on the dependency change.
   *
   * For example, for an `dom-repeat` with a filter of the following:
   *
   * ```js
   * isEngineer(item) {
   *   return item.type == 'engineer' || item.manager.type == 'engineer';
   * }
   * ```
   *
   * Then the `observe` property should be configured as follows:
   *
   * ```html
   * <dom-repeat items="{{employees}}" filter="isEngineer" observe="type manager.type">
   * ```
   *
   * @customElement
   * @polymer
   * @memberof Polymer
   * @extends {domRepeatBase}
   * @appliesMixin Polymer.OptionalMutableData
   * @summary Custom element for stamping instance of a template bound to
   *   items in an array.
   */
  class DomRepeat extends domRepeatBase {

    // Not needed to find template; can be removed once the analyzer
    // can find the tag name from customElements.define call
    static get is() { return 'dom-repeat'; }

    static get template() { return null; }

    static get properties() {

      /**
       * Fired whenever DOM is added or removed by this template (by
       * default, rendering occurs lazily).  To force immediate rendering, call
       * `render`.
       *
       * @event dom-change
       */
      return {

        /**
         * An array containing items determining how many instances of the template
         * to stamp and that that each template instance should bind to.
         */
        items: {
          type: Array
        },

        /**
         * The name of the variable to add to the binding scope for the array
         * element associated with a given template instance.
         */
        as: {
          type: String,
          value: 'item'
        },

        /**
         * The name of the variable to add to the binding scope with the index
         * of the instance in the sorted and filtered list of rendered items.
         * Note, for the index in the `this.items` array, use the value of the
         * `itemsIndexAs` property.
         */
        indexAs: {
          type: String,
          value: 'index'
        },

        /**
         * The name of the variable to add to the binding scope with the index
         * of the instance in the `this.items` array. Note, for the index of
         * this instance in the sorted and filtered list of rendered items,
         * use the value of the `indexAs` property.
         */
        itemsIndexAs: {
          type: String,
          value: 'itemsIndex'
        },

        /**
         * A function that should determine the sort order of the items.  This
         * property should either be provided as a string, indicating a method
         * name on the element's host, or else be an actual function.  The
         * function should match the sort function passed to `Array.sort`.
         * Using a sort function has no effect on the underlying `items` array.
         */
        sort: {
          type: Function,
          observer: '__sortChanged'
        },

        /**
         * A function that can be used to filter items out of the view.  This
         * property should either be provided as a string, indicating a method
         * name on the element's host, or else be an actual function.  The
         * function should match the sort function passed to `Array.filter`.
         * Using a filter function has no effect on the underlying `items` array.
         */
        filter: {
          type: Function,
          observer: '__filterChanged'
        },

        /**
         * When using a `filter` or `sort` function, the `observe` property
         * should be set to a space-separated list of the names of item
         * sub-fields that should trigger a re-sort or re-filter when changed.
         * These should generally be fields of `item` that the sort or filter
         * function depends on.
         */
        observe: {
          type: String,
          observer: '__observeChanged'
        },

        /**
         * When using a `filter` or `sort` function, the `delay` property
         * determines a debounce time in ms after a change to observed item
         * properties that must pass before the filter or sort is re-run.
         * This is useful in rate-limiting shuffling of the view when
         * item changes may be frequent.
         */
        delay: Number,

        /**
         * Count of currently rendered items after `filter` (if any) has been applied.
         * If "chunking mode" is enabled, `renderedItemCount` is updated each time a
         * set of template instances is rendered.
         *
         */
        renderedItemCount: {
          type: Number,
          notify: true,
          readOnly: true
        },

        /**
         * Defines an initial count of template instances to render after setting
         * the `items` array, before the next paint, and puts the `dom-repeat`
         * into "chunking mode".  The remaining items will be created and rendered
         * incrementally at each animation frame therof until all instances have
         * been rendered.
         */
        initialCount: {
          type: Number,
          observer: '__initializeChunking'
        },

        /**
         * When `initialCount` is used, this property defines a frame rate (in
         * fps) to target by throttling the number of instances rendered each
         * frame to not exceed the budget for the target frame rate.  The
         * framerate is effectively the number of `requestAnimationFrame`s that
         * it tries to allow to actually fire in a given second. It does this
         * by measuring the time between `rAF`s and continuously adjusting the
         * number of items created each `rAF` to maintain the target framerate.
         * Setting this to a higher number allows lower latency and higher
         * throughput for event handlers and other tasks, but results in a
         * longer time for the remaining items to complete rendering.
         */
        targetFramerate: {
          type: Number,
          value: 20
        },

        _targetFrameTime: {
          type: Number,
          computed: '__computeFrameTime(targetFramerate)'
        }

      };

    }

    static get observers() {
      return [ '__itemsChanged(items.*)' ];
    }

    constructor() {
      super();
      this.__instances = [];
      this.__limit = Infinity;
      this.__pool = [];
      this.__renderDebouncer = null;
      this.__itemsIdxToInstIdx = {};
      this.__chunkCount = null;
      this.__lastChunkTime = null;
      this.__sortFn = null;
      this.__filterFn = null;
      this.__observePaths = null;
      this.__ctor = null;
      this.__isDetached = true;
      this.template = null;
    }

    /**
     * @return {void}
     */
    disconnectedCallback() {
      super.disconnectedCallback();
      this.__isDetached = true;
      for (let i=0; i<this.__instances.length; i++) {
        this.__detachInstance(i);
      }
    }

    /**
     * @return {void}
     */
    connectedCallback() {
      super.connectedCallback();
      this.style.display = 'none';
      // only perform attachment if the element was previously detached.
      if (this.__isDetached) {
        this.__isDetached = false;
        let parent = this.parentNode;
        for (let i=0; i<this.__instances.length; i++) {
          this.__attachInstance(i, parent);
        }
      }
    }

    __ensureTemplatized() {
      // Templatizing (generating the instance constructor) needs to wait
      // until ready, since won't have its template content handed back to
      // it until then
      if (!this.__ctor) {
        let template = this.template = /** @type {HTMLTemplateElement} */(this.querySelector('template'));
        if (!template) {
          // // Wait until childList changes and template should be there by then
          let observer = new MutationObserver(() => {
            if (this.querySelector('template')) {
              observer.disconnect();
              this.__render();
            } else {
              throw new Error('dom-repeat requires a <template> child');
            }
          });
          observer.observe(this, {childList: true});
          return false;
        }
        // Template instance props that should be excluded from forwarding
        let instanceProps = {};
        instanceProps[this.as] = true;
        instanceProps[this.indexAs] = true;
        instanceProps[this.itemsIndexAs] = true;
        this.__ctor = Polymer.Templatize.templatize(template, this, {
          mutableData: this.mutableData,
          parentModel: true,
          instanceProps: instanceProps,
          /**
           * @this {this}
           * @param {string} prop Property to set
           * @param {*} value Value to set property to
           */
          forwardHostProp: function(prop, value) {
            let i$ = this.__instances;
            for (let i=0, inst; (i<i$.length) && (inst=i$[i]); i++) {
              inst.forwardHostProp(prop, value);
            }
          },
          /**
           * @this {this}
           * @param {Object} inst Instance to notify
           * @param {string} prop Property to notify
           * @param {*} value Value to notify
           */
          notifyInstanceProp: function(inst, prop, value) {
            if (Polymer.Path.matches(this.as, prop)) {
              let idx = inst[this.itemsIndexAs];
              if (prop == this.as) {
                this.items[idx] = value;
              }
              let path = Polymer.Path.translate(this.as, 'items.' + idx, prop);
              this.notifyPath(path, value);
            }
          }
        });
      }
      return true;
    }

    __getMethodHost() {
      // Technically this should be the owner of the outermost template.
      // In shadow dom, this is always getRootNode().host, but we can
      // approximate this via cooperation with our dataHost always setting
      // `_methodHost` as long as there were bindings (or id's) on this
      // instance causing it to get a dataHost.
      return this.__dataHost._methodHost || this.__dataHost;
    }

    __functionFromPropertyValue(functionOrMethodName) {
      if (typeof functionOrMethodName === 'string') {
        let methodName = functionOrMethodName;
        let obj = this.__getMethodHost();
        return function() { return obj[methodName].apply(obj, arguments); };
      }

      return functionOrMethodName;
    }

    __sortChanged(sort) {
      this.__sortFn = this.__functionFromPropertyValue(sort);
      if (this.items) { this.__debounceRender(this.__render); }
    }

    __filterChanged(filter) {
      this.__filterFn = this.__functionFromPropertyValue(filter);
      if (this.items) { this.__debounceRender(this.__render); }
    }

    __computeFrameTime(rate) {
      return Math.ceil(1000/rate);
    }

    __initializeChunking() {
      if (this.initialCount) {
        this.__limit = this.initialCount;
        this.__chunkCount = this.initialCount;
        this.__lastChunkTime = performance.now();
      }
    }

    __tryRenderChunk() {
      // Debounced so that multiple calls through `_render` between animation
      // frames only queue one new rAF (e.g. array mutation & chunked render)
      if (this.items && this.__limit < this.items.length) {
        this.__debounceRender(this.__requestRenderChunk);
      }
    }

    __requestRenderChunk() {
      requestAnimationFrame(()=>this.__renderChunk());
    }

    __renderChunk() {
      // Simple auto chunkSize throttling algorithm based on feedback loop:
      // measure actual time between frames and scale chunk count by ratio
      // of target/actual frame time
      let currChunkTime = performance.now();
      let ratio = this._targetFrameTime / (currChunkTime - this.__lastChunkTime);
      this.__chunkCount = Math.round(this.__chunkCount * ratio) || 1;
      this.__limit += this.__chunkCount;
      this.__lastChunkTime = currChunkTime;
      this.__debounceRender(this.__render);
    }

    __observeChanged() {
      this.__observePaths = this.observe &&
        this.observe.replace('.*', '.').split(' ');
    }

    __itemsChanged(change) {
      if (this.items && !Array.isArray(this.items)) {
        console.warn('dom-repeat expected array for `items`, found', this.items);
      }
      // If path was to an item (e.g. 'items.3' or 'items.3.foo'), forward the
      // path to that instance synchronously (returns false for non-item paths)
      if (!this.__handleItemPath(change.path, change.value)) {
        // Otherwise, the array was reset ('items') or spliced ('items.splices'),
        // so queue a full refresh
        this.__initializeChunking();
        this.__debounceRender(this.__render);
      }
    }

    __handleObservedPaths(path) {
      // Handle cases where path changes should cause a re-sort/filter
      if (this.__sortFn || this.__filterFn) {
        if (!path) {
          // Always re-render if the item itself changed
          this.__debounceRender(this.__render, this.delay);
        } else if (this.__observePaths) {
          // Otherwise, re-render if the path changed matches an observed path
          let paths = this.__observePaths;
          for (let i=0; i<paths.length; i++) {
            if (path.indexOf(paths[i]) === 0) {
              this.__debounceRender(this.__render, this.delay);
            }
          }
        }
      }
    }

    /**
     * @param {function(this:DomRepeat)} fn Function to debounce.
     * @param {number=} delay Delay in ms to debounce by.
     */
    __debounceRender(fn, delay = 0) {
      this.__renderDebouncer = Polymer.Debouncer.debounce(
            this.__renderDebouncer
          , delay > 0 ? Polymer.Async.timeOut.after(delay) : Polymer.Async.microTask
          , fn.bind(this));
      Polymer.enqueueDebouncer(this.__renderDebouncer);
    }

    /**
     * Forces the element to render its content. Normally rendering is
     * asynchronous to a provoking change. This is done for efficiency so
     * that multiple changes trigger only a single render. The render method
     * should be called if, for example, template rendering is required to
     * validate application state.
     * @return {void}
     */
    render() {
      // Queue this repeater, then flush all in order
      this.__debounceRender(this.__render);
      Polymer.flush();
    }

    __render() {
      if (!this.__ensureTemplatized()) {
        // No template found yet
        return;
      }
      this.__applyFullRefresh();
      // Reset the pool
      // TODO(kschaaf): Reuse pool across turns and nested templates
      // Now that objects/arrays are re-evaluated when set, we can safely
      // reuse pooled instances across turns, however we still need to decide
      // semantics regarding how long to hold, how many to hold, etc.
      this.__pool.length = 0;
      // Set rendered item count
      this._setRenderedItemCount(this.__instances.length);
      // Notify users
      this.dispatchEvent(new CustomEvent('dom-change', {
        bubbles: true,
        composed: true
      }));
      // Check to see if we need to render more items
      this.__tryRenderChunk();
    }

    __applyFullRefresh() {
      let items = this.items || [];
      let isntIdxToItemsIdx = new Array(items.length);
      for (let i=0; i<items.length; i++) {
        isntIdxToItemsIdx[i] = i;
      }
      // Apply user filter
      if (this.__filterFn) {
        isntIdxToItemsIdx = isntIdxToItemsIdx.filter((i, idx, array) =>
          this.__filterFn(items[i], idx, array));
      }
      // Apply user sort
      if (this.__sortFn) {
        isntIdxToItemsIdx.sort((a, b) => this.__sortFn(items[a], items[b]));
      }
      // items->inst map kept for item path forwarding
      const itemsIdxToInstIdx = this.__itemsIdxToInstIdx = {};
      let instIdx = 0;
      // Generate instances and assign items
      const limit = Math.min(isntIdxToItemsIdx.length, this.__limit);
      for (; instIdx<limit; instIdx++) {
        let inst = this.__instances[instIdx];
        let itemIdx = isntIdxToItemsIdx[instIdx];
        let item = items[itemIdx];
        itemsIdxToInstIdx[itemIdx] = instIdx;
        if (inst) {
          inst._setPendingProperty(this.as, item);
          inst._setPendingProperty(this.indexAs, instIdx);
          inst._setPendingProperty(this.itemsIndexAs, itemIdx);
          inst._flushProperties();
        } else {
          this.__insertInstance(item, instIdx, itemIdx);
        }
      }
      // Remove any extra instances from previous state
      for (let i=this.__instances.length-1; i>=instIdx; i--) {
        this.__detachAndRemoveInstance(i);
      }
    }

    __detachInstance(idx) {
      let inst = this.__instances[idx];
      for (let i=0; i<inst.children.length; i++) {
        let el = inst.children[i];
        inst.root.appendChild(el);
      }
      return inst;
    }

    __attachInstance(idx, parent) {
      let inst = this.__instances[idx];
      parent.insertBefore(inst.root, this);
    }

    __detachAndRemoveInstance(idx) {
      let inst = this.__detachInstance(idx);
      if (inst) {
        this.__pool.push(inst);
      }
      this.__instances.splice(idx, 1);
    }

    __stampInstance(item, instIdx, itemIdx) {
      let model = {};
      model[this.as] = item;
      model[this.indexAs] = instIdx;
      model[this.itemsIndexAs] = itemIdx;
      return new this.__ctor(model);
    }

    __insertInstance(item, instIdx, itemIdx) {
      let inst = this.__pool.pop();
      if (inst) {
        // TODO(kschaaf): If the pool is shared across turns, hostProps
        // need to be re-set to reused instances in addition to item
        inst._setPendingProperty(this.as, item);
        inst._setPendingProperty(this.indexAs, instIdx);
        inst._setPendingProperty(this.itemsIndexAs, itemIdx);
        inst._flushProperties();
      } else {
        inst = this.__stampInstance(item, instIdx, itemIdx);
      }
      let beforeRow = this.__instances[instIdx + 1];
      let beforeNode = beforeRow ? beforeRow.children[0] : this;
      this.parentNode.insertBefore(inst.root, beforeNode);
      this.__instances[instIdx] = inst;
      return inst;
    }

    // Implements extension point from Templatize mixin
    /**
     * Shows or hides the template instance top level child elements. For
     * text nodes, `textContent` is removed while "hidden" and replaced when
     * "shown."
     * @param {boolean} hidden Set to true to hide the children;
     * set to false to show them.
     * @return {void}
     * @protected
     */
    _showHideChildren(hidden) {
      for (let i=0; i<this.__instances.length; i++) {
        this.__instances[i]._showHideChildren(hidden);
      }
    }

    // Called as a side effect of a host items.<key>.<path> path change,
    // responsible for notifying item.<path> changes to inst for key
    __handleItemPath(path, value) {
      let itemsPath = path.slice(6); // 'items.'.length == 6
      let dot = itemsPath.indexOf('.');
      let itemsIdx = dot < 0 ? itemsPath : itemsPath.substring(0, dot);
      // If path was index into array...
      if (itemsIdx == parseInt(itemsIdx, 10)) {
        let itemSubPath = dot < 0 ? '' : itemsPath.substring(dot+1);
        // If the path is observed, it will trigger a full refresh
        this.__handleObservedPaths(itemSubPath);
        // Note, even if a rull refresh is triggered, always do the path
        // notification because unless mutableData is used for dom-repeat
        // and all elements in the instance subtree, a full refresh may
        // not trigger the proper update.
        let instIdx = this.__itemsIdxToInstIdx[itemsIdx];
        let inst = this.__instances[instIdx];
        if (inst) {
          let itemPath = this.as + (itemSubPath ? '.' + itemSubPath : '');
          // This is effectively `notifyPath`, but avoids some of the overhead
          // of the public API
          inst._setPendingPropertyOrPath(itemPath, value, false, true);
          inst._flushProperties();
        }
        return true;
      }
    }

    /**
     * Returns the item associated with a given element stamped by
     * this `dom-repeat`.
     *
     * Note, to modify sub-properties of the item,
     * `modelForElement(el).set('item.<sub-prop>', value)`
     * should be used.
     *
     * @param {!HTMLElement} el Element for which to return the item.
     * @return {*} Item associated with the element.
     */
    itemForElement(el) {
      let instance = this.modelForElement(el);
      return instance && instance[this.as];
    }

    /**
     * Returns the inst index for a given element stamped by this `dom-repeat`.
     * If `sort` is provided, the index will reflect the sorted order (rather
     * than the original array order).
     *
     * @param {!HTMLElement} el Element for which to return the index.
     * @return {?number} Row index associated with the element (note this may
     *   not correspond to the array index if a user `sort` is applied).
     */
    indexForElement(el) {
      let instance = this.modelForElement(el);
      return instance && instance[this.indexAs];
    }

    /**
     * Returns the template "model" associated with a given element, which
     * serves as the binding scope for the template instance the element is
     * contained in. A template model is an instance of `Polymer.Base`, and
     * should be used to manipulate data associated with this template instance.
     *
     * Example:
     *
     *   let model = modelForElement(el);
     *   if (model.index < 10) {
     *     model.set('item.checked', true);
     *   }
     *
     * @param {!HTMLElement} el Element for which to return a template model.
     * @return {TemplateInstanceBase} Model representing the binding scope for
     *   the element.
     */
    modelForElement(el) {
      return Polymer.Templatize.modelForElement(this.template, el);
    }

  }

  customElements.define(DomRepeat.is, DomRepeat);

  /** @const */
  Polymer.DomRepeat = DomRepeat;

})();




(function() {
  'use strict';

  /**
   * The `<dom-if>` element will stamp a light-dom `<template>` child when
   * the `if` property becomes truthy, and the template can use Polymer
   * data-binding and declarative event features when used in the context of
   * a Polymer element's template.
   *
   * When `if` becomes falsy, the stamped content is hidden but not
   * removed from dom. When `if` subsequently becomes truthy again, the content
   * is simply re-shown. This approach is used due to its favorable performance
   * characteristics: the expense of creating template content is paid only
   * once and lazily.
   *
   * Set the `restamp` property to true to force the stamped content to be
   * created / destroyed when the `if` condition changes.
   *
   * @customElement
   * @polymer
   * @extends Polymer.Element
   * @memberof Polymer
   * @summary Custom element that conditionally stamps and hides or removes
   *   template content based on a boolean flag.
   */
  class DomIf extends Polymer.Element {

    // Not needed to find template; can be removed once the analyzer
    // can find the tag name from customElements.define call
    static get is() { return 'dom-if'; }

    static get template() { return null; }

    static get properties() {

      return {

        /**
         * Fired whenever DOM is added or removed/hidden by this template (by
         * default, rendering occurs lazily).  To force immediate rendering, call
         * `render`.
         *
         * @event dom-change
         */

        /**
         * A boolean indicating whether this template should stamp.
         */
        if: {
          type: Boolean,
          observer: '__debounceRender'
        },

        /**
         * When true, elements will be removed from DOM and discarded when `if`
         * becomes false and re-created and added back to the DOM when `if`
         * becomes true.  By default, stamped elements will be hidden but left
         * in the DOM when `if` becomes false, which is generally results
         * in better performance.
         */
        restamp: {
          type: Boolean,
          observer: '__debounceRender'
        }

      };

    }

    constructor() {
      super();
      this.__renderDebouncer = null;
      this.__invalidProps = null;
      this.__instance = null;
      this._lastIf = false;
      this.__ctor = null;
    }

    __debounceRender() {
      // Render is async for 2 reasons:
      // 1. To eliminate dom creation trashing if user code thrashes `if` in the
      //    same turn. This was more common in 1.x where a compound computed
      //    property could result in the result changing multiple times, but is
      //    mitigated to a large extent by batched property processing in 2.x.
      // 2. To avoid double object propagation when a bag including values bound
      //    to the `if` property as well as one or more hostProps could enqueue
      //    the <dom-if> to flush before the <template>'s host property
      //    forwarding. In that scenario creating an instance would result in
      //    the host props being set once, and then the enqueued changes on the
      //    template would set properties a second time, potentially causing an
      //    object to be set to an instance more than once.  Creating the
      //    instance async from flushing data ensures this doesn't happen. If
      //    we wanted a sync option in the future, simply having <dom-if> flush
      //    (or clear) its template's pending host properties before creating
      //    the instance would also avoid the problem.
      this.__renderDebouncer = Polymer.Debouncer.debounce(
            this.__renderDebouncer
          , Polymer.Async.microTask
          , () => this.__render());
      Polymer.enqueueDebouncer(this.__renderDebouncer);
    }

    /**
     * @return {void}
     */
    disconnectedCallback() {
      super.disconnectedCallback();
      if (!this.parentNode ||
          (this.parentNode.nodeType == Node.DOCUMENT_FRAGMENT_NODE &&
           !this.parentNode.host)) {
        this.__teardownInstance();
      }
    }

    /**
     * @return {void}
     */
    connectedCallback() {
      super.connectedCallback();
      this.style.display = 'none';
      if (this.if) {
        this.__debounceRender();
      }
    }

    /**
     * Forces the element to render its content. Normally rendering is
     * asynchronous to a provoking change. This is done for efficiency so
     * that multiple changes trigger only a single render. The render method
     * should be called if, for example, template rendering is required to
     * validate application state.
     * @return {void}
     */
    render() {
      Polymer.flush();
    }

    __render() {
      if (this.if) {
        if (!this.__ensureInstance()) {
          // No template found yet
          return;
        }
        this._showHideChildren();
      } else if (this.restamp) {
        this.__teardownInstance();
      }
      if (!this.restamp && this.__instance) {
        this._showHideChildren();
      }
      if (this.if != this._lastIf) {
        this.dispatchEvent(new CustomEvent('dom-change', {
          bubbles: true,
          composed: true
        }));
        this._lastIf = this.if;
      }
    }

    __ensureInstance() {
      let parentNode = this.parentNode;
      // Guard against element being detached while render was queued
      if (parentNode) {
        if (!this.__ctor) {
          let template = /** @type {HTMLTemplateElement} */(this.querySelector('template'));
          if (!template) {
            // Wait until childList changes and template should be there by then
            let observer = new MutationObserver(() => {
              if (this.querySelector('template')) {
                observer.disconnect();
                this.__render();
              } else {
                throw new Error('dom-if requires a <template> child');
              }
            });
            observer.observe(this, {childList: true});
            return false;
          }
          this.__ctor = Polymer.Templatize.templatize(template, this, {
            // dom-if templatizer instances require `mutable: true`, as
            // `__syncHostProperties` relies on that behavior to sync objects
            mutableData: true,
            /**
             * @param {string} prop Property to forward
             * @param {*} value Value of property
             * @this {this}
             */
            forwardHostProp: function(prop, value) {
              if (this.__instance) {
                if (this.if) {
                  this.__instance.forwardHostProp(prop, value);
                } else {
                  // If we have an instance but are squelching host property
                  // forwarding due to if being false, note the invalidated
                  // properties so `__syncHostProperties` can sync them the next
                  // time `if` becomes true
                  this.__invalidProps = this.__invalidProps || Object.create(null);
                  this.__invalidProps[Polymer.Path.root(prop)] = true;
                }
              }
            }
          });
        }
        if (!this.__instance) {
          this.__instance = new this.__ctor();
          parentNode.insertBefore(this.__instance.root, this);
        } else {
          this.__syncHostProperties();
          let c$ = this.__instance.children;
          if (c$ && c$.length) {
            // Detect case where dom-if was re-attached in new position
            let lastChild = this.previousSibling;
            if (lastChild !== c$[c$.length-1]) {
              for (let i=0, n; (i<c$.length) && (n=c$[i]); i++) {
                parentNode.insertBefore(n, this);
              }
            }
          }
        }
      }
      return true;
    }

    __syncHostProperties() {
      let props = this.__invalidProps;
      if (props) {
        for (let prop in props) {
          this.__instance._setPendingProperty(prop, this.__dataHost[prop]);
        }
        this.__invalidProps = null;
        this.__instance._flushProperties();
      }
    }

    __teardownInstance() {
      if (this.__instance) {
        let c$ = this.__instance.children;
        if (c$ && c$.length) {
          // use first child parent, for case when dom-if may have been detached
          let parent = c$[0].parentNode;
          // Instance children may be disconnected from parents when dom-if
          // detaches if a tree was innerHTML'ed
          if (parent) {
            for (let i=0, n; (i<c$.length) && (n=c$[i]); i++) {
              parent.removeChild(n);
            }
          }
        }
        this.__instance = null;
        this.__invalidProps = null;
      }
    }

    /**
     * Shows or hides the template instance top level child elements. For
     * text nodes, `textContent` is removed while "hidden" and replaced when
     * "shown."
     * @return {void}
     * @protected
     */
    _showHideChildren() {
      let hidden = this.__hideTemplateChildren__ || !this.if;
      if (this.__instance) {
        this.__instance._showHideChildren(hidden);
      }
    }

  }

  customElements.define(DomIf.is, DomIf);

  /** @const */
  Polymer.DomIf = DomIf;

})();


(function() {
  'use strict';

  /**
   * Element mixin for recording dynamic associations between item paths in a
   * master `items` array and a `selected` array such that path changes to the
   * master array (at the host) element or elsewhere via data-binding) are
   * correctly propagated to items in the selected array and vice-versa.
   *
   * The `items` property accepts an array of user data, and via the
   * `select(item)` and `deselect(item)` API, updates the `selected` property
   * which may be bound to other parts of the application, and any changes to
   * sub-fields of `selected` item(s) will be kept in sync with items in the
   * `items` array.  When `multi` is false, `selected` is a property
   * representing the last selected item.  When `multi` is true, `selected`
   * is an array of multiply selected items.
   *
   * @polymer
   * @mixinFunction
   * @appliesMixin Polymer.ElementMixin
   * @memberof Polymer
   * @summary Element mixin for recording dynamic associations between item paths in a
   * master `items` array and a `selected` array
   */
  let ArraySelectorMixin = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_ElementMixin}
     * @private
     */
    let elementBase = Polymer.ElementMixin(superClass);

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_ArraySelectorMixin}
     * @unrestricted
     */
    class ArraySelectorMixin extends elementBase {

      static get properties() {

        return {

          /**
           * An array containing items from which selection will be made.
           */
          items: {
            type: Array,
          },

          /**
           * When `true`, multiple items may be selected at once (in this case,
           * `selected` is an array of currently selected items).  When `false`,
           * only one item may be selected at a time.
           */
          multi: {
            type: Boolean,
            value: false,
          },

          /**
           * When `multi` is true, this is an array that contains any selected.
           * When `multi` is false, this is the currently selected item, or `null`
           * if no item is selected.
           * @type {?(Object|Array<!Object>)}
           */
          selected: {
            type: Object,
            notify: true
          },

          /**
           * When `multi` is false, this is the currently selected item, or `null`
           * if no item is selected.
           * @type {?Object}
           */
          selectedItem: {
            type: Object,
            notify: true
          },

          /**
           * When `true`, calling `select` on an item that is already selected
           * will deselect the item.
           */
          toggle: {
            type: Boolean,
            value: false
          }

        };
      }

      static get observers() {
        return ['__updateSelection(multi, items.*)'];
      }

      constructor() {
        super();
        this.__lastItems = null;
        this.__lastMulti = null;
        this.__selectedMap = null;
      }

      __updateSelection(multi, itemsInfo) {
        let path = itemsInfo.path;
        if (path == 'items') {
          // Case 1 - items array changed, so diff against previous array and
          // deselect any removed items and adjust selected indices
          let newItems = itemsInfo.base || [];
          let lastItems = this.__lastItems;
          let lastMulti = this.__lastMulti;
          if (multi !== lastMulti) {
            this.clearSelection();
          }
          if (lastItems) {
            let splices = Polymer.ArraySplice.calculateSplices(newItems, lastItems);
            this.__applySplices(splices);
          }
          this.__lastItems = newItems;
          this.__lastMulti = multi;
        } else if (itemsInfo.path == 'items.splices') {
          // Case 2 - got specific splice information describing the array mutation:
          // deselect any removed items and adjust selected indices
          this.__applySplices(itemsInfo.value.indexSplices);
        } else {
          // Case 3 - an array element was changed, so deselect the previous
          // item for that index if it was previously selected
          let part = path.slice('items.'.length);
          let idx = parseInt(part, 10);
          if ((part.indexOf('.') < 0) && part == idx) {
            this.__deselectChangedIdx(idx);
          }
        }
      }

      __applySplices(splices) {
        let selected = this.__selectedMap;
        // Adjust selected indices and mark removals
        for (let i=0; i<splices.length; i++) {
          let s = splices[i];
          selected.forEach((idx, item) => {
            if (idx < s.index) {
              // no change
            } else if (idx >= s.index + s.removed.length) {
              // adjust index
              selected.set(item, idx + s.addedCount - s.removed.length);
            } else {
              // remove index
              selected.set(item, -1);
            }
          });
          for (let j=0; j<s.addedCount; j++) {
            let idx = s.index + j;
            if (selected.has(this.items[idx])) {
              selected.set(this.items[idx], idx);
            }
          }
        }
        // Update linked paths
        this.__updateLinks();
        // Remove selected items that were removed from the items array
        let sidx = 0;
        selected.forEach((idx, item) => {
          if (idx < 0) {
            if (this.multi) {
              this.splice('selected', sidx, 1);
            } else {
              this.selected = this.selectedItem = null;
            }
            selected.delete(item);
          } else {
            sidx++;
          }
        });
      }

      __updateLinks() {
        this.__dataLinkedPaths = {};
        if (this.multi) {
          let sidx = 0;
          this.__selectedMap.forEach(idx => {
            if (idx >= 0) {
              this.linkPaths('items.' + idx, 'selected.' + sidx++);
            }
          });
        } else {
          this.__selectedMap.forEach(idx => {
            this.linkPaths('selected', 'items.' + idx);
            this.linkPaths('selectedItem', 'items.' + idx);
          });
        }
      }

      /**
       * Clears the selection state.
       * @return {void}
       */
      clearSelection() {
        // Unbind previous selection
        this.__dataLinkedPaths = {};
        // The selected map stores 3 pieces of information:
        // key: items array object
        // value: items array index
        // order: selected array index
        this.__selectedMap = new Map();
        // Initialize selection
        this.selected = this.multi ? [] : null;
        this.selectedItem = null;
      }

      /**
       * Returns whether the item is currently selected.
       *
       * @param {*} item Item from `items` array to test
       * @return {boolean} Whether the item is selected
       */
      isSelected(item) {
        return this.__selectedMap.has(item);
      }

      /**
       * Returns whether the item is currently selected.
       *
       * @param {number} idx Index from `items` array to test
       * @return {boolean} Whether the item is selected
       */
      isIndexSelected(idx) {
        return this.isSelected(this.items[idx]);
      }

      __deselectChangedIdx(idx) {
        let sidx = this.__selectedIndexForItemIndex(idx);
        if (sidx >= 0) {
          let i = 0;
          this.__selectedMap.forEach((idx, item) => {
            if (sidx == i++) {
              this.deselect(item);
            }
          });
        }
      }

      __selectedIndexForItemIndex(idx) {
        let selected = this.__dataLinkedPaths['items.' + idx];
        if (selected) {
          return parseInt(selected.slice('selected.'.length), 10);
        }
      }

      /**
       * Deselects the given item if it is already selected.
       *
       * @param {*} item Item from `items` array to deselect
       * @return {void}
       */
      deselect(item) {
        let idx = this.__selectedMap.get(item);
        if (idx >= 0) {
          this.__selectedMap.delete(item);
          let sidx;
          if (this.multi) {
            sidx = this.__selectedIndexForItemIndex(idx);
          }
          this.__updateLinks();
          if (this.multi) {
            this.splice('selected', sidx, 1);
          } else {
            this.selected = this.selectedItem = null;
          }
        }
      }

      /**
       * Deselects the given index if it is already selected.
       *
       * @param {number} idx Index from `items` array to deselect
       * @return {void}
       */
      deselectIndex(idx) {
        this.deselect(this.items[idx]);
      }

      /**
       * Selects the given item.  When `toggle` is true, this will automatically
       * deselect the item if already selected.
       *
       * @param {*} item Item from `items` array to select
       * @return {void}
       */
      select(item) {
        this.selectIndex(this.items.indexOf(item));
      }

      /**
       * Selects the given index.  When `toggle` is true, this will automatically
       * deselect the item if already selected.
       *
       * @param {number} idx Index from `items` array to select
       * @return {void}
       */
      selectIndex(idx) {
        let item = this.items[idx];
        if (!this.isSelected(item)) {
          if (!this.multi) {
            this.__selectedMap.clear();
          }
          this.__selectedMap.set(item, idx);
          this.__updateLinks();
          if (this.multi) {
            this.push('selected', item);
          } else {
            this.selected = this.selectedItem = item;
          }
        } else if (this.toggle) {
          this.deselectIndex(idx);
        }
      }

    }

    return ArraySelectorMixin;

  });

  // export mixin
  Polymer.ArraySelectorMixin = ArraySelectorMixin;

  /**
   * @constructor
   * @extends {Polymer.Element}
   * @implements {Polymer_ArraySelectorMixin}
   * @private
   */
  let baseArraySelector = ArraySelectorMixin(Polymer.Element);

  /**
   * Element implementing the `Polymer.ArraySelector` mixin, which records
   * dynamic associations between item paths in a master `items` array and a
   * `selected` array such that path changes to the master array (at the host)
   * element or elsewhere via data-binding) are correctly propagated to items
   * in the selected array and vice-versa.
   *
   * The `items` property accepts an array of user data, and via the
   * `select(item)` and `deselect(item)` API, updates the `selected` property
   * which may be bound to other parts of the application, and any changes to
   * sub-fields of `selected` item(s) will be kept in sync with items in the
   * `items` array.  When `multi` is false, `selected` is a property
   * representing the last selected item.  When `multi` is true, `selected`
   * is an array of multiply selected items.
   *
   * Example:
   *
   * ```html
   * <dom-module id="employee-list">
   *
   *   <template>
   *
   *     <div> Employee list: </div>
   *     <dom-repeat id="employeeList" items="{{employees}}">
   *       <template>
   *         <div>First name: <span>{{item.first}}</span></div>
   *           <div>Last name: <span>{{item.last}}</span></div>
   *           <button on-click="toggleSelection">Select</button>
   *       </template>
   *     </dom-repeat>
   *
   *     <array-selector id="selector" items="{{employees}}" selected="{{selected}}" multi toggle></array-selector>
   *
   *     <div> Selected employees: </div>
   *     <dom-repeat items="{{selected}}">
   *       <template>
   *         <div>First name: <span>{{item.first}}</span></div>
   *         <div>Last name: <span>{{item.last}}</span></div>
   *       </template>
   *     </dom-repeat>
   *
   *   </template>
   *
   * </dom-module>
   * ```
   *
   * ```js
   *class EmployeeList extends Polymer.Element {
   *  static get is() { return 'employee-list'; }
   *  static get properties() {
   *    return {
   *      employees: {
   *        value() {
   *          return [
   *            {first: 'Bob', last: 'Smith'},
   *            {first: 'Sally', last: 'Johnson'},
   *            ...
   *          ];
   *        }
   *      }
   *    };
   *  }
   *  toggleSelection(e) {
   *    let item = this.$.employeeList.itemForElement(e.target);
   *    this.$.selector.select(item);
   *  }
   *}
   * ```
   *
   * @polymer
   * @customElement
   * @extends {baseArraySelector}
   * @appliesMixin Polymer.ArraySelectorMixin
   * @memberof Polymer
   * @summary Custom element that links paths between an input `items` array and
   *   an output `selected` item or array based on calls to its selection API.
   */
  class ArraySelector extends baseArraySelector {
    // Not needed to find template; can be removed once the analyzer
    // can find the tag name from customElements.define call
    static get is() { return 'array-selector'; }
  }
  customElements.define(ArraySelector.is, ArraySelector);

  /** @const */
  Polymer.ArraySelector = ArraySelector;

})();


(function(){/*

Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
Code distributed by Google as part of the polymer project is also
subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
'use strict';var c=null,f=window.HTMLImports&&window.HTMLImports.whenReady||null,g;function h(a){requestAnimationFrame(function(){f?f(a):(c||(c=new Promise(function(a){g=a}),"complete"===document.readyState?g():document.addEventListener("readystatechange",function(){"complete"===document.readyState&&g()})),c.then(function(){a&&a()}))})};var k=null,l=null;function m(){this.customStyles=[];this.enqueued=!1;h(function(){window.ShadyCSS.flushCustomStyles&&window.ShadyCSS.flushCustomStyles()})}function n(a){!a.enqueued&&l&&(a.enqueued=!0,h(l))}m.prototype.c=function(a){a.__seenByShadyCSS||(a.__seenByShadyCSS=!0,this.customStyles.push(a),n(this))};m.prototype.b=function(a){if(a.__shadyCSSCachedStyle)return a.__shadyCSSCachedStyle;var b;a.getStyle?b=a.getStyle():b=a;return b};
m.prototype.a=function(){for(var a=this.customStyles,b=0;b<a.length;b++){var d=a[b];if(!d.__shadyCSSCachedStyle){var e=this.b(d);e&&(e=e.__appliedElement||e,k&&k(e),d.__shadyCSSCachedStyle=e)}}return a};m.prototype.addCustomStyle=m.prototype.c;m.prototype.getStyleForCustomStyle=m.prototype.b;m.prototype.processStyles=m.prototype.a;
Object.defineProperties(m.prototype,{transformCallback:{get:function(){return k},set:function(a){k=a}},validateCallback:{get:function(){return l},set:function(a){var b=!1;l||(b=!0);l=a;b&&n(this)}}});function p(a,b){for(var d in b)null===d?a.style.removeProperty(d):a.style.setProperty(d,b[d])};var q=!(window.ShadyDOM&&window.ShadyDOM.inUse),r;function t(a){r=a&&a.shimcssproperties?!1:q||!(navigator.userAgent.match(/AppleWebKit\/601|Edge\/15/)||!window.CSS||!CSS.supports||!CSS.supports("box-shadow","0 0 0 var(--foo)"))}var u;window.ShadyCSS&&void 0!==window.ShadyCSS.cssBuild&&(u=window.ShadyCSS.cssBuild);var v=!(!window.ShadyCSS||!window.ShadyCSS.disableRuntime);
window.ShadyCSS&&void 0!==window.ShadyCSS.nativeCss?r=window.ShadyCSS.nativeCss:window.ShadyCSS?(t(window.ShadyCSS),window.ShadyCSS=void 0):t(window.WebComponents&&window.WebComponents.flags);var w=r,x=u;var y=new m;window.ShadyCSS||(window.ShadyCSS={prepareTemplate:function(){},prepareTemplateDom:function(){},prepareTemplateStyles:function(){},styleSubtree:function(a,b){y.a();p(a,b)},styleElement:function(){y.a()},styleDocument:function(a){y.a();p(document.body,a)},getComputedStyleValue:function(a,b){return(a=window.getComputedStyle(a).getPropertyValue(b))?a.trim():""},flushCustomStyles:function(){},nativeCss:w,nativeShadow:q,cssBuild:x,disableRuntime:v});window.ShadyCSS.CustomStyleInterface=y;}).call(this);




(function() {
  'use strict';

  const attr = 'include';

  const CustomStyleInterface = window.ShadyCSS.CustomStyleInterface;

  /**
   * Custom element for defining styles in the main document that can take
   * advantage of [shady DOM](https://github.com/webcomponents/shadycss) shims
   * for style encapsulation, custom properties, and custom mixins.
   *
   * - Document styles defined in a `<custom-style>` are shimmed to ensure they
   *   do not leak into local DOM when running on browsers without native
   *   Shadow DOM.
   * - Custom properties can be defined in a `<custom-style>`. Use the `html` selector
   *   to define custom properties that apply to all custom elements.
   * - Custom mixins can be defined in a `<custom-style>`, if you import the optional
   *   [apply shim](https://github.com/webcomponents/shadycss#about-applyshim)
   *   (`shadycss/apply-shim.html`).
   *
   * To use:
   *
   * - Import `custom-style.html`.
   * - Place a `<custom-style>` element in the main document, wrapping an inline `<style>` tag that
   *   contains the CSS rules you want to shim.
   *
   * For example:
   *
   * ```html
   * <!-- import apply shim--only required if using mixins -->
   * <link rel="import" href="bower_components/shadycss/apply-shim.html">
   * <!-- import custom-style element -->
   * <link rel="import" href="bower_components/polymer/lib/elements/custom-style.html">
   *
   * <custom-style>
   *   <style>
   *     html {
   *       --custom-color: blue;
   *       --custom-mixin: {
   *         font-weight: bold;
   *         color: red;
   *       };
   *     }
   *   </style>
   * </custom-style>
   * ```
   *
   * @customElement
   * @extends HTMLElement
   * @memberof Polymer
   * @summary Custom element for defining styles in the main document that can
   *   take advantage of Polymer's style scoping and custom properties shims.
   */
  class CustomStyle extends HTMLElement {
    constructor() {
      super();
      this._style = null;
      CustomStyleInterface.addCustomStyle(this);
    }
    /**
     * Returns the light-DOM `<style>` child this element wraps.  Upon first
     * call any style modules referenced via the `include` attribute will be
     * concatenated to this element's `<style>`.
     *
     * @return {HTMLStyleElement} This element's light-DOM `<style>`
     */
    getStyle() {
      if (this._style) {
        return this._style;
      }
      const style = /** @type {HTMLStyleElement} */(this.querySelector('style'));
      if (!style) {
        return null;
      }
      this._style = style;
      const include = style.getAttribute(attr);
      if (include) {
        style.removeAttribute(attr);
        style.textContent = Polymer.StyleGather.cssFromModules(include) + style.textContent;
      }
      /*
      HTML Imports styling the main document are deprecated in Chrome
      https://crbug.com/523952

      If this element is not in the main document, then it must be in an HTML Import document.
      In that case, move the custom style to the main document.

      The ordering of `<custom-style>` should stay the same as when loaded by HTML Imports, but there may be odd
      cases of ordering w.r.t the main document styles.
      */
      if (this.ownerDocument !== window.document) {
        window.document.head.appendChild(this);
      }
      return this._style;
    }
  }

  window.customElements.define('custom-style', CustomStyle);

  /** @const */
  Polymer.CustomStyle = CustomStyle;
})();


(function() {
  'use strict';

  let mutablePropertyChange;
  /** @suppress {missingProperties} */
  (() => {
    mutablePropertyChange = Polymer.MutableData._mutablePropertyChange;
  })();

  /**
   * Legacy element behavior to skip strict dirty-checking for objects and arrays,
   * (always consider them to be "dirty") for use on legacy API Polymer elements.
   *
   * By default, `Polymer.PropertyEffects` performs strict dirty checking on
   * objects, which means that any deep modifications to an object or array will
   * not be propagated unless "immutable" data patterns are used (i.e. all object
   * references from the root to the mutation were changed).
   *
   * Polymer also provides a proprietary data mutation and path notification API
   * (e.g. `notifyPath`, `set`, and array mutation API's) that allow efficient
   * mutation and notification of deep changes in an object graph to all elements
   * bound to the same object graph.
   *
   * In cases where neither immutable patterns nor the data mutation API can be
   * used, applying this mixin will cause Polymer to skip dirty checking for
   * objects and arrays (always consider them to be "dirty").  This allows a
   * user to make a deep modification to a bound object graph, and then either
   * simply re-set the object (e.g. `this.items = this.items`) or call `notifyPath`
   * (e.g. `this.notifyPath('items')`) to update the tree.  Note that all
   * elements that wish to be updated based on deep mutations must apply this
   * mixin or otherwise skip strict dirty checking for objects/arrays.
   * Specifically, any elements in the binding tree between the source of a
   * mutation and the consumption of it must apply this behavior or enable the
   * `Polymer.OptionalMutableDataBehavior`.
   *
   * In order to make the dirty check strategy configurable, see
   * `Polymer.OptionalMutableDataBehavior`.
   *
   * Note, the performance characteristics of propagating large object graphs
   * will be worse as opposed to using strict dirty checking with immutable
   * patterns or Polymer's path notification API.
   *
   * @polymerBehavior
   * @memberof Polymer
   * @summary Behavior to skip strict dirty-checking for objects and
   *   arrays
   */
  Polymer.MutableDataBehavior = {

    /**
     * Overrides `Polymer.PropertyEffects` to provide option for skipping
     * strict equality checking for Objects and Arrays.
     *
     * This method pulls the value to dirty check against from the `__dataTemp`
     * cache (rather than the normal `__data` cache) for Objects.  Since the temp
     * cache is cleared at the end of a turn, this implementation allows
     * side-effects of deep object changes to be processed by re-setting the
     * same object (using the temp cache as an in-turn backstop to prevent
     * cycles due to 2-way notification).
     *
     * @param {string} property Property name
     * @param {*} value New property value
     * @param {*} old Previous property value
     * @return {boolean} Whether the property should be considered a change
     * @protected
     */
    _shouldPropertyChange(property, value, old) {
      return mutablePropertyChange(this, property, value, old, true);
    }
  };

  /**
   * Legacy element behavior to add the optional ability to skip strict
   * dirty-checking for objects and arrays (always consider them to be
   * "dirty") by setting a `mutable-data` attribute on an element instance.
   *
   * By default, `Polymer.PropertyEffects` performs strict dirty checking on
   * objects, which means that any deep modifications to an object or array will
   * not be propagated unless "immutable" data patterns are used (i.e. all object
   * references from the root to the mutation were changed).
   *
   * Polymer also provides a proprietary data mutation and path notification API
   * (e.g. `notifyPath`, `set`, and array mutation API's) that allow efficient
   * mutation and notification of deep changes in an object graph to all elements
   * bound to the same object graph.
   *
   * In cases where neither immutable patterns nor the data mutation API can be
   * used, applying this mixin will allow Polymer to skip dirty checking for
   * objects and arrays (always consider them to be "dirty").  This allows a
   * user to make a deep modification to a bound object graph, and then either
   * simply re-set the object (e.g. `this.items = this.items`) or call `notifyPath`
   * (e.g. `this.notifyPath('items')`) to update the tree.  Note that all
   * elements that wish to be updated based on deep mutations must apply this
   * mixin or otherwise skip strict dirty checking for objects/arrays.
   * Specifically, any elements in the binding tree between the source of a
   * mutation and the consumption of it must enable this behavior or apply the
   * `Polymer.OptionalMutableDataBehavior`.
   *
   * While this behavior adds the ability to forgo Object/Array dirty checking,
   * the `mutableData` flag defaults to false and must be set on the instance.
   *
   * Note, the performance characteristics of propagating large object graphs
   * will be worse by relying on `mutableData: true` as opposed to using
   * strict dirty checking with immutable patterns or Polymer's path notification
   * API.
   *
   * @polymerBehavior
   * @memberof Polymer
   * @summary Behavior to optionally skip strict dirty-checking for objects and
   *   arrays
   */
  Polymer.OptionalMutableDataBehavior = {

    properties: {
      /**
       * Instance-level flag for configuring the dirty-checking strategy
       * for this element.  When true, Objects and Arrays will skip dirty
       * checking, otherwise strict equality checking will be used.
       */
      mutableData: Boolean
    },

    /**
     * Overrides `Polymer.PropertyEffects` to skip strict equality checking
     * for Objects and Arrays.
     *
     * Pulls the value to dirty check against from the `__dataTemp` cache
     * (rather than the normal `__data` cache) for Objects.  Since the temp
     * cache is cleared at the end of a turn, this implementation allows
     * side-effects of deep object changes to be processed by re-setting the
     * same object (using the temp cache as an in-turn backstop to prevent
     * cycles due to 2-way notification).
     *
     * @param {string} property Property name
     * @param {*} value New property value
     * @param {*} old Previous property value
     * @return {boolean} Whether the property should be considered a change
     * @this {this}
     * @protected
     */
    _shouldPropertyChange(property, value, old) {
      return mutablePropertyChange(this, property, value, old, this.mutableData);
    }
  };

})();



  // bc
  Polymer.Base = Polymer.LegacyElementMixin(HTMLElement).prototype;

  // NOTE: this is here for modulizer to export `html` for the module version of this file
  Polymer.html = Polymer.html;

//# sourceURL=build://iron-flex-layout/iron-flex-layout.html.js
(function(){var b=document.createElement("style");b.textContent="[hidden] { display: none !important; }";document.head.appendChild(b)})();

//# sourceURL=build://iron-a11y-keys-behavior/iron-a11y-keys-behavior.html.js
(function(){function b(w,C){var G="";if(w)if(w=w.toLowerCase()," "===w||A.test(w))G="space";else if(y.test(w))G="esc";else if(1==w.length){if(!C||q.test(w))G=w}else G=x.test(w)?w.replace("arrow",""):"multiply"==w?"*":w;return G}function d(w){var C="";w&&(w in p?C=p[w]:u.test(w)?(w=parseInt(w.replace("U+","0x"),16),C=String.fromCharCode(w).toLowerCase()):C=w.toLowerCase());return C}function f(w){var C="";Number(w)&&(C=65<=w&&90>=w?String.fromCharCode(32+w):112<=w&&123>=w?"f"+(w-112+1):48<=w&&57>=w?
String(w-48):96<=w&&105>=w?String(w-96):m[w]);return C}function h(w,C){return w.key?b(w.key,C):w.detail&&w.detail.key?b(w.detail.key,C):d(w.keyIdentifier)||f(w.keyCode)||""}function k(w,C){return h(C,w.hasModifiers)===w.key&&(!w.hasModifiers||!!C.shiftKey===!!w.shiftKey&&!!C.ctrlKey===!!w.ctrlKey&&!!C.altKey===!!w.altKey&&!!C.metaKey===!!w.metaKey)}function t(w){return 1===w.length?{combo:w,key:w,event:"keydown"}:w.split("+").reduce(function(C,G){var D=G.split(":");G=D[0];D=D[1];G in n?(C[n[G]]=!0,
C.hasModifiers=!0):(C.key=G,C.event=D||"keydown");return C},{combo:w.split(":").shift()})}function l(w){return w.trim().split(" ").map(function(C){return t(C)})}var p={"U+0008":"backspace","U+0009":"tab","U+001B":"esc","U+0020":"space","U+007F":"del"},m={8:"backspace",9:"tab",13:"enter",27:"esc",33:"pageup",34:"pagedown",35:"end",36:"home",32:"space",37:"left",38:"up",39:"right",40:"down",46:"del",106:"*"},n={shift:"shiftKey",ctrl:"ctrlKey",alt:"altKey",meta:"metaKey"},q=/[a-z0-9*]/,u=/U\+/,x=/^arrow/,
A=/^space(bar)?/,y=/^escape$/;Polymer.IronA11yKeysBehavior={properties:{keyEventTarget:{type:Object,value:function(){return this}},stopKeyboardEventPropagation:{type:Boolean,value:!1},_boundKeyHandlers:{type:Array,value:function(){return[]}},_imperativeKeyBindings:{type:Object,value:function(){return{}}}},observers:["_resetKeyEventListeners(keyEventTarget, _boundKeyHandlers)"],keyBindings:{},registered:function(){this._prepKeyBindings()},attached:function(){this._listenKeyEventListeners()},detached:function(){this._unlistenKeyEventListeners()},
addOwnKeyBinding:function(w,C){this._imperativeKeyBindings[w]=C;this._prepKeyBindings();this._resetKeyEventListeners()},removeOwnKeyBindings:function(){this._imperativeKeyBindings={};this._prepKeyBindings();this._resetKeyEventListeners()},keyboardEventMatchesKeys:function(w,C){C=l(C);for(var G=0;G<C.length;++G)if(k(C[G],w))return!0;return!1},_collectKeyBindings:function(){var w=this.behaviors.map(function(C){return C.keyBindings});-1===w.indexOf(this.keyBindings)&&w.push(this.keyBindings);return w},
_prepKeyBindings:function(){this._keyBindings={};this._collectKeyBindings().forEach(function(G){for(var D in G)this._addKeyBinding(D,G[D])},this);for(var w in this._imperativeKeyBindings)this._addKeyBinding(w,this._imperativeKeyBindings[w]);for(var C in this._keyBindings)this._keyBindings[C].sort(function(G,D){G=G[0].hasModifiers;return G===D[0].hasModifiers?0:G?-1:1})},_addKeyBinding:function(w,C){l(w).forEach(function(G){this._keyBindings[G.event]=this._keyBindings[G.event]||[];this._keyBindings[G.event].push([G,
C])},this)},_resetKeyEventListeners:function(){this._unlistenKeyEventListeners();this.isAttached&&this._listenKeyEventListeners()},_listenKeyEventListeners:function(){this.keyEventTarget&&Object.keys(this._keyBindings).forEach(function(w){var C=this._onKeyBindingEvent.bind(this,this._keyBindings[w]);this._boundKeyHandlers.push([this.keyEventTarget,w,C]);this.keyEventTarget.addEventListener(w,C)},this)},_unlistenKeyEventListeners:function(){for(var w,C,G;this._boundKeyHandlers.length;)w=this._boundKeyHandlers.pop(),
C=w[0],G=w[1],w=w[2],C.removeEventListener(G,w)},_onKeyBindingEvent:function(w,C){this.stopKeyboardEventPropagation&&C.stopPropagation();if(!C.defaultPrevented)for(var G=0;G<w.length;G++){var D=w[G][0],B=w[G][1];if(k(D,C)&&(this._triggerKeyHandler(D,B,C),C.defaultPrevented))break}},_triggerKeyHandler:function(w,C,G){var D=Object.create(w);D.keyboardEvent=G;w=new CustomEvent(w.event,{detail:D,cancelable:!0});this[C].call(this,w);w.defaultPrevented&&G.preventDefault()}}})();

//# sourceURL=build://iron-behaviors/iron-control-state.html.js
Polymer.IronControlState={properties:{focused:{type:Boolean,value:!1,notify:!0,readOnly:!0,reflectToAttribute:!0},disabled:{type:Boolean,value:!1,notify:!0,observer:"_disabledChanged",reflectToAttribute:!0},_oldTabIndex:{type:String},_boundFocusBlurHandler:{type:Function,value:function(){return this._focusBlurHandler.bind(this)}},__handleEventRetargeting:{type:Boolean,value:function(){return!this.shadowRoot&&!Polymer.Element}}},observers:["_changedControlState(focused, disabled)"],ready:function(){this.addEventListener("focus",
this._boundFocusBlurHandler,!0);this.addEventListener("blur",this._boundFocusBlurHandler,!0)},_focusBlurHandler:function(b){if(Polymer.Element)this._setFocused("focus"===b.type);else if(b.target===this)this._setFocused("focus"===b.type);else if(this.__handleEventRetargeting){var d=Polymer.dom(b).localTarget;this.isLightDescendant(d)||this.fire(b.type,{sourceEvent:b},{node:this,bubbles:b.bubbles,cancelable:b.cancelable})}},_disabledChanged:function(b){this.setAttribute("aria-disabled",b?"true":"false");
this.style.pointerEvents=b?"none":"";b?(this._oldTabIndex=this.getAttribute("tabindex"),this._setFocused(!1),this.tabIndex=-1,this.blur()):void 0!==this._oldTabIndex&&(null===this._oldTabIndex?this.removeAttribute("tabindex"):this.setAttribute("tabindex",this._oldTabIndex))},_changedControlState:function(){this._controlStateChanged&&this._controlStateChanged()}};

//# sourceURL=build://iron-behaviors/iron-button-state.html.js
Polymer.IronButtonStateImpl={properties:{pressed:{type:Boolean,readOnly:!0,value:!1,reflectToAttribute:!0,observer:"_pressedChanged"},toggles:{type:Boolean,value:!1,reflectToAttribute:!0},active:{type:Boolean,value:!1,notify:!0,reflectToAttribute:!0},pointerDown:{type:Boolean,readOnly:!0,value:!1},receivedFocusFromKeyboard:{type:Boolean,readOnly:!0},ariaActiveAttribute:{type:String,value:"aria-pressed",observer:"_ariaActiveAttributeChanged"}},listeners:{down:"_downHandler",up:"_upHandler",tap:"_tapHandler"},
observers:["_focusChanged(focused)","_activeChanged(active, ariaActiveAttribute)"],keyBindings:{"enter:keydown":"_asyncClick","space:keydown":"_spaceKeyDownHandler","space:keyup":"_spaceKeyUpHandler"},_mouseEventRe:/^mouse/,_tapHandler:function(){this.toggles?this._userActivate(!this.active):this.active=!1},_focusChanged:function(b){this._detectKeyboardFocus(b);b||this._setPressed(!1)},_detectKeyboardFocus:function(b){this._setReceivedFocusFromKeyboard(!this.pointerDown&&b)},_userActivate:function(b){this.active!==
b&&(this.active=b,this.fire("change"))},_downHandler:function(){this._setPointerDown(!0);this._setPressed(!0);this._setReceivedFocusFromKeyboard(!1)},_upHandler:function(){this._setPointerDown(!1);this._setPressed(!1)},_spaceKeyDownHandler:function(b){b=b.detail.keyboardEvent;var d=Polymer.dom(b).localTarget;this.isLightDescendant(d)||(b.preventDefault(),b.stopImmediatePropagation(),this._setPressed(!0))},_spaceKeyUpHandler:function(b){b=Polymer.dom(b.detail.keyboardEvent).localTarget;this.isLightDescendant(b)||
(this.pressed&&this._asyncClick(),this._setPressed(!1))},_asyncClick:function(){this.async(function(){this.click()},1)},_pressedChanged:function(){this._changedButtonState()},_ariaActiveAttributeChanged:function(b,d){d&&d!=b&&this.hasAttribute(d)&&this.removeAttribute(d)},_activeChanged:function(b){this.toggles?this.setAttribute(this.ariaActiveAttribute,b?"true":"false"):this.removeAttribute(this.ariaActiveAttribute);this._changedButtonState()},_controlStateChanged:function(){this.disabled?this._setPressed(!1):
this._changedButtonState()},_changedButtonState:function(){this._buttonStateChanged&&this._buttonStateChanged()}};Polymer.IronButtonState=[Polymer.IronA11yKeysBehavior,Polymer.IronButtonStateImpl];

//# sourceURL=build://paper-ripple/paper-ripple.html.js
(function(){function b(h){this.element=h;this.width=this.boundingRect.width;this.height=this.boundingRect.height;this.size=Math.max(this.width,this.height)}function d(h){this.element=h;this.color=window.getComputedStyle(h).color;this.wave=document.createElement("div");this.waveContainer=document.createElement("div");this.wave.style.backgroundColor=this.color;this.wave.classList.add("wave");this.waveContainer.classList.add("wave-container");Polymer.dom(this.waveContainer).appendChild(this.wave);this.resetInteractionState()}
var f={distance:function(h,k,t,l){h-=t;k-=l;return Math.sqrt(h*h+k*k)},now:window.performance&&window.performance.now?window.performance.now.bind(window.performance):Date.now};b.prototype={get boundingRect(){return this.element.getBoundingClientRect()},furthestCornerDistanceFrom:function(h,k){var t=f.distance(h,k,0,0),l=f.distance(h,k,this.width,0),p=f.distance(h,k,0,this.height);h=f.distance(h,k,this.width,this.height);return Math.max(t,l,p,h)}};d.MAX_RADIUS=300;d.prototype={get recenters(){return this.element.recenters},
get center(){return this.element.center},get mouseDownElapsed(){if(!this.mouseDownStart)return 0;var h=f.now()-this.mouseDownStart;this.mouseUpStart&&(h-=this.mouseUpElapsed);return h},get mouseUpElapsed(){return this.mouseUpStart?f.now()-this.mouseUpStart:0},get mouseDownElapsedSeconds(){return this.mouseDownElapsed/1E3},get mouseUpElapsedSeconds(){return this.mouseUpElapsed/1E3},get mouseInteractionSeconds(){return this.mouseDownElapsedSeconds+this.mouseUpElapsedSeconds},get initialOpacity(){return this.element.initialOpacity},
get opacityDecayVelocity(){return this.element.opacityDecayVelocity},get radius(){var h=1.1*Math.min(Math.sqrt(this.containerMetrics.width*this.containerMetrics.width+this.containerMetrics.height*this.containerMetrics.height),d.MAX_RADIUS)+5;return Math.abs(h*(1-Math.pow(80,-(this.mouseInteractionSeconds/(1.1-h/d.MAX_RADIUS*.2)))))},get opacity(){return this.mouseUpStart?Math.max(0,this.initialOpacity-this.mouseUpElapsedSeconds*this.opacityDecayVelocity):this.initialOpacity},get outerOpacity(){return Math.max(0,
Math.min(.3*this.mouseUpElapsedSeconds,this.opacity))},get isOpacityFullyDecayed(){return.01>this.opacity&&this.radius>=Math.min(this.maxRadius,d.MAX_RADIUS)},get isRestingAtMaxRadius(){return this.opacity>=this.initialOpacity&&this.radius>=Math.min(this.maxRadius,d.MAX_RADIUS)},get isAnimationComplete(){return this.mouseUpStart?this.isOpacityFullyDecayed:this.isRestingAtMaxRadius},get translationFraction(){return Math.min(1,this.radius/this.containerMetrics.size*2/Math.sqrt(2))},get xNow(){return this.xEnd?
this.xStart+this.translationFraction*(this.xEnd-this.xStart):this.xStart},get yNow(){return this.yEnd?this.yStart+this.translationFraction*(this.yEnd-this.yStart):this.yStart},get isMouseDown(){return this.mouseDownStart&&!this.mouseUpStart},resetInteractionState:function(){this.slideDistance=this.yEnd=this.xEnd=this.yStart=this.xStart=this.mouseUpStart=this.mouseDownStart=this.maxRadius=0;this.containerMetrics=new b(this.element)},draw:function(){this.wave.style.opacity=this.opacity;var h=this.radius/
(this.containerMetrics.size/2);var k=this.xNow-this.containerMetrics.width/2;var t=this.yNow-this.containerMetrics.height/2;this.waveContainer.style.webkitTransform="translate("+k+"px, "+t+"px)";this.waveContainer.style.transform="translate3d("+k+"px, "+t+"px, 0)";this.wave.style.webkitTransform="scale("+h+","+h+")";this.wave.style.transform="scale3d("+h+","+h+",1)"},downAction:function(h){var k=this.containerMetrics.width/2,t=this.containerMetrics.height/2;this.resetInteractionState();this.mouseDownStart=
f.now();this.center?(this.xStart=k,this.yStart=t,this.slideDistance=f.distance(this.xStart,this.yStart,this.xEnd,this.yEnd)):(this.xStart=h?h.detail.x-this.containerMetrics.boundingRect.left:this.containerMetrics.width/2,this.yStart=h?h.detail.y-this.containerMetrics.boundingRect.top:this.containerMetrics.height/2);this.recenters&&(this.xEnd=k,this.yEnd=t,this.slideDistance=f.distance(this.xStart,this.yStart,this.xEnd,this.yEnd));this.maxRadius=this.containerMetrics.furthestCornerDistanceFrom(this.xStart,
this.yStart);this.waveContainer.style.top=(this.containerMetrics.height-this.containerMetrics.size)/2+"px";this.waveContainer.style.left=(this.containerMetrics.width-this.containerMetrics.size)/2+"px";this.waveContainer.style.width=this.containerMetrics.size+"px";this.waveContainer.style.height=this.containerMetrics.size+"px"},upAction:function(){this.isMouseDown&&(this.mouseUpStart=f.now())},remove:function(){Polymer.dom(this.waveContainer.parentNode).removeChild(this.waveContainer)}};Polymer({is:"paper-ripple",
behaviors:[Polymer.IronA11yKeysBehavior],properties:{initialOpacity:{type:Number,value:.25},opacityDecayVelocity:{type:Number,value:.8},recenters:{type:Boolean,value:!1},center:{type:Boolean,value:!1},ripples:{type:Array,value:function(){return[]}},animating:{type:Boolean,readOnly:!0,reflectToAttribute:!0,value:!1},holdDown:{type:Boolean,value:!1,observer:"_holdDownChanged"},noink:{type:Boolean,value:!1},_animating:{type:Boolean},_boundAnimate:{type:Function,value:function(){return this.animate.bind(this)}}},
get target(){return this.keyEventTarget},keyBindings:{"enter:keydown":"_onEnterKeydown","space:keydown":"_onSpaceKeydown","space:keyup":"_onSpaceKeyup"},attached:function(){var h=this.keyEventTarget=11==this.parentNode.nodeType?Polymer.dom(this).getOwnerRoot().host:this.parentNode;this.listen(h,"up","uiUpAction");this.listen(h,"down","uiDownAction")},detached:function(){this.unlisten(this.keyEventTarget,"up","uiUpAction");this.unlisten(this.keyEventTarget,"down","uiDownAction");this.keyEventTarget=
null},get shouldKeepAnimating(){for(var h=0;h<this.ripples.length;++h)if(!this.ripples[h].isAnimationComplete)return!0;return!1},simulatedRipple:function(){this.downAction(null);this.async(function(){this.upAction()},1)},uiDownAction:function(h){this.noink||this.downAction(h)},downAction:function(h){this.holdDown&&0<this.ripples.length||(this.addRipple().downAction(h),this._animating||(this._animating=!0,this.animate()))},uiUpAction:function(h){this.noink||this.upAction(h)},upAction:function(h){this.holdDown||
(this.ripples.forEach(function(k){k.upAction(h)}),this._animating=!0,this.animate())},onAnimationComplete:function(){this._animating=!1;this.$.background.style.backgroundColor=null;this.fire("transitionend")},addRipple:function(){var h=new d(this);Polymer.dom(this.$.waves).appendChild(h.waveContainer);this.$.background.style.backgroundColor=h.color;this.ripples.push(h);this._setAnimating(!0);return h},removeRipple:function(h){var k=this.ripples.indexOf(h);0>k||(this.ripples.splice(k,1),h.remove(),
this.ripples.length||this._setAnimating(!1))},animate:function(){if(this._animating){var h;for(h=0;h<this.ripples.length;++h){var k=this.ripples[h];k.draw();this.$.background.style.opacity=k.outerOpacity;k.isOpacityFullyDecayed&&!k.isRestingAtMaxRadius&&this.removeRipple(k)}if(this.shouldKeepAnimating||0!==this.ripples.length)window.requestAnimationFrame(this._boundAnimate);else this.onAnimationComplete()}},_onEnterKeydown:function(){this.uiDownAction();this.async(this.uiUpAction,1)},_onSpaceKeydown:function(){this.uiDownAction()},
_onSpaceKeyup:function(){this.uiUpAction()},_holdDownChanged:function(h,k){void 0!==k&&(h?this.downAction():this.upAction())}})})();

//# sourceURL=build://paper-behaviors/paper-ripple-behavior.html.js
Polymer.PaperRippleBehavior={properties:{noink:{type:Boolean,observer:"_noinkChanged"},_rippleContainer:{type:Object}},_buttonStateChanged:function(){this.focused&&this.ensureRipple()},_downHandler:function(b){Polymer.IronButtonStateImpl._downHandler.call(this,b);this.pressed&&this.ensureRipple(b)},ensureRipple:function(b){if(!this.hasRipple()){this._ripple=this._createRipple();this._ripple.noink=this.noink;var d=this._rippleContainer||this.root;d&&Polymer.dom(d).appendChild(this._ripple);if(b){d=
Polymer.dom(this._rippleContainer||this);var f=Polymer.dom(b).rootTarget;d.deepContains(f)&&this._ripple.uiDownAction(b)}}},getRipple:function(){this.ensureRipple();return this._ripple},hasRipple:function(){return!!this._ripple},_createRipple:function(){return document.createElement("paper-ripple")},_noinkChanged:function(b){this.hasRipple()&&(this._ripple.noink=b)}};

//# sourceURL=build://paper-behaviors/paper-button-behavior.html.js
Polymer.PaperButtonBehaviorImpl={properties:{elevation:{type:Number,reflectToAttribute:!0,readOnly:!0}},observers:["_calculateElevation(focused, disabled, active, pressed, receivedFocusFromKeyboard)","_computeKeyboardClass(receivedFocusFromKeyboard)"],hostAttributes:{role:"button",tabindex:"0",animated:!0},_calculateElevation:function(){var b=1;this.disabled?b=0:this.active||this.pressed?b=4:this.receivedFocusFromKeyboard&&(b=3);this._setElevation(b)},_computeKeyboardClass:function(b){this.toggleClass("keyboard-focus",
b)},_spaceKeyDownHandler:function(b){Polymer.IronButtonStateImpl._spaceKeyDownHandler.call(this,b);this.hasRipple()&&1>this.getRipple().ripples.length&&this._ripple.uiDownAction()},_spaceKeyUpHandler:function(b){Polymer.IronButtonStateImpl._spaceKeyUpHandler.call(this,b);this.hasRipple()&&this._ripple.uiUpAction()}};Polymer.PaperButtonBehavior=[Polymer.IronButtonState,Polymer.IronControlState,Polymer.PaperRippleBehavior,Polymer.PaperButtonBehaviorImpl];

//# sourceURL=build://paper-button/paper-button.html.js
Polymer({is:"paper-button",behaviors:[Polymer.PaperButtonBehavior],properties:{raised:{type:Boolean,reflectToAttribute:!0,value:!1,observer:"_calculateElevation"}},_calculateElevation:function(){this.raised?Polymer.PaperButtonBehaviorImpl._calculateElevation.apply(this):this._setElevation(0)}});

//# sourceURL=build://iron-meta/iron-meta.html.js
(function(){Polymer.IronMeta=function(d){Polymer.IronMeta[" "](d);this.type=d&&d.type||"default";this.key=d&&d.key;d&&"value"in d&&(this.value=d.value)};Polymer.IronMeta[" "]=function(){};Polymer.IronMeta.types={};Polymer.IronMeta.prototype={get value(){var d=this.type,f=this.key;if(d&&f)return Polymer.IronMeta.types[d]&&Polymer.IronMeta.types[d][f]},set value(d){var f=this.type,h=this.key;f&&h&&(f=Polymer.IronMeta.types[f]=Polymer.IronMeta.types[f]||{},null==d?delete f[h]:f[h]=d)},get list(){if(this.type){var d=
Polymer.IronMeta.types[this.type];return d?Object.keys(d).map(function(f){return b[this.type][f]},this):[]}},byKey:function(d){this.key=d;return this.value}};var b=Polymer.IronMeta.types;Polymer({is:"iron-meta",properties:{type:{type:String,value:"default"},key:{type:String},value:{type:String,notify:!0},self:{type:Boolean,observer:"_selfChanged"},__meta:{type:Boolean,computed:"__computeMeta(type, key, value)"}},hostAttributes:{hidden:!0},__computeMeta:function(d,f,h){d=new Polymer.IronMeta({type:d,
key:f});void 0!==h&&h!==d.value?d.value=h:this.value!==d.value&&(this.value=d.value);return d},get list(){return this.__meta&&this.__meta.list},_selfChanged:function(d){d&&(this.value=this)},byKey:function(d){return(new Polymer.IronMeta({type:this.type,key:d})).value}})})();

//# sourceURL=build://iron-validatable-behavior/iron-validatable-behavior.html.js
Polymer.IronValidatableBehaviorMeta=null;
Polymer.IronValidatableBehavior={properties:{validator:{type:String},invalid:{notify:!0,reflectToAttribute:!0,type:Boolean,value:!1,observer:"_invalidChanged"}},registered:function(){Polymer.IronValidatableBehaviorMeta=new Polymer.IronMeta({type:"validator"})},_invalidChanged:function(){this.invalid?this.setAttribute("aria-invalid","true"):this.removeAttribute("aria-invalid")},get _validator(){return Polymer.IronValidatableBehaviorMeta&&Polymer.IronValidatableBehaviorMeta.byKey(this.validator)},hasValidator:function(){return null!=
this._validator},validate:function(b){this.invalid=void 0===b&&void 0!==this.value?!this._getValidity(this.value):!this._getValidity(b);return!this.invalid},_getValidity:function(b){return this.hasValidator()?this._validator.validate(b):!0}};

//# sourceURL=build://iron-form-element-behavior/iron-form-element-behavior.html.js
Polymer.IronFormElementBehavior={properties:{name:{type:String},value:{notify:!0,type:String},required:{type:Boolean,value:!1},_parentForm:{type:Object}},attached:function(){Polymer.Element||this.fire("iron-form-element-register")},detached:function(){!Polymer.Element&&this._parentForm&&this._parentForm.fire("iron-form-element-unregister",{target:this})}};

//# sourceURL=build://iron-checked-element-behavior/iron-checked-element-behavior.html.js
Polymer.IronCheckedElementBehaviorImpl={properties:{checked:{type:Boolean,value:!1,reflectToAttribute:!0,notify:!0,observer:"_checkedChanged"},toggles:{type:Boolean,value:!0,reflectToAttribute:!0},value:{type:String,value:"on",observer:"_valueChanged"}},observers:["_requiredChanged(required)"],created:function(){this._hasIronCheckedElementBehavior=!0},_getValidity:function(){return this.disabled||!this.required||this.checked},_requiredChanged:function(){this.required?this.setAttribute("aria-required",
"true"):this.removeAttribute("aria-required")},_checkedChanged:function(){this.active=this.checked;this.fire("iron-change")},_valueChanged:function(){if(void 0===this.value||null===this.value)this.value="on"}};Polymer.IronCheckedElementBehavior=[Polymer.IronFormElementBehavior,Polymer.IronValidatableBehavior,Polymer.IronCheckedElementBehaviorImpl];

//# sourceURL=build://paper-behaviors/paper-inky-focus-behavior.html.js
Polymer.PaperInkyFocusBehaviorImpl={observers:["_focusedChanged(receivedFocusFromKeyboard)"],_focusedChanged:function(b){b&&this.ensureRipple();this.hasRipple()&&(this._ripple.holdDown=b)},_createRipple:function(){var b=Polymer.PaperRippleBehavior._createRipple();b.id="ink";b.setAttribute("center","");b.classList.add("circle");return b}};Polymer.PaperInkyFocusBehavior=[Polymer.IronButtonState,Polymer.IronControlState,Polymer.PaperRippleBehavior,Polymer.PaperInkyFocusBehaviorImpl];

//# sourceURL=build://paper-behaviors/paper-checked-element-behavior.html.js
Polymer.PaperCheckedElementBehaviorImpl={_checkedChanged:function(){Polymer.IronCheckedElementBehaviorImpl._checkedChanged.call(this);this.hasRipple()&&(this.checked?this._ripple.setAttribute("checked",""):this._ripple.removeAttribute("checked"))},_buttonStateChanged:function(){Polymer.PaperRippleBehavior._buttonStateChanged.call(this);!this.disabled&&this.isAttached&&(this.checked=this.active)}};
Polymer.PaperCheckedElementBehavior=[Polymer.PaperInkyFocusBehavior,Polymer.IronCheckedElementBehavior,Polymer.PaperCheckedElementBehaviorImpl];

//# sourceURL=build://paper-checkbox/paper-checkbox.html.js
Polymer({is:"paper-checkbox",behaviors:[Polymer.PaperCheckedElementBehavior],hostAttributes:{role:"checkbox","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},attached:function(){Polymer.RenderStatus.afterNextRender(this,function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-checkbox-ink-size").trim()){var b=this.getComputedStyleValue("--calculated-paper-checkbox-size").trim(),d="px",f=b.match(/[A-Za-z]+$/);null!==f&&(d=f[0]);b=parseFloat(b);
f=8/3*b;"px"===d&&(f=Math.floor(f),f%2!==b%2&&f++);this.updateStyles({"--paper-checkbox-ink-size":f+d})}})},_computeCheckboxClass:function(b,d){var f="";b&&(f+="checked ");d&&(f+="invalid");return f},_computeCheckmarkClass:function(b){return b?"":"hidden"},_createRipple:function(){this._rippleContainer=this.$.checkboxContainer;return Polymer.PaperInkyFocusBehaviorImpl._createRipple.call(this)}});

//# sourceURL=build://iron-icon/iron-icon.html.js
Polymer({is:"iron-icon",properties:{icon:{type:String},theme:{type:String},src:{type:String},_meta:{value:Polymer.Base.create("iron-meta",{type:"iconset"})}},observers:["_updateIcon(_meta, isAttached)","_updateIcon(theme, isAttached)","_srcChanged(src, isAttached)","_iconChanged(icon, isAttached)"],_DEFAULT_ICONSET:"icons",_iconChanged:function(b){b=(b||"").split(":");this._iconName=b.pop();this._iconsetName=b.pop()||this._DEFAULT_ICONSET;this._updateIcon()},_srcChanged:function(){this._updateIcon()},
_usesIconset:function(){return this.icon||!this.src},_updateIcon:function(){this._usesIconset()?(this._img&&this._img.parentNode&&Polymer.dom(this.root).removeChild(this._img),""===this._iconName?this._iconset&&this._iconset.removeIcon(this):this._iconsetName&&this._meta&&((this._iconset=this._meta.byKey(this._iconsetName))?(this._iconset.applyIcon(this,this._iconName,this.theme),this.unlisten(window,"iron-iconset-added","_updateIcon")):this.listen(window,"iron-iconset-added","_updateIcon"))):(this._iconset&&
this._iconset.removeIcon(this),this._img||(this._img=document.createElement("img"),this._img.style.width="100%",this._img.style.height="100%",this._img.draggable=!1),this._img.src=this.src,Polymer.dom(this.root).appendChild(this._img))}});

//# sourceURL=build://iron-a11y-announcer/iron-a11y-announcer.html.js
(function(){Polymer.IronA11yAnnouncer=function(){};Polymer.IronA11yAnnouncer=Polymer({is:"iron-a11y-announcer",properties:{mode:{type:String,value:"polite"},_text:{type:String,value:""}},created:function(){Polymer.IronA11yAnnouncer.instance||(Polymer.IronA11yAnnouncer.instance=this);document.body.addEventListener("iron-announce",this._onIronAnnounce.bind(this))},announce:function(b){this._text="";this.async(function(){this._text=b},100)},_onIronAnnounce:function(b){b.detail&&b.detail.text&&this.announce(b.detail.text)}});
Polymer.IronA11yAnnouncer.instance=null;Polymer.IronA11yAnnouncer.requestAvailability=function(){Polymer.IronA11yAnnouncer.instance||(Polymer.IronA11yAnnouncer.instance=document.createElement("iron-a11y-announcer"));document.body.appendChild(Polymer.IronA11yAnnouncer.instance)}})();

//# sourceURL=build://iron-input/iron-input.html.js
Polymer({is:"iron-input",behaviors:[Polymer.IronValidatableBehavior],properties:{bindValue:{type:String,value:""},value:{type:String,computed:"_computeValue(bindValue)"},allowedPattern:{type:String},autoValidate:{type:Boolean,value:!1},_inputElement:Object},observers:["_bindValueChanged(bindValue, _inputElement)"],listeners:{input:"_onInput",keypress:"_onKeypress"},created:function(){Polymer.IronA11yAnnouncer.requestAvailability();this._previousValidInput="";this._patternAlreadyChecked=!1},attached:function(){this._observer=
Polymer.dom(this).observeNodes(function(){this._initSlottedInput()}.bind(this))},detached:function(){this._observer&&(Polymer.dom(this).unobserveNodes(this._observer),this._observer=null)},get inputElement(){return this._inputElement},_initSlottedInput:function(){this._inputElement=this.getEffectiveChildren()[0];this.inputElement&&this.inputElement.value&&(this.bindValue=this.inputElement.value);this.fire("iron-input-ready")},get _patternRegExp(){if(this.allowedPattern)var b=new RegExp(this.allowedPattern);
else switch(this.inputElement.type){case "number":b=/[0-9.,e-]/}return b},_bindValueChanged:function(b,d){d&&(void 0===b?d.value=null:b!==d.value&&(this.inputElement.value=b),this.autoValidate&&this.validate(),this.fire("bind-value-changed",{value:b}))},_onInput:function(){!this.allowedPattern||this._patternAlreadyChecked||this._checkPatternValidity()||(this._announceInvalidCharacter("Invalid string of characters not entered."),this.inputElement.value=this._previousValidInput);this.bindValue=this._previousValidInput=
this.inputElement.value;this._patternAlreadyChecked=!1},_isPrintable:function(b){var d=19==b.keyCode||20==b.keyCode||45==b.keyCode||46==b.keyCode||144==b.keyCode||145==b.keyCode||32<b.keyCode&&41>b.keyCode||111<b.keyCode&&124>b.keyCode;return!(8==b.keyCode||9==b.keyCode||13==b.keyCode||27==b.keyCode)&&!(0==b.charCode&&d)},_onKeypress:function(b){if(this.allowedPattern||"number"===this.inputElement.type){var d=this._patternRegExp;if(d&&!(b.metaKey||b.ctrlKey||b.altKey)){this._patternAlreadyChecked=
!0;var f=String.fromCharCode(b.charCode);this._isPrintable(b)&&!d.test(f)&&(b.preventDefault(),this._announceInvalidCharacter("Invalid character "+f+" not entered."))}}},_checkPatternValidity:function(){var b=this._patternRegExp;if(!b)return!0;for(var d=0;d<this.inputElement.value.length;d++)if(!b.test(this.inputElement.value[d]))return!1;return!0},validate:function(){if(!this.inputElement)return this.invalid=!1,!0;var b=this.inputElement.checkValidity();b&&(this.required&&""===this.bindValue?b=!1:
this.hasValidator()&&(b=Polymer.IronValidatableBehavior.validate.call(this,this.bindValue)));this.invalid=!b;this.fire("iron-input-validate");return b},_announceInvalidCharacter:function(b){this.fire("iron-announce",{text:b})},_computeValue:function(b){return b}});

//# sourceURL=build://paper-input/paper-input-behavior.html.js
Polymer.PaperInputHelper={};Polymer.PaperInputHelper.NextLabelID=1;Polymer.PaperInputHelper.NextAddonID=1;Polymer.PaperInputHelper.NextInputID=1;
Polymer.PaperInputBehaviorImpl={properties:{label:{type:String},value:{notify:!0,type:String},disabled:{type:Boolean,value:!1},invalid:{type:Boolean,value:!1,notify:!0},allowedPattern:{type:String},type:{type:String},list:{type:String},pattern:{type:String},required:{type:Boolean,value:!1},errorMessage:{type:String},charCounter:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},autoValidate:{type:Boolean,value:!1},validator:{type:String},autocomplete:{type:String,
value:"off"},autofocus:{type:Boolean,observer:"_autofocusChanged"},inputmode:{type:String},minlength:{type:Number},maxlength:{type:Number},min:{type:String},max:{type:String},step:{type:String},name:{type:String},placeholder:{type:String,value:""},readonly:{type:Boolean,value:!1},size:{type:Number},autocapitalize:{type:String,value:"none"},autocorrect:{type:String,value:"off"},autosave:{type:String},results:{type:Number},accept:{type:String},multiple:{type:Boolean},_ariaDescribedBy:{type:String,value:""},
_ariaLabelledBy:{type:String,value:""},_inputId:{type:String,value:""}},listeners:{"addon-attached":"_onAddonAttached"},keyBindings:{"shift+tab:keydown":"_onShiftTabDown"},hostAttributes:{tabindex:0},get inputElement(){this.$||(this.$={});this.$.input||(this._generateInputId(),this.$.input=this.$$("#"+this._inputId));return this.$.input},get _focusableElement(){return this.inputElement},created:function(){this._typesThatHaveText="date datetime datetime-local month time week file".split(" ")},attached:function(){this._updateAriaLabelledBy();
!Polymer.Element&&this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.inputElement.type)&&(this.alwaysFloatLabel=!0)},_appendStringWithSpace:function(b,d){return b?b+" "+d:d},_onAddonAttached:function(b){b=Polymer.dom(b).rootTarget;if(b.id)this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,b.id);else{var d="paper-input-add-on-"+Polymer.PaperInputHelper.NextAddonID++;b.id=d;this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,d)}},validate:function(){return this.inputElement.validate()},
_focusBlurHandler:function(b){Polymer.IronControlState._focusBlurHandler.call(this,b);this.focused&&!this._shiftTabPressed&&this._focusableElement&&this._focusableElement.focus()},_onShiftTabDown:function(){var b=this.getAttribute("tabindex");this._shiftTabPressed=!0;this.setAttribute("tabindex","-1");this.async(function(){this.setAttribute("tabindex",b);this._shiftTabPressed=!1},1)},_handleAutoValidate:function(){this.autoValidate&&this.validate()},updateValueAndPreserveCaret:function(b){try{var d=
this.inputElement.selectionStart;this.value=b;this.inputElement.selectionStart=d;this.inputElement.selectionEnd=d}catch(f){this.value=b}},_computeAlwaysFloatLabel:function(b,d){return d||b},_updateAriaLabelledBy:function(){var b=Polymer.dom(this.root).querySelector("label");if(b){if(b.id)var d=b.id;else d="paper-input-label-"+Polymer.PaperInputHelper.NextLabelID++,b.id=d;this._ariaLabelledBy=d}else this._ariaLabelledBy=""},_generateInputId:function(){this._inputId&&""!==this._inputId||(this._inputId=
"input-"+Polymer.PaperInputHelper.NextInputID++)},_onChange:function(b){this.shadowRoot&&this.fire(b.type,{sourceEvent:b},{node:this,bubbles:b.bubbles,cancelable:b.cancelable})},_autofocusChanged:function(){if(this.autofocus&&this._focusableElement){var b=document.activeElement;b instanceof HTMLElement&&b!==document.body&&b!==document.documentElement||this._focusableElement.focus()}}};Polymer.PaperInputBehavior=[Polymer.IronControlState,Polymer.IronA11yKeysBehavior,Polymer.PaperInputBehaviorImpl];

//# sourceURL=build://paper-input/paper-input-addon-behavior.html.js
Polymer.PaperInputAddonBehavior={attached:function(){this.fire("addon-attached")},update:function(){}};

//# sourceURL=build://paper-input/paper-input-char-counter.html.js
Polymer({is:"paper-input-char-counter",behaviors:[Polymer.PaperInputAddonBehavior],properties:{_charCounterStr:{type:String,value:"0"}},update:function(b){if(b.inputElement){b.value=b.value||"";var d=b.value.toString().length.toString();b.inputElement.hasAttribute("maxlength")&&(d+="/"+b.inputElement.getAttribute("maxlength"));this._charCounterStr=d}}});

//# sourceURL=build://paper-input/paper-input-container.html.js
Polymer({is:"paper-input-container",properties:{noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},attrForValue:{type:String,value:"bind-value"},autoValidate:{type:Boolean,value:!1},invalid:{observer:"_invalidChanged",type:Boolean,value:!1},focused:{readOnly:!0,type:Boolean,value:!1,notify:!0},_addons:{type:Array},_inputHasContent:{type:Boolean,value:!1},_inputSelector:{type:String,value:"input,iron-input,textarea,.paper-input-input"},_boundOnFocus:{type:Function,value:function(){return this._onFocus.bind(this)}},
_boundOnBlur:{type:Function,value:function(){return this._onBlur.bind(this)}},_boundOnInput:{type:Function,value:function(){return this._onInput.bind(this)}},_boundValueChanged:{type:Function,value:function(){return this._onValueChanged.bind(this)}}},listeners:{"addon-attached":"_onAddonAttached","iron-input-validate":"_onIronInputValidate"},get _valueChangedEvent(){return this.attrForValue+"-changed"},get _propertyForValue(){return Polymer.CaseMap.dashToCamelCase(this.attrForValue)},get _inputElement(){return Polymer.dom(this).querySelector(this._inputSelector)},
get _inputElementValue(){return this._inputElement[this._propertyForValue]||this._inputElement.value},ready:function(){this.__isFirstValueUpdate=!0;this._addons||(this._addons=[]);this.addEventListener("focus",this._boundOnFocus,!0);this.addEventListener("blur",this._boundOnBlur,!0)},attached:function(){this.attrForValue?this._inputElement.addEventListener(this._valueChangedEvent,this._boundValueChanged):this.addEventListener("input",this._onInput);this._inputElementValue&&""!=this._inputElementValue?
this._handleValueAndAutoValidate(this._inputElement):this._handleValue(this._inputElement)},_onAddonAttached:function(b){this._addons||(this._addons=[]);b=b.target;-1===this._addons.indexOf(b)&&(this._addons.push(b),this.isAttached&&this._handleValue(this._inputElement))},_onFocus:function(){this._setFocused(!0)},_onBlur:function(){this._setFocused(!1);this._handleValueAndAutoValidate(this._inputElement)},_onInput:function(b){this._handleValueAndAutoValidate(b.target)},_onValueChanged:function(b){var d=
b.target;if(this.__isFirstValueUpdate&&(this.__isFirstValueUpdate=!1,void 0===d.value||""===d.value))return;this._handleValueAndAutoValidate(b.target)},_handleValue:function(b){var d=this._inputElementValue;d||0===d||"number"===b.type&&!b.checkValidity()?this._inputHasContent=!0:this._inputHasContent=!1;this.updateAddons({inputElement:b,value:d,invalid:this.invalid})},_handleValueAndAutoValidate:function(b){this.autoValidate&&b&&(this.invalid=!(b.validate?b.validate(this._inputElementValue):b.checkValidity()));
this._handleValue(b)},_onIronInputValidate:function(){this.invalid=this._inputElement.invalid},_invalidChanged:function(){this._addons&&this.updateAddons({invalid:this.invalid})},updateAddons:function(b){for(var d,f=0;d=this._addons[f];f++)d.update(b)},_computeInputContentClass:function(b,d,f,h,k){var t="input-content";b?(k&&(t+=" label-is-hidden"),h&&(t+=" is-invalid")):(b=this.querySelector("label"),d||k?(t+=" label-is-floating",this.$.labelAndInputContainer.style.position="static",h?t+=" is-invalid":
f&&(t+=" label-is-highlighted")):(b&&(this.$.labelAndInputContainer.style.position="relative"),h&&(t+=" is-invalid")));f&&(t+=" focused");return t},_computeUnderlineClass:function(b,d){var f="underline";d?f+=" is-invalid":b&&(f+=" is-highlighted");return f},_computeAddOnContentClass:function(b,d){var f="add-on-content";d?f+=" is-invalid":b&&(f+=" is-highlighted");return f}});

//# sourceURL=build://paper-input/paper-input-error.html.js
Polymer({is:"paper-input-error",behaviors:[Polymer.PaperInputAddonBehavior],properties:{invalid:{readOnly:!0,reflectToAttribute:!0,type:Boolean}},update:function(b){this._setInvalid(b.invalid)}});

//# sourceURL=build://paper-input/paper-input.html.js
Polymer({is:"paper-input",behaviors:[Polymer.PaperInputBehavior,Polymer.IronFormElementBehavior],properties:{value:{type:String}},beforeRegister:function(){var b="function"==typeof document.createElement("iron-input")._initSlottedInput?"v1":"v0",d=Polymer.DomModule.import("paper-input","template");b=Polymer.DomModule.import("paper-input","template#"+b);(d=d.content.querySelector("#template-placeholder"))&&d.parentNode.replaceChild(b.content,d)},get _focusableElement(){return Polymer.Element?this.inputElement._inputElement:
this.inputElement},listeners:{"iron-input-ready":"_onIronInputReady"},_onIronInputReady:function(){this.$.nativeInput||(this.$.nativeInput=this.$$("input"));this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.$.nativeInput.type)&&(this.alwaysFloatLabel=!0);this.inputElement.bindValue&&this.$.container._handleValueAndAutoValidate(this.inputElement)}});

//# sourceURL=build://iron-fit-behavior/iron-fit-behavior.html.js
Polymer.IronFitBehavior={properties:{sizingTarget:{type:Object,value:function(){return this}},fitInto:{type:Object,value:window},noOverlap:{type:Boolean},positionTarget:{type:Element},horizontalAlign:{type:String},verticalAlign:{type:String},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},autoFitOnAttach:{type:Boolean,value:!1},_fitInfo:{type:Object}},get _fitWidth(){return this.fitInto===window?this.fitInto.innerWidth:this.fitInto.getBoundingClientRect().width},
get _fitHeight(){return this.fitInto===window?this.fitInto.innerHeight:this.fitInto.getBoundingClientRect().height},get _fitLeft(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().left},get _fitTop(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().top},get _defaultPositionTarget(){var b=Polymer.dom(this).parentNode;b&&b.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&(b=b.host);return b},get _localeHorizontalAlign(){if(this._isRTL){if("right"===this.horizontalAlign)return"left";
if("left"===this.horizontalAlign)return"right"}return this.horizontalAlign},get __shouldPosition(){return(this.horizontalAlign||this.verticalAlign)&&this.positionTarget},attached:function(){"undefined"===typeof this._isRTL&&(this._isRTL="rtl"==window.getComputedStyle(this).direction);this.positionTarget=this.positionTarget||this._defaultPositionTarget;this.autoFitOnAttach&&("none"===window.getComputedStyle(this).display?setTimeout(function(){this.fit()}.bind(this)):(window.ShadyDOM&&ShadyDOM.flush(),
this.fit()))},detached:function(){this.__deferredFit&&(clearTimeout(this.__deferredFit),this.__deferredFit=null)},fit:function(){this.position();this.constrain();this.center()},_discoverInfo:function(){if(!this._fitInfo){var b=window.getComputedStyle(this),d=window.getComputedStyle(this.sizingTarget);this._fitInfo={inlineStyle:{top:this.style.top||"",left:this.style.left||"",position:this.style.position||""},sizerInlineStyle:{maxWidth:this.sizingTarget.style.maxWidth||"",maxHeight:this.sizingTarget.style.maxHeight||
"",boxSizing:this.sizingTarget.style.boxSizing||""},positionedBy:{vertically:"auto"!==b.top?"top":"auto"!==b.bottom?"bottom":null,horizontally:"auto"!==b.left?"left":"auto"!==b.right?"right":null},sizedBy:{height:"none"!==d.maxHeight,width:"none"!==d.maxWidth,minWidth:parseInt(d.minWidth,10)||0,minHeight:parseInt(d.minHeight,10)||0},margin:{top:parseInt(b.marginTop,10)||0,right:parseInt(b.marginRight,10)||0,bottom:parseInt(b.marginBottom,10)||0,left:parseInt(b.marginLeft,10)||0}}}},resetFit:function(){var b=
this._fitInfo||{},d;for(d in b.sizerInlineStyle)this.sizingTarget.style[d]=b.sizerInlineStyle[d];for(d in b.inlineStyle)this.style[d]=b.inlineStyle[d];this._fitInfo=null},refit:function(){var b=this.sizingTarget.scrollLeft,d=this.sizingTarget.scrollTop;this.resetFit();this.fit();this.sizingTarget.scrollLeft=b;this.sizingTarget.scrollTop=d},position:function(){if(this.__shouldPosition){this._discoverInfo();this.style.position="fixed";this.sizingTarget.style.boxSizing="border-box";this.style.left="0px";
this.style.top="0px";var b=this.getBoundingClientRect(),d=this.__getNormalizedRect(this.positionTarget),f=this.__getNormalizedRect(this.fitInto),h=this._fitInfo.margin,k=this.__getPosition(this._localeHorizontalAlign,this.verticalAlign,{width:b.width+h.left+h.right,height:b.height+h.top+h.bottom},b,d,f);d=k.left+h.left;k=k.top+h.top;var t=Math.min(f.right-h.right,d+b.width),l=Math.min(f.bottom-h.bottom,k+b.height);d=Math.max(f.left+h.left,Math.min(d,t-this._fitInfo.sizedBy.minWidth));k=Math.max(f.top+
h.top,Math.min(k,l-this._fitInfo.sizedBy.minHeight));this.sizingTarget.style.maxWidth=Math.max(t-d,this._fitInfo.sizedBy.minWidth)+"px";this.sizingTarget.style.maxHeight=Math.max(l-k,this._fitInfo.sizedBy.minHeight)+"px";this.style.left=d-b.left+"px";this.style.top=k-b.top+"px"}},constrain:function(){if(!this.__shouldPosition){this._discoverInfo();var b=this._fitInfo;b.positionedBy.vertically||(this.style.position="fixed",this.style.top="0px");b.positionedBy.horizontally||(this.style.position="fixed",
this.style.left="0px");this.sizingTarget.style.boxSizing="border-box";var d=this.getBoundingClientRect();b.sizedBy.height||this.__sizeDimension(d,b.positionedBy.vertically,"top","bottom","Height");b.sizedBy.width||this.__sizeDimension(d,b.positionedBy.horizontally,"left","right","Width")}},_sizeDimension:function(b,d,f,h,k){this.__sizeDimension(b,d,f,h,k)},__sizeDimension:function(b,d,f,h,k){var t=this._fitInfo,l=this.__getNormalizedRect(this.fitInto);l="Width"===k?l.width:l.height;d=d===h;var p=
"offset"+k;this.sizingTarget.style["max"+k]=l-t.margin[d?f:h]-(d?l-b[h]:b[f])-(this[p]-this.sizingTarget[p])+"px"},center:function(){if(!this.__shouldPosition){this._discoverInfo();var b=this._fitInfo.positionedBy;if(!b.vertically||!b.horizontally){this.style.position="fixed";b.vertically||(this.style.top="0px");b.horizontally||(this.style.left="0px");var d=this.getBoundingClientRect(),f=this.__getNormalizedRect(this.fitInto);b.vertically||(this.style.top=f.top-d.top+(f.height-d.height)/2+"px");b.horizontally||
(this.style.left=f.left-d.left+(f.width-d.width)/2+"px")}}},__getNormalizedRect:function(b){return b===document.documentElement||b===window?{top:0,left:0,width:window.innerWidth,height:window.innerHeight,right:window.innerWidth,bottom:window.innerHeight}:b.getBoundingClientRect()},__getOffscreenArea:function(b,d,f){return Math.abs(Math.min(0,b.top)+Math.min(0,f.bottom-(b.top+d.height)))*d.width+Math.abs(Math.min(0,b.left)+Math.min(0,f.right-(b.left+d.width)))*d.height},__getPosition:function(b,d,
f,h,k,t){var l=[{verticalAlign:"top",horizontalAlign:"left",top:k.top+this.verticalOffset,left:k.left+this.horizontalOffset},{verticalAlign:"top",horizontalAlign:"right",top:k.top+this.verticalOffset,left:k.right-f.width-this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"left",top:k.bottom-f.height-this.verticalOffset,left:k.left+this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"right",top:k.bottom-f.height-this.verticalOffset,left:k.right-f.width-this.horizontalOffset}];
if(this.noOverlap){for(var p=0,m=l.length;p<m;p++){var n={},q;for(q in l[p])n[q]=l[p][q];l.push(n)}l[0].top=l[1].top+=k.height;l[2].top=l[3].top-=k.height;l[4].left=l[6].left+=k.width;l[5].left=l[7].left-=k.width}d="auto"===d?null:d;b="auto"===b?null:b;b&&"center"!==b||(l.push({verticalAlign:"top",horizontalAlign:"center",top:k.top+this.verticalOffset+(this.noOverlap?k.height:0),left:k.left-h.width/2+k.width/2+this.horizontalOffset}),l.push({verticalAlign:"bottom",horizontalAlign:"center",top:k.bottom-
f.height-this.verticalOffset-(this.noOverlap?k.height:0),left:k.left-h.width/2+k.width/2+this.horizontalOffset}));d&&"middle"!==d||(l.push({verticalAlign:"middle",horizontalAlign:"left",top:k.top-h.height/2+k.height/2+this.verticalOffset,left:k.left+this.horizontalOffset+(this.noOverlap?k.width:0)}),l.push({verticalAlign:"middle",horizontalAlign:"right",top:k.top-h.height/2+k.height/2+this.verticalOffset,left:k.right-f.width-this.horizontalOffset-(this.noOverlap?k.width:0)}));"middle"===d&&"center"===
b&&l.push({verticalAlign:"middle",horizontalAlign:"center",top:k.top-h.height/2+k.height/2+this.verticalOffset,left:k.left-h.width/2+k.width/2+this.horizontalOffset});for(p=0;p<l.length;p++){h=l[p];k=h.verticalAlign===d;m=h.horizontalAlign===b;if(!this.dynamicAlign&&!this.noOverlap&&k&&m){var u=h;break}n=(!d||k)&&(!b||m);if(this.dynamicAlign||n){h.offscreenArea=this.__getOffscreenArea(h,f,t);if(0===h.offscreenArea&&n){u=h;break}u=u||h;n=h.offscreenArea-u.offscreenArea;if(0>n||0===n&&(k||m))u=h}}return u}};

//# sourceURL=build://iron-resizable-behavior/iron-resizable-behavior.html.js
Polymer.IronResizableBehavior={properties:{_parentResizable:{type:Object,observer:"_parentResizableChanged"},_notifyingDescendant:{type:Boolean,value:!1}},listeners:{"iron-request-resize-notifications":"_onIronRequestResizeNotifications"},created:function(){this._interestedResizables=[];this._boundNotifyResize=this.notifyResize.bind(this)},attached:function(){this._requestResizeNotifications()},detached:function(){this._parentResizable?this._parentResizable.stopResizeNotificationsFor(this):window.removeEventListener("resize",
this._boundNotifyResize);this._parentResizable=null},notifyResize:function(){this.isAttached&&(this._interestedResizables.forEach(function(b){this.resizerShouldNotify(b)&&this._notifyDescendant(b)},this),this._fireResize())},assignParentResizable:function(b){this._parentResizable=b},stopResizeNotificationsFor:function(b){var d=this._interestedResizables.indexOf(b);-1<d&&(this._interestedResizables.splice(d,1),this.unlisten(b,"iron-resize","_onDescendantIronResize"))},resizerShouldNotify:function(){return!0},
_onDescendantIronResize:function(b){this._notifyingDescendant?b.stopPropagation():Polymer.Settings.useShadow||this._fireResize()},_fireResize:function(){this.fire("iron-resize",null,{node:this,bubbles:!1})},_onIronRequestResizeNotifications:function(b){var d=Polymer.dom(b).rootTarget;d!==this&&(-1===this._interestedResizables.indexOf(d)&&(this._interestedResizables.push(d),this.listen(d,"iron-resize","_onDescendantIronResize")),d.assignParentResizable(this),this._notifyDescendant(d),b.stopPropagation())},
_parentResizableChanged:function(b){b&&window.removeEventListener("resize",this._boundNotifyResize)},_notifyDescendant:function(b){this.isAttached&&(this._notifyingDescendant=!0,b.notifyResize(),this._notifyingDescendant=!1)},_requestResizeNotifications:function(){if(this.isAttached)if("loading"===document.readyState){var b=this._requestResizeNotifications.bind(this);document.addEventListener("readystatechange",function f(){document.removeEventListener("readystatechange",f);b()})}else this.fire("iron-request-resize-notifications",
null,{node:this,bubbles:!0,cancelable:!0}),this._parentResizable||(window.addEventListener("resize",this._boundNotifyResize),this.notifyResize())}};

//# sourceURL=build://iron-overlay-behavior/iron-overlay-backdrop.html.js
(function(){Polymer({is:"iron-overlay-backdrop",properties:{opened:{reflectToAttribute:!0,type:Boolean,value:!1,observer:"_openedChanged"}},listeners:{transitionend:"_onTransitionend"},created:function(){this.__openedRaf=null},attached:function(){this.opened&&this._openedChanged(this.opened)},prepare:function(){this.opened&&!this.parentNode&&Polymer.dom(document.body).appendChild(this)},open:function(){this.opened=!0},close:function(){this.opened=!1},complete:function(){this.opened||this.parentNode!==
document.body||Polymer.dom(this.parentNode).removeChild(this)},_onTransitionend:function(b){b&&b.target===this&&this.complete()},_openedChanged:function(b){b?this.prepare():(b=window.getComputedStyle(this),"0s"!==b.transitionDuration&&0!=b.opacity||this.complete());this.isAttached&&(this.__openedRaf&&(window.cancelAnimationFrame(this.__openedRaf),this.__openedRaf=null),this.scrollTop=this.scrollTop,this.__openedRaf=window.requestAnimationFrame(function(){this.__openedRaf=null;this.toggleClass("opened",
this.opened)}.bind(this)))}})})();

//# sourceURL=build://iron-overlay-behavior/iron-overlay-manager.html.js
Polymer.IronOverlayManagerClass=function(){this._overlays=[];this._minimumZ=101;this._backdropElement=null;Polymer.Gestures.add(document.documentElement,"tap",function(){});document.addEventListener("tap",this._onCaptureClick.bind(this),!0);document.addEventListener("focus",this._onCaptureFocus.bind(this),!0);document.addEventListener("keydown",this._onCaptureKeyDown.bind(this),!0)};
Polymer.IronOverlayManagerClass.prototype={constructor:Polymer.IronOverlayManagerClass,get backdropElement(){this._backdropElement||(this._backdropElement=document.createElement("iron-overlay-backdrop"));return this._backdropElement},get deepActiveElement(){var b=document.activeElement;b&&!1!==b instanceof Element||(b=document.body);for(;b.root&&Polymer.dom(b.root).activeElement;)b=Polymer.dom(b.root).activeElement;return b},_bringOverlayAtIndexToFront:function(b){var d=this._overlays[b];if(d){var f=
this._overlays.length-1,h=this._overlays[f];h&&this._shouldBeBehindOverlay(d,h)&&f--;if(!(b>=f)){h=Math.max(this.currentOverlayZ(),this._minimumZ);for(this._getZ(d)<=h&&this._applyOverlayZ(d,h);b<f;)this._overlays[b]=this._overlays[b+1],b++;this._overlays[f]=d}}},addOrRemoveOverlay:function(b){b.opened?this.addOverlay(b):this.removeOverlay(b)},addOverlay:function(b){var d=this._overlays.indexOf(b);if(0<=d)this._bringOverlayAtIndexToFront(d);else{d=this._overlays.length;var f=this._overlays[d-1],h=
Math.max(this._getZ(f),this._minimumZ),k=this._getZ(b);f&&this._shouldBeBehindOverlay(b,f)&&(this._applyOverlayZ(f,h),d--,h=Math.max(this._getZ(this._overlays[d-1]),this._minimumZ));k<=h&&this._applyOverlayZ(b,h);this._overlays.splice(d,0,b)}this.trackBackdrop()},removeOverlay:function(b){b=this._overlays.indexOf(b);-1!==b&&(this._overlays.splice(b,1),this.trackBackdrop())},currentOverlay:function(){return this._overlays[this._overlays.length-1]},currentOverlayZ:function(){return this._getZ(this.currentOverlay())},
ensureMinimumZ:function(b){this._minimumZ=Math.max(this._minimumZ,b)},focusOverlay:function(){var b=this.currentOverlay();b&&b._applyFocus()},trackBackdrop:function(){var b=this._overlayWithBackdrop();if(b||this._backdropElement)this.backdropElement.style.zIndex=this._getZ(b)-1,this.backdropElement.opened=!!b,this.backdropElement.prepare()},getBackdrops:function(){for(var b=[],d=0;d<this._overlays.length;d++)this._overlays[d].withBackdrop&&b.push(this._overlays[d]);return b},backdropZ:function(){return this._getZ(this._overlayWithBackdrop())-
1},_overlayWithBackdrop:function(){for(var b=this._overlays.length-1;0<=b;b--)if(this._overlays[b].withBackdrop)return this._overlays[b]},_getZ:function(b){var d=this._minimumZ;b&&(b=Number(b.style.zIndex||window.getComputedStyle(b).zIndex),b===b&&(d=b));return d},_setZ:function(b,d){b.style.zIndex=d},_applyOverlayZ:function(b,d){this._setZ(b,d+2)},_overlayInPath:function(b){b=b||[];for(var d=0;d<b.length;d++)if(b[d]._manager===this)return b[d]},_onCaptureClick:function(b){var d=this._overlays.length-
1;if(-1!==d)for(var f=Polymer.dom(b).path,h;(h=this._overlays[d])&&this._overlayInPath(f)!==h;)if(h._onCaptureClick(b),h.allowClickThrough)d--;else break},_onCaptureFocus:function(b){var d=this.currentOverlay();d&&d._onCaptureFocus(b)},_onCaptureKeyDown:function(b){var d=this.currentOverlay();d&&(Polymer.IronA11yKeysBehavior.keyboardEventMatchesKeys(b,"esc")?d._onCaptureEsc(b):Polymer.IronA11yKeysBehavior.keyboardEventMatchesKeys(b,"tab")&&d._onCaptureTab(b))},_shouldBeBehindOverlay:function(b,d){return!b.alwaysOnTop&&
d.alwaysOnTop}};Polymer.IronOverlayManager=new Polymer.IronOverlayManagerClass;

//# sourceURL=build://iron-overlay-behavior/iron-scroll-manager.html.js
(function(){var b=0,d=0,f=null,h=[],k=["wheel","mousewheel","DOMMouseScroll","touchstart","touchmove"];Polymer.IronScrollManager={get currentLockingElement(){return this._lockingElements[this._lockingElements.length-1]},elementIsScrollLocked:function(t){var l=this.currentLockingElement;if(void 0===l)return!1;if(this._hasCachedLockedElement(t))return!0;if(this._hasCachedUnlockedElement(t))return!1;(l=!!l&&l!==t&&!this._composedTreeContains(l,t))?this._lockedElementCache.push(t):this._unlockedElementCache.push(t);
return l},pushScrollLock:function(t){0<=this._lockingElements.indexOf(t)||(0===this._lockingElements.length&&this._lockScrollInteractions(),this._lockingElements.push(t),this._lockedElementCache=[],this._unlockedElementCache=[])},removeScrollLock:function(t){t=this._lockingElements.indexOf(t);-1!==t&&(this._lockingElements.splice(t,1),this._lockedElementCache=[],this._unlockedElementCache=[],0===this._lockingElements.length&&this._unlockScrollInteractions())},_lockingElements:[],_lockedElementCache:null,
_unlockedElementCache:null,_hasCachedLockedElement:function(t){return-1<this._lockedElementCache.indexOf(t)},_hasCachedUnlockedElement:function(t){return-1<this._unlockedElementCache.indexOf(t)},_composedTreeContains:function(t,l){var p,m;if(t.contains(l))return!0;t=Polymer.dom(t).querySelectorAll("content,slot");for(p=0;p<t.length;++p){var n=Polymer.dom(t[p]).getDistributedNodes();for(m=0;m<n.length;++m)if(n[m].nodeType===Node.ELEMENT_NODE&&this._composedTreeContains(n[m],l))return!0}return!1},_scrollInteractionHandler:function(t){t.cancelable&&
this._shouldPreventScrolling(t)&&t.preventDefault();t.targetTouches&&(t=t.targetTouches[0],b=t.pageX,d=t.pageY)},_lockScrollInteractions:function(){this._boundScrollHandler=this._boundScrollHandler||this._scrollInteractionHandler.bind(this);for(var t=0,l=k.length;t<l;t++)document.addEventListener(k[t],this._boundScrollHandler,{capture:!0,passive:!1})},_unlockScrollInteractions:function(){for(var t=0,l=k.length;t<l;t++)document.removeEventListener(k[t],this._boundScrollHandler,{capture:!0,passive:!1})},
_shouldPreventScrolling:function(t){var l=Polymer.dom(t).rootTarget;"touchmove"!==t.type&&f!==l&&(f=l,h=this._getScrollableNodes(Polymer.dom(t).path));if(!h.length)return!0;if("touchstart"===t.type)return!1;t=this._getScrollInfo(t);return!this._getScrollingNode(h,t.deltaX,t.deltaY)},_getScrollableNodes:function(t){for(var l=[],p=t.indexOf(this.currentLockingElement),m=0;m<=p;m++)if(t[m].nodeType===Node.ELEMENT_NODE){var n=t[m],q=n.style;"scroll"!==q.overflow&&"auto"!==q.overflow&&(q=window.getComputedStyle(n));
"scroll"!==q.overflow&&"auto"!==q.overflow||l.push(n)}return l},_getScrollingNode:function(t,l,p){if(l||p)for(var m=Math.abs(p)>=Math.abs(l),n=0;n<t.length;n++){var q=t[n];if(m?0>p?0<q.scrollTop:q.scrollTop<q.scrollHeight-q.clientHeight:0>l?0<q.scrollLeft:q.scrollLeft<q.scrollWidth-q.clientWidth)return q}},_getScrollInfo:function(t){var l={deltaX:t.deltaX,deltaY:t.deltaY};"deltaX"in t||("wheelDeltaX"in t&&"wheelDeltaY"in t?(l.deltaX=-t.wheelDeltaX,l.deltaY=-t.wheelDeltaY):"wheelDelta"in t?(l.deltaX=
0,l.deltaY=-t.wheelDelta):"axis"in t?(l.deltaX=1===t.axis?t.detail:0,l.deltaY=2===t.axis?t.detail:0):t.targetTouches&&(t=t.targetTouches[0],l.deltaX=b-t.pageX,l.deltaY=d-t.pageY));return l}}})();

//# sourceURL=build://iron-overlay-behavior/iron-focusables-helper.html.js
(function(){var b=Element.prototype,d=b.matches||b.matchesSelector||b.mozMatchesSelector||b.msMatchesSelector||b.oMatchesSelector||b.webkitMatchesSelector;Polymer.IronFocusablesHelper={getTabbableNodes:function(f){var h=[];return this._collectTabbableNodes(f,h)?this._sortByTabIndex(h):h},isFocusable:function(f){return d.call(f,"input, select, textarea, button, object")?d.call(f,":not([disabled])"):d.call(f,"a[href], area[href], iframe, [tabindex], [contentEditable]")},isTabbable:function(f){return this.isFocusable(f)&&
d.call(f,':not([tabindex\x3d"-1"])')&&this._isVisible(f)},_normalizedTabIndex:function(f){return this.isFocusable(f)?(f=f.getAttribute("tabindex")||0,Number(f)):-1},_collectTabbableNodes:function(f,h){if(f.nodeType!==Node.ELEMENT_NODE||!this._isVisible(f))return!1;var k=this._normalizedTabIndex(f),t=0<k;0<=k&&h.push(f);f="content"===f.localName||"slot"===f.localName?Polymer.dom(f).getDistributedNodes():Polymer.dom(f.root||f).children;for(k=0;k<f.length;k++)t=this._collectTabbableNodes(f[k],h)||t;
return t},_isVisible:function(f){var h=f.style;return"hidden"!==h.visibility&&"none"!==h.display?(h=window.getComputedStyle(f),"hidden"!==h.visibility&&"none"!==h.display):!1},_sortByTabIndex:function(f){var h=f.length;if(2>h)return f;var k=Math.ceil(h/2);h=this._sortByTabIndex(f.slice(0,k));f=this._sortByTabIndex(f.slice(k));return this._mergeSortByTabIndex(h,f)},_mergeSortByTabIndex:function(f,h){for(var k=[];0<f.length&&0<h.length;)this._hasLowerTabOrder(f[0],h[0])?k.push(h.shift()):k.push(f.shift());
return k.concat(f,h)},_hasLowerTabOrder:function(f,h){f=Math.max(f.tabIndex,0);h=Math.max(h.tabIndex,0);return 0===f||0===h?h>f:f>h}}})();

//# sourceURL=build://iron-overlay-behavior/iron-overlay-behavior.html.js
(function(){Polymer.IronOverlayBehaviorImpl={properties:{opened:{observer:"_openedChanged",type:Boolean,value:!1,notify:!0},canceled:{observer:"_canceledChanged",readOnly:!0,type:Boolean,value:!1},withBackdrop:{observer:"_withBackdropChanged",type:Boolean},noAutoFocus:{type:Boolean,value:!1},noCancelOnEscKey:{type:Boolean,value:!1},noCancelOnOutsideClick:{type:Boolean,value:!1},closingReason:{type:Object},restoreFocusOnClose:{type:Boolean,value:!1},allowClickThrough:{type:Boolean},alwaysOnTop:{type:Boolean},
scrollAction:{type:String},_manager:{type:Object,value:Polymer.IronOverlayManager},_focusedChild:{type:Object}},listeners:{"iron-resize":"_onIronResize"},observers:["__updateScrollObservers(isAttached, opened, scrollAction)"],get backdropElement(){return this._manager.backdropElement},get _focusNode(){return this._focusedChild||Polymer.dom(this).querySelector("[autofocus]")||this},get _focusableNodes(){return Polymer.IronFocusablesHelper.getTabbableNodes(this)},ready:function(){this.__shouldRemoveTabIndex=
this.__isAnimating=!1;this.__firstFocusableNode=this.__lastFocusableNode=null;this.__rafs={};this.__scrollTop=this.__scrollLeft=this.__restoreFocusNode=null;this.__onCaptureScroll=this.__onCaptureScroll.bind(this);this.__rootNodes=null;this._ensureSetup()},attached:function(){this.opened&&this._openedChanged(this.opened);this._observer=Polymer.dom(this).observeNodes(this._onNodesChange)},detached:function(){Polymer.dom(this).unobserveNodes(this._observer);this._observer=null;for(var b in this.__rafs)null!==
this.__rafs[b]&&cancelAnimationFrame(this.__rafs[b]);this.__rafs={};this._manager.removeOverlay(this);this.__isAnimating&&(this.opened?this._finishRenderOpened():(this._applyFocus(),this._finishRenderClosed()))},toggle:function(){this._setCanceled(!1);this.opened=!this.opened},open:function(){this._setCanceled(!1);this.opened=!0},close:function(){this._setCanceled(!1);this.opened=!1},cancel:function(b){this.fire("iron-overlay-canceled",b,{cancelable:!0}).defaultPrevented||(this._setCanceled(!0),this.opened=
!1)},invalidateTabbables:function(){this.__firstFocusableNode=this.__lastFocusableNode=null},_ensureSetup:function(){this._overlaySetup||(this._overlaySetup=!0,this.style.outline="none",this.style.display="none")},_openedChanged:function(b){b?this.removeAttribute("aria-hidden"):this.setAttribute("aria-hidden","true");this.isAttached&&(this.__isAnimating=!0,this.__deraf("__openedChanged",this.__openedChanged))},_canceledChanged:function(){this.closingReason=this.closingReason||{};this.closingReason.canceled=
this.canceled},_withBackdropChanged:function(){this.withBackdrop&&!this.hasAttribute("tabindex")?(this.setAttribute("tabindex","-1"),this.__shouldRemoveTabIndex=!0):this.__shouldRemoveTabIndex&&(this.removeAttribute("tabindex"),this.__shouldRemoveTabIndex=!1);this.opened&&this.isAttached&&this._manager.trackBackdrop()},_prepareRenderOpened:function(){this.__restoreFocusNode=this._manager.deepActiveElement;this._preparePositioning();this.refit();this._finishPositioning();this.noAutoFocus&&document.activeElement===
this._focusNode&&(this._focusNode.blur(),this.__restoreFocusNode.focus())},_renderOpened:function(){this._finishRenderOpened()},_renderClosed:function(){this._finishRenderClosed()},_finishRenderOpened:function(){this.notifyResize();this.__isAnimating=!1;this.fire("iron-overlay-opened")},_finishRenderClosed:function(){this.style.display="none";this.style.zIndex="";this.notifyResize();this.__isAnimating=!1;this.fire("iron-overlay-closed",this.closingReason)},_preparePositioning:function(){this.style.transition=
this.style.webkitTransition="none";this.style.transform=this.style.webkitTransform="none";this.style.display=""},_finishPositioning:function(){this.style.display="none";this.scrollTop=this.scrollTop;this.style.transition=this.style.webkitTransition="";this.style.transform=this.style.webkitTransform="";this.style.display="";this.scrollTop=this.scrollTop},_applyFocus:function(){if(this.opened)this.noAutoFocus||this._focusNode.focus();else{this._focusNode.blur();this._focusedChild=null;if(this.restoreFocusOnClose&&
this.__restoreFocusNode){var b=this._manager.deepActiveElement;(b===document.body||Polymer.dom(this).deepContains(b))&&this.__restoreFocusNode.focus()}this.__restoreFocusNode=null;(b=this._manager.currentOverlay())&&this!==b&&b._applyFocus()}},_onCaptureClick:function(b){this.noCancelOnOutsideClick||this.cancel(b)},_onCaptureFocus:function(b){if(this.withBackdrop){var d=Polymer.dom(b).path;-1===d.indexOf(this)?(b.stopPropagation(),this._applyFocus()):this._focusedChild=d[0]}},_onCaptureEsc:function(b){this.noCancelOnEscKey||
this.cancel(b)},_onCaptureTab:function(b){if(this.withBackdrop){this.__ensureFirstLastFocusables();var d=b.shiftKey,f=d?this.__firstFocusableNode:this.__lastFocusableNode;d=d?this.__lastFocusableNode:this.__firstFocusableNode;if(f===d)f=!0;else{var h=this._manager.deepActiveElement;f=h===f||h===this}f&&(b.preventDefault(),this._focusedChild=d,this._applyFocus())}},_onIronResize:function(){this.opened&&!this.__isAnimating&&this.__deraf("refit",this.refit)},_onNodesChange:function(){this.opened&&!this.__isAnimating&&
(this.invalidateTabbables(),this.notifyResize())},__ensureFirstLastFocusables:function(){if(!this.__firstFocusableNode||!this.__lastFocusableNode){var b=this._focusableNodes;this.__firstFocusableNode=b[0];this.__lastFocusableNode=b[b.length-1]}},__openedChanged:function(){this.opened?(this._prepareRenderOpened(),this._manager.addOverlay(this),this._applyFocus(),this._renderOpened()):(this._manager.removeOverlay(this),this._applyFocus(),this._renderClosed())},__deraf:function(b,d){var f=this.__rafs;
null!==f[b]&&cancelAnimationFrame(f[b]);f[b]=requestAnimationFrame(function(){f[b]=null;d.call(this)}.bind(this))},__updateScrollObservers:function(b,d,f){b&&d&&this.__isValidScrollAction(f)?("lock"===f&&(this.__saveScrollPosition(),Polymer.IronScrollManager.pushScrollLock(this)),this.__addScrollListeners()):(Polymer.IronScrollManager.removeScrollLock(this),this.__removeScrollListeners())},__addScrollListeners:function(){if(!this.__rootNodes){this.__rootNodes=[];if(Polymer.Settings.useShadow)for(var b=
this;b;)b.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&b.host&&this.__rootNodes.push(b),b=b.host||b.assignedSlot||b.parentNode;this.__rootNodes.push(document)}this.__rootNodes.forEach(function(d){d.addEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})},this)},__removeScrollListeners:function(){this.__rootNodes&&this.__rootNodes.forEach(function(b){b.removeEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})},this);this.isAttached||(this.__rootNodes=null)},__isValidScrollAction:function(b){return"lock"===
b||"refit"===b||"cancel"===b},__onCaptureScroll:function(b){if(!(this.__isAnimating||0<=Polymer.dom(b).path.indexOf(this)))switch(this.scrollAction){case "lock":this.__restoreScrollPosition();break;case "refit":this.__deraf("refit",this.refit);break;case "cancel":this.cancel(b)}},__saveScrollPosition:function(){document.scrollingElement?(this.__scrollTop=document.scrollingElement.scrollTop,this.__scrollLeft=document.scrollingElement.scrollLeft):(this.__scrollTop=Math.max(document.documentElement.scrollTop,
document.body.scrollTop),this.__scrollLeft=Math.max(document.documentElement.scrollLeft,document.body.scrollLeft))},__restoreScrollPosition:function(){document.scrollingElement?(document.scrollingElement.scrollTop=this.__scrollTop,document.scrollingElement.scrollLeft=this.__scrollLeft):(document.documentElement.scrollTop=document.body.scrollTop=this.__scrollTop,document.documentElement.scrollLeft=document.body.scrollLeft=this.__scrollLeft)}};Polymer.IronOverlayBehavior=[Polymer.IronFitBehavior,Polymer.IronResizableBehavior,
Polymer.IronOverlayBehaviorImpl]})();

//# sourceURL=build://neon-animation/neon-animatable-behavior.html.js
Polymer.NeonAnimatableBehavior={properties:{animationConfig:{type:Object},entryAnimation:{observer:"_entryAnimationChanged",type:String},exitAnimation:{observer:"_exitAnimationChanged",type:String}},_entryAnimationChanged:function(){this.animationConfig=this.animationConfig||{};this.animationConfig.entry=[{name:this.entryAnimation,node:this}]},_exitAnimationChanged:function(){this.animationConfig=this.animationConfig||{};this.animationConfig.exit=[{name:this.exitAnimation,node:this}]},_copyProperties:function(b,
d){for(var f in d)b[f]=d[f]},_cloneConfig:function(b){var d={isClone:!0};this._copyProperties(d,b);return d},_getAnimationConfigRecursive:function(b,d,f){if(this.animationConfig)if(this.animationConfig.value&&"function"===typeof this.animationConfig.value)this._warn(this._logf("playAnimation","Please put 'animationConfig' inside of your components 'properties' object instead of outside of it."));else{var h=b?this.animationConfig[b]:this.animationConfig;Array.isArray(h)||(h=[h]);if(h)for(var k,t=0;k=
h[t];t++)if(k.animatable)k.animatable._getAnimationConfigRecursive(k.type||b,d,f);else if(k.id){var l=d[k.id];l?(l.isClone||(d[k.id]=this._cloneConfig(l),l=d[k.id]),this._copyProperties(l,k)):d[k.id]=k}else f.push(k)}},getAnimationConfig:function(b){var d={},f=[];this._getAnimationConfigRecursive(b,d,f);for(var h in d)f.push(d[h]);return f}};

//# sourceURL=build://neon-animation/neon-animation-runner-behavior.html.js
Polymer.NeonAnimationRunnerBehaviorImpl={_configureAnimations:function(b){var d=[],f=[];if(0<b.length)for(var h,k=0;h=b[k];k++){var t=document.createElement(h.name);if(t.isNeonAnimation){var l=null;t.configure||(t.configure=function(){return null});l=t.configure(h);f.push({result:l,config:h})}else console.warn(this.is+":",h.name,"not found!")}for(b=0;b<f.length;b++){l=f[b].result;h=f[b].config;try{"function"!=typeof l.cancel&&(l=document.timeline.play(l))}catch(p){l=null,console.warn("Couldnt play",
"(",h.name,").",p)}l&&d.push({neonAnimation:t,config:h,animation:l})}return d},_shouldComplete:function(b){for(var d=!0,f=0;f<b.length;f++)if("finished"!=b[f].animation.playState){d=!1;break}return d},_complete:function(b){for(var d=0;d<b.length;d++)b[d].neonAnimation.complete(b[d].config);for(d=0;d<b.length;d++)b[d].animation.cancel()},playAnimation:function(b,d){var f=this.getAnimationConfig(b);if(f){this._active=this._active||{};this._active[b]&&(this._complete(this._active[b]),delete this._active[b]);
var h=this._configureAnimations(f);if(0==h.length)this.fire("neon-animation-finish",d,{bubbles:!1});else for(this._active[b]=h,f=0;f<h.length;f++)h[f].animation.onfinish=function(){this._shouldComplete(h)&&(this._complete(h),delete this._active[b],this.fire("neon-animation-finish",d,{bubbles:!1}))}.bind(this)}},cancelAnimation:function(){for(var b in this._active){var d=this._active[b],f;for(f in d)d[f].animation.cancel()}this._active={}}};
Polymer.NeonAnimationRunnerBehavior=[Polymer.NeonAnimatableBehavior,Polymer.NeonAnimationRunnerBehaviorImpl];

//# sourceURL=build://iron-dropdown/iron-dropdown-scroll-manager.html.js
(function(){Polymer.IronDropdownScrollManager=Polymer.IronScrollManager})();

//# sourceURL=build://iron-dropdown/iron-dropdown.html.js
(function(){Polymer({is:"iron-dropdown",behaviors:[Polymer.IronControlState,Polymer.IronA11yKeysBehavior,Polymer.IronOverlayBehavior,Polymer.NeonAnimationRunnerBehavior],properties:{horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},openAnimationConfig:{type:Object},closeAnimationConfig:{type:Object},focusTarget:{type:Object},noAnimations:{type:Boolean,value:!1},allowOutsideScroll:{type:Boolean,value:!1,observer:"_allowOutsideScrollChanged"}},
listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},observers:["_updateOverlayPosition(positionTarget, verticalAlign, horizontalAlign, verticalOffset, horizontalOffset)"],get containedElement(){for(var b=Polymer.dom(this.$.content).getDistributedNodes(),d=0,f=b.length;d<f;d++)if(b[d].nodeType===Node.ELEMENT_NODE)return b[d]},ready:function(){this.scrollAction||(this.scrollAction=this.allowOutsideScroll?"refit":"lock");this._readied=!0},attached:function(){this.sizingTarget&&this.sizingTarget!==
this||(this.sizingTarget=this.containedElement||this)},detached:function(){this.cancelAnimation()},_openedChanged:function(){this.opened&&this.disabled?this.cancel():(this.cancelAnimation(),this._updateAnimationConfig(),Polymer.IronOverlayBehaviorImpl._openedChanged.apply(this,arguments))},_renderOpened:function(){!this.noAnimations&&this.animationConfig.open?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("open")):Polymer.IronOverlayBehaviorImpl._renderOpened.apply(this,arguments)},
_renderClosed:function(){!this.noAnimations&&this.animationConfig.close?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("close")):Polymer.IronOverlayBehaviorImpl._renderClosed.apply(this,arguments)},_onNeonAnimationFinish:function(){this.$.contentWrapper.classList.remove("animating");this.opened?this._finishRenderOpened():this._finishRenderClosed()},_updateAnimationConfig:function(){for(var b=this.containedElement,d=[].concat(this.openAnimationConfig||[]).concat(this.closeAnimationConfig||
[]),f=0;f<d.length;f++)d[f].node=b;this.animationConfig={open:this.openAnimationConfig,close:this.closeAnimationConfig}},_updateOverlayPosition:function(){this.isAttached&&this.notifyResize()},_allowOutsideScrollChanged:function(b){this._readied&&(b?this.scrollAction&&"lock"!==this.scrollAction||(this.scrollAction="refit"):this.scrollAction="lock")},_applyFocus:function(){var b=this.focusTarget||this.containedElement;b&&this.opened&&!this.noAutoFocus?b.focus():Polymer.IronOverlayBehaviorImpl._applyFocus.apply(this,
arguments)}})})();

//# sourceURL=build://neon-animation/neon-animation-behavior.html.js
Polymer.NeonAnimationBehavior={properties:{animationTiming:{type:Object,value:function(){return{duration:500,easing:"cubic-bezier(0.4, 0, 0.2, 1)",fill:"both"}}}},isNeonAnimation:!0,created:function(){document.body.animate||console.warn("No web animations detected. This element will not function without a web animations polyfill.")},timingFromConfig:function(b){if(b.timing)for(var d in b.timing)this.animationTiming[d]=b.timing[d];return this.animationTiming},setPrefixedProperty:function(b,d,f){for(var h=
{transform:["webkitTransform"],transformOrigin:["mozTransformOrigin","webkitTransformOrigin"]}[d],k,t=0;k=h[t];t++)b.style[k]=f;b.style[d]=f},complete:function(){}};

//# sourceURL=build://neon-animation/animations/fade-in-animation.html.js
Polymer({is:"fade-in-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){return this._effect=new KeyframeEffect(b.node,[{opacity:"0"},{opacity:"1"}],this.timingFromConfig(b))}});

//# sourceURL=build://neon-animation/animations/fade-out-animation.html.js
Polymer({is:"fade-out-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){return this._effect=new KeyframeEffect(b.node,[{opacity:"1"},{opacity:"0"}],this.timingFromConfig(b))}});

//# sourceURL=build://paper-menu-button/paper-menu-button-animations.html.js
Polymer({is:"paper-menu-grow-height-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){var d=b.node,f=d.getBoundingClientRect().height;return this._effect=new KeyframeEffect(d,[{height:f/2+"px"},{height:f+"px"}],this.timingFromConfig(b))}});Polymer({is:"paper-menu-grow-width-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){var d=b.node,f=d.getBoundingClientRect().width;return this._effect=new KeyframeEffect(d,[{width:f/2+"px"},{width:f+"px"}],this.timingFromConfig(b))}});
Polymer({is:"paper-menu-shrink-width-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){var d=b.node,f=d.getBoundingClientRect().width;return this._effect=new KeyframeEffect(d,[{width:f+"px"},{width:f-f/20+"px"}],this.timingFromConfig(b))}});
Polymer({is:"paper-menu-shrink-height-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){var d=b.node,f=d.getBoundingClientRect().height;this.setPrefixedProperty(d,"transformOrigin","0 0");return this._effect=new KeyframeEffect(d,[{height:f+"px",transform:"translateY(0)"},{height:f/2+"px",transform:"translateY(-20px)"}],this.timingFromConfig(b))}});

//# sourceURL=build://paper-menu-button/paper-menu-button.html.js
(function(){var b={ANIMATION_CUBIC_BEZIER:"cubic-bezier(.3,.95,.5,1)",MAX_ANIMATION_TIME_MS:400};Polymer.PaperMenuButton=function(){};Polymer.PaperMenuButton.prototype.registered=function(){};Polymer.PaperMenuButton.prototype.addOwnKeyBinding=function(){};Polymer.PaperMenuButton.prototype.removeOwnKeyBindings=function(){};Polymer.PaperMenuButton.prototype.keyboardEventMatchesKeys=function(){};Polymer.PaperMenuButton.prototype._collectKeyBindings=function(){};Polymer.PaperMenuButton.prototype._prepKeyBindings=
function(){};Polymer.PaperMenuButton.prototype._addKeyBinding=function(){};Polymer.PaperMenuButton.prototype._resetKeyEventListeners=function(){};Polymer.PaperMenuButton.prototype._listenKeyEventListeners=function(){};Polymer.PaperMenuButton.prototype._unlistenKeyEventListeners=function(){};Polymer.PaperMenuButton.prototype._onKeyBindingEvent=function(){};Polymer.PaperMenuButton.prototype._triggerKeyHandler=function(){};Polymer.PaperMenuButton.prototype._focusBlurHandler=function(d){if(Polymer.Element)this._setFocused("focus"===
d.type);else if(d.target===this)this._setFocused("focus"===d.type);else if(this.__handleEventRetargeting){var f=Polymer.dom(d).localTarget;this.isLightDescendant(f)||this.fire(d.type,{sourceEvent:d},{node:this,bubbles:d.bubbles,cancelable:d.cancelable})}};Polymer.PaperMenuButton.prototype._changedControlState=function(){this._controlStateChanged&&this._controlStateChanged()};Polymer.PaperMenuButton.prototype._setFocused=function(){};Polymer.PaperMenuButton=Polymer({is:"paper-menu-button",behaviors:[Polymer.IronA11yKeysBehavior,
Polymer.IronControlState],properties:{opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},noOverlap:{type:Boolean},noAnimations:{type:Boolean,value:!1},ignoreSelect:{type:Boolean,value:!1},closeOnActivate:{type:Boolean,value:!1},openAnimationConfig:{type:Object,
value:function(){return[{name:"fade-in-animation",timing:{delay:100,duration:200}},{name:"paper-menu-grow-width-animation",timing:{delay:100,duration:150,easing:b.ANIMATION_CUBIC_BEZIER}},{name:"paper-menu-grow-height-animation",timing:{delay:100,duration:275,easing:b.ANIMATION_CUBIC_BEZIER}}]}},closeAnimationConfig:{type:Object,value:function(){return[{name:"fade-out-animation",timing:{duration:150}},{name:"paper-menu-shrink-width-animation",timing:{delay:100,duration:50,easing:b.ANIMATION_CUBIC_BEZIER}},
{name:"paper-menu-shrink-height-animation",timing:{duration:200,easing:"ease-in"}}]}},allowOutsideScroll:{type:Boolean,value:!1},restoreFocusOnClose:{type:Boolean,value:!0},_dropdownContent:{type:Object}},hostAttributes:{role:"group","aria-haspopup":"true"},listeners:{"iron-activate":"_onIronActivate","iron-select":"_onIronSelect"},get contentElement(){for(var d=Polymer.dom(this.$.content).getDistributedNodes(),f=0,h=d.length;f<h;f++)if(d[f].nodeType===Node.ELEMENT_NODE)return d[f]},toggle:function(){this.opened?
this.close():this.open()},open:function(){this.disabled||this.$.dropdown.open()},close:function(){this.$.dropdown.close()},_onIronSelect:function(){this.ignoreSelect||this.close()},_onIronActivate:function(){this.closeOnActivate&&this.close()},_openedChanged:function(d,f){d?(this._dropdownContent=this.contentElement,this.fire("paper-dropdown-open")):null!=f&&this.fire("paper-dropdown-close")},_disabledChanged:function(d){Polymer.IronControlState._disabledChanged.apply(this,arguments);d&&this.opened&&
this.close()},__onIronOverlayCanceled:function(d){var f=this.$.trigger;-1<Polymer.dom(d.detail).path.indexOf(f)&&d.preventDefault()}});Object.keys(b).forEach(function(d){Polymer.PaperMenuButton[d]=b[d]})})();

//# sourceURL=build://iron-iconset-svg/iron-iconset-svg.html.js
Polymer({is:"iron-iconset-svg",properties:{name:{type:String,observer:"_nameChanged"},size:{type:Number,value:24},rtlMirroring:{type:Boolean,value:!1},useGlobalRtlAttribute:{type:Boolean,value:!1}},created:function(){this._meta=new Polymer.IronMeta({type:"iconset",key:null,value:null})},attached:function(){this.style.display="none"},getIconNames:function(){this._icons=this._createIconMap();return Object.keys(this._icons).map(function(b){return this.name+":"+b},this)},applyIcon:function(b,d){this.removeIcon(b);
if(d=this._cloneIcon(d,this.rtlMirroring&&this._targetIsRTL(b))){var f=Polymer.dom(b.root||b);f.insertBefore(d,f.childNodes[0]);return b._svgIcon=d}return null},removeIcon:function(b){b._svgIcon&&(Polymer.dom(b.root||b).removeChild(b._svgIcon),b._svgIcon=null)},_targetIsRTL:function(b){null==this.__targetIsRTL&&(this.useGlobalRtlAttribute?this.__targetIsRTL="rtl"===(document.body&&document.body.hasAttribute("dir")?document.body:document.documentElement).getAttribute("dir"):(b&&b.nodeType!==Node.ELEMENT_NODE&&
(b=b.host),this.__targetIsRTL=b&&"rtl"===window.getComputedStyle(b).direction));return this.__targetIsRTL},_nameChanged:function(){this._meta.value=null;this._meta.key=this.name;this._meta.value=this;this.async(function(){this.fire("iron-iconset-added",this,{node:window})})},_createIconMap:function(){var b=Object.create(null);Polymer.dom(this).querySelectorAll("[id]").forEach(function(d){b[d.id]=d});return b},_cloneIcon:function(b,d){this._icons=this._icons||this._createIconMap();return this._prepareSvgClone(this._icons[b],
this.size,d)},_prepareSvgClone:function(b,d,f){if(b){b=b.cloneNode(!0);var h=document.createElementNS("http://www.w3.org/2000/svg","svg");d=b.getAttribute("viewBox")||"0 0 "+d+" "+d;var k="pointer-events: none; display: block; width: 100%; height: 100%;";f&&b.hasAttribute("mirror-in-rtl")&&(k+="-webkit-transform:scale(-1,1);transform:scale(-1,1);transform-origin:center;");h.setAttribute("viewBox",d);h.setAttribute("preserveAspectRatio","xMidYMid meet");h.setAttribute("focusable","false");h.style.cssText=
k;h.appendChild(b).removeAttribute("id");return h}return null}});

//# sourceURL=build://paper-dropdown-menu/paper-dropdown-menu.html.js
(function(){Polymer({is:"paper-dropdown-menu",behaviors:[Polymer.IronButtonState,Polymer.IronControlState,Polymer.IronFormElementBehavior,Polymer.IronValidatableBehavior],properties:{selectedItemLabel:{type:String,notify:!0,readOnly:!0},selectedItem:{type:Object,notify:!0,readOnly:!0},value:{type:String,notify:!0},label:{type:String},placeholder:{type:String},errorMessage:{type:String},opened:{type:Boolean,notify:!0,value:!1,observer:"_openedChanged"},allowOutsideScroll:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,
value:!1,reflectToAttribute:!0},alwaysFloatLabel:{type:Boolean,value:!1},noAnimations:{type:Boolean,value:!1},horizontalAlign:{type:String,value:"right"},verticalAlign:{type:String,value:"top"},verticalOffset:Number,dynamicAlign:{type:Boolean},restoreFocusOnClose:{type:Boolean,value:!0}},listeners:{tap:"_onTap"},keyBindings:{"up down":"open",esc:"close"},hostAttributes:{role:"combobox","aria-autocomplete":"none","aria-haspopup":"true"},observers:["_selectedItemChanged(selectedItem)"],attached:function(){var b=
this.contentElement;b&&b.selectedItem&&this._setSelectedItem(b.selectedItem)},get contentElement(){for(var b=Polymer.dom(this.$.content).getDistributedNodes(),d=0,f=b.length;d<f;d++)if(b[d].nodeType===Node.ELEMENT_NODE)return b[d]},open:function(){this.$.menuButton.open()},close:function(){this.$.menuButton.close()},_onIronSelect:function(b){this._setSelectedItem(b.detail.item)},_onIronDeselect:function(){this._setSelectedItem(null)},_onTap:function(b){Polymer.Gestures.findOriginalTarget(b)===this&&
this.open()},_selectedItemChanged:function(b){this.value=b=b?b.label||b.getAttribute("label")||b.textContent.trim():"";this._setSelectedItemLabel(b)},_computeMenuVerticalOffset:function(b,d){return d?d:b?-4:8},_getValidity:function(){return this.disabled||!this.required||this.required&&!!this.value},_openedChanged:function(){var b=this.opened?"true":"false",d=this.contentElement;d&&d.setAttribute("aria-expanded",b)}})})();

//# sourceURL=build://iron-selector/iron-selection.html.js
Polymer.IronSelection=function(b){this.selection=[];this.selectCallback=b};
Polymer.IronSelection.prototype={get:function(){return this.multi?this.selection.slice():this.selection[0]},clear:function(b){this.selection.slice().forEach(function(d){(!b||0>b.indexOf(d))&&this.setItemSelected(d,!1)},this)},isSelected:function(b){return 0<=this.selection.indexOf(b)},setItemSelected:function(b,d){if(null!=b&&d!==this.isSelected(b)){if(d)this.selection.push(b);else{var f=this.selection.indexOf(b);0<=f&&this.selection.splice(f,1)}this.selectCallback&&this.selectCallback(b,d)}},select:function(b){this.multi?
this.toggle(b):this.get()!==b&&(this.setItemSelected(this.get(),!1),this.setItemSelected(b,!0))},toggle:function(b){this.setItemSelected(b,!this.isSelected(b))}};

//# sourceURL=build://iron-selector/iron-selectable.html.js
Polymer.IronSelectableBehavior={properties:{attrForSelected:{type:String,value:null},selected:{type:String,notify:!0},selectedItem:{type:Object,readOnly:!0,notify:!0},activateEvent:{type:String,value:"tap",observer:"_activateEventChanged"},selectable:String,selectedClass:{type:String,value:"iron-selected"},selectedAttribute:{type:String,value:null},fallbackSelection:{type:String,value:null},items:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}},_excludedLocalNames:{type:Object,value:function(){return{template:1,
"dom-bind":1,"dom-if":1,"dom-repeat":1}}}},observers:["_updateAttrForSelected(attrForSelected)","_updateSelected(selected)","_checkFallback(fallbackSelection)"],created:function(){this._bindFilterItem=this._filterItem.bind(this);this._selection=new Polymer.IronSelection(this._applySelection.bind(this))},attached:function(){this._observer=this._observeItems(this);this._addListener(this.activateEvent)},detached:function(){this._observer&&Polymer.dom(this).unobserveNodes(this._observer);this._removeListener(this.activateEvent)},
indexOf:function(b){return this.items?this.items.indexOf(b):-1},select:function(b){this.selected=b},selectPrevious:function(){var b=this.items.length;b=(Number(this._valueToIndex(this.selected))-1+b)%b;this.selected=this._indexToValue(b)},selectNext:function(){var b=(Number(this._valueToIndex(this.selected))+1)%this.items.length;this.selected=this._indexToValue(b)},selectIndex:function(b){this.select(this._indexToValue(b))},forceSynchronousItemUpdate:function(){this._observer&&"function"===typeof this._observer.flush?
this._observer.flush():this._updateItems()},get _shouldUpdateSelection(){return null!=this.selected},_checkFallback:function(){this._updateSelected()},_addListener:function(b){this.listen(this,b,"_activateHandler")},_removeListener:function(b){this.unlisten(this,b,"_activateHandler")},_activateEventChanged:function(b,d){this._removeListener(d);this._addListener(b)},_updateItems:function(){var b=Polymer.dom(this).queryDistributedElements(this.selectable||"*");b=Array.prototype.filter.call(b,this._bindFilterItem);
this._setItems(b)},_updateAttrForSelected:function(){this.selectedItem&&(this.selected=this._valueForItem(this.selectedItem))},_updateSelected:function(){this._selectSelected(this.selected)},_selectSelected:function(){if(this.items){var b=this._valueToItem(this.selected);b?this._selection.select(b):this._selection.clear();this.fallbackSelection&&this.items.length&&void 0===this._selection.get()&&(this.selected=this.fallbackSelection)}},_filterItem:function(b){return!this._excludedLocalNames[b.localName]},
_valueToItem:function(b){return null==b?null:this.items[this._valueToIndex(b)]},_valueToIndex:function(b){if(this.attrForSelected)for(var d=0,f;f=this.items[d];d++){if(this._valueForItem(f)==b)return d}else return Number(b)},_indexToValue:function(b){if(this.attrForSelected){if(b=this.items[b])return this._valueForItem(b)}else return b},_valueForItem:function(b){if(!b)return null;if(!this.attrForSelected)return b=this.indexOf(b),-1===b?null:b;var d=b[Polymer.CaseMap.dashToCamelCase(this.attrForSelected)];
return void 0!=d?d:b.getAttribute(this.attrForSelected)},_applySelection:function(b,d){this.selectedClass&&this.toggleClass(this.selectedClass,d,b);this.selectedAttribute&&this.toggleAttribute(this.selectedAttribute,d,b);this._selectionChange();this.fire("iron-"+(d?"select":"deselect"),{item:b})},_selectionChange:function(){this._setSelectedItem(this._selection.get())},_observeItems:function(b){return Polymer.dom(b).observeNodes(function(d){this._updateItems();this._updateSelected();this.fire("iron-items-changed",
d,{bubbles:!1,cancelable:!1})})},_activateHandler:function(b){b=b.target;for(var d=this.items;b&&b!=this;){var f=d.indexOf(b);if(0<=f){d=this._indexToValue(f);this._itemActivate(d,b);break}b=b.parentNode}},_itemActivate:function(b,d){this.fire("iron-activate",{selected:b,item:d},{cancelable:!0}).defaultPrevented||this.select(b)}};

//# sourceURL=build://iron-selector/iron-multi-selectable.html.js
Polymer.IronMultiSelectableBehaviorImpl={properties:{multi:{type:Boolean,value:!1,observer:"multiChanged"},selectedValues:{type:Array,notify:!0,value:function(){return[]}},selectedItems:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}}},observers:["_updateSelected(selectedValues.splices)"],select:function(b){this.multi?this._toggleSelected(b):this.selected=b},multiChanged:function(b){this._selection.multi=b;this._updateSelected()},get _shouldUpdateSelection(){return null!=this.selected||
null!=this.selectedValues&&this.selectedValues.length},_updateAttrForSelected:function(){this.multi?this.selectedItems&&0<this.selectedItems.length&&(this.selectedValues=this.selectedItems.map(function(b){return this._indexToValue(this.indexOf(b))},this).filter(function(b){return null!=b},this)):Polymer.IronSelectableBehavior._updateAttrForSelected.apply(this)},_updateSelected:function(){this.multi?this._selectMulti(this.selectedValues):this._selectSelected(this.selected)},_selectMulti:function(b){b=
b||[];b=(this._valuesToItems(b)||[]).filter(function(f){return null!==f&&void 0!==f});this._selection.clear(b);for(var d=0;d<b.length;d++)this._selection.setItemSelected(b[d],!0);this.fallbackSelection&&!this._selection.get().length&&this._valueToItem(this.fallbackSelection)&&this.select(this.fallbackSelection)},_selectionChange:function(){var b=this._selection.get();this.multi?(this._setSelectedItems(b),this._setSelectedItem(b.length?b[0]:null)):null!==b&&void 0!==b?(this._setSelectedItems([b]),
this._setSelectedItem(b)):(this._setSelectedItems([]),this._setSelectedItem(null))},_toggleSelected:function(b){var d=this.selectedValues.indexOf(b);0>d?this.push("selectedValues",b):this.splice("selectedValues",d,1)},_valuesToItems:function(b){return null==b?null:b.map(function(d){return this._valueToItem(d)},this)}};Polymer.IronMultiSelectableBehavior=[Polymer.IronSelectableBehavior,Polymer.IronMultiSelectableBehaviorImpl];

//# sourceURL=build://iron-menu-behavior/iron-menu-behavior.html.js
Polymer.IronMenuBehaviorImpl={properties:{focusedItem:{observer:"_focusedItemChanged",readOnly:!0,type:Object},attrForItemTitle:{type:String},disabled:{type:Boolean,value:!1,observer:"_disabledChanged"}},_MODIFIER_KEYS:"Alt AltGraph CapsLock Control Fn FnLock Hyper Meta NumLock OS ScrollLock Shift Super Symbol SymbolLock".split(" "),_SEARCH_RESET_TIMEOUT_MS:1E3,_previousTabIndex:0,hostAttributes:{role:"menu"},observers:["_updateMultiselectable(multi)"],listeners:{focus:"_onFocus",keydown:"_onKeydown",
"iron-items-changed":"_onIronItemsChanged"},keyBindings:{up:"_onUpKey",down:"_onDownKey",esc:"_onEscKey","shift+tab:keydown":"_onShiftTabDown"},attached:function(){this._resetTabindices()},select:function(b){this._defaultFocusAsync&&(this.cancelAsync(this._defaultFocusAsync),this._defaultFocusAsync=null);var d=this._valueToItem(b);d&&d.hasAttribute("disabled")||(this._setFocusedItem(d),Polymer.IronMultiSelectableBehaviorImpl.select.apply(this,arguments))},_resetTabindices:function(){var b=this.multi?
this.selectedItems&&this.selectedItems[0]:this.selectedItem;this.items.forEach(function(d){d.setAttribute("tabindex",d===b?"0":"-1")},this)},_updateMultiselectable:function(b){b?this.setAttribute("aria-multiselectable","true"):this.removeAttribute("aria-multiselectable")},_focusWithKeyboardEvent:function(b){if(-1===this._MODIFIER_KEYS.indexOf(b.key)){this.cancelDebouncer("_clearSearchText");var d=this._searchText||"";d+=(b.key&&1==b.key.length?b.key:String.fromCharCode(b.keyCode)).toLocaleLowerCase();
b=d.length;for(var f=0,h;h=this.items[f];f++)if(!h.hasAttribute("disabled")){var k=this.attrForItemTitle||"textContent";k=(h[k]||h.getAttribute(k)||"").trim();if(!(k.length<b)&&k.slice(0,b).toLocaleLowerCase()==d){this._setFocusedItem(h);break}}this._searchText=d;this.debounce("_clearSearchText",this._clearSearchText,this._SEARCH_RESET_TIMEOUT_MS)}},_clearSearchText:function(){this._searchText=""},_focusPrevious:function(){for(var b=this.items.length,d=Number(this.indexOf(this.focusedItem)),f=1;f<
b+1;f++){var h=this.items[(d-f+b)%b];if(!h.hasAttribute("disabled")){var k=Polymer.dom(h).getOwnerRoot()||document;this._setFocusedItem(h);if(Polymer.dom(k).activeElement==h)break}}},_focusNext:function(){for(var b=this.items.length,d=Number(this.indexOf(this.focusedItem)),f=1;f<b+1;f++){var h=this.items[(d+f)%b];if(!h.hasAttribute("disabled")){var k=Polymer.dom(h).getOwnerRoot()||document;this._setFocusedItem(h);if(Polymer.dom(k).activeElement==h)break}}},_applySelection:function(b,d){d?b.setAttribute("aria-selected",
"true"):b.removeAttribute("aria-selected");Polymer.IronSelectableBehavior._applySelection.apply(this,arguments)},_focusedItemChanged:function(b,d){d&&d.setAttribute("tabindex","-1");!b||b.hasAttribute("disabled")||this.disabled||(b.setAttribute("tabindex","0"),b.focus())},_onIronItemsChanged:function(b){b.detail.addedNodes.length&&this._resetTabindices()},_onShiftTabDown:function(){var b=this.getAttribute("tabindex");Polymer.IronMenuBehaviorImpl._shiftTabPressed=!0;this._setFocusedItem(null);this.setAttribute("tabindex",
"-1");this.async(function(){this.setAttribute("tabindex",b);Polymer.IronMenuBehaviorImpl._shiftTabPressed=!1},1)},_onFocus:function(b){!Polymer.IronMenuBehaviorImpl._shiftTabPressed&&(b=Polymer.dom(b).rootTarget,b===this||"undefined"===typeof b.tabIndex||this.isLightDescendant(b))&&(this._defaultFocusAsync=this.async(function(){var d=this.multi?this.selectedItems&&this.selectedItems[0]:this.selectedItem;this._setFocusedItem(null);d?this._setFocusedItem(d):this.items[0]&&this._focusNext()}))},_onUpKey:function(b){this._focusPrevious();
b.detail.keyboardEvent.preventDefault()},_onDownKey:function(b){this._focusNext();b.detail.keyboardEvent.preventDefault()},_onEscKey:function(){var b=this.focusedItem;b&&b.blur()},_onKeydown:function(b){this.keyboardEventMatchesKeys(b,"up down esc")||this._focusWithKeyboardEvent(b);b.stopPropagation()},_activateHandler:function(b){Polymer.IronSelectableBehavior._activateHandler.call(this,b);b.stopPropagation()},_disabledChanged:function(b){b?(this._previousTabIndex=this.hasAttribute("tabindex")?this.tabIndex:
0,this.removeAttribute("tabindex")):this.hasAttribute("tabindex")||this.setAttribute("tabindex",this._previousTabIndex)}};Polymer.IronMenuBehaviorImpl._shiftTabPressed=!1;Polymer.IronMenuBehavior=[Polymer.IronMultiSelectableBehavior,Polymer.IronA11yKeysBehavior,Polymer.IronMenuBehaviorImpl];

//# sourceURL=build://paper-listbox/paper-listbox.html.js
(function(){Polymer({is:"paper-listbox",behaviors:[Polymer.IronMenuBehavior],hostAttributes:{role:"listbox"}})})();

//# sourceURL=build://paper-item/paper-item-behavior.html.js
Polymer.PaperItemBehaviorImpl={hostAttributes:{role:"option",tabindex:"0"}};Polymer.PaperItemBehavior=[Polymer.IronButtonState,Polymer.IronControlState,Polymer.PaperItemBehaviorImpl];

//# sourceURL=build://paper-item/paper-item.html.js
Polymer({is:"paper-item",behaviors:[Polymer.PaperItemBehavior]});

/*

 Lodash <https://lodash.com/>
 Copyright JS Foundation and other contributors <https://js.foundation/>
 Released under MIT license <https://lodash.com/license>
 Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE>
 Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
*/
(function(){var undefined;var VERSION="4.17.5";var LARGE_ARRAY_SIZE=200;var CORE_ERROR_TEXT="Unsupported core-js use. Try https://npms.io/search?q\x3dponyfill.",FUNC_ERROR_TEXT="Expected a function";var HASH_UNDEFINED="__lodash_hash_undefined__";var MAX_MEMOIZE_SIZE=500;var PLACEHOLDER="__lodash_placeholder__";var CLONE_DEEP_FLAG=1,CLONE_FLAT_FLAG=2,CLONE_SYMBOLS_FLAG=4;var COMPARE_PARTIAL_FLAG=1,COMPARE_UNORDERED_FLAG=2;var WRAP_BIND_FLAG=1,WRAP_BIND_KEY_FLAG=2,WRAP_CURRY_BOUND_FLAG=4,WRAP_CURRY_FLAG=
8,WRAP_CURRY_RIGHT_FLAG=16,WRAP_PARTIAL_FLAG=32,WRAP_PARTIAL_RIGHT_FLAG=64,WRAP_ARY_FLAG=128,WRAP_REARG_FLAG=256,WRAP_FLIP_FLAG=512;var DEFAULT_TRUNC_LENGTH=30,DEFAULT_TRUNC_OMISSION="...";var HOT_COUNT=800,HOT_SPAN=16;var LAZY_FILTER_FLAG=1,LAZY_MAP_FLAG=2,LAZY_WHILE_FLAG=3;var INFINITY=1/0,MAX_SAFE_INTEGER=9007199254740991,MAX_INTEGER=1.7976931348623157E308,NAN=0/0;var MAX_ARRAY_LENGTH=4294967295,MAX_ARRAY_INDEX=MAX_ARRAY_LENGTH-1,HALF_MAX_ARRAY_LENGTH=MAX_ARRAY_LENGTH>>>1;var wrapFlags=[["ary",
WRAP_ARY_FLAG],["bind",WRAP_BIND_FLAG],["bindKey",WRAP_BIND_KEY_FLAG],["curry",WRAP_CURRY_FLAG],["curryRight",WRAP_CURRY_RIGHT_FLAG],["flip",WRAP_FLIP_FLAG],["partial",WRAP_PARTIAL_FLAG],["partialRight",WRAP_PARTIAL_RIGHT_FLAG],["rearg",WRAP_REARG_FLAG]];var argsTag="[object Arguments]",arrayTag="[object Array]",asyncTag="[object AsyncFunction]",boolTag="[object Boolean]",dateTag="[object Date]",domExcTag="[object DOMException]",errorTag="[object Error]",funcTag="[object Function]",genTag="[object GeneratorFunction]",
mapTag="[object Map]",numberTag="[object Number]",nullTag="[object Null]",objectTag="[object Object]",promiseTag="[object Promise]",proxyTag="[object Proxy]",regexpTag="[object RegExp]",setTag="[object Set]",stringTag="[object String]",symbolTag="[object Symbol]",undefinedTag="[object Undefined]",weakMapTag="[object WeakMap]",weakSetTag="[object WeakSet]";var arrayBufferTag="[object ArrayBuffer]",dataViewTag="[object DataView]",float32Tag="[object Float32Array]",float64Tag="[object Float64Array]",
int8Tag="[object Int8Array]",int16Tag="[object Int16Array]",int32Tag="[object Int32Array]",uint8Tag="[object Uint8Array]",uint8ClampedTag="[object Uint8ClampedArray]",uint16Tag="[object Uint16Array]",uint32Tag="[object Uint32Array]";var reEmptyStringLeading=/\b__p \+= '';/g,reEmptyStringMiddle=/\b(__p \+=) '' \+/g,reEmptyStringTrailing=/(__e\(.*?\)|\b__t\)) \+\n'';/g;var reEscapedHtml=/&(?:amp|lt|gt|quot|#39);/g,reUnescapedHtml=/[&<>"']/g,reHasEscapedHtml=RegExp(reEscapedHtml.source),reHasUnescapedHtml=
RegExp(reUnescapedHtml.source);var reEscape=/<%-([\s\S]+?)%>/g,reEvaluate=/<%([\s\S]+?)%>/g,reInterpolate=/<%=([\s\S]+?)%>/g;var reIsDeepProp=/\.|\[(?:[^[\]]*|(["'])(?:(?!\1)[^\\]|\\.)*?\1)\]/,reIsPlainProp=/^\w*$/,rePropName=/[^.[\]]+|\[(?:(-?\d+(?:\.\d+)?)|(["'])((?:(?!\2)[^\\]|\\.)*?)\2)\]|(?=(?:\.|\[\])(?:\.|\[\]|$))/g;var reRegExpChar=/[\\^$.*+?()[\]{}|]/g,reHasRegExpChar=RegExp(reRegExpChar.source);var reTrim=/^\s+|\s+$/g,reTrimStart=/^\s+/,reTrimEnd=/\s+$/;var reWrapComment=/\{(?:\n\/\* \[wrapped with .+\] \*\/)?\n?/,
reWrapDetails=/\{\n\/\* \[wrapped with (.+)\] \*/,reSplitDetails=/,? & /;var reAsciiWord=/[^\x00-\x2f\x3a-\x40\x5b-\x60\x7b-\x7f]+/g;var reEscapeChar=/\\(\\)?/g;var reEsTemplate=/\$\{([^\\}]*(?:\\.[^\\}]*)*)\}/g;var reFlags=/\w*$/;var reIsBadHex=/^[-+]0x[0-9a-f]+$/i;var reIsBinary=/^0b[01]+$/i;var reIsHostCtor=/^\[object .+?Constructor\]$/;var reIsOctal=/^0o[0-7]+$/i;var reIsUint=/^(?:0|[1-9]\d*)$/;var reLatin=/[\xc0-\xd6\xd8-\xf6\xf8-\xff\u0100-\u017f]/g;var reNoMatch=/($^)/;var reUnescapedString=
/['\n\r\u2028\u2029\\]/g;var rsAstralRange="\\ud800-\\udfff",rsComboMarksRange="\\u0300-\\u036f",reComboHalfMarksRange="\\ufe20-\\ufe2f",rsComboSymbolsRange="\\u20d0-\\u20ff",rsComboRange=rsComboMarksRange+reComboHalfMarksRange+rsComboSymbolsRange,rsDingbatRange="\\u2700-\\u27bf",rsLowerRange="a-z\\xdf-\\xf6\\xf8-\\xff",rsMathOpRange="\\xac\\xb1\\xd7\\xf7",rsNonCharRange="\\x00-\\x2f\\x3a-\\x40\\x5b-\\x60\\x7b-\\xbf",rsPunctuationRange="\\u2000-\\u206f",rsSpaceRange=" \\t\\x0b\\f\\xa0\\ufeff\\n\\r\\u2028\\u2029\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000",
rsUpperRange="A-Z\\xc0-\\xd6\\xd8-\\xde",rsVarRange="\\ufe0e\\ufe0f",rsBreakRange=rsMathOpRange+rsNonCharRange+rsPunctuationRange+rsSpaceRange;var rsApos="['\u2019]",rsAstral="["+rsAstralRange+"]",rsBreak="["+rsBreakRange+"]",rsCombo="["+rsComboRange+"]",rsDigits="\\d+",rsDingbat="["+rsDingbatRange+"]",rsLower="["+rsLowerRange+"]",rsMisc="[^"+rsAstralRange+rsBreakRange+rsDigits+rsDingbatRange+rsLowerRange+rsUpperRange+"]",rsFitz="\\ud83c[\\udffb-\\udfff]",rsModifier="(?:"+rsCombo+"|"+rsFitz+")",rsNonAstral=
"[^"+rsAstralRange+"]",rsRegional="(?:\\ud83c[\\udde6-\\uddff]){2}",rsSurrPair="[\\ud800-\\udbff][\\udc00-\\udfff]",rsUpper="["+rsUpperRange+"]",rsZWJ="\\u200d";var rsMiscLower="(?:"+rsLower+"|"+rsMisc+")",rsMiscUpper="(?:"+rsUpper+"|"+rsMisc+")",rsOptContrLower="(?:"+rsApos+"(?:d|ll|m|re|s|t|ve))?",rsOptContrUpper="(?:"+rsApos+"(?:D|LL|M|RE|S|T|VE))?",reOptMod=rsModifier+"?",rsOptVar="["+rsVarRange+"]?",rsOptJoin="(?:"+rsZWJ+"(?:"+[rsNonAstral,rsRegional,rsSurrPair].join("|")+")"+rsOptVar+reOptMod+
")*",rsOrdLower="\\d*(?:1st|2nd|3rd|(?![123])\\dth)(?\x3d\\b|[A-Z_])",rsOrdUpper="\\d*(?:1ST|2ND|3RD|(?![123])\\dTH)(?\x3d\\b|[a-z_])",rsSeq=rsOptVar+reOptMod+rsOptJoin,rsEmoji="(?:"+[rsDingbat,rsRegional,rsSurrPair].join("|")+")"+rsSeq,rsSymbol="(?:"+[rsNonAstral+rsCombo+"?",rsCombo,rsRegional,rsSurrPair,rsAstral].join("|")+")";var reApos=RegExp(rsApos,"g");var reComboMark=RegExp(rsCombo,"g");var reUnicode=RegExp(rsFitz+"(?\x3d"+rsFitz+")|"+rsSymbol+rsSeq,"g");var reUnicodeWord=RegExp([rsUpper+"?"+
rsLower+"+"+rsOptContrLower+"(?\x3d"+[rsBreak,rsUpper,"$"].join("|")+")",rsMiscUpper+"+"+rsOptContrUpper+"(?\x3d"+[rsBreak,rsUpper+rsMiscLower,"$"].join("|")+")",rsUpper+"?"+rsMiscLower+"+"+rsOptContrLower,rsUpper+"+"+rsOptContrUpper,rsOrdUpper,rsOrdLower,rsDigits,rsEmoji].join("|"),"g");var reHasUnicode=RegExp("["+rsZWJ+rsAstralRange+rsComboRange+rsVarRange+"]");var reHasUnicodeWord=/[a-z][A-Z]|[A-Z]{2,}[a-z]|[0-9][a-zA-Z]|[a-zA-Z][0-9]|[^a-zA-Z0-9 ]/;var contextProps=["Array","Buffer","DataView",
"Date","Error","Float32Array","Float64Array","Function","Int8Array","Int16Array","Int32Array","Map","Math","Object","Promise","RegExp","Set","String","Symbol","TypeError","Uint8Array","Uint8ClampedArray","Uint16Array","Uint32Array","WeakMap","_","clearTimeout","isFinite","parseInt","setTimeout"];var templateCounter=-1;var typedArrayTags={};typedArrayTags[float32Tag]=typedArrayTags[float64Tag]=typedArrayTags[int8Tag]=typedArrayTags[int16Tag]=typedArrayTags[int32Tag]=typedArrayTags[uint8Tag]=typedArrayTags[uint8ClampedTag]=
typedArrayTags[uint16Tag]=typedArrayTags[uint32Tag]=true;typedArrayTags[argsTag]=typedArrayTags[arrayTag]=typedArrayTags[arrayBufferTag]=typedArrayTags[boolTag]=typedArrayTags[dataViewTag]=typedArrayTags[dateTag]=typedArrayTags[errorTag]=typedArrayTags[funcTag]=typedArrayTags[mapTag]=typedArrayTags[numberTag]=typedArrayTags[objectTag]=typedArrayTags[regexpTag]=typedArrayTags[setTag]=typedArrayTags[stringTag]=typedArrayTags[weakMapTag]=false;var cloneableTags={};cloneableTags[argsTag]=cloneableTags[arrayTag]=
cloneableTags[arrayBufferTag]=cloneableTags[dataViewTag]=cloneableTags[boolTag]=cloneableTags[dateTag]=cloneableTags[float32Tag]=cloneableTags[float64Tag]=cloneableTags[int8Tag]=cloneableTags[int16Tag]=cloneableTags[int32Tag]=cloneableTags[mapTag]=cloneableTags[numberTag]=cloneableTags[objectTag]=cloneableTags[regexpTag]=cloneableTags[setTag]=cloneableTags[stringTag]=cloneableTags[symbolTag]=cloneableTags[uint8Tag]=cloneableTags[uint8ClampedTag]=cloneableTags[uint16Tag]=cloneableTags[uint32Tag]=true;
cloneableTags[errorTag]=cloneableTags[funcTag]=cloneableTags[weakMapTag]=false;var deburredLetters={"\u00c0":"A","\u00c1":"A","\u00c2":"A","\u00c3":"A","\u00c4":"A","\u00c5":"A","\u00e0":"a","\u00e1":"a","\u00e2":"a","\u00e3":"a","\u00e4":"a","\u00e5":"a","\u00c7":"C","\u00e7":"c","\u00d0":"D","\u00f0":"d","\u00c8":"E","\u00c9":"E","\u00ca":"E","\u00cb":"E","\u00e8":"e","\u00e9":"e","\u00ea":"e","\u00eb":"e","\u00cc":"I","\u00cd":"I","\u00ce":"I","\u00cf":"I","\u00ec":"i","\u00ed":"i","\u00ee":"i",
"\u00ef":"i","\u00d1":"N","\u00f1":"n","\u00d2":"O","\u00d3":"O","\u00d4":"O","\u00d5":"O","\u00d6":"O","\u00d8":"O","\u00f2":"o","\u00f3":"o","\u00f4":"o","\u00f5":"o","\u00f6":"o","\u00f8":"o","\u00d9":"U","\u00da":"U","\u00db":"U","\u00dc":"U","\u00f9":"u","\u00fa":"u","\u00fb":"u","\u00fc":"u","\u00dd":"Y","\u00fd":"y","\u00ff":"y","\u00c6":"Ae","\u00e6":"ae","\u00de":"Th","\u00fe":"th","\u00df":"ss","\u0100":"A","\u0102":"A","\u0104":"A","\u0101":"a","\u0103":"a","\u0105":"a","\u0106":"C","\u0108":"C",
"\u010a":"C","\u010c":"C","\u0107":"c","\u0109":"c","\u010b":"c","\u010d":"c","\u010e":"D","\u0110":"D","\u010f":"d","\u0111":"d","\u0112":"E","\u0114":"E","\u0116":"E","\u0118":"E","\u011a":"E","\u0113":"e","\u0115":"e","\u0117":"e","\u0119":"e","\u011b":"e","\u011c":"G","\u011e":"G","\u0120":"G","\u0122":"G","\u011d":"g","\u011f":"g","\u0121":"g","\u0123":"g","\u0124":"H","\u0126":"H","\u0125":"h","\u0127":"h","\u0128":"I","\u012a":"I","\u012c":"I","\u012e":"I","\u0130":"I","\u0129":"i","\u012b":"i",
"\u012d":"i","\u012f":"i","\u0131":"i","\u0134":"J","\u0135":"j","\u0136":"K","\u0137":"k","\u0138":"k","\u0139":"L","\u013b":"L","\u013d":"L","\u013f":"L","\u0141":"L","\u013a":"l","\u013c":"l","\u013e":"l","\u0140":"l","\u0142":"l","\u0143":"N","\u0145":"N","\u0147":"N","\u014a":"N","\u0144":"n","\u0146":"n","\u0148":"n","\u014b":"n","\u014c":"O","\u014e":"O","\u0150":"O","\u014d":"o","\u014f":"o","\u0151":"o","\u0154":"R","\u0156":"R","\u0158":"R","\u0155":"r","\u0157":"r","\u0159":"r","\u015a":"S",
"\u015c":"S","\u015e":"S","\u0160":"S","\u015b":"s","\u015d":"s","\u015f":"s","\u0161":"s","\u0162":"T","\u0164":"T","\u0166":"T","\u0163":"t","\u0165":"t","\u0167":"t","\u0168":"U","\u016a":"U","\u016c":"U","\u016e":"U","\u0170":"U","\u0172":"U","\u0169":"u","\u016b":"u","\u016d":"u","\u016f":"u","\u0171":"u","\u0173":"u","\u0174":"W","\u0175":"w","\u0176":"Y","\u0177":"y","\u0178":"Y","\u0179":"Z","\u017b":"Z","\u017d":"Z","\u017a":"z","\u017c":"z","\u017e":"z","\u0132":"IJ","\u0133":"ij","\u0152":"Oe",
"\u0153":"oe","\u0149":"'n","\u017f":"s"};var htmlEscapes={"\x26":"\x26amp;","\x3c":"\x26lt;","\x3e":"\x26gt;",'"':"\x26quot;","'":"\x26#39;"};var htmlUnescapes={"\x26amp;":"\x26","\x26lt;":"\x3c","\x26gt;":"\x3e","\x26quot;":'"',"\x26#39;":"'"};var stringEscapes={"\\":"\\","'":"'","\n":"n","\r":"r","\u2028":"u2028","\u2029":"u2029"};var freeParseFloat=parseFloat,freeParseInt=parseInt;var freeGlobal=typeof global=="object"&&global&&global.Object===Object&&global;var freeSelf=typeof self=="object"&&
self&&self.Object===Object&&self;var root=freeGlobal||freeSelf||Function("return this")();var freeExports=typeof exports=="object"&&exports&&!exports.nodeType&&exports;var freeModule=freeExports&&typeof module=="object"&&module&&!module.nodeType&&module;var moduleExports=freeModule&&freeModule.exports===freeExports;var freeProcess=moduleExports&&freeGlobal.process;var nodeUtil=function(){try{return freeProcess&&freeProcess.binding&&freeProcess.binding("util")}catch(e){}}();var nodeIsArrayBuffer=nodeUtil&&
nodeUtil.isArrayBuffer,nodeIsDate=nodeUtil&&nodeUtil.isDate,nodeIsMap=nodeUtil&&nodeUtil.isMap,nodeIsRegExp=nodeUtil&&nodeUtil.isRegExp,nodeIsSet=nodeUtil&&nodeUtil.isSet,nodeIsTypedArray=nodeUtil&&nodeUtil.isTypedArray;function apply(func,thisArg,args){switch(args.length){case 0:return func.call(thisArg);case 1:return func.call(thisArg,args[0]);case 2:return func.call(thisArg,args[0],args[1]);case 3:return func.call(thisArg,args[0],args[1],args[2])}return func.apply(thisArg,args)}function arrayAggregator(array,
setter,iteratee,accumulator){var index=-1,length=array==null?0:array.length;while(++index<length){var value=array[index];setter(accumulator,value,iteratee(value),array)}return accumulator}function arrayEach(array,iteratee){var index=-1,length=array==null?0:array.length;while(++index<length)if(iteratee(array[index],index,array)===false)break;return array}function arrayEachRight(array,iteratee){var length=array==null?0:array.length;while(length--)if(iteratee(array[length],length,array)===false)break;
return array}function arrayEvery(array,predicate){var index=-1,length=array==null?0:array.length;while(++index<length)if(!predicate(array[index],index,array))return false;return true}function arrayFilter(array,predicate){var index=-1,length=array==null?0:array.length,resIndex=0,result=[];while(++index<length){var value=array[index];if(predicate(value,index,array))result[resIndex++]=value}return result}function arrayIncludes(array,value){var length=array==null?0:array.length;return!!length&&baseIndexOf(array,
value,0)>-1}function arrayIncludesWith(array,value,comparator){var index=-1,length=array==null?0:array.length;while(++index<length)if(comparator(value,array[index]))return true;return false}function arrayMap(array,iteratee){var index=-1,length=array==null?0:array.length,result=Array(length);while(++index<length)result[index]=iteratee(array[index],index,array);return result}function arrayPush(array,values){var index=-1,length=values.length,offset=array.length;while(++index<length)array[offset+index]=
values[index];return array}function arrayReduce(array,iteratee,accumulator,initAccum){var index=-1,length=array==null?0:array.length;if(initAccum&&length)accumulator=array[++index];while(++index<length)accumulator=iteratee(accumulator,array[index],index,array);return accumulator}function arrayReduceRight(array,iteratee,accumulator,initAccum){var length=array==null?0:array.length;if(initAccum&&length)accumulator=array[--length];while(length--)accumulator=iteratee(accumulator,array[length],length,array);
return accumulator}function arraySome(array,predicate){var index=-1,length=array==null?0:array.length;while(++index<length)if(predicate(array[index],index,array))return true;return false}var asciiSize=baseProperty("length");function asciiToArray(string){return string.split("")}function asciiWords(string){return string.match(reAsciiWord)||[]}function baseFindKey(collection,predicate,eachFunc){var result;eachFunc(collection,function(value,key,collection){if(predicate(value,key,collection)){result=key;
return false}});return result}function baseFindIndex(array,predicate,fromIndex,fromRight){var length=array.length,index=fromIndex+(fromRight?1:-1);while(fromRight?index--:++index<length)if(predicate(array[index],index,array))return index;return-1}function baseIndexOf(array,value,fromIndex){return value===value?strictIndexOf(array,value,fromIndex):baseFindIndex(array,baseIsNaN,fromIndex)}function baseIndexOfWith(array,value,fromIndex,comparator){var index=fromIndex-1,length=array.length;while(++index<
length)if(comparator(array[index],value))return index;return-1}function baseIsNaN(value){return value!==value}function baseMean(array,iteratee){var length=array==null?0:array.length;return length?baseSum(array,iteratee)/length:NAN}function baseProperty(key){return function(object){return object==null?undefined:object[key]}}function basePropertyOf(object){return function(key){return object==null?undefined:object[key]}}function baseReduce(collection,iteratee,accumulator,initAccum,eachFunc){eachFunc(collection,
function(value,index,collection){accumulator=initAccum?(initAccum=false,value):iteratee(accumulator,value,index,collection)});return accumulator}function baseSortBy(array,comparer){var length=array.length;array.sort(comparer);while(length--)array[length]=array[length].value;return array}function baseSum(array,iteratee){var result,index=-1,length=array.length;while(++index<length){var current=iteratee(array[index]);if(current!==undefined)result=result===undefined?current:result+current}return result}
function baseTimes(n,iteratee){var index=-1,result=Array(n);while(++index<n)result[index]=iteratee(index);return result}function baseToPairs(object,props){return arrayMap(props,function(key){return[key,object[key]]})}function baseUnary(func){return function(value){return func(value)}}function baseValues(object,props){return arrayMap(props,function(key){return object[key]})}function cacheHas(cache,key){return cache.has(key)}function charsStartIndex(strSymbols,chrSymbols){var index=-1,length=strSymbols.length;
while(++index<length&&baseIndexOf(chrSymbols,strSymbols[index],0)>-1);return index}function charsEndIndex(strSymbols,chrSymbols){var index=strSymbols.length;while(index--&&baseIndexOf(chrSymbols,strSymbols[index],0)>-1);return index}function countHolders(array,placeholder){var length=array.length,result=0;while(length--)if(array[length]===placeholder)++result;return result}var deburrLetter=basePropertyOf(deburredLetters);var escapeHtmlChar=basePropertyOf(htmlEscapes);function escapeStringChar(chr){return"\\"+
stringEscapes[chr]}function getValue(object,key){return object==null?undefined:object[key]}function hasUnicode(string){return reHasUnicode.test(string)}function hasUnicodeWord(string){return reHasUnicodeWord.test(string)}function iteratorToArray(iterator){var data,result=[];while(!(data=iterator.next()).done)result.push(data.value);return result}function mapToArray(map){var index=-1,result=Array(map.size);map.forEach(function(value,key){result[++index]=[key,value]});return result}function overArg(func,
transform){return function(arg){return func(transform(arg))}}function replaceHolders(array,placeholder){var index=-1,length=array.length,resIndex=0,result=[];while(++index<length){var value=array[index];if(value===placeholder||value===PLACEHOLDER){array[index]=PLACEHOLDER;result[resIndex++]=index}}return result}function safeGet(object,key){return key=="__proto__"?undefined:object[key]}function setToArray(set){var index=-1,result=Array(set.size);set.forEach(function(value){result[++index]=value});
return result}function setToPairs(set){var index=-1,result=Array(set.size);set.forEach(function(value){result[++index]=[value,value]});return result}function strictIndexOf(array,value,fromIndex){var index=fromIndex-1,length=array.length;while(++index<length)if(array[index]===value)return index;return-1}function strictLastIndexOf(array,value,fromIndex){var index=fromIndex+1;while(index--)if(array[index]===value)return index;return index}function stringSize(string){return hasUnicode(string)?unicodeSize(string):
asciiSize(string)}function stringToArray(string){return hasUnicode(string)?unicodeToArray(string):asciiToArray(string)}var unescapeHtmlChar=basePropertyOf(htmlUnescapes);function unicodeSize(string){var result=reUnicode.lastIndex=0;while(reUnicode.test(string))++result;return result}function unicodeToArray(string){return string.match(reUnicode)||[]}function unicodeWords(string){return string.match(reUnicodeWord)||[]}var runInContext=function runInContext(context){context=context==null?root:_.defaults(root.Object(),
context,_.pick(root,contextProps));var Array=context.Array,Date=context.Date,Error=context.Error,Function=context.Function,Math=context.Math,Object=context.Object,RegExp=context.RegExp,String=context.String,TypeError=context.TypeError;var arrayProto=Array.prototype,funcProto=Function.prototype,objectProto=Object.prototype;var coreJsData=context["__core-js_shared__"];var funcToString=funcProto.toString;var hasOwnProperty=objectProto.hasOwnProperty;var idCounter=0;var maskSrcKey=function(){var uid=
/[^.]+$/.exec(coreJsData&&coreJsData.keys&&coreJsData.keys.IE_PROTO||"");return uid?"Symbol(src)_1."+uid:""}();var nativeObjectToString=objectProto.toString;var objectCtorString=funcToString.call(Object);var oldDash=root._;var reIsNative=RegExp("^"+funcToString.call(hasOwnProperty).replace(reRegExpChar,"\\$\x26").replace(/hasOwnProperty|(function).*?(?=\\\()| for .+?(?=\\\])/g,"$1.*?")+"$");var Buffer=moduleExports?context.Buffer:undefined,Symbol=context.Symbol,Uint8Array=context.Uint8Array,allocUnsafe=
Buffer?Buffer.allocUnsafe:undefined,getPrototype=overArg(Object.getPrototypeOf,Object),objectCreate=Object.create,propertyIsEnumerable=objectProto.propertyIsEnumerable,splice=arrayProto.splice,spreadableSymbol=Symbol?Symbol.isConcatSpreadable:undefined,symIterator=Symbol?Symbol.iterator:undefined,symToStringTag=Symbol?Symbol.toStringTag:undefined;var defineProperty=function(){try{var func=getNative(Object,"defineProperty");func({},"",{});return func}catch(e){}}();var ctxClearTimeout=context.clearTimeout!==
root.clearTimeout&&context.clearTimeout,ctxNow=Date&&Date.now!==root.Date.now&&Date.now,ctxSetTimeout=context.setTimeout!==root.setTimeout&&context.setTimeout;var nativeCeil=Math.ceil,nativeFloor=Math.floor,nativeGetSymbols=Object.getOwnPropertySymbols,nativeIsBuffer=Buffer?Buffer.isBuffer:undefined,nativeIsFinite=context.isFinite,nativeJoin=arrayProto.join,nativeKeys=overArg(Object.keys,Object),nativeMax=Math.max,nativeMin=Math.min,nativeNow=Date.now,nativeParseInt=context.parseInt,nativeRandom=
Math.random,nativeReverse=arrayProto.reverse;var DataView=getNative(context,"DataView"),Map=getNative(context,"Map"),Promise=getNative(context,"Promise"),Set=getNative(context,"Set"),WeakMap=getNative(context,"WeakMap"),nativeCreate=getNative(Object,"create");var metaMap=WeakMap&&new WeakMap;var realNames={};var dataViewCtorString=toSource(DataView),mapCtorString=toSource(Map),promiseCtorString=toSource(Promise),setCtorString=toSource(Set),weakMapCtorString=toSource(WeakMap);var symbolProto=Symbol?
Symbol.prototype:undefined,symbolValueOf=symbolProto?symbolProto.valueOf:undefined,symbolToString=symbolProto?symbolProto.toString:undefined;function lodash(value){if(isObjectLike(value)&&!isArray(value)&&!(value instanceof LazyWrapper)){if(value instanceof LodashWrapper)return value;if(hasOwnProperty.call(value,"__wrapped__"))return wrapperClone(value)}return new LodashWrapper(value)}var baseCreate=function(){function object(){}return function(proto){if(!isObject(proto))return{};if(objectCreate)return objectCreate(proto);
object.prototype=proto;var result=new object;object.prototype=undefined;return result}}();function baseLodash(){}function LodashWrapper(value,chainAll){this.__wrapped__=value;this.__actions__=[];this.__chain__=!!chainAll;this.__index__=0;this.__values__=undefined}lodash.templateSettings={"escape":reEscape,"evaluate":reEvaluate,"interpolate":reInterpolate,"variable":"","imports":{"_":lodash}};lodash.prototype=baseLodash.prototype;lodash.prototype.constructor=lodash;LodashWrapper.prototype=baseCreate(baseLodash.prototype);
LodashWrapper.prototype.constructor=LodashWrapper;function LazyWrapper(value){this.__wrapped__=value;this.__actions__=[];this.__dir__=1;this.__filtered__=false;this.__iteratees__=[];this.__takeCount__=MAX_ARRAY_LENGTH;this.__views__=[]}function lazyClone(){var result=new LazyWrapper(this.__wrapped__);result.__actions__=copyArray(this.__actions__);result.__dir__=this.__dir__;result.__filtered__=this.__filtered__;result.__iteratees__=copyArray(this.__iteratees__);result.__takeCount__=this.__takeCount__;
result.__views__=copyArray(this.__views__);return result}function lazyReverse(){if(this.__filtered__){var result=new LazyWrapper(this);result.__dir__=-1;result.__filtered__=true}else{result=this.clone();result.__dir__*=-1}return result}function lazyValue(){var array=this.__wrapped__.value(),dir=this.__dir__,isArr=isArray(array),isRight=dir<0,arrLength=isArr?array.length:0,view=getView(0,arrLength,this.__views__),start=view.start,end=view.end,length=end-start,index=isRight?end:start-1,iteratees=this.__iteratees__,
iterLength=iteratees.length,resIndex=0,takeCount=nativeMin(length,this.__takeCount__);if(!isArr||!isRight&&arrLength==length&&takeCount==length)return baseWrapperValue(array,this.__actions__);var result=[];outer:while(length--&&resIndex<takeCount){index+=dir;var iterIndex=-1,value=array[index];while(++iterIndex<iterLength){var data=iteratees[iterIndex],iteratee=data.iteratee,type=data.type,computed=iteratee(value);if(type==LAZY_MAP_FLAG)value=computed;else if(!computed)if(type==LAZY_FILTER_FLAG)continue outer;
else break outer}result[resIndex++]=value}return result}LazyWrapper.prototype=baseCreate(baseLodash.prototype);LazyWrapper.prototype.constructor=LazyWrapper;function Hash(entries){var index=-1,length=entries==null?0:entries.length;this.clear();while(++index<length){var entry=entries[index];this.set(entry[0],entry[1])}}function hashClear(){this.__data__=nativeCreate?nativeCreate(null):{};this.size=0}function hashDelete(key){var result=this.has(key)&&delete this.__data__[key];this.size-=result?1:0;
return result}function hashGet(key){var data=this.__data__;if(nativeCreate){var result=data[key];return result===HASH_UNDEFINED?undefined:result}return hasOwnProperty.call(data,key)?data[key]:undefined}function hashHas(key){var data=this.__data__;return nativeCreate?data[key]!==undefined:hasOwnProperty.call(data,key)}function hashSet(key,value){var data=this.__data__;this.size+=this.has(key)?0:1;data[key]=nativeCreate&&value===undefined?HASH_UNDEFINED:value;return this}Hash.prototype.clear=hashClear;
Hash.prototype["delete"]=hashDelete;Hash.prototype.get=hashGet;Hash.prototype.has=hashHas;Hash.prototype.set=hashSet;function ListCache(entries){var index=-1,length=entries==null?0:entries.length;this.clear();while(++index<length){var entry=entries[index];this.set(entry[0],entry[1])}}function listCacheClear(){this.__data__=[];this.size=0}function listCacheDelete(key){var data=this.__data__,index=assocIndexOf(data,key);if(index<0)return false;var lastIndex=data.length-1;if(index==lastIndex)data.pop();
else splice.call(data,index,1);--this.size;return true}function listCacheGet(key){var data=this.__data__,index=assocIndexOf(data,key);return index<0?undefined:data[index][1]}function listCacheHas(key){return assocIndexOf(this.__data__,key)>-1}function listCacheSet(key,value){var data=this.__data__,index=assocIndexOf(data,key);if(index<0){++this.size;data.push([key,value])}else data[index][1]=value;return this}ListCache.prototype.clear=listCacheClear;ListCache.prototype["delete"]=listCacheDelete;ListCache.prototype.get=
listCacheGet;ListCache.prototype.has=listCacheHas;ListCache.prototype.set=listCacheSet;function MapCache(entries){var index=-1,length=entries==null?0:entries.length;this.clear();while(++index<length){var entry=entries[index];this.set(entry[0],entry[1])}}function mapCacheClear(){this.size=0;this.__data__={"hash":new Hash,"map":new (Map||ListCache),"string":new Hash}}function mapCacheDelete(key){var result=getMapData(this,key)["delete"](key);this.size-=result?1:0;return result}function mapCacheGet(key){return getMapData(this,
key).get(key)}function mapCacheHas(key){return getMapData(this,key).has(key)}function mapCacheSet(key,value){var data=getMapData(this,key),size=data.size;data.set(key,value);this.size+=data.size==size?0:1;return this}MapCache.prototype.clear=mapCacheClear;MapCache.prototype["delete"]=mapCacheDelete;MapCache.prototype.get=mapCacheGet;MapCache.prototype.has=mapCacheHas;MapCache.prototype.set=mapCacheSet;function SetCache(values){var index=-1,length=values==null?0:values.length;this.__data__=new MapCache;
while(++index<length)this.add(values[index])}function setCacheAdd(value){this.__data__.set(value,HASH_UNDEFINED);return this}function setCacheHas(value){return this.__data__.has(value)}SetCache.prototype.add=SetCache.prototype.push=setCacheAdd;SetCache.prototype.has=setCacheHas;function Stack(entries){var data=this.__data__=new ListCache(entries);this.size=data.size}function stackClear(){this.__data__=new ListCache;this.size=0}function stackDelete(key){var data=this.__data__,result=data["delete"](key);
this.size=data.size;return result}function stackGet(key){return this.__data__.get(key)}function stackHas(key){return this.__data__.has(key)}function stackSet(key,value){var data=this.__data__;if(data instanceof ListCache){var pairs=data.__data__;if(!Map||pairs.length<LARGE_ARRAY_SIZE-1){pairs.push([key,value]);this.size=++data.size;return this}data=this.__data__=new MapCache(pairs)}data.set(key,value);this.size=data.size;return this}Stack.prototype.clear=stackClear;Stack.prototype["delete"]=stackDelete;
Stack.prototype.get=stackGet;Stack.prototype.has=stackHas;Stack.prototype.set=stackSet;function arrayLikeKeys(value,inherited){var isArr=isArray(value),isArg=!isArr&&isArguments(value),isBuff=!isArr&&!isArg&&isBuffer(value),isType=!isArr&&!isArg&&!isBuff&&isTypedArray(value),skipIndexes=isArr||isArg||isBuff||isType,result=skipIndexes?baseTimes(value.length,String):[],length=result.length;for(var key in value)if((inherited||hasOwnProperty.call(value,key))&&!(skipIndexes&&(key=="length"||isBuff&&(key==
"offset"||key=="parent")||isType&&(key=="buffer"||key=="byteLength"||key=="byteOffset")||isIndex(key,length))))result.push(key);return result}function arraySample(array){var length=array.length;return length?array[baseRandom(0,length-1)]:undefined}function arraySampleSize(array,n){return shuffleSelf(copyArray(array),baseClamp(n,0,array.length))}function arrayShuffle(array){return shuffleSelf(copyArray(array))}function assignMergeValue(object,key,value){if(value!==undefined&&!eq(object[key],value)||
value===undefined&&!(key in object))baseAssignValue(object,key,value)}function assignValue(object,key,value){var objValue=object[key];if(!(hasOwnProperty.call(object,key)&&eq(objValue,value))||value===undefined&&!(key in object))baseAssignValue(object,key,value)}function assocIndexOf(array,key){var length=array.length;while(length--)if(eq(array[length][0],key))return length;return-1}function baseAggregator(collection,setter,iteratee,accumulator){baseEach(collection,function(value,key,collection){setter(accumulator,
value,iteratee(value),collection)});return accumulator}function baseAssign(object,source){return object&&copyObject(source,keys(source),object)}function baseAssignIn(object,source){return object&&copyObject(source,keysIn(source),object)}function baseAssignValue(object,key,value){if(key=="__proto__"&&defineProperty)defineProperty(object,key,{"configurable":true,"enumerable":true,"value":value,"writable":true});else object[key]=value}function baseAt(object,paths){var index=-1,length=paths.length,result=
Array(length),skip=object==null;while(++index<length)result[index]=skip?undefined:get(object,paths[index]);return result}function baseClamp(number,lower,upper){if(number===number){if(upper!==undefined)number=number<=upper?number:upper;if(lower!==undefined)number=number>=lower?number:lower}return number}function baseClone(value,bitmask,customizer,key,object,stack){var result,isDeep=bitmask&CLONE_DEEP_FLAG,isFlat=bitmask&CLONE_FLAT_FLAG,isFull=bitmask&CLONE_SYMBOLS_FLAG;if(customizer)result=object?
customizer(value,key,object,stack):customizer(value);if(result!==undefined)return result;if(!isObject(value))return value;var isArr=isArray(value);if(isArr){result=initCloneArray(value);if(!isDeep)return copyArray(value,result)}else{var tag=getTag(value),isFunc=tag==funcTag||tag==genTag;if(isBuffer(value))return cloneBuffer(value,isDeep);if(tag==objectTag||tag==argsTag||isFunc&&!object){result=isFlat||isFunc?{}:initCloneObject(value);if(!isDeep)return isFlat?copySymbolsIn(value,baseAssignIn(result,
value)):copySymbols(value,baseAssign(result,value))}else{if(!cloneableTags[tag])return object?value:{};result=initCloneByTag(value,tag,isDeep)}}stack||(stack=new Stack);var stacked=stack.get(value);if(stacked)return stacked;stack.set(value,result);if(isSet(value)){value.forEach(function(subValue){result.add(baseClone(subValue,bitmask,customizer,subValue,value,stack))});return result}if(isMap(value)){value.forEach(function(subValue,key){result.set(key,baseClone(subValue,bitmask,customizer,key,value,
stack))});return result}var keysFunc=isFull?isFlat?getAllKeysIn:getAllKeys:isFlat?keysIn:keys;var props=isArr?undefined:keysFunc(value);arrayEach(props||value,function(subValue,key){if(props){key=subValue;subValue=value[key]}assignValue(result,key,baseClone(subValue,bitmask,customizer,key,value,stack))});return result}function baseConforms(source){var props=keys(source);return function(object){return baseConformsTo(object,source,props)}}function baseConformsTo(object,source,props){var length=props.length;
if(object==null)return!length;object=Object(object);while(length--){var key=props[length],predicate=source[key],value=object[key];if(value===undefined&&!(key in object)||!predicate(value))return false}return true}function baseDelay(func,wait,args){if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);return setTimeout(function(){func.apply(undefined,args)},wait)}function baseDifference(array,values,iteratee,comparator){var index=-1,includes=arrayIncludes,isCommon=true,length=array.length,
result=[],valuesLength=values.length;if(!length)return result;if(iteratee)values=arrayMap(values,baseUnary(iteratee));if(comparator){includes=arrayIncludesWith;isCommon=false}else if(values.length>=LARGE_ARRAY_SIZE){includes=cacheHas;isCommon=false;values=new SetCache(values)}outer:while(++index<length){var value=array[index],computed=iteratee==null?value:iteratee(value);value=comparator||value!==0?value:0;if(isCommon&&computed===computed){var valuesIndex=valuesLength;while(valuesIndex--)if(values[valuesIndex]===
computed)continue outer;result.push(value)}else if(!includes(values,computed,comparator))result.push(value)}return result}var baseEach=createBaseEach(baseForOwn);var baseEachRight=createBaseEach(baseForOwnRight,true);function baseEvery(collection,predicate){var result=true;baseEach(collection,function(value,index,collection){result=!!predicate(value,index,collection);return result});return result}function baseExtremum(array,iteratee,comparator){var index=-1,length=array.length;while(++index<length){var value=
array[index],current=iteratee(value);if(current!=null&&(computed===undefined?current===current&&!isSymbol(current):comparator(current,computed)))var computed=current,result=value}return result}function baseFill(array,value,start,end){var length=array.length;start=toInteger(start);if(start<0)start=-start>length?0:length+start;end=end===undefined||end>length?length:toInteger(end);if(end<0)end+=length;end=start>end?0:toLength(end);while(start<end)array[start++]=value;return array}function baseFilter(collection,
predicate){var result=[];baseEach(collection,function(value,index,collection){if(predicate(value,index,collection))result.push(value)});return result}function baseFlatten(array,depth,predicate,isStrict,result){var index=-1,length=array.length;predicate||(predicate=isFlattenable);result||(result=[]);while(++index<length){var value=array[index];if(depth>0&&predicate(value))if(depth>1)baseFlatten(value,depth-1,predicate,isStrict,result);else arrayPush(result,value);else if(!isStrict)result[result.length]=
value}return result}var baseFor=createBaseFor();var baseForRight=createBaseFor(true);function baseForOwn(object,iteratee){return object&&baseFor(object,iteratee,keys)}function baseForOwnRight(object,iteratee){return object&&baseForRight(object,iteratee,keys)}function baseFunctions(object,props){return arrayFilter(props,function(key){return isFunction(object[key])})}function baseGet(object,path){path=castPath(path,object);var index=0,length=path.length;while(object!=null&&index<length)object=object[toKey(path[index++])];
return index&&index==length?object:undefined}function baseGetAllKeys(object,keysFunc,symbolsFunc){var result=keysFunc(object);return isArray(object)?result:arrayPush(result,symbolsFunc(object))}function baseGetTag(value){if(value==null)return value===undefined?undefinedTag:nullTag;return symToStringTag&&symToStringTag in Object(value)?getRawTag(value):objectToString(value)}function baseGt(value,other){return value>other}function baseHas(object,key){return object!=null&&hasOwnProperty.call(object,
key)}function baseHasIn(object,key){return object!=null&&key in Object(object)}function baseInRange(number,start,end){return number>=nativeMin(start,end)&&number<nativeMax(start,end)}function baseIntersection(arrays,iteratee,comparator){var includes=comparator?arrayIncludesWith:arrayIncludes,length=arrays[0].length,othLength=arrays.length,othIndex=othLength,caches=Array(othLength),maxLength=Infinity,result=[];while(othIndex--){var array=arrays[othIndex];if(othIndex&&iteratee)array=arrayMap(array,
baseUnary(iteratee));maxLength=nativeMin(array.length,maxLength);caches[othIndex]=!comparator&&(iteratee||length>=120&&array.length>=120)?new SetCache(othIndex&&array):undefined}array=arrays[0];var index=-1,seen=caches[0];outer:while(++index<length&&result.length<maxLength){var value=array[index],computed=iteratee?iteratee(value):value;value=comparator||value!==0?value:0;if(!(seen?cacheHas(seen,computed):includes(result,computed,comparator))){othIndex=othLength;while(--othIndex){var cache=caches[othIndex];
if(!(cache?cacheHas(cache,computed):includes(arrays[othIndex],computed,comparator)))continue outer}if(seen)seen.push(computed);result.push(value)}}return result}function baseInverter(object,setter,iteratee,accumulator){baseForOwn(object,function(value,key,object){setter(accumulator,iteratee(value),key,object)});return accumulator}function baseInvoke(object,path,args){path=castPath(path,object);object=parent(object,path);var func=object==null?object:object[toKey(last(path))];return func==null?undefined:
apply(func,object,args)}function baseIsArguments(value){return isObjectLike(value)&&baseGetTag(value)==argsTag}function baseIsArrayBuffer(value){return isObjectLike(value)&&baseGetTag(value)==arrayBufferTag}function baseIsDate(value){return isObjectLike(value)&&baseGetTag(value)==dateTag}function baseIsEqual(value,other,bitmask,customizer,stack){if(value===other)return true;if(value==null||other==null||!isObjectLike(value)&&!isObjectLike(other))return value!==value&&other!==other;return baseIsEqualDeep(value,
other,bitmask,customizer,baseIsEqual,stack)}function baseIsEqualDeep(object,other,bitmask,customizer,equalFunc,stack){var objIsArr=isArray(object),othIsArr=isArray(other),objTag=objIsArr?arrayTag:getTag(object),othTag=othIsArr?arrayTag:getTag(other);objTag=objTag==argsTag?objectTag:objTag;othTag=othTag==argsTag?objectTag:othTag;var objIsObj=objTag==objectTag,othIsObj=othTag==objectTag,isSameTag=objTag==othTag;if(isSameTag&&isBuffer(object)){if(!isBuffer(other))return false;objIsArr=true;objIsObj=
false}if(isSameTag&&!objIsObj){stack||(stack=new Stack);return objIsArr||isTypedArray(object)?equalArrays(object,other,bitmask,customizer,equalFunc,stack):equalByTag(object,other,objTag,bitmask,customizer,equalFunc,stack)}if(!(bitmask&COMPARE_PARTIAL_FLAG)){var objIsWrapped=objIsObj&&hasOwnProperty.call(object,"__wrapped__"),othIsWrapped=othIsObj&&hasOwnProperty.call(other,"__wrapped__");if(objIsWrapped||othIsWrapped){var objUnwrapped=objIsWrapped?object.value():object,othUnwrapped=othIsWrapped?other.value():
other;stack||(stack=new Stack);return equalFunc(objUnwrapped,othUnwrapped,bitmask,customizer,stack)}}if(!isSameTag)return false;stack||(stack=new Stack);return equalObjects(object,other,bitmask,customizer,equalFunc,stack)}function baseIsMap(value){return isObjectLike(value)&&getTag(value)==mapTag}function baseIsMatch(object,source,matchData,customizer){var index=matchData.length,length=index,noCustomizer=!customizer;if(object==null)return!length;object=Object(object);while(index--){var data=matchData[index];
if(noCustomizer&&data[2]?data[1]!==object[data[0]]:!(data[0]in object))return false}while(++index<length){data=matchData[index];var key=data[0],objValue=object[key],srcValue=data[1];if(noCustomizer&&data[2]){if(objValue===undefined&&!(key in object))return false}else{var stack=new Stack;if(customizer)var result=customizer(objValue,srcValue,key,object,source,stack);if(!(result===undefined?baseIsEqual(srcValue,objValue,COMPARE_PARTIAL_FLAG|COMPARE_UNORDERED_FLAG,customizer,stack):result))return false}}return true}
function baseIsNative(value){if(!isObject(value)||isMasked(value))return false;var pattern=isFunction(value)?reIsNative:reIsHostCtor;return pattern.test(toSource(value))}function baseIsRegExp(value){return isObjectLike(value)&&baseGetTag(value)==regexpTag}function baseIsSet(value){return isObjectLike(value)&&getTag(value)==setTag}function baseIsTypedArray(value){return isObjectLike(value)&&isLength(value.length)&&!!typedArrayTags[baseGetTag(value)]}function baseIteratee(value){if(typeof value=="function")return value;
if(value==null)return identity;if(typeof value=="object")return isArray(value)?baseMatchesProperty(value[0],value[1]):baseMatches(value);return property(value)}function baseKeys(object){if(!isPrototype(object))return nativeKeys(object);var result=[];for(var key in Object(object))if(hasOwnProperty.call(object,key)&&key!="constructor")result.push(key);return result}function baseKeysIn(object){if(!isObject(object))return nativeKeysIn(object);var isProto=isPrototype(object),result=[];for(var key in object)if(!(key==
"constructor"&&(isProto||!hasOwnProperty.call(object,key))))result.push(key);return result}function baseLt(value,other){return value<other}function baseMap(collection,iteratee){var index=-1,result=isArrayLike(collection)?Array(collection.length):[];baseEach(collection,function(value,key,collection){result[++index]=iteratee(value,key,collection)});return result}function baseMatches(source){var matchData=getMatchData(source);if(matchData.length==1&&matchData[0][2])return matchesStrictComparable(matchData[0][0],
matchData[0][1]);return function(object){return object===source||baseIsMatch(object,source,matchData)}}function baseMatchesProperty(path,srcValue){if(isKey(path)&&isStrictComparable(srcValue))return matchesStrictComparable(toKey(path),srcValue);return function(object){var objValue=get(object,path);return objValue===undefined&&objValue===srcValue?hasIn(object,path):baseIsEqual(srcValue,objValue,COMPARE_PARTIAL_FLAG|COMPARE_UNORDERED_FLAG)}}function baseMerge(object,source,srcIndex,customizer,stack){if(object===
source)return;baseFor(source,function(srcValue,key){if(isObject(srcValue)){stack||(stack=new Stack);baseMergeDeep(object,source,key,srcIndex,baseMerge,customizer,stack)}else{var newValue=customizer?customizer(safeGet(object,key),srcValue,key+"",object,source,stack):undefined;if(newValue===undefined)newValue=srcValue;assignMergeValue(object,key,newValue)}},keysIn)}function baseMergeDeep(object,source,key,srcIndex,mergeFunc,customizer,stack){var objValue=safeGet(object,key),srcValue=safeGet(source,
key),stacked=stack.get(srcValue);if(stacked){assignMergeValue(object,key,stacked);return}var newValue=customizer?customizer(objValue,srcValue,key+"",object,source,stack):undefined;var isCommon=newValue===undefined;if(isCommon){var isArr=isArray(srcValue),isBuff=!isArr&&isBuffer(srcValue),isTyped=!isArr&&!isBuff&&isTypedArray(srcValue);newValue=srcValue;if(isArr||isBuff||isTyped)if(isArray(objValue))newValue=objValue;else if(isArrayLikeObject(objValue))newValue=copyArray(objValue);else if(isBuff){isCommon=
false;newValue=cloneBuffer(srcValue,true)}else if(isTyped){isCommon=false;newValue=cloneTypedArray(srcValue,true)}else newValue=[];else if(isPlainObject(srcValue)||isArguments(srcValue)){newValue=objValue;if(isArguments(objValue))newValue=toPlainObject(objValue);else if(!isObject(objValue)||srcIndex&&isFunction(objValue))newValue=initCloneObject(srcValue)}else isCommon=false}if(isCommon){stack.set(srcValue,newValue);mergeFunc(newValue,srcValue,srcIndex,customizer,stack);stack["delete"](srcValue)}assignMergeValue(object,
key,newValue)}function baseNth(array,n){var length=array.length;if(!length)return;n+=n<0?length:0;return isIndex(n,length)?array[n]:undefined}function baseOrderBy(collection,iteratees,orders){var index=-1;iteratees=arrayMap(iteratees.length?iteratees:[identity],baseUnary(getIteratee()));var result=baseMap(collection,function(value,key,collection){var criteria=arrayMap(iteratees,function(iteratee){return iteratee(value)});return{"criteria":criteria,"index":++index,"value":value}});return baseSortBy(result,
function(object,other){return compareMultiple(object,other,orders)})}function basePick(object,paths){return basePickBy(object,paths,function(value,path){return hasIn(object,path)})}function basePickBy(object,paths,predicate){var index=-1,length=paths.length,result={};while(++index<length){var path=paths[index],value=baseGet(object,path);if(predicate(value,path))baseSet(result,castPath(path,object),value)}return result}function basePropertyDeep(path){return function(object){return baseGet(object,path)}}
function basePullAll(array,values,iteratee,comparator){var indexOf=comparator?baseIndexOfWith:baseIndexOf,index=-1,length=values.length,seen=array;if(array===values)values=copyArray(values);if(iteratee)seen=arrayMap(array,baseUnary(iteratee));while(++index<length){var fromIndex=0,value=values[index],computed=iteratee?iteratee(value):value;while((fromIndex=indexOf(seen,computed,fromIndex,comparator))>-1){if(seen!==array)splice.call(seen,fromIndex,1);splice.call(array,fromIndex,1)}}return array}function basePullAt(array,
indexes){var length=array?indexes.length:0,lastIndex=length-1;while(length--){var index=indexes[length];if(length==lastIndex||index!==previous){var previous=index;if(isIndex(index))splice.call(array,index,1);else baseUnset(array,index)}}return array}function baseRandom(lower,upper){return lower+nativeFloor(nativeRandom()*(upper-lower+1))}function baseRange(start,end,step,fromRight){var index=-1,length=nativeMax(nativeCeil((end-start)/(step||1)),0),result=Array(length);while(length--){result[fromRight?
length:++index]=start;start+=step}return result}function baseRepeat(string,n){var result="";if(!string||n<1||n>MAX_SAFE_INTEGER)return result;do{if(n%2)result+=string;n=nativeFloor(n/2);if(n)string+=string}while(n);return result}function baseRest(func,start){return setToString(overRest(func,start,identity),func+"")}function baseSample(collection){return arraySample(values(collection))}function baseSampleSize(collection,n){var array=values(collection);return shuffleSelf(array,baseClamp(n,0,array.length))}
function baseSet(object,path,value,customizer){if(!isObject(object))return object;path=castPath(path,object);var index=-1,length=path.length,lastIndex=length-1,nested=object;while(nested!=null&&++index<length){var key=toKey(path[index]),newValue=value;if(index!=lastIndex){var objValue=nested[key];newValue=customizer?customizer(objValue,key,nested):undefined;if(newValue===undefined)newValue=isObject(objValue)?objValue:isIndex(path[index+1])?[]:{}}assignValue(nested,key,newValue);nested=nested[key]}return object}
var baseSetData=!metaMap?identity:function(func,data){metaMap.set(func,data);return func};var baseSetToString=!defineProperty?identity:function(func,string){return defineProperty(func,"toString",{"configurable":true,"enumerable":false,"value":constant(string),"writable":true})};function baseShuffle(collection){return shuffleSelf(values(collection))}function baseSlice(array,start,end){var index=-1,length=array.length;if(start<0)start=-start>length?0:length+start;end=end>length?length:end;if(end<0)end+=
length;length=start>end?0:end-start>>>0;start>>>=0;var result=Array(length);while(++index<length)result[index]=array[index+start];return result}function baseSome(collection,predicate){var result;baseEach(collection,function(value,index,collection){result=predicate(value,index,collection);return!result});return!!result}function baseSortedIndex(array,value,retHighest){var low=0,high=array==null?low:array.length;if(typeof value=="number"&&value===value&&high<=HALF_MAX_ARRAY_LENGTH){while(low<high){var mid=
low+high>>>1,computed=array[mid];if(computed!==null&&!isSymbol(computed)&&(retHighest?computed<=value:computed<value))low=mid+1;else high=mid}return high}return baseSortedIndexBy(array,value,identity,retHighest)}function baseSortedIndexBy(array,value,iteratee,retHighest){value=iteratee(value);var low=0,high=array==null?0:array.length,valIsNaN=value!==value,valIsNull=value===null,valIsSymbol=isSymbol(value),valIsUndefined=value===undefined;while(low<high){var mid=nativeFloor((low+high)/2),computed=
iteratee(array[mid]),othIsDefined=computed!==undefined,othIsNull=computed===null,othIsReflexive=computed===computed,othIsSymbol=isSymbol(computed);if(valIsNaN)var setLow=retHighest||othIsReflexive;else if(valIsUndefined)setLow=othIsReflexive&&(retHighest||othIsDefined);else if(valIsNull)setLow=othIsReflexive&&othIsDefined&&(retHighest||!othIsNull);else if(valIsSymbol)setLow=othIsReflexive&&othIsDefined&&!othIsNull&&(retHighest||!othIsSymbol);else if(othIsNull||othIsSymbol)setLow=false;else setLow=
retHighest?computed<=value:computed<value;if(setLow)low=mid+1;else high=mid}return nativeMin(high,MAX_ARRAY_INDEX)}function baseSortedUniq(array,iteratee){var index=-1,length=array.length,resIndex=0,result=[];while(++index<length){var value=array[index],computed=iteratee?iteratee(value):value;if(!index||!eq(computed,seen)){var seen=computed;result[resIndex++]=value===0?0:value}}return result}function baseToNumber(value){if(typeof value=="number")return value;if(isSymbol(value))return NAN;return+value}
function baseToString(value){if(typeof value=="string")return value;if(isArray(value))return arrayMap(value,baseToString)+"";if(isSymbol(value))return symbolToString?symbolToString.call(value):"";var result=value+"";return result=="0"&&1/value==-INFINITY?"-0":result}function baseUniq(array,iteratee,comparator){var index=-1,includes=arrayIncludes,length=array.length,isCommon=true,result=[],seen=result;if(comparator){isCommon=false;includes=arrayIncludesWith}else if(length>=LARGE_ARRAY_SIZE){var set=
iteratee?null:createSet(array);if(set)return setToArray(set);isCommon=false;includes=cacheHas;seen=new SetCache}else seen=iteratee?[]:result;outer:while(++index<length){var value=array[index],computed=iteratee?iteratee(value):value;value=comparator||value!==0?value:0;if(isCommon&&computed===computed){var seenIndex=seen.length;while(seenIndex--)if(seen[seenIndex]===computed)continue outer;if(iteratee)seen.push(computed);result.push(value)}else if(!includes(seen,computed,comparator)){if(seen!==result)seen.push(computed);
result.push(value)}}return result}function baseUnset(object,path){path=castPath(path,object);object=parent(object,path);return object==null||delete object[toKey(last(path))]}function baseUpdate(object,path,updater,customizer){return baseSet(object,path,updater(baseGet(object,path)),customizer)}function baseWhile(array,predicate,isDrop,fromRight){var length=array.length,index=fromRight?length:-1;while((fromRight?index--:++index<length)&&predicate(array[index],index,array));return isDrop?baseSlice(array,
fromRight?0:index,fromRight?index+1:length):baseSlice(array,fromRight?index+1:0,fromRight?length:index)}function baseWrapperValue(value,actions){var result=value;if(result instanceof LazyWrapper)result=result.value();return arrayReduce(actions,function(result,action){return action.func.apply(action.thisArg,arrayPush([result],action.args))},result)}function baseXor(arrays,iteratee,comparator){var length=arrays.length;if(length<2)return length?baseUniq(arrays[0]):[];var index=-1,result=Array(length);
while(++index<length){var array=arrays[index],othIndex=-1;while(++othIndex<length)if(othIndex!=index)result[index]=baseDifference(result[index]||array,arrays[othIndex],iteratee,comparator)}return baseUniq(baseFlatten(result,1),iteratee,comparator)}function baseZipObject(props,values,assignFunc){var index=-1,length=props.length,valsLength=values.length,result={};while(++index<length){var value=index<valsLength?values[index]:undefined;assignFunc(result,props[index],value)}return result}function castArrayLikeObject(value){return isArrayLikeObject(value)?
value:[]}function castFunction(value){return typeof value=="function"?value:identity}function castPath(value,object){if(isArray(value))return value;return isKey(value,object)?[value]:stringToPath(toString(value))}var castRest=baseRest;function castSlice(array,start,end){var length=array.length;end=end===undefined?length:end;return!start&&end>=length?array:baseSlice(array,start,end)}var clearTimeout=ctxClearTimeout||function(id){return root.clearTimeout(id)};function cloneBuffer(buffer,isDeep){if(isDeep)return buffer.slice();
var length=buffer.length,result=allocUnsafe?allocUnsafe(length):new buffer.constructor(length);buffer.copy(result);return result}function cloneArrayBuffer(arrayBuffer){var result=new arrayBuffer.constructor(arrayBuffer.byteLength);(new Uint8Array(result)).set(new Uint8Array(arrayBuffer));return result}function cloneDataView(dataView,isDeep){var buffer=isDeep?cloneArrayBuffer(dataView.buffer):dataView.buffer;return new dataView.constructor(buffer,dataView.byteOffset,dataView.byteLength)}function cloneRegExp(regexp){var result=
new regexp.constructor(regexp.source,reFlags.exec(regexp));result.lastIndex=regexp.lastIndex;return result}function cloneSymbol(symbol){return symbolValueOf?Object(symbolValueOf.call(symbol)):{}}function cloneTypedArray(typedArray,isDeep){var buffer=isDeep?cloneArrayBuffer(typedArray.buffer):typedArray.buffer;return new typedArray.constructor(buffer,typedArray.byteOffset,typedArray.length)}function compareAscending(value,other){if(value!==other){var valIsDefined=value!==undefined,valIsNull=value===
null,valIsReflexive=value===value,valIsSymbol=isSymbol(value);var othIsDefined=other!==undefined,othIsNull=other===null,othIsReflexive=other===other,othIsSymbol=isSymbol(other);if(!othIsNull&&!othIsSymbol&&!valIsSymbol&&value>other||valIsSymbol&&othIsDefined&&othIsReflexive&&!othIsNull&&!othIsSymbol||valIsNull&&othIsDefined&&othIsReflexive||!valIsDefined&&othIsReflexive||!valIsReflexive)return 1;if(!valIsNull&&!valIsSymbol&&!othIsSymbol&&value<other||othIsSymbol&&valIsDefined&&valIsReflexive&&!valIsNull&&
!valIsSymbol||othIsNull&&valIsDefined&&valIsReflexive||!othIsDefined&&valIsReflexive||!othIsReflexive)return-1}return 0}function compareMultiple(object,other,orders){var index=-1,objCriteria=object.criteria,othCriteria=other.criteria,length=objCriteria.length,ordersLength=orders.length;while(++index<length){var result=compareAscending(objCriteria[index],othCriteria[index]);if(result){if(index>=ordersLength)return result;var order=orders[index];return result*(order=="desc"?-1:1)}}return object.index-
other.index}function composeArgs(args,partials,holders,isCurried){var argsIndex=-1,argsLength=args.length,holdersLength=holders.length,leftIndex=-1,leftLength=partials.length,rangeLength=nativeMax(argsLength-holdersLength,0),result=Array(leftLength+rangeLength),isUncurried=!isCurried;while(++leftIndex<leftLength)result[leftIndex]=partials[leftIndex];while(++argsIndex<holdersLength)if(isUncurried||argsIndex<argsLength)result[holders[argsIndex]]=args[argsIndex];while(rangeLength--)result[leftIndex++]=
args[argsIndex++];return result}function composeArgsRight(args,partials,holders,isCurried){var argsIndex=-1,argsLength=args.length,holdersIndex=-1,holdersLength=holders.length,rightIndex=-1,rightLength=partials.length,rangeLength=nativeMax(argsLength-holdersLength,0),result=Array(rangeLength+rightLength),isUncurried=!isCurried;while(++argsIndex<rangeLength)result[argsIndex]=args[argsIndex];var offset=argsIndex;while(++rightIndex<rightLength)result[offset+rightIndex]=partials[rightIndex];while(++holdersIndex<
holdersLength)if(isUncurried||argsIndex<argsLength)result[offset+holders[holdersIndex]]=args[argsIndex++];return result}function copyArray(source,array){var index=-1,length=source.length;array||(array=Array(length));while(++index<length)array[index]=source[index];return array}function copyObject(source,props,object,customizer){var isNew=!object;object||(object={});var index=-1,length=props.length;while(++index<length){var key=props[index];var newValue=customizer?customizer(object[key],source[key],
key,object,source):undefined;if(newValue===undefined)newValue=source[key];if(isNew)baseAssignValue(object,key,newValue);else assignValue(object,key,newValue)}return object}function copySymbols(source,object){return copyObject(source,getSymbols(source),object)}function copySymbolsIn(source,object){return copyObject(source,getSymbolsIn(source),object)}function createAggregator(setter,initializer){return function(collection,iteratee){var func=isArray(collection)?arrayAggregator:baseAggregator,accumulator=
initializer?initializer():{};return func(collection,setter,getIteratee(iteratee,2),accumulator)}}function createAssigner(assigner){return baseRest(function(object,sources){var index=-1,length=sources.length,customizer=length>1?sources[length-1]:undefined,guard=length>2?sources[2]:undefined;customizer=assigner.length>3&&typeof customizer=="function"?(length--,customizer):undefined;if(guard&&isIterateeCall(sources[0],sources[1],guard)){customizer=length<3?undefined:customizer;length=1}object=Object(object);
while(++index<length){var source=sources[index];if(source)assigner(object,source,index,customizer)}return object})}function createBaseEach(eachFunc,fromRight){return function(collection,iteratee){if(collection==null)return collection;if(!isArrayLike(collection))return eachFunc(collection,iteratee);var length=collection.length,index=fromRight?length:-1,iterable=Object(collection);while(fromRight?index--:++index<length)if(iteratee(iterable[index],index,iterable)===false)break;return collection}}function createBaseFor(fromRight){return function(object,
iteratee,keysFunc){var index=-1,iterable=Object(object),props=keysFunc(object),length=props.length;while(length--){var key=props[fromRight?length:++index];if(iteratee(iterable[key],key,iterable)===false)break}return object}}function createBind(func,bitmask,thisArg){var isBind=bitmask&WRAP_BIND_FLAG,Ctor=createCtor(func);function wrapper(){var fn=this&&this!==root&&this instanceof wrapper?Ctor:func;return fn.apply(isBind?thisArg:this,arguments)}return wrapper}function createCaseFirst(methodName){return function(string){string=
toString(string);var strSymbols=hasUnicode(string)?stringToArray(string):undefined;var chr=strSymbols?strSymbols[0]:string.charAt(0);var trailing=strSymbols?castSlice(strSymbols,1).join(""):string.slice(1);return chr[methodName]()+trailing}}function createCompounder(callback){return function(string){return arrayReduce(words(deburr(string).replace(reApos,"")),callback,"")}}function createCtor(Ctor){return function(){var args=arguments;switch(args.length){case 0:return new Ctor;case 1:return new Ctor(args[0]);
case 2:return new Ctor(args[0],args[1]);case 3:return new Ctor(args[0],args[1],args[2]);case 4:return new Ctor(args[0],args[1],args[2],args[3]);case 5:return new Ctor(args[0],args[1],args[2],args[3],args[4]);case 6:return new Ctor(args[0],args[1],args[2],args[3],args[4],args[5]);case 7:return new Ctor(args[0],args[1],args[2],args[3],args[4],args[5],args[6])}var thisBinding=baseCreate(Ctor.prototype),result=Ctor.apply(thisBinding,args);return isObject(result)?result:thisBinding}}function createCurry(func,
bitmask,arity){var Ctor=createCtor(func);function wrapper(){var length=arguments.length,args=Array(length),index=length,placeholder=getHolder(wrapper);while(index--)args[index]=arguments[index];var holders=length<3&&args[0]!==placeholder&&args[length-1]!==placeholder?[]:replaceHolders(args,placeholder);length-=holders.length;if(length<arity)return createRecurry(func,bitmask,createHybrid,wrapper.placeholder,undefined,args,holders,undefined,undefined,arity-length);var fn=this&&this!==root&&this instanceof
wrapper?Ctor:func;return apply(fn,this,args)}return wrapper}function createFind(findIndexFunc){return function(collection,predicate,fromIndex){var iterable=Object(collection);if(!isArrayLike(collection)){var iteratee=getIteratee(predicate,3);collection=keys(collection);predicate=function(key){return iteratee(iterable[key],key,iterable)}}var index=findIndexFunc(collection,predicate,fromIndex);return index>-1?iterable[iteratee?collection[index]:index]:undefined}}function createFlow(fromRight){return flatRest(function(funcs){var length=
funcs.length,index=length,prereq=LodashWrapper.prototype.thru;if(fromRight)funcs.reverse();while(index--){var func=funcs[index];if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);if(prereq&&!wrapper&&getFuncName(func)=="wrapper")var wrapper=new LodashWrapper([],true)}index=wrapper?index:length;while(++index<length){func=funcs[index];var funcName=getFuncName(func),data=funcName=="wrapper"?getData(func):undefined;if(data&&isLaziable(data[0])&&data[1]==(WRAP_ARY_FLAG|WRAP_CURRY_FLAG|WRAP_PARTIAL_FLAG|
WRAP_REARG_FLAG)&&!data[4].length&&data[9]==1)wrapper=wrapper[getFuncName(data[0])].apply(wrapper,data[3]);else wrapper=func.length==1&&isLaziable(func)?wrapper[funcName]():wrapper.thru(func)}return function(){var args=arguments,value=args[0];if(wrapper&&args.length==1&&isArray(value))return wrapper.plant(value).value();var index=0,result=length?funcs[index].apply(this,args):value;while(++index<length)result=funcs[index].call(this,result);return result}})}function createHybrid(func,bitmask,thisArg,
partials,holders,partialsRight,holdersRight,argPos,ary,arity){var isAry=bitmask&WRAP_ARY_FLAG,isBind=bitmask&WRAP_BIND_FLAG,isBindKey=bitmask&WRAP_BIND_KEY_FLAG,isCurried=bitmask&(WRAP_CURRY_FLAG|WRAP_CURRY_RIGHT_FLAG),isFlip=bitmask&WRAP_FLIP_FLAG,Ctor=isBindKey?undefined:createCtor(func);function wrapper(){var length=arguments.length,args=Array(length),index=length;while(index--)args[index]=arguments[index];if(isCurried)var placeholder=getHolder(wrapper),holdersCount=countHolders(args,placeholder);
if(partials)args=composeArgs(args,partials,holders,isCurried);if(partialsRight)args=composeArgsRight(args,partialsRight,holdersRight,isCurried);length-=holdersCount;if(isCurried&&length<arity){var newHolders=replaceHolders(args,placeholder);return createRecurry(func,bitmask,createHybrid,wrapper.placeholder,thisArg,args,newHolders,argPos,ary,arity-length)}var thisBinding=isBind?thisArg:this,fn=isBindKey?thisBinding[func]:func;length=args.length;if(argPos)args=reorder(args,argPos);else if(isFlip&&length>
1)args.reverse();if(isAry&&ary<length)args.length=ary;if(this&&this!==root&&this instanceof wrapper)fn=Ctor||createCtor(fn);return fn.apply(thisBinding,args)}return wrapper}function createInverter(setter,toIteratee){return function(object,iteratee){return baseInverter(object,setter,toIteratee(iteratee),{})}}function createMathOperation(operator,defaultValue){return function(value,other){var result;if(value===undefined&&other===undefined)return defaultValue;if(value!==undefined)result=value;if(other!==
undefined){if(result===undefined)return other;if(typeof value=="string"||typeof other=="string"){value=baseToString(value);other=baseToString(other)}else{value=baseToNumber(value);other=baseToNumber(other)}result=operator(value,other)}return result}}function createOver(arrayFunc){return flatRest(function(iteratees){iteratees=arrayMap(iteratees,baseUnary(getIteratee()));return baseRest(function(args){var thisArg=this;return arrayFunc(iteratees,function(iteratee){return apply(iteratee,thisArg,args)})})})}
function createPadding(length,chars){chars=chars===undefined?" ":baseToString(chars);var charsLength=chars.length;if(charsLength<2)return charsLength?baseRepeat(chars,length):chars;var result=baseRepeat(chars,nativeCeil(length/stringSize(chars)));return hasUnicode(chars)?castSlice(stringToArray(result),0,length).join(""):result.slice(0,length)}function createPartial(func,bitmask,thisArg,partials){var isBind=bitmask&WRAP_BIND_FLAG,Ctor=createCtor(func);function wrapper(){var argsIndex=-1,argsLength=
arguments.length,leftIndex=-1,leftLength=partials.length,args=Array(leftLength+argsLength),fn=this&&this!==root&&this instanceof wrapper?Ctor:func;while(++leftIndex<leftLength)args[leftIndex]=partials[leftIndex];while(argsLength--)args[leftIndex++]=arguments[++argsIndex];return apply(fn,isBind?thisArg:this,args)}return wrapper}function createRange(fromRight){return function(start,end,step){if(step&&typeof step!="number"&&isIterateeCall(start,end,step))end=step=undefined;start=toFinite(start);if(end===
undefined){end=start;start=0}else end=toFinite(end);step=step===undefined?start<end?1:-1:toFinite(step);return baseRange(start,end,step,fromRight)}}function createRelationalOperation(operator){return function(value,other){if(!(typeof value=="string"&&typeof other=="string")){value=toNumber(value);other=toNumber(other)}return operator(value,other)}}function createRecurry(func,bitmask,wrapFunc,placeholder,thisArg,partials,holders,argPos,ary,arity){var isCurry=bitmask&WRAP_CURRY_FLAG,newHolders=isCurry?
holders:undefined,newHoldersRight=isCurry?undefined:holders,newPartials=isCurry?partials:undefined,newPartialsRight=isCurry?undefined:partials;bitmask|=isCurry?WRAP_PARTIAL_FLAG:WRAP_PARTIAL_RIGHT_FLAG;bitmask&=~(isCurry?WRAP_PARTIAL_RIGHT_FLAG:WRAP_PARTIAL_FLAG);if(!(bitmask&WRAP_CURRY_BOUND_FLAG))bitmask&=~(WRAP_BIND_FLAG|WRAP_BIND_KEY_FLAG);var newData=[func,bitmask,thisArg,newPartials,newHolders,newPartialsRight,newHoldersRight,argPos,ary,arity];var result=wrapFunc.apply(undefined,newData);if(isLaziable(func))setData(result,
newData);result.placeholder=placeholder;return setWrapToString(result,func,bitmask)}function createRound(methodName){var func=Math[methodName];return function(number,precision){number=toNumber(number);precision=precision==null?0:nativeMin(toInteger(precision),292);if(precision){var pair=(toString(number)+"e").split("e"),value=func(pair[0]+"e"+(+pair[1]+precision));pair=(toString(value)+"e").split("e");return+(pair[0]+"e"+(+pair[1]-precision))}return func(number)}}var createSet=!(Set&&1/setToArray(new Set([,
-0]))[1]==INFINITY)?noop:function(values){return new Set(values)};function createToPairs(keysFunc){return function(object){var tag=getTag(object);if(tag==mapTag)return mapToArray(object);if(tag==setTag)return setToPairs(object);return baseToPairs(object,keysFunc(object))}}function createWrap(func,bitmask,thisArg,partials,holders,argPos,ary,arity){var isBindKey=bitmask&WRAP_BIND_KEY_FLAG;if(!isBindKey&&typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);var length=partials?partials.length:
0;if(!length){bitmask&=~(WRAP_PARTIAL_FLAG|WRAP_PARTIAL_RIGHT_FLAG);partials=holders=undefined}ary=ary===undefined?ary:nativeMax(toInteger(ary),0);arity=arity===undefined?arity:toInteger(arity);length-=holders?holders.length:0;if(bitmask&WRAP_PARTIAL_RIGHT_FLAG){var partialsRight=partials,holdersRight=holders;partials=holders=undefined}var data=isBindKey?undefined:getData(func);var newData=[func,bitmask,thisArg,partials,holders,partialsRight,holdersRight,argPos,ary,arity];if(data)mergeData(newData,
data);func=newData[0];bitmask=newData[1];thisArg=newData[2];partials=newData[3];holders=newData[4];arity=newData[9]=newData[9]===undefined?isBindKey?0:func.length:nativeMax(newData[9]-length,0);if(!arity&&bitmask&(WRAP_CURRY_FLAG|WRAP_CURRY_RIGHT_FLAG))bitmask&=~(WRAP_CURRY_FLAG|WRAP_CURRY_RIGHT_FLAG);if(!bitmask||bitmask==WRAP_BIND_FLAG)var result=createBind(func,bitmask,thisArg);else if(bitmask==WRAP_CURRY_FLAG||bitmask==WRAP_CURRY_RIGHT_FLAG)result=createCurry(func,bitmask,arity);else if((bitmask==
WRAP_PARTIAL_FLAG||bitmask==(WRAP_BIND_FLAG|WRAP_PARTIAL_FLAG))&&!holders.length)result=createPartial(func,bitmask,thisArg,partials);else result=createHybrid.apply(undefined,newData);var setter=data?baseSetData:setData;return setWrapToString(setter(result,newData),func,bitmask)}function customDefaultsAssignIn(objValue,srcValue,key,object){if(objValue===undefined||eq(objValue,objectProto[key])&&!hasOwnProperty.call(object,key))return srcValue;return objValue}function customDefaultsMerge(objValue,srcValue,
key,object,source,stack){if(isObject(objValue)&&isObject(srcValue)){stack.set(srcValue,objValue);baseMerge(objValue,srcValue,undefined,customDefaultsMerge,stack);stack["delete"](srcValue)}return objValue}function customOmitClone(value){return isPlainObject(value)?undefined:value}function equalArrays(array,other,bitmask,customizer,equalFunc,stack){var isPartial=bitmask&COMPARE_PARTIAL_FLAG,arrLength=array.length,othLength=other.length;if(arrLength!=othLength&&!(isPartial&&othLength>arrLength))return false;
var stacked=stack.get(array);if(stacked&&stack.get(other))return stacked==other;var index=-1,result=true,seen=bitmask&COMPARE_UNORDERED_FLAG?new SetCache:undefined;stack.set(array,other);stack.set(other,array);while(++index<arrLength){var arrValue=array[index],othValue=other[index];if(customizer)var compared=isPartial?customizer(othValue,arrValue,index,other,array,stack):customizer(arrValue,othValue,index,array,other,stack);if(compared!==undefined){if(compared)continue;result=false;break}if(seen){if(!arraySome(other,
function(othValue,othIndex){if(!cacheHas(seen,othIndex)&&(arrValue===othValue||equalFunc(arrValue,othValue,bitmask,customizer,stack)))return seen.push(othIndex)})){result=false;break}}else if(!(arrValue===othValue||equalFunc(arrValue,othValue,bitmask,customizer,stack))){result=false;break}}stack["delete"](array);stack["delete"](other);return result}function equalByTag(object,other,tag,bitmask,customizer,equalFunc,stack){switch(tag){case dataViewTag:if(object.byteLength!=other.byteLength||object.byteOffset!=
other.byteOffset)return false;object=object.buffer;other=other.buffer;case arrayBufferTag:if(object.byteLength!=other.byteLength||!equalFunc(new Uint8Array(object),new Uint8Array(other)))return false;return true;case boolTag:case dateTag:case numberTag:return eq(+object,+other);case errorTag:return object.name==other.name&&object.message==other.message;case regexpTag:case stringTag:return object==other+"";case mapTag:var convert=mapToArray;case setTag:var isPartial=bitmask&COMPARE_PARTIAL_FLAG;convert||
(convert=setToArray);if(object.size!=other.size&&!isPartial)return false;var stacked=stack.get(object);if(stacked)return stacked==other;bitmask|=COMPARE_UNORDERED_FLAG;stack.set(object,other);var result=equalArrays(convert(object),convert(other),bitmask,customizer,equalFunc,stack);stack["delete"](object);return result;case symbolTag:if(symbolValueOf)return symbolValueOf.call(object)==symbolValueOf.call(other)}return false}function equalObjects(object,other,bitmask,customizer,equalFunc,stack){var isPartial=
bitmask&COMPARE_PARTIAL_FLAG,objProps=getAllKeys(object),objLength=objProps.length,othProps=getAllKeys(other),othLength=othProps.length;if(objLength!=othLength&&!isPartial)return false;var index=objLength;while(index--){var key=objProps[index];if(!(isPartial?key in other:hasOwnProperty.call(other,key)))return false}var stacked=stack.get(object);if(stacked&&stack.get(other))return stacked==other;var result=true;stack.set(object,other);stack.set(other,object);var skipCtor=isPartial;while(++index<objLength){key=
objProps[index];var objValue=object[key],othValue=other[key];if(customizer)var compared=isPartial?customizer(othValue,objValue,key,other,object,stack):customizer(objValue,othValue,key,object,other,stack);if(!(compared===undefined?objValue===othValue||equalFunc(objValue,othValue,bitmask,customizer,stack):compared)){result=false;break}skipCtor||(skipCtor=key=="constructor")}if(result&&!skipCtor){var objCtor=object.constructor,othCtor=other.constructor;if(objCtor!=othCtor&&("constructor"in object&&"constructor"in
other)&&!(typeof objCtor=="function"&&objCtor instanceof objCtor&&typeof othCtor=="function"&&othCtor instanceof othCtor))result=false}stack["delete"](object);stack["delete"](other);return result}function flatRest(func){return setToString(overRest(func,undefined,flatten),func+"")}function getAllKeys(object){return baseGetAllKeys(object,keys,getSymbols)}function getAllKeysIn(object){return baseGetAllKeys(object,keysIn,getSymbolsIn)}var getData=!metaMap?noop:function(func){return metaMap.get(func)};
function getFuncName(func){var result=func.name+"",array=realNames[result],length=hasOwnProperty.call(realNames,result)?array.length:0;while(length--){var data=array[length],otherFunc=data.func;if(otherFunc==null||otherFunc==func)return data.name}return result}function getHolder(func){var object=hasOwnProperty.call(lodash,"placeholder")?lodash:func;return object.placeholder}function getIteratee(){var result=lodash.iteratee||iteratee;result=result===iteratee?baseIteratee:result;return arguments.length?
result(arguments[0],arguments[1]):result}function getMapData(map,key){var data=map.__data__;return isKeyable(key)?data[typeof key=="string"?"string":"hash"]:data.map}function getMatchData(object){var result=keys(object),length=result.length;while(length--){var key=result[length],value=object[key];result[length]=[key,value,isStrictComparable(value)]}return result}function getNative(object,key){var value=getValue(object,key);return baseIsNative(value)?value:undefined}function getRawTag(value){var isOwn=
hasOwnProperty.call(value,symToStringTag),tag=value[symToStringTag];try{value[symToStringTag]=undefined;var unmasked=true}catch(e){}var result=nativeObjectToString.call(value);if(unmasked)if(isOwn)value[symToStringTag]=tag;else delete value[symToStringTag];return result}var getSymbols=!nativeGetSymbols?stubArray:function(object){if(object==null)return[];object=Object(object);return arrayFilter(nativeGetSymbols(object),function(symbol){return propertyIsEnumerable.call(object,symbol)})};var getSymbolsIn=
!nativeGetSymbols?stubArray:function(object){var result=[];while(object){arrayPush(result,getSymbols(object));object=getPrototype(object)}return result};var getTag=baseGetTag;if(DataView&&getTag(new DataView(new ArrayBuffer(1)))!=dataViewTag||Map&&getTag(new Map)!=mapTag||Promise&&getTag(Promise.resolve())!=promiseTag||Set&&getTag(new Set)!=setTag||WeakMap&&getTag(new WeakMap)!=weakMapTag)getTag=function(value){var result=baseGetTag(value),Ctor=result==objectTag?value.constructor:undefined,ctorString=
Ctor?toSource(Ctor):"";if(ctorString)switch(ctorString){case dataViewCtorString:return dataViewTag;case mapCtorString:return mapTag;case promiseCtorString:return promiseTag;case setCtorString:return setTag;case weakMapCtorString:return weakMapTag}return result};function getView(start,end,transforms){var index=-1,length=transforms.length;while(++index<length){var data=transforms[index],size=data.size;switch(data.type){case "drop":start+=size;break;case "dropRight":end-=size;break;case "take":end=nativeMin(end,
start+size);break;case "takeRight":start=nativeMax(start,end-size);break}}return{"start":start,"end":end}}function getWrapDetails(source){var match=source.match(reWrapDetails);return match?match[1].split(reSplitDetails):[]}function hasPath(object,path,hasFunc){path=castPath(path,object);var index=-1,length=path.length,result=false;while(++index<length){var key=toKey(path[index]);if(!(result=object!=null&&hasFunc(object,key)))break;object=object[key]}if(result||++index!=length)return result;length=
object==null?0:object.length;return!!length&&isLength(length)&&isIndex(key,length)&&(isArray(object)||isArguments(object))}function initCloneArray(array){var length=array.length,result=new array.constructor(length);if(length&&typeof array[0]=="string"&&hasOwnProperty.call(array,"index")){result.index=array.index;result.input=array.input}return result}function initCloneObject(object){return typeof object.constructor=="function"&&!isPrototype(object)?baseCreate(getPrototype(object)):{}}function initCloneByTag(object,
tag,isDeep){var Ctor=object.constructor;switch(tag){case arrayBufferTag:return cloneArrayBuffer(object);case boolTag:case dateTag:return new Ctor(+object);case dataViewTag:return cloneDataView(object,isDeep);case float32Tag:case float64Tag:case int8Tag:case int16Tag:case int32Tag:case uint8Tag:case uint8ClampedTag:case uint16Tag:case uint32Tag:return cloneTypedArray(object,isDeep);case mapTag:return new Ctor;case numberTag:case stringTag:return new Ctor(object);case regexpTag:return cloneRegExp(object);
case setTag:return new Ctor;case symbolTag:return cloneSymbol(object)}}function insertWrapDetails(source,details){var length=details.length;if(!length)return source;var lastIndex=length-1;details[lastIndex]=(length>1?"\x26 ":"")+details[lastIndex];details=details.join(length>2?", ":" ");return source.replace(reWrapComment,"{\n/* [wrapped with "+details+"] */\n")}function isFlattenable(value){return isArray(value)||isArguments(value)||!!(spreadableSymbol&&value&&value[spreadableSymbol])}function isIndex(value,
length){var type=typeof value;length=length==null?MAX_SAFE_INTEGER:length;return!!length&&(type=="number"||type!="symbol"&&reIsUint.test(value))&&(value>-1&&value%1==0&&value<length)}function isIterateeCall(value,index,object){if(!isObject(object))return false;var type=typeof index;if(type=="number"?isArrayLike(object)&&isIndex(index,object.length):type=="string"&&index in object)return eq(object[index],value);return false}function isKey(value,object){if(isArray(value))return false;var type=typeof value;
if(type=="number"||type=="symbol"||type=="boolean"||value==null||isSymbol(value))return true;return reIsPlainProp.test(value)||!reIsDeepProp.test(value)||object!=null&&value in Object(object)}function isKeyable(value){var type=typeof value;return type=="string"||type=="number"||type=="symbol"||type=="boolean"?value!=="__proto__":value===null}function isLaziable(func){var funcName=getFuncName(func),other=lodash[funcName];if(typeof other!="function"||!(funcName in LazyWrapper.prototype))return false;
if(func===other)return true;var data=getData(other);return!!data&&func===data[0]}function isMasked(func){return!!maskSrcKey&&maskSrcKey in func}var isMaskable=coreJsData?isFunction:stubFalse;function isPrototype(value){var Ctor=value&&value.constructor,proto=typeof Ctor=="function"&&Ctor.prototype||objectProto;return value===proto}function isStrictComparable(value){return value===value&&!isObject(value)}function matchesStrictComparable(key,srcValue){return function(object){if(object==null)return false;
return object[key]===srcValue&&(srcValue!==undefined||key in Object(object))}}function memoizeCapped(func){var result=memoize(func,function(key){if(cache.size===MAX_MEMOIZE_SIZE)cache.clear();return key});var cache=result.cache;return result}function mergeData(data,source){var bitmask=data[1],srcBitmask=source[1],newBitmask=bitmask|srcBitmask,isCommon=newBitmask<(WRAP_BIND_FLAG|WRAP_BIND_KEY_FLAG|WRAP_ARY_FLAG);var isCombo=srcBitmask==WRAP_ARY_FLAG&&bitmask==WRAP_CURRY_FLAG||srcBitmask==WRAP_ARY_FLAG&&
bitmask==WRAP_REARG_FLAG&&data[7].length<=source[8]||srcBitmask==(WRAP_ARY_FLAG|WRAP_REARG_FLAG)&&source[7].length<=source[8]&&bitmask==WRAP_CURRY_FLAG;if(!(isCommon||isCombo))return data;if(srcBitmask&WRAP_BIND_FLAG){data[2]=source[2];newBitmask|=bitmask&WRAP_BIND_FLAG?0:WRAP_CURRY_BOUND_FLAG}var value=source[3];if(value){var partials=data[3];data[3]=partials?composeArgs(partials,value,source[4]):value;data[4]=partials?replaceHolders(data[3],PLACEHOLDER):source[4]}value=source[5];if(value){partials=
data[5];data[5]=partials?composeArgsRight(partials,value,source[6]):value;data[6]=partials?replaceHolders(data[5],PLACEHOLDER):source[6]}value=source[7];if(value)data[7]=value;if(srcBitmask&WRAP_ARY_FLAG)data[8]=data[8]==null?source[8]:nativeMin(data[8],source[8]);if(data[9]==null)data[9]=source[9];data[0]=source[0];data[1]=newBitmask;return data}function nativeKeysIn(object){var result=[];if(object!=null)for(var key in Object(object))result.push(key);return result}function objectToString(value){return nativeObjectToString.call(value)}
function overRest(func,start,transform){start=nativeMax(start===undefined?func.length-1:start,0);return function(){var args=arguments,index=-1,length=nativeMax(args.length-start,0),array=Array(length);while(++index<length)array[index]=args[start+index];index=-1;var otherArgs=Array(start+1);while(++index<start)otherArgs[index]=args[index];otherArgs[start]=transform(array);return apply(func,this,otherArgs)}}function parent(object,path){return path.length<2?object:baseGet(object,baseSlice(path,0,-1))}
function reorder(array,indexes){var arrLength=array.length,length=nativeMin(indexes.length,arrLength),oldArray=copyArray(array);while(length--){var index=indexes[length];array[length]=isIndex(index,arrLength)?oldArray[index]:undefined}return array}var setData=shortOut(baseSetData);var setTimeout=ctxSetTimeout||function(func,wait){return root.setTimeout(func,wait)};var setToString=shortOut(baseSetToString);function setWrapToString(wrapper,reference,bitmask){var source=reference+"";return setToString(wrapper,
insertWrapDetails(source,updateWrapDetails(getWrapDetails(source),bitmask)))}function shortOut(func){var count=0,lastCalled=0;return function(){var stamp=nativeNow(),remaining=HOT_SPAN-(stamp-lastCalled);lastCalled=stamp;if(remaining>0){if(++count>=HOT_COUNT)return arguments[0]}else count=0;return func.apply(undefined,arguments)}}function shuffleSelf(array,size){var index=-1,length=array.length,lastIndex=length-1;size=size===undefined?length:size;while(++index<size){var rand=baseRandom(index,lastIndex),
value=array[rand];array[rand]=array[index];array[index]=value}array.length=size;return array}var stringToPath=memoizeCapped(function(string){var result=[];if(string.charCodeAt(0)===46)result.push("");string.replace(rePropName,function(match,number,quote,subString){result.push(quote?subString.replace(reEscapeChar,"$1"):number||match)});return result});function toKey(value){if(typeof value=="string"||isSymbol(value))return value;var result=value+"";return result=="0"&&1/value==-INFINITY?"-0":result}
function toSource(func){if(func!=null){try{return funcToString.call(func)}catch(e){}try{return func+""}catch(e$0){}}return""}function updateWrapDetails(details,bitmask){arrayEach(wrapFlags,function(pair){var value="_."+pair[0];if(bitmask&pair[1]&&!arrayIncludes(details,value))details.push(value)});return details.sort()}function wrapperClone(wrapper){if(wrapper instanceof LazyWrapper)return wrapper.clone();var result=new LodashWrapper(wrapper.__wrapped__,wrapper.__chain__);result.__actions__=copyArray(wrapper.__actions__);
result.__index__=wrapper.__index__;result.__values__=wrapper.__values__;return result}function chunk(array,size,guard){if(guard?isIterateeCall(array,size,guard):size===undefined)size=1;else size=nativeMax(toInteger(size),0);var length=array==null?0:array.length;if(!length||size<1)return[];var index=0,resIndex=0,result=Array(nativeCeil(length/size));while(index<length)result[resIndex++]=baseSlice(array,index,index+=size);return result}function compact(array){var index=-1,length=array==null?0:array.length,
resIndex=0,result=[];while(++index<length){var value=array[index];if(value)result[resIndex++]=value}return result}function concat(){var length=arguments.length;if(!length)return[];var args=Array(length-1),array=arguments[0],index=length;while(index--)args[index-1]=arguments[index];return arrayPush(isArray(array)?copyArray(array):[array],baseFlatten(args,1))}var difference=baseRest(function(array,values){return isArrayLikeObject(array)?baseDifference(array,baseFlatten(values,1,isArrayLikeObject,true)):
[]});var differenceBy=baseRest(function(array,values){var iteratee=last(values);if(isArrayLikeObject(iteratee))iteratee=undefined;return isArrayLikeObject(array)?baseDifference(array,baseFlatten(values,1,isArrayLikeObject,true),getIteratee(iteratee,2)):[]});var differenceWith=baseRest(function(array,values){var comparator=last(values);if(isArrayLikeObject(comparator))comparator=undefined;return isArrayLikeObject(array)?baseDifference(array,baseFlatten(values,1,isArrayLikeObject,true),undefined,comparator):
[]});function drop(array,n,guard){var length=array==null?0:array.length;if(!length)return[];n=guard||n===undefined?1:toInteger(n);return baseSlice(array,n<0?0:n,length)}function dropRight(array,n,guard){var length=array==null?0:array.length;if(!length)return[];n=guard||n===undefined?1:toInteger(n);n=length-n;return baseSlice(array,0,n<0?0:n)}function dropRightWhile(array,predicate){return array&&array.length?baseWhile(array,getIteratee(predicate,3),true,true):[]}function dropWhile(array,predicate){return array&&
array.length?baseWhile(array,getIteratee(predicate,3),true):[]}function fill(array,value,start,end){var length=array==null?0:array.length;if(!length)return[];if(start&&typeof start!="number"&&isIterateeCall(array,value,start)){start=0;end=length}return baseFill(array,value,start,end)}function findIndex(array,predicate,fromIndex){var length=array==null?0:array.length;if(!length)return-1;var index=fromIndex==null?0:toInteger(fromIndex);if(index<0)index=nativeMax(length+index,0);return baseFindIndex(array,
getIteratee(predicate,3),index)}function findLastIndex(array,predicate,fromIndex){var length=array==null?0:array.length;if(!length)return-1;var index=length-1;if(fromIndex!==undefined){index=toInteger(fromIndex);index=fromIndex<0?nativeMax(length+index,0):nativeMin(index,length-1)}return baseFindIndex(array,getIteratee(predicate,3),index,true)}function flatten(array){var length=array==null?0:array.length;return length?baseFlatten(array,1):[]}function flattenDeep(array){var length=array==null?0:array.length;
return length?baseFlatten(array,INFINITY):[]}function flattenDepth(array,depth){var length=array==null?0:array.length;if(!length)return[];depth=depth===undefined?1:toInteger(depth);return baseFlatten(array,depth)}function fromPairs(pairs){var index=-1,length=pairs==null?0:pairs.length,result={};while(++index<length){var pair=pairs[index];result[pair[0]]=pair[1]}return result}function head(array){return array&&array.length?array[0]:undefined}function indexOf(array,value,fromIndex){var length=array==
null?0:array.length;if(!length)return-1;var index=fromIndex==null?0:toInteger(fromIndex);if(index<0)index=nativeMax(length+index,0);return baseIndexOf(array,value,index)}function initial(array){var length=array==null?0:array.length;return length?baseSlice(array,0,-1):[]}var intersection=baseRest(function(arrays){var mapped=arrayMap(arrays,castArrayLikeObject);return mapped.length&&mapped[0]===arrays[0]?baseIntersection(mapped):[]});var intersectionBy=baseRest(function(arrays){var iteratee=last(arrays),
mapped=arrayMap(arrays,castArrayLikeObject);if(iteratee===last(mapped))iteratee=undefined;else mapped.pop();return mapped.length&&mapped[0]===arrays[0]?baseIntersection(mapped,getIteratee(iteratee,2)):[]});var intersectionWith=baseRest(function(arrays){var comparator=last(arrays),mapped=arrayMap(arrays,castArrayLikeObject);comparator=typeof comparator=="function"?comparator:undefined;if(comparator)mapped.pop();return mapped.length&&mapped[0]===arrays[0]?baseIntersection(mapped,undefined,comparator):
[]});function join(array,separator){return array==null?"":nativeJoin.call(array,separator)}function last(array){var length=array==null?0:array.length;return length?array[length-1]:undefined}function lastIndexOf(array,value,fromIndex){var length=array==null?0:array.length;if(!length)return-1;var index=length;if(fromIndex!==undefined){index=toInteger(fromIndex);index=index<0?nativeMax(length+index,0):nativeMin(index,length-1)}return value===value?strictLastIndexOf(array,value,index):baseFindIndex(array,
baseIsNaN,index,true)}function nth(array,n){return array&&array.length?baseNth(array,toInteger(n)):undefined}var pull=baseRest(pullAll);function pullAll(array,values){return array&&array.length&&values&&values.length?basePullAll(array,values):array}function pullAllBy(array,values,iteratee){return array&&array.length&&values&&values.length?basePullAll(array,values,getIteratee(iteratee,2)):array}function pullAllWith(array,values,comparator){return array&&array.length&&values&&values.length?basePullAll(array,
values,undefined,comparator):array}var pullAt=flatRest(function(array,indexes){var length=array==null?0:array.length,result=baseAt(array,indexes);basePullAt(array,arrayMap(indexes,function(index){return isIndex(index,length)?+index:index}).sort(compareAscending));return result});function remove(array,predicate){var result=[];if(!(array&&array.length))return result;var index=-1,indexes=[],length=array.length;predicate=getIteratee(predicate,3);while(++index<length){var value=array[index];if(predicate(value,
index,array)){result.push(value);indexes.push(index)}}basePullAt(array,indexes);return result}function reverse(array){return array==null?array:nativeReverse.call(array)}function slice(array,start,end){var length=array==null?0:array.length;if(!length)return[];if(end&&typeof end!="number"&&isIterateeCall(array,start,end)){start=0;end=length}else{start=start==null?0:toInteger(start);end=end===undefined?length:toInteger(end)}return baseSlice(array,start,end)}function sortedIndex(array,value){return baseSortedIndex(array,
value)}function sortedIndexBy(array,value,iteratee){return baseSortedIndexBy(array,value,getIteratee(iteratee,2))}function sortedIndexOf(array,value){var length=array==null?0:array.length;if(length){var index=baseSortedIndex(array,value);if(index<length&&eq(array[index],value))return index}return-1}function sortedLastIndex(array,value){return baseSortedIndex(array,value,true)}function sortedLastIndexBy(array,value,iteratee){return baseSortedIndexBy(array,value,getIteratee(iteratee,2),true)}function sortedLastIndexOf(array,
value){var length=array==null?0:array.length;if(length){var index=baseSortedIndex(array,value,true)-1;if(eq(array[index],value))return index}return-1}function sortedUniq(array){return array&&array.length?baseSortedUniq(array):[]}function sortedUniqBy(array,iteratee){return array&&array.length?baseSortedUniq(array,getIteratee(iteratee,2)):[]}function tail(array){var length=array==null?0:array.length;return length?baseSlice(array,1,length):[]}function take(array,n,guard){if(!(array&&array.length))return[];
n=guard||n===undefined?1:toInteger(n);return baseSlice(array,0,n<0?0:n)}function takeRight(array,n,guard){var length=array==null?0:array.length;if(!length)return[];n=guard||n===undefined?1:toInteger(n);n=length-n;return baseSlice(array,n<0?0:n,length)}function takeRightWhile(array,predicate){return array&&array.length?baseWhile(array,getIteratee(predicate,3),false,true):[]}function takeWhile(array,predicate){return array&&array.length?baseWhile(array,getIteratee(predicate,3)):[]}var union=baseRest(function(arrays){return baseUniq(baseFlatten(arrays,
1,isArrayLikeObject,true))});var unionBy=baseRest(function(arrays){var iteratee=last(arrays);if(isArrayLikeObject(iteratee))iteratee=undefined;return baseUniq(baseFlatten(arrays,1,isArrayLikeObject,true),getIteratee(iteratee,2))});var unionWith=baseRest(function(arrays){var comparator=last(arrays);comparator=typeof comparator=="function"?comparator:undefined;return baseUniq(baseFlatten(arrays,1,isArrayLikeObject,true),undefined,comparator)});function uniq(array){return array&&array.length?baseUniq(array):
[]}function uniqBy(array,iteratee){return array&&array.length?baseUniq(array,getIteratee(iteratee,2)):[]}function uniqWith(array,comparator){comparator=typeof comparator=="function"?comparator:undefined;return array&&array.length?baseUniq(array,undefined,comparator):[]}function unzip(array){if(!(array&&array.length))return[];var length=0;array=arrayFilter(array,function(group){if(isArrayLikeObject(group)){length=nativeMax(group.length,length);return true}});return baseTimes(length,function(index){return arrayMap(array,
baseProperty(index))})}function unzipWith(array,iteratee){if(!(array&&array.length))return[];var result=unzip(array);if(iteratee==null)return result;return arrayMap(result,function(group){return apply(iteratee,undefined,group)})}var without=baseRest(function(array,values){return isArrayLikeObject(array)?baseDifference(array,values):[]});var xor=baseRest(function(arrays){return baseXor(arrayFilter(arrays,isArrayLikeObject))});var xorBy=baseRest(function(arrays){var iteratee=last(arrays);if(isArrayLikeObject(iteratee))iteratee=
undefined;return baseXor(arrayFilter(arrays,isArrayLikeObject),getIteratee(iteratee,2))});var xorWith=baseRest(function(arrays){var comparator=last(arrays);comparator=typeof comparator=="function"?comparator:undefined;return baseXor(arrayFilter(arrays,isArrayLikeObject),undefined,comparator)});var zip=baseRest(unzip);function zipObject(props,values){return baseZipObject(props||[],values||[],assignValue)}function zipObjectDeep(props,values){return baseZipObject(props||[],values||[],baseSet)}var zipWith=
baseRest(function(arrays){var length=arrays.length,iteratee=length>1?arrays[length-1]:undefined;iteratee=typeof iteratee=="function"?(arrays.pop(),iteratee):undefined;return unzipWith(arrays,iteratee)});function chain(value){var result=lodash(value);result.__chain__=true;return result}function tap(value,interceptor){interceptor(value);return value}function thru(value,interceptor){return interceptor(value)}var wrapperAt=flatRest(function(paths){var length=paths.length,start=length?paths[0]:0,value=
this.__wrapped__,interceptor=function(object){return baseAt(object,paths)};if(length>1||this.__actions__.length||!(value instanceof LazyWrapper)||!isIndex(start))return this.thru(interceptor);value=value.slice(start,+start+(length?1:0));value.__actions__.push({"func":thru,"args":[interceptor],"thisArg":undefined});return(new LodashWrapper(value,this.__chain__)).thru(function(array){if(length&&!array.length)array.push(undefined);return array})});function wrapperChain(){return chain(this)}function wrapperCommit(){return new LodashWrapper(this.value(),
this.__chain__)}function wrapperNext(){if(this.__values__===undefined)this.__values__=toArray(this.value());var done=this.__index__>=this.__values__.length,value=done?undefined:this.__values__[this.__index__++];return{"done":done,"value":value}}function wrapperToIterator(){return this}function wrapperPlant(value){var result,parent=this;while(parent instanceof baseLodash){var clone=wrapperClone(parent);clone.__index__=0;clone.__values__=undefined;if(result)previous.__wrapped__=clone;else result=clone;
var previous=clone;parent=parent.__wrapped__}previous.__wrapped__=value;return result}function wrapperReverse(){var value=this.__wrapped__;if(value instanceof LazyWrapper){var wrapped=value;if(this.__actions__.length)wrapped=new LazyWrapper(this);wrapped=wrapped.reverse();wrapped.__actions__.push({"func":thru,"args":[reverse],"thisArg":undefined});return new LodashWrapper(wrapped,this.__chain__)}return this.thru(reverse)}function wrapperValue(){return baseWrapperValue(this.__wrapped__,this.__actions__)}
var countBy=createAggregator(function(result,value,key){if(hasOwnProperty.call(result,key))++result[key];else baseAssignValue(result,key,1)});function every(collection,predicate,guard){var func=isArray(collection)?arrayEvery:baseEvery;if(guard&&isIterateeCall(collection,predicate,guard))predicate=undefined;return func(collection,getIteratee(predicate,3))}function filter(collection,predicate){var func=isArray(collection)?arrayFilter:baseFilter;return func(collection,getIteratee(predicate,3))}var find=
createFind(findIndex);var findLast=createFind(findLastIndex);function flatMap(collection,iteratee){return baseFlatten(map(collection,iteratee),1)}function flatMapDeep(collection,iteratee){return baseFlatten(map(collection,iteratee),INFINITY)}function flatMapDepth(collection,iteratee,depth){depth=depth===undefined?1:toInteger(depth);return baseFlatten(map(collection,iteratee),depth)}function forEach(collection,iteratee){var func=isArray(collection)?arrayEach:baseEach;return func(collection,getIteratee(iteratee,
3))}function forEachRight(collection,iteratee){var func=isArray(collection)?arrayEachRight:baseEachRight;return func(collection,getIteratee(iteratee,3))}var groupBy=createAggregator(function(result,value,key){if(hasOwnProperty.call(result,key))result[key].push(value);else baseAssignValue(result,key,[value])});function includes(collection,value,fromIndex,guard){collection=isArrayLike(collection)?collection:values(collection);fromIndex=fromIndex&&!guard?toInteger(fromIndex):0;var length=collection.length;
if(fromIndex<0)fromIndex=nativeMax(length+fromIndex,0);return isString(collection)?fromIndex<=length&&collection.indexOf(value,fromIndex)>-1:!!length&&baseIndexOf(collection,value,fromIndex)>-1}var invokeMap=baseRest(function(collection,path,args){var index=-1,isFunc=typeof path=="function",result=isArrayLike(collection)?Array(collection.length):[];baseEach(collection,function(value){result[++index]=isFunc?apply(path,value,args):baseInvoke(value,path,args)});return result});var keyBy=createAggregator(function(result,
value,key){baseAssignValue(result,key,value)});function map(collection,iteratee){var func=isArray(collection)?arrayMap:baseMap;return func(collection,getIteratee(iteratee,3))}function orderBy(collection,iteratees,orders,guard){if(collection==null)return[];if(!isArray(iteratees))iteratees=iteratees==null?[]:[iteratees];orders=guard?undefined:orders;if(!isArray(orders))orders=orders==null?[]:[orders];return baseOrderBy(collection,iteratees,orders)}var partition=createAggregator(function(result,value,
key){result[key?0:1].push(value)},function(){return[[],[]]});function reduce(collection,iteratee,accumulator){var func=isArray(collection)?arrayReduce:baseReduce,initAccum=arguments.length<3;return func(collection,getIteratee(iteratee,4),accumulator,initAccum,baseEach)}function reduceRight(collection,iteratee,accumulator){var func=isArray(collection)?arrayReduceRight:baseReduce,initAccum=arguments.length<3;return func(collection,getIteratee(iteratee,4),accumulator,initAccum,baseEachRight)}function reject(collection,
predicate){var func=isArray(collection)?arrayFilter:baseFilter;return func(collection,negate(getIteratee(predicate,3)))}function sample(collection){var func=isArray(collection)?arraySample:baseSample;return func(collection)}function sampleSize(collection,n,guard){if(guard?isIterateeCall(collection,n,guard):n===undefined)n=1;else n=toInteger(n);var func=isArray(collection)?arraySampleSize:baseSampleSize;return func(collection,n)}function shuffle(collection){var func=isArray(collection)?arrayShuffle:
baseShuffle;return func(collection)}function size(collection){if(collection==null)return 0;if(isArrayLike(collection))return isString(collection)?stringSize(collection):collection.length;var tag=getTag(collection);if(tag==mapTag||tag==setTag)return collection.size;return baseKeys(collection).length}function some(collection,predicate,guard){var func=isArray(collection)?arraySome:baseSome;if(guard&&isIterateeCall(collection,predicate,guard))predicate=undefined;return func(collection,getIteratee(predicate,
3))}var sortBy=baseRest(function(collection,iteratees){if(collection==null)return[];var length=iteratees.length;if(length>1&&isIterateeCall(collection,iteratees[0],iteratees[1]))iteratees=[];else if(length>2&&isIterateeCall(iteratees[0],iteratees[1],iteratees[2]))iteratees=[iteratees[0]];return baseOrderBy(collection,baseFlatten(iteratees,1),[])});var now=ctxNow||function(){return root.Date.now()};function after(n,func){if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);n=toInteger(n);
return function(){if(--n<1)return func.apply(this,arguments)}}function ary(func,n,guard){n=guard?undefined:n;n=func&&n==null?func.length:n;return createWrap(func,WRAP_ARY_FLAG,undefined,undefined,undefined,undefined,n)}function before(n,func){var result;if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);n=toInteger(n);return function(){if(--n>0)result=func.apply(this,arguments);if(n<=1)func=undefined;return result}}var bind=baseRest(function(func,thisArg,partials){var bitmask=WRAP_BIND_FLAG;
if(partials.length){var holders=replaceHolders(partials,getHolder(bind));bitmask|=WRAP_PARTIAL_FLAG}return createWrap(func,bitmask,thisArg,partials,holders)});var bindKey=baseRest(function(object,key,partials){var bitmask=WRAP_BIND_FLAG|WRAP_BIND_KEY_FLAG;if(partials.length){var holders=replaceHolders(partials,getHolder(bindKey));bitmask|=WRAP_PARTIAL_FLAG}return createWrap(key,bitmask,object,partials,holders)});function curry(func,arity,guard){arity=guard?undefined:arity;var result=createWrap(func,
WRAP_CURRY_FLAG,undefined,undefined,undefined,undefined,undefined,arity);result.placeholder=curry.placeholder;return result}function curryRight(func,arity,guard){arity=guard?undefined:arity;var result=createWrap(func,WRAP_CURRY_RIGHT_FLAG,undefined,undefined,undefined,undefined,undefined,arity);result.placeholder=curryRight.placeholder;return result}function debounce(func,wait,options){var lastArgs,lastThis,maxWait,result,timerId,lastCallTime,lastInvokeTime=0,leading=false,maxing=false,trailing=true;
if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);wait=toNumber(wait)||0;if(isObject(options)){leading=!!options.leading;maxing="maxWait"in options;maxWait=maxing?nativeMax(toNumber(options.maxWait)||0,wait):maxWait;trailing="trailing"in options?!!options.trailing:trailing}function invokeFunc(time){var args=lastArgs,thisArg=lastThis;lastArgs=lastThis=undefined;lastInvokeTime=time;result=func.apply(thisArg,args);return result}function leadingEdge(time){lastInvokeTime=time;timerId=setTimeout(timerExpired,
wait);return leading?invokeFunc(time):result}function remainingWait(time){var timeSinceLastCall=time-lastCallTime,timeSinceLastInvoke=time-lastInvokeTime,timeWaiting=wait-timeSinceLastCall;return maxing?nativeMin(timeWaiting,maxWait-timeSinceLastInvoke):timeWaiting}function shouldInvoke(time){var timeSinceLastCall=time-lastCallTime,timeSinceLastInvoke=time-lastInvokeTime;return lastCallTime===undefined||timeSinceLastCall>=wait||timeSinceLastCall<0||maxing&&timeSinceLastInvoke>=maxWait}function timerExpired(){var time=
now();if(shouldInvoke(time))return trailingEdge(time);timerId=setTimeout(timerExpired,remainingWait(time))}function trailingEdge(time){timerId=undefined;if(trailing&&lastArgs)return invokeFunc(time);lastArgs=lastThis=undefined;return result}function cancel(){if(timerId!==undefined)clearTimeout(timerId);lastInvokeTime=0;lastArgs=lastCallTime=lastThis=timerId=undefined}function flush(){return timerId===undefined?result:trailingEdge(now())}function debounced(){var time=now(),isInvoking=shouldInvoke(time);
lastArgs=arguments;lastThis=this;lastCallTime=time;if(isInvoking){if(timerId===undefined)return leadingEdge(lastCallTime);if(maxing){timerId=setTimeout(timerExpired,wait);return invokeFunc(lastCallTime)}}if(timerId===undefined)timerId=setTimeout(timerExpired,wait);return result}debounced.cancel=cancel;debounced.flush=flush;return debounced}var defer=baseRest(function(func,args){return baseDelay(func,1,args)});var delay=baseRest(function(func,wait,args){return baseDelay(func,toNumber(wait)||0,args)});
function flip(func){return createWrap(func,WRAP_FLIP_FLAG)}function memoize(func,resolver){if(typeof func!="function"||resolver!=null&&typeof resolver!="function")throw new TypeError(FUNC_ERROR_TEXT);var memoized=function(){var args=arguments,key=resolver?resolver.apply(this,args):args[0],cache=memoized.cache;if(cache.has(key))return cache.get(key);var result=func.apply(this,args);memoized.cache=cache.set(key,result)||cache;return result};memoized.cache=new (memoize.Cache||MapCache);return memoized}
memoize.Cache=MapCache;function negate(predicate){if(typeof predicate!="function")throw new TypeError(FUNC_ERROR_TEXT);return function(){var args=arguments;switch(args.length){case 0:return!predicate.call(this);case 1:return!predicate.call(this,args[0]);case 2:return!predicate.call(this,args[0],args[1]);case 3:return!predicate.call(this,args[0],args[1],args[2])}return!predicate.apply(this,args)}}function once(func){return before(2,func)}var overArgs=castRest(function(func,transforms){transforms=transforms.length==
1&&isArray(transforms[0])?arrayMap(transforms[0],baseUnary(getIteratee())):arrayMap(baseFlatten(transforms,1),baseUnary(getIteratee()));var funcsLength=transforms.length;return baseRest(function(args){var index=-1,length=nativeMin(args.length,funcsLength);while(++index<length)args[index]=transforms[index].call(this,args[index]);return apply(func,this,args)})});var partial=baseRest(function(func,partials){var holders=replaceHolders(partials,getHolder(partial));return createWrap(func,WRAP_PARTIAL_FLAG,
undefined,partials,holders)});var partialRight=baseRest(function(func,partials){var holders=replaceHolders(partials,getHolder(partialRight));return createWrap(func,WRAP_PARTIAL_RIGHT_FLAG,undefined,partials,holders)});var rearg=flatRest(function(func,indexes){return createWrap(func,WRAP_REARG_FLAG,undefined,undefined,undefined,indexes)});function rest(func,start){if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);start=start===undefined?start:toInteger(start);return baseRest(func,start)}
function spread(func,start){if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);start=start==null?0:nativeMax(toInteger(start),0);return baseRest(function(args){var array=args[start],otherArgs=castSlice(args,0,start);if(array)arrayPush(otherArgs,array);return apply(func,this,otherArgs)})}function throttle(func,wait,options){var leading=true,trailing=true;if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);if(isObject(options)){leading="leading"in options?!!options.leading:
leading;trailing="trailing"in options?!!options.trailing:trailing}return debounce(func,wait,{"leading":leading,"maxWait":wait,"trailing":trailing})}function unary(func){return ary(func,1)}function wrap(value,wrapper){return partial(castFunction(wrapper),value)}function castArray(){if(!arguments.length)return[];var value=arguments[0];return isArray(value)?value:[value]}function clone(value){return baseClone(value,CLONE_SYMBOLS_FLAG)}function cloneWith(value,customizer){customizer=typeof customizer==
"function"?customizer:undefined;return baseClone(value,CLONE_SYMBOLS_FLAG,customizer)}function cloneDeep(value){return baseClone(value,CLONE_DEEP_FLAG|CLONE_SYMBOLS_FLAG)}function cloneDeepWith(value,customizer){customizer=typeof customizer=="function"?customizer:undefined;return baseClone(value,CLONE_DEEP_FLAG|CLONE_SYMBOLS_FLAG,customizer)}function conformsTo(object,source){return source==null||baseConformsTo(object,source,keys(source))}function eq(value,other){return value===other||value!==value&&
other!==other}var gt=createRelationalOperation(baseGt);var gte=createRelationalOperation(function(value,other){return value>=other});var isArguments=baseIsArguments(function(){return arguments}())?baseIsArguments:function(value){return isObjectLike(value)&&hasOwnProperty.call(value,"callee")&&!propertyIsEnumerable.call(value,"callee")};var isArray=Array.isArray;var isArrayBuffer=nodeIsArrayBuffer?baseUnary(nodeIsArrayBuffer):baseIsArrayBuffer;function isArrayLike(value){return value!=null&&isLength(value.length)&&
!isFunction(value)}function isArrayLikeObject(value){return isObjectLike(value)&&isArrayLike(value)}function isBoolean(value){return value===true||value===false||isObjectLike(value)&&baseGetTag(value)==boolTag}var isBuffer=nativeIsBuffer||stubFalse;var isDate=nodeIsDate?baseUnary(nodeIsDate):baseIsDate;function isElement(value){return isObjectLike(value)&&value.nodeType===1&&!isPlainObject(value)}function isEmpty(value){if(value==null)return true;if(isArrayLike(value)&&(isArray(value)||typeof value==
"string"||typeof value.splice=="function"||isBuffer(value)||isTypedArray(value)||isArguments(value)))return!value.length;var tag=getTag(value);if(tag==mapTag||tag==setTag)return!value.size;if(isPrototype(value))return!baseKeys(value).length;for(var key in value)if(hasOwnProperty.call(value,key))return false;return true}function isEqual(value,other){return baseIsEqual(value,other)}function isEqualWith(value,other,customizer){customizer=typeof customizer=="function"?customizer:undefined;var result=
customizer?customizer(value,other):undefined;return result===undefined?baseIsEqual(value,other,undefined,customizer):!!result}function isError(value){if(!isObjectLike(value))return false;var tag=baseGetTag(value);return tag==errorTag||tag==domExcTag||typeof value.message=="string"&&typeof value.name=="string"&&!isPlainObject(value)}function isFinite(value){return typeof value=="number"&&nativeIsFinite(value)}function isFunction(value){if(!isObject(value))return false;var tag=baseGetTag(value);return tag==
funcTag||tag==genTag||tag==asyncTag||tag==proxyTag}function isInteger(value){return typeof value=="number"&&value==toInteger(value)}function isLength(value){return typeof value=="number"&&value>-1&&value%1==0&&value<=MAX_SAFE_INTEGER}function isObject(value){var type=typeof value;return value!=null&&(type=="object"||type=="function")}function isObjectLike(value){return value!=null&&typeof value=="object"}var isMap=nodeIsMap?baseUnary(nodeIsMap):baseIsMap;function isMatch(object,source){return object===
source||baseIsMatch(object,source,getMatchData(source))}function isMatchWith(object,source,customizer){customizer=typeof customizer=="function"?customizer:undefined;return baseIsMatch(object,source,getMatchData(source),customizer)}function isNaN(value){return isNumber(value)&&value!=+value}function isNative(value){if(isMaskable(value))throw new Error(CORE_ERROR_TEXT);return baseIsNative(value)}function isNull(value){return value===null}function isNil(value){return value==null}function isNumber(value){return typeof value==
"number"||isObjectLike(value)&&baseGetTag(value)==numberTag}function isPlainObject(value){if(!isObjectLike(value)||baseGetTag(value)!=objectTag)return false;var proto=getPrototype(value);if(proto===null)return true;var Ctor=hasOwnProperty.call(proto,"constructor")&&proto.constructor;return typeof Ctor=="function"&&Ctor instanceof Ctor&&funcToString.call(Ctor)==objectCtorString}var isRegExp=nodeIsRegExp?baseUnary(nodeIsRegExp):baseIsRegExp;function isSafeInteger(value){return isInteger(value)&&value>=
-MAX_SAFE_INTEGER&&value<=MAX_SAFE_INTEGER}var isSet=nodeIsSet?baseUnary(nodeIsSet):baseIsSet;function isString(value){return typeof value=="string"||!isArray(value)&&isObjectLike(value)&&baseGetTag(value)==stringTag}function isSymbol(value){return typeof value=="symbol"||isObjectLike(value)&&baseGetTag(value)==symbolTag}var isTypedArray=nodeIsTypedArray?baseUnary(nodeIsTypedArray):baseIsTypedArray;function isUndefined(value){return value===undefined}function isWeakMap(value){return isObjectLike(value)&&
getTag(value)==weakMapTag}function isWeakSet(value){return isObjectLike(value)&&baseGetTag(value)==weakSetTag}var lt=createRelationalOperation(baseLt);var lte=createRelationalOperation(function(value,other){return value<=other});function toArray(value){if(!value)return[];if(isArrayLike(value))return isString(value)?stringToArray(value):copyArray(value);if(symIterator&&value[symIterator])return iteratorToArray(value[symIterator]());var tag=getTag(value),func=tag==mapTag?mapToArray:tag==setTag?setToArray:
values;return func(value)}function toFinite(value){if(!value)return value===0?value:0;value=toNumber(value);if(value===INFINITY||value===-INFINITY){var sign=value<0?-1:1;return sign*MAX_INTEGER}return value===value?value:0}function toInteger(value){var result=toFinite(value),remainder=result%1;return result===result?remainder?result-remainder:result:0}function toLength(value){return value?baseClamp(toInteger(value),0,MAX_ARRAY_LENGTH):0}function toNumber(value){if(typeof value=="number")return value;
if(isSymbol(value))return NAN;if(isObject(value)){var other=typeof value.valueOf=="function"?value.valueOf():value;value=isObject(other)?other+"":other}if(typeof value!="string")return value===0?value:+value;value=value.replace(reTrim,"");var isBinary=reIsBinary.test(value);return isBinary||reIsOctal.test(value)?freeParseInt(value.slice(2),isBinary?2:8):reIsBadHex.test(value)?NAN:+value}function toPlainObject(value){return copyObject(value,keysIn(value))}function toSafeInteger(value){return value?
baseClamp(toInteger(value),-MAX_SAFE_INTEGER,MAX_SAFE_INTEGER):value===0?value:0}function toString(value){return value==null?"":baseToString(value)}var assign=createAssigner(function(object,source){if(isPrototype(source)||isArrayLike(source)){copyObject(source,keys(source),object);return}for(var key in source)if(hasOwnProperty.call(source,key))assignValue(object,key,source[key])});var assignIn=createAssigner(function(object,source){copyObject(source,keysIn(source),object)});var assignInWith=createAssigner(function(object,
source,srcIndex,customizer){copyObject(source,keysIn(source),object,customizer)});var assignWith=createAssigner(function(object,source,srcIndex,customizer){copyObject(source,keys(source),object,customizer)});var at=flatRest(baseAt);function create(prototype,properties){var result=baseCreate(prototype);return properties==null?result:baseAssign(result,properties)}var defaults=baseRest(function(object,sources){object=Object(object);var index=-1;var length=sources.length;var guard=length>2?sources[2]:
undefined;if(guard&&isIterateeCall(sources[0],sources[1],guard))length=1;while(++index<length){var source=sources[index];var props=keysIn(source);var propsIndex=-1;var propsLength=props.length;while(++propsIndex<propsLength){var key=props[propsIndex];var value=object[key];if(value===undefined||eq(value,objectProto[key])&&!hasOwnProperty.call(object,key))object[key]=source[key]}}return object});var defaultsDeep=baseRest(function(args){args.push(undefined,customDefaultsMerge);return apply(mergeWith,
undefined,args)});function findKey(object,predicate){return baseFindKey(object,getIteratee(predicate,3),baseForOwn)}function findLastKey(object,predicate){return baseFindKey(object,getIteratee(predicate,3),baseForOwnRight)}function forIn(object,iteratee){return object==null?object:baseFor(object,getIteratee(iteratee,3),keysIn)}function forInRight(object,iteratee){return object==null?object:baseForRight(object,getIteratee(iteratee,3),keysIn)}function forOwn(object,iteratee){return object&&baseForOwn(object,
getIteratee(iteratee,3))}function forOwnRight(object,iteratee){return object&&baseForOwnRight(object,getIteratee(iteratee,3))}function functions(object){return object==null?[]:baseFunctions(object,keys(object))}function functionsIn(object){return object==null?[]:baseFunctions(object,keysIn(object))}function get(object,path,defaultValue){var result=object==null?undefined:baseGet(object,path);return result===undefined?defaultValue:result}function has(object,path){return object!=null&&hasPath(object,
path,baseHas)}function hasIn(object,path){return object!=null&&hasPath(object,path,baseHasIn)}var invert=createInverter(function(result,value,key){if(value!=null&&typeof value.toString!="function")value=nativeObjectToString.call(value);result[value]=key},constant(identity));var invertBy=createInverter(function(result,value,key){if(value!=null&&typeof value.toString!="function")value=nativeObjectToString.call(value);if(hasOwnProperty.call(result,value))result[value].push(key);else result[value]=[key]},
getIteratee);var invoke=baseRest(baseInvoke);function keys(object){return isArrayLike(object)?arrayLikeKeys(object):baseKeys(object)}function keysIn(object){return isArrayLike(object)?arrayLikeKeys(object,true):baseKeysIn(object)}function mapKeys(object,iteratee){var result={};iteratee=getIteratee(iteratee,3);baseForOwn(object,function(value,key,object){baseAssignValue(result,iteratee(value,key,object),value)});return result}function mapValues(object,iteratee){var result={};iteratee=getIteratee(iteratee,
3);baseForOwn(object,function(value,key,object){baseAssignValue(result,key,iteratee(value,key,object))});return result}var merge=createAssigner(function(object,source,srcIndex){baseMerge(object,source,srcIndex)});var mergeWith=createAssigner(function(object,source,srcIndex,customizer){baseMerge(object,source,srcIndex,customizer)});var omit=flatRest(function(object,paths){var result={};if(object==null)return result;var isDeep=false;paths=arrayMap(paths,function(path){path=castPath(path,object);isDeep||
(isDeep=path.length>1);return path});copyObject(object,getAllKeysIn(object),result);if(isDeep)result=baseClone(result,CLONE_DEEP_FLAG|CLONE_FLAT_FLAG|CLONE_SYMBOLS_FLAG,customOmitClone);var length=paths.length;while(length--)baseUnset(result,paths[length]);return result});function omitBy(object,predicate){return pickBy(object,negate(getIteratee(predicate)))}var pick=flatRest(function(object,paths){return object==null?{}:basePick(object,paths)});function pickBy(object,predicate){if(object==null)return{};
var props=arrayMap(getAllKeysIn(object),function(prop){return[prop]});predicate=getIteratee(predicate);return basePickBy(object,props,function(value,path){return predicate(value,path[0])})}function result(object,path,defaultValue){path=castPath(path,object);var index=-1,length=path.length;if(!length){length=1;object=undefined}while(++index<length){var value=object==null?undefined:object[toKey(path[index])];if(value===undefined){index=length;value=defaultValue}object=isFunction(value)?value.call(object):
value}return object}function set(object,path,value){return object==null?object:baseSet(object,path,value)}function setWith(object,path,value,customizer){customizer=typeof customizer=="function"?customizer:undefined;return object==null?object:baseSet(object,path,value,customizer)}var toPairs=createToPairs(keys);var toPairsIn=createToPairs(keysIn);function transform(object,iteratee,accumulator){var isArr=isArray(object),isArrLike=isArr||isBuffer(object)||isTypedArray(object);iteratee=getIteratee(iteratee,
4);if(accumulator==null){var Ctor=object&&object.constructor;if(isArrLike)accumulator=isArr?new Ctor:[];else if(isObject(object))accumulator=isFunction(Ctor)?baseCreate(getPrototype(object)):{};else accumulator={}}(isArrLike?arrayEach:baseForOwn)(object,function(value,index,object){return iteratee(accumulator,value,index,object)});return accumulator}function unset(object,path){return object==null?true:baseUnset(object,path)}function update(object,path,updater){return object==null?object:baseUpdate(object,
path,castFunction(updater))}function updateWith(object,path,updater,customizer){customizer=typeof customizer=="function"?customizer:undefined;return object==null?object:baseUpdate(object,path,castFunction(updater),customizer)}function values(object){return object==null?[]:baseValues(object,keys(object))}function valuesIn(object){return object==null?[]:baseValues(object,keysIn(object))}function clamp(number,lower,upper){if(upper===undefined){upper=lower;lower=undefined}if(upper!==undefined){upper=
toNumber(upper);upper=upper===upper?upper:0}if(lower!==undefined){lower=toNumber(lower);lower=lower===lower?lower:0}return baseClamp(toNumber(number),lower,upper)}function inRange(number,start,end){start=toFinite(start);if(end===undefined){end=start;start=0}else end=toFinite(end);number=toNumber(number);return baseInRange(number,start,end)}function random(lower,upper,floating){if(floating&&typeof floating!="boolean"&&isIterateeCall(lower,upper,floating))upper=floating=undefined;if(floating===undefined)if(typeof upper==
"boolean"){floating=upper;upper=undefined}else if(typeof lower=="boolean"){floating=lower;lower=undefined}if(lower===undefined&&upper===undefined){lower=0;upper=1}else{lower=toFinite(lower);if(upper===undefined){upper=lower;lower=0}else upper=toFinite(upper)}if(lower>upper){var temp=lower;lower=upper;upper=temp}if(floating||lower%1||upper%1){var rand=nativeRandom();return nativeMin(lower+rand*(upper-lower+freeParseFloat("1e-"+((rand+"").length-1))),upper)}return baseRandom(lower,upper)}var camelCase=
createCompounder(function(result,word,index){word=word.toLowerCase();return result+(index?capitalize(word):word)});function capitalize(string){return upperFirst(toString(string).toLowerCase())}function deburr(string){string=toString(string);return string&&string.replace(reLatin,deburrLetter).replace(reComboMark,"")}function endsWith(string,target,position){string=toString(string);target=baseToString(target);var length=string.length;position=position===undefined?length:baseClamp(toInteger(position),
0,length);var end=position;position-=target.length;return position>=0&&string.slice(position,end)==target}function escape(string){string=toString(string);return string&&reHasUnescapedHtml.test(string)?string.replace(reUnescapedHtml,escapeHtmlChar):string}function escapeRegExp(string){string=toString(string);return string&&reHasRegExpChar.test(string)?string.replace(reRegExpChar,"\\$\x26"):string}var kebabCase=createCompounder(function(result,word,index){return result+(index?"-":"")+word.toLowerCase()});
var lowerCase=createCompounder(function(result,word,index){return result+(index?" ":"")+word.toLowerCase()});var lowerFirst=createCaseFirst("toLowerCase");function pad(string,length,chars){string=toString(string);length=toInteger(length);var strLength=length?stringSize(string):0;if(!length||strLength>=length)return string;var mid=(length-strLength)/2;return createPadding(nativeFloor(mid),chars)+string+createPadding(nativeCeil(mid),chars)}function padEnd(string,length,chars){string=toString(string);
length=toInteger(length);var strLength=length?stringSize(string):0;return length&&strLength<length?string+createPadding(length-strLength,chars):string}function padStart(string,length,chars){string=toString(string);length=toInteger(length);var strLength=length?stringSize(string):0;return length&&strLength<length?createPadding(length-strLength,chars)+string:string}function parseInt(string,radix,guard){if(guard||radix==null)radix=0;else if(radix)radix=+radix;return nativeParseInt(toString(string).replace(reTrimStart,
""),radix||0)}function repeat(string,n,guard){if(guard?isIterateeCall(string,n,guard):n===undefined)n=1;else n=toInteger(n);return baseRepeat(toString(string),n)}function replace(){var args=arguments,string=toString(args[0]);return args.length<3?string:string.replace(args[1],args[2])}var snakeCase=createCompounder(function(result,word,index){return result+(index?"_":"")+word.toLowerCase()});function split(string,separator,limit){if(limit&&typeof limit!="number"&&isIterateeCall(string,separator,limit))separator=
limit=undefined;limit=limit===undefined?MAX_ARRAY_LENGTH:limit>>>0;if(!limit)return[];string=toString(string);if(string&&(typeof separator=="string"||separator!=null&&!isRegExp(separator))){separator=baseToString(separator);if(!separator&&hasUnicode(string))return castSlice(stringToArray(string),0,limit)}return string.split(separator,limit)}var startCase=createCompounder(function(result,word,index){return result+(index?" ":"")+upperFirst(word)});function startsWith(string,target,position){string=
toString(string);position=position==null?0:baseClamp(toInteger(position),0,string.length);target=baseToString(target);return string.slice(position,position+target.length)==target}function template(string,options,guard){var settings=lodash.templateSettings;if(guard&&isIterateeCall(string,options,guard))options=undefined;string=toString(string);options=assignInWith({},options,settings,customDefaultsAssignIn);var imports=assignInWith({},options.imports,settings.imports,customDefaultsAssignIn),importsKeys=
keys(imports),importsValues=baseValues(imports,importsKeys);var isEscaping,isEvaluating,index=0,interpolate=options.interpolate||reNoMatch,source="__p +\x3d '";var reDelimiters=RegExp((options.escape||reNoMatch).source+"|"+interpolate.source+"|"+(interpolate===reInterpolate?reEsTemplate:reNoMatch).source+"|"+(options.evaluate||reNoMatch).source+"|$","g");var sourceURL="//# sourceURL\x3d"+("sourceURL"in options?options.sourceURL:"lodash.templateSources["+ ++templateCounter+"]")+"\n";string.replace(reDelimiters,
function(match,escapeValue,interpolateValue,esTemplateValue,evaluateValue,offset){interpolateValue||(interpolateValue=esTemplateValue);source+=string.slice(index,offset).replace(reUnescapedString,escapeStringChar);if(escapeValue){isEscaping=true;source+="' +\n__e("+escapeValue+") +\n'"}if(evaluateValue){isEvaluating=true;source+="';\n"+evaluateValue+";\n__p +\x3d '"}if(interpolateValue)source+="' +\n((__t \x3d ("+interpolateValue+")) \x3d\x3d null ? '' : __t) +\n'";index=offset+match.length;return match});
source+="';\n";var variable=options.variable;if(!variable)source="with (obj) {\n"+source+"\n}\n";source=(isEvaluating?source.replace(reEmptyStringLeading,""):source).replace(reEmptyStringMiddle,"$1").replace(reEmptyStringTrailing,"$1;");source="function("+(variable||"obj")+") {\n"+(variable?"":"obj || (obj \x3d {});\n")+"var __t, __p \x3d ''"+(isEscaping?", __e \x3d _.escape":"")+(isEvaluating?", __j \x3d Array.prototype.join;\n"+"function print() { __p +\x3d __j.call(arguments, '') }\n":";\n")+source+
"return __p\n}";var result=attempt(function(){return Function(importsKeys,sourceURL+"return "+source).apply(undefined,importsValues)});result.source=source;if(isError(result))throw result;return result}function toLower(value){return toString(value).toLowerCase()}function toUpper(value){return toString(value).toUpperCase()}function trim(string,chars,guard){string=toString(string);if(string&&(guard||chars===undefined))return string.replace(reTrim,"");if(!string||!(chars=baseToString(chars)))return string;
var strSymbols=stringToArray(string),chrSymbols=stringToArray(chars),start=charsStartIndex(strSymbols,chrSymbols),end=charsEndIndex(strSymbols,chrSymbols)+1;return castSlice(strSymbols,start,end).join("")}function trimEnd(string,chars,guard){string=toString(string);if(string&&(guard||chars===undefined))return string.replace(reTrimEnd,"");if(!string||!(chars=baseToString(chars)))return string;var strSymbols=stringToArray(string),end=charsEndIndex(strSymbols,stringToArray(chars))+1;return castSlice(strSymbols,
0,end).join("")}function trimStart(string,chars,guard){string=toString(string);if(string&&(guard||chars===undefined))return string.replace(reTrimStart,"");if(!string||!(chars=baseToString(chars)))return string;var strSymbols=stringToArray(string),start=charsStartIndex(strSymbols,stringToArray(chars));return castSlice(strSymbols,start).join("")}function truncate(string,options){var length=DEFAULT_TRUNC_LENGTH,omission=DEFAULT_TRUNC_OMISSION;if(isObject(options)){var separator="separator"in options?
options.separator:separator;length="length"in options?toInteger(options.length):length;omission="omission"in options?baseToString(options.omission):omission}string=toString(string);var strLength=string.length;if(hasUnicode(string)){var strSymbols=stringToArray(string);strLength=strSymbols.length}if(length>=strLength)return string;var end=length-stringSize(omission);if(end<1)return omission;var result=strSymbols?castSlice(strSymbols,0,end).join(""):string.slice(0,end);if(separator===undefined)return result+
omission;if(strSymbols)end+=result.length-end;if(isRegExp(separator)){if(string.slice(end).search(separator)){var match,substring=result;if(!separator.global)separator=RegExp(separator.source,toString(reFlags.exec(separator))+"g");separator.lastIndex=0;while(match=separator.exec(substring))var newEnd=match.index;result=result.slice(0,newEnd===undefined?end:newEnd)}}else if(string.indexOf(baseToString(separator),end)!=end){var index=result.lastIndexOf(separator);if(index>-1)result=result.slice(0,index)}return result+
omission}function unescape(string){string=toString(string);return string&&reHasEscapedHtml.test(string)?string.replace(reEscapedHtml,unescapeHtmlChar):string}var upperCase=createCompounder(function(result,word,index){return result+(index?" ":"")+word.toUpperCase()});var upperFirst=createCaseFirst("toUpperCase");function words(string,pattern,guard){string=toString(string);pattern=guard?undefined:pattern;if(pattern===undefined)return hasUnicodeWord(string)?unicodeWords(string):asciiWords(string);return string.match(pattern)||
[]}var attempt=baseRest(function(func,args){try{return apply(func,undefined,args)}catch(e){return isError(e)?e:new Error(e)}});var bindAll=flatRest(function(object,methodNames){arrayEach(methodNames,function(key){key=toKey(key);baseAssignValue(object,key,bind(object[key],object))});return object});function cond(pairs){var length=pairs==null?0:pairs.length,toIteratee=getIteratee();pairs=!length?[]:arrayMap(pairs,function(pair){if(typeof pair[1]!="function")throw new TypeError(FUNC_ERROR_TEXT);return[toIteratee(pair[0]),
pair[1]]});return baseRest(function(args){var index=-1;while(++index<length){var pair=pairs[index];if(apply(pair[0],this,args))return apply(pair[1],this,args)}})}function conforms(source){return baseConforms(baseClone(source,CLONE_DEEP_FLAG))}function constant(value){return function(){return value}}function defaultTo(value,defaultValue){return value==null||value!==value?defaultValue:value}var flow=createFlow();var flowRight=createFlow(true);function identity(value){return value}function iteratee(func){return baseIteratee(typeof func==
"function"?func:baseClone(func,CLONE_DEEP_FLAG))}function matches(source){return baseMatches(baseClone(source,CLONE_DEEP_FLAG))}function matchesProperty(path,srcValue){return baseMatchesProperty(path,baseClone(srcValue,CLONE_DEEP_FLAG))}var method=baseRest(function(path,args){return function(object){return baseInvoke(object,path,args)}});var methodOf=baseRest(function(object,args){return function(path){return baseInvoke(object,path,args)}});function mixin(object,source,options){var props=keys(source),
methodNames=baseFunctions(source,props);if(options==null&&!(isObject(source)&&(methodNames.length||!props.length))){options=source;source=object;object=this;methodNames=baseFunctions(source,keys(source))}var chain=!(isObject(options)&&"chain"in options)||!!options.chain,isFunc=isFunction(object);arrayEach(methodNames,function(methodName){var func=source[methodName];object[methodName]=func;if(isFunc)object.prototype[methodName]=function(){var chainAll=this.__chain__;if(chain||chainAll){var result=
object(this.__wrapped__),actions=result.__actions__=copyArray(this.__actions__);actions.push({"func":func,"args":arguments,"thisArg":object});result.__chain__=chainAll;return result}return func.apply(object,arrayPush([this.value()],arguments))}});return object}function noConflict(){if(root._===this)root._=oldDash;return this}function noop(){}function nthArg(n){n=toInteger(n);return baseRest(function(args){return baseNth(args,n)})}var over=createOver(arrayMap);var overEvery=createOver(arrayEvery);
var overSome=createOver(arraySome);function property(path){return isKey(path)?baseProperty(toKey(path)):basePropertyDeep(path)}function propertyOf(object){return function(path){return object==null?undefined:baseGet(object,path)}}var range=createRange();var rangeRight=createRange(true);function stubArray(){return[]}function stubFalse(){return false}function stubObject(){return{}}function stubString(){return""}function stubTrue(){return true}function times(n,iteratee){n=toInteger(n);if(n<1||n>MAX_SAFE_INTEGER)return[];
var index=MAX_ARRAY_LENGTH,length=nativeMin(n,MAX_ARRAY_LENGTH);iteratee=getIteratee(iteratee);n-=MAX_ARRAY_LENGTH;var result=baseTimes(length,iteratee);while(++index<n)iteratee(index);return result}function toPath(value){if(isArray(value))return arrayMap(value,toKey);return isSymbol(value)?[value]:copyArray(stringToPath(toString(value)))}function uniqueId(prefix){var id=++idCounter;return toString(prefix)+id}var add=createMathOperation(function(augend,addend){return augend+addend},0);var ceil=createRound("ceil");
var divide=createMathOperation(function(dividend,divisor){return dividend/divisor},1);var floor=createRound("floor");function max(array){return array&&array.length?baseExtremum(array,identity,baseGt):undefined}function maxBy(array,iteratee){return array&&array.length?baseExtremum(array,getIteratee(iteratee,2),baseGt):undefined}function mean(array){return baseMean(array,identity)}function meanBy(array,iteratee){return baseMean(array,getIteratee(iteratee,2))}function min(array){return array&&array.length?
baseExtremum(array,identity,baseLt):undefined}function minBy(array,iteratee){return array&&array.length?baseExtremum(array,getIteratee(iteratee,2),baseLt):undefined}var multiply=createMathOperation(function(multiplier,multiplicand){return multiplier*multiplicand},1);var round=createRound("round");var subtract=createMathOperation(function(minuend,subtrahend){return minuend-subtrahend},0);function sum(array){return array&&array.length?baseSum(array,identity):0}function sumBy(array,iteratee){return array&&
array.length?baseSum(array,getIteratee(iteratee,2)):0}lodash.after=after;lodash.ary=ary;lodash.assign=assign;lodash.assignIn=assignIn;lodash.assignInWith=assignInWith;lodash.assignWith=assignWith;lodash.at=at;lodash.before=before;lodash.bind=bind;lodash.bindAll=bindAll;lodash.bindKey=bindKey;lodash.castArray=castArray;lodash.chain=chain;lodash.chunk=chunk;lodash.compact=compact;lodash.concat=concat;lodash.cond=cond;lodash.conforms=conforms;lodash.constant=constant;lodash.countBy=countBy;lodash.create=
create;lodash.curry=curry;lodash.curryRight=curryRight;lodash.debounce=debounce;lodash.defaults=defaults;lodash.defaultsDeep=defaultsDeep;lodash.defer=defer;lodash.delay=delay;lodash.difference=difference;lodash.differenceBy=differenceBy;lodash.differenceWith=differenceWith;lodash.drop=drop;lodash.dropRight=dropRight;lodash.dropRightWhile=dropRightWhile;lodash.dropWhile=dropWhile;lodash.fill=fill;lodash.filter=filter;lodash.flatMap=flatMap;lodash.flatMapDeep=flatMapDeep;lodash.flatMapDepth=flatMapDepth;
lodash.flatten=flatten;lodash.flattenDeep=flattenDeep;lodash.flattenDepth=flattenDepth;lodash.flip=flip;lodash.flow=flow;lodash.flowRight=flowRight;lodash.fromPairs=fromPairs;lodash.functions=functions;lodash.functionsIn=functionsIn;lodash.groupBy=groupBy;lodash.initial=initial;lodash.intersection=intersection;lodash.intersectionBy=intersectionBy;lodash.intersectionWith=intersectionWith;lodash.invert=invert;lodash.invertBy=invertBy;lodash.invokeMap=invokeMap;lodash.iteratee=iteratee;lodash.keyBy=
keyBy;lodash.keys=keys;lodash.keysIn=keysIn;lodash.map=map;lodash.mapKeys=mapKeys;lodash.mapValues=mapValues;lodash.matches=matches;lodash.matchesProperty=matchesProperty;lodash.memoize=memoize;lodash.merge=merge;lodash.mergeWith=mergeWith;lodash.method=method;lodash.methodOf=methodOf;lodash.mixin=mixin;lodash.negate=negate;lodash.nthArg=nthArg;lodash.omit=omit;lodash.omitBy=omitBy;lodash.once=once;lodash.orderBy=orderBy;lodash.over=over;lodash.overArgs=overArgs;lodash.overEvery=overEvery;lodash.overSome=
overSome;lodash.partial=partial;lodash.partialRight=partialRight;lodash.partition=partition;lodash.pick=pick;lodash.pickBy=pickBy;lodash.property=property;lodash.propertyOf=propertyOf;lodash.pull=pull;lodash.pullAll=pullAll;lodash.pullAllBy=pullAllBy;lodash.pullAllWith=pullAllWith;lodash.pullAt=pullAt;lodash.range=range;lodash.rangeRight=rangeRight;lodash.rearg=rearg;lodash.reject=reject;lodash.remove=remove;lodash.rest=rest;lodash.reverse=reverse;lodash.sampleSize=sampleSize;lodash.set=set;lodash.setWith=
setWith;lodash.shuffle=shuffle;lodash.slice=slice;lodash.sortBy=sortBy;lodash.sortedUniq=sortedUniq;lodash.sortedUniqBy=sortedUniqBy;lodash.split=split;lodash.spread=spread;lodash.tail=tail;lodash.take=take;lodash.takeRight=takeRight;lodash.takeRightWhile=takeRightWhile;lodash.takeWhile=takeWhile;lodash.tap=tap;lodash.throttle=throttle;lodash.thru=thru;lodash.toArray=toArray;lodash.toPairs=toPairs;lodash.toPairsIn=toPairsIn;lodash.toPath=toPath;lodash.toPlainObject=toPlainObject;lodash.transform=
transform;lodash.unary=unary;lodash.union=union;lodash.unionBy=unionBy;lodash.unionWith=unionWith;lodash.uniq=uniq;lodash.uniqBy=uniqBy;lodash.uniqWith=uniqWith;lodash.unset=unset;lodash.unzip=unzip;lodash.unzipWith=unzipWith;lodash.update=update;lodash.updateWith=updateWith;lodash.values=values;lodash.valuesIn=valuesIn;lodash.without=without;lodash.words=words;lodash.wrap=wrap;lodash.xor=xor;lodash.xorBy=xorBy;lodash.xorWith=xorWith;lodash.zip=zip;lodash.zipObject=zipObject;lodash.zipObjectDeep=
zipObjectDeep;lodash.zipWith=zipWith;lodash.entries=toPairs;lodash.entriesIn=toPairsIn;lodash.extend=assignIn;lodash.extendWith=assignInWith;mixin(lodash,lodash);lodash.add=add;lodash.attempt=attempt;lodash.camelCase=camelCase;lodash.capitalize=capitalize;lodash.ceil=ceil;lodash.clamp=clamp;lodash.clone=clone;lodash.cloneDeep=cloneDeep;lodash.cloneDeepWith=cloneDeepWith;lodash.cloneWith=cloneWith;lodash.conformsTo=conformsTo;lodash.deburr=deburr;lodash.defaultTo=defaultTo;lodash.divide=divide;lodash.endsWith=
endsWith;lodash.eq=eq;lodash.escape=escape;lodash.escapeRegExp=escapeRegExp;lodash.every=every;lodash.find=find;lodash.findIndex=findIndex;lodash.findKey=findKey;lodash.findLast=findLast;lodash.findLastIndex=findLastIndex;lodash.findLastKey=findLastKey;lodash.floor=floor;lodash.forEach=forEach;lodash.forEachRight=forEachRight;lodash.forIn=forIn;lodash.forInRight=forInRight;lodash.forOwn=forOwn;lodash.forOwnRight=forOwnRight;lodash.get=get;lodash.gt=gt;lodash.gte=gte;lodash.has=has;lodash.hasIn=hasIn;
lodash.head=head;lodash.identity=identity;lodash.includes=includes;lodash.indexOf=indexOf;lodash.inRange=inRange;lodash.invoke=invoke;lodash.isArguments=isArguments;lodash.isArray=isArray;lodash.isArrayBuffer=isArrayBuffer;lodash.isArrayLike=isArrayLike;lodash.isArrayLikeObject=isArrayLikeObject;lodash.isBoolean=isBoolean;lodash.isBuffer=isBuffer;lodash.isDate=isDate;lodash.isElement=isElement;lodash.isEmpty=isEmpty;lodash.isEqual=isEqual;lodash.isEqualWith=isEqualWith;lodash.isError=isError;lodash.isFinite=
isFinite;lodash.isFunction=isFunction;lodash.isInteger=isInteger;lodash.isLength=isLength;lodash.isMap=isMap;lodash.isMatch=isMatch;lodash.isMatchWith=isMatchWith;lodash.isNaN=isNaN;lodash.isNative=isNative;lodash.isNil=isNil;lodash.isNull=isNull;lodash.isNumber=isNumber;lodash.isObject=isObject;lodash.isObjectLike=isObjectLike;lodash.isPlainObject=isPlainObject;lodash.isRegExp=isRegExp;lodash.isSafeInteger=isSafeInteger;lodash.isSet=isSet;lodash.isString=isString;lodash.isSymbol=isSymbol;lodash.isTypedArray=
isTypedArray;lodash.isUndefined=isUndefined;lodash.isWeakMap=isWeakMap;lodash.isWeakSet=isWeakSet;lodash.join=join;lodash.kebabCase=kebabCase;lodash.last=last;lodash.lastIndexOf=lastIndexOf;lodash.lowerCase=lowerCase;lodash.lowerFirst=lowerFirst;lodash.lt=lt;lodash.lte=lte;lodash.max=max;lodash.maxBy=maxBy;lodash.mean=mean;lodash.meanBy=meanBy;lodash.min=min;lodash.minBy=minBy;lodash.stubArray=stubArray;lodash.stubFalse=stubFalse;lodash.stubObject=stubObject;lodash.stubString=stubString;lodash.stubTrue=
stubTrue;lodash.multiply=multiply;lodash.nth=nth;lodash.noConflict=noConflict;lodash.noop=noop;lodash.now=now;lodash.pad=pad;lodash.padEnd=padEnd;lodash.padStart=padStart;lodash.parseInt=parseInt;lodash.random=random;lodash.reduce=reduce;lodash.reduceRight=reduceRight;lodash.repeat=repeat;lodash.replace=replace;lodash.result=result;lodash.round=round;lodash.runInContext=runInContext;lodash.sample=sample;lodash.size=size;lodash.snakeCase=snakeCase;lodash.some=some;lodash.sortedIndex=sortedIndex;lodash.sortedIndexBy=
sortedIndexBy;lodash.sortedIndexOf=sortedIndexOf;lodash.sortedLastIndex=sortedLastIndex;lodash.sortedLastIndexBy=sortedLastIndexBy;lodash.sortedLastIndexOf=sortedLastIndexOf;lodash.startCase=startCase;lodash.startsWith=startsWith;lodash.subtract=subtract;lodash.sum=sum;lodash.sumBy=sumBy;lodash.template=template;lodash.times=times;lodash.toFinite=toFinite;lodash.toInteger=toInteger;lodash.toLength=toLength;lodash.toLower=toLower;lodash.toNumber=toNumber;lodash.toSafeInteger=toSafeInteger;lodash.toString=
toString;lodash.toUpper=toUpper;lodash.trim=trim;lodash.trimEnd=trimEnd;lodash.trimStart=trimStart;lodash.truncate=truncate;lodash.unescape=unescape;lodash.uniqueId=uniqueId;lodash.upperCase=upperCase;lodash.upperFirst=upperFirst;lodash.each=forEach;lodash.eachRight=forEachRight;lodash.first=head;mixin(lodash,function(){var source={};baseForOwn(lodash,function(func,methodName){if(!hasOwnProperty.call(lodash.prototype,methodName))source[methodName]=func});return source}(),{"chain":false});lodash.VERSION=
VERSION;arrayEach(["bind","bindKey","curry","curryRight","partial","partialRight"],function(methodName){lodash[methodName].placeholder=lodash});arrayEach(["drop","take"],function(methodName,index){LazyWrapper.prototype[methodName]=function(n){n=n===undefined?1:nativeMax(toInteger(n),0);var result=this.__filtered__&&!index?new LazyWrapper(this):this.clone();if(result.__filtered__)result.__takeCount__=nativeMin(n,result.__takeCount__);else result.__views__.push({"size":nativeMin(n,MAX_ARRAY_LENGTH),
"type":methodName+(result.__dir__<0?"Right":"")});return result};LazyWrapper.prototype[methodName+"Right"]=function(n){return this.reverse()[methodName](n).reverse()}});arrayEach(["filter","map","takeWhile"],function(methodName,index){var type=index+1,isFilter=type==LAZY_FILTER_FLAG||type==LAZY_WHILE_FLAG;LazyWrapper.prototype[methodName]=function(iteratee){var result=this.clone();result.__iteratees__.push({"iteratee":getIteratee(iteratee,3),"type":type});result.__filtered__=result.__filtered__||
isFilter;return result}});arrayEach(["head","last"],function(methodName,index){var takeName="take"+(index?"Right":"");LazyWrapper.prototype[methodName]=function(){return this[takeName](1).value()[0]}});arrayEach(["initial","tail"],function(methodName,index){var dropName="drop"+(index?"":"Right");LazyWrapper.prototype[methodName]=function(){return this.__filtered__?new LazyWrapper(this):this[dropName](1)}});LazyWrapper.prototype.compact=function(){return this.filter(identity)};LazyWrapper.prototype.find=
function(predicate){return this.filter(predicate).head()};LazyWrapper.prototype.findLast=function(predicate){return this.reverse().find(predicate)};LazyWrapper.prototype.invokeMap=baseRest(function(path,args){if(typeof path=="function")return new LazyWrapper(this);return this.map(function(value){return baseInvoke(value,path,args)})});LazyWrapper.prototype.reject=function(predicate){return this.filter(negate(getIteratee(predicate)))};LazyWrapper.prototype.slice=function(start,end){start=toInteger(start);
var result=this;if(result.__filtered__&&(start>0||end<0))return new LazyWrapper(result);if(start<0)result=result.takeRight(-start);else if(start)result=result.drop(start);if(end!==undefined){end=toInteger(end);result=end<0?result.dropRight(-end):result.take(end-start)}return result};LazyWrapper.prototype.takeRightWhile=function(predicate){return this.reverse().takeWhile(predicate).reverse()};LazyWrapper.prototype.toArray=function(){return this.take(MAX_ARRAY_LENGTH)};baseForOwn(LazyWrapper.prototype,
function(func,methodName){var checkIteratee=/^(?:filter|find|map|reject)|While$/.test(methodName),isTaker=/^(?:head|last)$/.test(methodName),lodashFunc=lodash[isTaker?"take"+(methodName=="last"?"Right":""):methodName],retUnwrapped=isTaker||/^find/.test(methodName);if(!lodashFunc)return;lodash.prototype[methodName]=function(){var value=this.__wrapped__,args=isTaker?[1]:arguments,isLazy=value instanceof LazyWrapper,iteratee=args[0],useLazy=isLazy||isArray(value);var interceptor=function(value){var result=
lodashFunc.apply(lodash,arrayPush([value],args));return isTaker&&chainAll?result[0]:result};if(useLazy&&checkIteratee&&typeof iteratee=="function"&&iteratee.length!=1)isLazy=useLazy=false;var chainAll=this.__chain__,isHybrid=!!this.__actions__.length,isUnwrapped=retUnwrapped&&!chainAll,onlyLazy=isLazy&&!isHybrid;if(!retUnwrapped&&useLazy){value=onlyLazy?value:new LazyWrapper(this);var result=func.apply(value,args);result.__actions__.push({"func":thru,"args":[interceptor],"thisArg":undefined});return new LodashWrapper(result,
chainAll)}if(isUnwrapped&&onlyLazy)return func.apply(this,args);result=this.thru(interceptor);return isUnwrapped?isTaker?result.value()[0]:result.value():result}});arrayEach(["pop","push","shift","sort","splice","unshift"],function(methodName){var func=arrayProto[methodName],chainName=/^(?:push|sort|unshift)$/.test(methodName)?"tap":"thru",retUnwrapped=/^(?:pop|shift)$/.test(methodName);lodash.prototype[methodName]=function(){var args=arguments;if(retUnwrapped&&!this.__chain__){var value=this.value();
return func.apply(isArray(value)?value:[],args)}return this[chainName](function(value){return func.apply(isArray(value)?value:[],args)})}});baseForOwn(LazyWrapper.prototype,function(func,methodName){var lodashFunc=lodash[methodName];if(lodashFunc){var key=lodashFunc.name+"",names=realNames[key]||(realNames[key]=[]);names.push({"name":methodName,"func":lodashFunc})}});realNames[createHybrid(undefined,WRAP_BIND_KEY_FLAG).name]=[{"name":"wrapper","func":undefined}];LazyWrapper.prototype.clone=lazyClone;
LazyWrapper.prototype.reverse=lazyReverse;LazyWrapper.prototype.value=lazyValue;lodash.prototype.at=wrapperAt;lodash.prototype.chain=wrapperChain;lodash.prototype.commit=wrapperCommit;lodash.prototype.next=wrapperNext;lodash.prototype.plant=wrapperPlant;lodash.prototype.reverse=wrapperReverse;lodash.prototype.toJSON=lodash.prototype.valueOf=lodash.prototype.value=wrapperValue;lodash.prototype.first=lodash.prototype.head;if(symIterator)lodash.prototype[symIterator]=wrapperToIterator;return lodash};
var _=runInContext();if(typeof define=="function"&&typeof define.amd=="object"&&define.amd){root._=_;define(function(){return _})}else if(freeModule){(freeModule.exports=_)._=_;freeExports._=_}else root._=_}).call(this);
//# sourceURL=build://vz-sorting/sorting.js
var rc;
(function(b){function d(k,t){let l;(function(m){m[m.NATURAL=0]="NATURAL";m[m.REAL=1]="REAL";m[m.EXPONENT_SIGN=2]="EXPONENT_SIGN";m[m.EXPONENT=3]="EXPONENT"})(l||(l={}));let p=l.NATURAL;for(;t<k.length;t++)if(p===l.NATURAL)if("."===k[t])p=l.REAL;else if("e"===k[t]||"E"===k[t])p=l.EXPONENT_SIGN;else{if(!f(k[t]))break}else if(p===l.REAL)if("e"===k[t]||"E"===k[t])p=l.EXPONENT_SIGN;else{if(!f(k[t]))break}else if(p===l.EXPONENT_SIGN)if(f(k[t])||"+"===k[t]||"-"===k[t])p=l.EXPONENT;else break;else if(p===l.EXPONENT&&
!f(k[t]))break;return t}function f(k){return"0"<=k&&"9">=k}function h(k){return"/"===k||"_"===k||f(k)}b.compareTagNames=function(k,t){let l=0,p=0;for(;;){if(l===k.length)return p===t.length?0:-1;if(p===t.length)return 1;if(f(k[l])&&f(t[p])){var m=l,n=p;l=d(k,l+1);p=d(t,p+1);m=parseFloat(k.slice(m,l));n=parseFloat(t.slice(n,p));if(m<n)return-1;if(m>n)return 1}else{if(h(k[l])){if(!h(t[p]))return-1}else{if(h(t[p]))return 1;if(k[l]<t[p])return-1;if(k[l]>t[p])return 1}l++;p++}}}})(rc||(rc={}));

//# sourceURL=build://tf-backend/requestManager.js
var vc;
(function(b){function d(q,u,x,A){const y=new XMLHttpRequest;y.open(q,u);x&&(y.withCredentials=x);A&&y.setRequestHeader("Content-Type",A);return y}function f(q){const u=new m;if(!q)return u.methodType=p.GET,u;u.methodType=p.POST;u.body=h(q);return u}function h(q){const u=new FormData;for(let x in q)x&&u.append(x,q[x]);return u}class k extends Error{constructor(){super(...arguments);this.name="RequestCancellationError"}}b.RequestCancellationError=k;class t extends Error{constructor(q){super(q);this.name=
"InvalidRequestOptionsError";Object.setPrototypeOf(this,t.prototype)}}b.InvalidRequestOptionsError=t;class l extends Error{constructor(q,u){super();this.message=`RequestNetworkError: ${q.status} at ${u}`;this.name="RequestNetworkError";this.req=q;this.url=u}}b.RequestNetworkError=l;let p;(function(q){q.GET="GET";q.POST="POST"})(p=b.HttpMethodType||(b.HttpMethodType={}));class m{validate(){if(this.methodType===p.GET&&this.body)throw new t("body must be missing for a GET request.");}}b.RequestOptions=
m;class n{constructor(q=1E3,u=3){this._queue=[];this._nActiveRequests=0;this._nSimultaneousRequests=q;this._maxRetries=u}request(q,u){u=f(u);return this.requestWithOptions(q,u)}requestWithOptions(q,u){u.validate();return(new Promise((x,A)=>{this._queue.push({resolve:x,reject:A});this.launchRequests()})).then(()=>this.promiseWithRetries(q,this._maxRetries,u)).then(x=>{this._nActiveRequests--;this.launchRequests();return x},x=>{"RequestNetworkError"===x.name&&(this._nActiveRequests--,this.launchRequests());
return Promise.reject(x)})}fetch(q,u){return(new Promise((x,A)=>{this._queue.push({resolve:x,reject:A});this.launchRequests()})).then(()=>{let x=1;return new Promise(A=>{const y=()=>{fetch(q,u).then(w=>{!w.ok&&this._maxRetries>x?(x++,y()):(A(w),this._nActiveRequests--,this.launchRequests())})};y()})})}clearQueue(){for(;0<this._queue.length;)this._queue.pop().reject(new k("Request cancelled by clearQueue"))}activeRequests(){return this._nActiveRequests}outstandingRequests(){return this._nActiveRequests+
this._queue.length}launchRequests(){for(;this._nActiveRequests<this._nSimultaneousRequests&&0<this._queue.length;)this._nActiveRequests++,this._queue.pop().resolve()}promiseWithRetries(q,u,x){return this._promiseFromUrl(q,x).then(A=>A,A=>0<u?this.promiseWithRetries(q,u-1,x):Promise.reject(A))}_promiseFromUrl(q,u){return new Promise((x,A)=>{const y=d(u.methodType,q,u.withCredentials,u.contentType);y.onload=function(){200===y.status?x(JSON.parse(y.responseText)):A(new l(y,q))};y.onerror=function(){A(new l(y,
q))};u.body?y.send(u.body):y.send()})}}b.RequestManager=n})(vc||(vc={}));

//# sourceURL=build://tf-backend/urlPathHelpers.js
(function(b){function d(f){return encodeURIComponent(f).replace(/\(/g,"%28").replace(/\)/g,"%29")}b.addParams=function(f,h){var k=Object.keys(h).sort().filter(l=>void 0!==h[l]);if(!k.length)return f;const t=-1!==f.indexOf("?")?"\x26":"?";k=[].concat(...k.map(l=>{const p=h[l];return(Array.isArray(p)?p:[p]).map(m=>`${l}=${d(m)}`)})).join("\x26");return f+t+k}})(vc||(vc={}));

//# sourceURL=build://tf-backend/router.js
(function(b){function d(t="data",l=new URLSearchParams(window.location.search)){"/"===t[t.length-1]&&(t=t.slice(0,t.length-1));return{environment:()=>f(t,"/environment"),experiments:()=>f(t,"/experiments"),pluginRoute:(p,m,n)=>f(t+"/plugin",`/${p}${m}`,n),pluginsListing:()=>f(t,"/plugins_listing",h({["experimentalPlugin"]:l.getAll("experimentalPlugin")})),runs:()=>f(t,"/runs"),runsForExperiment:p=>f(t,"/experiment_runs",h({experiment:String(p)}))}}function f(t,l,p=new URLSearchParams){t+=l;String(p)&&
(l=l.includes("?")?"\x26":"?",t+=l+String(p));return t}function h(t={}){const l=Object.keys(t).sort().filter(m=>t[m]),p=new URLSearchParams;l.forEach(m=>{const n=t[m];(Array.isArray(n)?n:[n]).forEach(q=>p.append(m,q))});return p}let k=d();b.createRouter=d;b.getRouter=function(){return k};b.setRouter=function(t){if(null==t)throw Error("Router required, but got: "+t);k=t};b.createSearchParam=h})(vc||(vc={}));

//# sourceURL=build://tf-backend/baseStore.js
(function(b){class d{constructor(h){this.listener=h}}b.ListenKey=d;class f{constructor(){this.requestManager=new b.RequestManager(1);this._listeners=new Set;this.initialized=!1}refresh(){return this.load().then(()=>{this.initialized=!0})}addListener(h){h=new d(h);this._listeners.add(h);return h}removeListenerByKey(h){this._listeners.delete(h)}emitChange(){this._listeners.forEach(h=>{try{h.listener()}catch(k){}})}}b.BaseStore=f})(vc||(vc={}));

//# sourceURL=build://tf-backend/environmentStore.js
(function(b){class d extends b.BaseStore{load(){const f=b.getRouter().environment();return this.requestManager.request(f).then(h=>{const k={dataLocation:h.data_location,windowTitle:h.window_title};void 0!==h.experiment_name&&(k.experimentName=h.experiment_name);void 0!==h.experiment_description&&(k.experimentDescription=h.experiment_description);void 0!==h.creation_time&&(k.creationTime=h.creation_time);_.isEqual(this.environment,k)||(this.environment=k,this.emitChange())})}getDataLocation(){return this.environment?
this.environment.dataLocation:""}getWindowTitle(){return this.environment?this.environment.windowTitle:""}getExperimentName(){return this.environment?this.environment.experimentName:""}getExperimentDescription(){return this.environment?this.environment.experimentDescription:""}getCreationTime(){return this.environment?this.environment.creationTime:null}}b.EnvironmentStore=d;b.environmentStore=new d})(vc||(vc={}));

//# sourceURL=build://tf-backend/experimentsStore.js
(function(b){class d extends b.BaseStore{constructor(){super(...arguments);this._experiments=[]}load(){const f=b.getRouter().experiments();return this.requestManager.request(f).then(h=>{_.isEqual(this._experiments,h)||(this._experiments=h,this.emitChange())})}getExperiments(){return this._experiments.slice()}}b.ExperimentsStore=d;b.experimentsStore=new d})(vc||(vc={}));

//# sourceURL=build://tf-backend/runsStore.js
(function(b){class d extends b.BaseStore{constructor(){super(...arguments);this._runs=[]}load(){const f=b.getRouter().runs();return this.requestManager.request(f).then(h=>{_.isEqual(this._runs,h)||(this._runs=h,this.emitChange())})}getRuns(){return this._runs.slice()}}b.RunsStore=d;b.runsStore=new d})(vc||(vc={}));

//# sourceURL=build://tf-backend/backend.js
(function(b){b.TYPES=[];b.getRunsNamed=function(d){return _.keys(d).sort(rc.compareTagNames)};b.getTags=function(d){return _.union.apply(null,_.values(d)).sort(rc.compareTagNames)};b.filterTags=function(d,f){let h=[];f.forEach(k=>h=h.concat(d[k]));return _.uniq(h).sort(rc.compareTagNames)}})(vc||(vc={}));

//# sourceURL=build://tf-backend/canceller.js
(function(b){class d{constructor(){this.cancellationCount=0}cancellable(f){const h=this.cancellationCount;return k=>f({value:k,cancelled:this.cancellationCount!==h})}cancelAll(){this.cancellationCount++}}b.Canceller=d})(vc||(vc={}));

//# sourceURL=build://tf-backend/tf-backend-polymer.js
(function(b){Polymer({is:"tf-backend",_template:null,tf_backend:b})})(vc||(vc={}));

//# sourceURL=build://tf-categorization-utils/categorizationUtils.js
var $c;
(function(b){function d(m,n){const q=(()=>{try{return new RegExp(n)}catch(u){return null}})();return{name:n,metadata:{type:p.SEARCH_RESULTS,validRegex:!!q,universalRegex:".*"===n},items:q?m.filter(u=>u.match(q)):[]}}function f(m,n="/"){const q=[],u={};m.forEach(x=>{var A=x.indexOf(n);A=0<=A?x.slice(0,A):x;if(!u[A]){const y={name:A,metadata:{type:p.PREFIX_GROUP},items:[]};u[A]=y;q.push(y)}u[A].items.push(x)});return q}function h(m,n=""){n=[d(m,n)];m=f(m);return[].concat(n,m)}function k(m,n,q){const u=
vc.getTags(m);q=h(u,q);const x=t(_.pick(m,n));return q.map(({name:A,metadata:y,items:w})=>({name:A,metadata:y,items:w.map(C=>({tag:C,runs:(x.get(C)||[]).slice()}))}))}function t(m){const n=new Map;Object.keys(m).forEach(q=>{m[q].forEach(u=>{const x=n.get(u)||[];x.push(q);n.set(u,x)})});return n}function l(m,n){const q=rc.compareTagNames(m.tag,n.tag);return 0!=q?q:rc.compareTagNames(m.run,n.run)}let p;(function(m){m[m.SEARCH_RESULTS=0]="SEARCH_RESULTS";m[m.PREFIX_GROUP=1]="PREFIX_GROUP"})(p=b.CategoryType||
(b.CategoryType={}));b.categorizeBySearchQuery=d;b.categorizeByPrefix=f;b.categorize=h;b.categorizeTags=k;b.categorizeRunTagCombinations=function(m,n,q){return k(m,n,q).map(function(u){const x=_.flatten(u.items.map(({tag:A,runs:y})=>y.map(w=>({tag:A,run:w}))));x.sort(l);return{name:u.name,metadata:u.metadata,items:x}})}})($c||($c={}));

//# sourceURL=build://tf-globals/globals.js
var ad;(function(b){let d=!1;b.setUseHash=function(h){d=h};b.useHash=function(){return d};let f="";b.setFakeHash=function(h){f=h};b.getFakeHash=function(){return f}})(ad||(ad={}));

//# sourceURL=build://tf-globals/globals-polymer.js
(function(b){Polymer({is:"tf-globals",_template:null,tf_globals:b})})(ad||(ad={}));

//# sourceURL=build://tf-storage/listeners.js
var pd;
(function(b){class d{constructor(k){this.listener=k}}b.ListenKey=d;const f=new Set,h=new Set;window.addEventListener("hashchange",()=>{f.forEach(k=>k.listener())});window.addEventListener("storage",()=>{h.forEach(k=>k.listener())});b.addHashListener=function(k){k=new d(k);f.add(k);return k};b.addStorageListener=function(k){k=new d(k);h.add(k);return k};b.fireStorageChanged=function(){h.forEach(k=>k.listener())};b.removeHashListenerByKey=function(k){f.delete(k)};b.removeStorageListenerByKey=function(k){h.delete(k)}})(pd||
(pd={}));

//# sourceURL=build://tf-storage/storage.js
(function(b){function d(n,q){function u(w,C={}){const {defaultValue:G,useLocalStorage:D=!1}=C;w=D?window.localStorage.getItem(w):l(h())[w];return void 0==w?_.cloneDeep(G):n(w)}function x(w,C,G={}){const {defaultValue:D,useLocalStorage:B=!1,useLocationReplace:I=!1}=G;G=q(C);B?(window.localStorage.setItem(w,G),b.fireStorageChanged()):_.isEqual(C,u(w,{useLocalStorage:B}))||(_.isEqual(C,D)?p(w):(C=l(h()),C[w]=G,k(t(C),I)))}const A=[],y=[];return{get:u,set:x,getInitializer:function(w,C){const G=Object.assign({defaultValue:C.defaultValue,
polymerProperty:w,useLocalStorage:!1},C);return function(){const D=f(this,w),B=()=>{const N=u(D,G);_.isEqual(N,this[G.polymerProperty])||(this[G.polymerProperty]=N)},I=(G.useLocalStorage?b.addStorageListener:b.addHashListener)(()=>B());G.useLocalStorage?y.push(I):A.push(I);B();return this[G.polymerProperty]}},getObserver:function(w,C){const G=Object.assign({defaultValue:C.defaultValue,polymerProperty:w,useLocalStorage:!1},C);return function(){const D=f(this,w);x(D,this[G.polymerProperty],G)}},disposeBinding:function(){A.forEach(w=>
b.removeHashListenerByKey(w));y.forEach(w=>b.removeStorageListenerByKey(w))}}}function f(n,q){n=n[b.DISAMBIGUATOR];return(null==n?[q]:[n,q]).join(".")}function h(){return ad.useHash()?window.location.hash.slice(1):ad.getFakeHash()}function k(n,q=!1){ad.useHash()?q?window.location.replace("#"+n):window.location.hash=n:ad.setFakeHash(n)}function t(n){let q="";void 0!==n[b.TAB]&&(q+=n[b.TAB]);const u=Object.keys(n).map(x=>[x,n[x]]).filter(x=>x[0]!==b.TAB).map(x=>encodeURIComponent(x[0])+"\x3d"+encodeURIComponent(x[1])).join("\x26");
return 0<u.length?q+"\x26"+u:q}function l(n){const q={};n.split("\x26").forEach(u=>{u=u.split("\x3d");1===u.length?q[b.TAB]=u[0]:2===u.length&&(q[decodeURIComponent(u[0])]=decodeURIComponent(u[1]))});return q}function p(n){const q=l(h());delete q[n];k(t(q))}b.TAB="__tab__";b.DISAMBIGUATOR="disambiguator";b.urlDict=l(h());b.addHashListener(()=>{b.urlDict=l(h())});var m=d(n=>n,n=>n);b.getString=m.get;b.setString=m.set;b.getStringInitializer=m.getInitializer;b.getStringObserver=m.getObserver;b.disposeStringBinding=
m.disposeBinding;m=d(n=>"true"===n?!0:"false"===n?!1:void 0,n=>n.toString());b.getBoolean=m.get;b.setBoolean=m.set;b.getBooleanInitializer=m.getInitializer;b.getBooleanObserver=m.getObserver;b.disposeBooleanBinding=m.disposeBinding;m=d(n=>+n,n=>n.toString());b.getNumber=m.get;b.setNumber=m.set;b.getNumberInitializer=m.getInitializer;b.getNumberObserver=m.getObserver;b.disposeNumberBinding=m.disposeBinding;m=d(n=>JSON.parse(atob(n)),n=>btoa(JSON.stringify(n)));b.getObject=m.get;b.setObject=m.set;b.getObjectInitializer=
m.getInitializer;b.getObjectObserver=m.getObserver;b.disposeObjectBinding=m.disposeBinding;b.makeBindings=d;b.migrateLegacyURLScheme=function(){const n=new Set("examplesPath hideModelPane2 modelName1 modelName2 inferenceAddress1 inferenceAddress2 modelType modelVersion1 modelVersion2 modelSignature1 modelSignature2 maxExamples labelVocabPath multiClass sequenceExamples maxClassesToDisplay samplingOdds usePredictApi predictInputTensor predictOutputTensor".split(" ")),q=l(h());if("whatif"===q[b.TAB])for(let u of n)u in
q&&(q[`p.whatif.${u}`]=q[u]);k(t(q));this.urlDict=q}})(pd||(pd={}));

//# sourceURL=build://tf-storage/tf-storage-polymer.js
(function(b){Polymer({is:"tf-storage",_template:null,tf_storage:b})})(pd||(pd={}));

//# sourceURL=build://tf-categorization-utils/tf-tag-filterer.html.js
Polymer({is:"tf-tag-filterer",properties:{tagFilter:{type:String,notify:!0,computed:"_computeTagFilter(_tagFilter)"},_tagFilter:{type:String,value:pd.getStringInitializer("tagFilter",{defaultValue:"",useLocalStorage:!1,polymerProperty:"_tagFilter"}),observer:"_tagFilterObserver"}},_tagFilterObserver:pd.getStringObserver("tagFilter",{defaultValue:"",useLocalStorage:!1,polymerProperty:"_tagFilter"}),_computeTagFilter(){return this._tagFilter}});

//# sourceURL=build://tf-dashboard-common/array-update-helper.js
var qd;(function(b){b.ArrayUpdateHelper={updateArrayProp(d,f,h){let k=this.get(d);if(!Array.isArray(f))throw RangeError(`Expected new value to '${d}' to be an array.`);Array.isArray(k)||(k=[],this.set(d,k));const t=new Set(f.map((m,n)=>h(m,n)));let l=0,p=0;for(;l<k.length&&p<f.length;)t.has(h(k[l],l))?(h(k[l],l)==h(f[p],p)?this.set(`${d}.${l}`,f[p]):this.splice(d,l,0,f[p]),p++,l++):this.splice(d,l,1);l<k.length&&this.splice(d,l);p<f.length&&this.push(d,...f.slice(p))}}})(qd||(qd={}));

//# sourceURL=build://tf-dashboard-common/tf-dashboard-layout.html.js
Polymer({is:"tf-dashboard-layout"});

//# sourceURL=build://tf-dashboard-common/tf-option-selector.html.js
Polymer({is:"tf-option-selector",properties:{name:String,selectedId:{type:String,notify:!0,observer:"_selectedIdChanged"}},attached:function(){this.async(function(){this.getEffectiveChildren().forEach(function(b){this.listen(b,"tap","_selectTarget")}.bind(this))})},_selectTarget:function(b){this.selectedId=b.currentTarget.id},_selectedIdChanged:function(){var b=this.queryEffectiveChildren("#"+this.selectedId);b&&(this.getEffectiveChildren().forEach(function(d){d.classList.remove("selected")}),b.classList.add("selected"))}});

//# sourceURL=build://iron-collapse/iron-collapse.html.js
Polymer({is:"iron-collapse",behaviors:[Polymer.IronResizableBehavior],properties:{horizontal:{type:Boolean,value:!1,observer:"_horizontalChanged"},opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},transitioning:{type:Boolean,notify:!0,readOnly:!0},noAnimation:{type:Boolean},_desiredSize:{type:String,value:""}},get dimension(){return this.horizontal?"width":"height"},get _dimensionMax(){return this.horizontal?"maxWidth":"maxHeight"},get _dimensionMaxCss(){return this.horizontal?"max-width":
"max-height"},hostAttributes:{role:"group","aria-hidden":"true"},listeners:{transitionend:"_onTransitionEnd"},toggle:function(){this.opened=!this.opened},show:function(){this.opened=!0},hide:function(){this.opened=!1},updateSize:function(b,d){b="auto"===b?"":b;d=d&&!this.noAnimation&&this.isAttached&&this._desiredSize!==b;this._desiredSize=b;this._updateTransition(!1);d&&(d=this._calcSize(),""===b&&(this.style[this._dimensionMax]="",b=this._calcSize()),this.style[this._dimensionMax]=d,this.scrollTop=
this.scrollTop,this._updateTransition(!0),d=b!==d);this.style[this._dimensionMax]=b;d||this._transitionEnd()},enableTransition:function(b){Polymer.Base._warn("`enableTransition()` is deprecated, use `noAnimation` instead.");this.noAnimation=!b},_updateTransition:function(b){this.style.transitionDuration=b&&!this.noAnimation?"":"0s"},_horizontalChanged:function(){this.style.transitionProperty=this._dimensionMaxCss;this.style["maxWidth"===this._dimensionMax?"maxHeight":"maxWidth"]="";this.updateSize(this.opened?
"auto":"0px",!1)},_openedChanged:function(){this.setAttribute("aria-hidden",!this.opened);this._setTransitioning(!0);this.toggleClass("iron-collapse-closed",!1);this.toggleClass("iron-collapse-opened",!1);this.updateSize(this.opened?"auto":"0px",!0);this.opened&&this.focus()},_transitionEnd:function(){this.style[this._dimensionMax]=this._desiredSize;this.toggleClass("iron-collapse-closed",!this.opened);this.toggleClass("iron-collapse-opened",this.opened);this._updateTransition(!1);this.notifyResize();
this._setTransitioning(!1)},_onTransitionEnd:function(b){Polymer.dom(b).rootTarget===this&&this._transitionEnd()},_calcSize:function(){return this.getBoundingClientRect()[this.dimension]+"px"}});

//# sourceURL=build://tf-paginated-view/tf-dom-repeat.html.js
var Jd;
(function(b){b.TfDomRepeatBehavior=[qd.ArrayUpdateHelper,{properties:{as:{type:String,value:"item"},_contentActive:{type:Boolean,value:!0},_domBootstrapped:{type:Boolean,value:!1},_ctor:{type:Function,value:()=>null},_renderedItems:{type:Array,value:()=>[]},_renderedTemplateInst:{type:Object,value:()=>new Map},_lruCachedItems:{type:Object,value:()=>new Map},_cacheSize:{type:Number,value:10},_getItemKey:{type:Function,value:()=>d=>JSON.stringify(d)}},observers:["_bootstrapDom(_itemsRendered, isAttached)","_updateDom(_renderedItems.*, _domBootstrapped)",
"_updateActive(_contentActive)","_trimCache(_cacheSize)"],setCacheSize(d){this._cacheSize=d},setGetItemKey(d){this._getItemKey=d},updateDom(d){this.updateArrayProp("_renderedItems",d,this._getItemKey)},_ensureTemplatized(){if(!this.isAttached)return!1;this._ctor||(this._ctor=Polymer.Templatize.templatize(this.querySelector("template"),this,{parentModel:!0,instanceProps:{[this.as]:!0,active:this._contentActive},forwardHostProp:function(d,f){this._renderedTemplateInst.forEach(h=>{h.forwardHostProp(d,
f)})}}));return!0},_bootstrapDom(){this._itemsRendered&&this._ensureTemplatized()&&!this._domBootstrapped&&(Array.from(this.children).forEach(d=>{Polymer.dom(this).removeChild(d)}),this._lruCachedItems.clear(),this._renderedItems.forEach((d,f)=>this._insertItem(d,f)),this._domBootstrapped=!0)},_updateActive(){this._domBootstrapped&&Array.from(this._renderedTemplateInst.values()).forEach(d=>{d.notifyPath("active",this._contentActive)})},_updateDom(d){if(this._domBootstrapped&&"_renderedItems"!=d.path&&
"_renderedItems.length"!=d.path)if("_renderedItems.splices"===d.path)d.value.indexSplices.forEach(f=>{const h=f.index,k=f.addedCount,t=f.object;f.removed.forEach(l=>{this._removeItem(l,this.children[h])});t.slice(h,h+k).forEach((l,p)=>this._insertItem(l,h+p));this._trimCache()});else{const f=this._getItemKey(d.value);this._renderedTemplateInst.has(f)?this._renderedTemplateInst.get(f).notifyPath(this.as,d.value):console.warn(`Expected '${f}' to exist in the DOM but `+"could not find one.")}},_insertItem(d,
f){if(!this._ensureTemplatized())throw Error("Expected templatized before inserting an item");const h=this._getItemKey(d);if(this._lruCachedItems.has(h))d=this._lruCachedItems.get(h),this._lruCachedItems.delete(h),this._renderedTemplateInst.get(h).notifyPath("active",this._contentActive);else{const k=new this._ctor({[this.as]:d,active:this._contentActive});d=k.root;this._renderedTemplateInst.set(h,k)}this.children[f]?Polymer.dom(this).insertBefore(d,this.children[f]):((d.nodeType==Node.DOCUMENT_FRAGMENT_NODE?
Array.from(d.children):[d]).forEach(k=>k.setAttribute("slot","items")),Polymer.dom(this).appendChild(d))},_removeItem(d,f){Polymer.dom(f.parentNode).removeChild(f);d=this._getItemKey(d);this._lruCachedItems.set(d,f);this._renderedTemplateInst.get(d).notifyPath("active",!1)},_trimCache(){for(;this._lruCachedItems.size>this._cacheSize;){const [d]=this._lruCachedItems.keys();this._lruCachedItems.delete(d);this._renderedTemplateInst.delete(d)}}}]})(Jd||(Jd={}));

//# sourceURL=build://tf-paginated-view/paginatedViewStore.js
var ne;
(function(b){let d=null;const f=new Set;b.addLimitListener=function(h){f.add(h)};b.removeLimitListener=function(h){f.delete(h)};b.getLimit=function(){null==d&&(d=pd.getNumber("TF.TensorBoard.PaginatedView.limit",{useLocalStorage:!0}),null==d||!isFinite(d)||0>=d)&&(d=12);return d};b.setLimit=function(h){if(h!==Math.floor(h))throw Error(`limit must be an integer, but got: ${h}`);if(0>=h)throw Error(`limit must be positive, but got: ${h}`);h!==d&&(d=h,pd.setNumber("TF.TensorBoard.PaginatedView.limit",d,
{useLocalStorage:!0}),f.forEach(k=>{k()}))}})(ne||(ne={}));

//# sourceURL=build://tf-paginated-view/tf-paginated-view-store.html.js
Polymer({is:"tf-paginated-view-store",_template:null,tf_paginated_view:ne});

//# sourceURL=build://tf-paginated-view/tf-category-paginated-view.html.js
Polymer({is:"tf-category-paginated-view",properties:{category:Object,initialOpened:Boolean,opened:{type:Boolean,notify:!0,readOnly:!0},_contentActive:{type:Boolean,computed:"_computeContentActive(opened)"},disablePagination:{type:Boolean,value:!1},_count:{type:Number,computed:"_computeCount(category.items.*)"},_hasMultiple:{type:Boolean,computed:"_computeHasMultiple(_count)"},_paneRendered:{type:Boolean,computed:"_computePaneRendered(category)",observer:"_onPaneRenderedChanged"},_itemsRendered:{type:Boolean,
computed:"_computeItemsRendered(opened, _paneRendered)"},_isSearchResults:{type:Boolean,computed:"_computeIsSearchResults(category.metadata.type)"},_isInvalidSearchResults:{type:Boolean,computed:"_computeIsInvalidSearchResults(category.metadata)"},_isUniversalSearchQuery:{type:Boolean,computed:"_computeIsUniversalSearchQuery(category.metadata)"},getCategoryItemKey:{type:Function,value:()=>b=>JSON.stringify(b),observer:"_getCategoryItemKeyChanged"},_limit:{type:Number,value:12,observer:"_limitChanged"},
_activeIndex:{type:Number,value:0},_currentPage:{type:Number,computed:"_computeCurrentPage(_limit, _activeIndex)"},_pageCount:{type:Number,computed:"_computePageCount(category.items.*, _limit)"},_multiplePagesExist:{type:Boolean,computed:"_computeMultiplePagesExist(_pageCount, disablePagination)"},_hasPreviousPage:{type:Boolean,computed:"_computeHasPreviousPage(_currentPage)"},_hasNextPage:{type:Boolean,computed:"_computeHasNextPage(_currentPage, _pageCount)"},_inputWidth:{type:String,computed:"_computeInputWidth(_pageCount)",
observer:"_updateInputWidth"},_pageInputValue:{type:String,computed:"_computePageInputValue(_pageInputFocused, _pageInputRawValue, _currentPage)",observer:"_updatePageInputValue"},_pageInputRawValue:{type:String,value:""},_pageInputFocused:{type:Boolean,value:!1}},observers:["_clampActiveIndex(category.items.*)","_updateRenderedItems(_itemsRendered, category.items.*, _limit, _activeIndex, _pageCount, disablePagination)"],behaviors:[Jd.TfDomRepeatBehavior],_computeCount(){return this.category.items.length},
_computeHasMultiple(){return 1<this._count},_togglePane(){this._setOpened(!this.opened)},_computeContentActive(){return this.opened},_onPaneRenderedChanged(b,d){b&&b!==d&&this.$.ifRendered.render()},_computePaneRendered(b){return!(b.metadata.type===$c.CategoryType.SEARCH_RESULTS&&""===b.name)},_computeItemsRendered(){return this._paneRendered&&this.opened},_computeIsSearchResults(b){return b===$c.CategoryType.SEARCH_RESULTS},_computeIsInvalidSearchResults(b){return b.type===$c.CategoryType.SEARCH_RESULTS&&
!b.validRegex},_computeIsUniversalSearchQuery(b){return b.type===$c.CategoryType.SEARCH_RESULTS&&b.universalRegex},_isCompositeSearch(){const b=this.category.metadata.type;return this.category.metadata.compositeSearch&&b===$c.CategoryType.SEARCH_RESULTS},ready(){this._setOpened(null==this.initialOpened?!0:this.initialOpened);this._limitListener=()=>{this.set("_limit",ne.getLimit())};ne.addLimitListener(this._limitListener);this._limitListener()},detached(){ne.removeLimitListener(this._limitListener)},
_updateRenderedItems(b,d,f,h,k,t){b&&(b=Math.floor(h/f),d=this.category.items||[],this.updateDom(t?d:d.slice(b*f,(b+1)*f),this.getCategoryItemKey))},_limitChanged(b){this.setCacheSize(2*b)},_getCategoryItemKeyChanged(){this.setGetItemKey(this.getCategoryItemKey)},_computeCurrentPage(b,d){return Math.floor(d/b)+1},_computePageCount(b,d){return this.category?Math.ceil(this.category.items.length/d):0},_computeMultiplePagesExist(b,d){return!d&&1<b},_computeHasPreviousPage(b){return 1<b},_computeHasNextPage(b,
d){return b<d},_computeInputWidth(b){return`calc(${b.toString().length}em + 20px)`},_setActiveIndex(b){const d=(this.category.items||[]).length-1;b>d&&(b=d);0>b&&(b=0);this.set("_activeIndex",b)},_clampActiveIndex(){this._setActiveIndex(this._activeIndex)},_performPreviousPage(){this._setActiveIndex(this._activeIndex-this._limit)},_performNextPage(){this._setActiveIndex(this._activeIndex+this._limit)},_computePageInputValue(b,d,f){return b?d:f.toString()},_handlePageInputEvent(b){this.set("_pageInputRawValue",
b.target.value);b=Number(b.target.value||NaN);isNaN(b)||this._setActiveIndex(this._limit*(Math.max(1,Math.min(b,this._pageCount))-1))},_handlePageChangeEvent(){this.set("_pageInputRawValue",this._currentPage.toString())},_handlePageFocusEvent(){this.set("_pageInputRawValue",this._pageInputValue);this.set("_pageInputFocused",!0)},_handlePageBlurEvent(){this.set("_pageInputFocused",!1)},_updatePageInputValue(b){const d=this.$$("#page-input input");d&&(d.value=b)},_updateInputWidth(){this.updateStyles({"--tf-category-paginated-view-page-input-width":this._inputWidth})}});

//# sourceURL=build://paper-dialog-behavior/paper-dialog-behavior.html.js
(function(){Polymer.PaperDialogBehaviorImpl={hostAttributes:{role:"dialog",tabindex:"-1"},properties:{modal:{type:Boolean,value:!1},__readied:{type:Boolean,value:!1}},observers:["_modalChanged(modal, __readied)"],listeners:{tap:"_onDialogClick"},ready:function(){this.__prevNoCancelOnOutsideClick=this.noCancelOnOutsideClick;this.__prevNoCancelOnEscKey=this.noCancelOnEscKey;this.__prevWithBackdrop=this.withBackdrop;this.__readied=!0},_modalChanged:function(b,d){d&&(b?(this.__prevNoCancelOnOutsideClick=
this.noCancelOnOutsideClick,this.__prevNoCancelOnEscKey=this.noCancelOnEscKey,this.__prevWithBackdrop=this.withBackdrop,this.withBackdrop=this.noCancelOnEscKey=this.noCancelOnOutsideClick=!0):(this.noCancelOnOutsideClick=this.noCancelOnOutsideClick&&this.__prevNoCancelOnOutsideClick,this.noCancelOnEscKey=this.noCancelOnEscKey&&this.__prevNoCancelOnEscKey,this.withBackdrop=this.withBackdrop&&this.__prevWithBackdrop))},_updateClosingReasonConfirmed:function(b){this.closingReason=this.closingReason||
{};this.closingReason.confirmed=b},_onDialogClick:function(b){for(var d=Polymer.dom(b).path,f=0,h=d.indexOf(this);f<h;f++){var k=d[f];if(k.hasAttribute&&(k.hasAttribute("dialog-dismiss")||k.hasAttribute("dialog-confirm"))){this._updateClosingReasonConfirmed(k.hasAttribute("dialog-confirm"));this.close();b.stopPropagation();break}}}};Polymer.PaperDialogBehavior=[Polymer.IronOverlayBehavior,Polymer.PaperDialogBehaviorImpl]})();

//# sourceURL=build://paper-dialog/paper-dialog.html.js
Polymer({is:"paper-dialog",behaviors:[Polymer.PaperDialogBehavior,Polymer.NeonAnimationRunnerBehavior],listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},_renderOpened:function(){this.cancelAnimation();this.playAnimation("entry")},_renderClosed:function(){this.cancelAnimation();this.playAnimation("exit")},_onNeonAnimationFinish:function(){this.opened?this._finishRenderOpened():this._finishRenderClosed()}});

// https://d3js.org v5.7.0 Copyright 2018 Mike Bostock
!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?n(exports):"function"==typeof define&&define.amd?define(["exports"],n):n(t.d3=t.d3||{})}(this,function(t){"use strict";function n(t,n){return t<n?-1:t>n?1:t>=n?0:NaN}function e(t){var e;return 1===t.length&&(e=t,t=function(t,r){return n(e(t),r)}),{left:function(n,e,r,i){for(null==r&&(r=0),null==i&&(i=n.length);r<i;){var o=r+i>>>1;t(n[o],e)<0?r=o+1:i=o}return r},right:function(n,e,r,i){for(null==r&&(r=0),null==i&&(i=n.length);r<i;){var o=r+i>>>1;t(n[o],e)>0?i=o:r=o+1}return r}}}var r=e(n),i=r.right,o=r.left;function a(t,n){return[t,n]}function u(t){return null===t?NaN:+t}function f(t,n){var e,r,i=t.length,o=0,a=-1,f=0,c=0;if(null==n)for(;++a<i;)isNaN(e=u(t[a]))||(c+=(r=e-f)*(e-(f+=r/++o)));else for(;++a<i;)isNaN(e=u(n(t[a],a,t)))||(c+=(r=e-f)*(e-(f+=r/++o)));if(o>1)return c/(o-1)}function c(t,n){var e=f(t,n);return e?Math.sqrt(e):e}function s(t,n){var e,r,i,o=t.length,a=-1;if(null==n){for(;++a<o;)if(null!=(e=t[a])&&e>=e)for(r=i=e;++a<o;)null!=(e=t[a])&&(r>e&&(r=e),i<e&&(i=e))}else for(;++a<o;)if(null!=(e=n(t[a],a,t))&&e>=e)for(r=i=e;++a<o;)null!=(e=n(t[a],a,t))&&(r>e&&(r=e),i<e&&(i=e));return[r,i]}var l=Array.prototype,h=l.slice,d=l.map;function p(t){return function(){return t}}function v(t){return t}function g(t,n,e){t=+t,n=+n,e=(i=arguments.length)<2?(n=t,t=0,1):i<3?1:+e;for(var r=-1,i=0|Math.max(0,Math.ceil((n-t)/e)),o=new Array(i);++r<i;)o[r]=t+r*e;return o}var y=Math.sqrt(50),_=Math.sqrt(10),b=Math.sqrt(2);function m(t,n,e){var r,i,o,a,u=-1;if(e=+e,(t=+t)===(n=+n)&&e>0)return[t];if((r=n<t)&&(i=t,t=n,n=i),0===(a=x(t,n,e))||!isFinite(a))return[];if(a>0)for(t=Math.ceil(t/a),n=Math.floor(n/a),o=new Array(i=Math.ceil(n-t+1));++u<i;)o[u]=(t+u)*a;else for(t=Math.floor(t*a),n=Math.ceil(n*a),o=new Array(i=Math.ceil(t-n+1));++u<i;)o[u]=(t-u)/a;return r&&o.reverse(),o}function x(t,n,e){var r=(n-t)/Math.max(0,e),i=Math.floor(Math.log(r)/Math.LN10),o=r/Math.pow(10,i);return i>=0?(o>=y?10:o>=_?5:o>=b?2:1)*Math.pow(10,i):-Math.pow(10,-i)/(o>=y?10:o>=_?5:o>=b?2:1)}function w(t,n,e){var r=Math.abs(n-t)/Math.max(0,e),i=Math.pow(10,Math.floor(Math.log(r)/Math.LN10)),o=r/i;return o>=y?i*=10:o>=_?i*=5:o>=b&&(i*=2),n<t?-i:i}function M(t){return Math.ceil(Math.log(t.length)/Math.LN2)+1}function A(t,n,e){if(null==e&&(e=u),r=t.length){if((n=+n)<=0||r<2)return+e(t[0],0,t);if(n>=1)return+e(t[r-1],r-1,t);var r,i=(r-1)*n,o=Math.floor(i),a=+e(t[o],o,t);return a+(+e(t[o+1],o+1,t)-a)*(i-o)}}function T(t,n){var e,r,i=t.length,o=-1;if(null==n){for(;++o<i;)if(null!=(e=t[o])&&e>=e)for(r=e;++o<i;)null!=(e=t[o])&&e>r&&(r=e)}else for(;++o<i;)if(null!=(e=n(t[o],o,t))&&e>=e)for(r=e;++o<i;)null!=(e=n(t[o],o,t))&&e>r&&(r=e);return r}function N(t){for(var n,e,r,i=t.length,o=-1,a=0;++o<i;)a+=t[o].length;for(e=new Array(a);--i>=0;)for(n=(r=t[i]).length;--n>=0;)e[--a]=r[n];return e}function S(t,n){var e,r,i=t.length,o=-1;if(null==n){for(;++o<i;)if(null!=(e=t[o])&&e>=e)for(r=e;++o<i;)null!=(e=t[o])&&r>e&&(r=e)}else for(;++o<i;)if(null!=(e=n(t[o],o,t))&&e>=e)for(r=e;++o<i;)null!=(e=n(t[o],o,t))&&r>e&&(r=e);return r}function E(t){if(!(i=t.length))return[];for(var n=-1,e=S(t,k),r=new Array(e);++n<e;)for(var i,o=-1,a=r[n]=new Array(i);++o<i;)a[o]=t[o][n];return r}function k(t){return t.length}var C=Array.prototype.slice;function P(t){return t}var z=1,R=2,L=3,D=4,U=1e-6;function q(t){return"translate("+(t+.5)+",0)"}function O(t){return"translate(0,"+(t+.5)+")"}function Y(){return!this.__axis}function B(t,n){var e=[],r=null,i=null,o=6,a=6,u=3,f=t===z||t===D?-1:1,c=t===D||t===R?"x":"y",s=t===z||t===L?q:O;function l(l){var h=null==r?n.ticks?n.ticks.apply(n,e):n.domain():r,d=null==i?n.tickFormat?n.tickFormat.apply(n,e):P:i,p=Math.max(o,0)+u,v=n.range(),g=+v[0]+.5,y=+v[v.length-1]+.5,_=(n.bandwidth?function(t){var n=Math.max(0,t.bandwidth()-1)/2;return t.round()&&(n=Math.round(n)),function(e){return+t(e)+n}}:function(t){return function(n){return+t(n)}})(n.copy()),b=l.selection?l.selection():l,m=b.selectAll(".domain").data([null]),x=b.selectAll(".tick").data(h,n).order(),w=x.exit(),M=x.enter().append("g").attr("class","tick"),A=x.select("line"),T=x.select("text");m=m.merge(m.enter().insert("path",".tick").attr("class","domain").attr("stroke","currentColor")),x=x.merge(M),A=A.merge(M.append("line").attr("stroke","currentColor").attr(c+"2",f*o)),T=T.merge(M.append("text").attr("fill","currentColor").attr(c,f*p).attr("dy",t===z?"0em":t===L?"0.71em":"0.32em")),l!==b&&(m=m.transition(l),x=x.transition(l),A=A.transition(l),T=T.transition(l),w=w.transition(l).attr("opacity",U).attr("transform",function(t){return isFinite(t=_(t))?s(t):this.getAttribute("transform")}),M.attr("opacity",U).attr("transform",function(t){var n=this.parentNode.__axis;return s(n&&isFinite(n=n(t))?n:_(t))})),w.remove(),m.attr("d",t===D||t==R?a?"M"+f*a+","+g+"H0.5V"+y+"H"+f*a:"M0.5,"+g+"V"+y:a?"M"+g+","+f*a+"V0.5H"+y+"V"+f*a:"M"+g+",0.5H"+y),x.attr("opacity",1).attr("transform",function(t){return s(_(t))}),A.attr(c+"2",f*o),T.attr(c,f*p).text(d),b.filter(Y).attr("fill","none").attr("font-size",10).attr("font-family","sans-serif").attr("text-anchor",t===R?"start":t===D?"end":"middle"),b.each(function(){this.__axis=_})}return l.scale=function(t){return arguments.length?(n=t,l):n},l.ticks=function(){return e=C.call(arguments),l},l.tickArguments=function(t){return arguments.length?(e=null==t?[]:C.call(t),l):e.slice()},l.tickValues=function(t){return arguments.length?(r=null==t?null:C.call(t),l):r&&r.slice()},l.tickFormat=function(t){return arguments.length?(i=t,l):i},l.tickSize=function(t){return arguments.length?(o=a=+t,l):o},l.tickSizeInner=function(t){return arguments.length?(o=+t,l):o},l.tickSizeOuter=function(t){return arguments.length?(a=+t,l):a},l.tickPadding=function(t){return arguments.length?(u=+t,l):u},l}var F={value:function(){}};function I(){for(var t,n=0,e=arguments.length,r={};n<e;++n){if(!(t=arguments[n]+"")||t in r)throw new Error("illegal type: "+t);r[t]=[]}return new H(r)}function H(t){this._=t}function j(t,n){for(var e,r=0,i=t.length;r<i;++r)if((e=t[r]).name===n)return e.value}function X(t,n,e){for(var r=0,i=t.length;r<i;++r)if(t[r].name===n){t[r]=F,t=t.slice(0,r).concat(t.slice(r+1));break}return null!=e&&t.push({name:n,value:e}),t}H.prototype=I.prototype={constructor:H,on:function(t,n){var e,r,i=this._,o=(r=i,(t+"").trim().split(/^|\s+/).map(function(t){var n="",e=t.indexOf(".");if(e>=0&&(n=t.slice(e+1),t=t.slice(0,e)),t&&!r.hasOwnProperty(t))throw new Error("unknown type: "+t);return{type:t,name:n}})),a=-1,u=o.length;if(!(arguments.length<2)){if(null!=n&&"function"!=typeof n)throw new Error("invalid callback: "+n);for(;++a<u;)if(e=(t=o[a]).type)i[e]=X(i[e],t.name,n);else if(null==n)for(e in i)i[e]=X(i[e],t.name,null);return this}for(;++a<u;)if((e=(t=o[a]).type)&&(e=j(i[e],t.name)))return e},copy:function(){var t={},n=this._;for(var e in n)t[e]=n[e].slice();return new H(t)},call:function(t,n){if((e=arguments.length-2)>0)for(var e,r,i=new Array(e),o=0;o<e;++o)i[o]=arguments[o+2];if(!this._.hasOwnProperty(t))throw new Error("unknown type: "+t);for(o=0,e=(r=this._[t]).length;o<e;++o)r[o].value.apply(n,i)},apply:function(t,n,e){if(!this._.hasOwnProperty(t))throw new Error("unknown type: "+t);for(var r=this._[t],i=0,o=r.length;i<o;++i)r[i].value.apply(n,e)}};var G="http://www.w3.org/1999/xhtml",V={svg:"http://www.w3.org/2000/svg",xhtml:G,xlink:"http://www.w3.org/1999/xlink",xml:"http://www.w3.org/XML/1998/namespace",xmlns:"http://www.w3.org/2000/xmlns/"};function $(t){var n=t+="",e=n.indexOf(":");return e>=0&&"xmlns"!==(n=t.slice(0,e))&&(t=t.slice(e+1)),V.hasOwnProperty(n)?{space:V[n],local:t}:t}function W(t){var n=$(t);return(n.local?function(t){return function(){return this.ownerDocument.createElementNS(t.space,t.local)}}:function(t){return function(){var n=this.ownerDocument,e=this.namespaceURI;return e===G&&n.documentElement.namespaceURI===G?n.createElement(t):n.createElementNS(e,t)}})(n)}function Z(){}function Q(t){return null==t?Z:function(){return this.querySelector(t)}}function J(){return[]}function K(t){return null==t?J:function(){return this.querySelectorAll(t)}}var tt=function(t){return function(){return this.matches(t)}};if("undefined"!=typeof document){var nt=document.documentElement;if(!nt.matches){var et=nt.webkitMatchesSelector||nt.msMatchesSelector||nt.mozMatchesSelector||nt.oMatchesSelector;tt=function(t){return function(){return et.call(this,t)}}}}var rt=tt;function it(t){return new Array(t.length)}function ot(t,n){this.ownerDocument=t.ownerDocument,this.namespaceURI=t.namespaceURI,this._next=null,this._parent=t,this.__data__=n}ot.prototype={constructor:ot,appendChild:function(t){return this._parent.insertBefore(t,this._next)},insertBefore:function(t,n){return this._parent.insertBefore(t,n)},querySelector:function(t){return this._parent.querySelector(t)},querySelectorAll:function(t){return this._parent.querySelectorAll(t)}};var at="$";function ut(t,n,e,r,i,o){for(var a,u=0,f=n.length,c=o.length;u<c;++u)(a=n[u])?(a.__data__=o[u],r[u]=a):e[u]=new ot(t,o[u]);for(;u<f;++u)(a=n[u])&&(i[u]=a)}function ft(t,n,e,r,i,o,a){var u,f,c,s={},l=n.length,h=o.length,d=new Array(l);for(u=0;u<l;++u)(f=n[u])&&(d[u]=c=at+a.call(f,f.__data__,u,n),c in s?i[u]=f:s[c]=f);for(u=0;u<h;++u)(f=s[c=at+a.call(t,o[u],u,o)])?(r[u]=f,f.__data__=o[u],s[c]=null):e[u]=new ot(t,o[u]);for(u=0;u<l;++u)(f=n[u])&&s[d[u]]===f&&(i[u]=f)}function ct(t,n){return t<n?-1:t>n?1:t>=n?0:NaN}function st(t){return t.ownerDocument&&t.ownerDocument.defaultView||t.document&&t||t.defaultView}function lt(t,n){return t.style.getPropertyValue(n)||st(t).getComputedStyle(t,null).getPropertyValue(n)}function ht(t){return t.trim().split(/^|\s+/)}function dt(t){return t.classList||new pt(t)}function pt(t){this._node=t,this._names=ht(t.getAttribute("class")||"")}function vt(t,n){for(var e=dt(t),r=-1,i=n.length;++r<i;)e.add(n[r])}function gt(t,n){for(var e=dt(t),r=-1,i=n.length;++r<i;)e.remove(n[r])}function yt(){this.textContent=""}function _t(){this.innerHTML=""}function bt(){this.nextSibling&&this.parentNode.appendChild(this)}function mt(){this.previousSibling&&this.parentNode.insertBefore(this,this.parentNode.firstChild)}function xt(){return null}function wt(){var t=this.parentNode;t&&t.removeChild(this)}function Mt(){return this.parentNode.insertBefore(this.cloneNode(!1),this.nextSibling)}function At(){return this.parentNode.insertBefore(this.cloneNode(!0),this.nextSibling)}pt.prototype={add:function(t){this._names.indexOf(t)<0&&(this._names.push(t),this._node.setAttribute("class",this._names.join(" ")))},remove:function(t){var n=this._names.indexOf(t);n>=0&&(this._names.splice(n,1),this._node.setAttribute("class",this._names.join(" ")))},contains:function(t){return this._names.indexOf(t)>=0}};var Tt={};(t.event=null,"undefined"!=typeof document)&&("onmouseenter"in document.documentElement||(Tt={mouseenter:"mouseover",mouseleave:"mouseout"}));function Nt(t,n,e){return t=St(t,n,e),function(n){var e=n.relatedTarget;e&&(e===this||8&e.compareDocumentPosition(this))||t.call(this,n)}}function St(n,e,r){return function(i){var o=t.event;t.event=i;try{n.call(this,this.__data__,e,r)}finally{t.event=o}}}function Et(t){return function(){var n=this.__on;if(n){for(var e,r=0,i=-1,o=n.length;r<o;++r)e=n[r],t.type&&e.type!==t.type||e.name!==t.name?n[++i]=e:this.removeEventListener(e.type,e.listener,e.capture);++i?n.length=i:delete this.__on}}}function kt(t,n,e){var r=Tt.hasOwnProperty(t.type)?Nt:St;return function(i,o,a){var u,f=this.__on,c=r(n,o,a);if(f)for(var s=0,l=f.length;s<l;++s)if((u=f[s]).type===t.type&&u.name===t.name)return this.removeEventListener(u.type,u.listener,u.capture),this.addEventListener(u.type,u.listener=c,u.capture=e),void(u.value=n);this.addEventListener(t.type,c,e),u={type:t.type,name:t.name,value:n,listener:c,capture:e},f?f.push(u):this.__on=[u]}}function Ct(n,e,r,i){var o=t.event;n.sourceEvent=t.event,t.event=n;try{return e.apply(r,i)}finally{t.event=o}}function Pt(t,n,e){var r=st(t),i=r.CustomEvent;"function"==typeof i?i=new i(n,e):(i=r.document.createEvent("Event"),e?(i.initEvent(n,e.bubbles,e.cancelable),i.detail=e.detail):i.initEvent(n,!1,!1)),t.dispatchEvent(i)}var zt=[null];function Rt(t,n){this._groups=t,this._parents=n}function Lt(){return new Rt([[document.documentElement]],zt)}function Dt(t){return"string"==typeof t?new Rt([[document.querySelector(t)]],[document.documentElement]):new Rt([[t]],zt)}Rt.prototype=Lt.prototype={constructor:Rt,select:function(t){"function"!=typeof t&&(t=Q(t));for(var n=this._groups,e=n.length,r=new Array(e),i=0;i<e;++i)for(var o,a,u=n[i],f=u.length,c=r[i]=new Array(f),s=0;s<f;++s)(o=u[s])&&(a=t.call(o,o.__data__,s,u))&&("__data__"in o&&(a.__data__=o.__data__),c[s]=a);return new Rt(r,this._parents)},selectAll:function(t){"function"!=typeof t&&(t=K(t));for(var n=this._groups,e=n.length,r=[],i=[],o=0;o<e;++o)for(var a,u=n[o],f=u.length,c=0;c<f;++c)(a=u[c])&&(r.push(t.call(a,a.__data__,c,u)),i.push(a));return new Rt(r,i)},filter:function(t){"function"!=typeof t&&(t=rt(t));for(var n=this._groups,e=n.length,r=new Array(e),i=0;i<e;++i)for(var o,a=n[i],u=a.length,f=r[i]=[],c=0;c<u;++c)(o=a[c])&&t.call(o,o.__data__,c,a)&&f.push(o);return new Rt(r,this._parents)},data:function(t,n){if(!t)return p=new Array(this.size()),s=-1,this.each(function(t){p[++s]=t}),p;var e,r=n?ft:ut,i=this._parents,o=this._groups;"function"!=typeof t&&(e=t,t=function(){return e});for(var a=o.length,u=new Array(a),f=new Array(a),c=new Array(a),s=0;s<a;++s){var l=i[s],h=o[s],d=h.length,p=t.call(l,l&&l.__data__,s,i),v=p.length,g=f[s]=new Array(v),y=u[s]=new Array(v);r(l,h,g,y,c[s]=new Array(d),p,n);for(var _,b,m=0,x=0;m<v;++m)if(_=g[m]){for(m>=x&&(x=m+1);!(b=y[x])&&++x<v;);_._next=b||null}}return(u=new Rt(u,i))._enter=f,u._exit=c,u},enter:function(){return new Rt(this._enter||this._groups.map(it),this._parents)},exit:function(){return new Rt(this._exit||this._groups.map(it),this._parents)},merge:function(t){for(var n=this._groups,e=t._groups,r=n.length,i=e.length,o=Math.min(r,i),a=new Array(r),u=0;u<o;++u)for(var f,c=n[u],s=e[u],l=c.length,h=a[u]=new Array(l),d=0;d<l;++d)(f=c[d]||s[d])&&(h[d]=f);for(;u<r;++u)a[u]=n[u];return new Rt(a,this._parents)},order:function(){for(var t=this._groups,n=-1,e=t.length;++n<e;)for(var r,i=t[n],o=i.length-1,a=i[o];--o>=0;)(r=i[o])&&(a&&a!==r.nextSibling&&a.parentNode.insertBefore(r,a),a=r);return this},sort:function(t){function n(n,e){return n&&e?t(n.__data__,e.__data__):!n-!e}t||(t=ct);for(var e=this._groups,r=e.length,i=new Array(r),o=0;o<r;++o){for(var a,u=e[o],f=u.length,c=i[o]=new Array(f),s=0;s<f;++s)(a=u[s])&&(c[s]=a);c.sort(n)}return new Rt(i,this._parents).order()},call:function(){var t=arguments[0];return arguments[0]=this,t.apply(null,arguments),this},nodes:function(){var t=new Array(this.size()),n=-1;return this.each(function(){t[++n]=this}),t},node:function(){for(var t=this._groups,n=0,e=t.length;n<e;++n)for(var r=t[n],i=0,o=r.length;i<o;++i){var a=r[i];if(a)return a}return null},size:function(){var t=0;return this.each(function(){++t}),t},empty:function(){return!this.node()},each:function(t){for(var n=this._groups,e=0,r=n.length;e<r;++e)for(var i,o=n[e],a=0,u=o.length;a<u;++a)(i=o[a])&&t.call(i,i.__data__,a,o);return this},attr:function(t,n){var e=$(t);if(arguments.length<2){var r=this.node();return e.local?r.getAttributeNS(e.space,e.local):r.getAttribute(e)}return this.each((null==n?e.local?function(t){return function(){this.removeAttributeNS(t.space,t.local)}}:function(t){return function(){this.removeAttribute(t)}}:"function"==typeof n?e.local?function(t,n){return function(){var e=n.apply(this,arguments);null==e?this.removeAttributeNS(t.space,t.local):this.setAttributeNS(t.space,t.local,e)}}:function(t,n){return function(){var e=n.apply(this,arguments);null==e?this.removeAttribute(t):this.setAttribute(t,e)}}:e.local?function(t,n){return function(){this.setAttributeNS(t.space,t.local,n)}}:function(t,n){return function(){this.setAttribute(t,n)}})(e,n))},style:function(t,n,e){return arguments.length>1?this.each((null==n?function(t){return function(){this.style.removeProperty(t)}}:"function"==typeof n?function(t,n,e){return function(){var r=n.apply(this,arguments);null==r?this.style.removeProperty(t):this.style.setProperty(t,r,e)}}:function(t,n,e){return function(){this.style.setProperty(t,n,e)}})(t,n,null==e?"":e)):lt(this.node(),t)},property:function(t,n){return arguments.length>1?this.each((null==n?function(t){return function(){delete this[t]}}:"function"==typeof n?function(t,n){return function(){var e=n.apply(this,arguments);null==e?delete this[t]:this[t]=e}}:function(t,n){return function(){this[t]=n}})(t,n)):this.node()[t]},classed:function(t,n){var e=ht(t+"");if(arguments.length<2){for(var r=dt(this.node()),i=-1,o=e.length;++i<o;)if(!r.contains(e[i]))return!1;return!0}return this.each(("function"==typeof n?function(t,n){return function(){(n.apply(this,arguments)?vt:gt)(this,t)}}:n?function(t){return function(){vt(this,t)}}:function(t){return function(){gt(this,t)}})(e,n))},text:function(t){return arguments.length?this.each(null==t?yt:("function"==typeof t?function(t){return function(){var n=t.apply(this,arguments);this.textContent=null==n?"":n}}:function(t){return function(){this.textContent=t}})(t)):this.node().textContent},html:function(t){return arguments.length?this.each(null==t?_t:("function"==typeof t?function(t){return function(){var n=t.apply(this,arguments);this.innerHTML=null==n?"":n}}:function(t){return function(){this.innerHTML=t}})(t)):this.node().innerHTML},raise:function(){return this.each(bt)},lower:function(){return this.each(mt)},append:function(t){var n="function"==typeof t?t:W(t);return this.select(function(){return this.appendChild(n.apply(this,arguments))})},insert:function(t,n){var e="function"==typeof t?t:W(t),r=null==n?xt:"function"==typeof n?n:Q(n);return this.select(function(){return this.insertBefore(e.apply(this,arguments),r.apply(this,arguments)||null)})},remove:function(){return this.each(wt)},clone:function(t){return this.select(t?At:Mt)},datum:function(t){return arguments.length?this.property("__data__",t):this.node().__data__},on:function(t,n,e){var r,i,o=function(t){return t.trim().split(/^|\s+/).map(function(t){var n="",e=t.indexOf(".");return e>=0&&(n=t.slice(e+1),t=t.slice(0,e)),{type:t,name:n}})}(t+""),a=o.length;if(!(arguments.length<2)){for(u=n?kt:Et,null==e&&(e=!1),r=0;r<a;++r)this.each(u(o[r],n,e));return this}var u=this.node().__on;if(u)for(var f,c=0,s=u.length;c<s;++c)for(r=0,f=u[c];r<a;++r)if((i=o[r]).type===f.type&&i.name===f.name)return f.value},dispatch:function(t,n){return this.each(("function"==typeof n?function(t,n){return function(){return Pt(this,t,n.apply(this,arguments))}}:function(t,n){return function(){return Pt(this,t,n)}})(t,n))}};var Ut=0;function qt(){return new Ot}function Ot(){this._="@"+(++Ut).toString(36)}function Yt(){for(var n,e=t.event;n=e.sourceEvent;)e=n;return e}function Bt(t,n){var e=t.ownerSVGElement||t;if(e.createSVGPoint){var r=e.createSVGPoint();return r.x=n.clientX,r.y=n.clientY,[(r=r.matrixTransform(t.getScreenCTM().inverse())).x,r.y]}var i=t.getBoundingClientRect();return[n.clientX-i.left-t.clientLeft,n.clientY-i.top-t.clientTop]}function Ft(t){var n=Yt();return n.changedTouches&&(n=n.changedTouches[0]),Bt(t,n)}function It(t,n,e){arguments.length<3&&(e=n,n=Yt().changedTouches);for(var r,i=0,o=n?n.length:0;i<o;++i)if((r=n[i]).identifier===e)return Bt(t,r);return null}function Ht(){t.event.stopImmediatePropagation()}function jt(){t.event.preventDefault(),t.event.stopImmediatePropagation()}function Xt(t){var n=t.document.documentElement,e=Dt(t).on("dragstart.drag",jt,!0);"onselectstart"in n?e.on("selectstart.drag",jt,!0):(n.__noselect=n.style.MozUserSelect,n.style.MozUserSelect="none")}function Gt(t,n){var e=t.document.documentElement,r=Dt(t).on("dragstart.drag",null);n&&(r.on("click.drag",jt,!0),setTimeout(function(){r.on("click.drag",null)},0)),"onselectstart"in e?r.on("selectstart.drag",null):(e.style.MozUserSelect=e.__noselect,delete e.__noselect)}function Vt(t){return function(){return t}}function $t(t,n,e,r,i,o,a,u,f,c){this.target=t,this.type=n,this.subject=e,this.identifier=r,this.active=i,this.x=o,this.y=a,this.dx=u,this.dy=f,this._=c}function Wt(){return!t.event.button}function Zt(){return this.parentNode}function Qt(n){return null==n?{x:t.event.x,y:t.event.y}:n}function Jt(){return"ontouchstart"in this}function Kt(t,n,e){t.prototype=n.prototype=e,e.constructor=t}function tn(t,n){var e=Object.create(t.prototype);for(var r in n)e[r]=n[r];return e}function nn(){}Ot.prototype=qt.prototype={constructor:Ot,get:function(t){for(var n=this._;!(n in t);)if(!(t=t.parentNode))return;return t[n]},set:function(t,n){return t[this._]=n},remove:function(t){return this._ in t&&delete t[this._]},toString:function(){return this._}},$t.prototype.on=function(){var t=this._.on.apply(this._,arguments);return t===this._?this:t};var en="\\s*([+-]?\\d+)\\s*",rn="\\s*([+-]?\\d*\\.?\\d+(?:[eE][+-]?\\d+)?)\\s*",on="\\s*([+-]?\\d*\\.?\\d+(?:[eE][+-]?\\d+)?)%\\s*",an=/^#([0-9a-f]{3})$/,un=/^#([0-9a-f]{6})$/,fn=new RegExp("^rgb\\("+[en,en,en]+"\\)$"),cn=new RegExp("^rgb\\("+[on,on,on]+"\\)$"),sn=new RegExp("^rgba\\("+[en,en,en,rn]+"\\)$"),ln=new RegExp("^rgba\\("+[on,on,on,rn]+"\\)$"),hn=new RegExp("^hsl\\("+[rn,on,on]+"\\)$"),dn=new RegExp("^hsla\\("+[rn,on,on,rn]+"\\)$"),pn={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074};function vn(t){var n;return t=(t+"").trim().toLowerCase(),(n=an.exec(t))?new mn((n=parseInt(n[1],16))>>8&15|n>>4&240,n>>4&15|240&n,(15&n)<<4|15&n,1):(n=un.exec(t))?gn(parseInt(n[1],16)):(n=fn.exec(t))?new mn(n[1],n[2],n[3],1):(n=cn.exec(t))?new mn(255*n[1]/100,255*n[2]/100,255*n[3]/100,1):(n=sn.exec(t))?yn(n[1],n[2],n[3],n[4]):(n=ln.exec(t))?yn(255*n[1]/100,255*n[2]/100,255*n[3]/100,n[4]):(n=hn.exec(t))?wn(n[1],n[2]/100,n[3]/100,1):(n=dn.exec(t))?wn(n[1],n[2]/100,n[3]/100,n[4]):pn.hasOwnProperty(t)?gn(pn[t]):"transparent"===t?new mn(NaN,NaN,NaN,0):null}function gn(t){return new mn(t>>16&255,t>>8&255,255&t,1)}function yn(t,n,e,r){return r<=0&&(t=n=e=NaN),new mn(t,n,e,r)}function _n(t){return t instanceof nn||(t=vn(t)),t?new mn((t=t.rgb()).r,t.g,t.b,t.opacity):new mn}function bn(t,n,e,r){return 1===arguments.length?_n(t):new mn(t,n,e,null==r?1:r)}function mn(t,n,e,r){this.r=+t,this.g=+n,this.b=+e,this.opacity=+r}function xn(t){return((t=Math.max(0,Math.min(255,Math.round(t)||0)))<16?"0":"")+t.toString(16)}function wn(t,n,e,r){return r<=0?t=n=e=NaN:e<=0||e>=1?t=n=NaN:n<=0&&(t=NaN),new An(t,n,e,r)}function Mn(t,n,e,r){return 1===arguments.length?function(t){if(t instanceof An)return new An(t.h,t.s,t.l,t.opacity);if(t instanceof nn||(t=vn(t)),!t)return new An;if(t instanceof An)return t;var n=(t=t.rgb()).r/255,e=t.g/255,r=t.b/255,i=Math.min(n,e,r),o=Math.max(n,e,r),a=NaN,u=o-i,f=(o+i)/2;return u?(a=n===o?(e-r)/u+6*(e<r):e===o?(r-n)/u+2:(n-e)/u+4,u/=f<.5?o+i:2-o-i,a*=60):u=f>0&&f<1?0:a,new An(a,u,f,t.opacity)}(t):new An(t,n,e,null==r?1:r)}function An(t,n,e,r){this.h=+t,this.s=+n,this.l=+e,this.opacity=+r}function Tn(t,n,e){return 255*(t<60?n+(e-n)*t/60:t<180?e:t<240?n+(e-n)*(240-t)/60:n)}Kt(nn,vn,{displayable:function(){return this.rgb().displayable()},hex:function(){return this.rgb().hex()},toString:function(){return this.rgb()+""}}),Kt(mn,bn,tn(nn,{brighter:function(t){return t=null==t?1/.7:Math.pow(1/.7,t),new mn(this.r*t,this.g*t,this.b*t,this.opacity)},darker:function(t){return t=null==t?.7:Math.pow(.7,t),new mn(this.r*t,this.g*t,this.b*t,this.opacity)},rgb:function(){return this},displayable:function(){return 0<=this.r&&this.r<=255&&0<=this.g&&this.g<=255&&0<=this.b&&this.b<=255&&0<=this.opacity&&this.opacity<=1},hex:function(){return"#"+xn(this.r)+xn(this.g)+xn(this.b)},toString:function(){var t=this.opacity;return(1===(t=isNaN(t)?1:Math.max(0,Math.min(1,t)))?"rgb(":"rgba(")+Math.max(0,Math.min(255,Math.round(this.r)||0))+", "+Math.max(0,Math.min(255,Math.round(this.g)||0))+", "+Math.max(0,Math.min(255,Math.round(this.b)||0))+(1===t?")":", "+t+")")}})),Kt(An,Mn,tn(nn,{brighter:function(t){return t=null==t?1/.7:Math.pow(1/.7,t),new An(this.h,this.s,this.l*t,this.opacity)},darker:function(t){return t=null==t?.7:Math.pow(.7,t),new An(this.h,this.s,this.l*t,this.opacity)},rgb:function(){var t=this.h%360+360*(this.h<0),n=isNaN(t)||isNaN(this.s)?0:this.s,e=this.l,r=e+(e<.5?e:1-e)*n,i=2*e-r;return new mn(Tn(t>=240?t-240:t+120,i,r),Tn(t,i,r),Tn(t<120?t+240:t-120,i,r),this.opacity)},displayable:function(){return(0<=this.s&&this.s<=1||isNaN(this.s))&&0<=this.l&&this.l<=1&&0<=this.opacity&&this.opacity<=1}}));var Nn=Math.PI/180,Sn=180/Math.PI,En=.96422,kn=1,Cn=.82521,Pn=4/29,zn=6/29,Rn=3*zn*zn,Ln=zn*zn*zn;function Dn(t){if(t instanceof qn)return new qn(t.l,t.a,t.b,t.opacity);if(t instanceof jn){if(isNaN(t.h))return new qn(t.l,0,0,t.opacity);var n=t.h*Nn;return new qn(t.l,Math.cos(n)*t.c,Math.sin(n)*t.c,t.opacity)}t instanceof mn||(t=_n(t));var e,r,i=Fn(t.r),o=Fn(t.g),a=Fn(t.b),u=On((.2225045*i+.7168786*o+.0606169*a)/kn);return i===o&&o===a?e=r=u:(e=On((.4360747*i+.3850649*o+.1430804*a)/En),r=On((.0139322*i+.0971045*o+.7141733*a)/Cn)),new qn(116*u-16,500*(e-u),200*(u-r),t.opacity)}function Un(t,n,e,r){return 1===arguments.length?Dn(t):new qn(t,n,e,null==r?1:r)}function qn(t,n,e,r){this.l=+t,this.a=+n,this.b=+e,this.opacity=+r}function On(t){return t>Ln?Math.pow(t,1/3):t/Rn+Pn}function Yn(t){return t>zn?t*t*t:Rn*(t-Pn)}function Bn(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function Fn(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function In(t){if(t instanceof jn)return new jn(t.h,t.c,t.l,t.opacity);if(t instanceof qn||(t=Dn(t)),0===t.a&&0===t.b)return new jn(NaN,0,t.l,t.opacity);var n=Math.atan2(t.b,t.a)*Sn;return new jn(n<0?n+360:n,Math.sqrt(t.a*t.a+t.b*t.b),t.l,t.opacity)}function Hn(t,n,e,r){return 1===arguments.length?In(t):new jn(t,n,e,null==r?1:r)}function jn(t,n,e,r){this.h=+t,this.c=+n,this.l=+e,this.opacity=+r}Kt(qn,Un,tn(nn,{brighter:function(t){return new qn(this.l+18*(null==t?1:t),this.a,this.b,this.opacity)},darker:function(t){return new qn(this.l-18*(null==t?1:t),this.a,this.b,this.opacity)},rgb:function(){var t=(this.l+16)/116,n=isNaN(this.a)?t:t+this.a/500,e=isNaN(this.b)?t:t-this.b/200;return new mn(Bn(3.1338561*(n=En*Yn(n))-1.6168667*(t=kn*Yn(t))-.4906146*(e=Cn*Yn(e))),Bn(-.9787684*n+1.9161415*t+.033454*e),Bn(.0719453*n-.2289914*t+1.4052427*e),this.opacity)}})),Kt(jn,Hn,tn(nn,{brighter:function(t){return new jn(this.h,this.c,this.l+18*(null==t?1:t),this.opacity)},darker:function(t){return new jn(this.h,this.c,this.l-18*(null==t?1:t),this.opacity)},rgb:function(){return Dn(this).rgb()}}));var Xn=-.14861,Gn=1.78277,Vn=-.29227,$n=-.90649,Wn=1.97294,Zn=Wn*$n,Qn=Wn*Gn,Jn=Gn*Vn-$n*Xn;function Kn(t,n,e,r){return 1===arguments.length?function(t){if(t instanceof te)return new te(t.h,t.s,t.l,t.opacity);t instanceof mn||(t=_n(t));var n=t.r/255,e=t.g/255,r=t.b/255,i=(Jn*r+Zn*n-Qn*e)/(Jn+Zn-Qn),o=r-i,a=(Wn*(e-i)-Vn*o)/$n,u=Math.sqrt(a*a+o*o)/(Wn*i*(1-i)),f=u?Math.atan2(a,o)*Sn-120:NaN;return new te(f<0?f+360:f,u,i,t.opacity)}(t):new te(t,n,e,null==r?1:r)}function te(t,n,e,r){this.h=+t,this.s=+n,this.l=+e,this.opacity=+r}function ne(t,n,e,r,i){var o=t*t,a=o*t;return((1-3*t+3*o-a)*n+(4-6*o+3*a)*e+(1+3*t+3*o-3*a)*r+a*i)/6}function ee(t){var n=t.length-1;return function(e){var r=e<=0?e=0:e>=1?(e=1,n-1):Math.floor(e*n),i=t[r],o=t[r+1],a=r>0?t[r-1]:2*i-o,u=r<n-1?t[r+2]:2*o-i;return ne((e-r/n)*n,a,i,o,u)}}function re(t){var n=t.length;return function(e){var r=Math.floor(((e%=1)<0?++e:e)*n),i=t[(r+n-1)%n],o=t[r%n],a=t[(r+1)%n],u=t[(r+2)%n];return ne((e-r/n)*n,i,o,a,u)}}function ie(t){return function(){return t}}function oe(t,n){return function(e){return t+e*n}}function ae(t,n){var e=n-t;return e?oe(t,e>180||e<-180?e-360*Math.round(e/360):e):ie(isNaN(t)?n:t)}function ue(t){return 1==(t=+t)?fe:function(n,e){return e-n?function(t,n,e){return t=Math.pow(t,e),n=Math.pow(n,e)-t,e=1/e,function(r){return Math.pow(t+r*n,e)}}(n,e,t):ie(isNaN(n)?e:n)}}function fe(t,n){var e=n-t;return e?oe(t,e):ie(isNaN(t)?n:t)}Kt(te,Kn,tn(nn,{brighter:function(t){return t=null==t?1/.7:Math.pow(1/.7,t),new te(this.h,this.s,this.l*t,this.opacity)},darker:function(t){return t=null==t?.7:Math.pow(.7,t),new te(this.h,this.s,this.l*t,this.opacity)},rgb:function(){var t=isNaN(this.h)?0:(this.h+120)*Nn,n=+this.l,e=isNaN(this.s)?0:this.s*n*(1-n),r=Math.cos(t),i=Math.sin(t);return new mn(255*(n+e*(Xn*r+Gn*i)),255*(n+e*(Vn*r+$n*i)),255*(n+e*(Wn*r)),this.opacity)}}));var ce=function t(n){var e=ue(n);function r(t,n){var r=e((t=bn(t)).r,(n=bn(n)).r),i=e(t.g,n.g),o=e(t.b,n.b),a=fe(t.opacity,n.opacity);return function(n){return t.r=r(n),t.g=i(n),t.b=o(n),t.opacity=a(n),t+""}}return r.gamma=t,r}(1);function se(t){return function(n){var e,r,i=n.length,o=new Array(i),a=new Array(i),u=new Array(i);for(e=0;e<i;++e)r=bn(n[e]),o[e]=r.r||0,a[e]=r.g||0,u[e]=r.b||0;return o=t(o),a=t(a),u=t(u),r.opacity=1,function(t){return r.r=o(t),r.g=a(t),r.b=u(t),r+""}}}var le=se(ee),he=se(re);function de(t,n){var e,r=n?n.length:0,i=t?Math.min(r,t.length):0,o=new Array(i),a=new Array(r);for(e=0;e<i;++e)o[e]=me(t[e],n[e]);for(;e<r;++e)a[e]=n[e];return function(t){for(e=0;e<i;++e)a[e]=o[e](t);return a}}function pe(t,n){var e=new Date;return n-=t=+t,function(r){return e.setTime(t+n*r),e}}function ve(t,n){return n-=t=+t,function(e){return t+n*e}}function ge(t,n){var e,r={},i={};for(e in null!==t&&"object"==typeof t||(t={}),null!==n&&"object"==typeof n||(n={}),n)e in t?r[e]=me(t[e],n[e]):i[e]=n[e];return function(t){for(e in r)i[e]=r[e](t);return i}}var ye=/[-+]?(?:\d+\.?\d*|\.?\d+)(?:[eE][-+]?\d+)?/g,_e=new RegExp(ye.source,"g");function be(t,n){var e,r,i,o=ye.lastIndex=_e.lastIndex=0,a=-1,u=[],f=[];for(t+="",n+="";(e=ye.exec(t))&&(r=_e.exec(n));)(i=r.index)>o&&(i=n.slice(o,i),u[a]?u[a]+=i:u[++a]=i),(e=e[0])===(r=r[0])?u[a]?u[a]+=r:u[++a]=r:(u[++a]=null,f.push({i:a,x:ve(e,r)})),o=_e.lastIndex;return o<n.length&&(i=n.slice(o),u[a]?u[a]+=i:u[++a]=i),u.length<2?f[0]?function(t){return function(n){return t(n)+""}}(f[0].x):function(t){return function(){return t}}(n):(n=f.length,function(t){for(var e,r=0;r<n;++r)u[(e=f[r]).i]=e.x(t);return u.join("")})}function me(t,n){var e,r=typeof n;return null==n||"boolean"===r?ie(n):("number"===r?ve:"string"===r?(e=vn(n))?(n=e,ce):be:n instanceof vn?ce:n instanceof Date?pe:Array.isArray(n)?de:"function"!=typeof n.valueOf&&"function"!=typeof n.toString||isNaN(n)?ge:ve)(t,n)}function xe(t,n){return n-=t=+t,function(e){return Math.round(t+n*e)}}var we,Me,Ae,Te,Ne=180/Math.PI,Se={translateX:0,translateY:0,rotate:0,skewX:0,scaleX:1,scaleY:1};function Ee(t,n,e,r,i,o){var a,u,f;return(a=Math.sqrt(t*t+n*n))&&(t/=a,n/=a),(f=t*e+n*r)&&(e-=t*f,r-=n*f),(u=Math.sqrt(e*e+r*r))&&(e/=u,r/=u,f/=u),t*r<n*e&&(t=-t,n=-n,f=-f,a=-a),{translateX:i,translateY:o,rotate:Math.atan2(n,t)*Ne,skewX:Math.atan(f)*Ne,scaleX:a,scaleY:u}}function ke(t,n,e,r){function i(t){return t.length?t.pop()+" ":""}return function(o,a){var u=[],f=[];return o=t(o),a=t(a),function(t,r,i,o,a,u){if(t!==i||r!==o){var f=a.push("translate(",null,n,null,e);u.push({i:f-4,x:ve(t,i)},{i:f-2,x:ve(r,o)})}else(i||o)&&a.push("translate("+i+n+o+e)}(o.translateX,o.translateY,a.translateX,a.translateY,u,f),function(t,n,e,o){t!==n?(t-n>180?n+=360:n-t>180&&(t+=360),o.push({i:e.push(i(e)+"rotate(",null,r)-2,x:ve(t,n)})):n&&e.push(i(e)+"rotate("+n+r)}(o.rotate,a.rotate,u,f),function(t,n,e,o){t!==n?o.push({i:e.push(i(e)+"skewX(",null,r)-2,x:ve(t,n)}):n&&e.push(i(e)+"skewX("+n+r)}(o.skewX,a.skewX,u,f),function(t,n,e,r,o,a){if(t!==e||n!==r){var u=o.push(i(o)+"scale(",null,",",null,")");a.push({i:u-4,x:ve(t,e)},{i:u-2,x:ve(n,r)})}else 1===e&&1===r||o.push(i(o)+"scale("+e+","+r+")")}(o.scaleX,o.scaleY,a.scaleX,a.scaleY,u,f),o=a=null,function(t){for(var n,e=-1,r=f.length;++e<r;)u[(n=f[e]).i]=n.x(t);return u.join("")}}}var Ce=ke(function(t){return"none"===t?Se:(we||(we=document.createElement("DIV"),Me=document.documentElement,Ae=document.defaultView),we.style.transform=t,t=Ae.getComputedStyle(Me.appendChild(we),null).getPropertyValue("transform"),Me.removeChild(we),Ee(+(t=t.slice(7,-1).split(","))[0],+t[1],+t[2],+t[3],+t[4],+t[5]))},"px, ","px)","deg)"),Pe=ke(function(t){return null==t?Se:(Te||(Te=document.createElementNS("http://www.w3.org/2000/svg","g")),Te.setAttribute("transform",t),(t=Te.transform.baseVal.consolidate())?Ee((t=t.matrix).a,t.b,t.c,t.d,t.e,t.f):Se)},", ",")",")"),ze=Math.SQRT2,Re=2,Le=4,De=1e-12;function Ue(t){return((t=Math.exp(t))+1/t)/2}function qe(t,n){var e,r,i=t[0],o=t[1],a=t[2],u=n[0],f=n[1],c=n[2],s=u-i,l=f-o,h=s*s+l*l;if(h<De)r=Math.log(c/a)/ze,e=function(t){return[i+t*s,o+t*l,a*Math.exp(ze*t*r)]};else{var d=Math.sqrt(h),p=(c*c-a*a+Le*h)/(2*a*Re*d),v=(c*c-a*a-Le*h)/(2*c*Re*d),g=Math.log(Math.sqrt(p*p+1)-p),y=Math.log(Math.sqrt(v*v+1)-v);r=(y-g)/ze,e=function(t){var n,e=t*r,u=Ue(g),f=a/(Re*d)*(u*(n=ze*e+g,((n=Math.exp(2*n))-1)/(n+1))-function(t){return((t=Math.exp(t))-1/t)/2}(g));return[i+f*s,o+f*l,a*u/Ue(ze*e+g)]}}return e.duration=1e3*r,e}function Oe(t){return function(n,e){var r=t((n=Mn(n)).h,(e=Mn(e)).h),i=fe(n.s,e.s),o=fe(n.l,e.l),a=fe(n.opacity,e.opacity);return function(t){return n.h=r(t),n.s=i(t),n.l=o(t),n.opacity=a(t),n+""}}}var Ye=Oe(ae),Be=Oe(fe);function Fe(t){return function(n,e){var r=t((n=Hn(n)).h,(e=Hn(e)).h),i=fe(n.c,e.c),o=fe(n.l,e.l),a=fe(n.opacity,e.opacity);return function(t){return n.h=r(t),n.c=i(t),n.l=o(t),n.opacity=a(t),n+""}}}var Ie=Fe(ae),He=Fe(fe);function je(t){return function n(e){function r(n,r){var i=t((n=Kn(n)).h,(r=Kn(r)).h),o=fe(n.s,r.s),a=fe(n.l,r.l),u=fe(n.opacity,r.opacity);return function(t){return n.h=i(t),n.s=o(t),n.l=a(Math.pow(t,e)),n.opacity=u(t),n+""}}return e=+e,r.gamma=n,r}(1)}var Xe=je(ae),Ge=je(fe);var Ve,$e,We=0,Ze=0,Qe=0,Je=1e3,Ke=0,tr=0,nr=0,er="object"==typeof performance&&performance.now?performance:Date,rr="object"==typeof window&&window.requestAnimationFrame?window.requestAnimationFrame.bind(window):function(t){setTimeout(t,17)};function ir(){return tr||(rr(or),tr=er.now()+nr)}function or(){tr=0}function ar(){this._call=this._time=this._next=null}function ur(t,n,e){var r=new ar;return r.restart(t,n,e),r}function fr(){ir(),++We;for(var t,n=Ve;n;)(t=tr-n._time)>=0&&n._call.call(null,t),n=n._next;--We}function cr(){tr=(Ke=er.now())+nr,We=Ze=0;try{fr()}finally{We=0,function(){var t,n,e=Ve,r=1/0;for(;e;)e._call?(r>e._time&&(r=e._time),t=e,e=e._next):(n=e._next,e._next=null,e=t?t._next=n:Ve=n);$e=t,lr(r)}(),tr=0}}function sr(){var t=er.now(),n=t-Ke;n>Je&&(nr-=n,Ke=t)}function lr(t){We||(Ze&&(Ze=clearTimeout(Ze)),t-tr>24?(t<1/0&&(Ze=setTimeout(cr,t-er.now()-nr)),Qe&&(Qe=clearInterval(Qe))):(Qe||(Ke=er.now(),Qe=setInterval(sr,Je)),We=1,rr(cr)))}function hr(t,n,e){var r=new ar;return n=null==n?0:+n,r.restart(function(e){r.stop(),t(e+n)},n,e),r}ar.prototype=ur.prototype={constructor:ar,restart:function(t,n,e){if("function"!=typeof t)throw new TypeError("callback is not a function");e=(null==e?ir():+e)+(null==n?0:+n),this._next||$e===this||($e?$e._next=this:Ve=this,$e=this),this._call=t,this._time=e,lr()},stop:function(){this._call&&(this._call=null,this._time=1/0,lr())}};var dr=I("start","end","interrupt"),pr=[],vr=0,gr=1,yr=2,_r=3,br=4,mr=5,xr=6;function wr(t,n,e,r,i,o){var a=t.__transition;if(a){if(e in a)return}else t.__transition={};!function(t,n,e){var r,i=t.__transition;function o(f){var c,s,l,h;if(e.state!==gr)return u();for(c in i)if((h=i[c]).name===e.name){if(h.state===_r)return hr(o);h.state===br?(h.state=xr,h.timer.stop(),h.on.call("interrupt",t,t.__data__,h.index,h.group),delete i[c]):+c<n&&(h.state=xr,h.timer.stop(),delete i[c])}if(hr(function(){e.state===_r&&(e.state=br,e.timer.restart(a,e.delay,e.time),a(f))}),e.state=yr,e.on.call("start",t,t.__data__,e.index,e.group),e.state===yr){for(e.state=_r,r=new Array(l=e.tween.length),c=0,s=-1;c<l;++c)(h=e.tween[c].value.call(t,t.__data__,e.index,e.group))&&(r[++s]=h);r.length=s+1}}function a(n){for(var i=n<e.duration?e.ease.call(null,n/e.duration):(e.timer.restart(u),e.state=mr,1),o=-1,a=r.length;++o<a;)r[o].call(null,i);e.state===mr&&(e.on.call("end",t,t.__data__,e.index,e.group),u())}function u(){for(var r in e.state=xr,e.timer.stop(),delete i[n],i)return;delete t.__transition}i[n]=e,e.timer=ur(function(t){e.state=gr,e.timer.restart(o,e.delay,e.time),e.delay<=t&&o(t-e.delay)},0,e.time)}(t,e,{name:n,index:r,group:i,on:dr,tween:pr,time:o.time,delay:o.delay,duration:o.duration,ease:o.ease,timer:null,state:vr})}function Mr(t,n){var e=Tr(t,n);if(e.state>vr)throw new Error("too late; already scheduled");return e}function Ar(t,n){var e=Tr(t,n);if(e.state>yr)throw new Error("too late; already started");return e}function Tr(t,n){var e=t.__transition;if(!e||!(e=e[n]))throw new Error("transition not found");return e}function Nr(t,n){var e,r,i,o=t.__transition,a=!0;if(o){for(i in n=null==n?null:n+"",o)(e=o[i]).name===n?(r=e.state>yr&&e.state<mr,e.state=xr,e.timer.stop(),r&&e.on.call("interrupt",t,t.__data__,e.index,e.group),delete o[i]):a=!1;a&&delete t.__transition}}function Sr(t,n,e){var r=t._id;return t.each(function(){var t=Ar(this,r);(t.value||(t.value={}))[n]=e.apply(this,arguments)}),function(t){return Tr(t,r).value[n]}}function Er(t,n){var e;return("number"==typeof n?ve:n instanceof vn?ce:(e=vn(n))?(n=e,ce):be)(t,n)}var kr=Lt.prototype.constructor;var Cr=0;function Pr(t,n,e,r){this._groups=t,this._parents=n,this._name=e,this._id=r}function zr(t){return Lt().transition(t)}function Rr(){return++Cr}var Lr=Lt.prototype;function Dr(t){return((t*=2)<=1?t*t:--t*(2-t)+1)/2}function Ur(t){return((t*=2)<=1?t*t*t:(t-=2)*t*t+2)/2}Pr.prototype=zr.prototype={constructor:Pr,select:function(t){var n=this._name,e=this._id;"function"!=typeof t&&(t=Q(t));for(var r=this._groups,i=r.length,o=new Array(i),a=0;a<i;++a)for(var u,f,c=r[a],s=c.length,l=o[a]=new Array(s),h=0;h<s;++h)(u=c[h])&&(f=t.call(u,u.__data__,h,c))&&("__data__"in u&&(f.__data__=u.__data__),l[h]=f,wr(l[h],n,e,h,l,Tr(u,e)));return new Pr(o,this._parents,n,e)},selectAll:function(t){var n=this._name,e=this._id;"function"!=typeof t&&(t=K(t));for(var r=this._groups,i=r.length,o=[],a=[],u=0;u<i;++u)for(var f,c=r[u],s=c.length,l=0;l<s;++l)if(f=c[l]){for(var h,d=t.call(f,f.__data__,l,c),p=Tr(f,e),v=0,g=d.length;v<g;++v)(h=d[v])&&wr(h,n,e,v,d,p);o.push(d),a.push(f)}return new Pr(o,a,n,e)},filter:function(t){"function"!=typeof t&&(t=rt(t));for(var n=this._groups,e=n.length,r=new Array(e),i=0;i<e;++i)for(var o,a=n[i],u=a.length,f=r[i]=[],c=0;c<u;++c)(o=a[c])&&t.call(o,o.__data__,c,a)&&f.push(o);return new Pr(r,this._parents,this._name,this._id)},merge:function(t){if(t._id!==this._id)throw new Error;for(var n=this._groups,e=t._groups,r=n.length,i=e.length,o=Math.min(r,i),a=new Array(r),u=0;u<o;++u)for(var f,c=n[u],s=e[u],l=c.length,h=a[u]=new Array(l),d=0;d<l;++d)(f=c[d]||s[d])&&(h[d]=f);for(;u<r;++u)a[u]=n[u];return new Pr(a,this._parents,this._name,this._id)},selection:function(){return new kr(this._groups,this._parents)},transition:function(){for(var t=this._name,n=this._id,e=Rr(),r=this._groups,i=r.length,o=0;o<i;++o)for(var a,u=r[o],f=u.length,c=0;c<f;++c)if(a=u[c]){var s=Tr(a,n);wr(a,t,e,c,u,{time:s.time+s.delay+s.duration,delay:0,duration:s.duration,ease:s.ease})}return new Pr(r,this._parents,t,e)},call:Lr.call,nodes:Lr.nodes,node:Lr.node,size:Lr.size,empty:Lr.empty,each:Lr.each,on:function(t,n){var e=this._id;return arguments.length<2?Tr(this.node(),e).on.on(t):this.each(function(t,n,e){var r,i,o=function(t){return(t+"").trim().split(/^|\s+/).every(function(t){var n=t.indexOf(".");return n>=0&&(t=t.slice(0,n)),!t||"start"===t})}(n)?Mr:Ar;return function(){var a=o(this,t),u=a.on;u!==r&&(i=(r=u).copy()).on(n,e),a.on=i}}(e,t,n))},attr:function(t,n){var e=$(t),r="transform"===e?Pe:Er;return this.attrTween(t,"function"==typeof n?(e.local?function(t,n,e){var r,i,o;return function(){var a,u=e(this);if(null!=u)return(a=this.getAttributeNS(t.space,t.local))===u?null:a===r&&u===i?o:o=n(r=a,i=u);this.removeAttributeNS(t.space,t.local)}}:function(t,n,e){var r,i,o;return function(){var a,u=e(this);if(null!=u)return(a=this.getAttribute(t))===u?null:a===r&&u===i?o:o=n(r=a,i=u);this.removeAttribute(t)}})(e,r,Sr(this,"attr."+t,n)):null==n?(e.local?function(t){return function(){this.removeAttributeNS(t.space,t.local)}}:function(t){return function(){this.removeAttribute(t)}})(e):(e.local?function(t,n,e){var r,i;return function(){var o=this.getAttributeNS(t.space,t.local);return o===e?null:o===r?i:i=n(r=o,e)}}:function(t,n,e){var r,i;return function(){var o=this.getAttribute(t);return o===e?null:o===r?i:i=n(r=o,e)}})(e,r,n+""))},attrTween:function(t,n){var e="attr."+t;if(arguments.length<2)return(e=this.tween(e))&&e._value;if(null==n)return this.tween(e,null);if("function"!=typeof n)throw new Error;var r=$(t);return this.tween(e,(r.local?function(t,n){function e(){var e=this,r=n.apply(e,arguments);return r&&function(n){e.setAttributeNS(t.space,t.local,r(n))}}return e._value=n,e}:function(t,n){function e(){var e=this,r=n.apply(e,arguments);return r&&function(n){e.setAttribute(t,r(n))}}return e._value=n,e})(r,n))},style:function(t,n,e){var r="transform"==(t+="")?Ce:Er;return null==n?this.styleTween(t,function(t,n){var e,r,i;return function(){var o=lt(this,t),a=(this.style.removeProperty(t),lt(this,t));return o===a?null:o===e&&a===r?i:i=n(e=o,r=a)}}(t,r)).on("end.style."+t,function(t){return function(){this.style.removeProperty(t)}}(t)):this.styleTween(t,"function"==typeof n?function(t,n,e){var r,i,o;return function(){var a=lt(this,t),u=e(this);return null==u&&(this.style.removeProperty(t),u=lt(this,t)),a===u?null:a===r&&u===i?o:o=n(r=a,i=u)}}(t,r,Sr(this,"style."+t,n)):function(t,n,e){var r,i;return function(){var o=lt(this,t);return o===e?null:o===r?i:i=n(r=o,e)}}(t,r,n+""),e)},styleTween:function(t,n,e){var r="style."+(t+="");if(arguments.length<2)return(r=this.tween(r))&&r._value;if(null==n)return this.tween(r,null);if("function"!=typeof n)throw new Error;return this.tween(r,function(t,n,e){function r(){var r=this,i=n.apply(r,arguments);return i&&function(n){r.style.setProperty(t,i(n),e)}}return r._value=n,r}(t,n,null==e?"":e))},text:function(t){return this.tween("text","function"==typeof t?function(t){return function(){var n=t(this);this.textContent=null==n?"":n}}(Sr(this,"text",t)):function(t){return function(){this.textContent=t}}(null==t?"":t+""))},remove:function(){return this.on("end.remove",(t=this._id,function(){var n=this.parentNode;for(var e in this.__transition)if(+e!==t)return;n&&n.removeChild(this)}));var t},tween:function(t,n){var e=this._id;if(t+="",arguments.length<2){for(var r,i=Tr(this.node(),e).tween,o=0,a=i.length;o<a;++o)if((r=i[o]).name===t)return r.value;return null}return this.each((null==n?function(t,n){var e,r;return function(){var i=Ar(this,t),o=i.tween;if(o!==e)for(var a=0,u=(r=e=o).length;a<u;++a)if(r[a].name===n){(r=r.slice()).splice(a,1);break}i.tween=r}}:function(t,n,e){var r,i;if("function"!=typeof e)throw new Error;return function(){var o=Ar(this,t),a=o.tween;if(a!==r){i=(r=a).slice();for(var u={name:n,value:e},f=0,c=i.length;f<c;++f)if(i[f].name===n){i[f]=u;break}f===c&&i.push(u)}o.tween=i}})(e,t,n))},delay:function(t){var n=this._id;return arguments.length?this.each(("function"==typeof t?function(t,n){return function(){Mr(this,t).delay=+n.apply(this,arguments)}}:function(t,n){return n=+n,function(){Mr(this,t).delay=n}})(n,t)):Tr(this.node(),n).delay},duration:function(t){var n=this._id;return arguments.length?this.each(("function"==typeof t?function(t,n){return function(){Ar(this,t).duration=+n.apply(this,arguments)}}:function(t,n){return n=+n,function(){Ar(this,t).duration=n}})(n,t)):Tr(this.node(),n).duration},ease:function(t){var n=this._id;return arguments.length?this.each(function(t,n){if("function"!=typeof n)throw new Error;return function(){Ar(this,t).ease=n}}(n,t)):Tr(this.node(),n).ease}};var qr=function t(n){function e(t){return Math.pow(t,n)}return n=+n,e.exponent=t,e}(3),Or=function t(n){function e(t){return 1-Math.pow(1-t,n)}return n=+n,e.exponent=t,e}(3),Yr=function t(n){function e(t){return((t*=2)<=1?Math.pow(t,n):2-Math.pow(2-t,n))/2}return n=+n,e.exponent=t,e}(3),Br=Math.PI,Fr=Br/2;function Ir(t){return(1-Math.cos(Br*t))/2}function Hr(t){return((t*=2)<=1?Math.pow(2,10*t-10):2-Math.pow(2,10-10*t))/2}function jr(t){return((t*=2)<=1?1-Math.sqrt(1-t*t):Math.sqrt(1-(t-=2)*t)+1)/2}var Xr=4/11,Gr=6/11,Vr=8/11,$r=.75,Wr=9/11,Zr=10/11,Qr=.9375,Jr=21/22,Kr=63/64,ti=1/Xr/Xr;function ni(t){return(t=+t)<Xr?ti*t*t:t<Vr?ti*(t-=Gr)*t+$r:t<Zr?ti*(t-=Wr)*t+Qr:ti*(t-=Jr)*t+Kr}var ei=function t(n){function e(t){return t*t*((n+1)*t-n)}return n=+n,e.overshoot=t,e}(1.70158),ri=function t(n){function e(t){return--t*t*((n+1)*t+n)+1}return n=+n,e.overshoot=t,e}(1.70158),ii=function t(n){function e(t){return((t*=2)<1?t*t*((n+1)*t-n):(t-=2)*t*((n+1)*t+n)+2)/2}return n=+n,e.overshoot=t,e}(1.70158),oi=2*Math.PI,ai=function t(n,e){var r=Math.asin(1/(n=Math.max(1,n)))*(e/=oi);function i(t){return n*Math.pow(2,10*--t)*Math.sin((r-t)/e)}return i.amplitude=function(n){return t(n,e*oi)},i.period=function(e){return t(n,e)},i}(1,.3),ui=function t(n,e){var r=Math.asin(1/(n=Math.max(1,n)))*(e/=oi);function i(t){return 1-n*Math.pow(2,-10*(t=+t))*Math.sin((t+r)/e)}return i.amplitude=function(n){return t(n,e*oi)},i.period=function(e){return t(n,e)},i}(1,.3),fi=function t(n,e){var r=Math.asin(1/(n=Math.max(1,n)))*(e/=oi);function i(t){return((t=2*t-1)<0?n*Math.pow(2,10*t)*Math.sin((r-t)/e):2-n*Math.pow(2,-10*t)*Math.sin((r+t)/e))/2}return i.amplitude=function(n){return t(n,e*oi)},i.period=function(e){return t(n,e)},i}(1,.3),ci={time:null,delay:0,duration:250,ease:Ur};function si(t,n){for(var e;!(e=t.__transition)||!(e=e[n]);)if(!(t=t.parentNode))return ci.time=ir(),ci;return e}Lt.prototype.interrupt=function(t){return this.each(function(){Nr(this,t)})},Lt.prototype.transition=function(t){var n,e;t instanceof Pr?(n=t._id,t=t._name):(n=Rr(),(e=ci).time=ir(),t=null==t?null:t+"");for(var r=this._groups,i=r.length,o=0;o<i;++o)for(var a,u=r[o],f=u.length,c=0;c<f;++c)(a=u[c])&&wr(a,t,n,c,u,e||si(a,n));return new Pr(r,this._parents,t,n)};var li=[null];function hi(t){return function(){return t}}function di(t,n,e){this.target=t,this.type=n,this.selection=e}function pi(){t.event.stopImmediatePropagation()}function vi(){t.event.preventDefault(),t.event.stopImmediatePropagation()}var gi={name:"drag"},yi={name:"space"},_i={name:"handle"},bi={name:"center"},mi={name:"x",handles:["e","w"].map(Ei),input:function(t,n){return t&&[[t[0],n[0][1]],[t[1],n[1][1]]]},output:function(t){return t&&[t[0][0],t[1][0]]}},xi={name:"y",handles:["n","s"].map(Ei),input:function(t,n){return t&&[[n[0][0],t[0]],[n[1][0],t[1]]]},output:function(t){return t&&[t[0][1],t[1][1]]}},wi={name:"xy",handles:["n","e","s","w","nw","ne","se","sw"].map(Ei),input:function(t){return t},output:function(t){return t}},Mi={overlay:"crosshair",selection:"move",n:"ns-resize",e:"ew-resize",s:"ns-resize",w:"ew-resize",nw:"nwse-resize",ne:"nesw-resize",se:"nwse-resize",sw:"nesw-resize"},Ai={e:"w",w:"e",nw:"ne",ne:"nw",se:"sw",sw:"se"},Ti={n:"s",s:"n",nw:"sw",ne:"se",se:"ne",sw:"nw"},Ni={overlay:1,selection:1,n:null,e:1,s:null,w:-1,nw:-1,ne:1,se:1,sw:-1},Si={overlay:1,selection:1,n:-1,e:null,s:1,w:null,nw:-1,ne:-1,se:1,sw:1};function Ei(t){return{type:t}}function ki(){return!t.event.button}function Ci(){var t=this.ownerSVGElement||this;return[[0,0],[t.width.baseVal.value,t.height.baseVal.value]]}function Pi(t){for(;!t.__brush;)if(!(t=t.parentNode))return;return t.__brush}function zi(t){return t[0][0]===t[1][0]||t[0][1]===t[1][1]}function Ri(n){var e,r=Ci,i=ki,o=I(u,"start","brush","end"),a=6;function u(t){var e=t.property("__brush",h).selectAll(".overlay").data([Ei("overlay")]);e.enter().append("rect").attr("class","overlay").attr("pointer-events","all").attr("cursor",Mi.overlay).merge(e).each(function(){var t=Pi(this).extent;Dt(this).attr("x",t[0][0]).attr("y",t[0][1]).attr("width",t[1][0]-t[0][0]).attr("height",t[1][1]-t[0][1])}),t.selectAll(".selection").data([Ei("selection")]).enter().append("rect").attr("class","selection").attr("cursor",Mi.selection).attr("fill","#777").attr("fill-opacity",.3).attr("stroke","#fff").attr("shape-rendering","crispEdges");var r=t.selectAll(".handle").data(n.handles,function(t){return t.type});r.exit().remove(),r.enter().append("rect").attr("class",function(t){return"handle handle--"+t.type}).attr("cursor",function(t){return Mi[t.type]}),t.each(f).attr("fill","none").attr("pointer-events","all").style("-webkit-tap-highlight-color","rgba(0,0,0,0)").on("mousedown.brush touchstart.brush",l)}function f(){var t=Dt(this),n=Pi(this).selection;n?(t.selectAll(".selection").style("display",null).attr("x",n[0][0]).attr("y",n[0][1]).attr("width",n[1][0]-n[0][0]).attr("height",n[1][1]-n[0][1]),t.selectAll(".handle").style("display",null).attr("x",function(t){return"e"===t.type[t.type.length-1]?n[1][0]-a/2:n[0][0]-a/2}).attr("y",function(t){return"s"===t.type[0]?n[1][1]-a/2:n[0][1]-a/2}).attr("width",function(t){return"n"===t.type||"s"===t.type?n[1][0]-n[0][0]+a:a}).attr("height",function(t){return"e"===t.type||"w"===t.type?n[1][1]-n[0][1]+a:a})):t.selectAll(".selection,.handle").style("display","none").attr("x",null).attr("y",null).attr("width",null).attr("height",null)}function c(t,n){return t.__brush.emitter||new s(t,n)}function s(t,n){this.that=t,this.args=n,this.state=t.__brush,this.active=0}function l(){if(t.event.touches){if(t.event.changedTouches.length<t.event.touches.length)return vi()}else if(e)return;if(i.apply(this,arguments)){var r,o,a,u,s,l,h,d,p,v,g,y,_,b=this,m=t.event.target.__data__.type,x="selection"===(t.event.metaKey?m="overlay":m)?gi:t.event.altKey?bi:_i,w=n===xi?null:Ni[m],M=n===mi?null:Si[m],A=Pi(b),T=A.extent,N=A.selection,S=T[0][0],E=T[0][1],k=T[1][0],C=T[1][1],P=w&&M&&t.event.shiftKey,z=Ft(b),R=z,L=c(b,arguments).beforestart();"overlay"===m?A.selection=N=[[r=n===xi?S:z[0],a=n===mi?E:z[1]],[s=n===xi?k:r,h=n===mi?C:a]]:(r=N[0][0],a=N[0][1],s=N[1][0],h=N[1][1]),o=r,u=a,l=s,d=h;var D=Dt(b).attr("pointer-events","none"),U=D.selectAll(".overlay").attr("cursor",Mi[m]);if(t.event.touches)D.on("touchmove.brush",O,!0).on("touchend.brush touchcancel.brush",B,!0);else{var q=Dt(t.event.view).on("keydown.brush",function(){switch(t.event.keyCode){case 16:P=w&&M;break;case 18:x===_i&&(w&&(s=l-p*w,r=o+p*w),M&&(h=d-v*M,a=u+v*M),x=bi,Y());break;case 32:x!==_i&&x!==bi||(w<0?s=l-p:w>0&&(r=o-p),M<0?h=d-v:M>0&&(a=u-v),x=yi,U.attr("cursor",Mi.selection),Y());break;default:return}vi()},!0).on("keyup.brush",function(){switch(t.event.keyCode){case 16:P&&(y=_=P=!1,Y());break;case 18:x===bi&&(w<0?s=l:w>0&&(r=o),M<0?h=d:M>0&&(a=u),x=_i,Y());break;case 32:x===yi&&(t.event.altKey?(w&&(s=l-p*w,r=o+p*w),M&&(h=d-v*M,a=u+v*M),x=bi):(w<0?s=l:w>0&&(r=o),M<0?h=d:M>0&&(a=u),x=_i),U.attr("cursor",Mi[m]),Y());break;default:return}vi()},!0).on("mousemove.brush",O,!0).on("mouseup.brush",B,!0);Xt(t.event.view)}pi(),Nr(b),f.call(b),L.start()}function O(){var t=Ft(b);!P||y||_||(Math.abs(t[0]-R[0])>Math.abs(t[1]-R[1])?_=!0:y=!0),R=t,g=!0,vi(),Y()}function Y(){var t;switch(p=R[0]-z[0],v=R[1]-z[1],x){case yi:case gi:w&&(p=Math.max(S-r,Math.min(k-s,p)),o=r+p,l=s+p),M&&(v=Math.max(E-a,Math.min(C-h,v)),u=a+v,d=h+v);break;case _i:w<0?(p=Math.max(S-r,Math.min(k-r,p)),o=r+p,l=s):w>0&&(p=Math.max(S-s,Math.min(k-s,p)),o=r,l=s+p),M<0?(v=Math.max(E-a,Math.min(C-a,v)),u=a+v,d=h):M>0&&(v=Math.max(E-h,Math.min(C-h,v)),u=a,d=h+v);break;case bi:w&&(o=Math.max(S,Math.min(k,r-p*w)),l=Math.max(S,Math.min(k,s+p*w))),M&&(u=Math.max(E,Math.min(C,a-v*M)),d=Math.max(E,Math.min(C,h+v*M)))}l<o&&(w*=-1,t=r,r=s,s=t,t=o,o=l,l=t,m in Ai&&U.attr("cursor",Mi[m=Ai[m]])),d<u&&(M*=-1,t=a,a=h,h=t,t=u,u=d,d=t,m in Ti&&U.attr("cursor",Mi[m=Ti[m]])),A.selection&&(N=A.selection),y&&(o=N[0][0],l=N[1][0]),_&&(u=N[0][1],d=N[1][1]),N[0][0]===o&&N[0][1]===u&&N[1][0]===l&&N[1][1]===d||(A.selection=[[o,u],[l,d]],f.call(b),L.brush())}function B(){if(pi(),t.event.touches){if(t.event.touches.length)return;e&&clearTimeout(e),e=setTimeout(function(){e=null},500),D.on("touchmove.brush touchend.brush touchcancel.brush",null)}else Gt(t.event.view,g),q.on("keydown.brush keyup.brush mousemove.brush mouseup.brush",null);D.attr("pointer-events","all"),U.attr("cursor",Mi.overlay),A.selection&&(N=A.selection),zi(N)&&(A.selection=null,f.call(b)),L.end()}}function h(){var t=this.__brush||{selection:null};return t.extent=r.apply(this,arguments),t.dim=n,t}return u.move=function(t,e){t.selection?t.on("start.brush",function(){c(this,arguments).beforestart().start()}).on("interrupt.brush end.brush",function(){c(this,arguments).end()}).tween("brush",function(){var t=this,r=t.__brush,i=c(t,arguments),o=r.selection,a=n.input("function"==typeof e?e.apply(this,arguments):e,r.extent),u=me(o,a);function s(n){r.selection=1===n&&zi(a)?null:u(n),f.call(t),i.brush()}return o&&a?s:s(1)}):t.each(function(){var t=arguments,r=this.__brush,i=n.input("function"==typeof e?e.apply(this,t):e,r.extent),o=c(this,t).beforestart();Nr(this),r.selection=null==i||zi(i)?null:i,f.call(this),o.start().brush().end()})},s.prototype={beforestart:function(){return 1==++this.active&&(this.state.emitter=this,this.starting=!0),this},start:function(){return this.starting&&(this.starting=!1,this.emit("start")),this},brush:function(){return this.emit("brush"),this},end:function(){return 0==--this.active&&(delete this.state.emitter,this.emit("end")),this},emit:function(t){Ct(new di(u,t,n.output(this.state.selection)),o.apply,o,[t,this.that,this.args])}},u.extent=function(t){return arguments.length?(r="function"==typeof t?t:hi([[+t[0][0],+t[0][1]],[+t[1][0],+t[1][1]]]),u):r},u.filter=function(t){return arguments.length?(i="function"==typeof t?t:hi(!!t),u):i},u.handleSize=function(t){return arguments.length?(a=+t,u):a},u.on=function(){var t=o.on.apply(o,arguments);return t===o?u:t},u}var Li=Math.cos,Di=Math.sin,Ui=Math.PI,qi=Ui/2,Oi=2*Ui,Yi=Math.max;var Bi=Array.prototype.slice;function Fi(t){return function(){return t}}var Ii=Math.PI,Hi=2*Ii,ji=Hi-1e-6;function Xi(){this._x0=this._y0=this._x1=this._y1=null,this._=""}function Gi(){return new Xi}function Vi(t){return t.source}function $i(t){return t.target}function Wi(t){return t.radius}function Zi(t){return t.startAngle}function Qi(t){return t.endAngle}Xi.prototype=Gi.prototype={constructor:Xi,moveTo:function(t,n){this._+="M"+(this._x0=this._x1=+t)+","+(this._y0=this._y1=+n)},closePath:function(){null!==this._x1&&(this._x1=this._x0,this._y1=this._y0,this._+="Z")},lineTo:function(t,n){this._+="L"+(this._x1=+t)+","+(this._y1=+n)},quadraticCurveTo:function(t,n,e,r){this._+="Q"+ +t+","+ +n+","+(this._x1=+e)+","+(this._y1=+r)},bezierCurveTo:function(t,n,e,r,i,o){this._+="C"+ +t+","+ +n+","+ +e+","+ +r+","+(this._x1=+i)+","+(this._y1=+o)},arcTo:function(t,n,e,r,i){t=+t,n=+n,e=+e,r=+r,i=+i;var o=this._x1,a=this._y1,u=e-t,f=r-n,c=o-t,s=a-n,l=c*c+s*s;if(i<0)throw new Error("negative radius: "+i);if(null===this._x1)this._+="M"+(this._x1=t)+","+(this._y1=n);else if(l>1e-6)if(Math.abs(s*u-f*c)>1e-6&&i){var h=e-o,d=r-a,p=u*u+f*f,v=h*h+d*d,g=Math.sqrt(p),y=Math.sqrt(l),_=i*Math.tan((Ii-Math.acos((p+l-v)/(2*g*y)))/2),b=_/y,m=_/g;Math.abs(b-1)>1e-6&&(this._+="L"+(t+b*c)+","+(n+b*s)),this._+="A"+i+","+i+",0,0,"+ +(s*h>c*d)+","+(this._x1=t+m*u)+","+(this._y1=n+m*f)}else this._+="L"+(this._x1=t)+","+(this._y1=n);else;},arc:function(t,n,e,r,i,o){t=+t,n=+n;var a=(e=+e)*Math.cos(r),u=e*Math.sin(r),f=t+a,c=n+u,s=1^o,l=o?r-i:i-r;if(e<0)throw new Error("negative radius: "+e);null===this._x1?this._+="M"+f+","+c:(Math.abs(this._x1-f)>1e-6||Math.abs(this._y1-c)>1e-6)&&(this._+="L"+f+","+c),e&&(l<0&&(l=l%Hi+Hi),l>ji?this._+="A"+e+","+e+",0,1,"+s+","+(t-a)+","+(n-u)+"A"+e+","+e+",0,1,"+s+","+(this._x1=f)+","+(this._y1=c):l>1e-6&&(this._+="A"+e+","+e+",0,"+ +(l>=Ii)+","+s+","+(this._x1=t+e*Math.cos(i))+","+(this._y1=n+e*Math.sin(i))))},rect:function(t,n,e,r){this._+="M"+(this._x0=this._x1=+t)+","+(this._y0=this._y1=+n)+"h"+ +e+"v"+ +r+"h"+-e+"Z"},toString:function(){return this._}};function Ji(){}function Ki(t,n){var e=new Ji;if(t instanceof Ji)t.each(function(t,n){e.set(n,t)});else if(Array.isArray(t)){var r,i=-1,o=t.length;if(null==n)for(;++i<o;)e.set(i,t[i]);else for(;++i<o;)e.set(n(r=t[i],i,t),r)}else if(t)for(var a in t)e.set(a,t[a]);return e}function to(){return{}}function no(t,n,e){t[n]=e}function eo(){return Ki()}function ro(t,n,e){t.set(n,e)}function io(){}Ji.prototype=Ki.prototype={constructor:Ji,has:function(t){return"$"+t in this},get:function(t){return this["$"+t]},set:function(t,n){return this["$"+t]=n,this},remove:function(t){var n="$"+t;return n in this&&delete this[n]},clear:function(){for(var t in this)"$"===t[0]&&delete this[t]},keys:function(){var t=[];for(var n in this)"$"===n[0]&&t.push(n.slice(1));return t},values:function(){var t=[];for(var n in this)"$"===n[0]&&t.push(this[n]);return t},entries:function(){var t=[];for(var n in this)"$"===n[0]&&t.push({key:n.slice(1),value:this[n]});return t},size:function(){var t=0;for(var n in this)"$"===n[0]&&++t;return t},empty:function(){for(var t in this)if("$"===t[0])return!1;return!0},each:function(t){for(var n in this)"$"===n[0]&&t(this[n],n.slice(1),this)}};var oo=Ki.prototype;function ao(t,n){var e=new io;if(t instanceof io)t.each(function(t){e.add(t)});else if(t){var r=-1,i=t.length;if(null==n)for(;++r<i;)e.add(t[r]);else for(;++r<i;)e.add(n(t[r],r,t))}return e}io.prototype=ao.prototype={constructor:io,has:oo.has,add:function(t){return this["$"+(t+="")]=t,this},remove:oo.remove,clear:oo.clear,values:oo.keys,size:oo.size,empty:oo.empty,each:oo.each};var uo=Array.prototype.slice;function fo(t,n){return t-n}function co(t){return function(){return t}}function so(t,n){for(var e,r=-1,i=n.length;++r<i;)if(e=lo(t,n[r]))return e;return 0}function lo(t,n){for(var e=n[0],r=n[1],i=-1,o=0,a=t.length,u=a-1;o<a;u=o++){var f=t[o],c=f[0],s=f[1],l=t[u],h=l[0],d=l[1];if(ho(f,l,n))return 0;s>r!=d>r&&e<(h-c)*(r-s)/(d-s)+c&&(i=-i)}return i}function ho(t,n,e){var r,i,o,a;return function(t,n,e){return(n[0]-t[0])*(e[1]-t[1])==(e[0]-t[0])*(n[1]-t[1])}(t,n,e)&&(i=t[r=+(t[0]===n[0])],o=e[r],a=n[r],i<=o&&o<=a||a<=o&&o<=i)}function po(){}var vo=[[],[[[1,1.5],[.5,1]]],[[[1.5,1],[1,1.5]]],[[[1.5,1],[.5,1]]],[[[1,.5],[1.5,1]]],[[[1,1.5],[.5,1]],[[1,.5],[1.5,1]]],[[[1,.5],[1,1.5]]],[[[1,.5],[.5,1]]],[[[.5,1],[1,.5]]],[[[1,1.5],[1,.5]]],[[[.5,1],[1,.5]],[[1.5,1],[1,1.5]]],[[[1.5,1],[1,.5]]],[[[.5,1],[1.5,1]]],[[[1,1.5],[1.5,1]]],[[[.5,1],[1,1.5]]],[]];function go(){var t=1,n=1,e=M,r=u;function i(t){var n=e(t);if(Array.isArray(n))n=n.slice().sort(fo);else{var r=s(t),i=r[0],a=r[1];n=w(i,a,n),n=g(Math.floor(i/n)*n,Math.floor(a/n)*n,n)}return n.map(function(n){return o(t,n)})}function o(e,i){var o=[],u=[];return function(e,r,i){var o,u,f,c,s,l,h=new Array,d=new Array;o=u=-1,c=e[0]>=r,vo[c<<1].forEach(p);for(;++o<t-1;)f=c,c=e[o+1]>=r,vo[f|c<<1].forEach(p);vo[c<<0].forEach(p);for(;++u<n-1;){for(o=-1,c=e[u*t+t]>=r,s=e[u*t]>=r,vo[c<<1|s<<2].forEach(p);++o<t-1;)f=c,c=e[u*t+t+o+1]>=r,l=s,s=e[u*t+o+1]>=r,vo[f|c<<1|s<<2|l<<3].forEach(p);vo[c|s<<3].forEach(p)}o=-1,s=e[u*t]>=r,vo[s<<2].forEach(p);for(;++o<t-1;)l=s,s=e[u*t+o+1]>=r,vo[s<<2|l<<3].forEach(p);function p(t){var n,e,r=[t[0][0]+o,t[0][1]+u],f=[t[1][0]+o,t[1][1]+u],c=a(r),s=a(f);(n=d[c])?(e=h[s])?(delete d[n.end],delete h[e.start],n===e?(n.ring.push(f),i(n.ring)):h[n.start]=d[e.end]={start:n.start,end:e.end,ring:n.ring.concat(e.ring)}):(delete d[n.end],n.ring.push(f),d[n.end=s]=n):(n=h[s])?(e=d[c])?(delete h[n.start],delete d[e.end],n===e?(n.ring.push(f),i(n.ring)):h[e.start]=d[n.end]={start:e.start,end:n.end,ring:e.ring.concat(n.ring)}):(delete h[n.start],n.ring.unshift(r),h[n.start=c]=n):h[c]=d[s]={start:c,end:s,ring:[r,f]}}vo[s<<3].forEach(p)}(e,i,function(t){r(t,e,i),function(t){for(var n=0,e=t.length,r=t[e-1][1]*t[0][0]-t[e-1][0]*t[0][1];++n<e;)r+=t[n-1][1]*t[n][0]-t[n-1][0]*t[n][1];return r}(t)>0?o.push([t]):u.push(t)}),u.forEach(function(t){for(var n,e=0,r=o.length;e<r;++e)if(-1!==so((n=o[e])[0],t))return void n.push(t)}),{type:"MultiPolygon",value:i,coordinates:o}}function a(n){return 2*n[0]+n[1]*(t+1)*4}function u(e,r,i){e.forEach(function(e){var o,a=e[0],u=e[1],f=0|a,c=0|u,s=r[c*t+f];a>0&&a<t&&f===a&&(o=r[c*t+f-1],e[0]=a+(i-o)/(s-o)-.5),u>0&&u<n&&c===u&&(o=r[(c-1)*t+f],e[1]=u+(i-o)/(s-o)-.5)})}return i.contour=o,i.size=function(e){if(!arguments.length)return[t,n];var r=Math.ceil(e[0]),o=Math.ceil(e[1]);if(!(r>0&&o>0))throw new Error("invalid size");return t=r,n=o,i},i.thresholds=function(t){return arguments.length?(e="function"==typeof t?t:Array.isArray(t)?co(uo.call(t)):co(t),i):e},i.smooth=function(t){return arguments.length?(r=t?u:po,i):r===u},i}function yo(t,n,e){for(var r=t.width,i=t.height,o=1+(e<<1),a=0;a<i;++a)for(var u=0,f=0;u<r+e;++u)u<r&&(f+=t.data[u+a*r]),u>=e&&(u>=o&&(f-=t.data[u-o+a*r]),n.data[u-e+a*r]=f/Math.min(u+1,r-1+o-u,o))}function _o(t,n,e){for(var r=t.width,i=t.height,o=1+(e<<1),a=0;a<r;++a)for(var u=0,f=0;u<i+e;++u)u<i&&(f+=t.data[a+u*r]),u>=e&&(u>=o&&(f-=t.data[a+(u-o)*r]),n.data[a+(u-e)*r]=f/Math.min(u+1,i-1+o-u,o))}function bo(t){return t[0]}function mo(t){return t[1]}function xo(){return 1}var wo={},Mo={},Ao=34,To=10,No=13;function So(t){return new Function("d","return {"+t.map(function(t,n){return JSON.stringify(t)+": d["+n+"]"}).join(",")+"}")}function Eo(t){var n=new RegExp('["'+t+"\n\r]"),e=t.charCodeAt(0);function r(t,n){var r,i=[],o=t.length,a=0,u=0,f=o<=0,c=!1;function s(){if(f)return Mo;if(c)return c=!1,wo;var n,r,i=a;if(t.charCodeAt(i)===Ao){for(;a++<o&&t.charCodeAt(a)!==Ao||t.charCodeAt(++a)===Ao;);return(n=a)>=o?f=!0:(r=t.charCodeAt(a++))===To?c=!0:r===No&&(c=!0,t.charCodeAt(a)===To&&++a),t.slice(i+1,n-1).replace(/""/g,'"')}for(;a<o;){if((r=t.charCodeAt(n=a++))===To)c=!0;else if(r===No)c=!0,t.charCodeAt(a)===To&&++a;else if(r!==e)continue;return t.slice(i,n)}return f=!0,t.slice(i,o)}for(t.charCodeAt(o-1)===To&&--o,t.charCodeAt(o-1)===No&&--o;(r=s())!==Mo;){for(var l=[];r!==wo&&r!==Mo;)l.push(r),r=s();n&&null==(l=n(l,u++))||i.push(l)}return i}function i(n){return n.map(o).join(t)}function o(t){return null==t?"":n.test(t+="")?'"'+t.replace(/"/g,'""')+'"':t}return{parse:function(t,n){var e,i,o=r(t,function(t,r){if(e)return e(t,r-1);i=t,e=n?function(t,n){var e=So(t);return function(r,i){return n(e(r),i,t)}}(t,n):So(t)});return o.columns=i||[],o},parseRows:r,format:function(n,e){return null==e&&(e=function(t){var n=Object.create(null),e=[];return t.forEach(function(t){for(var r in t)r in n||e.push(n[r]=r)}),e}(n)),[e.map(o).join(t)].concat(n.map(function(n){return e.map(function(t){return o(n[t])}).join(t)})).join("\n")},formatRows:function(t){return t.map(i).join("\n")}}}var ko=Eo(","),Co=ko.parse,Po=ko.parseRows,zo=ko.format,Ro=ko.formatRows,Lo=Eo("\t"),Do=Lo.parse,Uo=Lo.parseRows,qo=Lo.format,Oo=Lo.formatRows;function Yo(t){if(!t.ok)throw new Error(t.status+" "+t.statusText);return t.blob()}function Bo(t){if(!t.ok)throw new Error(t.status+" "+t.statusText);return t.arrayBuffer()}function Fo(t){if(!t.ok)throw new Error(t.status+" "+t.statusText);return t.text()}function Io(t,n){return fetch(t,n).then(Fo)}function Ho(t){return function(n,e,r){return 2===arguments.length&&"function"==typeof e&&(r=e,e=void 0),Io(n,e).then(function(n){return t(n,r)})}}var jo=Ho(Co),Xo=Ho(Do);function Go(t){if(!t.ok)throw new Error(t.status+" "+t.statusText);return t.json()}function Vo(t){return function(n,e){return Io(n,e).then(function(n){return(new DOMParser).parseFromString(n,t)})}}var $o=Vo("application/xml"),Wo=Vo("text/html"),Zo=Vo("image/svg+xml");function Qo(t){return function(){return t}}function Jo(){return 1e-6*(Math.random()-.5)}function Ko(t,n,e,r){if(isNaN(n)||isNaN(e))return t;var i,o,a,u,f,c,s,l,h,d=t._root,p={data:r},v=t._x0,g=t._y0,y=t._x1,_=t._y1;if(!d)return t._root=p,t;for(;d.length;)if((c=n>=(o=(v+y)/2))?v=o:y=o,(s=e>=(a=(g+_)/2))?g=a:_=a,i=d,!(d=d[l=s<<1|c]))return i[l]=p,t;if(u=+t._x.call(null,d.data),f=+t._y.call(null,d.data),n===u&&e===f)return p.next=d,i?i[l]=p:t._root=p,t;do{i=i?i[l]=new Array(4):t._root=new Array(4),(c=n>=(o=(v+y)/2))?v=o:y=o,(s=e>=(a=(g+_)/2))?g=a:_=a}while((l=s<<1|c)==(h=(f>=a)<<1|u>=o));return i[h]=d,i[l]=p,t}function ta(t,n,e,r,i){this.node=t,this.x0=n,this.y0=e,this.x1=r,this.y1=i}function na(t){return t[0]}function ea(t){return t[1]}function ra(t,n,e){var r=new ia(null==n?na:n,null==e?ea:e,NaN,NaN,NaN,NaN);return null==t?r:r.addAll(t)}function ia(t,n,e,r,i,o){this._x=t,this._y=n,this._x0=e,this._y0=r,this._x1=i,this._y1=o,this._root=void 0}function oa(t){for(var n={data:t.data},e=n;t=t.next;)e=e.next={data:t.data};return n}var aa=ra.prototype=ia.prototype;function ua(t){return t.x+t.vx}function fa(t){return t.y+t.vy}function ca(t){return t.index}function sa(t,n){var e=t.get(n);if(!e)throw new Error("missing: "+n);return e}function la(t){return t.x}function ha(t){return t.y}aa.copy=function(){var t,n,e=new ia(this._x,this._y,this._x0,this._y0,this._x1,this._y1),r=this._root;if(!r)return e;if(!r.length)return e._root=oa(r),e;for(t=[{source:r,target:e._root=new Array(4)}];r=t.pop();)for(var i=0;i<4;++i)(n=r.source[i])&&(n.length?t.push({source:n,target:r.target[i]=new Array(4)}):r.target[i]=oa(n));return e},aa.add=function(t){var n=+this._x.call(null,t),e=+this._y.call(null,t);return Ko(this.cover(n,e),n,e,t)},aa.addAll=function(t){var n,e,r,i,o=t.length,a=new Array(o),u=new Array(o),f=1/0,c=1/0,s=-1/0,l=-1/0;for(e=0;e<o;++e)isNaN(r=+this._x.call(null,n=t[e]))||isNaN(i=+this._y.call(null,n))||(a[e]=r,u[e]=i,r<f&&(f=r),r>s&&(s=r),i<c&&(c=i),i>l&&(l=i));for(s<f&&(f=this._x0,s=this._x1),l<c&&(c=this._y0,l=this._y1),this.cover(f,c).cover(s,l),e=0;e<o;++e)Ko(this,a[e],u[e],t[e]);return this},aa.cover=function(t,n){if(isNaN(t=+t)||isNaN(n=+n))return this;var e=this._x0,r=this._y0,i=this._x1,o=this._y1;if(isNaN(e))i=(e=Math.floor(t))+1,o=(r=Math.floor(n))+1;else{if(!(e>t||t>i||r>n||n>o))return this;var a,u,f=i-e,c=this._root;switch(u=(n<(r+o)/2)<<1|t<(e+i)/2){case 0:do{(a=new Array(4))[u]=c,c=a}while(o=r+(f*=2),t>(i=e+f)||n>o);break;case 1:do{(a=new Array(4))[u]=c,c=a}while(o=r+(f*=2),(e=i-f)>t||n>o);break;case 2:do{(a=new Array(4))[u]=c,c=a}while(r=o-(f*=2),t>(i=e+f)||r>n);break;case 3:do{(a=new Array(4))[u]=c,c=a}while(r=o-(f*=2),(e=i-f)>t||r>n)}this._root&&this._root.length&&(this._root=c)}return this._x0=e,this._y0=r,this._x1=i,this._y1=o,this},aa.data=function(){var t=[];return this.visit(function(n){if(!n.length)do{t.push(n.data)}while(n=n.next)}),t},aa.extent=function(t){return arguments.length?this.cover(+t[0][0],+t[0][1]).cover(+t[1][0],+t[1][1]):isNaN(this._x0)?void 0:[[this._x0,this._y0],[this._x1,this._y1]]},aa.find=function(t,n,e){var r,i,o,a,u,f,c,s=this._x0,l=this._y0,h=this._x1,d=this._y1,p=[],v=this._root;for(v&&p.push(new ta(v,s,l,h,d)),null==e?e=1/0:(s=t-e,l=n-e,h=t+e,d=n+e,e*=e);f=p.pop();)if(!(!(v=f.node)||(i=f.x0)>h||(o=f.y0)>d||(a=f.x1)<s||(u=f.y1)<l))if(v.length){var g=(i+a)/2,y=(o+u)/2;p.push(new ta(v[3],g,y,a,u),new ta(v[2],i,y,g,u),new ta(v[1],g,o,a,y),new ta(v[0],i,o,g,y)),(c=(n>=y)<<1|t>=g)&&(f=p[p.length-1],p[p.length-1]=p[p.length-1-c],p[p.length-1-c]=f)}else{var _=t-+this._x.call(null,v.data),b=n-+this._y.call(null,v.data),m=_*_+b*b;if(m<e){var x=Math.sqrt(e=m);s=t-x,l=n-x,h=t+x,d=n+x,r=v.data}}return r},aa.remove=function(t){if(isNaN(o=+this._x.call(null,t))||isNaN(a=+this._y.call(null,t)))return this;var n,e,r,i,o,a,u,f,c,s,l,h,d=this._root,p=this._x0,v=this._y0,g=this._x1,y=this._y1;if(!d)return this;if(d.length)for(;;){if((c=o>=(u=(p+g)/2))?p=u:g=u,(s=a>=(f=(v+y)/2))?v=f:y=f,n=d,!(d=d[l=s<<1|c]))return this;if(!d.length)break;(n[l+1&3]||n[l+2&3]||n[l+3&3])&&(e=n,h=l)}for(;d.data!==t;)if(r=d,!(d=d.next))return this;return(i=d.next)&&delete d.next,r?(i?r.next=i:delete r.next,this):n?(i?n[l]=i:delete n[l],(d=n[0]||n[1]||n[2]||n[3])&&d===(n[3]||n[2]||n[1]||n[0])&&!d.length&&(e?e[h]=d:this._root=d),this):(this._root=i,this)},aa.removeAll=function(t){for(var n=0,e=t.length;n<e;++n)this.remove(t[n]);return this},aa.root=function(){return this._root},aa.size=function(){var t=0;return this.visit(function(n){if(!n.length)do{++t}while(n=n.next)}),t},aa.visit=function(t){var n,e,r,i,o,a,u=[],f=this._root;for(f&&u.push(new ta(f,this._x0,this._y0,this._x1,this._y1));n=u.pop();)if(!t(f=n.node,r=n.x0,i=n.y0,o=n.x1,a=n.y1)&&f.length){var c=(r+o)/2,s=(i+a)/2;(e=f[3])&&u.push(new ta(e,c,s,o,a)),(e=f[2])&&u.push(new ta(e,r,s,c,a)),(e=f[1])&&u.push(new ta(e,c,i,o,s)),(e=f[0])&&u.push(new ta(e,r,i,c,s))}return this},aa.visitAfter=function(t){var n,e=[],r=[];for(this._root&&e.push(new ta(this._root,this._x0,this._y0,this._x1,this._y1));n=e.pop();){var i=n.node;if(i.length){var o,a=n.x0,u=n.y0,f=n.x1,c=n.y1,s=(a+f)/2,l=(u+c)/2;(o=i[0])&&e.push(new ta(o,a,u,s,l)),(o=i[1])&&e.push(new ta(o,s,u,f,l)),(o=i[2])&&e.push(new ta(o,a,l,s,c)),(o=i[3])&&e.push(new ta(o,s,l,f,c))}r.push(n)}for(;n=r.pop();)t(n.node,n.x0,n.y0,n.x1,n.y1);return this},aa.x=function(t){return arguments.length?(this._x=t,this):this._x},aa.y=function(t){return arguments.length?(this._y=t,this):this._y};var da=10,pa=Math.PI*(3-Math.sqrt(5));function va(t,n){if((e=(t=n?t.toExponential(n-1):t.toExponential()).indexOf("e"))<0)return null;var e,r=t.slice(0,e);return[r.length>1?r[0]+r.slice(2):r,+t.slice(e+1)]}function ga(t){return(t=va(Math.abs(t)))?t[1]:NaN}var ya,_a=/^(?:(.)?([<>=^]))?([+\-( ])?([$#])?(0)?(\d+)?(,)?(\.\d+)?(~)?([a-z%])?$/i;function ba(t){return new ma(t)}function ma(t){if(!(n=_a.exec(t)))throw new Error("invalid format: "+t);var n;this.fill=n[1]||" ",this.align=n[2]||">",this.sign=n[3]||"-",this.symbol=n[4]||"",this.zero=!!n[5],this.width=n[6]&&+n[6],this.comma=!!n[7],this.precision=n[8]&&+n[8].slice(1),this.trim=!!n[9],this.type=n[10]||""}function xa(t,n){var e=va(t,n);if(!e)return t+"";var r=e[0],i=e[1];return i<0?"0."+new Array(-i).join("0")+r:r.length>i+1?r.slice(0,i+1)+"."+r.slice(i+1):r+new Array(i-r.length+2).join("0")}ba.prototype=ma.prototype,ma.prototype.toString=function(){return this.fill+this.align+this.sign+this.symbol+(this.zero?"0":"")+(null==this.width?"":Math.max(1,0|this.width))+(this.comma?",":"")+(null==this.precision?"":"."+Math.max(0,0|this.precision))+(this.trim?"~":"")+this.type};var wa={"%":function(t,n){return(100*t).toFixed(n)},b:function(t){return Math.round(t).toString(2)},c:function(t){return t+""},d:function(t){return Math.round(t).toString(10)},e:function(t,n){return t.toExponential(n)},f:function(t,n){return t.toFixed(n)},g:function(t,n){return t.toPrecision(n)},o:function(t){return Math.round(t).toString(8)},p:function(t,n){return xa(100*t,n)},r:xa,s:function(t,n){var e=va(t,n);if(!e)return t+"";var r=e[0],i=e[1],o=i-(ya=3*Math.max(-8,Math.min(8,Math.floor(i/3))))+1,a=r.length;return o===a?r:o>a?r+new Array(o-a+1).join("0"):o>0?r.slice(0,o)+"."+r.slice(o):"0."+new Array(1-o).join("0")+va(t,Math.max(0,n+o-1))[0]},X:function(t){return Math.round(t).toString(16).toUpperCase()},x:function(t){return Math.round(t).toString(16)}};function Ma(t){return t}var Aa,Ta=["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"];function Na(t){var n,e,r=t.grouping&&t.thousands?(n=t.grouping,e=t.thousands,function(t,r){for(var i=t.length,o=[],a=0,u=n[0],f=0;i>0&&u>0&&(f+u+1>r&&(u=Math.max(1,r-f)),o.push(t.substring(i-=u,i+u)),!((f+=u+1)>r));)u=n[a=(a+1)%n.length];return o.reverse().join(e)}):Ma,i=t.currency,o=t.decimal,a=t.numerals?function(t){return function(n){return n.replace(/[0-9]/g,function(n){return t[+n]})}}(t.numerals):Ma,u=t.percent||"%";function f(t){var n=(t=ba(t)).fill,e=t.align,f=t.sign,c=t.symbol,s=t.zero,l=t.width,h=t.comma,d=t.precision,p=t.trim,v=t.type;"n"===v?(h=!0,v="g"):wa[v]||(null==d&&(d=12),p=!0,v="g"),(s||"0"===n&&"="===e)&&(s=!0,n="0",e="=");var g="$"===c?i[0]:"#"===c&&/[boxX]/.test(v)?"0"+v.toLowerCase():"",y="$"===c?i[1]:/[%p]/.test(v)?u:"",_=wa[v],b=/[defgprs%]/.test(v);function m(t){var i,u,c,m=g,x=y;if("c"===v)x=_(t)+x,t="";else{var w=(t=+t)<0;if(t=_(Math.abs(t),d),p&&(t=function(t){t:for(var n,e=t.length,r=1,i=-1;r<e;++r)switch(t[r]){case".":i=n=r;break;case"0":0===i&&(i=r),n=r;break;default:if(i>0){if(!+t[r])break t;i=0}}return i>0?t.slice(0,i)+t.slice(n+1):t}(t)),w&&0==+t&&(w=!1),m=(w?"("===f?f:"-":"-"===f||"("===f?"":f)+m,x=("s"===v?Ta[8+ya/3]:"")+x+(w&&"("===f?")":""),b)for(i=-1,u=t.length;++i<u;)if(48>(c=t.charCodeAt(i))||c>57){x=(46===c?o+t.slice(i+1):t.slice(i))+x,t=t.slice(0,i);break}}h&&!s&&(t=r(t,1/0));var M=m.length+t.length+x.length,A=M<l?new Array(l-M+1).join(n):"";switch(h&&s&&(t=r(A+t,A.length?l-x.length:1/0),A=""),e){case"<":t=m+t+x+A;break;case"=":t=m+A+t+x;break;case"^":t=A.slice(0,M=A.length>>1)+m+t+x+A.slice(M);break;default:t=A+m+t+x}return a(t)}return d=null==d?6:/[gprs]/.test(v)?Math.max(1,Math.min(21,d)):Math.max(0,Math.min(20,d)),m.toString=function(){return t+""},m}return{format:f,formatPrefix:function(t,n){var e=f(((t=ba(t)).type="f",t)),r=3*Math.max(-8,Math.min(8,Math.floor(ga(n)/3))),i=Math.pow(10,-r),o=Ta[8+r/3];return function(t){return e(i*t)+o}}}}function Sa(n){return Aa=Na(n),t.format=Aa.format,t.formatPrefix=Aa.formatPrefix,Aa}function Ea(t){return Math.max(0,-ga(Math.abs(t)))}function ka(t,n){return Math.max(0,3*Math.max(-8,Math.min(8,Math.floor(ga(n)/3)))-ga(Math.abs(t)))}function Ca(t,n){return t=Math.abs(t),n=Math.abs(n)-t,Math.max(0,ga(n)-ga(t))+1}function Pa(){return new za}function za(){this.reset()}Sa({decimal:".",thousands:",",grouping:[3],currency:["$",""]}),za.prototype={constructor:za,reset:function(){this.s=this.t=0},add:function(t){La(Ra,t,this.t),La(this,Ra.s,this.s),this.s?this.t+=Ra.t:this.s=Ra.t},valueOf:function(){return this.s}};var Ra=new za;function La(t,n,e){var r=t.s=n+e,i=r-n,o=r-i;t.t=n-o+(e-i)}var Da=1e-6,Ua=1e-12,qa=Math.PI,Oa=qa/2,Ya=qa/4,Ba=2*qa,Fa=180/qa,Ia=qa/180,Ha=Math.abs,ja=Math.atan,Xa=Math.atan2,Ga=Math.cos,Va=Math.ceil,$a=Math.exp,Wa=Math.log,Za=Math.pow,Qa=Math.sin,Ja=Math.sign||function(t){return t>0?1:t<0?-1:0},Ka=Math.sqrt,tu=Math.tan;function nu(t){return t>1?0:t<-1?qa:Math.acos(t)}function eu(t){return t>1?Oa:t<-1?-Oa:Math.asin(t)}function ru(t){return(t=Qa(t/2))*t}function iu(){}function ou(t,n){t&&uu.hasOwnProperty(t.type)&&uu[t.type](t,n)}var au={Feature:function(t,n){ou(t.geometry,n)},FeatureCollection:function(t,n){for(var e=t.features,r=-1,i=e.length;++r<i;)ou(e[r].geometry,n)}},uu={Sphere:function(t,n){n.sphere()},Point:function(t,n){t=t.coordinates,n.point(t[0],t[1],t[2])},MultiPoint:function(t,n){for(var e=t.coordinates,r=-1,i=e.length;++r<i;)t=e[r],n.point(t[0],t[1],t[2])},LineString:function(t,n){fu(t.coordinates,n,0)},MultiLineString:function(t,n){for(var e=t.coordinates,r=-1,i=e.length;++r<i;)fu(e[r],n,0)},Polygon:function(t,n){cu(t.coordinates,n)},MultiPolygon:function(t,n){for(var e=t.coordinates,r=-1,i=e.length;++r<i;)cu(e[r],n)},GeometryCollection:function(t,n){for(var e=t.geometries,r=-1,i=e.length;++r<i;)ou(e[r],n)}};function fu(t,n,e){var r,i=-1,o=t.length-e;for(n.lineStart();++i<o;)r=t[i],n.point(r[0],r[1],r[2]);n.lineEnd()}function cu(t,n){var e=-1,r=t.length;for(n.polygonStart();++e<r;)fu(t[e],n,1);n.polygonEnd()}function su(t,n){t&&au.hasOwnProperty(t.type)?au[t.type](t,n):ou(t,n)}var lu,hu,du,pu,vu,gu=Pa(),yu=Pa(),_u={point:iu,lineStart:iu,lineEnd:iu,polygonStart:function(){gu.reset(),_u.lineStart=bu,_u.lineEnd=mu},polygonEnd:function(){var t=+gu;yu.add(t<0?Ba+t:t),this.lineStart=this.lineEnd=this.point=iu},sphere:function(){yu.add(Ba)}};function bu(){_u.point=xu}function mu(){wu(lu,hu)}function xu(t,n){_u.point=wu,lu=t,hu=n,du=t*=Ia,pu=Ga(n=(n*=Ia)/2+Ya),vu=Qa(n)}function wu(t,n){var e=(t*=Ia)-du,r=e>=0?1:-1,i=r*e,o=Ga(n=(n*=Ia)/2+Ya),a=Qa(n),u=vu*a,f=pu*o+u*Ga(i),c=u*r*Qa(i);gu.add(Xa(c,f)),du=t,pu=o,vu=a}function Mu(t){return[Xa(t[1],t[0]),eu(t[2])]}function Au(t){var n=t[0],e=t[1],r=Ga(e);return[r*Ga(n),r*Qa(n),Qa(e)]}function Tu(t,n){return t[0]*n[0]+t[1]*n[1]+t[2]*n[2]}function Nu(t,n){return[t[1]*n[2]-t[2]*n[1],t[2]*n[0]-t[0]*n[2],t[0]*n[1]-t[1]*n[0]]}function Su(t,n){t[0]+=n[0],t[1]+=n[1],t[2]+=n[2]}function Eu(t,n){return[t[0]*n,t[1]*n,t[2]*n]}function ku(t){var n=Ka(t[0]*t[0]+t[1]*t[1]+t[2]*t[2]);t[0]/=n,t[1]/=n,t[2]/=n}var Cu,Pu,zu,Ru,Lu,Du,Uu,qu,Ou,Yu,Bu,Fu,Iu,Hu,ju,Xu,Gu,Vu,$u,Wu,Zu,Qu,Ju,Ku,tf,nf,ef=Pa(),rf={point:of,lineStart:uf,lineEnd:ff,polygonStart:function(){rf.point=cf,rf.lineStart=sf,rf.lineEnd=lf,ef.reset(),_u.polygonStart()},polygonEnd:function(){_u.polygonEnd(),rf.point=of,rf.lineStart=uf,rf.lineEnd=ff,gu<0?(Cu=-(zu=180),Pu=-(Ru=90)):ef>Da?Ru=90:ef<-Da&&(Pu=-90),Yu[0]=Cu,Yu[1]=zu}};function of(t,n){Ou.push(Yu=[Cu=t,zu=t]),n<Pu&&(Pu=n),n>Ru&&(Ru=n)}function af(t,n){var e=Au([t*Ia,n*Ia]);if(qu){var r=Nu(qu,e),i=Nu([r[1],-r[0],0],r);ku(i),i=Mu(i);var o,a=t-Lu,u=a>0?1:-1,f=i[0]*Fa*u,c=Ha(a)>180;c^(u*Lu<f&&f<u*t)?(o=i[1]*Fa)>Ru&&(Ru=o):c^(u*Lu<(f=(f+360)%360-180)&&f<u*t)?(o=-i[1]*Fa)<Pu&&(Pu=o):(n<Pu&&(Pu=n),n>Ru&&(Ru=n)),c?t<Lu?hf(Cu,t)>hf(Cu,zu)&&(zu=t):hf(t,zu)>hf(Cu,zu)&&(Cu=t):zu>=Cu?(t<Cu&&(Cu=t),t>zu&&(zu=t)):t>Lu?hf(Cu,t)>hf(Cu,zu)&&(zu=t):hf(t,zu)>hf(Cu,zu)&&(Cu=t)}else Ou.push(Yu=[Cu=t,zu=t]);n<Pu&&(Pu=n),n>Ru&&(Ru=n),qu=e,Lu=t}function uf(){rf.point=af}function ff(){Yu[0]=Cu,Yu[1]=zu,rf.point=of,qu=null}function cf(t,n){if(qu){var e=t-Lu;ef.add(Ha(e)>180?e+(e>0?360:-360):e)}else Du=t,Uu=n;_u.point(t,n),af(t,n)}function sf(){_u.lineStart()}function lf(){cf(Du,Uu),_u.lineEnd(),Ha(ef)>Da&&(Cu=-(zu=180)),Yu[0]=Cu,Yu[1]=zu,qu=null}function hf(t,n){return(n-=t)<0?n+360:n}function df(t,n){return t[0]-n[0]}function pf(t,n){return t[0]<=t[1]?t[0]<=n&&n<=t[1]:n<t[0]||t[1]<n}var vf={sphere:iu,point:gf,lineStart:_f,lineEnd:xf,polygonStart:function(){vf.lineStart=wf,vf.lineEnd=Mf},polygonEnd:function(){vf.lineStart=_f,vf.lineEnd=xf}};function gf(t,n){t*=Ia;var e=Ga(n*=Ia);yf(e*Ga(t),e*Qa(t),Qa(n))}function yf(t,n,e){Iu+=(t-Iu)/++Bu,Hu+=(n-Hu)/Bu,ju+=(e-ju)/Bu}function _f(){vf.point=bf}function bf(t,n){t*=Ia;var e=Ga(n*=Ia);Ku=e*Ga(t),tf=e*Qa(t),nf=Qa(n),vf.point=mf,yf(Ku,tf,nf)}function mf(t,n){t*=Ia;var e=Ga(n*=Ia),r=e*Ga(t),i=e*Qa(t),o=Qa(n),a=Xa(Ka((a=tf*o-nf*i)*a+(a=nf*r-Ku*o)*a+(a=Ku*i-tf*r)*a),Ku*r+tf*i+nf*o);Fu+=a,Xu+=a*(Ku+(Ku=r)),Gu+=a*(tf+(tf=i)),Vu+=a*(nf+(nf=o)),yf(Ku,tf,nf)}function xf(){vf.point=gf}function wf(){vf.point=Af}function Mf(){Tf(Qu,Ju),vf.point=gf}function Af(t,n){Qu=t,Ju=n,t*=Ia,n*=Ia,vf.point=Tf;var e=Ga(n);Ku=e*Ga(t),tf=e*Qa(t),nf=Qa(n),yf(Ku,tf,nf)}function Tf(t,n){t*=Ia;var e=Ga(n*=Ia),r=e*Ga(t),i=e*Qa(t),o=Qa(n),a=tf*o-nf*i,u=nf*r-Ku*o,f=Ku*i-tf*r,c=Ka(a*a+u*u+f*f),s=eu(c),l=c&&-s/c;$u+=l*a,Wu+=l*u,Zu+=l*f,Fu+=s,Xu+=s*(Ku+(Ku=r)),Gu+=s*(tf+(tf=i)),Vu+=s*(nf+(nf=o)),yf(Ku,tf,nf)}function Nf(t){return function(){return t}}function Sf(t,n){function e(e,r){return e=t(e,r),n(e[0],e[1])}return t.invert&&n.invert&&(e.invert=function(e,r){return(e=n.invert(e,r))&&t.invert(e[0],e[1])}),e}function Ef(t,n){return[t>qa?t-Ba:t<-qa?t+Ba:t,n]}function kf(t,n,e){return(t%=Ba)?n||e?Sf(Pf(t),zf(n,e)):Pf(t):n||e?zf(n,e):Ef}function Cf(t){return function(n,e){return[(n+=t)>qa?n-Ba:n<-qa?n+Ba:n,e]}}function Pf(t){var n=Cf(t);return n.invert=Cf(-t),n}function zf(t,n){var e=Ga(t),r=Qa(t),i=Ga(n),o=Qa(n);function a(t,n){var a=Ga(n),u=Ga(t)*a,f=Qa(t)*a,c=Qa(n),s=c*e+u*r;return[Xa(f*i-s*o,u*e-c*r),eu(s*i+f*o)]}return a.invert=function(t,n){var a=Ga(n),u=Ga(t)*a,f=Qa(t)*a,c=Qa(n),s=c*i-f*o;return[Xa(f*i+c*o,u*e+s*r),eu(s*e-u*r)]},a}function Rf(t){function n(n){return(n=t(n[0]*Ia,n[1]*Ia))[0]*=Fa,n[1]*=Fa,n}return t=kf(t[0]*Ia,t[1]*Ia,t.length>2?t[2]*Ia:0),n.invert=function(n){return(n=t.invert(n[0]*Ia,n[1]*Ia))[0]*=Fa,n[1]*=Fa,n},n}function Lf(t,n,e,r,i,o){if(e){var a=Ga(n),u=Qa(n),f=r*e;null==i?(i=n+r*Ba,o=n-f/2):(i=Df(a,i),o=Df(a,o),(r>0?i<o:i>o)&&(i+=r*Ba));for(var c,s=i;r>0?s>o:s<o;s-=f)c=Mu([a,-u*Ga(s),-u*Qa(s)]),t.point(c[0],c[1])}}function Df(t,n){(n=Au(n))[0]-=t,ku(n);var e=nu(-n[1]);return((-n[2]<0?-e:e)+Ba-Da)%Ba}function Uf(){var t,n=[];return{point:function(n,e){t.push([n,e])},lineStart:function(){n.push(t=[])},lineEnd:iu,rejoin:function(){n.length>1&&n.push(n.pop().concat(n.shift()))},result:function(){var e=n;return n=[],t=null,e}}}function qf(t,n){return Ha(t[0]-n[0])<Da&&Ha(t[1]-n[1])<Da}function Of(t,n,e,r){this.x=t,this.z=n,this.o=e,this.e=r,this.v=!1,this.n=this.p=null}function Yf(t,n,e,r,i){var o,a,u=[],f=[];if(t.forEach(function(t){if(!((n=t.length-1)<=0)){var n,e,r=t[0],a=t[n];if(qf(r,a)){for(i.lineStart(),o=0;o<n;++o)i.point((r=t[o])[0],r[1]);i.lineEnd()}else u.push(e=new Of(r,t,null,!0)),f.push(e.o=new Of(r,null,e,!1)),u.push(e=new Of(a,t,null,!1)),f.push(e.o=new Of(a,null,e,!0))}}),u.length){for(f.sort(n),Bf(u),Bf(f),o=0,a=f.length;o<a;++o)f[o].e=e=!e;for(var c,s,l=u[0];;){for(var h=l,d=!0;h.v;)if((h=h.n)===l)return;c=h.z,i.lineStart();do{if(h.v=h.o.v=!0,h.e){if(d)for(o=0,a=c.length;o<a;++o)i.point((s=c[o])[0],s[1]);else r(h.x,h.n.x,1,i);h=h.n}else{if(d)for(c=h.p.z,o=c.length-1;o>=0;--o)i.point((s=c[o])[0],s[1]);else r(h.x,h.p.x,-1,i);h=h.p}c=(h=h.o).z,d=!d}while(!h.v);i.lineEnd()}}}function Bf(t){if(n=t.length){for(var n,e,r=0,i=t[0];++r<n;)i.n=e=t[r],e.p=i,i=e;i.n=e=t[0],e.p=i}}Ef.invert=Ef;var Ff=Pa();function If(t,n){var e=n[0],r=n[1],i=Qa(r),o=[Qa(e),-Ga(e),0],a=0,u=0;Ff.reset(),1===i?r=Oa+Da:-1===i&&(r=-Oa-Da);for(var f=0,c=t.length;f<c;++f)if(l=(s=t[f]).length)for(var s,l,h=s[l-1],d=h[0],p=h[1]/2+Ya,v=Qa(p),g=Ga(p),y=0;y<l;++y,d=b,v=x,g=w,h=_){var _=s[y],b=_[0],m=_[1]/2+Ya,x=Qa(m),w=Ga(m),M=b-d,A=M>=0?1:-1,T=A*M,N=T>qa,S=v*x;if(Ff.add(Xa(S*A*Qa(T),g*w+S*Ga(T))),a+=N?M+A*Ba:M,N^d>=e^b>=e){var E=Nu(Au(h),Au(_));ku(E);var k=Nu(o,E);ku(k);var C=(N^M>=0?-1:1)*eu(k[2]);(r>C||r===C&&(E[0]||E[1]))&&(u+=N^M>=0?1:-1)}}return(a<-Da||a<Da&&Ff<-Da)^1&u}function Hf(t,n,e,r){return function(i){var o,a,u,f=n(i),c=Uf(),s=n(c),l=!1,h={point:d,lineStart:v,lineEnd:g,polygonStart:function(){h.point=y,h.lineStart=_,h.lineEnd=b,a=[],o=[]},polygonEnd:function(){h.point=d,h.lineStart=v,h.lineEnd=g,a=N(a);var t=If(o,r);a.length?(l||(i.polygonStart(),l=!0),Yf(a,Xf,t,e,i)):t&&(l||(i.polygonStart(),l=!0),i.lineStart(),e(null,null,1,i),i.lineEnd()),l&&(i.polygonEnd(),l=!1),a=o=null},sphere:function(){i.polygonStart(),i.lineStart(),e(null,null,1,i),i.lineEnd(),i.polygonEnd()}};function d(n,e){t(n,e)&&i.point(n,e)}function p(t,n){f.point(t,n)}function v(){h.point=p,f.lineStart()}function g(){h.point=d,f.lineEnd()}function y(t,n){u.push([t,n]),s.point(t,n)}function _(){s.lineStart(),u=[]}function b(){y(u[0][0],u[0][1]),s.lineEnd();var t,n,e,r,f=s.clean(),h=c.result(),d=h.length;if(u.pop(),o.push(u),u=null,d)if(1&f){if((n=(e=h[0]).length-1)>0){for(l||(i.polygonStart(),l=!0),i.lineStart(),t=0;t<n;++t)i.point((r=e[t])[0],r[1]);i.lineEnd()}}else d>1&&2&f&&h.push(h.pop().concat(h.shift())),a.push(h.filter(jf))}return h}}function jf(t){return t.length>1}function Xf(t,n){return((t=t.x)[0]<0?t[1]-Oa-Da:Oa-t[1])-((n=n.x)[0]<0?n[1]-Oa-Da:Oa-n[1])}var Gf=Hf(function(){return!0},function(t){var n,e=NaN,r=NaN,i=NaN;return{lineStart:function(){t.lineStart(),n=1},point:function(o,a){var u=o>0?qa:-qa,f=Ha(o-e);Ha(f-qa)<Da?(t.point(e,r=(r+a)/2>0?Oa:-Oa),t.point(i,r),t.lineEnd(),t.lineStart(),t.point(u,r),t.point(o,r),n=0):i!==u&&f>=qa&&(Ha(e-i)<Da&&(e-=i*Da),Ha(o-u)<Da&&(o-=u*Da),r=function(t,n,e,r){var i,o,a=Qa(t-e);return Ha(a)>Da?ja((Qa(n)*(o=Ga(r))*Qa(e)-Qa(r)*(i=Ga(n))*Qa(t))/(i*o*a)):(n+r)/2}(e,r,o,a),t.point(i,r),t.lineEnd(),t.lineStart(),t.point(u,r),n=0),t.point(e=o,r=a),i=u},lineEnd:function(){t.lineEnd(),e=r=NaN},clean:function(){return 2-n}}},function(t,n,e,r){var i;if(null==t)i=e*Oa,r.point(-qa,i),r.point(0,i),r.point(qa,i),r.point(qa,0),r.point(qa,-i),r.point(0,-i),r.point(-qa,-i),r.point(-qa,0),r.point(-qa,i);else if(Ha(t[0]-n[0])>Da){var o=t[0]<n[0]?qa:-qa;i=e*o/2,r.point(-o,i),r.point(0,i),r.point(o,i)}else r.point(n[0],n[1])},[-qa,-Oa]);function Vf(t){var n=Ga(t),e=6*Ia,r=n>0,i=Ha(n)>Da;function o(t,e){return Ga(t)*Ga(e)>n}function a(t,e,r){var i=[1,0,0],o=Nu(Au(t),Au(e)),a=Tu(o,o),u=o[0],f=a-u*u;if(!f)return!r&&t;var c=n*a/f,s=-n*u/f,l=Nu(i,o),h=Eu(i,c);Su(h,Eu(o,s));var d=l,p=Tu(h,d),v=Tu(d,d),g=p*p-v*(Tu(h,h)-1);if(!(g<0)){var y=Ka(g),_=Eu(d,(-p-y)/v);if(Su(_,h),_=Mu(_),!r)return _;var b,m=t[0],x=e[0],w=t[1],M=e[1];x<m&&(b=m,m=x,x=b);var A=x-m,T=Ha(A-qa)<Da;if(!T&&M<w&&(b=w,w=M,M=b),T||A<Da?T?w+M>0^_[1]<(Ha(_[0]-m)<Da?w:M):w<=_[1]&&_[1]<=M:A>qa^(m<=_[0]&&_[0]<=x)){var N=Eu(d,(-p+y)/v);return Su(N,h),[_,Mu(N)]}}}function u(n,e){var i=r?t:qa-t,o=0;return n<-i?o|=1:n>i&&(o|=2),e<-i?o|=4:e>i&&(o|=8),o}return Hf(o,function(t){var n,e,f,c,s;return{lineStart:function(){c=f=!1,s=1},point:function(l,h){var d,p=[l,h],v=o(l,h),g=r?v?0:u(l,h):v?u(l+(l<0?qa:-qa),h):0;if(!n&&(c=f=v)&&t.lineStart(),v!==f&&(!(d=a(n,p))||qf(n,d)||qf(p,d))&&(p[0]+=Da,p[1]+=Da,v=o(p[0],p[1])),v!==f)s=0,v?(t.lineStart(),d=a(p,n),t.point(d[0],d[1])):(d=a(n,p),t.point(d[0],d[1]),t.lineEnd()),n=d;else if(i&&n&&r^v){var y;g&e||!(y=a(p,n,!0))||(s=0,r?(t.lineStart(),t.point(y[0][0],y[0][1]),t.point(y[1][0],y[1][1]),t.lineEnd()):(t.point(y[1][0],y[1][1]),t.lineEnd(),t.lineStart(),t.point(y[0][0],y[0][1])))}!v||n&&qf(n,p)||t.point(p[0],p[1]),n=p,f=v,e=g},lineEnd:function(){f&&t.lineEnd(),n=null},clean:function(){return s|(c&&f)<<1}}},function(n,r,i,o){Lf(o,t,e,i,n,r)},r?[0,-t]:[-qa,t-qa])}var $f=1e9,Wf=-$f;function Zf(t,n,e,r){function i(i,o){return t<=i&&i<=e&&n<=o&&o<=r}function o(i,o,u,c){var s=0,l=0;if(null==i||(s=a(i,u))!==(l=a(o,u))||f(i,o)<0^u>0)do{c.point(0===s||3===s?t:e,s>1?r:n)}while((s=(s+u+4)%4)!==l);else c.point(o[0],o[1])}function a(r,i){return Ha(r[0]-t)<Da?i>0?0:3:Ha(r[0]-e)<Da?i>0?2:1:Ha(r[1]-n)<Da?i>0?1:0:i>0?3:2}function u(t,n){return f(t.x,n.x)}function f(t,n){var e=a(t,1),r=a(n,1);return e!==r?e-r:0===e?n[1]-t[1]:1===e?t[0]-n[0]:2===e?t[1]-n[1]:n[0]-t[0]}return function(a){var f,c,s,l,h,d,p,v,g,y,_,b=a,m=Uf(),x={point:w,lineStart:function(){x.point=M,c&&c.push(s=[]);y=!0,g=!1,p=v=NaN},lineEnd:function(){f&&(M(l,h),d&&g&&m.rejoin(),f.push(m.result()));x.point=w,g&&b.lineEnd()},polygonStart:function(){b=m,f=[],c=[],_=!0},polygonEnd:function(){var n=function(){for(var n=0,e=0,i=c.length;e<i;++e)for(var o,a,u=c[e],f=1,s=u.length,l=u[0],h=l[0],d=l[1];f<s;++f)o=h,a=d,l=u[f],h=l[0],d=l[1],a<=r?d>r&&(h-o)*(r-a)>(d-a)*(t-o)&&++n:d<=r&&(h-o)*(r-a)<(d-a)*(t-o)&&--n;return n}(),e=_&&n,i=(f=N(f)).length;(e||i)&&(a.polygonStart(),e&&(a.lineStart(),o(null,null,1,a),a.lineEnd()),i&&Yf(f,u,n,o,a),a.polygonEnd());b=a,f=c=s=null}};function w(t,n){i(t,n)&&b.point(t,n)}function M(o,a){var u=i(o,a);if(c&&s.push([o,a]),y)l=o,h=a,d=u,y=!1,u&&(b.lineStart(),b.point(o,a));else if(u&&g)b.point(o,a);else{var f=[p=Math.max(Wf,Math.min($f,p)),v=Math.max(Wf,Math.min($f,v))],m=[o=Math.max(Wf,Math.min($f,o)),a=Math.max(Wf,Math.min($f,a))];!function(t,n,e,r,i,o){var a,u=t[0],f=t[1],c=0,s=1,l=n[0]-u,h=n[1]-f;if(a=e-u,l||!(a>0)){if(a/=l,l<0){if(a<c)return;a<s&&(s=a)}else if(l>0){if(a>s)return;a>c&&(c=a)}if(a=i-u,l||!(a<0)){if(a/=l,l<0){if(a>s)return;a>c&&(c=a)}else if(l>0){if(a<c)return;a<s&&(s=a)}if(a=r-f,h||!(a>0)){if(a/=h,h<0){if(a<c)return;a<s&&(s=a)}else if(h>0){if(a>s)return;a>c&&(c=a)}if(a=o-f,h||!(a<0)){if(a/=h,h<0){if(a>s)return;a>c&&(c=a)}else if(h>0){if(a<c)return;a<s&&(s=a)}return c>0&&(t[0]=u+c*l,t[1]=f+c*h),s<1&&(n[0]=u+s*l,n[1]=f+s*h),!0}}}}}(f,m,t,n,e,r)?u&&(b.lineStart(),b.point(o,a),_=!1):(g||(b.lineStart(),b.point(f[0],f[1])),b.point(m[0],m[1]),u||b.lineEnd(),_=!1)}p=o,v=a,g=u}return x}}var Qf,Jf,Kf,tc=Pa(),nc={sphere:iu,point:iu,lineStart:function(){nc.point=rc,nc.lineEnd=ec},lineEnd:iu,polygonStart:iu,polygonEnd:iu};function ec(){nc.point=nc.lineEnd=iu}function rc(t,n){Qf=t*=Ia,Jf=Qa(n*=Ia),Kf=Ga(n),nc.point=ic}function ic(t,n){t*=Ia;var e=Qa(n*=Ia),r=Ga(n),i=Ha(t-Qf),o=Ga(i),a=r*Qa(i),u=Kf*e-Jf*r*o,f=Jf*e+Kf*r*o;tc.add(Xa(Ka(a*a+u*u),f)),Qf=t,Jf=e,Kf=r}function oc(t){return tc.reset(),su(t,nc),+tc}var ac=[null,null],uc={type:"LineString",coordinates:ac};function fc(t,n){return ac[0]=t,ac[1]=n,oc(uc)}var cc={Feature:function(t,n){return lc(t.geometry,n)},FeatureCollection:function(t,n){for(var e=t.features,r=-1,i=e.length;++r<i;)if(lc(e[r].geometry,n))return!0;return!1}},sc={Sphere:function(){return!0},Point:function(t,n){return hc(t.coordinates,n)},MultiPoint:function(t,n){for(var e=t.coordinates,r=-1,i=e.length;++r<i;)if(hc(e[r],n))return!0;return!1},LineString:function(t,n){return dc(t.coordinates,n)},MultiLineString:function(t,n){for(var e=t.coordinates,r=-1,i=e.length;++r<i;)if(dc(e[r],n))return!0;return!1},Polygon:function(t,n){return pc(t.coordinates,n)},MultiPolygon:function(t,n){for(var e=t.coordinates,r=-1,i=e.length;++r<i;)if(pc(e[r],n))return!0;return!1},GeometryCollection:function(t,n){for(var e=t.geometries,r=-1,i=e.length;++r<i;)if(lc(e[r],n))return!0;return!1}};function lc(t,n){return!(!t||!sc.hasOwnProperty(t.type))&&sc[t.type](t,n)}function hc(t,n){return 0===fc(t,n)}function dc(t,n){var e=fc(t[0],t[1]);return fc(t[0],n)+fc(n,t[1])<=e+Da}function pc(t,n){return!!If(t.map(vc),gc(n))}function vc(t){return(t=t.map(gc)).pop(),t}function gc(t){return[t[0]*Ia,t[1]*Ia]}function yc(t,n,e){var r=g(t,n-Da,e).concat(n);return function(t){return r.map(function(n){return[t,n]})}}function _c(t,n,e){var r=g(t,n-Da,e).concat(n);return function(t){return r.map(function(n){return[n,t]})}}function bc(){var t,n,e,r,i,o,a,u,f,c,s,l,h=10,d=h,p=90,v=360,y=2.5;function _(){return{type:"MultiLineString",coordinates:b()}}function b(){return g(Va(r/p)*p,e,p).map(s).concat(g(Va(u/v)*v,a,v).map(l)).concat(g(Va(n/h)*h,t,h).filter(function(t){return Ha(t%p)>Da}).map(f)).concat(g(Va(o/d)*d,i,d).filter(function(t){return Ha(t%v)>Da}).map(c))}return _.lines=function(){return b().map(function(t){return{type:"LineString",coordinates:t}})},_.outline=function(){return{type:"Polygon",coordinates:[s(r).concat(l(a).slice(1),s(e).reverse().slice(1),l(u).reverse().slice(1))]}},_.extent=function(t){return arguments.length?_.extentMajor(t).extentMinor(t):_.extentMinor()},_.extentMajor=function(t){return arguments.length?(r=+t[0][0],e=+t[1][0],u=+t[0][1],a=+t[1][1],r>e&&(t=r,r=e,e=t),u>a&&(t=u,u=a,a=t),_.precision(y)):[[r,u],[e,a]]},_.extentMinor=function(e){return arguments.length?(n=+e[0][0],t=+e[1][0],o=+e[0][1],i=+e[1][1],n>t&&(e=n,n=t,t=e),o>i&&(e=o,o=i,i=e),_.precision(y)):[[n,o],[t,i]]},_.step=function(t){return arguments.length?_.stepMajor(t).stepMinor(t):_.stepMinor()},_.stepMajor=function(t){return arguments.length?(p=+t[0],v=+t[1],_):[p,v]},_.stepMinor=function(t){return arguments.length?(h=+t[0],d=+t[1],_):[h,d]},_.precision=function(h){return arguments.length?(y=+h,f=yc(o,i,90),c=_c(n,t,y),s=yc(u,a,90),l=_c(r,e,y),_):y},_.extentMajor([[-180,-90+Da],[180,90-Da]]).extentMinor([[-180,-80-Da],[180,80+Da]])}function mc(t){return t}var xc,wc,Mc,Ac,Tc=Pa(),Nc=Pa(),Sc={point:iu,lineStart:iu,lineEnd:iu,polygonStart:function(){Sc.lineStart=Ec,Sc.lineEnd=Pc},polygonEnd:function(){Sc.lineStart=Sc.lineEnd=Sc.point=iu,Tc.add(Ha(Nc)),Nc.reset()},result:function(){var t=Tc/2;return Tc.reset(),t}};function Ec(){Sc.point=kc}function kc(t,n){Sc.point=Cc,xc=Mc=t,wc=Ac=n}function Cc(t,n){Nc.add(Ac*t-Mc*n),Mc=t,Ac=n}function Pc(){Cc(xc,wc)}var zc=1/0,Rc=zc,Lc=-zc,Dc=Lc,Uc={point:function(t,n){t<zc&&(zc=t);t>Lc&&(Lc=t);n<Rc&&(Rc=n);n>Dc&&(Dc=n)},lineStart:iu,lineEnd:iu,polygonStart:iu,polygonEnd:iu,result:function(){var t=[[zc,Rc],[Lc,Dc]];return Lc=Dc=-(Rc=zc=1/0),t}};var qc,Oc,Yc,Bc,Fc=0,Ic=0,Hc=0,jc=0,Xc=0,Gc=0,Vc=0,$c=0,Wc=0,Zc={point:Qc,lineStart:Jc,lineEnd:ns,polygonStart:function(){Zc.lineStart=es,Zc.lineEnd=rs},polygonEnd:function(){Zc.point=Qc,Zc.lineStart=Jc,Zc.lineEnd=ns},result:function(){var t=Wc?[Vc/Wc,$c/Wc]:Gc?[jc/Gc,Xc/Gc]:Hc?[Fc/Hc,Ic/Hc]:[NaN,NaN];return Fc=Ic=Hc=jc=Xc=Gc=Vc=$c=Wc=0,t}};function Qc(t,n){Fc+=t,Ic+=n,++Hc}function Jc(){Zc.point=Kc}function Kc(t,n){Zc.point=ts,Qc(Yc=t,Bc=n)}function ts(t,n){var e=t-Yc,r=n-Bc,i=Ka(e*e+r*r);jc+=i*(Yc+t)/2,Xc+=i*(Bc+n)/2,Gc+=i,Qc(Yc=t,Bc=n)}function ns(){Zc.point=Qc}function es(){Zc.point=is}function rs(){os(qc,Oc)}function is(t,n){Zc.point=os,Qc(qc=Yc=t,Oc=Bc=n)}function os(t,n){var e=t-Yc,r=n-Bc,i=Ka(e*e+r*r);jc+=i*(Yc+t)/2,Xc+=i*(Bc+n)/2,Gc+=i,Vc+=(i=Bc*t-Yc*n)*(Yc+t),$c+=i*(Bc+n),Wc+=3*i,Qc(Yc=t,Bc=n)}function as(t){this._context=t}as.prototype={_radius:4.5,pointRadius:function(t){return this._radius=t,this},polygonStart:function(){this._line=0},polygonEnd:function(){this._line=NaN},lineStart:function(){this._point=0},lineEnd:function(){0===this._line&&this._context.closePath(),this._point=NaN},point:function(t,n){switch(this._point){case 0:this._context.moveTo(t,n),this._point=1;break;case 1:this._context.lineTo(t,n);break;default:this._context.moveTo(t+this._radius,n),this._context.arc(t,n,this._radius,0,Ba)}},result:iu};var us,fs,cs,ss,ls,hs=Pa(),ds={point:iu,lineStart:function(){ds.point=ps},lineEnd:function(){us&&vs(fs,cs),ds.point=iu},polygonStart:function(){us=!0},polygonEnd:function(){us=null},result:function(){var t=+hs;return hs.reset(),t}};function ps(t,n){ds.point=vs,fs=ss=t,cs=ls=n}function vs(t,n){ss-=t,ls-=n,hs.add(Ka(ss*ss+ls*ls)),ss=t,ls=n}function gs(){this._string=[]}function ys(t){return"m0,"+t+"a"+t+","+t+" 0 1,1 0,"+-2*t+"a"+t+","+t+" 0 1,1 0,"+2*t+"z"}function _s(t){return function(n){var e=new bs;for(var r in t)e[r]=t[r];return e.stream=n,e}}function bs(){}function ms(t,n,e){var r=t.clipExtent&&t.clipExtent();return t.scale(150).translate([0,0]),null!=r&&t.clipExtent(null),su(e,t.stream(Uc)),n(Uc.result()),null!=r&&t.clipExtent(r),t}function xs(t,n,e){return ms(t,function(e){var r=n[1][0]-n[0][0],i=n[1][1]-n[0][1],o=Math.min(r/(e[1][0]-e[0][0]),i/(e[1][1]-e[0][1])),a=+n[0][0]+(r-o*(e[1][0]+e[0][0]))/2,u=+n[0][1]+(i-o*(e[1][1]+e[0][1]))/2;t.scale(150*o).translate([a,u])},e)}function ws(t,n,e){return xs(t,[[0,0],n],e)}function Ms(t,n,e){return ms(t,function(e){var r=+n,i=r/(e[1][0]-e[0][0]),o=(r-i*(e[1][0]+e[0][0]))/2,a=-i*e[0][1];t.scale(150*i).translate([o,a])},e)}function As(t,n,e){return ms(t,function(e){var r=+n,i=r/(e[1][1]-e[0][1]),o=-i*e[0][0],a=(r-i*(e[1][1]+e[0][1]))/2;t.scale(150*i).translate([o,a])},e)}gs.prototype={_radius:4.5,_circle:ys(4.5),pointRadius:function(t){return(t=+t)!==this._radius&&(this._radius=t,this._circle=null),this},polygonStart:function(){this._line=0},polygonEnd:function(){this._line=NaN},lineStart:function(){this._point=0},lineEnd:function(){0===this._line&&this._string.push("Z"),this._point=NaN},point:function(t,n){switch(this._point){case 0:this._string.push("M",t,",",n),this._point=1;break;case 1:this._string.push("L",t,",",n);break;default:null==this._circle&&(this._circle=ys(this._radius)),this._string.push("M",t,",",n,this._circle)}},result:function(){if(this._string.length){var t=this._string.join("");return this._string=[],t}return null}},bs.prototype={constructor:bs,point:function(t,n){this.stream.point(t,n)},sphere:function(){this.stream.sphere()},lineStart:function(){this.stream.lineStart()},lineEnd:function(){this.stream.lineEnd()},polygonStart:function(){this.stream.polygonStart()},polygonEnd:function(){this.stream.polygonEnd()}};var Ts=16,Ns=Ga(30*Ia);function Ss(t,n){return+n?function(t,n){function e(r,i,o,a,u,f,c,s,l,h,d,p,v,g){var y=c-r,_=s-i,b=y*y+_*_;if(b>4*n&&v--){var m=a+h,x=u+d,w=f+p,M=Ka(m*m+x*x+w*w),A=eu(w/=M),T=Ha(Ha(w)-1)<Da||Ha(o-l)<Da?(o+l)/2:Xa(x,m),N=t(T,A),S=N[0],E=N[1],k=S-r,C=E-i,P=_*k-y*C;(P*P/b>n||Ha((y*k+_*C)/b-.5)>.3||a*h+u*d+f*p<Ns)&&(e(r,i,o,a,u,f,S,E,T,m/=M,x/=M,w,v,g),g.point(S,E),e(S,E,T,m,x,w,c,s,l,h,d,p,v,g))}}return function(n){var r,i,o,a,u,f,c,s,l,h,d,p,v={point:g,lineStart:y,lineEnd:b,polygonStart:function(){n.polygonStart(),v.lineStart=m},polygonEnd:function(){n.polygonEnd(),v.lineStart=y}};function g(e,r){e=t(e,r),n.point(e[0],e[1])}function y(){s=NaN,v.point=_,n.lineStart()}function _(r,i){var o=Au([r,i]),a=t(r,i);e(s,l,c,h,d,p,s=a[0],l=a[1],c=r,h=o[0],d=o[1],p=o[2],Ts,n),n.point(s,l)}function b(){v.point=g,n.lineEnd()}function m(){y(),v.point=x,v.lineEnd=w}function x(t,n){_(r=t,n),i=s,o=l,a=h,u=d,f=p,v.point=_}function w(){e(s,l,c,h,d,p,i,o,r,a,u,f,Ts,n),v.lineEnd=b,b()}return v}}(t,n):function(t){return _s({point:function(n,e){n=t(n,e),this.stream.point(n[0],n[1])}})}(t)}var Es=_s({point:function(t,n){this.stream.point(t*Ia,n*Ia)}});function ks(t,n,e,r){var i=Ga(r),o=Qa(r),a=i*t,u=o*t,f=i/t,c=o/t,s=(o*e-i*n)/t,l=(o*n+i*e)/t;function h(t,r){return[a*t-u*r+n,e-u*t-a*r]}return h.invert=function(t,n){return[f*t-c*n+s,l-c*t-f*n]},h}function Cs(t){return Ps(function(){return t})()}function Ps(t){var n,e,r,i,o,a,u,f,c,s,l=150,h=480,d=250,p=0,v=0,g=0,y=0,_=0,b=0,m=null,x=Gf,w=null,M=mc,A=.5;function T(t){return f(t[0]*Ia,t[1]*Ia)}function N(t){return(t=f.invert(t[0],t[1]))&&[t[0]*Fa,t[1]*Fa]}function S(){var t=ks(l,0,0,b).apply(null,n(p,v)),r=(b?ks:function(t,n,e){function r(r,i){return[n+t*r,e-t*i]}return r.invert=function(r,i){return[(r-n)/t,(e-i)/t]},r})(l,h-t[0],d-t[1],b);return e=kf(g,y,_),u=Sf(n,r),f=Sf(e,u),a=Ss(u,A),E()}function E(){return c=s=null,T}return T.stream=function(t){return c&&s===t?c:c=Es(function(t){return _s({point:function(n,e){var r=t(n,e);return this.stream.point(r[0],r[1])}})}(e)(x(a(M(s=t)))))},T.preclip=function(t){return arguments.length?(x=t,m=void 0,E()):x},T.postclip=function(t){return arguments.length?(M=t,w=r=i=o=null,E()):M},T.clipAngle=function(t){return arguments.length?(x=+t?Vf(m=t*Ia):(m=null,Gf),E()):m*Fa},T.clipExtent=function(t){return arguments.length?(M=null==t?(w=r=i=o=null,mc):Zf(w=+t[0][0],r=+t[0][1],i=+t[1][0],o=+t[1][1]),E()):null==w?null:[[w,r],[i,o]]},T.scale=function(t){return arguments.length?(l=+t,S()):l},T.translate=function(t){return arguments.length?(h=+t[0],d=+t[1],S()):[h,d]},T.center=function(t){return arguments.length?(p=t[0]%360*Ia,v=t[1]%360*Ia,S()):[p*Fa,v*Fa]},T.rotate=function(t){return arguments.length?(g=t[0]%360*Ia,y=t[1]%360*Ia,_=t.length>2?t[2]%360*Ia:0,S()):[g*Fa,y*Fa,_*Fa]},T.angle=function(t){return arguments.length?(b=t%360*Ia,S()):b*Fa},T.precision=function(t){return arguments.length?(a=Ss(u,A=t*t),E()):Ka(A)},T.fitExtent=function(t,n){return xs(T,t,n)},T.fitSize=function(t,n){return ws(T,t,n)},T.fitWidth=function(t,n){return Ms(T,t,n)},T.fitHeight=function(t,n){return As(T,t,n)},function(){return n=t.apply(this,arguments),T.invert=n.invert&&N,S()}}function zs(t){var n=0,e=qa/3,r=Ps(t),i=r(n,e);return i.parallels=function(t){return arguments.length?r(n=t[0]*Ia,e=t[1]*Ia):[n*Fa,e*Fa]},i}function Rs(t,n){var e=Qa(t),r=(e+Qa(n))/2;if(Ha(r)<Da)return function(t){var n=Ga(t);function e(t,e){return[t*n,Qa(e)/n]}return e.invert=function(t,e){return[t/n,eu(e*n)]},e}(t);var i=1+e*(2*r-e),o=Ka(i)/r;function a(t,n){var e=Ka(i-2*r*Qa(n))/r;return[e*Qa(t*=r),o-e*Ga(t)]}return a.invert=function(t,n){var e=o-n;return[Xa(t,Ha(e))/r*Ja(e),eu((i-(t*t+e*e)*r*r)/(2*r))]},a}function Ls(){return zs(Rs).scale(155.424).center([0,33.6442])}function Ds(){return Ls().parallels([29.5,45.5]).scale(1070).translate([480,250]).rotate([96,0]).center([-.6,38.7])}function Us(t){return function(n,e){var r=Ga(n),i=Ga(e),o=t(r*i);return[o*i*Qa(n),o*Qa(e)]}}function qs(t){return function(n,e){var r=Ka(n*n+e*e),i=t(r),o=Qa(i),a=Ga(i);return[Xa(n*o,r*a),eu(r&&e*o/r)]}}var Os=Us(function(t){return Ka(2/(1+t))});Os.invert=qs(function(t){return 2*eu(t/2)});var Ys=Us(function(t){return(t=nu(t))&&t/Qa(t)});function Bs(t,n){return[t,Wa(tu((Oa+n)/2))]}function Fs(t){var n,e,r,i=Cs(t),o=i.center,a=i.scale,u=i.translate,f=i.clipExtent,c=null;function s(){var o=qa*a(),u=i(Rf(i.rotate()).invert([0,0]));return f(null==c?[[u[0]-o,u[1]-o],[u[0]+o,u[1]+o]]:t===Bs?[[Math.max(u[0]-o,c),n],[Math.min(u[0]+o,e),r]]:[[c,Math.max(u[1]-o,n)],[e,Math.min(u[1]+o,r)]])}return i.scale=function(t){return arguments.length?(a(t),s()):a()},i.translate=function(t){return arguments.length?(u(t),s()):u()},i.center=function(t){return arguments.length?(o(t),s()):o()},i.clipExtent=function(t){return arguments.length?(null==t?c=n=e=r=null:(c=+t[0][0],n=+t[0][1],e=+t[1][0],r=+t[1][1]),s()):null==c?null:[[c,n],[e,r]]},s()}function Is(t){return tu((Oa+t)/2)}function Hs(t,n){var e=Ga(t),r=t===n?Qa(t):Wa(e/Ga(n))/Wa(Is(n)/Is(t)),i=e*Za(Is(t),r)/r;if(!r)return Bs;function o(t,n){i>0?n<-Oa+Da&&(n=-Oa+Da):n>Oa-Da&&(n=Oa-Da);var e=i/Za(Is(n),r);return[e*Qa(r*t),i-e*Ga(r*t)]}return o.invert=function(t,n){var e=i-n,o=Ja(r)*Ka(t*t+e*e);return[Xa(t,Ha(e))/r*Ja(e),2*ja(Za(i/o,1/r))-Oa]},o}function js(t,n){return[t,n]}function Xs(t,n){var e=Ga(t),r=t===n?Qa(t):(e-Ga(n))/(n-t),i=e/r+t;if(Ha(r)<Da)return js;function o(t,n){var e=i-n,o=r*t;return[e*Qa(o),i-e*Ga(o)]}return o.invert=function(t,n){var e=i-n;return[Xa(t,Ha(e))/r*Ja(e),i-Ja(r)*Ka(t*t+e*e)]},o}Ys.invert=qs(function(t){return t}),Bs.invert=function(t,n){return[t,2*ja($a(n))-Oa]},js.invert=js;var Gs=1.340264,Vs=-.081106,$s=893e-6,Ws=.003796,Zs=Ka(3)/2;function Qs(t,n){var e=eu(Zs*Qa(n)),r=e*e,i=r*r*r;return[t*Ga(e)/(Zs*(Gs+3*Vs*r+i*(7*$s+9*Ws*r))),e*(Gs+Vs*r+i*($s+Ws*r))]}function Js(t,n){var e=Ga(n),r=Ga(t)*e;return[e*Qa(t)/r,Qa(n)/r]}function Ks(t,n,e,r){return 1===t&&1===n&&0===e&&0===r?mc:_s({point:function(i,o){this.stream.point(i*t+e,o*n+r)}})}function tl(t,n){var e=n*n,r=e*e;return[t*(.8707-.131979*e+r*(r*(.003971*e-.001529*r)-.013791)),n*(1.007226+e*(.015085+r*(.028874*e-.044475-.005916*r)))]}function nl(t,n){return[Ga(n)*Qa(t),Qa(n)]}function el(t,n){var e=Ga(n),r=1+Ga(t)*e;return[e*Qa(t)/r,Qa(n)/r]}function rl(t,n){return[Wa(tu((Oa+n)/2)),-t]}function il(t,n){return t.parent===n.parent?1:2}function ol(t,n){return t+n.x}function al(t,n){return Math.max(t,n.y)}function ul(t){var n=0,e=t.children,r=e&&e.length;if(r)for(;--r>=0;)n+=e[r].value;else n=1;t.value=n}function fl(t,n){var e,r,i,o,a,u=new hl(t),f=+t.value&&(u.value=t.value),c=[u];for(null==n&&(n=cl);e=c.pop();)if(f&&(e.value=+e.data.value),(i=n(e.data))&&(a=i.length))for(e.children=new Array(a),o=a-1;o>=0;--o)c.push(r=e.children[o]=new hl(i[o])),r.parent=e,r.depth=e.depth+1;return u.eachBefore(ll)}function cl(t){return t.children}function sl(t){t.data=t.data.data}function ll(t){var n=0;do{t.height=n}while((t=t.parent)&&t.height<++n)}function hl(t){this.data=t,this.depth=this.height=0,this.parent=null}Qs.invert=function(t,n){for(var e,r=n,i=r*r,o=i*i*i,a=0;a<12&&(o=(i=(r-=e=(r*(Gs+Vs*i+o*($s+Ws*i))-n)/(Gs+3*Vs*i+o*(7*$s+9*Ws*i)))*r)*i*i,!(Ha(e)<Ua));++a);return[Zs*t*(Gs+3*Vs*i+o*(7*$s+9*Ws*i))/Ga(r),eu(Qa(r)/Zs)]},Js.invert=qs(ja),tl.invert=function(t,n){var e,r=n,i=25;do{var o=r*r,a=o*o;r-=e=(r*(1.007226+o*(.015085+a*(.028874*o-.044475-.005916*a)))-n)/(1.007226+o*(.045255+a*(.259866*o-.311325-.005916*11*a)))}while(Ha(e)>Da&&--i>0);return[t/(.8707+(o=r*r)*(o*(o*o*o*(.003971-.001529*o)-.013791)-.131979)),r]},nl.invert=qs(eu),el.invert=qs(function(t){return 2*ja(t)}),rl.invert=function(t,n){return[-n,2*ja($a(t))-Oa]},hl.prototype=fl.prototype={constructor:hl,count:function(){return this.eachAfter(ul)},each:function(t){var n,e,r,i,o=this,a=[o];do{for(n=a.reverse(),a=[];o=n.pop();)if(t(o),e=o.children)for(r=0,i=e.length;r<i;++r)a.push(e[r])}while(a.length);return this},eachAfter:function(t){for(var n,e,r,i=this,o=[i],a=[];i=o.pop();)if(a.push(i),n=i.children)for(e=0,r=n.length;e<r;++e)o.push(n[e]);for(;i=a.pop();)t(i);return this},eachBefore:function(t){for(var n,e,r=this,i=[r];r=i.pop();)if(t(r),n=r.children)for(e=n.length-1;e>=0;--e)i.push(n[e]);return this},sum:function(t){return this.eachAfter(function(n){for(var e=+t(n.data)||0,r=n.children,i=r&&r.length;--i>=0;)e+=r[i].value;n.value=e})},sort:function(t){return this.eachBefore(function(n){n.children&&n.children.sort(t)})},path:function(t){for(var n=this,e=function(t,n){if(t===n)return t;var e=t.ancestors(),r=n.ancestors(),i=null;for(t=e.pop(),n=r.pop();t===n;)i=t,t=e.pop(),n=r.pop();return i}(n,t),r=[n];n!==e;)n=n.parent,r.push(n);for(var i=r.length;t!==e;)r.splice(i,0,t),t=t.parent;return r},ancestors:function(){for(var t=this,n=[t];t=t.parent;)n.push(t);return n},descendants:function(){var t=[];return this.each(function(n){t.push(n)}),t},leaves:function(){var t=[];return this.eachBefore(function(n){n.children||t.push(n)}),t},links:function(){var t=this,n=[];return t.each(function(e){e!==t&&n.push({source:e.parent,target:e})}),n},copy:function(){return fl(this).eachBefore(sl)}};var dl=Array.prototype.slice;function pl(t){for(var n,e,r=0,i=(t=function(t){for(var n,e,r=t.length;r;)e=Math.random()*r--|0,n=t[r],t[r]=t[e],t[e]=n;return t}(dl.call(t))).length,o=[];r<i;)n=t[r],e&&yl(e,n)?++r:(e=bl(o=vl(o,n)),r=0);return e}function vl(t,n){var e,r;if(_l(n,t))return[n];for(e=0;e<t.length;++e)if(gl(n,t[e])&&_l(ml(t[e],n),t))return[t[e],n];for(e=0;e<t.length-1;++e)for(r=e+1;r<t.length;++r)if(gl(ml(t[e],t[r]),n)&&gl(ml(t[e],n),t[r])&&gl(ml(t[r],n),t[e])&&_l(xl(t[e],t[r],n),t))return[t[e],t[r],n];throw new Error}function gl(t,n){var e=t.r-n.r,r=n.x-t.x,i=n.y-t.y;return e<0||e*e<r*r+i*i}function yl(t,n){var e=t.r-n.r+1e-6,r=n.x-t.x,i=n.y-t.y;return e>0&&e*e>r*r+i*i}function _l(t,n){for(var e=0;e<n.length;++e)if(!yl(t,n[e]))return!1;return!0}function bl(t){switch(t.length){case 1:return{x:(n=t[0]).x,y:n.y,r:n.r};case 2:return ml(t[0],t[1]);case 3:return xl(t[0],t[1],t[2])}var n}function ml(t,n){var e=t.x,r=t.y,i=t.r,o=n.x,a=n.y,u=n.r,f=o-e,c=a-r,s=u-i,l=Math.sqrt(f*f+c*c);return{x:(e+o+f/l*s)/2,y:(r+a+c/l*s)/2,r:(l+i+u)/2}}function xl(t,n,e){var r=t.x,i=t.y,o=t.r,a=n.x,u=n.y,f=n.r,c=e.x,s=e.y,l=e.r,h=r-a,d=r-c,p=i-u,v=i-s,g=f-o,y=l-o,_=r*r+i*i-o*o,b=_-a*a-u*u+f*f,m=_-c*c-s*s+l*l,x=d*p-h*v,w=(p*m-v*b)/(2*x)-r,M=(v*g-p*y)/x,A=(d*b-h*m)/(2*x)-i,T=(h*y-d*g)/x,N=M*M+T*T-1,S=2*(o+w*M+A*T),E=w*w+A*A-o*o,k=-(N?(S+Math.sqrt(S*S-4*N*E))/(2*N):E/S);return{x:r+w+M*k,y:i+A+T*k,r:k}}function wl(t,n,e){var r,i,o,a,u=t.x-n.x,f=t.y-n.y,c=u*u+f*f;c?(i=n.r+e.r,i*=i,a=t.r+e.r,i>(a*=a)?(r=(c+a-i)/(2*c),o=Math.sqrt(Math.max(0,a/c-r*r)),e.x=t.x-r*u-o*f,e.y=t.y-r*f+o*u):(r=(c+i-a)/(2*c),o=Math.sqrt(Math.max(0,i/c-r*r)),e.x=n.x+r*u-o*f,e.y=n.y+r*f+o*u)):(e.x=n.x+e.r,e.y=n.y)}function Ml(t,n){var e=t.r+n.r-1e-6,r=n.x-t.x,i=n.y-t.y;return e>0&&e*e>r*r+i*i}function Al(t){var n=t._,e=t.next._,r=n.r+e.r,i=(n.x*e.r+e.x*n.r)/r,o=(n.y*e.r+e.y*n.r)/r;return i*i+o*o}function Tl(t){this._=t,this.next=null,this.previous=null}function Nl(t){if(!(i=t.length))return 0;var n,e,r,i,o,a,u,f,c,s,l;if((n=t[0]).x=0,n.y=0,!(i>1))return n.r;if(e=t[1],n.x=-e.r,e.x=n.r,e.y=0,!(i>2))return n.r+e.r;wl(e,n,r=t[2]),n=new Tl(n),e=new Tl(e),r=new Tl(r),n.next=r.previous=e,e.next=n.previous=r,r.next=e.previous=n;t:for(u=3;u<i;++u){wl(n._,e._,r=t[u]),r=new Tl(r),f=e.next,c=n.previous,s=e._.r,l=n._.r;do{if(s<=l){if(Ml(f._,r._)){e=f,n.next=e,e.previous=n,--u;continue t}s+=f._.r,f=f.next}else{if(Ml(c._,r._)){(n=c).next=e,e.previous=n,--u;continue t}l+=c._.r,c=c.previous}}while(f!==c.next);for(r.previous=n,r.next=e,n.next=e.previous=e=r,o=Al(n);(r=r.next)!==e;)(a=Al(r))<o&&(n=r,o=a);e=n.next}for(n=[e._],r=e;(r=r.next)!==e;)n.push(r._);for(r=pl(n),u=0;u<i;++u)(n=t[u]).x-=r.x,n.y-=r.y;return r.r}function Sl(t){if("function"!=typeof t)throw new Error;return t}function El(){return 0}function kl(t){return function(){return t}}function Cl(t){return Math.sqrt(t.value)}function Pl(t){return function(n){n.children||(n.r=Math.max(0,+t(n)||0))}}function zl(t,n){return function(e){if(r=e.children){var r,i,o,a=r.length,u=t(e)*n||0;if(u)for(i=0;i<a;++i)r[i].r+=u;if(o=Nl(r),u)for(i=0;i<a;++i)r[i].r-=u;e.r=o+u}}}function Rl(t){return function(n){var e=n.parent;n.r*=t,e&&(n.x=e.x+t*n.x,n.y=e.y+t*n.y)}}function Ll(t){t.x0=Math.round(t.x0),t.y0=Math.round(t.y0),t.x1=Math.round(t.x1),t.y1=Math.round(t.y1)}function Dl(t,n,e,r,i){for(var o,a=t.children,u=-1,f=a.length,c=t.value&&(r-n)/t.value;++u<f;)(o=a[u]).y0=e,o.y1=i,o.x0=n,o.x1=n+=o.value*c}var Ul="$",ql={depth:-1},Ol={};function Yl(t){return t.id}function Bl(t){return t.parentId}function Fl(t,n){return t.parent===n.parent?1:2}function Il(t){var n=t.children;return n?n[0]:t.t}function Hl(t){var n=t.children;return n?n[n.length-1]:t.t}function jl(t,n,e){var r=e/(n.i-t.i);n.c-=r,n.s+=e,t.c+=r,n.z+=e,n.m+=e}function Xl(t,n,e){return t.a.parent===n.parent?t.a:e}function Gl(t,n){this._=t,this.parent=null,this.children=null,this.A=null,this.a=this,this.z=0,this.m=0,this.c=0,this.s=0,this.t=null,this.i=n}function Vl(t,n,e,r,i){for(var o,a=t.children,u=-1,f=a.length,c=t.value&&(i-e)/t.value;++u<f;)(o=a[u]).x0=n,o.x1=r,o.y0=e,o.y1=e+=o.value*c}Gl.prototype=Object.create(hl.prototype);var $l=(1+Math.sqrt(5))/2;function Wl(t,n,e,r,i,o){for(var a,u,f,c,s,l,h,d,p,v,g,y=[],_=n.children,b=0,m=0,x=_.length,w=n.value;b<x;){f=i-e,c=o-r;do{s=_[m++].value}while(!s&&m<x);for(l=h=s,g=s*s*(v=Math.max(c/f,f/c)/(w*t)),p=Math.max(h/g,g/l);m<x;++m){if(s+=u=_[m].value,u<l&&(l=u),u>h&&(h=u),g=s*s*v,(d=Math.max(h/g,g/l))>p){s-=u;break}p=d}y.push(a={value:s,dice:f<c,children:_.slice(b,m)}),a.dice?Dl(a,e,r,i,w?r+=c*s/w:o):Vl(a,e,r,w?e+=f*s/w:i,o),w-=s,b=m}return y}var Zl=function t(n){function e(t,e,r,i,o){Wl(n,t,e,r,i,o)}return e.ratio=function(n){return t((n=+n)>1?n:1)},e}($l);var Ql=function t(n){function e(t,e,r,i,o){if((a=t._squarify)&&a.ratio===n)for(var a,u,f,c,s,l=-1,h=a.length,d=t.value;++l<h;){for(f=(u=a[l]).children,c=u.value=0,s=f.length;c<s;++c)u.value+=f[c].value;u.dice?Dl(u,e,r,i,r+=(o-r)*u.value/d):Vl(u,e,r,e+=(i-e)*u.value/d,o),d-=u.value}else t._squarify=a=Wl(n,t,e,r,i,o),a.ratio=n}return e.ratio=function(n){return t((n=+n)>1?n:1)},e}($l);function Jl(t,n){return t[0]-n[0]||t[1]-n[1]}function Kl(t){for(var n,e,r,i=t.length,o=[0,1],a=2,u=2;u<i;++u){for(;a>1&&(n=t[o[a-2]],e=t[o[a-1]],r=t[u],(e[0]-n[0])*(r[1]-n[1])-(e[1]-n[1])*(r[0]-n[0])<=0);)--a;o[a++]=u}return o.slice(0,a)}function th(){return Math.random()}var nh=function t(n){function e(t,e){return t=null==t?0:+t,e=null==e?1:+e,1===arguments.length?(e=t,t=0):e-=t,function(){return n()*e+t}}return e.source=t,e}(th),eh=function t(n){function e(t,e){var r,i;return t=null==t?0:+t,e=null==e?1:+e,function(){var o;if(null!=r)o=r,r=null;else do{r=2*n()-1,o=2*n()-1,i=r*r+o*o}while(!i||i>1);return t+e*o*Math.sqrt(-2*Math.log(i)/i)}}return e.source=t,e}(th),rh=function t(n){function e(){var t=eh.source(n).apply(this,arguments);return function(){return Math.exp(t())}}return e.source=t,e}(th),ih=function t(n){function e(t){return function(){for(var e=0,r=0;r<t;++r)e+=n();return e}}return e.source=t,e}(th),oh=function t(n){function e(t){var e=ih.source(n)(t);return function(){return e()/t}}return e.source=t,e}(th),ah=function t(n){function e(t){return function(){return-Math.log(1-n())/t}}return e.source=t,e}(th),uh=Array.prototype,fh=uh.map,ch=uh.slice,sh={name:"implicit"};function lh(t){var n=Ki(),e=[],r=sh;function i(i){var o=i+"",a=n.get(o);if(!a){if(r!==sh)return r;n.set(o,a=e.push(i))}return t[(a-1)%t.length]}return t=null==t?[]:ch.call(t),i.domain=function(t){if(!arguments.length)return e.slice();e=[],n=Ki();for(var r,o,a=-1,u=t.length;++a<u;)n.has(o=(r=t[a])+"")||n.set(o,e.push(r));return i},i.range=function(n){return arguments.length?(t=ch.call(n),i):t.slice()},i.unknown=function(t){return arguments.length?(r=t,i):r},i.copy=function(){return lh().domain(e).range(t).unknown(r)},i}function hh(){var t,n,e=lh().unknown(void 0),r=e.domain,i=e.range,o=[0,1],a=!1,u=0,f=0,c=.5;function s(){var e=r().length,s=o[1]<o[0],l=o[s-0],h=o[1-s];t=(h-l)/Math.max(1,e-u+2*f),a&&(t=Math.floor(t)),l+=(h-l-t*(e-u))*c,n=t*(1-u),a&&(l=Math.round(l),n=Math.round(n));var d=g(e).map(function(n){return l+t*n});return i(s?d.reverse():d)}return delete e.unknown,e.domain=function(t){return arguments.length?(r(t),s()):r()},e.range=function(t){return arguments.length?(o=[+t[0],+t[1]],s()):o.slice()},e.rangeRound=function(t){return o=[+t[0],+t[1]],a=!0,s()},e.bandwidth=function(){return n},e.step=function(){return t},e.round=function(t){return arguments.length?(a=!!t,s()):a},e.padding=function(t){return arguments.length?(u=f=Math.max(0,Math.min(1,t)),s()):u},e.paddingInner=function(t){return arguments.length?(u=Math.max(0,Math.min(1,t)),s()):u},e.paddingOuter=function(t){return arguments.length?(f=Math.max(0,Math.min(1,t)),s()):f},e.align=function(t){return arguments.length?(c=Math.max(0,Math.min(1,t)),s()):c},e.copy=function(){return hh().domain(r()).range(o).round(a).paddingInner(u).paddingOuter(f).align(c)},s()}function dh(t){return function(){return t}}function ph(t){return+t}var vh=[0,1];function gh(t,n){return(n-=t=+t)?function(e){return(e-t)/n}:dh(n)}function yh(t,n,e,r){var i=t[0],o=t[1],a=n[0],u=n[1];return o<i?(i=e(o,i),a=r(u,a)):(i=e(i,o),a=r(a,u)),function(t){return a(i(t))}}function _h(t,n,e,r){var o=Math.min(t.length,n.length)-1,a=new Array(o),u=new Array(o),f=-1;for(t[o]<t[0]&&(t=t.slice().reverse(),n=n.slice().reverse());++f<o;)a[f]=e(t[f],t[f+1]),u[f]=r(n[f],n[f+1]);return function(n){var e=i(t,n,1,o)-1;return u[e](a[e](n))}}function bh(t,n){return n.domain(t.domain()).range(t.range()).interpolate(t.interpolate()).clamp(t.clamp())}function mh(t,n){var e,r,i,o=vh,a=vh,u=me,f=!1;function c(){return e=Math.min(o.length,a.length)>2?_h:yh,r=i=null,s}function s(n){return(r||(r=e(o,a,f?function(t){return function(n,e){var r=t(n=+n,e=+e);return function(t){return t<=n?0:t>=e?1:r(t)}}}(t):t,u)))(+n)}return s.invert=function(t){return(i||(i=e(a,o,gh,f?function(t){return function(n,e){var r=t(n=+n,e=+e);return function(t){return t<=0?n:t>=1?e:r(t)}}}(n):n)))(+t)},s.domain=function(t){return arguments.length?(o=fh.call(t,ph),c()):o.slice()},s.range=function(t){return arguments.length?(a=ch.call(t),c()):a.slice()},s.rangeRound=function(t){return a=ch.call(t),u=xe,c()},s.clamp=function(t){return arguments.length?(f=!!t,c()):f},s.interpolate=function(t){return arguments.length?(u=t,c()):u},c()}function xh(n){var e=n.domain;return n.ticks=function(t){var n=e();return m(n[0],n[n.length-1],null==t?10:t)},n.tickFormat=function(n,r){return function(n,e,r){var i,o=n[0],a=n[n.length-1],u=w(o,a,null==e?10:e);switch((r=ba(null==r?",f":r)).type){case"s":var f=Math.max(Math.abs(o),Math.abs(a));return null!=r.precision||isNaN(i=ka(u,f))||(r.precision=i),t.formatPrefix(r,f);case"":case"e":case"g":case"p":case"r":null!=r.precision||isNaN(i=Ca(u,Math.max(Math.abs(o),Math.abs(a))))||(r.precision=i-("e"===r.type));break;case"f":case"%":null!=r.precision||isNaN(i=Ea(u))||(r.precision=i-2*("%"===r.type))}return t.format(r)}(e(),n,r)},n.nice=function(t){null==t&&(t=10);var r,i=e(),o=0,a=i.length-1,u=i[o],f=i[a];return f<u&&(r=u,u=f,f=r,r=o,o=a,a=r),(r=x(u,f,t))>0?r=x(u=Math.floor(u/r)*r,f=Math.ceil(f/r)*r,t):r<0&&(r=x(u=Math.ceil(u*r)/r,f=Math.floor(f*r)/r,t)),r>0?(i[o]=Math.floor(u/r)*r,i[a]=Math.ceil(f/r)*r,e(i)):r<0&&(i[o]=Math.ceil(u*r)/r,i[a]=Math.floor(f*r)/r,e(i)),n},n}function wh(t,n){var e,r=0,i=(t=t.slice()).length-1,o=t[r],a=t[i];return a<o&&(e=r,r=i,i=e,e=o,o=a,a=e),t[r]=n.floor(o),t[i]=n.ceil(a),t}function Mh(t,n){return(n=Math.log(n/t))?function(e){return Math.log(e/t)/n}:dh(n)}function Ah(t,n){return t<0?function(e){return-Math.pow(-n,e)*Math.pow(-t,1-e)}:function(e){return Math.pow(n,e)*Math.pow(t,1-e)}}function Th(t){return isFinite(t)?+("1e"+t):t<0?0:t}function Nh(t){return 10===t?Th:t===Math.E?Math.exp:function(n){return Math.pow(t,n)}}function Sh(t){return t===Math.E?Math.log:10===t&&Math.log10||2===t&&Math.log2||(t=Math.log(t),function(n){return Math.log(n)/t})}function Eh(t){return function(n){return-t(-n)}}function kh(t,n){return t<0?-Math.pow(-t,n):Math.pow(t,n)}function Ch(){var t=1,n=mh(function(n,e){return(e=kh(e,t)-(n=kh(n,t)))?function(r){return(kh(r,t)-n)/e}:dh(e)},function(n,e){return e=kh(e,t)-(n=kh(n,t)),function(r){return kh(n+e*r,1/t)}}),e=n.domain;return n.exponent=function(n){return arguments.length?(t=+n,e(e())):t},n.copy=function(){return bh(n,Ch().exponent(t))},xh(n)}var Ph=new Date,zh=new Date;function Rh(t,n,e,r){function i(n){return t(n=new Date(+n)),n}return i.floor=i,i.ceil=function(e){return t(e=new Date(e-1)),n(e,1),t(e),e},i.round=function(t){var n=i(t),e=i.ceil(t);return t-n<e-t?n:e},i.offset=function(t,e){return n(t=new Date(+t),null==e?1:Math.floor(e)),t},i.range=function(e,r,o){var a,u=[];if(e=i.ceil(e),o=null==o?1:Math.floor(o),!(e<r&&o>0))return u;do{u.push(a=new Date(+e)),n(e,o),t(e)}while(a<e&&e<r);return u},i.filter=function(e){return Rh(function(n){if(n>=n)for(;t(n),!e(n);)n.setTime(n-1)},function(t,r){if(t>=t)if(r<0)for(;++r<=0;)for(;n(t,-1),!e(t););else for(;--r>=0;)for(;n(t,1),!e(t););})},e&&(i.count=function(n,r){return Ph.setTime(+n),zh.setTime(+r),t(Ph),t(zh),Math.floor(e(Ph,zh))},i.every=function(t){return t=Math.floor(t),isFinite(t)&&t>0?t>1?i.filter(r?function(n){return r(n)%t==0}:function(n){return i.count(0,n)%t==0}):i:null}),i}var Lh=Rh(function(){},function(t,n){t.setTime(+t+n)},function(t,n){return n-t});Lh.every=function(t){return t=Math.floor(t),isFinite(t)&&t>0?t>1?Rh(function(n){n.setTime(Math.floor(n/t)*t)},function(n,e){n.setTime(+n+e*t)},function(n,e){return(e-n)/t}):Lh:null};var Dh=Lh.range,Uh=6e4,qh=6048e5,Oh=Rh(function(t){t.setTime(1e3*Math.floor(t/1e3))},function(t,n){t.setTime(+t+1e3*n)},function(t,n){return(n-t)/1e3},function(t){return t.getUTCSeconds()}),Yh=Oh.range,Bh=Rh(function(t){t.setTime(Math.floor(t/Uh)*Uh)},function(t,n){t.setTime(+t+n*Uh)},function(t,n){return(n-t)/Uh},function(t){return t.getMinutes()}),Fh=Bh.range,Ih=Rh(function(t){var n=t.getTimezoneOffset()*Uh%36e5;n<0&&(n+=36e5),t.setTime(36e5*Math.floor((+t-n)/36e5)+n)},function(t,n){t.setTime(+t+36e5*n)},function(t,n){return(n-t)/36e5},function(t){return t.getHours()}),Hh=Ih.range,jh=Rh(function(t){t.setHours(0,0,0,0)},function(t,n){t.setDate(t.getDate()+n)},function(t,n){return(n-t-(n.getTimezoneOffset()-t.getTimezoneOffset())*Uh)/864e5},function(t){return t.getDate()-1}),Xh=jh.range;function Gh(t){return Rh(function(n){n.setDate(n.getDate()-(n.getDay()+7-t)%7),n.setHours(0,0,0,0)},function(t,n){t.setDate(t.getDate()+7*n)},function(t,n){return(n-t-(n.getTimezoneOffset()-t.getTimezoneOffset())*Uh)/qh})}var Vh=Gh(0),$h=Gh(1),Wh=Gh(2),Zh=Gh(3),Qh=Gh(4),Jh=Gh(5),Kh=Gh(6),td=Vh.range,nd=$h.range,ed=Wh.range,rd=Zh.range,id=Qh.range,od=Jh.range,ad=Kh.range,ud=Rh(function(t){t.setDate(1),t.setHours(0,0,0,0)},function(t,n){t.setMonth(t.getMonth()+n)},function(t,n){return n.getMonth()-t.getMonth()+12*(n.getFullYear()-t.getFullYear())},function(t){return t.getMonth()}),fd=ud.range,cd=Rh(function(t){t.setMonth(0,1),t.setHours(0,0,0,0)},function(t,n){t.setFullYear(t.getFullYear()+n)},function(t,n){return n.getFullYear()-t.getFullYear()},function(t){return t.getFullYear()});cd.every=function(t){return isFinite(t=Math.floor(t))&&t>0?Rh(function(n){n.setFullYear(Math.floor(n.getFullYear()/t)*t),n.setMonth(0,1),n.setHours(0,0,0,0)},function(n,e){n.setFullYear(n.getFullYear()+e*t)}):null};var sd=cd.range,ld=Rh(function(t){t.setUTCSeconds(0,0)},function(t,n){t.setTime(+t+n*Uh)},function(t,n){return(n-t)/Uh},function(t){return t.getUTCMinutes()}),hd=ld.range,dd=Rh(function(t){t.setUTCMinutes(0,0,0)},function(t,n){t.setTime(+t+36e5*n)},function(t,n){return(n-t)/36e5},function(t){return t.getUTCHours()}),pd=dd.range,vd=Rh(function(t){t.setUTCHours(0,0,0,0)},function(t,n){t.setUTCDate(t.getUTCDate()+n)},function(t,n){return(n-t)/864e5},function(t){return t.getUTCDate()-1}),gd=vd.range;function yd(t){return Rh(function(n){n.setUTCDate(n.getUTCDate()-(n.getUTCDay()+7-t)%7),n.setUTCHours(0,0,0,0)},function(t,n){t.setUTCDate(t.getUTCDate()+7*n)},function(t,n){return(n-t)/qh})}var _d=yd(0),bd=yd(1),md=yd(2),xd=yd(3),wd=yd(4),Md=yd(5),Ad=yd(6),Td=_d.range,Nd=bd.range,Sd=md.range,Ed=xd.range,kd=wd.range,Cd=Md.range,Pd=Ad.range,zd=Rh(function(t){t.setUTCDate(1),t.setUTCHours(0,0,0,0)},function(t,n){t.setUTCMonth(t.getUTCMonth()+n)},function(t,n){return n.getUTCMonth()-t.getUTCMonth()+12*(n.getUTCFullYear()-t.getUTCFullYear())},function(t){return t.getUTCMonth()}),Rd=zd.range,Ld=Rh(function(t){t.setUTCMonth(0,1),t.setUTCHours(0,0,0,0)},function(t,n){t.setUTCFullYear(t.getUTCFullYear()+n)},function(t,n){return n.getUTCFullYear()-t.getUTCFullYear()},function(t){return t.getUTCFullYear()});Ld.every=function(t){return isFinite(t=Math.floor(t))&&t>0?Rh(function(n){n.setUTCFullYear(Math.floor(n.getUTCFullYear()/t)*t),n.setUTCMonth(0,1),n.setUTCHours(0,0,0,0)},function(n,e){n.setUTCFullYear(n.getUTCFullYear()+e*t)}):null};var Dd=Ld.range;function Ud(t){if(0<=t.y&&t.y<100){var n=new Date(-1,t.m,t.d,t.H,t.M,t.S,t.L);return n.setFullYear(t.y),n}return new Date(t.y,t.m,t.d,t.H,t.M,t.S,t.L)}function qd(t){if(0<=t.y&&t.y<100){var n=new Date(Date.UTC(-1,t.m,t.d,t.H,t.M,t.S,t.L));return n.setUTCFullYear(t.y),n}return new Date(Date.UTC(t.y,t.m,t.d,t.H,t.M,t.S,t.L))}function Od(t){return{y:t,m:0,d:1,H:0,M:0,S:0,L:0}}function Yd(t){var n=t.dateTime,e=t.date,r=t.time,i=t.periods,o=t.days,a=t.shortDays,u=t.months,f=t.shortMonths,c=Vd(i),s=$d(i),l=Vd(o),h=$d(o),d=Vd(a),p=$d(a),v=Vd(u),g=$d(u),y=Vd(f),_=$d(f),b={a:function(t){return a[t.getDay()]},A:function(t){return o[t.getDay()]},b:function(t){return f[t.getMonth()]},B:function(t){return u[t.getMonth()]},c:null,d:pp,e:pp,f:bp,H:vp,I:gp,j:yp,L:_p,m:mp,M:xp,p:function(t){return i[+(t.getHours()>=12)]},Q:Wp,s:Zp,S:wp,u:Mp,U:Ap,V:Tp,w:Np,W:Sp,x:null,X:null,y:Ep,Y:kp,Z:Cp,"%":$p},m={a:function(t){return a[t.getUTCDay()]},A:function(t){return o[t.getUTCDay()]},b:function(t){return f[t.getUTCMonth()]},B:function(t){return u[t.getUTCMonth()]},c:null,d:Pp,e:Pp,f:Up,H:zp,I:Rp,j:Lp,L:Dp,m:qp,M:Op,p:function(t){return i[+(t.getUTCHours()>=12)]},Q:Wp,s:Zp,S:Yp,u:Bp,U:Fp,V:Ip,w:Hp,W:jp,x:null,X:null,y:Xp,Y:Gp,Z:Vp,"%":$p},x={a:function(t,n,e){var r=d.exec(n.slice(e));return r?(t.w=p[r[0].toLowerCase()],e+r[0].length):-1},A:function(t,n,e){var r=l.exec(n.slice(e));return r?(t.w=h[r[0].toLowerCase()],e+r[0].length):-1},b:function(t,n,e){var r=y.exec(n.slice(e));return r?(t.m=_[r[0].toLowerCase()],e+r[0].length):-1},B:function(t,n,e){var r=v.exec(n.slice(e));return r?(t.m=g[r[0].toLowerCase()],e+r[0].length):-1},c:function(t,e,r){return A(t,n,e,r)},d:ip,e:ip,f:sp,H:ap,I:ap,j:op,L:cp,m:rp,M:up,p:function(t,n,e){var r=c.exec(n.slice(e));return r?(t.p=s[r[0].toLowerCase()],e+r[0].length):-1},Q:hp,s:dp,S:fp,u:Zd,U:Qd,V:Jd,w:Wd,W:Kd,x:function(t,n,r){return A(t,e,n,r)},X:function(t,n,e){return A(t,r,n,e)},y:np,Y:tp,Z:ep,"%":lp};function w(t,n){return function(e){var r,i,o,a=[],u=-1,f=0,c=t.length;for(e instanceof Date||(e=new Date(+e));++u<c;)37===t.charCodeAt(u)&&(a.push(t.slice(f,u)),null!=(i=Fd[r=t.charAt(++u)])?r=t.charAt(++u):i="e"===r?" ":"0",(o=n[r])&&(r=o(e,i)),a.push(r),f=u+1);return a.push(t.slice(f,u)),a.join("")}}function M(t,n){return function(e){var r,i,o=Od(1900);if(A(o,t,e+="",0)!=e.length)return null;if("Q"in o)return new Date(o.Q);if("p"in o&&(o.H=o.H%12+12*o.p),"V"in o){if(o.V<1||o.V>53)return null;"w"in o||(o.w=1),"Z"in o?(i=(r=qd(Od(o.y))).getUTCDay(),r=i>4||0===i?bd.ceil(r):bd(r),r=vd.offset(r,7*(o.V-1)),o.y=r.getUTCFullYear(),o.m=r.getUTCMonth(),o.d=r.getUTCDate()+(o.w+6)%7):(i=(r=n(Od(o.y))).getDay(),r=i>4||0===i?$h.ceil(r):$h(r),r=jh.offset(r,7*(o.V-1)),o.y=r.getFullYear(),o.m=r.getMonth(),o.d=r.getDate()+(o.w+6)%7)}else("W"in o||"U"in o)&&("w"in o||(o.w="u"in o?o.u%7:"W"in o?1:0),i="Z"in o?qd(Od(o.y)).getUTCDay():n(Od(o.y)).getDay(),o.m=0,o.d="W"in o?(o.w+6)%7+7*o.W-(i+5)%7:o.w+7*o.U-(i+6)%7);return"Z"in o?(o.H+=o.Z/100|0,o.M+=o.Z%100,qd(o)):n(o)}}function A(t,n,e,r){for(var i,o,a=0,u=n.length,f=e.length;a<u;){if(r>=f)return-1;if(37===(i=n.charCodeAt(a++))){if(i=n.charAt(a++),!(o=x[i in Fd?n.charAt(a++):i])||(r=o(t,e,r))<0)return-1}else if(i!=e.charCodeAt(r++))return-1}return r}return b.x=w(e,b),b.X=w(r,b),b.c=w(n,b),m.x=w(e,m),m.X=w(r,m),m.c=w(n,m),{format:function(t){var n=w(t+="",b);return n.toString=function(){return t},n},parse:function(t){var n=M(t+="",Ud);return n.toString=function(){return t},n},utcFormat:function(t){var n=w(t+="",m);return n.toString=function(){return t},n},utcParse:function(t){var n=M(t,qd);return n.toString=function(){return t},n}}}var Bd,Fd={"-":"",_:" ",0:"0"},Id=/^\s*\d+/,Hd=/^%/,jd=/[\\^$*+?|[\]().{}]/g;function Xd(t,n,e){var r=t<0?"-":"",i=(r?-t:t)+"",o=i.length;return r+(o<e?new Array(e-o+1).join(n)+i:i)}function Gd(t){return t.replace(jd,"\\$&")}function Vd(t){return new RegExp("^(?:"+t.map(Gd).join("|")+")","i")}function $d(t){for(var n={},e=-1,r=t.length;++e<r;)n[t[e].toLowerCase()]=e;return n}function Wd(t,n,e){var r=Id.exec(n.slice(e,e+1));return r?(t.w=+r[0],e+r[0].length):-1}function Zd(t,n,e){var r=Id.exec(n.slice(e,e+1));return r?(t.u=+r[0],e+r[0].length):-1}function Qd(t,n,e){var r=Id.exec(n.slice(e,e+2));return r?(t.U=+r[0],e+r[0].length):-1}function Jd(t,n,e){var r=Id.exec(n.slice(e,e+2));return r?(t.V=+r[0],e+r[0].length):-1}function Kd(t,n,e){var r=Id.exec(n.slice(e,e+2));return r?(t.W=+r[0],e+r[0].length):-1}function tp(t,n,e){var r=Id.exec(n.slice(e,e+4));return r?(t.y=+r[0],e+r[0].length):-1}function np(t,n,e){var r=Id.exec(n.slice(e,e+2));return r?(t.y=+r[0]+(+r[0]>68?1900:2e3),e+r[0].length):-1}function ep(t,n,e){var r=/^(Z)|([+-]\d\d)(?::?(\d\d))?/.exec(n.slice(e,e+6));return r?(t.Z=r[1]?0:-(r[2]+(r[3]||"00")),e+r[0].length):-1}function rp(t,n,e){var r=Id.exec(n.slice(e,e+2));return r?(t.m=r[0]-1,e+r[0].length):-1}function ip(t,n,e){var r=Id.exec(n.slice(e,e+2));return r?(t.d=+r[0],e+r[0].length):-1}function op(t,n,e){var r=Id.exec(n.slice(e,e+3));return r?(t.m=0,t.d=+r[0],e+r[0].length):-1}function ap(t,n,e){var r=Id.exec(n.slice(e,e+2));return r?(t.H=+r[0],e+r[0].length):-1}function up(t,n,e){var r=Id.exec(n.slice(e,e+2));return r?(t.M=+r[0],e+r[0].length):-1}function fp(t,n,e){var r=Id.exec(n.slice(e,e+2));return r?(t.S=+r[0],e+r[0].length):-1}function cp(t,n,e){var r=Id.exec(n.slice(e,e+3));return r?(t.L=+r[0],e+r[0].length):-1}function sp(t,n,e){var r=Id.exec(n.slice(e,e+6));return r?(t.L=Math.floor(r[0]/1e3),e+r[0].length):-1}function lp(t,n,e){var r=Hd.exec(n.slice(e,e+1));return r?e+r[0].length:-1}function hp(t,n,e){var r=Id.exec(n.slice(e));return r?(t.Q=+r[0],e+r[0].length):-1}function dp(t,n,e){var r=Id.exec(n.slice(e));return r?(t.Q=1e3*+r[0],e+r[0].length):-1}function pp(t,n){return Xd(t.getDate(),n,2)}function vp(t,n){return Xd(t.getHours(),n,2)}function gp(t,n){return Xd(t.getHours()%12||12,n,2)}function yp(t,n){return Xd(1+jh.count(cd(t),t),n,3)}function _p(t,n){return Xd(t.getMilliseconds(),n,3)}function bp(t,n){return _p(t,n)+"000"}function mp(t,n){return Xd(t.getMonth()+1,n,2)}function xp(t,n){return Xd(t.getMinutes(),n,2)}function wp(t,n){return Xd(t.getSeconds(),n,2)}function Mp(t){var n=t.getDay();return 0===n?7:n}function Ap(t,n){return Xd(Vh.count(cd(t),t),n,2)}function Tp(t,n){var e=t.getDay();return t=e>=4||0===e?Qh(t):Qh.ceil(t),Xd(Qh.count(cd(t),t)+(4===cd(t).getDay()),n,2)}function Np(t){return t.getDay()}function Sp(t,n){return Xd($h.count(cd(t),t),n,2)}function Ep(t,n){return Xd(t.getFullYear()%100,n,2)}function kp(t,n){return Xd(t.getFullYear()%1e4,n,4)}function Cp(t){var n=t.getTimezoneOffset();return(n>0?"-":(n*=-1,"+"))+Xd(n/60|0,"0",2)+Xd(n%60,"0",2)}function Pp(t,n){return Xd(t.getUTCDate(),n,2)}function zp(t,n){return Xd(t.getUTCHours(),n,2)}function Rp(t,n){return Xd(t.getUTCHours()%12||12,n,2)}function Lp(t,n){return Xd(1+vd.count(Ld(t),t),n,3)}function Dp(t,n){return Xd(t.getUTCMilliseconds(),n,3)}function Up(t,n){return Dp(t,n)+"000"}function qp(t,n){return Xd(t.getUTCMonth()+1,n,2)}function Op(t,n){return Xd(t.getUTCMinutes(),n,2)}function Yp(t,n){return Xd(t.getUTCSeconds(),n,2)}function Bp(t){var n=t.getUTCDay();return 0===n?7:n}function Fp(t,n){return Xd(_d.count(Ld(t),t),n,2)}function Ip(t,n){var e=t.getUTCDay();return t=e>=4||0===e?wd(t):wd.ceil(t),Xd(wd.count(Ld(t),t)+(4===Ld(t).getUTCDay()),n,2)}function Hp(t){return t.getUTCDay()}function jp(t,n){return Xd(bd.count(Ld(t),t),n,2)}function Xp(t,n){return Xd(t.getUTCFullYear()%100,n,2)}function Gp(t,n){return Xd(t.getUTCFullYear()%1e4,n,4)}function Vp(){return"+0000"}function $p(){return"%"}function Wp(t){return+t}function Zp(t){return Math.floor(+t/1e3)}function Qp(n){return Bd=Yd(n),t.timeFormat=Bd.format,t.timeParse=Bd.parse,t.utcFormat=Bd.utcFormat,t.utcParse=Bd.utcParse,Bd}Qp({dateTime:"%x, %X",date:"%-m/%-d/%Y",time:"%-I:%M:%S %p",periods:["AM","PM"],days:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],shortDays:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],months:["January","February","March","April","May","June","July","August","September","October","November","December"],shortMonths:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]});var Jp=Date.prototype.toISOString?function(t){return t.toISOString()}:t.utcFormat("%Y-%m-%dT%H:%M:%S.%LZ");var Kp=+new Date("2000-01-01T00:00:00.000Z")?function(t){var n=new Date(t);return isNaN(n)?null:n}:t.utcParse("%Y-%m-%dT%H:%M:%S.%LZ"),tv=1e3,nv=60*tv,ev=60*nv,rv=24*ev,iv=7*rv,ov=30*rv,av=365*rv;function uv(t){return new Date(t)}function fv(t){return t instanceof Date?+t:+new Date(+t)}function cv(t,n,r,i,o,a,u,f,c){var s=mh(gh,ve),l=s.invert,h=s.domain,d=c(".%L"),p=c(":%S"),v=c("%I:%M"),g=c("%I %p"),y=c("%a %d"),_=c("%b %d"),b=c("%B"),m=c("%Y"),x=[[u,1,tv],[u,5,5*tv],[u,15,15*tv],[u,30,30*tv],[a,1,nv],[a,5,5*nv],[a,15,15*nv],[a,30,30*nv],[o,1,ev],[o,3,3*ev],[o,6,6*ev],[o,12,12*ev],[i,1,rv],[i,2,2*rv],[r,1,iv],[n,1,ov],[n,3,3*ov],[t,1,av]];function M(e){return(u(e)<e?d:a(e)<e?p:o(e)<e?v:i(e)<e?g:n(e)<e?r(e)<e?y:_:t(e)<e?b:m)(e)}function A(n,r,i,o){if(null==n&&(n=10),"number"==typeof n){var a=Math.abs(i-r)/n,u=e(function(t){return t[2]}).right(x,a);u===x.length?(o=w(r/av,i/av,n),n=t):u?(o=(u=x[a/x[u-1][2]<x[u][2]/a?u-1:u])[1],n=u[0]):(o=Math.max(w(r,i,n),1),n=f)}return null==o?n:n.every(o)}return s.invert=function(t){return new Date(l(t))},s.domain=function(t){return arguments.length?h(fh.call(t,fv)):h().map(uv)},s.ticks=function(t,n){var e,r=h(),i=r[0],o=r[r.length-1],a=o<i;return a&&(e=i,i=o,o=e),e=(e=A(t,i,o,n))?e.range(i,o+1):[],a?e.reverse():e},s.tickFormat=function(t,n){return null==n?M:c(n)},s.nice=function(t,n){var e=h();return(t=A(t,e[0],e[e.length-1],n))?h(wh(e,t)):s},s.copy=function(){return bh(s,cv(t,n,r,i,o,a,u,f,c))},s}function sv(t){for(var n=t.length/6|0,e=new Array(n),r=0;r<n;)e[r]="#"+t.slice(6*r,6*++r);return e}var lv=sv("1f77b4ff7f0e2ca02cd627289467bd8c564be377c27f7f7fbcbd2217becf"),hv=sv("7fc97fbeaed4fdc086ffff99386cb0f0027fbf5b17666666"),dv=sv("1b9e77d95f027570b3e7298a66a61ee6ab02a6761d666666"),pv=sv("a6cee31f78b4b2df8a33a02cfb9a99e31a1cfdbf6fff7f00cab2d66a3d9affff99b15928"),vv=sv("fbb4aeb3cde3ccebc5decbe4fed9a6ffffcce5d8bdfddaecf2f2f2"),gv=sv("b3e2cdfdcdaccbd5e8f4cae4e6f5c9fff2aef1e2cccccccc"),yv=sv("e41a1c377eb84daf4a984ea3ff7f00ffff33a65628f781bf999999"),_v=sv("66c2a5fc8d628da0cbe78ac3a6d854ffd92fe5c494b3b3b3"),bv=sv("8dd3c7ffffb3bebadafb807280b1d3fdb462b3de69fccde5d9d9d9bc80bdccebc5ffed6f");function mv(t){return le(t[t.length-1])}var xv=new Array(3).concat("d8b365f5f5f55ab4ac","a6611adfc27d80cdc1018571","a6611adfc27df5f5f580cdc1018571","8c510ad8b365f6e8c3c7eae55ab4ac01665e","8c510ad8b365f6e8c3f5f5f5c7eae55ab4ac01665e","8c510abf812ddfc27df6e8c3c7eae580cdc135978f01665e","8c510abf812ddfc27df6e8c3f5f5f5c7eae580cdc135978f01665e","5430058c510abf812ddfc27df6e8c3c7eae580cdc135978f01665e003c30","5430058c510abf812ddfc27df6e8c3f5f5f5c7eae580cdc135978f01665e003c30").map(sv),wv=mv(xv),Mv=new Array(3).concat("af8dc3f7f7f77fbf7b","7b3294c2a5cfa6dba0008837","7b3294c2a5cff7f7f7a6dba0008837","762a83af8dc3e7d4e8d9f0d37fbf7b1b7837","762a83af8dc3e7d4e8f7f7f7d9f0d37fbf7b1b7837","762a839970abc2a5cfe7d4e8d9f0d3a6dba05aae611b7837","762a839970abc2a5cfe7d4e8f7f7f7d9f0d3a6dba05aae611b7837","40004b762a839970abc2a5cfe7d4e8d9f0d3a6dba05aae611b783700441b","40004b762a839970abc2a5cfe7d4e8f7f7f7d9f0d3a6dba05aae611b783700441b").map(sv),Av=mv(Mv),Tv=new Array(3).concat("e9a3c9f7f7f7a1d76a","d01c8bf1b6dab8e1864dac26","d01c8bf1b6daf7f7f7b8e1864dac26","c51b7de9a3c9fde0efe6f5d0a1d76a4d9221","c51b7de9a3c9fde0eff7f7f7e6f5d0a1d76a4d9221","c51b7dde77aef1b6dafde0efe6f5d0b8e1867fbc414d9221","c51b7dde77aef1b6dafde0eff7f7f7e6f5d0b8e1867fbc414d9221","8e0152c51b7dde77aef1b6dafde0efe6f5d0b8e1867fbc414d9221276419","8e0152c51b7dde77aef1b6dafde0eff7f7f7e6f5d0b8e1867fbc414d9221276419").map(sv),Nv=mv(Tv),Sv=new Array(3).concat("998ec3f7f7f7f1a340","5e3c99b2abd2fdb863e66101","5e3c99b2abd2f7f7f7fdb863e66101","542788998ec3d8daebfee0b6f1a340b35806","542788998ec3d8daebf7f7f7fee0b6f1a340b35806","5427888073acb2abd2d8daebfee0b6fdb863e08214b35806","5427888073acb2abd2d8daebf7f7f7fee0b6fdb863e08214b35806","2d004b5427888073acb2abd2d8daebfee0b6fdb863e08214b358067f3b08","2d004b5427888073acb2abd2d8daebf7f7f7fee0b6fdb863e08214b358067f3b08").map(sv),Ev=mv(Sv),kv=new Array(3).concat("ef8a62f7f7f767a9cf","ca0020f4a58292c5de0571b0","ca0020f4a582f7f7f792c5de0571b0","b2182bef8a62fddbc7d1e5f067a9cf2166ac","b2182bef8a62fddbc7f7f7f7d1e5f067a9cf2166ac","b2182bd6604df4a582fddbc7d1e5f092c5de4393c32166ac","b2182bd6604df4a582fddbc7f7f7f7d1e5f092c5de4393c32166ac","67001fb2182bd6604df4a582fddbc7d1e5f092c5de4393c32166ac053061","67001fb2182bd6604df4a582fddbc7f7f7f7d1e5f092c5de4393c32166ac053061").map(sv),Cv=mv(kv),Pv=new Array(3).concat("ef8a62ffffff999999","ca0020f4a582bababa404040","ca0020f4a582ffffffbababa404040","b2182bef8a62fddbc7e0e0e09999994d4d4d","b2182bef8a62fddbc7ffffffe0e0e09999994d4d4d","b2182bd6604df4a582fddbc7e0e0e0bababa8787874d4d4d","b2182bd6604df4a582fddbc7ffffffe0e0e0bababa8787874d4d4d","67001fb2182bd6604df4a582fddbc7e0e0e0bababa8787874d4d4d1a1a1a","67001fb2182bd6604df4a582fddbc7ffffffe0e0e0bababa8787874d4d4d1a1a1a").map(sv),zv=mv(Pv),Rv=new Array(3).concat("fc8d59ffffbf91bfdb","d7191cfdae61abd9e92c7bb6","d7191cfdae61ffffbfabd9e92c7bb6","d73027fc8d59fee090e0f3f891bfdb4575b4","d73027fc8d59fee090ffffbfe0f3f891bfdb4575b4","d73027f46d43fdae61fee090e0f3f8abd9e974add14575b4","d73027f46d43fdae61fee090ffffbfe0f3f8abd9e974add14575b4","a50026d73027f46d43fdae61fee090e0f3f8abd9e974add14575b4313695","a50026d73027f46d43fdae61fee090ffffbfe0f3f8abd9e974add14575b4313695").map(sv),Lv=mv(Rv),Dv=new Array(3).concat("fc8d59ffffbf91cf60","d7191cfdae61a6d96a1a9641","d7191cfdae61ffffbfa6d96a1a9641","d73027fc8d59fee08bd9ef8b91cf601a9850","d73027fc8d59fee08bffffbfd9ef8b91cf601a9850","d73027f46d43fdae61fee08bd9ef8ba6d96a66bd631a9850","d73027f46d43fdae61fee08bffffbfd9ef8ba6d96a66bd631a9850","a50026d73027f46d43fdae61fee08bd9ef8ba6d96a66bd631a9850006837","a50026d73027f46d43fdae61fee08bffffbfd9ef8ba6d96a66bd631a9850006837").map(sv),Uv=mv(Dv),qv=new Array(3).concat("fc8d59ffffbf99d594","d7191cfdae61abdda42b83ba","d7191cfdae61ffffbfabdda42b83ba","d53e4ffc8d59fee08be6f59899d5943288bd","d53e4ffc8d59fee08bffffbfe6f59899d5943288bd","d53e4ff46d43fdae61fee08be6f598abdda466c2a53288bd","d53e4ff46d43fdae61fee08bffffbfe6f598abdda466c2a53288bd","9e0142d53e4ff46d43fdae61fee08be6f598abdda466c2a53288bd5e4fa2","9e0142d53e4ff46d43fdae61fee08bffffbfe6f598abdda466c2a53288bd5e4fa2").map(sv),Ov=mv(qv),Yv=new Array(3).concat("e5f5f999d8c92ca25f","edf8fbb2e2e266c2a4238b45","edf8fbb2e2e266c2a42ca25f006d2c","edf8fbccece699d8c966c2a42ca25f006d2c","edf8fbccece699d8c966c2a441ae76238b45005824","f7fcfde5f5f9ccece699d8c966c2a441ae76238b45005824","f7fcfde5f5f9ccece699d8c966c2a441ae76238b45006d2c00441b").map(sv),Bv=mv(Yv),Fv=new Array(3).concat("e0ecf49ebcda8856a7","edf8fbb3cde38c96c688419d","edf8fbb3cde38c96c68856a7810f7c","edf8fbbfd3e69ebcda8c96c68856a7810f7c","edf8fbbfd3e69ebcda8c96c68c6bb188419d6e016b","f7fcfde0ecf4bfd3e69ebcda8c96c68c6bb188419d6e016b","f7fcfde0ecf4bfd3e69ebcda8c96c68c6bb188419d810f7c4d004b").map(sv),Iv=mv(Fv),Hv=new Array(3).concat("e0f3dba8ddb543a2ca","f0f9e8bae4bc7bccc42b8cbe","f0f9e8bae4bc7bccc443a2ca0868ac","f0f9e8ccebc5a8ddb57bccc443a2ca0868ac","f0f9e8ccebc5a8ddb57bccc44eb3d32b8cbe08589e","f7fcf0e0f3dbccebc5a8ddb57bccc44eb3d32b8cbe08589e","f7fcf0e0f3dbccebc5a8ddb57bccc44eb3d32b8cbe0868ac084081").map(sv),jv=mv(Hv),Xv=new Array(3).concat("fee8c8fdbb84e34a33","fef0d9fdcc8afc8d59d7301f","fef0d9fdcc8afc8d59e34a33b30000","fef0d9fdd49efdbb84fc8d59e34a33b30000","fef0d9fdd49efdbb84fc8d59ef6548d7301f990000","fff7ecfee8c8fdd49efdbb84fc8d59ef6548d7301f990000","fff7ecfee8c8fdd49efdbb84fc8d59ef6548d7301fb300007f0000").map(sv),Gv=mv(Xv),Vv=new Array(3).concat("ece2f0a6bddb1c9099","f6eff7bdc9e167a9cf02818a","f6eff7bdc9e167a9cf1c9099016c59","f6eff7d0d1e6a6bddb67a9cf1c9099016c59","f6eff7d0d1e6a6bddb67a9cf3690c002818a016450","fff7fbece2f0d0d1e6a6bddb67a9cf3690c002818a016450","fff7fbece2f0d0d1e6a6bddb67a9cf3690c002818a016c59014636").map(sv),$v=mv(Vv),Wv=new Array(3).concat("ece7f2a6bddb2b8cbe","f1eef6bdc9e174a9cf0570b0","f1eef6bdc9e174a9cf2b8cbe045a8d","f1eef6d0d1e6a6bddb74a9cf2b8cbe045a8d","f1eef6d0d1e6a6bddb74a9cf3690c00570b0034e7b","fff7fbece7f2d0d1e6a6bddb74a9cf3690c00570b0034e7b","fff7fbece7f2d0d1e6a6bddb74a9cf3690c00570b0045a8d023858").map(sv),Zv=mv(Wv),Qv=new Array(3).concat("e7e1efc994c7dd1c77","f1eef6d7b5d8df65b0ce1256","f1eef6d7b5d8df65b0dd1c77980043","f1eef6d4b9dac994c7df65b0dd1c77980043","f1eef6d4b9dac994c7df65b0e7298ace125691003f","f7f4f9e7e1efd4b9dac994c7df65b0e7298ace125691003f","f7f4f9e7e1efd4b9dac994c7df65b0e7298ace125698004367001f").map(sv),Jv=mv(Qv),Kv=new Array(3).concat("fde0ddfa9fb5c51b8a","feebe2fbb4b9f768a1ae017e","feebe2fbb4b9f768a1c51b8a7a0177","feebe2fcc5c0fa9fb5f768a1c51b8a7a0177","feebe2fcc5c0fa9fb5f768a1dd3497ae017e7a0177","fff7f3fde0ddfcc5c0fa9fb5f768a1dd3497ae017e7a0177","fff7f3fde0ddfcc5c0fa9fb5f768a1dd3497ae017e7a017749006a").map(sv),tg=mv(Kv),ng=new Array(3).concat("edf8b17fcdbb2c7fb8","ffffcca1dab441b6c4225ea8","ffffcca1dab441b6c42c7fb8253494","ffffccc7e9b47fcdbb41b6c42c7fb8253494","ffffccc7e9b47fcdbb41b6c41d91c0225ea80c2c84","ffffd9edf8b1c7e9b47fcdbb41b6c41d91c0225ea80c2c84","ffffd9edf8b1c7e9b47fcdbb41b6c41d91c0225ea8253494081d58").map(sv),eg=mv(ng),rg=new Array(3).concat("f7fcb9addd8e31a354","ffffccc2e69978c679238443","ffffccc2e69978c67931a354006837","ffffccd9f0a3addd8e78c67931a354006837","ffffccd9f0a3addd8e78c67941ab5d238443005a32","ffffe5f7fcb9d9f0a3addd8e78c67941ab5d238443005a32","ffffe5f7fcb9d9f0a3addd8e78c67941ab5d238443006837004529").map(sv),ig=mv(rg),og=new Array(3).concat("fff7bcfec44fd95f0e","ffffd4fed98efe9929cc4c02","ffffd4fed98efe9929d95f0e993404","ffffd4fee391fec44ffe9929d95f0e993404","ffffd4fee391fec44ffe9929ec7014cc4c028c2d04","ffffe5fff7bcfee391fec44ffe9929ec7014cc4c028c2d04","ffffe5fff7bcfee391fec44ffe9929ec7014cc4c02993404662506").map(sv),ag=mv(og),ug=new Array(3).concat("ffeda0feb24cf03b20","ffffb2fecc5cfd8d3ce31a1c","ffffb2fecc5cfd8d3cf03b20bd0026","ffffb2fed976feb24cfd8d3cf03b20bd0026","ffffb2fed976feb24cfd8d3cfc4e2ae31a1cb10026","ffffccffeda0fed976feb24cfd8d3cfc4e2ae31a1cb10026","ffffccffeda0fed976feb24cfd8d3cfc4e2ae31a1cbd0026800026").map(sv),fg=mv(ug),cg=new Array(3).concat("deebf79ecae13182bd","eff3ffbdd7e76baed62171b5","eff3ffbdd7e76baed63182bd08519c","eff3ffc6dbef9ecae16baed63182bd08519c","eff3ffc6dbef9ecae16baed64292c62171b5084594","f7fbffdeebf7c6dbef9ecae16baed64292c62171b5084594","f7fbffdeebf7c6dbef9ecae16baed64292c62171b508519c08306b").map(sv),sg=mv(cg),lg=new Array(3).concat("e5f5e0a1d99b31a354","edf8e9bae4b374c476238b45","edf8e9bae4b374c47631a354006d2c","edf8e9c7e9c0a1d99b74c47631a354006d2c","edf8e9c7e9c0a1d99b74c47641ab5d238b45005a32","f7fcf5e5f5e0c7e9c0a1d99b74c47641ab5d238b45005a32","f7fcf5e5f5e0c7e9c0a1d99b74c47641ab5d238b45006d2c00441b").map(sv),hg=mv(lg),dg=new Array(3).concat("f0f0f0bdbdbd636363","f7f7f7cccccc969696525252","f7f7f7cccccc969696636363252525","f7f7f7d9d9d9bdbdbd969696636363252525","f7f7f7d9d9d9bdbdbd969696737373525252252525","fffffff0f0f0d9d9d9bdbdbd969696737373525252252525","fffffff0f0f0d9d9d9bdbdbd969696737373525252252525000000").map(sv),pg=mv(dg),vg=new Array(3).concat("efedf5bcbddc756bb1","f2f0f7cbc9e29e9ac86a51a3","f2f0f7cbc9e29e9ac8756bb154278f","f2f0f7dadaebbcbddc9e9ac8756bb154278f","f2f0f7dadaebbcbddc9e9ac8807dba6a51a34a1486","fcfbfdefedf5dadaebbcbddc9e9ac8807dba6a51a34a1486","fcfbfdefedf5dadaebbcbddc9e9ac8807dba6a51a354278f3f007d").map(sv),gg=mv(vg),yg=new Array(3).concat("fee0d2fc9272de2d26","fee5d9fcae91fb6a4acb181d","fee5d9fcae91fb6a4ade2d26a50f15","fee5d9fcbba1fc9272fb6a4ade2d26a50f15","fee5d9fcbba1fc9272fb6a4aef3b2ccb181d99000d","fff5f0fee0d2fcbba1fc9272fb6a4aef3b2ccb181d99000d","fff5f0fee0d2fcbba1fc9272fb6a4aef3b2ccb181da50f1567000d").map(sv),_g=mv(yg),bg=new Array(3).concat("fee6cefdae6be6550d","feeddefdbe85fd8d3cd94701","feeddefdbe85fd8d3ce6550da63603","feeddefdd0a2fdae6bfd8d3ce6550da63603","feeddefdd0a2fdae6bfd8d3cf16913d948018c2d04","fff5ebfee6cefdd0a2fdae6bfd8d3cf16913d948018c2d04","fff5ebfee6cefdd0a2fdae6bfd8d3cf16913d94801a636037f2704").map(sv),mg=mv(bg),xg=Ge(Kn(300,.5,0),Kn(-240,.5,1)),wg=Ge(Kn(-100,.75,.35),Kn(80,1.5,.8)),Mg=Ge(Kn(260,.75,.35),Kn(80,1.5,.8)),Ag=Kn();var Tg=bn(),Ng=Math.PI/3,Sg=2*Math.PI/3;function Eg(t){var n=t.length;return function(e){return t[Math.max(0,Math.min(n-1,Math.floor(e*n)))]}}var kg=Eg(sv("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")),Cg=Eg(sv("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")),Pg=Eg(sv("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")),zg=Eg(sv("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"));function Rg(t){return function(){return t}}var Lg=Math.abs,Dg=Math.atan2,Ug=Math.cos,qg=Math.max,Og=Math.min,Yg=Math.sin,Bg=Math.sqrt,Fg=1e-12,Ig=Math.PI,Hg=Ig/2,jg=2*Ig;function Xg(t){return t>=1?Hg:t<=-1?-Hg:Math.asin(t)}function Gg(t){return t.innerRadius}function Vg(t){return t.outerRadius}function $g(t){return t.startAngle}function Wg(t){return t.endAngle}function Zg(t){return t&&t.padAngle}function Qg(t,n,e,r,i,o,a){var u=t-e,f=n-r,c=(a?o:-o)/Bg(u*u+f*f),s=c*f,l=-c*u,h=t+s,d=n+l,p=e+s,v=r+l,g=(h+p)/2,y=(d+v)/2,_=p-h,b=v-d,m=_*_+b*b,x=i-o,w=h*v-p*d,M=(b<0?-1:1)*Bg(qg(0,x*x*m-w*w)),A=(w*b-_*M)/m,T=(-w*_-b*M)/m,N=(w*b+_*M)/m,S=(-w*_+b*M)/m,E=A-g,k=T-y,C=N-g,P=S-y;return E*E+k*k>C*C+P*P&&(A=N,T=S),{cx:A,cy:T,x01:-s,y01:-l,x11:A*(i/x-1),y11:T*(i/x-1)}}function Jg(t){this._context=t}function Kg(t){return new Jg(t)}function ty(t){return t[0]}function ny(t){return t[1]}function ey(){var t=ty,n=ny,e=Rg(!0),r=null,i=Kg,o=null;function a(a){var u,f,c,s=a.length,l=!1;for(null==r&&(o=i(c=Gi())),u=0;u<=s;++u)!(u<s&&e(f=a[u],u,a))===l&&((l=!l)?o.lineStart():o.lineEnd()),l&&o.point(+t(f,u,a),+n(f,u,a));if(c)return o=null,c+""||null}return a.x=function(n){return arguments.length?(t="function"==typeof n?n:Rg(+n),a):t},a.y=function(t){return arguments.length?(n="function"==typeof t?t:Rg(+t),a):n},a.defined=function(t){return arguments.length?(e="function"==typeof t?t:Rg(!!t),a):e},a.curve=function(t){return arguments.length?(i=t,null!=r&&(o=i(r)),a):i},a.context=function(t){return arguments.length?(null==t?r=o=null:o=i(r=t),a):r},a}function ry(){var t=ty,n=null,e=Rg(0),r=ny,i=Rg(!0),o=null,a=Kg,u=null;function f(f){var c,s,l,h,d,p=f.length,v=!1,g=new Array(p),y=new Array(p);for(null==o&&(u=a(d=Gi())),c=0;c<=p;++c){if(!(c<p&&i(h=f[c],c,f))===v)if(v=!v)s=c,u.areaStart(),u.lineStart();else{for(u.lineEnd(),u.lineStart(),l=c-1;l>=s;--l)u.point(g[l],y[l]);u.lineEnd(),u.areaEnd()}v&&(g[c]=+t(h,c,f),y[c]=+e(h,c,f),u.point(n?+n(h,c,f):g[c],r?+r(h,c,f):y[c]))}if(d)return u=null,d+""||null}function c(){return ey().defined(i).curve(a).context(o)}return f.x=function(e){return arguments.length?(t="function"==typeof e?e:Rg(+e),n=null,f):t},f.x0=function(n){return arguments.length?(t="function"==typeof n?n:Rg(+n),f):t},f.x1=function(t){return arguments.length?(n=null==t?null:"function"==typeof t?t:Rg(+t),f):n},f.y=function(t){return arguments.length?(e="function"==typeof t?t:Rg(+t),r=null,f):e},f.y0=function(t){return arguments.length?(e="function"==typeof t?t:Rg(+t),f):e},f.y1=function(t){return arguments.length?(r=null==t?null:"function"==typeof t?t:Rg(+t),f):r},f.lineX0=f.lineY0=function(){return c().x(t).y(e)},f.lineY1=function(){return c().x(t).y(r)},f.lineX1=function(){return c().x(n).y(e)},f.defined=function(t){return arguments.length?(i="function"==typeof t?t:Rg(!!t),f):i},f.curve=function(t){return arguments.length?(a=t,null!=o&&(u=a(o)),f):a},f.context=function(t){return arguments.length?(null==t?o=u=null:u=a(o=t),f):o},f}function iy(t,n){return n<t?-1:n>t?1:n>=t?0:NaN}function oy(t){return t}Jg.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._point=0},lineEnd:function(){(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._line?this._context.lineTo(t,n):this._context.moveTo(t,n);break;case 1:this._point=2;default:this._context.lineTo(t,n)}}};var ay=fy(Kg);function uy(t){this._curve=t}function fy(t){function n(n){return new uy(t(n))}return n._curve=t,n}function cy(t){var n=t.curve;return t.angle=t.x,delete t.x,t.radius=t.y,delete t.y,t.curve=function(t){return arguments.length?n(fy(t)):n()._curve},t}function sy(){return cy(ey().curve(ay))}function ly(){var t=ry().curve(ay),n=t.curve,e=t.lineX0,r=t.lineX1,i=t.lineY0,o=t.lineY1;return t.angle=t.x,delete t.x,t.startAngle=t.x0,delete t.x0,t.endAngle=t.x1,delete t.x1,t.radius=t.y,delete t.y,t.innerRadius=t.y0,delete t.y0,t.outerRadius=t.y1,delete t.y1,t.lineStartAngle=function(){return cy(e())},delete t.lineX0,t.lineEndAngle=function(){return cy(r())},delete t.lineX1,t.lineInnerRadius=function(){return cy(i())},delete t.lineY0,t.lineOuterRadius=function(){return cy(o())},delete t.lineY1,t.curve=function(t){return arguments.length?n(fy(t)):n()._curve},t}function hy(t,n){return[(n=+n)*Math.cos(t-=Math.PI/2),n*Math.sin(t)]}uy.prototype={areaStart:function(){this._curve.areaStart()},areaEnd:function(){this._curve.areaEnd()},lineStart:function(){this._curve.lineStart()},lineEnd:function(){this._curve.lineEnd()},point:function(t,n){this._curve.point(n*Math.sin(t),n*-Math.cos(t))}};var dy=Array.prototype.slice;function py(t){return t.source}function vy(t){return t.target}function gy(t){var n=py,e=vy,r=ty,i=ny,o=null;function a(){var a,u=dy.call(arguments),f=n.apply(this,u),c=e.apply(this,u);if(o||(o=a=Gi()),t(o,+r.apply(this,(u[0]=f,u)),+i.apply(this,u),+r.apply(this,(u[0]=c,u)),+i.apply(this,u)),a)return o=null,a+""||null}return a.source=function(t){return arguments.length?(n=t,a):n},a.target=function(t){return arguments.length?(e=t,a):e},a.x=function(t){return arguments.length?(r="function"==typeof t?t:Rg(+t),a):r},a.y=function(t){return arguments.length?(i="function"==typeof t?t:Rg(+t),a):i},a.context=function(t){return arguments.length?(o=null==t?null:t,a):o},a}function yy(t,n,e,r,i){t.moveTo(n,e),t.bezierCurveTo(n=(n+r)/2,e,n,i,r,i)}function _y(t,n,e,r,i){t.moveTo(n,e),t.bezierCurveTo(n,e=(e+i)/2,r,e,r,i)}function by(t,n,e,r,i){var o=hy(n,e),a=hy(n,e=(e+i)/2),u=hy(r,e),f=hy(r,i);t.moveTo(o[0],o[1]),t.bezierCurveTo(a[0],a[1],u[0],u[1],f[0],f[1])}var my={draw:function(t,n){var e=Math.sqrt(n/Ig);t.moveTo(e,0),t.arc(0,0,e,0,jg)}},xy={draw:function(t,n){var e=Math.sqrt(n/5)/2;t.moveTo(-3*e,-e),t.lineTo(-e,-e),t.lineTo(-e,-3*e),t.lineTo(e,-3*e),t.lineTo(e,-e),t.lineTo(3*e,-e),t.lineTo(3*e,e),t.lineTo(e,e),t.lineTo(e,3*e),t.lineTo(-e,3*e),t.lineTo(-e,e),t.lineTo(-3*e,e),t.closePath()}},wy=Math.sqrt(1/3),My=2*wy,Ay={draw:function(t,n){var e=Math.sqrt(n/My),r=e*wy;t.moveTo(0,-e),t.lineTo(r,0),t.lineTo(0,e),t.lineTo(-r,0),t.closePath()}},Ty=Math.sin(Ig/10)/Math.sin(7*Ig/10),Ny=Math.sin(jg/10)*Ty,Sy=-Math.cos(jg/10)*Ty,Ey={draw:function(t,n){var e=Math.sqrt(.8908130915292852*n),r=Ny*e,i=Sy*e;t.moveTo(0,-e),t.lineTo(r,i);for(var o=1;o<5;++o){var a=jg*o/5,u=Math.cos(a),f=Math.sin(a);t.lineTo(f*e,-u*e),t.lineTo(u*r-f*i,f*r+u*i)}t.closePath()}},ky={draw:function(t,n){var e=Math.sqrt(n),r=-e/2;t.rect(r,r,e,e)}},Cy=Math.sqrt(3),Py={draw:function(t,n){var e=-Math.sqrt(n/(3*Cy));t.moveTo(0,2*e),t.lineTo(-Cy*e,-e),t.lineTo(Cy*e,-e),t.closePath()}},zy=Math.sqrt(3)/2,Ry=1/Math.sqrt(12),Ly=3*(Ry/2+1),Dy={draw:function(t,n){var e=Math.sqrt(n/Ly),r=e/2,i=e*Ry,o=r,a=e*Ry+e,u=-o,f=a;t.moveTo(r,i),t.lineTo(o,a),t.lineTo(u,f),t.lineTo(-.5*r-zy*i,zy*r+-.5*i),t.lineTo(-.5*o-zy*a,zy*o+-.5*a),t.lineTo(-.5*u-zy*f,zy*u+-.5*f),t.lineTo(-.5*r+zy*i,-.5*i-zy*r),t.lineTo(-.5*o+zy*a,-.5*a-zy*o),t.lineTo(-.5*u+zy*f,-.5*f-zy*u),t.closePath()}},Uy=[my,xy,Ay,ky,Ey,Py,Dy];function qy(){}function Oy(t,n,e){t._context.bezierCurveTo((2*t._x0+t._x1)/3,(2*t._y0+t._y1)/3,(t._x0+2*t._x1)/3,(t._y0+2*t._y1)/3,(t._x0+4*t._x1+n)/6,(t._y0+4*t._y1+e)/6)}function Yy(t){this._context=t}function By(t){this._context=t}function Fy(t){this._context=t}function Iy(t,n){this._basis=new Yy(t),this._beta=n}Yy.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){switch(this._point){case 3:Oy(this,this._x1,this._y1);case 2:this._context.lineTo(this._x1,this._y1)}(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._line?this._context.lineTo(t,n):this._context.moveTo(t,n);break;case 1:this._point=2;break;case 2:this._point=3,this._context.lineTo((5*this._x0+this._x1)/6,(5*this._y0+this._y1)/6);default:Oy(this,t,n)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=n}},By.prototype={areaStart:qy,areaEnd:qy,lineStart:function(){this._x0=this._x1=this._x2=this._x3=this._x4=this._y0=this._y1=this._y2=this._y3=this._y4=NaN,this._point=0},lineEnd:function(){switch(this._point){case 1:this._context.moveTo(this._x2,this._y2),this._context.closePath();break;case 2:this._context.moveTo((this._x2+2*this._x3)/3,(this._y2+2*this._y3)/3),this._context.lineTo((this._x3+2*this._x2)/3,(this._y3+2*this._y2)/3),this._context.closePath();break;case 3:this.point(this._x2,this._y2),this.point(this._x3,this._y3),this.point(this._x4,this._y4)}},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._x2=t,this._y2=n;break;case 1:this._point=2,this._x3=t,this._y3=n;break;case 2:this._point=3,this._x4=t,this._y4=n,this._context.moveTo((this._x0+4*this._x1+t)/6,(this._y0+4*this._y1+n)/6);break;default:Oy(this,t,n)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=n}},Fy.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3;var e=(this._x0+4*this._x1+t)/6,r=(this._y0+4*this._y1+n)/6;this._line?this._context.lineTo(e,r):this._context.moveTo(e,r);break;case 3:this._point=4;default:Oy(this,t,n)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=n}},Iy.prototype={lineStart:function(){this._x=[],this._y=[],this._basis.lineStart()},lineEnd:function(){var t=this._x,n=this._y,e=t.length-1;if(e>0)for(var r,i=t[0],o=n[0],a=t[e]-i,u=n[e]-o,f=-1;++f<=e;)r=f/e,this._basis.point(this._beta*t[f]+(1-this._beta)*(i+r*a),this._beta*n[f]+(1-this._beta)*(o+r*u));this._x=this._y=null,this._basis.lineEnd()},point:function(t,n){this._x.push(+t),this._y.push(+n)}};var Hy=function t(n){function e(t){return 1===n?new Yy(t):new Iy(t,n)}return e.beta=function(n){return t(+n)},e}(.85);function jy(t,n,e){t._context.bezierCurveTo(t._x1+t._k*(t._x2-t._x0),t._y1+t._k*(t._y2-t._y0),t._x2+t._k*(t._x1-n),t._y2+t._k*(t._y1-e),t._x2,t._y2)}function Xy(t,n){this._context=t,this._k=(1-n)/6}Xy.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x2,this._y2);break;case 3:jy(this,this._x1,this._y1)}(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._line?this._context.lineTo(t,n):this._context.moveTo(t,n);break;case 1:this._point=2,this._x1=t,this._y1=n;break;case 2:this._point=3;default:jy(this,t,n)}this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=n}};var Gy=function t(n){function e(t){return new Xy(t,n)}return e.tension=function(n){return t(+n)},e}(0);function Vy(t,n){this._context=t,this._k=(1-n)/6}Vy.prototype={areaStart:qy,areaEnd:qy,lineStart:function(){this._x0=this._x1=this._x2=this._x3=this._x4=this._x5=this._y0=this._y1=this._y2=this._y3=this._y4=this._y5=NaN,this._point=0},lineEnd:function(){switch(this._point){case 1:this._context.moveTo(this._x3,this._y3),this._context.closePath();break;case 2:this._context.lineTo(this._x3,this._y3),this._context.closePath();break;case 3:this.point(this._x3,this._y3),this.point(this._x4,this._y4),this.point(this._x5,this._y5)}},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._x3=t,this._y3=n;break;case 1:this._point=2,this._context.moveTo(this._x4=t,this._y4=n);break;case 2:this._point=3,this._x5=t,this._y5=n;break;default:jy(this,t,n)}this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=n}};var $y=function t(n){function e(t){return new Vy(t,n)}return e.tension=function(n){return t(+n)},e}(0);function Wy(t,n){this._context=t,this._k=(1-n)/6}Wy.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3,this._line?this._context.lineTo(this._x2,this._y2):this._context.moveTo(this._x2,this._y2);break;case 3:this._point=4;default:jy(this,t,n)}this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=n}};var Zy=function t(n){function e(t){return new Wy(t,n)}return e.tension=function(n){return t(+n)},e}(0);function Qy(t,n,e){var r=t._x1,i=t._y1,o=t._x2,a=t._y2;if(t._l01_a>Fg){var u=2*t._l01_2a+3*t._l01_a*t._l12_a+t._l12_2a,f=3*t._l01_a*(t._l01_a+t._l12_a);r=(r*u-t._x0*t._l12_2a+t._x2*t._l01_2a)/f,i=(i*u-t._y0*t._l12_2a+t._y2*t._l01_2a)/f}if(t._l23_a>Fg){var c=2*t._l23_2a+3*t._l23_a*t._l12_a+t._l12_2a,s=3*t._l23_a*(t._l23_a+t._l12_a);o=(o*c+t._x1*t._l23_2a-n*t._l12_2a)/s,a=(a*c+t._y1*t._l23_2a-e*t._l12_2a)/s}t._context.bezierCurveTo(r,i,o,a,t._x2,t._y2)}function Jy(t,n){this._context=t,this._alpha=n}Jy.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x2,this._y2);break;case 3:this.point(this._x2,this._y2)}(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){if(t=+t,n=+n,this._point){var e=this._x2-t,r=this._y2-n;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(e*e+r*r,this._alpha))}switch(this._point){case 0:this._point=1,this._line?this._context.lineTo(t,n):this._context.moveTo(t,n);break;case 1:this._point=2;break;case 2:this._point=3;default:Qy(this,t,n)}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=n}};var Ky=function t(n){function e(t){return n?new Jy(t,n):new Xy(t,0)}return e.alpha=function(n){return t(+n)},e}(.5);function t_(t,n){this._context=t,this._alpha=n}t_.prototype={areaStart:qy,areaEnd:qy,lineStart:function(){this._x0=this._x1=this._x2=this._x3=this._x4=this._x5=this._y0=this._y1=this._y2=this._y3=this._y4=this._y5=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){switch(this._point){case 1:this._context.moveTo(this._x3,this._y3),this._context.closePath();break;case 2:this._context.lineTo(this._x3,this._y3),this._context.closePath();break;case 3:this.point(this._x3,this._y3),this.point(this._x4,this._y4),this.point(this._x5,this._y5)}},point:function(t,n){if(t=+t,n=+n,this._point){var e=this._x2-t,r=this._y2-n;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(e*e+r*r,this._alpha))}switch(this._point){case 0:this._point=1,this._x3=t,this._y3=n;break;case 1:this._point=2,this._context.moveTo(this._x4=t,this._y4=n);break;case 2:this._point=3,this._x5=t,this._y5=n;break;default:Qy(this,t,n)}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=n}};var n_=function t(n){function e(t){return n?new t_(t,n):new Vy(t,0)}return e.alpha=function(n){return t(+n)},e}(.5);function e_(t,n){this._context=t,this._alpha=n}e_.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){if(t=+t,n=+n,this._point){var e=this._x2-t,r=this._y2-n;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(e*e+r*r,this._alpha))}switch(this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3,this._line?this._context.lineTo(this._x2,this._y2):this._context.moveTo(this._x2,this._y2);break;case 3:this._point=4;default:Qy(this,t,n)}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=n}};var r_=function t(n){function e(t){return n?new e_(t,n):new Wy(t,0)}return e.alpha=function(n){return t(+n)},e}(.5);function i_(t){this._context=t}function o_(t){return t<0?-1:1}function a_(t,n,e){var r=t._x1-t._x0,i=n-t._x1,o=(t._y1-t._y0)/(r||i<0&&-0),a=(e-t._y1)/(i||r<0&&-0),u=(o*i+a*r)/(r+i);return(o_(o)+o_(a))*Math.min(Math.abs(o),Math.abs(a),.5*Math.abs(u))||0}function u_(t,n){var e=t._x1-t._x0;return e?(3*(t._y1-t._y0)/e-n)/2:n}function f_(t,n,e){var r=t._x0,i=t._y0,o=t._x1,a=t._y1,u=(o-r)/3;t._context.bezierCurveTo(r+u,i+u*n,o-u,a-u*e,o,a)}function c_(t){this._context=t}function s_(t){this._context=new l_(t)}function l_(t){this._context=t}function h_(t){this._context=t}function d_(t){var n,e,r=t.length-1,i=new Array(r),o=new Array(r),a=new Array(r);for(i[0]=0,o[0]=2,a[0]=t[0]+2*t[1],n=1;n<r-1;++n)i[n]=1,o[n]=4,a[n]=4*t[n]+2*t[n+1];for(i[r-1]=2,o[r-1]=7,a[r-1]=8*t[r-1]+t[r],n=1;n<r;++n)e=i[n]/o[n-1],o[n]-=e,a[n]-=e*a[n-1];for(i[r-1]=a[r-1]/o[r-1],n=r-2;n>=0;--n)i[n]=(a[n]-i[n+1])/o[n];for(o[r-1]=(t[r]+i[r-1])/2,n=0;n<r-1;++n)o[n]=2*t[n+1]-i[n+1];return[i,o]}function p_(t,n){this._context=t,this._t=n}function v_(t,n){if((i=t.length)>1)for(var e,r,i,o=1,a=t[n[0]],u=a.length;o<i;++o)for(r=a,a=t[n[o]],e=0;e<u;++e)a[e][1]+=a[e][0]=isNaN(r[e][1])?r[e][0]:r[e][1]}function g_(t){for(var n=t.length,e=new Array(n);--n>=0;)e[n]=n;return e}function y_(t,n){return t[n]}function __(t){var n=t.map(b_);return g_(t).sort(function(t,e){return n[t]-n[e]})}function b_(t){for(var n,e=0,r=-1,i=t.length;++r<i;)(n=+t[r][1])&&(e+=n);return e}function m_(t){return function(){return t}}function x_(t){return t[0]}function w_(t){return t[1]}function M_(){this._=null}function A_(t){t.U=t.C=t.L=t.R=t.P=t.N=null}function T_(t,n){var e=n,r=n.R,i=e.U;i?i.L===e?i.L=r:i.R=r:t._=r,r.U=i,e.U=r,e.R=r.L,e.R&&(e.R.U=e),r.L=e}function N_(t,n){var e=n,r=n.L,i=e.U;i?i.L===e?i.L=r:i.R=r:t._=r,r.U=i,e.U=r,e.L=r.R,e.L&&(e.L.U=e),r.R=e}function S_(t){for(;t.L;)t=t.L;return t}function E_(t,n,e,r){var i=[null,null],o=J_.push(i)-1;return i.left=t,i.right=n,e&&C_(i,t,n,e),r&&C_(i,n,t,r),Z_[t.index].halfedges.push(o),Z_[n.index].halfedges.push(o),i}function k_(t,n,e){var r=[n,e];return r.left=t,r}function C_(t,n,e,r){t[0]||t[1]?t.left===e?t[1]=r:t[0]=r:(t[0]=r,t.left=n,t.right=e)}function P_(t,n,e,r,i){var o,a=t[0],u=t[1],f=a[0],c=a[1],s=0,l=1,h=u[0]-f,d=u[1]-c;if(o=n-f,h||!(o>0)){if(o/=h,h<0){if(o<s)return;o<l&&(l=o)}else if(h>0){if(o>l)return;o>s&&(s=o)}if(o=r-f,h||!(o<0)){if(o/=h,h<0){if(o>l)return;o>s&&(s=o)}else if(h>0){if(o<s)return;o<l&&(l=o)}if(o=e-c,d||!(o>0)){if(o/=d,d<0){if(o<s)return;o<l&&(l=o)}else if(d>0){if(o>l)return;o>s&&(s=o)}if(o=i-c,d||!(o<0)){if(o/=d,d<0){if(o>l)return;o>s&&(s=o)}else if(d>0){if(o<s)return;o<l&&(l=o)}return!(s>0||l<1)||(s>0&&(t[0]=[f+s*h,c+s*d]),l<1&&(t[1]=[f+l*h,c+l*d]),!0)}}}}}function z_(t,n,e,r,i){var o=t[1];if(o)return!0;var a,u,f=t[0],c=t.left,s=t.right,l=c[0],h=c[1],d=s[0],p=s[1],v=(l+d)/2,g=(h+p)/2;if(p===h){if(v<n||v>=r)return;if(l>d){if(f){if(f[1]>=i)return}else f=[v,e];o=[v,i]}else{if(f){if(f[1]<e)return}else f=[v,i];o=[v,e]}}else if(u=g-(a=(l-d)/(p-h))*v,a<-1||a>1)if(l>d){if(f){if(f[1]>=i)return}else f=[(e-u)/a,e];o=[(i-u)/a,i]}else{if(f){if(f[1]<e)return}else f=[(i-u)/a,i];o=[(e-u)/a,e]}else if(h<p){if(f){if(f[0]>=r)return}else f=[n,a*n+u];o=[r,a*r+u]}else{if(f){if(f[0]<n)return}else f=[r,a*r+u];o=[n,a*n+u]}return t[0]=f,t[1]=o,!0}function R_(t,n){var e=t.site,r=n.left,i=n.right;return e===i&&(i=r,r=e),i?Math.atan2(i[1]-r[1],i[0]-r[0]):(e===r?(r=n[1],i=n[0]):(r=n[0],i=n[1]),Math.atan2(r[0]-i[0],i[1]-r[1]))}function L_(t,n){return n[+(n.left!==t.site)]}function D_(t,n){return n[+(n.left===t.site)]}i_.prototype={areaStart:qy,areaEnd:qy,lineStart:function(){this._point=0},lineEnd:function(){this._point&&this._context.closePath()},point:function(t,n){t=+t,n=+n,this._point?this._context.lineTo(t,n):(this._point=1,this._context.moveTo(t,n))}},c_.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=this._t0=NaN,this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x1,this._y1);break;case 3:f_(this,this._t0,u_(this,this._t0))}(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){var e=NaN;if(n=+n,(t=+t)!==this._x1||n!==this._y1){switch(this._point){case 0:this._point=1,this._line?this._context.lineTo(t,n):this._context.moveTo(t,n);break;case 1:this._point=2;break;case 2:this._point=3,f_(this,u_(this,e=a_(this,t,n)),e);break;default:f_(this,this._t0,e=a_(this,t,n))}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=n,this._t0=e}}},(s_.prototype=Object.create(c_.prototype)).point=function(t,n){c_.prototype.point.call(this,n,t)},l_.prototype={moveTo:function(t,n){this._context.moveTo(n,t)},closePath:function(){this._context.closePath()},lineTo:function(t,n){this._context.lineTo(n,t)},bezierCurveTo:function(t,n,e,r,i,o){this._context.bezierCurveTo(n,t,r,e,o,i)}},h_.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x=[],this._y=[]},lineEnd:function(){var t=this._x,n=this._y,e=t.length;if(e)if(this._line?this._context.lineTo(t[0],n[0]):this._context.moveTo(t[0],n[0]),2===e)this._context.lineTo(t[1],n[1]);else for(var r=d_(t),i=d_(n),o=0,a=1;a<e;++o,++a)this._context.bezierCurveTo(r[0][o],i[0][o],r[1][o],i[1][o],t[a],n[a]);(this._line||0!==this._line&&1===e)&&this._context.closePath(),this._line=1-this._line,this._x=this._y=null},point:function(t,n){this._x.push(+t),this._y.push(+n)}},p_.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x=this._y=NaN,this._point=0},lineEnd:function(){0<this._t&&this._t<1&&2===this._point&&this._context.lineTo(this._x,this._y),(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line>=0&&(this._t=1-this._t,this._line=1-this._line)},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._line?this._context.lineTo(t,n):this._context.moveTo(t,n);break;case 1:this._point=2;default:if(this._t<=0)this._context.lineTo(this._x,n),this._context.lineTo(t,n);else{var e=this._x*(1-this._t)+t*this._t;this._context.lineTo(e,this._y),this._context.lineTo(e,n)}}this._x=t,this._y=n}},M_.prototype={constructor:M_,insert:function(t,n){var e,r,i;if(t){if(n.P=t,n.N=t.N,t.N&&(t.N.P=n),t.N=n,t.R){for(t=t.R;t.L;)t=t.L;t.L=n}else t.R=n;e=t}else this._?(t=S_(this._),n.P=null,n.N=t,t.P=t.L=n,e=t):(n.P=n.N=null,this._=n,e=null);for(n.L=n.R=null,n.U=e,n.C=!0,t=n;e&&e.C;)e===(r=e.U).L?(i=r.R)&&i.C?(e.C=i.C=!1,r.C=!0,t=r):(t===e.R&&(T_(this,e),e=(t=e).U),e.C=!1,r.C=!0,N_(this,r)):(i=r.L)&&i.C?(e.C=i.C=!1,r.C=!0,t=r):(t===e.L&&(N_(this,e),e=(t=e).U),e.C=!1,r.C=!0,T_(this,r)),e=t.U;this._.C=!1},remove:function(t){t.N&&(t.N.P=t.P),t.P&&(t.P.N=t.N),t.N=t.P=null;var n,e,r,i=t.U,o=t.L,a=t.R;if(e=o?a?S_(a):o:a,i?i.L===t?i.L=e:i.R=e:this._=e,o&&a?(r=e.C,e.C=t.C,e.L=o,o.U=e,e!==a?(i=e.U,e.U=t.U,t=e.R,i.L=t,e.R=a,a.U=e):(e.U=i,i=e,t=e.R)):(r=t.C,t=e),t&&(t.U=i),!r)if(t&&t.C)t.C=!1;else{do{if(t===this._)break;if(t===i.L){if((n=i.R).C&&(n.C=!1,i.C=!0,T_(this,i),n=i.R),n.L&&n.L.C||n.R&&n.R.C){n.R&&n.R.C||(n.L.C=!1,n.C=!0,N_(this,n),n=i.R),n.C=i.C,i.C=n.R.C=!1,T_(this,i),t=this._;break}}else if((n=i.L).C&&(n.C=!1,i.C=!0,N_(this,i),n=i.L),n.L&&n.L.C||n.R&&n.R.C){n.L&&n.L.C||(n.R.C=!1,n.C=!0,T_(this,n),n=i.L),n.C=i.C,i.C=n.L.C=!1,N_(this,i),t=this._;break}n.C=!0,t=i,i=i.U}while(!t.C);t&&(t.C=!1)}}};var U_,q_=[];function O_(){A_(this),this.x=this.y=this.arc=this.site=this.cy=null}function Y_(t){var n=t.P,e=t.N;if(n&&e){var r=n.site,i=t.site,o=e.site;if(r!==o){var a=i[0],u=i[1],f=r[0]-a,c=r[1]-u,s=o[0]-a,l=o[1]-u,h=2*(f*l-c*s);if(!(h>=-tb)){var d=f*f+c*c,p=s*s+l*l,v=(l*d-c*p)/h,g=(f*p-s*d)/h,y=q_.pop()||new O_;y.arc=t,y.site=i,y.x=v+a,y.y=(y.cy=g+u)+Math.sqrt(v*v+g*g),t.circle=y;for(var _=null,b=Q_._;b;)if(y.y<b.y||y.y===b.y&&y.x<=b.x){if(!b.L){_=b.P;break}b=b.L}else{if(!b.R){_=b;break}b=b.R}Q_.insert(_,y),_||(U_=y)}}}}function B_(t){var n=t.circle;n&&(n.P||(U_=n.N),Q_.remove(n),q_.push(n),A_(n),t.circle=null)}var F_=[];function I_(){A_(this),this.edge=this.site=this.circle=null}function H_(t){var n=F_.pop()||new I_;return n.site=t,n}function j_(t){B_(t),W_.remove(t),F_.push(t),A_(t)}function X_(t){var n=t.circle,e=n.x,r=n.cy,i=[e,r],o=t.P,a=t.N,u=[t];j_(t);for(var f=o;f.circle&&Math.abs(e-f.circle.x)<K_&&Math.abs(r-f.circle.cy)<K_;)o=f.P,u.unshift(f),j_(f),f=o;u.unshift(f),B_(f);for(var c=a;c.circle&&Math.abs(e-c.circle.x)<K_&&Math.abs(r-c.circle.cy)<K_;)a=c.N,u.push(c),j_(c),c=a;u.push(c),B_(c);var s,l=u.length;for(s=1;s<l;++s)c=u[s],f=u[s-1],C_(c.edge,f.site,c.site,i);f=u[0],(c=u[l-1]).edge=E_(f.site,c.site,null,i),Y_(f),Y_(c)}function G_(t){for(var n,e,r,i,o=t[0],a=t[1],u=W_._;u;)if((r=V_(u,a)-o)>K_)u=u.L;else{if(!((i=o-$_(u,a))>K_)){r>-K_?(n=u.P,e=u):i>-K_?(n=u,e=u.N):n=e=u;break}if(!u.R){n=u;break}u=u.R}!function(t){Z_[t.index]={site:t,halfedges:[]}}(t);var f=H_(t);if(W_.insert(n,f),n||e){if(n===e)return B_(n),e=H_(n.site),W_.insert(f,e),f.edge=e.edge=E_(n.site,f.site),Y_(n),void Y_(e);if(e){B_(n),B_(e);var c=n.site,s=c[0],l=c[1],h=t[0]-s,d=t[1]-l,p=e.site,v=p[0]-s,g=p[1]-l,y=2*(h*g-d*v),_=h*h+d*d,b=v*v+g*g,m=[(g*_-d*b)/y+s,(h*b-v*_)/y+l];C_(e.edge,c,p,m),f.edge=E_(c,t,null,m),e.edge=E_(t,p,null,m),Y_(n),Y_(e)}else f.edge=E_(n.site,f.site)}}function V_(t,n){var e=t.site,r=e[0],i=e[1],o=i-n;if(!o)return r;var a=t.P;if(!a)return-1/0;var u=(e=a.site)[0],f=e[1],c=f-n;if(!c)return u;var s=u-r,l=1/o-1/c,h=s/c;return l?(-h+Math.sqrt(h*h-2*l*(s*s/(-2*c)-f+c/2+i-o/2)))/l+r:(r+u)/2}function $_(t,n){var e=t.N;if(e)return V_(e,n);var r=t.site;return r[1]===n?r[0]:1/0}var W_,Z_,Q_,J_,K_=1e-6,tb=1e-12;function nb(t,n){return n[1]-t[1]||n[0]-t[0]}function eb(t,n){var e,r,i,o=t.sort(nb).pop();for(J_=[],Z_=new Array(t.length),W_=new M_,Q_=new M_;;)if(i=U_,o&&(!i||o[1]<i.y||o[1]===i.y&&o[0]<i.x))o[0]===e&&o[1]===r||(G_(o),e=o[0],r=o[1]),o=t.pop();else{if(!i)break;X_(i.arc)}if(function(){for(var t,n,e,r,i=0,o=Z_.length;i<o;++i)if((t=Z_[i])&&(r=(n=t.halfedges).length)){var a=new Array(r),u=new Array(r);for(e=0;e<r;++e)a[e]=e,u[e]=R_(t,J_[n[e]]);for(a.sort(function(t,n){return u[n]-u[t]}),e=0;e<r;++e)u[e]=n[a[e]];for(e=0;e<r;++e)n[e]=u[e]}}(),n){var a=+n[0][0],u=+n[0][1],f=+n[1][0],c=+n[1][1];!function(t,n,e,r){for(var i,o=J_.length;o--;)z_(i=J_[o],t,n,e,r)&&P_(i,t,n,e,r)&&(Math.abs(i[0][0]-i[1][0])>K_||Math.abs(i[0][1]-i[1][1])>K_)||delete J_[o]}(a,u,f,c),function(t,n,e,r){var i,o,a,u,f,c,s,l,h,d,p,v,g=Z_.length,y=!0;for(i=0;i<g;++i)if(o=Z_[i]){for(a=o.site,u=(f=o.halfedges).length;u--;)J_[f[u]]||f.splice(u,1);for(u=0,c=f.length;u<c;)p=(d=D_(o,J_[f[u]]))[0],v=d[1],l=(s=L_(o,J_[f[++u%c]]))[0],h=s[1],(Math.abs(p-l)>K_||Math.abs(v-h)>K_)&&(f.splice(u,0,J_.push(k_(a,d,Math.abs(p-t)<K_&&r-v>K_?[t,Math.abs(l-t)<K_?h:r]:Math.abs(v-r)<K_&&e-p>K_?[Math.abs(h-r)<K_?l:e,r]:Math.abs(p-e)<K_&&v-n>K_?[e,Math.abs(l-e)<K_?h:n]:Math.abs(v-n)<K_&&p-t>K_?[Math.abs(h-n)<K_?l:t,n]:null))-1),++c);c&&(y=!1)}if(y){var _,b,m,x=1/0;for(i=0,y=null;i<g;++i)(o=Z_[i])&&(m=(_=(a=o.site)[0]-t)*_+(b=a[1]-n)*b)<x&&(x=m,y=o);if(y){var w=[t,n],M=[t,r],A=[e,r],T=[e,n];y.halfedges.push(J_.push(k_(a=y.site,w,M))-1,J_.push(k_(a,M,A))-1,J_.push(k_(a,A,T))-1,J_.push(k_(a,T,w))-1)}}for(i=0;i<g;++i)(o=Z_[i])&&(o.halfedges.length||delete Z_[i])}(a,u,f,c)}this.edges=J_,this.cells=Z_,W_=Q_=J_=Z_=null}function rb(t){return function(){return t}}function ib(t,n,e){this.target=t,this.type=n,this.transform=e}function ob(t,n,e){this.k=t,this.x=n,this.y=e}eb.prototype={constructor:eb,polygons:function(){var t=this.edges;return this.cells.map(function(n){var e=n.halfedges.map(function(e){return L_(n,t[e])});return e.data=n.site.data,e})},triangles:function(){var t=[],n=this.edges;return this.cells.forEach(function(e,r){if(o=(i=e.halfedges).length)for(var i,o,a,u,f,c,s=e.site,l=-1,h=n[i[o-1]],d=h.left===s?h.right:h.left;++l<o;)a=d,d=(h=n[i[l]]).left===s?h.right:h.left,a&&d&&r<a.index&&r<d.index&&(f=a,c=d,((u=s)[0]-c[0])*(f[1]-u[1])-(u[0]-f[0])*(c[1]-u[1])<0)&&t.push([s.data,a.data,d.data])}),t},links:function(){return this.edges.filter(function(t){return t.right}).map(function(t){return{source:t.left.data,target:t.right.data}})},find:function(t,n,e){for(var r,i,o=this,a=o._found||0,u=o.cells.length;!(i=o.cells[a]);)if(++a>=u)return null;var f=t-i.site[0],c=n-i.site[1],s=f*f+c*c;do{i=o.cells[r=a],a=null,i.halfedges.forEach(function(e){var r=o.edges[e],u=r.left;if(u!==i.site&&u||(u=r.right)){var f=t-u[0],c=n-u[1],l=f*f+c*c;l<s&&(s=l,a=u.index)}})}while(null!==a);return o._found=r,null==e||s<=e*e?i.site:null}},ob.prototype={constructor:ob,scale:function(t){return 1===t?this:new ob(this.k*t,this.x,this.y)},translate:function(t,n){return 0===t&0===n?this:new ob(this.k,this.x+this.k*t,this.y+this.k*n)},apply:function(t){return[t[0]*this.k+this.x,t[1]*this.k+this.y]},applyX:function(t){return t*this.k+this.x},applyY:function(t){return t*this.k+this.y},invert:function(t){return[(t[0]-this.x)/this.k,(t[1]-this.y)/this.k]},invertX:function(t){return(t-this.x)/this.k},invertY:function(t){return(t-this.y)/this.k},rescaleX:function(t){return t.copy().domain(t.range().map(this.invertX,this).map(t.invert,t))},rescaleY:function(t){return t.copy().domain(t.range().map(this.invertY,this).map(t.invert,t))},toString:function(){return"translate("+this.x+","+this.y+") scale("+this.k+")"}};var ab=new ob(1,0,0);function ub(t){return t.__zoom||ab}function fb(){t.event.stopImmediatePropagation()}function cb(){t.event.preventDefault(),t.event.stopImmediatePropagation()}function sb(){return!t.event.button}function lb(){var t,n,e=this;return e instanceof SVGElement?(t=(e=e.ownerSVGElement||e).width.baseVal.value,n=e.height.baseVal.value):(t=e.clientWidth,n=e.clientHeight),[[0,0],[t,n]]}function hb(){return this.__zoom||ab}function db(){return-t.event.deltaY*(t.event.deltaMode?120:1)/500}function pb(){return"ontouchstart"in this}function vb(t,n,e){var r=t.invertX(n[0][0])-e[0][0],i=t.invertX(n[1][0])-e[1][0],o=t.invertY(n[0][1])-e[0][1],a=t.invertY(n[1][1])-e[1][1];return t.translate(i>r?(r+i)/2:Math.min(0,r)||Math.max(0,i),a>o?(o+a)/2:Math.min(0,o)||Math.max(0,a))}ub.prototype=ob.prototype,t.version="5.7.0",t.bisect=i,t.bisectRight=i,t.bisectLeft=o,t.ascending=n,t.bisector=e,t.cross=function(t,n,e){var r,i,o,u,f=t.length,c=n.length,s=new Array(f*c);for(null==e&&(e=a),r=o=0;r<f;++r)for(u=t[r],i=0;i<c;++i,++o)s[o]=e(u,n[i]);return s},t.descending=function(t,n){return n<t?-1:n>t?1:n>=t?0:NaN},t.deviation=c,t.extent=s,t.histogram=function(){var t=v,n=s,e=M;function r(r){var o,a,u=r.length,f=new Array(u);for(o=0;o<u;++o)f[o]=t(r[o],o,r);var c=n(f),s=c[0],l=c[1],h=e(f,s,l);Array.isArray(h)||(h=w(s,l,h),h=g(Math.ceil(s/h)*h,l,h));for(var d=h.length;h[0]<=s;)h.shift(),--d;for(;h[d-1]>l;)h.pop(),--d;var p,v=new Array(d+1);for(o=0;o<=d;++o)(p=v[o]=[]).x0=o>0?h[o-1]:s,p.x1=o<d?h[o]:l;for(o=0;o<u;++o)s<=(a=f[o])&&a<=l&&v[i(h,a,0,d)].push(r[o]);return v}return r.value=function(n){return arguments.length?(t="function"==typeof n?n:p(n),r):t},r.domain=function(t){return arguments.length?(n="function"==typeof t?t:p([t[0],t[1]]),r):n},r.thresholds=function(t){return arguments.length?(e="function"==typeof t?t:Array.isArray(t)?p(h.call(t)):p(t),r):e},r},t.thresholdFreedmanDiaconis=function(t,e,r){return t=d.call(t,u).sort(n),Math.ceil((r-e)/(2*(A(t,.75)-A(t,.25))*Math.pow(t.length,-1/3)))},t.thresholdScott=function(t,n,e){return Math.ceil((e-n)/(3.5*c(t)*Math.pow(t.length,-1/3)))},t.thresholdSturges=M,t.max=T,t.mean=function(t,n){var e,r=t.length,i=r,o=-1,a=0;if(null==n)for(;++o<r;)isNaN(e=u(t[o]))?--i:a+=e;else for(;++o<r;)isNaN(e=u(n(t[o],o,t)))?--i:a+=e;if(i)return a/i},t.median=function(t,e){var r,i=t.length,o=-1,a=[];if(null==e)for(;++o<i;)isNaN(r=u(t[o]))||a.push(r);else for(;++o<i;)isNaN(r=u(e(t[o],o,t)))||a.push(r);return A(a.sort(n),.5)},t.merge=N,t.min=S,t.pairs=function(t,n){null==n&&(n=a);for(var e=0,r=t.length-1,i=t[0],o=new Array(r<0?0:r);e<r;)o[e]=n(i,i=t[++e]);return o},t.permute=function(t,n){for(var e=n.length,r=new Array(e);e--;)r[e]=t[n[e]];return r},t.quantile=A,t.range=g,t.scan=function(t,e){if(r=t.length){var r,i,o=0,a=0,u=t[a];for(null==e&&(e=n);++o<r;)(e(i=t[o],u)<0||0!==e(u,u))&&(u=i,a=o);return 0===e(u,u)?a:void 0}},t.shuffle=function(t,n,e){for(var r,i,o=(null==e?t.length:e)-(n=null==n?0:+n);o;)i=Math.random()*o--|0,r=t[o+n],t[o+n]=t[i+n],t[i+n]=r;return t},t.sum=function(t,n){var e,r=t.length,i=-1,o=0;if(null==n)for(;++i<r;)(e=+t[i])&&(o+=e);else for(;++i<r;)(e=+n(t[i],i,t))&&(o+=e);return o},t.ticks=m,t.tickIncrement=x,t.tickStep=w,t.transpose=E,t.variance=f,t.zip=function(){return E(arguments)},t.axisTop=function(t){return B(z,t)},t.axisRight=function(t){return B(R,t)},t.axisBottom=function(t){return B(L,t)},t.axisLeft=function(t){return B(D,t)},t.brush=function(){return Ri(wi)},t.brushX=function(){return Ri(mi)},t.brushY=function(){return Ri(xi)},t.brushSelection=function(t){var n=t.__brush;return n?n.dim.output(n.selection):null},t.chord=function(){var t=0,n=null,e=null,r=null;function i(i){var o,a,u,f,c,s,l=i.length,h=[],d=g(l),p=[],v=[],y=v.groups=new Array(l),_=new Array(l*l);for(o=0,c=-1;++c<l;){for(a=0,s=-1;++s<l;)a+=i[c][s];h.push(a),p.push(g(l)),o+=a}for(n&&d.sort(function(t,e){return n(h[t],h[e])}),e&&p.forEach(function(t,n){t.sort(function(t,r){return e(i[n][t],i[n][r])})}),f=(o=Yi(0,Oi-t*l)/o)?t:Oi/l,a=0,c=-1;++c<l;){for(u=a,s=-1;++s<l;){var b=d[c],m=p[b][s],x=i[b][m],w=a,M=a+=x*o;_[m*l+b]={index:b,subindex:m,startAngle:w,endAngle:M,value:x}}y[b]={index:b,startAngle:u,endAngle:a,value:h[b]},a+=f}for(c=-1;++c<l;)for(s=c-1;++s<l;){var A=_[s*l+c],T=_[c*l+s];(A.value||T.value)&&v.push(A.value<T.value?{source:T,target:A}:{source:A,target:T})}return r?v.sort(r):v}return i.padAngle=function(n){return arguments.length?(t=Yi(0,n),i):t},i.sortGroups=function(t){return arguments.length?(n=t,i):n},i.sortSubgroups=function(t){return arguments.length?(e=t,i):e},i.sortChords=function(t){return arguments.length?(null==t?r=null:(n=t,r=function(t,e){return n(t.source.value+t.target.value,e.source.value+e.target.value)})._=t,i):r&&r._;var n},i},t.ribbon=function(){var t=Vi,n=$i,e=Wi,r=Zi,i=Qi,o=null;function a(){var a,u=Bi.call(arguments),f=t.apply(this,u),c=n.apply(this,u),s=+e.apply(this,(u[0]=f,u)),l=r.apply(this,u)-qi,h=i.apply(this,u)-qi,d=s*Li(l),p=s*Di(l),v=+e.apply(this,(u[0]=c,u)),g=r.apply(this,u)-qi,y=i.apply(this,u)-qi;if(o||(o=a=Gi()),o.moveTo(d,p),o.arc(0,0,s,l,h),l===g&&h===y||(o.quadraticCurveTo(0,0,v*Li(g),v*Di(g)),o.arc(0,0,v,g,y)),o.quadraticCurveTo(0,0,d,p),o.closePath(),a)return o=null,a+""||null}return a.radius=function(t){return arguments.length?(e="function"==typeof t?t:Fi(+t),a):e},a.startAngle=function(t){return arguments.length?(r="function"==typeof t?t:Fi(+t),a):r},a.endAngle=function(t){return arguments.length?(i="function"==typeof t?t:Fi(+t),a):i},a.source=function(n){return arguments.length?(t=n,a):t},a.target=function(t){return arguments.length?(n=t,a):n},a.context=function(t){return arguments.length?(o=null==t?null:t,a):o},a},t.nest=function(){var t,n,e,r=[],i=[];function o(e,i,a,u){if(i>=r.length)return null!=t&&e.sort(t),null!=n?n(e):e;for(var f,c,s,l=-1,h=e.length,d=r[i++],p=Ki(),v=a();++l<h;)(s=p.get(f=d(c=e[l])+""))?s.push(c):p.set(f,[c]);return p.each(function(t,n){u(v,n,o(t,i,a,u))}),v}return e={object:function(t){return o(t,0,to,no)},map:function(t){return o(t,0,eo,ro)},entries:function(t){return function t(e,o){if(++o>r.length)return e;var a,u=i[o-1];return null!=n&&o>=r.length?a=e.entries():(a=[],e.each(function(n,e){a.push({key:e,values:t(n,o)})})),null!=u?a.sort(function(t,n){return u(t.key,n.key)}):a}(o(t,0,eo,ro),0)},key:function(t){return r.push(t),e},sortKeys:function(t){return i[r.length-1]=t,e},sortValues:function(n){return t=n,e},rollup:function(t){return n=t,e}}},t.set=ao,t.map=Ki,t.keys=function(t){var n=[];for(var e in t)n.push(e);return n},t.values=function(t){var n=[];for(var e in t)n.push(t[e]);return n},t.entries=function(t){var n=[];for(var e in t)n.push({key:e,value:t[e]});return n},t.color=vn,t.rgb=bn,t.hsl=Mn,t.lab=Un,t.hcl=Hn,t.lch=function(t,n,e,r){return 1===arguments.length?In(t):new jn(e,n,t,null==r?1:r)},t.gray=function(t,n){return new qn(t,0,0,null==n?1:n)},t.cubehelix=Kn,t.contours=go,t.contourDensity=function(){var t=bo,n=mo,e=xo,r=960,i=500,o=20,a=2,u=3*o,f=r+2*u>>a,c=i+2*u>>a,s=co(20);function l(r){var i=new Float32Array(f*c),l=new Float32Array(f*c);r.forEach(function(r,o,s){var l=+t(r,o,s)+u>>a,h=+n(r,o,s)+u>>a,d=+e(r,o,s);l>=0&&l<f&&h>=0&&h<c&&(i[l+h*f]+=d)}),yo({width:f,height:c,data:i},{width:f,height:c,data:l},o>>a),_o({width:f,height:c,data:l},{width:f,height:c,data:i},o>>a),yo({width:f,height:c,data:i},{width:f,height:c,data:l},o>>a),_o({width:f,height:c,data:l},{width:f,height:c,data:i},o>>a),yo({width:f,height:c,data:i},{width:f,height:c,data:l},o>>a),_o({width:f,height:c,data:l},{width:f,height:c,data:i},o>>a);var d=s(i);if(!Array.isArray(d)){var p=T(i);d=w(0,p,d),(d=g(0,Math.floor(p/d)*d,d)).shift()}return go().thresholds(d).size([f,c])(i).map(h)}function h(t){return t.value*=Math.pow(2,-2*a),t.coordinates.forEach(d),t}function d(t){t.forEach(p)}function p(t){t.forEach(v)}function v(t){t[0]=t[0]*Math.pow(2,a)-u,t[1]=t[1]*Math.pow(2,a)-u}function y(){return f=r+2*(u=3*o)>>a,c=i+2*u>>a,l}return l.x=function(n){return arguments.length?(t="function"==typeof n?n:co(+n),l):t},l.y=function(t){return arguments.length?(n="function"==typeof t?t:co(+t),l):n},l.weight=function(t){return arguments.length?(e="function"==typeof t?t:co(+t),l):e},l.size=function(t){if(!arguments.length)return[r,i];var n=Math.ceil(t[0]),e=Math.ceil(t[1]);if(!(n>=0||n>=0))throw new Error("invalid size");return r=n,i=e,y()},l.cellSize=function(t){if(!arguments.length)return 1<<a;if(!((t=+t)>=1))throw new Error("invalid cell size");return a=Math.floor(Math.log(t)/Math.LN2),y()},l.thresholds=function(t){return arguments.length?(s="function"==typeof t?t:Array.isArray(t)?co(uo.call(t)):co(t),l):s},l.bandwidth=function(t){if(!arguments.length)return Math.sqrt(o*(o+1));if(!((t=+t)>=0))throw new Error("invalid bandwidth");return o=Math.round((Math.sqrt(4*t*t+1)-1)/2),y()},l},t.dispatch=I,t.drag=function(){var n,e,r,i,o=Wt,a=Zt,u=Qt,f=Jt,c={},s=I("start","drag","end"),l=0,h=0;function d(t){t.on("mousedown.drag",p).filter(f).on("touchstart.drag",y).on("touchmove.drag",_).on("touchend.drag touchcancel.drag",b).style("touch-action","none").style("-webkit-tap-highlight-color","rgba(0,0,0,0)")}function p(){if(!i&&o.apply(this,arguments)){var u=m("mouse",a.apply(this,arguments),Ft,this,arguments);u&&(Dt(t.event.view).on("mousemove.drag",v,!0).on("mouseup.drag",g,!0),Xt(t.event.view),Ht(),r=!1,n=t.event.clientX,e=t.event.clientY,u("start"))}}function v(){if(jt(),!r){var i=t.event.clientX-n,o=t.event.clientY-e;r=i*i+o*o>h}c.mouse("drag")}function g(){Dt(t.event.view).on("mousemove.drag mouseup.drag",null),Gt(t.event.view,r),jt(),c.mouse("end")}function y(){if(o.apply(this,arguments)){var n,e,r=t.event.changedTouches,i=a.apply(this,arguments),u=r.length;for(n=0;n<u;++n)(e=m(r[n].identifier,i,It,this,arguments))&&(Ht(),e("start"))}}function _(){var n,e,r=t.event.changedTouches,i=r.length;for(n=0;n<i;++n)(e=c[r[n].identifier])&&(jt(),e("drag"))}function b(){var n,e,r=t.event.changedTouches,o=r.length;for(i&&clearTimeout(i),i=setTimeout(function(){i=null},500),n=0;n<o;++n)(e=c[r[n].identifier])&&(Ht(),e("end"))}function m(n,e,r,i,o){var a,f,h,p=r(e,n),v=s.copy();if(Ct(new $t(d,"beforestart",a,n,l,p[0],p[1],0,0,v),function(){return null!=(t.event.subject=a=u.apply(i,o))&&(f=a.x-p[0]||0,h=a.y-p[1]||0,!0)}))return function t(u){var s,g=p;switch(u){case"start":c[n]=t,s=l++;break;case"end":delete c[n],--l;case"drag":p=r(e,n),s=l}Ct(new $t(d,u,a,n,s,p[0]+f,p[1]+h,p[0]-g[0],p[1]-g[1],v),v.apply,v,[u,i,o])}}return d.filter=function(t){return arguments.length?(o="function"==typeof t?t:Vt(!!t),d):o},d.container=function(t){return arguments.length?(a="function"==typeof t?t:Vt(t),d):a},d.subject=function(t){return arguments.length?(u="function"==typeof t?t:Vt(t),d):u},d.touchable=function(t){return arguments.length?(f="function"==typeof t?t:Vt(!!t),d):f},d.on=function(){var t=s.on.apply(s,arguments);return t===s?d:t},d.clickDistance=function(t){return arguments.length?(h=(t=+t)*t,d):Math.sqrt(h)},d},t.dragDisable=Xt,t.dragEnable=Gt,t.dsvFormat=Eo,t.csvParse=Co,t.csvParseRows=Po,t.csvFormat=zo,t.csvFormatRows=Ro,t.tsvParse=Do,t.tsvParseRows=Uo,t.tsvFormat=qo,t.tsvFormatRows=Oo,t.easeLinear=function(t){return+t},t.easeQuad=Dr,t.easeQuadIn=function(t){return t*t},t.easeQuadOut=function(t){return t*(2-t)},t.easeQuadInOut=Dr,t.easeCubic=Ur,t.easeCubicIn=function(t){return t*t*t},t.easeCubicOut=function(t){return--t*t*t+1},t.easeCubicInOut=Ur,t.easePoly=Yr,t.easePolyIn=qr,t.easePolyOut=Or,t.easePolyInOut=Yr,t.easeSin=Ir,t.easeSinIn=function(t){return 1-Math.cos(t*Fr)},t.easeSinOut=function(t){return Math.sin(t*Fr)},t.easeSinInOut=Ir,t.easeExp=Hr,t.easeExpIn=function(t){return Math.pow(2,10*t-10)},t.easeExpOut=function(t){return 1-Math.pow(2,-10*t)},t.easeExpInOut=Hr,t.easeCircle=jr,t.easeCircleIn=function(t){return 1-Math.sqrt(1-t*t)},t.easeCircleOut=function(t){return Math.sqrt(1- --t*t)},t.easeCircleInOut=jr,t.easeBounce=ni,t.easeBounceIn=function(t){return 1-ni(1-t)},t.easeBounceOut=ni,t.easeBounceInOut=function(t){return((t*=2)<=1?1-ni(1-t):ni(t-1)+1)/2},t.easeBack=ii,t.easeBackIn=ei,t.easeBackOut=ri,t.easeBackInOut=ii,t.easeElastic=ui,t.easeElasticIn=ai,t.easeElasticOut=ui,t.easeElasticInOut=fi,t.blob=function(t,n){return fetch(t,n).then(Yo)},t.buffer=function(t,n){return fetch(t,n).then(Bo)},t.dsv=function(t,n,e,r){3===arguments.length&&"function"==typeof e&&(r=e,e=void 0);var i=Eo(t);return Io(n,e).then(function(t){return i.parse(t,r)})},t.csv=jo,t.tsv=Xo,t.image=function(t,n){return new Promise(function(e,r){var i=new Image;for(var o in n)i[o]=n[o];i.onerror=r,i.onload=function(){e(i)},i.src=t})},t.json=function(t,n){return fetch(t,n).then(Go)},t.text=Io,t.xml=$o,t.html=Wo,t.svg=Zo,t.forceCenter=function(t,n){var e;function r(){var r,i,o=e.length,a=0,u=0;for(r=0;r<o;++r)a+=(i=e[r]).x,u+=i.y;for(a=a/o-t,u=u/o-n,r=0;r<o;++r)(i=e[r]).x-=a,i.y-=u}return null==t&&(t=0),null==n&&(n=0),r.initialize=function(t){e=t},r.x=function(n){return arguments.length?(t=+n,r):t},r.y=function(t){return arguments.length?(n=+t,r):n},r},t.forceCollide=function(t){var n,e,r=1,i=1;function o(){for(var t,o,u,f,c,s,l,h=n.length,d=0;d<i;++d)for(o=ra(n,ua,fa).visitAfter(a),t=0;t<h;++t)u=n[t],s=e[u.index],l=s*s,f=u.x+u.vx,c=u.y+u.vy,o.visit(p);function p(t,n,e,i,o){var a=t.data,h=t.r,d=s+h;if(!a)return n>f+d||i<f-d||e>c+d||o<c-d;if(a.index>u.index){var p=f-a.x-a.vx,v=c-a.y-a.vy,g=p*p+v*v;g<d*d&&(0===p&&(g+=(p=Jo())*p),0===v&&(g+=(v=Jo())*v),g=(d-(g=Math.sqrt(g)))/g*r,u.vx+=(p*=g)*(d=(h*=h)/(l+h)),u.vy+=(v*=g)*d,a.vx-=p*(d=1-d),a.vy-=v*d)}}}function a(t){if(t.data)return t.r=e[t.data.index];for(var n=t.r=0;n<4;++n)t[n]&&t[n].r>t.r&&(t.r=t[n].r)}function u(){if(n){var r,i,o=n.length;for(e=new Array(o),r=0;r<o;++r)i=n[r],e[i.index]=+t(i,r,n)}}return"function"!=typeof t&&(t=Qo(null==t?1:+t)),o.initialize=function(t){n=t,u()},o.iterations=function(t){return arguments.length?(i=+t,o):i},o.strength=function(t){return arguments.length?(r=+t,o):r},o.radius=function(n){return arguments.length?(t="function"==typeof n?n:Qo(+n),u(),o):t},o},t.forceLink=function(t){var n,e,r,i,o,a=ca,u=function(t){return 1/Math.min(i[t.source.index],i[t.target.index])},f=Qo(30),c=1;function s(r){for(var i=0,a=t.length;i<c;++i)for(var u,f,s,l,h,d,p,v=0;v<a;++v)f=(u=t[v]).source,l=(s=u.target).x+s.vx-f.x-f.vx||Jo(),h=s.y+s.vy-f.y-f.vy||Jo(),l*=d=((d=Math.sqrt(l*l+h*h))-e[v])/d*r*n[v],h*=d,s.vx-=l*(p=o[v]),s.vy-=h*p,f.vx+=l*(p=1-p),f.vy+=h*p}function l(){if(r){var u,f,c=r.length,s=t.length,l=Ki(r,a);for(u=0,i=new Array(c);u<s;++u)(f=t[u]).index=u,"object"!=typeof f.source&&(f.source=sa(l,f.source)),"object"!=typeof f.target&&(f.target=sa(l,f.target)),i[f.source.index]=(i[f.source.index]||0)+1,i[f.target.index]=(i[f.target.index]||0)+1;for(u=0,o=new Array(s);u<s;++u)f=t[u],o[u]=i[f.source.index]/(i[f.source.index]+i[f.target.index]);n=new Array(s),h(),e=new Array(s),d()}}function h(){if(r)for(var e=0,i=t.length;e<i;++e)n[e]=+u(t[e],e,t)}function d(){if(r)for(var n=0,i=t.length;n<i;++n)e[n]=+f(t[n],n,t)}return null==t&&(t=[]),s.initialize=function(t){r=t,l()},s.links=function(n){return arguments.length?(t=n,l(),s):t},s.id=function(t){return arguments.length?(a=t,s):a},s.iterations=function(t){return arguments.length?(c=+t,s):c},s.strength=function(t){return arguments.length?(u="function"==typeof t?t:Qo(+t),h(),s):u},s.distance=function(t){return arguments.length?(f="function"==typeof t?t:Qo(+t),d(),s):f},s},t.forceManyBody=function(){var t,n,e,r,i=Qo(-30),o=1,a=1/0,u=.81;function f(r){var i,o=t.length,a=ra(t,la,ha).visitAfter(s);for(e=r,i=0;i<o;++i)n=t[i],a.visit(l)}function c(){if(t){var n,e,o=t.length;for(r=new Array(o),n=0;n<o;++n)e=t[n],r[e.index]=+i(e,n,t)}}function s(t){var n,e,i,o,a,u=0,f=0;if(t.length){for(i=o=a=0;a<4;++a)(n=t[a])&&(e=Math.abs(n.value))&&(u+=n.value,f+=e,i+=e*n.x,o+=e*n.y);t.x=i/f,t.y=o/f}else{(n=t).x=n.data.x,n.y=n.data.y;do{u+=r[n.data.index]}while(n=n.next)}t.value=u}function l(t,i,f,c){if(!t.value)return!0;var s=t.x-n.x,l=t.y-n.y,h=c-i,d=s*s+l*l;if(h*h/u<d)return d<a&&(0===s&&(d+=(s=Jo())*s),0===l&&(d+=(l=Jo())*l),d<o&&(d=Math.sqrt(o*d)),n.vx+=s*t.value*e/d,n.vy+=l*t.value*e/d),!0;if(!(t.length||d>=a)){(t.data!==n||t.next)&&(0===s&&(d+=(s=Jo())*s),0===l&&(d+=(l=Jo())*l),d<o&&(d=Math.sqrt(o*d)));do{t.data!==n&&(h=r[t.data.index]*e/d,n.vx+=s*h,n.vy+=l*h)}while(t=t.next)}}return f.initialize=function(n){t=n,c()},f.strength=function(t){return arguments.length?(i="function"==typeof t?t:Qo(+t),c(),f):i},f.distanceMin=function(t){return arguments.length?(o=t*t,f):Math.sqrt(o)},f.distanceMax=function(t){return arguments.length?(a=t*t,f):Math.sqrt(a)},f.theta=function(t){return arguments.length?(u=t*t,f):Math.sqrt(u)},f},t.forceRadial=function(t,n,e){var r,i,o,a=Qo(.1);function u(t){for(var a=0,u=r.length;a<u;++a){var f=r[a],c=f.x-n||1e-6,s=f.y-e||1e-6,l=Math.sqrt(c*c+s*s),h=(o[a]-l)*i[a]*t/l;f.vx+=c*h,f.vy+=s*h}}function f(){if(r){var n,e=r.length;for(i=new Array(e),o=new Array(e),n=0;n<e;++n)o[n]=+t(r[n],n,r),i[n]=isNaN(o[n])?0:+a(r[n],n,r)}}return"function"!=typeof t&&(t=Qo(+t)),null==n&&(n=0),null==e&&(e=0),u.initialize=function(t){r=t,f()},u.strength=function(t){return arguments.length?(a="function"==typeof t?t:Qo(+t),f(),u):a},u.radius=function(n){return arguments.length?(t="function"==typeof n?n:Qo(+n),f(),u):t},u.x=function(t){return arguments.length?(n=+t,u):n},u.y=function(t){return arguments.length?(e=+t,u):e},u},t.forceSimulation=function(t){var n,e=1,r=.001,i=1-Math.pow(r,1/300),o=0,a=.6,u=Ki(),f=ur(s),c=I("tick","end");function s(){l(),c.call("tick",n),e<r&&(f.stop(),c.call("end",n))}function l(){var n,r,f=t.length;for(e+=(o-e)*i,u.each(function(t){t(e)}),n=0;n<f;++n)null==(r=t[n]).fx?r.x+=r.vx*=a:(r.x=r.fx,r.vx=0),null==r.fy?r.y+=r.vy*=a:(r.y=r.fy,r.vy=0)}function h(){for(var n,e=0,r=t.length;e<r;++e){if((n=t[e]).index=e,isNaN(n.x)||isNaN(n.y)){var i=da*Math.sqrt(e),o=e*pa;n.x=i*Math.cos(o),n.y=i*Math.sin(o)}(isNaN(n.vx)||isNaN(n.vy))&&(n.vx=n.vy=0)}}function d(n){return n.initialize&&n.initialize(t),n}return null==t&&(t=[]),h(),n={tick:l,restart:function(){return f.restart(s),n},stop:function(){return f.stop(),n},nodes:function(e){return arguments.length?(t=e,h(),u.each(d),n):t},alpha:function(t){return arguments.length?(e=+t,n):e},alphaMin:function(t){return arguments.length?(r=+t,n):r},alphaDecay:function(t){return arguments.length?(i=+t,n):+i},alphaTarget:function(t){return arguments.length?(o=+t,n):o},velocityDecay:function(t){return arguments.length?(a=1-t,n):1-a},force:function(t,e){return arguments.length>1?(null==e?u.remove(t):u.set(t,d(e)),n):u.get(t)},find:function(n,e,r){var i,o,a,u,f,c=0,s=t.length;for(null==r?r=1/0:r*=r,c=0;c<s;++c)(a=(i=n-(u=t[c]).x)*i+(o=e-u.y)*o)<r&&(f=u,r=a);return f},on:function(t,e){return arguments.length>1?(c.on(t,e),n):c.on(t)}}},t.forceX=function(t){var n,e,r,i=Qo(.1);function o(t){for(var i,o=0,a=n.length;o<a;++o)(i=n[o]).vx+=(r[o]-i.x)*e[o]*t}function a(){if(n){var o,a=n.length;for(e=new Array(a),r=new Array(a),o=0;o<a;++o)e[o]=isNaN(r[o]=+t(n[o],o,n))?0:+i(n[o],o,n)}}return"function"!=typeof t&&(t=Qo(null==t?0:+t)),o.initialize=function(t){n=t,a()},o.strength=function(t){return arguments.length?(i="function"==typeof t?t:Qo(+t),a(),o):i},o.x=function(n){return arguments.length?(t="function"==typeof n?n:Qo(+n),a(),o):t},o},t.forceY=function(t){var n,e,r,i=Qo(.1);function o(t){for(var i,o=0,a=n.length;o<a;++o)(i=n[o]).vy+=(r[o]-i.y)*e[o]*t}function a(){if(n){var o,a=n.length;for(e=new Array(a),r=new Array(a),o=0;o<a;++o)e[o]=isNaN(r[o]=+t(n[o],o,n))?0:+i(n[o],o,n)}}return"function"!=typeof t&&(t=Qo(null==t?0:+t)),o.initialize=function(t){n=t,a()},o.strength=function(t){return arguments.length?(i="function"==typeof t?t:Qo(+t),a(),o):i},o.y=function(n){return arguments.length?(t="function"==typeof n?n:Qo(+n),a(),o):t},o},t.formatDefaultLocale=Sa,t.formatLocale=Na,t.formatSpecifier=ba,t.precisionFixed=Ea,t.precisionPrefix=ka,t.precisionRound=Ca,t.geoArea=function(t){return yu.reset(),su(t,_u),2*yu},t.geoBounds=function(t){var n,e,r,i,o,a,u;if(Ru=zu=-(Cu=Pu=1/0),Ou=[],su(t,rf),e=Ou.length){for(Ou.sort(df),n=1,o=[r=Ou[0]];n<e;++n)pf(r,(i=Ou[n])[0])||pf(r,i[1])?(hf(r[0],i[1])>hf(r[0],r[1])&&(r[1]=i[1]),hf(i[0],r[1])>hf(r[0],r[1])&&(r[0]=i[0])):o.push(r=i);for(a=-1/0,n=0,r=o[e=o.length-1];n<=e;r=i,++n)i=o[n],(u=hf(r[1],i[0]))>a&&(a=u,Cu=i[0],zu=r[1])}return Ou=Yu=null,Cu===1/0||Pu===1/0?[[NaN,NaN],[NaN,NaN]]:[[Cu,Pu],[zu,Ru]]},t.geoCentroid=function(t){Bu=Fu=Iu=Hu=ju=Xu=Gu=Vu=$u=Wu=Zu=0,su(t,vf);var n=$u,e=Wu,r=Zu,i=n*n+e*e+r*r;return i<Ua&&(n=Xu,e=Gu,r=Vu,Fu<Da&&(n=Iu,e=Hu,r=ju),(i=n*n+e*e+r*r)<Ua)?[NaN,NaN]:[Xa(e,n)*Fa,eu(r/Ka(i))*Fa]},t.geoCircle=function(){var t,n,e=Nf([0,0]),r=Nf(90),i=Nf(6),o={point:function(e,r){t.push(e=n(e,r)),e[0]*=Fa,e[1]*=Fa}};function a(){var a=e.apply(this,arguments),u=r.apply(this,arguments)*Ia,f=i.apply(this,arguments)*Ia;return t=[],n=kf(-a[0]*Ia,-a[1]*Ia,0).invert,Lf(o,u,f,1),a={type:"Polygon",coordinates:[t]},t=n=null,a}return a.center=function(t){return arguments.length?(e="function"==typeof t?t:Nf([+t[0],+t[1]]),a):e},a.radius=function(t){return arguments.length?(r="function"==typeof t?t:Nf(+t),a):r},a.precision=function(t){return arguments.length?(i="function"==typeof t?t:Nf(+t),a):i},a},t.geoClipAntimeridian=Gf,t.geoClipCircle=Vf,t.geoClipExtent=function(){var t,n,e,r=0,i=0,o=960,a=500;return e={stream:function(e){return t&&n===e?t:t=Zf(r,i,o,a)(n=e)},extent:function(u){return arguments.length?(r=+u[0][0],i=+u[0][1],o=+u[1][0],a=+u[1][1],t=n=null,e):[[r,i],[o,a]]}}},t.geoClipRectangle=Zf,t.geoContains=function(t,n){return(t&&cc.hasOwnProperty(t.type)?cc[t.type]:lc)(t,n)},t.geoDistance=fc,t.geoGraticule=bc,t.geoGraticule10=function(){return bc()()},t.geoInterpolate=function(t,n){var e=t[0]*Ia,r=t[1]*Ia,i=n[0]*Ia,o=n[1]*Ia,a=Ga(r),u=Qa(r),f=Ga(o),c=Qa(o),s=a*Ga(e),l=a*Qa(e),h=f*Ga(i),d=f*Qa(i),p=2*eu(Ka(ru(o-r)+a*f*ru(i-e))),v=Qa(p),g=p?function(t){var n=Qa(t*=p)/v,e=Qa(p-t)/v,r=e*s+n*h,i=e*l+n*d,o=e*u+n*c;return[Xa(i,r)*Fa,Xa(o,Ka(r*r+i*i))*Fa]}:function(){return[e*Fa,r*Fa]};return g.distance=p,g},t.geoLength=oc,t.geoPath=function(t,n){var e,r,i=4.5;function o(t){return t&&("function"==typeof i&&r.pointRadius(+i.apply(this,arguments)),su(t,e(r))),r.result()}return o.area=function(t){return su(t,e(Sc)),Sc.result()},o.measure=function(t){return su(t,e(ds)),ds.result()},o.bounds=function(t){return su(t,e(Uc)),Uc.result()},o.centroid=function(t){return su(t,e(Zc)),Zc.result()},o.projection=function(n){return arguments.length?(e=null==n?(t=null,mc):(t=n).stream,o):t},o.context=function(t){return arguments.length?(r=null==t?(n=null,new gs):new as(n=t),"function"!=typeof i&&r.pointRadius(i),o):n},o.pointRadius=function(t){return arguments.length?(i="function"==typeof t?t:(r.pointRadius(+t),+t),o):i},o.projection(t).context(n)},t.geoAlbers=Ds,t.geoAlbersUsa=function(){var t,n,e,r,i,o,a=Ds(),u=Ls().rotate([154,0]).center([-2,58.5]).parallels([55,65]),f=Ls().rotate([157,0]).center([-3,19.9]).parallels([8,18]),c={point:function(t,n){o=[t,n]}};function s(t){var n=t[0],a=t[1];return o=null,e.point(n,a),o||(r.point(n,a),o)||(i.point(n,a),o)}function l(){return t=n=null,s}return s.invert=function(t){var n=a.scale(),e=a.translate(),r=(t[0]-e[0])/n,i=(t[1]-e[1])/n;return(i>=.12&&i<.234&&r>=-.425&&r<-.214?u:i>=.166&&i<.234&&r>=-.214&&r<-.115?f:a).invert(t)},s.stream=function(e){return t&&n===e?t:(r=[a.stream(n=e),u.stream(e),f.stream(e)],i=r.length,t={point:function(t,n){for(var e=-1;++e<i;)r[e].point(t,n)},sphere:function(){for(var t=-1;++t<i;)r[t].sphere()},lineStart:function(){for(var t=-1;++t<i;)r[t].lineStart()},lineEnd:function(){for(var t=-1;++t<i;)r[t].lineEnd()},polygonStart:function(){for(var t=-1;++t<i;)r[t].polygonStart()},polygonEnd:function(){for(var t=-1;++t<i;)r[t].polygonEnd()}});var r,i},s.precision=function(t){return arguments.length?(a.precision(t),u.precision(t),f.precision(t),l()):a.precision()},s.scale=function(t){return arguments.length?(a.scale(t),u.scale(.35*t),f.scale(t),s.translate(a.translate())):a.scale()},s.translate=function(t){if(!arguments.length)return a.translate();var n=a.scale(),o=+t[0],s=+t[1];return e=a.translate(t).clipExtent([[o-.455*n,s-.238*n],[o+.455*n,s+.238*n]]).stream(c),r=u.translate([o-.307*n,s+.201*n]).clipExtent([[o-.425*n+Da,s+.12*n+Da],[o-.214*n-Da,s+.234*n-Da]]).stream(c),i=f.translate([o-.205*n,s+.212*n]).clipExtent([[o-.214*n+Da,s+.166*n+Da],[o-.115*n-Da,s+.234*n-Da]]).stream(c),l()},s.fitExtent=function(t,n){return xs(s,t,n)},s.fitSize=function(t,n){return ws(s,t,n)},s.fitWidth=function(t,n){return Ms(s,t,n)},s.fitHeight=function(t,n){return As(s,t,n)},s.scale(1070)},t.geoAzimuthalEqualArea=function(){return Cs(Os).scale(124.75).clipAngle(179.999)},t.geoAzimuthalEqualAreaRaw=Os,t.geoAzimuthalEquidistant=function(){return Cs(Ys).scale(79.4188).clipAngle(179.999)},t.geoAzimuthalEquidistantRaw=Ys,t.geoConicConformal=function(){return zs(Hs).scale(109.5).parallels([30,30])},t.geoConicConformalRaw=Hs,t.geoConicEqualArea=Ls,t.geoConicEqualAreaRaw=Rs,t.geoConicEquidistant=function(){return zs(Xs).scale(131.154).center([0,13.9389])},t.geoConicEquidistantRaw=Xs,t.geoEqualEarth=function(){return Cs(Qs).scale(177.158)},t.geoEqualEarthRaw=Qs,t.geoEquirectangular=function(){return Cs(js).scale(152.63)},t.geoEquirectangularRaw=js,t.geoGnomonic=function(){return Cs(Js).scale(144.049).clipAngle(60)},t.geoGnomonicRaw=Js,t.geoIdentity=function(){var t,n,e,r,i,o,a=1,u=0,f=0,c=1,s=1,l=mc,h=null,d=mc;function p(){return r=i=null,o}return o={stream:function(t){return r&&i===t?r:r=l(d(i=t))},postclip:function(r){return arguments.length?(d=r,h=t=n=e=null,p()):d},clipExtent:function(r){return arguments.length?(d=null==r?(h=t=n=e=null,mc):Zf(h=+r[0][0],t=+r[0][1],n=+r[1][0],e=+r[1][1]),p()):null==h?null:[[h,t],[n,e]]},scale:function(t){return arguments.length?(l=Ks((a=+t)*c,a*s,u,f),p()):a},translate:function(t){return arguments.length?(l=Ks(a*c,a*s,u=+t[0],f=+t[1]),p()):[u,f]},reflectX:function(t){return arguments.length?(l=Ks(a*(c=t?-1:1),a*s,u,f),p()):c<0},reflectY:function(t){return arguments.length?(l=Ks(a*c,a*(s=t?-1:1),u,f),p()):s<0},fitExtent:function(t,n){return xs(o,t,n)},fitSize:function(t,n){return ws(o,t,n)},fitWidth:function(t,n){return Ms(o,t,n)},fitHeight:function(t,n){return As(o,t,n)}}},t.geoProjection=Cs,t.geoProjectionMutator=Ps,t.geoMercator=function(){return Fs(Bs).scale(961/Ba)},t.geoMercatorRaw=Bs,t.geoNaturalEarth1=function(){return Cs(tl).scale(175.295)},t.geoNaturalEarth1Raw=tl,t.geoOrthographic=function(){return Cs(nl).scale(249.5).clipAngle(90+Da)},t.geoOrthographicRaw=nl,t.geoStereographic=function(){return Cs(el).scale(250).clipAngle(142)},t.geoStereographicRaw=el,t.geoTransverseMercator=function(){var t=Fs(rl),n=t.center,e=t.rotate;return t.center=function(t){return arguments.length?n([-t[1],t[0]]):[(t=n())[1],-t[0]]},t.rotate=function(t){return arguments.length?e([t[0],t[1],t.length>2?t[2]+90:90]):[(t=e())[0],t[1],t[2]-90]},e([0,0,90]).scale(159.155)},t.geoTransverseMercatorRaw=rl,t.geoRotation=Rf,t.geoStream=su,t.geoTransform=function(t){return{stream:_s(t)}},t.cluster=function(){var t=il,n=1,e=1,r=!1;function i(i){var o,a=0;i.eachAfter(function(n){var e=n.children;e?(n.x=function(t){return t.reduce(ol,0)/t.length}(e),n.y=function(t){return 1+t.reduce(al,0)}(e)):(n.x=o?a+=t(n,o):0,n.y=0,o=n)});var u=function(t){for(var n;n=t.children;)t=n[0];return t}(i),f=function(t){for(var n;n=t.children;)t=n[n.length-1];return t}(i),c=u.x-t(u,f)/2,s=f.x+t(f,u)/2;return i.eachAfter(r?function(t){t.x=(t.x-i.x)*n,t.y=(i.y-t.y)*e}:function(t){t.x=(t.x-c)/(s-c)*n,t.y=(1-(i.y?t.y/i.y:1))*e})}return i.separation=function(n){return arguments.length?(t=n,i):t},i.size=function(t){return arguments.length?(r=!1,n=+t[0],e=+t[1],i):r?null:[n,e]},i.nodeSize=function(t){return arguments.length?(r=!0,n=+t[0],e=+t[1],i):r?[n,e]:null},i},t.hierarchy=fl,t.pack=function(){var t=null,n=1,e=1,r=El;function i(i){return i.x=n/2,i.y=e/2,t?i.eachBefore(Pl(t)).eachAfter(zl(r,.5)).eachBefore(Rl(1)):i.eachBefore(Pl(Cl)).eachAfter(zl(El,1)).eachAfter(zl(r,i.r/Math.min(n,e))).eachBefore(Rl(Math.min(n,e)/(2*i.r))),i}return i.radius=function(n){return arguments.length?(t=null==(e=n)?null:Sl(e),i):t;var e},i.size=function(t){return arguments.length?(n=+t[0],e=+t[1],i):[n,e]},i.padding=function(t){return arguments.length?(r="function"==typeof t?t:kl(+t),i):r},i},t.packSiblings=function(t){return Nl(t),t},t.packEnclose=pl,t.partition=function(){var t=1,n=1,e=0,r=!1;function i(i){var o=i.height+1;return i.x0=i.y0=e,i.x1=t,i.y1=n/o,i.eachBefore(function(t,n){return function(r){r.children&&Dl(r,r.x0,t*(r.depth+1)/n,r.x1,t*(r.depth+2)/n);var i=r.x0,o=r.y0,a=r.x1-e,u=r.y1-e;a<i&&(i=a=(i+a)/2),u<o&&(o=u=(o+u)/2),r.x0=i,r.y0=o,r.x1=a,r.y1=u}}(n,o)),r&&i.eachBefore(Ll),i}return i.round=function(t){return arguments.length?(r=!!t,i):r},i.size=function(e){return arguments.length?(t=+e[0],n=+e[1],i):[t,n]},i.padding=function(t){return arguments.length?(e=+t,i):e},i},t.stratify=function(){var t=Yl,n=Bl;function e(e){var r,i,o,a,u,f,c,s=e.length,l=new Array(s),h={};for(i=0;i<s;++i)r=e[i],u=l[i]=new hl(r),null!=(f=t(r,i,e))&&(f+="")&&(h[c=Ul+(u.id=f)]=c in h?Ol:u);for(i=0;i<s;++i)if(u=l[i],null!=(f=n(e[i],i,e))&&(f+="")){if(!(a=h[Ul+f]))throw new Error("missing: "+f);if(a===Ol)throw new Error("ambiguous: "+f);a.children?a.children.push(u):a.children=[u],u.parent=a}else{if(o)throw new Error("multiple roots");o=u}if(!o)throw new Error("no root");if(o.parent=ql,o.eachBefore(function(t){t.depth=t.parent.depth+1,--s}).eachBefore(ll),o.parent=null,s>0)throw new Error("cycle");return o}return e.id=function(n){return arguments.length?(t=Sl(n),e):t},e.parentId=function(t){return arguments.length?(n=Sl(t),e):n},e},t.tree=function(){var t=Fl,n=1,e=1,r=null;function i(i){var f=function(t){for(var n,e,r,i,o,a=new Gl(t,0),u=[a];n=u.pop();)if(r=n._.children)for(n.children=new Array(o=r.length),i=o-1;i>=0;--i)u.push(e=n.children[i]=new Gl(r[i],i)),e.parent=n;return(a.parent=new Gl(null,0)).children=[a],a}(i);if(f.eachAfter(o),f.parent.m=-f.z,f.eachBefore(a),r)i.eachBefore(u);else{var c=i,s=i,l=i;i.eachBefore(function(t){t.x<c.x&&(c=t),t.x>s.x&&(s=t),t.depth>l.depth&&(l=t)});var h=c===s?1:t(c,s)/2,d=h-c.x,p=n/(s.x+h+d),v=e/(l.depth||1);i.eachBefore(function(t){t.x=(t.x+d)*p,t.y=t.depth*v})}return i}function o(n){var e=n.children,r=n.parent.children,i=n.i?r[n.i-1]:null;if(e){!function(t){for(var n,e=0,r=0,i=t.children,o=i.length;--o>=0;)(n=i[o]).z+=e,n.m+=e,e+=n.s+(r+=n.c)}(n);var o=(e[0].z+e[e.length-1].z)/2;i?(n.z=i.z+t(n._,i._),n.m=n.z-o):n.z=o}else i&&(n.z=i.z+t(n._,i._));n.parent.A=function(n,e,r){if(e){for(var i,o=n,a=n,u=e,f=o.parent.children[0],c=o.m,s=a.m,l=u.m,h=f.m;u=Hl(u),o=Il(o),u&&o;)f=Il(f),(a=Hl(a)).a=n,(i=u.z+l-o.z-c+t(u._,o._))>0&&(jl(Xl(u,n,r),n,i),c+=i,s+=i),l+=u.m,c+=o.m,h+=f.m,s+=a.m;u&&!Hl(a)&&(a.t=u,a.m+=l-s),o&&!Il(f)&&(f.t=o,f.m+=c-h,r=n)}return r}(n,i,n.parent.A||r[0])}function a(t){t._.x=t.z+t.parent.m,t.m+=t.parent.m}function u(t){t.x*=n,t.y=t.depth*e}return i.separation=function(n){return arguments.length?(t=n,i):t},i.size=function(t){return arguments.length?(r=!1,n=+t[0],e=+t[1],i):r?null:[n,e]},i.nodeSize=function(t){return arguments.length?(r=!0,n=+t[0],e=+t[1],i):r?[n,e]:null},i},t.treemap=function(){var t=Zl,n=!1,e=1,r=1,i=[0],o=El,a=El,u=El,f=El,c=El;function s(t){return t.x0=t.y0=0,t.x1=e,t.y1=r,t.eachBefore(l),i=[0],n&&t.eachBefore(Ll),t}function l(n){var e=i[n.depth],r=n.x0+e,s=n.y0+e,l=n.x1-e,h=n.y1-e;l<r&&(r=l=(r+l)/2),h<s&&(s=h=(s+h)/2),n.x0=r,n.y0=s,n.x1=l,n.y1=h,n.children&&(e=i[n.depth+1]=o(n)/2,r+=c(n)-e,s+=a(n)-e,(l-=u(n)-e)<r&&(r=l=(r+l)/2),(h-=f(n)-e)<s&&(s=h=(s+h)/2),t(n,r,s,l,h))}return s.round=function(t){return arguments.length?(n=!!t,s):n},s.size=function(t){return arguments.length?(e=+t[0],r=+t[1],s):[e,r]},s.tile=function(n){return arguments.length?(t=Sl(n),s):t},s.padding=function(t){return arguments.length?s.paddingInner(t).paddingOuter(t):s.paddingInner()},s.paddingInner=function(t){return arguments.length?(o="function"==typeof t?t:kl(+t),s):o},s.paddingOuter=function(t){return arguments.length?s.paddingTop(t).paddingRight(t).paddingBottom(t).paddingLeft(t):s.paddingTop()},s.paddingTop=function(t){return arguments.length?(a="function"==typeof t?t:kl(+t),s):a},s.paddingRight=function(t){return arguments.length?(u="function"==typeof t?t:kl(+t),s):u},s.paddingBottom=function(t){return arguments.length?(f="function"==typeof t?t:kl(+t),s):f},s.paddingLeft=function(t){return arguments.length?(c="function"==typeof t?t:kl(+t),s):c},s},t.treemapBinary=function(t,n,e,r,i){var o,a,u=t.children,f=u.length,c=new Array(f+1);for(c[0]=a=o=0;o<f;++o)c[o+1]=a+=u[o].value;!function t(n,e,r,i,o,a,f){if(n>=e-1){var s=u[n];return s.x0=i,s.y0=o,s.x1=a,void(s.y1=f)}for(var l=c[n],h=r/2+l,d=n+1,p=e-1;d<p;){var v=d+p>>>1;c[v]<h?d=v+1:p=v}h-c[d-1]<c[d]-h&&n+1<d&&--d;var g=c[d]-l,y=r-g;if(a-i>f-o){var _=(i*y+a*g)/r;t(n,d,g,i,o,_,f),t(d,e,y,_,o,a,f)}else{var b=(o*y+f*g)/r;t(n,d,g,i,o,a,b),t(d,e,y,i,b,a,f)}}(0,f,t.value,n,e,r,i)},t.treemapDice=Dl,t.treemapSlice=Vl,t.treemapSliceDice=function(t,n,e,r,i){(1&t.depth?Vl:Dl)(t,n,e,r,i)},t.treemapSquarify=Zl,t.treemapResquarify=Ql,t.interpolate=me,t.interpolateArray=de,t.interpolateBasis=ee,t.interpolateBasisClosed=re,t.interpolateDate=pe,t.interpolateDiscrete=function(t){var n=t.length;return function(e){return t[Math.max(0,Math.min(n-1,Math.floor(e*n)))]}},t.interpolateHue=function(t,n){var e=ae(+t,+n);return function(t){var n=e(t);return n-360*Math.floor(n/360)}},t.interpolateNumber=ve,t.interpolateObject=ge,t.interpolateRound=xe,t.interpolateString=be,t.interpolateTransformCss=Ce,t.interpolateTransformSvg=Pe,t.interpolateZoom=qe,t.interpolateRgb=ce,t.interpolateRgbBasis=le,t.interpolateRgbBasisClosed=he,t.interpolateHsl=Ye,t.interpolateHslLong=Be,t.interpolateLab=function(t,n){var e=fe((t=Un(t)).l,(n=Un(n)).l),r=fe(t.a,n.a),i=fe(t.b,n.b),o=fe(t.opacity,n.opacity);return function(n){return t.l=e(n),t.a=r(n),t.b=i(n),t.opacity=o(n),t+""}},t.interpolateHcl=Ie,t.interpolateHclLong=He,t.interpolateCubehelix=Xe,t.interpolateCubehelixLong=Ge,t.piecewise=function(t,n){for(var e=0,r=n.length-1,i=n[0],o=new Array(r<0?0:r);e<r;)o[e]=t(i,i=n[++e]);return function(t){var n=Math.max(0,Math.min(r-1,Math.floor(t*=r)));return o[n](t-n)}},t.quantize=function(t,n){for(var e=new Array(n),r=0;r<n;++r)e[r]=t(r/(n-1));return e},t.path=Gi,t.polygonArea=function(t){for(var n,e=-1,r=t.length,i=t[r-1],o=0;++e<r;)n=i,i=t[e],o+=n[1]*i[0]-n[0]*i[1];return o/2},t.polygonCentroid=function(t){for(var n,e,r=-1,i=t.length,o=0,a=0,u=t[i-1],f=0;++r<i;)n=u,u=t[r],f+=e=n[0]*u[1]-u[0]*n[1],o+=(n[0]+u[0])*e,a+=(n[1]+u[1])*e;return[o/(f*=3),a/f]},t.polygonHull=function(t){if((e=t.length)<3)return null;var n,e,r=new Array(e),i=new Array(e);for(n=0;n<e;++n)r[n]=[+t[n][0],+t[n][1],n];for(r.sort(Jl),n=0;n<e;++n)i[n]=[r[n][0],-r[n][1]];var o=Kl(r),a=Kl(i),u=a[0]===o[0],f=a[a.length-1]===o[o.length-1],c=[];for(n=o.length-1;n>=0;--n)c.push(t[r[o[n]][2]]);for(n=+u;n<a.length-f;++n)c.push(t[r[a[n]][2]]);return c},t.polygonContains=function(t,n){for(var e,r,i=t.length,o=t[i-1],a=n[0],u=n[1],f=o[0],c=o[1],s=!1,l=0;l<i;++l)e=(o=t[l])[0],(r=o[1])>u!=c>u&&a<(f-e)*(u-r)/(c-r)+e&&(s=!s),f=e,c=r;return s},t.polygonLength=function(t){for(var n,e,r=-1,i=t.length,o=t[i-1],a=o[0],u=o[1],f=0;++r<i;)n=a,e=u,n-=a=(o=t[r])[0],e-=u=o[1],f+=Math.sqrt(n*n+e*e);return f},t.quadtree=ra,t.randomUniform=nh,t.randomNormal=eh,t.randomLogNormal=rh,t.randomBates=oh,t.randomIrwinHall=ih,t.randomExponential=ah,t.scaleBand=hh,t.scalePoint=function(){return function t(n){var e=n.copy;return n.padding=n.paddingOuter,delete n.paddingInner,delete n.paddingOuter,n.copy=function(){return t(e())},n}(hh().paddingInner(1))},t.scaleIdentity=function t(){var n=[0,1];function e(t){return+t}return e.invert=e,e.domain=e.range=function(t){return arguments.length?(n=fh.call(t,ph),e):n.slice()},e.copy=function(){return t().domain(n)},xh(e)},t.scaleLinear=function t(){var n=mh(gh,ve);return n.copy=function(){return bh(n,t())},xh(n)},t.scaleLog=function n(){var e=mh(Mh,Ah).domain([1,10]),r=e.domain,i=10,o=Sh(10),a=Nh(10);function u(){return o=Sh(i),a=Nh(i),r()[0]<0&&(o=Eh(o),a=Eh(a)),e}return e.base=function(t){return arguments.length?(i=+t,u()):i},e.domain=function(t){return arguments.length?(r(t),u()):r()},e.ticks=function(t){var n,e=r(),u=e[0],f=e[e.length-1];(n=f<u)&&(h=u,u=f,f=h);var c,s,l,h=o(u),d=o(f),p=null==t?10:+t,v=[];if(!(i%1)&&d-h<p){if(h=Math.round(h)-1,d=Math.round(d)+1,u>0){for(;h<d;++h)for(s=1,c=a(h);s<i;++s)if(!((l=c*s)<u)){if(l>f)break;v.push(l)}}else for(;h<d;++h)for(s=i-1,c=a(h);s>=1;--s)if(!((l=c*s)<u)){if(l>f)break;v.push(l)}}else v=m(h,d,Math.min(d-h,p)).map(a);return n?v.reverse():v},e.tickFormat=function(n,r){if(null==r&&(r=10===i?".0e":","),"function"!=typeof r&&(r=t.format(r)),n===1/0)return r;null==n&&(n=10);var u=Math.max(1,i*n/e.ticks().length);return function(t){var n=t/a(Math.round(o(t)));return n*i<i-.5&&(n*=i),n<=u?r(t):""}},e.nice=function(){return r(wh(r(),{floor:function(t){return a(Math.floor(o(t)))},ceil:function(t){return a(Math.ceil(o(t)))}}))},e.copy=function(){return bh(e,n().base(i))},e},t.scaleOrdinal=lh,t.scaleImplicit=sh,t.scalePow=Ch,t.scaleSqrt=function(){return Ch().exponent(.5)},t.scaleQuantile=function t(){var e=[],r=[],o=[];function a(){var t=0,n=Math.max(1,r.length);for(o=new Array(n-1);++t<n;)o[t-1]=A(e,t/n);return u}function u(t){if(!isNaN(t=+t))return r[i(o,t)]}return u.invertExtent=function(t){var n=r.indexOf(t);return n<0?[NaN,NaN]:[n>0?o[n-1]:e[0],n<o.length?o[n]:e[e.length-1]]},u.domain=function(t){if(!arguments.length)return e.slice();e=[];for(var r,i=0,o=t.length;i<o;++i)null==(r=t[i])||isNaN(r=+r)||e.push(r);return e.sort(n),a()},u.range=function(t){return arguments.length?(r=ch.call(t),a()):r.slice()},u.quantiles=function(){return o.slice()},u.copy=function(){return t().domain(e).range(r)},u},t.scaleQuantize=function t(){var n=0,e=1,r=1,o=[.5],a=[0,1];function u(t){if(t<=t)return a[i(o,t,0,r)]}function f(){var t=-1;for(o=new Array(r);++t<r;)o[t]=((t+1)*e-(t-r)*n)/(r+1);return u}return u.domain=function(t){return arguments.length?(n=+t[0],e=+t[1],f()):[n,e]},u.range=function(t){return arguments.length?(r=(a=ch.call(t)).length-1,f()):a.slice()},u.invertExtent=function(t){var i=a.indexOf(t);return i<0?[NaN,NaN]:i<1?[n,o[0]]:i>=r?[o[r-1],e]:[o[i-1],o[i]]},u.copy=function(){return t().domain([n,e]).range(a)},xh(u)},t.scaleThreshold=function t(){var n=[.5],e=[0,1],r=1;function o(t){if(t<=t)return e[i(n,t,0,r)]}return o.domain=function(t){return arguments.length?(n=ch.call(t),r=Math.min(n.length,e.length-1),o):n.slice()},o.range=function(t){return arguments.length?(e=ch.call(t),r=Math.min(n.length,e.length-1),o):e.slice()},o.invertExtent=function(t){var r=e.indexOf(t);return[n[r-1],n[r]]},o.copy=function(){return t().domain(n).range(e)},o},t.scaleTime=function(){return cv(cd,ud,Vh,jh,Ih,Bh,Oh,Lh,t.timeFormat).domain([new Date(2e3,0,1),new Date(2e3,0,2)])},t.scaleUtc=function(){return cv(Ld,zd,_d,vd,dd,ld,Oh,Lh,t.utcFormat).domain([Date.UTC(2e3,0,1),Date.UTC(2e3,0,2)])},t.scaleSequential=function t(n){var e=0,r=1,i=1,o=!1;function a(t){var r=(t-e)*i;return n(o?Math.max(0,Math.min(1,r)):r)}return a.domain=function(t){return arguments.length?(e=+t[0],r=+t[1],i=e===r?0:1/(r-e),a):[e,r]},a.clamp=function(t){return arguments.length?(o=!!t,a):o},a.interpolator=function(t){return arguments.length?(n=t,a):n},a.copy=function(){return t(n).domain([e,r]).clamp(o)},xh(a)},t.scaleDiverging=function t(n){var e=0,r=.5,i=1,o=1,a=1,u=!1;function f(t){var e=.5+((t=+t)-r)*(t<r?o:a);return n(u?Math.max(0,Math.min(1,e)):e)}return f.domain=function(t){return arguments.length?(e=+t[0],r=+t[1],i=+t[2],o=e===r?0:.5/(r-e),a=r===i?0:.5/(i-r),f):[e,r,i]},f.clamp=function(t){return arguments.length?(u=!!t,f):u},f.interpolator=function(t){return arguments.length?(n=t,f):n},f.copy=function(){return t(n).domain([e,r,i]).clamp(u)},xh(f)},t.schemeCategory10=lv,t.schemeAccent=hv,t.schemeDark2=dv,t.schemePaired=pv,t.schemePastel1=vv,t.schemePastel2=gv,t.schemeSet1=yv,t.schemeSet2=_v,t.schemeSet3=bv,t.interpolateBrBG=wv,t.schemeBrBG=xv,t.interpolatePRGn=Av,t.schemePRGn=Mv,t.interpolatePiYG=Nv,t.schemePiYG=Tv,t.interpolatePuOr=Ev,t.schemePuOr=Sv,t.interpolateRdBu=Cv,t.schemeRdBu=kv,t.interpolateRdGy=zv,t.schemeRdGy=Pv,t.interpolateRdYlBu=Lv,t.schemeRdYlBu=Rv,t.interpolateRdYlGn=Uv,t.schemeRdYlGn=Dv,t.interpolateSpectral=Ov,t.schemeSpectral=qv,t.interpolateBuGn=Bv,t.schemeBuGn=Yv,t.interpolateBuPu=Iv,t.schemeBuPu=Fv,t.interpolateGnBu=jv,t.schemeGnBu=Hv,t.interpolateOrRd=Gv,t.schemeOrRd=Xv,t.interpolatePuBuGn=$v,t.schemePuBuGn=Vv,t.interpolatePuBu=Zv,t.schemePuBu=Wv,t.interpolatePuRd=Jv,t.schemePuRd=Qv,t.interpolateRdPu=tg,t.schemeRdPu=Kv,t.interpolateYlGnBu=eg,t.schemeYlGnBu=ng,t.interpolateYlGn=ig,t.schemeYlGn=rg,t.interpolateYlOrBr=ag,t.schemeYlOrBr=og,t.interpolateYlOrRd=fg,t.schemeYlOrRd=ug,t.interpolateBlues=sg,t.schemeBlues=cg,t.interpolateGreens=hg,t.schemeGreens=lg,t.interpolateGreys=pg,t.schemeGreys=dg,t.interpolatePurples=gg,t.schemePurples=vg,t.interpolateReds=_g,t.schemeReds=yg,t.interpolateOranges=mg,t.schemeOranges=bg,t.interpolateCubehelixDefault=xg,t.interpolateRainbow=function(t){(t<0||t>1)&&(t-=Math.floor(t));var n=Math.abs(t-.5);return Ag.h=360*t-100,Ag.s=1.5-1.5*n,Ag.l=.8-.9*n,Ag+""},t.interpolateWarm=wg,t.interpolateCool=Mg,t.interpolateSinebow=function(t){var n;return t=(.5-t)*Math.PI,Tg.r=255*(n=Math.sin(t))*n,Tg.g=255*(n=Math.sin(t+Ng))*n,Tg.b=255*(n=Math.sin(t+Sg))*n,Tg+""},t.interpolateViridis=kg,t.interpolateMagma=Cg,t.interpolateInferno=Pg,t.interpolatePlasma=zg,t.create=function(t){return Dt(W(t).call(document.documentElement))},t.creator=W,t.local=qt,t.matcher=rt,t.mouse=Ft,t.namespace=$,t.namespaces=V,t.clientPoint=Bt,t.select=Dt,t.selectAll=function(t){return"string"==typeof t?new Rt([document.querySelectorAll(t)],[document.documentElement]):new Rt([null==t?[]:t],zt)},t.selection=Lt,t.selector=Q,t.selectorAll=K,t.style=lt,t.touch=It,t.touches=function(t,n){null==n&&(n=Yt().touches);for(var e=0,r=n?n.length:0,i=new Array(r);e<r;++e)i[e]=Bt(t,n[e]);return i},t.window=st,t.customEvent=Ct,t.arc=function(){var t=Gg,n=Vg,e=Rg(0),r=null,i=$g,o=Wg,a=Zg,u=null;function f(){var f,c,s,l=+t.apply(this,arguments),h=+n.apply(this,arguments),d=i.apply(this,arguments)-Hg,p=o.apply(this,arguments)-Hg,v=Lg(p-d),g=p>d;if(u||(u=f=Gi()),h<l&&(c=h,h=l,l=c),h>Fg)if(v>jg-Fg)u.moveTo(h*Ug(d),h*Yg(d)),u.arc(0,0,h,d,p,!g),l>Fg&&(u.moveTo(l*Ug(p),l*Yg(p)),u.arc(0,0,l,p,d,g));else{var y,_,b=d,m=p,x=d,w=p,M=v,A=v,T=a.apply(this,arguments)/2,N=T>Fg&&(r?+r.apply(this,arguments):Bg(l*l+h*h)),S=Og(Lg(h-l)/2,+e.apply(this,arguments)),E=S,k=S;if(N>Fg){var C=Xg(N/l*Yg(T)),P=Xg(N/h*Yg(T));(M-=2*C)>Fg?(x+=C*=g?1:-1,w-=C):(M=0,x=w=(d+p)/2),(A-=2*P)>Fg?(b+=P*=g?1:-1,m-=P):(A=0,b=m=(d+p)/2)}var z=h*Ug(b),R=h*Yg(b),L=l*Ug(w),D=l*Yg(w);if(S>Fg){var U=h*Ug(m),q=h*Yg(m),O=l*Ug(x),Y=l*Yg(x);if(v<Ig){var B=M>Fg?function(t,n,e,r,i,o,a,u){var f=e-t,c=r-n,s=a-i,l=u-o,h=(s*(n-o)-l*(t-i))/(l*f-s*c);return[t+h*f,n+h*c]}(z,R,O,Y,U,q,L,D):[L,D],F=z-B[0],I=R-B[1],H=U-B[0],j=q-B[1],X=1/Yg(((s=(F*H+I*j)/(Bg(F*F+I*I)*Bg(H*H+j*j)))>1?0:s<-1?Ig:Math.acos(s))/2),G=Bg(B[0]*B[0]+B[1]*B[1]);E=Og(S,(l-G)/(X-1)),k=Og(S,(h-G)/(X+1))}}A>Fg?k>Fg?(y=Qg(O,Y,z,R,h,k,g),_=Qg(U,q,L,D,h,k,g),u.moveTo(y.cx+y.x01,y.cy+y.y01),k<S?u.arc(y.cx,y.cy,k,Dg(y.y01,y.x01),Dg(_.y01,_.x01),!g):(u.arc(y.cx,y.cy,k,Dg(y.y01,y.x01),Dg(y.y11,y.x11),!g),u.arc(0,0,h,Dg(y.cy+y.y11,y.cx+y.x11),Dg(_.cy+_.y11,_.cx+_.x11),!g),u.arc(_.cx,_.cy,k,Dg(_.y11,_.x11),Dg(_.y01,_.x01),!g))):(u.moveTo(z,R),u.arc(0,0,h,b,m,!g)):u.moveTo(z,R),l>Fg&&M>Fg?E>Fg?(y=Qg(L,D,U,q,l,-E,g),_=Qg(z,R,O,Y,l,-E,g),u.lineTo(y.cx+y.x01,y.cy+y.y01),E<S?u.arc(y.cx,y.cy,E,Dg(y.y01,y.x01),Dg(_.y01,_.x01),!g):(u.arc(y.cx,y.cy,E,Dg(y.y01,y.x01),Dg(y.y11,y.x11),!g),u.arc(0,0,l,Dg(y.cy+y.y11,y.cx+y.x11),Dg(_.cy+_.y11,_.cx+_.x11),g),u.arc(_.cx,_.cy,E,Dg(_.y11,_.x11),Dg(_.y01,_.x01),!g))):u.arc(0,0,l,w,x,g):u.lineTo(L,D)}else u.moveTo(0,0);if(u.closePath(),f)return u=null,f+""||null}return f.centroid=function(){var e=(+t.apply(this,arguments)+ +n.apply(this,arguments))/2,r=(+i.apply(this,arguments)+ +o.apply(this,arguments))/2-Ig/2;return[Ug(r)*e,Yg(r)*e]},f.innerRadius=function(n){return arguments.length?(t="function"==typeof n?n:Rg(+n),f):t},f.outerRadius=function(t){return arguments.length?(n="function"==typeof t?t:Rg(+t),f):n},f.cornerRadius=function(t){return arguments.length?(e="function"==typeof t?t:Rg(+t),f):e},f.padRadius=function(t){return arguments.length?(r=null==t?null:"function"==typeof t?t:Rg(+t),f):r},f.startAngle=function(t){return arguments.length?(i="function"==typeof t?t:Rg(+t),f):i},f.endAngle=function(t){return arguments.length?(o="function"==typeof t?t:Rg(+t),f):o},f.padAngle=function(t){return arguments.length?(a="function"==typeof t?t:Rg(+t),f):a},f.context=function(t){return arguments.length?(u=null==t?null:t,f):u},f},t.area=ry,t.line=ey,t.pie=function(){var t=oy,n=iy,e=null,r=Rg(0),i=Rg(jg),o=Rg(0);function a(a){var u,f,c,s,l,h=a.length,d=0,p=new Array(h),v=new Array(h),g=+r.apply(this,arguments),y=Math.min(jg,Math.max(-jg,i.apply(this,arguments)-g)),_=Math.min(Math.abs(y)/h,o.apply(this,arguments)),b=_*(y<0?-1:1);for(u=0;u<h;++u)(l=v[p[u]=u]=+t(a[u],u,a))>0&&(d+=l);for(null!=n?p.sort(function(t,e){return n(v[t],v[e])}):null!=e&&p.sort(function(t,n){return e(a[t],a[n])}),u=0,c=d?(y-h*b)/d:0;u<h;++u,g=s)f=p[u],s=g+((l=v[f])>0?l*c:0)+b,v[f]={data:a[f],index:u,value:l,startAngle:g,endAngle:s,padAngle:_};return v}return a.value=function(n){return arguments.length?(t="function"==typeof n?n:Rg(+n),a):t},a.sortValues=function(t){return arguments.length?(n=t,e=null,a):n},a.sort=function(t){return arguments.length?(e=t,n=null,a):e},a.startAngle=function(t){return arguments.length?(r="function"==typeof t?t:Rg(+t),a):r},a.endAngle=function(t){return arguments.length?(i="function"==typeof t?t:Rg(+t),a):i},a.padAngle=function(t){return arguments.length?(o="function"==typeof t?t:Rg(+t),a):o},a},t.areaRadial=ly,t.radialArea=ly,t.lineRadial=sy,t.radialLine=sy,t.pointRadial=hy,t.linkHorizontal=function(){return gy(yy)},t.linkVertical=function(){return gy(_y)},t.linkRadial=function(){var t=gy(by);return t.angle=t.x,delete t.x,t.radius=t.y,delete t.y,t},t.symbol=function(){var t=Rg(my),n=Rg(64),e=null;function r(){var r;if(e||(e=r=Gi()),t.apply(this,arguments).draw(e,+n.apply(this,arguments)),r)return e=null,r+""||null}return r.type=function(n){return arguments.length?(t="function"==typeof n?n:Rg(n),r):t},r.size=function(t){return arguments.length?(n="function"==typeof t?t:Rg(+t),r):n},r.context=function(t){return arguments.length?(e=null==t?null:t,r):e},r},t.symbols=Uy,t.symbolCircle=my,t.symbolCross=xy,t.symbolDiamond=Ay,t.symbolSquare=ky,t.symbolStar=Ey,t.symbolTriangle=Py,t.symbolWye=Dy,t.curveBasisClosed=function(t){return new By(t)},t.curveBasisOpen=function(t){return new Fy(t)},t.curveBasis=function(t){return new Yy(t)},t.curveBundle=Hy,t.curveCardinalClosed=$y,t.curveCardinalOpen=Zy,t.curveCardinal=Gy,t.curveCatmullRomClosed=n_,t.curveCatmullRomOpen=r_,t.curveCatmullRom=Ky,t.curveLinearClosed=function(t){return new i_(t)},t.curveLinear=Kg,t.curveMonotoneX=function(t){return new c_(t)},t.curveMonotoneY=function(t){return new s_(t)},t.curveNatural=function(t){return new h_(t)},t.curveStep=function(t){return new p_(t,.5)},t.curveStepAfter=function(t){return new p_(t,1)},t.curveStepBefore=function(t){return new p_(t,0)},t.stack=function(){var t=Rg([]),n=g_,e=v_,r=y_;function i(i){var o,a,u=t.apply(this,arguments),f=i.length,c=u.length,s=new Array(c);for(o=0;o<c;++o){for(var l,h=u[o],d=s[o]=new Array(f),p=0;p<f;++p)d[p]=l=[0,+r(i[p],h,p,i)],l.data=i[p];d.key=h}for(o=0,a=n(s);o<c;++o)s[a[o]].index=o;return e(s,a),s}return i.keys=function(n){return arguments.length?(t="function"==typeof n?n:Rg(dy.call(n)),i):t},i.value=function(t){return arguments.length?(r="function"==typeof t?t:Rg(+t),i):r},i.order=function(t){return arguments.length?(n=null==t?g_:"function"==typeof t?t:Rg(dy.call(t)),i):n},i.offset=function(t){return arguments.length?(e=null==t?v_:t,i):e},i},t.stackOffsetExpand=function(t,n){if((r=t.length)>0){for(var e,r,i,o=0,a=t[0].length;o<a;++o){for(i=e=0;e<r;++e)i+=t[e][o][1]||0;if(i)for(e=0;e<r;++e)t[e][o][1]/=i}v_(t,n)}},t.stackOffsetDiverging=function(t,n){if((u=t.length)>1)for(var e,r,i,o,a,u,f=0,c=t[n[0]].length;f<c;++f)for(o=a=0,e=0;e<u;++e)(i=(r=t[n[e]][f])[1]-r[0])>=0?(r[0]=o,r[1]=o+=i):i<0?(r[1]=a,r[0]=a+=i):r[0]=o},t.stackOffsetNone=v_,t.stackOffsetSilhouette=function(t,n){if((e=t.length)>0){for(var e,r=0,i=t[n[0]],o=i.length;r<o;++r){for(var a=0,u=0;a<e;++a)u+=t[a][r][1]||0;i[r][1]+=i[r][0]=-u/2}v_(t,n)}},t.stackOffsetWiggle=function(t,n){if((i=t.length)>0&&(r=(e=t[n[0]]).length)>0){for(var e,r,i,o=0,a=1;a<r;++a){for(var u=0,f=0,c=0;u<i;++u){for(var s=t[n[u]],l=s[a][1]||0,h=(l-(s[a-1][1]||0))/2,d=0;d<u;++d){var p=t[n[d]];h+=(p[a][1]||0)-(p[a-1][1]||0)}f+=l,c+=h*l}e[a-1][1]+=e[a-1][0]=o,f&&(o-=c/f)}e[a-1][1]+=e[a-1][0]=o,v_(t,n)}},t.stackOrderAscending=__,t.stackOrderDescending=function(t){return __(t).reverse()},t.stackOrderInsideOut=function(t){var n,e,r=t.length,i=t.map(b_),o=g_(t).sort(function(t,n){return i[n]-i[t]}),a=0,u=0,f=[],c=[];for(n=0;n<r;++n)e=o[n],a<u?(a+=i[e],f.push(e)):(u+=i[e],c.push(e));return c.reverse().concat(f)},t.stackOrderNone=g_,t.stackOrderReverse=function(t){return g_(t).reverse()},t.timeInterval=Rh,t.timeMillisecond=Lh,t.timeMilliseconds=Dh,t.utcMillisecond=Lh,t.utcMilliseconds=Dh,t.timeSecond=Oh,t.timeSeconds=Yh,t.utcSecond=Oh,t.utcSeconds=Yh,t.timeMinute=Bh,t.timeMinutes=Fh,t.timeHour=Ih,t.timeHours=Hh,t.timeDay=jh,t.timeDays=Xh,t.timeWeek=Vh,t.timeWeeks=td,t.timeSunday=Vh,t.timeSundays=td,t.timeMonday=$h,t.timeMondays=nd,t.timeTuesday=Wh,t.timeTuesdays=ed,t.timeWednesday=Zh,t.timeWednesdays=rd,t.timeThursday=Qh,t.timeThursdays=id,t.timeFriday=Jh,t.timeFridays=od,t.timeSaturday=Kh,t.timeSaturdays=ad,t.timeMonth=ud,t.timeMonths=fd,t.timeYear=cd,t.timeYears=sd,t.utcMinute=ld,t.utcMinutes=hd,t.utcHour=dd,t.utcHours=pd,t.utcDay=vd,t.utcDays=gd,t.utcWeek=_d,t.utcWeeks=Td,t.utcSunday=_d,t.utcSundays=Td,t.utcMonday=bd,t.utcMondays=Nd,t.utcTuesday=md,t.utcTuesdays=Sd,t.utcWednesday=xd,t.utcWednesdays=Ed,t.utcThursday=wd,t.utcThursdays=kd,t.utcFriday=Md,t.utcFridays=Cd,t.utcSaturday=Ad,t.utcSaturdays=Pd,t.utcMonth=zd,t.utcMonths=Rd,t.utcYear=Ld,t.utcYears=Dd,t.timeFormatDefaultLocale=Qp,t.timeFormatLocale=Yd,t.isoFormat=Jp,t.isoParse=Kp,t.now=ir,t.timer=ur,t.timerFlush=fr,t.timeout=hr,t.interval=function(t,n,e){var r=new ar,i=n;return null==n?(r.restart(t,n,e),r):(n=+n,e=null==e?ir():+e,r.restart(function o(a){a+=i,r.restart(o,i+=n,e),t(a)},n,e),r)},t.transition=zr,t.active=function(t,n){var e,r,i=t.__transition;if(i)for(r in n=null==n?null:n+"",i)if((e=i[r]).state>gr&&e.name===n)return new Pr([[t]],li,n,+r);return null},t.interrupt=Nr,t.voronoi=function(){var t=x_,n=w_,e=null;function r(r){return new eb(r.map(function(e,i){var o=[Math.round(t(e,i,r)/K_)*K_,Math.round(n(e,i,r)/K_)*K_];return o.index=i,o.data=e,o}),e)}return r.polygons=function(t){return r(t).polygons()},r.links=function(t){return r(t).links()},r.triangles=function(t){return r(t).triangles()},r.x=function(n){return arguments.length?(t="function"==typeof n?n:m_(+n),r):t},r.y=function(t){return arguments.length?(n="function"==typeof t?t:m_(+t),r):n},r.extent=function(t){return arguments.length?(e=null==t?null:[[+t[0][0],+t[0][1]],[+t[1][0],+t[1][1]]],r):e&&[[e[0][0],e[0][1]],[e[1][0],e[1][1]]]},r.size=function(t){return arguments.length?(e=null==t?null:[[0,0],[+t[0],+t[1]]],r):e&&[e[1][0]-e[0][0],e[1][1]-e[0][1]]},r},t.zoom=function(){var n,e,r=sb,i=lb,o=vb,a=db,u=pb,f=[0,1/0],c=[[-1/0,-1/0],[1/0,1/0]],s=250,l=qe,h=[],d=I("start","zoom","end"),p=500,v=150,g=0;function y(t){t.property("__zoom",hb).on("wheel.zoom",A).on("mousedown.zoom",T).on("dblclick.zoom",N).filter(u).on("touchstart.zoom",S).on("touchmove.zoom",E).on("touchend.zoom touchcancel.zoom",k).style("touch-action","none").style("-webkit-tap-highlight-color","rgba(0,0,0,0)")}function _(t,n){return(n=Math.max(f[0],Math.min(f[1],n)))===t.k?t:new ob(n,t.x,t.y)}function b(t,n,e){var r=n[0]-e[0]*t.k,i=n[1]-e[1]*t.k;return r===t.x&&i===t.y?t:new ob(t.k,r,i)}function m(t){return[(+t[0][0]+ +t[1][0])/2,(+t[0][1]+ +t[1][1])/2]}function x(t,n,e){t.on("start.zoom",function(){w(this,arguments).start()}).on("interrupt.zoom end.zoom",function(){w(this,arguments).end()}).tween("zoom",function(){var t=arguments,r=w(this,t),o=i.apply(this,t),a=e||m(o),u=Math.max(o[1][0]-o[0][0],o[1][1]-o[0][1]),f=this.__zoom,c="function"==typeof n?n.apply(this,t):n,s=l(f.invert(a).concat(u/f.k),c.invert(a).concat(u/c.k));return function(t){if(1===t)t=c;else{var n=s(t),e=u/n[2];t=new ob(e,a[0]-n[0]*e,a[1]-n[1]*e)}r.zoom(null,t)}})}function w(t,n){for(var e,r=0,i=h.length;r<i;++r)if((e=h[r]).that===t)return e;return new M(t,n)}function M(t,n){this.that=t,this.args=n,this.index=-1,this.active=0,this.extent=i.apply(t,n)}function A(){if(r.apply(this,arguments)){var t=w(this,arguments),n=this.__zoom,e=Math.max(f[0],Math.min(f[1],n.k*Math.pow(2,a.apply(this,arguments)))),i=Ft(this);if(t.wheel)t.mouse[0][0]===i[0]&&t.mouse[0][1]===i[1]||(t.mouse[1]=n.invert(t.mouse[0]=i)),clearTimeout(t.wheel);else{if(n.k===e)return;t.mouse=[i,n.invert(i)],Nr(this),t.start()}cb(),t.wheel=setTimeout(function(){t.wheel=null,t.end()},v),t.zoom("mouse",o(b(_(n,e),t.mouse[0],t.mouse[1]),t.extent,c))}}function T(){if(!e&&r.apply(this,arguments)){var n=w(this,arguments),i=Dt(t.event.view).on("mousemove.zoom",function(){if(cb(),!n.moved){var e=t.event.clientX-u,r=t.event.clientY-f;n.moved=e*e+r*r>g}n.zoom("mouse",o(b(n.that.__zoom,n.mouse[0]=Ft(n.that),n.mouse[1]),n.extent,c))},!0).on("mouseup.zoom",function(){i.on("mousemove.zoom mouseup.zoom",null),Gt(t.event.view,n.moved),cb(),n.end()},!0),a=Ft(this),u=t.event.clientX,f=t.event.clientY;Xt(t.event.view),fb(),n.mouse=[a,this.__zoom.invert(a)],Nr(this),n.start()}}function N(){if(r.apply(this,arguments)){var n=this.__zoom,e=Ft(this),a=n.invert(e),u=n.k*(t.event.shiftKey?.5:2),f=o(b(_(n,u),e,a),i.apply(this,arguments),c);cb(),s>0?Dt(this).transition().duration(s).call(x,f,e):Dt(this).call(y.transform,f)}}function S(){if(r.apply(this,arguments)){var e,i,o,a,u=w(this,arguments),f=t.event.changedTouches,c=f.length;for(fb(),i=0;i<c;++i)a=[a=It(this,f,(o=f[i]).identifier),this.__zoom.invert(a),o.identifier],u.touch0?u.touch1||(u.touch1=a):(u.touch0=a,e=!0);if(n&&(n=clearTimeout(n),!u.touch1))return u.end(),void((a=Dt(this).on("dblclick.zoom"))&&a.apply(this,arguments));e&&(n=setTimeout(function(){n=null},p),Nr(this),u.start())}}function E(){var e,r,i,a,u=w(this,arguments),f=t.event.changedTouches,s=f.length;for(cb(),n&&(n=clearTimeout(n)),e=0;e<s;++e)i=It(this,f,(r=f[e]).identifier),u.touch0&&u.touch0[2]===r.identifier?u.touch0[0]=i:u.touch1&&u.touch1[2]===r.identifier&&(u.touch1[0]=i);if(r=u.that.__zoom,u.touch1){var l=u.touch0[0],h=u.touch0[1],d=u.touch1[0],p=u.touch1[1],v=(v=d[0]-l[0])*v+(v=d[1]-l[1])*v,g=(g=p[0]-h[0])*g+(g=p[1]-h[1])*g;r=_(r,Math.sqrt(v/g)),i=[(l[0]+d[0])/2,(l[1]+d[1])/2],a=[(h[0]+p[0])/2,(h[1]+p[1])/2]}else{if(!u.touch0)return;i=u.touch0[0],a=u.touch0[1]}u.zoom("touch",o(b(r,i,a),u.extent,c))}function k(){var n,r,i=w(this,arguments),o=t.event.changedTouches,a=o.length;for(fb(),e&&clearTimeout(e),e=setTimeout(function(){e=null},p),n=0;n<a;++n)r=o[n],i.touch0&&i.touch0[2]===r.identifier?delete i.touch0:i.touch1&&i.touch1[2]===r.identifier&&delete i.touch1;i.touch1&&!i.touch0&&(i.touch0=i.touch1,delete i.touch1),i.touch0?i.touch0[1]=this.__zoom.invert(i.touch0[0]):i.end()}return y.transform=function(t,n){var e=t.selection?t.selection():t;e.property("__zoom",hb),t!==e?x(t,n):e.interrupt().each(function(){w(this,arguments).start().zoom(null,"function"==typeof n?n.apply(this,arguments):n).end()})},y.scaleBy=function(t,n){y.scaleTo(t,function(){return this.__zoom.k*("function"==typeof n?n.apply(this,arguments):n)})},y.scaleTo=function(t,n){y.transform(t,function(){var t=i.apply(this,arguments),e=this.__zoom,r=m(t),a=e.invert(r),u="function"==typeof n?n.apply(this,arguments):n;return o(b(_(e,u),r,a),t,c)})},y.translateBy=function(t,n,e){y.transform(t,function(){return o(this.__zoom.translate("function"==typeof n?n.apply(this,arguments):n,"function"==typeof e?e.apply(this,arguments):e),i.apply(this,arguments),c)})},y.translateTo=function(t,n,e){y.transform(t,function(){var t=i.apply(this,arguments),r=this.__zoom,a=m(t);return o(ab.translate(a[0],a[1]).scale(r.k).translate("function"==typeof n?-n.apply(this,arguments):-n,"function"==typeof e?-e.apply(this,arguments):-e),t,c)})},M.prototype={start:function(){return 1==++this.active&&(this.index=h.push(this)-1,this.emit("start")),this},zoom:function(t,n){return this.mouse&&"mouse"!==t&&(this.mouse[1]=n.invert(this.mouse[0])),this.touch0&&"touch"!==t&&(this.touch0[1]=n.invert(this.touch0[0])),this.touch1&&"touch"!==t&&(this.touch1[1]=n.invert(this.touch1[0])),this.that.__zoom=n,this.emit("zoom"),this},end:function(){return 0==--this.active&&(h.splice(this.index,1),this.index=-1,this.emit("end")),this},emit:function(t){Ct(new ib(y,t,this.that.__zoom),d.apply,d,[t,this.that,this.args])}},y.wheelDelta=function(t){return arguments.length?(a="function"==typeof t?t:rb(+t),y):a},y.filter=function(t){return arguments.length?(r="function"==typeof t?t:rb(!!t),y):r},y.touchable=function(t){return arguments.length?(u="function"==typeof t?t:rb(!!t),y):u},y.extent=function(t){return arguments.length?(i="function"==typeof t?t:rb([[+t[0][0],+t[0][1]],[+t[1][0],+t[1][1]]]),y):i},y.scaleExtent=function(t){return arguments.length?(f[0]=+t[0],f[1]=+t[1],y):[f[0],f[1]]},y.translateExtent=function(t){return arguments.length?(c[0][0]=+t[0][0],c[1][0]=+t[1][0],c[0][1]=+t[0][1],c[1][1]=+t[1][1],y):[[c[0][0],c[0][1]],[c[1][0],c[1][1]]]},y.constrain=function(t){return arguments.length?(o=t,y):o},y.duration=function(t){return arguments.length?(s=+t,y):s},y.interpolate=function(t){return arguments.length?(l=t,y):l},y.on=function(){var t=d.on.apply(d,arguments);return t===d?y:t},y.clickDistance=function(t){return arguments.length?(g=(t=+t)*t,y):Math.sqrt(g)},y},t.zoomTransform=ub,t.zoomIdentity=ab,Object.defineProperty(t,"__esModule",{value:!0})});

//# sourceURL=build://tf-color-scale/palettes.js
var pf;
(function(b){b.palettes={googleStandard:"#db4437 #ff7043 #f4b400 #0f9d58 #00796b #00acc1 #4285f4 #5c6bc0 #ab47bc".split(" "),googleCool:"#9e9d24 #0f9d58 #00796b #00acc1 #4285f4 #5c6bc0 #607d8b".split(" "),googleWarm:"#795548 #ab47bc #f06292 #c2185b #db4437 #ff7043 #f4b400".split(" "),googleColorBlindAssist:"#ff7043 #00ACC1 #AB47BC #2A56C6 #0b8043 #F7CB4D #c0ca33 #5e35b1 #A52714".split(" "),tensorboardColorBlindAssist:"#ff7043 #0077bb #cc3311 #33bbee #ee3377 #009988 #bbbbbb".split(" "),colorBlindAssist1:"#4477aa #44aaaa #aaaa44 #aa7744 #aa4455 #aa4488".split(" "),colorBlindAssist2:"#88ccee #44aa99 #117733 #999933 #ddcc77 #cc6677 #882255 #aa4499".split(" "),
colorBlindAssist3:"#332288 #6699cc #88ccee #44aa99 #117733 #999933 #ddcc77 #cc6677 #aa4466 #882255 #661100 #aa4499".split(" "),colorBlindAssist4:"#4477aa #66ccee #228833 #ccbb44 #ee6677 #aa3377 #bbbbbb".split(" "),colorBlindAssist5:"#FF6DB6 #920000 #924900 #DBD100 #24FF24 #006DDB #490092".split(" "),mldash:"#E47EAD #F4640D #FAA300 #F5E636 #00A077 #0077B8 #00B7ED".split(" ")};b.standard=b.palettes.tensorboardColorBlindAssist})(pf||(pf={}));

//# sourceURL=build://tf-color-scale/colorScale.js
(function(b){function d(h,k){function t(){l.setDomain(k())}const l=new f;h.addListener(t);t();return p=>l.getColor(p)}class f{constructor(h=b.standard){this.palette=h;this.identifiers=d3.map()}setDomain(h){this.identifiers=d3.map();h.forEach((k,t)=>{this.identifiers.set(k,this.palette[t%this.palette.length])})}getColor(h){if(!this.identifiers.has(h))throw Error(`String ${h} was not in the domain.`);return this.identifiers.get(h)}}b.ColorScale=f;b.runsColorScale=d(vc.runsStore,()=>vc.runsStore.getRuns());
b.experimentsColorScale=d(vc.experimentsStore,()=>vc.experimentsStore.getExperiments().map(({name:h})=>h))})(pf||(pf={}));

//# sourceURL=build://paper-icon-button/paper-icon-button.html.js
Polymer({is:"paper-icon-button",hostAttributes:{role:"button",tabindex:"0"},behaviors:[Polymer.PaperInkyFocusBehavior],properties:{src:{type:String},icon:{type:String},alt:{type:String,observer:"_altChanged"}},_altChanged:function(b,d){var f=this.getAttribute("aria-label");f&&d!=f||this.setAttribute("aria-label",b)}});

//# sourceURL=build://tf-dashboard-common/tf-multi-checkbox.js
(function(){Polymer({is:"tf-multi-checkbox",properties:{names:{type:Array,value:()=>[]},coloring:{type:Object,value:{getColor:()=>""}},regex:{type:String,notify:!0,value:""},_regex:{type:Object,computed:"_makeRegex(regex)"},namesMatchingRegex:{type:Array,computed:"computeNamesMatchingRegex(names.*, _regex)"},selectionState:{type:Object,notify:!0,value:()=>({})},outSelected:{type:Array,notify:!0,computed:"computeOutSelected(namesMatchingRegex.*, selectionState.*)"},maxNamesToEnableByDefault:{type:Number,
value:40},_debouncedRegexChange:{type:Object,value:function(){var b=_.debounce(d=>{this.regex=d},150,{leading:!1});return function(){var d=this.$$("#names-regex").value;""==d?this.async(()=>{this.regex=d},30):b(d)}}}},observers:["_setIsolatorIcon(selectionState, names)"],_makeRegex:function(b){try{return new RegExp(b)}catch(d){return null}},_setIsolatorIcon:function(){var b=this.selectionState,d=_.filter(_.values(b)).length;Array.prototype.slice.call(this.root.querySelectorAll(".isolator")).forEach(function(f){f.icon=
1===d&&b[f.name]?"radio-button-checked":"radio-button-unchecked"})},computeNamesMatchingRegex:function(){const b=this._regex;return b?this.names.filter(d=>b.test(d)):this.names},computeOutSelected:function(){var b=this.selectionState,d=this.namesMatchingRegex.length<=this.maxNamesToEnableByDefault;return this.namesMatchingRegex.filter(f=>null==b[f]?d:b[f])},synchronizeColors:function(){this._setIsolatorIcon();this.root.querySelectorAll("paper-checkbox").forEach(b=>{const d=this.coloring.getColor(b.name);
b.updateStyles({"--paper-checkbox-checked-color":d,"--paper-checkbox-checked-ink-color":d,"--paper-checkbox-unchecked-color":d,"--paper-checkbox-unchecked-ink-color":d})});this.root.querySelectorAll(".isolator").forEach(b=>{const d=this.coloring.getColor(b.name);b.style.color=d});window.requestAnimationFrame(()=>{this.updateStyles()})},_isolateName:function(b){var d=Polymer.dom(b).localTarget.name,f={};this.names.forEach(function(h){f[h]=h==d});this.selectionState=f},_checkboxChange:function(b){b=
Polymer.dom(b).localTarget;const d=_.clone(this.selectionState);d[b.name]=b.checked;this.selectionState=d},_isChecked:function(b){return-1!=this.outSelected.indexOf(b)},toggleAll:function(){const b=this.namesMatchingRegex.some(f=>this.outSelected.includes(f)),d={};this.names.forEach(f=>{d[f]=!b});this.selectionState=d}})})(qd||(qd={}));

//# sourceURL=build://tf-runs-selector/tf-wbr-string.html.js
Polymer({is:"tf-wbr-string",properties:{value:String,_parts:{type:Array,computed:"_computeParts(value)"}},_computeParts(b){const d=[],f=/[/=_,-]/;for(null==b&&(b="");;){const h=b.search(f);if(-1===h){d.push(b);break}else d.push(b.slice(0,h+1)),b=b.slice(h+1)}return d}});

//# sourceURL=build://tf-runs-selector/tf-runs-selector.html.js
Polymer({is:"tf-runs-selector",properties:{runSelectionState:{type:Object,observer:"_storeRunSelectionState",value:pd.getObjectInitializer("runSelectionState",{defaultValue:{}})},regexInput:{type:String,value:pd.getStringInitializer("regexInput",{defaultValue:""}),observer:"_regexObserver"},selectedRuns:{type:Array,notify:!0},runs:Array,dataLocation:{type:String,notify:!0},_clippedDataLocation:{type:String,computed:"_getClippedDataLocation(dataLocation, _dataLocationClipLength)"},_dataLocationClipLength:{type:Number,
value:250,readOnly:!0},coloring:{type:Object,value:{getColor:pf.runsColorScale}}},attached(){this._runStoreListener=vc.runsStore.addListener(()=>{this.set("runs",vc.runsStore.getRuns())});this.set("runs",vc.runsStore.getRuns());this._envStoreListener=vc.environmentStore.addListener(()=>{this.set("dataLocation",vc.environmentStore.getDataLocation())});this.set("dataLocation",vc.environmentStore.getDataLocation())},detached(){vc.runsStore.removeListenerByKey(this._runStoreListener);vc.environmentStore.removeListenerByKey(this._envStoreListener)},
_toggleAll:function(){this.$.multiCheckbox.toggleAll()},_getClippedDataLocation:function(b,d){if(void 0!==b&&!(b.length>d))return b},_openDataLocationDialog:function(b){b.preventDefault();this.$$("#data-location-dialog").open()},_shouldShowExpandDataLocationButton(b,d){return b&&b.length>d},_storeRunSelectionState:pd.getObjectObserver("runSelectionState",{defaultValue:{}}),_regexObserver:pd.getStringObserver("regexInput",{defaultValue:""})});

//# sourceURL=build://tf-tensorboard/registry.js
var qf;
(function(b){(function(d){d.NOT_LOADED="NOT_LOADED";d.LOADED="LOADED";d.FAILED="FAILED"})(b.ActiveDashboardsLoadState||(b.ActiveDashboardsLoadState={}));b.dashboardRegistry={};b.registerDashboard=function(){var d={plugin:"beholder",elementName:"tf-beholder-dashboard",shouldRemoveDom:!0};if(!d.plugin)throw Error("Dashboard.plugin must be present");if(!d.elementName)throw Error("Dashboard.elementName must be present");if(d.plugin in b.dashboardRegistry)throw Error(`Plugin already registered: ${d.plugin}`);d.tabName||
(d.tabName=d.plugin);b.dashboardRegistry[d.plugin]=d}})(qf||(qf={}));

//# sourceURL=build://tf-utils/utils.js
var rf;
(function(b){function d(f,h,k){return 1===f?h:k}b.aggregateTagInfo=function(f,h){let k=void 0;const t={};Object.keys(f).forEach(p=>{const m=f[p];void 0===k&&(k=m.displayName);k!==m.displayName&&(k=null);void 0===t[m.description]&&(t[m.description]=[]);t[m.description].push(p)});h=null!=k?k:h;const l=(()=>{const p=Object.keys(t);return 0===p.length?"":1===p.length?p[0]:`${"\x3cp\x3e\x3cstrong\x3eMultiple descriptions:\x3c/strong\x3e\x3c/p\x3e"}<ul>${p.map(m=>{const n=t[m].map(u=>`<code>${u.replace(/</g,"\x26lt;").replace(/>/g,
"\x26gt;").replace(/&/g,"\x26amp;")}</code>`),q=2<n.length?n.slice(0,n.length-1).join(", ")+", and "+n[n.length-1]:n.join(" and ");return`<li><p>For ${d(n.length,"run","runs")} ${q}:</p>${m}</li>`}).join("")}</ul>`})();return{displayName:h,description:l}}})(rf||(rf={}));

//# sourceURL=build://paper-spinner/paper-spinner-behavior.html.js
Polymer.PaperSpinnerBehavior={properties:{active:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"__activeChanged"},alt:{type:String,value:"loading",observer:"__altChanged"},__coolingDown:{type:Boolean,value:!1}},__computeContainerClasses:function(b,d){return[b||d?"active":"",d?"cooldown":""].join(" ")},__activeChanged:function(b,d){this.__setAriaHidden(!b);this.__coolingDown=!b&&d},__altChanged:function(b){"loading"===b?this.alt=this.getAttribute("aria-label")||b:(this.__setAriaHidden(""===
b),this.setAttribute("aria-label",b))},__setAriaHidden:function(b){b?this.setAttribute("aria-hidden","true"):this.removeAttribute("aria-hidden")},__reset:function(){this.__coolingDown=this.active=!1}};

//# sourceURL=build://paper-spinner/paper-spinner-lite.html.js
Polymer({is:"paper-spinner-lite",behaviors:[Polymer.PaperSpinnerBehavior]});

//# sourceURL=build://tf-dashboard-common/data-loader-behavior.js
(function(b){let d;(function(f){f[f.LOADING=0]="LOADING";f[f.LOADED=1]="LOADED"})(d||(d={}));b.DataLoaderBehavior={properties:{active:{type:Boolean,observer:"_loadDataIfActive"},loadKey:{type:String,value:""},dataToLoad:{type:Array,value:()=>[]},getDataLoadName:{type:Function,value:()=>f=>String(f)},loadDataCallback:Function,requestData:{type:Function,value:function(){return f=>this.requestManager.request(this.getDataLoadUrl(f))}},getDataLoadUrl:Function,dataLoading:{type:Boolean,readOnly:!0,reflectToAttribute:!0,
value:!1},_dataLoadState:{type:Object,value:()=>new Map},_canceller:{type:Object,value:()=>new vc.Canceller},_loadDataAsync:{type:Number,value:null}},observers:["_dataToLoadChanged(isAttached, dataToLoad.*)"],onLoadFinish(){},reload(){this._dataLoadState.clear();this._loadData()},reset(){null!=this._loadDataAsync&&(this.cancelAsync(this._loadDataAsync),this._loadDataAsync=null);this._canceller&&this._canceller.cancelAll();this._dataLoadState&&this._dataLoadState.clear();this.isAttached&&this._loadData()},
_dataToLoadChanged(){this.isAttached&&this._loadData()},created(){this._loadData=_.throttle(this._loadDataImpl,100,{leading:!0,trailing:!0})},detached(){null!=this._loadDataAsync&&(this.cancelAsync(this._loadDataAsync),this._loadDataAsync=null)},_loadDataIfActive(){this.active&&this._loadData()},_loadDataImpl(){this.active&&(this.cancelAsync(this._loadDataAsync),this._loadDataAsync=this.async(this._canceller.cancellable(f=>{if(!f.cancelled)return this._setDataLoading(!0),f=this.dataToLoad.filter(h=>
{h=this.getDataLoadName(h);return!this._dataLoadState.has(h)}).map(h=>{const k=this.getDataLoadName(h);this._dataLoadState.set(k,d.LOADING);return this.requestData(h).then(this._canceller.cancellable(t=>{t.cancelled||(this._dataLoadState.set(k,d.LOADED),this.loadDataCallback(this,h,t.value));return k}))}),Promise.all(f).then(this._canceller.cancellable(h=>{if(!h.cancelled){const k=new Set(h.value);if(this.dataToLoad.some(t=>k.has(this.getDataLoadName(t))))this.onLoadFinish()}Array.from(this._dataLoadState.values()).some(k=>
k===d.LOADING)||this._setDataLoading(!1)}),()=>{}).then(this._canceller.cancellable(({cancelled:h})=>{h||(this._loadDataAsync=null)}))})))}}})(qd||(qd={}));

//# sourceURL=build://tf-imports/plottable.js
/*
 MIT
 MIT
 @fileoverview Implements the Signature API to help in comparing when two
 Plottable objects have "changed".

 Memoization in Plottable is complicated by mutable scales and datasets. We cannot simply
 reference compare two e.g. scales since it may have internally mutated. To resolve this,
 we write a recursive Signature interface that holds an immutable snapshot of whatever
 state the scale/data was in at the time. Then on memoized function invocation we sign the
 new inputs and compare the signatures to decide if we should recompute.

 We must hand-write a signature for each custom class we wish to support.
 MIT

 @fileoverview manually add d3-selection-multi to d3 default bundle. Most of this code is
 copied from d3-selection-multi@1.0.0.
 See https://github.com/d3/d3-selection-multi/issues/11 for why we have to do this
 MIT
 @fileoverview Implements a convenient thunk function to handle the common case
 of creating a memoized function that takes its inputs from mutable class properties.
 MIT
 @fileoverview Implements a function memoizer using the Signature API.
 Plottable 3.7.0 (https://github.com/palantir/plottable)
 Copyright 2014-2017 Palantir Technologies
 Licensed under MIT (https://github.com/palantir/plottable/blob/master/LICENSE)
 is-plain-object <https://github.com/jonschlinkert/is-plain-object>

 Copyright (c) 2014-2017, Jon Schlinkert.
 Released under the MIT License.
 isobject <https://github.com/jonschlinkert/isobject>

 Copyright (c) 2014-2017, Jon Schlinkert.
 Released under the MIT License.
*/
(function(b,d){"object"===typeof exports&&"object"===typeof module?module.exports=d(require("d3")):"function"===typeof define&&define.amd?define(["d3"],d):"object"===typeof exports?exports.Plottable=d(require("d3")):b.Plottable=d(b.d3)})(this,function(b){return function(d){function f(k){if(h[k])return h[k].exports;var t=h[k]={i:k,l:!1,exports:{}};d[k].call(t.exports,t,t.exports,f);t.l=!0;return t.exports}var h={};f.m=d;f.c=h;f.i=function(k){return k};f.d=function(k,t,l){f.o(k,t)||Object.defineProperty(k,
t,{configurable:!1,enumerable:!0,get:l})};f.n=function(k){var t=k&&k.__esModule?function(){return k["default"]}:function(){return k};f.d(t,"a",t);return t};f.o=function(k,t){return Object.prototype.hasOwnProperty.call(k,t)};f.p="";return f(f.s=140)}([function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}d=h(107);f.Array=d;d=h(110);f.Color=d;d=h(55);f.DOM=d;d=h(56);f.Math=d;d=h(113);f.Object=d;d=h(57);f.RTree=d;d=h(115);f.Stacking=d;d=h(35);f.Window=d;k(h(108));k(h(109));k(h(12));
k(h(111));k(h(112));k(h(58));k(h(116))},function(d){d.exports=b},function(d,f,h){function k(G,D,B){var I=D.accessor;D=D.scale;if(null==D)return[];var N=G.data();null!=B&&(N=N.filter(function(O,H){return B(O,H,G)}));N=N.map(function(O,H){return I(O,H,G)});return D.extentOfValues(N)}var t=this&&this.__extends||function(G,D){function B(){this.constructor=G}for(var I in D)D.hasOwnProperty(I)&&(G[I]=D[I]);G.prototype=null===D?Object.create(D):(B.prototype=D.prototype,new B)},l=h(1),p=h(7);d=h(4);var m=
h(18),n=h(6),q=h(9),u=h(20),x=h(0),A=h(12),y=h(10),w=h(51),C=h(52);f.Renderer=y.makeEnum(["svg","canvas"]);h=function(G){function D(){var B=G.call(this)||this;B._dataChanged=!1;B._attrExtents={};B._animate=!1;B._animators={};B._propertyExtents={};B._resetEntityStore=function(){B._cachedEntityStore=void 0};B._overflowHidden=!0;B.addClass("plot");B._datasetToDrawer=new x.Map;B._attrBindings=l.map();B._includedValuesProvider=function(N,O){return B._includedValuesForScale(N,O)};B._renderCallback=function(){return B.render()};
B._onDatasetUpdateCallback=function(){return B._onDatasetUpdate()};B._propertyBindings=l.map();var I=(new p.Easing).maxTotalDuration(D._ANIMATION_MAX_DURATION);B.animator(w.Animator.MAIN,I);B.animator(w.Animator.RESET,new p.Null);B._deferredResetEntityStore=x.Window.debounce(C.DeferredRenderer.DEFERRED_RENDERING_DELAY,B._resetEntityStore);return B}t(D,G);D.getTotalDrawTime=function(B,I){return I.reduce(function(N,O){return N+O.animator.totalTime(B.length)},0)};D.applyDrawSteps=function(B,I){return B.map(function(N){var O=
N.attrToProjector,H={};Object.keys(O).forEach(function(K){H[K]=function(M,L){return O[K](M,L,I)}});return{attrToAppliedProjector:H,animator:N.animator}})};D.prototype.anchor=function(B){B=A.coerceExternalD3(B);G.prototype.anchor.call(this,B);this._dataChanged=!0;this._resetEntityStore();this._updateExtents();return this};D.prototype._setup=function(){var B=this;this._isSetup||(G.prototype._setup.call(this),null!=this._canvas&&this._appendCanvasNode(),this._renderArea=this.content().append("g").classed("render-area",
!0),this.datasets().forEach(function(I){return B._createNodesForDataset(I)}))};D.prototype._appendCanvasNode=function(){var B=this.element().select(".plot-canvas-container");B.empty()&&(B=this.element().append("div").classed("plot-canvas-container",!0),B.node().appendChild(this._canvas.node()))};D.prototype.setBounds=function(B,I,N,O){G.prototype.setBounds.call(this,B,I,N,O);this._resetEntityStore();null!=this._canvas&&(this._bufferCanvas&&!this._bufferCanvasValid&&(this._bufferCanvas.attr("width",
this._canvas.attr("width")),this._bufferCanvas.attr("height",this._canvas.attr("height")),(N=this._bufferCanvas.node().getContext("2d"))&&N.drawImage(this._canvas.node(),0,0),this._bufferCanvasValid=!0),N=null!=window.devicePixelRatio?window.devicePixelRatio:1,this._canvas.attr("width",B*N),this._canvas.attr("height",I*N),O=this._canvas.node().getContext("2d"))&&(O.setTransform(N,0,0,N,0,0),this._bufferCanvas&&O.drawImage(this._bufferCanvas.node(),0,0,B,I))};D.prototype.destroy=function(){var B=this;
G.prototype.destroy.call(this);this._scales().forEach(function(I){return I.offUpdate(B._renderCallback)});this.datasets([])};D.prototype._createNodesForDataset=function(B){B=this._datasetToDrawer.get(B);"svg"===this.renderer()?B.useSVG(this._renderArea):B.useCanvas(this._canvas);return B};D.prototype._createDrawer=function(){return new n.ProxyDrawer(function(){return new q.SVGDrawer("path","")},function(B){return new m.CanvasDrawer(B,function(){})})};D.prototype._getAnimator=function(B){return this._animateOnNextRender()?
this._animators[B]||new p.Null:new p.Null};D.prototype._onDatasetUpdate=function(){this._updateExtents();this._dataChanged=!0;this._resetEntityStore();this.renderLowPriority()};D.prototype.attr=function(B,I,N){if(null==I)return this._attrBindings.get(B);this._bindAttr(B,I,N);this.render();return this};D.prototype._bindProperty=function(B,I,N,O){var H=this._propertyBindings.get(B);H=null!=H?H.scale:null;this._propertyBindings.set(B,{accessor:"function"===typeof I?I:function(){return I},scale:N,postScale:O});
null!=H&&this._uninstallScaleForKey(H,B);null!=N&&this._installScaleForKey(N,B);this._clearAttrToProjectorCache()};D.prototype._bindAttr=function(B,I,N){var O=this._attrBindings.get(B);O=null!=O?O.scale:null;this._attrBindings.set(B,{accessor:"function"===typeof I?I:function(){return I},scale:N});null!=O&&this._uninstallScaleForKey(O,B);null!=N&&this._installScaleForKey(N,B);this._clearAttrToProjectorCache()};D.prototype._clearAttrToProjectorCache=function(){delete this._cachedAttrToProjector};D.prototype._getAttrToProjector=
function(){null==this._cachedAttrToProjector&&(this._cachedAttrToProjector=this._generateAttrToProjector());return x.Object.assign({},this._cachedAttrToProjector)};D.prototype._generateAttrToProjector=function(){var B={};this._attrBindings.each(function(N,O){B[O]=D._scaledAccessor(N)});var I=this._propertyProjectors();Object.keys(I).forEach(function(N){null==B[N]&&(B[N]=I[N])});return B};D.prototype.renderImmediately=function(){G.prototype.renderImmediately.call(this);this._isAnchored&&(this._paint(),
this._dataChanged=!1);return this};D.prototype.renderLowPriority=function(){this._renderCallback()};D.prototype.animated=function(B){if(null==B)return this._animate;this._animate=B;return this};D.prototype.detach=function(){G.prototype.detach.call(this);this._updateExtents();return this};D.prototype._scales=function(){var B=[];this._attrBindings.each(function(I){I=I.scale;null!=I&&-1===B.indexOf(I)&&B.push(I)});this._propertyBindings.each(function(I){I=I.scale;null!=I&&-1===B.indexOf(I)&&B.push(I)});
return B};D.prototype._updateExtents=function(){var B=this;this._resetEntityStore();this._scales().forEach(function(I){return I.addIncludedValuesProvider(B._includedValuesProvider)})};D.prototype._filterForProperty=function(){return null};D.prototype.getExtentsForAttr=function(B){var I=this;null==this._attrExtents[B]&&(this._attrExtents[B]=u.memThunk(function(){return I.datasets()},function(){return I._attrBindings.get(B)},function(N,O){return null==O||null==O.accessor?null:N.map(function(H){return k(H,
O,null)})}));return this._attrExtents[B]()};D.prototype.getExtentsForProperty=function(B){var I=this;null==this._propertyExtents[B]&&(this._propertyExtents[B]=u.memThunk(function(){return I.datasets()},function(){return I._propertyBindings.get(B)},function(){return I._filterForProperty(B)},function(N,O,H){return null==O||null==O.accessor?null:N.map(function(K){return k(K,O,H)})}));return this._propertyExtents[B]()};D.prototype._includedValuesForScale=function(B,I){var N=this;if(!this._isAnchored&&
!I)return[];var O=[];this._attrBindings.each(function(H,K){H.scale===B&&(H=N.getExtentsForAttr(K),null!=H&&(O=O.concat(l.merge(H))))});this._propertyBindings.each(function(H,K){H.scale===B&&(H=N.getExtentsForProperty(K),null!=H&&(O=O.concat(l.merge(H))))});return O};D.prototype.animator=function(B,I){if(void 0===I)return this._animators[B];this._animators[B]=I;return this};D.prototype.renderer=function(){return null==this._canvas?"svg":"canvas"};D.prototype.addDataset=function(B){this._addDataset(B);
this._onDatasetUpdate()};D.prototype._addDataset=function(B){this._removeDataset(B);var I=this._createDrawer(B);this._datasetToDrawer.set(B,I);this._isSetup&&this._createNodesForDataset(B);B.onUpdate(this._onDatasetUpdateCallback);return this};D.prototype.removeDataset=function(B){this._removeDataset(B);this._onDatasetUpdate()};D.prototype._removeDataset=function(B){if(-1===this.datasets().indexOf(B))return this;this._removeDatasetNodes(B);B.offUpdate(this._onDatasetUpdateCallback);this._datasetToDrawer.delete(B);
return this};D.prototype._removeDatasetNodes=function(B){this._datasetToDrawer.get(B).remove()};D.prototype.datasets=function(B){var I=this,N=[];this._datasetToDrawer.forEach(function(O,H){return N.push(H)});if(null==B)return N;N.forEach(function(O){return I._removeDataset(O)});B.forEach(function(O){return I._addDataset(O)});this._onDatasetUpdate();return this};D.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new p.Null}]};D.prototype._additionalPaint=
function(){};D.prototype._buildLightweightPlotEntities=function(B){var I=this,N=[];B.forEach(function(O,H){var K=I._datasetToDrawer.get(O),M=0;O.data().forEach(function(L,Q){var T=I._pixelPoint(L,Q,O);x.Math.isNaN(T.x)||x.Math.isNaN(T.y)||(N.push({datum:L,get position(){return I._pixelPoint.call(I,L,Q,O)},index:Q,dataset:O,datasetIndex:H,component:I,drawer:K,validDatumIndex:M}),M++)})});return N};D.prototype._getDataToDraw=function(){var B=new x.Map;this.datasets().forEach(function(I){return B.set(I,
I.data())});return B};D.prototype._paint=function(){var B=this;delete this._cachedAttrToProjector;var I=this._generateDrawSteps(),N=this._getDataToDraw(),O=this.datasets().map(function(K){return B._datasetToDrawer.get(K)});if("canvas"===this.renderer()){var H=this._canvas.node();H.getContext("2d").clearRect(0,0,H.clientWidth,H.clientHeight);this._bufferCanvasValid=!1}this.datasets().forEach(function(K,M){var L=D.applyDrawSteps(I,K);O[M].draw(N.get(K),L)});H=this.datasets().map(function(K){return D.getTotalDrawTime(N.get(K),
I)});H=x.Math.max(H,0);this._additionalPaint(H)};D.prototype.selections=function(B){var I=this;void 0===B&&(B=this.datasets());if("canvas"===this.renderer())return l.selectAll();var N=[];B.forEach(function(O){O=I._datasetToDrawer.get(O);null!=O&&(O=O.getVisualPrimitives(),N.push.apply(N,O))});return l.selectAll(N)};D.prototype.entities=function(B){var I=this;return this._getEntityStore(B).entities().map(function(N){return I._lightweightPlotEntityToPlotEntity(N)})};D.prototype._getEntityStore=function(B){function I(H){return N._entityBounds(H)}
var N=this;if(void 0!==B){var O=new x.EntityStore;O.addAll(this._buildLightweightPlotEntities(B),I,this._localOriginBounds());return O}void 0===this._cachedEntityStore&&(O=new x.EntityStore,O.addAll(this._buildLightweightPlotEntities(this.datasets()),I,this._localOriginBounds()),this._cachedEntityStore=O);return this._cachedEntityStore};D.prototype._localOriginBounds=function(){return{topLeft:{x:0,y:0},bottomRight:{x:this.width(),y:this.height()}}};D.prototype._entityBounds=function(B){B=this._pixelPoint(B.datum,
B.index,B.dataset);return{x:B.x,y:B.y,width:0,height:0}};D.prototype._lightweightPlotEntityToPlotEntity=function(B){return{bounds:this._entityBounds(B),component:B.component,dataset:B.dataset,datasetIndex:B.datasetIndex,datum:B.datum,index:B.index,position:B.position,selection:l.select(B.drawer.getVisualPrimitives()[B.validDatumIndex])}};D.prototype.entitiesAt=function(){throw Error("plots must implement entitiesAt");};D.prototype.entityNearest=function(B){B=this._getEntityStore().entityNearest(B);
return void 0===B?void 0:this._lightweightPlotEntityToPlotEntity(B)};D.prototype.entitiesIn=function(B,I){return this.entitiesInBounds(null==I?{x:B.topLeft.x,y:B.topLeft.y,width:B.bottomRight.x-B.topLeft.x,height:B.bottomRight.y-B.topLeft.y}:{x:B.min,y:I.min,width:B.max-B.min,height:I.max-I.min})};D.prototype.entitiesInBounds=function(B){var I=this;if(B=this._getEntityStore().entitiesInBounds(B))return B.map(function(N){return I._lightweightPlotEntityToPlotEntity(N)})};D.prototype.entitiesInXBounds=
function(B){var I=this;if(B=this._getEntityStore().entitiesInXBounds(B))return B.map(function(N){return I._lightweightPlotEntityToPlotEntity(N)})};D.prototype.entitiesInYBounds=function(B){var I=this;if(B=this._getEntityStore().entitiesInYBounds(B))return B.map(function(N){return I._lightweightPlotEntityToPlotEntity(N)})};D.prototype._uninstallScaleForKey=function(B){B.offUpdate(this._renderCallback);B.offUpdate(this._deferredResetEntityStore);B.removeIncludedValuesProvider(this._includedValuesProvider)};
D.prototype._installScaleForKey=function(B){B.onUpdate(this._renderCallback);B.onUpdate(this._deferredResetEntityStore);B.addIncludedValuesProvider(this._includedValuesProvider)};D.prototype._propertyProjectors=function(){return{}};D._scaledAccessor=function(B){var I=B.scale,N=B.accessor,O=B.postScale,H=null==I?N:function(K,M,L){return I.scale(N(K,M,L))};return null==O?H:function(K,M,L){return O(H(K,M,L),K,M,L)}};D.prototype._pixelPoint=function(){return{x:0,y:0}};D.prototype._animateOnNextRender=
function(){return this._animate&&this._dataChanged};return D}(d.Component);h._ANIMATION_MAX_DURATION=600;f.Plot=h},function(d,f,h){function k(p){for(var m in p)f.hasOwnProperty(m)||(f[m]=p[m])}d=h(105);f.TickGenerators=d;k(h(54));k(h(101));k(h(102));k(h(103));k(h(104));k(h(106));var t=h(54),l=h(11);f.isTransformable=function(p){return p instanceof l.QuantitativeScale||p instanceof t.Category}},function(d,f,h){var k=h(1),t=h(30),l=h(0),p=h(12);d=h(10);f.XAlignment=d.makeEnum(["left","center","right"]);
f.YAlignment=d.makeEnum(["top","center","bottom"]);d=function(){function m(){this._overflowHidden=!1;this._origin={x:0,y:0};this._xAlignment="left";this._yAlignment="top";this._isAnchored=this._isSetup=!1;this._cssClasses=new l.Set;this._destroyed=!1;this._onAnchorCallbacks=new l.CallbackSet;this._onDetachCallbacks=new l.CallbackSet;this._cssClasses.add("component")}m.prototype.anchor=function(n){n=p.coerceExternalD3(n);if(this._destroyed)throw Error("Can't reuse destroy()-ed Components!");this.isRoot()&&
(this._rootElement=n,this._rootElement.classed("plottable",!0));null!=this._element?n.node().appendChild(this._element.node()):(this._element=n.append("div"),this._setup());this._isAnchored=!0;this._onAnchorCallbacks.callCallbacks(this);return this};m.prototype.onAnchor=function(n){this._isAnchored&&n(this);this._onAnchorCallbacks.add(n)};m.prototype.offAnchor=function(n){this._onAnchorCallbacks.delete(n)};m.prototype._setup=function(){var n=this;this._isSetup||(this._cssClasses.forEach(function(q){n._element.classed(q,
!0)}),this._cssClasses=new l.Set,this._backgroundContainer=this._element.append("svg").classed("background-container",!0),this._content=this._element.append("svg").classed("content",!0),this._foregroundContainer=this._element.append("svg").classed("foreground-container",!0),this._overflowHidden?this._content.classed("component-overflow-hidden",!0):this._content.classed("component-overflow-visible",!0),this._isSetup=!0)};m.prototype.requestedSpace=function(){return{minWidth:0,minHeight:0}};m.prototype.computeLayout=
function(n,q,u){if(null==n||null==q||null==u){if(null==this._element)throw Error("anchor() must be called before computeLayout()");if(null!=this._rootElement)n={x:0,y:0},u=this._rootElement.node(),q=l.DOM.elementWidth(u),u=l.DOM.elementHeight(u);else throw Error("null arguments cannot be passed to computeLayout() on a non-root, unanchored node");}var x=this._sizeFromOffer(q,u),A=x.height;x=x.width;this.setBounds(x,A,n.x+(q-x)*m._xAlignToProportion[this._xAlignment],n.y+(u-A)*m._yAlignToProportion[this._yAlignment]);
return this};m.prototype.setBounds=function(n,q,u,x){void 0===u&&(u=0);void 0===x&&(x=0);this._width=n;this._height=q;this._origin={x:u,y:x};null!=this._element&&this._element.styles({left:u+"px",height:q+"px",top:x+"px",width:n+"px"});null!=this._resizeHandler&&this._resizeHandler({width:n,height:q})};m.prototype._sizeFromOffer=function(n,q){var u=this.requestedSpace(n,q);return{width:this.fixedWidth()?Math.min(n,u.minWidth):n,height:this.fixedHeight()?Math.min(q,u.minHeight):q}};m.prototype.render=
function(){this._isAnchored&&this._isSetup&&0<=this.width()&&0<=this.height()&&t.registerToRender(this);return this};m.prototype.renderLowPriority=function(){this.render()};m.prototype._scheduleComputeLayout=function(){this._isAnchored&&this._isSetup&&t.registerToComputeLayoutAndRender(this)};m.prototype.onResize=function(n){this._resizeHandler=n;return this};m.prototype.renderImmediately=function(){return this};m.prototype.redraw=function(){this._isAnchored&&this._isSetup&&(this.isRoot()?this._scheduleComputeLayout():
this.parent().redraw());return this};m.prototype.invalidateCache=function(){};m.prototype.renderTo=function(n){this.detach();if(null!=n){n="string"===typeof n?k.select(n):n instanceof Element?k.select(n):p.coerceExternalD3(n);if(!n.node()||null==n.node().nodeName)throw Error("Plottable requires a valid Element to renderTo");if("svg"===n.node().nodeName)throw Error("Plottable 3.x and later can only renderTo an HTML component; pass a div instead!");this.anchor(n)}if(null==this._element)throw Error("If a Component has never been rendered before, then renderTo must be given a node to render to, or a d3.Selection, or a selector string");
t.registerToComputeLayoutAndRender(this);t.flush()};m.prototype.xAlignment=function(n){if(null==n)return this._xAlignment;n=n.toLowerCase();if(null==m._xAlignToProportion[n])throw Error("Unsupported alignment: "+n);this._xAlignment=n;this.redraw();return this};m.prototype.yAlignment=function(n){if(null==n)return this._yAlignment;n=n.toLowerCase();if(null==m._yAlignToProportion[n])throw Error("Unsupported alignment: "+n);this._yAlignment=n;this.redraw();return this};m.prototype.hasClass=function(n){return null==
n?!1:null==this._element?this._cssClasses.has(n):this._element.classed(n)};m.prototype.addClass=function(n){null!=n&&(null==this._element?this._cssClasses.add(n):this._element.classed(n,!0))};m.prototype.removeClass=function(n){null!=n&&(null==this._element?this._cssClasses.delete(n):this._element.classed(n,!1))};m.prototype.fixedWidth=function(){return!1};m.prototype.fixedHeight=function(){return!1};m.prototype.detach=function(){this.parent(null);this._isAnchored&&this._element.remove();this._isAnchored=
!1;this._onDetachCallbacks.callCallbacks(this);return this};m.prototype.onDetach=function(n){this._onDetachCallbacks.add(n)};m.prototype.offDetach=function(n){this._onDetachCallbacks.delete(n)};m.prototype.parent=function(n){if(void 0===n)return this._parent;if(null!==n&&!n.has(this))throw Error("Passed invalid parent");this._parent=n;return this};m.prototype.bounds=function(){var n=this.origin();return{topLeft:n,bottomRight:{x:n.x+this.width(),y:n.y+this.height()}}};m.prototype.destroy=function(){this._destroyed=
!0;this.detach()};m.prototype.width=function(){return this._width};m.prototype.height=function(){return this._height};m.prototype.origin=function(){return{x:this._origin.x,y:this._origin.y}};m.prototype.originToRoot=function(){for(var n=this.origin(),q=this.parent();null!=q;){var u=q.origin();n.x+=u.x;n.y+=u.y;q=q.parent()}return n};m.prototype.root=function(){for(var n=this;!n.isRoot();)n=n.parent();return n};m.prototype.isRoot=function(){return null==this.parent()};m.prototype.foreground=function(){return this._foregroundContainer};
m.prototype.content=function(){return this._content};m.prototype.element=function(){return this._element};m.prototype.rootElement=function(){return this.root()._rootElement};m.prototype.background=function(){return this._backgroundContainer};return m}();d._xAlignToProportion={left:0,center:.5,right:1};d._yAlignToProportion={top:0,center:.5,bottom:1};f.Component=d},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(59));k(h(62));k(h(133));k(h(21));k(h(64));k(h(66))},
function(d,f){d=function(){function h(k,t){this._svgDrawerFactory=k;this._canvasDrawerFactory=t}h.prototype.useSVG=function(k){null!=this._currentDrawer&&this._currentDrawer.remove();var t=this._svgDrawerFactory();t.attachTo(k);this._currentDrawer=t};h.prototype.useCanvas=function(k){null!=this._currentDrawer&&this._currentDrawer.remove();this._currentDrawer=this._canvasDrawerFactory(k.node().getContext("2d"))};h.prototype.getDrawer=function(){return this._currentDrawer};h.prototype.remove=function(){null!=
this._currentDrawer&&this._currentDrawer.remove()};h.prototype.draw=function(k,t){this._currentDrawer.draw(k,t)};h.prototype.getVisualPrimitives=function(){return this._currentDrawer.getVisualPrimitives()};h.prototype.getVisualPrimitiveAtIndex=function(k){return this._currentDrawer.getVisualPrimitiveAtIndex(k)};return h}();f.ProxyDrawer=d},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(70));k(h(71))},function(d,f,h){function k(p){void 0===p&&(p=3);t(p);return function(m){return m.toFixed(p)}}
function t(p){if(0>p||20<p)throw new RangeError("Formatter precision must be between 0 and 20");if(p!==Math.floor(p))throw new RangeError("Formatter precision must be an integer");}var l=h(1);f.currency=function(p,m,n){void 0===p&&(p=2);void 0===m&&(m="$");void 0===n&&(n=!0);var q=k(p);return function(u){var x=q(Math.abs(u));""!==x&&(x=n?m+x:x+m,0>u&&(x="-"+x));return x}};f.fixed=k;f.general=function(){var p;void 0===p&&(p=3);t(p);return function(m){if("number"===typeof m){var n=Math.pow(10,p);return String(Math.round(m*
n)/n)}return String(m)}};f.identity=function(){return function(p){return String(p)}};f.percentage=function(p){void 0===p&&(p=0);var m=k(p);return function(n){var q=n.toString();q=Math.pow(10,q.length-(q.indexOf(".")+1));return m(parseInt((100*n*q).toString(),10)/q)+"%"}};f.siSuffix=function(p){void 0===p&&(p=3);t(p);return function(m){return l.format("."+p+"s")(m)}};f.shortScale=function(p){void 0===p&&(p=3);t(p);var m=l.format("."+p+"e"),n=l.format("."+p+"f"),q=Math.pow(10,18),u=Math.pow(10,-p);
return function(x){var A=Math.abs(x);if((A<u||A>=q)&&0!==A)return m(x);for(var y=-1;A>=Math.pow(1E3,y+2)&&4>y;)y++;A=-1===y?n(x):n(x/Math.pow(1E3,y+1))+"KMBTQ"[y];if(0<x&&"1000"===A.substr(0,4)||0>x&&"-1000"===A.substr(0,5))4>y?(y++,A=n(x/Math.pow(1E3,y+1))+"KMBTQ"[y]):A=m(x);return A}};f.multiTime=function(){var p=[{specifier:".%L",predicate:function(m){return 0!==m.getMilliseconds()}},{specifier:":%S",predicate:function(m){return 0!==m.getSeconds()}},{specifier:"%I:%M",predicate:function(m){return 0!==
m.getMinutes()}},{specifier:"%I %p",predicate:function(m){return 0!==m.getHours()}},{specifier:"%a %d",predicate:function(m){return 0!==m.getDay()&&1!==m.getDate()}},{specifier:"%b %d",predicate:function(m){return 1!==m.getDate()}},{specifier:"%b",predicate:function(m){return 0!==m.getMonth()}}];return function(m){var n=p.filter(function(q){return q.predicate(m)});return l.timeFormat(0<n.length?n[0].specifier:"%Y")(m)}};f.time=function(p){return l.timeFormat(p)}},function(d,f,h){var k=h(1),t=h(0);
d=function(){function l(p,m){this._root=k.select(document.createElementNS("http://www.w3.org/2000/svg","g"));this._className=m;this._svgElementName=p}l.prototype.draw=function(p,m){var n=this;this._createAndDestroyDOMElements(p);var q=0;m.forEach(function(u){t.Window.setTimeout(function(){return n._drawStep(u)},q);q+=u.animator.totalTime(p.length)})};l.prototype.getVisualPrimitives=function(){null==this._cachedVisualPrimitivesNodes&&(this._cachedVisualPrimitivesNodes=this._selection.nodes());return this._cachedVisualPrimitivesNodes};
l.prototype.getVisualPrimitiveAtIndex=function(p){return this.getVisualPrimitives()[p]};l.prototype.remove=function(){this._root.remove()};l.prototype.attachTo=function(p){p.node().appendChild(this._root.node())};l.prototype.getRoot=function(){return this._root};l.prototype.selector=function(){return this._svgElementName};l.prototype._applyDefaultAttributes=function(){};l.prototype._createAndDestroyDOMElements=function(p){p=p.filter(function(m){return null!=m});p=this._root.selectAll(this.selector()).data(p);
this._selection=p.enter().append(this._svgElementName).merge(p);p.exit().remove();this._cachedVisualPrimitivesNodes=null;null!=this._className&&this._selection.classed(this._className,!0);this._applyDefaultAttributes(this._selection)};l.prototype._drawStep=function(p){var m=this;["fill","stroke"].forEach(function(n){null!=p.attrToAppliedProjector[n]&&m._selection.attr(n,p.attrToAppliedProjector[n])});p.animator.animate(this._selection,p.attrToAppliedProjector);null!=this._className&&this._selection.classed(this._className,
!0)};return l}();f.SVGDrawer=d},function(d,f){f.makeEnum=function(h){return h.reduce(function(k,t){k[t]=t;return k},{})}},function(d,f,h){var k=this&&this.__extends||function(m,n){function q(){this.constructor=m}for(var u in n)n.hasOwnProperty(u)&&(m[u]=n[u]);m.prototype=null===n?Object.create(n):(q.prototype=n.prototype,new q)},t=h(1),l=h(26),p=h(0);d=function(m){function n(){var q=m.call(this)||this;q._tickGenerator=function(u){return u.defaultTicks()};q._padProportion=.05;q._snappingDomainEnabled=
!0;q._paddingExceptionsProviders=new p.Set;return q}k(n,m);n.prototype.autoDomain=function(){this._domainMax=this._domainMin=null;m.prototype.autoDomain.call(this)};n.prototype._autoDomainIfAutomaticMode=function(){if(null!=this._domainMin&&null!=this._domainMax)this._setDomain([this._domainMin,this._domainMax]);else{var q=this._getExtent();null!=this._domainMin?(q=q[1],this._domainMin>=q&&(q=this._expandSingleValueDomain([this._domainMin,this._domainMin])[1]),this._setDomain([this._domainMin,q])):
null!=this._domainMax?(q=q[0],this._domainMax<=q&&(q=this._expandSingleValueDomain([this._domainMax,this._domainMax])[0]),this._setDomain([q,this._domainMax])):m.prototype._autoDomainIfAutomaticMode.call(this)}};n.prototype._getUnboundedExtent=function(q){void 0===q&&(q=!1);q=this._getAllIncludedValues(q);var u=this._defaultExtent();0!==q.length&&(q=[p.Math.min(q,u[0]),p.Math.max(q,u[1])],u=this._padDomain(q));return u};n.prototype._getExtent=function(){var q=this._getUnboundedExtent();null!=this._domainMin&&
(q[0]=this._domainMin);null!=this._domainMax&&(q[1]=this._domainMax);return q};n.prototype.addPaddingExceptionsProvider=function(q){this._paddingExceptionsProviders.add(q);this._autoDomainIfAutomaticMode()};n.prototype.removePaddingExceptionsProvider=function(q){this._paddingExceptionsProviders.delete(q);this._autoDomainIfAutomaticMode()};n.prototype.padProportion=function(q){if(null==q)return this._padProportion;if(0>q)throw Error("padProportion must be non-negative");this._padProportion=q;this._autoDomainIfAutomaticMode();
return this};n.prototype._padDomain=function(q){var u=this;if(q[0].valueOf()===q[1].valueOf())return this._expandSingleValueDomain(q);if(0===this._padProportion)return q;var x=this._padProportion/2,A=q[0],y=q[1],w=!1,C=!1;this._paddingExceptionsProviders.forEach(function(D){D(u).forEach(function(B){B.valueOf()===A.valueOf()&&(w=!0);B.valueOf()===y.valueOf()&&(C=!0)})});var G=this._backingScaleDomain();this._backingScaleDomain(q);q=w?A:this.invert(this.scale(A)-(this.scale(y)-this.scale(A))*x);x=C?
y:this.invert(this.scale(y)+(this.scale(y)-this.scale(A))*x);this._backingScaleDomain(G);return this._snappingDomainEnabled?this._niceDomain([q,x]):[q,x]};n.prototype.snappingDomainEnabled=function(q){null!=q&&(this._snappingDomainEnabled=q,this._autoDomainIfAutomaticMode())};n.prototype._expandSingleValueDomain=function(q){return q};n.prototype.invert=function(){throw Error("Subclasses should override invert");};n.prototype.domain=function(q){null!=q&&(this._domainMin=q[0],this._domainMax=q[1]);
return m.prototype.domain.call(this,q)};n.prototype.domainMin=function(q){if(null==q)return this.domain()[0];this._domainMin=q;this._autoDomainIfAutomaticMode();return this};n.prototype.domainMax=function(q){if(null==q)return this.domain()[1];this._domainMax=q;this._autoDomainIfAutomaticMode();return this};n.prototype.extentOfValues=function(q){q=t.extent(q.filter(function(u){return p.Math.isValidNumber(+u)}));return null==q[0]||null==q[1]?[]:q};n.prototype.zoom=function(q,u){var x=this;this.domain(this.range().map(function(A){return x.invert(l.zoomOut(A,
q,u))}))};n.prototype.pan=function(q){var u=this;this.domain(this.range().map(function(x){return u.invert(x+q)}))};n.prototype.scaleTransformation=function(){throw Error("Subclasses should override scaleTransformation");};n.prototype.invertedTransformation=function(){throw Error("Subclasses should override invertedTransformation");};n.prototype.getTransformationExtent=function(){throw Error("Subclasses should override getTransformationExtent");};n.prototype.getTransformationDomain=function(){throw Error("Subclasses should override getTransformationDomain");
};n.prototype.setTransformationDomain=function(){throw Error("Subclasses should override setTransformationDomain");};n.prototype._setDomain=function(q){function u(x){return p.Math.isNaN(x)||Infinity===x||-Infinity===x}u(q[0])||u(q[1])?p.Window.warn("Warning: QuantitativeScales cannot take NaN or Infinity as a domain value. Ignoring."):m.prototype._setDomain.call(this,q)};n.prototype.defaultTicks=function(){throw Error("Subclasses should override _getDefaultTicks");};n.prototype.ticks=function(){return this._tickGenerator(this)};
n.prototype._niceDomain=function(){throw Error("Subclasses should override _niceDomain");};n.prototype._defaultExtent=function(){throw Error("Subclasses should override _defaultExtent");};n.prototype.tickGenerator=function(){var q=Plottable.Scales.TickGenerators.integerTickGenerator();null!=q&&(this._tickGenerator=q)};return n}(h(17).Scale);d._DEFAULT_NUM_TICKS=10;f.QuantitativeScale=d},function(d,f,h){var k=h(1);f.coerceExternalD3=function(t){if(null==t.attrs){if(null==t.nodes){var l=[];t.each(function(){l.push(this)});
return k.selectAll(l)}return k.selectAll(t.nodes())}return t}},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(83));k(h(84));k(h(85))},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(44));k(h(45));k(h(46));k(h(18));k(h(6));k(h(33));k(h(34));k(h(47));k(h(9));k(h(48))},function(d,f){d=function(){function h(){var k=this;this._anchorCallback=function(t){return k._anchor(t)};this._enabled=!0}h.prototype.attachTo=function(k){this._disconnect();
this._componentAttachedTo=k;this._connect();return this};h.prototype.detachFrom=function(){this.detach()};h.prototype.detach=function(){this._disconnect();this._componentAttachedTo=null;return this};h.prototype.enabled=function(k){if(null==k)return this._enabled;(this._enabled=k)?this._connect():this._disconnect();return this};h.prototype._anchor=function(){this._isAnchored=!0};h.prototype._unanchor=function(){this._isAnchored=!1};h.prototype._translateToComponentSpace=function(k){var t=this._componentAttachedTo.originToRoot();
return{x:k.x-t.x,y:k.y-t.y}};h.prototype._isInsideComponent=function(k){return 0<=k.x&&0<=k.y&&k.x<=this._componentAttachedTo.width()&&k.y<=this._componentAttachedTo.height()};h.prototype._connect=function(){if(this.enabled()&&null!=this._componentAttachedTo&&!this._isAnchored)this._componentAttachedTo.onAnchor(this._anchorCallback)};h.prototype._disconnect=function(){this._isAnchored&&this._unanchor();null!=this._componentAttachedTo&&this._componentAttachedTo.offAnchor(this._anchorCallback)};return h}();
f.Interaction=d},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var x in q)q.hasOwnProperty(x)&&(n[x]=q[x]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},t=h(3),l=h(0),p=h(52),m=h(2);d=function(n){function q(){var u=n.call(this)||this;u._autoAdjustXScaleDomain=!1;u._autoAdjustYScaleDomain=!1;u._deferredRendering=!1;u._applyDeferredRenderingTransform=function(x,A,y,w){u._isAnchored&&(null!=u._renderArea&&u._renderArea.attr("transform",
"translate("+x+", "+A+") scale("+y+", "+w+")"),null!=u._canvas&&u._canvas.style("transform","translate("+x+"px, "+A+"px) scale("+y+", "+w+")"))};u.addClass("xy-plot");u._adjustYDomainOnChangeFromXCallback=function(){return u._adjustYDomainOnChangeFromX()};u._adjustXDomainOnChangeFromYCallback=function(){return u._adjustXDomainOnChangeFromY()};u._renderCallback=function(){if(u.deferredRendering()){var x=u.x()&&u.x().scale,A=u.y()&&u.y().scale;u._deferredRenderer.updateDomains(x,A)}else u.render()};
u._deferredRenderer=new p.DeferredRenderer(function(){return u.render()},u._applyDeferredRenderingTransform);return u}k(q,n);q.prototype.render=function(){this.deferredRendering()&&this._deferredRenderer.resetTransforms();return n.prototype.render.call(this)};q.prototype.deferredRendering=function(){return this._deferredRendering};q.prototype.x=function(u,x,A){if(null==u)return this._propertyBindings.get(q._X_KEY);this._bindProperty(q._X_KEY,u,x,A);u=this.width();null!=x&&null!=u&&x.range([0,u]);
this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain();this.render();return this};q.prototype.y=function(u,x,A){if(null==u)return this._propertyBindings.get(q._Y_KEY);this._bindProperty(q._Y_KEY,u,x,A);u=this.height();null!=x&&null!=u&&(x instanceof t.Category?x.range([0,u]):x.range([u,0]));this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain();this.render();return this};q.prototype._filterForProperty=function(u){return"x"===u&&this._autoAdjustXScaleDomain?this._makeFilterByProperty("y"):
"y"===u&&this._autoAdjustYScaleDomain?this._makeFilterByProperty("x"):null};q.prototype._makeFilterByProperty=function(u){u=this._propertyBindings.get(u);if(null!=u){var x=u.accessor,A=u.scale;if(null!=A)return function(y,w,C){var G=A.range();return l.Math.inRange(A.scale(x(y,w,C)),G[0],G[1])}}return null};q.prototype._uninstallScaleForKey=function(u,x){n.prototype._uninstallScaleForKey.call(this,u,x);u.offUpdate(x===q._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)};
q.prototype._installScaleForKey=function(u,x){n.prototype._installScaleForKey.call(this,u,x);u.onUpdate(x===q._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)};q.prototype.destroy=function(){n.prototype.destroy.call(this);this.x().scale&&this.x().scale.offUpdate(this._adjustYDomainOnChangeFromXCallback);this.y().scale&&this.y().scale.offUpdate(this._adjustXDomainOnChangeFromYCallback);return this};q.prototype.autorangeMode=function(u){if(null==u)return this._autoAdjustXScaleDomain?
"x":this._autoAdjustYScaleDomain?"y":"none";switch(u){case "x":this._autoAdjustXScaleDomain=!0;this._autoAdjustYScaleDomain=!1;this._adjustXDomainOnChangeFromY();break;case "y":this._autoAdjustXScaleDomain=!1;this._autoAdjustYScaleDomain=!0;this._adjustYDomainOnChangeFromX();break;case "none":this._autoAdjustYScaleDomain=this._autoAdjustXScaleDomain=!1;break;default:throw Error("Invalid scale name '"+u+"', must be 'x', 'y' or 'none'");}return this};q.prototype.computeLayout=function(u,x,A){n.prototype.computeLayout.call(this,
u,x,A);u=(u=this.x())&&u.scale;null!=u&&u.range([0,this.width()]);u=(u=this.y())&&u.scale;null!=u&&(u instanceof t.Category?u.range([0,this.height()]):u.range([this.height(),0]));return this};q.prototype._updateXExtentsAndAutodomain=function(){var u=this.x().scale;null!=u&&u.autoDomain()};q.prototype._updateYExtentsAndAutodomain=function(){var u=this.y().scale;null!=u&&u.autoDomain()};q.prototype.showAllData=function(){this._updateXExtentsAndAutodomain();this._updateYExtentsAndAutodomain();return this};
q.prototype._adjustYDomainOnChangeFromX=function(){this._projectorsReady()&&this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain()};q.prototype._adjustXDomainOnChangeFromY=function(){this._projectorsReady()&&this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain()};q.prototype._projectorsReady=function(){var u=this.x(),x=this.y();return null!=u&&null!=u.accessor&&null!=x&&null!=x.accessor};q.prototype._pixelPoint=function(u,x,A){var y=m.Plot._scaledAccessor(this.x()),w=m.Plot._scaledAccessor(this.y());
return{x:y(u,x,A),y:w(u,x,A)}};q.prototype._getDataToDraw=function(){function u(y,w,C){var G=m.Plot._scaledAccessor(x.x())(y,w,C);y=m.Plot._scaledAccessor(x.y())(y,w,C);return l.Math.isValidNumber(G)&&l.Math.isValidNumber(y)}var x=this,A=n.prototype._getDataToDraw.call(this);this.datasets().forEach(function(y){A.set(y,A.get(y).filter(function(w,C){return u(w,C,y)}))});return A};return q}(m.Plot);d._X_KEY="x";d._Y_KEY="y";f.XYPlot=d},function(d,f,h){var k=h(0);d=function(){function t(){this._autoDomainAutomatically=
!0;this._domainModificationInProgress=!1;this._updateId=0;this._callbacks=new k.CallbackSet;this._includedValuesProviders=new k.Set}t.prototype.extentOfValues=function(){return[]};t.prototype._getAllIncludedValues=function(l){var p=this;void 0===l&&(l=!1);var m=[];this._includedValuesProviders.forEach(function(n){n=n(p,l);m=m.concat(n)});return m};t.prototype._getExtent=function(){return[]};t.prototype.onUpdate=function(l){this._callbacks.add(l);return this};t.prototype.offUpdate=function(l){this._callbacks.delete(l);
return this};t.prototype._dispatchUpdate=function(){this._updateId++;this._callbacks.callCallbacks(this)};t.prototype.autoDomain=function(){this._autoDomainAutomatically=!0;this._setDomain(this._getExtent())};t.prototype._autoDomainIfAutomaticMode=function(){this._autoDomainAutomatically&&this.autoDomain()};t.prototype.scale=function(){throw Error("Subclasses should override scale");};t.prototype.ticks=function(){return this.domain()};t.prototype.domain=function(l){if(null==l)return this._getDomain();
this._autoDomainAutomatically=!1;this._setDomain(l);return this};t.prototype._getDomain=function(){throw Error("Subclasses should override _getDomain");};t.prototype._setDomain=function(l){this._domainModificationInProgress||(this._domainModificationInProgress=!0,this._backingScaleDomain(l),this._dispatchUpdate(),this._domainModificationInProgress=!1)};t.prototype._backingScaleDomain=function(){throw Error("Subclasses should override _backingDomain");};t.prototype.range=function(l){if(null==l)return this._getRange();
this._setRange(l);return this};t.prototype._getRange=function(){throw Error("Subclasses should override _getRange");};t.prototype._setRange=function(){throw Error("Subclasses should override _setRange");};t.prototype.addIncludedValuesProvider=function(l){this._includedValuesProviders.add(l);this._autoDomainIfAutomaticMode();return this};t.prototype.removeIncludedValuesProvider=function(l){this._includedValuesProviders.delete(l);this._autoDomainIfAutomaticMode()};t.prototype.updateId=function(){return this._updateId};
return t}();f.Scale=d},function(d,f,h){function k(q,u,x,A){for(var y={},w=0;w<u.length;w++){var C=u[w];q.hasOwnProperty(C)&&(y[C]=q[C](x,A))}return y}function t(q){return(null!=q["stroke-opacity"]?parseFloat(q["stroke-opacity"]):1)*(null!=q.opacity?parseFloat(q.opacity):1)}function l(q){return(null!=q["fill-opacity"]?parseFloat(q["fill-opacity"]):1)*(null!=q.opacity?parseFloat(q.opacity):1)}function p(q){return null!=q["stroke-width"]?parseFloat(q["stroke-width"]):1}function m(q,u){if(u.stroke){q.lineWidth=
p(u);var x=n.color(u.stroke);x.opacity*=t(u);q.strokeStyle=x.toString();q.stroke()}u.fill&&(x=n.color(u.fill),x.opacity*=l(u),q.fillStyle=x.toString(),q.fill())}var n=h(1);d=function(){function q(u,x){this._context=u;this._drawStep=x}q.prototype.getDrawStep=function(){return this._drawStep};q.prototype.draw=function(u,x){x=x[x.length-1].attrToAppliedProjector;this._context.save();this._drawStep(this._context,u,x);this._context.restore()};q.prototype.getVisualPrimitives=function(){return[]};q.prototype.getVisualPrimitiveAtIndex=
function(){return null};q.prototype.remove=function(){};return q}();f.CanvasDrawer=d;f.ContextStyleAttrs="fill-opacity fill opacity stroke-opacity stroke-width stroke".split(" ");f.resolveAttributesSubsetWithStyles=function(q,u,x,A){return k(q,f.ContextStyleAttrs.concat(u),x,A)};f.resolveAttributes=k;f.getStrokeWidth=p;f.renderArea=function(q,u,x,A){q.save();q.beginPath();u.context(q);u(x);q.lineJoin="round";m(q,A);q.restore()};f.renderLine=function(q,u,x,A){q.save();q.beginPath();u.context(q);u(x);
q.lineJoin="round";m(q,A);q.restore()};f.renderPathWithStyle=m},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(50));k(h(27));k(h(51));k(h(93));k(h(53));k(h(94));k(h(95));k(h(96));k(h(97));k(h(98));k(h(99));k(h(100))},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(92));k(h(91));d=h(49);f.sign=d.sign},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(134));k(h(135));k(h(136));k(h(137))},function(d,
f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var x in q)q.hasOwnProperty(x)&&(n[x]=q[x]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},t=h(1),l=h(5);d=h(4);var p=h(8),m=h(0);h=h(10);f.AxisOrientation=h.makeEnum(["bottom","left","right","top"]);h=function(n){function q(u,x){var A=n.call(this)||this;A._endTickLength=5;A._innerTickLength=5;A._tickLabelPadding=10;A._margin=15;A._showEndTickLabels=!1;A._annotationsEnabled=!1;A._annotationTierCount=
1;if(null==u||null==x)throw Error("Axis requires a scale and orientation");A._scale=u;A.orientation(x);A._setDefaultAlignment();A.addClass("axis");A.isHorizontal()?A.addClass("x-axis"):A.addClass("y-axis");A.formatter(p.identity());A._rescaleCallback=function(){return A._rescale()};A._scale.onUpdate(A._rescaleCallback);A._annotatedTicks=[];A._annotationFormatter=p.identity();return A}k(q,n);q.prototype.destroy=function(){n.prototype.destroy.call(this);this._scale.offUpdate(this._rescaleCallback)};
q.prototype.tickLabelDataOnElement=function(u){if(null!=u){for(var x;null!=u&&u.classList&&void 0===x;)u.classList.contains(q.TICK_LABEL_CLASS)?x=u:u=u.parentNode;return void 0===u?void 0:t.select(u).datum()}};q.prototype._computeWidth=function(){return this._maxLabelTickLength()};q.prototype._computeHeight=function(){return this._maxLabelTickLength()};q.prototype.requestedSpace=function(){var u=0,x=0;if(this.isHorizontal()){if(x=this._computeHeight()+this._margin,this.annotationsEnabled()){var A=
this._annotationMeasurer.measure().height+2*q._ANNOTATION_LABEL_PADDING;x+=A*this.annotationTierCount()}}else u=this._computeWidth()+this._margin,this.annotationsEnabled()&&(A=this._annotationMeasurer.measure().height+2*q._ANNOTATION_LABEL_PADDING,u+=A*this.annotationTierCount());return{minWidth:u,minHeight:x}};q.prototype.fixedHeight=function(){return this.isHorizontal()};q.prototype.fixedWidth=function(){return!this.isHorizontal()};q.prototype._rescale=function(){this.render()};q.prototype.computeLayout=
function(u,x,A){n.prototype.computeLayout.call(this,u,x,A);this.isHorizontal()?this._scale.range([0,this.width()]):this._scale.range([this.height(),0]);return this};q.prototype._sizeFromOffer=function(u,x){var A=this.requestedSpace(u,x);return this.isHorizontal()?{width:u,height:A.minHeight}:{height:x,width:A.minWidth}};q.prototype._setup=function(){n.prototype._setup.call(this);this._tickMarkContainer=this.content().append("g").classed(q.TICK_MARK_CLASS+"-container",!0);this._tickLabelContainer=
this.content().append("g").classed(q.TICK_LABEL_CLASS+"-container",!0);this._baseline=this.content().append("line").classed("baseline",!0);this._annotationContainer=this.content().append("g").classed("annotation-container",!0);this._annotationContainer.append("g").classed("annotation-line-container",!0);this._annotationContainer.append("g").classed("annotation-circle-container",!0);this._annotationContainer.append("g").classed("annotation-rect-container",!0);var u=this._annotationContainer.append("g").classed("annotation-label-container",
!0);u=new l.SvgContext(u.node());this._annotationMeasurer=new l.CacheMeasurer(u);this._annotationWriter=new l.Writer(this._annotationMeasurer,u)};q.prototype._getTickValues=function(){return[]};q.prototype.renderImmediately=function(){var u=this._getTickValues(),x=this._tickMarkContainer.selectAll("."+q.TICK_MARK_CLASS).data(u),A=x.enter().append("line").classed(q.TICK_MARK_CLASS,!0).merge(x);A.attrs(this._generateTickMarkAttrHash());t.select(A.nodes()[0]).classed(q.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0));
t.select(A.nodes()[u.length-1]).classed(q.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0));x.exit().remove();this._baseline.attrs(this._generateBaselineAttrHash());this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations();return this};q.prototype.annotatedTicks=function(){return this._annotatedTicks};q.prototype.annotationFormatter=function(u){if(null==u)return this._annotationFormatter;this._annotationFormatter=u;this.render();return this};q.prototype.annotationsEnabled=
function(){return this._annotationsEnabled};q.prototype.annotationTierCount=function(){return this._annotationTierCount};q.prototype._drawAnnotations=function(){function u(aa){switch(C.orientation()){case "bottom":case "right":return y(aa);case "top":case "left":return y(aa)-D.get(aa).height}}function x(aa){return O.has(aa)?"hidden":"visible"}function A(aa){return C._scale.scale(aa)}function y(aa){switch(C.orientation()){case "bottom":case "right":return N.get(aa)*I+K;case "top":case "left":return H-
K-N.get(aa)*I}}function w(aa,la,Z){aa=aa.selectAll("."+Z).data(B);la=aa.enter().append(la).classed(Z,!0).merge(aa);aa.exit().remove();return la}var C=this,G=q._ANNOTATION_LABEL_PADDING,D=new m.Map,B=this._annotatedTicksToRender();B.forEach(function(aa){var la=C._annotationMeasurer.measure(C.annotationFormatter()(aa));D.set(aa,{width:la.width+2*G,height:la.height+2*G})});var I=this._annotationMeasurer.measure().height+2*G,N=this._annotationToTier(D),O=new m.Set,H=this.isHorizontal()?this.height():
this.width(),K=this._coreSize(),M=Math.min(this.annotationTierCount(),Math.floor((H-K)/I));N.forEach(function(aa,la){(-1===aa||aa>=M)&&O.add(la)});switch(this.orientation()){case "bottom":case "right":var L=0;break;case "top":L=this.height();break;case "left":L=this.width()}var Q=this.isHorizontal();w(this._annotationContainer.select(".annotation-line-container"),"line",q.ANNOTATION_LINE_CLASS).attrs({x1:Q?A:L,x2:Q?A:y,y1:Q?L:A,y2:Q?y:A,visibility:x});w(this._annotationContainer.select(".annotation-circle-container"),
"circle",q.ANNOTATION_CIRCLE_CLASS).attrs({cx:Q?A:L,cy:Q?L:A,r:3});w(this._annotationContainer.select(".annotation-rect-container"),"rect",q.ANNOTATION_RECT_CLASS).attrs({x:Q?A:u,y:Q?u:A,width:Q?function(aa){return D.get(aa).width}:function(aa){return D.get(aa).height},height:Q?function(aa){return D.get(aa).height}:function(aa){return D.get(aa).width},visibility:x});var T=this._annotationWriter,X=this.annotationFormatter();L=w(this._annotationContainer.select(".annotation-label-container"),"g",q.ANNOTATION_LABEL_CLASS);
L.selectAll(".text-container").remove();L.attrs({transform:function(aa){var la=Q?A(aa):u(aa);aa=Q?u(aa):A(aa);return"translate("+la+","+aa+")"},visibility:x}).each(function(aa){T.write(X(aa),Q?D.get(aa).width:D.get(aa).height,Q?D.get(aa).height:D.get(aa).width,{xAlign:"center",yAlign:"center",textRotation:Q?0:90},t.select(this).node())})};q.prototype._annotatedTicksToRender=function(){var u=this,x=this._scale.range();return m.Array.uniq(this.annotatedTicks().filter(function(A){return null==A?!1:m.Math.inRange(u._scale.scale(A),
x[0],x[1])}))};q.prototype._coreSize=function(){var u=this.isHorizontal()?this.height():this.width(),x=this.isHorizontal()?this._computeHeight():this._computeWidth();return Math.min(x,u)};q.prototype._annotationTierHeight=function(){return this._annotationMeasurer.measure().height+2*q._ANNOTATION_LABEL_PADDING};q.prototype._annotationToTier=function(u){var x=this,A=[[]],y=new m.Map,w=this.isHorizontal()?this.width():this.height();this._annotatedTicksToRender().forEach(function(C){var G=x._scale.scale(C),
D=u.get(C).width;if(0>G||G+D>w)y.set(C,-1);else{for(var B=function(N){return A[N].some(function(O){var H=x._scale.scale(O);O=u.get(O).width;return G+D>=H&&G<=H+O})},I=0;B(I);)I++,A.length===I&&A.push([]);A[I].push(C);y.set(C,I)}});return y};q.prototype._removeAnnotations=function(){this._annotationContainer.selectAll(".annotation-line").remove();this._annotationContainer.selectAll(".annotation-circle").remove();this._annotationContainer.selectAll(".annotation-rect").remove();this._annotationContainer.selectAll(".annotation-label").remove()};
q.prototype._generateBaselineAttrHash=function(){var u={x1:0,y1:0,x2:0,y2:0};switch(this._orientation){case "bottom":u.x2=this.width();break;case "top":u.x2=this.width();u.y1=this.height();u.y2=this.height();break;case "left":u.x1=this.width();u.x2=this.width();u.y2=this.height();break;case "right":u.y2=this.height()}return u};q.prototype._generateTickMarkAttrHash=function(u){function x(w){return A._scale.scale(w)}var A=this;void 0===u&&(u=!1);var y={x1:0,y1:0,x2:0,y2:0};this.isHorizontal()?(y.x1=
x,y.x2=x):(y.y1=x,y.y2=x);u=u?this._endTickLength:this._innerTickLength;switch(this._orientation){case "bottom":y.y2=u;break;case "top":y.y1=this.height();y.y2=this.height()-u;break;case "left":y.x1=this.width();y.x2=this.width()-u;break;case "right":y.x2=u}return y};q.prototype._setDefaultAlignment=function(){switch(this._orientation){case "bottom":this.yAlignment("top");break;case "top":this.yAlignment("bottom");break;case "left":this.xAlignment("right");break;case "right":this.xAlignment("left")}};
q.prototype.isHorizontal=function(){return"top"===this._orientation||"bottom"===this._orientation};q.prototype.getScale=function(){return this._scale};q.prototype.formatter=function(u){if(null==u)return this._formatter;this._formatter=u;this.redraw();return this};q.prototype.innerTickLength=function(){return this._innerTickLength};q.prototype.endTickLength=function(){return this._endTickLength};q.prototype._maxLabelTickLength=function(){return this.showEndTickLabels()?Math.max(this.innerTickLength(),
this.endTickLength()):this.innerTickLength()};q.prototype.tickLabelPadding=function(u){if(null==u)return this._tickLabelPadding;if(0>u)throw Error("tick label padding must be positive");this._tickLabelPadding=u;this.redraw();return this};q.prototype.margin=function(u){if(null==u)return this._margin;if(0>u)throw Error("margin size must be positive");this._margin=u;this.redraw();return this};q.prototype.orientation=function(u){if(null==u)return this._orientation;u=u.toLowerCase();if("top"!==u&&"bottom"!==
u&&"left"!==u&&"right"!==u)throw Error("unsupported orientation");this._orientation=u;this.redraw();return this};q.prototype.showEndTickLabels=function(){return this._showEndTickLabels};q.prototype._showAllTickMarks=function(){this._tickMarkContainer.selectAll("."+q.TICK_MARK_CLASS).each(function(){t.select(this).style("visibility","inherit")})};q.prototype._showAllTickLabels=function(){this._tickLabelContainer.selectAll("."+q.TICK_LABEL_CLASS).each(function(){t.select(this).style("visibility","inherit")})};
q.prototype._hideOverflowingTickLabels=function(){var u=this.element().node().getBoundingClientRect(),x=this._tickLabelContainer.selectAll("."+q.TICK_LABEL_CLASS);x.empty()||x.each(function(){m.DOM.clientRectInside(this.getBoundingClientRect(),u)||t.select(this).style("visibility","hidden")})};q.prototype._hideTickMarksWithoutLabel=function(){var u=this._tickMarkContainer.selectAll("."+q.TICK_MARK_CLASS),x=this._tickLabelContainer.selectAll("."+q.TICK_LABEL_CLASS).filter(function(){var A=t.select(this).style("visibility");
return"inherit"===A||"visible"===A}).data();u.each(function(A){-1===x.indexOf(A)&&t.select(this).style("visibility","hidden")})};q.prototype.invalidateCache=function(){n.prototype.invalidateCache.call(this);this._annotationMeasurer.reset()};return q}(d.Component);h.END_TICK_MARK_CLASS="end-tick-mark";h.TICK_MARK_CLASS="tick-mark";h.TICK_LABEL_CLASS="tick-label";h.ANNOTATION_LINE_CLASS="annotation-line";h.ANNOTATION_RECT_CLASS="annotation-rect";h.ANNOTATION_CIRCLE_CLASS="annotation-circle";h.ANNOTATION_LABEL_CLASS=
"annotation-label";h._ANNOTATION_LABEL_PADDING=4;f.Axis=h},function(d,f){f.SHOW_WARNINGS=!0;f.ADD_TITLE_ELEMENTS=!0},function(d,f,h){var k=h(0);d=function(){function t(){this._eventToProcessingFunction={};this._eventTarget=document;this._eventNameToCallbackSet={};this._connected=!1}t.prototype._hasNoCallbacks=function(){for(var l=Object.keys(this._eventNameToCallbackSet),p=0;p<l.length;p++)if(0!==this._eventNameToCallbackSet[l[p]].size)return!1;return!0};t.prototype._connect=function(){var l=this;
this._connected||(Object.keys(this._eventToProcessingFunction).forEach(function(p){l._eventTarget.addEventListener(p,l._eventToProcessingFunction[p])}),this._connected=!0)};t.prototype._disconnect=function(){var l=this;this._connected&&this._hasNoCallbacks()&&(Object.keys(this._eventToProcessingFunction).forEach(function(p){l._eventTarget.removeEventListener(p,l._eventToProcessingFunction[p])}),this._connected=!1)};t.prototype._addCallbackForEvent=function(l,p){null==this._eventNameToCallbackSet[l]&&
(this._eventNameToCallbackSet[l]=new k.CallbackSet);this._eventNameToCallbackSet[l].add(p);this._connect()};t.prototype._removeCallbackForEvent=function(l,p){null!=this._eventNameToCallbackSet[l]&&this._eventNameToCallbackSet[l].delete(p);this._disconnect()};t.prototype._callCallbacksForEvent=function(l){for(var p=[],m=1;m<arguments.length;m++)p[m-1]=arguments[m];m=this._eventNameToCallbackSet[l];null!=m&&m.callCallbacks.apply(m,p)};return t}();f.Dispatcher=d},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||
(f[l]=t[l])}k(h(87));k(h(88));k(h(40));k(h(89));k(h(90));d=h(26);f.zoomOut=d.zoomOut},function(d,f){function h(n,q,u){return u-(u-n)*q}function k(n,q,u){return(n*q-u)/(q-1)}function t(n,q,u,x){var A=1<q;u=A?x:u;if(null==u)return q;n=n.getTransformationDomain();return(A?Math.min:Math.max)(q,u/Math.abs(n[1]-n[0]))}function l(n,q,u,x,A){if(1>=q)return{centerPoint:u,zoomAmount:q};if(null==x&&null==A)return{centerPoint:u,zoomAmount:q};var y=p(n),w=m(n),C=w?Infinity:-Infinity;w=w?-Infinity:Infinity;x=null==
x?C:x;A=null==A?w:A;w=n.getTransformationDomain();C=w[0];w=w[1];A=n.scaleTransformation(A);w=n.scaleTransformation(w);var G=h(w,q,u);x=n.scaleTransformation(x);n=n.scaleTransformation(C);C=h(n,q,u);return Math.abs(G-C)>Math.abs(A-x)?(q=(A-x)/(w-n),1!==q?{centerPoint:k(w,q,A),zoomAmount:q}:{centerPoint:u,zoomAmount:q}):G>A!=y?{centerPoint:k(w,q,A),zoomAmount:q}:C<x!=y?{centerPoint:k(n,q,x),zoomAmount:q}:{centerPoint:u,zoomAmount:q}}function p(n){n=n.range();return n[1]<n[0]}function m(n){n=n.getTransformationDomain();
return n[1]<n[0]}f.zoomOut=h;f.constrainedZoom=function(n,q,u,x,A,y,w){q=t(n,q,x,A);return l(n,q,u,y,w)};f.constrainZoomExtents=t;f.constrainZoomValues=l;f.constrainedTranslation=function(n,q,u,x){var A=n.getTransformationDomain(),y=A[0],w=A[1];A=p(n);0<q!==A?(u=x,null!=u&&(y=n.scaleTransformation(w),n=n.scaleTransformation(u),q=(A?Math.max:Math.min)(y+q,n)-y)):null!=u&&(y=n.scaleTransformation(y),n=n.scaleTransformation(u),q=(A?Math.min:Math.max)(y+q,n)-y);return q}},function(d,f,h){function k(I,
N,O){var H=I.scale;if(H instanceof y.Category)O=H.rangeBand();else{var K=I.accessor;I=l.set(C.Array.flatten(N.map(function(M){return M.data().map(function(L,Q){return K(L,Q,M)}).filter(function(L){return null!=L}).map(function(L){return L.valueOf()})}))).values().map(function(M){return+M});I.sort(function(M,L){return M-L});I=I.map(function(M){return H.scale(M)});I=l.pairs(I);O=C.Math.min(I,function(M){return Math.abs(M[1]-M[0])},O*B._SINGLE_BAR_DIMENSION_RATIO);O*=B._BAR_THICKNESS_RATIO}return O}
var t=this&&this.__extends||function(I,N){function O(){this.constructor=I}for(var H in N)N.hasOwnProperty(H)&&(I[H]=N[H]);I.prototype=null===N?Object.create(N):(O.prototype=N.prototype,new O)},l=h(1),p=h(5),m=h(7),n=h(8),q=h(14),u=h(6),x=h(34),A=h(20),y=h(3),w=h(11),C=h(0);d=h(10);var G=h(19),D=h(2);h=h(16);f.BarOrientation=d.makeEnum(["vertical","horizontal"]);f.LabelsPosition=d.makeEnum(["start","middle","end","outside"]);f.BarAlignment=d.makeEnum(["start","middle","end"]);var B=function(I){function N(O){void 0===
O&&(O="vertical");var H=I.call(this)||this;H._labelFormatter=n.identity();H._labelsEnabled=!1;H._labelsPosition=f.LabelsPosition.end;H._hideBarsIfAnyAreTooWide=!0;H._barAlignment="middle";H._computeBarPixelThickness=A.memoize(k);H._fixedBarPixelThickness=!0;H.addClass("bar-plot");if("vertical"!==O&&"horizontal"!==O)throw Error(O+" is not a valid orientation for Plots.Bar");H._isVertical="vertical"===O;H.animator("baseline",new m.Null);H.attr("fill",(new y.Color).range()[0]);H.attr(N._BAR_THICKNESS_KEY,
function(){return H._barPixelThickness()});H._labelConfig=new C.Map;H._baselineValueProvider=function(){return[H.baselineValue()]};return H}t(N,I);N.prototype.computeLayout=function(O,H,K){I.prototype.computeLayout.call(this,O,H,K);this._updateExtents();return this};N.prototype.x=function(O,H){if(null==O)return I.prototype.x.call(this);null==H?I.prototype.x.call(this,O):I.prototype.x.call(this,O,H);this._updateThicknessAttr();this._updateLengthScale();return this};N.prototype.y=function(O,H){if(null==
O)return I.prototype.y.call(this);null==H?I.prototype.y.call(this,O):I.prototype.y.call(this,O,H);this._updateLengthScale();return this};N.prototype.length=function(){return this._isVertical?this.y():this.x()};N.prototype.position=function(){return this._isVertical?this.x():this.y()};N.prototype.barEnd=function(){return this._propertyBindings.get(N._BAR_END_KEY)};N.prototype.barAlignment=function(O){if(null==O)return this._barAlignment;this._barAlignment=O;this._clearAttrToProjectorCache();this.render();
return this};N.prototype.orientation=function(){return this._isVertical?"vertical":"horizontal"};N.prototype._createDrawer=function(){return new u.ProxyDrawer(function(){return new x.RectangleSVGDrawer(N._BAR_AREA_CLASS)},function(O){return new q.RectangleCanvasDrawer(O)})};N.prototype._setup=function(){I.prototype._setup.call(this);this._baseline=this._renderArea.append("line").classed("baseline",!0)};N.prototype.baselineValue=function(){if(null!=this._baselineValue)return this._baselineValue;if(!this._projectorsReady())return 0;
var O=this.length().scale;return O?O instanceof y.Time?new Date(0):0:0};N.prototype.addDataset=function(O){I.prototype.addDataset.call(this,O)};N.prototype._addDataset=function(O){I.prototype._addDataset.call(this,O);return this};N.prototype.removeDataset=function(O){I.prototype.removeDataset.call(this,O)};N.prototype._removeDataset=function(O){I.prototype._removeDataset.call(this,O);return this};N.prototype.datasets=function(O){if(null==O)return I.prototype.datasets.call(this);I.prototype.datasets.call(this,
O);return this};N.prototype.labelsEnabled=function(O,H){if(null==O)return this._labelsEnabled;this._labelsEnabled=O;null!=H&&(this._labelsPosition=H);this._clearAttrToProjectorCache();this.render();return this};N.prototype.labelFormatter=function(O){if(null==O)return this._labelFormatter;this._labelFormatter=O;this._clearAttrToProjectorCache();this.render();return this};N.prototype._createNodesForDataset=function(O){var H=I.prototype._createNodesForDataset.call(this,O),K=this._renderArea.append("g").classed(N._LABEL_AREA_CLASS,
!0),M=new p.SvgContext(K.node()),L=new p.CacheMeasurer(M);M=new p.Writer(L,M);this._labelConfig.set(O,{labelArea:K,measurer:L,writer:M});return H};N.prototype._removeDatasetNodes=function(O){I.prototype._removeDatasetNodes.call(this,O);var H=this._labelConfig.get(O);null!=H&&(H.labelArea.remove(),this._labelConfig.delete(O))};N.prototype.entityNearest=function(O){var H=this;return this._computeBarPixelThickness.doLocked(function(){function K(ca,ka,Y,Ea){return H._pixelPointBar(aa(ca,ka,Y),la,Ea)}
var M=H._isVertical?O.x:O.y,L=H._isVertical?O.y:O.x,Q=H.bounds(),T={min:0,max:Q.bottomRight.x-Q.topLeft.x},X={min:0,max:Q.bottomRight.y-Q.topLeft.y},aa=D.Plot._scaledAccessor(H.length()),la=H.length().scale.scale(H.baselineValue()),Z=Infinity,ba=Infinity,ea;H._getEntityStore().entities().forEach(function(ca){var ka=H._entityBounds(ca);if(C.DOM.intersectsBBox(T,X,ka)){var Y=0,Ea=0;if(!C.DOM.intersectsBBox(O.x,O.y,ka,.5)){Ea=K(ca.datum,ca.index,ca.dataset,ka);Y=Math.abs(M-(H._isVertical?Ea.x:Ea.y));
var va=H._isVertical?ka.y:ka.x;ka=va+(H._isVertical?ka.height:ka.width);Ea=L>=va-.5&&L<=ka+.5?0:Math.abs(L-(H._isVertical?Ea.y:Ea.x))}if(Y<Z||Y===Z&&Ea<ba)ea=ca,Z=Y,ba=Ea}});if(void 0!==ea)return H._lightweightPlotEntityToPlotEntity(ea)})};N.prototype.entitiesAt=function(O){return this._entitiesIntersecting(O.x,O.y)};N.prototype._entitiesIntersecting=function(O,H){var K=this,M=[];this._getEntityStore().entities().forEach(function(L){C.DOM.intersectsBBox(O,H,K._entityBounds(L))&&M.push(K._lightweightPlotEntityToPlotEntity(L))});
return M};N.prototype._updateLengthScale=function(){if(this._projectorsReady()){var O=this.length().scale;O instanceof w.QuantitativeScale&&(O.addPaddingExceptionsProvider(this._baselineValueProvider),O.addIncludedValuesProvider(this._baselineValueProvider))}};N.prototype.renderImmediately=function(){var O=this;this._barPixelThickness();return this._computeBarPixelThickness.doLocked(function(){return I.prototype.renderImmediately.call(O)})};N.prototype._additionalPaint=function(O){var H=this,K=this.length().scale.scale(this.baselineValue());
K={x1:this._isVertical?0:K,y1:this._isVertical?K:0,x2:this._isVertical?this.width():K,y2:this._isVertical?K:this.height()};this._getAnimator("baseline").animate(this._baseline,K);this.datasets().forEach(function(M){return H._labelConfig.get(M).labelArea.selectAll("g").remove()});this._labelsEnabled&&C.Window.setTimeout(function(){return H._drawLabels()},O)};N.prototype.getExtentsForProperty=function(O){var H=this,K=I.prototype.getExtentsForProperty.call(this,O);if("x"===O&&this._isVertical)O=this.x();
else{if("y"!==O||this._isVertical)return K;O=this.y()}if(!(O&&O.scale&&O.scale instanceof w.QuantitativeScale))return K;var M=O.scale,L=this._barPixelThickness();return K=K.map(function(Q){return l.extent([M.invert(H._getPositionAttr(M.scale(Q[0]),L)),M.invert(H._getPositionAttr(M.scale(Q[0]),L)+L),M.invert(H._getPositionAttr(M.scale(Q[1]),L)),M.invert(H._getPositionAttr(M.scale(Q[1]),L)+L)])})};N.prototype._getPositionAttr=function(O,H){this._isVertical||(O-=H,H*=-1);switch(this._barAlignment){case "start":return O;
case "end":return O-H;default:return O-H/2}};N.prototype._drawLabels=function(){var O=this,H=this._getDataToDraw(),K=this._getAttrToProjector(),M=this.datasets().some(function(L){return H.get(L).some(function(Q,T){return null==Q?!1:O._drawLabel(Q,T,L,K)})});this._hideBarsIfAnyAreTooWide&&M&&this.datasets().forEach(function(L){return O._labelConfig.get(L).labelArea.selectAll("g").remove()})};N.prototype._drawLabel=function(O,H,K,M){var L=this._labelConfig.get(K),Q=L.labelArea,T=L.measurer;L=L.writer;
var X=this.length().accessor,aa=X(O,H,K);X=this.length().scale;var la=null!=X?X.scale(aa):aa,Z=null!=X?X.scale(this.baselineValue()):this.baselineValue(),ba={x:M.x(O,H,K),y:M.y(O,H,K)};X={width:M.width(O,H,K),height:M.height(O,H,K)};aa=this._labelFormatter(aa,O,H,K);T=T.measure(aa);var ea=this._shouldShowLabelOnBar(ba,X,T);ba=this._calculateLabelProperties(ba,X,T,ea,this._isVertical?la<=Z:la<Z);la=ba.containerDimensions;Z=ba.labelContainerOrigin;ba=ba.alignment;O=M.fill(O,H,K);Q=this._createLabelContainer(Q,
Z,ea,O);L.write(aa,la.width,la.height,{xAlign:ba.x,yAlign:ba.y},Q.node());return this._isVertical?X.width<T.width:X.height<T.height};N.prototype._shouldShowLabelOnBar=function(O,H,K){if(this._labelsPosition===f.LabelsPosition.outside)return!1;O=this._isVertical?O.y:O.x;var M=this._isVertical?H.height:H.width;H=this._isVertical?this.height():this.width();K=this._isVertical?K.height:K.width;var L=O+M;L>H?M=H-O:0>O&&(M=L);return K+N._LABEL_MARGIN_INSIDE_BAR<=M};N.prototype._calculateLabelProperties=
function(O,H,K,M,L){function Q(ka){switch(ka){case "topLeft":Z=T._isVertical?"top":"left";ea+=N._LABEL_MARGIN_INSIDE_BAR;ca+=N._LABEL_MARGIN_INSIDE_BAR;break;case "center":ca+=(aa+la)/2;break;case "bottomRight":Z=T._isVertical?"bottom":"right",ea-=N._LABEL_MARGIN_INSIDE_BAR,ca+=ba-N._LABEL_MARGIN_INSIDE_BAR-la}}var T=this,X=this._isVertical?O.y:O.x,aa=this._isVertical?H.height:H.width,la=this._isVertical?K.height:K.width,Z="center",ba=aa,ea=X,ca=X;if(M)switch(this._labelsPosition){case f.LabelsPosition.start:L?
Q("bottomRight"):Q("topLeft");break;case f.LabelsPosition.middle:Q("center");break;case f.LabelsPosition.end:L?Q("topLeft"):Q("bottomRight")}else L?(Z=this._isVertical?"top":"left",ba=aa+N._LABEL_MARGIN_INSIDE_BAR+la,ea-=N._LABEL_MARGIN_INSIDE_BAR+la,ca-=N._LABEL_MARGIN_INSIDE_BAR+la):(Z=this._isVertical?"bottom":"right",ba=aa+N._LABEL_MARGIN_INSIDE_BAR+la,ca+=aa+N._LABEL_MARGIN_INSIDE_BAR);return{containerDimensions:{width:this._isVertical?H.width:ba,height:this._isVertical?ba:H.height},labelContainerOrigin:{x:this._isVertical?
O.x:ea,y:this._isVertical?ea:O.y},labelOrigin:{x:this._isVertical?O.x+H.width/2-K.width/2:ca,y:this._isVertical?ca:O.y+H.height/2-K.height/2},alignment:{x:this._isVertical?"center":Z,y:this._isVertical?Z:"center"}}};N.prototype._createLabelContainer=function(O,H,K,M){O=O.append("g").attr("transform","translate("+H.x+", "+H.y+")");K?(O.classed("on-bar-label",!0),K=1.6*C.Color.contrast("white",M)<C.Color.contrast("black",M),O.classed(K?"dark-label":"light-label",!0)):O.classed("off-bar-label",!0);return O};
N.prototype._generateDrawSteps=function(){var O=[];if(this._animateOnNextRender()){var H=this._getAttrToProjector(),K=this.length().scale.scale(this.baselineValue()),M=this._isVertical?"height":"width";H[this._isVertical?"y":"x"]=function(){return K};H[M]=function(){return 0};O.push({attrToProjector:H,animator:this._getAnimator(G.Animator.RESET)})}O.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(G.Animator.MAIN)});return O};N.prototype._generateAttrToProjector=function(){function O(ba,
ea,ca){return Math.abs(M-X(ba,ea,ca))}var H=this,K=I.prototype._generateAttrToProjector.call(this),M=this.length().scale.scale(this.baselineValue()),L=this._isVertical?"y":"x",Q=this._isVertical?"x":"y",T=D.Plot._scaledAccessor(this.position()),X=D.Plot._scaledAccessor(this.length()),aa=K[N._BAR_THICKNESS_KEY],la=K.gap,Z=null==la?aa:function(ba,ea,ca){return aa(ba,ea,ca)-la(ba,ea,ca)};K.width=this._isVertical?Z:O;K.height=this._isVertical?O:Z;K[L]=function(ba,ea,ca){ba=X(ba,ea,ca);return ba>M?M:ba};
K[Q]=function(ba,ea,ca){return H._getPositionAttr(T(ba,ea,ca),aa(ba,ea,ca))};return K};N.prototype._updateThicknessAttr=function(){var O=this,H=this.position(),K=this.barEnd();null!=H&&null!=K?(this._fixedBarPixelThickness=!1,this.attr(N._BAR_THICKNESS_KEY,function(M,L,Q){var T=H.accessor(M,L,Q);M=K.accessor(M,L,Q);T=H.scale?H.scale.scale(T):T;M=K.scale?K.scale.scale(M):M;return Math.abs(M-T)})):(this._fixedBarPixelThickness=!0,this.attr(N._BAR_THICKNESS_KEY,function(){return O._barPixelThickness()}))};
N.prototype._barPixelThickness=function(){return this._fixedBarPixelThickness?this._projectorsReady()?this._computeBarPixelThickness(this.position(),this.datasets(),this._isVertical?this.width():this.height()):0:0};N.prototype.entities=function(O){void 0===O&&(O=this.datasets());return this._projectorsReady()?I.prototype.entities.call(this,O):[]};N.prototype._entityBounds=function(O){return this._pixelBounds(O.datum,O.index,O.dataset)};N.prototype._pixelBounds=function(O,H,K){var M=this._getAttrToProjector();
return{x:M.x(O,H,K),y:M.y(O,H,K),width:M.width(O,H,K),height:M.height(O,H,K)}};N.prototype._pixelPoint=function(O,H,K){var M=this._pixelBounds(O,H,K);O=(this._isVertical?D.Plot._scaledAccessor(this.y()):D.Plot._scaledAccessor(this.x()))(O,H,K);H=(this._isVertical?this.y().scale:this.x().scale).scale(this.baselineValue());return this._pixelPointBar(O,H,M)};N.prototype._pixelPointBar=function(O,H,K){if(this._isVertical){var M=K.x+K.width/2;O=O<=H?K.y:K.y+K.height}else M=O>=H?K.x+K.width:K.x,O=K.y+K.height/
2;return{x:M,y:O}};N.prototype._uninstallScaleForKey=function(O,H){I.prototype._uninstallScaleForKey.call(this,O,H)};N.prototype._getDataToDraw=function(){var O=this,H=new C.Map,K=this._getAttrToProjector(),M=this.width(),L=this.height();this.datasets().forEach(function(Q){var T=Q.data().map(function(X,aa){return O._isDatumOnScreen(K,M,L,X,aa,Q)?X:null});H.set(Q,T)});return H};N.prototype._isDatumOnScreen=function(O,H,K,M,L,Q){var T=O.x(M,L,Q),X=O.y(M,L,Q),aa=O.width(M,L,Q);O=O.height(M,L,Q);return C.Math.isValidNumber(T)&&
C.Math.isValidNumber(X)&&C.Math.isValidNumber(aa)&&C.Math.isValidNumber(O)?C.Math.boundsIntersects(T,X,aa,O,H,K):!1};return N}(h.XYPlot);B._BAR_THICKNESS_RATIO=.95;B._SINGLE_BAR_DIMENSION_RATIO=.4;B._BAR_AREA_CLASS="bar-area";B._BAR_END_KEY="barEnd";B._BAR_THICKNESS_KEY="width";B._LABEL_AREA_CLASS="bar-label-text-area";B._LABEL_MARGIN_INSIDE_BAR=10;f.Bar=B},function(d,f,h){var k=this&&this.__extends||function(x,A){function y(){this.constructor=x}for(var w in A)A.hasOwnProperty(w)&&(x[w]=A[w]);x.prototype=
null===A?Object.create(A):(y.prototype=A.prototype,new y)},t=h(1),l=h(5),p=h(8),m=h(3),n=h(0);d=h(10);var q=h(22);f.TimeInterval=d.makeEnum("second minute hour day week month year".split(" "));f.TimeAxisOrientation=d.makeEnum(["top","bottom"]);f.TierLabelPosition=d.makeEnum(["between","center"]);h=function(x){function A(y,w){y=x.call(this,y,w)||this;y._maxTimeIntervalPrecision=null;y._tierLabelPositions=[];y.addClass("time-axis");y.tickLabelPadding(5);y.axisConfigurations(A._DEFAULT_TIME_AXIS_CONFIGURATIONS);
y.annotationFormatter(p.time("%a %b %d, %Y"));return y}k(A,x);A.prototype.tierLabelPositions=function(y){if(null==y)return this._tierLabelPositions;if(!y.every(function(w){return"between"===w.toLowerCase()||"center"===w.toLowerCase()}))throw Error("Unsupported position for tier labels");this._tierLabelPositions=y;this.redraw();return this};A.prototype.maxTimeIntervalPrecision=function(y){if(null==y)return this._maxTimeIntervalPrecision;this._maxTimeIntervalPrecision=y;this.redraw();return this};A.prototype.currentAxisConfiguration=
function(){return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex]};A.prototype.axisConfigurations=function(y){if(null!=y){this._possibleTimeAxisConfigurations=y;this._numTiers=n.Math.max(this._possibleTimeAxisConfigurations.map(function(G){return G.length}),0);this._isAnchored&&this._setupDomElements();y=this.tierLabelPositions();for(var w=[],C=0;C<this._numTiers;C++)w.push(y[C]||"between");this.tierLabelPositions(w);this.redraw()}};A.prototype._getMostPreciseConfigurationIndex=
function(){var y=this,w=this._possibleTimeAxisConfigurations.length;this._possibleTimeAxisConfigurations.forEach(function(C,G){G<w&&C.every(function(D){return y._checkTimeAxisTierConfiguration(D)})&&(w=G)});w===this._possibleTimeAxisConfigurations.length&&(n.Window.warn("zoomed out too far: could not find suitable interval to display labels"),--w);return w};A.prototype.orientation=function(y){if(y&&("right"===y.toLowerCase()||"left"===y.toLowerCase()))throw Error(y+" is not a supported orientation for TimeAxis - only horizontal orientations are supported");
return x.prototype.orientation.call(this,y)};A.prototype._computeHeight=function(){var y=this._measurer.measure().height;this._tierHeights=[];for(var w=0;w<this._numTiers;w++)this._tierHeights.push(y+this.tickLabelPadding()+("between"===this._tierLabelPositions[w]?0:this._maxLabelTickLength()));return t.sum(this._tierHeights)};A.prototype._getIntervalLength=function(y){var w=this._scale.domain()[0];y=m.Time.timeIntervalToD3Time(y.interval).offset(w,y.step);return y>this._scale.domain()[1]?this.width():
Math.abs(this._scale.scale(y)-this._scale.scale(w))};A.prototype._maxWidthForInterval=function(y){return this._measurer.measure(y.formatter(A._LONG_DATE)).width};A.prototype._checkTimeAxisTierConfiguration=function(y){if(null!=this._maxTimeIntervalPrecision){var w=A._SORTED_TIME_INTERVAL_INDEX[this._maxTimeIntervalPrecision],C=A._SORTED_TIME_INTERVAL_INDEX[y.interval];if(null!=w&&null!=C&&C<w)return!1}w=this._maxWidthForInterval(y)+2*this.tickLabelPadding();return Math.min(this._getIntervalLength(y),
this.width())>=w};A.prototype._sizeFromOffer=function(y,w){var C=x.prototype._sizeFromOffer.call(this,y,w);y=this._tierHeights.reduce(function(G,D){return G+D>C.height?G:G+D});w=this.margin()+(this.annotationsEnabled()?this.annotationTierCount()*this._annotationTierHeight():0);C.height=Math.min(C.height,y+w);return C};A.prototype._setup=function(){x.prototype._setup.call(this);this._setupDomElements()};A.prototype._setupDomElements=function(){this.content().selectAll("."+A.TIME_AXIS_TIER_CLASS).remove();
this._tierLabelContainers=[];this._tierMarkContainers=[];this._tierBaselines=[];this._tickLabelContainer.remove();this._baseline.remove();for(var y=0;y<this._numTiers;++y){var w=this.content().append("g").classed(A.TIME_AXIS_TIER_CLASS,!0);this._tierLabelContainers.push(w.append("g").classed(q.Axis.TICK_LABEL_CLASS+"-container",!0));this._tierMarkContainers.push(w.append("g").classed(q.Axis.TICK_MARK_CLASS+"-container",!0));this._tierBaselines.push(w.append("line").classed("baseline",!0))}y=new l.SvgContext(this._tierLabelContainers[0].node());
this._measurer=new l.CacheMeasurer(y)};A.prototype._getTickIntervalValues=function(y){return this._scale.tickInterval(y.interval,y.step)};A.prototype._getTickValues=function(){var y=this;return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex].reduce(function(w,C){return w.concat(y._getTickIntervalValues(C))},[])};A.prototype._cleanTiers=function(){for(var y=0;y<this._tierLabelContainers.length;y++)this._tierLabelContainers[y].selectAll("."+q.Axis.TICK_LABEL_CLASS).remove(),this._tierMarkContainers[y].selectAll("."+
q.Axis.TICK_MARK_CLASS).remove(),this._tierBaselines[y].style("visibility","hidden")};A.prototype._getTickValuesForConfiguration=function(y){y=this._scale.tickInterval(y.interval,y.step);var w=this._scale.domain(),C=y.map(function(G){return G.valueOf()});-1===C.indexOf(w[0].valueOf())&&y.unshift(w[0]);-1===C.indexOf(w[1].valueOf())&&y.push(w[1]);return y};A.prototype._renderTierLabels=function(y,w,C){var G=this,D=this._getTickValuesForConfiguration(w),B=[];"between"===this._tierLabelPositions[C]&&
1===w.step?D.map(function(K,M){M+1>=D.length||B.push(new Date((D[M+1].valueOf()-D[M].valueOf())/2+D[M].valueOf()))}):B=D;y=y.selectAll("."+q.Axis.TICK_LABEL_CLASS).data(B,function(K){return String(K.valueOf())});var I=y.enter().append("g").classed(q.Axis.TICK_LABEL_CLASS,!0);I.append("text");var N="center"===this._tierLabelPositions[C]||1===w.step?0:this.tickLabelPadding();var O="bottom"===this.orientation()?t.sum(this._tierHeights.slice(0,C+1))-this.tickLabelPadding():"center"===this._tierLabelPositions[C]?
this.height()-t.sum(this._tierHeights.slice(0,C))-this.tickLabelPadding()-this._maxLabelTickLength():this.height()-t.sum(this._tierHeights.slice(0,C))-this.tickLabelPadding();I=y.merge(I);var H=I.selectAll("text");0<H.size()&&H.attr("transform","translate("+N+","+O+")");y.exit().remove();I.attr("transform",function(K){return"translate("+G._scale.scale(K)+",0)"});C="center"===this._tierLabelPositions[C]||1===w.step?"middle":"start";I.selectAll("text").text(w.formatter).style("text-anchor",C)};A.prototype._renderTickMarks=
function(y,w){y=this._tierMarkContainers[w].selectAll("."+q.Axis.TICK_MARK_CLASS).data(y);var C=y.enter().append("line").classed(q.Axis.TICK_MARK_CLASS,!0).merge(y),G=this._generateTickMarkAttrHash(),D=this._tierHeights.slice(0,w).reduce(function(B,I){return B+I},0);"bottom"===this.orientation()?(G.y1=D,G.y2=D+("center"===this._tierLabelPositions[w]?this.innerTickLength():this._tierHeights[w])):(G.y1=this.height()-D,G.y2=this.height()-(D+("center"===this._tierLabelPositions[w]?this.innerTickLength():
this._tierHeights[w])));C.attrs(G);"bottom"===this.orientation()?(G.y1=D,G.y2=D+("center"===this._tierLabelPositions[w]?this.endTickLength():this._tierHeights[w])):(G.y1=this.height()-D,G.y2=this.height()-(D+("center"===this._tierLabelPositions[w]?this.endTickLength():this._tierHeights[w])));t.select(C.nodes()[0]).attrs(G);t.select(C.nodes()[C.size()-1]).attrs(G);t.select(C.nodes()[0]).classed(q.Axis.END_TICK_MARK_CLASS,!0);t.select(C.nodes()[C.size()-1]).classed(q.Axis.END_TICK_MARK_CLASS,!0);y.exit().remove()};
A.prototype._renderLabellessTickMarks=function(y){y=this._tickMarkContainer.selectAll("."+q.Axis.TICK_MARK_CLASS).data(y);var w=y.enter().append("line").classed(q.Axis.TICK_MARK_CLASS,!0).merge(y),C=this._generateTickMarkAttrHash();C.y2="bottom"===this.orientation()?this.tickLabelPadding():this.height()-this.tickLabelPadding();w.attrs(C);y.exit().remove()};A.prototype._generateLabellessTicks=function(){return 1>this._mostPreciseConfigIndex?[]:this._getTickIntervalValues(this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex-
1][0])};A.prototype.renderImmediately=function(){var y=this;this._mostPreciseConfigIndex=this._getMostPreciseConfigurationIndex();var w=this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex];this._cleanTiers();w.forEach(function(I,N){return y._renderTierLabels(y._tierLabelContainers[N],I,N)});for(var C=w.map(function(I){return y._getTickValuesForConfiguration(I)}),G=0,D=0;D<Math.max(w.length,1);++D){var B=this._generateBaselineAttrHash();B.y1+="bottom"===this.orientation()?G:-G;B.y2=B.y1;
this._tierBaselines[D].attrs(B).style("visibility","inherit");G+=this._tierHeights[D]}G=[];D=this._scale.domain();D=this._scale.scale(D[1])-this._scale.scale(D[0]);1.5*this._getIntervalLength(w[0])>=D&&(G=this._generateLabellessTicks());this._renderLabellessTickMarks(G);this._hideOverflowingTiers();for(D=0;D<w.length;++D)this._renderTickMarks(C[D],D),this._hideOverlappingAndCutOffLabels(D);this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations();return this};A.prototype._hideOverflowingTiers=
function(){var y=this,w=this.height(),C=0;this.content().selectAll("."+A.TIME_AXIS_TIER_CLASS).attr("visibility",function(G,D){C+=y._tierHeights[D];return C<=w?"inherit":"hidden"})};A.prototype._hideOverlappingAndCutOffLabels=function(y){function w(I){return Math.floor(G.left)<=Math.ceil(I.left)&&Math.floor(G.top)<=Math.ceil(I.top)&&Math.floor(I.right)<=Math.ceil(G.left+C.width())&&Math.floor(I.bottom)<=Math.ceil(G.top+C.height())}var C=this,G=this.element().node().getBoundingClientRect(),D=this._tierMarkContainers[y].selectAll("."+
q.Axis.TICK_MARK_CLASS).filter(function(){var I=t.select(this).style("visibility");return"visible"===I||"inherit"===I}).nodes().map(function(I){return I.getBoundingClientRect()}),B;this._tierLabelContainers[y].selectAll("."+q.Axis.TICK_LABEL_CLASS).filter(function(){var I=t.select(this).style("visibility");return"visible"===I||"inherit"===I}).each(function(I,N){I=this.getBoundingClientRect();var O=t.select(this),H=D[N],K=D[N+1];N=null!=B&&n.DOM.clientRectsOverlap(I,B);H=null!=H&&n.DOM.clientRectsOverlap(I,
H);K=null!=K&&n.DOM.clientRectsOverlap(I,K);!w(I)||N||H||K?O.style("visibility","hidden"):(B=I,O.style("visibility","inherit"))})};A.prototype.invalidateCache=function(){x.prototype.invalidateCache.call(this);this._measurer.reset()};return A}(q.Axis);h.TIME_AXIS_TIER_CLASS="time-axis-tier";h._SORTED_TIME_INTERVAL_INDEX=(u={},u[f.TimeInterval.second]=0,u[f.TimeInterval.minute]=1,u[f.TimeInterval.hour]=2,u[f.TimeInterval.day]=3,u[f.TimeInterval.week]=4,u[f.TimeInterval.month]=5,u[f.TimeInterval.year]=
6,u);h._DEFAULT_TIME_AXIS_CONFIGURATIONS=[[{interval:f.TimeInterval.second,step:1,formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.second,step:5,formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.second,step:10,formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.second,step:15,
formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.second,step:30,formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.minute,step:1,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.minute,step:5,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],
[{interval:f.TimeInterval.minute,step:10,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.minute,step:15,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.minute,step:30,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.hour,step:1,formatter:p.time("%I %p")},{interval:f.TimeInterval.day,
step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.hour,step:3,formatter:p.time("%I %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.hour,step:6,formatter:p.time("%I %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.hour,step:12,formatter:p.time("%I %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.day,step:1,formatter:p.time("%a %e")},
{interval:f.TimeInterval.month,step:1,formatter:p.time("%B %Y")}],[{interval:f.TimeInterval.day,step:1,formatter:p.time("%e")},{interval:f.TimeInterval.month,step:1,formatter:p.time("%B %Y")}],[{interval:f.TimeInterval.month,step:1,formatter:p.time("%B")},{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.month,step:1,formatter:p.time("%b")},{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.month,step:3,formatter:p.time("%b")},
{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.month,step:6,formatter:p.time("%b")},{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:1,formatter:p.time("%y")}],[{interval:f.TimeInterval.year,step:5,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:25,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:50,formatter:p.time("%Y")}],
[{interval:f.TimeInterval.year,step:100,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:200,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:500,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:1E3,formatter:p.time("%Y")}]];h._LONG_DATE=new Date(9999,8,29,12,59,9999);f.Time=h;var u},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=
p.prototype,new m)},t=h(12);d=function(l){function p(){var m=l.call(this)||this;m._detachCallback=function(n){return m.remove(n)};return m}k(p,l);p.prototype.anchor=function(m){var n=this;m=t.coerceExternalD3(m);l.prototype.anchor.call(this,m);this._forEach(function(q){return q.anchor(n.element())});return this};p.prototype.render=function(){this._forEach(function(m){return m.render()});return this};p.prototype.has=function(){throw Error("has() is not implemented on ComponentContainer");};p.prototype._adoptAndAnchor=
function(m){m.parent(this);m.onDetach(this._detachCallback);this._isAnchored&&m.anchor(this.element())};p.prototype.remove=function(m){this.has(m)&&(m.offDetach(this._detachCallback),this._remove(m),m.detach(),this.redraw());return this};p.prototype._remove=function(){};p.prototype._forEach=function(){throw Error("_forEach() is not implemented on ComponentContainer");};p.prototype.destroy=function(){l.prototype.destroy.call(this);this._forEach(function(m){return m.destroy()})};p.prototype.invalidateCache=
function(){this._forEach(function(m){return m.invalidateCache()})};return p}(h(4).Component);f.ComponentContainer=d},function(d,f,h){function k(A){n.add(A);m.add(A);t()}function t(){q||(q=!0,x.render())}var l=h(0);d=h(10);var p=h(39),m=new l.Set,n=new l.Set,q=!1,u=!1;f.Policy=d.makeEnum(["immediate","animationFrame","timeout"]);var x=new p.AnimationFrame;f.renderPolicy=function(A){if(null==A)return x;switch(A){case f.Policy.immediate:x=new p.Immediate;break;case f.Policy.animationFrame:x=new p.AnimationFrame;
break;case f.Policy.timeout:x=new p.Timeout;break;default:l.Window.warn("Unrecognized renderPolicy: "+A)}};f.registerToRender=function(A){u&&l.Window.warn("Registered to render while other components are flushing: request may be ignored");m.add(A);t()};f.registerToComputeLayoutAndRender=k;f.registerToComputeLayout=function(A){k(A)};f.flush=function(){if(q){n.forEach(function(y){return y.computeLayout()});m.forEach(function(y){return y.render()});u=!0;var A=new l.Set;m.forEach(function(y){try{y.renderImmediately()}catch(w){window.setTimeout(function(){throw w;
},0),A.add(y)}});n=new l.Set;m=A;u=q=!1}}},function(d,f,h){var k=h(1);f.circle=function(){return function(l){return k.symbol().type(k.symbolCircle).size(Math.PI*Math.pow(l/2,2))}};f.square=function(){return function(l){return k.symbol().type(k.symbolSquare).size(Math.pow(l,2))}};f.cross=function(){return function(l){return k.symbol().type(k.symbolCross).size(5/9*Math.pow(l,2))}};f.diamond=function(){return function(l){return k.symbol().type(k.symbolDiamond).size(Math.tan(Math.PI/6)*Math.pow(l,2)/
2)}};f.triangle=function(){return function(l){return k.symbol().type(k.symbolTriangle).size(Math.sqrt(3)*Math.pow(l/2,2))}};f.star=function(){return function(l){return k.symbol().type(k.symbolStar).size(.8908130915292852*Math.pow(l/2,2))}};var t=3*(1/Math.sqrt(12)/2+1);f.wye=function(){return function(l){return k.symbol().type(k.symbolWye).size(t*Math.pow(l/2.4,2))}}},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var x in q)q.hasOwnProperty(x)&&(n[x]=
q[x]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},t=h(25),l=h(0),p=h(12),m=h(37);d=function(n){function q(){var u=n.call(this)||this;u._detectionRadius=3;u._resizable=!1;u._movable=!1;u._hasCorners=!0;u.addClass("drag-box-layer");u._dragInteraction=new t.Drag;u._setUpCallbacks();u._dragInteraction.attachTo(u);u._dragStartCallbacks=new l.CallbackSet;u._dragCallbacks=new l.CallbackSet;u._dragEndCallbacks=new l.CallbackSet;return u}k(q,n);q.prototype._setUpCallbacks=function(){function u(I,
N){0===B&&I.x===N.x&&I.y===N.y&&y.boxVisible(!1);y._dragEndCallbacks.callCallbacks(y.bounds())}function x(I,N){switch(B){case 0:G.x=N.x;G.y=N.y;break;case 1:w.bottom?G.y=N.y:w.top&&(C.y=N.y);w.right?G.x=N.x:w.left&&(C.x=N.x);break;case 2:I=N.x-D.x;var O=N.y-D.y;C.x+=I;C.y+=O;G.x+=I;G.y+=O;D=N}y._setBounds({topLeft:C,bottomRight:G});y._xBoundsMode===m.PropertyMode.VALUE&&null!=y.xScale()&&y._setXExtent([y.xScale().invert(C.x),y.xScale().invert(G.x)]);y._yBoundsMode===m.PropertyMode.VALUE&&null!=y.yScale()&&
y._setYExtent([y.yScale().invert(C.y),y.yScale().invert(G.y)]);y.render();y._dragCallbacks.callCallbacks(y.bounds())}function A(I){w=y._getResizingEdges(I);var N=y.bounds();N=N.topLeft.x<=I.x&&I.x<=N.bottomRight.x&&N.topLeft.y<=I.y&&I.y<=N.bottomRight.y;y.boxVisible()&&(w.top||w.bottom||w.left||w.right)?B=1:y.boxVisible()&&y.movable()&&N?B=2:(B=0,y._setBounds({topLeft:I,bottomRight:I}),y._xBoundsMode===m.PropertyMode.VALUE&&null!=y.xScale()&&y._setXExtent([y.xScale().invert(I.x),y.xScale().invert(I.x)]),
y._yBoundsMode===m.PropertyMode.VALUE&&null!=y.yScale()&&y._setYExtent([y.yScale().invert(I.y),y.yScale().invert(I.y)]),y.render());y.boxVisible(!0);N=y.bounds();C={x:N.topLeft.x,y:N.topLeft.y};G={x:N.bottomRight.x,y:N.bottomRight.y};D=I;y._dragStartCallbacks.callCallbacks(N)}var y=this,w,C,G,D,B=0;this._dragInteraction.onDragStart(A);this._dragInteraction.onDrag(x);this._dragInteraction.onDragEnd(u);this._disconnectInteraction=function(){y._dragInteraction.offDragStart(A);y._dragInteraction.offDrag(x);
y._dragInteraction.offDragEnd(u);y._dragInteraction.detach()}};q.prototype._setup=function(){function u(){return x._box.append("line").styles({opacity:0,stroke:"pink","pointer-events":"visibleStroke"})}var x=this;n.prototype._setup.call(this);this._detectionEdgeT=u().classed("drag-edge-tb",!0);this._detectionEdgeB=u().classed("drag-edge-tb",!0);this._detectionEdgeL=u().classed("drag-edge-lr",!0);this._detectionEdgeR=u().classed("drag-edge-lr",!0);if(this._hasCorners){var A=function(){return x._box.append("circle").styles({opacity:0,
fill:"pink","pointer-events":"visibleFill"})};this._detectionCornerTL=A().classed("drag-corner-tl",!0);this._detectionCornerTR=A().classed("drag-corner-tr",!0);this._detectionCornerBL=A().classed("drag-corner-bl",!0);this._detectionCornerBR=A().classed("drag-corner-br",!0)}};q.prototype._getResizingEdges=function(u){var x={top:!1,bottom:!1,left:!1,right:!1};if(!this.resizable())return x;var A=this.bounds(),y=A.topLeft.y,w=A.bottomRight.y,C=A.topLeft.x;A=A.bottomRight.x;var G=this._detectionRadius;
C-G<=u.x&&u.x<=A+G&&(x.top=y-G<=u.y&&u.y<=y+G,x.bottom=w-G<=u.y&&u.y<=w+G);y-G<=u.y&&u.y<=w+G&&(x.left=C-G<=u.x&&u.x<=C+G,x.right=A-G<=u.x&&u.x<=A+G);return x};q.prototype.renderImmediately=function(){n.prototype.renderImmediately.call(this);if(this.boxVisible()){var u=this.bounds(),x=u.topLeft.y,A=u.bottomRight.y,y=u.topLeft.x;u=u.bottomRight.x;this._detectionEdgeT.attrs({x1:y,y1:x,x2:u,y2:x,"stroke-width":2*this._detectionRadius});this._detectionEdgeB.attrs({x1:y,y1:A,x2:u,y2:A,"stroke-width":2*
this._detectionRadius});this._detectionEdgeL.attrs({x1:y,y1:x,x2:y,y2:A,"stroke-width":2*this._detectionRadius});this._detectionEdgeR.attrs({x1:u,y1:x,x2:u,y2:A,"stroke-width":2*this._detectionRadius});this._hasCorners&&(this._detectionCornerTL.attrs({cx:y,cy:x,r:this._detectionRadius}),this._detectionCornerTR.attrs({cx:u,cy:x,r:this._detectionRadius}),this._detectionCornerBL.attrs({cx:y,cy:A,r:this._detectionRadius}),this._detectionCornerBR.attrs({cx:u,cy:A,r:this._detectionRadius}))}return this};
q.prototype.detectionRadius=function(){return this._detectionRadius};q.prototype.resizable=function(u){if(null==u)return this._resizable;this._resizable=u;this._setResizableClasses(u);return this};q.prototype._setResizableClasses=function(u){u&&this.enabled()?(this.addClass("x-resizable"),this.addClass("y-resizable")):(this.removeClass("x-resizable"),this.removeClass("y-resizable"))};q.prototype.movable=function(){return this._movable};q.prototype._setMovableClass=function(){this.movable()&&this.enabled()?
this.addClass("movable"):this.removeClass("movable")};q.prototype.onDragStart=function(u){this._dragStartCallbacks.add(u)};q.prototype.offDragStart=function(u){this._dragStartCallbacks.delete(u)};q.prototype.onDrag=function(u){this._dragCallbacks.add(u);return this};q.prototype.offDrag=function(u){this._dragCallbacks.delete(u)};q.prototype.onDragEnd=function(u){this._dragEndCallbacks.add(u)};q.prototype.offDragEnd=function(u){this._dragEndCallbacks.delete(u)};q.prototype.dragInteraction=function(){return this._dragInteraction};
q.prototype.enabled=function(u){if(null==u)return this._dragInteraction.enabled();this._dragInteraction.enabled(u);this._setResizableClasses(this.resizable());this._setMovableClass();return this};q.prototype.destroy=function(){var u=this;n.prototype.destroy.call(this);this._dragStartCallbacks.forEach(function(x){return u._dragCallbacks.delete(x)});this._dragCallbacks.forEach(function(x){return u._dragCallbacks.delete(x)});this._dragEndCallbacks.forEach(function(x){return u._dragEndCallbacks.delete(x)});
this._disconnectInteraction()};q.prototype.detach=function(){this._resetState();this._dragInteraction.detach();n.prototype.detach.call(this);return this};q.prototype.anchor=function(u){u=p.coerceExternalD3(u);this._dragInteraction.attachTo(this);n.prototype.anchor.call(this,u);return this};q.prototype._resetState=function(){this.bounds({topLeft:{x:0,y:0},bottomRight:{x:0,y:0}})};return q}(h(43).SelectionBoxLayer);f.DragBoxLayer=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(18);d=function(p){function m(){return p.call(this,"path","line")||this}k(m,p);m.prototype._applyDefaultAttributes=function(n){n.style("fill","none")};m.prototype.getVisualPrimitiveAtIndex=function(){return p.prototype.getVisualPrimitiveAtIndex.call(this,0)};return m}(h(9).SVGDrawer);f.LineSVGDrawer=d;var l=["opacity","stroke-opacity","stroke-width","stroke"];f.makeLineCanvasDrawStep=
function(p){return function(m,n,q){q=t.resolveAttributes(q,l,n[0],0);t.renderLine(m,p(),n[0],q)}}},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(18);d=function(p){function m(n){void 0===n&&(n="");var q=p.call(this,"rect","")||this;q._rootClassName=n;q._root.classed(q._rootClassName,!0);return q}k(m,p);return m}(h(9).SVGDrawer);f.RectangleSVGDrawer=
d;var l=["x","y","width","height"];f.RectangleCanvasDrawStep=function(p,m,n){p.save();m.forEach(function(q,u){null!=q&&(q=t.resolveAttributesSubsetWithStyles(n,l,q,u),p.beginPath(),p.rect(q.x,q.y,q.width,q.height),t.renderPathWithStyle(p,q))});p.restore()};d=function(p){function m(n){return p.call(this,n,f.RectangleCanvasDrawStep)||this}k(m,p);return m}(t.CanvasDrawer);f.RectangleCanvasDrawer=d},function(d,f,h){function k(p){l.SHOW_WARNINGS&&console.warn(p)}function t(p,m){for(var n=[],q=2;q<arguments.length;q++)n[q-
2]=arguments[q];return 0===m?(p(n),-1):window.setTimeout(p,m,n)}var l=h(23);f.warn=k;f.setTimeout=t;f.debounce=function(p,m,n){function q(){m.apply(n,x)}var u=null,x=[];return function(){x=Array.prototype.slice.call(arguments);clearTimeout(u);u=t(q,p)}};f.deprecated=function(p,m,n){void 0===n&&(n="");k("Method "+p+" has been deprecated in version "+m+". Please refer to the release notes. "+n)}},function(d,f){d=function(){function h(k){this.ruler=null!=k.createRuler?k.createRuler():k}h.prototype.measure=
function(k){void 0===k&&(k=h.HEIGHT_TEXT);return this.ruler(k)};return h}();d.HEIGHT_TEXT="bdpql";f.AbstractMeasurer=d},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(32));k(h(74));k(h(75));k(h(41));k(h(42));k(h(76));k(h(77));k(h(78));k(h(79));k(h(43));k(h(80));k(h(81));k(h(82))},function(d,f,h){var k=h(0);d=function(){function t(l,p){void 0===l&&(l=[]);void 0===p&&(p={});this._updateId=0;this._data=l;this._metadata=p;this._callbacks=new k.CallbackSet}t.prototype.onUpdate=
function(l){this._callbacks.add(l);return this};t.prototype.offUpdate=function(l){this._callbacks.delete(l);return this};t.prototype.data=function(l){if(null==l)return this._data;this._data=l;this._dispatchUpdate();return this};t.prototype.metadata=function(l){if(null==l)return this._metadata;this._metadata=l;this._dispatchUpdate();return this};t.prototype.updateId=function(){return this._updateId};t.prototype._dispatchUpdate=function(){this._updateId++;this._callbacks.callCallbacks(this)};return t}();
f.Dataset=d},function(d,f,h){var k=h(0),t=h(30);d=function(){function l(){}l.prototype.render=function(){t.flush()};return l}();f.Immediate=d;d=function(){function l(){}l.prototype.render=function(){k.DOM.requestAnimationFramePolyfill(t.flush)};return l}();f.AnimationFrame=d;d=function(){function l(){this._timeoutMsec=k.DOM.SCREEN_REFRESH_RATE_MILLISECONDS}l.prototype.render=function(){setTimeout(t.flush,this._timeoutMsec)};return l}();f.Timeout=d},function(d,f,h){var k=this&&this.__extends||function(p,
m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(13),l=h(0);d=function(p){function m(){var n=null!==p&&p.apply(this,arguments)||this;n._keyPressCallbacks={};n._keyReleaseCallbacks={};n._mouseMoveCallback=function(){return!1};n._downedKeys=new l.Set;n._keyDownCallback=function(q,u){return n._handleKeyDownEvent(q,u)};n._keyUpCallback=function(q){return n._handleKeyUpEvent(q)};return n}k(m,p);
m.prototype._anchor=function(n){p.prototype._anchor.call(this,n);this._positionDispatcher=t.Mouse.getDispatcher(this._componentAttachedTo);this._positionDispatcher.onMouseMove(this._mouseMoveCallback);this._keyDispatcher=t.Key.getDispatcher();this._keyDispatcher.onKeyDown(this._keyDownCallback);this._keyDispatcher.onKeyUp(this._keyUpCallback)};m.prototype._unanchor=function(){p.prototype._unanchor.call(this);this._positionDispatcher.offMouseMove(this._mouseMoveCallback);this._positionDispatcher=null;
this._keyDispatcher.offKeyDown(this._keyDownCallback);this._keyDispatcher.offKeyUp(this._keyUpCallback);this._keyDispatcher=null};m.prototype._handleKeyDownEvent=function(n,q){var u=this._translateToComponentSpace(this._positionDispatcher.lastMousePosition());this._isInsideComponent(u)&&!q.repeat&&(this._keyPressCallbacks[n]&&this._keyPressCallbacks[n].callCallbacks(n),this._downedKeys.add(n))};m.prototype._handleKeyUpEvent=function(n){this._downedKeys.has(n)&&this._keyReleaseCallbacks[n]&&this._keyReleaseCallbacks[n].callCallbacks(n);
this._downedKeys.delete(n)};m.prototype.onKeyPress=function(n,q){this._keyPressCallbacks[n]||(this._keyPressCallbacks[n]=new l.CallbackSet);this._keyPressCallbacks[n].add(q);return this};m.prototype.offKeyPress=function(n,q){this._keyPressCallbacks[n].delete(q);0===this._keyPressCallbacks[n].size&&delete this._keyPressCallbacks[n];return this};m.prototype.onKeyRelease=function(n,q){this._keyReleaseCallbacks[n]||(this._keyReleaseCallbacks[n]=new l.CallbackSet);this._keyReleaseCallbacks[n].add(q);return this};
m.prototype.offKeyRelease=function(n,q){this._keyReleaseCallbacks[n].delete(q);0===this._keyReleaseCallbacks[n].size&&delete this._keyReleaseCallbacks[n];return this};return m}(h(15).Interaction);f.Key=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},t=h(0);d=function(l){function p(m){void 0===m&&(m=[]);var n=l.call(this)||this;n._components=
[];n.addClass("component-group");m.forEach(function(q){return n.append(q)});return n}k(p,l);p.prototype._forEach=function(m){this.components().forEach(m)};p.prototype.has=function(m){return 0<=this._components.indexOf(m)};p.prototype.requestedSpace=function(m,n){var q=this._components.map(function(u){return u.requestedSpace(m,n)});return{minWidth:t.Math.max(q,function(u){return u.minWidth},0),minHeight:t.Math.max(q,function(u){return u.minHeight},0)}};p.prototype.computeLayout=function(m,n,q){var u=
this;l.prototype.computeLayout.call(this,m,n,q);this._forEach(function(x){x.computeLayout({x:0,y:0},u.width(),u.height())});return this};p.prototype._sizeFromOffer=function(m,n){return{width:m,height:n}};p.prototype.fixedWidth=function(){return this._components.every(function(m){return m.fixedWidth()})};p.prototype.fixedHeight=function(){return this._components.every(function(m){return m.fixedHeight()})};p.prototype.components=function(){return this._components.slice()};p.prototype.append=function(m){null==
m||this.has(m)||(m.detach(),this._components.push(m),this._adoptAndAnchor(m),this.redraw());return this};p.prototype._remove=function(m){m=this._components.indexOf(m);0<=m&&this._components.splice(m,1)};return p}(h(29).ComponentContainer);f.Group=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)};h(0);d=h(4);var t;(function(l){l[l.VALUE=0]=
"VALUE";l[l.PIXEL=1]="PIXEL"})(t||(t={}));d=function(l){function p(m){var n=l.call(this)||this;n._mode=t.VALUE;if(m!==p.ORIENTATION_VERTICAL&&m!==p.ORIENTATION_HORIZONTAL)throw Error(m+" is not a valid orientation for GuideLineLayer");n._orientation=m;n._overflowHidden=!0;n.addClass("guide-line-layer");n._isVertical()?n.addClass("vertical"):n.addClass("horizontal");n._scaleUpdateCallback=function(){n._syncPixelPositionAndValue();n.render()};return n}k(p,l);p.prototype._setup=function(){l.prototype._setup.call(this);
this._guideLine=this.content().append("line").classed("guide-line",!0)};p.prototype._sizeFromOffer=function(m,n){return{width:m,height:n}};p.prototype._isVertical=function(){return this._orientation===p.ORIENTATION_VERTICAL};p.prototype.fixedWidth=function(){return!0};p.prototype.fixedHeight=function(){return!0};p.prototype.computeLayout=function(m,n,q){l.prototype.computeLayout.call(this,m,n,q);null!=this.scale()&&(this._isVertical()?this.scale().range([0,this.width()]):this.scale().range([this.height(),
0]));return this};p.prototype.renderImmediately=function(){l.prototype.renderImmediately.call(this);this._syncPixelPositionAndValue();this._guideLine.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition()});return this};p.prototype._syncPixelPositionAndValue=function(){null!=this.scale()&&(this._mode===t.VALUE&&null!=this.value()?this._pixelPosition=
this.scale().scale(this.value()):this._mode===t.PIXEL&&null!=this.pixelPosition()&&(this._value=this.scale().invert(this.pixelPosition())))};p.prototype._setPixelPositionWithoutChangingMode=function(m){this._pixelPosition=m;null!=this.scale()&&(this._value=this.scale().invert(this.pixelPosition()));this.render()};p.prototype.scale=function(m){if(null==m)return this._scale;var n=this._scale;null!=n&&n.offUpdate(this._scaleUpdateCallback);this._scale=m;this._scale.onUpdate(this._scaleUpdateCallback);
this._syncPixelPositionAndValue();this.redraw();return this};p.prototype.value=function(m){if(null==m)return this._value;this._value=m;this._mode=t.VALUE;this._syncPixelPositionAndValue();this.render();return this};p.prototype.pixelPosition=function(){return this._pixelPosition};p.prototype.destroy=function(){l.prototype.destroy.call(this);null!=this.scale()&&this.scale().offUpdate(this._scaleUpdateCallback)};return p}(d.Component);d.ORIENTATION_VERTICAL="vertical";d.ORIENTATION_HORIZONTAL="horizontal";
f.GuideLineLayer=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(0);d=h(4);var l;(function(p){p[p.VALUE=0]="VALUE";p[p.PIXEL=1]="PIXEL"})(l=f.PropertyMode||(f.PropertyMode={}));d=function(p){function m(){var n=p.call(this)||this;n._boxVisible=!1;n._boxBounds={topLeft:{x:0,y:0},bottomRight:{x:0,y:0}};n._xBoundsMode=l.PIXEL;n._yBoundsMode=
l.PIXEL;n.addClass("selection-box-layer");n._adjustBoundsCallback=function(){n.render()};n._overflowHidden=!0;n._xExtent=[void 0,void 0];n._yExtent=[void 0,void 0];return n}k(m,p);m.prototype._setup=function(){p.prototype._setup.call(this);this._box=this.content().append("g").classed("selection-box",!0).remove();this._boxArea=this._box.append("rect").classed("selection-area",!0)};m.prototype._sizeFromOffer=function(n,q){return{width:n,height:q}};m.prototype.bounds=function(n){if(null==n)return this._getBounds();
this._setBounds(n);this._yBoundsMode=this._xBoundsMode=l.PIXEL;this.render();return this};m.prototype._setBounds=function(n){this._boxBounds={topLeft:{x:Math.min(n.topLeft.x,n.bottomRight.x),y:Math.min(n.topLeft.y,n.bottomRight.y)},bottomRight:{x:Math.max(n.topLeft.x,n.bottomRight.x),y:Math.max(n.topLeft.y,n.bottomRight.y)}}};m.prototype._getBounds=function(){return{topLeft:{x:this._xBoundsMode===l.PIXEL?this._boxBounds.topLeft.x:null==this._xScale?0:Math.min(this.xScale().scale(this.xExtent()[0]),
this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===l.PIXEL?this._boxBounds.topLeft.y:null==this._yScale?0:Math.min(this.yScale().scale(this.yExtent()[0]),this.yScale().scale(this.yExtent()[1]))},bottomRight:{x:this._xBoundsMode===l.PIXEL?this._boxBounds.bottomRight.x:null==this._xScale?0:Math.max(this.xScale().scale(this.xExtent()[0]),this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===l.PIXEL?this._boxBounds.bottomRight.y:null==this._yScale?0:Math.max(this.yScale().scale(this.yExtent()[0]),
this.yScale().scale(this.yExtent()[1]))}}};m.prototype.renderImmediately=function(){p.prototype.renderImmediately.call(this);if(this._boxVisible){var n=this.bounds(),q=n.topLeft.y,u=n.bottomRight.y,x=n.topLeft.x;n=n.bottomRight.x;if(!(t.Math.isValidNumber(q)&&t.Math.isValidNumber(u)&&t.Math.isValidNumber(x)&&t.Math.isValidNumber(n)))throw Error("bounds have not been properly set");this._boxArea.attrs({x,y:q,width:n-x,height:u-q});this.content().node().appendChild(this._box.node())}else this._box.remove();
return this};m.prototype.boxVisible=function(n){if(null==n)return this._boxVisible;this._boxVisible=n;this.render();return this};m.prototype.fixedWidth=function(){return!0};m.prototype.fixedHeight=function(){return!0};m.prototype.xScale=function(n){if(null==n)return this._xScale;null!=this._xScale&&this._xScale.offUpdate(this._adjustBoundsCallback);this._xScale=n;this._xBoundsMode=l.VALUE;this._xScale.onUpdate(this._adjustBoundsCallback);this.render();return this};m.prototype.yScale=function(n){if(null==
n)return this._yScale;null!=this._yScale&&this._yScale.offUpdate(this._adjustBoundsCallback);this._yScale=n;this._yBoundsMode=l.VALUE;this._yScale.onUpdate(this._adjustBoundsCallback);this.render();return this};m.prototype.xExtent=function(){return this._getXExtent()};m.prototype._getXExtent=function(){return this._xBoundsMode===l.VALUE?this._xExtent:null==this._xScale?[void 0,void 0]:[this._xScale.invert(this._boxBounds.topLeft.x),this._xScale.invert(this._boxBounds.bottomRight.x)]};m.prototype._setXExtent=
function(n){this._xExtent=n};m.prototype.yExtent=function(){return this._getYExtent()};m.prototype._getYExtent=function(){return this._yBoundsMode===l.VALUE?this._yExtent:null==this._yScale?[void 0,void 0]:[this._yScale.invert(this._boxBounds.topLeft.y),this._yScale.invert(this._boxBounds.bottomRight.y)]};m.prototype._setYExtent=function(n){this._yExtent=n};m.prototype.destroy=function(){p.prototype.destroy.call(this);null!=this._xScale&&this.xScale().offUpdate(this._adjustBoundsCallback);null!=this._yScale&&
this.yScale().offUpdate(this._adjustBoundsCallback)};return m}(d.Component);f.SelectionBoxLayer=d},function(d,f,h){var k=this&&this.__extends||function(t,l){function p(){this.constructor=t}for(var m in l)l.hasOwnProperty(m)&&(t[m]=l[m]);t.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(t){function l(){return t.call(this,"path","arc fill")||this}k(l,t);l.prototype._applyDefaultAttributes=function(p){p.style("stroke","none")};return l}(h(9).SVGDrawer);f.ArcSVGDrawer=
d},function(d,f,h){var k=this&&this.__extends||function(t,l){function p(){this.constructor=t}for(var m in l)l.hasOwnProperty(m)&&(t[m]=l[m]);t.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(t){function l(){return t.call(this,"path","arc outline")||this}k(l,t);l.prototype._applyDefaultAttributes=function(p){p.style("fill","none")};return l}(h(9).SVGDrawer);f.ArcOutlineSVGDrawer=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(18);d=function(p){function m(){return p.call(this,"path","area")||this}k(m,p);m.prototype._applyDefaultAttributes=function(n){n.style("stroke","none")};m.prototype.getVisualPrimitiveAtIndex=function(){return p.prototype.getVisualPrimitiveAtIndex.call(this,0)};return m}(h(9).SVGDrawer);f.AreaSVGDrawer=d;var l=["fill","opacity","fill-opacity"];f.makeAreaCanvasDrawStep=function(p){return function(m,
n,q){q=t.resolveAttributes(q,l,n[0],0);t.renderArea(m,p(),n[0],q)}}},function(d,f,h){var k=this&&this.__extends||function(t,l){function p(){this.constructor=t}for(var m in l)l.hasOwnProperty(m)&&(t[m]=l[m]);t.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(t){function l(){return t.call(this,"line","")||this}k(l,t);return l}(h(9).SVGDrawer);f.SegmentSVGDrawer=d},function(d,f,h){function k(n,q,u,x,A){return 0<=u+A&&u-A<=n&&0<=x+A&&x-A<=q}function t(n,q,u){if(null==n)return!1;
for(var x=0;x<u.length;x++){var A=u[x];if(n[A]!=q[A])return!1}return!0}var l=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var x in q)q.hasOwnProperty(x)&&(n[x]=q[x]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},p=h(86),m=h(18);d=function(n){function q(){return n.call(this,"path","symbol")||this}l(q,n);return q}(h(9).SVGDrawer);f.SymbolSVGDrawer=d;f.makeSymbolCanvasDrawStep=function(n,q,u){var x=this;return function(A,y,w){var C=A.canvas,G=C.clientWidth;
C=C.clientHeight;for(var D=new p.CanvasBuffer(0,0),B=q(),I=u(),N=null,O=null,H=null,K=0;K<y.length;K++){var M=y[K];if(null!=M){var L=m.resolveAttributesSubsetWithStyles(w,["x","y"],M,K),Q=I(M,K,n);if(k(G,C,L.x,L.y,Q)){var T=t(N,L,m.ContextStyleAttrs);M=B(M,K,x._dataset);T&&H==Q&&O==M||(N=m.getStrokeWidth(L),N=Q+N+1,(N>D.screenWidth||N>D.screenHeight)&&D.resize(N,N,!0),D.clear(),N=D.ctx,N.beginPath(),M(Q).context(N)(null),N.closePath(),m.renderPathWithStyle(N,L),O=M,H=Q,N=L);D.blitCenter(A,L.x,L.y)}}}}}},
function(d,f,h){function k(D){return D instanceof y?D:D instanceof Date?p(D.valueOf()):D instanceof A.Scale?t(D):D instanceof x.Dataset?l(D):u(D)?n(D):Array.isArray(D)?m(D):p(D)}function t(D){D={domain:D.domain(),range:D.range(),updateId:D.updateId(),ref:p(D)};return n(D)}function l(D){D={ref:p(D),updateId:D.updateId()};return n(D)}function p(D){return new C(D)}function m(D){return new w(D.map(function(B){return k(B)}))}function n(D){var B={},I;for(I in D)D.hasOwnProperty(I)&&(B[I]=k(D[I]));return new G(B)}
var q=this&&this.__extends||function(D,B){function I(){this.constructor=D}for(var N in B)B.hasOwnProperty(N)&&(D[N]=B[N]);D.prototype=null===B?Object.create(B):(I.prototype=B.prototype,new I)},u=h(128),x=h(38),A=h(17);f.sign=k;f.signScale=t;f.signDataset=l;f.signRef=p;f.signArray=m;f.signObj=n;var y=function(){function D(){}D.prototype.isDifferent=function(B){return B instanceof this.constructor?this.isSignatureDifferent(B):!0};return D}();f.Signature=y;var w=function(D){function B(I){var N=D.call(this)||
this;N.array=I;return N}q(B,D);B.prototype.isSignatureDifferent=function(I){if(I.array.length!==this.array.length)return!0;for(var N=0;N<this.array.length;N++)if(this.array[N].isDifferent(I.array[N]))return!0;return!1};return B}(y);f.ArraySignature=w;var C=function(D){function B(I){var N=D.call(this)||this;N.ref=I;return N}q(B,D);B.prototype.isSignatureDifferent=function(I){return this.ref!==I.ref};return B}(y);f.ReferenceSignature=C;var G=function(D){function B(I){var N=D.call(this)||this;N.obj=
I;return N}q(B,D);B.prototype.isSignatureDifferent=function(I){var N=Object.keys(this.obj),O=Object.keys(I.obj);if(N.length!==O.length)return!0;for(O=0;O<N.length;O++){var H=N[O];if(!I.obj.hasOwnProperty(H)||this.obj[H].isDifferent(I.obj[H]))return!0}return!1};return B}(y);f.ObjectSignature=G},function(d,f,h){var k=this&&this.__extends||function(y,w){function C(){this.constructor=y}for(var G in w)w.hasOwnProperty(G)&&(y[G]=w[G]);y.prototype=null===w?Object.create(w):(C.prototype=w.prototype,new C)},
t=h(1),l=h(3),p=h(0),m=h(14),n=h(46),q=h(6),u=h(33),x=h(19);d=h(53);var A=h(2);h=function(y){function w(){var C=y.call(this)||this;C.addClass("area-plot");C.y0(0);C.attr("fill-opacity",.25);C.attr("fill",(new l.Color).range()[0]);C._lineDrawers=new p.Map;return C}k(w,y);w.prototype.y=function(C,G){if(null==C)return y.prototype.y.call(this);null==G?y.prototype.y.call(this,C):y.prototype.y.call(this,C,G);null!=G&&(C=this.y0().accessor,null!=C&&this._bindProperty(w._Y0_KEY,C,G),this._updateYScale());
return this};w.prototype.y0=function(C){if(null==C)return this._propertyBindings.get(w._Y0_KEY);var G=this.y();this._bindProperty(w._Y0_KEY,C,G&&G.scale);this._updateYScale();this.render();return this};w.prototype._onDatasetUpdate=function(){y.prototype._onDatasetUpdate.call(this);this._updateYScale()};w.prototype._addDataset=function(C){var G=this;this._lineDrawers.set(C,new m.ProxyDrawer(function(){return new u.LineSVGDrawer},function(D){return new m.CanvasDrawer(D,u.makeLineCanvasDrawStep(function(){var B=
A.Plot._scaledAccessor(G.x()),I=A.Plot._scaledAccessor(G.y());return G._d3LineFactory(C,B,I)}))}));y.prototype._addDataset.call(this,C);return this};w.prototype._createNodesForDataset=function(C){y.prototype._createNodesForDataset.call(this,C);C=this._lineDrawers.get(C);"svg"===this.renderer()?C.useSVG(this._renderArea):C.useCanvas(this._canvas);return C};w.prototype._removeDatasetNodes=function(C){y.prototype._removeDatasetNodes.call(this,C);this._lineDrawers.get(C).remove()};w.prototype._additionalPaint=
function(){var C=this,G=this._generateLineDrawSteps(),D=this._getDataToDraw();this.datasets().forEach(function(B){var I=A.Plot.applyDrawSteps(G,B);C._lineDrawers.get(B).draw(D.get(B),I)})};w.prototype._generateLineDrawSteps=function(){var C=[];if(this._animateOnNextRender()){var G=this._generateLineAttrToProjector();G.d=this._constructLineProjector(A.Plot._scaledAccessor(this.x()),this._getResetYFunction());C.push({attrToProjector:G,animator:this._getAnimator(x.Animator.RESET)})}C.push({attrToProjector:this._generateLineAttrToProjector(),
animator:this._getAnimator(x.Animator.MAIN)});return C};w.prototype._generateLineAttrToProjector=function(){var C=this._getAttrToProjector();C.d=this._constructLineProjector(A.Plot._scaledAccessor(this.x()),A.Plot._scaledAccessor(this.y()));return C};w.prototype._createDrawer=function(C){var G=this;return new q.ProxyDrawer(function(){return new n.AreaSVGDrawer},function(D){return new m.CanvasDrawer(D,n.makeAreaCanvasDrawStep(function(){var B=A.Plot._scaledAccessor(G.x()),I=A.Plot._scaledAccessor(G.y()),
N=A.Plot._scaledAccessor(G.y0());return G._createAreaGenerator(B,I,N,G._createDefinedProjector(B,I),C)}))})};w.prototype._generateDrawSteps=function(){var C=[];if(this._animateOnNextRender()){var G=this._getAttrToProjector();G.d=this._constructAreaProjector(A.Plot._scaledAccessor(this.x()),this._getResetYFunction(),A.Plot._scaledAccessor(this.y0()));C.push({attrToProjector:G,animator:this._getAnimator(x.Animator.RESET)})}C.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(x.Animator.MAIN)});
return C};w.prototype._updateYScale=function(){var C=this.getExtentsForProperty("y0");C=p.Array.uniq(p.Array.flatten(C));var G=1===C.length?C[0]:null;C=(C=this.y())&&C.scale;null!=C&&(null!=this._constantBaselineValueProvider&&(C.removePaddingExceptionsProvider(this._constantBaselineValueProvider),this._constantBaselineValueProvider=null),null!=G&&(this._constantBaselineValueProvider=function(){return[G]},C.addPaddingExceptionsProvider(this._constantBaselineValueProvider)))};w.prototype._getResetYFunction=
function(){return A.Plot._scaledAccessor(this.y0())};w.prototype._propertyProjectors=function(){var C=y.prototype._propertyProjectors.call(this);C.d=this._constructAreaProjector(A.Plot._scaledAccessor(this.x()),A.Plot._scaledAccessor(this.y()),A.Plot._scaledAccessor(this.y0()));return C};w.prototype.selections=function(C){var G=this;void 0===C&&(C=this.datasets());if("canvas"===this.renderer())return t.selectAll();var D=y.prototype.selections.call(this,C).nodes();C.map(function(B){return G._lineDrawers.get(B)}).filter(function(B){return null!=
B}).forEach(function(B){return D.push.apply(D,B.getVisualPrimitives())});return t.selectAll(D)};w.prototype._constructAreaProjector=function(C,G,D){var B=this,I=this._createDefinedProjector(A.Plot._scaledAccessor(this.x()),A.Plot._scaledAccessor(this.y()));return function(N,O,H){return B._createAreaGenerator(C,G,D,I,H)(N)}};w.prototype._createDefinedProjector=function(C,G){return function(D,B,I){var N=C(D,B,I);D=G(D,B,I);return p.Math.isValidNumber(N)&&p.Math.isValidNumber(D)}};w.prototype._createAreaGenerator=
function(C,G,D,B,I){var N=this._getCurveFactory();return t.area().x(function(O,H){return C(O,H,I)}).y1(function(O,H){return G(O,H,I)}).y0(function(O,H){return D(O,H,I)}).curve(N).defined(function(O,H){return B(O,H,I)})};return w}(d.Line);h._Y0_KEY="y0";f.Area=h},function(d,f){(function(h){h.MAIN="main";h.RESET="reset"})(f.Animator||(f.Animator={}))},function(d,f){var h=function(){function k(){var t=this;this.translate=this.scale=0;this.cachedDomain=[null,null];this.lastSeenDomain=[null,null];this.updateDomain=
function(l){t.lastSeenDomain=l.getTransformationDomain();var p=l.scaleTransformation(t.cachedDomain[1])-l.scaleTransformation(t.cachedDomain[0]),m=l.scaleTransformation(t.lastSeenDomain[1])-l.scaleTransformation(t.lastSeenDomain[0]);t.scale=p/m||1;t.translate=l.scaleTransformation(t.cachedDomain[0])-l.scaleTransformation(t.lastSeenDomain[0])*t.scale||0}}k.prototype.reset=function(){this.scale=1;this.translate=0;this.cachedDomain=this.lastSeenDomain};k.prototype.setDomain=function(t){this.cachedDomain=
t.getTransformationDomain()};return k}();d=function(){function k(t,l){var p=this;this.renderCallback=t;this.applyTransformCallback=l;this.domainTransformX=new h;this.domainTransformY=new h;this.renderDeferred=function(){p.applyTransform();clearTimeout(p.timeoutToken);p.timeoutToken=setTimeout(function(){p.renderCallback()},k.DEFERRED_RENDERING_DELAY)}}k.prototype.setDomains=function(t,l){t&&this.domainTransformX.setDomain(t);l&&this.domainTransformY.setDomain(l);this.renderDeferred()};k.prototype.updateDomains=
function(t,l){t&&this.domainTransformX.updateDomain(t);l&&this.domainTransformY.updateDomain(l);this.renderDeferred()};k.prototype.resetTransforms=function(){this.domainTransformX.reset();this.domainTransformY.reset();this.applyTransform()};k.prototype.applyTransform=function(){this.applyTransformCallback(this.domainTransformX.translate,this.domainTransformY.translate,this.domainTransformX.scale,this.domainTransformY.scale)};return k}();d.DEFERRED_RENDERING_DELAY=200;f.DeferredRenderer=d},function(d,
f,h){var k=this&&this.__extends||function(C,G){function D(){this.constructor=C}for(var B in G)G.hasOwnProperty(B)&&(C[B]=G[B]);C.prototype=null===G?Object.create(G):(D.prototype=G.prototype,new D)},t=h(1),l=h(7),p=h(14),m=h(6),n=h(33),q=h(3),u=h(11),x=h(0);d=h(10);var A=h(19),y=h(2);h=h(16);var w={linear:t.curveLinear,linearClosed:t.curveLinearClosed,step:t.curveStep,stepBefore:t.curveStepBefore,stepAfter:t.curveStepAfter,basis:t.curveBasis,basisOpen:t.curveBasisOpen,basisClosed:t.curveBasisClosed,
bundle:t.curveBundle,cardinal:t.curveCardinal,cardinalOpen:t.curveCardinalOpen,cardinalClosed:t.curveCardinalClosed,monotone:t.curveMonotoneX};f.CurveName=d.makeEnum("linear linearClosed step stepBefore stepAfter basis basisOpen basisClosed bundle cardinal cardinalOpen cardinalClosed monotone".split(" "));h=function(C){function G(){var D=C.call(this)||this;D._curve="linear";D._autorangeSmooth=!1;D._croppedRenderingEnabled=!0;D._collapseDenseVerticalLinesEnabled=!1;D._downsamplingEnabled=!1;D.addClass("line-plot");
var B=new l.Easing;B.stepDuration(y.Plot._ANIMATION_MAX_DURATION);B.easingMode("expInOut");B.maxTotalDuration(y.Plot._ANIMATION_MAX_DURATION);D.animator(A.Animator.MAIN,B);D.attr("stroke",(new q.Color).range()[0]);D.attr("stroke-width","2px");return D}k(G,C);G.prototype.x=function(D,B,I){if(null==D)return C.prototype.x.call(this);C.prototype.x.call(this,D,B,I);this._setScaleSnapping();return this};G.prototype.y=function(D,B,I){if(null==D)return C.prototype.y.call(this);C.prototype.y.call(this,D,B,
I);this._setScaleSnapping();return this};G.prototype.autorangeMode=function(D){if(null==D)return C.prototype.autorangeMode.call(this);C.prototype.autorangeMode.call(this,D);this._setScaleSnapping();return this};G.prototype.autorangeSmooth=function(){return this._autorangeSmooth};G.prototype._setScaleSnapping=function(){"x"===this.autorangeMode()&&this.x()&&this.x().scale&&this.x().scale instanceof u.QuantitativeScale&&this.x().scale.snappingDomainEnabled(!this.autorangeSmooth());"y"===this.autorangeMode()&&
this.y()&&this.y().scale&&this.y().scale instanceof u.QuantitativeScale&&this.y().scale.snappingDomainEnabled(!this.autorangeSmooth())};G.prototype.curve=function(D){if(null==D)return this._curve;this._curve=D;this.render();return this};G.prototype.downsamplingEnabled=function(){return this._downsamplingEnabled};G.prototype.croppedRenderingEnabled=function(D){if(null==D)return this._croppedRenderingEnabled;this._croppedRenderingEnabled=D;this.render();return this};G.prototype.collapseDenseLinesEnabled=
function(D){if(null==D)return this._collapseDenseVerticalLinesEnabled;this._collapseDenseVerticalLinesEnabled=D;this.render();return this};G.prototype._createDrawer=function(D){var B=this;return new m.ProxyDrawer(function(){return new n.LineSVGDrawer},function(I){return new p.CanvasDrawer(I,n.makeLineCanvasDrawStep(function(){return B._d3LineFactory(D)}))})};G.prototype.getExtentsForProperty=function(D){var B=C.prototype.getExtentsForProperty.call(this,D);if(!this._autorangeSmooth||this.autorangeMode()!==
D||"x"!==this.autorangeMode()&&"y"!==this.autorangeMode())return B;D=this._getEdgeIntersectionPoints();var I="y"===this.autorangeMode()?D.left.concat(D.right).map(function(N){return N.y}):D.top.concat(D.bottom).map(function(N){return N.x});return B.map(function(N){return t.extent(t.merge([N,I]))})};G.prototype._getEdgeIntersectionPoints=function(){var D=this;if(!(this.y().scale instanceof u.QuantitativeScale&&this.x().scale instanceof u.QuantitativeScale))return{left:[],right:[],top:[],bottom:[]};
var B=this.y().scale,I=this.x().scale,N={left:[],right:[],top:[],bottom:[]},O=I.scale(I.domain()[0]),H=I.scale(I.domain()[1]),K=B.scale(B.domain()[0]),M=B.scale(B.domain()[1]);this.datasets().forEach(function(L){for(var Q=L.data(),T,X,aa,la,Z,ba,ea,ca=1;ca<Q.length;ca++)la=ba||I.scale(D.x().accessor(Q[ca-1],ca-1,L)),Z=ea||B.scale(D.y().accessor(Q[ca-1],ca-1,L)),ba=I.scale(D.x().accessor(Q[ca],ca,L)),ea=B.scale(D.y().accessor(Q[ca],ca,L)),la<O===O<=ba&&(T=O-la,X=ba-la,aa=ea-Z,T=T*aa/X,N.left.push({x:O,
y:B.invert(Z+T)})),la<H===H<=ba&&(T=H-la,X=ba-la,aa=ea-Z,T=T*aa/X,N.right.push({x:H,y:B.invert(Z+T)})),Z<M===M<=ea&&(X=ba-la,T=M-Z,aa=ea-Z,T=T*X/aa,N.top.push({x:I.invert(la+T),y:M})),Z<K===K<=ea&&(X=ba-la,T=K-Z,aa=ea-Z,T=T*X/aa,N.bottom.push({x:I.invert(la+T),y:K}))});return N};G.prototype._getResetYFunction=function(){var D=this.y().scale.domain(),B=Math.max(D[0],D[1]);D=Math.min(D[0],D[1]);B=0>B&&B||0<D&&D||0;var I=this.y().scale.scale(B);return function(){return I}};G.prototype._generateDrawSteps=
function(){var D=[];if(this._animateOnNextRender()){var B=this._getAttrToProjector();B.d=this._constructLineProjector(y.Plot._scaledAccessor(this.x()),this._getResetYFunction());D.push({attrToProjector:B,animator:this._getAnimator(A.Animator.RESET)})}D.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(A.Animator.MAIN)});return D};G.prototype._generateAttrToProjector=function(){var D=C.prototype._generateAttrToProjector.call(this);Object.keys(D).forEach(function(B){if("d"!==
B){var I=D[B];D[B]=function(N,O,H){return 0<N.length?I(N[0],O,H):null}}});return D};G.prototype.entitiesAt=function(D){D=this.entityNearestByXThenY(D);return null!=D?[D]:[]};G.prototype.entityNearestByXThenY=function(D){var B=Infinity,I=Infinity,N,O=this.bounds();this.entities().forEach(function(H){if(x.Math.within(H.position,O)){var K=Math.abs(D.x-H.position.x),M=Math.abs(D.y-H.position.y);if(K<B||K===B&&M<I)N=H,B=K,I=M}});return N};G.prototype._propertyProjectors=function(){var D=C.prototype._propertyProjectors.call(this);
D.d=this._constructLineProjector(y.Plot._scaledAccessor(this.x()),y.Plot._scaledAccessor(this.y()));return D};G.prototype._constructLineProjector=function(D,B){var I=this;return function(N,O,H){return I._d3LineFactory(H,D,B)(N)}};G.prototype._d3LineFactory=function(D,B,I){function N(O,H,K){var M=B(O,H,K);O=I(O,H,K);return x.Math.isValidNumber(M)&&x.Math.isValidNumber(O)}void 0===B&&(B=y.Plot._scaledAccessor(this.x()));void 0===I&&(I=y.Plot._scaledAccessor(this.y()));return t.line().x(function(O,H){return B(O,
H,D)}).y(function(O,H){return I(O,H,D)}).curve(this._getCurveFactory()).defined(function(O,H){return N(O,H,D)})};G.prototype._getCurveFactory=function(){var D=this.curve();return"string"===typeof D?(D=w[D],null==D?w.linear:D):D};G.prototype._getDataToDraw=function(){var D=this,B=new x.Map;this.datasets().forEach(function(I){var N=I.data();if(D._croppedRenderingEnabled||D._downsamplingEnabled){var O=N.map(function(H,K){return K});D._croppedRenderingEnabled&&(O=D._filterCroppedRendering(I,O));D._downsamplingEnabled&&
(O=D._filterDownsampling(I,O));D._collapseDenseVerticalLinesEnabled&&(O=D._filterDenseLines(I,O));B.set(I,[O.map(function(H){return N[H]})])}else B.set(I,[N])});return B};G.prototype._filterCroppedRendering=function(D,B){function I(aa,la){return x.Math.inRange(aa,0,N.width())&&x.Math.inRange(la,0,N.height())}for(var N=this,O=y.Plot._scaledAccessor(this.x()),H=y.Plot._scaledAccessor(this.y()),K=D.data(),M=[],L=0;L<B.length;L++){var Q=O(K[B[L]],B[L],D),T=H(K[B[L]],B[L],D);Q=I(Q,T);if(!Q&&null!=B[L-
1]&&null!=K[B[L-1]]){T=O(K[B[L-1]],B[L-1],D);var X=H(K[B[L-1]],B[L-1],D);Q=Q||I(T,X)}Q||null==B[L+1]||null==K[B[L+1]]||(T=O(K[B[L+1]],B[L+1],D),X=H(K[B[L+1]],B[L+1],D),Q=Q||I(T,X));Q&&M.push(B[L])}return M};G.prototype._filterDownsampling=function(D,B){function I(ea,ca){var ka=O(N[B[ea]],B[ea],D),Y=H(N[B[ea]],B[ea],D),Ea=O(N[B[ea+1]],B[ea+1],D);ea=H(N[B[ea+1]],B[ea+1],D);return Infinity===ca?Math.floor(ka)===Math.floor(Ea):Math.floor(ea)===Math.floor(Y+(Ea-ka)*ca)}if(0===B.length)return[];for(var N=
D.data(),O=y.Plot._scaledAccessor(this.x()),H=y.Plot._scaledAccessor(this.y()),K=[B[0]],M=0;M<B.length-1;){var L=B[M],Q=O(N[B[M]],B[M],D),T=H(N[B[M]],B[M],D),X=O(N[B[M+1]],B[M+1],D),aa=H(N[B[M+1]],B[M+1],D);aa=Math.floor(Q)===Math.floor(X)?Infinity:(aa-T)/(X-Q);X=B[M];T=Infinity===aa?T:Q;Q=X;for(var la=T,Z=!0;M<B.length-1&&(Z||I(M,aa));){M++;Z=!1;var ba=Infinity===aa?H(N[B[M]],B[M],D):O(N[B[M]],B[M],D);ba>la&&(la=ba,Q=B[M]);ba<T&&(T=ba,X=B[M])}aa=B[M];X!==L&&K.push(X);Q!==X&&Q!==L&&K.push(Q);aa!==
L&&aa!==X&&aa!==Q&&K.push(aa)}return K};G.prototype._filterDenseLines=function(D,B){if(0===B.length)return[];var I=D.data(),N=y.Plot._scaledAccessor(this.x()),O=y.Plot._scaledAccessor(this.y());return this._bucketByX(D,B,function(H){return N(I[H],H,D)},function(H){return O(I[H],H,D)})};G.prototype._bucketByX=function(D,B,I,N){var O=[];D=D.data();for(var H=null,K=0;K<=B.length;++K){var M=B[K];if(null!=D[M]){var L=Math.floor(I(M)),Q=N(M);null==H?H=new x.Bucket(M,L,Q):H.isInBucket(L)?H.addToBucket(Q,
M):(O.push.apply(O,H.getUniqueIndices()),H=new x.Bucket(M,L,Q))}}null!=H&&O.push.apply(O,H.getUniqueIndices());return O};return G}(h.XYPlot);f.Line=h},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var x in q)q.hasOwnProperty(x)&&(n[x]=q[x]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},t=h(1),l=h(26),p=h(0),m=[0,1];d=function(n){function q(){var u=n.call(this)||this;u._range=[0,1];u._d3Scale=t.scaleBand();u._d3Scale.range(m);
u._d3TransformationScale=t.scaleLinear();u._d3TransformationScale.domain(m);u._innerPadding=q._convertToPlottableInnerPadding();u._outerPadding=q._convertToPlottableOuterPadding();return u}k(q,n);q.prototype.cloneWithoutProviders=function(){var u=(new q).domain(this.domain()).range(this.range()).innerPadding(this.innerPadding()).outerPadding(this.outerPadding());u._d3TransformationScale.domain(this._d3TransformationScale.domain());return u};q.prototype.extentOfValues=function(u){return p.Array.uniq(u)};
q.prototype._getExtent=function(){return p.Array.uniq(this._getAllIncludedValues())};q.prototype.domain=function(u){return n.prototype.domain.call(this,u)};q.prototype.invertRange=function(){var u,x=this;void 0===u&&(u=this.range());var A=this._d3Scale.bandwidth(),y=this.invertedTransformation(u[0]),w=this.invertedTransformation(u[1]);u=this._d3Scale.domain();var C=u.map(function(G){return x._d3Scale(G)+A/2});y=t.bisect(C,y);w=t.bisect(C,w);return u.slice(y,w)};q.prototype.range=function(u){return n.prototype.range.call(this,
u)};q._convertToPlottableInnerPadding=function(){return 1/.7-1};q._convertToPlottableOuterPadding=function(){return.5/.7};q.prototype._setBands=function(){var u=1-1/(1+this.innerPadding()),x=this.outerPadding()/(1+this.innerPadding());this._d3Scale.paddingInner(u);this._d3Scale.paddingOuter(x)};q.prototype.rangeBand=function(){return this._rescaleBand(this._d3Scale.bandwidth())};q.prototype.stepWidth=function(){return this._rescaleBand(this._d3Scale.bandwidth()*(1+this.innerPadding()))};q.prototype.ticks=
function(){return this.domain()};q.prototype.innerPadding=function(u){if(null==u)return this._innerPadding;this._innerPadding=u;this.range(this.range());this._dispatchUpdate();return this};q.prototype.outerPadding=function(u){if(null==u)return this._outerPadding;this._outerPadding=u;this.range(this.range());this._dispatchUpdate();return this};q.prototype.scale=function(u){u=this._d3Scale(u)+this._d3Scale.bandwidth()/2;return this._d3TransformationScale(u)};q.prototype.zoom=function(u,x){var A=this;
this._d3TransformationScale.domain(this._d3TransformationScale.range().map(function(y){return A._d3TransformationScale.invert(l.zoomOut(y,u,x))}));this._dispatchUpdate()};q.prototype.pan=function(u){var x=this;this._d3TransformationScale.domain(this._d3TransformationScale.range().map(function(A){return x._d3TransformationScale.invert(A+u)}));this._dispatchUpdate()};q.prototype.scaleTransformation=function(u){return this._d3TransformationScale(u)};q.prototype.invertedTransformation=function(u){return this._d3TransformationScale.invert(u)};
q.prototype.getTransformationExtent=function(){return m};q.prototype.getTransformationDomain=function(){return this._d3TransformationScale.domain()};q.prototype.setTransformationDomain=function(u){this._d3TransformationScale.domain(u);this._dispatchUpdate()};q.prototype._getDomain=function(){return this._backingScaleDomain()};q.prototype._backingScaleDomain=function(u){if(null==u)return this._d3Scale.domain();this._d3Scale.domain(u);this._setBands();return this};q.prototype._getRange=function(){return this._range};
q.prototype._setRange=function(u){this._range=u;this._d3TransformationScale.range(u);this._setBands()};q.prototype._rescaleBand=function(u){return Math.abs(this._d3TransformationScale(u)-this._d3TransformationScale(0))};return q}(h(17).Scale);f.Category=d},function(d,f,h){function k(w){try{var C=w.node().getBBox()}catch(G){C={x:0,y:0,width:0,height:0}}return C}function t(w){if("number"===typeof w)return{min:w,max:w};if(w instanceof Object&&"min"in w&&"max"in w)return w;throw Error("input '"+w+"' can't be parsed as an Range");
}function l(w,C){w=w.getPropertyValue(C);return parseFloat(w)||0}function p(w){if(null==w||"none"===w)return null;w=w.match(A);if(null==w||2>w.length)return null;w=w[1].split(y).map(function(C){return parseFloat(C)});return 6!=w.length?null:w}var m=h(1),n=Math;f.contains=function(w,C){for(;null!=C&&C!==w;)C=C.parentNode;return C===w};f.elementBBox=k;f.entityBounds=function(w){return w instanceof SVGElement?k(m.select(w)):w instanceof HTMLElement?(w=w.getBoundingClientRect(),{x:w.left,y:w.top,width:w.width,
height:w.height}):{x:0,y:0,width:0,height:0}};f.SCREEN_REFRESH_RATE_MILLISECONDS=1E3/60;f.requestAnimationFramePolyfill=function(w){null!=window.requestAnimationFrame?window.requestAnimationFrame(w):setTimeout(w,f.SCREEN_REFRESH_RATE_MILLISECONDS)};f.elementWidth=function(w){w=w instanceof m.selection?w.node():w;w=window.getComputedStyle(w);return l(w,"width")+l(w,"padding-left")+l(w,"padding-right")+l(w,"border-left-width")+l(w,"border-right-width")};f.elementHeight=function(w){w=w instanceof m.selection?
w.node():w;w=window.getComputedStyle(w);return l(w,"height")+l(w,"padding-top")+l(w,"padding-bottom")+l(w,"border-top-width")+l(w,"border-bottom-width")};var q=/translate\s*\(\s*((?:[-+]?[0-9]*\.?[0-9]+))(?:(?:(?:\s+,?\s*)|(?:,\s*))((?:[-+]?[0-9]*\.?[0-9]+)))?\s*\)/,u=/rotate\s*\(\s*((?:[-+]?[0-9]*\.?[0-9]+))\s*\)/,x=/scale\s*\(\s*((?:[-+]?[0-9]*\.?[0-9]+))(?:(?:(?:\s+,?\s*)|(?:,\s*))((?:[-+]?[0-9]*\.?[0-9]+)))?\s*\)/;f.getTranslateValues=function(w){w=q.exec(w.attr("transform"));if(null!=w){var C=
w[2];return[+w[1],+(void 0===C?0:C)]}return[0,0]};f.getRotate=function(w){w=u.exec(w.attr("transform"));return null!=w?+w[1]:0};f.getScaleValues=function(w){var C=x.exec(w.attr("transform"));return null!=C?(w=C[1],C=C[2],[+w,null==C?+w:+C]):[0,0]};f.clientRectsOverlap=function(w,C){return n.floor(w.right)<=n.ceil(C.left)||n.ceil(w.left)>=n.floor(C.right)||n.floor(w.bottom)<=n.ceil(C.top)||n.ceil(w.top)>=n.floor(C.bottom)?!1:!0};f.expandRect=function(w,C){return{left:w.left-C,top:w.top-C,right:w.right+
C,bottom:w.bottom+C,width:w.width+2*C,height:w.height+2*C}};f.clientRectInside=function(w,C){return n.floor(C.left)<=n.ceil(w.left)&&n.floor(C.top)<=n.ceil(w.top)&&n.floor(w.right)<=n.ceil(C.right)&&n.floor(w.bottom)<=n.ceil(C.bottom)};f.intersectsBBox=function(w,C,G,D){void 0===D&&(D=.5);w=t(w);C=t(C);return G.x+G.width>=w.min-D&&G.x<=w.max+D&&G.y+G.height>=C.min-D&&G.y<=C.max+D};f.getHtmlElementAncestors=function(w){for(var C=[];w&&w instanceof HTMLElement;)C.push(w),w=w.parentElement;return C};
f.getElementTransform=function(w){w=window.getComputedStyle(w,null);w=w.getPropertyValue("-webkit-transform")||w.getPropertyValue("-moz-transform")||w.getPropertyValue("-ms-transform")||w.getPropertyValue("-o-transform")||w.getPropertyValue("transform");return p(w)};var A=/^matrix\(([^)]+)\)$/,y=/[, ]+/},function(d,f,h){function k(u,x){return[u[0]*x[0]+u[2]*x[1],u[1]*x[0]+u[3]*x[1],u[0]*x[2]+u[2]*x[3],u[1]*x[2]+u[3]*x[3],u[0]*x[4]+u[2]*x[5]+u[4],u[1]*x[4]+u[3]*x[5]+u[5]]}function t(u,x){return[u[0],
u[1],u[2],u[3],u[0]*x[0]+u[2]*x[1]+u[4],u[1]*x[0]+u[3]*x[1]+u[5]]}function l(u){var x=u[0]*u[3]-u[1]*u[2];if(0===x)throw Error("singular matrix");x=1/x;return[x*u[3],x*-u[1],x*-u[2],x*u[0],x*(-u[3]*u[4]+u[2]*u[5]),x*(u[1]*u[4]+-u[0]*u[5])]}var p=h(1),m=h(55),n=Math,q=[1,0,0,1,0,0];f.inRange=function(u,x,A){return n.min(x,A)<=u&&u<=n.max(x,A)};f.clamp=function(u,x,A){return n.min(n.max(x,u),A)};f.max=function(u,x,A){var y="function"===typeof x?x:null;x=null==y?x:A;u=null==y?p.max(u):p.max(u,y);return void 0!==
u?u:x};f.min=function(u,x,A){var y="function"===typeof x?x:null;x=null==y?x:A;u=null==y?p.min(u):p.min(u,y);return void 0!==u?u:x};f.isNaN=function(u){return u!==u};f.isValidNumber=function(u){return"number"===typeof u&&1>u-u};f.range=function(u,x,A){void 0===A&&(A=1);if(0===A)throw Error("step cannot be 0");x=n.max(n.ceil((x-u)/A),0);for(var y=[],w=0;w<x;++w)y[w]=u+A*w;return y};f.distanceSquared=function(u,x){return n.pow(x.y-u.y,2)+n.pow(x.x-u.x,2)};f.degreesToRadians=function(u){return u/360*
n.PI*2};f.within=function(u,x){return x.topLeft.x<=u.x&&x.bottomRight.x>=u.x&&x.topLeft.y<=u.y&&x.bottomRight.y>=u.y};f.boundsIntersects=function(u,x,A,y,w,C){return u<=0+w&&0<=u+A&&x<=0+C&&0<=x+y};f.getCumulativeTransform=function(u){u=m.getHtmlElementAncestors(u);for(var x=q,A=null,y=0;y<u.length;y++){var w=u[y],C=m.getElementTransform(w);if(null!=C){var G=w.clientWidth/2,D=w.clientHeight/2;x=t(x,[G,D]);x=k(x,l(C));x=t(x,[-G,-D])}C=w.scrollLeft;G=w.scrollTop;if(null===A||w===A)C-=w.offsetLeft+w.clientLeft,
G-=w.offsetTop+w.clientTop,A=w.offsetParent;x=t(x,[C,G])}return x};f.multiplyMatrix=k;f.premultiplyTranslate=function(u,x){return[x[0],x[1],x[2],x[3],x[4]+u[0],x[5]+u[1]]};f.multiplyTranslate=t;f.invertMatrix=l;f.applyTransform=function(u,x){return{x:u[0]*x.x+u[2]*x.y+u[4],y:u[1]*x.x+u[3]*x.y+u[5]}}},function(d,f,h){var k=new (h(114).SplitStrategyLinear);d=function(){function p(m,n){void 0===m&&(m=5);void 0===n&&(n=k);this.maxNodeChildren=m;this.splitStrategy=n;this.root=new t(!0);this.size=0}p.prototype.getRoot=
function(){return this.root};p.prototype.clear=function(){this.root=new t(!0);this.size=0};p.prototype.insert=function(m,n){for(var q=this.root;!q.leaf;)q=q.subtree(m);m=t.valueNode(m,n);q.insert(m);for(this.size+=1;q.overflow(this.maxNodeChildren);)q=q.split(this.splitStrategy),null==q.parent&&(this.root=q)};p.prototype.locate=function(m){return this.query(function(n){return n.contains(m)})};p.prototype.intersect=function(m){return this.query(function(n){return l.isBoundsOverlapBounds(n,m)})};p.prototype.intersectX=
function(m){return this.query(function(n){return l.isBoundsOverlapX(n,m)})};p.prototype.intersectY=function(m){return this.query(function(n){return l.isBoundsOverlapY(n,m)})};p.prototype.query=function(m){var n=[];if(null!=this.root.bounds&&!m(this.root.bounds))return n;for(var q=[this.root];0<q.length;)for(var u=q.shift(),x=0;x<u.entries.length;x++){var A=u.entries[x];m(A.bounds)&&(u.leaf?n.push(A.value):q.push(A))}return n};return p}();f.RTree=d;var t=function(){function p(m){this.leaf=m;this.bounds=
null;this.entries=[];this.value=this.parent=null}p.valueNode=function(m,n){var q=new p(!0);q.bounds=m;q.value=n;return q};p.prototype.overflow=function(m){return this.entries.length>m};p.prototype.insert=function(m){this.entries.push(m);m.parent=this;for(var n=this;null!=n;)n.bounds=l.unionAll([n.bounds,m.bounds]),n=n.parent};p.prototype.remove=function(m){m=this.entries.indexOf(m);if(0<=m)for(this.entries.splice(m,1),m=this;null!=m;)m.bounds=l.unionAll(m.entries.map(function(n){return n.bounds})),
m=m.parent;return this};p.prototype.subtree=function(m){for(var n=null,q=0;q<this.entries.length;q++){var u=this.entries[q],x=u.unionAreaDifference(m);if(Infinity>x||Infinity===x&&null!=n&&u.entries.length<n.entries.length)n=u}return n};p.prototype.split=function(m){null!=this.parent&&this.parent.remove(this);var n=[new p(this.leaf),new p(this.leaf)];m.split(this.entries,n);m=null!=this.parent?this.parent:new p(!1);m.insert(n[0]);m.insert(n[1]);return m};p.prototype.unionAreaDifference=function(m){return Math.abs(l.union(this.bounds,
m).area()-this.bounds.area())};p.prototype.maxDepth=function(){return this.leaf?1:1+this.entries.map(function(m){return m.maxDepth()}).reduce(function(m,n){return Math.max(m,n)})};return p}();f.RTreeNode=t;var l=function(){function p(m,n,q,u){this.xl=m;this.yl=n;this.xh=q;this.yh=u;this.width=this.xh-this.xl;this.height=this.yh-this.yl}p.xywh=function(m,n,q,u){return new p(m,n,m+q,n+u)};p.entityBounds=function(m){return new p(m.x,m.y,m.x+m.width,m.y+m.height)};p.bounds=function(m){return p.pointPair(m.topLeft,
m.bottomRight)};p.pointPair=function(m,n){return new p(Math.min(m.x,n.x),Math.min(m.y,n.y),Math.max(m.x,n.x),Math.max(m.y,n.y))};p.points=function(m){if(2>m.length)throw Error("need at least 2 points to create bounds");var n=m.map(function(q){return q.x});m=m.map(function(q){return q.y});return new p(n.reduce(function(q,u){return Math.min(q,u)}),m.reduce(function(q,u){return Math.min(q,u)}),n.reduce(function(q,u){return Math.max(q,u)}),m.reduce(function(q,u){return Math.max(q,u)}))};p.union=function(m,
n){return new p(Math.min(m.xl,n.xl),Math.min(m.yl,n.yl),Math.max(m.xh,n.xh),Math.max(m.yh,n.yh))};p.unionAll=function(m){m=m.filter(function(n){return null!=n});return 0===m.length?null:m.reduce(function(n,q){return p.union(n,q)})};p.isBoundsOverlapBounds=function(m,n){return p.isBoundsOverlapX(m,n)&&p.isBoundsOverlapY(m,n)};p.isBoundsOverlapX=function(m,n){return!(m.xh<n.xl)&&!(m.xl>n.xh)};p.isBoundsOverlapY=function(m,n){return!(m.yh<n.yl)&&!(m.yl>n.yh)};p.prototype.area=function(){null==this.areaCached&&
(this.areaCached=(this.xh-this.xl)*(this.yh-this.yl));return this.areaCached};p.prototype.contains=function(m){return this.xl<=m.x&&this.xh>=m.x&&this.yl<=m.y&&this.yh>=m.y};return p}();f.RTreeBounds=l},function(d,f){d=function(){function h(){"function"===typeof window.Set?this._es6Set=new window.Set:this._values=[];this.size=0}h.prototype.add=function(k){if(null!=this._es6Set)return this._es6Set.add(k),this.size=this._es6Set.size,this;this.has(k)||(this._values.push(k),this.size=this._values.length);
return this};h.prototype.delete=function(k){if(null!=this._es6Set)return k=this._es6Set.delete(k),this.size=this._es6Set.size,k;k=this._values.indexOf(k);return-1!==k?(this._values.splice(k,1),this.size=this._values.length,!0):!1};h.prototype.has=function(k){return null!=this._es6Set?this._es6Set.has(k):-1!==this._values.indexOf(k)};h.prototype.forEach=function(k,t){var l=this;null!=this._es6Set?this._es6Set.forEach(function(p,m){return k.call(t,p,m,l)},t):this._values.forEach(function(p){k.call(t,
p,p,l)})};return h}();f.Set=d},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(131));k(h(130))},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},t=h(21);d=function(l){function p(m,n){var q=l.call(this,m,n)||this;q.cache=new t.Cache(function(u){return q._measureCharacterNotFromCache(u)});return q}k(p,l);p.prototype._measureCharacterNotFromCache=
function(m){return l.prototype._measureCharacter.call(this,m)};p.prototype._measureCharacter=function(m){return this.cache.get(m)};p.prototype.reset=function(){this.cache.clear()};return p}(h(61).CharacterMeasurer);f.CacheCharacterMeasurer=d},function(d,f,h){var k=this&&this.__extends||function(t,l){function p(){this.constructor=t}for(var m in l)l.hasOwnProperty(m)&&(t[m]=l[m]);t.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(t){function l(){return t.apply(this,arguments)||
this}k(l,t);l.prototype._measureCharacter=function(p){return t.prototype._measureLine.call(this,p)};l.prototype._measureLine=function(p){var m=this;p=p.split("").map(function(n){return m._measureCharacter(n)});return{height:p.reduce(function(n,q){return Math.max(n,q.height)},0),width:p.reduce(function(n,q){return n+q.width},0)}};return l}(h(63).Measurer);f.CharacterMeasurer=d},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(36));k(h(60));k(h(132));k(h(61));k(h(63))},
function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},t=h(36);d=function(l){function p(m,n){void 0===n&&(n=!1);m=l.call(this,m)||this;m.useGuards=n;return m}k(p,l);p.prototype._addGuards=function(m){return t.AbstractMeasurer.HEIGHT_TEXT+m+t.AbstractMeasurer.HEIGHT_TEXT};p.prototype._measureLine=function(m){var n;void 0===n&&(n=!1);n=this.useGuards||
n||/^[\t ]$/.test(m);m=l.prototype.measure.call(this,n?this._addGuards(m):m);m.width-=n?2*this.getGuardWidth():0;return m};p.prototype.measure=function(m){var n=this;void 0===m&&(m=t.AbstractMeasurer.HEIGHT_TEXT);if(""===m.trim())return{width:0,height:0};m=m.trim().split("\n").map(function(q){return n._measureLine(q)});return{height:m.reduce(function(q,u){return q+u.height},0),width:m.reduce(function(q,u){return Math.max(q,u.width)},0)}};p.prototype.getGuardWidth=function(){null==this.guardWidth&&
(this.guardWidth=l.prototype.measure.call(this).width);return this.guardWidth};return p}(t.AbstractMeasurer);f.Measurer=d},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(138));k(h(65))},function(d,f,h){var k=h(21);d=function(){function t(){this.maxLines(Infinity);this.textTrimming();this.allowBreakingWords();this._tokenizer=new k.Tokenizer;this._breakingCharacter="-"}t.prototype.maxLines=function(l){if(null==l)return this._maxLines;this._maxLines=l;return this};
t.prototype.textTrimming=function(){this._textTrimming="ellipsis"};t.prototype.allowBreakingWords=function(){this._allowBreakingWords=!0};t.prototype.wrap=function(l,p,m,n){var q=this;void 0===n&&(n=Infinity);var u={noBrokeWords:0,noLines:0,originalText:l,truncatedText:"",wrappedText:""};m={availableLines:Math.min(Math.floor(n/p.measure().height),this._maxLines),availableWidth:m,canFitText:!0,currentLine:"",wrapping:u};var x=l.split("\n");return x.reduce(function(A,y,w){return q.breakLineToFitWidth(A,
y,w!==x.length-1,p)},m).wrapping};t.prototype.breakLineToFitWidth=function(l,p,m,n){var q=this;l.canFitText||""===l.wrapping.truncatedText||(l.wrapping.truncatedText+="\n");l=this._tokenizer.tokenize(p).reduce(function(u,x){return q.wrapNextToken(x,u,n)},l);p=k.StringMethods.trimEnd(l.currentLine);l.wrapping.noLines+=+(""!==p);l.wrapping.noLines===l.availableLines&&"none"!==this._textTrimming&&m?l.canFitText=!1:l.wrapping.wrappedText+=p;l.currentLine="\n";return l};t.prototype.canFitToken=function(l,
p,m){var n=this,q=this._allowBreakingWords?l.split("").map(function(u,x){return x!==l.length-1?u+n._breakingCharacter:u}):[l];return m.measure(l).width<=p||q.every(function(u){return m.measure(u).width<=p})};t.prototype.addEllipsis=function(l,p,m){if("none"===this._textTrimming)return{remainingToken:"",wrappedToken:l};var n=l.substring(0).trim(),q=m.measure(n).width,u=m.measure("...").width,x=0<l.length&&"\n"===l[0]?"\n":"";if(p<=u)return{remainingToken:l,wrappedToken:x+"...".substr(0,Math.floor(p/
(u/3)))};for(;q+u>p;)n=k.StringMethods.trimEnd(n.substr(0,n.length-1)),q=m.measure(n).width;return{remainingToken:k.StringMethods.trimEnd(l.substring(n.length),"-").trim(),wrappedToken:x+n+"..."}};t.prototype.wrapNextToken=function(l,p,m){if(!p.canFitText||p.availableLines===p.wrapping.noLines||!this.canFitToken(l,p.availableWidth,m))return this.finishWrapping(l,p,m);for(;l;){var n=this.breakTokenToFitInWidth(l,p.currentLine,p.availableWidth,m);p.currentLine=n.line;l=n.remainingToken;if(null!=l)if(p.wrapping.noBrokeWords+=
+n.breakWord,++p.wrapping.noLines,p.availableLines===p.wrapping.noLines){m=this.addEllipsis(p.currentLine,p.availableWidth,m);p.wrapping.wrappedText+=m.wrappedToken;p.wrapping.truncatedText+=m.remainingToken+l;p.currentLine="\n";break}else p.wrapping.wrappedText+=k.StringMethods.trimEnd(p.currentLine),p.currentLine="\n"}return p};t.prototype.finishWrapping=function(l,p,m){p.canFitText&&p.availableLines!==p.wrapping.noLines&&this._allowBreakingWords&&"none"!==this._textTrimming?(m=this.addEllipsis(p.currentLine+
l,p.availableWidth,m),p.wrapping.wrappedText+=m.wrappedToken,p.wrapping.truncatedText+=m.remainingToken,p.wrapping.noBrokeWords+=+(m.remainingToken.length<l.length),p.wrapping.noLines+=+(0<m.wrappedToken.length),p.currentLine=""):p.wrapping.truncatedText+=l;p.canFitText=!1;return p};t.prototype.breakTokenToFitInWidth=function(l,p,m,n){if(void 0===q)var q=this._breakingCharacter;if(n.measure(p+l).width<=m)return{breakWord:!1,line:p+l,remainingToken:null};if(""===l.trim())return{breakWord:!1,line:p,
remainingToken:""};if(!this._allowBreakingWords)return{breakWord:!1,line:p,remainingToken:l};for(var u=0;u<l.length;)if(n.measure(p+l.substring(0,u+1)+q).width<=m)++u;else break;m="";0<u&&(m=q);return{breakWord:0<u,line:p+l.substring(0,u)+m,remainingToken:l.substring(u)}};return t}();f.Wrapper=d},function(d,f,h){(function(k){for(var t in k)f.hasOwnProperty(t)||(f[t]=k[t])})(h(139))},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}k(h(72));k(h(73));k(h(28))},function(d,
f){f.version="3.7.0"},function(d,f,h){function k(w,C){return w.each(function(){var G=C.apply(this,arguments),D=y.select(this),B;for(B in G)D.attr(B,G[B])})}function t(w,C){for(var G in C)w.attr(G,C[G]);return w}function l(w,C,G){return w.each(function(){var D=C.apply(this,arguments),B=y.select(this),I;for(I in D)B.style(I,D[I],G)})}function p(w,C,G){for(var D in C)w.style(D,C[D],G);return w}function m(w,C){return w.each(function(){var G=C.apply(this,arguments),D=y.select(this),B;for(B in G)D.property(B,
G[B])})}function n(w,C){for(var G in C)w.property(G,C[G]);return w}function q(w,C){return w.each(function(){var G=C.apply(this,arguments),D=y.select(this).transition(w),B;for(B in G)D.attr(B,G[B])})}function u(w,C){for(var G in C)w.attr(G,C[G]);return w}function x(w,C,G){return w.each(function(){var D=C.apply(this,arguments),B=y.select(this).transition(w),I;for(I in D)B.style(I,D[I],G)})}function A(w,C,G){for(var D in C)w.style(D,C[D],G);return w}var y=d=h(1);y.selection.prototype.attrs=function(w){return("function"===
typeof w?k:t)(this,w)};y.selection.prototype.styles=function(w){return("function"===typeof w?l:p)(this,w,"")};y.selection.prototype.properties=function(w){return("function"===typeof w?m:n)(this,w)};d.transition.prototype.attrs=function(w){return("function"===typeof w?q:u)(this,w)};d.transition.prototype.styles=function(w){return("function"===typeof w?x:A)(this,w,"")}},function(d,f,h){d=h(117);var k=h(12);h=h(10);var t={linear:d.easeLinear,quad:d.easeQuad,quadIn:d.easeQuadIn,quadOut:d.easeQuadOut,
quadInOut:d.easeQuadInOut,cubic:d.easeCubic,cubicIn:d.easeCubicIn,cubicOut:d.easeCubicOut,cubicInOut:d.easeCubicInOut,poly:d.easePoly,polyIn:d.easePolyIn,polyOut:d.easePolyOut,polyInOut:d.easePolyInOut,sin:d.easeSin,sinIn:d.easeSinIn,sinOut:d.easeSinOut,sinInOut:d.easeSinInOut,exp:d.easeExp,expIn:d.easeExpIn,expOut:d.easeExpOut,expInOut:d.easeExpInOut,circle:d.easeCircle,circleIn:d.easeCircleIn,circleOut:d.easeCircleOut,circleInOut:d.easeCircleInOut,bounce:d.easeBounce,bounceIn:d.easeBounceIn,bounceOut:d.easeBounceOut,
bounceInOut:d.easeBounceInOut,back:d.easeBack,backIn:d.easeBackIn,backOut:d.easeBackOut,backInOut:d.easeBackInOut,elastic:d.easeElastic,elasticIn:d.easeElasticIn,elasticOut:d.easeElasticOut,elasticInOut:d.easeElasticInOut};f.EaseName=h.makeEnum("linear quad quadIn quadOut quadInOut cubic cubicIn cubicOut cubicInOut poly polyIn polyOut polyInOut sin sinIn sinOut sinInOut exp expIn expOut expInOut circle circleIn circleOut circleInOut bounce bounceIn bounceOut bounceInOut back backIn backOut backInOut elastic elasticIn elasticOut elasticInOut".split(" "));
h=function(){function l(){this._startDelay=l._DEFAULT_START_DELAY_MILLISECONDS;this._stepDuration=l._DEFAULT_STEP_DURATION_MILLISECONDS;this._stepDelay=l._DEFAULT_ITERATIVE_DELAY_MILLISECONDS;this._maxTotalDuration=l._DEFAULT_MAX_TOTAL_DURATION_MILLISECONDS;this._easingMode=l._DEFAULT_EASING_MODE}l.prototype.totalTime=function(p){var m=this._getAdjustedIterativeDelay(p);return this.startDelay()+m*Math.max(p-1,0)+this.stepDuration()};l.prototype.animate=function(p,m){var n=this;p=k.coerceExternalD3(p);
var q=p.size(),u=this._getAdjustedIterativeDelay(q);return p.transition().ease(this._getEaseFactory()).duration(this.stepDuration()).delay(function(x,A){return n.startDelay()+u*A}).attrs(m)};l.prototype.startDelay=function(p){if(null==p)return this._startDelay;this._startDelay=p;return this};l.prototype.stepDuration=function(p){if(null==p)return Math.min(this._stepDuration,this._maxTotalDuration);this._stepDuration=p;return this};l.prototype.stepDelay=function(){return this._stepDelay};l.prototype.maxTotalDuration=
function(p){if(null==p)return this._maxTotalDuration;this._maxTotalDuration=p;return this};l.prototype.easingMode=function(p){if(null==p)return this._easingMode;this._easingMode=p;return this};l.prototype._getEaseFactory=function(){var p=this.easingMode();return"string"===typeof p?(p=t[p],null==p?t.linear:p):p};l.prototype._getAdjustedIterativeDelay=function(p){var m=this.maxTotalDuration()-this.stepDuration();m=Math.max(m,0);p=m/Math.max(p-1,1);return Math.min(this.stepDelay(),p)};return l}();h._DEFAULT_START_DELAY_MILLISECONDS=
0;h._DEFAULT_STEP_DURATION_MILLISECONDS=300;h._DEFAULT_ITERATIVE_DELAY_MILLISECONDS=15;h._DEFAULT_MAX_TOTAL_DURATION_MILLISECONDS=Infinity;h._DEFAULT_EASING_MODE="expOut";f.Easing=h},function(d,f,h){var k=h(12);d=function(){function t(){}t.prototype.totalTime=function(){return 0};t.prototype.animate=function(l,p){l=k.coerceExternalD3(l);return l.attrs(p)};return t}();f.Null=d},function(d,f,h){var k=this&&this.__extends||function(q,u){function x(){this.constructor=q}for(var A in u)u.hasOwnProperty(A)&&
(q[A]=u[A]);q.prototype=null===u?Object.create(u):(x.prototype=u.prototype,new x)},t=h(1),l=h(5),p=h(4),m=h(0),n=h(22);d=function(q){function u(x,A){void 0===A&&(A="bottom");x=q.call(this,x,A)||this;x._tickLabelAngle=0;x._tickLabelShearAngle=0;x.addClass("category-axis");return x}k(u,q);Object.defineProperty(u.prototype,"_wrapper",{get:function(){var x=new l.Wrapper;null!=this._tickLabelMaxLines&&x.maxLines(this._tickLabelMaxLines);return x},enumerable:!0,configurable:!0});Object.defineProperty(u.prototype,
"_writer",{get:function(){return new l.Writer(this._measurer,this._typesetterContext,this._wrapper)},enumerable:!0,configurable:!0});u.prototype._setup=function(){q.prototype._setup.call(this);this._typesetterContext=new l.SvgContext(this._tickLabelContainer.node());this._measurer=new l.CacheMeasurer(this._typesetterContext)};u.prototype._rescale=function(){return this.redraw()};u.prototype.requestedSpace=function(x,A){var y=this.isHorizontal()?0:this._tickSpaceRequired()+this.margin(),w=this.isHorizontal()?
this._tickSpaceRequired()+this.margin():0;if(0===this._scale.domain().length)return{minWidth:0,minHeight:0};if(this.annotationsEnabled()){var C=this._annotationTierHeight()*this.annotationTierCount();this.isHorizontal()?w+=C:y+=C}x=this._measureTickLabels(x,A);return{minWidth:x.usedWidth+y,minHeight:x.usedHeight+w}};u.prototype._coreSize=function(){var x=this.isHorizontal()?this.height():this.width(),A=this.isHorizontal()?this.requestedSpace(this.width(),this.height()).minHeight:this.requestedSpace(this.width(),
this.height()).minWidth,y=this.margin()+this._annotationTierHeight();return Math.min(A-y,x)};u.prototype._getTickValues=function(){return this.getDownsampleInfo().domain};u.prototype._sizeFromOffer=function(x,A){return p.Component.prototype._sizeFromOffer.call(this,x,A)};u.prototype.getDownsampleInfo=function(x){var A;void 0===x&&(x=this._scale);void 0===A&&(A=x.invertRange());var y=Math.ceil(u._MINIMUM_WIDTH_PER_LABEL_PX*(0===this._tickLabelAngle?1:1/Math.cos(this._tickLabelShearAngle/180*Math.PI))/
x.stepWidth());return{domain:A.filter(function(w,C){return 0===C%y}),stepWidth:y*x.stepWidth()}};u.prototype.tickLabelAngle=function(){return this._tickLabelAngle;throw Error("Angle undefined not supported; only 0, 90, and -90 are valid values");};u.prototype.tickLabelShearAngle=function(){return this._tickLabelShearAngle};u.prototype.tickLabelMaxWidth=function(x){if(0===arguments.length)return this._tickLabelMaxWidth;this._tickLabelMaxWidth=x;this.redraw();return this};u.prototype.tickLabelMaxLines=
function(x){if(0===arguments.length)return this._tickLabelMaxLines;this._tickLabelMaxLines=x;this.redraw();return this};u.prototype._tickSpaceRequired=function(){return this._maxLabelTickLength()+this.tickLabelPadding()};u.prototype._drawTicks=function(x,A){var y=this;switch(this.tickLabelAngle()){case 0:var w={left:"right",right:"left",top:"center",bottom:"center"};var C={left:"center",right:"center",top:"bottom",bottom:"top"};break;case 90:w={left:"center",right:"center",top:"right",bottom:"left"};
C={left:"top",right:"bottom",top:"center",bottom:"center"};break;case -90:w={left:"center",right:"center",top:"left",bottom:"right"},C={left:"bottom",right:"top",top:"center",bottom:"center"}}A.each(function(G){var D=t.select(this),B=y.isHorizontal()?x:y.width()-y._tickSpaceRequired(),I=y.isHorizontal()?y.height()-y._tickSpaceRequired():x,N={xAlign:w[y.orientation()],yAlign:C[y.orientation()],textRotation:y.tickLabelAngle(),textShear:y.tickLabelShearAngle()};if(null!=y._tickLabelMaxWidth){if("left"===
y.orientation()&&B>y._tickLabelMaxWidth){var O=B-y._tickLabelMaxWidth;O=D.attr("transform")+" translate("+O+", 0)";D.attr("transform",O)}B=Math.min(B,y._tickLabelMaxWidth)}y._writer.write(y.formatter()(G),B,I,N,D.node())})};u.prototype._measureTickLabels=function(x,A){var y=this,w=this._scale.cloneWithoutProviders().range([0,this.isHorizontal()?x:A]),C=this.getDownsampleInfo(w);w=C.domain;C=C.stepWidth;var G=x-this._tickSpaceRequired();this.isHorizontal()&&(G=C,0!==this._tickLabelAngle&&(G=A-this._tickSpaceRequired()),
G=Math.max(G,0));var D=C;this.isHorizontal()&&(D=A-this._tickSpaceRequired(),0!==this._tickLabelAngle&&(D=x-this._tickSpaceRequired()),D=Math.max(D,0));null!=this._tickLabelMaxWidth&&(G=Math.min(G,this._tickLabelMaxWidth));A=w.map(function(B){return y._wrapper.wrap(y.formatter()(B),y._measurer,G,D)});x=this.isHorizontal()&&0===this._tickLabelAngle?t.sum:m.Math.max;w=this.isHorizontal()&&0===this._tickLabelAngle?m.Math.max:t.sum;x=x(A,function(B){return y._measurer.measure(B.wrappedText).width},0);
A=w(A,function(B){return y._measurer.measure(B.wrappedText).height},0);0!==this._tickLabelAngle&&(A=[A,x],x=A[0],A=A[1]);return{usedWidth:x,usedHeight:A}};u.prototype.renderImmediately=function(){var x=this;q.prototype.renderImmediately.call(this);var A=this._scale,y=this.getDownsampleInfo(A),w=y.domain,C=y=y.stepWidth;this.isHorizontal()&&null!=this._tickLabelMaxWidth&&(C=Math.min(C,this._tickLabelMaxWidth));w=this._tickLabelContainer.selectAll("."+n.Axis.TICK_LABEL_CLASS).data(w);var G=w.enter().append("g").classed(n.Axis.TICK_LABEL_CLASS,
!0).merge(w);w.exit().remove();G.attr("transform",function(D){var B=A.scale(D)-C/2;D=x.isHorizontal()?B:0;B=x.isHorizontal()?0:B;return"translate("+D+","+B+")"});G.text("");this._drawTicks(y,G);y="right"===this.orientation()?this._tickSpaceRequired():0;w="bottom"===this.orientation()?this._tickSpaceRequired():0;this._tickLabelContainer.attr("transform","translate("+y+","+w+")");this._showAllTickMarks();this._showAllTickLabels();this._hideTickMarksWithoutLabel();return this};u.prototype.computeLayout=
function(x,A,y){q.prototype.computeLayout.call(this,x,A,y);this.isHorizontal()||this._scale.range([0,this.height()]);return this};u.prototype.invalidateCache=function(){q.prototype.invalidateCache.call(this);this._measurer.reset()};return u}(n.Axis);d._MINIMUM_WIDTH_PER_LABEL_PX=15;f.Category=d},function(d,f,h){var k=this&&this.__extends||function(q,u){function x(){this.constructor=q}for(var A in u)u.hasOwnProperty(A)&&(q[A]=u[A]);q.prototype=null===u?Object.create(u):(x.prototype=u.prototype,new x)},
t=h(1),l=h(5),p=h(8),m=h(0),n=h(22);d=function(q){function u(x,A){x=q.call(this,x,A)||this;x._tickLabelPositioning="center";x._usesTextWidthApproximation=!1;x.formatter(p.general());return x}k(u,q);u.prototype._setup=function(){q.prototype._setup.call(this);var x=new l.SvgContext(this._tickLabelContainer.node(),n.Axis.TICK_LABEL_CLASS);this._measurer=new l.CacheMeasurer(x);this._wrapper=(new l.Wrapper).maxLines(1)};u.prototype._computeWidth=function(){var x=this._usesTextWidthApproximation?this._computeApproximateTextWidth():
this._computeExactTextWidth();return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+x:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+x)};u.prototype._computeExactTextWidth=function(){var x=this,A=this._getTickValues().map(function(y){y=x.formatter()(y);return x._measurer.measure(y).width});return m.Math.max(A,0)};u.prototype._computeApproximateTextWidth=function(){var x=this,A=this._getTickValues(),y=this._measurer.measure("M").width;A=A.map(function(w){return x.formatter()(w).length*
y});return m.Math.max(A,0)};u.prototype._computeHeight=function(){var x=this._measurer.measure().height;return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+x:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+x)};u.prototype._getTickValues=function(){var x=this._scale,A=x.domain(),y=A[0]<=A[1]?A[0]:A[1],w=A[0]>=A[1]?A[0]:A[1];return x.ticks().filter(function(C){return C>=y&&C<=w})};u.prototype._rescale=function(){if(this._isSetup){if(!this.isHorizontal()){var x=
this._computeWidth();if(x>this.width()||x<this.width()-this.margin()){this.redraw();return}}this.render()}};u.prototype.renderImmediately=function(){var x=this;q.prototype.renderImmediately.call(this);var A={x:0,y:0,dx:"0em",dy:"0.3em"},y=this._maxLabelTickLength(),w=this.tickLabelPadding(),C="middle",G=0,D=0,B=0,I=0;if(this.isHorizontal())switch(this._tickLabelPositioning){case "left":C="end";G=-w;I=w;break;case "center":I=y+w;break;case "right":C="start",I=G=w}else switch(this._tickLabelPositioning){case "top":A.dy=
"-0.3em";B=w;D=-w;break;case "center":B=y+w;break;case "bottom":A.dy="1em",D=B=w}y=this._generateTickMarkAttrHash();switch(this.orientation()){case "bottom":A.x=y.x1;A.dy="0.95em";D=y.y1+I;break;case "top":A.x=y.x1;A.dy="-.25em";D=y.y1-I;break;case "left":C="end";G=y.x1-B;A.y=y.y1;break;case "right":C="start",G=y.x1+B,A.y=y.y1}B=this._getTickValues();B=this._tickLabelContainer.selectAll("."+n.Axis.TICK_LABEL_CLASS).data(B);B.exit().remove();B.enter().append("text").classed(n.Axis.TICK_LABEL_CLASS,
!0).merge(B).style("text-anchor",C).style("visibility","inherit").attrs(A).text(function(N){return x.formatter()(N)});this._tickLabelContainer.attr("transform","translate("+G+", "+D+")");this._showAllTickMarks();this.showEndTickLabels()||this._hideEndTickLabels();this._hideOverflowingTickLabels();this._hideOverlappingTickLabels();"center"!==this._tickLabelPositioning&&this._hideTickMarksWithoutLabel();return this};u.prototype.tickLabelPosition=function(x){if(null==x)return this._tickLabelPositioning;
x=x.toLowerCase();if(this.isHorizontal()){if("left"!==x&&"center"!==x&&"right"!==x)throw Error(x+" is not a valid tick label position for a horizontal NumericAxis");}else if("top"!==x&&"center"!==x&&"bottom"!==x)throw Error(x+" is not a valid tick label position for a vertical NumericAxis");this._tickLabelPositioning=x;this.redraw();return this};u.prototype.usesTextWidthApproximation=function(){this._usesTextWidthApproximation=!0};u.prototype._hideEndTickLabels=function(){var x=this.element().node().getBoundingClientRect(),
A=this._tickLabelContainer.selectAll("."+n.Axis.TICK_LABEL_CLASS);if(0!==A.size()){var y=A.nodes()[0];m.DOM.clientRectInside(y.getBoundingClientRect(),x)||t.select(y).style("visibility","hidden");A=A.nodes()[A.size()-1];m.DOM.clientRectInside(A.getBoundingClientRect(),x)||t.select(A).style("visibility","hidden")}};u.prototype._hideOverlappingTickLabels=function(){for(var x=this._tickLabelContainer.selectAll("."+n.Axis.TICK_LABEL_CLASS).filter(function(){var w=t.select(this).style("visibility");return"inherit"===
w||"visible"===w}),A=x.nodes().map(function(w){return w.getBoundingClientRect()}),y=1;!this._hasOverlapWithInterval(y,A)&&y<A.length;)y+=1;x.each(function(w,C){w=t.select(this);0!==C%y&&w.style("visibility","hidden")})};u.prototype._hasOverlapWithInterval=function(x,A){var y="center"===this._tickLabelPositioning?this.tickLabelPadding():3*this.tickLabelPadding();A=A.map(function(C){return m.DOM.expandRect(C,y)});for(var w=0;w<A.length-x;w+=x)if(m.DOM.clientRectsOverlap(A[w],A[w+x]))return!1;return!0};
u.prototype.invalidateCache=function(){q.prototype.invalidateCache.call(this);this._measurer.reset()};return u}(n.Axis);f.Numeric=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)};d=h(42);var t=h(25),l=h(0);h=function(p){function m(n){function q(){w&&(w=!1,y._dragEndCallbacks.callCallbacks(y))}function u(C,G){w&&(y._setPixelPositionWithoutChangingMode(y._isVertical()?
G.x:G.y),y._dragCallbacks.callCallbacks(y))}function x(C){A(C)&&(w=!0,y._dragStartCallbacks.callCallbacks(y))}function A(C){return y._isVertical()&&y.pixelPosition()-y.detectionRadius()<=C.x&&C.x<=y.pixelPosition()+y.detectionRadius()||!y._isVertical()&&y.pixelPosition()-y.detectionRadius()<=C.y&&C.y<=y.pixelPosition()+y.detectionRadius()}var y=p.call(this,n)||this;y._detectionRadius=3;y._enabled=!0;y.addClass("drag-line-layer");y.addClass("enabled");y._dragInteraction=new t.Drag;y._dragInteraction.attachTo(y);
var w=!1;y._dragInteraction.onDragStart(x);y._dragInteraction.onDrag(u);y._dragInteraction.onDragEnd(q);y._disconnectInteraction=function(){y._dragInteraction.offDragStart(x);y._dragInteraction.offDrag(u);y._dragInteraction.offDragEnd(q);y._dragInteraction.detach()};y._dragStartCallbacks=new l.CallbackSet;y._dragCallbacks=new l.CallbackSet;y._dragEndCallbacks=new l.CallbackSet;return y}k(m,p);m.prototype._setup=function(){p.prototype._setup.call(this);this._detectionEdge=this.content().append("line").styles({opacity:0,
stroke:"pink","pointer-events":"visibleStroke"}).classed("drag-edge",!0)};m.prototype.renderImmediately=function(){p.prototype.renderImmediately.call(this);this._detectionEdge.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition(),"stroke-width":2*this._detectionRadius});return this};m.prototype.detectionRadius=function(){return this._detectionRadius};
m.prototype.enabled=function(n){if(null==n)return this._enabled;(this._enabled=n)?this.addClass("enabled"):this.removeClass("enabled");this._dragInteraction.enabled(n);return this};m.prototype.onDragStart=function(n){this._dragStartCallbacks.add(n)};m.prototype.offDragStart=function(n){this._dragStartCallbacks.delete(n)};m.prototype.onDrag=function(n){this._dragCallbacks.add(n);return this};m.prototype.offDrag=function(n){this._dragCallbacks.delete(n)};m.prototype.onDragEnd=function(n){this._dragEndCallbacks.add(n)};
m.prototype.offDragEnd=function(n){this._dragEndCallbacks.delete(n)};m.prototype.destroy=function(){var n=this;p.prototype.destroy.call(this);this._dragStartCallbacks.forEach(function(q){return n._dragStartCallbacks.delete(q)});this._dragCallbacks.forEach(function(q){return n._dragCallbacks.delete(q)});this._dragEndCallbacks.forEach(function(q){return n._dragEndCallbacks.delete(q)});this._disconnectInteraction()};return m}(d.GuideLineLayer);f.DragLineLayer=h},function(d,f,h){function k(l,p,m){var n=
{};if(void 0!==m)for(var q=0;q<m.length;q++)n[m[q]]=m[q-1];return function(u){var x=l.scale(u);if(!p)return x;var A;u=void 0===n[u]?void 0:l.scale(n[u]);void 0!==u&&(A=u+(x-u)/2);return A}}var t=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)};d=function(l){function p(m,n){var q=l.call(this)||this;q.addClass("gridlines");q._xScale=m;q._yScale=n;q._renderCallback=
function(){return q.render()};if(q._xScale)q._xScale.onUpdate(q._renderCallback);if(q._yScale)q._yScale.onUpdate(q._renderCallback);return q}t(p,l);p.prototype.betweenX=function(){return this._betweenX};p.prototype.betweenY=function(){return this._betweenY};p.prototype.destroy=function(){l.prototype.destroy.call(this);this._xScale&&this._xScale.offUpdate(this._renderCallback);this._yScale&&this._yScale.offUpdate(this._renderCallback);return this};p.prototype._setup=function(){l.prototype._setup.call(this);
this._xLinesContainer=this.content().append("g").classed("x-gridlines",!0);this._yLinesContainer=this.content().append("g").classed("y-gridlines",!0)};p.prototype.renderImmediately=function(){l.prototype.renderImmediately.call(this);this._redrawXLines();this._redrawYLines();return this};p.prototype.computeLayout=function(m,n,q){l.prototype.computeLayout.call(this,m,n,q);null!=this._xScale&&this._xScale.range([0,this.width()]);null!=this._yScale&&this._yScale.range([this.height(),0]);return this};
p.prototype._redrawXLines=function(){if(this._xScale){var m=this.betweenX(),n=this._xScale.ticks().slice(m?1:0);n=this._xLinesContainer.selectAll("line").data(n);n.enter().append("line").merge(n).attr("x1",k(this._xScale,m,this._xScale.ticks())).attr("y1",0).attr("x2",k(this._xScale,m,this._xScale.ticks())).attr("y2",this.height()).classed("betweenline",m).classed("zeroline",function(q){return 0===q});n.exit().remove()}};p.prototype._redrawYLines=function(){if(this._yScale){var m=this.betweenY(),
n=this._yScale.ticks().slice(m?1:0);n=this._yLinesContainer.selectAll("line").data(n);n.enter().append("line").merge(n).attr("x1",0).attr("y1",k(this._yScale,m,this._yScale.ticks())).attr("x2",this.width()).attr("y2",k(this._yScale,m,this._yScale.ticks())).classed("betweenline",m).classed("zeroline",function(q){return 0===q});n.exit().remove()}};return p}(h(4).Component);f.Gridlines=d},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var x in q)q.hasOwnProperty(x)&&
(n[x]=q[x]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},t=h(5),l=h(23),p=h(8),m=h(0);d=function(n){function q(u){var x=n.call(this)||this;x._textPadding=5;if(null==u)throw Error("InterpolatedColorLegend requires a interpolatedColorScale");x._scale=u;x._redrawCallback=function(){return x.redraw()};x._scale.onUpdate(x._redrawCallback);x._formatter=p.general();x._orientation="horizontal";x._expands=!1;x.addClass("legend");x.addClass("interpolated-color-legend");return x}k(q,
n);q.prototype.destroy=function(){n.prototype.destroy.call(this);this._scale.offUpdate(this._redrawCallback)};q.prototype.formatter=function(u){if(void 0===u)return this._formatter;this._formatter=u;this.redraw();return this};q.prototype.expands=function(){return this._expands};q._ensureOrientation=function(u){u=u.toLowerCase();if("horizontal"===u||"left"===u||"right"===u)return u;throw Error('"'+u+'" is not a valid orientation for InterpolatedColorLegend');};q.prototype.orientation=function(u){if(null==
u)return this._orientation;this._orientation=q._ensureOrientation(u);this.redraw();return this};q.prototype.fixedWidth=function(){return!this.expands()||this._isVertical()};q.prototype.fixedHeight=function(){return!this.expands()||!this._isVertical()};q.prototype._generateTicks=function(u){void 0===u&&(u=q._DEFAULT_NUM_SWATCHES);var x=this._scale.domain();if(1===u)return[x[0]];for(var A=(x[1]-x[0])/(u-1),y=[],w=0;w<u;w++)y.push(x[0]+A*w);return y};q.prototype._setup=function(){n.prototype._setup.call(this);
this._swatchContainer=this.content().append("g").classed("swatch-container",!0);this._swatchBoundingBox=this.content().append("rect").classed("swatch-bounding-box",!0);this._lowerLabel=this.content().append("g").classed(q.LEGEND_LABEL_CLASS,!0);this._upperLabel=this.content().append("g").classed(q.LEGEND_LABEL_CLASS,!0);var u=new t.SvgContext(this.content().node());this._measurer=new t.Measurer(u);this._wrapper=new t.Wrapper;this._writer=new t.Writer(this._measurer,u,this._wrapper)};q.prototype.requestedSpace=
function(){var u=this,x=this._measurer.measure().height,A=this._scale.domain().map(function(C){return u._measurer.measure(u._formatter(C)).width}),y=q._DEFAULT_NUM_SWATCHES;if(this._isVertical()){var w=m.Math.max(A,0);A=x+x+this._textPadding+w+this._textPadding;w=y*x}else w=x+x+x,A=this._textPadding+A[0]+y*x+A[1]+this._textPadding;return{minWidth:A,minHeight:w}};q.prototype._isVertical=function(){return"horizontal"!==this._orientation};q.prototype.renderImmediately=function(){var u=this;n.prototype.renderImmediately.call(this);
var x=this._scale.domain(),A=this._formatter(x[0]),y=this._measurer.measure(A).width,w=this._formatter(x[1]);x=this._measurer.measure(w).width;var C=this._measurer.measure().height,G=this._textPadding,D=0,B=0,I=0,N=0,O={xAlign:"center",yAlign:"center",textRotation:0},H={xAlign:"center",yAlign:"center",textRotation:0},K={x:0,y:0,width:0,height:0};if(this._isVertical()){var M=Math.floor(this.height());var L=Math.max(y,x);var Q=(this.width()-L-2*this._textPadding)/2;x=Math.max(this.width()-Q-2*G-L,0);
C=1;var T=function(aa,la){return u.height()-(la+1)};H.yAlign="top";B=0;O.yAlign="bottom";N=0;if("left"===this._orientation){var X=function(){return G+L+G};H.xAlign="right";D=-(Q+x+G);O.xAlign="right";I=-(Q+x+G)}else X=function(){return Q},H.xAlign="left",D=Q+x+G,O.xAlign="left",I=Q+x+G;K.width=x;K.height=M*C}else Q=Math.max(G,(this.height()-C)/2),M=Math.max(Math.floor(this.width()-4*G-y-x),0),x=1,C=Math.max(this.height()-2*Q,0),X=function(aa,la){return Math.floor(y+2*G)+la},T=function(){return Q},
H.xAlign="right",D=-G,O.xAlign="left",I=G,K.y=Q,K.width=M*x,K.height=C;K.x=X(null,0);this._upperLabel.text("");this._writer.write(w,this.width(),this.height(),H,this._upperLabel.node());this._upperLabel.attr("transform","translate("+D+", "+B+")");this._lowerLabel.text("");this._writer.write(A,this.width(),this.height(),O,this._lowerLabel.node());this._lowerLabel.attr("transform","translate("+I+", "+N+")");this._swatchBoundingBox.attrs(K);A=this._generateTicks(M);A=this._swatchContainer.selectAll("rect.swatch").data(A);
w=A.enter().append("rect").classed("swatch",!0);D=A.merge(w);A.exit().remove();D.attrs({fill:function(aa){return u._scale.scale(aa)},width:x,height:C,x:X,y:T,"shape-rendering":"crispEdges"});l.ADD_TITLE_ELEMENTS&&w.append("title").text(function(aa){return u._formatter(aa)});return this};return q}(h(4).Component);d._DEFAULT_NUM_SWATCHES=11;d.LEGEND_LABEL_CLASS="legend-label";f.InterpolatedColorLegend=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&
(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},t=h(5);d=function(l){function p(m,n){void 0===m&&(m="");void 0===n&&(n=0);var q=l.call(this)||this;q.addClass("label");q.text(m);q.angle(n);q.xAlignment("center").yAlignment("center");q._padding=0;return q}k(p,l);p.prototype.requestedSpace=function(){var m=this._measurer.measure(this._text),n=(0===this.angle()?m.width:m.height)+2*this.padding();m=(0===this.angle()?m.height:m.width)+2*this.padding();return{minWidth:n,
minHeight:m}};p.prototype._setup=function(){l.prototype._setup.call(this);this._textContainer=this.content().append("g");var m=new t.SvgContext(this._textContainer.node());this._measurer=new t.CacheMeasurer(m);this._wrapper=new t.Wrapper;this._writer=new t.Writer(this._measurer,m,this._wrapper);this.text(this._text)};p.prototype.text=function(m){if(null==m)return this._text;if("string"!==typeof m)throw Error("Label.text() only takes strings as input");this._text=m;this.redraw();return this};p.prototype.angle=
function(m){if(null==m)return this._angle;m%=360;180<m?m-=360:-180>m&&(m+=360);if(-90===m||0===m||90===m)this._angle=m;else throw Error(m+" is not a valid angle for Label");this.redraw();return this};p.prototype.padding=function(m){if(null==m)return this._padding;m=+m;if(0>m)throw Error(m+" is not a valid padding value. Cannot be less than 0.");this._padding=m;this.redraw();return this};p.prototype.fixedWidth=function(){return!0};p.prototype.fixedHeight=function(){return!0};p.prototype.renderImmediately=
function(){l.prototype.renderImmediately.call(this);this._textContainer.selectAll("g").remove();var m=this._measurer.measure(this._text),n=Math.max(Math.min((this.height()-m.height)/2,this.padding()),0);m=Math.max(Math.min((this.width()-m.width)/2,this.padding()),0);this._textContainer.attr("transform","translate("+m+","+n+")");m=this.width()-2*m;n=this.height()-2*n;var q={xAlign:this.xAlignment(),yAlign:this.yAlignment(),textRotation:this.angle()};this._writer.write(this._text,m,n,q);return this};
p.prototype.invalidateCache=function(){l.prototype.invalidateCache.call(this);this._measurer.reset()};return p}(h(4).Component);f.Label=d;h=function(l){function p(m,n){m=l.call(this,m,n)||this;m.addClass(p.TITLE_LABEL_CLASS);return m}k(p,l);return p}(d);h.TITLE_LABEL_CLASS="title-label";f.TitleLabel=h;d=function(l){function p(m,n){m=l.call(this,m,n)||this;m.addClass(p.AXIS_LABEL_CLASS);return m}k(p,l);return p}(d);d.AXIS_LABEL_CLASS="axis-label";f.AxisLabel=d},function(d,f,h){var k=this&&this.__extends||
function(A,y){function w(){this.constructor=A}for(var C in y)y.hasOwnProperty(C)&&(A[C]=y[C]);A.prototype=null===y?Object.create(y):(w.prototype=y.prototype,new w)},t=h(1),l=h(5),p=h(23),m=h(8),n=h(31),q=h(0);d=h(4);var u=function(){function A(y,w,C){void 0===y&&(y=[]);void 0===w&&(w=0);void 0===C&&(C=Infinity);this.columns=y;this.bottomPadding=w;this.maxWidth=C}A.prototype.addColumn=function(y){var w=y.width,C=this.getWidthAvailable();y.width=Math.min(C,w);this.columns.push(y)};A.prototype.getBounds=
function(y){for(var w=this.columns[y],C=0,G=0;G<y;G++)C+=this.columns[G].width;return{topLeft:{x:C,y:0},bottomRight:{x:C+w.width,y:w.height}}};A.prototype.getHeight=function(){return q.Math.max(this.columns.map(function(y){return y.height}),0)+this.bottomPadding};A.prototype.getWidth=function(){return Math.min(this.columns.reduce(function(y,w){return y+w.width},0),this.maxWidth)};A.prototype.getWidthAvailable=function(){var y=this.getWidth();return Math.max(this.maxWidth-y,0)};return A}(),x=function(){function A(y,
w,C,G){void 0===y&&(y=Infinity);void 0===w&&(w=Infinity);void 0===C&&(C=0);void 0===G&&(G=[]);this.maxWidth=y;this.maxHeight=w;this.padding=C;this.rows=G}A.prototype.addRow=function(y){y.maxWidth=this.maxWidth-2*this.padding;this.rows.push(y)};A.prototype.getColumnBounds=function(y,w){var C=this.getRowBounds(y);y=this.rows[y].getBounds(w);y.topLeft.x+=C.topLeft.x;y.bottomRight.x+=C.topLeft.x;y.topLeft.y+=C.topLeft.y;y.bottomRight.y+=C.topLeft.y;return y};A.prototype.getRowBounds=function(y){for(var w=
this.padding,C=this.padding,G=0;G<y;G++)C+=this.rows[G].getHeight();return{topLeft:{x:w,y:C},bottomRight:{x:w+this.rows[y].getWidth(),y:C+this.rows[y].getHeight()}}};A.prototype.getHeight=function(){return Math.min(this.rows.reduce(function(y,w){return y+w.getHeight()},0)+2*this.padding,this.maxHeight)};A.prototype.getWidth=function(){return Math.min(q.Math.max(this.rows.map(function(y){return y.getWidth()}),0)+2*this.padding,this.maxWidth)};return A}();d=function(A){function y(w){var C=A.call(this)||
this;C._padding=5;C._rowBottomPadding=3;C.addClass("legend");C.maxEntriesPerRow(1);if(null==w)throw Error("Legend requires a colorScale");C._colorScale=w;C._redrawCallback=function(){return C.redraw()};C._colorScale.onUpdate(C._redrawCallback);C._formatter=m.identity();C.maxLinesPerEntry(1);C.xAlignment("right").yAlignment("top");C.comparator(function(G,D){var B=C._colorScale.domain().slice().map(function(I){return C._formatter(I)});return B.indexOf(G)-B.indexOf(D)});C._symbolFactoryAccessor=function(){return n.circle()};
C._symbolOpacityAccessor=function(){return 1};return C}k(y,A);y.prototype._setup=function(){A.prototype._setup.call(this);var w=this.content().append("g").classed(y.LEGEND_ROW_CLASS,!0);w.append("g").classed(y.LEGEND_ENTRY_CLASS,!0).append("text");w=new l.SvgContext(w.node(),null,p.ADD_TITLE_ELEMENTS);this._measurer=new l.CacheMeasurer(w);this._wrapper=(new l.Wrapper).maxLines(this.maxLinesPerEntry());this._writer=new l.Writer(this._measurer,w,this._wrapper)};y.prototype.formatter=function(w){if(null==
w)return this._formatter;this._formatter=w;this.redraw();return this};y.prototype.maxEntriesPerRow=function(w){if(null==w)return this._maxEntriesPerRow;this._maxEntriesPerRow=w;this.redraw();return this};y.prototype.maxLinesPerEntry=function(w){if(null==w)return this._maxLinesPerEntry;this._maxLinesPerEntry=w;this.redraw();return this};y.prototype.maxWidth=function(w){if(null==w)return this._maxWidth;this._maxWidth=w;this.redraw();return this};y.prototype.comparator=function(w){null!=w&&(this._comparator=
w,this.redraw())};y.prototype.colorScale=function(w){return null!=w?(this._colorScale.offUpdate(this._redrawCallback),this._colorScale=w,this._colorScale.onUpdate(this._redrawCallback),this.redraw(),this):this._colorScale};y.prototype.destroy=function(){A.prototype.destroy.call(this);this._colorScale.offUpdate(this._redrawCallback)};y.prototype._buildLegendTable=function(w,C){var G=this,D=this._measurer.measure().height,B=new x(w,C,this._padding);w=this._colorScale.domain().slice().sort(function(N,
O){return G._comparator(G._formatter(N),G._formatter(O))});var I=new u;B.addRow(I);I.bottomPadding=this._rowBottomPadding;w.forEach(function(N){I.columns.length/2===G.maxEntriesPerRow()&&(I=new u,I.bottomPadding=G._rowBottomPadding,B.addRow(I));var O=I.getWidthAvailable(),H=G._formatter(N),K=G._measurer.measure(H).width;0>O-D-K&&1<I.columns.length&&(I=new u,I.bottomPadding=G._rowBottomPadding,B.addRow(I));I.addColumn({width:D,height:D,data:{name:N,type:"symbol"}});O=I.getWidthAvailable();O=Math.min(O,
K);G._wrapper.maxLines(G.maxLinesPerEntry());H=G._wrapper.wrap(H,G._measurer,O).noLines*D;I.addColumn({width:O,height:H,data:{name:N,type:"text"}})});return B};y.prototype.requestedSpace=function(w,C){w=this._buildLegendTable(q.Math.min([this.maxWidth(),w],w),C);return{minHeight:w.getHeight(),minWidth:w.getWidth()}};y.prototype.entitiesAt=function(w){var C=this;if(!this._isSetup)return[];var G=this._buildLegendTable(this.width(),this.height());return G.rows.reduce(function(D,B,I){if(0!==D.length)return D;
var N=G.getRowBounds(I);return q.Math.within(w,N)?B.columns.reduce(function(O,H,K){var M=G.getColumnBounds(I,K);if(q.Math.within(w,M)){O=C.content().selectAll("."+y.LEGEND_ROW_CLASS).nodes()[I];K=t.select(O).selectAll("."+y.LEGEND_ENTRY_CLASS).nodes()[Math.floor(K/2)];var L=t.select(K).select("."+y.LEGEND_SYMBOL_CLASS);M=q.DOM.getTranslateValues(t.select(O));L=q.DOM.getTranslateValues(L);return[{bounds:q.DOM.elementBBox(t.select(O)),datum:H.data.name,position:{x:M[0]+L[0],y:M[1]+L[1]},selection:t.select(K),
component:C}]}return O},D):D},[])};y.prototype.renderImmediately=function(){A.prototype.renderImmediately.call(this);var w=this._buildLegendTable(this.width(),this.height());this.content().selectAll("*").remove();var C=this.content().selectAll("g."+y.LEGEND_ROW_CLASS).data(w.rows),G=C.enter().append("g").classed(y.LEGEND_ROW_CLASS,!0).merge(C);C.exit().remove();G.attr("transform",function(B,I){B=w.getRowBounds(I);return"translate("+B.topLeft.x+", "+B.topLeft.y+")"});var D=this;G.each(function(B,I){for(var N=
[],O=0;O<B.columns.length;O+=2)N.push([B.columns[O],B.columns[O+1]]);B=t.select(this).selectAll("g."+y.LEGEND_ENTRY_CLASS).data(N);N=B.enter().append("g").classed(y.LEGEND_ENTRY_CLASS,!0).merge(B);N.append("path").attr("d",function(H){H=H[0];return D.symbol()(H.data.name,I)(.6*H.height)(null)}).attr("transform",function(H){H=H[0];return"translate("+(w.getColumnBounds(I,w.rows[I].columns.indexOf(H)).topLeft.x+H.width/2)+", "+H.height/2+")"}).attr("fill",function(H){return D._colorScale.scale(H[0].data.name)}).attr("opacity",
function(H){return D.symbolOpacity()(H[0].data.name,I)}).classed(y.LEGEND_SYMBOL_CLASS,!0);N.append("g").classed("text-container",!0).attr("transform",function(H){return"translate("+w.getColumnBounds(I,w.rows[I].columns.indexOf(H[1])).topLeft.x+", 0)"}).each(function(H){var K=t.select(this);H=H[1];D._writer.write(D._formatter(H.data.name),H.width,D.height(),{xAlign:"left",yAlign:"top",textRotation:0},K.node())});B.exit().remove()});return this};y.prototype.symbol=function(w){if(null==w)return this._symbolFactoryAccessor;
this._symbolFactoryAccessor=w;this.render();return this};y.prototype.symbolOpacity=function(){return this._symbolOpacityAccessor};y.prototype.fixedWidth=function(){return!0};y.prototype.fixedHeight=function(){return!0};y.prototype.invalidateCache=function(){this._measurer.reset()};return y}(d.Component);d.LEGEND_ROW_CLASS="legend-row";d.LEGEND_ENTRY_CLASS="legend-entry";d.LEGEND_SYMBOL_CLASS="legend-symbol";f.Legend=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(2),l=h(0);d=function(p){function m(){return null!==p&&p.apply(this,arguments)||this}k(m,p);m.prototype.entityNearest=function(n){var q,u=Infinity;this.components().forEach(function(x){x=x.entityNearest(n);if(null!=x){var A=l.Math.distanceSquared(x.position,n);A<=u&&(u=A,q=x)}});return q};m.prototype.append=function(n){if(null!=n&&!(n instanceof t.Plot))throw Error("Plot Group only accepts plots");
p.prototype.append.call(this,n);return this};return m}(h(41).Group);f.PlotGroup=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(1),l=h(0);d=function(p){function m(n){void 0===n&&(n=[]);var q=p.call(this)||this;q._rowPadding=0;q._columnPadding=0;q._rows=[];q._rowWeights=[];q._columnWeights=[];q._nRows=0;q._nCols=0;q._calculatedLayout=
null;q.addClass("table");n.forEach(function(u,x){u.forEach(function(A,y){null!=A&&q.add(A,x,y)})});return q}k(m,p);m.prototype._forEach=function(n){for(var q=0;q<this._nRows;q++)for(var u=0;u<this._nCols;u++)null!=this._rows[q][u]&&n(this._rows[q][u])};m.prototype.has=function(n){for(var q=0;q<this._nRows;q++)for(var u=0;u<this._nCols;u++)if(this._rows[q][u]===n)return!0;return!1};m.prototype.componentAt=function(n,q){return 0>n||n>=this._nRows||0>q||q>=this._nCols?null:this._rows[n][q]};m.prototype.add=
function(n,q,u){if(null==n)throw Error("Cannot add null to a table cell");if(!this.has(n)){if(null!=(this._rows[q]&&this._rows[q][u]))throw Error("cell is occupied");n.detach();this._nRows=Math.max(q+1,this._nRows);this._nCols=Math.max(u+1,this._nCols);this._padTableToSize(this._nRows,this._nCols);this._rows[q][u]=n;this._adoptAndAnchor(n);this.redraw()}return this};m.prototype._remove=function(n){for(var q=0;q<this._nRows;q++)for(var u=0;u<this._nCols;u++)if(this._rows[q][u]===n){this._rows[q][u]=
null;return}};m.prototype._iterateLayout=function(n,q,u){void 0===u&&(u=!1);var x=this._rows,A=t.transpose(this._rows);n-=this._columnPadding*(this._nCols-1);q-=this._rowPadding*(this._nRows-1);x=m._calcComponentWeights(this._rowWeights,x,function(L){return null==L||L.fixedHeight()});A=m._calcComponentWeights(this._columnWeights,A,function(L){return null==L||L.fixedWidth()});var y=A.map(function(L){return 0===L?.5:L}),w=x.map(function(L){return 0===L?.5:L});y=m._calcProportionalSpace(y,n);var C=m._calcProportionalSpace(w,
q),G=l.Array.createFilledArray(0,this._nCols),D=l.Array.createFilledArray(0,this._nRows);w=0;for(var B,I,N;;){D=l.Array.add(D,C);y=l.Array.add(G,y);B=this._determineGuarantees(y,D,u);G=B.guaranteedWidths;D=B.guaranteedHeights;I=B.wantsWidthArr.some(function(L){return L});N=B.wantsHeightArr.some(function(L){return L});var O=K,H=M;var K=n-t.sum(B.guaranteedWidths);var M=q-t.sum(B.guaranteedHeights);y=void 0;I?(y=B.wantsWidthArr.map(function(L){return L?.1:0}),y=l.Array.add(y,A)):y=A;C=void 0;N?(C=B.wantsHeightArr.map(function(L){return L?
.1:0}),C=l.Array.add(C,x)):C=x;y=m._calcProportionalSpace(y,K);C=m._calcProportionalSpace(C,M);w++;H=0<M&&M!==H;if(!(0<K&&K!==O||H))break;if(5<w)break}K=n-t.sum(B.guaranteedWidths);M=q-t.sum(B.guaranteedHeights);y=m._calcProportionalSpace(A,K);C=m._calcProportionalSpace(x,M);return{colProportionalSpace:y,rowProportionalSpace:C,guaranteedWidths:B.guaranteedWidths,guaranteedHeights:B.guaranteedHeights,wantsWidth:I,wantsHeight:N}};m.prototype._determineGuarantees=function(n,q,u){void 0===u&&(u=!1);var x=
l.Array.createFilledArray(0,this._nCols),A=l.Array.createFilledArray(0,this._nRows),y=l.Array.createFilledArray(!1,this._nCols),w=l.Array.createFilledArray(!1,this._nRows);this._rows.forEach(function(C,G){C.forEach(function(D,B){D=null!=D?D.requestedSpace(n[B],q[G]):{minWidth:0,minHeight:0};x[B]=Math.max(x[B],u?Math.min(D.minWidth,n[B]):D.minWidth);A[G]=Math.max(A[G],u?Math.min(D.minHeight,q[G]):D.minHeight);var I=D.minWidth>n[B];y[B]=y[B]||I;B=D.minHeight>q[G];w[G]=w[G]||B})});return{guaranteedWidths:x,
guaranteedHeights:A,wantsWidthArr:y,wantsHeightArr:w}};m.prototype.requestedSpace=function(n,q){this._calculatedLayout=this._iterateLayout(n,q);return{minWidth:t.sum(this._calculatedLayout.guaranteedWidths),minHeight:t.sum(this._calculatedLayout.guaranteedHeights)}};m.prototype.computeLayout=function(n,q,u){var x=this;p.prototype.computeLayout.call(this,n,q,u);n=t.sum(this._calculatedLayout.guaranteedWidths);q=t.sum(this._calculatedLayout.guaranteedHeights);u=this._calculatedLayout;if(n>this.width()||
q>this.height())u=this._iterateLayout(this.width(),this.height(),!0);var A=0,y=l.Array.add(u.rowProportionalSpace,u.guaranteedHeights),w=l.Array.add(u.colProportionalSpace,u.guaranteedWidths);this._rows.forEach(function(C,G){var D=0;C.forEach(function(B,I){null!=B&&B.computeLayout({x:D,y:A},w[I],y[G]);D+=w[I]+x._columnPadding});A+=y[G]+x._rowPadding});return this};m.prototype.rowPadding=function(n){if(null==n)return this._rowPadding;if(!l.Math.isValidNumber(n)||0>n)throw Error("rowPadding must be a non-negative finite value");
this._rowPadding=n;this.redraw();return this};m.prototype.columnPadding=function(n){if(null==n)return this._columnPadding;if(!l.Math.isValidNumber(n)||0>n)throw Error("columnPadding must be a non-negative finite value");this._columnPadding=n;this.redraw();return this};m.prototype.rowWeight=function(n,q){if(null==q)return this._rowWeights[n];if(!l.Math.isValidNumber(q)||0>q)throw Error("rowWeight must be a non-negative finite value");this._rowWeights[n]=q;this.redraw();return this};m.prototype.columnWeight=
function(n,q){if(null==q)return this._columnWeights[n];if(!l.Math.isValidNumber(q)||0>q)throw Error("columnWeight must be a non-negative finite value");this._columnWeights[n]=q;this.redraw();return this};m.prototype.fixedWidth=function(){var n=t.transpose(this._rows);return m._fixedSpace(n,function(q){return null==q||q.fixedWidth()})};m.prototype.fixedHeight=function(){return m._fixedSpace(this._rows,function(n){return null==n||n.fixedHeight()})};m.prototype._padTableToSize=function(n,q){for(var u=
0;u<n;u++){void 0===this._rows[u]&&(this._rows[u]=[],this._rowWeights[u]=null);for(var x=0;x<q;x++)void 0===this._rows[u][x]&&(this._rows[u][x]=null)}for(x=0;x<q;x++)void 0===this._columnWeights[x]&&(this._columnWeights[x]=null)};m._calcComponentWeights=function(n,q,u){return n.map(function(x,A){return null!=x?x:q[A].map(u).reduce(function(y,w){return y&&w},!0)?0:1})};m._calcProportionalSpace=function(n,q){var u=t.sum(n);return 0===u?l.Array.createFilledArray(0,n.length):n.map(function(x){return q*
x/u})};m._fixedSpace=function(n,q){function u(x){return x.reduce(function(A,y){return A&&y},!0)}return u(n.map(function(x){return u(x.map(q))}))};return m}(h(29).ComponentContainer);f.Table=d},function(d,f,h){var k=this&&this.__extends||function(t,l){function p(){this.constructor=t}for(var m in l)l.hasOwnProperty(m)&&(t[m]=l[m]);t.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(t){function l(){var p=t.call(this)||this;p.addClass("x-drag-box-layer");p._hasCorners=!1;
return p}k(l,t);l.prototype.computeLayout=function(p,m,n){t.prototype.computeLayout.call(this,p,m,n);this._setBounds(this.bounds());return this};l.prototype._setBounds=function(p){t.prototype._setBounds.call(this,{topLeft:{x:p.topLeft.x,y:0},bottomRight:{x:p.bottomRight.x,y:this.height()}})};l.prototype._setResizableClasses=function(p){p&&this.enabled()?this.addClass("x-resizable"):this.removeClass("x-resizable")};l.prototype.yScale=function(p){if(null==p)return t.prototype.yScale.call(this);throw Error("yScales cannot be set on an XDragBoxLayer");
};l.prototype.yExtent=function(){return t.prototype.yExtent.call(this);throw Error("XDragBoxLayer has no yExtent");};return l}(h(32).DragBoxLayer);f.XDragBoxLayer=d},function(d,f,h){var k=this&&this.__extends||function(t,l){function p(){this.constructor=t}for(var m in l)l.hasOwnProperty(m)&&(t[m]=l[m]);t.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(t){function l(){var p=t.call(this)||this;p.addClass("y-drag-box-layer");p._hasCorners=!1;return p}k(l,t);l.prototype.computeLayout=
function(p,m,n){t.prototype.computeLayout.call(this,p,m,n);this._setBounds(this.bounds());return this};l.prototype._setBounds=function(p){t.prototype._setBounds.call(this,{topLeft:{x:0,y:p.topLeft.y},bottomRight:{x:this.width(),y:p.bottomRight.y}})};l.prototype._setResizableClasses=function(p){p&&this.enabled()?this.addClass("y-resizable"):this.removeClass("y-resizable")};l.prototype.xScale=function(p){if(null==p)return t.prototype.xScale.call(this);throw Error("xScales cannot be set on an YDragBoxLayer");
};l.prototype.xExtent=function(){return t.prototype.xExtent.call(this);throw Error("YDragBoxLayer has no xExtent");};return l}(h(32).DragBoxLayer);f.YDragBoxLayer=d},function(d,f,h){var k=this&&this.__extends||function(t,l){function p(){this.constructor=t}for(var m in l)l.hasOwnProperty(m)&&(t[m]=l[m]);t.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(t){function l(){var p=t.call(this)||this;p._eventToProcessingFunction[l._KEYDOWN_EVENT_NAME]=function(m){return p._processKeydown(m)};
p._eventToProcessingFunction[l._KEYUP_EVENT_NAME]=function(m){return p._processKeyup(m)};return p}k(l,t);l.getDispatcher=function(){var p=document[l._DISPATCHER_KEY];null==p&&(p=new l,document[l._DISPATCHER_KEY]=p);return p};l.prototype._processKeydown=function(p){this._callCallbacksForEvent(l._KEYDOWN_EVENT_NAME,p.keyCode,p)};l.prototype._processKeyup=function(p){this._callCallbacksForEvent(l._KEYUP_EVENT_NAME,p.keyCode,p)};l.prototype.onKeyDown=function(p){this._addCallbackForEvent(l._KEYDOWN_EVENT_NAME,
p)};l.prototype.offKeyDown=function(p){this._removeCallbackForEvent(l._KEYDOWN_EVENT_NAME,p)};l.prototype.onKeyUp=function(p){this._addCallbackForEvent(l._KEYUP_EVENT_NAME,p)};l.prototype.offKeyUp=function(p){this._removeCallbackForEvent(l._KEYUP_EVENT_NAME,p)};return l}(h(24).Dispatcher);d._DISPATCHER_KEY="__Plottable_Dispatcher_Key";d._KEYDOWN_EVENT_NAME="keydown";d._KEYUP_EVENT_NAME="keyup";f.Key=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&
(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},t=h(0);d=function(l){function p(m){function n(u){return q._measureAndDispatch(m,u,p._MOUSEMOVE_EVENT_NAME,"page")}var q=l.call(this)||this;q._lastMousePosition={x:-1,y:-1};q._translator=t.getTranslator(m);q._eventToProcessingFunction[p._MOUSEOVER_EVENT_NAME]=n;q._eventToProcessingFunction[p._MOUSEMOVE_EVENT_NAME]=n;q._eventToProcessingFunction[p._MOUSEOUT_EVENT_NAME]=n;q._eventToProcessingFunction[p._MOUSEDOWN_EVENT_NAME]=
function(u){return q._measureAndDispatch(m,u,p._MOUSEDOWN_EVENT_NAME)};q._eventToProcessingFunction[p._MOUSEUP_EVENT_NAME]=function(u){return q._measureAndDispatch(m,u,p._MOUSEUP_EVENT_NAME,"page")};q._eventToProcessingFunction[p._WHEEL_EVENT_NAME]=function(u){return q._measureAndDispatch(m,u,p._WHEEL_EVENT_NAME)};q._eventToProcessingFunction[p._DBLCLICK_EVENT_NAME]=function(u){return q._measureAndDispatch(m,u,p._DBLCLICK_EVENT_NAME)};return q}k(p,l);p.getDispatcher=function(m){var n=m.root().rootElement(),
q=n[p._DISPATCHER_KEY];null==q&&(q=new p(m),n[p._DISPATCHER_KEY]=q);return q};p.prototype.onMouseMove=function(m){this._addCallbackForEvent(p._MOUSEMOVE_EVENT_NAME,m)};p.prototype.offMouseMove=function(m){this._removeCallbackForEvent(p._MOUSEMOVE_EVENT_NAME,m)};p.prototype.onMouseDown=function(m){this._addCallbackForEvent(p._MOUSEDOWN_EVENT_NAME,m)};p.prototype.offMouseDown=function(m){this._removeCallbackForEvent(p._MOUSEDOWN_EVENT_NAME,m)};p.prototype.onMouseUp=function(m){this._addCallbackForEvent(p._MOUSEUP_EVENT_NAME,
m)};p.prototype.offMouseUp=function(m){this._removeCallbackForEvent(p._MOUSEUP_EVENT_NAME,m)};p.prototype.onWheel=function(m){this._addCallbackForEvent(p._WHEEL_EVENT_NAME,m);return this};p.prototype.offWheel=function(m){this._removeCallbackForEvent(p._WHEEL_EVENT_NAME,m)};p.prototype.onDblClick=function(m){this._addCallbackForEvent(p._DBLCLICK_EVENT_NAME,m)};p.prototype.offDblClick=function(m){this._removeCallbackForEvent(p._DBLCLICK_EVENT_NAME,m)};p.prototype._measureAndDispatch=function(m,n,q,
u){void 0===u&&(u="element");if("page"!==u&&"element"!==u)throw Error("Invalid scope '"+u+"', must be 'element' or 'page'");if("page"===u||this.eventInside(m,n))this._lastMousePosition=this._translator.computePosition(n.clientX,n.clientY),this._callCallbacksForEvent(q,this.lastMousePosition(),n)};p.prototype.eventInside=function(m,n){return t.Translator.isEventInside(m,n)};p.prototype.lastMousePosition=function(){return this._lastMousePosition};return p}(h(24).Dispatcher);d._DISPATCHER_KEY="__Plottable_Dispatcher_Mouse";
d._MOUSEOVER_EVENT_NAME="mouseover";d._MOUSEMOVE_EVENT_NAME="mousemove";d._MOUSEOUT_EVENT_NAME="mouseout";d._MOUSEDOWN_EVENT_NAME="mousedown";d._MOUSEUP_EVENT_NAME="mouseup";d._WHEEL_EVENT_NAME="wheel";d._DBLCLICK_EVENT_NAME="dblclick";f.Mouse=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},t=h(0);d=function(l){function p(m){var n=l.call(this)||
this;n._translator=t.getTranslator(m);n._eventToProcessingFunction[p._TOUCHSTART_EVENT_NAME]=function(q){return n._measureAndDispatch(m,q,p._TOUCHSTART_EVENT_NAME,"page")};n._eventToProcessingFunction[p._TOUCHMOVE_EVENT_NAME]=function(q){return n._measureAndDispatch(m,q,p._TOUCHMOVE_EVENT_NAME,"page")};n._eventToProcessingFunction[p._TOUCHEND_EVENT_NAME]=function(q){return n._measureAndDispatch(m,q,p._TOUCHEND_EVENT_NAME,"page")};n._eventToProcessingFunction[p._TOUCHCANCEL_EVENT_NAME]=function(q){return n._measureAndDispatch(m,
q,p._TOUCHCANCEL_EVENT_NAME,"page")};return n}k(p,l);p.getDispatcher=function(m){var n=m.root().rootElement(),q=n[p._DISPATCHER_KEY];null==q&&(q=new p(m),n[p._DISPATCHER_KEY]=q);return q};p.prototype.onTouchStart=function(m){this._addCallbackForEvent(p._TOUCHSTART_EVENT_NAME,m)};p.prototype.offTouchStart=function(m){this._removeCallbackForEvent(p._TOUCHSTART_EVENT_NAME,m)};p.prototype.onTouchMove=function(m){this._addCallbackForEvent(p._TOUCHMOVE_EVENT_NAME,m)};p.prototype.offTouchMove=function(m){this._removeCallbackForEvent(p._TOUCHMOVE_EVENT_NAME,
m)};p.prototype.onTouchEnd=function(m){this._addCallbackForEvent(p._TOUCHEND_EVENT_NAME,m)};p.prototype.offTouchEnd=function(m){this._removeCallbackForEvent(p._TOUCHEND_EVENT_NAME,m)};p.prototype.onTouchCancel=function(m){this._addCallbackForEvent(p._TOUCHCANCEL_EVENT_NAME,m)};p.prototype.offTouchCancel=function(m){this._removeCallbackForEvent(p._TOUCHCANCEL_EVENT_NAME,m)};p.prototype._measureAndDispatch=function(m,n,q,u){void 0===u&&(u="element");if("page"!==u&&"element"!==u)throw Error("Invalid scope '"+
u+"', must be 'element' or 'page'");if("element"!==u||this.eventInside(m,n)){m=n.changedTouches;u={};for(var x=[],A=0;A<m.length;A++){var y=m[A],w=y.identifier;y=this._translator.computePosition(y.clientX,y.clientY);null!=y&&(u[w]=y,x.push(w))}0<x.length&&this._callCallbacksForEvent(q,x,u,n)}};p.prototype.eventInside=function(m,n){return t.Translator.isEventInside(m,n)};return p}(h(24).Dispatcher);d._DISPATCHER_KEY="__Plottable_Dispatcher_Touch";d._TOUCHSTART_EVENT_NAME="touchstart";d._TOUCHMOVE_EVENT_NAME=
"touchmove";d._TOUCHEND_EVENT_NAME="touchend";d._TOUCHCANCEL_EVENT_NAME="touchcancel";f.Touch=d},function(d,f){d=function(){function h(k,t,l){void 0===l&&(l=window.devicePixelRatio);this.screenWidth=k;this.screenHeight=t;this.devicePixelRatio=l;this.pixelWidth=k*l;this.pixelHeight=t*l;this.canvas=document.createElement("canvas");this.ctx=this.canvas.getContext("2d");h.sizePixels(this.ctx,k,t,l)}h.sizePixels=function(k,t,l,p){var m=k.canvas;m.width=t*p;m.height=l*p;m.style.width=t+"px";m.style.height=
l+"px";k.setTransform(1,0,0,1,0,0);k.scale(p,p)};h.prototype.blit=function(k,t,l){void 0===t&&(t=0);void 0===l&&(l=0);k.drawImage(this.canvas,t,l,this.screenWidth,this.screenHeight)};h.prototype.blitCenter=function(k,t,l){void 0===t&&(t=0);void 0===l&&(l=0);this.blit(k,Math.floor(t-this.screenWidth/2),Math.floor(l-this.screenHeight/2))};h.prototype.resize=function(k,t,l){void 0===l&&(l=!1);var p=this.devicePixelRatio;this.screenWidth=k;this.screenHeight=t;this.pixelWidth=k*p;this.pixelHeight=t*p;
h.sizePixels(this.ctx,k,t,p);l&&this.ctx.translate(k/2,k/2);return this};h.prototype.clear=function(k){var t=this.pixelWidth,l=this.pixelHeight,p=this.ctx;p.save();p.setTransform(1,0,0,1,0,0);null==k?p.clearRect(0,0,t,l):(p.fillStyle=k,p.fillRect(0,0,t,l));p.restore();return this};h.prototype.getImageData=function(){return this.ctx.getImageData(0,0,this.pixelWidth,this.pixelHeight)};return h}();f.CanvasBuffer=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(13),l=h(0);d=function(p){function m(){var n=null!==p&&p.apply(this,arguments)||this;n._clickedDown=!1;n._doubleClicking=!1;n._onClickCallbacks=new l.CallbackSet;n._onDoubleClickCallbacks=new l.CallbackSet;n._mouseDownCallback=function(q){return n._handleClickDown(q)};n._mouseUpCallback=function(q,u){return n._handleClickUp(q,u)};n._dblClickCallback=function(q,u){return n._handleDblClick(q,
u)};n._touchStartCallback=function(q,u){return n._handleClickDown(u[q[0]])};n._touchEndCallback=function(q,u,x){return n._handleClickUp(u[q[0]],x)};n._touchCancelCallback=function(){return n._clickedDown=!1};return n}k(m,p);m.prototype._anchor=function(n){p.prototype._anchor.call(this,n);this._mouseDispatcher=t.Mouse.getDispatcher(n);this._mouseDispatcher.onMouseDown(this._mouseDownCallback);this._mouseDispatcher.onMouseUp(this._mouseUpCallback);this._mouseDispatcher.onDblClick(this._dblClickCallback);
this._touchDispatcher=t.Touch.getDispatcher(n);this._touchDispatcher.onTouchStart(this._touchStartCallback);this._touchDispatcher.onTouchEnd(this._touchEndCallback);this._touchDispatcher.onTouchCancel(this._touchCancelCallback)};m.prototype._unanchor=function(){p.prototype._unanchor.call(this);this._mouseDispatcher.offMouseDown(this._mouseDownCallback);this._mouseDispatcher.offMouseUp(this._mouseUpCallback);this._mouseDispatcher.offDblClick(this._dblClickCallback);this._mouseDispatcher=null;this._touchDispatcher.offTouchStart(this._touchStartCallback);
this._touchDispatcher.offTouchEnd(this._touchEndCallback);this._touchDispatcher.offTouchCancel(this._touchCancelCallback);this._touchDispatcher=null};m.prototype._handleClickDown=function(n){n=this._translateToComponentSpace(n);this._isInsideComponent(n)&&(this._clickedDown=!0,this._clickedPoint=n)};m.prototype._handleClickUp=function(n,q){var u=this,x=this._translateToComponentSpace(n);this._clickedDown&&m._pointsEqual(x,this._clickedPoint)&&setTimeout(function(){u._doubleClicking||u._onClickCallbacks.callCallbacks(x,
q)},0);this._clickedDown=!1};m.prototype._handleDblClick=function(n,q){var u=this;n=this._translateToComponentSpace(n);this._doubleClicking=!0;this._onDoubleClickCallbacks.callCallbacks(n,q);setTimeout(function(){return u._doubleClicking=!1},0)};m._pointsEqual=function(n,q){return n.x===q.x&&n.y===q.y};m.prototype.onClick=function(n){this._onClickCallbacks.add(n);return this};m.prototype.offClick=function(n){this._onClickCallbacks.delete(n);return this};m.prototype.onDoubleClick=function(n){this._onDoubleClickCallbacks.add(n)};
m.prototype.offDoubleClick=function(n){this._onDoubleClickCallbacks.delete(n);return this};return m}(h(15).Interaction);f.Click=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(13),l=h(0);d=function(p){function m(){var n=null!==p&&p.apply(this,arguments)||this;n._dragging=!1;n._constrainedToComponent=!0;n._mouseFilter=m._DEFAULT_MOUSE_FILTER;
n._dragStartCallbacks=new l.CallbackSet;n._dragCallbacks=new l.CallbackSet;n._dragEndCallbacks=new l.CallbackSet;n._mouseDownCallback=function(q,u){return n._startDrag(q,u)};n._mouseMoveCallback=function(q){return n._doDrag(q)};n._mouseUpCallback=function(q,u){return n._endDrag(q,u)};n._touchStartCallback=function(q,u,x){return n._startDrag(u[q[0]],x)};n._touchMoveCallback=function(q,u){return n._doDrag(u[q[0]])};n._touchEndCallback=function(q,u,x){return n._endDrag(u[q[0]],x)};return n}k(m,p);m.prototype._anchor=
function(n){p.prototype._anchor.call(this,n);this._mouseDispatcher=t.Mouse.getDispatcher(this._componentAttachedTo);this._mouseDispatcher.onMouseDown(this._mouseDownCallback);this._mouseDispatcher.onMouseMove(this._mouseMoveCallback);this._mouseDispatcher.onMouseUp(this._mouseUpCallback);this._touchDispatcher=t.Touch.getDispatcher(this._componentAttachedTo);this._touchDispatcher.onTouchStart(this._touchStartCallback);this._touchDispatcher.onTouchMove(this._touchMoveCallback);this._touchDispatcher.onTouchEnd(this._touchEndCallback)};
m.prototype._unanchor=function(){p.prototype._unanchor.call(this);this._mouseDispatcher.offMouseDown(this._mouseDownCallback);this._mouseDispatcher.offMouseMove(this._mouseMoveCallback);this._mouseDispatcher.offMouseUp(this._mouseUpCallback);this._mouseDispatcher=null;this._touchDispatcher.offTouchStart(this._touchStartCallback);this._touchDispatcher.offTouchMove(this._touchMoveCallback);this._touchDispatcher.offTouchEnd(this._touchEndCallback);this._touchDispatcher=null};m.prototype._translateAndConstrain=
function(n){n=this._translateToComponentSpace(n);return this._constrainedToComponent?{x:l.Math.clamp(n.x,0,this._componentAttachedTo.width()),y:l.Math.clamp(n.y,0,this._componentAttachedTo.height())}:n};m.prototype._startDrag=function(n,q){q instanceof MouseEvent&&!this._mouseFilter(q)||(n=this._translateToComponentSpace(n),this._isInsideComponent(n)&&(q.preventDefault(),this._dragging=!0,this._dragOrigin=n,this._dragStartCallbacks.callCallbacks(this._dragOrigin)))};m.prototype._doDrag=function(n){this._dragging&&
this._dragCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(n))};m.prototype._endDrag=function(n,q){q instanceof MouseEvent&&0!==q.button||!this._dragging||(this._dragging=!1,this._dragEndCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(n)))};m.prototype.constrainedToComponent=function(){this._constrainedToComponent=!1};m.prototype.mouseFilter=function(n){0!==arguments.length&&(this._mouseFilter=n)};m.prototype.onDragStart=function(n){this._dragStartCallbacks.add(n)};
m.prototype.offDragStart=function(n){this._dragStartCallbacks.delete(n)};m.prototype.onDrag=function(n){this._dragCallbacks.add(n);return this};m.prototype.offDrag=function(n){this._dragCallbacks.delete(n)};m.prototype.onDragEnd=function(n){this._dragEndCallbacks.add(n)};m.prototype.offDragEnd=function(n){this._dragEndCallbacks.delete(n)};return m}(h(15).Interaction);d._DEFAULT_MOUSE_FILTER=function(p){return 0===p.button};f.Drag=d},function(d,f,h){var k=this&&this.__extends||function(u,x){function A(){this.constructor=
u}for(var y in x)x.hasOwnProperty(y)&&(u[y]=x[y]);u.prototype=null===x?Object.create(x):(A.prototype=x.prototype,new A)},t=h(1),l=h(13),p=h(3),m=h(0),n=h(25);d=h(15);var q=h(26);h=function(u){function x(A,y){var w=u.call(this)||this;w._wheelFilter=function(){return!0};w._wheelCallback=function(C,G){return w._handleWheelEvent(C,G)};w._touchStartCallback=function(C,G){return w._handleTouchStart(C,G)};w._touchMoveCallback=function(C,G){return w._handlePinch(C,G)};w._touchEndCallback=function(C){return w._handleTouchEnd(C)};
w._touchCancelCallback=function(C){return w._handleTouchEnd(C)};w._panEndCallbacks=new m.CallbackSet;w._zoomEndCallbacks=new m.CallbackSet;w._panZoomUpdateCallbacks=new m.CallbackSet;w._xScales=new m.Set;w._yScales=new m.Set;w._dragInteraction=new n.Drag;w._setupDragInteraction();w._touchIds=t.map();w._minDomainExtents=new m.Map;w._maxDomainExtents=new m.Map;w._minDomainValues=new m.Map;w._maxDomainValues=new m.Map;null!=A&&w.addXScale(A);null!=y&&w.addYScale(y);return w}k(x,u);x.prototype.dragInteraction=
function(){return this._dragInteraction};x.prototype.wheelFilter=function(A){0!==arguments.length&&(this._wheelFilter=A)};x.prototype.pan=function(A){var y=this;this.xScales().forEach(function(w){w.pan(y._constrainedTranslation(w,A.x))});this.yScales().forEach(function(w){w.pan(y._constrainedTranslation(w,A.y))});this._panZoomUpdateCallbacks.callCallbacks()};x.prototype.zoom=function(A,y,w){var C=this;void 0===w&&(w=!0);if(null!=y){var G=y.x;var D=y.y;w&&(this.xScales().forEach(function(B){B=C._constrainedZoom(B,
A,G);G=B.centerPoint;A=B.zoomAmount}),this.yScales().forEach(function(B){B=C._constrainedZoom(B,A,D);D=B.centerPoint;A=B.zoomAmount}))}this.xScales().forEach(function(B){var I=B.range();B.zoom(A,null==G?(I[1]+I[0])/2:G)});this.yScales().forEach(function(B){var I=B.range();B.zoom(A,null==D?(I[1]+I[0])/2:D)});this._panZoomUpdateCallbacks.callCallbacks();return{zoomAmount:A,centerValue:{centerX:G,centerY:D}}};x.prototype._anchor=function(A){u.prototype._anchor.call(this,A);this._dragInteraction.attachTo(A);
this._mouseDispatcher=l.Mouse.getDispatcher(this._componentAttachedTo);this._mouseDispatcher.onWheel(this._wheelCallback);this._touchDispatcher=l.Touch.getDispatcher(this._componentAttachedTo);this._touchDispatcher.onTouchStart(this._touchStartCallback);this._touchDispatcher.onTouchMove(this._touchMoveCallback);this._touchDispatcher.onTouchEnd(this._touchEndCallback);this._touchDispatcher.onTouchCancel(this._touchCancelCallback)};x.prototype._unanchor=function(){u.prototype._unanchor.call(this);this._mouseDispatcher.offWheel(this._wheelCallback);
this._mouseDispatcher=null;this._touchDispatcher.offTouchStart(this._touchStartCallback);this._touchDispatcher.offTouchMove(this._touchMoveCallback);this._touchDispatcher.offTouchEnd(this._touchEndCallback);this._touchDispatcher.offTouchCancel(this._touchCancelCallback);this._touchDispatcher=null;this._dragInteraction.detach()};x.prototype._handleTouchStart=function(A,y){for(var w=0;w<A.length&&2>this._touchIds.size();w++){var C=A[w];this._touchIds.set(C.toString(),this._translateToComponentSpace(y[C]))}};
x.prototype._handlePinch=function(A,y){var w=this;if(!(2>this._touchIds.size())){var C=this._touchIds.values();if(this._isInsideComponent(this._translateToComponentSpace(C[0]))&&this._isInsideComponent(this._translateToComponentSpace(C[1]))){var G=x._pointDistance(C[0],C[1]);if(0!==G){A.forEach(function(O){w._touchIds.has(O.toString())&&w._touchIds.set(O.toString(),w._translateToComponentSpace(y[O]))});A=this._touchIds.values();var D=x._pointDistance(A[0],A[1]);if(0!==D){var B=G/D,I=A.map(function(O,
H){return{x:(O.x-C[H].x)/B,y:(O.y-C[H].y)/B}});G=x.centerPoint(C[0],C[1]);G=this.zoom(B,G);A=G.centerValue;var N=G.zoomAmount;G=A.centerX;A=A.centerY;D=C.map(function(O,H){return{x:I[H].x*N+O.x,y:I[H].y*N+O.y}});this.pan({x:G-(D[0].x+D[1].x)/2,y:A-(D[0].y+D[1].y)/2})}}}}};x.centerPoint=function(A,y){return{x:(Math.min(A.x,y.x)+Math.max(A.x,y.x))/2,y:(Math.max(A.y,y.y)+Math.min(A.y,y.y))/2}};x._pointDistance=function(A,y){return Math.sqrt(Math.pow(Math.max(A.x,y.x)-Math.min(A.x,y.x),2)+Math.pow(Math.max(A.y,
y.y)-Math.min(A.y,y.y),2))};x.prototype._handleTouchEnd=function(A){var y=this;A.forEach(function(w){y._touchIds.remove(w.toString())});0<this._touchIds.size()&&this._zoomEndCallbacks.callCallbacks()};x.prototype._handleWheelEvent=function(A,y){this._wheelFilter(y)&&(A=this._translateToComponentSpace(A),this._isInsideComponent(A)&&(y.preventDefault(),this.zoom(Math.pow(2,(0!==y.deltaY?y.deltaY:y.deltaX)*(y.deltaMode?x._PIXELS_PER_LINE:1)*.002),A),this._zoomEndCallbacks.callCallbacks()))};x.prototype._constrainedZoom=
function(A,y,w){return q.constrainedZoom(A,y,w,this.minDomainExtent(A),this.maxDomainExtent(A),this.minDomainValue(A),this.maxDomainValue(A))};x.prototype._constrainedTranslation=function(A,y){return q.constrainedTranslation(A,y,this.minDomainValue(A),this.maxDomainValue(A))};x.prototype._setupDragInteraction=function(){var A=this;this._dragInteraction.constrainedToComponent();var y;this._dragInteraction.onDragStart(function(){return y=null});this._dragInteraction.onDrag(function(w,C){2<=A._touchIds.size()||
(A.pan({x:(null==y?w.x:y.x)-C.x,y:(null==y?w.y:y.y)-C.y}),y=C)});this._dragInteraction.onDragEnd(function(){return A._panEndCallbacks.callCallbacks()})};x.prototype._nonLinearScaleWithExtents=function(A){return null!=this.minDomainExtent(A)&&null!=this.maxDomainExtent(A)&&!(A instanceof p.Linear)&&!(A instanceof p.Time)};x.prototype.xScales=function(){var A=[];this._xScales.forEach(function(y){A.push(y)});return A};x.prototype.yScales=function(){var A=[];this._yScales.forEach(function(y){A.push(y)});
return A};x.prototype.addXScale=function(A){this._xScales.add(A)};x.prototype.removeXScale=function(A){this._xScales.delete(A);this._minDomainExtents.delete(A);this._maxDomainExtents.delete(A);this._minDomainValues.delete(A);this._maxDomainValues.delete(A);return this};x.prototype.addYScale=function(A){this._yScales.add(A)};x.prototype.removeYScale=function(A){this._yScales.delete(A);this._minDomainExtents.delete(A);this._maxDomainExtents.delete(A);this._minDomainValues.delete(A);this._maxDomainValues.delete(A);
return this};x.prototype.minDomainExtent=function(A){return this._minDomainExtents.get(A)};x.prototype.maxDomainExtent=function(A){return this._maxDomainExtents.get(A)};x.prototype.minDomainValue=function(A,y){if(null==y)return this._minDomainValues.get(A);this._minDomainValues.set(A,y);return this};x.prototype.maxDomainValue=function(A,y){if(null==y)return this._maxDomainValues.get(A);this._maxDomainValues.set(A,y);return this};x.prototype.setMinMaxDomainValuesTo=function(A){this._minDomainValues.delete(A);
this._maxDomainValues.delete(A);var y=A.getTransformationDomain(),w=y[1];this.minDomainValue(A,y[0]);this.maxDomainValue(A,w);return this};x.prototype.onPanEnd=function(A){this._panEndCallbacks.add(A)};x.prototype.offPanEnd=function(A){this._panEndCallbacks.delete(A);return this};x.prototype.onZoomEnd=function(A){this._zoomEndCallbacks.add(A)};x.prototype.offZoomEnd=function(A){this._zoomEndCallbacks.delete(A);return this};x.prototype.onPanZoomUpdate=function(A){this._panZoomUpdateCallbacks.add(A);
return this};x.prototype.offPanZoomUpdate=function(A){this._panZoomUpdateCallbacks.delete(A);return this};return x}(d.Interaction);h._PIXELS_PER_LINE=120;f.PanZoom=h},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(13),l=h(0);d=function(p){function m(){var n=null!==p&&p.apply(this,arguments)||this;n._overComponent=!1;n._pointerEnterCallbacks=
new l.CallbackSet;n._pointerMoveCallbacks=new l.CallbackSet;n._pointerExitCallbacks=new l.CallbackSet;n._mouseMoveCallback=function(q,u){return n._handleMouseEvent(q,u)};n._touchStartCallback=function(q,u,x){return n._handleTouchEvent(u[q[0]],x)};return n}k(m,p);m.prototype._anchor=function(n){p.prototype._anchor.call(this,n);this._mouseDispatcher=t.Mouse.getDispatcher(this._componentAttachedTo);this._mouseDispatcher.onMouseMove(this._mouseMoveCallback);this._touchDispatcher=t.Touch.getDispatcher(this._componentAttachedTo);
this._touchDispatcher.onTouchStart(this._touchStartCallback)};m.prototype._unanchor=function(){p.prototype._unanchor.call(this);this._mouseDispatcher.offMouseMove(this._mouseMoveCallback);this._mouseDispatcher=null;this._touchDispatcher.offTouchStart(this._touchStartCallback);this._touchDispatcher=null};m.prototype._handleMouseEvent=function(n,q){q=this._mouseDispatcher.eventInside(this._componentAttachedTo,q);this._handlePointerEvent(n,q)};m.prototype._handleTouchEvent=function(n,q){q=this._touchDispatcher.eventInside(this._componentAttachedTo,
q);this._handlePointerEvent(n,q)};m.prototype._handlePointerEvent=function(n,q){n=this._translateToComponentSpace(n);var u=this._isInsideComponent(n);u&&q?(this._overComponent||this._pointerEnterCallbacks.callCallbacks(n),this._pointerMoveCallbacks.callCallbacks(n)):this._overComponent&&this._pointerExitCallbacks.callCallbacks(n);this._overComponent=u&&q};m.prototype.onPointerEnter=function(n){this._pointerEnterCallbacks.add(n);return this};m.prototype.offPointerEnter=function(n){this._pointerEnterCallbacks.delete(n);
return this};m.prototype.onPointerMove=function(n){this._pointerMoveCallbacks.add(n)};m.prototype.offPointerMove=function(n){this._pointerMoveCallbacks.delete(n);return this};m.prototype.onPointerExit=function(n){this._pointerExitCallbacks.add(n)};m.prototype.offPointerExit=function(n){this._pointerExitCallbacks.delete(n);return this};return m}(h(15).Interaction);f.Pointer=d},function(d,f,h){var k=h(20);f.memThunk=function(){for(var t=[],l=0;l<arguments.length;l++)t[l]=arguments[l];var p=t.slice(0,
-1),m=k.memoize(t[t.length-1]);return function(){var n=this,q=p.map(function(u){return u.apply(n)});return m.apply(void 0,q)}}},function(d,f,h){var k=h(49);f.memoize=function(t){function l(){for(var u=[],x=0;x<arguments.length;x++)u[x]=arguments[x];if(n)return m;x=k.signArray(u);void 0===p||p.isDifferent(x)?(q&&console.log("cache miss! computing"),p=x,m=t.apply(this,u)):q&&console.log("cache hit!");return m}var p=void 0,m,n=!1,q=!1;l.doLocked=function(u){if(n)throw Error("Locking an already locked memoize function!");
n=!0;u=u.apply(this);n=!1;return u};l.logPerformance=function(u){void 0===u&&(u=!0);q=u;return this};return l}},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var x in q)q.hasOwnProperty(x)&&(n[x]=q[x]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},t=h(3),l=h(0),p=h(27),m=h(2);d=function(n){function q(u){void 0===u&&(u="vertical");u=n.call(this,u)||this;u._clusterOffsets=new l.Map;return u}k(q,n);q.prototype._generateAttrToProjector=
function(){function u(){return y.rangeBand()}var x=this,A=n.prototype._generateAttrToProjector.call(this),y=this._makeInnerScale();A.width=this._isVertical?u:A.width;A.height=this._isVertical?A.height:u;var w=A.x,C=A.y;A.x=this._isVertical?function(G,D,B){return w(G,D,B)+x._clusterOffsets.get(B)}:function(G,D,B){return w(G,D,B)};A.y=this._isVertical?function(G,D,B){return C(G,D,B)}:function(G,D,B){return C(G,D,B)+x._clusterOffsets.get(B)};return A};q.prototype._updateClusterPosition=function(){var u=
this,x=this._makeInnerScale();this.datasets().forEach(function(A,y){return u._clusterOffsets.set(A,x.scale(String(y))-x.rangeBand()/2)})};q.prototype._makeInnerScale=function(){var u=new t.Category;u.domain(this.datasets().map(function(A,y){return String(y)}));var x=m.Plot._scaledAccessor(this.attr(p.Bar._BAR_THICKNESS_KEY));u.range([0,x(null,0,null)]);return u};q.prototype._getDataToDraw=function(){this._updateClusterPosition();return n.prototype._getDataToDraw.call(this)};return q}(p.Bar);f.ClusteredBar=
d},function(d,f,h){var k=this&&this.__extends||function(C,G){function D(){this.constructor=C}for(var B in G)G.hasOwnProperty(B)&&(C[B]=G[B]);C.prototype=null===G?Object.create(G):(D.prototype=G.prototype,new D)},t=h(1),l=h(5),p=h(7),m=h(8),n=h(3),q=h(0),u=h(44),x=h(45),A=h(6),y=h(35),w=h(2);d=function(C){function G(){var D=C.call(this)||this;D._startAngle=0;D._endAngle=2*Math.PI;D._labelFormatter=m.identity();D._labelsEnabled=!1;D.innerRadius(0);D.outerRadius(function(){var B=D._pieCenter();return Math.min(Math.max(D.width()-
B.x,B.x),Math.max(D.height()-B.y,B.y))});D.addClass("pie-plot");D.attr("fill",function(B,I){return String(I)},new n.Color);D._strokeDrawers=new q.Map;return D}k(G,C);G.prototype._setup=function(){var D=this;C.prototype._setup.call(this);this._strokeDrawers.forEach(function(B){return B.attachTo(D._renderArea)})};G.prototype.computeLayout=function(D,B,I){C.prototype.computeLayout.call(this,D,B,I);D=this._pieCenter();this._renderArea.attr("transform","translate("+D.x+","+D.y+")");D=Math.min(Math.max(this.width()-
D.x,D.x),Math.max(this.height()-D.y,D.y));null!=this.innerRadius().scale&&this.innerRadius().scale.range([0,D]);null!=this.outerRadius().scale&&this.outerRadius().scale.range([0,D]);return this};G.prototype.addDataset=function(D){C.prototype.addDataset.call(this,D)};G.prototype._addDataset=function(D){if(1===this.datasets().length)return q.Window.warn("Only one dataset is supported in Pie plots"),this;this._updatePieAngles();var B=new x.ArcOutlineSVGDrawer;this._isSetup&&B.attachTo(this._renderArea);
this._strokeDrawers.set(D,B);C.prototype._addDataset.call(this,D);return this};G.prototype.removeDataset=function(D){C.prototype.removeDataset.call(this,D)};G.prototype._removeDatasetNodes=function(D){C.prototype._removeDatasetNodes.call(this,D);this._strokeDrawers.get(D).remove()};G.prototype._removeDataset=function(D){C.prototype._removeDataset.call(this,D);this._startAngles=[];this._endAngles=[];return this};G.prototype.selections=function(D){var B=this;void 0===D&&(D=this.datasets());var I=C.prototype.selections.call(this,
D).nodes();D.forEach(function(N){N=B._strokeDrawers.get(N);null!=N&&I.push.apply(I,N.getVisualPrimitives())});return t.selectAll(I)};G.prototype._onDatasetUpdate=function(){C.prototype._onDatasetUpdate.call(this);this._updatePieAngles();this.render()};G.prototype._createDrawer=function(){return new A.ProxyDrawer(function(){return new u.ArcSVGDrawer},function(){y.warn("canvas renderer is not supported on Pie Plot!");return null})};G.prototype.entities=function(D){var B=this;void 0===D&&(D=this.datasets());
return C.prototype.entities.call(this,D).map(function(I){I.position.x+=B.width()/2;I.position.y+=B.height()/2;var N=t.select(B._strokeDrawers.get(I.dataset).getVisualPrimitiveAtIndex(I.index));I.strokeSelection=N;return I})};G.prototype.sectorValue=function(){return this._propertyBindings.get(G._SECTOR_VALUE_KEY)};G.prototype.innerRadius=function(D,B){if(null==D)return this._propertyBindings.get(G._INNER_RADIUS_KEY);this._bindProperty(G._INNER_RADIUS_KEY,D,B);this.render();return this};G.prototype.outerRadius=
function(D,B){if(null==D)return this._propertyBindings.get(G._OUTER_RADIUS_KEY);this._bindProperty(G._OUTER_RADIUS_KEY,D,B);this.render();return this};G.prototype.startAngle=function(D){if(null==D)return this._startAngle;this._startAngle=D;this._updatePieAngles();this.render();return this};G.prototype.endAngle=function(D){if(null==D)return this._endAngle;this._endAngle=D;this._updatePieAngles();this.render();return this};G.prototype.labelsEnabled=function(D){if(null==D)return this._labelsEnabled;
this._labelsEnabled=D;this.render();return this};G.prototype.labelFormatter=function(D){if(null==D)return this._labelFormatter;this._labelFormatter=D;this.render();return this};G.prototype.entitiesAt=function(D){var B=this.width()/2,I=this.height()/2;D=this._sliceIndexForPoint({x:D.x-B,y:D.y-I});return null==D?[]:[this.entities()[D]]};G.prototype._propertyProjectors=function(){var D=this,B=C.prototype._propertyProjectors.call(this),I=w.Plot._scaledAccessor(this.innerRadius()),N=w.Plot._scaledAccessor(this.outerRadius());
B.d=function(O,H,K){return t.arc().innerRadius(I(O,H,K)).outerRadius(N(O,H,K)).startAngle(D._startAngles[H]).endAngle(D._endAngles[H])(O,H)};return B};G.prototype._updatePieAngles=function(){if(null!=this.sectorValue()&&0!==this.datasets().length){var D=w.Plot._scaledAccessor(this.sectorValue()),B=this.datasets()[0],I=this._getDataToDraw().get(B);I=t.pie().sort(null).startAngle(this._startAngle).endAngle(this._endAngle).value(function(N,O){return D(N,O,B)})(I);this._startAngles=I.map(function(N){return N.startAngle});
this._endAngles=I.map(function(N){return N.endAngle})}};G.prototype._pieCenter=function(){var D=this._startAngle<this._endAngle?this._startAngle:this._endAngle,B=this._startAngle<this._endAngle?this._endAngle:this._startAngle,I=Math.sin(D);D=Math.cos(D);var N=Math.sin(B);B=Math.cos(B);var O;if(0<=I&&0<=N)if(0<=D&&0<=B){var H=D;var K=O=0;var M=N}else 0>D&&0>B?(H=0,O=-B,K=0,M=I):0<=D&&0>B?(H=D,O=-B,K=0,M=I):0>D&&0<=B&&(K=O=H=1,M=Math.max(I,N));else 0<=I&&0>N?0<=D&&0<=B?(H=Math.max(D,B),M=K=O=1):0>D&&
0>B?(H=0,O=1,K=-N,M=I):0<=D&&0>B?(H=D,O=1,K=-N,M=1):0>D&&0<=B&&(H=B,K=O=1,M=I):0>I&&0<=N?0<=D&&0<=B?(H=1,O=0,K=-I,M=N):0>D&&0>B?(H=1,O=Math.max(-D,-B),M=K=1):0<=D&&0>B?(H=1,O=-B,K=-I,M=1):0>D&&0<=B&&(H=1,O=-D,K=1,M=N):0>I&&0>N&&(0<=D&&0<=B?(H=B,O=0,K=-I,M=0):0>D&&0>B?(H=0,O=-D,K=-N,M=0):0<=D&&0>B?(O=H=1,K=Math.max(D,-B),M=1):0>D&&0<=B&&(H=B,O=-D,K=1,M=0));return{x:0==K+M?0:K/(K+M)*this.width(),y:0==H+O?0:H/(H+O)*this.height()}};G.prototype._getDataToDraw=function(){var D=C.prototype._getDataToDraw.call(this);
if(0===this.datasets().length)return D;var B=w.Plot._scaledAccessor(this.sectorValue()),I=this.datasets()[0],N=D.get(I).filter(function(O,H){return G._isValidData(B(O,H,I))});D.set(I,N);return D};G._isValidData=function(D){return q.Math.isValidNumber(D)&&0<=D};G.prototype._pixelPoint=function(D,B,I){var N=w.Plot._scaledAccessor(this.sectorValue());if(!G._isValidData(N(D,B,I)))return{x:NaN,y:NaN};var O=w.Plot._scaledAccessor(this.innerRadius())(D,B,I);D=w.Plot._scaledAccessor(this.outerRadius())(D,
B,I);O=(O+D)/2;D=t.pie().sort(null).value(function(H,K){H=N(H,K,I);return G._isValidData(H)?H:0}).startAngle(this._startAngle).endAngle(this._endAngle)(I.data());B=(D[B].startAngle+D[B].endAngle)/2;return{x:O*Math.sin(B),y:-O*Math.cos(B)}};G.prototype._additionalPaint=function(D){var B=this;this._renderArea.select(".label-area").remove();this._labelsEnabled&&q.Window.setTimeout(function(){return B._drawLabels()},D);var I=this._generateStrokeDrawSteps(),N=this._getDataToDraw();this.datasets().forEach(function(O){var H=
w.Plot.applyDrawSteps(I,O);B._strokeDrawers.get(O).draw(N.get(O),H)})};G.prototype._generateStrokeDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new p.Null}]};G.prototype._sliceIndexForPoint=function(D){var B=Math.sqrt(Math.pow(D.x,2)+Math.pow(D.y,2)),I=Math.acos(-D.y/B);0>D.x&&(I=2*Math.PI-I);for(D=0;D<this._startAngles.length;D++)if(this._startAngles[D]<I&&this._endAngles[D]>I){var N=D;break}if(void 0!==N){D=this.datasets()[0];var O=D.data()[N];I=this.innerRadius().accessor(O,
N,D);D=this.outerRadius().accessor(O,N,D);if(B>I&&B<D)return N}return null};G.prototype._drawLabels=function(){var D=this,B=this._getAttrToProjector(),I=this._renderArea.append("g").classed("label-area",!0),N=new l.SvgContext(I.node()),O=new l.CacheMeasurer(N),H=new l.Writer(O,N),K=this.datasets()[0];this._getDataToDraw().get(K).forEach(function(M,L){var Q=D.sectorValue().accessor(M,L,K);if(q.Math.isValidNumber(Q)){Q=D._labelFormatter(Q,M,L,K);var T=O.measure(Q),X=(D._endAngles[L]+D._startAngles[L])/
2,aa=D.outerRadius().accessor(M,L,K);D.outerRadius().scale&&(aa=D.outerRadius().scale.scale(aa));var la=D.innerRadius().accessor(M,L,K);D.innerRadius().scale&&(la=D.innerRadius().scale.scale(la));la=(aa+la)/2;aa=Math.sin(X)*la-T.width/2;la=-Math.cos(X)*la-T.height/2;var Z=[{x:aa,y:la},{x:aa,y:la+T.height},{x:aa+T.width,y:la},{x:aa+T.width,y:la+T.height}];(X=Z.every(function(ba){return Math.abs(ba.x)<=D.width()/2&&Math.abs(ba.y)<=D.height()/2}))&&(X=Z.map(function(ba){return D._sliceIndexForPoint(ba)}).every(function(ba){return ba===
L}));M=B.fill(M,L,K);M=1.6*q.Color.contrast("white",M)<q.Color.contrast("black",M);aa=I.append("g").attr("transform","translate("+aa+","+la+")");aa.classed(M?"dark-label":"light-label",!0);aa.style("visibility",X?"inherit":"hidden");H.write(Q,T.width,T.height,{xAlign:"center",yAlign:"center"},aa.node())}})};return G}(w.Plot);d._INNER_RADIUS_KEY="inner-radius";d._OUTER_RADIUS_KEY="outer-radius";d._SECTOR_VALUE_KEY="sector-value";f.Pie=d},function(d,f,h){var k=this&&this.__extends||function(y,w){function C(){this.constructor=
y}for(var G in w)w.hasOwnProperty(G)&&(y[G]=w[G]);y.prototype=null===w?Object.create(w):(C.prototype=w.prototype,new C)},t=h(1),l=h(5),p=h(7),m=h(14),n=h(6),q=h(34),u=h(3),x=h(0),A=h(2);d=function(y){function w(){var C=y.call(this)||this;C._labelsEnabled=!1;C._label=null;C.animator("rectangles",new p.Null);C.addClass("rectangle-plot");C.attr("fill",(new u.Color).range()[0]);return C}k(w,y);w.prototype._createDrawer=function(){return new n.ProxyDrawer(function(){return new q.RectangleSVGDrawer},function(C){return new m.RectangleCanvasDrawer(C)})};
w.prototype._generateAttrToProjector=function(){var C=this,G=y.prototype._generateAttrToProjector.call(this),D=A.Plot._scaledAccessor(this.x()),B=G[w._X2_KEY],I=A.Plot._scaledAccessor(this.y()),N=G[w._Y2_KEY],O=this.x().scale,H=this.y().scale;null!=B?(G.width=function(K,M,L){return Math.abs(B(K,M,L)-D(K,M,L))},G.x=function(K,M,L){return Math.min(B(K,M,L),D(K,M,L))}):(G.width=function(){return C._rectangleWidth(O)},G.x=function(K,M,L){return D(K,M,L)-.5*G.width(K,M,L)});null!=N?(G.height=function(K,
M,L){return Math.abs(N(K,M,L)-I(K,M,L))},G.y=function(K,M,L){return Math.max(N(K,M,L),I(K,M,L))-G.height(K,M,L)}):(G.height=function(){return C._rectangleWidth(H)},G.y=function(K,M,L){return I(K,M,L)-.5*G.height(K,M,L)});delete G[w._X2_KEY];delete G[w._Y2_KEY];return G};w.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:this._getAnimator("rectangles")}]};w.prototype._filterForProperty=function(C){return"x2"===C?y.prototype._filterForProperty.call(this,
"x"):"y2"===C?y.prototype._filterForProperty.call(this,"y"):y.prototype._filterForProperty.call(this,C)};w.prototype.x=function(C,G,D){if(null==C)return y.prototype.x.call(this);null==G?y.prototype.x.call(this,C):y.prototype.x.call(this,C,G,D);null!=G&&(D=(C=this.x2())&&C.accessor,null!=D&&this._bindProperty(w._X2_KEY,D,G,C.postScale));G instanceof u.Category&&G.innerPadding(0).outerPadding(0);return this};w.prototype.x2=function(){return this._propertyBindings.get(w._X2_KEY)};w.prototype.y=function(C,
G,D){if(null==C)return y.prototype.y.call(this);null==G?y.prototype.y.call(this,C):y.prototype.y.call(this,C,G,D);null!=G&&(D=(C=this.y2())&&C.accessor,null!=D&&this._bindProperty(w._Y2_KEY,D,G,C.postScale));G instanceof u.Category&&G.innerPadding(0).outerPadding(0);return this};w.prototype.y2=function(){return this._propertyBindings.get(w._Y2_KEY)};w.prototype.entitiesAt=function(C){var G=this._getAttrToProjector();return this.entities().filter(function(D){var B=D.datum,I=D.index,N=D.dataset;D=G.x(B,
I,N);var O=G.y(B,I,N),H=G.width(B,I,N);B=G.height(B,I,N);return D<=C.x&&C.x<=D+H&&O<=C.y&&C.y<=O+B})};w.prototype._entityBounds=function(C){return this._entityBBox(C.datum,C.index,C.dataset,this._getAttrToProjector())};w.prototype._entityBBox=function(C,G,D,B){return{x:B.x(C,G,D),y:B.y(C,G,D),width:B.width(C,G,D),height:B.height(C,G,D)}};w.prototype.label=function(C){if(null==C)return this._label;this._label=C;this.render();return this};w.prototype.labelsEnabled=function(C){if(null==C)return this._labelsEnabled;
this._labelsEnabled=C;this.render();return this};w.prototype._propertyProjectors=function(){var C=y.prototype._propertyProjectors.call(this);null!=this.x2()&&(C.x2=A.Plot._scaledAccessor(this.x2()));null!=this.y2()&&(C.y2=A.Plot._scaledAccessor(this.y2()));return C};w.prototype._pixelPoint=function(C,G,D){var B=this._getAttrToProjector(),I=B.x(C,G,D),N=B.y(C,G,D),O=B.width(C,G,D);C=B.height(C,G,D);return{x:I+O/2,y:N+C/2}};w.prototype._rectangleWidth=function(C){if(C instanceof u.Category)return C.rangeBand();
var G=C===this.x().scale?this.x().accessor:this.y().accessor,D=t.set(x.Array.flatten(this.datasets().map(function(N){return N.data().map(function(O,H){return G(O,H,N).valueOf()})}))).values().map(function(N){return+N}),B=x.Math.min(D,0);D=x.Math.max(D,0);var I=C.scale(B);return(C.scale(D)-I)/Math.abs(D-B)};w.prototype._getDataToDraw=function(){var C=new x.Map,G=this._getAttrToProjector();this.datasets().forEach(function(D){var B=D.data().map(function(I,N){return x.Math.isValidNumber(G.x(I,N,D))&&
x.Math.isValidNumber(G.y(I,N,D))&&x.Math.isValidNumber(G.width(I,N,D))&&x.Math.isValidNumber(G.height(I,N,D))?I:null});C.set(D,B)});return C};w.prototype._additionalPaint=function(C){var G=this;this._renderArea.selectAll(".label-area").remove();this._labelsEnabled&&null!=this.label()&&x.Window.setTimeout(function(){return G._drawLabels()},C)};w.prototype._drawLabels=function(){var C=this,G=this._getDataToDraw();this.datasets().forEach(function(D,B){return C._drawLabel(G,D,B)})};w.prototype._drawLabel=
function(C,G,D){var B=this,I=this._getAttrToProjector(),N=this._renderArea.append("g").classed("label-area",!0),O=new l.SvgContext(N.node()),H=new l.CacheMeasurer(O),K=new l.Writer(H,O);O=this.x().scale.range();var M=this.y().scale.range(),L=Math.min.apply(null,O),Q=Math.max.apply(null,O),T=Math.min.apply(null,M),X=Math.max.apply(null,M);C.get(G).forEach(function(aa,la){if(null!=aa){var Z=""+B.label()(aa,la,G),ba=H.measure(Z),ea=I.x(aa,la,G),ca=I.y(aa,la,G),ka=I.width(aa,la,G),Y=I.height(aa,la,G);
ba.height<=Y&&ba.width<=ka&&(Y=(Y-ba.height)/2,ea+=(ka-ba.width)/2,ca+=Y,ka={min:ea,max:ea+ba.width},Y={min:ca,max:ca+ba.height},ka.min<L||ka.max>Q||Y.min<T||Y.max>X||B._overlayLabel(ka,Y,la,D,C)||(aa=I.fill(aa,la,G),aa=1.6*x.Color.contrast("white",aa)<x.Color.contrast("black",aa),ea=N.append("g").attr("transform","translate("+ea+","+ca+")"),ea.classed(aa?"dark-label":"light-label",!0),K.write(Z,ba.width,ba.height,{xAlign:"center",yAlign:"center"},ea.node())))}})};w.prototype._overlayLabel=function(C,
G,D,B,I){for(var N=this._getAttrToProjector(),O=this.datasets(),H=B;H<O.length;H++)for(var K=O[H],M=I.get(K),L=H===B?D+1:0;L<M.length;L++)if(x.DOM.intersectsBBox(C,G,this._entityBBox(M[L],L,K,N)))return!0;return!1};return w}(h(16).XYPlot);d._X2_KEY="x2";d._Y2_KEY="y2";f.Rectangle=d},function(d,f,h){var k=this&&this.__extends||function(y,w){function C(){this.constructor=y}for(var G in w)w.hasOwnProperty(G)&&(y[G]=w[G]);y.prototype=null===w?Object.create(w):(C.prototype=w.prototype,new C)},t=h(31),
l=h(6),p=h(48),m=h(7),n=h(14),q=h(3),u=h(0),x=h(19),A=h(2);d=function(y){function w(){var C=y.call(this)||this;C.addClass("scatter-plot");var G=new m.Easing;G.startDelay(5);G.stepDuration(250);G.maxTotalDuration(A.Plot._ANIMATION_MAX_DURATION);C.animator(x.Animator.MAIN,G);C.attr("opacity",.6);C.attr("fill",(new q.Color).range()[0]);C.size(6);var D=t.circle();C.symbol(function(){return D});return C}k(w,y);w.prototype._buildLightweightPlotEntities=function(C){var G=this;return y.prototype._buildLightweightPlotEntities.call(this,
C).map(function(D){var B=A.Plot._scaledAccessor(G.size())(D.datum,D.index,D.dataset);D.diameter=B;return D})};w.prototype._createDrawer=function(C){var G=this;return new l.ProxyDrawer(function(){return new p.SymbolSVGDrawer},function(D){return new n.CanvasDrawer(D,p.makeSymbolCanvasDrawStep(C,function(){return A.Plot._scaledAccessor(G.symbol())},function(){return A.Plot._scaledAccessor(G.size())}))})};w.prototype.size=function(C,G){if(null==C)return this._propertyBindings.get(w._SIZE_KEY);this._bindProperty(w._SIZE_KEY,
C,G);this.render();return this};w.prototype.symbol=function(C){if(null==C)return this._propertyBindings.get(w._SYMBOL_KEY);this._propertyBindings.set(w._SYMBOL_KEY,{accessor:C});this.render();return this};w.prototype._generateDrawSteps=function(){var C=[];if(this._animateOnNextRender()){var G=this._getAttrToProjector(),D=A.Plot._scaledAccessor(this.symbol());G.d=function(B,I,N){return D(B,I,N)(0)(null)};C.push({attrToProjector:G,animator:this._getAnimator(x.Animator.RESET)})}C.push({attrToProjector:this._getAttrToProjector(),
animator:this._getAnimator(x.Animator.MAIN)});return C};w.prototype._propertyProjectors=function(){var C=y.prototype._propertyProjectors.call(this),G=A.Plot._scaledAccessor(this.x()),D=A.Plot._scaledAccessor(this.y());C.x=G;C.y=D;C.transform=function(B,I,N){return"translate("+G(B,I,N)+","+D(B,I,N)+")"};C.d=this._constructSymbolGenerator();return C};w.prototype._constructSymbolGenerator=function(){var C=A.Plot._scaledAccessor(this.symbol()),G=A.Plot._scaledAccessor(this.size());return function(D,B,
I){return C(D,B,I)(G(D,B,I))(null)}};w.prototype._entityBounds=function(C){return{x:C.position.x-C.diameter/2,y:C.position.y-C.diameter/2,width:C.diameter,height:C.diameter}};w.prototype._entityVisibleOnPlot=function(C,G){var D={min:G.topLeft.x,max:G.bottomRight.x};G={min:G.topLeft.y,max:G.bottomRight.y};C=this._entityBounds(C);return u.DOM.intersectsBBox(D,G,C)};w.prototype.entitiesAt=function(C){var G=A.Plot._scaledAccessor(this.x()),D=A.Plot._scaledAccessor(this.y()),B=A.Plot._scaledAccessor(this.size());
return this.entities().filter(function(I){var N=I.datum,O=I.index,H=I.dataset;I=G(N,O,H);var K=D(N,O,H);N=B(N,O,H);return I-N/2<=C.x&&C.x<=I+N/2&&K-N/2<=C.y&&C.y<=K+N/2})};return w}(h(16).XYPlot);d._SIZE_KEY="size";d._SYMBOL_KEY="symbol";f.Scatter=d},function(d,f,h){var k=this&&this.__extends||function(u,x){function A(){this.constructor=u}for(var y in x)x.hasOwnProperty(y)&&(u[y]=x[y]);u.prototype=null===x?Object.create(x):(A.prototype=x.prototype,new A)},t=h(7),l=h(6),p=h(47),m=h(3),n=h(35),q=h(2);
d=function(u){function x(){var A=u.call(this)||this;A.addClass("segment-plot");A.attr("stroke",(new m.Color).range()[0]);A.attr("stroke-width","2px");return A}k(x,u);x.prototype._createDrawer=function(){return new l.ProxyDrawer(function(){return new p.SegmentSVGDrawer},function(){n.warn("canvas renderer is not supported on Segment Plot!");return null})};x.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new t.Null}]};x.prototype._filterForProperty=
function(A){return"x2"===A?u.prototype._filterForProperty.call(this,"x"):"y2"===A?u.prototype._filterForProperty.call(this,"y"):u.prototype._filterForProperty.call(this,A)};x.prototype.x=function(A,y){if(null==A)return u.prototype.x.call(this);null==y?u.prototype.x.call(this,A):(u.prototype.x.call(this,A,y),A=(A=this.x2())&&A.accessor,null!=A&&this._bindProperty(x._X2_KEY,A,y));return this};x.prototype.x2=function(){return this._propertyBindings.get(x._X2_KEY)};x.prototype.y=function(A,y){if(null==
A)return u.prototype.y.call(this);null==y?u.prototype.y.call(this,A):(u.prototype.y.call(this,A,y),A=(A=this.y2())&&A.accessor,null!=A&&this._bindProperty(x._Y2_KEY,A,y));return this};x.prototype.y2=function(){return this._propertyBindings.get(x._Y2_KEY)};x.prototype._propertyProjectors=function(){var A=u.prototype._propertyProjectors.call(this);A.x1=q.Plot._scaledAccessor(this.x());A.x2=null==this.x2()?q.Plot._scaledAccessor(this.x()):q.Plot._scaledAccessor(this.x2());A.y1=q.Plot._scaledAccessor(this.y());
A.y2=null==this.y2()?q.Plot._scaledAccessor(this.y()):q.Plot._scaledAccessor(this.y2());return A};x.prototype.entitiesAt=function(A){A=this.entityNearest(A);return null!=A?[A]:[]};x.prototype.entitiesIn=function(A,y){if(null==y){var w={min:A.topLeft.x,max:A.bottomRight.x};A={min:A.topLeft.y,max:A.bottomRight.y}}else w=A,A=y;return this._entitiesIntersecting(w,A)};x.prototype._entitiesIntersecting=function(A,y){var w=this,C=[],G=this._getAttrToProjector();this.entities().forEach(function(D){w._lineIntersectsBox(D,
A,y,G)&&C.push(D)});return C};x.prototype._lineIntersectsBox=function(A,y,w,C){var G=this,D=C.x1(A.datum,A.index,A.dataset),B=C.x2(A.datum,A.index,A.dataset),I=C.y1(A.datum,A.index,A.dataset);A=C.y2(A.datum,A.index,A.dataset);if(y.min<=D&&D<=y.max&&w.min<=I&&I<=w.max||y.min<=B&&B<=y.max&&w.min<=A&&A<=w.max)return!0;var N={x:D,y:I},O={x:B,y:A},H=[{x:y.min,y:w.min},{x:y.min,y:w.max},{x:y.max,y:w.max},{x:y.max,y:w.min}];return 0<H.filter(function(K,M){return 0!==M?G._lineIntersectsSegment(N,O,K,H[M-
1])&&G._lineIntersectsSegment(K,H[M-1],N,O):!1}).length};x.prototype._lineIntersectsSegment=function(A,y,w,C){function G(D,B,I){return(B.x-D.x)*(I.y-B.y)-(B.y-D.y)*(I.x-B.x)}return 0>G(A,y,w)*G(A,y,C)};return x}(h(16).XYPlot);d._X2_KEY="x2";d._Y2_KEY="y2";f.Segment=d},function(d,f,h){var k=this&&this.__extends||function(q,u){function x(){this.constructor=q}for(var A in u)u.hasOwnProperty(A)&&(q[A]=u[A]);q.prototype=null===u?Object.create(u):(x.prototype=u.prototype,new x)},t=h(1),l=h(7),p=h(20),m=
h(0);d=h(50);var n=h(2);h=function(q){function u(){var x=q.call(this)||this;x._stackingResult=p.memThunk(function(){return x.datasets()},function(){return x.x().accessor},function(){return x.y().accessor},function(){return x._stackingOrder},function(A,y,w,C){return m.Stacking.stack(A,y,w,C)});x._stackedExtent=p.memThunk(x._stackingResult,function(){return x.x().accessor},function(){return x._filterForProperty("y")},function(A,y,w){return m.Stacking.stackedExtent(A,y,w)});x._baselineValue=0;x._stackingOrder=
"bottomup";x.addClass("stacked-area-plot");x.attr("fill-opacity",1);x._baselineValueProvider=function(){return[x._baselineValue]};x.croppedRenderingEnabled(!1);return x}k(u,q);u.prototype.croppedRenderingEnabled=function(x){return null==x?q.prototype.croppedRenderingEnabled.call(this):x?(m.Window.warn("Warning: Stacked Area Plot does not support cropped rendering."),this):q.prototype.croppedRenderingEnabled.call(this,x)};u.prototype._getAnimator=function(){return new l.Null};u.prototype._setup=function(){q.prototype._setup.call(this);
this._baseline=this._renderArea.append("line").classed("baseline",!0)};u.prototype.x=function(x,A){if(null==x)return q.prototype.x.call(this);null==A?q.prototype.x.call(this,x):q.prototype.x.call(this,x,A);this._checkSameDomain();return this};u.prototype.y=function(x,A){if(null==x)return q.prototype.y.call(this);null==A?q.prototype.y.call(this,x):q.prototype.y.call(this,x,A);this._checkSameDomain();return this};u.prototype.stackingOrder=function(x){if(null==x)return this._stackingOrder;this._stackingOrder=
x;this._onDatasetUpdate();return this};u.prototype.downsamplingEnabled=function(){return q.prototype.downsamplingEnabled.call(this)};u.prototype._additionalPaint=function(){var x=this.y().scale.scale(this._baselineValue);x={x1:0,y1:x,x2:this.width(),y2:x};this._getAnimator("baseline").animate(this._baseline,x)};u.prototype._updateYScale=function(){var x=this.y();x=x&&x.scale;null!=x&&(x.addPaddingExceptionsProvider(this._baselineValueProvider),x.addIncludedValuesProvider(this._baselineValueProvider))};
u.prototype._onDatasetUpdate=function(){this._checkSameDomain();q.prototype._onDatasetUpdate.call(this);return this};u.prototype.getExtentsForProperty=function(x){return"y"===x?[this._stackedExtent()]:q.prototype.getExtentsForProperty.call(this,x)};u.prototype._checkSameDomain=function(){if(this._projectorsReady()){var x=this.datasets(),A=this.x().accessor,y=x.map(function(C){return t.set(C.data().map(function(G,D){return A(G,D,C).toString()})).values()}),w=u._domainKeys(x,A);y.some(function(C){return C.length!==
w.length})&&m.Window.warn("the domains across the datasets are not the same. Plot may produce unintended behavior.")}};u._domainKeys=function(x,A){var y=t.set();x.forEach(function(w){w.data().forEach(function(C,G){y.add(A(C,G,w))})});return y.values()};u.prototype._propertyProjectors=function(){function x(D,B,I){return m.Stacking.normalizeKey(C(D,B,I))}var A=this,y=q.prototype._propertyProjectors.call(this),w=this.y().accessor,C=this.x().accessor,G=this._stackingResult();y.d=this._constructAreaProjector(n.Plot._scaledAccessor(this.x()),
function(D,B,I){return A.y().scale.scale(+w(D,B,I)+G.get(I).get(x(D,B,I)).offset)},function(D,B,I){return A.y().scale.scale(G.get(I).get(x(D,B,I)).offset)});return y};u.prototype._pixelPoint=function(x,A,y){var w=q.prototype._pixelPoint.call(this,x,A,y),C=this.x().accessor(x,A,y);x=this.y().accessor(x,A,y);y=this.y().scale.scale(+x+this._stackingResult().get(y).get(m.Stacking.normalizeKey(C)).offset);return{x:w.x,y}};return u}(d.Area);f.StackedArea=h},function(d,f,h){var k=this&&this.__extends||function(u,
x){function A(){this.constructor=u}for(var y in x)x.hasOwnProperty(y)&&(u[y]=x[y]);u.prototype=null===x?Object.create(x):(A.prototype=x.prototype,new A)},t=h(5),l=h(8),p=h(20),m=h(0),n=h(27),q=h(2);d=function(u){function x(A){void 0===A&&(A="vertical");var y=u.call(this,A)||this;y._extremaFormatter=l.identity();y._stackingResult=p.memThunk(function(){return y.datasets()},function(){return y.position().accessor},function(){return y.length().accessor},function(){return y._stackingOrder},function(w,
C,G,D){return m.Stacking.stack(w,C,G,D)});y._stackedExtent=p.memThunk(y._stackingResult,function(){return y.position().accessor},function(){return y._filterForProperty(y._isVertical?"y":"x")},function(w,C,G){return m.Stacking.stackedExtent(w,C,G)});y.addClass("stacked-bar-plot");y._stackingOrder="bottomup";return y}k(x,u);x.prototype.stackingOrder=function(A){if(null==A)return this._stackingOrder;this._stackingOrder=A;this._onDatasetUpdate();return this};x.prototype.extremaFormatter=function(A){if(0===
arguments.length)return this._extremaFormatter;this._extremaFormatter=A;this.render();return this};x.prototype._setup=function(){u.prototype._setup.call(this);this._labelArea=this._renderArea.append("g").classed(n.Bar._LABEL_AREA_CLASS,!0);var A=new t.SvgContext(this._labelArea.node());this._measurer=new t.CacheMeasurer(A);this._writer=new t.Writer(this._measurer,A)};x.prototype._drawLabels=function(){function A(O,H){var K=w._generateAttrToProjector(),M=w.width(),L=w.height();O.forEach(function(Q){if(Q.extent!==
C){var T=w.extremaFormatter()(Q.extent),X=w._measurer.measure(T),aa=Q.stackedDatum,la=aa.originalDatum,Z=aa.originalIndex;aa=aa.originalDataset;w._isDatumOnScreen(K,M,L,la,Z,aa)&&(la=q.Plot._scaledAccessor(w.attr(n.Bar._BAR_THICKNESS_KEY))(la,Z,aa),Z=D.scale(Q.extent),Q=w._getPositionAttr(G.scale(Q.axisValue),la)+la/2,Q=H(w._isVertical?{x:Q,y:Z}:{x:Z,y:Q},X,la),T=y(T,{topLeft:Q,bottomRight:{x:Q.x+X.width,y:Q.y+X.height}},la),N.push(T))}})}function y(O,H,K){var M=H.topLeft,L=M.x,Q=M.y;M=H.bottomRight.x-
H.topLeft.x;H=H.bottomRight.y-H.topLeft.y;K=w._isVertical?M>K:H>K;K||(L=w._labelArea.append("g").attr("transform","translate("+L+", "+Q+")"),L.classed("stacked-bar-label",!0),w._writer.write(O,M,H,{xAlign:"center",yAlign:"center"},L.node()));return K}var w=this;u.prototype._drawLabels.call(this);this._labelArea.selectAll("g").remove();var C=+this.baselineValue(),G=this.position().scale,D=this.length().scale,B=m.Stacking.stackedExtents(this._stackingResult()),I=B.minimumExtents,N=[];A(B.maximumExtents,
function(O,H){var K=w._isVertical?H.width:H.height;H=w._isVertical?H.height:H.width;return{x:w._isVertical?O.x-K/2:O.x+x._EXTREMA_LABEL_MARGIN_FROM_BAR,y:w._isVertical?O.y-H:O.y-K/2}});A(I,function(O,H){var K=w._isVertical?H.width:H.height;H=w._isVertical?H.height:H.width;return{x:w._isVertical?O.x-K/2:O.x-H,y:w._isVertical?O.y+x._EXTREMA_LABEL_MARGIN_FROM_BAR:O.y-K/2}});N.some(function(O){return O})&&this._labelArea.selectAll("g").remove()};x.prototype._generateAttrToProjector=function(){function A(M,
L,Q){return 0>+O(M,L,Q)?C(M,L,Q):w(M,L,Q)}function y(M,L,Q){return Math.abs(w(M,L,Q)-C(M,L,Q))}function w(M,L,Q){return N.scale(+O(M,L,Q)+K.get(Q).get(G(M,L,Q)).offset)}function C(M,L,Q){return N.scale(K.get(Q).get(G(M,L,Q)).offset)}function G(M,L,Q){return m.Stacking.normalizeKey(H(M,L,Q))}var D=this,B=u.prototype._generateAttrToProjector.call(this),I=this._isVertical?"y":"x",N=this.length().scale,O=this.length().accessor,H=this.position().accessor,K=this._stackingResult();B[this._isVertical?"height":
"width"]=y;B[I]=function(M,L,Q){return D._isVertical?A(M,L,Q):A(M,L,Q)-y(M,L,Q)};return B};x.prototype.getExtentsForProperty=function(A){return A===(this._isVertical?"y":"x")?[this._stackedExtent()]:u.prototype.getExtentsForProperty.call(this,A)};x.prototype.invalidateCache=function(){u.prototype.invalidateCache.call(this);this._measurer.reset()};return x}(n.Bar);d._EXTREMA_LABEL_MARGIN_FROM_BAR=5;f.StackedBar=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(0);d=h(27);var l=h(2);h=function(p){function m(){var n=p.call(this)||this;n._connectorsEnabled=!1;n.addClass("waterfall-plot");return n}k(m,p);m.prototype.connectorsEnabled=function(n){if(null==n)return this._connectorsEnabled;this._connectorsEnabled=n;return this};m.prototype.total=function(n){if(null==n)return this._propertyBindings.get(m._TOTAL_KEY);this._bindProperty(m._TOTAL_KEY,
n,null);return this};m.prototype._additionalPaint=function(n){var q=this;this._connectorArea.selectAll("line").remove();this._connectorsEnabled&&t.Window.setTimeout(function(){return q._drawConnectors()},n)};m.prototype._createNodesForDataset=function(n){n=p.prototype._createNodesForDataset.call(this,n);this._connectorArea=this._renderArea.append("g").classed(m._CONNECTOR_AREA_CLASS,!0);return n};m.prototype.getExtentsForProperty=function(n){return"y"===n?[this._extent]:p.prototype.getExtentsForProperty.call(this,
n)};m.prototype._generateAttrToProjector=function(){var n=this,q=p.prototype._generateAttrToProjector.call(this),u=this.y().scale,x=l.Plot._scaledAccessor(this.total());null==this.attr("y")&&(q.y=function(A,y,w){var C=n.y().accessor(A,y,w);if(x(A,y,w))return Math.min(u.scale(C),u.scale(0));A=n._subtotals[y];if(0===y)return 0>C?u.scale(A-C):u.scale(A);y=n._subtotals[y-1];return A>y?u.scale(A):u.scale(y)});null==this.attr("height")&&(q.height=function(A,y,w){var C=x(A,y,w);A=n.y().accessor(A,y,w);if(C)return Math.abs(u.scale(A)-
u.scale(0));C=n._subtotals[y];if(0===y)return Math.abs(u.scale(C)-u.scale(C-A));y=n._subtotals[y-1];return Math.abs(u.scale(C)-u.scale(y))});q["class"]=function(A,y,w){var C="";null!=n.attr("class")&&(C=n.attr("class").accessor(A,y,w)+" ");if(x(A,y,w))return C+m._BAR_TOTAL_CLASS;A=n.y().accessor(A,y,w);return C+(0<A?m._BAR_GROWTH_CLASS:m._BAR_DECLINE_CLASS)};return q};m.prototype._onDatasetUpdate=function(){this._updateSubtotals();p.prototype._onDatasetUpdate.call(this);return this};m.prototype._calculateSubtotalsAndExtent=
function(n){var q=this,u=Number.MAX_VALUE,x=Number.MIN_VALUE,A=0,y=!1;n.data().forEach(function(w,C){var G=q.y().accessor(w,C,n);(w=q.total().accessor(w,C,n))&&0!==C||(A+=G);q._subtotals.push(A);A<u&&(u=A);A>x&&(x=A);w&&(G<u&&(u=G),G>x&&(x=G));if(!y&&w){C=G-A;for(G=0;G<q._subtotals.length;G++)q._subtotals[G]+=C;y=!0;A+=C;u+=C;x+=C}});this._extent=[u,x]};m.prototype._drawConnectors=function(){for(var n=this._getAttrToProjector(),q=this.datasets()[0],u=1;u<q.data().length;u++){var x=u-1,A=q.data()[u],
y=q.data()[x];y=n.x(y,x,q);var w=n.x(A,u,q)+n.width(A,u,q),C=n.y(A,u,q);if(0<this._subtotals[u]&&this._subtotals[u]>this._subtotals[x]||0>this._subtotals[u]&&this._subtotals[u]>=this._subtotals[x])C=n.y(A,u,q)+n.height(A,u,q);this._connectorArea.append("line").classed(m._CONNECTOR_CLASS,!0).attr("x1",y).attr("x2",w).attr("y1",C).attr("y2",C)}};m.prototype._updateSubtotals=function(){var n=this.datasets();0<n.length&&(n=n[n.length-1],this._subtotals=[],this._calculateSubtotalsAndExtent(n))};return m}(d.Bar);
h._BAR_DECLINE_CLASS="waterfall-decline";h._BAR_GROWTH_CLASS="waterfall-growth";h._BAR_TOTAL_CLASS="waterfall-total";h._CONNECTOR_CLASS="connector";h._CONNECTOR_AREA_CLASS="connector-area";h._TOTAL_KEY="total";f.Waterfall=h},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(1),l=h(0);d=function(p){function m(n){var q=p.call(this)||this;
switch(n){case null:case void 0:null==m._plottableColorCache&&(m._plottableColorCache=m._getPlottableColors());n=t.scaleOrdinal().range(m._plottableColorCache);break;case "Category10":case "category10":case "10":n=t.scaleOrdinal(t.schemeCategory10);break;case "Category20":case "category20":case "20":n=t.scaleOrdinal(t.schemeCategory20);break;case "Category20b":case "category20b":case "20b":n=t.scaleOrdinal(t.schemeCategory20b);break;case "Category20c":case "category20c":case "20c":n=t.scaleOrdinal(t.schemeCategory20c);
break;default:throw Error("Unsupported ColorScale type");}q._d3Scale=n;return q}k(m,p);m.prototype.extentOfValues=function(n){return l.Array.uniq(n)};m.prototype._getExtent=function(){return l.Array.uniq(this._getAllIncludedValues())};m.invalidateColorCache=function(){m._plottableColorCache=null};m._getPlottableColors=function(){for(var n=[],q=t.select("body").append("plottable-color-tester"),u=l.Color.colorTest(q,""),x=0,A=l.Color.colorTest(q,"plottable-colors-0");null!=A&&x<this._MAXIMUM_COLORS_FROM_CSS&&
(A!==u||A!==n[n.length-1]);)n.push(A),x++,A=l.Color.colorTest(q,"plottable-colors-"+x);q.remove();return n};m.prototype.scale=function(n){var q=this._d3Scale(n);n=this.domain().indexOf(n);n=Math.floor(n/this.range().length);return l.Color.lightenColor(q,Math.log(n*m._LOOP_LIGHTEN_FACTOR+1))};m.prototype._getDomain=function(){return this._backingScaleDomain()};m.prototype._backingScaleDomain=function(n){if(null==n)return this._d3Scale.domain();this._d3Scale.domain(n);return this};m.prototype._getRange=
function(){return this._d3Scale.range()};m.prototype._setRange=function(n){this._d3Scale.range(n)};return m}(h(17).Scale);d._LOOP_LIGHTEN_FACTOR=1.6;d._MAXIMUM_COLORS_FROM_CSS=256;f.Color=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(1),l=h(0);d=function(p){function m(n){void 0===n&&(n="linear");var q=p.call(this)||this;switch(n){case "linear":q._colorScale=
t.scaleLinear();break;case "log":q._colorScale=t.scaleLog();break;case "sqrt":q._colorScale=t.scaleSqrt();break;case "pow":q._colorScale=t.scalePow()}if(null==q._colorScale)throw Error("unknown QuantitativeScale scale type "+n);q.range(m.REDS);return q}k(m,p);m.prototype.extentOfValues=function(n){n=t.extent(n);return null==n[0]||null==n[1]?[]:n};m.prototype._d3InterpolatedScale=function(){return this._colorScale.range([0,1]).interpolate(this._interpolateColors())};m.prototype._interpolateColors=
function(){var n=this._colorRange;if(2>n.length)throw Error("Color scale arrays must have at least two elements.");return function(){return function(q){q=Math.max(0,Math.min(1,q));q*=n.length-1;var u=Math.floor(q),x=q-u;return t.interpolateLab(n[u],n[Math.ceil(q)])(x)}}};m.prototype._resetScale=function(){this._d3Scale=this._d3InterpolatedScale();this._autoDomainIfAutomaticMode();this._dispatchUpdate()};m.prototype.autoDomain=function(){var n=this._getAllIncludedValues();0<n.length&&this._setDomain([l.Math.min(n,
0),l.Math.max(n,0)])};m.prototype.scale=function(n){return this._d3Scale(n)};m.prototype._getDomain=function(){return this._backingScaleDomain()};m.prototype._backingScaleDomain=function(n){if(null==n)return this._d3Scale.domain();this._d3Scale.domain(n);return this};m.prototype._getRange=function(){return this._colorRange};m.prototype._setRange=function(n){this._colorRange=n;this._resetScale()};return m}(h(17).Scale);d.REDS="#FFFFFF #FFF6E1 #FEF4C0 #FED976 #FEB24C #FD8D3C #FC4E2A #E31A1C #B10026".split(" ");
d.BLUES="#FFFFFF #CCFFFF #A5FFFD #85F7FB #6ED3EF #55A7E0 #417FD0 #2545D3 #0B02E1".split(" ");d.POSNEG="#0B02E1 #2545D3 #417FD0 #55A7E0 #6ED3EF #85F7FB #A5FFFD #CCFFFF #FFFFFF #FFF6E1 #FEF4C0 #FED976 #FEB24C #FD8D3C #FC4E2A #E31A1C #B10026".split(" ");f.InterpolatedColor=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},t=h(1);d=function(l){function p(){var m=
l.call(this)||this;m._d3Scale=t.scaleLinear();return m}k(p,l);p.prototype._defaultExtent=function(){return[0,1]};p.prototype._expandSingleValueDomain=function(m){return m[0]===m[1]?[m[0]-1,m[1]+1]:m};p.prototype.scale=function(m){return this._d3Scale(m)};p.prototype.scaleTransformation=function(m){return this.scale(m)};p.prototype.invertedTransformation=function(m){return this.invert(m)};p.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)};p.prototype.getTransformationDomain=
function(){return this.domain()};p.prototype.setTransformationDomain=function(m){this.domain(m)};p.prototype._getDomain=function(){return this._backingScaleDomain()};p.prototype._backingScaleDomain=function(m){if(null==m)return this._d3Scale.domain();this._d3Scale.domain(m);return this};p.prototype._getRange=function(){return this._d3Scale.range()};p.prototype._setRange=function(m){this._d3Scale.range(m)};p.prototype.invert=function(m){return this._d3Scale.invert(m)};p.prototype.defaultTicks=function(){return this._d3Scale.ticks()};
p.prototype._niceDomain=function(m,n){return this._d3Scale.copy().domain(m).nice(n).domain()};return p}(h(11).QuantitativeScale);f.Linear=d},function(d,f,h){var k=this&&this.__extends||function(m,n){function q(){this.constructor=m}for(var u in n)n.hasOwnProperty(u)&&(m[u]=n[u]);m.prototype=null===n?Object.create(n):(q.prototype=n.prototype,new q)},t=h(1),l=h(0),p=h(3);d=function(m){function n(q){void 0===q&&(q=10);var u=m.call(this)||this;u._d3Scale=t.scaleLinear();u._base=q;u._pivot=u._base;u._setDomain(u._defaultExtent());
if(1>=q)throw Error("ModifiedLogScale: The base must be \x3e 1");return u}k(n,m);n.prototype._adjustedLog=function(q){var u=0>q?-1:1;q*=u;q<this._pivot&&(q+=(this._pivot-q)/this._pivot);q=Math.log(q)/Math.log(this._base);return q*u};n.prototype._invertedAdjustedLog=function(q){var u=0>q?-1:1;q=Math.pow(this._base,q*u);q<this._pivot&&(q=this._pivot*(q-1)/(this._pivot-1));return q*u};n.prototype.scale=function(q){return this._d3Scale(this._adjustedLog(q))};n.prototype.invert=function(q){return this._invertedAdjustedLog(this._d3Scale.invert(q))};
n.prototype.scaleTransformation=function(q){return this.scale(q)};n.prototype.invertedTransformation=function(q){return this.invert(q)};n.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)};n.prototype.getTransformationDomain=function(){return this.domain()};n.prototype.setTransformationDomain=function(q){this.domain(q)};n.prototype._getDomain=function(){return this._untransformedDomain};n.prototype._setDomain=function(q){this._untransformedDomain=q;m.prototype._setDomain.call(this,
[this._adjustedLog(q[0]),this._adjustedLog(q[1])])};n.prototype._backingScaleDomain=function(q){if(null==q)return this._d3Scale.domain();this._d3Scale.domain(q);return this};n.prototype.ticks=function(){function q(G,D,B){return[G,D,B].sort(function(I,N){return I-N})[1]}var u=l.Math.min(this._untransformedDomain,0),x=l.Math.max(this._untransformedDomain,0),A=q(u,x,-this._pivot),y=q(u,x,this._pivot);A=this._logTicks(-A,-u).map(function(G){return-G}).reverse();y=this._logTicks(y,x);var w=Math.max(u,
-this._pivot),C=Math.min(x,this._pivot);w=t.scaleLinear().domain([w,C]).ticks(this._howManyTicks(w,C));A=A.concat(w).concat(y);1>=A.length&&(A=t.scaleLinear().domain([u,x]).ticks());return A};n.prototype._logTicks=function(q,u){var x=this,A=this._howManyTicks(q,u);if(0===A)return[];var y=Math.floor(Math.log(q)/Math.log(this._base)),w=Math.ceil(Math.log(u)/Math.log(this._base));A=t.range(w,y,-Math.ceil((w-y)/A));y=t.range(this._base,1,-(this._base-1)).map(Math.floor);var C=l.Array.uniq(y);A=A.map(function(G){return C.map(function(D){return Math.pow(x._base,
G-1)*D})});return l.Array.flatten(A).filter(function(G){return q<=G&&G<=u}).sort(function(G,D){return G-D})};n.prototype._howManyTicks=function(q,u){var x=this._adjustedLog(l.Math.min(this._untransformedDomain,0)),A=this._adjustedLog(l.Math.max(this._untransformedDomain,0));return Math.ceil((this._adjustedLog(u)-this._adjustedLog(q))/(A-x)*p.ModifiedLog._DEFAULT_NUM_TICKS)};n.prototype._niceDomain=function(q){return q};n.prototype._defaultExtent=function(){return[0,this._base]};n.prototype._expandSingleValueDomain=
function(q){return q[0]===q[1]?(q=q[0],0<q?[q/this._base,q*this._base]:0===q?[-this._base,this._base]:[q*this._base,q/this._base]):q};n.prototype._getRange=function(){return this._d3Scale.range()};n.prototype._setRange=function(q){this._d3Scale.range(q)};n.prototype.defaultTicks=function(){return this._d3Scale.ticks()};return n}(h(11).QuantitativeScale);f.ModifiedLog=d},function(d,f,h){var k=h(0);f.intervalTickGenerator=function(t){if(0>=t)throw Error("interval must be positive number");return function(l){l=
l.domain();var p=Math.min(l[0],l[1]);l=Math.max(l[0],l[1]);var m=Math.ceil(p/t)*t;p=0===p%t?[]:[p];var n=k.Math.range(0,Math.floor((l-m)/t)+1).map(function(q){return m+q*t});return p.concat(n).concat(0===l%t?[]:[l])}};f.integerTickGenerator=function(){return function(t){var l=t.defaultTicks();return l.filter(function(p,m){return 0===p%1||0===m||m===l.length-1})}}},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);
p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(1),l=h(28);d=function(p){function m(){var n=p.call(this)||this;n._d3Scale=t.scaleTime();n.autoDomain();return n}k(m,p);m.prototype.tickInterval=function(n,q){void 0===q&&(q=1);var u=t.scaleTime();m.timeIntervalToD3Time(n).every(q);u.domain(this.domain());u.range(this.range());return u.ticks()};m.prototype._setDomain=function(n){if(n[1]<n[0])throw Error("Scale.Time domain values must be in chronological order");return p.prototype._setDomain.call(this,
n)};m.prototype._defaultExtent=function(){return[new Date("1970-01-01"),new Date("1970-01-02")]};m.prototype._expandSingleValueDomain=function(n){var q=n[0].getTime(),u=n[1].getTime();return q===u?(n=new Date(q),n.setDate(n.getDate()-1),u=new Date(u),u.setDate(u.getDate()+1),[n,u]):n};m.prototype.scale=function(n){return this._d3Scale(n)};m.prototype.scaleTransformation=function(n){return this.scale(new Date(n))};m.prototype.invertedTransformation=function(n){return this.invert(n).getTime()};m.prototype.getTransformationExtent=
function(){var n=this._getUnboundedExtent(!0);return[n[0].valueOf(),n[1].valueOf()]};m.prototype.getTransformationDomain=function(){var n=this.domain();return[n[0].valueOf(),n[1].valueOf()]};m.prototype.setTransformationDomain=function(n){this.domain([new Date(n[0]),new Date(n[1])])};m.prototype._getDomain=function(){return this._backingScaleDomain()};m.prototype._backingScaleDomain=function(n){if(null==n)return this._d3Scale.domain();this._d3Scale.domain(n);return this};m.prototype._getRange=function(){return this._d3Scale.range()};
m.prototype._setRange=function(n){this._d3Scale.range(n)};m.prototype.invert=function(n){return this._d3Scale.invert(n)};m.prototype.defaultTicks=function(){return this._d3Scale.ticks()};m.prototype._niceDomain=function(n){return this._d3Scale.copy().domain(n).nice().domain()};m.timeIntervalToD3Time=function(n){switch(n){case l.TimeInterval.second:return t.timeSecond;case l.TimeInterval.minute:return t.timeMinute;case l.TimeInterval.hour:return t.timeHour;case l.TimeInterval.day:return t.timeDay;
case l.TimeInterval.week:return t.timeWeek;case l.TimeInterval.month:return t.timeMonth;case l.TimeInterval.year:return t.timeYear;default:throw Error("TimeInterval specified does not exist: "+n);}};return m}(h(11).QuantitativeScale);f.Time=d},function(d,f,h){var k=h(1),t=Array;f.add=function(l,p){if(l.length!==p.length)throw Error("attempted to add arrays of unequal length");return l.map(function(m,n){return l[n]+p[n]})};f.uniq=function(l){var p=k.set(),m=[];l.forEach(function(n){p.has(String(n))||
(p.add(String(n)),m.push(n))});return m};f.flatten=function(l){return t.prototype.concat.apply([],l)};f.createFilledArray=function(l,p){for(var m=[],n=0;n<p;n++)m[n]="function"===typeof l?l(n):l;return m}},function(d,f){d=function(){function h(k,t,l){this.maxIndex=this.minIndex=this.exitIndex=this.entryIndex=k;this.bucketValue=t;this.maxValue=this.minValue=l}h.prototype.isInBucket=function(k){return k==this.bucketValue};h.prototype.addToBucket=function(k,t){k<this.minValue&&(this.minValue=k,this.minIndex=
t);k>this.maxValue&&(this.maxValue=k,this.maxIndex=t);this.exitIndex=t};h.prototype.getUniqueIndices=function(){var k=[this.entryIndex,this.maxIndex,this.minIndex,this.exitIndex];return k.filter(function(t,l){return 0==l||t!=k[l-1]})};return h}();f.Bucket=d},function(d,f,h){var k=this&&this.__extends||function(t,l){function p(){this.constructor=t}for(var m in l)l.hasOwnProperty(m)&&(t[m]=l[m]);t.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(t){function l(){return null!==
t&&t.apply(this,arguments)||this}k(l,t);l.prototype.callCallbacks=function(){for(var p=this,m=[],n=0;n<arguments.length;n++)m[n]=arguments[n];this.forEach(function(q){q.apply(p,m)});return this};return l}(h(58).Set);f.CallbackSet=d},function(d,f,h){function k(p){function m(u){u/=255;return.03928>=u?u/12.92:l.pow((u+.055)/1.055,2.4)}var n=t.rgb(p);p=m(n.r);var q=m(n.g);n=m(n.b);return.2126*p+.7152*q+.0722*n}var t=h(1),l=Math;f.contrast=function(p,m){p=k(p)+.05;m=k(m)+.05;return p>m?p/m:m/p};f.lightenColor=
function(p,m){return t.color(p).brighter(m).rgb().toString()};f.colorTest=function(p,m){p.classed(m,!0);var n=p.style("background-color");if("transparent"===n)return null;n=/\((.+)\)/.exec(n);if(!n)return null;n=n[1].split(",").map(function(q){q=+q;var u=q.toString(16);return 16>q?"0"+u:u});if(4===n.length&&"00"===n[3])return null;n="#"+n.join("");p.classed(m,!1);return n}},function(d,f,h){var k=h(1),t=h(57);d=function(){function l(){this._entities=[];this._rtree=new t.RTree;this._tree=k.quadtree().x(function(p){return Math.floor(p.position.x)}).y(function(p){return Math.floor(p.position.y)})}
l.prototype.addAll=function(p,m,n){(x=this._entities).push.apply(x,p);if(void 0!==n)for(n=t.RTreeBounds.bounds(n),x=0;x<p.length;x++){var q=p[x],u=t.RTreeBounds.entityBounds(m(q));t.RTreeBounds.isBoundsOverlapBounds(n,u)&&(this._tree.add(q),this._rtree.insert(u,q))}else for(this._tree.addAll(p),x=0;x<p.length;x++)q=p[x],u=t.RTreeBounds.entityBounds(m(q)),this._rtree.insert(u,q);var x};l.prototype.entityNearest=function(p){return this._tree.find(p.x,p.y)};l.prototype.entitiesInBounds=function(p){return this._rtree.intersect(t.RTreeBounds.entityBounds(p))};
l.prototype.entitiesInXBounds=function(p){return this._rtree.intersectX(t.RTreeBounds.entityBounds(p))};l.prototype.entitiesInYBounds=function(p){return this._rtree.intersectY(t.RTreeBounds.entityBounds(p))};l.prototype.entities=function(){return this._entities};return l}();f.EntityStore=d},function(d,f,h){var k=h(56);d=function(){function t(){"function"===typeof window.Map?this._es6Map=new window.Map:this._keyValuePairs=[]}t.prototype.set=function(l,p){if(k.isNaN(l))throw Error("NaN may not be used as a key to the Map");
if(null!=this._es6Map)return this._es6Map.set(l,p),this;for(var m=0;m<this._keyValuePairs.length;m++)if(this._keyValuePairs[m].key===l)return this._keyValuePairs[m].value=p,this;this._keyValuePairs.push({key:l,value:p});return this};t.prototype.get=function(l){if(null!=this._es6Map)return this._es6Map.get(l);for(var p=0;p<this._keyValuePairs.length;p++)if(this._keyValuePairs[p].key===l)return this._keyValuePairs[p].value};t.prototype.has=function(l){if(null!=this._es6Map)return this._es6Map.has(l);
for(var p=0;p<this._keyValuePairs.length;p++)if(this._keyValuePairs[p].key===l)return!0;return!1};t.prototype.forEach=function(l,p){var m=this;null!=this._es6Map?this._es6Map.forEach(function(n,q){return l.call(p,n,q,m)},p):this._keyValuePairs.forEach(function(n){l.call(p,n.value,n.key,m)})};t.prototype.delete=function(l){if(null!=this._es6Map)return this._es6Map.delete(l);for(var p=0;p<this._keyValuePairs.length;p++)if(this._keyValuePairs[p].key===l)return this._keyValuePairs.splice(p,1),!0;return!1};
return t}();f.Map=d},function(d,f){f.assign=function(){for(var h=[],k=0;k<arguments.length;k++)h[k]=arguments[k];k={};for(var t=0;t<h.length;t++)for(var l=h[t],p=0,m=Object.keys(l);p<m.length;p++){var n=m[p];k[n]=l[n]}return k}},function(d,f){d=function(){function h(){}h.prototype.split=function(k,t){for(var l=Math.ceil(k.length/2),p=0;p<l;p++)t[0].insert(k[p]);for(p=l;p<k.length;p++)t[1].insert(k[p])};return h}();f.SplitStrategyTrivial=d;d=function(){function h(){}h.prototype.split=function(k,t){k=
k.slice();for(this.chooseFirstSplit(k,t);0<k.length;)this.addNext(k,t)};h.prototype.chooseFirstSplit=function(k,t){for(var l=0,p=0,m=k.length-1,n=k.length-1,q=1;q<k.length-1;q++){var u=k[q];u.bounds.xl>k[m].bounds.xl?m=q:u.bounds.xh<k[l].bounds.xh&&(l=q);u.bounds.yl>k[n].bounds.yl?n=q:u.bounds.yh<k[p].bounds.yh&&(p=q)}p=Math.abs(k[l].bounds.xh-k[m].bounds.xl)>Math.abs(k[p].bounds.yh-k[n].bounds.yl)?[l,m]:[p,n];l=p[0];p=p[1];l===p&&(l=0,p=k.length-1);t[0].insert(k.splice(Math.max(l,p),1)[0]);t[1].insert(k.splice(Math.min(l,
p),1)[0])};h.prototype.addNext=function(k,t){for(var l=null,p=null,m=null,n=0;n<k.length;n++){var q=k[n],u=t[0].unionAreaDifference(q.bounds);q=t[1].unionAreaDifference(q.bounds);if(u<p||null==l)l=n,p=u,m=t[0];q<p&&(l=n,p=q,m=t[1])}m.insert(k.splice(l,1)[0])};return h}();f.SplitStrategyLinear=d},function(d,f,h){function k(m){return String(m)}var t=h(1),l=h(0);d=h(10);f.IStackingOrder=d.makeEnum(["topdown","bottomup"]);var p=Math;f.stack=function(m,n,q,u){void 0===u&&(u="bottomup");var x=t.map(),A=
t.map(),y=new l.Map;"topdown"===u&&(m=m.slice(),m.reverse());m.forEach(function(w){var C=new l.Map;w.data().forEach(function(G,D){var B=k(n(G,D,w)),I=+q(G,D,w),N=0<=I?x:A;if(N.has(B)){var O=N.get(B);N.set(B,O+I)}else O=0,N.set(B,I);C.set(B,{offset:O,value:I,axisValue:n(G,D,w),originalDatum:G,originalDataset:w,originalIndex:D})});y.set(w,C)});return y};f.stackedExtents=function(m){var n=new l.Map,q=new l.Map;m.forEach(function(u){u.forEach(function(x,A){var y=l.Math.max([x.offset+x.value,x.offset],
x.offset),w=l.Math.min([x.offset+x.value,x.offset],x.offset),C=x.axisValue;n.has(A)?n.get(A).extent<y&&n.set(A,{extent:y,axisValue:C,stackedDatum:x}):n.set(A,{extent:y,axisValue:C,stackedDatum:x});q.has(A)?q.get(A).extent>w&&q.set(A,{extent:w,axisValue:C,stackedDatum:x}):q.set(A,{extent:w,axisValue:C,stackedDatum:x})})});return{maximumExtents:n,minimumExtents:q}};f.stackedExtent=function(m,n,q){var u=[];m.forEach(function(A,y){y.data().forEach(function(w,C){if(null==q||q(w,C,y))w=A.get(k(n(w,C,y))),
u.push(w.value+w.offset)})});m=l.Math.max(u,0);var x=l.Math.min(u,0);return[p.min(x,0),p.max(0,m)]};f.normalizeKey=k},function(d,f,h){var k=h(0);f.getTranslator=function(l){l=l.root().rootElement().node();var p=l.__Plottable_ClientTranslator;null==p&&(p=new t(l),l.__Plottable_ClientTranslator=p);return p};var t=function(){function l(p){this._rootElement=p}l.prototype.computePosition=function(p,m){p={x:p,y:m};m=k.Math.getCumulativeTransform(this._rootElement);return null==m?p:k.Math.applyTransform(m,
p)};l.isEventInside=function(p,m){return k.DOM.contains(p.root().rootElement().node(),m.target)};return l}();f.Translator=t},function(d,f,h){Object.defineProperty(f,"__esModule",{value:!0});var k=h(124);h.d(f,"easeLinear",function(){return k.a});var t=h(126);h.d(f,"easeQuad",function(){return t.a});h.d(f,"easeQuadIn",function(){return t.b});h.d(f,"easeQuadOut",function(){return t.c});h.d(f,"easeQuadInOut",function(){return t.a});var l=h(121);h.d(f,"easeCubic",function(){return l.a});h.d(f,"easeCubicIn",
function(){return l.b});h.d(f,"easeCubicOut",function(){return l.c});h.d(f,"easeCubicInOut",function(){return l.a});var p=h(125);h.d(f,"easePoly",function(){return p.a});h.d(f,"easePolyIn",function(){return p.b});h.d(f,"easePolyOut",function(){return p.c});h.d(f,"easePolyInOut",function(){return p.a});var m=h(127);h.d(f,"easeSin",function(){return m.a});h.d(f,"easeSinIn",function(){return m.b});h.d(f,"easeSinOut",function(){return m.c});h.d(f,"easeSinInOut",function(){return m.a});var n=h(123);h.d(f,
"easeExp",function(){return n.a});h.d(f,"easeExpIn",function(){return n.b});h.d(f,"easeExpOut",function(){return n.c});h.d(f,"easeExpInOut",function(){return n.a});var q=h(120);h.d(f,"easeCircle",function(){return q.a});h.d(f,"easeCircleIn",function(){return q.b});h.d(f,"easeCircleOut",function(){return q.c});h.d(f,"easeCircleInOut",function(){return q.a});var u=h(119);h.d(f,"easeBounce",function(){return u.a});h.d(f,"easeBounceIn",function(){return u.b});h.d(f,"easeBounceOut",function(){return u.a});
h.d(f,"easeBounceInOut",function(){return u.c});var x=h(118);h.d(f,"easeBack",function(){return x.a});h.d(f,"easeBackIn",function(){return x.b});h.d(f,"easeBackOut",function(){return x.c});h.d(f,"easeBackInOut",function(){return x.a});var A=h(122);h.d(f,"easeElastic",function(){return A.a});h.d(f,"easeElasticIn",function(){return A.b});h.d(f,"easeElasticOut",function(){return A.a});h.d(f,"easeElasticInOut",function(){return A.c})},function(d,f,h){h.d(f,"b",function(){return k});h.d(f,"c",function(){return t});
h.d(f,"a",function(){return l});var k=function n(m){function q(u){return u*u*((m+1)*u-m)}m=+m;q.overshoot=n;return q}(1.70158),t=function q(n){function u(x){return--x*x*((n+1)*x+n)+1}n=+n;u.overshoot=q;return u}(1.70158),l=function u(q){function x(A){return(1>(A*=2)?A*A*((q+1)*A-q):(A-=2)*A*((q+1)*A+q)+2)/2}q=+q;x.overshoot=u;return x}(1.70158)},function(d,f){function h(y){return(y=+y)<k?A*y*y:y<l?A*(y-=t)*y+p:y<n?A*(y-=m)*y+q:A*(y-=u)*y+x}f.b=function(y){return 1-h(1-y)};f.a=h;f.c=function(y){return(1>=
(y*=2)?1-h(1-y):h(y-1)+1)/2};var k=4/11,t=6/11,l=8/11,p=.75,m=9/11,n=10/11,q=.9375,u=21/22,x=.984375,A=1/k/k},function(d,f){f.b=function(h){return 1-Math.sqrt(1-h*h)};f.c=function(h){return Math.sqrt(1- --h*h)};f.a=function(h){return(1>=(h*=2)?1-Math.sqrt(1-h*h):Math.sqrt(1-(h-=2)*h)+1)/2}},function(d,f){f.b=function(h){return h*h*h};f.c=function(h){return--h*h*h+1};f.a=function(h){return(1>=(h*=2)?h*h*h:(h-=2)*h*h+2)/2}},function(d,f,h){h.d(f,"b",function(){return t});h.d(f,"a",function(){return l});
h.d(f,"c",function(){return p});var k=2*Math.PI,t=function u(n,q){function x(y){return n*Math.pow(2,10*--y)*Math.sin((A-y)/q)}var A=Math.asin(1/(n=Math.max(1,n)))*(q/=k);x.amplitude=function(y){return u(y,q*k)};x.period=function(y){return u(n,y)};return x}(1,.3),l=function x(q,u){function A(w){return 1-q*Math.pow(2,-10*(w=+w))*Math.sin((w+y)/u)}var y=Math.asin(1/(q=Math.max(1,q)))*(u/=k);A.amplitude=function(w){return x(w,u*k)};A.period=function(w){return x(q,w)};return A}(1,.3),p=function A(u,x){function y(C){return(0>
(C=2*C-1)?u*Math.pow(2,10*C)*Math.sin((w-C)/x):2-u*Math.pow(2,-10*C)*Math.sin((w+C)/x))/2}var w=Math.asin(1/(u=Math.max(1,u)))*(x/=k);y.amplitude=function(C){return A(C,x*k)};y.period=function(C){return A(u,C)};return y}(1,.3)},function(d,f){f.b=function(h){return Math.pow(2,10*h-10)};f.c=function(h){return 1-Math.pow(2,-10*h)};f.a=function(h){return(1>=(h*=2)?Math.pow(2,10*h-10):2-Math.pow(2,10-10*h))/2}},function(d,f){f.a=function(h){return+h}},function(d,f,h){h.d(f,"b",function(){return k});h.d(f,
"c",function(){return t});h.d(f,"a",function(){return l});var k=function n(m){function q(u){return Math.pow(u,m)}m=+m;q.exponent=n;return q}(3),t=function q(n){function u(x){return 1-Math.pow(1-x,n)}n=+n;u.exponent=q;return u}(3),l=function u(q){function x(A){return(1>=(A*=2)?Math.pow(A,q):2-Math.pow(2-A,q))/2}q=+q;x.exponent=u;return x}(3)},function(d,f){f.b=function(h){return h*h};f.c=function(h){return h*(2-h)};f.a=function(h){return(1>=(h*=2)?h*h:--h*(2-h)+1)/2}},function(d,f){f.b=function(t){return 1-
Math.cos(t*k)};f.c=function(t){return Math.sin(t*k)};f.a=function(t){return(1-Math.cos(h*t))/2};var h=Math.PI,k=h/2},function(d,f,h){function k(l){return!0===t(l)&&"[object Object]"===Object.prototype.toString.call(l)}var t=h(129);d.exports=function(l){if(!1===k(l))return!1;l=l.constructor;if("function"!==typeof l)return!1;l=l.prototype;return!1===k(l)||!1===l.hasOwnProperty("isPrototypeOf")?!1:!0}},function(d){d.exports=function(f){return null!=f&&"object"===typeof f&&!1===Array.isArray(f)}},function(d,
f){d=function(){function h(k,t,l){void 0===t&&(t=10);void 0===l&&(l={});var p=this;this.ctx=k;this.lineHeight=t;this.style=l;this.createRuler=function(){return function(m){p.ctx.font=p.style.font;return{width:p.ctx.measureText(m).width,height:p.lineHeight}}};this.createPen=function(m,n,q){null==q&&(q=p.ctx);q.save();q.translate(n.translate[0],n.translate[1]);q.rotate(n.rotate*Math.PI/180);return p.createCanvasPen(q)};void 0===this.style.fill&&(this.style.fill="#444")}h.prototype.createCanvasPen=function(k){var t=
this;return{destroy:function(){k.restore()},write:function(l,p,m,n){k.textAlign=p;null!=t.style.font&&(k.font=t.style.font);null!=t.style.fill&&(k.fillStyle=t.style.fill,k.fillText(l,m,n));null!=t.style.stroke&&(k.strokeStyle=t.style.fill,k.strokeText(l,m,n))}}};return h}();f.CanvasContext=d},function(d,f){var h=function(){function k(){}k.append=function(t,l){for(var p=[],m=2;m<arguments.length;m++)p[m-2]=arguments[m];p=k.create.apply(k,[l].concat(p));t.appendChild(p);return p};k.create=function(t){for(var l=
[],p=1;p<arguments.length;p++)l[p-1]=arguments[p];p=document.createElementNS(k.SVG_NS,t);k.addClasses.apply(k,[p].concat(l));return p};k.addClasses=function(t){for(var l=[],p=1;p<arguments.length;p++)l[p-1]=arguments[p];l=l.filter(function(m){return null!=m});null!=t.classList?l.forEach(function(m){t.classList.add(m)}):t.setAttribute("class",l.join(" "))};k.getDimensions=function(t){if(t.getBBox)try{var l=t.getBBox();return{width:l.width,height:l.height}}catch(p){}return{height:0,width:0}};return k}();
h.SVG_NS="http://www.w3.org/2000/svg";f.SvgUtils=h;d=function(){function k(t,l,p){void 0===p&&(p=!1);var m=this;this.element=t;this.className=l;this.addTitleElement=p;this.createRuler=function(){var n=m.getTextElements(m.element),q=n.parentElement,u=n.containerElement,x=n.textElement;return function(A){q.appendChild(u);x.textContent=A;A=h.getDimensions(x);q.removeChild(u);return A}};this.createPen=function(n,q,u){null==u&&(u=m.element);u=h.append(u,"g","text-container",m.className);m.addTitleElement&&
(h.append(u,"title").textContent=n,u.setAttribute("title",n));n=h.append(u,"g","text-area");n.setAttribute("transform","translate("+q.translate[0]+","+q.translate[1]+")rotate("+(q.rotate+")"));return m.createSvgLinePen(n)}}k.prototype.setAddTitleElement=function(t){this.addTitleElement=t};k.prototype.createSvgLinePen=function(t){return{write:function(l,p,m,n){var q=h.append(t,"text","text-line");q.textContent=l;q.setAttribute("text-anchor",p);q.setAttribute("transform","translate("+m+","+n+")");q.setAttribute("y",
"-0.25em")}}};k.prototype.getTextElements=function(t){if("text"===t.tagName){var l=t.parentElement;null==l&&(l=t.parentNode);l.removeChild(t);return{containerElement:t,parentElement:l,textElement:t}}var p=t.querySelector("text");if(null!=p)return l=t.parentElement,null==l&&(l=t.parentNode),l.removeChild(t),{containerElement:t,parentElement:l,textElement:p};l=h.create("text",this.className);return{containerElement:l,parentElement:t,textElement:l}};return k}();f.SvgContext=d},function(d,f,h){var k=
this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},t=h(21),l=h(36);d=function(p){function m(n){var q=p.call(this,n)||this;q.dimCache=new t.Cache(function(u){return q._measureNotFromCache(u)});return q}k(m,p);m.prototype._measureNotFromCache=function(n){return p.prototype.measure.call(this,n)};m.prototype.measure=function(n){void 0===n&&(n=l.AbstractMeasurer.HEIGHT_TEXT);
return this.dimCache.get(n)};m.prototype.reset=function(){this.dimCache.clear();p.prototype.reset.call(this)};return m}(h(60).CacheCharacterMeasurer);f.CacheMeasurer=d},function(d,f,h){var k=h(59),t=h(62),l=h(64),p=h(66);d=function(){function m(n){this.context=n;this.measurer=new t.CacheMeasurer(this.context);this.wrapper=new l.Wrapper;this.writer=new p.Writer(this.measurer,this.context,this.wrapper)}m.svg=function(n,q,u){return new m(new k.SvgContext(n,q,u))};m.canvas=function(n,q,u){return new m(new k.CanvasContext(n,
q,u))};m.prototype.write=function(n,q,u,x,A){this.writer.write(n,q,u,x,A)};m.prototype.clearMeasurerCache=function(){this.measurer.reset()};return m}();f.Typesetter=d},function(d,f){d=function(){function h(k){this.cache={};this.compute=k}h.prototype.get=function(k){this.cache.hasOwnProperty(k)||(this.cache[k]=this.compute(k));return this.cache[k]};h.prototype.clear=function(){this.cache={};return this};return h}();f.Cache=d},function(d,f){f.Methods=function(){function h(){}h.arrayEq=function(k,t){if(null==
k||null==t)return k===t;if(k.length!==t.length)return!1;for(var l=0;l<k.length;l++)if(k[l]!==t[l])return!1;return!0};h.objEq=function(k,t){if(null==k||null==t)return k===t;var l=Object.keys(k).sort(),p=Object.keys(t).sort(),m=l.map(function(q){return k[q]}),n=p.map(function(q){return t[q]});return h.arrayEq(l,p)&&h.arrayEq(m,n)};h.strictEq=function(k,t){return k===t};h.defaults=function(k){for(var t=[],l=1;l<arguments.length;l++)t[l-1]=arguments[l];if(null==k)throw new TypeError("Cannot convert undefined or null to object");
var p=Object(k);t.forEach(function(m){if(null!=m)for(var n in m)Object.prototype.hasOwnProperty.call(m,n)&&(p[n]=m[n])});return p};return h}()},function(d,f){f.StringMethods=function(){function h(){}h.combineWhitespace=function(k){return k.replace(/[ \t]+/g," ")};h.isNotEmptyString=function(k){return k&&""!==k.trim()};h.trimStart=function(k,t){if(!k)return k;k=k.split("");var l=t?function(p){return p.split(t).some(h.isNotEmptyString)}:h.isNotEmptyString;return k.reduce(function(p,m){return l(p+m)?
p+m:p},"")};h.trimEnd=function(k,t){if(!k)return k;k=k.split("");k.reverse();k=h.trimStart(k.join(""),t).split("");k.reverse();return k.join("")};return h}()},function(d,f){d=function(){function h(){this.WordDividerRegExp=/\W/;this.WhitespaceRegExp=/\s/}h.prototype.tokenize=function(k){var t=this;return k.split("").reduce(function(l,p){return l.slice(0,-1).concat(t.shouldCreateNewToken(l[l.length-1],p))},[""])};h.prototype.shouldCreateNewToken=function(k,t){if(!k)return[t];var l=k[k.length-1];return this.WhitespaceRegExp.test(l)&&
this.WhitespaceRegExp.test(t)?[k+t]:this.WhitespaceRegExp.test(l)||this.WhitespaceRegExp.test(t)?[k,t]:this.WordDividerRegExp.test(l)||this.WordDividerRegExp.test(t)?l===t?[k+t]:[k,t]:[k+t]};return h}();f.Tokenizer=d},function(d,f,h){var k=this&&this.__extends||function(t,l){function p(){this.constructor=t}for(var m in l)l.hasOwnProperty(m)&&(t[m]=l[m]);t.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(t){function l(){return t.apply(this,arguments)||this}k(l,t);l.prototype.wrap=
function(p,m,n,q){function u(D){return t.prototype.wrap.call(x,p,m,D,q)}var x=this;void 0===q&&(q=Infinity);if(1<p.split("\n").length)throw Error("SingleLineWrapper is designed to work only on single line");var A=u(n);if(2>A.noLines)return A;for(var y=0,w=0;w<l.NO_WRAP_ITERATIONS&&n>y;++w){var C=(n+y)/2,G=u(C);this.areSameResults(A,G)?(n=C,A=G):y=C}return A};l.prototype.areSameResults=function(p,m){return p.noLines===m.noLines&&p.truncatedText===m.truncatedText};return l}(h(65).Wrapper);d.NO_WRAP_ITERATIONS=
5;f.SingleLineWrapper=d},function(d,f,h){var k=h(21),t={textRotation:0,textShear:0,xAlign:"left",yAlign:"top"};d=function(){function l(p,m,n){this._measurer=p;this._penFactory=m;this._wrapper=n}l.prototype.measurer=function(p){this._measurer=p;return this};l.prototype.wrapper=function(p){this._wrapper=p;return this};l.prototype.penFactory=function(p){this._penFactory=p;return this};l.prototype.write=function(p,m,n,q,u){void 0===q&&(q={});q=k.Methods.defaults({},t,q);if(-1===l.SupportedRotation.indexOf(q.textRotation))throw Error("unsupported rotation - "+
q.textRotation+". Supported rotations are "+l.SupportedRotation.join(", "));if(null!=q.textShear&&-80>q.textShear||80<q.textShear)throw Error("unsupported shear angle - "+q.textShear+". Must be between -80 and 80");var x=45<Math.abs(Math.abs(q.textRotation)-90),A=x?m:n,y=x?n:m,w=q.textShear,C=w*Math.PI/180;x=this._measurer.measure().height;var G=x*Math.tan(C);A=A/Math.cos(C)-Math.abs(G);var D=y*Math.cos(C);y=k.StringMethods.combineWhitespace(p);y=(this._wrapper?this._wrapper.wrap(y,this._measurer,
A,D).wrappedText:y).split("\n");C=l.XOffsetFactor[q.xAlign]*A*Math.sin(C)-l.YOffsetFactor[q.yAlign]*(D-y.length*x);w=q.textRotation+w;switch(q.textRotation){case 90:m=[m+C,0];break;case -90:m=[-C,n];break;case 180:m=[m,n+C];break;default:m=[0,-C]}p=this._penFactory.createPen(p,{translate:m,rotate:w},u);this.writeLines(y,p,A,x,G,q.xAlign);null!=p.destroy&&p.destroy()};l.prototype.writeLines=function(p,m,n,q,u,x){p.forEach(function(A,y){m.write(A,l.AnchorConverter[x],(0<u?(y+1)*u:y*u)+n*l.XOffsetFactor[x],
(y+1)*q)})};return l}();d.SupportedRotation=[-90,0,180,90];d.AnchorConverter={center:"middle",left:"start",right:"end"};d.XOffsetFactor={center:.5,left:0,right:1};d.YOffsetFactor={bottom:1,center:.5,top:0};f.Writer=d},function(d,f,h){function k(t){for(var l in t)f.hasOwnProperty(l)||(f[l]=t[l])}h(69);d=h(7);f.Animators=d;d=h(67);f.Axes=d;d=h(37);f.Components=d;d=h(23);f.Configs=d;d=h(8);f.Formatters=d;d=h(30);f.RenderController=d;d=h(39);f.RenderPolicies=d;d=h(31);f.SymbolFactories=d;d=h(13);f.Dispatchers=
d;d=h(14);f.Drawers=d;d=h(25);f.Interactions=d;d=h(19);f.Plots=d;d=h(3);f.Scales=d;d=h(0);f.Utils=d;k(h(22));d=h(28);f.TimeInterval=d.TimeInterval;k(h(4));k(h(29));k(h(38));d=h(68);f.version=d.version;k(h(24));k(h(6));k(h(15));k(h(40));k(h(16));k(h(2));k(h(11));k(h(17))}])});

//# sourceURL=build://vz-chart-helpers/plottable-interactions.js
var rg;
(function(b){function d(m){const n=[];for(;m&&m instanceof HTMLElement;)if(n.push(m),m.assignedSlot)m=m.assignedSlot;else if(m.parentElement)m=m.parentElement;else{const q=m.parentNode;m=q instanceof DocumentFragment?q.host:q!==m?q:null}return n}function f(m){var n=d(m);m=h;let q=null;for(const x of n){n=Plottable.Utils.DOM.getElementTransform(x);if(null!=n){var u=x.clientWidth/2;const A=x.clientHeight/2;m=Plottable.Utils.Math.multiplyTranslate(m,[u,A]);m=Plottable.Utils.Math.multiplyMatrix(m,Plottable.Utils.Math.invertMatrix(n));
m=Plottable.Utils.Math.multiplyTranslate(m,[-u,-A])}n=x.scrollLeft;u=x.scrollTop;if(null===q||x===q)n-=x.offsetLeft+x.clientLeft,u-=x.offsetTop+x.clientTop,q=x.offsetParent;m=Plottable.Utils.Math.multiplyTranslate(m,[n,u])}return m}const h=[1,0,0,1,0,0];class k extends Plottable.Utils.Translator{computePosition(m,n){m={x:m,y:n};n=f(this._rootElement);return null==n?m:Plottable.Utils.Math.applyTransform(n,m)}}class t extends Plottable.Dispatchers.Mouse{constructor(m){super(m);this._eventTarget=m.root().rootElement().node();
this._translator=new k(m.root().rootElement().node())}static getDispatcher(m){const n=m.root().rootElement();let q=n[t._DISPATCHER_KEY];q||(q=new t(m),n[t._DISPATCHER_KEY]=q);return q}}class l extends Plottable.Dispatchers.Touch{constructor(m){super(m);this._eventTarget=m.root().rootElement().node();this._translator=new k(m.root().rootElement().node())}static getDispatcher(m){const n=m.root().rootElement();let q=n[l._DISPATCHER_KEY];q||(q=new l(m),n[l._DISPATCHER_KEY]=q);return q}}Plottable.Interaction.prototype._isInsideComponent=
function(m){return 0<=m.x&&0<=m.y&&m.x<this._componentAttachedTo.width()&&m.y<this._componentAttachedTo.height()};class p extends Plottable.Interactions.Pointer{_anchor(){this._isAnchored=!0;this._mouseDispatcher=t.getDispatcher(this._componentAttachedTo);this._mouseDispatcher.onMouseMove(this._mouseMoveCallback);this._touchDispatcher=l.getDispatcher(this._componentAttachedTo);this._touchDispatcher.onTouchStart(this._touchStartCallback)}}b.PointerInteraction=p})(rg||(rg={}));

//# sourceURL=build://vz-chart-helpers/vz-chart-helpers.js
(function(b){function d(){let t=new Plottable.Scales.Linear;t.tickGenerator();let l=new Plottable.Axes.Numeric(t,"bottom");l.formatter(b.stepFormatter);return{scale:t,axis:l,accessor:p=>p.step}}function f(){let t=new Plottable.Scales.Time;return{scale:t,axis:new Plottable.Axes.Time(t,"bottom"),accessor:l=>l.wall_time}}function h(){let t=new Plottable.Scales.Linear;return{scale:t,axis:new Plottable.Axes.Numeric(t,"bottom"),accessor:b.relativeAccessor}}b.SYMBOLS_LIST=[{character:"\u25fc",method:Plottable.SymbolFactories.square},
{character:"\u25c6",method:Plottable.SymbolFactories.diamond},{character:"\u25b2",method:Plottable.SymbolFactories.triangle},{character:"\u2605",method:Plottable.SymbolFactories.star},{character:"\u271a",method:Plottable.SymbolFactories.cross}];let k;(function(t){t.STEP="step";t.RELATIVE="relative";t.WALL_TIME="wall_time"})(k=b.XType||(b.XType={}));b.Y_TOOLTIP_FORMATTER_PRECISION=4;b.STEP_FORMATTER_PRECISION=4;b.Y_AXIS_FORMATTER_PRECISION=3;b.TOOLTIP_Y_PIXEL_OFFSET=20;b.TOOLTIP_CIRCLE_SIZE=4;b.NAN_SYMBOL_SIZE=
6;b.multiscaleFormatter=function(t){return l=>{let p=Math.abs(l);1E-15>p&&(p=0);return(1E4<=p?d3.format("."+t+"~e"):0<p&&.01>p?d3.format("."+t+"~e"):d3.format("."+t+"~g"))(l)}};b.computeDomain=function(t,l){t=t.filter(n=>isFinite(n));if(0===t.length)return[-.1,1.1];l?(t=_.sortBy(t),l=d3.quantile(t,.05),t=d3.quantile(t,.95)):(l=d3.min(t),t=d3.max(t));let p,m=t-l;p=0===m?1.1*Math.abs(l)+1.1:.2*m;l=[0<=l&&l<m?-.1*t:l-p,t+p];return l=d3.scaleLinear().domain(l).nice().domain()};b.accessorize=function(t){return l=>
l[t]};b.stepFormatter=d3.format(`.${b.STEP_FORMATTER_PRECISION}~s`);b.stepX=d;b.timeFormatter=Plottable.Formatters.time("%a %b %e, %H:%M:%S");b.wallX=f;b.relativeAccessor=(t,l,p)=>{if(null!=t.relative)return t.relative;l=p.data();return(+t.wall_time-(0<l.length?+l[0].wall_time:0))/36E5};b.relativeFormatter=t=>{let l="",p=Math.floor(t/24);t-=24*p;p&&(l+=p+"d ");let m=Math.floor(t);t=60*(t-m);if(m||p)l+=m+"h ";let n=Math.floor(t);t=60*(t-n);if(n||m||p)l+=n+"m ";return l+Math.floor(t)+"s"};b.relativeX=
h;b.getXComponents=function(t){switch(t){case k.STEP:return d();case k.WALL_TIME:return f();case k.RELATIVE:return h();default:throw Error("invalid xType: "+t);}}})(rg||(rg={}));

//# sourceURL=build://vz-chart-helpers/vz-chart-tooltip.js
var Eh;
(function(b){let d;(function(h){h.AUTO="auto";h.BOTTOM="bottom";h.RIGHT="right"})(d=b.TooltipPosition||(b.TooltipPosition={}));const f={boxShadow:"0 1px 4px rgba(0, 0, 0, .3)",opacity:0,position:"fixed",willChange:"transform",zIndex:5};Polymer({is:"vz-chart-tooltip",_template:null,properties:{contentComponentName:String,position:{type:String,value:d.AUTO},minDistFromEdge:{type:Number,value:15}},ready(){this._tunnel=this._raf=this._styleCache=null},attached(){this._tunnel=this._createTunnel();this._hideOnBlur=
()=>{document.hidden&&this.hide()};window.addEventListener("visibilitychange",this._hideOnBlur)},detached(){this.hide();this._removeTunnel(this._tunnel);this._tunnel=null;window.removeEventListener("visibilitychange",this._hideOnBlur)},content(){return this._tunnel.shadowRoot},hide(){window.cancelAnimationFrame(this._raf);this._styleCache=null;this._tunnel.style.opacity=0},updateAndPosition(h){window.cancelAnimationFrame(this._raf);this._raf=window.requestAnimationFrame(()=>{this.isAttached&&this._repositionImpl(h)})},
_repositionImpl(h){const k=this._tunnel;h=h.getBoundingClientRect();const t=k.getBoundingClientRect(),l=window.innerHeight,p=document.body.clientWidth,m=h.top,n=m+h.height,q=t.height+rg.TOOLTIP_Y_PIXEL_OFFSET;let u=null,x=Math.max(this.minDistFromEdge,h.left),A=null,y=m;this.position==d.RIGHT?x=h.right:(y=n+rg.TOOLTIP_Y_PIXEL_OFFSET,p<x+t.width+this.minDistFromEdge&&(x=null,A=this.minDistFromEdge));this.position==d.AUTO&&0<h.top-q&&l<h.top+h.height+q&&(y=null,u=l-m+rg.TOOLTIP_Y_PIXEL_OFFSET);h={opacity:1,
left:x?`${x}px`:null,right:A?`${A}px`:null,top:y?`${y}px`:null,bottom:u?`${u}px`:null};_.isEqual(this._styleCache,h)||(Object.assign(k.style,h),this._styleCache=h)},_createTunnel(){if(!this.contentComponentName)throw new RangeError("Require `contentComponentName` to be a name of a Polymer component");const h=document.createElement(this.contentComponentName);Object.assign(h.style,f);document.body.appendChild(h);return h},_removeTunnel(h){document.body.removeChild(h)}})})(Eh||(Eh={}));

//# sourceURL=build://vz-line-chart/dragZoomInteraction.js
var Fh;
(function(b){class d extends Plottable.Components.SelectionBoxLayer{constructor(f,h,k){super();this.easeFn=d3.easeCubicInOut;this._animationTime=750;this.xScale(f);this.yScale(h);this._dragInteraction=new Plottable.Interactions.Drag;this._doubleClickInteraction=new Plottable.Interactions.Click;this.setupCallbacks();this.unzoomMethod=k;this.onDetach(()=>{this._doubleClickInteraction.detachFrom();this._dragInteraction.detachFrom()});this.onAnchor(()=>{this._doubleClickInteraction.attachTo(this);this._dragInteraction.attachTo(this)})}interactionStart(f){this.onStart=
f}interactionEnd(f){this.onEnd=f}dragInteraction(){return this._dragInteraction}setupCallbacks(){let f=!1;this._dragInteraction.onDragStart(h=>{this.bounds({topLeft:h,bottomRight:h});this.onStart()});this._dragInteraction.onDrag((h,k)=>{this.bounds({topLeft:h,bottomRight:k});this.boxVisible(!0);f=!0});this._dragInteraction.onDragEnd((h,k)=>{this.boxVisible(!1);this.bounds({topLeft:h,bottomRight:k});if(f)this.zoom();else this.onEnd();f=!1});this._doubleClickInteraction.onDoubleClick(this.unzoom.bind(this))}animationTime(f){if(null==
f)return this._animationTime;if(0>f)throw Error("animationTime cannot be negative");this._animationTime=f;return this}ease(f){if("function"!==typeof f)throw Error("ease function must be a function");0===f(0)&&1===f(1)||Plottable.Utils.Window.warn("Easing function does not maintain invariant f(0)\x3d\x3d0 \x26\x26 f(1)\x3d\x3d1. Bad behavior may result.");this.easeFn=f;return this}zoom(){let f=this.xExtent()[0].valueOf(),h=this.xExtent()[1].valueOf(),k=this.yExtent()[1].valueOf(),t=this.yExtent()[0].valueOf();
f!==h&&k!==t&&this.interpolateZoom(f,h,k,t)}unzoom(){var f=this.xScale();f._domainMin=null;f._domainMax=null;f=f._getExtent();this.xScale().domain(f);this.unzoomMethod()}isZooming(f){this._dragInteraction.enabled(!f);this._doubleClickInteraction.enabled(!f)}interpolateZoom(f,h,k,t){let l=this.xScale().domain()[0].valueOf(),p=this.xScale().domain()[1].valueOf(),m=this.yScale().domain()[0].valueOf(),n=this.yScale().domain()[1].valueOf(),q=this.easeFn,u=(y,w,C)=>d3.interpolateNumber(y,w)(q(C));this.isZooming(!0);
let x=Date.now(),A=()=>{var y=Date.now()-x;y=0===this._animationTime?1:Math.min(1,y/this._animationTime);let w=u(l,f,y),C=u(p,h,y),G=u(m,k,y),D=u(n,t,y);this.xScale().domain([w,C]);this.yScale().domain([G,D]);1>y?Plottable.Utils.DOM.requestAnimationFramePolyfill(A):(this.onEnd(),this.isZooming(!1))};A()}}b.DragZoomLayer=d})(Fh||(Fh={}));

//# sourceURL=build://vz-line-chart2/panZoomDragLayer.js
var Gh;
(function(b){let d;(function(h){h[h.NONE=0]="NONE";h[h.DRAG_ZOOMING=1]="DRAG_ZOOMING";h[h.PANNING=2]="PANNING"})(d||(d={}));class f extends Plottable.Components.Group{constructor(h,k,t){super();this.state=d.NONE;this.panStartCallback=new Plottable.Utils.CallbackSet;this.panEndCallback=new Plottable.Utils.CallbackSet;this.panZoom=new Plottable.Interactions.PanZoom(h,k);this.panZoom.dragInteraction().mouseFilter(p=>f.isPanKey(p)&&0===p.button);this.panZoom.wheelFilter(this.canScrollZoom);this.dragZoomLayer=new Fh.DragZoomLayer(h,
k,t);this.dragZoomLayer.dragInteraction().mouseFilter(p=>!f.isPanKey(p)&&0===p.button);this.append(this.dragZoomLayer);const l=this.onWheel.bind(this);this.onAnchor(()=>{this._mouseDispatcher=Plottable.Dispatchers.Mouse.getDispatcher(this);this._mouseDispatcher.onWheel(l);this.panZoom.attachTo(this)});this.onDetach(()=>{this.panZoom.detachFrom();this._mouseDispatcher&&(this._mouseDispatcher.offWheel(l),this._mouseDispatcher=null)});this.panZoom.dragInteraction().onDragStart(()=>{this.state==d.NONE&&
this.setState(d.PANNING)});this.panZoom.dragInteraction().onDragEnd(()=>{this.state==d.PANNING&&this.setState(d.NONE)});this.dragZoomLayer.dragInteraction().onDragStart(()=>{this.state==d.NONE&&this.setState(d.DRAG_ZOOMING)});this.dragZoomLayer.dragInteraction().onDragEnd(()=>{this.state==d.DRAG_ZOOMING&&this.setState(d.NONE)})}onWheel(h,k){if(!this.canScrollZoom(k)&&(h=this.element(),h.select(".help").empty())){var t=h.append("div").classed("help",!0);t.append("span").text("Alt + Scroll to Zoom");
t.on("animationend",()=>void t.remove())}}static isPanKey(h){return!!h.altKey||!!h.shiftKey}canScrollZoom(h){return h.altKey}setState(h){if(this.state!=h){var k=this.state;this.state=h;this.root().removeClass(this.stateClassName(k));this.root().addClass(this.stateClassName(h));k==d.PANNING&&this.panEndCallback.callCallbacks();h==d.PANNING&&this.panStartCallback.callCallbacks()}}stateClassName(h){switch(h){case d.PANNING:return"panning";case d.DRAG_ZOOMING:return"drag-zooming";default:return""}}onPanStart(h){this.panStartCallback.add(h)}onPanEnd(h){this.panEndCallback.add(h)}onScrollZoom(h){this.panZoom.onZoomEnd(h)}onDragZoomStart(h){this.dragZoomLayer.interactionStart(h)}onDragZoomEnd(h){this.dragZoomLayer.interactionEnd(h)}}
b.PanZoomDragLayer=f})(Gh||(Gh={}));

//# sourceURL=build://vz-line-chart2/tf-scale.js
(function(b){class d extends Plottable.QuantitativeScale{constructor(){super(...arguments);this._ignoreOutlier=!1}setValueProviderForDomain(f){this._valueProviderForDomain=f}ignoreOutlier(f){return"boolean"==typeof f?(this._ignoreOutlier=f,this):this._ignoreOutlier}_getAllIncludedValues(){const f=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(f)}}b.TfScale=d})(Gh||(Gh={}));

//# sourceURL=build://vz-line-chart2/linear-scale.js
(function(b){class d extends Plottable.Scales.Linear{constructor(){super();this._ignoreOutlier=!1;this.padProportion(.2)}setValueProviderForDomain(f){this._valueProviderForDomain=f}_niceDomain(f,h){const [k,t]=f,l=t-k;f=0===l?1.1*Math.abs(k)+1.1:l*this.padProportion();return super._niceDomain([0<=k&&k<l?-.1*t:k-f,t+f],h)}_getUnboundedExtent(f){f=this._getAllIncludedValues(f);let h=this._defaultExtent();0!==f.length&&(f=[Plottable.Utils.Math.min(f,h[0]),Plottable.Utils.Math.max(f,h[1])],h=this._niceDomain(f));
return h}_getAllIncludedValues(){const f=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(f)}extentOfValues(f){var h=f=f.filter(k=>Plottable.Utils.Math.isValidNumber(k));if(this.ignoreOutlier()){h=f.sort((l,p)=>l-p);const k=d3.quantile(h,.05),t=d3.quantile(h,.95);h=f.filter(l=>l>=k&&l<=t)}f=d3.extent(h);return null==f[0]||null==f[1]?[]:f}ignoreOutlier(f){return"boolean"==typeof f?(this._ignoreOutlier=f,this):this._ignoreOutlier}}b.LinearScale=d})(Gh||(Gh={}));

//# sourceURL=build://vz-line-chart2/log-scale.js
(function(b){function d(k){return Math.log10(k)}function f(k){return Math.pow(10,k)}b.MIN_POSITIVE_VALUE=Math.pow(2,-1074);class h extends b.TfScale{constructor(){super();this._d3LogScale=d3.scaleLog();this.padProportion(.2)}scale(k){return 0>=k?NaN:this._d3LogScale(k)}invert(k){return this._d3LogScale.invert(k)}scaleTransformation(k){return this.scale(k)}invertedTransformation(k){return this.invert(k)}getTransformationDomain(){return this.domain()}_getDomain(){return this._untransformedDomain}_setDomain(k){this._untransformedDomain=
k;const [t,l]=k;super._setDomain([Math.max(b.MIN_POSITIVE_VALUE,t),l])}_niceDomain(k){const [t,l]=k;k=Math.max(d(b.MIN_POSITIVE_VALUE),d(t));const p=d(l);var m=p-k;m=m?m*this.padProportion():1;return[f(Math.max(d(b.MIN_POSITIVE_VALUE),k-m)),f(p+m)]}_getUnboundedExtent(k){k=this._getAllIncludedValues(k);let t=this._defaultExtent();0!==k.length&&(k=[Plottable.Utils.Math.min(k,t[0]),Plottable.Utils.Math.max(k,t[1])],t=this._niceDomain(k));return t}_getAllIncludedValues(){return super._getAllIncludedValues().map(k=>
0<k?k:b.MIN_POSITIVE_VALUE)}_defaultExtent(){return[1,10]}_backingScaleDomain(k){if(null==k)return this._d3LogScale.domain();this._d3LogScale.domain(k);return this}_getRange(){return this._d3LogScale.range()}_setRange(k){this._d3LogScale.range(k)}defaultTicks(){return this._d3LogScale.ticks()}ticks(){return this._d3LogScale.ticks()}extentOfValues(k){let t=k=k.filter(l=>Plottable.Utils.Math.isValidNumber(l)&&0<l);if(this.ignoreOutlier()){k=k.map(d).sort((m,n)=>m-n);const l=d3.quantile(k,.05),p=d3.quantile(k,
.95);t=k.filter(m=>m>=l&&m<=p).map(f)}k=d3.extent(t);return null==k[0]||null==k[1]?[]:k}}b.LogScale=h})(Gh||(Gh={}));

//# sourceURL=build://vz-line-chart2/line-chart.js
(function(b){let d;(function(k){k[k.TEXT=0]="TEXT";k[k.DOM=1]="DOM"})(d||(d={}));let f;(function(k){k.LOG="log";k.LINEAR="linear"})(f||(f={}));class h{constructor(k,t,l,p,m,n,q,u,x,A,y){this.seriesNames=[];this.name2datasets={};this.colorScale=p;this.tooltip=m;this.datasets=[];this._ignoreYOutliers=!1;this.lastPointsDataset=new Plottable.Dataset;this.nanDataset=new Plottable.Dataset;this.yValueAccessor=t;this.symbolFunction=A;this.onDatasetChanged=this._onDatasetChanged.bind(this);this._defaultXRange=
u;this._defaultYRange=x;this.tooltipColumns=n;this.buildChart(k,l,q,y)}buildChart(k,t,l,p){this.destroy();k=k();this.xAccessor=k.accessor;this.xScale=k.scale;this.xAxis=k.axis;this.xAxis.margin(0).tickLabelPadding(3);p&&this.xAxis.formatter(p);this.yScale=h.getYScaleFromType(t);this.yScale.setValueProviderForDomain(()=>this.getValuesForYAxisDomainCompute());this.yAxis=new Plottable.Axes.Numeric(this.yScale,"left");p=rg.multiscaleFormatter(rg.Y_AXIS_FORMATTER_PRECISION);this.yAxis.margin(0).tickLabelPadding(5).formatter(p);
this.yAxis.usesTextWidthApproximation();this.fillArea=l;p=new b.PanZoomDragLayer(this.xScale,this.yScale,()=>this.resetDomain());this.tooltipInteraction=this.createTooltipInteraction(p);this.tooltipPointsComponent=new Plottable.Component;l=this.buildPlot(this.xScale,this.yScale,l);this.gridlines=new Plottable.Components.Gridlines(this.xScale,this.yScale);k=null;t!==f.LOG&&(k=new Plottable.Components.GuideLineLayer("horizontal"),k.scale(this.yScale).value(0));t=new Plottable.Components.GuideLineLayer("vertical");
t.scale(this.xScale).value(0);this.center=new Plottable.Components.Group([this.gridlines,k,t,l,this.tooltipPointsComponent,p]);this.center.addClass("main");this.outer=new Plottable.Components.Table([[this.yAxis,this.center],[null,this.xAxis]])}buildPlot(k,t,l){l&&(this.marginAreaPlot=new Plottable.Plots.Area,this.marginAreaPlot.x(this.xAccessor,k),this.marginAreaPlot.y(l.higherAccessor,t),this.marginAreaPlot.y0(l.lowerAccessor),this.marginAreaPlot.attr("fill",(q,u,x)=>this.colorScale.scale(x.metadata().name)),
this.marginAreaPlot.attr("fill-opacity",.3),this.marginAreaPlot.attr("stroke-width",0));this.smoothedAccessor=q=>q.smoothed;l=new Plottable.Plots.Line;l.x(this.xAccessor,k);l.y(this.yValueAccessor,t);l.attr("stroke",(q,u,x)=>this.colorScale.scale(x.metadata().name));this.linePlot=l;this.setupTooltips(l);let p=new Plottable.Plots.Line;p.x(this.xAccessor,k);p.y(this.smoothedAccessor,t);p.attr("stroke",(q,u,x)=>this.colorScale.scale(x.metadata().name));this.smoothLinePlot=p;if(this.symbolFunction){var m=
new Plottable.Plots.Scatter;m.x(this.xAccessor,k);m.y(this.yValueAccessor,t);m.attr("fill",(q,u,x)=>this.colorScale.scale(x.metadata().name));m.attr("opacity",1);m.size(2*rg.TOOLTIP_CIRCLE_SIZE);m.symbol((q,u,x)=>this.symbolFunction(x.metadata().name));this.markersScatterPlot=m}m=new Plottable.Plots.Scatter;m.x(this.xAccessor,k);m.y(this.yValueAccessor,t);m.attr("fill",q=>this.colorScale.scale(q.name));m.attr("opacity",1);m.size(2*rg.TOOLTIP_CIRCLE_SIZE);m.datasets([this.lastPointsDataset]);this.scatterPlot=
m;let n=new Plottable.Plots.Scatter;n.x(this.xAccessor,k);n.y(q=>q.displayY,t);n.attr("fill",q=>this.colorScale.scale(q.name));n.attr("opacity",1);n.size(2*rg.NAN_SYMBOL_SIZE);n.datasets([this.nanDataset]);n.symbol(Plottable.SymbolFactories.triangle);this.nanDisplay=n;k=[n,m,p,l];this.marginAreaPlot&&k.push(this.marginAreaPlot);this.markersScatterPlot&&k.push(this.markersScatterPlot);return new Plottable.Components.Group(k)}_onDatasetChanged(k){this.smoothingEnabled&&this.resmoothDataset(k);this.updateSpecialDatasets()}ignoreYOutliers(k){k!==
this._ignoreYOutliers&&(this._ignoreYOutliers=k,this.updateSpecialDatasets(),this.yScale.ignoreOutlier(k),this.resetYDomain())}getValuesForYAxisDomainCompute(){const k=this.getAccessorsForComputingYRange();return _.flattenDeep(this.datasets.map(t=>k.map(l=>t.data().map(p=>l(p,-1,t))))).filter(isFinite)}updateSpecialDatasets(){const k=this.getYAxisAccessor();var t=this.datasets.map(l=>{let p=null,m=l.data().filter(n=>!isNaN(k(n,-1,l)));0<m.length&&(p=m[m.length-1],p.name=l.metadata().name,p.relative=
rg.relativeAccessor(p,-1,l));return p}).filter(l=>null!=l);this.lastPointsDataset.data(t);this.markersScatterPlot&&this.markersScatterPlot.datasets(this.datasets.map(this.createSampledDatasetForMarkers));t=_.flatten(this.datasets.map(l=>{let p=null,m=l.data(),n=0;for(;n<m.length&&null==p;)isNaN(k(m[n],-1,l))||(p=k(m[n],-1,l)),n++;null==p&&(p=0);let q=[];for(n=0;n<m.length;n++)isNaN(k(m[n],-1,l))?(m[n].name=l.metadata().name,m[n].displayY=p,m[n].relative=rg.relativeAccessor(m[n],-1,l),q.push(m[n])):
p=k(m[n],-1,l);return q}));this.nanDataset.data(t)}resetDomain(){this.resetXDomain();this.resetYDomain()}resetXDomain(){if(null!=this._defaultXRange)var k=this._defaultXRange;else k=this.xScale,k._domainMin=null,k._domainMax=null,k=k._getExtent();this.xScale.domain(k)}resetYDomain(){null!=this._defaultYRange?this.yScale.domain(this._defaultYRange):(this.yScale.autoDomain(),this.yScale.domain(this.yScale.domain()))}getAccessorsForComputingYRange(){const k=[this.getYAxisAccessor()];this.fillArea&&k.push(this.fillArea.lowerAccessor,
this.fillArea.higherAccessor);return k}getYAxisAccessor(){return this.smoothingEnabled?this.smoothedAccessor:this.yValueAccessor}createTooltipInteraction(k){const t=new rg.PointerInteraction,l=()=>{t.enabled(!1);this.hideTooltips()},p=()=>t.enabled(!0);k.onPanStart(l);k.onDragZoomStart(l);k.onPanEnd(p);k.onDragZoomEnd(p);k.onScrollZoom(()=>this.updateTooltipContent(this._lastMousePosition));t.onPointerMove(m=>{this._lastMousePosition=m;this.updateTooltipContent(m)});t.onPointerExit(()=>this.hideTooltips());
return t}updateTooltipContent(k){this.linePlot&&(window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame),this._tooltipUpdateAnimationFrame=window.requestAnimationFrame(()=>{let t={x:k.x,y:k.y,datum:null,dataset:null},l=this.gridlines.content().node().getBBox();var p=this.linePlot.datasets().map(u=>this.findClosestPoint(t,u)).filter(Boolean);let m=Plottable.Utils.DOM.intersectsBBox,n=p.filter(u=>m(u.x,u.y,l)||isNaN(this.yValueAccessor(u.datum,0,u.dataset))),q=n.filter(u=>!isNaN(this.yValueAccessor(u.datum,
0,u.dataset)));0!==p.length?(this.scatterPlot.attr("display","none"),p=this.tooltipPointsComponent.content().selectAll(".point").data(q,u=>u.dataset.metadata().name),p.enter().append("circle").classed("point",!0),p.attr("r",rg.TOOLTIP_CIRCLE_SIZE).attr("cx",u=>u.x).attr("cy",u=>u.y).style("stroke","none").attr("fill",u=>this.colorScale.scale(u.dataset.metadata().name)),p.exit().remove(),this.drawTooltips(n,t,this.tooltipColumns)):this.hideTooltips()}))}hideTooltips(){window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame);
this.tooltip.hide();this.scatterPlot.attr("display","block");this.tooltipPointsComponent.content().selectAll(".point").remove()}setupTooltips(k){k.onDetach(()=>{this.tooltipInteraction.detachFrom();this.tooltipInteraction.enabled(!1)});k.onAnchor(()=>{this.tooltipInteraction.attachTo(k);this.tooltipInteraction.enabled(!0)})}drawTooltips(k,t,l){if(k.length){var p=this.colorScale;l=[{title:"",static:!1,evalType:d.DOM,evaluate(y){d3.select(this).select("span").style("background-color",()=>p.scale(y.dataset.metadata().name));
return""},enter(y){d3.select(this).append("span").classed("swatch",!0).style("background-color",()=>p.scale(y.dataset.metadata().name))}},...l];var m=y=>Math.pow(y.x-t.x,2)+Math.pow(y.y-t.y,2),n=_.min(k.map(m)),q=this.smoothingEnabled?this.smoothedAccessor:this.yValueAccessor;k="ascending"===this.tooltipSortingMethod?_.sortBy(k,y=>q(y.datum,-1,y.dataset)):"descending"===this.tooltipSortingMethod?_.sortBy(k,y=>q(y.datum,-1,y.dataset)).reverse():"nearest"===this.tooltipSortingMethod?_.sortBy(k,m):k.slice(0).reverse();
var u=this,x=d3.select(this.tooltip.content()).select("table"),A=x.select("thead").selectAll("th").data(l,y=>y.title);A.enter().append("th").text(y=>y.title).nodes();A.exit().remove();k=x.select("tbody").selectAll("tr").data(k,y=>y.dataset.metadata().name);k.classed("distant",y=>{var w=y.dataset.data()[0],C=_.last(y.dataset.data());w=this.xScale.scale(this.xAccessor(w,0,y.dataset));C=this.xScale.scale(this.xAccessor(C,0,y.dataset));y=this.smoothingEnabled?y.datum.smoothed:this.yValueAccessor(y.datum,
0,y.dataset);return t.x<w||t.x>C||isNaN(y)}).classed("closest",y=>m(y)===n).each(function(y){u.drawTooltipRow(this,l,y)}).order();k.exit().remove();k.enter().append("tr").each(function(y){u.drawTooltipRow(this,l,y)}).nodes();this.tooltip.updateAndPosition(this.targetSVG.node())}else this.tooltip.hide()}drawTooltipRow(k,t,l){const p=this;k=d3.select(k).selectAll("td").data(t);k.each(function(m){m.static||p.drawTooltipColumn.call(p,this,m,l)});k.enter().append("td").each(function(m){m.enter&&m.enter.call(this,
l);p.drawTooltipColumn.call(p,this,m,l)})}drawTooltipColumn(k,t,l){const p=this.smoothingEnabled;t.evalType==d.DOM?t.evaluate.call(k,l,{smoothingEnabled:p}):d3.select(k).text(t.evaluate.call(k,l,{smoothingEnabled:p}))}findClosestPoint(k,t){const l=t.data().map((n,q)=>this.xScale.scale(this.xAccessor(n,q,t)));let p=_.sortedIndex(l,k.x);if(0==l.length)return null;p===l.length?--p:0!==p&&(p=Math.abs(l[p-1]-k.x)<Math.abs(l[p]-k.x)?p-1:p);k=t.data()[p];const m=this.smoothingEnabled?this.smoothedAccessor(k,
p,t):this.yValueAccessor(k,p,t);return{x:l[p],y:this.yScale.scale(m),datum:k,dataset:t}}resmoothDataset(k){let t=k.data();const l=this.smoothingWeight;let p=0<t.length?0:NaN,m=0;const n=t.map((u,x)=>this.yValueAccessor(u,x,k)),q=n.every(u=>u==n[0]);t.forEach((u,x)=>{x=n[x];q||!Number.isFinite(x)?u.smoothed=x:(p=p*l+(1-l)*x,m++,x=1,1!==l&&(x=1-Math.pow(l,m)),u.smoothed=p/x)})}getDataset(k){void 0===this.name2datasets[k]&&(this.name2datasets[k]=new Plottable.Dataset([],{name:k,meta:null}));return this.name2datasets[k]}static getYScaleFromType(k){if(k===
f.LOG)return new b.LogScale;if(k===f.LINEAR)return new b.LinearScale;throw Error("Unrecognized yScale type "+k);}setVisibleSeries(k){this.seriesNames=k=k.sort();k.reverse();this.datasets.forEach(t=>t.offUpdate(this.onDatasetChanged));this.datasets=k.map(t=>this.getDataset(t));this.datasets.forEach(t=>t.onUpdate(this.onDatasetChanged));this.linePlot.datasets(this.datasets);this.smoothingEnabled&&this.smoothLinePlot.datasets(this.datasets);this.marginAreaPlot&&this.marginAreaPlot.datasets(this.datasets);
this.updateSpecialDatasets()}createSampledDatasetForMarkers(k){const t=k.data();if(20>=t.length)return k;const l=Math.ceil(t.length/20),p=Array(Math.floor(t.length/l));for(let m=0,n=0;m<p.length;m++,n+=l)p[m]=t[n];return new Plottable.Dataset(p,k.metadata())}setSeriesData(k,t){this.getDataset(k).data(t);this.measureBBoxAndMaybeInvalidateLayoutInRaf()}setSeriesMetadata(k,t){t=Object.assign({},this.getDataset(k).metadata(),{meta:t});this.getDataset(k).metadata(t)}smoothingUpdate(k){this.smoothingWeight=
k;this.datasets.forEach(t=>this.resmoothDataset(t));this.smoothingEnabled||(this.linePlot.addClass("ghost"),this.scatterPlot.y(this.smoothedAccessor,this.yScale),this.smoothingEnabled=!0,this.smoothLinePlot.datasets(this.datasets));this.markersScatterPlot&&this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale);this.updateSpecialDatasets()}smoothingDisable(){this.smoothingEnabled&&(this.linePlot.removeClass("ghost"),this.scatterPlot.y(this.yValueAccessor,this.yScale),this.smoothLinePlot.datasets([]),
this.smoothingEnabled=!1,this.updateSpecialDatasets());this.markersScatterPlot&&this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale)}setTooltipSortingMethod(k){this.tooltipSortingMethod=k}renderTo(k){this.targetSVG=k;this.outer.renderTo(k);null!=this._defaultXRange&&this.resetXDomain();null!=this._defaultYRange&&this.resetYDomain();this.measureBBoxAndMaybeInvalidateLayoutInRaf()}redraw(){window.cancelAnimationFrame(this._redrawRaf);this._redrawRaf=window.requestAnimationFrame(()=>{this.measureBBoxAndMaybeInvalidateLayout();
this.outer.redraw()})}measureBBoxAndMaybeInvalidateLayoutInRaf(){window.cancelAnimationFrame(this._invalidateLayoutRaf);this._invalidateLayoutRaf=window.requestAnimationFrame(()=>{this.measureBBoxAndMaybeInvalidateLayout()})}measureBBoxAndMaybeInvalidateLayout(){if(this._lastDrawBBox){const k=this._lastDrawBBox.width,{width:t}=this.targetSVG.node().getBoundingClientRect();0==k&&k<t&&this.outer.invalidateCache()}this._lastDrawBBox=this.targetSVG.node().getBoundingClientRect()}destroy(){window.cancelAnimationFrame(this._redrawRaf);
window.cancelAnimationFrame(this._invalidateLayoutRaf);this.outer&&this.outer.destroy()}onAnchor(k){if(this.outer)this.outer.onAnchor(k)}}b.LineChart=h})(Gh||(Gh={}));

//# sourceURL=build://vz-line-chart2/vz-line-chart2.js
(function(b){const d=rg.multiscaleFormatter(rg.Y_TOOLTIP_FORMATTER_PRECISION),f=h=>isNaN(h)?"NaN":d(h);b.DEFAULT_TOOLTIP_COLUMNS=[{title:"Name",evaluate:h=>h.dataset.metadata().name},{title:"Smoothed",evaluate(h,k){return f(k.smoothingEnabled?h.datum.smoothed:h.datum.scalar)}},{title:"Value",evaluate:h=>f(h.datum.scalar)},{title:"Step",evaluate:h=>rg.stepFormatter(h.datum.step)},{title:"Time",evaluate:h=>rg.timeFormatter(h.datum.wall_time)},{title:"Relative",evaluate:h=>rg.relativeFormatter(rg.relativeAccessor(h.datum,
-1,h.dataset))}];Polymer({is:"vz-line-chart2",properties:{colorScale:{type:Object,value:function(){return(new Plottable.Scales.Color).range(d3.schemeCategory10)}},symbolFunction:Object,smoothingEnabled:{type:Boolean,notify:!0,value:!1},smoothingWeight:{type:Number,value:.6},xType:{type:String,value:""},xComponentsCreationMethod:{type:Object,value:""},xAxisFormatter:Object,yValueAccessor:{type:Object,value:()=>h=>h.scalar},tooltipColumns:{type:Array,value:()=>b.DEFAULT_TOOLTIP_COLUMNS},fillArea:Object,
defaultXRange:Array,defaultYRange:Array,yScaleType:{type:String,value:"linear"},ignoreYOutliers:{type:Boolean,value:!1},tooltipSortingMethod:{type:String,value:"default"},tooltipPosition:{type:String,value:Eh.TooltipPosition.BOTTOM},_chart:Object,_visibleSeriesCache:{type:Array,value:()=>[]},_seriesDataCache:{type:Object,value:()=>({})},_seriesMetadataCache:{type:Object,value:()=>({})},_makeChartAsyncCallbackId:{type:Number,value:null}},observers:["_makeChart(xComponentsCreationMethod, xType, yValueAccessor, yScaleType, tooltipColumns, colorScale, isAttached)",
"_reloadFromCache(_chart, _visibleSeriesCache)","_smoothingChanged(smoothingEnabled, smoothingWeight, _chart)","_tooltipSortingMethodChanged(tooltipSortingMethod, _chart)","_outliersChanged(ignoreYOutliers, _chart)"],ready(){this.scopeSubtree(this.$.chartdiv,!0)},attached(){const h={capture:!0,passive:!0};this._listen(this,"mousedown",this._onMouseDown.bind(this),h);this._listen(this,"mouseup",this._onMouseUp.bind(this),h);this._listen(window,"keydown",this._onKeyDown.bind(this),h);this._listen(window,
"keyup",this._onKeyUp.bind(this),h)},detached(){this.cancelAsync(this._makeChartAsyncCallbackId);this._chart&&this._chart.destroy();this._listeners&&(this._listeners.forEach(({node:h,eventName:k,func:t,option:l})=>{h.removeEventListener(k,t,l)}),this._listeners.clear())},_listen(h,k,t,l={}){this._listeners||(this._listeners=new Set);this._listeners.add({node:h,eventName:k,func:t,option:l});h.addEventListener(k,t,l)},_onKeyDown(h){this.toggleClass("pankey",b.PanZoomDragLayer.isPanKey(h))},_onKeyUp(h){this.toggleClass("pankey",
b.PanZoomDragLayer.isPanKey(h))},_onMouseDown(){this.toggleClass("mousedown",!0)},_onMouseUp(){this.toggleClass("mousedown",!1)},setVisibleSeries:function(h){_.isEqual(this._visibleSeriesCache,h)||(this._visibleSeriesCache=h)},setSeriesData:function(h,k){this._seriesDataCache[h]=k;this._chart&&this._chart.setSeriesData(h,k)},setSeriesMetadata(h,k){this._seriesMetadataCache[h]=k;this._chart&&this._chart.setSeriesMetadata(h,k)},resetDomain:function(){this._chart&&this._chart.resetDomain()},redraw:function(){this._chart&&
this._chart.redraw()},_makeChart:function(h,k,t,l,p,m){k||h?k&&(h=()=>rg.getXComponents(k)):h=rg.stepX;null!==this._makeChartAsyncCallbackId&&(this.cancelAsync(this._makeChartAsyncCallbackId),this._makeChartAsyncCallbackId=null);this._makeChartAsyncCallbackId=this.async(function(){this._makeChartAsyncCallbackId=null;if(h&&this.yValueAccessor&&this.tooltipColumns){var n=new b.LineChart(h,this.yValueAccessor,l,m,this.$.tooltip,this.tooltipColumns,this.fillArea,this.defaultXRange,this.defaultYRange,
this.symbolFunction,this.xAxisFormatter),q=d3.select(this.$.chartdiv);n.renderTo(q);this._chart&&this._chart.destroy();this._chart=n;this._chart.onAnchor(()=>this.fire("chart-attached"))}},350)},_reloadFromCache:function(){this._chart&&(this._visibleSeriesCache.forEach(h=>{this._chart.setSeriesData(h,this._seriesDataCache[h]||[])}),this._visibleSeriesCache.filter(h=>this._seriesMetadataCache[h]).forEach(h=>{this._chart.setSeriesMetadata(h,this._seriesMetadataCache[h])}),this._chart.setVisibleSeries(this._visibleSeriesCache))},
_smoothingChanged:function(){this._chart&&(this.smoothingEnabled?this._chart.smoothingUpdate(this.smoothingWeight):this._chart.smoothingDisable())},_outliersChanged:function(){this._chart&&this._chart.ignoreYOutliers(this.ignoreYOutliers)},_tooltipSortingMethodChanged:function(){this._chart&&this._chart.setTooltipSortingMethod(this.tooltipSortingMethod)},getExporter(){return new b.LineChartExporter(this.$.chartdiv)}})})(Gh||(Gh={}));

//# sourceURL=build://vz-line-chart2/vz-line-chart2.html.js
Polymer({is:"vz-line-chart-tooltip"});

//# sourceURL=build://vz-line-chart2/line-chart-exporter.js
(function(b){let d;(function(k){k.GROUP="G";k.DIV="DIV";k.SVG="SVG";k.TEXT="TEXT"})(d||(d={}));class f{constructor(k){this.uniqueId=0;this.root=k}exportAsString(){const k=this.convert(this.root);if(!k)return"";const t=this.createRootSvg();t.appendChild(k);return t.outerHTML}createUniqueId(){return`${"clip"}_${this.uniqueId++}`}getSize(){return this.root.getBoundingClientRect()}createRootSvg(){const k=document.createElement("svg"),t=this.getSize();k.setAttributeNS("svg","viewBox",`0 0 ${t.width} ${t.height}`);
k.setAttribute("xmlns","http://www.w3.org/2000/svg");return k}convert(k){let t=null;var l=k.nodeName.toUpperCase();if(k.nodeType!=Node.ELEMENT_NODE||l!=d.DIV&&l!=d.SVG)t=k.cloneNode();else{t=document.createElement(d.GROUP);var p=window.getComputedStyle(k),m=parseInt(p.left,10),n=parseInt(p.top,10);if(m||n)l=this.createUniqueId(),t.setAttribute("transform",`translate(${m}, ${n})`),t.setAttribute("clip-path",`url(#${l})`),n=parseInt(p.height,10),m=document.createElement("rect"),m.setAttribute("width",
String(parseInt(p.width,10))),m.setAttribute("height",String(n)),p=document.createElementNS("svg","clipPath"),p.id=l,p.appendChild(m),t.appendChild(p)}Array.from(k.childNodes).map(q=>this.convert(q)).filter(Boolean).forEach(q=>t.appendChild(q));return t.nodeName.toUpperCase()==d.GROUP&&!t.hasChildNodes()||this.shouldOmitNode(k)?null:this.stripClass(this.transferStyle(k,t))}stripClass(k){k.nodeType==Node.ELEMENT_NODE&&k.removeAttribute("class");return k}transferStyle(k,t){if(t.nodeType!=Node.ELEMENT_NODE)return t;
const l=t.nodeName.toUpperCase();k=window.getComputedStyle(k);l==d.TEXT&&Object.assign(t.style,{fontFamily:k.fontFamily,fontSize:k.fontSize,fontWeight:k.fontWeight});l!=d.GROUP&&(t.setAttribute("fill",k.fill),t.setAttribute("stroke",k.stroke),t.setAttribute("stroke-width",k.strokeWidth));"1"!=k.opacity&&t.setAttribute("opacity",k.opacity);return t}shouldOmitNode(){return!1}}b.PlottableExporter=f;class h extends f{shouldOmitNode(k){return k.nodeType==Node.ELEMENT_NODE?k.classList.contains("scatter-plot"):
!1}}b.LineChartExporter=h})(Gh||(Gh={}));

//# sourceURL=build://tf-line-chart-data-loader/tf-line-chart-data-loader.html.js
(function(){const b=[],d=function(){return _.throttle(function h(){if(0!=b.length){var k=b.shift();k.active&&(k.redraw(),k._maybeRenderedInBadState=!1);window.cancelAnimationFrame(0);window.requestAnimationFrame(h)}},100)}();Polymer({is:"tf-line-chart-data-loader",properties:{active:{type:Boolean,observer:"_fixBadStateWhenActive"},dataSeries:Array,requestManager:Object,logScaleActive:{type:Boolean,observer:"_logScaleChanged"},xComponentsCreationMethod:Object,xType:String,yValueAccessor:Object,fillArea:Object,
smoothingEnabled:Boolean,smoothingWeight:Number,tooltipColumns:Array,tooltipSortingMethod:String,tooltipPosition:String,ignoreYOutliers:Boolean,defaultXRange:Array,defaultYRange:Array,symbolFunction:Object,colorScale:{type:Object,value:()=>({scale:pf.runsColorScale})},_resetDomainOnNextLoad:{type:Boolean,value:!0},_maybeRenderedInBadState:{type:Boolean,value:!1,reflectToAttribute:!0}},behaviors:[qd.DataLoaderBehavior],observers:["_dataSeriesChanged(dataSeries.*)","_loadKeyChanged(loadKey)"],onLoadFinish(){0<
this.dataToLoad.length&&this._resetDomainOnNextLoad&&(this._resetDomainOnNextLoad=!1,this.$.chart.resetDomain());this.redraw()},detached(){cancelAnimationFrame(this._redrawRaf)},exportAsSvgString(){return this.$.chart.getExporter().exportAsString()},resetDomain(){this.$.chart.resetDomain()},setSeriesData(f,h){this.$.chart.setSeriesData(f,h)},setSeriesMetadata(f,h){this.$.chart.setSeriesMetadata(f,h)},redraw(){cancelAnimationFrame(this._redrawRaf);this._redrawRaf=window.requestAnimationFrame(()=>{this.active?
this.$.chart.redraw():this._maybeRenderedInBadState=!0})},_loadKeyChanged(){this.reset();this._resetDomainOnNextLoad=!0},_dataSeriesChanged(){this.$.chart.setVisibleSeries(this.dataSeries)},_logScaleChanged(f){this.$.chart.yScaleType=f?"log":"linear";this.redraw()},_fixBadStateWhenActive(){this.active&&this._maybeRenderedInBadState&&(b.push(this),d())},_onChartAttached(){this.active||(this._maybeRenderedInBadState=!0)}})})();

//# sourceURL=build://paper-dialog-scrollable/paper-dialog-scrollable.html.js
Polymer({is:"paper-dialog-scrollable",properties:{dialogElement:{type:Object}},get scrollTarget(){return this.$.scrollable},ready:function(){this._ensureTarget();this.classList.add("no-padding")},attached:function(){this._ensureTarget();requestAnimationFrame(this.updateScrollState.bind(this))},updateScrollState:function(){this.toggleClass("is-scrolled",0<this.scrollTarget.scrollTop);this.toggleClass("can-scroll",this.scrollTarget.offsetHeight<this.scrollTarget.scrollHeight);this.toggleClass("scrolled-to-bottom",
this.scrollTarget.scrollTop+this.scrollTarget.offsetHeight>=this.scrollTarget.scrollHeight)},_ensureTarget:function(){(this.dialogElement=this.dialogElement||this.parentElement)&&this.dialogElement.behaviors&&0<=this.dialogElement.behaviors.indexOf(Polymer.PaperDialogBehaviorImpl)?(this.dialogElement.sizingTarget=this.scrollTarget,this.scrollTarget.classList.remove("fit")):this.dialogElement&&this.scrollTarget.classList.add("fit")}});

//# sourceURL=build://tf-markdown-view/tf-markdown-view.html.js
Polymer({is:"tf-markdown-view",properties:{html:{type:String,value:""}},attached(){window.requestAnimationFrame(()=>{this.scopeSubtree(this.$.markdown,!0)})}});

//# sourceURL=build://tf-card-heading/util.js
var Hh;(function(b){function d(f){if(!f)return null;let h=f.match(/^#([0-9a-f]{1,2})([0-9a-f]{1,2})([0-9a-f]{1,2})$/);if(!h)return null;if(4==f.length)for(f=1;3>=f;f++)h[f]+=h[f];return[parseInt(h[1],16),parseInt(h[2],16),parseInt(h[3],16)]}b.formatDate=function(f){return f?f.toString().replace(/GMT-\d+ \(([^)]+)\)/,"$1"):""};b.pickTextColor=function(f){return(f=d(f))?125<Math.round((299*f[0]+587*f[1]+114*f[2])/1E3)?"inherit":"#eee":"inherit"}})(Hh||(Hh={}));

//# sourceURL=build://tf-card-heading/tf-card-heading.html.js
Polymer({is:"tf-card-heading",properties:{displayName:{type:String,value:null},tag:{type:String,value:null},run:{type:String,value:null},description:{type:String,value:null},color:{type:String,value:null},_runBackground:{type:String,computed:"_computeRunBackground(color)",readOnly:!0,observer:"_updateHeadingStyle"},_runColor:{type:String,computed:"_computeRunColor(color)",readOnly:!0,observer:"_updateHeadingStyle"},_nameLabel:{type:String,computed:"_computeNameLabel(displayName, tag)"},_tagLabel:{type:String,
computed:"_computeTagLabel(displayName, tag)"}},_updateHeadingStyle(){this.updateStyles({"--tf-card-heading-background-color":this._runBackground,"--tf-card-heading-color":this._runColor})},_computeRunBackground(b){return b||"none"},_computeRunColor(b){return Hh.pickTextColor(b)},_computeNameLabel(b,d){return b||d||""},_computeTagLabel(b,d){return d&&d!==b?d:""},_toggleDescriptionDialog(b){this.$.descriptionDialog.positionTarget=b.target;this.$.descriptionDialog.toggle()}});

//# sourceURL=build://tf-dashboard-common/tf-downloader.html.js
Polymer({is:"tf-downloader",properties:{_run:{type:String,value:""},runs:Array,tag:String,urlFn:Function},_csvUrl(b,d,f){return d?vc.addParams(f(b,d),{format:"csv"}):""},_jsonUrl(b,d,f){return d?f(b,d):""},_csvName(b,d){return d?`run-${d}-tag-${b}.csv`:""},_jsonName(b,d){return d?`run-${d}-tag-${b}.json`:""}});

//# sourceURL=build://tf-scalar-dashboard/tf-scalar-card.html.js
Polymer({is:"tf-scalar-card",properties:{tag:String,dataToLoad:Array,xType:String,active:Boolean,ignoreYOutliers:Boolean,requestManager:Object,showDownLinks:Boolean,smoothingEnabled:Boolean,smoothingWeight:Number,tagMetadata:Object,colorScale:{type:Object,value:null},tooltipSortingMethod:String,_loadDataCallback:{type:Object,value:function(){return(b,d,f)=>{f=f.map(k=>({wall_time:new Date(1E3*k[0]),step:k[1],scalar:k[2]}));const h=this._getSeriesNameFromDatum(d);b.setSeriesMetadata(h,d);b.setSeriesData(h,
f)}},readOnly:!0},getDataLoadUrl:{type:Function,value:function(){return({tag:b,run:d})=>vc.getRouter().pluginRoute("scalars","/scalars",new URLSearchParams({tag:b,run:d}))}},_downloadUrlFn:{type:Function,value:function(){return(b,d)=>this.getDataLoadUrl({tag:b,run:d})}},requestData:Function,_getDataLoadName:{type:Function,value:function(){return b=>this._getSeriesNameFromDatum(b)}},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},_logScaleActive:Boolean,_tooltipColumns:{type:Array,value:function(){const b=
Gh.DEFAULT_TOOLTIP_COLUMNS.slice(),d=b.findIndex(f=>"Name"==f.title);b.splice(d,1,{title:"Name",evaluate:f=>{f=f.dataset.metadata().meta;return this._getSeriesDisplayNameFromDatum(f)}});return b}}},reload(){this.$$("tf-line-chart-data-loader").reload()},redraw(){this.$$("tf-line-chart-data-loader").redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()},_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)},_resetDomain(){const b=this.$$("tf-line-chart-data-loader");
b&&b.resetDomain()},_updateDownloadLink(){const b=this.$$("tf-line-chart-data-loader").exportAsSvgString();this.$$("#svgLink").href=`data:image/svg+xml;base64,${btoa(b)}`},_runsFromData(b){return b.map(d=>d.run)},_getDataSeries(){return this.dataToLoad.map(b=>this._getSeriesNameFromDatum(b))},_getSeriesNameFromDatum({run:b,experiment:d={name:"_default"}}){return JSON.stringify([d.name,b])},_getSeriesDisplayNameFromDatum(b){return b.run},_getColorScale(){return null!==this.colorScale?this.colorScale:
{scale:b=>{[,b]=JSON.parse(b);return pf.runsColorScale(b)}}}});

//# sourceURL=build://iron-range-behavior/iron-range-behavior.html.js
Polymer.IronRangeBehavior={properties:{value:{type:Number,value:0,notify:!0,reflectToAttribute:!0},min:{type:Number,value:0,notify:!0},max:{type:Number,value:100,notify:!0},step:{type:Number,value:1,notify:!0},ratio:{type:Number,value:0,readOnly:!0,notify:!0}},observers:["_update(value, min, max, step)"],_calcRatio:function(b){return(this._clampValue(b)-this.min)/(this.max-this.min)},_clampValue:function(b){return Math.min(this.max,Math.max(this.min,this._calcStep(b)))},_calcStep:function(b){b=parseFloat(b);
if(!this.step)return b;b=Math.round((b-this.min)/this.step);return 1>this.step?b/(1/this.step)+this.min:b*this.step+this.min},_validateValue:function(){var b=this._clampValue(this.value);this.value=this.oldValue=isNaN(b)?this.oldValue:b;return this.value!==b},_update:function(){this._validateValue();this._setRatio(100*this._calcRatio(this.value))}};

//# sourceURL=build://paper-progress/paper-progress.html.js
Polymer({is:"paper-progress",behaviors:[Polymer.IronRangeBehavior],properties:{secondaryProgress:{type:Number,value:0},secondaryRatio:{type:Number,value:0,readOnly:!0},indeterminate:{type:Boolean,value:!1,observer:"_toggleIndeterminate"},disabled:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"_disabledChanged"}},observers:["_progressChanged(secondaryProgress, value, min, max, indeterminate)"],hostAttributes:{role:"progressbar"},_toggleIndeterminate:function(b){this.toggleClass("indeterminate",
b,this.$.primaryProgress)},_transformProgress:function(b,d){b.style.transform=b.style.webkitTransform="scaleX("+d/100+")"},_mainRatioChanged:function(b){this._transformProgress(this.$.primaryProgress,b)},_progressChanged:function(b,d,f,h,k){b=this._clampValue(b);d=this._clampValue(d);var t=100*this._calcRatio(b),l=100*this._calcRatio(d);this._setSecondaryRatio(t);this._transformProgress(this.$.secondaryProgress,t);this._transformProgress(this.$.primaryProgress,l);this.secondaryProgress=b;k?this.removeAttribute("aria-valuenow"):
this.setAttribute("aria-valuenow",d);this.setAttribute("aria-valuemin",f);this.setAttribute("aria-valuemax",h)},_disabledChanged:function(b){this.setAttribute("aria-disabled",b?"true":"false")},_hideSecondaryProgress:function(b){return 0===b}});

//# sourceURL=build://paper-slider/paper-slider.html.js
Polymer({is:"paper-slider",behaviors:[Polymer.IronA11yKeysBehavior,Polymer.IronFormElementBehavior,Polymer.PaperInkyFocusBehavior,Polymer.IronRangeBehavior],properties:{snaps:{type:Boolean,value:!1,notify:!0},pin:{type:Boolean,value:!1,notify:!0},secondaryProgress:{type:Number,value:0,notify:!0,observer:"_secondaryProgressChanged"},editable:{type:Boolean,value:!1},immediateValue:{type:Number,value:0,readOnly:!0,notify:!0},maxMarkers:{type:Number,value:0,notify:!0},expand:{type:Boolean,value:!1,readOnly:!0},
ignoreBarTouch:{type:Boolean,value:!1},dragging:{type:Boolean,value:!1,readOnly:!0,notify:!0},transiting:{type:Boolean,value:!1,readOnly:!0},markers:{type:Array,readOnly:!0,value:function(){return[]}}},observers:["_updateKnob(value, min, max, snaps, step)","_valueChanged(value)","_immediateValueChanged(immediateValue)","_updateMarkers(maxMarkers, min, max, snaps)"],hostAttributes:{role:"slider",tabindex:0},keyBindings:{left:"_leftKey",right:"_rightKey","down pagedown home":"_decrementKey","up pageup end":"_incrementKey"},
ready:function(){this.ignoreBarTouch&&Polymer.Gestures.setTouchAction(this.$.sliderBar,"auto")},increment:function(){this.value=this._clampValue(this.value+this.step)},decrement:function(){this.value=this._clampValue(this.value-this.step)},_updateKnob:function(b,d,f){this.setAttribute("aria-valuemin",d);this.setAttribute("aria-valuemax",f);this.setAttribute("aria-valuenow",b);this._positionKnob(100*this._calcRatio(b))},_valueChanged:function(){this.fire("value-change",{composed:!0})},_immediateValueChanged:function(){this.dragging?
this.fire("immediate-value-change",{composed:!0}):this.value=this.immediateValue},_secondaryProgressChanged:function(){this.secondaryProgress=this._clampValue(this.secondaryProgress)},_expandKnob:function(){this._setExpand(!0)},_resetKnob:function(){this.cancelDebouncer("expandKnob");this._setExpand(!1)},_positionKnob:function(b){this._setImmediateValue(this._calcStep(this._calcKnobPosition(b)));this._setRatio(100*this._calcRatio(this.immediateValue));this.$.sliderKnob.style.left=this.ratio+"%";this.dragging&&
(this._knobstartx=this.ratio*this._w/100,this.translate3d(0,0,0,this.$.sliderKnob))},_calcKnobPosition:function(b){return(this.max-this.min)*b/100+this.min},_onTrack:function(b){b.stopPropagation();switch(b.detail.state){case "start":this._trackStart(b);break;case "track":this._trackX(b);break;case "end":this._trackEnd()}},_trackStart:function(){this._setTransiting(!1);this._w=this.$.sliderBar.offsetWidth;this._knobstartx=this._startx=this._x=this.ratio*this._w/100;this._minx=-this._startx;this._maxx=
this._w-this._startx;this.$.sliderKnob.classList.add("dragging");this._setDragging(!0)},_trackX:function(b){this.dragging||this._trackStart(b);this._x=this._startx+Math.min(this._maxx,Math.max(this._minx,b.detail.dx*(this._isRTL?-1:1)));this._setImmediateValue(this._calcStep(this._calcKnobPosition(this._x/this._w*100)));this.translate3d(this._calcRatio(this.immediateValue)*this._w-this._knobstartx+"px",0,0,this.$.sliderKnob)},_trackEnd:function(){var b=this.$.sliderKnob.style;this.$.sliderKnob.classList.remove("dragging");
this._setDragging(!1);this._resetKnob();this.value=this.immediateValue;b.transform=b.webkitTransform="";this.fire("change",{composed:!0})},_knobdown:function(b){this._expandKnob();b.preventDefault();this.focus()},_bartrack:function(b){this._allowBarEvent(b)&&this._onTrack(b)},_barclick:function(b){this._w=this.$.sliderBar.offsetWidth;var d=this.$.sliderBar.getBoundingClientRect();d=(b.detail.x-d.left)/this._w*100;this._isRTL&&(d=100-d);var f=this.ratio;this._setTransiting(!0);this._positionKnob(d);
f===this.ratio&&this._setTransiting(!1);this.async(function(){this.fire("change",{composed:!0})});b.preventDefault();this.focus()},_bardown:function(b){this._allowBarEvent(b)&&(this.debounce("expandKnob",this._expandKnob,60),this._barclick(b))},_knobTransitionEnd:function(b){b.target===this.$.sliderKnob&&this._setTransiting(!1)},_updateMarkers:function(b,d,f,h){h||this._setMarkers([]);d=Math.round((f-d)/this.step);d>b&&(d=b);if(0>d||!isFinite(d))d=0;this._setMarkers(Array(d))},_mergeClasses:function(b){return Object.keys(b).filter(function(d){return b[d]}).join(" ")},
_getClassNames:function(){return this._mergeClasses({disabled:this.disabled,pin:this.pin,snaps:this.snaps,ring:this.immediateValue<=this.min,expand:this.expand,dragging:this.dragging,transiting:this.transiting,editable:this.editable})},_allowBarEvent:function(b){return!this.ignoreBarTouch||b.detail.sourceEvent instanceof MouseEvent},get _isRTL(){void 0===this.__isRTL&&(this.__isRTL="rtl"===window.getComputedStyle(this).direction);return this.__isRTL},_leftKey:function(b){this._isRTL?this._incrementKey(b):
this._decrementKey(b)},_rightKey:function(b){this._isRTL?this._decrementKey(b):this._incrementKey(b)},_incrementKey:function(b){this.disabled||("end"===b.detail.key?this.value=this.max:this.increment(),this.fire("change"),b.preventDefault())},_decrementKey:function(b){this.disabled||("home"===b.detail.key?this.value=this.min:this.decrement(),this.fire("change"),b.preventDefault())},_changeValue:function(b){this.value=b.target.value;this.fire("change",{composed:!0})},_inputKeyDown:function(b){b.stopPropagation()},
_createRipple:function(){this._rippleContainer=this.$.sliderKnob;return Polymer.PaperInkyFocusBehaviorImpl._createRipple.call(this)},_focusedChanged:function(b){b&&this.ensureRipple();this.hasRipple()&&(this._ripple.style.display=b?"":"none",this._ripple.holdDown=b)}});

//# sourceURL=build://tf-scalar-dashboard/tf-smoothing-input.html.js
Polymer({is:"tf-smoothing-input",properties:{step:Number,max:Number,min:Number,weight:{type:Number,value:.6,notify:!0},_immediateWeightNumberForPaperSlider:{type:Number,notify:!0,observer:"_immediateWeightNumberForPaperSliderChanged"},_inputWeightStringForPaperInput:{type:String,notify:!0,observer:"_inputWeightStringForPaperInputChanged"}},_updateWeight:_.debounce(function(b){this.weight=b},250),_immediateWeightNumberForPaperSliderChanged:function(){this._inputWeightStringForPaperInput=this._immediateWeightNumberForPaperSlider.toString();
this._updateWeight.call(this,this._immediateWeightNumberForPaperSlider)},_inputWeightStringForPaperInputChanged:function(){0>+this._inputWeightStringForPaperInput?this._inputWeightStringForPaperInput="0":1<+this._inputWeightStringForPaperInput&&(this._inputWeightStringForPaperInput="1");var b=+this._inputWeightStringForPaperInput;isNaN(b)||this._updateWeight.call(this,b)}});

//# sourceURL=build://tf-scalar-dashboard/tf-scalar-dashboard.html.js
Polymer({is:"tf-scalar-dashboard",properties:{_showDownloadLinks:{type:Boolean,notify:!0,value:pd.getBooleanInitializer("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),observer:"_showDownloadLinksObserver"},_smoothingWeight:{type:Number,notify:!0,value:pd.getNumberInitializer("_smoothingWeight",{defaultValue:.6}),observer:"_smoothingWeightObserver"},_smoothingEnabled:{type:Boolean,computed:"_computeSmoothingEnabled(_smoothingWeight)"},_ignoreYOutliers:{type:Boolean,value:pd.getBooleanInitializer("_ignoreYOutliers",
{defaultValue:!0,useLocalStorage:!0}),observer:"_ignoreYOutliersObserver"},_xType:{type:String,value:rg.XType.STEP},_selectedRuns:{type:Array,value:()=>[]},_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:{type:String,value:""},_categoriesDomReady:Boolean,_categories:{type:Array,value:()=>[]},_getCategoryItemKey:{type:Function,value:()=>b=>b.tag},_requestManager:{type:Object,value:()=>new vc.RequestManager(50)}},behaviors:[qd.ArrayUpdateHelper],observers:["_updateCategories(_runToTagInfo, _selectedRuns, _tagFilter, _categoriesDomReady)"],
_showDownloadLinksObserver:pd.getBooleanObserver("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),_smoothingWeightObserver:pd.getNumberObserver("_smoothingWeight",{defaultValue:.6}),_ignoreYOutliersObserver:pd.getBooleanObserver("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}),_computeSmoothingEnabled(b){return 0<b},_getCategoryKey(b){return b.metadata.type==$c.CategoryType.SEARCH_RESULTS?"":b.name},_shouldOpen(b){return 2>=b},ready(){this.reload()},reload(){this._fetchTags().then(()=>
{this._reloadCharts()})},_fetchTags(){const b=vc.getRouter().pluginRoute("scalars","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,h=>Object.keys(h));f=vc.getTags(f);this.set("_dataNotFound",0===f.length);this.set("_runToTagInfo",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_reloadCharts(){this.root.querySelectorAll("tf-scalar-card").forEach(b=>{b.reload()})},_updateCategories(b,d,f){b=_.mapValues(b,h=>Object.keys(h));
d=$c.categorizeTags(b,d,f);d.forEach(h=>{h.items=h.items.map(k=>({tag:k.tag,series:k.runs.map(t=>({run:t,tag:k.tag}))}))});this.updateArrayProp("_categories",d,this._getCategoryKey)},_tagMetadata(b,d,f){const h=f.tag,k={};f.series.forEach(({run:p})=>{k[p]=d[p][h]});f=h.replace(/\/scalar_summary$/,"");let {description:t,displayName:l}=rf.aggregateTagInfo(k,f);b.metadata.type==$c.CategoryType.PREFIX_GROUP&&l.startsWith(b.name+"/")&&(l=l.slice(b.name.length+1));return{description:t,displayName:l}}});

//# sourceURL=build://tf-custom-scalar-dashboard/tf-custom-scalar-helpers.js
var Ti;
(function(b){class d{constructor(h,k,t,l,p){this.run=h;this.tag=k;this.name=t;this.scalarData=l;this.symbol=p}getName(){return this.name}setData(h){this.scalarData=h}getData(){return this.scalarData}getRun(){return this.run}getTag(){return this.tag}getSymbol(){return this.symbol}}b.DataSeries=d;b.generateDataSeriesName=function(h,k){return`${k} (${h})`};class f{constructor(h){this.runBasedColorScale=h}scale(h){return this.runBasedColorScale.scale(this.parseRunName(h))}parseRunName(h){return(h=h.match(/\((.*)\)$/))?
h[1]:""}}b.DataSeriesColorScale=f})(Ti||(Ti={}));

//# sourceURL=build://tf-custom-scalar-dashboard/tf-custom-scalar-margin-chart-card.html.js
Polymer({is:"tf-custom-scalar-margin-chart-card",properties:{runs:Array,xType:String,active:{type:Boolean,value:!0,readOnly:!0},title:String,marginChartSeries:Array,ignoreYOutliers:Boolean,requestManager:Object,showDownloadLinks:Boolean,tagMetadata:Object,tooltipSortingMethod:String,_colorScale:{type:Object,value:new Ti.DataSeriesColorScale({scale:pf.runsColorScale}),readOnly:!0},_tagFilter:{type:String,computed:"_computeTagFilter(marginChartSeries)"},_tagFilterInvalid:Boolean,_nameToDataSeries:{type:Object,
value:()=>({})},_seriesNames:{type:Object,computed:"_computeSeriesNames(_nameToDataSeries, runs)"},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},_logScaleActive:Boolean,_dataUrl:{type:Function,value:function(){return b=>{const d=this._tagFilter;return vc.addParams(vc.getRouter().pluginRoute("custom_scalars","/scalars"),{tag:d,run:b})}}},_runToNextAvailableSymbolIndex:{type:Object,value:{}},_matchesListOpened:{type:Boolean,value:!1},_titleDisplayString:{type:String,computed:"_computeTitleDisplayString(title)"},
_fillArea:{type:Object,readOnly:!0,value:{lowerAccessor:b=>b.lower,higherAccessor:b=>b.upper}},_tooltipColumns:{type:Array,value:function(){const b=rg.multiscaleFormatter(rg.Y_TOOLTIP_FORMATTER_PRECISION),d=f=>isNaN(f)?"NaN":b(f);return[{title:"Name",evaluate:f=>f.dataset.metadata().name},{title:"Value",evaluate:f=>d(f.datum.scalar)},{title:"Lower Margin",evaluate:f=>d(f.datum.lower)},{title:"Upper Margin",evaluate:f=>d(f.datum.upper)},{title:"Step",evaluate:f=>rg.stepFormatter(f.datum.step)},{title:"Time",
evaluate:f=>rg.timeFormatter(f.datum.wall_time)},{title:"Relative",evaluate:f=>rg.relativeFormatter(rg.relativeAccessor(f.datum,-1,f.dataset))}]}},_missingTags:{type:Array,value:[]},_missingTagsCollapsibleOpened:{type:Boolean,value:!1},_stepsMismatch:Object},observers:["_updateChart(_nameToDataSeries)","_refreshDataSeries(_tagFilter)"],reload(){this.$.loader.reload()},redraw(){this.$.loader.redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()},_toggleLogScale(){this.set("_logScaleActive",
!this._logScaleActive)},_resetDomain(){const b=this.$.loader;b&&b.resetDomain()},_csvUrl(b,d){if(!d)return"";b=this._downloadDataUrl(b,d);return vc.addParams(b,{format:"csv"})},_jsonUrl(b,d){if(!d)return"";b=this._downloadDataUrl(b,d);return vc.addParams(b,{format:"json"})},_downloadDataUrl(b,d){b=b[d];b={tag:b.getTag(),run:b.getRun()};return vc.addParams(vc.getRouter().pluginRoute("custom_scalars","/download_data"),b)},_createProcessDataFunction(b){return(d,f,h)=>{if(h.regex_valid){var k=_.clone(this._nameToDataSeries),
t=[];_.forEach(b,l=>{var p=!1,m=h.tag_to_events[l.value];const n=h.tag_to_events[l.lower],q=h.tag_to_events[l.upper];_.isUndefined(m)&&(t.push(l.value),p=!0);_.isUndefined(n)&&(t.push(l.lower),p=!0);_.isUndefined(q)&&(t.push(l.upper),p=!0);if(!p){var u=y=>y[1];if(p=this._findStepMismatch(l,m.map(u),n.map(u),q.map(u)))this.set("_stepsMismatch",p);else{var x=y=>y[2];p=m.map((y,w)=>({wall_time:new Date(1E3*y[0]),step:u(y),scalar:x(y),lower:x(n[w]),upper:x(q[w])}));m=Ti.generateDataSeriesName(f,l.value);
var A=k[m];A?A.setData(p):(l=this._createNewDataSeries(f,l.value,m,p),k[m]=l)}}});this.set("_nameToDataSeries",k);d=_.findIndex(this._missingTags,l=>l.run===f);if(t.length&&3!=t.length){const l={run:f,tags:t};0<=d?this.splice("_missingTags",d,1,l):this.push("_missingTags",l)}else 0<=d&&this.splice("_missingTags",d,1)}else this.set("_tagFilterInvalid",!0)}},_findStepMismatch(b,d,f,h){return _.isEqual(f,d)&&_.isEqual(h,d)?null:{seriesObject:b,valueSteps:d,lowerSteps:f,upperSteps:h}},_createNewDataSeries(b,
d,f,h){this._runToNextAvailableSymbolIndex[b]|=0;d=new Ti.DataSeries(b,d,f,h,rg.SYMBOLS_LIST[this._runToNextAvailableSymbolIndex[b]]);this._runToNextAvailableSymbolIndex[b]=(this._runToNextAvailableSymbolIndex[b]+1)%rg.SYMBOLS_LIST.length;return d},_updateChart(b){_.forOwn(b,d=>{this.$.loader.setSeriesData(d.getName(),d.getData())})},_computeSeriesNames(){const b=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter(([,d])=>b.has(d.run)).map(([d])=>d)},_determineColor(b,d){return b.scale(d)},
_refreshDataSeries(){this.set("_nameToDataSeries",{})},_createSymbolFunction(){return b=>this._nameToDataSeries[b].getSymbol().method()},_determineSymbol(b,d){return b[d].getSymbol().character},_computeTagFilter(b){return _.flatten(b.map(d=>[d.value,d.lower,d.upper])).map(d=>"("+this._escapeRegexCharacters(d)+")").join("|")},_escapeRegexCharacters(b){return b.replace(/[.*+?^${}()|[\]\\]/g,"\\$\x26")},_getToggleCollapsibleIcon(b){return b?"expand-less":"expand-more"},_toggleMatchesOpen(){this.set("_matchesListOpened",
!this._matchesListOpened)},_computeTitleDisplayString(b){return b||"untitled"},_separateWithCommas(b){return b.join(", ")},_toggleMissingTagsCollapsibleOpen(){this.set("_missingTagsCollapsibleOpened",!this._missingTagsCollapsibleOpened)},_matchListEntryColorUpdated(){const b=this.$$("#match-list-repeat");b&&this.root.querySelectorAll(".match-list-entry").forEach(d=>{const f=b.itemForElement(d);d.style.color=this._determineColor(this._colorScale,f)})}});

//# sourceURL=build://tf-custom-scalar-dashboard/tf-custom-scalar-multi-line-chart-card.html.js
Polymer({is:"tf-custom-scalar-multi-line-chart-card",properties:{runs:Array,xType:String,active:{type:Boolean,value:!0,readOnly:!0},title:String,tagRegexes:Array,ignoreYOutliers:Boolean,requestManager:Object,showDownloadLinks:Boolean,smoothingEnabled:Boolean,smoothingWeight:Number,tagMetadata:Object,tooltipSortingMethod:String,_colorScale:{type:Object,value:new Ti.DataSeriesColorScale({scale:pf.runsColorScale}),readOnly:!0},_tagFilter:{type:String,computed:"_computeTagFilter(tagRegexes)"},_nameToDataSeries:{type:Object,
value:()=>({})},_seriesNames:{type:Object,computed:"_computeSeriesNames(_nameToDataSeries, runs)"},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},_logScaleActive:Boolean,_dataUrl:{type:Function,value:function(){return b=>{const d=this._tagFilter;return vc.addParams(vc.getRouter().pluginRoute("custom_scalars","/scalars"),{tag:d,run:b})}}},_runToNextAvailableSymbolIndex:{type:Object,value:{}},_matchesListOpened:{type:Boolean,value:!1},_titleDisplayString:{type:String,computed:"_computeTitleDisplayString(title)"}},
observers:["_updateChart(_nameToDataSeries)","_refreshDataSeries(_tagFilter)"],reload(){this.$.loader.reload()},redraw(){this.$.loader.redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()},_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)},_resetDomain(){const b=this.$.loader;b&&b.resetDomain()},_csvUrl(b,d){if(!d)return"";b=this._downloadDataUrl(b,d);return vc.addParams(b,{format:"csv"})},_jsonUrl(b,d){if(!d)return"";b=this._downloadDataUrl(b,d);return vc.addParams(b,
{format:"json"})},_downloadDataUrl(b,d){b=b[d];b={tag:b.getTag(),run:b.getRun()};return vc.addParams(vc.getRouter().pluginRoute("custom_scalars","/download_data"),b)},_createProcessDataFunction(){return(b,d,f)=>{if(f.regex_valid){const h=_.clone(this._nameToDataSeries);_.forOwn(f.tag_to_events,(k,t)=>{const l=k.map(m=>({wall_time:new Date(1E3*m[0]),step:m[1],scalar:m[2]}));k=Ti.generateDataSeriesName(d,t);const p=h[k];p?p.setData(l):(_.isUndefined(this._runToNextAvailableSymbolIndex[d])&&(this._runToNextAvailableSymbolIndex[d]=
0),t=new Ti.DataSeries(d,t,k,l,rg.SYMBOLS_LIST[this._runToNextAvailableSymbolIndex[d]]),h[k]=t,this._runToNextAvailableSymbolIndex[d]=(this._runToNextAvailableSymbolIndex[d]+1)%rg.SYMBOLS_LIST.length)});this.set("_nameToDataSeries",h)}}},_updateChart(b){Object.entries(b).forEach(([d,f])=>{this.$.loader.setSeriesData(d,f.getData())})},_computeSelectedRunsSet(b){const d={};_.forEach(b,f=>{d[f]=1});return d},_computeSeriesNames(){const b=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter(([,
d])=>b.has(d.run)).map(([d])=>d)},_determineColor(b,d){return b.scale(d)},_refreshDataSeries(){this.set("_nameToDataSeries",{})},_createSymbolFunction(){return b=>this._nameToDataSeries[b].getSymbol().method()},_determineSymbol(b,d){return b[d].getSymbol().character},_computeTagFilter(b){return 1===b.length?b[0]:b.map(d=>"("+d+")").join("|")},_getToggleMatchesIcon(b){return b?"expand-less":"expand-more"},_toggleMatchesOpen(){this.set("_matchesListOpened",!this._matchesListOpened)},_computeTitleDisplayString(b){return b||
"untitled"},_matchListEntryColorUpdated(){const b=this.$$("#match-list-repeat");b&&this.root.querySelectorAll(".match-list-entry").forEach(d=>{const f=b.itemForElement(d);d.style.color=this._determineColor(this._colorScale,f)})}});

//# sourceURL=build://tf-custom-scalar-dashboard/tf-custom-scalar-dashboard.html.js
Polymer({is:"tf-custom-scalar-dashboard",properties:{_requestManager:{type:Object,value:()=>new vc.RequestManager(50)},_canceller:{type:Object,value:()=>new vc.Canceller},_selectedRuns:Array,_showDownloadLinks:{type:Boolean,notify:!0,value:pd.getBooleanInitializer("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),observer:"_showDownloadLinksObserver"},_smoothingEnabled:{type:Boolean,computed:"_computeSmoothingEnabled(_smoothingWeight)"},_smoothingWeight:{type:Number,notify:!0,value:pd.getNumberInitializer("_smoothingWeight",
{defaultValue:.6}),observer:"_smoothingWeightObserver"},_ignoreYOutliers:{type:Boolean,value:pd.getBooleanInitializer("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}),observer:"_ignoreYOutliersObserver"},_xType:{type:String,value:"step"},_layout:Object,_dataNotFound:Boolean,_categories:{type:Array,computed:"_makeCategories(_layout)"},_openedCategories:{type:Object},_active:{type:Boolean,value:!0,readOnly:!0}},ready(){this.reload()},reload(){const b=vc.getRouter().pluginsListing(),d=this._canceller.cancellable(f=>
{f.cancelled||(this.set("_dataNotFound",!f.value.custom_scalars),this._dataNotFound||this._retrieveLayoutAndData())});this._requestManager.request(b).then(d)},_reloadCharts(){this.root.querySelectorAll("tf-custom-scalar-margin-chart-card, tf-custom-scalar-multi-line-chart-card").forEach(b=>{b.reload()})},_retrieveLayoutAndData(){const b=vc.getRouter().pluginRoute("custom_scalars","/layout"),d=this._canceller.cancellable(f=>{f.cancelled||(this.set("_layout",f.value),this._dataNotFound||this._reloadCharts())});
this._requestManager.request(b).then(d)},_showDownloadLinksObserver:pd.getBooleanObserver("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),_smoothingWeightObserver:pd.getNumberObserver("_smoothingWeight",{defaultValue:.6}),_ignoreYOutliersObserver:pd.getBooleanObserver("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}),_computeSmoothingEnabled(b){return 0<b},_makeCategories(b){if(!b.category)return[];let d=!1;this._openedCategories||(d=!0,this._openedCategories={});return b.category.map(f=>
{d&&!f.closed&&(this._openedCategories[f.title]=!0);return{name:f.title,items:f.chart,metadata:{opened:!!this._openedCategories[f.title]}}})},_categoryOpenedToggled(b){b=b.target;b.opened?this._openedCategories[b.category.name]=!0:delete this._openedCategories[b.category.name]}});

//# sourceURL=build://tf-image-dashboard/tf-image-loader.html.js
Polymer({is:"tf-image-loader",properties:{run:String,tag:String,sample:Number,ofSamples:Number,tagMetadata:Object,_runColor:{type:String,computed:"_computeRunColor(run)"},actualSize:{type:Boolean,value:!1,reflectToAttribute:!0},brightnessAdjustment:{type:Number,value:.5},contrastPercentage:{type:Number,value:0},requestManager:Object,_metadataCanceller:{type:Object,value:()=>new vc.Canceller},_imageCanceller:{type:Object,value:()=>new vc.Canceller},_steps:{type:Array,value:[],notify:!0},_stepIndex:{type:Number,
notify:!0},_currentStep:{type:Object,computed:"_computeCurrentStep(_steps, _stepIndex)"},_hasAtLeastOneStep:{type:Boolean,computed:"_computeHasAtLeastOneStep(_steps)"},_hasMultipleSteps:{type:Boolean,computed:"_computeHasMultipleSteps(_steps)"},_stepValue:{type:Number,computed:"_computeStepValue(_currentStep)"},_currentWallTime:{type:String,computed:"_computeCurrentWallTime(_currentStep)"},_maxStepIndex:{type:Number,computed:"_computeMaxStepIndex(_steps)"},_sampleText:{type:String,computed:"_computeSampleText(sample)"},
_hasMultipleSamples:{type:Boolean,computed:"_computeHasMultipleSamples(ofSamples)"},_isImageLoading:{type:Boolean,value:!1}},observers:["reload(run, tag)","_updateImageUrl(_currentStep, brightnessAdjustment, contrastPercentage)"],_computeRunColor(b){return pf.runsColorScale(b)},_computeHasAtLeastOneStep(b){return!!b&&0<b.length},_computeHasMultipleSteps(b){return!!b&&1<b.length},_computeCurrentStep(b,d){return b[d]||null},_computeStepValue(b){return b?b.step:0},_computeCurrentWallTime(b){return b?
Hh.formatDate(b.wall_time):""},_computeMaxStepIndex(b){return b.length-1},_computeSampleText(b){return`${b+1}`},_computeHasMultipleSamples(b){return 1<b},_getAriaExpanded(){return this.actualSize?"true":"false"},attached(){this._attached=!0;this.reload()},reload(){if(this._attached){this._metadataCanceller.cancelAll();var b=vc.addParams(vc.getRouter().pluginRoute("images","/images"),{tag:this.tag,run:this.run,sample:this.sample}),d=this._metadataCanceller.cancellable(f=>{f.cancelled||(f=f.value.map(this._createStepDatum.bind(this)),
this.set("_steps",f),this.set("_stepIndex",f.length-1))});this.requestManager.request(b).then(d)}},_createStepDatum(b){let d=vc.getRouter().pluginRoute("images","/individualImage");d=vc.addParams(d,{ts:b.wall_time});d+="\x26"+b.query;return{wall_time:new Date(1E3*b.wall_time),step:b.step,url:d}},_updateImageUrl(b,d,f){if(b){var h=new Image;this._imageCanceller.cancelAll();h.onload=h.onerror=this._imageCanceller.cancellable(k=>{k.cancelled||(k=this.$$("#main-image-container"),k.innerHTML="",Polymer.dom(k).appendChild(h),
this.set("_isImageLoading",!1))}).bind(this);h.style.filter=`contrast(${f}%) `;h.style.filter+=`brightness(${d})`;this.set("_isImageLoading",!0);h.src=b.url}},_handleTap(){this.set("actualSize",!this.actualSize)},_toLocaleString(b){return b.toLocaleString()}});

//# sourceURL=build://tf-image-dashboard/tf-image-dashboard.html.js
Polymer({is:"tf-image-dashboard",properties:{_selectedRuns:Array,_runToTagInfo:Object,_dataNotFound:Boolean,_actualSize:Boolean,_defaultBrightnessAdjustment:{type:Number,value:1,readOnly:!0},_defaultContrastPercentage:{type:Number,value:100,readOnly:!0},_brightnessAdjustment:{type:Number,value:1},_contrastPercentage:{type:Number,value:100},_tagFilter:String,_brightnessIsDefault:{type:Boolean,computed:"_computeBrightnessIsDefault(_brightnessAdjustment)"},_contrastIsDefault:{type:Boolean,computed:"_computeContrastIsDefault(_contrastPercentage)"},
_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTagInfo, _selectedRuns, _tagFilter, _categoriesDomReady)"},_requestManager:{type:Object,value:()=>new vc.RequestManager}},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadImages()})},_fetchTags(){const b=vc.getRouter().pluginRoute("images","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,h=>Object.keys(h));f=vc.getTags(f);this.set("_dataNotFound",
0===f.length);this.set("_runToTagInfo",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_reloadImages(){this.root.querySelectorAll("tf-image-loader").forEach(b=>{b.reload()})},_shouldOpen(b){return 2>=b},_resetBrightness(){this._brightnessAdjustment=this._defaultBrightnessAdjustment},_resetContrast(){this._contrastPercentage=this._defaultContrastPercentage},_computeBrightnessIsDefault(b){return b===this._defaultBrightnessAdjustment},_computeContrastIsDefault(b){return b===this._defaultContrastPercentage},
_makeCategories(b,d,f){function h(t){const l=b[t.run][t.tag].samples;return _.range(l).map(p=>Object.assign({},t,{sample:p,ofSamples:l}))}const k=_.mapValues(b,t=>Object.keys(t));return $c.categorizeRunTagCombinations(k,d,f).map(t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(h))}))},_tagMetadata(b,d,f){return b[d][f]}});

//# sourceURL=build://tf-audio-dashboard/tf-audio-loader.html.js
Polymer({is:"tf-audio-loader",properties:{run:String,tag:String,sample:Number,totalSamples:Number,tagMetadata:Object,_runColor:{type:String,computed:"_computeRunColor(run)"},requestManager:Object,_metadataCanceller:{type:Object,value:()=>new vc.Canceller},_steps:{type:Array,value:()=>[]},_stepIndex:Number,_hasAtLeastOneStep:{type:Boolean,computed:"_computeHasAtLeastOneStep(_steps)"},_hasMultipleSteps:{type:Boolean,computed:"_computeHasMultipleSteps(_steps)"},_currentDatum:{type:Object,computed:"_computeCurrentDatum(_steps, _stepIndex)"},
_maxStepIndex:{type:Number,computed:"_computeMaxStepIndex(_steps)"},_sampleText:{type:String,computed:"_computeSampleText(sample)"},_hasMultipleSamples:{type:Boolean,computed:"_computeHasMultipleSamples(totalSamples)"}},observers:["reload(run, tag)"],_computeRunColor(b){return pf.runsColorScale(b)},_computeHasAtLeastOneStep(b){return!!b&&0<b.length},_computeHasMultipleSteps(b){return!!b&&1<b.length},_computeMaxStepIndex(b){return b.length-1},_computeCurrentDatum(b,d){return b[d]},_computeSampleText(b){return`${b+
1}`},_computeHasMultipleSamples(b){return 1<b},attached(){this._attached=!0;this.reload()},reload(){if(this._attached){this._metadataCanceller.cancelAll();var b=vc.getRouter().pluginRoute("audio","/audio",new URLSearchParams({tag:this.tag,run:this.run,sample:this.sample})),d=this._metadataCanceller.cancellable(f=>{f.cancelled||(f=f.value.map(this._createStepDatum.bind(this)),this.set("_steps",f),this.set("_stepIndex",f.length-1))});this.requestManager.request(b).then(d)}},_createStepDatum(b){var d=
new URLSearchParams(b.query);d.append("ts",b.wall_time);d=vc.getRouter().pluginRoute("audio","/individualAudio",d);return{wall_time:Hh.formatDate(new Date(1E3*b.wall_time)),step:b.step,label:b.label,contentType:b.contentType,url:d}}});

//# sourceURL=build://tf-audio-dashboard/tf-audio-dashboard.html.js
Polymer({is:"tf-audio-dashboard",properties:{_selectedRuns:Array,_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:{type:String,value:""},_categories:{type:Array,computed:"_makeCategories(_runToTagInfo, _selectedRuns, _tagFilter)"},_requestManager:{type:Object,value:()=>new vc.RequestManager}},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadAudio()})},_fetchTags(){const b=vc.getRouter().pluginRoute("audio","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,
this._runToTagInfo)){var f=_.mapValues(d,h=>Object.keys(h));f=vc.getTags(f);this.set("_dataNotFound",0===f.length);this.set("_runToTagInfo",d)}})},_reloadAudio(){this.root.querySelectorAll("tf-audio-loader").forEach(b=>{b.reload()})},_shouldOpen(b){return 2>=b},_makeCategories(b,d,f){function h(t){const l=b[t.run][t.tag].samples;return _.range(l).map(p=>Object.assign({},t,{sample:p,totalSamples:l}))}const k=_.mapValues(b,t=>Object.keys(t));return $c.categorizeRunTagCombinations(k,d,f).map(t=>Object.assign({},
t,{items:[].concat.apply([],t.items.map(h))}))},_tagMetadata(b,d,f){return b[d][f]}});

//# sourceURL=build://iron-autogrow-textarea/iron-autogrow-textarea.html.js
Polymer({is:"iron-autogrow-textarea",behaviors:[Polymer.IronValidatableBehavior,Polymer.IronControlState],properties:{value:{observer:"_valueChanged",type:String,notify:!0},bindValue:{observer:"_bindValueChanged",type:String,notify:!0},rows:{type:Number,value:1,observer:"_updateCached"},maxRows:{type:Number,value:0,observer:"_updateCached"},autocomplete:{type:String,value:"off"},autofocus:{type:Boolean,value:!1},inputmode:{type:String},placeholder:{type:String},readonly:{type:String},required:{type:Boolean},
minlength:{type:Number},maxlength:{type:Number},label:{type:String}},listeners:{input:"_onInput"},get textarea(){return this.$.textarea},get selectionStart(){return this.$.textarea.selectionStart},get selectionEnd(){return this.$.textarea.selectionEnd},set selectionStart(b){this.$.textarea.selectionStart=b},set selectionEnd(b){this.$.textarea.selectionEnd=b},attached:function(){navigator.userAgent.match(/iP(?:[oa]d|hone)/)&&(this.$.textarea.style.marginLeft="-3px")},validate:function(){var b=this.$.textarea.validity.valid;
b&&(this.required&&""===this.value?b=!1:this.hasValidator()&&(b=Polymer.IronValidatableBehavior.validate.call(this,this.value)));this.invalid=!b;this.fire("iron-input-validate");return b},_bindValueChanged:function(b){this.value=b},_valueChanged:function(b){var d=this.textarea;d&&(d.value!==b&&(d.value=b||0===b?b:""),this.bindValue=b,this.$.mirror.innerHTML=this._valueForMirror(),this.fire("bind-value-changed",{value:this.bindValue}))},_onInput:function(b){var d=Polymer.dom(b).path;this.value=d?d[0].value:
b.target.value},_constrain:function(b){b=b||[""];for(b=0<this.maxRows&&b.length>this.maxRows?b.slice(0,this.maxRows):b.slice(0);0<this.rows&&b.length<this.rows;)b.push("");return b.join("\x3cbr/\x3e")+"\x26#160;"},_valueForMirror:function(){var b=this.textarea;if(b)return this.tokens=b&&b.value?b.value.replace(/&/gm,"\x26amp;").replace(/"/gm,"\x26quot;").replace(/'/gm,"\x26#39;").replace(/</gm,"\x26lt;").replace(/>/gm,"\x26gt;").split("\n"):[""],this._constrain(this.tokens)},_updateCached:function(){this.$.mirror.innerHTML=
this._constrain(this.tokens)}});

//# sourceURL=build://paper-input/paper-textarea.html.js
Polymer({is:"paper-textarea",behaviors:[Polymer.PaperInputBehavior,Polymer.IronFormElementBehavior],properties:{_ariaLabelledBy:{observer:"_ariaLabelledByChanged",type:String},_ariaDescribedBy:{observer:"_ariaDescribedByChanged",type:String},value:{type:String},rows:{type:Number,value:1},maxRows:{type:Number,value:0}},get selectionStart(){return this.$.input.textarea.selectionStart},set selectionStart(b){this.$.input.textarea.selectionStart=b},get selectionEnd(){return this.$.input.textarea.selectionEnd},
set selectionEnd(b){this.$.input.textarea.selectionEnd=b},_ariaLabelledByChanged:function(b){this._focusableElement.setAttribute("aria-labelledby",b)},_ariaDescribedByChanged:function(b){this._focusableElement.setAttribute("aria-describedby",b)},get _focusableElement(){return this.inputElement.textarea}});

//# sourceURL=build://paper-toast/paper-toast.html.js
(function(){var b=null;Polymer({is:"paper-toast",behaviors:[Polymer.IronOverlayBehavior],properties:{fitInto:{type:Object,value:window,observer:"_onFitIntoChanged"},horizontalAlign:{type:String,value:"left"},verticalAlign:{type:String,value:"bottom"},duration:{type:Number,value:3E3},text:{type:String,value:""},noCancelOnOutsideClick:{type:Boolean,value:!0},noAutoFocus:{type:Boolean,value:!0}},listeners:{transitionend:"__onTransitionEnd"},get visible(){Polymer.Base._warn("`visible` is deprecated, use `opened` instead");
return this.opened},get _canAutoClose(){return 0<this.duration&&Infinity!==this.duration},created:function(){this._autoClose=null;Polymer.IronA11yAnnouncer.requestAvailability()},show:function(d){"string"==typeof d&&(d={text:d});for(var f in d)0===f.indexOf("_")?Polymer.Base._warn('The property "'+f+'" is private and was not set.'):f in this?this[f]=d[f]:Polymer.Base._warn('The property "'+f+'" is not valid.');this.open()},hide:function(){this.close()},__onTransitionEnd:function(d){d&&d.target===
this&&"opacity"===d.propertyName&&(this.opened?this._finishRenderOpened():this._finishRenderClosed())},_openedChanged:function(){null!==this._autoClose&&(this.cancelAsync(this._autoClose),this._autoClose=null);this.opened?(b&&b!==this&&b.close(),b=this,this.fire("iron-announce",{text:this.text}),this._canAutoClose&&(this._autoClose=this.async(this.close,this.duration))):b===this&&(b=null);Polymer.IronOverlayBehaviorImpl._openedChanged.apply(this,arguments)},_renderOpened:function(){this.classList.add("paper-toast-open")},
_renderClosed:function(){this.classList.remove("paper-toast-open")},_onFitIntoChanged:function(d){this.positionTarget=d}})})();

//# sourceURL=build://paper-toggle-button/paper-toggle-button.html.js
Polymer({is:"paper-toggle-button",behaviors:[Polymer.PaperCheckedElementBehavior],hostAttributes:{role:"button","aria-pressed":"false",tabindex:0},properties:{},listeners:{track:"_ontrack"},attached:function(){Polymer.RenderStatus.afterNextRender(this,function(){Polymer.Gestures.setTouchAction(this,"pan-y")})},_ontrack:function(b){b=b.detail;"start"===b.state?this._trackStart(b):"track"===b.state?this._trackMove(b):"end"===b.state&&this._trackEnd(b)},_trackStart:function(){this._width=this.$.toggleBar.offsetWidth/
2;this._trackChecked=this.checked;this.$.toggleButton.classList.add("dragging")},_trackMove:function(b){b=b.dx;this._x=Math.min(this._width,Math.max(0,this._trackChecked?this._width+b:b));this.translate3d(this._x+"px",0,0,this.$.toggleButton);this._userActivate(this._x>this._width/2)},_trackEnd:function(){this.$.toggleButton.classList.remove("dragging");this.transform("",this.$.toggleButton)},_createRipple:function(){this._rippleContainer=this.$.toggleButton;var b=Polymer.PaperRippleBehavior._createRipple();
b.id="ink";b.setAttribute("recenters","");b.classList.add("circle","toggle-ink");return b}});

(function(f){if(typeof exports==="object"&&typeof module!=="undefined")module.exports=f();else if(typeof define==="function"&&define.amd)define([],f);else{var g;if(typeof window!=="undefined")g=window;else if(typeof global!=="undefined")g=global;else if(typeof self!=="undefined")g=self;else g=this;g.graphlib=f()}})(function(){var define,module,exports;return function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);
var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f;}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}({1:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":17,"./lib/alg":8,"./lib/json":18}],
2:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length)cmpts.push(cmpt)});return cmpts}},{"../lodash":19}],3:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs))vs=
[vs];var navigation=(g.isDirected()?g.successors:g.neighbors).bind(g);var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v))throw new Error("Graph does not have node: "+v);doDfs(g,v,order==="post",visited,navigation,acc)});return acc}function doDfs(g,v,postorder,visited,navigation,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder)acc.push(v);_.each(navigation(v),function(w){doDfs(g,w,postorder,visited,navigation,acc)});if(postorder)acc.push(v)}}},{"../lodash":19}],4:[function(require,
module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"../lodash":19,"./dijkstra":5}],5:[function(require,module,exports){var _=require("../lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,
String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0)throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight);if(distance<wEntry.distance){wEntry.distance=distance;wEntry.predecessor=
v;pq.decrease(w,distance)}};g.nodes().forEach(function(v){var distance=v===source?0:Number.POSITIVE_INFINITY;results[v]={distance:distance};pq.add(v,distance)});while(pq.size()>0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY)break;edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":15,"../lodash":19}],6:[function(require,module,exports){var _=require("../lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),
function(cmpt){return cmpt.length>1||cmpt.length===1&&g.hasEdge(cmpt[0],cmpt[0])})}},{"../lodash":19,"./tarjan":13}],7:[function(require,module,exports){var _=require("../lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]=
{};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w)results[v][w]={distance:Number.POSITIVE_INFINITY}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance<ij.distance){ij.distance=altDistance;ij.predecessor=
kj.predecessor}})})});return results}},{"../lodash":19}],8:[function(require,module,exports){module.exports={components:require("./components"),dijkstra:require("./dijkstra"),dijkstraAll:require("./dijkstra-all"),findCycles:require("./find-cycles"),floydWarshall:require("./floyd-warshall"),isAcyclic:require("./is-acyclic"),postorder:require("./postorder"),preorder:require("./preorder"),prim:require("./prim"),tarjan:require("./tarjan"),topsort:require("./topsort")}},{"./components":2,"./dijkstra":5,
"./dijkstra-all":4,"./find-cycles":6,"./floyd-warshall":7,"./is-acyclic":9,"./postorder":10,"./preorder":11,"./prim":12,"./tarjan":13,"./topsort":14}],9:[function(require,module,exports){var topsort=require("./topsort");module.exports=isAcyclic;function isAcyclic(g){try{topsort(g)}catch(e){if(e instanceof topsort.CycleException)return false;throw e;}return true}},{"./topsort":14}],10:[function(require,module,exports){var dfs=require("./dfs");module.exports=postorder;function postorder(g,vs){return dfs(g,
vs,"post")}},{"./dfs":3}],11:[function(require,module,exports){var dfs=require("./dfs");module.exports=preorder;function preorder(g,vs){return dfs(g,vs,"pre")}},{"./dfs":3}],12:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graph"),PriorityQueue=require("../data/priority-queue");module.exports=prim;function prim(g,weightFunc){var result=new Graph,parents={},pq=new PriorityQueue,v;function updateNeighbors(edge){var w=edge.v===v?edge.w:edge.v,pri=pq.priority(w);if(pri!==
undefined){var edgeWeight=weightFunc(edge);if(edgeWeight<pri){parents[w]=v;pq.decrease(w,edgeWeight)}}}if(g.nodeCount()===0)return result;_.each(g.nodes(),function(v){pq.add(v,Number.POSITIVE_INFINITY);result.setNode(v)});pq.decrease(g.nodes()[0],0);var init=false;while(pq.size()>0){v=pq.removeMin();if(_.has(parents,v))result.setEdge(v,parents[v]);else if(init)throw new Error("Input graph is not connected: "+g);else init=true;g.nodeEdges(v).forEach(updateNeighbors)}return result}},{"../data/priority-queue":15,
"../graph":16,"../lodash":19}],13:[function(require,module,exports){var _=require("../lodash");module.exports=tarjan;function tarjan(g){var index=0,stack=[],visited={},results=[];function dfs(v){var entry=visited[v]={onStack:true,lowlink:index,index:index++};stack.push(v);g.successors(v).forEach(function(w){if(!_.has(visited,w)){dfs(w);entry.lowlink=Math.min(entry.lowlink,visited[w].lowlink)}else if(visited[w].onStack)entry.lowlink=Math.min(entry.lowlink,visited[w].index)});if(entry.lowlink===entry.index){var cmpt=
[],w;do{w=stack.pop();visited[w].onStack=false;cmpt.push(w)}while(v!==w);results.push(cmpt)}}g.nodes().forEach(function(v){if(!_.has(visited,v))dfs(v)});return results}},{"../lodash":19}],14:[function(require,module,exports){var _=require("../lodash");module.exports=topsort;topsort.CycleException=CycleException;function topsort(g){var visited={},stack={},results=[];function visit(node){if(_.has(stack,node))throw new CycleException;if(!_.has(visited,node)){stack[node]=true;visited[node]=true;_.each(g.predecessors(node),
visit);delete stack[node];results.push(node)}}_.each(g.sinks(),visit);if(_.size(visited)!==g.nodeCount())throw new CycleException;return results}function CycleException(){}},{"../lodash":19}],15:[function(require,module,exports){var _=require("../lodash");module.exports=PriorityQueue;function PriorityQueue(){this._arr=[];this._keyIndices={}}PriorityQueue.prototype.size=function(){return this._arr.length};PriorityQueue.prototype.keys=function(){return this._arr.map(function(x){return x.key})};PriorityQueue.prototype.has=
function(key){return _.has(this._keyIndices,key)};PriorityQueue.prototype.priority=function(key){var index=this._keyIndices[key];if(index!==undefined)return this._arr[index].priority};PriorityQueue.prototype.min=function(){if(this.size()===0)throw new Error("Queue underflow");return this._arr[0].key};PriorityQueue.prototype.add=function(key,priority){var keyIndices=this._keyIndices;key=String(key);if(!_.has(keyIndices,key)){var arr=this._arr;var index=arr.length;keyIndices[key]=index;arr.push({key:key,
priority:priority});this._decrease(index);return true}return false};PriorityQueue.prototype.removeMin=function(){this._swap(0,this._arr.length-1);var min=this._arr.pop();delete this._keyIndices[min.key];this._heapify(0);return min.key};PriorityQueue.prototype.decrease=function(key,priority){var index=this._keyIndices[key];if(priority>this._arr[index].priority)throw new Error("New priority is greater than current priority. "+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority);this._arr[index].priority=
priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l<arr.length){largest=arr[l].priority<arr[largest].priority?l:largest;if(r<arr.length)largest=arr[r].priority<arr[largest].priority?r:largest;if(largest!==i){this._swap(i,largest);this._heapify(largest)}}};PriorityQueue.prototype._decrease=function(index){var arr=this._arr;var priority=arr[index].priority;var parent;while(index!==0){parent=index>>1;if(arr[parent].priority<priority)break;
this._swap(index,parent);index=parent}};PriorityQueue.prototype._swap=function(i,j){var arr=this._arr;var keyIndices=this._keyIndices;var origArrI=arr[i];var origArrJ=arr[j];arr[i]=origArrJ;arr[j]=origArrI;keyIndices[origArrJ.key]=i;keyIndices[origArrI.key]=j}},{"../lodash":19}],16:[function(require,module,exports){var _=require("./lodash");module.exports=Graph;var DEFAULT_EDGE_NAME="\x00",GRAPH_NODE="\x00",EDGE_KEY_DELIM="\u0001";function Graph(opts){this._isDirected=_.has(opts,"directed")?opts.directed:
true;this._isMultigraph=_.has(opts,"multigraph")?opts.multigraph:false;this._isCompound=_.has(opts,"compound")?opts.compound:false;this._label=undefined;this._defaultNodeLabelFn=_.constant(undefined);this._defaultEdgeLabelFn=_.constant(undefined);this._nodes={};if(this._isCompound){this._parent={};this._children={};this._children[GRAPH_NODE]={}}this._in={};this._preds={};this._out={};this._sucs={};this._edgeObjs={};this._edgeLabels={}}Graph.prototype._nodeCount=0;Graph.prototype._edgeCount=0;Graph.prototype.isDirected=
function(){return this._isDirected};Graph.prototype.isMultigraph=function(){return this._isMultigraph};Graph.prototype.isCompound=function(){return this._isCompound};Graph.prototype.setGraph=function(label){this._label=label;return this};Graph.prototype.graph=function(){return this._label};Graph.prototype.setDefaultNodeLabel=function(newDefault){if(!_.isFunction(newDefault))newDefault=_.constant(newDefault);this._defaultNodeLabelFn=newDefault;return this};Graph.prototype.nodeCount=function(){return this._nodeCount};
Graph.prototype.nodes=function(){return _.keys(this._nodes)};Graph.prototype.sources=function(){var self=this;return _.filter(this.nodes(),function(v){return _.isEmpty(self._in[v])})};Graph.prototype.sinks=function(){var self=this;return _.filter(this.nodes(),function(v){return _.isEmpty(self._out[v])})};Graph.prototype.setNodes=function(vs,value){var args=arguments;var self=this;_.each(vs,function(v){if(args.length>1)self.setNode(v,value);else self.setNode(v)});return this};Graph.prototype.setNode=
function(v,value){if(_.has(this._nodes,v)){if(arguments.length>1)this._nodes[v]=value;return this}this._nodes[v]=arguments.length>1?value:this._defaultNodeLabelFn(v);if(this._isCompound){this._parent[v]=GRAPH_NODE;this._children[v]={};this._children[GRAPH_NODE][v]=true}this._in[v]={};this._preds[v]={};this._out[v]={};this._sucs[v]={};++this._nodeCount;return this};Graph.prototype.node=function(v){return this._nodes[v]};Graph.prototype.hasNode=function(v){return _.has(this._nodes,v)};Graph.prototype.removeNode=
function(v){var self=this;if(_.has(this._nodes,v)){var removeEdge=function(e){self.removeEdge(self._edgeObjs[e])};delete this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){self.setParent(child)});delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=
function(v,parent){if(!this._isCompound)throw new Error("Cannot set parent in a non-compound graph");if(_.isUndefined(parent))parent=GRAPH_NODE;else{parent+="";for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor))if(ancestor===v)throw new Error("Setting "+parent+" as parent of "+v+" would create a cycle");this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=
function(v){delete this._children[this._parent[v]][v]};Graph.prototype.parent=function(v){if(this._isCompound){var parent=this._parent[v];if(parent!==GRAPH_NODE)return parent}};Graph.prototype.children=function(v){if(_.isUndefined(v))v=GRAPH_NODE;if(this._isCompound){var children=this._children[v];if(children)return _.keys(children)}else if(v===GRAPH_NODE)return this.nodes();else if(this.hasNode(v))return[]};Graph.prototype.predecessors=function(v){var predsV=this._preds[v];if(predsV)return _.keys(predsV)};
Graph.prototype.successors=function(v){var sucsV=this._sucs[v];if(sucsV)return _.keys(sucsV)};Graph.prototype.neighbors=function(v){var preds=this.predecessors(v);if(preds)return _.union(preds,this.successors(v))};Graph.prototype.isLeaf=function(v){var neighbors;if(this.isDirected())neighbors=this.successors(v);else neighbors=this.neighbors(v);return neighbors.length===0};Graph.prototype.filterNodes=function(filter){var copy=new this.constructor({directed:this._isDirected,multigraph:this._isMultigraph,
compound:this._isCompound});copy.setGraph(this.graph());var self=this;_.each(this._nodes,function(value,v){if(filter(v))copy.setNode(v,value)});_.each(this._edgeObjs,function(e){if(copy.hasNode(e.v)&&copy.hasNode(e.w))copy.setEdge(e,self.edge(e))});var parents={};function findParent(v){var parent=self.parent(v);if(parent===undefined||copy.hasNode(parent)){parents[v]=parent;return parent}else if(parent in parents)return parents[parent];else return findParent(parent)}if(this._isCompound)_.each(copy.nodes(),
function(v){copy.setParent(v,findParent(v))});return copy};Graph.prototype.setDefaultEdgeLabel=function(newDefault){if(!_.isFunction(newDefault))newDefault=_.constant(newDefault);this._defaultEdgeLabelFn=newDefault;return this};Graph.prototype.edgeCount=function(){return this._edgeCount};Graph.prototype.edges=function(){return _.values(this._edgeObjs)};Graph.prototype.setPath=function(vs,value){var self=this,args=arguments;_.reduce(vs,function(v,w){if(args.length>1)self.setEdge(v,w,value);else self.setEdge(v,
w);return w});return this};Graph.prototype.setEdge=function(){var v,w,name,value,valueSpecified=false,arg0=arguments[0];if(typeof arg0==="object"&&arg0!==null&&"v"in arg0){v=arg0.v;w=arg0.w;name=arg0.name;if(arguments.length===2){value=arguments[1];valueSpecified=true}}else{v=arg0;w=arguments[1];name=arguments[3];if(arguments.length>2){value=arguments[2];valueSpecified=true}}v=""+v;w=""+w;if(!_.isUndefined(name))name=""+name;var e=edgeArgsToId(this._isDirected,v,w,name);if(_.has(this._edgeLabels,
e)){if(valueSpecified)this._edgeLabels[e]=value;return this}if(!_.isUndefined(name)&&!this._isMultigraph)throw new Error("Cannot set a named edge when isMultigraph \x3d false");this.setNode(v);this.setNode(w);this._edgeLabels[e]=valueSpecified?value:this._defaultEdgeLabelFn(v,w,name);var edgeObj=edgeArgsToObj(this._isDirected,v,w,name);v=edgeObj.v;w=edgeObj.w;Object.freeze(edgeObj);this._edgeObjs[e]=edgeObj;incrementOrInitEntry(this._preds[w],v);incrementOrInitEntry(this._sucs[v],w);this._in[w][e]=
edgeObj;this._out[v][e]=edgeObj;this._edgeCount++;return this};Graph.prototype.edge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return this._edgeLabels[e]};Graph.prototype.hasEdge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return _.has(this._edgeLabels,e)};Graph.prototype.removeEdge=function(v,w,name){var e=arguments.length===1?
edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name),edge=this._edgeObjs[e];if(edge){v=edge.v;w=edge.w;delete this._edgeLabels[e];delete this._edgeObjs[e];decrementOrRemoveEntry(this._preds[w],v);decrementOrRemoveEntry(this._sucs[v],w);delete this._in[w][e];delete this._out[v][e];this._edgeCount--}return this};Graph.prototype.inEdges=function(v,u){var inV=this._in[v];if(inV){var edges=_.values(inV);if(!u)return edges;return _.filter(edges,function(edge){return edge.v===
u})}};Graph.prototype.outEdges=function(v,w){var outV=this._out[v];if(outV){var edges=_.values(outV);if(!w)return edges;return _.filter(edges,function(edge){return edge.w===w})}};Graph.prototype.nodeEdges=function(v,w){var inEdges=this.inEdges(v,w);if(inEdges)return inEdges.concat(this.outEdges(v,w))};function incrementOrInitEntry(map,k){if(map[k])map[k]++;else map[k]=1}function decrementOrRemoveEntry(map,k){if(!--map[k])delete map[k]}function edgeArgsToId(isDirected,v_,w_,name){var v=""+v_;var w=
""+w_;if(!isDirected&&v>w){var tmp=v;v=w;w=tmp}return v+EDGE_KEY_DELIM+w+EDGE_KEY_DELIM+(_.isUndefined(name)?DEFAULT_EDGE_NAME:name)}function edgeArgsToObj(isDirected,v_,w_,name){var v=""+v_;var w=""+w_;if(!isDirected&&v>w){var tmp=v;v=w;w=tmp}var edgeObj={v:v,w:w};if(name)edgeObj.name=name;return edgeObj}function edgeObjToId(isDirected,edgeObj){return edgeArgsToId(isDirected,edgeObj.v,edgeObj.w,edgeObj.name)}},{"./lodash":19}],17:[function(require,module,exports){module.exports={Graph:require("./graph"),
version:require("./version")}},{"./graph":16,"./version":20}],18:[function(require,module,exports){var _=require("./lodash"),Graph=require("./graph");module.exports={write:write,read:read};function write(g){var json={options:{directed:g.isDirected(),multigraph:g.isMultigraph(),compound:g.isCompound()},nodes:writeNodes(g),edges:writeEdges(g)};if(!_.isUndefined(g.graph()))json.value=_.clone(g.graph());return json}function writeNodes(g){return _.map(g.nodes(),function(v){var nodeValue=g.node(v),parent=
g.parent(v),node={v:v};if(!_.isUndefined(nodeValue))node.value=nodeValue;if(!_.isUndefined(parent))node.parent=parent;return node})}function writeEdges(g){return _.map(g.edges(),function(e){var edgeValue=g.edge(e),edge={v:e.v,w:e.w};if(!_.isUndefined(e.name))edge.name=e.name;if(!_.isUndefined(edgeValue))edge.value=edgeValue;return edge})}function read(json){var g=(new Graph(json.options)).setGraph(json.value);_.each(json.nodes,function(entry){g.setNode(entry.v,entry.value);if(entry.parent)g.setParent(entry.v,
entry.parent)});_.each(json.edges,function(entry){g.setEdge({v:entry.v,w:entry.w,name:entry.name},entry.value)});return g}},{"./graph":16,"./lodash":19}],19:[function(require,module,exports){var lodash;if(typeof require==="function")try{lodash=require("lodash")}catch(e){}if(!lodash)lodash=window._;module.exports=lodash},{"lodash":undefined}],20:[function(require,module,exports){module.exports="2.1.5"},{}]},{},[1])(1)});
(function(f){if(typeof exports==="object"&&typeof module!=="undefined")module.exports=f();else if(typeof define==="function"&&define.amd)define([],f);else{var g;if(typeof window!=="undefined")g=window;else if(typeof global!=="undefined")g=global;else if(typeof self!=="undefined")g=self;else g=this;g.dagre=f()}})(function(){var define,module,exports;return function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=
new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f;}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}({1:[function(require,module,exports){module.exports={graphlib:require("./lib/graphlib"),layout:require("./lib/layout"),debug:require("./lib/debug"),util:{time:require("./lib/util").time,notime:require("./lib/util").notime},
version:require("./lib/version")}},{"./lib/debug":6,"./lib/graphlib":7,"./lib/layout":9,"./lib/util":29,"./lib/version":30}],2:[function(require,module,exports){var _=require("./lodash"),greedyFAS=require("./greedy-fas");module.exports={run:run,undo:undo};function run(g){var fas=g.graph().acyclicer==="greedy"?greedyFAS(g,weightFn(g)):dfsFAS(g);_.forEach(fas,function(e){var label=g.edge(e);g.removeEdge(e);label.forwardName=e.name;label.reversed=true;g.setEdge(e.w,e.v,label,_.uniqueId("rev"))});function weightFn(g){return function(e){return g.edge(e).weight}}
}function dfsFAS(g){var fas=[],stack={},visited={};function dfs(v){if(_.has(visited,v))return;visited[v]=true;stack[v]=true;_.forEach(g.outEdges(v),function(e){if(_.has(stack,e.w))fas.push(e);else dfs(e.w)});delete stack[v]}_.forEach(g.nodes(),dfs);return fas}function undo(g){_.forEach(g.edges(),function(e){var label=g.edge(e);if(label.reversed){g.removeEdge(e);var forwardName=label.forwardName;delete label.reversed;delete label.forwardName;g.setEdge(e.w,e.v,label,forwardName)}})}},{"./greedy-fas":8,
"./lodash":10}],3:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports=addBorderSegments;function addBorderSegments(g){function dfs(v){var children=g.children(v),node=g.node(v);if(children.length)_.forEach(children,dfs);if(_.has(node,"minRank")){node.borderLeft=[];node.borderRight=[];for(var rank=node.minRank,maxRank=node.maxRank+1;rank<maxRank;++rank){addBorderNode(g,"borderLeft","_bl",v,node,rank);addBorderNode(g,"borderRight","_br",v,node,rank)}}}_.forEach(g.children(),
dfs)}function addBorderNode(g,prop,prefix,sg,sgNode,rank){var label={width:0,height:0,rank:rank,borderType:prop},prev=sgNode[prop][rank-1],curr=util.addDummyNode(g,"border",label,prefix);sgNode[prop][rank]=curr;g.setParent(curr,sg);if(prev)g.setEdge(prev,curr,{weight:1})}},{"./lodash":10,"./util":29}],4:[function(require,module,exports){var _=require("./lodash");module.exports={adjust:adjust,undo:undo};function adjust(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="lr"||rankDir==="rl")swapWidthHeight(g)}
function undo(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="bt"||rankDir==="rl")reverseY(g);if(rankDir==="lr"||rankDir==="rl"){swapXY(g);swapWidthHeight(g)}}function swapWidthHeight(g){_.forEach(g.nodes(),function(v){swapWidthHeightOne(g.node(v))});_.forEach(g.edges(),function(e){swapWidthHeightOne(g.edge(e))})}function swapWidthHeightOne(attrs){var w=attrs.width;attrs.width=attrs.height;attrs.height=w}function reverseY(g){_.forEach(g.nodes(),function(v){reverseYOne(g.node(v))});_.forEach(g.edges(),
function(e){var edge=g.edge(e);_.forEach(edge.points,reverseYOne);if(_.has(edge,"y"))reverseYOne(edge)})}function reverseYOne(attrs){attrs.y=-attrs.y}function swapXY(g){_.forEach(g.nodes(),function(v){swapXYOne(g.node(v))});_.forEach(g.edges(),function(e){var edge=g.edge(e);_.forEach(edge.points,swapXYOne);if(_.has(edge,"x"))swapXYOne(edge)})}function swapXYOne(attrs){var x=attrs.x;attrs.x=attrs.y;attrs.y=x}},{"./lodash":10}],5:[function(require,module,exports){module.exports=List;function List(){var sentinel=
{};sentinel._next=sentinel._prev=sentinel;this._sentinel=sentinel}List.prototype.dequeue=function(){var sentinel=this._sentinel,entry=sentinel._prev;if(entry!==sentinel){unlink(entry);return entry}};List.prototype.enqueue=function(entry){var sentinel=this._sentinel;if(entry._prev&&entry._next)unlink(entry);entry._next=sentinel._next;sentinel._next._prev=entry;sentinel._next=entry;entry._prev=sentinel};List.prototype.toString=function(){var strs=[],sentinel=this._sentinel,curr=sentinel._prev;while(curr!==
sentinel){strs.push(JSON.stringify(curr,filterOutLinks));curr=curr._prev}return"["+strs.join(", ")+"]"};function unlink(entry){entry._prev._next=entry._next;entry._next._prev=entry._prev;delete entry._next;delete entry._prev}function filterOutLinks(k,v){if(k!=="_next"&&k!=="_prev")return v}},{}],6:[function(require,module,exports){var _=require("./lodash"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports={debugOrdering:debugOrdering};function debugOrdering(g){var layerMatrix=
util.buildLayerMatrix(g);var h=(new Graph({compound:true,multigraph:true})).setGraph({});_.forEach(g.nodes(),function(v){h.setNode(v,{label:v});h.setParent(v,"layer"+g.node(v).rank)});_.forEach(g.edges(),function(e){h.setEdge(e.v,e.w,{},e.name)});_.forEach(layerMatrix,function(layer,i){var layerV="layer"+i;h.setNode(layerV,{rank:"same"});_.reduce(layer,function(u,v){h.setEdge(u,v,{style:"invis"});return v})});return h}},{"./graphlib":7,"./lodash":10,"./util":29}],7:[function(require,module,exports){var graphlib;
if(typeof require==="function")try{graphlib=require("graphlib")}catch(e){}if(!graphlib)graphlib=window.graphlib;module.exports=graphlib},{"graphlib":undefined}],8:[function(require,module,exports){var _=require("./lodash"),Graph=require("./graphlib").Graph,List=require("./data/list");module.exports=greedyFAS;var DEFAULT_WEIGHT_FN=_.constant(1);function greedyFAS(g,weightFn){if(g.nodeCount()<=1)return[];var state=buildState(g,weightFn||DEFAULT_WEIGHT_FN);var results=doGreedyFAS(state.graph,state.buckets,
state.zeroIdx);return _.flatten(_.map(results,function(e){return g.outEdges(e.v,e.w)}),true)}function doGreedyFAS(g,buckets,zeroIdx){var results=[],sources=buckets[buckets.length-1],sinks=buckets[0];var entry;while(g.nodeCount()){while(entry=sinks.dequeue())removeNode(g,buckets,zeroIdx,entry);while(entry=sources.dequeue())removeNode(g,buckets,zeroIdx,entry);if(g.nodeCount())for(var i=buckets.length-2;i>0;--i){entry=buckets[i].dequeue();if(entry){results=results.concat(removeNode(g,buckets,zeroIdx,
entry,true));break}}}return results}function removeNode(g,buckets,zeroIdx,entry,collectPredecessors){var results=collectPredecessors?[]:undefined;_.forEach(g.inEdges(entry.v),function(edge){var weight=g.edge(edge),uEntry=g.node(edge.v);if(collectPredecessors)results.push({v:edge.v,w:edge.w});uEntry.out-=weight;assignBucket(buckets,zeroIdx,uEntry)});_.forEach(g.outEdges(entry.v),function(edge){var weight=g.edge(edge),w=edge.w,wEntry=g.node(w);wEntry["in"]-=weight;assignBucket(buckets,zeroIdx,wEntry)});
g.removeNode(entry.v);return results}function buildState(g,weightFn){var fasGraph=new Graph,maxIn=0,maxOut=0;_.forEach(g.nodes(),function(v){fasGraph.setNode(v,{v:v,"in":0,out:0})});_.forEach(g.edges(),function(e){var prevWeight=fasGraph.edge(e.v,e.w)||0,weight=weightFn(e),edgeWeight=prevWeight+weight;fasGraph.setEdge(e.v,e.w,edgeWeight);maxOut=Math.max(maxOut,fasGraph.node(e.v).out+=weight);maxIn=Math.max(maxIn,fasGraph.node(e.w)["in"]+=weight)});var buckets=_.range(maxOut+maxIn+3).map(function(){return new List});
var zeroIdx=maxIn+1;_.forEach(fasGraph.nodes(),function(v){assignBucket(buckets,zeroIdx,fasGraph.node(v))});return{graph:fasGraph,buckets:buckets,zeroIdx:zeroIdx}}function assignBucket(buckets,zeroIdx,entry){if(!entry.out)buckets[0].enqueue(entry);else if(!entry["in"])buckets[buckets.length-1].enqueue(entry);else buckets[entry.out-entry["in"]+zeroIdx].enqueue(entry)}},{"./data/list":5,"./graphlib":7,"./lodash":10}],9:[function(require,module,exports){var _=require("./lodash"),acyclic=require("./acyclic"),
normalize=require("./normalize"),rank=require("./rank"),normalizeRanks=require("./util").normalizeRanks,parentDummyChains=require("./parent-dummy-chains"),removeEmptyRanks=require("./util").removeEmptyRanks,nestingGraph=require("./nesting-graph"),addBorderSegments=require("./add-border-segments"),coordinateSystem=require("./coordinate-system"),order=require("./order"),position=require("./position"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports=layout;function layout(g,opts){var time=
opts&&opts.debugTiming?util.time:util.notime;time("layout",function(){var layoutGraph=time("  buildLayoutGraph",function(){return buildLayoutGraph(g)});time("  runLayout",function(){runLayout(layoutGraph,time)});time("  updateInputGraph",function(){updateInputGraph(g,layoutGraph)})})}function runLayout(g,time){time("    makeSpaceForEdgeLabels",function(){makeSpaceForEdgeLabels(g)});time("    removeSelfEdges",function(){removeSelfEdges(g)});time("    acyclic",function(){acyclic.run(g)});time("    nestingGraph.run",
function(){nestingGraph.run(g)});time("    rank",function(){rank(util.asNonCompoundGraph(g))});time("    injectEdgeLabelProxies",function(){injectEdgeLabelProxies(g)});time("    removeEmptyRanks",function(){removeEmptyRanks(g)});time("    nestingGraph.cleanup",function(){nestingGraph.cleanup(g)});time("    normalizeRanks",function(){normalizeRanks(g)});time("    assignRankMinMax",function(){assignRankMinMax(g)});time("    removeEdgeLabelProxies",function(){removeEdgeLabelProxies(g)});time("    normalize.run",
function(){normalize.run(g)});time("    parentDummyChains",function(){parentDummyChains(g)});time("    addBorderSegments",function(){addBorderSegments(g)});time("    order",function(){order(g)});time("    insertSelfEdges",function(){insertSelfEdges(g)});time("    adjustCoordinateSystem",function(){coordinateSystem.adjust(g)});time("    position",function(){position(g)});time("    positionSelfEdges",function(){positionSelfEdges(g)});time("    removeBorderNodes",function(){removeBorderNodes(g)});time("    normalize.undo",
function(){normalize.undo(g)});time("    fixupEdgeLabelCoords",function(){fixupEdgeLabelCoords(g)});time("    undoCoordinateSystem",function(){coordinateSystem.undo(g)});time("    translateGraph",function(){translateGraph(g)});time("    assignNodeIntersects",function(){assignNodeIntersects(g)});time("    reversePoints",function(){reversePointsForReversedEdges(g)});time("    acyclic.undo",function(){acyclic.undo(g)})}function updateInputGraph(inputGraph,layoutGraph){_.forEach(inputGraph.nodes(),function(v){var inputLabel=
inputGraph.node(v),layoutLabel=layoutGraph.node(v);if(inputLabel){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y;if(layoutGraph.children(v).length){inputLabel.width=layoutLabel.width;inputLabel.height=layoutLabel.height}}});_.forEach(inputGraph.edges(),function(e){var inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;
inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=
new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs)));_.forEach(inputGraph.nodes(),function(v){var node=canonicalize(inputGraph.node(v));g.setNode(v,_.defaults(selectNumberAttrs(node,nodeNumAttrs),nodeDefaults));g.setParent(v,inputGraph.parent(v))});_.forEach(inputGraph.edges(),function(e){var edge=canonicalize(inputGraph.edge(e));g.setEdge(e,_.merge({},edgeDefaults,selectNumberAttrs(edge,
edgeNumAttrs),_.pick(edge,edgeAttrs)))});return g}function makeSpaceForEdgeLabels(g){var graph=g.graph();graph.ranksep/=2;_.forEach(g.edges(),function(e){var edge=g.edge(e);edge.minlen*=2;if(edge.labelpos.toLowerCase()!=="c")if(graph.rankdir==="TB"||graph.rankdir==="BT")edge.width+=edge.labeloffset;else edge.height+=edge.labeloffset})}function injectEdgeLabelProxies(g){_.forEach(g.edges(),function(e){var edge=g.edge(e);if(edge.width&&edge.height){var v=g.node(e.v),w=g.node(e.w),label={rank:(w.rank-
v.rank)/2+v.rank,e:e};util.addDummyNode(g,"edge-proxy",label,"_ep")}})}function assignRankMinMax(g){var maxRank=0;_.forEach(g.nodes(),function(v){var node=g.node(v);if(node.borderTop){node.minRank=g.node(node.borderTop).rank;node.maxRank=g.node(node.borderBottom).rank;maxRank=_.max(maxRank,node.maxRank)}});g.graph().maxRank=maxRank}function removeEdgeLabelProxies(g){_.forEach(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="edge-proxy"){g.edge(node.e).labelRank=node.rank;g.removeNode(v)}})}
function translateGraph(g){var minX=Number.POSITIVE_INFINITY,maxX=0,minY=Number.POSITIVE_INFINITY,maxY=0,graphLabel=g.graph(),marginX=graphLabel.marginx||0,marginY=graphLabel.marginy||0;function getExtremes(attrs){var x=attrs.x,y=attrs.y,w=attrs.width,h=attrs.height;minX=Math.min(minX,x-w/2);maxX=Math.max(maxX,x+w/2);minY=Math.min(minY,y-h/2);maxY=Math.max(maxY,y+h/2)}_.forEach(g.nodes(),function(v){getExtremes(g.node(v))});_.forEach(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x"))getExtremes(edge)});
minX-=marginX;minY-=marginY;_.forEach(g.nodes(),function(v){var node=g.node(v);node.x-=minX;node.y-=minY});_.forEach(g.edges(),function(e){var edge=g.edge(e);_.forEach(edge.points,function(p){p.x-=minX;p.y-=minY});if(_.has(edge,"x"))edge.x-=minX;if(_.has(edge,"y"))edge.y-=minY});graphLabel.width=maxX-minX+marginX;graphLabel.height=maxY-minY+marginY}function assignNodeIntersects(g){_.forEach(g.edges(),function(e){var edge=g.edge(e),nodeV=g.node(e.v),nodeW=g.node(e.w),p1,p2;if(!edge.points){edge.points=
[];p1=nodeW;p2=nodeV}else{p1=edge.points[0];p2=edge.points[edge.points.length-1]}edge.points.unshift(util.intersectRect(nodeV,p1));edge.points.push(util.intersectRect(nodeW,p2))})}function fixupEdgeLabelCoords(g){_.forEach(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){if(edge.labelpos==="l"||edge.labelpos==="r")edge.width-=edge.labeloffset;switch(edge.labelpos){case "l":edge.x-=edge.width/2+edge.labeloffset;break;case "r":edge.x+=edge.width/2+edge.labeloffset;break}}})}function reversePointsForReversedEdges(g){_.forEach(g.edges(),
function(e){var edge=g.edge(e);if(edge.reversed)edge.points.reverse()})}function removeBorderNodes(g){_.forEach(g.nodes(),function(v){if(g.children(v).length){var node=g.node(v),t=g.node(node.borderTop),b=g.node(node.borderBottom),l=g.node(_.last(node.borderLeft)),r=g.node(_.last(node.borderRight));node.width=Math.abs(r.x-l.x);node.height=Math.abs(b.y-t.y);node.x=l.x+node.width/2;node.y=t.y+node.height/2}});_.forEach(g.nodes(),function(v){if(g.node(v).dummy==="border")g.removeNode(v)})}function removeSelfEdges(g){_.forEach(g.edges(),
function(e){if(e.v===e.w){var node=g.node(e.v);if(!node.selfEdges)node.selfEdges=[];node.selfEdges.push({e:e,label:g.edge(e)});g.removeEdge(e)}})}function insertSelfEdges(g){var layers=util.buildLayerMatrix(g);_.forEach(layers,function(layer){var orderShift=0;_.forEach(layer,function(v,i){var node=g.node(v);node.order=i+orderShift;_.forEach(node.selfEdges,function(selfEdge){util.addDummyNode(g,"selfedge",{width:selfEdge.label.width,height:selfEdge.label.height,rank:node.rank,order:i+ ++orderShift,
e:selfEdge.e,label:selfEdge.label},"_se")});delete node.selfEdges})})}function positionSelfEdges(g){_.forEach(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="selfedge"){var selfNode=g.node(node.e.v),x=selfNode.x+selfNode.width/2,y=selfNode.y,dx=node.x-x,dy=selfNode.height/2;g.setEdge(node.e,node.label);g.removeNode(v);node.label.points=[{x:x+2*dx/3,y:y-dy},{x:x+5*dx/6,y:y-dy},{x:x+dx,y:y},{x:x+5*dx/6,y:y+dy},{x:x+2*dx/3,y:y+dy}];node.label.x=node.x;node.label.y=node.y}})}function selectNumberAttrs(obj,
attrs){return _.mapValues(_.pick(obj,attrs),Number)}function canonicalize(attrs){var newAttrs={};_.forEach(attrs,function(v,k){newAttrs[k.toLowerCase()]=v});return newAttrs}},{"./acyclic":2,"./add-border-segments":3,"./coordinate-system":4,"./graphlib":7,"./lodash":10,"./nesting-graph":11,"./normalize":12,"./order":17,"./parent-dummy-chains":22,"./position":24,"./rank":26,"./util":29}],10:[function(require,module,exports){var lodash;if(typeof require==="function")try{lodash=require("lodash")}catch(e){}if(!lodash)lodash=
window._;module.exports=lodash},{"lodash":undefined}],11:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,cleanup:cleanup};function run(g){var root=util.addDummyNode(g,"root",{},"_root");var depths=treeDepths(g);var height=_.max(_.values(depths))-1;var nodeSep=2*height+1;g.graph().nestingRoot=root;_.forEach(g.edges(),function(e){g.edge(e).minlen*=nodeSep});var weight=sumWeights(g)+1;_.forEach(g.children(),function(child){dfs(g,root,nodeSep,
weight,height,depths,child)});g.graph().nodeRankFactor=nodeSep}function dfs(g,root,nodeSep,weight,height,depths,v){var children=g.children(v);if(!children.length){if(v!==root)g.setEdge(root,v,{weight:0,minlen:nodeSep});return}var top=util.addBorderNode(g,"_bt"),bottom=util.addBorderNode(g,"_bb"),label=g.node(v);g.setParent(top,v);label.borderTop=top;g.setParent(bottom,v);label.borderBottom=bottom;_.forEach(children,function(child){dfs(g,root,nodeSep,weight,height,depths,child);var childNode=g.node(child),
childTop=childNode.borderTop?childNode.borderTop:child,childBottom=childNode.borderBottom?childNode.borderBottom:child,thisWeight=childNode.borderTop?weight:2*weight,minlen=childTop!==childBottom?1:height-depths[v]+1;g.setEdge(top,childTop,{weight:thisWeight,minlen:minlen,nestingEdge:true});g.setEdge(childBottom,bottom,{weight:thisWeight,minlen:minlen,nestingEdge:true})});if(!g.parent(v))g.setEdge(root,top,{weight:0,minlen:height+depths[v]})}function treeDepths(g){var depths={};function dfs(v,depth){var children=
g.children(v);if(children&&children.length)_.forEach(children,function(child){dfs(child,depth+1)});depths[v]=depth}_.forEach(g.children(),function(v){dfs(v,1)});return depths}function sumWeights(g){return _.reduce(g.edges(),function(acc,e){return acc+g.edge(e).weight},0)}function cleanup(g){var graphLabel=g.graph();g.removeNode(graphLabel.nestingRoot);delete graphLabel.nestingRoot;_.forEach(g.edges(),function(e){var edge=g.edge(e);if(edge.nestingEdge)g.removeEdge(e)})}},{"./lodash":10,"./util":29}],
12:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,undo:undo};function run(g){g.graph().dummyChains=[];_.forEach(g.edges(),function(edge){normalizeEdge(g,edge)})}function normalizeEdge(g,e){var v=e.v,vRank=g.node(v).rank,w=e.w,wRank=g.node(w).rank,name=e.name,edgeLabel=g.edge(e),labelRank=edgeLabel.labelRank;if(wRank===vRank+1)return;g.removeEdge(e);var dummy,attrs,i;for(i=0,++vRank;vRank<wRank;++i,++vRank){edgeLabel.points=[];attrs={width:0,
height:0,edgeLabel:edgeLabel,edgeObj:e,rank:vRank};dummy=util.addDummyNode(g,"edge",attrs,"_d");if(vRank===labelRank){attrs.width=edgeLabel.width;attrs.height=edgeLabel.height;attrs.dummy="edge-label";attrs.labelpos=edgeLabel.labelpos}g.setEdge(v,dummy,{weight:edgeLabel.weight},name);if(i===0)g.graph().dummyChains.push(dummy);v=dummy}g.setEdge(v,w,{weight:edgeLabel.weight},name)}function undo(g){_.forEach(g.graph().dummyChains,function(v){var node=g.node(v),origLabel=node.edgeLabel,w;g.setEdge(node.edgeObj,
origLabel);while(node.dummy){w=g.successors(v)[0];g.removeNode(v);origLabel.points.push({x:node.x,y:node.y});if(node.dummy==="edge-label"){origLabel.x=node.x;origLabel.y=node.y;origLabel.width=node.width;origLabel.height=node.height}v=w;node=g.node(v)}})}},{"./lodash":10,"./util":29}],13:[function(require,module,exports){var _=require("../lodash");module.exports=addSubgraphConstraints;function addSubgraphConstraints(g,cg,vs){var prev={},rootPrev;_.forEach(vs,function(v){var child=g.parent(v),parent,
prevChild;while(child){parent=g.parent(child);if(parent){prevChild=prev[parent];prev[parent]=child}else{prevChild=rootPrev;rootPrev=child}if(prevChild&&prevChild!==child){cg.setEdge(prevChild,child);return}child=parent}})}},{"../lodash":10}],14:[function(require,module,exports){var _=require("../lodash");module.exports=barycenter;function barycenter(g,movable){return _.map(movable,function(v){var inV=g.inEdges(v);if(!inV.length)return{v:v};else{var result=_.reduce(inV,function(acc,e){var edge=g.edge(e),
nodeU=g.node(e.v);return{sum:acc.sum+edge.weight*nodeU.order,weight:acc.weight+edge.weight}},{sum:0,weight:0});return{v:v,barycenter:result.sum/result.weight,weight:result.weight}}})}},{"../lodash":10}],15:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graphlib").Graph;module.exports=buildLayerGraph;function buildLayerGraph(g,rank,relationship){var root=createRootNode(g),result=(new Graph({compound:true})).setGraph({root:root}).setDefaultNodeLabel(function(v){return g.node(v)});
_.forEach(g.nodes(),function(v){var node=g.node(v),parent=g.parent(v);if(node.rank===rank||node.minRank<=rank&&rank<=node.maxRank){result.setNode(v);result.setParent(v,parent||root);_.forEach(g[relationship](v),function(e){var u=e.v===v?e.w:e.v,edge=result.edge(u,v),weight=!_.isUndefined(edge)?edge.weight:0;result.setEdge(u,v,{weight:g.edge(e).weight+weight})});if(_.has(node,"minRank"))result.setNode(v,{borderLeft:node.borderLeft[rank],borderRight:node.borderRight[rank]})}});return result}function createRootNode(g){var v;
while(g.hasNode(v=_.uniqueId("_root")));return v}},{"../graphlib":7,"../lodash":10}],16:[function(require,module,exports){var _=require("../lodash");module.exports=crossCount;function crossCount(g,layering){var cc=0;for(var i=1;i<layering.length;++i)cc+=twoLayerCrossCount(g,layering[i-1],layering[i]);return cc}function twoLayerCrossCount(g,northLayer,southLayer){var southPos=_.zipObject(southLayer,_.map(southLayer,function(v,i){return i}));var southEntries=_.flatten(_.map(northLayer,function(v){return _.chain(g.outEdges(v)).map(function(e){return{pos:southPos[e.w],
weight:g.edge(e).weight}}).sortBy("pos").value()}),true);var firstIndex=1;while(firstIndex<southLayer.length)firstIndex<<=1;var treeSize=2*firstIndex-1;firstIndex-=1;var tree=_.map(new Array(treeSize),function(){return 0});var cc=0;_.forEach(southEntries.forEach(function(entry){var index=entry.pos+firstIndex;tree[index]+=entry.weight;var weightSum=0;while(index>0){if(index%2)weightSum+=tree[index+1];index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":10}],
17:[function(require,module,exports){var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,
_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc<bestCC){lastBest=0;best=_.cloneDeep(layering);bestCC=cc}}assignOrder(g,best)}function buildLayerGraphs(g,ranks,relationship){return _.map(ranks,function(rank){return buildLayerGraph(g,rank,relationship)})}
function sweepLayerGraphs(layerGraphs,biasRight){var cg=new Graph;_.forEach(layerGraphs,function(lg){var root=lg.graph().root;var sorted=sortSubgraph(lg,root,cg,biasRight);_.forEach(sorted.vs,function(v,i){lg.node(v).order=i});addSubgraphConstraints(lg,cg,sorted.vs)})}function assignOrder(g,layering){_.forEach(layering,function(layer){_.forEach(layer,function(v,i){g.node(v).order=i})})}},{"../graphlib":7,"../lodash":10,"../util":29,"./add-subgraph-constraints":13,"./build-layer-graph":15,"./cross-count":16,
"./init-order":18,"./sort-subgraph":20}],18:[function(require,module,exports){var _=require("../lodash");module.exports=initOrder;function initOrder(g){var visited={},simpleNodes=_.filter(g.nodes(),function(v){return!g.children(v).length}),maxRank=_.max(_.map(simpleNodes,function(v){return g.node(v).rank})),layers=_.map(_.range(maxRank+1),function(){return[]});function dfs(v){if(_.has(visited,v))return;visited[v]=true;var node=g.node(v);layers[node.rank].push(v);_.forEach(g.successors(v),dfs)}var orderedVs=
_.sortBy(simpleNodes,function(v){return g.node(v).rank});_.forEach(orderedVs,dfs);return layers}},{"../lodash":10}],19:[function(require,module,exports){var _=require("../lodash");module.exports=resolveConflicts;function resolveConflicts(entries,cg){var mappedEntries={};_.forEach(entries,function(entry,i){var tmp=mappedEntries[entry.v]={indegree:0,"in":[],out:[],vs:[entry.v],i:i};if(!_.isUndefined(entry.barycenter)){tmp.barycenter=entry.barycenter;tmp.weight=entry.weight}});_.forEach(cg.edges(),function(e){var entryV=
mappedEntries[e.v],entryW=mappedEntries[e.w];if(!_.isUndefined(entryV)&&!_.isUndefined(entryW)){entryW.indegree++;entryV.out.push(mappedEntries[e.w])}});var sourceSet=_.filter(mappedEntries,function(entry){return!entry.indegree});return doResolveConflicts(sourceSet)}function doResolveConflicts(sourceSet){var entries=[];function handleIn(vEntry){return function(uEntry){if(uEntry.merged)return;if(_.isUndefined(uEntry.barycenter)||_.isUndefined(vEntry.barycenter)||uEntry.barycenter>=vEntry.barycenter)mergeEntries(vEntry,
uEntry)}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0)sourceSet.push(wEntry)}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.forEach(entry["in"].reverse(),handleIn(entry));_.forEach(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;
if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":10}],20:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=
sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl)movable=_.filter(movable,function(w){return w!==bl&&w!==br});var barycenters=barycenter(g,movable);_.forEach(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter"))mergeBarycenters(entry,
subgraphResult)}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,
subgraphs){_.forEach(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v])return subgraphs[v].vs;return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":10,"./barycenter":14,"./resolve-conflicts":19,"./sort":21}],
21:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.forEach(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);
sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenter<entryW.barycenter)return-1;else if(entryV.barycenter>
entryW.barycenter)return 1;return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":10,"../util":29}],22:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.forEach(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=
g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRank<node.rank)pathIdx++;if(pathV===lca)ascending=false}if(!ascending){while(pathIdx<path.length-1&&g.node(pathV=path[pathIdx+1]).minRank<=node.rank)pathIdx++;pathV=path[pathIdx]}g.setParent(v,pathV);v=g.successors(v)[0]}})}function findPath(g,postorderNums,v,w){var vPath=[],wPath=[],low=Math.min(postorderNums[v].low,postorderNums[w].low),lim=Math.max(postorderNums[v].lim,postorderNums[w].lim),parent,lca;parent=v;do{parent=
g.parent(parent);vPath.push(parent)}while(parent&&(postorderNums[parent].low>low||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca)wPath.push(parent);return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function dfs(v){var low=lim;_.forEach(g.children(v),dfs);result[v]={low:low,lim:lim++}}_.forEach(g.children(),dfs);return result}},{"./lodash":10}],23:[function(require,module,exports){var _=require("../lodash"),Graph=
require("../graphlib").Graph,util=require("../util");module.exports={positionX:positionX,findType1Conflicts:findType1Conflicts,findType2Conflicts:findType2Conflicts,addConflict:addConflict,hasConflict:hasConflict,verticalAlignment:verticalAlignment,horizontalCompaction:horizontalCompaction,alignCoordinates:alignCoordinates,findSmallestWidthAlignment:findSmallestWidthAlignment,balance:balance};function findType1Conflicts(g,layering){var conflicts={};function visitLayer(prevLayer,layer){var k0=0,scanPos=
0,prevLayerLength=prevLayer.length,lastNode=_.last(layer);_.forEach(layer,function(v,i){var w=findOtherInnerSegmentNode(g,v),k1=w?g.node(w).order:prevLayerLength;if(w||v===lastNode){_.forEach(layer.slice(scanPos,i+1),function(scanNode){_.forEach(g.predecessors(scanNode),function(u){var uLabel=g.node(u),uPos=uLabel.order;if((uPos<k0||k1<uPos)&&!(uLabel.dummy&&g.node(scanNode).dummy))addConflict(conflicts,u,scanNode)})});scanPos=i+1;k0=k1}});return layer}_.reduce(layering,visitLayer);return conflicts}
function findType2Conflicts(g,layering){var conflicts={};function scan(south,southPos,southEnd,prevNorthBorder,nextNorthBorder){var v;_.forEach(_.range(southPos,southEnd),function(i){v=south[i];if(g.node(v).dummy)_.forEach(g.predecessors(v),function(u){var uNode=g.node(u);if(uNode.dummy&&(uNode.order<prevNorthBorder||uNode.order>nextNorthBorder))addConflict(conflicts,u,v)})})}function visitLayer(north,south){var prevNorthPos=-1,nextNorthPos,southPos=0;_.forEach(south,function(v,southLookahead){if(g.node(v).dummy===
"border"){var predecessors=g.predecessors(v);if(predecessors.length){nextNorthPos=g.node(predecessors[0]).order;scan(south,southPos,southLookahead,prevNorthPos,nextNorthPos);southPos=southLookahead;prevNorthPos=nextNorthPos}}scan(south,southPos,south.length,nextNorthPos,north.length)});return south}_.reduce(layering,visitLayer);return conflicts}function findOtherInnerSegmentNode(g,v){if(g.node(v).dummy)return _.find(g.predecessors(v),function(u){return g.node(u).dummy})}function addConflict(conflicts,
v,w){if(v>w){var tmp=v;v=w;w=tmp}var conflictsV=conflicts[v];if(!conflictsV)conflicts[v]=conflictsV={};conflictsV[w]=true}function hasConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}return _.has(conflicts[v],w)}function verticalAlignment(g,layering,conflicts,neighborFn){var root={},align={},pos={};_.forEach(layering,function(layer){_.forEach(layer,function(v,order){root[v]=v;align[v]=v;pos[v]=order})});_.forEach(layering,function(layer){var prevIdx=-1;_.forEach(layer,function(v){var ws=neighborFn(v);
if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdx<pos[w]&&!hasConflict(conflicts,v,w)){align[w]=v;align[v]=root[v]=root[w];prevIdx=pos[w]}}}})});return{root:root,align:align}}function horizontalCompaction(g,layering,root,align,reverseSep){var xs={},blockG=buildBlockGraph(g,layering,root,reverseSep),borderType=reverseSep?"borderLeft":"borderRight";function iterate(setXsFunc,nextNodesFunc){var stack=
blockG.nodes();var elem=stack.pop();var visited={};while(elem){if(visited[elem])setXsFunc(elem);else{visited[elem]=true;stack.push(elem);stack=stack.concat(nextNodesFunc(elem))}elem=stack.pop()}}function pass1(elem){xs[elem]=blockG.inEdges(elem).reduce(function(acc,e){return Math.max(acc,xs[e.v]+blockG.edge(e))},0)}function pass2(elem){var min=blockG.outEdges(elem).reduce(function(acc,e){return Math.min(acc,xs[e.w]-blockG.edge(e))},Number.POSITIVE_INFINITY);var node=g.node(elem);if(min!==Number.POSITIVE_INFINITY&&
node.borderType!==borderType)xs[elem]=Math.max(xs[elem],min)}iterate(pass1,_.bind(blockG.predecessors,blockG));iterate(pass2,_.bind(blockG.successors,blockG));_.forEach(align,function(v){xs[v]=xs[root[v]]});return xs}function buildBlockGraph(g,layering,root,reverseSep){var blockGraph=new Graph,graphLabel=g.graph(),sepFn=sep(graphLabel.nodesep,graphLabel.edgesep,reverseSep);_.forEach(layering,function(layer){var u;_.forEach(layer,function(v){var vRoot=root[v];blockGraph.setNode(vRoot);if(u){var uRoot=
root[u],prevMax=blockGraph.edge(uRoot,vRoot);blockGraph.setEdge(uRoot,vRoot,Math.max(sepFn(g,v,u),prevMax||0))}u=v})});return blockGraph}function findSmallestWidthAlignment(g,xss){return _.minBy(_.values(xss),function(xs){var max=Number.NEGATIVE_INFINITY;var min=Number.POSITIVE_INFINITY;_.forIn(xs,function(x,v){var halfWidth=width(g,v)/2;max=Math.max(x+halfWidth,max);min=Math.min(x-halfWidth,min)});return max-min})}function alignCoordinates(xss,alignTo){var alignToVals=_.values(alignTo),alignToMin=
_.min(alignToVals),alignToMax=_.max(alignToVals);_.forEach(["u","d"],function(vert){_.forEach(["l","r"],function(horiz){var alignment=vert+horiz,xs=xss[alignment],delta;if(xs===alignTo)return;var xsVals=_.values(xs);delta=horiz==="l"?alignToMin-_.min(xsVals):alignToMax-_.max(xsVals);if(delta)xss[alignment]=_.mapValues(xs,function(x){return x+delta})})})}function balance(xss,align){return _.mapValues(xss.ul,function(ignore,v){if(align)return xss[align.toLowerCase()][v];else{var xs=_.sortBy(_.map(xss,
v));return(xs[1]+xs[2])/2}})}function positionX(g){var layering=util.buildLayerMatrix(g),conflicts=_.merge(findType1Conflicts(g,layering),findType2Conflicts(g,layering));var xss={},adjustedLayering;_.forEach(["u","d"],function(vert){adjustedLayering=vert==="u"?layering:_.values(layering).reverse();_.forEach(["l","r"],function(horiz){if(horiz==="r")adjustedLayering=_.map(adjustedLayering,function(inner){return _.values(inner).reverse()});var neighborFn=_.bind(vert==="u"?g.predecessors:g.successors,
g);var align=verticalAlignment(g,adjustedLayering,conflicts,neighborFn);var xs=horizontalCompaction(g,adjustedLayering,align.root,align.align,horiz==="r");if(horiz==="r")xs=_.mapValues(xs,function(x){return-x});xss[vert+horiz]=xs})});var smallestWidth=findSmallestWidthAlignment(g,xss);alignCoordinates(xss,smallestWidth);return balance(xss,g.graph().align)}function sep(nodeSep,edgeSep,reverseSep){return function(g,v,w){var vLabel=g.node(v),wLabel=g.node(w),sum=0,delta;sum+=vLabel.width/2;if(_.has(vLabel,
"labelpos"))switch(vLabel.labelpos.toLowerCase()){case "l":delta=-vLabel.width/2;break;case "r":delta=vLabel.width/2;break}if(delta)sum+=reverseSep?delta:-delta;delta=0;sum+=(vLabel.dummy?edgeSep:nodeSep)/2;sum+=(wLabel.dummy?edgeSep:nodeSep)/2;sum+=wLabel.width/2;if(_.has(wLabel,"labelpos"))switch(wLabel.labelpos.toLowerCase()){case "l":delta=wLabel.width/2;break;case "r":delta=-wLabel.width/2;break}if(delta)sum+=reverseSep?delta:-delta;delta=0;return sum}}function width(g,v){return g.node(v).width}
},{"../graphlib":7,"../lodash":10,"../util":29}],24:[function(require,module,exports){var _=require("../lodash"),util=require("../util"),positionX=require("./bk").positionX;module.exports=position;function position(g){g=util.asNonCompoundGraph(g);positionY(g);_.forEach(positionX(g),function(x,v){g.node(v).x=x})}function positionY(g){var layering=util.buildLayerMatrix(g),rankSep=g.graph().ranksep,prevY=0;_.forEach(layering,function(layer){var maxHeight=_.max(_.map(layer,function(v){return g.node(v).height}));
_.forEach(layer,function(v){g.node(v).y=prevY+maxHeight/2});prevY+=maxHeight+rankSep})}},{"../lodash":10,"../util":29,"./bk":23}],25:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graphlib").Graph,slack=require("./util").slack;module.exports=feasibleTree;function feasibleTree(g){var t=new Graph({directed:false});var start=g.nodes()[0],size=g.nodeCount();t.setNode(start,{});var edge,delta;while(tightTree(t,g)<size){edge=findMinSlackEdge(t,g);delta=t.hasNode(edge.v)?
slack(g,edge):-slack(g,edge);shiftRanks(t,g,delta)}return t}function tightTree(t,g){function dfs(v){_.forEach(g.nodeEdges(v),function(e){var edgeV=e.v,w=v===edgeV?e.w:edgeV;if(!t.hasNode(w)&&!slack(g,e)){t.setNode(w,{});t.setEdge(v,w,{});dfs(w)}})}_.forEach(t.nodes(),dfs);return t.nodeCount()}function findMinSlackEdge(t,g){return _.minBy(g.edges(),function(e){if(t.hasNode(e.v)!==t.hasNode(e.w))return slack(g,e)})}function shiftRanks(t,g,delta){_.forEach(t.nodes(),function(v){g.node(v).rank+=delta})}
},{"../graphlib":7,"../lodash":10,"./util":28}],26:[function(require,module,exports){var rankUtil=require("./util"),longestPath=rankUtil.longestPath,feasibleTree=require("./feasible-tree"),networkSimplex=require("./network-simplex");module.exports=rank;function rank(g){switch(g.graph().ranker){case "network-simplex":networkSimplexRanker(g);break;case "tight-tree":tightTreeRanker(g);break;case "longest-path":longestPathRanker(g);break;default:networkSimplexRanker(g)}}var longestPathRanker=longestPath;
function tightTreeRanker(g){longestPath(g);feasibleTree(g)}function networkSimplexRanker(g){networkSimplex(g)}},{"./feasible-tree":25,"./network-simplex":27,"./util":28}],27:[function(require,module,exports){var _=require("../lodash"),feasibleTree=require("./feasible-tree"),slack=require("./util").slack,initRank=require("./util").longestPath,preorder=require("../graphlib").alg.preorder,postorder=require("../graphlib").alg.postorder,simplify=require("../util").simplify;module.exports=networkSimplex;
networkSimplex.initLowLimValues=initLowLimValues;networkSimplex.initCutValues=initCutValues;networkSimplex.calcCutValue=calcCutValue;networkSimplex.leaveEdge=leaveEdge;networkSimplex.enterEdge=enterEdge;networkSimplex.exchangeEdges=exchangeEdges;function networkSimplex(g){g=simplify(g);initRank(g);var t=feasibleTree(g);initLowLimValues(t);initCutValues(t,g);var e,f;while(e=leaveEdge(t)){f=enterEdge(t,g,e);exchangeEdges(t,g,e,f)}}function initCutValues(t,g){var vs=postorder(t,t.nodes());vs=vs.slice(0,
vs.length-1);_.forEach(vs,function(v){assignCutValue(t,g,v)})}function assignCutValue(t,g,child){var childLab=t.node(child),parent=childLab.parent;t.edge(child,parent).cutvalue=calcCutValue(t,g,child)}function calcCutValue(t,g,child){var childLab=t.node(child),parent=childLab.parent,childIsTail=true,graphEdge=g.edge(child,parent),cutValue=0;if(!graphEdge){childIsTail=false;graphEdge=g.edge(parent,child)}cutValue=graphEdge.weight;_.forEach(g.nodeEdges(child),function(e){var isOutEdge=e.v===child,other=
isOutEdge?e.w:e.v;if(other!==parent){var pointsToHead=isOutEdge===childIsTail,otherWeight=g.edge(e).weight;cutValue+=pointsToHead?otherWeight:-otherWeight;if(isTreeEdge(t,child,other)){var otherCutValue=t.edge(child,other).cutvalue;cutValue+=pointsToHead?-otherCutValue:otherCutValue}}});return cutValue}function initLowLimValues(tree,root){if(arguments.length<2)root=tree.nodes()[0];dfsAssignLowLim(tree,{},1,root)}function dfsAssignLowLim(tree,visited,nextLim,v,parent){var low=nextLim,label=tree.node(v);
visited[v]=true;_.forEach(tree.neighbors(v),function(w){if(!_.has(visited,w))nextLim=dfsAssignLowLim(tree,visited,nextLim,w,v)});label.low=low;label.lim=nextLim++;if(parent)label.parent=parent;else delete label.parent;return nextLim}function leaveEdge(tree){return _.find(tree.edges(),function(e){return tree.edge(e).cutvalue<0})}function enterEdge(t,g,edge){var v=edge.v,w=edge.w;if(!g.hasEdge(v,w)){v=edge.w;w=edge.v}var vLabel=t.node(v),wLabel=t.node(w),tailLabel=vLabel,flip=false;if(vLabel.lim>wLabel.lim){tailLabel=
wLabel;flip=true}var candidates=_.filter(g.edges(),function(edge){return flip===isDescendant(t,t.node(edge.v),tailLabel)&&flip!==isDescendant(t,t.node(edge.w),tailLabel)});return _.minBy(candidates,function(edge){return slack(g,edge)})}function exchangeEdges(t,g,e,f){var v=e.v,w=e.w;t.removeEdge(v,w);t.setEdge(f.v,f.w,{});initLowLimValues(t);initCutValues(t,g);updateRanks(t,g)}function updateRanks(t,g){var root=_.find(t.nodes(),function(v){return!g.node(v).parent}),vs=preorder(t,root);vs=vs.slice(1);
_.forEach(vs,function(v){var parent=t.node(v).parent,edge=g.edge(v,parent),flipped=false;if(!edge){edge=g.edge(parent,v);flipped=true}g.node(v).rank=g.node(parent).rank+(flipped?edge.minlen:-edge.minlen)})}function isTreeEdge(tree,u,v){return tree.hasEdge(u,v)}function isDescendant(tree,vLabel,rootLabel){return rootLabel.low<=vLabel.lim&&vLabel.lim<=rootLabel.lim}},{"../graphlib":7,"../lodash":10,"../util":29,"./feasible-tree":25,"./util":28}],28:[function(require,module,exports){var _=require("../lodash");
module.exports={longestPath:longestPath,slack:slack};function longestPath(g){var visited={};function dfs(v){var label=g.node(v);if(_.has(visited,v))return label.rank;visited[v]=true;var rank=_.minBy(_.map(g.outEdges(v),function(e){return dfs(e.w)-g.edge(e).minlen}));if(rank===Number.POSITIVE_INFINITY||rank===undefined||rank===null)rank=0;return label.rank=rank}_.forEach(g.sources(),dfs)}function slack(g,e){return g.node(e.w).rank-g.node(e.v).rank-g.edge(e).minlen}},{"../lodash":10}],29:[function(require,
module,exports){var _=require("./lodash"),Graph=require("./graphlib").Graph;module.exports={addDummyNode:addDummyNode,simplify:simplify,asNonCompoundGraph:asNonCompoundGraph,successorWeights:successorWeights,predecessorWeights:predecessorWeights,intersectRect:intersectRect,buildLayerMatrix:buildLayerMatrix,normalizeRanks:normalizeRanks,removeEmptyRanks:removeEmptyRanks,addBorderNode:addBorderNode,maxRank:maxRank,partition:partition,time:time,notime:notime};function addDummyNode(g,type,attrs,name){var v;
do v=_.uniqueId(name);while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.forEach(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.forEach(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){var simplified=
(new Graph({multigraph:g.isMultigraph()})).setGraph(g.graph());_.forEach(g.nodes(),function(v){if(!g.children(v).length)simplified.setNode(v,g.node(v))});_.forEach(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.forEach(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),
function(v){var preds={};_.forEach(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy)throw new Error("Not possible to find intersection inside of the rectangle");var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0)h=-h;sx=h*dx/dy;sy=h}else{if(dx<0)w=-w;sx=w;sy=w*dy/dx}return{x:x+
sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.forEach(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank))layering[rank][node.order]=v});return layering}function normalizeRanks(g){var min=_.minBy(_.map(g.nodes(),function(v){return g.node(v).rank}));_.forEach(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank"))node.rank-=min})}function removeEmptyRanks(g){var offset=_.minBy(_.map(g.nodes(),function(v){return g.node(v).rank}));
var layers=[];_.forEach(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!layers[rank])layers[rank]=[];layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.forEach(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0)--delta;else if(delta)_.forEach(vs,function(v){g.node(v).rank+=delta})})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}
function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank))return rank}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.forEach(collection,function(value){if(fn(value))result.lhs.push(value);else result.rhs.push(value)});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":7,"./lodash":10}],30:[function(require,
module,exports){module.exports="0.8.2"},{}]},{},[1])(1)});
//# sourceURL=build://tf-graph-common/annotation.js
var tf;
(function(b){(function(d){(function(f){(function(h){function k(q){return(d.render.AnnotationType[q]||"").toLowerCase()||null}function t(q,u){u.annotationType===d.render.AnnotationType.SUMMARY?f.selectOrCreateChild(q,"use").attr("class","summary").attr("xlink:href","#summary-icon").attr("cursor","pointer"):(q=f.node.buildShape(q,u,f.Class.Annotation.NODE),f.selectOrCreateChild(q,"title").text(u.node.name))}function l(q,u){let x=u.node.name.split("/");return p(q,x[x.length-1],u,null)}function p(q,u,
x,A){let y=f.Class.Annotation.LABEL;A&&(y+=" "+A);q=q.append("text").attr("class",y).attr("dy",".35em").attr("text-anchor",x.isIn?"end":"start").text(u);return b.graph.scene.node.enforceLabelWidth(q,-1)}function m(q,u,x,A){q.on("mouseover",y=>{A.fire("annotation-highlight",{name:y.node.name,hostName:u.node.name})}).on("mouseout",y=>{A.fire("annotation-unhighlight",{name:y.node.name,hostName:u.node.name})}).on("click",y=>{d3.event.stopPropagation();A.fire("annotation-select",{name:y.node.name,hostName:u.node.name})});
if(x.annotationType!==d.render.AnnotationType.SUMMARY&&x.annotationType!==d.render.AnnotationType.CONSTANT)q.on("contextmenu",f.contextmenu.getMenu(A,f.node.getContextMenu(x.node,A)))}function n(q,u,x,A){let y=d.layout.computeCXPositionOfNodeShape(u);x.renderNodeInfo&&x.annotationType!==d.render.AnnotationType.ELLIPSIS&&f.node.stylize(q,x.renderNodeInfo,A,f.Class.Annotation.NODE);x.annotationType===d.render.AnnotationType.SUMMARY&&(x.width+=10);q.select("text."+f.Class.Annotation.LABEL).transition().attr("x",
y+x.dx+(x.isIn?-1:1)*(x.width/2+x.labelOffset)).attr("y",u.y+x.dy);q.select("use.summary").transition().attr("x",y+x.dx-3).attr("y",u.y+x.dy-6);f.positionEllipse(q.select("."+f.Class.Annotation.NODE+" ellipse"),y+x.dx,u.y+x.dy,x.width,x.height);f.positionRect(q.select("."+f.Class.Annotation.NODE+" rect"),y+x.dx,u.y+x.dy,x.width,x.height);f.positionRect(q.select("."+f.Class.Annotation.NODE+" use"),y+x.dx,u.y+x.dy,x.width,x.height);q.select("path."+f.Class.Annotation.EDGE).transition().attr("d",w=>
{w=w.points.map(C=>({x:C.dx+y,y:C.dy+u.y}));return f.edge.interpolate(w)})}h.buildGroup=function(q,u,x,A){q=q.selectAll(function(){return this.childNodes}).data(u.list,y=>y.node.name);q.enter().append("g").attr("data-name",y=>y.node.name).each(function(y){let w=d3.select(this);A.addAnnotationGroup(y,x,w);let C=f.Class.Annotation.EDGE,G=y.renderMetaedgeInfo&&y.renderMetaedgeInfo.metaedge;G&&!G.numRegularEdges&&(C+=" "+f.Class.Annotation.CONTROL_EDGE);G&&G.numRefEdges&&(C+=" "+f.Class.Edge.REF_LINE);
f.edge.appendEdge(w,y,A,C);y.annotationType!==d.render.AnnotationType.ELLIPSIS?(l(w,y),t(w,y)):p(w,y.node.name,y,f.Class.Annotation.ELLIPSIS)}).merge(q).attr("class",y=>f.Class.Annotation.GROUP+" "+k(y.annotationType)+" "+f.node.nodeClass(y)).each(function(y){let w=d3.select(this);n(w,x,y,A);y.annotationType!==d.render.AnnotationType.ELLIPSIS&&m(w,x,y,A)});q.exit().each(function(y){let w=d3.select(this);A.removeAnnotationGroup(y,x,w)}).remove();return q}})(f.annotation||(f.annotation={}))})(d.scene||
(d.scene={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/colors.js
(function(b){b.COLORS=[{name:"Google Blue",color:"#4184f3",active:"#3a53c5",disabled:"#cad8fc"},{name:"Google Red",color:"#db4437",active:"#8f2a0c",disabled:"#e8c6c1"},{name:"Google Yellow",color:"#f4b400",active:"#db9200",disabled:"#f7e8b0"},{name:"Google Green",color:"#0f9d58",active:"#488046",disabled:"#c2e1cc"},{name:"Purple",color:"#aa46bb",active:"#5c1398",disabled:"#d7bce6"},{name:"Teal",color:"#00abc0",active:"#47828e",disabled:"#c2eaf2"},{name:"Deep Orange",color:"#ff6f42",active:"#ca4a06",
disabled:"#f2cbba"},{name:"Lime",color:"#9d9c23",active:"#7f771d",disabled:"#f1f4c2"},{name:"Indigo",color:"#5b6abf",active:"#3e47a9",disabled:"#c5c8e8"},{name:"Pink",color:"#ef6191",active:"#ca1c60",disabled:"#e9b9ce"},{name:"Deep Teal",color:"#00786a",active:"#2b4f43",disabled:"#bededa"},{name:"Deep Pink",color:"#c1175a",active:"#75084f",disabled:"#de8cae"},{name:"Gray",color:"#9E9E9E",active:"#424242",disabled:"F5F5F5"}].reduce((d,f)=>{d[f.name]=f;return d},{});b.OP_GROUP_COLORS=[{color:"Google Red",
groups:"gen_legacy_ops legacy_ops legacy_flogs_input legacy_image_input legacy_input_example_input legacy_sequence_input legacy_seti_input_input".split(" ")},{color:"Deep Orange",groups:["constant_ops"]},{color:"Indigo",groups:["state_ops"]},{color:"Purple",groups:["nn_ops","nn"]},{color:"Google Green",groups:["math_ops"]},{color:"Lime",groups:["array_ops"]},{color:"Teal",groups:["control_flow_ops","data_flow_ops"]},{color:"Pink",groups:["summary_ops"]},{color:"Deep Pink",groups:["io_ops"]}].reduce((d,
f)=>{f.groups.forEach(function(h){d[h]=f.color});return d},{})})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/common.js
(function(b){(function(d){(function(f){f.OP_GRAPH="op_graph";f.CONCEPTUAL_GRAPH="conceptual_graph";f.PROFILE="profile"})(d.SelectionType||(d.SelectionType={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/contextmenu.js
(function(b){(function(d){(function(f){(function(h){function k(t){let l=0,p=0;for(;t&&0<=t.offsetLeft&&0<=t.offsetTop;)l+=t.offsetLeft-t.scrollLeft,p+=t.offsetTop-t.scrollTop,t=t.offsetParent;return{left:l,top:p}}h.getMenu=function(t,l){const p=t.getContextMenu(),m=d3.select(t.getContextMenu());return function(n,q){function u(y){y&&y.composedPath().includes(p)||(m.style("display","none"),document.body.removeEventListener("mousedown",u,{capture:!0}))}let x=d3.event;const A=k(t);m.style("display","block").style("left",
x.clientX-A.left+1+"px").style("top",x.clientY-A.top+1+"px");x.preventDefault();x.stopPropagation();document.body.addEventListener("mousedown",u,{capture:!0});m.html("");m.append("ul").selectAll("li").data(l).enter().append("li").on("click",y=>{y.action(this,n,q);u()}).html(function(y){return y.title(n)})}}})(f.contextmenu||(f.contextmenu={}))})(d.scene||(d.scene={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/edge.js
(function(b){(function(d){(function(f){(function(h){function k(y){return y.v+d.EDGE_KEY_DELIM+y.w}function t(y,w){w=w.getNodeByName(y.v);if(null==w.outputShapes||_.isEmpty(w.outputShapes))return null;y=w.outputShapes[y.outputTensorKey];return null==y?null:0===y.length?"scalar":y.map(C=>-1===C?"?":C).join("\u00d7")}function l(y,w){return w.edgeLabelFunction?w.edgeLabelFunction(y,w):1<y.baseEdgeList.length?y.baseEdgeList.length+" tensors":t(y.baseEdgeList[0],w)}function p(y,w,C){const G=document.createElementNS(b.graph.scene.SVG_NAMESPACE,
"path");for(let D=1;D<y.length;D++)if(G.setAttribute("d",C(y.slice(0,D))),G.getTotalLength()>w)return D-1;return y.length-1}function m(y,w,C){var G=d3.line().x(N=>N.x).y(N=>N.y),D=d3.select(document.createElementNS("http://www.w3.org/2000/svg","path")).attr("d",G(y)),B=+w.attr("markerWidth"),I=w.attr("viewBox").split(" ").map(Number);I=I[2]-I[0];w=+w.attr("refX");D=D.node();if(C)return B*=1-w/I,C=D.getPointAtLength(B),G=p(y,B,G),y[G-1]={x:C.x,y:C.y},y.slice(G-1);C=1-w/I;B=D.getTotalLength()-B*C;C=
D.getPointAtLength(B);G=p(y,B,G);y[G]={x:C.x,y:C.y};return y.slice(0,G+1)}function n(y,w,C,G){G=G||f.Class.Edge.LINE;w.label&&w.label.structural&&(G+=" "+f.Class.Edge.STRUCTURAL);w.label&&w.label.metaedge&&w.label.metaedge.numRefEdges&&(G+=" "+f.Class.Edge.REFERENCE_EDGE);C.handleEdgeSelected&&(G+=" "+f.Class.Edge.SELECTABLE);let D="path_"+k(w);if(C.renderHierarchy.edgeWidthFunction)var B=C.renderHierarchy.edgeWidthFunction(w,G);else B=1,null!=w.label&&null!=w.label.metaedge&&(B=w.label.metaedge.totalSize),
B=C.renderHierarchy.edgeWidthSizedBasedScale(B);G=y.append("path").attr("id",D).attr("class",G).style("stroke-width",B+"px");w.label&&w.label.metaedge&&(w.label.metaedge.numRefEdges?(B=`reference-arrowhead-${A(B)}`,G.style("marker-start",`url(#${B})`),w.label.startMarkerId=B):(B=`dataflow-arrowhead-${A(B)}`,G.style("marker-end",`url(#${B})`),w.label.endMarkerId=B));null!=w.label&&null!=w.label.metaedge&&(w=l(w.label.metaedge,C.renderHierarchy),null!=w&&y.append("text").append("textPath").attr("xlink:href",
"#"+D).attr("startOffset","50%").attr("text-anchor","middle").attr("dominant-baseline","central").text(w))}function q(y,w,C,G,D){G=C.label;let B=G.adjoiningMetaedge,I=G.points;y=y.shadowRoot;C.label.startMarkerId&&(I=m(I,d3.select(y.querySelector("#"+C.label.startMarkerId)),!0));C.label.endMarkerId&&(I=m(I,d3.select(y.querySelector("#"+C.label.endMarkerId)),!1));if(!B)return d3.interpolate(D,h.interpolate(I));let N=B.edgeGroup.node().firstChild,O=G.metaedge.inbound;return function(){let H=N.getPointAtLength(O?
N.getTotalLength():0).matrixTransform(N.getCTM()).matrixTransform(w.getCTM().inverse()),K=O?0:I.length-1;I[K].x=H.x;I[K].y=H.y;return h.interpolate(I)}}function u(y,w){d3.select(w).select("path."+f.Class.Edge.LINE).transition().attrTween("d",function(C,G,D){return q(y,this,C,G,D)})}function x(y,w){y.classed("faded",w.label.isFadedOut);w=w.label.metaedge;y.select("path."+f.Class.Edge.LINE).classed("control-dep",w&&!w.numRegularEdges)}h.MIN_EDGE_WIDTH=.75;h.MAX_EDGE_WIDTH=12;h.EDGE_WIDTH_SIZE_BASED_SCALE=
d3.scalePow().exponent(.3).domain([1,5E6]).range([h.MIN_EDGE_WIDTH,h.MAX_EDGE_WIDTH]).clamp(!0);let A=d3.scaleQuantize().domain([h.MIN_EDGE_WIDTH,h.MAX_EDGE_WIDTH]).range(["small","medium","large","xlarge"]);h.getEdgeKey=k;h.buildGroup=function(y,w,C){let G=[];G=_.reduce(w.edges(),(D,B)=>{let I=w.edge(B);D.push({v:B.v,w:B.w,label:I});return D},G);y=f.selectOrCreateChild(y,"g",f.Class.Edge.CONTAINER).selectAll(function(){return this.childNodes}).data(G,k);y.enter().append("g").attr("class",f.Class.Edge.GROUP).attr("data-edge",
k).each(function(D){let B=d3.select(this);D.label.edgeGroup=B;C._edgeGroupIndex[k(D)]=B;if(C.handleEdgeSelected)B.on("click",I=>{d3.event.stopPropagation();C.fire("edge-select",{edgeData:I,edgeGroup:B})});n(B,D,C)}).merge(y).each(function(){u(C,this)}).each(function(D){x(d3.select(this),D,C)});y.exit().each(D=>{delete C._edgeGroupIndex[k(D)]}).remove();return y};h.getLabelForBaseEdge=t;h.getLabelForEdge=l;h.appendEdge=n;h.interpolate=d3.line().curve(d3.curveBasis).x(y=>y.x).y(y=>y.y)})(f.edge||(f.edge=
{}))})(d.scene||(d.scene={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/externs.js

//# sourceURL=build://tf-graph-common/graph.js
(function(b){(function(d){function f(H,K,M,L,Q){return(M?M+"/":"")+(H+("undefined"!==typeof L&&"undefined"!==typeof Q?"["+L+"-"+Q+"]":"#")+K)}function h(H){if(!H)return null;for(let K=0;K<H.length;K++){let {key:M,value:L}=H[K];if("_output_shapes"===M){if(!L.list.shape)break;let Q=L.list.shape.map(T=>T.unknown_rank?null:null==T.dim||1===T.dim.length&&null==T.dim[0].size?[]:T.dim.map(X=>X.size));H.splice(K,1);return Q}}return null}function k(H){if(!H)return null;for(let K=0;K<H.length;K++)if("_XlaCluster"===
H[K].key)return H[K].value.s||null;return null}function t(H){let K=[];_.each(H,M=>{let L="^"===M[0];L&&(M=M.substring(1));let Q=M,T="0",X=M.match(/(.*):(\w+:\d+)$/);if(X)Q=X[1],T=X[2];else if(X=M.match(/(.*):(\d+)$/))Q=X[1],T=X[2];0!==K.length&&Q===K[K.length-1].name||K.push({name:Q,outputTensorKey:T,isControlDependency:L})});return K}function l(H,K,M,L,Q,T){K!==M.name&&H.edges.push({v:K,w:M.name,outputTensorKey:L.outputTensorKey,isControlDependency:L.isControlDependency,isReferenceEdge:!0===Q.refEdges[M.op+
" "+T]})}function p(H,K,M){M=M||{};let L=new graphlib.Graph(M);L.setGraph({name:H,rankdir:M.rankdir||"BT",type:K});return L}function m(H){return function(K){for(let M=0;M<H.length;M++){let L=new RegExp(H[M]);if("string"===typeof K.op&&K.op.match(L))return!0}return!1}}function n(H){let K=H.split(d.NAMESPACE_DELIM);return H+d.NAMESPACE_DELIM+"("+K[K.length-1]+")"}function q(H,K){let M={},L={};H.sort();for(let Q=0;Q<H.length-1;++Q){let T=H[Q];_.each(x(T).slice(0,-1),X=>{L[X]=!0});for(let X=Q+1;X<H.length;++X){let aa=
H[X];if(_.startsWith(aa,T)){if(aa.length>T.length&&aa.charAt(T.length)===d.NAMESPACE_DELIM){M[T]=n(T);break}}else break}}_.each(K,Q=>{Q in L&&(M[Q]=n(Q))});return M}function u(H){let K=H.nodes().map(function(M){return H.neighbors(M).length});K.sort();return K}function x(H,K){let M=[],L=H.indexOf(d.NAMESPACE_DELIM);for(;0<=L;)M.push(H.substring(0,L)),L=H.indexOf(d.NAMESPACE_DELIM,L+1);K&&(K=K[H])&&M.push(K);M.push(H);return M}d.NAMESPACE_DELIM="/";d.ROOT_NAME="__root__";d.FUNCTION_LIBRARY_NODE_PREFIX=
"__function_library__";d.LARGE_ATTRS_KEY="_too_large_attrs";d.LIMIT_ATTR_SIZE=1024;d.EDGE_KEY_DELIM="--";let A;(function(H){H[H.FULL=0]="FULL";H[H.EMBEDDED=1]="EMBEDDED";H[H.META=2]="META";H[H.SERIES=3]="SERIES";H[H.CORE=4]="CORE";H[H.SHADOW=5]="SHADOW";H[H.BRIDGE=6]="BRIDGE";H[H.EDGE=7]="EDGE"})(A=d.GraphType||(d.GraphType={}));let y;(function(H){H[H.META=0]="META";H[H.OP=1]="OP";H[H.SERIES=2]="SERIES";H[H.BRIDGE=3]="BRIDGE";H[H.ELLIPSIS=4]="ELLIPSIS"})(y=d.NodeType||(d.NodeType={}));let w;(function(H){H[H.INCLUDE=
0]="INCLUDE";H[H.EXCLUDE=1]="EXCLUDE";H[H.UNSPECIFIED=2]="UNSPECIFIED"})(w=d.InclusionType||(d.InclusionType={}));(function(H){H[H.GROUP=0]="GROUP";H[H.UNGROUP=1]="UNGROUP"})(d.SeriesGroupingType||(d.SeriesGroupingType={}));class C{constructor(){this.nodes={};this.edges=[]}}d.SlimGraph=C;class G{constructor(H){this.type=y.ELLIPSIS;this.isGroupNode=!1;this.cardinality=1;this.stats=this.parentNode=null;this.setNumMoreNodes(H);this.include=w.UNSPECIFIED}setNumMoreNodes(H){this.numMoreNodes=H;this.name=
"... "+H+" more"}}d.EllipsisNodeImpl=G;class D{constructor(H){this.op=H.op;this.name=H.name;this.device=H.device;this.attr=H.attr;this.inputs=t(H.input);this.outputShapes=h(H.attr);this.xlaCluster=k(H.attr);this.compatible=!1;this.type=y.OP;this.isGroupNode=!1;this.cardinality=1;this.inEmbeddings=[];this.outEmbeddings=[];this.parentNode=null;this.include=w.UNSPECIFIED;this.owningSeries=null}}d.OpNodeImpl=D;d.createMetanode=function(H,K={}){return new I(H,K)};d.joinStatsInfoWithGraph=function(H,K,
M){_.each(H.nodes,L=>{L.stats=null});_.each(K.dev_stats,L=>{M&&!M[L.device]||_.each(L.node_stats,Q=>{let T=Q.node_name in H.nodes?Q.node_name:n(Q.node_name);if(T in H.nodes){var X=0;Q.memory&&_.each(Q.memory,la=>{la.total_bytes&&(0<la.total_bytes?X+=Number(la.total_bytes):console.log("ignoring negative memory allocation for "+T))});var aa=null;Q.output&&(aa=_.map(Q.output,la=>_.map(la.tensor_description.shape.dim,Z=>Number(Z.size))));H.nodes[T].device=L.device;null==H.nodes[T].stats&&(H.nodes[T].stats=
new B(aa));H.nodes[T].stats.addBytesAllocation(X);Q.all_end_rel_micros&&(0<Q.all_end_rel_micros?H.nodes[T].stats.addExecutionTime(Q.all_start_micros,Q.all_start_micros+Q.all_end_rel_micros):console.log("ignoring negative runtime for "+T))}})})};class B{constructor(H){this.totalBytes=0;this.outputSize=H}addExecutionTime(H,K){this.startTime=null!=this.startTime?Math.min(this.startTime,H):H;this.endTime=null!=this.endTime?Math.max(this.endTime,K):K}addBytesAllocation(H){this.totalBytes=null!=this.totalBytes?
Math.max(this.totalBytes,H):H}combine(H){null!=H.totalBytes&&(this.totalBytes+=H.totalBytes);null!=H.getTotalMicros()&&this.addExecutionTime(H.startTime,H.endTime)}getTotalMicros(){return null==this.startTime||null==this.endTime?null:this.endTime-this.startTime}}d.NodeStats=B;class I{constructor(H,K={}){this.name=H;this.type=y.META;this.depth=1;this.isGroupNode=!0;this.cardinality=0;this.metagraph=p(H,A.META,K);this.bridgegraph=null;this.opHistogram={};this.deviceHistogram={};this.xlaClusterHistogram=
{};this.compatibilityHistogram={compatible:0,incompatible:0};this.parentNode=this.templateId=null;this.hasNonControlEdges=!1;this.include=w.UNSPECIFIED;this.associatedFunction=""}getFirstChild(){return this.metagraph.node(this.metagraph.nodes()[0])}getRootOp(){let H=this.name.split("/");return this.metagraph.node(this.name+"/("+H[H.length-1]+")")}leaves(){let H=[],K=[this],M;for(;K.length;){let L=K.shift();L.isGroupNode?(M=L.metagraph,_.each(M.nodes(),Q=>K.push(M.node(Q)))):H.push(L.name)}return H}}
d.MetanodeImpl=I;d.createMetaedge=function(H,K){return new N(H,K)};class N{constructor(H,K){this.v=H;this.w=K;this.baseEdgeList=[];this.inbound=null;this.totalSize=this.numRefEdges=this.numControlEdges=this.numRegularEdges=0}addBaseEdge(H,K){this.baseEdgeList.push(H);H.isControlDependency?this.numControlEdges+=1:this.numRegularEdges+=1;H.isReferenceEdge&&(this.numRefEdges+=1);this.totalSize+=N.computeSizeOfEdge(H,K);K.maxMetaEdgeSize=Math.max(K.maxMetaEdgeSize,this.totalSize)}static computeSizeOfEdge(H,
K){let M=K.node(H.v);if(!M.outputShapes)return 1;K.hasShapeInfo=!0;H=Object.keys(M.outputShapes).map(L=>M.outputShapes[L]).map(L=>null==L?1:L.reduce((Q,T)=>{-1===T&&(T=1);return Q*T},1));return _.sum(H)}}d.MetaedgeImpl=N;d.createSeriesNode=function(H,K,M,L,Q,T){return new O(H,K,M,L,Q,T)};d.getSeriesNodeName=f;class O{constructor(H,K,M,L,Q,T){this.name=Q||f(H,K,M);this.type=y.SERIES;this.hasLoop=!1;this.prefix=H;this.suffix=K;this.clusterId=L;this.ids=[];this.parent=M;this.isGroupNode=!0;this.cardinality=
0;this.metagraph=p(Q,A.SERIES,T);this.parentNode=this.bridgegraph=null;this.deviceHistogram={};this.xlaClusterHistogram={};this.compatibilityHistogram={compatible:0,incompatible:0};this.hasNonControlEdges=!1;this.include=w.UNSPECIFIED}}d.DefaultBuildParams={enableEmbedding:!0,inEmbeddingTypes:["Const"],outEmbeddingTypes:["^[a-zA-Z]+Summary$"],refEdges:{"Assign 0":!0,"AssignAdd 0":!0,"AssignSub 0":!0,"assign 0":!0,"assign_add 0":!0,"assign_sub 0":!0,"count_up_to 0":!0,"ScatterAdd 0":!0,"ScatterSub 0":!0,
"ScatterUpdate 0":!0,"scatter_add 0":!0,"scatter_sub 0":!0,"scatter_update 0":!0}};d.build=function(H,K,M){let L={},Q={},T={},X=m(K.inEmbeddingTypes),aa=m(K.outEmbeddingTypes),la=[],Z=H.node,ba=Array(Z.length);return b.graph.util.runAsyncTask("Normalizing names",30,()=>{let ea=Array(Z.length),ca=0;const ka=Ea=>{let va=new D(Ea);if(X(va))return la.push(va.name),L[va.name]=va;if(aa(va))return la.push(va.name),Q[va.name]=va,_.each(va.inputs,xa=>{xa=xa.name;T[xa]=T[xa]||[];T[xa].push(va)}),va;ea[ca]=
va;ba[ca]=va.name;ca++;return va};_.each(Z,ka);const Y=Ea=>{const va=d.FUNCTION_LIBRARY_NODE_PREFIX+Ea.signature.name;ka({name:va,input:[],device:"",op:"",attr:[]});if(Ea.signature.input_arg){let ya=0;var xa=Sa=>{ka({name:va+d.NAMESPACE_DELIM+Sa.name,input:[],device:"",op:"input_arg",attr:[{key:"T",value:{type:Sa.type}}]}).functionInputIndex=ya;ya++};Ea.signature.input_arg.name?xa(Ea.signature.input_arg):_.each(Ea.signature.input_arg,xa)}let Aa=0;const Fa={};Ea.signature.output_arg&&(xa=ya=>{Fa[va+
d.NAMESPACE_DELIM+ya.name]=Aa;Aa++},Ea.signature.output_arg.name?xa(Ea.signature.output_arg):_.each(Ea.signature.output_arg,xa));_.each(Ea.node_def,ya=>{ya.name=va+"/"+ya.name;"string"===typeof ya.input&&(ya.input=[ya.input]);const Sa=ka(ya);_.isNumber(Fa[ya.name])&&(Sa.functionOutputIndex=Fa[ya.name]);_.each(Sa.inputs,Xa=>{Xa.name=va+d.NAMESPACE_DELIM+Xa.name})})};H.library&&H.library.function&&_.each(H.library.function,Y);ea.splice(ca);ba.splice(ca);return ea},M).then(ea=>b.graph.util.runAsyncTask("Building the data structure",
70,()=>{let ca=q(ba,la),ka=new C;_.each(ea,Y=>{let Ea=ca[Y.name]||Y.name;ka.nodes[Ea]=Y;Y.name in T&&(Y.outEmbeddings=T[Y.name],_.each(Y.outEmbeddings,va=>{va.name=ca[va.name]||va.name}));Y.name=Ea});_.each(ea,Y=>{_.each(Y.inputs,(Ea,va)=>{let xa=Ea.name;if(xa in L){Ea=L[xa];Y.inEmbeddings.push(Ea);for(var Aa of Ea.inputs)l(ka,ca[Aa.name]||Aa.name,Y,Aa,K,va)}else if(xa in Q){Aa=Q[xa];for(let Fa of Aa.inputs)l(ka,ca[Fa.name]||Fa.name,Y,Ea,K,va)}else l(ka,ca[xa]||xa,Y,Ea,K,va)})});_.each(L,Y=>{Y.name=
ca[Y.name]||Y.name});return ka},M))};d.createGraph=p;d.getStrictName=n;d.hasSimilarDegreeSequence=function(H,K){H=u(H);K=u(K);for(let M=0;M<H.length;M++)if(H[M]!==K[M])return!1;return!0};d.getHierarchicalPath=x;d.getIncludeNodeButtonString=function(H){return H===b.graph.InclusionType.EXCLUDE?"Add to main graph":"Remove from main graph"};d.getGroupSeriesNodeButtonString=function(H){return H===b.graph.SeriesGroupingType.GROUP?"Ungroup this series of nodes":"Group this series of nodes"};d.toggleNodeSeriesGroup=
function(H,K){H[K]=K in H&&H[K]!==b.graph.SeriesGroupingType.GROUP?b.graph.SeriesGroupingType.GROUP:b.graph.SeriesGroupingType.UNGROUP}})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/hierarchy.js
(function(b){(function(d){(function(f){function h(x,A,y,w){A=y?x.inEdges(A.name):x.outEdges(A.name);_.each(A,C=>{C=x.edge(C);(C.numRegularEdges?w.regular:w.control).push(C)})}function k(x,A){const y={};_.each(A.nodes,w=>{let C=d.getHierarchicalPath(w.name),G=x.root;G.depth=Math.max(C.length,G.depth);y[w.op]||(y[w.op]=[]);y[w.op].push(w);for(let B=0;B<C.length;B++){G.depth=Math.max(G.depth,C.length-B);G.cardinality+=w.cardinality;G.opHistogram[w.op]=(G.opHistogram[w.op]||0)+1;null!=w.device&&(G.deviceHistogram[w.device]=
(G.deviceHistogram[w.device]||0)+1);null!=w.xlaCluster&&(G.xlaClusterHistogram[w.xlaCluster]=(G.xlaClusterHistogram[w.xlaCluster]||0)+1);w.compatible?G.compatibilityHistogram.compatible=(G.compatibilityHistogram.compatible||0)+1:G.compatibilityHistogram.incompatible=(G.compatibilityHistogram.incompatible||0)+1;_.each(w.inEmbeddings,N=>{N.compatible?G.compatibilityHistogram.compatible=(G.compatibilityHistogram.compatible||0)+1:G.compatibilityHistogram.incompatible=(G.compatibilityHistogram.incompatible||
0)+1});_.each(w.outEmbeddings,N=>{N.compatible?G.compatibilityHistogram.compatible=(G.compatibilityHistogram.compatible||0)+1:G.compatibilityHistogram.incompatible=(G.compatibilityHistogram.incompatible||0)+1});if(B===C.length-1)break;var D=C[B];let I=x.node(D);I||(I=d.createMetanode(D,x.graphOptions),I.parentNode=G,x.setNode(D,I),G.metagraph.setNode(D,I),0===D.indexOf(b.graph.FUNCTION_LIBRARY_NODE_PREFIX)&&G.name===b.graph.ROOT_NAME&&(D=D.substring(b.graph.FUNCTION_LIBRARY_NODE_PREFIX.length),y[D]||
(y[D]=[]),x.libraryFunctions[D]={node:I,usages:y[D]},I.associatedFunction=D));G=I}x.setNode(w.name,w);w.parentNode=G;G.metagraph.setNode(w.name,w);_.each(w.inEmbeddings,function(B){x.setNode(B.name,B);B.parentNode=w});_.each(w.outEmbeddings,function(B){x.setNode(B.name,B);B.parentNode=w})})}function t(x,A){let y=x.getNodeMap(),w=[],C=[],G=(D,B)=>{let I=0;for(;D;)B[I++]=D.name,D=D.parentNode;return I-1};_.each(A.edges,D=>{var B=G(A.nodes[D.v],w),I=G(A.nodes[D.w],C);if(-1!==B&&-1!==I){for(;w[B]===C[I];)if(B--,
I--,0>B||0>I)throw Error("No difference found between ancestor paths.");var N=y[w[B+1]];B=w[B];I=C[I];var O=N.metagraph.edge(B,I);O||(O=d.createMetaedge(B,I),N.metagraph.setEdge(B,I,O));N.hasNonControlEdges||D.isControlDependency||(N.hasNonControlEdges=!0);O.addBaseEdge(D,x)}})}function l(x,A,y,w,C,G){let D=x.metagraph;_.each(D.nodes(),B=>{B=D.node(B);B.type===b.graph.NodeType.META&&l(B,A,y,w,C,G)});x=p(D);x=(G?n:m)(x,D,A.graphOptions);_.each(x,function(B,I){let N=B.metagraph.nodes();_.each(N,O=>
{O=D.node(O);O.owningSeries||(O.owningSeries=I)});N.length<w&&!(B.name in C)&&(C[B.name]=b.graph.SeriesGroupingType.UNGROUP);B.name in C&&C[B.name]===b.graph.SeriesGroupingType.UNGROUP||(A.setNode(I,B),D.setNode(I,B),_.each(N,O=>{let H=D.node(O);B.metagraph.setNode(O,H);B.parentNode=H.parentNode;B.cardinality++;null!=H.device&&(B.deviceHistogram[H.device]=(B.deviceHistogram[H.device]||0)+1);null!=H.xlaCluster&&(B.xlaClusterHistogram[H.xlaCluster]=(B.xlaClusterHistogram[H.xlaCluster]||0)+1);H.compatible?
B.compatibilityHistogram.compatible=(B.compatibilityHistogram.compatible||0)+1:B.compatibilityHistogram.incompatible=(B.compatibilityHistogram.incompatible||0)+1;_.each(H.inEmbeddings,K=>{K.compatible?B.compatibilityHistogram.compatible=(B.compatibilityHistogram.compatible||0)+1:B.compatibilityHistogram.incompatible=(B.compatibilityHistogram.incompatible||0)+1});_.each(H.outEmbeddings,K=>{K.compatible?B.compatibilityHistogram.compatible=(B.compatibilityHistogram.compatible||0)+1:B.compatibilityHistogram.incompatible=
(B.compatibilityHistogram.incompatible||0)+1});H.parentNode=B;y[O]=I;D.removeNode(O)}))})}function p(x){return _.reduce(x.nodes(),(A,y)=>{y=x.node(y);if(y.type===d.NodeType.META)return A;let w=y.op;w&&(A[w]=A[w]||[],A[w].push(y.name));return A},{})}function m(x,A,y){let w={};_.each(x,function(C,G){if(!(1>=C.length)){var D={};_.each(C,function(B){var I="*"===B.charAt(B.length-1),N=B.split("/"),O=N[N.length-1];N=N.slice(0,N.length-1).join("/");var H=O.match(/^(\D*)_(\d+)$/);let K="";H?(O=H[1],H=H[2]):
(O=I?O.substr(0,O.length-1):O,H=0,K=I?"*":"");I=d.getSeriesNodeName(O,K,N);D[I]=D[I]||[];B=d.createSeriesNode(O,K,N,+H,B,y);D[I].push(B)});_.each(D,function(B){if(!(2>B.length)){B.sort(function(N,O){return+N.clusterId-+O.clusterId});var I=[B[0]];for(let N=1;N<B.length;N++){let O=B[N];O.clusterId===I[I.length-1].clusterId+1?I.push(O):(q(I,w,+G,A,y),I=[O])}q(I,w,+G,A,y)}})}});return w}function n(x,A,y){let w={};_.each(x,function(C,G){if(!(1>=C.length)){var D={},B={};_.each(C,function(N){let O="*"===
N.charAt(N.length-1);var H=N.split("/");let K=H[H.length-1];H=H.slice(0,H.length-1).join("/");const M=/(\d+)/g;var L;let Q,T,X,aa=0;for(;L=M.exec(K);)++aa,Q=K.slice(0,L.index),T=L[0],L=K.slice(L.index+L[0].length),X=d.getSeriesNodeName(Q,L,H),D[X]=D[X],D[X]||(D[X]=d.createSeriesNode(Q,L,H,+T,N,y)),D[X].ids.push(T),B[N]=B[N]||[],B[N].push([X,T]);1>aa&&(Q=O?K.substr(0,K.length-1):K,T=0,L=O?"*":"",X=d.getSeriesNodeName(Q,L,H),D[X]=D[X],D[X]||(D[X]=d.createSeriesNode(Q,L,H,+T,N,y)),D[X].ids.push(T),B[N]=
B[N]||[],B[N].push([X,T]))});var I={};_.each(B,function(N,O){N.sort(function(M,L){return D[L[0]].ids.length-D[M[0]].ids.length});var H=N[0][0];N=N[0][1];I[H]=I[H]||[];const K=O.split("/");O=d.createSeriesNode(D[H].prefix,D[H].suffix,K.slice(0,K.length-1).join("/"),+N,O,y);I[H].push(O)});_.each(I,function(N){if(!(2>N.length)){N.sort(function(H,K){return+H.clusterId-+K.clusterId});var O=[N[0]];for(let H=1;H<N.length;H++){let K=N[H];K.clusterId===O[O.length-1].clusterId+1?O.push(K):(q(O,w,+G,A,y),O=
[K])}q(O,w,+G,A,y)}})}});return w}function q(x,A,y,w,C){if(1<x.length){let G=d.getSeriesNodeName(x[0].prefix,x[0].suffix,x[0].parent,x[0].clusterId,x[x.length-1].clusterId),D=d.createSeriesNode(x[0].prefix,x[0].suffix,x[0].parent,y,G,C);_.each(x,function(B){D.ids.push(B.clusterId);D.metagraph.setNode(B.name,w.node(B.name))});A[G]=D}}class u{constructor(x){this.hasShapeInfo=!1;this.maxMetaEdgeSize=1;this.graphOptions=x||{};this.graphOptions.compound=!0;this.root=d.createMetanode(d.ROOT_NAME,this.graphOptions);
this.libraryFunctions={};this.xlaClusters=this.devices=this.templates=null;this.index={};this.index[d.ROOT_NAME]=this.root;this.orderings={}}getNodeMap(){return this.index}node(x){return this.index[x]}setNode(x,A){this.index[x]=A}getBridgegraph(x){var A=this.index[x];if(!A)throw Error("Could not find node in hierarchy: "+x);if(!("metagraph"in A))return null;if(A.bridgegraph)return A.bridgegraph;let y=A.bridgegraph=d.createGraph("BRIDGEGRAPH",d.GraphType.BRIDGE,this.graphOptions);if(!(A.parentNode&&
"metagraph"in A.parentNode))return y;var w=A.parentNode;A=w.metagraph;w=this.getBridgegraph(w.name);_.each([A,w],C=>{C.edges().filter(G=>G.v===x||G.w===x).forEach(G=>{let D=G.w===x,B=C.edge(G);_.each(B.baseEdgeList,I=>{let [N,O]=D?[I.w,G.v]:[I.v,G.w];var H=this.getChildName(x,N);H={v:D?O:H,w:D?H:O};let K=y.edge(H);K||(K=d.createMetaedge(H.v,H.w),K.inbound=D,y.setEdge(H.v,H.w,K));K.addBaseEdge(I,this)})})});return y}getChildName(x,A){let y=this.index[A];for(;y;){if(y.parentNode&&y.parentNode.name===
x)return y.name;y=y.parentNode}throw Error("Could not find immediate child for descendant: "+A);}getPredecessors(x){let A=this.index[x];if(!A)throw Error("Could not find node with name: "+x);let y=this.getOneWayEdges(A,!0);A.isGroupNode||_.each(A.inEmbeddings,w=>{_.each(A.inputs,C=>{if(C.name===w.name){let G=new d.MetaedgeImpl(w.name,x);G.addBaseEdge({isControlDependency:C.isControlDependency,outputTensorKey:C.outputTensorKey,isReferenceEdge:!1,v:w.name,w:x},this);y.regular.push(G)}})});return y}getSuccessors(x){let A=
this.index[x];if(!A)throw Error("Could not find node with name: "+x);let y=this.getOneWayEdges(A,!1);A.isGroupNode||_.each(A.outEmbeddings,w=>{_.each(w.inputs,C=>{if(C.name===x){let G=new d.MetaedgeImpl(x,w.name);G.addBaseEdge({isControlDependency:C.isControlDependency,outputTensorKey:C.outputTensorKey,isReferenceEdge:!1,v:x,w:w.name},this);y.regular.push(G)}})});return y}getOneWayEdges(x,A){let y={control:[],regular:[]};if(!x.parentNode||!x.parentNode.isGroupNode)return y;var w=x.parentNode;let C=
w.metagraph;w=this.getBridgegraph(w.name);h(C,x,A,y);h(w,x,A,y);return y}getTopologicalOrdering(x){var A=this.index[x];if(!A)throw Error("Could not find node with name: "+x);if(!A.isGroupNode)return null;if(x in this.orderings)return this.orderings[x];let y={},w={},C=A.metagraph;_.each(C.edges(),D=>{C.edge(D).numRegularEdges&&(D.v in y||(y[D.v]=[]),y[D.v].push(D.w),w[D.w]=!0)});let G=_.difference(_.keys(y),_.keys(w));x=this.orderings[x]={};for(A=0;G.length;){let D=G.shift();x[D]=A++;_.each(y[D],B=>
G.push(B));delete y[D]}return x}getTemplateIndex(){let x=d3.keys(this.templates),A=d3.scaleOrdinal().domain(x).range(d3.range(0,x.length));return y=>A(y)}}f.DefaultHierarchyParams={verifyTemplate:!0,seriesNodeMinSize:5,seriesMap:{},rankDirection:"BT",useGeneralizedSeriesPatterns:!1};f.build=function(x,A,y){let w=new u({rankdir:A.rankDirection}),C={};return b.graph.util.runAsyncTask("Adding nodes",20,()=>{let G={},D={};_.each(x.nodes,B=>{B.device&&(G[B.device]=!0);B.xlaCluster&&(D[B.xlaCluster]=!0)});
w.devices=_.keys(G);w.xlaClusters=_.keys(D);k(w,x)},y).then(()=>b.graph.util.runAsyncTask("Detect series",20,()=>{0<A.seriesNodeMinSize&&l(w.root,w,C,A.seriesNodeMinSize,A.seriesMap,A.useGeneralizedSeriesPatterns)},y)).then(()=>b.graph.util.runAsyncTask("Adding edges",30,()=>{t(w,x,C)},y)).then(()=>b.graph.util.runAsyncTask("Finding similar subgraphs",30,()=>{w.templates=d.template.detect(w,A.verifyTemplate)},y)).then(()=>w)};f.joinAndAggregateStats=function(x){let A={},y={};_.each(x.root.leaves(),
w=>{w=x.node(w);null!=w.device&&(A[w.device]=!0);null!=w.xlaCluster&&(y[w.xlaCluster]=!0)});x.devices=_.keys(A);x.xlaClusters=_.keys(y);_.each(x.getNodeMap(),w=>{w.isGroupNode&&(w.stats=new d.NodeStats(null),w.deviceHistogram={})});_.each(x.root.leaves(),w=>{let C=w=x.node(w);for(;null!=C.parentNode;){if(null!=w.device){var G=C.parentNode.deviceHistogram;G[w.device]=(G[w.device]||0)+1}null!=w.xlaCluster&&(G=C.parentNode.xlaClusterHistogram,G[w.xlaCluster]=(G[w.xlaCluster]||0)+1);null!=w.stats&&C.parentNode.stats.combine(w.stats);
C=C.parentNode}})};f.getIncompatibleOps=function(x,A){let y=[],w={};_.each(x.root.leaves(),C=>{C=x.node(C);if(C.type==d.NodeType.OP){if(!C.compatible)if(C.owningSeries)if(A&&A.seriesMap[C.owningSeries]===b.graph.SeriesGroupingType.UNGROUP)y.push(C);else{if(!w[C.owningSeries]){let G=x.node(C.owningSeries);G&&(w[C.owningSeries]=G,y.push(G))}}else y.push(C);_.each(C.inEmbeddings,G=>{G.compatible||y.push(G)});_.each(C.outEmbeddings,G=>{G.compatible||y.push(G)})}});return y}})(d.hierarchy||(d.hierarchy=
{}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/layout.js
(function(b){(function(d){(function(f){function h(w){w.node.isGroupNode&&t(w);w.node.type===d.NodeType.META?p(w):w.node.type===d.NodeType.SERIES&&m(w)}function k(w){w.inboxWidth=0<w.inAnnotations.list.length?f.PARAMS.annotations.inboxWidth:0;w.outboxWidth=0<w.outAnnotations.list.length?f.PARAMS.annotations.outboxWidth:0;w.coreBox.width=w.width;w.coreBox.height=w.height;w.width=Math.max(w.coreBox.width+w.inboxWidth+w.outboxWidth,3*w.displayName.length)}function t(w){let C=w.coreGraph.nodes().map(G=>
w.coreGraph.node(G)).concat(w.isolatedInExtract,w.isolatedOutExtract,w.libraryFunctionsExtract);_.each(C,G=>{switch(G.node.type){case d.NodeType.OP:_.extend(G,f.PARAMS.nodeSize.op);break;case d.NodeType.BRIDGE:_.extend(G,f.PARAMS.nodeSize.bridge);break;case d.NodeType.META:G.expanded?h(G):(_.extend(G,f.PARAMS.nodeSize.meta),G.height=f.PARAMS.nodeSize.meta.height(G.node.cardinality));break;case d.NodeType.SERIES:G.expanded?(_.extend(G,f.PARAMS.nodeSize.series.expanded),h(G)):_.extend(G,G.node.hasNonControlEdges?
f.PARAMS.nodeSize.series.vertical:f.PARAMS.nodeSize.series.horizontal);break;default:throw Error("Unrecognized node type: "+G.node.type);}G.expanded||k(G);n(G)})}function l(w,C){_.extend(w.graph(),{nodesep:C.nodeSep,ranksep:C.rankSep,edgesep:C.edgeSep});let G=[],D=[];_.each(w.nodes(),H=>{w.node(H).node.type===d.NodeType.BRIDGE?G.push(H):D.push(H)});if(!D.length)return{width:0,height:0};dagre.layout(w);let B=Infinity,I=Infinity,N=-Infinity,O=-Infinity;_.each(D,H=>{H=w.node(H);var K=.5*H.width,M=H.x-
K;K=H.x+K;B=M<B?M:B;N=K>N?K:N;K=.5*H.height;M=H.y-K;H=H.y+K;I=M<I?M:I;O=H>O?H:O});_.each(w.edges(),H=>{H=w.edge(H);if(!H.structural){var K=w.node(H.metaedge.v),M=w.node(H.metaedge.w);if(3===H.points.length&&A(H.points)){if(null!=K){var L=K.expanded?K.x:u(K);H.points[0].x=L}null!=M&&(L=M.expanded?M.x:u(M),H.points[2].x=L);H.points=[H.points[0],H.points[1]]}L=H.points[H.points.length-2];null!=M&&(H.points[H.points.length-1]=y(L,M));M=H.points[1];null!=K&&(H.points[0]=y(M,K));_.each(H.points,Q=>{B=Q.x<
B?Q.x:B;N=Q.x>N?Q.x:N;I=Q.y<I?Q.y:I;O=Q.y>O?Q.y:O})}});_.each(w.nodes(),H=>{H=w.node(H);H.x-=B;H.y-=I});_.each(w.edges(),H=>{_.each(w.edge(H).points,K=>{K.x-=B;K.y-=I})});return{width:N-B,height:O-I}}function p(w){let C=f.PARAMS.subscene.meta;_.extend(w,C);_.extend(w.coreBox,l(w.coreGraph,f.PARAMS.graph.meta));var G=w.isolatedInExtract.length?_.max(w.isolatedInExtract,B=>B.width).width:null;w.inExtractBox.width=null!=G?G:0;w.inExtractBox.height=_.reduce(w.isolatedInExtract,(B,I,N)=>{N=0<N?C.extractYOffset:
0;I.x=0;I.y=B+N+I.height/2;return B+N+I.height},0);G=w.isolatedOutExtract.length?_.max(w.isolatedOutExtract,B=>B.width).width:null;w.outExtractBox.width=null!=G?G:0;w.outExtractBox.height=_.reduce(w.isolatedOutExtract,(B,I,N)=>{N=0<N?C.extractYOffset:0;I.x=0;I.y=B+N+I.height/2;return B+N+I.height},0);G=w.libraryFunctionsExtract.length?_.max(w.libraryFunctionsExtract,B=>B.width).width:null;w.libraryFunctionsBox.width=null!=G?G:0;w.libraryFunctionsBox.height=_.reduce(w.libraryFunctionsExtract,(B,I,
N)=>{N=0<N?C.extractYOffset:0;I.x=0;I.y=B+N+I.height/2;return B+N+I.height},0);G=0;0<w.isolatedInExtract.length&&G++;0<w.isolatedOutExtract.length&&G++;0<w.libraryFunctionsExtract.length&&G++;0<w.coreGraph.nodeCount()&&G++;let D=f.PARAMS.subscene.meta.extractXOffset;G=1>=G?0:G*D;w.coreBox.width+=Math.max(f.MIN_AUX_WIDTH,w.inExtractBox.width+w.outExtractBox.width)+G+w.libraryFunctionsBox.width+G;w.coreBox.height=C.labelHeight+Math.max(w.inExtractBox.height,w.coreBox.height,w.libraryFunctionsBox.height,
w.outExtractBox.height);w.width=w.coreBox.width+C.paddingLeft+C.paddingRight;w.height=w.paddingTop+w.coreBox.height+w.paddingBottom}function m(w){let C=w.coreGraph,G=f.PARAMS.subscene.series;_.extend(w,G);_.extend(w.coreBox,l(w.coreGraph,f.PARAMS.graph.series));_.each(C.nodes(),D=>{C.node(D).excluded=!1});w.width=w.coreBox.width+G.paddingLeft+G.paddingRight;w.height=w.coreBox.height+G.paddingTop+G.paddingBottom}function n(w){if(!w.expanded){var C=w.inAnnotations.list,G=w.outAnnotations.list;_.each(C,
K=>q(K));_.each(G,K=>q(K));var D=f.PARAMS.annotations,B=_.reduce(C,(K,M,L)=>{L=0<L?D.yOffset:0;M.dx=-(w.coreBox.width+M.width)/2-D.xOffset;M.dy=K+L+M.height/2;return K+L+M.height},0);_.each(C,K=>{K.dy-=B/2;K.labelOffset=D.labelOffset});var I=_.reduce(G,(K,M,L)=>{L=0<L?D.yOffset:0;M.dx=(w.coreBox.width+M.width)/2+D.xOffset;M.dy=K+L+M.height/2;return K+L+M.height},0);_.each(G,K=>{K.dy-=I/2;K.labelOffset=D.labelOffset});var N=Math.min(w.height/2-w.radius,B/2);N=0>N?0:N;var O=d3.scaleLinear().domain([0,
C.length-1]).range([-N,N]);_.each(C,(K,M)=>{K.points=[{dx:K.dx+K.width/2,dy:K.dy},{dx:-w.coreBox.width/2,dy:1<C.length?O(M):0}]});N=Math.min(w.height/2-w.radius,I/2);N=0>N?0:N;var H=d3.scaleLinear().domain([0,G.length-1]).range([-N,N]);_.each(G,(K,M)=>{K.points=[{dx:w.coreBox.width/2,dy:1<G.length?H(M):0},{dx:K.dx-K.width/2,dy:K.dy}]});w.height=Math.max(w.height,B,I)}}function q(w){switch(w.annotationType){case d.render.AnnotationType.CONSTANT:_.extend(w,f.PARAMS.constant.size);break;case d.render.AnnotationType.SHORTCUT:if(w.node.type===
d.NodeType.OP)_.extend(w,f.PARAMS.shortcutSize.op);else if(w.node.type===d.NodeType.META)_.extend(w,f.PARAMS.shortcutSize.meta);else if(w.node.type===d.NodeType.SERIES)_.extend(w,f.PARAMS.shortcutSize.series);else throw Error("Invalid node type: "+w.node.type);break;case d.render.AnnotationType.SUMMARY:_.extend(w,f.PARAMS.constant.size)}}function u(w){return w.expanded?w.x:w.x-w.width/2+(w.inAnnotations.list.length?w.inboxWidth:0)+w.coreBox.width/2}function x(w,C){return 180*Math.atan((C.y-w.y)/(C.x-
w.x))/Math.PI}function A(w){let C=x(w[0],w[1]);for(let G=1;G<w.length-1;G++){let D=x(w[G],w[G+1]);if(1<Math.abs(D-C))return!1;C=D}return!0}function y(w,C){let G=C.expanded?C.x:u(C),D=C.y;var B=w.x-G;w=w.y-D;let I=C.expanded?C.width:C.coreBox.width,N=C.expanded?C.height:C.coreBox.height;Math.abs(w)*I/2>Math.abs(B)*N/2?(0>w&&(N=-N),C=0===w?0:N/2*B/w,B=N/2):(0>B&&(I=-I),C=I/2,B=0===B?0:I/2*w/B);return{x:G+C,y:D+B}}f.PARAMS={animation:{duration:250},graph:{meta:{nodeSep:5,rankSep:25,edgeSep:5},series:{nodeSep:5,
rankSep:25,edgeSep:5},padding:{paddingTop:40,paddingLeft:20}},subscene:{meta:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:20,extractXOffset:15,extractYOffset:20},series:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:10}},nodeSize:{meta:{radius:5,width:60,maxLabelWidth:52,height:d3.scaleLinear().domain([1,200]).range([15,60]).clamp(!0),expandButtonRadius:3},op:{width:15,height:6,radius:3,labelOffset:-8,maxLabelWidth:30},series:{expanded:{radius:10,
labelOffset:0},vertical:{width:16,height:13,labelOffset:-13},horizontal:{width:24,height:8,radius:10,labelOffset:-10}},bridge:{width:20,height:20,radius:2,labelOffset:0}},shortcutSize:{op:{width:10,height:4},meta:{width:12,height:4,radius:1},series:{width:14,height:4}},annotations:{inboxWidth:50,outboxWidth:50,xOffset:10,yOffset:3,labelOffset:2,maxLabelWidth:120},constant:{size:{width:4,height:4}},series:{maxStackCount:3,parallelStackOffsetRatio:.2,towerStackOffsetRatio:.5},minimap:{size:150}};f.MIN_AUX_WIDTH=
140;f.layoutScene=h;f.computeCXPositionOfNodeShape=u})(d.layout||(d.layout={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/loader.js
var Ui=this&&this.__awaiter||function(b,d,f,h){return new (f||(f=Promise))(function(k,t){function l(n){try{m(h.next(n))}catch(q){t(q)}}function p(n){try{m(h["throw"](n))}catch(q){t(q)}}function m(n){n.done?k(n.value):(new f(function(q){q(n.value)})).then(l,p)}m((h=h.apply(b,d||[])).next())})};
(function(b){(function(d){(function(f){f.fetchAndConstructHierarchicalGraph=function(h,k,t,l=new d.op.TpuCompatibilityProvider,p=d.hierarchy.DefaultHierarchyParams){const m=d.util.getSubtaskTracker(h,20,"Graph"),n=d.util.getSubtaskTracker(h,50,"Namespace hierarchy");return d.parser.fetchAndParseGraphData(k,t,d.util.getSubtaskTracker(h,30,"Data")).then(function(q){if(!q.node)throw Error("The graph is empty. This can happen when TensorFlow could not trace any graph. Please refer to https://github.com/tensorflow/tensorboard/issues/1961 for more information.");
return d.build(q,d.DefaultBuildParams,m)},()=>{throw Error("Malformed GraphDef. This can sometimes be caused by a bad network connection or difficulty reconciling multiple GraphDefs; for the latter case, please refer to https://github.com/tensorflow/tensorboard/issues/1929.");}).then(q=>Ui(this,void 0,void 0,function*(){d.op.checkOpsForCompatibility(q,l);const u=yield d.hierarchy.build(q,p,n);return{graph:q,graphHierarchy:u}})).catch(q=>{h.reportError(`Graph visualization failed.\n\n${q}`,q);throw q;
})}})(d.loader||(d.loader={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/node.js
(function(b){(function(d){(function(f){(function(h){function k(Z,ba,ea){if(ba.node.isGroupNode){if(ba.expanded)return f.buildGroup(Z,ba,ea,f.Class.Subscene.GROUP);f.selectChild(Z,"g",f.Class.Subscene.GROUP).remove()}return null}function t(Z,ba){let ea=ba.x-ba.width/2+ba.paddingLeft;ba=ba.y-ba.height/2+ba.paddingTop;Z=f.selectChild(Z,"g",f.Class.Subscene.GROUP);f.translate(Z,ea,ba)}function l(Z,ba,ea){Z=f.selectOrCreateChild(Z,"g",f.Class.Node.BUTTON_CONTAINER);f.selectOrCreateChild(Z,"circle",f.Class.Node.BUTTON_CIRCLE);
f.selectOrCreateChild(Z,"path",f.Class.Node.EXPAND_BUTTON).attr("d","M0,-2.2 V2.2 M-2.2,0 H2.2");f.selectOrCreateChild(Z,"path",f.Class.Node.COLLAPSE_BUTTON).attr("d","M-2.2,0 H2.2");Z.on("click",ca=>{d3.event.stopPropagation();ea.fire("node-toggle-expand",{name:ca.node.name})});f.positionButton(Z,ba)}function p(Z,ba,ea,ca){if(ca)Z.attr("pointer-events","none");else{var ka=f.contextmenu.getMenu(ea,m(ba.node,ea));Z.on("dblclick",Y=>{ea.fire("node-toggle-expand",{name:Y.node.name})}).on("mouseover",
Y=>{ea.isNodeExpanded(Y)||ea.fire("node-highlight",{name:Y.node.name})}).on("mouseout",Y=>{ea.isNodeExpanded(Y)||ea.fire("node-unhighlight",{name:Y.node.name})}).on("click",Y=>{d3.event.stopPropagation();ea.fire("node-select",{name:Y.node.name})}).on("contextmenu",(Y,Ea)=>{ea.fire("node-select",{name:Y.node.name});ka.call(Y,Ea)})}}function m(Z,ba){let ea=[{title:()=>d.getIncludeNodeButtonString(Z.include),action:()=>{ba.fire("node-toggle-extract",{name:Z.name})}}];ba.nodeContextMenuItems&&(ea=ea.concat(ba.nodeContextMenuItems));
n(Z)&&ea.push({title:()=>x(Z),action:()=>{ba.fire("node-toggle-seriesgroup",{name:q(Z)})}});return ea}function n(Z){return null!==q(Z)}function q(Z){return Z?Z.type===d.NodeType.SERIES?Z.name:Z.type===d.NodeType.OP?Z.owningSeries:null:null}function u(Z){let ba=null;if(!Z)return null;Z.type===d.NodeType.SERIES?ba=Z:Z.parentNode&&Z.parentNode.type===d.NodeType.SERIES&&(ba=Z.parentNode);return ba}function x(Z){return b.graph.getGroupSeriesNodeButtonString(null!==u(Z)?b.graph.SeriesGroupingType.GROUP:
b.graph.SeriesGroupingType.UNGROUP)}function A(Z,ba,ea){var ca=ba.displayName;let ka=ba.node.type===d.NodeType.META&&!ba.expanded;Z=f.selectOrCreateChild(Z,"text",f.Class.Node.LABEL);let Y=Z.node();Y.parentNode.appendChild(Y);Z.attr("dy",".35em").attr("text-anchor","middle");ka&&(ca.length>ea.maxMetanodeLabelLength&&(ca=ca.substr(0,ea.maxMetanodeLabelLength-2)+"..."),ea=w(ea),Z.attr("font-size",ea(ca.length)+"px"));ca=Z.text(ca);y(ca,ba.node.type,ba);return Z}function y(Z,ba,ea){let ca=Z.node();var ka=
ca.getComputedTextLength();let Y=ca.textContent,Ea=null;switch(ba){case d.NodeType.META:ea&&!ea.expanded&&(Ea=d.layout.PARAMS.nodeSize.meta.maxLabelWidth);break;case d.NodeType.OP:Ea=d.layout.PARAMS.nodeSize.op.maxLabelWidth;break;case -1:Ea=d.layout.PARAMS.annotations.maxLabelWidth}if(!(null===Ea||ka<=Ea)){for(ka=1;ca.getSubStringLength(0,ka)<Ea;)ka++;ba=ca.textContent.substr(0,ka);do ba=ba.substr(0,ba.length-1),ca.textContent=ba+"...",ka=ca.getComputedTextLength();while(ka>Ea&&0<ba.length);return Z.append("title").text(Y)}}
function w(Z){aa||(aa=d3.scaleLinear().domain([Z.maxMetanodeLabelLengthLargeFont,Z.maxMetanodeLabelLength]).range([Z.maxMetanodeLabelLengthFontSize,Z.minMetanodeLabelLengthFontSize]).clamp(!0));return aa}function C(Z,ba,ea,ca){f.selectChild(Z,"text",f.Class.Node.LABEL).transition().attr("x",ba).attr("y",ea+ca)}function G(Z,ba,ea){Z=f.selectOrCreateChild(Z,"g",ea);switch(ba.node.type){case d.NodeType.OP:ba=ba.node;if(_.isNumber(ba.functionInputIndex)||_.isNumber(ba.functionOutputIndex)){f.selectOrCreateChild(Z,
"polygon",f.Class.Node.COLOR_TARGET);break}f.selectOrCreateChild(Z,"ellipse",f.Class.Node.COLOR_TARGET);break;case d.NodeType.SERIES:ea="annotation";ba.coreGraph&&(ea=ba.node.hasNonControlEdges?"vertical":"horizontal");let ca=[f.Class.Node.COLOR_TARGET];ba.isFadedOut&&ca.push("faded-ellipse");f.selectOrCreateChild(Z,"use",ca).attr("xlink:href","#op-series-"+ea+"-stamp");f.selectOrCreateChild(Z,"rect",f.Class.Node.COLOR_TARGET).attr("rx",ba.radius).attr("ry",ba.radius);break;case d.NodeType.BRIDGE:f.selectOrCreateChild(Z,
"rect",f.Class.Node.COLOR_TARGET).attr("rx",ba.radius).attr("ry",ba.radius);break;case d.NodeType.META:f.selectOrCreateChild(Z,"rect",f.Class.Node.COLOR_TARGET).attr("rx",ba.radius).attr("ry",ba.radius);break;default:throw Error("Unrecognized node type: "+ba.node.type);}return Z}function D(Z){switch(Z.node.type){case d.NodeType.OP:return f.Class.OPNODE;case d.NodeType.META:return f.Class.METANODE;case d.NodeType.SERIES:return f.Class.SERIESNODE;case d.NodeType.BRIDGE:return f.Class.BRIDGENODE;case d.NodeType.ELLIPSIS:return f.Class.ELLIPSISNODE}throw Error("Unrecognized node type: "+
Z.node.type);}function B(Z,ba){var ea=f.selectChild(Z,"g",f.Class.Node.SHAPE);let ca=d.layout.computeCXPositionOfNodeShape(ba);switch(ba.node.type){case d.NodeType.OP:{const ka=ba.node;_.isNumber(ka.functionInputIndex)||_.isNumber(ka.functionOutputIndex)?(ea=f.selectChild(ea,"polygon"),f.positionTriangle(ea,ba.x,ba.y,ba.coreBox.width,ba.coreBox.height)):(ea=f.selectChild(ea,"ellipse"),f.positionEllipse(ea,ca,ba.y,ba.coreBox.width,ba.coreBox.height));C(Z,ca,ba.y,ba.labelOffset);break}case d.NodeType.META:ea=
ea.selectAll("rect");ba.expanded?(f.positionRect(ea,ba.x,ba.y,ba.width,ba.height),t(Z,ba),C(Z,ca,ba.y,-ba.height/2+ba.labelHeight/2)):(f.positionRect(ea,ca,ba.y,ba.coreBox.width,ba.coreBox.height),C(Z,ca,ba.y,0));break;case d.NodeType.SERIES:ea=f.selectChild(ea,"use");ba.expanded?(f.positionRect(ea,ba.x,ba.y,ba.width,ba.height),t(Z,ba),C(Z,ca,ba.y,-ba.height/2+ba.labelHeight/2)):(f.positionRect(ea,ca,ba.y,ba.coreBox.width,ba.coreBox.height),C(Z,ca,ba.y,ba.labelOffset));break;case d.NodeType.BRIDGE:Z=
f.selectChild(ea,"rect");f.positionRect(Z,ba.x,ba.y,ba.width,ba.height);break;default:throw Error("Unrecognized node type: "+ba.node.type);}}function I(Z,ba,ea){let ca=b.graph.util.escapeQuerySelector(Z);if(!ea)return`url(#${ca})`;ea=d3.select(ea);let ka=ea.select("defs#_graph-gradients");ka.empty()&&(ka=ea.append("defs").attr("id","_graph-gradients"));let Y=ka.select("linearGradient#"+ca);if(Y.empty()){Y=ka.append("linearGradient").attr("id",Z);Y.selectAll("*").remove();let Ea=0;_.each(ba,va=>{let xa=
va.color;Y.append("stop").attr("offset",Ea).attr("stop-color",xa);Y.append("stop").attr("offset",Ea+va.proportion).attr("stop-color",xa);Ea+=va.proportion})}return`url(#${ca})`}function N(Z,ba,ea,ca,ka){let Y=d.render.MetanodeColors;switch(ba){case la.STRUCTURE:return ea.node.type===d.NodeType.META?(ba=ea.node.templateId,null===ba?Y.UNKNOWN:Y.STRUCTURE_PALETTE(Z(ba),ca)):ea.node.type===d.NodeType.SERIES?ca?Y.EXPANDED_COLOR:"white":ea.node.type===d.NodeType.BRIDGE?ea.structural?"#f0e":ea.node.inbound?
"#0ef":"#fe0":_.isNumber(ea.node.functionInputIndex)?"#795548":_.isNumber(ea.node.functionOutputIndex)?"#009688":"white";case la.DEVICE:return null==ea.deviceColors?Y.UNKNOWN:ca?Y.EXPANDED_COLOR:I("device-"+ea.node.name,ea.deviceColors,ka);case la.XLA_CLUSTER:return null==ea.xlaClusterColors?Y.UNKNOWN:ca?Y.EXPANDED_COLOR:I("xla-"+ea.node.name,ea.xlaClusterColors,ka);case la.COMPUTE_TIME:return ca?Y.EXPANDED_COLOR:ea.computeTimeColor||Y.UNKNOWN;case la.MEMORY:return ca?Y.EXPANDED_COLOR:ea.memoryColor||
Y.UNKNOWN;case la.OP_COMPATIBILITY:return null==ea.compatibilityColors?Y.UNKNOWN:ca?Y.EXPANDED_COLOR:I("op-compat-"+ea.node.name,ea.compatibilityColors,ka);default:throw Error("Unknown case to color nodes by");}}function O(Z,ba,ea,ca){ca=ca||f.Class.Node.SHAPE;let ka=ea.isNodeSelected(ba.node.name),Y=ba.isInExtract||ba.isOutExtract||ba.isLibraryFunction,Ea=ba.expanded&&ca!==f.Class.Annotation.NODE,va=ba.isFadedOut;Z.classed("highlighted",ea.isNodeHighlighted(ba.node.name));Z.classed("selected",ka);
Z.classed("extract",Y);Z.classed("expanded",Ea);Z.classed("faded",va);Z=Z.select("."+ca+" ."+f.Class.Node.COLOR_TARGET);ba=N(ea.templateIndex,la[ea.colorBy.toUpperCase()],ba,Ea,ea.getGraphSvgRoot());Z.style("fill",ba);Z.style("stroke",ka?null:H(ba))}function H(Z){return"url"===Z.substring(0,3)?d.render.MetanodeColors.GRADIENT_OUTLINE:d3.rgb(Z).darker().toString()}function K(Z,ba){let ea=[];Z=ba.getNodeByName(Z);if(Z instanceof b.graph.OpNodeImpl)return[Z].concat(Z.inEmbeddings);Z=Z.metagraph.nodes();
_.each(Z,function(ca){ea=ea.concat(K(ca,ba))});return ea}function M(Z,ba,ea,ca){if(ca[ea.name])return ca;ca[ea.name]=!0;var ka=ea.inputs;let Y=X(ba,ea);d3.select(Z).select(`.node[data-name="${Y.name}"]`).classed("input-highlight",!0);let Ea={};_.each(ka,function(Aa){Aa=ba.getNodeByName(Aa.name);if(void 0!==Aa){Aa instanceof d.MetanodeImpl&&(Aa=b.graph.getStrictName(Aa.name),Aa=ba.getNodeByName(Aa));var Fa=X(ba,Aa),ya=Ea[Fa.name];ya?ya.opNodes.push(Aa):Ea[Fa.name]={visibleParent:Fa,opNodes:[Aa]}}});
let va={},xa=[Y];va[Y.name]={traced:!1,index:0,connectionEndpoints:[]};ea=Y;for(ka=1;ea.name!==b.graph.ROOT_NAME;ka++)ea=ea.parentNode,va[ea.name]={traced:!1,index:ka,connectionEndpoints:[]},xa[ka]=ea;_.forOwn(Ea,function(Aa){let Fa=Aa.visibleParent;_.each(Aa.opNodes,function(ya){ca=M(Z,ba,ya,ca)});Fa.name!==Y.name&&L(Z,Fa,va,xa)});return ca}function L(Z,ba,ea,ca){var ka=ba,Y=ba;for(ba=[];!ea[ka.name];)Y.name!==ka.name&&ba.push([Y,ka]),Y=ka,ka=ka.parentNode;ea=ea[ka.name].index;let Ea=ca[Math.max(ea-
1,0)].name;Y=ka=Y.name;const va=d3.select(Z);va.selectAll(`[data-edge="${Y}--${Ea}"]`).classed("input-edge-highlight",!0);_.each(ba,function(xa){va.selectAll(`[data-edge="${xa[0].name}--${Ea}`+`~~${xa[1].name}~~OUT"]`).classed("input-edge-highlight",!0)});for(Z=1;Z<ea;Z++)va.selectAll(`[data-edge="${ka}~~${ca[Z].name}`+`~~IN--${ca[Z-1].name}"]`).classed("input-edge-highlight",!0)}function Q(Z,ba){let ea={};_.each(ba,function(ca){ca=Z.getNodeByName(ca);ca=X(Z,ca);ea[ca.name]=ca});return ea}function T(Z,
ba){_.forOwn(ba,function(ea){for(;ea.name!==b.graph.ROOT_NAME;){const ca=d3.select(Z).select(`.node[data-name="${ea.name}"]`);!ca.nodes().length||ca.classed("input-highlight")||ca.classed("selected")||ca.classed("op")||ca.classed("input-parent",!0);ea=ea.parentNode}})}function X(Z,ba){let ea=!1,ca=ba;for(;!ea;)if(ba=ca,ca=ba.parentNode,void 0===ca)ea=!0;else{let ka=Z.getRenderNodeByName(ca.name);ka&&(ka.expanded||ca instanceof d.OpNodeImpl)&&(ea=!0)}return ba}h.buildGroup=function(Z,ba,ea){Z=f.selectOrCreateChild(Z,
"g",f.Class.Node.CONTAINER).selectAll(function(){return this.childNodes}).data(ba,ca=>ca.node.name+":"+ca.node.type);Z.enter().append("g").attr("data-name",ca=>ca.node.name).each(function(ca){let ka=d3.select(this);ea.addNodeGroup(ca.node.name,ka)}).merge(Z).attr("class",ca=>f.Class.Node.GROUP+" "+D(ca)).each(function(ca){let ka=d3.select(this);var Y=f.selectOrCreateChild(ka,"g",f.Class.Annotation.INBOX);f.annotation.buildGroup(Y,ca.inAnnotations,ca,ea);Y=f.selectOrCreateChild(ka,"g",f.Class.Annotation.OUTBOX);
f.annotation.buildGroup(Y,ca.outAnnotations,ca,ea);Y=G(ka,ca,f.Class.Node.SHAPE);ca.node.isGroupNode&&l(Y,ca,ea);p(Y,ca,ea);k(ka,ca,ea);Y=A(ka,ca,ea);p(Y,ca,ea,ca.node.type===d.NodeType.META);O(ka,ca,ea);B(ka,ca)});Z.exit().each(function(ca){ea.removeNodeGroup(ca.node.name);let ka=d3.select(this);0<ca.inAnnotations.list.length&&ka.select("."+f.Class.Annotation.INBOX).selectAll("."+f.Class.Annotation.GROUP).each(Y=>{ea.removeAnnotationGroup(Y,ca)});0<ca.outAnnotations.list.length&&ka.select("."+f.Class.Annotation.OUTBOX).selectAll("."+
f.Class.Annotation.GROUP).each(Y=>{ea.removeAnnotationGroup(Y,ca)})}).remove();return Z};h.getContextMenu=m;h.canBeInSeries=n;h.getSeriesName=q;h.getGroupSettingLabel=x;h.enforceLabelWidth=y;let aa=null;h.buildShape=G;h.nodeClass=D;let la;(function(Z){Z[Z.STRUCTURE=0]="STRUCTURE";Z[Z.DEVICE=1]="DEVICE";Z[Z.XLA_CLUSTER=2]="XLA_CLUSTER";Z[Z.COMPUTE_TIME=3]="COMPUTE_TIME";Z[Z.MEMORY=4]="MEMORY";Z[Z.OP_COMPATIBILITY=5]="OP_COMPATIBILITY"})(la=h.ColorBy||(h.ColorBy={}));h.removeGradientDefinitions=function(Z){d3.select(Z).select("defs#_graph-gradients").remove()};
h.getFillForNode=N;h.stylize=O;h.getStrokeForFill=H;h.updateInputTrace=function(Z,ba,ea,ca){const ka=d3.select(Z);ka.selectAll(".input-highlight").classed("input-highlight",!1);ka.selectAll(".non-input").classed("non-input",!1);ka.selectAll(".input-parent").classed("input-parent",!1);ka.selectAll(".input-child").classed("input-child",!1);ka.selectAll(".input-edge-highlight").classed("input-edge-highlight",!1);ka.selectAll(".non-input-edge-highlight").classed("non-input-edge-highlight",!1);ka.selectAll(".input-highlight-selected").classed("input-highlight-selected",
!1);if(ba&&ca&&ea){ea=K(ea,ba);var Y={};_.each(ea,function(Ea){Y=M(Z,ba,Ea,Y)});ea=Object.keys(Y);ea=Q(ba,ea);T(Z,ea);ka.selectAll("g.node:not(.selected):not(.input-highlight):not(.input-parent):not(.input-children)").classed("non-input",!0).each(function(Ea){ka.selectAll(`[data-name="${Ea.node.name}"]`).classed("non-input",!0)});ka.selectAll("g.edge:not(.input-edge-highlight)").classed("non-input-edge-highlight",!0)}};h.getVisibleParent=X})(f.node||(f.node={}))})(d.scene||(d.scene={}))})(b.graph||
(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/op.js
(function(b){(function(d){(function(f){class h{isNotTpuOp(k){return-1!=k.toLowerCase().search("cpu:")||-1!=k.toLowerCase().search("gpu:")?!0:-1==k.toLowerCase().search("tpu")}opValid(k){return 0==k.name.search(d.FUNCTION_LIBRARY_NODE_PREFIX)||!k.op||k.device&&this.isNotTpuOp(k.device)||k.device&&-1!=k.device.search("TPU_SYSTEM")?!0:_.includes(h.WHITELIST,k.op)}}h.WHITELIST="Abs Acos Acosh Add AddN AdjustContrastv2 AdjustHue AdjustSaturation All Angle Any ApproximateEqual ArgMax ArgMin Asin Asinh Assert AssignAddVariableOp AssignSubVariableOp AssignVariableOp Atan Atan2 Atanh AvgPool AvgPool3D AvgPool3DGrad AvgPoolGrad BatchMatMul BatchToSpace BatchToSpaceND BiasAdd BiasAddGrad BiasAddV1 Bitcast BitwiseAnd BitwiseOr BitwiseXor BroadcastArgs BroadcastGradientArgs Bucketize Cast Ceil CheckNumerics Cholesky ClipByValue Complex ComplexAbs Concat ConcatOffset ConcatV2 Conj ConjugateTranspose Const ControlTrigger Conv2D Conv2DBackpropFilter Conv2DBackpropInput Conv3D Conv3DBackpropFilterV2 Conv3DBackpropInputV2 Cos Cosh Cross CrossReplicaSum Cumprod Cumsum DepthToSpace DepthwiseConv2dNative DepthwiseConv2dNativeBackpropFilter DepthwiseConv2dNativeBackpropInput Diag DiagPart Digamma Div DynamicStitch Elu EluGrad Empty Equal Erf Erfc Exp ExpandDims Expm1 ExtractImagePatches FFT FFT2D FFT3D FakeQuantWithMinMaxArgs FakeQuantWithMinMaxArgsGradient FakeQuantWithMinMaxVars FakeQuantWithMinMaxVarsGradient Fill Floor FloorDiv FloorMod FusedBatchNorm FusedBatchNormGrad FusedBatchNormGradV2 FusedBatchNormV2 Gather GatherNd GatherV2 GetItem Greater GreaterEqual HSVToRGB IFFT IFFT2D IFFT3D IRFFT IRFFT2D IRFFT3D Identity IdentityN If Imag InfeedDequeue InfeedDequeueTuple InplaceAdd InplaceUpdate Inv Invert InvertPermutation IsFinite IsInf IsNan L2Loss LRN LRNGrad LeftShift Less LessEqual Lgamma LinSpace ListDiff Log Log1p LogSoftmax LogicalAnd LogicalNot LogicalOr MatMul MatrixBandPart MatrixDiag MatrixDiagPart MatrixSetDiag MatrixTriangularSolve Max MaxPool MaxPool3D MaxPool3DGrad MaxPool3DGradGrad MaxPoolGrad MaxPoolGradGrad MaxPoolGradGradV2 MaxPoolGradV2 MaxPoolV2 Maximum Mean Min Minimum MirrorPad Mod Mul Multinomial Neg NoOp NonMaxSuppressionV4 NotEqual OneHot OnesLike OutfeedEnqueue OutfeedEnqueueTuple Pack Pad PadV2 ParallelDynamicStitch PlaceholderWithDefault Pow PreventGradient Prod Qr QuantizeAndDequantizeV2 QuantizeAndDequantizeV3 RFFT RFFT2D RFFT3D RGBToHSV RandomShuffle RandomStandardNormal RandomUniform RandomUniformInt Range Rank ReadVariableOp Real RealDiv Reciprocal ReciprocalGrad RecvTPUEmbeddingActivations Relu Relu6 Relu6Grad ReluGrad Reshape ResizeBilinear ResizeBilinearGrad ResourceApplyAdaMax ResourceApplyAdadelta ResourceApplyAdagrad ResourceApplyAdagradDA ResourceApplyAdam ResourceApplyAddSign ResourceApplyCenteredRMSProp ResourceApplyFtrl ResourceApplyFtrlV2 ResourceApplyGradientDescent ResourceApplyMomentum ResourceApplyPowerSign ResourceApplyProximalAdagrad ResourceApplyProximalGradientDescent ResourceApplyRMSProp ResourceGather ResourceScatterAdd ResourceScatterDiv ResourceScatterMax ResourceScatterMin ResourceScatterMul ResourceScatterNdAdd ResourceScatterNdUpdate ResourceScatterSub ResourceScatterUpdate ResourceStridedSliceAssign Reverse ReverseSequence ReverseV2 RightShift Rint Round Rsqrt RsqrtGrad ScatterNd Select Selu SeluGrad SendTPUEmbeddingGradients Shape ShapeN Sigmoid SigmoidGrad Sign Sin Sinh Size Slice Snapshot Softmax SoftmaxCrossEntropyWithLogits Softplus SoftplusGrad Softsign SoftsignGrad SpaceToBatch SpaceToBatchND SpaceToDepth SparseMatMul SparseSoftmaxCrossEntropyWithLogits SparseToDense Split SplitV Sqrt SqrtGrad Square SquaredDifference Squeeze StackCloseV2 StackPopV2 StackPushV2 StackV2 StatelessIf StatelessRandomNormal StatelessRandomUniform StatelessTruncatedNormal StatelessWhile StopGradient StridedSlice StridedSliceGrad Sub Sum SymbolicGradient TPUEmbeddingActivations Tan Tanh TanhGrad TensorArrayCloseV3 TensorArrayConcatV3 TensorArrayGatherV3 TensorArrayGradV3 TensorArrayReadV3 TensorArrayScatterV3 TensorArraySizeV3 TensorArraySplitV3 TensorArrayV3 TensorArrayWriteV3 Tile TopKV2 Transpose TruncateDiv TruncateMod TruncatedNormal Unpack UnsortedSegmentMax UnsortedSegmentMin UnsortedSegmentProd UnsortedSegmentSum VarIsInitializedOp VariableShape While XlaDynamicUpdateSlice XlaHostCompute XlaIf XlaRecv XlaReduceWindow XlaSend XlaSort XlaWhile ZerosLike Enter Exit LoopCond Merge NextIteration Switch _Arg _ParallelConcatUpdate _Retval _TPUCompile _TPUExecute TPUCompilationResult TPUReplicatedInput TPUReplicatedOutput TPUReplicateMetadata MergeV2Checkpoints RestoreV2 SaveV2 Abort Assert Assign Placeholder PlaceholderV2 ShardedFilename StringJoin Variable VariableV2 VarHandleOp AudioSummary AudioSummaryV2 DebugNumericSummary HistogramSummary ImageSummary MergeSummary ScalarSummary StatsAggregatorSummary".split(" ");
f.TpuCompatibilityProvider=h;f.checkOpsForCompatibility=function(k,t){if(null===t)throw Error("Compatibility provider required, but got: "+t);_.each(k.nodes,l=>{l.compatible=t.opValid(l);_.each(l.inEmbeddings,p=>{p.compatible=t.opValid(p)});_.each(l.outEmbeddings,p=>{p.compatible=t.opValid(p)})})}})(d.op||(d.op={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/parser.js
(function(b){(function(d){(function(f){function h(u){if("true"===u)return!0;if("false"===u)return!1;if('"'===u[0])return u.substring(1,u.length-1);let x=parseFloat(u);return isNaN(x)?u:x}function k(u){return new Promise((x,A)=>{fetch(u).then(y=>{y.ok?y.arrayBuffer().then(x,A):y.text().then(A,A)})})}function t(u,x,A=1E6,y="\n"){return new Promise(function(w,C){function G(D,B,I){var N=I>=u.byteLength;B=B.split(y);B[0]=D+B[0];const O=N?"":B.pop();for(let H of B)try{x(H)}catch(K){C(K);return}N?w(!0):
(D=new Blob([u.slice(I,I+A)]),N=new FileReader,N.onload=function(H){G(O,H.target.result,I+A)},N.readAsText(D))}G("","",0)})}function l(u){return m(u,n)}function p(u){return m(u,q).then(x=>x.step_stats)}function m(u,x){function A(B){let I=B.indexOf(":"),N=B.substring(0,I).trim();B=h(B.substring(I+2).trim());return{name:N,value:B}}function y(B,I,N,O){let H=B[I];null==H?B[I]=O.join(".")in x?[N]:N:Array.isArray(H)?H.push(N):B[I]=[H,N]}let w={},C=[],G=[],D=w;return t(u,function(B){if(B)switch(B=B.trim(),
B[B.length-1]){case "{":B=B.substring(0,B.length-2).trim();let I={};C.push(D);G.push(B);y(D,B,I,G);D=I;break;case "}":D=C.pop();G.pop();break;default:B=A(B),y(D,B.name,B.value,G.concat(B.name))}}).then(function(){return w})}f.fetchPbTxt=k;f.fetchAndParseMetadata=function(u,x){return b.graph.util.runTask(()=>null==u?Promise.resolve(null):k(u),x).then(A=>b.graph.util.runAsyncPromiseTask("Parsing metadata.pbtxt",60,()=>null!=A?p(A):Promise.resolve(null),x))};f.fetchAndParseGraphData=function(u,x,A){return b.graph.util.runAsyncPromiseTask("Reading graph pbtxt",
40,()=>x?new Promise(function(y,w){let C=new FileReader;C.onload=()=>y(C.result);C.onerror=()=>w(C.error);C.readAsArrayBuffer(x)}):k(u),A).then(y=>b.graph.util.runAsyncPromiseTask("Parsing graph.pbtxt",60,()=>l(y),A))};f.streamParse=t;const n={"library.function":!0,"library.function.node_def":!0,"library.function.node_def.input":!0,"library.function.node_def.attr":!0,"library.function.node_def.attr.value.list.b":!0,"library.function.node_def.attr.value.list.f":!0,"library.function.node_def.attr.value.list.func":!0,
"library.function.node_def.attr.value.list.i":!0,"library.function.node_def.attr.value.list.s":!0,"library.function.node_def.attr.value.list.shape":!0,"library.function.node_def.attr.value.list.shape.dim":!0,"library.function.node_def.attr.value.list.tensor":!0,"library.function.node_def.attr.value.list.type":!0,"library.function.node_def.attr.value.shape.dim":!0,"library.function.node_def.attr.value.tensor.string_val":!0,"library.function.node_def.attr.value.tensor.tensor_shape.dim":!0,"library.function.signature.input_arg":!0,
"library.function.signature.output_arg":!0,"library.versions":!0,node:!0,"node.input":!0,"node.attr":!0,"node.attr.value.list.b":!0,"node.attr.value.list.f":!0,"node.attr.value.list.func":!0,"node.attr.value.list.i":!0,"node.attr.value.list.s":!0,"node.attr.value.list.shape":!0,"node.attr.value.list.shape.dim":!0,"node.attr.value.list.tensor":!0,"node.attr.value.list.type":!0,"node.attr.value.shape.dim":!0,"node.attr.value.tensor.string_val":!0,"node.attr.value.tensor.tensor_shape.dim":!0},q={"step_stats.dev_stats":!0,
"step_stats.dev_stats.node_stats":!0,"step_stats.dev_stats.node_stats.output":!0,"step_stats.dev_stats.node_stats.memory":!0,"step_stats.dev_stats.node_stats.output.tensor_description.shape.dim":!0};f.parseGraphPbTxt=l;f.parseStatsPbTxt=p})(d.parser||(d.parser={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/proto.js

//# sourceURL=build://tf-graph-common/render.js
(function(b){(function(d){(function(f){function h(L,Q,T,X,aa){Q=new I(Q,T,X,aa,!0);L.inAnnotations.push(Q)}function k(L,Q,T,X,aa){Q=new I(Q,T,X,aa,!1);L.outAnnotations.push(Q)}function t(L,Q){_.each(L.nodes(),T=>{T=L.node(T);T.expanded=1<Q;if(0<Q)switch(T.node.type){case d.NodeType.META:case d.NodeType.SERIES:l(T,Q-1)}})}function l(L,Q){L.coreGraph&&t(L.coreGraph,Q)}function p(L,Q,T){let X=L.node(Q),aa=L.node(T),la=L.edge(Q,T);if(X.node.include!==d.InclusionType.INCLUDE&&aa.node.include!==d.InclusionType.INCLUDE||
X.node.include===d.InclusionType.EXCLUDE||aa.node.include===d.InclusionType.EXCLUDE)k(X,aa.node,aa,la,N.SHORTCUT),h(aa,X.node,X,la,N.SHORTCUT),L.removeEdge(Q,T)}function m(L,Q,T){let X=L.coreGraph,aa=X.node(Q);aa.isOutExtract=!0;_.each(X.predecessors(Q),la=>{p(X,la,Q)});(G.detachAllEdgesForHighDegree||T)&&_.each(X.successors(Q),la=>{p(X,Q,la)});0===X.neighbors(Q).length&&(aa.node.include=d.InclusionType.EXCLUDE,L.isolatedOutExtract.push(aa),X.removeNode(Q))}function n(L,Q,T){let X=L.coreGraph,aa=
X.node(Q);aa.isInExtract=!0;_.each(X.successors(Q),la=>{p(X,Q,la)});(G.detachAllEdgesForHighDegree||T)&&_.each(X.predecessors(Q),la=>{p(X,la,Q)});0===X.neighbors(Q).length&&(aa.node.include=d.InclusionType.EXCLUDE,L.isolatedInExtract.push(aa),X.removeNode(Q))}function q(L,Q){if(L.type===d.NodeType.OP)for(var T=0;T<Q.length;T++){if(L.op===Q[T])return!0}else if(L.type===d.NodeType.META&&(L=L.getRootOp()))for(T=0;T<Q.length;T++)if(L.op===Q[T])return!0;return!1}function u(L){let Q=L.coreGraph;_.each(Q.nodes(),
T=>{Q.node(T).node.include!==d.InclusionType.EXCLUDE||T.startsWith(b.graph.FUNCTION_LIBRARY_NODE_PREFIX)||(L.coreGraph.outEdges(T).length>L.coreGraph.inEdges(T).length?m(L,T,!0):n(L,T,!0))})}function x(L){let Q=L.coreGraph;_.each(Q.nodes(),T=>{let X=Q.node(T);X.node.include===d.InclusionType.UNSPECIFIED&&q(X.node,G.outExtractTypes)&&m(L,T)})}function A(L){let Q=L.coreGraph;_.each(Q.nodes(),T=>{let X=Q.node(T);X.node.include===d.InclusionType.UNSPECIFIED&&q(X.node,G.inExtractTypes)&&n(L,T)})}function y(L){let Q=
L.coreGraph,T={},X={},aa=0;_.each(Q.nodes(),ka=>{if(Q.node(ka).node.include===d.InclusionType.UNSPECIFIED){var Y=_.reduce(Q.predecessors(ka),(va,xa)=>{xa=Q.edge(xa,ka).metaedge;return va+(xa.numRegularEdges?1:0)},0);0===Y&&0<Q.predecessors(ka).length&&(Y=Q.predecessors(ka).length);var Ea=_.reduce(Q.successors(ka),(va,xa)=>{xa=Q.edge(ka,xa).metaedge;return va+(xa.numRegularEdges?1:0)},0);0===Ea&&0<Q.successors(ka).length&&(Ea=Q.successors(ka).length);T[ka]=Y;X[ka]=Ea;aa++}});if(!(aa<G.minNodeCountForExtraction)){var la=
G.minDegreeForExtraction-1,Z=Math.round(.75*aa),ba=Math.round(.25*aa),ea=Object.keys(T).sort((ka,Y)=>T[ka]-T[Y]),ca=T[ea[Z]];ca=ca+ca-T[ea[ba]];ca=Math.max(ca,la);for(let ka=aa-1;T[ea[ka]]>ca;ka--)n(L,ea[ka]);ea=Object.keys(X).sort((ka,Y)=>X[ka]-X[Y]);Z=X[ea[Z]];ba=Z+4*(Z-X[ea[ba]]);ba=Math.max(ba,la);for(la=aa-1;X[ea[la]]>ba;la--)(Z=Q.node(ea[la]))&&!Z.isInExtract&&m(L,ea[la])}}function w(L){let Q=L.coreGraph,T={};_.each(Q.edges(),X=>{Q.edge(X).metaedge.numRegularEdges||((T[X.v]=T[X.v]||[]).push(X),
(T[X.w]=T[X.w]||[]).push(X))});_.each(T,X=>{X.length>G.maxControlDegree&&_.each(X,aa=>p(Q,aa.v,aa.w))})}function C(L){u(L);G.outExtractTypes&&x(L);G.inExtractTypes&&A(L);y(L);G.maxControlDegree&&w(L);let Q=L.coreGraph;_.each(Q.nodes(),T=>{let X=Q.node(T);var aa=Q.neighbors(T).length;if(X.node.include===d.InclusionType.UNSPECIFIED&&0===aa){aa=0<X.outAnnotations.list.length;let la=0<X.inAnnotations.list.length;X.isInExtract?(L.isolatedInExtract.push(X),X.node.include=d.InclusionType.EXCLUDE,Q.removeNode(T)):
X.isOutExtract?(L.isolatedOutExtract.push(X),X.node.include=d.InclusionType.EXCLUDE,Q.removeNode(T)):G.extractIsolatedNodesWithAnnotationsOnOneSide&&(aa&&!la?(X.isInExtract=!0,L.isolatedInExtract.push(X),X.node.include=d.InclusionType.EXCLUDE,Q.removeNode(T)):la&&!aa&&(X.isOutExtract=!0,L.isolatedOutExtract.push(X),X.node.include=d.InclusionType.EXCLUDE,Q.removeNode(T)))}})}f.OpNodeColors={DEFAULT_FILL:"#ffffff",DEFAULT_STROKE:"#b2b2b2",COMPATIBLE:"#0f9d58",INCOMPATIBLE:"#db4437"};f.MetanodeColors=
{DEFAULT_FILL:"#d9d9d9",DEFAULT_STROKE:"#a6a6a6",SATURATION:.6,LIGHTNESS:.85,EXPANDED_COLOR:"#f0f0f0",HUES:[220,100,180,40,20,340,260,300,140,60],STRUCTURE_PALETTE(L,Q){var T=f.MetanodeColors.HUES;L=T[L%T.length];T=Math.sin(L*Math.PI/360);return d3.hsl(L,.01*(Q?30:90-60*T),.01*(Q?95:80)).toString()},DEVICE_PALETTE(L){return f.MetanodeColors.STRUCTURE_PALETTE(L)},XLA_CLUSTER_PALETTE(L){return f.MetanodeColors.STRUCTURE_PALETTE(L)},UNKNOWN:"#eee",GRADIENT_OUTLINE:"#888"};f.SeriesNodeColors={DEFAULT_FILL:"white",
DEFAULT_STROKE:"#b2b2b2"};const G={enableExtraction:!0,minNodeCountForExtraction:15,minDegreeForExtraction:5,maxControlDegree:4,maxBridgePathDegree:4,outExtractTypes:["NoOp"],inExtractTypes:[],detachAllEdgesForHighDegree:!0,extractIsolatedNodesWithAnnotationsOnOneSide:!0,enableBridgegraph:!0,minMaxColors:["#fff5f0","#fb6a4a"],maxAnnotations:5},D=new RegExp("^(?:"+b.graph.FUNCTION_LIBRARY_NODE_PREFIX+")?(\\w+)_[a-z0-9]{8}(?:_\\d+)?$");class B{constructor(L,Q){this.hierarchy=L;this.displayingStats=
Q;this.index={};this.renderedOpNames=[];this.computeScales();this.hasSubhierarchy={};this.root=new M(L.root,L.graphOptions);this.index[L.root.name]=this.root;this.renderedOpNames.push(L.root.name);this.buildSubhierarchy(L.root.name);this.root.expanded=!0;this.traceInputs=!1}computeScales(){this.deviceColorMap=d3.scaleOrdinal().domain(this.hierarchy.devices).range(_.map(d3.range(this.hierarchy.devices.length),f.MetanodeColors.DEVICE_PALETTE));this.xlaClusterColorMap=d3.scaleOrdinal().domain(this.hierarchy.xlaClusters).range(_.map(d3.range(this.hierarchy.xlaClusters.length),
f.MetanodeColors.XLA_CLUSTER_PALETTE));let L=this.hierarchy.root.metagraph;var Q=d3.max(L.nodes(),T=>{T=L.node(T);if(null!=T.stats)return T.stats.totalBytes});this.memoryUsageScale=d3.scaleLinear().domain([0,Q]).range(G.minMaxColors);Q=d3.max(L.nodes(),T=>{T=L.node(T);if(null!=T.stats)return T.stats.getTotalMicros()});this.computeTimeScale=d3.scaleLinear().domain([0,Q]).range(G.minMaxColors);this.edgeWidthSizedBasedScale=this.hierarchy.hasShapeInfo?d.scene.edge.EDGE_WIDTH_SIZE_BASED_SCALE:d3.scaleLinear().domain([1,
this.hierarchy.maxMetaEdgeSize]).range([d.scene.edge.MIN_EDGE_WIDTH,d.scene.edge.MAX_EDGE_WIDTH])}getRenderNodeByName(L){return this.index[L]}getNodeByName(L){return this.hierarchy.node(L)}colorHistogram(L,Q){if(0<Object.keys(L).length){const T=_.sum(Object.keys(L).map(X=>L[X]));return Object.keys(L).map(X=>({color:Q(X),proportion:L[X]/T}))}console.info("no pairs found!");return null}getOrCreateRenderNodeByName(L){if(!L)return null;if(L in this.index)return this.index[L];var Q=this.hierarchy.node(L);
if(!Q)return null;let T=Q.isGroupNode?new M(Q,this.hierarchy.graphOptions):new H(Q);this.index[L]=T;this.renderedOpNames.push(L);Q.stats&&(T.memoryColor=this.memoryUsageScale(Q.stats.totalBytes),T.computeTimeColor=this.computeTimeScale(Q.stats.getTotalMicros()));T.isFadedOut=this.displayingStats&&!b.graph.util.hasDisplayableNodeStats(Q.stats);var X=null,aa=null,la=null;if(Q.isGroupNode){X=Q.deviceHistogram;aa=Q.xlaClusterHistogram;var Z=Q.compatibilityHistogram.compatible;Q=Q.compatibilityHistogram.incompatible;
if(0!=Z||0!=Q)la=Z/(Z+Q)}else(Z=T.node.device)&&(X={[Z]:1}),(Z=T.node.xlaCluster)&&(aa={[Z]:1}),T.node.type===d.NodeType.OP&&(la=T.node.compatible?1:0);X&&(T.deviceColors=this.colorHistogram(X,this.deviceColorMap));aa&&(T.xlaClusterColors=this.colorHistogram(aa,this.xlaClusterColorMap));null!=la&&(T.compatibilityColors=[{color:b.graph.render.OpNodeColors.COMPATIBLE,proportion:la},{color:b.graph.render.OpNodeColors.INCOMPATIBLE,proportion:1-la}]);return this.index[L]}getNearestVisibleAncestor(L){var Q=
d.getHierarchicalPath(L);let T=0,X=null;for(;T<Q.length&&(L=Q[T],X=this.getRenderNodeByName(L),X.expanded);T++);return T==Q.length-2&&(Q=Q[T+1],X.inAnnotations.nodeNames[Q]||X.outAnnotations.nodeNames[Q])?Q:L}setDepth(L){l(this.root,+L)}isNodeAuxiliary(L){let Q=this.getRenderNodeByName(L.node.parentNode.name),T=_.find(Q.isolatedInExtract,X=>X.node.name===L.node.name);if(T)return!0;T=_.find(Q.isolatedOutExtract,X=>X.node.name===L.node.name);return!!T}getNamesOfRenderedOps(){return this.renderedOpNames}cloneAndAddFunctionOpNode(L,
Q,T,X){var aa=T.name.replace(Q,X);let la=L.metagraph.node(aa);if(la)return la;la=new d.OpNodeImpl({name:aa,input:[],device:T.device,op:T.op,attr:_.cloneDeep(T.attr)});la.cardinality=T.cardinality;la.include=T.include;la.outputShapes=_.cloneDeep(T.outputShapes);la.xlaCluster=T.xlaCluster;la.functionInputIndex=T.functionInputIndex;la.functionOutputIndex=T.functionOutputIndex;la.inputs=T.inputs.map(Z=>{const ba=_.clone(Z);ba.name=Z.name.replace(Q,X);return ba});la.parentNode=L;L.metagraph.setNode(la.name,
la);this.hierarchy.setNode(la.name,la);aa=Z=>this.cloneAndAddFunctionOpNode(L,Q,Z,X);la.inEmbeddings=T.inEmbeddings.map(aa);la.outEmbeddings=T.outEmbeddings.map(aa);return la}cloneFunctionLibraryMetanode(L,Q,T,X,aa){const la={};L=this.cloneFunctionLibraryMetanodeHelper(L,Q,T,X,aa,la);_.isEmpty(la)||this.patchEdgesFromFunctionOutputs(Q,la);return L}cloneFunctionLibraryMetanodeHelper(L,Q,T,X,aa,la){const Z=b.graph.createMetanode(T.name.replace(X,aa));Z.depth=T.depth;Z.cardinality=T.cardinality;Z.templateId=
T.templateId;Z.opHistogram=_.clone(T.opHistogram);Z.deviceHistogram=_.clone(T.deviceHistogram);Z.xlaClusterHistogram=_.clone(T.xlaClusterHistogram);Z.hasNonControlEdges=T.hasNonControlEdges;Z.include=T.include;Z.nodeAttributes=_.clone(T.nodeAttributes);Z.associatedFunction=T.associatedFunction;_.each(T.metagraph.nodes(),ba=>{ba=T.metagraph.node(ba);switch(ba.type){case d.NodeType.META:ba=this.cloneFunctionLibraryMetanodeHelper(L,Q,ba,X,aa,la);ba.parentNode=Z;Z.metagraph.setNode(ba.name,ba);this.hierarchy.setNode(ba.name,
ba);break;case d.NodeType.OP:ba=this.cloneAndAddFunctionOpNode(Z,X,ba,aa);_.isNumber(ba.functionInputIndex)&&this.patchEdgesIntoFunctionInputs(Q,ba);_.isNumber(ba.functionOutputIndex)&&(la[ba.functionOutputIndex]=ba);break;default:console.warn(ba.name+" is oddly neither a metanode nor an opnode.")}});this.cloneLibraryMetanodeEdges(T,Z,X,aa);return Z}cloneLibraryMetanodeEdges(L,Q,T,X){_.each(L.metagraph.edges(),aa=>{aa=L.metagraph.edge(aa);const la=aa.v.replace(T,X),Z=aa.w.replace(T,X),ba=new d.MetaedgeImpl(la,
Z);ba.inbound=aa.inbound;ba.numRegularEdges=aa.numRegularEdges;ba.numControlEdges=aa.numControlEdges;ba.numRefEdges=aa.numRefEdges;ba.totalSize=aa.totalSize;aa.baseEdgeList&&(ba.baseEdgeList=aa.baseEdgeList.map(ea=>{const ca=_.clone(ea);ca.v=ea.v.replace(T,X);ca.w=ea.w.replace(T,X);return ca}));Q.metagraph.node(Z)?Q.metagraph.setEdge(la,Z,ba):Q.metagraph.setEdge(Z,la,ba)})}patchEdgesIntoFunctionInputs(L,Q){let T=Math.min(Q.functionInputIndex,L.inputs.length-1);for(var X=_.clone(L.inputs[T]);X.isControlDependency;)T++,
X=L.inputs[T];Q.inputs.push(X);X=this.hierarchy.getPredecessors(L.name);let aa,la=0;_.each(X.regular,Z=>{la+=Z.numRegularEdges;if(la>T)return aa=Z,!1});_.each(aa.baseEdgeList,Z=>{Z.w===L.name&&(Z.w=Q.name);Z.v===L.name&&(Z.v=Q.name)})}patchEdgesFromFunctionOutputs(L,Q){const T=this.hierarchy.getSuccessors(L.name);_.each(T.regular,X=>{_.each(X.baseEdgeList,aa=>{const la=this.hierarchy.node(aa.w);_.each(la.inputs,Z=>{Z.name===L.name&&(Z.name=Q[Z.outputTensorKey].name,Z.outputTensorKey=aa.outputTensorKey)})});
_.each(X.baseEdgeList,aa=>{aa.v=Q[aa.outputTensorKey].name;aa.outputTensorKey="0"})})}buildSubhierarchy(L){if(!(L in this.hasSubhierarchy)){this.hasSubhierarchy[L]=!0;var Q=this.index[L];if(Q.node.type===d.NodeType.META||Q.node.type===d.NodeType.SERIES){var T=Q.node.metagraph,X=Q.coreGraph,aa=[],la=[];_.isEmpty(this.hierarchy.libraryFunctions)||(_.each(T.nodes(),xa=>{const Aa=T.node(xa),Fa=this.hierarchy.libraryFunctions[Aa.op];Fa&&0!==xa.indexOf(b.graph.FUNCTION_LIBRARY_NODE_PREFIX)&&(xa=this.cloneFunctionLibraryMetanode(T,
Aa,Fa.node,Fa.node.name,Aa.name),aa.push(Aa),la.push(xa))}),_.each(la,(xa,Aa)=>{Aa=aa[Aa];xa.parentNode=Aa.parentNode;T.setNode(Aa.name,xa);this.hierarchy.setNode(Aa.name,xa)}));_.each(T.nodes(),xa=>{let Aa=this.getOrCreateRenderNodeByName(xa),Fa=Aa.node;X.setNode(xa,Aa);Fa.isGroupNode||(_.each(Fa.inEmbeddings,ya=>{let Sa=new K(null),Xa=new H(ya);h(Aa,ya,Xa,Sa,N.CONSTANT);this.index[ya.name]=Xa}),_.each(Fa.outEmbeddings,ya=>{let Sa=new K(null),Xa=new H(ya);k(Aa,ya,Xa,Sa,N.SUMMARY);this.index[ya.name]=
Xa}))});_.each(T.edges(),xa=>{var Aa=T.edge(xa);Aa=new K(Aa);Aa.isFadedOut=this.index[xa.v].isFadedOut||this.index[xa.w].isFadedOut;X.setEdge(xa.v,xa.w,Aa)});G.enableExtraction&&Q.node.type===d.NodeType.META&&C(Q);_.isEmpty(this.hierarchy.libraryFunctions)||this.buildSubhierarchiesForNeededFunctions(T);L===b.graph.ROOT_NAME&&_.forOwn(this.hierarchy.libraryFunctions,xa=>{xa=xa.node;const Aa=this.getOrCreateRenderNodeByName(xa.name);Q.libraryFunctionsExtract.push(Aa);Aa.node.include=d.InclusionType.EXCLUDE;
X.removeNode(xa.name)});var Z=Q.node.parentNode;if(Z){var ba=this.index[Z.name],ea=(xa,...Aa)=>Aa.concat([xa?"IN":"OUT"]).join("~~"),ca=this.hierarchy.getBridgegraph(L),ka={},Y={},Ea={};_.each(ca.edges(),xa=>{let Aa=!!T.node(xa.w),Fa=Aa?xa.v:xa.w;ca.edge(xa).numRegularEdges?Aa?Y[Fa]=(Y[Fa]||0)+1:ka[Fa]=(ka[Fa]||0)+1:Ea[Fa]=(Ea[Fa]||0)+1});var va=this.hierarchy.getNodeMap();_.each(ca.edges(),xa=>{var Aa=ca.edge(xa);let Fa=!!T.node(xa.w),[ya,Sa]=Fa?[xa.w,xa.v]:[xa.v,xa.w];var Xa=this.index[ya],ub=this.index[Sa],
Bb=ub?ub.node:va[Sa],qb=!Aa.numRegularEdges&&Ea[Sa]>G.maxControlDegree,[,zb]=Fa?[Q.inAnnotations,Xa.inAnnotations]:[Q.outAnnotations,Xa.outAnnotations];let vb=(Fa?Y:ka)[Sa]>G.maxBridgePathDegree;xa=null;var Gb=!1;G.enableBridgegraph&&!vb&&!qb&&Xa.isInCore()&&(Gb=Nb=>ba.coreGraph.edge(Fa?{v:Nb,w:L}:{v:L,w:Nb}),(xa=Gb(Sa))||(xa=Gb(ea(Fa,Sa,Z.name))),Gb=!!xa);Xa=!1;if(xa&&!Aa.numRegularEdges){Xa=xa;for(qb=ba.node;Xa.adjoiningMetaedge;)Xa=Xa.adjoiningMetaedge,qb=qb.parentNode;qb=this.hierarchy.getTopologicalOrdering(qb.name);
Xa=Xa.metaedge;Xa=qb[Xa.v]>qb[Xa.w]}Gb&&!Xa?(Bb=ea(Fa,L),ub=ea(Fa,Sa,L),zb=X.node(ub),zb||(Gb=X.node(Bb),Gb||(Gb=new H({name:Bb,type:d.NodeType.BRIDGE,isGroupNode:!1,cardinality:0,parentNode:null,stats:null,include:d.InclusionType.UNSPECIFIED,inbound:Fa,nodeAttributes:{}}),this.index[Bb]=Gb,X.setNode(Bb,Gb)),zb=new H({name:ub,type:d.NodeType.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,include:d.InclusionType.UNSPECIFIED,inbound:Fa,nodeAttributes:{}}),this.index[ub]=zb,X.setNode(ub,
zb),X.setParent(ub,Bb),Gb.node.cardinality++),Aa=new K(Aa),Aa.adjoiningMetaedge=xa,Fa?X.setEdge(ub,ya,Aa):X.setEdge(ya,ub,Aa)):zb.push(new I(Bb,ub,new K(Aa),N.SHORTCUT,Fa))});_.each([!0,!1],xa=>{let Aa=ea(xa,L),Fa=X.node(Aa);Fa&&_.each(X.nodes(),ya=>{if(X.node(ya).node.type!==d.NodeType.BRIDGE&&(xa?!X.predecessors(ya).length:!X.successors(ya).length)){var Sa=ea(xa,L,"STRUCTURAL_TARGET"),Xa=X.node(Sa);Xa||(Xa=new H({name:Sa,type:d.NodeType.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,
include:d.InclusionType.UNSPECIFIED,inbound:xa,nodeAttributes:{}}),Xa.structural=!0,this.index[Sa]=Xa,X.setNode(Sa,Xa),Fa.node.cardinality++,X.setParent(Sa,Aa));Xa=new K(null);Xa.structural=!0;Xa.weight--;xa?X.setEdge(Sa,ya,Xa):X.setEdge(ya,Sa,Xa)}})})}}}}buildSubhierarchiesForNeededFunctions(L){_.each(L.edges(),Q=>{Q=L.edge(Q);Q=new K(Q);_.forEach(Q.metaedge.baseEdgeList,T=>{var X=T.v.split(b.graph.NAMESPACE_DELIM);for(var aa=X.length;0<=aa;aa--){T=X.slice(0,aa);const la=this.hierarchy.node(T.join(b.graph.NAMESPACE_DELIM));
if(la){if(la.type===d.NodeType.OP&&this.hierarchy.libraryFunctions[la.op])for(X=1;X<T.length;X++)(aa=T.slice(0,X).join(b.graph.NAMESPACE_DELIM))&&this.buildSubhierarchy(aa);break}}})})}}f.RenderGraphInfo=B;class I{constructor(L,Q,T,X,aa){this.node=L;this.renderNodeInfo=Q;this.renderMetaedgeInfo=T;this.annotationType=X;this.height=this.width=this.dy=this.dx=0;T&&T.metaedge&&(this.v=T.metaedge.v,this.w=T.metaedge.w);this.isIn=aa;this.points=[]}}f.Annotation=I;let N;(function(L){L[L.SHORTCUT=0]="SHORTCUT";
L[L.CONSTANT=1]="CONSTANT";L[L.SUMMARY=2]="SUMMARY";L[L.ELLIPSIS=3]="ELLIPSIS"})(N=f.AnnotationType||(f.AnnotationType={}));class O{constructor(){this.list=[];this.nodeNames={}}push(L){if(!(L.node.name in this.nodeNames))if(this.nodeNames[L.node.name]=!0,this.list.length<G.maxAnnotations)this.list.push(L);else{var Q=this.list[this.list.length-1];Q.annotationType===N.ELLIPSIS?(L=Q.node,L.setNumMoreNodes(++L.numMoreNodes)):(Q=new b.graph.EllipsisNodeImpl(1),this.list.push(new I(Q,new H(Q),null,N.ELLIPSIS,
L.isIn)))}}}f.AnnotationList=O;class H{constructor(L){this.node=L;this.expanded=!1;this.inAnnotations=new O;this.outAnnotations=new O;this.outboxWidth=this.inboxWidth=this.height=this.width=this.y=this.x=0;this.structural=this.excluded=!1;this.paddingBottom=this.paddingRight=this.paddingLeft=this.paddingTop=this.labelHeight=this.radius=this.labelOffset=0;this.isOutExtract=this.isInExtract=!1;this.coreBox={width:0,height:0};this.isFadedOut=!1;this.displayName=L.name.substring(L.name.lastIndexOf(b.graph.NAMESPACE_DELIM)+
1);L.type===d.NodeType.META&&L.associatedFunction&&((L=this.displayName.match(D))?this.displayName=L[1]:_.startsWith(this.displayName,b.graph.FUNCTION_LIBRARY_NODE_PREFIX)&&(this.displayName=this.displayName.substring(b.graph.FUNCTION_LIBRARY_NODE_PREFIX.length)))}isInCore(){return!this.isInExtract&&!this.isOutExtract&&!this.isLibraryFunction}}f.RenderNodeInfo=H;class K{constructor(L){this.metaedge=L;this.adjoiningMetaedge=null;this.structural=!1;this.weight=1;this.isFadedOut=!1}}f.RenderMetaedgeInfo=
K;class M extends H{constructor(L,Q){super(L);L=L.metagraph.graph();Q.compound=!0;this.coreGraph=d.createGraph(L.name,d.GraphType.CORE,Q);this.inExtractBox={width:0,height:0};this.outExtractBox={width:0,height:0};this.libraryFunctionsBox={width:0,height:0};this.isolatedInExtract=[];this.isolatedOutExtract=[];this.libraryFunctionsExtract=[]}}f.RenderGroupNodeInfo=M;f.makeInExtract=n;f.mapIndexToHue=function(L){return 1+579.2561679725*L%358};f.expandUntilNodeIsShown=function(L,Q){var T=document.getElementById("scene");
Q=Q.split("/");var X=Q[Q.length-1].match(/(.*):\w+/);2===X.length&&(Q[Q.length-1]=X[1]);X=Q[0];let aa=L.getRenderNodeByName(X);for(let la=1;la<Q.length&&aa.node.type!==b.graph.NodeType.OP;la++)L.buildSubhierarchy(X),aa.expanded=!0,T.setNodeExpanded(aa),X+="/"+Q[la],aa=L.getRenderNodeByName(X);return aa.node.name}})(d.render||(d.render={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/scene.js
(function(b){(function(d){(function(f){function h(q,u,x,A){var y=k(q,u,x);if(!y.empty())return y;u=document.createElementNS("http://www.w3.org/2000/svg",u);if(x instanceof Array)for(y=0;y<x.length;y++)u.classList.add(x[y]);else u.classList.add(x);A?q.node().insertBefore(u,A):q.node().appendChild(u);return d3.select(u).datum(q.datum())}function k(q,u,x){q=q.node().childNodes;for(let A=0;A<q.length;A++){let y=q[A];if(y.tagName===u)if(x instanceof Array){let w=!0;for(let C=0;C<x.length;C++)w=w&&y.classList.contains(x[C]);
if(w)return d3.select(y)}else if(!x||y.classList.contains(x))return d3.select(y)}return d3.select(null)}function t(q,u){let x=u.node.type===d.NodeType.SERIES?0:d.layout.PARAMS.subscene.meta.labelHeight;l(k(q,"g",f.Class.Scene.CORE),0,x);var A=0<u.isolatedInExtract.length,y=0<u.isolatedOutExtract.length;let w=0<u.libraryFunctionsExtract.length,C=d.layout.PARAMS.subscene.meta.extractXOffset,G=0;A&&(G+=u.outExtractBox.width);y&&(G+=u.outExtractBox.width);A&&(A=u.coreBox.width,A=G<d.layout.MIN_AUX_WIDTH?
A-d.layout.MIN_AUX_WIDTH+u.inExtractBox.width/2:A-u.inExtractBox.width/2-u.outExtractBox.width-(y?C:0),A=A-u.libraryFunctionsBox.width-(w?C:0),l(k(q,"g",f.Class.Scene.INEXTRACT),A,x));y&&(y=u.coreBox.width,y=G<d.layout.MIN_AUX_WIDTH?y-d.layout.MIN_AUX_WIDTH+u.outExtractBox.width/2:y-u.outExtractBox.width/2,y=y-u.libraryFunctionsBox.width-(w?C:0),l(k(q,"g",f.Class.Scene.OUTEXTRACT),y,x));w&&(u=u.coreBox.width-u.libraryFunctionsBox.width/2,l(k(q,"g",f.Class.Scene.FUNCTION_LIBRARY),u,x))}function l(q,
u,x){null!=q.attr("transform")&&(q=q.transition("position"));q.attr("transform","translate("+u+","+x+")")}function p(q,u){return u?q.toFixed(0):1<=Math.abs(q)?q.toFixed(1):q.toExponential(1)}function m(q,u,x,A){let y="Device: "+q.device_name+"\n";y+="dtype: "+q.dtype+"\n";let w="(scalar)";0<q.shape.length&&(w="("+q.shape.join(",")+")");y=y+("\nshape: "+w+"\n\n#(elements): ")+(u+"\n");q=[];for(u=0;u<x.length;u++)0<x[u]&&q.push("#("+f.healthPillEntries[u].label+"): "+x[u]);y+=q.join(", ")+"\n\n";A.max>=
A.min&&(y+="min: "+A.min+", max: "+A.max+"\n",y+="mean: "+A.mean+", stddev: "+A.stddev);return y}function n(q,u,x,A,y=60,w=10,C=0,G){d3.select(q.parentNode).selectAll(".health-pill").remove();if(u){var D=u.value,B=D.slice(2,8),I=B[0],N=B[1],O=B[5],H=D[1],K={min:D[8],max:D[9],mean:D[10],stddev:Math.sqrt(D[11])};null==y&&(y=60);null==w&&(w=10);null==C&&(C=0);null!=x&&x.node.type===b.graph.NodeType.OP&&(y/=2,w/=2);D=document.createElementNS(f.SVG_NAMESPACE,"g");D.classList.add("health-pill");var M=document.createElementNS(f.SVG_NAMESPACE,
"defs");D.appendChild(M);var L=document.createElementNS(f.SVG_NAMESPACE,"linearGradient");A="health-pill-gradient-"+A;L.setAttribute("id",A);var Q=0,T="0%";for(let aa=0;aa<B.length;aa++)if(B[aa]){Q+=B[aa];var X=document.createElementNS(f.SVG_NAMESPACE,"stop");X.setAttribute("offset",T);X.setAttribute("stop-color",f.healthPillEntries[aa].background_color);L.appendChild(X);T=document.createElementNS(f.SVG_NAMESPACE,"stop");X=100*Q/H+"%";T.setAttribute("offset",X);T.setAttribute("stop-color",f.healthPillEntries[aa].background_color);
L.appendChild(T);T=X}M.appendChild(L);M=document.createElementNS(f.SVG_NAMESPACE,"rect");M.setAttribute("fill","url(#"+A+")");M.setAttribute("width",String(y));M.setAttribute("height",String(w));M.setAttribute("y",String(C));D.appendChild(M);M=document.createElementNS(f.SVG_NAMESPACE,"title");M.textContent=m(u,H,B,K);D.appendChild(M);u=!1;if(null!=x&&(M=x.x-y/2,w=x.y-w-x.height/2-2,0>x.labelOffset&&(w+=x.labelOffset),D.setAttribute("transform","translate("+M+", "+w+")"),(B[2]||B[3]||B[4])&&(x=x.node.attr)&&
x.length))for(B=0;B<x.length;B++)if("T"===x[B].key){u=(x=x[B].value.type)&&/^DT_(BOOL|INT|UINT)/.test(x);break}x=document.createElementNS(f.SVG_NAMESPACE,"text");if(Number.isFinite(K.min)&&Number.isFinite(K.max)){if(B=p(K.min,u),K=p(K.max,u),x.textContent=1<H?B+" ~ "+K:B,0<I||0<N||0<O)x.textContent+=" (",H=[],0<I&&H.push(`NaN\u00d7${I}`),0<N&&H.push(`-\u221e\u00d7${N}`),0<O&&H.push(`+\u221e\u00d7${O}`),x.textContent+=H.join("; ")+")"}else x.textContent="(No finite elements)";x.classList.add("health-pill-stats");
null==G&&(G=y/2);x.setAttribute("x",String(G));x.setAttribute("y",String(C-2));D.appendChild(x);Polymer.dom(q.parentNode).appendChild(D)}}f.SVG_NAMESPACE="http://www.w3.org/2000/svg";f.Class={Node:{CONTAINER:"nodes",GROUP:"node",SHAPE:"nodeshape",COLOR_TARGET:"nodecolortarget",LABEL:"nodelabel",BUTTON_CONTAINER:"buttoncontainer",BUTTON_CIRCLE:"buttoncircle",EXPAND_BUTTON:"expandbutton",COLLAPSE_BUTTON:"collapsebutton"},Edge:{CONTAINER:"edges",GROUP:"edge",LINE:"edgeline",REFERENCE_EDGE:"referenceedge",
REF_LINE:"refline",SELECTABLE:"selectableedge",SELECTED:"selectededge",STRUCTURAL:"structural"},Annotation:{OUTBOX:"out-annotations",INBOX:"in-annotations",GROUP:"annotation",NODE:"annotation-node",EDGE:"annotation-edge",CONTROL_EDGE:"annotation-control-edge",LABEL:"annotation-label",ELLIPSIS:"annotation-ellipsis"},Scene:{GROUP:"scene",CORE:"core",FUNCTION_LIBRARY:"function-library",INEXTRACT:"in-extract",OUTEXTRACT:"out-extract"},Subscene:{GROUP:"subscene"},OPNODE:"op",METANODE:"meta",SERIESNODE:"series",
BRIDGENODE:"bridge",ELLIPSISNODE:"ellipsis"};f.healthPillEntries=[{background_color:"#CC2F2C",label:"NaN"},{background_color:"#FF8D00",label:"-\u221e"},{background_color:"#EAEAEA",label:"-"},{background_color:"#A5A5A5",label:"0"},{background_color:"#262626",label:"+"},{background_color:"#003ED4",label:"+\u221e"}];f.fit=function(q,u,x,A){var y=q.getBoundingClientRect();let w=null;try{if(w=u.getBBox(),0===w.width)return}catch(C){return}u=d.layout.PARAMS.graph;y=d3.zoomIdentity.scale(.9*Math.min(y.width/
w.width,y.height/w.height,2)).translate(u.padding.paddingLeft,u.padding.paddingTop);d3.select(q).transition().duration(500).call(x.transform,y).on("end.fitted",()=>{x.on("end.fitted",null);A()})};f.panToNode=function(q,u,x,A){x=d3.select(u).select(`[data-name="${q}"]`).node();if(!x)return console.warn(`panToNode() failed for node name "${q}"`),!1;var y=x.getBBox(),w=x.getScreenCTM();q=u.createSVGPoint();x=u.createSVGPoint();q.x=y.x;q.y=y.y;x.x=y.x+y.width;x.y=y.y+y.height;q=q.matrixTransform(w);x=
x.matrixTransform(w);w=(G,D,B,I)=>!(G>B&&D<I);y=u.getBoundingClientRect();const C=y.top+y.height-150;return w(q.x,x.x,y.left,y.left+y.width-320)||w(q.y,x.y,y.top,C)?(w=y.left+y.width/2-(q.x+x.x)/2,q=y.top+y.height/2-(q.y+x.y)/2,x=d3.zoomTransform(u),d3.select(u).transition().duration(500).call(A.translateBy,w/x.k,q/x.k),!0):!1};f.selectOrCreateChild=h;f.selectChild=k;f.buildGroup=function(q,u,x,A){A=A||f.Class.Scene.GROUP;let y=k(q,"g",A).empty();q=h(q,"g",A);A=h(q,"g",f.Class.Scene.CORE);let w=_.reduce(u.coreGraph.nodes(),
(C,G)=>{G=u.coreGraph.node(G);G.excluded||C.push(G);return C},[]);u.node.type===d.NodeType.SERIES&&w.reverse();f.edge.buildGroup(A,u.coreGraph,x);f.node.buildGroup(A,w,x);0<u.isolatedInExtract.length?(A=h(q,"g",f.Class.Scene.INEXTRACT),f.node.buildGroup(A,u.isolatedInExtract,x)):k(q,"g",f.Class.Scene.INEXTRACT).remove();0<u.isolatedOutExtract.length?(A=h(q,"g",f.Class.Scene.OUTEXTRACT),f.node.buildGroup(A,u.isolatedOutExtract,x)):k(q,"g",f.Class.Scene.OUTEXTRACT).remove();0<u.libraryFunctionsExtract.length?
(A=h(q,"g",f.Class.Scene.FUNCTION_LIBRARY),f.node.buildGroup(A,u.libraryFunctionsExtract,x)):k(q,"g",f.Class.Scene.FUNCTION_LIBRARY).remove();t(q,u);y&&q.attr("opacity",0).transition().attr("opacity",1);return q};f.addGraphClickListener=function(q,u){d3.select(q).on("click",()=>{u.fire("graph-select")})};f.translate=l;f.positionRect=function(q,u,x,A,y){q.transition().attr("x",u-A/2).attr("y",x-y/2).attr("width",A).attr("height",y)};f.positionTriangle=function(q,u,x,A,y){y/=2;A/=2;u=[[u,x-y],[u+A,
x+y],[u-A,x+y]];q.transition().attr("points",u.map(w=>w.join(",")).join(" "))};f.positionButton=function(q,u){let x=d.layout.computeCXPositionOfNodeShape(u)+(u.expanded?u.width:u.coreBox.width)/2-6,A=u.y-(u.expanded?u.height:u.coreBox.height)/2+6;u.node.type!==d.NodeType.SERIES||u.expanded||(x+=10,A-=2);u="translate("+x+","+A+")";q.selectAll("path").transition().attr("transform",u);q.select("circle").transition().attr({cx:x,cy:A,r:d.layout.PARAMS.nodeSize.meta.expandButtonRadius})};f.positionEllipse=
function(q,u,x,A,y){q.transition().attr("cx",u).attr("cy",x).attr("rx",A/2).attr("ry",y/2)};f.humanizeHealthPillStat=p;f.addHealthPill=n;f.addHealthPills=function(q,u,x){if(u){var A=1;d3.select(q).selectAll("g.nodeshape").each(function(y){const w=u[y.node.name];n(this,w?w[x]:null,y,A++)})}}})(d.scene||(d.scene={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/template.js
(function(b){(function(d){(function(f){function h(n){let q=_.map({depth:n.depth,"|V|":n.metagraph.nodes().length,"|E|":n.metagraph.edges().length},function(u,x){return x+"\x3d"+u}).join(" ");n=_.map(n.opHistogram,function(u,x){return x+"\x3d"+u}).join(",");return q+" [ops] "+n}function k(n){const q=n.getNodeMap();let u=Object.keys(q).reduce((x,A)=>{const y=q[A];if(y.type!==d.NodeType.META)return x;A=A.split("/").length-1;let w=h(y),C=x[w]||{nodes:[],level:A};x[w]=C;C.nodes.push(y);C.level>A&&(C.level=
A);return x},{});return Object.keys(u).map(x=>[x,u[x]]).filter(([,x])=>{x=x.nodes;if(1<x.length)return!0;x=x[0];return x.type===d.NodeType.META&&x.associatedFunction}).sort(([,x])=>x.nodes[0].depth)}function t(n,q){return _.reduce(n,function(u,x){let A=x[0],y=[];x[1].nodes.forEach(function(w){for(let C=0;C<y.length;C++)if(!q||p(y[C].metanode.metagraph,w.metagraph)){w.templateId=y[C].metanode.templateId;y[C].members.push(w.name);return}w.templateId=A+"["+y.length+"]";y.push({metanode:w,members:[w.name]})});
y.forEach(function(w){u[w.metanode.templateId]={level:x[1].level,nodes:w.members}});return u},{})}function l(n,q,u){return _.sortBy(n,[x=>q.node(x).op,x=>q.node(x).templateId,x=>q.neighbors(x).length,x=>q.predecessors(x).length,x=>q.successors(x).length,x=>x.substr(u.length)])}function p(n,q){function u(I,N){let O=I.substr(x.length),H=N.substr(A.length);if(y[O]^w[H])return console.warn("different visit pattern","["+x+"]",O,"["+A+"]",H),!0;y[O]||(y[O]=w[H]=!0,C.push({n1:I,n2:N}));return!1}if(!b.graph.hasSimilarDegreeSequence(n,
q))return!1;let x=n.graph().name,A=q.graph().name,y={},w={},C=[];var G=n.sources(),D=q.sources();if(G.length!==D.length)return console.log("different source length"),!1;G=l(G,n,x);D=l(D,q,A);for(var B=0;B<G.length;B++)if(u(G[B],D[B]))return!1;for(;0<C.length;){D=C.pop();if(!m(n.node(D.n1),q.node(D.n2)))return!1;G=n.successors(D.n1);D=q.successors(D.n2);if(G.length!==D.length)return console.log("# of successors mismatch",G,D),!1;G=l(G,n,x);D=l(D,q,A);for(B=0;B<G.length;B++)if(u(G[B],D[B]))return!1}return!0}
function m(n,q){if(n.type===d.NodeType.META)return n.templateId&&q.templateId&&n.templateId===q.templateId;if(n.type===d.NodeType.OP&&q.type===d.NodeType.OP)return n.op===q.op;if(n.type===d.NodeType.SERIES&&q.type===d.NodeType.SERIES){let u=n.metagraph.nodeCount();return u===q.metagraph.nodeCount()&&(0===u||n.metagraph.node(n.metagraph.nodes()[0]).op===q.metagraph.node(q.metagraph.nodes()[0]).op)}return!1}f.detect=function(n,q){n=k(n);let u=t(n,q);return Object.keys(u).sort(x=>u[x].level).reduce((x,
A)=>{x[A]=u[A];return x},{})}})(d.template||(d.template={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/util.js
(function(b){(function(d){(function(f){f.time=function(h,k){let t=Date.now();k=k();console.log(h,":",Date.now()-t,"ms");return k};f.getTracker=function(h){return{setMessage:function(k){h.set("progress",{value:h.progress.value,msg:k})},updateProgress:function(k){h.set("progress",{value:h.progress.value+k,msg:h.progress.msg})},reportError:function(k,t){console.error(t.stack);h.set("progress",{value:h.progress.value,msg:k,error:!0})}}};f.getSubtaskTracker=function(h,k,t){return{setMessage:function(l){h.setMessage(t+
": "+l)},updateProgress:function(l){h.updateProgress(l*k/100)},reportError:function(l,p){h.reportError(t+": "+l,p)}}};f.runTask=function(h,k){k.setMessage("Reading metadata pbtxt");try{let t=b.graph.util.time("Reading metadata pbtxt",h);k.updateProgress(40);return t}catch(t){k.reportError("Failed Reading metadata pbtxt",t)}};f.runAsyncTask=function(h,k,t,l){return new Promise(p=>{l.setMessage(h);setTimeout(function(){try{let m=b.graph.util.time(h,t);l.updateProgress(k);p(m)}catch(m){l.reportError("Failed "+
h,m)}},20)})};f.runAsyncPromiseTask=function(h,k,t,l){return new Promise((p,m)=>{function n(q){l.reportError("Failed "+h,q);m(q)}l.setMessage(h);setTimeout(function(){try{let q=Date.now();t().then(function(u){console.log(h,":",Date.now()-q,"ms");l.updateProgress(k);p(u)}).catch(n)}catch(q){n(q)}},20)})};f.escapeQuerySelector=function(h){return h.replace(/([:.\[\],/\\\(\)])/g,"\\$1")};f.MEMORY_UNITS=[{symbol:"B"},{symbol:"KB",numUnits:1024},{symbol:"MB",numUnits:1024},{symbol:"GB",numUnits:1024},{symbol:"TB",
numUnits:1024},{symbol:"PB",numUnits:1024}];f.TIME_UNITS=[{symbol:"\u00b5s"},{symbol:"ms",numUnits:1E3},{symbol:"s",numUnits:1E3},{symbol:"min",numUnits:60},{symbol:"hr",numUnits:60},{symbol:"days",numUnits:24}];f.convertUnitsToHumanReadable=function(h,k,t=0){return t+1<k.length&&h>=k[t+1].numUnits?b.graph.util.convertUnitsToHumanReadable(h/k[t+1].numUnits,k,t+1):Number(h.toPrecision(3))+" "+k[t].symbol};f.hasDisplayableNodeStats=function(h){return h&&(0<h.totalBytes||0<h.getTotalMicros()||h.outputSize)?
!0:!1};f.removeCommonPrefix=function(h){if(2>h.length)return h;let k=0,t=0,l=_.min(_.map(h,p=>p.length));for(;;){k++;let p=_.map(h,m=>m.substring(0,k));if(p.every((m,n)=>0===n?!0:m===p[n-1])){if(k>=l)return h;t=k}else break}return _.map(h,p=>p.substring(t))};f.computeHumanFriendlyTime=function(h){h=+new Date-+new Date(h/1E3);return 3E4>h?"just now":6E4>h?Math.floor(h/1E3)+" seconds ago":12E4>h?"a minute ago":36E5>h?Math.floor(h/6E4)+" minutes ago":1==Math.floor(h/36E5)?"an hour ago":864E5>h?Math.floor(h/
36E5)+" hours ago":1728E5>h?"yesterday":Math.floor(h/864E5)+" days ago"}})(d.util||(d.util={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/minimap.js
(function(b){(function(d){class f{constructor(h,k,t,l,p,m){this.svg=h;this.labelPadding=m;this.zoomG=k;this.mainZoom=t;this.maxWandH=p;h=d3.select(l.shadowRoot);let n=h.select("svg"),q=n.select("rect");this.viewpointCoord={x:0,y:0};k=d3.drag().subject(Object).on("drag",()=>{this.viewpointCoord.x=d3.event.x;this.viewpointCoord.y=d3.event.y;this.updateViewpoint()});q.datum(this.viewpointCoord).call(k);n.on("click",()=>{if(!d3.event.defaultPrevented){var u=Number(q.attr("width")),x=Number(q.attr("height")),
A=d3.mouse(n.node());this.viewpointCoord.x=A[0]-u/2;this.viewpointCoord.y=A[1]-x/2;this.updateViewpoint()}});this.viewpoint=q.node();this.minimapSvg=n.node();this.minimap=l;this.canvas=h.select("canvas.first").node();this.canvasBuffer=h.select("canvas.second").node();this.downloadCanvas=h.select("canvas.download").node();d3.select(this.downloadCanvas).style("display","none");this.update()}updateViewpoint(){d3.select(this.viewpoint).attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y);let h=
-this.viewpointCoord.x*this.scaleMain/this.scaleMinimap,k=-this.viewpointCoord.y*this.scaleMain/this.scaleMinimap;d3.select(this.svg).call(this.mainZoom.transform,d3.zoomIdentity.translate(h,k).scale(this.scaleMain))}update(){let h=null;try{if(h=this.zoomG.getBBox(),0===h.width)return}catch(u){return}var k=d3.select("#graphdownload");this.download=k.node();k.on("click",()=>{URL.revokeObjectURL(this.download.href);var u=this.downloadCanvas.toDataURL("image/png");const x=u.slice(0,u.indexOf(","));if(x.endsWith(";base64")){var A=
atob(u.slice(u.indexOf(",")+1));u=(new Uint8Array(A.length)).map((y,w)=>A.charCodeAt(w));this.download.href=URL.createObjectURL(new Blob([u],{type:"image/png"}))}else console.warn(`non-base64 data URL (${x}); cannot use blob download`),this.download.href=u});k=d3.select(this.svg);var t="",l=this.svg;l=(l.getRootNode?l.getRootNode():this.svg.parentNode).styleSheets;for(var p=0;p<l.length;p++)try{var m=l[p].cssRules||l[p].rules;if(null!=m)for(let u=0;u<m.length;u++)t+=m[u].cssText.replace(/ ?tf-[\w-]+ ?/g,
"")+"\n"}catch(u){if("SecurityError"!==u.name)throw u;}m=k.append("style");m.text(t);t=d3.select(this.zoomG);l=t.attr("transform");t.attr("transform",null);h.height+=h.y;h.width+=h.x;h.height+=2*this.labelPadding;h.width+=2*this.labelPadding;k.attr("width",h.width).attr("height",h.height);this.scaleMinimap=this.maxWandH/Math.max(h.width,h.height);this.minimapSize={width:h.width*this.scaleMinimap,height:h.height*this.scaleMinimap};d3.select(this.minimapSvg).attr(this.minimapSize);d3.select(this.canvasBuffer).attr(this.minimapSize);
p=d3.select(this.downloadCanvas);p.style("width",h.width);p.style("height",h.height);p.attr("width",3*h.width);p.attr("height",3*h.height);null!=this.translate&&null!=this.zoom&&requestAnimationFrame(()=>this.zoom());let n=(new XMLSerializer).serializeToString(this.svg);m.remove();k.attr("width",null).attr("height",null);t.attr("transform",l);let q=new Image;q.onload=()=>{var u=this.canvasBuffer.getContext("2d");u.clearRect(0,0,this.canvasBuffer.width,this.canvasBuffer.height);u.drawImage(q,0,0,this.minimapSize.width,
this.minimapSize.height);requestAnimationFrame(()=>{d3.select(this.canvasBuffer).style("display",null);d3.select(this.canvas).style("display","none");[this.canvas,this.canvasBuffer]=[this.canvasBuffer,this.canvas]});u=this.downloadCanvas.getContext("2d");u.clearRect(0,0,this.downloadCanvas.width,this.downloadCanvas.height);u.drawImage(q,0,0,this.downloadCanvas.width,this.downloadCanvas.height)};q.onerror=()=>{q.src=URL.createObjectURL(new Blob([n],{type:"image/svg+xml;charset\x3dutf-8"}))};q.src=
"data:image/svg+xml;charset\x3dutf-8,"+encodeURIComponent(n)}zoom(h){if(null!=this.scaleMinimap){h&&(this.translate=[h.x,h.y],this.scaleMain=h.k);var k=this.svg.getBoundingClientRect(),t=d3.select(this.viewpoint);this.viewpointCoord.x=-this.translate[0]*this.scaleMinimap/this.scaleMain;this.viewpointCoord.y=-this.translate[1]*this.scaleMinimap/this.scaleMain;h=k.width*this.scaleMinimap/this.scaleMain;k=k.height*this.scaleMinimap/this.scaleMain;t.attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y).attr("width",
h).attr("height",k);t=this.minimapSize.width;var l=this.minimapSize.height,p=this.viewpointCoord.x,m=this.viewpointCoord.y;.8>(Math.min(Math.max(0,p+h),t)-Math.min(Math.max(0,p),t))*(Math.min(Math.max(0,m+k),l)-Math.min(Math.max(0,m),l))/(t*l)?this.minimap.classList.remove("hidden"):this.minimap.classList.add("hidden")}}}d.Minimap=f})(b.scene||(b.scene={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph/tf-graph-minimap.html.js
Polymer({is:"tf-graph-minimap",init:function(b,d,f,h,k){return new tf.scene.Minimap(b,d,f,this,h,k)}});

//# sourceURL=build://tf-graph/tf-graph-scene.html.js
Polymer({is:"tf-graph-scene",properties:{renderHierarchy:Object,name:String,colorBy:String,traceInputs:Boolean,_hasRenderHierarchyBeenFitOnce:Boolean,_isAttached:Boolean,_zoom:Object,highlightedNode:{type:String,observer:"_highlightedNodeChanged"},selectedNode:{type:String,observer:"_selectedNodeChanged"},handleEdgeSelected:Object,_zoomed:{type:Boolean,observer:"_onZoomChanged",value:!1},_zoomStartCoords:{type:Object,value:null},_zoomTransform:{type:Object,value:null},_maxZoomDistanceForClick:{type:Number,
value:20},templateIndex:Function,minimap:Object,_nodeGroupIndex:{type:Object,value:function(){return{}}},_annotationGroupIndex:{type:Object,value:function(){return{}}},_edgeGroupIndex:{type:Object,value:function(){return{}}},maxMetanodeLabelLengthFontSize:{type:Number,value:9},minMetanodeLabelLengthFontSize:{type:Number,value:6},maxMetanodeLabelLengthLargeFont:{type:Number,value:11},maxMetanodeLabelLength:{type:Number,value:18},progress:Object,nodeContextMenuItems:Array,nodeNamesToHealthPills:Object,
healthPillStepIndex:Number},observers:["_colorByChanged(colorBy)","_renderHierarchyChanged(renderHierarchy)","_animateAndFit(_isAttached, renderHierarchy)","_updateHealthPills(nodeNamesToHealthPills, healthPillStepIndex)","_updateInputTrace(traceInputs, selectedNode)"],getNode:function(b){return this.renderHierarchy.getRenderNodeByName(b)},isNodeExpanded:function(b){return b.expanded},setNodeExpanded:function(){this._build(this.renderHierarchy);this._updateLabels(!this._zoomed)},panToNode(b){tf.graph.scene.panToNode(b,
this.$.svg,this.$.root,this._zoom)&&(this._zoomed=!0)},getGraphSvgRoot(){return this.$.svg},getContextMenu(){return this.$.contextMenu},_resetState:function(){this._nodeGroupIndex={};this._annotationGroupIndex={};this._edgeGroupIndex={};this._updateLabels(!1);d3.select(this.$.svg).select("#root").selectAll("*").remove();tf.graph.scene.node.removeGradientDefinitions(this.$.svg)},_build:function(b){this.templateIndex=b.hierarchy.getTemplateIndex();tf.graph.util.time("tf-graph-scene (layout):",function(){tf.graph.layout.layoutScene(b.root,
this)}.bind(this));tf.graph.util.time("tf-graph-scene (build scene):",function(){tf.graph.scene.buildGroup(d3.select(this.$.root),b.root,this);tf.graph.scene.addGraphClickListener(this.$.svg,this);this._updateInputTrace()}.bind(this));setTimeout(function(){this._updateHealthPills(this.nodeNamesToHealthPills,this.healthPillStepIndex);this.minimap.update()}.bind(this),tf.graph.layout.PARAMS.animation.duration)},ready:function(){this._zoom=d3.zoom().on("end",function(){this._zoomStartCoords&&(Math.sqrt(Math.pow(this._zoomStartCoords.x-
this._zoomTransform.x,2)+Math.pow(this._zoomStartCoords.y-this._zoomTransform.y,2))<this._maxZoomDistanceForClick?this._fireEnableClick():setTimeout(this._fireEnableClick.bind(this),50));this._zoomStartCoords=null}.bind(this)).on("zoom",function(){this._zoomTransform=d3.event.transform;this._zoomStartCoords||(this._zoomStartCoords=this._zoomTransform,this.fire("disable-click"));this._zoomed=!0;d3.select(this.$.root).attr("transform",d3.event.transform);this.minimap.zoom(d3.event.transform)}.bind(this));
d3.select(this.$.svg).call(this._zoom).on("dblclick.zoom",null);d3.select(window).on("resize",function(){this.minimap.zoom()}.bind(this));this.minimap=this.$.minimap.init(this.$.svg,this.$.root,this._zoom,tf.graph.layout.PARAMS.minimap.size,tf.graph.layout.PARAMS.subscene.meta.labelHeight)},attached:function(){this.set("_isAttached",!0)},detached:function(){this.set("_isAttached",!1)},_renderHierarchyChanged:function(b){this._hasRenderHierarchyBeenFitOnce=!1;this._resetState();this._build(b)},_animateAndFit:function(b){!this._hasRenderHierarchyBeenFitOnce&&
b&&setTimeout(this.fit.bind(this),tf.graph.layout.PARAMS.animation.duration)},_updateLabels:function(b){var d=this.$$(".title"),f=d.style,h=this.$$(".auxTitle"),k=h.style,t=this.$$(".functionLibraryTitle").style;const l=d3.select(this.$.svg);var p=l.select("."+tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.CORE).node();if(b&&p&&this.progress&&100===this.progress.value){b=l.select("."+tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.INEXTRACT).node()||l.select("."+
tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.OUTEXTRACT).node();var m=p.getCTM().e;p=b?b.getCTM().e:null;f.display="inline";f.left=m+"px";null!==p&&p!==m?(k.display="inline",p=Math.max(m+d.getBoundingClientRect().width,p),k.left=p+"px"):k.display="none";d=(d=l.select("."+tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.FUNCTION_LIBRARY).node())?d.getCTM().e:null;null!==d&&d!==p?(t.display="inline",d=Math.max(p+h.getBoundingClientRect().width,d),t.left=d+"px"):
t.display="none"}else f.display="none",k.display="none",t.display="none"},_colorByChanged:function(){null!=this.renderHierarchy&&(_.each(this._nodeGroupIndex,(b,d)=>{this._updateNodeState(d)}),this.minimap.update())},fit:function(){this._hasRenderHierarchyBeenFitOnce=!0;tf.graph.scene.fit(this.$.svg,this.$.root,this._zoom,function(){this._zoomed=!1}.bind(this))},isNodeSelected:function(b){return b===this.selectedNode},isNodeHighlighted:function(b){return b===this.highlightedNode},addAnnotationGroup:function(b,
d,f){b=b.node.name;this._annotationGroupIndex[b]=this._annotationGroupIndex[b]||{};this._annotationGroupIndex[b][d.node.name]=f},getAnnotationGroupsIndex:function(b){return this._annotationGroupIndex[b]},removeAnnotationGroup:function(b,d){delete this._annotationGroupIndex[b.node.name][d.node.name]},addNodeGroup:function(b,d){this._nodeGroupIndex[b]=d},getNodeGroup:function(b){return this._nodeGroupIndex[b]},removeNodeGroup:function(b){delete this._nodeGroupIndex[b]},addEdgeGroup:function(b,d){this._edgeGroupIndex[b]=
d},getEdgeGroup:function(b){return this._edgeGroupIndex[b]},_updateHealthPills:function(b,d){tf.graph.scene.addHealthPills(this.$.svg,b,d)},_updateNodeState:function(b){var d=this.getNode(b),f=this.getNodeGroup(b);f&&tf.graph.scene.node.stylize(f,d,this);d.node.type===tf.graph.NodeType.META&&d.node.associatedFunction&&!d.isLibraryFunction&&(f=d3.select("."+tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.FUNCTION_LIBRARY+' g[data-name\x3d"'+(tf.graph.FUNCTION_LIBRARY_NODE_PREFIX+
d.node.associatedFunction)+'"]'),tf.graph.scene.node.stylize(f,d,this));_.each(this.getAnnotationGroupsIndex(b),h=>{tf.graph.scene.node.stylize(h,d,this,tf.graph.scene.Class.Annotation.NODE)})},_selectedNodeChanged:function(b,d){if(b!==d&&(d&&this._updateNodeState(d),b)){this.minimap.update();d=this.renderHierarchy.hierarchy.node(b);for(var f=[];null!=d.parentNode&&d.parentNode.name!=tf.graph.ROOT_NAME;)d=d.parentNode,f.push(d.name);var h;_.forEachRight(f,k=>{this.renderHierarchy.buildSubhierarchy(k);
k=this.renderHierarchy.getRenderNodeByName(k);k.node.isGroupNode&&!k.expanded&&(k.expanded=!0,h||(h=k))});h&&(this.setNodeExpanded(h),this._zoomed=!0);b&&this._updateNodeState(b);setTimeout(()=>{this.panToNode(b)},tf.graph.layout.PARAMS.animation.duration)}},_highlightedNodeChanged:function(b,d){b!==d&&(b&&this._updateNodeState(b),d&&this._updateNodeState(d))},_onZoomChanged:function(){this._updateLabels(!this._zoomed)},_fireEnableClick:function(){this.fire("enable-click")},_updateInputTrace:function(){tf.graph.scene.node.updateInputTrace(this.getGraphSvgRoot(),
this.renderHierarchy,this.selectedNode,this.traceInputs)}});

//# sourceURL=build://tf-graph/tf-graph.html.js
Polymer({is:"tf-graph",properties:{graphHierarchy:{type:Object,notify:!0,observer:"_graphChanged"},basicGraph:Object,stats:Object,devicesForStats:Object,hierarchyParams:Object,progress:{type:Object,notify:!0},title:String,selectedNode:{type:String,notify:!0},selectedEdge:{type:Object,notify:!0},_lastSelectedEdgeGroup:Object,highlightedNode:{type:String,notify:!0},colorBy:String,colorByParams:{type:Object,notify:!0,readOnly:!0},renderHierarchy:{type:Object,readOnly:!0,notify:!0},traceInputs:Boolean,
nodeContextMenuItems:Array,_renderDepth:{type:Number,value:1},_allowGraphSelect:{type:Boolean,value:!0},nodeNamesToHealthPills:Object,healthPillStepIndex:Number,edgeWidthFunction:{type:Object,value:""},handleNodeSelected:{type:Object,value:""},edgeLabelFunction:{type:Object,value:""},handleEdgeSelected:{type:Object,value:""}},observers:["_statsChanged(stats, devicesForStats)","_buildNewRenderHierarchy(graphHierarchy, edgeWidthFunction, handleNodeSelected, edgeLabelFunction, handleEdgeSelected)","_selectedNodeChanged(selectedNode)",
"_selectedEdgeChanged(selectedEdge)"],panToNode(b){this.$$("tf-graph-scene").panToNode(b)},_buildNewRenderHierarchy(b){b&&this._buildRenderHierarchy(b)},_statsChanged:function(b,d){this.graphHierarchy&&(b&&d&&(tf.graph.joinStatsInfoWithGraph(this.basicGraph,b,d),tf.graph.hierarchy.joinAndAggregateStats(this.graphHierarchy)),this._buildRenderHierarchy(this.graphHierarchy))},_buildRenderHierarchy:function(b){tf.graph.util.time("new tf.graph.render.Hierarchy",function(){function d(h){return{minValue:h.domain()[0],
maxValue:h.domain()[1],startColor:h.range()[0],endColor:h.range()[1]}}if(b.root.type===tf.graph.NodeType.META){var f=new tf.graph.render.RenderGraphInfo(b,!!this.stats);f.edgeLabelFunction=this.edgeLabelFunction;f.edgeWidthFunction=this.edgeWidthFunction;this._setColorByParams({compute_time:d(f.computeTimeScale),memory:d(f.memoryUsageScale),device:_.map(f.deviceColorMap.domain(),function(h){return{device:h,color:f.deviceColorMap(h)}}),xla_cluster:_.map(f.xlaClusterColorMap.domain(),function(h){return{xla_cluster:h,
color:f.xlaClusterColorMap(h)}})});this._setRenderHierarchy(f);this.async(function(){this.fire("rendered")})}}.bind(this))},_getVisible:function(b){return b?this.renderHierarchy.getNearestVisibleAncestor(b):b},listeners:{"graph-select":"_graphSelected","disable-click":"_disableClick","enable-click":"_enableClick","node-toggle-expand":"_nodeToggleExpand","node-select":"_nodeSelected","node-highlight":"_nodeHighlighted","node-unhighlight":"_nodeUnhighlighted","node-toggle-extract":"_nodeToggleExtract",
"node-toggle-seriesgroup":"_nodeToggleSeriesGroup","edge-select":"_edgeSelected","annotation-select":"_nodeSelected","annotation-highlight":"_nodeHighlighted","annotation-unhighlight":"_nodeUnhighlighted"},fit:function(){this.$.scene.fit()},_graphChanged:function(){this.fire("graph-select")},_graphSelected:function(){this._allowGraphSelect&&(this.set("selectedNode",null),this.set("selectedEdge",null));this._allowGraphSelect=!0},_disableClick:function(){this._allowGraphSelect=!1},_enableClick:function(){this._allowGraphSelect=
!0},_selectedNodeChanged(b){this.handleNodeSelected&&this.handleNodeSelected(b)},_selectedEdgeChanged(b){this._deselectPreviousEdge();b&&(this._lastSelectedEdgeGroup.classed(tf.graph.scene.Class.Edge.SELECTED,!0),this._updateMarkerOfSelectedEdge(b));this.handleEdgeSelected&&this.handleEdgeSelected(b)},_nodeSelected:function(b){this._allowGraphSelect&&this.set("selectedNode",b.detail.name);this._allowGraphSelect=!0},_edgeSelected(b){this._allowGraphSelect&&(this.set("_lastSelectedEdgeGroup",b.detail.edgeGroup),
this.set("selectedEdge",b.detail.edgeData));this._allowGraphSelect=!0},_nodeHighlighted:function(b){this.set("highlightedNode",b.detail.name)},_nodeUnhighlighted:function(){this.set("highlightedNode",null)},_nodeToggleExpand:function(b){this._nodeSelected(b);b=b.detail.name;var d=this.renderHierarchy.getRenderNodeByName(b);d.node.type!==tf.graph.NodeType.OP&&(this.renderHierarchy.buildSubhierarchy(b),d.expanded=!d.expanded,this.async(function(){this.$.scene.setNodeExpanded(d)},75))},_nodeToggleExtract:function(b){this.nodeToggleExtract(b.detail.name)},
nodeToggleExtract:function(b){b=this.renderHierarchy.getRenderNodeByName(b);b.node.include=b.node.include==tf.graph.InclusionType.INCLUDE?tf.graph.InclusionType.EXCLUDE:b.node.include==tf.graph.InclusionType.EXCLUDE?tf.graph.InclusionType.INCLUDE:this.renderHierarchy.isNodeAuxiliary(b)?tf.graph.InclusionType.INCLUDE:tf.graph.InclusionType.EXCLUDE;this._buildRenderHierarchy(this.graphHierarchy)},_nodeToggleSeriesGroup:function(b){this.nodeToggleSeriesGroup(b.detail.name)},nodeToggleSeriesGroup:function(b){tf.graph.toggleNodeSeriesGroup(this.hierarchyParams.seriesMap,
b);this.set("progress",{value:0,msg:""});tf.graph.hierarchy.build(this.basicGraph,this.hierarchyParams,tf.graph.util.getSubtaskTracker(tf.graph.util.getTracker(this),100,"Namespace hierarchy")).then(function(d){this.set("graphHierarchy",d);this._buildRenderHierarchy(this.graphHierarchy)}.bind(this))},_deselectPreviousEdge(){d3.select("."+tf.graph.scene.Class.Edge.SELECTED).classed(tf.graph.scene.Class.Edge.SELECTED,!1).each(b=>{if(b.label){const d=d3.select(this).selectAll("path.edgeline");b.label.startMarkerId&&
d.style("marker-start",`url(#${b.label.startMarkerId})`);b.label.endMarkerId&&d.style("marker-end",`url(#${b.label.endMarkerId})`)}})},_updateMarkerOfSelectedEdge(b){if(b.label){var d=b.label.startMarkerId||b.label.endMarkerId;if(d){const f=d.replace("dataflow-","selected-");let h=this.$$("#"+f);h||(d=this.$.scene.querySelector("#"+d),h=d.cloneNode(!0),h.setAttribute("id",f),h.classList.add("selected-arrowhead"),d.parentNode.appendChild(h));b=b.label.startMarkerId?"marker-start":"marker-end";this._lastSelectedEdgeGroup.selectAll("path.edgeline").style(b,
`url(#${f})`)}}},not:function(b){return!b}});

//# sourceURL=build://tf-graph-loader/tf-graph-loader.js
(function(b){(function(d){(function(){Polymer({is:"tf-graph-loader",_template:null,properties:{datasets:Array,selectedData:{type:Number,value:0},selectedFile:Object,compatibilityProvider:{type:Object,value:()=>new b.graph.op.TpuCompatibilityProvider},overridingHierarchyParams:{type:Object,value:()=>({})},progress:{type:Object,notify:!0},outGraphHierarchy:{type:Object,readOnly:!0,notify:!0},outGraph:{type:Object,readOnly:!0,notify:!0},outHierarchyParams:{type:Object,readOnly:!0,notify:!0}},observers:["_loadData(datasets, selectedData, overridingHierarchyParams, compatibilityProvider)",
"_loadFile(selectedFile, overridingHierarchyParams, compatibilityProvider)"],_loadData(){this.debounce("load",()=>{const f=this.datasets[this.selectedData];f&&this._parseAndConstructHierarchicalGraph(f.path)})},_parseAndConstructHierarchicalGraph(f,h){const k=this.overridingHierarchyParams,t=this.compatibilityProvider;this.progress={value:0,msg:""};const l=b.graph.util.getTracker(this),p=Object.assign({},b.graph.hierarchy.DefaultHierarchyParams,k);b.graph.loader.fetchAndConstructHierarchicalGraph(l,
f,h,t,p).then(({graph:m,graphHierarchy:n})=>{this._setOutHierarchyParams(p);this._setOutGraph(m);this._setOutGraphHierarchy(n)})},_loadFile(f){if(f){f=f.target;var h=f.files[0];h&&(f.value="",this._parseAndConstructHierarchicalGraph(null,h))}}})})(d.loader||(d.loader={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-debugger-dashboard/health-pills.js
var Vi;
(function(b){function d(k,t){if(null==k)throw Error(`Missing refValue for condition (${t}).`);}function f(k){return null==k||0==k.length||1!==k[0]}const h={INF_OR_NAN:{description:"Contains +/-\u221e or NaN",predicate:k=>0<k[2]||0<k[3]||0<k[7]},INF:{description:"Contains +/-\u221e",predicate:k=>0<k[3]||0<k[7]},NAN:{description:"Contains NaN",predicate:k=>0<k[2]},MAX_GT:{description:"Max \x3e",predicate:(k,t)=>{d(t,"MAX_GT");return k[9]>t}},MAX_LT:{description:"Max \x3c",predicate:(k,t)=>{d(t,"MAX_LT");
return k[9]<t}},MIN_GT:{description:"Min \x3e",predicate:(k,t)=>{d(t,"MIN_GT");return k[8]>t}},MIN_LT:{description:"Min \x3c",predicate:(k,t)=>{d(t,"MIN_LT");return k[8]<t}},MEAN_GT:{description:"Mean \x3e",predicate:(k,t)=>{d(t,"MEAN_GT");return k[10]>t}},MEAN_LT:{description:"Mean \x3c",predicate:(k,t)=>{d(t,"MEAN_LT");return k[10]<t}},RANGE_GT:{description:"Max - Min \x3e",predicate:(k,t)=>{d(t,"RANGE_GT");return k[9]-k[8]>t}},RANGE_LT:{description:"Max - Min \x3c",predicate:(k,t)=>{d(t,"RANGE_LT");
return k[9]-k[8]<t}},STDDEV_GT:{description:"Standard deviation \x3e",predicate:(k,t)=>{d(t,"STDDEV_GT");return Math.sqrt(k[11])>t}},STDDEV_LT:{description:"Standard deviation \x3c",predicate:(k,t)=>{d(t,"STDDEV_LT");return Math.sqrt(k[11])<t}}};b.tensorConditionDescription2Key=function(k){for(const t in h)if(h.hasOwnProperty(t)&&h[t].description===k)return t;return null};b.checkHealthPillAgainstTensorConditionKey=function(k,t,l){if(f(t))return!1;k=h[k].predicate;return k(t,l)}})(Vi||(Vi={}));

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-continue-dialog.html.js
Polymer({is:"tf-debugger-continue-dialog",properties:{continueNum:{type:Number,value:5},sessionRunGo:Function,tensorConditionGo:Function,forceContinuationStop:Function,_continueButtonText:{type:String,value:"Continue..."},_continueButtonContinueText:{type:String,value:"Continue...",readonly:!0},_continueButtonStopText:{type:String,value:"Stop Continuation",readonly:!0},_selectedTensorCondition:String,_tensorConditionRefValue:{type:Number,value:0,notify:!0},_isRefValueInputHidden:{type:Boolean,value:!0,
notify:!0}},observers:["_onSelectedTensorConditionChanged(_selectedTensorCondition)"],notifyContinuationStop(){this.updateContinueButtonText(!1)},_openDialog(){this.$.continueDialog.open()},_closeDialog(){this.$.continueDialog.close()},_continueButtonCallback(){this._continueButtonText===this._continueButtonStopText?this.forceContinuationStop():this._openDialog()},updateContinueButtonText(b){this.set("_continueButtonText",b?this._continueButtonStopText:this._continueButtonContinueText)},_sessionRunGoButtonCallback(){0<
this.continueNum?(this.sessionRunGo(this.continueNum),this.updateContinueButtonText(!0),this._closeDialog()):this.set("continueNum",1)},_tensorContinueGoButtonCallback(){if(null!=this._selectedTensorCondition){var b=Vi.tensorConditionDescription2Key(this._selectedTensorCondition);null==b&&console.error("Invalid Tensor Condition name:"+this._selectedTensorCondition);var d=Number(this._tensorConditionRefValue);Number.isFinite(d)?(this.tensorConditionGo(b,d),this.updateContinueButtonText(!0),this._closeDialog()):
this.set("_tensorConditionRefValue",0)}},_onSelectedTensorConditionChanged(b){b=Vi.tensorConditionDescription2Key(b);this.set("_isRefValueInputHidden",-1!==["INF_OR_NAN","INF","NAN"].indexOf(b))}});

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-initial-dialog.html.js
Polymer({is:"tf-debugger-initial-dialog",properties:{_title:{type:String,value:null},_customMessage:{type:String,value:null},_hasCustomMessage:{type:Boolean,computed:"_computeHasCustomMessage(_customMessage)"},_host:{type:String,value:null},_port:{type:String,value:null},_open:{type:Boolean},_hidden:{type:Boolean,computed:"_computeHidden(_open)",reflectToAttribute:!0}},openDialog(b,d){this.set("_title","Debugger is waiting for Session.run() connections...");this.set("_customMessage",null);this.$.dialog.open();
null!=b&&null!=d&&(this.set("_host",b),this.set("_port",d))},closeDialog(){this.$.dialog.close()},openDisabledDialog(){this.set("_title","Debugger is not enabled in this TensorBoard instance");this.set("_customMessage","To enable the debugger in TensorBoard, use the flag: --debugger_port \x3cport_number\x3e");this.$.dialog.open()},_computeHidden(b){return!b},_computeHasCustomMessage(b){return!_.isEmpty(b)}});

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-resizer.html.js
Polymer({is:"tf-debugger-resizer",properties:{currentLength:{type:Number,notify:!0},minLength:Number,maxLength:Number,isHorizontal:{type:Boolean,value:!1,reflectToAttribute:!0},_resizerIdentifier:{type:Boolean,value:!0,readOnly:!0,reflectToAttribute:!0},_isVertical:{type:Boolean,computed:"_computeIsVertical(isHorizontal)",reflectToAttribute:!0,readOnly:!0},_dragStartPosition:Number,_dragStartLength:Number,_previousMouseMoveCallback:Object,_previousMouseUpCallback:Object},listeners:{mousedown:"_handleMouseDown"},
_handleMouseDown(b){b.preventDefault();this._endDrag();this._previousMouseMoveCallback=d=>{d.preventDefault();d=this._dragStartLength+(this._getPositionRelativeToViewport(d)-this._dragStartPosition);d=Math.max(d,this.minLength);d=Math.min(d,this.maxLength);this.set("currentLength",d)};this._previousMouseUpCallback=d=>{d.preventDefault();this._endDrag()};this.set("_dragStartPosition",this._getPositionRelativeToViewport(b));this.set("_dragStartLength",this.currentLength);window.addEventListener("mouseup",
this._previousMouseUpCallback,!1);window.addEventListener("mousemove",this._previousMouseMoveCallback,!1)},_getPositionRelativeToViewport(b){return this.isHorizontal?b.clientY:b.clientX},_endDrag(){window.removeEventListener("mousemove",this._previousMouseMoveCallback,!1);this._previousMouseMoveCallback=null;window.removeEventListener("mouseup",this._previousMouseUpCallback,!1);this._previousMouseUpCallback=null},_computeIsVertical(b){return!b}});

//# sourceURL=build://tf-debugger-dashboard/selection-tree-node.js
(function(b){b.NODE_NAME_SEPARATOR="/";b.DEVICE_NAME_PATTERN=/^\/job:[A-Za-z0-9_]+\/replica:[0-9_]+\/task:[0-9]+\/device:[A-Za-z0-9_]+:[0-9]+/;let d;(function(k){k[k.EMPTY=0]="EMPTY";k[k.CHECKED=1]="CHECKED";k[k.PARTIAL=2]="PARTIAL"})(d=b.CheckboxState||(b.CheckboxState={}));b.splitNodeName=function(k){let t=[];const l=k.match(b.DEVICE_NAME_PATTERN);null!=l&&(t.push(l[0]),"/"!==k[l[0].length]&&console.error('No slash ("/") after device name in node name:',k),k=k.slice(l[0].length+1));return t.concat(k.split(b.NODE_NAME_SEPARATOR))};
b.getCleanNodeName=function(k){let t=k;const l=k.match(b.DEVICE_NAME_PATTERN);null!=l?(t.length>l[0].length&&"/"!=t[l[0].length]&&console.error('No slash ("/") after device name in node name:',k),t=t.slice(l[0].length+1)):"/"===t[0]&&(t=t.slice(1));t.indexOf(")")===t.length-1&&(t=t.slice(0,t.indexOf("/(")));return t};b.sortAndBaseExpandDebugWatches=function(k){k.sort((l,p)=>l.node_name<p.node_name?-1:l.node_name>p.node_name?1:l.output_slot-p.output_slot);for(let l=0;l<k.length;++l){var t=k[l].node_name+
"/";let p=!1;for(let m=l+1;m<k.length;++m)if(0===k[m].node_name.indexOf(t)){p=!0;break}p&&(t=k[l].node_name.split("/"),k[l].node_name+="/("+t[t.length-1]+")")}};b.removeNodeNameBaseExpansion=function(k){return k.endsWith(")")?k.slice(0,k.lastIndexOf("/(")):k};b.assembleDeviceAndNodeNames=function(k){const t=[null,null];if(k[0].match(b.DEVICE_NAME_PATTERN)){let l=k[0];"/"===l[l.length-1]&&(l=l.slice(0,l.length-1));t[0]=l;t[1]=k.slice(1).join("/")}else t[1]=k.join("/");return t};let f;(function(k){k[k.NodeName=
0]="NodeName";k[k.OpType=1]="OpType"})(f=b.DebugWatchFilterMode||(b.DebugWatchFilterMode={}));b.filterDebugWatches=function(k,t,l){if(t===f.NodeName)return k.filter(p=>p.node_name.match(l));if(t===f.OpType)return k.filter(p=>p.op_type.match(l))};class h{constructor(k,t,l,p){this.debugWatchChange=t;this.debugWatch=p;this.name=k;this.debugWatch=p;this.checkboxState=d.EMPTY;this.parent=l;this.children={};this.checkbox=document.createElement("paper-checkbox");this.checkbox.addEventListener("change",()=>
{this._handleChange()},!1)}_handleChange(){if(this.avoidPropagation)this.debugWatch&&this.debugWatchChange(this.debugWatch,this.isCheckboxChecked());else if(this.debugWatch)this.setCheckboxState(this.isCheckboxChecked()?d.CHECKED:d.EMPTY,!0),this.isCheckboxChecked()?this.setNodesAboveToChecked():this.setNodesAboveToEmpty(),this.debugWatchChange(this.debugWatch,this.isCheckboxChecked());else if(this.setCheckboxState(this.isCheckboxChecked()?d.CHECKED:d.EMPTY,!0),this.isCheckboxChecked()){const t=_.values(this.children);
for(;t.length;){var k=t.pop();_.forEach(k.children,l=>t.push(l));k.setCheckboxState(d.CHECKED,!0)}this.setNodesAboveToChecked()}else{const t=_.values(this.children);for(;t.length;)k=t.pop(),_.forEach(k.children,l=>t.push(l)),k.setCheckboxState(d.EMPTY,!0);this.setNodesAboveToEmpty()}}isLeaf(){return!!this.debugWatch}setToAllCheckedExternally(){this.setCheckboxState(d.CHECKED);this._handleChange()}setCheckboxState(k,t){this.avoidPropagation=t;this.checkboxState=k;this.checkbox.classList.toggle("partial-checkbox",
k===d.PARTIAL);k===d.CHECKED?this.checkbox.setAttribute("checked","checked"):this.checkbox.removeAttribute("checked");this.avoidPropagation=!1}isCheckboxChecked(){return this.checkbox.hasAttribute("checked")}setNodesAboveToChecked(){let k=this.parent,t=!1;for(;k;)t?k.setCheckboxState(d.PARTIAL,!0):(t=-1!==_.findIndex(_.values(k.children),l=>l.checkboxState!==d.CHECKED),k.setCheckboxState(t?d.PARTIAL:d.CHECKED,!0)),k=k.parent}setNodesAboveToEmpty(){let k=this.parent,t=!1;for(;k;)t?k.setCheckboxState(d.PARTIAL,
!0):(t=-1!==_.findIndex(_.values(k.children),l=>l.checkboxState!==d.EMPTY),k.setCheckboxState(t?d.PARTIAL:d.EMPTY,!0)),k=k.parent}setLevelDom(k){this.levelDom=k}}b.SelectionTreeNode=h})(Vi||(Vi={}));

//# sourceURL=build://tf-debugger-dashboard/tf-op-selector.html.js
Polymer({is:"tf-op-selector",properties:{debugWatches:Array,debugWatchChange:Object,nodeClicked:Function,forceExpandAndCheckNodeName:{type:String,value:null},forceExpandNodeName:{type:String,value:null},_selectedDebugWatchMapping:{type:Object,value:()=>({})},_levelName2Container:{type:Object,value:null},_levelName2Node:{type:Object,value:null},_watchHierarchy:{type:Object,computed:"_computeWatchHierarchy(debugWatches, debugWatchChange, _filterMode, _filterInput)"},_filterMode:{type:String,value:"Node Name",
notify:!0},_filterInput:{type:String,value:"",notify:!0},_isLoading:{type:Boolean,value:!1},_highlightedLevelDom:{type:Object,value:null}},observers:["_renderHierarchyWithTimeout(_watchHierarchy, debugWatchChange)","_handleForceNodeExpandAndCheck(forceExpandAndCheckNodeName)","_handleForceNodeExpand(forceExpandNodeName)"],_computeWatchHierarchy(b,d,f,h){h=h.trim();let k=b;null!=f&&0<h.length&&(k=Vi.filterDebugWatches(b,Vi.DebugWatchFilterMode[f.replace(/\s/g,"")],new RegExp(h)));const t=new Vi.SelectionTreeNode("",
d);t.isRoot=!0;_.forEach(k,l=>{const p=Vi.splitNodeName(l.device_name+"/"+l.node_name);let m=t;_.forEach(p,(n,q)=>{q===p.length-1?(q=new Vi.SelectionTreeNode(n,d,m,l),m.children[n]=q):(m.children[n]||(m.children[n]=new Vi.SelectionTreeNode(n,d,m)),m=m.children[n])})});return t},_clearSelectorHierarchy(){const b=this.$$("#selector-hierarchy");for(;b.firstChild;)b.removeChild(b.firstChild)},_renderHierarchyWithTimeout(b,d,f,h){this._isLoading||(this.set("_isLoading",!0),this._clearSelectorHierarchy(),
setTimeout(()=>{this._renderHierarchy(b,d,f,h)},10))},_renderHierarchy(b,d){this.set("_levelName2Container",{});this.set("_levelName2Node",{});b=this._renderLevel(null,null,b,d);Polymer.dom(this.$$("#selector-hierarchy")).appendChild(b);this.set("_isLoading",!1)},_renderLevel(b,d,f,h){const k=document.createElement("div");null!=b&&k.setAttribute("level-name",b);let t;t=null==d?b:d+"/"+b;Polymer.dom(k).classList.add("level-container");const l=document.createElement("iron-collapse");if(b){this._levelName2Container[t]=
l;l.removeAttribute("opened");Polymer.dom(k).classList.add("indented-level-container");d=document.createElement("div");Polymer.dom(d).classList.add("level-title");const n=document.createElement("paper-icon-button");Polymer.dom(n).classList.add("node-expand-button");const q=()=>{n.setAttribute("icon",l.hasAttribute("opened")?"expand-less":"expand-more")};n.addEventListener("click",()=>{l.hasAttribute("opened")?l.removeAttribute("opened"):l.setAttribute("opened",!0);q()},!1);q();Polymer.dom(d).appendChild(n);
Polymer.dom(d).appendChild(f.checkbox);f.setLevelDom(d);const u=document.createElement("span");Polymer.dom(u).classList.add("level-title-text");u.textContent=b;Polymer.dom(d).appendChild(u);Polymer.dom(k).appendChild(d);(b.match(Vi.DEVICE_NAME_PATTERN)||1===Object.keys(f.children).length)&&l.setAttribute("opened",!0)}else l.setAttribute("opened",!0);const p=[],m=[];Polymer.dom(l).classList.add("content-container");_.forEach(f.children,(n,q)=>{const u=n.debugWatch;var x=t;null==t&&(x="");x+="/"+q;
this._levelName2Node[x]=n;null!=this._selectedDebugWatchMapping[x]&&(n.setCheckboxState(Vi.CheckboxState.CHECKED),n.setNodesAboveToChecked());if(u){x=document.createElement("div");Polymer.dom(x).classList.add("op-description");n.checkbox.addEventListener("change",y=>{this._handleLeafNodeSelected(h,u,y.target.checked)},!1);Polymer.dom(x).appendChild(n.checkbox);n.setLevelDom(x);var A=document.createElement("span");A.textContent="["+u.op_type+"]";A.setAttribute("class","op-type");Polymer.dom(x).appendChild(A);
A=document.createElement("span");A.textContent=q;A.setAttribute("class","op-title-leaf");A.addEventListener("click",()=>{const y=this._getDeviceAndNodeNames(q,k);this.nodeClicked(y[0],y[1])},!1);Polymer.dom(x).appendChild(A);m.push(x)}else n.checkbox.addEventListener("change",y=>{this._handleMetaNodeChange(n,h,y.target.checked)}),p.push(this._renderLevel(q,t,n,h))});b=n=>{Polymer.dom(l).appendChild(n)};_.forEach(m,b);_.forEach(p,b);Polymer.dom(k).appendChild(l);return k},_getLeafDebugWatches(b,d){b.debugWatch?
d.push(b.debugWatch):_.forEach(b.children,f=>{this._getLeafDebugWatches(f,d)})},_getDeviceAndNodeNames(b,d){for(b=[b];;){const f=d.getAttribute("level-name");if(null==f)break;else b.push(f);d=Polymer.dom(d).parentNode.parentNode}b.reverse();return Vi.assembleDeviceAndNodeNames(b)},_handleMetaNodeChange(b,d,f){let h=[];this._getLeafDebugWatches(b,h);_.forEach(h,k=>{this._handleLeafNodeSelected(d,k,f)})},_handleLeafNodeSelected(b,d,f){const h=d.device_name+"/"+d.node_name;f?this._selectedDebugWatchMapping[h]=
d:delete this._selectedDebugWatchMapping[h];b(d,f)},_handleForceNode(b,d){this.set("_filterInput","");setTimeout(()=>{if(null!=b&&null!=this._levelName2Container){var f=Vi.splitNodeName(b);for(let k=1;k<=f.length;++k){var h=f.slice(0,k).join("/");const t=this._levelName2Node[h];null!=t&&null!=t.levelDom&&t.levelDom.scrollIntoView({block:"center",behaviour:"smooth"});k<f.length?null!=this._levelName2Container[h]&&this._levelName2Container[h].setAttribute("opened",!0):(t.debugWatch||this._handleMetaNodeChange(t,
t.debugWatchChange,!0),d&&(t.setToAllCheckedExternally(),(h=t.debugWatch)&&null==this._selectedDebugWatchMapping[h.node_name]&&(this._selectedDebugWatchMapping[b]=h)),null!=this._highlightedLevelDom&&this._highlightedLevelDom.classList.remove("highlighted"),t.levelDom.classList.add("highlighted"),this.set("_highlightedLevelDom",t.levelDom))}}},20)},_handleForceNodeExpandAndCheck(b){this._handleForceNode(b,!0)},_handleForceNodeExpand(b){this._handleForceNode(b,!1)}});

//# sourceURL=build://tf-debugger-dashboard/tf-session-runs-view.html.js
Polymer({is:"tf-session-runs-view",properties:{latestSessionRun:Object,sessionRunKeyToDeviceNames:Object,soleActive:Boolean,nodeOrTensorClicked:Function,_runKey2Count:{type:Object,value:{}},_runKey2NumDevices:{type:Object,value:{}},_activeRunKey:String},observers:["renderLatest(latestSessionRun)","setSoleActiveStatus(soleActive)"],renderLatest(b){b=JSON.stringify(b);this._runKey2Count[b]=void 0===this._runKey2Count[b]?1:this._runKey2Count[b]+1;void 0===this._runKey2NumDevices[b]&&(this._runKey2NumDevices[b]=
0);this._activeRunKey=b;this._renderSessionRunTable()},updateNumDevices(b){null!=this._activeRunKey&&(this._runKey2NumDevices[this._activeRunKey]=b,this._renderSessionRunTable())},setSoleActiveStatus(){this._renderSessionRunTable()},_renderSessionRunTable(){this._clearTable();this._renderHeader();let b;for(const f in this._runKey2Count)if(this._runKey2Count.hasOwnProperty(f)){var d=JSON.parse(f);(d=this._renderRow(d,this._runKey2NumDevices[f],this._runKey2Count[f],this._activeRunKey===f,this.soleActive))&&
(b=d)}b&&(Polymer.dom(this.$$("#session-runs-table")).parentNode.parentNode.scrollTop=b.offsetTop)},_clearTable(){const b=this.$$("#session-runs-table");for(;b.firstChild;)b.removeChild(b.firstChild)},_renderHeader(){const b=document.createElement("tr"),d=document.createElement("th");d.textContent="Feeds";const f=document.createElement("th");f.textContent="Fetches";const h=document.createElement("th");h.textContent="Targets";const k=document.createElement("th");k.textContent="#(Devices)";const t=
document.createElement("th");t.textContent="Count";b.appendChild(d);b.appendChild(f);b.appendChild(h);b.appendChild(k);b.appendChild(t);Polymer.dom(this.$$("#session-runs-table")).appendChild(b)},_renderRow(b,d,f,h,k){const t=document.createElement("tr"),l=this._renderGraphElements(b.feeds),p=this._renderGraphElements(b.fetches);b=this._renderGraphElements(b.targets);const m=document.createElement("td");m.textContent=d;d=document.createElement("td");d.textContent=f;t.appendChild(l);t.appendChild(p);
t.appendChild(b);t.appendChild(m);t.appendChild(d);h&&(k?t.setAttribute("class","sole-active-session-run"):t.setAttribute("class","active-session-run"));Polymer.dom(this.$$("#session-runs-table")).appendChild(t);if(h)return t},_renderGraphElements(b){const d=document.createElement("td");_.forEach(b,f=>{const h=document.createElement("div");h.textContent=f;h.setAttribute("class","node-or-tensor-element");h.addEventListener("click",()=>{this.nodeOrTensorClicked(f)});d.appendChild(h)});return d}});

//# sourceURL=build://tf-debugger-dashboard/tf-source-code-view.html.js
Polymer({is:"tf-source-code-view",properties:{requestManager:{type:Object,value:null},focusNodeName:{type:String,value:null},_oldFocusNodeName:{type:String,value:null},debugWatches:{type:Array,value:[]},nodeClicked:{type:Function,value:null},continueToNode:{type:Function,value:null},_highlightedElements:{type:Array,value:[]},_filePathSelected:Number,_fullFilePaths:{type:Array,value:null},_shortFilePaths:{type:Array,value:null},_fileLines:{type:Array,value:null},_nodeName2DeviceName:{type:Object,value:null},
_nodeName2BaseExpandedNodeName:{type:Object,value:null},_nodeName2NodeElements:{type:Object,value:null},_nodeName2StackTopNodeElement:{type:Object,value:null},_setHightlightOriginNodeElement:{type:Object,value:null},_fullStackShown:{type:Boolean,value:!1},_fullStackNodeName:{type:String,value:null},_renderDelayMillis:{type:Number,value:50,readonly:!0}},observers:["_renderFile(_filePathSelected)","_focusOnNode(focusNodeName)"],render(b){null!=b&&this.set("_debugWatches",b);this._querySourceCodeEndPoint({mode:"paths"}).then(d=>
{this.set("_fullFilePaths",d.paths);const f=d.paths.map(h=>({id:h,name:this._shortenPath(h,d.paths)}));this.set("_shortFilePaths",f);0<f.length&&this.set("_filePathSelected",0)})},_shortenPath(b){b=b.replace(/\\/g,"/");b=b.split("/");return b[b.length-1]},_renderFile(b){if(null!=b){var d=this._shortFilePaths[b].id;this._querySourceCodeEndPoint({mode:"content",file_path:d}).then(f=>{const h=[],k=f.content[d],t=f.lineno_to_op_name_and_stack_pos;f={};for(var l in t)t.hasOwnProperty(l)&&(f[l]=t[l].length);
this._filterFileTracebacksByDebugWatches(t);for(l=0;l<k.length;++l){const m=l+1;h.push({lineno:m,numNodes:null!=t[m]?String(t[m].length)+"/"+String(f[m])+" \u25bc":"",text:this._htmlEscape(k[l])})}this.set("_fileLines",h);const p=this;setTimeout(()=>{const m={},n={};for(const u in t){if(!t.hasOwnProperty(u))continue;for(var q=p.$$("#source-line-nodes-"+u);q.firstChild;)q.removeChild(q.firstChild);const x=t[u];x.sort(function(A,y){return A[0]<y[0]?-1:A[0]>y[0]?1:0});for(let A=0;A<x.length;++A){const y=
x[A][0],w=x[A][1],C=document.createElement("div"),G=document.createElement("span");G.setAttribute("class","source-line-node-enttry");G.setAttribute("sourceLineno",u);G.textContent=y;G.addEventListener("tap",()=>{this.nodeClicked(this._nodeName2DeviceName[y],this._nodeName2BaseExpandedNodeName[y],!0)});const D=document.createElement("paper-icon-button");D.setAttribute("icon","filter-list");D.setAttribute("title","Show stack");D.addEventListener("tap",()=>{this._highlightNodeElements(y);this.set("_fullStackNodeName",
y);this.set("_fullStackShown",!0);this._populateFullStack(y,this._fullFilePaths[this._filePathSelected],Number(u))});const B=document.createElement("paper-icon-button");B.setAttribute("icon","forward");B.setAttribute("title","Continue to");B.addEventListener("tap",()=>{this.nodeClicked(this._nodeName2DeviceName[y],this._nodeName2BaseExpandedNodeName[y],!0);const I=this._nodeName2DeviceName[y],N=this._nodeName2BaseExpandedNodeName[y];this.set("_setHightlightOriginNodeElement",G);this.continueToNode(I,
N)});C.appendChild(D);C.appendChild(B);C.appendChild(G);q.appendChild(C);m.hasOwnProperty(y)||(m[y]=[]);m[y].push(G);n.hasOwnProperty(y)||(n[y]=[G,w]);w>n[y][1]&&(n[y]=[G,w])}q.setAttribute("hidden",!0);q=p.$$("#source-line-node-toggle-"+u);null==q.getAttribute("tapCallbackSet")&&(q.addEventListener("tap",()=>{p._toggleLineNodes(Number(u))}),q.setAttribute("tapCallbackSet",!0))}p.set("_nodeName2NodeElements",m);for(const u in n)n.hasOwnProperty(u)&&(n[u]=n[u][0]);p.set("_nodeName2StackTopNodeElement",
n)},this._renderDelayMillis)})}},_toggleLineNodes(b,d=!1){b=this.$$("#source-line-nodes-"+b);null==b.getAttribute("hidden")&&!0!==d?b.setAttribute("hidden",!0):b.removeAttribute("hidden")},_filterFileTracebacksByDebugWatches(b){const d=this.debugWatches.map(k=>Vi.removeNodeNameBaseExpansion(k.node_name)),f={},h={};for(const k of this.debugWatches){const t=Vi.removeNodeNameBaseExpansion(k.node_name);f[t]=k.device_name;h[t]=k.node_name}this.set("_nodeName2DeviceName",f);this.set("_nodeName2BaseExpandedNodeName",
h);for(const k in b)b.hasOwnProperty(k)&&(b[k]=b[k].filter(t=>_.includes(d,t[0])))},_querySourceCodeEndPoint(b){const d=vc.getRouter().pluginRoute("debugger","/source_code");b=vc.addParams(d,b);return this.requestManager.request(b)},_htmlEscape(b){return b.replace(/ /g,"\u00a0")},_focusOnNode(b){if(null!=b){var d=this._shortFilePaths[this._filePathSelected].id,f=this;this._querySourceCodeEndPoint({mode:"op_traceback",op_name:b}).then(h=>{const k=h.op_traceback[b];h=[];for(let l=0;l<k.length;++l){const p=
k[l][1];k[l][0]===d&&h.push(p)}for(var t of f._highlightedElements)t.classList.remove("highlighted-source-line");t=[];for(const l of h)h=this.$$("#source-line-"+l),t.push(h),h.classList.add("highlighted-source-line"),f._toggleLineNodes(l,!0);f.set("_highlightedElements",t);this._highlightNodeElements(b)})}},_highlightNodeElements(b){if(null!=this._oldFocusNodeName)for(const d of this._nodeName2NodeElements[this._oldFocusNodeName])d.style["font-weight"]="normal";for(const d of this._nodeName2NodeElements[b])d.style["font-weight"]=
"bold";null==this._setHightlightOriginNodeElement?this._nodeName2StackTopNodeElement[b].scrollIntoView({block:"center",behaviour:"smooth"}):this.set("_setHightlightOriginNodeElement",null);this.set("_oldFocusNodeName",b)},_populateFullStack(b,d,f){this._querySourceCodeEndPoint({mode:"op_traceback",op_name:b}).then(h=>{const k=this.$$("#full-stack-content");for(;k.firstChild;)k.removeChild(k.firstChild);for(const t of h.op_traceback[b]){const l=document.createElement("li"),p=t[0],m=Number(t[1]);l.textContent=
p+": "+String(m);_.includes(this._fullFilePaths,p)?(l.classList.add("stack-frame-clickable"),l.style.color="blue",l.style["text-decoration"]="underline",l.style.cursor="pointer",p===d&&m===f&&(l.style["font-weight"]="bold"),l.addEventListener("tap",()=>{this.set("_filePathSelected",this._fullFilePaths.indexOf(p));setTimeout(()=>{this._toggleLineNodes(m,!0);for(const n of this._nodeName2NodeElements[b])Number(n.getAttribute("sourceLineno"))===Number(m)&&(n.scrollIntoView({block:"center",behaviour:"smooth"}),
this.set("_setHightlightOriginNodeElement",l),this._highlightNodeElements(b),d===p&&f===m||this._populateFullStack(b,p,m))},2*this._renderDelayMillis)})):(l.classList.add("stack-frame-nonclickable"),l.style.color="#555");k.appendChild(l)}})},_closeFullStackDialog(){this.set("_fullStackShown",!1)}});

//# sourceURL=build://tf-debugger-dashboard/tf-tensor-data-summary.html.js
Polymer({is:"tf-tensor-data-summary",properties:{latestTensorData:Object,expandHandler:Object,continueToCallback:Function,highlightedNodeName:{type:String,value:null},tensorNameClicked:{type:Function,value:null},getHealthPill:Function,_healthPillsEnabled:{type:Boolean,value:!0,notify:!0},_watchKeys:{type:Array,value:[]},_watchKey2Data:{type:Object,value:{}},_watchKey2Count:{type:Object,value:{}},_watchKey2ExpandHandler:{type:Object,value:{}},_watchKey2ValueShort:{type:Object,value:{}},_watchKey2Row:{type:Object,
value:{}},_activeWatchKey:String,_healthPillWidth:{type:Number,value:200,readonly:!0},_healthPillHeight:{type:Number,value:32,readonly:!0}},observers:["_renderLatest(latestTensorData, expandHandler)","_highlight(highlightedNodeName)"],listeners:{"show-health-pills.change":"_showHealthPillsChanged"},ready(){this._renderHealthPillLegend()},enableHealthPills(){this.set("_healthPillsEnabled",!0);this._renderHealthPillLegend()},_showHealthPillsChanged(){this._healthPillsEnabled?this._renderHealthPillLegend():
this._clearHealthPillLegend();this._renderAll()},_renderAll(){this._clearTensorDataTable();for(const b of this._watchKeys)this._renderLatest(this._watchKey2Data[b],this._watchKey2ExpandHandler[b])},_tensorData2WatchKey(b){return b.deviceName+"/"+b.tensorName+":"+b.debugOp},_renderLatest(b,d){if(b){var f=this._tensorData2WatchKey(b),h=null;"Uninitialized"!==b.dtype&&"Unsupported"!==b.dtype&&(h=()=>d(b));var k=null!=b.value?JSON.stringify(b.value,(t,l)=>l.toFixed?Number(l.toFixed(3)):l):"(Click to view)";
this._watchKey2Data[f]=b;-1===this._watchKeys.indexOf(f)?(this._watchKeys.push(f),this._watchKey2Count[f]=1):this._watchKey2Count[f]+=1;this._watchKey2ExpandHandler[f]=h;this._watchKey2ValueShort[f]=k;this._activeWatchKey=f;this._removeActiveStatusFromAllRows();this._renderRow(f)}},_clearTensorDataTable(){for(const b in this._watchKey2Row)this._watchKey2Row.hasOwnProperty(b)&&(this._watchKey2Row[b].remove(),delete this._watchKey2Row[b])},_clearTensorDataRow(b){for(;b.firstChild;)b.removeChild(b.firstChild)},
_clearHealthPillLegend(){const b=this.$$("#health-pill-legend");for(;b.firstChild;)b.removeChild(b.firstChild)},_renderHealthPillLegend(){this._clearHealthPillLegend();const b=this.$$("#health-pill-legend");var d=document.createElement("div");d.textContent="Legend:";b.appendChild(d);d.style["margin-right"]="0.5em";d.style.display="inline-block";for(d=0;d<tf.graph.scene.healthPillEntries.length;++d){const f=tf.graph.scene.healthPillEntries[d],h=document.createElement("div");h.style.display="inline-block";
h.style["margin-right"]="0.25em";const k=document.createElement("span");k.textContent="\u25a0";k.style.color=f.background_color;const t=document.createElement("span");t.textContent=f.label;t.style.color=f.background_color;h.appendChild(k);h.appendChild(t);b.appendChild(h)}},_removeActiveStatusFromAllRows(){for(const b in this._watchKey2Row){if(!this._watchKey2Row.hasOwnProperty(b))continue;const d=this._watchKey2Row[b];Polymer.dom(d).classList.remove("active-tensor");Polymer.dom(d).classList.remove("highlighted")}},
_renderRow(b){let d,f=!1;null!=this._watchKey2Row[b]?(d=this._watchKey2Row[b],this._clearTensorDataRow(d),f=!1):(d=document.createElement("tr"),f=!0);const h=this._watchKey2Data[b].deviceName,k=this._watchKey2Data[b].maybeBaseExpandedNodeName,t=h+"/"+k;var l=this._watchKey2Count[b],p=this._watchKey2Data[b].tensorName,m=this._watchKey2Data[b].debugOp,n=this._watchKey2ValueShort[b];const q=this._watchKey2ExpandHandler[b],u=b===this._activeWatchKey,x=document.createElement("td");Polymer.dom(x).classList.add("tensor-name");
x.style["text-decoration"]="underline";x.style.cursor="pointer";x.textContent=p;x.addEventListener("tap",()=>{null!=this.tensorNameClicked&&this.tensorNameClicked(h,k)});const A=document.createElement("td");A.textContent=l;const y=this._watchKey2Data[b].dtype;l=document.createElement("td");const w=this._watchKey2Data[b].shape;l.textContent=y;const C=document.createElement("td");C.textContent=JSON.stringify(w);const G=document.createElement("td");G.textContent=n;Polymer.dom(G).classList.add("value-expansion-link");
null!=q&&(G.addEventListener("tap",q,!1),G.style["text-decoration"]="underline",G.style.cursor="pointer");n=null;n=this._healthPillsEnabled?this._renderHealthPill(p+":"+m,{device_name:h,node_name:k,dtype:y,shape:w,value:null},q):document.createElement("td");p=document.createElement("td");m=document.createElement("paper-icon-button");m.setAttribute("icon","forward");m.setAttribute("title","Continue to");m.addEventListener("click",()=>{this.continueToCallback(h,k)});p.appendChild(m);d.appendChild(x);
d.appendChild(A);d.appendChild(l);d.appendChild(C);d.appendChild(G);d.appendChild(n);d.appendChild(p);d.setAttribute("nodeNameWithDevice",t);u&&(Polymer.dom(d).classList.add("active-tensor"),Polymer.dom(d).classList.add("highlighted"));this._watchKey2Row[b]=d;f&&Polymer.dom(this.$$("#tensor-data-table tbody")).appendChild(d);d.scrollIntoView({block:"end",inline:"nearest",behaviour:"smooth"})},_renderHealthPill(b,d,f){const h=document.createElement("td");Polymer.dom(h).classList.add("health-pill");
null!=f&&h.addEventListener("tap",f,!1);f=document.createElementNS(tf.graph.scene.SVG_NAMESPACE,"svg");f.setAttribute("width",this._healthPillWidth);f.setAttribute("height",this._healthPillHeight);const k=document.createElementNS(tf.graph.scene.SVG_NAMESPACE,"g");f.appendChild(k);h.appendChild(f);const t="tdp/"+b;this.getHealthPill(b,d.device_name,d.node_name,l=>{null==l?(h.textContent="N/A",h.style.color="gray"):(d.value=l,tf.graph.scene.addHealthPill(k,d,null,t,this._healthPillWidth,this._healthPillHeight/
2,this._healthPillHeight/2,0))});return h},_highlight(b){Polymer.dom(this.$$("#tensor-data-table"));const d=[];for(const f in this._watchKey2Row){if(!this._watchKey2Row.hasOwnProperty(f))continue;const h=this._watchKey2Row[f];null!=h.getAttribute&&(h.getAttribute("nodeNameWithDevice")===b?d.push(h):Polymer.dom(h).classList.remove("highlighted"))}if(null!=b)for(b=0;b<d.length;++b)Polymer.dom(d[b]).classList.add("highlighted"),d[b].scrollIntoView({block:"end",inline:"nearest",behaviour:"smooth"})}});

//# sourceURL=build://tensor-widget/tensor_widget_binary.js
var Wi=this&&this.__extends||function(){function b(d,f){b=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(h,k){h.__proto__=k}||function(h,k){for(var t in k)k.hasOwnProperty(t)&&(h[t]=k[t])};return b(d,f)}return function(d,f){function h(){this.constructor=d}b(d,f);d.prototype=null===f?Object.create(f):(h.prototype=f.prototype,new h)}}(),Xi=this&&this.__generator||function(b,d){function f(n){return function(q){return h([n,q])}}function h(n){if(t)throw new TypeError("Generator is already executing.");
for(;k;)try{if(t=1,l&&(p=n[0]&2?l["return"]:n[0]?l["throw"]||((p=l["return"])&&p.call(l),0):l.next)&&!(p=p.call(l,n[1])).done)return p;if(l=0,p)n=[n[0]&2,p.value];switch(n[0]){case 0:case 1:p=n;break;case 4:return k.label++,{value:n[1],done:!1};case 5:k.label++;l=n[1];n=[0];continue;case 7:n=k.ops.pop();k.trys.pop();continue;default:if(!(p=k.trys,p=0<p.length&&p[p.length-1])&&(6===n[0]||2===n[0])){k=0;continue}if(3===n[0]&&(!p||n[1]>p[0]&&n[1]<p[3]))k.label=n[1];else if(6===n[0]&&k.label<p[1])k.label=
p[1],p=n;else if(p&&k.label<p[2])k.label=p[2],k.ops.push(n);else{p[2]&&k.ops.pop();k.trys.pop();continue}}n=d.call(b,k)}catch(q){n=[6,q],l=0}finally{t=p=0}if(n[0]&5)throw n[1];return{value:n[0]?n[1]:void 0,done:!0}}var k={label:0,sent:function(){if(p[0]&1)throw p[1];return p[1]},trys:[],ops:[]},t,l,p,m;return m={next:f(0),"throw":f(1),"return":f(2)},"function"===typeof Symbol&&(m[Symbol.iterator]=function(){return this}),m},Yi=this&&this.__read||function(b,d){var f="function"===typeof Symbol&&b[Symbol.iterator];
if(!f)return b;b=f.call(b);var h,k=[];try{for(;(void 0===d||0<d--)&&!(h=b.next()).done;)k.push(h.value)}catch(l){var t={error:l}}finally{try{h&&!h.done&&(f=b["return"])&&f.call(b)}finally{if(t)throw t.error;}}return k};
(function(){function b(H,K,M,L){return new (M||(M=Promise))(function(Q,T){function X(Z){try{la(L.next(Z))}catch(ba){T(ba)}}function aa(Z){try{la(L["throw"](Z))}catch(ba){T(ba)}}function la(Z){Z.done?Q(Z.value):(new M(function(ba){ba(Z.value)})).then(X,aa)}la((L=L.apply(H,K||[])).next())})}function d(H){return null!==H.match(/^int[0-9]+$/)||null!==H.match(/^uint[0-9]+$/)}function f(H){return null!==H.match(/^float[0-9]+$/)||null!==H.match(/^bfloat[0-9]+$/)}function h(H){return"bool"===H.toLowerCase()||
"boolean"===H.toLowerCase()}function k(H){return"str"===H.toLowerCase()||"string"===H.toLowerCase()}function t(H){var K=1;H.forEach(function(M){K*=M});return K}function l(H){return 0===H.length?"scalar":"["+H+"]"}function p(H){var K={slicingDimsAndIndices:[],viewingDims:[],verticalRange:null,horizontalRange:null},M=H.length;if(1===M)K.viewingDims=[0];else if(1<M){if(2<M)for(var L=0;L<M-2;++L)K.slicingDimsAndIndices.push({dim:L,index:0===H[L]?null:0});for(L=H.length-2;L<H.length;++L)K.viewingDims.push(L)}return K}
function m(H,K){if(H.viewingDims[0]!==K.viewingDims[0]||H.viewingDims[1]!==K.viewingDims[1])return!1;K=H.slicingDimsAndIndices.map(function(M){return M.dim});K.sort();H=H.slicingDimsAndIndices.map(function(M){return M.dim});H.sort();return JSON.stringify(K)===JSON.stringify(H)}function n(H){return 20>=H.length?H:H.slice(0,10)+"..."+H.slice(H.length-7,H.length)}function q(H,K,M,L){void 0===M&&(M=2);if(isNaN(H))return"NaN";if(-Infinity===H)return"-\u221e";if(Infinity===H)return"+\u221e";null==L&&(L=
Math.abs(H),L=1E3>L&&.01<=L||0===L?"fixed":"exponential");return null==L||"fixed"===L?K?""+H:H.toFixed(M):H.toExponential(M)}function u(H,K){void 0===K&&(K=!0);return H?K?"T":"True":K?"F":"False"}function x(H,K){void 0===K&&(K=4);return null===K||H.length<=K?H:H.slice(0,K-1)+"\u2026"}var A=function(){function H(K){this.isShown=!1;this.blurHideFunction=null;this.dropdown=document.createElement("div");this.dropdown.classList.add("tensor-widget-dim-dropdown");this.dropdown.style.position="fixed";this.dropdown.style.display=
"none";K.appendChild(this.dropdown)}H.prototype.show=function(K,M,L){var Q=this;L.forEach(function(X){var aa=document.createElement("div");aa.classList.add("tensor-widget-dim-dropdown-menu-item");aa.textContent=X.caption;Q.dropdown.appendChild(aa);X.disabled?aa.classList.add("tensor-widget-dim-dropdown-menu-item-disabled"):(aa.addEventListener("click",function(la){la.stopPropagation();Q.dropdown.click();if(null!==X.onClick)X.onClick(la);Q.hide()}),aa.addEventListener("mouseenter",function(la){if(null!==
X.onHover)X.onHover(la);aa.classList.add("tensor-widget-dim-dropdown-menu-item-active")}),aa.addEventListener("mouseleave",function(){aa.classList.remove("tensor-widget-dim-dropdown-menu-item-active");if(null!==X.onHover){for(var la=[],Z=0;Z<aa.children.length;++Z){var ba=aa.children[Z];ba.classList.contains("tensor-widget-dim-dropdown")&&la.push(ba)}la.forEach(function(ea){return aa.removeChild(ea)})}}))});this.dropdown.style.display="block";this.dropdown.style.top=K+"px";this.dropdown.style.left=
M+"px";L=this.dropdown.getBoundingClientRect();var T=L.left-M;this.dropdown.style.top=(K-(L.top-K)).toFixed(1)+"px";this.dropdown.style.left=(M-T).toFixed(1)+"px";this.isShown=!0;this.blurHideFunction=function(){Q.hide()};setTimeout(function(){return window.addEventListener("click",Q.blurHideFunction)},50)};H.prototype.hide=function(){for(this.dropdown.style.display="none";this.dropdown.firstChild;)this.dropdown.removeChild(this.dropdown.firstChild);this.isShown=!1;null!=this.blurHideFunction&&window.removeEventListener("click",
this.blurHideFunction)};H.prototype.shown=function(){return this.isShown};return H}(),y=function(){function H(K,M){var L=this;this.config=K;this.parentElement=M;this.baseFlatMenu=new A(this.parentElement);this.currentChoiceSelections={};this.config.items.forEach(function(Q,T){null!=Q.options&&(L.currentChoiceSelections[T]=Q.defaultSelection)})}H.prototype.show=function(K,M){var L=this,Q=[];this.config.items.forEach(function(T,X){var aa={caption:T.caption,onClick:null,onHover:null};if(null!=T.options){var la=
L.currentChoiceSelections[X];aa.onHover=function(Z){var ba=Z.target,ea=[];T.options.forEach(function(ca,ka){ea.push({caption:ka===la?ca+" (\u2713)":ca,onClick:function(){la!==ka&&(L.currentChoiceSelections[X]=ka,T.callback(ka))},onHover:null})});Z=new A(ba);ba=ba.getBoundingClientRect();Z.show(ba.top,ba.right,ea)}}else aa.onClick=T.callback;null==T.isEnabled||T.isEnabled()||(aa.disabled=!0);Q.push(aa)});this.baseFlatMenu.show(K,M,Q)};H.prototype.hide=function(){this.baseFlatMenu.hide()};H.prototype.shown=
function(){return this.baseFlatMenu.shown()};return H}(),w;(function(H){H[H.UP=1]="UP";H[H.DOWN=2]="DOWN";H[H.LEFT=3]="LEFT";H[H.RIGHT=4]="RIGHT"})(w||(w={}));var C=function(){function H(K,M,L,Q,T,X){this.shape=K;this.sliceDims=[];this.sliceIndices=[];if(0===t(this.shape))throw Error("TensorElementSelection doesn't support tensor with zero elements.");for(K=0;K<M.slicingDimsAndIndices.length;++K){this.sliceDims.push(M.slicingDimsAndIndices[K].dim);var aa=M.slicingDimsAndIndices[K].index;if(null===
aa)throw Error("Failed to create TensorElementSelection due to undetermined slicing index at dimension "+K);this.sliceIndices.push(aa)}this.rank=this.shape.length;if(0<this.rank&&this.sliceDims.length>=this.rank)throw Error("Expected sliceDims to have a length less than rank "+this.rank+", but got length "+this.sliceDims.length);this.viewDims=[];for(K=0;K<this.rank;++K)-1===this.sliceDims.indexOf(K)&&this.viewDims.push(K);if(2<this.viewDims.length)throw Error("Only selections in 1D and 2D are supported.");
this.rowStart=null==L?0:L;this.colStart=null==Q?0:Q;this.rowCount=null==T?1:T;this.colCount=null==X?1:X}H.prototype.getElementStatus=function(K){if(K.length!==this.rank)throw Error("Expected indices to have a rank of "+this.rank+", but got "+(K.length+" (["+K+"])"));for(var M=0;M<K.length;++M)if(-1!==this.sliceDims.indexOf(M)&&K[M]!==this.sliceIndices[this.sliceDims.indexOf(M)])return null;M=null;var L=this.rowStart+this.rowCount,Q=this.colStart+this.colCount;if(0===this.viewDims.length)0===K.length&&
(M={topEdge:!0,bottomEdge:!0,leftEdge:!0,rightEdge:!0});else if(1===this.viewDims.length){var T=this.viewDims[0];K[T]>=this.rowStart&&K[T]<L&&(M={topEdge:K[T]===this.rowStart,bottomEdge:K[T]===L-1,leftEdge:!0,rightEdge:!0})}else if(2===this.viewDims.length){T=this.viewDims[0];var X=this.viewDims[1];K[T]>=this.rowStart&&K[T]<L&&K[X]>=this.colStart&&K[X]<Q&&(M={topEdge:K[T]===this.rowStart,bottomEdge:K[T]===L-1,leftEdge:K[X]===this.colStart,rightEdge:K[X]===Q-1})}else throw Error("Unexpected length of viewDims: "+
this.viewDims);return M};H.prototype.move=function(K,M){var L=null;if(0===this.rank||1===this.rank&&(K===w.LEFT||K===w.RIGHT))return null;if(null===M.verticalRange||null===M.verticalRange[1])throw Error("Failed to move due to undetermined vertical range.");K===w.UP?0<this.rowStart&&(this.rowStart--,null!=M.verticalRange&&this.rowStart<M.verticalRange[0]&&(L=w.UP)):K===w.DOWN?null!=M.viewingDims&&null!=M.viewingDims[0]&&this.rowStart<this.shape[M.viewingDims[0]]-1&&(this.rowStart++,null!=M.verticalRange&&
this.rowStart>=M.verticalRange[1]&&(L=w.DOWN)):K===w.LEFT?0<this.colStart&&(this.colStart--,null!=M.horizontalRange&&this.colStart<M.horizontalRange[0]&&(L=w.LEFT)):K===w.RIGHT&&null!=M.viewingDims&&null!=M.viewingDims[1]&&this.colStart<this.shape[M.viewingDims[1]]-1&&(this.colStart++,null!=M.horizontalRange&&this.colStart>=M.horizontalRange[1]&&(L=w.RIGHT));this.colCount=this.rowCount=1;return L};H.prototype.getRowStart=function(){return this.rowStart};H.prototype.getRowCount=function(){return this.rowCount};
H.prototype.getColStart=function(){return this.colStart};H.prototype.getColCount=function(){return this.colCount};return H}(),G=function(){function H(K,M,L){void 0===L&&(L=function(){});this.rootDiv=K;this.shape=M;this.onSlicingSpecChange=L;this.dimControls=[];this.dimInputs=[];this.commas=[];this.dropdowns=[];this.bracketDivs=[null,null];this.dimControlsListenerAttached=[];this.rank=this.shape.length;if(3>this.rank)throw Error("Dimension control is not applicable to tensor shapes less than 3D: received "+
(this.rank+"D tensor shape: ")+(JSON.stringify(this.shape)+"."));this.createComponents();this.slicingSpec=p(M)}H.prototype.createComponents=function(){for(var K=this;this.rootDiv.firstChild;)this.rootDiv.removeChild(this.rootDiv.firstChild);this.dimControls=[];this.dimInputs=[];this.commas=[];this.dropdowns=[];this.dimControlsListenerAttached=[];this.bracketDivs[0]=document.createElement("div");this.bracketDivs[0].textContent="Slicing: [";this.bracketDivs[0].classList.add("tensor-widget-dim-brackets");
this.rootDiv.appendChild(this.bracketDivs[0]);for(var M=0;M<this.rank;++M){var L=document.createElement("div");L.classList.add("tensor-widget-dim");L.title="Dimension "+M+": size\x3d"+this.shape[M];this.rootDiv.appendChild(L);this.dimControls.push(L);this.dimControlsListenerAttached.push(!1);L=document.createElement("input");L.classList.add("tensor-widget-dim");L.style.display="none";this.rootDiv.appendChild(L);this.dimInputs.push(L);M<this.rank-1&&(L=document.createElement("div"),L.classList.add("tensor-widget-dim-comma"),
L.textContent=",",this.rootDiv.appendChild(L),this.commas.push(L));L=document.createElement("div");L.classList.add("tensor-widget-dim-dropdown");L.style.display="none";this.rootDiv.appendChild(L);this.dropdowns.push(L)}this.bracketDivs[1]=document.createElement("div");this.bracketDivs[1].textContent="]";this.bracketDivs[1].classList.add("tensor-widget-dim-brackets");this.rootDiv.appendChild(this.bracketDivs[1]);this.rootDiv.addEventListener("mouseleave",function(){K.clearAllDropdowns()})};H.prototype.render=
function(K){function M(aa){var la=X.dimControls[aa],Z=X.dimInputs[aa],ba=X.dropdowns[aa];if("none"!==Z.style.display)return"continue";var ea=X.shape[aa];if(-1!==Q.indexOf(aa)){var ca=T[Q.indexOf(aa)];la.textContent=String(ca);Z.classList.add("tensor-widget-dim");Z.type="number";Z.min="0";Z.max=String(ea-1);Z.value=String(ca);X.dimControlsListenerAttached[aa]||(la.addEventListener("click",function(){L.clearAllDropdowns();la.style.display="none";Z.style.display="inline-block"}),Z.addEventListener("change",
function(){if(null===L.slicingSpec)throw Error("Slicing control change callback failed due to missing spec.");var ka=parseInt(Z.value,10);!isFinite(ka)||0>ka||ka>=ea||Math.floor(ea)!=ea?Z.value=String(L.slicingSpec.slicingDimsAndIndices[Q.indexOf(aa)].index):(L.slicingSpec.slicingDimsAndIndices[Q.indexOf(aa)].index=ka,la.textContent=String(ka),L.onSlicingSpecChange(L.slicingSpec))}),Z.addEventListener("blur",function(){Z.style.display="none";la.style.display="inline-block"}),X.dimControlsListenerAttached[aa]=
!0)}else{if(X.slicingSpec.viewingDims[0]===aa){if(null===X.slicingSpec.verticalRange)throw Error("Missing vertical range.");la.textContent="\u2195 "+X.slicingSpec.verticalRange[0]+":"+X.slicingSpec.verticalRange[1]}else{if(null===X.slicingSpec.horizontalRange)throw Error("Missing horizontal range.");la.textContent="\u2194 "+X.slicingSpec.horizontalRange[0]+":"+X.slicingSpec.horizontalRange[1]}la.classList.add("tensor-widget-dim");X.dimControlsListenerAttached[aa]||(la.addEventListener("click",function(){var ka=
la.getBoundingClientRect();L.renderDropdownMenuItems(ba,ka.bottom,ka.left,aa)}),X.dimControlsListenerAttached[aa]=!0)}}var L=this;null!=K&&(this.slicingSpec=JSON.parse(JSON.stringify(K)));if(null===this.slicingSpec)throw Error("Slicing control rendering failed due to missing slicing spec.");var Q=this.slicingSpec.slicingDimsAndIndices.map(function(aa){return aa.dim}),T=this.slicingSpec.slicingDimsAndIndices.map(function(aa){return aa.index}),X=this;for(K=0;K<this.rank;++K)M(K)};H.prototype.renderDropdownMenuItems=
function(K,M,L,Q){function T(ea){if(-1===aa.indexOf(ea)||Q===la.slicingSpec.viewingDims[1]&&ea<=la.slicingSpec.viewingDims[0]||Q==la.slicingSpec.viewingDims[0]&&ea>=la.slicingSpec.viewingDims[1])return"continue";var ca=document.createElement("div");ca.classList.add("tensor-widget-dim-dropdown-menu-item");ca.textContent="Swap with dimension "+ea;K.appendChild(ca);ca.addEventListener("mouseenter",function(){ca.classList.add("tensor-widget-dim-dropdown-menu-item-active");X.dimControls[ea].classList.add("tensor-widget-dim-highlighted")});
ca.addEventListener("mouseleave",function(){ca.classList.remove("tensor-widget-dim-dropdown-menu-item-active");X.dimControls[ea].classList.remove("tensor-widget-dim-highlighted")});var ka=la.slicingSpec.viewingDims[0]===Q;ca.addEventListener("click",function(){if(null===X.slicingSpec)throw Error("Dimension swapping failed due to missing slicing spec");var Y=aa.indexOf(ea);X.slicingSpec.viewingDims[ka?0:1]=ea;X.slicingSpec.slicingDimsAndIndices[Y]={dim:Q,index:0};X.slicingSpec.verticalRange=null;X.slicingSpec.horizontalRange=
null;if(X.onSlicingSpecChange)X.onSlicingSpecChange(X.slicingSpec)})}var X=this;if(null===this.slicingSpec)throw Error("Slicing control cannot render dropdown menu items due to missing slicing spec.");this.clearAllDropdowns();for(var aa=this.slicingSpec.slicingDimsAndIndices.map(function(ea){return ea.dim}),la=this,Z=0;Z<this.rank;++Z)T(Z);K.addEventListener("mouseleave",function(){K.style.display="none"});if(K.firstChild){K.style.position="fixed";K.style.top=M+"px";K.style.left=L+"px";K.style.display=
"block";Z=K.getBoundingClientRect();var ba=Z.left-L;K.style.top=(M-(Z.top-M)).toFixed(1)+"px";K.style.left=(L-ba).toFixed(1)+"px"}};H.prototype.setSlicingSpec=function(K){this.slicingSpec=JSON.parse(JSON.stringify(K));if(null===this.slicingSpec)throw Error("Cannot set slicing spec to null.");this.render(this.slicingSpec)};H.prototype.clearAllDropdowns=function(){this.dropdowns.forEach(function(K){if(null!=K){for(;K.firstChild;)K.removeChild(K.firstChild);K.style.display="none"}})};return H}(),D=function(){function H(K){this.config=
K;if(!isFinite(K.min))throw Error("min value ("+K.min+") is not finite");if(!isFinite(K.max))throw Error("max value ("+K.max+") is not finite");if(K.max<K.min)throw Error("max ("+K.max+") is \x3c min ("+K.min+")");}H.prototype.render=function(K,M){if(this.config.min!==this.config.max){var L=K.getContext("2d");if(null!=L){for(var Q=K.width/100,T=K.height,X=.6*T,aa=0;100>aa;++aa){var la=Q*aa,Z=.2*T,ba=Yi(this.getRGB(aa/100*(this.config.max-this.config.min)+this.config.min),3),ea=ba[0],ca=ba[1];ba=ba[2];
L.beginPath();L.fillStyle="rgba("+ea+", "+ca+", "+ba+", 1)";L.fillRect(la,Z,Q,X);L.stroke()}null!=M&&M>=this.config.min&&M<=this.config.max&&(K=(M-this.config.min)/(this.config.max-this.config.min)*K.width,L.beginPath(),L.fillStyle="rgba(0, 0, 0, 1)",L.moveTo(K,.2*T),L.lineTo(K-4,0),L.lineTo(K+4,0),L.fill(),L.beginPath(),L.moveTo(K,.8*T),L.lineTo(K-4,T),L.lineTo(K+4,T),L.fill())}}};return H}(),B=function(H){function K(){return null!==H&&H.apply(this,arguments)||this}Wi(K,H);K.prototype.getRGB=function(M){if(isNaN(M))return[255,
0,0];if(!isFinite(M))return 0<M?[0,0,255]:[255,127.5,0];M=this.config.min===this.config.max?.5:(M-this.config.min)/(this.config.max-this.config.min);M=Math.max(Math.min(M,1),0);return[255*M,255*M,255*M]};return K}(D);D=function(H){function K(){return null!==H&&H.apply(this,arguments)||this}Wi(K,H);K.prototype.getRGB=function(M){if(isNaN(M))return[63.75,63.75,63.75];if(!isFinite(M))return 0>M?[127.5,127.5,127.5]:[191.25,191.25,191.25];var L=0,Q=0,T=0;M=this.config.min===this.config.max?.5:(M-this.config.min)/
(this.config.max-this.config.min);M=Math.max(Math.min(M,1),0);.35>=M?(Q=M/.35,T=1):.35<M&&.65>=M?(L=(M-.35)/(.65-.35),Q=1,T=(.65-M)/(.65-.35)):.65<M&&(L=1,Q=(1-M)/.35);return[255*L,255*Q,255*T]};return K}(D);var I;(function(H){H[H.TEXT=1]="TEXT";H[H.IMAGE=2]="IMAGE"})(I||(I={}));var N={Grayscale:B,Jet:D},O=function(){function H(K,M,L){this.rootElement=K;this.tensorView=M;this.baseRulerTick=this.topRuler=this.valueSection=this.slicingSpecRoot=this.menuThumb=this.infoSubsection=this.headerSection=null;
this.topRulerTicks=[];this.leftRulerTicks=[];this.valueRows=[];this.valueDivs=[];this.slicingControl=this.valueTooltip=null;this.colsCutoff=this.rowsCutoff=!1;this.menu=this.menuConfig=this.selection=null;this.colorMapName="Grayscale";this.colorMap=null;this.showIndicesOnTicks=!1;this.imageCellSize=16;this.minImageCellSize=4;this.maxImageCellSize=40;this.zoomStepRatio=1.2;this.numericSummary=null;this.options=L||{};this.slicingSpec=p(this.tensorView.spec.shape);this.rank=this.tensorView.spec.shape.length;
this.valueRenderMode=I.TEXT}H.prototype.render=function(){return b(this,void 0,void 0,function(){return Xi(this,function(K){switch(K.label){case 0:this.rootElement.classList.add("tensor-widget");this.renderHeader();if(!(d(this.tensorView.spec.dtype)||f(this.tensorView.spec.dtype)||h(this.tensorView.spec.dtype)||k(this.tensorView.spec.dtype)))throw Error("Rendering dtype "+this.tensorView.spec.dtype+" is not supported yet.");return[4,this.renderValues()];case 1:return K.sent(),[2]}})})};H.prototype.renderHeader=
function(){null==this.headerSection&&(this.headerSection=document.createElement("div"),this.headerSection.classList.add("tensor-widget-header"),this.rootElement.appendChild(this.headerSection),this.createMenu());this.renderInfo()};H.prototype.renderInfo=function(){if(null===this.headerSection)throw Error("Rendering tensor info failed due to mising header section");null==this.infoSubsection&&(this.infoSubsection=document.createElement("div"),this.infoSubsection.classList.add("tensor-widget-info"),
this.headerSection.appendChild(this.infoSubsection));for(;this.infoSubsection.firstChild;)this.infoSubsection.removeChild(this.infoSubsection.firstChild);this.renderName();this.renderDType();this.renderShape()};H.prototype.renderName=function(){if(null==this.infoSubsection)throw Error("Rendering tensor name failed due to missing info subsection.");if(null!=this.options.name&&0!==this.options.name.length){var K=document.createElement("div");K.classList.add("tensor-widget-tensor-name");K.textContent=
n(this.options.name);K.title=this.options.name;this.infoSubsection.appendChild(K)}};H.prototype.renderDType=function(){if(null==this.infoSubsection)throw Error("Rendering tensor dtype failed due to missing info subsection.");var K=document.createElement("div");K.classList.add("tensor-widget-dtype");var M=document.createElement("span");M.classList.add("tensor-widget-dtype-label");M.textContent="dtype:";K.appendChild(M);M=document.createElement("span");M.textContent=this.tensorView.spec.dtype;K.appendChild(M);
this.infoSubsection.appendChild(K)};H.prototype.renderShape=function(){if(null==this.infoSubsection)throw Error("Rendering tensor shape failed due to missing info subsection.");var K=document.createElement("div");K.classList.add("tensor-widget-shape");var M=document.createElement("div");M.classList.add("tensor-widget-shape-label");M.textContent="shape:";K.appendChild(M);M=document.createElement("div");M.classList.add("tensor-widget-shape-value");M.textContent=l(this.tensorView.spec.shape);K.appendChild(M);
this.infoSubsection.appendChild(K)};H.prototype.createMenu=function(){var K=this;this.menuConfig={items:[]};if(f(this.tensorView.spec.dtype)||d(this.tensorView.spec.dtype)||h(this.tensorView.spec.dtype))this.menuConfig.items.push({caption:"Select display mode...",options:["Text","Image"],defaultSelection:0,callback:function(M){0===M?(K.valueRenderMode=I.TEXT,K.renderValues()):(K.valueRenderMode=I.IMAGE,K.tensorView.getNumericSummary().then(function(L){K.numericSummary=L;K.renderValues()}))}}),this.menuConfig.items.push({caption:"Select color map...",
options:Object.keys(N),defaultSelection:0,callback:function(M){K.colorMapName=Object.keys(N)[M];K.renderValues()},isEnabled:function(){return K.valueRenderMode===I.IMAGE}}),this.menuConfig.items.push({caption:"Zoom in (Image mode)",callback:function(){K.zoomInOneStepAndRenderValues()},isEnabled:function(){return K.valueRenderMode===I.IMAGE}}),this.menuConfig.items.push({caption:"Zoom out (Image mode)",callback:function(){K.zoomOutOneStepAndRenderValues()},isEnabled:function(){return K.valueRenderMode===
I.IMAGE}});null!==this.menuConfig&&0<this.menuConfig.items.length&&(this.menu=new y(this.menuConfig,this.headerSection),this.renderMenuThumb())};H.prototype.zoomInOneStepAndRenderValues=function(){this.imageCellSize*this.zoomStepRatio<=this.maxImageCellSize&&(this.imageCellSize*=this.zoomStepRatio,this.renderValues())};H.prototype.zoomOutOneStepAndRenderValues=function(){this.imageCellSize/this.zoomStepRatio>=this.minImageCellSize&&(this.imageCellSize/=this.zoomStepRatio,this.renderValues())};H.prototype.renderMenuThumb=
function(){var K=this;if(null==this.headerSection)throw Error("Rendering menu thumb failed due to missing header section.");this.menuThumb=document.createElement("div");this.menuThumb.textContent="\u22ee";this.menuThumb.classList.add("tensor-widget-menu-thumb");this.headerSection.appendChild(this.menuThumb);this.menuThumb.addEventListener("click",function(){if(null!==K.menu)if(K.menu.shown())K.menu.hide();else{var M=K.menuThumb.getBoundingClientRect();K.menu.show(M.bottom,M.left)}})};H.prototype.renderValues=
function(){return b(this,void 0,void 0,function(){var K=this;return Xi(this,function(M){switch(M.label){case 0:return 2<this.rank&&null===this.slicingSpecRoot&&(this.slicingSpecRoot=document.createElement("div"),this.slicingSpecRoot.classList.add("tensor-widget-slicing-group"),this.rootElement.appendChild(this.slicingSpecRoot)),null==this.valueSection&&(this.valueSection=document.createElement("div"),this.valueSection.classList.add("tensor-widget-value-section"),this.rootElement.appendChild(this.valueSection),
this.valueSection.addEventListener("wheel",function(L){return b(K,void 0,void 0,function(){var Q;return Xi(this,function(T){switch(T.label){case 0:Q=!1;null==this.options.wheelZoomKey||"ctrl"===this.options.wheelZoomKey?Q=L.ctrlKey:"alt"===this.options.wheelZoomKey?Q=L.altKey:"shift"===this.options.wheelZoomKey&&(Q=L.shiftKey);if(Q&&this.valueRenderMode===I.IMAGE)return L.stopPropagation(),L.preventDefault(),0<L.deltaY?this.zoomOutOneStepAndRenderValues():this.zoomInOneStepAndRenderValues(),[2];if(null==
this.selection)return[2];L.stopPropagation();L.preventDefault();this.hideValueTooltip();return[4,this.scrollUpOrDown(0<L.deltaY?w.DOWN:w.UP)];case 1:return T.sent(),[2]}})})}),this.valueSection.tabIndex=1024,this.valueSection.addEventListener("keydown",function(L){var Q=[38,40,37,39];if(null!=K.selection&&-1!==Q.indexOf(L.keyCode)){L.stopPropagation();L.preventDefault();K.hideValueTooltip();var T=Q=null;38===L.keyCode?T=w.UP:40===L.keyCode?T=w.DOWN:37===L.keyCode?T=w.LEFT:39===L.keyCode&&(T=w.RIGHT);
null!==T&&(Q=K.selection.move(T,K.slicingSpec));null===Q?K.renderSelection():Q===w.UP||Q===w.DOWN?K.scrollUpOrDown(Q):(Q===w.LEFT||Q===w.RIGHT)&&K.scrollLeftOrRight(Q)}})),this.clearValueSection(),this.createTopRuler(),this.createLeftRuler(),this.createValueDivs(),[4,this.renderRulersAndValueDivs()];case 1:return M.sent(),2<this.rank&&(this.slicingControl=new G(this.slicingSpecRoot,this.tensorView.spec.shape,function(L){return b(K,void 0,void 0,function(){return Xi(this,function(Q){switch(Q.label){case 0:if(m(this.slicingSpec,
L))return[3,2];this.slicingSpec=JSON.parse(JSON.stringify(L));return[4,this.render()];case 1:return Q.sent(),[3,4];case 2:return this.slicingSpec=JSON.parse(JSON.stringify(L)),[4,this.renderRulersAndValueDivs()];case 3:Q.sent(),Q.label=4;case 4:return[2]}})})}),this.slicingControl.render(this.slicingSpec)),[2]}})})};H.prototype.clearValueSection=function(){if(null!==this.valueSection){for(;this.valueSection.firstChild;)this.valueSection.removeChild(this.valueSection.firstChild);this.topRuler=null;
this.valueRows=[]}};H.prototype.createTopRuler=function(){var K=this;if(null===this.valueSection)throw Error("Failed to create top ruler due to missing value section.");null==this.topRuler&&(this.topRuler=document.createElement("div"),this.topRuler.classList.add("tenesor-widget-top-ruler"),this.topRuler.style.whiteSpace="nowrap",this.valueSection.appendChild(this.topRuler),this.topRulerTicks=[],this.topRuler.addEventListener("wheel",function(X){return b(K,void 0,void 0,function(){return Xi(this,function(aa){switch(aa.label){case 0:if(null==
this.selection)return[2];X.stopPropagation();X.preventDefault();this.hideValueTooltip();return[4,this.scrollLeftOrRight(0<X.deltaY?w.RIGHT:w.LEFT)];case 1:return aa.sent(),[2]}})})}));for(;this.topRuler.firstChild;)this.topRuler.removeChild(this.topRuler.firstChild);this.baseRulerTick=document.createElement("div");this.baseRulerTick.classList.add("tensor-widget-top-ruler-tick");this.topRuler.appendChild(this.baseRulerTick);2<=this.rank&&(this.slicingSpec.horizontalRange=[0,null]);var M=1>=this.rank?
1:this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]];var L=this.rootElement.getBoundingClientRect().right;this.colsCutoff=!1;for(var Q=0;Q<M;++Q){var T=document.createElement("div");T.classList.add("tensor-widget-top-ruler-tick");this.valueRenderMode===I.IMAGE&&(T.style.width=this.imageCellSize+"px");this.topRuler.appendChild(T);this.topRulerTicks.push(T);if(T.getBoundingClientRect().right>=L){if(2<=this.rank){if(null===this.slicingSpec.horizontalRange)throw Error("Missing horizontal range for "+
this.rank+"D tensor.");this.slicingSpec.horizontalRange[1]=Q+1;this.colsCutoff=!0}break}}if(!this.colsCutoff&&2<=this.rank){if(null===this.slicingSpec.horizontalRange)throw Error("Missing horizontal range for "+this.rank+"D tensor.");this.slicingSpec.horizontalRange[1]=M}};H.prototype.createLeftRuler=function(){if(null===this.valueSection)throw Error("Failed to create left ruler due to missing value section.");this.valueRows=[];this.leftRulerTicks=[];1<=this.rank&&(this.slicingSpec.verticalRange=
[0,null]);var K=0===this.rank?1:this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]];this.rowsCutoff=!1;for(var M=this.rootElement.getBoundingClientRect().bottom,L=0;L<K;++L){var Q=document.createElement("div");Q.classList.add("tensor-widget-value-row");this.valueRenderMode===I.IMAGE&&(Q.style.height=this.imageCellSize+"px",Q.style.lineHeight=this.imageCellSize+"px");this.valueSection.appendChild(Q);this.valueRows.push(Q);var T=document.createElement("div");T.classList.add("tensor-widget-top-ruler-tick");
this.valueRenderMode===I.IMAGE&&(T.style.height=this.imageCellSize+"px",T.style.lineHeight=this.imageCellSize+"px");Q.appendChild(T);this.leftRulerTicks.push(T);if(T.getBoundingClientRect().bottom>=M){if(1<=this.rank){if(null===this.slicingSpec.verticalRange)throw Error("Missing vertical range for "+this.rank+"D tensor.");this.slicingSpec.verticalRange[1]=L+1;this.rowsCutoff=!0}break}}if(!this.rowsCutoff&&1<=this.rank){if(null===this.slicingSpec.verticalRange)throw Error("Missing vertical range for "+
this.rank+"D tensor.");this.slicingSpec.verticalRange[1]=K}};H.prototype.createValueDivs=function(){function K(aa){function la(ba){var ea=document.createElement("div");ea.classList.add("tensor-widget-value-div");T.valueRenderMode===I.IMAGE&&(ea.style.width=T.imageCellSize+"px",ea.style.height=T.imageCellSize+"px",ea.style.lineHeight=T.imageCellSize+"px");T.valueRows[aa].appendChild(ea);T.valueDivs[aa].push(ea);ea.addEventListener("click",function(){M.selection=new C(M.tensorView.spec.shape,M.slicingSpec,
null==M.slicingSpec.verticalRange||null==M.slicingSpec.verticalRange[0]?0:M.slicingSpec.verticalRange[0]+aa,null==M.slicingSpec.horizontalRange||null==M.slicingSpec.horizontalRange[0]?0:M.slicingSpec.horizontalRange[0]+ba,1,1);M.renderSelection()});ea.addEventListener("mouseenter",function(){var ca=ea.getAttribute("detailed-value");if(null!==ca){var ka=M.rootElement.getBoundingClientRect(),Y=ea.getBoundingClientRect(),Ea=Y.bottom-Y.top,va=Y.right-Y.left,xa=M.calculateIndices(aa,ba);M.drawValueTooltip(xa,
ca,Y.top-ka.top+.8*Ea,Y.left-ka.left+.75*va)}});ea.addEventListener("mouseleave",function(){M.hideValueTooltip()})}T.valueDivs[aa]=[];for(var Z=0;Z<L;++Z)la(Z)}var M=this;if(null===this.valueRows)throw Error("Value rows are unexpectedly uninitialized.");this.valueDivs=[];for(var L=this.topRulerTicks.length,Q=this.valueRows.length,T=this,X=0;X<Q;++X)K(X)};H.prototype.renderTopRuler=function(){if(2<=this.rank)for(var K=this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]],M=0;M<this.topRulerTicks.length;++M){if(null===
this.slicingSpec.horizontalRange)throw Error("Missing horizontal range for "+this.rank+"D tensor.");var L=this.slicingSpec.horizontalRange[0]+M;this.showIndicesOnTicks&&(this.topRulerTicks[M].textContent=L<K?""+L:"")}};H.prototype.renderLeftRuler=function(){if(1<=this.rank)for(var K=this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]],M=0;M<this.leftRulerTicks.length;++M){if(null===this.slicingSpec.verticalRange)throw Error("Missing vertcial range for "+this.rank+"D tensor.");var L=this.slicingSpec.verticalRange[0]+
M;this.showIndicesOnTicks&&(this.leftRulerTicks[M].textContent=L<K?""+L:"")}};H.prototype.renderValueDivs=function(){return b(this,void 0,void 0,function(){var K,M,L,Q,T,X,aa,la,Z,ba,ea,ca,ka,Y,Ea,va,xa;return Xi(this,function(Aa){switch(Aa.label){case 0:return K=this.valueDivs.length,M=this.valueDivs[0].length,[4,this.tensorView.view(this.slicingSpec)];case 1:L=Aa.sent();0===this.rank?L=[[L]]:1===this.rank&&(L=L.map(function(Fa){return[Fa]}));Q=this.getValueClass();T=this.valueRenderMode;if(T===
I.IMAGE){if(null==this.numericSummary)throw Error("Failed to render image representation of tensor due to missing numeric summary");X=this.numericSummary;aa=X.minimum;la=X.maximum;if(null==aa||null==la)throw Error("Failed to render image representation of tensor due to missing minimum or maximum values in numeric summary");Z={min:aa,max:la};this.colorMap=this.colorMapName in N?new N[this.colorMapName](Z):new B(Z)}for(ba=0;ba<K;++ba)for(ea=0;ea<M;++ea)ca=this.valueDivs[ba][ea],ba<L.length&&ea<L[ba].length?
(ka=L[ba][ea],T===I.IMAGE?(Y=Yi(this.colorMap.getRGB(ka),3),Ea=Y[0],va=Y[1],xa=Y[2],ca.style.backgroundColor="rgb("+Ea+", "+va+", "+xa+")"):"numeric"===Q?ca.textContent=q(ka,d(this.tensorView.spec.dtype)):"boolean"===Q?ca.textContent=u(ka):"string"===Q&&(ca.textContent=x(ka)),ca.setAttribute("detailed-value",this.getDetailedValueTooltipString(ka))):(ca.textContent="",ca.setAttribute("detailed-value",""));this.renderSelection();return[2]}})})};H.prototype.getDetailedValueTooltipString=function(K){return"boolean"===
this.getValueClass()?u(K,!1):"string"===this.getValueClass()?"Length-"+K.length+' string: "'+x(K,500)+'"':String(K)};H.prototype.renderSelection=function(){if(null!=this.selection)for(var K=this.valueDivs.length,M=this.valueDivs[0].length,L=0;L<K;++L)for(var Q=0;Q<M;++Q){var T=this.valueDivs[L][Q];T.classList.remove("tensor-widget-value-div-selection");T.classList.remove("tensor-widget-value-div-selection-top");T.classList.remove("tensor-widget-value-div-selection-bottom");T.classList.remove("tensor-widget-value-div-selection-left");
T.classList.remove("tensor-widget-value-div-selection-right");var X=this.calculateIndices(L,Q);X=this.selection.getElementStatus(X);null!==X&&(T.classList.add("tensor-widget-value-div-selection"),X.topEdge&&T.classList.add("tensor-widget-value-div-selection-top"),X.bottomEdge&&T.classList.add("tensor-widget-value-div-selection-bottom"),X.leftEdge&&T.classList.add("tensor-widget-value-div-selection-left"),X.rightEdge&&T.classList.add("tensor-widget-value-div-selection-right"))}};H.prototype.calculateIndices=
function(K,M){for(var L=[],Q=this.slicingSpec.slicingDimsAndIndices.map(function(la){return la.dim}),T=this.slicingSpec.slicingDimsAndIndices.map(function(la){return la.index}),X=0;X<this.rank;++X)if(-1!==Q.indexOf(X)){var aa=T[Q.indexOf(X)];if(null===aa)throw Error("Failed to calculate indices: Undetermined index at dimension "+X);L.push(aa)}else if(X===this.slicingSpec.viewingDims[0]){if(null===this.slicingSpec.verticalRange||null===this.slicingSpec.verticalRange[0])throw Error("Failed to calculate indices due to undertermined vertical range.");
L.push(this.slicingSpec.verticalRange[0]+K)}else if(X===this.slicingSpec.viewingDims[1]){if(null===this.slicingSpec.horizontalRange||null===this.slicingSpec.horizontalRange[0])throw Error("Failed to calculate indices due to undertermined vertical range.");L.push(this.slicingSpec.horizontalRange[0]+M)}return L};H.prototype.drawValueTooltip=function(K,M,L,Q){null===this.valueTooltip&&(this.valueTooltip=document.createElement("div"),this.valueTooltip.classList.add("tensor-widget-value-tooltip"),this.rootElement.appendChild(this.valueTooltip));
for(;this.valueTooltip.firstChild;)this.valueTooltip.removeChild(this.valueTooltip.firstChild);var T=document.createElement("div");T.classList.add("tensor-widget-value-tooltip-indices");T.textContent="Indices: "+JSON.stringify(K);this.valueTooltip.appendChild(T);K=document.createElement("div");K.classList.add("tensor-widget-value-tooltip-value");K.textContent=M;this.valueTooltip.appendChild(K);this.valueTooltip.style.top=L+"px";this.valueTooltip.style.left=Q+"px";this.valueTooltip.style.display="block";
this.valueRenderMode==I.IMAGE&&null!=this.colorMap&&(L=document.createElement("canvas"),L.classList.add("tensor-widget-value-tooltip-colorbar"),this.valueTooltip.appendChild(L),this.colorMap.render(L,parseFloat(M)))};H.prototype.hideValueTooltip=function(){null!=this.valueTooltip&&(this.valueTooltip.style.display="none")};H.prototype.renderRulersAndValueDivs=function(){return b(this,void 0,void 0,function(){return Xi(this,function(K){switch(K.label){case 0:return null!=this.slicingControl&&this.slicingControl.setSlicingSpec(this.slicingSpec),
this.calculateShowIndicesOnRulerTicks(),this.renderTopRuler(),this.renderLeftRuler(),[4,this.renderValueDivs()];case 1:return K.sent(),[2]}})})};H.prototype.calculateShowIndicesOnRulerTicks=function(){if(2<=this.rank){var K=this.topRulerTicks[0].getBoundingClientRect();this.showIndicesOnTicks=K.right-K.left>9*Math.ceil(Math.log(this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]])/Math.LN10)}else 1===this.rank?(K=this.leftRulerTicks[0].getBoundingClientRect(),this.showIndicesOnTicks=16<K.bottom-
K.top):this.showIndicesOnTicks=!1};H.prototype.scrollHorizontally=function(K){return b(this,void 0,void 0,function(){var M,L;return Xi(this,function(Q){switch(Q.label){case 0:if(1>=this.rank)return[2];if(null===this.slicingSpec.horizontalRange)throw Error("Missing horizontal range for "+this.rank+"D tensor.");M=this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]];if(0>K||K>=M)throw Error("Index out of bound: "+K+" is outside [0, "+M+"])");this.slicingSpec.horizontalRange[0]=K;this.slicingSpec.horizontalRange[1]=
K+this.topRulerTicks.length;L=this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]];this.slicingSpec.horizontalRange[1]>L&&(this.slicingSpec.horizontalRange[1]=L);return[4,this.renderRulersAndValueDivs()];case 1:return Q.sent(),[2]}})})};H.prototype.scrollVertically=function(K){return b(this,void 0,void 0,function(){var M,L;return Xi(this,function(Q){switch(Q.label){case 0:if(0===this.rank)return[2];if(null===this.slicingSpec.verticalRange)throw Error("Missing vertical range for "+this.rank+
"D tensor.");if(null===this.valueRows)throw Error("Vertical scrolling failed due to missing value rows.");M=this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]];if(0>K||K>=M)throw Error("Index out of bound: "+K+" is outside [0, "+M+"])");this.slicingSpec.verticalRange[0]=K;this.slicingSpec.verticalRange[1]=K+this.valueRows.length;L=this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]];this.slicingSpec.verticalRange[1]>L&&(this.slicingSpec.verticalRange[1]=L);return[4,this.renderRulersAndValueDivs()];
case 1:return Q.sent(),[2]}})})};H.prototype.scrollUpOrDown=function(K){return b(this,void 0,void 0,function(){var M,L,Q;return Xi(this,function(T){switch(T.label){case 0:if(0===this.rank||!this.rowsCutoff)return[2];if(null===this.slicingSpec.verticalRange)throw Error("Missing vertical range for "+this.rank+"D tensor.");if(null===this.valueRows)throw Error("Vertical scrolling failed due to missing value rows.");M=this.slicingSpec.verticalRange[0];if(K!==w.DOWN)return[3,3];L=this.valueRows.length-
1;Q=this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]]-L;return M<Q?[4,this.scrollVertically(M+1)]:[3,2];case 1:T.sent(),T.label=2;case 2:return[3,5];case 3:return 0<=M-1?[4,this.scrollVertically(M-1)]:[3,5];case 4:T.sent(),T.label=5;case 5:return[2]}})})};H.prototype.scrollLeftOrRight=function(K){return b(this,void 0,void 0,function(){var M,L,Q;return Xi(this,function(T){switch(T.label){case 0:if(1>=this.rank||!this.colsCutoff)return[2];if(null===this.slicingSpec.horizontalRange)throw Error("Horizontal scrolling failed due to missing horizontal range.");
M=this.slicingSpec.horizontalRange[0];if(K!==w.RIGHT)return[3,3];L=this.topRulerTicks.length-1;Q=this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]]-L;return M<Q?[4,this.scrollHorizontally(M+1)]:[3,2];case 1:T.sent(),T.label=2;case 2:return[3,5];case 3:return 0<=M-1?[4,this.scrollHorizontally(M-1)]:[3,5];case 4:T.sent(),T.label=5;case 5:return[2]}})})};H.prototype.navigateToIndices=function(){return b(this,void 0,void 0,function(){return Xi(this,function(){throw Error("navigateToIndices() is not implemented yet.");
})})};H.prototype.getValueClass=function(){var K=this.tensorView.spec.dtype;return d(K)||f(K)?"numeric":h(K)?"boolean":"string"};return H}();D=Object.freeze({tensorWidget:function(H,K,M){return new O(H,K,M)},VERSION:"0.0.0"});window.tensor_widget=D})();

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-line-chart.html.js
Polymer({is:"tf-debugger-line-chart",properties:{data:{type:Object,value:null},_defaultSeriesName:{type:String,value:"__debugger_data__",readonly:!0},_lineChartXComponentsCreationMethod:{type:Object,readOnly:!0,value:()=>()=>{const b=new Plottable.Scales.Linear;return{scale:b,axis:new Plottable.Axes.Numeric(b,"bottom"),accessor:d=>d.step}}},_lineChartYValueAccessor:{type:Object,readOnly:!0,value:()=>b=>b.scalar},_lineChartTooltipColumns:{type:Array,readOnly:!0,value:()=>[{title:"Name",evaluate:b=>
"step\x3d"+b.datum.step+"; scalar\x3d "+b.datum.scalar}]},_lineChartSmoothingEnabled:{type:Boolean,value:!1,readOnly:!0}},observers:["render(data)"],render(b){if(null!=b){var d=this.$$("vz-line-chart2");d.setVisibleSeries([this._defaultSeriesName]);var f=[],h=b.x;b=b.y;for(let k=0;k<h.length;++k)f.push({step:h[k],scalar:b[k]});d.setSeriesData(this._defaultSeriesName,f)}}});

//# sourceURL=build://tf-debugger-dashboard/tf-tensor-value-view.html.js
Polymer({is:"tf-tensor-value-view",properties:{viewId:String,tensorName:String,debugOp:String,deviceName:String,maybeBaseExpandedNodeName:String,slicing:String,timeIndices:String,dtype:String,shape:Array,continueToButtonCallback:Object,closeButtonCallback:Object,tensorNameCallback:Object,tensorWidget:Object,getHealthPill:Function,_isTensorValueScalar:{type:Boolean,value:!1},_isTensorValueLineChart:{type:Boolean,value:!1},_isTensorValueImage:{type:Boolean,value:!1},_dataScalar:{type:Number,value:null},
_lineChartData:{type:Array,value:null},_dataImageSrc:{type:String,value:null},_requestManager:{type:Object,value:()=>new vc.RequestManager(10)}},observers:["_updateTimeIndicesToggle(timeIndices)"],renderTensorValue(){if(this.tensorName)if(null==this.slicing){this.set("_useTensorWidget",!0);const d={spec:{dtype:this.dtype,shape:this.shape},get:()=>{throw Error("tensorView.get() is not implemented yet.");},view:f=>{const h=this;return hc(function*(){const k=h.shape.length,t=f.slicingDimsAndIndices.map(m=>
m.dim),l=f.slicingDimsAndIndices.map(m=>m.index);let p="[";for(let m=0;m<k;++m)-1!==t.indexOf(m)?p+=`${l[t.indexOf(m)]}`:f.viewingDims[0]===m?p+=`${f.verticalRange[0]}:${f.verticalRange[1]}`:f.viewingDims[1]===m&&(p+=`${f.horizontalRange[0]}:${f.horizontalRange[1]}`),m<k-1&&(p+=",");p+="]";return new Promise((m,n)=>{const q=h._getTensorDataURL({watch_key:h.tensorName+":"+h.debugOp,slicing:p,time_indices:h.timeIndices,mapping:"none"});h._requestManager.request(q).then(u=>{null==u.error?m(u.tensor_data[u.tensor_data.length-
1]):n(u.error)}).catch(u=>n(u))})})},getNumericSummary:()=>{const f=this;return hc(function*(){return new Promise((h,k)=>{const t=f.tensorName+":"+f.debugOp;f.getHealthPill(t,f.deviceName,f.maybeBaseExpandedNodeName,l=>{null==l?k(`Failed to get health pill for watch key ${t}`):h({elementCount:l[1],minimum:l[8],maximum:l[9]})})})})}};setTimeout(()=>{null==this.tensorWidget&&(this.tensorWidget=tensor_widget.tensorWidget(this.$$("#tensor-widget"),d,{wheelZoomKey:"alt"}));this.tensorWidget.render()},
10)}else{this.set("_useTensorWidget",!1);var b=this._rankFromSlicing(this.slicing.trim());const d=this._isTimeIndicesSingleStep(this.timeIndices);let f=b;if(!d){if(1<b){this._showToast("History for tensors \x3e 1D is not yet supported.");return}f+=1}b=this._getTensorDataURL({watch_key:this.tensorName+":"+this.debugOp,slicing:this.slicing,time_indices:this.timeIndices,mapping:2<=f?"image/png":"none"});this._requestManager.request(b).then(h=>{this.$$("#debug-op").textContent=this._calculateDebugOpToDisplay();
if(null!=h.error)this._showToast(h.error.type+": "+h.error.message);else if(h=d?h.tensor_data[0]:h.tensor_data,0===f)this._setVisualizationType("scalar"),this.set("_dataScalar",h);else if(1===f){this._setVisualizationType("lineChart");let k={x:[],y:h};for(let t=0;t<h.length;++t)k.x.push(t+1);this.set("_lineChartData",k)}else 2<=f?(this._setVisualizationType("image"),this.set("_dataImageSrc","data:image/png;base64,"+h)):this._showToast("Visualization of rank-"+f+" tensors is not yet supported.")})}},
refresh(){this.tensorName.trim()&&this.renderTensorValue()},_getTensorDataURL(b){const d=vc.getRouter().pluginRoute("debugger","/tensor_data");return vc.addParams(d,b)},_rankFromSlicing(b){b.startsWith("[")&&(b=b.slice(1,b.length-1));if(0===b.length)return 0;{b=b.split(",");let d=b.length;for(const f of b)isNaN(Number(f))||d--;return d}},_setVisualizationType(b){"scalar"===b?(this.set("_isValueScalar",!0),this.set("_isValueLineChart",!1),this.set("_isValueImage",!1)):"lineChart"===b?(this.set("_isValueScalar",
!1),this.set("_isValueLineChart",!0),this.set("_isValueImage",!1)):"image"===b?(this.set("_isValueScalar",!1),this.set("_isValueLineChart",!1),this.set("_isValueImage",!0)):console.error("Invalid visualizationType:",b)},_timeIndicesToggleButtonCallback(){"full history"===Polymer.dom(this.$$("#time-indices-toggle-button")).textContent.toLowerCase()?this.set("timeIndices",":"):this.set("timeIndices","-1");this.renderTensorValue()},_updateTimeIndicesToggle(b){this._isTimeIndicesSingleStep(b)?Polymer.dom(this.$$("#time-indices-toggle-button")).textContent=
"Full History":Polymer.dom(this.$$("#time-indices-toggle-button")).textContent="Latest Time Point"},_isTimeIndicesSingleStep(b){b.startsWith("[")&&(b=b.slice(1,b.length-1));return!isNaN(Number(b))},_calculateDebugOpToDisplay(){return"DebugIdentity"===this.debugOp?"":this.debugOp},_showToast(b){this.$.tensorValueToast.setAttribute("text",b);this.$.tensorValueToast.open()}});

//# sourceURL=build://tf-debugger-dashboard/tf-tensor-value-multi-view.html.js
Polymer({is:"tf-tensor-value-multi-view",properties:{continueToCallback:Function,tensorNameClicked:Function,_tensorViewCounter:{type:Number,value:0},getHealthPill:Function},addView(b){const d=this.$$("#multi-tensor-view-container"),f=document.createElement("tf-tensor-value-view");f.setAttribute("class","debugger-tensor-view");f.viewId=b.viewId;f.tensorName=b.tensorName;f.debugOp=b.debugOp;f.deviceName=b.deviceName;f.maybeBaseExpandedNodeName=b.maybeBaseExpandedNodeName;f.dtype=b.dtype;f.shape=b.shape;
f.slicing=b.slicing;f.timeIndices=b.timeIndices;f.closeButtonCallback=this._createCloseButtonCallback(b.viewId);f.continueToButtonCallback=()=>{this.continueToCallback(b.deviceName,b.maybeBaseExpandedNodeName)};f.tensorNameCallback=()=>{this.tensorNameClicked(b.deviceName,b.maybeBaseExpandedNodeName)};f.getHealthPill=this.getHealthPill;d.appendChild(f);f.refresh()},getViews(){const b=[];_.forEach(this.root.querySelectorAll(".debugger-tensor-view"),d=>{b.push({viewId:d.viewId,tensorName:d.tensorName,
debugOp:d.debugOp,slicing:d.slicing,timeIndices:d.timeIndices})});return b},renderTensorValues(){_.forEach(this.root.querySelectorAll(".debugger-tensor-view"),b=>{b.renderTensorValue()})},_redrawViews(b){const d=this.$$("#multi-tensor-view-container");_.forEach(this.root.querySelectorAll(".debugger-tensor-view"),f=>{d.removeChild(f)});_.forEach(b,f=>{this.addView(f)})},_createCloseButtonCallback(b){return()=>{const d=[],f=this.root.querySelectorAll(".debugger-tensor-view");for(let h=0;h<f.length;++h){const k=
f[h];k.viewId!==b&&d.push({viewId:k.viewId,tensorName:k.tensorName,debugOp:k.debugOp,dtype:k.dtype,shape:k.shape,slicing:k.slicing,timeIndices:k.timeIndices})}this._redrawViews(d)}}});

//# sourceURL=build://tf-debugger-dashboard/tensor-shape-helper.js
(function(b){function d(f,h){return f<=h?"::":"::"+Math.ceil(f/h)}b.getDefaultSlicing=function(f){return 0===f.length?"":1===f.length?"["+d(f[0],1E3)+"]":2===f.length?"["+d(f[0],250)+", "+d(f[1],250)+"]":null};b.rankFromSlicing=function(f){f.startsWith("[")&&(f=f.slice(1,f.length-1));if(0===f.length)return 0;{f=f.split(",");let h=f.length;for(const k of f)isNaN(Number(k))||h--;return h}}})(Vi||(Vi={}));

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-dashboard.html.js
const Zi=()=>window.innerHeight||document.documentElement.clientHeight||document.body.clientHeight,Hk=()=>window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth,Ik=(Zi()-70)/2;
Polymer({is:"tf-debugger-dashboard",properties:{_topRightTabs:{type:Array,value:[{id:"tab-runtime-graphs",name:"Runtime Graphs"},{id:"tab-tensor-values",name:"Tensor Values"}],readonly:!0},_isTopRightRuntimeGraphsActive:{type:Boolean,value:!0},_isTopRightTensorValuesActive:{type:Boolean,value:!1},_topRightSelected:{type:String,value:"0",observer:"_topRightSelectedChanged"},_longPollCount:{type:Number,value:0},_stepButtonText:{type:String,value:"Step"},_continueButtonText:{type:String,value:"Continue..."},
_tensorViewIdCounter:{type:Number,value:0},isReloadDisabled:{type:Boolean,value:!0,readOnly:!0},alreadyStarted:{type:Boolean,value:!1},_currentSessionRunInfo:{type:String,value:null},_sessionRunTotalCounter:{type:Number,value:0},_sessionRunCounters:{type:Object,value:{}},_sessionRunKey2DeviceNames:{type:Object,value:{}},_activeSessionRunKey:{type:String,value:null},_activeSessionRunDevices:{type:Array,value:[]},_activeSessionRunNumDevices:{type:Number,value:-1},_activeRuntimeGraphDeviceName:{type:String,
value:null,notify:!0},_highlightNodeName:{type:String,value:null},_continueToType:{type:String,value:""},_continueToCounter:{type:Number,value:0},_continueStop:{type:Boolean,value:!1},_continueToTarget:{type:String,value:""},_continueToCounterTarget:{type:Number,value:-1},_forceExpandAndCheckNodeName:String,_forceExpandNodeName:String,_sourceFocusNodeName:String,_sourceCodeShown:{type:Boolean,value:!1,observer:"_showSourceCodeChanged"},_graphProgress:{type:Object},_requestManager:{type:Object,value:()=>
new vc.RequestManager(50)},_busy:{type:Boolean,value:!1},_leftPaneWidth:{type:Number,value:pd.getNumberInitializer("_leftPaneWidth",{defaultValue:450}),observer:"_leftPaneWidthObserver"},_minleftPaneWidth:{type:Number,value:450,readOnly:!0},_maxleftPaneWidth:{type:Number,computed:"_computeMaxleftPaneWidth(_windowWidth, _maxMainContentWidth, _resizerWidth)"},_maxMainContentWidth:{type:Number,value:350,readOnly:!0},_topRightQuadrantHeight:{type:Number,value:pd.getNumberInitializer("_topRightQuadrantHeight",
{defaultValue:Ik}),observer:"_topRightQuadrantHeightObserver"},_minTopRightQuadrantHeight:{type:Number,value:200,readOnly:!0},_maxTopRightQuadrantHeight:{type:Number,computed:"_computeMaxTopRightQuadrantHeight(_windowHeight, _resizerWidth)"},_resizerWidth:{type:Number,value:30,readOnly:!0},_windowWidth:Number,_windowHeight:Number,_debugWatches:Array,_latestSessionRun:Object},observers:["_onActiveRuntimeGraphDeviceNameChange(_activeRuntimeGraphDeviceName)","_sizeDashboardRegions(_leftPaneWidth, _topRightQuadrantHeight, _windowWidth)",
"_graphProgressUpdated(_graphProgress)"],ready(){this._handleWindowResize();window.addEventListener("resize",()=>{this._handleWindowResize()},!1);this.reload()},long_poll(){const b={pos:++this._longPollCount};let d=vc.getRouter().pluginRoute("debugger","/comm");d=vc.addParams(d,b);this._requestManager.request(d).then(f=>{const h=f.type;f=f.data;if("meta"===h){var k=f.run_key,t=k[0].split(","),l=k[1].split(",");const m=k[2].split(",");var p=this._activeSessionRunKey;this.set("_activeSessionRunKey",
k);this.set("_latestSessionRun",{feeds:t,fetches:l,targets:m});this.set("_sessionRunSoleActive",!0);void 0===this._sessionRunKey2DeviceNames[k]?(this._sessionRunKey2DeviceNames[k]=[],this.set("_activeSessionRunDevices",[])):this.set("_activeSessionRunDevices",this._sessionRunKey2DeviceNames[k]);this._currentSessionRunInfo=t="Feeds: "+t+"; Fetches: "+l+"; Targets: "+m;this._sessionRunCounters.hasOwnProperty(t)?this._sessionRunCounters[t]+=1:this._sessionRunCounters[t]=1;this._sessionRunTotalCounter++;
this.$.initialDialog.closeDialog();this._continueToType&&_.isEqual(p,k)||(this._processGatedGrpcDebugOps(k,!1),this._announceNewSessionRun())}else"tensor"===h?(k=f.device_name,p=f.node_name,t=f.maybe_base_expanded_node_name,this._activeRuntimeGraphDeviceName!=k?this.set("_activeRuntimeGraphDeviceName",k):!this._continueToType&&this._isTopRightRuntimeGraphsActive&&(this._focusOnGraphNode(k,t),this.set("_forceExpandNodeName",k+"/"+t)),this.set("_sessionRunSoleActive",!1),l=p+":"+f.output_slot,this.set("_latestTensorData",
{deviceName:k,tensorName:l,nodeName:p,maybeBaseExpandedNodeName:t,debugOp:f.debug_op,dtype:f.dtype,shape:f.shape,value:f.values}),this._maybeUpdateTensorValueViews(l,f.debug_op),this.set("_busy",!1)):console.error("Invalid long-polling response type: ",h);null!=this._continueToType&&this._processContinueTo(h,f);this.long_poll()})},_processContinueTo(b,d){this._continueStop?this._clearContinueTo():"SessionRun"===this._continueToType?this._processContinueToSessionRun("meta"===b):"TensorCondition"===
this._continueToType?this._step():"op"===this._continueToType?this._processContinueToOp("meta"===b,d):null!=this._continueToType&&""!==this._continueToType&&console.error("Invalid _continueToType:",this._continueToType)},_processContinueToSessionRun(b){b&&this.set("_continueToCounter",this._continueToCounter+1);this._continueToCounter<this._continueToCounterTarget?this._step():this._clearContinueTo()},_processContinueToOp(b,d){b&&this._announceNewSessionRun();b=d.device_name;d=d.maybe_base_expanded_node_name;
const f=null==d?null:Vi.removeNodeNameBaseExpansion(d);b+"/"+d===this._continueToTarget||b+"/"+f===this._continueToTarget?(this._clearContinueTo(),this._sourceCodeShown&&this.set("_sourceFocusNodeName",f)):this._step()},_maybeUpdateTensorValueViews(b,d){const f=this.$$("#tensorValueMultiView");if(null!=f){var h=!1;_.forEach(f.getViews(),k=>{if(k.tensorName===b&&k.debugOp===d)return h=!0,!1});h&&f.renderTensorValues()}},reload(){if(!this.alreadyStarted){this.set("alreadyStarted",!0);var b=vc.getRouter().pluginRoute("debugger",
"/debugger_grpc_host_port");this._requestManager.request(b).then(d=>{0<d.port?(this.$.initialDialog.openDialog(d.host,d.port),this.long_poll()):this.$.initialDialog.openDisabledDialog()})}},_showSourceCodeChanged(){this._sourceCodeShown?(this.$$("#node-entries").style.height="40%",this.$.sourceCodeView.render()):this.$$("#node-entries").style.height="80%"},_showToast(b){this.$.toast.setAttribute("text",b);this.$.toast.open()},_announceNewSessionRun(){this._showToast("Session.run() #"+this._sessionRunTotalCounter+
" is starting.")},_displayGraph(b,d){b={run_key:JSON.stringify(b),device_name:d};b=vc.addParams("/data/plugin/debugger/debugger_graph",b);this.$.loader.datasets=[{name:"/debugger_graph",path:b}];this.$.loader.set("selectedDataset",0)},_processGatedGrpcDebugOps(b,d){d?console.log("Polling for first GraphDef for run key:",b):this.set("_activeRuntimeGraphDeviceName",null);var f={mode:"retrieve_all",run_key:JSON.stringify(b)};const h=vc.getRouter().pluginRoute("debugger","/gated_grpc");f=vc.addParams(h,
f);let k=[];this._requestManager.request(f).then(t=>{if(0==t.device_names.length)d||this._step(),this._processGatedGrpcDebugOps(b,!0);else{var l=null;for(const p in t.gated_grpc_tensors)if(t.gated_grpc_tensors.hasOwnProperty(p)){-1===this._sessionRunKey2DeviceNames[b].indexOf(p)&&(this._sessionRunKey2DeviceNames[b].push(p),this.$.sessionRunsView.updateNumDevices(this._sessionRunKey2DeviceNames[b].length));this.set("_activeSessionRunDevices",this._sessionRunKey2DeviceNames[b].slice());l=this._activeSessionRunDevices[this._activeSessionRunDevices.length-
1];const m=t.gated_grpc_tensors[p];for(let n=0;n<m.length;++n)k.push({device_name:p,node_name:m[n][0],op_type:m[n][1],output_slot:m[n][2],debug_op:m[n][3]})}null!=l&&(this.set("_activeRuntimeGraphDeviceName",l),t=Polymer.dom(this.$$("#active-runtime-graph-device-name")),null!=t&&t.setAttribute("selected",l));Vi.sortAndBaseExpandDebugWatches(k);this.set("_debugWatches",k);this.$.sourceCodeView.render(k)}})},_createDebugWatchChangeHandler(){return(b,d)=>{d=d?"break":"disable";this._requestBreakpointStateChange(Vi.getCleanNodeName(b.device_name+
"/"+b.node_name),b.output_slot,b.debug_op,d)}},_focusOnGraphNode(b,d){null!=b&&this._activeRuntimeGraphDeviceName!==b&&this.set("_activeRuntimeGraphDeviceName",b);this._setTopRightRuntimeGraphsToActive();const f=this.$$("#graph");if(f.selectedNode===d)f.panToNode(d);else{const h=f.get("renderHierarchy").hierarchy.getNodeMap();null==h[d]&&(d=Vi.removeNodeNameBaseExpansion(d));null!=h[d]&&f.set("selectedNode",d)}this.set("_highlightNodeName",b+"/"+d)},_createNodeClickedHandler(){return(b,d,f)=>{this._sourceCodeShown&&
!0!==f&&this.set("_sourceFocusNodeName",Vi.removeNodeNameBaseExpansion(d));this._focusOnGraphNode(b,d);this.set("_forceExpandNodeName",b+"/"+d)}},_createFeedFetchTargetClickedHandler(){return b=>{let d=b;-1!==d.indexOf(":")&&(d=d.slice(0,d.indexOf(":")));b=_.find(this._debugWatches,f=>f.node_name===d||0===f.node_name.indexOf(d)&&"("===f.node_name[d.length]);null==b?this._showToast("Node '"+d+"' is not in the runtime graph of the current Session.run or does not have a debug op attached."):this._focusOnGraphNode(b.device_name,
d)}},_createTensorDataExpandHandler(){return b=>{this._setTopRightTensorValuesToActive();setTimeout(()=>{this.$$("#tensorValueMultiView").addView({viewId:this._createTensorViewId(),deviceName:b.deviceName,tensorName:b.tensorName,nodeName:b.nodeName,maybeBaseExpandedNodeName:b.maybeBaseExpandedNodeName,debugOp:b.debugOp,dtype:b.dtype,shape:b.shape,slicing:Vi.getDefaultSlicing(b.shape),timeIndices:"-1"})},10)}},_createTensorViewId(){const b="debugger-tensor-view-"+this._tensorViewIdCounter;this._tensorViewIdCounter++;
return b},_createNodeContextMenuItems(){return[{title:()=>"Expand and highlight",action:b=>{const d=Vi.getCleanNodeName(b.node.name);b=this._activeRuntimeGraphDeviceName+"/"+b.node.name;this.set("_forceExpandNodeName",b);this.set("_highlightNodeName",b);this._sourceCodeShown&&this.set("_sourceFocusNodeName",Vi.removeNodeNameBaseExpansion(d))}},{title:()=>"Add breakpoint",action:b=>{const d=Vi.getCleanNodeName(b.node.name);this.set("_forceExpandAndCheckNodeName",this._activeRuntimeGraphDeviceName+
"/"+b.node.name);this._sourceCodeShown&&this.set("_sourceFocusNodeName",Vi.removeNodeNameBaseExpansion(d))}},{title:()=>"Continue to",action:b=>{-1!==["_Arg","_Retval"].indexOf(b.node.op)?this._showToast('Cannot continue to node "'+b.node.name+'", due to op type "'+b.node.op+'".'):this._continueToNode(this._activeRuntimeGraphDeviceName,b.node.name)}}]},_createGetHealthPill(){return(b,d,f,h)=>{var k={watch_key:b,time_indices:"-1",mapping:"health-pill"};const t=vc.getRouter().pluginRoute("debugger",
"/tensor_data");k=vc.addParams(t,k);this._requestManager.request(k).then(l=>{l=l.tensor_data[0];h(l);this._conditionalHealthPillStop(b,d,f,l)})}},_conditionalHealthPillStop(b,d,f,h){if("TensorCondition"===this._continueToType&&Vi.checkHealthPillAgainstTensorConditionKey(this._continueToTarget,h,this._continueToCounterTarget)){this.set("_continueStop",!0);h=Vi.removeNodeNameBaseExpansion(f);this._sourceCodeShown&&this.set("_sourceFocusNodeName",h);this._focusOnGraphNode(d,f);const k=d+"/"+f;this.set("_forceExpandNodeName",
k);setTimeout(()=>{this.set("_highlightNodeName",null);this.set("_highlightNodeName",k)},100);this._showToast('Tensor condition "'+this._continueToTarget+'" is met by watch key: "'+b+'".\nStopping continuation.')}},_continueToNode(b,d){const f=Vi.getCleanNodeName(d);b=b+"/"+d;this._requestBreakpointStateChange(f,0,"DebugIdentity","break");this.set("_forceExpandAndCheckNodeName",b);this._sourceCodeShown&&this.set("_sourceFocusNodeName",Vi.removeNodeNameBaseExpansion(f));this._setContinueTo("op",b);
this.$.continueDialog.updateContinueButtonText(!0);this._step()},_createContinueToNodeHandler(){return(b,d)=>{this._continueToNode(b,d)}},_onActiveRuntimeGraphDeviceNameChange(b){const d=Polymer.dom(this.$$("#runtime-graph-device-name"));if(0<this._activeSessionRunDevices.length){let f;f=b+(" (device "+(this._activeSessionRunDevices.indexOf(b)+1)+" of "+this._activeSessionRunDevices.length+")");this._isTopRightRuntimeGraphsActive&&null!=d&&(d.textContent=f)}else this._isTopRightRuntimeGraphsActive&&
null!=d&&(d.textContent="Waiting for device...");null!=b&&this._displayGraph(this._activeSessionRunKey,b)},_step(){if(null!=this._activeSessionRunKey){this.set("_busy",!0);var b={mode:"retrieve_device_names",run_key:JSON.stringify(this._activeSessionRunKey)},d=vc.getRouter().pluginRoute("debugger","/gated_grpc");b=vc.addParams(d,b);this._requestManager.request(b).then(f=>{let h=!1;for(let k=0;k<f.device_names.length;++k)if(-1===this._activeSessionRunDevices.indexOf(f.device_names[k])){h=!0;break}f=
vc.getRouter().pluginRoute("debugger","/ack");this._requestManager.request(f).then(()=>{h&&this._processGatedGrpcDebugOps(this._activeSessionRunKey,!1)})})}},_createSessionRunGo(){return b=>{this._setContinueTo("SessionRun",this._currentSessionRunInfo,b);this._step()}},_createTensorConditionGo(){return(b,d)=>{this._setContinueTo("TensorCondition",b,d);this.$.tensorDataSummary.enableHealthPills();this._step()}},_createForceContinuationStop(){return()=>{this._showToast('Continuation of type "'+this._continueToType+
'" was interrupted by user.');this.set("_continueStop",!0)}},_setContinueTo(b,d,f=-1){this._continueToType=b;this._continueToTarget=d;this._continueToCounterTarget=f;this._continueToCounter=0;this._continueStop=!1},_clearContinueTo(){this.$.continueDialog.notifyContinuationStop();this._continueToTarget=this._continueToType="";this._continueToCounterTarget=-1;this._continueToCounter=0;this._continueStop=!1;this.set("_busy",!1)},_createContinueToCallback(){return(b,d)=>{this._setContinueTo("op",b+"/"+
d);this._step();this._isTopRightRuntimeGraphsActive&&this._focusOnGraphNode(b,d);this.set("_forceExpandNodeName",b+"/"+d)}},_topRightSelectedChanged(b){b=this._topRightTabs[b].id;this.set("_isTopRightRuntimeGraphsActive","tab-runtime-graphs"===b);this.set("_isTopRightTensorValuesActive","tab-tensor-values"===b)},_setTopRightRuntimeGraphsToActive(){this.set("_topRightSelected","0");this.set("_isTopRightRuntimeGraphsActive",!0);this.set("_isTopRightTensorValuesActive",!1)},_setTopRightTensorValuesToActive(){this.set("_topRightSelected",
"1");this.set("_isTopRightRuntimeGraphsActive",!1);this.set("_isTopRightTensorValuesActive",!0)},_requestBreakpointStateChange(b,d,f,h){b={mode:"set_state",node_name:b,output_slot:d,debug_op:f,state:h};d=vc.getRouter().pluginRoute("debugger","/gated_grpc");b=vc.addParams(d,b);this.set("_busy",!0);this._requestManager.request(b).then(k=>{this.set("_busy",!1);console.log("Breakpoint set_state response: ",k)})},_graphProgressUpdated(b){const d=this.$$("#top-right-progress-bar");null==this._latestSessionRun?
(d.setAttribute("value",0),this.set("_busy",!1)):(d.setAttribute("value",b.value),this.set("_busy",100>b.value))},_handleWindowResize(){this.set("_windowWidth",Hk());this.set("_windowHeight",Zi());this._sizeDashboardRegions(this._leftPaneWidth,this._topRightQuadrantHeight,this._windowWidth)},_computeMaxleftPaneWidth(b,d,f){return b-d-f},_computeMaxTopRightQuadrantHeight(b,d){return b-d-70},_sizeDashboardRegions(b,d,f){this.$$("#left-pane").style.width=b+"px";b=f-b-this._resizerWidth-8;this.$$("#center-content").style.width=
b+"px";b=d-this._resizerWidth;this.$$("#top-right-quadrant").style.height=b+"px";this.$$("#tensor-data").style.top=d+"px"},_leftPaneWidthObserver:pd.getNumberObserver("_leftPaneWidth",{defaultValue:450}),_topRightQuadrantHeightObserver:pd.getNumberObserver("_topRightQuadrantHeight",{defaultValue:Ik})});

//# sourceURL=build://paper-material/paper-material.html.js
Polymer({is:"paper-material",properties:{elevation:{type:Number,reflectToAttribute:!0,value:1},animated:{type:Boolean,reflectToAttribute:!0,value:!1}}});

//# sourceURL=build://tf-graph-debugger-data-card/tf-graph-debugger-data-card.html.js
(function(){Polymer({is:"tf-graph-debugger-data-card",properties:{renderHierarchy:Object,debuggerNumericAlerts:{type:Array,notify:!0},nodeNamesToHealthPills:Object,healthPillStepIndex:{type:Number,notify:!0},specificHealthPillStep:{type:Number,value:0,notify:!0},selectedNode:{type:String,notify:!0},highlightedNode:{type:String,notify:!0},selectedNodeInclude:{type:Number,notify:!0},areHealthPillsLoading:Boolean,healthPillEntries:{type:Array,value:tf.graph.scene.healthPillEntries,readOnly:!0},healthPillValuesForSelectedNode:{type:Array,
computed:"_computeHealthPillForNode(nodeNamesToHealthPills, healthPillStepIndex, selectedNode, allStepsModeEnabled, areHealthPillsLoading)"},allStepsModeEnabled:{type:Boolean,notify:!0},_biggestStepEverSeen:{type:Number,computed:"_computeBiggestStepEverSeen(nodeNamesToHealthPills)"},_maxStepIndex:{type:Number,computed:"_computeMaxStepIndex(nodeNamesToHealthPills)"},_currentStepDisplayValue:{type:String,computed:"_computeCurrentStepDisplayValue(nodeNamesToHealthPills, healthPillStepIndex, allStepsModeEnabled, specificHealthPillStep, areHealthPillsLoading)"}},
observers:["_updateAlertsList(debuggerNumericAlerts)"],ready:function(){var b=document.getElementById("mainContainer"),d=document.querySelector("tf-dashboard-layout .scrollbar");b&&d&&(b.style.overflow="hidden",d.style.overflow="hidden")},_healthPillsAvailable:function(b,d){return b&&d},_computeTensorCountString:function(b,d){return b?b[d].toFixed(0):""},_computeHealthPillForNode:function(b,d,f,h,k){if(k||!f)return null;b=b[f];return b?(d=b[h?0:d])?d.value.slice(2,8):null:null},_computeCurrentStepDisplayValue:function(b,
d,f,h,k){if(f)return h.toFixed(0);if(k)return 0;for(let t in b)return b[t][d].step.toFixed(0);return 0},_computeBiggestStepEverSeen:function(b){for(let d in b)return b=b[d],Math.max(this._biggestStepEverSeen,b[b.length-1].step);return this._biggestStepEverSeen||0},_computeMaxStepIndex:function(b){for(let d in b)return b[d].length-1;return 0},_hasDebuggerNumericAlerts:function(b){return b&&b.length},_updateAlertsList:function(b){var d=this.$$("#numeric-alerts-body");if(d){d.innerHTML="";for(var f=
0;f<b.length;f++){var h=b[f],k=document.createElement("tr"),t=document.createElement("td");t.innerHTML=tf.graph.util.computeHumanFriendlyTime(h.first_timestamp);t.classList.add("first-offense-td");k.appendChild(t);t=document.createElement("td");t.classList.add("tensor-device-td");var l=document.createElement("div");l.classList.add("tensor-section-within-table");l.innerHTML=h.tensor_name;this._addOpExpansionListener(l,h.tensor_name);t.appendChild(l);l=document.createElement("div");l.classList.add("device-section-within-table");
l.innerHTML="("+h.device_name+")";t.appendChild(l);k.appendChild(t);t=document.createElement("div");t.classList.add("mini-health-pill");l=document.createElement("td");l.classList.add("mini-health-pill-td");l.appendChild(t);k.appendChild(l);h.neg_inf_event_count&&(l=document.createElement("div"),l.classList.add("negative-inf-mini-health-pill-section"),l.innerHTML=h.neg_inf_event_count,l.setAttribute("title",h.neg_inf_event_count+" events with -\u221e"),t.appendChild(l));h.pos_inf_event_count&&(l=document.createElement("div"),
l.classList.add("positive-inf-mini-health-pill-section"),l.innerHTML=h.pos_inf_event_count,l.setAttribute("title",h.pos_inf_event_count+" events with +\u221e"),t.appendChild(l));h.nan_event_count&&(l=document.createElement("div"),l.classList.add("nan-mini-health-pill-section"),l.innerHTML=h.nan_event_count,l.setAttribute("title",h.nan_event_count+" events with NaN"),t.appendChild(l));Polymer.dom(d).appendChild(k)}}},_addOpExpansionListener:function(b,d){b.addEventListener("click",()=>{var f=tf.graph.render.expandUntilNodeIsShown(this.renderHierarchy,
d),h,k=document.querySelector("tf-graph-info#graph-info");k&&(h=k.scrollHeight-k.scrollTop);var t=this.selectedNode;this.set("selectedNode",f);f=()=>{k.scrollTop=k.scrollHeight-h};k&&(t?f():window.setTimeout(f,20))})}})})();

//# sourceURL=build://iron-scroll-target-behavior/iron-scroll-target-behavior.html.js
Polymer.IronScrollTargetBehavior={properties:{scrollTarget:{type:HTMLElement,value:function(){return this._defaultScrollTarget}}},observers:["_scrollTargetChanged(scrollTarget, isAttached)"],_shouldHaveListener:!0,_scrollTargetChanged:function(b,d){this._oldScrollTarget&&(this._toggleScrollListener(!1,this._oldScrollTarget),this._oldScrollTarget=null);d&&("document"===b?this.scrollTarget=this._doc:"string"===typeof b?this.scrollTarget=(d=this.domHost)&&d.$?d.$[b]:Polymer.dom(this.ownerDocument).querySelector("#"+
b):this._isValidScrollTarget()&&(this._oldScrollTarget=b,this._toggleScrollListener(this._shouldHaveListener,b)))},_scrollHandler:function(){},get _defaultScrollTarget(){return this._doc},get _doc(){return this.ownerDocument.documentElement},get _scrollTop(){return this._isValidScrollTarget()?this.scrollTarget===this._doc?window.pageYOffset:this.scrollTarget.scrollTop:0},get _scrollLeft(){return this._isValidScrollTarget()?this.scrollTarget===this._doc?window.pageXOffset:this.scrollTarget.scrollLeft:
0},set _scrollTop(b){this.scrollTarget===this._doc?window.scrollTo(window.pageXOffset,b):this._isValidScrollTarget()&&(this.scrollTarget.scrollTop=b)},set _scrollLeft(b){this.scrollTarget===this._doc?window.scrollTo(b,window.pageYOffset):this._isValidScrollTarget()&&(this.scrollTarget.scrollLeft=b)},scroll:function(b,d){this.scrollTarget===this._doc?window.scrollTo(b,d):this._isValidScrollTarget()&&(this.scrollTarget.scrollLeft=b,this.scrollTarget.scrollTop=d)},get _scrollTargetWidth(){return this._isValidScrollTarget()?
this.scrollTarget===this._doc?window.innerWidth:this.scrollTarget.offsetWidth:0},get _scrollTargetHeight(){return this._isValidScrollTarget()?this.scrollTarget===this._doc?window.innerHeight:this.scrollTarget.offsetHeight:0},_isValidScrollTarget:function(){return this.scrollTarget instanceof HTMLElement},_toggleScrollListener:function(b,d){d=d===this._doc?window:d;b?this._boundScrollHandler||(this._boundScrollHandler=this._scrollHandler.bind(this),d.addEventListener("scroll",this._boundScrollHandler)):
this._boundScrollHandler&&(d.removeEventListener("scroll",this._boundScrollHandler),this._boundScrollHandler=null)},toggleScrollListener:function(b){this._shouldHaveListener=b;this._toggleScrollListener(b,this.scrollTarget)}};

//# sourceURL=build://iron-list/iron-list.html.js
(function(){var b=navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),d=b&&8<=b[1],f=null!=Polymer.flush,h=f?Polymer.Async.animationFrame:0,k=f?Polymer.Async.idlePeriod:1,t=f?Polymer.Async.microTask:2;Polymer.OptionalMutableDataBehavior||(Polymer.OptionalMutableDataBehavior={});Polymer({is:"iron-list",properties:{items:{type:Array},as:{type:String,value:"item"},indexAs:{type:String,value:"index"},selectedAs:{type:String,value:"selected"},grid:{type:Boolean,value:!1,reflectToAttribute:!0,
observer:"_gridChanged"},selectionEnabled:{type:Boolean,value:!1},selectedItem:{type:Object,notify:!0},selectedItems:{type:Object,notify:!0},multiSelection:{type:Boolean,value:!1},scrollOffset:{type:Number,value:0}},observers:["_itemsChanged(items.*)","_selectionEnabledChanged(selectionEnabled)","_multiSelectionChanged(multiSelection)","_setOverflow(scrollTarget, scrollOffset)"],behaviors:[Polymer.Templatizer,Polymer.IronResizableBehavior,Polymer.IronScrollTargetBehavior,Polymer.OptionalMutableDataBehavior],
_ratio:.5,_scrollerPaddingTop:0,_scrollPosition:0,_physicalSize:0,_physicalAverage:0,_physicalAverageCount:0,_physicalTop:0,_virtualCount:0,_estScrollHeight:0,_scrollHeight:0,_viewportHeight:0,_viewportWidth:0,_physicalItems:null,_physicalSizes:null,_firstVisibleIndexVal:null,_collection:null,_lastVisibleIndexVal:null,_maxPages:2,_focusedItem:null,_focusedVirtualIndex:-1,_focusedPhysicalIndex:-1,_offscreenFocusedItem:null,_focusBackfillItem:null,_itemsPerRow:1,_itemWidth:0,_rowHeight:0,_templateCost:0,
_parentModel:!0,get _physicalBottom(){return this._physicalTop+this._physicalSize},get _scrollBottom(){return this._scrollPosition+this._viewportHeight},get _virtualEnd(){return this._virtualStart+this._physicalCount-1},get _hiddenContentSize(){return(this.grid?this._physicalRows*this._rowHeight:this._physicalSize)-this._viewportHeight},get _itemsParent(){return Polymer.dom(Polymer.dom(this._userTemplate).parentNode)},get _maxScrollTop(){return this._estScrollHeight-this._viewportHeight+this._scrollOffset},
get _maxVirtualStart(){var l=this._convertIndexToCompleteRow(this._virtualCount);return Math.max(0,l-this._physicalCount)},set _virtualStart(l){l=this._clamp(l,0,this._maxVirtualStart);this.grid&&(l-=l%this._itemsPerRow);this._virtualStartVal=l},get _virtualStart(){return this._virtualStartVal||0},set _physicalStart(l){l%=this._physicalCount;0>l&&(l=this._physicalCount+l);this.grid&&(l-=l%this._itemsPerRow);this._physicalStartVal=l},get _physicalStart(){return this._physicalStartVal||0},get _physicalEnd(){return(this._physicalStart+
this._physicalCount-1)%this._physicalCount},set _physicalCount(l){this._physicalCountVal=l},get _physicalCount(){return this._physicalCountVal||0},get _optPhysicalSize(){return 0===this._viewportHeight?Infinity:this._viewportHeight*this._maxPages},get _isVisible(){return!(!this.offsetWidth&&!this.offsetHeight)},get firstVisibleIndex(){var l=this._firstVisibleIndexVal;if(null==l){var p=this._physicalTop+this._scrollOffset;this._firstVisibleIndexVal=l=this._iterateItems(function(m,n){p+=this._getPhysicalSizeIncrement(m);
if(p>this._scrollPosition)return this.grid?n-n%this._itemsPerRow:n;if(this.grid&&this._virtualCount-1===n)return n-n%this._itemsPerRow})||0}return l},get lastVisibleIndex(){var l=this._lastVisibleIndexVal;if(null==l){if(this.grid)l=Math.min(this._virtualCount,this.firstVisibleIndex+this._estRowsInView*this._itemsPerRow-1);else{var p=this._physicalTop+this._scrollOffset;this._iterateItems(function(m,n){p<this._scrollBottom&&(l=n);p+=this._getPhysicalSizeIncrement(m)})}this._lastVisibleIndexVal=l}return l},
get _defaultScrollTarget(){return this},get _virtualRowCount(){return Math.ceil(this._virtualCount/this._itemsPerRow)},get _estRowsInView(){return Math.ceil(this._viewportHeight/this._rowHeight)},get _physicalRows(){return Math.ceil(this._physicalCount/this._itemsPerRow)},get _scrollOffset(){return this._scrollerPaddingTop+this.scrollOffset},ready:function(){this.addEventListener("focus",this._didFocus.bind(this),!0)},attached:function(){this._debounce("_render",this._render,h);this.listen(this,"iron-resize",
"_resizeHandler");this.listen(this,"keydown","_keydownHandler")},detached:function(){this.unlisten(this,"iron-resize","_resizeHandler");this.unlisten(this,"keydown","_keydownHandler")},_setOverflow:function(l){this.style.webkitOverflowScrolling=l===this?"touch":"";this.style.overflowY=l===this?"auto":"";this._firstVisibleIndexVal=this._lastVisibleIndexVal=null;this._debounce("_render",this._render,h)},updateViewportBoundaries:function(){var l=window.getComputedStyle(this);this._scrollerPaddingTop=
this.scrollTarget===this?0:parseInt(l["padding-top"],10);this._isRTL="rtl"===l.direction;this._viewportWidth=this.$.items.offsetWidth;this._viewportHeight=this._scrollTargetHeight;this.grid&&this._updateGridMetrics()},_scrollHandler:function(){var l=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop)),p=l-this._scrollPosition,m=0<=p;this._scrollPosition=l;this._lastVisibleIndexVal=this._firstVisibleIndexVal=null;Math.abs(p)>this._physicalSize&&0<this._physicalSize?(p-=this._scrollOffset,m=Math.round(p/
this._physicalAverage)*this._itemsPerRow,this._virtualStart+=m,this._physicalStart+=m,this._physicalTop=Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage,this._update()):0<this._physicalCount&&(l=this._getReusables(m),m?(this._physicalTop=l.physicalTop,this._virtualStart+=l.indexes.length,this._physicalStart+=l.indexes.length):(this._virtualStart-=l.indexes.length,this._physicalStart-=l.indexes.length),this._update(l.indexes,m?null:l.indexes),this._debounce("_increasePoolIfNeeded",
this._increasePoolIfNeeded.bind(this,0),t))},_getReusables:function(l){var p=[],m=this._hiddenContentSize*this._ratio,n=this._virtualStart,q=this._virtualEnd,u=this._physicalCount,x=this._physicalTop+this._scrollOffset;var A=this._physicalBottom+this._scrollOffset;var y=this._scrollTop,w=this._scrollBottom;if(l){var C=this._physicalStart;A=y-x}else C=this._physicalEnd,A-=w;for(;;){var G=this._getPhysicalSizeIncrement(C);A-=G;if(p.length>=u||A<=m)break;if(l){if(q+p.length+1>=this._virtualCount)break;
if(x+G>=y-this._scrollOffset)break;p.push(C);x+=G;C=(C+1)%u}else{if(0>=n-p.length)break;if(x+this._physicalSize-G<=w)break;p.push(C);x-=G;C=0===C?u-1:C-1}}return{indexes:p,physicalTop:x-this._scrollOffset}},_update:function(l,p){if(!(l&&0===l.length||0===this._physicalCount)){this._manageFocus();this._assignModels(l);this._updateMetrics(l);if(p)for(;p.length;)l=p.pop(),this._physicalTop-=this._getPhysicalSizeIncrement(l);this._positionItems();this._updateScrollerSize()}},_createPool:function(l){this._ensureTemplatized();
var p,m=Array(l);for(p=0;p<l;p++){var n=this.stamp(null);m[p]=n.root.querySelector("*");this._itemsParent.appendChild(n.root)}return m},_isClientFull:function(){return 0!=this._scrollBottom&&this._physicalBottom-1>=this._scrollBottom&&this._physicalTop<=this._scrollPosition},_increasePoolIfNeeded:function(l){l=this._clamp(this._physicalCount+l,3,this._virtualCount-this._virtualStart);l=this._convertIndexToCompleteRow(l);if(this.grid){var p=l%this._itemsPerRow;p&&l-p<=this._physicalCount&&(l+=this._itemsPerRow);
l-=p}l-=this._physicalCount;p=Math.round(.5*this._physicalCount);if(!(0>l)){if(0<l){p=window.performance.now();[].push.apply(this._physicalItems,this._createPool(l));for(var m=0;m<l;m++)this._physicalSizes.push(0);this._physicalCount+=l;this._physicalStart>this._physicalEnd&&this._isIndexRendered(this._focusedVirtualIndex)&&this._getPhysicalIndex(this._focusedVirtualIndex)<this._physicalEnd&&(this._physicalStart+=l);this._update();this._templateCost=(window.performance.now()-p)/l;p=Math.round(.5*
this._physicalCount)}this._virtualEnd>=this._virtualCount-1||0===p||(this._isClientFull()?this._physicalSize<this._optPhysicalSize&&this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,this._clamp(Math.round(50/this._templateCost),1,p)),k):this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,p),t))}},_render:function(){if(this.isAttached&&this._isVisible)if(0!==this._physicalCount){var l=this._getReusables(!0);this._physicalTop=l.physicalTop;this._virtualStart+=
l.indexes.length;this._physicalStart+=l.indexes.length;this._update(l.indexes);this._update();this._increasePoolIfNeeded(0)}else 0<this._virtualCount&&(this.updateViewportBoundaries(),this._increasePoolIfNeeded(3))},_ensureTemplatized:function(){if(!this.ctor){(this._userTemplate=this.queryEffectiveChildren("template"))||console.warn("iron-list requires a template to be provided in light-dom");var l={__key__:!0};l[this.as]=!0;l[this.indexAs]=!0;l[this.selectedAs]=!0;l.tabIndex=!0;this._instanceProps=
l;this.templatize(this._userTemplate,this.mutableData)}},_gridChanged:function(l,p){"undefined"!==typeof p&&(this.notifyResize(),Polymer.flush?Polymer.flush():Polymer.dom.flush(),l&&this._updateGridMetrics())},_itemsChanged:function(l){if("items"===l.path)this._physicalTop=this._virtualStart=0,this._virtualCount=this.items?this.items.length:0,this._collection=this.items&&Polymer.Collection?Polymer.Collection.get(this.items):null,this._physicalIndexForKey={},this._lastVisibleIndexVal=this._firstVisibleIndexVal=
null,this._physicalCount=this._physicalCount||0,this._physicalItems=this._physicalItems||[],this._physicalSizes=this._physicalSizes||[],this._physicalStart=0,this._scrollTop>this._scrollOffset&&this._resetScrollPosition(0),this._removeFocusedItem(),this._debounce("_render",this._render,h);else if("items.splices"===l.path){this._adjustVirtualIndex(l.value.indexSplices);this._virtualCount=this.items?this.items.length:0;if(l.value.indexSplices.some(function(m){return 0<m.addedCount||0<m.removed.length})){var p=
this._getActiveElement();this.contains(p)&&p.blur()}l=l.value.indexSplices.some(function(m){return m.index+m.addedCount>=this._virtualStart&&m.index<=this._virtualEnd},this);this._isClientFull()&&!l||this._debounce("_render",this._render,h)}else"items.length"!==l.path&&this._forwardItemPath(l.path,l.value)},_forwardItemPath:function(l,p){l=l.slice(6);var m=l.indexOf(".");-1===m&&(m=l.length);var n,q=this.modelForElement(this._offscreenFocusedItem);if(f){var u=parseInt(l.substring(0,m),10);if(n=this._isIndexRendered(u)){var x=
this._getPhysicalIndex(u);var A=this.modelForElement(this._physicalItems[x])}else q&&(A=q);if(!A||A[this.indexAs]!==u)return}else if(u=l.substring(0,m),q&&q.__key__===u)A=q;else if(x=this._physicalIndexForKey[u],A=this.modelForElement(this._physicalItems[x]),!A||A.__key__!==u)return;l=l.substring(m+1);l=this.as+(l?"."+l:"");f?A._setPendingPropertyOrPath(l,p,!1,!0):A.notifyPath(l,p,!0);A._flushProperties&&A._flushProperties(!0);n&&(this._updateMetrics([x]),this._positionItems(),this._updateScrollerSize())},
_adjustVirtualIndex:function(l){l.forEach(function(p){p.removed.forEach(this._removeItem,this);p.index<this._virtualStart&&(p=Math.max(p.addedCount-p.removed.length,p.index-this._virtualStart),this._virtualStart+=p,0<=this._focusedVirtualIndex&&(this._focusedVirtualIndex+=p))},this)},_removeItem:function(l){this.$.selector.deselect(l);this._focusedItem&&this.modelForElement(this._focusedItem)[this.as]===l&&this._removeFocusedItem()},_iterateItems:function(l,p){var m,n;if(2===arguments.length&&p)for(n=
0;n<p.length;n++){var q=p[n];var u=this._computeVidx(q);if(null!=(m=l.call(this,q,u)))return m}else{q=this._physicalStart;for(u=this._virtualStart;q<this._physicalCount;q++,u++)if(null!=(m=l.call(this,q,u)))return m;for(q=0;q<this._physicalStart;q++,u++)if(null!=(m=l.call(this,q,u)))return m}},_computeVidx:function(l){return l>=this._physicalStart?this._virtualStart+(l-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+l},_assignModels:function(l){this._iterateItems(function(p,
m){var n=this._physicalItems[p],q=this.items&&this.items[m];if(null!=q){var u=this.modelForElement(n);u.__key__=this._collection?this._collection.getKey(q):null;this._forwardProperty(u,this.as,q);this._forwardProperty(u,this.selectedAs,this.$.selector.isSelected(q));this._forwardProperty(u,this.indexAs,m);this._forwardProperty(u,"tabIndex",this._focusedVirtualIndex===m?0:-1);this._physicalIndexForKey[u.__key__]=p;u._flushProperties&&u._flushProperties(!0);n.removeAttribute("hidden")}else n.setAttribute("hidden",
"")},l)},_updateMetrics:function(l){Polymer.flush?Polymer.flush():Polymer.dom.flush();var p=0,m=0,n=this._physicalAverageCount,q=this._physicalAverage;this._iterateItems(function(u){m+=this._physicalSizes[u];this._physicalSizes[u]=this._physicalItems[u].offsetHeight;p+=this._physicalSizes[u];this._physicalAverageCount+=this._physicalSizes[u]?1:0},l);this.grid?(this._updateGridMetrics(),this._physicalSize=Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight):(m=1===this._itemsPerRow?m:
Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight,this._physicalSize=this._physicalSize+p-m,this._itemsPerRow=1);this._physicalAverageCount!==n&&(this._physicalAverage=Math.round((q*n+p)/this._physicalAverageCount))},_updateGridMetrics:function(){this._itemWidth=0<this._physicalCount?this._physicalItems[0].getBoundingClientRect().width:200;this._rowHeight=0<this._physicalCount?this._physicalItems[0].offsetHeight:200;this._itemsPerRow=this._itemWidth?Math.floor(this._viewportWidth/this._itemWidth):
this._itemsPerRow},_positionItems:function(){this._adjustScrollPosition();var l=this._physicalTop;if(this.grid){var p=(this._viewportWidth-this._itemsPerRow*this._itemWidth)/2;this._iterateItems(function(m,n){var q=Math.floor(n%this._itemsPerRow*this._itemWidth+p);this._isRTL&&(q*=-1);this.translate3d(q+"px",l+"px",0,this._physicalItems[m]);this._shouldRenderNextRow(n)&&(l+=this._rowHeight)})}else this._iterateItems(function(m){this.translate3d(0,l+"px",0,this._physicalItems[m]);l+=this._physicalSizes[m]})},
_getPhysicalSizeIncrement:function(l){return this.grid?this._computeVidx(l)%this._itemsPerRow!==this._itemsPerRow-1?0:this._rowHeight:this._physicalSizes[l]},_shouldRenderNextRow:function(l){return l%this._itemsPerRow===this._itemsPerRow-1},_adjustScrollPosition:function(){var l=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);if(0!==l){this._physicalTop-=l;var p=this._scrollTop;!d&&0<p&&this._resetScrollPosition(p-l)}},_resetScrollPosition:function(l){this.scrollTarget&&
0<=l&&(this._scrollPosition=this._scrollTop=l)},_updateScrollerSize:function(l){this._estScrollHeight=this.grid?this._virtualRowCount*this._rowHeight:this._physicalBottom+Math.max(this._virtualCount-this._physicalCount-this._virtualStart,0)*this._physicalAverage;if((l=(l=(l=l||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||this.grid&&this.$.items.style.height<this._estScrollHeight)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._viewportHeight)this.$.items.style.height=
this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight},scrollToItem:function(l){return this.scrollToIndex(this.items.indexOf(l))},scrollToIndex:function(l){if(!("number"!==typeof l||0>l||l>this.items.length-1)&&(Polymer.flush?Polymer.flush():Polymer.dom.flush(),0!==this._physicalCount)){l=this._clamp(l,0,this._virtualCount-1);if(!this._isIndexRendered(l)||l>=this._maxVirtualStart)this._virtualStart=this.grid?l-2*this._itemsPerRow:l-1;this._manageFocus();this._assignModels();this._updateMetrics();
this._physicalTop=Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage;for(var p=this._physicalStart,m=this._virtualStart,n=0,q=this._hiddenContentSize;m<l&&n<=q;)n+=this._getPhysicalSizeIncrement(p),p=(p+1)%this._physicalCount,m++;this._updateScrollerSize(!0);this._positionItems();this._resetScrollPosition(this._physicalTop+this._scrollOffset+n);this._increasePoolIfNeeded(0);this._lastVisibleIndexVal=this._firstVisibleIndexVal=null}},_resetAverage:function(){this._physicalAverageCount=
this._physicalAverage=0},_resizeHandler:function(){this._debounce("_render",function(){this._lastVisibleIndexVal=this._firstVisibleIndexVal=null;this.updateViewportBoundaries();this._isVisible?(this.toggleScrollListener(!0),this._resetAverage(),this._render()):this.toggleScrollListener(!1)},h)},selectItem:function(l){return this.selectIndex(this.items.indexOf(l))},selectIndex:function(l){if(!(0>l||l>=this._virtualCount)){!this.multiSelection&&this.selectedItem&&this.clearSelection();if(this._isIndexRendered(l)){var p=
this.modelForElement(this._physicalItems[this._getPhysicalIndex(l)]);p&&(p[this.selectedAs]=!0);this.updateSizeForIndex(l)}this.$.selector.selectIndex?this.$.selector.selectIndex(l):this.$.selector.select(this.items[l])}},deselectItem:function(l){return this.deselectIndex(this.items.indexOf(l))},deselectIndex:function(l){0>l||l>=this._virtualCount||(this._isIndexRendered(l)&&(this.modelForElement(this._physicalItems[this._getPhysicalIndex(l)])[this.selectedAs]=!1,this.updateSizeForIndex(l)),this.$.selector.deselectIndex?
this.$.selector.deselectIndex(l):this.$.selector.deselect(this.items[l]))},toggleSelectionForItem:function(l){return this.toggleSelectionForIndex(this.items.indexOf(l))},toggleSelectionForIndex:function(l){(this.$.selector.isIndexSelected?this.$.selector.isIndexSelected(l):this.$.selector.isSelected(this.items[l]))?this.deselectIndex(l):this.selectIndex(l)},clearSelection:function(){this._iterateItems(function(l){this.modelForElement(this._physicalItems[l])[this.selectedAs]=!1});this.$.selector.clearSelection()},
_selectionEnabledChanged:function(l){(l?this.listen:this.unlisten).call(this,this,"tap","_selectionHandler")},_selectionHandler:function(l){var p=this.modelForElement(l.target);if(p){var m=Polymer.dom(l).path[0];l=this._getActiveElement();var n=this._physicalItems[this._getPhysicalIndex(p[this.indexAs])];if("input"!==m.localName&&"button"!==m.localName&&"select"!==m.localName){m=p.tabIndex;p.tabIndex=-100;var q=l?l.tabIndex:-1;p.tabIndex=m;l&&n!==l&&n.contains(l)&&-100!==q||this.toggleSelectionForItem(p[this.as])}}},
_multiSelectionChanged:function(l){this.clearSelection();this.$.selector.multi=l},updateSizeForItem:function(l){return this.updateSizeForIndex(this.items.indexOf(l))},updateSizeForIndex:function(l){if(!this._isIndexRendered(l))return null;this._updateMetrics([this._getPhysicalIndex(l)]);this._positionItems();return null},_manageFocus:function(){var l=this._focusedVirtualIndex;0<=l&&l<this._virtualCount?this._isIndexRendered(l)?this._restoreFocusedItem():this._createFocusBackfillItem():0<this._virtualCount&&
0<this._physicalCount&&(this._focusedPhysicalIndex=this._physicalStart,this._focusedVirtualIndex=this._virtualStart,this._focusedItem=this._physicalItems[this._physicalStart])},_convertIndexToCompleteRow:function(l){this._itemsPerRow=this._itemsPerRow||1;return this.grid?Math.ceil(l/this._itemsPerRow)*this._itemsPerRow:l},_isIndexRendered:function(l){return l>=this._virtualStart&&l<=this._virtualEnd},_isIndexVisible:function(l){return l>=this.firstVisibleIndex&&l<=this.lastVisibleIndex},_getPhysicalIndex:function(l){return f?
(this._physicalStart+(l-this._virtualStart))%this._physicalCount:this._physicalIndexForKey[this._collection.getKey(this.items[l])]},focusItem:function(l){this._focusPhysicalItem(l)},_focusPhysicalItem:function(l){if(!(0>l||l>=this._virtualCount)){this._restoreFocusedItem();this._isIndexRendered(l)||this.scrollToIndex(l);var p=this._physicalItems[this._getPhysicalIndex(l)],m=this.modelForElement(p),n;m.tabIndex=-100;-100===p.tabIndex&&(n=p);n||(n=Polymer.dom(p).querySelector('[tabindex\x3d"-100"]'));
m.tabIndex=0;this._focusedVirtualIndex=l;n&&n.focus()}},_removeFocusedItem:function(){this._offscreenFocusedItem&&this._itemsParent.removeChild(this._offscreenFocusedItem);this._focusedItem=this._focusBackfillItem=this._offscreenFocusedItem=null;this._focusedPhysicalIndex=this._focusedVirtualIndex=-1},_createFocusBackfillItem:function(){var l=this._focusedPhysicalIndex;if(!(this._offscreenFocusedItem||0>this._focusedVirtualIndex)){if(!this._focusBackfillItem){var p=this.stamp(null);this._focusBackfillItem=
p.root.querySelector("*");this._itemsParent.appendChild(p.root)}this._offscreenFocusedItem=this._physicalItems[l];this.modelForElement(this._offscreenFocusedItem).tabIndex=0;this._physicalItems[l]=this._focusBackfillItem;this._focusedPhysicalIndex=l;this.translate3d(0,"-10000px",0,this._offscreenFocusedItem)}},_restoreFocusedItem:function(){if(this._offscreenFocusedItem&&!(0>this._focusedVirtualIndex)){this._assignModels();var l=this._focusedPhysicalIndex=this._getPhysicalIndex(this._focusedVirtualIndex),
p=this._physicalItems[l];if(p){var m=this.modelForElement(p),n=this.modelForElement(this._offscreenFocusedItem);m[this.as]===n[this.as]?(this._focusBackfillItem=p,m.tabIndex=-1,this._physicalItems[l]=this._offscreenFocusedItem,this.translate3d(0,"-10000px",0,this._focusBackfillItem)):(this._removeFocusedItem(),this._focusBackfillItem=null);this._offscreenFocusedItem=null}}},_didFocus:function(l){l=this.modelForElement(l.target);var p=this.modelForElement(this._focusedItem),m=null!==this._offscreenFocusedItem,
n=this._focusedVirtualIndex;l&&(p===l?this._isIndexVisible(n)||this.scrollToIndex(n):(this._restoreFocusedItem(),p&&(p.tabIndex=-1),l.tabIndex=0,this._focusedVirtualIndex=n=l[this.indexAs],this._focusedPhysicalIndex=this._getPhysicalIndex(n),this._focusedItem=this._physicalItems[this._focusedPhysicalIndex],m&&!this._offscreenFocusedItem&&this._update()))},_keydownHandler:function(l){switch(l.keyCode){case 40:this._focusedVirtualIndex<this._virtualCount-1&&l.preventDefault();this._focusPhysicalItem(this._focusedVirtualIndex+
(this.grid?this._itemsPerRow:1));break;case 39:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?-1:1));break;case 38:0<this._focusedVirtualIndex&&l.preventDefault();this._focusPhysicalItem(this._focusedVirtualIndex-(this.grid?this._itemsPerRow:1));break;case 37:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?1:-1));break;case 13:this._focusPhysicalItem(this._focusedVirtualIndex),this.selectionEnabled&&this._selectionHandler(l)}},_clamp:function(l,
p,m){return Math.min(m,Math.max(p,l))},_debounce:function(l,p,m){f?(this._debouncers=this._debouncers||{},this._debouncers[l]=Polymer.Debouncer.debounce(this._debouncers[l],m,p.bind(this)),Polymer.enqueueDebouncer(this._debouncers[l])):Polymer.dom.addDebouncer(this.debounce(l,p))},_forwardProperty:function(l,p,m){f?l._setPendingProperty(p,m):l[p]=m},_forwardHostPropV2:function(l,p){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach(function(m){m&&this.modelForElement(m).forwardHostProp(l,
p)},this)},_notifyInstancePropV2:function(l,p,m){Polymer.Path.matches(this.as,p)&&(l=l[this.indexAs],p==this.as&&(this.items[l]=m),this.notifyPath(Polymer.Path.translate(this.as,"items."+l,p),m))},_getStampedChildren:function(){return this._physicalItems},_forwardInstancePath:function(l,p,m){0===p.indexOf(this.as+".")&&this.notifyPath("items."+l.__key__+"."+p.slice(this.as.length+1),m)},_forwardParentPath:function(l,p){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach(function(m){m&&
this.modelForElement(m).notifyPath(l,p,!0)},this)},_forwardParentProp:function(l,p){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach(function(m){m&&(this.modelForElement(m)[l]=p)},this)},_getActiveElement:function(){var l=this._itemsParent.node.domHost;return Polymer.dom(l?l.root:document).activeElement}})})();

//# sourceURL=build://paper-item/paper-item-body.html.js
Polymer({is:"paper-item-body"});

//# sourceURL=build://tf-graph-common/tf-graph-icon.js
(function(b){(function(d){(function(f){let h;(function(k){k.CONST="CONST";k.META="META";k.OP="OP";k.SERIES="SERIES";k.SUMMARY="SUMMARY"})(h=f.GraphIconType||(f.GraphIconType={}));Polymer({is:"tf-graph-icon",properties:{type:String,vertical:{type:Boolean,value:!1},fillOverride:{type:String,value:null},strokeOverride:{type:String,value:null},height:{type:Number,value:20},faded:{type:Boolean,value:!1},_fill:{type:String,computed:"_computeFill(type, fillOverride)"},_stroke:{type:String,computed:"_computeStroke(type, strokeOverride)"}},
getSvgDefinableElement(){return this.$.svgDefs},_computeFill(k,t){if(null!=t)return t;switch(k){case h.META:return b.graph.render.MetanodeColors.DEFAULT_FILL;case h.SERIES:return b.graph.render.SeriesNodeColors.DEFAULT_FILL;default:return b.graph.render.OpNodeColors.DEFAULT_FILL}},_computeStroke(k,t){if(null!=t)return t;switch(k){case h.META:return b.graph.render.MetanodeColors.DEFAULT_STROKE;case h.SERIES:return b.graph.render.SeriesNodeColors.DEFAULT_STROKE;default:return b.graph.render.OpNodeColors.DEFAULT_STROKE}},
_isType(k,t){return k===t},_fadedClass:function(k,t){return k?"faded-"+t:""}})})(d.icon||(d.icon={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/tf-node-icon.html.js
(function(){Polymer({is:"tf-node-icon",properties:{node:{type:Object,value:null},renderInfo:{type:Object,value:null},colorBy:{type:Object,value:"structural"},templateIndex:{type:Function,value:null},type:{type:String,value:null},vertical:{type:Boolean,value:!1},const:{type:Boolean,value:!1},summary:{type:Boolean,value:!1},fill:{type:String,value:null},height:{type:Number,value:20},_fillOverride:{type:String,computed:"_computeFillOverride(node, renderInfo, colorBy, templateIndex, fill)",observer:"_onFillOverrideChanged"}},
_computeFillOverride:function(b,d,f,h,k){return b&&d&&f&&h?(b=tf.graph.scene.node,b.getFillForNode(h,b.ColorBy[f.toUpperCase()],d,!1)):k},_getStrokeOverride:function(b){return b?tf.graph.scene.node.getStrokeForFill(b):null},_getType:function(b,d,f,h){const k=tf.graph.icon.GraphIconType;if(b)switch(b.type){case tf.graph.NodeType.OP:return b=b.op,"string"!==typeof b?k.OP:"Const"===b||f?k.CONST:b.endsWith("Summary")||d?k.SUMMARY:k.OP;case tf.graph.NodeType.META:return k.META;case tf.graph.NodeType.SERIES:return k.SERIES}return h},
_isVertical:function(b,d){return b?b.hasNonControlEdges:!!d},_getFaded:function(b){return b&&b.isFadedOut},_onFillOverrideChanged(b,d){const f=this.node,h=this.renderInfo,k=this.colorBy,t=this.templateIndex,l=tf.graph.scene.node;b!==d&&l.removeGradientDefinitions(this.$.icon.getSvgDefinableElement());f&&h&&k&&t&&l.getFillForNode(t,l.ColorBy[k.toUpperCase()],h,!1,this.$.icon.getSvgDefinableElement())}})})();

//# sourceURL=build://tf-graph-op-compat-card/tf-graph-op-compat-list-item.html.js
(function(){Polymer({is:"tf-graph-op-compat-list-item",properties:{cardNode:Object,itemNode:Object,edgeLabel:String,itemRenderInfo:Object,name:String,itemType:{type:String,observer:"_itemTypeChanged"},colorBy:String,colorByParams:Object,templateIndex:Function},_itemTypeChanged:function(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")},_nodeListener:function(b){this.fire("node-list-item-"+b.type,{nodeName:this.name,type:this.itemType})},
_fadedClass:function(b){return b&&b.isFadedOut?"faded":""}})})();

//# sourceURL=build://tf-graph-op-compat-card/tf-graph-op-compat-card.html.js
(function(){Polymer({is:"tf-graph-op-compat-card",properties:{graphHierarchy:Object,hierarchyParams:Object,renderHierarchy:Object,nodeTitle:String,_templateIndex:{type:Function,computed:"_getTemplateIndex(graphHierarchy)"},_incompatibleOpNodes:{type:Object,computed:"_getIncompatibleOpNodes(graphHierarchy, hierarchyParams)"},_expanded:{type:Boolean,value:!0},_opCompatScore:{type:Number,computed:"_computeOpCompatScore(graphHierarchy)"},_opCompatScoreLabel:{type:String,computed:"_getOpCompatScoreLabel(_opCompatScore)"},
_opCompatColor:{type:String,value:tf.graph.render.OpNodeColors.COMPATIBLE},_opIncompatColor:{type:String,value:tf.graph.render.OpNodeColors.INCOMPATIBLE},_totalIncompatOps:{type:Number,computed:"_getTotalIncompatibleOps(graphHierarchy)"}},_getTemplateIndex:function(b){return b.getTemplateIndex()},_getNode:function(b,d){return d.node(b)},_getPrintableHTMLNodeName:function(b){return(b||"").replace(/\//g,"\x3cwbr\x3e/")},_getRenderInfo:function(b){return this.renderHierarchy.getOrCreateRenderNodeByName(b)},
_toggleExpanded:function(){this._expanded=!this._expanded},_getToggleIcon:function(b){return b?"expand-less":"expand-more"},_resizeList:function(b){(b=document.querySelector(b))&&b.fire("iron-resize")},_getIncompatibleOpNodes:function(b,d){if(b&&b.root)return this.async(this._resizeList.bind(this,"#incompatibleOpsList")),tf.graph.hierarchy.getIncompatibleOps(b,d)},_computeOpCompatScore:function(b){if(b&&b.root){var d=b.root;b=d.compatibilityHistogram.compatible;d=d.compatibilityHistogram.incompatible;
return 0==b&&0==d?0:Math.floor(100*b/(b+d))/100}return 0},_getOpCompatScoreLabel:function(b){return d3.format(".0%")(b)},_getTotalIncompatibleOps:function(b){return b&&b.root?b.root.compatibilityHistogram.incompatible:0}})})();

//# sourceURL=build://tf-graph-info/tf-node-list-item.html.js
(function(){Polymer({is:"tf-node-list-item",properties:{cardNode:Object,itemNode:Object,edgeLabel:String,itemRenderInfo:Object,name:String,itemType:{type:String,observer:"_itemTypeChanged"},colorBy:String,colorByParams:Object,templateIndex:Function},_itemTypeChanged:function(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")},_nodeListener:function(b){this.fire("node-list-item-"+b.type,{cardNode:this.cardNode.name,nodeName:this.name,
type:this.itemType})},_fadedClass:function(b){return b&&b.isFadedOut?"faded":""}})})();

//# sourceURL=build://tf-graph-info/tf-node-info.html.js
(function(){Polymer({is:"tf-node-info",properties:{graphNodeName:String,graphHierarchy:Object,renderHierarchy:Object,colorBy:String,_templateIndex:{type:Function,computed:"_getTemplateIndex(graphHierarchy)"},_node:{type:Object,computed:"_getNode(graphNodeName, graphHierarchy)",observer:"_resetState"},_nodeStats:{type:Object,computed:"_getNodeStats(graphNodeName, graphHierarchy)",observer:"_resetState"},_hasDisplayableNodeStats:{type:Object,computed:"_getHasDisplayableNodeStats(_nodeStats)"},_nodeStatsFormattedBytes:{type:String,
computed:"_getNodeStatsFormattedBytes(_nodeStats)"},_nodeStatsFormattedComputeTime:{type:String,computed:"_getNodeStatsFormattedComputeTime(_nodeStats)"},_nodeStatsFormattedOutputSizes:{type:Array,computed:"_getNodeStatsFormattedOutputSizes(_nodeStats)"},nodeInclude:{type:Number,observer:"_nodeIncludeStateChanged"},_attributes:{type:Array,computed:"_getAttributes(_node)"},_device:{type:String,computed:"_getDevice(_node)"},_successors:{type:Object,computed:"_getSuccessors(_node, graphHierarchy)"},
_predecessors:{type:Object,computed:"_getPredecessors(_node, graphHierarchy)"},_functionUsages:{type:Array,computed:"_getFunctionUsages(_node, graphHierarchy)"},_subnodes:{type:Array,computed:"_getSubnodes(_node)"},_expanded:{type:Boolean,value:!0},_totalPredecessors:{type:Number,computed:"_getTotalPred(_predecessors)"},_totalSuccessors:{type:Number,computed:"_getTotalSucc(_successors)"},_openedControlPred:{type:Boolean,value:!1},_openedControlSucc:{type:Boolean,value:!1},_auxButtonText:String,_groupButtonText:String},
expandNode:function(){this.fire("_node.expand",this.node)},_getTemplateIndex:function(b){return b.getTemplateIndex()},_getNode:function(b,d){return d.node(b)},_getNodeStats:function(b,d){return(b=this._getNode(b,d))?b.stats:null},_getTotalMicros:function(b){return b?b.getTotalMicros():0},_getHasDisplayableNodeStats:function(b){return tf.graph.util.hasDisplayableNodeStats(b)},_getNodeStatsFormattedBytes:function(b){if(b&&b.totalBytes)return tf.graph.util.convertUnitsToHumanReadable(b.totalBytes,tf.graph.util.MEMORY_UNITS)},
_getNodeStatsFormattedComputeTime:function(b){if(b&&b.getTotalMicros())return tf.graph.util.convertUnitsToHumanReadable(b.getTotalMicros(),tf.graph.util.TIME_UNITS)},_getNodeStatsFormattedOutputSizes:function(b){if(b&&b.outputSize&&b.outputSize.length)return _.map(b.outputSize,function(d){return 0===d.length?"scalar":"["+d.join(", ")+"]"})},_getPrintableHTMLNodeName:function(b){return(b||"").replace(/\//g,"\x3cwbr\x3e/")},_getRenderInfo:function(b){return this.renderHierarchy.getOrCreateRenderNodeByName(b)},
_getAttributes:function(b){this.async(this._resizeList.bind(this,"#attributesList"));if(!b||!b.attr)return[];var d=[];_.each(b.attr,function(f){f.key===tf.graph.LARGE_ATTRS_KEY?d=d.concat(f.value.list.s.map(function(h){return{key:h,value:"Too large to show..."}})):d.push({key:f.key,value:JSON.stringify(f.value)})});return d},_getDevice:function(b){return b?b.device:null},_getSuccessors(b,d){this._refreshNodeItemList("inputsList");return b?this._convertEdgeListToEdgeInfoList(d.getSuccessors(b.name),
!1,b.isGroupNode):{regular:[],control:[]}},_getPredecessors(b,d){this._refreshNodeItemList("outputsList");return b?this._convertEdgeListToEdgeInfoList(d.getPredecessors(b.name),!0,b.isGroupNode):{regular:[],control:[]}},_getFunctionUsages(b,d){this._refreshNodeItemList("functionUsagesList");return b&&b.type===tf.graph.NodeType.META?(b=d.libraryFunctions[b.associatedFunction])?b.usages:[]:[]},_refreshNodeItemList(b){this.async(this._resizeList.bind(this,`#${b}`))},_convertEdgeListToEdgeInfoList:function(b,
d,f){var h=t=>_.map(t.baseEdgeList,l=>{var p=d?l.v:l.w;return{name:p,node:this._getNode(p,this.graphHierarchy),edgeLabel:tf.graph.scene.edge.getLabelForBaseEdge(l,this.renderHierarchy),renderInfo:this._getRenderInfo(p,this.renderHierarchy)}}),k=function(t){var l=[];_.each(t,p=>{var m=d?p.v:p.w;f&&1!=p.baseEdgeList.length?l.push({name:m,node:this._getNode(m,this.graphHierarchy),edgeLabel:tf.graph.scene.edge.getLabelForEdge(p,this.renderHierarchy),renderInfo:this._getRenderInfo(m,this.renderHierarchy)}):
l=l.concat(h(p))});return l}.bind(this);return{regular:k(b.regular),control:k(b.control)}},_getSubnodes:function(b){return b&&b.metagraph?b.metagraph.nodes():null},_getTotalPred:function(b){return b.regular.length+b.control.length},_getTotalSucc:function(b){return b.regular.length+b.control.length},_toggleControlPred:function(){this._openedControlPred=!this._openedControlPred},_toggleControlSucc:function(){this._openedControlSucc=!this._openedControlSucc},_toggleExpanded:function(){this._expanded=
!this._expanded},_getToggleIcon:function(b){return b?"expand-less":"expand-more"},_resetState:function(){this._openedControlSucc=this._openedControlPred=!1;this.set("_groupButtonText",tf.graph.scene.node.getGroupSettingLabel(this._node));this._node&&(Polymer.dom(this.$.nodetitle).innerHTML=this._getPrintableHTMLNodeName(this._node.name))},_resizeList:function(b){(b=document.querySelector(b))&&b.fire("iron-resize")},_toggleInclude:function(){this.fire("node-toggle-inclusion",{name:this.graphNodeName})},
_nodeIncludeStateChanged:function(b){this.set("_auxButtonText",tf.graph.getIncludeNodeButtonString(b))},_toggleGroup:function(){var b=tf.graph.scene.node.getSeriesName(this._node);this.fire("node-toggle-seriesgroup",{name:b})},_isLibraryFunction(b){return b&&b.name.startsWith(tf.graph.FUNCTION_LIBRARY_NODE_PREFIX)},_isInSeries:function(b){return tf.graph.scene.node.canBeInSeries(b)}})})();

//# sourceURL=build://tf-graph-info/tf-graph-info.html.js
(function(){Polymer({is:"tf-graph-info",properties:{title:String,graphHierarchy:Object,graph:Object,renderHierarchy:Object,nodeNamesToHealthPills:Object,healthPillStepIndex:{type:Number,notify:!0},colorBy:String,compatNodeTitle:String,selectedNode:{type:String,notify:!0},highlightedNode:{type:String,notify:!0},selectedNodeInclude:{type:Number,notify:!0},debuggerDataEnabled:Boolean},listeners:{"node-list-item-click":"_nodeListItemClicked","node-list-item-mouseover":"_nodeListItemMouseover","node-list-item-mouseout":"_nodeListItemMouseout"},
_nodeListItemClicked:function(b){this.selectedNode=b.detail.nodeName},_nodeListItemMouseover:function(b){this.highlightedNode=b.detail.nodeName},_nodeListItemMouseout:function(){this.highlightedNode=null},_healthPillsAvailable:function(b,d){return b&&d&&0<Object.keys(d).length},_equals:function(b,d){return b===d}})})();

//# sourceURL=build://tf-graph-board/tf-graph-board.html.js
Polymer({is:"tf-graph-board",properties:{graphHierarchy:Object,graph:Object,stats:Object,progress:Object,traceInputs:Boolean,colorBy:String,colorByParams:{type:Object,notify:!0},renderHierarchy:{type:Object,notify:!0},debuggerDataEnabled:Boolean,areHealthPillsLoading:Boolean,debuggerNumericAlerts:{type:Array,notify:!0},nodeNamesToHealthPills:Object,allStepsModeEnabled:{type:Boolean,notify:!0,value:!1},specificHealthPillStep:{type:Number,notify:!0,value:0},healthPillStepIndex:Number,selectedNode:{type:String,
notify:!0},compatNodeTitle:{type:String,value:"TPU Compatibility"},edgeWidthFunction:Object,_selectedNodeInclude:Number,_highlightedNode:String,handleNodeSelected:Object,edgeLabelFunction:Object,handleEdgeSelected:Object},observers:["_updateNodeInclude(selectedNode, renderHierarchy)"],fit:function(){this.$.graph.fit()},_isNotComplete:function(b){return 100>b.value},_getContainerClass:function(b){var d="container";b.error&&(d+=" error");this._isNotComplete(b)&&(d+=" loading");return d},_onNodeInclusionToggled(b){this.$.graph.nodeToggleExtract(b.detail.name)},
_onNodeSeriesGroupToggled(b){this.$.graph.nodeToggleSeriesGroup(b.detail.name)},_updateNodeInclude(){const b=this.renderHierarchy?this.renderHierarchy.getNodeByName(this.selectedNode):null;this._selectedNodeInclude=b?b.include:tf.graph.InclusionType.UNSPECIFIED}});

//# sourceURL=build://iron-menu-behavior/iron-menubar-behavior.html.js
Polymer.IronMenubarBehaviorImpl={hostAttributes:{role:"menubar"},keyBindings:{left:"_onLeftKey",right:"_onRightKey"},_onUpKey:function(b){this.focusedItem.click();b.detail.keyboardEvent.preventDefault()},_onDownKey:function(b){this.focusedItem.click();b.detail.keyboardEvent.preventDefault()},get _isRTL(){return"rtl"===window.getComputedStyle(this).direction},_onLeftKey:function(b){this._isRTL?this._focusNext():this._focusPrevious();b.detail.keyboardEvent.preventDefault()},_onRightKey:function(b){this._isRTL?
this._focusPrevious():this._focusNext();b.detail.keyboardEvent.preventDefault()},_onKeydown:function(b){this.keyboardEventMatchesKeys(b,"up down left right esc")||this._focusWithKeyboardEvent(b)}};Polymer.IronMenubarBehavior=[Polymer.IronMenuBehavior,Polymer.IronMenubarBehaviorImpl];

//# sourceURL=build://paper-radio-button/paper-radio-button.html.js
Polymer({is:"paper-radio-button",behaviors:[Polymer.PaperCheckedElementBehavior],hostAttributes:{role:"radio","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},ready:function(){this._rippleContainer=this.$.radioContainer},attached:function(){Polymer.RenderStatus.afterNextRender(this,function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-radio-button-ink-size").trim()){var b=parseFloat(this.getComputedStyleValue("--calculated-paper-radio-button-size").trim()),
d=Math.floor(3*b);d%2!==b%2&&d++;this.updateStyles({"--paper-radio-button-ink-size":d+"px"})}})}});

//# sourceURL=build://paper-radio-group/paper-radio-group.html.js
Polymer({is:"paper-radio-group",behaviors:[Polymer.IronMenubarBehavior],hostAttributes:{role:"radiogroup"},properties:{attrForSelected:{type:String,value:"name"},selectedAttribute:{type:String,value:"checked"},selectable:{type:String,value:"paper-radio-button"},allowEmptySelection:{type:Boolean,value:!1}},select:function(b){var d=this._valueToItem(b);if(!d||!d.hasAttribute("disabled")){if(this.selected){d=this._valueToItem(this.selected);if(this.selected==b)if(this.allowEmptySelection)b="";else{d&&
(d.checked=!0);return}d&&(d.checked=!1)}Polymer.IronSelectableBehavior.select.apply(this,[b]);this.fire("paper-radio-group-changed")}},_activateFocusedItem:function(){this._itemActivate(this._valueForItem(this.focusedItem),this.focusedItem)},_onUpKey:function(b){this._focusPrevious();b.preventDefault();this._activateFocusedItem()},_onDownKey:function(b){this._focusNext();b.preventDefault();this._activateFocusedItem()},_onLeftKey:function(b){Polymer.IronMenubarBehaviorImpl._onLeftKey.apply(this,arguments);
this._activateFocusedItem()},_onRightKey:function(b){Polymer.IronMenubarBehaviorImpl._onRightKey.apply(this,arguments);this._activateFocusedItem()}});

//# sourceURL=build://paper-tooltip/paper-tooltip.html.js
Polymer({is:"paper-tooltip",hostAttributes:{role:"tooltip",tabindex:-1},properties:{for:{type:String,observer:"_findTarget"},manualMode:{type:Boolean,value:!1,observer:"_manualModeChanged"},position:{type:String,value:"bottom"},fitToVisibleBounds:{type:Boolean,value:!1},offset:{type:Number,value:14},marginTop:{type:Number,value:14},animationDelay:{type:Number,value:500,observer:"_delayChange"},animationEntry:{type:String,value:""},animationExit:{type:String,value:""},animationConfig:{type:Object,
value:function(){return{entry:[{name:"fade-in-animation",node:this,timing:{delay:0}}],exit:[{name:"fade-out-animation",node:this}]}}},_showing:{type:Boolean,value:!1}},listeners:{webkitAnimationEnd:"_onAnimationEnd"},get target(){var b=Polymer.dom(this).parentNode,d=Polymer.dom(this).getOwnerRoot();return this.for?Polymer.dom(d).querySelector("#"+this.for):b.nodeType==Node.DOCUMENT_FRAGMENT_NODE?d.host:b},attached:function(){this._findTarget()},detached:function(){this.manualMode||this._removeListeners()},
playAnimation:function(b){"entry"===b?this.show():"exit"===b&&this.hide()},cancelAnimation:function(){this.$.tooltip.classList.add("cancel-animation")},show:function(){if(!this._showing){if(""===Polymer.dom(this).textContent.trim()){for(var b=!0,d=Polymer.dom(this).getEffectiveChildNodes(),f=0;f<d.length;f++)if(""!==d[f].textContent.trim()){b=!1;break}if(b)return}this._showing=!0;this.$.tooltip.classList.remove("hidden");this.$.tooltip.classList.remove("cancel-animation");this.$.tooltip.classList.remove(this._getAnimationType("exit"));
this.updatePosition();this._animationPlaying=!0;this.$.tooltip.classList.add(this._getAnimationType("entry"))}},hide:function(){this._showing&&(this._animationPlaying?(this._showing=!1,this._cancelAnimation()):(this._onAnimationFinish(),this._showing=!1,this._animationPlaying=!0))},updatePosition:function(){if(this._target&&this.offsetParent){var b=this.offset;14!=this.marginTop&&14==this.offset&&(b=this.marginTop);var d=this.offsetParent.getBoundingClientRect(),f=this._target.getBoundingClientRect(),
h=this.getBoundingClientRect(),k=(f.width-h.width)/2,t=(f.height-h.height)/2,l=f.left-d.left,p=f.top-d.top;switch(this.position){case "top":var m=l+k;var n=p-h.height-b;break;case "bottom":m=l+k;n=p+f.height+b;break;case "left":m=l-h.width-b;n=p+t;break;case "right":m=l+f.width+b,n=p+t}this.fitToVisibleBounds?(d.left+m+h.width>window.innerWidth?(this.style.right="0px",this.style.left="auto"):(this.style.left=Math.max(0,m)+"px",this.style.right="auto"),d.top+n+h.height>window.innerHeight?(this.style.bottom=
d.height+"px",this.style.top="auto"):(this.style.top=Math.max(-d.top,n)+"px",this.style.bottom="auto")):(this.style.left=m+"px",this.style.top=n+"px")}},_addListeners:function(){this._target&&(this.listen(this._target,"mouseenter","show"),this.listen(this._target,"focus","show"),this.listen(this._target,"mouseleave","hide"),this.listen(this._target,"blur","hide"),this.listen(this._target,"tap","hide"));this.listen(this.$.tooltip,"animationend","_onAnimationEnd");this.listen(this,"mouseenter","hide")},
_findTarget:function(){this.manualMode||this._removeListeners();this._target=this.target;this.manualMode||this._addListeners()},_delayChange:function(b){500!==b&&this.updateStyles({"--paper-tooltip-delay-in":b+"ms"})},_manualModeChanged:function(){this.manualMode?this._removeListeners():this._addListeners()},_cancelAnimation:function(){this.$.tooltip.classList.remove(this._getAnimationType("entry"));this.$.tooltip.classList.remove(this._getAnimationType("exit"));this.$.tooltip.classList.remove("cancel-animation");
this.$.tooltip.classList.add("hidden")},_onAnimationFinish:function(){this._showing&&(this.$.tooltip.classList.remove(this._getAnimationType("entry")),this.$.tooltip.classList.remove("cancel-animation"),this.$.tooltip.classList.add(this._getAnimationType("exit")))},_onAnimationEnd:function(){this._animationPlaying=!1;this._showing||(this.$.tooltip.classList.remove(this._getAnimationType("exit")),this.$.tooltip.classList.add("hidden"))},_getAnimationType:function(b){if("entry"===b&&""!==this.animationEntry)return this.animationEntry;
if("exit"===b&&""!==this.animationExit)return this.animationExit;if(this.animationConfig[b]&&"string"===typeof this.animationConfig[b][0].name){if(this.animationConfig[b][0].timing&&this.animationConfig[b][0].timing.delay&&0!==this.animationConfig[b][0].timing.delay){var d=this.animationConfig[b][0].timing.delay;"entry"===b?this.updateStyles({"--paper-tooltip-delay-in":d+"ms"}):"exit"===b&&this.updateStyles({"--paper-tooltip-delay-out":d+"ms"})}return this.animationConfig[b][0].name}},_removeListeners:function(){this._target&&
(this.unlisten(this._target,"mouseenter","show"),this.unlisten(this._target,"focus","show"),this.unlisten(this._target,"mouseleave","hide"),this.unlisten(this._target,"blur","hide"),this.unlisten(this._target,"tap","hide"));this.unlisten(this.$.tooltip,"animationend","_onAnimationEnd");this.unlisten(this,"mouseenter","hide")}});

//# sourceURL=build://tf-graph-node-search/tf-graph-node-search.html.js
Polymer({is:"tf-graph-node-search",properties:{renderHierarchy:Object,selectedNode:{type:String,notify:!0},_rawRegexInput:{type:String,value:""},_regexInput:{type:String,computed:"_computeRegexInput(renderHierarchy, _rawRegexInput)"},_previousRegexInput:{type:String,value:""},_searchTimeoutDelay:{type:Number,value:150,readOnly:!0},_searchPending:Boolean,_maxRegexResults:{type:Number,value:42},_regexMatches:Array},observers:["_regexInputChanged(_regexInput)"],_computeRegexInput(b,d){return d.trim()},
_regexInputChanged(){this._requestSearch()},_clearSearchResults(){this.set("_regexMatches",[])},_requestSearch(){this._searchPending||(this._regexInput===this._previousRegexInput?this._searchPending=!1:(this._searchPending=!0,this._executeSearch(),this.async(()=>{this._searchPending=!1;this._requestSearch()},this._searchTimeoutDelay)))},_executeSearch(){if(this._previousRegexInput=this._regexInput){try{var b=new RegExp(this._regexInput)}catch(f){this._clearSearchResults();return}var d=[];_.each(this.renderHierarchy.hierarchy.getNodeMap(),
(f,h)=>{if(d.length>=this._maxRegexResults)return!1;b.test(h)&&d.push(h)});this.set("_regexMatches",d)}else this._clearSearchResults()},_matchClicked(b){this.set("selectedNode",b.model.item)}});

//# sourceURL=build://tf-graph-controls/tf-graph-controls.js
(function(b){(function(d){(function(f){const h=/device:([^:]+:[0-9]+)$/,k=[{regex:h}],t=[];let l;(function(m){m.COMPUTE_TIME="compute_time";m.MEMORY="memory";m.STRUCTURE="structure";m.XLA_CLUSTER="xla_cluster";m.OP_COMPATIBILITY="op_compatibility"})(l=f.ColorBy||(f.ColorBy={}));const p=new Set([l.COMPUTE_TIME,l.MEMORY]);Polymer({is:"tf-graph-controls",properties:{stats:{value:null,type:Object,observer:"_statsChanged"},devicesForStats:{value:null,type:Object,notify:!0,readonly:!0},colorBy:{type:String,
value:l.STRUCTURE,notify:!0},colorByParams:{type:Object,notify:!0,readonly:!0},datasets:{type:Array,observer:"_datasetsChanged",value:()=>[]},renderHierarchy:{type:Object},selection:{type:Object,notify:!0,readOnly:!0,computed:"_computeSelection(datasets, _selectedRunIndex, _selectedTagIndex, _selectedGraphType)"},selectedFile:{type:Object,notify:!0},_selectedRunIndex:{type:Number,value:0,observer:"_selectedRunIndexChanged"},traceInputs:{type:Boolean,notify:!0,value:!1},_selectedTagIndex:{type:Number,
value:0,observer:"_selectedTagIndexChanged"},_selectedGraphType:{type:String,value:b.graph.SelectionType.OP_GRAPH},selectedNode:{type:String,notify:!0},_currentDevices:{type:Array,computed:"_getCurrentDevices(devicesForStats)"},_currentDeviceParams:{type:Array,computed:"_getCurrentDeviceParams(colorByParams)"},_currentXlaClusterParams:{type:Array,computed:"_getCurrentXlaClusterParams(colorByParams)"},_currentGradientParams:{type:Object,computed:"_getCurrentGradientParams(colorByParams, colorBy)"},
showSessionRunsDropdown:{type:Boolean,value:!0},showUploadButton:{type:Boolean,value:!0},healthPillsFeatureEnabled:Boolean,healthPillsToggledOn:{type:Boolean,notify:!0},_legendOpened:{type:Boolean,value:!0}},_xlaClustersProvided:function(m){return m&&m.hierarchy&&0<m.hierarchy.xlaClusters.length},_statsChanged:function(m){if(null!=m){var n={};_.each(m.dev_stats,function(q){var u=_.some(k,function(A){return A.regex.test(q.device)}),x=_.some(t,function(A){return A.regex.test(q.device)});u&&!x&&(n[q.device]=
!0)});this.set("devicesForStats",n)}},_getCurrentDevices:function(m){var n=this.stats;n=(n?n.dev_stats:[]).map(u=>u.device).filter(u=>k.some(x=>x.regex.test(u)));const q=b.graph.util.removeCommonPrefix(n);if(1==q.length){const u=q[0].match(h);u&&(q[0]=u[1])}return n.map((u,x)=>{let A=null;t.forEach(y=>{y.regex.test(u)&&(A=y.msg)});return{device:u,suffix:q[x],used:m[u],ignoredMsg:A}})},_deviceCheckboxClicked:function(m){m=m.target;const n=Object.assign({},this.devicesForStats),q=m.value;m.checked?
n[q]=!0:delete n[q];this.set("devicesForStats",n)},_numTags:function(m,n){return this._getTags(m,n).length},_getTags:function(m,n){return m&&m[n]?m[n].tags:[]},_fit:function(){this.fire("fit-tap")},_isGradientColoring:function(m,n){return p.has(n)&&null!=m},_equals:function(m,n){return m===n},_getCurrentDeviceParams:function(m){m=m.device.filter(u=>k.some(x=>x.regex.test(u.device)));const n=b.graph.util.removeCommonPrefix(m.map(u=>u.device));if(1==n.length){var q=n[0].match(h);q&&(n[0]=q[1])}return m.map((u,
x)=>({device:n[x],color:u.color}))},_getCurrentXlaClusterParams:function(m){return m.xla_cluster},_getCurrentGradientParams:function(m,n){if(this._isGradientColoring(this.stats,n)){m=m[n];var q=m.minValue,u=m.maxValue;n===l.MEMORY?(q=b.graph.util.convertUnitsToHumanReadable(q,b.graph.util.MEMORY_UNITS),u=b.graph.util.convertUnitsToHumanReadable(u,b.graph.util.MEMORY_UNITS)):n===l.COMPUTE_TIME&&(q=b.graph.util.convertUnitsToHumanReadable(q,b.graph.util.TIME_UNITS),u=b.graph.util.convertUnitsToHumanReadable(u,
b.graph.util.TIME_UNITS));return{minValue:q,maxValue:u,startColor:m.startColor,endColor:m.endColor}}},download:function(){this.$.graphdownload.click()},_updateFileInput:function(m){var n=m.target.files[0];if(n){n=n.name;var q=n.lastIndexOf(".");0<=q&&(n=n.substring(0,q));q=n.lastIndexOf("/");0<=q&&(n=n.substring(q+1));this._setDownloadFilename(n);this.set("selectedFile",m)}},_datasetsChanged:function(m,n){null!=n&&(this._selectedRunIndex=0)},_computeSelection:function(m,n,q,u){return m[n]&&m[n].tags[q]?
{run:m[n].name,tag:m[n].tags[q].tag,type:u}:null},_selectedRunIndexChanged:function(m){this.datasets&&(this.colorBy=l.STRUCTURE,this._selectedTagIndex=0,this._selectedGraphType=this._getDefaultSelectionType(),this.traceInputs=!1,this._setDownloadFilename(this.datasets[m]?this.datasets[m].name:""))},_selectedTagIndexChanged(){this._selectedGraphType=this._getDefaultSelectionType()},_getDefaultSelectionType(){const m=this.datasets,n=this._selectedRunIndex,q=this._selectedTagIndex;return m&&m[n]&&m[n].tags[q]&&
!m[n].tags[q].opGraph?m[n].tags[q].profile?b.graph.SelectionType.PROFILE:m[n].tags[q].conceptualGraph?b.graph.SelectionType.CONCEPTUAL_GRAPH:b.graph.SelectionType.OP_GRAPH:b.graph.SelectionType.OP_GRAPH},_getFile:function(){this.$$("#file").click()},_setDownloadFilename:function(m){this.$.graphdownload.setAttribute("download",m+".png")},_statsNotNull:function(m){return null!==m},_toggleLegendOpen(){this.set("_legendOpened",!this._legendOpened)},_getToggleText(m){return m?"Close legend.":"Expand legend."},
_getToggleLegendIcon(m){return m?"expand-more":"expand-less"},_getSelectionOpGraphDisabled(m,n,q){return!m[n]||!m[n].tags[q]||!m[n].tags[q].opGraph},_getSelectionProfileDisabled(m,n,q){return!m[n]||!m[n].tags[q]||!m[n].tags[q].profile},_getSelectionConceptualGraphDisabled(m,n,q){return!m[n]||!m[n].tags[q]||!m[n].tags[q].conceptualGraph}})})(d.controls||(d.controls={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-loader/tf-graph-dashboard-loader.js
Ui=this&&this.__awaiter||function(b,d,f,h){return new (f||(f=Promise))(function(k,t){function l(n){try{m(h.next(n))}catch(q){t(q)}}function p(n){try{m(h["throw"](n))}catch(q){t(q)}}function m(n){n.done?k(n.value):(new f(function(q){q(n.value)})).then(l,p)}m((h=h.apply(b,d||[])).next())})};
(function(b){(function(d){(function(){Polymer({is:"tf-graph-dashboard-loader",_template:null,properties:{datasets:Array,progress:{type:Object,notify:!0},selection:Object,selectedFile:Object,compatibilityProvider:{type:Object,value:()=>new b.graph.op.TpuCompatibilityProvider},hierarchyParams:{type:Object,value:()=>b.graph.hierarchy.DefaultHierarchyParams},outGraphHierarchy:{type:Object,readOnly:!0,notify:!0},outGraph:{type:Object,readOnly:!0,notify:!0},outStats:{type:Object,readOnly:!0,notify:!0},
_graphRunTag:Object},observers:["_selectionChanged(selection, compatibilityProvider)","_selectedFileChanged(selectedFile, compatibilityProvider)"],_selectionChanged(){this.debounce("selectionchange",()=>{this._load(this.selection)})},_load:function(f){const h=f.run,k=f.tag;f=f.type;switch(f){case b.graph.SelectionType.OP_GRAPH:case b.graph.SelectionType.CONCEPTUAL_GRAPH:this._setOutStats(null);var t=new URLSearchParams;t.set("run",h);t.set("conceptual",String(f===b.graph.SelectionType.CONCEPTUAL_GRAPH));
k&&t.set("tag",k);f=vc.getRouter().pluginRoute("graphs","/graph",t);return this._fetchAndConstructHierarchicalGraph(f).then(()=>{this._graphRunTag={run:h,tag:k}});case b.graph.SelectionType.PROFILE:{({tags:f}=this.datasets.find(({name:m})=>m===h));const l=f.find(m=>m.tag===k).opGraph?k:null;console.assert(f.find(m=>m.tag===l),`Required tag (${l}) is missing.`);f=this._graphRunTag&&this._graphRunTag.run===h&&this._graphRunTag.tag===l?Promise.resolve():this._load({run:h,tag:l,type:b.graph.SelectionType.OP_GRAPH});
t=new URLSearchParams;t.set("tag",k);t.set("run",h);const p=vc.getRouter().pluginRoute("graphs","/run_metadata",t);return f.then(()=>this._readAndParseMetadata(p))}default:return Promise.reject(Error(`Unknown selection type: ${f}`))}},_readAndParseMetadata:function(f){this.set("progress",{value:0,msg:""});b.graph.parser.fetchAndParseMetadata(f,b.graph.util.getTracker(this)).then(h=>{this._setOutStats(h)})},_fetchAndConstructHierarchicalGraph:function(f,h){return Ui(this,void 0,void 0,function*(){this.set("progress",
{value:0,msg:""});return b.graph.loader.fetchAndConstructHierarchicalGraph(b.graph.util.getTracker(this),f,h,this.compatibilityProvider,this.hierarchyParams).then(({graph:k,graphHierarchy:t})=>{this._setOutGraph(k);this._setOutGraphHierarchy(t)})})},_selectedFileChanged:function(f){if(f){f=f.target;var h=f.files[0];h&&(f.value="",this._fetchAndConstructHierarchicalGraph(null,h))}}})})(d.loader||(d.loader={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-dashboard/tf-graph-dashboard.html.js
Polymer({is:"tf-graph-dashboard",properties:{_datasets:{type:Array,value:()=>[]},_datasetsFetched:{type:Boolean,value:!1},_selectedDataset:{type:Number,value:0},_renderHierarchy:{type:Object,observer:"_renderHierarchyChanged"},_requestManager:{type:Object,value:()=>new vc.RequestManager},_canceller:{type:Object,value:()=>new vc.Canceller},_debuggerDataEnabled:Boolean,allStepsModeEnabled:Boolean,specificHealthPillStep:{type:Number,value:0},healthPillsToggledOn:{type:Boolean,value:!1,observer:"_healthPillsToggledOnChanged"},
selectedNode:{type:String,notify:!0},_isAttached:Boolean,_initialized:Boolean,_areHealthPillsLoading:Boolean,_debuggerNumericAlerts:{type:Array,value:[],notify:!0},_nodeNamesToHealthPills:{type:Object,value:{}},_healthPillStepIndex:Number,_healthPillRequestId:{type:Number,value:1},_healthPillStepRequestTimerId:Number,_healthPillStepRequestTimerDelay:{type:Number,value:500,readOnly:!0},runs:Array,run:{type:String,notify:!0,value:pd.getStringInitializer("run",{defaultValue:"",useLocalStorage:!1}),observer:"_runObserver"},
_selection:{type:Object},_compatibilityProvider:Object,_traceInputs:Boolean},listeners:{"node-toggle-expand":"_handleNodeToggleExpand"},observers:["_maybeFetchHealthPills(_debuggerDataEnabled, allStepsModeEnabled, specificHealthPillStep, _selectedNode)","_maybeInitializeDashboard(_isAttached)","_determineSelectedDataset(_datasetsFetched, _datasets, run)","_updateSelectedDatasetName(_datasetsFetched, _datasets, _selectedDataset)"],attached:function(){this.set("_isAttached",!0)},detached:function(){this.set("_isAttached",
!1)},reload:function(){this._debuggerDataEnabled||this._requestManager.request(vc.getRouter().pluginsListing()).then(this._canceller.cancellable(b=>{b.cancelled||b.value["debugger"]&&this.set("_debuggerDataEnabled",!0)}));this._maybeFetchHealthPills()},_fit:function(){this.$$("#graphboard").fit()},_runObserver:pd.getStringObserver("run",{defaultValue:"",polymerProperty:"run",useLocalStorage:!1}),_fetchDataset(){return this._requestManager.request(vc.getRouter().pluginRoute("graphs","/info"))},_fetchHealthPills(b,
d){b={node_names:JSON.stringify(b),run:"__debugger_data__"};void 0!==d&&(b.step=d);d=vc.getRouter().pluginRoute("debugger","/health_pills");return this._requestManager.request(d,b)},_fetchDebuggerNumericsAlerts(){return this._requestManager.request(vc.getRouter().pluginRoute("debugger","/numerics_alert_report"))},_graphUrl(b,d,f){return vc.getRouter().pluginRoute("graphs","/graph",new URLSearchParams({run:b,limit_attr_size:d,large_attrs_key:f}))},_shouldRequestHealthPills:function(){return this._debuggerDataEnabled&&
this.healthPillsToggledOn&&this._renderHierarchy&&this._datasetsState(this._datasetsFetched,this._datasets,"PRESENT")},_maybeInitializeDashboard:function(b){!this._initialized&&b&&(this.set("_compatibilityProvider",new tf.graph.op.TpuCompatibilityProvider),this._initialized=!0,this._fetchDataset().then(d=>{this._datasets=Object.keys(d).sort(rc.compareTagNames).map(f=>{const h=d[f];var k=Object.keys(h.tags).sort(rc.compareTagNames).map(t=>h.tags[t]).map(({tag:t,conceptual_graph:l,op_graph:p,profile:m})=>
({tag:t,displayName:t,conceptualGraph:l,opGraph:p,profile:m}));k=h.run_graph?[{tag:null,displayName:"Default",conceptualGraph:!1,opGraph:!0,profile:!1},...k]:k;return{name:f,tags:k}});this._datasetsFetched=!0}))},_determineSelectedDataset(b,d,f){f?(d=d.findIndex(h=>h.name===f),-1===d?b&&(b=this.$$("#error-dialog"),b.textContent=`No dataset named "${f}" could be found.`,b.open()):this.set("_selectedDataset",d)):this.set("_selectedDataset",0)},_updateSelectedDatasetName(b,d,f){b&&(d.length<=f||this.set("run",
d[f].name))},_requestHealthPills:function(){this.set("_areHealthPillsLoading",!0);var b=++this._healthPillRequestId;null!==this._healthPillStepRequestTimerId&&(window.clearTimeout(this._healthPillStepRequestTimerId),this._healthPillStepRequestTimerId=null);this.allStepsModeEnabled?this._healthPillStepRequestTimerId=setTimeout(function(){this._healthPillStepRequestTimerId=null;this._initiateNetworkRequestForHealthPills(b)}.bind(this),this._healthPillStepRequestTimerDelay):this._initiateNetworkRequestForHealthPills(b)},
_initiateNetworkRequestForHealthPills:function(b){if(this._healthPillRequestId===b){var d=this._fetchHealthPills(this._renderHierarchy.getNamesOfRenderedOps(),this.allStepsModeEnabled?this.specificHealthPillStep:void 0),f=this._fetchDebuggerNumericsAlerts();Promise.all([d,f]).then(function(h){var k=h[0];h=h[1];if(this.healthPillsToggledOn&&b===this._healthPillRequestId){for(var t in k){this.set("_healthPillStepIndex",k[t].length-1);break}this.set("_debuggerNumericAlerts",h);this.set("_nodeNamesToHealthPills",
k);this.set("_areHealthPillsLoading",!1);this.set("_healthPillStepRequestTimerId",null)}}.bind(this))}},_datasetsState:function(b,d,f){return b?d&&d.length?"PRESENT"===f:"EMPTY"===f:"NOT_LOADED"===f},_renderHierarchyChanged:function(){this.reload()},_handleNodeToggleExpand:function(){this._maybeFetchHealthPills()},_healthPillsToggledOnChanged:function(b){b?this.reload():this.set("_nodeNamesToHealthPills",{})},_maybeFetchHealthPills:function(){this._shouldRequestHealthPills()&&this._requestHealthPills()}});

//# sourceURL=build://vz-distribution-chart/vz-distribution-chart.js
var Jk;
(function(b){class d{constructor(f,h){this.run2datasets={};this.colorScale=h;this.buildChart(f)}getDataset(f){void 0===this.run2datasets[f]&&(this.run2datasets[f]=new Plottable.Dataset([],{run:f}));return this.run2datasets[f]}buildChart(f){this.outer&&this.outer.destroy();f=rg.getXComponents(f);this.xAccessor=f.accessor;this.xScale=f.scale;this.xAxis=f.axis;this.xAxis.margin(0).tickLabelPadding(3);this.yScale=new Plottable.Scales.Linear;this.yAxis=new Plottable.Axes.Numeric(this.yScale,"left");f=
rg.multiscaleFormatter(rg.Y_AXIS_FORMATTER_PRECISION);this.yAxis.margin(0).tickLabelPadding(5).formatter(f);this.yAxis.usesTextWidthApproximation();f=this.buildPlot(this.xAccessor,this.xScale,this.yScale);this.gridlines=new Plottable.Components.Gridlines(this.xScale,this.yScale);this.center=new Plottable.Components.Group([this.gridlines,f]);this.outer=new Plottable.Components.Table([[this.yAxis,this.center],[null,this.xAxis]])}buildPlot(f,h,k){let t=[0,228,1587,3085,5E3,6915,8413,9772,1E4],l=_.range(t.length-
1).map(u=>(t[u+1]-t[u])/2500),p=t.map((u,x)=>A=>A[x][1]),m=p[4],n=_.range(p.length-1).map(u=>{let x=new Plottable.Plots.Area;x.x(f,h);let A=4<u?p[u]:p[u+1];x.y(4<u?p[u+1]:p[u],k);x.y0(A);x.attr("fill",(y,w,C)=>this.colorScale.scale(C.metadata().run));x.attr("stroke",(y,w,C)=>this.colorScale.scale(C.metadata().run));x.attr("stroke-weight",()=>"0.5px");x.attr("stroke-opacity",()=>l[u]);x.attr("fill-opacity",()=>l[u]);return x}),q=new Plottable.Plots.Line;q.x(f,h);q.y(m,k);q.attr("stroke",(u,x,A)=>this.colorScale.scale(A.run));
this.plots=n;return new Plottable.Components.Group(n)}setVisibleSeries(f){this.runs=f;let h=f.map(k=>this.getDataset(k));this.plots.forEach(k=>k.datasets(h))}setSeriesData(f,h){this.getDataset(f).data(h)}renderTo(f){this.targetSVG=f;this.outer.renderTo(f)}redraw(){this.outer.redraw()}destroy(){this.outer.destroy()}}b.DistributionChart=d;Polymer({is:"vz-distribution-chart",properties:{colorScale:{type:Object,value:function(){return(new Plottable.Scales.Color).range(d3.schemeCategory10)}},xType:{type:String,
value:"step"},_attached:Boolean,_chart:Object,_visibleSeriesCache:{type:Array,value:function(){return[]}},_seriesDataCache:{type:Object,value:function(){return{}}},_makeChartAsyncCallbackId:{type:Number,value:null}},observers:["_makeChart(xType, colorScale, _attached)","_reloadFromCache(_chart)"],setVisibleSeries:function(f){this._visibleSeriesCache=f;this._chart&&(this._chart.setVisibleSeries(f),this.redraw())},setSeriesData:function(f,h){this._seriesDataCache[f]=h;this._chart&&this._chart.setSeriesData(f,
h)},redraw:function(){this._chart.redraw()},ready:function(){this.scopeSubtree(this.$.chartdiv,!0)},_makeChart:function(f,h,k){null===this._makeChartAsyncCallbackId&&this.cancelAsync(this._makeChartAsyncCallbackId);this._makeChartAsyncCallbackId=this.async(function(){this._makeChartAsyncCallbackId=null;if(k){this._chart&&this._chart.destroy();var t=new d(f,h),l=d3.select(this.$.chartdiv);t.renderTo(l);this._chart=t}},350)},_reloadFromCache:function(){this._chart&&(this._chart.setVisibleSeries(this._visibleSeriesCache),
this._visibleSeriesCache.forEach(function(f){this._chart.setSeriesData(f,this._seriesDataCache[f]||[])}.bind(this)))},attached:function(){this._attached=!0},detached:function(){this._attached=!1}})})(Jk||(Jk={}));

//# sourceURL=build://tf-distribution-dashboard/tf-distribution-loader.html.js
Polymer({is:"tf-distribution-loader",properties:{run:String,tag:String,tagMetadata:Object,xType:String,dataToLoad:{type:Array,computed:"_computeDataToLoad(run, tag)"},getDataLoadName:{type:Function,value:()=>({run:b})=>b},getDataLoadUrl:{type:Function,value:()=>({tag:b,run:d})=>vc.addParams(vc.getRouter().pluginRoute("distributions","/distributions"),{tag:b,run:d})},loadDataCallback:{type:Function,value:function(){return(b,d,f)=>{b=f.map(h=>{const [k,t,l]=h;l.wall_time=new Date(1E3*k);l.step=t;return l});
d=this.getDataLoadName(d);this.$.chart.setSeriesData(d,b);this.$.chart.setVisibleSeries([d])}}},_colorScale:{type:Object,value:()=>({scale:pf.runsColorScale}),readOnly:!0},_runColor:{type:String,computed:"_computeRunColor(run)"},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},requestManager:Object,_canceller:{type:Object,value:()=>new vc.Canceller}},observers:["reload(run, tag)"],behaviors:[qd.DataLoaderBehavior],_computeDataToLoad(b,d){return[{run:b,tag:d}]},_computeRunColor(b){return this._colorScale.scale(b)},
redraw(){this.$.chart.redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()}});

//# sourceURL=build://tf-distribution-dashboard/tf-distribution-dashboard.html.js
Polymer({is:"tf-distribution-dashboard",properties:{_xType:{type:String,value:"step"},_selectedRuns:Array,_runToTag:Object,_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:String,_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTag, _selectedRuns, _tagFilter, _categoriesDomReady)"},_requestManager:{type:Object,value:()=>new vc.RequestManager}},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadDistributions()})},_fetchTags(){const b=vc.getRouter().pluginRoute("distributions",
"/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,k=>Object.keys(k)),h=vc.getTags(f);this.set("_dataNotFound",0===h.length);this.set("_runToTag",f);this.set("_runToTagInfo",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_reloadDistributions(){this.root.querySelectorAll("tf-distribution-loader").forEach(b=>{b.reload()})},_shouldOpen(b){return 2>=b},_makeCategories(b,d,f){return $c.categorizeRunTagCombinations(b,d,f)},_tagMetadata(b,
d,f){return b[d][f]}});

//# sourceURL=build://vz-histogram-timeseries/vz-histogram-timeseries.html.js
Polymer({is:"vz-histogram-timeseries",properties:{mode:{type:String,value:"offset"},timeProperty:{type:String,value:"step"},bins:{type:String,value:"bins"},x:{type:String,value:"x"},dx:{type:String,value:"dx"},y:{type:String,value:"y"},colorScale:{type:Object,value:function(){return d3.scaleOrdinal(d3.schemeCategory10)}},modeTransitionDuration:{type:Number,value:500},_attached:Boolean,_name:{type:String,value:null},_data:{type:Array,value:null}},observers:["redraw(timeProperty, _attached)","_modeRedraw(mode)"],
ready:function(){this.scopeSubtree(this.$.svg,!0)},attached:function(){this._attached=!0},detached:function(){this._attached=!1},setSeriesData:function(b,d){this._name=b;this._data=d;this.redraw()},redraw:function(){this._draw(0)},_modeRedraw:function(){this._draw(this.modeTransitionDuration)},_draw:function(b){if(this._attached&&this._data){if(void 0===b)throw Error("vz-histogram-timeseries _draw needs duration");if(0>=this._data.length)throw Error("Not enough steps in the data");if(!this._data[0].hasOwnProperty(this.bins))throw Error("No bins property of '"+
this.bins+"' in data");if(0>=this._data[0][this.bins].length)throw Error("Must have at least one bin in bins in data");if(!this._data[0][this.bins][0].hasOwnProperty(this.x))throw Error("No x property '"+this.x+"' on bins data");if(!this._data[0][this.bins][0].hasOwnProperty(this.dx))throw Error("No dx property '"+this.dx+"' on bins data");if(!this._data[0][this.bins][0].hasOwnProperty(this.y))throw Error("No y property '"+this.y+"' on bins data");var d=this.timeProperty,f=this.x,h=this.bins,k=this.dx,
t=this.y,l=this._data,p=this.mode,m=d3.hcl(this.colorScale(this._name)),n=d3.select(this.$.tooltip),q=function(ya){return ya[f]},u=function(ya){return ya[t]},x=function(ya){return ya[f]+ya[k]},A=function(ya){return ya[d]};"relative"===d&&(A=function(ya){return ya.wall_time-l[0].wall_time});var y=this.$.svg.getBoundingClientRect(),w=y.width,C=y.height,G=5;if("offset"===p){var D=C/2.5;G=D+5}else D=C-G-20;var B=w-24-60,I=C-G-20;d3.min(l,q);d3.max(l,x);var N=d3.format(".3n");y=d3.format(".0f");"wall_time"===
d?y=d3.timeFormat("%m/%d %X"):"relative"===d&&(y=function(ya){return d3.format(".1r")(ya/36E5)+"h"});var O=l.map(function(ya){return[d3.min(ya[h],q),d3.max(ya[h],x)]}),H=l.map(function(ya){return d3.extent(ya[h],u)}),K=d3.extent(l,A),M=("wall_time"===d?d3.scaleTime():d3.scaleLinear()).domain(K).range([0,"offset"===p?I:0]),L=d3.scaleLinear().domain([0,d3.max(l,function(ya,Sa){return H[Sa][1]})]).range([D,0]),Q=d3.scaleLinear().domain(L.domain()).range([500,0]),T=d3.scaleLinear().domain([d3.min(l,function(ya,
Sa){return O[Sa][0]}),d3.max(l,function(ya,Sa){return O[Sa][1]})]).nice().range([0,B]),X=d3.scaleLinear().domain(T.domain()).range([0,500]),aa=d3.scaleLinear().domain(d3.extent(l,A)).range([m.darker(),m.brighter()]).interpolate(d3.interpolateHcl);m=d3.axisBottom(T).ticks();var la=d3.axisRight(M).ticks().tickFormat(y),Z=d3.axisRight(L).ticks().tickSize(B+5).tickFormat(N),ba=function(ya){return ya[f]+ya[k]/2},ea=d3.line().x(function(ya){return X(ba(ya))}).y(function(ya){return Q(ya[t])}),ca=function(ya){return"M"+
X(ba(ya[0]))+","+Q(0)+"L"+ea(ya).slice(1)+"L"+X(ba(ya[ya.length-1]))+","+Q(0)},ka=this.$.svg;y=d3.select(ka);b=y.transition().duration(b);y=y.select("g").classed("small",function(){return 0<B&&150>=B}).classed("medium",function(){return 150<B&&300>=B}).classed("large",function(){return 300<B});b=b.select("g").attr("transform","translate(24,"+G+")");var Y=d3.bisector(x).left;K=y.select(".stage").on("mouseover",function(){va.style("opacity",1);xa.style("opacity",1);Aa.style("opacity",1);Fa.style("opacity",
1);n.style("opacity",1)}).on("mouseout",function(){va.style("opacity",0);xa.style("opacity",0);Aa.style("opacity",0);Fa.style("opacity",0);va.classed("hover-closest",!1);Ea.classed("outline-hover",!1);n.style("opacity",0)}).on("mousemove",function(){function ya(Ab){return Math.min(Ab[h].length-1,Y(Ab[h],Xa))}var Sa=d3.mouse(this),Xa=T.invert(Sa[0]);M.invert(Sa[1]);var ub,Bb=Infinity,qb;va.attr("transform",function(Ab){var Hb=ya(Ab);qb=Ab;var ic=T(Ab[h][Hb][f]+Ab[h][Hb][k]/2);Hb=L(Ab[h][Hb][t]);var bc=
"offset"===p?M(A(Ab))-(D-Hb):Hb;bc=Math.abs(Sa[1]-bc);bc<Bb&&(Bb=bc,ub=Ab);return"translate("+ic+","+Hb+")"});va.select("text").text(function(Ab){var Hb=ya(Ab);return Ab[h][Hb][t]});va.classed("hover-closest",function(Ab){return Ab===ub});Ea.classed("outline-hover",function(Ab){return Ab===ub});var zb=ya(qb);xa.attr("transform",function(){return"translate("+T(qb[h][zb][f]+qb[h][zb][k]/2)+", "+I+")"}).select("text").text(function(){return N(qb[h][zb][f]+qb[h][zb][k]/2)});var vb=la.tickFormat();Aa.attr("transform",
function(){return"translate("+B+", "+("offset"===p?M(A(ub)):0)+")"}).style("display","offset"===p?"":"none").select("text").text(function(){return vb(A(ub))});var Gb=Z.tickFormat();Fa.attr("transform",function(){return"translate("+B+", "+("offset"===p?0:L(ub[h][zb][t]))+")"}).style("display","offset"===p?"none":"").select("text").text(function(){return Gb(ub[h][zb][t])});var Nb=d3.mouse(ka);n.style("transform","translate("+(Nb[0]+15)+"px,"+(Nb[1]-15)+"px)").select("span").text("offset"===p?Gb(ub[h][zb][t]):
("step"===d?"step ":"")+vb(A(ub)))});K.select(".background").attr("transform","translate(-24,"+-G+")").attr("width",w).attr("height",C);C=K.selectAll(".histogram").data(l);C.exit().remove();w=C.enter().append("g").attr("class","histogram");C=w.merge(C).sort(function(ya,Sa){return A(ya)-A(Sa)});G=b.selectAll(".histogram").attr("transform",function(ya){return"translate(0, "+("offset"===p?M(A(ya))-D:0)+")"});w.append("line").attr("class","baseline");G.select(".baseline").style("stroke-opacity",function(){return"offset"===
p?.1:0}).attr("y1",D).attr("y2",D).attr("x2",B);w.append("path").attr("class","outline");var Ea=C.select(".outline").attr("vector-effect","non-scaling-stroke").attr("d",function(ya){return ca(ya[h])}).style("stroke-width",1);G.select(".outline").attr("transform","scale("+B/500+", "+D/500+")").style("stroke",function(ya){return"offset"===p?"white":aa(A(ya))}).style("fill-opacity",function(){return"offset"===p?1:0}).style("fill",function(ya){return aa(A(ya))});w=w.append("g").attr("class","hover").style("fill",
function(ya){return aa(A(ya))});var va=C.select(".hover");w.append("circle").attr("r",2);w.append("text").style("display","none").attr("dx",4);w=y.select(".x-axis-hover").selectAll(".label").data(["x"]);C=w.enter().append("g").attr("class","label");var xa=w.merge(C);C.append("rect").attr("x",-20).attr("y",6).attr("width",40).attr("height",14);C.append("line").attr("x1",0).attr("x2",0).attr("y1",0).attr("y2",6);C.append("text").attr("dy",18);w=y.select(".y-axis-hover").selectAll(".label").data(["y"]);
C=w.enter().append("g").attr("class","label");var Aa=w.merge(C);C.append("rect").attr("x",8).attr("y",-6).attr("width",40).attr("height",14);C.append("line").attr("x1",0).attr("x2",6).attr("y1",0).attr("y2",0);C.append("text").attr("dx",8).attr("dy",4);y=y.select(".y-slice-axis-hover").selectAll(".label").data(["y"]);w=y.enter().append("g").attr("class","label");var Fa=y.merge(w);w.append("rect").attr("x",8).attr("y",-6).attr("width",40).attr("height",14);w.append("line").attr("x1",0).attr("x2",6).attr("y1",
0).attr("y2",0);w.append("text").attr("dx",8).attr("dy",4);b.select(".y.axis.slice").style("opacity","offset"===p?0:1).attr("transform","translate(0, "+("offset"===p?-D:0)+")").call(Z);b.select(".x.axis").attr("transform","translate(0, "+I+")").call(m);b.select(".y.axis").style("opacity","offset"===p?1:0).attr("transform","translate("+B+", "+("offset"===p?0:I)+")").call(la);b.selectAll(".tick text").attr("fill","#aaa");b.selectAll(".axis path.domain").attr("stroke","none")}}});

//# sourceURL=build://tf-histogram-dashboard/histogramCore.js
var Kk;
(function(b){function d(h){const [k,t,l]=h;return{wall_time:k,step:t,min:d3.min(l.map(([p])=>p)),max:d3.max(l.map(([,p])=>p)),buckets:l.map(([p,m,n])=>({left:p,right:m,count:n}))}}function f(h,k,t,l=30){t===k&&(t=1.1*k+1,k=k/1.1-1);const p=(t-k)/l;let m=0;return d3.range(k,t,p).map(n=>{const q=n+p;let u=0;for(;m<h.buckets.length;){const A=Math.min(t,h.buckets[m].right);var x=Math.max(k,h.buckets[m].left);const y=Math.min(A,q)-Math.max(x,n);x=y/(A-x)*h.buckets[m].count;u+=0<y?x:0;if(A>q)break;m++}return{x:n,
dx:p,y:u}})}b.backendToIntermediate=d;b.intermediateToD3=f;b.backendToVz=function(h){h=h.map(d);const k=d3.min(h,l=>l.min),t=d3.max(h,l=>l.max);return h.map(l=>({wall_time:l.wall_time,step:l.step,bins:f(l,k,t)}))}})(Kk||(Kk={}));

//# sourceURL=build://tf-histogram-dashboard/tf-histogram-loader.html.js
Polymer({is:"tf-histogram-loader",properties:{run:String,tag:String,dataToLoad:{type:Array,computed:"_computeDataToLoad(run, tag)"},getDataLoadName:{type:Function,value:()=>({run:b})=>b},getDataLoadUrl:{type:Function,value:()=>({tag:b,run:d})=>vc.addParams(vc.getRouter().pluginRoute("histograms","/histograms"),{tag:b,run:d})},loadDataCallback:{type:Function,value:function(){return(b,d,f)=>{b=Kk.backendToVz(f);d=this.getDataLoadName(d);this.$.chart.setSeriesData(d,b)}}},tagMetadata:Object,timeProperty:String,
histogramMode:String,_colorScaleFunction:{type:Object,value:()=>pf.runsColorScale},_runColor:{type:String,computed:"_computeRunColor(run)"},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0}},observers:["reload(run, tag, requestManager)"],behaviors:[qd.DataLoaderBehavior],_computeDataToLoad(b,d){return[{run:b,tag:d}]},_computeRunColor(b){return this._colorScaleFunction(b)},redraw(){this.$.chart.redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()}});

//# sourceURL=build://tf-histogram-dashboard/tf-histogram-dashboard.html.js
Polymer({is:"tf-histogram-dashboard",properties:{_histogramMode:{type:String,value:"offset"},_timeProperty:{type:String,value:"step"},_selectedRuns:Array,_runToTag:Object,_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:String,_restamp:{type:Boolean,value:!1},_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTag, _selectedRuns, _tagFilter, _categoriesDomReady)"},_requestManager:{type:Object,value:()=>new vc.RequestManager}},listeners:{"content-visibility-changed":"_redrawCategoryPane"},
_redrawCategoryPane(b,d){d&&b.target.querySelectorAll("tf-histogram-loader").forEach(f=>f.redraw())},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadHistograms()})},_fetchTags(){const b=vc.getRouter().pluginRoute("histograms","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,k=>Object.keys(k)),h=vc.getTags(f);this.set("_dataNotFound",0===h.length);this.set("_runToTag",f);this.set("_runToTagInfo",d);this.async(()=>
{this.set("_categoriesDomReady",!0)})}})},_reloadHistograms(){this.root.querySelectorAll("tf-histogram-loader").forEach(b=>{b.reload()})},_shouldOpen(b){return 2>=b},_makeCategories(b,d,f){return $c.categorizeRunTagCombinations(b,d,f)},_tagMetadata(b,d,f){return b[d][f]}});

//# sourceURL=build://tf-text-dashboard/tf-text-loader.html.js
Polymer({is:"tf-text-loader",properties:{run:String,tag:String,_runColor:{type:String,computed:"_computeRunColor(run)"},_texts:{type:Array,value:[]},requestManager:Object,_canceller:{type:Object,value:()=>new vc.Canceller}},_computeRunColor(b){return pf.runsColorScale(b)},attached(){this._attached=!0;this.reload()},reload(){if(this._attached){this._canceller.cancelAll();var b=vc.addParams(vc.getRouter().pluginRoute("text","/text"),{tag:this.tag,run:this.run}),d=this._canceller.cancellable(f=>{f.cancelled||
(f=f.value.map(h=>({wall_time:new Date(1E3*h.wall_time),step:h.step,text:h.text})),this.set("_texts",f.slice().reverse()))});this.requestManager.request(b).then(d)}},_formatStep(b){return d3.format(",")(b)}});

//# sourceURL=build://tf-text-dashboard/tf-text-dashboard.html.js
Polymer({is:"tf-text-dashboard",properties:{_selectedRuns:Array,_runToTag:Object,_dataNotFound:Boolean,_tagFilter:String,_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTag, _selectedRuns, _tagFilter, _categoriesDomReady)"},_requestManager:{type:Object,value:()=>new vc.RequestManager}},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadTexts()})},_shouldOpen(b){return 2>=b},_fetchTags(){const b=vc.getRouter().pluginRoute("text","/tags");return this._requestManager.request(b).then(d=>
{if(!_.isEqual(d,this._runToTag)){var f=vc.getTags(d);this.set("_dataNotFound",0===f.length);this.set("_runToTag",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_reloadTexts(){this.root.querySelectorAll("tf-text-loader").forEach(b=>{b.reload()})},_makeCategories(b,d,f){return $c.categorizeRunTagCombinations(b,d,f)}});

//# sourceURL=build://tf-pr-curve-dashboard/tf-pr-curve-card.html.js
Polymer({is:"tf-pr-curve-card",properties:{runs:Array,tag:String,tagMetadata:Object,runToStepCap:Object,requestManager:Object,active:Boolean,_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},_runToPrCurveEntry:{type:Object,value:()=>({})},_previousRunToPrCurveEntry:{type:Object,value:()=>({})},_runsWithStepAvailable:{type:Array,computed:"_computeRunsWithStepAvailable(runs, _runToPrCurveEntry)"},_setOfRelevantRuns:{type:Object,computed:"_computeSetOfRelevantRuns(_runsWithStepAvailable)"},_runToDataOverTime:Object,
_colorScaleFunction:{type:Object,value:()=>({scale:pf.runsColorScale})},_canceller:{type:Object,value:()=>new vc.Canceller},_attached:Boolean,_xComponentsCreationMethod:{type:Object,readOnly:!0,value:()=>()=>{const b=new Plottable.Scales.Linear;return{scale:b,axis:new Plottable.Axes.Numeric(b,"bottom"),accessor:d=>d.recall}}},_yValueAccessor:{type:Object,readOnly:!0,value:()=>b=>b.precision},_tooltipColumns:{type:Array,readOnly:!0,value:()=>{const b=rg.multiscaleFormatter(rg.Y_TOOLTIP_FORMATTER_PRECISION),
d=f=>isNaN(f)?"NaN":b(f);return[{title:"Run",evaluate:f=>f.dataset.metadata().name},{title:"Threshold",evaluate:f=>d(f.datum.thresholds)},{title:"Precision",evaluate:f=>d(f.datum.precision)},{title:"Recall",evaluate:f=>d(f.datum.recall)},{title:"TP",evaluate:f=>f.datum.true_positives},{title:"FP",evaluate:f=>f.datum.false_positives},{title:"TN",evaluate:f=>f.datum.true_negatives},{title:"FN",evaluate:f=>f.datum.false_negatives}]}},_seriesDataFields:{type:Array,value:"thresholds precision recall true_positives false_positives true_negatives false_negatives".split(" "),
readOnly:!0},_defaultXRange:{type:Array,value:[-.05,1.05],readOnly:!0},_defaultYRange:{type:Array,value:[-.05,1.05],readOnly:!0},_dataUrl:{type:Function,value:function(){return b=>{const d=this.tag;return vc.addParams(vc.getRouter().pluginRoute("pr_curves","/pr_curves"),{tag:d,run:b})}}},_smoothingEnabled:{type:Boolean,value:!1,readOnly:!0}},observers:["reload(runs, tag)","_setChartData(_runToPrCurveEntry, _previousRunToPrCurveEntry, _setOfRelevantRuns)","_updateRunToPrCurveEntry(_runToDataOverTime, runToStepCap)"],
_createProcessDataFunction(){return(b,d,f)=>{this.set("_runToDataOverTime",Object.assign({},this._runToDataOverTime,f))}},_computeRunColor(b){return this._colorScaleFunction.scale(b)},attached(){this._attached=!0;this.reload()},reload(){this._attached&&(0===this.runs.length?this.set("_runToDataOverTime",{}):this.$$("tf-line-chart-data-loader").reload())},_setChartData(b,d,f){_.forOwn(b,(h,k)=>{const t=d[k];t&&b[k].step===t.step||(f[k]?this._updateSeriesDataForRun(k,h):this._clearSeriesData(k))})},
_updateSeriesDataForRun(b,d){const f=_.reduce(this._seriesDataFields,(k,t)=>{k[t]=d[t].slice().reverse();return k},{}),h=Array(f[this._seriesDataFields[0]].length);for(let k=0;k<h.length;k++)h[k]=_.mapValues(f,t=>t[k]);this.$$("tf-line-chart-data-loader").setSeriesData(b,h)},_clearSeriesData(b){this.$$("tf-line-chart-data-loader").setSeriesData(b,[])},_updateRunToPrCurveEntry(b,d){const f={};_.forOwn(b,(h,k)=>{h&&h.length&&(f[k]=this._computeEntryClosestOrEqualToStepCap(d[k],h))});this.set("_previousRunToPrCurveEntry",
this._runToPrCurveEntry);this.set("_runToPrCurveEntry",f)},_computeEntryClosestOrEqualToStepCap(b,d){b=Math.min(_.sortedIndex(d.map(f=>f.step),b),d.length-1);return d[b]},_computeRunsWithStepAvailable(b,d){return _.filter(b,f=>d[f]).sort()},_computeSetOfRelevantRuns(b){const d={};_.forEach(b,f=>{d[f]=!0});return d},_computeCurrentStepForRun(b,d){return(b=b[d])?b.step:null},_computeCurrentWallTimeForRun(b,d){return(b=b[d])?(new Date(1E3*b.wall_time)).toString():null},_toggleExpanded(){this.set("_expanded",
!this._expanded);this.redraw()},_resetDomain(){this.$$("tf-line-chart-data-loader").resetDomain()},redraw(){this.$$("tf-line-chart-data-loader").redraw()}});

//# sourceURL=build://tf-pr-curve-dashboard/tf-pr-curve-steps-selector.html.js
Polymer({is:"tf-pr-curve-steps-selector",properties:{runs:Array,runToAvailableTimeEntries:Object,runToStep:{type:Object,notify:!0,computed:"_computeRunToStep(runToAvailableTimeEntries, _runToStepIndex)"},timeDisplayType:String,_runToStepIndex:{type:Object,value:()=>({})},_runsWithSliders:{type:Array,computed:"_computeRunsWithSliders(runs, runToAvailableTimeEntries)"}},observers:["_updateStepsForNewRuns(runToAvailableTimeEntries)"],_computeColorForRun(b){return pf.runsColorScale(b)},_computeTimeTextForRun(b,
d,f,h){d=d[f];if(!_.isNumber(d))return"";b=b[f];if(!b)return"";b=b[d][h];if("step"===h)return`step ${b}`;if("relative"===h)return 1>b?`${(1E3*b).toFixed(2)} ms`:`${b.toFixed(2)} s`;if("wall_time"===h)return(new Date(1E3*b)).toString();throw Error(`The display type of ${h} is not recognized.`);},_sliderValueChanged(b){const d=b.target.dataset.run,f=b.target.immediateValue,h=Object.assign({},this._runToStepIndex);isNaN(f)?delete h[d]:h[d]=b.target.immediateValue;this._runToStepIndex=h},_computeMaxStepIndexForRun(b,
d){return(b=b[d])&&b.length?b.length-1:0},_updateStepsForNewRuns(b){const d=Object.assign({},this._runToStepIndex);_.forOwn(b,(f,h)=>{_.isNumber(d[h])||(d[h]=f.length-1)});this._runToStepIndex=d},_getStep(b,d){return this._runToStepIndex?this._runToStepIndex[d]:0},_computeRunToStep(b,d){const f={};_.forOwn(d,(h,k)=>{const t=b[k];t&&(f[k]=t[h].step)});return f},_computeRunsWithSliders(b,d){return b.filter(f=>d[f])}});

//# sourceURL=build://tf-pr-curve-dashboard/tf-pr-curve-dashboard.html.js
Polymer({is:"tf-pr-curve-dashboard",properties:{_timeDisplayType:{type:String,value:"step"},_selectedRuns:{type:Array,value:()=>[]},_runToTagInfo:{type:Object,value:()=>({})},_runToAvailableTimeEntries:{type:Object,value:{}},_relevantSelectedRuns:{type:Array,computed:"_computeRelevantSelectedRuns(_selectedRuns, _runToTagInfo)"},_runsWithPrCurveData:Array,_runToStep:{type:Object,notify:!0},_dataNotFound:Boolean,_tagFilter:String,_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTagInfo, _selectedRuns, _tagFilter, _categoriesDomReady)"},
_getCategoryItemKey:{type:Function,value:()=>b=>b.tag},_requestManager:{type:Object,value:()=>new vc.RequestManager},_step:{type:Number,value:0,notify:!0}},ready(){this.reload()},reload(){Promise.all([this._fetchTags(),this._fetchTimeEntriesPerRun()]).then(()=>{this._reloadCards()})},_shouldOpen(b){return 2>=b},_fetchTags(){const b=vc.getRouter().pluginRoute("pr_curves","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,h=>_.keys(h));
f=vc.getTags(f);this.set("_dataNotFound",0===f.length);this.set("_runToTagInfo",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_fetchTimeEntriesPerRun(){const b=vc.getRouter().pluginRoute("pr_curves","/available_time_entries");return this._requestManager.request(b).then(d=>{_.forOwn(d,f=>{_.forEach(f,h=>{h.relative=h.wall_time-f[0].wall_time})});this.set("_runToAvailableTimeEntries",d);d=_.keys(d).slice().sort();_.isEqual(d,this._runsWithPrCurveData)||this.set("_runsWithPrCurveData",
d)})},_reloadCards(){_.forEach(this.root.querySelectorAll("tf-pr-curve-card"),b=>{b.reload()})},_makeCategories(b,d,f){b=_.mapValues(b,h=>Object.keys(h));return $c.categorizeTags(b,d,f)},_computeColorForRun(b){return pf.runsColorScale(b)},_computeRelevantSelectedRuns(b,d){return b.filter(f=>d[f])},_tagMetadata(b,d,f){const h={};d.forEach(k=>{h[k]=b[k][f]});d=f.replace(/\/pr_curves$/,"");return rf.aggregateTagInfo(h,d)}});

//# sourceURL=build://tf-profile-redirect-dashboard/tf-profile-redirect-dashboard.html.js
(function(){Polymer({is:"tf-profile-redirect-dashboard",properties:{_installCommand:{type:String,readOnly:!0,value:"pip install -U tensorboard_plugin_profile"}},_copyInstallCommand(){const b=this;return hc(function*(){const d=()=>hc(function*(){b.$.commandTextarea.select();try{yield navigator.clipboard.writeText(b._installCommand)}catch(f){if(!document.execCommand("copy"))return Promise.reject()}});try{yield d(),b.$.copiedMessage.innerText="Copied."}catch(f){b.$.copiedMessage.innerText="Failed to copy to clipboard."}})},
_removeCopiedMessage(){this.$.copiedMessage.innerText=""}})})();

//# sourceURL=build://tf-tensorboard/plugin-dialog.html.js
Polymer({is:"tf-plugin-dialog",properties:{_title:{type:String,value:null},_customMessage:{type:String,value:null},_open:{type:Boolean},_hidden:{type:Boolean,computed:"_computeHidden(_open)",reflectToAttribute:!0},_useNativeBackdrop:{type:Boolean,value:!1,readOnly:!0}},openNoTensorFlowDialog(){this.openDialog("This plugin is disabled without TensorFlow",'To enable this plugin in TensorBoard, install TensorFlow with "pip install tensorflow" or equivalent.')},openOldTensorFlowDialog(b){this.openDialog("This plugin is disabled without TensorFlow "+
b,"To enable this plugin in TensorBoard, install TensorFlow "+b+' or greater with "pip install tensorflow" or equivalent.')},openDialog(b,d){this.set("_title",b);this.set("_customMessage",d);this.$.dialog.open()},closeDialog(){this.$.dialog.close()},_computeHidden(b){return!b}});

//# sourceURL=build://tf-beholder-dashboard/tf-beholder-video.html.js
(function(){const b=vc.getRouter().pluginRoute("beholder","/beholder-frame"),d=vc.getRouter().pluginRoute("beholder","/ping");Polymer({is:"tf-beholder-video",properties:{fps:{type:Number,value:10,observer:"_fpsChanged"},pingSleep:{type:Number,value:1E3},xhrTimeout:{type:Number,value:2500},_imageURL:{type:String,value:"data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs\x3d"},_xhr:Object,_timer:Number,_isDead:Boolean},attached(){this.set("_imageURL",b);this._ping()},detached(){this._clear();
this.set("_imageURL","data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs\x3d")},_ping(){this._clear();this._xhr=new XMLHttpRequest;this._xhr.open("GET",d,!0);this._xhr.timeout=this.xhrTimeout;this._xhr.onload=this._onPingLoad.bind(this);this._xhr.onerror=this._onPing.bind(this,!1,this.pingSleep);this._xhr.ontimeout=this._onPing.bind(this,!1,1);this._xhr.send(null)},_onPingLoad(){if(200==this._xhr.status){const f=JSON.parse(this._xhr.responseText);this._onPing("alive"==f.status,this.pingSleep)}else this._onPing(!1,
this.pingSleep)},_onPing(f,h){f&&this._isDead&&this.set("_imageURL",b+"?t\x3d"+(new Date).getTime());this._isDead=!f;this._timer=window.setTimeout(()=>this._ping(),h)},_clear(){this._timer&&(window.clearTimeout(this._timer),this._timer=null);this._xhr&&(this._xhr.readyState<XMLHttpRequest.DONE&&this._xhr.abort(),this._xhr=null)},_fpsChanged(f,h){0==f?this._clear():0==h&&this._ping()}})})();

//# sourceURL=build://tf-beholder-dashboard/tf-beholder-info.html.js
(function(){const b=vc.getRouter().pluginRoute("beholder","/section-info");Polymer({is:"tf-beholder-info",properties:{fps:{type:Number,value:10,observer:"_fpsChanged"},xhrTimeout:{type:Number,value:1E4},_items:{type:Array,value:()=>[{name:"Loading..."}]},_xhr:Object,_timer:Number},attached(){this._load()},detached(){this._clear()},_load(){this._clear();this._xhr=new XMLHttpRequest;this._xhr.open("GET",b,!0);this._xhr.timeout=this.xhrTimeout;this._xhr.onload=this._onLoad.bind(this);this._xhr.onerror=
this._retry.bind(this,this._getSleep());this._xhr.ontimeout=this._retry.bind(this,1);this._xhr.send(null)},_onLoad(){if(200==this._xhr.status){const d=JSON.parse(this._xhr.responseText);console.assert(Array.isArray(d),"Expected response to be in an array");this._items=d}this._retry(this._getSleep())},_retry(d){this._timer=window.setTimeout(this._load.bind(this),d)},_getSleep(){return 1E3/(0===this.fps?1:this.fps)},_clear(){this._timer&&(window.clearTimeout(this._timer),this._timer=null);this._xhr&&
(this._xhr.readyState<XMLHttpRequest.DONE&&this._xhr.abort(),this._xhr=null)},_fpsChanged(d,f){0==d?this._clear():0==f&&this._load()}})})();

//# sourceURL=build://tf-beholder-dashboard/tf-beholder-dashboard.html.js
(function(){Polymer({is:"tf-beholder-dashboard",properties:{_requestManager:{type:Object,value:()=>new vc.RequestManager(10,0)},_isAvailable:Boolean,_values:{type:String,value:"trainable_variables",observer:"_configChanged"},_mode:{type:String,value:"variance",observer:"_configChanged"},_scaling:{type:String,value:"layer",observer:"_configChanged"},_windowSize:{type:Number,value:15,observer:"_configChanged"},_previousFPS:{type:Number,value:30},_FPS:{type:Number,value:10,observer:"_configChanged"},
_recordText:{type:String,value:"start recording"},_isRecording:{type:Boolean,value:!1,observer:"_configChanged"},_showAll:{type:Boolean,value:!1,observer:"_configChanged"},_colormap:{type:String,value:"magma",observer:"_configChanged"},_is_active:{type:Boolean,value:!1,observer:"_configChanged"},_controls_disabled:{type:Boolean,value:!1,observer:"_configChanged"}},_valuesNotFrame(b){return"frames"!==b},_varianceSelected(b){return"variance"===b},_configChanged(){if(this._is_active&&!this._controls_disabled){var b=
[this._values,this._mode,this._scaling,this._windowSize,this._FPS,this._isRecording,this._showAll,this._colormap],d;for(d of b)if("undefined"===typeof d||""===d)return;b=vc.getRouter().pluginRoute("beholder","/change-config");this._requestManager.request(b,{values:this._values,mode:this._mode,scaling:this._scaling,window_size:this._windowSize,FPS:this._FPS,is_recording:this._isRecording,show_all:this._showAll,colormap:this._colormap})}},_toggleRecord(){"start recording"==this._recordText?(this.set("_recordText",
"stop recording"),this.set("_isRecording",!0)):(this.set("_recordText","start recording"),this.set("_isRecording",!1));this.$.record_button.classList.toggle("is-recording")},attached:function(){this._requestManager.request(vc.getRouter().pluginsListing()).then(b=>{"beholder"in b?(this.$.initialDialog.closeDialog(),this.set("_isAvailable",!0)):(this.$.initialDialog.openNoTensorFlowDialog(),this.set("_isAvailable",!1))})},ready(){this.reload()},reload(){if(this._isAvailable){const b=vc.getRouter().pluginRoute("beholder",
"/is-active");this._requestManager.request(b).then(d=>{this.set("_is_active",d.is_active);this.set("_controls_disabled",!d.is_config_writable)})}}});qf.registerDashboard()})();

//# sourceURL=build://tf-hparams-utils/tf-hparams-utils.html.js
(function(b){(function(d){(function(f){function h(B){return""!==B.displayName&&void 0!==B.displayName?B.displayName:B.name}function k(B){if(""!==B.displayName&&void 0!==B.displayName)return B.displayName;let I=B.name.group;B=B.name.tag;void 0===I&&(I="");void 0===B&&(B="");return""===I?B:I+"."+B}function t(B){return B.hparamColumns.length}function l(B){return B.metricColumns.length}function p(B,I){return B[I]}function m(B,I){return B.find(N=>_.isEqual(N.name,I))}function n(B,I,N){return I.hparams[B.hparamColumns[N].hparamInfo.name]}
function q(B,I,N){B=m(I.metricValues,B.metricColumns[N].metricInfo.name);return void 0===B?void 0:B.value}function u(B,I,N){return N<B.hparamColumns.length?n(B,I,N):q(B,I,N-B.hparamColumns.length)}function x(B){return B.hparamInfos.length}function A(B){return B.metricInfos.length}function y(B,I,N){return I.hparams[B.hparamInfos[N].name]}function w(B,I,N){B=m(I.metricValues,B.metricInfos[N].name);return void 0===B?void 0:B.value}function C(B,I,N){return N<B.hparamInfos.length?y(B,I,N):w(B,I,N-B.hparamInfos.length)}
function G(B){return _.isNumber(B)?B.toPrecision(5):void 0===B?"":B.toString()}function D(B,I){return B*B+I*I}f.hparamName=h;f.metricName=k;f.schemaColumnName=function(B,I){return I<B.hparamColumns.length?h(B.hparamColumns[I].hparamInfo):k(B.metricColumns[I-B.hparamColumns.length].metricInfo)};f.numHParams=t;f.numMetrics=l;f.numColumns=function(B){return t(B)+l(B)};f.hparamValueByName=p;f.metricValueByName=m;f.hparamValueByIndex=n;f.metricValueByIndex=q;f.columnValueByIndex=u;f.numericColumnExtent=
function(B,I,N){return d3.extent(I,O=>u(B,O,N))};f.getAbsoluteColumnIndex=function(B,I,N){if(N<I.hparamInfos.length)B=B.hparamColumns.findIndex(O=>O.hparamInfo.name===I.hparamInfos[N].name);else{const O=I.metricInfos[N-I.hparamInfos.length].name;B=B.hparamColumns.length+B.metricColumns.findIndex(H=>H.metricInfo.name===O)}console.assert(-1!==B);return B};f.schemaVisibleColumnName=function(B,I){return I<B.hparamInfos.length?h(B.hparamInfos[I]):k(B.metricInfos[I-B.hparamInfos.length])};f.numVisibleHParams=
x;f.numVisibleMetrics=A;f.numVisibleColumns=function(B){return x(B)+A(B)};f.visibleNumericColumnExtent=function(B,I,N){return d3.extent(I,O=>C(B,O,N))};f.prettyPrintHParamValueByName=function(B,I){return G(p(B,I))};f.prettyPrintMetricValueByName=function(B,I){return G(m(B,I))};f.sessionGroupWithName=function(B,I){return B.find(N=>N.name===I)};f.hparamValueByVisibleIndex=y;f.metricValueByVisibleIndex=w;f.columnValueByVisibleIndex=C;f.prettyPrint=G;f.l2NormSquared=D;f.euclideanDist=function(B,I,N,O){return Math.sqrt(D(B-
N,I-O))};f.pointToRectangleDist=function(B,I,N,O,H,K){if(B<N&&I<O)return f.euclideanDist(B,I,N,O);if(N<=B&&B<H&&I<O)return O-I;if(H<=B&&I<O)return f.euclideanDist(B,I,H,O);if(B<N&&O<=I&&I<K)return N-B;if(N<=B&&B<H&&O<=I&&I<K)return 0;if(H<=B&&O<=I&&I<K)return B-H;if(B<N&&K<=I)return f.euclideanDist(B,I,N,K);if(N<=B&&B<H&&K<=I)return I-K;if(H<=B&&K<=I)return f.euclideanDist(B,I,H,K);throw"Point (x,y) must be in one of the regions defined above.";};f.translateStr=function(B,I){return void 0===I?"translate("+
B+")":"translate("+B+","+I+")"};f.rotateStr=function(B,I){let N="rotate(90";void 0!==B&&void 0!==I&&(N=N+","+B+","+I);return N+")"};f.isNullOrUndefined=function(B){return null===B||void 0===B};f.quadTreeVisitPointsInRect=function(B,I,N,O,H,K){B.visit((M,L,Q,T,X)=>{if(void 0===M.length){do L=B.x()(M.data),Q=B.y()(M.data),I<=L&&L<O&&N<=Q&&Q<H&&K(M.data);while(M=M.next);return!0}return L>=O||T<=I||Q>=H||X<=N})};f.quadTreeVisitPointsInDisk=function(B,I,N,O,H){B.visit((K,M,L,Q,T)=>{if(void 0===K.length){do M=
B.x()(K.data),L=B.y()(K.data),M=f.euclideanDist(I,N,M,L),M<=O&&H(K.data,M);while(K=K.next);return!0}return f.pointToRectangleDist(I,N,M,L,Q,T)>O})};f.filterSet=function(B,I){const N=new Set;B.forEach(O=>{I(O)&&N.add(O)});return N};f.setArrayObservably=function(B,I){const N=B.get("sessionGroups",B);Array.isArray(N)?B.splice.apply(B,["sessionGroups",0,N.length].concat(I)):B.set("sessionGroups",I)};f.hashOfString=function(B){let I=0;for(let N=0;N<B.length;++N)I=31*I+B.charCodeAt(N)&4294967295;return I+
Math.pow(2,31)}})(d.utils||(d.utils={}))})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://vaadin-split-layout/vaadin-split-layout.html.js
Polymer({is:"vaadin-split-layout",behaviors:[Polymer.IronResizableBehavior],properties:{vertical:{type:Boolean,reflectToAttribute:!0,value:!1},_previousPrimaryPointerEvents:String,_previousSecondaryPointerEvents:String},attached:function(){this._observer=Polymer.dom(this).observeNodes(this._processChildren)},detached:function(){Polymer.dom(this).unobserveNodes(this._observer)},_processChildren:function(){this.getEffectiveChildren().filter(function(b){return b.classList.contains("splitter-handle")?
(Polymer.dom(b).setAttribute("slot","handle"),!1):!0}).forEach(function(b,d){0===d?(this._primaryChild=b,Polymer.dom(b).setAttribute("slot","primary")):1==d?(this._secondaryChild=b,Polymer.dom(b).setAttribute("slot","secondary")):Polymer.dom(b).removeAttribute("slot")}.bind(this))},_setFlexBasis:function(b,d,f){d=Math.max(0,Math.min(d,f));0===d&&(d=1E-6);b.style.flex="1 1 "+d+"px"},_onHandleTrack:function(b){if(this._primaryChild&&this._secondaryChild){var d=this.vertical?"height":"width";"start"===
b.detail.state?(this._startSize={container:this.getBoundingClientRect()[d]-this.$.splitter.getBoundingClientRect()[d],primary:this._primaryChild.getBoundingClientRect()[d],secondary:this._secondaryChild.getBoundingClientRect()[d]},this._previousPrimaryPointerEvents=this._primaryChild.style.pointerEvents,this._previousSecondaryPointerEvents=this._secondaryChild.style.pointerEvents,this._primaryChild.style.pointerEvents="none",this._secondaryChild.style.pointerEvents="none"):(d=this.vertical?b.detail.dy:
b.detail.dx,this._setFlexBasis(this._primaryChild,this._startSize.primary+d,this._startSize.container),this._setFlexBasis(this._secondaryChild,this._startSize.secondary-d,this._startSize.container),this.notifyResize(),"end"===b.detail.state&&(delete this._startSize,this._primaryChild.style.pointerEvents=this._previousPrimaryPointerEvents,this._secondaryChild.style.pointerEvents=this._previousSecondaryPointerEvents))}},_preventDefault:function(b){b.preventDefault()}});

//# sourceURL=build://tf-hparams-query-pane/tf-hparams-query-pane.html.js
Polymer({is:"tf-hparams-query-pane",properties:{backend:Object,experimentName:String,configuration:{type:Object,value:()=>({schema:{hparamColumns:[],metricColumns:[]},columnsVisibility:[],visibleSchema:{hparamInfos:[],metricInfos:[]}}),readOnly:!0,notify:!0},sessionGroups:{type:Array,value:()=>[],readOnly:!0,notify:!0},_experiment:Object,_hparams:Array,_metrics:Array,_statuses:{type:Array,value:()=>[{value:"STATUS_UNKNOWN",displayName:"Unknown",allowed:!0},{value:"STATUS_SUCCESS",displayName:"Success",
allowed:!0},{value:"STATUS_FAILURE",displayName:"Failure",allowed:!0},{value:"STATUS_RUNNING",displayName:"Running",allowed:!0}]},_getExperimentResolved:{type:Object,value:function(){return new Promise(b=>{this._resolveGetExperiment=b})}},_resolveGetExperiment:Function,_listSessionGroupsCanceller:{type:Object,value:()=>new vc.Canceller},_sortByIndex:Number,_sortDirection:Number,_pageSizeInput:{type:Object,value:{value:"100",invalid:!1}},_pageNumberInput:{type:Object,value:{value:"1",invalid:!1}},
_pageCountStr:{type:String,value:"?"},_totalSessionGroupsCountStr:String,_sessionGroupsRequest:Object},observers:["_computeExperimentAndRelatedProps(backend, experimentName)","_updateConfiguration(_hparams.*, _metrics.*)"],reload(){this._queryServer()},_csvUrl(b,d){return this._downloadDataUrl(b,d,"csv")},_jsonUrl(b,d){return this._downloadDataUrl(b,d,"json")},_latexUrl(b,d){return this._downloadDataUrl(b,d,"latex")},_downloadDataUrl(b,d,f){return this.backend.getDownloadUrl(f,b,d.columnsVisibility)},
_computeExperimentAndRelatedProps(){const b=tf.hparams.utils;b.isNullOrUndefined(this.backend)||b.isNullOrUndefined(this.experimentName)||this.backend.getExperiment({experimentName:this.experimentName}).then(d=>{_.isEqual(d,this._experiment)||(this.set("_experiment",d),this._computeHParams(),this._computeMetrics(),this._queryServer(),this._resolveGetExperiment())})},_computeHParams(){const b=[];this._experiment.hparamInfos.forEach((d,f)=>{const h={info:d,displayed:5>f,filter:{}};h.info.hasOwnProperty("domainDiscrete")?
(h.filter.domainDiscrete=[],h.info.domainDiscrete.forEach(k=>{h.filter.domainDiscrete.push({value:k,checked:!0})})):"DATA_TYPE_BOOL"===h.info.type?h.filter.domainDiscrete=[{value:!1,checked:!0},{value:!0,checked:!0}]:"DATA_TYPE_FLOAT64"===h.info.type?h.filter.interval={min:{value:"",invalid:!1},max:{value:"",invalid:!1}}:"DATA_TYPE_STRING"===h.info.type?h.filter.regexp="":console.warn("unknown hparam.info.type: %s",h.info.type);b.push(h)});this.set("_hparams",b)},_computeMetrics(){const b=[];this._experiment.metricInfos.forEach((d,
f)=>{b.push({info:d,filter:{interval:{min:{value:"",invalid:!1},max:{value:"",invalid:!1}}},displayed:5>f})});this.set("_metrics",b)},_computeSchema(){return this._hparams&&this._metrics?{hparamColumns:this._hparams.map(b=>({hparamInfo:b.info})),metricColumns:this._metrics.map(b=>({metricInfo:b.info}))}:{hparamColumns:[],metricColumns:[]}},_updateConfiguration(){this.debounce("_updateConfiguration",()=>{this._setConfiguration({schema:this._computeSchema(),columnsVisibility:this._computeColumnsVisibility(),
visibleSchema:this._computeVisibleSchema()})})},_computeColumnsVisibility(){return this._hparams&&this._metrics?this._hparams.map(b=>b.displayed).concat(this._metrics.map(b=>b.displayed)):[]},_computeVisibleSchema(){if(!this._hparams||!this._metrics)return{hparamInfos:[],metricInfos:[]};const b=this._hparams.filter(f=>f.displayed).map(f=>f.info),d=this._metrics.filter(f=>f.displayed).map(f=>f.info);return{hparamInfos:b,metricInfos:d}},_queryServer(){this.debounce("queryServer",()=>this._queryServerNoDebounce(),
100)},_queryServerNoDebounce(){return this._sendListSessionGroupsRequest().then(this._listSessionGroupsCanceller.cancellable(({value:b,cancelled:d})=>{d||(0<=b.totalSize?(this.set("_pageCountStr",String(Math.ceil(b.totalSize/+this._pageSizeInput.value))),this.set("_totalSessionGroupsCountStr",b.totalSize)):(this.set("_pageCountStr","?"),this.set("_totalSessionGroupsCountStr","Unknown")),tf.hparams.utils.setArrayObservably(this,b.sessionGroups))}))},_sendListSessionGroupsRequest(){const b=this._buildListSessionGroupsRequest();
if(null!==b)return this.set("_sessionGroupsRequest",b),this._listSessionGroupsCanceller.cancelAll(),this.backend.listSessionGroups(b)},_buildListSessionGroupsRequest(){function b(m){var n=f.get(m+".min.value");console.assert(void 0!==n);n=""===n?"-Infinity":+n;f.set(m+".min.invalid",isNaN(n));h=h&&!isNaN(n);var q=f.get(m+".max.value");console.assert(void 0!==q);q=""===q?"Infinity":+q;f.set(m+".max.invalid",isNaN(q));h=h&&!isNaN(q);return isNaN(n)||isNaN(q)?null:{minValue:n,maxValue:q}}function d(m){var n=
f.get(m+".value");console.assert(void 0!==n);n=+n;const q=Number.isInteger(n)&&0<n;f.set(m+".invalid",!q);h=h&&q;return q?n:null}const f=this;let h=!0;const k=this._statuses.filter(m=>m.allowed).map(m=>m.value);let t=[];this._hparams.forEach((m,n)=>{let q={hparam:m.info.name};if(m.filter.domainDiscrete)q.filterDiscrete=[],m.filter.domainDiscrete.forEach(u=>{u.checked&&q.filterDiscrete.push(u.value)});else if(m.filter.interval)q.filterInterval=b("_hparams."+n+".filter.interval");else if(m.filter.regexp)q.filterRegexp=
m.filter.regexp;else return console.error("hparam.filter with no domainDiscrete, interval or regexp properties set: %s",m),null;t.push(q)});this._metrics.forEach((m,n)=>{m={metric:m.info.name,filterInterval:b("_metrics."+n+".filter.interval")};t.push(m)});if(void 0!==this._sortByIndex&&void 0!==this._sortDirection){if(!(this._sortByIndex in t))return console.error("No column in colParams with index sortByIndex: %s",this._sortByIndex),null;t[this._sortByIndex].order=0===this._sortDirection?"ORDER_ASC":
"ORDER_DESC"}const l=d("_pageNumberInput"),p=d("_pageSizeInput");return h?{experimentName:this.experimentName,allowedStatuses:k,colParams:t,startIndex:p*(l-1),sliceSize:p}:null},_metricSortByIndex(b){return b+this._hparams.length},_hparamName:tf.hparams.utils.hparamName,_metricName:tf.hparams.utils.metricName,_prettyPrint:tf.hparams.utils.prettyPrint});

//# sourceURL=build://iron-pages/iron-pages.html.js
Polymer({is:"iron-pages",behaviors:[Polymer.IronResizableBehavior,Polymer.IronSelectableBehavior],properties:{activateEvent:{type:String,value:null}},observers:["_selectedPageChanged(selected)"],_selectedPageChanged:function(){this.async(this.notifyResize)}});

//# sourceURL=build://paper-header-panel/paper-header-panel.html.js
(function(){var b={scroll:!0},d={standard:2,waterfall:1,"waterfall-tall":1},f={"waterfall-tall":!0};Polymer({is:"paper-header-panel",properties:{mode:{type:String,value:"standard",observer:"_modeChanged",reflectToAttribute:!0},shadow:{type:Boolean,value:!1},tallClass:{type:String,value:"tall"},atTop:{type:Boolean,value:!0,notify:!0,readOnly:!0,reflectToAttribute:!0}},observers:["_computeDropShadowHidden(atTop, mode, shadow)"],attached:function(){this._addListener();this._keepScrollingState()},detached:function(){this._removeListener()},
ready:function(){this.scrollHandler=this._scroll.bind(this);console.warn(this.is,"is deprecated. Please use app-layout instead!")},get header(){return Polymer.dom(this.$.headerSlot).getDistributedNodes()[0]},get scroller(){return this._getScrollerForMode(this.mode)},get visibleShadow(){return this.$.dropShadow.classList.contains("has-shadow")},_computeDropShadowHidden:function(h,k){k=d[k];this.shadow?this.toggleClass("has-shadow",!0,this.$.dropShadow):2===k?this.toggleClass("has-shadow",!0,this.$.dropShadow):
1!==k||h?this.toggleClass("has-shadow",!1,this.$.dropShadow):this.toggleClass("has-shadow",!0,this.$.dropShadow)},_computeMainContainerClass:function(h){var k={};k.flex="cover"!==h;return Object.keys(k).filter(function(t){return k[t]}).join(" ")},_addListener:function(){this.scroller.addEventListener("scroll",this.scrollHandler)},_removeListener:function(){this.scroller.removeEventListener("scroll",this.scrollHandler)},_modeChanged:function(h,k){var t=this.header;t&&(f[k]&&!f[h]?(t.classList.remove(this.tallClass),
this.async(function(){t.classList.remove("animate")},200)):this.toggleClass("animate",f[h],t));this._keepScrollingState()},_keepScrollingState:function(){var h=this.scroller,k=this.header;this._setAtTop(0===h.scrollTop);k&&this.tallClass&&f[this.mode]&&this.toggleClass(this.tallClass,this.atTop||k.classList.contains(this.tallClass)&&h.scrollHeight<this.offsetHeight,k)},_scroll:function(){this._keepScrollingState();this.fire("content-scroll",{target:this.scroller},{bubbles:!1})},_getScrollerForMode:function(h){return b[h]?
this:this.$.mainContainer}})})();

//# sourceURL=build://paper-tabs/paper-tab.html.js
Polymer({is:"paper-tab",behaviors:[Polymer.IronControlState,Polymer.IronButtonState,Polymer.PaperRippleBehavior],properties:{link:{type:Boolean,value:!1,reflectToAttribute:!0}},hostAttributes:{role:"tab"},listeners:{down:"_updateNoink",tap:"_onTap"},attached:function(){this._updateNoink()},get _parentNoink(){var b=Polymer.dom(this).parentNode;return!!b&&!!b.noink},_updateNoink:function(){this.noink=!!this.noink||!!this._parentNoink},_onTap:function(b){if(this.link){var d=this.queryEffectiveChildren("a");
d&&b.target!==d&&d.click()}}});

//# sourceURL=build://paper-tabs/paper-tabs.html.js
Polymer({is:"paper-tabs",behaviors:[Polymer.IronResizableBehavior,Polymer.IronMenubarBehavior],properties:{noink:{type:Boolean,value:!1,observer:"_noinkChanged"},noBar:{type:Boolean,value:!1},noSlide:{type:Boolean,value:!1},scrollable:{type:Boolean,value:!1},fitContainer:{type:Boolean,value:!1},disableDrag:{type:Boolean,value:!1},hideScrollButtons:{type:Boolean,value:!1},alignBottom:{type:Boolean,value:!1},selectable:{type:String,value:"paper-tab"},autoselect:{type:Boolean,value:!1},autoselectDelay:{type:Number,
value:0},_step:{type:Number,value:10},_holdDelay:{type:Number,value:1},_leftHidden:{type:Boolean,value:!1},_rightHidden:{type:Boolean,value:!1},_previousTab:{type:Object}},hostAttributes:{role:"tablist"},listeners:{"iron-resize":"_onTabSizingChanged","iron-items-changed":"_onTabSizingChanged","iron-select":"_onIronSelect","iron-deselect":"_onIronDeselect"},keyBindings:{"left:keyup right:keyup":"_onArrowKeyup"},created:function(){this._holdJob=null;this._pendingActivationTimeout=this._pendingActivationItem=
void 0;this._bindDelayedActivationHandler=this._delayedActivationHandler.bind(this);this.addEventListener("blur",this._onBlurCapture.bind(this),!0)},ready:function(){this.setScrollDirection("y",this.$.tabsContainer)},detached:function(){this._cancelPendingActivation()},_noinkChanged:function(b){Polymer.dom(this).querySelectorAll("paper-tab").forEach(b?this._setNoinkAttribute:this._removeNoinkAttribute)},_setNoinkAttribute:function(b){b.setAttribute("noink","")},_removeNoinkAttribute:function(b){b.removeAttribute("noink")},
_computeScrollButtonClass:function(b,d,f){return!d||f?"hidden":b?"not-visible":""},_computeTabsContentClass:function(b,d){return b?"scrollable"+(d?" fit-container":""):" fit-container"},_computeSelectionBarClass:function(b,d){return b?"hidden":d?"align-bottom":""},_onTabSizingChanged:function(){this.debounce("_onTabSizingChanged",function(){this._scroll();this._tabChanged(this.selectedItem)},10)},_onIronSelect:function(b){this._tabChanged(b.detail.item,this._previousTab);this._previousTab=b.detail.item;
this.cancelDebouncer("tab-changed")},_onIronDeselect:function(){this.debounce("tab-changed",function(){this._tabChanged(null,this._previousTab);this._previousTab=null},1)},_activateHandler:function(){this._cancelPendingActivation();Polymer.IronMenuBehaviorImpl._activateHandler.apply(this,arguments)},_scheduleActivation:function(b,d){this._pendingActivationItem=b;this._pendingActivationTimeout=this.async(this._bindDelayedActivationHandler,d)},_delayedActivationHandler:function(){var b=this._pendingActivationItem;
this._pendingActivationTimeout=this._pendingActivationItem=void 0;b.fire(this.activateEvent,null,{bubbles:!0,cancelable:!0})},_cancelPendingActivation:function(){void 0!==this._pendingActivationTimeout&&(this.cancelAsync(this._pendingActivationTimeout),this._pendingActivationTimeout=this._pendingActivationItem=void 0)},_onArrowKeyup:function(){this.autoselect&&this._scheduleActivation(this.focusedItem,this.autoselectDelay)},_onBlurCapture:function(b){b.target===this._pendingActivationItem&&this._cancelPendingActivation()},
get _tabContainerScrollSize(){return Math.max(0,this.$.tabsContainer.scrollWidth-this.$.tabsContainer.offsetWidth)},_scroll:function(b,d){this.scrollable&&this._affectScroll(d&&-d.ddx||0)},_down:function(){this.async(function(){this._defaultFocusAsync&&(this.cancelAsync(this._defaultFocusAsync),this._defaultFocusAsync=null)},1)},_affectScroll:function(b){this.$.tabsContainer.scrollLeft+=b;b=this.$.tabsContainer.scrollLeft;this._leftHidden=0===b;this._rightHidden=b===this._tabContainerScrollSize},
_onLeftScrollButtonDown:function(){this._scrollToLeft();this._holdJob=setInterval(this._scrollToLeft.bind(this),this._holdDelay)},_onRightScrollButtonDown:function(){this._scrollToRight();this._holdJob=setInterval(this._scrollToRight.bind(this),this._holdDelay)},_onScrollButtonUp:function(){clearInterval(this._holdJob);this._holdJob=null},_scrollToLeft:function(){this._affectScroll(-this._step)},_scrollToRight:function(){this._affectScroll(this._step)},_tabChanged:function(b,d){if(b){var f=this.$.tabsContent.getBoundingClientRect(),
h=f.width,k=b.getBoundingClientRect();f=k.left-f.left;this._pos={width:this._calcPercent(k.width,h),left:this._calcPercent(f,h)};if(this.noSlide||null==d)this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),this._positionBar(this._pos.width,this._pos.left);else{var t=d.getBoundingClientRect();d=this.items.indexOf(d);b=this.items.indexOf(b);this.$.selectionBar.classList.add("expand");b=d<b;this._isRTL&&(b=!b);b?this._positionBar(this._calcPercent(k.left+k.width-
t.left,h)-5,this._left):this._positionBar(this._calcPercent(t.left+t.width-k.left,h)-5,this._calcPercent(f,h)+5);this.scrollable&&this._scrollToSelectedIfNeeded(k.width,f)}}else this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),this._positionBar(0,0)},_scrollToSelectedIfNeeded:function(b,d){d-=this.$.tabsContainer.scrollLeft;0>d?this.$.tabsContainer.scrollLeft+=d:(d+=b-this.$.tabsContainer.offsetWidth,0<d&&(this.$.tabsContainer.scrollLeft+=d))},_calcPercent:function(b,
d){return 100*b/d},_positionBar:function(b,d){b=b||0;d=d||0;this._width=b;this._left=d;this.transform("translateX("+d+"%) scaleX("+b/100+")",this.$.selectionBar)},_onBarTransitionEnd:function(){var b=this.$.selectionBar.classList;b.contains("expand")?(b.remove("expand"),b.add("contract"),this._positionBar(this._pos.width,this._pos.left)):b.contains("contract")&&b.remove("contract")}});

//# sourceURL=build://paper-toolbar/paper-toolbar.html.js
Polymer({is:"paper-toolbar",hostAttributes:{role:"toolbar"},properties:{bottomJustify:{type:String,value:""},justify:{type:String,value:""},middleJustify:{type:String,value:""}},ready:function(){console.warn(this.is,"is deprecated. Please use app-layout instead!")},attached:function(){this._observer=this._observe(this);this._updateAriaLabelledBy()},detached:function(){this._observer&&this._observer.disconnect()},_observe:function(b){var d=new MutationObserver(function(){this._updateAriaLabelledBy()}.bind(this));
d.observe(b,{childList:!0,subtree:!0});return d},_updateAriaLabelledBy:function(){Polymer.dom.flush();for(var b=[],d=Array.prototype.slice.call(Polymer.dom(this.root).querySelectorAll("slot")).concat(Array.prototype.slice.call(Polymer.dom(this.root).querySelectorAll("content"))),f,h=0;f=d[h];h++){f=Polymer.dom(f).getDistributedNodes();for(var k,t=0;k=f[t];t++)if(k.classList&&k.classList.contains("title"))if(k.id)b.push(k.id);else{var l="paper-toolbar-label-"+Math.floor(1E4*Math.random());k.id=l;b.push(l)}}0<
b.length&&this.setAttribute("aria-labelledby",b.join(" "))},_computeBarExtraClasses:function(b){return b?b+("justified"===b?"":"-justified"):""}});

//# sourceURL=build://tf-hparams-scale-and-color-controls/tf-hparams-scale-and-color-controls.html.js
Polymer({is:"tf-hparams-scale-and-color-controls",properties:{configuration:Object,sessionGroups:Array,options:{type:Object,notify:!0,value:null}},observers:["_configurationChanged(configuration.*)","_unselectDisabledLogScales(sessionGroups.*)"],_configurationChanged(){const b=this.configuration.visibleSchema,d=this.configuration.schema,f={columns:b.hparamInfos.map((h,k)=>({name:tf.hparams.utils.hparamName(h),index:k,absoluteIndex:tf.hparams.utils.getAbsoluteColumnIndex(d,b,k),scale:this._isNumericColumn(k)?
"LINEAR":"NON_NUMERIC"})).concat(b.metricInfos.map((h,k)=>{k+=b.hparamInfos.length;return{scale:"LINEAR",name:tf.hparams.utils.metricName(h),index:k,absoluteIndex:tf.hparams.utils.getAbsoluteColumnIndex(d,b,k)}})),minColor:"#0000FF",maxColor:"#FF0000",configuration:this.configuration};this.set("options",f);Polymer.dom.flush();this.set("options.colorByColumnIndex",this._defaultColorByColumnIndex())},_unselectDisabledLogScales(){null!==this.options&&this.options.columns.forEach(b=>{const d="options.columns."+
b.index;this._allowLogScale(b)||"LOG"!==b.scale||this.set(d+".scale","LINEAR")})},_allowLogScale(b){if(!this._isNumericColumn(b.index)||!this.sessionGroups)return!1;const [d,f]=tf.hparams.utils.visibleNumericColumnExtent(this.configuration.visibleSchema,this.sessionGroups,b.index);return 0<d||0>f},_isNumericColumn(b){return b>=this.configuration.visibleSchema.hparamInfos.length||"DATA_TYPE_FLOAT64"===this.configuration.visibleSchema.hparamInfos[b].type},_defaultColorByColumnIndex(){if(0<this.configuration.visibleSchema.metricInfos.length)return this.configuration.visibleSchema.hparamInfos.length;
const b=this.configuration.visibleSchema.hparamInfos.findIndex(d=>"DATA_TYPE_FLOAT64"===d.type);if(-1!==b)return b}});

//# sourceURL=build://vaadin-grid/vaadin-grid-active-item-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.ActiveItemBehavior={properties:{activeItem:{type:Object,notify:!0,value:null}},listeners:{"cell-activate":"_activateItem"},observers:["_activeItemChanged(activeItem)"],_activateItem:function(b){var d=b.detail.model.item;this.activeItem=this.activeItem!==d?d:null;b.stopImmediatePropagation()},_activeItemChanged:function(){this.$.scroller._physicalItems&&this.$.scroller._physicalItems.forEach(function(b){this._updateItem(b,b.item)}.bind(this))}};

//# sourceURL=build://vaadin-grid/vaadin-grid-table-scroll-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.TableScrollBehaviorImpl={properties:{_vidxOffset:{type:Number,value:0},ios:{type:Boolean,value:navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),reflectToAttribute:!0},fixedSections:{type:Boolean,reflectToAttribute:!0,computed:"_hasFixedSections(scrollbarWidth)"},_frozenCells:{type:Array,value:function(){return[]}},scrolling:{type:Boolean,reflectToAttribute:!0}},ready:function(){this.scrollTarget=this.$.table},attached:function(){this.listen(this.scrollTarget,"wheel",
"_onWheel")},detached:function(){this.unlisten(this.scrollTarget,"wheel","_onWheel")},scrollToScaledIndex:function(b){this._pendingScrollToScaledIndex=null;this.$.items.style.borderTopWidth||(this._pendingScrollToScaledIndex=b);b=Math.min(Math.max(b,0),this.size-1);this.$.table.scrollTop=b/this.size*this.$.table.scrollHeight;this._scrollHandler();this.scrollToIndex(b-this._vidxOffset);this._resetScrollPosition(this._scrollPosition);this._scrollHandler();this._vidxOffset+this.lastVisibleIndex===this.size-
1&&(this.$.table.scrollTop=this.$.table.scrollHeight-this.$.table.offsetHeight,this._scrollHandler())},_hasFixedSections:function(b){return navigator.userAgent.match(/Edge/)&&0===b},_onWheel:function(b){if(!b.ctrlKey&&!this._hasScrolledAncestor(b.target,b.deltaX,b.deltaY)){var d=this.$.table,f=b.deltaY;1===b.deltaMode&&(f*=grid.$.scroller._physicalAverage);var h=Math.abs(b.deltaX)+Math.abs(f);this._canScroll(d,b.deltaX,f)?(b.preventDefault(),d.scrollTop+=f,d.scrollLeft+=b.deltaX,this._scrollHandler(),
this._hasResidualMomentum=!0,this._ignoreNewWheel=this.debounce("ignore-new-wheel",function(){this._ignoreNewWheel=null},500)):this._hasResidualMomentum&&h<=this._previousMomentum||this._ignoreNewWheel?b.preventDefault():h>this._previousMomentum&&(this._hasResidualMomentum=!1);this._previousMomentum=h}},_hasScrolledAncestor:function(b,d,f){if(this._canScroll(b,d,f))return!0;if("vaadin-grid-cell-content"!==b.localName&&b!==this&&b.parentElement)return this._hasScrolledAncestor(b.parentElement,d,f)},
_canScroll:function(b,d,f){return 0<f&&b.scrollTop<b.scrollHeight-b.offsetHeight||0>f&&0<b.scrollTop||0<d&&b.scrollLeft<b.scrollWidth-b.offsetWidth||0>d&&0<b.scrollLeft},_scrollHandler:function(){var b=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop)),d=b-this._scrollPosition,f=this._ratio,h=0,k=this._hiddenContentSize,t=f,l=[];this._scrollPosition=b;this._lastVisibleIndexVal=this._firstVisibleIndexVal=null;var p=this._scrollBottom;var m=this._physicalBottom;if(Math.abs(d)>this._physicalSize)this._physicalTop+=
d,h=Math.round(d/this._physicalAverage);else if(0>d){var n=b-this._physicalTop;l=this._virtualStart;var q=[];var u=this._physicalEnd;for(t=n/k;t<f&&h<this._physicalCount&&0<l-h&&m-this._getPhysicalSizeIncrement(u)>p;)n=this._getPhysicalSizeIncrement(u),t+=n/k,m-=n,q.push(u),h++,u=0===u?this._physicalCount-1:u-1;l=q;h=-h}else if(0<d){var x=this._virtualEnd,A=this._virtualCount-1;q=[];u=this._physicalStart;for(t=(m-p)/k;t<f&&h<this._physicalCount&&x+h<A&&this._physicalTop+this._getPhysicalSizeIncrement(u)<
b;)n=this._getPhysicalSizeIncrement(u),t+=n/k,this._physicalTop+=n,q.push(u),h++,u=(u+1)%this._physicalCount}this._virtualCount<this.size&&this._adjustVirtualIndexOffset(d);0===h?(m<p||this._physicalTop>b)&&this._increasePoolIfNeeded():(this._virtualStart+=h,this._physicalStart+=h,this._update(q,l));this._translateStationaryElements();this.hasAttribute("reordering")||(this.scrolling=!0);this.debounce("vaadin-grid-scrolling",function(){this.scrolling=!1;this._reorderRows()},100)},_adjustVirtualIndexOffset:function(b){if(1E4<
Math.abs(b))this._noScale?this._noScale=!1:(b=Math.round(this._scrollPosition/this._scrollHeight*1E3)/1E3,this._vidxOffset=Math.round(b*this.size-b*this._virtualCount),0===this._scrollTop&&this.scrollToIndex(0));else{b=this._vidxOffset||0;0===this._scrollTop?(this._vidxOffset=0,b!==this._vidxOffset&&this.scrollToIndex(0)):1E3>this.firstVisibleIndex&&0<this._vidxOffset&&(this._vidxOffset-=Math.min(this._vidxOffset,100),this.scrollToIndex(this.firstVisibleIndex+(b-this._vidxOffset)+1),this._noScale=
!0);var d=this.size-this._virtualCount;this._scrollTop>=this._maxScrollTop?(this._vidxOffset=d,b!==this._vidxOffset&&this.scrollToIndex(this._virtualCount)):this.firstVisibleIndex>this._virtualCount-1E3&&this._vidxOffset<d&&(this._vidxOffset+=Math.min(d-this._vidxOffset,100),this.scrollToIndex(this.firstVisibleIndex-(this._vidxOffset-b)),this._noScale=!0)}},_reorderRows:function(){var b=Polymer.dom(this.$.items),d=b.querySelectorAll(".vaadin-grid-row"),f=d.length-(d[0].index-(this._virtualStart+this._vidxOffset));
if(f<d.length/2)for(var h=0;h<f;h++)b.appendChild(d[h]);else for(;f<d.length;f++)b.insertBefore(d[f],d[0])},_frozenCellsChanged:function(){this.debounce("cache-elements",function(){Polymer.dom(this.domHost.root).querySelectorAll(".vaadin-grid-cell").forEach(function(b){b.style.transform=""});this._frozenCells=Array.prototype.slice.call(Polymer.dom(this.domHost.root).querySelectorAll("[frozen]"));this._translateStationaryElements()});this._updateLastFrozen()},_updateLastFrozen:function(){if(this.columnTree){var b=
this.columnTree[this.columnTree.length-1].slice(0);b.sort(function(f,h){return f._order-h._order});var d=b.reduce(function(f,h,k){h._lastFrozen=!1;return h.frozen&&!h.hidden?k:f},void 0);void 0!==d&&(b[d]._lastFrozen=!0)}},_translateStationaryElements:function(){this.fixedSections?(this.$.items.style.transform=this._getTranslate(-this._scrollLeft||0,-this._scrollTop||0),this.$.footer.style.transform=this.$.header.style.transform=this._getTranslate(-this._scrollLeft||0,0)):this.$.footer.style.transform=
this.$.header.style.transform=this._getTranslate(0,this._scrollTop);for(var b=this._getTranslate(this._scrollLeft,0),d=0;d<this._frozenCells.length;d++)this._frozenCells[d].style.transform=b},_getTranslate:function(b,d){return"translate("+b+"px,"+d+"px)"}};vaadin.elements.grid.TableScrollBehavior=[Polymer.IronScrollTargetBehavior,vaadin.elements.grid.TableScrollBehaviorImpl];

//# sourceURL=build://vaadin-grid/vaadin-grid-cell-click-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.CellClickBehavior={listeners:{click:"_onClick"},attached:function(){this._cellContentFocusHandler=function(b){b.target!==this._cellContent&&this.fire("cell-content-focus",{cell:this})}.bind(this);this.addEventListener("focus",this._cellContentFocusHandler,!0)},detached:function(){this.removeEventListener("focus",this._cellContentFocusHandler,!0)},_onClick:function(b){"vaadin-grid-sorter"!==this.localName&&this.fire("cell-focus",{cell:this});if(this._cellClick){var d=Polymer.dom(b).localTarget;
d.getDistributedNodes&&(d=Polymer.dom(d).getDistributedNodes()[0]);var f=Polymer.dom(b).path;f=Array.prototype.slice.call(f,0,f.indexOf(d)+1);d.contains(this.target&&this.target.root.activeElement||document.activeElement)||f.some(this._isFocusable)||this._cellClick(b)}},_isFocusable:function(b){var d=Polymer.dom(b).parentNode;d=-1!==Array.prototype.indexOf.call(Polymer.dom(d).querySelectorAll("[tabindex], button, input, select, textarea, object, iframe, label, a[href], area[href]"),b);return!b.disabled&&
d}};

//# sourceURL=build://vaadin-grid/vaadin-grid-table-cell.html.js
(function(){var b={properties:{column:Object,expanded:Boolean,flexGrow:Number,colSpan:Number,focused:{type:Boolean,reflectToAttribute:!0},frozen:{type:Boolean,reflectToAttribute:!0},lastFrozen:{type:Boolean,reflectToAttribute:!0},hidden:{type:Boolean,reflectToAttribute:!0},instance:Object,index:Number,item:Object,selected:Boolean,template:Object,target:Object,width:String,order:Number,reorderStatus:{type:String,reflectToAttribute:!0},_childColumns:Array,_cellContent:Object,_insertionPoint:Object,
_templatizer:Object},observers:"_columnChanged(column);_cellAttached(column, isAttached);_expandedChanged(expanded, instance);_flexGrowChanged(flexGrow);_indexChanged(index, instance);_itemChanged(item, instance);_instanceChanged(instance, target);_selectedChanged(selected, instance);_toggleContent(isAttached, _cellContent, _insertionPoint);_toggleInstance(isAttached, _templatizer, instance);_widthChanged(width);_orderChanged(order);_visibleChildColumnsChanged(_visibleChildColumns);_childColumnsChanged(_childColumns)".split(";"),
ready:function(){this.classList.add("vaadin-grid-cell");!1===Polymer.Settings.useShadow&&(this.classList.add("style-scope"),this.classList.add("vaadin-grid"))},_columnChanged:function(d){this.flexGrow=d.flexGrow;this.frozen=d.frozen;this.lastFrozen=d._lastFrozen;this.headerTemplate=d.headerTemplate;this.footerTemplate=d.footerTemplate;this.template=d.template;this.width=d.width;this.hidden=d.hidden;this.resizable=d.resizable;this._childColumns=d._childColumns;this.order=d._order;d.colSpan&&(this.colSpan=
d.colSpan);this.listen(d,"property-changed","_columnPropChanged")},_cellAttached:function(d,f){void 0!==d&&void 0!==f&&(f?this.listen(d,"property-changed","_columnPropChanged"):this.async(function(){this.isAttached||this.unlisten(d,"property-changed","_columnPropChanged")}))},_columnPropChanged:function(d){d.target==this.column&&(this[d.detail.path]=d.detail.value)},_expandedChanged:function(d,f){void 0!==d&&void 0!==f&&(f.__expanded__=d,f.expanded=d)},_flexGrowChanged:function(d){this.style.flexGrow=
d},_indexChanged:function(d,f){void 0!==d&&void 0!==f&&(f.index=d)},_itemChanged:function(d,f){void 0!==d&&void 0!==f&&(f.item=d)},_selectedChanged:function(d,f){void 0!==d&&void 0!==f&&(f.__selected__=d,f.selected=d)},_childColumnsChanged:function(d){this.colSpan=d.length},_toggleContent:function(d,f,h){void 0!==d&&void 0!==f&&void 0!==h&&(d?(Polymer.dom(f).parentNode!==this.target&&Polymer.dom(this.target).appendChild(f),Polymer.dom(this).appendChild(h)):this.async(function(){this.isAttached||Polymer.dom(f).parentNode!==
this.target||Polymer.dom(this.target).removeChild(f)}))},_toggleInstance:function(d,f,h){void 0!==d&&void 0!==f&&void 0!==h&&(d?f.addInstance(h):f.removeInstance(h))},_widthChanged:function(d){this.style.width=d},_orderChanged:function(d){this.style.order=d},_templateChanged:function(d){this.instance=d.templatizer.createInstance();this._templatizer=d.templatizer},_instanceChanged:function(d,f){void 0!==d&&void 0!==f&&(this.style.height="",this._cellContent=this._cellContent||document.createElement("vaadin-grid-cell-content"),
d="vaadin-grid-cell-content-"+(vaadin.elements.grid._contentIndex=vaadin.elements.grid._contentIndex+1||0),this._cellContent.innerHTML="",Polymer.dom(this._cellContent).appendChild(this.instance.root),this._cellContent.setAttribute("id",d),Polymer.Element?(this._cellContent.setAttribute("slot",d),this._insertionPoint=this._insertionPoint||document.createElement("slot"),this._insertionPoint.setAttribute("name",d)):(this._insertionPoint=this._insertionPoint||document.createElement("content"),this._insertionPoint.setAttribute("select",
"#"+d)))}};Polymer({is:"vaadin-grid-table-cell",behaviors:[b,vaadin.elements.grid.CellClickBehavior],observers:["_templateChanged(template)"],_cellClick:function(d){d.defaultPrevented||this.fire("cell-activate",{model:this.instance})}});Polymer({is:"vaadin-grid-table-header-cell",properties:{headerTemplate:Object,resizable:Boolean,columnResizing:{type:Boolean,reflectToAttribute:!0}},behaviors:[b,vaadin.elements.grid.CellClickBehavior],observers:["_headerTemplateChanged(headerTemplate)","_isEmptyChanged(_isEmpty, isAttached)",
"_resizableChanged(resizable)"],listeners:{mousedown:"_cancelMouseDownOnResize",mousemove:"_enableDrag",mouseout:"_disableDrag",touchstart:"_onTouchStart",touchmove:"_onTouchMove",touchend:"_onTouchEnd",contextmenu:"_onContextMenu"},_onContextMenu:function(d){this._reorderGhost&&d.preventDefault()},_onTouchStart:function(d){d.target!==this._resizeHandle&&this.target.columnReorderingAllowed&&(this._startReorderTimeout=setTimeout(this._startReorder.bind(this,d),100))},_startReorder:function(d){this._reorderGhost=
this._getGhost();this._reorderGhost.style.visibility="visible";var f=new CustomEvent("dragstart",{bubbles:!0});this._cellContent.dispatchEvent(f);this._reorderXY={x:d.touches[0].clientX-this.getBoundingClientRect().left,y:d.touches[0].clientY-this.getBoundingClientRect().top};this._updateGhostPosition(d.touches[0].clientX,d.touches[0].clientY)},_onTouchMove:function(d){if(this._reorderGhost){d.preventDefault();var f=new CustomEvent("dragover",{bubbles:!0});f.clientX=d.touches[0].clientX;f.clientY=
d.touches[0].clientY;var h=this._contentFromPoint(f.clientX,f.clientY);h&&h.dispatchEvent(f);this._updateGhostPosition(d.touches[0].clientX,d.touches[0].clientY)}else clearTimeout(this._startReorderTimeout)},_updateGhostPosition:function(d,f){d-=this._reorderXY.x;f=f-this._reorderXY.y-50;var h=parseInt(this._reorderGhost.style.left||0),k=parseInt(this._reorderGhost.style.top||0),t=this._reorderGhost.getBoundingClientRect();this._reorderGhost.style.left=h-(t.left-d)+"px";this._reorderGhost.style.top=
k-(t.top-f)+"px"},_onTouchEnd:function(d){clearTimeout(this._startReorderTimeout);this._reorderGhost&&(d.preventDefault(),d=new CustomEvent("dragend",{bubbles:!0}),this.dispatchEvent(d),this._reorderGhost.style.visibility="hidden",this._reorderGhost=null)},_contentFromPoint:function(d,f){if(Polymer.Settings.useShadow){var h=this.target.$.scroller;h.toggleAttribute("no-content-pointer-events",!0);d=this.domHost.root.elementFromPoint(d,f);h.toggleAttribute("no-content-pointer-events",!1);if(d&&d.getContentChildren)return d.getContentChildren(Polymer.Element?
"slot":"content")[0]}else return document.elementFromPoint(d,f)},_getGhost:function(){var d=this.target.$.scroller.$.reorderghost;d.innerText=this._cellContent.innerText;var f=window.getComputedStyle(this._cellContent);"boxSizing display width height background alignItems padding border flex-direction overflow".split(" ").forEach(function(h){d.style[h]=f[h]},this);return d},_enableDrag:function(){this._cellContent.draggable=this.target.columnReorderingAllowed&&!window.getSelection().toString()},_disableDrag:function(){this._cellContent.draggable=
!1},_cancelMouseDownOnResize:function(d){d.target===this._resizeHandle&&d.preventDefault()},_resizableChanged:function(d){d?(this._resizeHandle=document.createElement("div"),this._resizeHandle.classList.add("vaadin-grid-column-resize-handle"),this.listen(this._resizeHandle,"track","_onTrack"),Polymer.dom(this).appendChild(this._resizeHandle)):this._resizeHandle&&(this.unlisten(this._resizeHandle,"track","_onTrack"),Polymer.dom(this).removeChild(this._resizeHandle))},_onTrack:function(d){this.columnResizing=
!0;var f=this.column;"vaadin-grid-column-group"===f.localName&&(f=Array.prototype.slice.call(f._childColumns,0).sort(function(t,l){return t._order-l._order}).filter(function(t){return!t.hidden}).pop());var h=this._getHeaderCellByColumn(f);if(h.offsetWidth){var k=window.getComputedStyle(h._cellContent);f.width=Math.max(10+parseInt(k.paddingLeft)+parseInt(k.paddingRight),h.offsetWidth+d.detail.x-h.getBoundingClientRect().right)+"px";f.flexGrow=0}Array.prototype.slice.call(Polymer.dom(this.parentElement.parentElement).querySelectorAll(".vaadin-grid-row:last-child .vaadin-grid-cell")).sort(function(t,
l){return t.column._order-l.column._order}).forEach(function(t,l,p){l<p.indexOf(h)&&(t.column.width=t.offsetWidth+"px",t.column.flexGrow=0)});this.columnResizing&&"end"===d.detail.state&&(this.columnResizing=!1);this.fire("column-resizing")},_getHeaderCellByColumn:function(d){return Array.prototype.filter.call(Polymer.dom(this.parentElement.parentElement).querySelectorAll(".vaadin-grid-row:last-child .vaadin-grid-cell"),function(f){return f.column===d})[0]},_headerTemplateChanged:function(d){void 0!==
d&&(null===d||!this._isColumnRow&&"vaadin-grid-column-group"!==this.column.localName?(this.instance={root:document.createElement("div")},this._isEmpty=!0):(this.instance=d.templatizer.createInstance(),this._templatizer=d.templatizer,this._isEmpty=!1))},_isEmptyChanged:function(d,f){f&&this.fire("cell-empty-changed")}});Polymer({is:"vaadin-grid-table-footer-cell",properties:{footerTemplate:Object},behaviors:[b,vaadin.elements.grid.CellClickBehavior],observers:["_footerTemplateChanged(footerTemplate)",
"_isEmptyChanged(_isEmpty, isAttached)"],_footerTemplateChanged:function(d){void 0!==d&&(null===d||!this._isColumnRow&&"vaadin-grid-column-group"!==this.column.localName?(this.instance={root:document.createElement("div")},this._isEmpty=!0):(this.instance=d.templatizer.createInstance(),this._templatizer=d.templatizer,this._isEmpty=!1))},_isEmptyChanged:function(d,f){f&&this.fire("cell-empty-changed")}});Polymer({is:"vaadin-grid-sizer-cell",behaviors:[b]})})();

//# sourceURL=build://vaadin-grid/vaadin-grid-sizer.html.js
Polymer({is:"vaadin-grid-sizer",properties:{columnTree:Array,top:Number,_columns:Array},observers:["_columnTreeChanged(columnTree)","_topChanged(top)"],_columnTreeChanged:function(b){this._columns=b[b.length-1]},_topChanged:function(b){this.style.top=b+"px"}});

//# sourceURL=build://vaadin-grid/vaadin-grid-table-outer-scroller.html.js
Polymer({is:"vaadin-grid-table-outer-scroller",properties:{scrollTarget:{type:Object,observer:"_scrollTargetChanged"},passthrough:{type:Boolean,reflectToAttribute:!0,value:!0}},listeners:{scroll:"_syncScrollTarget"},attached:function(){this.listen(this.domHost,"mousemove","_onMouseMove");this.style.webkitOverflowScrolling="touch"},detached:function(){this.unlisten(this.domHost,"mousemove","_onMouseMove")},_scrollTargetChanged:function(b,d){d&&this.unlisten(d,"scroll","_syncOuterScroller");this.listen(b,
"scroll","_syncOuterScroller")},_onMouseMove:function(b){this.passthrough=b.offsetY<=this.clientHeight&&b.offsetX<=this.clientWidth},_syncOuterScroller:function(){this._syncingScrollTarget||(this._syncingOuterScroller=!0,this.scrollTop=this.domHost._scrollTop,this.scrollLeft=this.domHost._scrollLeft);this._syncingScrollTarget=!1},_syncScrollTarget:function(){this._syncingOuterScroller||(this._syncingScrollTarget=!0,this.scrollTarget.scrollTop=this.scrollTop,this.scrollTarget.scrollLeft=this.scrollLeft,
this.domHost._scrollHandler());this._syncingOuterScroller=!1}});

//# sourceURL=build://vaadin-grid/vaadin-grid-focusable-cell-container-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.FocusableCellContainerBehavior={properties:{focused:{type:Boolean,reflectToAttribute:!0},_focusedRow:Object,_focusedRowIndex:Number,_focusedCell:Object,_focusedCellIndex:Number,_lastFocusedCell:Object},observers:["_announceFocusedCell(_focusedCell, focused)","_dispatchEvents(_focusedCell, focused)","_focusedCellChanged(_focusedRowIndex, _focusedCellIndex)"],_announceFocusedCell:function(b,d){void 0!==b&&void 0!==d&&this.domHost.navigating&&d&&(d=Polymer.Element?b._cellContent.getAttribute("slot"):
b._cellContent.id,"vaadin-grid-table-body"!==this.is||b.hasAttribute("detailscell")||(b=Array.prototype.indexOf.call(Polymer.dom(b.parentElement).querySelectorAll(".vaadin-grid-cell"),b),d=this.domHost.$.header.lastElementChild.children[b]._cellContent.id+" "+d),this.domHost.$.footerFocusTrap.activeTarget=d)},_dispatchEvents:function(b,d){void 0!==b&&void 0!==d&&(this._lastFocusedCell&&(this._lastFocusedCell._cellContent.dispatchEvent(new CustomEvent("cell-focusout")),this._lastFocusedCell=void 0),
d&&(b._cellContent.dispatchEvent(new CustomEvent("cell-focusin")),this._lastFocusedCell=b))},_focusedCellChanged:function(b,d){void 0!==b&&void 0!==d&&Array.prototype.forEach.call(Polymer.dom(this).children,function(f,h){f.focused=h===b;f.focused&&(this._focusedRow=f,this._focusedCellIndex=Math.min(d,f.children.length-1),this._focusedCell=f.children[this._focusedCellIndex]);f.cells.forEach(function(k,t){k.focused=t===this._focusedCellIndex}.bind(this))}.bind(this))},focusLeft:function(){if(!this._focusedCell.hasAttribute("detailscell")){var b=
this._visibleCellIndexes();0<b.length&&(this._focusedCellIndex=b[Math.max(0,b.indexOf(this._focusedCellIndex)-1)])}},focusDown:function(){this._focusedRowIndex=Math.min(this._focusedRowIndex+1,this.children.length-1)},_visibleCellIndexes:function(){var b=[];if(this._focusedRow&&this._focusedRow.children){for(var d=this._focusedRow.children,f=0;f<d.length;f++)d[f].hidden||d[f]===this._focusedRow._rowDetailsCell||b.push(f);b.sort(function(h,k){return d[h].column._order<d[k].column._order?-1:1})}return b},
focusPageDown:function(){this._focusedRowIndex=Math.min(this._focusedRowIndex+10,this.children.length-1)},focusPageUp:function(){this._focusedRowIndex=Math.max(0,this._focusedRowIndex-10)},focusRight:function(){if(!this._focusedCell.hasAttribute("detailscell")){var b=this._visibleCellIndexes();0<b.length&&(this._focusedCellIndex=b[Math.min(b.indexOf(this._focusedCellIndex)+1,b.length-1)])}},focusUp:function(){this._focusedRowIndex=Math.max(0,this._focusedRowIndex-1)},focusHome:function(){if(!this._focusedCell.hasAttribute("detailscell")){var b=
this._visibleCellIndexes();0<b.length&&(this._focusedCellIndex=b[0])}},focusEnd:function(){if(!this._focusedCell.hasAttribute("detailscell")){var b=this._visibleCellIndexes();0<b.length&&(this._focusedCellIndex=b[b.length-1])}},focusFirst:function(){this._focusedRowIndex=0;this.focusHome()},focusLast:function(){this._focusedRowIndex=this.children.length-1;this.focusEnd()}};

//# sourceURL=build://vaadin-grid/vaadin-grid-table-header-footer.html.js
(function(){var b={properties:{columnTree:Array,target:Object,_rows:Array},observers:["_columnTreeChanged(columnTree, target)","_rowsChanged(_rows)"],_columnTreeChanged:function(d,f){if(void 0!==d&&void 0!==f){this._rows&&this._rows.forEach(function(l){Polymer.dom(l).innerHTML=""});for(var h=[],k=0;k<d.length;k++){var t=this._createRow();t.target=f;t._isColumnRow=k==d.length-1;t.columns=d[k];h.push(t)}this._rows="vaadin-grid-table-header"===this.localName?h:h.reverse()}},_rowsChanged:function(d){Polymer.dom(this).innerHTML=
"";d.forEach(function(f){Polymer.dom(this).appendChild(f)}.bind(this))}};Polymer({is:"vaadin-grid-table-header",behaviors:[b,vaadin.elements.grid.FocusableCellContainerBehavior],_createRow:function(){return document.createElement("vaadin-grid-table-header-row")}});Polymer({is:"vaadin-grid-table-body",behaviors:[vaadin.elements.grid.FocusableCellContainerBehavior],observers:["_announceFocusedRow(_focusedRow)"],_announceFocusedRow:function(d){this.fire("iron-announce",{text:"Row "+(d.index+1)+" of "+
this.domHost.size})},_moveFocusToDetailsCell:function(){this._focusedCell.focused=!1;this._focusedRow._rowDetailsCell.focused=!0;this._focusedCell=this._focusedRow._rowDetailsCell},_focusedRowHasDetailsCell:function(){return this._focusedRow&&this._focusedRow._rowDetailsCell&&this._focusedCell!==this._focusedRow._rowDetailsCell},focusDown:function(){this._focusedRowHasDetailsCell()?this._moveFocusToDetailsCell():this._focusedRowIndex=Math.min(this._focusedRowIndex+1,this.domHost.size-1)},focusUp:function(){this._focusedRow&&
this._focusedCell===this._focusedRow._rowDetailsCell?this._focusedCellChanged(this._focusedRowIndex,this._focusedCellIndex):(this._focusedRowIndex=Math.max(0,this._focusedRowIndex-1),this._focusedRowHasDetailsCell()&&this._moveFocusToDetailsCell())},focusLast:function(){this._focusedRowIndex=this.domHost.size-1;this.focusEnd()},_focusedCellChanged:function(d,f){void 0!==d&&void 0!==f&&Array.prototype.forEach.call(Polymer.dom(this).children,function(h){h.focused=h.index===d;h.index===d&&(this._focusedRow=
h,this._focusedCell=h.children[f]);h.iterateCells(function(k,t){k.focused=t===f})}.bind(this))}});Polymer({is:"vaadin-grid-table-footer",behaviors:[b,vaadin.elements.grid.FocusableCellContainerBehavior],_createRow:function(){return document.createElement("vaadin-grid-table-footer-row")}})})();

//# sourceURL=build://vaadin-grid/vaadin-grid-table-focus-trap.html.js
Polymer({is:"vaadin-grid-table-focus-trap",hostAttributes:{role:"gridcell"},properties:{activeTarget:{type:String,observer:"_activeTargetChanged"}},ready:function(){this._primary=Polymer.dom(this.root).querySelector(".primary");this._secondary=Polymer.dom(this.root).querySelector(".secondary");if(Polymer.Settings.useNativeShadow||Polymer.Settings.useShadow)Polymer.dom(this).appendChild(this._secondary),Polymer.dom(this).appendChild(this._primary)},focus:function(){this._focused!==this._primary?this._primary.focus():
this._secondary.focus()},_onBaitFocus:function(b){this._focused=b.target;this._movingFocusInternally||(this.fire("focus-gained"),this._primary.tabIndex=-1)},_onBaitBlur:function(){this._movingFocusInternally||(this.fire("focus-lost"),this._primary.tabIndex=0)},_activeTargetChanged:function(b){this._movingFocusInternally=!0;this._focused===this._primary?(this._secondary.setAttribute("aria-labelledby",b),this._secondary.focus()):(this._primary.setAttribute("aria-labelledby",b),this._primary.focus());
this._movingFocusInternally=!1},_reannounce:function(){this._movingFocusInternally=!0;this._focused===this._primary?(this._secondary.setAttribute("aria-labelledby",this.activeTarget),this._secondary.focus()):(this._primary.setAttribute("aria-labelledby",this.activeTarget),this._primary.focus());this._movingFocusInternally=!1}});

//# sourceURL=build://vaadin-grid/vaadin-grid-table-row.html.js
(function(){var b={properties:{active:{type:Boolean,reflectToAttribute:!0,value:!1},columns:Array,index:Number,cells:{value:[]},target:Object,expanded:{value:!1},focused:{type:Boolean,reflectToAttribute:!0},item:Object,selected:{reflectToAttribute:!0},_rowDetailsCell:Object,rowDetailsTemplate:Object},observers:"_columnsChanged(columns, target);_indexChanged(index, cells);_itemChanged(item, cells);_itemChangedForDetails(item, _rowDetailsCell);_rowDetailsChanged(expanded, rowDetailsTemplate, target);_rowDetailsCellIndexChanged(_rowDetailsCell, index);_rowDetailsCellChanged(_rowDetailsCell, target);_selectedChanged(selected, cells);_selectedChangedForDetails(selected, _rowDetailsCell)".split(";"),
ready:function(){this.classList.add("vaadin-grid-row");!1===Polymer.Settings.useShadow&&(this.classList.add("style-scope"),this.classList.add("vaadin-grid"))},iterateCells:function(d){this.cells.forEach(d);this._rowDetailsCell&&d(this._rowDetailsCell)},_rowDetailsChanged:function(d,f,h){if(void 0!==d&&void 0!==f&&void 0!==h){if(d){var k=document.createElement("vaadin-grid-table-cell");k.setAttribute("detailscell",!0);k.frozen=!0;k.target=h;k.template=f;k.toggleAttribute("lastcolumn",!0);Polymer.dom(this.root).appendChild(k);
Polymer.dom.flush();this._rowDetailsCell=k}else this._rowDetailsCell&&(Polymer.dom(this.root).removeChild(this._rowDetailsCell),this._rowDetailsCell=null);this.iterateCells(function(t){t.expanded=d});this.target.$.scroller._frozenCellsChanged()}},_updateRowVisibility:function(){this.hidden=this.cells.every(function(d){return d._isEmpty})},_rowDetailsCellChanged:function(d,f){void 0!==d&&void 0!==f&&f.$.scroller._update()},_rowDetailsCellIndexChanged:function(d,f){void 0!==d&&void 0!==f&&(d?(d.index=
f,Polymer.dom.flush(),this.updateRowDetailsCellMetrics()):this.style.paddingBottom="")},updateRowDetailsCellMetrics:function(){this._rowDetailsCell&&(this.target&&this.target._observer&&this.target._observer.flush&&this.target._observer.flush(),this._rowDetailsCell.style.height="",this.style.paddingBottom=this._rowDetailsCell.style.height=this._rowDetailsCell.clientHeight+"px")},_columnsChanged:function(d,f){if(void 0!==d&&void 0!==f){Polymer.dom(this).innerHTML="";var h=[];d.forEach(function(k){var t=
"_"+this.is.replace(/-/g,"_")+"_cells";t=k[t]=k[t]||[];var l=t.filter(function(m){return!Polymer.dom(m).parentNode})[0];if(!l){l=this._createCell();var p=Array.prototype.some.call(this.target.querySelectorAll("dom-repeat"),function(m){return!m.restamp});(p=p||"vaadin-grid-table-header-row"===this.is||"vaadin-grid-table-footer-row"===this.is)||t.push(l)}l.index=this.index;l.target=this.target;l._isColumnRow=this._isColumnRow;l.column=k;l.expanded=this.expanded;Polymer.dom(this).appendChild(l);h.push(l)}.bind(this));
this.cells=h}},_indexChanged:function(d,f){void 0!==d&&void 0!==f&&f.forEach(function(h){h.index=d})},_itemChanged:function(d,f){void 0!==d&&void 0!==f&&f.forEach(function(h){h.item=d})},_itemChangedForDetails:function(d,f){void 0!==d&&void 0!==f&&f&&(f.item=d)},_selectedChanged:function(d,f){void 0!==d&&void 0!==f&&f.forEach(function(h){h.selected=d})},_selectedChangedForDetails:function(d,f){void 0!==d&&void 0!==f&&f&&(f.selected=d)},updateLastColumn:function(){this.cells.slice(0).sort(function(d,
f){return d.column._order-f.column._order}).forEach(function(d,f,h){d.toggleAttribute("lastcolumn",f===h.length-1)})}};Polymer({is:"vaadin-grid-table-row",behaviors:[b],_createCell:function(){return document.createElement("vaadin-grid-table-cell")}});Polymer({is:"vaadin-grid-table-header-row",behaviors:[b],observers:["_updateRowVisibility(columns)"],listeners:{"cell-empty-changed":"_updateRowVisibility"},_createCell:function(){return document.createElement("vaadin-grid-table-header-cell")}});Polymer({is:"vaadin-grid-table-footer-row",
behaviors:[b],observers:["_updateRowVisibility(columns)"],listeners:{"cell-empty-changed":"_updateRowVisibility"},_createCell:function(){return document.createElement("vaadin-grid-table-footer-cell")}})})();

//# sourceURL=build://vaadin-grid/vaadin-grid-templatizer.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};vaadin.elements.grid.Templatizer=function(){};
vaadin.elements.grid.Templatizer=Polymer({is:"vaadin-grid-templatizer",behaviors:[Polymer.Templatizer],properties:{dataHost:Object,template:Object,_templateInstances:{type:Array,value:function(){return[]}},_parentPathValues:{value:function(){return{}}}},observers:["_templateInstancesChanged(_templateInstances.*, _parentPathValues.*)"],created:function(){this._parentModel=!0;this._instanceProps={expanded:!0,index:!0,item:!0,selected:!0}},createInstance:function(){this._ensureTemplatized();var b=this.stamp({});
this.addInstance(b);return b},addInstance:function(b){-1===this._templateInstances.indexOf(b)&&this.push("_templateInstances",b)},removeInstance:function(b){this.splice("_templateInstances",this._templateInstances.indexOf(b),1)},_ensureTemplatized:function(){this.template._templatized||(this.template._templatized=!0,this.templatize(this.template),this._parentProps=this._parentProps||{},Polymer.Element||Object.keys(this._parentProps).forEach(function(){},this))},_forwardInstanceProp:function(b,d,f){void 0!==
b["__"+d+"__"]&&b["__"+d+"__"]!==f&&this.fire("template-instance-changed",{prop:d,value:f,inst:b})},_forwardInstancePath:function(b,d,f){0!==d.indexOf("item.")||this._suppressItemChangeEvent||this.fire("item-changed",{item:b.item,path:d.substring(5),value:f})},_notifyInstancePropV2:function(b,d,f){this._forwardInstanceProp(b,d,f);this._forwardInstancePath(b,d,f)},_forwardParentProp:function(b,d){this._parentPathValues[b]=d;this._templateInstances.forEach(function(f){f.set(b,d)},this)},_forwardParentPath:function(b,
d){this.set(["_parentPathValues",b],d);this._templateInstances.forEach(function(f){f.notifyPath(b,d)},this)},_forwardHostPropV2:function(b,d){this._forwardParentProp(b,d);this._templateInstances&&this._templateInstances.forEach(function(f){f.notifyPath(b,d)},this)},_templateInstancesChanged:function(b){if("_templateInstances"===b.path){var d=0;var f=this._templateInstances.length}else if("_templateInstances.splices"===b.path)d=b.value.index,f=b.value.addedCount;else return;Object.keys(this._parentPathValues||
{}).forEach(function(h){for(var k=d;k<d+f;k++)this._templateInstances[k].set(h,this._parentPathValues[h])},this)}});

//# sourceURL=build://vaadin-grid/vaadin-grid-row-details-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.RowDetailsBehavior={properties:{expandedItems:{type:Array,value:function(){return[]}}},listeners:{"template-instance-changed":"_templateInstanceChangedExpanded"},observers:["_expandedItemsChanged(expandedItems.*, dataProvider)","_rowDetailsTemplateChanged(_rowDetailsTemplate)"],_expandedItemsChanged:function(b,d){void 0!==b&&void 0!==d&&(this._flushItemsDebouncer(),this.$.scroller._physicalItems&&this.$.scroller._physicalItems.forEach(function(f){f.expanded=this._isExpanded(f.item)}.bind(this)))},
_rowDetailsTemplateChanged:function(b){var d=new vaadin.elements.grid.Templatizer;d.dataHost=this.dataHost;d._instanceProps={expanded:!0,index:!0,item:!0,selected:!0};Polymer.dom(this.root).appendChild(d);d.template=b;b.templatizer=d},_isExpanded:function(b){return this.expandedItems&&-1!==this.expandedItems.indexOf(b)},expandItem:function(b){this._isExpanded(b)||this.push("expandedItems",b)},collapseItem:function(b){this._isExpanded(b)&&this.splice("expandedItems",this.expandedItems.indexOf(b),1)},
_templateInstanceChangedExpanded:function(b){"expanded"===b.detail.prop&&(b.detail.value?this.expandItem(b.detail.inst.item):this.collapseItem(b.detail.inst.item),b.stopPropagation())}};

//# sourceURL=build://vaadin-grid/vaadin-grid-data-provider-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.DataProviderBehavior={listeners:{"item-changed":"_templateItemChanged"},properties:{pageSize:{type:Number,value:50,observer:"_pageSizeChanged"},dataProvider:{type:Object,notify:!0,observer:"_dataProviderChanged"},_loading:Boolean,_cache:{type:Object,value:function(){return{}}},_pendingRequests:{type:Object,value:function(){return{}}}},_templateItemChanged:function(b){var d=b.detail.item;Array.prototype.forEach.call(Polymer.dom(this.$.items).children,function(f){f.item===d&&f.iterateCells(function(h){h._templatizer._suppressItemChangeEvent=
!0;h.instance.notifyPath("item."+b.detail.path,b.detail.value);h._templatizer._suppressItemChangeEvent=!1})})},_getCachedItem:function(b){var d=this._getPageForIndex(b),f=this._cache&&this._cache[d];return f?f[b-d*this.pageSize]:null},_getItem:function(b,d){this._updateItem(d,this._getCachedItem(b));this._eagerlyLoadPages();var f=this._uncachedPagesForPhysicalItems();0<f.length&&(this._loading=!0,this.debounce("load",function(){f.forEach(function(h){this._loadPage(h)}.bind(this))},100))},_cachedPagesForPhysicalItems:function(){return this._pagesForPhysicalItems().filter(function(b){return void 0!==
this._cache&&void 0!==this._cache[b]}.bind(this))},_uncachedPagesForPhysicalItems:function(){return this._pagesForPhysicalItems().filter(function(b){return void 0!==this._cache&&void 0===this._cache[b]}.bind(this))},_eagerlyLoadPages:function(){var b=this._cachedPagesForPhysicalItems().slice(0);if(0<b.length){b.sort(function(f,h){return f>h});var d=Math.min(b[b.length-1]+1,Math.max(0,Math.floor(this.size/this.pageSize)-1));this._loadPage(Math.max(0,b[0]-1));this._loadPage(d)}},_pagesForPhysicalItems:function(){return[this._getPageForIndex(this.$.scroller.firstVisibleIndex+
this.$.scroller._vidxOffset)].concat(this.$.scroller._physicalItems.filter(function(b){return b.index}).map(function(b){return this._getPageForIndex(b.index)}.bind(this))).reduce(function(b,d){-1===b.indexOf(d)&&b.push(d);return b},[])},_updateItems:function(b,d){for(var f=0;f<this.pageSize;f++){var h=this.$.scroller._virtualIndexToItem[b*this.pageSize+f];h&&(this._updateItem(h,d[f]),this.debounce("update-heights",function(){this.$.scroller._updateMetrics();this.$.scroller._positionItems();this.$.scroller._updateScrollerSize()},
1))}},_loadPage:function(b,d){d=d||this._updateItems.bind(this);if(!this._cache[b]&&!this._pendingRequests[b]&&this.dataProvider){this._pendingRequests[b]=!0;var f={page:b,pageSize:this.pageSize,sortOrders:this._mapSorters(),filters:this._mapFilters()};this.dataProvider(f,function(h){this._cache[b]=h;delete this._pendingRequests[b];d(b,h);this._loading=0<this._pendingRequests.length;this.debounce("check-size",this._checkSize,2E3)}.bind(this))}},_getPageForIndex:function(b){return Math.floor(b/this.pageSize)},
clearCache:function(){this._cache={};this._pendingRequests={};this.$.scroller.hasData&&this.$.scroller._update();this._flushItemsDebouncer()},_flushItemsDebouncer:function(){this.flushDebouncer("load")},_pageSizeChanged:function(b,d){void 0!==d&&b!==d&&this.clearCache()},_checkSize:function(){void 0===this.size&&console.warn('The \x3cvaadin-grid\x3e needs a value for "size" property in order to display rows.')},_dataProviderChanged:function(b,d){void 0!==d&&this.clearCache();this.$.scroller.hasData||
(this._loading=!0,this._loadPage(0,function(){this.$.scroller.hasData=!0}.bind(this)))}};

//# sourceURL=build://vaadin-grid/vaadin-grid-selection-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.SelectionBehavior={properties:{selectedItems:{type:Object,notify:!0,value:function(){return[]}}},observers:["_selectedItemsChanged(selectedItems.*)"],listeners:{"template-instance-changed":"_templateInstanceChangedSelection"},_templateInstanceChangedSelection:function(b){if("selected"===b.detail.prop){var d=b.detail.inst.item;(this._isSelected(d)?this.deselectItem:this.selectItem).bind(this)(d);this.fire("iron-announce",{text:(this._isSelected(d)?"Selected":"Deselected")+" Row "+
(b.detail.inst.index+1)+" of "+this.size});b.stopPropagation()}},_isSelected:function(b){return this.selectedItems&&-1<this.selectedItems.indexOf(b)},selectItem:function(b){b=this._takeItem(b);this._isSelected(b)||this.push("selectedItems",b)},deselectItem:function(b){b=this._takeItem(b);b=this.selectedItems.indexOf(b);-1<b&&this.splice("selectedItems",b,1)},_toggleItem:function(b){b=this._takeItem(b);-1===this.selectedItems.indexOf(b)?this.selectItem(b):this.deselectItem(b)},_takeItem:function(b){return"number"===
typeof b&&0<=b&&this.items&&this.items.length>b?this.items[b]:b},_selectedItemsChanged:function(b){!this.$.scroller._physicalItems||"selectedItems"!==b.path&&"selectedItems.splices"!==b.path||this.$.scroller._physicalItems.forEach(function(d){d.selected=this._isSelected(d.item)}.bind(this))}};

//# sourceURL=build://vaadin-grid/vaadin-grid-keyboard-navigation-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.TableKeyboardBehaviorImpl={hostAttributes:{role:"application",tabindex:0},keyBindings:{"ctrl+home":"_onCtrlHome","ctrl+end":"_onCtrlEnd",down:"_onArrowDown",end:"_onEnd",enter:"_onEnter",esc:"_onEscape",f2:"_onF2",home:"_onHome",left:"_onArrowLeft",pagedown:"_onPageDown",pageup:"_onPageUp",right:"_onArrowRight",space:"_onSpace",tab:"_onTab",up:"_onArrowUp"},attached:function(){Polymer.IronA11yAnnouncer.requestAvailability()},properties:{_virtualFocus:{type:Object,observer:"_virtualFocusChanged"},
interacting:{type:Boolean,reflectToAttribute:!0,value:!1},navigating:{type:Boolean,reflectToAttribute:!0,value:!1}},listeners:{focus:"_onFocus","cell-focus":"_onCellFocus","cell-content-focus":"_onCellContentFocus"},ready:function(){document.addEventListener("keydown",function(b){9===b.keyCode&&(this._tabbed=!0);9===b.keyCode&&b.shiftKey&&(this._shiftTabbed=!0)}.bind(this),!0);document.addEventListener("keyup",function(b){9===b.keyCode&&(this._tabbed=!1);9===b.keyCode&&b.shiftKey&&(this._shiftTabbed=
!1)}.bind(this),!0)},_isFooterVisible:function(){return 0<this.$.footer._rows.filter(function(b){return!b.hidden}).length},_onFocus:function(){this._tabbed&&!this._shiftTabbed&&this._activateNavigation()},_activateNavigation:function(){this.$.footerFocusTrap.focus()},_onFocusout:function(){this.interacting=this.navigating=!1},_onFooterFocus:function(){this.navigating=!0;this.interacting=!1;this._virtualFocus=this._virtualFocus||(this._shiftTabbed?this._isFooterVisible()?this.$.footer:this.$.items:
this.$.header)},_virtualFocusChanged:function(b,d){d&&(d.focused=!1);b&&(b._focusedCellIndex=b._focusedCellIndex||0,b._focusedRowIndex=b._focusedRowIndex||0,b.focused=!0,b===this.$.items&&this._ensureVirtualFocusInViewport())},_onTab:function(b){if(!this.interacting&&this._virtualFocus)if(this.navigating)if(b.detail.keyboardEvent.shiftKey)switch(this._virtualFocus){case this.$.footer:this._virtualFocus=this.$.items;b.preventDefault();break;case this.$.items:this._virtualFocus=this.$.header;b.preventDefault();
break;case this.$.header:this.focus(),this._virtualFocus=null}else switch(this._virtualFocus){case this.$.header:this._virtualFocus=this.$.items;b.preventDefault();break;case this.$.items:this._isFooterVisible()?(this._virtualFocus=this.$.footer,b.preventDefault()):this.async(function(){this._virtualFocus=null},1);break;case this.$.footer:this._virtualFocus=null}else this._activateNavigation(),b.preventDefault()},_isAboveViewport:function(b){return this.firstVisibleIndex>b},_onArrowDown:function(b){this.interacting||
(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusDown(),this._ensureVirtualFocusInViewport())},_scrollPageDown:function(){var b=this.$.header.getBoundingClientRect(),d=this.$.footer.getBoundingClientRect();this.$.scroller.$.table.scrollTop+=d.top-b.bottom;this.$.scroller._scrollHandler()},_onPageDown:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus===this.$.items?(b=this.$.scroller.lastVisibleIndex,this._scrollPageDown(),this._virtualFocus._focusedRowIndex+=
this.$.scroller.lastVisibleIndex-b||this.$.scroller.lastVisibleIndex-this._virtualFocus._focusedRowIndex,this._ensureVirtualFocusInViewport()):this._virtualFocus.focusPageDown())},_scrollPageUp:function(){var b=this.$.header.getBoundingClientRect(),d=this.$.footer.getBoundingClientRect();this.$.scroller.$.table.scrollTop-=d.top-b.bottom;this.$.scroller._scrollHandler()},_onPageUp:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus===this.$.items?(b=this.$.scroller.lastVisibleIndex,
this._scrollPageUp(),this._virtualFocus._focusedRowIndex-=b-this.$.scroller.lastVisibleIndex||this._virtualFocus._focusedRowIndex,this._ensureVirtualFocusInViewport()):this._virtualFocus.focusPageUp())},_onArrowUp:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusUp(),this._ensureVirtualFocusInViewport())},_onArrowRight:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusRight(),this._ensureVirtualFocusInViewport())},
_onArrowLeft:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusLeft(),this._ensureVirtualFocusInViewport())},_onHome:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusHome(),this._ensureVirtualFocusInViewport())},_onEnd:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusEnd(),this._ensureVirtualFocusInViewport())},_moveFocusToFocusTarget:function(){var b=this._virtualFocus._focusedCell._cellContent;
(b=b.querySelector("[focus-target]")||b.firstElementChild)&&b.focus()},_onEnter:function(b){this.interacting?"input"===b.detail.keyboardEvent.target.localName&&"text"===b.detail.keyboardEvent.target.type&&this.$.footerFocusTrap.focus():(b.preventDefault(),this._moveFocusToFocusTarget())},_onEscape:function(){this.interacting?this.$.footerFocusTrap.focus():this.navigating&&(this.navigating=!1)},_onF2:function(b){b.preventDefault();this.interacting?this.$.footerFocusTrap.focus():this._moveFocusToFocusTarget()},
_onCtrlHome:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusFirst(),this._ensureVirtualFocusInViewport())},_onCtrlEnd:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusLast(),this._ensureVirtualFocusInViewport())},_onSpace:function(b){if(!this.interacting){b.preventDefault();b=this._virtualFocus._focusedCell;var d=b.getContentChildren(Polymer.Element?"slot":"content")[0].firstElementChild;d?d.click():this.navigating&&
this.fire("cell-activate",{model:b.instance})}},_onCellContentFocus:function(b){this.interacting=!0;this._onCellFocus(b)},_onCellFocus:function(b){b=b.detail.cell;var d=b.parentElement,f=d.parentElement,h=Array.prototype.indexOf.call(Polymer.dom(f).children,d);f===this.$.items&&(h=d.index);f._focusedRowIndex=h;f._focusedCellIndex=Array.prototype.indexOf.call(Polymer.dom(d).children,b);this._virtualFocus=f;b.hasAttribute("detailscell")&&(f._focusedCellIndex=0,f._moveFocusToDetailsCell())},_ensureVirtualFocusInViewport:function(){var b=
this.$.scroller._vidxOffset+this.$.scroller._virtualStart,d=this._virtualFocus._focusedRowIndex;this._virtualFocus===this.$.items&&(d<b||d>b+this.$.scroller._physicalCount)&&(this.$.scroller.scrollToScaledIndex(d),this._virtualFocus._focusedCellChanged(d,this._virtualFocus._focusedCellIndex));this._ensureElementInViewport(this._virtualFocus._focusedCell)},_ensureElementInViewport:function(b){var d=b.getBoundingClientRect();if(this._virtualFocus===this.$.items){var f=this.$.footer.getBoundingClientRect().top,
h=this.$.header.getBoundingClientRect().bottom;d.bottom>f?this.$.scroller.$.table.scrollTop+=d.bottom-f:d.top<h&&(this.$.scroller.$.table.scrollTop+=d.top-h)}if(!b.hasAttribute("detailscell")){b=this.$.scroller.$.table.getBoundingClientRect().right;f=this.$.scroller.$.table.getBoundingClientRect().left;if(h=this._virtualFocus._focusedRow.querySelector("[last-frozen]"))f=h.getBoundingClientRect().right;d.right>b?this.$.scroller.$.table.scrollLeft+=d.right-b:d.left<f&&(this.$.scroller.$.table.scrollLeft+=
d.left-f)}}};vaadin.elements.grid.TableKeyboardBehavior=[vaadin.elements.grid.TableKeyboardBehaviorImpl,Polymer.IronA11yKeysBehavior];

//# sourceURL=build://vaadin-grid/vaadin-grid-column-reordering-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};vaadin.elements.grid.ColumnReorderingBehavior={properties:{columnReorderingAllowed:{type:Boolean,value:!1}}};
vaadin.elements.grid.TableColumnReorderingBehavior={properties:{_orderBaseScope:{type:Number,value:1E7}},listeners:{dragstart:"_onDragStart",dragover:"_onDragOver",dragend:"_onDragEnd"},observers:["_updateOrders(columnTree, columnTree.*)"],_updateOrders:function(b,d){void 0!==b&&void 0!==d&&b[0].forEach(function(f,h){f._order=(h+1)*this._orderBaseScope},this)},_onDragStart:function(b){if("vaadin-grid-cell-content"===b.target.localName){var d=this._getCellByCellContent(b.target);d&&(this.toggleAttribute("reordering",
!0),this._draggedColumn=d.column,this._setSiblingsReorderStatus(this._draggedColumn,"allowed"),this._draggedColumn._reorderStatus="dragging",b.dataTransfer&&(b.dataTransfer.setData("text",""),b.dataTransfer.effectAllowed="move"),this._autoScroller())}},_setSiblingsReorderStatus:function(b,d){Array.prototype.filter.call(Polymer.dom(Polymer.dom(b).parentNode).children,function(f){return/column/.test(f.localName)&&this._isSwapAllowed(f,b)},this).forEach(function(f){f._reorderStatus=d})},_onDragOver:function(b){if(this._draggedColumn){var d=
(Polymer.Element?b.composedPath():Polymer.dom(b).path).filter(function(f){return"vaadin-grid-cell-content"===f.localName})[0];d&&(b.preventDefault(),d=this._getCellByCellContent(d),(d=this._getTargetColumn(d,this._draggedColumn))&&this._isSwapAllowed(this._draggedColumn,d)&&this._isSwappableByPosition(d,b.clientX)&&this._swapColumnOrders(this._draggedColumn,d),this._lastDragClientX=b.clientX)}},_autoScroller:function(){if(this._lastDragClientX){var b=this._lastDragClientX-this.getBoundingClientRect().right+
50,d=this.getBoundingClientRect().left-this._lastDragClientX+50;0<b?this.$.table.scrollLeft+=b/10:0<d&&(this.$.table.scrollLeft-=d/10);this._scrollHandler()}this._draggedColumn&&this.async(this._autoScroller,10)},_onDragEnd:function(){this._draggedColumn&&(this.toggleAttribute("reordering",!1),this._draggedColumn._reorderStatus="",this._setSiblingsReorderStatus(this._draggedColumn,""),this._lastDragClientX=this._draggedColumn=null)},_isSwapAllowed:function(b,d){if(b&&d){var f=b.parentElement===d.parentElement,
h=b.frozen===d.frozen;return b!==d&&f&&h}},_isSwappableByPosition:function(b,d){var f=Array.prototype.filter.call(Polymer.dom(this.$.header).querySelectorAll(".vaadin-grid-cell"),function(k){return k.column===b})[0],h=this.$.header.querySelector("[reorder-status\x3ddragging]").getBoundingClientRect();return f.getBoundingClientRect().left>h.left?d>f.getBoundingClientRect().right-h.width:d<f.getBoundingClientRect().left+h.width},_getCellByCellContent:function(b){if(Polymer.Element)return b.assignedSlot.parentNode;
b=Polymer.dom(b).getDestinationInsertionPoints()[0];return Polymer.dom(b).parentNode},_swapColumnOrders:function(b,d){var f=b._order;b._order=d._order;d._order=f;this._updateLastFrozen();this._updateLastColumn()},_getTargetColumn:function(b,d){if(b&&d){for(var f=b.column;f.parentElement!==d.parentElement&&f!==this.target;)f=f.parentElement;return f.parentElement===d.parentElement?f:b.column}}};

//# sourceURL=build://vaadin-grid/iron-list-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.IronListBehaviorImpl=function(){var b=navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),d=b&&8<=b[1];return{is:"iron-list",properties:{maxPhysicalCount:{type:Number,value:500},as:{type:String,value:"item"},indexAs:{type:String,value:"index"}},_ratio:.5,_scrollerPaddingTop:0,_scrollPosition:0,_physicalSize:0,_physicalAverage:0,_physicalAverageCount:0,_physicalTop:0,_virtualCount:0,_physicalIndexForKey:null,_estScrollHeight:0,_scrollHeight:0,_viewportHeight:0,_viewportWidth:0,
_physicalItems:null,_physicalSizes:null,_firstVisibleIndexVal:null,_lastVisibleIndexVal:null,_collection:null,_itemsRendered:!1,_lastPage:null,_maxPages:3,_itemsPerRow:1,_itemWidth:0,_rowHeight:0,get _physicalBottom(){return this._physicalTop+this._physicalSize},get _scrollBottom(){return this._scrollPosition+this._viewportHeight},get _virtualEnd(){return this._virtualStart+this._physicalCount-1},get _hiddenContentSize(){return this._physicalSize-this._viewportHeight},get _maxScrollTop(){return this._estScrollHeight-
this._viewportHeight+this._scrollerPaddingTop},_minVirtualStart:0,get _maxVirtualStart(){return Math.max(0,this._virtualCount-this._physicalCount)},_virtualStartVal:0,set _virtualStart(f){this._virtualStartVal=Math.min(this._maxVirtualStart,Math.max(this._minVirtualStart,f))},get _virtualStart(){return this._virtualStartVal||0},_physicalStartVal:0,set _physicalStart(f){this._physicalStartVal=f%this._physicalCount;0>this._physicalStartVal&&(this._physicalStartVal=this._physicalCount+this._physicalStartVal);
this._physicalEnd=(this._physicalStart+this._physicalCount-1)%this._physicalCount},get _physicalStart(){return this._physicalStartVal||0},_physicalCountVal:0,set _physicalCount(f){this._physicalCountVal=f;this._physicalEnd=(this._physicalStart+this._physicalCount-1)%this._physicalCount},get _physicalCount(){return this._physicalCountVal},_physicalEnd:0,get _optPhysicalSize(){return this._viewportHeight*this._maxPages},get _optPhysicalCount(){return this._estRowsInView*this._itemsPerRow*this._maxPages},
get _isVisible(){return this.scrollTarget&&!(!this.scrollTarget.offsetWidth&&!this.scrollTarget.offsetHeight)},get firstVisibleIndex(){if(null===this._firstVisibleIndexVal){var f=Math.floor(this._physicalTop+this._scrollerPaddingTop);this._firstVisibleIndexVal=this._iterateItems(function(h,k){f+=this._getPhysicalSizeIncrement(h);if(f>this._scrollPosition)return k})||0}return this._firstVisibleIndexVal},get lastVisibleIndex(){if(null===this._lastVisibleIndexVal){var f=this._physicalTop;this._iterateItems(function(h,
k){if(f<this._scrollBottom)this._lastVisibleIndexVal=k;else return!0;f+=this._getPhysicalSizeIncrement(h)})}return this._lastVisibleIndexVal},get _defaultScrollTarget(){return this},get _virtualRowCount(){return Math.ceil(this._virtualCount/this._itemsPerRow)},get _estRowsInView(){return Math.ceil(this._viewportHeight/this._rowHeight)},get _physicalRows(){return Math.ceil(this._physicalCount/this._itemsPerRow)},attached:function(){this.updateViewportBoundaries();this._render();this.listen(this,"iron-resize",
"_resizeHandler")},detached:function(){this._itemsRendered=!1;this.unlisten(this,"iron-resize","_resizeHandler")},updateViewportBoundaries:function(){this._scrollerPaddingTop=this.scrollTarget===this?0:parseInt(window.getComputedStyle(this)["padding-top"]||0,10);this._viewportHeight=this._scrollTargetHeight},_update:function(f,h){this._assignModels(f);this._updateMetrics(f);if(h)for(;h.length;)f=h.pop(),this._physicalTop-=this._getPhysicalSizeIncrement(f);this._positionItems();this._updateScrollerSize();
this._increasePoolIfNeeded()},_increasePoolIfNeeded:function(){if(0===this._viewportHeight)return!1;var f=this._physicalSizes.reduce(function(k,t){return k+(t||100)},0),h=f>this._viewportHeight;if(f>=this._optPhysicalSize&&h)return!1;f=Math.floor(this._physicalSize/this._viewportHeight);0===f?this._debounceTemplate(this._increasePool.bind(this,Math.round(.5*this._physicalCount))):this._lastPage!==f&&h?Polymer.dom.addDebouncer(this.debounce("_debounceTemplate",this._increasePool.bind(this,this._itemsPerRow),
16)):this._debounceTemplate(this._increasePool.bind(this,Math.ceil(this._viewportHeight/(this._physicalSize/this._physicalCount)*this._maxPages-this._physicalCount)||1));this._lastPage=f;return!0},_debounceTemplate:function(f){Polymer.dom.addDebouncer(this.debounce("_debounceTemplate",f))},_increasePool:function(f){var h=this._physicalCount;f=Math.min(this._physicalCount+f,this._virtualCount-this._virtualStart,Math.max(this.maxPhysicalCount,25))-h;0>=f||([].push.apply(this._physicalItems,this._createPool(f)),
[].push.apply(this._physicalSizes,Array(f)),this._physicalCount=h+f,this._update())},_render:function(){var f=0<this._virtualCount||0<this._physicalCount;this.isAttached&&!this._itemsRendered&&this._isVisible&&f&&(this._lastPage=0,this._update(),this._itemsRendered=!0)},_iterateItems:function(f,h){var k,t;if(2===arguments.length&&h)for(t=0;t<h.length;t++){var l=h[t];var p=this._computeVidx(l);if(null!=(k=f.call(this,l,p)))return k}else{l=this._physicalStart;for(p=this._virtualStart;l<this._physicalCount;l++,
p++)if(null!=(k=f.call(this,l,p)))return k;for(l=0;l<this._physicalStart;l++,p++)if(null!=(k=f.call(this,l,p)))return k}},_computeVidx:function(f){return f>=this._physicalStart?this._virtualStart+(f-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+f},_updateMetrics:function(f){this.scrolling&&Polymer.dom.flush();var h=0,k=0,t=this._physicalAverageCount,l=this._physicalAverage;this._iterateItems(function(p){k+=this._physicalSizes[p]||0;this._physicalSizes[p]=this._physicalItems[p].offsetHeight;
h+=this._physicalSizes[p];this._physicalAverageCount+=this._physicalSizes[p]?1:0},f);this._viewportHeight=this._scrollTargetHeight;this._physicalSize=this._physicalSize+h-k;this._physicalAverageCount!==t&&(this._physicalAverage=Math.round((l*t+h)/this._physicalAverageCount))},_positionItems:function(){this._adjustScrollPosition();var f=this._physicalTop;this._iterateItems(function(h){this._physicalItems[h].style.transform=this._getTranslate(0,f);f+=this._physicalSizes[h]})},_getPhysicalSizeIncrement:function(f){return this._physicalSizes[f]},
_shouldRenderNextRow:function(f){return f%this._itemsPerRow===this._itemsPerRow-1},_adjustScrollPosition:function(){var f=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);f&&(this._physicalTop-=f,d||0===this._physicalTop||this._resetScrollPosition(this._scrollTop-f))},_resetScrollPosition:function(f){this.scrollTarget&&(this._scrollPosition=this._scrollTop=f)},_updateScrollerSize:function(f){this._estScrollHeight=this._physicalBottom+Math.max(this._virtualCount-
this._physicalCount-this._virtualStart,0)*this._physicalAverage;if((f=(f=f||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._optPhysicalSize)this.$.items.style.height=this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight},scrollToIndex:function(f){Polymer.dom.flush();f=Math.min(Math.max(f,0),this._virtualCount-1);if(!this._isIndexRendered(f)||f>=this._maxVirtualStart)this._virtualStart=
f-1;this._assignModels();this._updateMetrics();this._physicalTop=Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage;for(var h=this._physicalStart,k=this._virtualStart,t=0,l=this._hiddenContentSize;k<f&&t<=l;)t+=this._getPhysicalSizeIncrement(h),h=(h+1)%this._physicalCount,k++;this._updateScrollerSize(!0);this._positionItems();this._resetScrollPosition(this._physicalTop+this._scrollerPaddingTop+t);this._increasePoolIfNeeded();this._lastVisibleIndexVal=this._firstVisibleIndexVal=
null},_resetAverage:function(){this._physicalAverageCount=this._physicalAverage=0},_resizeHandler:function(){Polymer.dom.addDebouncer(this.debounce("_debounceTemplate",function(){this.updateViewportBoundaries();this._render();this._itemsRendered&&this._physicalItems&&this._isVisible&&(this._resetAverage(),this.scrollToIndex(this.firstVisibleIndex))}.bind(this),1))},updateSizeForItem:function(f){f=this._physicalIndexForKey[f];null!=f&&(this._updateMetrics([f]),this._positionItems())},_isIndexRendered:function(f){return f>=
this._virtualStart&&f<=this._virtualEnd},_isIndexVisible:function(f){return f>=this.firstVisibleIndex&&f<=this.lastVisibleIndex}}}();vaadin.elements.grid.IronListBehavior=[Polymer.Templatizer,Polymer.IronScrollTargetBehavior,vaadin.elements.grid.IronListBehaviorImpl];

//# sourceURL=build://vaadin-grid/vaadin-grid-table.html.js
Polymer({is:"vaadin-grid-table",behaviors:[vaadin.elements.grid.IronListBehavior,vaadin.elements.grid.TableScrollBehavior,vaadin.elements.grid.TableColumnReorderingBehavior,Polymer.Templatizer],properties:{size:Number,columnTree:Array,bindData:Function,rowDetailsTemplate:Object,columnReorderingAllowed:{type:Boolean,reflectToAttribute:!0},safari:{type:Boolean,value:/^((?!chrome|android).)*safari/i.test(navigator.userAgent)},scrollbarWidth:{type:Number,value:function(){var b=document.createElement("div");
b.style.width="100px";b.style.height="100px";b.style.overflow="scroll";b.style.position="absolute";b.style.top="-9999px";document.body.appendChild(b);var d=b.offsetWidth-b.clientWidth;document.body.removeChild(b);return d}},target:Object,hasData:Boolean},observers:["_columnTreeChanged(columnTree, _physicalItems, _physicalCountVal)","_sizeChanged(size, bindData, hasData)","_rowDetailsTemplateChanged(rowDetailsTemplate, _physicalItems, _physicalCountVal)"],listeners:{"property-changed":"_columnPropChanged",
animationend:"_onAnimationEnd","column-resizing":"_onColumnResize"},ready:function(){this.$=this.$||{};this.$.header=this.domHost.$.header;this.$.items=this.domHost.$.items;this.$.footer=this.domHost.$.footer},_onColumnResize:function(){this.toggleAttribute("column-resizing",this.$.header.querySelector("[column-resizing]"));this._gridResizeHandler()},_onAnimationEnd:function(b){/appear/.test(b.animationName)&&(this._render(),this._updateHeaderFooterMetrics(),b.stopPropagation())},_columnPropChanged:function(b){"headerTemplate"===
b.detail.path&&this.toggleAttribute("has-templates",!0,this.$.header);"footerTemplate"===b.detail.path&&this.toggleAttribute("has-templates",!0,this.$.footer);/frozen|hidden/.test(b.detail.path)&&this._frozenCellsChanged();"hidden"===b.detail.path&&this._gridResizeHandler()},_hideOuterScroller:function(b,d){return 0===b&&!d},_hideTableOverflow:function(b,d){return 0===b&&d},_rowDetailsTemplateChanged:function(b,d,f){void 0!==b&&d&&void 0!==f&&Array.prototype.forEach.call(d,function(h){h.rowDetailsTemplate=
b})},_columnTreeChanged:function(b,d,f){void 0!==b&&d&&void 0!==f&&(Polymer.RenderStatus.afterNextRender(this,this._update),this._frozenCellsChanged(),this._hasTemplatesChanged(b),Array.prototype.forEach.call(d,function(h){h.columns=b[b.length-1]}),this._gridResizeHandler(),Polymer.dom.flush(this),this._updateLastColumn())},_updateLastColumn:function(){Array.prototype.forEach.call(Polymer.dom(this.domHost.root).querySelectorAll(".vaadin-grid-row"),function(b){b.updateLastColumn()})},_updateHeaderFooterMetrics:function(){this._physicalSizes&&
Polymer.dom.flush();this._updateHeaderFooterMetricsSync();Polymer.RenderStatus.afterNextRender(this.$.header,function(){this._updateHeaderFooterMetricsSync();this._pendingScrollToScaledIndex&&this.scrollToScaledIndex(this._pendingScrollToScaledIndex)}.bind(this))},_updateHeaderFooterMetricsSync:function(){var b=this.$.header.clientHeight+"px",d=this.$.footer.clientHeight+"px";[this.$.outersizer,this.$.fixedsizer,this.$.items].forEach(function(f){f.style.borderTopWidth=b;f.style.borderBottomWidth=
d})},_hasTemplatesChanged:function(b){var d=!1,f=!1;b.forEach(function(h){return h.forEach(function(k){d=d||k.headerTemplate;f=f||k.footerTemplate})});this.toggleAttribute("has-templates",d,this.$.header);this.toggleAttribute("has-templates",f,this.$.footer)},_createPool:function(b){for(var d=Array(b),f=0;f<b;f++){var h=document.createElement("vaadin-grid-table-row");h.target=this.domHost;d[f]=h;h.setAttribute("hidden","");Polymer.dom(this.$.items).appendChild(h)}return d},_sizeChanged:function(b,
d,f){if(void 0!==b&&void 0!==d&&void 0!==f){var h=this._scrollTop,k=this.firstVisibleIndex+this._vidxOffset;this._virtualCount=Math.min(b,1E5);this._physicalIndexForKey={};this._lastVisibleIndexVal=this._firstVisibleIndexVal=null;this._vidxOffset=0;this._physicalItems||(this._physicalCount=Math.max(1,Math.min(25,this._virtualCount)),this._physicalItems=this._createPool(this._physicalCount),this._physicalSizes=Array(this._physicalCount));this._itemsRendered=!1;this._debounceTemplate(function(){this._render();
this._viewportHeight&&(this.scrollToScaledIndex(Math.min(k,this.size)),this._scrollTop=h,this._scrollHandler(),this.flushDebouncer("vaadin-grid-scrolling"))})}},_assignModels:function(b){this._virtualIndexToItem=this._virtualIndexToItem||{};this._iterateItems(function(d,f){d=this._physicalItems[d];d.index&&delete this._virtualIndexToItem[d.index];d.index=f+this._vidxOffset;this._virtualIndexToItem[d.index]=d;d.toggleAttribute("odd",d.index%2);d.toggleAttribute("lastrow",d.index===this.size-1);d.toggleAttribute("hidden",
d.index>=this.size);this.bindData(d.index,d)},b)},_gridResizeHandler:function(){this._updateHeaderFooterMetrics();this._physicalSizes&&(this._physicalItems.forEach(function(b){b.updateRowDetailsCellMetrics()}),this.debounce("vaadin-grid-resizing",function(){this._update()}.bind(this),1))}});

//# sourceURL=build://vaadin-grid/vaadin-grid-column.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.ColumnBaseBehavior={properties:{resizable:{type:Boolean,value:function(){if("vaadin-grid-column-group"!==this.localName){var b=Polymer.dom(this).parentNode;return b&&"vaadin-grid-column-group"===b.localName?b.resizable||!1:!1}}},headerTemplate:{type:Object},footerTemplate:{type:Object},frozen:{type:Boolean,notify:!0,value:!1},hidden:{type:Boolean,notify:!0},_lastFrozen:{type:Boolean,notify:!0,value:!1},_order:Number,_reorderStatus:Boolean},observers:["_footerTemplateChanged(footerTemplate)",
"_headerTemplateChanged(headerTemplate)","_lastFrozenChanged(_lastFrozen)"],created:function(){function b(d){0<=d.addedNodes.length&&(this.headerTemplate=this._prepareHeaderTemplate(),this.footerTemplate=this._prepareFooterTemplate(),this.template=this._prepareBodyTemplate())}this._templateObserver=Polymer.Element?new Polymer.FlattenedNodesObserver(this,b):Polymer.dom(this).observeNodes(b)},_prepareHeaderTemplate:function(){return this._prepareTemplatizer(this._findTemplate("template.header")||null,
{})},_prepareFooterTemplate:function(){return this._prepareTemplatizer(this._findTemplate("template.footer")||null,{})},_prepareBodyTemplate:function(){return this._prepareTemplatizer(this._findTemplate("template:not(.header):not(.footer)",{}))},_prepareTemplatizer:function(b,d){if(b&&!b.templatizer){var f=new vaadin.elements.grid.Templatizer;f.dataHost=this.dataHost;f._instanceProps=d||f._instanceProps;f.template=b;b.templatizer=f}return b},_selectFirstTemplate:function(b){return Array.prototype.filter.call(Polymer.dom(this).querySelectorAll(b),
function(d){return Polymer.dom(d).parentNode===this}.bind(this))[0]},_findTemplate:function(b){(b=this._selectFirstTemplate(b))&&this.dataHost&&(b._rootDataHost=this.dataHost._rootDataHost||this.dataHost);return b},_headerTemplateChanged:function(b){this.fire("property-changed",{path:"headerTemplate",value:b})},_footerTemplateChanged:function(b){this.fire("property-changed",{path:"footerTemplate",value:b})},_flexGrowChanged:function(b){this.fire("property-changed",{path:"flexGrow",value:b})},_widthChanged:function(b){this.fire("property-changed",
{path:"width",value:b})},_lastFrozenChanged:function(b){this.fire("property-changed",{path:"lastFrozen",value:b})}};
vaadin.elements.grid.ColumnBehaviorImpl={properties:{width:{type:String,value:"100px"},flexGrow:{type:Number,value:1},template:{type:Object}},observers:"_flexGrowChanged(flexGrow);_widthChanged(width);_templateChanged(template);_frozenChanged(frozen, isAttached);_hiddenChanged(hidden);_orderChanged(_order);_reorderStatusChanged(_reorderStatus);_resizableChanged(resizable)".split(";"),_frozenChanged:function(b,d){void 0!==b&&void 0!==d&&(void 0===this._oldFrozen&&!1===b||this.fire("property-changed",
{path:"frozen",value:b}),this._oldFrozen=b)},_templateChanged:function(b){b&&b.templatizer&&Polymer.dom(this.root).appendChild(b.templatizer);this.fire("property-changed",{path:"template",value:b},{bubbles:!1})},_hiddenChanged:function(b){this.fire("property-changed",{path:"hidden",value:b})},_orderChanged:function(b){this.fire("property-changed",{path:"order",value:b})},_reorderStatusChanged:function(b){this.fire("property-changed",{path:"reorderStatus",value:b})},_resizableChanged:function(b){this.fire("property-changed",
{path:"resizable",value:b})}};vaadin.elements.grid.ColumnBehavior=[vaadin.elements.grid.ColumnBaseBehavior,vaadin.elements.grid.ColumnBehaviorImpl];

//# sourceURL=build://vaadin-grid/vaadin-grid-column.html-2.js
Polymer({is:"vaadin-grid-column",behaviors:[vaadin.elements.grid.ColumnBehavior]});

//# sourceURL=build://vaadin-grid/vaadin-grid-array-data-provider-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.ArrayDataProviderBehavior={properties:{items:Array},observers:["_itemsChanged(items, items.*)"],_itemsChanged:function(b,d){void 0!==b&&void 0!==d&&(this.size=(b||[]).length,this.dataProvider=this.dataProvider||this._arrayDataProvider,this.clearCache())},_arrayDataProvider:function(b,d){var f=(this.items||[]).slice(0);this._checkPaths(this._filters,"filtering",f)&&(f=this._filter(f));this.size=f.length;b.sortOrders.length&&this._checkPaths(this._sorters,"sorting",f)&&(f=f.sort(this._multiSort.bind(this)));
var h=b.page*b.pageSize;d(f.slice(h,h+b.pageSize),f.length)},_checkPaths:function(b,d,f){if(!f.length)return!1;var h=!0,k;for(k in b){var t=b[k].path;if(t&&-1!==t.indexOf(".")){var l=t.replace(/\.[^\.]*$/,"");void 0===Polymer.Base.get(l,f[0])&&(console.warn('Path "'+t+'" used for '+d+" does not exist in all of the items, "+d+" is disabled."),h=!1)}}return h},_multiSort:function(b,d){return this._sorters.map(function(f){return"asc"===f.direction?this._compare(Polymer.Base.get(f.path,b),Polymer.Base.get(f.path,
d)):"desc"===f.direction?this._compare(Polymer.Base.get(f.path,d),Polymer.Base.get(f.path,b)):0},this).reduce(function(f,h){return f?f:h},0)},_normalizeEmptyValue:function(b){return 0<=[void 0,null].indexOf(b)?"":isNaN(b)?b.toString():b},_compare:function(b,d){b=this._normalizeEmptyValue(b);d=this._normalizeEmptyValue(d);return b<d?-1:b>d?1:0},_filter:function(b){return b.filter(function(d){return 0===this._filters.filter(function(f){return-1===this._normalizeEmptyValue(Polymer.Base.get(f.path,d)).toString().toLowerCase().indexOf(f.value.toString().toLowerCase())}.bind(this)).length},
this)}};

//# sourceURL=build://vaadin-grid/vaadin-grid-dynamic-columns-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.DynamicColumnsBehavior={ready:function(){this._addNodeObserver()},_hasColumnGroups:function(b){for(var d=0;d<b.length;d++)if("vaadin-grid-column-group"===b[d].localName)return!0;return!1},_getChildColumns:function(b){return Polymer.dom(b).queryDistributedElements("vaadin-grid-column, vaadin-grid-column-group, vaadin-grid-selection-column")},_flattenColumnGroups:function(b){return b.map(function(d){return"vaadin-grid-column-group"===d.localName?this._getChildColumns(d):[d]},this).reduce(function(d,
f){return d.concat(f)},[])},_getColumnTree:function(){for(var b=[],d=this.queryAllEffectiveChildren("vaadin-grid-column, vaadin-grid-column-group, vaadin-grid-selection-column");;){b.push(d);if(!this._hasColumnGroups(d))break;d=this._flattenColumnGroups(d)}return b},_updateColumnTree:function(){var b=this._getColumnTree();this._arrayEquals(b,this._columnTree)||(this._columnTree=b)},_addNodeObserver:function(){this._observer=Polymer.dom(this).observeNodes(function(b){function d(f){return f.nodeType===
Node.ELEMENT_NODE&&/^vaadin-grid-(column|selection)/i.test(f.localName)}(0<b.addedNodes.filter(d).length||0<b.removedNodes.filter(d).length)&&this._updateColumnTree();(Polymer.Settings.useNativeShadow||Polymer.Settings.useShadow)&&Polymer.dom(this).appendChild(this.$.footerFocusTrap);this.debounce("check-imports",this._checkImports,2E3)}.bind(this))},_arrayEquals:function(b,d){if(!b||!d||b.length!=d.length)return!1;for(var f=0,h=b.length;f<h;f++)if(b[f]instanceof Array&&d[f]instanceof Array){if(!this._arrayEquals(b[f],
d[f]))return!1}else if(b[f]!=d[f])return!1;return!0},_checkImports:function(){["vaadin-grid-column-group","vaadin-grid-sorter","vaadin-grid-filter","vaadin-grid-selection-column"].forEach(function(b){var d=Polymer.dom(this).querySelector(b);!d||(Polymer.isInstance?Polymer.isInstance(d):d instanceof Polymer.Element)||console.warn("Make sure you have imported the required module for \x3c"+b+"\x3e element.")},this)}};

//# sourceURL=build://vaadin-grid/vaadin-grid-sort-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.SortBehavior={properties:{multiSort:{type:Boolean,value:!1},_sorters:{type:Array,value:function(){return[]}},_previousSorters:{type:Array,value:function(){return[]}}},listeners:{"sorter-changed":"_onSorterChanged"},ready:function(){Polymer.Element&&!Polymer.Settings.useNativeShadow&&this.async(function(){var b=Polymer.dom(this).querySelectorAll("vaadin-grid-sorter");Array.prototype.forEach.call(b,function(d){d.fire&&d.fire("sorter-changed")})})},_onSorterChanged:function(b){var d=
b.target;this._removeArrayItem(this._sorters,d);d._order=null;this.multiSort?(d.direction&&this._sorters.unshift(d),this._sorters.forEach(function(f,h){f._order=1<this._sorters.length?h:null},this)):(this._sorters.forEach(function(f){f._order=null;f.direction=null}),d.direction&&(this._sorters=[d]));b.stopPropagation();this.dataProvider&&JSON.stringify(this._previousSorters)!==JSON.stringify(this._mapSorters())&&this.clearCache();this._previousSorters=this._mapSorters()},_mapSorters:function(){return this._sorters.map(function(b){return{path:b.path,
direction:b.direction}})},_removeArrayItem:function(b,d){d=b.indexOf(d);-1<d&&b.splice(d,1)}};

//# sourceURL=build://vaadin-grid/vaadin-grid-filter-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};vaadin.elements.grid.FilterBehavior={properties:{_filters:{type:Array,value:function(){return[]}}},listeners:{"filter-changed":"_filterChanged"},_filterChanged:function(b){-1===this._filters.indexOf(b.target)&&this._filters.push(b.target);b.stopPropagation();this.dataProvider&&this.clearCache()},_mapFilters:function(){return this._filters.map(function(b){return{path:b.path,value:b.value}})}};

//# sourceURL=build://vaadin-grid/vaadin-grid.html.js
Polymer({is:"vaadin-grid",properties:{_columnTree:{type:Array,notify:!0},size:Number,_rowDetailsTemplate:Object,_bindData:{type:Object,value:function(){return this._getItem.bind(this)}}},behaviors:[Polymer.IronA11yKeysBehavior,Polymer.IronResizableBehavior,vaadin.elements.grid.ActiveItemBehavior,vaadin.elements.grid.RowDetailsBehavior,vaadin.elements.grid.DataProviderBehavior,vaadin.elements.grid.DynamicColumnsBehavior,vaadin.elements.grid.ArrayDataProviderBehavior,vaadin.elements.grid.SelectionBehavior,
vaadin.elements.grid.SortBehavior,vaadin.elements.grid.FilterBehavior,vaadin.elements.grid.ColumnReorderingBehavior,vaadin.elements.grid.TableKeyboardBehavior],listeners:{"property-changed":"_columnPropChanged","iron-resize":"_gridResizeHandler"},_updateItem:function(b,d){b.style.minHeight=d?"":this.$.scroller._physicalAverage+"px";b.item=d;b.selected=this._isSelected(d);b.expanded=this._isExpanded(d);b.active=null!==d&&d==this.activeItem;b.focused=b.index===this.$.items._focusedRowIndex},_getContentTarget:function(){return this},
ready:function(){this._updateColumnTree();this._rowDetailsTemplate=Polymer.dom(this).querySelector("template.row-details")||void 0;this.$.scroller.target=this;null===document.doctype&&console.warn('\x3cvaadin-grid\x3e requires the "standards mode" declaration. Please add \x3c!DOCTYPE html\x3e to the HTML document.')},_columnPropChanged:function(b){"_childColumns"===b.detail.path&&this._updateColumnTree();b.stopPropagation()},_gridResizeHandler:function(){this.$.scroller._gridResizeHandler()}});

//# sourceURL=build://tf-hparams-session-group-details/tf-hparams-session-group-details.html.js
Polymer({is:"tf-hparams-session-group-details",properties:{backend:Object,experimentName:String,visibleSchema:Object,sessionGroup:Object,_xType:{type:String,value:rg.XType.STEP},_noMultiExperiments:{type:Boolean,value:!1},_indexOfSession:Object,_sessionGroupNameHash:Number,_requestData:{type:Function,value:function(){return({tag:b,run:d})=>this.backend.listMetricEvals({experimentName:this.experimentName,sessionName:d,metricName:b})}},_colorScale:{type:Object,value:function(){return{scale:b=>{b=JSON.parse(b)[1];
b=this._indexOfSession.get(b);const d=pf.standard;return d[(this._sessionGroupNameHash+b)%d.length]}}}}},behaviors:[Polymer.IronResizableBehavior],listeners:{"iron-resize":"redraw"},observers:["_sessionGroupChanged(sessionGroup.*)"],redraw(){Polymer.dom(this.root).querySelectorAll("tf-scalar-card").forEach(b=>b.redraw())},_sessionGroupChanged(){this.sessionGroup?(this._indexOfSession=new Map(this.sessionGroup.sessions.map((b,d)=>[b.name,d])),this._sessionGroupNameHash=tf.hparams.utils.hashOfString(this.sessionGroup.name)):
(this._indexOfSession=new Map,this._sessionGroupNameHash=0);Polymer.dom(this.root).querySelectorAll("tf-scalar-card").forEach(b=>{const d=b.get("tag");b.set("tag","");b.set("tag",d)})},_haveMetrics(){return this.visibleSchema&&Array.isArray(this.visibleSchema.metricInfos)&&0<this.visibleSchema.metricInfos.length},_haveMetricsAndSessionGroup(){return this.sessionGroup&&this._haveMetrics()},_computeSeriesForSessionGroupMetric(b,d){return null===b||null===d?[]:b.sessions.filter(f=>void 0!==tf.hparams.utils.metricValueByName(f.metricValues,
d.name)).map(f=>({tag:d.name,run:f.name}))},_computeTagMetadata(b){return{displayName:tf.hparams.utils.metricName(b),description:b.description||""}}});

//# sourceURL=build://tf-hparams-table-view/tf-hparams-table-view.html.js
Polymer({is:"tf-hparams-table-view",properties:{visibleSchema:Object,sessionGroups:Array,enableShowMetrics:Boolean,backend:Object,experimentName:String},observers:["_visibleSchemaOrSessionGroupsChanged(visibleSchema.*, sessionGroups.*)"],_visibleSchemaOrSessionGroupsChanged(){const b=this.$.sessionGroupsTable.get("expandedItems");this.$.sessionGroupsTable.set("expandedItems",[]);Polymer.dom.flush();const d=new Map;this.sessionGroups.forEach(f=>{d.set(f.name,f)});this.$.sessionGroupsTable.set("expandedItems",
b.map(f=>d.get(f.name)).filter(Boolean))},_hparamName:tf.hparams.utils.hparamName,_metricName:tf.hparams.utils.metricName,_sessionGroupHParam(b,d){return null!=b&&Object.prototype.hasOwnProperty.call(b.hparams,d)?tf.hparams.utils.prettyPrint(b.hparams[d]):""},_sessionGroupMetric(b,d){if(null==b)return null;for(let f=0;f<b.metricValues.length;++f){let h=b.metricValues[f];if(h.name.group===d.group&&h.name.tag==d.tag)return tf.hparams.utils.prettyPrint(h.value)}return""},_rowNumber(b){return b+1}});

//# sourceURL=build://tf-hparams-session-group-values/tf-hparams-session-group-values.html.js
Polymer({is:"tf-hparams-session-group-values",properties:{sessionGroup:{type:Object,value:null},visibleSchema:{type:Object,value:null}},_propertiesArePopulated:function(b,d){return void 0!==b&&null!==b&&void 0!==d&&null!==d},_singletonSessionGroups:function(b){return null===b||void 0===b?[]:[b]}});

//# sourceURL=build://tf-hparams-parallel-coords-plot/utils.html.js
(function(b){(function(d){(function(f){function h(k,t,l){function p(){if(0===k.length)return[1,2];const [m,n]=d3.extent(k);return m!==n?[m,n]:0<m?[.5*m,1.5*m]:0>m?[1.5*m,.5*m]:[-1,1]}if("LINEAR"===l)return d3.scaleLinear().domain(p()).range([t,0]);if("LOG"===l)return l=p(),0>=l[0]&&0<=l[1]?h(k,t,"LINEAR"):d3.scaleLog().domain(l).range([t,0]);if("QUANTILE"===l)return l=d3.range(20).map(m=>t-m*t/19),0===k.length&&(k=[1]),d3.scaleQuantile().domain(_.uniq(k)).range(l);if("NON_NUMERIC"===l)return d3.scalePoint().domain(_.uniq(k.sort())).range([t,
0]).padding(.1);throw RangeError("Unknown scale: "+l);}f.findClosestPath=function(k,t,l){function p(y,w,C,G){const D=y-C,B=w-G;C=m-C;G=n-G;const I=(D*C+B*G)/(D*D+B*B);return 0>=I?b.hparams.utils.l2NormSquared(C,G):1<=I?b.hparams.utils.l2NormSquared(y-m,w-n):b.hparams.utils.l2NormSquared(C-I*D,G-I*B)}if(2>t.length)return console.error("Less than two axes in parallel coordinates plot."),null;const m=l[0],n=l[1];if(m<=t[0]||m>=t[t.length-1])return null;const q=_.sortedIndex(t,m);console.assert(0<q);
console.assert(q<t.length);const u=q-1;let x=null,A=null;k.forEach(y=>{const w=p(y.controlPoints[u][0],y.controlPoints[u][1],y.controlPoints[q][0],y.controlPoints[q][1]);100<w||!(null===x||w<x)||(x=w,A=y)});return A};f.pointScaleInverseImage=function(k,t,l){return k.domain().filter(p=>{p=k(p);return t<=p&&p<=l})};f.quantileScaleInverseImage=function(k,t,l){const p=k.range(),m=p.filter(n=>t<=n&&n<=l).map(n=>{const q=k.invertExtent(n);return n===p[p.length-1]?[q[0],q[1]+1]:q});return 0==m.length?[0,
0]:d3.extent(d3.merge(m))};f.continuousScaleInverseImage=function(k,t,l){return[k.invert(t),k.invert(l)].sort((p,m)=>p-m)};f.createAxisScale=h})(d.parallel_coords_plot||(d.parallel_coords_plot={}))})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://tf-hparams-parallel-coords-plot/axes.js
(function(b){(function(d){(function(f){function h(q){return null!==q.sourceEvent}let k;(function(q){q.LINEAR="LINEAR";q.LOG="LOG";q.QUANTILE="QUANTILE";q.NON_NUMERIC="NON_NUMERIC"})(k=f.ScaleType||(f.ScaleType={}));class t{isPassing(){return!0}}class l{constructor(q,u,x,A){this._lower=q;this._upper=u;this._lowerOpen=x;this._upperOpen=A}isPassing(q){return this._before(this._lower,q,!this._lowerOpen)&&this._before(q,this._upper,!this._upperOpen)}_before(q,u,x){return x?q<=u:q<u}}class p{constructor(q){this._domainSet=
q}isPassing(q){return-1!==this._domainSet.findIndex(u=>u===q)}}class m{constructor(q,u,x,A){this._svgProps=q;this._schema=u;this._interactionManager=x;this._colIndex=A;this._isDisplayed=!1;this._scaleType=this._yScale=null;this.setBrushSelection(null)}colIndex(){return this._colIndex}yScale(){return this._yScale}scaleType(){return this._scaleType}brushSelection(){return this._brushSelection}isDisplayed(){return this._isDisplayed}setBrushSelection(q){this._brushSelection=q;this._brushFilter=this._buildBrushFilter(this.brushSelection(),
this.scaleType(),this.yScale())}setDomainAndScale(q,u){this._scaleType=u;this._yScale=b.hparams.parallel_coords_plot.createAxisScale(q.slice(),this._svgProps.height,this.scaleType());this._brushFilter=this._buildBrushFilter(this.brushSelection(),this.scaleType(),this.yScale())}brushFilter(){return this._brushFilter}updateDOM(q){var u=d3.axisLeft(this.yScale());this.scaleType()===k.QUANTILE&&(u=u.tickValues(this.yScale().quantiles()).tickFormat(d3.format("-.6g")));var x=d3.select(q);x.selectAll("g").remove();
x.append("g").classed("axis",!0).call(u).append("text").classed("axis-title",!0).style("cursor","move").style("text-anchor","middle").attr("y",-9).text(A=>b.hparams.utils.schemaColumnName(this._schema,A));x.call(d3.drag().on("start",()=>{q.setAttribute("is-dragging","");this._interactionManager.onDragStart(this.colIndex())}).on("drag",()=>this._interactionManager.onDrag(d3.event.x)).on("end",()=>{this._interactionManager.onDragEnd();q.removeAttribute("is-dragging")}));u=d3.brushY().extent([[-8,0],
[8,this._svgProps.height+1]]).on("start",()=>{h(d3.event)&&(q.setAttribute("is-brushing",""),this._interactionManager.onBrushChanged(this.colIndex()))}).on("brush",()=>{if(h(d3.event))this._interactionManager.onBrushChanged(this.colIndex())}).on("end",()=>{h(d3.event)&&(this._interactionManager.onBrushChanged(this.colIndex()),q.removeAttribute("is-brushing"))});x=d3.select(q).append("g").classed("brush",!0);x.call(u);u.move(x,this.brushSelection())}setDisplayed(q){this._isDisplayed=q}_buildBrushFilter(q,
u,x){if(null===q)return new t;if(null===u)return console.error("Scale type is null, but brushSelection isn't: ",q),new t;switch(u){case k.LINEAR:case k.LOG:{const [A,y]=b.hparams.parallel_coords_plot.continuousScaleInverseImage(x,q[0],q[1]);return new l(A,y,!1,!1)}case k.QUANTILE:{const [A,y]=b.hparams.parallel_coords_plot.quantileScaleInverseImage(x,q[0],q[1]);return new l(A,y,!1,!0)}case k.NON_NUMERIC:return new p(b.hparams.parallel_coords_plot.pointScaleInverseImage(x,q[0],q[1]))}console.error("Unknown scale type: ",
u);return new t}}f.Axis=m;class n{constructor(q,u,x){this._svgProps=q;this._schema=u;this._axes=this._createAxes(x);this._stationaryAxesPositions=d3.scalePoint().range([1,this._svgProps.width-1]).padding(.5);this._draggedAxis=null;this._svgProps.svgG.selectAll("g.axis-parent").remove();this._parentsSel=this._svgProps.svgG.selectAll(".axis-parent")}updateAxes(q,u){console.assert(!this.isAxisDragging());const x=new Set;q.columns.forEach(y=>{const w=y.absoluteIndex;let C=this._axes[w];C.setDisplayed(!0);
const G=u.map(D=>b.hparams.utils.columnValueByIndex(this._schema,D,w));C.setDomainAndScale(G,y.scale);x.add(w)});this._axes.forEach(y=>{x.has(y.colIndex())||y.setDisplayed(!1)});this._updateStationaryAxesPositions(x);this._parentsSel=this._parentsSel.data(Array.from(x),y=>y);this._parentsSel.exit().remove();this._parentsSel=this._parentsSel.enter().append("g").classed("axis-parent",!0).merge(this._parentsSel);const A=this;this._parentsSel.call(y=>this._updateAxesPositionsInDOM(y)).each(function(y){A._axes[y].updateDOM(this)})}mapVisibleAxes(q){return this._stationaryAxesPositions.domain().map(u=>
q(this.getAxisPosition(u),this._axes[u]))}allVisibleAxesSatisfy(q){return this._stationaryAxesPositions.domain().every(u=>q(this.getAxisPosition(u),this._axes[u]))}getAxisForColIndex(q){return this._axes[q]}dragStart(q){console.assert(!this.isAxisDragging());console.assert(this._axes[q].isDisplayed());this._draggedAxis=this._axes[q];this._draggedAxisPosition=this._stationaryAxesPositions(q)}drag(q){this._draggedAxisPosition=q=Math.min(Math.max(q,0),this._svgProps.width);q=this._stationaryAxesPositions.domain();
q.sort((u,x)=>this.getAxisPosition(u)-this.getAxisPosition(x));this._stationaryAxesPositions.domain(q);this._updateAxesPositionsInDOM(this._parentsSel)}dragEnd(){console.assert(this.isAxisDragging());this._draggedAxis=this._draggedAxisPosition=null;this._updateAxesPositionsInDOM(this._parentsSel.transition().duration(500))}isAxisDragging(){return null!==this._draggedAxis}getAxisPosition(q){return null!==this._draggedAxis&&this._draggedAxis.colIndex()===q?this._draggedAxisPosition:this._stationaryAxesPositions(q)}_updateStationaryAxesPositions(q){var u=
this._stationaryAxesPositions.domain().filter(x=>q.has(x));u=Array.from(new Set([...u,...Array.from(q)]));this._stationaryAxesPositions.domain(u)}_updateAxesPositionsInDOM(q){q.attr("transform",u=>b.hparams.utils.translateStr(this.getAxisPosition(u)))}_createAxes(q){return d3.range(b.hparams.utils.numColumns(this._schema)).map(u=>new m(this._svgProps,this._schema,q,u))}}f.AxesCollection=n})(d.parallel_coords_plot||(d.parallel_coords_plot={}))})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://tf-hparams-parallel-coords-plot/lines.js
(function(b){(function(d){(function(f){let h;(function(l){l[l.FOREGROUND=0]="FOREGROUND";l[l.BACKGROUND=1]="BACKGROUND"})(h=f.LineType||(f.LineType={}));class k{constructor(l){void 0===l&&(l=d3.selectAll(null));console.assert(1>=l.size());this._sessionGroupSel=l}sessionGroup(){return 1===this._sessionGroupSel.size()?this._sessionGroupSel.datum():null}isNull(){return null===this.sessionGroup()}selection(){return this._sessionGroupSel}equalsTo(l){return this.isNull()?l.isNull():l.isNull()?!1:l.sessionGroup().name==
this.sessionGroup().name}}f.SessionGroupHandle=k;class t{constructor(l,p,m){this._svgProps=l;this._schema=p;this._axesCollection=m;this._sessionGroups=[];this._svgProps.svgG.selectAll("g.background").remove();this._svgProps.svgG.selectAll("g.foreground").remove();this._bgPathsSel=this._svgProps.svgG.append("g").classed("background",!0).selectAll("path");this._fgPathsSel=this._svgProps.svgG.append("g").classed("foreground",!0).selectAll("path");this._updateVisibleFgPathsSel();this._peakedSessionGroupHandle=
new k;this._selectedSessionGroupHandle=new k;this._d3line=d3.line().curve(d3.curveLinear)}getSessionGroupHandle(l){return null===l||void 0===l?new k:new k(this._fgPathsSel.filter(p=>p.name===l.name))}hideBackgroundLines(){this._bgPathsSel.attr("visibility","hidden")}showBackgroundLines(){this._bgPathsSel.attr("visibility",null)}peakedSessionGroupHandle(){return this._peakedSessionGroupHandle}selectedSessionGroupHandle(){return this._selectedSessionGroupHandle}recomputeControlPoints(l,p=0){(l===h.FOREGROUND?
this._fgPathsSel:this._bgPathsSel).transition().duration(p).attr("d",m=>this._pathDAttribute(m));l===h.FOREGROUND&&window.setTimeout(()=>{const m=this;this._fgPathsSel.each(function(n){m._setControlPointsProperty(this,n)})})}recomputeForegroundLinesVisibility(){this._fgPathsSel.classed("invisible-path",l=>!this._axesCollection.allVisibleAxesSatisfy((p,m)=>m.brushFilter().isPassing(b.hparams.utils.columnValueByIndex(this._schema,l,m.colIndex()))));this._updateVisibleFgPathsSel()}setForegroundLinesColor(l,
p,m){l=this._createLineColorFunction(l,p,m);this._fgPathsSel.attr("stroke",l)}redraw(l,p,m,n){const q=this._peakedSessionGroupHandle.sessionGroup(),u=this._selectedSessionGroupHandle.sessionGroup();this._sessionGroups=l;this._fgPathsSel=this._recomputePathSelection(this._fgPathsSel);this._bgPathsSel=this._recomputePathSelection(this._bgPathsSel);this._peakedSessionGroupHandle=this.getSessionGroupHandle(q);this._selectedSessionGroupHandle=this.getSessionGroupHandle(u);this.recomputeControlPoints(h.FOREGROUND);
this.recomputeControlPoints(h.BACKGROUND);this.recomputeForegroundLinesVisibility();this.setForegroundLinesColor(p,m,n)}updatePeakedSessionGroup(l){this._peakedSessionGroupHandle.selection().classed("peaked-path",!1);this._peakedSessionGroupHandle=l;this._peakedSessionGroupHandle.selection().classed("peaked-path",!0)}clearPeakedSessionGroup(){this.updatePeakedSessionGroup(new k)}updateSelectedSessionGroup(l){this._selectedSessionGroupHandle.selection().classed("selected-path",!1);this._selectedSessionGroupHandle=
l;this._selectedSessionGroupHandle.selection().classed("selected-path",!0)}findClosestSessionGroup(l,p){const m=this._axesCollection.mapVisibleAxes(n=>n);l=b.hparams.parallel_coords_plot.findClosestPath(this._visibleFgPathsSel.nodes(),m,[l,p]);return null===l?new k:new k(d3.select(l))}_createLineColorFunction(l,p,m){if(null===l)return()=>"red";const n=d3.scaleLinear().domain(b.hparams.utils.numericColumnExtent(this._schema,this._sessionGroups,l)).range([p,m]).interpolate(d3.interpolateLab);return q=>
n(b.hparams.utils.columnValueByIndex(this._schema,q,l))}_recomputePathSelection(l){l=l.data(this._sessionGroups,p=>p.name);l.exit().remove();return l.enter().append("path").merge(l)}_setControlPointsProperty(l,p){l.controlPoints=this._computeControlPoints(p)}_computeControlPoints(l){return this._axesCollection.mapVisibleAxes((p,m)=>[p,m.yScale()(b.hparams.utils.columnValueByIndex(this._schema,l,m.colIndex()))])}_pathDAttribute(l){return this._d3line(this._computeControlPoints(l))}_updateVisibleFgPathsSel(){this._visibleFgPathsSel=
this._fgPathsSel.filter(":not(.invisible-path)")}}f.LinesCollection=t})(d.parallel_coords_plot||(d.parallel_coords_plot={}))})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://tf-hparams-parallel-coords-plot/interaction_manager.js
(function(b){(function(d){(function(f){class h{constructor(t,l){this.svg=d3.select(t);t=100*l+20;this.svg.attr("viewBox",`0 0 ${t} ${240}`);this.svg.attr("preserveAspectRatio","xMidYMid");this.svg.style("min-width",t+"px");this.svg.style("min-height","240px");this.width=t-10-10;this.height=200;this.svgG=this.svg.append("g").attr("transform",b.hparams.utils.translateStr(10,30))}}f.SVGProperties=h;class k{constructor(t,l,p,m){this._svgProps=t;this._schema=l;this._peakedSessionGroupChangedCB=p;this._selectedSessionGroupChangedCB=
m;this._axesCollection=new f.AxesCollection(t,l,this);this._linesCollection=new f.LinesCollection(t,l,this._axesCollection);this._svgProps.svg.on("click",()=>this.onClick()).on("mousemove mouseenter",()=>{const [n,q]=d3.mouse(this._svgProps.svgG.node());this.onMouseMoved(n,q)}).on("mouseleave",()=>this.onMouseLeave())}onDragStart(t){this._axesCollection.dragStart(t);this._linesCollection.hideBackgroundLines()}onDrag(t){this._axesCollection.drag(t);this._linesCollection.recomputeControlPoints(f.LineType.FOREGROUND)}onDragEnd(){this._axesCollection.dragEnd();
this._linesCollection.recomputeControlPoints(f.LineType.FOREGROUND,500);window.setTimeout(()=>{this._linesCollection.recomputeControlPoints(f.LineType.BACKGROUND);this._linesCollection.showBackgroundLines()},500)}onBrushChanged(t){this._axesCollection.getAxisForColIndex(t).setBrushSelection(d3.event.selection);this._linesCollection.recomputeForegroundLinesVisibility()}onMouseMoved(t,l){this._linesCollection.updatePeakedSessionGroup(this._linesCollection.findClosestSessionGroup(t,l));this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup())}onMouseLeave(){this._linesCollection.peakedSessionGroupHandle().isNull()||
(this._linesCollection.clearPeakedSessionGroup(),this._peakedSessionGroupChangedCB(null))}onClick(){this._linesCollection.peakedSessionGroupHandle().sessionGroup()===this._linesCollection.selectedSessionGroupHandle().sessionGroup()?this._linesCollection.updateSelectedSessionGroup(new f.SessionGroupHandle):this._linesCollection.updateSelectedSessionGroup(this._linesCollection.peakedSessionGroupHandle());this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}onOptionsOrSessionGroupsChanged(t,
l){this._axesCollection.updateAxes(t,l);const p=this._linesCollection.peakedSessionGroupHandle(),m=this._linesCollection.selectedSessionGroupHandle();this._linesCollection.redraw(l,void 0!==t.colorByColumnIndex?t.columns[t.colorByColumnIndex].absoluteIndex:null,t.minColor,t.maxColor);p.equalsTo(this._linesCollection.peakedSessionGroupHandle())||this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup());m.equalsTo(this._linesCollection.selectedSessionGroupHandle())||
this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}schema(){return this._schema}}f.InteractionManager=k})(d.parallel_coords_plot||(d.parallel_coords_plot={}))})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://tf-hparams-parallel-coords-plot/tf-hparams-parallel-coords-plot.html.js
Polymer({is:"tf-hparams-parallel-coords-plot",properties:{sessionGroups:Array,options:Object,selectedSessionGroup:{type:Object,value:null,readOnly:!0,notify:!0},closestSessionGroup:{type:Object,value:null,readOnly:!0,notify:!0},redrawCount:{type:Number,value:0},_validSessionGroups:Array,_interactionManager:Object},observers:["_optionsOrSessionGroupsChanged(options.*, sessionGroups.*)"],_optionsOrSessionGroupsChanged(){if(null!==this.options){var b=this.options.configuration;if(void 0===this._interactionManager||
!_.isEqual(this._interactionManager.schema(),b.schema)){d3.select(this.$.svg).selectAll("*").remove();const d=new tf.hparams.parallel_coords_plot.SVGProperties(this.$.svg,tf.hparams.utils.numColumns(b.schema));this.scopeSubtree(this.$.svg,!0);this._interactionManager=new tf.hparams.parallel_coords_plot.InteractionManager(d,b.schema,f=>this.closestSessionGroupChanged(f),f=>this.selectedSessionGroupChanged(f))}this._computeValidSessionGroups();this._interactionManager.onOptionsOrSessionGroupsChanged(this.options,
this._validSessionGroups);this.redrawCount++}},closestSessionGroupChanged(b){this._setClosestSessionGroup(b)},selectedSessionGroupChanged(b){this._setSelectedSessionGroup(b)},_computeValidSessionGroups(){const b=tf.hparams.utils;if(void 0===this.sessionGroups)this._validSessionGroups=void 0;else{var d=this.options.configuration.schema;this._validSessionGroups=this.sessionGroups.filter(f=>{for(let h=0;h<b.numColumns(d);++h)if(this.options.configuration.columnsVisibility[h]&&void 0===b.columnValueByIndex(d,
f,h))return!1;return!0})}}});

//# sourceURL=build://tf-hparams-parallel-coords-view/tf-hparams-parallel-coords-view.html.js
Polymer({is:"tf-hparams-parallel-coords-view",properties:{backend:Object,experimentName:String,configuration:Object,sessionGroups:Array},_closestOrSelected:function(b,d){return null!==b?b:d}});

//# sourceURL=build://tf-hparams-scatter-plot-matrix-plot/tf-hparams-scatter-plot-matrix-plot.html.js
Polymer({is:"tf-hparams-scatter-plot-matrix-plot",properties:{visibleSchema:Object,sessionGroups:Array,options:Object,selectedSessionGroup:{type:Object,value:null,readOnly:!0,notify:!0},closestSessionGroup:{type:Object,value:null,readOnly:!0,notify:!0},_container:{type:Object,value:null},_svg:{type:Object,value:null},width:{type:Number,value:0},height:{type:Number,value:0},_brushedCellIndex:{type:Object,value:null},_brushSelection:{type:Object,value:null}},observers:["_sessionGroupsChanged(sessionGroups.*)",
"_visibleSchemaChanged(visibleSchema.*)","_redraw(options.*)"],ready(){this._container=this.$.container;this._svg=d3.select(this.$.svg);this._redraw()},_sessionGroupsChanged(){null!==this.selectedSessionGroup&&this._setSelectedSessionGroup(tf.hparams.utils.sessionGroupWithName(this.sessionGroups,this.selectedSessionGroup.name)||null);this._redraw()},_visibleSchemaChanged(){this._brushSelection=this._brushedCellIndex=null;this._redraw()},_redraw(){this.debounce("_redraw",()=>{const b=tf.hparams.utils;
this.width=Math.max(150*b.numVisibleColumns(this.visibleSchema),1200);this.height=Math.max(112.5*b.numVisibleMetrics(this.visibleSchema),480);this._container.style.width=this.width+"px";this._container.style.height=this.height+"px";this._svg.attr("width",this.width).attr("height",this.height);this._svg.selectAll("g").remove();this._draw()},100)},_draw(){function b(ka){return"x-axis-clip-path-"+ka}function d(ka){return"x-label-clip-path-"+ka}function f(ka){return"y-axis-clip-path-"+ka}function h(ka){return"y-label-clip-path-"+
ka}function k(ka,Y,Ea,va,xa){Ea=Math.floor(Ea/va);va=Y.scale();if("QUANTILE"===xa){let Aa=va.quantiles();Aa=d3.range(0,Aa.length,Math.ceil(Aa.length/Ea)).map(Fa=>Aa[Fa]);Y.tickValues(Aa).tickFormat(d3.format("-.2g"))}"LINEAR"!==xa&&"LOG"!==xa||Y.ticks(Ea);ka.call(Y);ka.selectAll(".domain").remove();ka.selectAll(".tick line").attr("stroke","#ddd")}function t(ka,Y){return O[Y](w._colValue(ka,Y))}function l(ka,Y){return H[Y](w._metricValue(ka,Y))}function p(ka,Y){const Ea=[];T[ka][Y].each(function(){Ea.push(this)});
return d3.quadtree().x(va=>d3.select(va).datum().x).y(va=>d3.select(va).datum().y).addAll(Ea)}function m(){let ka=new Set(Q.nodes());x()||(ka=n(w._brushedCellIndex,w._brushSelection));d3.selectAll(Array.from(y.filterSet(ka,Y=>!Z.has(Y)))).attr("fill",L);d3.selectAll(Array.from(y.filterSet(Z,Y=>!ka.has(Y)))).attr("fill","#ddd");Z=ka}function n(ka,Y){console.assert(null!==ka);console.assert(null!==Y);const [Ea,va]=ka,xa=new Set;y.quadTreeVisitPointsInRect(aa[Ea][va],Y[0][0],Y[0][1],Y[1][0],Y[1][1],
Aa=>{d3.select(Aa).datum().sessionGroupMarkers.forEach(Fa=>{xa.add(Fa)})});return xa}function q(ka){const Y=d3.brushSelection(ka);!u()&&null===Y||u()&&ka===la.node()&&_.isEqual(Y,w._brushSelection)||(w._brushSelection=Y,null!==Y?(la=d3.select(ka),w._brushedCellIndex=la.datum()):(la=null,w._brushedCellIndex=null),m())}function u(){return null!==w._brushedCellIndex&&null!==w._brushSelection}function x(){return!u()||w._brushSelection[0][0]===w._brushSelection[1][0]||w._brushSelection[0][1]===w._brushSelection[1][1]}
function A(ka,Y,Ea,va,xa){let Aa=Infinity,Fa=null;y.quadTreeVisitPointsInDisk(aa[ka][Y],Ea,va,xa,(ya,Sa)=>{Z.has(ya)&&Sa<Aa&&(ya=d3.select(ya).datum(),Aa=Sa,Fa=ya.sessionGroup)});return null===Fa?null:d3.selectAll(X.get(Fa))}const y=tf.hparams.utils,w=this;if(this.sessionGroups&&0!=this.sessionGroups.length&&this.visibleSchema&&0!=this.visibleSchema.metricInfos.length){var C=d3.range(y.numVisibleColumns(w.visibleSchema)),G=d3.range(y.numVisibleMetrics(w.visibleSchema)),D=d3.scaleBand().domain(C).range([85,
this.width-1-5]).paddingInner(.1),B=d3.scaleBand().domain(G).range([this.height-1-5-50,5]).paddingInner(.1),I=D.bandwidth(),N=B.bandwidth(),O=C.map(ka=>w._cellScale(ka,[0,I-1])),H=G.map(ka=>w._cellScale(ka+y.numVisibleHParams(w.visibleSchema),[N-1,0])),K=this._svg.selectAll(".x-axis").data(C).enter().append("g").classed("x-axis",!0).attr("transform",ka=>y.translateStr(D(ka),0));K.append("clipPath").attr("id",b).append("rect").attr("x",-5).attr("y",0).attr("width",I+10).attr("height",w.height-25);
K.append("clipPath").attr("id",d).append("rect").attr("x",0).attr("y",w.height-25).attr("width",I).attr("height",25);K.append("g").attr("clip-path",ka=>"url(#"+b(ka)+")").each(function(ka){d3.select(this).call(k,d3.axisBottom(O[ka]).tickSize(w.height-50),I,40,w.options.columns[ka].scale)});K.append("g").classed("x-axis-label",!0).attr("clip-path",ka=>"url(#"+d(ka)+")").append("text").attr("text-anchor","middle").attr("x",I/2).attr("y",w.height-1-12.5).text(ka=>y.schemaVisibleColumnName(w.visibleSchema,
ka)).append("title").text(ka=>y.schemaVisibleColumnName(w.visibleSchema,ka));K=this._svg.selectAll(".y-axis").data(G).enter().append("g").classed("y-axis",!0).attr("transform",ka=>y.translateStr(w.width-1,B(ka)));K.append("clipPath").attr("id",f).append("rect").attr("x",-(w.width-40-1)).attr("y",-5).attr("width",w.width-40).attr("height",N+10);K.append("clipPath").attr("id",h).append("rect").attr("x",-(w.width-1)).attr("y",0).attr("width",40).attr("height",N);K.append("g").attr("clip-path",ka=>"url(#"+
f(ka)+")").each(function(ka){d3.select(this).call(k,d3.axisLeft(H[ka]).tickSize(w.width-80),N,20,w.options.columns[ka+y.numVisibleHParams(w.visibleSchema)].scale)});K.append("g").classed("y-axis-label",!0).attr("clip-path",ka=>"url(#"+h(ka)+")").append("text").attr("text-anchor","middle").attr("x",-(w.width-20-1)).attr("y",N/2).attr("transform",y.rotateStr(-(w.width-20-1),N/2)).text(ka=>y.metricName(w.visibleSchema.metricInfos[ka])).append("title").text(ka=>y.metricName(w.visibleSchema.metricInfos[ka]));
K=this._svg.selectAll(".cell").data(d3.cross(C,G)).enter().append("g").classed("cell",!0).attr("transform",([ka,Y])=>y.translateStr(D(ka),B(Y)));K.append("g").classed("frame",!0).append("rect").attr("x",-5).attr("y",-5).attr("width",I+10).attr("height",N+10).attr("stroke","#000").attr("fill","none").attr("shape-rendering","crispEdges");var M=null;void 0!==w.options.colorByColumnIndex&&(M=d3.scaleLinear().domain(this._colExtent(this.options.colorByColumnIndex)).range([this.options.minColor,this.options.maxColor]).interpolate(d3.interpolateLab));
var L=void 0===w.options.colorByColumnIndex?()=>"red":({sessionGroup:ka})=>M(this._colValue(ka,w.options.colorByColumnIndex)),[Q,T,X]=function(ka,Y){const Ea=ka.selectAll(".data-marker").data(([xa,Aa])=>w.sessionGroups.filter(Fa=>void 0!==w._colValue(Fa,xa)&&void 0!==w._metricValue(Fa,Aa)).map(Fa=>({col:xa,metric:Aa,sessionGroup:Fa,x:t(Fa,xa),y:l(Fa,Aa),sessionGroupMarkers:null}))).enter().append("circle").classed("data-marker",!0).attr("cx",({x:xa})=>xa).attr("cy",({y:xa})=>xa).attr("r",2).attr("fill",
Y),va=new Map;w.sessionGroups.forEach(xa=>{va.set(xa,[])});Ea.each(function(xa){va.get(xa.sessionGroup).push(this)});Ea.each(xa=>{const Aa=va.get(xa.sessionGroup);xa.sessionGroupMarkers=new Set(Aa)});ka=C.map(xa=>G.map(Aa=>Ea.filter(Fa=>Fa.col==xa&&Fa.metric==Aa)));return[Ea,ka,va]}(K.append("g"),L),aa=C.map(ka=>G.map(Y=>p(ka,Y))),la=null;u()&&(la=K.filter(ka=>_.isEqual(ka,w._brushedCellIndex)),console.assert(1==la.size(),la));var Z=new Set(Q.nodes());m();var ba=d3.brush().extent([[-4,-4],[I-1+5-
1,N-1+5-1]]).on("start",function(){u()&&la.node()!=this&&ba.move(la,null);q(this)}).on("brush",function(){q(this)}).on("end",function(){q(this)});K.call(ba);u()&&ba.move(la,w._brushSelection);var ea=null,ca=null;null!==this.selectedSessionGroup&&(ca=d3.selectAll(X.get(this.selectedSessionGroup)).classed("selected-marker",!0));K.on("click",function(){var ka=ea===ca?null:ea;ka!==ca&&(null!==ca&&ca.classed("selected-marker",!1),ca=ka,null!==ca&&ca.classed("selected-marker",!0),ka=null===ca?null:ca.datum().sessionGroup,
w._setSelectedSessionGroup(ka))}).on("mousemove mouseenter",function([ka,Y]){const [Ea,va]=d3.mouse(this);ka=A(ka,Y,Ea,va,20);ea!==ka&&(null!==ea&&ea.classed("closest-marker",!1),ea=ka,null!==ea?(ea.classed("closest-marker",!0),w._setClosestSessionGroup(ea.datum().sessionGroup)):w._setClosestSessionGroup(null))}).on("mouseleave",function(){null!==ea&&(ea.classed("closest-marker",!1),ea=null,w._setClosestSessionGroup(null))});this._svg.selectAll("*").classed("tf-hparams-scatter-plot-matrix-plot",!0)}},
_cellScale(b,d){var f=this._colExtent(b);const h=d3.scaleLinear().domain(f).range(d);if("LINEAR"===this.options.columns[b].scale)return h;if("LOG"===this.options.columns[b].scale)return 0>=f[0]&&0<=f[1]?h:d3.scaleLog().domain(f).range(d);if("QUANTILE"===this.options.columns[b].scale){const k=(d[1]-d[0])/19;f=d3.range(20).map(t=>d[0]+k*t);return d3.scaleQuantile().domain(_.uniq(this.sessionGroups.map(t=>this._colValue(t,b)))).range(f)}if("NON_NUMERIC"===this.options.columns[b].scale)return d3.scalePoint().domain(_.uniq(this.sessionGroups.map(k=>
this._colValue(k,b)).sort())).range(d).padding(.1);throw"Unknown scale for column: "+b+". options: "+this.options;},_colValue(b,d){return tf.hparams.utils.columnValueByVisibleIndex(this.visibleSchema,b,d)},_metricValue(b,d){return tf.hparams.utils.metricValueByVisibleIndex(this.visibleSchema,b,d)},_colExtent(b){return tf.hparams.utils.visibleNumericColumnExtent(this.visibleSchema,this.sessionGroups,b)}});

//# sourceURL=build://tf-hparams-scatter-plot-matrix-view/tf-hparams-scatter-plot-matrix-view.html.js
Polymer({is:"tf-hparams-scatter-plot-matrix-view",properties:{backend:Object,experimentName:String,configuration:Object,sessionGroups:Array},_closestOrSelected:function(b,d){return null!==b?b:d}});

//# sourceURL=build://tf-hparams-sessions-pane/tf-hparams-sessions-pane.html.js
Polymer({is:"tf-hparams-sessions-pane",properties:{backend:Object,helpUrl:String,bugReportUrl:String,experimentName:String,configuration:Object,sessionGroups:Array,_selectedTab:{type:Number,value:0}}});

//# sourceURL=build://tf-hparams-google-analytics-tracker/tf-hparams-google-analytics-tracker.html.js
(function(){Polymer({is:"tf-hparams-google-analytics-tracker",handleEvent:function(){}})})();

//# sourceURL=build://tf-hparams-main/tf-hparams-main.html.js
Polymer({is:"tf-hparams-main",properties:{backend:Object,experimentName:String,trackingId:String,helpUrl:String,bugReportUrl:String,_configuration:Object,_sessionGroups:Array,_throttledSendEventToGA:{type:Function,value:()=>_.throttle(function(){this._handleGAEvent({detail:{hitType:"event",eventCategory:"UserInteraction",eventLabel:"Experiment: "+this.experimentName}})},6E4,{leading:!0})}},listeners:{mousemove:"_sendEventToGA",tap:"_sendEventToGA","google-analytics-tracking":"_handleGAEvent"},attached(){this._handleGAEvent({detail:{hitType:"pageview"}})},
reload(){this.$["query-pane"].reload()},_sendEventToGA(){this._throttledSendEventToGA(this)},_handleGAEvent(b){this.$.tracker.handleEvent(b)}});

//# sourceURL=build://tf-hparams-backend/tf-hparams-backend.html.js
(function(b){(function(d){class f{constructor(h,k,t=!0){this._apiUrl=h;this._requestManager=k;this._useHttpGet=t}getExperiment(h){return this._sendRequest("experiment",h)}getDownloadUrl(h,k,t){return this._apiUrl+"/download_data?"+new URLSearchParams({format:h,columnsVisibility:JSON.stringify(t),request:JSON.stringify(k)})}listSessionGroups(h){return this._sendRequest("session_groups",h)}listMetricEvals(h){return this._sendRequest("metric_evals",h)}_sendRequest(h,k){if(this._useHttpGet)return k=encodeURIComponent(JSON.stringify(k)),
this._requestManager.request(this._apiUrl+"/"+h+"?request\x3d"+k);const t=new vc.RequestOptions;t.withCredentials=!0;t.methodType="POST";t.contentType="text/plain";t.body=JSON.stringify(k);return this._requestManager.requestWithOptions(this._apiUrl+"/"+h,t)}}d.Backend=f})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://tf-hparams-dashboard/tf-hparams-dashboard.html.js
(function(){Polymer({is:"tf-hparams-dashboard",properties:{_backend:{type:Object,value:()=>new tf.hparams.Backend(vc.getRouter().pluginRoute("hparams",""),new vc.RequestManager,!!(window.TENSORBOARD_ENV||{}).IN_COLAB)}},reload(){this.$["hparams-main"].reload()}})})();

//# sourceURL=build://tf-imports/three.js
(function(b,d){"object"===typeof exports&&"undefined"!==typeof module?d(exports):"function"===typeof define&&define.amd?define(["exports"],d):(b=b||self,d(b.THREE={}))})(this,function(b){function d(){}function f(a,c){this.x=a||0;this.y=c||0}function h(a,c,e,g){this._x=a||0;this._y=c||0;this._z=e||0;this._w=void 0!==g?g:1}function k(a,c,e){this.x=a||0;this.y=c||0;this.z=e||0}function t(){this.elements=[1,0,0,0,1,0,0,0,1];0<arguments.length&&console.error("THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.")}
function l(a,c,e,g,r,v,z,E,F,J){Object.defineProperty(this,"id",{value:Nk++});this.uuid=hb.generateUUID();this.name="";this.image=void 0!==a?a:l.DEFAULT_IMAGE;this.mipmaps=[];this.mapping=void 0!==c?c:l.DEFAULT_MAPPING;this.wrapS=void 0!==e?e:1001;this.wrapT=void 0!==g?g:1001;this.magFilter=void 0!==r?r:1006;this.minFilter=void 0!==v?v:1008;this.anisotropy=void 0!==F?F:1;this.format=void 0!==z?z:1023;this.type=void 0!==E?E:1009;this.offset=new f(0,0);this.repeat=new f(1,1);this.center=new f(0,0);
this.rotation=0;this.matrixAutoUpdate=!0;this.matrix=new t;this.generateMipmaps=!0;this.premultiplyAlpha=!1;this.flipY=!0;this.unpackAlignment=4;this.encoding=void 0!==J?J:3E3;this.version=0;this.onUpdate=null}function p(a,c,e,g){this.x=a||0;this.y=c||0;this.z=e||0;this.w=void 0!==g?g:1}function m(a,c,e){this.width=a;this.height=c;this.scissor=new p(0,0,a,c);this.scissorTest=!1;this.viewport=new p(0,0,a,c);e=e||{};this.texture=new l(void 0,void 0,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,
e.anisotropy,e.encoding);this.texture.image={};this.texture.image.width=a;this.texture.image.height=c;this.texture.generateMipmaps=void 0!==e.generateMipmaps?e.generateMipmaps:!1;this.texture.minFilter=void 0!==e.minFilter?e.minFilter:1006;this.depthBuffer=void 0!==e.depthBuffer?e.depthBuffer:!0;this.stencilBuffer=void 0!==e.stencilBuffer?e.stencilBuffer:!0;this.depthTexture=void 0!==e.depthTexture?e.depthTexture:null}function n(a,c,e){m.call(this,a,c,e);this.samples=4}function q(){this.elements=
[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];0<arguments.length&&console.error("THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.")}function u(a,c,e,g){this._x=a||0;this._y=c||0;this._z=e||0;this._order=g||u.DefaultOrder}function x(){this.mask=1}function A(){Object.defineProperty(this,"id",{value:Ok++});this.uuid=hb.generateUUID();this.name="";this.type="Object3D";this.parent=null;this.children=[];this.up=A.DefaultUp.clone();var a=new k,c=new u,e=new h,g=new k(1,1,1);c._onChange(function(){e.setFromEuler(c,
!1)});e._onChange(function(){c.setFromQuaternion(e,void 0,!1)});Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:a},rotation:{configurable:!0,enumerable:!0,value:c},quaternion:{configurable:!0,enumerable:!0,value:e},scale:{configurable:!0,enumerable:!0,value:g},modelViewMatrix:{value:new q},normalMatrix:{value:new t}});this.matrix=new q;this.matrixWorld=new q;this.matrixAutoUpdate=A.DefaultMatrixAutoUpdate;this.matrixWorldNeedsUpdate=!1;this.layers=new x;this.visible=!0;
this.receiveShadow=this.castShadow=!1;this.frustumCulled=!0;this.renderOrder=0;this.userData={}}function y(){A.call(this);this.type="Scene";this.overrideMaterial=this.fog=this.background=null;this.autoUpdate=!0;"undefined"!==typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}function w(a,c){this.min=void 0!==a?a:new k(Infinity,Infinity,Infinity);this.max=void 0!==c?c:new k(-Infinity,-Infinity,-Infinity)}function C(a,c,e,g,r){var v;var z=0;for(v=a.length-
3;z<=v;z+=3){Kd.fromArray(a,z);var E=c.dot(Kd),F=e.dot(Kd),J=g.dot(Kd);if(Math.max(-Math.max(E,F,J),Math.min(E,F,J))>r.x*Math.abs(Kd.x)+r.y*Math.abs(Kd.y)+r.z*Math.abs(Kd.z))return!1}return!0}function G(a,c){this.center=void 0!==a?a:new k;this.radius=void 0!==c?c:0}function D(a,c){this.origin=void 0!==a?a:new k;this.direction=void 0!==c?c:new k}function B(a,c,e){this.a=void 0!==a?a:new k;this.b=void 0!==c?c:new k;this.c=void 0!==e?e:new k}function I(a,c,e){return void 0===c&&void 0===e?this.set(a):
this.setRGB(a,c,e)}function N(a,c,e){0>e&&(e+=1);1<e&&--e;return e<1/6?a+6*(c-a)*e:.5>e?c:e<2/3?a+6*(c-a)*(2/3-e):a}function O(a){return.04045>a?.0773993808*a:Math.pow(.9478672986*a+.0521327014,2.4)}function H(a){return.0031308>a?12.92*a:1.055*Math.pow(a,.41666)-.055}function K(a,c,e,g,r,v){this.a=a;this.b=c;this.c=e;this.normal=g&&g.isVector3?g:new k;this.vertexNormals=Array.isArray(g)?g:[];this.color=r&&r.isColor?r:new I;this.vertexColors=Array.isArray(r)?r:[];this.materialIndex=void 0!==v?v:0}
function M(){Object.defineProperty(this,"id",{value:Pk++});this.uuid=hb.generateUUID();this.name="";this.type="Material";this.lights=this.fog=!0;this.blending=1;this.side=0;this.vertexTangents=this.flatShading=!1;this.vertexColors=0;this.opacity=1;this.transparent=!1;this.blendSrc=204;this.blendDst=205;this.blendEquation=100;this.blendEquationAlpha=this.blendDstAlpha=this.blendSrcAlpha=null;this.depthFunc=3;this.depthWrite=this.depthTest=!0;this.stencilFunc=519;this.stencilRef=0;this.stencilMask=
255;this.stencilZPass=this.stencilZFail=this.stencilFail=7680;this.stencilWrite=!1;this.clippingPlanes=null;this.clipShadows=this.clipIntersection=!1;this.shadowSide=null;this.colorWrite=!0;this.precision=null;this.polygonOffset=!1;this.polygonOffsetUnits=this.polygonOffsetFactor=0;this.dithering=!1;this.alphaTest=0;this.premultipliedAlpha=!1;this.toneMapped=this.visible=!0;this.userData={};this.needsUpdate=!0}function L(a){M.call(this);this.type="MeshBasicMaterial";this.color=new I(16777215);this.lightMap=
this.map=null;this.lightMapIntensity=1;this.aoMap=null;this.aoMapIntensity=1;this.envMap=this.alphaMap=this.specularMap=null;this.combine=0;this.reflectivity=1;this.refractionRatio=.98;this.wireframe=!1;this.wireframeLinewidth=1;this.wireframeLinejoin=this.wireframeLinecap="round";this.lights=this.morphTargets=this.skinning=!1;this.setValues(a)}function Q(a,c,e){if(Array.isArray(a))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.name="";this.array=a;this.itemSize=
c;this.count=void 0!==a?a.length/c:0;this.normalized=!0===e;this.dynamic=!1;this.updateRange={offset:0,count:-1};this.version=0}function T(a,c,e){Q.call(this,new Int8Array(a),c,e)}function X(a,c,e){Q.call(this,new Uint8Array(a),c,e)}function aa(a,c,e){Q.call(this,new Uint8ClampedArray(a),c,e)}function la(a,c,e){Q.call(this,new Int16Array(a),c,e)}function Z(a,c,e){Q.call(this,new Uint16Array(a),c,e)}function ba(a,c,e){Q.call(this,new Int32Array(a),c,e)}function ea(a,c,e){Q.call(this,new Uint32Array(a),
c,e)}function ca(a,c,e){Q.call(this,new Float32Array(a),c,e)}function ka(a,c,e){Q.call(this,new Float64Array(a),c,e)}function Y(){this.vertices=[];this.normals=[];this.colors=[];this.uvs=[];this.uvs2=[];this.groups=[];this.morphTargets={};this.skinWeights=[];this.skinIndices=[];this.boundingSphere=this.boundingBox=null;this.groupsNeedUpdate=this.uvsNeedUpdate=this.colorsNeedUpdate=this.normalsNeedUpdate=this.verticesNeedUpdate=!1}function Ea(a){if(0===a.length)return-Infinity;for(var c=a[0],e=1,g=
a.length;e<g;++e)a[e]>c&&(c=a[e]);return c}function va(){Object.defineProperty(this,"id",{value:Qk+=2});this.uuid=hb.generateUUID();this.name="";this.type="BufferGeometry";this.index=null;this.attributes={};this.morphAttributes={};this.groups=[];this.boundingSphere=this.boundingBox=null;this.drawRange={start:0,count:Infinity};this.userData={}}function xa(a,c){A.call(this);this.type="Mesh";this.geometry=void 0!==a?a:new va;this.material=void 0!==c?c:new L({color:16777215*Math.random()});this.drawMode=
0;this.updateMorphTargets()}function Aa(a,c,e,g,r,v,z,E){if(null===(1===c.side?g.intersectTriangle(z,v,r,!0,E):g.intersectTriangle(r,v,z,2!==c.side,E)))return null;sg.copy(E);sg.applyMatrix4(a.matrixWorld);c=e.ray.origin.distanceTo(sg);return c<e.near||c>e.far?null:{distance:c,point:sg.clone(),object:a}}function Fa(a,c,e,g,r,v,z,E,F,J,P){Ld.fromBufferAttribute(r,F);Md.fromBufferAttribute(r,J);Nd.fromBufferAttribute(r,P);r=a.morphTargetInfluences;if(c.morphTargets&&v&&r){Ih.set(0,0,0);Jh.set(0,0,0);
Kh.set(0,0,0);for(var R=0,S=v.length;R<S;R++){var V=r[R],W=v[R];0!==V&&($i.fromBufferAttribute(W,F),aj.fromBufferAttribute(W,J),bj.fromBufferAttribute(W,P),Ih.addScaledVector($i.sub(Ld),V),Jh.addScaledVector(aj.sub(Md),V),Kh.addScaledVector(bj.sub(Nd),V))}Ld.add(Ih);Md.add(Jh);Nd.add(Kh)}if(a=Aa(a,c,e,g,Ld,Md,Nd,sf))z&&(oe.fromBufferAttribute(z,F),pe.fromBufferAttribute(z,J),qe.fromBufferAttribute(z,P),a.uv=B.getUV(sf,Ld,Md,Nd,oe,pe,qe,new f)),E&&(oe.fromBufferAttribute(E,F),pe.fromBufferAttribute(E,
J),qe.fromBufferAttribute(E,P),a.uv2=B.getUV(sf,Ld,Md,Nd,oe,pe,qe,new f)),z=new K(F,J,P),B.getNormal(Ld,Md,Nd,z.normal),a.face=z;return a}function ya(){Object.defineProperty(this,"id",{value:Rk+=2});this.uuid=hb.generateUUID();this.name="";this.type="Geometry";this.vertices=[];this.colors=[];this.faces=[];this.faceVertexUvs=[[]];this.morphTargets=[];this.morphNormals=[];this.skinWeights=[];this.skinIndices=[];this.lineDistances=[];this.boundingSphere=this.boundingBox=null;this.groupsNeedUpdate=this.lineDistancesNeedUpdate=
this.colorsNeedUpdate=this.normalsNeedUpdate=this.uvsNeedUpdate=this.verticesNeedUpdate=this.elementsNeedUpdate=!1}function Sa(a,c,e,g,r,v){ya.call(this);this.type="BoxGeometry";this.parameters={width:a,height:c,depth:e,widthSegments:g,heightSegments:r,depthSegments:v};this.fromBufferGeometry(new Xa(a,c,e,g,r,v));this.mergeVertices()}function Xa(a,c,e,g,r,v){function z(W,ha,fa,ra,pa,qa,ua,oa,ta,Ba,Ta){var Ua=qa/ta,Ca=ua/Ba,Ha=qa/2,Da=ua/2,Ma=oa/2;ua=ta+1;var db=Ba+1,tb=qa=0,Ka,bb,jb=new k;for(bb=
0;bb<db;bb++){var Eb=bb*Ca-Da;for(Ka=0;Ka<ua;Ka++)jb[W]=(Ka*Ua-Ha)*ra,jb[ha]=Eb*pa,jb[fa]=Ma,J.push(jb.x,jb.y,jb.z),jb[W]=0,jb[ha]=0,jb[fa]=0<oa?1:-1,P.push(jb.x,jb.y,jb.z),R.push(Ka/ta),R.push(1-bb/Ba),qa+=1}for(bb=0;bb<Ba;bb++)for(Ka=0;Ka<ta;Ka++)W=S+Ka+ua*(bb+1),ha=S+(Ka+1)+ua*(bb+1),fa=S+(Ka+1)+ua*bb,F.push(S+Ka+ua*bb,W,fa),F.push(W,ha,fa),tb+=6;E.addGroup(V,tb,Ta);V+=tb;S+=qa}va.call(this);this.type="BoxBufferGeometry";this.parameters={width:a,height:c,depth:e,widthSegments:g,heightSegments:r,
depthSegments:v};var E=this;a=a||1;c=c||1;e=e||1;g=Math.floor(g)||1;r=Math.floor(r)||1;v=Math.floor(v)||1;var F=[],J=[],P=[],R=[],S=0,V=0;z("z","y","x",-1,-1,e,c,a,v,r,0);z("z","y","x",1,-1,e,c,-a,v,r,1);z("x","z","y",1,1,a,e,c,g,v,2);z("x","z","y",1,-1,a,e,-c,g,v,3);z("x","y","z",1,-1,a,c,e,g,r,4);z("x","y","z",-1,-1,a,c,-e,g,r,5);this.setIndex(F);this.addAttribute("position",new ca(J,3));this.addAttribute("normal",new ca(P,3));this.addAttribute("uv",new ca(R,2))}function ub(a){var c={},e;for(e in a){c[e]=
{};for(var g in a[e]){var r=a[e][g];c[e][g]=r&&(r.isColor||r.isMatrix3||r.isMatrix4||r.isVector2||r.isVector3||r.isVector4||r.isTexture)?r.clone():Array.isArray(r)?r.slice():r}}return c}function Bb(a){for(var c={},e=0;e<a.length;e++){var g=ub(a[e]),r;for(r in g)c[r]=g[r]}return c}function qb(a){M.call(this);this.type="ShaderMaterial";this.defines={};this.uniforms={};this.vertexShader="void main() {\n\tgl_Position \x3d projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}";this.fragmentShader=
"void main() {\n\tgl_FragColor \x3d vec4( 1.0, 0.0, 0.0, 1.0 );\n}";this.linewidth=1;this.wireframe=!1;this.wireframeLinewidth=1;this.morphNormals=this.morphTargets=this.skinning=this.clipping=this.lights=this.fog=!1;this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1};this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]};this.index0AttributeName=void 0;this.uniformsNeedUpdate=!1;void 0!==a&&(void 0!==a.attributes&&console.error("THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead."),
this.setValues(a))}function zb(){A.call(this);this.type="Camera";this.matrixWorldInverse=new q;this.projectionMatrix=new q;this.projectionMatrixInverse=new q}function vb(a,c,e,g){zb.call(this);this.type="PerspectiveCamera";this.fov=void 0!==a?a:50;this.zoom=1;this.near=void 0!==e?e:.1;this.far=void 0!==g?g:2E3;this.focus=10;this.aspect=void 0!==c?c:1;this.view=null;this.filmGauge=35;this.filmOffset=0;this.updateProjectionMatrix()}function Gb(a,c,e,g){A.call(this);this.type="CubeCamera";var r=new vb(90,
1,a,c);r.up.set(0,-1,0);r.lookAt(new k(1,0,0));this.add(r);var v=new vb(90,1,a,c);v.up.set(0,-1,0);v.lookAt(new k(-1,0,0));this.add(v);var z=new vb(90,1,a,c);z.up.set(0,0,1);z.lookAt(new k(0,1,0));this.add(z);var E=new vb(90,1,a,c);E.up.set(0,0,-1);E.lookAt(new k(0,-1,0));this.add(E);var F=new vb(90,1,a,c);F.up.set(0,-1,0);F.lookAt(new k(0,0,1));this.add(F);var J=new vb(90,1,a,c);J.up.set(0,-1,0);J.lookAt(new k(0,0,-1));this.add(J);g=g||{format:1022,magFilter:1006,minFilter:1006};this.renderTarget=
new Nb(e,e,g);this.renderTarget.texture.name="CubeCamera";this.update=function(P,R){null===this.parent&&this.updateMatrixWorld();var S=P.getRenderTarget(),V=this.renderTarget,W=V.texture.generateMipmaps;V.texture.generateMipmaps=!1;P.setRenderTarget(V,0);P.render(R,r);P.setRenderTarget(V,1);P.render(R,v);P.setRenderTarget(V,2);P.render(R,z);P.setRenderTarget(V,3);P.render(R,E);P.setRenderTarget(V,4);P.render(R,F);V.texture.generateMipmaps=W;P.setRenderTarget(V,5);P.render(R,J);P.setRenderTarget(S)};
this.clear=function(P,R,S,V){for(var W=P.getRenderTarget(),ha=this.renderTarget,fa=0;6>fa;fa++)P.setRenderTarget(ha,fa),P.clear(R,S,V);P.setRenderTarget(W)}}function Nb(a,c,e){m.call(this,a,c,e)}function Ab(a,c,e,g,r,v,z,E,F,J,P,R){l.call(this,null,v,z,E,F,J,g,r,P,R);this.image={data:a,width:c,height:e};this.magFilter=void 0!==F?F:1003;this.minFilter=void 0!==J?J:1003;this.flipY=this.generateMipmaps=!1;this.unpackAlignment=1}function Hb(a,c){this.normal=void 0!==a?a:new k(1,0,0);this.constant=void 0!==
c?c:0}function ic(a,c,e,g,r,v){this.planes=[void 0!==a?a:new Hb,void 0!==c?c:new Hb,void 0!==e?e:new Hb,void 0!==g?g:new Hb,void 0!==r?r:new Hb,void 0!==v?v:new Hb]}function bc(){function a(r,v){!1!==e&&(g(r,v),c.requestAnimationFrame(a))}var c=null,e=!1,g=null;return{start:function(){!0!==e&&null!==g&&(c.requestAnimationFrame(a),e=!0)},stop:function(){e=!1},setAnimationLoop:function(r){g=r},setContext:function(r){c=r}}}function Od(a){function c(r,v){var z=r.array,E=r.dynamic?35048:35044,F=a.createBuffer();
a.bindBuffer(v,F);a.bufferData(v,z,E);r.onUploadCallback();v=5126;z instanceof Float32Array?v=5126:z instanceof Float64Array?console.warn("THREE.WebGLAttributes: Unsupported data buffer format: Float64Array."):z instanceof Uint16Array?v=5123:z instanceof Int16Array?v=5122:z instanceof Uint32Array?v=5125:z instanceof Int32Array?v=5124:z instanceof Int8Array?v=5120:z instanceof Uint8Array&&(v=5121);return{buffer:F,type:v,bytesPerElement:z.BYTES_PER_ELEMENT,version:r.version}}function e(r,v,z){var E=
v.array,F=v.updateRange;a.bindBuffer(z,r);!1===v.dynamic?a.bufferData(z,E,35044):-1===F.count?a.bufferSubData(z,0,E):0===F.count?console.error("THREE.WebGLObjects.updateBuffer: dynamic THREE.BufferAttribute marked as needsUpdate but updateRange.count is 0, ensure you are using set methods or updating manually."):(a.bufferSubData(z,F.offset*E.BYTES_PER_ELEMENT,E.subarray(F.offset,F.offset+F.count)),F.count=-1)}var g=new WeakMap;return{get:function(r){r.isInterleavedBufferAttribute&&(r=r.data);return g.get(r)},
remove:function(r){r.isInterleavedBufferAttribute&&(r=r.data);var v=g.get(r);v&&(a.deleteBuffer(v.buffer),g.delete(r))},update:function(r,v){r.isInterleavedBufferAttribute&&(r=r.data);var z=g.get(r);void 0===z?g.set(r,c(r,v)):z.version<r.version&&(e(z.buffer,r,v),z.version=r.version)}}}function rd(a,c,e,g){ya.call(this);this.type="PlaneGeometry";this.parameters={width:a,height:c,widthSegments:e,heightSegments:g};this.fromBufferGeometry(new Lc(a,c,e,g));this.mergeVertices()}function Lc(a,c,e,g){va.call(this);
this.type="PlaneBufferGeometry";this.parameters={width:a,height:c,widthSegments:e,heightSegments:g};a=a||1;c=c||1;var r=a/2,v=c/2;e=Math.floor(e)||1;g=Math.floor(g)||1;var z=e+1,E=g+1,F=a/e,J=c/g,P=[],R=[],S=[],V=[];for(a=0;a<E;a++){var W=a*J-v;for(c=0;c<z;c++)R.push(c*F-r,-W,0),S.push(0,0,1),V.push(c/e),V.push(1-a/g)}for(a=0;a<g;a++)for(c=0;c<e;c++)r=c+z*(a+1),v=c+1+z*(a+1),E=c+1+z*a,P.push(c+z*a,r,E),P.push(r,v,E);this.setIndex(P);this.addAttribute("position",new ca(R,3));this.addAttribute("normal",
new ca(S,3));this.addAttribute("uv",new ca(V,2))}function sd(a,c,e,g){function r(R,S){c.buffers.color.setClear(R.r,R.g,R.b,S,g)}var v=new I(0),z=0,E,F,J=null,P=0;return{getClearColor:function(){return v},setClearColor:function(R,S){v.set(R);z=void 0!==S?S:1;r(v,z)},getClearAlpha:function(){return z},setClearAlpha:function(R){z=R;r(v,z)},render:function(R,S,V,W){S=S.background;V=a.vr;(V=V.getSession&&V.getSession())&&"additive"===V.environmentBlendMode&&(S=null);null===S?(r(v,z),J=null,P=0):S&&S.isColor&&
(r(S,1),W=!0,J=null,P=0);(a.autoClear||W)&&a.clear(a.autoClearColor,a.autoClearDepth,a.autoClearStencil);if(S&&(S.isCubeTexture||S.isWebGLRenderTargetCube)){void 0===F&&(F=new xa(new Xa(1,1,1),new qb({type:"BackgroundCubeMaterial",uniforms:ub(Mc.cube.uniforms),vertexShader:Mc.cube.vertexShader,fragmentShader:Mc.cube.fragmentShader,side:1,depthTest:!1,depthWrite:!1,fog:!1})),F.geometry.removeAttribute("normal"),F.geometry.removeAttribute("uv"),F.onBeforeRender=function(ha,fa,ra){this.matrixWorld.copyPosition(ra.matrixWorld)},
Object.defineProperty(F.material,"map",{get:function(){return this.uniforms.tCube.value}}),e.update(F));W=S.isWebGLRenderTargetCube?S.texture:S;F.material.uniforms.tCube.value=W;F.material.uniforms.tFlip.value=S.isWebGLRenderTargetCube?1:-1;if(J!==S||P!==W.version)F.material.needsUpdate=!0,J=S,P=W.version;R.unshift(F,F.geometry,F.material,0,0,null)}else if(S&&S.isTexture){void 0===E&&(E=new xa(new Lc(2,2),new qb({type:"BackgroundMaterial",uniforms:ub(Mc.background.uniforms),vertexShader:Mc.background.vertexShader,
fragmentShader:Mc.background.fragmentShader,side:0,depthTest:!1,depthWrite:!1,fog:!1})),E.geometry.removeAttribute("normal"),Object.defineProperty(E.material,"map",{get:function(){return this.uniforms.t2D.value}}),e.update(E));E.material.uniforms.t2D.value=S;!0===S.matrixAutoUpdate&&S.updateMatrix();E.material.uniforms.uvTransform.value.copy(S.matrix);if(J!==S||P!==S.version)E.material.needsUpdate=!0,J=S,P=S.version;R.unshift(E,E.geometry,E.material,0,0,null)}}}}function sa(a,c,e,g){var r;this.setMode=
function(v){r=v};this.render=function(v,z){a.drawArrays(r,v,z);e.update(z,r)};this.renderInstances=function(v,z,E){if(g.isWebGL2){var F=a;var J="drawArraysInstanced"}else if(F=c.get("ANGLE_instanced_arrays"),J="drawArraysInstancedANGLE",null===F){console.error("THREE.WebGLBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");return}F[J](r,z,E,v.maxInstancedCount);e.update(E,r,v.maxInstancedCount)}}function Mb(a,c,e){function g(qa){if("highp"===
qa){if(0<a.getShaderPrecisionFormat(35633,36338).precision&&0<a.getShaderPrecisionFormat(35632,36338).precision)return"highp";qa="mediump"}return"mediump"===qa&&0<a.getShaderPrecisionFormat(35633,36337).precision&&0<a.getShaderPrecisionFormat(35632,36337).precision?"mediump":"lowp"}var r,v="undefined"!==typeof WebGL2RenderingContext&&a instanceof WebGL2RenderingContext,z=void 0!==e.precision?e.precision:"highp",E=g(z);E!==z&&(console.warn("THREE.WebGLRenderer:",z,"not supported, using",E,"instead."),
z=E);e=!0===e.logarithmicDepthBuffer;E=a.getParameter(34930);var F=a.getParameter(35660),J=a.getParameter(3379),P=a.getParameter(34076),R=a.getParameter(34921),S=a.getParameter(36347),V=a.getParameter(36348),W=a.getParameter(36349),ha=0<F,fa=v||!!c.get("OES_texture_float"),ra=ha&&fa,pa=v?a.getParameter(36183):0;return{isWebGL2:v,getMaxAnisotropy:function(){if(void 0!==r)return r;var qa=c.get("EXT_texture_filter_anisotropic");return r=null!==qa?a.getParameter(qa.MAX_TEXTURE_MAX_ANISOTROPY_EXT):0},
getMaxPrecision:g,precision:z,logarithmicDepthBuffer:e,maxTextures:E,maxVertexTextures:F,maxTextureSize:J,maxCubemapSize:P,maxAttributes:R,maxVertexUniforms:S,maxVaryings:V,maxFragmentUniforms:W,vertexTextures:ha,floatFragmentTextures:fa,floatVertexTextures:ra,maxSamples:pa}}function wc(){function a(){J.value!==g&&(J.value=g,J.needsUpdate=0<r);e.numPlanes=r;e.numIntersection=0}function c(P,R,S,V){var W=null!==P?P.length:0,ha=null;if(0!==W){ha=J.value;if(!0!==V||null===ha){V=S+4*W;R=R.matrixWorldInverse;
F.getNormalMatrix(R);if(null===ha||ha.length<V)ha=new Float32Array(V);for(V=0;V!==W;++V,S+=4)E.copy(P[V]).applyMatrix4(R,F),E.normal.toArray(ha,S),ha[S+3]=E.constant}J.value=ha;J.needsUpdate=!0}e.numPlanes=W;return ha}var e=this,g=null,r=0,v=!1,z=!1,E=new Hb,F=new t,J={value:null,needsUpdate:!1};this.uniform=J;this.numIntersection=this.numPlanes=0;this.init=function(P,R,S){var V=0!==P.length||R||0!==r||v;v=R;g=c(P,S,0);r=P.length;return V};this.beginShadows=function(){z=!0;c(null)};this.endShadows=
function(){z=!1;a()};this.setState=function(P,R,S,V,W,ha){if(!v||null===P||0===P.length||z&&!S)z?c(null):a();else{S=z?0:r;var fa=4*S,ra=W.clippingState||null;J.value=ra;ra=c(P,V,fa,ha);for(P=0;P!==fa;++P)ra[P]=g[P];W.clippingState=ra;this.numIntersection=R?this.numPlanes:0;this.numPlanes+=S}}}function bd(a){var c={};return{get:function(e){if(void 0!==c[e])return c[e];switch(e){case "WEBGL_depth_texture":var g=a.getExtension("WEBGL_depth_texture")||a.getExtension("MOZ_WEBGL_depth_texture")||a.getExtension("WEBKIT_WEBGL_depth_texture");
break;case "EXT_texture_filter_anisotropic":g=a.getExtension("EXT_texture_filter_anisotropic")||a.getExtension("MOZ_EXT_texture_filter_anisotropic")||a.getExtension("WEBKIT_EXT_texture_filter_anisotropic");break;case "WEBGL_compressed_texture_s3tc":g=a.getExtension("WEBGL_compressed_texture_s3tc")||a.getExtension("MOZ_WEBGL_compressed_texture_s3tc")||a.getExtension("WEBKIT_WEBGL_compressed_texture_s3tc");break;case "WEBGL_compressed_texture_pvrtc":g=a.getExtension("WEBGL_compressed_texture_pvrtc")||
a.getExtension("WEBKIT_WEBGL_compressed_texture_pvrtc");break;default:g=a.getExtension(e)}null===g&&console.warn("THREE.WebGLRenderer: "+e+" extension not supported.");return c[e]=g}}}function td(a,c,e){function g(E){var F=E.target;E=v.get(F);null!==E.index&&c.remove(E.index);for(var J in E.attributes)c.remove(E.attributes[J]);F.removeEventListener("dispose",g);v.delete(F);if(J=z.get(E))c.remove(J),z.delete(E);e.memory.geometries--}function r(E){var F=[],J=E.index,P=E.attributes.position;if(null!==
J){var R=J.array;J=J.version;P=0;for(var S=R.length;P<S;P+=3){var V=R[P+0],W=R[P+1],ha=R[P+2];F.push(V,W,W,ha,ha,V)}}else for(R=P.array,J=P.version,P=0,S=R.length/3-1;P<S;P+=3)V=P+0,W=P+1,ha=P+2,F.push(V,W,W,ha,ha,V);F=new (65535<Ea(F)?ea:Z)(F,1);F.version=J;c.update(F,34963);(R=z.get(E))&&c.remove(R);z.set(E,F)}var v=new WeakMap,z=new WeakMap;return{get:function(E,F){var J=v.get(F);if(J)return J;F.addEventListener("dispose",g);F.isBufferGeometry?J=F:F.isGeometry&&(void 0===F._bufferGeometry&&(F._bufferGeometry=
(new va).setFromObject(E)),J=F._bufferGeometry);v.set(F,J);e.memory.geometries++;return J},update:function(E){var F=E.index,J=E.attributes;null!==F&&c.update(F,34963);for(var P in J)c.update(J[P],34962);E=E.morphAttributes;for(P in E){F=E[P];J=0;for(var R=F.length;J<R;J++)c.update(F[J],34962)}},getWireframeAttribute:function(E){var F=z.get(E);if(F){var J=E.index;null!==J&&F.version<J.version&&r(E)}else r(E);return z.get(E)}}}function tg(a,c,e,g){var r,v,z;this.setMode=function(E){r=E};this.setIndex=
function(E){v=E.type;z=E.bytesPerElement};this.render=function(E,F){a.drawElements(r,F,v,E*z);e.update(F,r)};this.renderInstances=function(E,F,J){if(g.isWebGL2){var P=a;var R="drawElementsInstanced"}else if(P=c.get("ANGLE_instanced_arrays"),R="drawElementsInstancedANGLE",null===P){console.error("THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");return}P[R](r,J,v,F*z,E.maxInstancedCount);e.update(J,r,E.maxInstancedCount)}}
function Sk(){var a={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:a,programs:null,autoReset:!0,reset:function(){a.frame++;a.calls=0;a.triangles=0;a.points=0;a.lines=0},update:function(c,e,g){g=g||1;a.calls++;switch(e){case 4:a.triangles+=c/3*g;break;case 5:case 6:a.triangles+=g*(c-2);break;case 1:a.lines+=c/2*g;break;case 3:a.lines+=g*(c-1);break;case 2:a.lines+=g*c;break;case 0:a.points+=g*c;break;default:console.error("THREE.WebGLInfo: Unknown draw mode:",
e)}}}}function Tk(a,c){return Math.abs(c[1])-Math.abs(a[1])}function Uk(a){var c={},e=new Float32Array(8);return{update:function(g,r,v,z){var E=g.morphTargetInfluences,F=E.length;g=c[r.id];if(void 0===g){g=[];for(var J=0;J<F;J++)g[J]=[J,0];c[r.id]=g}var P=v.morphTargets&&r.morphAttributes.position;v=v.morphNormals&&r.morphAttributes.normal;for(J=0;J<F;J++){var R=g[J];0!==R[1]&&(P&&r.removeAttribute("morphTarget"+J),v&&r.removeAttribute("morphNormal"+J))}for(J=0;J<F;J++)R=g[J],R[0]=J,R[1]=E[J];g.sort(Tk);
for(J=0;8>J;J++){if(R=g[J])if(E=R[0],F=R[1]){P&&r.addAttribute("morphTarget"+J,P[E]);v&&r.addAttribute("morphNormal"+J,v[E]);e[J]=F;continue}e[J]=0}z.getUniforms().setValue(a,"morphTargetInfluences",e)}}}function Vk(a,c){var e={};return{update:function(g){var r=c.render.frame,v=g.geometry,z=a.get(g,v);e[z.id]!==r&&(v.isGeometry&&z.updateFromObject(g),a.update(z),e[z.id]=r);return z},dispose:function(){e={}}}}function cd(a,c,e,g,r,v,z,E,F,J){a=void 0!==a?a:[];l.call(this,a,void 0!==c?c:301,e,g,r,v,
void 0!==z?z:1022,E,F,J);this.flipY=!1}function re(a,c,e,g){l.call(this,null);this.image={data:a,width:c,height:e,depth:g};this.minFilter=this.magFilter=1003;this.wrapR=1001;this.flipY=this.generateMipmaps=!1}function se(a,c,e,g){l.call(this,null);this.image={data:a,width:c,height:e,depth:g};this.minFilter=this.magFilter=1003;this.wrapR=1001;this.flipY=this.generateMipmaps=!1}function te(a,c,e){var g=a[0];if(0>=g||0<g)return a;var r=c*e,v=cj[r];void 0===v&&(v=new Float32Array(r),cj[r]=v);if(0!==c)for(g.toArray(v,
0),g=1,r=0;g!==c;++g)r+=e,a[g].toArray(v,r);return v}function sc(a,c){if(a.length!==c.length)return!1;for(var e=0,g=a.length;e<g;e++)if(a[e]!==c[e])return!1;return!0}function mc(a,c){for(var e=0,g=c.length;e<g;e++)a[e]=c[e]}function dj(a,c){var e=ej[c];void 0===e&&(e=new Int32Array(c),ej[c]=e);for(var g=0;g!==c;++g)e[g]=a.allocateTextureUnit();return e}function Wk(a,c){var e=this.cache;e[0]!==c&&(a.uniform1f(this.addr,c),e[0]=c)}function Xk(a,c){var e=this.cache;if(void 0!==c.x){if(e[0]!==c.x||e[1]!==
c.y)a.uniform2f(this.addr,c.x,c.y),e[0]=c.x,e[1]=c.y}else sc(e,c)||(a.uniform2fv(this.addr,c),mc(e,c))}function Yk(a,c){var e=this.cache;if(void 0!==c.x){if(e[0]!==c.x||e[1]!==c.y||e[2]!==c.z)a.uniform3f(this.addr,c.x,c.y,c.z),e[0]=c.x,e[1]=c.y,e[2]=c.z}else if(void 0!==c.r){if(e[0]!==c.r||e[1]!==c.g||e[2]!==c.b)a.uniform3f(this.addr,c.r,c.g,c.b),e[0]=c.r,e[1]=c.g,e[2]=c.b}else sc(e,c)||(a.uniform3fv(this.addr,c),mc(e,c))}function Zk(a,c){var e=this.cache;if(void 0!==c.x){if(e[0]!==c.x||e[1]!==c.y||
e[2]!==c.z||e[3]!==c.w)a.uniform4f(this.addr,c.x,c.y,c.z,c.w),e[0]=c.x,e[1]=c.y,e[2]=c.z,e[3]=c.w}else sc(e,c)||(a.uniform4fv(this.addr,c),mc(e,c))}function $k(a,c){var e=this.cache,g=c.elements;void 0===g?sc(e,c)||(a.uniformMatrix2fv(this.addr,!1,c),mc(e,c)):sc(e,g)||(fj.set(g),a.uniformMatrix2fv(this.addr,!1,fj),mc(e,g))}function al(a,c){var e=this.cache,g=c.elements;void 0===g?sc(e,c)||(a.uniformMatrix3fv(this.addr,!1,c),mc(e,c)):sc(e,g)||(gj.set(g),a.uniformMatrix3fv(this.addr,!1,gj),mc(e,g))}
function bl(a,c){var e=this.cache,g=c.elements;void 0===g?sc(e,c)||(a.uniformMatrix4fv(this.addr,!1,c),mc(e,c)):sc(e,g)||(hj.set(g),a.uniformMatrix4fv(this.addr,!1,hj),mc(e,g))}function cl(a,c,e){var g=this.cache,r=e.allocateTextureUnit();g[0]!==r&&(a.uniform1i(this.addr,r),g[0]=r);e.safeSetTexture2D(c||ij,r)}function dl(a,c,e){var g=this.cache,r=e.allocateTextureUnit();g[0]!==r&&(a.uniform1i(this.addr,r),g[0]=r);e.setTexture2DArray(c||el,r)}function fl(a,c,e){var g=this.cache,r=e.allocateTextureUnit();
g[0]!==r&&(a.uniform1i(this.addr,r),g[0]=r);e.setTexture3D(c||gl,r)}function hl(a,c,e){var g=this.cache,r=e.allocateTextureUnit();g[0]!==r&&(a.uniform1i(this.addr,r),g[0]=r);e.safeSetTextureCube(c||jj,r)}function il(a,c){var e=this.cache;e[0]!==c&&(a.uniform1i(this.addr,c),e[0]=c)}function jl(a,c){var e=this.cache;sc(e,c)||(a.uniform2iv(this.addr,c),mc(e,c))}function kl(a,c){var e=this.cache;sc(e,c)||(a.uniform3iv(this.addr,c),mc(e,c))}function ll(a,c){var e=this.cache;sc(e,c)||(a.uniform4iv(this.addr,
c),mc(e,c))}function ml(a){switch(a){case 5126:return Wk;case 35664:return Xk;case 35665:return Yk;case 35666:return Zk;case 35674:return $k;case 35675:return al;case 35676:return bl;case 35678:case 36198:return cl;case 35679:return fl;case 35680:return hl;case 36289:return dl;case 5124:case 35670:return il;case 35667:case 35671:return jl;case 35668:case 35672:return kl;case 35669:case 35673:return ll}}function nl(a,c){a.uniform1fv(this.addr,c)}function ol(a,c){a.uniform1iv(this.addr,c)}function pl(a,
c){a.uniform2iv(this.addr,c)}function ql(a,c){a.uniform3iv(this.addr,c)}function rl(a,c){a.uniform4iv(this.addr,c)}function sl(a,c){c=te(c,this.size,2);a.uniform2fv(this.addr,c)}function tl(a,c){c=te(c,this.size,3);a.uniform3fv(this.addr,c)}function ul(a,c){c=te(c,this.size,4);a.uniform4fv(this.addr,c)}function vl(a,c){c=te(c,this.size,4);a.uniformMatrix2fv(this.addr,!1,c)}function wl(a,c){c=te(c,this.size,9);a.uniformMatrix3fv(this.addr,!1,c)}function xl(a,c){c=te(c,this.size,16);a.uniformMatrix4fv(this.addr,
!1,c)}function yl(a,c,e){var g=c.length,r=dj(e,g);a.uniform1iv(this.addr,r);for(a=0;a!==g;++a)e.safeSetTexture2D(c[a]||ij,r[a])}function zl(a,c,e){var g=c.length,r=dj(e,g);a.uniform1iv(this.addr,r);for(a=0;a!==g;++a)e.safeSetTextureCube(c[a]||jj,r[a])}function Al(a){switch(a){case 5126:return nl;case 35664:return sl;case 35665:return tl;case 35666:return ul;case 35674:return vl;case 35675:return wl;case 35676:return xl;case 35678:return yl;case 35680:return zl;case 5124:case 35670:return ol;case 35667:case 35671:return pl;
case 35668:case 35672:return ql;case 35669:case 35673:return rl}}function Bl(a,c,e){this.id=a;this.addr=e;this.cache=[];this.setValue=ml(c.type)}function kj(a,c,e){this.id=a;this.addr=e;this.cache=[];this.size=c.size;this.setValue=Al(c.type)}function lj(a){this.id=a;this.seq=[];this.map={}}function mj(a,c){a.seq.push(c);a.map[c.id]=c}function Cl(a,c,e){var g=a.name,r=g.length;for(Lh.lastIndex=0;;){var v=Lh.exec(g),z=Lh.lastIndex,E=v[1],F=v[3];"]"===v[2]&&(E|=0);if(void 0===F||"["===F&&z+2===r){mj(e,
void 0===F?new Bl(E,a,c):new kj(E,a,c));break}else v=e.map[E],void 0===v&&(v=new lj(E),mj(e,v)),e=v}}function ud(a,c){this.seq=[];this.map={};for(var e=a.getProgramParameter(c,35718),g=0;g<e;++g){var r=a.getActiveUniform(c,g);Cl(r,a.getUniformLocation(c,r.name),this)}}function nj(a,c,e){c=a.createShader(c);a.shaderSource(c,e);a.compileShader(c);return c}function Dl(a){a=a.split("\n");for(var c=0;c<a.length;c++)a[c]=c+1+": "+a[c];return a.join("\n")}function oj(a){switch(a){case 3E3:return["Linear",
"( value )"];case 3001:return["sRGB","( value )"];case 3002:return["RGBE","( value )"];case 3004:return["RGBM","( value, 7.0 )"];case 3005:return["RGBM","( value, 16.0 )"];case 3006:return["RGBD","( value, 256.0 )"];case 3007:return["Gamma","( value, float( GAMMA_FACTOR ) )"];case 3003:return["LogLuv","( value )"];default:throw Error("unsupported encoding: "+a);}}function pj(a,c,e){var g=a.getShaderParameter(c,35713),r=a.getShaderInfoLog(c).trim();return g&&""===r?"":"THREE.WebGLShader: gl.getShaderInfoLog() "+
e+"\n"+r+Dl(a.getShaderSource(c))}function ug(a,c){c=oj(c);return"vec4 "+a+"( vec4 value ) { return "+c[0]+"ToLinear"+c[1]+"; }"}function El(a,c){c=oj(c);return"vec4 "+a+"( vec4 value ) { return LinearTo"+c[0]+c[1]+"; }"}function Fl(a,c){switch(c){case 1:c="Linear";break;case 2:c="Reinhard";break;case 3:c="Uncharted2";break;case 4:c="OptimizedCineon";break;case 5:c="ACESFilmic";break;default:throw Error("unsupported toneMapping: "+c);}return"vec3 "+a+"( vec3 color ) { return "+c+"ToneMapping( color ); }"}
function Gl(a,c,e){a=a||{};return[a.derivatives||c.envMapCubeUV||c.bumpMap||c.tangentSpaceNormalMap||c.clearcoatNormalMap||c.flatShading?"#extension GL_OES_standard_derivatives : enable":"",(a.fragDepth||c.logarithmicDepthBuffer)&&e.get("EXT_frag_depth")?"#extension GL_EXT_frag_depth : enable":"",a.drawBuffers&&e.get("WEBGL_draw_buffers")?"#extension GL_EXT_draw_buffers : require":"",(a.shaderTextureLOD||c.envMap)&&e.get("EXT_shader_texture_lod")?"#extension GL_EXT_shader_texture_lod : enable":""].filter(uf).join("\n")}
function Hl(a){var c=[],e;for(e in a){var g=a[e];!1!==g&&c.push("#define "+e+" "+g)}return c.join("\n")}function Il(a,c){for(var e={},g=a.getProgramParameter(c,35721),r=0;r<g;r++){var v=a.getActiveAttrib(c,r).name;e[v]=a.getAttribLocation(c,v)}return e}function uf(a){return""!==a}function qj(a,c){return a.replace(/NUM_DIR_LIGHTS/g,c.numDirLights).replace(/NUM_SPOT_LIGHTS/g,c.numSpotLights).replace(/NUM_RECT_AREA_LIGHTS/g,c.numRectAreaLights).replace(/NUM_POINT_LIGHTS/g,c.numPointLights).replace(/NUM_HEMI_LIGHTS/g,
c.numHemiLights).replace(/NUM_DIR_LIGHT_SHADOWS/g,c.numDirLightShadows).replace(/NUM_SPOT_LIGHT_SHADOWS/g,c.numSpotLightShadows).replace(/NUM_POINT_LIGHT_SHADOWS/g,c.numPointLightShadows)}function rj(a,c){return a.replace(/NUM_CLIPPING_PLANES/g,c.numClippingPlanes).replace(/UNION_CLIPPING_PLANES/g,c.numClippingPlanes-c.numClipIntersection)}function Mh(a){return a.replace(/^[ \t]*#include +<([\w\d./]+)>/gm,function(c,e){c=rb[e];if(void 0===c)throw Error("Can not resolve #include \x3c"+e+"\x3e");return Mh(c)})}
function sj(a){return a.replace(/#pragma unroll_loop[\s]+?for \( int i = (\d+); i < (\d+); i \+\+ \) \{([\s\S]+?)(?=\})\}/g,function(c,e,g,r){c="";for(e=parseInt(e);e<parseInt(g);e++)c+=r.replace(/\[ i \]/g,"[ "+e+" ]").replace(/UNROLLED_LOOP_INDEX/g,e);return c})}function Jl(a,c,e,g,r,v,z){var E=a.getContext(),F=g.defines,J=r.vertexShader,P=r.fragmentShader,R="SHADOWMAP_TYPE_BASIC";1===v.shadowMapType?R="SHADOWMAP_TYPE_PCF":2===v.shadowMapType?R="SHADOWMAP_TYPE_PCF_SOFT":3===v.shadowMapType&&(R=
"SHADOWMAP_TYPE_VSM");var S="ENVMAP_TYPE_CUBE",V="ENVMAP_MODE_REFLECTION",W="ENVMAP_BLENDING_MULTIPLY";if(v.envMap){switch(g.envMap.mapping){case 301:case 302:S="ENVMAP_TYPE_CUBE";break;case 306:case 307:S="ENVMAP_TYPE_CUBE_UV";break;case 303:case 304:S="ENVMAP_TYPE_EQUIREC";break;case 305:S="ENVMAP_TYPE_SPHERE"}switch(g.envMap.mapping){case 302:case 304:V="ENVMAP_MODE_REFRACTION"}switch(g.combine){case 0:W="ENVMAP_BLENDING_MULTIPLY";break;case 1:W="ENVMAP_BLENDING_MIX";break;case 2:W="ENVMAP_BLENDING_ADD"}}var ha=
0<a.gammaFactor?a.gammaFactor:1,fa=z.isWebGL2?"":Gl(g.extensions,v,c),ra=Hl(F),pa=E.createProgram();g.isRawShaderMaterial?(F=[ra].filter(uf).join("\n"),0<F.length&&(F+="\n"),c=[fa,ra].filter(uf).join("\n"),0<c.length&&(c+="\n")):(F=["precision "+v.precision+" float;","precision "+v.precision+" int;","highp"===v.precision?"#define HIGH_PRECISION":"","#define SHADER_NAME "+r.name,ra,v.supportsVertexTextures?"#define VERTEX_TEXTURES":"","#define GAMMA_FACTOR "+ha,"#define MAX_BONES "+v.maxBones,v.useFog&&
v.fog?"#define USE_FOG":"",v.useFog&&v.fogExp2?"#define FOG_EXP2":"",v.map?"#define USE_MAP":"",v.envMap?"#define USE_ENVMAP":"",v.envMap?"#define "+V:"",v.lightMap?"#define USE_LIGHTMAP":"",v.aoMap?"#define USE_AOMAP":"",v.emissiveMap?"#define USE_EMISSIVEMAP":"",v.bumpMap?"#define USE_BUMPMAP":"",v.normalMap?"#define USE_NORMALMAP":"",v.normalMap&&v.objectSpaceNormalMap?"#define OBJECTSPACE_NORMALMAP":"",v.normalMap&&v.tangentSpaceNormalMap?"#define TANGENTSPACE_NORMALMAP":"",v.clearcoatNormalMap?
"#define USE_CLEARCOAT_NORMALMAP":"",v.displacementMap&&v.supportsVertexTextures?"#define USE_DISPLACEMENTMAP":"",v.specularMap?"#define USE_SPECULARMAP":"",v.roughnessMap?"#define USE_ROUGHNESSMAP":"",v.metalnessMap?"#define USE_METALNESSMAP":"",v.alphaMap?"#define USE_ALPHAMAP":"",v.vertexTangents?"#define USE_TANGENT":"",v.vertexColors?"#define USE_COLOR":"",v.vertexUvs?"#define USE_UV":"",v.flatShading?"#define FLAT_SHADED":"",v.skinning?"#define USE_SKINNING":"",v.useVertexTexture?"#define BONE_TEXTURE":
"",v.morphTargets?"#define USE_MORPHTARGETS":"",v.morphNormals&&!1===v.flatShading?"#define USE_MORPHNORMALS":"",v.doubleSided?"#define DOUBLE_SIDED":"",v.flipSided?"#define FLIP_SIDED":"",v.shadowMapEnabled?"#define USE_SHADOWMAP":"",v.shadowMapEnabled?"#define "+R:"",v.sizeAttenuation?"#define USE_SIZEATTENUATION":"",v.logarithmicDepthBuffer?"#define USE_LOGDEPTHBUF":"",v.logarithmicDepthBuffer&&(z.isWebGL2||c.get("EXT_frag_depth"))?"#define USE_LOGDEPTHBUF_EXT":"","uniform mat4 modelMatrix;","uniform mat4 modelViewMatrix;",
"uniform mat4 projectionMatrix;","uniform mat4 viewMatrix;","uniform mat3 normalMatrix;","uniform vec3 cameraPosition;","attribute vec3 position;","attribute vec3 normal;","attribute vec2 uv;","#ifdef USE_TANGENT","\tattribute vec4 tangent;","#endif","#ifdef USE_COLOR","\tattribute vec3 color;","#endif","#ifdef USE_MORPHTARGETS","\tattribute vec3 morphTarget0;","\tattribute vec3 morphTarget1;","\tattribute vec3 morphTarget2;","\tattribute vec3 morphTarget3;","\t#ifdef USE_MORPHNORMALS","\t\tattribute vec3 morphNormal0;",
"\t\tattribute vec3 morphNormal1;","\t\tattribute vec3 morphNormal2;","\t\tattribute vec3 morphNormal3;","\t#else","\t\tattribute vec3 morphTarget4;","\t\tattribute vec3 morphTarget5;","\t\tattribute vec3 morphTarget6;","\t\tattribute vec3 morphTarget7;","\t#endif","#endif","#ifdef USE_SKINNING","\tattribute vec4 skinIndex;","\tattribute vec4 skinWeight;","#endif","\n"].filter(uf).join("\n"),c=[fa,"precision "+v.precision+" float;","precision "+v.precision+" int;","highp"===v.precision?"#define HIGH_PRECISION":
"","#define SHADER_NAME "+r.name,ra,v.alphaTest?"#define ALPHATEST "+v.alphaTest+(v.alphaTest%1?"":".0"):"","#define GAMMA_FACTOR "+ha,v.useFog&&v.fog?"#define USE_FOG":"",v.useFog&&v.fogExp2?"#define FOG_EXP2":"",v.map?"#define USE_MAP":"",v.matcap?"#define USE_MATCAP":"",v.envMap?"#define USE_ENVMAP":"",v.envMap?"#define "+S:"",v.envMap?"#define "+V:"",v.envMap?"#define "+W:"",v.lightMap?"#define USE_LIGHTMAP":"",v.aoMap?"#define USE_AOMAP":"",v.emissiveMap?"#define USE_EMISSIVEMAP":"",v.bumpMap?
"#define USE_BUMPMAP":"",v.normalMap?"#define USE_NORMALMAP":"",v.normalMap&&v.objectSpaceNormalMap?"#define OBJECTSPACE_NORMALMAP":"",v.normalMap&&v.tangentSpaceNormalMap?"#define TANGENTSPACE_NORMALMAP":"",v.clearcoatNormalMap?"#define USE_CLEARCOAT_NORMALMAP":"",v.specularMap?"#define USE_SPECULARMAP":"",v.roughnessMap?"#define USE_ROUGHNESSMAP":"",v.metalnessMap?"#define USE_METALNESSMAP":"",v.alphaMap?"#define USE_ALPHAMAP":"",v.sheen?"#define USE_SHEEN":"",v.vertexTangents?"#define USE_TANGENT":
"",v.vertexColors?"#define USE_COLOR":"",v.vertexUvs?"#define USE_UV":"",v.gradientMap?"#define USE_GRADIENTMAP":"",v.flatShading?"#define FLAT_SHADED":"",v.doubleSided?"#define DOUBLE_SIDED":"",v.flipSided?"#define FLIP_SIDED":"",v.shadowMapEnabled?"#define USE_SHADOWMAP":"",v.shadowMapEnabled?"#define "+R:"",v.premultipliedAlpha?"#define PREMULTIPLIED_ALPHA":"",v.physicallyCorrectLights?"#define PHYSICALLY_CORRECT_LIGHTS":"",v.logarithmicDepthBuffer?"#define USE_LOGDEPTHBUF":"",v.logarithmicDepthBuffer&&
(z.isWebGL2||c.get("EXT_frag_depth"))?"#define USE_LOGDEPTHBUF_EXT":"",(g.extensions&&g.extensions.shaderTextureLOD||v.envMap)&&(z.isWebGL2||c.get("EXT_shader_texture_lod"))?"#define TEXTURE_LOD_EXT":"","uniform mat4 viewMatrix;","uniform vec3 cameraPosition;",0!==v.toneMapping?"#define TONE_MAPPING":"",0!==v.toneMapping?rb.tonemapping_pars_fragment:"",0!==v.toneMapping?Fl("toneMapping",v.toneMapping):"",v.dithering?"#define DITHERING":"",v.outputEncoding||v.mapEncoding||v.matcapEncoding||v.envMapEncoding||
v.emissiveMapEncoding?rb.encodings_pars_fragment:"",v.mapEncoding?ug("mapTexelToLinear",v.mapEncoding):"",v.matcapEncoding?ug("matcapTexelToLinear",v.matcapEncoding):"",v.envMapEncoding?ug("envMapTexelToLinear",v.envMapEncoding):"",v.emissiveMapEncoding?ug("emissiveMapTexelToLinear",v.emissiveMapEncoding):"",v.outputEncoding?El("linearToOutputTexel",v.outputEncoding):"",v.depthPacking?"#define DEPTH_PACKING "+g.depthPacking:"","\n"].filter(uf).join("\n"));J=Mh(J);J=qj(J,v);J=rj(J,v);P=Mh(P);P=qj(P,
v);P=rj(P,v);J=sj(J);P=sj(P);z.isWebGL2&&!g.isRawShaderMaterial&&(z=!1,R=/^\s*#version\s+300\s+es\s*\n/,g.isShaderMaterial&&null!==J.match(R)&&null!==P.match(R)&&(z=!0,J=J.replace(R,""),P=P.replace(R,"")),F="#version 300 es\n\n#define attribute in\n#define varying out\n#define texture2D texture\n"+F,c=["#version 300 es\n\n#define varying in",z?"":"out highp vec4 pc_fragColor;",z?"":"#define gl_FragColor pc_fragColor","#define gl_FragDepthEXT gl_FragDepth\n#define texture2D texture\n#define textureCube texture\n#define texture2DProj textureProj\n#define texture2DLodEXT textureLod\n#define texture2DProjLodEXT textureProjLod\n#define textureCubeLodEXT textureLod\n#define texture2DGradEXT textureGrad\n#define texture2DProjGradEXT textureProjGrad\n#define textureCubeGradEXT textureGrad"].join("\n")+
"\n"+c);P=c+P;J=nj(E,35633,F+J);P=nj(E,35632,P);E.attachShader(pa,J);E.attachShader(pa,P);void 0!==g.index0AttributeName?E.bindAttribLocation(pa,0,g.index0AttributeName):!0===v.morphTargets&&E.bindAttribLocation(pa,0,"position");E.linkProgram(pa);if(a.debug.checkShaderErrors){a=E.getProgramInfoLog(pa).trim();v=E.getShaderInfoLog(J).trim();z=E.getShaderInfoLog(P).trim();S=R=!0;if(!1===E.getProgramParameter(pa,35714))R=!1,V=pj(E,J,"vertex"),W=pj(E,P,"fragment"),console.error("THREE.WebGLProgram: shader error: ",
E.getError(),"35715",E.getProgramParameter(pa,35715),"gl.getProgramInfoLog",a,V,W);else if(""!==a)console.warn("THREE.WebGLProgram: gl.getProgramInfoLog()",a);else if(""===v||""===z)S=!1;S&&(this.diagnostics={runnable:R,material:g,programLog:a,vertexShader:{log:v,prefix:F},fragmentShader:{log:z,prefix:c}})}E.deleteShader(J);E.deleteShader(P);var qa;this.getUniforms=function(){void 0===qa&&(qa=new ud(E,pa));return qa};var ua;this.getAttributes=function(){void 0===ua&&(ua=Il(E,pa));return ua};this.destroy=
function(){E.deleteProgram(pa);this.program=void 0};this.name=r.name;this.id=Kl++;this.code=e;this.usedTimes=1;this.program=pa;this.vertexShader=J;this.fragmentShader=P;return this}function Ll(a,c,e){function g(F){F=F.skeleton.bones;if(e.floatVertexTextures)return 1024;var J=Math.min(Math.floor((e.maxVertexUniforms-20)/4),F.length);return J<F.length?(console.warn("THREE.WebGLRenderer: Skeleton has "+F.length+" bones. This GPU supports "+J+"."),0):J}function r(F,J){if(F)F.isTexture?P=F.encoding:F.isWebGLRenderTarget&&
(console.warn("THREE.WebGLPrograms.getTextureEncodingFromMap: don't use render targets as textures. Use their .texture property instead."),P=F.texture.encoding);else var P=3E3;3E3===P&&J&&(P=3007);return P}var v=[],z={MeshDepthMaterial:"depth",MeshDistanceMaterial:"distanceRGBA",MeshNormalMaterial:"normal",MeshBasicMaterial:"basic",MeshLambertMaterial:"lambert",MeshPhongMaterial:"phong",MeshToonMaterial:"phong",MeshStandardMaterial:"physical",MeshPhysicalMaterial:"physical",MeshMatcapMaterial:"matcap",
LineBasicMaterial:"basic",LineDashedMaterial:"dashed",PointsMaterial:"points",ShadowMaterial:"shadow",SpriteMaterial:"sprite"},E="precision supportsVertexTextures map mapEncoding matcap matcapEncoding envMap envMapMode envMapEncoding lightMap aoMap emissiveMap emissiveMapEncoding bumpMap normalMap objectSpaceNormalMap tangentSpaceNormalMap clearcoatNormalMap displacementMap specularMap roughnessMap metalnessMap gradientMap alphaMap combine vertexColors vertexTangents fog useFog fogExp2 flatShading sizeAttenuation logarithmicDepthBuffer skinning maxBones useVertexTexture morphTargets morphNormals maxMorphTargets maxMorphNormals premultipliedAlpha numDirLights numPointLights numSpotLights numHemiLights numRectAreaLights shadowMapEnabled shadowMapType toneMapping physicallyCorrectLights alphaTest doubleSided flipSided numClippingPlanes numClipIntersection depthPacking dithering sheen".split(" ");
this.getParameters=function(F,J,P,R,S,V,W){var ha=z[F.type],fa=W.isSkinnedMesh?g(W):0,ra=e.precision;null!==F.precision&&(ra=e.getMaxPrecision(F.precision),ra!==F.precision&&console.warn("THREE.WebGLProgram.getParameters:",F.precision,"not supported, using",ra,"instead."));var pa=a.getRenderTarget();return{shaderID:ha,precision:ra,supportsVertexTextures:e.vertexTextures,outputEncoding:r(pa?pa.texture:null,a.gammaOutput),map:!!F.map,mapEncoding:r(F.map,a.gammaInput),matcap:!!F.matcap,matcapEncoding:r(F.matcap,
a.gammaInput),envMap:!!F.envMap,envMapMode:F.envMap&&F.envMap.mapping,envMapEncoding:r(F.envMap,a.gammaInput),envMapCubeUV:!!F.envMap&&(306===F.envMap.mapping||307===F.envMap.mapping),lightMap:!!F.lightMap,aoMap:!!F.aoMap,emissiveMap:!!F.emissiveMap,emissiveMapEncoding:r(F.emissiveMap,a.gammaInput),bumpMap:!!F.bumpMap,normalMap:!!F.normalMap,objectSpaceNormalMap:1===F.normalMapType,tangentSpaceNormalMap:0===F.normalMapType,clearcoatNormalMap:!!F.clearcoatNormalMap,displacementMap:!!F.displacementMap,
roughnessMap:!!F.roughnessMap,metalnessMap:!!F.metalnessMap,specularMap:!!F.specularMap,alphaMap:!!F.alphaMap,gradientMap:!!F.gradientMap,sheen:!!F.sheen,combine:F.combine,vertexTangents:F.normalMap&&F.vertexTangents,vertexColors:F.vertexColors,vertexUvs:!!F.map||!!F.bumpMap||!!F.normalMap||!!F.specularMap||!!F.alphaMap||!!F.emissiveMap||!!F.roughnessMap||!!F.metalnessMap||!!F.clearcoatNormalMap,fog:!!R,useFog:F.fog,fogExp2:R&&R.isFogExp2,flatShading:F.flatShading,sizeAttenuation:F.sizeAttenuation,
logarithmicDepthBuffer:e.logarithmicDepthBuffer,skinning:F.skinning&&0<fa,maxBones:fa,useVertexTexture:e.floatVertexTextures,morphTargets:F.morphTargets,morphNormals:F.morphNormals,maxMorphTargets:a.maxMorphTargets,maxMorphNormals:a.maxMorphNormals,numDirLights:J.directional.length,numPointLights:J.point.length,numSpotLights:J.spot.length,numRectAreaLights:J.rectArea.length,numHemiLights:J.hemi.length,numDirLightShadows:J.directionalShadowMap.length,numPointLightShadows:J.pointShadowMap.length,numSpotLightShadows:J.spotShadowMap.length,
numClippingPlanes:S,numClipIntersection:V,dithering:F.dithering,shadowMapEnabled:a.shadowMap.enabled&&W.receiveShadow&&0<P.length,shadowMapType:a.shadowMap.type,toneMapping:F.toneMapped?a.toneMapping:0,physicallyCorrectLights:a.physicallyCorrectLights,premultipliedAlpha:F.premultipliedAlpha,alphaTest:F.alphaTest,doubleSided:2===F.side,flipSided:1===F.side,depthPacking:void 0!==F.depthPacking?F.depthPacking:!1}};this.getProgramCode=function(F,J){var P=[];J.shaderID?P.push(J.shaderID):(P.push(F.fragmentShader),
P.push(F.vertexShader));if(void 0!==F.defines)for(var R in F.defines)P.push(R),P.push(F.defines[R]);for(R=0;R<E.length;R++)P.push(J[E[R]]);P.push(F.onBeforeCompile.toString());P.push(a.gammaOutput);P.push(a.gammaFactor);return P.join()};this.acquireProgram=function(F,J,P,R){for(var S,V=0,W=v.length;V<W;V++){var ha=v[V];if(ha.code===R){S=ha;++S.usedTimes;break}}void 0===S&&(S=new Jl(a,c,R,F,J,P,e),v.push(S));return S};this.releaseProgram=function(F){0===--F.usedTimes&&(v[v.indexOf(F)]=v[v.length-1],
v.pop(),F.destroy())};this.programs=v}function Ml(){var a=new WeakMap;return{get:function(c){var e=a.get(c);void 0===e&&(e={},a.set(c,e));return e},remove:function(c){a.delete(c)},update:function(c,e,g){a.get(c)[e]=g},dispose:function(){a=new WeakMap}}}function Nl(a,c){return a.groupOrder!==c.groupOrder?a.groupOrder-c.groupOrder:a.renderOrder!==c.renderOrder?a.renderOrder-c.renderOrder:a.program!==c.program?a.program.id-c.program.id:a.material.id!==c.material.id?a.material.id-c.material.id:a.z!==
c.z?a.z-c.z:a.id-c.id}function Ol(a,c){return a.groupOrder!==c.groupOrder?a.groupOrder-c.groupOrder:a.renderOrder!==c.renderOrder?a.renderOrder-c.renderOrder:a.z!==c.z?c.z-a.z:a.id-c.id}function tj(){function a(z,E,F,J,P,R){var S=c[e];void 0===S?(S={id:z.id,object:z,geometry:E,material:F,program:F.program||v,groupOrder:J,renderOrder:z.renderOrder,z:P,group:R},c[e]=S):(S.id=z.id,S.object=z,S.geometry=E,S.material=F,S.program=F.program||v,S.groupOrder=J,S.renderOrder=z.renderOrder,S.z=P,S.group=R);
e++;return S}var c=[],e=0,g=[],r=[],v={id:-1};return{opaque:g,transparent:r,init:function(){e=0;g.length=0;r.length=0},push:function(z,E,F,J,P,R){z=a(z,E,F,J,P,R);(!0===F.transparent?r:g).push(z)},unshift:function(z,E,F,J,P,R){z=a(z,E,F,J,P,R);(!0===F.transparent?r:g).unshift(z)},sort:function(){1<g.length&&g.sort(Nl);1<r.length&&r.sort(Ol)}}}function Pl(){function a(e){e=e.target;e.removeEventListener("dispose",a);c.delete(e)}var c=new WeakMap;return{get:function(e,g){var r=c.get(e);if(void 0===
r){var v=new tj;c.set(e,new WeakMap);c.get(e).set(g,v);e.addEventListener("dispose",a)}else v=r.get(g),void 0===v&&(v=new tj,r.set(g,v));return v},dispose:function(){c=new WeakMap}}}function Ql(){var a={};return{get:function(c){if(void 0!==a[c.id])return a[c.id];switch(c.type){case "DirectionalLight":var e={direction:new k,color:new I,shadow:!1,shadowBias:0,shadowRadius:1,shadowMapSize:new f};break;case "SpotLight":e={position:new k,direction:new k,color:new I,distance:0,coneCos:0,penumbraCos:0,decay:0,
shadow:!1,shadowBias:0,shadowRadius:1,shadowMapSize:new f};break;case "PointLight":e={position:new k,color:new I,distance:0,decay:0,shadow:!1,shadowBias:0,shadowRadius:1,shadowMapSize:new f,shadowCameraNear:1,shadowCameraFar:1E3};break;case "HemisphereLight":e={direction:new k,skyColor:new I,groundColor:new I};break;case "RectAreaLight":e={color:new I,position:new k,halfWidth:new k,halfHeight:new k}}return a[c.id]=e}}}function Rl(a,c){return(c.castShadow?1:0)-(a.castShadow?1:0)}function Sl(){for(var a=
new Ql,c={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],point:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[],numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},e=0;9>e;e++)c.probe.push(new k);var g=new k,r=new q,v=new q;return{setup:function(z,
E,F){for(var J=0,P=0,R=0,S=0;9>S;S++)c.probe[S].set(0,0,0);var V=E=0,W=0,ha=0,fa=0,ra=0,pa=0,qa=0;F=F.matrixWorldInverse;z.sort(Rl);S=0;for(var ua=z.length;S<ua;S++){var oa=z[S],ta=oa.color,Ba=oa.intensity,Ta=oa.distance,Ua=oa.shadow&&oa.shadow.map?oa.shadow.map.texture:null;if(oa.isAmbientLight)J+=ta.r*Ba,P+=ta.g*Ba,R+=ta.b*Ba;else if(oa.isLightProbe)for(Ua=0;9>Ua;Ua++)c.probe[Ua].addScaledVector(oa.sh.coefficients[Ua],Ba);else if(oa.isDirectionalLight){var Ca=a.get(oa);Ca.color.copy(oa.color).multiplyScalar(oa.intensity);
Ca.direction.setFromMatrixPosition(oa.matrixWorld);g.setFromMatrixPosition(oa.target.matrixWorld);Ca.direction.sub(g);Ca.direction.transformDirection(F);if(Ca.shadow=oa.castShadow)Ba=oa.shadow,Ca.shadowBias=Ba.bias,Ca.shadowRadius=Ba.radius,Ca.shadowMapSize=Ba.mapSize,c.directionalShadowMap[E]=Ua,c.directionalShadowMatrix[E]=oa.shadow.matrix,ra++;c.directional[E]=Ca;E++}else if(oa.isSpotLight){Ca=a.get(oa);Ca.position.setFromMatrixPosition(oa.matrixWorld);Ca.position.applyMatrix4(F);Ca.color.copy(ta).multiplyScalar(Ba);
Ca.distance=Ta;Ca.direction.setFromMatrixPosition(oa.matrixWorld);g.setFromMatrixPosition(oa.target.matrixWorld);Ca.direction.sub(g);Ca.direction.transformDirection(F);Ca.coneCos=Math.cos(oa.angle);Ca.penumbraCos=Math.cos(oa.angle*(1-oa.penumbra));Ca.decay=oa.decay;if(Ca.shadow=oa.castShadow)Ba=oa.shadow,Ca.shadowBias=Ba.bias,Ca.shadowRadius=Ba.radius,Ca.shadowMapSize=Ba.mapSize,c.spotShadowMap[W]=Ua,c.spotShadowMatrix[W]=oa.shadow.matrix,qa++;c.spot[W]=Ca;W++}else if(oa.isRectAreaLight)Ca=a.get(oa),
Ca.color.copy(ta).multiplyScalar(Ba),Ca.position.setFromMatrixPosition(oa.matrixWorld),Ca.position.applyMatrix4(F),v.identity(),r.copy(oa.matrixWorld),r.premultiply(F),v.extractRotation(r),Ca.halfWidth.set(.5*oa.width,0,0),Ca.halfHeight.set(0,.5*oa.height,0),Ca.halfWidth.applyMatrix4(v),Ca.halfHeight.applyMatrix4(v),c.rectArea[ha]=Ca,ha++;else if(oa.isPointLight){Ca=a.get(oa);Ca.position.setFromMatrixPosition(oa.matrixWorld);Ca.position.applyMatrix4(F);Ca.color.copy(oa.color).multiplyScalar(oa.intensity);
Ca.distance=oa.distance;Ca.decay=oa.decay;if(Ca.shadow=oa.castShadow)Ba=oa.shadow,Ca.shadowBias=Ba.bias,Ca.shadowRadius=Ba.radius,Ca.shadowMapSize=Ba.mapSize,Ca.shadowCameraNear=Ba.camera.near,Ca.shadowCameraFar=Ba.camera.far,c.pointShadowMap[V]=Ua,c.pointShadowMatrix[V]=oa.shadow.matrix,pa++;c.point[V]=Ca;V++}else oa.isHemisphereLight&&(Ca=a.get(oa),Ca.direction.setFromMatrixPosition(oa.matrixWorld),Ca.direction.transformDirection(F),Ca.direction.normalize(),Ca.skyColor.copy(oa.color).multiplyScalar(Ba),
Ca.groundColor.copy(oa.groundColor).multiplyScalar(Ba),c.hemi[fa]=Ca,fa++)}c.ambient[0]=J;c.ambient[1]=P;c.ambient[2]=R;z=c.hash;if(z.directionalLength!==E||z.pointLength!==V||z.spotLength!==W||z.rectAreaLength!==ha||z.hemiLength!==fa||z.numDirectionalShadows!==ra||z.numPointShadows!==pa||z.numSpotShadows!==qa)c.directional.length=E,c.spot.length=W,c.rectArea.length=ha,c.point.length=V,c.hemi.length=fa,c.directionalShadowMap.length=ra,c.pointShadowMap.length=pa,c.spotShadowMap.length=qa,c.directionalShadowMatrix.length=
ra,c.pointShadowMatrix.length=pa,c.spotShadowMatrix.length=qa,z.directionalLength=E,z.pointLength=V,z.spotLength=W,z.rectAreaLength=ha,z.hemiLength=fa,z.numDirectionalShadows=ra,z.numPointShadows=pa,z.numSpotShadows=qa,c.version=Tl++},state:c}}function uj(){var a=new Sl,c=[],e=[];return{init:function(){c.length=0;e.length=0},state:{lightsArray:c,shadowsArray:e,lights:a},setupLights:function(g){a.setup(c,e,g)},pushLight:function(g){c.push(g)},pushShadow:function(g){e.push(g)}}}function Ul(){function a(e){e=
e.target;e.removeEventListener("dispose",a);c.delete(e)}var c=new WeakMap;return{get:function(e,g){if(!1===c.has(e)){var r=new uj;c.set(e,new WeakMap);c.get(e).set(g,r);e.addEventListener("dispose",a)}else!1===c.get(e).has(g)?(r=new uj,c.get(e).set(g,r)):r=c.get(e).get(g);return r},dispose:function(){c=new WeakMap}}}function vd(a){M.call(this);this.type="MeshDepthMaterial";this.depthPacking=3200;this.morphTargets=this.skinning=!1;this.displacementMap=this.alphaMap=this.map=null;this.displacementScale=
1;this.displacementBias=0;this.wireframe=!1;this.wireframeLinewidth=1;this.lights=this.fog=!1;this.setValues(a)}function wd(a){M.call(this);this.type="MeshDistanceMaterial";this.referencePosition=new k;this.nearDistance=1;this.farDistance=1E3;this.morphTargets=this.skinning=!1;this.displacementMap=this.alphaMap=this.map=null;this.displacementScale=1;this.displacementBias=0;this.lights=this.fog=!1;this.setValues(a)}function vj(a,c,e){function g(ta,Ba){var Ta=c.update(ra);W.uniforms.shadow_pass.value=
ta.map.texture;W.uniforms.resolution.value=ta.mapSize;W.uniforms.radius.value=ta.radius;a.setRenderTarget(ta.mapPass);a.clear();a.renderBufferDirect(Ba,null,Ta,W,ra,null);ha.uniforms.shadow_pass.value=ta.mapPass.texture;ha.uniforms.resolution.value=ta.mapSize;ha.uniforms.radius.value=ta.radius;a.setRenderTarget(ta.map);a.clear();a.renderBufferDirect(Ba,null,Ta,ha,ra,null)}function r(ta,Ba,Ta,Ua,Ca,Ha){var Da=ta.geometry;var Ma=P;var db=ta.customDepthMaterial;Ta.isPointLight&&(Ma=R,db=ta.customDistanceMaterial);
db?Ma=db:(db=!1,Ba.morphTargets&&(Da&&Da.isBufferGeometry?db=Da.morphAttributes&&Da.morphAttributes.position&&0<Da.morphAttributes.position.length:Da&&Da.isGeometry&&(db=Da.morphTargets&&0<Da.morphTargets.length)),ta.isSkinnedMesh&&!1===Ba.skinning&&console.warn("THREE.WebGLShadowMap: THREE.SkinnedMesh with material.skinning set to false:",ta),ta=ta.isSkinnedMesh&&Ba.skinning,Da=0,db&&(Da|=1),ta&&(Da|=2),Ma=Ma[Da]);a.localClippingEnabled&&!0===Ba.clipShadows&&0!==Ba.clippingPlanes.length&&(Da=Ma.uuid,
db=Ba.uuid,ta=S[Da],void 0===ta&&(ta={},S[Da]=ta),Da=ta[db],void 0===Da&&(Da=Ma.clone(),ta[db]=Da),Ma=Da);Ma.visible=Ba.visible;Ma.wireframe=Ba.wireframe;Ma.side=3===Ha?null!=Ba.shadowSide?Ba.shadowSide:Ba.side:null!=Ba.shadowSide?Ba.shadowSide:V[Ba.side];Ma.clipShadows=Ba.clipShadows;Ma.clippingPlanes=Ba.clippingPlanes;Ma.clipIntersection=Ba.clipIntersection;Ma.wireframeLinewidth=Ba.wireframeLinewidth;Ma.linewidth=Ba.linewidth;Ta.isPointLight&&Ma.isMeshDistanceMaterial&&(Ma.referencePosition.setFromMatrixPosition(Ta.matrixWorld),
Ma.nearDistance=Ua,Ma.farDistance=Ca);return Ma}function v(ta,Ba,Ta,Ua,Ca){if(!1!==ta.visible){if(ta.layers.test(Ba.layers)&&(ta.isMesh||ta.isLine||ta.isPoints)&&(ta.castShadow||ta.receiveShadow&&3===Ca)&&(!ta.frustumCulled||z.intersectsObject(ta))){ta.modelViewMatrix.multiplyMatrices(Ta.matrixWorldInverse,ta.matrixWorld);var Ha=c.update(ta),Da=ta.material;if(Array.isArray(Da))for(var Ma=Ha.groups,db=0,tb=Ma.length;db<tb;db++){var Ka=Ma[db],bb=Da[Ka.materialIndex];bb&&bb.visible&&(bb=r(ta,bb,Ua,Ta.near,
Ta.far,Ca),a.renderBufferDirect(Ta,null,Ha,bb,ta,Ka))}else Da.visible&&(bb=r(ta,Da,Ua,Ta.near,Ta.far,Ca),a.renderBufferDirect(Ta,null,Ha,bb,ta,null))}ta=ta.children;Ha=0;for(Da=ta.length;Ha<Da;Ha++)v(ta[Ha],Ba,Ta,Ua,Ca)}}var z=new ic,E=new f,F=new f,J=new p,P=Array(4),R=Array(4),S={},V={0:1,1:0,2:2},W=new qb({defines:{SAMPLE_RATE:.25,HALF_SAMPLE_RATE:.125},uniforms:{shadow_pass:{value:null},resolution:{value:new f},radius:{value:4}},vertexShader:"void main() {\n\tgl_Position \x3d vec4( position, 1.0 );\n}",
fragmentShader:"uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include \x3cpacking\x3e\nvoid main() {\n  float mean \x3d 0.0;\n  float squared_mean \x3d 0.0;\n  \n\tfloat depth \x3d unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy  ) / resolution ) );\n  for ( float i \x3d -1.0; i \x3c 1.0 ; i +\x3d SAMPLE_RATE) {\n    #ifdef HORIZONAL_PASS\n      vec2 distribution \x3d decodeHalfRGBA ( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( i, 0.0 ) * radius ) / resolution ) );\n      mean +\x3d distribution.x;\n      squared_mean +\x3d distribution.y * distribution.y + distribution.x * distribution.x;\n    #else\n      float depth \x3d unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0,  i )  * radius ) / resolution ) );\n      mean +\x3d depth;\n      squared_mean +\x3d depth * depth;\n    #endif\n  }\n  mean \x3d mean * HALF_SAMPLE_RATE;\n  squared_mean \x3d squared_mean * HALF_SAMPLE_RATE;\n  float std_dev \x3d pow( squared_mean - mean * mean, 0.5 );\n  gl_FragColor \x3d encodeHalfRGBA( vec2( mean, std_dev ) );\n}"}),
ha=W.clone();ha.defines.HORIZONAL_PASS=1;var fa=new va;fa.addAttribute("position",new Q(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));var ra=new xa(fa,W);for(fa=0;4!==fa;++fa){var pa=0!==(fa&1),qa=0!==(fa&2),ua=new vd({depthPacking:3201,morphTargets:pa,skinning:qa});P[fa]=ua;pa=new wd({morphTargets:pa,skinning:qa});R[fa]=pa}var oa=this;this.enabled=!1;this.autoUpdate=!0;this.needsUpdate=!1;this.type=1;this.render=function(ta,Ba,Ta){if(!1!==oa.enabled&&(!1!==oa.autoUpdate||!1!==oa.needsUpdate)&&
0!==ta.length){var Ua=a.getRenderTarget(),Ca=a.getActiveCubeFace(),Ha=a.getActiveMipmapLevel(),Da=a.state;Da.setBlending(0);Da.buffers.color.setClear(1,1,1,1);Da.buffers.depth.setTest(!0);Da.setScissorTest(!1);for(var Ma=0,db=ta.length;Ma<db;Ma++){var tb=ta[Ma],Ka=tb.shadow;if(void 0===Ka)console.warn("THREE.WebGLShadowMap:",tb,"has no shadow.");else{E.copy(Ka.mapSize);var bb=Ka.getFrameExtents();E.multiply(bb);F.copy(Ka.mapSize);if(E.x>e||E.y>e)console.warn("THREE.WebGLShadowMap:",tb,"has shadow exceeding max texture size, reducing"),
E.x>e&&(F.x=Math.floor(e/bb.x),E.x=F.x*bb.x,Ka.mapSize.x=F.x),E.y>e&&(F.y=Math.floor(e/bb.y),E.y=F.y*bb.y,Ka.mapSize.y=F.y);null!==Ka.map||Ka.isPointLightShadow||3!==this.type||(bb={minFilter:1006,magFilter:1006,format:1023},Ka.map=new m(E.x,E.y,bb),Ka.map.texture.name=tb.name+".shadowMap",Ka.mapPass=new m(E.x,E.y,bb),Ka.camera.updateProjectionMatrix());null===Ka.map&&(bb={minFilter:1003,magFilter:1003,format:1023},Ka.map=new m(E.x,E.y,bb),Ka.map.texture.name=tb.name+".shadowMap",Ka.camera.updateProjectionMatrix());
a.setRenderTarget(Ka.map);a.clear();bb=Ka.getViewportCount();for(var jb=0;jb<bb;jb++){var Eb=Ka.getViewport(jb);J.set(F.x*Eb.x,F.y*Eb.y,F.x*Eb.z,F.y*Eb.w);Da.viewport(J);Ka.updateMatrices(tb,Ta,jb);z=Ka.getFrustum();v(Ba,Ta,Ka.camera,tb,this.type)}Ka.isPointLightShadow||3!==this.type||g(Ka,Ta)}}oa.needsUpdate=!1;a.setRenderTarget(Ua,Ca,Ha)}}}function Vl(a,c,e,g){function r(ja,Ga,La){var nb=new Uint8Array(4),Va=a.createTexture();a.bindTexture(ja,Va);a.texParameteri(ja,10241,9728);a.texParameteri(ja,
10240,9728);for(ja=0;ja<La;ja++)a.texImage2D(Ga+ja,0,6408,1,1,0,6408,5121,nb);return Va}function v(ja,Ga){ra[ja]=1;0===pa[ja]&&(a.enableVertexAttribArray(ja),pa[ja]=1);qa[ja]!==Ga&&((g.isWebGL2?a:c.get("ANGLE_instanced_arrays"))[g.isWebGL2?"vertexAttribDivisor":"vertexAttribDivisorANGLE"](ja,Ga),qa[ja]=Ga)}function z(ja){!0!==ua[ja]&&(a.enable(ja),ua[ja]=!0)}function E(ja){!1!==ua[ja]&&(a.disable(ja),ua[ja]=!1)}function F(ja,Ga,La,nb,Va,ib,kb,Qa){if(0===ja)Ba&&(E(3042),Ba=!1);else if(Ba||(z(3042),
Ba=!0),5!==ja){if(ja!==Ta||Qa!==tb){if(100!==Ua||100!==Da)a.blendEquation(32774),Da=Ua=100;if(Qa)switch(ja){case 1:a.blendFuncSeparate(1,771,1,771);break;case 2:a.blendFunc(1,1);break;case 3:a.blendFuncSeparate(0,0,769,771);break;case 4:a.blendFuncSeparate(0,768,0,770);break;default:console.error("THREE.WebGLState: Invalid blending: ",ja)}else switch(ja){case 1:a.blendFuncSeparate(770,771,1,771);break;case 2:a.blendFunc(770,1);break;case 3:a.blendFunc(0,769);break;case 4:a.blendFunc(0,768);break;
default:console.error("THREE.WebGLState: Invalid blending: ",ja)}db=Ma=Ha=Ca=null;Ta=ja;tb=Qa}}else{Va=Va||Ga;ib=ib||La;kb=kb||nb;if(Ga!==Ua||Va!==Da)a.blendEquationSeparate(e.convert(Ga),e.convert(Va)),Ua=Ga,Da=Va;if(La!==Ca||nb!==Ha||ib!==Ma||kb!==db)a.blendFuncSeparate(e.convert(La),e.convert(nb),e.convert(ib),e.convert(kb)),Ca=La,Ha=nb,Ma=ib,db=kb;Ta=ja;tb=null}}function J(ja){Ka!==ja&&(ja?a.frontFace(2304):a.frontFace(2305),Ka=ja)}function P(ja){0!==ja?(z(2884),ja!==bb&&(1===ja?a.cullFace(1029):
2===ja?a.cullFace(1028):a.cullFace(1032))):E(2884);bb=ja}function R(ja,Ga,La){if(ja){if(z(32823),Eb!==Ga||xb!==La)a.polygonOffset(Ga,La),Eb=Ga,xb=La}else E(32823)}function S(ja){void 0===ja&&(ja=33984+ia-1);za!==ja&&(a.activeTexture(ja),za=ja)}var V=new function(){var ja=!1,Ga=new p,La=null,nb=new p(0,0,0,0);return{setMask:function(Va){La===Va||ja||(a.colorMask(Va,Va,Va,Va),La=Va)},setLocked:function(Va){ja=Va},setClear:function(Va,ib,kb,Qa,eb){!0===eb&&(Va*=Qa,ib*=Qa,kb*=Qa);Ga.set(Va,ib,kb,Qa);
!1===nb.equals(Ga)&&(a.clearColor(Va,ib,kb,Qa),nb.copy(Ga))},reset:function(){ja=!1;La=null;nb.set(-1,0,0,0)}}},W=new function(){var ja=!1,Ga=null,La=null,nb=null;return{setTest:function(Va){Va?z(2929):E(2929)},setMask:function(Va){Ga===Va||ja||(a.depthMask(Va),Ga=Va)},setFunc:function(Va){if(La!==Va){if(Va)switch(Va){case 0:a.depthFunc(512);break;case 1:a.depthFunc(519);break;case 2:a.depthFunc(513);break;case 3:a.depthFunc(515);break;case 4:a.depthFunc(514);break;case 5:a.depthFunc(518);break;case 6:a.depthFunc(516);
break;case 7:a.depthFunc(517);break;default:a.depthFunc(515)}else a.depthFunc(515);La=Va}},setLocked:function(Va){ja=Va},setClear:function(Va){nb!==Va&&(a.clearDepth(Va),nb=Va)},reset:function(){ja=!1;nb=La=Ga=null}}},ha=new function(){var ja=!1,Ga=null,La=null,nb=null,Va=null,ib=null,kb=null,Qa=null,eb=null;return{setTest:function(mb){ja||(mb?z(2960):E(2960))},setMask:function(mb){Ga===mb||ja||(a.stencilMask(mb),Ga=mb)},setFunc:function(mb,pb,sb){if(La!==mb||nb!==pb||Va!==sb)a.stencilFunc(mb,pb,
sb),La=mb,nb=pb,Va=sb},setOp:function(mb,pb,sb){if(ib!==mb||kb!==pb||Qa!==sb)a.stencilOp(mb,pb,sb),ib=mb,kb=pb,Qa=sb},setLocked:function(mb){ja=mb},setClear:function(mb){eb!==mb&&(a.clearStencil(mb),eb=mb)},reset:function(){ja=!1;eb=Qa=kb=ib=Va=nb=La=Ga=null}}},fa=a.getParameter(34921),ra=new Uint8Array(fa),pa=new Uint8Array(fa),qa=new Uint8Array(fa),ua={},oa=null,ta=null,Ba=null,Ta=null,Ua=null,Ca=null,Ha=null,Da=null,Ma=null,db=null,tb=!1,Ka=null,bb=null,jb=null,Eb=null,xb=null,ia=a.getParameter(35661),
na=!1;fa=0;fa=a.getParameter(7938);-1!==fa.indexOf("WebGL")?(fa=parseFloat(/^WebGL ([0-9])/.exec(fa)[1]),na=1<=fa):-1!==fa.indexOf("OpenGL ES")&&(fa=parseFloat(/^OpenGL ES ([0-9])/.exec(fa)[1]),na=2<=fa);var za=null,Ja={},Ya=new p,Na=new p,cb={};cb[3553]=r(3553,3553,1);cb[34067]=r(34067,34069,6);V.setClear(0,0,0,1);W.setClear(1);ha.setClear(0);z(2929);W.setFunc(3);J(!1);P(1);z(2884);F(0);return{buffers:{color:V,depth:W,stencil:ha},initAttributes:function(){for(var ja=0,Ga=ra.length;ja<Ga;ja++)ra[ja]=
0},enableAttribute:function(ja){v(ja,0)},enableAttributeAndDivisor:v,disableUnusedAttributes:function(){for(var ja=0,Ga=pa.length;ja!==Ga;++ja)pa[ja]!==ra[ja]&&(a.disableVertexAttribArray(ja),pa[ja]=0)},enable:z,disable:E,getCompressedTextureFormats:function(){if(null===oa&&(oa=[],c.get("WEBGL_compressed_texture_pvrtc")||c.get("WEBGL_compressed_texture_s3tc")||c.get("WEBGL_compressed_texture_etc1")||c.get("WEBGL_compressed_texture_astc")))for(var ja=a.getParameter(34467),Ga=0;Ga<ja.length;Ga++)oa.push(ja[Ga]);
return oa},useProgram:function(ja){return ta!==ja?(a.useProgram(ja),ta=ja,!0):!1},setBlending:F,setMaterial:function(ja,Ga){2===ja.side?E(2884):z(2884);var La=1===ja.side;Ga&&(La=!La);J(La);1===ja.blending&&!1===ja.transparent?F(0):F(ja.blending,ja.blendEquation,ja.blendSrc,ja.blendDst,ja.blendEquationAlpha,ja.blendSrcAlpha,ja.blendDstAlpha,ja.premultipliedAlpha);W.setFunc(ja.depthFunc);W.setTest(ja.depthTest);W.setMask(ja.depthWrite);V.setMask(ja.colorWrite);Ga=ja.stencilWrite;ha.setTest(Ga);Ga&&
(ha.setFunc(ja.stencilFunc,ja.stencilRef,ja.stencilMask),ha.setOp(ja.stencilFail,ja.stencilZFail,ja.stencilZPass));R(ja.polygonOffset,ja.polygonOffsetFactor,ja.polygonOffsetUnits)},setFlipSided:J,setCullFace:P,setLineWidth:function(ja){ja!==jb&&(na&&a.lineWidth(ja),jb=ja)},setPolygonOffset:R,setScissorTest:function(ja){ja?z(3089):E(3089)},activeTexture:S,bindTexture:function(ja,Ga){null===za&&S();var La=Ja[za];void 0===La&&(La={type:void 0,texture:void 0},Ja[za]=La);if(La.type!==ja||La.texture!==
Ga)a.bindTexture(ja,Ga||cb[ja]),La.type=ja,La.texture=Ga},compressedTexImage2D:function(){try{a.compressedTexImage2D.apply(a,arguments)}catch(ja){console.error("THREE.WebGLState:",ja)}},texImage2D:function(){try{a.texImage2D.apply(a,arguments)}catch(ja){console.error("THREE.WebGLState:",ja)}},texImage3D:function(){try{a.texImage3D.apply(a,arguments)}catch(ja){console.error("THREE.WebGLState:",ja)}},scissor:function(ja){!1===Ya.equals(ja)&&(a.scissor(ja.x,ja.y,ja.z,ja.w),Ya.copy(ja))},viewport:function(ja){!1===
Na.equals(ja)&&(a.viewport(ja.x,ja.y,ja.z,ja.w),Na.copy(ja))},reset:function(){for(var ja=0;ja<pa.length;ja++)1===pa[ja]&&(a.disableVertexAttribArray(ja),pa[ja]=0);ua={};za=oa=null;Ja={};bb=Ka=Ta=ta=null;V.reset();W.reset();ha.reset()}}}function Wl(a,c,e,g,r,v,z){function E(ia,na){return bb?new OffscreenCanvas(ia,na):document.createElementNS("http://www.w3.org/1999/xhtml","canvas")}function F(ia,na,za,Ja){var Ya=1;if(ia.width>Ja||ia.height>Ja)Ya=Ja/Math.max(ia.width,ia.height);if(1>Ya||!0===na){if("undefined"!==
typeof HTMLImageElement&&ia instanceof HTMLImageElement||"undefined"!==typeof HTMLCanvasElement&&ia instanceof HTMLCanvasElement||"undefined"!==typeof ImageBitmap&&ia instanceof ImageBitmap)return Ja=na?hb.floorPowerOfTwo:Math.floor,na=Ja(Ya*ia.width),Ya=Ja(Ya*ia.height),void 0===Ka&&(Ka=E(na,Ya)),za=za?E(na,Ya):Ka,za.width=na,za.height=Ya,za.getContext("2d").drawImage(ia,0,0,na,Ya),console.warn("THREE.WebGLRenderer: Texture has been resized from ("+ia.width+"x"+ia.height+") to ("+na+"x"+Ya+")."),
za;"data"in ia&&console.warn("THREE.WebGLRenderer: Image in DataTexture is too big ("+ia.width+"x"+ia.height+").")}return ia}function J(ia){return hb.isPowerOfTwo(ia.width)&&hb.isPowerOfTwo(ia.height)}function P(ia){return r.isWebGL2?!1:1001!==ia.wrapS||1001!==ia.wrapT||1003!==ia.minFilter&&1006!==ia.minFilter}function R(ia,na){return ia.generateMipmaps&&na&&1003!==ia.minFilter&&1006!==ia.minFilter}function S(ia,na,za,Ja){a.generateMipmap(ia);g.get(na).__maxMipLevel=Math.log(Math.max(za,Ja))*Math.LOG2E}
function V(ia,na){if(!r.isWebGL2)return ia;var za=ia;6403===ia&&(5126===na&&(za=33326),5131===na&&(za=33325),5121===na&&(za=33321));6407===ia&&(5126===na&&(za=34837),5131===na&&(za=34843),5121===na&&(za=32849));6408===ia&&(5126===na&&(za=34836),5131===na&&(za=34842),5121===na&&(za=32856));33325===za||33326===za||34842===za||34836===za?c.get("EXT_color_buffer_float"):(34843===za||34837===za)&&console.warn("THREE.WebGLRenderer: Floating point textures with RGB format not supported. Please use RGBA instead.");
return za}function W(ia){return 1003===ia||1004===ia||1005===ia?9728:9729}function ha(ia){ia=ia.target;ia.removeEventListener("dispose",ha);ra(ia);ia.isVideoTexture&&tb.delete(ia);z.memory.textures--}function fa(ia){ia=ia.target;ia.removeEventListener("dispose",fa);pa(ia);z.memory.textures--}function ra(ia){var na=g.get(ia);void 0!==na.__webglInit&&(a.deleteTexture(na.__webglTexture),g.remove(ia))}function pa(ia){var na=g.get(ia),za=g.get(ia.texture);if(ia){void 0!==za.__webglTexture&&a.deleteTexture(za.__webglTexture);
ia.depthTexture&&ia.depthTexture.dispose();if(ia.isWebGLRenderTargetCube)for(za=0;6>za;za++)a.deleteFramebuffer(na.__webglFramebuffer[za]),na.__webglDepthbuffer&&a.deleteRenderbuffer(na.__webglDepthbuffer[za]);else a.deleteFramebuffer(na.__webglFramebuffer),na.__webglDepthbuffer&&a.deleteRenderbuffer(na.__webglDepthbuffer);g.remove(ia.texture);g.remove(ia)}}function qa(ia,na){var za=g.get(ia);ia.isVideoTexture&&db(ia);if(0<ia.version&&za.__version!==ia.version){var Ja=ia.image;if(void 0===Ja)console.warn("THREE.WebGLRenderer: Texture marked for update but image is undefined");
else if(!1===Ja.complete)console.warn("THREE.WebGLRenderer: Texture marked for update but image is incomplete");else{Ta(za,ia,na);return}}e.activeTexture(33984+na);e.bindTexture(3553,za.__webglTexture)}function ua(ia,na){if(6===ia.image.length){var za=g.get(ia);if(0<ia.version&&za.__version!==ia.version){Ba(za,ia);e.activeTexture(33984+na);e.bindTexture(34067,za.__webglTexture);a.pixelStorei(37440,ia.flipY);var Ja=ia&&ia.isCompressedTexture;na=ia.image[0]&&ia.image[0].isDataTexture;for(var Ya=[],
Na=0;6>Na;Na++)Ya[Na]=Ja||na?na?ia.image[Na].image:ia.image[Na]:F(ia.image[Na],!1,!0,r.maxCubemapSize);var cb=Ya[0],ja=J(cb)||r.isWebGL2,Ga=v.convert(ia.format),La=v.convert(ia.type),nb=V(Ga,La);ta(34067,ia,ja);if(Ja){for(Na=0;6>Na;Na++){var Va=Ya[Na].mipmaps;for(Ja=0;Ja<Va.length;Ja++){var ib=Va[Ja];1023!==ia.format&&1022!==ia.format?-1<e.getCompressedTextureFormats().indexOf(Ga)?e.compressedTexImage2D(34069+Na,Ja,nb,ib.width,ib.height,0,ib.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .setTextureCube()"):
e.texImage2D(34069+Na,Ja,nb,ib.width,ib.height,0,Ga,La,ib.data)}}za.__maxMipLevel=Va.length-1}else{Va=ia.mipmaps;for(Na=0;6>Na;Na++)if(na)for(e.texImage2D(34069+Na,0,nb,Ya[Na].width,Ya[Na].height,0,Ga,La,Ya[Na].data),Ja=0;Ja<Va.length;Ja++)ib=Va[Ja],ib=ib.image[Na].image,e.texImage2D(34069+Na,Ja+1,nb,ib.width,ib.height,0,Ga,La,ib.data);else for(e.texImage2D(34069+Na,0,nb,Ga,La,Ya[Na]),Ja=0;Ja<Va.length;Ja++)ib=Va[Ja],e.texImage2D(34069+Na,Ja+1,nb,Ga,La,ib.image[Na]);za.__maxMipLevel=Va.length}R(ia,
ja)&&S(34067,ia,cb.width,cb.height);za.__version=ia.version;if(ia.onUpdate)ia.onUpdate(ia)}else e.activeTexture(33984+na),e.bindTexture(34067,za.__webglTexture)}}function oa(ia,na){e.activeTexture(33984+na);e.bindTexture(34067,g.get(ia).__webglTexture)}function ta(ia,na,za){za?(a.texParameteri(ia,10242,v.convert(na.wrapS)),a.texParameteri(ia,10243,v.convert(na.wrapT)),32879!==ia&&35866!==ia||a.texParameteri(ia,32882,v.convert(na.wrapR)),a.texParameteri(ia,10240,v.convert(na.magFilter)),a.texParameteri(ia,
10241,v.convert(na.minFilter))):(a.texParameteri(ia,10242,33071),a.texParameteri(ia,10243,33071),32879!==ia&&35866!==ia||a.texParameteri(ia,32882,33071),1001===na.wrapS&&1001===na.wrapT||console.warn("THREE.WebGLRenderer: Texture is not power of two. Texture.wrapS and Texture.wrapT should be set to THREE.ClampToEdgeWrapping."),a.texParameteri(ia,10240,W(na.magFilter)),a.texParameteri(ia,10241,W(na.minFilter)),1003!==na.minFilter&&1006!==na.minFilter&&console.warn("THREE.WebGLRenderer: Texture is not power of two. Texture.minFilter should be set to THREE.NearestFilter or THREE.LinearFilter."));
!(za=c.get("EXT_texture_filter_anisotropic"))||1015===na.type&&null===c.get("OES_texture_float_linear")||1016===na.type&&null===(r.isWebGL2||c.get("OES_texture_half_float_linear"))||!(1<na.anisotropy||g.get(na).__currentAnisotropy)||(a.texParameterf(ia,za.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(na.anisotropy,r.getMaxAnisotropy())),g.get(na).__currentAnisotropy=na.anisotropy)}function Ba(ia,na){void 0===ia.__webglInit&&(ia.__webglInit=!0,na.addEventListener("dispose",ha),ia.__webglTexture=a.createTexture(),
z.memory.textures++)}function Ta(ia,na,za){var Ja=3553;na.isDataTexture2DArray&&(Ja=35866);na.isDataTexture3D&&(Ja=32879);Ba(ia,na);e.activeTexture(33984+za);e.bindTexture(Ja,ia.__webglTexture);a.pixelStorei(37440,na.flipY);a.pixelStorei(37441,na.premultiplyAlpha);a.pixelStorei(3317,na.unpackAlignment);za=P(na)&&!1===J(na.image);za=F(na.image,za,!1,r.maxTextureSize);var Ya=J(za)||r.isWebGL2,Na=v.convert(na.format),cb=v.convert(na.type),ja=V(Na,cb);ta(Ja,na,Ya);var Ga=na.mipmaps;if(na.isDepthTexture){ja=
6402;if(1015===na.type){if(!r.isWebGL2)throw Error("Float Depth Texture only supported in WebGL2.0");ja=36012}else r.isWebGL2&&(ja=33189);1026===na.format&&6402===ja&&1012!==na.type&&1014!==na.type&&(console.warn("THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture."),na.type=1012,cb=v.convert(na.type));1027===na.format&&(ja=34041,1020!==na.type&&(console.warn("THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture."),na.type=1020,cb=
v.convert(na.type)));e.texImage2D(3553,0,ja,za.width,za.height,0,Na,cb,null)}else if(na.isDataTexture)if(0<Ga.length&&Ya){for(var La=0,nb=Ga.length;La<nb;La++)Ja=Ga[La],e.texImage2D(3553,La,ja,Ja.width,Ja.height,0,Na,cb,Ja.data);na.generateMipmaps=!1;ia.__maxMipLevel=Ga.length-1}else e.texImage2D(3553,0,ja,za.width,za.height,0,Na,cb,za.data),ia.__maxMipLevel=0;else if(na.isCompressedTexture){La=0;for(nb=Ga.length;La<nb;La++)Ja=Ga[La],1023!==na.format&&1022!==na.format?-1<e.getCompressedTextureFormats().indexOf(Na)?
e.compressedTexImage2D(3553,La,ja,Ja.width,Ja.height,0,Ja.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()"):e.texImage2D(3553,La,ja,Ja.width,Ja.height,0,Na,cb,Ja.data);ia.__maxMipLevel=Ga.length-1}else if(na.isDataTexture2DArray)e.texImage3D(35866,0,ja,za.width,za.height,za.depth,0,Na,cb,za.data),ia.__maxMipLevel=0;else if(na.isDataTexture3D)e.texImage3D(32879,0,ja,za.width,za.height,za.depth,0,Na,cb,za.data),ia.__maxMipLevel=0;else if(0<
Ga.length&&Ya){La=0;for(nb=Ga.length;La<nb;La++)Ja=Ga[La],e.texImage2D(3553,La,ja,Na,cb,Ja);na.generateMipmaps=!1;ia.__maxMipLevel=Ga.length-1}else e.texImage2D(3553,0,ja,Na,cb,za),ia.__maxMipLevel=0;R(na,Ya)&&S(3553,na,za.width,za.height);ia.__version=na.version;if(na.onUpdate)na.onUpdate(na)}function Ua(ia,na,za,Ja){var Ya=v.convert(na.texture.format),Na=v.convert(na.texture.type),cb=V(Ya,Na);e.texImage2D(Ja,0,cb,na.width,na.height,0,Ya,Na,null);a.bindFramebuffer(36160,ia);a.framebufferTexture2D(36160,
za,Ja,g.get(na.texture).__webglTexture,0);a.bindFramebuffer(36160,null)}function Ca(ia,na,za){a.bindRenderbuffer(36161,ia);if(na.depthBuffer&&!na.stencilBuffer)za?(za=Ma(na),a.renderbufferStorageMultisample(36161,za,33189,na.width,na.height)):a.renderbufferStorage(36161,33189,na.width,na.height),a.framebufferRenderbuffer(36160,36096,36161,ia);else if(na.depthBuffer&&na.stencilBuffer)za?(za=Ma(na),a.renderbufferStorageMultisample(36161,za,35056,na.width,na.height)):a.renderbufferStorage(36161,34041,
na.width,na.height),a.framebufferRenderbuffer(36160,33306,36161,ia);else{ia=v.convert(na.texture.format);var Ja=v.convert(na.texture.type);ia=V(ia,Ja);za?(za=Ma(na),a.renderbufferStorageMultisample(36161,za,ia,na.width,na.height)):a.renderbufferStorage(36161,ia,na.width,na.height)}a.bindRenderbuffer(36161,null)}function Ha(ia,na){if(na&&na.isWebGLRenderTargetCube)throw Error("Depth Texture with cube render targets is not supported");a.bindFramebuffer(36160,ia);if(!na.depthTexture||!na.depthTexture.isDepthTexture)throw Error("renderTarget.depthTexture must be an instance of THREE.DepthTexture");
g.get(na.depthTexture).__webglTexture&&na.depthTexture.image.width===na.width&&na.depthTexture.image.height===na.height||(na.depthTexture.image.width=na.width,na.depthTexture.image.height=na.height,na.depthTexture.needsUpdate=!0);qa(na.depthTexture,0);ia=g.get(na.depthTexture).__webglTexture;if(1026===na.depthTexture.format)a.framebufferTexture2D(36160,36096,3553,ia,0);else if(1027===na.depthTexture.format)a.framebufferTexture2D(36160,33306,3553,ia,0);else throw Error("Unknown depthTexture format");
}function Da(ia){var na=g.get(ia),za=!0===ia.isWebGLRenderTargetCube;if(ia.depthTexture){if(za)throw Error("target.depthTexture not supported in Cube render targets");Ha(na.__webglFramebuffer,ia)}else if(za)for(na.__webglDepthbuffer=[],za=0;6>za;za++)a.bindFramebuffer(36160,na.__webglFramebuffer[za]),na.__webglDepthbuffer[za]=a.createRenderbuffer(),Ca(na.__webglDepthbuffer[za],ia);else a.bindFramebuffer(36160,na.__webglFramebuffer),na.__webglDepthbuffer=a.createRenderbuffer(),Ca(na.__webglDepthbuffer,
ia);a.bindFramebuffer(36160,null)}function Ma(ia){return r.isWebGL2&&ia.isWebGLMultisampleRenderTarget?Math.min(r.maxSamples,ia.samples):0}function db(ia){var na=z.render.frame;tb.get(ia)!==na&&(tb.set(ia,na),ia.update())}var tb=new WeakMap,Ka,bb="undefined"!==typeof OffscreenCanvas,jb=0,Eb=!1,xb=!1;this.allocateTextureUnit=function(){var ia=jb;ia>=r.maxTextures&&console.warn("THREE.WebGLTextures: Trying to use "+ia+" texture units while this GPU supports only "+r.maxTextures);jb+=1;return ia};this.resetTextureUnits=
function(){jb=0};this.setTexture2D=qa;this.setTexture2DArray=function(ia,na){var za=g.get(ia);0<ia.version&&za.__version!==ia.version?Ta(za,ia,na):(e.activeTexture(33984+na),e.bindTexture(35866,za.__webglTexture))};this.setTexture3D=function(ia,na){var za=g.get(ia);0<ia.version&&za.__version!==ia.version?Ta(za,ia,na):(e.activeTexture(33984+na),e.bindTexture(32879,za.__webglTexture))};this.setTextureCube=ua;this.setTextureCubeDynamic=oa;this.setupRenderTarget=function(ia){var na=g.get(ia),za=g.get(ia.texture);
ia.addEventListener("dispose",fa);za.__webglTexture=a.createTexture();z.memory.textures++;var Ja=!0===ia.isWebGLRenderTargetCube,Ya=!0===ia.isWebGLMultisampleRenderTarget,Na=J(ia)||r.isWebGL2;if(Ja)for(na.__webglFramebuffer=[],Ya=0;6>Ya;Ya++)na.__webglFramebuffer[Ya]=a.createFramebuffer();else if(na.__webglFramebuffer=a.createFramebuffer(),Ya)if(r.isWebGL2){na.__webglMultisampledFramebuffer=a.createFramebuffer();na.__webglColorRenderbuffer=a.createRenderbuffer();a.bindRenderbuffer(36161,na.__webglColorRenderbuffer);
Ya=v.convert(ia.texture.format);var cb=v.convert(ia.texture.type);Ya=V(Ya,cb);cb=Ma(ia);a.renderbufferStorageMultisample(36161,cb,Ya,ia.width,ia.height);a.bindFramebuffer(36160,na.__webglMultisampledFramebuffer);a.framebufferRenderbuffer(36160,36064,36161,na.__webglColorRenderbuffer);a.bindRenderbuffer(36161,null);ia.depthBuffer&&(na.__webglDepthRenderbuffer=a.createRenderbuffer(),Ca(na.__webglDepthRenderbuffer,ia,!0));a.bindFramebuffer(36160,null)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.");
if(Ja){e.bindTexture(34067,za.__webglTexture);ta(34067,ia.texture,Na);for(Ya=0;6>Ya;Ya++)Ua(na.__webglFramebuffer[Ya],ia,36064,34069+Ya);R(ia.texture,Na)&&S(34067,ia.texture,ia.width,ia.height);e.bindTexture(34067,null)}else e.bindTexture(3553,za.__webglTexture),ta(3553,ia.texture,Na),Ua(na.__webglFramebuffer,ia,36064,3553),R(ia.texture,Na)&&S(3553,ia.texture,ia.width,ia.height),e.bindTexture(3553,null);ia.depthBuffer&&Da(ia)};this.updateRenderTargetMipmap=function(ia){var na=ia.texture,za=J(ia)||
r.isWebGL2;if(R(na,za)){za=ia.isWebGLRenderTargetCube?34067:3553;var Ja=g.get(na).__webglTexture;e.bindTexture(za,Ja);S(za,na,ia.width,ia.height);e.bindTexture(za,null)}};this.updateMultisampleRenderTarget=function(ia){if(ia.isWebGLMultisampleRenderTarget)if(r.isWebGL2){var na=g.get(ia);a.bindFramebuffer(36008,na.__webglMultisampledFramebuffer);a.bindFramebuffer(36009,na.__webglFramebuffer);na=ia.width;var za=ia.height,Ja=16384;ia.depthBuffer&&(Ja|=256);ia.stencilBuffer&&(Ja|=1024);a.blitFramebuffer(0,
0,na,za,0,0,na,za,Ja,9728)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.")};this.safeSetTexture2D=function(ia,na){ia&&ia.isWebGLRenderTarget&&(!1===Eb&&(console.warn("THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead."),Eb=!0),ia=ia.texture);qa(ia,na)};this.safeSetTextureCube=function(ia,na){ia&&ia.isWebGLRenderTargetCube&&(!1===xb&&(console.warn("THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead."),
xb=!0),ia=ia.texture);ia&&ia.isCubeTexture||Array.isArray(ia.image)&&6===ia.image.length?ua(ia,na):oa(ia,na)}}function wj(a,c,e){return{convert:function(g){if(1E3===g)return 10497;if(1001===g)return 33071;if(1002===g)return 33648;if(1003===g)return 9728;if(1004===g)return 9984;if(1005===g)return 9986;if(1006===g)return 9729;if(1007===g)return 9985;if(1008===g)return 9987;if(1009===g)return 5121;if(1017===g)return 32819;if(1018===g)return 32820;if(1019===g)return 33635;if(1010===g)return 5120;if(1011===
g)return 5122;if(1012===g)return 5123;if(1013===g)return 5124;if(1014===g)return 5125;if(1015===g)return 5126;if(1016===g){if(e.isWebGL2)return 5131;var r=c.get("OES_texture_half_float");if(null!==r)return r.HALF_FLOAT_OES}if(1021===g)return 6406;if(1022===g)return 6407;if(1023===g)return 6408;if(1024===g)return 6409;if(1025===g)return 6410;if(1026===g)return 6402;if(1027===g)return 34041;if(1028===g)return 6403;if(100===g)return 32774;if(101===g)return 32778;if(102===g)return 32779;if(200===g)return 0;
if(201===g)return 1;if(202===g)return 768;if(203===g)return 769;if(204===g)return 770;if(205===g)return 771;if(206===g)return 772;if(207===g)return 773;if(208===g)return 774;if(209===g)return 775;if(210===g)return 776;if(33776===g||33777===g||33778===g||33779===g)if(r=c.get("WEBGL_compressed_texture_s3tc"),null!==r){if(33776===g)return r.COMPRESSED_RGB_S3TC_DXT1_EXT;if(33777===g)return r.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(33778===g)return r.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(33779===g)return r.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(35840===
g||35841===g||35842===g||35843===g)if(r=c.get("WEBGL_compressed_texture_pvrtc"),null!==r){if(35840===g)return r.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(35841===g)return r.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(35842===g)return r.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(35843===g)return r.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===g&&(r=c.get("WEBGL_compressed_texture_etc1"),null!==r))return r.COMPRESSED_RGB_ETC1_WEBGL;if(37808===g||37809===g||37810===g||37811===g||37812===g||37813===g||37814===g||37815===g||37816===
g||37817===g||37818===g||37819===g||37820===g||37821===g)if(r=c.get("WEBGL_compressed_texture_astc"),null!==r)return g;if(103===g||104===g){if(e.isWebGL2){if(103===g)return 32775;if(104===g)return 32776}r=c.get("EXT_blend_minmax");if(null!==r){if(103===g)return r.MIN_EXT;if(104===g)return r.MAX_EXT}}if(1020===g){if(e.isWebGL2)return 34042;r=c.get("WEBGL_depth_texture");if(null!==r)return r.UNSIGNED_INT_24_8_WEBGL}return 0}}}function ue(){A.call(this);this.type="Group"}function vf(a){vb.call(this);
this.cameras=a||[]}function xj(a,c,e){yj.setFromMatrixPosition(c.matrixWorld);zj.setFromMatrixPosition(e.matrixWorld);var g=yj.distanceTo(zj),r=c.projectionMatrix.elements,v=e.projectionMatrix.elements,z=r[14]/(r[10]-1);e=r[14]/(r[10]+1);var E=(r[9]+1)/r[5],F=(r[9]-1)/r[5],J=(r[8]-1)/r[0],P=(v[8]+1)/v[0];r=z*J;v=z*P;P=g/(-J+P);J=P*-J;c.matrixWorld.decompose(a.position,a.quaternion,a.scale);a.translateX(J);a.translateZ(P);a.matrixWorld.compose(a.position,a.quaternion,a.scale);a.matrixWorldInverse.getInverse(a.matrixWorld);
c=z+P;z=e+P;a.projectionMatrix.makePerspective(r-J,v+(g-J),E*e/z*c,F*e/z*c,c,z)}function Nh(a){function c(){return null!==J&&!0===J.isPresenting}function e(){if(c()){var Ha=J.getEyeParameters("left");z=2*Ha.renderWidth*ha;E=Ha.renderHeight*ha;Ta=a.getPixelRatio();a.getSize(Ba);a.setDrawingBufferSize(z,E,1);ua.viewport.set(0,0,z/2,E);oa.viewport.set(z/2,0,z/2,E);Ca.start();F.dispatchEvent({type:"sessionstart"})}else F.enabled&&a.setDrawingBufferSize(Ba.width,Ba.height,Ta),Ca.stop(),F.dispatchEvent({type:"sessionend"})}
function g(Ha){for(var Da=navigator.getGamepads&&navigator.getGamepads(),Ma=0,db=0,tb=Da.length;Ma<tb;Ma++){var Ka=Da[Ma];if(Ka&&("Daydream Controller"===Ka.id||"Gear VR Controller"===Ka.id||"Oculus Go Controller"===Ka.id||"OpenVR Gamepad"===Ka.id||Ka.id.startsWith("Oculus Touch")||Ka.id.startsWith("HTC Vive Focus")||Ka.id.startsWith("Spatial Controller"))){if(db===Ha)return Ka;db++}}}function r(){for(var Ha=0;Ha<S.length;Ha++){var Da=S[Ha],Ma=g(Ha);if(void 0!==Ma&&void 0!==Ma.pose){if(null===Ma.pose)break;
var db=Ma.pose;!1===db.hasPosition&&Da.position.set(.2,-.6,-.05);null!==db.position&&Da.position.fromArray(db.position);null!==db.orientation&&Da.quaternion.fromArray(db.orientation);Da.matrix.compose(Da.position,Da.quaternion,Da.scale);Da.matrix.premultiply(V);Da.matrix.decompose(Da.position,Da.quaternion,Da.scale);Da.matrixWorldNeedsUpdate=!0;Da.visible=!0;db="Daydream Controller"===Ma.id?0:1;void 0===Ua[Ha]&&(Ua[Ha]=!1);Ua[Ha]!==Ma.buttons[db].pressed&&(Ua[Ha]=Ma.buttons[db].pressed,!0===Ua[Ha]?
Da.dispatchEvent({type:"selectstart"}):(Da.dispatchEvent({type:"selectend"}),Da.dispatchEvent({type:"select"})))}else Da.visible=!1}}function v(Ha,Da){null!==Da&&4===Da.length&&Ha.set(Da[0]*z,Da[1]*E,Da[2]*z,Da[3]*E)}var z,E,F=this,J=null,P=null,R=null,S=[],V=new q,W=new q,ha=1,fa="local-floor";"undefined"!==typeof window&&"VRFrameData"in window&&(P=new window.VRFrameData,window.addEventListener("vrdisplaypresentchange",e,!1));var ra=new q,pa=new h,qa=new k,ua=new vb;ua.viewport=new p;ua.layers.enable(1);
var oa=new vb;oa.viewport=new p;oa.layers.enable(2);var ta=new vf([ua,oa]);ta.layers.enable(1);ta.layers.enable(2);var Ba=new f,Ta,Ua=[];this.enabled=!1;this.getController=function(Ha){var Da=S[Ha];void 0===Da&&(Da=new ue,Da.matrixAutoUpdate=!1,Da.visible=!1,S[Ha]=Da);return Da};this.getDevice=function(){return J};this.setDevice=function(Ha){void 0!==Ha&&(J=Ha);Ca.setContext(Ha)};this.setFramebufferScaleFactor=function(Ha){ha=Ha};this.setReferenceSpaceType=function(Ha){fa=Ha};this.setPoseTarget=function(Ha){void 0!==
Ha&&(R=Ha)};this.getCamera=function(Ha){var Da="local-floor"===fa?1.6:0;if(!1===c())return Ha.position.set(0,Da,0),Ha.rotation.set(0,0,0),Ha;J.depthNear=Ha.near;J.depthFar=Ha.far;J.getFrameData(P);if("local-floor"===fa){var Ma=J.stageParameters;Ma?V.fromArray(Ma.sittingToStandingTransform):V.makeTranslation(0,Da,0)}Da=P.pose;Ma=null!==R?R:Ha;Ma.matrix.copy(V);Ma.matrix.decompose(Ma.position,Ma.quaternion,Ma.scale);null!==Da.orientation&&(pa.fromArray(Da.orientation),Ma.quaternion.multiply(pa));null!==
Da.position&&(pa.setFromRotationMatrix(V),qa.fromArray(Da.position),qa.applyQuaternion(pa),Ma.position.add(qa));Ma.updateMatrixWorld();ua.near=Ha.near;oa.near=Ha.near;ua.far=Ha.far;oa.far=Ha.far;ua.matrixWorldInverse.fromArray(P.leftViewMatrix);oa.matrixWorldInverse.fromArray(P.rightViewMatrix);W.getInverse(V);"local-floor"===fa&&(ua.matrixWorldInverse.multiply(W),oa.matrixWorldInverse.multiply(W));Ha=Ma.parent;null!==Ha&&(ra.getInverse(Ha.matrixWorld),ua.matrixWorldInverse.multiply(ra),oa.matrixWorldInverse.multiply(ra));
ua.matrixWorld.getInverse(ua.matrixWorldInverse);oa.matrixWorld.getInverse(oa.matrixWorldInverse);ua.projectionMatrix.fromArray(P.leftProjectionMatrix);oa.projectionMatrix.fromArray(P.rightProjectionMatrix);xj(ta,ua,oa);Ha=J.getLayers();Ha.length&&(Ha=Ha[0],v(ua.viewport,Ha.leftBounds),v(oa.viewport,Ha.rightBounds));r();return ta};this.getStandingMatrix=function(){return V};this.isPresenting=c;var Ca=new bc;this.setAnimationLoop=function(Ha){Ca.setAnimationLoop(Ha);c()&&Ca.start()};this.submitFrame=
function(){c()&&J.submitFrame()};this.dispose=function(){"undefined"!==typeof window&&window.removeEventListener("vrdisplaypresentchange",e)};this.setFrameOfReferenceType=function(){console.warn("THREE.WebVRManager: setFrameOfReferenceType() has been deprecated.")}}function Aj(a,c){function e(){return null!==F&&null!==J}function g(qa){for(var ua=0;ua<S.length;ua++)V[ua]===qa.inputSource&&S[ua].dispatchEvent({type:qa.type})}function r(){a.setFramebuffer(null);a.setRenderTarget(a.getRenderTarget());
pa.stop();E.dispatchEvent({type:"sessionend"})}function v(qa){J=qa;pa.setContext(F);pa.start();E.dispatchEvent({type:"sessionstart"})}function z(qa,ua){null===ua?qa.matrixWorld.copy(qa.matrix):qa.matrixWorld.multiplyMatrices(ua.matrixWorld,qa.matrix);qa.matrixWorldInverse.getInverse(qa.matrixWorld)}var E=this,F=null,J=null,P="local-floor",R=null,S=[],V=[],W=new vb;W.layers.enable(1);W.viewport=new p;var ha=new vb;ha.layers.enable(2);ha.viewport=new p;var fa=new vf([W,ha]);fa.layers.enable(1);fa.layers.enable(2);
this.enabled=!1;this.getController=function(qa){var ua=S[qa];void 0===ua&&(ua=new ue,ua.matrixAutoUpdate=!1,ua.visible=!1,S[qa]=ua);return ua};this.setFramebufferScaleFactor=function(){};this.setReferenceSpaceType=function(qa){P=qa};this.getSession=function(){return F};this.setSession=function(qa){F=qa;null!==F&&(F.addEventListener("select",g),F.addEventListener("selectstart",g),F.addEventListener("selectend",g),F.addEventListener("end",r),F.updateRenderState({baseLayer:new XRWebGLLayer(F,c)}),F.requestReferenceSpace(P).then(v),
V=F.inputSources,F.addEventListener("inputsourceschange",function(){V=F.inputSources;console.log(V);for(var ua=0;ua<S.length;ua++)S[ua].userData.inputSource=V[ua]}))};this.getCamera=function(qa){if(e()){var ua=qa.parent,oa=fa.cameras;z(fa,ua);for(var ta=0;ta<oa.length;ta++)z(oa[ta],ua);qa.matrixWorld.copy(fa.matrixWorld);qa=qa.children;ta=0;for(ua=qa.length;ta<ua;ta++)qa[ta].updateMatrixWorld(!0);xj(fa,W,ha);return fa}return qa};this.isPresenting=e;var ra=null,pa=new bc;pa.setAnimationLoop(function(qa,
ua){R=ua.getViewerPose(J);if(null!==R){var oa=R.views,ta=F.renderState.baseLayer;a.setFramebuffer(ta.framebuffer);for(var Ba=0;Ba<oa.length;Ba++){var Ta=oa[Ba],Ua=ta.getViewport(Ta),Ca=fa.cameras[Ba];Ca.matrix.fromArray(Ta.transform.inverse.matrix).getInverse(Ca.matrix);Ca.projectionMatrix.fromArray(Ta.projectionMatrix);Ca.viewport.set(Ua.x,Ua.y,Ua.width,Ua.height);0===Ba&&fa.matrix.copy(Ca.matrix)}}for(Ba=0;Ba<S.length;Ba++){oa=S[Ba];if(ta=V[Ba])if(ta=ua.getPose(ta.targetRaySpace,J),null!==ta){oa.matrix.fromArray(ta.transform.matrix);
oa.matrix.decompose(oa.position,oa.rotation,oa.scale);oa.visible=!0;continue}oa.visible=!1}ra&&ra(qa)});this.setAnimationLoop=function(qa){ra=qa};this.dispose=function(){};this.getStandingMatrix=function(){console.warn("THREE.WebXRManager: getStandingMatrix() is no longer needed.");return new q};this.getDevice=function(){console.warn("THREE.WebXRManager: getDevice() has been deprecated.")};this.setDevice=function(){console.warn("THREE.WebXRManager: setDevice() has been deprecated.")};this.setFrameOfReferenceType=
function(){console.warn("THREE.WebXRManager: setFrameOfReferenceType() has been deprecated.")};this.submitFrame=function(){}}function Oh(a){var c;function e(){return null===ib?cc:1}function g(){Lb=new bd(Ra);dc=new Mb(Ra,Lb,a);dc.isWebGL2||(Lb.get("WEBGL_depth_texture"),Lb.get("OES_texture_float"),Lb.get("OES_texture_half_float"),Lb.get("OES_texture_half_float_linear"),Lb.get("OES_standard_derivatives"),Lb.get("OES_element_index_uint"),Lb.get("ANGLE_instanced_arrays"));Lb.get("OES_texture_float_linear");
Nc=new wj(Ra,Lb,dc);yb=new Vl(Ra,Lb,Nc,dc);yb.scissor(Sb.copy(ve).multiplyScalar(cc).floor());yb.viewport(Kb.copy(we).multiplyScalar(cc).floor());xd=new Sk(Ra);ec=new Ml;Oc=new Wl(Ra,Lb,yb,ec,dc,Nc,xd);vg=new Od(Ra);Ph=new td(Ra,vg,xd);xe=new Vk(Ph,xd);Bj=new Uk(Ra);Pd=new Ll(ja,Lb,dc);wg=new Pl;ye=new Ul;yd=new sd(ja,yb,xe,na);Cj=new sa(Ra,Lb,xd,dc);Dj=new tg(Ra,Lb,xd,dc);xd.programs=Pd.programs;ja.capabilities=dc;ja.extensions=Lb;ja.properties=ec;ja.renderLists=wg;ja.state=yb;ja.info=xd}function r(U){U.preventDefault();
console.log("THREE.WebGLRenderer: Context Lost.");Ga=!0}function v(){console.log("THREE.WebGLRenderer: Context Restored.");Ga=!1;g()}function z(U){U=U.target;U.removeEventListener("dispose",z);E(U)}function E(U){F(U);ec.remove(U)}function F(U){var da=ec.get(U).program;U.program=void 0;void 0!==da&&Pd.releaseProgram(da)}function J(U,da){U.render(function(ma){ja.renderBufferImmediate(ma,da)})}function P(U,da,ma){if(ma&&ma.isInstancedBufferGeometry&&!dc.isWebGL2&&null===Lb.get("ANGLE_instanced_arrays"))console.error("THREE.WebGLRenderer.setupVertexAttributes: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");
else{yb.initAttributes();var Ia=ma.attributes;da=da.getAttributes();U=U.defaultAttributeValues;for(var Oa in da){var ab=da[Oa];if(0<=ab){var Pa=Ia[Oa];if(void 0!==Pa){var fb=Pa.normalized,Cb=Pa.itemSize,ob=vg.get(Pa);if(void 0!==ob){var $a=ob.buffer,Pc=ob.type;ob=ob.bytesPerElement;if(Pa.isInterleavedBufferAttribute){var xc=Pa.data,ze=xc.stride;Pa=Pa.offset;xc&&xc.isInstancedInterleavedBuffer?(yb.enableAttributeAndDivisor(ab,xc.meshPerAttribute),void 0===ma.maxInstancedCount&&(ma.maxInstancedCount=
xc.meshPerAttribute*xc.count)):yb.enableAttribute(ab);Ra.bindBuffer(34962,$a);Ra.vertexAttribPointer(ab,Cb,Pc,fb,ze*ob,Pa*ob)}else Pa.isInstancedBufferAttribute?(yb.enableAttributeAndDivisor(ab,Pa.meshPerAttribute),void 0===ma.maxInstancedCount&&(ma.maxInstancedCount=Pa.meshPerAttribute*Pa.count)):yb.enableAttribute(ab),Ra.bindBuffer(34962,$a),Ra.vertexAttribPointer(ab,Cb,Pc,fb,0,0)}}else if(void 0!==U&&(fb=U[Oa],void 0!==fb))switch(fb.length){case 2:Ra.vertexAttrib2fv(ab,fb);break;case 3:Ra.vertexAttrib3fv(ab,
fb);break;case 4:Ra.vertexAttrib4fv(ab,fb);break;default:Ra.vertexAttrib1fv(ab,fb)}}}yb.disableUnusedAttributes()}}function R(U,da,ma,Ia){if(!1!==U.visible){if(U.layers.test(da.layers))if(U.isGroup)ma=U.renderOrder;else if(U.isLOD)!0===U.autoUpdate&&U.update(da);else if(U.isLight)cb.pushLight(U),U.castShadow&&cb.pushShadow(U);else if(U.isSprite){if(!U.frustumCulled||Qh.intersectsSprite(U)){Ia&&zd.setFromMatrixPosition(U.matrixWorld).applyMatrix4(wf);var Oa=xe.update(U),ab=U.material;ab.visible&&Na.push(U,
Oa,ab,ma,zd.z,null)}}else if(U.isImmediateRenderObject)Ia&&zd.setFromMatrixPosition(U.matrixWorld).applyMatrix4(wf),Na.push(U,null,U.material,ma,zd.z,null);else if(U.isMesh||U.isLine||U.isPoints)if(U.isSkinnedMesh&&U.skeleton.update(),!U.frustumCulled||Qh.intersectsObject(U))if(Ia&&zd.setFromMatrixPosition(U.matrixWorld).applyMatrix4(wf),Oa=xe.update(U),ab=U.material,Array.isArray(ab))for(var Pa=Oa.groups,fb=0,Cb=Pa.length;fb<Cb;fb++){var ob=Pa[fb],$a=ab[ob.materialIndex];$a&&$a.visible&&Na.push(U,
Oa,$a,ma,zd.z,ob)}else ab.visible&&Na.push(U,Oa,ab,ma,zd.z,null);U=U.children;fb=0;for(Cb=U.length;fb<Cb;fb++)R(U[fb],da,ma,Ia)}}function S(U,da,ma,Ia){for(var Oa=0,ab=U.length;Oa<ab;Oa++){var Pa=U[Oa],fb=Pa.object,Cb=Pa.geometry,ob=void 0===Ia?Pa.material:Ia;Pa=Pa.group;if(ma.isArrayCamera){sb=ma;for(var $a=ma.cameras,Pc=0,xc=$a.length;Pc<xc;Pc++){var ze=$a[Pc];fb.layers.test(ze.layers)&&(yb.viewport(Kb.copy(ze.viewport)),cb.setupLights(ze),V(fb,da,ze,Cb,ob,Pa))}}else sb=null,V(fb,da,ma,Cb,ob,Pa)}}
function V(U,da,ma,Ia,Oa,ab){U.onBeforeRender(ja,da,ma,Ia,Oa,ab);cb=ye.get(da,sb||ma);U.modelViewMatrix.multiplyMatrices(ma.matrixWorldInverse,U.matrixWorld);U.normalMatrix.getNormalMatrix(U.modelViewMatrix);U.isImmediateRenderObject?(yb.setMaterial(Oa),Ia=ha(ma,da.fog,Oa,U),eb=c=null,mb=!1,J(U,Ia)):ja.renderBufferDirect(ma,da.fog,Ia,Oa,U,ab);cb=ye.get(da,sb||ma)}function W(U,da,ma){var Ia=ec.get(U),Oa=cb.state.lights,ab=Oa.state.version;ma=Pd.getParameters(U,Oa.state,cb.state.shadowsArray,da,yc.numPlanes,
yc.numIntersection,ma);var Pa=Pd.getProgramCode(U,ma),fb=Ia.program,Cb=!0;if(void 0===fb)U.addEventListener("dispose",z);else if(fb.code!==Pa)F(U);else{if(Ia.lightsStateVersion!==ab)Ia.lightsStateVersion=ab;else if(void 0!==ma.shaderID)return;Cb=!1}Cb&&(ma.shaderID?(Pa=Mc[ma.shaderID],Ia.shader={name:U.type,uniforms:ub(Pa.uniforms),vertexShader:Pa.vertexShader,fragmentShader:Pa.fragmentShader}):Ia.shader={name:U.type,uniforms:U.uniforms,vertexShader:U.vertexShader,fragmentShader:U.fragmentShader},
Pa=Pd.getProgramCode(U,ma),fb=Pd.acquireProgram(U,Ia.shader,ma,Pa),Ia.program=fb,U.program=fb);ma=fb.getAttributes();if(U.morphTargets)for(Pa=U.numSupportedMorphTargets=0;Pa<ja.maxMorphTargets;Pa++)0<=ma["morphTarget"+Pa]&&U.numSupportedMorphTargets++;if(U.morphNormals)for(Pa=U.numSupportedMorphNormals=0;Pa<ja.maxMorphNormals;Pa++)0<=ma["morphNormal"+Pa]&&U.numSupportedMorphNormals++;ma=Ia.shader.uniforms;if(!U.isShaderMaterial&&!U.isRawShaderMaterial||!0===U.clipping)Ia.numClippingPlanes=yc.numPlanes,
Ia.numIntersection=yc.numIntersection,ma.clippingPlanes=yc.uniform;Ia.fog=da;Ia.lightsStateVersion=ab;U.lights&&(ma.ambientLightColor.value=Oa.state.ambient,ma.lightProbe.value=Oa.state.probe,ma.directionalLights.value=Oa.state.directional,ma.spotLights.value=Oa.state.spot,ma.rectAreaLights.value=Oa.state.rectArea,ma.pointLights.value=Oa.state.point,ma.hemisphereLights.value=Oa.state.hemi,ma.directionalShadowMap.value=Oa.state.directionalShadowMap,ma.directionalShadowMatrix.value=Oa.state.directionalShadowMatrix,
ma.spotShadowMap.value=Oa.state.spotShadowMap,ma.spotShadowMatrix.value=Oa.state.spotShadowMatrix,ma.pointShadowMap.value=Oa.state.pointShadowMap,ma.pointShadowMatrix.value=Oa.state.pointShadowMatrix);U=Ia.program.getUniforms();U=ud.seqWithValue(U.seq,ma);Ia.uniformsList=U}function ha(U,da,ma,Ia){Oc.resetTextureUnits();var Oa=ec.get(ma),ab=cb.state.lights;xg&&(Rh||U!==pb)&&yc.setState(ma.clippingPlanes,ma.clipIntersection,ma.clipShadows,U,Oa,U===pb&&ma.id===Qa);!1===ma.needsUpdate&&(void 0===Oa.program?
ma.needsUpdate=!0:ma.fog&&Oa.fog!==da?ma.needsUpdate=!0:ma.lights&&Oa.lightsStateVersion!==ab.state.version?ma.needsUpdate=!0:void 0===Oa.numClippingPlanes||Oa.numClippingPlanes===yc.numPlanes&&Oa.numIntersection===yc.numIntersection||(ma.needsUpdate=!0));ma.needsUpdate&&(W(ma,da,Ia),ma.needsUpdate=!1);var Pa=!1,fb=ab=!1,Cb=Oa.program,ob=Cb.getUniforms(),$a=Oa.shader.uniforms;yb.useProgram(Cb.program)&&(fb=ab=Pa=!0);ma.id!==Qa&&(Qa=ma.id,ab=!0);if(Pa||pb!==U){ob.setValue(Ra,"projectionMatrix",U.projectionMatrix);
dc.logarithmicDepthBuffer&&ob.setValue(Ra,"logDepthBufFC",2/(Math.log(U.far+1)/Math.LN2));pb!==U&&(pb=U,fb=ab=!0);if(ma.isShaderMaterial||ma.isMeshPhongMaterial||ma.isMeshStandardMaterial||ma.envMap)Pa=ob.map.cameraPosition,void 0!==Pa&&Pa.setValue(Ra,zd.setFromMatrixPosition(U.matrixWorld));(ma.isMeshPhongMaterial||ma.isMeshLambertMaterial||ma.isMeshBasicMaterial||ma.isMeshStandardMaterial||ma.isShaderMaterial||ma.skinning)&&ob.setValue(Ra,"viewMatrix",U.matrixWorldInverse)}if(ma.skinning&&(ob.setOptional(Ra,
Ia,"bindMatrix"),ob.setOptional(Ra,Ia,"bindMatrixInverse"),U=Ia.skeleton))if(Pa=U.bones,dc.floatVertexTextures){if(void 0===U.boneTexture){Pa=Math.sqrt(4*Pa.length);Pa=hb.ceilPowerOfTwo(Pa);Pa=Math.max(Pa,4);var Pc=new Float32Array(Pa*Pa*4);Pc.set(U.boneMatrices);var xc=new Ab(Pc,Pa,Pa,1023,1015);xc.needsUpdate=!0;U.boneMatrices=Pc;U.boneTexture=xc;U.boneTextureSize=Pa}ob.setValue(Ra,"boneTexture",U.boneTexture,Oc);ob.setValue(Ra,"boneTextureSize",U.boneTextureSize)}else ob.setOptional(Ra,U,"boneMatrices");
ab&&(ob.setValue(Ra,"toneMappingExposure",ja.toneMappingExposure),ob.setValue(Ra,"toneMappingWhitePoint",ja.toneMappingWhitePoint),ma.lights&&tb($a,fb),da&&ma.fog&&oa($a,da),ma.isMeshBasicMaterial?fa($a,ma):ma.isMeshLambertMaterial?(fa($a,ma),ta($a,ma)):ma.isMeshPhongMaterial?(fa($a,ma),ma.isMeshToonMaterial?Ta($a,ma):Ba($a,ma)):ma.isMeshStandardMaterial?(fa($a,ma),ma.isMeshPhysicalMaterial?Ca($a,ma):Ua($a,ma)):ma.isMeshMatcapMaterial?(fa($a,ma),Ha($a,ma)):ma.isMeshDepthMaterial?(fa($a,ma),Da($a,
ma)):ma.isMeshDistanceMaterial?(fa($a,ma),Ma($a,ma)):ma.isMeshNormalMaterial?(fa($a,ma),db($a,ma)):ma.isLineBasicMaterial?(ra($a,ma),ma.isLineDashedMaterial&&pa($a,ma)):ma.isPointsMaterial?qa($a,ma):ma.isSpriteMaterial?ua($a,ma):ma.isShadowMaterial&&($a.color.value.copy(ma.color),$a.opacity.value=ma.opacity),void 0!==$a.ltc_1&&($a.ltc_1.value=Wa.LTC_1),void 0!==$a.ltc_2&&($a.ltc_2.value=Wa.LTC_2),ud.upload(Ra,Oa.uniformsList,$a,Oc));ma.isShaderMaterial&&!0===ma.uniformsNeedUpdate&&(ud.upload(Ra,Oa.uniformsList,
$a,Oc),ma.uniformsNeedUpdate=!1);ma.isSpriteMaterial&&ob.setValue(Ra,"center",Ia.center);ob.setValue(Ra,"modelViewMatrix",Ia.modelViewMatrix);ob.setValue(Ra,"normalMatrix",Ia.normalMatrix);ob.setValue(Ra,"modelMatrix",Ia.matrixWorld);return Cb}function fa(U,da){U.opacity.value=da.opacity;da.color&&U.diffuse.value.copy(da.color);da.emissive&&U.emissive.value.copy(da.emissive).multiplyScalar(da.emissiveIntensity);da.map&&(U.map.value=da.map);da.alphaMap&&(U.alphaMap.value=da.alphaMap);da.specularMap&&
(U.specularMap.value=da.specularMap);da.envMap&&(U.envMap.value=da.envMap,U.flipEnvMap.value=da.envMap.isCubeTexture?-1:1,U.reflectivity.value=da.reflectivity,U.refractionRatio.value=da.refractionRatio,U.maxMipLevel.value=ec.get(da.envMap).__maxMipLevel);da.lightMap&&(U.lightMap.value=da.lightMap,U.lightMapIntensity.value=da.lightMapIntensity);da.aoMap&&(U.aoMap.value=da.aoMap,U.aoMapIntensity.value=da.aoMapIntensity);if(da.map)var ma=da.map;else da.specularMap?ma=da.specularMap:da.displacementMap?
ma=da.displacementMap:da.normalMap?ma=da.normalMap:da.bumpMap?ma=da.bumpMap:da.roughnessMap?ma=da.roughnessMap:da.metalnessMap?ma=da.metalnessMap:da.alphaMap?ma=da.alphaMap:da.emissiveMap&&(ma=da.emissiveMap);void 0!==ma&&(ma.isWebGLRenderTarget&&(ma=ma.texture),!0===ma.matrixAutoUpdate&&ma.updateMatrix(),U.uvTransform.value.copy(ma.matrix))}function ra(U,da){U.diffuse.value.copy(da.color);U.opacity.value=da.opacity}function pa(U,da){U.dashSize.value=da.dashSize;U.totalSize.value=da.dashSize+da.gapSize;
U.scale.value=da.scale}function qa(U,da){U.diffuse.value.copy(da.color);U.opacity.value=da.opacity;U.size.value=da.size*cc;U.scale.value=.5*zc;U.map.value=da.map;null!==da.map&&(!0===da.map.matrixAutoUpdate&&da.map.updateMatrix(),U.uvTransform.value.copy(da.map.matrix))}function ua(U,da){U.diffuse.value.copy(da.color);U.opacity.value=da.opacity;U.rotation.value=da.rotation;U.map.value=da.map;null!==da.map&&(!0===da.map.matrixAutoUpdate&&da.map.updateMatrix(),U.uvTransform.value.copy(da.map.matrix))}
function oa(U,da){U.fogColor.value.copy(da.color);da.isFog?(U.fogNear.value=da.near,U.fogFar.value=da.far):da.isFogExp2&&(U.fogDensity.value=da.density)}function ta(U,da){da.emissiveMap&&(U.emissiveMap.value=da.emissiveMap)}function Ba(U,da){U.specular.value.copy(da.specular);U.shininess.value=Math.max(da.shininess,1E-4);da.emissiveMap&&(U.emissiveMap.value=da.emissiveMap);da.bumpMap&&(U.bumpMap.value=da.bumpMap,U.bumpScale.value=da.bumpScale,1===da.side&&(U.bumpScale.value*=-1));da.normalMap&&(U.normalMap.value=
da.normalMap,U.normalScale.value.copy(da.normalScale),1===da.side&&U.normalScale.value.negate());da.displacementMap&&(U.displacementMap.value=da.displacementMap,U.displacementScale.value=da.displacementScale,U.displacementBias.value=da.displacementBias)}function Ta(U,da){Ba(U,da);da.gradientMap&&(U.gradientMap.value=da.gradientMap)}function Ua(U,da){U.roughness.value=da.roughness;U.metalness.value=da.metalness;da.roughnessMap&&(U.roughnessMap.value=da.roughnessMap);da.metalnessMap&&(U.metalnessMap.value=
da.metalnessMap);da.emissiveMap&&(U.emissiveMap.value=da.emissiveMap);da.bumpMap&&(U.bumpMap.value=da.bumpMap,U.bumpScale.value=da.bumpScale,1===da.side&&(U.bumpScale.value*=-1));da.normalMap&&(U.normalMap.value=da.normalMap,U.normalScale.value.copy(da.normalScale),1===da.side&&U.normalScale.value.negate());da.displacementMap&&(U.displacementMap.value=da.displacementMap,U.displacementScale.value=da.displacementScale,U.displacementBias.value=da.displacementBias);da.envMap&&(U.envMapIntensity.value=
da.envMapIntensity)}function Ca(U,da){Ua(U,da);U.reflectivity.value=da.reflectivity;U.clearcoat.value=da.clearcoat;U.clearcoatRoughness.value=da.clearcoatRoughness;da.sheen&&U.sheen.value.copy(da.sheen);da.clearcoatNormalMap&&(U.clearcoatNormalScale.value.copy(da.clearcoatNormalScale),U.clearcoatNormalMap.value=da.clearcoatNormalMap,1===da.side&&U.clearcoatNormalScale.value.negate());U.transparency.value=da.transparency}function Ha(U,da){da.matcap&&(U.matcap.value=da.matcap);da.bumpMap&&(U.bumpMap.value=
da.bumpMap,U.bumpScale.value=da.bumpScale,1===da.side&&(U.bumpScale.value*=-1));da.normalMap&&(U.normalMap.value=da.normalMap,U.normalScale.value.copy(da.normalScale),1===da.side&&U.normalScale.value.negate());da.displacementMap&&(U.displacementMap.value=da.displacementMap,U.displacementScale.value=da.displacementScale,U.displacementBias.value=da.displacementBias)}function Da(U,da){da.displacementMap&&(U.displacementMap.value=da.displacementMap,U.displacementScale.value=da.displacementScale,U.displacementBias.value=
da.displacementBias)}function Ma(U,da){da.displacementMap&&(U.displacementMap.value=da.displacementMap,U.displacementScale.value=da.displacementScale,U.displacementBias.value=da.displacementBias);U.referencePosition.value.copy(da.referencePosition);U.nearDistance.value=da.nearDistance;U.farDistance.value=da.farDistance}function db(U,da){da.bumpMap&&(U.bumpMap.value=da.bumpMap,U.bumpScale.value=da.bumpScale,1===da.side&&(U.bumpScale.value*=-1));da.normalMap&&(U.normalMap.value=da.normalMap,U.normalScale.value.copy(da.normalScale),
1===da.side&&U.normalScale.value.negate());da.displacementMap&&(U.displacementMap.value=da.displacementMap,U.displacementScale.value=da.displacementScale,U.displacementBias.value=da.displacementBias)}function tb(U,da){U.ambientLightColor.needsUpdate=da;U.lightProbe.needsUpdate=da;U.directionalLights.needsUpdate=da;U.pointLights.needsUpdate=da;U.spotLights.needsUpdate=da;U.rectAreaLights.needsUpdate=da;U.hemisphereLights.needsUpdate=da}a=a||{};var Ka=void 0!==a.canvas?a.canvas:document.createElementNS("http://www.w3.org/1999/xhtml",
"canvas"),bb=void 0!==a.context?a.context:null,jb=void 0!==a.alpha?a.alpha:!1,Eb=void 0!==a.depth?a.depth:!0,xb=void 0!==a.stencil?a.stencil:!0,ia=void 0!==a.antialias?a.antialias:!1,na=void 0!==a.premultipliedAlpha?a.premultipliedAlpha:!0,za=void 0!==a.preserveDrawingBuffer?a.preserveDrawingBuffer:!1,Ja=void 0!==a.powerPreference?a.powerPreference:"default",Ya=void 0!==a.failIfMajorPerformanceCaveat?a.failIfMajorPerformanceCaveat:!1,Na=null,cb=null;this.domElement=Ka;this.debug={checkShaderErrors:!0};
this.sortObjects=this.autoClearStencil=this.autoClearDepth=this.autoClearColor=this.autoClear=!0;this.clippingPlanes=[];this.localClippingEnabled=!1;this.gammaFactor=2;this.physicallyCorrectLights=this.gammaOutput=this.gammaInput=!1;this.toneMappingWhitePoint=this.toneMappingExposure=this.toneMapping=1;this.maxMorphTargets=8;this.maxMorphNormals=4;var ja=this,Ga=!1,La=null,nb=0,Va=0,ib=null,kb=null,Qa=-1;var eb=c=null;var mb=!1;var pb=null,sb=null,Kb=new p,Sb=new p,nc=null,Qc=Ka.width,zc=Ka.height,
cc=1,we=new p(0,0,Qc,zc),ve=new p(0,0,Qc,zc),Sh=!1,Qh=new ic,yc=new wc,xg=!1,Rh=!1,wf=new q,zd=new k;try{jb={alpha:jb,depth:Eb,stencil:xb,antialias:ia,premultipliedAlpha:na,preserveDrawingBuffer:za,powerPreference:Ja,failIfMajorPerformanceCaveat:Ya,xrCompatible:!0};Ka.addEventListener("webglcontextlost",r,!1);Ka.addEventListener("webglcontextrestored",v,!1);var Ra=bb||Ka.getContext("webgl",jb)||Ka.getContext("experimental-webgl",jb);if(null===Ra){if(null!==Ka.getContext("webgl"))throw Error("Error creating WebGL context with your selected attributes.");
throw Error("Error creating WebGL context.");}void 0===Ra.getShaderPrecisionFormat&&(Ra.getShaderPrecisionFormat=function(){return{rangeMin:1,rangeMax:1,precision:1}})}catch(U){throw console.error("THREE.WebGLRenderer: "+U.message),U;}var Lb,dc,yb,xd,ec,Oc,vg,Ph,xe,Pd,wg,ye,yd,Bj,Cj,Dj,Nc;g();var dd="undefined"!==typeof navigator&&"xr"in navigator&&"supportsSession"in navigator.xr?new Aj(ja,Ra):new Nh(ja);this.vr=dd;var Ej=new vj(ja,xe,dc.maxTextureSize);this.shadowMap=Ej;this.getContext=function(){return Ra};
this.getContextAttributes=function(){return Ra.getContextAttributes()};this.forceContextLoss=function(){var U=Lb.get("WEBGL_lose_context");U&&U.loseContext()};this.forceContextRestore=function(){var U=Lb.get("WEBGL_lose_context");U&&U.restoreContext()};this.getPixelRatio=function(){return cc};this.setPixelRatio=function(){var U=window.devicePixelRatio;void 0!==U&&(cc=U,this.setSize(Qc,zc,!1))};this.getSize=function(U){void 0===U&&(console.warn("WebGLRenderer: .getsize() now requires a Vector2 as an argument"),
U=new f);return U.set(Qc,zc)};this.setSize=function(U,da,ma){dd.isPresenting()?console.warn("THREE.WebGLRenderer: Can't change size while VR device is presenting."):(Qc=U,zc=da,Ka.width=Math.floor(U*cc),Ka.height=Math.floor(da*cc),!1!==ma&&(Ka.style.width=U+"px",Ka.style.height=da+"px"),this.setViewport(U,da))};this.getDrawingBufferSize=function(U){void 0===U&&(console.warn("WebGLRenderer: .getdrawingBufferSize() now requires a Vector2 as an argument"),U=new f);return U.set(Qc*cc,zc*cc).floor()};
this.setDrawingBufferSize=function(U,da,ma){Qc=U;zc=da;cc=ma;Ka.width=Math.floor(U*ma);Ka.height=Math.floor(da*ma);this.setViewport(U,da)};this.getCurrentViewport=function(U){void 0===U&&(console.warn("WebGLRenderer: .getCurrentViewport() now requires a Vector4 as an argument"),U=new p);return U.copy(Kb)};this.getViewport=function(U){return U.copy(we)};this.setViewport=function(U,da){(0).isVector4?we.set((0).x,(0).y,(0).z,(0).w):we.set(0,0,U,da);yb.viewport(Kb.copy(we).multiplyScalar(cc).floor())};
this.getScissor=function(U){return U.copy(ve)};this.setScissor=function(U,da,ma,Ia){U.isVector4?ve.set(U.x,U.y,U.z,U.w):ve.set(U,da,ma,Ia);yb.scissor(Sb.copy(ve).multiplyScalar(cc).floor())};this.getScissorTest=function(){return Sh};this.setScissorTest=function(U){yb.setScissorTest(Sh=U)};this.getClearColor=function(){return yd.getClearColor()};this.setClearColor=function(){yd.setClearColor.apply(yd,arguments)};this.getClearAlpha=function(){return yd.getClearAlpha()};this.setClearAlpha=function(){yd.setClearAlpha.apply(yd,
arguments)};this.clear=function(U,da,ma){var Ia=0;if(void 0===U||U)Ia|=16384;if(void 0===da||da)Ia|=256;if(void 0===ma||ma)Ia|=1024;Ra.clear(Ia)};this.clearColor=function(){this.clear(!0,!1,!1)};this.clearDepth=function(){this.clear(!1,!0,!1)};this.clearStencil=function(){this.clear(!1,!1,!0)};this.dispose=function(){Ka.removeEventListener("webglcontextlost",r,!1);Ka.removeEventListener("webglcontextrestored",v,!1);wg.dispose();ye.dispose();ec.dispose();xe.dispose();dd.dispose();yg.stop()};this.renderBufferImmediate=
function(U,da){yb.initAttributes();var ma=ec.get(U);U.hasPositions&&!ma.position&&(ma.position=Ra.createBuffer());U.hasNormals&&!ma.normal&&(ma.normal=Ra.createBuffer());U.hasUvs&&!ma.uv&&(ma.uv=Ra.createBuffer());U.hasColors&&!ma.color&&(ma.color=Ra.createBuffer());da=da.getAttributes();U.hasPositions&&(Ra.bindBuffer(34962,ma.position),Ra.bufferData(34962,U.positionArray,35048),yb.enableAttribute(da.position),Ra.vertexAttribPointer(da.position,3,5126,!1,0,0));U.hasNormals&&(Ra.bindBuffer(34962,ma.normal),
Ra.bufferData(34962,U.normalArray,35048),yb.enableAttribute(da.normal),Ra.vertexAttribPointer(da.normal,3,5126,!1,0,0));U.hasUvs&&(Ra.bindBuffer(34962,ma.uv),Ra.bufferData(34962,U.uvArray,35048),yb.enableAttribute(da.uv),Ra.vertexAttribPointer(da.uv,2,5126,!1,0,0));U.hasColors&&(Ra.bindBuffer(34962,ma.color),Ra.bufferData(34962,U.colorArray,35048),yb.enableAttribute(da.color),Ra.vertexAttribPointer(da.color,3,5126,!1,0,0));yb.disableUnusedAttributes();Ra.drawArrays(4,0,U.count);U.count=0};this.renderBufferDirect=
function(U,da,ma,Ia,Oa,ab){yb.setMaterial(Ia,Oa.isMesh&&0>Oa.matrixWorld.determinant());var Pa=ha(U,da,Ia,Oa),fb=!1;if(c!==ma.id||eb!==Pa.id||mb!==(!0===Ia.wireframe))c=ma.id,eb=Pa.id,mb=!0===Ia.wireframe,fb=!0;Oa.morphTargetInfluences&&(Bj.update(Oa,ma,Ia,Pa),fb=!0);var Cb=ma.index,ob=ma.attributes.position;da=1;!0===Ia.wireframe&&(Cb=Ph.getWireframeAttribute(ma),da=2);U=Cj;if(null!==Cb){var $a=vg.get(Cb);U=Dj;U.setIndex($a)}fb&&(P(Ia,Pa,ma),null!==Cb&&Ra.bindBuffer(34963,$a.buffer));$a=Infinity;
null!==Cb?$a=Cb.count:void 0!==ob&&($a=ob.count);ob=ma.drawRange.start*da;Pa=null!==ab?ab.start*da:0;Cb=Math.max(ob,Pa);ab=Math.max(0,Math.min($a,ob+ma.drawRange.count*da,Pa+(null!==ab?ab.count*da:Infinity))-1-Cb+1);if(0!==ab){if(Oa.isMesh)if(!0===Ia.wireframe)yb.setLineWidth(Ia.wireframeLinewidth*e()),U.setMode(1);else switch(Oa.drawMode){case 0:U.setMode(4);break;case 1:U.setMode(5);break;case 2:U.setMode(6)}else Oa.isLine?(Ia=Ia.linewidth,void 0===Ia&&(Ia=1),yb.setLineWidth(Ia*e()),Oa.isLineSegments?
U.setMode(1):Oa.isLineLoop?U.setMode(2):U.setMode(3)):Oa.isPoints?U.setMode(0):Oa.isSprite&&U.setMode(4);ma&&ma.isInstancedBufferGeometry?0<ma.maxInstancedCount&&U.renderInstances(ma,Cb,ab):U.render(Cb,ab)}};this.compile=function(U,da){cb=ye.get(U,da);cb.init();U.traverse(function(ma){ma.isLight&&(cb.pushLight(ma),ma.castShadow&&cb.pushShadow(ma))});cb.setupLights(da);U.traverse(function(ma){if(ma.material)if(Array.isArray(ma.material))for(var Ia=0;Ia<ma.material.length;Ia++)W(ma.material[Ia],U.fog,
ma);else W(ma.material,U.fog,ma)})};var Th=null,yg=new bc;yg.setAnimationLoop(function(U){dd.isPresenting()||Th&&Th(U)});"undefined"!==typeof window&&yg.setContext(window);this.setAnimationLoop=function(U){Th=U;dd.setAnimationLoop(U);yg.start()};this.render=function(U,da,ma,Ia){if(void 0!==ma){console.warn("THREE.WebGLRenderer.render(): the renderTarget argument has been removed. Use .setRenderTarget() instead.");var Oa=ma}if(void 0!==Ia){console.warn("THREE.WebGLRenderer.render(): the forceClear argument has been removed. Use .clear() instead.");
var ab=Ia}da&&da.isCamera?Ga||(eb=c=null,mb=!1,Qa=-1,pb=null,!0===U.autoUpdate&&U.updateMatrixWorld(),null===da.parent&&da.updateMatrixWorld(),dd.enabled&&(da=dd.getCamera(da)),cb=ye.get(U,da),cb.init(),U.onBeforeRender(ja,U,da,Oa||ib),wf.multiplyMatrices(da.projectionMatrix,da.matrixWorldInverse),Qh.setFromMatrix(wf),Rh=this.localClippingEnabled,xg=yc.init(this.clippingPlanes,Rh,da),Na=wg.get(U,da),Na.init(),R(U,da,0,ja.sortObjects),!0===ja.sortObjects&&Na.sort(),xg&&yc.beginShadows(),Ej.render(cb.state.shadowsArray,
U,da),cb.setupLights(da),xg&&yc.endShadows(),this.info.autoReset&&this.info.reset(),void 0!==Oa&&this.setRenderTarget(Oa),yd.render(Na,U,da,ab),ma=Na.opaque,Ia=Na.transparent,U.overrideMaterial?(Oa=U.overrideMaterial,ma.length&&S(ma,U,da,Oa),Ia.length&&S(Ia,U,da,Oa)):(ma.length&&S(ma,U,da),Ia.length&&S(Ia,U,da)),null!==ib&&(Oc.updateRenderTargetMipmap(ib),Oc.updateMultisampleRenderTarget(ib)),yb.buffers.depth.setTest(!0),yb.buffers.depth.setMask(!0),yb.buffers.color.setMask(!0),yb.setPolygonOffset(!1),
dd.enabled&&dd.submitFrame(),cb=Na=null):console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.")};this.setFramebuffer=function(U){La!==U&&Ra.bindFramebuffer(36160,U);La=U};this.getActiveCubeFace=function(){return nb};this.getActiveMipmapLevel=function(){return Va};this.getRenderTarget=function(){return ib};this.setRenderTarget=function(U,da,ma){ib=U;nb=da;Va=ma;U&&void 0===ec.get(U).__webglFramebuffer&&Oc.setupRenderTarget(U);var Ia=La,Oa=!1;U?(Ia=ec.get(U).__webglFramebuffer,
U.isWebGLRenderTargetCube?(Ia=Ia[da||0],Oa=!0):Ia=U.isWebGLMultisampleRenderTarget?ec.get(U).__webglMultisampledFramebuffer:Ia,Kb.copy(U.viewport),Sb.copy(U.scissor),nc=U.scissorTest):(Kb.copy(we).multiplyScalar(cc).floor(),Sb.copy(ve).multiplyScalar(cc).floor(),nc=Sh);kb!==Ia&&(Ra.bindFramebuffer(36160,Ia),kb=Ia);yb.viewport(Kb);yb.scissor(Sb);yb.setScissorTest(nc);Oa&&(U=ec.get(U.texture),Ra.framebufferTexture2D(36160,36064,34069+(da||0),U.__webglTexture,ma||0))};this.readRenderTargetPixels=function(U,
da,ma,Ia,Oa,ab,Pa){if(U&&U.isWebGLRenderTarget){var fb=ec.get(U).__webglFramebuffer;U.isWebGLRenderTargetCube&&void 0!==Pa&&(fb=fb[Pa]);if(fb){Pa=!1;fb!==kb&&(Ra.bindFramebuffer(36160,fb),Pa=!0);try{var Cb=U.texture,ob=Cb.format,$a=Cb.type;1023!==ob&&Nc.convert(ob)!==Ra.getParameter(35739)?console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format."):1009===$a||Nc.convert($a)===Ra.getParameter(35738)||1015===$a&&(dc.isWebGL2||Lb.get("OES_texture_float")||
Lb.get("WEBGL_color_buffer_float"))||1016===$a&&(dc.isWebGL2?Lb.get("EXT_color_buffer_float"):Lb.get("EXT_color_buffer_half_float"))?36053===Ra.checkFramebufferStatus(36160)?0<=da&&da<=U.width-Ia&&0<=ma&&ma<=U.height-Oa&&Ra.readPixels(da,ma,Ia,Oa,Nc.convert(ob),Nc.convert($a),ab):console.error("THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete."):console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.")}finally{Pa&&
Ra.bindFramebuffer(36160,kb)}}}else console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.")};this.copyFramebufferToTexture=function(U,da,ma){var Ia=da.image.width,Oa=da.image.height,ab=Nc.convert(da.format);Oc.setTexture2D(da,0);Ra.copyTexImage2D(3553,ma||0,ab,U.x,U.y,Ia,Oa,0)};this.copyTextureToTexture=function(U,da,ma,Ia){var Oa=da.image.width,ab=da.image.height,Pa=Nc.convert(ma.format),fb=Nc.convert(ma.type);Oc.setTexture2D(ma,0);da.isDataTexture?
Ra.texSubImage2D(3553,Ia||0,U.x,U.y,Oa,ab,Pa,fb,da.image.data):Ra.texSubImage2D(3553,Ia||0,U.x,U.y,Pa,fb,da.image)};"undefined"!==typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}function zg(a,c){this.name="";this.color=new I(a);this.density=void 0!==c?c:2.5E-4}function Ag(a,c,e){this.name="";this.color=new I(a);this.near=void 0!==c?c:1;this.far=void 0!==e?e:1E3}function Qd(a,c){this.array=a;this.stride=c;this.count=void 0!==a?a.length/c:0;this.dynamic=
!1;this.updateRange={offset:0,count:-1};this.version=0}function xf(a,c,e,g){this.data=a;this.itemSize=c;this.offset=e;this.normalized=!0===g}function Ad(a){M.call(this);this.type="SpriteMaterial";this.color=new I(16777215);this.map=null;this.rotation=0;this.sizeAttenuation=!0;this.lights=!1;this.transparent=!0;this.setValues(a)}function yf(a){A.call(this);this.type="Sprite";if(void 0===Ae){Ae=new va;var c=new Qd(new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),5);Ae.setIndex([0,
1,2,0,2,3]);Ae.addAttribute("position",new xf(c,3,0,!1));Ae.addAttribute("uv",new xf(c,2,3,!1))}this.geometry=Ae;this.material=void 0!==a?a:new Ad;this.center=new f(.5,.5)}function Bg(a,c,e,g,r,v){Be.subVectors(a,e).addScalar(.5).multiply(g);void 0!==r?(zf.x=v*Be.x-r*Be.y,zf.y=r*Be.x+v*Be.y):zf.copy(Be);a.copy(c);a.x+=zf.x;a.y+=zf.y;a.applyMatrix4(Fj)}function Af(){A.call(this);this.type="LOD";Object.defineProperties(this,{levels:{enumerable:!0,value:[]}});this.autoUpdate=!0}function Bf(a,c){a&&a.isGeometry&&
console.error("THREE.SkinnedMesh no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");xa.call(this,a,c);this.type="SkinnedMesh";this.bindMode="attached";this.bindMatrix=new q;this.bindMatrixInverse=new q}function Cg(a,c){a=a||[];this.bones=a.slice(0);this.boneMatrices=new Float32Array(16*this.bones.length);if(void 0===c)this.calculateInverses();else if(this.bones.length===c.length)this.boneInverses=c.slice(0);else for(console.warn("THREE.Skeleton boneInverses is the wrong length."),
this.boneInverses=[],a=0,c=this.bones.length;a<c;a++)this.boneInverses.push(new q)}function Uh(){A.call(this);this.type="Bone"}function Fb(a){M.call(this);this.type="LineBasicMaterial";this.color=new I(16777215);this.linewidth=1;this.linejoin=this.linecap="round";this.lights=!1;this.setValues(a)}function Vb(a,c,e){1===e&&console.error("THREE.Line: parameter THREE.LinePieces no longer supported. Use THREE.LineSegments instead.");A.call(this);this.type="Line";this.geometry=void 0!==a?a:new va;this.material=
void 0!==c?c:new Fb({color:16777215*Math.random()})}function Ib(a,c){Vb.call(this,a,c);this.type="LineSegments"}function Dg(a,c){Vb.call(this,a,c);this.type="LineLoop"}function Ac(a){M.call(this);this.type="PointsMaterial";this.color=new I(16777215);this.map=null;this.size=1;this.sizeAttenuation=!0;this.lights=this.morphTargets=!1;this.setValues(a)}function Ce(a,c){A.call(this);this.type="Points";this.geometry=void 0!==a?a:new va;this.material=void 0!==c?c:new Ac({color:16777215*Math.random()});this.updateMorphTargets()}
function Vh(a,c,e,g,r,v,z){var E=Wh.distanceSqToPoint(a);E<e&&(e=new k,Wh.closestPointToPoint(a,e),e.applyMatrix4(g),a=r.ray.origin.distanceTo(e),a<r.near||a>r.far||v.push({distance:a,distanceToRay:Math.sqrt(E),point:e,index:c,face:null,object:z}))}function Xh(a,c,e,g,r,v,z,E,F){l.call(this,a,c,e,g,r,v,z,E,F);this.format=void 0!==z?z:1022;this.minFilter=void 0!==v?v:1006;this.magFilter=void 0!==r?r:1006;this.generateMipmaps=!1}function De(a,c,e,g,r,v,z,E,F,J,P,R){l.call(this,null,v,z,E,F,J,g,r,P,
R);this.image={width:c,height:e};this.mipmaps=a;this.generateMipmaps=this.flipY=!1}function Cf(a,c,e,g,r,v,z,E,F){l.call(this,a,c,e,g,r,v,z,E,F);this.needsUpdate=!0}function Df(a,c,e,g,r,v,z,E,F,J){J=void 0!==J?J:1026;if(1026!==J&&1027!==J)throw Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");void 0===e&&1026===J&&(e=1012);void 0===e&&1027===J&&(e=1020);l.call(this,null,g,r,v,z,E,J,e,F);this.image={width:a,height:c};this.magFilter=void 0!==z?z:1003;this.minFilter=
void 0!==E?E:1003;this.generateMipmaps=this.flipY=!1}function Ee(a){va.call(this);this.type="WireframeGeometry";var c=[],e,g,r,v=[0,0],z={},E=["a","b","c"];if(a&&a.isGeometry){var F=a.faces;var J=0;for(g=F.length;J<g;J++){var P=F[J];for(e=0;3>e;e++){var R=P[E[e]];var S=P[E[(e+1)%3]];v[0]=Math.min(R,S);v[1]=Math.max(R,S);R=v[0]+","+v[1];void 0===z[R]&&(z[R]={index1:v[0],index2:v[1]})}}for(R in z)J=z[R],E=a.vertices[J.index1],c.push(E.x,E.y,E.z),E=a.vertices[J.index2],c.push(E.x,E.y,E.z)}else if(a&&
a.isBufferGeometry)if(E=new k,null!==a.index){F=a.attributes.position;P=a.index;var V=a.groups;0===V.length&&(V=[{start:0,count:P.count,materialIndex:0}]);a=0;for(r=V.length;a<r;++a)for(J=V[a],e=J.start,g=J.count,J=e,g=e+g;J<g;J+=3)for(e=0;3>e;e++)R=P.getX(J+e),S=P.getX(J+(e+1)%3),v[0]=Math.min(R,S),v[1]=Math.max(R,S),R=v[0]+","+v[1],void 0===z[R]&&(z[R]={index1:v[0],index2:v[1]});for(R in z)J=z[R],E.fromBufferAttribute(F,J.index1),c.push(E.x,E.y,E.z),E.fromBufferAttribute(F,J.index2),c.push(E.x,
E.y,E.z)}else for(F=a.attributes.position,J=0,g=F.count/3;J<g;J++)for(e=0;3>e;e++)z=3*J+e,E.fromBufferAttribute(F,z),c.push(E.x,E.y,E.z),z=3*J+(e+1)%3,E.fromBufferAttribute(F,z),c.push(E.x,E.y,E.z);this.addAttribute("position",new ca(c,3))}function Ef(a,c,e){ya.call(this);this.type="ParametricGeometry";this.parameters={func:a,slices:c,stacks:e};this.fromBufferGeometry(new Fe(a,c,e));this.mergeVertices()}function Fe(a,c,e){va.call(this);this.type="ParametricBufferGeometry";this.parameters={func:a,
slices:c,stacks:e};var g=[],r=[],v=[],z=[],E=new k,F=new k,J=new k,P=new k,R=new k,S,V;3>a.length&&console.error("THREE.ParametricGeometry: Function must now modify a Vector3 as third parameter.");var W=c+1;for(S=0;S<=e;S++){var ha=S/e;for(V=0;V<=c;V++){var fa=V/c;a(fa,ha,F);r.push(F.x,F.y,F.z);0<=fa-1E-5?(a(fa-1E-5,ha,J),P.subVectors(F,J)):(a(fa+1E-5,ha,J),P.subVectors(J,F));0<=ha-1E-5?(a(fa,ha-1E-5,J),R.subVectors(F,J)):(a(fa,ha+1E-5,J),R.subVectors(J,F));E.crossVectors(P,R).normalize();v.push(E.x,
E.y,E.z);z.push(fa,ha)}}for(S=0;S<e;S++)for(V=0;V<c;V++)a=S*W+V+1,E=(S+1)*W+V+1,F=(S+1)*W+V,g.push(S*W+V,a,F),g.push(a,E,F);this.setIndex(g);this.addAttribute("position",new ca(r,3));this.addAttribute("normal",new ca(v,3));this.addAttribute("uv",new ca(z,2))}function Ff(a,c,e,g){ya.call(this);this.type="PolyhedronGeometry";this.parameters={vertices:a,indices:c,radius:e,detail:g};this.fromBufferGeometry(new jc(a,c,e,g));this.mergeVertices()}function jc(a,c,e,g){function r(W,ha,fa,ra){ra=Math.pow(2,
ra);var pa=[],qa,ua;for(qa=0;qa<=ra;qa++){pa[qa]=[];var oa=W.clone().lerp(fa,qa/ra),ta=ha.clone().lerp(fa,qa/ra),Ba=ra-qa;for(ua=0;ua<=Ba;ua++)pa[qa][ua]=0===ua&&qa===ra?oa:oa.clone().lerp(ta,ua/Ba)}for(qa=0;qa<ra;qa++)for(ua=0;ua<2*(ra-qa)-1;ua++)W=Math.floor(ua/2),0===ua%2?(z(pa[qa][W+1]),z(pa[qa+1][W]),z(pa[qa][W])):(z(pa[qa][W+1]),z(pa[qa+1][W+1]),z(pa[qa+1][W]))}function v(){for(var W=0;W<V.length;W+=6){var ha=V[W+0],fa=V[W+2],ra=V[W+4],pa=Math.min(ha,fa,ra);.9<Math.max(ha,fa,ra)&&.1>pa&&(.2>
ha&&(V[W+0]+=1),.2>fa&&(V[W+2]+=1),.2>ra&&(V[W+4]+=1))}}function z(W){S.push(W.x,W.y,W.z)}function E(W,ha){W*=3;ha.x=a[W+0];ha.y=a[W+1];ha.z=a[W+2]}function F(){for(var W=new k,ha=new k,fa=new k,ra=new k,pa=new f,qa=new f,ua=new f,oa=0,ta=0;oa<S.length;oa+=9,ta+=6){W.set(S[oa+0],S[oa+1],S[oa+2]);ha.set(S[oa+3],S[oa+4],S[oa+5]);fa.set(S[oa+6],S[oa+7],S[oa+8]);pa.set(V[ta+0],V[ta+1]);qa.set(V[ta+2],V[ta+3]);ua.set(V[ta+4],V[ta+5]);ra.copy(W).add(ha).add(fa).divideScalar(3);var Ba=P(ra);J(pa,ta+0,W,
Ba);J(qa,ta+2,ha,Ba);J(ua,ta+4,fa,Ba)}}function J(W,ha,fa,ra){0>ra&&1===W.x&&(V[ha]=W.x-1);0===fa.x&&0===fa.z&&(V[ha]=ra/2/Math.PI+.5)}function P(W){return Math.atan2(W.z,-W.x)}function R(W){return Math.atan2(-W.y,Math.sqrt(W.x*W.x+W.z*W.z))}va.call(this);this.type="PolyhedronBufferGeometry";this.parameters={vertices:a,indices:c,radius:e,detail:g};e=e||1;g=g||0;var S=[],V=[];(function(W){for(var ha=new k,fa=new k,ra=new k,pa=0;pa<c.length;pa+=3)E(c[pa+0],ha),E(c[pa+1],fa),E(c[pa+2],ra),r(ha,fa,ra,
W)})(g);(function(W){for(var ha=new k,fa=0;fa<S.length;fa+=3)ha.x=S[fa+0],ha.y=S[fa+1],ha.z=S[fa+2],ha.normalize().multiplyScalar(W),S[fa+0]=ha.x,S[fa+1]=ha.y,S[fa+2]=ha.z})(e);(function(){for(var W=new k,ha=0;ha<S.length;ha+=3){W.x=S[ha+0];W.y=S[ha+1];W.z=S[ha+2];var fa=P(W)/2/Math.PI+.5,ra=R(W)/Math.PI+.5;V.push(fa,1-ra)}F();v()})();this.addAttribute("position",new ca(S,3));this.addAttribute("normal",new ca(S.slice(),3));this.addAttribute("uv",new ca(V,2));0===g?this.computeVertexNormals():this.normalizeNormals()}
function Gf(a,c){ya.call(this);this.type="TetrahedronGeometry";this.parameters={radius:a,detail:c};this.fromBufferGeometry(new Ge(a,c));this.mergeVertices()}function Ge(a,c){jc.call(this,[1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],a,c);this.type="TetrahedronBufferGeometry";this.parameters={radius:a,detail:c}}function Hf(a,c){ya.call(this);this.type="OctahedronGeometry";this.parameters={radius:a,detail:c};this.fromBufferGeometry(new Rd(a,c));this.mergeVertices()}function Rd(a,c){jc.call(this,
[1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],a,c);this.type="OctahedronBufferGeometry";this.parameters={radius:a,detail:c}}function If(a,c){ya.call(this);this.type="IcosahedronGeometry";this.parameters={radius:a,detail:c};this.fromBufferGeometry(new He(a,c));this.mergeVertices()}function He(a,c){var e=(1+Math.sqrt(5))/2;jc.call(this,[-1,e,0,1,e,0,-1,-e,0,1,-e,0,0,-1,e,0,1,e,0,-1,-e,0,1,-e,e,0,-1,e,0,1,-e,0,-1,-e,0,1],[0,11,5,0,5,1,0,1,7,0,7,10,0,10,11,
1,5,9,5,11,4,11,10,2,10,7,6,7,1,8,3,9,4,3,4,2,3,2,6,3,6,8,3,8,9,4,9,5,2,4,11,6,2,10,8,6,7,9,8,1],a,c);this.type="IcosahedronBufferGeometry";this.parameters={radius:a,detail:c}}function Jf(a,c){ya.call(this);this.type="DodecahedronGeometry";this.parameters={radius:a,detail:c};this.fromBufferGeometry(new Ie(a,c));this.mergeVertices()}function Ie(a,c){var e=(1+Math.sqrt(5))/2,g=1/e;jc.call(this,[-1,-1,-1,-1,-1,1,-1,1,-1,-1,1,1,1,-1,-1,1,-1,1,1,1,-1,1,1,1,0,-g,-e,0,-g,e,0,g,-e,0,g,e,-g,-e,0,-g,e,0,g,
-e,0,g,e,0,-e,0,-g,e,0,-g,-e,0,g,e,0,g],[3,11,7,3,7,15,3,15,13,7,19,17,7,17,6,7,6,15,17,4,8,17,8,10,17,10,6,8,0,16,8,16,2,8,2,10,0,12,1,0,1,18,0,18,16,6,10,2,6,2,13,6,13,15,2,16,18,2,18,3,2,3,13,18,1,9,18,9,11,18,11,3,4,14,12,4,12,0,4,0,8,11,9,5,11,5,19,11,19,7,19,5,14,19,14,4,19,4,17,1,12,14,1,14,5,1,5,9],a,c);this.type="DodecahedronBufferGeometry";this.parameters={radius:a,detail:c}}function Kf(a,c,e,g,r,v){ya.call(this);this.type="TubeGeometry";this.parameters={path:a,tubularSegments:c,radius:e,
radialSegments:g,closed:r};void 0!==v&&console.warn("THREE.TubeGeometry: taper has been removed.");a=new Sd(a,c,e,g,r);this.tangents=a.tangents;this.normals=a.normals;this.binormals=a.binormals;this.fromBufferGeometry(a);this.mergeVertices()}function Sd(a,c,e,g,r){function v(qa){S=a.getPointAt(qa/c,S);var ua=F.normals[qa];qa=F.binormals[qa];for(W=0;W<=g;W++){var oa=W/g*Math.PI*2,ta=Math.sin(oa);oa=-Math.cos(oa);P.x=oa*ua.x+ta*qa.x;P.y=oa*ua.y+ta*qa.y;P.z=oa*ua.z+ta*qa.z;P.normalize();fa.push(P.x,
P.y,P.z);J.x=S.x+e*P.x;J.y=S.y+e*P.y;J.z=S.z+e*P.z;ha.push(J.x,J.y,J.z)}}function z(){for(W=1;W<=c;W++)for(V=1;V<=g;V++){var qa=(g+1)*W+(V-1),ua=(g+1)*W+V,oa=(g+1)*(W-1)+V;pa.push((g+1)*(W-1)+(V-1),qa,oa);pa.push(qa,ua,oa)}}function E(){for(V=0;V<=c;V++)for(W=0;W<=g;W++)R.x=V/c,R.y=W/g,ra.push(R.x,R.y)}va.call(this);this.type="TubeBufferGeometry";this.parameters={path:a,tubularSegments:c,radius:e,radialSegments:g,closed:r};c=c||64;e=e||1;g=g||8;r=r||!1;var F=a.computeFrenetFrames(c,r);this.tangents=
F.tangents;this.normals=F.normals;this.binormals=F.binormals;var J=new k,P=new k,R=new f,S=new k,V,W,ha=[],fa=[],ra=[],pa=[];(function(){for(V=0;V<c;V++)v(V);v(!1===r?c:0);E();z()})();this.setIndex(pa);this.addAttribute("position",new ca(ha,3));this.addAttribute("normal",new ca(fa,3));this.addAttribute("uv",new ca(ra,2))}function Lf(a,c,e,g,r,v,z){ya.call(this);this.type="TorusKnotGeometry";this.parameters={radius:a,tube:c,tubularSegments:e,radialSegments:g,p:r,q:v};void 0!==z&&console.warn("THREE.TorusKnotGeometry: heightScale has been deprecated. Use .scale( x, y, z ) instead.");
this.fromBufferGeometry(new Je(a,c,e,g,r,v));this.mergeVertices()}function Je(a,c,e,g,r,v){function z(ta,Ba,Ta,Ua,Ca){var Ha=Math.sin(ta);Ba=Ta/Ba*ta;Ta=Math.cos(Ba);Ca.x=Ua*(2+Ta)*.5*Math.cos(ta);Ca.y=Ua*(2+Ta)*Ha*.5;Ca.z=Ua*Math.sin(Ba)*.5}va.call(this);this.type="TorusKnotBufferGeometry";this.parameters={radius:a,tube:c,tubularSegments:e,radialSegments:g,p:r,q:v};a=a||1;c=c||.4;e=Math.floor(e)||64;g=Math.floor(g)||8;r=r||2;v=v||3;var E=[],F=[],J=[],P=[],R,S=new k,V=new k,W=new k,ha=new k,fa=new k,
ra=new k,pa=new k;for(R=0;R<=e;++R){var qa=R/e*r*Math.PI*2;z(qa,r,v,a,W);z(qa+.01,r,v,a,ha);ra.subVectors(ha,W);pa.addVectors(ha,W);fa.crossVectors(ra,pa);pa.crossVectors(fa,ra);fa.normalize();pa.normalize();for(qa=0;qa<=g;++qa){var ua=qa/g*Math.PI*2,oa=-c*Math.cos(ua);ua=c*Math.sin(ua);S.x=W.x+(oa*pa.x+ua*fa.x);S.y=W.y+(oa*pa.y+ua*fa.y);S.z=W.z+(oa*pa.z+ua*fa.z);F.push(S.x,S.y,S.z);V.subVectors(S,W).normalize();J.push(V.x,V.y,V.z);P.push(R/e);P.push(qa/g)}}for(qa=1;qa<=e;qa++)for(R=1;R<=g;R++)a=
(g+1)*qa+(R-1),c=(g+1)*qa+R,r=(g+1)*(qa-1)+R,E.push((g+1)*(qa-1)+(R-1),a,r),E.push(a,c,r);this.setIndex(E);this.addAttribute("position",new ca(F,3));this.addAttribute("normal",new ca(J,3));this.addAttribute("uv",new ca(P,2))}function Mf(a,c,e,g,r){ya.call(this);this.type="TorusGeometry";this.parameters={radius:a,tube:c,radialSegments:e,tubularSegments:g,arc:r};this.fromBufferGeometry(new Ke(a,c,e,g,r));this.mergeVertices()}function Ke(a,c,e,g,r){va.call(this);this.type="TorusBufferGeometry";this.parameters=
{radius:a,tube:c,radialSegments:e,tubularSegments:g,arc:r};a=a||1;c=c||.4;e=Math.floor(e)||8;g=Math.floor(g)||6;r=r||2*Math.PI;var v=[],z=[],E=[],F=[],J=new k,P=new k,R=new k,S,V;for(S=0;S<=e;S++)for(V=0;V<=g;V++){var W=V/g*r,ha=S/e*Math.PI*2;P.x=(a+c*Math.cos(ha))*Math.cos(W);P.y=(a+c*Math.cos(ha))*Math.sin(W);P.z=c*Math.sin(ha);z.push(P.x,P.y,P.z);J.x=a*Math.cos(W);J.y=a*Math.sin(W);R.subVectors(P,J).normalize();E.push(R.x,R.y,R.z);F.push(V/g);F.push(S/e)}for(S=1;S<=e;S++)for(V=1;V<=g;V++)a=(g+
1)*(S-1)+V-1,c=(g+1)*(S-1)+V,r=(g+1)*S+V,v.push((g+1)*S+V-1,a,r),v.push(a,c,r);this.setIndex(v);this.addAttribute("position",new ca(z,3));this.addAttribute("normal",new ca(E,3));this.addAttribute("uv",new ca(F,2))}function Gj(a,c,e,g,r){if(r===0<Xl(a,c,e,g))for(r=c;r<e;r+=g)var v=Hj(r,a[r],a[r+1],v);else for(r=e-g;r>=c;r-=g)v=Hj(r,a[r],a[r+1],v);v&&Td(v,v.next)&&(Nf(v),v=v.next);return v}function Of(a,c){if(!a)return a;c||(c=a);do{var e=!1;if(a.steiner||!Td(a,a.next)&&0!==Wb(a.prev,a,a.next))a=a.next;
else{Nf(a);a=c=a.prev;if(a===a.next)break;e=!0}}while(e||a!==c);return c}function Pf(a,c,e,g,r,v,z){if(a){!z&&v&&Yl(a,g,r,v);for(var E=a,F,J;a.prev!==a.next;)if(F=a.prev,J=a.next,v?Zl(a,g,r,v):$l(a))c.push(F.i/e),c.push(a.i/e),c.push(J.i/e),Nf(a),E=a=J.next;else if(a=J,a===E){z?1===z?(a=am(a,c,e),Pf(a,c,e,g,r,v,2)):2===z&&bm(a,c,e,g,r,v):Pf(Of(a),c,e,g,r,v,1);break}}}function $l(a){var c=a.prev,e=a.next;if(0<=Wb(c,a,e))return!1;for(var g=a.next.next;g!==a.prev;){if(Le(c.x,c.y,a.x,a.y,e.x,e.y,g.x,
g.y)&&0<=Wb(g.prev,g,g.next))return!1;g=g.next}return!0}function Zl(a,c,e,g){var r=a.prev,v=a.next;if(0<=Wb(r,a,v))return!1;var z=r.x>a.x?r.x>v.x?r.x:v.x:a.x>v.x?a.x:v.x,E=r.y>a.y?r.y>v.y?r.y:v.y:a.y>v.y?a.y:v.y,F=Yh(r.x<a.x?r.x<v.x?r.x:v.x:a.x<v.x?a.x:v.x,r.y<a.y?r.y<v.y?r.y:v.y:a.y<v.y?a.y:v.y,c,e,g);c=Yh(z,E,c,e,g);e=a.prevZ;for(g=a.nextZ;e&&e.z>=F&&g&&g.z<=c;){if(e!==a.prev&&e!==a.next&&Le(r.x,r.y,a.x,a.y,v.x,v.y,e.x,e.y)&&0<=Wb(e.prev,e,e.next))return!1;e=e.prevZ;if(g!==a.prev&&g!==a.next&&Le(r.x,
r.y,a.x,a.y,v.x,v.y,g.x,g.y)&&0<=Wb(g.prev,g,g.next))return!1;g=g.nextZ}for(;e&&e.z>=F;){if(e!==a.prev&&e!==a.next&&Le(r.x,r.y,a.x,a.y,v.x,v.y,e.x,e.y)&&0<=Wb(e.prev,e,e.next))return!1;e=e.prevZ}for(;g&&g.z<=c;){if(g!==a.prev&&g!==a.next&&Le(r.x,r.y,a.x,a.y,v.x,v.y,g.x,g.y)&&0<=Wb(g.prev,g,g.next))return!1;g=g.nextZ}return!0}function am(a,c,e){var g=a;do{var r=g.prev,v=g.next.next;!Td(r,v)&&Ij(r,g,g.next,v)&&Qf(r,v)&&Qf(v,r)&&(c.push(r.i/e),c.push(g.i/e),c.push(v.i/e),Nf(g),Nf(g.next),g=a=v);g=g.next}while(g!==
a);return g}function bm(a,c,e,g,r,v){var z=a;do{for(var E=z.next.next;E!==z.prev;){if(z.i!==E.i&&cm(z,E)){a=Jj(z,E);z=Of(z,z.next);a=Of(a,a.next);Pf(z,c,e,g,r,v);Pf(a,c,e,g,r,v);return}E=E.next}z=z.next}while(z!==a)}function dm(a,c,e,g){var r=[],v;var z=0;for(v=c.length;z<v;z++){var E=c[z]*g;var F=z<v-1?c[z+1]*g:a.length;E=Gj(a,E,F,g,!1);E===E.next&&(E.steiner=!0);r.push(em(E))}r.sort(fm);for(z=0;z<r.length;z++)gm(r[z],e),e=Of(e,e.next);return e}function fm(a,c){return a.x-c.x}function gm(a,c){if(c=
hm(a,c))a=Jj(c,a),Of(a,a.next)}function hm(a,c){var e=c,g=a.x,r=a.y,v=-Infinity;do{if(r<=e.y&&r>=e.next.y&&e.next.y!==e.y){var z=e.x+(r-e.y)*(e.next.x-e.x)/(e.next.y-e.y);if(z<=g&&z>v){v=z;if(z===g){if(r===e.y)return e;if(r===e.next.y)return e.next}var E=e.x<e.next.x?e:e.next}}e=e.next}while(e!==c);if(!E)return null;if(g===v)return E.prev;c=E;z=E.x;var F=E.y,J=Infinity;for(e=E.next;e!==c;){if(g>=e.x&&e.x>=z&&g!==e.x&&Le(r<F?g:v,r,z,F,r<F?v:g,r,e.x,e.y)){var P=Math.abs(r-e.y)/(g-e.x);(P<J||P===J&&
e.x>E.x)&&Qf(e,a)&&(E=e,J=P)}e=e.next}return E}function Yl(a,c,e,g){var r=a;do null===r.z&&(r.z=Yh(r.x,r.y,c,e,g)),r.prevZ=r.prev,r=r.nextZ=r.next;while(r!==a);r.prevZ.nextZ=null;r.prevZ=null;im(r)}function im(a){var c,e,g,r,v=1;do{var z=a;var E=a=null;for(e=0;z;){e++;var F=z;for(c=g=0;c<v&&(g++,F=F.nextZ,F);c++);for(r=v;0<g||0<r&&F;)0!==g&&(0===r||!F||z.z<=F.z)?(c=z,z=z.nextZ,g--):(c=F,F=F.nextZ,r--),E?E.nextZ=c:a=c,c.prevZ=E,E=c;z=F}E.nextZ=null;v*=2}while(1<e);return a}function Yh(a,c,e,g,r){a=
32767*(a-e)*r;c=32767*(c-g)*r;a=(a|a<<8)&16711935;a=(a|a<<4)&252645135;a=(a|a<<2)&858993459;c=(c|c<<8)&16711935;c=(c|c<<4)&252645135;c=(c|c<<2)&858993459;return(a|a<<1)&1431655765|((c|c<<1)&1431655765)<<1}function em(a){var c=a,e=a;do{if(c.x<e.x||c.x===e.x&&c.y<e.y)e=c;c=c.next}while(c!==a);return e}function Le(a,c,e,g,r,v,z,E){return 0<=(r-z)*(c-E)-(a-z)*(v-E)&&0<=(a-z)*(g-E)-(e-z)*(c-E)&&0<=(e-z)*(v-E)-(r-z)*(g-E)}function cm(a,c){return a.next.i!==c.i&&a.prev.i!==c.i&&!jm(a,c)&&Qf(a,c)&&Qf(c,a)&&
km(a,c)}function Wb(a,c,e){return(c.y-a.y)*(e.x-c.x)-(c.x-a.x)*(e.y-c.y)}function Td(a,c){return a.x===c.x&&a.y===c.y}function Ij(a,c,e,g){return Td(a,e)&&Td(c,g)||Td(a,g)&&Td(e,c)?!0:0<Wb(a,c,e)!==0<Wb(a,c,g)&&0<Wb(e,g,a)!==0<Wb(e,g,c)}function jm(a,c){var e=a;do{if(e.i!==a.i&&e.next.i!==a.i&&e.i!==c.i&&e.next.i!==c.i&&Ij(e,e.next,a,c))return!0;e=e.next}while(e!==a);return!1}function Qf(a,c){return 0>Wb(a.prev,a,a.next)?0<=Wb(a,c,a.next)&&0<=Wb(a,a.prev,c):0>Wb(a,c,a.prev)||0>Wb(a,a.next,c)}function km(a,
c){var e=a,g=!1,r=(a.x+c.x)/2;c=(a.y+c.y)/2;do e.y>c!==e.next.y>c&&e.next.y!==e.y&&r<(e.next.x-e.x)*(c-e.y)/(e.next.y-e.y)+e.x&&(g=!g),e=e.next;while(e!==a);return g}function Jj(a,c){var e=new Zh(a.i,a.x,a.y),g=new Zh(c.i,c.x,c.y),r=a.next,v=c.prev;a.next=c;c.prev=a;e.next=r;r.prev=e;g.next=e;e.prev=g;v.next=g;g.prev=v;return g}function Hj(a,c,e,g){a=new Zh(a,c,e);g?(a.next=g.next,a.prev=g,g.next.prev=a,g.next=a):(a.prev=a,a.next=a);return a}function Nf(a){a.next.prev=a.prev;a.prev.next=a.next;a.prevZ&&
(a.prevZ.nextZ=a.nextZ);a.nextZ&&(a.nextZ.prevZ=a.prevZ)}function Zh(a,c,e){this.i=a;this.x=c;this.y=e;this.nextZ=this.prevZ=this.z=this.next=this.prev=null;this.steiner=!1}function Xl(a,c,e,g){for(var r=0,v=e-g;c<e;c+=g)r+=(a[v]-a[c])*(a[c+1]+a[v+1]),v=c;return r}function Kj(a){var c=a.length;2<c&&a[c-1].equals(a[0])&&a.pop()}function Lj(a,c){for(var e=0;e<c.length;e++)a.push(c[e].x),a.push(c[e].y)}function Ud(a,c){ya.call(this);this.type="ExtrudeGeometry";this.parameters={shapes:a,options:c};this.fromBufferGeometry(new Rc(a,
c));this.mergeVertices()}function Rc(a,c){function e(F){function J(Qa,eb,mb){eb||console.error("THREE.ExtrudeGeometry: vec does not exist");return eb.clone().multiplyScalar(mb).add(Qa)}function P(Qa,eb,mb){var pb=Qa.x-eb.x;var sb=Qa.y-eb.y;var Kb=mb.x-Qa.x;var Sb=mb.y-Qa.y,nc=pb*pb+sb*sb;if(Math.abs(pb*Sb-sb*Kb)>Number.EPSILON){var Qc=Math.sqrt(nc),zc=Math.sqrt(Kb*Kb+Sb*Sb);nc=eb.x-sb/Qc;eb=eb.y+pb/Qc;Sb=((mb.x-Sb/zc-nc)*Sb-(mb.y+Kb/zc-eb)*Kb)/(pb*Sb-sb*Kb);Kb=nc+pb*Sb-Qa.x;pb=eb+sb*Sb-Qa.y;sb=Kb*
Kb+pb*pb;if(2>=sb)return new f(Kb,pb);sb=Math.sqrt(sb/2)}else Qa=!1,pb>Number.EPSILON?Kb>Number.EPSILON&&(Qa=!0):pb<-Number.EPSILON?Kb<-Number.EPSILON&&(Qa=!0):Math.sign(sb)===Math.sign(Sb)&&(Qa=!0),Qa?(Kb=-sb,sb=Math.sqrt(nc)):(Kb=pb,pb=sb,sb=Math.sqrt(nc/2));return new f(Kb/sb,pb/sb)}function R(Qa,eb){for(ja=Qa.length;0<=--ja;){var mb=ja;var pb=ja-1;0>pb&&(pb=Qa.length-1);var sb,Kb=qa+2*Ua;for(sb=0;sb<Kb;sb++){var Sb=Ya*sb,nc=Ya*(sb+1);W(eb+mb+Sb,eb+pb+Sb,eb+pb+nc,eb+mb+nc)}}}function S(Qa,eb,mb){ra.push(Qa);
ra.push(eb);ra.push(mb)}function V(Qa,eb,mb){ha(Qa);ha(eb);ha(mb);Qa=r.length/3;Qa=Ha.generateTopUV(g,r,Qa-3,Qa-2,Qa-1);fa(Qa[0]);fa(Qa[1]);fa(Qa[2])}function W(Qa,eb,mb,pb){ha(Qa);ha(eb);ha(pb);ha(eb);ha(mb);ha(pb);Qa=r.length/3;Qa=Ha.generateSideWallUV(g,r,Qa-6,Qa-3,Qa-2,Qa-1);fa(Qa[0]);fa(Qa[1]);fa(Qa[3]);fa(Qa[1]);fa(Qa[2]);fa(Qa[3])}function ha(Qa){r.push(ra[3*Qa]);r.push(ra[3*Qa+1]);r.push(ra[3*Qa+2])}function fa(Qa){v.push(Qa.x);v.push(Qa.y)}var ra=[],pa=void 0!==c.curveSegments?c.curveSegments:
12,qa=void 0!==c.steps?c.steps:1,ua=void 0!==c.depth?c.depth:100,oa=void 0!==c.bevelEnabled?c.bevelEnabled:!0,ta=void 0!==c.bevelThickness?c.bevelThickness:6,Ba=void 0!==c.bevelSize?c.bevelSize:ta-2,Ta=void 0!==c.bevelOffset?c.bevelOffset:0,Ua=void 0!==c.bevelSegments?c.bevelSegments:3,Ca=c.extrudePath,Ha=void 0!==c.UVGenerator?c.UVGenerator:lm;void 0!==c.amount&&(console.warn("THREE.ExtrudeBufferGeometry: amount has been renamed to depth."),ua=c.amount);var Da=!1;if(Ca){var Ma=Ca.getSpacedPoints(qa);
Da=!0;oa=!1;var db=Ca.computeFrenetFrames(qa,!1);var tb=new k;var Ka=new k;var bb=new k}oa||(Ta=Ba=ta=Ua=0);var jb;pa=F.extractPoints(pa);F=pa.shape;var Eb=pa.holes;if(!ed.isClockWise(F)){F=F.reverse();var xb=0;for(jb=Eb.length;xb<jb;xb++){var ia=Eb[xb];ed.isClockWise(ia)&&(Eb[xb]=ia.reverse())}}var na=ed.triangulateShape(F,Eb),za=F;xb=0;for(jb=Eb.length;xb<jb;xb++)ia=Eb[xb],F=F.concat(ia);var Ja,Ya=F.length,Na,cb=na.length;pa=[];var ja=0;var Ga=za.length;var La=Ga-1;for(Ja=ja+1;ja<Ga;ja++,La++,Ja++)La===
Ga&&(La=0),Ja===Ga&&(Ja=0),pa[ja]=P(za[ja],za[La],za[Ja]);Ca=[];var nb=pa.concat();xb=0;for(jb=Eb.length;xb<jb;xb++){ia=Eb[xb];var Va=[];ja=0;Ga=ia.length;La=Ga-1;for(Ja=ja+1;ja<Ga;ja++,La++,Ja++)La===Ga&&(La=0),Ja===Ga&&(Ja=0),Va[ja]=P(ia[ja],ia[La],ia[Ja]);Ca.push(Va);nb=nb.concat(Va)}for(La=0;La<Ua;La++){Ga=La/Ua;var ib=ta*Math.cos(Ga*Math.PI/2);Ja=Ba*Math.sin(Ga*Math.PI/2)+Ta;ja=0;for(Ga=za.length;ja<Ga;ja++){var kb=J(za[ja],pa[ja],Ja);S(kb.x,kb.y,-ib)}xb=0;for(jb=Eb.length;xb<jb;xb++)for(ia=
Eb[xb],Va=Ca[xb],ja=0,Ga=ia.length;ja<Ga;ja++)kb=J(ia[ja],Va[ja],Ja),S(kb.x,kb.y,-ib)}Ja=Ba+Ta;for(ja=0;ja<Ya;ja++)kb=oa?J(F[ja],nb[ja],Ja):F[ja],Da?(Ka.copy(db.normals[0]).multiplyScalar(kb.x),tb.copy(db.binormals[0]).multiplyScalar(kb.y),bb.copy(Ma[0]).add(Ka).add(tb),S(bb.x,bb.y,bb.z)):S(kb.x,kb.y,0);for(Ga=1;Ga<=qa;Ga++)for(ja=0;ja<Ya;ja++)kb=oa?J(F[ja],nb[ja],Ja):F[ja],Da?(Ka.copy(db.normals[Ga]).multiplyScalar(kb.x),tb.copy(db.binormals[Ga]).multiplyScalar(kb.y),bb.copy(Ma[Ga]).add(Ka).add(tb),
S(bb.x,bb.y,bb.z)):S(kb.x,kb.y,ua/qa*Ga);for(La=Ua-1;0<=La;La--){Ga=La/Ua;ib=ta*Math.cos(Ga*Math.PI/2);Ja=Ba*Math.sin(Ga*Math.PI/2)+Ta;ja=0;for(Ga=za.length;ja<Ga;ja++)kb=J(za[ja],pa[ja],Ja),S(kb.x,kb.y,ua+ib);xb=0;for(jb=Eb.length;xb<jb;xb++)for(ia=Eb[xb],Va=Ca[xb],ja=0,Ga=ia.length;ja<Ga;ja++)kb=J(ia[ja],Va[ja],Ja),Da?S(kb.x,kb.y+Ma[qa-1].y,Ma[qa-1].x+ib):S(kb.x,kb.y,ua+ib)}(function(){var Qa=r.length/3;if(oa){var eb=0*Ya;for(ja=0;ja<cb;ja++)Na=na[ja],V(Na[2]+eb,Na[1]+eb,Na[0]+eb);eb=Ya*(qa+2*Ua);
for(ja=0;ja<cb;ja++)Na=na[ja],V(Na[0]+eb,Na[1]+eb,Na[2]+eb)}else{for(ja=0;ja<cb;ja++)Na=na[ja],V(Na[2],Na[1],Na[0]);for(ja=0;ja<cb;ja++)Na=na[ja],V(Na[0]+Ya*qa,Na[1]+Ya*qa,Na[2]+Ya*qa)}g.addGroup(Qa,r.length/3-Qa,0)})();(function(){var Qa=r.length/3,eb=0;R(za,eb);eb+=za.length;xb=0;for(jb=Eb.length;xb<jb;xb++)ia=Eb[xb],R(ia,eb),eb+=ia.length;g.addGroup(Qa,r.length/3-Qa,1)})()}va.call(this);this.type="ExtrudeBufferGeometry";this.parameters={shapes:a,options:c};a=Array.isArray(a)?a:[a];for(var g=this,
r=[],v=[],z=0,E=a.length;z<E;z++)e(a[z]);this.addAttribute("position",new ca(r,3));this.addAttribute("uv",new ca(v,2));this.computeVertexNormals()}function Mj(a,c,e){e.shapes=[];if(Array.isArray(a))for(var g=0,r=a.length;g<r;g++)e.shapes.push(a[g].uuid);else e.shapes.push(a.uuid);void 0!==c.extrudePath&&(e.options.extrudePath=c.extrudePath.toJSON());return e}function Rf(a,c){ya.call(this);this.type="TextGeometry";this.parameters={text:a,parameters:c};this.fromBufferGeometry(new Me(a,c));this.mergeVertices()}
function Me(a,c){c=c||{};var e=c.font;if(!e||!e.isFont)return console.error("THREE.TextGeometry: font parameter is not an instance of THREE.Font."),new ya;a=e.generateShapes(a,c.size);c.depth=void 0!==c.height?c.height:50;void 0===c.bevelThickness&&(c.bevelThickness=10);void 0===c.bevelSize&&(c.bevelSize=8);void 0===c.bevelEnabled&&(c.bevelEnabled=!1);Rc.call(this,a,c);this.type="TextBufferGeometry"}function Sf(a,c,e,g,r,v,z){ya.call(this);this.type="SphereGeometry";this.parameters={radius:a,widthSegments:c,
heightSegments:e,phiStart:g,phiLength:r,thetaStart:v,thetaLength:z};this.fromBufferGeometry(new Bd(a,c,e,g,r,v,z));this.mergeVertices()}function Bd(a,c,e,g,r,v,z){va.call(this);this.type="SphereBufferGeometry";this.parameters={radius:a,widthSegments:c,heightSegments:e,phiStart:g,phiLength:r,thetaStart:v,thetaLength:z};a=a||1;c=Math.max(3,Math.floor(c)||8);e=Math.max(2,Math.floor(e)||6);g=void 0!==g?g:0;r=void 0!==r?r:2*Math.PI;v=void 0!==v?v:0;z=void 0!==z?z:Math.PI;var E=Math.min(v+z,Math.PI),F,
J,P=0,R=[],S=new k,V=new k,W=[],ha=[],fa=[],ra=[];for(J=0;J<=e;J++){var pa=[],qa=J/e,ua=0;0==J&&0==v?ua=.5/c:J==e&&E==Math.PI&&(ua=-.5/c);for(F=0;F<=c;F++){var oa=F/c;S.x=-a*Math.cos(g+oa*r)*Math.sin(v+qa*z);S.y=a*Math.cos(v+qa*z);S.z=a*Math.sin(g+oa*r)*Math.sin(v+qa*z);ha.push(S.x,S.y,S.z);V.copy(S).normalize();fa.push(V.x,V.y,V.z);ra.push(oa+ua,1-qa);pa.push(P++)}R.push(pa)}for(J=0;J<e;J++)for(F=0;F<c;F++)a=R[J][F+1],g=R[J][F],r=R[J+1][F],z=R[J+1][F+1],(0!==J||0<v)&&W.push(a,g,z),(J!==e-1||E<Math.PI)&&
W.push(g,r,z);this.setIndex(W);this.addAttribute("position",new ca(ha,3));this.addAttribute("normal",new ca(fa,3));this.addAttribute("uv",new ca(ra,2))}function Tf(a,c,e,g,r,v){ya.call(this);this.type="RingGeometry";this.parameters={innerRadius:a,outerRadius:c,thetaSegments:e,phiSegments:g,thetaStart:r,thetaLength:v};this.fromBufferGeometry(new Ne(a,c,e,g,r,v));this.mergeVertices()}function Ne(a,c,e,g,r,v){va.call(this);this.type="RingBufferGeometry";this.parameters={innerRadius:a,outerRadius:c,thetaSegments:e,
phiSegments:g,thetaStart:r,thetaLength:v};a=a||.5;c=c||1;r=void 0!==r?r:0;v=void 0!==v?v:2*Math.PI;e=void 0!==e?Math.max(3,e):8;g=void 0!==g?Math.max(1,g):1;var z=[],E=[],F=[],J=[],P=a,R=(c-a)/g,S=new k,V=new f,W,ha;for(W=0;W<=g;W++){for(ha=0;ha<=e;ha++)a=r+ha/e*v,S.x=P*Math.cos(a),S.y=P*Math.sin(a),E.push(S.x,S.y,S.z),F.push(0,0,1),V.x=(S.x/c+1)/2,V.y=(S.y/c+1)/2,J.push(V.x,V.y);P+=R}for(W=0;W<g;W++)for(c=W*(e+1),ha=0;ha<e;ha++)a=ha+c,r=a+e+1,v=a+e+2,P=a+1,z.push(a,r,P),z.push(r,v,P);this.setIndex(z);
this.addAttribute("position",new ca(E,3));this.addAttribute("normal",new ca(F,3));this.addAttribute("uv",new ca(J,2))}function Uf(a,c,e,g){ya.call(this);this.type="LatheGeometry";this.parameters={points:a,segments:c,phiStart:e,phiLength:g};this.fromBufferGeometry(new Oe(a,c,e,g));this.mergeVertices()}function Oe(a,c,e,g){va.call(this);this.type="LatheBufferGeometry";this.parameters={points:a,segments:c,phiStart:e,phiLength:g};c=Math.floor(c)||12;e=e||0;g=g||2*Math.PI;g=hb.clamp(g,0,2*Math.PI);var r=
[],v=[],z=[],E=1/c,F=new k,J=new f,P;for(P=0;P<=c;P++){var R=e+P*E*g;var S=Math.sin(R),V=Math.cos(R);for(R=0;R<=a.length-1;R++)F.x=a[R].x*S,F.y=a[R].y,F.z=a[R].x*V,v.push(F.x,F.y,F.z),J.x=P/c,J.y=R/(a.length-1),z.push(J.x,J.y)}for(P=0;P<c;P++)for(R=0;R<a.length-1;R++)e=R+P*a.length,E=e+a.length,F=e+a.length+1,J=e+1,r.push(e,E,J),r.push(E,F,J);this.setIndex(r);this.addAttribute("position",new ca(v,3));this.addAttribute("uv",new ca(z,2));this.computeVertexNormals();if(g===2*Math.PI)for(g=this.attributes.normal.array,
r=new k,v=new k,z=new k,e=c*a.length*3,R=P=0;P<a.length;P++,R+=3)r.x=g[R+0],r.y=g[R+1],r.z=g[R+2],v.x=g[e+R+0],v.y=g[e+R+1],v.z=g[e+R+2],z.addVectors(r,v).normalize(),g[R+0]=g[e+R+0]=z.x,g[R+1]=g[e+R+1]=z.y,g[R+2]=g[e+R+2]=z.z}function Vd(a,c){ya.call(this);this.type="ShapeGeometry";"object"===typeof c&&(console.warn("THREE.ShapeGeometry: Options parameter has been removed."),c=c.curveSegments);this.parameters={shapes:a,curveSegments:c};this.fromBufferGeometry(new Wd(a,c));this.mergeVertices()}function Wd(a,
c){function e(P){var R,S=r.length/3;P=P.extractPoints(c);var V=P.shape,W=P.holes;!1===ed.isClockWise(V)&&(V=V.reverse());P=0;for(R=W.length;P<R;P++){var ha=W[P];!0===ed.isClockWise(ha)&&(W[P]=ha.reverse())}var fa=ed.triangulateShape(V,W);P=0;for(R=W.length;P<R;P++)ha=W[P],V=V.concat(ha);P=0;for(R=V.length;P<R;P++)ha=V[P],r.push(ha.x,ha.y,0),v.push(0,0,1),z.push(ha.x,ha.y);P=0;for(R=fa.length;P<R;P++)V=fa[P],g.push(V[0]+S,V[1]+S,V[2]+S),F+=3}va.call(this);this.type="ShapeBufferGeometry";this.parameters=
{shapes:a,curveSegments:c};c=c||12;var g=[],r=[],v=[],z=[],E=0,F=0;if(!1===Array.isArray(a))e(a);else for(var J=0;J<a.length;J++)e(a[J]),this.addGroup(E,F,J),E+=F,F=0;this.setIndex(g);this.addAttribute("position",new ca(r,3));this.addAttribute("normal",new ca(v,3));this.addAttribute("uv",new ca(z,2))}function Nj(a,c){c.shapes=[];if(Array.isArray(a))for(var e=0,g=a.length;e<g;e++)c.shapes.push(a[e].uuid);else c.shapes.push(a.uuid);return c}function Pe(a,c){va.call(this);this.type="EdgesGeometry";this.parameters=
{thresholdAngle:c};var e=[];c=Math.cos(hb.DEG2RAD*(void 0!==c?c:1));var g=[0,0],r={},v=["a","b","c"];if(a.isBufferGeometry){var z=new ya;z.fromBufferGeometry(a)}else z=a.clone();z.mergeVertices();z.computeFaceNormals();a=z.vertices;z=z.faces;for(var E=0,F=z.length;E<F;E++)for(var J=z[E],P=0;3>P;P++){var R=J[v[P]];var S=J[v[(P+1)%3]];g[0]=Math.min(R,S);g[1]=Math.max(R,S);R=g[0]+","+g[1];void 0===r[R]?r[R]={index1:g[0],index2:g[1],face1:E,face2:void 0}:r[R].face2=E}for(R in r)if(g=r[R],void 0===g.face2||
z[g.face1].normal.dot(z[g.face2].normal)<=c)v=a[g.index1],e.push(v.x,v.y,v.z),v=a[g.index2],e.push(v.x,v.y,v.z);this.addAttribute("position",new ca(e,3))}function Xd(a,c,e,g,r,v,z,E){ya.call(this);this.type="CylinderGeometry";this.parameters={radiusTop:a,radiusBottom:c,height:e,radialSegments:g,heightSegments:r,openEnded:v,thetaStart:z,thetaLength:E};this.fromBufferGeometry(new fd(a,c,e,g,r,v,z,E));this.mergeVertices()}function fd(a,c,e,g,r,v,z,E){function F(pa){var qa,ua=new f,oa=new k,ta=0,Ba=!0===
pa?a:c,Ta=!0===pa?1:-1;var Ua=W;for(qa=1;qa<=g;qa++)R.push(0,fa*Ta,0),S.push(0,Ta,0),V.push(.5,.5),W++;var Ca=W;for(qa=0;qa<=g;qa++){var Ha=qa/g*E+z,Da=Math.cos(Ha);Ha=Math.sin(Ha);oa.x=Ba*Ha;oa.y=fa*Ta;oa.z=Ba*Da;R.push(oa.x,oa.y,oa.z);S.push(0,Ta,0);ua.x=.5*Da+.5;ua.y=.5*Ha*Ta+.5;V.push(ua.x,ua.y);W++}for(qa=0;qa<g;qa++)ua=Ua+qa,oa=Ca+qa,!0===pa?P.push(oa,oa+1,ua):P.push(oa+1,oa,ua),ta+=3;J.addGroup(ra,ta,!0===pa?1:2);ra+=ta}va.call(this);this.type="CylinderBufferGeometry";this.parameters={radiusTop:a,
radiusBottom:c,height:e,radialSegments:g,heightSegments:r,openEnded:v,thetaStart:z,thetaLength:E};var J=this;a=void 0!==a?a:1;c=void 0!==c?c:1;e=e||1;g=Math.floor(g)||8;r=Math.floor(r)||1;v=void 0!==v?v:!1;z=void 0!==z?z:0;E=void 0!==E?E:2*Math.PI;var P=[],R=[],S=[],V=[],W=0,ha=[],fa=e/2,ra=0;(function(){var pa,qa,ua=new k,oa=new k,ta=0,Ba=(c-a)/e;for(qa=0;qa<=r;qa++){var Ta=[],Ua=qa/r,Ca=Ua*(c-a)+a;for(pa=0;pa<=g;pa++){var Ha=pa/g,Da=Ha*E+z,Ma=Math.sin(Da);Da=Math.cos(Da);oa.x=Ca*Ma;oa.y=-Ua*e+fa;
oa.z=Ca*Da;R.push(oa.x,oa.y,oa.z);ua.set(Ma,Ba,Da).normalize();S.push(ua.x,ua.y,ua.z);V.push(Ha,1-Ua);Ta.push(W++)}ha.push(Ta)}for(pa=0;pa<g;pa++)for(qa=0;qa<r;qa++)ua=ha[qa+1][pa],oa=ha[qa+1][pa+1],Ba=ha[qa][pa+1],P.push(ha[qa][pa],ua,Ba),P.push(ua,oa,Ba),ta+=6;J.addGroup(ra,ta,0);ra+=ta})();!1===v&&(0<a&&F(!0),0<c&&F(!1));this.setIndex(P);this.addAttribute("position",new ca(R,3));this.addAttribute("normal",new ca(S,3));this.addAttribute("uv",new ca(V,2))}function Vf(a,c,e,g,r,v,z){Xd.call(this,
0,a,c,e,g,r,v,z);this.type="ConeGeometry";this.parameters={radius:a,height:c,radialSegments:e,heightSegments:g,openEnded:r,thetaStart:v,thetaLength:z}}function Wf(a,c,e,g,r,v,z){fd.call(this,0,a,c,e,g,r,v,z);this.type="ConeBufferGeometry";this.parameters={radius:a,height:c,radialSegments:e,heightSegments:g,openEnded:r,thetaStart:v,thetaLength:z}}function Xf(a,c,e,g){ya.call(this);this.type="CircleGeometry";this.parameters={radius:a,segments:c,thetaStart:e,thetaLength:g};this.fromBufferGeometry(new Qe(a,
c,e,g));this.mergeVertices()}function Qe(a,c,e,g){va.call(this);this.type="CircleBufferGeometry";this.parameters={radius:a,segments:c,thetaStart:e,thetaLength:g};a=a||1;c=void 0!==c?Math.max(3,c):8;e=void 0!==e?e:0;g=void 0!==g?g:2*Math.PI;var r=[],v=[],z=[],E=[],F,J=new k,P=new f;v.push(0,0,0);z.push(0,0,1);E.push(.5,.5);var R=0;for(F=3;R<=c;R++,F+=3){var S=e+R/c*g;J.x=a*Math.cos(S);J.y=a*Math.sin(S);v.push(J.x,J.y,J.z);z.push(0,0,1);P.x=(v[F]/a+1)/2;P.y=(v[F+1]/a+1)/2;E.push(P.x,P.y)}for(F=1;F<=
c;F++)r.push(F,F+1,0);this.setIndex(r);this.addAttribute("position",new ca(v,3));this.addAttribute("normal",new ca(z,3));this.addAttribute("uv",new ca(E,2))}function Yd(a){M.call(this);this.type="ShadowMaterial";this.color=new I(0);this.transparent=!0;this.setValues(a)}function Re(a){qb.call(this,a);this.type="RawShaderMaterial"}function Sc(a){M.call(this);this.defines={STANDARD:""};this.type="MeshStandardMaterial";this.color=new I(16777215);this.metalness=this.roughness=.5;this.lightMap=this.map=
null;this.lightMapIntensity=1;this.aoMap=null;this.aoMapIntensity=1;this.emissive=new I(0);this.emissiveIntensity=1;this.bumpMap=this.emissiveMap=null;this.bumpScale=1;this.normalMap=null;this.normalMapType=0;this.normalScale=new f(1,1);this.displacementMap=null;this.displacementScale=1;this.displacementBias=0;this.envMap=this.alphaMap=this.metalnessMap=this.roughnessMap=null;this.envMapIntensity=1;this.refractionRatio=.98;this.wireframe=!1;this.wireframeLinewidth=1;this.wireframeLinejoin=this.wireframeLinecap=
"round";this.morphNormals=this.morphTargets=this.skinning=!1;this.setValues(a)}function Zd(a){Sc.call(this);this.defines={STANDARD:"",PHYSICAL:""};this.type="MeshPhysicalMaterial";this.reflectivity=.5;this.clearcoatRoughness=this.clearcoat=0;this.sheen=null;this.clearcoatNormalScale=new f(1,1);this.clearcoatNormalMap=null;this.transparency=0;this.setValues(a)}function Bc(a){M.call(this);this.type="MeshPhongMaterial";this.color=new I(16777215);this.specular=new I(1118481);this.shininess=30;this.lightMap=
this.map=null;this.lightMapIntensity=1;this.aoMap=null;this.aoMapIntensity=1;this.emissive=new I(0);this.emissiveIntensity=1;this.bumpMap=this.emissiveMap=null;this.bumpScale=1;this.normalMap=null;this.normalMapType=0;this.normalScale=new f(1,1);this.displacementMap=null;this.displacementScale=1;this.displacementBias=0;this.envMap=this.alphaMap=this.specularMap=null;this.combine=0;this.reflectivity=1;this.refractionRatio=.98;this.wireframe=!1;this.wireframeLinewidth=1;this.wireframeLinejoin=this.wireframeLinecap=
"round";this.morphNormals=this.morphTargets=this.skinning=!1;this.setValues(a)}function $d(a){Bc.call(this);this.defines={TOON:""};this.type="MeshToonMaterial";this.gradientMap=null;this.setValues(a)}function ae(a){M.call(this);this.type="MeshNormalMaterial";this.bumpMap=null;this.bumpScale=1;this.normalMap=null;this.normalMapType=0;this.normalScale=new f(1,1);this.displacementMap=null;this.displacementScale=1;this.displacementBias=0;this.wireframe=!1;this.wireframeLinewidth=1;this.morphNormals=this.morphTargets=
this.skinning=this.lights=this.fog=!1;this.setValues(a)}function be(a){M.call(this);this.type="MeshLambertMaterial";this.color=new I(16777215);this.lightMap=this.map=null;this.lightMapIntensity=1;this.aoMap=null;this.aoMapIntensity=1;this.emissive=new I(0);this.emissiveIntensity=1;this.envMap=this.alphaMap=this.specularMap=this.emissiveMap=null;this.combine=0;this.reflectivity=1;this.refractionRatio=.98;this.wireframe=!1;this.wireframeLinewidth=1;this.wireframeLinejoin=this.wireframeLinecap="round";
this.morphNormals=this.morphTargets=this.skinning=!1;this.setValues(a)}function ce(a){M.call(this);this.defines={MATCAP:""};this.type="MeshMatcapMaterial";this.color=new I(16777215);this.bumpMap=this.map=this.matcap=null;this.bumpScale=1;this.normalMap=null;this.normalMapType=0;this.normalScale=new f(1,1);this.displacementMap=null;this.displacementScale=1;this.displacementBias=0;this.alphaMap=null;this.lights=this.morphNormals=this.morphTargets=this.skinning=!1;this.setValues(a)}function de(a){Fb.call(this);
this.type="LineDashedMaterial";this.scale=1;this.dashSize=3;this.gapSize=1;this.setValues(a)}function oc(a,c,e,g){this.parameterPositions=a;this._cachedIndex=0;this.resultBuffer=void 0!==g?g:new c.constructor(e);this.sampleValues=c;this.valueSize=e}function Eg(a,c,e,g){oc.call(this,a,c,e,g);this._offsetNext=this._weightNext=this._offsetPrev=this._weightPrev=-0}function Yf(a,c,e,g){oc.call(this,a,c,e,g)}function Fg(a,c,e,g){oc.call(this,a,c,e,g)}function Xb(a,c,e,g){if(void 0===a)throw Error("THREE.KeyframeTrack: track name is undefined");
if(void 0===c||0===c.length)throw Error("THREE.KeyframeTrack: no keyframes in track named "+a);this.name=a;this.times=Tb.convertArray(c,this.TimeBufferType);this.values=Tb.convertArray(e,this.ValueBufferType);this.setInterpolation(g||this.DefaultInterpolation)}function Gg(a,c,e){Xb.call(this,a,c,e)}function Hg(a,c,e,g){Xb.call(this,a,c,e,g)}function Se(a,c,e,g){Xb.call(this,a,c,e,g)}function Ig(a,c,e,g){oc.call(this,a,c,e,g)}function Zf(a,c,e,g){Xb.call(this,a,c,e,g)}function Jg(a,c,e,g){Xb.call(this,
a,c,e,g)}function Te(a,c,e,g){Xb.call(this,a,c,e,g)}function tc(a,c,e){this.name=a;this.tracks=e;this.duration=void 0!==c?c:-1;this.uuid=hb.generateUUID();0>this.duration&&this.resetDuration()}function mm(a){switch(a.toLowerCase()){case "scalar":case "double":case "float":case "number":case "integer":return Se;case "vector":case "vector2":case "vector3":case "vector4":return Te;case "color":return Hg;case "quaternion":return Zf;case "bool":case "boolean":return Gg;case "string":return Jg}throw Error("THREE.KeyframeTrack: Unsupported typeName: "+
a);}function nm(a){if(void 0===a.type)throw Error("THREE.KeyframeTrack: track type undefined, can not parse");var c=mm(a.type);if(void 0===a.times){var e=[],g=[];Tb.flattenJSON(a.keys,e,g,"value");a.times=e;a.values=g}return void 0!==c.parse?c.parse(a):new c(a.name,a.times,a.values,a.interpolation)}function $h(a,c,e){var g=this,r=!1,v=0,z=0,E=void 0;this.onStart=void 0;this.onLoad=a;this.onProgress=c;this.onError=e;this.itemStart=function(F){z++;if(!1===r&&void 0!==g.onStart)g.onStart(F,v,z);r=!0};
this.itemEnd=function(F){v++;if(void 0!==g.onProgress)g.onProgress(F,v,z);if(v===z&&(r=!1,void 0!==g.onLoad))g.onLoad()};this.itemError=function(F){if(void 0!==g.onError)g.onError(F)};this.resolveURL=function(F){return E?E(F):F};this.setURLModifier=function(F){E=F;return this}}function Db(a){this.manager=void 0!==a?a:Oj;this.crossOrigin="anonymous";this.resourcePath=this.path=""}function uc(a){Db.call(this,a)}function ai(a){Db.call(this,a)}function bi(a){Db.call(this,a);this._parser=null}function Kg(a){Db.call(this,
a);this._parser=null}function Ue(a){Db.call(this,a)}function Lg(a){Db.call(this,a)}function Mg(a){Db.call(this,a)}function Za(){this.type="Curve";this.arcLengthDivisions=200}function pc(a,c,e,g,r,v,z,E){Za.call(this);this.type="EllipseCurve";this.aX=a||0;this.aY=c||0;this.xRadius=e||1;this.yRadius=g||1;this.aStartAngle=r||0;this.aEndAngle=v||2*Math.PI;this.aClockwise=z||!1;this.aRotation=E||0}function Ve(a,c,e,g,r,v){pc.call(this,a,c,e,e,g,r,v);this.type="ArcCurve"}function ci(){function a(v,z,E,
F){c=v;e=E;g=-3*v+3*z-2*E-F;r=2*v-2*z+E+F}var c=0,e=0,g=0,r=0;return{initCatmullRom:function(v,z,E,F,J){a(z,E,J*(E-v),J*(F-z))},initNonuniformCatmullRom:function(v,z,E,F,J,P,R){a(z,E,((z-v)/J-(E-v)/(J+P)+(E-z)/P)*P,((E-z)/P-(F-z)/(P+R)+(F-E)/R)*P)},calc:function(v){var z=v*v;return c+e*v+g*z+r*z*v}}}function Zb(a,c,e,g){Za.call(this);this.type="CatmullRomCurve3";this.points=a||[];this.closed=c||!1;this.curveType=e||"centripetal";this.tension=g||.5}function Pj(a,c,e,g,r){c=.5*(g-c);r=.5*(r-e);var v=
a*a;return(2*e-2*g+c+r)*a*v+(-3*e+3*g-2*c-r)*v+c*a+e}function om(a,c){a=1-a;return a*a*c}function pm(a,c){return 2*(1-a)*a*c}function qm(a,c){return a*a*c}function $f(a,c,e,g){return om(a,c)+pm(a,e)+qm(a,g)}function rm(a,c){a=1-a;return a*a*a*c}function sm(a,c){var e=1-a;return 3*e*e*a*c}function tm(a,c){return 3*(1-a)*a*a*c}function um(a,c){return a*a*a*c}function ag(a,c,e,g,r){return rm(a,c)+sm(a,e)+tm(a,g)+um(a,r)}function Cc(a,c,e,g){Za.call(this);this.type="CubicBezierCurve";this.v0=a||new f;
this.v1=c||new f;this.v2=e||new f;this.v3=g||new f}function Tc(a,c,e,g){Za.call(this);this.type="CubicBezierCurve3";this.v0=a||new k;this.v1=c||new k;this.v2=e||new k;this.v3=g||new k}function kc(a,c){Za.call(this);this.type="LineCurve";this.v1=a||new f;this.v2=c||new f}function Dc(a,c){Za.call(this);this.type="LineCurve3";this.v1=a||new k;this.v2=c||new k}function Ec(a,c,e){Za.call(this);this.type="QuadraticBezierCurve";this.v0=a||new f;this.v1=c||new f;this.v2=e||new f}function Uc(a,c,e){Za.call(this);
this.type="QuadraticBezierCurve3";this.v0=a||new k;this.v1=c||new k;this.v2=e||new k}function Fc(a){Za.call(this);this.type="SplineCurve";this.points=a||[]}function gd(){Za.call(this);this.type="CurvePath";this.curves=[];this.autoClose=!1}function Gc(a){gd.call(this);this.type="Path";this.currentPoint=new f;a&&this.setFromPoints(a)}function Cd(a){Gc.call(this,a);this.uuid=hb.generateUUID();this.type="Shape";this.holes=[]}function Jb(a,c){A.call(this);this.type="Light";this.color=new I(a);this.intensity=
void 0!==c?c:1;this.receiveShadow=void 0}function Ng(a,c,e){Jb.call(this,a,e);this.type="HemisphereLight";this.castShadow=void 0;this.position.copy(A.DefaultUp);this.updateMatrix();this.groundColor=new I(c)}function Vc(a){this.camera=a;this.bias=0;this.radius=1;this.mapSize=new f(512,512);this.mapPass=this.map=null;this.matrix=new q;this._frustum=new ic;this._frameExtents=new f(1,1);this._viewportCount=1;this._viewports=[new p(0,0,1,1)]}function Og(){Vc.call(this,new vb(50,1,.5,500))}function Pg(a,
c,e,g,r,v){Jb.call(this,a,c);this.type="SpotLight";this.position.copy(A.DefaultUp);this.updateMatrix();this.target=new A;Object.defineProperty(this,"power",{get:function(){return this.intensity*Math.PI},set:function(z){this.intensity=z/Math.PI}});this.distance=void 0!==e?e:0;this.angle=void 0!==g?g:Math.PI/3;this.penumbra=void 0!==r?r:0;this.decay=void 0!==v?v:1;this.shadow=new Og}function di(){Vc.call(this,new vb(90,1,.5,500));this._frameExtents=new f(4,2);this._viewportCount=6;this._viewports=[new p(2,
1,1,1),new p(0,1,1,1),new p(3,1,1,1),new p(1,1,1,1),new p(3,0,1,1),new p(1,0,1,1)];this._cubeDirections=[new k(1,0,0),new k(-1,0,0),new k(0,0,1),new k(0,0,-1),new k(0,1,0),new k(0,-1,0)];this._cubeUps=[new k(0,1,0),new k(0,1,0),new k(0,1,0),new k(0,1,0),new k(0,0,1),new k(0,0,-1)]}function Qg(a,c,e,g){Jb.call(this,a,c);this.type="PointLight";Object.defineProperty(this,"power",{get:function(){return 4*this.intensity*Math.PI},set:function(r){this.intensity=r/(4*Math.PI)}});this.distance=void 0!==e?
e:0;this.decay=void 0!==g?g:1;this.shadow=new di}function bg(a,c,e,g,r,v){zb.call(this);this.type="OrthographicCamera";this.zoom=1;this.view=null;this.left=void 0!==a?a:-1;this.right=void 0!==c?c:1;this.top=void 0!==e?e:1;this.bottom=void 0!==g?g:-1;this.near=void 0!==r?r:.1;this.far=void 0!==v?v:2E3;this.updateProjectionMatrix()}function Rg(){Vc.call(this,new bg(-5,5,5,-5,.5,500))}function Sg(a,c){Jb.call(this,a,c);this.type="DirectionalLight";this.position.copy(A.DefaultUp);this.updateMatrix();
this.target=new A;this.shadow=new Rg}function Tg(a,c){Jb.call(this,a,c);this.type="AmbientLight";this.castShadow=void 0}function Ug(a,c,e,g){Jb.call(this,a,c);this.type="RectAreaLight";this.width=void 0!==e?e:10;this.height=void 0!==g?g:10}function Vg(a){Db.call(this,a);this.textures={}}function Wg(){va.call(this);this.type="InstancedBufferGeometry";this.maxInstancedCount=void 0}function Xg(a,c,e,g){"number"===typeof e&&(g=e,e=!1,console.error("THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument."));
Q.call(this,a,c,e);this.meshPerAttribute=g||1}function Yg(a){Db.call(this,a)}function Zg(a){Db.call(this,a)}function ei(a){"undefined"===typeof createImageBitmap&&console.warn("THREE.ImageBitmapLoader: createImageBitmap() not supported.");"undefined"===typeof fetch&&console.warn("THREE.ImageBitmapLoader: fetch() not supported.");Db.call(this,a);this.options=void 0}function fi(){this.type="ShapePath";this.color=new I;this.subPaths=[];this.currentPath=null}function gi(a){this.type="Font";this.data=
a}function vm(a,c,e){a=Array.from?Array.from(a):String(a).split("");c/=e.resolution;for(var g=(e.boundingBox.yMax-e.boundingBox.yMin+e.underlineThickness)*c,r=[],v=0,z=0,E=0;E<a.length;E++){var F=a[E];"\n"===F?(v=0,z-=g):(F=wm(F,c,v,z,e),v+=F.offsetX,r.push(F.path))}return r}function wm(a,c,e,g,r){var v=r.glyphs[a]||r.glyphs["?"];if(v){a=new fi;if(v.o){r=v._cachedOutline||(v._cachedOutline=v.o.split(" "));for(var z=0,E=r.length;z<E;)switch(r[z++]){case "m":var F=r[z++]*c+e;var J=r[z++]*c+g;a.moveTo(F,
J);break;case "l":F=r[z++]*c+e;J=r[z++]*c+g;a.lineTo(F,J);break;case "q":F=r[z++]*c+e;J=r[z++]*c+g;var P=r[z++]*c+e;var R=r[z++]*c+g;a.quadraticCurveTo(P,R,F,J);break;case "b":F=r[z++]*c+e;J=r[z++]*c+g;P=r[z++]*c+e;R=r[z++]*c+g;var S=r[z++]*c+e;var V=r[z++]*c+g;a.bezierCurveTo(P,R,S,V,F,J)}}return{offsetX:v.ha*c,path:a}}console.error('THREE.Font: character "'+a+'" does not exists in font family '+r.familyName+".")}function hi(a){Db.call(this,a)}function $g(a){Db.call(this,a)}function ah(){this.coefficients=
[];for(var a=0;9>a;a++)this.coefficients.push(new k)}function Hc(a,c){Jb.call(this,void 0,c);this.sh=void 0!==a?a:new ah}function ii(a,c,e){Hc.call(this,void 0,e);a=(new I).set(a);e=(new I).set(c);c=new k(a.r,a.g,a.b);a=new k(e.r,e.g,e.b);e=Math.sqrt(Math.PI);var g=e*Math.sqrt(.75);this.sh.coefficients[0].copy(c).add(a).multiplyScalar(e);this.sh.coefficients[1].copy(c).sub(a).multiplyScalar(g)}function ji(a,c){Hc.call(this,void 0,c);a=(new I).set(a);this.sh.coefficients[0].set(a.r,a.g,a.b).multiplyScalar(2*
Math.sqrt(Math.PI))}function Qj(){this.type="StereoCamera";this.aspect=1;this.eyeSep=.064;this.cameraL=new vb;this.cameraL.layers.enable(1);this.cameraL.matrixAutoUpdate=!1;this.cameraR=new vb;this.cameraR.layers.enable(2);this.cameraR.matrixAutoUpdate=!1;this._cache={focus:null,fov:null,aspect:null,near:null,far:null,zoom:null,eyeSep:null}}function ki(a){this.autoStart=void 0!==a?a:!0;this.elapsedTime=this.oldTime=this.startTime=0;this.running=!1}function li(){A.call(this);this.type="AudioListener";
this.context=mi.getContext();this.gain=this.context.createGain();this.gain.connect(this.context.destination);this.filter=null;this.timeDelta=0;this._clock=new ki}function We(a){A.call(this);this.type="Audio";this.listener=a;this.context=a.context;this.gain=this.context.createGain();this.gain.connect(a.getInput());this.autoplay=!1;this.buffer=null;this.detune=0;this.loop=!1;this.offset=this.startTime=0;this.duration=void 0;this.playbackRate=1;this.isPlaying=!1;this.hasPlaybackControl=!0;this.sourceType=
"empty";this.filters=[]}function ni(a){We.call(this,a);this.panner=this.context.createPanner();this.panner.panningModel="HRTF";this.panner.connect(this.gain)}function oi(a,c){this.analyser=a.context.createAnalyser();this.analyser.fftSize=void 0!==c?c:2048;this.data=new Uint8Array(this.analyser.frequencyBinCount);a.getOutput().connect(this.analyser)}function pi(a,c,e){this.binding=a;this.valueSize=e;a=Float64Array;switch(c){case "quaternion":c=this._slerp;break;case "string":case "bool":a=Array;c=
this._select;break;default:c=this._lerp}this.buffer=new a(4*e);this._mixBufferRegion=c;this.referenceCount=this.useCount=this.cumulativeWeight=0}function Rj(a,c,e){e=e||$b.parseTrackName(c);this._targetGroup=a;this._bindings=a.subscribe_(c,e)}function $b(a,c,e){this.path=c;this.parsedPath=e||$b.parseTrackName(c);this.node=$b.findNode(a,this.parsedPath.nodeName)||a;this.rootNode=a}function Sj(){this.uuid=hb.generateUUID();this._objects=Array.prototype.slice.call(arguments);this.nCachedObjects_=0;var a=
{};this._indicesByUUID=a;for(var c=0,e=arguments.length;c!==e;++c)a[arguments[c].uuid]=c;this._paths=[];this._parsedPaths=[];this._bindings=[];this._bindingsIndicesByPath={};var g=this;this.stats={objects:{get total(){return g._objects.length},get inUse(){return this.total-g.nCachedObjects_}},get bindingsPerObject(){return g._bindings.length}}}function Tj(a,c,e){this._mixer=a;this._clip=c;this._localRoot=e||null;a=c.tracks;c=a.length;e=Array(c);for(var g={endingStart:2400,endingEnd:2400},r=0;r!==
c;++r){var v=a[r].createInterpolant(null);e[r]=v;v.settings=g}this._interpolantSettings=g;this._interpolants=e;this._propertyBindings=Array(c);this._weightInterpolant=this._timeScaleInterpolant=this._byClipCacheIndex=this._cacheIndex=null;this.loop=2201;this._loopCount=-1;this._startTime=null;this.time=0;this._effectiveWeight=this.weight=this._effectiveTimeScale=this.timeScale=1;this.repetitions=Infinity;this.paused=!1;this.enabled=!0;this.clampWhenFinished=!1;this.zeroSlopeAtEnd=this.zeroSlopeAtStart=
!0}function qi(a){this._root=a;this._initMemoryManager();this.time=this._accuIndex=0;this.timeScale=1}function bh(a,c){"string"===typeof a&&(console.warn("THREE.Uniform: Type parameter is no longer needed."),a=c);this.value=a}function ri(a,c,e){Qd.call(this,a,c);this.meshPerAttribute=e||1}function Uj(a,c,e,g){this.ray=new D(a,c);this.near=e||0;this.far=g||Infinity;this.camera=null;this.params={Mesh:{},Line:{},LOD:{},Points:{threshold:1},Sprite:{}};Object.defineProperties(this.params,{PointCloud:{get:function(){console.warn("THREE.Raycaster: params.PointCloud has been renamed to params.Points.");
return this.Points}}})}function Vj(a,c){return a.distance-c.distance}function si(a,c,e,g){if(!1!==a.visible&&(a.raycast(c,e),!0===g)){a=a.children;g=0;for(var r=a.length;g<r;g++)si(a[g],c,e,!0)}}function Wj(a,c,e){this.radius=void 0!==a?a:1;this.phi=void 0!==c?c:0;this.theta=void 0!==e?e:0;return this}function Xj(a,c,e){this.radius=void 0!==a?a:1;this.theta=void 0!==c?c:0;this.y=void 0!==e?e:0;return this}function ti(a,c){this.min=void 0!==a?a:new f(Infinity,Infinity);this.max=void 0!==c?c:new f(-Infinity,
-Infinity)}function ui(a,c){this.start=void 0!==a?a:new k;this.end=void 0!==c?c:new k}function cg(a){A.call(this);this.material=a;this.render=function(){}}function dg(a,c,e,g){this.object=a;this.size=void 0!==c?c:1;a=void 0!==e?e:16711680;g=void 0!==g?g:1;c=0;(e=this.object.geometry)&&e.isGeometry?c=3*e.faces.length:e&&e.isBufferGeometry&&(c=e.attributes.normal.count);e=new va;c=new ca(6*c,3);e.addAttribute("position",c);Ib.call(this,e,new Fb({color:a,linewidth:g}));this.matrixAutoUpdate=!1;this.update()}
function Xe(a,c){A.call(this);this.light=a;this.light.updateMatrixWorld();this.matrix=a.matrixWorld;this.matrixAutoUpdate=!1;this.color=c;a=new va;c=[0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,-1,0,1,0,0,0,0,1,1,0,0,0,0,-1,1];for(var e=0,g=1;32>e;e++,g++){var r=e/32*Math.PI*2,v=g/32*Math.PI*2;c.push(Math.cos(r),Math.sin(r),1,Math.cos(v),Math.sin(v),1)}a.addAttribute("position",new ca(c,3));c=new Fb({fog:!1});this.cone=new Ib(a,c);this.add(this.cone);this.update()}function Yj(a){var c=[];a&&a.isBone&&c.push(a);
for(var e=0;e<a.children.length;e++)c.push.apply(c,Yj(a.children[e]));return c}function Ye(a){for(var c=Yj(a),e=new va,g=[],r=[],v=new I(0,0,1),z=new I(0,1,0),E=0;E<c.length;E++){var F=c[E];F.parent&&F.parent.isBone&&(g.push(0,0,0),g.push(0,0,0),r.push(v.r,v.g,v.b),r.push(z.r,z.g,z.b))}e.addAttribute("position",new ca(g,3));e.addAttribute("color",new ca(r,3));g=new Fb({vertexColors:2,depthTest:!1,depthWrite:!1,transparent:!0});Ib.call(this,e,g);this.root=a;this.bones=c;this.matrix=a.matrixWorld;this.matrixAutoUpdate=
!1}function Ze(a,c,e){this.light=a;this.light.updateMatrixWorld();this.color=e;a=new Bd(c,4,2);c=new L({wireframe:!0,fog:!1});xa.call(this,a,c);this.matrix=this.light.matrixWorld;this.matrixAutoUpdate=!1;this.update()}function $e(a,c){this.type="RectAreaLightHelper";this.light=a;this.color=c;a=new va;a.addAttribute("position",new ca([1,1,0,-1,1,0,-1,-1,0,1,-1,0,1,1,0],3));a.computeBoundingSphere();c=new Fb({fog:!1});Vb.call(this,a,c);a=new va;a.addAttribute("position",new ca([1,1,0,-1,1,0,-1,-1,0,
1,1,0,-1,-1,0,1,-1,0],3));a.computeBoundingSphere();this.add(new xa(a,new L({side:1,fog:!1})));this.update()}function af(a,c,e){A.call(this);this.light=a;this.light.updateMatrixWorld();this.matrix=a.matrixWorld;this.matrixAutoUpdate=!1;this.color=e;a=new Rd(c);a.rotateY(.5*Math.PI);this.material=new L({wireframe:!0,fog:!1});void 0===this.color&&(this.material.vertexColors=2);c=a.getAttribute("position");a.addAttribute("color",new Q(new Float32Array(3*c.count),3));this.add(new xa(a,this.material));
this.update()}function bf(a,c){this.lightProbe=a;this.size=c;a=new qb({defines:{GAMMA_OUTPUT:""},uniforms:{sh:{value:this.lightProbe.sh.coefficients},intensity:{value:this.lightProbe.intensity}},vertexShader:"varying vec3 vNormal;\nvoid main() {\n\tvNormal \x3d normalize( normalMatrix * normal );\n\tgl_Position \x3d projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",fragmentShader:"#define RECIPROCAL_PI 0.318309886\nvec3 inverseTransformDirection( in vec3 normal, in mat4 matrix ) {\n\t// matrix is assumed to be orthogonal\n\treturn normalize( ( vec4( normal, 0.0 ) * matrix ).xyz );\n}\nvec3 linearToOutput( in vec3 a ) {\n\t#ifdef GAMMA_OUTPUT\n\t\treturn pow( a, vec3( 1.0 / float( GAMMA_FACTOR ) ) );\n\t#else\n\t\treturn a;\n\t#endif\n}\n// source: https://graphics.stanford.edu/papers/envmap/envmap.pdf\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\t// normal is assumed to have unit length\n\tfloat x \x3d normal.x, y \x3d normal.y, z \x3d normal.z;\n\t// band 0\n\tvec3 result \x3d shCoefficients[ 0 ] * 0.886227;\n\t// band 1\n\tresult +\x3d shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult +\x3d shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult +\x3d shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\t// band 2\n\tresult +\x3d shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult +\x3d shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult +\x3d shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult +\x3d shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult +\x3d shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nuniform vec3 sh[ 9 ]; // sh coefficients\nuniform float intensity; // light probe intensity\nvarying vec3 vNormal;\nvoid main() {\n\tvec3 normal \x3d normalize( vNormal );\n\tvec3 worldNormal \x3d inverseTransformDirection( normal, viewMatrix );\n\tvec3 irradiance \x3d shGetIrradianceAt( worldNormal, sh );\n\tvec3 outgoingLight \x3d RECIPROCAL_PI * irradiance * intensity;\n\toutgoingLight \x3d linearToOutput( outgoingLight );\n\tgl_FragColor \x3d vec4( outgoingLight, 1.0 );\n}"});
c=new Bd(1,32,16);xa.call(this,c,a);this.onBeforeRender()}function ch(a,c,e,g){a=a||10;c=c||10;e=new I(void 0!==e?e:4473924);g=new I(void 0!==g?g:8947848);var r=c/2,v=a/c,z=a/2;a=[];for(var E=[],F=0,J=0,P=-z;F<=c;F++,P+=v){a.push(-z,0,P,z,0,P);a.push(P,0,-z,P,0,z);var R=F===r?e:g;R.toArray(E,J);J+=3;R.toArray(E,J);J+=3;R.toArray(E,J);J+=3;R.toArray(E,J);J+=3}c=new va;c.addAttribute("position",new ca(a,3));c.addAttribute("color",new ca(E,3));e=new Fb({vertexColors:2});Ib.call(this,c,e)}function dh(a,
c,e,g,r,v){a=a||10;c=c||16;e=e||8;g=g||64;r=new I(void 0!==r?r:4473924);v=new I(void 0!==v?v:8947848);var z=[],E=[],F;for(F=0;F<=c;F++){var J=F/c*2*Math.PI;var P=Math.sin(J)*a;J=Math.cos(J)*a;z.push(0,0,0);z.push(P,0,J);var R=F&1?r:v;E.push(R.r,R.g,R.b);E.push(R.r,R.g,R.b)}for(F=0;F<=e;F++){R=F&1?r:v;var S=a-a/e*F;for(c=0;c<g;c++)J=c/g*2*Math.PI,P=Math.sin(J)*S,J=Math.cos(J)*S,z.push(P,0,J),E.push(R.r,R.g,R.b),J=(c+1)/g*2*Math.PI,P=Math.sin(J)*S,J=Math.cos(J)*S,z.push(P,0,J),E.push(R.r,R.g,R.b)}a=
new va;a.addAttribute("position",new ca(z,3));a.addAttribute("color",new ca(E,3));z=new Fb({vertexColors:2});Ib.call(this,a,z)}function cf(a,c,e,g){this.audio=a;this.range=c||1;this.divisionsInnerAngle=e||16;this.divisionsOuterAngle=g||2;a=new va;a.addAttribute("position",new Q(new Float32Array(3*(3*(this.divisionsInnerAngle+2*this.divisionsOuterAngle)+3)),3));c=new Fb({color:65280});e=new Fb({color:16776960});Vb.call(this,a,[e,c]);this.update()}function eg(a,c,e,g){this.object=a;this.size=void 0!==
c?c:1;a=void 0!==e?e:16776960;g=void 0!==g?g:1;c=0;(e=this.object.geometry)&&e.isGeometry?c=e.faces.length:console.warn("THREE.FaceNormalsHelper: only THREE.Geometry is supported. Use THREE.VertexNormalsHelper, instead.");e=new va;c=new ca(6*c,3);e.addAttribute("position",c);Ib.call(this,e,new Fb({color:a,linewidth:g}));this.matrixAutoUpdate=!1;this.update()}function df(a,c,e){A.call(this);this.light=a;this.light.updateMatrixWorld();this.matrix=a.matrixWorld;this.matrixAutoUpdate=!1;this.color=e;
void 0===c&&(c=1);a=new va;a.addAttribute("position",new ca([-c,c,0,c,c,0,c,-c,0,-c,-c,0,-c,c,0],3));c=new Fb({fog:!1});this.lightPlane=new Vb(a,c);this.add(this.lightPlane);a=new va;a.addAttribute("position",new ca([0,0,0,0,0,1],3));this.targetLine=new Vb(a,c);this.add(this.targetLine);this.update()}function fg(a){function c(V,W,ha){e(V,ha);e(W,ha)}function e(V,W){v.push(0,0,0);z.push(W.r,W.g,W.b);void 0===E[V]&&(E[V]=[]);E[V].push(v.length/3-1)}var g=new va,r=new Fb({color:16777215,vertexColors:1}),
v=[],z=[],E={},F=new I(16755200),J=new I(16711680),P=new I(43775),R=new I(16777215),S=new I(3355443);c("n1","n2",F);c("n2","n4",F);c("n4","n3",F);c("n3","n1",F);c("f1","f2",F);c("f2","f4",F);c("f4","f3",F);c("f3","f1",F);c("n1","f1",F);c("n2","f2",F);c("n3","f3",F);c("n4","f4",F);c("p","n1",J);c("p","n2",J);c("p","n3",J);c("p","n4",J);c("u1","u2",P);c("u2","u3",P);c("u3","u1",P);c("c","t",R);c("p","c",S);c("cn1","cn2",S);c("cn3","cn4",S);c("cf1","cf2",S);c("cf3","cf4",S);g.addAttribute("position",
new ca(v,3));g.addAttribute("color",new ca(z,3));Ib.call(this,g,r);this.camera=a;this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix();this.matrix=a.matrixWorld;this.matrixAutoUpdate=!1;this.pointMap=E;this.update()}function Pb(a,c,e,g,r,v,z){eh.set(r,v,z).unproject(g);a=c[a];if(void 0!==a)for(e=e.getAttribute("position"),c=0,g=a.length;c<g;c++)e.setXYZ(a[c],eh.x,eh.y,eh.z)}function hd(a,c){this.object=a;void 0===c&&(c=16776960);a=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,
4,0,4,1,5,2,6,3,7]);var e=new Float32Array(24),g=new va;g.setIndex(new Q(a,1));g.addAttribute("position",new Q(e,3));Ib.call(this,g,new Fb({color:c}));this.matrixAutoUpdate=!1;this.update()}function gg(a,c){this.type="Box3Helper";this.box=a;c=c||16776960;a=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,4,0,4,1,5,2,6,3,7]);var e=new va;e.setIndex(new Q(a,1));e.addAttribute("position",new ca([1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,-1,-1,1,-1,-1,-1,-1,1,-1,-1],3));Ib.call(this,e,new Fb({color:c}));this.geometry.computeBoundingSphere()}
function hg(a,c,e){this.type="PlaneHelper";this.plane=a;this.size=void 0===c?1:c;a=void 0!==e?e:16776960;c=new va;c.addAttribute("position",new ca([1,-1,1,-1,1,1,-1,-1,1,1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,1,0,0,1,0,0,0],3));c.computeBoundingSphere();Vb.call(this,c,new Fb({color:a}));c=new va;c.addAttribute("position",new ca([1,1,1,-1,1,1,-1,-1,1,1,1,1,-1,-1,1,1,-1,1],3));c.computeBoundingSphere();this.add(new xa(c,new L({color:a,opacity:.2,transparent:!0,depthWrite:!1})))}function id(a,c,e,g,r,v){A.call(this);
void 0===a&&(a=new k(0,0,1));void 0===c&&(c=new k(0,0,0));void 0===e&&(e=1);void 0===g&&(g=16776960);void 0===r&&(r=.2*e);void 0===v&&(v=.2*r);void 0===fh&&(fh=new va,fh.addAttribute("position",new ca([0,0,0,0,1,0],3)),vi=new fd(0,.5,1,5,1),vi.translate(0,-.5,0));this.position.copy(c);this.line=new Vb(fh,new Fb({color:g}));this.line.matrixAutoUpdate=!1;this.add(this.line);this.cone=new xa(vi,new L({color:g}));this.cone.matrixAutoUpdate=!1;this.add(this.cone);this.setDirection(a);this.setLength(e,
r,v)}function ig(a){a=a||1;var c=[0,0,0,a,0,0,0,0,0,0,a,0,0,0,0,0,0,a];a=new va;a.addAttribute("position",new ca(c,3));a.addAttribute("color",new ca([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));c=new Fb({vertexColors:2});Ib.call(this,a,c)}function Zj(a){console.warn("THREE.ClosedSplineCurve3 has been deprecated. Use THREE.CatmullRomCurve3 instead.");Zb.call(this,a);this.type="catmullrom";this.closed=!0}function ak(a){console.warn("THREE.SplineCurve3 has been deprecated. Use THREE.CatmullRomCurve3 instead.");
Zb.call(this,a);this.type="catmullrom"}function wi(a){console.warn("THREE.Spline has been removed. Use THREE.CatmullRomCurve3 instead.");Zb.call(this,a);this.type="catmullrom"}void 0===Number.EPSILON&&(Number.EPSILON=Math.pow(2,-52));void 0===Number.isInteger&&(Number.isInteger=function(a){return"number"===typeof a&&isFinite(a)&&Math.floor(a)===a});void 0===Math.sign&&(Math.sign=function(a){return 0>a?-1:0<a?1:+a});!1==="name"in Function.prototype&&Object.defineProperty(Function.prototype,"name",
{get:function(){return this.toString().match(/^\s*function\s*([^\(\s]*)/)[1]}});void 0===Object.assign&&(Object.assign=function(a){if(void 0===a||null===a)throw new TypeError("Cannot convert undefined or null to object");for(var c=Object(a),e=1;e<arguments.length;e++){var g=arguments[e];if(void 0!==g&&null!==g)for(var r in g)Object.prototype.hasOwnProperty.call(g,r)&&(c[r]=g[r])}return c});Object.assign(d.prototype,{addEventListener:function(a,c){void 0===this._listeners&&(this._listeners={});var e=
this._listeners;void 0===e[a]&&(e[a]=[]);-1===e[a].indexOf(c)&&e[a].push(c)},hasEventListener:function(a,c){if(void 0===this._listeners)return!1;var e=this._listeners;return void 0!==e[a]&&-1!==e[a].indexOf(c)},removeEventListener:function(a,c){void 0!==this._listeners&&(a=this._listeners[a],void 0!==a&&(c=a.indexOf(c),-1!==c&&a.splice(c,1)))},dispatchEvent:function(a){if(void 0!==this._listeners){var c=this._listeners[a.type];if(void 0!==c){a.target=this;c=c.slice(0);for(var e=0,g=c.length;e<g;e++)c[e].call(this,
a)}}}});for(var Yb=[],jg=0;256>jg;jg++)Yb[jg]=(16>jg?"0":"")+jg.toString(16);var hb={DEG2RAD:Math.PI/180,RAD2DEG:180/Math.PI,generateUUID:function(){var a=4294967295*Math.random()|0,c=4294967295*Math.random()|0,e=4294967295*Math.random()|0,g=4294967295*Math.random()|0;return(Yb[a&255]+Yb[a>>8&255]+Yb[a>>16&255]+Yb[a>>24&255]+"-"+Yb[c&255]+Yb[c>>8&255]+"-"+Yb[c>>16&15|64]+Yb[c>>24&255]+"-"+Yb[e&63|128]+Yb[e>>8&255]+"-"+Yb[e>>16&255]+Yb[e>>24&255]+Yb[g&255]+Yb[g>>8&255]+Yb[g>>16&255]+Yb[g>>24&255]).toUpperCase()},
clamp:function(a,c,e){return Math.max(c,Math.min(e,a))},euclideanModulo:function(a,c){return(a%c+c)%c},mapLinear:function(a,c,e,g,r){return g+(a-c)*(r-g)/(e-c)},lerp:function(a,c,e){return(1-e)*a+e*c},smoothstep:function(a,c,e){if(a<=c)return 0;if(a>=e)return 1;a=(a-c)/(e-c);return a*a*(3-2*a)},smootherstep:function(a,c,e){if(a<=c)return 0;if(a>=e)return 1;a=(a-c)/(e-c);return a*a*a*(a*(6*a-15)+10)},randInt:function(a,c){return a+Math.floor(Math.random()*(c-a+1))},randFloat:function(a,c){return a+
Math.random()*(c-a)},randFloatSpread:function(a){return a*(.5-Math.random())},degToRad:function(a){return a*hb.DEG2RAD},radToDeg:function(a){return a*hb.RAD2DEG},isPowerOfTwo:function(a){return 0===(a&a-1)&&0!==a},ceilPowerOfTwo:function(a){return Math.pow(2,Math.ceil(Math.log(a)/Math.LN2))},floorPowerOfTwo:function(a){return Math.pow(2,Math.floor(Math.log(a)/Math.LN2))}};Object.defineProperties(f.prototype,{width:{get:function(){return this.x},set:function(a){this.x=a}},height:{get:function(){return this.y},
set:function(a){this.y=a}}});Object.assign(f.prototype,{isVector2:!0,set:function(a,c){this.x=a;this.y=c;return this},setScalar:function(a){this.y=this.x=a;return this},setX:function(a){this.x=a;return this},setY:function(a){this.y=a;return this},setComponent:function(a,c){switch(a){case 0:this.x=c;break;case 1:this.y=c;break;default:throw Error("index is out of range: "+a);}return this},getComponent:function(a){switch(a){case 0:return this.x;case 1:return this.y;default:throw Error("index is out of range: "+
a);}},clone:function(){return new this.constructor(this.x,this.y)},copy:function(a){this.x=a.x;this.y=a.y;return this},add:function(a,c){if(void 0!==c)return console.warn("THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(a,c);this.x+=a.x;this.y+=a.y;return this},addScalar:function(a){this.x+=a;this.y+=a;return this},addVectors:function(a,c){this.x=a.x+c.x;this.y=a.y+c.y;return this},addScaledVector:function(a,c){this.x+=a.x*c;this.y+=a.y*c;return this},
sub:function(a,c){if(void 0!==c)return console.warn("THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(a,c);this.x-=a.x;this.y-=a.y;return this},subScalar:function(a){this.x-=a;this.y-=a;return this},subVectors:function(a,c){this.x=a.x-c.x;this.y=a.y-c.y;return this},multiply:function(a){this.x*=a.x;this.y*=a.y;return this},multiplyScalar:function(a){this.x*=a;this.y*=a;return this},divide:function(a){this.x/=a.x;this.y/=a.y;return this},divideScalar:function(a){return this.multiplyScalar(1/
a)},applyMatrix3:function(a){var c=this.x,e=this.y;a=a.elements;this.x=a[0]*c+a[3]*e+a[6];this.y=a[1]*c+a[4]*e+a[7];return this},min:function(a){this.x=Math.min(this.x,a.x);this.y=Math.min(this.y,a.y);return this},max:function(a){this.x=Math.max(this.x,a.x);this.y=Math.max(this.y,a.y);return this},clamp:function(a,c){this.x=Math.max(a.x,Math.min(c.x,this.x));this.y=Math.max(a.y,Math.min(c.y,this.y));return this},clampScalar:function(a,c){this.x=Math.max(a,Math.min(c,this.x));this.y=Math.max(a,Math.min(c,
this.y));return this},clampLength:function(a,c){var e=this.length();return this.divideScalar(e||1).multiplyScalar(Math.max(a,Math.min(c,e)))},floor:function(){this.x=Math.floor(this.x);this.y=Math.floor(this.y);return this},ceil:function(){this.x=Math.ceil(this.x);this.y=Math.ceil(this.y);return this},round:function(){this.x=Math.round(this.x);this.y=Math.round(this.y);return this},roundToZero:function(){this.x=0>this.x?Math.ceil(this.x):Math.floor(this.x);this.y=0>this.y?Math.ceil(this.y):Math.floor(this.y);
return this},negate:function(){this.x=-this.x;this.y=-this.y;return this},dot:function(a){return this.x*a.x+this.y*a.y},cross:function(a){return this.x*a.y-this.y*a.x},lengthSq:function(){return this.x*this.x+this.y*this.y},length:function(){return Math.sqrt(this.x*this.x+this.y*this.y)},manhattanLength:function(){return Math.abs(this.x)+Math.abs(this.y)},normalize:function(){return this.divideScalar(this.length()||1)},angle:function(){var a=Math.atan2(this.y,this.x);0>a&&(a+=2*Math.PI);return a},
distanceTo:function(a){return Math.sqrt(this.distanceToSquared(a))},distanceToSquared:function(a){var c=this.x-a.x;a=this.y-a.y;return c*c+a*a},manhattanDistanceTo:function(a){return Math.abs(this.x-a.x)+Math.abs(this.y-a.y)},setLength:function(a){return this.normalize().multiplyScalar(a)},lerp:function(a,c){this.x+=(a.x-this.x)*c;this.y+=(a.y-this.y)*c;return this},lerpVectors:function(a,c,e){return this.subVectors(c,a).multiplyScalar(e).add(a)},equals:function(a){return a.x===this.x&&a.y===this.y},
fromArray:function(a,c){void 0===c&&(c=0);this.x=a[c];this.y=a[c+1];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this.x;a[c+1]=this.y;return a},fromBufferAttribute:function(a,c,e){void 0!==e&&console.warn("THREE.Vector2: offset has been removed from .fromBufferAttribute().");this.x=a.getX(c);this.y=a.getY(c);return this},rotateAround:function(a,c){var e=Math.cos(c);c=Math.sin(c);var g=this.x-a.x,r=this.y-a.y;this.x=g*e-r*c+a.x;this.y=g*c+r*e+a.y;return this}});Object.assign(h,
{slerp:function(a,c,e,g){return e.copy(a).slerp(c,g)},slerpFlat:function(a,c,e,g,r,v,z){var E=e[g+0],F=e[g+1],J=e[g+2];e=e[g+3];g=r[v+0];var P=r[v+1],R=r[v+2];r=r[v+3];if(e!==r||E!==g||F!==P||J!==R){v=1-z;var S=E*g+F*P+J*R+e*r,V=0<=S?1:-1,W=1-S*S;W>Number.EPSILON&&(W=Math.sqrt(W),S=Math.atan2(W,S*V),v=Math.sin(v*S)/W,z=Math.sin(z*S)/W);V*=z;E=E*v+g*V;F=F*v+P*V;J=J*v+R*V;e=e*v+r*V;v===1-z&&(z=1/Math.sqrt(E*E+F*F+J*J+e*e),E*=z,F*=z,J*=z,e*=z)}a[c]=E;a[c+1]=F;a[c+2]=J;a[c+3]=e}});Object.defineProperties(h.prototype,
{x:{get:function(){return this._x},set:function(a){this._x=a;this._onChangeCallback()}},y:{get:function(){return this._y},set:function(a){this._y=a;this._onChangeCallback()}},z:{get:function(){return this._z},set:function(a){this._z=a;this._onChangeCallback()}},w:{get:function(){return this._w},set:function(a){this._w=a;this._onChangeCallback()}}});Object.assign(h.prototype,{isQuaternion:!0,set:function(a,c,e,g){this._x=a;this._y=c;this._z=e;this._w=g;this._onChangeCallback();return this},clone:function(){return new this.constructor(this._x,
this._y,this._z,this._w)},copy:function(a){this._x=a.x;this._y=a.y;this._z=a.z;this._w=a.w;this._onChangeCallback();return this},setFromEuler:function(a,c){if(!a||!a.isEuler)throw Error("THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.");var e=a._x,g=a._y,r=a._z;a=a.order;var v=Math.cos,z=Math.sin,E=v(e/2),F=v(g/2);v=v(r/2);e=z(e/2);g=z(g/2);r=z(r/2);"XYZ"===a?(this._x=e*F*v+E*g*r,this._y=E*g*v-e*F*r,this._z=E*F*r+e*g*v,this._w=E*F*v-e*g*r):"YXZ"===
a?(this._x=e*F*v+E*g*r,this._y=E*g*v-e*F*r,this._z=E*F*r-e*g*v,this._w=E*F*v+e*g*r):"ZXY"===a?(this._x=e*F*v-E*g*r,this._y=E*g*v+e*F*r,this._z=E*F*r+e*g*v,this._w=E*F*v-e*g*r):"ZYX"===a?(this._x=e*F*v-E*g*r,this._y=E*g*v+e*F*r,this._z=E*F*r-e*g*v,this._w=E*F*v+e*g*r):"YZX"===a?(this._x=e*F*v+E*g*r,this._y=E*g*v+e*F*r,this._z=E*F*r-e*g*v,this._w=E*F*v-e*g*r):"XZY"===a&&(this._x=e*F*v-E*g*r,this._y=E*g*v-e*F*r,this._z=E*F*r+e*g*v,this._w=E*F*v+e*g*r);!1!==c&&this._onChangeCallback();return this},setFromAxisAngle:function(a,
c){c/=2;var e=Math.sin(c);this._x=a.x*e;this._y=a.y*e;this._z=a.z*e;this._w=Math.cos(c);this._onChangeCallback();return this},setFromRotationMatrix:function(a){var c=a.elements,e=c[0];a=c[4];var g=c[8],r=c[1],v=c[5],z=c[9],E=c[2],F=c[6];c=c[10];var J=e+v+c;0<J?(e=.5/Math.sqrt(J+1),this._w=.25/e,this._x=(F-z)*e,this._y=(g-E)*e,this._z=(r-a)*e):e>v&&e>c?(e=2*Math.sqrt(1+e-v-c),this._w=(F-z)/e,this._x=.25*e,this._y=(a+r)/e,this._z=(g+E)/e):v>c?(e=2*Math.sqrt(1+v-e-c),this._w=(g-E)/e,this._x=(a+r)/e,
this._y=.25*e,this._z=(z+F)/e):(e=2*Math.sqrt(1+c-e-v),this._w=(r-a)/e,this._x=(g+E)/e,this._y=(z+F)/e,this._z=.25*e);this._onChangeCallback();return this},setFromUnitVectors:function(a,c){var e=a.dot(c)+1;1E-6>e?(e=0,Math.abs(a.x)>Math.abs(a.z)?(this._x=-a.y,this._y=a.x,this._z=0):(this._x=0,this._y=-a.z,this._z=a.y)):(this._x=a.y*c.z-a.z*c.y,this._y=a.z*c.x-a.x*c.z,this._z=a.x*c.y-a.y*c.x);this._w=e;return this.normalize()},angleTo:function(a){return 2*Math.acos(Math.abs(hb.clamp(this.dot(a),-1,
1)))},rotateTowards:function(a,c){var e=this.angleTo(a);if(0===e)return this;this.slerp(a,Math.min(1,c/e));return this},inverse:function(){return this.conjugate()},conjugate:function(){this._x*=-1;this._y*=-1;this._z*=-1;this._onChangeCallback();return this},dot:function(a){return this._x*a._x+this._y*a._y+this._z*a._z+this._w*a._w},lengthSq:function(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w},length:function(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*
this._z+this._w*this._w)},normalize:function(){var a=this.length();0===a?(this._z=this._y=this._x=0,this._w=1):(a=1/a,this._x*=a,this._y*=a,this._z*=a,this._w*=a);this._onChangeCallback();return this},multiply:function(a,c){return void 0!==c?(console.warn("THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead."),this.multiplyQuaternions(a,c)):this.multiplyQuaternions(this,a)},premultiply:function(a){return this.multiplyQuaternions(a,this)},multiplyQuaternions:function(a,
c){var e=a._x,g=a._y,r=a._z;a=a._w;var v=c._x,z=c._y,E=c._z;c=c._w;this._x=e*c+a*v+g*E-r*z;this._y=g*c+a*z+r*v-e*E;this._z=r*c+a*E+e*z-g*v;this._w=a*c-e*v-g*z-r*E;this._onChangeCallback();return this},slerp:function(a,c){if(0===c)return this;if(1===c)return this.copy(a);var e=this._x,g=this._y,r=this._z,v=this._w,z=v*a._w+e*a._x+g*a._y+r*a._z;0>z?(this._w=-a._w,this._x=-a._x,this._y=-a._y,this._z=-a._z,z=-z):this.copy(a);if(1<=z)return this._w=v,this._x=e,this._y=g,this._z=r,this;a=1-z*z;if(a<=Number.EPSILON)return z=
1-c,this._w=z*v+c*this._w,this._x=z*e+c*this._x,this._y=z*g+c*this._y,this._z=z*r+c*this._z,this.normalize(),this._onChangeCallback(),this;a=Math.sqrt(a);var E=Math.atan2(a,z);z=Math.sin((1-c)*E)/a;c=Math.sin(c*E)/a;this._w=v*z+this._w*c;this._x=e*z+this._x*c;this._y=g*z+this._y*c;this._z=r*z+this._z*c;this._onChangeCallback();return this},equals:function(a){return a._x===this._x&&a._y===this._y&&a._z===this._z&&a._w===this._w},fromArray:function(a,c){void 0===c&&(c=0);this._x=a[c];this._y=a[c+1];
this._z=a[c+2];this._w=a[c+3];this._onChangeCallback();return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this._x;a[c+1]=this._y;a[c+2]=this._z;a[c+3]=this._w;return a},_onChange:function(a){this._onChangeCallback=a;return this},_onChangeCallback:function(){}});var xi=new k,bk=new h;Object.assign(k.prototype,{isVector3:!0,set:function(a,c,e){this.x=a;this.y=c;this.z=e;return this},setScalar:function(a){this.z=this.y=this.x=a;return this},setX:function(a){this.x=a;return this},
setY:function(a){this.y=a;return this},setZ:function(a){this.z=a;return this},setComponent:function(a,c){switch(a){case 0:this.x=c;break;case 1:this.y=c;break;case 2:this.z=c;break;default:throw Error("index is out of range: "+a);}return this},getComponent:function(a){switch(a){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw Error("index is out of range: "+a);}},clone:function(){return new this.constructor(this.x,this.y,this.z)},copy:function(a){this.x=a.x;this.y=a.y;
this.z=a.z;return this},add:function(a,c){if(void 0!==c)return console.warn("THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(a,c);this.x+=a.x;this.y+=a.y;this.z+=a.z;return this},addScalar:function(a){this.x+=a;this.y+=a;this.z+=a;return this},addVectors:function(a,c){this.x=a.x+c.x;this.y=a.y+c.y;this.z=a.z+c.z;return this},addScaledVector:function(a,c){this.x+=a.x*c;this.y+=a.y*c;this.z+=a.z*c;return this},sub:function(a,c){if(void 0!==c)return console.warn("THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),
this.subVectors(a,c);this.x-=a.x;this.y-=a.y;this.z-=a.z;return this},subScalar:function(a){this.x-=a;this.y-=a;this.z-=a;return this},subVectors:function(a,c){this.x=a.x-c.x;this.y=a.y-c.y;this.z=a.z-c.z;return this},multiply:function(a,c){if(void 0!==c)return console.warn("THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead."),this.multiplyVectors(a,c);this.x*=a.x;this.y*=a.y;this.z*=a.z;return this},multiplyScalar:function(a){this.x*=a;this.y*=a;this.z*=
a;return this},multiplyVectors:function(a,c){this.x=a.x*c.x;this.y=a.y*c.y;this.z=a.z*c.z;return this},applyEuler:function(a){a&&a.isEuler||console.error("THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.");return this.applyQuaternion(bk.setFromEuler(a))},applyAxisAngle:function(a,c){return this.applyQuaternion(bk.setFromAxisAngle(a,c))},applyMatrix3:function(a){var c=this.x,e=this.y,g=this.z;a=a.elements;this.x=a[0]*c+a[3]*e+a[6]*g;this.y=a[1]*c+a[4]*e+a[7]*
g;this.z=a[2]*c+a[5]*e+a[8]*g;return this},applyMatrix4:function(a){var c=this.x,e=this.y,g=this.z;a=a.elements;var r=1/(a[3]*c+a[7]*e+a[11]*g+a[15]);this.x=(a[0]*c+a[4]*e+a[8]*g+a[12])*r;this.y=(a[1]*c+a[5]*e+a[9]*g+a[13])*r;this.z=(a[2]*c+a[6]*e+a[10]*g+a[14])*r;return this},applyQuaternion:function(a){var c=this.x,e=this.y,g=this.z,r=a.x,v=a.y,z=a.z;a=a.w;var E=a*c+v*g-z*e,F=a*e+z*c-r*g,J=a*g+r*e-v*c;c=-r*c-v*e-z*g;this.x=E*a+c*-r+F*-z-J*-v;this.y=F*a+c*-v+J*-r-E*-z;this.z=J*a+c*-z+E*-v-F*-r;return this},
project:function(a){return this.applyMatrix4(a.matrixWorldInverse).applyMatrix4(a.projectionMatrix)},unproject:function(a){return this.applyMatrix4(a.projectionMatrixInverse).applyMatrix4(a.matrixWorld)},transformDirection:function(a){var c=this.x,e=this.y,g=this.z;a=a.elements;this.x=a[0]*c+a[4]*e+a[8]*g;this.y=a[1]*c+a[5]*e+a[9]*g;this.z=a[2]*c+a[6]*e+a[10]*g;return this.normalize()},divide:function(a){this.x/=a.x;this.y/=a.y;this.z/=a.z;return this},divideScalar:function(a){return this.multiplyScalar(1/
a)},min:function(a){this.x=Math.min(this.x,a.x);this.y=Math.min(this.y,a.y);this.z=Math.min(this.z,a.z);return this},max:function(a){this.x=Math.max(this.x,a.x);this.y=Math.max(this.y,a.y);this.z=Math.max(this.z,a.z);return this},clamp:function(a,c){this.x=Math.max(a.x,Math.min(c.x,this.x));this.y=Math.max(a.y,Math.min(c.y,this.y));this.z=Math.max(a.z,Math.min(c.z,this.z));return this},clampScalar:function(a,c){this.x=Math.max(a,Math.min(c,this.x));this.y=Math.max(a,Math.min(c,this.y));this.z=Math.max(a,
Math.min(c,this.z));return this},clampLength:function(a,c){var e=this.length();return this.divideScalar(e||1).multiplyScalar(Math.max(a,Math.min(c,e)))},floor:function(){this.x=Math.floor(this.x);this.y=Math.floor(this.y);this.z=Math.floor(this.z);return this},ceil:function(){this.x=Math.ceil(this.x);this.y=Math.ceil(this.y);this.z=Math.ceil(this.z);return this},round:function(){this.x=Math.round(this.x);this.y=Math.round(this.y);this.z=Math.round(this.z);return this},roundToZero:function(){this.x=
0>this.x?Math.ceil(this.x):Math.floor(this.x);this.y=0>this.y?Math.ceil(this.y):Math.floor(this.y);this.z=0>this.z?Math.ceil(this.z):Math.floor(this.z);return this},negate:function(){this.x=-this.x;this.y=-this.y;this.z=-this.z;return this},dot:function(a){return this.x*a.x+this.y*a.y+this.z*a.z},lengthSq:function(){return this.x*this.x+this.y*this.y+this.z*this.z},length:function(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)},manhattanLength:function(){return Math.abs(this.x)+Math.abs(this.y)+
Math.abs(this.z)},normalize:function(){return this.divideScalar(this.length()||1)},setLength:function(a){return this.normalize().multiplyScalar(a)},lerp:function(a,c){this.x+=(a.x-this.x)*c;this.y+=(a.y-this.y)*c;this.z+=(a.z-this.z)*c;return this},lerpVectors:function(a,c,e){return this.subVectors(c,a).multiplyScalar(e).add(a)},cross:function(a,c){return void 0!==c?(console.warn("THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead."),this.crossVectors(a,c)):this.crossVectors(this,
a)},crossVectors:function(a,c){var e=a.x,g=a.y;a=a.z;var r=c.x,v=c.y;c=c.z;this.x=g*c-a*v;this.y=a*r-e*c;this.z=e*v-g*r;return this},projectOnVector:function(a){var c=a.dot(this)/a.lengthSq();return this.copy(a).multiplyScalar(c)},projectOnPlane:function(a){xi.copy(this).projectOnVector(a);return this.sub(xi)},reflect:function(a){return this.sub(xi.copy(a).multiplyScalar(2*this.dot(a)))},angleTo:function(a){return Math.acos(hb.clamp(this.dot(a)/Math.sqrt(this.lengthSq()*a.lengthSq()),-1,1))},distanceTo:function(a){return Math.sqrt(this.distanceToSquared(a))},
distanceToSquared:function(a){var c=this.x-a.x,e=this.y-a.y;a=this.z-a.z;return c*c+e*e+a*a},manhattanDistanceTo:function(a){return Math.abs(this.x-a.x)+Math.abs(this.y-a.y)+Math.abs(this.z-a.z)},setFromSpherical:function(a){return this.setFromSphericalCoords(a.radius,a.phi,a.theta)},setFromSphericalCoords:function(a,c,e){var g=Math.sin(c)*a;this.x=g*Math.sin(e);this.y=Math.cos(c)*a;this.z=g*Math.cos(e);return this},setFromCylindrical:function(a){return this.setFromCylindricalCoords(a.radius,a.theta,
a.y)},setFromCylindricalCoords:function(a,c,e){this.x=a*Math.sin(c);this.y=e;this.z=a*Math.cos(c);return this},setFromMatrixPosition:function(a){a=a.elements;this.x=a[12];this.y=a[13];this.z=a[14];return this},setFromMatrixScale:function(a){var c=this.setFromMatrixColumn(a,0).length(),e=this.setFromMatrixColumn(a,1).length();a=this.setFromMatrixColumn(a,2).length();this.x=c;this.y=e;this.z=a;return this},setFromMatrixColumn:function(a,c){return this.fromArray(a.elements,4*c)},equals:function(a){return a.x===
this.x&&a.y===this.y&&a.z===this.z},fromArray:function(a,c){void 0===c&&(c=0);this.x=a[c];this.y=a[c+1];this.z=a[c+2];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this.x;a[c+1]=this.y;a[c+2]=this.z;return a},fromBufferAttribute:function(a,c,e){void 0!==e&&console.warn("THREE.Vector3: offset has been removed from .fromBufferAttribute().");this.x=a.getX(c);this.y=a.getY(c);this.z=a.getZ(c);return this}});var ee=new k;Object.assign(t.prototype,{isMatrix3:!0,set:function(a,
c,e,g,r,v,z,E,F){var J=this.elements;J[0]=a;J[1]=g;J[2]=z;J[3]=c;J[4]=r;J[5]=E;J[6]=e;J[7]=v;J[8]=F;return this},identity:function(){this.set(1,0,0,0,1,0,0,0,1);return this},clone:function(){return(new this.constructor).fromArray(this.elements)},copy:function(a){var c=this.elements;a=a.elements;c[0]=a[0];c[1]=a[1];c[2]=a[2];c[3]=a[3];c[4]=a[4];c[5]=a[5];c[6]=a[6];c[7]=a[7];c[8]=a[8];return this},setFromMatrix4:function(a){a=a.elements;this.set(a[0],a[4],a[8],a[1],a[5],a[9],a[2],a[6],a[10]);return this},
applyToBufferAttribute:function(a){for(var c=0,e=a.count;c<e;c++)ee.x=a.getX(c),ee.y=a.getY(c),ee.z=a.getZ(c),ee.applyMatrix3(this),a.setXYZ(c,ee.x,ee.y,ee.z);return a},multiply:function(a){return this.multiplyMatrices(this,a)},premultiply:function(a){return this.multiplyMatrices(a,this)},multiplyMatrices:function(a,c){var e=a.elements,g=c.elements;c=this.elements;a=e[0];var r=e[3],v=e[6],z=e[1],E=e[4],F=e[7],J=e[2],P=e[5];e=e[8];var R=g[0],S=g[3],V=g[6],W=g[1],ha=g[4],fa=g[7],ra=g[2],pa=g[5];g=g[8];
c[0]=a*R+r*W+v*ra;c[3]=a*S+r*ha+v*pa;c[6]=a*V+r*fa+v*g;c[1]=z*R+E*W+F*ra;c[4]=z*S+E*ha+F*pa;c[7]=z*V+E*fa+F*g;c[2]=J*R+P*W+e*ra;c[5]=J*S+P*ha+e*pa;c[8]=J*V+P*fa+e*g;return this},multiplyScalar:function(a){var c=this.elements;c[0]*=a;c[3]*=a;c[6]*=a;c[1]*=a;c[4]*=a;c[7]*=a;c[2]*=a;c[5]*=a;c[8]*=a;return this},determinant:function(){var a=this.elements,c=a[0],e=a[1],g=a[2],r=a[3],v=a[4],z=a[5],E=a[6],F=a[7];a=a[8];return c*v*a-c*z*F-e*r*a+e*z*E+g*r*F-g*v*E},getInverse:function(a,c){a&&a.isMatrix4&&
console.error("THREE.Matrix3: .getInverse() no longer takes a Matrix4 argument.");var e=a.elements;a=this.elements;var g=e[0],r=e[1],v=e[2],z=e[3],E=e[4],F=e[5],J=e[6],P=e[7];e=e[8];var R=e*E-F*P,S=F*J-e*z,V=P*z-E*J,W=g*R+r*S+v*V;if(0===W){if(!0===c)throw Error("THREE.Matrix3: .getInverse() can't invert matrix, determinant is 0");console.warn("THREE.Matrix3: .getInverse() can't invert matrix, determinant is 0");return this.identity()}c=1/W;a[0]=R*c;a[1]=(v*P-e*r)*c;a[2]=(F*r-v*E)*c;a[3]=S*c;a[4]=
(e*g-v*J)*c;a[5]=(v*z-F*g)*c;a[6]=V*c;a[7]=(r*J-P*g)*c;a[8]=(E*g-r*z)*c;return this},transpose:function(){var a=this.elements;var c=a[1];a[1]=a[3];a[3]=c;c=a[2];a[2]=a[6];a[6]=c;c=a[5];a[5]=a[7];a[7]=c;return this},getNormalMatrix:function(a){return this.setFromMatrix4(a).getInverse(this).transpose()},transposeIntoArray:function(a){var c=this.elements;a[0]=c[0];a[1]=c[3];a[2]=c[6];a[3]=c[1];a[4]=c[4];a[5]=c[7];a[6]=c[2];a[7]=c[5];a[8]=c[8];return this},setUvTransform:function(a,c,e,g,r,v,z){var E=
Math.cos(r);r=Math.sin(r);this.set(e*E,e*r,-e*(E*v+r*z)+v+a,-g*r,g*E,-g*(-r*v+E*z)+z+c,0,0,1)},scale:function(a,c){var e=this.elements;e[0]*=a;e[3]*=a;e[6]*=a;e[1]*=c;e[4]*=c;e[7]*=c;return this},rotate:function(a){var c=Math.cos(a);a=Math.sin(a);var e=this.elements,g=e[0],r=e[3],v=e[6],z=e[1],E=e[4],F=e[7];e[0]=c*g+a*z;e[3]=c*r+a*E;e[6]=c*v+a*F;e[1]=-a*g+c*z;e[4]=-a*r+c*E;e[7]=-a*v+c*F;return this},translate:function(a,c){var e=this.elements;e[0]+=a*e[2];e[3]+=a*e[5];e[6]+=a*e[8];e[1]+=c*e[2];e[4]+=
c*e[5];e[7]+=c*e[8];return this},equals:function(a){var c=this.elements;a=a.elements;for(var e=0;9>e;e++)if(c[e]!==a[e])return!1;return!0},fromArray:function(a,c){void 0===c&&(c=0);for(var e=0;9>e;e++)this.elements[e]=a[e+c];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);var e=this.elements;a[c]=e[0];a[c+1]=e[1];a[c+2]=e[2];a[c+3]=e[3];a[c+4]=e[4];a[c+5]=e[5];a[c+6]=e[6];a[c+7]=e[7];a[c+8]=e[8];return a}});var ef,Dd={getDataURL:function(a){if("undefined"==typeof HTMLCanvasElement)return a.src;
if(!(a instanceof HTMLCanvasElement)){void 0===ef&&(ef=document.createElementNS("http://www.w3.org/1999/xhtml","canvas"));ef.width=a.width;ef.height=a.height;var c=ef.getContext("2d");a instanceof ImageData?c.putImageData(a,0,0):c.drawImage(a,0,0,a.width,a.height);a=ef}return 2048<a.width||2048<a.height?a.toDataURL("image/jpeg",.6):a.toDataURL("image/png")}},Nk=0;l.DEFAULT_IMAGE=void 0;l.DEFAULT_MAPPING=300;l.prototype=Object.assign(Object.create(d.prototype),{constructor:l,isTexture:!0,updateMatrix:function(){this.matrix.setUvTransform(this.offset.x,
this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.name=a.name;this.image=a.image;this.mipmaps=a.mipmaps.slice(0);this.mapping=a.mapping;this.wrapS=a.wrapS;this.wrapT=a.wrapT;this.magFilter=a.magFilter;this.minFilter=a.minFilter;this.anisotropy=a.anisotropy;this.format=a.format;this.type=a.type;this.offset.copy(a.offset);this.repeat.copy(a.repeat);this.center.copy(a.center);this.rotation=
a.rotation;this.matrixAutoUpdate=a.matrixAutoUpdate;this.matrix.copy(a.matrix);this.generateMipmaps=a.generateMipmaps;this.premultiplyAlpha=a.premultiplyAlpha;this.flipY=a.flipY;this.unpackAlignment=a.unpackAlignment;this.encoding=a.encoding;return this},toJSON:function(a){var c=void 0===a||"string"===typeof a;if(!c&&void 0!==a.textures[this.uuid])return a.textures[this.uuid];var e={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,mapping:this.mapping,
repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){var g=this.image;void 0===g.uuid&&(g.uuid=hb.generateUUID());if(!c&&void 0===a.images[g.uuid]){if(Array.isArray(g)){var r=
[];for(var v=0,z=g.length;v<z;v++)r.push(Dd.getDataURL(g[v]))}else r=Dd.getDataURL(g);a.images[g.uuid]={uuid:g.uuid,url:r}}e.image=g.uuid}c||(a.textures[this.uuid]=e);return e},dispose:function(){this.dispatchEvent({type:"dispose"})},transformUv:function(a){if(300!==this.mapping)return a;a.applyMatrix3(this.matrix);if(0>a.x||1<a.x)switch(this.wrapS){case 1E3:a.x-=Math.floor(a.x);break;case 1001:a.x=0>a.x?0:1;break;case 1002:a.x=1===Math.abs(Math.floor(a.x)%2)?Math.ceil(a.x)-a.x:a.x-Math.floor(a.x)}if(0>
a.y||1<a.y)switch(this.wrapT){case 1E3:a.y-=Math.floor(a.y);break;case 1001:a.y=0>a.y?0:1;break;case 1002:a.y=1===Math.abs(Math.floor(a.y)%2)?Math.ceil(a.y)-a.y:a.y-Math.floor(a.y)}this.flipY&&(a.y=1-a.y);return a}});Object.defineProperty(l.prototype,"needsUpdate",{set:function(a){!0===a&&this.version++}});Object.defineProperties(p.prototype,{width:{get:function(){return this.z},set:function(a){this.z=a}},height:{get:function(){return this.w},set:function(a){this.w=a}}});Object.assign(p.prototype,
{isVector4:!0,set:function(a,c,e,g){this.x=a;this.y=c;this.z=e;this.w=g;return this},setScalar:function(a){this.w=this.z=this.y=this.x=a;return this},setX:function(a){this.x=a;return this},setY:function(a){this.y=a;return this},setZ:function(a){this.z=a;return this},setW:function(a){this.w=a;return this},setComponent:function(a,c){switch(a){case 0:this.x=c;break;case 1:this.y=c;break;case 2:this.z=c;break;case 3:this.w=c;break;default:throw Error("index is out of range: "+a);}return this},getComponent:function(a){switch(a){case 0:return this.x;
case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw Error("index is out of range: "+a);}},clone:function(){return new this.constructor(this.x,this.y,this.z,this.w)},copy:function(a){this.x=a.x;this.y=a.y;this.z=a.z;this.w=void 0!==a.w?a.w:1;return this},add:function(a,c){if(void 0!==c)return console.warn("THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(a,c);this.x+=a.x;this.y+=a.y;this.z+=a.z;this.w+=a.w;return this},
addScalar:function(a){this.x+=a;this.y+=a;this.z+=a;this.w+=a;return this},addVectors:function(a,c){this.x=a.x+c.x;this.y=a.y+c.y;this.z=a.z+c.z;this.w=a.w+c.w;return this},addScaledVector:function(a,c){this.x+=a.x*c;this.y+=a.y*c;this.z+=a.z*c;this.w+=a.w*c;return this},sub:function(a,c){if(void 0!==c)return console.warn("THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(a,c);this.x-=a.x;this.y-=a.y;this.z-=a.z;this.w-=a.w;return this},subScalar:function(a){this.x-=
a;this.y-=a;this.z-=a;this.w-=a;return this},subVectors:function(a,c){this.x=a.x-c.x;this.y=a.y-c.y;this.z=a.z-c.z;this.w=a.w-c.w;return this},multiplyScalar:function(a){this.x*=a;this.y*=a;this.z*=a;this.w*=a;return this},applyMatrix4:function(a){var c=this.x,e=this.y,g=this.z,r=this.w;a=a.elements;this.x=a[0]*c+a[4]*e+a[8]*g+a[12]*r;this.y=a[1]*c+a[5]*e+a[9]*g+a[13]*r;this.z=a[2]*c+a[6]*e+a[10]*g+a[14]*r;this.w=a[3]*c+a[7]*e+a[11]*g+a[15]*r;return this},divideScalar:function(a){return this.multiplyScalar(1/
a)},setAxisAngleFromQuaternion:function(a){this.w=2*Math.acos(a.w);var c=Math.sqrt(1-a.w*a.w);1E-4>c?(this.x=1,this.z=this.y=0):(this.x=a.x/c,this.y=a.y/c,this.z=a.z/c);return this},setAxisAngleFromRotationMatrix:function(a){a=a.elements;var c=a[0];var e=a[4];var g=a[8],r=a[1],v=a[5],z=a[9];var E=a[2];var F=a[6];var J=a[10];if(.01>Math.abs(e-r)&&.01>Math.abs(g-E)&&.01>Math.abs(z-F)){if(.1>Math.abs(e+r)&&.1>Math.abs(g+E)&&.1>Math.abs(z+F)&&.1>Math.abs(c+v+J-3))return this.set(1,0,0,0),this;a=Math.PI;
c=(c+1)/2;v=(v+1)/2;J=(J+1)/2;e=(e+r)/4;g=(g+E)/4;z=(z+F)/4;c>v&&c>J?.01>c?(F=0,e=E=.707106781):(F=Math.sqrt(c),E=e/F,e=g/F):v>J?.01>v?(F=.707106781,E=0,e=.707106781):(E=Math.sqrt(v),F=e/E,e=z/E):.01>J?(E=F=.707106781,e=0):(e=Math.sqrt(J),F=g/e,E=z/e);this.set(F,E,e,a);return this}a=Math.sqrt((F-z)*(F-z)+(g-E)*(g-E)+(r-e)*(r-e));.001>Math.abs(a)&&(a=1);this.x=(F-z)/a;this.y=(g-E)/a;this.z=(r-e)/a;this.w=Math.acos((c+v+J-1)/2);return this},min:function(a){this.x=Math.min(this.x,a.x);this.y=Math.min(this.y,
a.y);this.z=Math.min(this.z,a.z);this.w=Math.min(this.w,a.w);return this},max:function(a){this.x=Math.max(this.x,a.x);this.y=Math.max(this.y,a.y);this.z=Math.max(this.z,a.z);this.w=Math.max(this.w,a.w);return this},clamp:function(a,c){this.x=Math.max(a.x,Math.min(c.x,this.x));this.y=Math.max(a.y,Math.min(c.y,this.y));this.z=Math.max(a.z,Math.min(c.z,this.z));this.w=Math.max(a.w,Math.min(c.w,this.w));return this},clampScalar:function(a,c){this.x=Math.max(a,Math.min(c,this.x));this.y=Math.max(a,Math.min(c,
this.y));this.z=Math.max(a,Math.min(c,this.z));this.w=Math.max(a,Math.min(c,this.w));return this},clampLength:function(a,c){var e=this.length();return this.divideScalar(e||1).multiplyScalar(Math.max(a,Math.min(c,e)))},floor:function(){this.x=Math.floor(this.x);this.y=Math.floor(this.y);this.z=Math.floor(this.z);this.w=Math.floor(this.w);return this},ceil:function(){this.x=Math.ceil(this.x);this.y=Math.ceil(this.y);this.z=Math.ceil(this.z);this.w=Math.ceil(this.w);return this},round:function(){this.x=
Math.round(this.x);this.y=Math.round(this.y);this.z=Math.round(this.z);this.w=Math.round(this.w);return this},roundToZero:function(){this.x=0>this.x?Math.ceil(this.x):Math.floor(this.x);this.y=0>this.y?Math.ceil(this.y):Math.floor(this.y);this.z=0>this.z?Math.ceil(this.z):Math.floor(this.z);this.w=0>this.w?Math.ceil(this.w):Math.floor(this.w);return this},negate:function(){this.x=-this.x;this.y=-this.y;this.z=-this.z;this.w=-this.w;return this},dot:function(a){return this.x*a.x+this.y*a.y+this.z*
a.z+this.w*a.w},lengthSq:function(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w},length:function(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)},manhattanLength:function(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)},normalize:function(){return this.divideScalar(this.length()||1)},setLength:function(a){return this.normalize().multiplyScalar(a)},lerp:function(a,c){this.x+=(a.x-this.x)*c;this.y+=(a.y-this.y)*c;this.z+=(a.z-
this.z)*c;this.w+=(a.w-this.w)*c;return this},lerpVectors:function(a,c,e){return this.subVectors(c,a).multiplyScalar(e).add(a)},equals:function(a){return a.x===this.x&&a.y===this.y&&a.z===this.z&&a.w===this.w},fromArray:function(a,c){void 0===c&&(c=0);this.x=a[c];this.y=a[c+1];this.z=a[c+2];this.w=a[c+3];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this.x;a[c+1]=this.y;a[c+2]=this.z;a[c+3]=this.w;return a},fromBufferAttribute:function(a,c,e){void 0!==e&&console.warn("THREE.Vector4: offset has been removed from .fromBufferAttribute().");
this.x=a.getX(c);this.y=a.getY(c);this.z=a.getZ(c);this.w=a.getW(c);return this}});m.prototype=Object.assign(Object.create(d.prototype),{constructor:m,isWebGLRenderTarget:!0,setSize:function(a,c){if(this.width!==a||this.height!==c)this.width=a,this.height=c,this.texture.image.width=a,this.texture.image.height=c,this.dispose();this.viewport.set(0,0,a,c);this.scissor.set(0,0,a,c)},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.width=a.width;this.height=a.height;this.viewport.copy(a.viewport);
this.texture=a.texture.clone();this.depthBuffer=a.depthBuffer;this.stencilBuffer=a.stencilBuffer;this.depthTexture=a.depthTexture;return this},dispose:function(){this.dispatchEvent({type:"dispose"})}});n.prototype=Object.assign(Object.create(m.prototype),{constructor:n,isWebGLMultisampleRenderTarget:!0,copy:function(a){m.prototype.copy.call(this,a);this.samples=a.samples;return this}});var qc=new k,Qb=new q,xm=new k(0,0,0),ym=new k(1,1,1),Ed=new k,gh=new k,fc=new k;Object.assign(q.prototype,{isMatrix4:!0,
set:function(a,c,e,g,r,v,z,E,F,J,P,R,S,V,W,ha){var fa=this.elements;fa[0]=a;fa[4]=c;fa[8]=e;fa[12]=g;fa[1]=r;fa[5]=v;fa[9]=z;fa[13]=E;fa[2]=F;fa[6]=J;fa[10]=P;fa[14]=R;fa[3]=S;fa[7]=V;fa[11]=W;fa[15]=ha;return this},identity:function(){this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1);return this},clone:function(){return(new q).fromArray(this.elements)},copy:function(a){var c=this.elements;a=a.elements;c[0]=a[0];c[1]=a[1];c[2]=a[2];c[3]=a[3];c[4]=a[4];c[5]=a[5];c[6]=a[6];c[7]=a[7];c[8]=a[8];c[9]=a[9];c[10]=
a[10];c[11]=a[11];c[12]=a[12];c[13]=a[13];c[14]=a[14];c[15]=a[15];return this},copyPosition:function(a){var c=this.elements;a=a.elements;c[12]=a[12];c[13]=a[13];c[14]=a[14];return this},extractBasis:function(a,c,e){a.setFromMatrixColumn(this,0);c.setFromMatrixColumn(this,1);e.setFromMatrixColumn(this,2);return this},makeBasis:function(a,c,e){this.set(a.x,c.x,e.x,0,a.y,c.y,e.y,0,a.z,c.z,e.z,0,0,0,0,1);return this},extractRotation:function(a){var c=this.elements,e=a.elements,g=1/qc.setFromMatrixColumn(a,
0).length(),r=1/qc.setFromMatrixColumn(a,1).length();a=1/qc.setFromMatrixColumn(a,2).length();c[0]=e[0]*g;c[1]=e[1]*g;c[2]=e[2]*g;c[3]=0;c[4]=e[4]*r;c[5]=e[5]*r;c[6]=e[6]*r;c[7]=0;c[8]=e[8]*a;c[9]=e[9]*a;c[10]=e[10]*a;c[11]=0;c[12]=0;c[13]=0;c[14]=0;c[15]=1;return this},makeRotationFromEuler:function(a){a&&a.isEuler||console.error("THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.");var c=this.elements,e=a.x,g=a.y,r=a.z,v=Math.cos(e);e=Math.sin(e);
var z=Math.cos(g);g=Math.sin(g);var E=Math.cos(r);r=Math.sin(r);if("XYZ"===a.order){a=v*E;var F=v*r,J=e*E,P=e*r;c[0]=z*E;c[4]=-z*r;c[8]=g;c[1]=F+J*g;c[5]=a-P*g;c[9]=-e*z;c[2]=P-a*g;c[6]=J+F*g;c[10]=v*z}else"YXZ"===a.order?(a=z*E,F=z*r,J=g*E,P=g*r,c[0]=a+P*e,c[4]=J*e-F,c[8]=v*g,c[1]=v*r,c[5]=v*E,c[9]=-e,c[2]=F*e-J,c[6]=P+a*e,c[10]=v*z):"ZXY"===a.order?(a=z*E,F=z*r,J=g*E,P=g*r,c[0]=a-P*e,c[4]=-v*r,c[8]=J+F*e,c[1]=F+J*e,c[5]=v*E,c[9]=P-a*e,c[2]=-v*g,c[6]=e,c[10]=v*z):"ZYX"===a.order?(a=v*E,F=v*r,J=e*
E,P=e*r,c[0]=z*E,c[4]=J*g-F,c[8]=a*g+P,c[1]=z*r,c[5]=P*g+a,c[9]=F*g-J,c[2]=-g,c[6]=e*z,c[10]=v*z):"YZX"===a.order?(a=v*z,F=v*g,J=e*z,P=e*g,c[0]=z*E,c[4]=P-a*r,c[8]=J*r+F,c[1]=r,c[5]=v*E,c[9]=-e*E,c[2]=-g*E,c[6]=F*r+J,c[10]=a-P*r):"XZY"===a.order&&(a=v*z,F=v*g,J=e*z,P=e*g,c[0]=z*E,c[4]=-r,c[8]=g*E,c[1]=a*r+P,c[5]=v*E,c[9]=F*r-J,c[2]=J*r-F,c[6]=e*E,c[10]=P*r+a);c[3]=0;c[7]=0;c[11]=0;c[12]=0;c[13]=0;c[14]=0;c[15]=1;return this},makeRotationFromQuaternion:function(a){return this.compose(xm,a,ym)},lookAt:function(a,
c,e){var g=this.elements;fc.subVectors(a,c);0===fc.lengthSq()&&(fc.z=1);fc.normalize();Ed.crossVectors(e,fc);0===Ed.lengthSq()&&(1===Math.abs(e.z)?fc.x+=1E-4:fc.z+=1E-4,fc.normalize(),Ed.crossVectors(e,fc));Ed.normalize();gh.crossVectors(fc,Ed);g[0]=Ed.x;g[4]=gh.x;g[8]=fc.x;g[1]=Ed.y;g[5]=gh.y;g[9]=fc.y;g[2]=Ed.z;g[6]=gh.z;g[10]=fc.z;return this},multiply:function(a,c){return void 0!==c?(console.warn("THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead."),
this.multiplyMatrices(a,c)):this.multiplyMatrices(this,a)},premultiply:function(a){return this.multiplyMatrices(a,this)},multiplyMatrices:function(a,c){var e=a.elements,g=c.elements;c=this.elements;a=e[0];var r=e[4],v=e[8],z=e[12],E=e[1],F=e[5],J=e[9],P=e[13],R=e[2],S=e[6],V=e[10],W=e[14],ha=e[3],fa=e[7],ra=e[11];e=e[15];var pa=g[0],qa=g[4],ua=g[8],oa=g[12],ta=g[1],Ba=g[5],Ta=g[9],Ua=g[13],Ca=g[2],Ha=g[6],Da=g[10],Ma=g[14],db=g[3],tb=g[7],Ka=g[11];g=g[15];c[0]=a*pa+r*ta+v*Ca+z*db;c[4]=a*qa+r*Ba+v*
Ha+z*tb;c[8]=a*ua+r*Ta+v*Da+z*Ka;c[12]=a*oa+r*Ua+v*Ma+z*g;c[1]=E*pa+F*ta+J*Ca+P*db;c[5]=E*qa+F*Ba+J*Ha+P*tb;c[9]=E*ua+F*Ta+J*Da+P*Ka;c[13]=E*oa+F*Ua+J*Ma+P*g;c[2]=R*pa+S*ta+V*Ca+W*db;c[6]=R*qa+S*Ba+V*Ha+W*tb;c[10]=R*ua+S*Ta+V*Da+W*Ka;c[14]=R*oa+S*Ua+V*Ma+W*g;c[3]=ha*pa+fa*ta+ra*Ca+e*db;c[7]=ha*qa+fa*Ba+ra*Ha+e*tb;c[11]=ha*ua+fa*Ta+ra*Da+e*Ka;c[15]=ha*oa+fa*Ua+ra*Ma+e*g;return this},multiplyScalar:function(a){var c=this.elements;c[0]*=a;c[4]*=a;c[8]*=a;c[12]*=a;c[1]*=a;c[5]*=a;c[9]*=a;c[13]*=a;c[2]*=
a;c[6]*=a;c[10]*=a;c[14]*=a;c[3]*=a;c[7]*=a;c[11]*=a;c[15]*=a;return this},applyToBufferAttribute:function(a){for(var c=0,e=a.count;c<e;c++)qc.x=a.getX(c),qc.y=a.getY(c),qc.z=a.getZ(c),qc.applyMatrix4(this),a.setXYZ(c,qc.x,qc.y,qc.z);return a},determinant:function(){var a=this.elements,c=a[0],e=a[4],g=a[8],r=a[12],v=a[1],z=a[5],E=a[9],F=a[13],J=a[2],P=a[6],R=a[10],S=a[14];return a[3]*(+r*E*P-g*F*P-r*z*R+e*F*R+g*z*S-e*E*S)+a[7]*(+c*E*S-c*F*R+r*v*R-g*v*S+g*F*J-r*E*J)+a[11]*(+c*F*P-c*z*S-r*v*P+e*v*S+
r*z*J-e*F*J)+a[15]*(-g*z*J-c*E*P+c*z*R+g*v*P-e*v*R+e*E*J)},transpose:function(){var a=this.elements;var c=a[1];a[1]=a[4];a[4]=c;c=a[2];a[2]=a[8];a[8]=c;c=a[6];a[6]=a[9];a[9]=c;c=a[3];a[3]=a[12];a[12]=c;c=a[7];a[7]=a[13];a[13]=c;c=a[11];a[11]=a[14];a[14]=c;return this},setPosition:function(a,c,e){var g=this.elements;a.isVector3?(g[12]=a.x,g[13]=a.y,g[14]=a.z):(g[12]=a,g[13]=c,g[14]=e);return this},getInverse:function(a,c){var e=this.elements,g=a.elements;a=g[0];var r=g[1],v=g[2],z=g[3],E=g[4],F=g[5],
J=g[6],P=g[7],R=g[8],S=g[9],V=g[10],W=g[11],ha=g[12],fa=g[13],ra=g[14];g=g[15];var pa=S*ra*P-fa*V*P+fa*J*W-F*ra*W-S*J*g+F*V*g,qa=ha*V*P-R*ra*P-ha*J*W+E*ra*W+R*J*g-E*V*g,ua=R*fa*P-ha*S*P+ha*F*W-E*fa*W-R*F*g+E*S*g,oa=ha*S*J-R*fa*J-ha*F*V+E*fa*V+R*F*ra-E*S*ra,ta=a*pa+r*qa+v*ua+z*oa;if(0===ta){if(!0===c)throw Error("THREE.Matrix4: .getInverse() can't invert matrix, determinant is 0");console.warn("THREE.Matrix4: .getInverse() can't invert matrix, determinant is 0");return this.identity()}c=1/ta;e[0]=
pa*c;e[1]=(fa*V*z-S*ra*z-fa*v*W+r*ra*W+S*v*g-r*V*g)*c;e[2]=(F*ra*z-fa*J*z+fa*v*P-r*ra*P-F*v*g+r*J*g)*c;e[3]=(S*J*z-F*V*z-S*v*P+r*V*P+F*v*W-r*J*W)*c;e[4]=qa*c;e[5]=(R*ra*z-ha*V*z+ha*v*W-a*ra*W-R*v*g+a*V*g)*c;e[6]=(ha*J*z-E*ra*z-ha*v*P+a*ra*P+E*v*g-a*J*g)*c;e[7]=(E*V*z-R*J*z+R*v*P-a*V*P-E*v*W+a*J*W)*c;e[8]=ua*c;e[9]=(ha*S*z-R*fa*z-ha*r*W+a*fa*W+R*r*g-a*S*g)*c;e[10]=(E*fa*z-ha*F*z+ha*r*P-a*fa*P-E*r*g+a*F*g)*c;e[11]=(R*F*z-E*S*z-R*r*P+a*S*P+E*r*W-a*F*W)*c;e[12]=oa*c;e[13]=(R*fa*v-ha*S*v+ha*r*V-a*fa*V-
R*r*ra+a*S*ra)*c;e[14]=(ha*F*v-E*fa*v-ha*r*J+a*fa*J+E*r*ra-a*F*ra)*c;e[15]=(E*S*v-R*F*v+R*r*J-a*S*J-E*r*V+a*F*V)*c;return this},scale:function(a){var c=this.elements,e=a.x,g=a.y;a=a.z;c[0]*=e;c[4]*=g;c[8]*=a;c[1]*=e;c[5]*=g;c[9]*=a;c[2]*=e;c[6]*=g;c[10]*=a;c[3]*=e;c[7]*=g;c[11]*=a;return this},getMaxScaleOnAxis:function(){var a=this.elements;return Math.sqrt(Math.max(a[0]*a[0]+a[1]*a[1]+a[2]*a[2],a[4]*a[4]+a[5]*a[5]+a[6]*a[6],a[8]*a[8]+a[9]*a[9]+a[10]*a[10]))},makeTranslation:function(a,c,e){this.set(1,
0,0,a,0,1,0,c,0,0,1,e,0,0,0,1);return this},makeRotationX:function(a){var c=Math.cos(a);a=Math.sin(a);this.set(1,0,0,0,0,c,-a,0,0,a,c,0,0,0,0,1);return this},makeRotationY:function(a){var c=Math.cos(a);a=Math.sin(a);this.set(c,0,a,0,0,1,0,0,-a,0,c,0,0,0,0,1);return this},makeRotationZ:function(a){var c=Math.cos(a);a=Math.sin(a);this.set(c,-a,0,0,a,c,0,0,0,0,1,0,0,0,0,1);return this},makeRotationAxis:function(a,c){var e=Math.cos(c);c=Math.sin(c);var g=1-e,r=a.x,v=a.y;a=a.z;var z=g*r,E=g*v;this.set(z*
r+e,z*v-c*a,z*a+c*v,0,z*v+c*a,E*v+e,E*a-c*r,0,z*a-c*v,E*a+c*r,g*a*a+e,0,0,0,0,1);return this},makeScale:function(a,c,e){this.set(a,0,0,0,0,c,0,0,0,0,e,0,0,0,0,1);return this},makeShear:function(a,c,e){this.set(1,c,e,0,a,1,e,0,a,c,1,0,0,0,0,1);return this},compose:function(a,c,e){var g=this.elements,r=c._x,v=c._y,z=c._z,E=c._w,F=r+r,J=v+v,P=z+z;c=r*F;var R=r*J;r*=P;var S=v*J;v*=P;z*=P;F*=E;J*=E;E*=P;P=e.x;var V=e.y;e=e.z;g[0]=(1-(S+z))*P;g[1]=(R+E)*P;g[2]=(r-J)*P;g[3]=0;g[4]=(R-E)*V;g[5]=(1-(c+z))*
V;g[6]=(v+F)*V;g[7]=0;g[8]=(r+J)*e;g[9]=(v-F)*e;g[10]=(1-(c+S))*e;g[11]=0;g[12]=a.x;g[13]=a.y;g[14]=a.z;g[15]=1;return this},decompose:function(a,c,e){var g=this.elements,r=qc.set(g[0],g[1],g[2]).length(),v=qc.set(g[4],g[5],g[6]).length(),z=qc.set(g[8],g[9],g[10]).length();0>this.determinant()&&(r=-r);a.x=g[12];a.y=g[13];a.z=g[14];Qb.copy(this);a=1/r;g=1/v;var E=1/z;Qb.elements[0]*=a;Qb.elements[1]*=a;Qb.elements[2]*=a;Qb.elements[4]*=g;Qb.elements[5]*=g;Qb.elements[6]*=g;Qb.elements[8]*=E;Qb.elements[9]*=
E;Qb.elements[10]*=E;c.setFromRotationMatrix(Qb);e.x=r;e.y=v;e.z=z;return this},makePerspective:function(a,c,e,g,r,v){void 0===v&&console.warn("THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.");var z=this.elements;z[0]=2*r/(c-a);z[4]=0;z[8]=(c+a)/(c-a);z[12]=0;z[1]=0;z[5]=2*r/(e-g);z[9]=(e+g)/(e-g);z[13]=0;z[2]=0;z[6]=0;z[10]=-(v+r)/(v-r);z[14]=-2*v*r/(v-r);z[3]=0;z[7]=0;z[11]=-1;z[15]=0;return this},makeOrthographic:function(a,c,e,g,r,v){var z=
this.elements,E=1/(c-a),F=1/(e-g),J=1/(v-r);z[0]=2*E;z[4]=0;z[8]=0;z[12]=-((c+a)*E);z[1]=0;z[5]=2*F;z[9]=0;z[13]=-((e+g)*F);z[2]=0;z[6]=0;z[10]=-2*J;z[14]=-((v+r)*J);z[3]=0;z[7]=0;z[11]=0;z[15]=1;return this},equals:function(a){var c=this.elements;a=a.elements;for(var e=0;16>e;e++)if(c[e]!==a[e])return!1;return!0},fromArray:function(a,c){void 0===c&&(c=0);for(var e=0;16>e;e++)this.elements[e]=a[e+c];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);var e=this.elements;a[c]=e[0];
a[c+1]=e[1];a[c+2]=e[2];a[c+3]=e[3];a[c+4]=e[4];a[c+5]=e[5];a[c+6]=e[6];a[c+7]=e[7];a[c+8]=e[8];a[c+9]=e[9];a[c+10]=e[10];a[c+11]=e[11];a[c+12]=e[12];a[c+13]=e[13];a[c+14]=e[14];a[c+15]=e[15];return a}});var ck=new q,dk=new h;u.RotationOrders="XYZ YZX ZXY XZY YXZ ZYX".split(" ");u.DefaultOrder="XYZ";Object.defineProperties(u.prototype,{x:{get:function(){return this._x},set:function(a){this._x=a;this._onChangeCallback()}},y:{get:function(){return this._y},set:function(a){this._y=a;this._onChangeCallback()}},
z:{get:function(){return this._z},set:function(a){this._z=a;this._onChangeCallback()}},order:{get:function(){return this._order},set:function(a){this._order=a;this._onChangeCallback()}}});Object.assign(u.prototype,{isEuler:!0,set:function(a,c,e,g){this._x=a;this._y=c;this._z=e;this._order=g||this._order;this._onChangeCallback();return this},clone:function(){return new this.constructor(this._x,this._y,this._z,this._order)},copy:function(a){this._x=a._x;this._y=a._y;this._z=a._z;this._order=a._order;
this._onChangeCallback();return this},setFromRotationMatrix:function(a,c,e){var g=hb.clamp,r=a.elements;a=r[0];var v=r[4],z=r[8],E=r[1],F=r[5],J=r[9],P=r[2],R=r[6];r=r[10];c=c||this._order;"XYZ"===c?(this._y=Math.asin(g(z,-1,1)),.9999999>Math.abs(z)?(this._x=Math.atan2(-J,r),this._z=Math.atan2(-v,a)):(this._x=Math.atan2(R,F),this._z=0)):"YXZ"===c?(this._x=Math.asin(-g(J,-1,1)),.9999999>Math.abs(J)?(this._y=Math.atan2(z,r),this._z=Math.atan2(E,F)):(this._y=Math.atan2(-P,a),this._z=0)):"ZXY"===c?(this._x=
Math.asin(g(R,-1,1)),.9999999>Math.abs(R)?(this._y=Math.atan2(-P,r),this._z=Math.atan2(-v,F)):(this._y=0,this._z=Math.atan2(E,a))):"ZYX"===c?(this._y=Math.asin(-g(P,-1,1)),.9999999>Math.abs(P)?(this._x=Math.atan2(R,r),this._z=Math.atan2(E,a)):(this._x=0,this._z=Math.atan2(-v,F))):"YZX"===c?(this._z=Math.asin(g(E,-1,1)),.9999999>Math.abs(E)?(this._x=Math.atan2(-J,F),this._y=Math.atan2(-P,a)):(this._x=0,this._y=Math.atan2(z,r))):"XZY"===c?(this._z=Math.asin(-g(v,-1,1)),.9999999>Math.abs(v)?(this._x=
Math.atan2(R,F),this._y=Math.atan2(z,a)):(this._x=Math.atan2(-J,r),this._y=0)):console.warn("THREE.Euler: .setFromRotationMatrix() given unsupported order: "+c);this._order=c;!1!==e&&this._onChangeCallback();return this},setFromQuaternion:function(a,c,e){ck.makeRotationFromQuaternion(a);return this.setFromRotationMatrix(ck,c,e)},setFromVector3:function(a,c){return this.set(a.x,a.y,a.z,c||this._order)},reorder:function(a){dk.setFromEuler(this);return this.setFromQuaternion(dk,a)},equals:function(a){return a._x===
this._x&&a._y===this._y&&a._z===this._z&&a._order===this._order},fromArray:function(a){this._x=a[0];this._y=a[1];this._z=a[2];void 0!==a[3]&&(this._order=a[3]);this._onChangeCallback();return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this._x;a[c+1]=this._y;a[c+2]=this._z;a[c+3]=this._order;return a},toVector3:function(a){return a?a.set(this._x,this._y,this._z):new k(this._x,this._y,this._z)},_onChange:function(a){this._onChangeCallback=a;return this},_onChangeCallback:function(){}});
Object.assign(x.prototype,{set:function(a){this.mask=1<<a|0},enable:function(a){this.mask=this.mask|1<<a|0},enableAll:function(){this.mask=-1},toggle:function(a){this.mask^=1<<a|0},disable:function(a){this.mask&=~(1<<a|0)},disableAll:function(){this.mask=0},test:function(a){return 0!==(this.mask&a.mask)}});var Ok=0,ek=new k,ff=new h,jd=new q,hh=new k,kg=new k,zm=new k,Am=new h,fk=new k(1,0,0),gk=new k(0,1,0),hk=new k(0,0,1),Bm={type:"added"},Cm={type:"removed"};A.DefaultUp=new k(0,1,0);A.DefaultMatrixAutoUpdate=
!0;A.prototype=Object.assign(Object.create(d.prototype),{constructor:A,isObject3D:!0,onBeforeRender:function(){},onAfterRender:function(){},applyMatrix:function(a){this.matrixAutoUpdate&&this.updateMatrix();this.matrix.premultiply(a);this.matrix.decompose(this.position,this.quaternion,this.scale)},applyQuaternion:function(a){this.quaternion.premultiply(a);return this},setRotationFromAxisAngle:function(a,c){this.quaternion.setFromAxisAngle(a,c)},setRotationFromEuler:function(a){this.quaternion.setFromEuler(a,
!0)},setRotationFromMatrix:function(a){this.quaternion.setFromRotationMatrix(a)},setRotationFromQuaternion:function(a){this.quaternion.copy(a)},rotateOnAxis:function(a,c){ff.setFromAxisAngle(a,c);this.quaternion.multiply(ff);return this},rotateOnWorldAxis:function(a,c){ff.setFromAxisAngle(a,c);this.quaternion.premultiply(ff);return this},rotateX:function(a){return this.rotateOnAxis(fk,a)},rotateY:function(a){return this.rotateOnAxis(gk,a)},rotateZ:function(a){return this.rotateOnAxis(hk,a)},translateOnAxis:function(a,
c){ek.copy(a).applyQuaternion(this.quaternion);this.position.add(ek.multiplyScalar(c));return this},translateX:function(a){return this.translateOnAxis(fk,a)},translateY:function(a){return this.translateOnAxis(gk,a)},translateZ:function(a){return this.translateOnAxis(hk,a)},localToWorld:function(a){return a.applyMatrix4(this.matrixWorld)},worldToLocal:function(a){return a.applyMatrix4(jd.getInverse(this.matrixWorld))},lookAt:function(a,c,e){a.isVector3?hh.copy(a):hh.set(a,c,e);a=this.parent;this.updateWorldMatrix(!0,
!1);kg.setFromMatrixPosition(this.matrixWorld);this.isCamera||this.isLight?jd.lookAt(kg,hh,this.up):jd.lookAt(hh,kg,this.up);this.quaternion.setFromRotationMatrix(jd);a&&(jd.extractRotation(a.matrixWorld),ff.setFromRotationMatrix(jd),this.quaternion.premultiply(ff.inverse()))},add:function(a){if(1<arguments.length){for(var c=0;c<arguments.length;c++)this.add(arguments[c]);return this}if(a===this)return console.error("THREE.Object3D.add: object can't be added as a child of itself.",a),this;a&&a.isObject3D?
(null!==a.parent&&a.parent.remove(a),a.parent=this,this.children.push(a),a.dispatchEvent(Bm)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",a);return this},remove:function(a){if(1<arguments.length){for(var c=0;c<arguments.length;c++)this.remove(arguments[c]);return this}c=this.children.indexOf(a);-1!==c&&(a.parent=null,this.children.splice(c,1),a.dispatchEvent(Cm));return this},attach:function(a){this.updateWorldMatrix(!0,!1);jd.getInverse(this.matrixWorld);null!==
a.parent&&(a.parent.updateWorldMatrix(!0,!1),jd.multiply(a.parent.matrixWorld));a.applyMatrix(jd);a.updateWorldMatrix(!1,!1);this.add(a);return this},getObjectById:function(a){return this.getObjectByProperty("id",a)},getObjectByName:function(a){return this.getObjectByProperty("name",a)},getObjectByProperty:function(a,c){if(this[a]===c)return this;for(var e=0,g=this.children.length;e<g;e++){var r=this.children[e].getObjectByProperty(a,c);if(void 0!==r)return r}},getWorldPosition:function(a){void 0===
a&&(console.warn("THREE.Object3D: .getWorldPosition() target is now required"),a=new k);this.updateMatrixWorld(!0);return a.setFromMatrixPosition(this.matrixWorld)},getWorldQuaternion:function(a){void 0===a&&(console.warn("THREE.Object3D: .getWorldQuaternion() target is now required"),a=new h);this.updateMatrixWorld(!0);this.matrixWorld.decompose(kg,a,zm);return a},getWorldScale:function(a){void 0===a&&(console.warn("THREE.Object3D: .getWorldScale() target is now required"),a=new k);this.updateMatrixWorld(!0);
this.matrixWorld.decompose(kg,Am,a);return a},getWorldDirection:function(a){void 0===a&&(console.warn("THREE.Object3D: .getWorldDirection() target is now required"),a=new k);this.updateMatrixWorld(!0);var c=this.matrixWorld.elements;return a.set(c[8],c[9],c[10]).normalize()},raycast:function(){},traverse:function(a){a(this);for(var c=this.children,e=0,g=c.length;e<g;e++)c[e].traverse(a)},traverseVisible:function(a){if(!1!==this.visible){a(this);for(var c=this.children,e=0,g=c.length;e<g;e++)c[e].traverseVisible(a)}},
traverseAncestors:function(a){var c=this.parent;null!==c&&(a(c),c.traverseAncestors(a))},updateMatrix:function(){this.matrix.compose(this.position,this.quaternion,this.scale);this.matrixWorldNeedsUpdate=!0},updateMatrixWorld:function(a){this.matrixAutoUpdate&&this.updateMatrix();if(this.matrixWorldNeedsUpdate||a)null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,a=!0;for(var c=this.children,e=
0,g=c.length;e<g;e++)c[e].updateMatrixWorld(a)},updateWorldMatrix:function(a,c){var e=this.parent;!0===a&&null!==e&&e.updateWorldMatrix(!0,!1);this.matrixAutoUpdate&&this.updateMatrix();null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix);if(!0===c)for(a=this.children,c=0,e=a.length;c<e;c++)a[c].updateWorldMatrix(!1,!0)},toJSON:function(a){function c(P,R){void 0===P[R.uuid]&&(P[R.uuid]=R.toJSON(a));return R.uuid}function e(P){var R=
[],S;for(S in P){var V=P[S];delete V.metadata;R.push(V)}return R}var g=void 0===a||"string"===typeof a,r={};g&&(a={geometries:{},materials:{},textures:{},images:{},shapes:{}},r.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});var v={};v.uuid=this.uuid;v.type=this.type;""!==this.name&&(v.name=this.name);!0===this.castShadow&&(v.castShadow=!0);!0===this.receiveShadow&&(v.receiveShadow=!0);!1===this.visible&&(v.visible=!1);!1===this.frustumCulled&&(v.frustumCulled=!1);0!==this.renderOrder&&
(v.renderOrder=this.renderOrder);"{}"!==JSON.stringify(this.userData)&&(v.userData=this.userData);v.layers=this.layers.mask;v.matrix=this.matrix.toArray();!1===this.matrixAutoUpdate&&(v.matrixAutoUpdate=!1);this.isMesh&&0!==this.drawMode&&(v.drawMode=this.drawMode);if(this.isMesh||this.isLine||this.isPoints){v.geometry=c(a.geometries,this.geometry);var z=this.geometry.parameters;if(void 0!==z&&void 0!==z.shapes)if(z=z.shapes,Array.isArray(z))for(var E=0,F=z.length;E<F;E++)c(a.shapes,z[E]);else c(a.shapes,
z)}if(void 0!==this.material)if(Array.isArray(this.material)){z=[];E=0;for(F=this.material.length;E<F;E++)z.push(c(a.materials,this.material[E]));v.material=z}else v.material=c(a.materials,this.material);if(0<this.children.length)for(v.children=[],E=0;E<this.children.length;E++)v.children.push(this.children[E].toJSON(a).object);if(g){g=e(a.geometries);E=e(a.materials);F=e(a.textures);var J=e(a.images);z=e(a.shapes);0<g.length&&(r.geometries=g);0<E.length&&(r.materials=E);0<F.length&&(r.textures=F);
0<J.length&&(r.images=J);0<z.length&&(r.shapes=z)}r.object=v;return r},clone:function(a){return(new this.constructor).copy(this,a)},copy:function(a,c){void 0===c&&(c=!0);this.name=a.name;this.up.copy(a.up);this.position.copy(a.position);this.quaternion.copy(a.quaternion);this.scale.copy(a.scale);this.matrix.copy(a.matrix);this.matrixWorld.copy(a.matrixWorld);this.matrixAutoUpdate=a.matrixAutoUpdate;this.matrixWorldNeedsUpdate=a.matrixWorldNeedsUpdate;this.layers.mask=a.layers.mask;this.visible=a.visible;
this.castShadow=a.castShadow;this.receiveShadow=a.receiveShadow;this.frustumCulled=a.frustumCulled;this.renderOrder=a.renderOrder;this.userData=JSON.parse(JSON.stringify(a.userData));if(!0===c)for(c=0;c<a.children.length;c++)this.add(a.children[c].clone());return this}});y.prototype=Object.assign(Object.create(A.prototype),{constructor:y,isScene:!0,copy:function(a,c){A.prototype.copy.call(this,a,c);null!==a.background&&(this.background=a.background.clone());null!==a.fog&&(this.fog=a.fog.clone());
null!==a.overrideMaterial&&(this.overrideMaterial=a.overrideMaterial.clone());this.autoUpdate=a.autoUpdate;this.matrixAutoUpdate=a.matrixAutoUpdate;return this},toJSON:function(a){var c=A.prototype.toJSON.call(this,a);null!==this.background&&(c.object.background=this.background.toJSON(a));null!==this.fog&&(c.object.fog=this.fog.toJSON());return c},dispose:function(){this.dispatchEvent({type:"dispose"})}});var kd=[new k,new k,new k,new k,new k,new k,new k,new k],Wc=new k,gf=new k,hf=new k,jf=new k,
Fd=new k,Gd=new k,fe=new k,lg=new k,ih=new k,jh=new k,Kd=new k;Object.assign(w.prototype,{isBox3:!0,set:function(a,c){this.min.copy(a);this.max.copy(c);return this},setFromArray:function(a){for(var c=Infinity,e=Infinity,g=Infinity,r=-Infinity,v=-Infinity,z=-Infinity,E=0,F=a.length;E<F;E+=3){var J=a[E],P=a[E+1],R=a[E+2];J<c&&(c=J);P<e&&(e=P);R<g&&(g=R);J>r&&(r=J);P>v&&(v=P);R>z&&(z=R)}this.min.set(c,e,g);this.max.set(r,v,z);return this},setFromBufferAttribute:function(a){for(var c=Infinity,e=Infinity,
g=Infinity,r=-Infinity,v=-Infinity,z=-Infinity,E=0,F=a.count;E<F;E++){var J=a.getX(E),P=a.getY(E),R=a.getZ(E);J<c&&(c=J);P<e&&(e=P);R<g&&(g=R);J>r&&(r=J);P>v&&(v=P);R>z&&(z=R)}this.min.set(c,e,g);this.max.set(r,v,z);return this},setFromPoints:function(a){this.makeEmpty();for(var c=0,e=a.length;c<e;c++)this.expandByPoint(a[c]);return this},setFromCenterAndSize:function(a,c){c=Wc.copy(c).multiplyScalar(.5);this.min.copy(a).sub(c);this.max.copy(a).add(c);return this},setFromObject:function(a){this.makeEmpty();
return this.expandByObject(a)},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.min.copy(a.min);this.max.copy(a.max);return this},makeEmpty:function(){this.min.x=this.min.y=this.min.z=Infinity;this.max.x=this.max.y=this.max.z=-Infinity;return this},isEmpty:function(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z},getCenter:function(a){void 0===a&&(console.warn("THREE.Box3: .getCenter() target is now required"),a=new k);return this.isEmpty()?
a.set(0,0,0):a.addVectors(this.min,this.max).multiplyScalar(.5)},getSize:function(a){void 0===a&&(console.warn("THREE.Box3: .getSize() target is now required"),a=new k);return this.isEmpty()?a.set(0,0,0):a.subVectors(this.max,this.min)},expandByPoint:function(a){this.min.min(a);this.max.max(a);return this},expandByVector:function(a){this.min.sub(a);this.max.add(a);return this},expandByScalar:function(a){this.min.addScalar(-a);this.max.addScalar(a);return this},expandByObject:function(a){var c;a.updateWorldMatrix(!1,
!1);var e=a.geometry;if(void 0!==e)if(e.isGeometry){var g=e.vertices;e=0;for(c=g.length;e<c;e++)Wc.copy(g[e]),Wc.applyMatrix4(a.matrixWorld),this.expandByPoint(Wc)}else if(e.isBufferGeometry&&(g=e.attributes.position,void 0!==g))for(e=0,c=g.count;e<c;e++)Wc.fromBufferAttribute(g,e).applyMatrix4(a.matrixWorld),this.expandByPoint(Wc);a=a.children;e=0;for(c=a.length;e<c;e++)this.expandByObject(a[e]);return this},containsPoint:function(a){return a.x<this.min.x||a.x>this.max.x||a.y<this.min.y||a.y>this.max.y||
a.z<this.min.z||a.z>this.max.z?!1:!0},containsBox:function(a){return this.min.x<=a.min.x&&a.max.x<=this.max.x&&this.min.y<=a.min.y&&a.max.y<=this.max.y&&this.min.z<=a.min.z&&a.max.z<=this.max.z},getParameter:function(a,c){void 0===c&&(console.warn("THREE.Box3: .getParameter() target is now required"),c=new k);return c.set((a.x-this.min.x)/(this.max.x-this.min.x),(a.y-this.min.y)/(this.max.y-this.min.y),(a.z-this.min.z)/(this.max.z-this.min.z))},intersectsBox:function(a){return a.max.x<this.min.x||
a.min.x>this.max.x||a.max.y<this.min.y||a.min.y>this.max.y||a.max.z<this.min.z||a.min.z>this.max.z?!1:!0},intersectsSphere:function(a){this.clampPoint(a.center,Wc);return Wc.distanceToSquared(a.center)<=a.radius*a.radius},intersectsPlane:function(a){if(0<a.normal.x){var c=a.normal.x*this.min.x;var e=a.normal.x*this.max.x}else c=a.normal.x*this.max.x,e=a.normal.x*this.min.x;0<a.normal.y?(c+=a.normal.y*this.min.y,e+=a.normal.y*this.max.y):(c+=a.normal.y*this.max.y,e+=a.normal.y*this.min.y);0<a.normal.z?
(c+=a.normal.z*this.min.z,e+=a.normal.z*this.max.z):(c+=a.normal.z*this.max.z,e+=a.normal.z*this.min.z);return c<=-a.constant&&e>=-a.constant},intersectsTriangle:function(a){if(this.isEmpty())return!1;this.getCenter(lg);ih.subVectors(this.max,lg);gf.subVectors(a.a,lg);hf.subVectors(a.b,lg);jf.subVectors(a.c,lg);Fd.subVectors(hf,gf);Gd.subVectors(jf,hf);fe.subVectors(gf,jf);a=[0,-Fd.z,Fd.y,0,-Gd.z,Gd.y,0,-fe.z,fe.y,Fd.z,0,-Fd.x,Gd.z,0,-Gd.x,fe.z,0,-fe.x,-Fd.y,Fd.x,0,-Gd.y,Gd.x,0,-fe.y,fe.x,0];if(!C(a,
gf,hf,jf,ih))return!1;a=[1,0,0,0,1,0,0,0,1];if(!C(a,gf,hf,jf,ih))return!1;jh.crossVectors(Fd,Gd);a=[jh.x,jh.y,jh.z];return C(a,gf,hf,jf,ih)},clampPoint:function(a,c){void 0===c&&(console.warn("THREE.Box3: .clampPoint() target is now required"),c=new k);return c.copy(a).clamp(this.min,this.max)},distanceToPoint:function(a){return Wc.copy(a).clamp(this.min,this.max).sub(a).length()},getBoundingSphere:function(a){void 0===a&&console.error("THREE.Box3: .getBoundingSphere() target is now required");this.getCenter(a.center);
a.radius=.5*this.getSize(Wc).length();return a},intersect:function(a){this.min.max(a.min);this.max.min(a.max);this.isEmpty()&&this.makeEmpty();return this},union:function(a){this.min.min(a.min);this.max.max(a.max);return this},applyMatrix4:function(a){if(this.isEmpty())return this;kd[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(a);kd[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(a);kd[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(a);kd[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(a);
kd[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(a);kd[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(a);kd[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(a);kd[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(a);this.setFromPoints(kd);return this},translate:function(a){this.min.add(a);this.max.add(a);return this},equals:function(a){return a.min.equals(this.min)&&a.max.equals(this.max)}});var Dm=new w;Object.assign(G.prototype,{set:function(a,c){this.center.copy(a);this.radius=
c;return this},setFromPoints:function(a,c){var e=this.center;void 0!==c?e.copy(c):Dm.setFromPoints(a).getCenter(e);for(var g=c=0,r=a.length;g<r;g++)c=Math.max(c,e.distanceToSquared(a[g]));this.radius=Math.sqrt(c);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.center.copy(a.center);this.radius=a.radius;return this},empty:function(){return 0>=this.radius},containsPoint:function(a){return a.distanceToSquared(this.center)<=this.radius*this.radius},distanceToPoint:function(a){return a.distanceTo(this.center)-
this.radius},intersectsSphere:function(a){var c=this.radius+a.radius;return a.center.distanceToSquared(this.center)<=c*c},intersectsBox:function(a){return a.intersectsSphere(this)},intersectsPlane:function(a){return Math.abs(a.distanceToPoint(this.center))<=this.radius},clampPoint:function(a,c){var e=this.center.distanceToSquared(a);void 0===c&&(console.warn("THREE.Sphere: .clampPoint() target is now required"),c=new k);c.copy(a);e>this.radius*this.radius&&(c.sub(this.center).normalize(),c.multiplyScalar(this.radius).add(this.center));
return c},getBoundingBox:function(a){void 0===a&&(console.warn("THREE.Sphere: .getBoundingBox() target is now required"),a=new w);a.set(this.center,this.center);a.expandByScalar(this.radius);return a},applyMatrix4:function(a){this.center.applyMatrix4(a);this.radius*=a.getMaxScaleOnAxis();return this},translate:function(a){this.center.add(a);return this},equals:function(a){return a.center.equals(this.center)&&a.radius===this.radius}});var ld=new k,yi=new k,kh=new k,Hd=new k,zi=new k,lh=new k,Ai=new k;
Object.assign(D.prototype,{set:function(a,c){this.origin.copy(a);this.direction.copy(c);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.origin.copy(a.origin);this.direction.copy(a.direction);return this},at:function(a,c){void 0===c&&(console.warn("THREE.Ray: .at() target is now required"),c=new k);return c.copy(this.direction).multiplyScalar(a).add(this.origin)},lookAt:function(a){this.direction.copy(a).sub(this.origin).normalize();return this},recast:function(a){this.origin.copy(this.at(a,
ld));return this},closestPointToPoint:function(a,c){void 0===c&&(console.warn("THREE.Ray: .closestPointToPoint() target is now required"),c=new k);c.subVectors(a,this.origin);a=c.dot(this.direction);return 0>a?c.copy(this.origin):c.copy(this.direction).multiplyScalar(a).add(this.origin)},distanceToPoint:function(a){return Math.sqrt(this.distanceSqToPoint(a))},distanceSqToPoint:function(a){var c=ld.subVectors(a,this.origin).dot(this.direction);if(0>c)return this.origin.distanceToSquared(a);ld.copy(this.direction).multiplyScalar(c).add(this.origin);
return ld.distanceToSquared(a)},distanceSqToSegment:function(a,c,e,g){yi.copy(a).add(c).multiplyScalar(.5);kh.copy(c).sub(a).normalize();Hd.copy(this.origin).sub(yi);var r=.5*a.distanceTo(c),v=-this.direction.dot(kh),z=Hd.dot(this.direction),E=-Hd.dot(kh),F=Hd.lengthSq(),J=Math.abs(1-v*v);if(0<J){a=v*E-z;c=v*z-E;var P=r*J;0<=a?c>=-P?c<=P?(r=1/J,a*=r,c*=r,v=a*(a+v*c+2*z)+c*(v*a+c+2*E)+F):(c=r,a=Math.max(0,-(v*c+z)),v=-a*a+c*(c+2*E)+F):(c=-r,a=Math.max(0,-(v*c+z)),v=-a*a+c*(c+2*E)+F):c<=-P?(a=Math.max(0,
-(-v*r+z)),c=0<a?-r:Math.min(Math.max(-r,-E),r),v=-a*a+c*(c+2*E)+F):c<=P?(a=0,c=Math.min(Math.max(-r,-E),r),v=c*(c+2*E)+F):(a=Math.max(0,-(v*r+z)),c=0<a?r:Math.min(Math.max(-r,-E),r),v=-a*a+c*(c+2*E)+F)}else c=0<v?-r:r,a=Math.max(0,-(v*c+z)),v=-a*a+c*(c+2*E)+F;e&&e.copy(this.direction).multiplyScalar(a).add(this.origin);g&&g.copy(kh).multiplyScalar(c).add(yi);return v},intersectSphere:function(a,c){ld.subVectors(a.center,this.origin);var e=ld.dot(this.direction),g=ld.dot(ld)-e*e;a=a.radius*a.radius;
if(g>a)return null;a=Math.sqrt(a-g);g=e-a;e+=a;return 0>g&&0>e?null:0>g?this.at(e,c):this.at(g,c)},intersectsSphere:function(a){return this.distanceSqToPoint(a.center)<=a.radius*a.radius},distanceToPlane:function(a){var c=a.normal.dot(this.direction);if(0===c)return 0===a.distanceToPoint(this.origin)?0:null;a=-(this.origin.dot(a.normal)+a.constant)/c;return 0<=a?a:null},intersectPlane:function(a,c){a=this.distanceToPlane(a);return null===a?null:this.at(a,c)},intersectsPlane:function(a){var c=a.distanceToPoint(this.origin);
return 0===c||0>a.normal.dot(this.direction)*c?!0:!1},intersectBox:function(a,c){var e=1/this.direction.x;var g=1/this.direction.y;var r=1/this.direction.z,v=this.origin;if(0<=e){var z=(a.min.x-v.x)*e;e*=a.max.x-v.x}else z=(a.max.x-v.x)*e,e*=a.min.x-v.x;if(0<=g){var E=(a.min.y-v.y)*g;g*=a.max.y-v.y}else E=(a.max.y-v.y)*g,g*=a.min.y-v.y;if(z>g||E>e)return null;if(E>z||z!==z)z=E;if(g<e||e!==e)e=g;0<=r?(E=(a.min.z-v.z)*r,a=(a.max.z-v.z)*r):(E=(a.max.z-v.z)*r,a=(a.min.z-v.z)*r);if(z>a||E>e)return null;
if(E>z||z!==z)z=E;if(a<e||e!==e)e=a;return 0>e?null:this.at(0<=z?z:e,c)},intersectsBox:function(a){return null!==this.intersectBox(a,ld)},intersectTriangle:function(a,c,e,g,r){zi.subVectors(c,a);lh.subVectors(e,a);Ai.crossVectors(zi,lh);c=this.direction.dot(Ai);if(0<c){if(g)return null;g=1}else if(0>c)g=-1,c=-c;else return null;Hd.subVectors(this.origin,a);a=g*this.direction.dot(lh.crossVectors(Hd,lh));if(0>a)return null;e=g*this.direction.dot(zi.cross(Hd));if(0>e||a+e>c)return null;a=-g*Hd.dot(Ai);
return 0>a?null:this.at(a/c,r)},applyMatrix4:function(a){this.origin.applyMatrix4(a);this.direction.transformDirection(a);return this},equals:function(a){return a.origin.equals(this.origin)&&a.direction.equals(this.direction)}});var Ic=new k,md=new k,Bi=new k,nd=new k,kf=new k,lf=new k,ik=new k,Ci=new k,Di=new k,Ei=new k;Object.assign(B,{getNormal:function(a,c,e,g){void 0===g&&(console.warn("THREE.Triangle: .getNormal() target is now required"),g=new k);g.subVectors(e,c);Ic.subVectors(a,c);g.cross(Ic);
a=g.lengthSq();return 0<a?g.multiplyScalar(1/Math.sqrt(a)):g.set(0,0,0)},getBarycoord:function(a,c,e,g,r){Ic.subVectors(g,c);md.subVectors(e,c);Bi.subVectors(a,c);a=Ic.dot(Ic);c=Ic.dot(md);e=Ic.dot(Bi);var v=md.dot(md);g=md.dot(Bi);var z=a*v-c*c;void 0===r&&(console.warn("THREE.Triangle: .getBarycoord() target is now required"),r=new k);if(0===z)return r.set(-2,-1,-1);z=1/z;v=(v*e-c*g)*z;a=(a*g-c*e)*z;return r.set(1-v-a,a,v)},containsPoint:function(a,c,e,g){B.getBarycoord(a,c,e,g,nd);return 0<=nd.x&&
0<=nd.y&&1>=nd.x+nd.y},getUV:function(a,c,e,g,r,v,z,E){this.getBarycoord(a,c,e,g,nd);E.set(0,0);E.addScaledVector(r,nd.x);E.addScaledVector(v,nd.y);E.addScaledVector(z,nd.z);return E},isFrontFacing:function(a,c,e,g){Ic.subVectors(e,c);md.subVectors(a,c);return 0>Ic.cross(md).dot(g)?!0:!1}});Object.assign(B.prototype,{set:function(a,c,e){this.a.copy(a);this.b.copy(c);this.c.copy(e);return this},setFromPointsAndIndices:function(a,c,e,g){this.a.copy(a[c]);this.b.copy(a[e]);this.c.copy(a[g]);return this},
clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.a.copy(a.a);this.b.copy(a.b);this.c.copy(a.c);return this},getArea:function(){Ic.subVectors(this.c,this.b);md.subVectors(this.a,this.b);return.5*Ic.cross(md).length()},getMidpoint:function(a){void 0===a&&(console.warn("THREE.Triangle: .getMidpoint() target is now required"),a=new k);return a.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)},getNormal:function(a){return B.getNormal(this.a,this.b,this.c,a)},getPlane:function(a){void 0===
a&&(console.warn("THREE.Triangle: .getPlane() target is now required"),a=new k);return a.setFromCoplanarPoints(this.a,this.b,this.c)},getBarycoord:function(a,c){return B.getBarycoord(a,this.a,this.b,this.c,c)},getUV:function(a,c,e,g,r){return B.getUV(a,this.a,this.b,this.c,c,e,g,r)},containsPoint:function(a){return B.containsPoint(a,this.a,this.b,this.c)},isFrontFacing:function(a){return B.isFrontFacing(this.a,this.b,this.c,a)},intersectsBox:function(a){return a.intersectsTriangle(this)},closestPointToPoint:function(a,
c){void 0===c&&(console.warn("THREE.Triangle: .closestPointToPoint() target is now required"),c=new k);var e=this.a,g=this.b,r=this.c;kf.subVectors(g,e);lf.subVectors(r,e);Ci.subVectors(a,e);var v=kf.dot(Ci),z=lf.dot(Ci);if(0>=v&&0>=z)return c.copy(e);Di.subVectors(a,g);var E=kf.dot(Di),F=lf.dot(Di);if(0<=E&&F<=E)return c.copy(g);var J=v*F-E*z;if(0>=J&&0<=v&&0>=E)return g=v/(v-E),c.copy(e).addScaledVector(kf,g);Ei.subVectors(a,r);a=kf.dot(Ei);var P=lf.dot(Ei);if(0<=P&&a<=P)return c.copy(r);v=a*z-
v*P;if(0>=v&&0<=z&&0>=P)return J=z/(z-P),c.copy(e).addScaledVector(lf,J);z=E*P-a*F;if(0>=z&&0<=F-E&&0<=a-P)return ik.subVectors(r,g),J=(F-E)/(F-E+(a-P)),c.copy(g).addScaledVector(ik,J);r=1/(z+v+J);g=v*r;J*=r;return c.copy(e).addScaledVector(kf,g).addScaledVector(lf,J)},equals:function(a){return a.a.equals(this.a)&&a.b.equals(this.b)&&a.c.equals(this.c)}});var Em={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,
blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,
darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,
lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,
mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,
rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},lc={h:0,
s:0,l:0},mh={h:0,s:0,l:0};Object.assign(I.prototype,{isColor:!0,r:1,g:1,b:1,set:function(a){a&&a.isColor?this.copy(a):"number"===typeof a?this.setHex(a):"string"===typeof a&&this.setStyle(a);return this},setScalar:function(a){this.b=this.g=this.r=a;return this},setHex:function(a){a=Math.floor(a);this.r=(a>>16&255)/255;this.g=(a>>8&255)/255;this.b=(a&255)/255;return this},setRGB:function(a,c,e){this.r=a;this.g=c;this.b=e;return this},setHSL:function(a,c,e){a=hb.euclideanModulo(a,1);c=hb.clamp(c,0,
1);e=hb.clamp(e,0,1);0===c?this.r=this.g=this.b=e:(c=.5>=e?e*(1+c):e+c-e*c,e=2*e-c,this.r=N(e,c,a+1/3),this.g=N(e,c,a),this.b=N(e,c,a-1/3));return this},setStyle:function(a){function c(z){void 0!==z&&1>parseFloat(z)&&console.warn("THREE.Color: Alpha component of "+a+" will be ignored.")}var e;if(e=/^((?:rgb|hsl)a?)\(\s*([^\)]*)\)/.exec(a)){var g=e[2];switch(e[1]){case "rgb":case "rgba":if(e=/^(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(,\s*([0-9]*\.?[0-9]+)\s*)?$/.exec(g))return this.r=Math.min(255,parseInt(e[1],
10))/255,this.g=Math.min(255,parseInt(e[2],10))/255,this.b=Math.min(255,parseInt(e[3],10))/255,c(e[5]),this;if(e=/^(\d+)%\s*,\s*(\d+)%\s*,\s*(\d+)%\s*(,\s*([0-9]*\.?[0-9]+)\s*)?$/.exec(g))return this.r=Math.min(100,parseInt(e[1],10))/100,this.g=Math.min(100,parseInt(e[2],10))/100,this.b=Math.min(100,parseInt(e[3],10))/100,c(e[5]),this;break;case "hsl":case "hsla":if(e=/^([0-9]*\.?[0-9]+)\s*,\s*(\d+)%\s*,\s*(\d+)%\s*(,\s*([0-9]*\.?[0-9]+)\s*)?$/.exec(g)){g=parseFloat(e[1])/360;var r=parseInt(e[2],
10)/100,v=parseInt(e[3],10)/100;c(e[5]);return this.setHSL(g,r,v)}}}else if(e=/^#([A-Fa-f0-9]+)$/.exec(a)){e=e[1];g=e.length;if(3===g)return this.r=parseInt(e.charAt(0)+e.charAt(0),16)/255,this.g=parseInt(e.charAt(1)+e.charAt(1),16)/255,this.b=parseInt(e.charAt(2)+e.charAt(2),16)/255,this;if(6===g)return this.r=parseInt(e.charAt(0)+e.charAt(1),16)/255,this.g=parseInt(e.charAt(2)+e.charAt(3),16)/255,this.b=parseInt(e.charAt(4)+e.charAt(5),16)/255,this}a&&0<a.length&&(e=Em[a],void 0!==e?this.setHex(e):
console.warn("THREE.Color: Unknown color "+a));return this},clone:function(){return new this.constructor(this.r,this.g,this.b)},copy:function(a){this.r=a.r;this.g=a.g;this.b=a.b;return this},copyGammaToLinear:function(a,c){void 0===c&&(c=2);this.r=Math.pow(a.r,c);this.g=Math.pow(a.g,c);this.b=Math.pow(a.b,c);return this},copyLinearToGamma:function(a,c){void 0===c&&(c=2);c=0<c?1/c:1;this.r=Math.pow(a.r,c);this.g=Math.pow(a.g,c);this.b=Math.pow(a.b,c);return this},convertGammaToLinear:function(a){this.copyGammaToLinear(this,
a);return this},convertLinearToGamma:function(a){this.copyLinearToGamma(this,a);return this},copySRGBToLinear:function(a){this.r=O(a.r);this.g=O(a.g);this.b=O(a.b);return this},copyLinearToSRGB:function(a){this.r=H(a.r);this.g=H(a.g);this.b=H(a.b);return this},convertSRGBToLinear:function(){this.copySRGBToLinear(this);return this},convertLinearToSRGB:function(){this.copyLinearToSRGB(this);return this},getHex:function(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0},getHexString:function(){return("000000"+
this.getHex().toString(16)).slice(-6)},getHSL:function(a){void 0===a&&(console.warn("THREE.Color: .getHSL() target is now required"),a={h:0,s:0,l:0});var c=this.r,e=this.g,g=this.b,r=Math.max(c,e,g),v=Math.min(c,e,g),z,E=(v+r)/2;if(v===r)v=z=0;else{var F=r-v;v=.5>=E?F/(r+v):F/(2-r-v);switch(r){case c:z=(e-g)/F+(e<g?6:0);break;case e:z=(g-c)/F+2;break;case g:z=(c-e)/F+4}z/=6}a.h=z;a.s=v;a.l=E;return a},getStyle:function(){return"rgb("+(255*this.r|0)+","+(255*this.g|0)+","+(255*this.b|0)+")"},offsetHSL:function(a,
c,e){this.getHSL(lc);lc.h+=a;lc.s+=c;lc.l+=e;this.setHSL(lc.h,lc.s,lc.l);return this},add:function(a){this.r+=a.r;this.g+=a.g;this.b+=a.b;return this},addColors:function(a,c){this.r=a.r+c.r;this.g=a.g+c.g;this.b=a.b+c.b;return this},addScalar:function(a){this.r+=a;this.g+=a;this.b+=a;return this},sub:function(a){this.r=Math.max(0,this.r-a.r);this.g=Math.max(0,this.g-a.g);this.b=Math.max(0,this.b-a.b);return this},multiply:function(a){this.r*=a.r;this.g*=a.g;this.b*=a.b;return this},multiplyScalar:function(a){this.r*=
a;this.g*=a;this.b*=a;return this},lerp:function(a,c){this.r+=(a.r-this.r)*c;this.g+=(a.g-this.g)*c;this.b+=(a.b-this.b)*c;return this},lerpHSL:function(a,c){this.getHSL(lc);a.getHSL(mh);a=hb.lerp(lc.h,mh.h,c);var e=hb.lerp(lc.s,mh.s,c);c=hb.lerp(lc.l,mh.l,c);this.setHSL(a,e,c);return this},equals:function(a){return a.r===this.r&&a.g===this.g&&a.b===this.b},fromArray:function(a,c){void 0===c&&(c=0);this.r=a[c];this.g=a[c+1];this.b=a[c+2];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===
c&&(c=0);a[c]=this.r;a[c+1]=this.g;a[c+2]=this.b;return a},toJSON:function(){return this.getHex()}});Object.assign(K.prototype,{clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.a=a.a;this.b=a.b;this.c=a.c;this.normal.copy(a.normal);this.color.copy(a.color);this.materialIndex=a.materialIndex;for(var c=0,e=a.vertexNormals.length;c<e;c++)this.vertexNormals[c]=a.vertexNormals[c].clone();c=0;for(e=a.vertexColors.length;c<e;c++)this.vertexColors[c]=a.vertexColors[c].clone();
return this}});var Pk=0;M.prototype=Object.assign(Object.create(d.prototype),{constructor:M,isMaterial:!0,onBeforeCompile:function(){},setValues:function(a){if(void 0!==a)for(var c in a){var e=a[c];if(void 0===e)console.warn("THREE.Material: '"+c+"' parameter is undefined.");else if("shading"===c)console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===e?!0:!1;else{var g=this[c];void 0===g?console.warn("THREE."+this.type+": '"+c+"' is not a property of this material."):
g&&g.isColor?g.set(e):g&&g.isVector3&&e&&e.isVector3?g.copy(e):this[c]=e}}},toJSON:function(a){function c(r){var v=[],z;for(z in r){var E=r[z];delete E.metadata;v.push(E)}return v}var e=void 0===a||"string"===typeof a;e&&(a={textures:{},images:{}});var g={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};g.uuid=this.uuid;g.type=this.type;""!==this.name&&(g.name=this.name);this.color&&this.color.isColor&&(g.color=this.color.getHex());void 0!==this.roughness&&(g.roughness=this.roughness);
void 0!==this.metalness&&(g.metalness=this.metalness);this.emissive&&this.emissive.isColor&&(g.emissive=this.emissive.getHex());this.emissiveIntensity&&1!==this.emissiveIntensity&&(g.emissiveIntensity=this.emissiveIntensity);this.specular&&this.specular.isColor&&(g.specular=this.specular.getHex());void 0!==this.shininess&&(g.shininess=this.shininess);void 0!==this.clearcoat&&(g.clearcoat=this.clearcoat);void 0!==this.clearcoatRoughness&&(g.clearcoatRoughness=this.clearcoatRoughness);this.clearcoatNormalMap&&
this.clearcoatNormalMap.isTexture&&(g.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(a).uuid,g.clearcoatNormalScale=this.clearcoatNormalScale.toArray());this.map&&this.map.isTexture&&(g.map=this.map.toJSON(a).uuid);this.matcap&&this.matcap.isTexture&&(g.matcap=this.matcap.toJSON(a).uuid);this.alphaMap&&this.alphaMap.isTexture&&(g.alphaMap=this.alphaMap.toJSON(a).uuid);this.lightMap&&this.lightMap.isTexture&&(g.lightMap=this.lightMap.toJSON(a).uuid);this.aoMap&&this.aoMap.isTexture&&(g.aoMap=this.aoMap.toJSON(a).uuid,
g.aoMapIntensity=this.aoMapIntensity);this.bumpMap&&this.bumpMap.isTexture&&(g.bumpMap=this.bumpMap.toJSON(a).uuid,g.bumpScale=this.bumpScale);this.normalMap&&this.normalMap.isTexture&&(g.normalMap=this.normalMap.toJSON(a).uuid,g.normalMapType=this.normalMapType,g.normalScale=this.normalScale.toArray());this.displacementMap&&this.displacementMap.isTexture&&(g.displacementMap=this.displacementMap.toJSON(a).uuid,g.displacementScale=this.displacementScale,g.displacementBias=this.displacementBias);this.roughnessMap&&
this.roughnessMap.isTexture&&(g.roughnessMap=this.roughnessMap.toJSON(a).uuid);this.metalnessMap&&this.metalnessMap.isTexture&&(g.metalnessMap=this.metalnessMap.toJSON(a).uuid);this.emissiveMap&&this.emissiveMap.isTexture&&(g.emissiveMap=this.emissiveMap.toJSON(a).uuid);this.specularMap&&this.specularMap.isTexture&&(g.specularMap=this.specularMap.toJSON(a).uuid);this.envMap&&this.envMap.isTexture&&(g.envMap=this.envMap.toJSON(a).uuid,g.reflectivity=this.reflectivity,g.refractionRatio=this.refractionRatio,
void 0!==this.combine&&(g.combine=this.combine),void 0!==this.envMapIntensity&&(g.envMapIntensity=this.envMapIntensity));this.gradientMap&&this.gradientMap.isTexture&&(g.gradientMap=this.gradientMap.toJSON(a).uuid);void 0!==this.size&&(g.size=this.size);void 0!==this.sizeAttenuation&&(g.sizeAttenuation=this.sizeAttenuation);1!==this.blending&&(g.blending=this.blending);!0===this.flatShading&&(g.flatShading=this.flatShading);0!==this.side&&(g.side=this.side);0!==this.vertexColors&&(g.vertexColors=
this.vertexColors);1>this.opacity&&(g.opacity=this.opacity);!0===this.transparent&&(g.transparent=this.transparent);g.depthFunc=this.depthFunc;g.depthTest=this.depthTest;g.depthWrite=this.depthWrite;g.stencilWrite=this.stencilWrite;g.stencilFunc=this.stencilFunc;g.stencilRef=this.stencilRef;g.stencilMask=this.stencilMask;g.stencilFail=this.stencilFail;g.stencilZFail=this.stencilZFail;g.stencilZPass=this.stencilZPass;this.rotation&&0!==this.rotation&&(g.rotation=this.rotation);!0===this.polygonOffset&&
(g.polygonOffset=!0);0!==this.polygonOffsetFactor&&(g.polygonOffsetFactor=this.polygonOffsetFactor);0!==this.polygonOffsetUnits&&(g.polygonOffsetUnits=this.polygonOffsetUnits);this.linewidth&&1!==this.linewidth&&(g.linewidth=this.linewidth);void 0!==this.dashSize&&(g.dashSize=this.dashSize);void 0!==this.gapSize&&(g.gapSize=this.gapSize);void 0!==this.scale&&(g.scale=this.scale);!0===this.dithering&&(g.dithering=!0);0<this.alphaTest&&(g.alphaTest=this.alphaTest);!0===this.premultipliedAlpha&&(g.premultipliedAlpha=
this.premultipliedAlpha);!0===this.wireframe&&(g.wireframe=this.wireframe);1<this.wireframeLinewidth&&(g.wireframeLinewidth=this.wireframeLinewidth);"round"!==this.wireframeLinecap&&(g.wireframeLinecap=this.wireframeLinecap);"round"!==this.wireframeLinejoin&&(g.wireframeLinejoin=this.wireframeLinejoin);!0===this.morphTargets&&(g.morphTargets=!0);!0===this.morphNormals&&(g.morphNormals=!0);!0===this.skinning&&(g.skinning=!0);!1===this.visible&&(g.visible=!1);!1===this.toneMapped&&(g.toneMapped=!1);
"{}"!==JSON.stringify(this.userData)&&(g.userData=this.userData);e&&(e=c(a.textures),a=c(a.images),0<e.length&&(g.textures=e),0<a.length&&(g.images=a));return g},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.name=a.name;this.fog=a.fog;this.lights=a.lights;this.blending=a.blending;this.side=a.side;this.flatShading=a.flatShading;this.vertexColors=a.vertexColors;this.opacity=a.opacity;this.transparent=a.transparent;this.blendSrc=a.blendSrc;this.blendDst=a.blendDst;this.blendEquation=
a.blendEquation;this.blendSrcAlpha=a.blendSrcAlpha;this.blendDstAlpha=a.blendDstAlpha;this.blendEquationAlpha=a.blendEquationAlpha;this.depthFunc=a.depthFunc;this.depthTest=a.depthTest;this.depthWrite=a.depthWrite;this.stencilWrite=a.stencilWrite;this.stencilFunc=a.stencilFunc;this.stencilRef=a.stencilRef;this.stencilMask=a.stencilMask;this.stencilFail=a.stencilFail;this.stencilZFail=a.stencilZFail;this.stencilZPass=a.stencilZPass;this.colorWrite=a.colorWrite;this.precision=a.precision;this.polygonOffset=
a.polygonOffset;this.polygonOffsetFactor=a.polygonOffsetFactor;this.polygonOffsetUnits=a.polygonOffsetUnits;this.dithering=a.dithering;this.alphaTest=a.alphaTest;this.premultipliedAlpha=a.premultipliedAlpha;this.visible=a.visible;this.toneMapped=a.toneMapped;this.userData=JSON.parse(JSON.stringify(a.userData));this.clipShadows=a.clipShadows;this.clipIntersection=a.clipIntersection;var c=a.clippingPlanes,e=null;if(null!==c){var g=c.length;e=Array(g);for(var r=0;r!==g;++r)e[r]=c[r].clone()}this.clippingPlanes=
e;this.shadowSide=a.shadowSide;return this},dispose:function(){this.dispatchEvent({type:"dispose"})}});L.prototype=Object.create(M.prototype);L.prototype.constructor=L;L.prototype.isMeshBasicMaterial=!0;L.prototype.copy=function(a){M.prototype.copy.call(this,a);this.color.copy(a.color);this.map=a.map;this.lightMap=a.lightMap;this.lightMapIntensity=a.lightMapIntensity;this.aoMap=a.aoMap;this.aoMapIntensity=a.aoMapIntensity;this.specularMap=a.specularMap;this.alphaMap=a.alphaMap;this.envMap=a.envMap;
this.combine=a.combine;this.reflectivity=a.reflectivity;this.refractionRatio=a.refractionRatio;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.wireframeLinecap=a.wireframeLinecap;this.wireframeLinejoin=a.wireframeLinejoin;this.skinning=a.skinning;this.morphTargets=a.morphTargets;return this};Object.defineProperty(Q.prototype,"needsUpdate",{set:function(a){!0===a&&this.version++}});Object.assign(Q.prototype,{isBufferAttribute:!0,onUploadCallback:function(){},setArray:function(a){if(Array.isArray(a))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");
this.count=void 0!==a?a.length/this.itemSize:0;this.array=a;return this},setDynamic:function(a){this.dynamic=a;return this},copy:function(a){this.name=a.name;this.array=new a.array.constructor(a.array);this.itemSize=a.itemSize;this.count=a.count;this.normalized=a.normalized;this.dynamic=a.dynamic;return this},copyAt:function(a,c,e){a*=this.itemSize;e*=c.itemSize;for(var g=0,r=this.itemSize;g<r;g++)this.array[a+g]=c.array[e+g];return this},copyArray:function(a){this.array.set(a);return this},copyColorsArray:function(a){for(var c=
this.array,e=0,g=0,r=a.length;g<r;g++){var v=a[g];void 0===v&&(console.warn("THREE.BufferAttribute.copyColorsArray(): color is undefined",g),v=new I);c[e++]=v.r;c[e++]=v.g;c[e++]=v.b}return this},copyVector2sArray:function(a){for(var c=this.array,e=0,g=0,r=a.length;g<r;g++){var v=a[g];void 0===v&&(console.warn("THREE.BufferAttribute.copyVector2sArray(): vector is undefined",g),v=new f);c[e++]=v.x;c[e++]=v.y}return this},copyVector3sArray:function(a){for(var c=this.array,e=0,g=0,r=a.length;g<r;g++){var v=
a[g];void 0===v&&(console.warn("THREE.BufferAttribute.copyVector3sArray(): vector is undefined",g),v=new k);c[e++]=v.x;c[e++]=v.y;c[e++]=v.z}return this},copyVector4sArray:function(a){for(var c=this.array,e=0,g=0,r=a.length;g<r;g++){var v=a[g];void 0===v&&(console.warn("THREE.BufferAttribute.copyVector4sArray(): vector is undefined",g),v=new p);c[e++]=v.x;c[e++]=v.y;c[e++]=v.z;c[e++]=v.w}return this},set:function(a,c){void 0===c&&(c=0);this.array.set(a,c);return this},getX:function(a){return this.array[a*
this.itemSize]},setX:function(a,c){this.array[a*this.itemSize]=c;return this},getY:function(a){return this.array[a*this.itemSize+1]},setY:function(a,c){this.array[a*this.itemSize+1]=c;return this},getZ:function(a){return this.array[a*this.itemSize+2]},setZ:function(a,c){this.array[a*this.itemSize+2]=c;return this},getW:function(a){return this.array[a*this.itemSize+3]},setW:function(a,c){this.array[a*this.itemSize+3]=c;return this},setXY:function(a,c,e){a*=this.itemSize;this.array[a+0]=c;this.array[a+
1]=e;return this},setXYZ:function(a,c,e,g){a*=this.itemSize;this.array[a+0]=c;this.array[a+1]=e;this.array[a+2]=g;return this},setXYZW:function(a,c,e,g,r){a*=this.itemSize;this.array[a+0]=c;this.array[a+1]=e;this.array[a+2]=g;this.array[a+3]=r;return this},onUpload:function(a){this.onUploadCallback=a;return this},clone:function(){return(new this.constructor(this.array,this.itemSize)).copy(this)},toJSON:function(){return{itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),
normalized:this.normalized}}});T.prototype=Object.create(Q.prototype);T.prototype.constructor=T;X.prototype=Object.create(Q.prototype);X.prototype.constructor=X;aa.prototype=Object.create(Q.prototype);aa.prototype.constructor=aa;la.prototype=Object.create(Q.prototype);la.prototype.constructor=la;Z.prototype=Object.create(Q.prototype);Z.prototype.constructor=Z;ba.prototype=Object.create(Q.prototype);ba.prototype.constructor=ba;ea.prototype=Object.create(Q.prototype);ea.prototype.constructor=ea;ca.prototype=
Object.create(Q.prototype);ca.prototype.constructor=ca;ka.prototype=Object.create(Q.prototype);ka.prototype.constructor=ka;Object.assign(Y.prototype,{computeGroups:function(a){var c=[],e=void 0;a=a.faces;for(var g=0;g<a.length;g++){var r=a[g];if(r.materialIndex!==e){e=r.materialIndex;void 0!==v&&(v.count=3*g-v.start,c.push(v));var v={start:3*g,materialIndex:e}}}void 0!==v&&(v.count=3*g-v.start,c.push(v));this.groups=c},fromGeometry:function(a){var c=a.faces,e=a.vertices,g=a.faceVertexUvs,r=g[0]&&
0<g[0].length,v=g[1]&&0<g[1].length,z=a.morphTargets,E=z.length;if(0<E){var F=[];for(var J=0;J<E;J++)F[J]={name:z[J].name,data:[]};this.morphTargets.position=F}var P=a.morphNormals,R=P.length;if(0<R){var S=[];for(J=0;J<R;J++)S[J]={name:P[J].name,data:[]};this.morphTargets.normal=S}var V=a.skinIndices,W=a.skinWeights,ha=V.length===e.length,fa=W.length===e.length;0<e.length&&0===c.length&&console.error("THREE.DirectGeometry: Faceless geometries are not supported.");for(J=0;J<c.length;J++){var ra=c[J];
this.vertices.push(e[ra.a],e[ra.b],e[ra.c]);var pa=ra.vertexNormals;3===pa.length?this.normals.push(pa[0],pa[1],pa[2]):(pa=ra.normal,this.normals.push(pa,pa,pa));pa=ra.vertexColors;3===pa.length?this.colors.push(pa[0],pa[1],pa[2]):(pa=ra.color,this.colors.push(pa,pa,pa));!0===r&&(pa=g[0][J],void 0!==pa?this.uvs.push(pa[0],pa[1],pa[2]):(console.warn("THREE.DirectGeometry.fromGeometry(): Undefined vertexUv ",J),this.uvs.push(new f,new f,new f)));!0===v&&(pa=g[1][J],void 0!==pa?this.uvs2.push(pa[0],
pa[1],pa[2]):(console.warn("THREE.DirectGeometry.fromGeometry(): Undefined vertexUv2 ",J),this.uvs2.push(new f,new f,new f)));for(pa=0;pa<E;pa++){var qa=z[pa].vertices;F[pa].data.push(qa[ra.a],qa[ra.b],qa[ra.c])}for(pa=0;pa<R;pa++)qa=P[pa].vertexNormals[J],S[pa].data.push(qa.a,qa.b,qa.c);ha&&this.skinIndices.push(V[ra.a],V[ra.b],V[ra.c]);fa&&this.skinWeights.push(W[ra.a],W[ra.b],W[ra.c])}this.computeGroups(a);this.verticesNeedUpdate=a.verticesNeedUpdate;this.normalsNeedUpdate=a.normalsNeedUpdate;
this.colorsNeedUpdate=a.colorsNeedUpdate;this.uvsNeedUpdate=a.uvsNeedUpdate;this.groupsNeedUpdate=a.groupsNeedUpdate;null!==a.boundingSphere&&(this.boundingSphere=a.boundingSphere.clone());null!==a.boundingBox&&(this.boundingBox=a.boundingBox.clone());return this}});var Qk=1,Xc=new q,Fi=new A,nh=new k,ge=new w,Gi=new w,Jc=new k;va.prototype=Object.assign(Object.create(d.prototype),{constructor:va,isBufferGeometry:!0,getIndex:function(){return this.index},setIndex:function(a){this.index=Array.isArray(a)?
new (65535<Ea(a)?ea:Z)(a,1):a},addAttribute:function(a,c,e){if(!(c&&c.isBufferAttribute||c&&c.isInterleavedBufferAttribute))return console.warn("THREE.BufferGeometry: .addAttribute() now expects ( name, attribute )."),this.addAttribute(a,new Q(c,e));if("index"===a)return console.warn("THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute."),this.setIndex(c),this;this.attributes[a]=c;return this},getAttribute:function(a){return this.attributes[a]},removeAttribute:function(a){delete this.attributes[a];
return this},addGroup:function(a,c,e){this.groups.push({start:a,count:c,materialIndex:void 0!==e?e:0})},clearGroups:function(){this.groups=[]},setDrawRange:function(a,c){this.drawRange.start=a;this.drawRange.count=c},applyMatrix:function(a){var c=this.attributes.position;void 0!==c&&(a.applyToBufferAttribute(c),c.needsUpdate=!0);var e=this.attributes.normal;void 0!==e&&(c=(new t).getNormalMatrix(a),c.applyToBufferAttribute(e),e.needsUpdate=!0);e=this.attributes.tangent;void 0!==e&&(c=(new t).getNormalMatrix(a),
c.applyToBufferAttribute(e),e.needsUpdate=!0);null!==this.boundingBox&&this.computeBoundingBox();null!==this.boundingSphere&&this.computeBoundingSphere();return this},rotateX:function(a){Xc.makeRotationX(a);this.applyMatrix(Xc);return this},rotateY:function(a){Xc.makeRotationY(a);this.applyMatrix(Xc);return this},rotateZ:function(a){Xc.makeRotationZ(a);this.applyMatrix(Xc);return this},translate:function(a,c,e){Xc.makeTranslation(a,c,e);this.applyMatrix(Xc);return this},scale:function(a,c,e){Xc.makeScale(a,
c,e);this.applyMatrix(Xc);return this},lookAt:function(a){Fi.lookAt(a);Fi.updateMatrix();this.applyMatrix(Fi.matrix);return this},center:function(){this.computeBoundingBox();this.boundingBox.getCenter(nh).negate();this.translate(nh.x,nh.y,nh.z);return this},setFromObject:function(a){var c=a.geometry;if(a.isPoints||a.isLine){a=new ca(3*c.vertices.length,3);var e=new ca(3*c.colors.length,3);this.addAttribute("position",a.copyVector3sArray(c.vertices));this.addAttribute("color",e.copyColorsArray(c.colors));
c.lineDistances&&c.lineDistances.length===c.vertices.length&&(a=new ca(c.lineDistances.length,1),this.addAttribute("lineDistance",a.copyArray(c.lineDistances)));null!==c.boundingSphere&&(this.boundingSphere=c.boundingSphere.clone());null!==c.boundingBox&&(this.boundingBox=c.boundingBox.clone())}else a.isMesh&&c&&c.isGeometry&&this.fromGeometry(c);return this},setFromPoints:function(a){for(var c=[],e=0,g=a.length;e<g;e++){var r=a[e];c.push(r.x,r.y,r.z||0)}this.addAttribute("position",new ca(c,3));
return this},updateFromObject:function(a){var c=a.geometry;if(a.isMesh){var e=c.__directGeometry;!0===c.elementsNeedUpdate&&(e=void 0,c.elementsNeedUpdate=!1);if(void 0===e)return this.fromGeometry(c);e.verticesNeedUpdate=c.verticesNeedUpdate;e.normalsNeedUpdate=c.normalsNeedUpdate;e.colorsNeedUpdate=c.colorsNeedUpdate;e.uvsNeedUpdate=c.uvsNeedUpdate;e.groupsNeedUpdate=c.groupsNeedUpdate;c.verticesNeedUpdate=!1;c.normalsNeedUpdate=!1;c.colorsNeedUpdate=!1;c.uvsNeedUpdate=!1;c.groupsNeedUpdate=!1;
c=e}!0===c.verticesNeedUpdate&&(e=this.attributes.position,void 0!==e&&(e.copyVector3sArray(c.vertices),e.needsUpdate=!0),c.verticesNeedUpdate=!1);!0===c.normalsNeedUpdate&&(e=this.attributes.normal,void 0!==e&&(e.copyVector3sArray(c.normals),e.needsUpdate=!0),c.normalsNeedUpdate=!1);!0===c.colorsNeedUpdate&&(e=this.attributes.color,void 0!==e&&(e.copyColorsArray(c.colors),e.needsUpdate=!0),c.colorsNeedUpdate=!1);c.uvsNeedUpdate&&(e=this.attributes.uv,void 0!==e&&(e.copyVector2sArray(c.uvs),e.needsUpdate=
!0),c.uvsNeedUpdate=!1);c.lineDistancesNeedUpdate&&(e=this.attributes.lineDistance,void 0!==e&&(e.copyArray(c.lineDistances),e.needsUpdate=!0),c.lineDistancesNeedUpdate=!1);c.groupsNeedUpdate&&(c.computeGroups(a.geometry),this.groups=c.groups,c.groupsNeedUpdate=!1);return this},fromGeometry:function(a){a.__directGeometry=(new Y).fromGeometry(a);return this.fromDirectGeometry(a.__directGeometry)},fromDirectGeometry:function(a){this.addAttribute("position",(new Q(new Float32Array(3*a.vertices.length),
3)).copyVector3sArray(a.vertices));0<a.normals.length&&this.addAttribute("normal",(new Q(new Float32Array(3*a.normals.length),3)).copyVector3sArray(a.normals));0<a.colors.length&&this.addAttribute("color",(new Q(new Float32Array(3*a.colors.length),3)).copyColorsArray(a.colors));0<a.uvs.length&&this.addAttribute("uv",(new Q(new Float32Array(2*a.uvs.length),2)).copyVector2sArray(a.uvs));0<a.uvs2.length&&this.addAttribute("uv2",(new Q(new Float32Array(2*a.uvs2.length),2)).copyVector2sArray(a.uvs2));
this.groups=a.groups;for(var c in a.morphTargets){for(var e=[],g=a.morphTargets[c],r=0,v=g.length;r<v;r++){var z=g[r],E=new ca(3*z.data.length,3);E.name=z.name;e.push(E.copyVector3sArray(z.data))}this.morphAttributes[c]=e}0<a.skinIndices.length&&(c=new ca(4*a.skinIndices.length,4),this.addAttribute("skinIndex",c.copyVector4sArray(a.skinIndices)));0<a.skinWeights.length&&(c=new ca(4*a.skinWeights.length,4),this.addAttribute("skinWeight",c.copyVector4sArray(a.skinWeights)));null!==a.boundingSphere&&
(this.boundingSphere=a.boundingSphere.clone());null!==a.boundingBox&&(this.boundingBox=a.boundingBox.clone());return this},computeBoundingBox:function(){null===this.boundingBox&&(this.boundingBox=new w);var a=this.attributes.position,c=this.morphAttributes.position;if(void 0!==a){if(this.boundingBox.setFromBufferAttribute(a),c){a=0;for(var e=c.length;a<e;a++)ge.setFromBufferAttribute(c[a]),this.boundingBox.expandByPoint(ge.min),this.boundingBox.expandByPoint(ge.max)}}else this.boundingBox.makeEmpty();
(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox: Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)},computeBoundingSphere:function(){null===this.boundingSphere&&(this.boundingSphere=new G);var a=this.attributes.position,c=this.morphAttributes.position;if(a){var e=this.boundingSphere.center;ge.setFromBufferAttribute(a);if(c)for(var g=0,r=c.length;g<r;g++){var v=
c[g];Gi.setFromBufferAttribute(v);ge.expandByPoint(Gi.min);ge.expandByPoint(Gi.max)}ge.getCenter(e);var z=0;g=0;for(r=a.count;g<r;g++)Jc.fromBufferAttribute(a,g),z=Math.max(z,e.distanceToSquared(Jc));if(c)for(g=0,r=c.length;g<r;g++){v=c[g];a=0;for(var E=v.count;a<E;a++)Jc.fromBufferAttribute(v,a),z=Math.max(z,e.distanceToSquared(Jc))}this.boundingSphere.radius=Math.sqrt(z);isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',
this)}},computeFaceNormals:function(){},computeVertexNormals:function(){var a=this.index,c=this.attributes;if(c.position){var e=c.position.array;if(void 0===c.normal)this.addAttribute("normal",new Q(new Float32Array(e.length),3));else for(var g=c.normal.array,r=0,v=g.length;r<v;r++)g[r]=0;g=c.normal.array;var z=new k,E=new k,F=new k,J=new k,P=new k;if(a){var R=a.array;r=0;for(v=a.count;r<v;r+=3){a=3*R[r+0];var S=3*R[r+1];var V=3*R[r+2];z.fromArray(e,a);E.fromArray(e,S);F.fromArray(e,V);J.subVectors(F,
E);P.subVectors(z,E);J.cross(P);g[a]+=J.x;g[a+1]+=J.y;g[a+2]+=J.z;g[S]+=J.x;g[S+1]+=J.y;g[S+2]+=J.z;g[V]+=J.x;g[V+1]+=J.y;g[V+2]+=J.z}}else for(r=0,v=e.length;r<v;r+=9)z.fromArray(e,r),E.fromArray(e,r+3),F.fromArray(e,r+6),J.subVectors(F,E),P.subVectors(z,E),J.cross(P),g[r]=J.x,g[r+1]=J.y,g[r+2]=J.z,g[r+3]=J.x,g[r+4]=J.y,g[r+5]=J.z,g[r+6]=J.x,g[r+7]=J.y,g[r+8]=J.z;this.normalizeNormals();c.normal.needsUpdate=!0}},merge:function(a,c){if(a&&a.isBufferGeometry){void 0===c&&(c=0,console.warn("THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset\x3d0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge."));
var e=this.attributes,g;for(g in e)if(void 0!==a.attributes[g]){var r=e[g].array,v=a.attributes[g],z=v.array,E=v.itemSize*c;v=Math.min(z.length,r.length-E);for(var F=0;F<v;F++,E++)r[E]=z[F]}return this}console.error("THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.",a)},normalizeNormals:function(){for(var a=this.attributes.normal,c=0,e=a.count;c<e;c++)Jc.x=a.getX(c),Jc.y=a.getY(c),Jc.z=a.getZ(c),Jc.normalize(),a.setXYZ(c,Jc.x,Jc.y,Jc.z)},toNonIndexed:function(){function a(P,
R){var S=P.array;P=P.itemSize;for(var V=new S.constructor(R.length*P),W,ha=0,fa=0,ra=R.length;fa<ra;fa++){W=R[fa]*P;for(var pa=0;pa<P;pa++)V[ha++]=S[W++]}return new Q(V,P)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): Geometry is already non-indexed."),this;var c=new va,e=this.index.array,g=this.attributes,r;for(r in g){var v=g[r];v=a(v,e);c.addAttribute(r,v)}var z=this.morphAttributes;for(r in z){var E=[],F=z[r];g=0;for(var J=F.length;g<J;g++)v=F[g],v=a(v,e),E.push(v);
c.morphAttributes[r]=E}e=this.groups;g=0;for(r=e.length;g<r;g++)v=e[g],c.addGroup(v.start,v.count,v.materialIndex);return c},toJSON:function(){var a={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};a.uuid=this.uuid;a.type=this.type;""!==this.name&&(a.name=this.name);0<Object.keys(this.userData).length&&(a.userData=this.userData);if(void 0!==this.parameters){var c=this.parameters;for(J in c)void 0!==c[J]&&(a[J]=c[J]);return a}a.data={attributes:{}};c=this.index;null!==
c&&(a.data.index={type:c.array.constructor.name,array:Array.prototype.slice.call(c.array)});var e=this.attributes;for(J in e){c=e[J];var g=c.toJSON();""!==c.name&&(g.name=c.name);a.data.attributes[J]=g}e={};var r=!1;for(J in this.morphAttributes){for(var v=this.morphAttributes[J],z=[],E=0,F=v.length;E<F;E++)c=v[E],g=c.toJSON(),""!==c.name&&(g.name=c.name),z.push(g);0<z.length&&(e[J]=z,r=!0)}r&&(a.data.morphAttributes=e);var J=this.groups;0<J.length&&(a.data.groups=JSON.parse(JSON.stringify(J)));J=
this.boundingSphere;null!==J&&(a.data.boundingSphere={center:J.center.toArray(),radius:J.radius});return a},clone:function(){return(new va).copy(this)},copy:function(a){var c;this.index=null;this.attributes={};this.morphAttributes={};this.groups=[];this.boundingSphere=this.boundingBox=null;this.name=a.name;var e=a.index;null!==e&&this.setIndex(e.clone());e=a.attributes;for(z in e)this.addAttribute(z,e[z].clone());var g=a.morphAttributes;for(z in g){var r=[],v=g[z];e=0;for(c=v.length;e<c;e++)r.push(v[e].clone());
this.morphAttributes[z]=r}var z=a.groups;e=0;for(c=z.length;e<c;e++)g=z[e],this.addGroup(g.start,g.count,g.materialIndex);z=a.boundingBox;null!==z&&(this.boundingBox=z.clone());z=a.boundingSphere;null!==z&&(this.boundingSphere=z.clone());this.drawRange.start=a.drawRange.start;this.drawRange.count=a.drawRange.count;this.userData=a.userData;return this},dispose:function(){this.dispatchEvent({type:"dispose"})}});var jk=new q,he=new D,Hi=new G,Ld=new k,Md=new k,Nd=new k,$i=new k,aj=new k,bj=new k,Ih=
new k,Jh=new k,Kh=new k,oe=new f,pe=new f,qe=new f,sf=new k,sg=new k;xa.prototype=Object.assign(Object.create(A.prototype),{constructor:xa,isMesh:!0,setDrawMode:function(a){this.drawMode=a},copy:function(a){A.prototype.copy.call(this,a);this.drawMode=a.drawMode;void 0!==a.morphTargetInfluences&&(this.morphTargetInfluences=a.morphTargetInfluences.slice());void 0!==a.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},a.morphTargetDictionary));return this},updateMorphTargets:function(){var a=
this.geometry;if(a.isBufferGeometry){a=a.morphAttributes;var c=Object.keys(a);if(0<c.length){var e=a[c[0]];if(void 0!==e)for(this.morphTargetInfluences=[],this.morphTargetDictionary={},a=0,c=e.length;a<c;a++){var g=e[a].name||String(a);this.morphTargetInfluences.push(0);this.morphTargetDictionary[g]=a}}}else a=a.morphTargets,void 0!==a&&0<a.length&&console.error("THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")},raycast:function(a,c){var e=this.geometry,
g=this.material,r=this.matrixWorld;if(void 0!==g&&(null===e.boundingSphere&&e.computeBoundingSphere(),Hi.copy(e.boundingSphere),Hi.applyMatrix4(r),!1!==a.ray.intersectsSphere(Hi)&&(jk.getInverse(r),he.copy(a.ray).applyMatrix4(jk),null===e.boundingBox||!1!==he.intersectsBox(e.boundingBox))))if(e.isBufferGeometry){var v=e.index;r=e.attributes.position;var z=e.morphAttributes.position,E=e.attributes.uv,F=e.attributes.uv2,J=e.groups,P=e.drawRange,R,S;if(null!==v)if(Array.isArray(g)){var V=0;for(R=J.length;V<
R;V++){var W=J[V];var ha=g[W.materialIndex];var fa=Math.max(W.start,P.start);for(S=e=Math.min(W.start+W.count,P.start+P.count);fa<S;fa+=3){e=v.getX(fa);var ra=v.getX(fa+1);var pa=v.getX(fa+2);if(e=Fa(this,ha,a,he,r,z,E,F,e,ra,pa))e.faceIndex=Math.floor(fa/3),e.face.materialIndex=W.materialIndex,c.push(e)}}}else for(fa=Math.max(0,P.start),e=Math.min(v.count,P.start+P.count),V=fa,R=e;V<R;V+=3){if(e=v.getX(V),ra=v.getX(V+1),pa=v.getX(V+2),e=Fa(this,g,a,he,r,z,E,F,e,ra,pa))e.faceIndex=Math.floor(V/3),
c.push(e)}else if(void 0!==r)if(Array.isArray(g))for(V=0,R=J.length;V<R;V++)for(W=J[V],ha=g[W.materialIndex],fa=Math.max(W.start,P.start),S=e=Math.min(W.start+W.count,P.start+P.count);fa<S;fa+=3){if(e=fa,ra=fa+1,pa=fa+2,e=Fa(this,ha,a,he,r,z,E,F,e,ra,pa))e.faceIndex=Math.floor(fa/3),e.face.materialIndex=W.materialIndex,c.push(e)}else for(fa=Math.max(0,P.start),e=Math.min(r.count,P.start+P.count),V=fa,R=e;V<R;V+=3)if(e=V,ra=V+1,pa=V+2,e=Fa(this,g,a,he,r,z,E,F,e,ra,pa))e.faceIndex=Math.floor(V/3),c.push(e)}else if(e.isGeometry)for(r=
Array.isArray(g),z=e.vertices,E=e.faces,e=e.faceVertexUvs[0],0<e.length&&(v=e),V=0,R=E.length;V<R;V++)if(W=E[V],e=r?g[W.materialIndex]:g,void 0!==e&&(F=z[W.a],J=z[W.b],P=z[W.c],e=Aa(this,e,a,he,F,J,P,sf)))v&&v[V]&&(ha=v[V],oe.copy(ha[0]),pe.copy(ha[1]),qe.copy(ha[2]),e.uv=B.getUV(sf,F,J,P,oe,pe,qe,new f)),e.face=W,e.faceIndex=V,c.push(e)},clone:function(){return(new this.constructor(this.geometry,this.material)).copy(this)}});var Rk=0,Yc=new q,Ii=new A,oh=new k;ya.prototype=Object.assign(Object.create(d.prototype),
{constructor:ya,isGeometry:!0,applyMatrix:function(a){for(var c=(new t).getNormalMatrix(a),e=0,g=this.vertices.length;e<g;e++)this.vertices[e].applyMatrix4(a);e=0;for(g=this.faces.length;e<g;e++){a=this.faces[e];a.normal.applyMatrix3(c).normalize();for(var r=0,v=a.vertexNormals.length;r<v;r++)a.vertexNormals[r].applyMatrix3(c).normalize()}null!==this.boundingBox&&this.computeBoundingBox();null!==this.boundingSphere&&this.computeBoundingSphere();this.normalsNeedUpdate=this.verticesNeedUpdate=!0;return this},
rotateX:function(a){Yc.makeRotationX(a);this.applyMatrix(Yc);return this},rotateY:function(a){Yc.makeRotationY(a);this.applyMatrix(Yc);return this},rotateZ:function(a){Yc.makeRotationZ(a);this.applyMatrix(Yc);return this},translate:function(a,c,e){Yc.makeTranslation(a,c,e);this.applyMatrix(Yc);return this},scale:function(a,c,e){Yc.makeScale(a,c,e);this.applyMatrix(Yc);return this},lookAt:function(a){Ii.lookAt(a);Ii.updateMatrix();this.applyMatrix(Ii.matrix);return this},fromBufferGeometry:function(a){function c(V,
W,ha,fa){var ra=void 0===E?[]:[e.colors[V].clone(),e.colors[W].clone(),e.colors[ha].clone()],pa=void 0===z?[]:[(new k).fromArray(z,3*V),(new k).fromArray(z,3*W),(new k).fromArray(z,3*ha)];fa=new K(V,W,ha,pa,ra,fa);e.faces.push(fa);void 0!==F&&e.faceVertexUvs[0].push([(new f).fromArray(F,2*V),(new f).fromArray(F,2*W),(new f).fromArray(F,2*ha)]);void 0!==J&&e.faceVertexUvs[1].push([(new f).fromArray(J,2*V),(new f).fromArray(J,2*W),(new f).fromArray(J,2*ha)])}var e=this,g=null!==a.index?a.index.array:
void 0,r=a.attributes,v=r.position.array,z=void 0!==r.normal?r.normal.array:void 0,E=void 0!==r.color?r.color.array:void 0,F=void 0!==r.uv?r.uv.array:void 0,J=void 0!==r.uv2?r.uv2.array:void 0;void 0!==J&&(this.faceVertexUvs[1]=[]);for(r=0;r<v.length;r+=3)e.vertices.push((new k).fromArray(v,r)),void 0!==E&&e.colors.push((new I).fromArray(E,r));var P=a.groups;if(0<P.length)for(r=0;r<P.length;r++){v=P[r];var R=v.start,S=R;for(R+=v.count;S<R;S+=3)void 0!==g?c(g[S],g[S+1],g[S+2],v.materialIndex):c(S,
S+1,S+2,v.materialIndex)}else if(void 0!==g)for(r=0;r<g.length;r+=3)c(g[r],g[r+1],g[r+2]);else for(r=0;r<v.length/3;r+=3)c(r,r+1,r+2);this.computeFaceNormals();null!==a.boundingBox&&(this.boundingBox=a.boundingBox.clone());null!==a.boundingSphere&&(this.boundingSphere=a.boundingSphere.clone());return this},center:function(){this.computeBoundingBox();this.boundingBox.getCenter(oh).negate();this.translate(oh.x,oh.y,oh.z);return this},normalize:function(){this.computeBoundingSphere();var a=this.boundingSphere.center,
c=this.boundingSphere.radius;c=0===c?1:1/c;var e=new q;e.set(c,0,0,-c*a.x,0,c,0,-c*a.y,0,0,c,-c*a.z,0,0,0,1);this.applyMatrix(e);return this},computeFaceNormals:function(){for(var a=new k,c=new k,e=0,g=this.faces.length;e<g;e++){var r=this.faces[e],v=this.vertices[r.a],z=this.vertices[r.b];a.subVectors(this.vertices[r.c],z);c.subVectors(v,z);a.cross(c);a.normalize();r.normal.copy(a)}},computeVertexNormals:function(a){void 0===a&&(a=!0);var c;var e=Array(this.vertices.length);var g=0;for(c=this.vertices.length;g<
c;g++)e[g]=new k;if(a){var r=new k,v=new k;a=0;for(g=this.faces.length;a<g;a++){c=this.faces[a];var z=this.vertices[c.a];var E=this.vertices[c.b];var F=this.vertices[c.c];r.subVectors(F,E);v.subVectors(z,E);r.cross(v);e[c.a].add(r);e[c.b].add(r);e[c.c].add(r)}}else for(this.computeFaceNormals(),a=0,g=this.faces.length;a<g;a++)c=this.faces[a],e[c.a].add(c.normal),e[c.b].add(c.normal),e[c.c].add(c.normal);g=0;for(c=this.vertices.length;g<c;g++)e[g].normalize();a=0;for(g=this.faces.length;a<g;a++)c=
this.faces[a],z=c.vertexNormals,3===z.length?(z[0].copy(e[c.a]),z[1].copy(e[c.b]),z[2].copy(e[c.c])):(z[0]=e[c.a].clone(),z[1]=e[c.b].clone(),z[2]=e[c.c].clone());0<this.faces.length&&(this.normalsNeedUpdate=!0)},computeFlatVertexNormals:function(){var a;this.computeFaceNormals();var c=0;for(a=this.faces.length;c<a;c++){var e=this.faces[c];var g=e.vertexNormals;3===g.length?(g[0].copy(e.normal),g[1].copy(e.normal),g[2].copy(e.normal)):(g[0]=e.normal.clone(),g[1]=e.normal.clone(),g[2]=e.normal.clone())}0<
this.faces.length&&(this.normalsNeedUpdate=!0)},computeMorphNormals:function(){var a,c;var e=0;for(c=this.faces.length;e<c;e++){var g=this.faces[e];g.__originalFaceNormal?g.__originalFaceNormal.copy(g.normal):g.__originalFaceNormal=g.normal.clone();g.__originalVertexNormals||(g.__originalVertexNormals=[]);var r=0;for(a=g.vertexNormals.length;r<a;r++)g.__originalVertexNormals[r]?g.__originalVertexNormals[r].copy(g.vertexNormals[r]):g.__originalVertexNormals[r]=g.vertexNormals[r].clone()}var v=new ya;
v.faces=this.faces;r=0;for(a=this.morphTargets.length;r<a;r++){if(!this.morphNormals[r]){this.morphNormals[r]={};this.morphNormals[r].faceNormals=[];this.morphNormals[r].vertexNormals=[];g=this.morphNormals[r].faceNormals;var z=this.morphNormals[r].vertexNormals;e=0;for(c=this.faces.length;e<c;e++){var E=new k;var F={a:new k,b:new k,c:new k};g.push(E);z.push(F)}}z=this.morphNormals[r];v.vertices=this.morphTargets[r].vertices;v.computeFaceNormals();v.computeVertexNormals();e=0;for(c=this.faces.length;e<
c;e++)g=this.faces[e],E=z.faceNormals[e],F=z.vertexNormals[e],E.copy(g.normal),F.a.copy(g.vertexNormals[0]),F.b.copy(g.vertexNormals[1]),F.c.copy(g.vertexNormals[2])}e=0;for(c=this.faces.length;e<c;e++)g=this.faces[e],g.normal=g.__originalFaceNormal,g.vertexNormals=g.__originalVertexNormals},computeBoundingBox:function(){null===this.boundingBox&&(this.boundingBox=new w);this.boundingBox.setFromPoints(this.vertices)},computeBoundingSphere:function(){null===this.boundingSphere&&(this.boundingSphere=
new G);this.boundingSphere.setFromPoints(this.vertices)},merge:function(a,c,e){if(a&&a.isGeometry){var g,r=this.vertices.length,v=this.vertices,z=a.vertices,E=this.faces,F=a.faces,J=this.colors,P=a.colors;void 0===e&&(e=0);void 0!==c&&(g=(new t).getNormalMatrix(c));for(var R=0,S=z.length;R<S;R++){var V=z[R].clone();void 0!==c&&V.applyMatrix4(c);v.push(V)}R=0;for(S=P.length;R<S;R++)J.push(P[R].clone());R=0;for(S=F.length;R<S;R++){z=F[R];var W=z.vertexNormals;P=z.vertexColors;J=new K(z.a+r,z.b+r,z.c+
r);J.normal.copy(z.normal);void 0!==g&&J.normal.applyMatrix3(g).normalize();c=0;for(v=W.length;c<v;c++)V=W[c].clone(),void 0!==g&&V.applyMatrix3(g).normalize(),J.vertexNormals.push(V);J.color.copy(z.color);c=0;for(v=P.length;c<v;c++)V=P[c],J.vertexColors.push(V.clone());J.materialIndex=z.materialIndex+e;E.push(J)}R=0;for(S=a.faceVertexUvs.length;R<S;R++)for(e=a.faceVertexUvs[R],void 0===this.faceVertexUvs[R]&&(this.faceVertexUvs[R]=[]),c=0,v=e.length;c<v;c++){g=e[c];r=[];E=0;for(F=g.length;E<F;E++)r.push(g[E].clone());
this.faceVertexUvs[R].push(r)}}else console.error("THREE.Geometry.merge(): geometry not an instance of THREE.Geometry.",a)},mergeMesh:function(a){a&&a.isMesh?(a.matrixAutoUpdate&&a.updateMatrix(),this.merge(a.geometry,a.matrix)):console.error("THREE.Geometry.mergeMesh(): mesh not an instance of THREE.Mesh.",a)},mergeVertices:function(){var a={},c=[],e=[],g=Math.pow(10,4),r;var v=0;for(r=this.vertices.length;v<r;v++){var z=this.vertices[v];z=Math.round(z.x*g)+"_"+Math.round(z.y*g)+"_"+Math.round(z.z*
g);void 0===a[z]?(a[z]=v,c.push(this.vertices[v]),e[v]=c.length-1):e[v]=e[a[z]]}a=[];v=0;for(r=this.faces.length;v<r;v++)for(g=this.faces[v],g.a=e[g.a],g.b=e[g.b],g.c=e[g.c],g=[g.a,g.b,g.c],z=0;3>z;z++)if(g[z]===g[(z+1)%3]){a.push(v);break}for(v=a.length-1;0<=v;v--)for(g=a[v],this.faces.splice(g,1),e=0,r=this.faceVertexUvs.length;e<r;e++)this.faceVertexUvs[e].splice(g,1);v=this.vertices.length-c.length;this.vertices=c;return v},setFromPoints:function(a){this.vertices=[];for(var c=0,e=a.length;c<e;c++){var g=
a[c];this.vertices.push(new k(g.x,g.y,g.z||0))}return this},sortFacesByMaterialIndex:function(){for(var a=this.faces,c=a.length,e=0;e<c;e++)a[e]._id=e;a.sort(function(F,J){return F.materialIndex-J.materialIndex});var g=this.faceVertexUvs[0],r=this.faceVertexUvs[1],v,z;g&&g.length===c&&(v=[]);r&&r.length===c&&(z=[]);for(e=0;e<c;e++){var E=a[e]._id;v&&v.push(g[E]);z&&z.push(r[E])}v&&(this.faceVertexUvs[0]=v);z&&(this.faceVertexUvs[1]=z)},toJSON:function(){function a(oa,ta,Ba){return Ba?oa|1<<ta:oa&
~(1<<ta)}function c(oa){var ta=oa.x.toString()+oa.y.toString()+oa.z.toString();if(void 0!==J[ta])return J[ta];J[ta]=F.length/3;F.push(oa.x,oa.y,oa.z);return J[ta]}function e(oa){var ta=oa.r.toString()+oa.g.toString()+oa.b.toString();if(void 0!==R[ta])return R[ta];R[ta]=P.length;P.push(oa.getHex());return R[ta]}function g(oa){var ta=oa.x.toString()+oa.y.toString();if(void 0!==V[ta])return V[ta];V[ta]=S.length/2;S.push(oa.x,oa.y);return V[ta]}var r={metadata:{version:4.5,type:"Geometry",generator:"Geometry.toJSON"}};
r.uuid=this.uuid;r.type=this.type;""!==this.name&&(r.name=this.name);if(void 0!==this.parameters){var v=this.parameters,z;for(z in v)void 0!==v[z]&&(r[z]=v[z]);return r}v=[];for(z=0;z<this.vertices.length;z++){var E=this.vertices[z];v.push(E.x,E.y,E.z)}E=[];var F=[],J={},P=[],R={},S=[],V={};for(z=0;z<this.faces.length;z++){var W=this.faces[z],ha=void 0!==this.faceVertexUvs[0][z],fa=0<W.normal.length(),ra=0<W.vertexNormals.length,pa=1!==W.color.r||1!==W.color.g||1!==W.color.b,qa=0<W.vertexColors.length,
ua=0;ua=a(ua,0,0);ua=a(ua,1,!0);ua=a(ua,2,!1);ua=a(ua,3,ha);ua=a(ua,4,fa);ua=a(ua,5,ra);ua=a(ua,6,pa);ua=a(ua,7,qa);E.push(ua);E.push(W.a,W.b,W.c);E.push(W.materialIndex);ha&&(ha=this.faceVertexUvs[0][z],E.push(g(ha[0]),g(ha[1]),g(ha[2])));fa&&E.push(c(W.normal));ra&&(fa=W.vertexNormals,E.push(c(fa[0]),c(fa[1]),c(fa[2])));pa&&E.push(e(W.color));qa&&(W=W.vertexColors,E.push(e(W[0]),e(W[1]),e(W[2])))}r.data={};r.data.vertices=v;r.data.normals=F;0<P.length&&(r.data.colors=P);0<S.length&&(r.data.uvs=
[S]);r.data.faces=E;return r},clone:function(){return(new ya).copy(this)},copy:function(a){var c,e,g;this.vertices=[];this.colors=[];this.faces=[];this.faceVertexUvs=[[]];this.morphTargets=[];this.morphNormals=[];this.skinWeights=[];this.skinIndices=[];this.lineDistances=[];this.boundingSphere=this.boundingBox=null;this.name=a.name;var r=a.vertices;var v=0;for(c=r.length;v<c;v++)this.vertices.push(r[v].clone());r=a.colors;v=0;for(c=r.length;v<c;v++)this.colors.push(r[v].clone());r=a.faces;v=0;for(c=
r.length;v<c;v++)this.faces.push(r[v].clone());v=0;for(c=a.faceVertexUvs.length;v<c;v++){var z=a.faceVertexUvs[v];void 0===this.faceVertexUvs[v]&&(this.faceVertexUvs[v]=[]);r=0;for(e=z.length;r<e;r++){var E=z[r],F=[];var J=0;for(g=E.length;J<g;J++)F.push(E[J].clone());this.faceVertexUvs[v].push(F)}}J=a.morphTargets;v=0;for(c=J.length;v<c;v++){g={};g.name=J[v].name;if(void 0!==J[v].vertices)for(g.vertices=[],r=0,e=J[v].vertices.length;r<e;r++)g.vertices.push(J[v].vertices[r].clone());if(void 0!==J[v].normals)for(g.normals=
[],r=0,e=J[v].normals.length;r<e;r++)g.normals.push(J[v].normals[r].clone());this.morphTargets.push(g)}J=a.morphNormals;v=0;for(c=J.length;v<c;v++){g={};if(void 0!==J[v].vertexNormals)for(g.vertexNormals=[],r=0,e=J[v].vertexNormals.length;r<e;r++)z=J[v].vertexNormals[r],E={},E.a=z.a.clone(),E.b=z.b.clone(),E.c=z.c.clone(),g.vertexNormals.push(E);if(void 0!==J[v].faceNormals)for(g.faceNormals=[],r=0,e=J[v].faceNormals.length;r<e;r++)g.faceNormals.push(J[v].faceNormals[r].clone());this.morphNormals.push(g)}r=
a.skinWeights;v=0;for(c=r.length;v<c;v++)this.skinWeights.push(r[v].clone());r=a.skinIndices;v=0;for(c=r.length;v<c;v++)this.skinIndices.push(r[v].clone());r=a.lineDistances;v=0;for(c=r.length;v<c;v++)this.lineDistances.push(r[v]);v=a.boundingBox;null!==v&&(this.boundingBox=v.clone());v=a.boundingSphere;null!==v&&(this.boundingSphere=v.clone());this.elementsNeedUpdate=a.elementsNeedUpdate;this.verticesNeedUpdate=a.verticesNeedUpdate;this.uvsNeedUpdate=a.uvsNeedUpdate;this.normalsNeedUpdate=a.normalsNeedUpdate;
this.colorsNeedUpdate=a.colorsNeedUpdate;this.lineDistancesNeedUpdate=a.lineDistancesNeedUpdate;this.groupsNeedUpdate=a.groupsNeedUpdate;return this},dispose:function(){this.dispatchEvent({type:"dispose"})}});Sa.prototype=Object.create(ya.prototype);Sa.prototype.constructor=Sa;Xa.prototype=Object.create(va.prototype);Xa.prototype.constructor=Xa;var Fm={clone:ub,merge:Bb};qb.prototype=Object.create(M.prototype);qb.prototype.constructor=qb;qb.prototype.isShaderMaterial=!0;qb.prototype.copy=function(a){M.prototype.copy.call(this,
a);this.fragmentShader=a.fragmentShader;this.vertexShader=a.vertexShader;this.uniforms=ub(a.uniforms);this.defines=Object.assign({},a.defines);this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.lights=a.lights;this.clipping=a.clipping;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;this.extensions=a.extensions;return this};qb.prototype.toJSON=function(a){var c=M.prototype.toJSON.call(this,a);c.uniforms={};for(var e in this.uniforms){var g=
this.uniforms[e].value;c.uniforms[e]=g&&g.isTexture?{type:"t",value:g.toJSON(a).uuid}:g&&g.isColor?{type:"c",value:g.getHex()}:g&&g.isVector2?{type:"v2",value:g.toArray()}:g&&g.isVector3?{type:"v3",value:g.toArray()}:g&&g.isVector4?{type:"v4",value:g.toArray()}:g&&g.isMatrix3?{type:"m3",value:g.toArray()}:g&&g.isMatrix4?{type:"m4",value:g.toArray()}:{value:g}}0<Object.keys(this.defines).length&&(c.defines=this.defines);c.vertexShader=this.vertexShader;c.fragmentShader=this.fragmentShader;a={};for(var r in this.extensions)!0===
this.extensions[r]&&(a[r]=!0);0<Object.keys(a).length&&(c.extensions=a);return c};zb.prototype=Object.assign(Object.create(A.prototype),{constructor:zb,isCamera:!0,copy:function(a,c){A.prototype.copy.call(this,a,c);this.matrixWorldInverse.copy(a.matrixWorldInverse);this.projectionMatrix.copy(a.projectionMatrix);this.projectionMatrixInverse.copy(a.projectionMatrixInverse);return this},getWorldDirection:function(a){void 0===a&&(console.warn("THREE.Camera: .getWorldDirection() target is now required"),
a=new k);this.updateMatrixWorld(!0);var c=this.matrixWorld.elements;return a.set(-c[8],-c[9],-c[10]).normalize()},updateMatrixWorld:function(a){A.prototype.updateMatrixWorld.call(this,a);this.matrixWorldInverse.getInverse(this.matrixWorld)},clone:function(){return(new this.constructor).copy(this)}});vb.prototype=Object.assign(Object.create(zb.prototype),{constructor:vb,isPerspectiveCamera:!0,copy:function(a,c){zb.prototype.copy.call(this,a,c);this.fov=a.fov;this.zoom=a.zoom;this.near=a.near;this.far=
a.far;this.focus=a.focus;this.aspect=a.aspect;this.view=null===a.view?null:Object.assign({},a.view);this.filmGauge=a.filmGauge;this.filmOffset=a.filmOffset;return this},setFocalLength:function(a){this.fov=2*hb.RAD2DEG*Math.atan(.5*this.getFilmHeight()/a);this.updateProjectionMatrix()},getFocalLength:function(){return.5*this.getFilmHeight()/Math.tan(.5*hb.DEG2RAD*this.fov)},getEffectiveFOV:function(){return 2*hb.RAD2DEG*Math.atan(Math.tan(.5*hb.DEG2RAD*this.fov)/this.zoom)},getFilmWidth:function(){return this.filmGauge*
Math.min(this.aspect,1)},getFilmHeight:function(){return this.filmGauge/Math.max(this.aspect,1)},setViewOffset:function(a,c,e,g,r,v){this.aspect=a/c;null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1});this.view.enabled=!0;this.view.fullWidth=a;this.view.fullHeight=c;this.view.offsetX=e;this.view.offsetY=g;this.view.width=r;this.view.height=v;this.updateProjectionMatrix()},clearViewOffset:function(){null!==this.view&&(this.view.enabled=!1);this.updateProjectionMatrix()},
updateProjectionMatrix:function(){var a=this.near,c=a*Math.tan(.5*hb.DEG2RAD*this.fov)/this.zoom,e=2*c,g=this.aspect*e,r=-.5*g,v=this.view;if(null!==this.view&&this.view.enabled){var z=v.fullWidth,E=v.fullHeight;r+=v.offsetX*g/z;c-=v.offsetY*e/E;g*=v.width/z;e*=v.height/E}v=this.filmOffset;0!==v&&(r+=a*v/this.getFilmWidth());this.projectionMatrix.makePerspective(r,r+g,c,c-e,a,this.far);this.projectionMatrixInverse.getInverse(this.projectionMatrix)},toJSON:function(a){a=A.prototype.toJSON.call(this,
a);a.object.fov=this.fov;a.object.zoom=this.zoom;a.object.near=this.near;a.object.far=this.far;a.object.focus=this.focus;a.object.aspect=this.aspect;null!==this.view&&(a.object.view=Object.assign({},this.view));a.object.filmGauge=this.filmGauge;a.object.filmOffset=this.filmOffset;return a}});Gb.prototype=Object.create(A.prototype);Gb.prototype.constructor=Gb;Nb.prototype=Object.create(m.prototype);Nb.prototype.constructor=Nb;Nb.prototype.isWebGLRenderTargetCube=!0;Nb.prototype.fromEquirectangularTexture=
function(a,c){this.texture.type=c.type;this.texture.format=c.format;this.texture.encoding=c.encoding;var e=new y,g=new qb({type:"CubemapFromEquirect",uniforms:ub({tEquirect:{value:null}}),vertexShader:"varying vec3 vWorldDirection;\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvoid main() {\n\tvWorldDirection \x3d transformDirection( position, modelMatrix );\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n}",
fragmentShader:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#define RECIPROCAL_PI 0.31830988618\n#define RECIPROCAL_PI2 0.15915494\nvoid main() {\n\tvec3 direction \x3d normalize( vWorldDirection );\n\tvec2 sampleUV;\n\tsampleUV.y \x3d asin( clamp( direction.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\tsampleUV.x \x3d atan( direction.z, direction.x ) * RECIPROCAL_PI2 + 0.5;\n\tgl_FragColor \x3d texture2D( tEquirect, sampleUV );\n}",side:1,blending:0});g.uniforms.tEquirect.value=c;
c=new xa(new Xa(5,5,5),g);e.add(c);g=new Gb(1,10,1);g.renderTarget=this;g.renderTarget.texture.name="CubeCameraTexture";g.update(a,e);c.geometry.dispose();c.material.dispose();return this};Ab.prototype=Object.create(l.prototype);Ab.prototype.constructor=Ab;Ab.prototype.isDataTexture=!0;var Ji=new k,Gm=new k,Hm=new t;Object.assign(Hb.prototype,{isPlane:!0,set:function(a,c){this.normal.copy(a);this.constant=c;return this},setComponents:function(a,c,e,g){this.normal.set(a,c,e);this.constant=g;return this},
setFromNormalAndCoplanarPoint:function(a,c){this.normal.copy(a);this.constant=-c.dot(this.normal);return this},setFromCoplanarPoints:function(a,c,e){c=Ji.subVectors(e,c).cross(Gm.subVectors(a,c)).normalize();this.setFromNormalAndCoplanarPoint(c,a);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.normal.copy(a.normal);this.constant=a.constant;return this},normalize:function(){var a=1/this.normal.length();this.normal.multiplyScalar(a);this.constant*=a;return this},
negate:function(){this.constant*=-1;this.normal.negate();return this},distanceToPoint:function(a){return this.normal.dot(a)+this.constant},distanceToSphere:function(a){return this.distanceToPoint(a.center)-a.radius},projectPoint:function(a,c){void 0===c&&(console.warn("THREE.Plane: .projectPoint() target is now required"),c=new k);return c.copy(this.normal).multiplyScalar(-this.distanceToPoint(a)).add(a)},intersectLine:function(a,c){void 0===c&&(console.warn("THREE.Plane: .intersectLine() target is now required"),
c=new k);var e=a.delta(Ji),g=this.normal.dot(e);if(0===g){if(0===this.distanceToPoint(a.start))return c.copy(a.start)}else if(g=-(a.start.dot(this.normal)+this.constant)/g,!(0>g||1<g))return c.copy(e).multiplyScalar(g).add(a.start)},intersectsLine:function(a){var c=this.distanceToPoint(a.start);a=this.distanceToPoint(a.end);return 0>c&&0<a||0>a&&0<c},intersectsBox:function(a){return a.intersectsPlane(this)},intersectsSphere:function(a){return a.intersectsPlane(this)},coplanarPoint:function(a){void 0===
a&&(console.warn("THREE.Plane: .coplanarPoint() target is now required"),a=new k);return a.copy(this.normal).multiplyScalar(-this.constant)},applyMatrix4:function(a,c){c=c||Hm.getNormalMatrix(a);a=this.coplanarPoint(Ji).applyMatrix4(a);c=this.normal.applyMatrix3(c).normalize();this.constant=-a.dot(c);return this},translate:function(a){this.constant-=a.dot(this.normal);return this},equals:function(a){return a.normal.equals(this.normal)&&a.constant===this.constant}});var mf=new G,ph=new k;Object.assign(ic.prototype,
{set:function(a,c,e,g,r,v){var z=this.planes;z[0].copy(a);z[1].copy(c);z[2].copy(e);z[3].copy(g);z[4].copy(r);z[5].copy(v);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){for(var c=this.planes,e=0;6>e;e++)c[e].copy(a.planes[e]);return this},setFromMatrix:function(a){var c=this.planes,e=a.elements;a=e[0];var g=e[1],r=e[2],v=e[3],z=e[4],E=e[5],F=e[6],J=e[7],P=e[8],R=e[9],S=e[10],V=e[11],W=e[12],ha=e[13],fa=e[14];e=e[15];c[0].setComponents(v-a,J-z,V-P,e-W).normalize();
c[1].setComponents(v+a,J+z,V+P,e+W).normalize();c[2].setComponents(v+g,J+E,V+R,e+ha).normalize();c[3].setComponents(v-g,J-E,V-R,e-ha).normalize();c[4].setComponents(v-r,J-F,V-S,e-fa).normalize();c[5].setComponents(v+r,J+F,V+S,e+fa).normalize();return this},intersectsObject:function(a){var c=a.geometry;null===c.boundingSphere&&c.computeBoundingSphere();mf.copy(c.boundingSphere).applyMatrix4(a.matrixWorld);return this.intersectsSphere(mf)},intersectsSprite:function(a){mf.center.set(0,0,0);mf.radius=
.7071067811865476;mf.applyMatrix4(a.matrixWorld);return this.intersectsSphere(mf)},intersectsSphere:function(a){var c=this.planes,e=a.center;a=-a.radius;for(var g=0;6>g;g++)if(c[g].distanceToPoint(e)<a)return!1;return!0},intersectsBox:function(a){for(var c=this.planes,e=0;6>e;e++){var g=c[e];ph.x=0<g.normal.x?a.max.x:a.min.x;ph.y=0<g.normal.y?a.max.y:a.min.y;ph.z=0<g.normal.z?a.max.z:a.min.z;if(0>g.distanceToPoint(ph))return!1}return!0},containsPoint:function(a){for(var c=this.planes,e=0;6>e;e++)if(0>
c[e].distanceToPoint(a))return!1;return!0}});var rb={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *\x3d texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef ALPHATEST\n\tif ( diffuseColor.a \x3c ALPHATEST ) discard;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion \x3d ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *\x3d ambientOcclusion;\n\t#if defined( USE_ENVMAP ) \x26\x26 defined( STANDARD )\n\t\tfloat dotNV \x3d saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *\x3d computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\n\t#endif\n#endif",
aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed \x3d vec3( position );",beginnormal_vertex:"vec3 objectNormal \x3d vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent \x3d vec3( tangent.xyz );\n#endif",bsdfs:"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\n\tconst vec4 c0 \x3d vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 \x3d vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r \x3d roughness * c0 + c1;\n\tfloat a004 \x3d min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\n}\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\tfloat distanceFalloff \x3d 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\tif( cutoffDistance \x3e 0.0 ) {\n\t\tdistanceFalloff *\x3d pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t}\n\treturn distanceFalloff;\n#else\n\tif( cutoffDistance \x3e 0.0 \x26\x26 decayExponent \x3e 0.0 ) {\n\t\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t}\n\treturn 1.0;\n#endif\n}\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 specularColor, const in float dotLH ) {\n\tfloat fresnel \x3d exp2( ( -5.55473 * dotLH - 6.98316 ) * dotLH );\n\treturn ( 1.0 - specularColor ) * fresnel + specularColor;\n}\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\n\tfloat fresnel \x3d exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\n\tvec3 Fr \x3d max( vec3( 1.0 - roughness ), F0 ) - F0;\n\treturn Fr * fresnel + F0;\n}\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 \x3d pow2( alpha );\n\tfloat gl \x3d dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\tfloat gv \x3d dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\treturn 1.0 / ( gl * gv );\n}\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 \x3d pow2( alpha );\n\tfloat gv \x3d dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl \x3d dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 \x3d pow2( alpha );\n\tfloat denom \x3d pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat alpha \x3d pow2( roughness );\n\tvec3 halfDir \x3d normalize( incidentLight.direction + viewDir );\n\tfloat dotNL \x3d saturate( dot( normal, incidentLight.direction ) );\n\tfloat dotNV \x3d saturate( dot( normal, viewDir ) );\n\tfloat dotNH \x3d saturate( dot( normal, halfDir ) );\n\tfloat dotLH \x3d saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F \x3d F_Schlick( specularColor, dotLH );\n\tfloat G \x3d G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D \x3d D_GGX( alpha, dotNH );\n\treturn F * ( G * D );\n}\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE  \x3d 64.0;\n\tconst float LUT_SCALE \x3d ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS  \x3d 0.5 / LUT_SIZE;\n\tfloat dotNV \x3d saturate( dot( N, V ) );\n\tvec2 uv \x3d vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv \x3d uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l \x3d length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x \x3d dot( v1, v2 );\n\tfloat y \x3d abs( x );\n\tfloat a \x3d 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b \x3d 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v \x3d a / b;\n\tfloat theta_sintheta \x3d ( x \x3e 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 \x3d rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 \x3d rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal \x3d cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) \x3c 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 \x3d normalize( V - N * dot( V, N ) );\n\tT2 \x3d - cross( N, T1 );\n\tmat3 mat \x3d mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] \x3d mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] \x3d mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] \x3d mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] \x3d mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] \x3d normalize( coords[ 0 ] );\n\tcoords[ 1 ] \x3d normalize( coords[ 1 ] );\n\tcoords[ 2 ] \x3d normalize( coords[ 2 ] );\n\tcoords[ 3 ] \x3d normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor \x3d vec3( 0.0 );\n\tvectorFormFactor +\x3d LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor +\x3d LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor +\x3d LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor +\x3d LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result \x3d LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat dotNV \x3d saturate( dot( normal, viewDir ) );\n\tvec2 brdf \x3d integrateSpecularBRDF( dotNV, roughness );\n\treturn specularColor * brdf.x + brdf.y;\n}\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n\tfloat dotNV \x3d saturate( dot( geometry.normal, geometry.viewDir ) );\n\tvec3 F \x3d F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\n\tvec2 brdf \x3d integrateSpecularBRDF( dotNV, roughness );\n\tvec3 FssEss \x3d F * brdf.x + brdf.y;\n\tfloat Ess \x3d brdf.x + brdf.y;\n\tfloat Ems \x3d 1.0 - Ess;\n\tvec3 Favg \x3d specularColor + ( 1.0 - specularColor ) * 0.047619;\tvec3 Fms \x3d FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter +\x3d FssEss;\n\tmultiScatter +\x3d Fms * Ems;\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir \x3d normalize( incidentLight.direction + geometry.viewDir );\n\tfloat dotNH \x3d saturate( dot( geometry.normal, halfDir ) );\n\tfloat dotLH \x3d saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F \x3d F_Schlick( specularColor, dotLH );\n\tfloat G \x3d G_BlinnPhong_Implicit( );\n\tfloat D \x3d D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\n\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\n}\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\n\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie(float roughness, float NoH) {\n\tfloat invAlpha  \x3d 1.0 / roughness;\n\tfloat cos2h \x3d NoH * NoH;\n\tfloat sin2h \x3d max(1.0 - cos2h, 0.0078125);\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\n}\nfloat V_Neubelt(float NoV, float NoL) {\n\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\n}\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\n\tvec3 N \x3d geometry.normal;\n\tvec3 V \x3d geometry.viewDir;\n\tvec3 H \x3d normalize( V + L );\n\tfloat dotNH \x3d saturate( dot( N, H ) );\n\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\n}\n#endif",
bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx \x3d dFdx( vUv );\n\t\tvec2 dSTdy \x3d dFdy( vUv );\n\t\tfloat Hll \x3d bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx \x3d bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy \x3d bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy ) {\n\t\tvec3 vSigmaX \x3d vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\n\t\tvec3 vSigmaY \x3d vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\n\t\tvec3 vN \x3d surf_norm;\n\t\tvec3 R1 \x3d cross( vSigmaY, vN );\n\t\tvec3 R2 \x3d cross( vN, vSigmaX );\n\t\tfloat fDet \x3d dot( vSigmaX, R1 );\n\t\tfDet *\x3d ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t\tvec3 vGrad \x3d sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",
clipping_planes_fragment:"#if NUM_CLIPPING_PLANES \x3e 0\n\tvec4 plane;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane \x3d clippingPlanes[ i ];\n\t\tif ( dot( vViewPosition, plane.xyz ) \x3e plane.w ) discard;\n\t}\n\t#if UNION_CLIPPING_PLANES \x3c NUM_CLIPPING_PLANES\n\t\tbool clipped \x3d true;\n\t\t#pragma unroll_loop\n\t\tfor ( int i \x3d UNION_CLIPPING_PLANES; i \x3c NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane \x3d clippingPlanes[ i ];\n\t\t\tclipped \x3d ( dot( vViewPosition, plane.xyz ) \x3e plane.w ) \x26\x26 clipped;\n\t\t}\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",
clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES \x3e 0\n\t#if ! defined( STANDARD ) \x26\x26 ! defined( PHONG ) \x26\x26 ! defined( MATCAP )\n\t\tvarying vec3 vViewPosition;\n\t#endif\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES \x3e 0 \x26\x26 ! defined( STANDARD ) \x26\x26 ! defined( PHONG ) \x26\x26 ! defined( MATCAP )\n\tvarying vec3 vViewPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES \x3e 0 \x26\x26 ! defined( STANDARD ) \x26\x26 ! defined( PHONG ) \x26\x26 ! defined( MATCAP )\n\tvViewPosition \x3d - mvPosition.xyz;\n#endif",
color_fragment:"#ifdef USE_COLOR\n\tdiffuseColor.rgb *\x3d vColor;\n#endif",color_pars_fragment:"#ifdef USE_COLOR\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#ifdef USE_COLOR\n\tvarying vec3 vColor;\n#endif",color_vertex:"#ifdef USE_COLOR\n\tvColor.xyz \x3d color.xyz;\n#endif",common:"#define PI 3.14159265359\n#define PI2 6.28318530718\n#define PI_HALF 1.5707963267949\n#define RECIPROCAL_PI 0.31830988618\n#define RECIPROCAL_PI2 0.15915494\n#define LOG2 1.442695\n#define EPSILON 1e-6\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#define whiteComplement(a) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 \x3d x*x; return x2*x2; }\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a \x3d 12.9898, b \x3d 78.233, c \x3d 43758.5453;\n\thighp float dt \x3d dot( uv.xy, vec2( a,b ) ), sn \x3d mod( dt, PI );\n\treturn fract(sin(sn) * c);\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat max3( vec3 v ) { return max( max( v.x, v.y ), v.z ); }\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent \x3d max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\tfloat distance \x3d dot( planeNormal, point - pointOnPlane );\n\treturn - distance * planeNormal + point;\n}\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn sign( dot( point - pointOnPlane, planeNormal ) );\n}\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] \x3d vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] \x3d vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] \x3d vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat linearToRelativeLuminance( const in vec3 color ) {\n\tvec3 weights \x3d vec3( 0.2126, 0.7152, 0.0722 );\n\treturn dot( weights, color.rgb );\n}",
cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n#define cubeUV_textureSize (1024.0)\nint getFaceFromDirection(vec3 direction) {\n\tvec3 absDirection \x3d abs(direction);\n\tint face \x3d -1;\n\tif( absDirection.x \x3e absDirection.z ) {\n\t\tif(absDirection.x \x3e absDirection.y )\n\t\t\tface \x3d direction.x \x3e 0.0 ? 0 : 3;\n\t\telse\n\t\t\tface \x3d direction.y \x3e 0.0 ? 1 : 4;\n\t}\n\telse {\n\t\tif(absDirection.z \x3e absDirection.y )\n\t\t\tface \x3d direction.z \x3e 0.0 ? 2 : 5;\n\t\telse\n\t\t\tface \x3d direction.y \x3e 0.0 ? 1 : 4;\n\t}\n\treturn face;\n}\n#define cubeUV_maxLods1  (log2(cubeUV_textureSize*0.25) - 1.0)\n#define cubeUV_rangeClamp (exp2((6.0 - 1.0) * 2.0))\nvec2 MipLevelInfo( vec3 vec, float roughnessLevel, float roughness ) {\n\tfloat scale \x3d exp2(cubeUV_maxLods1 - roughnessLevel);\n\tfloat dxRoughness \x3d dFdx(roughness);\n\tfloat dyRoughness \x3d dFdy(roughness);\n\tvec3 dx \x3d dFdx( vec * scale * dxRoughness );\n\tvec3 dy \x3d dFdy( vec * scale * dyRoughness );\n\tfloat d \x3d max( dot( dx, dx ), dot( dy, dy ) );\n\td \x3d clamp(d, 1.0, cubeUV_rangeClamp);\n\tfloat mipLevel \x3d 0.5 * log2(d);\n\treturn vec2(floor(mipLevel), fract(mipLevel));\n}\n#define cubeUV_maxLods2 (log2(cubeUV_textureSize*0.25) - 2.0)\n#define cubeUV_rcpTextureSize (1.0 / cubeUV_textureSize)\nvec2 getCubeUV(vec3 direction, float roughnessLevel, float mipLevel) {\n\tmipLevel \x3d roughnessLevel \x3e cubeUV_maxLods2 - 3.0 ? 0.0 : mipLevel;\n\tfloat a \x3d 16.0 * cubeUV_rcpTextureSize;\n\tvec2 exp2_packed \x3d exp2( vec2( roughnessLevel, mipLevel ) );\n\tvec2 rcp_exp2_packed \x3d vec2( 1.0 ) / exp2_packed;\n\tfloat powScale \x3d exp2_packed.x * exp2_packed.y;\n\tfloat scale \x3d rcp_exp2_packed.x * rcp_exp2_packed.y * 0.25;\n\tfloat mipOffset \x3d 0.75*(1.0 - rcp_exp2_packed.y) * rcp_exp2_packed.x;\n\tbool bRes \x3d mipLevel \x3d\x3d 0.0;\n\tscale \x3d  bRes \x26\x26 (scale \x3c a) ? a : scale;\n\tvec3 r;\n\tvec2 offset;\n\tint face \x3d getFaceFromDirection(direction);\n\tfloat rcpPowScale \x3d 1.0 / powScale;\n\tif( face \x3d\x3d 0) {\n\t\tr \x3d vec3(direction.x, -direction.z, direction.y);\n\t\toffset \x3d vec2(0.0+mipOffset,0.75 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? a : offset.y;\n\t}\n\telse if( face \x3d\x3d 1) {\n\t\tr \x3d vec3(direction.y, direction.x, direction.z);\n\t\toffset \x3d vec2(scale+mipOffset, 0.75 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? a : offset.y;\n\t}\n\telse if( face \x3d\x3d 2) {\n\t\tr \x3d vec3(direction.z, direction.x, direction.y);\n\t\toffset \x3d vec2(2.0*scale+mipOffset, 0.75 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? a : offset.y;\n\t}\n\telse if( face \x3d\x3d 3) {\n\t\tr \x3d vec3(direction.x, direction.z, direction.y);\n\t\toffset \x3d vec2(0.0+mipOffset,0.5 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? 0.0 : offset.y;\n\t}\n\telse if( face \x3d\x3d 4) {\n\t\tr \x3d vec3(direction.y, direction.x, -direction.z);\n\t\toffset \x3d vec2(scale+mipOffset, 0.5 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? 0.0 : offset.y;\n\t}\n\telse {\n\t\tr \x3d vec3(direction.z, -direction.x, direction.y);\n\t\toffset \x3d vec2(2.0*scale+mipOffset, 0.5 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? 0.0 : offset.y;\n\t}\n\tr \x3d normalize(r);\n\tfloat texelOffset \x3d 0.5 * cubeUV_rcpTextureSize;\n\tvec2 s \x3d ( r.yz / abs( r.x ) + vec2( 1.0 ) ) * 0.5;\n\tvec2 base \x3d offset + vec2( texelOffset );\n\treturn base + s * ( scale - 2.0 * texelOffset );\n}\n#define cubeUV_maxLods3 (log2(cubeUV_textureSize*0.25) - 3.0)\nvec4 textureCubeUV( sampler2D envMap, vec3 reflectedDirection, float roughness ) {\n\tfloat roughnessVal \x3d roughness* cubeUV_maxLods3;\n\tfloat r1 \x3d floor(roughnessVal);\n\tfloat r2 \x3d r1 + 1.0;\n\tfloat t \x3d fract(roughnessVal);\n\tvec2 mipInfo \x3d MipLevelInfo(reflectedDirection, r1, roughness);\n\tfloat s \x3d mipInfo.y;\n\tfloat level0 \x3d mipInfo.x;\n\tfloat level1 \x3d level0 + 1.0;\n\tlevel1 \x3d level1 \x3e 5.0 ? 5.0 : level1;\n\tlevel0 +\x3d min( floor( s + 0.5 ), 5.0 );\n\tvec2 uv_10 \x3d getCubeUV(reflectedDirection, r1, level0);\n\tvec4 color10 \x3d envMapTexelToLinear(texture2D(envMap, uv_10));\n\tvec2 uv_20 \x3d getCubeUV(reflectedDirection, r2, level0);\n\tvec4 color20 \x3d envMapTexelToLinear(texture2D(envMap, uv_20));\n\tvec4 result \x3d mix(color10, color20, t);\n\treturn vec4(result.rgb, 1.0);\n}\n#endif",
defaultnormal_vertex:"vec3 transformedNormal \x3d normalMatrix * objectNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal \x3d - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent \x3d normalMatrix * objectTangent;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent \x3d - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed +\x3d normalize( objectNormal ) * ( texture2D( displacementMap, uv ).x * displacementScale + displacementBias );\n#endif",
emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor \x3d texture2D( emissiveMap, vUv );\n\temissiveColor.rgb \x3d emissiveMapTexelToLinear( emissiveColor ).rgb;\n\ttotalEmissiveRadiance *\x3d emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor \x3d linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"\nvec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\n}\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\n}\nvec4 sRGBToLinear( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}\nvec4 RGBEToLinear( in vec4 value ) {\n\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\n}\nvec4 LinearToRGBE( in vec4 value ) {\n\tfloat maxComponent \x3d max( max( value.r, value.g ), value.b );\n\tfloat fExp \x3d clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\n\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\n}\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\n}\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\n\tfloat maxRGB \x3d max( value.r, max( value.g, value.b ) );\n\tfloat M \x3d clamp( maxRGB / maxRange, 0.0, 1.0 );\n\tM \x3d ceil( M * 255.0 ) / 255.0;\n\treturn vec4( value.rgb / ( M * maxRange ), M );\n}\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\n}\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\n\tfloat maxRGB \x3d max( value.r, max( value.g, value.b ) );\n\tfloat D \x3d max( maxRange / maxRGB, 1.0 );\n\tD \x3d min( floor( D ) / 255.0, 1.0 );\n\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\n}\nconst mat3 cLogLuvM \x3d mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\nvec4 LinearToLogLuv( in vec4 value )  {\n\tvec3 Xp_Y_XYZp \x3d cLogLuvM * value.rgb;\n\tXp_Y_XYZp \x3d max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\n\tvec4 vResult;\n\tvResult.xy \x3d Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\n\tfloat Le \x3d 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\n\tvResult.w \x3d fract( Le );\n\tvResult.z \x3d ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\n\treturn vResult;\n}\nconst mat3 cLogLuvInverseM \x3d mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\nvec4 LogLuvToLinear( in vec4 value ) {\n\tfloat Le \x3d value.z * 255.0 + value.w;\n\tvec3 Xp_Y_XYZp;\n\tXp_Y_XYZp.y \x3d exp2( ( Le - 127.0 ) / 2.0 );\n\tXp_Y_XYZp.z \x3d Xp_Y_XYZp.y / value.y;\n\tXp_Y_XYZp.x \x3d value.x * Xp_Y_XYZp.z;\n\tvec3 vRGB \x3d cLogLuvInverseM * Xp_Y_XYZp.rgb;\n\treturn vec4( max( vRGB, 0.0 ), 1.0 );\n}",
envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToVertex \x3d normalize( vWorldPosition - cameraPosition );\n\t\tvec3 worldNormal \x3d inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec \x3d reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec \x3d refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec \x3d vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor \x3d textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#elif defined( ENVMAP_TYPE_EQUIREC )\n\t\tvec2 sampleUV;\n\t\treflectVec \x3d normalize( reflectVec );\n\t\tsampleUV.y \x3d asin( clamp( reflectVec.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\t\tsampleUV.x \x3d atan( reflectVec.z, reflectVec.x ) * RECIPROCAL_PI2 + 0.5;\n\t\tvec4 envColor \x3d texture2D( envMap, sampleUV );\n\t#elif defined( ENVMAP_TYPE_SPHERE )\n\t\treflectVec \x3d normalize( reflectVec );\n\t\tvec3 reflectView \x3d normalize( ( viewMatrix * vec4( reflectVec, 0.0 ) ).xyz + vec3( 0.0, 0.0, 1.0 ) );\n\t\tvec4 envColor \x3d texture2D( envMap, reflectView.xy * 0.5 + 0.5 );\n\t#else\n\t\tvec4 envColor \x3d vec4( 0.0 );\n\t#endif\n\tenvColor \x3d envMapTexelToLinear( envColor );\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight \x3d mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight \x3d mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight +\x3d envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",
envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\tuniform int maxMipLevel;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",
envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\t#ifdef ENVMAP_MODE_REFRACTION\n\t\tuniform float refractionRatio;\n\t#endif\n\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\n\t\tvec3 worldNormal \x3d inverseTransformDirection( geometry.normal, viewMatrix );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryVec \x3d vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor \x3d textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor \x3d textureCube( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb \x3d envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 queryVec \x3d vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\tvec4 envMapColor \x3d textureCubeUV( envMap, queryVec, 1.0 );\n\t\t#else\n\t\t\tvec4 envMapColor \x3d vec4( 0.0 );\n\t\t#endif\n\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t}\n\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\n\t\tfloat maxMIPLevelScalar \x3d float( maxMIPLevel );\n\t\tfloat sigma \x3d PI * roughness * roughness / ( 1.0 + roughness );\n\t\tfloat desiredMIPLevel \x3d maxMIPLevelScalar + log2( sigma );\n\t\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\n\t}\n\tvec3 getLightProbeIndirectRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t  vec3 reflectVec \x3d reflect( -viewDir, normal );\n\t\t  reflectVec \x3d normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t#else\n\t\t  vec3 reflectVec \x3d refract( -viewDir, normal, refractionRatio );\n\t\t#endif\n\t\treflectVec \x3d inverseTransformDirection( reflectVec, viewMatrix );\n\t\tfloat specularMIPLevel \x3d getSpecularMIPLevel( roughness, maxMIPLevel );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryReflectVec \x3d vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor \x3d textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor \x3d textureCube( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb \x3d envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 queryReflectVec \x3d vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\tvec4 envMapColor \x3d textureCubeUV( envMap, queryReflectVec, roughness );\n\t\t#elif defined( ENVMAP_TYPE_EQUIREC )\n\t\t\tvec2 sampleUV;\n\t\t\tsampleUV.y \x3d asin( clamp( reflectVec.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\t\t\tsampleUV.x \x3d atan( reflectVec.z, reflectVec.x ) * RECIPROCAL_PI2 + 0.5;\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor \x3d texture2DLodEXT( envMap, sampleUV, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor \x3d texture2D( envMap, sampleUV, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb \x3d envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_SPHERE )\n\t\t\tvec3 reflectView \x3d normalize( ( viewMatrix * vec4( reflectVec, 0.0 ) ).xyz + vec3( 0.0,0.0,1.0 ) );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor \x3d texture2DLodEXT( envMap, reflectView.xy * 0.5 + 0.5, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor \x3d texture2D( envMap, reflectView.xy * 0.5 + 0.5, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb \x3d envMapTexelToLinear( envMapColor ).rgb;\n\t\t#endif\n\t\treturn envMapColor.rgb * envMapIntensity;\n\t}\n#endif",
envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition \x3d worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex \x3d normalize( worldPosition.xyz - cameraPosition );\n\t\tvec3 worldNormal \x3d inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect \x3d reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect \x3d refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tfogDepth \x3d -mvPosition.z;\n#endif",
fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float fogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor \x3d 1.0 - exp( - fogDensity * fogDensity * fogDepth * fogDepth );\n\t#else\n\t\tfloat fogFactor \x3d smoothstep( fogNear, fogFar, fogDepth );\n\t#endif\n\tgl_FragColor.rgb \x3d mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float fogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",
gradientmap_pars_fragment:"#ifdef TOON\n\tuniform sampler2D gradientMap;\n\tvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\t\tfloat dotNL \x3d dot( normal, lightDirection );\n\t\tvec2 coord \x3d vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t\t#ifdef USE_GRADIENTMAP\n\t\t\treturn texture2D( gradientMap, coord ).rgb;\n\t\t#else\n\t\t\treturn ( coord.x \x3c 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\n\t\t#endif\n\t}\n#endif",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\treflectedLight.indirectDiffuse +\x3d PI * texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;\n#endif",
lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_vertex:"vec3 diffuse \x3d vec3( 1.0 );\nGeometricContext geometry;\ngeometry.position \x3d mvPosition.xyz;\ngeometry.normal \x3d normalize( transformedNormal );\ngeometry.viewDir \x3d normalize( -mvPosition.xyz );\nGeometricContext backGeometry;\nbackGeometry.position \x3d geometry.position;\nbackGeometry.normal \x3d -geometry.normal;\nbackGeometry.viewDir \x3d geometry.viewDir;\nvLightFront \x3d vec3( 0.0 );\nvIndirectFront \x3d vec3( 0.0 );\n#ifdef DOUBLE_SIDED\n\tvLightBack \x3d vec3( 0.0 );\n\tvIndirectBack \x3d vec3( 0.0 );\n#endif\nIncidentLight directLight;\nfloat dotNL;\nvec3 directLightColor_Diffuse;\n#if NUM_POINT_LIGHTS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_POINT_LIGHTS; i ++ ) {\n\t\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\n\t\tdotNL \x3d dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse \x3d PI * directLight.color;\n\t\tvLightFront +\x3d saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack +\x3d saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n#endif\n#if NUM_SPOT_LIGHTS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_SPOT_LIGHTS; i ++ ) {\n\t\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\n\t\tdotNL \x3d dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse \x3d PI * directLight.color;\n\t\tvLightFront +\x3d saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack +\x3d saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n#endif\n#if NUM_DIR_LIGHTS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_DIR_LIGHTS; i ++ ) {\n\t\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\n\t\tdotNL \x3d dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse \x3d PI * directLight.color;\n\t\tvLightFront +\x3d saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack +\x3d saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n#endif\n#if NUM_HEMI_LIGHTS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_HEMI_LIGHTS; i ++ ) {\n\t\tvIndirectFront +\x3d getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvIndirectBack +\x3d getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\n\t\t#endif\n\t}\n#endif",
lights_pars_begin:"uniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x \x3d normal.x, y \x3d normal.y, z \x3d normal.z;\n\tvec3 result \x3d shCoefficients[ 0 ] * 0.886227;\n\tresult +\x3d shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult +\x3d shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult +\x3d shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult +\x3d shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult +\x3d shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult +\x3d shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult +\x3d shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult +\x3d shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\n\tvec3 worldNormal \x3d inverseTransformDirection( geometry.normal, viewMatrix );\n\tvec3 irradiance \x3d shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance \x3d ambientLightColor;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *\x3d PI;\n\t#endif\n\treturn irradiance;\n}\n#if NUM_DIR_LIGHTS \x3e 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tint shadow;\n\t\tfloat shadowBias;\n\t\tfloat shadowRadius;\n\t\tvec2 shadowMapSize;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tdirectLight.color \x3d directionalLight.color;\n\t\tdirectLight.direction \x3d directionalLight.direction;\n\t\tdirectLight.visible \x3d true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS \x3e 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tint shadow;\n\t\tfloat shadowBias;\n\t\tfloat shadowRadius;\n\t\tvec2 shadowMapSize;\n\t\tfloat shadowCameraNear;\n\t\tfloat shadowCameraFar;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector \x3d pointLight.position - geometry.position;\n\t\tdirectLight.direction \x3d normalize( lVector );\n\t\tfloat lightDistance \x3d length( lVector );\n\t\tdirectLight.color \x3d pointLight.color;\n\t\tdirectLight.color *\x3d punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\n\t\tdirectLight.visible \x3d ( directLight.color !\x3d vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS \x3e 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t\tint shadow;\n\t\tfloat shadowBias;\n\t\tfloat shadowRadius;\n\t\tvec2 shadowMapSize;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight  ) {\n\t\tvec3 lVector \x3d spotLight.position - geometry.position;\n\t\tdirectLight.direction \x3d normalize( lVector );\n\t\tfloat lightDistance \x3d length( lVector );\n\t\tfloat angleCos \x3d dot( directLight.direction, spotLight.direction );\n\t\tif ( angleCos \x3e spotLight.coneCos ) {\n\t\t\tfloat spotEffect \x3d smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\t\tdirectLight.color \x3d spotLight.color;\n\t\t\tdirectLight.color *\x3d spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tdirectLight.visible \x3d true;\n\t\t} else {\n\t\t\tdirectLight.color \x3d vec3( 0.0 );\n\t\t\tdirectLight.visible \x3d false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS \x3e 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS \x3e 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\n\t\tfloat dotNL \x3d dot( geometry.normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight \x3d 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance \x3d mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tirradiance *\x3d PI;\n\t\t#endif\n\t\treturn irradiance;\n\t}\n#endif",
lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor \x3d diffuseColor.rgb;\nmaterial.specularColor \x3d specular;\nmaterial.specularShininess \x3d shininess;\nmaterial.specularStrength \x3d specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct BlinnPhongMaterial {\n\tvec3\tdiffuseColor;\n\tvec3\tspecularColor;\n\tfloat\tspecularShininess;\n\tfloat\tspecularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\t#ifdef TOON\n\t\tvec3 irradiance \x3d getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\t#else\n\t\tfloat dotNL \x3d saturate( dot( geometry.normal, directLight.direction ) );\n\t\tvec3 irradiance \x3d dotNL * directLight.color;\n\t#endif\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *\x3d PI;\n\t#endif\n\treflectedLight.directDiffuse +\x3d irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular +\x3d irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse +\x3d irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong\n#define Material_LightProbeLOD( material )\t(0)",
lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor \x3d diffuseColor.rgb * ( 1.0 - metalnessFactor );\nmaterial.specularRoughness \x3d clamp( roughnessFactor, 0.04, 1.0 );\n#ifdef REFLECTIVITY\n\tmaterial.specularColor \x3d mix( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ), diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor \x3d mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\n#endif\n#ifdef CLEARCOAT\n\tmaterial.clearcoat \x3d saturate( clearcoat );\tmaterial.clearcoatRoughness \x3d clamp( clearcoatRoughness, 0.04, 1.0 );\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor \x3d sheen;\n#endif",
lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3\tdiffuseColor;\n\tfloat\tspecularRoughness;\n\tvec3\tspecularColor;\n#ifdef CLEARCOAT\n\tfloat clearcoat;\n\tfloat clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tvec3 sheenColor;\n#endif\n};\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\n\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\n}\n#if NUM_RECT_AREA_LIGHTS \x3e 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal \x3d geometry.normal;\n\t\tvec3 viewDir \x3d geometry.viewDir;\n\t\tvec3 position \x3d geometry.position;\n\t\tvec3 lightPos \x3d rectAreaLight.position;\n\t\tvec3 halfWidth \x3d rectAreaLight.halfWidth;\n\t\tvec3 halfHeight \x3d rectAreaLight.halfHeight;\n\t\tvec3 lightColor \x3d rectAreaLight.color;\n\t\tfloat roughness \x3d material.specularRoughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] \x3d lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] \x3d lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] \x3d lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] \x3d lightPos + halfWidth + halfHeight;\n\t\tvec2 uv \x3d LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 \x3d texture2D( ltc_1, uv );\n\t\tvec4 t2 \x3d texture2D( ltc_2, uv );\n\t\tmat3 mInv \x3d mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel \x3d ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular +\x3d lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse +\x3d lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL \x3d saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance \x3d dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *\x3d PI;\n\t#endif\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNL \x3d saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance \x3d ccDotNL * directLight.color;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tccIrradiance *\x3d PI;\n\t\t#endif\n\t\tfloat clearcoatDHR \x3d material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t\treflectedLight.directSpecular +\x3d ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t#else\n\t\tfloat clearcoatDHR \x3d 0.0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\treflectedLight.directSpecular +\x3d ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_Sheen(\n\t\t\tmaterial.specularRoughness,\n\t\t\tdirectLight.direction,\n\t\t\tgeometry,\n\t\t\tmaterial.sheenColor\n\t\t);\n\t#else\n\t\treflectedLight.directSpecular +\x3d ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.normal, material.specularColor, material.specularRoughness);\n\t#endif\n\treflectedLight.directDiffuse +\x3d ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse +\x3d irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNV \x3d saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular +\x3d clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t\tfloat ccDotNL \x3d ccDotNV;\n\t\tfloat clearcoatDHR \x3d material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t#else\n\t\tfloat clearcoatDHR \x3d 0.0;\n\t#endif\n\tfloat clearcoatInv \x3d 1.0 - clearcoatDHR;\n\tvec3 singleScattering \x3d vec3( 0.0 );\n\tvec3 multiScattering \x3d vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance \x3d irradiance * RECIPROCAL_PI;\n\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\n\tvec3 diffuse \x3d material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\n\treflectedLight.indirectSpecular +\x3d clearcoatInv * radiance * singleScattering;\n\treflectedLight.indirectDiffuse +\x3d multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse +\x3d diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",
lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position \x3d - vViewPosition;\ngeometry.normal \x3d normal;\ngeometry.viewDir \x3d normalize( vViewPosition );\n#ifdef CLEARCOAT\n\tgeometry.clearcoatNormal \x3d clearcoatNormal;\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS \x3e 0 ) \x26\x26 defined( RE_Direct )\n\tPointLight pointLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight \x3d pointLights[ i ];\n\t\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) \x26\x26 ( UNROLLED_LOOP_INDEX \x3c NUM_POINT_LIGHT_SHADOWS )\n\t\tdirectLight.color *\x3d all( bvec2( pointLight.shadow, directLight.visible ) ) ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n#endif\n#if ( NUM_SPOT_LIGHTS \x3e 0 ) \x26\x26 defined( RE_Direct )\n\tSpotLight spotLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight \x3d spotLights[ i ];\n\t\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) \x26\x26 ( UNROLLED_LOOP_INDEX \x3c NUM_SPOT_LIGHT_SHADOWS )\n\t\tdirectLight.color *\x3d all( bvec2( spotLight.shadow, directLight.visible ) ) ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n#endif\n#if ( NUM_DIR_LIGHTS \x3e 0 ) \x26\x26 defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight \x3d directionalLights[ i ];\n\t\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) \x26\x26 ( UNROLLED_LOOP_INDEX \x3c NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectLight.color *\x3d all( bvec2( directionalLight.shadow, directLight.visible ) ) ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n#endif\n#if ( NUM_RECT_AREA_LIGHTS \x3e 0 ) \x26\x26 defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight \x3d rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance \x3d vec3( 0.0 );\n\tvec3 irradiance \x3d getAmbientLightIrradiance( ambientLightColor );\n\tirradiance +\x3d getLightProbeIrradiance( lightProbe, geometry );\n\t#if ( NUM_HEMI_LIGHTS \x3e 0 )\n\t\t#pragma unroll_loop\n\t\tfor ( int i \x3d 0; i \x3c NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance +\x3d getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t}\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance \x3d vec3( 0.0 );\n\tvec3 clearcoatRadiance \x3d vec3( 0.0 );\n#endif",
lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec3 lightMapIrradiance \x3d texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tlightMapIrradiance *\x3d PI;\n\t\t#endif\n\t\tirradiance +\x3d lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) \x26\x26 defined( STANDARD ) \x26\x26 defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance +\x3d getLightProbeIndirectIrradiance( geometry, maxMipLevel );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) \x26\x26 defined( RE_IndirectSpecular )\n\tradiance +\x3d getLightProbeIndirectRadiance( geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\n\t#ifdef CLEARCOAT\n\t\tclearcoatRadiance +\x3d getLightProbeIndirectRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\n\t#endif\n#endif",
lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) \x26\x26 defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT \x3d log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) \x26\x26 defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n#endif",
logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth \x3d 1.0 + gl_Position.w;\n\t#else\n\t\tgl_Position.z \x3d log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\tgl_Position.z *\x3d gl_Position.w;\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 texelColor \x3d texture2D( map, vUv );\n\ttexelColor \x3d mapTexelToLinear( texelColor );\n\tdiffuseColor *\x3d texelColor;\n#endif",
map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#ifdef USE_MAP\n\tvec2 uv \x3d ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n\tvec4 mapTexel \x3d texture2D( map, uv );\n\tdiffuseColor *\x3d mapTexelToLinear( mapTexel );\n#endif",map_particle_pars_fragment:"#ifdef USE_MAP\n\tuniform mat3 uvTransform;\n\tuniform sampler2D map;\n#endif",metalnessmap_fragment:"float metalnessFactor \x3d metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness \x3d texture2D( metalnessMap, vUv );\n\tmetalnessFactor *\x3d texelMetalness.b;\n#endif",
metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal +\x3d ( morphNormal0 - normal ) * morphTargetInfluences[ 0 ];\n\tobjectNormal +\x3d ( morphNormal1 - normal ) * morphTargetInfluences[ 1 ];\n\tobjectNormal +\x3d ( morphNormal2 - normal ) * morphTargetInfluences[ 2 ];\n\tobjectNormal +\x3d ( morphNormal3 - normal ) * morphTargetInfluences[ 3 ];\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\t#ifndef USE_MORPHNORMALS\n\tuniform float morphTargetInfluences[ 8 ];\n\t#else\n\tuniform float morphTargetInfluences[ 4 ];\n\t#endif\n#endif",
morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed +\x3d ( morphTarget0 - position ) * morphTargetInfluences[ 0 ];\n\ttransformed +\x3d ( morphTarget1 - position ) * morphTargetInfluences[ 1 ];\n\ttransformed +\x3d ( morphTarget2 - position ) * morphTargetInfluences[ 2 ];\n\ttransformed +\x3d ( morphTarget3 - position ) * morphTargetInfluences[ 3 ];\n\t#ifndef USE_MORPHNORMALS\n\ttransformed +\x3d ( morphTarget4 - position ) * morphTargetInfluences[ 4 ];\n\ttransformed +\x3d ( morphTarget5 - position ) * morphTargetInfluences[ 5 ];\n\ttransformed +\x3d ( morphTarget6 - position ) * morphTargetInfluences[ 6 ];\n\ttransformed +\x3d ( morphTarget7 - position ) * morphTargetInfluences[ 7 ];\n\t#endif\n#endif",
normal_fragment_begin:"#ifdef FLAT_SHADED\n\tvec3 fdx \x3d vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\n\tvec3 fdy \x3d vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\n\tvec3 normal \x3d normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal \x3d normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal \x3d normal * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent \x3d normalize( vTangent );\n\t\tvec3 bitangent \x3d normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent \x3d tangent * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t\t\tbitangent \x3d bitangent * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal \x3d normal;",
normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal \x3d texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal \x3d - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal \x3d normal * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t#endif\n\tnormal \x3d normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\t#ifdef USE_TANGENT\n\t\tmat3 vTBN \x3d mat3( tangent, bitangent, normal );\n\t\tvec3 mapN \x3d texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t\tmapN.xy \x3d normalScale * mapN.xy;\n\t\tnormal \x3d normalize( vTBN * mapN );\n\t#else\n\t\tnormal \x3d perturbNormal2Arb( -vViewPosition, normal, normalScale, normalMap );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal \x3d perturbNormalArb( -vViewPosition, normal, dHdxy_fwd() );\n#endif",
normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) \x26\x26 ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec2 normalScale, in sampler2D normalMap ) {\n\t\tvec3 q0 \x3d vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\n\t\tvec3 q1 \x3d vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\n\t\tvec2 st0 \x3d dFdx( vUv.st );\n\t\tvec2 st1 \x3d dFdy( vUv.st );\n\t\tfloat scale \x3d sign( st1.t * st0.s - st0.t * st1.s );\n\t\tvec3 S \x3d normalize( ( q0 * st1.t - q1 * st0.t ) * scale );\n\t\tvec3 T \x3d normalize( ( - q0 * st1.s + q1 * st0.s ) * scale );\n\t\tvec3 N \x3d normalize( surf_norm );\n\t\tvec3 mapN \x3d texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t\tmapN.xy *\x3d normalScale;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvec3 NfromST \x3d cross( S, T );\n\t\t\tif( dot( NfromST, N ) \x3e 0.0 ) {\n\t\t\t\tS *\x3d -1.0;\n\t\t\t\tT *\x3d -1.0;\n\t\t\t}\n\t\t#else\n\t\t\tmapN.xy *\x3d ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t\t#endif\n\t\tmat3 tsn \x3d mat3( S, T, N );\n\t\treturn normalize( tsn * mapN );\n\t}\n#endif",
clearcoat_normal_fragment_begin:"#ifdef CLEARCOAT\n\tvec3 clearcoatNormal \x3d geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\t#ifdef USE_TANGENT\n\t\tmat3 vTBN \x3d mat3( tangent, bitangent, clearcoatNormal );\n\t\tvec3 mapN \x3d texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t\tmapN.xy \x3d clearcoatNormalScale * mapN.xy;\n\t\tclearcoatNormal \x3d normalize( vTBN * mapN );\n\t#else\n\t\tclearcoatNormal \x3d perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatNormalScale, clearcoatNormalMap );\n\t#endif\n#endif",
clearcoat_normalmap_pars_fragment:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale \x3d 256. / 255.;const float UnpackDownscale \x3d 255. / 256.;\nconst vec3 PackFactors \x3d vec3( 256. * 256. * 256., 256. * 256.,  256. );\nconst vec4 UnpackFactors \x3d UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 \x3d 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r \x3d vec4( fract( v * PackFactors ), v );\n\tr.yzw -\x3d r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec4 encodeHalfRGBA ( vec2 v ) {\n\tvec4 encoded \x3d vec4( 0.0 );\n\tconst vec2 offset \x3d vec2( 1.0 / 255.0, 0.0 );\n\tencoded.xy \x3d vec2( v.x, fract( v.x * 255.0 ) );\n\tencoded.xy \x3d encoded.xy - ( encoded.yy * offset );\n\tencoded.zw \x3d vec2( v.y, fract( v.y * 255.0 ) );\n\tencoded.zw \x3d encoded.zw - ( encoded.ww * offset );\n\treturn encoded;\n}\nvec2 decodeHalfRGBA( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",
premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *\x3d gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition \x3d modelViewMatrix * vec4( transformed, 1.0 );\ngl_Position \x3d projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb \x3d dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position \x3d rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB \x3d vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB \x3d mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",
roughnessmap_fragment:"float roughnessFactor \x3d roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness \x3d texture2D( roughnessMap, vUv );\n\troughnessFactor *\x3d texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS \x3e 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS \x3e 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS \x3e 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn decodeHalfRGBA( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion \x3d 1.0;\n\t\tvec2 distribution \x3d texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow \x3d step( compare , distribution.x );\n\t\tif (hard_shadow !\x3d 1.0 ) {\n\t\t\tfloat distance \x3d compare - distribution.x ;\n\t\t\tfloat variance \x3d max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability \x3d variance / (variance + distance * distance );\t\t\tsoftness_probability \x3d clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion \x3d clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat texture2DShadowLerp( sampler2D depths, vec2 size, vec2 uv, float compare ) {\n\t\tconst vec2 offset \x3d vec2( 0.0, 1.0 );\n\t\tvec2 texelSize \x3d vec2( 1.0 ) / size;\n\t\tvec2 centroidUV \x3d ( floor( uv * size - 0.5 ) + 0.5 ) * texelSize;\n\t\tfloat lb \x3d texture2DCompare( depths, centroidUV + texelSize * offset.xx, compare );\n\t\tfloat lt \x3d texture2DCompare( depths, centroidUV + texelSize * offset.xy, compare );\n\t\tfloat rb \x3d texture2DCompare( depths, centroidUV + texelSize * offset.yx, compare );\n\t\tfloat rt \x3d texture2DCompare( depths, centroidUV + texelSize * offset.yy, compare );\n\t\tvec2 f \x3d fract( uv * size + 0.5 );\n\t\tfloat a \x3d mix( lb, lt, f.y );\n\t\tfloat b \x3d mix( rb, rt, f.y );\n\t\tfloat c \x3d mix( a, b, f.x );\n\t\treturn c;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow \x3d 1.0;\n\t\tshadowCoord.xyz /\x3d shadowCoord.w;\n\t\tshadowCoord.z +\x3d shadowBias;\n\t\tbvec4 inFrustumVec \x3d bvec4 ( shadowCoord.x \x3e\x3d 0.0, shadowCoord.x \x3c\x3d 1.0, shadowCoord.y \x3e\x3d 0.0, shadowCoord.y \x3c\x3d 1.0 );\n\t\tbool inFrustum \x3d all( inFrustumVec );\n\t\tbvec2 frustumTestVec \x3d bvec2( inFrustum, shadowCoord.z \x3c\x3d 1.0 );\n\t\tbool frustumTest \x3d all( frustumTestVec );\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize \x3d vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 \x3d - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 \x3d - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 \x3d + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 \x3d + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 \x3d dx0 / 2.0;\n\t\t\tfloat dy2 \x3d dy0 / 2.0;\n\t\t\tfloat dx3 \x3d dx1 / 2.0;\n\t\t\tfloat dy3 \x3d dy1 / 2.0;\n\t\t\tshadow \x3d (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize \x3d vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 \x3d - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 \x3d - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 \x3d + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 \x3d + texelSize.y * shadowRadius;\n\t\t\tshadow \x3d (\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow \x3d VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow \x3d texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV \x3d abs( v );\n\t\tfloat scaleToCube \x3d 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *\x3d scaleToCube;\n\t\tv *\x3d scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar \x3d v.xy;\n\t\tfloat almostATexel \x3d 1.5 * texelSizeY;\n\t\tfloat almostOne \x3d 1.0 - almostATexel;\n\t\tif ( absV.z \x3e\x3d almostOne ) {\n\t\t\tif ( v.z \x3e 0.0 )\n\t\t\t\tplanar.x \x3d 4.0 - v.x;\n\t\t} else if ( absV.x \x3e\x3d almostOne ) {\n\t\t\tfloat signX \x3d sign( v.x );\n\t\t\tplanar.x \x3d v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y \x3e\x3d almostOne ) {\n\t\t\tfloat signY \x3d sign( v.y );\n\t\t\tplanar.x \x3d v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y \x3d v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize \x3d vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition \x3d shadowCoord.xyz;\n\t\tfloat dp \x3d ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp +\x3d shadowBias;\n\t\tvec3 bd3D \x3d normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset \x3d vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",
shadowmap_pars_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS \x3e 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS \x3e 0\n\t\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS \x3e 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",
shadowmap_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tvDirectionalShadowCoord[ i ] \x3d directionalShadowMatrix[ i ] * worldPosition;\n\t}\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tvSpotShadowCoord[ i ] \x3d spotShadowMatrix[ i ] * worldPosition;\n\t}\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tvPointShadowCoord[ i ] \x3d pointShadowMatrix[ i ] * worldPosition;\n\t}\n\t#endif\n#endif",
shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow \x3d 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS \x3e 0\n\tDirectionalLight directionalLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight \x3d directionalLights[ i ];\n\t\tshadow *\x3d bool( directionalLight.shadow ) ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS \x3e 0\n\tSpotLight spotLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight \x3d spotLights[ i ];\n\t\tshadow *\x3d bool( spotLight.shadow ) ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t}\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS \x3e 0\n\tPointLight pointLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight \x3d pointLights[ i ];\n\t\tshadow *\x3d bool( pointLight.shadow ) ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#endif\n\t#endif\n\treturn shadow;\n}",
skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX \x3d getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY \x3d getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ \x3d getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW \x3d getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\t#ifdef BONE_TEXTURE\n\t\tuniform highp sampler2D boneTexture;\n\t\tuniform int boneTextureSize;\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tfloat j \x3d i * 4.0;\n\t\t\tfloat x \x3d mod( j, float( boneTextureSize ) );\n\t\t\tfloat y \x3d floor( j / float( boneTextureSize ) );\n\t\t\tfloat dx \x3d 1.0 / float( boneTextureSize );\n\t\t\tfloat dy \x3d 1.0 / float( boneTextureSize );\n\t\t\ty \x3d dy * ( y + 0.5 );\n\t\t\tvec4 v1 \x3d texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\t\tvec4 v2 \x3d texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\t\tvec4 v3 \x3d texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\t\tvec4 v4 \x3d texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\t\tmat4 bone \x3d mat4( v1, v2, v3, v4 );\n\t\t\treturn bone;\n\t\t}\n\t#else\n\t\tuniform mat4 boneMatrices[ MAX_BONES ];\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tmat4 bone \x3d boneMatrices[ int(i) ];\n\t\t\treturn bone;\n\t\t}\n\t#endif\n#endif",
skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex \x3d bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned \x3d vec4( 0.0 );\n\tskinned +\x3d boneMatX * skinVertex * skinWeight.x;\n\tskinned +\x3d boneMatY * skinVertex * skinWeight.y;\n\tskinned +\x3d boneMatZ * skinVertex * skinWeight.z;\n\tskinned +\x3d boneMatW * skinVertex * skinWeight.w;\n\ttransformed \x3d ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix \x3d mat4( 0.0 );\n\tskinMatrix +\x3d skinWeight.x * boneMatX;\n\tskinMatrix +\x3d skinWeight.y * boneMatY;\n\tskinMatrix +\x3d skinWeight.z * boneMatZ;\n\tskinMatrix +\x3d skinWeight.w * boneMatW;\n\tskinMatrix  \x3d bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal \x3d vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent \x3d vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",
specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular \x3d texture2D( specularMap, vUv );\n\tspecularStrength \x3d texelSpecular.r;\n#else\n\tspecularStrength \x3d 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb \x3d toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n\t#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nuniform float toneMappingWhitePoint;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *\x3d toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\n#define Uncharted2Helper( x ) max( ( ( x * ( 0.15 * x + 0.10 * 0.50 ) + 0.20 * 0.02 ) / ( x * ( 0.15 * x + 0.50 ) + 0.20 * 0.30 ) ) - 0.02 / 0.30, vec3( 0.0 ) )\nvec3 Uncharted2ToneMapping( vec3 color ) {\n\tcolor *\x3d toneMappingExposure;\n\treturn saturate( Uncharted2Helper( color ) / Uncharted2Helper( vec3( toneMappingWhitePoint ) ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *\x3d toneMappingExposure;\n\tcolor \x3d max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tcolor *\x3d toneMappingExposure;\n\treturn saturate( ( color * ( 2.51 * color + 0.03 ) ) / ( color * ( 2.43 * color + 0.59 ) + 0.14 ) );\n}",
uv_pars_fragment:"#ifdef USE_UV\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\tvarying vec2 vUv;\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv \x3d ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 \x3d uv2;\n#endif",
worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP )\n\tvec4 worldPosition \x3d modelMatrix * vec4( transformed, 1.0 );\n#endif",background_frag:"uniform sampler2D t2D;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor \x3d texture2D( t2D, vUv );\n\tgl_FragColor \x3d mapTexelToLinear( texColor );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n}",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv \x3d ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position \x3d vec4( position.xy, 1.0, 1.0 );\n}",
cube_frag:"uniform samplerCube tCube;\nuniform float tFlip;\nuniform float opacity;\nvarying vec3 vWorldDirection;\nvoid main() {\n\tvec4 texColor \x3d textureCube( tCube, vec3( tFlip * vWorldDirection.x, vWorldDirection.yz ) );\n\tgl_FragColor \x3d mapTexelToLinear( texColor );\n\tgl_FragColor.a *\x3d opacity;\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n}",cube_vert:"varying vec3 vWorldDirection;\n#include \x3ccommon\x3e\nvoid main() {\n\tvWorldDirection \x3d transformDirection( position, modelMatrix );\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\tgl_Position.z \x3d gl_Position.w;\n}",
depth_frag:"#if DEPTH_PACKING \x3d\x3d 3200\n\tuniform float opacity;\n#endif\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( 1.0 );\n\t#if DEPTH_PACKING \x3d\x3d 3200\n\t\tdiffuseColor.a \x3d opacity;\n\t#endif\n\t#include \x3cmap_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#if DEPTH_PACKING \x3d\x3d 3200\n\t\tgl_FragColor \x3d vec4( vec3( 1.0 - gl_FragCoord.z ), opacity );\n\t#elif DEPTH_PACKING \x3d\x3d 3201\n\t\tgl_FragColor \x3d packDepthToRGBA( gl_FragCoord.z );\n\t#endif\n}",
depth_vert:"#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include \x3cbeginnormal_vertex\x3e\n\t\t#include \x3cmorphnormal_vertex\x3e\n\t\t#include \x3cskinnormal_vertex\x3e\n\t#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n}",
distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main () {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( 1.0 );\n\t#include \x3cmap_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\tfloat dist \x3d length( vWorldPosition - referencePosition );\n\tdist \x3d ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist \x3d saturate( dist );\n\tgl_FragColor \x3d packDepthToRGBA( dist );\n}",
distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include \x3cbeginnormal_vertex\x3e\n\t\t#include \x3cmorphnormal_vertex\x3e\n\t\t#include \x3cskinnormal_vertex\x3e\n\t#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\tvWorldPosition \x3d worldPosition.xyz;\n}",
equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include \x3ccommon\x3e\nvoid main() {\n\tvec3 direction \x3d normalize( vWorldDirection );\n\tvec2 sampleUV;\n\tsampleUV.y \x3d asin( clamp( direction.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\tsampleUV.x \x3d atan( direction.z, direction.x ) * RECIPROCAL_PI2 + 0.5;\n\tvec4 texColor \x3d texture2D( tEquirect, sampleUV );\n\tgl_FragColor \x3d mapTexelToLinear( texColor );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n}",
equirect_vert:"varying vec3 vWorldDirection;\n#include \x3ccommon\x3e\nvoid main() {\n\tvWorldDirection \x3d transformDirection( position, modelMatrix );\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tif ( mod( vLineDistance, totalSize ) \x3e dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight \x3d vec3( 0.0 );\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\toutgoingLight \x3d diffuseColor.rgb;\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3ccolor_vertex\x3e\n\tvLineDistance \x3d scale * lineDistance;\n\tvec4 mvPosition \x3d modelViewMatrix * vec4( position, 1.0 );\n\tgl_Position \x3d projectionMatrix * mvPosition;\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cuv2_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3caomap_pars_fragment\x3e\n#include \x3clightmap_pars_fragment\x3e\n#include \x3cenvmap_common_pars_fragment\x3e\n#include \x3cenvmap_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cspecularmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3cspecularmap_fragment\x3e\n\tReflectedLight reflectedLight \x3d ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\treflectedLight.indirectDiffuse +\x3d texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;\n\t#else\n\t\treflectedLight.indirectDiffuse +\x3d vec3( 1.0 );\n\t#endif\n\t#include \x3caomap_fragment\x3e\n\treflectedLight.indirectDiffuse *\x3d diffuseColor.rgb;\n\tvec3 outgoingLight \x3d reflectedLight.indirectDiffuse;\n\t#include \x3cenvmap_fragment\x3e\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
meshbasic_vert:"#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cuv2_pars_vertex\x3e\n#include \x3cenvmap_pars_vertex\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cuv2_vertex\x3e\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#ifdef USE_ENVMAP\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n\t#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cenvmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
meshlambert_frag:"uniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cdithering_pars_fragment\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cuv2_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3caomap_pars_fragment\x3e\n#include \x3clightmap_pars_fragment\x3e\n#include \x3cemissivemap_pars_fragment\x3e\n#include \x3cenvmap_common_pars_fragment\x3e\n#include \x3cenvmap_pars_fragment\x3e\n#include \x3cbsdfs\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cshadowmap_pars_fragment\x3e\n#include \x3cshadowmask_pars_fragment\x3e\n#include \x3cspecularmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\tReflectedLight reflectedLight \x3d ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance \x3d emissive;\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3cspecularmap_fragment\x3e\n\t#include \x3cemissivemap_fragment\x3e\n\treflectedLight.indirectDiffuse \x3d getAmbientLightIrradiance( ambientLightColor );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.indirectDiffuse +\x3d ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\n\t#else\n\t\treflectedLight.indirectDiffuse +\x3d vIndirectFront;\n\t#endif\n\t#include \x3clightmap_fragment\x3e\n\treflectedLight.indirectDiffuse *\x3d BRDF_Diffuse_Lambert( diffuseColor.rgb );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.directDiffuse \x3d ( gl_FrontFacing ) ? vLightFront : vLightBack;\n\t#else\n\t\treflectedLight.directDiffuse \x3d vLightFront;\n\t#endif\n\treflectedLight.directDiffuse *\x3d BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\n\t#include \x3caomap_fragment\x3e\n\tvec3 outgoingLight \x3d reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include \x3cenvmap_fragment\x3e\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3cdithering_fragment\x3e\n}",
meshlambert_vert:"#define LAMBERT\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cuv2_pars_vertex\x3e\n#include \x3cenvmap_pars_vertex\x3e\n#include \x3cbsdfs\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3cshadowmap_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cuv2_vertex\x3e\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cenvmap_vertex\x3e\n\t#include \x3clights_lambert_vertex\x3e\n\t#include \x3cshadowmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cbumpmap_pars_fragment\x3e\n#include \x3cnormalmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3cnormal_fragment_begin\x3e\n\t#include \x3cnormal_fragment_maps\x3e\n\tvec3 viewDir \x3d normalize( vViewPosition );\n\tvec3 x \x3d normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y \x3d cross( viewDir, x );\n\tvec2 uv \x3d vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor \x3d texture2D( matcap, uv );\n\t\tmatcapColor \x3d matcapTexelToLinear( matcapColor );\n\t#else\n\t\tvec4 matcapColor \x3d vec4( 1.0 );\n\t#endif\n\tvec3 outgoingLight \x3d diffuseColor.rgb * matcapColor.rgb;\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n\t#ifndef FLAT_SHADED\n\t\tvNormal \x3d normalize( transformedNormal );\n\t#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n\tvViewPosition \x3d - mvPosition.xyz;\n}",
meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cdithering_pars_fragment\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cuv2_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3caomap_pars_fragment\x3e\n#include \x3clightmap_pars_fragment\x3e\n#include \x3cemissivemap_pars_fragment\x3e\n#include \x3cenvmap_common_pars_fragment\x3e\n#include \x3cenvmap_pars_fragment\x3e\n#include \x3cgradientmap_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cbsdfs\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3clights_phong_pars_fragment\x3e\n#include \x3cshadowmap_pars_fragment\x3e\n#include \x3cbumpmap_pars_fragment\x3e\n#include \x3cnormalmap_pars_fragment\x3e\n#include \x3cspecularmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\tReflectedLight reflectedLight \x3d ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance \x3d emissive;\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3cspecularmap_fragment\x3e\n\t#include \x3cnormal_fragment_begin\x3e\n\t#include \x3cnormal_fragment_maps\x3e\n\t#include \x3cemissivemap_fragment\x3e\n\t#include \x3clights_phong_fragment\x3e\n\t#include \x3clights_fragment_begin\x3e\n\t#include \x3clights_fragment_maps\x3e\n\t#include \x3clights_fragment_end\x3e\n\t#include \x3caomap_fragment\x3e\n\tvec3 outgoingLight \x3d reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include \x3cenvmap_fragment\x3e\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3cdithering_fragment\x3e\n}",
meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cuv2_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cenvmap_pars_vertex\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3cshadowmap_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cuv2_vertex\x3e\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n#ifndef FLAT_SHADED\n\tvNormal \x3d normalize( transformedNormal );\n#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\tvViewPosition \x3d - mvPosition.xyz;\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cenvmap_vertex\x3e\n\t#include \x3cshadowmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define REFLECTIVITY\n\t#define CLEARCOAT\n\t#define TRANSPARENCY\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef TRANSPARENCY\n\tuniform float transparency;\n#endif\n#ifdef REFLECTIVITY\n\tuniform float reflectivity;\n#endif\n#ifdef CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheen;\n#endif\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cdithering_pars_fragment\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cuv2_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3caomap_pars_fragment\x3e\n#include \x3clightmap_pars_fragment\x3e\n#include \x3cemissivemap_pars_fragment\x3e\n#include \x3cbsdfs\x3e\n#include \x3ccube_uv_reflection_fragment\x3e\n#include \x3cenvmap_common_pars_fragment\x3e\n#include \x3cenvmap_physical_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3clights_physical_pars_fragment\x3e\n#include \x3cshadowmap_pars_fragment\x3e\n#include \x3cbumpmap_pars_fragment\x3e\n#include \x3cnormalmap_pars_fragment\x3e\n#include \x3cclearcoat_normalmap_pars_fragment\x3e\n#include \x3croughnessmap_pars_fragment\x3e\n#include \x3cmetalnessmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\tReflectedLight reflectedLight \x3d ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance \x3d emissive;\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3croughnessmap_fragment\x3e\n\t#include \x3cmetalnessmap_fragment\x3e\n\t#include \x3cnormal_fragment_begin\x3e\n\t#include \x3cnormal_fragment_maps\x3e\n\t#include \x3cclearcoat_normal_fragment_begin\x3e\n\t#include \x3cclearcoat_normal_fragment_maps\x3e\n\t#include \x3cemissivemap_fragment\x3e\n\t#include \x3clights_physical_fragment\x3e\n\t#include \x3clights_fragment_begin\x3e\n\t#include \x3clights_fragment_maps\x3e\n\t#include \x3clights_fragment_end\x3e\n\t#include \x3caomap_fragment\x3e\n\tvec3 outgoingLight \x3d reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#ifdef TRANSPARENCY\n\t\tdiffuseColor.a *\x3d saturate( 1. - transparency + linearToRelativeLuminance( reflectedLight.directSpecular + reflectedLight.indirectSpecular ) );\n\t#endif\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3cdithering_fragment\x3e\n}",
meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cuv2_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3cshadowmap_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cuv2_vertex\x3e\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n#ifndef FLAT_SHADED\n\tvNormal \x3d normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent \x3d normalize( transformedTangent );\n\t\tvBitangent \x3d normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\tvViewPosition \x3d - mvPosition.xyz;\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cshadowmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
normal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include \x3cpacking\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cbumpmap_pars_fragment\x3e\n#include \x3cnormalmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cnormal_fragment_begin\x3e\n\t#include \x3cnormal_fragment_maps\x3e\n\tgl_FragColor \x3d vec4( packNormalToRGB( normal ), opacity );\n}",
normal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include \x3cuv_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n#ifndef FLAT_SHADED\n\tvNormal \x3d normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent \x3d normalize( transformedTangent );\n\t\tvBitangent \x3d normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition \x3d - mvPosition.xyz;\n#endif\n}",
points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cmap_particle_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec3 outgoingLight \x3d vec3( 0.0 );\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_particle_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\toutgoingLight \x3d diffuseColor.rgb;\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
points_vert:"uniform float size;\nuniform float scale;\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\tgl_PointSize \x3d size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective \x3d ( projectionMatrix[ 2 ][ 3 ] \x3d\x3d - 1.0 );\n\t\tif ( isPerspective ) gl_PointSize *\x3d ( scale / - mvPosition.z );\n\t#endif\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
shadow_frag:"uniform vec3 color;\nuniform float opacity;\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cbsdfs\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3cshadowmap_pars_fragment\x3e\n#include \x3cshadowmask_pars_fragment\x3e\nvoid main() {\n\tgl_FragColor \x3d vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n\t#include \x3cfog_fragment\x3e\n}",shadow_vert:"#include \x3cfog_pars_vertex\x3e\n#include \x3cshadowmap_pars_vertex\x3e\nvoid main() {\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cshadowmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
sprite_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include \x3ccommon\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec3 outgoingLight \x3d vec3( 0.0 );\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\toutgoingLight \x3d diffuseColor.rgb;\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
sprite_vert:"uniform float rotation;\nuniform vec2 center;\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\tvec4 mvPosition \x3d modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\n\tvec2 scale;\n\tscale.x \x3d length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\n\tscale.y \x3d length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\n\t#ifndef USE_SIZEATTENUATION\n\t\tbool isPerspective \x3d ( projectionMatrix[ 2 ][ 3 ] \x3d\x3d - 1.0 );\n\t\tif ( isPerspective ) scale *\x3d - mvPosition.z;\n\t#endif\n\tvec2 alignedPosition \x3d ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\n\tvec2 rotatedPosition;\n\trotatedPosition.x \x3d cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\n\trotatedPosition.y \x3d sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\n\tmvPosition.xy +\x3d rotatedPosition;\n\tgl_Position \x3d projectionMatrix * mvPosition;\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}"},
Wa={common:{diffuse:{value:new I(15658734)},opacity:{value:1},map:{value:null},uvTransform:{value:new t},alphaMap:{value:null}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},
normalScale:{value:new f(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:2.5E-4},fogNear:{value:1},fogFar:{value:2E3},fogColor:{value:new I(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{},shadow:{},shadowBias:{},
shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{},shadow:{},shadowBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{},shadow:{},shadowBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},
pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}}},points:{diffuse:{value:new I(15658734)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},uvTransform:{value:new t}},sprite:{diffuse:{value:new I(15658734)},opacity:{value:1},center:{value:new f(.5,.5)},rotation:{value:0},map:{value:null},uvTransform:{value:new t}}},Mc=
{basic:{uniforms:Bb([Wa.common,Wa.specularmap,Wa.envmap,Wa.aomap,Wa.lightmap,Wa.fog]),vertexShader:rb.meshbasic_vert,fragmentShader:rb.meshbasic_frag},lambert:{uniforms:Bb([Wa.common,Wa.specularmap,Wa.envmap,Wa.aomap,Wa.lightmap,Wa.emissivemap,Wa.fog,Wa.lights,{emissive:{value:new I(0)}}]),vertexShader:rb.meshlambert_vert,fragmentShader:rb.meshlambert_frag},phong:{uniforms:Bb([Wa.common,Wa.specularmap,Wa.envmap,Wa.aomap,Wa.lightmap,Wa.emissivemap,Wa.bumpmap,Wa.normalmap,Wa.displacementmap,Wa.gradientmap,
Wa.fog,Wa.lights,{emissive:{value:new I(0)},specular:{value:new I(1118481)},shininess:{value:30}}]),vertexShader:rb.meshphong_vert,fragmentShader:rb.meshphong_frag},standard:{uniforms:Bb([Wa.common,Wa.envmap,Wa.aomap,Wa.lightmap,Wa.emissivemap,Wa.bumpmap,Wa.normalmap,Wa.displacementmap,Wa.roughnessmap,Wa.metalnessmap,Wa.fog,Wa.lights,{emissive:{value:new I(0)},roughness:{value:.5},metalness:{value:.5},envMapIntensity:{value:1}}]),vertexShader:rb.meshphysical_vert,fragmentShader:rb.meshphysical_frag},
matcap:{uniforms:Bb([Wa.common,Wa.bumpmap,Wa.normalmap,Wa.displacementmap,Wa.fog,{matcap:{value:null}}]),vertexShader:rb.meshmatcap_vert,fragmentShader:rb.meshmatcap_frag},points:{uniforms:Bb([Wa.points,Wa.fog]),vertexShader:rb.points_vert,fragmentShader:rb.points_frag},dashed:{uniforms:Bb([Wa.common,Wa.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:rb.linedashed_vert,fragmentShader:rb.linedashed_frag},depth:{uniforms:Bb([Wa.common,Wa.displacementmap]),vertexShader:rb.depth_vert,
fragmentShader:rb.depth_frag},normal:{uniforms:Bb([Wa.common,Wa.bumpmap,Wa.normalmap,Wa.displacementmap,{opacity:{value:1}}]),vertexShader:rb.normal_vert,fragmentShader:rb.normal_frag},sprite:{uniforms:Bb([Wa.sprite,Wa.fog]),vertexShader:rb.sprite_vert,fragmentShader:rb.sprite_frag},background:{uniforms:{uvTransform:{value:new t},t2D:{value:null}},vertexShader:rb.background_vert,fragmentShader:rb.background_frag},cube:{uniforms:{tCube:{value:null},tFlip:{value:-1},opacity:{value:1}},vertexShader:rb.cube_vert,
fragmentShader:rb.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:rb.equirect_vert,fragmentShader:rb.equirect_frag},distanceRGBA:{uniforms:Bb([Wa.common,Wa.displacementmap,{referencePosition:{value:new k},nearDistance:{value:1},farDistance:{value:1E3}}]),vertexShader:rb.distanceRGBA_vert,fragmentShader:rb.distanceRGBA_frag},shadow:{uniforms:Bb([Wa.lights,Wa.fog,{color:{value:new I(0)},opacity:{value:1}}]),vertexShader:rb.shadow_vert,fragmentShader:rb.shadow_frag}};Mc.physical=
{uniforms:Bb([Mc.standard.uniforms,{transparency:{value:0},clearcoat:{value:0},clearcoatRoughness:{value:0},sheen:{value:new I(0)},clearcoatNormalScale:{value:new f(1,1)},clearcoatNormalMap:{value:null}}]),vertexShader:rb.meshphysical_vert,fragmentShader:rb.meshphysical_frag};rd.prototype=Object.create(ya.prototype);rd.prototype.constructor=rd;Lc.prototype=Object.create(va.prototype);Lc.prototype.constructor=Lc;cd.prototype=Object.create(l.prototype);cd.prototype.constructor=cd;cd.prototype.isCubeTexture=
!0;Object.defineProperty(cd.prototype,"images",{get:function(){return this.image},set:function(a){this.image=a}});re.prototype=Object.create(l.prototype);re.prototype.constructor=re;re.prototype.isDataTexture2DArray=!0;se.prototype=Object.create(l.prototype);se.prototype.constructor=se;se.prototype.isDataTexture3D=!0;var ij=new l,el=new re,gl=new se,jj=new cd,cj=[],ej=[],hj=new Float32Array(16),gj=new Float32Array(9),fj=new Float32Array(4);kj.prototype.updateCache=function(a){var c=this.cache;a instanceof
Float32Array&&c.length!==a.length&&(this.cache=new Float32Array(a.length));mc(c,a)};lj.prototype.setValue=function(a,c,e){for(var g=this.seq,r=0,v=g.length;r!==v;++r){var z=g[r];z.setValue(a,c[z.id],e)}};var Lh=/([\w\d_]+)(\])?(\[|\.)?/g;ud.prototype.setValue=function(a,c,e,g){c=this.map[c];void 0!==c&&c.setValue(a,e,g)};ud.prototype.setOptional=function(a,c,e){c=c[e];void 0!==c&&this.setValue(a,e,c)};ud.upload=function(a,c,e,g){for(var r=0,v=c.length;r!==v;++r){var z=c[r],E=e[z.id];!1!==E.needsUpdate&&
z.setValue(a,E.value,g)}};ud.seqWithValue=function(a,c){for(var e=[],g=0,r=a.length;g!==r;++g){var v=a[g];v.id in c&&e.push(v)}return e};var Kl=0,Tl=0;vd.prototype=Object.create(M.prototype);vd.prototype.constructor=vd;vd.prototype.isMeshDepthMaterial=!0;vd.prototype.copy=function(a){M.prototype.copy.call(this,a);this.depthPacking=a.depthPacking;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.map=a.map;this.alphaMap=a.alphaMap;this.displacementMap=a.displacementMap;this.displacementScale=
a.displacementScale;this.displacementBias=a.displacementBias;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;return this};wd.prototype=Object.create(M.prototype);wd.prototype.constructor=wd;wd.prototype.isMeshDistanceMaterial=!0;wd.prototype.copy=function(a){M.prototype.copy.call(this,a);this.referencePosition.copy(a.referencePosition);this.nearDistance=a.nearDistance;this.farDistance=a.farDistance;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.map=a.map;this.alphaMap=
a.alphaMap;this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;return this};ue.prototype=Object.assign(Object.create(A.prototype),{constructor:ue,isGroup:!0});vf.prototype=Object.assign(Object.create(vb.prototype),{constructor:vf,isArrayCamera:!0});var yj=new k,zj=new k;Object.assign(Nh.prototype,d.prototype);Object.assign(Aj.prototype,d.prototype);Object.assign(zg.prototype,{isFogExp2:!0,clone:function(){return new zg(this.color,
this.density)},toJSON:function(){return{type:"FogExp2",color:this.color.getHex(),density:this.density}}});Object.assign(Ag.prototype,{isFog:!0,clone:function(){return new Ag(this.color,this.near,this.far)},toJSON:function(){return{type:"Fog",color:this.color.getHex(),near:this.near,far:this.far}}});Object.defineProperty(Qd.prototype,"needsUpdate",{set:function(a){!0===a&&this.version++}});Object.assign(Qd.prototype,{isInterleavedBuffer:!0,onUploadCallback:function(){},setArray:function(a){if(Array.isArray(a))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");
this.count=void 0!==a?a.length/this.stride:0;this.array=a;return this},setDynamic:function(a){this.dynamic=a;return this},copy:function(a){this.array=new a.array.constructor(a.array);this.count=a.count;this.stride=a.stride;this.dynamic=a.dynamic;return this},copyAt:function(a,c,e){a*=this.stride;e*=c.stride;for(var g=0,r=this.stride;g<r;g++)this.array[a+g]=c.array[e+g];return this},set:function(a,c){void 0===c&&(c=0);this.array.set(a,c);return this},clone:function(){return(new this.constructor).copy(this)},
onUpload:function(a){this.onUploadCallback=a;return this}});Object.defineProperties(xf.prototype,{count:{get:function(){return this.data.count}},array:{get:function(){return this.data.array}}});Object.assign(xf.prototype,{isInterleavedBufferAttribute:!0,setX:function(a,c){this.data.array[a*this.data.stride+this.offset]=c;return this},setY:function(a,c){this.data.array[a*this.data.stride+this.offset+1]=c;return this},setZ:function(a,c){this.data.array[a*this.data.stride+this.offset+2]=c;return this},
setW:function(a,c){this.data.array[a*this.data.stride+this.offset+3]=c;return this},getX:function(a){return this.data.array[a*this.data.stride+this.offset]},getY:function(a){return this.data.array[a*this.data.stride+this.offset+1]},getZ:function(a){return this.data.array[a*this.data.stride+this.offset+2]},getW:function(a){return this.data.array[a*this.data.stride+this.offset+3]},setXY:function(a,c,e){a=a*this.data.stride+this.offset;this.data.array[a+0]=c;this.data.array[a+1]=e;return this},setXYZ:function(a,
c,e,g){a=a*this.data.stride+this.offset;this.data.array[a+0]=c;this.data.array[a+1]=e;this.data.array[a+2]=g;return this},setXYZW:function(a,c,e,g,r){a=a*this.data.stride+this.offset;this.data.array[a+0]=c;this.data.array[a+1]=e;this.data.array[a+2]=g;this.data.array[a+3]=r;return this}});Ad.prototype=Object.create(M.prototype);Ad.prototype.constructor=Ad;Ad.prototype.isSpriteMaterial=!0;Ad.prototype.copy=function(a){M.prototype.copy.call(this,a);this.color.copy(a.color);this.map=a.map;this.rotation=
a.rotation;this.sizeAttenuation=a.sizeAttenuation;return this};var Ae,mg=new k,nf=new k,of=new k,Be=new f,zf=new f,Fj=new q,qh=new k,ng=new k,rh=new k,kk=new f,Ki=new f,lk=new f;yf.prototype=Object.assign(Object.create(A.prototype),{constructor:yf,isSprite:!0,raycast:function(a,c){null===a.camera&&console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.');nf.setFromMatrixScale(this.matrixWorld);Fj.copy(a.camera.matrixWorld);this.modelViewMatrix.multiplyMatrices(a.camera.matrixWorldInverse,
this.matrixWorld);of.setFromMatrixPosition(this.modelViewMatrix);a.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&nf.multiplyScalar(-of.z);var e=this.material.rotation;if(0!==e){var g=Math.cos(e);var r=Math.sin(e)}e=this.center;Bg(qh.set(-.5,-.5,0),of,e,nf,r,g);Bg(ng.set(.5,-.5,0),of,e,nf,r,g);Bg(rh.set(.5,.5,0),of,e,nf,r,g);kk.set(0,0);Ki.set(1,0);lk.set(1,1);var v=a.ray.intersectTriangle(qh,ng,rh,!1,mg);if(null===v&&(Bg(ng.set(-.5,.5,0),of,e,nf,r,g),Ki.set(0,1),v=a.ray.intersectTriangle(qh,
rh,ng,!1,mg),null===v))return;r=a.ray.origin.distanceTo(mg);r<a.near||r>a.far||c.push({distance:r,point:mg.clone(),uv:B.getUV(mg,qh,ng,rh,kk,Ki,lk,new f),face:null,object:this})},clone:function(){return(new this.constructor(this.material)).copy(this)},copy:function(a){A.prototype.copy.call(this,a);void 0!==a.center&&this.center.copy(a.center);return this}});var sh=new k,mk=new k;Af.prototype=Object.assign(Object.create(A.prototype),{constructor:Af,isLOD:!0,copy:function(a){A.prototype.copy.call(this,
a,!1);a=a.levels;for(var c=0,e=a.length;c<e;c++){var g=a[c];this.addLevel(g.object.clone(),g.distance)}return this},addLevel:function(a,c){void 0===c&&(c=0);c=Math.abs(c);for(var e=this.levels,g=0;g<e.length&&!(c<e[g].distance);g++);e.splice(g,0,{distance:c,object:a});this.add(a);return this},getObjectForDistance:function(a){for(var c=this.levels,e=1,g=c.length;e<g&&!(a<c[e].distance);e++);return c[e-1].object},raycast:function(a,c){sh.setFromMatrixPosition(this.matrixWorld);this.getObjectForDistance(a.ray.origin.distanceTo(sh)).raycast(a,
c)},update:function(a){var c=this.levels;if(1<c.length){sh.setFromMatrixPosition(a.matrixWorld);mk.setFromMatrixPosition(this.matrixWorld);a=sh.distanceTo(mk);c[0].object.visible=!0;for(var e=1,g=c.length;e<g;e++)if(a>=c[e].distance)c[e-1].object.visible=!1,c[e].object.visible=!0;else break;for(;e<g;e++)c[e].object.visible=!1}},toJSON:function(a){a=A.prototype.toJSON.call(this,a);a.object.levels=[];for(var c=this.levels,e=0,g=c.length;e<g;e++){var r=c[e];a.object.levels.push({object:r.object.uuid,
distance:r.distance})}return a}});Bf.prototype=Object.assign(Object.create(xa.prototype),{constructor:Bf,isSkinnedMesh:!0,bind:function(a,c){this.skeleton=a;void 0===c&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),c=this.matrixWorld);this.bindMatrix.copy(c);this.bindMatrixInverse.getInverse(c)},pose:function(){this.skeleton.pose()},normalizeSkinWeights:function(){for(var a=new p,c=this.geometry.attributes.skinWeight,e=0,g=c.count;e<g;e++){a.x=c.getX(e);a.y=c.getY(e);a.z=c.getZ(e);
a.w=c.getW(e);var r=1/a.manhattanLength();Infinity!==r?a.multiplyScalar(r):a.set(1,0,0,0);c.setXYZW(e,a.x,a.y,a.z,a.w)}},updateMatrixWorld:function(a){xa.prototype.updateMatrixWorld.call(this,a);"attached"===this.bindMode?this.bindMatrixInverse.getInverse(this.matrixWorld):"detached"===this.bindMode?this.bindMatrixInverse.getInverse(this.bindMatrix):console.warn("THREE.SkinnedMesh: Unrecognized bindMode: "+this.bindMode)},clone:function(){return(new this.constructor(this.geometry,this.material)).copy(this)}});
var nk=new q,Im=new q;Object.assign(Cg.prototype,{calculateInverses:function(){this.boneInverses=[];for(var a=0,c=this.bones.length;a<c;a++){var e=new q;this.bones[a]&&e.getInverse(this.bones[a].matrixWorld);this.boneInverses.push(e)}},pose:function(){var a,c;var e=0;for(c=this.bones.length;e<c;e++)(a=this.bones[e])&&a.matrixWorld.getInverse(this.boneInverses[e]);e=0;for(c=this.bones.length;e<c;e++)if(a=this.bones[e])a.parent&&a.parent.isBone?(a.matrix.getInverse(a.parent.matrixWorld),a.matrix.multiply(a.matrixWorld)):
a.matrix.copy(a.matrixWorld),a.matrix.decompose(a.position,a.quaternion,a.scale)},update:function(){for(var a=this.bones,c=this.boneInverses,e=this.boneMatrices,g=this.boneTexture,r=0,v=a.length;r<v;r++)nk.multiplyMatrices(a[r]?a[r].matrixWorld:Im,c[r]),nk.toArray(e,16*r);void 0!==g&&(g.needsUpdate=!0)},clone:function(){return new Cg(this.bones,this.boneInverses)},getBoneByName:function(a){for(var c=0,e=this.bones.length;c<e;c++){var g=this.bones[c];if(g.name===a)return g}}});Uh.prototype=Object.assign(Object.create(A.prototype),
{constructor:Uh,isBone:!0});Fb.prototype=Object.create(M.prototype);Fb.prototype.constructor=Fb;Fb.prototype.isLineBasicMaterial=!0;Fb.prototype.copy=function(a){M.prototype.copy.call(this,a);this.color.copy(a.color);this.linewidth=a.linewidth;this.linecap=a.linecap;this.linejoin=a.linejoin;return this};var ok=new k,pk=new k,qk=new q,th=new D,og=new G;Vb.prototype=Object.assign(Object.create(A.prototype),{constructor:Vb,isLine:!0,computeLineDistances:function(){var a=this.geometry;if(a.isBufferGeometry)if(null===
a.index){for(var c=a.attributes.position,e=[0],g=1,r=c.count;g<r;g++)ok.fromBufferAttribute(c,g-1),pk.fromBufferAttribute(c,g),e[g]=e[g-1],e[g]+=ok.distanceTo(pk);a.addAttribute("lineDistance",new ca(e,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else if(a.isGeometry)for(c=a.vertices,e=a.lineDistances,e[0]=0,g=1,r=c.length;g<r;g++)e[g]=e[g-1],e[g]+=c[g-1].distanceTo(c[g]);return this},raycast:function(a,c){var e=a.linePrecision,
g=this.geometry,r=this.matrixWorld;null===g.boundingSphere&&g.computeBoundingSphere();og.copy(g.boundingSphere);og.applyMatrix4(r);og.radius+=e;if(!1!==a.ray.intersectsSphere(og)){qk.getInverse(r);th.copy(a.ray).applyMatrix4(qk);e/=(this.scale.x+this.scale.y+this.scale.z)/3;e*=e;var v=new k,z=new k;r=new k;var E=new k,F=this&&this.isLineSegments?2:1;if(g.isBufferGeometry){var J=g.index,P=g.attributes.position.array;if(null!==J){J=J.array;g=0;for(var R=J.length-1;g<R;g+=F){var S=J[g+1];v.fromArray(P,
3*J[g]);z.fromArray(P,3*S);S=th.distanceSqToSegment(v,z,E,r);S>e||(E.applyMatrix4(this.matrixWorld),S=a.ray.origin.distanceTo(E),S<a.near||S>a.far||c.push({distance:S,point:r.clone().applyMatrix4(this.matrixWorld),index:g,face:null,faceIndex:null,object:this}))}}else for(g=0,R=P.length/3-1;g<R;g+=F)v.fromArray(P,3*g),z.fromArray(P,3*g+3),S=th.distanceSqToSegment(v,z,E,r),S>e||(E.applyMatrix4(this.matrixWorld),S=a.ray.origin.distanceTo(E),S<a.near||S>a.far||c.push({distance:S,point:r.clone().applyMatrix4(this.matrixWorld),
index:g,face:null,faceIndex:null,object:this}))}else if(g.isGeometry)for(v=g.vertices,z=v.length,g=0;g<z-1;g+=F)S=th.distanceSqToSegment(v[g],v[g+1],E,r),S>e||(E.applyMatrix4(this.matrixWorld),S=a.ray.origin.distanceTo(E),S<a.near||S>a.far||c.push({distance:S,point:r.clone().applyMatrix4(this.matrixWorld),index:g,face:null,faceIndex:null,object:this}))}},clone:function(){return(new this.constructor(this.geometry,this.material)).copy(this)}});var uh=new k,vh=new k;Ib.prototype=Object.assign(Object.create(Vb.prototype),
{constructor:Ib,isLineSegments:!0,computeLineDistances:function(){var a=this.geometry;if(a.isBufferGeometry)if(null===a.index){for(var c=a.attributes.position,e=[],g=0,r=c.count;g<r;g+=2)uh.fromBufferAttribute(c,g),vh.fromBufferAttribute(c,g+1),e[g]=0===g?0:e[g-1],e[g+1]=e[g]+uh.distanceTo(vh);a.addAttribute("lineDistance",new ca(e,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else if(a.isGeometry)for(c=a.vertices,e=
a.lineDistances,g=0,r=c.length;g<r;g+=2)uh.copy(c[g]),vh.copy(c[g+1]),e[g]=0===g?0:e[g-1],e[g+1]=e[g]+uh.distanceTo(vh);return this}});Dg.prototype=Object.assign(Object.create(Vb.prototype),{constructor:Dg,isLineLoop:!0});Ac.prototype=Object.create(M.prototype);Ac.prototype.constructor=Ac;Ac.prototype.isPointsMaterial=!0;Ac.prototype.copy=function(a){M.prototype.copy.call(this,a);this.color.copy(a.color);this.map=a.map;this.size=a.size;this.sizeAttenuation=a.sizeAttenuation;this.morphTargets=a.morphTargets;
return this};var rk=new q,Wh=new D,pg=new G,wh=new k;Ce.prototype=Object.assign(Object.create(A.prototype),{constructor:Ce,isPoints:!0,raycast:function(a,c){var e=this.geometry,g=this.matrixWorld,r=a.params.Points.threshold;null===e.boundingSphere&&e.computeBoundingSphere();pg.copy(e.boundingSphere);pg.applyMatrix4(g);pg.radius+=r;if(!1!==a.ray.intersectsSphere(pg))if(rk.getInverse(g),Wh.copy(a.ray).applyMatrix4(rk),r/=(this.scale.x+this.scale.y+this.scale.z)/3,r*=r,e.isBufferGeometry){var v=e.index;
e=e.attributes.position.array;if(null!==v){var z=v.array;v=0;for(var E=z.length;v<E;v++){var F=z[v];wh.fromArray(e,3*F);Vh(wh,F,r,g,a,c,this)}}else for(v=0,z=e.length/3;v<z;v++)wh.fromArray(e,3*v),Vh(wh,v,r,g,a,c,this)}else for(e=e.vertices,v=0,z=e.length;v<z;v++)Vh(e[v],v,r,g,a,c,this)},updateMorphTargets:function(){var a=this.geometry;if(a.isBufferGeometry){a=a.morphAttributes;var c=Object.keys(a);if(0<c.length){var e=a[c[0]];if(void 0!==e)for(this.morphTargetInfluences=[],this.morphTargetDictionary=
{},a=0,c=e.length;a<c;a++){var g=e[a].name||String(a);this.morphTargetInfluences.push(0);this.morphTargetDictionary[g]=a}}}else a=a.morphTargets,void 0!==a&&0<a.length&&console.error("THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")},clone:function(){return(new this.constructor(this.geometry,this.material)).copy(this)}});Xh.prototype=Object.assign(Object.create(l.prototype),{constructor:Xh,isVideoTexture:!0,update:function(){var a=this.image;a.readyState>=
a.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}});De.prototype=Object.create(l.prototype);De.prototype.constructor=De;De.prototype.isCompressedTexture=!0;Cf.prototype=Object.create(l.prototype);Cf.prototype.constructor=Cf;Cf.prototype.isCanvasTexture=!0;Df.prototype=Object.create(l.prototype);Df.prototype.constructor=Df;Df.prototype.isDepthTexture=!0;Ee.prototype=Object.create(va.prototype);Ee.prototype.constructor=Ee;Ef.prototype=Object.create(ya.prototype);Ef.prototype.constructor=Ef;Fe.prototype=Object.create(va.prototype);
Fe.prototype.constructor=Fe;Ff.prototype=Object.create(ya.prototype);Ff.prototype.constructor=Ff;jc.prototype=Object.create(va.prototype);jc.prototype.constructor=jc;Gf.prototype=Object.create(ya.prototype);Gf.prototype.constructor=Gf;Ge.prototype=Object.create(jc.prototype);Ge.prototype.constructor=Ge;Hf.prototype=Object.create(ya.prototype);Hf.prototype.constructor=Hf;Rd.prototype=Object.create(jc.prototype);Rd.prototype.constructor=Rd;If.prototype=Object.create(ya.prototype);If.prototype.constructor=
If;He.prototype=Object.create(jc.prototype);He.prototype.constructor=He;Jf.prototype=Object.create(ya.prototype);Jf.prototype.constructor=Jf;Ie.prototype=Object.create(jc.prototype);Ie.prototype.constructor=Ie;Kf.prototype=Object.create(ya.prototype);Kf.prototype.constructor=Kf;Sd.prototype=Object.create(va.prototype);Sd.prototype.constructor=Sd;Sd.prototype.toJSON=function(){var a=va.prototype.toJSON.call(this);a.path=this.parameters.path.toJSON();return a};Lf.prototype=Object.create(ya.prototype);
Lf.prototype.constructor=Lf;Je.prototype=Object.create(va.prototype);Je.prototype.constructor=Je;Mf.prototype=Object.create(ya.prototype);Mf.prototype.constructor=Mf;Ke.prototype=Object.create(va.prototype);Ke.prototype.constructor=Ke;var Jm={triangulate:function(a,c,e){e=e||2;var g=c&&c.length,r=g?c[0]*e:a.length,v=Gj(a,0,r,e,!0),z=[];if(!v||v.next===v.prev)return z;g&&(v=dm(a,c,v,e));if(a.length>80*e){var E=c=a[0];var F=g=a[1];for(var J=e;J<r;J+=e){var P=a[J];var R=a[J+1];P<E&&(E=P);R<F&&(F=R);
P>c&&(c=P);R>g&&(g=R)}P=Math.max(c-E,g-F);P=0!==P?1/P:0}Pf(v,z,e,E,F,P);return z}},ed={area:function(a){for(var c=a.length,e=0,g=c-1,r=0;r<c;g=r++)e+=a[g].x*a[r].y-a[r].x*a[g].y;return.5*e},isClockWise:function(a){return 0>ed.area(a)},triangulateShape:function(a,c){var e=[],g=[],r=[];Kj(a);Lj(e,a);var v=a.length;c.forEach(Kj);for(a=0;a<c.length;a++)g.push(v),v+=c[a].length,Lj(e,c[a]);c=Jm.triangulate(e,g);for(a=0;a<c.length;a+=3)r.push(c.slice(a,a+3));return r}};Ud.prototype=Object.create(ya.prototype);
Ud.prototype.constructor=Ud;Ud.prototype.toJSON=function(){var a=ya.prototype.toJSON.call(this);return Mj(this.parameters.shapes,this.parameters.options,a)};Rc.prototype=Object.create(va.prototype);Rc.prototype.constructor=Rc;Rc.prototype.toJSON=function(){var a=va.prototype.toJSON.call(this);return Mj(this.parameters.shapes,this.parameters.options,a)};var lm={generateTopUV:function(a,c,e,g,r){a=c[3*g];g=c[3*g+1];var v=c[3*r];r=c[3*r+1];return[new f(c[3*e],c[3*e+1]),new f(a,g),new f(v,r)]},generateSideWallUV:function(a,
c,e,g,r,v){a=c[3*e];var z=c[3*e+1];e=c[3*e+2];var E=c[3*g],F=c[3*g+1];g=c[3*g+2];var J=c[3*r],P=c[3*r+1];r=c[3*r+2];var R=c[3*v],S=c[3*v+1];c=c[3*v+2];return.01>Math.abs(z-F)?[new f(a,1-e),new f(E,1-g),new f(J,1-r),new f(R,1-c)]:[new f(z,1-e),new f(F,1-g),new f(P,1-r),new f(S,1-c)]}};Rf.prototype=Object.create(ya.prototype);Rf.prototype.constructor=Rf;Me.prototype=Object.create(Rc.prototype);Me.prototype.constructor=Me;Sf.prototype=Object.create(ya.prototype);Sf.prototype.constructor=Sf;Bd.prototype=
Object.create(va.prototype);Bd.prototype.constructor=Bd;Tf.prototype=Object.create(ya.prototype);Tf.prototype.constructor=Tf;Ne.prototype=Object.create(va.prototype);Ne.prototype.constructor=Ne;Uf.prototype=Object.create(ya.prototype);Uf.prototype.constructor=Uf;Oe.prototype=Object.create(va.prototype);Oe.prototype.constructor=Oe;Vd.prototype=Object.create(ya.prototype);Vd.prototype.constructor=Vd;Vd.prototype.toJSON=function(){var a=ya.prototype.toJSON.call(this);return Nj(this.parameters.shapes,
a)};Wd.prototype=Object.create(va.prototype);Wd.prototype.constructor=Wd;Wd.prototype.toJSON=function(){var a=va.prototype.toJSON.call(this);return Nj(this.parameters.shapes,a)};Pe.prototype=Object.create(va.prototype);Pe.prototype.constructor=Pe;Xd.prototype=Object.create(ya.prototype);Xd.prototype.constructor=Xd;fd.prototype=Object.create(va.prototype);fd.prototype.constructor=fd;Vf.prototype=Object.create(Xd.prototype);Vf.prototype.constructor=Vf;Wf.prototype=Object.create(fd.prototype);Wf.prototype.constructor=
Wf;Xf.prototype=Object.create(ya.prototype);Xf.prototype.constructor=Xf;Qe.prototype=Object.create(va.prototype);Qe.prototype.constructor=Qe;var gc=Object.freeze({WireframeGeometry:Ee,ParametricGeometry:Ef,ParametricBufferGeometry:Fe,TetrahedronGeometry:Gf,TetrahedronBufferGeometry:Ge,OctahedronGeometry:Hf,OctahedronBufferGeometry:Rd,IcosahedronGeometry:If,IcosahedronBufferGeometry:He,DodecahedronGeometry:Jf,DodecahedronBufferGeometry:Ie,PolyhedronGeometry:Ff,PolyhedronBufferGeometry:jc,TubeGeometry:Kf,
TubeBufferGeometry:Sd,TorusKnotGeometry:Lf,TorusKnotBufferGeometry:Je,TorusGeometry:Mf,TorusBufferGeometry:Ke,TextGeometry:Rf,TextBufferGeometry:Me,SphereGeometry:Sf,SphereBufferGeometry:Bd,RingGeometry:Tf,RingBufferGeometry:Ne,PlaneGeometry:rd,PlaneBufferGeometry:Lc,LatheGeometry:Uf,LatheBufferGeometry:Oe,ShapeGeometry:Vd,ShapeBufferGeometry:Wd,ExtrudeGeometry:Ud,ExtrudeBufferGeometry:Rc,EdgesGeometry:Pe,ConeGeometry:Vf,ConeBufferGeometry:Wf,CylinderGeometry:Xd,CylinderBufferGeometry:fd,CircleGeometry:Xf,
CircleBufferGeometry:Qe,BoxGeometry:Sa,BoxBufferGeometry:Xa});Yd.prototype=Object.create(M.prototype);Yd.prototype.constructor=Yd;Yd.prototype.isShadowMaterial=!0;Yd.prototype.copy=function(a){M.prototype.copy.call(this,a);this.color.copy(a.color);return this};Re.prototype=Object.create(qb.prototype);Re.prototype.constructor=Re;Re.prototype.isRawShaderMaterial=!0;Sc.prototype=Object.create(M.prototype);Sc.prototype.constructor=Sc;Sc.prototype.isMeshStandardMaterial=!0;Sc.prototype.copy=function(a){M.prototype.copy.call(this,
a);this.defines={STANDARD:""};this.color.copy(a.color);this.roughness=a.roughness;this.metalness=a.metalness;this.map=a.map;this.lightMap=a.lightMap;this.lightMapIntensity=a.lightMapIntensity;this.aoMap=a.aoMap;this.aoMapIntensity=a.aoMapIntensity;this.emissive.copy(a.emissive);this.emissiveMap=a.emissiveMap;this.emissiveIntensity=a.emissiveIntensity;this.bumpMap=a.bumpMap;this.bumpScale=a.bumpScale;this.normalMap=a.normalMap;this.normalMapType=a.normalMapType;this.normalScale.copy(a.normalScale);
this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;this.roughnessMap=a.roughnessMap;this.metalnessMap=a.metalnessMap;this.alphaMap=a.alphaMap;this.envMap=a.envMap;this.envMapIntensity=a.envMapIntensity;this.refractionRatio=a.refractionRatio;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.wireframeLinecap=a.wireframeLinecap;this.wireframeLinejoin=a.wireframeLinejoin;this.skinning=a.skinning;this.morphTargets=
a.morphTargets;this.morphNormals=a.morphNormals;return this};Zd.prototype=Object.create(Sc.prototype);Zd.prototype.constructor=Zd;Zd.prototype.isMeshPhysicalMaterial=!0;Zd.prototype.copy=function(a){Sc.prototype.copy.call(this,a);this.defines={STANDARD:"",PHYSICAL:""};this.reflectivity=a.reflectivity;this.clearcoat=a.clearcoat;this.clearcoatRoughness=a.clearcoatRoughness;this.sheen=a.sheen?(this.sheen||new I).copy(a.sheen):null;this.clearcoatNormalMap=a.clearcoatNormalMap;this.clearcoatNormalScale.copy(a.clearcoatNormalScale);
this.transparency=a.transparency;return this};Bc.prototype=Object.create(M.prototype);Bc.prototype.constructor=Bc;Bc.prototype.isMeshPhongMaterial=!0;Bc.prototype.copy=function(a){M.prototype.copy.call(this,a);this.color.copy(a.color);this.specular.copy(a.specular);this.shininess=a.shininess;this.map=a.map;this.lightMap=a.lightMap;this.lightMapIntensity=a.lightMapIntensity;this.aoMap=a.aoMap;this.aoMapIntensity=a.aoMapIntensity;this.emissive.copy(a.emissive);this.emissiveMap=a.emissiveMap;this.emissiveIntensity=
a.emissiveIntensity;this.bumpMap=a.bumpMap;this.bumpScale=a.bumpScale;this.normalMap=a.normalMap;this.normalMapType=a.normalMapType;this.normalScale.copy(a.normalScale);this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;this.specularMap=a.specularMap;this.alphaMap=a.alphaMap;this.envMap=a.envMap;this.combine=a.combine;this.reflectivity=a.reflectivity;this.refractionRatio=a.refractionRatio;this.wireframe=a.wireframe;this.wireframeLinewidth=
a.wireframeLinewidth;this.wireframeLinecap=a.wireframeLinecap;this.wireframeLinejoin=a.wireframeLinejoin;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;return this};$d.prototype=Object.create(Bc.prototype);$d.prototype.constructor=$d;$d.prototype.isMeshToonMaterial=!0;$d.prototype.copy=function(a){Bc.prototype.copy.call(this,a);this.gradientMap=a.gradientMap;return this};ae.prototype=Object.create(M.prototype);ae.prototype.constructor=ae;ae.prototype.isMeshNormalMaterial=
!0;ae.prototype.copy=function(a){M.prototype.copy.call(this,a);this.bumpMap=a.bumpMap;this.bumpScale=a.bumpScale;this.normalMap=a.normalMap;this.normalMapType=a.normalMapType;this.normalScale.copy(a.normalScale);this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;return this};
be.prototype=Object.create(M.prototype);be.prototype.constructor=be;be.prototype.isMeshLambertMaterial=!0;be.prototype.copy=function(a){M.prototype.copy.call(this,a);this.color.copy(a.color);this.map=a.map;this.lightMap=a.lightMap;this.lightMapIntensity=a.lightMapIntensity;this.aoMap=a.aoMap;this.aoMapIntensity=a.aoMapIntensity;this.emissive.copy(a.emissive);this.emissiveMap=a.emissiveMap;this.emissiveIntensity=a.emissiveIntensity;this.specularMap=a.specularMap;this.alphaMap=a.alphaMap;this.envMap=
a.envMap;this.combine=a.combine;this.reflectivity=a.reflectivity;this.refractionRatio=a.refractionRatio;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.wireframeLinecap=a.wireframeLinecap;this.wireframeLinejoin=a.wireframeLinejoin;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;return this};ce.prototype=Object.create(M.prototype);ce.prototype.constructor=ce;ce.prototype.isMeshMatcapMaterial=!0;ce.prototype.copy=function(a){M.prototype.copy.call(this,
a);this.defines={MATCAP:""};this.color.copy(a.color);this.matcap=a.matcap;this.map=a.map;this.bumpMap=a.bumpMap;this.bumpScale=a.bumpScale;this.normalMap=a.normalMap;this.normalMapType=a.normalMapType;this.normalScale.copy(a.normalScale);this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;this.alphaMap=a.alphaMap;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;return this};de.prototype=
Object.create(Fb.prototype);de.prototype.constructor=de;de.prototype.isLineDashedMaterial=!0;de.prototype.copy=function(a){Fb.prototype.copy.call(this,a);this.scale=a.scale;this.dashSize=a.dashSize;this.gapSize=a.gapSize;return this};var Km=Object.freeze({ShadowMaterial:Yd,SpriteMaterial:Ad,RawShaderMaterial:Re,ShaderMaterial:qb,PointsMaterial:Ac,MeshPhysicalMaterial:Zd,MeshStandardMaterial:Sc,MeshPhongMaterial:Bc,MeshToonMaterial:$d,MeshNormalMaterial:ae,MeshLambertMaterial:be,MeshDepthMaterial:vd,
MeshDistanceMaterial:wd,MeshBasicMaterial:L,MeshMatcapMaterial:ce,LineDashedMaterial:de,LineBasicMaterial:Fb,Material:M}),Tb={arraySlice:function(a,c,e){return Tb.isTypedArray(a)?new a.constructor(a.subarray(c,void 0!==e?e:a.length)):a.slice(c,e)},convertArray:function(a,c,e){return!a||!e&&a.constructor===c?a:"number"===typeof c.BYTES_PER_ELEMENT?new c(a):Array.prototype.slice.call(a)},isTypedArray:function(a){return ArrayBuffer.isView(a)&&!(a instanceof DataView)},getKeyframeOrder:function(a){for(var c=
a.length,e=Array(c),g=0;g!==c;++g)e[g]=g;e.sort(function(r,v){return a[r]-a[v]});return e},sortedArray:function(a,c,e){for(var g=a.length,r=new a.constructor(g),v=0,z=0;z!==g;++v)for(var E=e[v]*c,F=0;F!==c;++F)r[z++]=a[E+F];return r},flattenJSON:function(a,c,e,g){for(var r=1,v=a[0];void 0!==v&&void 0===v[g];)v=a[r++];if(void 0!==v){var z=v[g];if(void 0!==z)if(Array.isArray(z)){do z=v[g],void 0!==z&&(c.push(v.time),e.push.apply(e,z)),v=a[r++];while(void 0!==v)}else if(void 0!==z.toArray){do z=v[g],
void 0!==z&&(c.push(v.time),z.toArray(e,e.length)),v=a[r++];while(void 0!==v)}else{do z=v[g],void 0!==z&&(c.push(v.time),e.push(z)),v=a[r++];while(void 0!==v)}}}};Object.assign(oc.prototype,{evaluate:function(a){var c=this.parameterPositions,e=this._cachedIndex,g=c[e],r=c[e-1];a:{b:{c:{d:if(!(a<g)){for(var v=e+2;;){if(void 0===g){if(a<r)break d;this._cachedIndex=e=c.length;return this.afterEnd_(e-1,a,r)}if(e===v)break;r=g;g=c[++e];if(a<g)break b}g=c.length;break c}if(a>=r)break a;else{v=c[1];a<v&&
(e=2,r=v);for(v=e-2;;){if(void 0===r)return this._cachedIndex=0,this.beforeStart_(0,a,g);if(e===v)break;g=r;r=c[--e-1];if(a>=r)break b}g=e;e=0}}for(;e<g;)r=e+g>>>1,a<c[r]?g=r:e=r+1;g=c[e];r=c[e-1];if(void 0===r)return this._cachedIndex=0,this.beforeStart_(0,a,g);if(void 0===g)return this._cachedIndex=e=c.length,this.afterEnd_(e-1,r,a)}this._cachedIndex=e;this.intervalChanged_(e,r,g)}return this.interpolate_(e,r,a,g)},settings:null,DefaultSettings_:{},getSettings_:function(){return this.settings||
this.DefaultSettings_},copySampleValue_:function(a){var c=this.resultBuffer,e=this.sampleValues,g=this.valueSize;a*=g;for(var r=0;r!==g;++r)c[r]=e[a+r];return c},interpolate_:function(){throw Error("call to abstract method");},intervalChanged_:function(){}});Object.assign(oc.prototype,{beforeStart_:oc.prototype.copySampleValue_,afterEnd_:oc.prototype.copySampleValue_});Eg.prototype=Object.assign(Object.create(oc.prototype),{constructor:Eg,DefaultSettings_:{endingStart:2400,endingEnd:2400},intervalChanged_:function(a,
c,e){var g=this.parameterPositions,r=a-2,v=a+1,z=g[r],E=g[v];if(void 0===z)switch(this.getSettings_().endingStart){case 2401:r=a;z=2*c-e;break;case 2402:r=g.length-2;z=c+g[r]-g[r+1];break;default:r=a,z=e}if(void 0===E)switch(this.getSettings_().endingEnd){case 2401:v=a;E=2*e-c;break;case 2402:v=1;E=e+g[1]-g[0];break;default:v=a-1,E=c}a=.5*(e-c);g=this.valueSize;this._weightPrev=a/(c-z);this._weightNext=a/(E-e);this._offsetPrev=r*g;this._offsetNext=v*g},interpolate_:function(a,c,e,g){var r=this.resultBuffer,
v=this.sampleValues,z=this.valueSize;a*=z;var E=a-z,F=this._offsetPrev,J=this._offsetNext,P=this._weightPrev,R=this._weightNext,S=(e-c)/(g-c);e=S*S;g=e*S;c=-P*g+2*P*e-P*S;P=(1+P)*g+(-1.5-2*P)*e+(-.5+P)*S+1;S=(-1-R)*g+(1.5+R)*e+.5*S;R=R*g-R*e;for(e=0;e!==z;++e)r[e]=c*v[F+e]+P*v[E+e]+S*v[a+e]+R*v[J+e];return r}});Yf.prototype=Object.assign(Object.create(oc.prototype),{constructor:Yf,interpolate_:function(a,c,e,g){var r=this.resultBuffer,v=this.sampleValues,z=this.valueSize;a*=z;var E=a-z;c=(e-c)/(g-
c);e=1-c;for(g=0;g!==z;++g)r[g]=v[E+g]*e+v[a+g]*c;return r}});Fg.prototype=Object.assign(Object.create(oc.prototype),{constructor:Fg,interpolate_:function(a){return this.copySampleValue_(a-1)}});Object.assign(Xb,{toJSON:function(a){var c=a.constructor;if(void 0!==c.toJSON)c=c.toJSON(a);else{c={name:a.name,times:Tb.convertArray(a.times,Array),values:Tb.convertArray(a.values,Array)};var e=a.getInterpolation();e!==a.DefaultInterpolation&&(c.interpolation=e)}c.type=a.ValueTypeName;return c}});Object.assign(Xb.prototype,
{constructor:Xb,TimeBufferType:Float32Array,ValueBufferType:Float32Array,DefaultInterpolation:2301,InterpolantFactoryMethodDiscrete:function(a){return new Fg(this.times,this.values,this.getValueSize(),a)},InterpolantFactoryMethodLinear:function(a){return new Yf(this.times,this.values,this.getValueSize(),a)},InterpolantFactoryMethodSmooth:function(a){return new Eg(this.times,this.values,this.getValueSize(),a)},setInterpolation:function(a){switch(a){case 2300:var c=this.InterpolantFactoryMethodDiscrete;
break;case 2301:c=this.InterpolantFactoryMethodLinear;break;case 2302:c=this.InterpolantFactoryMethodSmooth}if(void 0===c){c="unsupported interpolation for "+this.ValueTypeName+" keyframe track named "+this.name;if(void 0===this.createInterpolant)if(a!==this.DefaultInterpolation)this.setInterpolation(this.DefaultInterpolation);else throw Error(c);console.warn("THREE.KeyframeTrack:",c);return this}this.createInterpolant=c;return this},getInterpolation:function(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return 2300;
case this.InterpolantFactoryMethodLinear:return 2301;case this.InterpolantFactoryMethodSmooth:return 2302}},getValueSize:function(){return this.values.length/this.times.length},shift:function(a){if(0!==a)for(var c=this.times,e=0,g=c.length;e!==g;++e)c[e]+=a;return this},scale:function(a){if(1!==a)for(var c=this.times,e=0,g=c.length;e!==g;++e)c[e]*=a;return this},trim:function(a,c){for(var e=this.times,g=e.length,r=0,v=g-1;r!==g&&e[r]<a;)++r;for(;-1!==v&&e[v]>c;)--v;++v;if(0!==r||v!==g)r>=v&&(v=Math.max(v,
1),r=v-1),a=this.getValueSize(),this.times=Tb.arraySlice(e,r,v),this.values=Tb.arraySlice(this.values,r*a,v*a);return this},validate:function(){var a=!0,c=this.getValueSize();0!==c-Math.floor(c)&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),a=!1);var e=this.times;c=this.values;var g=e.length;0===g&&(console.error("THREE.KeyframeTrack: Track is empty.",this),a=!1);for(var r=null,v=0;v!==g;v++){var z=e[v];if("number"===typeof z&&isNaN(z)){console.error("THREE.KeyframeTrack: Time is not a valid number.",
this,v,z);a=!1;break}if(null!==r&&r>z){console.error("THREE.KeyframeTrack: Out of order keys.",this,v,z,r);a=!1;break}r=z}if(void 0!==c&&Tb.isTypedArray(c))for(v=0,e=c.length;v!==e;++v)if(g=c[v],isNaN(g)){console.error("THREE.KeyframeTrack: Value is not a valid number.",this,v,g);a=!1;break}return a},optimize:function(){for(var a=this.times,c=this.values,e=this.getValueSize(),g=2302===this.getInterpolation(),r=1,v=a.length-1,z=1;z<v;++z){var E=!1,F=a[z];if(F!==a[z+1]&&(1!==z||F!==F[0]))if(g)E=!0;
else{var J=z*e,P=J-e,R=J+e;for(F=0;F!==e;++F){var S=c[J+F];if(S!==c[P+F]||S!==c[R+F]){E=!0;break}}}if(E){if(z!==r)for(a[r]=a[z],E=z*e,J=r*e,F=0;F!==e;++F)c[J+F]=c[E+F];++r}}if(0<v){a[r]=a[v];E=v*e;J=r*e;for(F=0;F!==e;++F)c[J+F]=c[E+F];++r}r!==a.length&&(this.times=Tb.arraySlice(a,0,r),this.values=Tb.arraySlice(c,0,r*e));return this},clone:function(){var a=Tb.arraySlice(this.times,0),c=Tb.arraySlice(this.values,0);a=new this.constructor(this.name,a,c);a.createInterpolant=this.createInterpolant;return a}});
Gg.prototype=Object.assign(Object.create(Xb.prototype),{constructor:Gg,ValueTypeName:"bool",ValueBufferType:Array,DefaultInterpolation:2300,InterpolantFactoryMethodLinear:void 0,InterpolantFactoryMethodSmooth:void 0});Hg.prototype=Object.assign(Object.create(Xb.prototype),{constructor:Hg,ValueTypeName:"color"});Se.prototype=Object.assign(Object.create(Xb.prototype),{constructor:Se,ValueTypeName:"number"});Ig.prototype=Object.assign(Object.create(oc.prototype),{constructor:Ig,interpolate_:function(a,
c,e,g){var r=this.resultBuffer,v=this.sampleValues,z=this.valueSize;a*=z;c=(e-c)/(g-c);for(e=a+z;a!==e;a+=4)h.slerpFlat(r,0,v,a-z,v,a,c);return r}});Zf.prototype=Object.assign(Object.create(Xb.prototype),{constructor:Zf,ValueTypeName:"quaternion",DefaultInterpolation:2301,InterpolantFactoryMethodLinear:function(a){return new Ig(this.times,this.values,this.getValueSize(),a)},InterpolantFactoryMethodSmooth:void 0});Jg.prototype=Object.assign(Object.create(Xb.prototype),{constructor:Jg,ValueTypeName:"string",
ValueBufferType:Array,DefaultInterpolation:2300,InterpolantFactoryMethodLinear:void 0,InterpolantFactoryMethodSmooth:void 0});Te.prototype=Object.assign(Object.create(Xb.prototype),{constructor:Te,ValueTypeName:"vector"});Object.assign(tc,{parse:function(a){for(var c=[],e=a.tracks,g=1/(a.fps||1),r=0,v=e.length;r!==v;++r)c.push(nm(e[r]).scale(g));return new tc(a.name,a.duration,c)},toJSON:function(a){var c=[],e=a.tracks;a={name:a.name,duration:a.duration,tracks:c,uuid:a.uuid};for(var g=0,r=e.length;g!==
r;++g)c.push(Xb.toJSON(e[g]));return a},CreateFromMorphTargetSequence:function(a,c,e,g){for(var r=c.length,v=[],z=0;z<r;z++){var E=[],F=[];E.push((z+r-1)%r,z,(z+1)%r);F.push(0,1,0);var J=Tb.getKeyframeOrder(E);E=Tb.sortedArray(E,1,J);F=Tb.sortedArray(F,1,J);g||0!==E[0]||(E.push(r),F.push(F[0]));v.push((new Se(".morphTargetInfluences["+c[z].name+"]",E,F)).scale(1/e))}return new tc(a,-1,v)},findByName:function(a,c){var e=a;Array.isArray(a)||(e=a.geometry&&a.geometry.animations||a.animations);for(a=
0;a<e.length;a++)if(e[a].name===c)return e[a];return null},CreateClipsFromMorphTargetSequences:function(a,c,e){for(var g={},r=/^([\w-]*?)([\d]+)$/,v=0,z=a.length;v<z;v++){var E=a[v],F=E.name.match(r);if(F&&1<F.length){var J=F[1];(F=g[J])||(g[J]=F=[]);F.push(E)}}a=[];for(J in g)a.push(tc.CreateFromMorphTargetSequence(J,g[J],c,e));return a},parseAnimation:function(a,c){function e(ha,fa,ra,pa,qa){if(0!==ra.length){var ua=[],oa=[];Tb.flattenJSON(ra,ua,oa,pa);0!==ua.length&&qa.push(new ha(fa,ua,oa))}}
if(!a)return console.error("THREE.AnimationClip: No animation in JSONLoader data."),null;var g=[],r=a.name||"default",v=a.length||-1,z=a.fps||30;a=a.hierarchy||[];for(var E=0;E<a.length;E++){var F=a[E].keys;if(F&&0!==F.length)if(F[0].morphTargets){v={};for(var J=0;J<F.length;J++)if(F[J].morphTargets)for(var P=0;P<F[J].morphTargets.length;P++)v[F[J].morphTargets[P]]=-1;for(var R in v){var S=[],V=[];for(P=0;P!==F[J].morphTargets.length;++P){var W=F[J];S.push(W.time);V.push(W.morphTarget===R?1:0)}g.push(new Se(".morphTargetInfluence["+
R+"]",S,V))}v=v.length*(z||1)}else J=".bones["+c[E].name+"]",e(Te,J+".position",F,"pos",g),e(Zf,J+".quaternion",F,"rot",g),e(Te,J+".scale",F,"scl",g)}return 0===g.length?null:new tc(r,v,g)}});Object.assign(tc.prototype,{resetDuration:function(){for(var a=0,c=0,e=this.tracks.length;c!==e;++c){var g=this.tracks[c];a=Math.max(a,g.times[g.times.length-1])}this.duration=a;return this},trim:function(){for(var a=0;a<this.tracks.length;a++)this.tracks[a].trim(0,this.duration);return this},validate:function(){for(var a=
!0,c=0;c<this.tracks.length;c++)a=a&&this.tracks[c].validate();return a},optimize:function(){for(var a=0;a<this.tracks.length;a++)this.tracks[a].optimize();return this},clone:function(){for(var a=[],c=0;c<this.tracks.length;c++)a.push(this.tracks[c].clone());return new tc(this.name,this.duration,a)}});var ie={enabled:!1,files:{},add:function(a,c){!1!==this.enabled&&(this.files[a]=c)},get:function(a){if(!1!==this.enabled)return this.files[a]},remove:function(a){delete this.files[a]},clear:function(){this.files=
{}}},Oj=new $h;Object.assign(Db.prototype,{load:function(){},parse:function(){},setCrossOrigin:function(a){this.crossOrigin=a;return this},setPath:function(a){this.path=a;return this},setResourcePath:function(a){this.resourcePath=a;return this}});Db.Handlers={handlers:[],add:function(a,c){this.handlers.push(a,c)},get:function(a){for(var c=this.handlers,e=0,g=c.length;e<g;e+=2){var r=c[e+1];if(c[e].test(a))return r}return null}};var Kc={};uc.prototype=Object.assign(Object.create(Db.prototype),{constructor:uc,
load:function(a,c,e,g){void 0===a&&(a="");void 0!==this.path&&(a=this.path+a);a=this.manager.resolveURL(a);var r=this,v=ie.get(a);if(void 0!==v)return r.manager.itemStart(a),setTimeout(function(){c&&c(v);r.manager.itemEnd(a)},0),v;if(void 0!==Kc[a])Kc[a].push({onLoad:c,onProgress:e,onError:g});else{var z=a.match(/^data:(.*?)(;base64)?,(.*)$/);if(z){e=z[1];var E=!!z[2];z=z[3];z=decodeURIComponent(z);E&&(z=atob(z));try{var F=(this.responseType||"").toLowerCase();switch(F){case "arraybuffer":case "blob":var J=
new Uint8Array(z.length);for(E=0;E<z.length;E++)J[E]=z.charCodeAt(E);var P="blob"===F?new Blob([J.buffer],{type:e}):J.buffer;break;case "document":P=(new DOMParser).parseFromString(z,e);break;case "json":P=JSON.parse(z);break;default:P=z}setTimeout(function(){c&&c(P);r.manager.itemEnd(a)},0)}catch(S){setTimeout(function(){g&&g(S);r.manager.itemError(a);r.manager.itemEnd(a)},0)}}else{Kc[a]=[];Kc[a].push({onLoad:c,onProgress:e,onError:g});var R=new XMLHttpRequest;R.open("GET",a,!0);R.addEventListener("load",
function(S){var V=this.response;ie.add(a,V);var W=Kc[a];delete Kc[a];if(200===this.status||0===this.status){0===this.status&&console.warn("THREE.FileLoader: HTTP Status 0 received.");for(var ha=0,fa=W.length;ha<fa;ha++){var ra=W[ha];if(ra.onLoad)ra.onLoad(V)}}else{ha=0;for(fa=W.length;ha<fa;ha++)if(ra=W[ha],ra.onError)ra.onError(S);r.manager.itemError(a)}r.manager.itemEnd(a)},!1);R.addEventListener("progress",function(S){for(var V=Kc[a],W=0,ha=V.length;W<ha;W++){var fa=V[W];if(fa.onProgress)fa.onProgress(S)}},
!1);R.addEventListener("error",function(S){var V=Kc[a];delete Kc[a];for(var W=0,ha=V.length;W<ha;W++){var fa=V[W];if(fa.onError)fa.onError(S)}r.manager.itemError(a);r.manager.itemEnd(a)},!1);R.addEventListener("abort",function(S){var V=Kc[a];delete Kc[a];for(var W=0,ha=V.length;W<ha;W++){var fa=V[W];if(fa.onError)fa.onError(S)}r.manager.itemError(a);r.manager.itemEnd(a)},!1);void 0!==this.responseType&&(R.responseType=this.responseType);void 0!==this.withCredentials&&(R.withCredentials=this.withCredentials);
R.overrideMimeType&&R.overrideMimeType(void 0!==this.mimeType?this.mimeType:"text/plain");for(E in this.requestHeader)R.setRequestHeader(E,this.requestHeader[E]);R.send(null)}r.manager.itemStart(a);return R}},setResponseType:function(a){this.responseType=a;return this},setWithCredentials:function(a){this.withCredentials=a;return this},setMimeType:function(a){this.mimeType=a;return this},setRequestHeader:function(a){this.requestHeader=a;return this}});ai.prototype=Object.assign(Object.create(Db.prototype),
{constructor:ai,load:function(a,c,e,g){var r=this,v=new uc(r.manager);v.setPath(r.path);v.load(a,function(z){c(r.parse(JSON.parse(z)))},e,g)},parse:function(a){for(var c=[],e=0;e<a.length;e++){var g=tc.parse(a[e]);c.push(g)}return c}});bi.prototype=Object.assign(Object.create(Db.prototype),{constructor:bi,load:function(a,c,e,g){function r(S){F.load(a[S],function(V){V=v._parser(V,!0);z[S]={width:V.width,height:V.height,format:V.format,mipmaps:V.mipmaps};J+=1;6===J&&(1===V.mipmapCount&&(E.minFilter=
1006),E.format=V.format,E.needsUpdate=!0,c&&c(E))},e,g)}var v=this,z=[],E=new De;E.image=z;var F=new uc(this.manager);F.setPath(this.path);F.setResponseType("arraybuffer");if(Array.isArray(a))for(var J=0,P=0,R=a.length;P<R;++P)r(P);else F.load(a,function(S){S=v._parser(S,!0);if(S.isCubemap)for(var V=S.mipmaps.length/S.mipmapCount,W=0;W<V;W++){z[W]={mipmaps:[]};for(var ha=0;ha<S.mipmapCount;ha++)z[W].mipmaps.push(S.mipmaps[W*S.mipmapCount+ha]),z[W].format=S.format,z[W].width=S.width,z[W].height=S.height}else E.image.width=
S.width,E.image.height=S.height,E.mipmaps=S.mipmaps;1===S.mipmapCount&&(E.minFilter=1006);E.format=S.format;E.needsUpdate=!0;c&&c(E)},e,g);return E}});Kg.prototype=Object.assign(Object.create(Db.prototype),{constructor:Kg,load:function(a,c,e,g){var r=this,v=new Ab,z=new uc(this.manager);z.setResponseType("arraybuffer");z.setPath(this.path);z.load(a,function(E){if(E=r._parser(E))void 0!==E.image?v.image=E.image:void 0!==E.data&&(v.image.width=E.width,v.image.height=E.height,v.image.data=E.data),v.wrapS=
void 0!==E.wrapS?E.wrapS:1001,v.wrapT=void 0!==E.wrapT?E.wrapT:1001,v.magFilter=void 0!==E.magFilter?E.magFilter:1006,v.minFilter=void 0!==E.minFilter?E.minFilter:1008,v.anisotropy=void 0!==E.anisotropy?E.anisotropy:1,void 0!==E.format&&(v.format=E.format),void 0!==E.type&&(v.type=E.type),void 0!==E.mipmaps&&(v.mipmaps=E.mipmaps),1===E.mipmapCount&&(v.minFilter=1006),v.needsUpdate=!0,c&&c(v,E)},e,g);return v}});Ue.prototype=Object.assign(Object.create(Db.prototype),{constructor:Ue,load:function(a,
c,e,g){function r(){F.removeEventListener("load",r,!1);F.removeEventListener("error",v,!1);ie.add(a,this);c&&c(this);z.manager.itemEnd(a)}function v(J){F.removeEventListener("load",r,!1);F.removeEventListener("error",v,!1);g&&g(J);z.manager.itemError(a);z.manager.itemEnd(a)}void 0!==this.path&&(a=this.path+a);a=this.manager.resolveURL(a);var z=this,E=ie.get(a);if(void 0!==E)return z.manager.itemStart(a),setTimeout(function(){c&&c(E);z.manager.itemEnd(a)},0),E;var F=document.createElementNS("http://www.w3.org/1999/xhtml",
"img");F.addEventListener("load",r,!1);F.addEventListener("error",v,!1);"data:"!==a.substr(0,5)&&void 0!==this.crossOrigin&&(F.crossOrigin=this.crossOrigin);z.manager.itemStart(a);F.src=a;return F}});Lg.prototype=Object.assign(Object.create(Db.prototype),{constructor:Lg,load:function(a,c,e,g){function r(F){z.load(a[F],function(J){v.images[F]=J;E++;6===E&&(v.needsUpdate=!0,c&&c(v))},void 0,g)}var v=new cd,z=new Ue(this.manager);z.setCrossOrigin(this.crossOrigin);z.setPath(this.path);var E=0;for(e=
0;e<a.length;++e)r(e);return v}});Mg.prototype=Object.assign(Object.create(Db.prototype),{constructor:Mg,load:function(a,c,e,g){var r=new l,v=new Ue(this.manager);v.setCrossOrigin(this.crossOrigin);v.setPath(this.path);v.load(a,function(z){r.image=z;z=0<a.search(/\.jpe?g($|\?)/i)||0===a.search(/^data:image\/jpeg/);r.format=z?1022:1023;r.needsUpdate=!0;void 0!==c&&c(r)},e,g);return r}});Object.assign(Za.prototype,{getPoint:function(){console.warn("THREE.Curve: .getPoint() not implemented.");return null},
getPointAt:function(a,c){a=this.getUtoTmapping(a);return this.getPoint(a,c)},getPoints:function(a){void 0===a&&(a=5);for(var c=[],e=0;e<=a;e++)c.push(this.getPoint(e/a));return c},getSpacedPoints:function(a){void 0===a&&(a=5);for(var c=[],e=0;e<=a;e++)c.push(this.getPointAt(e/a));return c},getLength:function(){var a=this.getLengths();return a[a.length-1]},getLengths:function(a){void 0===a&&(a=this.arcLengthDivisions);if(this.cacheArcLengths&&this.cacheArcLengths.length===a+1&&!this.needsUpdate)return this.cacheArcLengths;
this.needsUpdate=!1;var c=[],e=this.getPoint(0),g,r=0;c.push(0);for(g=1;g<=a;g++){var v=this.getPoint(g/a);r+=v.distanceTo(e);c.push(r);e=v}return this.cacheArcLengths=c},updateArcLengths:function(){this.needsUpdate=!0;this.getLengths()},getUtoTmapping:function(a,c){var e=this.getLengths(),g=e.length;c=c?c:a*e[g-1];for(var r=0,v=g-1,z;r<=v;)if(a=Math.floor(r+(v-r)/2),z=e[a]-c,0>z)r=a+1;else if(0<z)v=a-1;else{v=a;break}a=v;if(e[a]===c)return a/(g-1);r=e[a];return(a+(c-r)/(e[a+1]-r))/(g-1)},getTangent:function(a){var c=
a-1E-4;a+=1E-4;0>c&&(c=0);1<a&&(a=1);c=this.getPoint(c);return this.getPoint(a).clone().sub(c).normalize()},getTangentAt:function(a){a=this.getUtoTmapping(a);return this.getTangent(a)},computeFrenetFrames:function(a,c){var e=new k,g=[],r=[],v=[],z=new k,E=new q,F;for(F=0;F<=a;F++){var J=F/a;g[F]=this.getTangentAt(J);g[F].normalize()}r[0]=new k;v[0]=new k;F=Number.MAX_VALUE;J=Math.abs(g[0].x);var P=Math.abs(g[0].y),R=Math.abs(g[0].z);J<=F&&(F=J,e.set(1,0,0));P<=F&&(F=P,e.set(0,1,0));R<=F&&e.set(0,
0,1);z.crossVectors(g[0],e).normalize();r[0].crossVectors(g[0],z);v[0].crossVectors(g[0],r[0]);for(F=1;F<=a;F++)r[F]=r[F-1].clone(),v[F]=v[F-1].clone(),z.crossVectors(g[F-1],g[F]),z.length()>Number.EPSILON&&(z.normalize(),e=Math.acos(hb.clamp(g[F-1].dot(g[F]),-1,1)),r[F].applyMatrix4(E.makeRotationAxis(z,e))),v[F].crossVectors(g[F],r[F]);if(!0===c)for(e=Math.acos(hb.clamp(r[0].dot(r[a]),-1,1)),e/=a,0<g[0].dot(z.crossVectors(r[0],r[a]))&&(e=-e),F=1;F<=a;F++)r[F].applyMatrix4(E.makeRotationAxis(g[F],
e*F)),v[F].crossVectors(g[F],r[F]);return{tangents:g,normals:r,binormals:v}},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.arcLengthDivisions=a.arcLengthDivisions;return this},toJSON:function(){var a={metadata:{version:4.5,type:"Curve",generator:"Curve.toJSON"}};a.arcLengthDivisions=this.arcLengthDivisions;a.type=this.type;return a},fromJSON:function(a){this.arcLengthDivisions=a.arcLengthDivisions;return this}});pc.prototype=Object.create(Za.prototype);pc.prototype.constructor=
pc;pc.prototype.isEllipseCurve=!0;pc.prototype.getPoint=function(a,c){c=c||new f;for(var e=2*Math.PI,g=this.aEndAngle-this.aStartAngle,r=Math.abs(g)<Number.EPSILON;0>g;)g+=e;for(;g>e;)g-=e;g<Number.EPSILON&&(g=r?0:e);!0!==this.aClockwise||r||(g=g===e?-e:g-e);e=this.aStartAngle+a*g;a=this.aX+this.xRadius*Math.cos(e);var v=this.aY+this.yRadius*Math.sin(e);0!==this.aRotation&&(e=Math.cos(this.aRotation),g=Math.sin(this.aRotation),r=a-this.aX,v-=this.aY,a=r*e-v*g+this.aX,v=r*g+v*e+this.aY);return c.set(a,
v)};pc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.aX=a.aX;this.aY=a.aY;this.xRadius=a.xRadius;this.yRadius=a.yRadius;this.aStartAngle=a.aStartAngle;this.aEndAngle=a.aEndAngle;this.aClockwise=a.aClockwise;this.aRotation=a.aRotation;return this};pc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.aX=this.aX;a.aY=this.aY;a.xRadius=this.xRadius;a.yRadius=this.yRadius;a.aStartAngle=this.aStartAngle;a.aEndAngle=this.aEndAngle;a.aClockwise=this.aClockwise;a.aRotation=
this.aRotation;return a};pc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.aX=a.aX;this.aY=a.aY;this.xRadius=a.xRadius;this.yRadius=a.yRadius;this.aStartAngle=a.aStartAngle;this.aEndAngle=a.aEndAngle;this.aClockwise=a.aClockwise;this.aRotation=a.aRotation;return this};Ve.prototype=Object.create(pc.prototype);Ve.prototype.constructor=Ve;Ve.prototype.isArcCurve=!0;var xh=new k,Li=new ci,Mi=new ci,Ni=new ci;Zb.prototype=Object.create(Za.prototype);Zb.prototype.constructor=Zb;
Zb.prototype.isCatmullRomCurve3=!0;Zb.prototype.getPoint=function(a,c){c=c||new k;var e=this.points,g=e.length;a*=g-(this.closed?0:1);var r=Math.floor(a);a-=r;this.closed?r+=0<r?0:(Math.floor(Math.abs(r)/g)+1)*g:0===a&&r===g-1&&(r=g-2,a=1);if(this.closed||0<r)var v=e[(r-1)%g];else xh.subVectors(e[0],e[1]).add(e[0]),v=xh;var z=e[r%g];var E=e[(r+1)%g];this.closed||r+2<g?e=e[(r+2)%g]:(xh.subVectors(e[g-1],e[g-2]).add(e[g-1]),e=xh);if("centripetal"===this.curveType||"chordal"===this.curveType){var F=
"chordal"===this.curveType?.5:.25;g=Math.pow(v.distanceToSquared(z),F);r=Math.pow(z.distanceToSquared(E),F);F=Math.pow(E.distanceToSquared(e),F);1E-4>r&&(r=1);1E-4>g&&(g=r);1E-4>F&&(F=r);Li.initNonuniformCatmullRom(v.x,z.x,E.x,e.x,g,r,F);Mi.initNonuniformCatmullRom(v.y,z.y,E.y,e.y,g,r,F);Ni.initNonuniformCatmullRom(v.z,z.z,E.z,e.z,g,r,F)}else"catmullrom"===this.curveType&&(Li.initCatmullRom(v.x,z.x,E.x,e.x,this.tension),Mi.initCatmullRom(v.y,z.y,E.y,e.y,this.tension),Ni.initCatmullRom(v.z,z.z,E.z,
e.z,this.tension));c.set(Li.calc(a),Mi.calc(a),Ni.calc(a));return c};Zb.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.points=[];for(var c=0,e=a.points.length;c<e;c++)this.points.push(a.points[c].clone());this.closed=a.closed;this.curveType=a.curveType;this.tension=a.tension;return this};Zb.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.points=[];for(var c=0,e=this.points.length;c<e;c++)a.points.push(this.points[c].toArray());a.closed=this.closed;a.curveType=this.curveType;
a.tension=this.tension;return a};Zb.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.points=[];for(var c=0,e=a.points.length;c<e;c++){var g=a.points[c];this.points.push((new k).fromArray(g))}this.closed=a.closed;this.curveType=a.curveType;this.tension=a.tension;return this};Cc.prototype=Object.create(Za.prototype);Cc.prototype.constructor=Cc;Cc.prototype.isCubicBezierCurve=!0;Cc.prototype.getPoint=function(a,c){c=c||new f;var e=this.v0,g=this.v1,r=this.v2,v=this.v3;c.set(ag(a,
e.x,g.x,r.x,v.x),ag(a,e.y,g.y,r.y,v.y));return c};Cc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v0.copy(a.v0);this.v1.copy(a.v1);this.v2.copy(a.v2);this.v3.copy(a.v3);return this};Cc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.v0=this.v0.toArray();a.v1=this.v1.toArray();a.v2=this.v2.toArray();a.v3=this.v3.toArray();return a};Cc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v0.fromArray(a.v0);this.v1.fromArray(a.v1);this.v2.fromArray(a.v2);
this.v3.fromArray(a.v3);return this};Tc.prototype=Object.create(Za.prototype);Tc.prototype.constructor=Tc;Tc.prototype.isCubicBezierCurve3=!0;Tc.prototype.getPoint=function(a,c){c=c||new k;var e=this.v0,g=this.v1,r=this.v2,v=this.v3;c.set(ag(a,e.x,g.x,r.x,v.x),ag(a,e.y,g.y,r.y,v.y),ag(a,e.z,g.z,r.z,v.z));return c};Tc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v0.copy(a.v0);this.v1.copy(a.v1);this.v2.copy(a.v2);this.v3.copy(a.v3);return this};Tc.prototype.toJSON=function(){var a=
Za.prototype.toJSON.call(this);a.v0=this.v0.toArray();a.v1=this.v1.toArray();a.v2=this.v2.toArray();a.v3=this.v3.toArray();return a};Tc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v0.fromArray(a.v0);this.v1.fromArray(a.v1);this.v2.fromArray(a.v2);this.v3.fromArray(a.v3);return this};kc.prototype=Object.create(Za.prototype);kc.prototype.constructor=kc;kc.prototype.isLineCurve=!0;kc.prototype.getPoint=function(a,c){c=c||new f;1===a?c.copy(this.v2):(c.copy(this.v2).sub(this.v1),
c.multiplyScalar(a).add(this.v1));return c};kc.prototype.getPointAt=function(a,c){return this.getPoint(a,c)};kc.prototype.getTangent=function(){return this.v2.clone().sub(this.v1).normalize()};kc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v1.copy(a.v1);this.v2.copy(a.v2);return this};kc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.v1=this.v1.toArray();a.v2=this.v2.toArray();return a};kc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v1.fromArray(a.v1);
this.v2.fromArray(a.v2);return this};Dc.prototype=Object.create(Za.prototype);Dc.prototype.constructor=Dc;Dc.prototype.isLineCurve3=!0;Dc.prototype.getPoint=function(a,c){c=c||new k;1===a?c.copy(this.v2):(c.copy(this.v2).sub(this.v1),c.multiplyScalar(a).add(this.v1));return c};Dc.prototype.getPointAt=function(a,c){return this.getPoint(a,c)};Dc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v1.copy(a.v1);this.v2.copy(a.v2);return this};Dc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);
a.v1=this.v1.toArray();a.v2=this.v2.toArray();return a};Dc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v1.fromArray(a.v1);this.v2.fromArray(a.v2);return this};Ec.prototype=Object.create(Za.prototype);Ec.prototype.constructor=Ec;Ec.prototype.isQuadraticBezierCurve=!0;Ec.prototype.getPoint=function(a,c){c=c||new f;var e=this.v0,g=this.v1,r=this.v2;c.set($f(a,e.x,g.x,r.x),$f(a,e.y,g.y,r.y));return c};Ec.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v0.copy(a.v0);
this.v1.copy(a.v1);this.v2.copy(a.v2);return this};Ec.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.v0=this.v0.toArray();a.v1=this.v1.toArray();a.v2=this.v2.toArray();return a};Ec.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v0.fromArray(a.v0);this.v1.fromArray(a.v1);this.v2.fromArray(a.v2);return this};Uc.prototype=Object.create(Za.prototype);Uc.prototype.constructor=Uc;Uc.prototype.isQuadraticBezierCurve3=!0;Uc.prototype.getPoint=function(a,c){c=c||
new k;var e=this.v0,g=this.v1,r=this.v2;c.set($f(a,e.x,g.x,r.x),$f(a,e.y,g.y,r.y),$f(a,e.z,g.z,r.z));return c};Uc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v0.copy(a.v0);this.v1.copy(a.v1);this.v2.copy(a.v2);return this};Uc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.v0=this.v0.toArray();a.v1=this.v1.toArray();a.v2=this.v2.toArray();return a};Uc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v0.fromArray(a.v0);this.v1.fromArray(a.v1);
this.v2.fromArray(a.v2);return this};Fc.prototype=Object.create(Za.prototype);Fc.prototype.constructor=Fc;Fc.prototype.isSplineCurve=!0;Fc.prototype.getPoint=function(a,c){c=c||new f;var e=this.points,g=(e.length-1)*a;a=Math.floor(g);g-=a;var r=e[0===a?a:a-1],v=e[a],z=e[a>e.length-2?e.length-1:a+1];e=e[a>e.length-3?e.length-1:a+2];c.set(Pj(g,r.x,v.x,z.x,e.x),Pj(g,r.y,v.y,z.y,e.y));return c};Fc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.points=[];for(var c=0,e=a.points.length;c<
e;c++)this.points.push(a.points[c].clone());return this};Fc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.points=[];for(var c=0,e=this.points.length;c<e;c++)a.points.push(this.points[c].toArray());return a};Fc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.points=[];for(var c=0,e=a.points.length;c<e;c++){var g=a.points[c];this.points.push((new f).fromArray(g))}return this};var Oi=Object.freeze({ArcCurve:Ve,CatmullRomCurve3:Zb,CubicBezierCurve:Cc,CubicBezierCurve3:Tc,
EllipseCurve:pc,LineCurve:kc,LineCurve3:Dc,QuadraticBezierCurve:Ec,QuadraticBezierCurve3:Uc,SplineCurve:Fc});gd.prototype=Object.assign(Object.create(Za.prototype),{constructor:gd,add:function(a){this.curves.push(a)},closePath:function(){var a=this.curves[0].getPoint(0),c=this.curves[this.curves.length-1].getPoint(1);a.equals(c)||this.curves.push(new kc(c,a))},getPoint:function(a){var c=a*this.getLength(),e=this.getCurveLengths();for(a=0;a<e.length;){if(e[a]>=c)return c=e[a]-c,a=this.curves[a],e=
a.getLength(),a.getPointAt(0===e?0:1-c/e);a++}return null},getLength:function(){var a=this.getCurveLengths();return a[a.length-1]},updateArcLengths:function(){this.needsUpdate=!0;this.cacheLengths=null;this.getCurveLengths()},getCurveLengths:function(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;for(var a=[],c=0,e=0,g=this.curves.length;e<g;e++)c+=this.curves[e].getLength(),a.push(c);return this.cacheLengths=a},getSpacedPoints:function(a){void 0===
a&&(a=40);for(var c=[],e=0;e<=a;e++)c.push(this.getPoint(e/a));this.autoClose&&c.push(c[0]);return c},getPoints:function(a){a=a||12;for(var c=[],e,g=0,r=this.curves;g<r.length;g++){var v=r[g];v=v.getPoints(v&&v.isEllipseCurve?2*a:v&&(v.isLineCurve||v.isLineCurve3)?1:v&&v.isSplineCurve?a*v.points.length:a);for(var z=0;z<v.length;z++){var E=v[z];e&&e.equals(E)||(c.push(E),e=E)}}this.autoClose&&1<c.length&&!c[c.length-1].equals(c[0])&&c.push(c[0]);return c},copy:function(a){Za.prototype.copy.call(this,
a);this.curves=[];for(var c=0,e=a.curves.length;c<e;c++)this.curves.push(a.curves[c].clone());this.autoClose=a.autoClose;return this},toJSON:function(){var a=Za.prototype.toJSON.call(this);a.autoClose=this.autoClose;a.curves=[];for(var c=0,e=this.curves.length;c<e;c++)a.curves.push(this.curves[c].toJSON());return a},fromJSON:function(a){Za.prototype.fromJSON.call(this,a);this.autoClose=a.autoClose;this.curves=[];for(var c=0,e=a.curves.length;c<e;c++){var g=a.curves[c];this.curves.push((new Oi[g.type]).fromJSON(g))}return this}});
Gc.prototype=Object.assign(Object.create(gd.prototype),{constructor:Gc,setFromPoints:function(a){this.moveTo(a[0].x,a[0].y);for(var c=1,e=a.length;c<e;c++)this.lineTo(a[c].x,a[c].y)},moveTo:function(a,c){this.currentPoint.set(a,c)},lineTo:function(a,c){var e=new kc(this.currentPoint.clone(),new f(a,c));this.curves.push(e);this.currentPoint.set(a,c)},quadraticCurveTo:function(a,c,e,g){a=new Ec(this.currentPoint.clone(),new f(a,c),new f(e,g));this.curves.push(a);this.currentPoint.set(e,g)},bezierCurveTo:function(a,
c,e,g,r,v){a=new Cc(this.currentPoint.clone(),new f(a,c),new f(e,g),new f(r,v));this.curves.push(a);this.currentPoint.set(r,v)},splineThru:function(a){var c=[this.currentPoint.clone()].concat(a);c=new Fc(c);this.curves.push(c);this.currentPoint.copy(a[a.length-1])},arc:function(a,c,e,g,r,v){this.absarc(a+this.currentPoint.x,c+this.currentPoint.y,e,g,r,v)},absarc:function(a,c,e,g,r,v){this.absellipse(a,c,e,e,g,r,v)},ellipse:function(a,c,e,g,r,v,z,E){this.absellipse(a+this.currentPoint.x,c+this.currentPoint.y,
e,g,r,v,z,E)},absellipse:function(a,c,e,g,r,v,z,E){a=new pc(a,c,e,g,r,v,z,E);0<this.curves.length&&(c=a.getPoint(0),c.equals(this.currentPoint)||this.lineTo(c.x,c.y));this.curves.push(a);a=a.getPoint(1);this.currentPoint.copy(a)},copy:function(a){gd.prototype.copy.call(this,a);this.currentPoint.copy(a.currentPoint);return this},toJSON:function(){var a=gd.prototype.toJSON.call(this);a.currentPoint=this.currentPoint.toArray();return a},fromJSON:function(a){gd.prototype.fromJSON.call(this,a);this.currentPoint.fromArray(a.currentPoint);
return this}});Cd.prototype=Object.assign(Object.create(Gc.prototype),{constructor:Cd,getPointsHoles:function(a){for(var c=[],e=0,g=this.holes.length;e<g;e++)c[e]=this.holes[e].getPoints(a);return c},extractPoints:function(a){return{shape:this.getPoints(a),holes:this.getPointsHoles(a)}},copy:function(a){Gc.prototype.copy.call(this,a);this.holes=[];for(var c=0,e=a.holes.length;c<e;c++)this.holes.push(a.holes[c].clone());return this},toJSON:function(){var a=Gc.prototype.toJSON.call(this);a.uuid=this.uuid;
a.holes=[];for(var c=0,e=this.holes.length;c<e;c++)a.holes.push(this.holes[c].toJSON());return a},fromJSON:function(a){Gc.prototype.fromJSON.call(this,a);this.uuid=a.uuid;this.holes=[];for(var c=0,e=a.holes.length;c<e;c++){var g=a.holes[c];this.holes.push((new Gc).fromJSON(g))}return this}});Jb.prototype=Object.assign(Object.create(A.prototype),{constructor:Jb,isLight:!0,copy:function(a){A.prototype.copy.call(this,a);this.color.copy(a.color);this.intensity=a.intensity;return this},toJSON:function(a){a=
A.prototype.toJSON.call(this,a);a.object.color=this.color.getHex();a.object.intensity=this.intensity;void 0!==this.groundColor&&(a.object.groundColor=this.groundColor.getHex());void 0!==this.distance&&(a.object.distance=this.distance);void 0!==this.angle&&(a.object.angle=this.angle);void 0!==this.decay&&(a.object.decay=this.decay);void 0!==this.penumbra&&(a.object.penumbra=this.penumbra);void 0!==this.shadow&&(a.object.shadow=this.shadow.toJSON());return a}});Ng.prototype=Object.assign(Object.create(Jb.prototype),
{constructor:Ng,isHemisphereLight:!0,copy:function(a){Jb.prototype.copy.call(this,a);this.groundColor.copy(a.groundColor);return this}});Object.assign(Vc.prototype,{_projScreenMatrix:new q,_lightPositionWorld:new k,_lookTarget:new k,getViewportCount:function(){return this._viewportCount},getFrustum:function(){return this._frustum},updateMatrices:function(a){var c=this.camera,e=this.matrix,g=this._projScreenMatrix,r=this._lookTarget,v=this._lightPositionWorld;v.setFromMatrixPosition(a.matrixWorld);
c.position.copy(v);r.setFromMatrixPosition(a.target.matrixWorld);c.lookAt(r);c.updateMatrixWorld();g.multiplyMatrices(c.projectionMatrix,c.matrixWorldInverse);this._frustum.setFromMatrix(g);e.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1);e.multiply(c.projectionMatrix);e.multiply(c.matrixWorldInverse)},getViewport:function(a){return this._viewports[a]},getFrameExtents:function(){return this._frameExtents},copy:function(a){this.camera=a.camera.clone();this.bias=a.bias;this.radius=a.radius;this.mapSize.copy(a.mapSize);
return this},clone:function(){return(new this.constructor).copy(this)},toJSON:function(){var a={};0!==this.bias&&(a.bias=this.bias);1!==this.radius&&(a.radius=this.radius);if(512!==this.mapSize.x||512!==this.mapSize.y)a.mapSize=this.mapSize.toArray();a.camera=this.camera.toJSON(!1).object;delete a.camera.matrix;return a}});Og.prototype=Object.assign(Object.create(Vc.prototype),{constructor:Og,isSpotLightShadow:!0,updateMatrices:function(a,c,e){var g=this.camera,r=2*hb.RAD2DEG*a.angle,v=this.mapSize.width/
this.mapSize.height,z=a.distance||g.far;if(r!==g.fov||v!==g.aspect||z!==g.far)g.fov=r,g.aspect=v,g.far=z,g.updateProjectionMatrix();Vc.prototype.updateMatrices.call(this,a,c,e)}});Pg.prototype=Object.assign(Object.create(Jb.prototype),{constructor:Pg,isSpotLight:!0,copy:function(a){Jb.prototype.copy.call(this,a);this.distance=a.distance;this.angle=a.angle;this.penumbra=a.penumbra;this.decay=a.decay;this.target=a.target.clone();this.shadow=a.shadow.clone();return this}});di.prototype=Object.assign(Object.create(Vc.prototype),
{constructor:di,isPointLightShadow:!0,updateMatrices:function(a,c,e){c=this.camera;var g=this.matrix,r=this._lightPositionWorld,v=this._lookTarget,z=this._projScreenMatrix;r.setFromMatrixPosition(a.matrixWorld);c.position.copy(r);v.copy(c.position);v.add(this._cubeDirections[e]);c.up.copy(this._cubeUps[e]);c.lookAt(v);c.updateMatrixWorld();g.makeTranslation(-r.x,-r.y,-r.z);z.multiplyMatrices(c.projectionMatrix,c.matrixWorldInverse);this._frustum.setFromMatrix(z)}});Qg.prototype=Object.assign(Object.create(Jb.prototype),
{constructor:Qg,isPointLight:!0,copy:function(a){Jb.prototype.copy.call(this,a);this.distance=a.distance;this.decay=a.decay;this.shadow=a.shadow.clone();return this}});bg.prototype=Object.assign(Object.create(zb.prototype),{constructor:bg,isOrthographicCamera:!0,copy:function(a,c){zb.prototype.copy.call(this,a,c);this.left=a.left;this.right=a.right;this.top=a.top;this.bottom=a.bottom;this.near=a.near;this.far=a.far;this.zoom=a.zoom;this.view=null===a.view?null:Object.assign({},a.view);return this},
setViewOffset:function(a,c,e,g,r,v){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1});this.view.enabled=!0;this.view.fullWidth=a;this.view.fullHeight=c;this.view.offsetX=e;this.view.offsetY=g;this.view.width=r;this.view.height=v;this.updateProjectionMatrix()},clearViewOffset:function(){null!==this.view&&(this.view.enabled=!1);this.updateProjectionMatrix()},updateProjectionMatrix:function(){var a=(this.right-this.left)/(2*this.zoom),c=(this.top-
this.bottom)/(2*this.zoom),e=(this.right+this.left)/2,g=(this.top+this.bottom)/2,r=e-a;e+=a;a=g+c;c=g-c;if(null!==this.view&&this.view.enabled){e=this.zoom/(this.view.width/this.view.fullWidth);c=this.zoom/(this.view.height/this.view.fullHeight);var v=(this.right-this.left)/this.view.width;g=(this.top-this.bottom)/this.view.height;r+=this.view.offsetX/e*v;e=r+this.view.width/e*v;a-=this.view.offsetY/c*g;c=a-this.view.height/c*g}this.projectionMatrix.makeOrthographic(r,e,a,c,this.near,this.far);this.projectionMatrixInverse.getInverse(this.projectionMatrix)},
toJSON:function(a){a=A.prototype.toJSON.call(this,a);a.object.zoom=this.zoom;a.object.left=this.left;a.object.right=this.right;a.object.top=this.top;a.object.bottom=this.bottom;a.object.near=this.near;a.object.far=this.far;null!==this.view&&(a.object.view=Object.assign({},this.view));return a}});Rg.prototype=Object.assign(Object.create(Vc.prototype),{constructor:Rg,isDirectionalLightShadow:!0,updateMatrices:function(a,c,e){Vc.prototype.updateMatrices.call(this,a,c,e)}});Sg.prototype=Object.assign(Object.create(Jb.prototype),
{constructor:Sg,isDirectionalLight:!0,copy:function(a){Jb.prototype.copy.call(this,a);this.target=a.target.clone();this.shadow=a.shadow.clone();return this}});Tg.prototype=Object.assign(Object.create(Jb.prototype),{constructor:Tg,isAmbientLight:!0});Ug.prototype=Object.assign(Object.create(Jb.prototype),{constructor:Ug,isRectAreaLight:!0,copy:function(a){Jb.prototype.copy.call(this,a);this.width=a.width;this.height=a.height;return this},toJSON:function(a){a=Jb.prototype.toJSON.call(this,a);a.object.width=
this.width;a.object.height=this.height;return a}});Vg.prototype=Object.assign(Object.create(Db.prototype),{constructor:Vg,load:function(a,c,e,g){var r=this,v=new uc(r.manager);v.setPath(r.path);v.load(a,function(z){c(r.parse(JSON.parse(z)))},e,g)},parse:function(a){function c(E){void 0===e[E]&&console.warn("THREE.MaterialLoader: Undefined texture",E);return e[E]}var e=this.textures,g=new Km[a.type];void 0!==a.uuid&&(g.uuid=a.uuid);void 0!==a.name&&(g.name=a.name);void 0!==a.color&&g.color.setHex(a.color);
void 0!==a.roughness&&(g.roughness=a.roughness);void 0!==a.metalness&&(g.metalness=a.metalness);void 0!==a.emissive&&g.emissive.setHex(a.emissive);void 0!==a.specular&&g.specular.setHex(a.specular);void 0!==a.shininess&&(g.shininess=a.shininess);void 0!==a.clearcoat&&(g.clearcoat=a.clearcoat);void 0!==a.clearcoatRoughness&&(g.clearcoatRoughness=a.clearcoatRoughness);void 0!==a.vertexColors&&(g.vertexColors=a.vertexColors);void 0!==a.fog&&(g.fog=a.fog);void 0!==a.flatShading&&(g.flatShading=a.flatShading);
void 0!==a.blending&&(g.blending=a.blending);void 0!==a.combine&&(g.combine=a.combine);void 0!==a.side&&(g.side=a.side);void 0!==a.opacity&&(g.opacity=a.opacity);void 0!==a.transparent&&(g.transparent=a.transparent);void 0!==a.alphaTest&&(g.alphaTest=a.alphaTest);void 0!==a.depthTest&&(g.depthTest=a.depthTest);void 0!==a.depthWrite&&(g.depthWrite=a.depthWrite);void 0!==a.colorWrite&&(g.colorWrite=a.colorWrite);void 0!==a.wireframe&&(g.wireframe=a.wireframe);void 0!==a.wireframeLinewidth&&(g.wireframeLinewidth=
a.wireframeLinewidth);void 0!==a.wireframeLinecap&&(g.wireframeLinecap=a.wireframeLinecap);void 0!==a.wireframeLinejoin&&(g.wireframeLinejoin=a.wireframeLinejoin);void 0!==a.rotation&&(g.rotation=a.rotation);1!==a.linewidth&&(g.linewidth=a.linewidth);void 0!==a.dashSize&&(g.dashSize=a.dashSize);void 0!==a.gapSize&&(g.gapSize=a.gapSize);void 0!==a.scale&&(g.scale=a.scale);void 0!==a.polygonOffset&&(g.polygonOffset=a.polygonOffset);void 0!==a.polygonOffsetFactor&&(g.polygonOffsetFactor=a.polygonOffsetFactor);
void 0!==a.polygonOffsetUnits&&(g.polygonOffsetUnits=a.polygonOffsetUnits);void 0!==a.skinning&&(g.skinning=a.skinning);void 0!==a.morphTargets&&(g.morphTargets=a.morphTargets);void 0!==a.morphNormals&&(g.morphNormals=a.morphNormals);void 0!==a.dithering&&(g.dithering=a.dithering);void 0!==a.visible&&(g.visible=a.visible);void 0!==a.toneMapped&&(g.toneMapped=a.toneMapped);void 0!==a.userData&&(g.userData=a.userData);if(void 0!==a.uniforms)for(var r in a.uniforms){var v=a.uniforms[r];g.uniforms[r]=
{};switch(v.type){case "t":g.uniforms[r].value=c(v.value);break;case "c":g.uniforms[r].value=(new I).setHex(v.value);break;case "v2":g.uniforms[r].value=(new f).fromArray(v.value);break;case "v3":g.uniforms[r].value=(new k).fromArray(v.value);break;case "v4":g.uniforms[r].value=(new p).fromArray(v.value);break;case "m3":g.uniforms[r].value=(new t).fromArray(v.value);case "m4":g.uniforms[r].value=(new q).fromArray(v.value);break;default:g.uniforms[r].value=v.value}}void 0!==a.defines&&(g.defines=a.defines);
void 0!==a.vertexShader&&(g.vertexShader=a.vertexShader);void 0!==a.fragmentShader&&(g.fragmentShader=a.fragmentShader);if(void 0!==a.extensions)for(var z in a.extensions)g.extensions[z]=a.extensions[z];void 0!==a.shading&&(g.flatShading=1===a.shading);void 0!==a.size&&(g.size=a.size);void 0!==a.sizeAttenuation&&(g.sizeAttenuation=a.sizeAttenuation);void 0!==a.map&&(g.map=c(a.map));void 0!==a.matcap&&(g.matcap=c(a.matcap));void 0!==a.alphaMap&&(g.alphaMap=c(a.alphaMap),g.transparent=!0);void 0!==
a.bumpMap&&(g.bumpMap=c(a.bumpMap));void 0!==a.bumpScale&&(g.bumpScale=a.bumpScale);void 0!==a.normalMap&&(g.normalMap=c(a.normalMap));void 0!==a.normalMapType&&(g.normalMapType=a.normalMapType);void 0!==a.normalScale&&(r=a.normalScale,!1===Array.isArray(r)&&(r=[r,r]),g.normalScale=(new f).fromArray(r));void 0!==a.displacementMap&&(g.displacementMap=c(a.displacementMap));void 0!==a.displacementScale&&(g.displacementScale=a.displacementScale);void 0!==a.displacementBias&&(g.displacementBias=a.displacementBias);
void 0!==a.roughnessMap&&(g.roughnessMap=c(a.roughnessMap));void 0!==a.metalnessMap&&(g.metalnessMap=c(a.metalnessMap));void 0!==a.emissiveMap&&(g.emissiveMap=c(a.emissiveMap));void 0!==a.emissiveIntensity&&(g.emissiveIntensity=a.emissiveIntensity);void 0!==a.specularMap&&(g.specularMap=c(a.specularMap));void 0!==a.envMap&&(g.envMap=c(a.envMap));void 0!==a.envMapIntensity&&(g.envMapIntensity=a.envMapIntensity);void 0!==a.reflectivity&&(g.reflectivity=a.reflectivity);void 0!==a.refractionRatio&&(g.refractionRatio=
a.refractionRatio);void 0!==a.lightMap&&(g.lightMap=c(a.lightMap));void 0!==a.lightMapIntensity&&(g.lightMapIntensity=a.lightMapIntensity);void 0!==a.aoMap&&(g.aoMap=c(a.aoMap));void 0!==a.aoMapIntensity&&(g.aoMapIntensity=a.aoMapIntensity);void 0!==a.gradientMap&&(g.gradientMap=c(a.gradientMap));void 0!==a.clearcoatNormalMap&&(g.clearcoatNormalMap=c(a.clearcoatNormalMap));void 0!==a.clearcoatNormalScale&&(g.clearcoatNormalScale=(new f).fromArray(a.clearcoatNormalScale));return g},setTextures:function(a){this.textures=
a;return this}});var Pi={decodeText:function(a){if("undefined"!==typeof TextDecoder)return(new TextDecoder).decode(a);for(var c="",e=0,g=a.length;e<g;e++)c+=String.fromCharCode(a[e]);try{return decodeURIComponent(escape(c))}catch(r){return c}},extractUrlBase:function(a){var c=a.lastIndexOf("/");return-1===c?"./":a.substr(0,c+1)}};Wg.prototype=Object.assign(Object.create(va.prototype),{constructor:Wg,isInstancedBufferGeometry:!0,copy:function(a){va.prototype.copy.call(this,a);this.maxInstancedCount=
a.maxInstancedCount;return this},clone:function(){return(new this.constructor).copy(this)},toJSON:function(){var a=va.prototype.toJSON.call(this);a.maxInstancedCount=this.maxInstancedCount;a.isInstancedBufferGeometry=!0;return a}});Xg.prototype=Object.assign(Object.create(Q.prototype),{constructor:Xg,isInstancedBufferAttribute:!0,copy:function(a){Q.prototype.copy.call(this,a);this.meshPerAttribute=a.meshPerAttribute;return this},toJSON:function(){var a=Q.prototype.toJSON.call(this);a.meshPerAttribute=
this.meshPerAttribute;a.isInstancedBufferAttribute=!0;return a}});Yg.prototype=Object.assign(Object.create(Db.prototype),{constructor:Yg,load:function(a,c,e,g){var r=this,v=new uc(r.manager);v.setPath(r.path);v.load(a,function(z){c(r.parse(JSON.parse(z)))},e,g)},parse:function(a){var c=a.isInstancedBufferGeometry?new Wg:new va,e=a.data.index;if(void 0!==e){var g=new Qi[e.type](e.array);c.setIndex(new Q(g,1))}e=a.data.attributes;for(var r in e){var v=e[r];g=new Qi[v.type](v.array);g=new (v.isInstancedBufferAttribute?
Xg:Q)(g,v.itemSize,v.normalized);void 0!==v.name&&(g.name=v.name);c.addAttribute(r,g)}var z=a.data.morphAttributes;if(z)for(r in z){var E=z[r],F=[];e=0;for(var J=E.length;e<J;e++)v=E[e],g=new Qi[v.type](v.array),g=new Q(g,v.itemSize,v.normalized),void 0!==v.name&&(g.name=v.name),F.push(g);c.morphAttributes[r]=F}r=a.data.groups||a.data.drawcalls||a.data.offsets;if(void 0!==r)for(e=0,v=r.length;e!==v;++e)g=r[e],c.addGroup(g.start,g.count,g.materialIndex);e=a.data.boundingSphere;void 0!==e&&(r=new k,
void 0!==e.center&&r.fromArray(e.center),c.boundingSphere=new G(r,e.radius));a.name&&(c.name=a.name);a.userData&&(c.userData=a.userData);return c}});var Qi={Int8Array,Uint8Array,Uint8ClampedArray:"undefined"!==typeof Uint8ClampedArray?Uint8ClampedArray:Uint8Array,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array};Zg.prototype=Object.assign(Object.create(Db.prototype),{constructor:Zg,load:function(a,c,e,g){var r=this,v=""===this.path?Pi.extractUrlBase(a):this.path;this.resourcePath=
this.resourcePath||v;v=new uc(r.manager);v.setPath(this.path);v.load(a,function(z){var E=null;try{E=JSON.parse(z)}catch(F){void 0!==g&&g(F);console.error("THREE:ObjectLoader: Can't parse "+a+".",F.message);return}z=E.metadata;void 0===z||void 0===z.type||"geometry"===z.type.toLowerCase()?console.error("THREE.ObjectLoader: Can't load "+a):r.parse(E,c)},e,g)},parse:function(a,c){var e=this.parseShape(a.shapes);e=this.parseGeometries(a.geometries,e);var g=this.parseImages(a.images,function(){void 0!==
c&&c(r)});g=this.parseTextures(a.textures,g);g=this.parseMaterials(a.materials,g);var r=this.parseObject(a.object,e,g);a.animations&&(r.animations=this.parseAnimations(a.animations));void 0!==a.images&&0!==a.images.length||void 0===c||c(r);return r},parseShape:function(a){var c={};if(void 0!==a)for(var e=0,g=a.length;e<g;e++){var r=(new Cd).fromJSON(a[e]);c[r.uuid]=r}return c},parseGeometries:function(a,c){var e={};if(void 0!==a)for(var g=new Yg,r=0,v=a.length;r<v;r++){var z=a[r];switch(z.type){case "PlaneGeometry":case "PlaneBufferGeometry":var E=
new gc[z.type](z.width,z.height,z.widthSegments,z.heightSegments);break;case "BoxGeometry":case "BoxBufferGeometry":case "CubeGeometry":E=new gc[z.type](z.width,z.height,z.depth,z.widthSegments,z.heightSegments,z.depthSegments);break;case "CircleGeometry":case "CircleBufferGeometry":E=new gc[z.type](z.radius,z.segments,z.thetaStart,z.thetaLength);break;case "CylinderGeometry":case "CylinderBufferGeometry":E=new gc[z.type](z.radiusTop,z.radiusBottom,z.height,z.radialSegments,z.heightSegments,z.openEnded,
z.thetaStart,z.thetaLength);break;case "ConeGeometry":case "ConeBufferGeometry":E=new gc[z.type](z.radius,z.height,z.radialSegments,z.heightSegments,z.openEnded,z.thetaStart,z.thetaLength);break;case "SphereGeometry":case "SphereBufferGeometry":E=new gc[z.type](z.radius,z.widthSegments,z.heightSegments,z.phiStart,z.phiLength,z.thetaStart,z.thetaLength);break;case "DodecahedronGeometry":case "DodecahedronBufferGeometry":case "IcosahedronGeometry":case "IcosahedronBufferGeometry":case "OctahedronGeometry":case "OctahedronBufferGeometry":case "TetrahedronGeometry":case "TetrahedronBufferGeometry":E=
new gc[z.type](z.radius,z.detail);break;case "RingGeometry":case "RingBufferGeometry":E=new gc[z.type](z.innerRadius,z.outerRadius,z.thetaSegments,z.phiSegments,z.thetaStart,z.thetaLength);break;case "TorusGeometry":case "TorusBufferGeometry":E=new gc[z.type](z.radius,z.tube,z.radialSegments,z.tubularSegments,z.arc);break;case "TorusKnotGeometry":case "TorusKnotBufferGeometry":E=new gc[z.type](z.radius,z.tube,z.tubularSegments,z.radialSegments,z.p,z.q);break;case "TubeGeometry":case "TubeBufferGeometry":E=
new gc[z.type]((new Oi[z.path.type]).fromJSON(z.path),z.tubularSegments,z.radius,z.radialSegments,z.closed);break;case "LatheGeometry":case "LatheBufferGeometry":E=new gc[z.type](z.points,z.segments,z.phiStart,z.phiLength);break;case "PolyhedronGeometry":case "PolyhedronBufferGeometry":E=new gc[z.type](z.vertices,z.indices,z.radius,z.details);break;case "ShapeGeometry":case "ShapeBufferGeometry":E=[];for(var F=0,J=z.shapes.length;F<J;F++){var P=c[z.shapes[F]];E.push(P)}E=new gc[z.type](E,z.curveSegments);
break;case "ExtrudeGeometry":case "ExtrudeBufferGeometry":E=[];F=0;for(J=z.shapes.length;F<J;F++)P=c[z.shapes[F]],E.push(P);F=z.options.extrudePath;void 0!==F&&(z.options.extrudePath=(new Oi[F.type]).fromJSON(F));E=new gc[z.type](E,z.options);break;case "BufferGeometry":case "InstancedBufferGeometry":E=g.parse(z);break;case "Geometry":"THREE"in window&&"LegacyJSONLoader"in THREE?E=(new THREE.LegacyJSONLoader).parse(z,this.resourcePath).geometry:console.error('THREE.ObjectLoader: You have to import LegacyJSONLoader in order load geometry data of type "Geometry".');
break;default:console.warn('THREE.ObjectLoader: Unsupported geometry type "'+z.type+'"');continue}E.uuid=z.uuid;void 0!==z.name&&(E.name=z.name);!0===E.isBufferGeometry&&void 0!==z.userData&&(E.userData=z.userData);e[z.uuid]=E}return e},parseMaterials:function(a,c){var e={},g={};if(void 0!==a){var r=new Vg;r.setTextures(c);c=0;for(var v=a.length;c<v;c++){var z=a[c];if("MultiMaterial"===z.type){for(var E=[],F=0;F<z.materials.length;F++){var J=z.materials[F];void 0===e[J.uuid]&&(e[J.uuid]=r.parse(J));
E.push(e[J.uuid])}g[z.uuid]=E}else void 0===e[z.uuid]&&(e[z.uuid]=r.parse(z)),g[z.uuid]=e[z.uuid]}}return g},parseAnimations:function(a){for(var c=[],e=0;e<a.length;e++){var g=a[e],r=tc.parse(g);void 0!==g.uuid&&(r.uuid=g.uuid);c.push(r)}return c},parseImages:function(a,c){function e(S){g.manager.itemStart(S);return v.load(S,function(){g.manager.itemEnd(S)},void 0,function(){g.manager.itemError(S);g.manager.itemEnd(S)})}var g=this,r={};if(void 0!==a&&0<a.length){c=new $h(c);var v=new Ue(c);v.setCrossOrigin(this.crossOrigin);
c=0;for(var z=a.length;c<z;c++){var E=a[c],F=E.url;if(Array.isArray(F)){r[E.uuid]=[];for(var J=0,P=F.length;J<P;J++){var R=F[J];R=/^(\/\/)|([a-z]+:(\/\/)?)/i.test(R)?R:g.resourcePath+R;r[E.uuid].push(e(R))}}else R=/^(\/\/)|([a-z]+:(\/\/)?)/i.test(E.url)?E.url:g.resourcePath+E.url,r[E.uuid]=e(R)}}return r},parseTextures:function(a,c){function e(F,J){if("number"===typeof F)return F;console.warn("THREE.ObjectLoader.parseTexture: Constant should be in numeric form.",F);return J[F]}var g={};if(void 0!==
a)for(var r=0,v=a.length;r<v;r++){var z=a[r];void 0===z.image&&console.warn('THREE.ObjectLoader: No "image" specified for',z.uuid);void 0===c[z.image]&&console.warn("THREE.ObjectLoader: Undefined image",z.image);var E=Array.isArray(c[z.image])?new cd(c[z.image]):new l(c[z.image]);E.needsUpdate=!0;E.uuid=z.uuid;void 0!==z.name&&(E.name=z.name);void 0!==z.mapping&&(E.mapping=e(z.mapping,Lm));void 0!==z.offset&&E.offset.fromArray(z.offset);void 0!==z.repeat&&E.repeat.fromArray(z.repeat);void 0!==z.center&&
E.center.fromArray(z.center);void 0!==z.rotation&&(E.rotation=z.rotation);void 0!==z.wrap&&(E.wrapS=e(z.wrap[0],sk),E.wrapT=e(z.wrap[1],sk));void 0!==z.format&&(E.format=z.format);void 0!==z.type&&(E.type=z.type);void 0!==z.encoding&&(E.encoding=z.encoding);void 0!==z.minFilter&&(E.minFilter=e(z.minFilter,tk));void 0!==z.magFilter&&(E.magFilter=e(z.magFilter,tk));void 0!==z.anisotropy&&(E.anisotropy=z.anisotropy);void 0!==z.flipY&&(E.flipY=z.flipY);void 0!==z.premultiplyAlpha&&(E.premultiplyAlpha=
z.premultiplyAlpha);void 0!==z.unpackAlignment&&(E.unpackAlignment=z.unpackAlignment);g[z.uuid]=E}return g},parseObject:function(a,c,e){function g(J){void 0===c[J]&&console.warn("THREE.ObjectLoader: Undefined geometry",J);return c[J]}function r(J){if(void 0!==J){if(Array.isArray(J)){for(var P=[],R=0,S=J.length;R<S;R++){var V=J[R];void 0===e[V]&&console.warn("THREE.ObjectLoader: Undefined material",V);P.push(e[V])}return P}void 0===e[J]&&console.warn("THREE.ObjectLoader: Undefined material",J);return e[J]}}
switch(a.type){case "Scene":var v=new y;void 0!==a.background&&Number.isInteger(a.background)&&(v.background=new I(a.background));void 0!==a.fog&&("Fog"===a.fog.type?v.fog=new Ag(a.fog.color,a.fog.near,a.fog.far):"FogExp2"===a.fog.type&&(v.fog=new zg(a.fog.color,a.fog.density)));break;case "PerspectiveCamera":v=new vb(a.fov,a.aspect,a.near,a.far);void 0!==a.focus&&(v.focus=a.focus);void 0!==a.zoom&&(v.zoom=a.zoom);void 0!==a.filmGauge&&(v.filmGauge=a.filmGauge);void 0!==a.filmOffset&&(v.filmOffset=
a.filmOffset);void 0!==a.view&&(v.view=Object.assign({},a.view));break;case "OrthographicCamera":v=new bg(a.left,a.right,a.top,a.bottom,a.near,a.far);void 0!==a.zoom&&(v.zoom=a.zoom);void 0!==a.view&&(v.view=Object.assign({},a.view));break;case "AmbientLight":v=new Tg(a.color,a.intensity);break;case "DirectionalLight":v=new Sg(a.color,a.intensity);break;case "PointLight":v=new Qg(a.color,a.intensity,a.distance,a.decay);break;case "RectAreaLight":v=new Ug(a.color,a.intensity,a.width,a.height);break;
case "SpotLight":v=new Pg(a.color,a.intensity,a.distance,a.angle,a.penumbra,a.decay);break;case "HemisphereLight":v=new Ng(a.color,a.groundColor,a.intensity);break;case "SkinnedMesh":console.warn("THREE.ObjectLoader.parseObject() does not support SkinnedMesh yet.");case "Mesh":v=g(a.geometry);var z=r(a.material);v=v.bones&&0<v.bones.length?new Bf(v,z):new xa(v,z);void 0!==a.drawMode&&v.setDrawMode(a.drawMode);break;case "LOD":v=new Af;break;case "Line":v=new Vb(g(a.geometry),r(a.material),a.mode);
break;case "LineLoop":v=new Dg(g(a.geometry),r(a.material));break;case "LineSegments":v=new Ib(g(a.geometry),r(a.material));break;case "PointCloud":case "Points":v=new Ce(g(a.geometry),r(a.material));break;case "Sprite":v=new yf(r(a.material));break;case "Group":v=new ue;break;default:v=new A}v.uuid=a.uuid;void 0!==a.name&&(v.name=a.name);void 0!==a.matrix?(v.matrix.fromArray(a.matrix),void 0!==a.matrixAutoUpdate&&(v.matrixAutoUpdate=a.matrixAutoUpdate),v.matrixAutoUpdate&&v.matrix.decompose(v.position,
v.quaternion,v.scale)):(void 0!==a.position&&v.position.fromArray(a.position),void 0!==a.rotation&&v.rotation.fromArray(a.rotation),void 0!==a.quaternion&&v.quaternion.fromArray(a.quaternion),void 0!==a.scale&&v.scale.fromArray(a.scale));void 0!==a.castShadow&&(v.castShadow=a.castShadow);void 0!==a.receiveShadow&&(v.receiveShadow=a.receiveShadow);a.shadow&&(void 0!==a.shadow.bias&&(v.shadow.bias=a.shadow.bias),void 0!==a.shadow.radius&&(v.shadow.radius=a.shadow.radius),void 0!==a.shadow.mapSize&&
v.shadow.mapSize.fromArray(a.shadow.mapSize),void 0!==a.shadow.camera&&(v.shadow.camera=this.parseObject(a.shadow.camera)));void 0!==a.visible&&(v.visible=a.visible);void 0!==a.frustumCulled&&(v.frustumCulled=a.frustumCulled);void 0!==a.renderOrder&&(v.renderOrder=a.renderOrder);void 0!==a.userData&&(v.userData=a.userData);void 0!==a.layers&&(v.layers.mask=a.layers);if(void 0!==a.children){z=a.children;for(var E=0;E<z.length;E++)v.add(this.parseObject(z[E],c,e))}if("LOD"===a.type)for(a=a.levels,z=
0;z<a.length;z++){E=a[z];var F=v.getObjectByProperty("uuid",E.object);void 0!==F&&v.addLevel(F,E.distance)}return v}});var Lm={UVMapping:300,CubeReflectionMapping:301,CubeRefractionMapping:302,EquirectangularReflectionMapping:303,EquirectangularRefractionMapping:304,SphericalReflectionMapping:305,CubeUVReflectionMapping:306,CubeUVRefractionMapping:307},sk={RepeatWrapping:1E3,ClampToEdgeWrapping:1001,MirroredRepeatWrapping:1002},tk={NearestFilter:1003,NearestMipmapNearestFilter:1004,NearestMipmapLinearFilter:1005,
LinearFilter:1006,LinearMipmapNearestFilter:1007,LinearMipmapLinearFilter:1008};ei.prototype=Object.assign(Object.create(Db.prototype),{constructor:ei,setOptions:function(a){this.options=a;return this},load:function(a,c,e,g){void 0===a&&(a="");void 0!==this.path&&(a=this.path+a);a=this.manager.resolveURL(a);var r=this,v=ie.get(a);if(void 0!==v)return r.manager.itemStart(a),setTimeout(function(){c&&c(v);r.manager.itemEnd(a)},0),v;fetch(a).then(function(z){return z.blob()}).then(function(z){return void 0===
r.options?createImageBitmap(z):createImageBitmap(z,r.options)}).then(function(z){ie.add(a,z);c&&c(z);r.manager.itemEnd(a)}).catch(function(z){g&&g(z);r.manager.itemError(a);r.manager.itemEnd(a)});r.manager.itemStart(a)}});Object.assign(fi.prototype,{moveTo:function(a,c){this.currentPath=new Gc;this.subPaths.push(this.currentPath);this.currentPath.moveTo(a,c)},lineTo:function(a,c){this.currentPath.lineTo(a,c)},quadraticCurveTo:function(a,c,e,g){this.currentPath.quadraticCurveTo(a,c,e,g)},bezierCurveTo:function(a,
c,e,g,r,v){this.currentPath.bezierCurveTo(a,c,e,g,r,v)},splineThru:function(a){this.currentPath.splineThru(a)},toShapes:function(a,c){function e(fa){for(var ra=[],pa=0,qa=fa.length;pa<qa;pa++){var ua=fa[pa],oa=new Cd;oa.curves=ua.curves;ra.push(oa)}return ra}function g(fa,ra){for(var pa=ra.length,qa=!1,ua=pa-1,oa=0;oa<pa;ua=oa++){var ta=ra[ua],Ba=ra[oa],Ta=Ba.x-ta.x,Ua=Ba.y-ta.y;if(Math.abs(Ua)>Number.EPSILON){if(0>Ua&&(ta=ra[oa],Ta=-Ta,Ba=ra[ua],Ua=-Ua),!(fa.y<ta.y||fa.y>Ba.y))if(fa.y===ta.y){if(fa.x===
ta.x)return!0}else{ua=Ua*(fa.x-ta.x)-Ta*(fa.y-ta.y);if(0===ua)return!0;0>ua||(qa=!qa)}}else if(fa.y===ta.y&&(Ba.x<=fa.x&&fa.x<=ta.x||ta.x<=fa.x&&fa.x<=Ba.x))return!0}return qa}var r=ed.isClockWise,v=this.subPaths;if(0===v.length)return[];if(!0===c)return e(v);c=[];if(1===v.length){var z=v[0];var E=new Cd;E.curves=z.curves;c.push(E);return c}var F=!r(v[0].getPoints());F=a?!F:F;E=[];var J=[],P=[],R=0;J[R]=void 0;P[R]=[];for(var S=0,V=v.length;S<V;S++){z=v[S];var W=z.getPoints();var ha=r(W);(ha=a?!ha:
ha)?(!F&&J[R]&&R++,J[R]={s:new Cd,p:W},J[R].s.curves=z.curves,F&&R++,P[R]=[]):P[R].push({h:z,p:W[0]})}if(!J[0])return e(v);if(1<J.length){S=!1;a=[];r=0;for(v=J.length;r<v;r++)E[r]=[];r=0;for(v=J.length;r<v;r++)for(z=P[r],ha=0;ha<z.length;ha++){F=z[ha];R=!0;for(W=0;W<J.length;W++)g(F.p,J[W].p)&&(r!==W&&a.push({froms:r,tos:W,hole:ha}),R?(R=!1,E[W].push(F)):S=!0);R&&E[r].push(F)}0<a.length&&(S||(P=E))}S=0;for(r=J.length;S<r;S++)for(E=J[S].s,c.push(E),a=P[S],v=0,z=a.length;v<z;v++)E.holes.push(a[v].h);
return c}});Object.assign(gi.prototype,{isFont:!0,generateShapes:function(a,c){void 0===c&&(c=100);var e=[];a=vm(a,c,this.data);c=0;for(var g=a.length;c<g;c++)Array.prototype.push.apply(e,a[c].toShapes());return e}});hi.prototype=Object.assign(Object.create(Db.prototype),{constructor:hi,load:function(a,c,e,g){var r=this,v=new uc(this.manager);v.setPath(this.path);v.load(a,function(z){try{var E=JSON.parse(z)}catch(F){console.warn("THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead."),
E=JSON.parse(z.substring(65,z.length-2))}z=r.parse(E);c&&c(z)},e,g)},parse:function(a){return new gi(a)}});var yh,mi={getContext:function(){void 0===yh&&(yh=new (window.AudioContext||window.webkitAudioContext));return yh},setContext:function(a){yh=a}};$g.prototype=Object.assign(Object.create(Db.prototype),{constructor:$g,load:function(a,c,e,g){var r=new uc(this.manager);r.setResponseType("arraybuffer");r.setPath(this.path);r.load(a,function(v){v=v.slice(0);mi.getContext().decodeAudioData(v,function(z){c(z)})},
e,g)}});Object.assign(ah.prototype,{isSphericalHarmonics3:!0,set:function(a){for(var c=0;9>c;c++)this.coefficients[c].copy(a[c]);return this},zero:function(){for(var a=0;9>a;a++)this.coefficients[a].set(0,0,0);return this},getAt:function(a,c){var e=a.x,g=a.y;a=a.z;var r=this.coefficients;c.copy(r[0]).multiplyScalar(.282095);c.addScale(r[1],.488603*g);c.addScale(r[2],.488603*a);c.addScale(r[3],.488603*e);c.addScale(r[4],1.092548*e*g);c.addScale(r[5],1.092548*g*a);c.addScale(r[6],.315392*(3*a*a-1));
c.addScale(r[7],1.092548*e*a);c.addScale(r[8],.546274*(e*e-g*g));return c},getIrradianceAt:function(a,c){var e=a.x,g=a.y;a=a.z;var r=this.coefficients;c.copy(r[0]).multiplyScalar(.886227);c.addScale(r[1],1.023328*g);c.addScale(r[2],1.023328*a);c.addScale(r[3],1.023328*e);c.addScale(r[4],.858086*e*g);c.addScale(r[5],.858086*g*a);c.addScale(r[6],.743125*a*a-.247708);c.addScale(r[7],.858086*e*a);c.addScale(r[8],.429043*(e*e-g*g));return c},add:function(a){for(var c=0;9>c;c++)this.coefficients[c].add(a.coefficients[c]);
return this},scale:function(a){for(var c=0;9>c;c++)this.coefficients[c].multiplyScalar(a);return this},lerp:function(a,c){for(var e=0;9>e;e++)this.coefficients[e].lerp(a.coefficients[e],c);return this},equals:function(a){for(var c=0;9>c;c++)if(!this.coefficients[c].equals(a.coefficients[c]))return!1;return!0},copy:function(a){return this.set(a.coefficients)},clone:function(){return(new this.constructor).copy(this)},fromArray:function(a,c){void 0===c&&(c=0);for(var e=this.coefficients,g=0;9>g;g++)e[g].fromArray(a,
c+3*g);return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);for(var e=this.coefficients,g=0;9>g;g++)e[g].toArray(a,c+3*g);return a}});Object.assign(ah,{getBasisAt:function(a,c){var e=a.x,g=a.y;a=a.z;c[0]=.282095;c[1]=.488603*g;c[2]=.488603*a;c[3]=.488603*e;c[4]=1.092548*e*g;c[5]=1.092548*g*a;c[6]=.315392*(3*a*a-1);c[7]=1.092548*e*a;c[8]=.546274*(e*e-g*g)}});Hc.prototype=Object.assign(Object.create(Jb.prototype),{constructor:Hc,isLightProbe:!0,copy:function(a){Jb.prototype.copy.call(this,
a);this.sh.copy(a.sh);this.intensity=a.intensity;return this},toJSON:function(a){return Jb.prototype.toJSON.call(this,a)}});ii.prototype=Object.assign(Object.create(Hc.prototype),{constructor:ii,isHemisphereLightProbe:!0,copy:function(a){Hc.prototype.copy.call(this,a);return this},toJSON:function(a){return Hc.prototype.toJSON.call(this,a)}});ji.prototype=Object.assign(Object.create(Hc.prototype),{constructor:ji,isAmbientLightProbe:!0,copy:function(a){Hc.prototype.copy.call(this,a);return this},toJSON:function(a){return Hc.prototype.toJSON.call(this,
a)}});var uk=new q,vk=new q;Object.assign(Qj.prototype,{update:function(a){var c=this._cache;if(c.focus!==a.focus||c.fov!==a.fov||c.aspect!==a.aspect*this.aspect||c.near!==a.near||c.far!==a.far||c.zoom!==a.zoom||c.eyeSep!==this.eyeSep){c.focus=a.focus;c.fov=a.fov;c.aspect=a.aspect*this.aspect;c.near=a.near;c.far=a.far;c.zoom=a.zoom;c.eyeSep=this.eyeSep;var e=a.projectionMatrix.clone(),g=c.eyeSep/2,r=g*c.near/c.focus,v=c.near*Math.tan(hb.DEG2RAD*c.fov*.5)/c.zoom;vk.elements[12]=-g;uk.elements[12]=
g;g=-v*c.aspect+r;var z=v*c.aspect+r;e.elements[0]=2*c.near/(z-g);e.elements[8]=(z+g)/(z-g);this.cameraL.projectionMatrix.copy(e);g=-v*c.aspect-r;z=v*c.aspect-r;e.elements[0]=2*c.near/(z-g);e.elements[8]=(z+g)/(z-g);this.cameraR.projectionMatrix.copy(e)}this.cameraL.matrixWorld.copy(a.matrixWorld).multiply(vk);this.cameraR.matrixWorld.copy(a.matrixWorld).multiply(uk)}});Object.assign(ki.prototype,{start:function(){this.oldTime=this.startTime=("undefined"===typeof performance?Date:performance).now();
this.elapsedTime=0;this.running=!0},stop:function(){this.getElapsedTime();this.autoStart=this.running=!1},getElapsedTime:function(){this.getDelta();return this.elapsedTime},getDelta:function(){var a=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){var c=("undefined"===typeof performance?Date:performance).now();a=(c-this.oldTime)/1E3;this.oldTime=c;this.elapsedTime+=a}return a}});var je=new k,wk=new h,Mm=new k,ke=new k;li.prototype=Object.assign(Object.create(A.prototype),
{constructor:li,getInput:function(){return this.gain},removeFilter:function(){null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null);return this},getFilter:function(){return this.filter},setFilter:function(a){null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination);this.filter=a;this.gain.connect(this.filter);
this.filter.connect(this.context.destination);return this},getMasterVolume:function(){return this.gain.gain.value},setMasterVolume:function(a){this.gain.gain.setTargetAtTime(a,this.context.currentTime,.01);return this},updateMatrixWorld:function(a){A.prototype.updateMatrixWorld.call(this,a);a=this.context.listener;var c=this.up;this.timeDelta=this._clock.getDelta();this.matrixWorld.decompose(je,wk,Mm);ke.set(0,0,-1).applyQuaternion(wk);if(a.positionX){var e=this.context.currentTime+this.timeDelta;
a.positionX.linearRampToValueAtTime(je.x,e);a.positionY.linearRampToValueAtTime(je.y,e);a.positionZ.linearRampToValueAtTime(je.z,e);a.forwardX.linearRampToValueAtTime(ke.x,e);a.forwardY.linearRampToValueAtTime(ke.y,e);a.forwardZ.linearRampToValueAtTime(ke.z,e);a.upX.linearRampToValueAtTime(c.x,e);a.upY.linearRampToValueAtTime(c.y,e);a.upZ.linearRampToValueAtTime(c.z,e)}else a.setPosition(je.x,je.y,je.z),a.setOrientation(ke.x,ke.y,ke.z,c.x,c.y,c.z)}});We.prototype=Object.assign(Object.create(A.prototype),
{constructor:We,getOutput:function(){return this.gain},setNodeSource:function(a){this.hasPlaybackControl=!1;this.sourceType="audioNode";this.source=a;this.connect();return this},setMediaElementSource:function(a){this.hasPlaybackControl=!1;this.sourceType="mediaNode";this.source=this.context.createMediaElementSource(a);this.connect();return this},setBuffer:function(a){this.buffer=a;this.sourceType="buffer";this.autoplay&&this.play();return this},play:function(){if(!0===this.isPlaying)console.warn("THREE.Audio: Audio is already playing.");
else if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");else{var a=this.context.createBufferSource();a.buffer=this.buffer;a.loop=this.loop;a.onended=this.onEnded.bind(this);this.startTime=this.context.currentTime;a.start(this.startTime,this.offset,this.duration);this.isPlaying=!0;this.source=a;this.setDetune(this.detune);this.setPlaybackRate(this.playbackRate);return this.connect()}},pause:function(){if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");
else return!0===this.isPlaying&&(this.source.stop(),this.source.onended=null,this.offset+=(this.context.currentTime-this.startTime)*this.playbackRate,this.isPlaying=!1),this},stop:function(){if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");else return this.source.stop(),this.source.onended=null,this.offset=0,this.isPlaying=!1,this},connect:function(){if(0<this.filters.length){this.source.connect(this.filters[0]);for(var a=1,c=this.filters.length;a<c;a++)this.filters[a-
1].connect(this.filters[a]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this},disconnect:function(){if(0<this.filters.length){this.source.disconnect(this.filters[0]);for(var a=1,c=this.filters.length;a<c;a++)this.filters[a-1].disconnect(this.filters[a]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this},getFilters:function(){return this.filters},setFilters:function(a){a||
(a=[]);!0===this.isPlaying?(this.disconnect(),this.filters=a,this.connect()):this.filters=a;return this},setDetune:function(a){this.detune=a;if(void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this},getDetune:function(){return this.detune},getFilter:function(){return this.getFilters()[0]},setFilter:function(a){return this.setFilters(a?[a]:[])},setPlaybackRate:function(a){if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");
else return this.playbackRate=a,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this},getPlaybackRate:function(){return this.playbackRate},onEnded:function(){this.isPlaying=!1},getLoop:function(){return!1===this.hasPlaybackControl?(console.warn("THREE.Audio: this Audio has no playback control."),!1):this.loop},setLoop:function(a){if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");else return this.loop=
a,!0===this.isPlaying&&(this.source.loop=this.loop),this},getVolume:function(){return this.gain.gain.value},setVolume:function(a){this.gain.gain.setTargetAtTime(a,this.context.currentTime,.01);return this}});var le=new k,xk=new h,Nm=new k,me=new k;ni.prototype=Object.assign(Object.create(We.prototype),{constructor:ni,getOutput:function(){return this.panner},getRefDistance:function(){return this.panner.refDistance},setRefDistance:function(a){this.panner.refDistance=a;return this},getRolloffFactor:function(){return this.panner.rolloffFactor},
setRolloffFactor:function(a){this.panner.rolloffFactor=a;return this},getDistanceModel:function(){return this.panner.distanceModel},setDistanceModel:function(a){this.panner.distanceModel=a;return this},getMaxDistance:function(){return this.panner.maxDistance},setMaxDistance:function(a){this.panner.maxDistance=a;return this},setDirectionalCone:function(a,c,e){this.panner.coneInnerAngle=a;this.panner.coneOuterAngle=c;this.panner.coneOuterGain=e;return this},updateMatrixWorld:function(a){A.prototype.updateMatrixWorld.call(this,
a);if(!0!==this.hasPlaybackControl||!1!==this.isPlaying)if(this.matrixWorld.decompose(le,xk,Nm),me.set(0,0,1).applyQuaternion(xk),a=this.panner,a.positionX){var c=this.context.currentTime+this.listener.timeDelta;a.positionX.linearRampToValueAtTime(le.x,c);a.positionY.linearRampToValueAtTime(le.y,c);a.positionZ.linearRampToValueAtTime(le.z,c);a.orientationX.linearRampToValueAtTime(me.x,c);a.orientationY.linearRampToValueAtTime(me.y,c);a.orientationZ.linearRampToValueAtTime(me.z,c)}else a.setPosition(le.x,
le.y,le.z),a.setOrientation(me.x,me.y,me.z)}});Object.assign(oi.prototype,{getFrequencyData:function(){this.analyser.getByteFrequencyData(this.data);return this.data},getAverageFrequency:function(){for(var a=0,c=this.getFrequencyData(),e=0;e<c.length;e++)a+=c[e];return a/c.length}});Object.assign(pi.prototype,{accumulate:function(a,c){var e=this.buffer,g=this.valueSize;a=a*g+g;var r=this.cumulativeWeight;if(0===r){for(r=0;r!==g;++r)e[a+r]=e[r];r=c}else r+=c,this._mixBufferRegion(e,a,0,c/r,g);this.cumulativeWeight=
r},apply:function(a){var c=this.valueSize,e=this.buffer;a=a*c+c;var g=this.cumulativeWeight,r=this.binding;this.cumulativeWeight=0;1>g&&this._mixBufferRegion(e,a,3*c,1-g,c);g=c;for(var v=c+c;g!==v;++g)if(e[g]!==e[g+c]){r.setValue(e,a);break}},saveOriginalState:function(){var a=this.buffer,c=this.valueSize,e=3*c;this.binding.getValue(a,e);for(var g=c;g!==e;++g)a[g]=a[e+g%c];this.cumulativeWeight=0},restoreOriginalState:function(){this.binding.setValue(this.buffer,3*this.valueSize)},_select:function(a,
c,e,g,r){if(.5<=g)for(g=0;g!==r;++g)a[c+g]=a[e+g]},_slerp:function(a,c,e,g){h.slerpFlat(a,c,a,c,a,e,g)},_lerp:function(a,c,e,g,r){for(var v=1-g,z=0;z!==r;++z){var E=c+z;a[E]=a[E]*v+a[e+z]*g}}});var Om=/[\[\]\.:\/]/g,Pm="[^"+"\\[\\]\\.:\\/".replace("\\.","")+"]",Qm=/((?:WC+[\/:])*)/.source.replace("WC","[^\\[\\]\\.:\\/]"),Rm=/(WCOD+)?/.source.replace("WCOD",Pm),Sm=/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC","[^\\[\\]\\.:\\/]"),Tm=/\.(WC+)(?:\[(.+)\])?/.source.replace("WC","[^\\[\\]\\.:\\/]"),Um=
new RegExp("^"+Qm+Rm+Sm+Tm+"$"),Vm=["material","materials","bones"];Object.assign(Rj.prototype,{getValue:function(a,c){this.bind();var e=this._bindings[this._targetGroup.nCachedObjects_];void 0!==e&&e.getValue(a,c)},setValue:function(a,c){for(var e=this._bindings,g=this._targetGroup.nCachedObjects_,r=e.length;g!==r;++g)e[g].setValue(a,c)},bind:function(){for(var a=this._bindings,c=this._targetGroup.nCachedObjects_,e=a.length;c!==e;++c)a[c].bind()},unbind:function(){for(var a=this._bindings,c=this._targetGroup.nCachedObjects_,
e=a.length;c!==e;++c)a[c].unbind()}});Object.assign($b,{Composite:Rj,create:function(a,c,e){return a&&a.isAnimationObjectGroup?new $b.Composite(a,c,e):new $b(a,c,e)},sanitizeNodeName:function(a){return a.replace(/\s/g,"_").replace(Om,"")},parseTrackName:function(a){var c=Um.exec(a);if(!c)throw Error("PropertyBinding: Cannot parse trackName: "+a);c={nodeName:c[2],objectName:c[3],objectIndex:c[4],propertyName:c[5],propertyIndex:c[6]};var e=c.nodeName&&c.nodeName.lastIndexOf(".");if(void 0!==e&&-1!==
e){var g=c.nodeName.substring(e+1);-1!==Vm.indexOf(g)&&(c.nodeName=c.nodeName.substring(0,e),c.objectName=g)}if(null===c.propertyName||0===c.propertyName.length)throw Error("PropertyBinding: can not parse propertyName from trackName: "+a);return c},findNode:function(a,c){if(!c||""===c||"root"===c||"."===c||-1===c||c===a.name||c===a.uuid)return a;if(a.skeleton){var e=a.skeleton.getBoneByName(c);if(void 0!==e)return e}if(a.children){var g=function(r){for(var v=0;v<r.length;v++){var z=r[v];if(z.name===
c||z.uuid===c)return z;if(z=g(z.children))return z}return null};if(a=g(a.children))return a}return null}});Object.assign($b.prototype,{_getValue_unavailable:function(){},_setValue_unavailable:function(){},BindingType:{Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},Versioning:{None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},GetterByBindingType:[function(a,c){a[c]=this.node[this.propertyName]},function(a,c){for(var e=this.resolvedProperty,g=0,r=e.length;g!==r;++g)a[c++]=e[g]},function(a,c){a[c]=
this.resolvedProperty[this.propertyIndex]},function(a,c){this.resolvedProperty.toArray(a,c)}],SetterByBindingTypeAndVersioning:[[function(a,c){this.targetObject[this.propertyName]=a[c]},function(a,c){this.targetObject[this.propertyName]=a[c];this.targetObject.needsUpdate=!0},function(a,c){this.targetObject[this.propertyName]=a[c];this.targetObject.matrixWorldNeedsUpdate=!0}],[function(a,c){for(var e=this.resolvedProperty,g=0,r=e.length;g!==r;++g)e[g]=a[c++]},function(a,c){for(var e=this.resolvedProperty,
g=0,r=e.length;g!==r;++g)e[g]=a[c++];this.targetObject.needsUpdate=!0},function(a,c){for(var e=this.resolvedProperty,g=0,r=e.length;g!==r;++g)e[g]=a[c++];this.targetObject.matrixWorldNeedsUpdate=!0}],[function(a,c){this.resolvedProperty[this.propertyIndex]=a[c]},function(a,c){this.resolvedProperty[this.propertyIndex]=a[c];this.targetObject.needsUpdate=!0},function(a,c){this.resolvedProperty[this.propertyIndex]=a[c];this.targetObject.matrixWorldNeedsUpdate=!0}],[function(a,c){this.resolvedProperty.fromArray(a,
c)},function(a,c){this.resolvedProperty.fromArray(a,c);this.targetObject.needsUpdate=!0},function(a,c){this.resolvedProperty.fromArray(a,c);this.targetObject.matrixWorldNeedsUpdate=!0}]],getValue:function(a,c){this.bind();this.getValue(a,c)},setValue:function(a,c){this.bind();this.setValue(a,c)},bind:function(){var a=this.node,c=this.parsedPath,e=c.objectName,g=c.propertyName,r=c.propertyIndex;a||(this.node=a=$b.findNode(this.rootNode,c.nodeName)||this.rootNode);this.getValue=this._getValue_unavailable;
this.setValue=this._setValue_unavailable;if(a){if(e){var v=c.objectIndex;switch(e){case "materials":if(!a.material){console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);return}if(!a.material.materials){console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);return}a=a.material.materials;break;case "bones":if(!a.skeleton){console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",
this);return}a=a.skeleton.bones;for(e=0;e<a.length;e++)if(a[e].name===v){v=e;break}break;default:if(void 0===a[e]){console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);return}a=a[e]}if(void 0!==v){if(void 0===a[v]){console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,a);return}a=a[v]}}v=a[g];if(void 0===v)console.error("THREE.PropertyBinding: Trying to update property for track: "+c.nodeName+"."+g+" but it wasn't found.",
a);else{c=this.Versioning.None;this.targetObject=a;void 0!==a.needsUpdate?c=this.Versioning.NeedsUpdate:void 0!==a.matrixWorldNeedsUpdate&&(c=this.Versioning.MatrixWorldNeedsUpdate);e=this.BindingType.Direct;if(void 0!==r){if("morphTargetInfluences"===g){if(!a.geometry){console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);return}if(a.geometry.isBufferGeometry){if(!a.geometry.morphAttributes){console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",
this);return}for(e=0;e<this.node.geometry.morphAttributes.position.length;e++)if(a.geometry.morphAttributes.position[e].name===r){r=e;break}}else{if(!a.geometry.morphTargets){console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphTargets.",this);return}for(e=0;e<this.node.geometry.morphTargets.length;e++)if(a.geometry.morphTargets[e].name===r){r=e;break}}}e=this.BindingType.ArrayElement;this.resolvedProperty=v;this.propertyIndex=r}else void 0!==
v.fromArray&&void 0!==v.toArray?(e=this.BindingType.HasFromToArray,this.resolvedProperty=v):Array.isArray(v)?(e=this.BindingType.EntireArray,this.resolvedProperty=v):this.propertyName=g;this.getValue=this.GetterByBindingType[e];this.setValue=this.SetterByBindingTypeAndVersioning[e][c]}}else console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.")},unbind:function(){this.node=null;this.getValue=this._getValue_unbound;this.setValue=this._setValue_unbound}});
Object.assign($b.prototype,{_getValue_unbound:$b.prototype.getValue,_setValue_unbound:$b.prototype.setValue});Object.assign(Sj.prototype,{isAnimationObjectGroup:!0,add:function(){for(var a=this._objects,c=a.length,e=this.nCachedObjects_,g=this._indicesByUUID,r=this._paths,v=this._parsedPaths,z=this._bindings,E=z.length,F=void 0,J=0,P=arguments.length;J!==P;++J){var R=arguments[J],S=R.uuid,V=g[S];if(void 0===V){V=c++;g[S]=V;a.push(R);S=0;for(var W=E;S!==W;++S)z[S].push(new $b(R,r[S],v[S]))}else if(V<
e){F=a[V];var ha=--e;W=a[ha];g[W.uuid]=V;a[V]=W;g[S]=ha;a[ha]=R;S=0;for(W=E;S!==W;++S){var fa=z[S],ra=fa[V];fa[V]=fa[ha];void 0===ra&&(ra=new $b(R,r[S],v[S]));fa[ha]=ra}}else a[V]!==F&&console.error("THREE.AnimationObjectGroup: Different objects with the same UUID detected. Clean the caches or recreate your infrastructure when reloading scenes.")}this.nCachedObjects_=e},remove:function(){for(var a=this._objects,c=this.nCachedObjects_,e=this._indicesByUUID,g=this._bindings,r=g.length,v=0,z=arguments.length;v!==
z;++v){var E=arguments[v],F=E.uuid,J=e[F];if(void 0!==J&&J>=c){var P=c++,R=a[P];e[R.uuid]=J;a[J]=R;e[F]=P;a[P]=E;E=0;for(F=r;E!==F;++E){R=g[E];var S=R[J];R[J]=R[P];R[P]=S}}}this.nCachedObjects_=c},uncache:function(){for(var a=this._objects,c=a.length,e=this.nCachedObjects_,g=this._indicesByUUID,r=this._bindings,v=r.length,z=0,E=arguments.length;z!==E;++z){var F=arguments[z].uuid,J=g[F];if(void 0!==J)if(delete g[F],J<e){F=--e;var P=a[F],R=--c,S=a[R];g[P.uuid]=J;a[J]=P;g[S.uuid]=F;a[F]=S;a.pop();P=
0;for(S=v;P!==S;++P){var V=r[P],W=V[R];V[J]=V[F];V[F]=W;V.pop()}}else for(R=--c,S=a[R],g[S.uuid]=J,a[J]=S,a.pop(),P=0,S=v;P!==S;++P)V=r[P],V[J]=V[R],V.pop()}this.nCachedObjects_=e},subscribe_:function(a,c){var e=this._bindingsIndicesByPath,g=e[a],r=this._bindings;if(void 0!==g)return r[g];var v=this._paths,z=this._parsedPaths,E=this._objects,F=this.nCachedObjects_,J=Array(E.length);g=r.length;e[a]=g;v.push(a);z.push(c);r.push(J);e=F;for(g=E.length;e!==g;++e)J[e]=new $b(E[e],a,c);return J},unsubscribe_:function(a){var c=
this._bindingsIndicesByPath,e=c[a];if(void 0!==e){var g=this._paths,r=this._parsedPaths,v=this._bindings,z=v.length-1,E=v[z];c[a[z]]=e;v[e]=E;v.pop();r[e]=r[z];r.pop();g[e]=g[z];g.pop()}}});Object.assign(Tj.prototype,{play:function(){this._mixer._activateAction(this);return this},stop:function(){this._mixer._deactivateAction(this);return this.reset()},reset:function(){this.paused=!1;this.enabled=!0;this.time=0;this._loopCount=-1;this._startTime=null;return this.stopFading().stopWarping()},isRunning:function(){return this.enabled&&
!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)},isScheduled:function(){return this._mixer._isActiveAction(this)},startAt:function(a){this._startTime=a;return this},setLoop:function(a,c){this.loop=a;this.repetitions=c;return this},setEffectiveWeight:function(a){this.weight=a;this._effectiveWeight=this.enabled?a:0;return this.stopFading()},getEffectiveWeight:function(){return this._effectiveWeight},fadeIn:function(a){return this._scheduleFading(a,0,1)},fadeOut:function(a){return this._scheduleFading(a,
1,0)},crossFadeFrom:function(a,c,e){a.fadeOut(c);this.fadeIn(c);if(e){e=this._clip.duration;var g=a._clip.duration,r=e/g;a.warp(1,g/e,c);this.warp(r,1,c)}return this},crossFadeTo:function(a,c,e){return a.crossFadeFrom(this,c,e)},stopFading:function(){var a=this._weightInterpolant;null!==a&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(a));return this},setEffectiveTimeScale:function(a){this.timeScale=a;this._effectiveTimeScale=this.paused?0:a;return this.stopWarping()},getEffectiveTimeScale:function(){return this._effectiveTimeScale},
setDuration:function(a){this.timeScale=this._clip.duration/a;return this.stopWarping()},syncWith:function(a){this.time=a.time;this.timeScale=a.timeScale;return this.stopWarping()},halt:function(a){return this.warp(this._effectiveTimeScale,0,a)},warp:function(a,c,e){var g=this._mixer,r=g.time,v=this._timeScaleInterpolant,z=this.timeScale;null===v&&(this._timeScaleInterpolant=v=g._lendControlInterpolant());g=v.parameterPositions;v=v.sampleValues;g[0]=r;g[1]=r+e;v[0]=a/z;v[1]=c/z;return this},stopWarping:function(){var a=
this._timeScaleInterpolant;null!==a&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(a));return this},getMixer:function(){return this._mixer},getClip:function(){return this._clip},getRoot:function(){return this._localRoot||this._mixer._root},_update:function(a,c,e,g){if(this.enabled){var r=this._startTime;if(null!==r){c=(a-r)*e;if(0>c||0===e)return;this._startTime=null;c*=e}c*=this._updateTimeScale(a);e=this._updateTime(c);a=this._updateWeight(a);if(0<a){c=this._interpolants;
r=this._propertyBindings;for(var v=0,z=c.length;v!==z;++v)c[v].evaluate(e),r[v].accumulate(g,a)}}else this._updateWeight(a)},_updateWeight:function(a){var c=0;if(this.enabled){c=this.weight;var e=this._weightInterpolant;if(null!==e){var g=e.evaluate(a)[0];c*=g;a>e.parameterPositions[1]&&(this.stopFading(),0===g&&(this.enabled=!1))}}return this._effectiveWeight=c},_updateTimeScale:function(a){var c=0;if(!this.paused){c=this.timeScale;var e=this._timeScaleInterpolant;if(null!==e){var g=e.evaluate(a)[0];
c*=g;a>e.parameterPositions[1]&&(this.stopWarping(),0===c?this.paused=!0:this.timeScale=c)}}return this._effectiveTimeScale=c},_updateTime:function(a){var c=this.time+a,e=this._clip.duration,g=this.loop,r=this._loopCount,v=2202===g;if(0===a)return-1===r?c:v&&1===(r&1)?e-c:c;if(2200===g)a:{if(-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1)),c>=e)c=e;else if(0>c)c=0;else{this.time=c;break a}this.clampWhenFinished?this.paused=!0:this.enabled=!1;this.time=c;this._mixer.dispatchEvent({type:"finished",
action:this,direction:0>a?-1:1})}else{-1===r&&(0<=a?(r=0,this._setEndings(!0,0===this.repetitions,v)):this._setEndings(0===this.repetitions,!0,v));if(c>=e||0>c){g=Math.floor(c/e);c-=e*g;r+=Math.abs(g);var z=this.repetitions-r;0>=z?(this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=c=0<a?e:0,this._mixer.dispatchEvent({type:"finished",action:this,direction:0<a?1:-1})):(1===z?(a=0>a,this._setEndings(a,!a,v)):this._setEndings(!1,!1,v),this._loopCount=r,this.time=c,this._mixer.dispatchEvent({type:"loop",
action:this,loopDelta:g}))}else this.time=c;if(v&&1===(r&1))return e-c}return c},_setEndings:function(a,c,e){var g=this._interpolantSettings;e?(g.endingStart=2401,g.endingEnd=2401):(g.endingStart=a?this.zeroSlopeAtStart?2401:2400:2402,g.endingEnd=c?this.zeroSlopeAtEnd?2401:2400:2402)},_scheduleFading:function(a,c,e){var g=this._mixer,r=g.time,v=this._weightInterpolant;null===v&&(this._weightInterpolant=v=g._lendControlInterpolant());g=v.parameterPositions;v=v.sampleValues;g[0]=r;v[0]=c;g[1]=r+a;v[1]=
e;return this}});qi.prototype=Object.assign(Object.create(d.prototype),{constructor:qi,_bindAction:function(a,c){var e=a._localRoot||this._root,g=a._clip.tracks,r=g.length,v=a._propertyBindings;a=a._interpolants;var z=e.uuid,E=this._bindingsByRootAndName,F=E[z];void 0===F&&(F={},E[z]=F);for(E=0;E!==r;++E){var J=g[E],P=J.name,R=F[P];if(void 0===R){R=v[E];if(void 0!==R){null===R._cacheIndex&&(++R.referenceCount,this._addInactiveBinding(R,z,P));continue}R=new pi($b.create(e,P,c&&c._propertyBindings[E].binding.parsedPath),
J.ValueTypeName,J.getValueSize());++R.referenceCount;this._addInactiveBinding(R,z,P)}v[E]=R;a[E].resultBuffer=R.buffer}},_activateAction:function(a){if(!this._isActiveAction(a)){if(null===a._cacheIndex){var c=(a._localRoot||this._root).uuid,e=a._clip.uuid,g=this._actionsByClip[e];this._bindAction(a,g&&g.knownActions[0]);this._addInactiveAction(a,e,c)}c=a._propertyBindings;e=0;for(g=c.length;e!==g;++e){var r=c[e];0===r.useCount++&&(this._lendBinding(r),r.saveOriginalState())}this._lendAction(a)}},
_deactivateAction:function(a){if(this._isActiveAction(a)){for(var c=a._propertyBindings,e=0,g=c.length;e!==g;++e){var r=c[e];0===--r.useCount&&(r.restoreOriginalState(),this._takeBackBinding(r))}this._takeBackAction(a)}},_initMemoryManager:function(){this._actions=[];this._nActiveActions=0;this._actionsByClip={};this._bindings=[];this._nActiveBindings=0;this._bindingsByRootAndName={};this._controlInterpolants=[];this._nActiveControlInterpolants=0;var a=this;this.stats={actions:{get total(){return a._actions.length},
get inUse(){return a._nActiveActions}},bindings:{get total(){return a._bindings.length},get inUse(){return a._nActiveBindings}},controlInterpolants:{get total(){return a._controlInterpolants.length},get inUse(){return a._nActiveControlInterpolants}}}},_isActiveAction:function(a){a=a._cacheIndex;return null!==a&&a<this._nActiveActions},_addInactiveAction:function(a,c,e){var g=this._actions,r=this._actionsByClip,v=r[c];void 0===v?(v={knownActions:[a],actionByRoot:{}},a._byClipCacheIndex=0,r[c]=v):(c=
v.knownActions,a._byClipCacheIndex=c.length,c.push(a));a._cacheIndex=g.length;g.push(a);v.actionByRoot[e]=a},_removeInactiveAction:function(a){var c=this._actions,e=c[c.length-1],g=a._cacheIndex;e._cacheIndex=g;c[g]=e;c.pop();a._cacheIndex=null;c=a._clip.uuid;e=this._actionsByClip;g=e[c];var r=g.knownActions,v=r[r.length-1],z=a._byClipCacheIndex;v._byClipCacheIndex=z;r[z]=v;r.pop();a._byClipCacheIndex=null;delete g.actionByRoot[(a._localRoot||this._root).uuid];0===r.length&&delete e[c];this._removeInactiveBindingsForAction(a)},
_removeInactiveBindingsForAction:function(a){a=a._propertyBindings;for(var c=0,e=a.length;c!==e;++c){var g=a[c];0===--g.referenceCount&&this._removeInactiveBinding(g)}},_lendAction:function(a){var c=this._actions,e=a._cacheIndex,g=this._nActiveActions++,r=c[g];a._cacheIndex=g;c[g]=a;r._cacheIndex=e;c[e]=r},_takeBackAction:function(a){var c=this._actions,e=a._cacheIndex,g=--this._nActiveActions,r=c[g];a._cacheIndex=g;c[g]=a;r._cacheIndex=e;c[e]=r},_addInactiveBinding:function(a,c,e){var g=this._bindingsByRootAndName,
r=g[c],v=this._bindings;void 0===r&&(r={},g[c]=r);r[e]=a;a._cacheIndex=v.length;v.push(a)},_removeInactiveBinding:function(a){var c=this._bindings,e=a.binding,g=e.rootNode.uuid;e=e.path;var r=this._bindingsByRootAndName,v=r[g],z=c[c.length-1];a=a._cacheIndex;z._cacheIndex=a;c[a]=z;c.pop();delete v[e];0===Object.keys(v).length&&delete r[g]},_lendBinding:function(a){var c=this._bindings,e=a._cacheIndex,g=this._nActiveBindings++,r=c[g];a._cacheIndex=g;c[g]=a;r._cacheIndex=e;c[e]=r},_takeBackBinding:function(a){var c=
this._bindings,e=a._cacheIndex,g=--this._nActiveBindings,r=c[g];a._cacheIndex=g;c[g]=a;r._cacheIndex=e;c[e]=r},_lendControlInterpolant:function(){var a=this._controlInterpolants,c=this._nActiveControlInterpolants++,e=a[c];void 0===e&&(e=new Yf(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),e.__cacheIndex=c,a[c]=e);return e},_takeBackControlInterpolant:function(a){var c=this._controlInterpolants,e=a.__cacheIndex,g=--this._nActiveControlInterpolants,r=c[g];a.__cacheIndex=
g;c[g]=a;r.__cacheIndex=e;c[e]=r},_controlInterpolantsResultBuffer:new Float32Array(1),clipAction:function(a,c){var e=c||this._root,g=e.uuid;e="string"===typeof a?tc.findByName(e,a):a;a=null!==e?e.uuid:a;var r=this._actionsByClip[a],v=null;if(void 0!==r){v=r.actionByRoot[g];if(void 0!==v)return v;v=r.knownActions[0];null===e&&(e=v._clip)}if(null===e)return null;c=new Tj(this,e,c);this._bindAction(c,v);this._addInactiveAction(c,a,g);return c},existingAction:function(a,c){var e=c||this._root;c=e.uuid;
e="string"===typeof a?tc.findByName(e,a):a;a=this._actionsByClip[e?e.uuid:a];return void 0!==a?a.actionByRoot[c]||null:null},stopAllAction:function(){for(var a=this._actions,c=this._nActiveActions,e=this._bindings,g=this._nActiveBindings,r=this._nActiveBindings=this._nActiveActions=0;r!==c;++r)a[r].reset();for(r=0;r!==g;++r)e[r].useCount=0;return this},update:function(a){a*=this.timeScale;for(var c=this._actions,e=this._nActiveActions,g=this.time+=a,r=Math.sign(a),v=this._accuIndex^=1,z=0;z!==e;++z)c[z]._update(g,
a,r,v);a=this._bindings;c=this._nActiveBindings;for(z=0;z!==c;++z)a[z].apply(v);return this},getRoot:function(){return this._root},uncacheClip:function(a){var c=this._actions;a=a.uuid;var e=this._actionsByClip,g=e[a];if(void 0!==g){g=g.knownActions;for(var r=0,v=g.length;r!==v;++r){var z=g[r];this._deactivateAction(z);var E=z._cacheIndex,F=c[c.length-1];z._cacheIndex=null;z._byClipCacheIndex=null;F._cacheIndex=E;c[E]=F;c.pop();this._removeInactiveBindingsForAction(z)}delete e[a]}},uncacheRoot:function(a){a=
a.uuid;var c=this._actionsByClip;for(g in c){var e=c[g].actionByRoot[a];void 0!==e&&(this._deactivateAction(e),this._removeInactiveAction(e))}var g=this._bindingsByRootAndName[a];if(void 0!==g)for(var r in g)a=g[r],a.restoreOriginalState(),this._removeInactiveBinding(a)},uncacheAction:function(a,c){a=this.existingAction(a,c);null!==a&&(this._deactivateAction(a),this._removeInactiveAction(a))}});bh.prototype.clone=function(){return new bh(void 0===this.value.clone?this.value:this.value.clone())};ri.prototype=
Object.assign(Object.create(Qd.prototype),{constructor:ri,isInstancedInterleavedBuffer:!0,copy:function(a){Qd.prototype.copy.call(this,a);this.meshPerAttribute=a.meshPerAttribute;return this}});Object.assign(Uj.prototype,{linePrecision:1,set:function(a,c){this.ray.set(a,c)},setFromCamera:function(a,c){c&&c.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(c.matrixWorld),this.ray.direction.set(a.x,a.y,.5).unproject(c).sub(this.ray.origin).normalize(),this.camera=c):c&&c.isOrthographicCamera?
(this.ray.origin.set(a.x,a.y,(c.near+c.far)/(c.near-c.far)).unproject(c),this.ray.direction.set(0,0,-1).transformDirection(c.matrixWorld),this.camera=c):console.error("THREE.Raycaster: Unsupported camera type.")},intersectObject:function(a,c,e){e=e||[];si(a,this,e,c);e.sort(Vj);return e},intersectObjects:function(a,c,e){e=e||[];if(!1===Array.isArray(a))return console.warn("THREE.Raycaster.intersectObjects: objects is not an Array."),e;for(var g=0,r=a.length;g<r;g++)si(a[g],this,e,c);e.sort(Vj);return e}});
Object.assign(Wj.prototype,{set:function(a,c,e){this.radius=a;this.phi=c;this.theta=e;return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.radius=a.radius;this.phi=a.phi;this.theta=a.theta;return this},makeSafe:function(){this.phi=Math.max(1E-6,Math.min(Math.PI-1E-6,this.phi));return this},setFromVector3:function(a){return this.setFromCartesianCoords(a.x,a.y,a.z)},setFromCartesianCoords:function(a,c,e){this.radius=Math.sqrt(a*a+c*c+e*e);0===this.radius?this.phi=
this.theta=0:(this.theta=Math.atan2(a,e),this.phi=Math.acos(hb.clamp(c/this.radius,-1,1)));return this}});Object.assign(Xj.prototype,{set:function(a,c,e){this.radius=a;this.theta=c;this.y=e;return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.radius=a.radius;this.theta=a.theta;this.y=a.y;return this},setFromVector3:function(a){return this.setFromCartesianCoords(a.x,a.y,a.z)},setFromCartesianCoords:function(a,c,e){this.radius=Math.sqrt(a*a+e*e);this.theta=Math.atan2(a,
e);this.y=c;return this}});var yk=new f;Object.assign(ti.prototype,{set:function(a,c){this.min.copy(a);this.max.copy(c);return this},setFromPoints:function(a){this.makeEmpty();for(var c=0,e=a.length;c<e;c++)this.expandByPoint(a[c]);return this},setFromCenterAndSize:function(a,c){c=yk.copy(c).multiplyScalar(.5);this.min.copy(a).sub(c);this.max.copy(a).add(c);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.min.copy(a.min);this.max.copy(a.max);return this},
makeEmpty:function(){this.min.x=this.min.y=Infinity;this.max.x=this.max.y=-Infinity;return this},isEmpty:function(){return this.max.x<this.min.x||this.max.y<this.min.y},getCenter:function(a){void 0===a&&(console.warn("THREE.Box2: .getCenter() target is now required"),a=new f);return this.isEmpty()?a.set(0,0):a.addVectors(this.min,this.max).multiplyScalar(.5)},getSize:function(a){void 0===a&&(console.warn("THREE.Box2: .getSize() target is now required"),a=new f);return this.isEmpty()?a.set(0,0):a.subVectors(this.max,
this.min)},expandByPoint:function(a){this.min.min(a);this.max.max(a);return this},expandByVector:function(a){this.min.sub(a);this.max.add(a);return this},expandByScalar:function(a){this.min.addScalar(-a);this.max.addScalar(a);return this},containsPoint:function(a){return a.x<this.min.x||a.x>this.max.x||a.y<this.min.y||a.y>this.max.y?!1:!0},containsBox:function(a){return this.min.x<=a.min.x&&a.max.x<=this.max.x&&this.min.y<=a.min.y&&a.max.y<=this.max.y},getParameter:function(a,c){void 0===c&&(console.warn("THREE.Box2: .getParameter() target is now required"),
c=new f);return c.set((a.x-this.min.x)/(this.max.x-this.min.x),(a.y-this.min.y)/(this.max.y-this.min.y))},intersectsBox:function(a){return a.max.x<this.min.x||a.min.x>this.max.x||a.max.y<this.min.y||a.min.y>this.max.y?!1:!0},clampPoint:function(a,c){void 0===c&&(console.warn("THREE.Box2: .clampPoint() target is now required"),c=new f);return c.copy(a).clamp(this.min,this.max)},distanceToPoint:function(a){return yk.copy(a).clamp(this.min,this.max).sub(a).length()},intersect:function(a){this.min.max(a.min);
this.max.min(a.max);return this},union:function(a){this.min.min(a.min);this.max.max(a.max);return this},translate:function(a){this.min.add(a);this.max.add(a);return this},equals:function(a){return a.min.equals(this.min)&&a.max.equals(this.max)}});var zk=new k,zh=new k;Object.assign(ui.prototype,{set:function(a,c){this.start.copy(a);this.end.copy(c);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.start.copy(a.start);this.end.copy(a.end);return this},getCenter:function(a){void 0===
a&&(console.warn("THREE.Line3: .getCenter() target is now required"),a=new k);return a.addVectors(this.start,this.end).multiplyScalar(.5)},delta:function(a){void 0===a&&(console.warn("THREE.Line3: .delta() target is now required"),a=new k);return a.subVectors(this.end,this.start)},distanceSq:function(){return this.start.distanceToSquared(this.end)},distance:function(){return this.start.distanceTo(this.end)},at:function(a,c){void 0===c&&(console.warn("THREE.Line3: .at() target is now required"),c=
new k);return this.delta(c).multiplyScalar(a).add(this.start)},closestPointToPointParameter:function(a,c){zk.subVectors(a,this.start);zh.subVectors(this.end,this.start);a=zh.dot(zk)/zh.dot(zh);c&&(a=hb.clamp(a,0,1));return a},closestPointToPoint:function(a,c,e){a=this.closestPointToPointParameter(a,c);void 0===e&&(console.warn("THREE.Line3: .closestPointToPoint() target is now required"),e=new k);return this.delta(e).multiplyScalar(a).add(this.start)},applyMatrix4:function(a){this.start.applyMatrix4(a);
this.end.applyMatrix4(a);return this},equals:function(a){return a.start.equals(this.start)&&a.end.equals(this.end)}});cg.prototype=Object.create(A.prototype);cg.prototype.constructor=cg;cg.prototype.isImmediateRenderObject=!0;var Zc=new k,od=new k,Ri=new t,Wm=["a","b","c"];dg.prototype=Object.create(Ib.prototype);dg.prototype.constructor=dg;dg.prototype.update=function(){this.object.updateMatrixWorld(!0);Ri.getNormalMatrix(this.object.matrixWorld);var a=this.object.matrixWorld,c=this.geometry.attributes.position,
e=this.object.geometry;if(e&&e.isGeometry)for(var g=e.vertices,r=e.faces,v=e=0,z=r.length;v<z;v++)for(var E=r[v],F=0,J=E.vertexNormals.length;F<J;F++){var P=E.vertexNormals[F];Zc.copy(g[E[Wm[F]]]).applyMatrix4(a);od.copy(P).applyMatrix3(Ri).normalize().multiplyScalar(this.size).add(Zc);c.setXYZ(e,Zc.x,Zc.y,Zc.z);e+=1;c.setXYZ(e,od.x,od.y,od.z);e+=1}else if(e&&e.isBufferGeometry)for(g=e.attributes.position,r=e.attributes.normal,F=e=0,J=g.count;F<J;F++)Zc.set(g.getX(F),g.getY(F),g.getZ(F)).applyMatrix4(a),
od.set(r.getX(F),r.getY(F),r.getZ(F)),od.applyMatrix3(Ri).normalize().multiplyScalar(this.size).add(Zc),c.setXYZ(e,Zc.x,Zc.y,Zc.z),e+=1,c.setXYZ(e,od.x,od.y,od.z),e+=1;c.needsUpdate=!0};var Ak=new k;Xe.prototype=Object.create(A.prototype);Xe.prototype.constructor=Xe;Xe.prototype.dispose=function(){this.cone.geometry.dispose();this.cone.material.dispose()};Xe.prototype.update=function(){this.light.updateMatrixWorld();var a=this.light.distance?this.light.distance:1E3,c=a*Math.tan(this.light.angle);
this.cone.scale.set(c,c,a);Ak.setFromMatrixPosition(this.light.target.matrixWorld);this.cone.lookAt(Ak);void 0!==this.color?this.cone.material.color.set(this.color):this.cone.material.color.copy(this.light.color)};var Id=new k,Ah=new q,Si=new q;Ye.prototype=Object.create(Ib.prototype);Ye.prototype.constructor=Ye;Ye.prototype.updateMatrixWorld=function(a){var c=this.bones,e=this.geometry,g=e.getAttribute("position");Si.getInverse(this.root.matrixWorld);for(var r=0,v=0;r<c.length;r++){var z=c[r];z.parent&&
z.parent.isBone&&(Ah.multiplyMatrices(Si,z.matrixWorld),Id.setFromMatrixPosition(Ah),g.setXYZ(v,Id.x,Id.y,Id.z),Ah.multiplyMatrices(Si,z.parent.matrixWorld),Id.setFromMatrixPosition(Ah),g.setXYZ(v+1,Id.x,Id.y,Id.z),v+=2)}e.getAttribute("position").needsUpdate=!0;A.prototype.updateMatrixWorld.call(this,a)};Ze.prototype=Object.create(xa.prototype);Ze.prototype.constructor=Ze;Ze.prototype.dispose=function(){this.geometry.dispose();this.material.dispose()};Ze.prototype.update=function(){void 0!==this.color?
this.material.color.set(this.color):this.material.color.copy(this.light.color)};$e.prototype=Object.create(Vb.prototype);$e.prototype.constructor=$e;$e.prototype.update=function(){this.scale.set(.5*this.light.width,.5*this.light.height,1);if(void 0!==this.color)this.material.color.set(this.color),this.children[0].material.color.set(this.color);else{this.material.color.copy(this.light.color).multiplyScalar(this.light.intensity);var a=this.material.color,c=Math.max(a.r,a.g,a.b);1<c&&a.multiplyScalar(1/
c);this.children[0].material.color.copy(this.material.color)}};$e.prototype.dispose=function(){this.geometry.dispose();this.material.dispose();this.children[0].geometry.dispose();this.children[0].material.dispose()};var Xm=new k,Bk=new I,Ck=new I;af.prototype=Object.create(A.prototype);af.prototype.constructor=af;af.prototype.dispose=function(){this.children[0].geometry.dispose();this.children[0].material.dispose()};af.prototype.update=function(){var a=this.children[0];if(void 0!==this.color)this.material.color.set(this.color);
else{var c=a.geometry.getAttribute("color");Bk.copy(this.light.color);Ck.copy(this.light.groundColor);for(var e=0,g=c.count;e<g;e++){var r=e<g/2?Bk:Ck;c.setXYZ(e,r.r,r.g,r.b)}c.needsUpdate=!0}a.lookAt(Xm.setFromMatrixPosition(this.light.matrixWorld).negate())};bf.prototype=Object.create(xa.prototype);bf.prototype.constructor=bf;bf.prototype.dispose=function(){this.geometry.dispose();this.material.dispose()};bf.prototype.onBeforeRender=function(){this.position.copy(this.lightProbe.position);this.scale.set(1,
1,1).multiplyScalar(this.size);this.material.uniforms.intensity.value=this.lightProbe.intensity};ch.prototype=Object.assign(Object.create(Ib.prototype),{constructor:ch,copy:function(a){Ib.prototype.copy.call(this,a);this.geometry.copy(a.geometry);this.material.copy(a.material);return this},clone:function(){return(new this.constructor).copy(this)}});dh.prototype=Object.create(Ib.prototype);dh.prototype.constructor=dh;cf.prototype=Object.create(Vb.prototype);cf.prototype.constructor=cf;cf.prototype.update=
function(){function a(W,ha,fa,ra){fa=(ha-W)/fa;V.setXYZ(F,0,0,0);J++;for(P=W;P<ha;P+=fa)R=F+J,V.setXYZ(R,Math.sin(P)*e,0,Math.cos(P)*e),V.setXYZ(R+1,Math.sin(Math.min(P+fa,ha))*e,0,Math.cos(Math.min(P+fa,ha))*e),V.setXYZ(R+2,0,0,0),J+=3;S.addGroup(F,J,ra);F+=J;J=0}var c=this.audio,e=this.range,g=this.divisionsInnerAngle,r=this.divisionsOuterAngle,v=hb.degToRad(c.panner.coneInnerAngle);c=hb.degToRad(c.panner.coneOuterAngle);var z=v/2,E=c/2,F=0,J=0,P,R,S=this.geometry,V=S.attributes.position;S.clearGroups();
a(-E,-z,r,0);a(-z,z,g,1);a(z,E,r,0);V.needsUpdate=!0;v===c&&(this.material[0].visible=!1)};cf.prototype.dispose=function(){this.geometry.dispose();this.material[0].dispose();this.material[1].dispose()};var qg=new k,Bh=new k,Dk=new t;eg.prototype=Object.create(Ib.prototype);eg.prototype.constructor=eg;eg.prototype.update=function(){this.object.updateMatrixWorld(!0);Dk.getNormalMatrix(this.object.matrixWorld);var a=this.object.matrixWorld,c=this.geometry.attributes.position,e=this.object.geometry,g=
e.vertices;e=e.faces;for(var r=0,v=0,z=e.length;v<z;v++){var E=e[v],F=E.normal;qg.copy(g[E.a]).add(g[E.b]).add(g[E.c]).divideScalar(3).applyMatrix4(a);Bh.copy(F).applyMatrix3(Dk).normalize().multiplyScalar(this.size).add(qg);c.setXYZ(r,qg.x,qg.y,qg.z);r+=1;c.setXYZ(r,Bh.x,Bh.y,Bh.z);r+=1}c.needsUpdate=!0};var Ek=new k,Ch=new k,Fk=new k;df.prototype=Object.create(A.prototype);df.prototype.constructor=df;df.prototype.dispose=function(){this.lightPlane.geometry.dispose();this.lightPlane.material.dispose();
this.targetLine.geometry.dispose();this.targetLine.material.dispose()};df.prototype.update=function(){Ek.setFromMatrixPosition(this.light.matrixWorld);Ch.setFromMatrixPosition(this.light.target.matrixWorld);Fk.subVectors(Ch,Ek);this.lightPlane.lookAt(Ch);void 0!==this.color?(this.lightPlane.material.color.set(this.color),this.targetLine.material.color.set(this.color)):(this.lightPlane.material.color.copy(this.light.color),this.targetLine.material.color.copy(this.light.color));this.targetLine.lookAt(Ch);
this.targetLine.scale.z=Fk.length()};var eh=new k,Ob=new zb;fg.prototype=Object.create(Ib.prototype);fg.prototype.constructor=fg;fg.prototype.update=function(){var a=this.geometry,c=this.pointMap;Ob.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse);Pb("c",c,a,Ob,0,0,-1);Pb("t",c,a,Ob,0,0,1);Pb("n1",c,a,Ob,-1,-1,-1);Pb("n2",c,a,Ob,1,-1,-1);Pb("n3",c,a,Ob,-1,1,-1);Pb("n4",c,a,Ob,1,1,-1);Pb("f1",c,a,Ob,-1,-1,1);Pb("f2",c,a,Ob,1,-1,1);Pb("f3",c,a,Ob,-1,1,1);Pb("f4",c,a,Ob,1,1,1);Pb("u1",
c,a,Ob,.7,1.1,-1);Pb("u2",c,a,Ob,-.7,1.1,-1);Pb("u3",c,a,Ob,0,2,-1);Pb("cf1",c,a,Ob,-1,0,1);Pb("cf2",c,a,Ob,1,0,1);Pb("cf3",c,a,Ob,0,-1,1);Pb("cf4",c,a,Ob,0,1,1);Pb("cn1",c,a,Ob,-1,0,-1);Pb("cn2",c,a,Ob,1,0,-1);Pb("cn3",c,a,Ob,0,-1,-1);Pb("cn4",c,a,Ob,0,1,-1);a.getAttribute("position").needsUpdate=!0};var Dh=new w;hd.prototype=Object.create(Ib.prototype);hd.prototype.constructor=hd;hd.prototype.update=function(a){void 0!==a&&console.warn("THREE.BoxHelper: .update() has no longer arguments.");void 0!==
this.object&&Dh.setFromObject(this.object);if(!Dh.isEmpty()){a=Dh.min;var c=Dh.max,e=this.geometry.attributes.position,g=e.array;g[0]=c.x;g[1]=c.y;g[2]=c.z;g[3]=a.x;g[4]=c.y;g[5]=c.z;g[6]=a.x;g[7]=a.y;g[8]=c.z;g[9]=c.x;g[10]=a.y;g[11]=c.z;g[12]=c.x;g[13]=c.y;g[14]=a.z;g[15]=a.x;g[16]=c.y;g[17]=a.z;g[18]=a.x;g[19]=a.y;g[20]=a.z;g[21]=c.x;g[22]=a.y;g[23]=a.z;e.needsUpdate=!0;this.geometry.computeBoundingSphere()}};hd.prototype.setFromObject=function(a){this.object=a;this.update();return this};hd.prototype.copy=
function(a){Ib.prototype.copy.call(this,a);this.object=a.object;return this};hd.prototype.clone=function(){return(new this.constructor).copy(this)};gg.prototype=Object.create(Ib.prototype);gg.prototype.constructor=gg;gg.prototype.updateMatrixWorld=function(a){var c=this.box;c.isEmpty()||(c.getCenter(this.position),c.getSize(this.scale),this.scale.multiplyScalar(.5),A.prototype.updateMatrixWorld.call(this,a))};hg.prototype=Object.create(Vb.prototype);hg.prototype.constructor=hg;hg.prototype.updateMatrixWorld=
function(a){var c=-this.plane.constant;1E-8>Math.abs(c)&&(c=1E-8);this.scale.set(.5*this.size,.5*this.size,c);this.children[0].material.side=0>c?1:0;this.lookAt(this.plane.normal);A.prototype.updateMatrixWorld.call(this,a)};var Gk=new k,fh,vi;id.prototype=Object.create(A.prototype);id.prototype.constructor=id;id.prototype.setDirection=function(a){.99999<a.y?this.quaternion.set(0,0,0,1):-.99999>a.y?this.quaternion.set(1,0,0,0):(Gk.set(a.z,0,-a.x).normalize(),this.quaternion.setFromAxisAngle(Gk,Math.acos(a.y)))};
id.prototype.setLength=function(a,c,e){void 0===c&&(c=.2*a);void 0===e&&(e=.2*c);this.line.scale.set(1,Math.max(0,a-c),1);this.line.updateMatrix();this.cone.scale.set(e,c,e);this.cone.position.y=a;this.cone.updateMatrix()};id.prototype.setColor=function(a){this.line.material.color.set(a);this.cone.material.color.set(a)};id.prototype.copy=function(a){A.prototype.copy.call(this,a,!1);this.line.copy(a.line);this.cone.copy(a.cone);return this};id.prototype.clone=function(){return(new this.constructor).copy(this)};
ig.prototype=Object.create(Ib.prototype);ig.prototype.constructor=ig;Za.create=function(a,c){console.log("THREE.Curve.create() has been deprecated");a.prototype=Object.create(Za.prototype);a.prototype.constructor=a;a.prototype.getPoint=c;return a};Object.assign(gd.prototype,{createPointsGeometry:function(a){console.warn("THREE.CurvePath: .createPointsGeometry() has been removed. Use new THREE.Geometry().setFromPoints( points ) instead.");a=this.getPoints(a);return this.createGeometry(a)},createSpacedPointsGeometry:function(a){console.warn("THREE.CurvePath: .createSpacedPointsGeometry() has been removed. Use new THREE.Geometry().setFromPoints( points ) instead.");
a=this.getSpacedPoints(a);return this.createGeometry(a)},createGeometry:function(a){console.warn("THREE.CurvePath: .createGeometry() has been removed. Use new THREE.Geometry().setFromPoints( points ) instead.");for(var c=new ya,e=0,g=a.length;e<g;e++){var r=a[e];c.vertices.push(new k(r.x,r.y,r.z||0))}return c}});Object.assign(Gc.prototype,{fromPoints:function(a){console.warn("THREE.Path: .fromPoints() has been renamed to .setFromPoints().");this.setFromPoints(a)}});Zj.prototype=Object.create(Zb.prototype);
ak.prototype=Object.create(Zb.prototype);wi.prototype=Object.create(Zb.prototype);Object.assign(wi.prototype,{initFromArray:function(){console.error("THREE.Spline: .initFromArray() has been removed.")},getControlPointsArray:function(){console.error("THREE.Spline: .getControlPointsArray() has been removed.")},reparametrizeByArcLength:function(){console.error("THREE.Spline: .reparametrizeByArcLength() has been removed.")}});ch.prototype.setColors=function(){console.error("THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.")};
Ye.prototype.update=function(){console.error("THREE.SkeletonHelper: update() no longer needs to be called.")};Object.assign(Db.prototype,{extractUrlBase:function(a){console.warn("THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead.");return Pi.extractUrlBase(a)}});Object.assign(Zg.prototype,{setTexturePath:function(a){console.warn("THREE.ObjectLoader: .setTexturePath() has been renamed to .setResourcePath().");return this.setResourcePath(a)}});Object.assign(ti.prototype,
{center:function(a){console.warn("THREE.Box2: .center() has been renamed to .getCenter().");return this.getCenter(a)},empty:function(){console.warn("THREE.Box2: .empty() has been renamed to .isEmpty().");return this.isEmpty()},isIntersectionBox:function(a){console.warn("THREE.Box2: .isIntersectionBox() has been renamed to .intersectsBox().");return this.intersectsBox(a)},size:function(a){console.warn("THREE.Box2: .size() has been renamed to .getSize().");return this.getSize(a)}});Object.assign(w.prototype,
{center:function(a){console.warn("THREE.Box3: .center() has been renamed to .getCenter().");return this.getCenter(a)},empty:function(){console.warn("THREE.Box3: .empty() has been renamed to .isEmpty().");return this.isEmpty()},isIntersectionBox:function(a){console.warn("THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox().");return this.intersectsBox(a)},isIntersectionSphere:function(a){console.warn("THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere().");return this.intersectsSphere(a)},
size:function(a){console.warn("THREE.Box3: .size() has been renamed to .getSize().");return this.getSize(a)}});ui.prototype.center=function(a){console.warn("THREE.Line3: .center() has been renamed to .getCenter().");return this.getCenter(a)};Object.assign(hb,{random16:function(){console.warn("THREE.Math: .random16() has been deprecated. Use Math.random() instead.");return Math.random()},nearestPowerOfTwo:function(a){console.warn("THREE.Math: .nearestPowerOfTwo() has been renamed to .floorPowerOfTwo().");
return hb.floorPowerOfTwo(a)},nextPowerOfTwo:function(a){console.warn("THREE.Math: .nextPowerOfTwo() has been renamed to .ceilPowerOfTwo().");return hb.ceilPowerOfTwo(a)}});Object.assign(t.prototype,{flattenToArrayOffset:function(a,c){console.warn("THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead.");return this.toArray(a,c)},multiplyVector3:function(a){console.warn("THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead.");return a.applyMatrix3(this)},
multiplyVector3Array:function(){console.error("THREE.Matrix3: .multiplyVector3Array() has been removed.")},applyToBuffer:function(a){console.warn("THREE.Matrix3: .applyToBuffer() has been removed. Use matrix.applyToBufferAttribute( attribute ) instead.");return this.applyToBufferAttribute(a)},applyToVector3Array:function(){console.error("THREE.Matrix3: .applyToVector3Array() has been removed.")}});Object.assign(q.prototype,{extractPosition:function(a){console.warn("THREE.Matrix4: .extractPosition() has been renamed to .copyPosition().");
return this.copyPosition(a)},flattenToArrayOffset:function(a,c){console.warn("THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead.");return this.toArray(a,c)},getPosition:function(){console.warn("THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead.");return(new k).setFromMatrixColumn(this,3)},setRotationFromQuaternion:function(a){console.warn("THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion().");
return this.makeRotationFromQuaternion(a)},multiplyToArray:function(){console.warn("THREE.Matrix4: .multiplyToArray() has been removed.")},multiplyVector3:function(a){console.warn("THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead.");return a.applyMatrix4(this)},multiplyVector4:function(a){console.warn("THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead.");return a.applyMatrix4(this)},multiplyVector3Array:function(){console.error("THREE.Matrix4: .multiplyVector3Array() has been removed.")},
rotateAxis:function(a){console.warn("THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead.");a.transformDirection(this)},crossVector:function(a){console.warn("THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead.");return a.applyMatrix4(this)},translate:function(){console.error("THREE.Matrix4: .translate() has been removed.")},rotateX:function(){console.error("THREE.Matrix4: .rotateX() has been removed.")},rotateY:function(){console.error("THREE.Matrix4: .rotateY() has been removed.")},
rotateZ:function(){console.error("THREE.Matrix4: .rotateZ() has been removed.")},rotateByAxis:function(){console.error("THREE.Matrix4: .rotateByAxis() has been removed.")},applyToBuffer:function(a){console.warn("THREE.Matrix4: .applyToBuffer() has been removed. Use matrix.applyToBufferAttribute( attribute ) instead.");return this.applyToBufferAttribute(a)},applyToVector3Array:function(){console.error("THREE.Matrix4: .applyToVector3Array() has been removed.")},makeFrustum:function(a,c,e,g,r,v){console.warn("THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead.");
return this.makePerspective(a,c,g,e,r,v)}});Hb.prototype.isIntersectionLine=function(a){console.warn("THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine().");return this.intersectsLine(a)};h.prototype.multiplyVector3=function(a){console.warn("THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead.");return a.applyQuaternion(this)};Object.assign(D.prototype,{isIntersectionBox:function(a){console.warn("THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox().");
return this.intersectsBox(a)},isIntersectionPlane:function(a){console.warn("THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane().");return this.intersectsPlane(a)},isIntersectionSphere:function(a){console.warn("THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere().");return this.intersectsSphere(a)}});Object.assign(B.prototype,{area:function(){console.warn("THREE.Triangle: .area() has been renamed to .getArea().");return this.getArea()},barycoordFromPoint:function(a,
c){console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord().");return this.getBarycoord(a,c)},midpoint:function(a){console.warn("THREE.Triangle: .midpoint() has been renamed to .getMidpoint().");return this.getMidpoint(a)},normal:function(a){console.warn("THREE.Triangle: .normal() has been renamed to .getNormal().");return this.getNormal(a)},plane:function(a){console.warn("THREE.Triangle: .plane() has been renamed to .getPlane().");return this.getPlane(a)}});Object.assign(B,
{barycoordFromPoint:function(a,c,e,g,r){console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord().");return B.getBarycoord(a,c,e,g,r)},normal:function(a,c,e,g){console.warn("THREE.Triangle: .normal() has been renamed to .getNormal().");return B.getNormal(a,c,e,g)}});Object.assign(Cd.prototype,{extractAllPoints:function(a){console.warn("THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead.");return this.extractPoints(a)},extrude:function(a){console.warn("THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead.");
return new Ud(this,a)},makeGeometry:function(a){console.warn("THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead.");return new Vd(this,a)}});Object.assign(f.prototype,{fromAttribute:function(a,c,e){console.warn("THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute().");return this.fromBufferAttribute(a,c,e)},distanceToManhattan:function(a){console.warn("THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo().");return this.manhattanDistanceTo(a)},
lengthManhattan:function(){console.warn("THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength().");return this.manhattanLength()}});Object.assign(k.prototype,{setEulerFromRotationMatrix:function(){console.error("THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.")},setEulerFromQuaternion:function(){console.error("THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.")},getPositionFromMatrix:function(a){console.warn("THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition().");
return this.setFromMatrixPosition(a)},getScaleFromMatrix:function(a){console.warn("THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale().");return this.setFromMatrixScale(a)},getColumnFromMatrix:function(a,c){console.warn("THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn().");return this.setFromMatrixColumn(c,a)},applyProjection:function(a){console.warn("THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead.");return this.applyMatrix4(a)},
fromAttribute:function(a,c,e){console.warn("THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute().");return this.fromBufferAttribute(a,c,e)},distanceToManhattan:function(a){console.warn("THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo().");return this.manhattanDistanceTo(a)},lengthManhattan:function(){console.warn("THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength().");return this.manhattanLength()}});Object.assign(p.prototype,
{fromAttribute:function(a,c,e){console.warn("THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute().");return this.fromBufferAttribute(a,c,e)},lengthManhattan:function(){console.warn("THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength().");return this.manhattanLength()}});Object.assign(ya.prototype,{computeTangents:function(){console.error("THREE.Geometry: .computeTangents() has been removed.")},computeLineDistances:function(){console.error("THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.")}});
Object.assign(A.prototype,{getChildByName:function(a){console.warn("THREE.Object3D: .getChildByName() has been renamed to .getObjectByName().");return this.getObjectByName(a)},renderDepth:function(){console.warn("THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.")},translate:function(a,c){console.warn("THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead.");return this.translateOnAxis(c,a)},getWorldRotation:function(){console.error("THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.")}});
Object.defineProperties(A.prototype,{eulerOrder:{get:function(){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order.");return this.rotation.order},set:function(a){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order.");this.rotation.order=a}},useQuaternion:{get:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")},set:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")}}});
Object.defineProperties(Af.prototype,{objects:{get:function(){console.warn("THREE.LOD: .objects has been renamed to .levels.");return this.levels}}});Object.defineProperty(Cg.prototype,"useVertexTexture",{get:function(){console.warn("THREE.Skeleton: useVertexTexture has been removed.")},set:function(){console.warn("THREE.Skeleton: useVertexTexture has been removed.")}});Bf.prototype.initBones=function(){console.error("THREE.SkinnedMesh: initBones() has been removed.")};Object.defineProperty(Za.prototype,
"__arcLengthDivisions",{get:function(){console.warn("THREE.Curve: .__arcLengthDivisions is now .arcLengthDivisions.");return this.arcLengthDivisions},set:function(a){console.warn("THREE.Curve: .__arcLengthDivisions is now .arcLengthDivisions.");this.arcLengthDivisions=a}});vb.prototype.setLens=function(a,c){console.warn("THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup.");void 0!==c&&(this.filmGauge=c);this.setFocalLength(a)};Object.defineProperties(Jb.prototype,
{onlyShadow:{set:function(){console.warn("THREE.Light: .onlyShadow has been removed.")}},shadowCameraFov:{set:function(a){console.warn("THREE.Light: .shadowCameraFov is now .shadow.camera.fov.");this.shadow.camera.fov=a}},shadowCameraLeft:{set:function(a){console.warn("THREE.Light: .shadowCameraLeft is now .shadow.camera.left.");this.shadow.camera.left=a}},shadowCameraRight:{set:function(a){console.warn("THREE.Light: .shadowCameraRight is now .shadow.camera.right.");this.shadow.camera.right=a}},shadowCameraTop:{set:function(a){console.warn("THREE.Light: .shadowCameraTop is now .shadow.camera.top.");
this.shadow.camera.top=a}},shadowCameraBottom:{set:function(a){console.warn("THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom.");this.shadow.camera.bottom=a}},shadowCameraNear:{set:function(a){console.warn("THREE.Light: .shadowCameraNear is now .shadow.camera.near.");this.shadow.camera.near=a}},shadowCameraFar:{set:function(a){console.warn("THREE.Light: .shadowCameraFar is now .shadow.camera.far.");this.shadow.camera.far=a}},shadowCameraVisible:{set:function(){console.warn("THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.")}},
shadowBias:{set:function(a){console.warn("THREE.Light: .shadowBias is now .shadow.bias.");this.shadow.bias=a}},shadowDarkness:{set:function(){console.warn("THREE.Light: .shadowDarkness has been removed.")}},shadowMapWidth:{set:function(a){console.warn("THREE.Light: .shadowMapWidth is now .shadow.mapSize.width.");this.shadow.mapSize.width=a}},shadowMapHeight:{set:function(a){console.warn("THREE.Light: .shadowMapHeight is now .shadow.mapSize.height.");this.shadow.mapSize.height=a}}});Object.defineProperties(Q.prototype,
{length:{get:function(){console.warn("THREE.BufferAttribute: .length has been deprecated. Use .count instead.");return this.array.length}},copyIndicesArray:function(){console.error("THREE.BufferAttribute: .copyIndicesArray() has been removed.")}});Object.assign(va.prototype,{addIndex:function(a){console.warn("THREE.BufferGeometry: .addIndex() has been renamed to .setIndex().");this.setIndex(a)},addDrawCall:function(a,c,e){void 0!==e&&console.warn("THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset.");
console.warn("THREE.BufferGeometry: .addDrawCall() is now .addGroup().");this.addGroup(a,c)},clearDrawCalls:function(){console.warn("THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups().");this.clearGroups()},computeTangents:function(){console.warn("THREE.BufferGeometry: .computeTangents() has been removed.")},computeOffsets:function(){console.warn("THREE.BufferGeometry: .computeOffsets() has been removed.")}});Object.defineProperties(va.prototype,{drawcalls:{get:function(){console.error("THREE.BufferGeometry: .drawcalls has been renamed to .groups.");
return this.groups}},offsets:{get:function(){console.warn("THREE.BufferGeometry: .offsets has been renamed to .groups.");return this.groups}}});Object.assign(Rc.prototype,{getArrays:function(){console.error("THREE.ExtrudeBufferGeometry: .getArrays() has been removed.")},addShapeList:function(){console.error("THREE.ExtrudeBufferGeometry: .addShapeList() has been removed.")},addShape:function(){console.error("THREE.ExtrudeBufferGeometry: .addShape() has been removed.")}});Object.defineProperties(bh.prototype,
{dynamic:{set:function(){console.warn("THREE.Uniform: .dynamic has been removed. Use object.onBeforeRender() instead.")}},onUpdate:{value:function(){console.warn("THREE.Uniform: .onUpdate() has been removed. Use object.onBeforeRender() instead.");return this}}});Object.defineProperties(M.prototype,{wrapAround:{get:function(){console.warn("THREE.Material: .wrapAround has been removed.")},set:function(){console.warn("THREE.Material: .wrapAround has been removed.")}},overdraw:{get:function(){console.warn("THREE.Material: .overdraw has been removed.")},
set:function(){console.warn("THREE.Material: .overdraw has been removed.")}},wrapRGB:{get:function(){console.warn("THREE.Material: .wrapRGB has been removed.");return new I}},shading:{get:function(){console.error("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.")},set:function(a){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.");this.flatShading=1===a}}});Object.defineProperties(Bc.prototype,{metal:{get:function(){console.warn("THREE.MeshPhongMaterial: .metal has been removed. Use THREE.MeshStandardMaterial instead.");
return!1},set:function(){console.warn("THREE.MeshPhongMaterial: .metal has been removed. Use THREE.MeshStandardMaterial instead")}}});Object.defineProperties(qb.prototype,{derivatives:{get:function(){console.warn("THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives.");return this.extensions.derivatives},set:function(a){console.warn("THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives.");this.extensions.derivatives=a}}});Object.assign(Oh.prototype,
{clearTarget:function(a,c,e,g){console.warn("THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead.");this.setRenderTarget(a);this.clear(c,e,g)},animate:function(a){console.warn("THREE.WebGLRenderer: .animate() is now .setAnimationLoop().");this.setAnimationLoop(a)},getCurrentRenderTarget:function(){console.warn("THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget().");return this.getRenderTarget()},getMaxAnisotropy:function(){console.warn("THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy().");
return this.capabilities.getMaxAnisotropy()},getPrecision:function(){console.warn("THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision.");return this.capabilities.precision},resetGLState:function(){console.warn("THREE.WebGLRenderer: .resetGLState() is now .state.reset().");return this.state.reset()},supportsFloatTextures:function(){console.warn("THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' ).");return this.extensions.get("OES_texture_float")},
supportsHalfFloatTextures:function(){console.warn("THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' ).");return this.extensions.get("OES_texture_half_float")},supportsStandardDerivatives:function(){console.warn("THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' ).");return this.extensions.get("OES_standard_derivatives")},supportsCompressedTextureS3TC:function(){console.warn("THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' ).");
return this.extensions.get("WEBGL_compressed_texture_s3tc")},supportsCompressedTexturePVRTC:function(){console.warn("THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' ).");return this.extensions.get("WEBGL_compressed_texture_pvrtc")},supportsBlendMinMax:function(){console.warn("THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' ).");return this.extensions.get("EXT_blend_minmax")},supportsVertexTextures:function(){console.warn("THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures.");
return this.capabilities.vertexTextures},supportsInstancedArrays:function(){console.warn("THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' ).");return this.extensions.get("ANGLE_instanced_arrays")},enableScissorTest:function(a){console.warn("THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest().");this.setScissorTest(a)},initMaterial:function(){console.warn("THREE.WebGLRenderer: .initMaterial() has been removed.")},addPrePlugin:function(){console.warn("THREE.WebGLRenderer: .addPrePlugin() has been removed.")},
addPostPlugin:function(){console.warn("THREE.WebGLRenderer: .addPostPlugin() has been removed.")},updateShadowMap:function(){console.warn("THREE.WebGLRenderer: .updateShadowMap() has been removed.")},setFaceCulling:function(){console.warn("THREE.WebGLRenderer: .setFaceCulling() has been removed.")},allocTextureUnit:function(){console.warn("THREE.WebGLRenderer: .allocTextureUnit() has been removed.")},setTexture:function(){console.warn("THREE.WebGLRenderer: .setTexture() has been removed.")},setTexture2D:function(){console.warn("THREE.WebGLRenderer: .setTexture2D() has been removed.")},
setTextureCube:function(){console.warn("THREE.WebGLRenderer: .setTextureCube() has been removed.")},getActiveMipMapLevel:function(){console.warn("THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel().");return this.getActiveMipmapLevel()}});Object.defineProperties(Oh.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(a){console.warn("THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled.");this.shadowMap.enabled=a}},shadowMapType:{get:function(){return this.shadowMap.type},
set:function(a){console.warn("THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type.");this.shadowMap.type=a}},shadowMapCullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")}},context:{get:function(){console.warn("THREE.WebGLRenderer: .context has been removed. Use .getContext() instead.");return this.getContext()}}});
Object.defineProperties(vj.prototype,{cullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")}},renderReverseSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")}},
renderSingleSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")}}});Object.defineProperties(Nb.prototype,{activeCubeFace:{set:function(){console.warn("THREE.WebGLRenderTargetCube: .activeCubeFace has been removed. It is now the second parameter of WebGLRenderer.setRenderTarget().")}},
activeMipMapLevel:{set:function(){console.warn("THREE.WebGLRenderTargetCube: .activeMipMapLevel has been removed. It is now the third parameter of WebGLRenderer.setRenderTarget().")}}});Object.defineProperties(m.prototype,{wrapS:{get:function(){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS.");return this.texture.wrapS},set:function(a){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS.");this.texture.wrapS=a}},wrapT:{get:function(){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT.");
return this.texture.wrapT},set:function(a){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT.");this.texture.wrapT=a}},magFilter:{get:function(){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter.");return this.texture.magFilter},set:function(a){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter.");this.texture.magFilter=a}},minFilter:{get:function(){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter.");return this.texture.minFilter},
set:function(a){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter.");this.texture.minFilter=a}},anisotropy:{get:function(){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy.");return this.texture.anisotropy},set:function(a){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy.");this.texture.anisotropy=a}},offset:{get:function(){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset.");return this.texture.offset},
set:function(a){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset.");this.texture.offset=a}},repeat:{get:function(){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat.");return this.texture.repeat},set:function(a){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat.");this.texture.repeat=a}},format:{get:function(){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format.");return this.texture.format},set:function(a){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format.");
this.texture.format=a}},type:{get:function(){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type.");return this.texture.type},set:function(a){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type.");this.texture.type=a}},generateMipmaps:{get:function(){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps.");return this.texture.generateMipmaps},set:function(a){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps.");
this.texture.generateMipmaps=a}}});Object.defineProperties(Nh.prototype,{standing:{set:function(){console.warn("THREE.WebVRManager: .standing has been removed.")}},userHeight:{set:function(){console.warn("THREE.WebVRManager: .userHeight has been removed.")}}});We.prototype.load=function(a){console.warn("THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.");var c=this;(new $g).load(a,function(e){c.setBuffer(e)});return this};oi.prototype.getData=function(){console.warn("THREE.AudioAnalyser: .getData() is now .getFrequencyData().");
return this.getFrequencyData()};Gb.prototype.updateCubeMap=function(a,c){console.warn("THREE.CubeCamera: .updateCubeMap() is now .update().");return this.update(a,c)};Dd.crossOrigin=void 0;Dd.loadTexture=function(a,c,e,g){console.warn("THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.");var r=new Mg;r.setCrossOrigin(this.crossOrigin);a=r.load(a,e,void 0,g);c&&(a.mapping=c);return a};Dd.loadTextureCube=function(a,c,e,g){console.warn("THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.");
var r=new Lg;r.setCrossOrigin(this.crossOrigin);a=r.load(a,e,void 0,g);c&&(a.mapping=c);return a};Dd.loadCompressedTexture=function(){console.error("THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.")};Dd.loadCompressedTextureCube=function(){console.error("THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.")};b.ACESFilmicToneMapping=5;b.AddEquation=100;b.AddOperation=2;b.AdditiveBlending=2;b.AlphaFormat=1021;b.AlwaysDepth=
1;b.AlwaysStencilFunc=519;b.AmbientLight=Tg;b.AmbientLightProbe=ji;b.AnimationClip=tc;b.AnimationLoader=ai;b.AnimationMixer=qi;b.AnimationObjectGroup=Sj;b.AnimationUtils=Tb;b.ArcCurve=Ve;b.ArrayCamera=vf;b.ArrowHelper=id;b.Audio=We;b.AudioAnalyser=oi;b.AudioContext=mi;b.AudioListener=li;b.AudioLoader=$g;b.AxesHelper=ig;b.AxisHelper=function(a){console.warn("THREE.AxisHelper has been renamed to THREE.AxesHelper.");return new ig(a)};b.BackSide=1;b.BasicDepthPacking=3200;b.BasicShadowMap=0;b.BinaryTextureLoader=
function(a){console.warn("THREE.BinaryTextureLoader has been renamed to THREE.DataTextureLoader.");return new Kg(a)};b.Bone=Uh;b.BooleanKeyframeTrack=Gg;b.BoundingBoxHelper=function(a,c){console.warn("THREE.BoundingBoxHelper has been deprecated. Creating a THREE.BoxHelper instead.");return new hd(a,c)};b.Box2=ti;b.Box3=w;b.Box3Helper=gg;b.BoxBufferGeometry=Xa;b.BoxGeometry=Sa;b.BoxHelper=hd;b.BufferAttribute=Q;b.BufferGeometry=va;b.BufferGeometryLoader=Yg;b.ByteType=1010;b.Cache=ie;b.Camera=zb;b.CameraHelper=
fg;b.CanvasRenderer=function(){console.error("THREE.CanvasRenderer has been removed")};b.CanvasTexture=Cf;b.CatmullRomCurve3=Zb;b.CineonToneMapping=4;b.CircleBufferGeometry=Qe;b.CircleGeometry=Xf;b.ClampToEdgeWrapping=1001;b.Clock=ki;b.ClosedSplineCurve3=Zj;b.Color=I;b.ColorKeyframeTrack=Hg;b.CompressedTexture=De;b.CompressedTextureLoader=bi;b.ConeBufferGeometry=Wf;b.ConeGeometry=Vf;b.CubeCamera=Gb;b.CubeGeometry=Sa;b.CubeReflectionMapping=301;b.CubeRefractionMapping=302;b.CubeTexture=cd;b.CubeTextureLoader=
Lg;b.CubeUVReflectionMapping=306;b.CubeUVRefractionMapping=307;b.CubicBezierCurve=Cc;b.CubicBezierCurve3=Tc;b.CubicInterpolant=Eg;b.CullFaceBack=1;b.CullFaceFront=2;b.CullFaceFrontBack=3;b.CullFaceNone=0;b.Curve=Za;b.CurvePath=gd;b.CustomBlending=5;b.CylinderBufferGeometry=fd;b.CylinderGeometry=Xd;b.Cylindrical=Xj;b.DataTexture=Ab;b.DataTexture2DArray=re;b.DataTexture3D=se;b.DataTextureLoader=Kg;b.DecrementStencilOp=7683;b.DecrementWrapStencilOp=34056;b.DefaultLoadingManager=Oj;b.DepthFormat=1026;
b.DepthStencilFormat=1027;b.DepthTexture=Df;b.DirectionalLight=Sg;b.DirectionalLightHelper=df;b.DirectionalLightShadow=Rg;b.DiscreteInterpolant=Fg;b.DodecahedronBufferGeometry=Ie;b.DodecahedronGeometry=Jf;b.DoubleSide=2;b.DstAlphaFactor=206;b.DstColorFactor=208;b.DynamicBufferAttribute=function(a,c){console.warn("THREE.DynamicBufferAttribute has been removed. Use new THREE.BufferAttribute().setDynamic( true ) instead.");return(new Q(a,c)).setDynamic(!0)};b.EdgesGeometry=Pe;b.EdgesHelper=function(a,
c){console.warn("THREE.EdgesHelper has been removed. Use THREE.EdgesGeometry instead.");return new Ib(new Pe(a.geometry),new Fb({color:void 0!==c?c:16777215}))};b.EllipseCurve=pc;b.EqualDepth=4;b.EqualStencilFunc=514;b.EquirectangularReflectionMapping=303;b.EquirectangularRefractionMapping=304;b.Euler=u;b.EventDispatcher=d;b.ExtrudeBufferGeometry=Rc;b.ExtrudeGeometry=Ud;b.Face3=K;b.Face4=function(a,c,e,g,r,v,z){console.warn("THREE.Face4 has been removed. A THREE.Face3 will be created instead.");return new K(a,
c,e,r,v,z)};b.FaceColors=1;b.FaceNormalsHelper=eg;b.FileLoader=uc;b.FlatShading=1;b.Float32Attribute=function(a,c){console.warn("THREE.Float32Attribute has been removed. Use new THREE.Float32BufferAttribute() instead.");return new ca(a,c)};b.Float32BufferAttribute=ca;b.Float64Attribute=function(a,c){console.warn("THREE.Float64Attribute has been removed. Use new THREE.Float64BufferAttribute() instead.");return new ka(a,c)};b.Float64BufferAttribute=ka;b.FloatType=1015;b.Fog=Ag;b.FogExp2=zg;b.Font=gi;
b.FontLoader=hi;b.FrontFaceDirectionCCW=1;b.FrontFaceDirectionCW=0;b.FrontSide=0;b.Frustum=ic;b.GammaEncoding=3007;b.Geometry=ya;b.GeometryUtils={merge:function(a,c,e){console.warn("THREE.GeometryUtils: .merge() has been moved to Geometry. Use geometry.merge( geometry2, matrix, materialIndexOffset ) instead.");if(c.isMesh){c.matrixAutoUpdate&&c.updateMatrix();var g=c.matrix;c=c.geometry}a.merge(c,g,e)},center:function(a){console.warn("THREE.GeometryUtils: .center() has been moved to Geometry. Use geometry.center() instead.");
return a.center()}};b.GreaterDepth=6;b.GreaterEqualDepth=5;b.GreaterEqualStencilFunc=518;b.GreaterStencilFunc=516;b.GridHelper=ch;b.Group=ue;b.HalfFloatType=1016;b.HemisphereLight=Ng;b.HemisphereLightHelper=af;b.HemisphereLightProbe=ii;b.IcosahedronBufferGeometry=He;b.IcosahedronGeometry=If;b.ImageBitmapLoader=ei;b.ImageLoader=Ue;b.ImageUtils=Dd;b.ImmediateRenderObject=cg;b.IncrementStencilOp=7682;b.IncrementWrapStencilOp=34055;b.InstancedBufferAttribute=Xg;b.InstancedBufferGeometry=Wg;b.InstancedInterleavedBuffer=
ri;b.Int16Attribute=function(a,c){console.warn("THREE.Int16Attribute has been removed. Use new THREE.Int16BufferAttribute() instead.");return new la(a,c)};b.Int16BufferAttribute=la;b.Int32Attribute=function(a,c){console.warn("THREE.Int32Attribute has been removed. Use new THREE.Int32BufferAttribute() instead.");return new ba(a,c)};b.Int32BufferAttribute=ba;b.Int8Attribute=function(a,c){console.warn("THREE.Int8Attribute has been removed. Use new THREE.Int8BufferAttribute() instead.");return new T(a,
c)};b.Int8BufferAttribute=T;b.IntType=1013;b.InterleavedBuffer=Qd;b.InterleavedBufferAttribute=xf;b.Interpolant=oc;b.InterpolateDiscrete=2300;b.InterpolateLinear=2301;b.InterpolateSmooth=2302;b.InvertStencilOp=5386;b.JSONLoader=function(){console.error("THREE.JSONLoader has been removed.")};b.KeepStencilOp=7680;b.KeyframeTrack=Xb;b.LOD=Af;b.LatheBufferGeometry=Oe;b.LatheGeometry=Uf;b.Layers=x;b.LensFlare=function(){console.error("THREE.LensFlare has been moved to /examples/js/objects/Lensflare.js")};
b.LessDepth=2;b.LessEqualDepth=3;b.LessEqualStencilFunc=515;b.LessStencilFunc=513;b.Light=Jb;b.LightProbe=Hc;b.LightProbeHelper=bf;b.LightShadow=Vc;b.Line=Vb;b.Line3=ui;b.LineBasicMaterial=Fb;b.LineCurve=kc;b.LineCurve3=Dc;b.LineDashedMaterial=de;b.LineLoop=Dg;b.LinePieces=1;b.LineSegments=Ib;b.LineStrip=0;b.LinearEncoding=3E3;b.LinearFilter=1006;b.LinearInterpolant=Yf;b.LinearMipMapLinearFilter=1008;b.LinearMipMapNearestFilter=1007;b.LinearMipmapLinearFilter=1008;b.LinearMipmapNearestFilter=1007;
b.LinearToneMapping=1;b.Loader=Db;b.LoaderUtils=Pi;b.LoadingManager=$h;b.LogLuvEncoding=3003;b.LoopOnce=2200;b.LoopPingPong=2202;b.LoopRepeat=2201;b.LuminanceAlphaFormat=1025;b.LuminanceFormat=1024;b.MOUSE={LEFT:0,MIDDLE:1,RIGHT:2,ROTATE:0,DOLLY:1,PAN:2};b.Material=M;b.MaterialLoader=Vg;b.Math=hb;b.Matrix3=t;b.Matrix4=q;b.MaxEquation=104;b.Mesh=xa;b.MeshBasicMaterial=L;b.MeshDepthMaterial=vd;b.MeshDistanceMaterial=wd;b.MeshFaceMaterial=function(a){console.warn("THREE.MeshFaceMaterial has been removed. Use an Array instead.");
return a};b.MeshLambertMaterial=be;b.MeshMatcapMaterial=ce;b.MeshNormalMaterial=ae;b.MeshPhongMaterial=Bc;b.MeshPhysicalMaterial=Zd;b.MeshStandardMaterial=Sc;b.MeshToonMaterial=$d;b.MinEquation=103;b.MirroredRepeatWrapping=1002;b.MixOperation=1;b.MultiMaterial=function(a){void 0===a&&(a=[]);console.warn("THREE.MultiMaterial has been removed. Use an Array instead.");a.isMultiMaterial=!0;a.materials=a;a.clone=function(){return a.slice()};return a};b.MultiplyBlending=4;b.MultiplyOperation=0;b.NearestFilter=
1003;b.NearestMipMapLinearFilter=1005;b.NearestMipMapNearestFilter=1004;b.NearestMipmapLinearFilter=1005;b.NearestMipmapNearestFilter=1004;b.NeverDepth=0;b.NeverStencilFunc=512;b.NoBlending=0;b.NoColors=0;b.NoToneMapping=0;b.NormalBlending=1;b.NotEqualDepth=7;b.NotEqualStencilFunc=517;b.NumberKeyframeTrack=Se;b.Object3D=A;b.ObjectLoader=Zg;b.ObjectSpaceNormalMap=1;b.OctahedronBufferGeometry=Rd;b.OctahedronGeometry=Hf;b.OneFactor=201;b.OneMinusDstAlphaFactor=207;b.OneMinusDstColorFactor=209;b.OneMinusSrcAlphaFactor=
205;b.OneMinusSrcColorFactor=203;b.OrthographicCamera=bg;b.PCFShadowMap=1;b.PCFSoftShadowMap=2;b.ParametricBufferGeometry=Fe;b.ParametricGeometry=Ef;b.Particle=function(a){console.warn("THREE.Particle has been renamed to THREE.Sprite.");return new yf(a)};b.ParticleBasicMaterial=function(a){console.warn("THREE.ParticleBasicMaterial has been renamed to THREE.PointsMaterial.");return new Ac(a)};b.ParticleSystem=function(a,c){console.warn("THREE.ParticleSystem has been renamed to THREE.Points.");return new Ce(a,
c)};b.ParticleSystemMaterial=function(a){console.warn("THREE.ParticleSystemMaterial has been renamed to THREE.PointsMaterial.");return new Ac(a)};b.Path=Gc;b.PerspectiveCamera=vb;b.Plane=Hb;b.PlaneBufferGeometry=Lc;b.PlaneGeometry=rd;b.PlaneHelper=hg;b.PointCloud=function(a,c){console.warn("THREE.PointCloud has been renamed to THREE.Points.");return new Ce(a,c)};b.PointCloudMaterial=function(a){console.warn("THREE.PointCloudMaterial has been renamed to THREE.PointsMaterial.");return new Ac(a)};b.PointLight=
Qg;b.PointLightHelper=Ze;b.Points=Ce;b.PointsMaterial=Ac;b.PolarGridHelper=dh;b.PolyhedronBufferGeometry=jc;b.PolyhedronGeometry=Ff;b.PositionalAudio=ni;b.PositionalAudioHelper=cf;b.PropertyBinding=$b;b.PropertyMixer=pi;b.QuadraticBezierCurve=Ec;b.QuadraticBezierCurve3=Uc;b.Quaternion=h;b.QuaternionKeyframeTrack=Zf;b.QuaternionLinearInterpolant=Ig;b.REVISION="108";b.RGBADepthPacking=3201;b.RGBAFormat=1023;b.RGBA_ASTC_10x10_Format=37819;b.RGBA_ASTC_10x5_Format=37816;b.RGBA_ASTC_10x6_Format=37817;b.RGBA_ASTC_10x8_Format=
37818;b.RGBA_ASTC_12x10_Format=37820;b.RGBA_ASTC_12x12_Format=37821;b.RGBA_ASTC_4x4_Format=37808;b.RGBA_ASTC_5x4_Format=37809;b.RGBA_ASTC_5x5_Format=37810;b.RGBA_ASTC_6x5_Format=37811;b.RGBA_ASTC_6x6_Format=37812;b.RGBA_ASTC_8x5_Format=37813;b.RGBA_ASTC_8x6_Format=37814;b.RGBA_ASTC_8x8_Format=37815;b.RGBA_PVRTC_2BPPV1_Format=35843;b.RGBA_PVRTC_4BPPV1_Format=35842;b.RGBA_S3TC_DXT1_Format=33777;b.RGBA_S3TC_DXT3_Format=33778;b.RGBA_S3TC_DXT5_Format=33779;b.RGBDEncoding=3006;b.RGBEEncoding=3002;b.RGBEFormat=
1023;b.RGBFormat=1022;b.RGBM16Encoding=3005;b.RGBM7Encoding=3004;b.RGB_ETC1_Format=36196;b.RGB_PVRTC_2BPPV1_Format=35841;b.RGB_PVRTC_4BPPV1_Format=35840;b.RGB_S3TC_DXT1_Format=33776;b.RawShaderMaterial=Re;b.Ray=D;b.Raycaster=Uj;b.RectAreaLight=Ug;b.RectAreaLightHelper=$e;b.RedFormat=1028;b.ReinhardToneMapping=2;b.RepeatWrapping=1E3;b.ReplaceStencilOp=7681;b.ReverseSubtractEquation=102;b.RingBufferGeometry=Ne;b.RingGeometry=Tf;b.Scene=y;b.SceneUtils={createMultiMaterialObject:function(){console.error("THREE.SceneUtils has been moved to /examples/js/utils/SceneUtils.js")},
detach:function(){console.error("THREE.SceneUtils has been moved to /examples/js/utils/SceneUtils.js")},attach:function(){console.error("THREE.SceneUtils has been moved to /examples/js/utils/SceneUtils.js")}};b.ShaderChunk=rb;b.ShaderLib=Mc;b.ShaderMaterial=qb;b.ShadowMaterial=Yd;b.Shape=Cd;b.ShapeBufferGeometry=Wd;b.ShapeGeometry=Vd;b.ShapePath=fi;b.ShapeUtils=ed;b.ShortType=1011;b.Skeleton=Cg;b.SkeletonHelper=Ye;b.SkinnedMesh=Bf;b.SmoothShading=2;b.Sphere=G;b.SphereBufferGeometry=Bd;b.SphereGeometry=
Sf;b.Spherical=Wj;b.SphericalHarmonics3=ah;b.SphericalReflectionMapping=305;b.Spline=wi;b.SplineCurve=Fc;b.SplineCurve3=ak;b.SpotLight=Pg;b.SpotLightHelper=Xe;b.SpotLightShadow=Og;b.Sprite=yf;b.SpriteMaterial=Ad;b.SrcAlphaFactor=204;b.SrcAlphaSaturateFactor=210;b.SrcColorFactor=202;b.StereoCamera=Qj;b.StringKeyframeTrack=Jg;b.SubtractEquation=101;b.SubtractiveBlending=3;b.TOUCH={ROTATE:0,PAN:1,DOLLY_PAN:2,DOLLY_ROTATE:3};b.TangentSpaceNormalMap=0;b.TetrahedronBufferGeometry=Ge;b.TetrahedronGeometry=
Gf;b.TextBufferGeometry=Me;b.TextGeometry=Rf;b.Texture=l;b.TextureLoader=Mg;b.TorusBufferGeometry=Ke;b.TorusGeometry=Mf;b.TorusKnotBufferGeometry=Je;b.TorusKnotGeometry=Lf;b.Triangle=B;b.TriangleFanDrawMode=2;b.TriangleStripDrawMode=1;b.TrianglesDrawMode=0;b.TubeBufferGeometry=Sd;b.TubeGeometry=Kf;b.UVMapping=300;b.Uint16Attribute=function(a,c){console.warn("THREE.Uint16Attribute has been removed. Use new THREE.Uint16BufferAttribute() instead.");return new Z(a,c)};b.Uint16BufferAttribute=Z;b.Uint32Attribute=
function(a,c){console.warn("THREE.Uint32Attribute has been removed. Use new THREE.Uint32BufferAttribute() instead.");return new ea(a,c)};b.Uint32BufferAttribute=ea;b.Uint8Attribute=function(a,c){console.warn("THREE.Uint8Attribute has been removed. Use new THREE.Uint8BufferAttribute() instead.");return new X(a,c)};b.Uint8BufferAttribute=X;b.Uint8ClampedAttribute=function(a,c){console.warn("THREE.Uint8ClampedAttribute has been removed. Use new THREE.Uint8ClampedBufferAttribute() instead.");return new aa(a,
c)};b.Uint8ClampedBufferAttribute=aa;b.Uncharted2ToneMapping=3;b.Uniform=bh;b.UniformsLib=Wa;b.UniformsUtils=Fm;b.UnsignedByteType=1009;b.UnsignedInt248Type=1020;b.UnsignedIntType=1014;b.UnsignedShort4444Type=1017;b.UnsignedShort5551Type=1018;b.UnsignedShort565Type=1019;b.UnsignedShortType=1012;b.VSMShadowMap=3;b.Vector2=f;b.Vector3=k;b.Vector4=p;b.VectorKeyframeTrack=Te;b.Vertex=function(a,c,e){console.warn("THREE.Vertex has been removed. Use THREE.Vector3 instead.");return new k(a,c,e)};b.VertexColors=
2;b.VertexNormalsHelper=dg;b.VideoTexture=Xh;b.WebGLMultisampleRenderTarget=n;b.WebGLRenderTarget=m;b.WebGLRenderTargetCube=Nb;b.WebGLRenderer=Oh;b.WebGLUtils=wj;b.WireframeGeometry=Ee;b.WireframeHelper=function(a,c){console.warn("THREE.WireframeHelper has been removed. Use THREE.WireframeGeometry instead.");return new Ib(new Ee(a.geometry),new Fb({color:void 0!==c?c:16777215}))};b.WrapAroundEnding=2402;b.XHRLoader=function(a){console.warn("THREE.XHRLoader has been renamed to THREE.FileLoader.");
return new uc(a)};b.ZeroCurvatureEnding=2400;b.ZeroFactor=200;b.ZeroSlopeEnding=2401;b.ZeroStencilOp=0;b.sRGBEncoding=3001;Object.defineProperty(b,"__esModule",{value:!0})});

//# sourceURL=build://tf-imports/OrbitControls.js
THREE.OrbitControls=function(b,d){function f(){return 2*Math.PI/60/60*Y.autoRotateSpeed}function h(){return Math.pow(.95,Y.zoomSpeed)}function k(sa){Xa.theta-=sa}function t(sa){Xa.phi-=sa}function l(sa){Y.object.isPerspectiveCamera?ub/=sa:Y.object.isOrthographicCamera?(Y.object.zoom=Math.max(Y.minZoom,Math.min(Y.maxZoom,Y.object.zoom*sa)),Y.object.updateProjectionMatrix(),qb=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),Y.enableZoom=!1)}function p(sa){Y.object.isPerspectiveCamera?
ub*=sa:Y.object.isOrthographicCamera?(Y.object.zoom=Math.max(Y.minZoom,Math.min(Y.maxZoom,Y.object.zoom/sa)),Y.object.updateProjectionMatrix(),qb=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),Y.enableZoom=!1)}function m(sa){zb.set(sa.clientX,sa.clientY)}function n(sa){ic.set(sa.clientX,sa.clientY)}function q(sa){Nb.set(sa.clientX,sa.clientY)}function u(sa){vb.set(sa.clientX,sa.clientY);Gb.subVectors(vb,zb).multiplyScalar(Y.rotateSpeed);sa=
Y.domElement===document?Y.domElement.body:Y.domElement;k(2*Math.PI*Gb.x/sa.clientHeight);t(2*Math.PI*Gb.y/sa.clientHeight);zb.copy(vb);Y.update()}function x(sa){bc.set(sa.clientX,sa.clientY);Od.subVectors(bc,ic);0<Od.y?l(h()):0>Od.y&&p(h());ic.copy(bc);Y.update()}function A(sa){Ab.set(sa.clientX,sa.clientY);Hb.subVectors(Ab,Nb).multiplyScalar(Y.panSpeed);sd(Hb.x,Hb.y);Nb.copy(Ab);Y.update()}function y(){}function w(sa){0>sa.deltaY?p(h()):0<sa.deltaY&&l(h());Y.update()}function C(sa){var Mb=!1;switch(sa.keyCode){case Y.keys.UP:sd(0,
Y.keyPanSpeed);Mb=!0;break;case Y.keys.BOTTOM:sd(0,-Y.keyPanSpeed);Mb=!0;break;case Y.keys.LEFT:sd(Y.keyPanSpeed,0);Mb=!0;break;case Y.keys.RIGHT:sd(-Y.keyPanSpeed,0),Mb=!0}Mb&&(sa.preventDefault(),Y.update())}function G(sa){1==sa.touches.length?zb.set(sa.touches[0].pageX,sa.touches[0].pageY):zb.set(.5*(sa.touches[0].pageX+sa.touches[1].pageX),.5*(sa.touches[0].pageY+sa.touches[1].pageY))}function D(sa){1==sa.touches.length?Nb.set(sa.touches[0].pageX,sa.touches[0].pageY):Nb.set(.5*(sa.touches[0].pageX+
sa.touches[1].pageX),.5*(sa.touches[0].pageY+sa.touches[1].pageY))}function B(sa){var Mb=sa.touches[0].pageX-sa.touches[1].pageX;sa=sa.touches[0].pageY-sa.touches[1].pageY;ic.set(0,Math.sqrt(Mb*Mb+sa*sa))}function I(sa){Y.enableZoom&&B(sa);Y.enablePan&&D(sa)}function N(sa){Y.enableZoom&&B(sa);Y.enableRotate&&G(sa)}function O(sa){1==sa.touches.length?vb.set(sa.touches[0].pageX,sa.touches[0].pageY):vb.set(.5*(sa.touches[0].pageX+sa.touches[1].pageX),.5*(sa.touches[0].pageY+sa.touches[1].pageY));Gb.subVectors(vb,
zb).multiplyScalar(Y.rotateSpeed);sa=Y.domElement===document?Y.domElement.body:Y.domElement;k(2*Math.PI*Gb.x/sa.clientHeight);t(2*Math.PI*Gb.y/sa.clientHeight);zb.copy(vb)}function H(sa){1==sa.touches.length?Ab.set(sa.touches[0].pageX,sa.touches[0].pageY):Ab.set(.5*(sa.touches[0].pageX+sa.touches[1].pageX),.5*(sa.touches[0].pageY+sa.touches[1].pageY));Hb.subVectors(Ab,Nb).multiplyScalar(Y.panSpeed);sd(Hb.x,Hb.y);Nb.copy(Ab)}function K(sa){var Mb=sa.touches[0].pageX-sa.touches[1].pageX;sa=sa.touches[0].pageY-
sa.touches[1].pageY;bc.set(0,Math.sqrt(Mb*Mb+sa*sa));Od.set(0,Math.pow(bc.y/ic.y,Y.zoomSpeed));l(Od.y);ic.copy(bc)}function M(sa){Y.enableZoom&&K(sa);Y.enablePan&&H(sa)}function L(sa){Y.enableZoom&&K(sa);Y.enableRotate&&O(sa)}function Q(){}function T(sa){if(!1!==Y.enabled){sa.preventDefault();Y.domElement.focus?Y.domElement.focus():window.focus();switch(sa.button){case 0:switch(Y.mouseButtons.LEFT){case THREE.MOUSE.ROTATE:if(sa.ctrlKey||sa.metaKey||sa.shiftKey){if(!1===Y.enablePan)return;q(sa);Fa=
Aa.PAN}else{if(!1===Y.enableRotate)return;m(sa);Fa=Aa.ROTATE}break;case THREE.MOUSE.PAN:if(sa.ctrlKey||sa.metaKey||sa.shiftKey){if(!1===Y.enableRotate)return;m(sa);Fa=Aa.ROTATE}else{if(!1===Y.enablePan)return;q(sa);Fa=Aa.PAN}break;default:Fa=Aa.NONE}break;case 1:switch(Y.mouseButtons.MIDDLE){case THREE.MOUSE.DOLLY:if(!1===Y.enableZoom)return;n(sa);Fa=Aa.DOLLY;break;default:Fa=Aa.NONE}break;case 2:switch(Y.mouseButtons.RIGHT){case THREE.MOUSE.ROTATE:if(!1===Y.enableRotate)return;m(sa);Fa=Aa.ROTATE;
break;case THREE.MOUSE.PAN:if(!1===Y.enablePan)return;q(sa);Fa=Aa.PAN;break;default:Fa=Aa.NONE}}Fa!==Aa.NONE&&(document.addEventListener("mousemove",X,!1),document.addEventListener("mouseup",aa,!1),Y.dispatchEvent(va))}}function X(sa){if(!1!==Y.enabled)switch(sa.preventDefault(),Fa){case Aa.ROTATE:if(!1===Y.enableRotate)break;u(sa);break;case Aa.DOLLY:if(!1===Y.enableZoom)break;x(sa);break;case Aa.PAN:!1!==Y.enablePan&&A(sa)}}function aa(sa){!1!==Y.enabled&&(y(sa),document.removeEventListener("mousemove",
X,!1),document.removeEventListener("mouseup",aa,!1),Y.dispatchEvent(xa),Fa=Aa.NONE)}function la(sa){!1===Y.enabled||!1===Y.enableZoom||Fa!==Aa.NONE&&Fa!==Aa.ROTATE||(sa.preventDefault(),sa.stopPropagation(),Y.dispatchEvent(va),w(sa),Y.dispatchEvent(xa))}function Z(sa){!1!==Y.enabled&&!1!==Y.enableKeys&&!1!==Y.enablePan&&C(sa)}function ba(sa){if(!1!==Y.enabled){sa.preventDefault();switch(sa.touches.length){case 1:switch(Y.touches.ONE){case THREE.TOUCH.ROTATE:if(!1===Y.enableRotate)return;G(sa);Fa=
Aa.TOUCH_ROTATE;break;case THREE.TOUCH.PAN:if(!1===Y.enablePan)return;D(sa);Fa=Aa.TOUCH_PAN;break;default:Fa=Aa.NONE}break;case 2:switch(Y.touches.TWO){case THREE.TOUCH.DOLLY_PAN:if(!1===Y.enableZoom&&!1===Y.enablePan)return;I(sa);Fa=Aa.TOUCH_DOLLY_PAN;break;case THREE.TOUCH.DOLLY_ROTATE:if(!1===Y.enableZoom&&!1===Y.enableRotate)return;N(sa);Fa=Aa.TOUCH_DOLLY_ROTATE;break;default:Fa=Aa.NONE}break;default:Fa=Aa.NONE}Fa!==Aa.NONE&&Y.dispatchEvent(va)}}function ea(sa){if(!1!==Y.enabled)switch(sa.preventDefault(),
sa.stopPropagation(),Fa){case Aa.TOUCH_ROTATE:if(!1===Y.enableRotate)break;O(sa);Y.update();break;case Aa.TOUCH_PAN:if(!1===Y.enablePan)break;H(sa);Y.update();break;case Aa.TOUCH_DOLLY_PAN:if(!1===Y.enableZoom&&!1===Y.enablePan)break;M(sa);Y.update();break;case Aa.TOUCH_DOLLY_ROTATE:if(!1===Y.enableZoom&&!1===Y.enableRotate)break;L(sa);Y.update();break;default:Fa=Aa.NONE}}function ca(sa){!1!==Y.enabled&&(Q(sa),Y.dispatchEvent(xa),Fa=Aa.NONE)}function ka(sa){!1!==Y.enabled&&sa.preventDefault()}this.object=
b;this.domElement=void 0!==d?d:document;this.enabled=!0;this.target=new THREE.Vector3;this.minDistance=0;this.maxDistance=Infinity;this.minZoom=0;this.maxZoom=Infinity;this.minPolarAngle=0;this.maxPolarAngle=Math.PI;this.minAzimuthAngle=-Infinity;this.maxAzimuthAngle=Infinity;this.enableDamping=!1;this.dampingFactor=.05;this.enableZoom=!0;this.zoomSpeed=1;this.enableRotate=!0;this.rotateSpeed=1;this.enablePan=!0;this.panSpeed=1;this.screenSpacePanning=!1;this.keyPanSpeed=7;this.autoRotate=!1;this.autoRotateSpeed=
2;this.enableKeys=!0;this.keys={LEFT:37,UP:38,RIGHT:39,BOTTOM:40};this.mouseButtons={LEFT:THREE.MOUSE.ROTATE,MIDDLE:THREE.MOUSE.DOLLY,RIGHT:THREE.MOUSE.PAN};this.touches={ONE:THREE.TOUCH.ROTATE,TWO:THREE.TOUCH.DOLLY_PAN};this.target0=this.target.clone();this.position0=this.object.position.clone();this.zoom0=this.object.zoom;this.getPolarAngle=function(){return Sa.phi};this.getAzimuthalAngle=function(){return Sa.theta};this.saveState=function(){Y.target0.copy(Y.target);Y.position0.copy(Y.object.position);
Y.zoom0=Y.object.zoom};this.reset=function(){Y.target.copy(Y.target0);Y.object.position.copy(Y.position0);Y.object.zoom=Y.zoom0;Y.object.updateProjectionMatrix();Y.dispatchEvent(Ea);Y.update();Fa=Aa.NONE};this.update=function(){var sa=new THREE.Vector3,Mb=(new THREE.Quaternion).setFromUnitVectors(b.up,new THREE.Vector3(0,1,0)),wc=Mb.clone().inverse(),bd=new THREE.Vector3,td=new THREE.Quaternion;return function(){var tg=Y.object.position;sa.copy(tg).sub(Y.target);sa.applyQuaternion(Mb);Sa.setFromVector3(sa);
Y.autoRotate&&Fa===Aa.NONE&&k(f());Y.enableDamping?(Sa.theta+=Xa.theta*Y.dampingFactor,Sa.phi+=Xa.phi*Y.dampingFactor):(Sa.theta+=Xa.theta,Sa.phi+=Xa.phi);Sa.theta=Math.max(Y.minAzimuthAngle,Math.min(Y.maxAzimuthAngle,Sa.theta));Sa.phi=Math.max(Y.minPolarAngle,Math.min(Y.maxPolarAngle,Sa.phi));Sa.makeSafe();Sa.radius*=ub;Sa.radius=Math.max(Y.minDistance,Math.min(Y.maxDistance,Sa.radius));!0===Y.enableDamping?Y.target.addScaledVector(Bb,Y.dampingFactor):Y.target.add(Bb);sa.setFromSpherical(Sa);sa.applyQuaternion(wc);
tg.copy(Y.target).add(sa);Y.object.lookAt(Y.target);!0===Y.enableDamping?(Xa.theta*=1-Y.dampingFactor,Xa.phi*=1-Y.dampingFactor,Bb.multiplyScalar(1-Y.dampingFactor)):(Xa.set(0,0,0),Bb.set(0,0,0));ub=1;return qb||bd.distanceToSquared(Y.object.position)>ya||8*(1-td.dot(Y.object.quaternion))>ya?(Y.dispatchEvent(Ea),bd.copy(Y.object.position),td.copy(Y.object.quaternion),qb=!1,!0):!1}}();this.dispose=function(){Y.domElement.removeEventListener("contextmenu",ka,!1);Y.domElement.removeEventListener("mousedown",
T,!1);Y.domElement.removeEventListener("wheel",la,!1);Y.domElement.removeEventListener("touchstart",ba,!1);Y.domElement.removeEventListener("touchend",ca,!1);Y.domElement.removeEventListener("touchmove",ea,!1);document.removeEventListener("mousemove",X,!1);document.removeEventListener("mouseup",aa,!1);window.removeEventListener("keydown",Z,!1)};var Y=this,Ea={type:"change"},va={type:"start"},xa={type:"end"},Aa={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6},
Fa=Aa.NONE,ya=1E-6,Sa=new THREE.Spherical,Xa=new THREE.Spherical,ub=1,Bb=new THREE.Vector3,qb=!1,zb=new THREE.Vector2,vb=new THREE.Vector2,Gb=new THREE.Vector2,Nb=new THREE.Vector2,Ab=new THREE.Vector2,Hb=new THREE.Vector2,ic=new THREE.Vector2,bc=new THREE.Vector2,Od=new THREE.Vector2,rd=function(){var sa=new THREE.Vector3;return function(Mb,wc){sa.setFromMatrixColumn(wc,0);sa.multiplyScalar(-Mb);Bb.add(sa)}}(),Lc=function(){var sa=new THREE.Vector3;return function(Mb,wc){!0===Y.screenSpacePanning?
sa.setFromMatrixColumn(wc,1):(sa.setFromMatrixColumn(wc,0),sa.crossVectors(Y.object.up,sa));sa.multiplyScalar(Mb);Bb.add(sa)}}(),sd=function(){var sa=new THREE.Vector3;return function(Mb,wc){var bd=Y.domElement===document?Y.domElement.body:Y.domElement;if(Y.object.isPerspectiveCamera){sa.copy(Y.object.position).sub(Y.target);var td=sa.length();td*=Math.tan(Y.object.fov/2*Math.PI/180);rd(2*Mb*td/bd.clientHeight,Y.object.matrix);Lc(2*wc*td/bd.clientHeight,Y.object.matrix)}else Y.object.isOrthographicCamera?
(rd(Mb*(Y.object.right-Y.object.left)/Y.object.zoom/bd.clientWidth,Y.object.matrix),Lc(wc*(Y.object.top-Y.object.bottom)/Y.object.zoom/bd.clientHeight,Y.object.matrix)):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - pan disabled."),Y.enablePan=!1)}}();Y.domElement.addEventListener("contextmenu",ka,!1);Y.domElement.addEventListener("mousedown",T,!1);Y.domElement.addEventListener("wheel",la,!1);Y.domElement.addEventListener("touchstart",ba,!1);Y.domElement.addEventListener("touchend",
ca,!1);Y.domElement.addEventListener("touchmove",ea,!1);window.addEventListener("keydown",Z,!1);this.update()};THREE.OrbitControls.prototype=Object.create(THREE.EventDispatcher.prototype);THREE.OrbitControls.prototype.constructor=THREE.OrbitControls;THREE.MapControls=function(b,d){THREE.OrbitControls.call(this,b,d);this.mouseButtons.LEFT=THREE.MOUSE.PAN;this.mouseButtons.RIGHT=THREE.MOUSE.ROTATE;this.touches.ONE=THREE.TOUCH.PAN;this.touches.TWO=THREE.TOUCH.DOLLY_ROTATE};
THREE.MapControls.prototype=Object.create(THREE.EventDispatcher.prototype);THREE.MapControls.prototype.constructor=THREE.MapControls;

//# sourceURL=build://tf-imports/array-buffer-data-provider.js
var Lk;
(function(b){b.ErrorCodes={CANCELLED:1};const d={VERTEX:1,FACE:2,COLOR:3},f={VERTEX:"float32",FACE:"int32",COLOR:"uint8"};class h{constructor(k){this._requestManager=k;this._canceller=new vc.Canceller}reload(k,t,l){this._canceller.cancelAll();return this._fetchMetadata(k,t,l)}_fetchDataByStep(k,t,l,p,m,n){function q(u){let x=[];for(let A=0;A<u.length/3;A++){let y=[];for(let w=0;3>w;w++)y.push(u[3*A+w]);x.push(y)}return x}k=vc.getRouter().pluginRoute("mesh","/data",new URLSearchParams({tag:t,run:k,
content_type:l,sample:p,step:m}));t=this._canceller.cancellable(u=>{if(u.cancelled)return Promise.reject({code:b.ErrorCodes.CANCELLED,message:"Response was invalidated."});u=u.value;switch(l){case "VERTEX":n.vertices=q(new Float32Array(u));break;case "FACE":n.faces=q(new Int32Array(u));break;case "COLOR":n.colors=q(new Uint8Array(u))}return n});return this._requestManager.fetch(k,null,"arraybuffer",f[l]).then(u=>u.arrayBuffer()).then(t)}fetchData(k,t,l,p){let m=[],n=new Map;Object.keys(d).forEach(q=>
{k.components&1<<d[q]&&m.push(this._fetchDataByStep(t,l,q,p,k.step,n))});return Promise.all(m)}_fetchMetadata(k,t,l){this._canceller.cancelAll();k=vc.getRouter().pluginRoute("mesh","/meshes",new URLSearchParams({tag:t,run:k,sample:l}));t=this._canceller.cancellable(p=>p.cancelled?Promise.reject({code:b.ErrorCodes.CANCELLED,message:"Response was invalidated."}):p.value);return this._requestManager.fetch(k).then(p=>p.json()).then(t).then(this._processMetadata.bind(this))}_processMetadata(k){if(k){var t=
new Map;for(let p=0;p<k.length;p++){let m=k[p];t.has(m.step)||t.set(m.step,[]);t.get(m.step).push(m)}var l=[];t.forEach(p=>{p=this._createStepDatum(p[0]);l.push(p)});return l}}_createStepDatum(k){return{wall_time:new Date(1E3*k.wall_time),step:k.step,config:k.config,content_type:k.content_type,components:k.components}}}b.ArrayBufferDataProvider=h})(Lk||(Lk={}));

//# sourceURL=build://tf-imports/mesh-viewer.js
(function(b){class d extends THREE.EventDispatcher{constructor(f){super();this._lastMesh=null;this._clock=new THREE.Clock;this._canvasSize=null;this._runColor=f}_isObject(f){return"object"==typeof f&&null!=f&&!Array.isArray(f)}_applyDefaults(f,h){let k={};f=[f,h];for(h=0;h<f.length;h++){const t=f[h];for(let l in t){const p=l in k;this._isObject(t[l])?k[l]=this._applyDefaults(k[l]||{},t[l]):p||(k[l]=t[l])}}return k}_createWorld(f,h){this.isReady()||(this._scene=new THREE.Scene,this._camera=f=new THREE[f.camera.cls](f.camera.fov,
this._canvasSize.width/this._canvasSize.height,f.camera.near,f.camera.far),h=new THREE.OrbitControls(f,h),h.lookSpeed=.4,h.movementSpeed=20,h.noFly=!0,h.lookVertical=!0,h.constrainVertical=!0,h.verticalMin=1,h.verticalMax=2,h.addEventListener("change",this._onCameraPositionChange.bind(this)),this._cameraControls=h,this._renderer=new THREE.WebGLRenderer({antialias:!0}),this._renderer.setPixelRatio(),this._renderer.setSize(this._canvasSize.width,this._canvasSize.height),this._renderer.setClearColor(16777215,
1))}_clearScene(){for(;0<this._scene.children.length;)this._scene.remove(this._scene.children[0])}getRenderer(){return this._renderer}getCameraControls(){return this._cameraControls}isReady(){return!!this._camera&&!!this._cameraControls}getCameraPosition(){return{far:this._camera.far,position:this._camera.position.clone(),target:this._cameraControls.target.clone()}}setCanvasSize(f){this._canvasSize=f}draw(){this._animationFrameIndex&&cancelAnimationFrame(this._animationFrameIndex);this._camera.aspect=
this._canvasSize.width/this._canvasSize.height;this._camera.updateProjectionMatrix();this._renderer.setSize(this._canvasSize.width,this._canvasSize.height);const f=function(){var h=this._clock.getDelta();this._cameraControls.update(h);this._animationFrameIndex=requestAnimationFrame(f);this._renderer.render(this._scene,this._camera)}.bind(this);f()}updateScene(f,h){let k={};"config"in f&&f.config&&(k=JSON.parse(f.config));this.dispatchEvent({type:"beforeUpdateScene"});k=this._applyDefaults(k,{camera:{cls:"PerspectiveCamera",
fov:75,near:.1,far:1E3},lights:[{cls:"AmbientLight",color:"#ffffff",intensity:.75},{cls:"DirectionalLight",color:"#ffffff",intensity:.75,position:[0,-1,2]}]});this._createWorld(k,h);this._clearScene();this._createLights(this._scene,k);this._createGeometry(f,k);this.draw()}resetView(){if(this.isReady()){this._cameraControls.reset();if(!f&&this._lastMesh)var f=this._lastMesh;f&&(this._fitObjectToViewport(f),this._lastMesh=f);this._cameraControls.update()}}_createGeometry(f,h){f=f.mesh;f.vertices&&f.faces&&
f.faces.length?this._createMesh(f,h):this._createPointCloud(f,h)}_createPointCloud(f,h){var k=f.vertices;f=f.colors;let t={material:{cls:"PointsMaterial",size:.005}};f&&f.length==k.length?t.material.vertexColors=THREE.VertexColors:t.material.color=this._runColor;h=this._applyDefaults(h,t);var l=new THREE.Geometry;k.forEach(function(p){var m=new THREE.Vector3(p[0],p[1],p[2]);m.x=1*p[0];m.y=1*p[1];m.z=1*p[2];l.vertices.push(m)});f&&f.length==k.length&&f.forEach(function(p){p=new THREE.Color(p[0]/255,
p[1]/255,p[2]/255);l.colors.push(p)});k=new THREE[h.material.cls](h.material);k=new THREE.Points(l,k);this._scene.add(k);this._lastMesh=k}setCameraViewpoint(f,h,k){this._silent=!0;this._camera.far=h;this._camera.position.set(f.x,f.y,f.z);this._camera.lookAt(k.clone());this._camera.updateProjectionMatrix();this._cameraControls.target=k.clone();this._cameraControls.update();this._silent=!1}_onCameraPositionChange(f){this._silent||this.dispatchEvent({type:"cameraPositionChange",event:f})}_fitObjectToViewport(f){var h=
new THREE.Box3;h.setFromObject(f);f=h.center();var k=h.size();k=1.25*Math.abs(Math.max(k.x,k.y,k.z)/(2*Math.tan(Math.PI/180*this._camera.fov/2)));h=h.min.z;this.setCameraViewpoint({x:f.x,y:f.y,z:k},3*(0>h?-h+k:k-h),f)}_createMesh(f,h){var k=f.vertices;const t=f.faces,l=f.colors;f=this._applyDefaults(h,{material:{cls:"MeshStandardMaterial",color:"#a0a0a0",roughness:1,metalness:0}});let p=new THREE.Geometry;k.forEach(function(m){let n=new THREE.Vector3(m[0],m[1],m[2]);n.x=1*m[0];n.y=1*m[1];n.z=1*m[2];
p.vertices.push(n)});t.forEach(function(m){let n=new THREE.Face3(m[0],m[1],m[2]);if(l&&l.length){m=[l[m[0]],l[m[1]],l[m[2]]];for(let u=0;u<m.length;u++){var q=m[u];q=new THREE.Color(q[0]/255,q[1]/255,q[2]/255);n.vertexColors.push(q)}}p.faces.push(n)});l&&l.length&&(f.material=f.material||{},f.material.vertexColors=THREE.VertexColors);p.center();p.computeBoundingSphere();p.computeVertexNormals();k=new THREE[f.material.cls](f.material);k=new THREE.Mesh(p,k);k.castShadow=!0;k.receiveShadow=!0;this._scene.add(k);
this._lastMesh=k}_createLights(f,h){for(let k=0;k<h.lights.length;k++){const t=h.lights[k];let l=new THREE[t.cls](t.color,t.intensity);t.position&&l.position.set(t.position[0],t.position[1],t.position[2]);f.add(l)}}}b.MeshViewer=d})(Lk||(Lk={}));

//# sourceURL=build://tf-mesh-dashboard/mesh-loader.js
(function(b){Polymer({is:"tf-mesh-loader",properties:{run:String,tag:String,sample:Number,ofSamples:Number,selectedView:{type:String,value:"all"},active:{type:Boolean,value:!1},requestManager:Object,_meshViewer:{type:Object},_dataProvider:{type:Object},_colorScaleFunction:{type:Object,value:()=>pf.runsColorScale},_runColor:{type:String,computed:"_computeRunColor(run)"},_steps:{type:Array,value:()=>[],notify:!0},_stepIndex:{type:Number,notify:!0},_currentStep:{type:Object,computed:"_computeCurrentStep(_steps, _stepIndex)"},
_meshViewerAttached:{type:Boolean,value:!1},_cameraPositionInitialized:{type:Boolean,value:!1},_stepValue:{type:Number,computed:"_computeStepValue(_currentStep)"},_currentWallTime:{type:String,computed:"_computeCurrentWallTime(_currentStep)"},_isMeshLoading:{type:Boolean,value:!1}},observers:["reload(run, tag, active, _dataProvider, _meshViewer)","_updateScene(_currentStep.*, _meshViewer)","_debouncedFetchMesh(_currentStep)","_updateView(selectedView)"],_computeRunColor:function(d){return this._colorScaleFunction(d)},
attached:function(){this._dataProvider=new b.ArrayBufferDataProvider(this.requestManager);const d=new b.MeshViewer(this._runColor);d.addEventListener("beforeUpdateScene",this._updateCanvasSize.bind(this));d.addEventListener("cameraPositionChange",this._onCameraPositionChange.bind(this));this._meshViewer=d},reload:function(){this.active&&this._dataProvider&&(this.set("_isMeshLoading",!0),this._dataProvider.reload(this.run,this.tag,this.sample).then(d=>{d&&(this.set("_steps",d),this.set("_stepIndex",
d.length-1))}).catch(d=>{if(!d||!d.code||d.code!=b.ErrorCodes.CANCELLED)throw Error(d||"Response processing failed.");}))},_updateScene:function(){const d=this._currentStep;d&&d.mesh&&(this._meshViewer.updateScene(d,this),this._cameraPositionInitialized||(this._meshViewer.resetView(),this._cameraPositionInitialized=!0),this._meshViewerAttached||(this.root.appendChild(this._meshViewer.getRenderer().domElement),this._meshViewerAttached=!0))},_debouncedFetchMesh(){this.debounce("fetchMesh",()=>this._maybeFetchMesh(),
100)},_maybeFetchMesh(){const d=this;return hc(function*(){const f=d._currentStep;if(f&&!f.mesh&&!f.meshFetching){f.meshFetching=!0;d._isMeshLoading=!0;try{const h=yield d._dataProvider.fetchData(f,d.run,d.tag,d.sample);f.mesh=h[0];d.notifyPath("_currentStep.mesh")}catch(h){if(!h||!h.code||h.code!=b.ErrorCodes.CANCELLED)throw h=h||"Response processing failed.",Error(h);}finally{d._isMeshLoading=!1,f.meshFetching=!1}}})},_onCameraPositionChange:function(){if(this._meshViewer.isReady()){var d=new CustomEvent("camera-position-change",
{detail:this._meshViewer.getCameraPosition()});this.dispatchEvent(d)}},setCameraViewpoint:function(d,f,h){this._meshViewer.setCameraViewpoint(d,f,h)},_updateCanvasSize:function(){const d=this.offsetWidth,f=this.$$(".tf-mesh-loader-header").offsetHeight;this._meshViewer.setCanvasSize({width:d,height:d-f})},redraw:function(){this._updateCanvasSize();this.isConnected&&this._meshViewer.draw()},_hasAtLeastOneStep:function(d){return!!d&&0<d.length},_hasMultipleSteps:function(d){return!!d&&1<d.length},_computeCurrentStep:function(d,
f){return d[f]||null},_computeStepValue:function(d){return d?d.step:0},_computeCurrentWallTime:function(d){return d?Hh.formatDate(d.wall_time):""},_getMaxStepIndex:function(d){return d.length-1},_getSampleText:function(d){return String(d+1)},_hasMultipleSamples:function(d){return 1<d},_updateView:function(d){this._meshViewer&&"all"==d&&this._meshViewer.resetView()},toLocaleString_:function(d){return d.toLocaleString()}})})(Lk||(Lk={}));

//# sourceURL=build://tf-mesh-dashboard/tf-mesh-dashboard.html.js
(function(){Polymer({is:"mesh-dashboard",properties:{_selectedRuns:Array,_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:{type:String,value:".*"},_selectedView:{type:String,notify:!0,value:"all"},_categories:{type:Array,computed:"_makeCategories(_runToTagInfo, _selectedRuns, _tagFilter)"},_requestManager:{type:Object,value:()=>new vc.RequestManager}},ready(){window.addEventListener("resize",()=>{this._handleWindowResize()},!1);this.reload()},_getAllChildren(){return this.root.querySelectorAll("tf-mesh-loader")},
_onCameraPositionChanged(b){"share"==this._selectedView&&this._getAllChildren().forEach(d=>{b.target!=d&&d.setCameraViewpoint(b.detail.position,b.detail.far,b.detail.target)})},_shouldOpen(b){return 2>=b},reload(){this._fetchTags().then(this._reloadMeshes.bind(this))},_handleWindowResize(){this._getAllChildren().forEach(b=>{b.redraw()})},_fetchTags(){const b=vc.getRouter().pluginRoute("mesh","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,
h=>Object.keys(h));f=vc.getTags(f);this.set("_dataNotFound",0===f.length);this.set("_runToTagInfo",d)}})},_reloadMeshes(){this._getAllChildren().forEach(b=>{b.reload()})},_makeCategories(b,d,f){function h(t){const l=b[t.run][t.tag].samples;return _.range(l).map(p=>Object.assign({},t,{sample:p,ofSamples:l}))}const k=_.mapValues(b,t=>Object.keys(t));return $c.categorizeRunTagCombinations(k,d,f).map(t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(h))}))}})})();

//# sourceURL=build://tf-plugin-util/message.js
Ui=this&&this.__awaiter||function(b,d,f,h){return new (f||(f=Promise))(function(k,t){function l(n){try{m(h.next(n))}catch(q){t(q)}}function p(n){try{m(h["throw"](n))}catch(q){t(q)}}function m(n){n.done?k(n.value):(new f(function(q){q(n.value)})).then(l,p)}m((h=h.apply(b,d||[])).next())})};var Mk;
(function(b){(function(d){(function(f){class h{constructor(k){this.port=k;this.id=0;this.responseWaits=new Map;this.listeners=new Map;this.port.addEventListener("message",t=>this.onMessage(t))}listen(k,t){this.listeners.set(k,t)}unlisten(k){this.listeners.delete(k)}onMessage(k){return Ui(this,void 0,void 0,function*(){var t=JSON.parse(k.data);const l=t.type,p=t.id,m=t.payload;var n=t.error;if(t.isReply){if(this.responseWaits.has(p)){var {resolve:q,reject:u}=this.responseWaits.get(p);this.responseWaits.delete(p);
n?u(Error(n)):q(m)}}else{n=t=null;if(this.listeners.has(l)){const x=this.listeners.get(l);try{t=yield x(m)}catch(A){n=A}}this.postMessage({["type"]:l,["id"]:p,["payload"]:t,["error"]:n,["isReply"]:!0})}})}postMessage(k){this.port.postMessage(JSON.stringify(k))}sendMessage(k){const t=this.id++;this.postMessage({type:"experimental.RunsChanged",id:t,payload:k,error:null,isReply:!1});return new Promise((l,p)=>{this.responseWaits.set(t,{resolve:l,reject:p})})}}f.IPC=h})(d.DO_NOT_USE_INTERNAL||(d.DO_NOT_USE_INTERNAL=
{}))})(b.lib||(b.lib={}))})(Mk||(Mk={}));

//# sourceURL=build://tf-plugin-util/plugin-host-ipc.js
(function(b){(function(d){function f(m,n){const q=new b.lib.DO_NOT_USE_INTERNAL.IPC(m);t.add(q);p.set(q,n);m.start();for(const [u,x]of l)m=h(x,q),q.listen(u,m)}function h(m,n){return q=>{var u=p.get(n);u=k.get(u)||null;return m(u,q)}}d.registerPluginIframe=function(m,n){k.set(m,{pluginName:n})};const k=new WeakMap,t=new Set,l=new Map,p=new Map;window.addEventListener("message",m=>{if("experimental.bootstrap"===m.data){var n=m.ports[0];n&&(m=m.source?m.source.frameElement:null)&&f(n,m)}});d.broadcast=
function(){var m=vc.runsStore.getRuns();for(var n of t)p.get(n).isConnected||(t.delete(n),p.delete(n));n=[...t].map(q=>q.sendMessage(m));return Promise.all(n)};d.listen=function(m,n){l.set(m,n);for(const q of t){const u=h(n,q);q.listen(m,u)}};d.unlisten=function(m){l.delete(m);for(const n of t)n.unlisten(m)}})(b.host||(b.host={}))})(Mk||(Mk={}));

//# sourceURL=build://tf-plugin-util/core-host-impl.js
Mk.host.listen("experimental.GetURLPluginData",b=>{if(b){b=`p.${b.pluginName}.`;var d={};for(let f in pd.urlDict)f.startsWith(b)&&(d[f.substring(b.length)]=pd.urlDict[f]);return d}});

//# sourceURL=build://tf-plugin-util/runs-host-impl.js
Mk.host.listen("experimental.GetRuns",()=>vc.runsStore.getRuns());vc.runsStore.addListener(()=>Mk.host.broadcast());

//# sourceURL=build://tf-tensorboard/autoReloadBehavior.js
(function(b){function d(){return(new URLSearchParams(window.location.search)).has("_DisableAutoReload")}b.AUTORELOAD_LOCALSTORAGE_KEY="TF.TensorBoard.autoReloadEnabled";b.AutoReloadBehavior={properties:{autoReloadEnabled:{type:Boolean,observer:"_autoReloadObserver",value:()=>{var f=window.localStorage.getItem(b.AUTORELOAD_LOCALSTORAGE_KEY);return"true"===f||null==f}},_autoReloadId:{type:Number},_missedAutoReload:{type:Boolean,value:!1},_boundHandleVisibilityChange:{type:Object},autoReloadIntervalSecs:{type:Number,
value:30}},attached:function(){this._boundHandleVisibilityChange=this._handleVisibilityChange.bind(this);document.addEventListener("visibilitychange",this._boundHandleVisibilityChange)},detached:function(){window.clearTimeout(this._autoReloadId);document.removeEventListener("visibilitychange",this._boundHandleVisibilityChange)},_autoReloadObserver:function(f){window.localStorage.setItem(b.AUTORELOAD_LOCALSTORAGE_KEY,f);f&&!d()?this._autoReloadId=window.setTimeout(()=>this._doAutoReload(),1E3*this.autoReloadIntervalSecs):
window.clearTimeout(this._autoReloadId)},_doAutoReload:function(){this._isDocumentVisible()?this._doReload():this._missedAutoReload=!0;this._autoReloadId=window.setTimeout(()=>this._doAutoReload(),1E3*this.autoReloadIntervalSecs)},_doReload:function(){if(null==this.reload)throw Error("AutoReloadBehavior requires a reload method");this.reload()},_handleVisibilityChange:function(){this._isDocumentVisible()&&this._missedAutoReload&&(this._missedAutoReload=!1,this._doReload())},_isDocumentVisible:function(){return"visible"===
document.visibilityState}}})(qf||(qf={}));

//# sourceURL=build://tf-tensorboard/tf-tensorboard.html.js
const Ym={getLocation(){return window.location}};
Polymer({is:"tf-tensorboard",behaviors:[qf.AutoReloadBehavior],properties:{brand:{type:String,value:"TensorBoard-X"},homePath:{type:String,value:""},_homePath:{type:String,computed:"_sanitizeHomePath(homePath)"},title:{type:String,observer:"_updateTitle"},router:{type:Object,observer:"_updateRouter"},demoDir:{type:String,value:null},useHash:{type:Boolean,value:!1},disabledDashboards:{type:String,value:""},_dashboardData:{type:Array,computed:"_computeDashboardData(_dashboardRegistry)"},_dashboardRegistry:{type:Object,
computed:"_computeDashboardRegistry(_pluginsListing)"},_pluginsListing:{type:Object,value:()=>({})},_activeDashboards:{type:Array,computed:"_computeActiveDashboard(_dashboardData, _pluginsListing)"},_activeDashboardsLoadState:{type:String,value:qf.ActiveDashboardsLoadState.NOT_LOADED},_activeDashboardsNotLoaded:{type:Boolean,computed:"_computeActiveDashboardsNotLoaded(_activeDashboardsLoadState)"},_activeDashboardsLoaded:{type:Boolean,computed:"_computeActiveDashboardsLoaded(_activeDashboardsLoadState)"},
_activeDashboardsFailedToLoad:{type:Boolean,computed:"_computeActiveDashboardsFailedToLoad(_activeDashboardsLoadState)"},_showNoDashboardsMessage:{type:Boolean,computed:"_computeShowNoDashboardsMessage(_activeDashboardsLoaded, _activeDashboards, _selectedDashboard)"},_showNoSuchDashboardMessage:{type:Boolean,computed:"_computeShowNoSuchDashboardMessage(_activeDashboardsLoaded, _dashboardRegistry, _selectedDashboard)"},_selectedDashboard:{type:String,value:pd.getString(pd.TAB)||null,observer:"_selectedDashboardChanged"},
_dashboardToMaybeRemove:String,_dashboardContainersStamped:{type:Object,value:()=>({})},_isReloadDisabled:{type:Boolean,value:!1},_lastReloadTime:{type:String,value:"not yet loaded"},_lastReloadTimeShort:{type:String,value:"Not yet loaded"},_dataLocation:{type:String,value:null},_requestManager:{type:Object,value:()=>new vc.RequestManager},_canceller:{type:Object,value:()=>new vc.Canceller},_refreshing:{type:Boolean,value:!1}},observers:["_updateSelectedDashboardFromActive(_selectedDashboard, _activeDashboards)",
"_ensureSelectedDashboardStamped(_dashboardRegistry, _dashboardContainersStamped, _activeDashboards, _selectedDashboard)"],_sanitizeHomePath(b){if(!b)return"";const d=Ym.getLocation(),f=new URL(b,d.href),h="http:"===f.protocol||"https:"===f.protocol,k=f.origin===d.origin;if(!h)throw new RangeError(`Expect 'homePath' to be of http: or https:. ${b}`);if(!k)throw new RangeError(`Expect 'homePath' be a path or have the same origin. ${b} vs. ${d.origin}`);return h&&k?f.toString():""},_activeDashboardsUpdated(){},
_isDashboardActive(b,d,f){return 0<=(b||"").split(",").indexOf(f.plugin)||!(d||[]).includes(f.plugin)?!1:!0},_isDashboardInactive(b,d,f){return 0<=(b||"").split(",").indexOf(f.plugin)?!1:(d||[]).includes(f.plugin)?!1:!0},_inactiveDashboardsExist(b,d,f){if(!f)return!1;const h=new Set;b.forEach(k=>{h.add(k.plugin)});(d||"").split(",").forEach(k=>{h.delete(k.plugin)});f.forEach(k=>{h.delete(k)});return 0<h.size},_getDashboardFromIndex(b,d){return b[d]},_selectedStatus(b,d){return b===d},_selectedDashboardChanged(b){b=
b||"";pd.setString(pd.TAB,b);let d=window.location.pathname;d+=d.endsWith("/")?b:"/"+b;ga("set","page",d);ga("send","pageview")},_updateSelectedDashboardFromActive(b,d){d&&null==b&&(b=d[0]||null,null!=b&&(pd.setString(pd.TAB,b,{useLocationReplace:!0}),this._selectedDashboard=b))},_updateSelectedDashboardFromHash(){const b=pd.getString(pd.TAB);this.set("_selectedDashboard",b||null)},_ensureSelectedDashboardStamped(b,d,f,h){if(f&&h&&d[h]&&(d=this._dashboardToMaybeRemove,this._dashboardToMaybeRemove=
h,d&&d!=h&&b[d].removeDom&&(d=this.$$(`.dashboard-container[data-dashboard=${d}]`),d.firstChild&&d.firstChild.remove()),d=this.$$(`.dashboard-container[data-dashboard=${h}]`))){b=b[h];if(0===d.children.length)switch(f=b.loadingMechanism,f.type){case "CUSTOM_ELEMENT":h=document.createElement(f.elementName);h.id="dashboard";d.appendChild(h);break;case "IFRAME":this._renderPluginIframe(d,h,f);break;default:console.warn("Invariant violation:",f)}this.set("_isReloadDisabled",b.disableReload)}},_renderPluginIframe(b,
d){const f=document.createElement("iframe");f.id="dashboard";Mk.host.registerPluginIframe(f,d);const h=new URL("data/plugin_entry.html",window.location.href);h.searchParams.set("name",d);f.setAttribute("src",h.toString());b.appendChild(f)},_selectedDashboardComponent(){return this.$$(`.dashboard-container[data-dashboard=${this._selectedDashboard}] #dashboard`)},ready(){ad.setUseHash(this.useHash);this._updateSelectedDashboardFromHash();window.addEventListener("hashchange",()=>{this._updateSelectedDashboardFromHash()},
!1);vc.environmentStore.addListener(()=>{this._dataLocation=vc.environmentStore.getDataLocation();const b=vc.environmentStore.getWindowTitle();b&&(window.document.title=b)});pd.migrateLegacyURLScheme();this._reloadData();this._lastReloadTime=(new Date).toString()},_computeActiveDashboard(){return this._dashboardData?this._dashboardData.map(b=>b.plugin).filter(b=>{b=this._pluginsListing[b];return"boolean"===typeof b?b:b&&b.enabled}):[]},_onTemplateChanged(){const b={};for(const d of this.root.querySelectorAll(".dashboard-container"))b[d.dataset.dashboard]=
!0;this._dashboardContainersStamped=b},_computeDashboardRegistry(b){const d={};for(const [h,k]of Object.entries(qf.dashboardRegistry))d[h]={plugin:k.plugin,loadingMechanism:{type:"CUSTOM_ELEMENT",elementName:k.elementName},tabName:k.tabName.toUpperCase(),disableReload:k.isReloadDisabled||!1,removeDom:k.removeDom||!1};if(null!=b)for(const [h,k]of Object.entries(b))if("boolean"!==typeof k){switch(k.loading_mechanism.type){case "NONE":null==d[h]&&console.warn("Plugin has no loading mechanism and no baked-in registry entry: %s",
h);continue;case "CUSTOM_ELEMENT":var f={type:"CUSTOM_ELEMENT",elementName:k.loading_mechanism.element_name};break;case "IFRAME":f={type:"IFRAME",modulePath:k.loading_mechanism.module_path};break;default:console.warn("Unknown loading mechanism for plugin %s: %s",h,k.loading_mechanism);continue}null==f&&console.error("Invariant violation: loadingMechanism is %s for %s",f,h);d[h]={plugin:h,loadingMechanism:f,tabName:k.tab_name.toUpperCase(),disableReload:k.disable_reload,removeDom:k.remove_dom}}f={};
for(const h of Object.keys(b))d[h]&&(f[h]=d[h]);Object.assign(f,d);return f},_computeDashboardData(b){return Object.values(b)},_fetchPluginsListing(){this._canceller.cancelAll();const b=this._canceller.cancellable(d=>{d.cancelled||(this._pluginsListing=d.value,this._activeDashboardsLoadState=qf.ActiveDashboardsLoadState.LOADED)});return this._requestManager.request(vc.getRouter().pluginsListing()).then(b,()=>{this._activeDashboardsLoadState===qf.ActiveDashboardsLoadState.NOT_LOADED?this._activeDashboardsLoadState=
qf.ActiveDashboardsLoadState.FAILED:console.warn("Failed to reload the set of active plugins; using old value.")})},_computeActiveDashboardsNotLoaded(b){return b===qf.ActiveDashboardsLoadState.NOT_LOADED},_computeActiveDashboardsLoaded(b){return b===qf.ActiveDashboardsLoadState.LOADED},_computeActiveDashboardsFailedToLoad(b){return b===qf.ActiveDashboardsLoadState.FAILED},_computeShowNoDashboardsMessage(b,d,f){return b&&0===d.length&&null==f},_computeShowNoSuchDashboardMessage(b,d,f){return b&&!!f&&
null==d[f]},_updateRouter(b){vc.setRouter(b)},_updateTitle(b){b&&this.set("brand",b)},reload(){this._isReloadDisabled||(this._reloadData().then(()=>{const b=this._selectedDashboardComponent();b&&b.reload&&b.reload()}),this._lastReloadTime=(new Date).toString())},_reloadData(){this._refreshing=!0;return Promise.all([this._fetchPluginsListing(),vc.environmentStore.refresh(),vc.runsStore.refresh(),vc.experimentsStore.refresh()]).then(()=>{this._lastReloadTimeShort=(new Date).toLocaleDateString(void 0,
{month:"long",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric"})}).finally(()=>{this._refreshing=!1})},_getDataRefreshingClass(){return this._refreshing?"refreshing":""},openSettings(){this.$.settings.open();this.$.paginationLimitInput.value=ne.getLimit()},_paginationLimitValidate(b){b.target.validate()},_paginationLimitChanged(b){b=Number.parseInt(b.target.value,10);b===+b&&0<b&&ne.setLimit(b)}});
", "ok": true, "headers": [["content-type", "application/javascript; charset=utf-8"]], "status": 200, "status_text": ""}, "https://localhost:6006/data/experiments": {"data": "W10=", "ok": true, "headers": [["content-type", "application/json"]], "status": 200, "status_text": ""}, "https://localhost:6006/data/environment": {"data": "eyJkYXRhX2xvY2F0aW9uIjogIkdyYXBoMSIsICJ3aW5kb3dfdGl0bGUiOiAiIn0=", "ok": true, "headers": [["content-type", "application/json"]], "status": 200, "status_text": ""}, "https://localhost:6006/data/plugins_listing": {"data": "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", "ok": true, "headers": [["content-type", "application/json"]], "status": 200, "status_text": ""}, "https://localhost:6006/data/runs": {"data": "WyJ0cmFpbiIsICJ2YWxpZGF0aW9uIl0=", "ok": true, "headers": [["content-type", "application/json"]], "status": 200, "status_text": ""}, "https://localhost:6006/font-roboto/RxZJdnzeo3R5zSexge8UUZBw1xU1rKptJj_0jans920.woff2": {"data": "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", "ok": true, "headers": [["content-type", "font/woff2"]], "status": 200, "status_text": ""}, "https://localhost:6006/data/plugin/scalars/tags": {"data": "eyJ0cmFpbiI6IHsiZXBvY2hfbG9zcyI6IHsiZGlzcGxheU5hbWUiOiAiZXBvY2hfbG9zcyIsICJkZXNjcmlwdGlvbiI6ICIifSwgImVwb2NoX21lYW5fYWJzb2x1dGVfZXJyb3IiOiB7ImRpc3BsYXlOYW1lIjogImVwb2NoX21lYW5fYWJzb2x1dGVfZXJyb3IiLCAiZGVzY3JpcHRpb24iOiAiIn19LCAidmFsaWRhdGlvbiI6IHsiZXBvY2hfbG9zcyI6IHsiZGlzcGxheU5hbWUiOiAiZXBvY2hfbG9zcyIsICJkZXNjcmlwdGlvbiI6ICIifSwgImVwb2NoX21lYW5fYWJzb2x1dGVfZXJyb3IiOiB7ImRpc3BsYXlOYW1lIjogImVwb2NoX21lYW5fYWJzb2x1dGVfZXJyb3IiLCAiZGVzY3JpcHRpb24iOiAiIn19fQ==", "ok": true, "headers": [["content-type", "application/json"]], "status": 200, "status_text": ""}, "https://localhost:6006/data/plugin/scalars/scalars?tag=epoch_loss&run=train": {"data": "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", "ok": true, "headers": [["content-type", "application/json"]], "status": 200, "status_text": ""}, "https://localhost:6006/data/plugin/scalars/scalars?tag=epoch_loss&run=validation": {"data": "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", "ok": true, "headers": [["content-type", "application/json"]], "status": 200, "status_text": ""}, "https://localhost:6006/data/plugin/scalars/scalars?tag=epoch_mean_absolute_error&run=train": {"data": "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", "ok": true, "headers": [["content-type", "application/json"]], "status": 200, "status_text": ""}, "https://localhost:6006/data/plugin/scalars/scalars?tag=epoch_mean_absolute_error&run=validation": {"data": "W1sxNTg5MDA5MTc4Ljg1MzIyLCAwLCAxMzg2Ny4wMjM0Mzc1XSwgWzE1ODkwMDkxNzguOTE5Nzg5LCAxLCAxMzg2Ny4wMjM0Mzc1XSwgWzE1ODkwMDkxNzguOTg0NTIzLCAyLCAxMzg2Ny4wMjM0Mzc1XSwgWzE1ODkwMDkxNzkuMDQ5MTA1LCAzLCAxMzg2Ny4wMjM0Mzc1XSwgWzE1ODkwMDkxNzkuMTI2OTAzLCA0LCAxMzg2Ny4wMjM0Mzc1XSwgWzE1ODkwMDkxNzkuMTg0MjksIDUsIDEzODY3LjAyMzQzNzVdLCBbMTU4OTAwOTE3OS4yNDc5NCwgNiwgMTM4NjcuMDIzNDM3NV0sIFsxNTg5MDA5MTc5LjMxNzgzMSwgNywgMTM4NjcuMDIzNDM3NV0sIFsxNTg5MDA5MTc5LjM5MDI0MiwgOCwgMTM4NjQuMzY1MjM0Mzc1XSwgWzE1ODkwMDkxNzkuNDUyMzU5LCA5LCAxMzg1My4yODgwODU5Mzc1XSwgWzE1ODkwMDkxNzkuNTE1NzI2LCAxMCwgMTM4MzAuNTI3MzQzNzVdLCBbMTU4OTAwOTE3OS41NzUyNTQsIDExLCAxMzc5MC42OTE0MDYyNV0sIFsxNTg5MDA5MTc5LjYzNjMyMiwgMTIsIDEzNzIyLjA2MjVdLCBbMTU4OTAwOTE3OS42OTY3ODMsIDEzLCAxMzYxNS42MDI1MzkwNjI1XSwgWzE1ODkwMDkxNzkuNzU5NzA2LCAxNCwgMTM0NDcuMzgzNzg5MDYyNV0sIFsxNTg5MDA5MTc5LjgzODQ3MSwgMTUsIDEzMjA5Ljk1NzAzMTI1XSwgWzE1ODkwMDkxNzkuOTAyOTQsIDE2LCAxMjg4My43NTc4MTI1XSwgWzE1ODkwMDkxNzkuOTY5MzA2LCAxNywgMTI0OTAuMTczODI4MTI1XSwgWzE1ODkwMDkxODAuMDMxMTA1LCAxOCwgMTE5NzUuMjAwMTk1MzEyNV0sIFsxNTg5MDA5MTgwLjA5MTEzNCwgMTksIDExMzU3LjM4NzY5NTMxMjVdLCBbMTU4OTAwOTE4MC4xNTUwNDksIDIwLCAxMDcxMC4zNzk4ODI4MTI1XSwgWzE1ODkwMDkxODAuMjIyMzUsIDIxLCA5OTEzLjY0MDYyNV0sIFsxNTg5MDA5MTgwLjI4NTk2OCwgMjIsIDkwNTkuMDEzNjcxODc1XSwgWzE1ODkwMDkxODAuMzQ2NTY2LCAyMywgODI4My43MTM4NjcxODc1XSwgWzE1ODkwMDkxODAuNDE1NjYzLCAyNCwgNzc1OS4xOTkyMTg3NV0sIFsxNTg5MDA5MTgwLjQ4NjU5MiwgMjUsIDc2OTIuNTA3MzI0MjE4NzVdLCBbMTU4OTAwOTE4MC41NDgwMjMsIDI2LCA4MDE1LjI1MjkyOTY4NzVdLCBbMTU4OTAwOTE4MC42MTUxMzMsIDI3LCA4Mzg1LjE4MzU5Mzc1XSwgWzE1ODkwMDkxODAuNjc3NjksIDI4LCA4NjYyLjU0MDAzOTA2MjVdLCBbMTU4OTAwOTE4MC43NDIwNDMsIDI5LCA4ODEzLjQ1NDEwMTU2MjVdLCBbMTU4OTAwOTE4MC44MDMyMjUsIDMwLCA4OTUwLjQ1NzAzMTI1XSwgWzE1ODkwMDkxODAuODY2NDEzLCAzMSwgODk2OC4xNTgyMDMxMjVdLCBbMTU4OTAwOTE4MC45MjczNSwgMzIsIDkwMzguNTk2Njc5Njg3NV0sIFsxNTg5MDA5MTgxLjAwMDEwNiwgMzMsIDkwMDkuMjI5NDkyMTg3NV0sIFsxNTg5MDA5MTgxLjA1OTgsIDM0LCA5MDI5LjQ1MzEyNV0sIFsxNTg5MDA5MTgxLjEzMzI2NCwgMzUsIDkwODIuODk3NDYwOTM3NV0sIFsxNTg5MDA5MTgxLjIwMzgzNSwgMzYsIDkxMDYuOTYxOTE0MDYyNV0sIFsxNTg5MDA5MTgxLjI2NTQ4NCwgMzcsIDkwNzMuNzE1ODIwMzEyNV0sIFsxNTg5MDA5MTgxLjMyNjc4MiwgMzgsIDkwNDAuOTU4OTg0Mzc1XSwgWzE1ODkwMDkxODEuMzk0NzUsIDM5LCA5MDk5LjY3MzgyODEyNV0sIFsxNTg5MDA5MTgxLjQ1OTI1NCwgNDAsIDkwNzIuNDkyMTg3NV0sIFsxNTg5MDA5MTgxLjUxNzk2NywgNDEsIDkwNTAuODIxMjg5MDYyNV0sIFsxNTg5MDA5MTgxLjU4MDY4NiwgNDIsIDkwNTMuMzg3Njk1MzEyNV0sIFsxNTg5MDA5MTgxLjY0MDc5NCwgNDMsIDkwMzUuMzY1MjM0Mzc1XSwgWzE1ODkwMDkxODEuNzAzMzE5LCA0NCwgOTA4Ni4yNjk1MzEyNV0sIFsxNTg5MDA5MTgxLjc3MDA2LCA0NSwgOTA0Ny4xNTYyNV0sIFsxNTg5MDA5MTgxLjg1MTQ0NiwgNDYsIDkwNzAuODM5ODQzNzVdLCBbMTU4OTAwOTE4MS45MTUyMzEsIDQ3LCA5MDQ0LjM5NDUzMTI1XSwgWzE1ODkwMDkxODEuOTczMDk4LCA0OCwgOTA4MS45Mjg3MTA5Mzc1XSwgWzE1ODkwMDkxODIuMDM4MDQzLCA0OSwgOTA1MC43NTc4MTI1XSwgWzE1ODkwMDkxODIuMDk5OTE2LCA1MCwgOTA2My41ODY5MTQwNjI1XSwgWzE1ODkwMDkxODIuMTYyODgyLCA1MSwgOTA0Mi4wNzcxNDg0Mzc1XSwgWzE1ODkwMDkxODIuMjI1OTk2LCA1MiwgOTAxNy4wMjM0Mzc1XSwgWzE1ODkwMDkxODIuMjg0MDE2LCA1MywgOTA1My4yMjY1NjI1XSwgWzE1ODkwMDkxODIuMzQ0Nzg1LCA1NCwgOTAwOS45NjM4NjcxODc1XSwgWzE1ODkwMDkxODIuNDI1NzYyLCA1NSwgODk4Mi44Mzk4NDM3NV0sIFsxNTg5MDA5MTgyLjQ5NjE2NiwgNTYsIDg5NzMuMzM5ODQzNzVdLCBbMTU4OTAwOTE4Mi41NTM3NjksIDU3LCA5MDQ4LjIyNzUzOTA2MjVdLCBbMTU4OTAwOTE4Mi42MjA3NTUsIDU4LCA5MDAyLjI2NzU3ODEyNV0sIFsxNTg5MDA5MTgyLjY4MjI2OCwgNTksIDkwMjcuOTQ2Mjg5MDYyNV0sIFsxNTg5MDA5MTgyLjc0MjgzOSwgNjAsIDkwMjkuMDA3ODEyNV0sIFsxNTg5MDA5MTgyLjgxMjgwNywgNjEsIDkwNDAuMTY4OTQ1MzEyNV0sIFsxNTg5MDA5MTgyLjg3MzkxLCA2MiwgOTAxMC4wNDAwMzkwNjI1XSwgWzE1ODkwMDkxODIuOTMzNDM1LCA2MywgOTAyNi41MzQxNzk2ODc1XSwgWzE1ODkwMDkxODIuOTk1MzU1LCA2NCwgOTAwMS4xMjAxMTcxODc1XSwgWzE1ODkwMDkxODMuMDU1NTg1LCA2NSwgOTAyNy44NDA4MjAzMTI1XSwgWzE1ODkwMDkxODMuMTIwNjExLCA2NiwgOTA1My4yMDQxMDE1NjI1XSwgWzE1ODkwMDkxODMuMTg1NjY4LCA2NywgODk4MS45OTAyMzQzNzVdLCBbMTU4OTAwOTE4My4yNDgxNjEsIDY4LCA5MDI0LjExNDI1NzgxMjVdLCBbMTU4OTAwOTE4My4zMTE2NjEsIDY5LCA5MDA2LjE2NjAxNTYyNV0sIFsxNTg5MDA5MTgzLjM3NDcyMiwgNzAsIDg5NzcuMDcxMjg5MDYyNV0sIFsxNTg5MDA5MTgzLjQzOTM3NywgNzEsIDg5ODQuOTAyMzQzNzVdLCBbMTU4OTAwOTE4My41MDAxNjYsIDcyLCA5MDUwLjQ1MTE3MTg3NV0sIFsxNTg5MDA5MTgzLjU2MzE4MywgNzMsIDkwMTkuNDg4MjgxMjVdLCBbMTU4OTAwOTE4My42MjI5MjYsIDc0LCA4OTUyLjY4MjYxNzE4NzVdLCBbMTU4OTAwOTE4My42ODQ5NzUsIDc1LCA5MDE5LjczNTM1MTU2MjVdLCBbMTU4OTAwOTE4My43NDkzMDUsIDc2LCA5MDQ4Ljk0OTIxODc1XSwgWzE1ODkwMDkxODMuODA3NDMxLCA3NywgOTAwMi45MjI4NTE1NjI1XSwgWzE1ODkwMDkxODMuODY1ODA4LCA3OCwgODk4Mi43NTI5Mjk2ODc1XSwgWzE1ODkwMDkxODMuOTQyODQ4LCA3OSwgOTAwOS4wNjkzMzU5Mzc1XSwgWzE1ODkwMDkxODQuMDA5MzcsIDgwLCA4OTg2Ljk0NTMxMjVdLCBbMTU4OTAwOTE4NC4wNzE0NjUsIDgxLCA4OTg1LjU1ODU5Mzc1XSwgWzE1ODkwMDkxODQuMTM3MzM3LCA4MiwgOTAyNS4zMjcxNDg0Mzc1XSwgWzE1ODkwMDkxODQuMTk5MjAyLCA4MywgOTAwOC4yOTU4OTg0Mzc1XSwgWzE1ODkwMDkxODQuMjYyNzQ2LCA4NCwgODk4OC45ODYzMjgxMjVdLCBbMTU4OTAwOTE4NC4zMjE4MTEsIDg1LCA4OTk0LjUwNzgxMjVdLCBbMTU4OTAwOTE4NC4zODM3NiwgODYsIDg5NTcuMDc3MTQ4NDM3NV0sIFsxNTg5MDA5MTg0LjQ1NDQ1NiwgODcsIDg5OTMuMzgzNzg5MDYyNV0sIFsxNTg5MDA5MTg0LjUxNDg4LCA4OCwgODk0OC4yMDExNzE4NzVdLCBbMTU4OTAwOTE4NC41Nzg0NjEsIDg5LCA5MDEwLjk3MjY1NjI1XSwgWzE1ODkwMDkxODQuNjQ1MzU0LCA5MCwgODk5MC40Njc3NzM0Mzc1XSwgWzE1ODkwMDkxODQuNzA4MTczLCA5MSwgOTAxNS43NzM0Mzc1XSwgWzE1ODkwMDkxODQuNzY2OTAyLCA5MiwgODk5Mi44OTE2MDE1NjI1XSwgWzE1ODkwMDkxODQuODI5NDQyLCA5MywgODk3Ny42MDc0MjE4NzVdLCBbMTU4OTAwOTE4NC44OTI0MTMsIDk0LCA4OTYxLjc0NzA3MDMxMjVdLCBbMTU4OTAwOTE4NC45NTE5NDIsIDk1LCA5MDEwLjg2NDI1NzgxMjVdLCBbMTU4OTAwOTE4NS4wMTQ2MzQsIDk2LCA4OTQ4LjQyMjg1MTU2MjVdLCBbMTU4OTAwOTE4NS4wODg5ODQsIDk3LCA4OTU3LjM0NjY3OTY4NzVdLCBbMTU4OTAwOTE4NS4xNTQwMDksIDk4LCA5MDE4LjU1ODU5Mzc1XSwgWzE1ODkwMDkxODUuMjE1NjM2LCA5OSwgOTAwNS4xMzI4MTI1XV0=", "ok": true, "headers": [["content-type", "application/json"]], "status": 200, "status_text": ""}, "https://localhost:6006/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2": {"data": "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", "ok": true, "headers": [["content-type", "font/woff2"]], "status": 200, "status_text": ""}}, "base_uri": "https://localhost:8080/", "height": 856, "output_embedded_package_id": "1bf4DdL5GlPuL3hPabHYA7bem95YmgYDu"}
# %load_ext tensorboard
# %tensorboard --logdir Graph1
| 5,266,346 |
/project_1.ipynb | b7a91b94e8538fd6278334b6a13a29b551acfad6 | [
"MIT"
] | permissive | texasroh/Linear-Regression_with_interaction_diminishing_return_terms | https://github.com/texasroh/Linear-Regression_with_interaction_diminishing_return_terms | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 571,475 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# ## Explanatory Data Analysis (EDA)
#
# Academic project from UTA
#
# Data has 520 rows and 4 columns<br>
# Each row represents a week
#
#
# <h3>Independant Variable</h3>
# <ul>
# <li><strong>rebate:</strong> \$ pay back spent on each purchase</li>
# <li><strong>ad.spent:</strong> MIL $ spent on Advertisement</li>
# <li><strong>xmas:</strong> every last 6 weeks in each year. 0 is off-season, 1 is christmas season</li>
# </ul>
#
# <h3>Dependant Variable</h3>
# <ul>
# <li><strong>sales:</strong> BIL $ revenue</li>
# </ul>
# <h2>Import necessary modules</h2>
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %matplotlib inline
# <h2>Data load</h2>
data = pd.read_csv("project1.2.csv")
data.head()
# <h2>Visualization</h2>
g = sns.scatterplot(x="ad.spent", y="sales", hue="xmas", data=data)
g = sns.scatterplot(x="rebate", y="sales", hue="xmas", data=data)
data['rebate_ca'] = data['rebate'] // 1
a = data.groupby('rebate_ca').sales.mean().reset_index(name = 'sales_avg')
plt.plot(a.rebate_ca, a.sales_avg)
plt.plot([a.rebate_ca.iloc[0], a.rebate_ca.iloc[-1]], [a.sales_avg.iloc[0], a.sales_avg.iloc[-1]])
plt.xlabel('Rebate')
plt.ylabel('Sales')
xmas = data.groupby('xmas').mean().sales
plt.grid()
plt.boxplot((data.loc[data.xmas==0,'sales'], data.loc[data.xmas==1, 'sales']), labels=[0,1])
plt.scatter([1,2],xmas)
plt.xlabel('X-mas')
plt.ylabel('Sales')
# <h2>Scipy optimize w/o xmas</h2>
from scipy.optimize import least_squares
# +
def simple_regression(c, x):
#c[0] : intercept
#x[0] : rebate
#c[1] : coefficient for rebate
#x[1] : ad.spent
#c[2] : coefficient for ad.spent
#x[2] : xmas
#c[3] : coefficient for xmas
return c[0] + c[1]*x[0] + c[2]*x[1] + c[3]*x[2]
def diminishing_return(x, r):
return (1-np.exp((-1)*x*r)) / r
def regression_with_diminishing_return(c, x):
#c[4] : r for rebate
#c[5] : r for ad.spent
return c[0] + c[1]*diminishing_return(x[0],c[4]) + c[2]*diminishing_return(x[1],c[5]) + c[3]*x[2]
def simple_func(c,x,y):
return y - simple_regression(c,x)
def diminishing_func(c,x,y):
return y - regression_with_diminishing_return(c,x)
# -
c_sim = np.random.rand(4)
c_dim = np.random.rand(6)
c_dim
x = data[['rebate','ad.spent','xmas']].values.T
y = data['sales'].values
x
# <h2>Simple Regression</h2>
# simple_regression
c_sim = np.random.rand(4)
solv1 = least_squares(simple_func, c_sim, args = (x, y))
print(solv1.x)
#ad.spent
plt.scatter(x[1], y)
plt.plot(x[1], solv1.x[0]+solv1.x[2]*x[1], color='red')
#rebate
plt.scatter(x[0],y)
plt.plot(x[0], solv1.x[0]+solv1.x[1]*x[0], color = 'red')
# predicted vs real
plt.scatter(simple_regression(solv1.x, x), y)
plt.plot([y.min(),y.max()], [y.min(),y.max()], color='red')
plt.xlabel('Predicted')
plt.ylabel('Real')
# <h2>Diminishing return</h2>
#diminishing return
solv2 = least_squares(diminishing_func, c_dim, args=(x, y))
print(solv2.x)
#ad.spent
plt.scatter(x[1], y)
plt.plot(x[1], solv2.x[0]+solv2.x[2]*diminishing_return(x[1],solv2.x[5]), color='red')
#rebate
plt.scatter(x[0],y)
plt.plot(x[0], solv2.x[0]+solv1.x[1]*diminishing_return(x[0], solv2.x[4]), color='red')
# predicted vs real simple regression
plt.scatter(simple_regression(solv1.x, x), y)
plt.plot([y.min(),y.max()], [y.min(),y.max()], color='red')
plt.xlabel('Predicted')
plt.ylabel('Real')
# predicted vs real Diminishing Return
plt.scatter(regression_with_diminishing_return(solv2.x,x),y)
plt.plot([y.min(),y.max()], [y.min(),y.max()], color='red')
plt.xlabel('Predicted')
plt.ylabel('Real')
# <h2>Using dummy data for validating the model (Algorithm validation)</h2>
# simple_regression
np.random.seed(0)
e = np.random.randn(10000)
x_rand = np.random.randint(1,10000,(2,10000))
xmas_rand = np.random.randint(0,2,(1,10000))
x_rand = np.vstack((x_rand, xmas_rand))
y_rand_sim = simple_regression(solv1.x, x_rand) + e
solv4 = least_squares(simple_func, c_sim, args=(x_rand, y_rand_sim))
print(solv4.x)
print(solv1.x)
print(solv4.x - solv1.x)
## Both calculated outputs look so similar which means Algorithm works well
# diminishing return
np.random.seed(0)
e = np.random.randn(10000)
x_rand = np.random.randint(1,10000, (2,10000))
xmas_rand = np.random.randint(0,2, (1,10000))
x_rand = np.vstack((x_rand, xmas_rand))
y_rand_dim = regression_with_diminishing_return(solv2.x, x_rand) + e
solv3 = least_squares(diminishing_func, c_dim, args=(x_rand, y_rand_dim))
print(solv3.x)
print(solv2.x)
print(solv3.x - solv2.x)
# <h1>With standardized data</h1>
# n_data = (data - data.mean())/data.std()
#normalize only continuous variables
n_data = data.copy()
n_data['rebate'] = (n_data['rebate'] - n_data['rebate'].mean())/n_data['rebate'].std()
n_data['ad.spent'] = (n_data['ad.spent'] - n_data['ad.spent'].mean())/n_data['ad.spent'].std()
n_data['sales'] = (n_data['sales'] - n_data['sales'].mean())/n_data['sales'].std()
plt.scatter(n_data['rebate'], n_data['sales'])
n_x = n_data[['rebate','ad.spent','xmas']].values.T
n_y = n_data['sales'].values
n_x[0].min()
# simple_regression
c_sim = np.random.randn(4)
n_solv1 = least_squares(simple_func, c_sim, args = (n_x, n_y))
print(n_solv1.x)
print(n_solv1.x)
print(data.mean())
print(data.std())
#diminishing return
n_solv2 = least_squares(diminishing_func, c_dim, args=(n_x, n_y))
print(n_solv2.x)
# <h2>Scipy w/ xmas interaction</h2>
# +
def simple_regression_interaction(c, x):
#c[0] : intercept
#x[0] : rebate
#c[1] : coefficient for rebate
#x[1] : ad.spent
#c[2] : coefficient for ad.spent
#x[2] : xmas
#c[3] : coefficient for xmas
#c[4] : coefficient for rebate * xmas
#c[5] : coefficient for ad.spent * xmas
#c[6] : coefficient for rebate * ad.spent
return c[0] + c[1]*x[0] + c[2]*x[1] + c[3]*x[2] + c[4]*x[0]*x[2] + c[5]*x[1]*x[2] + c[6]*x[0]*x[1]
def diminishing_return(x, r):
return (1-np.exp((-1)*x*r)) / r
def regression_with_diminishing_return_interaction(c, x):
#c[7] : r for rebate
#c[8] : r for ad.spent
return c[0] + c[1]*diminishing_return(x[0],c[7]) + c[2]*diminishing_return(x[1],c[8]) + c[3]*x[2] + c[4]*x[0]*x[2] + c[5]*x[1]*x[2] + c[6]*x[0]*x[1]
def simple_func_interaction(c,x,y):
return y - simple_regression_interaction(c,x)
def diminishing_func_interaction(c,x,y):
return y - regression_with_diminishing_return_interaction(c,x)
# -
c_sim_inter = np.random.rand(7)
c_dim_inter = np.random.rand(9)
# simple_regression_interaction
c_sim_inter = np.random.rand(7)
solv1_inter = least_squares(simple_func_interaction, c_sim_inter, args = (x, y))
print(c_sim_inter)
print(solv1_inter.x)
# predicted vs real simpe regression _ interaction
plt.scatter(simple_regression_interaction(solv1_inter.x, x), y)
plt.plot([y.min(),y.max()], [y.min(),y.max()], color='red')
plt.xlabel('Predicted')
plt.ylabel('Real')
# diminishing_return_interaction
solv2_inter = least_squares(diminishing_func_interaction, c_dim_inter, args = (x, y))
print(solv2_inter.x)
# predicted vs real Diminishing Return
plt.scatter(regression_with_diminishing_return_interaction(solv2_inter.x, x), y)
plt.plot([y.min(),y.max()], [y.min(),y.max()], color='red')
plt.xlabel('Predicted')
plt.ylabel('Real')
plt.scatter(regression_with_diminishing_return_interaction(solv2_inter.x, x), y)
plt.plot([y.min(),y.max()], [y.min(),y.max()], color='red')
plt.xlabel('Predicted')
plt.ylabel('Real')
# <h2>Hypothesis test</h2>
# <h3>parameters</h3>
# <ul>
# <li>c[0] : intercept</li>
# <li>x[0] : rebate</li>
# <li>c[1] : coefficient for rebate</li>
# <li>x[1] : ad.spent</li>
# <li>c[2] : coefficient for ad.spent</li>
# <li>x[2] : xmas</li>
# <li>c[3] : coefficient for xmas</li>
# <li>c[4] : coefficient for rebate * xmas</li>
# <li>c[5] : coefficient for ad.spent * xmas</li>
# <li>c[6] : coefficient for rebate * ad.spent</li>
# <li>c[7] : coefficient for rebate * ad.spent * xmas</li>
# <li>c[7] : r for rebate</li>
# <li>c[8] : r for ad.spent</li>
# </ul>
#
# <h3>Equation</h3>
# <div>$Sales = c[0] + c[1] \left [ \frac{1-e^{-x[0]\cdot c[7]}}{b} \right ] + c[2] \left [ \frac{1-e^{-x[1]\cdot c[8]}}{b} \right ] + c[3]\cdot x[2] + c[4]( x[0]\cdot x[2])+c[5]( x[1]\cdot x[2])+c[6](x[0]\cdot x[1])$</div>
# <a hidden="True" href="https://www.codecogs.com/eqnedit.php?latex=Sales&space;=&space;c[0]&space;+&space;c[1]&space;\left&space;[&space;\frac{1-e^{-x[0]\cdot&space;c[7]}}{b}&space;\right&space;]&space;+&space;c[2]&space;\left&space;[&space;\frac{1-e^{-x[1]\cdot&space;c[8]}}{b}&space;\right&space;]&space;+&space;c[3]\cdot&space;x[2]&space;+&space;c[4](&space;x[0]\cdot&space;x[2])+c[5](&space;x[1]\cdot&space;x[2])+c[6](x[0]\cdot&space;x[1])" target="_blank"><img src="https://latex.codecogs.com/gif.latex?Sales&space;=&space;c[0]&space;+&space;c[1]&space;\left&space;[&space;\frac{1-e^{-x[0]\cdot&space;c[7]}}{b}&space;\right&space;]&space;+&space;c[2]&space;\left&space;[&space;\frac{1-e^{-x[1]\cdot&space;c[8]}}{b}&space;\right&space;]&space;+&space;c[3]\cdot&space;x[2]&space;+&space;c[4](&space;x[0]\cdot&space;x[2])+c[5](&space;x[1]\cdot&space;x[2])+c[6](x[0]\cdot&space;x[1])" title="Sales = c[0] + c[1] \left [ \frac{1-e^{-x[0]\cdot c[7]}}{b} \right ] + c[2] \left [ \frac{1-e^{-x[1]\cdot c[8]}}{b} \right ] + c[3]\cdot x[2] + c[4]( x[0]\cdot x[2])+c[5]( x[1]\cdot x[2])+c[6](x[0]\cdot x[1])" /></a>
# +
def test_c0(c,x):
return 0*c[0] + c[1]*diminishing_return(x[0],c[7]) + c[2]*diminishing_return(x[1],c[8]) + c[3]*x[2] + c[4]*x[0]*x[2] + c[5]*x[1]*x[2] + c[6]*x[0]*x[1]
def test_c1(c,x):
return c[0] + c[2]*diminishing_return(x[1],c[8]) + c[3]*x[2] + c[4]*x[0]*x[2] + c[5]*x[1]*x[2] + c[6]*x[0]*x[1]
def test_c2(c,x):
return c[0] + c[1]*diminishing_return(x[0],c[7]) + c[3]*x[2] + c[4]*x[0]*x[2] + c[5]*x[1]*x[2] + c[6]*x[0]*x[1]
def test_c3(c,x):
return c[0] + c[1]*diminishing_return(x[0],c[7]) + c[2]*diminishing_return(x[1],c[8]) + c[4]*x[0]*x[2] + c[5]*x[1]*x[2] + c[6]*x[0]*x[1]
def test_c4(c,x):
return c[0] + c[1]*diminishing_return(x[0],c[7]) + c[2]*diminishing_return(x[1],c[8]) + c[3]*x[2] + c[5]*x[1]*x[2] + c[6]*x[0]*x[1]
def test_c5(c,x):
return c[0] + c[1]*diminishing_return(x[0],c[7]) + c[2]*diminishing_return(x[1],c[8]) + c[3]*x[2] + c[4]*x[0]*x[2] + c[6]*x[0]*x[1]
def test_c6(c,x):
return c[0] + c[1]*diminishing_return(x[0],c[7]) + c[2]*diminishing_return(x[1],c[8]) + c[3]*x[2] + c[4]*x[0]*x[2] + c[5]*x[1]*x[2]
def test_c7(c,x):
return c[0] + c[1]*x[0] + c[2]*diminishing_return(x[1],c[8]) + c[3]*x[2] + c[4]*x[0]*x[2] + c[5]*x[1]*x[2] + c[6]*x[0]*x[1]
def test_c8(c,x):
return c[0] + c[1]*diminishing_return(x[0],c[7]) + c[2]*x[1] + c[3]*x[2] + c[4]*x[0]*x[2] + c[5]*x[1]*x[2] + c[6]*x[0]*x[1]
def test_c7_c8(c,x):
return simple_regression_interaction(c, x)
def least_square_func(c,x,y, funcc):
return y - funcc(c,x)
# +
def llk(c,x,y,test_func):
return -len(y)/2*(np.log(np.sum(np.power(y-test_func(c,x),2)))+1+np.log(2*np.pi)-np.log(len(y)))
def lrt(c,x,y, full_func, restricted_func):
full_solv = least_squares(least_square_func, c_dim_inter, args = (x, y, full_func))
restricted_solv = least_squares(least_square_func, c_dim_inter, args = (x, y, restricted_func))
return 2*(llk(full_solv.x,x,y,full_func) - llk(restricted_solv.x,x,y,restricted_func))
# -
solv2_inter.x
llk(solv2_inter.x,x,y,regression_with_diminishing_return_interaction)
c_dim_inter = np.ones(9)/100
test_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,test_c0))
print(llk(test_solv.x, x, y, test_c0))
test_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,test_c1))
print(llk(test_solv.x, x, y, test_c1))
test_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,test_c2))
print(llk(test_solv.x, x, y, test_c2))
test_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,test_c3))
print(llk(test_solv.x, x, y, test_c3))
test_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,test_c4))
print(llk(test_solv.x, x, y, test_c4))
test_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,test_c5))
print(llk(test_solv.x, x, y, test_c5))
test_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,test_c6))
print(llk(test_solv.x, x, y, test_c6))
test_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,test_c7))
print(llk(test_solv.x, x, y, test_c7))
test_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,test_c8))
print(llk(test_solv.x, x, y, test_c8))
# +
# find LRT and p-value
from scipy.stats import chi2
test_model = [test_c0, test_c1, test_c2, test_c3, test_c4, test_c5, test_c6, test_c7, test_c8]
for model in test_model:
lrt_value = lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, model)
p_value = chi2.sf(lrt_value,1)
print(lrt_value,'\t', p_value)
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c0))
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c1))
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c2))
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c3))
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c4))
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c5))
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c6))
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c7))
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c8))
# print(lrt(c_dim_inter, x, y, regression_with_diminishing_return_interaction, test_c7_c8))
## c[6] and c[8] are not significant
## c[6] : interaction term - rebate * ad.spent
## c[8] : r - ad.spent
# -
# <h3>Final Model</h3>
# <div>$Sales = c[0] + c[1] \left [ \frac{1-e^{-x[0]\cdot c[6]}}{b} \right ] + c[2] \cdot x[1]+ c[3]\cdot x[2] + c[4]( x[0]\cdot x[2])+c[5]( x[1]\cdot x[2])$</div>
# <a hidden = "True" href="https://www.codecogs.com/eqnedit.php?latex=Sales&space;=&space;c[0]&space;+&space;c[1]&space;\left&space;[&space;\frac{1-e^{-x[0]\cdot&space;c[6]}}{b}&space;\right&space;]&space;+&space;c[2]&space;\cdot&space;x[1]+&space;c[3]\cdot&space;x[2]&space;+&space;c[4](&space;x[0]\cdot&space;x[2])+c[5](&space;x[1]\cdot&space;x[2])" target="_blank"><img src="https://latex.codecogs.com/gif.latex?Sales&space;=&space;c[0]&space;+&space;c[1]&space;\left&space;[&space;\frac{1-e^{-x[0]\cdot&space;c[6]}}{b}&space;\right&space;]&space;+&space;c[2]&space;\cdot&space;x[1]+&space;c[3]\cdot&space;x[2]&space;+&space;c[4](&space;x[0]\cdot&space;x[2])+c[5](&space;x[1]\cdot&space;x[2])" title="Sales = c[0] + c[1] \left [ \frac{1-e^{-x[0]\cdot c[6]}}{b} \right ] + c[2] \cdot x[1]+ c[3]\cdot x[2] + c[4]( x[0]\cdot x[2])+c[5]( x[1]\cdot x[2])" /></a>
def final_model(c,x):
return c[0] + c[1]*diminishing_return(x[0],c[6]) + c[2]*x[1] + c[3]*x[2] + c[4]*x[0]*x[2] + c[5]*x[1]*x[2]
final_c = np.random.randn(7)
full_solv = least_squares(least_square_func, c_dim_inter, args = (x,y,regression_with_diminishing_return_interaction))
final_solv = least_squares(least_square_func, final_c, args = (x,y,final_model))
print(full_solv.x)
final_solv.x
# rebate interpretation
tmp_x = np.linspace(data.rebate.min(), data.rebate.max(), num=1000)
tmp_y= final_solv.x[1] * diminishing_return(tmp_x, final_solv.x[6]) / final_solv.x[6]
plt.plot(tmp_x, tmp_y)
tmp_y2 = final_solv.x[1] * diminishing_return(tmp_x, final_solv.x[6]) / final_solv.x[6] + final_solv.x[4] * tmp_x
plt.plot(tmp_x, tmp_y2)
plt.xlabel('rebate')
plt.ylabel('sales')
# ad.spent interpretation
tmp_x = np.linspace(data['ad.spent'].min(), data['ad.spent'].max(), num=10000)
tmp_y = final_solv.x[2]*tmp_x
plt.plot(tmp_x, tmp_y)
tmp_y2 = final_solv.x[2]*tmp_x + final_solv.x[5]*tmp_x
plt.plot(tmp_x, tmp_y2)
plt.xlabel('ad.spent')
plt.ylabel('sales')
# <h2> Interprete with normalized data</h2>
n_final_solv = least_squares(least_square_func, final_c, args = (n_x,n_y,final_model))
n_final_solv.x
r_x = np.linspace(n_data['rebate'].min(), n_data['rebate'].max(), num=1000)
r_r = n_final_solv.x[6]
plt.plot(r_x, diminishing_return(r_x, r_r))
r_y = n_final_solv.x[1] * diminishing_return(r_x,r_r) + (n_final_solv.x[4]*r_x*n_data['xmas'].max())
plt.plot(r_x, r_y)
plt.axhline(r_y[364], color='black', linestyle='--', alpha=.3)
plt.axvline(r_x[364], color='black', linestyle='--', alpha=.3)
plt.xlabel('n_rebate')
plt.ylabel('n_sales')
plt.grid()
r_y.argmax()
r_x[364]
best_rebate = r_x[364]*data['rebate'].std()+data['rebate'].mean()
best_rebate
r_y = n_final_solv.x[1] * diminishing_return(r_x,r_r) + (n_final_solv.x[4]*r_x*n_data['xmas'].min())
plt.plot(r_x, r_y)
plt.grid()
plt.xlabel('n_rebate')
plt.ylabel('n_sales')
a_x = np.linspace(n_data['ad.spent'].min(), n_data['ad.spent'].max(), num=100)
a_y = n_final_solv.x[2] * a_x + (n_final_solv.x[5] * a_x * n_data['xmas'].max())
plt.plot(a_x, a_y)
plt.grid()
plt.xlabel('n_ad.spent')
plt.ylabel('n_sales')
n_final_solv.x[2]
a_x = np.linspace(n_data['ad.spent'].min(), n_data['ad.spent'].max(), num=100)
a_y = n_final_solv.x[2] * a_x + (n_final_solv.x[5] * a_x * n_data['xmas'].min())
plt.plot(a_x, a_y)
plt.grid()
n_x[2].max()
plt.xlabel('n_ad.spent')
plt.ylabel('n_sales')
# xmas best choice
tmp_x = np.linspace(n_data['ad.spent'].min(), n_data['ad.spent'].max(), num=1000)
tmp_y = n_final_solv.x[3] + n_final_solv.x[4] * best_rebate + n_final_solv.x[5]*tmp_x
plt.plot(tmp_x, tmp_y)
| 17,962 |
/computer-vision/06_Tensorflow_ConvNet_MNIST_Steven_Mi.ipynb | 4082cde6dd046634f86d6b15b7f52c483b76e1d7 | [] | no_license | steven-mi/machine-learning-basics | https://github.com/steven-mi/machine-learning-basics | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 16,630 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] id="zXlk954chnX6"
# <h1>Convolutional Neural Networks anhand von MNIST</h1>
#
# <p>Das neuronale Netzwerk der letzten Übung brauchte nur einige hundert Megabyte Arbeitsspeicher. Die 60000 MNIST Bilder mit ihren 784 Pixeln, die Gewichtsmatrizen und alle Zwischenergebnisse, die im Netzwerk beim Vorwärtspass berechnet wurden, sind keine 350MB groß:<br />
# (60000∗ 784 + 784∗ 300 + 60000 ∗ 300 + 300 ∗ 10 + 60000 ∗ 10) ∗ 4 = 335MB <br />(MNISTData + Weights1 + Intermediate + Weight2 + Predictions) ∗ Float32</p>
#
# <p>Dieser Verbrauch steigt rasant an, wenn Konvolutionsfilter ins Spiel kommen. Werden die Eingangsdaten mit 10 Filtern beliebiger Größe (z.b. 3x3) gefalten, sind die ausgehenden Daten 10 mal so groß:<br />
# (60000∗784+10∗3∗3+60000∗10∗784)∗4=2GB</p>
#
# <p>Um das zu verhindern, sollten in Zukunft nicht mehr alle Daten auf einmal im Netzwerk verarbeitet werden. Stattdessen werden Mini-Batches benötigt.</p>
#
# <p>Das komplette Notebook steht wieder zum <a href="06_Tensorflow_ConvNet_MNIST_Vorlage.ipynb">download</a> bereit.</p>
#
# <hr>
#
# <h2>Neural Networks mit Mini-Batches</h2>
#
# <p>Im folgenden ist ein zweischichtiges neuronales Netzwerk implementiert, welches alle MNIST Ziffern auf einmal verarbeitet. Bauen Sie den Code so um, dass er stattdessen mit Mini-Batches funktioniert. Dabei können Sie Numpy verwenden und die Daten als Mini-Batch in den Computation-Graph von Tensorflow geben oder Sie benutzen Tensorflows Batch-Methoden, um die Batches innerhalb eines Graphens zu erzeugen. Die Königsdiziplin sind Tensorflow Esitmators, die die Arbeit des Batchings übernehmen, aber viele andere Anforderungen an das Netzwerk haben. Wichtig ist in allen Fällen, dass auch die Testdaten gebatched werden.</p>
#
# <ul>
# <li><strong>Numpy</strong>: Es ist möglich die Daten einmalig in kleine Batches zu unterteilen und diese dann in zufälliger Reihnfolge in den Computation-Graph zu geben. Besser jedoch ist die Variante, bei der erst im letzten Moment ein Batch aus dem gesamten Datensatz extrahiert wird. Der Extraktionsbereich sollte dabei zufällig gewählt sein. </li>
# <li><strong>Tensorflow <a href="https://www.tensorflow.org/guide/datasets" target="_blank">Dataset</a></strong>: Sind die Daten klein genug, dass Sie in den Arbeitsspeicher, aber nicht mit einen Durchlauf durch das Netzwerk passen, können sie zunächst komplett in den Graphen geladen werden und von dort in <a href="https://www.tensorflow.org/guide/datasets#batching_dataset_elements" target="_blank">kleine Batches</a> zerlegt werden. Mithilfe von <a href="https://www.tensorflow.org/guide/datasets#creating_an_iterator" target="_blank">Iteratoren</a> können diese Batches dann einzeln in das Netzwerk geschickt werden.</li>
# <li><strong>Tensorflow Estimator</strong>: Innerhalb der High-Level API von Tensorflow gibt es die Möglichkeit, Estimators zu verwenden. Diese übernehmen sämtliche Batching-Arbeiten, verlangen aber bestimmte Eigenschaften vom Computation-Graphen. So müssen Trainings- und Evaluierungsmethoden in einen sogenannten <a href="https://www.tensorflow.org/api_docs/python/tf/estimator/EstimatorSpec">EstimatorSpec</a> beschrieben werden, um später mit einen <a href="http://www.tensorflow.org/api_docs/python/tf/estimator">Estimator Model</a> arbeiten zu können.</li>
# </ul>
# + id="I2Z4xzBKiGUt"
# !pip install tensorflow-gpu==1.15.0
# !pip install deep-teaching-commons
# + id="tf8aHW8YhnX9"
import tensorflow as tf
import numpy as np
import time
from matplotlib import pyplot as plt
from tqdm import tqdm
from shutil import copyfileobj
from sklearn.datasets.base import get_data_home
from deep_teaching_commons.data.fundamentals import mnist
from sklearn.utils import check_random_state
from sklearn.preprocessing import OneHotEncoder
# + id="-R_fu0-_hnYJ"
X_train, y_train, X_test, y_test = mnist.Mnist().get_all_data(normalized=True, flatten=False)
X_train = X_train.reshape((-1, 28, 28, 1))
X_test = X_test.reshape((-1, 28, 28, 1))
# only shuffle train dataset
random_state = check_random_state(0)
permutation = random_state.permutation(X_train.shape[0])
X_train = X_train[permutation]
y_train = y_train[permutation]
print(X_train.shape, y_train.shape)
print(X_test.shape, y_test.shape)
enc = OneHotEncoder()
y_train = enc.fit_transform(np.expand_dims(y_train, axis=1)).toarray()
y_test = enc.fit_transform(np.expand_dims(y_test, axis=1)).toarray()
print(y_train.shape, y_test.shape)
# + id="sr4cPY8fhnYS"
def minibatcher(inputs, targets, batchsize, shuffle=True):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt]
# + [markdown] id="FAJpMQ1shnYY"
# <hr>
#
# <h2>Convolutional Neural Network mit MNIST Ziffern</h2>
#
# <p>Nachdem das neuronale Netzwerk mit Mini-Batches arbeitet, können die Fully-Connected (Dense) Layer mit Konvolutionsschichten ersetzt werden. Sinnvoll sind z.B. zwei Schichten mit 64 5x5 und 96 3x3 Filterkerneln. Um die Dimensionalität der Daten langsam zu reduzieren, können entweder Schrittweiten bei den Konvolutionsschichten eingestellt werden oder Pooling angewendet werden. Zum Schluss ist es hilfreich, die hochdimensionalen Daten zu flatten, um sie in Dense Layern auf 10 Dimensionen herunterzubrechen. Berechnen Sie wieder den Trainingsfehler und die Testgenauigkeit. Zu erwarten sind Genauigkeiten von bis zu 99%. </p>
#
# <p>Je nachdem welche Tensorflow Version Sie nutzen (mindestens aber Version >= 1.0), sind folgende Methoden hilfreich:</p>
#
# <ul>
# <li><a href="https://www.tensorflow.org/api_docs/python/tf/nn/max_pool" target="_blank">tf.nn.max_pool</a> oder <a href="https://www.tensorflow.org/versions/master/api_docs/python/tf/layers/max_pooling2d" target="_blank">tf.layers.max_pooling2d</a></li>
# <li><a href="https://www.tensorflow.org/versions/master/api_docs/python/tf/nn/conv2d" target="_blank">tf.nn.conv2d</a> oder <a href="https://www.tensorflow.org/versions/master/api_docs/python/tf/layers/conv2d" target="_blank">tf.layers.conv2d</a></li>
# <li><a href="https://www.tensorflow.org/versions/master/api_docs/python/tf/reshape" target="_blank">tf.reshape</a> oder <a href="https://www.tensorflow.org/versions/master/api_docs/python/tf/layers/flatten" target="_blank">tf.layers.Flatten</a></li>
# </ul>
#
# <p><strong>Optional</strong>: Yann LeCun hat vor fast 20 Jahren das MNIST Datenset herausgebracht und die Convolutionsnetzwerke erfunden. Damals gab es nicht die nötige Rechenleistung um in kurzer Zeit die notwendigen Filterkernel mittels Backpropagation und Gradient Descent zu erlernen. Seine Netzwerke sind daher sehr minimalistisch. Implementieren Sie das <a href="http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf" target="_blank">LeNet5</a> Netzwerk nach seinen Vorbild. Padden Sie dazu die Eingangsdaten, damit die Bilder 32x32 Pixel haben und verwenden Sie nur 6, 16 und 120 Filterkernel (je 5x5 Pixel groß) für die drei Konvolutionsschichten in LeNet5. Natürlich können Sie auch LeCun's <a href="http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf" target="_blank">Stochastic gradient descent</a> nutzen um ihr Netzwerk zu trainieren oder gar den <a href="https://arxiv.org/pdf/1412.6980v8.pdf" target="_blank">Adam Optimizer</a>.</p>
#
# <p> </p>
#
# 
# + id="OdeQA_3hhnYZ"
# pixel count
num_input = 28
# num of classes
num_classes = 10
# learn rate
learning_rate = 0.001
batch_size = 128
# + id="YhOJw9j3hnYf"
# computation graph
graph = tf.Graph()
with graph.as_default():
# input data with fix shape to infer shapes of other graph nodes a build time
x_input = tf.placeholder(dtype=tf.float32, shape=[None, num_input, num_input, 1], name='x')
y_input = tf.placeholder(tf.int64, shape=[None, num_classes], name='y')
layer1 = tf.layers.conv2d(inputs=x_input, filters=64, kernel_size=(5,5), activation=tf.nn.relu)
layer2 = tf.layers.conv2d(inputs=layer1, filters=64, kernel_size=(5,5), activation=tf.nn.relu)
flatten = tf.layers.flatten(layer2)
dense = tf.layers.dense(inputs=flatten, units=128, activation=tf.nn.relu)
prediction = tf.layers.dense(inputs=dense, units=num_classes)
# compute trainings error
cost = tf.losses.softmax_cross_entropy(onehot_labels=y_input, logits=prediction)
# use the Adam optimizer to derive the cost function and update the weights
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# accuracy for multiple batches
softmax = tf.nn.softmax(prediction)
acc, update_acc = tf.metrics.accuracy(labels=tf.argmax(y_input, 1), predictions=tf.argmax(softmax, axis=-1))
# + id="dMkB6t8ThnYi"
# start a new session
with tf.Session(graph=graph) as session:
# initialize weights and bias variables
session.run(tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()))
# check against test set
print("Test accuracy ", session.run(update_acc, feed_dict={x_input: X_test, y_input: y_test}))
# reset accuracy
session.run(tf.local_variables_initializer())
# train for a few iterations
ts = time.time()
train_errors = []
for i in range(100):
train_batcher = minibatcher(X_train, y_train, batch_size)
temp = []
for i, (X_batch, y_batch) in enumerate(train_batcher):
c, _ = session.run([cost, optimizer], feed_dict={x_input: X_batch, y_input: y_batch})
temp.append(c)
train_errors.append(np.mean(temp))
print("Improved train error from ", train_errors[0], " to ", train_errors[-1], " in ", str(time.time()-ts), "secs")
test_batcher = minibatcher(X_test, y_test, batch_size)
for i, (X_batch, y_batch) in enumerate(test_batcher):
session.run(update_acc, feed_dict={x_input: X_batch, y_input: y_batch})
# check against test set
print("Test accuracy ", session.run(acc))
# + id="D1YZPl1QhnYl"
# plot the train errors
plt.plot(train_errors)
plt.show()
# + [markdown] id="0Q-Jrlq7hnYn"
# <hr />
#
# <h2>Abgabe</h2>
#
# Bevor sie das Notebook in Moodle hochladen entfernen sie bitte über "Kernel" -> "Restart and Clear Output" sämtlichen von Python erstellten Inhalt und speichern anschließend das Notebook "File" -> "Save and Checkpoint" erneut ab. Sorgen sie bitte außerdem dafür das im Dateinamen ihr Vor- und Nachname steht, ich empfehle folgende Namensgebung: "06_Tensorflow_ConvNet_MNIST_VORNAME_NACHNAME.ipynb"
| 11,273 |
/Intermediate_Linear_Algebra_Assignment.ipynb | cdc63bf911818d2c2ae5993674435acd73614da3 | [] | no_license | Captmoonshot/DS-Unit-2-Sprint-1-Linear-Algebra | https://github.com/Captmoonshot/DS-Unit-2-Sprint-1-Linear-Algebra | 0 | 0 | null | 2019-01-07T13:22:27 | 2019-01-06T09:50:43 | null | Jupyter Notebook | false | false | .py | 209,020 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# New York City has published data on student SAT scores by high school, along with additional demographic data sets. we have to combine the following data sets into a single, clean pandas dataframe combined:
#
# ## SAT scores by school - SAT scores for each high school in New York City
# ## School attendance - Attendance information for each school in New York City
# ## Class size - Information on class size for each school
# ## AP test results - Advanced Placement (AP) exam results for each high school (passing an optional AP exam in a particular subject can earn a student college credit in that subject)
# ## Graduation outcomes - The percentage of students who graduated, and other outcome information
# ## Demographics - Demographic information for each school
# ## School survey - Surveys of parents, teachers, and students at each school
#
# The Goal : New York City has a significant immigrant population and is very diverse, so comparing demographic factors such as race, income, and gender with SAT scores is a good way to determine whether the SAT is a fair test. For example, if certain racial groups consistently perform better on the SAT, we would have some evidence that the SAT is unfair.
# # Read in the data
#
# +
import pandas as pd
import numpy
import re
# %matplotlib inline
data_files = [
"ap_2010.csv",
"class_size.csv",
"demographics.csv",
"graduation.csv",
"hs_directory.csv",
"sat_results.csv"
]
data = {}
for f in data_files:
d = pd.read_csv("schools/{0}".format(f))
data[f.replace(".csv", "")] = d
# -
# # Read in the surveys
# +
all_survey = pd.read_csv("schools/survey_all.txt", delimiter="\t", encoding='windows-1252')
d75_survey = pd.read_csv("schools/survey_d75.txt", delimiter="\t", encoding='windows-1252')
survey = pd.concat([all_survey, d75_survey], axis=0)
survey["DBN"] = survey["dbn"]
survey_fields = [
"DBN",
"rr_s",
"rr_t",
"rr_p",
"N_s",
"N_t",
"N_p",
"saf_p_11",
"com_p_11",
"eng_p_11",
"aca_p_11",
"saf_t_11",
"com_t_11",
"eng_t_11",
"aca_t_11",
"saf_s_11",
"com_s_11",
"eng_s_11",
"aca_s_11",
"saf_tot_11",
"com_tot_11",
"eng_tot_11",
"aca_tot_11",
]
survey = survey.loc[:,survey_fields]
data["survey"] = survey
# -
# # Add DBN columns
# +
data["hs_directory"]["DBN"] = data["hs_directory"]["dbn"]
def pad_csd(num):
string_representation = str(num)
if len(string_representation) > 1:
return string_representation
else:
return "0" + string_representation
data["class_size"]["padded_csd"] = data["class_size"]["CSD"].apply(pad_csd)
data["class_size"]["DBN"] = data["class_size"]["padded_csd"] + data["class_size"]["SCHOOL CODE"]
# -
# # Convert columns to numeric
# +
cols = ['SAT Math Avg. Score', 'SAT Critical Reading Avg. Score', 'SAT Writing Avg. Score']
for c in cols:
data["sat_results"][c] = pd.to_numeric(data["sat_results"][c], errors="coerce")
data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]
def find_lat(loc):
coords = re.findall("\(.+, .+\)", loc)
lat = coords[0].split(",")[0].replace("(", "")
return lat
def find_lon(loc):
coords = re.findall("\(.+, .+\)", loc)
lon = coords[0].split(",")[1].replace(")", "").strip()
return lon
data["hs_directory"]["lat"] = data["hs_directory"]["Location 1"].apply(find_lat)
data["hs_directory"]["lon"] = data["hs_directory"]["Location 1"].apply(find_lon)
data["hs_directory"]["lat"] = pd.to_numeric(data["hs_directory"]["lat"], errors="coerce")
data["hs_directory"]["lon"] = pd.to_numeric(data["hs_directory"]["lon"], errors="coerce")
# -
# # Condense datasets
# +
class_size = data["class_size"]
class_size = class_size[class_size["GRADE "] == "09-12"]
class_size = class_size[class_size["PROGRAM TYPE"] == "GEN ED"]
class_size = class_size.groupby("DBN").agg(numpy.mean)
class_size.reset_index(inplace=True)
data["class_size"] = class_size
data["demographics"] = data["demographics"][data["demographics"]["schoolyear"] == 20112012]
data["graduation"] = data["graduation"][data["graduation"]["Cohort"] == "2006"]
data["graduation"] = data["graduation"][data["graduation"]["Demographic"] == "Total Cohort"]
# -
# # Convert AP scores to numeric
# +
cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']
for col in cols:
data["ap_2010"][col] = pd.to_numeric(data["ap_2010"][col], errors="coerce")
# -
# # Combine the datasets
# +
combined = data["sat_results"]
combined = combined.merge(data["ap_2010"], on="DBN", how="left")
combined = combined.merge(data["graduation"], on="DBN", how="left")
to_merge = ["class_size", "demographics", "survey", "hs_directory"]
for m in to_merge:
combined = combined.merge(data[m], on="DBN", how="inner")
combined = combined.fillna(combined.mean())
combined = combined.fillna(0)
# -
# # Add a school district column for mapping
# +
def get_first_two_chars(dbn):
return dbn[0:2]
combined["school_dist"] = combined["DBN"].apply(get_first_two_chars)
# -
# # Find correlations
correlations = combined.corr()
correlations = correlations["sat_score"]
print(correlations)
# # Plotting survey correlations
# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.
combined[survey_fields]
sur_corr1 =combined[survey_fields].copy()
sur_corr1["sat_score"] = combined["sat_score"]
corr_sur = sur_corr1.corr()
corr_sur["sat_score"]
combined.corr()["sat_score"][survey_fields].plot.bar()
# some columns like N_s,N_t,N_p have high correlations and the com_p_11 has a negative correlation
combined.plot.scatter("saf_s_11","sat_score")
# It appears to be a correlation between SAT scores and safety, eventhough it isn't strong. It looks like there are some schools with high SAT scores and high safety scores. There are a few schools with low safety scores and low SAT scores. No school with a safety score lower than 6.5 has an average SAT score higher than 1500 or so
# +
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
districts = combined.groupby("school_dist").agg(numpy.mean)
districts.reset_index(inplace=True)
m = Basemap(
projection='merc',
llcrnrlat=40.496044,
urcrnrlat=40.915256,
llcrnrlon=-74.255735,
urcrnrlon=-73.700272,
resolution='i'
)
m.drawmapboundary(fill_color='#85A6D9')
m.drawcoastlines(color='#6D5F47', linewidth=.4)
m.drawrivers(color='#6D5F47', linewidth=.4)
longitudes = districts["lon"].tolist()
latitudes = districts["lat"].tolist()
m.scatter(longitudes, latitudes, s=50, zorder=2, latlon=True, c=districts["saf_s_11"], cmap="summer")
plt.show()
# -
# # Analysis based on race
race_fields = ["white_per", "asian_per", "black_per", "hispanic_per"]
combined.corr()["sat_score"][race_fields].plot.bar()
# It looks like a higher percentage of white or asian students at a school correlates positively with sat, whereas a higher percentage of black or hispanic students correlates negatively with sat score.
combined.plot.scatter("hispanic_per", "sat_score")
print(combined[combined["hispanic_per"] > 95]["SCHOOL NAME"])
print(combined[(combined["hispanic_per"] < 10) & (combined["sat_score"] > 1800)]["SCHOOL NAME"])
# The reason i think the schools perform well is the students are from all over New York who did well on a standardized test.
# # Gender differences in SAT scores
gender_fields = ["male_per", "female_per"]
combined.corr()["sat_score"][gender_fields].plot.bar()
# it is clear that high percentage of female positively correlates with the sat score where a high percent of the male students correlates negatively
combined.plot.scatter("female_per", "sat_score")
# Based on the scatterplot, there doesn't seem to be any real correlation between sat_score and female_per. However, there is a cluster of schools with a high percentage of females (60 to 80), and high SAT scores.
print(combined[(combined["female_per"] > 60) & (combined["sat_score"] > 1700)]["SCHOOL NAME"])
# # AP Exam Scores vs SAT Scores
# +
combined["ap_per"] = combined["AP Test Takers "] / combined["total_enrollment"]
combined.plot.scatter(x='ap_per', y='sat_score')
# -
# It appears that there is a relationship between the percentage of students who take the AP exam, and their average SAT scores. It's not a strong correlation, though.
| 8,717 |
/SF Salaries - LSR.ipynb | ac74dec2fd993173cbb78053c19e1d2c86d92d58 | [] | no_license | Prabir1/python_files | https://github.com/Prabir1/python_files | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 13,322 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import requests
import json
url = "https://api.github.com/users/{}"
username = input("Please privide your username : ")
output = requests.get(url.format(username))
profile = json.loads(output.text)
profile["name"]
profile["avatar_url"]
pic_out = requests.get(profile["avatar_url"])
data = pic_out.content
f = open("{}.png".format(username), "wb")
f.write(data)
f.close()
# -
Year"].value_counts
# ** Use the .info() method to find out how many entries there are.**
# **What is the average BasePay ?**
# ** What is the highest amount of OvertimePay in the dataset ? **
# ** What is the job title of JOSEPH DRISCOLL ? Note: Use all caps, otherwise you may get an answer that doesn't match up (there is also a lowercase Joseph Driscoll). **
# ** How much does JOSEPH DRISCOLL make (including benefits)? **
# ** What is the name of highest paid person (including benefits)?**
# ** What is the name of lowest paid person (including benefits)? Do you notice something strange about how much he or she is paid?**
# ** What was the average (mean) BasePay of all employees per year? (2011-2014) ? **
# ** How many unique job titles are there? **
# ** What are the top 5 most common jobs? **
# ** How many Job Titles were represented by only one person in 2013? (e.g. Job Titles with only one occurence in 2013?) **
| 1,625 |
/notebooks/TensorFlow/images/9-building-autoencoders-in-keras.ipynb | 86c6f82d2558f25c65a8ea1b260348d5bf8f7229 | [] | no_license | tex2e/notebook | https://github.com/tex2e/notebook | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 213,628 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# [Building Autoencoders in Keras](https://blog.keras.io/building-autoencoders-in-keras.html)
# Autoencoders について
#
# <img src="data:image/png;base64,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" />
# + _cell_guid="79c7e3d0-c299-4dcb-8224-4455121ee9b0" _uuid="d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
# -
# ### MNISTの数字画像データの準備
from keras.datasets import mnist
import numpy as np
(x_train, _), (x_test, _) = mnist.load_data()
# +
# 正規化
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# Flatten
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print(x_train.shape)
print(x_test.shape)
# -
# ### モデルの構築
# +
# 入力画像のサイズ
IMG_SIZE = 28
# Autoencoderの作成
# 配列の要素数は 784 --encoder--> 32 --decoder--> 784 のように変化する
autoencoder = tf.keras.models.Sequential([
Dense(32, activation='relu', input_shape=(IMG_SIZE*IMG_SIZE,)),
Dense(IMG_SIZE*IMG_SIZE, activation='sigmoid')
])
autoencoder.compile(optimizer='adam',
loss='binary_crossentropy')
autoencoder.summary()
# -
autoencoder.fit(x_train, x_train, # データとラベルが同じであることに注意
epochs=10,
batch_size=256,
shuffle=True,
validation_data=(x_test, x_test))
# ## 画像化して確認する
#
# - 上の行がオリジナルの画像
# - 下の行がAutoencoderによって再構築した画像
# +
decoded_imgs = autoencoder.predict(x_test)
n = 10 # 表示したい画像の個数
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(IMG_SIZE, IMG_SIZE))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(IMG_SIZE, IMG_SIZE))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
# -
# ## Deep autoencoder
#
# 層の数を増やすことも可能
# +
autoencoder = tf.keras.models.Sequential([
# Begin Encoder
Dense(128, activation='relu', input_shape=(IMG_SIZE*IMG_SIZE,)),
Dense(64, activation='relu'),
Dense(32, activation='relu'),
# End Encoder
# Begin Decoder
Dense(64, activation='relu'),
Dense(128, activation='relu'),
Dense(IMG_SIZE*IMG_SIZE, activation='sigmoid')
# End Decoder
])
autoencoder.compile(optimizer='adam',
loss='binary_crossentropy')
autoencoder.summary()
# -
autoencoder.fit(x_train, x_train, # データとラベルが同じであることに注意
epochs=20,
batch_size=256,
shuffle=True,
validation_data=(x_test, x_test))
# ### 画像化して確認する
#
# - 上の行がオリジナルの画像
# - 下の行がAutoencoderによって再構築した画像
# +
decoded_imgs = autoencoder.predict(x_test)
n = 10 # 表示したい画像の個数
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(IMG_SIZE, IMG_SIZE))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(IMG_SIZE, IMG_SIZE))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
# -
# ### Convolutional autoencoder
#
# 畳み込みをすることも可能
#
# (ただし、Dense層を使わないので Sequential でモデルを定義できない点に注意)
# +
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
input_img = Input(shape=(28, 28, 1)) # adapt this if using `channels_first` image data format
x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
# at this point the representation is (4, 4, 8) i.e. 128-dimensional
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = Model(inputs=input_img, outputs=decoded)
autoencoder.compile(optimizer='adadelta',
loss='binary_crossentropy')
autoencoder.summary()
# +
(x_train, _), (x_test, _) = mnist.load_data()
# 正規化
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# 次元を (?, 28, 28) から (?, 28, 28, 1) に変換する
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
autoencoder.fit(x_train, x_train, # データとラベルが同じであることに注意
epochs=10,
batch_size=128,
shuffle=True,
validation_data=(x_test, x_test))
# -
# ### 画像化して確認する
#
# - 上の行がオリジナルの画像
# - 下の行がAutoencoderによって再構築した画像
# +
decoded_imgs = autoencoder.predict(x_test)
n = 10 # 表示したい画像の個数
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(IMG_SIZE, IMG_SIZE))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(IMG_SIZE, IMG_SIZE))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
# -
# ### 画像のノイズ除去
#
# Autoencodersの応用として、画像のノイズ除去がある
# +
# ノイズを加える
noise_factor = 0.5
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
# 範囲が0〜1となるように、はみ出た値を最大値/最小値にする
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
# ノイズを加えた画像の表示
n = 10
plt.figure(figsize=(20, 2))
for i in range(n):
ax = plt.subplot(1, n, i+1)
plt.imshow(x_test_noisy[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
# +
input_img = Input(shape=(28, 28, 1))
x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
# at this point the representation is (7, 7, 32)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = Model(inputs=input_img, outputs=decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.summary()
# -
# ### モデルの訓練
#
# - 入力データ : ノイズを加えた画像
# - 教師データ : 元の画像
autoencoder.fit(x_train_noisy, x_train,
epochs=5,
batch_size=128,
shuffle=True,
validation_data=(x_test_noisy, x_test))
# ### 画像化して確認する
#
# - 上の行がオリジナルの画像
# - 下の行がAutoencoderによって再構築した画像
# +
decoded_imgs = autoencoder.predict(x_train_noisy)
n = 10 # 表示したい画像の個数
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_train_noisy[i].reshape(IMG_SIZE, IMG_SIZE))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(IMG_SIZE, IMG_SIZE))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
# -
# 見た感じでは、上手にノイズ除去ができている
| 52,374 |
/dog-project/dog_app.ipynb | b51ee5a3140ed61073169be8b1a2e559ce37a756 | [] | no_license | xuzhe0628/mlnd-projects | https://github.com/xuzhe0628/mlnd-projects | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 1,565,490 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python [default]
# language: python
# name: python3
# ---
# + [markdown] deletable=true editable=true
# # Artificial Intelligence Nanodegree
#
# ## Convolutional Neural Networks
#
# ## Project: Write an Algorithm for a Dog Identification App
#
# ---
#
# In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with **'(IMPLEMENTATION)'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
#
# > **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n",
# "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission.
#
# In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
#
# >**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
#
# The rubric contains _optional_ "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.
#
#
#
# ---
# ### Why We're Here
#
# In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).
#
# 
#
# In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
#
# ### The Road Ahead
#
# We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
#
# * [Step 0](#step0): Import Datasets
# * [Step 1](#step1): Detect Humans
# * [Step 2](#step2): Detect Dogs
# * [Step 3](#step3): Create a CNN to Classify Dog Breeds (from Scratch)
# * [Step 4](#step4): Use a CNN to Classify Dog Breeds (using Transfer Learning)
# * [Step 5](#step5): Create a CNN to Classify Dog Breeds (using Transfer Learning)
# * [Step 6](#step6): Write your Algorithm
# * [Step 7](#step7): Test Your Algorithm
#
# ---
# <a id='step0'></a>
# ## Step 0: Import Datasets
#
# ### Import Dog Dataset
#
# In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the `load_files` function from the scikit-learn library:
# - `train_files`, `valid_files`, `test_files` - numpy arrays containing file paths to images
# - `train_targets`, `valid_targets`, `test_targets` - numpy arrays containing onehot-encoded classification labels
# - `dog_names` - list of string-valued dog breed names for translating labels
# + deletable=true editable=true
from sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
# define function to load train, test, and validation datasets
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
return dog_files, dog_targets
# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')
# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]
# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
# + [markdown] deletable=true editable=true
# ### Import Human Dataset
#
# In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array `human_files`.
# + deletable=true editable=true
import random
random.seed(8675309)
# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)
# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
# + [markdown] deletable=true editable=true
# ---
# <a id='step1'></a>
# ## Step 1: Detect Humans
#
# We use OpenCV's implementation of [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on [github](https://github.com/opencv/opencv/tree/master/data/haarcascades). We have downloaded one of these detectors and stored it in the `haarcascades` directory.
#
# In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
# + deletable=true editable=true
import cv2
import matplotlib.pyplot as plt
# %matplotlib inline
# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
# + [markdown] deletable=true editable=true
# Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The `detectMultiScale` function executes the classifier stored in `face_cascade` and takes the grayscale image as a parameter.
#
# In the above code, `faces` is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as `x` and `y`) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as `w` and `h`) specify the width and height of the box.
#
# ### Write a Human Face Detector
#
# We can use this procedure to write a function that returns `True` if a human face is detected in an image and `False` otherwise. This function, aptly named `face_detector`, takes a string-valued file path to an image as input and appears in the code block below.
# + deletable=true editable=true
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Assess the Human Face Detector
#
# __Question 1:__ Use the code cell below to test the performance of the `face_detector` function.
# - What percentage of the first 100 images in `human_files` have a detected human face?
# - What percentage of the first 100 images in `dog_files` have a detected human face?
#
# Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays `human_files_short` and `dog_files_short`.
#
# __Answer:__
#
# Regarding the first 100 images in `human_files`, 99% of them have a detected human face.<br>
# Regarding the first 100 images in `dog_files`, 11% of them have a detected human face.<br>
# + deletable=true editable=true
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
human_result = np.array([face_detector(image) for image in human_files_short])
dog_result = np.array([face_detector(image) for image in dog_files_short])
print(np.count_nonzero(human_result))
print(np.count_nonzero(dog_result))
# + [markdown] deletable=true editable=true
# __Question 2:__ This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?
#
# __Answer:__
#
# I think it is a resonable expectation to pose on the user as there is no perfect algorithm. No algorithm can achieve 100% accuracy without constraint on input. Although we can find another face detector that performs better on blurry images, I still think it will be more effiencient and effective to tell users that this function may not work well on blurry images. Tell user the limitation, it is good for managing users' expectation.
#
# We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this _optional_ task, report performance on each of the datasets.
# + deletable=true editable=true
## (Optional) TODO: Report the performance of another
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.
# + [markdown] deletable=true editable=true
# ---
# <a id='step2'></a>
# ## Step 2: Detect Dogs
#
# In this section, we use a pre-trained [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on [ImageNet](http://www.image-net.org/), a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a). Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.
# + deletable=true editable=true
from keras.applications.resnet50 import ResNet50
# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
# + [markdown] deletable=true editable=true
# ### Pre-process the Data
#
# When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape
#
# $$
# (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}),
# $$
#
# where `nb_samples` corresponds to the total number of images (or samples), and `rows`, `columns`, and `channels` correspond to the number of rows, columns, and channels for each image, respectively.
#
# The `path_to_tensor` function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape
#
# $$
# (1, 224, 224, 3).
# $$
#
# The `paths_to_tensor` function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape
#
# $$
# (\text{nb_samples}, 224, 224, 3).
# $$
#
# Here, `nb_samples` is the number of samples, or number of images, in the supplied array of image paths. It is best to think of `nb_samples` as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!
# + deletable=true editable=true
from keras.preprocessing import image
from tqdm import tqdm
def path_to_tensor(img_path):
# loads RGB image as PIL.Image.Image type
img = image.load_img(img_path, target_size=(224, 224))
# convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
x = image.img_to_array(img)
# convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
return np.expand_dims(x, axis=0)
def paths_to_tensor(img_paths):
list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
return np.vstack(list_of_tensors)
# + [markdown] deletable=true editable=true
# ### Making Predictions with ResNet-50
#
# Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function `preprocess_input`. If you're curious, you can check the code for `preprocess_input` [here](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py).
#
# Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the `predict` method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the `ResNet50_predict_labels` function below.
#
# By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a).
# + deletable=true editable=true
from keras.applications.resnet50 import preprocess_input, decode_predictions
def ResNet50_predict_labels(img_path):
# returns prediction vector for image located at img_path
img = preprocess_input(path_to_tensor(img_path))
return np.argmax(ResNet50_model.predict(img))
# + [markdown] deletable=true editable=true
# ### Write a Dog Detector
#
# While looking at the [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a), you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from `'Chihuahua'` to `'Mexican hairless'`. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the `ResNet50_predict_labels` function above returns a value between 151 and 268 (inclusive).
#
# We use these ideas to complete the `dog_detector` function below, which returns `True` if a dog is detected in an image (and `False` if not).
# + deletable=true editable=true
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
prediction = ResNet50_predict_labels(img_path)
return ((prediction <= 268) & (prediction >= 151))
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Assess the Dog Detector
#
# __Question 3:__ Use the code cell below to test the performance of your `dog_detector` function.
# - What percentage of the images in `human_files_short` have a detected dog?
# - What percentage of the images in `dog_files_short` have a detected dog?
#
# __Answer:__
#
# Regarding `human_files_short`, 1% of them have a detected dog.<br>
# Regarding `dog_files_short`, 100% of them have a detected dog.<br>
# + deletable=true editable=true
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
human_result = np.array([dog_detector(image) for image in human_files_short])
dog_result = np.array([dog_detector(image) for image in dog_files_short])
print(np.count_nonzero(human_result))
print(np.count_nonzero(dog_result))
# + [markdown] deletable=true editable=true
# ---
# <a id='step3'></a>
# ## Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
#
# Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN _from scratch_ (so, you can't use transfer learning _yet_!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
#
# Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.
#
# We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that *even a human* would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.
#
# Brittany | Welsh Springer Spaniel
# - | -
# <img src="images/Brittany_02625.jpg" width="100"> | <img src="images/Welsh_springer_spaniel_08203.jpg" width="200">
#
# It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
#
# Curly-Coated Retriever | American Water Spaniel
# - | -
# <img src="images/Curly-coated_retriever_03896.jpg" width="200"> | <img src="images/American_water_spaniel_00648.jpg" width="200">
#
#
# Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
#
# Yellow Labrador | Chocolate Labrador | Black Labrador
# - | -
# <img src="images/Labrador_retriever_06457.jpg" width="150"> | <img src="images/Labrador_retriever_06455.jpg" width="240"> | <img src="images/Labrador_retriever_06449.jpg" width="220">
#
# We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
#
# Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
#
# ### Pre-process the Data
#
# We rescale the images by dividing every pixel in every image by 255.
# + deletable=true editable=true
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Model Architecture
#
# Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
#
# model.summary()
#
# We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:
#
# 
#
# __Question 4:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.
#
# __Answer:__
#
# I use the recommended CNN architecture considering the long training and testing time.The accuracy is 1.55%. I think this architecture is a good start as it is a good way to use a combination of convolutional layers with max pooling layers. It will reduce the spatial size of inputs and find local patterns. This architecture uses 16, 32 and 64 filters at each convolutional layers. I think this is a resonable parameters as we may need this number of filters to detect edges of dog legs, bodys and heads, which will help on deceide the dog breed. At last, we use a global average pooling layer to convert all arrays into an array with 64 features. The combination of 64 features will be enough for detecting different dog breeds.
# + deletable=true editable=true
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
model = Sequential()
### TODO: Define your architecture.
model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(GlobalAveragePooling2D())
model.add(Dense(133, activation='softmax'))
model.summary()
# + [markdown] deletable=true editable=true
# ### Compile the Model
# + deletable=true editable=true
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Train the Model
#
# Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
#
# You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement.
# + deletable=true editable=true
from keras.callbacks import ModelCheckpoint
### TODO: specify the number of epochs that you would like to use to train the model.
epochs = 3
### Do NOT modify the code below this line.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5',
verbose=1, save_best_only=True)
model.fit(train_tensors, train_targets,
validation_data=(valid_tensors, valid_targets),
epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
# + [markdown] deletable=true editable=true
# ### Load the Model with the Best Validation Loss
# + deletable=true editable=true
model.load_weights('saved_models/weights.best.from_scratch.hdf5')
# + [markdown] deletable=true editable=true
# ### Test the Model
#
# Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.
# + deletable=true editable=true
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]
# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
# + [markdown] deletable=true editable=true
# ---
# <a id='step4'></a>
# ## Step 4: Use a CNN to Classify Dog Breeds
#
# To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.
#
# ### Obtain Bottleneck Features
# + deletable=true editable=true
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']
# + [markdown] deletable=true editable=true
# ### Model Architecture
#
# The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.
# + deletable=true editable=true
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))
VGG16_model.summary()
# + [markdown] deletable=true editable=true
# ### Compile the Model
# + deletable=true editable=true
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
# + [markdown] deletable=true editable=true
# ### Train the Model
# + deletable=true editable=true
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5',
verbose=1, save_best_only=True)
VGG16_model.fit(train_VGG16, train_targets,
validation_data=(valid_VGG16, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
# + [markdown] deletable=true editable=true
# ### Load the Model with the Best Validation Loss
# + deletable=true editable=true
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')
# + [markdown] deletable=true editable=true
# ### Test the Model
#
# Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.
# + deletable=true editable=true
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]
# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
# + [markdown] deletable=true editable=true
# ### Predict Dog Breed with the Model
# + deletable=true editable=true
from extract_bottleneck_features import *
def VGG16_predict_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = VGG16_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
# + [markdown] deletable=true editable=true
# ---
# <a id='step5'></a>
# ## Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
#
# You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
#
# In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:
# - [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features
# - [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features
# - [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features
# - [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features
#
# The files are encoded as such:
#
# Dog{network}Data.npz
#
# where `{network}`, in the above filename, can be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the `bottleneck_features/` folder in the repository.
#
# ### (IMPLEMENTATION) Obtain Bottleneck Features
#
# In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:
#
# bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
# train_{network} = bottleneck_features['train']
# valid_{network} = bottleneck_features['valid']
# test_{network} = bottleneck_features['test']
# + deletable=true editable=true
### TODO: Obtain bottleneck features from another pre-trained CNN.
bottleneck_features = np.load('bottleneck_features/DogResnet50Data.npz')
train_Resnet50 = bottleneck_features['train']
valid_Resnet50 = bottleneck_features['valid']
test_Resnet50 = bottleneck_features['test']
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Model Architecture
#
# Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
#
# <your model's name>.summary()
#
# __Question 5:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
#
# __Answer:__
#
# As Resnet50 is a tested and effective architecture in image recognition, I would like to use it as feature extractor. Thus, I use Resnet50 output as my model's input. It will provide good features for recognizing dogs. Then I add a global averaging layer to reduce the spacial size of the features, which will increase the computing speed. At last, I add a dense layer as the output layer with 133 possible labels, which is the number of all dog breeds in our data.
#
# I believe it will work or at least imrpove the performance as Resnet50 extracts good features from images that clearly reflect patterns of different dog breads.
# + deletable=true editable=true
### TODO: Define your architecture.
Resnet50_model = Sequential()
Resnet50_model.add(GlobalAveragePooling2D(input_shape=train_Resnet50.shape[1:]))
Resnet50_model.add(Dense(133, activation='softmax'))
Resnet50_model.summary()
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Compile the Model
# + deletable=true editable=true
### TODO: Compile the model.
Resnet50_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Train the Model
#
# Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
#
# You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement.
# + deletable=true editable=true
### TODO: Train the model.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Resnet50.hdf5',
verbose=1, save_best_only=True)
Resnet50_model.fit(train_Resnet50, train_targets,
validation_data=(valid_Resnet50, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Load the Model with the Best Validation Loss
# + deletable=true editable=true
### TODO: Load the model weights with the best validation loss.
Resnet50_model.load_weights('saved_models/weights.best.Resnet50.hdf5')
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Test the Model
#
# Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.
# + deletable=true editable=true
### TODO: Calculate classification accuracy on the test dataset.
# get index of predicted dog breed for each image in test set
Resnet50_predictions = [np.argmax(Resnet50_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Resnet50]
# report test accuracy
test_accuracy = 100*np.sum(np.array(Resnet50_predictions)==np.argmax(test_targets, axis=1))/len(Resnet50_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
# + [markdown] deletable=true editable=true
# ### (IMPLEMENTATION) Predict Dog Breed with the Model
#
# Write a function that takes an image path as input and returns the dog breed (`Affenpinscher`, `Afghan_hound`, etc) that is predicted by your model.
#
# Similar to the analogous function in Step 5, your function should have three steps:
# 1. Extract the bottleneck features corresponding to the chosen CNN model.
# 2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
# 3. Use the `dog_names` array defined in Step 0 of this notebook to return the corresponding breed.
#
# The functions to extract the bottleneck features can be found in `extract_bottleneck_features.py`, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function
#
# extract_{network}
#
# where `{network}`, in the above filename, should be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`.
# + deletable=true editable=true
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
def Resnet50_predict_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_Resnet50(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = Resnet50_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
# + [markdown] deletable=true editable=true
# ---
# <a id='step6'></a>
# ## Step 6: Write your Algorithm
#
# Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
# - if a __dog__ is detected in the image, return the predicted breed.
# - if a __human__ is detected in the image, return the resembling dog breed.
# - if __neither__ is detected in the image, provide output that indicates an error.
#
# You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the `face_detector` and `dog_detector` functions developed above. You are __required__ to use your CNN from Step 5 to predict dog breed.
#
# Some sample output for our algorithm is provided below, but feel free to design your own user experience!
#
# 
#
#
# ### (IMPLEMENTATION) Write your Algorithm
# + deletable=true editable=true
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
def dog_breed_classifier(image_path):
img = cv2.imread(image_path)
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(cv_rgb)
if dog_detector(image_path):
print('Hello, dog.')
print('You are a ...{}'.format(Resnet50_predict_breed(image_path)))
elif face_detector(image_path):
print('Hello, human!')
print('You look like a ...{}'.format(Resnet50_predict_breed(image_path)))
else:
print('Neighter dog or human face detected.')
# + [markdown] deletable=true editable=true
# ---
# <a id='step7'></a>
# ## Step 7: Test Your Algorithm
#
# In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that __you__ look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
#
# ### (IMPLEMENTATION) Test Your Algorithm on Sample Images!
#
# Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
#
# __Question 6:__ Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
#
# __Answer:__
#
# I use 3 images of Labrador, 1 image of bulldog and 3 images of famous people. The algorithm successfully dectect all dogs and 2 of 3 humans. The dog breed are successfully detected. I think the output is better than my expectation. To increase the algorithm, I can use image augmentation and use more deep architectures and include dropout to avoid overfitting.
# + deletable=true editable=true
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
# + deletable=true editable=true
dog_breed_classifier('test_images/Labrador1.jpg')
# + deletable=true editable=true
dog_breed_classifier('test_images/Labrador2.jpg')
# + deletable=true editable=true
dog_breed_classifier('test_images/Labrador3.jpg')
# + deletable=true editable=true
dog_breed_classifier('test_images/bulldog.jpg')
# + deletable=true editable=true
dog_breed_classifier('test_images/eminem.jpg')
# + deletable=true editable=true
dog_breed_classifier('test_images/trump.jpg')
# + deletable=true editable=true
dog_breed_classifier('test_images/putin.jpg')
| 39,748 |
/Assignment1_Nevin.ipynb | f72cb4d7fa2c92421b6217d1eb99ab5bca18fbdd | [] | no_license | nevinmathews/assignment | https://github.com/nevinmathews/assignment | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,568 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Introductory applied machine learning (INFR10069)
# # Assignment 3 (Part B): Mini-Challenge [25%]
# ## Important Instructions
#
# **It is important that you follow the instructions below to the letter - we will not be responsible for incorrect marking due to non-standard practices.**
#
# 1. <font color='red'>We have split Assignment 3 into two parts to make it easier for you to work on them separately and for the markers to give you feedback. This is part B of Assignment 3 - Part A is an introduction to Object Recognition. Both Assignments together are still worth 50% of CourseWork 2. **Remember to submit both notebooks (you can submit them separately).**</font>
#
# 1. You *MUST* have your environment set up as in the [README](https://github.com/michael-camilleri/IAML2018) and you *must activate this environment before running this notebook*:
# ```
# source activate py3iaml
# # cd [DIRECTORY CONTAINING GIT REPOSITORY]
# jupyter notebook
# # Navigate to this file
# ```
#
# 1. Read the instructions carefully, especially where asked to name variables with a specific name. Wherever you are required to produce code you should use code cells, otherwise you should use markdown cells to report results and explain answers. In most cases we indicate the nature of answer we are expecting (code/text), and also provide the code/markdown cell where to put it
#
# 1. This part of the Assignment is the same for all students i.e. irrespective of whether you are taking the Level 10 version (INFR10069) or the Level-11 version of the course (INFR11182 and INFR11152).
#
# 1. The .csv files that you will be using are located at `./datasets` (i.e. use the `datasets` directory **adjacent** to this file).
#
# 1. In the textual answer, you are given a word-count limit of 600 words: exceeding this will lead to penalisation.
#
# 1. Make sure to distinguish between **attributes** (columns of the data) and **features** (which typically refers only to the independent variables, i.e. excluding the target variables).
#
# 1. Make sure to show **all** your code/working.
#
# 1. Write readable code. While we do not expect you to follow [PEP8](https://www.python.org/dev/peps/pep-0008/) to the letter, the code should be adequately understandable, with plots/visualisations correctly labelled. **Do** use inline comments when doing something non-standard. When asked to present numerical values, make sure to represent real numbers in the appropriate precision to exemplify your answer. Marks *WILL* be deducted if the marker cannot understand your logic/results.
#
# 1. **Collaboration:** You may discuss the assignment with your colleagues, provided that the writing that you submit is entirely your own. That is, you must NOT borrow actual text or code from others. We ask that you provide a list of the people who you've had discussions with (if any). Please refer to the [Academic Misconduct](http://web.inf.ed.ac.uk/infweb/admin/policies/academic-misconduct) page for what consistutes a breach of the above.
#
#
# ### SUBMISSION Mechanics
#
# **IMPORTANT:** You must submit this assignment by **Thursday 15/11/2018 at 16:00**.
#
# **Late submissions:** The policy stated in the School of Informatics is that normally you will not be allowed to submit coursework late. See the [ITO webpage](http://web.inf.ed.ac.uk/infweb/student-services/ito/admin/coursework-projects/late-coursework-extension-requests) for exceptions to this, e.g. in case of serious medical illness or serious personal problems.
#
# **Resubmission:** If you submit your file(s) again, the previous submission is **overwritten**. We will mark the version that is in the submission folder at the deadline.
#
# **N.B.**: This Assignment requires submitting **two files (electronically as described below)**:
# 1. This Jupyter Notebook (Part B), *and*
# 1. The Jupyter Notebook for Part A
#
# All submissions happen electronically. To submit:
#
# 1. Fill out this notebook (as well as Part A), making sure to:
# 1. save it with **all code/text and visualisations**: markers are NOT expected to run any cells,
# 1. keep the name of the file **UNCHANGED**, *and*
# 1. **keep the same structure**: retain the questions, **DO NOT** delete any cells and **avoid** adding unnecessary cells unless absolutely necessary, as this makes the job harder for the markers. This is especially important for the textual description and probability output (below).
#
# 1. Submit it using the `submit` functionality. To do this, you must be on a DICE environment. Open a Terminal, and:
# 1. **On-Campus Students**: navigate to the location of this notebook and execute the following command:
#
# ```submit iaml cw2 03_A_ObjectRecognition.ipynb 03_B_MiniChallenge.ipynb```
#
# 1. **Distance Learners:** These instructions also apply to those students who work on their own computer. First you need to copy your work onto DICE (so that you can use the `submit` command). For this, you can use `scp` or `rsync` (you may need to install these yourself). You can copy files to `student.ssh.inf.ed.ac.uk`, then ssh into it in order to submit. The following is an example. Replace entries in `[square brackets]` with your specific details: i.e. if your student number is for example s1234567, then `[YOUR USERNAME]` becomes `s1234567`.
#
# ```
# scp -r [FULL PATH TO 03_A_ObjectRecognition.ipynb] [YOUR USERNAME]@student.ssh.inf.ed.ac.uk:03_A_ObjectRecognition.ipynb
# scp -r [FULL PATH TO 03_B_MiniChallenge.ipynb] [YOUR USERNAME]@student.ssh.inf.ed.ac.uk:03_B_MiniChallenge.ipynb
# ssh [YOUR USERNAME]@student.ssh.inf.ed.ac.uk
# ssh student.login
# submit iaml cw2 03_A_ObjectRecognition.ipynb 03_B_MiniChallenge.ipynb
# ```
#
# What actually happens in the background is that your file is placed in a folder available to markers. If you submit a file with the same name into the same location, **it will *overwrite* your previous submission**. You should receive an automatic email confirmation after submission.
#
#
#
# ### Marking Breakdown
#
# The Level 10 and Level 11 points are marked out of different totals, however these are all normalised to 100%. Note that Part A (Object Recognition) is worth 75% of the total Mark for Assignment 3, while Part B (this notebook) is worth 25%. Keep this in mind when allocating time for this assignment.
#
# **70-100%** results/answer correct plus extra achievement at understanding or analysis of results. Clear explanations, evidence of creative or deeper thought will contribute to a higher grade.
#
# **60-69%** results/answer correct or nearly correct and well explained.
#
# **50-59%** results/answer in right direction but significant errors.
#
# **40-49%** some evidence that the student has gained some understanding, but not answered the questions
# properly.
#
# **0-39%** serious error or slack work.
#
# Note that while this is not a programming assignment, in questions which involve visualisation of results and/or long cold snippets, some marks may be deducted if the code is not adequately readable.
# ## Imports
#
# Use the cell below to include any imports you deem necessary.
# +
# Nice Formatting within Jupyter Notebook
# %matplotlib inline
from IPython.display import display # Allows multiple displays from a single code-cell
# System functionality
import sys
sys.path.append('..')
# Import Here any Additional modules you use. To import utilities we provide, use something like:
# from utils.plotter import plot_hinton
# Your Code goes here:
import os
import sys
import sklearn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.dummy import DummyClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import log_loss
from sklearn.preprocessing import StandardScaler,normalize
from sklearn.metrics import log_loss
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from numpy import savetxt
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from utils.plotter import plot_voronoi
from sklearn.model_selection import GridSearchCV
# -
# # Mini challenge
#
# In this second part of the assignment we will have a mini object-recognition challenge. Using the same type of data as in Part A, you are asked to find the best classifier for the person/no person classification task. You can apply any preprocessing steps to the data that you think fit and employ any classifier you like (with the provision that you can explain what the classifier is/preprocessing steps are doing). You can also employ any lessons learnt during the course, either from previous Assignments, the Labs or the lecture material to try and squeeze out as much performance as you possibly can. The only restriction is that all steps must be performed in `Python` by using the `numpy`, `pandas` and `sklearn` packages. You can also make use of `matplotlib` and `seaborn` for visualisation.
#
# ### DataSet Description
#
# The datasets we use here are similar in composition but not the same as the ones used in Part A: *it will be useful to revise the description in that notebook*. Specifically, you have access to three new datasets: a training set (`Images_C_Train.csv`), a validation set (`Images_C_Validate.csv`), and a test set (`Images_C_Test.csv`). You must use the former two for training and evaluating your models (as you see fit). As before, the full data-set has 520 attributes (dimensions). Of these you only have access to the 500 features (`dim1` through `dim500`) to test your model on: i.e. the test set does not have any of the class labels.
#
# ### Model Evaluation
#
# Your results will be evaluated in terms of the logarithmic loss metric, specifically the [logloss](http://scikit-learn.org/0.19/modules/model_evaluation.html#log-loss) function from SKLearn. You should familiarise yourself with this. To estimate this metric you will need to provide probability outputs, as opposed to discrete predictions which we have used so far to compute classification accuracies. Most models in `sklearn` implement a `predict_proba()` method which returns the probabilities for each class. For instance, if your test set consists of `N` datapoints and there are `K` class-labels, the method will return an `N` x `K` matrix (with rows summing to 1).
#
# ### Submission and Scoring
#
# This part of Assignment 3 carries 25% of the total marks. Within this, you will be scored on two criteria:
# 1. 80% of the mark will depend on the thoroughness of the exploration of various approaches. This will be assessed through your code, as well as a brief description (<600 words) justifying the approaches you considered, your exploration pattern and your suggested final approach (and why you chose it).
# 1. 20% of the mark will depend on the quality of your predictions: this will be evaluated based on the logarithmic loss metric.
# Note here that just getting exceptional performance is not enough: in fact, you should focus more on analysing your results that just getting the best score!
#
# You have to submit the following:
# 1. **All Code-Cells** which show your **working** with necessary output/plots already generated.
# 1. In **TEXT** cell `#ANSWER_TEXT#` you are to write your explanation (<600 words) as described above. Keep this brief and to the point. **Make sure** to keep the token `#ANSWER_TEXT#` as the first line of the cell!
# 1. In **CODE** cell `#ANSWER_PROB#` you are to submit your predictions. To do this:
# 1. Once you have chosen your favourite model (and pre-processing steps) apply it to the test-set and estimate the posterior proabilities for the data points in the test set.
# 1. Store these probabilities in a 2D numpy array named `pred_probabilities`, with predictions along the rows i.e. each row should be a complete probability distribution over whether the image contains a person or not. Note that due to the encoding of the `is_person` class, the negative case (i.e. there is no person) comes first.
# 1. Execute the `#ANSWER_PROB#` code cell, making sure to not change anything. This cell will do some checks to ensure that you are submitting the right shape of array.
#
# You may create as many code cells as you need (within reason) for training your models, evaluating the data etc: however, the text cell `#ANSWER_TEXT#` and code-cell `#ANSWER_PROB#` showing your answers must be the last two cells in the notebook.
# ## Exploring the datatsets
# +
# This is where your working code should start. Fell free to add as many code-cells as necessary.
# Make sure however that all working code cells come BEFORE the #ANSWER_TEXT# and #ANSWER_PROB#
# cells below.
# Your Code goes here:
# Loading the dataset
data_path_train = os.path.join(os.getcwd(), "datasets", "Images_C_Train.csv")
C_Train= pd.read_csv(data_path_train, delimiter = ",")
data_path_valid = os.path.join(os.getcwd(), "datasets", "Images_C_Validate.csv")
C_Val = pd.read_csv(data_path_valid, delimiter = ",")
data_path_test = os.path.join(os.getcwd(), "datasets", "Images_C_Test.csv")
C_Test = pd.read_csv(data_path_test, delimiter = ",")
print(C_Train.info())
print(C_Val.info())
print(C_Test.info())
# -
C_Train.head(3)
C_Val.head(3)
C_Test.head(3)
C_Train.describe()
dims=C_Train[C_Train.columns[pd.Series(C_Train.columns).str.startswith('dim')]]
X_tr =pd.concat([dims], axis=1)
dims=C_Val[C_Val.columns[pd.Series(C_Val.columns).str.startswith('dim')]]
X_val = pd.concat([dims], axis=1)
dims=C_Test[C_Test.columns[pd.Series(C_Test.columns).str.startswith('dim')]]
X_tst =pd.concat([dims], axis=1)
y_tr = C_Train["is_person"].values
y_val = C_Val["is_person"].values
y_tst = C_Test["is_person"].values
# ## Scatter plots
plt.scatter(x=X_tr["dim1"],y=y_tr)
plt.scatter(x=X_tr["dim500"],y=y_tr)
# ## Preprocessing
# +
#StandardScaler
stc = StandardScaler()
stc.fit(X_tr)
X_tr_std = stc.transform(X_tr)
X_val_std = stc.transform(X_val)
#Normalisation
X_tr_norm = normalize(X_tr, norm='l2')
X_val_norm = normalize(X_val, norm='l2')
# -
# ## Supervised Techniques
# ### Dummy Classifier
#
# +
#with Standard Scaler
dummy = DummyClassifier()#strategy='prior')
dummy.fit(X_tr_std,y_tr)
dummy_pred = dummy.predict(X_val_std)
print("Accuracy Score for X_val_std using the Dummy Classifier: {:.5f}".format(accuracy_score(y_val,dummy_pred)))
print("Logarithmic Loss for X_val_std using the Dummy Classifier: {:.5f}".format(log_loss(y_val,dummy_pred)))
#with Normalisation
dummy = DummyClassifier()#strategy='prior')
dummy.fit(X_tr_norm,y_tr)
dummy_pred = dummy.predict(X_val_norm)
print("Accuracy Score for X_val_norm using the Dummy Classifier: {:.5f}".format(accuracy_score(y_val,dummy_pred)))
print("Logarithmic Loss for X_val_norm using the Dummy Classifier: {:.5f}".format(log_loss(y_val,dummy_pred)))
# -
# ### Trying Logistic Regression, the RandomForestClassifier, and SVC.
# +
# Logistic Regression with StandardScale
LR = LogisticRegression(solver='lbfgs')
LR.fit(X_tr_std,y_tr)
LR_pred = LR.predict(X_val_std)
print("Accuracy Score for X_val_std using Logistic Regression: {:.5f}".format(accuracy_score(y_val,LR_pred)))
print("Logarithmic Loss for X_val_std using Logistic Regression: {:.5f}".format(log_loss(y_val,LR_pred)))
# Logistic Regression with Normalization
LR = LogisticRegression(solver='lbfgs')
LR.fit(X_tr_norm,y_tr)
LR_pred = LR.predict(X_val_norm)
print("\nAccuracy Score for X_val_norm using Logistic Regression: {:.5f}".format(accuracy_score(y_val,LR_pred)))
print("Logarithmic Loss for X_val_norm using Logistic Regression: {:.5f}".format(log_loss(y_val,LR_pred)))
# Random Forrest Classifier with StandardScale
RFM = RandomForestClassifier(random_state=0,n_estimators=250)
RFM .fit(X_tr_std,y_tr)
RFM_pred = RFM.predict(X_val_std)
print("\nAccuracy Score for X_val_std using a Random Forest Classifier: {:.5f}".format(accuracy_score(y_val,RFM_pred)))
print("Logarithmic Loss for X_val_std using a Random Forest Classifierr: {:.5f}".format(log_loss(y_val,RFM_pred)))
# Random Forrest Classifier with Normalization
RFM = RandomForestClassifier(random_state=0,n_estimators=250)
RFM .fit(X_tr_norm,y_tr)
RFM_pred = RFM.predict(X_val_norm)
print("\nAccuracy Score for X_val_norm using a Random Forest Classifier: {:.5f}".format(accuracy_score(y_val,RFM_pred)))
print("Logarithmic Loss for X_val_norm using a Random Forest Classifierr: {:.5f}".format(log_loss(y_val,RFM_pred)))
from sklearn.svm import SVC
# SVC with StandardScale
SVC = SVC(kernel='rbf', probability=True)
SVC.fit(X_tr_std,y_tr)
SVC_pred = SVC.predict(X_val_std)
print("\nAccuracy Score for X_val using SVC: {:.5f}".format(accuracy_score(y_val,SVC_pred)))
print("Logarithmic Loss for X_val_std using SVC: {:.5f}".format(log_loss(y_val,SVC_pred)))
from sklearn.svm import SVC
# SVC with Normalization
SVC = SVC(kernel='rbf', probability=True)
SVC.fit(X_tr_norm,y_tr)
SVC_pred = SVC.predict(X_val_norm)
print("\nAccuracy Score for X_val_norm using SVC: {:.5f}".format(accuracy_score(y_val,SVC_pred)))
print("Logarithmic Loss for X_val_norm using SVC: {:.5f}".format(log_loss(y_val,SVC_pred)))
# -
# ## Unsupervised Techniques
# ### KMeans with PCA
print("Number of photos classified as a person: {}".format(len(y_tr[y_tr==1])))
print("Number of photos classified as not a person: {}".format(len(y_tr[y_tr==0])))
# +
reduced_data = PCA(n_components=2).fit_transform(X_tr_std)
kmeans_pca1 = KMeans(n_clusters=2, random_state=1000)
kmeans_pca1.fit(reduced_data)
centrocolor=['blue', 'magenta']
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
plt.figure(figsize=(10,8))
plot_voronoi(kmeans_pca1,[x_min,x_max,y_min,y_max])
# Plot the centroids as X
centroids = kmeans_pca1.cluster_centers_
plt.plot(reduced_data [:,0],reduced_data [:,1],".w",markersize=6)
plt.title('K-means clustering on the training dataset (PCA-reduced data)\n'
'Centroids are marked with a cross')
labels= ["not_person","person"]
for it in range(centroids.shape[0]):
plt.scatter(centroids[it,0], centroids[it,1],
marker='x', s=169, linewidths=3,
color=centrocolor[it], zorder=10,
label=labels[it])
plt.legend(loc='center left', scatterpoints=1, bbox_to_anchor=[1.01, 0.5])
# -
# ## Parameter estimation using grid search with cross-validation
# ### Optimising Logistic Regressor
# +
clf2 = LogisticRegression(solver='lbfgs',class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
random_state=0, tol=0.0001,
verbose=0, warm_start=False)
param_grid2 = {'C': np.logspace(-6,8,20) }
grid_search2 = GridSearchCV(clf2, param_grid=param_grid2,scoring='neg_log_loss') # Use default 3 fold cross validation
grid_search2.fit(X_tr_norm, y_tr)
pass
# -
top1 = pd.DataFrame.from_dict(grid_search2.cv_results_)
top1[top1['rank_test_score']<6] # show top 5
# From the above table, we can identify that including an optimal value for the paramter C would increase the accuracy.
optimalC = top1[top1['rank_test_score'] == 1]['param_C'].real[0]
LR_new = LogisticRegression(solver='lbfgs',C=optimalC)
LR_new.fit(X_tr_norm,y_tr)
LR_new_pred = LR_new.predict(X_val_norm)
print("Accuracy Score for X_val_norm using Logistic Regression: {:.5f}".format(accuracy_score(y_val,LR_new_pred)))
print("Logarithmic Loss for X_val_norm using Logistic Regression: {:.5f}".format(log_loss(y_val,LR_new_pred)))
# +
clf = RandomForestClassifier(n_estimators=250,random_state=0)
param_grid = {"max_depth": [None, 5],
"max_features": ['auto','log2'],
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
grid_search = GridSearchCV(clf, param_grid=param_grid,scoring='neg_log_loss') # Use default 3 fold cross validation
grid_search.fit(X_tr_norm, y_tr)
pass
# -
top2 = pd.DataFrame.from_dict(grid_search.cv_results_)
top2[top2['rank_test_score']<6] # show top 5
# From the above table, we can identify which parameters it would be best to include in our RandomForestClassifier model for higher accuracy.
# The paramters are:
# * bootstrap=False
# * criterion='entropy'
# * max_depth=None
# * max_features='auto'
RFM_new = RandomForestClassifier(random_state=0,n_estimators=500,bootstrap=False,criterion='entropy',max_depth=None,max_features='auto')
RFM_new.fit(X_tr_norm,y_tr)
RFM_new_pred = RFM_new.predict(X_val_norm)
print("\nAccuracy Score for X_val using a Random Forest Classifier: {:.5f}".format(accuracy_score(y_val,RFM_new_pred)))
print("Logarithmic Loss for X_val_std using a Random Forest Classifier: {:.5f}".format(log_loss(y_val,RFM_new_pred)))
# +
from sklearn.svm import SVC
clf3 = SVC(kernel='rbf',probability=True)
param_grid3 = {"C": np.logspace(-2,2,10),
"gamma": np.logspace(-4,0,10)}
# scoring is socre instead of neg_log_loss
grid_search3 = GridSearchCV(clf3, param_grid=param_grid3,scoring='neg_log_loss',n_jobs=8) # Use default 3 fold cross validation
grid_search3.fit(X_tr_norm, y_tr)
pass
# -
top3 = pd.DataFrame.from_dict(grid_search3.cv_results_)
top3[top3['rank_test_score']<6] # show top 5
# +
# SVC_new = SVC(kernel='rbf', probability=True)
# SVC_new.fit(X_tr_std,y_tr)
# SVC_new_pred = SVC.predict(X_val_std)
# print("\nAccuracy Score for X_val using SVC: {:.5f}".format(accuracy_score(y_val,SVC_new_pred)))
# print("Logarithmic Loss for X_val_std using SVC: {:.5f}".format(log_loss(y_val,SVC_new_pred)))
opt_c2 = top3[top3['rank_test_score'] == 1]['param_C'].real[0]
opt_gamma = top3[top3['rank_test_score'] == 1]['param_gamma'].real[0]
svc = SVC(kernel='rbf',C=opt_c2,gamma=opt_gamma,probability=True)
svc.fit(X_tr_norm, y_tr)
pp3 = svc.predict_proba(X_val_norm)
print('Optimal SVC with C = {}, gamma = {} has a log loss = {}.'.format(opt_c2,opt_gamma,
log_loss(y_val,pp3)))
print('Score with this classifier = {:.3f}.'.format(svc.score(X_val_norm,y_val)))
# -
# #ANSWER_TEXT#
#
# ***Your answer goes here:***
# First, I start by exploring the datasets.
# ### Description
# All three datasets, are very similar. Their common attributes are the 500 dimensions and the is_person. Training and Validation sets have some more attributes like is_dog,is_cow and so on.
# The training set has 2113 entries while the Validation set has 1113. Following that the Testing set has 1114 entries.
#
# ### Preprocessing
#
# From my observations above, I concluded that it would be best to drop some attributes from the Training and Testing sets as they don't affect in any way if the observation is a person or not. The attribues I decided to drop are:
#
# * is_cow
# * is_distingtable
# * is_dog
# * is_horse
# * is_motorbike
# * is_pottedplant
# * is_sheep
# * is_sofa
# * is_tvmonitor
#
#
# ### Splitting the Data
# Following that, I split my data into training and testing sets(as we did in the labs)
# The training sets contain all observations from each data set yet only the attributes that represent the dimensions.
# The testing sets contain all observation but only the is_person attribute.
#
# ### Scatter plots
#
# I also tried to see some scatter plots in order to see if there are any outliers in the data. I created 2, one for the first dimension and one for the last dimension. From the plots above we can see that most likely there are no outliers in the dataset.
#
# ### Preprocessing (II)
# I also decided to use both the StandardScaler and Normalization and see which one produces the highest accuracy.
#
# ### Supervised Techniques
# I then started using some supervised techniques to see which one gives the highest accuracy
# #### Baseline Classifier
# My dummy classifier with the stratified method gave the following results:
#
# *`Accuracy Score for X_val_std using the Dummy Classifier: 0.52291
# Logarithmic Loss for X_val_std using the Dummy Classifier: 16.47825
# Accuracy Score for X_val_norm using the Dummy Classifier: 0.50584
# Logarithmic Loss for X_val_norm using the Dummy Classifier: 17.06785`*
#
# From this we can see that Normalisation may be a better method for preprocessing to carry out.
#
# ### Logistic Regression, RandomForestClassifier, SVC
#
# Logistic Regression Results :
#
# *`Accuracy Score for X_val_std using Logistic Regression: 0.60737
# Logarithmic Loss for X_val_std using Logistic Regression: 13.56107
# Accuracy Score for X_val_norm using Logistic Regression: 0.69452
# Logarithmic Loss for X_val_norm using Logistic Regression: 10.55102`*
#
# Random Forrest Classifier Results:
#
# *`Accuracy Score for X_val_std using a Random Forest Classifier: 0.69003
# Logarithmic Loss for X_val_std using a Random Forest Classifierr: 10.70619
# Accuracy Score for X_val_norm using a Random Forest Classifier: 0.69452
# Logarithmic Loss for X_val_norm using a Random Forest Classifierr: 10.55103`*
#
# SVC Results:
#
# *`Accuracy Score for X_val using SVC: 0.52650
# Logarithmic Loss for X_val_std using SVC: 16.35394
# Accuracy Score for X_val_norm using SVC: 0.52650
# Logarithmic Loss for X_val_norm using SVC: 16.35394`*
#
# From the above metrics,we can identify that the Random Forest Classifier is more accurate. It's accuracy is highest when compared with Logistic Regression or an SVC and its Logarithmic Loss is lower than the others. Also, Normalisation seems to be a better methos to use when comapred with Standard Scale.
#
# ### Unsupervised Techniques
# #### KMeans with PCA
#
# Note: 947 documents in the training set are marked as person and 1166 documents are marked as not a person.
#
# From the voronoi plot above we can conclude that an unsupervised classification technique such that kmeans with pca shouldn't being used.The reason is that only one document is classified as not a person where as this is completely wrong if we compare it with their actual labels.
#
# ### Parameter estimation using grid search with cross-validation
#
# #### Logistic Regression:
# Using grid search, cross-validation and the normalized datasets we get:
#
# *`Accuracy Score for X_val_norm using Logistic Regression: 0.69452
# Logarithmic Loss for X_val_norm using Logistic Regression: 10.55103`*
#
# These results are much higher than by not using the grid-search.
#
# #### Random Forrest Classifier:
# Using grid search, cross-validation and the normalized datasets we get:
#
# *`Accuracy Score for X_val using a Random Forest Classifier: 0.69362
# Logarithmic Loss for X_val_std using a Random Forest Classifier: 10.58206`*
#
# #### SVC
# Using grid search, cross-validation and the normalized datasets we get:
#
# *` Optimal SVC with C = 1.6681005372000592, gamma = 1.0 has a log loss = 0.5599244408904153.
# Score with this classifier = 0.708. `*
#
# ## FINAL model selected
#
# From the results above, I concluded that it would be best to use the svc model using the grid search, cross-validation and the normalized dataset of X_tst.
# I also saved the probabilities in a text file called 'assignment_3_predictions.txt'.
X_tst_norm = normalize(X_tst, norm='l2')
pred_probabilities=svc.predict_proba(X_tst_norm)
print(pred_probabilities.shape)
savetxt('assignment_3_predictions.txt', pred_probabilities)
# +
#ANSWER_PROB#
# Run this cell when you are ready to submit your test-set probabilities. This cell will generate some
# warning messages if something is not right: make sure to address them!
if pred_probabilities.shape != (1114, 2):
print('Array is of incorrect shape. Rectify this before submitting.')
elif (pred_probabilities.sum(axis=1) != 1.0).all():
print('Submitted values are not correct probabilities. Rectify this before submitting.')
else:
for _prob in pred_probabilities:
print('{:.8f}, {:.8f}'.format(_prob[0], _prob[1]))
| 28,205 |
/Lecture Materials/Day 1/PMIM102_Day_1/PMIM102_Tidyverse_4.ipynb | e6e2a65e6fb4ac2562b10547c024449f5a633d6a | [] | no_license | codingWithAndy/PMIM102 | https://github.com/codingWithAndy/PMIM102 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .r | 26,987 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .r
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: R
# language: R
# name: ir
# ---
# <table width='100%'><tr>
# <td style='background-color:red; text-align:center; color: white;'><!--Foundation<!--hr size='5' style='border-color:red; background-color:red;'--></td>
# <td style='background-color:yellow; text-align:center;'><!--Level 1<!--hr size='5' style='border-color:yellow; background-color:yellow;'--></td>
# <td style='background-color:orange; text-align:center;'><!--Level 2<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:green; text-align:center; color: white;'><!--Level 3<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:blue; text-align:center; color: white;'><!--Level 4<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:purple; text-align:center; color: white;'><!--Level 5<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:brown; text-align:center; color: white;'><!--Level 6<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:black; text-align:center; color: white;'><!--Level 7<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# </tr></table>
# <table style='border-left:10px solid orange;'><tr>
# <td style='padding-left:20px;'>
# <h2><i>Swansea University Medical School</i><br/><b>MSc Health Data Science</b></h2>
# <h3>PMIM-102 Introduction to Scientific Computing in Healthcare</h3>
# <h1><b>Introduction to Programming in R</b></h1>
# <h2><b>3. The Tidyverse</b></h2>
# <h2><i>Part 4: Data types in the tidyverse.</i></h2>
# <h3><i>September 2020</i></h3>
# <h3><b>To-do</b></h3>
# <ul><li>Nothing.</li></ul>
# </td>
# <td><img height='300' width='500' src='images/cover.jpg'/></td>
# </tr></table>
# ## __Aim__: Use the tools available in R to manipulate tables of data.
#
# The aim of this session is to concentrate on the core activities in working with large datasets: moving, cleaning and transforming table data to facilitate analyses. Whilst this is possible using base-R, the facilities provided by the libraries in the __Tidyverse__ make it considerably __easier__ and the resulting code __more readable__.
#
# ### __A map of where we're going__
#
# 1. <b>Introduction</b> - What is the process, the problems with standard R and the structure of 'tidy' data.
#
# 1. <b>Acquiring data</b> - Getting data into R from files (<b>readr</b>).
#
# 1. <b>Tidying the data</b> - Handling missing data and reshaping the tables (<b>tidyr</b>).
#
# 1. <b>Transforming the data</b> - Selecting and converting the data ready to analyse (<b>dplyr</b>).
#
# 1. <div style="background-color:yellow;"><b>Working with specific data types in tidyverse</b>: strings (<b>stringr</b>), dates (<b>lubridate</b>), factors (<b>forcats</b>).</div>
#
# 1. <b>Plotting & Data visualisation</b> - beyond the simple R plot etc. (<b>ggplot2</b>).
#
# 1. <b>Extras</b> - Things worth knowing of so that you can use them if you ever need them.
# * Applying functions and working with lists (purrr).
# * Tidy evaluation (rlang).
# * Communicating your results with a dynamic, R-based website (shiny).
# ## __Load the Tidyverse__
#
# The first thing to do is make sure the library is loaded. If you have not already installed it, do so not using the <code>install.packages()</code> function.
# +
## install.packages('tidyverse')
#library(tidyverse)
# -
# ## __Working with specific data types: strings (stringr), dates (lubridate), factors (forcats)__
#
# There are three different data types in R that often introduce additional problems or subtleties to the work: strings, dates and factors.
#
# ### __Strings__
#
# Strings are sequences of characters: words, text etc. As such they are naturally used to convey meaning and naturally subject to spelling mistakes, odd use of capitalisation etc. and we frequently need to process text to make sure all variations of any text are caught (which is one of the strengths of regular expressions). Often it is simply a case of making sure we catch the outliers, for example, with the gleason codes used in prostate cancer, we expect:
#
# `GLEASON 3+4=7`
#
# but we might get:
#
# `3+4=7`, `3+4`, `-+-=7`, `7`, `GLEASON 4+3=8`
#
# and we have to spot these and do something defined in all cases.
#
# [See the stringr cheatsheet.](https://github.com/rstudio/cheatsheets/blob/master/strings.pdf)
#
# Searching, creating or changing strings is simplified in R with the `stringr` library. There are functions to find, count or locate matches in strings, to select parts of strings and to measure or adjust the length of strings. We can also change parts of strings, join or split them and sort them. A full list of these functions is shown in the cheatsheet.
#
# The most useful functions are:
# * `str_length(x)` which returns the length of the string, x.
# * `str_sub(x, start, stop)` which returns the substring in x that starts at position start (left-most is 1) and ends at position stop. You can use negative numbers to count from the end of the string (right-most is -1).
# +
#str_sub('GLEASON 3+4=7', 1, 7)
#str_sub('GLEASON 3+4=7', 9, -1)
#str_sub('GLEASON 3+4=7', -5, -3)
# +
## library(stringr)
#pregnancy <- read_csv(file="data/pregnancy.csv")
#head(pregnancy)
#string_test <- pregnancy %>% select(PARENT_ID, DATE_OF_BIRTH, OTHER_LANGUAGE, OTHER_ETHNICITY) %>% filter(!is.na(OTHER_LANGUAGE)|!is.na(OTHER_ETHNICITY))
#string_test %>% filter(str_detect(OTHER_LANGUAGE, str_sub('Polish', 2, str_length('Polish'))))
# +
#x <- tibble(Gleason=c('GLEASON 3+4=7', 'DUKES=B', 'T1a'))
#x
#x %>% mutate(Gleason=ifelse(str_detect(str_sub(Gleason, 1, 7), 'GLEASON'), str_sub(Gleason, 9, str_length(Gleason)), NA)) %>%
# mutate(Gleason=str_sub(Gleason, str_locate(Gleason, '=')[,1] + 1, str_length(Gleason)))
# -
# ## __Exercise__: Something devilish to do with string handling.
#
# 1. List the number of attacks which involved each shirt colour and each monster type with: 'The \<monster\> attacked redshirts \<number\> times.'
# 1. Choose a monster and find out how many attacks it made and on which shirt colours.
# 1. Create a list of words and their frequencies.
# +
#events <- c(
# 'The redshirt was bitten by a red vampire.',
# 'The basilisk froze seven securty officers',
# 'Spock was attacked by the zombies',
# 'Werewolves ate four redshirts and one yellowshirt',
# 'The captain spontaneously combusted'
#)
#str_length(events)
#events[str_detect(events, 'vampire')]
#str_locate(events[str_detect(events, 'vampire')], 'red')
#str_locate_all(events[str_detect(events, 'vampire')], 'red')
#str_sub(events[str_detect(events, 'vampire')], str_locate(events[str_detect(events, 'vampire')], 'red')) <-'blue'
#str_sub(events[str_detect(events, 'vampire')], str_locate(events[str_detect(events, 'vampire')], 'vampire')) <- 'green'
#events[str_detect(events, 'vampire')]
#word <- str_to_lower(unlist(str_split(events, ' ')))
#word
#t <- table(word)
#print(t)
#class(t)
#u <- as.data.frame(t)
#print(u %>% arrange(desc(Freq)))
# -
# ## _Regular Expressions_
# Regular expressions are a way to describe pattern in strings and is the defaullt form of the 'pattern' used in stringr, e.g. `str_match(string, pattern)`. They may seem obscure to start with but you quickly get used to them with practice. The rules define the patterns that match:
#
# * 'x' --- match the character x
# * 'x|y' --- match the character x or the character y
# * '\[xyz\]' --- match any one of x, y or z
# * '\[^xyz\]' --- match anything but x, y or z
# * '\[t-z\]' --- match anything from t to z
# * '^a' --- match anything that starts with a
# * 'a\$' --- match anything that ends with a
# * '^abcdef\$' --- match abcdef only
# * 'a?' --- match zero or one a
# * 'a*' --- match zero or more a's
# * 'a+' --- match one or more a's
# * 'a{n}' --- match exactly n a's
# * 'a{n,}' --- match n or more a's
# * 'a{n,m}' --- match n to m a's
# * '.' --- match any character (except a new-line)
# * '\[:digit:\]' --- match a digit (see also: alpha, lower, upper, alnum, punct, graph, space, blank)
# * '(ab|c)(d|ef)' --- match the first group followed by the second group i.e. 'ab' or 'c' followed by 'd' or 'ef'
#
# For example, if we wish to find the character, 'x', we simply use 'x'. If we want to find 'x' or 'y', we use 'x|y'. If we want to find one of a set of characters, we use '\[xyz\]' which will find one of 'x', ''y' or 'z'. If we want to find 'abc' or 'd' followed by 'e', we use '(abc|d)e'.
#
# | Regular Expression | Matches, for example |
# | --- | --- |
# | ^a | a, apple, aardvark, a00001 |
# | \[a\] | a, baa, people with a problem, aaaaaaaaaaaaaa |
# | \[ab\] | a, baa, boo, oboe |
# | \[a\]\*\[b\]\* | bbb, aaa, aaaabbbb, aaaaacccc |
# | \[a\]+\[b\]+ | abbbb, aaaaab, abc |
# | \[A-Za-z\]+ | any, word, butnotanumberbyitself |
# | ^\[a-z-\]+\[@\]{1}\[a-z\]+(\[.\]{1}\[a-z\]+)*$ | [email protected], [email protected] |
#
# You can use other forms of pattern if you convert them with one of the helper functions:
# * regex() --- the default
# * fixed() --- matches raw bytes but, some characters can be represented in multiple ways and these will be missed
# * coll() --- matches raw bytes and uses locale collation rules to match multiply represented characters (slow)
# * boundary() --- matches boundaries between characters
# +
#s <- 'How many redshirts were killed in episode 7?'
#p <- '7\\?$'
#str_match(s, p)
#str_split(s, boundary('word'))
# -
# ## __Exercise__: Using regular expressions to find codes.
# Load the package, 'pccc' and use regular expressions to look for specific codes in the 'pcc_icd10_dataset' data.
# +
##install.packages('pccc')
##install.packages('data.table')
#library(pccc)
#head(pccc_icd10_dataset)
#p <- pccc_icd10_dataset[str_which(pccc_icd10_dataset$dx1, '^S66'),]
#head(p)
# -
# <table style="text-align:center;"><tr><td width="100" height="20" style="background-color:greenyellow"></td><td width="100" height="20" style="background-color:hotpink"></td></tr></table>
# ### __Dates__
#
# As we found in earlier exercises, dates in R can require considerable care, not only are their formats likely to vary but R will not always treat dates as you would like it to. In addition, people frequently enter dates badly as strings (for example, 31.06.2005 where someone may simply have added 3 to 31.03.2005 to indicate a date 3 months later). There are further complications when you need to calculate intervals from dates - leap years, what is a month, a year etc.
#
# The `lubridate` package has been designed to make handling dates easier and to provide a simple way to perform mathematical functions.
#
# [See the lubridate cheatsheet.](https://github.com/rstudio/cheatsheets/raw/master/lubridate.pdf) Especially for additional information on timestamps and timezones.
#
# There are three components in a lubridate date/time:
# 1. A datetime which is stored as the number of seconds since January 1st 1970 and represents a date with a time.
# 1. A date is stored as the number of days since January 1st 1970.
# 1. An hms is the number of seconds since 00:00 and represents the time.
# +
#library(lubridate)
## datetime
#dt <- as_datetime('2020-09-22 12:00:00')
#dt
#as.integer(dt)
## date
#dt <- as_date('2019-09-22')
#dt
#as.integer(dt)
## hms
#hms <- hms::as_hms('12:00:00')
#hms
#as.integer(hms)
# -
# ### _Specifying datetimes_
#
# You can specify datetimes with string and numbers using a series of functions that match the format of the strings and numbers you want to use:
# 1. ymd_hms(), ymd_hm(), ymd_h(), ymd()
# 1. ydm_hms() etc.
# 1. mdy_hms() etc.
# 1. yq(), hms(), hm(), ms() (may need to specify lubridate here, for example, lubridate::hms() rather than hms::hms())
# +
#dt <- ymd_hms('2020/09/22 17:26:30')
#dt
# -
# ### _Getting and setting components_
#
# It is straightforward to set the components of a datetime, such as date(), year(), month(), day(), wday(), hour(), minute(), second(), week(), semester() and check values such as am(), pm(), dst() - daylight saving time?, leap_year().
# +
#semester(dt)
#wday(dt)
#pm(dt)
#leap_year(dt)
#dst(dt)
#month(dt) <- 12
#dt
# -
# ### _Maths with dates_
#
# Date calculations are a series of traps and problems. You need to consider:
# 1. Leap days and leap seconds
# 1. Daylight saving hours
# 1. Adding months when on 31st of the month (31st February --> NA)
#
# The Tidyverse tries to help by including three kinds of timespan:
# 1. Period --- tracks the change in clock time
# 1. Duration --- tracks the actual amount of time irrespective of clock manipulations, so not clock time when changes occur
# 1. Interval --- represents specific intervals on the timeline, bounded by a start and end datetime.
#
# To create one of these timespans, you can use the constructor:
# * `period()`
# * `duration()`
# * `interval()` (or, in this case, %--%)
#
# or the and the conversion functions:
# * `as.period()`
# * `as.duration()`
# * `as.interval()`
#
# Some functions are also provided to help round dates appropriately:
# * floor_date(x, unit='second') --- rounds down to the specified unit
# * round_date(x, unit='second') --- rounds to the nearest unit
# * ceiling_date(x, unit='second') --- rounds up to the nearest unit
# * rollback(dates, roll_to_first=FALSE, preserve_hms=TRUE) -- rollback to the last day of the previous month (this is also done with the operators, %m+% and %m-%)
# +
#per <- months(2)
#per
#dur <- dweeks(4)
#dur
#int <- interval(ymd('2019-09-28'), ymd('2019-09-01'))
#int
#dt <- ymd('2016-01-31') + months(1)
#dt
#dt <- ymd('2016-01-31') %m+% months(1)
#dt
# -
# And there are some handy extra functions:
# * now() --- current time
# * today() --- current date
# +
#today() + dur
#now() + dminutes(10)
# -
# ## __Exercise__: Play with dates and durations.
# What happens if you add or subtract a day, a month, a year?
# +
#n <- now()
#n
#m <- months(1)
#m
#m1 <- as.duration(m)
#m1
#m2 <- as.period(m)
#m2
#m3 <- as.interval(m, now())
#m3
#n <- n + months(1)
#n
# -
# ## __Exercise__: Convert the dates in the `pregnancy` dataset to a date type and process them.
# Convert all (some) of the date fields in the pregnancy dataset to dates. Check for problems. Determine how many children were born in the summer months. Calculate the interval between children for those parents with more than one child.
# +
#library(lubridate)
#p <- pregnancy %>% select(PARENT_ID, DOBCHILD1, DOBCHILD2, DOBCHILD3, DOBCHILD4, DOBCHILD5, DOBCHILD6) %>%
# mutate(DOBCHILD1 = as.Date(DOBCHILD1, format='%d/%m/%Y')) %>%
# mutate(MONTH_CHILD1 = month(DOBCHILD1)) %>%
# mutate(SUMMER_CHILD1 = (MONTH_CHILD1 >= 6 & MONTH_CHILD1 <= 8))
#table(p$SUMMER_CHILD1)
#head(p, 20)
#q <- pregnancy %>% select(PARENT_ID, DOBCHILD1, DOBCHILD2, DOBCHILD3, DOBCHILD4, DOBCHILD5, DOBCHILD6) %>%
# mutate(DOBCHILD1 = as.Date(DOBCHILD1, format='%d/%m/%Y')) %>%
# mutate(DOBCHILD2 = as.Date(DOBCHILD2, format='%d/%m/%Y')) %>%
# mutate(GAP_12 = ifelse(!is.na(DOBCHILD1) & !is.na(DOBCHILD2), DOBCHILD2 - DOBCHILD1, NA))
#head(q, 20)
# -
# <table style="text-align:center;"><tr><td width="100" height="20" style="background-color:greenyellow"></td><td width="100" height="20" style="background-color:hotpink"></td></tr></table>
# ### __Factors__
#
# [See the forcats cheatsheet.](https://github.com/rstudio/cheatsheets/raw/master/factors.pdf)
#
# The Tidyverse provides a library, `forcats`, with a number of utilities for handling factors.
#
# A factor is a categorical variable (a vector with a fixed set of values). In R a factor is displayed with string values but stored as integers with a hidden look-up table to do the conversion. This avoids spelling mistakes and problems with capitalisation (or, rather, these things show up as errors).
#
# The factors are usually ordered alphabetically which can be inconvenient when it comes to plotting by categories for example.
#
# We create a factor with `factor(x=character(), levels)` or from a vector with `as.factor()`. The levels can be seen and set by calling `levels(x)`.
# +
#bmi <- factor(c('healthy', 'underweight', 'obese', 'healthy', 'healthy', 'overweight', 'healthy', 'obese', 'overweight'))
#bmi
## Notice that the levels are listed alphabetically. Note also that the print(bmi) automatically prints the levels.
#levels(bmi)
#bmi <- factor(c('healthy', 'underweight', 'obese', 'healthy', 'healthy', 'overweight', 'healthy', 'obese', 'overweight'),
# levels=c('underweight', 'healthy', 'overweight', 'obese'))
#bmi
## Notice that the levels are listed ordinally.
#levels(bmi)
## Notice that the following changes the labels not the order of the labels so the data has changed too.
#levels(bmi) <- c('obese', 'overweight', 'healthy', 'underweight')
#bmi
#levels(bmi)
# -
# There are some useful forcats functions:
# * fct_count() --- count the frequency of each level
# * fct_unique() --- get the unique values
# * fct_c() --- combine factors (with possibly different levels)
# * fct_unify() --- unify the levels across different factors
# * fct_relevel() --- relevel the factor
# * fct_infreq() --- relevel the factor by th efrequency eacch occurs
# * fct_collapse() --- collapse the specified levels into a new (or existing) level
# * fct_other() --- allows you to select some levels to keep, the rest collapsed into a level, 'Other'
#
# There are several additional re-ordering functions - see the cheatsheet.
# +
#library(forcats)
#bmi <- factor(c('healthy', 'underweight', 'obese', 'healthy', 'healthy', 'overweight', 'healthy', 'obese', 'overweight'))
#fct_count(bmi)
#fct_unique(bmi)
## Note that the order of the levels below has changed not the data.
#fct_relevel(bmi, c('obese', 'overweight', 'healthy', 'underweight'))
## Now re-ordered according to frequency of each factor (useful for listing and plotting).
#fct_infreq(bmi)
## Collapse some factors into the same value.
#fct_collapse(bmi, heavy=c('overweight', 'obese'))
# -
# ## __Exercise__: Factors in the pregnancy dataset.
# 1. Create a factor for season of child birth.
# 1. Create a factor for length of gap between births with a non-linear increment, e.g. <1 year, <18 months, <2 years, <3 years, <5 years, >5 years.
# +
#head(pregnancy)
#seasons <- pregnancy %>% select(PARENT_ID, INFANT_ID, EDD) %>% mutate(CHILD_BIRTH=dmy(EDD))
#seasons <- seasons %>% mutate(SEASON=ifelse(month(CHILD_BIRTH) %in% c(12, 1, 2), 'winter',
# ifelse(month(CHILD_BIRTH) %in% c(3, 4, 5), 'spring',
# ifelse(month(CHILD_BIRTH) %in% c(6, 7, 8), 'summer',
# 'autumn'))))
#seasons <- seasons %>% mutate(SEASON=as.factor(SEASON))
#head(seasons)
# -
# <table style="text-align:center;"><tr><td width="100" height="20" style="background-color:greenyellow"></td><td width="100" height="20" style="background-color:hotpink"></td></tr></table>
# <table width='100%'><tr>
# <td style='background-color:red; text-align:center; color: white;'><!--Foundation<!--hr size='5' style='border-color:red; background-color:red;'--></td>
# <td style='background-color:yellow; text-align:center;'><!--Level 1<!--hr size='5' style='border-color:yellow; background-color:yellow;'--></td>
# <td style='background-color:orange; text-align:center;'><!--Level 2<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:green; text-align:center; color: white;'><!--Level 3<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:blue; text-align:center; color: white;'><!--Level 4<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:purple; text-align:center; color: white;'><!--Level 5<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:brown; text-align:center; color: white;'><!--Level 6<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# <td style='background-color:black; text-align:center; color: white;'><!--Level 7<!--hr size='5' style='border-color:orange; background-color:orange;'--></td>
# </tr></table>
| 20,712 |
/TASK1.ipynb | 0e9dc8d79f2e318fe09bab08c2a5b2ae1834e574 | [] | no_license | apurvakadam28/TASK-1_Linear_Regression | https://github.com/apurvakadam28/TASK-1_Linear_Regression | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 51,866 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Neural networks with PyTorch
#
# Deep learning networks tend to be massive with dozens or hundreds of layers, that's where the term "deep" comes from. You can build one of these deep networks using only weight matrices as we did in the previous notebook, but in general it's very cumbersome and difficult to implement. PyTorch has a nice module `nn` that provides a nice way to efficiently build large neural networks.
# +
# Import necessary packages
# %matplotlib inline
# %config InlineBackend.figure_format = 'retina'
import numpy as np
import torch
import helper
import matplotlib.pyplot as plt
# -
#
# Now we're going to build a larger network that can solve a (formerly) difficult problem, identifying text in an image. Here we'll use the MNIST dataset which consists of greyscale handwritten digits. Each image is 28x28 pixels, you can see a sample below
#
# <img src='assets/mnist.png'>
#
# Our goal is to build a neural network that can take one of these images and predict the digit in the image.
#
# First up, we need to get our dataset. This is provided through the `torchvision` package. The code below will download the MNIST dataset, then create training and test datasets for us. Don't worry too much about the details here, you'll learn more about this later.
# +
### Run this cell
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
])
# Download and load the training data
trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
# -
# We have the training data loaded into `trainloader` and we make that an iterator with `iter(trainloader)`. Later, we'll use this to loop through the dataset for training, like
#
# ```python
# for image, label in trainloader:
# ## do things with images and labels
# ```
#
# You'll notice I created the `trainloader` with a batch size of 64, and `shuffle=True`. The batch size is the number of images we get in one iteration from the data loader and pass through our network, often called a *batch*. And `shuffle=True` tells it to shuffle the dataset every time we start going through the data loader again. But here I'm just grabbing the first batch so we can check out the data. We can see below that `images` is just a tensor with size `(64, 1, 28, 28)`. So, 64 images per batch, 1 color channel, and 28x28 images.
dataiter = iter(trainloader)
images, labels = dataiter.next()
print(type(images))
print(images.shape)
print(labels.shape)
# This is what one of the images looks like.
plt.imshow(images[1].numpy().squeeze(), cmap='Greys_r');
# First, let's try to build a simple network for this dataset using weight matrices and matrix multiplications. Then, we'll see how to do it using PyTorch's `nn` module which provides a much more convenient and powerful method for defining network architectures.
#
# The networks you've seen so far are called *fully-connected* or *dense* networks. Each unit in one layer is connected to each unit in the next layer. In fully-connected networks, the input to each layer must be a one-dimensional vector (which can be stacked into a 2D tensor as a batch of multiple examples). However, our images are 28x28 2D tensors, so we need to convert them into 1D vectors. Thinking about sizes, we need to convert the batch of images with shape `(64, 1, 28, 28)` to a have a shape of `(64, 784)`, 784 is 28 times 28. This is typically called *flattening*, we flattened the 2D images into 1D vectors.
#
# Previously you built a network with one output unit. Here we need 10 output units, one for each digit. We want our network to predict the digit shown in an image, so what we'll do is calculate probabilities that the image is of any one digit or class. This ends up being a discrete probability distribution over the classes (digits) that tells us the most likely class for the image. That means we need 10 output units for the 10 classes (digits). We'll see how to convert the network output into a probability distribution next.
#
# > **Exercise:** Flatten the batch of images `images`. Then build a multi-layer network with 784 input units, 256 hidden units, and 10 output units using random tensors for the weights and biases. For now, use a sigmoid activation for the hidden layer. Leave the output layer without an activation, we'll add one that gives us a probability distribution next.
def activation(x):
""" Sigmoid activation function
Arguments
---------
x: torch.Tensor
"""
return 1/(1+torch.exp(-x))
# +
## Your solution
# Flatten the input images
inputs = images.view(images.shape[0], -1)
# inputs.shape
# Create parameters
w1 = torch.randn(784, 256)
b1 = torch.randn(256)
w2 = torch.randn(256, 10)
b2 = torch.randn(10)
h = activation(torch.mm(inputs, w1) + b1)
out = torch.mm(h, w2) + b2 # output of your network, should have shape (64,10)
out.shape
# -
# Now we have 10 outputs for our network. We want to pass in an image to our network and get out a probability distribution over the classes that tells us the likely class(es) the image belongs to. Something that looks like this:
# <img src='assets/image_distribution.png' width=500px>
#
# Here we see that the probability for each class is roughly the same. This is representing an untrained network, it hasn't seen any data yet so it just returns a uniform distribution with equal probabilities for each class.
#
# To calculate this probability distribution, we often use the [**softmax** function](https://en.wikipedia.org/wiki/Softmax_function). Mathematically this looks like
#
# $$
# \Large \sigma(x_i) = \cfrac{e^{x_i}}{\sum_k^K{e^{x_k}}}
# $$
#
# What this does is squish each input $x_i$ between 0 and 1 and normalizes the values to give you a proper probability distribution where the probabilites sum up to one.
#
# > **Exercise:** Implement a function `softmax` that performs the softmax calculation and returns probability distributions for each example in the batch. Note that you'll need to pay attention to the shapes when doing this. If you have a tensor `a` with shape `(64, 10)` and a tensor `b` with shape `(64,)`, doing `a/b` will give you an error because PyTorch will try to do the division across the columns (called broadcasting) but you'll get a size mismatch. The way to think about this is for each of the 64 examples, you only want to divide by one value, the sum in the denominator. So you need `b` to have a shape of `(64, 1)`. This way PyTorch will divide the 10 values in each row of `a` by the one value in each row of `b`. Pay attention to how you take the sum as well. You'll need to define the `dim` keyword in `torch.sum`. Setting `dim=0` takes the sum across the rows while `dim=1` takes the sum across the columns.
# +
def softmax(x):
## TODO: Implement the softmax function here
a = torch.exp(x)
# print(a.shape)
b = torch.exp(x).sum(dim=1).view(-1,1)
# print(b.shape)
return a/b
# Here, out should be the output of the network in the previous excercise with shape (64,10)
probabilities = softmax(out)
# Does it have the right shape? Should be (64, 10)
print(probabilities.shape)
# Does it sum to 1?
print(probabilities.sum(dim=1))
# -
# ## Building networks with PyTorch
#
# PyTorch provides a module `nn` that makes building networks much simpler. Here I'll show you how to build the same one as above with 784 inputs, 256 hidden units, 10 output units and a softmax output.
from torch import nn
class Network(nn.Module):
def __init__(self):
super().__init__()
# Inputs to hidden layer linear transformation
self.hidden = nn.Linear(784, 256)
# Output layer, 10 units - one for each digit
self.output = nn.Linear(256, 10)
# Define sigmoid activation and softmax output
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
# Pass the input tensor through each of our operations
x = self.hidden(x)
x = self.sigmoid(x)
x = self.output(x)
x = self.softmax(x)
return x
# Let's go through this bit by bit.
#
# ```python
# class Network(nn.Module):
# ```
#
# Here we're inheriting from `nn.Module`. Combined with `super().__init__()` this creates a class that tracks the architecture and provides a lot of useful methods and attributes. It is mandatory to inherit from `nn.Module` when you're creating a class for your network. The name of the class itself can be anything.
#
# ```python
# self.hidden = nn.Linear(784, 256)
# ```
#
# This line creates a module for a linear transformation, $x\mathbf{W} + b$, with 784 inputs and 256 outputs and assigns it to `self.hidden`. The module automatically creates the weight and bias tensors which we'll use in the `forward` method. You can access the weight and bias tensors once the network (`net`) is created with `net.hidden.weight` and `net.hidden.bias`.
#
# ```python
# self.output = nn.Linear(256, 10)
# ```
#
# Similarly, this creates another linear transformation with 256 inputs and 10 outputs.
#
# ```python
# self.sigmoid = nn.Sigmoid()
# self.softmax = nn.Softmax(dim=1)
# ```
#
# Here I defined operations for the sigmoid activation and softmax output. Setting `dim=1` in `nn.Softmax(dim=1)` calculates softmax across the columns.
#
# ```python
# def forward(self, x):
# ```
#
# PyTorch networks created with `nn.Module` must have a `forward` method defined. It takes in a tensor `x` and passes it through the operations you defined in the `__init__` method.
#
# ```python
# x = self.hidden(x)
# x = self.sigmoid(x)
# x = self.output(x)
# x = self.softmax(x)
# ```
#
# Here the input tensor `x` is passed through each operation and reassigned to `x`. We can see that the input tensor goes through the hidden layer, then a sigmoid function, then the output layer, and finally the softmax function. It doesn't matter what you name the variables here, as long as the inputs and outputs of the operations match the network architecture you want to build. The order in which you define things in the `__init__` method doesn't matter, but you'll need to sequence the operations correctly in the `forward` method.
#
# Now we can create a `Network` object.
# Create the network and look at it's text representation
model = Network()
model
# You can define the network somewhat more concisely and clearly using the `torch.nn.functional` module. This is the most common way you'll see networks defined as many operations are simple element-wise functions. We normally import this module as `F`, `import torch.nn.functional as F`.
# +
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self):
super().__init__()
# Inputs to hidden layer linear transformation
self.hidden = nn.Linear(784, 256)
# Output layer, 10 units - one for each digit
self.output = nn.Linear(256, 10)
def forward(self, x):
# Hidden layer with sigmoid activation
x = F.sigmoid(self.hidden(x))
# Output layer with softmax activation
x = F.softmax(self.output(x), dim=1)
return x
# -
# ### Activation functions
#
# So far we've only been looking at the sigmoid activation function, but in general any function can be used as an activation function. The only requirement is that for a network to approximate a non-linear function, the activation functions must be non-linear. Here are a few more examples of common activation functions: Tanh (hyperbolic tangent), and ReLU (rectified linear unit).
#
# <img src="assets/activation.png" width=700px>
#
# In practice, the ReLU function is used almost exclusively as the activation function for hidden layers.
# ### Your Turn to Build a Network
#
# <img src="assets/mlp_mnist.png" width=600px>
#
# > **Exercise:** Create a network with 784 input units, a hidden layer with 128 units and a ReLU activation, then a hidden layer with 64 units and a ReLU activation, and finally an output layer with a softmax activation as shown above. You can use a ReLU activation with the `nn.ReLU` module or `F.relu` function.
#
# It's good practice to name your layers by their type of network, for instance 'fc' to represent a fully-connected layer. As you code your solution, use `fc1`, `fc2`, and `fc3` as your layer names.
# +
## Your solution here
## Your solution here
import torch.nn.functional as F
class MyNetwork(nn.Module):
def __init__(self):
super().__init__()
# inputs to first hidden layer linear transformation
self.fc1 = nn.Linear(784, 128)
# inputs to second hidden layer linear transformation
self.fc2 = nn.Linear(128, 64)
# Output layer, 10 units - one for each digit
self.output = nn.Linear(64, 10)
def forward(self, x):
# Hidden layer 1 with relu activation
x = F.relu(self.fc1(x))
# Hidden layer 2 with relu activation
x = F.relu(self.fc2(x))
# Output layer with softmax activation
x = F.softmax(self.output(x), dim=1)
return x
# Create the network and look at it's text representation
model = MyNetwork()
model
# -
# ### Initializing weights and biases
#
# The weights and such are automatically initialized for you, but it's possible to customize how they are initialized. The weights and biases are tensors attached to the layer you defined, you can get them with `model.fc1.weight` for instance.
print(model.fc1.weight)
print(model.fc1.bias)
# For custom initialization, we want to modify these tensors in place. These are actually autograd *Variables*, so we need to get back the actual tensors with `model.fc1.weight.data`. Once we have the tensors, we can fill them with zeros (for biases) or random normal values.
# Set biases to all zeros
model.fc1.bias.data.fill_(0)
# sample from random normal with standard dev = 0.01
model.fc1.weight.data.normal_(std=0.01)
# ### Forward pass
#
# Now that we have a network, let's see what happens when we pass in an image.
# +
# Grab some data
dataiter = iter(trainloader)
images, labels = dataiter.next()
# Resize images into a 1D vector, new shape is (batch size, color channels, image pixels)
images.resize_(64, 1, 784)
# or images.resize_(images.shape[0], 1, 784) to automatically get batch size
# Forward pass through the network
img_idx = 0
ps = model.forward(images[img_idx,:])
img = images[img_idx]
helper.view_classify(img.view(1, 28, 28), ps)
# -
# As you can see above, our network has basically no idea what this digit is. It's because we haven't trained it yet, all the weights are random!
#
# ### Using `nn.Sequential`
#
# PyTorch provides a convenient way to build networks like this where a tensor is passed sequentially through operations, `nn.Sequential` ([documentation](https://pytorch.org/docs/master/nn.html#torch.nn.Sequential)). Using this to build the equivalent network:
# +
# Hyperparameters for our network
input_size = 784
hidden_sizes = [128, 64]
output_size = 10
# Build a feed-forward network
model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
nn.ReLU(),
nn.Linear(hidden_sizes[0], hidden_sizes[1]),
nn.ReLU(),
nn.Linear(hidden_sizes[1], output_size),
nn.Softmax(dim=1))
print(model)
# Forward pass through the network and display output
images, labels = next(iter(trainloader))
images.resize_(images.shape[0], 1, 784)
ps = model.forward(images[0,:])
helper.view_classify(images[0].view(1, 28, 28), ps)
# -
# Here our model is the same as before: 784 input units, a hidden layer with 128 units, ReLU activation, 64 unit hidden layer, another ReLU, then the output layer with 10 units, and the softmax output.
#
# The operations are available by passing in the appropriate index. For example, if you want to get first Linear operation and look at the weights, you'd use `model[0]`.
print(model[0])
model[0].weight
# You can also pass in an `OrderedDict` to name the individual layers and operations, instead of using incremental integers. Note that dictionary keys must be unique, so _each operation must have a different name_.
from collections import OrderedDict
model = nn.Sequential(OrderedDict([
('fc1', nn.Linear(input_size, hidden_sizes[0])),
('relu1', nn.ReLU()),
('fc2', nn.Linear(hidden_sizes[0], hidden_sizes[1])),
('relu2', nn.ReLU()),
('output', nn.Linear(hidden_sizes[1], output_size)),
('softmax', nn.Softmax(dim=1))]))
model
# Now you can access layers either by integer or the name
print(model[0])
print(model.fc1)
# In the next notebook, we'll see how we can train a neural network to accuractly predict the numbers appearing in the MNIST images.
| 17,423 |
/assignment3/assignment3.ipynb | 074967e2b369b02f275169bac1bcf8264774db09 | [] | no_license | Weijiang-Xiong/DLcourse | https://github.com/Weijiang-Xiong/DLcourse | 3 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 162,308 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="vIv32qoYkoCO"
# ## Global Terrorism Database
# + [markdown] id="n-eYfhnokz0b"
# ### GTD
# + colab={"base_uri": "https://localhost:8080/"} id="OSPLoKjTOITa" outputId="af1be95b-68fe-48cd-ad0d-19d1849a36d1"
from google.colab import drive
drive.mount("/content/gdrive")
# + colab={"base_uri": "https://localhost:8080/"} id="SPjF0a4sOoqH" outputId="d3932e45-2bf1-4257-d46c-6e762d87c705"
import pandas as pd
df = pd.read_csv('/content/gdrive/My Drive/CPE-695/Crime Data/GTD/globalterrorismdb.csv')
# + [markdown] id="H5Hd-m1blImz"
# ### Understanding the dataframe
#
#
# + colab={"base_uri": "https://localhost:8080/"} id="pS_-LcFhiRJW" outputId="6c4da565-be83-4f2c-c3ea-c046f25a94ea"
# GTD
pd.set_option('display.max_columns', None)
df.head()
# + colab={"base_uri": "https://localhost:8080/"} id="ndVvaW9Fix1w" outputId="bd14e7f7-6687-415f-ef6e-4823c9349bde"
type(df)
# + colab={"base_uri": "https://localhost:8080/"} id="EeJ9erKFi9b-" outputId="2bde5f23-5d1f-4b27-8415-e76b7009828f"
# Datatypes
pd.set_option('display.max_rows', None)
df.dtypes
# + colab={"base_uri": "https://localhost:8080/"} id="OmqbXSyrjCqr" outputId="dcfc1774-e89a-42b5-8098-032ff8399e2e"
# Basic statistical details
df.describe()
# + colab={"base_uri": "https://localhost:8080/"} id="u8mzuw_1jFD7" outputId="6af924bb-e2b4-42eb-e016-ebb8a4c96341"
# Dataframe information
df.info()
# + colab={"base_uri": "https://localhost:8080/"} id="3Egt___5jK09" outputId="784c1944-53c9-4598-d6db-e9d5bd5dd112"
# Range index of the dataframe
df.index
# + colab={"base_uri": "https://localhost:8080/"} id="36XO8wpJjNby" outputId="fcac652f-94ca-4cec-e2e9-e65069b613e6"
# Length of the dataframe
len(df)
# + colab={"base_uri": "https://localhost:8080/"} id="WXsqyQ4Hf6B0" outputId="0a3c3302-5c9e-404d-8fe3-b3c5e43c4a3c"
# Checking if there are any missing values in the dataframe
df.isna().any().any()
# + colab={"base_uri": "https://localhost:8080/"} id="T2yZWqirfb36" outputId="7c115432-99be-4a41-a32b-723269bf0db2"
# Counting the number of missing values in the dataframe
df.isna().sum().sum()
# + colab={"base_uri": "https://localhost:8080/"} id="vPFWahoWXV7Z" outputId="b1b84143-a4e9-4928-e775-fa0329337507"
# Identifying columns with missing values
pd.options.display.max_seq_items = 200
df.loc[:, df.isnull().any()].columns
# + colab={"base_uri": "https://localhost:8080/"} id="yVD3Nx4VlNAx" outputId="786a9713-c163-464f-e261-1f277fad67ad"
# Counting the number of missing values in each column of the dataframe
pd.set_option('display.max_rows', None)
df.isna().sum()
# + colab={"base_uri": "https://localhost:8080/"} id="slB6XnkLc-Ee" outputId="2b40e072-b4ab-4faf-f934-6073820fe610"
# The relative frequency of missings per column
pd.set_option('display.max_rows', None)
df.isna().sum()/(len(df))*100
# + [markdown] id="WbdFZrAcs-nj"
# ### Exploratory Data Analysis
# + id="CFthpA49XWVk"
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sb
import io
# + [markdown] id="x7EgQw0nu0et"
# #### General Analysis
# + colab={"base_uri": "https://localhost:8080/"} id="nQa48HtuvMHY" outputId="b1e36c9d-dd6d-4555-de7b-a2cd46e7dd2b"
pd.set_option('display.max_columns', None)
df.head()
# + colab={"base_uri": "https://localhost:8080/"} id="M-ys77uw3_kA" outputId="c578b212-5305-4cd1-ca8d-cf329ca2ae01"
print('Country with Highest Terrorist Attacks:',df['country_txt'].value_counts().index[0])
print('Region with Highest Terrorist Attacks:',df['region_txt'].value_counts().index[0])
print('Maximum people killed in an attack:',df['nkill'].max(),'that took place in',df.loc[df['nkill'].idxmax()].country_txt)
print('Maximum people injured in an attack:',df['nwound'].max(),'that took place in',df.loc[df['nwound'].idxmax()].country_txt,'in',df.loc[df['nwound'].idxmax()].iyear)
df['casualities']=df['nkill']+df['nwound']
print('Maximum casualities in an attack:',df['casualities'].max(),'that took place in',df.loc[df['casualities'].idxmax()].country_txt)
print('Maximum property damage in an attack (in USD):',df['propvalue'].max(),'that took place in',df.loc[df['propvalue'].idxmax()].country_txt)
print('Most common weapon type:',df['weaptype1_txt'].value_counts().index[0])
print('Most common target type:',df['targtype1_txt'].value_counts().index[0])
# + colab={"base_uri": "https://localhost:8080/"} id="U-sFpMwbWyAI" outputId="a54cc5ba-1a18-4874-935d-535f3cace141"
plt.subplots(figsize=(15,10))
sb.set(font_scale=1.3)
sb.set(style='darkgrid', rc={"grid.linewidth": 0.1})
plt_nattacks = sb.countplot(x='iyear', data=df, palette='seismic', edgecolor=sb.color_palette('dark',7))
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Count', fontsize = 14)
plt.xticks(rotation=60)
plt.title('Number Of Terrorist Activities by Year', fontsize = 18)
plt.show()
# + [markdown] id="eZPJQGoLc-im"
# **Inference**: Terrorism has only increased through the years.
#
# * 1970 - 1992: increase in terror activity
# * 1994 - 2004: fluctuating activity
# * 2004 - 2014: steep rise in terror activity
# * 2015 - 2018: decrease in terror activity in relation to the period (2004 - 2014)
# + colab={"base_uri": "https://localhost:8080/"} id="gTw875BufJO7" outputId="380fc0f8-d818-46dc-a58d-6578b82fa6a2"
plt.subplots(figsize=(15,6))
sb.set(font_scale=1.3)
sb.set(style='darkgrid', rc={"grid.linewidth": 0.1})
plt_nattacks = sb.countplot(y='region_txt', data=df, palette='nipy_spectral', edgecolor=sb.color_palette('dark',7))
plt.xlabel('Region', fontsize = 14)
plt.ylabel('Count', fontsize = 14)
plt.xticks(rotation=0)
plt.title('Number Of Terrorist Activities by Region', fontsize = 18)
plt.tight_layout(pad=4.0)
plt.show()
# + [markdown] id="WHhOHLHJfUVW"
# **Inference**: The following regions are prone to terror the most,
#
# * Middle East & North Africa
# * South Asia
# * Sub-Saharan Africa
# * South America
# + colab={"base_uri": "https://localhost:8080/"} id="7ZR7XWHNvDGV" outputId="b4c27dd5-c7c2-4616-fcaa-0e2bc3f6de6a"
pd.crosstab(df.iyear, df.region_txt).plot.line(color=sb.color_palette('tab20', 10), linewidth=2.5)
fig = plt.gcf()
fig.set_size_inches(15, 10)
plt.xlabel('Year', fontsize = 14)
fig.suptitle('Trends in Terror Activity by Region', fontsize = 18)
plt.legend(title='Region')
plt.subplots_adjust(top=0.95)
plt.show()
# + [markdown] id="hgjiAHP3f0Qi"
# **Inference**: This plot reinforces inferences drawn from the previous ones and provides the following insights in addition,
#
# * South America dominated the world of terror during the period (1980 - 1993); it saw a steep decrease in terror activity following this period. This is an interesting trend because the region has managed to reduce terror activity over the years.
# * Middle East & North Africa and South Asia see a sharp, amplified increase in terror activity during the period (2000 - 2014)
# * Sub-Saharan Africa sees periods of fluctuating terror over the years with a sharp increase (on average) in terror during the period (2005 - 2018)
# + colab={"base_uri": "https://localhost:8080/"} id="vctJP5ShvGit" outputId="622edd93-06bb-4b77-ef7f-743c5d12cc7e"
fig, ax = plt.subplots(figsize=(15, 5))
sb.set(font_scale=1.25)
w2 = sb.countplot(y='country_txt', data=df, palette='gist_ncar',order=df['country_txt'].value_counts()[:10].index)
w2.set(ylabel='Country', xlabel='Terror Count')
ax.tick_params(labelrotation=0)
fig.autofmt_xdate()
fig.tight_layout()
plt.suptitle('Countries most affected by Terrorism', fontsize = 18)
plt.subplots_adjust(top=0.92)
plt.show()
# + [markdown] id="zFzM2D2skwtH"
# **Inference**: The following countries have suffered the most,
#
# * Iraq
# * Pakistan
# * Afghanistan
# * India
# * Colombia
# * Philippines
# + [markdown] id="CRPwI7tSxKsU"
# #### Terror Groups
# + colab={"base_uri": "https://localhost:8080/"} id="eJxZjx7bxsBy" outputId="65b2db78-037b-4ad8-805e-8fbc6cd031de"
sb.scatterplot(x=df['gname'].value_counts()[1:15].values, y=df['gname'].value_counts()[1:15].index, s=200)
sb.set(font_scale=1.3)
plt.xticks(rotation=0)
fig=plt.gcf()
fig.set_size_inches(10, 5)
plt.title('Most Notorious Terror Groups')
plt.show()
# + [markdown] id="6r4yfngJlO-R"
# **Inference**: The following are the most notorious terrorist organizations,
#
# * Taliban
# * Islamic State of Iraq and the Levant (ISIL)
# * Shining Path (ISL)
# * Al-Shabaab
# * Farabundo Marti National Liberation Front (FMLN)
# * New People's Army
# * Irish Republican Army (IRA)
# + colab={"base_uri": "https://localhost:8080/"} id="r9YwEhQoyAvR" outputId="5bb24e51-d062-4e8d-b850-30458ad3deb9"
fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(20, 12))
sb.set(font_scale=1.5)
sb.set(style='darkgrid', rc={"grid.linewidth": 0.1})
g1 = sb.countplot(y='gname', data=df, palette='bright',order=df['gname'].value_counts().index[:7], ax=ax[0])
g2 = sb.countplot(y='gname2', data=df, palette='muted',order=df['gname2'].value_counts().index[:7], ax=ax[1])
g3 = sb.countplot(y='gname3', data=df, palette='pastel',order=df['gname3'].value_counts().index[:7], ax=ax[2])
g1.set(ylabel='Primary Perpetrators of Terror', xlabel='Incident Count')
g2.set(ylabel='Secondary Perpetrators of Terror', xlabel='Incident Count')
g3.set(ylabel='Tertiary Perpetrators of Terror', xlabel='Incident Count')
for ax in fig.axes:
ax.tick_params(labelrotation=0)
fig.tight_layout(pad=5.0)
plt.suptitle('Notorious Terror Groups, Multiple Perpetrators', fontsize = 18)
plt.subplots_adjust(top=0.95)
plt.show()
# + [markdown] id="HU8RPMWurgAa"
# **Background**: These plots apply individually (standalone) and also when responsibility for an attack is attributed to more than one perpetrator. Primary perpetrators of terror are terror groups that are most notorious for organizing a terror attack. Secondary and tertiary perpetrator groups are terror groups that either aided or contributed to an attack.
#
# NOTE: Multiple perpetrator group attributions do not necessarily indicate that perpetrator groups collaborated to execute an attack. This could represent competing attributions, competing claims of responsibility, competing accusations, or a combination of these.
#
# **Inference**: Most notorious groups of terror,
#
# * Taliban
# * Islamic State of Iraq and the Levant (ISIL)
# * Shining Path (ISL)
# * Al-Shabaab
# * Farabundo Marti National Liberation Front (FMLN)
# * New People's Army (NPA)
# * Khorasan Chapter of the Islamic State
# * Al-Nusrah Front
# * Lashkar-e-Taiba (LeT)
# * Badr Brigades
# * National Liberation Army of Colombia
# * National Democratic Alliance Army (NDAA-ESSA)
# + colab={"base_uri": "https://localhost:8080/"} id="3KBBSqfT3B-u" outputId="7c221938-db18-49d4-e7db-bcaa116a0bab"
df_filter = df[df['gname'] == "Taliban"]
df_filter = df_filter.groupby(['region_txt','iyear'])['gname'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(20, 5))
g = sb.heatmap(df_filter[0:3],cmap='YlGnBu',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Region', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Taliban, Region of Operation', fontsize = 18)
plt.show()
# + colab={"base_uri": "https://localhost:8080/"} id="WZWtg2b_ztAz" outputId="cff5b3c2-c037-477d-d768-e13faf8e9c7b"
df_filter = df[df['gname'] == "Taliban"]
df_filter = df_filter.groupby(['country_txt','iyear'])['gname'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(20, 5))
g = sb.heatmap(df_filter[0:3],cmap='YlGnBu',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Country', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Countries affected by Taliban', fontsize = 18)
plt.show()
# + [markdown] id="bL3dZCRrv4s5"
# **Inference**: Taliban operates in South Asia. It predominantly wages terror in the country of Afghanistan.
# + colab={"base_uri": "https://localhost:8080/"} id="2Z8Fz_4J3TlM" outputId="a1cf0530-bc86-4312-fec7-0e7965d37918"
df_filter = df[df['gname'] == "Islamic State of Iraq and the Levant (ISIL)"]
df_filter = df_filter.groupby(['region_txt','iyear'])['gname'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(20, 5))
g = sb.heatmap(df_filter[0:3],cmap='YlGnBu',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Region', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Islamic State of Iraq and the Levant (ISIL), Region of Operation', fontsize = 18)
plt.show()
# + colab={"base_uri": "https://localhost:8080/"} id="ArfUB2Dt2dL_" outputId="e680b37b-e254-4665-c29e-2ebb52a98b51"
df_filter = df[df['gname'] == "Islamic State of Iraq and the Levant (ISIL)"]
df_filter = df_filter.groupby(['country_txt','iyear'])['gname'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(20, 5))
g = sb.heatmap(df_filter[0:3],cmap='YlGnBu',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Country', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Countries affected by Islamic State of Iraq and the Levant (ISIL)', fontsize = 18)
plt.show()
# + [markdown] id="SPIuFNC-wjqo"
# **Inference**: Islamic State of Iraq and the Levant (ISIL) operates in Middle East & North Africa. It predominantly wages terror in Iraq and Syria.
# + colab={"base_uri": "https://localhost:8080/"} id="qHiPjXH43tx0" outputId="6dfbb823-f824-406b-a87d-e1af58e0d9e1"
df_filter = df[df['gname'] == "Shining Path (SL)"]
df_filter = df_filter.groupby(['region_txt','iyear'])['gname'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(30, 5))
g = sb.heatmap(df_filter[0:3],cmap='YlGnBu',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Region', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Shining Path (SL), Region of Operation', fontsize = 18)
plt.show()
# + colab={"base_uri": "https://localhost:8080/"} id="4jaQkErq2kdU" outputId="e35001a1-2bed-403c-f228-b3f758e27340"
df_filter = df[df['gname'] == "Shining Path (SL)"]
df_filter = df_filter.groupby(['country_txt','iyear'])['gname'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(30, 5))
g = sb.heatmap(df_filter[0:3],cmap='YlGnBu',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Country', fontsize = 14)
ax.tick_params(labelrotation=0)
fig.tight_layout()
fig = plt.gcf()
fig.suptitle('Countries affected by Shining Path (SL)', fontsize = 18)
plt.show()
# + [markdown] id="N_kFi9KmxBhz"
# **Inference**: Shining Path (ISL) operates in South America. It predominantly wages terror in Peru.
# + [markdown] id="tzggCbOD46-k"
# #### Terror Targets
# + colab={"base_uri": "https://localhost:8080/"} id="-eYhmg265Mxu" outputId="69051884-fce5-4527-b0d8-154b70f4817a"
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(20, 10))
sb.set(font_scale=1.25)
sb.set(style='darkgrid', rc={"grid.linewidth": 0.1})
a1 = sb.countplot(y='targtype1_txt', data=df, palette='bright',order=df['targtype1_txt'].value_counts().index, ax=ax[0])
a2 = sb.countplot(y='targtype2_txt', data=df, palette='muted',order=df['targtype2_txt'].value_counts().index, ax=ax[1])
a3 = sb.countplot(y='targtype3_txt', data=df, palette='pastel',order=df['targtype3_txt'].value_counts().index, ax=ax[2])
a1.set(xlabel='Primary Target Types', ylabel='Count')
a2.set(xlabel='Secondary Target Types', ylabel='Count')
a3.set(xlabel='Tertiary Target Types', ylabel='Count')
for ax in fig.axes:
ax.tick_params(labelrotation=0)
fig.tight_layout(pad=4.0)
fig.autofmt_xdate()
plt.suptitle('Terror Target Types, Multiple Targets', fontsize = 18)
plt.subplots_adjust(top=0.93)
plt.show()
# + [markdown] id="IBbpaDOkxyLI"
# **Background**: The target/victim type field captures the general type of target/victim. When a victim is attacked specifically because of his or her relationship to a particular person, such as a prominent figure, the target type reflects that motive. For example, if a family member of a government official is attacked because of his or her relationship to that individual, the type of target is “government.” This variable consists of 22 different categories.
#
# **Inference**: Primary Terror Targets,
#
# * Private Citizens & Property
# * Military
# * Police
# * Government (General)
# * Business
# * Transportation
#
# Secondary and Tertiary Terror Targets are second and third target types in terror attacks or incidents. The field target type contains information on both intended targets and incidental bystanders, and therefore, intentionality should be carefully considered in each case.
# + colab={"base_uri": "https://localhost:8080/"} id="AzMPBZJb5bdp" outputId="c460e028-e223-460f-a4a1-fb74258ca757"
df_filter = df[df['targtype1_txt'] == "Private Citizens & Property"]
df_filter = df_filter.groupby(['country_txt','iyear'])['targtype1_txt'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(15, 10))
g = sb.heatmap(df_filter[0:10],cmap='Blues',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Country', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Time Series Analysis for Target Type: Private Citizens & Property', fontsize = 18)
plt.show()
# + [markdown] id="QYGGupPuzLrH"
# **Inference**: Private Citizens & Property in the following countries suffered the maximum amount of damage,
#
# * Iraq
# * India
# * Pakistan
# * Nigeria
# * Afghanistan
# + colab={"base_uri": "https://localhost:8080/"} id="QQN0DJal6AKY" outputId="11f5a896-ad8b-4182-9449-90a68899493a"
df_filter = df[df['targtype1_txt'] == "Military"]
df_filter = df_filter.groupby(['country_txt','iyear'])['targtype1_txt'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(15, 10))
g = sb.heatmap(df_filter[0:10],cmap='BuPu',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Country', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Time Series Analysis for Target Type: Military', fontsize = 18)
plt.show()
# + [markdown] id="hWrOcCRtzqG4"
# **Inference**: Military groups targeted the most by terror groups belong to the following countries,
#
# * Afghanistan
# * Iraq
# * Somalia
# * Philippines
# * Pakistan
# * India
# * Yemen
# + colab={"base_uri": "https://localhost:8080/"} id="o5mc1wZS5823" outputId="8c343fa0-aaa6-4221-9ee6-c2482a986163"
df_filter = df[df['targtype1_txt'] == "Police"]
df_filter = df_filter.groupby(['country_txt','iyear'])['targtype1_txt'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(15, 10))
g = sb.heatmap(df_filter[0:10],cmap='Greens',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Country', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Time Series Analysis for Target Type: Police', fontsize = 18)
plt.show()
# + [markdown] id="oLGgstNa0IlC"
# **Inference**: Police groups targeted the most by terror groups belong to the following countries,
#
# * Afghanistan
# * Iraq
# * Pakistan
# * India
# * Colombia
# + colab={"base_uri": "https://localhost:8080/"} id="uGQXmwv46F5-" outputId="7d5f763d-aac2-4f52-becc-cc2d56839b07"
df_filter = df[df['targtype1_txt'] == "Government (General)"]
df_filter = df_filter.groupby(['country_txt','iyear'])['targtype1_txt'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(15, 10))
g = sb.heatmap(df_filter[0:10],cmap='Purples',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Country', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Time Series Analysis for Target Type: Government (General)', fontsize = 18)
plt.show()
# + [markdown] id="OW5DB56Z1irO"
# **Inference**: Governments of the following countries suffered the most damage due to terrorism,
#
# * Afghanistan
# * Philippines
# * Iraq
# * Yemen
# * Somalia
# * India
# * Pakistan
# * Nigeria
# * Yemen
# * Mali
# + colab={"base_uri": "https://localhost:8080/"} id="hOhAgPjC2Vaz" outputId="d7935ebb-3101-4d3f-ee83-1f1d4ad3a2b5"
df_filter = df[df['targtype1_txt'] == "Government (Diplomatic)"]
df_filter = df_filter.groupby(['country_txt','iyear'])['targtype1_txt'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(15, 10))
g = sb.heatmap(df_filter[0:10],cmap='Reds',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Country', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Time Series Analysis for Target Type: Government (Diplomatic)', fontsize = 18)
plt.show()
# + [markdown] id="kivyueFK0iWX"
# **Inference**: Attacks carried out against foreign missions, including embassies, consulates, etc. in the following countries make them the most vulnerable to terror attacks on Government personnel or property etc.
#
# * Mali
# * Central African Republic
# * Democratic Republic of the Congo
# * Yemen
# * Somalia
# * South Sudan
# * Afghanistan
# * Iraq
# * Pakistan
# * Nigeria
# + colab={"base_uri": "https://localhost:8080/"} id="Bf85xqec6L9T" outputId="e2c0ee05-a64b-4cd9-cf61-cb3223111bd0"
df_filter = df[df['targtype1_txt'] == "Business"]
df_filter = df_filter.groupby(['country_txt','iyear'])['targtype1_txt'].count().unstack()
df_filter = df_filter.sort_values([2018], ascending=False)
df_filter = df_filter.fillna(0)
f, ax = plt.subplots(figsize=(15, 10))
g = sb.heatmap(df_filter[0:10],cmap='Oranges',linewidths=.6,vmin=0.01)
plt.xlabel('Year', fontsize = 14)
plt.ylabel('Country', fontsize = 14)
fig = plt.gcf()
fig.suptitle('Time Series Analysis for Target Type: Business', fontsize = 18)
plt.show()
# + [markdown] id="1gr4Q6ha2xZW"
# **Inference**: Businesses in the following countries suffered the most due to terrorism,
#
# * India
# * Iraq
# * Philippines
# * Afghanistan
# * Thailand
# * Chile
# * Libya
# + [markdown] id="P5Z7eSJM67u7"
# #### Attack Types and Weaponry
# + colab={"base_uri": "https://localhost:8080/"} id="4O4_CEc17B83" outputId="384494ef-fb68-4af5-feda-ebe89394dec6"
fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(20, 10))
sb.set(font_scale=1.25)
sb.set(style='darkgrid', rc={"grid.linewidth": 0.1})
a1 = sb.countplot(y='attacktype1_txt', data=df, palette='bright',order=df['attacktype1_txt'].value_counts().index, ax=ax[0])
a2 = sb.countplot(y='attacktype2_txt', data=df, palette='muted',order=df['attacktype2_txt'].value_counts().index, ax=ax[1])
a3 = sb.countplot(y='attacktype3_txt', data=df, palette='pastel',order=df['attacktype3_txt'].value_counts().index, ax=ax[2])
a1.set(ylabel='Primary Attack Types')
a2.set(ylabel='Secondary Attack Types')
a3.set(ylabel='Tertiary Attack Types')
for ax in fig.axes:
ax.tick_params(labelrotation=0)
fig.tight_layout(pad=4.0)
plt.suptitle('Multiple Attack Methods used by Terrorists', fontsize = 18)
plt.subplots_adjust(top=0.93)
plt.show()
# + [markdown] id="CjmF2m2JwJcL"
# **Background**: This field captures the general method of attack and often reflects the broad class of tactics used. It consists of nine categories, given below. Up to three attack types can be recorded for each incident. Typically, only one attack type is recorded for each incident unless the attack is comprised of a sequence of events. When multiple attack types may apply, the most appropriate value is determined based on the hierarchy below.
#
# Attack Type Hierarchy:
# * Assassination
# * Hijacking
# * Kidnapping
# * Barricade Incident
# * Bombing/Explosion
# * Armed Assault
# * Unarmed Assault
# * Facility/Infrastructure Attack
# * Unknown
#
# **Inference**: As can be inferred from the plots above, the most popular attack types used by terrorists are (in descending order),
#
# * Bombing/Explossion
# * Armed Assault
# * Assassination
# * Hostage Taking (Kidnapping)
# * Facility/Infrastructure Attack
# * Unarmed Assault
# + colab={"base_uri": "https://localhost:8080/"} id="E4faOGaQ7KuR" outputId="46651167-081a-4e98-d57c-4d4591b0d0b0"
pd.crosstab(df.targtype1_txt, df.attacktype1_txt).plot.barh(stacked=True, width=1.0, color=sb.color_palette('dark', 10))
fig = plt.gcf()
fig.set_size_inches(20, 10)
plt.ylabel('Target Type', fontsize = 14)
fig.suptitle('Common Attack Types used for Prominent Terror Targets', fontsize = 18)
plt.legend(title='Attack Types')
plt.subplots_adjust(top=0.93)
plt.show()
# + [markdown] id="ZIgV-JM_xFtM"
# **Inference**: The plot is self-evident. For example, it can be inferred that the three most popular attack types on "Private Citizens & Property" are,
#
# * Bombing/Explosion
# * Armed Assault
# * Hostage Taking (Kidnapping)
# + colab={"base_uri": "https://localhost:8080/"} id="uMoxuMwntnil" outputId="c8515957-25ba-4628-94af-71c5d6bb3825"
sb.set(font_scale=1.25)
region_dictionary = {3: 'South America', 6: 'South Asia', 10: 'Middle East and North Africa'}
def generate_graph(by_region_list):
fig = plt.figure(figsize=(15,50))
i = 1
for element in by_region_list:
ax1 = fig.add_subplot(11,2,i)
ax1.set(title = 'Attack Region: %s ' % region_dictionary[element[2]],
ylabel = 'Attack Count', xlabel = 'Year')
#entering data
ax1.plot(element[0].index, element[0].eventid, label = 'Successfull attacks' )
ax1.plot(element[1].index, element[1].eventid, label = 'Failed attacks' )
i+=1
#add legend
ax1.legend(loc = 'upper center', frameon = True, edgecolor = 'black', bbox_to_anchor =(-0.1,-0.4))
plt.subplots_adjust(top=0.95)
ax1.tick_params(labelrotation=30)
fig.tight_layout()
plt.show()
def by_region():
for region_number in region_dictionary:
region_df = df[(df.region == region_number)] #for each region group data by year
region_grouped_success = region_df[(region_df.success == 1)].groupby('iyear').count() #filter on success and group by year
region_grouped_failure = region_df[(region_df.success == 0)].groupby('iyear').count() #filter on failure and group by year
by_region_list.append([region_grouped_success, region_grouped_failure, region_number])
#create line plot for region grouped by year
generate_graph(by_region_list)
by_region_list = []
by_region()
# + [markdown] id="ACJ_l6yL9cyH"
# **Inference**: Plots show no clear trend through time. South Asia and Middle & North Africa display strong increase in terror activity from the year 2005 and beyond. South America has a similar increase during the years 1970 to 1994.
# + colab={"base_uri": "https://localhost:8080/"} id="YhW5OJF0B59L" outputId="2102b762-43ed-479f-a3b0-2c0e36e7da44"
fig, ax = plt.subplots(figsize=(10, 15))
sb.set(font_scale=1.25)
w1 = sb.countplot(y='weaptype1_txt', hue='weapsubtype1_txt', data=df, palette='bright',order=df['weaptype1_txt'].value_counts()[:5].index)
w1.set(xlabel='Count', ylabel='Weapon Types')
ax.tick_params(labelrotation=0)
plt.legend(title='Weapon Subtypes', fontsize='small', loc='lower right')
fig.tight_layout(pad=3.0)
fig.autofmt_xdate()
plt.suptitle('Favorite Weapon Types, Primary', fontsize = 18)
plt.subplots_adjust(top=0.95)
plt.show()
# + colab={"base_uri": "https://localhost:8080/"} id="wz9mYxtaB8er" outputId="0ac01c73-4996-4f64-b9cc-03fc889f6b69"
fig, ax = plt.subplots(figsize=(10, 15))
sb.set(font_scale=1.25)
w2 = sb.countplot(y='weaptype2_txt', hue='weapsubtype2_txt', data=df, palette='Paired',order=df['weaptype2_txt'].value_counts()[:5].index)
w2.set(ylabel='Weapon Types', xlabel='Count')
ax.tick_params(labelrotation=0)
plt.legend(title='Weapon Subtypes', fontsize='small', loc='lower right')
fig.tight_layout(pad=3.0)
fig.autofmt_xdate()
plt.suptitle('Favorite Weapon Types, Secondary', fontsize = 18)
plt.subplots_adjust(top=0.95)
plt.show()
# + colab={"base_uri": "https://localhost:8080/"} id="tzboJGpSB_eg" outputId="0ddbb358-aed6-4f54-a8f2-fb3549e8d131"
fig, ax = plt.subplots(figsize=(10, 15))
sb.set(font_scale=1.25)
w3 = sb.countplot(y='weaptype3_txt', hue='weapsubtype3_txt', data=df, palette='pastel',order=df['weaptype3_txt'].value_counts()[:5].index)
w3.set(ylabel='Weapon Types', xlabel='Count')
ax.ti | 28,668 |
/R-basics/03_R_Basic _Data _Types.ipynb | ec9329490b8b803ba79877366f85db396992c339 | [] | no_license | anatulea/R-Udemy | https://github.com/anatulea/R-Udemy | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .r | 6,027 | # -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .r
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: R
# language: R
# name: ir
# ---
# # R Data Types
# Numerics
#
# Decimal (floating point values) are part of the numeric class in R
n <- 2.2
# Integers
#
# Natural (whole) numbers are known as integers and are also part of the numeric class
i <- 5
# Logical
#
# Boolean values (True and False) are part of the logical class. In R these are written in All Caps.
t <- TRUE
f <- FALSE
t
f
# Characters
#
# Text/string values are known as characters in R. You use quotation marks to create a text character string:
char <- "Hello World!"
char
# # Checking Data Type Classes¶
class(t)
class(f)
class(char)
class(n)
class(i)
| 877 |
/Radar_plot.ipynb | 6bc2ac6bf03a2f6c5905d2afd914fc3f5e4e5521 | [] | no_license | nirmitktripathii/Football-Dataset-Analysis | https://github.com/nirmitktripathii/Football-Dataset-Analysis | 0 | 0 | null | 2020-08-08T08:10:38 | 2020-07-01T12:19:29 | null | Jupyter Notebook | false | false | .py | 2,845,075 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
# This mounts your Google Drive to the Colab VM.
from google.colab import drive
drive.mount('/content/drive')
# TODO: Enter the foldername in your Drive where you have saved the unzipped
# assignment folder, e.g. 'cs231n/assignments/assignment1/'
FOLDERNAME = None
assert FOLDERNAME is not None, "[!] Enter the foldername."
# Now that we've mounted your Drive, this ensures that
# the Python interpreter of the Colab VM can load
# python files from within it.
import sys
sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))
# This downloads the CIFAR-10 dataset to your Drive
# if it doesn't already exist.
# %cd /content/drive/My\ Drive/$FOLDERNAME/cs231n/datasets/
# !bash get_datasets.sh
# %cd /content/drive/My\ Drive/$FOLDERNAME
# -
# This downloads the CIFAR-10 dataset to your Drive
# if it doesn't already exist.
# %cd cs231n/datasets/
# !bash get_datasets.sh
# %cd -
# # Multi-Layer Fully Connected Network
# In this exercise, you will implement a fully connected network with an arbitrary number of hidden layers.
# Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.
#
# Implement the network initialization, forward pass, and backward pass. Throughout this assignment, you will be implementing layers in `cs231n/layers.py`. You can re-use your implementations for `affine_forward`, `affine_backward`, `relu_forward`, `relu_backward`, and `softmax_loss` from Assignment 1. For right now, don't worry about implementing dropout or batch/layer normalization yet, as you will add those features later.
#
# + tags=["pdf-ignore"]
# Setup cell.
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
# %matplotlib inline
plt.rcParams["figure.figsize"] = (10.0, 8.0) # Set default size of plots.
plt.rcParams["image.interpolation"] = "nearest"
plt.rcParams["image.cmap"] = "gray"
# %load_ext autoreload
# %autoreload 2
def rel_error(x, y):
"""Returns relative error."""
return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))
# -
# Load the (preprocessed) CIFAR-10 data.
data = get_CIFAR10_data()
for k, v in list(data.items()):
print(f"{k}: {v.shape}")
# ## Initial Loss and Gradient Check
#
# As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. This is a good way to see if the initial losses seem reasonable.
#
# For gradient checking, you should expect to see errors around 1e-7 or less.
# +
np.random.seed(231)
N, D, H1, H2, C = 2, 15, 20, 30, 10
X = np.random.randn(N, D)
y = np.random.randint(C, size=(N,))
for reg in [0, 3.14]:
print("Running check with reg = ", reg)
model = FullyConnectedNet(
[H1, H2],
input_dim=D,
num_classes=C,
reg=reg,
weight_scale=5e-2,
dtype=np.float64
)
loss, grads = model.loss(X, y)
print("Initial loss: ", loss)
# Most of the errors should be on the order of e-7 or smaller.
# NOTE: It is fine however to see an error for W2 on the order of e-5
# for the check when reg = 0.0
for name in sorted(grads):
f = lambda _: model.loss(X, y)[0]
grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)
print(f"{name} relative error: {rel_error(grad_num, grads[name])}")
# -
# As another sanity check, make sure your network can overfit on a small dataset of 50 images. First, we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the **learning rate** and **weight initialization scale** to overfit and achieve 100% training accuracy within 20 epochs.
# +
# TODO: Use a three-layer Net to overfit 50 training examples by
# tweaking just the learning rate and initialization scale.
num_train = 50
small_data = {
"X_train": data["X_train"][:num_train],
"y_train": data["y_train"][:num_train],
"X_val": data["X_val"],
"y_val": data["y_val"],
}
weight_scale = 1e-2 # Experiment with this!
learning_rate = 5e-3 # Experiment with this!
model = FullyConnectedNet(
[100, 100],
weight_scale=weight_scale,
dtype=np.float64
)
solver = Solver(
model,
small_data,
print_every=10,
num_epochs=20,
batch_size=25,
update_rule="sgd",
optim_config={"learning_rate": learning_rate},
)
solver.train()
plt.plot(solver.loss_history)
plt.title("Training loss history")
plt.xlabel("Iteration")
plt.ylabel("Training loss")
plt.grid(linestyle='--', linewidth=0.5)
plt.show()
# -
# Now, try to use a five-layer network with 100 units on each layer to overfit on 50 training examples. Again, you will have to adjust the learning rate and weight initialization scale, but you should be able to achieve 100% training accuracy within 20 epochs.
# +
# TODO: Use a five-layer Net to overfit 50 training examples by
# tweaking just the learning rate and initialization scale.
num_train = 50
small_data = {
'X_train': data['X_train'][:num_train],
'y_train': data['y_train'][:num_train],
'X_val': data['X_val'],
'y_val': data['y_val'],
}
learning_rate = 5e-4 # Experiment with this!
weight_scale = 7e-2 # Experiment with this!
model = FullyConnectedNet(
[100, 100, 100, 100],
weight_scale=weight_scale,
dtype=np.float64
)
solver = Solver(
model,
small_data,
print_every=10,
num_epochs=20,
batch_size=25,
update_rule='sgd',
optim_config={'learning_rate': learning_rate},
)
solver.train()
plt.plot(solver.loss_history)
plt.title('Training loss history')
plt.xlabel('Iteration')
plt.ylabel('Training loss')
plt.grid(linestyle='--', linewidth=0.5)
plt.show()
# + [markdown] tags=["pdf-inline"]
# ## Inline Question 1:
# Did you notice anything about the comparative difficulty of training the three-layer network vs. training the five-layer network? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?
#
# ## Answer:
# Training deeper network is harder to train since the early layers are more likely to suffer from diminishing gradients.
#
# -
# # Update rules
# So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD.
# ## SGD+Momentum
# Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.
#
# Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8.
# +
from cs231n.optim import sgd_momentum
N, D = 4, 5
w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)
dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)
v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)
config = {"learning_rate": 1e-3, "velocity": v}
next_w, _ = sgd_momentum(w, dw, config=config)
expected_next_w = np.asarray([
[ 0.1406, 0.20738947, 0.27417895, 0.34096842, 0.40775789],
[ 0.47454737, 0.54133684, 0.60812632, 0.67491579, 0.74170526],
[ 0.80849474, 0.87528421, 0.94207368, 1.00886316, 1.07565263],
[ 1.14244211, 1.20923158, 1.27602105, 1.34281053, 1.4096 ]])
expected_velocity = np.asarray([
[ 0.5406, 0.55475789, 0.56891579, 0.58307368, 0.59723158],
[ 0.61138947, 0.62554737, 0.63970526, 0.65386316, 0.66802105],
[ 0.68217895, 0.69633684, 0.71049474, 0.72465263, 0.73881053],
[ 0.75296842, 0.76712632, 0.78128421, 0.79544211, 0.8096 ]])
# Should see relative errors around e-8 or less
print("next_w error: ", rel_error(next_w, expected_next_w))
print("velocity error: ", rel_error(expected_velocity, config["velocity"]))
# -
# Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster.
# +
num_train = 4000
small_data = {
'X_train': data['X_train'][:num_train],
'y_train': data['y_train'][:num_train],
'X_val': data['X_val'],
'y_val': data['y_val'],
}
solvers = {}
for update_rule in ['sgd', 'sgd_momentum']:
print('Running with ', update_rule)
model = FullyConnectedNet(
[100, 100, 100, 100, 100],
weight_scale=5e-2
)
solver = Solver(
model,
small_data,
num_epochs=5,
batch_size=100,
update_rule=update_rule,
optim_config={'learning_rate': 5e-3},
verbose=True,
)
solvers[update_rule] = solver
solver.train()
fig, axes = plt.subplots(3, 1, figsize=(15, 15))
axes[0].set_title('Training loss')
axes[0].set_xlabel('Iteration')
axes[1].set_title('Training accuracy')
axes[1].set_xlabel('Epoch')
axes[2].set_title('Validation accuracy')
axes[2].set_xlabel('Epoch')
for update_rule, solver in solvers.items():
axes[0].plot(solver.loss_history, label=f"loss_{update_rule}")
axes[1].plot(solver.train_acc_history, label=f"train_acc_{update_rule}")
axes[2].plot(solver.val_acc_history, label=f"val_acc_{update_rule}")
for ax in axes:
ax.legend(loc="best", ncol=4)
ax.grid(linestyle='--', linewidth=0.5)
plt.show()
# -
# ## RMSProp and Adam
# RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.
#
# In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.
#
# **NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes.
#
# [1] Tijmen Tieleman and Geoffrey Hinton. "Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude." COURSERA: Neural Networks for Machine Learning 4 (2012).
#
# [2] Diederik Kingma and Jimmy Ba, "Adam: A Method for Stochastic Optimization", ICLR 2015.
# +
# Test RMSProp implementation
from cs231n.optim import rmsprop
N, D = 4, 5
w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)
dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)
cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)
config = {'learning_rate': 1e-2, 'cache': cache}
next_w, _ = rmsprop(w, dw, config=config)
expected_next_w = np.asarray([
[-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],
[-0.132737, -0.08078555, -0.02881884, 0.02316247, 0.07515774],
[ 0.12716641, 0.17918792, 0.23122175, 0.28326742, 0.33532447],
[ 0.38739248, 0.43947102, 0.49155973, 0.54365823, 0.59576619]])
expected_cache = np.asarray([
[ 0.5976, 0.6126277, 0.6277108, 0.64284931, 0.65804321],
[ 0.67329252, 0.68859723, 0.70395734, 0.71937285, 0.73484377],
[ 0.75037008, 0.7659518, 0.78158892, 0.79728144, 0.81302936],
[ 0.82883269, 0.84469141, 0.86060554, 0.87657507, 0.8926 ]])
# You should see relative errors around e-7 or less
print('next_w error: ', rel_error(expected_next_w, next_w))
print('cache error: ', rel_error(expected_cache, config['cache']))
# +
# Test Adam implementation
from cs231n.optim import adam
N, D = 4, 5
w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)
dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)
m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)
v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)
config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}
next_w, _ = adam(w, dw, config=config)
expected_next_w = np.asarray([
[-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],
[-0.1380274, -0.08544591, -0.03286534, 0.01971428, 0.0722929],
[ 0.1248705, 0.17744702, 0.23002243, 0.28259667, 0.33516969],
[ 0.38774145, 0.44031188, 0.49288093, 0.54544852, 0.59801459]])
expected_v = np.asarray([
[ 0.69966, 0.68908382, 0.67851319, 0.66794809, 0.65738853,],
[ 0.64683452, 0.63628604, 0.6257431, 0.61520571, 0.60467385,],
[ 0.59414753, 0.58362676, 0.57311152, 0.56260183, 0.55209767,],
[ 0.54159906, 0.53110598, 0.52061845, 0.51013645, 0.49966, ]])
expected_m = np.asarray([
[ 0.48, 0.49947368, 0.51894737, 0.53842105, 0.55789474],
[ 0.57736842, 0.59684211, 0.61631579, 0.63578947, 0.65526316],
[ 0.67473684, 0.69421053, 0.71368421, 0.73315789, 0.75263158],
[ 0.77210526, 0.79157895, 0.81105263, 0.83052632, 0.85 ]])
# You should see relative errors around e-7 or less
print('next_w error: ', rel_error(expected_next_w, next_w))
print('v error: ', rel_error(expected_v, config['v']))
print('m error: ', rel_error(expected_m, config['m']))
# -
# Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:
# +
learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}
for update_rule in ['adam', 'rmsprop']:
print('Running with ', update_rule)
model = FullyConnectedNet(
[100, 100, 100, 100, 100],
weight_scale=5e-2
)
solver = Solver(
model,
small_data,
num_epochs=5,
batch_size=100,
update_rule=update_rule,
optim_config={'learning_rate': learning_rates[update_rule]},
verbose=True
)
solvers[update_rule] = solver
solver.train()
print()
fig, axes = plt.subplots(3, 1, figsize=(15, 15))
axes[0].set_title('Training loss')
axes[0].set_xlabel('Iteration')
axes[1].set_title('Training accuracy')
axes[1].set_xlabel('Epoch')
axes[2].set_title('Validation accuracy')
axes[2].set_xlabel('Epoch')
for update_rule, solver in solvers.items():
axes[0].plot(solver.loss_history, label=f"{update_rule}")
axes[1].plot(solver.train_acc_history, label=f"{update_rule}")
axes[2].plot(solver.val_acc_history, label=f"{update_rule}")
for ax in axes:
ax.legend(loc='best', ncol=4)
ax.grid(linestyle='--', linewidth=0.5)
plt.show()
# + [markdown] tags=["pdf-inline"]
# ## Inline Question 2:
#
# AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:
#
# ```
# cache += dw**2
# w += - learning_rate * dw / (np.sqrt(cache) + eps)
# ```
#
# John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?
#
#
# ## Answer:
# The variable cache gets increasingly larger which has a learning rate decay effect. Adam eliminates this issue by keeping a moving average of this cache that is not necessarily increasing, same as RMSprop.
#
# -
# # Train a Good Model!
# Train the best fully connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully connected network.
#
# If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional networks rather than fully connected networks.
#
# **Note:** You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models.
# +
best_model = None
################################################################################
# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might #
# find batch/layer normalization and dropout useful. Store your best model in #
# the best_model variable. #
################################################################################
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
lrs = [5e-4]
regs = [1e1, 1e2, 5e2, 1e3]
#learning_rate = 5e-4 # Experiment with this!
#weight_scale = 7e-2 # Experiment with this!
best_acc = -1
best_params = None
for lr in lrs:
for reg in regs:
model = FullyConnectedNet([100, 100, 100, 100],
weight_scale=7e-2)
solver = Solver(
model,
data,
num_epochs=50,
batch_size=100,
update_rule='adam',
optim_config={'learning_rate': lr},
verbose=True
)
solver.train()
res = 'lr: {}, reg: {}, val_acc: {}'.format(lr, reg, solver.best_val_acc)
print(res)
if solver.best_val_acc > best_acc:
best_acc = solver.best_val_acc
best_params = solver.best_params
best_hyperp = {'lr': lr, 'reg': reg}
best_model = model
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
################################################################################
# END OF YOUR CODE #
################################################################################
# -
best_acc
best_hyperp
# # Test Your Model!
# Run your best model on the validation and test sets. You should achieve at least 50% accuracy on the validation set.
y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)
y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)
print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())
print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())
| 18,444 |
/LoadBostonReg.ipynb | c384dc651c50b5109eaecb69142a14bab390274e | [] | no_license | Wadiprasetyo/Boston_Regression | https://github.com/Wadiprasetyo/Boston_Regression | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 61,704 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# sklern toy datasets
import pandas as pd
import numpy as np
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
# +
# load Boston house prices dataset
# +
dataBoston = load_boston()
# print(dataBoston)
# print(dir(dataBoston))
# ['DESCR', 'data', 'feature_names', 'filename', 'target']
# print(dataBoston['DESCR'])
# print(dataBoston['data'].shape)
# print(dataBoston['data'][0])
df = pd.DataFrame(
dataBoston['data'],
columns= dataBoston['feature_names']
)
df['price'] = dataBoston['target']
df
# +
korelasi = df.corr()
plt.imshow(korelasi, cmap='BuPu_r')
plt.colorbar()
plt.xticks(np.arange(14), list(df.columns), rotat)
plt.yticks(np.arange(14), list(df.columns))
plt.show()
# -
model = LinearRegression()
model.fit(df[['ZN','CHAS','RM','DIS','B']], df['price'])
model.predict(df.head(1)[['ZN','CHAS','RM','DIS','B']])
df.head(1)['price']
| 1,195 |
/melspectrum_Turnkey.ipynb | ad230bfb3acb20db0c93e735becb45d4b8bc87d4 | [] | no_license | sujitha-msit/CGL3 | https://github.com/sujitha-msit/CGL3 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 37,125 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/sujitha-msit/CGL3/blob/main/melspectrum_Turnkey.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + id="_bNIHMBzGICK"
# !pip install -q kaggle
# + colab={"resources": {"http://localhost:8080/nbextensions/google.colab/files.js": {"data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7CgpmdW5jdGlvbiBfdXBsb2FkRmlsZXMoaW5wdXRJZCwgb3V0cHV0SWQpIHsKICBjb25zdCBzdGVwcyA9IHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCk7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICAvLyBDYWNoZSBzdGVwcyBvbiB0aGUgb3V0cHV0RWxlbWVudCB0byBtYWtlIGl0IGF2YWlsYWJsZSBmb3IgdGhlIG5leHQgY2FsbAogIC8vIHRvIHVwbG9hZEZpbGVzQ29udGludWUgZnJvbSBQeXRob24uCiAgb3V0cHV0RWxlbWVudC5zdGVwcyA9IHN0ZXBzOwoKICByZXR1cm4gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpOwp9CgovLyBUaGlzIGlzIHJvdWdobHkgYW4gYXN5bmMgZ2VuZXJhdG9yIChub3Qgc3VwcG9ydGVkIGluIHRoZSBicm93c2VyIHlldCksCi8vIHdoZXJlIHRoZXJlIGFyZSBtdWx0aXBsZSBhc3luY2hyb25vdXMgc3RlcHMgYW5kIHRoZSBQeXRob24gc2lkZSBpcyBnb2luZwovLyB0byBwb2xsIGZvciBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcC4KLy8gVGhpcyB1c2VzIGEgUHJvbWlzZSB0byBibG9jayB0aGUgcHl0aG9uIHNpZGUgb24gY29tcGxldGlvbiBvZiBlYWNoIHN0ZXAsCi8vIHRoZW4gcGFzc2VzIHRoZSByZXN1bHQgb2YgdGhlIHByZXZpb3VzIHN0ZXAgYXMgdGhlIGlucHV0IHRvIHRoZSBuZXh0IHN0ZXAuCmZ1bmN0aW9uIF91cGxvYWRGaWxlc0NvbnRpbnVlKG91dHB1dElkKSB7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICBjb25zdCBzdGVwcyA9IG91dHB1dEVsZW1lbnQuc3RlcHM7CgogIGNvbnN0IG5leHQgPSBzdGVwcy5uZXh0KG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSk7CiAgcmV0dXJuIFByb21pc2UucmVzb2x2ZShuZXh0LnZhbHVlLnByb21pc2UpLnRoZW4oKHZhbHVlKSA9PiB7CiAgICAvLyBDYWNoZSB0aGUgbGFzdCBwcm9taXNlIHZhbHVlIHRvIG1ha2UgaXQgYXZhaWxhYmxlIHRvIHRoZSBuZXh0CiAgICAvLyBzdGVwIG9mIHRoZSBnZW5lcmF0b3IuCiAgICBvdXRwdXRFbGVtZW50Lmxhc3RQcm9taXNlVmFsdWUgPSB2YWx1ZTsKICAgIHJldHVybiBuZXh0LnZhbHVlLnJlc3BvbnNlOwogIH0pOwp9CgovKioKICogR2VuZXJhdG9yIGZ1bmN0aW9uIHdoaWNoIGlzIGNhbGxlZCBiZXR3ZWVuIGVhY2ggYXN5bmMgc3RlcCBvZiB0aGUgdXBsb2FkCiAqIHByb2Nlc3MuCiAqIEBwYXJhbSB7c3RyaW5nfSBpbnB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIGlucHV0IGZpbGUgcGlja2VyIGVsZW1lbnQuCiAqIEBwYXJhbSB7c3RyaW5nfSBvdXRwdXRJZCBFbGVtZW50IElEIG9mIHRoZSBvdXRwdXQgZGlzcGxheS4KICogQHJldHVybiB7IUl0ZXJhYmxlPCFPYmplY3Q+fSBJdGVyYWJsZSBvZiBuZXh0IHN0ZXBzLgogKi8KZnVuY3Rpb24qIHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IGlucHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKGlucHV0SWQpOwogIGlucHV0RWxlbWVudC5kaXNhYmxlZCA9IGZhbHNlOwoKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIG91dHB1dEVsZW1lbnQuaW5uZXJIVE1MID0gJyc7CgogIGNvbnN0IHBpY2tlZFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgaW5wdXRFbGVtZW50LmFkZEV2ZW50TGlzdGVuZXIoJ2NoYW5nZScsIChlKSA9PiB7CiAgICAgIHJlc29sdmUoZS50YXJnZXQuZmlsZXMpOwogICAgfSk7CiAgfSk7CgogIGNvbnN0IGNhbmNlbCA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2J1dHRvbicpOwogIGlucHV0RWxlbWVudC5wYXJlbnRFbGVtZW50LmFwcGVuZENoaWxkKGNhbmNlbCk7CiAgY2FuY2VsLnRleHRDb250ZW50ID0gJ0NhbmNlbCB1cGxvYWQnOwogIGNvbnN0IGNhbmNlbFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgY2FuY2VsLm9uY2xpY2sgPSAoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9OwogIH0pOwoKICAvLyBXYWl0IGZvciB0aGUgdXNlciB0byBwaWNrIHRoZSBmaWxlcy4KICBjb25zdCBmaWxlcyA9IHlpZWxkIHsKICAgIHByb21pc2U6IFByb21pc2UucmFjZShbcGlja2VkUHJvbWlzZSwgY2FuY2VsUHJvbWlzZV0pLAogICAgcmVzcG9uc2U6IHsKICAgICAgYWN0aW9uOiAnc3RhcnRpbmcnLAogICAgfQogIH07CgogIGNhbmNlbC5yZW1vdmUoKTsKCiAgLy8gRGlzYWJsZSB0aGUgaW5wdXQgZWxlbWVudCBzaW5jZSBmdXJ0aGVyIHBpY2tzIGFyZSBub3QgYWxsb3dlZC4KICBpbnB1dEVsZW1lbnQuZGlzYWJsZWQgPSB0cnVlOwoKICBpZiAoIWZpbGVzKSB7CiAgICByZXR1cm4gewogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgICAgfQogICAgfTsKICB9CgogIGZvciAoY29uc3QgZmlsZSBvZiBmaWxlcykgewogICAgY29uc3QgbGkgPSBkb2N1bWVudC5jcmVhdGVFbGVtZW50KCdsaScpOwogICAgbGkuYXBwZW5kKHNwYW4oZmlsZS5uYW1lLCB7Zm9udFdlaWdodDogJ2JvbGQnfSkpOwogICAgbGkuYXBwZW5kKHNwYW4oCiAgICAgICAgYCgke2ZpbGUudHlwZSB8fCAnbi9hJ30pIC0gJHtmaWxlLnNpemV9IGJ5dGVzLCBgICsKICAgICAgICBgbGFzdCBtb2RpZmllZDogJHsKICAgICAgICAgICAgZmlsZS5sYXN0TW9kaWZpZWREYXRlID8gZmlsZS5sYXN0TW9kaWZpZWREYXRlLnRvTG9jYWxlRGF0ZVN0cmluZygpIDoKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgJ24vYSd9IC0gYCkpOwogICAgY29uc3QgcGVyY2VudCA9IHNwYW4oJzAlIGRvbmUnKTsKICAgIGxpLmFwcGVuZENoaWxkKHBlcmNlbnQpOwoKICAgIG91dHB1dEVsZW1lbnQuYXBwZW5kQ2hpbGQobGkpOwoKICAgIGNvbnN0IGZpbGVEYXRhUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICAgIGNvbnN0IHJlYWRlciA9IG5ldyBGaWxlUmVhZGVyKCk7CiAgICAgIHJlYWRlci5vbmxvYWQgPSAoZSkgPT4gewogICAgICAgIHJlc29sdmUoZS50YXJnZXQucmVzdWx0KTsKICAgICAgfTsKICAgICAgcmVhZGVyLnJlYWRBc0FycmF5QnVmZmVyKGZpbGUpOwogICAgfSk7CiAgICAvLyBXYWl0IGZvciB0aGUgZGF0YSB0byBiZSByZWFkeS4KICAgIGxldCBmaWxlRGF0YSA9IHlpZWxkIHsKICAgICAgcHJvbWlzZTogZmlsZURhdGFQcm9taXNlLAogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbnRpbnVlJywKICAgICAgfQogICAgfTsKCiAgICAvLyBVc2UgYSBjaHVua2VkIHNlbmRpbmcgdG8gYXZvaWQgbWVzc2FnZSBzaXplIGxpbWl0cy4gU2VlIGIvNjIxMTU2NjAuCiAgICBsZXQgcG9zaXRpb24gPSAwOwogICAgd2hpbGUgKHBvc2l0aW9uIDwgZmlsZURhdGEuYnl0ZUxlbmd0aCkgewogICAgICBjb25zdCBsZW5ndGggPSBNYXRoLm1pbihmaWxlRGF0YS5ieXRlTGVuZ3RoIC0gcG9zaXRpb24sIE1BWF9QQVlMT0FEX1NJWkUpOwogICAgICBjb25zdCBjaHVuayA9IG5ldyBVaW50OEFycmF5KGZpbGVEYXRhLCBwb3NpdGlvbiwgbGVuZ3RoKTsKICAgICAgcG9zaXRpb24gKz0gbGVuZ3RoOwoKICAgICAgY29uc3QgYmFzZTY0ID0gYnRvYShTdHJpbmcuZnJvbUNoYXJDb2RlLmFwcGx5KG51bGwsIGNodW5rKSk7CiAgICAgIHlpZWxkIHsKICAgICAgICByZXNwb25zZTogewogICAgICAgICAgYWN0aW9uOiAnYXBwZW5kJywKICAgICAgICAgIGZpbGU6IGZpbGUubmFtZSwKICAgICAgICAgIGRhdGE6IGJhc2U2NCwKICAgICAgICB9LAogICAgICB9OwogICAgICBwZXJjZW50LnRleHRDb250ZW50ID0KICAgICAgICAgIGAke01hdGgucm91bmQoKHBvc2l0aW9uIC8gZmlsZURhdGEuYnl0ZUxlbmd0aCkgKiAxMDApfSUgZG9uZWA7CiAgICB9CiAgfQoKICAvLyBBbGwgZG9uZS4KICB5aWVsZCB7CiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICB9CiAgfTsKfQoKc2NvcGUuZ29vZ2xlID0gc2NvcGUuZ29vZ2xlIHx8IHt9OwpzY29wZS5nb29nbGUuY29sYWIgPSBzY29wZS5nb29nbGUuY29sYWIgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYi5fZmlsZXMgPSB7CiAgX3VwbG9hZEZpbGVzLAogIF91cGxvYWRGaWxlc0NvbnRpbnVlLAp9Owp9KShzZWxmKTsK", "headers": [["content-type", "application/javascript"]], "ok": true, "status": 200, "status_text": ""}}, "base_uri": "https://localhost:8080/", "height": 90} id="XZhMiAONGebA" outputId="8a31c9b7-4cb3-40c1-f777-06c2cda744e1"
from google.colab import files
files.upload()
# + id="lRd7TCslGo56"
# !mkdir ~/.kaggle
# ! cp kaggle.json ~/.kaggle/
# ! chmod 600 ~/.kaggle/kaggle.json
# + colab={"base_uri": "https://localhost:8080/"} id="e5Rm347ZHCJI" outputId="9942c5bf-cc5d-4be0-be1c-b5c89bf2435c"
# ! kaggle competitions download -c '11785-Spring2021-Hw1P2'
# + id="QHGPRNNcHOqp"
# !mkdir test
# !mkdir train
# !mkdir dev
# + colab={"background_save": true, "base_uri": "https://localhost:8080/"} id="6O9ixy1nHf9u" outputId="807b2be0-48a0-4ed5-9625-176ae9c72bab"
# ! unzip train.npy.zip -d train
# !unzip test.npy.zip -d test
# !unzip dev.npy.zip -d dev
# + colab={"base_uri": "https://localhost:8080/"} id="wxHY4V-eIWlS" outputId="77788797-f83d-4aa2-cb9a-a6961b3bd261"
# !unzip dev_labels.npy.zip -d dev
# + id="FzfUSXoCJYcc" colab={"base_uri": "https://localhost:8080/"} outputId="c32541fd-0db0-4544-a31c-c3f272c8a5a7"
import numpy as np
# !rm *.npy.zip
# + id="umSnKIrQIaYo"
import numpy as np
train_data=np.load('train/train.npy',allow_pickle=True)
train_labels=np.load('train_labels.npy',allow_pickle=True)
# test_data=np.load('test/test.npy',allow_pickle=True)
# validation_data=np.load('dev/dev.npy',allow_pickle=True)
# validation_labels=np.load('dev/dev_labels.npy',allow_pickle=True)
# + id="7I9qsA1oKPLN"
# print("train_data",train_data[1])
# print("test_data",test_data[1].shape)
# print("train_labels",train_labels[1].shape)
# print("validation_data",validation_data[1].shape)
# print("validation_labels",validation_labels[1].shape)
# + id="DsPkaEhHQK0D"
# + colab={"base_uri": "https://localhost:8080/"} id="2uu0W6qJD67i" outputId="1881650d-956a-472e-973a-629ab79d8ca5"
# import numpy as np
# np.vstack(data_x)
# data_x.shape
# + colab={"base_uri": "https://localhost:8080/"} id="NkafjeEeYtbC" outputId="d2adb1cd-6300-40f1-fd72-5fa5258b2df2"
# validation_data.shape
# validation_labels.shape
# + id="D-tDLqK-Y4-v"
import numpy as np
train_data=np.concatenate(train_data,axis=0)
# + colab={"base_uri": "https://localhost:8080/", "height": 166} id="UZn8SweOUw22" outputId="12ae92e1-6d18-4912-a227-1191d22a7e17"
# + id="X7SkWAtGLmnC"
import numpy as np
import torch
torch.utils.data.Dataset
class MyDataset(torch.utils.data.Dataset):
def __init__(self, X, Y, context):
# Store paramters as class variables
self.X=X
self.Y=Y
self.context=context
# Taking the data in variable x and arraning all the frames in one list
da=[]
for x in X:
for i in x:
da.append(i)
self.X=np.array(da,dtype=np.float)
# padding the data at starting of the data and ending of the data with context no of rows
self.X=np.pad(self.X,pad_width=((self.context,self.context),(0,0)),mode="constant",constant_values=0)
da=[]
for y in Y:
for j in y:
da.append(j)
self.Y=np.array(da,dtype=np.float)
# print("self.Y at index 0 istype is:",self.Y[0])
def __len__(self):
# print(self.Y.shape[0])
return len(self.Y)
def __getitem__(self,index):
start_index=index
end_index=index+2*self.context+1
xx=self.X[start_index:end_index,:].flatten()
# print(xx)
# xx=self.X[index]
yy=self.Y[index]
return xx,yy
def collate_fn(batch):
### Select all data from batch (1 line)
batch_x = [x for x,y in batch]
# print(batch_x)
### Select all labels from batch (1 line)
batch_y = [y for x,y in batch]
### Convert batched data and labels to tensors (2 lines)
batch_x = torch.as_tensor(batch_x)
batch_y = torch.as_tensor(batch_y)
### Return batched data and labels (1 line)
return batch_x, batch_y
# + id="-GZQd4z6QDOz"
# + id="2qos8mt5GJqu"
# dataset4 = MyDataset(data_x, data_y,context=2)
# dataloader4 = torch.utils.data.DataLoader(dataset4,
# batch_size=3,
# shuffle=False,
# collate_fn=MyDataset.collate_fn)
# for i, batch in enumerate(dataloader4):
# if i==0:
# print("Batch", i, ":\n", batch, "\n")
# + id="Ic1Y3-L5Y61k"
# + id="D_ExjvMuOTDf"
cuda = torch.cuda.is_available()
num_workers=2 if cuda else 0
context=5
dataset=MyDataset(train_data,train_labels,context)
train_loader_args = dict(shuffle=True, batch_size=512, num_workers=num_workers, pin_memory=True) if cuda\
else dict(shuffle=True, batch_size=64)
train_loader=torch.utils.data.DataLoader(dataset,**train_loader_args)
# + id="fattWZv9UXw0"
import torch.nn as nn
class Simple_MLP(nn.Module):
def __init__(self,input_output_data):
super(Simple_MLP, self).__init__()
self.input_output_data=input_output_data
layers=[]
for i in range(len(self.input_output_data)-2):
layers.append(nn.Linear(input_output_data[i],input_output_data[i+1]))
layers.append(nn.ReLU())
layers.append(nn.Linear(input_output_data[-2],input_output_data[-1]))
self.net=nn.Sequential(*layers)
def forward(self, x):
# make sure to return the output after
# call the network created above
return self.net(x.float())
# + id="q8A8ZFZxZu7V"
import torch.optim as optim
model=Simple_MLP([440,320,225,71])
criterion=nn.CrossEntropyLoss()
optimizer=optim.Adam(model.parameters())
device=torch.device("cuda" if cuda else "cpu")
model.to(device)
print(model)
# + id="saR5bL5Pb64r"
import time
def train_epoch(model, train_loader, criterion, optimizer):
print("Training....")
model.train()
running_loss=0.0
total_predictions=0.0
correct_predictions=0.0
start_time=time.time()
for batch_idx, (data, target) in enumerate(train_loader):
print(batch_idx)
optimizer.zero_grad() #hackward() accumulates gradients
data=data.to(device)
target=target.to(device) #data & sodel on same device
outputs=model(data)
loss=criterion(outputs, target.long())
running_loss+=loss.item()
loss.backward()
optimizer.step()
# Here the accuracy can be calculaated with the no training examples being
# correclty predicted i,e target is the y and outputs is y^)
# correct_predictions=torch.sum(outputs==target)
# total_predictions=outputs.shape[0]
# print(accuracy)
# print(outputs.shape,target.shape)
value,index=torch.max(outputs,1)
correct_predictions=torch.sum(index==target)
total_predictions=outputs.shape[0]
# print(value,index,"--------------------")
# print(outputs[1],target[1],"===============================","index is :",value)
# if index==target[1]:
# print(index,target[1])
# # ,target[1])
end_time=time.time()
running_loss/=len(train_loader)
print("Training Loss:", running_loss, "Time: ",end_time-start_time,'s')
acc=(correct_predictions/total_predictions)*100.0
print('Training Accuracy:', acc, "% ")
return running_loss ,acc
# + id="XSDC4v9JWVV7"
cuda = torch.cuda.is_available()
num_workers=2 if cuda else 0
context=5
val_dataset=MyDataset(validation_data,validation_labels,context)
val_loader_args = dict(shuffle=False, batch_size=512, num_workers=num_workers, pin_memory=True) if cuda\
else dict(shuffle=False, batch_size=64)
val_loader=torch.utils.data.DataLoader(val_dataset,**val_loader_args)
# + id="h9Wc-RV4WLDf"
# validation data model lis tested.
import time
def val_epoch(model, val_loader, criterion, optimizer):
print("validating....")
model.eval()
running_loss=0.0
total_predictions=0.0
correct_predictions=0.0
start_time=time.time()
for batch_idx, (data, target) in enumerate(train_loader):
print(batch_idx)
optimizer.zero_grad() #hackward() accumulates gradients
data=data.to(device)
target=target.to(device) #data & sodel on same device
outputs=model(data)
loss=criterion(outputs, target.long())
running_loss+=loss.item()
loss.backward()
optimizer.step()
# Here the accuracy can be calculaated with the no training examples being
# correclty predicted i,e target is the y and outputs is y^)
# correct_predictions=torch.sum(outputs==target)
# total_predictions=outputs.shape[0]
# print(accuracy)
# print(outputs.shape,target.shape)
value,index=torch.max(outputs,1)
correct_predictions=torch.sum(index==target)
total_predictions=outputs.shape[0]
# print(value,index,"--------------------")
# print(outputs[1],target[1],"===============================","index is :",value)
# if index==target[1]:
# print(index,target[1])
# # ,target[1])
end_time=time.time()
running_loss/=len(train_loader)
print("testing Loss:", running_loss, "Time: ",end_time-start_time,'s')
acc=(correct_predictions/total_predictions)*100.0
print('Testing Accuracy:', acc, "% ")
return running_loss ,acc
# + id="OQRy0igEfRBk"
train_epoch(model, train_loader, criterion, optimizer)
# + id="8-otQq28VfGx"
val_epoch(model, val_loader, criterion, optimizer)
# + id="e25u3Sx6q3Tj"
import numpy as np
import torch
torch.utils.data.Dataset
class MyDatasettest(torch.utils.data.Dataset):
def __init__(self, X, context):
# Store paramters as class variables
self.X=X
self.context=context
# Taking the data in variable x and arraning all the frames in one list
da=[]
for x in X:
for i in x:
da.append(i)
self.X=np.array(da,dtype=np.float)
# padding the data at starting of the data and ending of the data with context no of rows
self.length=self.X.shape[0]
self.X=np.pad(self.X,pad_width=((self.context,self.context),(0,0)),mode="constant",constant_values=0)
# da=[]
# for y in Y:
# for j in y:
# da.append(j)
# self.Y=np.array(da,dtype=np.float)
# print("self.Y at index 0 istype is:",self.Y[0])
def __len__(self):
# print(self.Y.shape[0])
return self.length
def __getitem__(self,index):
start_index=index
end_index=index+2*self.context+1
xx=self.X[start_index:end_index,:].flatten()
# print(xx)
# xx=self.X[index]
# yy=self.Y[index]
return xx
def collate_fn(batch):
### Select all data from batch (1 line)
batch_x =batch
# print(batch_x)
### Select all labels from batch (1 line)
# batch_y = [y for x in batch]
### Convert batched data and labels to tensors (2 lines)
batch_x = torch.as_tensor(batch_x)
# batch_y = torch.as_tensor(batch_y)
### Return batched data and labels (1 line)
return batch_x
# + id="k9SSEj97qT4R"
cuda = torch.cuda.is_available()
num_workers=2 if cuda else 0
context=5
dataset=MyDatasettest(test_data,context)
train_loader_args = dict(shuffle=True, batch_size=512, num_workers=num_workers, pin_memory=True) if cuda\
else dict(shuffle=True, batch_size=64)
test_loader=torch.utils.data.DataLoader(dataset,**train_loader_args)
# + id="39_yKimypIZx"
# test data model is tested.
import time
def test_epoch(model, test_loader):
indexes=[]
print("testing data is taken....")
model.eval()
running_loss=0.0
total_predictions=0.0
correct_predictions=0.0
start_time=time.time()
result=[]
for batch_idx, data in enumerate(test_loader):
data=data.to(device)
outputs=model(data)
# print(outputs.shape)
value,index=torch.max(outputs,1)
result.append(index.tolist())
return result
# Here the accuracy can be calculaated with the no training examples being
# correclty predicted i,e target is the y and outputs is y^)
# correct_predictions=torch.sum(outputs==target)
# total_predictions=outputs.shape[0]
# print(accuracy)
# print(outputs.shape,target.shape)
# print(value,index,"--------------------")
# print(outputs[1],target[1],"===============================","index is :",value)
# if index==target[1]:
# print(index,target[1])
# # ,target[1])
result=test_epoch(model, test_loader)
# + id="zgCpZIt20Ans"
tensor_result=torch.as_tensor(result[0:-1]).flatten()
tensor_result.shape
last=torch.as_tensor(result[-1])
final_tensor=torch.hstack((tensor_result,last))
final=final_tensor.tolist()
from pandas import DataFrame
# your_list = ['item1', 'item2', 'item3',...]
df = DataFrame (final,columns=['labels'])
df.to_csv("sample.csv",index_label='id')
df.head
# + colab={"base_uri": "https://localhost:8080/"} id="Yq1FDsXimwDS" outputId="78fe427b-bb79-4734-b07d-c97770b9711a"
import torch
import numpy as np
X = np.array([ np.array([[ 2, 3, 4],
[ 4, 6, 8],
[ 6, 9, 12],
[ 8, 12, 16]]),
np.array([[10, 15, 20],
[12, 18, 24]]) ], dtype=object)
Y = np.array([ np.array([1, 2, 3, 4]),
np.array([5, 6])], dtype=object)
X.shape
np.vstack(X)
| 20,909 |
/Week 2/Course_3_Week_2_Lesson_1.ipynb | 0d4eb572894c3985b4d1e1c7a91b545cac54802e | [] | no_license | AbdulBasit0044/Natural-Language-Processing-in-Tensorflow | https://github.com/AbdulBasit0044/Natural-Language-Processing-in-Tensorflow | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 870,294 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + id="P-AhVYeBWgQ3" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 79} outputId="a5279239-e64c-4e8f-a418-54c45ee16cd3"
# NOTE: PLEASE MAKE SURE YOU ARE RUNNING THIS IN A PYTHON3 ENVIRONMENT
import tensorflow as tf
print(tf.__version__)
# This is needed for the iterator over the data
# But not necessary if you have TF 2.0 installed
# #!pip install tensorflow==2.0.0-beta0
tf.enable_eager_execution()
# # !pip install -q tensorflow-datasets
# + id="_IoM4VFxWpMR" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 319, "referenced_widgets": ["829124bafa424805b223526624278661", "03ba532711c94af49cb7588202860031", "3e1b9332b4f4432c917455dd46edeed6", "a1d9c3b786b44e2db0341dc40169c454", "18540577e0fb41dbb4431d4fa3418dd5", "bc65c4848c594ada97601d9fc1c1cc9a", "2887246df0bb4987926d12d41bf40b82", "78bb5fd3370c48998a4a801f29c60a07", "bd68128c201846fc9f4c1d18c5e90082", "f42d669872514c56871a3a2ab5f0275d", "6102d789ada8488da590b1c845f346e2", "b23526637d284e8f991b07d9d53c00e5", "730d92d54e904daa9f1632a14bfd2489", "cb4406b7124c44a4b390223ee1ad3386", "b11bffb8cf544ac19a11b29436a8a444", "fbe1a0404c11454a852c83a43b8c8f51", "dc532453b63b4002b24fa82332b7c36e", "dcdede60476d4686ad81b9ce374b7588", "558b828c523741e79a74feb8b27a46f6", "495d5ae0d6844dd5b5c0c048ed7c342f", "f9b85383a4be4a258d65181a6ddad1db", "b549a340ba78443cb70c150fb2d38ade", "7ce553fa1f854ba08705f962387a9af6", "35996a90745e4c73ae1700418a9143a6", "95ac3938efde47fda3ee2b86f23bb8ae", "33516468ee0944739fdaabac25f8c3e3", "ef5788bc3f564644b07ed0faa114d164", "bc73eac8841848a9bc490ffb2f5a14de", "509d66f8a93c480dbf800d524aa5cd87", "29479d2a202b4737977e94673f3f488d", "4b75bbb64fbe4f6089a98847fec3efff", "3d58f7345c5848b4bc36dccba719cd42", "dafe0c7a42144f1ba46bf9b6a1597a5a", "b8614c5156c440fd889013a5486d6307", "58f13035404a4e95b2a2760986411b29", "d60a16ec75b64736a28e3f60dbc07e7d", "d611a9711024452886a05aebc0fdf46d", "b79a88e2aab847e8b9c2c1bef53d3841", "78deeb1960bd4f0e90d4f0d63a7d1267", "52a057483ef54264876245e9411c1b31", "d8db64af8e3840c1a4da5757bfc6f890", "d27aad78afc24f4b80d117c19a471002", "aa6e98ba8c6d4a708cbaf2c9748fbdb6", "2bce7de04eeb47efad50c3609a44021d", "6c9f3b1e971a411fbb76bd7fbacbf729", "a8ae4fe42b1f490198e705ddbcd5589c", "ffade1e91c3945acb391c356efe97c5c", "15addce4da3643a38d531f8a44d33fa1", "78bb34d59bf340a98513d985b15dfe12", "ffd04404372a43e4ac4b5a91698f02ac", "8d3b7e4e42604677b199b48687230ab1", "fdd7279c5dd942a28766c0399369577c", "35656a209437455183c22956aa6175fc", "a880a331bbaf42498036f0d4fb8104e3", "fd4f84f1ae7e4b28baef7f54b6965766", "1fe1e8bf8c5a4127a925aa3fa9a54752", "da98ec06e34945639672a4a7e401705d", "0444f9969fab4e05b90a51cb2c06e98d", "b8ba93a371c342a59f89c716c643cc2a", "317207be359847fa88cf0ffadc8bc84e", "36b031b08e934bafbea7a21128adf2e1", "9d1a47e04f6f46acb6dd501328795181", "93bce95bf018453c8f1cce315cf63b11", "4509be0678394cff92bf9655efb863ba", "31d792cad46f465aba5490e8d3661083", "4d1ce6720b0149efbe1676e6ec2c59d7", "cf0ea6f5dce64774a411328261cbe7e7", "0666869a85ff4796a0600375a6b09181", "fe830a85d4a8470a8418171b12041b06", "c68cf0a9cafe49bfa6e068e92554abe5", "4b729aadecf54247b58331659b6e2657", "ab805d3571314e28b316f4a4cd466c5e", "1712f4e3350342869566fe70ba3f6f9c", "56c70313b8064c54bfa389cdb4b34991", "7c84b213cd28438cba525a4f1028b5e1", "6db32779ada34c4da20fb41f957fb0b8", "d35482f5636e409f97e721de84f2cbf5", "e591357ecd2b43559d6756e78e148907", "d7b43b6e63764758a94b3563d972d299", "b701443f84304a3780e9d3a349419453", "842fcae6bf674a99a4bb89dc761723f2", "9f63d32f1ab340faae4133ef3e96e297", "927407b1d54748f9896abab635f39a28", "1592276db52c4b0eb5f100993cb4ca40", "1ad4004c10f044a1a53ad040d4d30f32", "2f9eb70f580d4f52a4043897a30b9e1f", "b534724810234e8f87a7f07a0c6eb1d3", "30c72f52477749f08a3d35ca1bcfef20", "38376316e39c4cf3953e3936e5f1423c", "67babdb3de7449978b07f53cc36d968a", "5073c2d622704762944efb269b1a7366", "c69d5a4b39574774a95582bae22e57a9", "b61e70c20af54c9b9aa9dde044dd13b2", "210fc65c82be45a8a4ac4b6f32cdbf87", "1d7dea52fc084521b627a32a53d548f7", "97b94c2a70014d10a13a45b18fd6508f", "48b133ddbc0644459afba107dbae9c4e", "05f825aba4b04e419eb5f701f2f80f05", "b7a54ea1af04487aa8b459b94c44eb22", "c715c159767d4b1ebab04178803c6042", "62ec2ec4e1794819b3a7b85962c8739b", "872a2019156b4a35b29c1d534203a2f2", "f7756b43648b4b63819d9ad96cb164d1", "c151b940e24e41fda02b1f200ee8dd48", "a5258a28554e429485ae9d0e1f6e05c2", "e82e0f6d743042c1b0538cd43310e02b", "fdf14d8efdc84f1b87abbb359ad8c6a1", "9f221b883d63427fa63a9f8756967769", "4407753f14924bdebb49831a31a01806", "d8a1680238f54ce496a476260c1e0d77", "9bacb23410514828824966564d434e75", "9e3af21b072d44a483b76d2a22ed658f", "37e4ba36173b403abe0b968a2be2aa0c", "d2dfff855f034f9c9702a07f4283a35b", "3069bfdc5e7e4e58b5dc56041bf09f3b", "5823faac947945ac80fd3cbde76837c2", "784f139fc3594c01a2e0e10247ef6c4a", "545307a262f14d5b80308515d490deaf", "fb0189b049994e5a879c480cc2bdf573", "2bc953c95fa7434f9e796b92f525717a", "636ae756e294492996b9cd59ee813d4d", "200f92ed6d844f589cd32731dcee71a3", "c13b8a61815b43068f40c01af2607f44", "bb19ba2772a041e0aab674b2aea829a7", "05cb7522fc394c7f9aef7736137c7997", "b6effb21dce74da5a77d9d455db84b0f", "1800680a9cb84e8d9dc8a94a372423a6", "f0e4c8702fbe48a08bc3dfb071e5bbf6", "655a036c23eb470893905a74e2c9e432", "d6174d48c19044ad9f05ca58f6fae07c", "1177980f5e354fd19ddf124418bdef97", "c4139288a4ad47cda43de272063dd89f", "eb5995be28d84a5c8524975f34e39716", "d1f7dbd202b643beb37d5b403c1fca99", "7e9e0e9f57c045c6b6e426a64d642419", "7f12c539d3ac45cc8ce8f695720ed972", "1bb7b5dedf1446e9a8f415560d7184f2", "c145a241cb55478eb70eb8cc4b377d5c", "7b7134ebb3994864a5ee699646f9acd4", "98682574a9a042f0bf02b39ff8d7e6fa", "aaa336b63ff44cc7b78a904f20a4d5e8", "9395d0b92bae40e5a59cfc5eed2c06c5", "9338ba9a95d74aae89faee7384654f44", "b9d7c8e056a04017ad15d548e3a8355b", "74fd285a5d6f4df4ab3b99987f8ee499", "5621e8b833f54ff0982524c9a4816821", "b8f12f947b7e4200b8bfd002b20e12e6", "2a17c5f71ab640b797264bab0c920e76", "63fac6a20ae247518f50904c1d8f6b22", "a7841e97b8684bb7b05a18231bb93d9f", "072d1fdd1b354cce8b3c600041dc0d4a", "04a657731fea49d1a853618827f6941f", "50f8510fd30e4bcebeb85780c039d923", "26a3c1a2de5a4207b288a20c7c1934df", "f936ab35f2cc497b91da953d97ad50d8", "f3ab24f20ccf4b2e893c4a24f63d4603", "d0ad73b65d9348bca0cc2cd487f1293b", "83de2cc3c11e435fbd63310f1e2c9ba3", "ac151a2122d847e0a2f6e2686cb60991", "f8ee8f2f89ba46f6be89496682d8d230", "fa8309d2c619493eb0b4b7b1e335b620", "15d2b036a766477abf31d46efe250811", "2694fabb3e6443b3894a4d4f24dd870c", "876269de29844a8195a27ab29a6e9327", "57e1076a688f40fda52d7182d47a09dd", "af5ccc4c9ce0422598e35430a44feb35", "1127890eae9b46bda9c2b790bc4f152b", "d5629d081eca47a6b5f7d51b1f2fc601", "3028c257f74b44a7895b94fc4ce7ee15", "57adf774234440c9814d1cdc19d1d124", "4e24d131cc0d40ba815f03bccd0131db", "c245255c2e6e4063bbc0d0e6a13b9d0c", "b2cf3dadca8b4c23a1abfb0bcd3ba3bb", "f798abb5f37f40ea9792589b527b55c9", "7837052831864fcdb7103de6543e9be4", "994dd6d8289843178a56b1e0238f2bb1", "81527b4145814b7facb1842d0f657571", "1a0e8a3869bf4bb180cb5047b639d8c5", "16da8202217f413cb6d9b5543ec498ba", "04b172507dda40e98a847b9db06c626c", "7faba5eff79147e18229555d871f0fa6", "52a26e221a0d4333b507fb46ce4ce596", "f518d09ad25f4b1bac2891cfd6f669f2", "85957946c12e4866afdf43ff0c4385c6", "8eba9ed9bc7640619e8b14dcde205d7c", "37adba6dfea445a79889312ac2ad8b39", "b7c80cc567404cdd9d48f0172f78b595", "93dc10feb5c3438d8981672a8f2ab670", "8270e1efe86e49b0971edcb2261dc716", "dec4680046024ba68e8b85b4028e2de7", "5b7d73bfaec7428fa896fd60dcc32115", "c905ef2c10cb4648bf7c5727af15c5ee", "843a687b932b4f9e81ed97aa44bdb664", "b3e29dd6ce76418b8c60abd28c60685a", "b76e7b9d7817400897c9b4ddfbfa4c13", "9696267b664c4afa99f19add25d16a8c", "2a3d7f23589941cf8fb52deea4a6a4cf", "2d3a05acb77440d4b114b69f3ee0f1f6", "d022220656ef404ea82541ee3c1dc88f", "6c068ee507e1470eac72a7876ca7eb75", "903cfb35852e4a4a989edc1aeec46845", "8559d311824743d296ca8b0d9ef7bafa", "5774b33c79f54ffb9f4cde524b93ba58", "a54e5c12ae08418c9ed9efeb137b9e66", "88dcbd40625945659d65cff729d835f7", "7829a2e30fbb448ba95b04adfc343d82", "e6f65aaa250d4c48bcb0109ae2188787", "d73b25bde2194b6585f8b6a1aa57a7bd", "dc9cd78284644686ac7130155739f487", "f1535bb264ad4447b8067237b6e3988e", "8f7315806bc04c238fd72eb7dbb2a240", "20566662f32846f7b7c6d3899f8ae2ed", "2c330a39b0f04e9a948295c1cbb89f14", "d679411555db48f28236e52f20af0152", "cb320ee0ff6545e4b9b5e6c2dc99a332", "f377fc6655584ec2a4b66f51e51f8f98", "fa23ffad638c403b8a2bb24d4adaeb8a", "20f417d9decd41cfaf2e6f9ae3b829db", "b165935fdcb443058632cdbf1897edeb", "fc7e2a1e1ae54128a03b44df1a418de6", "da09f975e4ad49a5a8a037186815d29a", "a47c57ee15f449c39d008f233d2159cf", "4252d51f925749069ccc514ab72f644e", "2c489c6406f346e0af8810c729836a40", "502f90d32fc64cc0b207be9abac8a222", "1496fa6dc0ae43d4a6e9444f3f0afb0d", "9f51dfec81c349e8ab913c9b41072864", "3c3b840dd3ae43bf93514ff7bc69b3e5", "628c0dd9055142c3a1181fb7c4e354f9", "5520e3e942ad42a49ec4858f49a1afa1", "b2b70c1eecf1495a8cd94f598f6af702", "e4b10521ac0347d4adae0178645cc209", "d6429919e36e48e8a82b1839ca6e60df", "95d1dc57c41149048380baf99e9be26e", "b526ed15cb5f48dd98d438acd7df2c89", "5814e5edeb034e0aa160b87b483a6a6b", "43c06ce7c144446cb1cc4df7190b09b8", "45ced1bcae714fecaddf804df0d604b1", "3293c7524ef34f9fbb830c096778201c", "f0478375292a448e8212289b61c380d7", "6c44489886354bed8140928fce3acaac", "1a623ccacf27419cad63eb147e41c793", "ad127be7cb714901929e6899ae55801e", "4d0d10047dad444b9f1b32a080c84214", "249676c7f4ef451394fd208e8e2445cd", "ebd7afd405ef4f37baea40e5180d2b25", "c4e5ab3acbb04b3bbcbb2ca600cf511a", "7b7d68c5dae5495a8f8b495bf89efc7f", "6363442a6eb242848889bcae4e7d49e2", "df1c96b0d88e4a4db9c6450058b76bab", "c9c446149bfc4e748875f17e3f093f9a", "d85acb4417754b84afc3bfad5ffb13de", "10b156b95b5d400aaf9851d59ce5ce79", "193f6a0d88034434841b3022afe80d6a", "582f06cfb92b4b218085329535f4d430", "2b78e1f829a1450990e0763c32ff7119", "1faec7858b694b2599b236e1ad6bcf14", "c55f60cd8e3a43cb9988e3b7f3e35b4e", "416b868da5244abcbb767aaeb1b4d968", "0ba8b69ae6ee43b5ad9ae2cd7276b377", "969520a630c648459f9c80057a9ff15a", "8ff73f824709407b8b3ba1e339ad5bf0", "b6532bbbc1c94f56ac92f17d8d654ff7", "7cf7f132df5d4b51b7773674f65f1dab", "abe018740b174db4b22fcaaddf03fc08", "8ff88d7b411b47a192e0e2dc9d0a8f15", "c563cc5627634181bec26af003e57c74", "7f1519faee104edfa08401e68c8116c3", "3fa1bbea6c1c4fb286eb30f066fc061e", "8fa809dc250a49bdabc1f4ae335ed148", "22dde5a2fdd24cf1a013a320c179e06f", "e71149fd0ae14bdb9e4f53a19118026e", "bd4b23fc41084ae1b8d73212a229edbf", "5822601023b9477eaef627a97c86ccf0", "c26fa95624f34f3db03598adbf2fcb34", "57f8a20cbc214dda845c541b45104f66", "a364e548d9e9403190fefaa12638a2cf", "7095140b7b6547b88b1593473475423a", "ad49e0f0ab304028ab643130d3c9a6a9", "df99ff422d284f63a8adc2e0f0bbc96c", "e118bd66303942c18128777d5bc45afb", "75c2affc8bbb43fa96ca2c0868dd022d", "130663e56de745e987164f75f993532c", "6fb591cee4dd4df0bc2cf42f27a1b0cb", "3e026cbf5d74482fa8ef7c92abda8237", "0861c9febec6429791017a535636614d", "fcde946a4dfa4fd8b46c1c71adcb11f4", "2d4b9bd285fe4a9498a090007a4b7895", "7d924c4c588e4bed9c1aadd5533f12f3", "3a67e6979fa9467f901af2f39c652a2c", "83f63554f90b4d6d800f91a0b7f8622d", "eb80a56864f740169c3d54760ed392ce", "ba2b4f96d1074eb7a23fd3696e690851", "658722ff808543afb921573f8bec1889", "97e4ffb745dc44fdabd3d77c65fd441a", "2e5128d0d777445dacb72e7e0cbac0ba", "2e6927f561d8474e9cffc1331b7db1eb", "9e3b479ad5c841588336d9b76c955500", "debbd669ff384408a0a4e05a7f474e72", "9c9be49122ba434abf65e652abfaa252", "ff77bba5f4184313be24af55fd8f9e6e", "20a4c22d4e1142c78bf7f266c70a3d9f", "6417b84ff0f544dda2e211ecb405caca", "684cae6b9b2e4f52b53c9592ac68530e", "73eb015a578748f2b3d3a87b73e4f0ba", "24b4a1ad0379488c954cb6ad0df898a8", "acaebe07b1464bcca4cfb4ef84ad1ac8", "9435387b913a4ebebf20760c3f508a9e", "295c443c0e2046d08b91867978e59840", "8314e7eb725649b08c1ad6d845a70c1c", "b07aaed6f04641749f1212f696196f67", "712d706bab1f430a913a6c62dfbb8711", "7dddb82a1c2c4b9e90175c4b315193a9", "98d3b5c30bb548fb9b5d9e5e6d7deef9", "7a9b1facc1fa46be9847abdf45ce2e20", "2c4cbacd038e4b0483de2f320f99de48", "73c1accf943b4285a3b28c0c1571c851", "f59bb6803d0e4d959289119b7a3f28c3", "eb4c973474064f53ab1edf2bae80d95e", "ea3acf579b8d446383444b9bad84acf3", "49b56268afcb4cd2bdcbef908fecea20", "66753e8347fb4a75abef3f5408128fa4", "12185a9122a74c019358fa2385a7848e", "7aa0175e1e5e4c54873df53bd3904975", "b2bf3e5b843c40c08a35484e8dab45e0", "cfb791de17e4438b90bfe1de71d6fbfd", "a3f62faf27624cd2aabec58df2a2b893", "12deaca898a24b5597304264a50a95ad", "30c31f724d234675b804eda80d3bffb7", "fb1c3adc76c54095a32fdd84aac28b6c", "7bea4dfbc3844e82af5863ed4f0f0637", "e97ebf9bb3f74bc1b5a6c80e2e5ae9b6", "7acf1f8fd193412a8ddd9bd7d7f67b40", "5a1e34cf8fec44269a3d36f5e21a772e", "708b445092964aa0af87a74ec468d949", "78f306d3c00b4153b572a5094a55b579", "cddb0e2924a34fdcb87ddb8d3fe1e06a", "cb05730211064f01acd79f469004d434", "a845ba9b95354e7e8d26efc75c7dd5ac", "ac7de8dbf1df4ed9941ee3cc7d1d2578", "372f43dd339c4cc591834033bb288820", "16f6956430084d24be02542a2ee1c2b9", "90bba3b35caa4f6f872ae7ba57ae7992", "f74351b674c14bbeb640d62f0bee8ff4", "d3688b17d3f9445d8fad66c5490d1927", "ead73001b01d4d4d88cd841775b05186", "d9112a4becc94a91801f6ced361429e6", "c15e6201b2334f27887359a748c37dac", "962acdd18a394eaabc66512c4dc7ff51", "7745c494c9e043b8818ff4944d4917d3", "00f63aa99bb748febd8dd9e4abd6802b", "791f451453984cdcb413032de4dbec88", "8f41225a776f4bbcb38ac691a6945814", "ac40f5b8cc524b20843312ed5358a463", "b00966158b7a43bdac7c9ca463584170", "bf30deee44094ff4be7837f3f33ac751", "d2ae808f969444cfa5b711b700bf7ef5", "8b8204a615d64dae8deeb8bee749e0f4", "27e4275077914b768852c4f2fd14355d", "ed21993441804db98ea1e34a70e6069d", "483817468f4a4b3ca56bd979c9d146b6", "584ae9c9edf946109c78454223343ffd", "3662cc579e4945a89160e1c5d8c171aa", "e2fe915fe86b4e76b8828d430af6ab82", "e2121e7308724bcb8d2bce34eb5acb9d", "fddf1e9342c1451f82d73fcc4abb8c4d", "b69d4c56e0f24ed4b0e176d80f174a0c", "f7b6cd5684534b29af447d1535eba144", "bc26e8e8908648bf8e01636805391fb1", "bbeaa941d11542159f8f054619fd2eb3", "6ab0d33d25914025825535739123fc2f", "cc9db798be7a4ceaa78535acae05d48a", "bc82aaf154964b848174585d2a11ff2f", "79a9a34cd5b3488d8f5059b050c5032b", "8fcd9a27a9254802b31e12f20fa0c3c8", "0758bbbaae4d4554b2790081ae160e7c", "8bb9d853ba58475d967518425811615a", "1989325078c7478b961bf7777a477573", "c6cd61efc43f4a0b88240befb59f69c1", "57070230d8d64bce80ead37a51545d5d", "86399aafd76f4136a9b5d395881206c6", "147be0e03b1d4f859d1513f0de76913a", "9fae4aec4c254ede9a6f9482158cefcc", "9f0c4af0a9cd4e5a9088810981a8ba09", "fd819a3bac884496b8f78b063a08a0a3", "201393da1cdc4749b8d30bb49ad99d18", "fbe8a1a9ba264db495732a344e0da34b", "1f7af9f0dff843c4a71a1e1f4f32151e", "dfff21712ead4290a742bdd3179bda81", "b4b812bc37bd4c2fa6bb4107c051eafc", "76a9b2cf11db47f5971d380cc6b12b7c", "6ebc05c993b8494885eaadfbae9af61d", "26e4efc9660a4f3d8054179559825824", "eb9c03d5271f40aaac3294c93f5767dd", "76a517dccd804842bdb8637eae7ede4e", "ed503b4284d94e289755341640bda998", "300c90cf6415445fa0b38d9f5f196662", "b321cfa1897f4a109a63b204a348a792", "b3286b5f9f034f2cb2585833afd4443e", "9ebdb2915e86441c990c1f649e2074e5", "cd52c43005c34054936332f2fd717edd", "cf13df03b3304a309b7cb910266550a5", "b1af0d15c0504ed7ace86bf38769a313", "b8d24b32fa0e4f11819e3f375b6ad05b", "50015cbf79b84e2b95dabec562639a33", "0c2a2caf380f41f5b597fc1889cc39fc", "2273baf7536647098b687a5690545be7", "7db4be3315294cf1b0881ab8dafee5b1", "033954f0185a441a9e6443b2c199bdc8", "a8fba1f0855043a0be86312176361a14", "914124d9a663406eb55d519226eafd4c", "c0fa46b337fc4887b653f62222908880", "2ea4998f66674430bd4dd73ec7d8b0c2", "a38082457fff4d948693405f586ffd7c", "b29ec481ae544285bb6660a9ae0de9d5", "31527443008e4d3a87c91d898ed08341", "b140a8a1ad91497ba8b4f2a6c79a31cd", "db56fef74e634e7a8fb76aa5b72ba018", "5a4d48fbfd7044269beeb3193f8d6a62", "dbb3c9a8ff6a4e6ebf8a4bc63d546981", "53f43d06ee684cecac71601c58702f5f", "fd30c5536f824da3940829791e001c85", "78ea1875a36942d89e4852153bc5f277", "74c30f0cd1624a65848d01b03376ba3e", "c8da0a311f6c412098cba417e4a491f8", "cfd2e900a97b4e67b642932543d71242", "3b8e6cd819de47a6a7e58358df88aa21", "46dc81d52ac44d04980e306d7bacc7c0", "39a1c79a1d6e490c93e2e5467aa1487b", "d2aeffc94fda4bc7a0a74d8872554dea", "9e4cf1d0c97c4d38b594674e25f7fbdb", "45eef4e1ed0940599982516dd11ff453", "a923f686b02c470abf2ccd690c2be5c6", "c9bd3388e29442ad86338dfc8ad70398", "1d2ac7e9c590405194c850386f5eed6e", "dc74d15fd7f643a99f20cdaf9a5bcbdf", "fbb5a869ee3d4cb79b32d8bf16fbc80e", "4322fc4e2d514daa935d3e7485fd6f51", "7cbbea2cbd2a4ede97136cb17ccbb53e", "6b6a74be241343209770acd130bab027", "382d92117c0e44bd986b3d517313c2d6", "4b3bc0092eda445487d84df1cc6d47a9", "61c21fd203334825a99e7df22176d7e2", "484b8cea3ae249d28b1473f11069ffd8", "d9247e44a46e45a599b520a2a80585df", "b2a9a09516d043a79ffb30ca5eafc40a", "d5f8c1edf5c947eeace864b884530743", "d9cdcba0147a4e7ea3043fed2ede4f90", "e6cb35821d9f487d830085b12c54bf06", "96a70f0d2996455e83369fd69cee9646", "590e40fa86874297b32b2d64a507cca2", "254ff4c3cec24080abd858addb951898", "87418c217bca4151bad700941c6a01e3", "507bbad9b218436fbbe3c4ec2ccdb5f8", "59b757f7de124e2881c42c6aecaff7dc", "ae181f07dbbd44478e6cfd84710ad004", "915802675b2e4d70b3e5fbb9a7b200ad", "8a8d01386d204b508ece0e0776a056d0", "5b5e9564c53d4a09b3799998450112a4", "389ba8db186c401382bccac77d6caec0", "d8419625b17c4225b2e31022e8907f13", "087de3b1cbfc4c479c9007e60c2096c6", "4f48778cd8eb4a1d9c211724c33b69e4", "06853b731bc24c98a524ad45dc2886dd", "2ccb213437f945a59ecbc630967b0564", "9342fc45508f4557b870d35949553c03", "a22d97db09cb4e91807b7578fcafd72b", "2d0098f0135e49dab217eaa4c908c165", "4e9ca850980e4b8ba5de87a37e044d5e", "248308efdc8a4485b742c11a2ed4a876", "15220ca6fae340c1a54705ecb66f734e", "64a687883a42477b9f975395bbfec5dc", "0b1fe7d2a7a14c8e90bda3dc98663ebc", "12d936f52db442c89c5d0079b0bfce14", "ab03a4821941417da90f59fab0b4cf66", "99d9594205954777a3fa052ec949aef8", "1833b6f5e6a74846a69b2d440917ad66", "702cbaebc33045c68a7b5b0014d21ab2", "b05d640fa1f7419aaf93f532d1a27d92", "e610314f9e184bd4b855d247cbc2c42a", "ec7f78021f8a444dab0894b273b909c9", "f3df8d57f51241ccab5fee7961bbce57", "1038fba2cfdc4cc88b27ba6038615c5d", "f7113b8d42334e54938cb731b54ee62e", "0917e5ef5e05417684deb35f8a5367c5", "d71ec352da974df5993f2bad77fc8e70", "8d027dc28358442ba09b7a382c7b0ab2", "03e3f73bb792423a968205db70df901e", "bebe6703bcb3496da76528c4a442f005", "4a8ad847285c41e0a874eb23c7d7d406", "b5810d5b4c1942e09e6d2ad498396cde", "57b36a4bf55c4e78b9d15ba57d037c4b", "647e4c787b414f58b0adfd5876734388", "2cefd736bb7a4e569bc87c1970973e3d", "31f86554505f4f7abc7f661a4861f919", "082963b91e304c98b1f3dc48827a5857", "0d6aef9be0d74c82b9cac76ecba78bfa", "05a3df0fb4e24f3e93a3ec109884111e", "dd29f6e962df404284ca55e60730d1c0", "01044d2b3cf64b70bd03af7dae6df22a", "afd3d53b9d52498b8a494d325b312f2f", "a2b8cb59d52f43d889fb246f391ed2f6", "e4b543086ec945ee905c4c6aa9dee146", "a6bf8caf2f8c433ca4a15b348692fff7", "984d1f70abdb45beb51c0c63f30f838d", "4839eac5127b4ab0810a4801cada506a", "f459ebff72a64e2eb6bf39e75646fb92", "84e7b364203f45c58b76a956cb5314ec", "76ba6b2227c54ae9acf98bd2d7c94bc1", "f46a762ab48d478784d9db78d53167dc", "0528adc788fb43da9c23470e708750c2", "23a754ca79aa43dbbd058929c249c6b7", "3860ec8a8a8843c5ac00ea8d5a3e6a5d", "64692c5108d446278d2cbeb4fa7bfd52", "62e873f4bd674b2f96eb7ce80ec9b79c", "78bc013cda3745ad9e0cc76c0d8eb7af", "9fbd1dc8442743cc84e7e71b8e4dd786", "ea44dccf059d493ca4288a175fde2011", "c834d0c5f45d4a44989b118baa20d42e", "9444e7bdcd674a92aceee81fed350828", "407612e8f8734763ac6d7c52c23f721b", "e3a1cc8cf7fb4f9381337058bb8ea8da", "75f8c902891e475e9711456e41eb66a4", "ff55a1912bcc4b56910e4712903decdb", "176bd0e7e0ea487181c4a68eb579729c", "f70175b623324cfe895016d66542b4fa", "ad287b66bc4947f88431ddc493fff013", "8ac987b5c3cc430ba92854ef189fa8fe", "4c33a33a753348dcad2b3288c706ba91", "0b5521a901584c75b4e42332ef20c8c2", "ad802cc73b9a4894810c08a1e6301bbf", "2417e109af384312b4943c47ae657f8c", "7ad147c4340547ec8d08d8b656b2f212", "e6cf556ef24a48139dc8feadb5773376", "bdb32a8e8e7a4071a325754b1807000c", "e4940fa5f3034d5b94f4903afe918e06", "478d6d0dba76497ba6dc2beec2d082e1", "4ba825334557437abc652fe300b2f1b2", "d08a36f0de394ec5835c735492019cc9", "74747d3f4f044df5a2537476852b44df", "065b378e961c4bcebf12e6d742d457d0", "110a5172de1940fb86fb0d6803943142", "4cb7b25869c14d2f9493ab19fec004b1", "c8531bed73864081afe999383bfcbf4d", "ebb2d1a211024153bc7674f5ed175edb", "237332d6c0de4df8a4526570ae046877", "bb96d898a0d84e19879cb285d4cfadbc", "2252cd7aee7d4639a08c9abd6175193f", "b81a7fffc7e8476a9a5502a17ee565e4", "187f60a28d954ab5900a586c04e5a12e", "8d4e30897b424ae6beb57300cbf82346", "068309f9b45149a3862510d3428c154c", "c46d24caea604f6f8b1205301b840daa", "f0c1fdb7a0a24d7eb19b3211410f928c", "d7880d6b02d944e6a39ef0d9012516cc", "dba2014d64e842a8bea08ab42afe60b5", "df2aa934afdc427b85220432e8094bed", "f9371814fef6413f96edcb9283a25e9e", "a53b76bdba194082bb572115805187a9", "a65afb379b6b445f93480c06e117052b", "4f377dbde1804d4290ff2206efcdb051", "6407a76ff0d94239b18fe0c8cc27c823", "b07f4ce64f5343bfacdaa59eb047f290", "9d79d1746c164bc3901f30435e3a9d71", "75187328463f496d8595eab0cef57c46", "1ddd4b0a34aa4e9c8a41348f41d2871f", "3ac4337ab44a4689bd9f6b17570aacd4", "7cda0cf940894f4ca042e92071b7d572", "bd824e4df31747349074263eef6311ed", "dc191f49dc574de984d84ab7af0d937b", "de09856004504e7ba6ab1fd00ed7cb1d", "20c7a7a9d2e4477089f6b8c75c211c93", "04e695190ef74bc08f333acc23aec20a", "2f2abc683d85495ea4a24aa713211386", "701ea8da56cb4b189456bed09cf56ffc", "73d678fcae2b410cb08962060147fa8e", "f26cb7b9992b48b8bac8534214ddec12", "f01774ad324d4a0d876660ff36843f8a", "e4c585f6c98b45819780fbb7b0c39586", "149ee3427ddc4797945120b93d9768a2", "dd507d66d5be45d38154c5954d5fb802", "d7be70bc73e34f82801b052594621c00", "1872fc5b639247c085e1c7077790a3e2", "9c1bdeaa6902496ea3566297891a03de", "3ac16caf403e4adcbcedec800099292b", "454a2180129e40e1ae864e748eecaba7", "e204c2971ad7413c8db2bba60ecc6943", "16a28beb04194160a9a65d4f1bd4bf46", "f2f4c3468c044861a27c0c80266f39f4", "789f498b892b49f080eb0508c59e1982", "d436bcd75174484b8c21ce209828277b", "b0d2edcb2e214f2495f0a58b37fa5383", "b5b6b26657f0439685dcefc0bd2ada36", "dbefb4aa339d484aa1ff6aa8c6cb3f5c", "5841bc391c9a451f8fa2aff2f8df0686", "d9b19e58079a46d89d68be610db17082", "a9ce4d27cad749559623601730f9d6c1", "ce5d576e89b14483a2eeee188f593643", "9463c21f25c2435698e8f5cdc4c9924d", "f3e50a23ad8e4cf48a0ceeae89159a36", "8683e3b2972e4a1c864b72a4e95ea959", "f309186897e749e7ad00f0ae5c9a6c52", "502a573c4dca4df58cc5391205dddd7d", "4151f7f3d9574d489acb0e9e903da388", "0e5c3e0b8e2a4e27a7e9f598a73d640f", "c4731c25f0ab4e8ca95aa9383ca7b9a4", "b51219c67fc541669e865121268d873b", "60b22b6d7c034faeb6c11485031bf9d1", "6e5dd7af21344db7a1a2047d8d96afd4", "b9ccc405a0f646ed81741b0eaf624248", "d7de36ab7dfb44ea96369273931d2fe9", "9301fe588b7345ab9cdd334bb6244ae0", "58877673f4eb4387959fdf9e9ff8f284", "63cab90e07ed4665a9ac89d3c3229c07", "25d7fc24e9434aeea54c2ab9bea25be8", "9f3f9168f2c74c08b2dced62a5f77f2b", "a1b2a9aa74ec49e29dce505216ca0357", "634f4f684f144048ba1cb2767928d58f", "b4fa052ae2c043d48e342dfcf5b59ed6", "2b817d624c534219bb77b962f1d54ddd", "c5df52cf096f4489a702016661849a1b", "a1401312366849a0a844618b1c2fbd26", "150371db5ff54dd08e40826e77b9b132", "608de030443d41b8ba82c8ce576ed0f0", "41549c72133f42309d8f0f2ba22bb524", "209305a825264611aea4dd0de2d20b92", "ba01c9df7fa846ea9eb5b4c8f4103ebb", "063c1540d4f24e4d8589b91fa3d8db9d", "e4e98a8bd41f4b8c9fc21724b18bc365", "70e4f29eee20437eae31ec1daa4d4587", "988f61fc4cb5452587ef503bc1574494", "5d615f6efc704b1ea3d76a8214f5b1b2", "2383e29cd1cc4d0ba008df7360d0f28b", "e74c2e608812459a8aa4a890901c62dd", "41c618e54a7c4eff9575455a34f39da2", "df447005c5f249e2b5237ce638b3feba", "f10a627f65d445c1ad1cf340c0ade44b", "a684aa56b69c405b9812f74e320944cb", "bff56b23b9b34aad9d46dada127bb4f7", "8f26ecbf4855450f923759f2ca7f1b8c", "26dc984903b3408da7dde6a291c50eef", "b444933f21ad45ae87bc7f5384f17401", "bca2920a735f437189ee491abc07df91", "00b263fe856141548a11983c88cbf885", "19cc8b5c2a5d44fbbccdeb2cad8850cf", "b8fb92ac356246ec894abd2e097cb9dd", "b7ff07e535fa44bbaa0590dcbc880fa8", "fe06862a46774498a60373964b4e8026", "4eb0c0db8b304d139a15d56ee1c8fc1b", "40aea4fd4e2d4f04949f27d854876c9b", "fa240ce4e9b14252b7b7537ea481d047", "fd97806d844d4571957407aa8c17a361", "814c2308eb9742eca969aa30bb2c97cf", "e4687033c0d1491fbf0ee7bd71f1d01f", "380093a0d97149d299515eb1f2c71f2a", "eeb1ec18eb58448aa9a0963ca4a1d5b3", "c749c6631b214c599052ab2939f9d147", "02e94bdbbb7d45f28eede0e7fee65921", "bbc03c0c66d5405880fa9606f3257018", "93e76caa883c4511a56a357235950ba1", "0e5951a41ec448d6be89e824f31cbc90", "e6ca9c6b3f16456b901318b8403d3dbb", "e72b5ad93da0481e88c15b564b999ced", "2663dee251f14441b337a7abfa236afa", "1d4d606d62564ee4b68de8f85ccb6a77", "298c46a7d8ad469cb1c6ccc1b41d8299", "0504791841c34b648c10c6811685bd5f", "64a19f440b554fc7ba7347af5a6156da", "5857c7e1daf04d59915b48bd5fb01f2d", "f7e5b8e10d454a50a6ce914eab11eb9f", "e4a0715dccb444e982103d28c80b447c", "6b555fda3fe44cea918ebe554050c0d6", "036f955668a5437381833ca6a0d6371d", "a015d30565814237a4dd4f0dadcb689c", "731d0126c35540b9b8fa1b02a25dadb4", "d577b2c4223b4bb396b3c1462f324a20", "5ce00b7f6bd149eab3486322f366351e", "447b6d5210f84dc08d5c634023c7453f", "3560ef4a21dc4353b89f57427bc089de", "aa2da6ed7dd644cf92eb2b58506d94b1", "efb4d7fb4f9a473eb4d287f5d507781e", "d19b1888123c481db4af9e886c8ab8b9", "c880f4f983064426bde5f529a0a881e2", "c482792dc7e64113bf9a201b52e936de", "186da99333cc4434a7ade226a7e0de5b", "d5adb90946f848d695e1428e80d86c86", "39f47e8f46b1460e86b81104c8e8889f", "5f1aebc46565476ca45fb2dac665d32e", "addfdeeaad7141b3adc5cee148633aca", "3f6fef0e849d4ca2b513c3163e25d33c", "a03633f3c3f14de89df93902925257ce", "f668d1f90d0140e4ba414ae802546243", "b0214503fe67428a8018754d728e6071", "c52a92d0160e4c0f942d01c71f9d84b4", "98b625c8b8cf481ab133cba64396e429", "21017ef0da184688861ee9f0867d7505", "a7a0b1758b2c4e8fba3e87d8aae4ad79", "98bd6b13db084832bead597e5ffb0153", "0da112468fbc4cb29fb22c85b90db02e", "37c2be85c7844127a6100641bcd3f97d", "90fdc39bdaaf42a797cb08e5f55dec3a", "fa3a6e93d453426ab671487073fbde09", "0e4be0cc0e9348349ea6526df5811762", "b1fdbfcbb81f4f16a029bdf5d63d820a"]} outputId="158a376c-8b35-47b5-d82e-b7459284300f"
import tensorflow_datasets as tfds
imdb, info = tfds.load("imdb_reviews", with_info=True, as_supervised=True)
# + id="wHQ2Ko0zl7M4" colab_type="code" colab={}
import numpy as np
train_data, test_data = imdb['train'], imdb['test']
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
# str(s.tonumpy()) is needed in Python3 instead of just s.numpy()
for s,l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.numpy())
for s,l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy())
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)
# + id="7n15yyMdmoH1" colab_type="code" colab={}
vocab_size = 10000
embedding_dim = 16
max_length = 120
trunc_type='post'
oov_tok = "<OOV>"
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(training_sentences)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(training_sentences)
padded = pad_sequences(sequences,maxlen=max_length, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences,maxlen=max_length)
# + colab_type="code" id="9axf0uIXVMhO" colab={"base_uri": "https://localhost:8080/", "height": 70} outputId="a52faac6-d5e1-4071-a29b-f6ce8b5aaef4"
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
def decode_review(text):
return ' '.join([reverse_word_index.get(i, '?') for i in text])
print(decode_review(padded[1]))
print(training_sentences[1])
# + id="5NEpdhb8AxID" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 286} outputId="a2501426-5bae-4acc-a6a8-c53837c1ffa5"
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(6, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
model.summary()
# + id="V5LLrXC-uNX6" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 507} outputId="110fd2a4-3c46-403e-91d6-af4607f85fff"
num_epochs = 10
model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))
# + id="yAmjJqEyCOF_" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="690ee156-96c9-4d3e-9088-42563623718e"
e = model.layers[0]
weights = e.get_weights()[0]
print(weights.shape) # shape: (vocab_size, embedding_dim)
# + id="jmB0Uxk0ycP6" colab_type="code" colab={}
import io
out_v = io.open('vecs.tsv', 'w', encoding='utf-8')
out_m = io.open('meta.tsv', 'w', encoding='utf-8')
for word_num in range(1, vocab_size):
word = reverse_word_index[word_num]
embeddings = weights[word_num]
out_m.write(word + "\n")
out_v.write('\t'.join([str(x) for x in embeddings]) + "\n")
out_v.close()
out_m.close()
# + id="VDeqpOCVydtq" colab_type="code" colab={}
try:
from google.colab import files
except ImportError:
pass
else:
files.download('vecs.tsv')
files.download('meta.tsv')
# + id="YRxoxc2apscY" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 54} outputId="252745c2-5d47-49dc-da24-76366a11999e"
sentence = "I really think this is amazing. honest."
sequence = tokenizer.texts_to_sequences(sentence)
print(sequence)
| 29,875 |
/opencv/12_colorspaces.ipynb | 3e87962fb8a24e48ec63ec9297b5f3d3b66a5cd8 | [] | no_license | GreenGhostMan/ml | https://github.com/GreenGhostMan/ml | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 153,616 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
# BGR VS RGB
# COLORSPACES ==> GRAY,RGB,BGR,CMY,CMYK,HSV(Hue,Satuation,Value)
import cv2 as cv
import matplotlib.pyplot as plt
def main():
img = cv.imread('images/lena_color_512.tif',1)
img = cv.cvtColor(img,cv.COLOR_BGR2RGB)
plt.imshow(img)
plt.title('Color')
plt.xticks([])
plt.yticks([])
plt.show()
if __name__ == "__main__":
main()
# -
| 667 |
/voice/train classifier active passive proxy bert.ipynb | ba9bd0d65f2d210516fa839831bfef69389bca73 | [] | no_license | kai-pinckard/thesis | https://github.com/kai-pinckard/thesis | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 950,767 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import torch
# If there's a GPU available...
if torch.cuda.is_available():
# Tell PyTorch to use the GPU.
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
# If not...
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
# +
import json
with open("\\Users\\kaidpinck\\thesis\\thesis\\classifier\\semeval2010task8\\voice_dataset.json", "r") as f:
data = json.load(f)
print(data)
print(len(data))
# -
sentences = [ item["sent"].lower() for item in data]
sentences[0]
labels = [ item["label"] for item in data]
labels[0]
# 1 corresponds to active and 0 to passive
# +
from transformers import BertTokenizer
# Load the BERT tokenizer.
print('Loading BERT tokenizer...')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
# +
# Print the original sentence.
print(' Original: ', sentences[0])
# Print the sentence split into tokens.
print('Tokenized: ', tokenizer.tokenize(sentences[0]))
# Print the sentence mapped to token ids.
print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sentences[0])))
# +
max_len = 0
# For every sentence...
for sent in sentences:
# Tokenize the text and add `[CLS]` and `[SEP]` tokens.
input_ids = tokenizer.encode(sent, add_special_tokens=True)
# Update the maximum sentence length.
max_len = max(max_len, len(input_ids))
print('Max sentence length: ', max_len)
# +
# Tokenize all of the sentences and map the tokens to thier word IDs.
input_ids = []
attention_masks = []
# For every sentence...
for sent in sentences:
# `encode_plus` will:
# (1) Tokenize the sentence.
# (2) Prepend the `[CLS]` token to the start.
# (3) Append the `[SEP]` token to the end.
# (4) Map tokens to their IDs.
# (5) Pad or truncate the sentence to `max_length`
# (6) Create attention masks for [PAD] tokens.
encoded_dict = tokenizer.encode_plus(
sent, # Sentence to encode.
add_special_tokens = True, # Add '[CLS]' and '[SEP]'
max_length = 128, # Pad & truncate all sentences.
pad_to_max_length = True,
return_attention_mask = True, # Construct attn. masks.
return_tensors = 'pt', # Return pytorch tensors.
truncation=True,
padding="max_length"
)
# Add the encoded sentence to the list.
input_ids.append(encoded_dict['input_ids'])
# And its attention mask (simply differentiates padding from non-padding).
attention_masks.append(encoded_dict['attention_mask'])
# Convert the lists into tensors.
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
labels = torch.tensor(labels)
# Print sentence 0, now as a list of IDs.
print('Original: ', sentences[0])
print('Token IDs:', input_ids[0])
# +
from torch.utils.data import TensorDataset, random_split
# Combine the training inputs into a TensorDataset.
dataset = TensorDataset(input_ids, attention_masks, labels)
# Create a 90-10 train-validation split.
# Calculate the number of samples to include in each set.
train_size = int(0.9 * len(dataset))
val_size = len(dataset) - train_size
# Divide the dataset by randomly selecting samples.
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
print('{:>5,} training samples'.format(train_size))
print('{:>5,} validation samples'.format(val_size))
# +
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
# The DataLoader needs to know our batch size for training, so we specify it
# here. For fine-tuning BERT on a specific task, the authors recommend a batch
# size of 16 or 32.
batch_size = 8
# Create the DataLoaders for our training and validation sets.
# We'll take training samples in random order.
train_dataloader = DataLoader(
train_dataset, # The training samples.
sampler = RandomSampler(train_dataset), # Select batches randomly
batch_size = batch_size # Trains with this batch size.
)
# For validation the order doesn't matter, so we'll just read them sequentially.
validation_dataloader = DataLoader(
val_dataset, # The validation samples.
sampler = SequentialSampler(val_dataset), # Pull out batches sequentially.
batch_size = batch_size # Evaluate with this batch size.
)
# +
from transformers import BertForSequenceClassification, AdamW, BertConfig
# Load BertForSequenceClassification, the pretrained BERT model with a single
# linear classification layer on top.
model = BertForSequenceClassification.from_pretrained(
"bert-base-uncased", # Use the 12-layer BERT model, with an uncased vocab.
num_labels = 2, # The number of output labels--2 for binary classification.
# You can increase this for multi-class tasks.
output_attentions = False, # Whether the model returns attentions weights.
output_hidden_states = False, # Whether the model returns all hidden-states.
)
# Tell pytorch to run this model on the GPU.
model.cuda()
# -
# Note: AdamW is a class from the huggingface library (as opposed to pytorch)
# I believe the 'W' stands for 'Weight Decay fix"
optimizer = AdamW(model.parameters(),
lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5
eps = 1e-8 # args.adam_epsilon - default is 1e-8.
)
# +
from transformers import get_linear_schedule_with_warmup
# Number of training epochs. The BERT authors recommend between 2 and 4.
# We chose to run for 4, but we'll see later that this may be over-fitting the
# training data.
epochs = 1
# Total number of training steps is [number of batches] x [number of epochs].
# (Note that this is not the same as the number of training samples).
total_steps = len(train_dataloader) * epochs
# Create the learning rate scheduler.
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps = 0, # Default value in run_glue.py
num_training_steps = total_steps)
# +
import numpy as np
# Function to calculate the accuracy of our predictions vs labels
def flat_accuracy(preds, labels):
pred_flat = np.argmax(preds, axis=1).flatten()
labels_flat = labels.flatten()
return np.sum(pred_flat == labels_flat) / len(labels_flat)
# +
import time
import datetime
def format_time(elapsed):
'''
Takes a time in seconds and returns a string hh:mm:ss
'''
# Round to the nearest second.
elapsed_rounded = int(round((elapsed)))
# Format as hh:mm:ss
return str(datetime.timedelta(seconds=elapsed_rounded))
# +
import random
import numpy as np
# This training code is based on the `run_glue.py` script here:
# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128
# Set the seed value all over the place to make this reproducible.
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
# We'll store a number of quantities such as training and validation loss,
# validation accuracy, and timings.
training_stats = []
# Measure the total training time for the whole run.
total_t0 = time.time()
# For each epoch...
for epoch_i in range(0, epochs):
# ========================================
# Training
# ========================================
# Perform one full pass over the training set.
print("")
print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
print('Training...')
# Measure how long the training epoch takes.
t0 = time.time()
# Reset the total loss for this epoch.
total_train_loss = 0
# Put the model into training mode. Don't be mislead--the call to
# `train` just changes the *mode*, it doesn't *perform* the training.
# `dropout` and `batchnorm` layers behave differently during training
# vs. test (source: https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch)
model.train()
# For each batch of training data...
for step, batch in enumerate(train_dataloader):
# Progress update every 40 batches.
if step % 40 == 0 and not step == 0:
# Calculate elapsed time in minutes.
elapsed = format_time(time.time() - t0)
# Report progress.
print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))
# Unpack this training batch from our dataloader.
#
# As we unpack the batch, we'll also copy each tensor to the GPU using the
# `to` method.
#
# `batch` contains three pytorch tensors:
# [0]: input ids
# [1]: attention masks
# [2]: labels
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
# Always clear any previously calculated gradients before performing a
# backward pass. PyTorch doesn't do this automatically because
# accumulating the gradients is "convenient while training RNNs".
# (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
model.zero_grad()
# Perform a forward pass (evaluate the model on this training batch).
# The documentation for this `model` function is here:
# https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
# It returns different numbers of parameters depending on what arguments
# arge given and what flags are set. For our useage here, it returns
# the loss (because we provided labels) and the "logits"--the model
# outputs prior to activation.
loss, logits = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
# Accumulate the training loss over all of the batches so that we can
# calculate the average loss at the end. `loss` is a Tensor containing a
# single value; the `.item()` function just returns the Python value
# from the tensor.
total_train_loss += loss.item()
# Perform a backward pass to calculate the gradients.
loss.backward()
# Clip the norm of the gradients to 1.0.
# This is to help prevent the "exploding gradients" problem.
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# Update parameters and take a step using the computed gradient.
# The optimizer dictates the "update rule"--how the parameters are
# modified based on their gradients, the learning rate, etc.
optimizer.step()
# Update the learning rate.
scheduler.step()
# Calculate the average loss over all of the batches.
avg_train_loss = total_train_loss / len(train_dataloader)
# Measure how long this epoch took.
training_time = format_time(time.time() - t0)
print("")
print(" Average training loss: {0:.2f}".format(avg_train_loss))
print(" Training epcoh took: {:}".format(training_time))
# ========================================
# Validation
# ========================================
# After the completion of each training epoch, measure our performance on
# our validation set.
print("")
print("Running Validation...")
t0 = time.time()
# Put the model in evaluation mode--the dropout layers behave differently
# during evaluation.
model.eval()
# Tracking variables
total_eval_accuracy = 0
total_eval_loss = 0
nb_eval_steps = 0
# Evaluate data for one epoch
for batch in validation_dataloader:
# Unpack this training batch from our dataloader.
#
# As we unpack the batch, we'll also copy each tensor to the GPU using
# the `to` method.
#
# `batch` contains three pytorch tensors:
# [0]: input ids
# [1]: attention masks
# [2]: labels
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
# Tell pytorch not to bother with constructing the compute graph during
# the forward pass, since this is only needed for backprop (training).
with torch.no_grad():
# Forward pass, calculate logit predictions.
# token_type_ids is the same as the "segment ids", which
# differentiates sentence 1 and 2 in 2-sentence tasks.
# The documentation for this `model` function is here:
# https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
# Get the "logits" output by the model. The "logits" are the output
# values prior to applying an activation function like the softmax.
(loss, logits) = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
# Accumulate the validation loss.
total_eval_loss += loss.item()
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# Calculate the accuracy for this batch of test sentences, and
# accumulate it over all batches.
total_eval_accuracy += flat_accuracy(logits, label_ids)
# Report the final accuracy for this validation run.
avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)
print(" Accuracy: {0:.2f}".format(avg_val_accuracy))
# Calculate the average loss over all of the batches.
avg_val_loss = total_eval_loss / len(validation_dataloader)
# Measure how long the validation run took.
validation_time = format_time(time.time() - t0)
print(" Validation Loss: {0:.2f}".format(avg_val_loss))
print(" Validation took: {:}".format(validation_time))
# Record all statistics from this epoch.
training_stats.append(
{
'epoch': epoch_i + 1,
'Training Loss': avg_train_loss,
'Valid. Loss': avg_val_loss,
'Valid. Accur.': avg_val_accuracy,
'Training Time': training_time,
'Validation Time': validation_time
}
)
print("")
print("Training complete!")
print("Total training took {:} (h:mm:ss)".format(format_time(time.time()-total_t0)))
# +
import pandas as pd
# Display floats with two decimal places.
pd.set_option('precision', 2)
# Create a DataFrame from our training statistics.
df_stats = pd.DataFrame(data=training_stats)
# Use the 'epoch' as the row index.
df_stats = df_stats.set_index('epoch')
# A hack to force the column headers to wrap.
#df = df.style.set_table_styles([dict(selector="th",props=[('max-width', '70px')])])
# Display the table.
df_stats
# +
import matplotlib.pyplot as plt
# %matplotlib inline
import seaborn as sns
# Use plot styling from seaborn.
sns.set(style='darkgrid')
# Increase the plot size and font size.
sns.set(font_scale=1.5)
plt.rcParams["figure.figsize"] = (12,6)
# Plot the learning curve.
plt.plot(df_stats['Training Loss'], 'b-o', label="Training")
plt.plot(df_stats['Valid. Loss'], 'g-o', label="Validation")
# Label the plot.
plt.title("Training & Validation Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.xticks([1, 2, 3, 4])
plt.show()
# +
#Option 1 previously handwritten causal sentences
with open("\\Users\\kaidpinck\\thesis\\thesis\\benchmark\\statements.json", "r") as f:
data = json.load(f)
#print(data)
# Report the number of sentences.
print('Number of test sentences: {:,}\n'.format(len(data)))
# Create sentence and label lists
sentences = [ item["sent"].lower() for item in data]
labels = [ 1 for item in data]
for i in range(len(data)//2,len(data)):
labels[i] = 0
print(sentences)
print(labels)
# +
#Option 2 previously handwritten causal sentences
with open("voice_dataset.json", "r") as f:
data = json.load(f)
#print(data)
# Report the number of sentences.
print('Number of test sentences: {:,}\n'.format(len(data)))
# Create sentence and label lists
sentences = [ item["sent"].lower() for item in data]
labels = [ item["label"] for item in data]
for i in range(len(data)//2,len(data)):
labels[i] = 0
print(sentences)
print(labels)
# +
# Tokenize all of the sentences and map the tokens to thier word IDs.
input_ids = []
attention_masks = []
# For every sentence...
for sent in sentences:
# `encode_plus` will:
# (1) Tokenize the sentence.
# (2) Prepend the `[CLS]` token to the start.
# (3) Append the `[SEP]` token to the end.
# (4) Map tokens to their IDs.
# (5) Pad or truncate the sentence to `max_length`
# (6) Create attention masks for [PAD] tokens.
encoded_dict = tokenizer.encode_plus(
sent, # Sentence to encode.
add_special_tokens = True, # Add '[CLS]' and '[SEP]'
max_length = 128, # Pad & truncate all sentences.
pad_to_max_length = True,
return_attention_mask = True, # Construct attn. masks.
return_tensors = 'pt', # Return pytorch tensors.
truncation=True,
padding="max_length"
)
# Add the encoded sentence to the list.
input_ids.append(encoded_dict['input_ids'])
# And its attention mask (simply differentiates padding from non-padding).
attention_masks.append(encoded_dict['attention_mask'])
# Convert the lists into tensors.
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
labels = torch.tensor(labels)
# Set the batch size.
batch_size = 8
# Create the DataLoader.
prediction_data = TensorDataset(input_ids, attention_masks, labels)
prediction_sampler = SequentialSampler(prediction_data)
prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)
# +
# Prediction on test set
print('Predicting labels for {:,} test sentences...'.format(len(input_ids)))
# Put model in evaluation mode
model.eval()
# Tracking variables
predictions , true_labels = [], []
# Predict
for batch in prediction_dataloader:
# Add batch to GPU
batch = tuple(t.to(device) for t in batch)
# Unpack the inputs from our dataloader
b_input_ids, b_input_mask, b_labels = batch
# Telling the model not to compute or store gradients, saving memory and
# speeding up prediction
with torch.no_grad():
# Forward pass, calculate logit predictions
outputs = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask)
logits = outputs[0]
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# Store predictions and true labels
predictions.append(logits)
true_labels.append(label_ids)
print(' DONE.')
# +
from sklearn.metrics import matthews_corrcoef
matthews_set = []
# Evaluate each test batch using Matthew's correlation coefficient
print('Calculating Matthews Corr. Coef. for each batch...')
# For each input batch...
for i in range(len(true_labels)):
# The predictions for this batch are a 2-column ndarray (one column for "0"
# and one column for "1"). Pick the label with the highest value and turn this
# in to a list of 0s and 1s.
pred_labels_i = np.argmax(predictions[i], axis=1).flatten()
# Calculate and store the coef for this batch.
matthews = matthews_corrcoef(true_labels[i], pred_labels_i)
matthews_set.append(matthews)
# +
# Create a barplot showing the MCC score for each batch of test samples.
ax = sns.barplot(x=list(range(len(matthews_set))), y=matthews_set, ci=None)
plt.title('MCC Score per Batch')
plt.ylabel('MCC Score (-1 to +1)')
plt.xlabel('Batch #')
plt.show()
# +
# Combine the results across all batches.
flat_predictions = np.concatenate(predictions, axis=0)
# For each sample, pick the label (0 or 1) with the higher score.
flat_predictions = np.argmax(flat_predictions, axis=1).flatten()
print("flat predictions:", flat_predictions )
# Combine the correct labels for each batch into a single list.
flat_true_labels = np.concatenate(true_labels, axis=0)
print("true labels:", flat_true_labels)
# Calculate the MCC
mcc = matthews_corrcoef(flat_true_labels, flat_predictions)
print('Total MCC: %.3f' % mcc)
# +
# Accuracy by type
#Correctly identified active
#overall accuracy
total = 0
total_active = 0
total_passive = 0
correct = 0
num_correct_active = 0
num_correct_passive = 0
for i, pred in enumerate(flat_predictions):
if flat_true_labels[i] == 1:
total_active += 1
if pred == 1:
num_correct_active += 1
correct += 1
else:
total_passive += 1
if pred == 0:
num_correct_passive += 1
correct += 1
total += 1
print("overall accuracy", float(correct)/total)
print("active voice accuracy", float(num_correct_active)/total_active)
print("passive voice accuracy", float(num_correct_passive)/total_passive)
# -
and backward propagation. You need to compute the cost, because you want to check if your model is actually learning.
#
# **Exercise**: Compute the cross-entropy cost $J$, using the following formula: $$-\frac{1}{m} \sum\limits_{i = 1}^{m} (y^{(i)}\log\left(a^{[L] (i)}\right) + (1-y^{(i)})\log\left(1- a^{[L](i)}\right)) \tag{7}$$
#
# +
# GRADED FUNCTION: compute_cost
def compute_cost(AL, Y):
"""
Implement the cost function defined by equation (7).
Arguments:
AL -- probability vector corresponding to your label predictions, shape (1, number of examples)
Y -- true "label" vector (for example: containing 0 if non-cat, 1 if cat), shape (1, number of examples)
Returns:
cost -- cross-entropy cost
"""
m = Y.shape[1]
# Compute loss from aL and y.
### START CODE HERE ### (≈ 1 lines of code)
cost = (np.dot(Y, np.log(AL.T)) + np.dot(1 - Y, np.log(1 - AL.T)))/-m
### END CODE HERE ###
cost = np.squeeze(cost) # To make sure your cost's shape is what we expect (e.g. this turns [[17]] into 17).
assert(cost.shape == ())
return cost
# +
Y, AL = compute_cost_test_case()
print("cost = " + str(compute_cost(AL, Y)))
# -
# **Expected Output**:
#
# <table>
#
# <tr>
# <td>**cost** </td>
# <td> 0.2797765635793422</td>
# </tr>
# </table>
# ## 6 - Backward propagation module
#
# Just like with forward propagation, you will implement helper functions for backpropagation. Remember that back propagation is used to calculate the gradient of the loss function with respect to the parameters.
#
# **Reminder**:
# <img src="images/backprop_kiank.png" style="width:650px;height:250px;">
# <caption><center> **Figure 3** : Forward and Backward propagation for *LINEAR->RELU->LINEAR->SIGMOID* <br> *The purple blocks represent the forward propagation, and the red blocks represent the backward propagation.* </center></caption>
#
# <!--
# For those of you who are expert in calculus (you don't need to be to do this assignment), the chain rule of calculus can be used to derive the derivative of the loss $\mathcal{L}$ with respect to $z^{[1]}$ in a 2-layer network as follows:
#
# $$\frac{d \mathcal{L}(a^{[2]},y)}{{dz^{[1]}}} = \frac{d\mathcal{L}(a^{[2]},y)}{{da^{[2]}}}\frac{{da^{[2]}}}{{dz^{[2]}}}\frac{{dz^{[2]}}}{{da^{[1]}}}\frac{{da^{[1]}}}{{dz^{[1]}}} \tag{8} $$
#
# In order to calculate the gradient $dW^{[1]} = \frac{\partial L}{\partial W^{[1]}}$, you use the previous chain rule and you do $dW^{[1]} = dz^{[1]} \times \frac{\partial z^{[1]} }{\partial W^{[1]}}$. During the backpropagation, at each step you multiply your current gradient by the gradient corresponding to the specific layer to get the gradient you wanted.
#
# Equivalently, in order to calculate the gradient $db^{[1]} = \frac{\partial L}{\partial b^{[1]}}$, you use the previous chain rule and you do $db^{[1]} = dz^{[1]} \times \frac{\partial z^{[1]} }{\partial b^{[1]}}$.
#
# This is why we talk about **backpropagation**.
# !-->
#
# Now, similar to forward propagation, you are going to build the backward propagation in three steps:
# - LINEAR backward
# - LINEAR -> ACTIVATION backward where ACTIVATION computes the derivative of either the ReLU or sigmoid activation
# - [LINEAR -> RELU] $\times$ (L-1) -> LINEAR -> SIGMOID backward (whole model)
# ### 6.1 - Linear backward
#
# For layer $l$, the linear part is: $Z^{[l]} = W^{[l]} A^{[l-1]} + b^{[l]}$ (followed by an activation).
#
# Suppose you have already calculated the derivative $dZ^{[l]} = \frac{\partial \mathcal{L} }{\partial Z^{[l]}}$. You want to get $(dW^{[l]}, db^{[l]}, dA^{[l-1]})$.
#
# <img src="images/linearback_kiank.png" style="width:250px;height:300px;">
# <caption><center> **Figure 4** </center></caption>
#
# The three outputs $(dW^{[l]}, db^{[l]}, dA^{[l-1]})$ are computed using the input $dZ^{[l]}$.Here are the formulas you need:
# $$ dW^{[l]} = \frac{\partial \mathcal{J} }{\partial W^{[l]}} = \frac{1}{m} dZ^{[l]} A^{[l-1] T} \tag{8}$$
# $$ db^{[l]} = \frac{\partial \mathcal{J} }{\partial b^{[l]}} = \frac{1}{m} \sum_{i = 1}^{m} dZ^{[l](i)}\tag{9}$$
# $$ dA^{[l-1]} = \frac{\partial \mathcal{L} }{\partial A^{[l-1]}} = W^{[l] T} dZ^{[l]} \tag{10}$$
#
# **Exercise**: Use the 3 formulas above to implement linear_backward().
# +
# GRADED FUNCTION: linear_backward
def linear_backward(dZ, cache):
"""
Implement the linear portion of backward propagation for a single layer (layer l)
Arguments:
dZ -- Gradient of the cost with respect to the linear output (of current layer l)
cache -- tuple of values (A_prev, W, b) coming from the forward propagation in the current layer
Returns:
dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev
dW -- Gradient of the cost with respect to W (current layer l), same shape as W
db -- Gradient of the cost with respect to b (current layer l), same shape as b
"""
A_prev, W, b = cache
m = A_prev.shape[1]
### START CODE HERE ### (≈ 3 lines of code)
dW = np.dot(dZ, A_prev.T) / m
db = np.sum(dZ, axis = 1, keepdims = True) / m
dA_prev = np.dot(W.T, dZ)
### END CODE HERE ###
assert (dA_prev.shape == A_prev.shape)
assert (dW.shape == W.shape)
assert (db.shape == b.shape)
return dA_prev, dW, db
# +
# Set up some test inputs
dZ, linear_cache = linear_backward_test_case()
dA_prev, dW, db = linear_backward(dZ, linear_cache)
print ("dA_prev = "+ str(dA_prev))
print ("dW = " + str(dW))
print ("db = " + str(db))
# -
# ** Expected Output**:
#
# ```
# dA_prev =
# [[-1.15171336 0.06718465 -0.3204696 2.09812712]
# [ 0.60345879 -3.72508701 5.81700741 -3.84326836]
# [-0.4319552 -1.30987417 1.72354705 0.05070578]
# [-0.38981415 0.60811244 -1.25938424 1.47191593]
# [-2.52214926 2.67882552 -0.67947465 1.48119548]]
# dW =
# [[ 0.07313866 -0.0976715 -0.87585828 0.73763362 0.00785716]
# [ 0.85508818 0.37530413 -0.59912655 0.71278189 -0.58931808]
# [ 0.97913304 -0.24376494 -0.08839671 0.55151192 -0.10290907]]
# db =
# [[-0.14713786]
# [-0.11313155]
# [-0.13209101]]
# ```
# ### 6.2 - Linear-Activation backward
#
# Next, you will create a function that merges the two helper functions: **`linear_backward`** and the backward step for the activation **`linear_activation_backward`**.
#
# To help you implement `linear_activation_backward`, we provided two backward functions:
# - **`sigmoid_backward`**: Implements the backward propagation for SIGMOID unit. You can call it as follows:
#
# ```python
# dZ = sigmoid_backward(dA, activation_cache)
# ```
#
# - **`relu_backward`**: Implements the backward propagation for RELU unit. You can call it as follows:
#
# ```python
# dZ = relu_backward(dA, activation_cache)
# ```
#
# If $g(.)$ is the activation function,
# `sigmoid_backward` and `relu_backward` compute $$dZ^{[l]} = dA^{[l]} * g'(Z^{[l]}) \tag{11}$$.
#
# **Exercise**: Implement the backpropagation for the *LINEAR->ACTIVATION* layer.
# +
# GRADED FUNCTION: linear_activation_backward
def linear_activation_backward(dA, cache, activation):
"""
Implement the backward propagation for the LINEAR->ACTIVATION layer.
Arguments:
dA -- post-activation gradient for current layer l
cache -- tuple of values (linear_cache, activation_cache) we store for computing backward propagation efficiently
activation -- the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"
Returns:
dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev
dW -- Gradient of the cost with respect to W (current layer l), same shape as W
db -- Gradient of the cost with respect to b (current layer l), same shape as b
"""
linear_cache, activation_cache = cache
if activation == "relu":
### START CODE HERE ### (≈ 2 lines of code)
dZ = relu_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
### END CODE HERE ###
elif activation == "sigmoid":
### START CODE HERE ### (≈ 2 lines of code)
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
### END CODE HERE ###
return dA_prev, dW, db
# +
dAL, linear_activation_cache = linear_activation_backward_test_case()
dA_prev, dW, db = linear_activation_backward(dAL, linear_activation_cache, activation = "sigmoid")
print ("sigmoid:")
print ("dA_prev = "+ str(dA_prev))
print ("dW = " + str(dW))
print ("db = " + str(db) + "\n")
dA_prev, dW, db = linear_activation_backward(dAL, linear_activation_cache, activation = "relu")
print ("relu:")
print ("dA_prev = "+ str(dA_prev))
print ("dW = " + str(dW))
print ("db = " + str(db))
# -
# **Expected output with sigmoid:**
#
# <table style="width:100%">
# <tr>
# <td > dA_prev </td>
# <td >[[ 0.11017994 0.01105339]
# [ 0.09466817 0.00949723]
# [-0.05743092 -0.00576154]] </td>
#
# </tr>
#
# <tr>
# <td > dW </td>
# <td > [[ 0.10266786 0.09778551 -0.01968084]] </td>
# </tr>
#
# <tr>
# <td > db </td>
# <td > [[-0.05729622]] </td>
# </tr>
# </table>
#
#
# **Expected output with relu:**
#
# <table style="width:100%">
# <tr>
# <td > dA_prev </td>
# <td > [[ 0.44090989 0. ]
# [ 0.37883606 0. ]
# [-0.2298228 0. ]] </td>
#
# </tr>
#
# <tr>
# <td > dW </td>
# <td > [[ 0.44513824 0.37371418 -0.10478989]] </td>
# </tr>
#
# <tr>
# <td > db </td>
# <td > [[-0.20837892]] </td>
# </tr>
# </table>
#
#
# ### 6.3 - L-Model Backward
#
# Now you will implement the backward function for the whole network. Recall that when you implemented the `L_model_forward` function, at each iteration, you stored a cache which contains (X,W,b, and z). In the back propagation module, you will use those variables to compute the gradients. Therefore, in the `L_model_backward` function, you will iterate through all the hidden layers backward, starting from layer $L$. On each step, you will use the cached values for layer $l$ to backpropagate through layer $l$. Figure 5 below shows the backward pass.
#
#
# <img src="images/mn_backward.png" style="width:450px;height:300px;">
# <caption><center> **Figure 5** : Backward pass </center></caption>
#
# ** Initializing backpropagation**:
# To backpropagate through this network, we know that the output is,
# $A^{[L]} = \sigma(Z^{[L]})$. Your code thus needs to compute `dAL` $= \frac{\partial \mathcal{L}}{\partial A^{[L]}}$.
# To do so, use this formula (derived using calculus which you don't need in-depth knowledge of):
# ```python
# dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL)) # derivative of cost with respect to AL
# ```
#
# You can then use this post-activation gradient `dAL` to keep going backward. As seen in Figure 5, you can now feed in `dAL` into the LINEAR->SIGMOID backward function you implemented (which will use the cached values stored by the L_model_forward function). After that, you will have to use a `for` loop to iterate through all the other layers using the LINEAR->RELU backward function. You should store each dA, dW, and db in the grads dictionary. To do so, use this formula :
#
# $$grads["dW" + str(l)] = dW^{[l]}\tag{15} $$
#
# For example, for $l=3$ this would store $dW^{[l]}$ in `grads["dW3"]`.
#
# **Exercise**: Implement backpropagation for the *[LINEAR->RELU] $\times$ (L-1) -> LINEAR -> SIGMOID* model.
# +
# GRADED FUNCTION: L_model_backward
def L_model_backward(AL, Y, caches):
"""
Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group
Arguments:
AL -- probability vector, output of the forward propagation (L_model_forward())
Y -- true "label" vector (containing 0 if non-cat, 1 if cat)
caches -- list of caches containing:
every cache of linear_activation_forward() with "relu" (it's caches[l], for l in range(L-1) i.e l = 0...L-2)
the cache of linear_activation_forward() with "sigmoid" (it's caches[L-1])
Returns:
grads -- A dictionary with the gradients
grads["dA" + str(l)] = ...
grads["dW" + str(l)] = ...
grads["db" + str(l)] = ...
"""
grads = {}
L = len(caches) # the number of layers
m = AL.shape[1]
Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL
# Initializing the backpropagation
### START CODE HERE ### (1 line of code)
dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
### END CODE HERE ###
# Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "dAL, current_cache". Outputs: "grads["dAL-1"], grads["dWL"], grads["dbL"]
### START CODE HERE ### (approx. 2 lines)
current_cache = caches[L-1]
grads["dA" + str(L-1)], grads["dW" + str(L)], grads["db" + str(L)] = linear_activation_backward(dAL, current_cache, "sigmoid")
### END CODE HERE ###
# Loop from l=L-2 to l=0
for l in reversed(range(L-1)):
# lth layer: (RELU -> LINEAR) gradients.
# Inputs: "grads["dA" + str(l + 1)], current_cache". Outputs: "grads["dA" + str(l)] , grads["dW" + str(l + 1)] , grads["db" + str(l + 1)]
### START CODE HERE ### (approx. 5 lines)
current_cache = caches[l]
dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads["dA" + str(l + 1)], current_cache, "relu")
grads["dA" + str(l)] = dA_prev_temp
grads["dW" + str(l + 1)] = dW_temp
grads["db" + str(l + 1)] = db_temp
### END CODE HERE ###
return grads
# -
AL, Y_assess, caches = L_model_backward_test_case()
grads = L_model_backward(AL, Y_assess, caches)
print_grads(grads)
# **Expected Output**
#
# <table style="width:60%">
#
# <tr>
# <td > dW1 </td>
# <td > [[ 0.41010002 0.07807203 0.13798444 0.10502167]
# [ 0. 0. 0. 0. ]
# [ 0.05283652 0.01005865 0.01777766 0.0135308 ]] </td>
# </tr>
#
# <tr>
# <td > db1 </td>
# <td > [[-0.22007063]
# [ 0. ]
# [-0.02835349]] </td>
# </tr>
#
# <tr>
# <td > dA1 </td>
# <td > [[ 0.12913162 -0.44014127]
# [-0.14175655 0.48317296]
# [ 0.01663708 -0.05670698]] </td>
#
# </tr>
# </table>
#
#
# ### 6.4 - Update Parameters
#
# In this section you will update the parameters of the model, using gradient descent:
#
# $$ W^{[l]} = W^{[l]} - \alpha \text{ } dW^{[l]} \tag{16}$$
# $$ b^{[l]} = b^{[l]} - \alpha \text{ } db^{[l]} \tag{17}$$
#
# where $\alpha$ is the learning rate. After computing the updated parameters, store them in the parameters dictionary.
# **Exercise**: Implement `update_parameters()` to update your parameters using gradient descent.
#
# **Instructions**:
# Update parameters using gradient descent on every $W^{[l]}$ and $b^{[l]}$ for $l = 1, 2, ..., L$.
#
# +
# GRADED FUNCTION: update_parameters
def update_parameters(parameters, grads, learning_rate):
"""
Update parameters using gradient descent
Arguments:
parameters -- python dictionary containing your parameters
grads -- python dictionary containing your gradients, output of L_model_backward
Returns:
parameters -- python dictionary containing your updated parameters
parameters["W" + str(l)] = ...
parameters["b" + str(l)] = ...
"""
L = len(parameters) // 2 # number of layers in the neural network
# Update rule for each parameter. Use a for loop.
### START CODE HERE ### (≈ 3 lines of code)
for l in range(L):
parameters["W" + str(l+1)] = parameters["W" + str(l+1)] - learning_rate * grads["dW" + str(l+1)]
parameters["b" + str(l+1)] = parameters["b" + str(l+1)] - learning_rate * grads["db" + str(l+1)]
### END CODE HERE ###
return parameters
# +
parameters, grads = update_parameters_test_case()
parameters = update_parameters(parameters, grads, 0.1)
print ("W1 = "+ str(parameters["W1"]))
print ("b1 = "+ str(parameters["b1"]))
print ("W2 = "+ str(parameters["W2"]))
print ("b2 = "+ str(parameters["b2"]))
# -
# **Expected Output**:
#
# <table style="width:100%">
# <tr>
# <td > W1 </td>
# <td > [[-0.59562069 -0.09991781 -2.14584584 1.82662008]
# [-1.76569676 -0.80627147 0.51115557 -1.18258802]
# [-1.0535704 -0.86128581 0.68284052 2.20374577]] </td>
# </tr>
#
# <tr>
# <td > b1 </td>
# <td > [[-0.04659241]
# [-1.28888275]
# [ 0.53405496]] </td>
# </tr>
# <tr>
# <td > W2 </td>
# <td > [[-0.55569196 0.0354055 1.32964895]]</td>
# </tr>
#
# <tr>
# <td > b2 </td>
# <td > [[-0.84610769]] </td>
# </tr>
# </table>
#
#
# ## 7 - Conclusion
#
# Congrats on implementing all the functions required for building a deep neural network!
#
# We know it was a long assignment but going forward it will only get better. The next part of the assignment is easier.
#
# In the next assignment you will put all these together to build two models:
# - A two-layer neural network
# - An L-layer neural network
#
# You will in fact use these models to classify cat vs non-cat images!
| 39,732 |
/lab11-students/lab11-students/Lab 1 Load Prediction in ERCOT Markets-Students.ipynb | a01cb1a2d066491d245fe11d8c76fceef6b33432 | [] | no_license | YichenZhou113/ECE398BD | https://github.com/YichenZhou113/ECE398BD | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 554,963 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Lab 1: Day-ahead load prediction for ERCOT (Texas) markets.
#
# In this lab, you train a neural network to predict 24-hour aggregate load from Texas for a day using history of demands. The goals for this lab are:
# 1. Load the data and analyze to find patterns.
# 2. Define a neural network for the regression. Try different number of layers, learning rates, linear v/s nonlinear regression, activation functions, number of epochs, etc.
# 3. Explore the effects of wind energy on load prediction.
# +
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import random
import datetime
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
import matplotlib.pyplot as plt
# The following line suppresses certain warnings.
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
# -
# ## Load the ERCOT data from 2015.
#
# The load data is given in the column named 'ERCOT Load, MW' in the csv file provided.
# +
year = 2015
dfDemand = pd.read_csv("ERCOT_Hourly_Wind_Output_" + str(year) + ".csv")
demands = dfDemand['ERCOT Load, MW']
# Count the number of days for which we have demand data.
numberOfDays = int(len(demands)/24)
print("Hourly demand data loaded for %d days." % numberOfDays)
# -
# ## Understand the data.
#
# It is always useful to get accustomed to the data you are trying to learn. Visualize it if you can.
#
# #### Q1. How does load vary over the year in Texas?
# +
fig = plt.figure()
plt.plot([hour/24 for hour in range(numberOfDays * 24)], demands.values)
plt.xlabel("Days in " + str(year))
plt.ylabel("Net demand of Texas (in MW)")
# -
# **Fact.** A significant portion of the demand is usually thermal, i.e., for air conditioners and heating systems.
#
# **Question (5 points).** From the above plot, what can you infer about the climate of Texas? What would you expect if you plotted the same in Illinois?
#
# **Your answer.** The climate is hot during the summer in Texas, because the demand in Summer is much higher than other time. For Illinois, I expect a plot with higher peaks at the two sides, because the climate here is cold and demand during winter would be larger.
#
# #### Q2. How does day of week affect the load profiles?
# +
# Plot the load data of the same day of the week over several weeks.
dayStart = 30
numberOfWeeks = 4
DayOfWeek = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
print("The first day in the first plot is Jan 31, " + str(year) + ".")
print("Day 1", "was a", DayOfWeek[datetime.date(year, 1, 31).weekday()] + ".")
fig, axs = plt.subplots(7, 1, sharex=True, figsize=(5,10))
axs = axs.ravel()
for dayInFirstWeek in range(7):
for week in range(numberOfWeeks):
axs[dayInFirstWeek].plot(range(24), dfDemand.loc[(dayStart + 7 * week + dayInFirstWeek) * 24:
(dayStart + 7 * week + dayInFirstWeek + 1) * 24 - 1,
'ERCOT Load, MW'].values.flatten())
axs[dayInFirstWeek].set_ylim(bottom=20000, top=60000)
axs[dayInFirstWeek].set_title("Day " + str(dayInFirstWeek + 1))
fig.tight_layout()
plt.show()
# -
# **Question (5 points).** Can you find any discernible change in the load profiles of different days of the week?
#
# **Your answer.** The demand in first two days are generally lower. And in those two days between the weeks, the demands are more identical.
# **Question (15 points).** Redo the above exercise for the month of August. Make 'Day 1' correspond to August 15th. What do you observe differently? Do your observations agree with Q1?
#
# **Your answer (comments here, code below).** I noticed the overall demand is much larger than what is in January and Feburary, I need to change the y lim because the peak demand is bigger than 60000. This observation agrees with Q1 in that the demand in summer is much larger.
# +
print(datetime.date(year, 8, 15))
dayStart = 227
numberOfWeeks = 4
DayOfWeek = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
print("The first day in the first plot is Aug 15th, " + str(year) + ".")
print("Day 1", "was a", DayOfWeek[datetime.date(year, 8, 15).weekday()] + ".")
fig, axs = plt.subplots(7, 1, sharex=True, figsize=(5,10))
axs = axs.ravel()
for dayInFirstWeek in range(7):
for week in range(numberOfWeeks):
axs[dayInFirstWeek].plot(range(24), dfDemand.loc[(dayStart + 7 * week + dayInFirstWeek) * 24:
(dayStart + 7 * week + dayInFirstWeek + 1) * 24 - 1,
'ERCOT Load, MW'].values.flatten())
axs[dayInFirstWeek].set_ylim(bottom=30000, top=70000)
axs[dayInFirstWeek].set_title("Day " + str(dayInFirstWeek + 1))
fig.tight_layout()
plt.show()
# -
# ## Define the demand prediction module.
#
# Use past demand profiles to predict demands a day in advance. We draw two conclusions from the above analysis:
# 1. Demand profiles have seasonal effects. Therefore, data from the past few days will help in predicting the demands tomorrow.
# 2. Demand profiles have weekly dependencies. Therefore, data from the same days but a week or two before can be useful in load prediction.
#
# How much past data you want to train over depends on two considerations:
# 1. Which data in the past is useful in prediction?
# 2. How complex you want your training process to be? The more features of past data you want to train on, the more complex your neural network should be, and it will require more time to train it.
#
# To strike a balance, use the demand profile from $d-7, d-2, d-1$ to predict the load profile of day $d$.
# +
daysToTrainOn = [-7, -2, -1]
rangeOfDays = range(-np.min(daysToTrainOn), numberOfDays)
X = [np.concatenate([dfDemand.loc[(day + h) * 24: (day + h + 1) * 24 -1, 'ERCOT Load, MW'].values.flatten()
for h in daysToTrainOn]) for day in rangeOfDays]
Y = [dfDemand.loc[day * 24: (day + 1) * 24 - 1, 'ERCOT Load, MW'].values.flatten() for day in rangeOfDays]
# -
# When you perform regression, it is often desirable to scale the inputs so that it has zero mean and unit variance. Other types of scaling are possible. Here, we cheat a little and scale both the training and test data together. Ideally, they should be scaled separately.
#
# Split the data into two sets: training set and testing set. Train the neural network on the training set, and test how well it performs on the testing set. You should typically never sample from the training set to test your algorithms. The learnt model for prediction should work well on data that the algorithm has never encountered before.
#
# The function 'train_test_split' helps you to split the data into two parts, where 'test_size'
# indicates the fraction of the data you want to test on.
# +
X = preprocessing.StandardScaler().fit_transform(X)
trainX, testX, trainY, testY = train_test_split(X, Y, test_size=0.2)
print("Scaled and split the data into two parts:")
nTrain = np.shape(trainX)[0]
nTest = np.shape(testX)[0]
print("Neural network will train on data from %d days, and test on %d days." % (nTrain, nTest))
# -
# ### Design the neural network (NN) for demand prediction with only one hidden layer.
#
# Recall that TensorFlow defines a computation graph where the weights and biases associated with the NN are variables. The goal is to optimize the weights and biases of the NN to minimize prediction error using data.
#
#
# To define the computation graph, create the inputs and outputs as 'placeholders'.
# The algorithm only expects them to be specified at the time of computation. The
# first element of the shape attribute for both inputs and outputs are 'None'. This
# means that they are left unspecified, and will be provided at runtime. It will help
# in batch training for prediction, where the size of the batch will determine
# this value. Batch training is useful because training the NN with one data point at a
# time can be time consuming.
#
# In this lab, we begin with a 'relu' activation. We additionally implement 'dropouts' that basically
# prevents certain paramters from updating in each round. This is known to prevent overfitting. The number'0.995' in the description below updates 99.5% of all weights, leaving out 0.5%.
#
# Design the optimizer and the loss. For reporting the accuracy of prediction, we choose in this lab the idea of mean absolute error (MAE). For a data set, if the true values are scalars $y_1, \ldots, y_m$ and the predictions are $\hat{y}_1, \ldots, \hat{y}_m$, then its MAE is given by
# $$ MAE = \frac{1}{m}\sum|y_i - \hat{y}_i|.$$
# If $y$ and $\hat{y}$ are multidimensional, it computes the average across each coordinate of $y$ and $\hat{y}$.
#
# **Question (5 points). Insert a line of code for the output of layer 1 below (use the relu function)**
# +
nHidden = 150
# Store the dimension of each row of 'X' in 'nDimX' and that of 'Y' in 'nDimY' .
nDimX = np.shape(trainX)[1]
nDimY = np.shape(trainY)[1]
# Define the inputs and the target outputs for the NN.
inputNN = tf.placeholder(dtype=tf.float32, shape=[None, nDimX])
targetOutputNN = tf.placeholder(dtype=tf.float32, shape=[None, nDimY])
# Define the weights and biases of the first layer.
W1 = tf.Variable(tf.truncated_normal(shape=[nDimX, nHidden]))
b1 = tf.Variable(tf.zeros(nHidden))
# Define the output of layer 1.
## use the function 'tf.nn.relu' to define 'OutputLayer1'.
# ....
outputLayer1 = tf.nn.relu(tf.matmul(inputNN, W1) + b1)
outputLayer1 = tf.nn.dropout(outputLayer1, 0.995)
# Define the weights and biases of the second layer.
W2 = tf.Variable(tf.truncated_normal(shape=[nHidden, nDimY]))
b2 = tf.Variable(tf.zeros(nDimY))
# Define the output of layer 2.
outputNN = tf.nn.dropout((tf.matmul(outputLayer1, W2) + b2), 0.995)
# Define the loss function and the optimizer.
loss = tf.losses.mean_squared_error(labels=targetOutputNN, predictions=outputNN)
optimizer = tf.train.AdagradOptimizer(learning_rate=0.25).minimize(loss)
# Compute the MAE metric to judge accuracy of prediction.
_, maeY = tf.metrics.mean_absolute_error(labels=targetOutputNN, predictions=outputNN)
# -
#
# ### Train the neural network.
#
# Create the training module for the NN. Feed the training data in batches of size 'batchSize'
# and ask Tensorflow to run the function 'optimizer'. The number of batches, denoted by 'nBatches'
# is then given by the size of your training dataset divided by 'batchSize. Usually, going through
# the training data once does not train your NN. You train over the same data multiple
# times. More precisely, train it 'nEpochs' times. It is similar to the idea that you never learn
# a material by reading through it once!
# +
batchSize = 50
nBatches = int(nTrain/batchSize)
nEpochs = 7000
# Define a session.
sess = tf.Session()
with sess.as_default():
# Initialize the computation graph.
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
print("Started the training module.")
for epoch in range(nEpochs):
lossEpoch = 0
# In each epoch, use 'optimizer' to reduce the 'loss' over batches of data.
for n in range(nBatches):
# Define the batch to train on.
batchX = trainX[n * batchSize: (n + 1) * batchSize]
batchY = trainY[n * batchSize: (n + 1) * batchSize]
# Run the optimizer, and specify the placeholders with the inputs and
# target outputs from the batch.
_, lossBatch = sess.run([optimizer, loss], feed_dict={inputNN: batchX, targetOutputNN: batchY})
# Keep track of the total loss over an entire epoch.
lossEpoch += lossBatch
if (epoch + 1) % 200 == 0:
# Output the loss over an epoch, every few epochs or so.
print("Epoch: %d - Average loss in last epoch = %1.1f" % (epoch + 1, lossEpoch/nBatches))
print("Training process completed.")
# -
# ### Test the accuracy of prediction via NN.
#
# Here, you report the mean absolute error of your predictions over the 'testX' dataset. Finally, plot the actual demand profile versus the predicted demand profile for a few days from the test data.
predictedY, maeOfPrediction = sess.run([outputNN, maeY], feed_dict={inputNN: testX, targetOutputNN: testY})
print("Mean absolute error of forecast = ", maeOfPrediction)
# **Question (5 points).** Comment whether your MAE is high or low.
#
# **Hint.** Compare the mean absolute error to the maximum demands.
#
# **Your answer.** My MAE is low, only 2414 comparing to the maximum demands of more than 60000.
# ### Let us visualize the results.
#
# Plot the predicted load and compare against the actual load from the test data.
assert(nTest >= 16)
days = random.sample(range(nTest), 16)
fig, axs = plt.subplots(4, 4, sharex=True, sharey=True, figsize=(10,10))
axs = axs.ravel()
for dd, day in enumerate(days):
testYDay = testY[day]
predictedYDay = predictedY[day]
l1 = axs[dd].plot(range(1, 25), testYDay, label='Measured')
l2 = axs[dd].plot(range(1, 25), predictedYDay, label='Predicted')
axs[dd].set_ylim(bottom=0, top=75000)
axs[dd].legend()
fig.text(0.5, 0.07, 'Time of day (in hour)', ha='center')
fig.text(0.04, 0.5, 'Demand in Texas (in MW)', va='center', rotation='vertical')
plt.show()
# **Question (20 points).** Explore how the number of epochs affects the accuracy and speed of training. Start with 10 epochs, and increase it to 100, 1000, 5000, 10000, and maybe more (do not exceed 20000 unless you have a powerful computer, you are only required to do up to 10000 for this lab). Make comments based on your observations. As an engineer, what is your favorite number of epochs, and why?
#
# **Your answer.** As the number of epochs become larger, at first the predicted Demand is more identical to the measured ones, However, when the number of epochs comes bigger to for example 10000, the MAE slightly increases, and the predicted value have some great errors on the plot. The speed becomes slower as I increase number of epochs. My favorite number of epochs is 7000 becaue the MAE at this case is only 2117 and the plot looks fine.
#
# **Question (20 points).** Fix the number of echos to your favorite one, and then explore how the number of neurons affects the accuracy and speed of training. Start with 6 , and increase it to 12, 24, 48, 100, and more. Make comments based on your observations. As an engineer, what is your favorite number of neurons, and why?
#
# **Your answer.** First when the number of neuron increases, the accuracy becomes higher. However, the accuracy goes down after certain threshold. And the speed is always becoming slower as the number of neurons increases. My favorite number of neurons is 150 because it gives the smallest MAE.
#
# **Question (30 points).** Fix the number of epochs and neurons to your favorite ones. Then, add another layer to the network. Discuss what your observe in terms of speed and accuracy. If the training becomes too slow, you may alter the number of epochs/neurons.
#
# **Your answer (comments here, code below)**. I observed that the speed becomes slower. My MAE becomes drops by 20 which means the accuracy raises.
#
#
#
# **Your code should show the results for the 2 layers case. Go back to the codes above for the 1 layer case and run it again for the same number of epochs/neurons**
# +
nHidden = 150
# Store the dimension of each row of 'X' in 'nDimX' and that of 'Y' in 'nDimY' .
nDimX = np.shape(trainX)[1]
nDimY = np.shape(trainY)[1]
# Define the inputs and the target outputs for the NN.
inputNN = tf.placeholder(dtype=tf.float32, shape=[None, nDimX])
targetOutputNN = tf.placeholder(dtype=tf.float32, shape=[None, nDimY])
# Define the weights and biases of the first layer.
W1 = tf.Variable(tf.truncated_normal(shape=[nDimX, nHidden]))
b1 = tf.Variable(tf.zeros(nHidden))
# Define the output of layer 1.
## use the function 'tf.nn.relu' to define 'OutputLayer1'.
# ....
outputLayer1 = tf.nn.relu(tf.matmul(inputNN, W1) + b1)
outputLayer1 = tf.nn.dropout(outputLayer1, 0.995)
print(outputLayer1.shape)
# Define the weights and biases of the second layer.
W2 = tf.Variable(tf.truncated_normal(shape=[nHidden, nHidden]))
b2 = tf.Variable(tf.zeros(nHidden))
# Define the output of layer 2.
outputLayer2 = tf.nn.relu(tf.matmul(outputLayer1, W2) + b2)
outputLayer2 = tf.nn.dropout(outputLayer2, 0.995)
print(outputLayer2.shape)
W3 = tf.Variable(tf.truncated_normal(shape=[nHidden, nDimY]))
b3 = tf.Variable(tf.zeros(nDimY))
# Define the output of layer 3.
outputNN = tf.nn.dropout((tf.matmul(outputLayer2, W3) + b3), 0.995)
print(outputNN.shape)
# Define the loss function and the optimizer.
loss = tf.losses.mean_squared_error(labels=targetOutputNN, predictions=outputNN)
optimizer = tf.train.AdagradOptimizer(learning_rate=0.25).minimize(loss)
# Compute the MAE metric to judge accuracy of prediction.
_, maeY = tf.metrics.mean_absolute_error(labels=targetOutputNN, predictions=outputNN)
# +
batchSize = 50
nBatches = int(nTrain/batchSize)
nEpochs = 7000
# Define a session.
sess = tf.Session()
with sess.as_default():
# Initialize the computation graph.
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
print("Started the training module.")
for epoch in range(nEpochs):
lossEpoch = 0
# In each epoch, use 'optimizer' to reduce the 'loss' over batches of data.
for n in range(nBatches):
# Define the batch to train on.
batchX = trainX[n * batchSize: (n + 1) * batchSize]
batchY = trainY[n * batchSize: (n + 1) * batchSize]
# Run the optimizer, and specify the placeholders with the inputs and
# target outputs from the batch.
_, lossBatch = sess.run([optimizer, loss], feed_dict={inputNN: batchX, targetOutputNN: batchY})
# Keep track of the total loss over an entire epoch.
lossEpoch += lossBatch
if (epoch + 1) % 200 == 0:
# Output the loss over an epoch, every few epochs or so.
print("Epoch: %d - Average loss in last epoch = %1.1f" % (epoch + 1, lossEpoch/nBatches))
print("Training process completed.")
# -
predictedY, maeOfPrediction = sess.run([outputNN, maeY], feed_dict={inputNN: testX, targetOutputNN: testY})
print("Mean absolute error of forecast = ", maeOfPrediction)
# Plot the predicted load and compare against the actual load from the test data.
assert(nTest >= 16)
days = random.sample(range(nTest), 16)
fig, axs = plt.subplots(4, 4, sharex=True, sharey=True, figsize=(10,10))
axs = axs.ravel()
for dd, day in enumerate(days):
testYDay = testY[day]
predictedYDay = predictedY[day]
l1 = axs[dd].plot(range(1, 25), testYDay, label='Measured')
l2 = axs[dd].plot(range(1, 25), predictedYDay, label='Predicted')
axs[dd].set_ylim(bottom=0, top=75000)
axs[dd].legend()
fig.text(0.5, 0.07, 'Time of day (in hour)', ha='center')
fig.text(0.04, 0.5, 'Demand in Texas (in MW)', va='center', rotation='vertical')
plt.show()
# ### The effect of wind energy (bonus).
#Let's check the raw data
dfDemand = pd.read_csv("ERCOT_Hourly_Wind_Output_" + str(year) + ".csv")
dfDemand[:]
# Note that in addition to the load data, we have some wind data!
#
# **Question (20 points).** Subtract the wind data from the load, and redo the above experiment and observe how does wind energy affect the forecasting process. How does the accuracy change? Why? Write down your MAE before and after considering wind energy.
#
# The accuracy drops. The MAE before was 2414, now it is 2589. The MAE did not raise that much because the wind output is mainly only 2 percent of the total load.
#
# **Your answer (comments here, code below).**
# +
daysToTrainOn = [-7, -2, -1]
rangeOfDays = range(-np.min(daysToTrainOn), numberOfDays)
X = [np.concatenate([(dfDemand.loc[(day + h) * 24: (day + h + 1) * 24 -1, 'ERCOT Load, MW']-dfDemand.loc[(day + h) * 24: (day + h + 1) * 24 -1, 'Total Wind Output, MW']).values.flatten()
for h in daysToTrainOn]) for day in rangeOfDays]
Y = [(dfDemand.loc[day * 24: (day + 1) * 24 - 1, 'ERCOT Load, MW']-dfDemand.loc[day * 24: (day + 1) * 24 - 1, 'Total Wind Output, MW']).values.flatten() for day in rangeOfDays]
# +
X = preprocessing.StandardScaler().fit_transform(X)
trainX, testX, trainY, testY = train_test_split(X, Y, test_size=0.2)
print("Scaled and split the data into two parts:")
nTrain = np.shape(trainX)[0]
nTest = np.shape(testX)[0]
print("Neural network will train on data from %d days, and test on %d days." % (nTrain, nTest))
# +
nHidden = 150
# Store the dimension of each row of 'X' in 'nDimX' and that of 'Y' in 'nDimY' .
nDimX = np.shape(trainX)[1]
nDimY = np.shape(trainY)[1]
# Define the inputs and the target outputs for the NN.
inputNN = tf.placeholder(dtype=tf.float32, shape=[None, nDimX])
targetOutputNN = tf.placeholder(dtype=tf.float32, shape=[None, nDimY])
# Define the weights and biases of the first layer.
W1 = tf.Variable(tf.truncated_normal(shape=[nDimX, nHidden]))
b1 = tf.Variable(tf.zeros(nHidden))
# Define the output of layer 1.
## use the function 'tf.nn.relu' to define 'OutputLayer1'.
# ....
outputLayer1 = tf.nn.relu(tf.matmul(inputNN, W1) + b1)
outputLayer1 = tf.nn.dropout(outputLayer1, 0.995)
# Define the weights and biases of the second layer.
W2 = tf.Variable(tf.truncated_normal(shape=[nHidden, nDimY]))
b2 = tf.Variable(tf.zeros(nDimY))
# Define the output of layer 2.
outputNN = tf.nn.dropout((tf.matmul(outputLayer1, W2) + b2), 0.995)
# Define the loss function and the optimizer.
loss = tf.losses.mean_squared_error(labels=targetOutputNN, predictions=outputNN)
optimizer = tf.train.AdagradOptimizer(learning_rate=0.25).minimize(loss)
# Compute the MAE metric to judge accuracy of prediction.
_, maeY = tf.metrics.mean_absolute_error(labels=targetOutputNN, predictions=outputNN)
# +
batchSize = 50
nBatches = int(nTrain/batchSize)
nEpochs = 7000
# Define a session.
sess = tf.Session()
with sess.as_default():
# Initialize the computation graph.
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
print("Started the training module.")
for epoch in range(nEpochs):
lossEpoch = 0
# In each epoch, use 'optimizer' to reduce the 'loss' over batches of data.
for n in range(nBatches):
# Define the batch to train on.
batchX = trainX[n * batchSize: (n + 1) * batchSize]
batchY = trainY[n * batchSize: (n + 1) * batchSize]
# Run the optimizer, and specify the placeholders with the inputs and
# target outputs from the batch.
_, lossBatch = sess.run([optimizer, loss], feed_dict={inputNN: batchX, targetOutputNN: batchY})
# Keep track of the total loss over an entire epoch.
lossEpoch += lossBatch
if (epoch + 1) % 200 == 0:
# Output the loss over an epoch, every few epochs or so.
print("Epoch: %d - Average loss in last epoch = %1.1f" % (epoch + 1, lossEpoch/nBatches))
print("Training process completed.")
# -
predictedY, maeOfPrediction = sess.run([outputNN, maeY], feed_dict={inputNN: testX, targetOutputNN: testY})
print("Mean absolute error of forecast = ", maeOfPrediction)
# Plot the predicted load and compare against the actual load from the test data.
assert(nTest >= 16)
days = random.sample(range(nTest), 16)
fig, axs = plt.subplots(4, 4, sharex=True, sharey=True, figsize=(10,10))
axs = axs.ravel()
for dd, day in enumerate(days):
testYDay = testY[day]
predictedYDay = predictedY[day]
l1 = axs[dd].plot(range(1, 25), testYDay, label='Measured')
l2 = axs[dd].plot(range(1, 25), predictedYDay, label='Predicted')
axs[dd].set_ylim(bottom=0, top=75000)
axs[dd].legend()
fig.text(0.5, 0.07, 'Time of day (in hour)', ha='center')
fig.text(0.04, 0.5, 'Demand in Texas (in MW)', va='center', rotation='vertical')
plt.show()
| 24,645 |
/Practice on Sites/Hackerrank Practices/Python.py4e/Find a string.ipynb | cf5fa2d50e0d5e90080e822576e33402ba291db1 | [] | no_license | suryaanshah/My-Programming-Practices | https://github.com/suryaanshah/My-Programming-Practices | 0 | 0 | null | 2021-03-06T08:27:51 | 2021-02-26T03:31:44 | Python | Jupyter Notebook | false | false | .py | 3,328 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="rlQ4BfarSqsz"
# [Finding Sub string in a string.](https://www.hackerrank.com/challenges/find-a-string/problem)
# + [markdown] id="cwPciRCOTjTj"
# # Method 1(simple to understand, for loops)
# 1. Defining a variable count.
# 2. Repeating the indented text length of the string times.
# 3. If we found our sustring from i(the iterator) to the last letter in our string, we add 1 to the string.
# + id="j8e_AsMJSn1p" colab={"base_uri": "https://localhost:8080/"} outputId="51054e5e-be7f-4e78-ae2a-b83ce27b47cf"
def count_substring(string, sub_string):
count = 0
for i in range(len(string)):
if string[i:].startswith(sub_string):
count += 1
return(count)
if __name__ == '__main__':
string = input().strip()
sub_string = input().strip()
count = count_substring(string, sub_string)
print(count)
# + [markdown] id="BZTJmP0_VY7s"
# # Method 2(List comprehensions|Sophisticated|One-Liner|Complex)
# 1. Computing the count by substracting the length of smaller string from bigger string.
# 2. For each slide, we compare that part of bigger string with our smaller string and append 1 in a list if match found.
# 3. Adding the list elements and getting the total number.
# ## Note: In method 1, to count the number of times, we created a variale and kept adding 1 to it. But here, we made a list and kept appending 1 to it and them summed up all those 1s.
# + colab={"base_uri": "https://localhost:8080/"} id="5lyAaDExWqgK" outputId="89d51d36-a2f6-4bfb-b117-0672555a522e"
string, substring = (input().strip(), input().strip())
print(sum([ 1 for i in range(len(string)-len(substring)+1) if string[i:i+len(substring)] == substring]))
| 1,941 |
/AttackClassifier_train_valid_set.ipynb | b0c055a6cf9bfcc651445d6a790c2cc71a04eaa1 | [] | no_license | sweagle07/attack_classification | https://github.com/sweagle07/attack_classification | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 139,916 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import time
import queue
from pysnmp.hlapi.asyncore import *
t = time.time()
myq = queue.Queue()
def getOID(strword):
s1 = "."
strword = strword.replace("SNMPv2-SMI::mib-2", "1.3.6.1.2.1")
arr = strword.split(".")
del arr[-1]
return s1.join(arr)
#回调函数。在有数据返回时触发
def cbFun(snmpEngine, sendRequestHandle, errorIndication, errorStatus, errorIndex, varBinds, cbCtx):
myq.put((time.time()-t, varBinds, errorIndication, errorStatus, errorIndex))
hosts = ["140.134.207.74","140.134.207.73"]
oids = ['1.3.6.1.2.1.1','1.3.6.1.2.1.2.2','1.3.6.1.2.1.25.1','1.3.6.1.2.1.25.2','1.3.6.1.2.1.25.4','1.3.6.1.2.1.25.4.2.1.1','1.3.6.1.2.1.25.4.2.1.2','1.3.6.1.2.1.25.5','1.3.6.1.2.1.25.6']
snmpEngine = SnmpEngine()
#添加任务
for oid in oids:
for h in hosts:
print("ip:", h)
nextCmd(snmpEngine,
CommunityData('rmc2772'),
UdpTransportTarget((h, 161), timeout=3, retries=0,),
ContextData(),
ObjectType(ObjectIdentity(oid)),
cbFun=cbFun)
time1 = time.time() - t
#执行异步获取snmp
snmpEngine.transportDispatcher.runDispatcher()
#打印结果
while True:
try:
info = myq.get(block=False)
# print(info)
if info[2]: # SNMP agent errors
print("errorIndication:", info[2])
else:
if info[3]:
print("here")
print('%s at %s' % (info[3].prettyPrint(), info[1][int(info[4])-1] if info[4] else '?'))
else:
for row in info[1]:
#print("row:", row)
for oid, val in row:
print("oid:", getOID(oid.prettyPrint()), "val:", val.prettyPrint())
except queue.Empty:
print(time1)
print(time.time() - t)
break
# -
creations',
'num_shells', 'num_access_files', 'num_outbound_cmds', 'is_host_login',
'is_guest_login', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate',
'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate',
'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate',
'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate',
'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate',
'dst_host_srv_rerror_rate','category'])
# +
# saved off copies of the data with headers added
#train_df.to_csv('train_df.csv')
#train_df.to_pickle('train_df.pkl')
# -
# ### Get and load the data into a DF
train_df_orig.shape
# +
#### Save off a clean copy of imported data frame
# -
train_df = train_df_orig.copy()
train_df.head()
train_df.describe()
train_df.info(verbose=True,null_counts=True)
# ### Create overarching groups of attacks and prep data for modelling
DOS = ['back.','land.','neptune.','pod.','smurf.','teardrop.']
R2L = ['ftp_write.','guess_passwd.','imap.','multihop.','phf.','spy.','warezclient.','warezmaster.']
U2R = ['buffer_overflow.', 'loadmodule.','perl.','rootkit.']
probing = ['ipsweep.','nmap.','portsweep.','satan.']
normal = 'normal.'
def get_group(x):
if x in R2L:
return 4
elif x in U2R:
return 3
elif x in DOS:
return 2
elif x in probing:
return 1
elif x == normal:
return 0
else: return 10
# #### Add column with mapping to various Attack Types
# #### Will build the model based on these Attack Types
train_df['attack_type'] = train_df['category'].apply(get_group)
train_df.head()
# #### Found that "num_outbound_cmds" is always 0
# #### Since it's constant, won't help with prediction,
train_df.num_outbound_cmds.value_counts()
# #### Remove initial columns I won't use in the model
train_df.drop(columns=['category','num_outbound_cmds'], inplace=True)
# #### Encode catagorical features
cat_feats = ['protocol_type','flag','service' ]
final_data_df = pd.get_dummies(train_df,columns=cat_feats,drop_first=True)
final_data_df.head()
final_data_df.shape
# ### Create train and validation sets for iniital training/test
X = final_data_df.drop(['attack_type'],axis=1)
y = final_data_df['attack_type']
# +
# separate train and validation sets
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=10)
# -
X_train.shape, X_valid.shape
y_train.shape, y_valid.shape
# ### Check for correlated features
# #### I didn't end up using this feature selection technique as it did not significantly improve the performance of the model.
# #### Leaving the code in for reference, if I want to try it again at a later stage
# +
# # find and remove correlated features
# def correlation(dataset, threshold):
# col_corr = set() # Set of all the names of correlated columns
# corr_matrix = dataset.corr()
# for i in range(len(corr_matrix.columns)):
# for j in range(i):
# if abs(corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value
# colname = corr_matrix.columns[i] # getting the name of column
# col_corr.add(colname)
# return col_corr
# corr_features = correlation(X_train, 0.90)
# print('correlated features: ', len(set(corr_features)) )
# +
# corr_features
# +
# corrmat = X_train.corr()
# # we can make a heatmap with the package seaborn
# # and customise the colours of searborn's heatmap
# cmap = sns.diverging_palette(220, 20, as_cmap=True)
# # some more parameters for the figure
# fig, ax = plt.subplots()
# fig.set_size_inches(11,11)
# # and now plot the correlation matrix
# sns.heatmap(corrmat, cmap=cmap)
# +
# X_train.drop(labels=corr_features, axis=1, inplace=True)
# X_valid.drop(labels=corr_features, axis=1, inplace=True)
# -
# ### Select Feature by importance using random forest algorithm
# +
sel_ = SelectFromModel(RandomForestClassifier(n_estimators=50, random_state=10))
tic = time.perf_counter()
sel_.fit(X_train, y_train)
toc = time.perf_counter()
print(f"Time to fit feature selection: {toc - tic:0.4f} seconds")
# remove features with zero coefficient from dataset
# and parse again as dataframe
X_train_rf = pd.DataFrame(sel_.transform(X_train))
X_test_rf = pd.DataFrame(sel_.transform(X_valid))
# add the columns name
X_train_rf.columns = X_train.columns[(sel_.get_support())]
X_test_rf.columns = X_train.columns[(sel_.get_support())]
# -
X_train_rf.head()
X_test_rf.head()
# #### List of features recommended below; there are 21
X_test_rf.columns
len(X_train.columns[(sel_.get_support())])
tic = time.perf_counter()
rfc = RandomForestClassifier(n_estimators=200, random_state=10, max_depth=4)
rfc.fit(X_train_rf, y_train)
toc = time.perf_counter()
print(f"Time to train classifier {toc - tic:0.4f} seconds")
# ## Save model to disk for re-use
w_file = open(model_file_name,'wb')
pickle.dump(rfc,w_file)
w_file.close()
y_pred_test = rfc.predict(X_test_rf)
X_test_rf.shape
#y_pred_test
X_test_rf.head()
# ## Evaluating the model
# #### The model performed quite well with an F1 score of 99% and 100% for normal and DOS, respecitively
# #### it was pretty good at classifying probing attacks with 87% F1 score.
# #### The model did not perform well at all for the U2R and R2L attacks.
# #### More data to balance the tree, or perhaps create separate models
# normal= 0
# probing = 1
# DOS = 2
# U2R = 3
# R2L = 4
print(confusion_matrix(y_valid,y_pred_test))
print(classification_report(y_valid, y_pred_test))
# ## Test exported model to confirm saved appropiately
# #### Do this by comparing confusion matrix and report numbers
i_file = open(model_file_name,'rb')
loaded_model = pickle.load(i_file)
i_file.close()
loaded_model_pred_test = loaded_model.predict(X_test_rf)
print(confusion_matrix(y_valid,loaded_model_pred_test))
print(classification_report(y_valid, loaded_model_pred_test))
| 8,361 |
/python3/1.5.ipynb | 20e40e2d796ba396a5ec87dd90f5f5320cee4e96 | [] | no_license | rygao/cryptopals | https://github.com/rygao/cryptopals | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 3,392 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Implement repeating-key XOR
# Here is the opening stanza of an important work of the English language:
# ```
# Burning 'em, if you ain't quick and nimble
# I go crazy when I hear a cymbal
# ```
# Encrypt it, under the key "ICE", using repeating-key XOR.
#
# In repeating-key XOR, you'll sequentially apply each byte of the key; the first byte of plaintext will be XOR'd against I, the next C, the next E, then I again for the 4th byte, and so on.
#
# It should come out to:
# ```
# 0b3637272a2b2e63622c2e69692a23693a2a3c6324202d623d63343c2a26226324272765272
# a282b2f20430a652e2c652a3124333a653e2b2027630c692b20283165286326302e27282f
# ```
# Encrypt a bunch of stuff using your repeating-key XOR function. Encrypt your mail. Encrypt your password file. Your .sig file. Get a feel for it. I promise, we aren't wasting your time with this.
from cryptopals import *
# +
def repeating_key_xor(bs, key):
'''Encrypts a byte array using repeating-key (byte array) XOR'''
key_multiplier = len(bs) // len(key) + 1
repeated_key = (key * key_multiplier)[:len(bs)]
return fixed_xor(bs, repeated_key)
def repeating_strkey_xor(bs, strkey):
'''Encrypts a byte array using repeating-key (string) XOR'''
return repeating_key_xor(bs, strkey.encode())
# -
pt = "Burning 'em, if you ain't quick and nimble\nI go crazy when I hear a cymbal"
ct = '0b3637272a2b2e63622c2e69692a23693a2a3c6324202d623d63343c2a26226324272765272a282b2f20430a652e2c652a3124333a653e2b2027630c692b20283165286326302e27282f'
print(bytes_to_hex(repeating_strkey_xor(pt.encode(), 'ICE')))
print(ct)
print(bytes_to_hex(repeating_strkey_xor(pt.encode(), 'ICE')) == ct)
| 1,924 |
/08_convolutional_networks/homework_cnn_old.ipynb | 42f3260bb933aa98169aaca0a676f5f849a800cb | [] | no_license | nalysann/dlschool | https://github.com/nalysann/dlschool | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 44,780 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] colab_type="text" id="view-in-github"
# <a href="https://colab.research.google.com/github/pabloinsente/CovNet_Human_Drawings/blob/master/code/baseline_binary_classification_methods_MDA.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] colab_type="text" id="IkqvnxR44en3"
# #Baseline for binary classification of child/adult from drawings with MDA vectors
# + colab={"base_uri": "https://localhost:8080/", "height": 193} colab_type="code" id="SR14WE_QlF76" outputId="4e7a9d4a-728c-4377-ccc3-75a8f7376a7d"
# !pip install rarfile
# + [markdown] colab_type="text" id="UzsmE5Ez4-1n"
# ##Data Preparation
# + colab={} colab_type="code" id="NKxEuVPy4ykq"
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
import rarfile, csv
from numpy import genfromtxt
import pandas as pd
from collections import Counter
import matplotlib.pyplot as plt
# + colab={} colab_type="code" id="VfHNxgoP7kf0"
# Clone the data into Colab
# ! git clone https://github.com/pabloinsente/CovNet_Human_Drawings
# + colab={"base_uri": "https://localhost:8080/", "height": 139} colab_type="code" id="TvCowCOIlRkg" outputId="3c85f16e-2054-4860-c376-90ce5516906a"
# Read csv files from compressed rar file and convert into a dataframe
rar_path = rarfile.RarFile("CovNet_Human_Drawings/data/merged_dataframes_prediction/x_drawings_features_max_pool_5.rar")
csv_file_name = "x_drawings_features_max_pool_5.csv"
rar_file = rarfile.RarFile.open(rar_path, csv_file_name)
x = pd.read_csv(rar_file, sep=",", header=None)
print(x.shape)
print(x.iloc[0:5,0:5])
# + colab={"base_uri": "https://localhost:8080/", "height": 52} colab_type="code" id="1ImOc2dx4XE3" outputId="daf27582-d4c5-4321-ae76-d21621781e5b"
# Read labels vector
y_path = 'CovNet_Human_Drawings/data/merged_dataframes_prediction/y_age_adult_labels.csv'
y = genfromtxt(y_path, delimiter=',')
print(y.shape)
print(y[0:5])
# + [markdown] colab_type="text" id="kq5L5JR-9N4-"
# ##Multidimensional scaling
# + colab={} colab_type="code" id="uVE9ztLQ9KJx"
from sklearn.manifold import MDS
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="TyExc3Qi9P9L" outputId="1f584cdf-1a72-4483-ff6c-cf72460ff8f3"
from sklearn.manifold import MDS
embedding = MDS(n_components=2)
x_transformed = embedding.fit_transform(x)
x = x_transformed
x.shape
# + colab={"base_uri": "https://localhost:8080/", "height": 87} colab_type="code" id="lt-ai_0r-oR5" outputId="50b1e5fe-d16c-4135-b69f-2de59a6dea1e"
# Split data into train and test sets
# Since we have an small sample, we will do a 70/30 split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state=44, stratify=y)
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
# + [markdown] colab_type="text" id="Vucc_DCC_cXb"
# ##Resampling Imbalanced Data
# Since our classes are imbalanced, we will use oversampling of the "adult" class **on the training set** to help training on that class. *imblearn* implement oversampling for us
#
# **See documentantion at** https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.RandomOverSampler.html
# + colab={"base_uri": "https://localhost:8080/", "height": 69} colab_type="code" id="oG3pLrJa_c3w" outputId="752d6b0e-e994-44fb-ed27-dd99b083ea86"
#ros = RandomOverSampler(random_state=42)
ros = SMOTE(random_state=42)
x_train_res, y_train_res = ros.fit_resample(x_train, y_train)
print(x_train_res.shape) # (258, 200)
print(y_train_res.shape) # (258,)
print('Resampled dataset shape %s' % Counter(y_train_res))
# Now we have 50/50 balanced classes
# + colab={"base_uri": "https://localhost:8080/", "height": 299} colab_type="code" id="iadb3lSSAB5w" outputId="e0152dee-9ad2-4d72-8253-500208495678"
x_train = x_train_res
y_train = y_train_res
print('Resampled dataset shape %s' % Counter(y_train))
plt.hist(y_train, bins='auto') # arguments are passed to np.histogram
plt.title("Histogram train dataset classes")
plt.show()
# + colab={"base_uri": "https://localhost:8080/", "height": 299} colab_type="code" id="1IYy8P6KADpB" outputId="9cda18a4-466d-40c8-cff1-f205d4408afb"
print('Test dataset shape %s' % Counter(y_test))
plt.hist(y_test, bins='auto') # arguments are passed to np.histogram
plt.title("Histogram test dataset classes")
plt.show()
# + [markdown] colab_type="text" id="ZISSB87_CIpk"
# ## Logistic Regression
# + colab={} colab_type="code" id="jKy9gzK5CHgp"
from sklearn.linear_model import LogisticRegression
# + [markdown] colab_type="text" id="w5LJ8GSzCdAM"
# ###Train logistic regression
# + colab={"base_uri": "https://localhost:8080/", "height": 72} colab_type="code" id="ZaAiRnI2CTfd" outputId="13741d59-f49d-4bff-fa97-77e8667ae89f"
#Train classifier
log_classifier = LogisticRegression(random_state=0)
log_classifier.fit(x_train, y_train)
y_pred = log_classifier.predict(x_test)
# + [markdown] colab_type="text" id="WDcHvUIACZJ5"
# ###Test logistic regression
# + colab={"base_uri": "https://localhost:8080/", "height": 312} colab_type="code" id="gZheMsJHCT8D" outputId="ac1901ee-e21e-4d33-e9fb-5ebfd6452068"
# Accuracy
acc = accuracy_score(y_test, y_pred)
print("Accuracy", "\n", acc, "\n")
# Confusion matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix", "\n", cm, "\n")
# Classification report
target_names = ['Child', 'Adult']
cr = classification_report(y_test, y_pred, target_names=target_names)
print("Calssification report", "\n", cr)
# + [markdown] colab_type="text" id="7PurYKkGCuXK"
# ##Suport Vector Machine
# + colab={} colab_type="code" id="2vPQz84eCvfw"
from sklearn.svm import SVC
# + [markdown] colab_type="text" id="fVTvtM5dEfHP"
# ###Training SVM
# + colab={} colab_type="code" id="rWghQTHECyQr"
svm = SVC(gamma='auto')
svm.fit(x_train, y_train)
y_pred = svm.predict(x_test)
# + [markdown] colab_type="text" id="DSwUg50xEhpU"
# ###Testing SVM
# + colab={"base_uri": "https://localhost:8080/", "height": 312} colab_type="code" id="rJ4k2RikC0Cj" outputId="1547c811-f9ed-4598-aadf-829992dfe585"
# Accuracy
acc = accuracy_score(y_test, y_pred)
print("Accuracy", "\n", acc, "\n")
# Confusion matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix", "\n", cm, "\n")
# Classification report
target_names = ['Child', 'Adult']
cr = classification_report(y_test, y_pred, target_names=target_names)
print("Calssification report", "\n", cr)
# + [markdown] colab_type="text" id="qAzWQqEMDBo4"
# ##Decision Tree
# + colab={} colab_type="code" id="nJlaghTyC3CP"
from sklearn import tree
# + [markdown] colab_type="text" id="4Zp6zihOEkNp"
# ###Training Decision Tree
# + colab={} colab_type="code" id="28D1jnNtDE2-"
d_tree = tree.DecisionTreeClassifier()
d_tree.fit(x_train, y_train)
y_pred = d_tree.predict(x_test)
# + [markdown] colab_type="text" id="givblE85EnI4"
# ###Testing Decision Tree
# + colab={"base_uri": "https://localhost:8080/", "height": 312} colab_type="code" id="m-ozyzjVDGss" outputId="37741498-055b-40c3-8e4b-015e3aa9cf91"
# Accuracy
acc = accuracy_score(y_test, y_pred)
print("Accuracy", "\n", acc, "\n")
# Confusion matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix", "\n", cm, "\n")
# Classification report
target_names = ['Child', 'Adult']
cr = classification_report(y_test, y_pred, target_names=target_names)
print("Calssification report", "\n", cr)
# + [markdown] colab_type="text" id="0Qe5iaLxDRk0"
# ##Gaussian Naive Bayes
# + colab={} colab_type="code" id="LSQUQeQ3DY6K"
from sklearn.naive_bayes import GaussianNB
# + [markdown] colab_type="text" id="Z_LcphfnEqSn"
# ###Training Naive Bayes
# + colab={} colab_type="code" id="NBqmF9G4DJ3e"
bayes = GaussianNB()
bayes.fit(x_train, y_train)
y_pred = bayes.predict(x_test)
# + [markdown] colab_type="text" id="MFWkaQ4EEtAr"
# ###Testing Naive Bayes
# + colab={"base_uri": "https://localhost:8080/", "height": 312} colab_type="code" id="mwMIZR5ODVJh" outputId="5fd6b7a2-025f-4cea-ae89-b40be5b0774e"
# Accuracy
acc = accuracy_score(y_test, y_pred)
print("Accuracy", "\n", acc, "\n")
# Confusion matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix", "\n", cm, "\n")
# Classification report
target_names = ['Child', 'Adult']
cr = classification_report(y_test, y_pred, target_names=target_names)
print("Calssification report", "\n", cr)
# + [markdown] colab_type="text" id="ycNH1eTHDlLt"
# ##Random Forest
# + colab={} colab_type="code" id="ineHNAqiDgF-"
from sklearn.ensemble import RandomForestClassifier
# + [markdown] colab_type="text" id="8xrxdERbEwDw"
# ###Training Random Forest
# + colab={} colab_type="code" id="yQGZoKO9DnWi"
forest = RandomForestClassifier(n_estimators=100)
forest.fit(x_train, y_train)
y_pred = forest.predict(x_test)
# + [markdown] colab_type="text" id="xRR861gKE8e3"
# ###Testing Random Forest
# + colab={"base_uri": "https://localhost:8080/", "height": 312} colab_type="code" id="RGgcTBsgDpB5" outputId="3b391d45-e70d-47f2-8a6a-d7b432d20f99"
# Accuracy
acc = accuracy_score(y_test, y_pred)
print("Accuracy", "\n", acc, "\n")
# Confusion matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix", "\n", cm, "\n")
# Classification report
target_names = ['Child', 'Adult']
cr = classification_report(y_test, y_pred, target_names=target_names)
print("Calssification report", "\n", cr)
# + [markdown] colab_type="text" id="XUQ6PtNODudA"
# ##Gradient Boosting Classifier
# + colab={} colab_type="code" id="ZW8juoFqDq8O"
from sklearn.ensemble import GradientBoostingClassifier
# + [markdown] colab_type="text" id="M_nOyh3jE_0L"
# ###Training Boosting
# + colab={} colab_type="code" id="0YGye5LyDwxO"
boosting = GradientBoostingClassifier(n_estimators=100, learning_rate = 0.1)
boosting.fit(x_train, y_train)
boosting = forest.predict(x_test)
# + [markdown] colab_type="text" id="bUAOJs3NFL3-"
# ###Testing Boosting
# + colab={"base_uri": "https://localhost:8080/", "height": 312} colab_type="code" id="EzoXGDhtD2NQ" outputId="d892edae-d0bb-48f0-a390-4ea86f2b7ec3"
# Accuracy
acc = accuracy_score(y_test, y_pred)
print("Accuracy", "\n", acc, "\n")
# Confusion matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix", "\n", cm, "\n")
# Classification report
target_names = ['Child', 'Adult']
cr = classification_report(y_test, y_pred, target_names=target_names)
print("Calssification report", "\n", cr)
=3,label="null distribution")
plt.plot(xn,stats.beta.pdf(xn,0.2,1)+1-0.3,color=newcol[0],lw=3,label="alternative distribution")
plt.xlim([0,1])
plt.ylim([0,4])
plt.title("")
plt.xlabel("")
plt.ylabel("")
plt.legend(loc="upper right",frameon=False)
plt.show()
plt.figure(figsize=(5,3))
plt.xlim([2,6])
plt.ylim([0,1])
plt.plot(xn,neuropower.nulprobdens(2,xn)*0.3,color=newcol[3],lw=3,label="null distribution")
plt.plot(xn,neuropower.altprobdens(3,1,2,xn)*0.7,color=newcol[1],lw=3, label="alternative distribution")
plt.plot(xn,neuropower.mixprobdens(3,1,0.7,2,xn),color=newcol[0],lw=3,label="total distribution")
plt.title("")
plt.xlabel("")
plt.ylabel("")
plt.legend(loc="upper right",frameon=False)
plt.show()
# +
y1 = []
ran = range(10,51)
for n in ran:
delta = 3/10**0.5
new = delta*n**0.5
y1.append(1-neuropower.altcumdens(new,1,2,4))
plt.figure(figsize=(5,3))
plt.plot(ran,y1,color=newcol[0],lw=3)
plt.xlim([10,np.max(ran)])
plt.ylim([0,1])
plt.title("")
plt.xlabel("")
plt.ylabel("")
plt.show()
# -
рую Вы сделали выше и замените сумму на произвдение.
Это очень просто сделать, если свертка реализована как два вложенных цикла
"""
kernel_y, kernel_x = kernel.shape[:2]
img_y, img_x = img.shape[:2]
<Ваш код здесь>
return result
# + colab={} colab_type="code" id="R6c718sVGnFb" outputId="31ee6815-91b4-4ca7-e00d-324c78806465"
# применим новую свертку и возьмем сумму
<Ваш код здесь>
# + [markdown] colab_type="text" id="zq1AfqrdGnFe"
# ## Свертка для извлечения локальной информации
# + [markdown] colab_type="text" id="z25YaUWaGnFf"
# **[Advanced]**
#
# Если еще раз посмотреть на определение свертки, то мы видим, что ее значение в точке является взвешенной суммой значений функции $f(x)$, но с одним интересным свойством, чтобы получить значение свертки в точке x, мы двигаем функцию $g(x)$, которая и задает веса, на х. (Чуть позже будет иллюстрация, которая поможет понять, что это значит).
# $$(f*g)(x) = \int \limits^{+\infty}_{-\infty} f(\tau)g(x - \tau) d\tau$$
#
#
# **[Not advanced]**
#
#
# Теперь вернемся к сверткам в нейронных сетях. На лекции было сказано, что сверточные слои намного лучше обрабатывают картинки, чем полносвязные. Причина в том, что сверточные слои эксплуатируют внешние знания о структуре данных:
#
# * Пиксели находящиеся рядом намного сильнее связаны между собой, чем дальние.
# * Мы можем сдвинуть объект на картинке и он останется собой.
#
# Эти предположения можно переформулировать в более сжатом виде: в данных важна именно локальная структура. Такие жесткие ограничения позволяют сверточным слоям использовать намного меньше весов, применяя один и тот же небольшой фильтр ко всем частям картинки. Это, в свою очередь, упрощает обучение нейронной сети.
# + [markdown] colab_type="text" id="h9HlHk-QGnFg"
# ## Свертка для нахождения похожих паттернов
# + [markdown] colab_type="text" id="WzCYjxzzGnFh"
# Давайте посмотрим на небольшую иллюстрацию того, как просчитывается свертка в одномерном случае
# + code_folding=[0, 7] colab={} colab_type="code" id="P0Lkm6TgGnFi"
def f(x):
"""
Просто красивая функция.
"""
return 1/(2 + x**2 * (0.1 + np.sin(x)**2))
def g(x):
"""
Эта функци - немного измененная плотность нормального распределения, потому что она тоже красивая.
"""
return np.exp(-x**2/2) / np.sqrt(2 * np.pi)
x = np.linspace(-10, 10, 100)
@interact(g_offset=FloatSlider(min=-10, max=10, step=0.5))
def plot_and_calc(g_offset):
plt.figure(figsize=(10, 7))
f_val = f(x)
g_val = g(g_offset - x)
mul_vals = f_val * g_val
plt.plot(x, f_val, label='f(x)')
plt.plot(x, g_val, label='g(x)')
plt.plot(x, mul_vals, label='f(x)*g(x)')
plt.gca().fill_between(x, 0, mul_vals)
plt.legend()
plt.text(-10.5, 0.55,
"Approximate conv value at {} = {:.2f}".format(g_offset, mul_vals.sum()))
# + [markdown] colab_type="text" id="-Hc5GnPbGnFl"
# Перемещая ползунок, Вы видите как перемещается функция $g(x)$, задающая веса для аггрегирования. Значение свертки в точке, которую Вы задаете g_offset, равно площади под кривой $f(x)\cdot g(x)$.
#
# Легко заметить, что чем лучше совпадают две функции, чем более они похожи, тем выше значение свертки. Максимум доостигается при нулевом сдвиге $g(x)$, когда совпадают два толстых пика. Но большие значения получаются и при совпадении малых пиков с $g(x)$.
#
# **Посмотрев на значения свертки мы можем догадаться, где на функции $f(x)$ находятся пики.**
# + [markdown] colab_type="text" id="P5uzZB1BGnFl"
# Использование свертки для нахождения каких-то особых частей в функции - одна из самых интересных трактовок с точки зрения Deep Learning. В случае картинок в качестве $f(x)$ выступает картинка, а в качестве $g(x)$ кернел, который мы перемещаем по картинке и ищем совпадения. Только кернелы не заданы заранее, а выучиваются самой нейронной сетью в зависимости от того, какие паттерны ей понадобятся.
#
# У хорошо обученных нейросетей на первом слое можно увижеть паттерны, которые они распознают:
# <img src="http://cs231n.github.io/assets/nn3/cnnweights.jpg" width=600>
# (Изображение взято из http://cs231n.github.io/neural-networks-3/#vis)
# + [markdown] colab_type="text" id="CRtdGvwIGnFm"
# # [Задание 2]
# + [markdown] colab_type="text" id="x8ETT-mXGnFn"
# <img src="https://i.imgflip.com/2yq5nl.jpg">
# + colab={} colab_type="code" id="oZkCIfc8GnFo"
# Пришло время поиграть в сыщиков
# Загрузите файл noisy_data.txt и с помощью свертки найдите в нем кресты(плюсики) высотой 5 и шириной 5.
# Такие кресты состоят из одинаковых положительных чисел и их можно легко найти, подобрав нужный паттерн.
# Все значения, кроме крестов, являются числами из нормального распределения со средним 0 и норм. отклонением 1
noise = np.loadtxt('./noisy_data.txt')
# Для начала зададим паттерн размера 5*5 для поиска.
# Учитывая описание данных вверху, попробуйте подобрать нужный паттерн.
pattern = <Ваш код здесь>
# Лучше всего использовать свою свертку, написанную ранее.
convolution_activation = <Ваш код здесь>
# + colab={} colab_type="code" id="ko_EXM-8GnFr"
# Выберем среди активаций три наибольших (потому что крестов именно три :)
# В нахождении индексов этих максимальных активаций Вам может помочь функция numpy.where и оператор сравнения
<Ваш код здесь>
# + [markdown] colab_type="text" id="qo1YlH-dGnFw"
# **!!!Чтобы получить ответ, Вы должны найти центры трех крестов и сложить все их координаты. Будьте внимательны, потому что координаты, которые вы получили выше могут не являются центрами крестов!!!**
# + [markdown] colab_type="text" id="7Ox73PZ3GnFx"
# ## Сверточный слой
# + [markdown] colab_type="text" id="CpnIcJNXGnFz"
# Надеюсь, что вы уже поняли, что такое свертка и как она считается. Теперь перейдем к применению сверток в нейросетях. Для начала мы сами напишем свой сверточный слой. Вы уже писали свертку, но только для двумерной картинки,
# пришло время понять, как это делать для батча картинок с несколькими фильтрами. **Так как теперь у нашей картинки есть несколько каналов, то и фильтры теперь имеют несколько каналов, по одному на каждый канал входной картинки, чтобы их все так же можно было накладывать друг на друга, поэлементно умножать и складывать.**
#
# Сейчас вам предстоит написать ConvLayer(in_channels, out_channels, kernel_size). Я думаю, что лучше всего это получится сделать опираясь на иллюстрацию, находящуюся на этой странице http://cs231n.github.io/convolutional-networks (чтобы перейти к ней нажмите Ctrl+F и введите Convolution Demo).
# + colab={} colab_type="code" id="JOnnPFj_GnF0" outputId="f877e9d1-2fdc-41b5-dd1d-22470de1d2dc"
class ConvLayer:
def __init__(self, in_channels, out_channels, kernel_size):
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
# Теперь инициализируем массив с кернелами
self.kernels =seed_random((out_channels, kernel_size, kernel_size, in_channels), 42)
# Чтобы разобраться, что здесь написано, вспомним, что происзодит в сверточном слое.
# 1) В сверточном слое применеяется несколько фильтров, каждый из них ищет в картинке какую-то особенность,
# каждый фильтр производит свою двумерную картинку, состоящую из активаций. На иллюстрации, которая
# приложена выше это как раз можно наблюдать. После этого каждая карта активаций становится каналом
# в новой катинке, которая и является выходом сети. Поэтому число фильтров равно числу выходных каналов.
# 2) При этом каждый фильтр содержит несколько шаблонов размера kernel_size*kernel_size, чтобы
# собираться информацию с каждого из входных каналов. Количество таких двумерных шаблонов равно количеству
# каналов во входной картинке.
self.biases = seed_random((out_channels), 13)
def forward(self, X):
# Инициализируем массив с реузльтатом работы всертки
res = np.zeros((X.shape[0], X.shape[1] - self.kernel_size + 1,
X.shape[2] - self.kernel_size + 1, self.out_channels))
# используем четыре вложенных цикла, чтобы посчитать свертку. Сначала по картинкам в батче, потом по
# фильтрам, а потом по координатам. !!!Не забудьте добавить bias!!!
# применять функцию активации не нужно.
for i, img in enumerate(X):
<Ваш код здесь>
return res
# + [markdown] colab_type="text" id="V5tasERfGnF5"
# Чтобы протестировать свертку опять вернемся к картинке с котиком и попробуем сделать так, чтобы после применения фильтра резкости у нас получалась не серая картинка. Самый простой способ - применить фильтр отедеьно к каждому каналу. Для этого нужно сделать три кернела, каждый из которых работает только со своим каналом в исходной картинке.
# + colab={} colab_type="code" id="D3k89BHDGnF6"
# Создадим наш слой
conv = ConvLayer(3, 3, 3)
r_filter = np.zeros((3, 3, 3))
r_filter[:, :, 0] = blur_filter
g_filter = np.zeros((3, 3, 3))
g_filter[:, :, 1] = blur_filter
b_filter = np.zeros((3, 3, 3))
b_filter[:, :, 2] = blur_filter
# Поставим в качестве кернелов фильтры, которые мы уже определили и применим к картинке
# Мы добавляем им новое измерение, чтобы их можно было применять к трехмерным картинкам
conv.kernels = np.array([r_filter, g_filter, b_filter])
conv.biases = np.zeros((3))
# + colab={} colab_type="code" id="O8nrA7qeGnF9"
img = load_img('./img.jpeg')
res = conv.forward(img[np.newaxis, :, :, :])
# Выведем размытую картинку
show_img((res[0]).clip(0, 1))
# + [markdown] colab_type="text" id="loStoqcpGnF_"
# # [Задание 3]
# + [markdown] colab_type="text" id="aon37VnkGnGB"
# А теперь протестируем, что вы правильно написали свертку и она хорошо работает.
#
# **!!!Как и в задании 1 скопируйте весь код свертки в класс ниже и замените сумму на умножение. (сумму на умножение нужно менять только внутри подсчета кернела, bias все так же нужно добавлять)!!!**
#
# **Создайте слой, применяющий модифицированную свертку с тремя фильтрами, примените ее к картинке с котиком и возьмите сумму всех чисел в выходе, клипать не нужно. (не забудьте добавить массиву img еще одно измерение, так как наш класс рассчитан на работу с батчами картинок)**
# + colab={} colab_type="code" id="K2i44vtoGnGB"
class ModifiedConvLayer:
def __init__(self, in_channels, out_channels, kernel_size):
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
# Теперь инициализируем массив с кернелами
self.kernels =seed_random((out_channels, kernel_size, kernel_size, in_channels), 42)
self.biases = seed_random((out_channels), 13)
def forward(self, X):
# Инициализируем массив с реузльтатом работы всертки
res = np.zeros((X.shape[0], X.shape[1] - self.kernel_size + 1,
X.shape[2] - self.kernel_size + 1, self.out_channels))
# испоьзуем четыре вложенных цикла, чтобы посчитать свертку. Сначала по картинкам в батче, потом по
# фильтрам, а потом по координатам. Не забудьте добавить bias!!!
<Ваш код здесь>
return res
# + colab={} colab_type="code" id="P715Wi7uGnGH"
# Создайте объект ModifiedConvLayer с тремя фильтрами и размером фильтров 3 и примените его к котику, а потом
# посчитайте нужную статистику. Изменять сгенерированный в конструкторе фильтр не нужно.
img = load_img('./img.jpeg')
<Ваш код здесь>
# + [markdown] colab_type="text" id="AyOT0DExGnGJ"
# ## Pytorch Convolutions
# + [markdown] colab_type="text" id="ST6co4gxGnGK"
# ### Реализации сверток
# Вы уже достаточно много поработали со сверткой, реализованной на for циклах, и понимаете, что она работает слишком долго. Обработка одной кратинки среднего размера занимает около 10 секунд, что непозволительно долго. В реальном мире свертки реализованы с помощью продвинутых алгоритмов.
#
# Для маленьких размеров фильтров обычно используется метод, в котором свертка заменяется на одно матричное умножение. Это можно сделать, потому что на свертку можно смотреть как на полносвязный слой с некоторыми ограничениями.
#
# Для больших фильтров используется агоритм быстрого преобразования Фурье, с помощью которого также можно выполнить свертку.
#
# Это достатчоно сложные вещи и поэтому не стоит обраoать на них внимание, если вы только начали изучать Deep Learning. Если же Вам интересно, то на arxiv есть статья про то, какие эффективные методы для подсчета сверток существуют https://arxiv.org/abs/1509.09308.
# + [markdown] colab_type="text" id="vp-4auh5GnGM"
# ### PyTorch
#
# Хорошая новость заключается в том, что в PyTorch уже есть быстрые свертки и нам не придется писать их самим.
#
# Давайте потренируемся писать сверточные нейросети в PyTorch.
# + [markdown] colab_type="text" id="E5mGZdmXGnGN"
# # [Задание 4 и 5]
#
# **4. В этом задании мы хотим выбрать лучшую модель для работы с cifar10. Ниже будет несколько возможных архитектур, реализуйте их, потренируйте по 3 эпохи и выберите ту, которая в конце обучения достигает наименьшего лосса на тренировочном датасете (имеется в виду последний выведенный функцией train лосс)**
#
# **5. Ответом на задание 5 является лосс на !!!тестовом!!! датасете модели, которую Вы выбрали в задании 4. (средений лосс по кртинке, который выводит print_test_loss)**
# + colab={} colab_type="code" id="TU-K0w7BGnGO"
import torch.nn as nn
import torch.nn.functional as F
import torch
import torchvision
from torchvision import transforms
from tqdm import tqdm_notebook
# + colab={} colab_type="code" id="FqWcskLaGnGR"
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# + code_folding=[] colab={} colab_type="code" id="iRGU4srbGnGU"
def print_test_loss(net):
# выбираем функцию потерь
loss_fn = torch.nn.CrossEntropyLoss()
running_loss = 0.0
for i, batch in enumerate(tqdm_notebook(testloader)):
# так получаем текущий батч
<Ваш код здесь>
# forward + loss calc
<Ваш код здесь>
# добавляем лосс
<Ваш код здесь>
print("Test Loss: {}".format(running_loss / len(testloader)))
def train(net):
# выбираем функцию потерь
<Ваш код здесь>
# выбираем алгоритм оптимизации и learning_rate
learning_rate = 1e-4
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
# итерируемся
for epoch in tqdm_notebook(range(3)):
running_loss = 0.0
for i, batch in enumerate(tqdm_notebook(trainloader)):
# так получаем текущий батч
<Ваш код здесь>
# обнуляем градиент
<Ваш код здесь>
# forward + backward + optimize
<Ваш код здесь>
# добавим лосс
<Ваш код здесь>
# выведем качество каждые 2000 батчей
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Обучение закончено')
return net
# + [markdown] colab_type="text" id="AUfEH7_kGnGW"
# # Простые архитектуры
# + [markdown] colab_type="text" id="oRiWK6b7GnGX"
# **!!!ВАЖНО, Чтобы добиться воспроизводимости результатов, вставьте перед кажджым объявлением слоя
# torch.manual_seed(0) + запукайте код в колабе + не используйте GPU!!!**
# + [markdown] colab_type="text" id="gR1aXzGrGnGY"
# #### Архитектура 1:
# Активация - ReLu
# 1. Conv(in_channels=3, out_channels=6, kernel_size=5)
# 2. MaxPool(kernel_size=2, stride=2)
# 3. Conv(in_channels=6, out_channels=16, kernel_size=5)
# 4. MaxPool(kernel_size=2, stride=2)
# 4. Linear(output=120)
# 5. Linear(output=84)
# 6. Linear(output=10)
# + code_folding=[] colab={} colab_type="code" id="sdd3HFIAGnGZ"
class SimpleConvNet1(nn.Module):
def __init__(self):
# вызов конструктора предка
super(SimpleConvNet1, self).__init__()
# необходмо заранее знать, сколько каналов у картинки (сейчас = 1),
# которую будем подавать в сеть, больше ничего
# про входящие картинки знать не нужно
<Ваш код здесь>
def forward(self, x):
<Ваш код здесь>
return x
# + colab={} colab_type="code" id="x03waDHKGnGb"
net1 = train(SimpleConvNet1())
# + colab={} colab_type="code" id="M6TZq4HzGnGd"
print_test_loss(net1)
# + [markdown] colab_type="text" id="T3JVuBZYGnGf"
# **!!!ВАЖНО, Чтобы добиться воспроизводимости результатов, вставьте перед кажджым объявлением слоя
# torch.manual_seed(0)!!!**
# + [markdown] colab_type="text" id="pHj8VJd9GnGg"
#
# #### Архитектура 2:
# В этой архитектуре мы немного поэкспериментируем и используем необычные вещи, а именно активацию tanh и вместо MaxPooling используем AveragePooling.
#
# Активация - tanh
# 1. Conv(in_channels=3, out_channels=6, kernel_size=5)
# 2. AvgPool(kernel_size=2, stride=2)
# 3. Conv(in_channels=6, out_channels=16, kernel_size=5)
# 4. AvgPool(kernel_size=2, stride=2)
# 4. Linear(output=120)
# 5. Linear(output=84)
# 6. Linear(output=10)
# + code_folding=[] colab={} colab_type="code" id="2DVhNkPDGnGh"
class SimpleConvNet2(nn.Module):
def __init__(self):
# вызов конструктора предка
super(SimpleConvNet2, self).__init__()
# необходмо заранее знать, сколько каналов у картинки (сейчас = 1),
# которую будем подавать в сеть, больше ничего
# про входящие картинки знать не нужно
<Ваш код здесь>
def forward(self, x):
<Ваш код здесь>
return x
# + colab={} colab_type="code" id="lDGcR-tYGnGk"
net2 = train(SimpleConvNet2())
# + colab={} colab_type="code" id="wm4l-2UBGnGm"
print_test_loss(net2)
# + [markdown] colab_type="text" id="AbXdIA5MGnGo"
# # Более сложные архитектуры
# + [markdown] colab_type="text" id="VQxCZAinGnGp"
# **!!!ВАЖНО, Чтобы добиться воспроизводимости результатов, вставьте перед кажджым объявлением слоя
# torch.manual_seed(0)!!!**
# + [markdown] colab_type="text" id="1U4EXT3_GnGq"
# #### Архитектура 3:
# А здесь мы добавим еще сверточных слоев и уберем один полносвязный!
#
# Активация - ReLu
# 1. Conv(in_channels=3, out_channels=64, kernel_size=3)
# * Conv(in_channels=64, out_channels=64, kernel_size=3)
# * MaxPool(kernel_size=2, stride=2)
# * Conv(in_channels=64, out_channels=64, kernel_size=3)
# * Conv(in_channels=64, out_channels=64, kernel_size=3)
# * MaxPool(kernel_size=2, stride=2)
# * Linear(output=60)
# * Linear(output=10)
# + colab={} colab_type="code" id="2_rwgOpcGnGq"
class SimpleConvNet3(nn.Module):
def __init__(self):
# вызов конструктора предка
super(SimpleConvNet3, self).__init__()
# необходмо заранее знать, сколько каналов у картинки (сейчас = 1),
# которую будем подавать в сеть, больше ничего
# про входящие картинки знать не нужно
<Ваш код здесь>
def forward(self, x):
<Ваш код здесь>
return x
# + colab={} colab_type="code" id="PxBxY2KkGnGs"
net3 = train(SimpleConvNet3())
# + colab={} colab_type="code" id="UtJeYyjiGnGu"
print_test_loss(net3)
# + [markdown] colab_type="text" id="ulIJN5lWGnGx"
# **!!!ВАЖНО, Чтобы добиться воспроизводимости результатов, вставьте перед кажджым объявлением слоя
# torch.manual_seed(0)!!!**
# + [markdown] colab_type="text" id="ysoVYiPqGnGx"
# #### Архитектура 4:
# А что если мы пойдем еще глубже!!! (Осторожнее, тренироваться будет час, поэтому обновите подключение к ноутбуку, прежде чем запускать ячейку, иначе может быть таймаут)
#
#
# Активация ReLu
# 1. Conv(in_channels=3, out_channels=64, kernel_size=3)
# * Conv(in_channels=64, out_channels=64, kernel_size=3)
# 1. Conv(in_channels=64, out_channels=64, kernel_size=3)
# * Conv(in_channels=64, out_channels=64, kernel_size=3)
# * MaxPool(kernel_size=2, stride=2)
# * Conv(in_channels=64, out_channels=128, kernel_size=3)
# * Conv(in_channels=128, out_channels=128, kernel_size=3)
# 1. Conv(in_channels=128, out_channels=128, kernel_size=3)
# * Conv(in_channels=128, out_channels=128, kernel_size=3)
# * MaxPool(kernel_size=2, stride=2)
# * Linear(output=10)
# + colab={} colab_type="code" id="MyijC7GPGnGy"
class SimpleConvNet4(nn.Module):
def __init__(self):
# вызов конструктора предка
super(SimpleConvNet4, self).__init__()
# которую будем подавать в сеть, больше ничего
# про входящие картинки знать не нужно
<Ваш код здесь>
def forward(self, x):
<Ваш код здесь>
return x
# + colab={} colab_type="code" id="KVpf7Y3OGnG1"
net4 = train(SimpleConvNet4())
# + colab={} colab_type="code" id="Hb_VCKECGnG5"
print_test_loss(net4)
# + [markdown] colab={} colab_type="code" id="XHqfI8vOGnG8"
# ### p.s. чтобы получить правильные ответы, нужно запустить все задания в Google Colab (кроме визуализации с "ползунком")
| 33,447 |
/A4/PCA_StudentVersion/pca_StudentVersion.ipynb | 2bc6ee4c23c4c605cdf59d9b011eca1c9658c0ac | [] | no_license | jsonwulff/MAD_afleveringer | https://github.com/jsonwulff/MAD_afleveringer | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 90,004 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Assignment: Principal Components Analysis (PCA)
# Task 1: Implement PCA on the diatoms database. Please output the proportion of variance explained by each of the first 10 components (5 points)
#
# Task 2: Visualize fourth component of the PCA (3 points)
#
#
#
# We start by loading the dataset found in the file 'diatoms.txt', which contains a set of *diatom* outlines. A diatom is a type of algae, whose species is strongly correlated with its outline shape; in the following, we will be using these outlines as a descriptive feature of the diatom.
#
# The file 'diatoms.txt' contains 780 diatoms described by 90 successive "landmark points" (x_i, y_i) along the outline, recorded as (x_0, y_0, x_1, y_1, ..., x_89, y_89).
#
# The file 'diatoms_classes.txt' contains one class assignment per diatom, into species classified by the integers 1-37.
# +
import numpy as np
np.set_printoptions(precision=4, suppress=True)
diatoms = np.loadtxt('diatoms.txt', delimiter=',').T
diatoms_classes = np.loadtxt('diatoms_classes.txt', delimiter=',')
print('Shape of diatoms:', diatoms.shape)
print('Shape of diatoms_classes:', diatoms_classes.shape)
#print('Classes:', diatoms_classes)
d,N = diatoms.shape
print('Dimension:', d)
print('Sample size:', N)
print(diatoms)
# -
# Here's a function that will plot a given diatom. Let's try it on the first diatom in the dataset.
# +
import matplotlib.pyplot as plt
def plot_diatom(diatom):
xs = np.zeros(91)
ys = np.zeros(91)
for i in range(90):
# Loop from 0 - 179
xs[i] = diatom[2*i]
ys[i] = diatom[2*i+1]
# Loop around to first landmark point to get a connected shape
xs[90] = xs[0]
ys[90] = ys[0]
plt.plot(xs, ys)
plt.axis('equal')
plot_diatom(diatoms[:,0])
# -
# Let's next compute the mean diatom and plot it.
mean_diatom = np.mean(diatoms, 1)
print(mean_diatom.shape)
print(mean_diatom)
plot_diatom(mean_diatom)
# ### Task1: Implementing PCA
#
# To implement PCA, please check the algorithm explaination from the lecture.
# Hits:
#
# 1) Noramilize data subtracting the mean shape. No need to use Procrustes Analysis or other more complex types of normalization
#
# 2) Compute covariance matrix (check np.cov)
#
# 3) Compute eigenvectors and values (check np.linalg.eigh)
# +
import numpy.matlib
def pca(data):
mean = np.mean(data, 1)
data_cent = np.array(data)
for i in range(data.shape[1]):
data_cent[:,i] = data[:,i] - mean
cov = np.cov(data_cent)
w, v = np.linalg.eigh(cov)
PCevals = np.flip(w)
PCevecs = np.flip(v)
#PCevecs = np.flip(v, axis=1) //
return PCevals, PCevecs, data_cent
PCevals, PCevecs, data_cent = pca(diatoms)
# PCevals is a vector of eigenvalues in decreasing order. To verify, uncomment:
print(PCevecs)
# PCevecs is a matrix whose columns are the eigenvectors listed in the order of decreasing eigenvectors
# -
# ***Recall:***
# * The eigenvalues represent the variance of the data projected to the corresponding eigenvectors.
# * Thus, the 2D linear subspace with highest projected variance is spanned by the eigenvectors corresponding to the two largest eigenvalues.
# * We extract these eigenvectors and plot the data projected onto the corresponding space.
# ### Compute variance of the first 10 components
#
# How many components you need to cover 90%, 95% and 99% of variantion. Submit the resulting numbers for grading.
# +
variance_explained_per_component = PCevals/np.sum(PCevals)
cumulative_variance_explained = np.cumsum(variance_explained_per_component)
plt.plot(cumulative_variance_explained)
plt.xlabel('Number of principal components included')
plt.ylabel('Proportion of variance explained')
plt.title('Proportion of variance explained as a function of number of PCs included')
# Let's print out the proportion of variance explained by the first 10 PCs
for i in range(10):
print('Proportion of variance explained by the first '+str(i+1)+' principal components:', cumulative_variance_explained[i])
# -
# ### Task2: Plot varianace accosiated with the first component
#
# Please fill the gaps in the code to plot mean diatom shape with added FOURTH eigenvector mulitplied by [-3,-2,-1,0,1,2,3] standard deviations corresponding to this eigenvector.
#
# Submit the resulting plot for grading.
# +
e4 = PCevecs[:, 3] # gets the fourth eigenvector
print(e4.shape)
print(e4)
lambda4 = PCevals[3] # gets the fourth eigenvalue
print(lambda4)
std4 = np.sqrt(lambda4) # In case the naming std is confusing -- the eigenvalues have a statistical interpretation
diatoms_along_pc = np.zeros((7, 180))
for i in range(7):
#.......
diatoms_along_pc[i] = mean_diatom + (i-3)*std4*e4
for i in range(7):
plot_diatom(diatoms_along_pc[i])
plt.title('Diatom shape along PC1')
# -
| 5,087 |
/RecurrentNeuralNetworks/TimeSeriesWithMultilayerPerceptrons.ipynb | d17efd7917faa7bb78855bc7c4a00c1ee836a639 | [] | no_license | abdulbaruwa/DeepLearningPython | https://github.com/abdulbaruwa/DeepLearningPython | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 24,956 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# примеры взяты [отсюда](https://habr.com/ru/company/wunderfund/blog/316826/)
# list available python magics
# %lsmagic
# ### Используйте `%run` для выполнения кода на Python
#
# # %run может выполнить код на языке Python из файлов с расширением .py — это поведение хорошо задокументировано.
#
# Но эта команда может выполнять и другие блокноты из Jupyter! Иногда это очень полезно.
#
# Обратите внимание, что %run — это не то же, что импорт python-модуля.
# %run ./files/example.ipynb
# # ### %load
#
# Загрузит код напрямую в ячейку. Можно выбрать файл локально или из сети.
#
# Если раскомментировать и выполнить код ниже, содержание ячейки заменится на содержание файла.
# # %load ./files/some.txt
42342
Now that!!!
sdfsdf
# +
# %%writefile ./files/some.txt
sdfsdf
# -
# ### Тайминг
#
# Если вы хотите замерить время выполнения программы или найти узкое место в коде, на помощь придет IPython.
# %%time
import time
time.sleep(2) # sleep for two seconds
# +
def some_foo(n):
a = 0
for i in range(int(n)):
a += i
return a
# %timeit some_foo(1e6)
# -
# ### Профилирование: `%prun`
# +
import numpy as np
def append_if_not_exists(arr, x):
if x in arr:
arr.append(x)
def some_useless_slow_function():
arr = list()
for i in range(10000):
x = np.random.randint(0, 10000)
append_if_not_exists(arr, x)
# %prun some_useless_slow_function()
# -
# %timeit some_useless_slow_function()
def some_useless_slow_function2():
arr = list()
xs = np.random.randint(0, 10000, size=10000)
for x in xs:
append_if_not_exists(arr, x)
# %timeit some_useless_slow_function2()
# %load_ext line_profiler
# %lprun?
# # ###### %lprun -f append_if_not_exists some_useless_slow_function2()
| 2,032 |
/covid19_x-ray_images/Experiment8/algorithm_pneumonia.ipynb | bbc55d48b14d6d3bbf9f2ee0dcfa4204977a7e42 | [] | no_license | jaidenmeiden/arf | https://github.com/jaidenmeiden/arf | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 168,448 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# <span class='h2'>C - Sugar Water</span>
# <hr>
# <p>Time Limit: 3 sec / Memory Limit: 256 MB</p>
#
# <div id='task-statement'>
# <span class='lang'>
# <span class='lang-ja'>
# <p>配点: <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-1'><span class='MJXp-mn' id='MJXp-Span-2'>300</span></span></span><script type='math/tex' id='MathJax-Element-1'>300</script></var> 点</p>
#
# <div class='part'>
# <section>
# <h3>問題文</h3><p>すぬけ君はビーカーに砂糖水を作ろうとしています。
# 最初ビーカーは空です。すぬけ君は以下の <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-3'><span class='MJXp-mn' id='MJXp-Span-4'>4</span></span></span><script type='math/tex' id='MathJax-Element-2'>4</script></var> 種類の操作をそれぞれ何回でも行うことができます。一度も行わない操作があっても構いません。</p>
# <ul>
# <li>操作 1: ビーカーに水を <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-5'><span class='MJXp-mn' id='MJXp-Span-6'>100</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-7'>A</span></span></span><script type='math/tex' id='MathJax-Element-3'>100A</script></var> [g] 入れる。</li>
# <li>操作 2: ビーカーに水を <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-8'><span class='MJXp-mn' id='MJXp-Span-9'>100</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-10'>B</span></span></span><script type='math/tex' id='MathJax-Element-4'>100B</script></var> [g] 入れる。</li>
# <li>操作 3: ビーカーに砂糖を <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-11'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-12'>C</span></span></span><script type='math/tex' id='MathJax-Element-5'>C</script></var> [g] 入れる。</li>
# <li>操作 4: ビーカーに砂糖を <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-13'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-14'>D</span></span></span><script type='math/tex' id='MathJax-Element-6'>D</script></var> [g] 入れる。</li>
# </ul>
# <p>すぬけ君の実験環境下では、水 <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-15'><span class='MJXp-mn' id='MJXp-Span-16'>100</span></span></span><script type='math/tex' id='MathJax-Element-7'>100</script></var> [g] あたり砂糖は <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-17'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-18'>E</span></span></span><script type='math/tex' id='MathJax-Element-8'>E</script></var> [g] 溶けます。</p>
# <p>すぬけ君はできるだけ濃度の高い砂糖水を作りたいと考えています。</p>
# <p>ビーカーに入れられる物質の質量 (水の質量と砂糖の質量の合計) が <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-19'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-20'>F</span></span></span><script type='math/tex' id='MathJax-Element-9'>F</script></var> [g] 以下であり、
# ビーカーの中に砂糖を溶け残らせてはいけないとき、
# すぬけ君が作る砂糖水の質量と、それに溶けている砂糖の質量を求めてください。
# 答えが複数ある場合はどれを答えても構いません。</p>
# <p>水 <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-21'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-22'>a</span></span></span><script type='math/tex' id='MathJax-Element-10'>a</script></var> [g] と砂糖 <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-23'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-24'>b</span></span></span><script type='math/tex' id='MathJax-Element-11'>b</script></var> [g] を混ぜた砂糖水の濃度は <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-25'><span class='MJXp-mfrac' id='MJXp-Span-26' style='vertical-align: 0.25em;'><span class='MJXp-box MJXp-script'><span class='MJXp-mn' id='MJXp-Span-27'>100</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-28'>b</span></span><span class='MJXp-box' style='margin-top: -0.9em;'><span class='MJXp-denom'><span><span class='MJXp-rule' style='height: 1em; border-top: none; border-bottom: 1px solid; margin: 0.1em 0px;'></span></span><span><span class='MJXp-box MJXp-script'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-29'>a</span><span class='MJXp-mo' id='MJXp-Span-30'>+</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-31'>b</span></span></span></span></span></span></span></span><script type='math/tex' id='MathJax-Element-12'>\frac{100b}{a + b}</script></var> [%]です。
# また、この問題では、砂糖が全く溶けていない水も濃度 <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-32'><span class='MJXp-mn' id='MJXp-Span-33'>0</span></span></span><script type='math/tex' id='MathJax-Element-13'>0</script></var> [%] の砂糖水と考えることにします。</p>
# </section>
# </div>
#
# <div class='part'>
# <section>
# <h3>制約</h3><ul>
# <li><var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-34'><span class='MJXp-mn' id='MJXp-Span-35'>1</span><span class='MJXp-mo' id='MJXp-Span-36' style='margin-left: 0.333em; margin-right: 0.333em;'>≦</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-37'>A</span><span class='MJXp-mo' id='MJXp-Span-38' style='margin-left: 0.333em; margin-right: 0.333em;'><</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-39'>B</span><span class='MJXp-mo' id='MJXp-Span-40' style='margin-left: 0.333em; margin-right: 0.333em;'>≦</span><span class='MJXp-mn' id='MJXp-Span-41'>30</span></span></span><script type='math/tex' id='MathJax-Element-14'>1 ≦ A < B ≦ 30</script></var></li>
# <li><var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-42'><span class='MJXp-mn' id='MJXp-Span-43'>1</span><span class='MJXp-mo' id='MJXp-Span-44' style='margin-left: 0.333em; margin-right: 0.333em;'>≦</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-45'>C</span><span class='MJXp-mo' id='MJXp-Span-46' style='margin-left: 0.333em; margin-right: 0.333em;'><</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-47'>D</span><span class='MJXp-mo' id='MJXp-Span-48' style='margin-left: 0.333em; margin-right: 0.333em;'>≦</span><span class='MJXp-mn' id='MJXp-Span-49'>30</span></span></span><script type='math/tex' id='MathJax-Element-15'>1 ≦ C < D ≦ 30</script></var></li>
# <li><var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-50'><span class='MJXp-mn' id='MJXp-Span-51'>1</span><span class='MJXp-mo' id='MJXp-Span-52' style='margin-left: 0.333em; margin-right: 0.333em;'>≦</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-53'>E</span><span class='MJXp-mo' id='MJXp-Span-54' style='margin-left: 0.333em; margin-right: 0.333em;'>≦</span><span class='MJXp-mn' id='MJXp-Span-55'>100</span></span></span><script type='math/tex' id='MathJax-Element-16'>1≦ E ≦ 100</script></var></li>
# <li><var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-56'><span class='MJXp-mn' id='MJXp-Span-57'>100</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-58'>A</span><span class='MJXp-mo' id='MJXp-Span-59' style='margin-left: 0.333em; margin-right: 0.333em;'>≦</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-60'>F</span><span class='MJXp-mo' id='MJXp-Span-61' style='margin-left: 0.333em; margin-right: 0.333em;'>≦</span><span class='MJXp-mn' id='MJXp-Span-62'>3</span><span class='MJXp-mo' id='MJXp-Span-63' style='margin-left: 0em; margin-right: 0.222em;'>,</span><span class='MJXp-mn' id='MJXp-Span-64'>000</span></span></span><script type='math/tex' id='MathJax-Element-17'>100A ≦ F ≦ 3,000</script></var></li>
# <li><var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-65'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-66'>A</span><span class='MJXp-mo' id='MJXp-Span-67' style='margin-left: 0em; margin-right: 0.222em;'>,</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-68'>B</span><span class='MJXp-mo' id='MJXp-Span-69' style='margin-left: 0em; margin-right: 0.222em;'>,</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-70'>C</span><span class='MJXp-mo' id='MJXp-Span-71' style='margin-left: 0em; margin-right: 0.222em;'>,</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-72'>D</span><span class='MJXp-mo' id='MJXp-Span-73' style='margin-left: 0em; margin-right: 0.222em;'>,</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-74'>E</span><span class='MJXp-mo' id='MJXp-Span-75' style='margin-left: 0em; margin-right: 0.222em;'>,</span><span class='MJXp-mi MJXp-italic' id='MJXp-Span-76'>F</span></span></span><script type='math/tex' id='MathJax-Element-18'>A, B, C, D, E, F</script></var> はすべて整数である。</li>
# </ul>
# </section>
# </div>
#
# <hr>
#
# <div class='io-style'>
# <div class='part'>
# <section>
# <h3>入力</h3><p>入力は以下の形式で標準入力から与えられる。</p>
# <pre><var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-77'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-78'>A</span></span></span><script type='math/tex' id='MathJax-Element-19'>A</script></var> <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-79'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-80'>B</span></span></span><script type='math/tex' id='MathJax-Element-20'>B</script></var> <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-81'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-82'>C</span></span></span><script type='math/tex' id='MathJax-Element-21'>C</script></var> <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-83'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-84'>D</span></span></span><script type='math/tex' id='MathJax-Element-22'>D</script></var> <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-85'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-86'>E</span></span></span><script type='math/tex' id='MathJax-Element-23'>E</script></var> <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-87'><span class='MJXp-mi MJXp-italic' id='MJXp-Span-88'>F</span></span></span><script type='math/tex' id='MathJax-Element-24'>F</script></var>
# </pre>
#
# </section>
# </div>
#
# <div class='part'>
# <section>
# <h3>出力</h3><p>整数を空白区切りで <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-89'><span class='MJXp-mn' id='MJXp-Span-90'>2</span></span></span><script type='math/tex' id='MathJax-Element-25'>2</script></var> つ出力せよ。
# <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-91'><span class='MJXp-mn' id='MJXp-Span-92'>1</span></span></span><script type='math/tex' id='MathJax-Element-26'>1</script></var> つ目は求める砂糖水の質量、<var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-93'><span class='MJXp-mn' id='MJXp-Span-94'>2</span></span></span><script type='math/tex' id='MathJax-Element-27'>2</script></var> つ目はそれに溶けている砂糖の質量とせよ。</p>
# </section>
# </div>
# </div>
#
# <hr>
#
# <div class='part'>
# <section>
# <h3>入力例 1 <span class='btn btn-default btn-sm btn-copy' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' data-target='pre-sample0' data-original-title='Copied!'>Copy</span></h3><div class='div-btn-copy'><span class='btn-copy btn-pre' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' style='display: none;' data-target='pre-sample0' data-original-title='Copied!'>Copy</span></div><pre id='pre-sample0'>1 2 10 20 15 200
# </pre>
#
# </section>
# </div>
#
# <div class='part'>
# <section>
# <h3>出力例 1 <span class='btn btn-default btn-sm btn-copy' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' data-target='pre-sample1' data-original-title='Copied!'>Copy</span></h3><div class='div-btn-copy'><span class='btn-copy btn-pre' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' style='display: none;' data-target='pre-sample1' data-original-title='Copied!'>Copy</span></div><pre id='pre-sample1'>110 10
# </pre>
#
# <p>この入力例の状況では、水 <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-95'><span class='MJXp-mn' id='MJXp-Span-96'>100</span></span></span><script type='math/tex' id='MathJax-Element-28'>100</script></var> [g] あたり砂糖は <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-97'><span class='MJXp-mn' id='MJXp-Span-98'>15</span></span></span><script type='math/tex' id='MathJax-Element-29'>15</script></var> [g] 溶けます。
# また、ビーカーに物質を <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-99'><span class='MJXp-mn' id='MJXp-Span-100'>200</span></span></span><script type='math/tex' id='MathJax-Element-30'>200</script></var> [g] まで入れることができます。</p>
# <p>操作 1 と操作 3 を <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-101'><span class='MJXp-mn' id='MJXp-Span-102'>1</span></span></span><script type='math/tex' id='MathJax-Element-31'>1</script></var> 回ずつ行うことで <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-103'><span class='MJXp-mn' id='MJXp-Span-104'>110</span></span></span><script type='math/tex' id='MathJax-Element-32'>110</script></var> [g] の砂糖水を作ることができます。
# また、これ以上濃度の高い砂糖水を作ることはできません。
# たとえば、以下のような操作は条件を満たしません。</p>
# <ul>
# <li>操作 1 と操作 4 を <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-105'><span class='MJXp-mn' id='MJXp-Span-106'>1</span></span></span><script type='math/tex' id='MathJax-Element-33'>1</script></var> 回ずつ行うと、ビーカーに砂糖が溶け残ってしまいます。</li>
# <li>操作 2 を <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-107'><span class='MJXp-mn' id='MJXp-Span-108'>1</span></span></span><script type='math/tex' id='MathJax-Element-34'>1</script></var> 回と操作 3 を <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-109'><span class='MJXp-mn' id='MJXp-Span-110'>3</span></span></span><script type='math/tex' id='MathJax-Element-35'>3</script></var> 回行うと、ビーカーの中の物質の量が <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-111'><span class='MJXp-mn' id='MJXp-Span-112'>200</span></span></span><script type='math/tex' id='MathJax-Element-36'>200</script></var> [g] を超えてしまいます。</li>
# </ul>
# </section>
# </div>
#
# <hr>
#
# <div class='part'>
# <section>
# <h3>入力例 2 <span class='btn btn-default btn-sm btn-copy' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' data-target='pre-sample2' data-original-title='Copied!'>Copy</span></h3><div class='div-btn-copy'><span class='btn-copy btn-pre' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' style='display: none;' data-target='pre-sample2' data-original-title='Copied!'>Copy</span></div><pre id='pre-sample2'>1 2 1 2 100 1000
# </pre>
#
# </section>
# </div>
#
# <div class='part'>
# <section>
# <h3>出力例 2 <span class='btn btn-default btn-sm btn-copy' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' data-target='pre-sample3' data-original-title='Copied!'>Copy</span></h3><div class='div-btn-copy'><span class='btn-copy btn-pre' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' style='display: none;' data-target='pre-sample3' data-original-title='Copied!'>Copy</span></div><pre id='pre-sample3'>200 100
# </pre>
#
# <p>ほかに、たとえば以下の出力も正解となります。</p>
# <pre>400 200
# </pre>
#
# <p>一方、以下の出力は不正解となります。</p>
# <pre>300 150
# </pre>
#
# <p>なぜなら、砂糖が <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-113'><span class='MJXp-mn' id='MJXp-Span-114'>150</span></span></span><script type='math/tex' id='MathJax-Element-37'>150</script></var> [g] 溶けた <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-115'><span class='MJXp-mn' id='MJXp-Span-116'>300</span></span></span><script type='math/tex' id='MathJax-Element-38'>300</script></var> [g] の砂糖水を作るにはビーカーに水をちょうど <var><span class='MathJax_Preview' style='color: inherit;'><span class='MJXp-math' id='MJXp-Span-117'><span class='MJXp-mn' id='MJXp-Span-118'>150</span></span></span><script type='math/tex' id='MathJax-Element-39'>150</script></var> [g] 入れる必要がありますが、そのようなことは不可能だからです。</p>
# </section>
# </div>
#
# <hr>
#
# <div class='part'>
# <section>
# <h3>入力例 3 <span class='btn btn-default btn-sm btn-copy' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' data-target='pre-sample4' data-original-title='Copied!'>Copy</span></h3><div class='div-btn-copy'><span class='btn-copy btn-pre' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' style='display: none;' data-target='pre-sample4' data-original-title='Copied!'>Copy</span></div><pre id='pre-sample4'>17 19 22 26 55 2802
# </pre>
#
# </section>
# </div>
#
# <div class='part'>
# <section>
# <h3>出力例 3 <span class='btn btn-default btn-sm btn-copy' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' data-target='pre-sample5' data-original-title='Copied!'>Copy</span></h3><div class='div-btn-copy'><span class='btn-copy btn-pre' tabindex='0' data-toggle='tooltip' data-trigger='manual' title='' style='display: none;' data-target='pre-sample5' data-original-title='Copied!'>Copy</span></div><pre id='pre-sample5'>2634 934
# </pre></section>
# </div>
# </span>
#
# +
from ipywidgets import Textarea
import sys
sys.path.append('../../..')
from utils.multi_line_input import multi_line_input
text_area = Textarea()
input = multi_line_input()
display(text_area)
| 18,035 |
/Sao_Paolo.ipynb | 33bb545fb5009fff0a263fe2f9cf25e61b0669b8 | [] | no_license | abdurrehman98/SaoPaolo-Homicide-Rate_Analysis | https://github.com/abdurrehman98/SaoPaolo-Homicide-Rate_Analysis | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .r | 147,676 | # -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .r
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: R
# language: R
# name: ir
# ---
# +
#####################
### Data Analysis ###
#####################
## Please set your working directory to the data/ folder
# Clear the workspace
rm(list = ls())
# Load necessary packages
library(dplyr) # data manipulation
library(Synth) # models
# Load data
df <- read.csv("df.csv", header = TRUE)
# Prepare dataset
df$state <- as.character(df$state) # required by dataprep()
# Plot: Homicide rates for Sao Paulo and Brazil (average)
df1 <- df %>%
mutate(homicide.sp = ifelse(homicide.rates & state == "São Paulo", homicide.rates, NA)) %>%
select(year, homicide.sp)
df2 <- df %>%
mutate(homicide.rates1 = ifelse(homicide.rates & state != "São Paulo", homicide.rates, NA)) %>%
group_by(year) %>%
summarise(homicide.br = mean(homicide.rates1, na.rm = TRUE))
# -
setwd("C:/Users/abdur/Downloads")
# Prepare data for synth
dataprep.out <-
dataprep(df,
predictors = c("state.gdp.capita",
"state.gdp.growth.percent",
"population.projection.ln",
"years.schooling.imp"
),
special.predictors = list(
list("homicide.rates", 1990:1998, "mean"),
list("proportion.extreme.poverty", 1990:1998, "mean"),
list("gini.imp", 1990:1998, "mean")
),
predictors.op = "mean",
dependent = "homicide.rates",
unit.variable = "code",
time.variable = "year",
unit.names.variable = "state",
treatment.identifier = 35,
controls.identifier = c(11:17, 21:27, 31:33, 50:53),
time.predictors.prior = c(1990:1998),
time.optimize.ssr = c(1990:1998),
time.plot = c(1990:2009)
)
# +
# Run synth
synth.out <- synth(dataprep.out)
# Get result tables
print(synth.tables <- synth.tab(
dataprep.res = dataprep.out,
synth.res = synth.out)
)
# +
path.plot(synth.res = synth.out,
dataprep.res = dataprep.out,
Ylab = c("Homicide Rates"),
Xlab = c("Year"),
Legend = c("São Paulo","Synthetic São Paulo"),
Legend.position = c("bottomleft")
)
abline(v = 1999,
lty = 2)
arrows(1997, 50, 1999, 50,
col = "black",
length = .1)
text(1995, 50,
"Policy Change",
cex = .8)
# +
gaps.plot(synth.res = synth.out,
dataprep.res = dataprep.out,
Ylab = c("Gap in Homicide Rates"),
Xlab = c("Year"),
Ylim = c(-30, 30),
Main = ""
)
abline(v = 1999,
lty = 2)
arrows(1997, 20, 1999, 20,
col = "black",
length = .1)
text(1995, 20,
"Policy Change",
cex = .8)
invisible(dev.off())
# +
######
#Placebo across time
######
results <- list()
results_synth <- list()
gaps <- list()
#Years
treatment.years <- c(1993, 1994, 1995, 1996, 1997, 1998, 1999) # For treatment years
for (i in 1:7) {
# Prepare data for synth
dataprep.out <-
dataprep(df,
predictors = c("state.gdp.capita",
"state.gdp.growth.percent",
"population.projection.ln",
"years.schooling.imp"
),
special.predictors = list(
list("homicide.rates", 1990:treatment.years[i], "mean"),
list("proportion.extreme.poverty", 1990:treatment.years[i], "mean"),
list("gini.imp", 1990:treatment.years[i], "mean")
),
predictors.op = "mean",
dependent = "homicide.rates",
unit.variable = "code",
time.variable = "year",
unit.names.variable = "state",
treatment.identifier = 35,
controls.identifier = c(11:17, 21:27, 31:33, 41:43, 50:53),
time.predictors.prior = c(1990:treatment.years[i]),
time.optimize.ssr = c(1990:treatment.years[i]),
time.plot = c(1990:2009)
)
results[[as.character(i)]] <- dataprep.out
results_synth[[as.character(i)]] <- synth(results[[as.character(i)]])
gaps[[as.character(i)]] <- results[[as.character(i)]]$Y1plot - (results[[as.character(i)]]$Y0plot %*% results_synth[[as.character(i)]]$solution.w)
}
plot(1990:2009,
ylim = c(-30, 30),
xlim = c(1990,2009),
ylab = "Gap in Homicide Rates",
xlab = "Year"
)
for (i in 1:6) {
lines(1990:2009,
gaps[[as.character(i)]],
col = "lightgrey",
lty = "solid",
lwd = 2
)
}
lines(1990:2009,
gaps[[as.character(7)]], # São Paulo
col = "black",
lty = "solid",
lwd = 2
)
abline(v = 1999,
lty = 2)
abline(h = 0,
lty = 1,
lwd = 1)
arrows(1997, 25, 1999, 25,
col = "black",
length = .1)
text(1995, 25,
"Policy Change",
cex = .8)
legend(x = "bottomleft",
legend = c("Treatment in policy change",
"Placebo treatment (from 1993 to 1998)"),
lty = c("solid", "solid"),
col = c("black", "darkgrey"),
cex = .8,
bg = "white",
lwdc(2, 2, 1)
)
# -
# Placebo in Year for 1993
dataprep.out <-
dataprep(df,
predictors = c("state.gdp.capita",
"state.gdp.growth.percent",
"population.projection.ln",
"years.schooling.imp"
),
special.predictors = list(
list("homicide.rates", 1990:1993, "mean"),
list("proportion.extreme.poverty", 1990:1993, "mean"),
list("gini.imp", 1990:1993, "mean")
),
predictors.op = "mean",
dependent = "homicide.rates",
unit.variable = "code",
time.variable = "year",
unit.names.variable = "state",
treatment.identifier = 35,
controls.identifier = c(11:17, 21:27, 31:33, 50:53),
time.predictors.prior = c(1990:1993),
time.optimize.ssr = c(1990:1993),
time.plot = c(1990:2009)
)
# +
synth.out <- synth(dataprep.out)
# +
path.plot(synth.res = synth.out,
dataprep.res = dataprep.out,
Ylab = c("Homicide Rates"),
Xlab = c("Year"),
Legend = c("São Paulo","Synthetic São Paulo"),
Legend.position = c("bottomleft")
)
abline(v = 1993,
lty = 2)
arrows(1995, 50, 1993, 50,
col = "black",
length = .1)
text(1997, 50,
"Placebo Year",
cex = .8)
# -
# Placebo in Time for 1996
dataprep.out <-
dataprep(df,
predictors = c("state.gdp.capita",
"state.gdp.growth.percent",
"population.projection.ln",
"years.schooling.imp"
),
special.predictors = list(
list("homicide.rates", 1990:1996, "mean"),
list("proportion.extreme.poverty", 1990:1996, "mean"),
list("gini.imp", 1990:1996, "mean")
),
predictors.op = "mean",
dependent = "homicide.rates",
unit.variable = "code",
time.variable = "year",
unit.names.variable = "state",
treatment.identifier = 35,
controls.identifier = c(11:17, 21:27, 31:33, 50:53),
time.predictors.prior = c(1990:1996),
time.optimize.ssr = c(1990:1996),
time.plot = c(1990:2009)
)
synth.out <- synth(dataprep.out)
# +
path.plot(synth.res = synth.out,
dataprep.res = dataprep.out,
Ylab = c("Homicide Rates"),
Xlab = c("Year"),
Legend = c("São Paulo","Synthetic São Paulo"),
Legend.position = c("bottomleft")
)
abline(v = 1996,
lty = 2)
arrows(1994, 50, 1996, 50,
col = "black",
length = .1)
text(1992, 50.5,
"Placebo Year",
cex = .8)
# -
#Robustness test of Synth Control by removing most heavily weighted component of Control
#Mato Grase De sul, number 50 makes up 33.5% of the Synthetic Control, so that can be removed to see the effect
dataprep.out <-
dataprep(df,
predictors = c("state.gdp.capita",
"state.gdp.growth.percent",
"population.projection.ln",
"years.schooling.imp"
),
special.predictors = list(
list("homicide.rates", 1990:1998, "mean"),
list("proportion.extreme.poverty", 1990:1998, "mean"),
list("gini.imp", 1990:1998, "mean")
),
predictors.op = "mean",
dependent = "homicide.rates",
unit.variable = "code",
time.variable = "year",
unit.names.variable = "state",
treatment.identifier = 35,
controls.identifier = c(11:17, 21:27, 31:33, 51:53),
time.predictors.prior = c(1990:1998),
time.optimize.ssr = c(1990:1998),
time.plot = c(1990:2009)
)
# +
# Run synth
synth.out <- synth(dataprep.out)
# Get result tables
print(synth.tables <- synth.tab(
dataprep.res = dataprep.out,
synth.res = synth.out)
)
# +
path.plot(synth.res = synth.out,
dataprep.res = dataprep.out,
Ylab = c("Homicide Rates"),
Xlab = c("Year"),
Legend = c("São Paulo","Synthetic São Paulo"),
Legend.position = c("bottomleft")
)
abline(v = 1999,
lty = 2)
arrows(1997, 50, 1999, 50,
col = "black",
length = .1)
text(1995, 50,
"Policy Change",
cex = .8) ### Now we see that Rio De Janeiro, number 33 is the biggest contributor to the Synth control
#Now we see that the Synthetic Control does not perform too well
# +
#Now Lets remove number 53 which is distrito federal
dataprep.out <-
dataprep(df,
predictors = c("state.gdp.capita",
"state.gdp.growth.percent",
"population.projection.ln",
"years.schooling.imp"
),
special.predictors = list(
list("homicide.rates", 1990:1998, "mean"),
list("proportion.extreme.poverty", 1990:1998, "mean"),
list("gini.imp", 1990:1998, "mean")
),
predictors.op = "mean",
dependent = "homicide.rates",
unit.variable = "code",
time.variable = "year",
unit.names.variable = "state",
treatment.identifier = 35,
controls.identifier = c(11:17, 21:27, 31:33, 51,52),
time.predictors.prior = c(1990:1998),
time.optimize.ssr = c(1990:1998),
time.plot = c(1990:2009)
)
# Run synth
synth.out <- synth(dataprep.out)
# Get result tables
print(synth.tables <- synth.tab(
dataprep.res = dataprep.out,
synth.res = synth.out)
)
# +
path.plot(synth.res = synth.out,
dataprep.res = dataprep.out,
Ylab = c("Homicide Rates"),
Xlab = c("Year"),
Legend = c("São Paulo","Synthetic São Paulo"),
Legend.position = c("bottomleft")
)
abline(v = 1999,
lty = 2)
arrows(1997, 50, 1999, 50,
col = "black",
length = .1)
text(1995, 50,
"Policy Change",
cex = .8) ### Now we see that Rio De Janeiro, number 33 is the biggest contributor to the Synth control
#The performance has gone worse, specifically at the treatment introduction time period
# +
#Now Lets remove number 15 as it is the most heavily weighted - Parai is number 15
dataprep.out <-
dataprep(df,
predictors = c("state.gdp.capita",
"state.gdp.growth.percent",
"population.projection.ln",
"years.schooling.imp"
),
special.predictors = list(
list("homicide.rates", 1990:1998, "mean"),
list("proportion.extreme.poverty", 1990:1998, "mean"),
list("gini.imp", 1990:1998, "mean")
),
predictors.op = "mean",
dependent = "homicide.rates",
unit.variable = "code",
time.variable = "year",
unit.names.variable = "state",
treatment.identifier = 35,
controls.identifier = c(11:14,16,17, 21:27, 31:33, 51,52),
time.predictors.prior = c(1990:1998),
time.optimize.ssr = c(1990:1998),
time.plot = c(1990:2009)
)
# Run synth
synth.out <- synth(dataprep.out)
# Get result tables
print(synth.tables <- synth.tab(
dataprep.res = dataprep.out,
synth.res = synth.out)
)
# +
path.plot(synth.res = synth.out,
dataprep.res = dataprep.out,
Ylab = c("Homicide Rates"),
Xlab = c("Year"),
Legend = c("São Paulo","Synthetic São Paulo"),
Legend.position = c("bottomleft")
)
abline(v = 1999,
lty = 2)
arrows(1997, 50, 1999, 50,
col = "black",
length = .1)
text(1995, 50,
"Policy Change",
cex = .8)
#It is increasingly evident that the performance is not improving so this will end the test.
# -
| 15,146 |
/Minimum_Window_Substring.ipynb | dab07bff000586031fe8f10598e46fd57793bf96 | [] | no_license | Zavi77/pythonCodes | https://github.com/Zavi77/pythonCodes | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,220 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Tensorflow(GPU)
# language: python
# name: tensorflow
# ---
# # Image features exercise
# *Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*
#
# We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.
#
# All of your work for this exercise will be done in this notebook.
# +
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from __future__ import print_function
# %matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloading extenrnal modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
# %load_ext autoreload
# %autoreload 2
# -
# ## Load data
# Similar to previous exercises, we will load CIFAR-10 data from disk.
# +
from cs231n.features import color_histogram_hsv, hog_feature
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):
# Load the raw CIFAR-10 data
cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'
X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)
# Subsample the data
mask = list(range(num_training, num_training + num_validation))
X_val = X_train[mask]
y_val = y_train[mask]
mask = list(range(num_training))
X_train = X_train[mask]
y_train = y_train[mask]
mask = list(range(num_test))
X_test = X_test[mask]
y_test = y_test[mask]
return X_train, y_train, X_val, y_val, X_test, y_test
# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)
try:
del X_train, y_train
del X_test, y_test
print('Clear previously loaded data.')
except:
pass
X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()
print(X_train.shape)
print(y_train.shape)
print(X_val.shape)
print(y_val.shape)
print(X_test.shape)
print(y_test.shape)
# -
# ## Extract Features
# For each image we will compute a Histogram of Oriented
# Gradients (HOG) as well as a color histogram using the hue channel in HSV
# color space. We form our final feature vector for each image by concatenating
# the HOG and color histogram feature vectors.
#
# Roughly speaking, HOG should capture the texture of the image while ignoring
# color information, and the color histogram represents the color of the input
# image while ignoring texture. As a result, we expect that using both together
# ought to work better than using either alone. Verifying this assumption would
# be a good thing to try for your interests.
#
# The `hog_feature` and `color_histogram_hsv` functions both operate on a single
# image and return a feature vector for that image. The extract_features
# function takes a set of images and a list of feature functions and evaluates
# each feature function on each image, storing the results in a matrix where
# each column is the concatenation of all feature vectors for a single image.
# +
from cs231n.features import *
num_color_bins = 10 # Number of bins in the color histogram
feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]
X_train_feats = extract_features(X_train, feature_fns, verbose=True)
X_val_feats = extract_features(X_val, feature_fns)
X_test_feats = extract_features(X_test, feature_fns)
print(X_train_feats.shape)
print(X_val_feats.shape)
print(X_test_feats.shape)
# Preprocessing: Subtract the mean feature
mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)
X_train_feats -= mean_feat
X_val_feats -= mean_feat
X_test_feats -= mean_feat
# Preprocessing: Divide by standard deviation. This ensures that each feature
# has roughly the same scale.
std_feat = np.std(X_train_feats, axis=0, keepdims=True)
X_train_feats /= std_feat
X_val_feats /= std_feat
X_test_feats /= std_feat
# Preprocessing: Add a bias dimension
X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])
X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])
X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])
# -
# ## Train SVM on features
# Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels.
# +
# Use the validation set to tune the learning rate and regularization strength
from cs231n.classifiers.linear_classifier import LinearSVM
learning_rates = [1e-9, 1e-8, 1e-7]
regularization_strengths = [5e4, 5e5, 5e6]
results = {}
best_val = -1
best_svm = None
################################################################################
# TODO: #
# Use the validation set to set the learning rate and regularization strength. #
# This should be identical to the validation that you did for the SVM; save #
# the best trained classifer in best_svm. You might also want to play #
# with different numbers of bins in the color histogram. If you are careful #
# you should be able to get accuracy of near 0.44 on the validation set. #
################################################################################
################################################################################
for lr in learning_rates:
for rs in regularization_strengths:
# 训练
current_svm=LinearSVM()
loss_hist=current_svm.train(X_train_feats, y_train,
learning_rate=lr,
reg=rs,
num_iters=5000,
batch_size=200,
verbose=True)
y_train_pred=current_svm.predict(X_train_feats)
training_acc=np.mean(y_train_pred==y_train)
y_val_pred=current_svm.predict(X_val_feats)
val_acc=np.mean(y_val==y_val_pred)
results[(lr,rs)]=(training_acc,val_acc)
if val_acc>best_val:
best_val=val_acc
best_svm=current_svm
# END OF YOUR CODE #
################################################################################
# Print out results.
for lr, reg in sorted(results):
train_accuracy, val_accuracy = results[(lr, reg)]
print('lr %e reg %e train accuracy: %f val accuracy: %f' % (
lr, reg, train_accuracy, val_accuracy))
print('best validation accuracy achieved during cross-validation: %f' % best_val)
# -
# Evaluate your trained SVM on the test set
y_test_pred = best_svm.predict(X_test_feats)
test_accuracy = np.mean(y_test == y_test_pred)
print(test_accuracy)
# +
# An important way to gain intuition about how an algorithm works is to
# visualize the mistakes that it makes. In this visualization, we show examples
# of images that are misclassified by our current system. The first column
# shows images that our system labeled as "plane" but whose true label is
# something other than "plane".
examples_per_class = 8
classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
for cls, cls_name in enumerate(classes):
idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]
idxs = np.random.choice(idxs, examples_per_class, replace=False)
for i, idx in enumerate(idxs):
plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)
plt.imshow(X_test[idx].astype('uint8'))
plt.axis('off')
if i == 0:
plt.title(cls_name)
plt.show()
# -
# ### Inline question 1:
# Describe the misclassification results that you see. Do they make sense?
# ## Neural Network on image features
# Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels.
#
# For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy.
# +
# Preprocessing: Remove the bias dimension
# Make sure to run this cell only ONCE
print(X_train_feats.shape)
X_train_feats = X_train_feats[:, :-1]
X_val_feats = X_val_feats[:, :-1]
X_test_feats = X_test_feats[:, :-1]
print(X_train_feats.shape)
# +
from cs231n.classifiers.neural_net import TwoLayerNet
input_dim = X_train_feats.shape[1]
hidden_dim=200
num_classes = 10
best_acc=-1
best_net = None
################################################################################
# TODO: Train a two-layer neural network on image features. You may want to #
# cross-validate various parameters as in previous sections. Store your best #
# model in the best_net variable. #
################################################################################
# Your code
# for hd in [200]:
# for lr in [3e-1,1]:
# for reg in [3e-4,1e-3,3e-3]:
# net = TwoLayerNet(input_dim, hd, num_classes)
# stats = net.train(X_train_feats, y_train, X_val_feats, y_val,
# num_iters=3000, batch_size=300,
# learning_rate=lr, learning_rate_decay=0.99,
# reg=reg, verbose=True)
# # Predict on the validation set
# val_acc = (net.predict(X_val_feats)==y_val).mean()
# print ("regularization=%f, lr = %f, hidden dim = %f, Valid_accuracy: %f" %(reg, lr, hd,val_acc))
# if val_acc > best_acc:
# best_acc = val_acc
# best_net = net
# print('best validation accuracy achieved during cross-validation: %f' % best_acc)
for hidden_size in [hidden_dim]:
for learning_rate in [1e-1,5e-1]:
for learning_rate_decay in [0.999]:
for reg in [3e-4,1e-3,3e-3]:
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
# Train the network
stats = net.train(X_train_feats, y_train, X_val_feats, y_val,
num_iters=3000, batch_size=500,
learning_rate=learning_rate, learning_rate_decay=learning_rate_decay,
reg=reg, verbose=True)
# Predict on the validation set
val_acc = (net.predict(X_val_feats) == y_val).mean()
print('hidden_size = %d,learning_rate = %f,learning_rate_decay = %f,reg = %f,Validation accuracy =%f '%(hidden_size,learning_rate,learning_rate_decay,reg,val_acc))
if best_acc<val_acc:
best_acc = val_acc
best_net = net
print('best validation accuracy achieved during cross-validation: %f' % best_acc)
################################################################################
# END OF YOUR CODE #
################################################################################
# +
# Run your best neural net classifier on the test set. You should be able
# to get more than 55% accuracy.
test_acc = (best_net.predict(X_test_feats) == y_test).mean()
print(test_acc)
pe="text" id="XftzX9CN_uGT"
# たとえば、データセットに `[False, 4, bytes('goat'), 0.9876]` という1つの観測記録があるとします。`create_message()` を使うとこの観測記録から `tf.Example` メッセージを作成し印字できます。上記のように、観測記録一つ一つが `Features` メッセージとして書かれています。`tf.Example` [メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88)は、この `Features` メッセージを包むラッパーに過ぎないことに注意してください。
# + colab_type="code" id="N8BtSx2RjYcb" colab={}
# データセットからの観測記録の例
example_observation = []
serialized_example = serialize_example(False, 4, b'goat', 0.9876)
serialized_example
# + [markdown] colab_type="text" id="_pbGATlG6u-4"
# メッセージをデコードするには、`tf.train.Example.FromString` メソッドを使用します。
# + colab_type="code" id="dGim-mEm6vit" colab={}
example_proto = tf.train.Example.FromString(serialized_example)
example_proto
# + [markdown] colab_type="text" id="y-Hjmee-fbLH"
# ## TFRecord フォーマットの詳細
#
# TFRecord ファイルにはレコードのシーケンスが含まれます。このファイルはシーケンシャル読み取りのみが可能です。
#
# それぞれのレコードには、データを格納するためのバイト文字列とデータ長、そして整合性チェックのための CRC32C（Castagnoli 多項式を使った 32 ビットの CRC ）ハッシュ値が含まれます。
#
# 各レコードのフォーマットは
#
# uint64 長さ
# uint32 長さのマスク済み crc32 ハッシュ値
# byte data[長さ]
# uint32 データのマスク済み crc32 ハッシュ値
#
# 複数のレコードが結合されてファイルを構成します。CRC については[ここ](https://en.wikipedia.org/wiki/Cyclic_redundancy_check)に說明があります。CRC のマスクは下記のとおりです。
#
# masked_crc = ((crc >> 15) | (crc << 17)) + 0xa282ead8ul
#
# 注：TFRecord ファイルを作るのに、`tf.Example` を使わなければならないということはありません。tf.Example は、ディクショナリをバイト文字列にシリアライズする方法の1つです。エンコードされた画像データや、（`tf.io.serialize_tensor` を使ってシリアライズされ、`tf.io.parse_tensor` で読み込まれる）シリアライズされたテンソルもあります。そのほかのオプションについては、`tf.io` モジュールを参照してください。
# + [markdown] colab_type="text" id="LYnQzvAvfchQ"
# ## `tf.data` を使用した TFRecord ファイル
# + [markdown] colab_type="text" id="GmehkCCT81Ez"
# `tf.data` モジュールには、TensorFlow でデータを読み書きするツールが含まれます。
# + [markdown] colab_type="text" id="1FISEuz8ubu3"
# ### TFRecord ファイルの書き出し
#
# データをデータセットにするもっとも簡単な方法は `from_tensor_slices` メソッドです。
#
# 配列に適用すると、このメソッドはスカラー値のデータセットを返します。
# + colab_type="code" id="mXeaukvwu5_-" colab={}
tf.data.Dataset.from_tensor_slices(feature1)
# + [markdown] colab_type="text" id="f-q0VKyZvcad"
# 配列のタプルに適用すると、タプルのデータセットが返されます。
# + colab_type="code" id="H5sWyu1kxnvg" colab={}
features_dataset = tf.data.Dataset.from_tensor_slices((feature0, feature1, feature2, feature3))
features_dataset
# + colab_type="code" id="m1C-t71Nywze" colab={}
# データセットから1つのサンプルだけを取り出すには `take(1)` を使います。
for f0,f1,f2,f3 in features_dataset.take(1):
print(f0)
print(f1)
print(f2)
print(f3)
# + [markdown] colab_type="text" id="mhIe63awyZYd"
# `Dataset` のそれぞれの要素に関数を適用するには、`tf.data.Dataset.map` メソッドを使用します。
#
# マップされる関数は TensorFlow のグラフモードで動作する必要があります。関数は `tf.Tensors` を処理し、返す必要があります。`create_example` のような非テンソル関数は、互換性のため `tf.py_func` でラップすることができます。
#
# `tf.py_func` を使用する際には、シェイプと型は取得できないため、指定する必要があります。
# + colab_type="code" id="apB5KYrJzjPI" colab={}
def tf_serialize_example(f0,f1,f2,f3):
tf_string = tf.py_function(
serialize_example,
(f0,f1,f2,f3), # 上記の関数にこれらの引数を渡す
tf.string) # 戻り値の型は tf.string
return tf.reshape(tf_string, ()) # 結果はスカラー
# + id="mckRzbHlfchm" colab_type="code" colab={}
tf_serialize_example(f0,f1,f2,f3)
# + [markdown] colab_type="text" id="CrFZ9avE3HUF"
# この関数をデータセットのそれぞれの要素に適用します。
# + colab_type="code" id="VDeqYVbW3ww9" colab={}
serialized_features_dataset = features_dataset.map(tf_serialize_example)
serialized_features_dataset
# + id="CRtx4Cjpfch2" colab_type="code" colab={}
def generator():
for features in features_dataset:
yield serialize_example(*features)
# + id="sDl1JG09fch4" colab_type="code" colab={}
serialized_features_dataset = tf.data.Dataset.from_generator(
generator, output_types=tf.string, output_shapes=())
# + id="_ZVqJdH5fch6" colab_type="code" colab={}
serialized_features_dataset
# + [markdown] colab_type="text" id="p6lw5VYpjZZC"
# TFRecord ファイルに書き出します。
# + colab_type="code" id="vP1VgTO44UIE" colab={}
filename = 'test.tfrecord'
writer = tf.data.experimental.TFRecordWriter(filename)
writer.write(serialized_features_dataset)
# + [markdown] colab_type="text" id="6aV0GQhV8tmp"
# ### TFRecord ファイルの読み込み
# + [markdown] colab_type="text" id="o3J5D4gcSy8N"
# `tf.data.TFRecordDataset` クラスを使って TFRecord ファイルを読み込むこともできます。
#
# `tf.data` を使って TFRecord ファイルを取り扱う際の詳細については、[こちら](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data)を参照ください。
#
# `TFRecordDataset` を使うことは、入力データを標準化し、パフォーマンスを最適化するのに有用です。
# + colab_type="code" id="6OjX6UZl-bHC" colab={}
filenames = [filename]
raw_dataset = tf.data.TFRecordDataset(filenames)
raw_dataset
# + [markdown] colab_type="text" id="6_EQ9i2E_-Fz"
# この時点で、データセットにはシリアライズされた `tf.train.Example` メッセージが含まれています。データセットをイテレートすると、スカラーの文字列テンソルが返ってきます。
#
# `.take` メソッドを使って最初の 10 レコードだけを表示します。
#
# 注：`tf.data.Dataset` をイテレートできるのは、Eager Execution が有効になっている場合のみです。
# + colab_type="code" id="hxVXpLz_AJlm" colab={}
for raw_record in raw_dataset.take(10):
print(repr(raw_record))
# + [markdown] colab_type="text" id="W-6oNzM4luFQ"
# これらのテンソルは下記の関数でパースできます。
#
# 注：ここでは、`feature_description` が必要です。データセットはグラフ実行を使用するため、この記述を使ってシェイプと型を構築するのです。
# + colab_type="code" id="zQjbIR1nleiy" colab={}
# 特徴の記述
feature_description = {
'feature0': tf.io.FixedLenFeature([], tf.int64, default_value=0),
'feature1': tf.io.FixedLenFeature([], tf.int64, default_value=0),
'feature2': tf.io.FixedLenFeature([], tf.string, default_value=''),
'feature3': tf.io.FixedLenFeature([], tf.float32, default_value=0.0),
}
def _parse_function(example_proto):
# 上記の記述を使って入力の tf.Example を処理
return tf.io.parse_single_example(example_proto, feature_description)
# + [markdown] colab_type="text" id="gWETjUqhEQZf"
# あるいは、`tf.parse example` を使ってバッチ全体を一度にパースします。
# + [markdown] colab_type="text" id="AH73hav6Bnmg"
# `tf.data.Dataset.map` メソッドを使って、データセットの各アイテムにこの関数を適用します。
# + colab_type="code" id="6Ob7D-zmBm1w" colab={}
parsed_dataset = raw_dataset.map(_parse_function)
parsed_dataset
# + [markdown] colab_type="text" id="sNV-XclGnOvn"
# Eager Execution を使ってデータセット中の観測記録を表示します。このデータセットには 10,000 件の観測記録がありますが、最初の 10 個だけ表示します。
# データは特徴量のディクショナリの形で表示されます。それぞれの項目は `tf.Tensor` であり、このテンソルの `numpy` 要素は特徴量を表します。
# + colab_type="code" id="x2LT2JCqhoD_" colab={}
for parsed_record in parsed_dataset.take(10):
print(repr(raw_record))
# + [markdown] colab_type="text" id="Cig9EodTlDmg"
# ここでは、`tf.parse_example` が`tf.Example` のフィールドを通常のテンソルに展開しています。
# + [markdown] colab_type="text" id="jyg1g3gU7DNn"
# ## tf.python_io を使った TFRecord ファイル
# + [markdown] colab_type="text" id="3FXG3miA7Kf1"
# `tf.python_io` モジュールには、TFRecord ファイルの読み書きのための純粋な Python 関数も含まれています。
# + [markdown] colab_type="text" id="CKn5uql2lAaN"
# ### TFRecord ファイルの書き出し
# + [markdown] colab_type="text" id="LNW_FA-GQWXs"
# 次にこの 10,000 件の観測記録を `test.tfrecords` ファイルに出力します。観測記録はそれぞれ `tf.Example` メッセージに変換され、ファイルに出力されます。その後、`test.tfrecords` ファイルが作成されたことを確認することができます。
# + colab_type="code" id="MKPHzoGv7q44" colab={}
# `tf.Example` 観測記録をファイルに出力
with tf.io.TFRecordWriter(filename) as writer:
for i in range(n_observations):
example = serialize_example(feature0[i], feature1[i], feature2[i], feature3[i])
writer.write(example)
# + colab_type="code" id="EjdFHHJMpUUo" colab={}
# !du -sh {filename}
# + [markdown] colab_type="text" id="wtQ7k0YWQ1cz"
# ### TFRecord ファイルの読み込み
#
# これらのシリアライズされたテンソルは、`tf.train.Example.ParseFromString` を使って簡単にパースできます。
# + colab_type="code" id="36ltP9B8OezA" colab={}
filenames = [filename]
raw_dataset = tf.data.TFRecordDataset(filenames)
raw_dataset
# + id="BpS-R4MLfcic" colab_type="code" colab={}
for raw_record in raw_dataset.take(1):
example = tf.train.Example()
example.ParseFromString(raw_record.numpy())
print(example)
# + [markdown] colab_type="text" id="S0tFDrwdoj3q"
# ## ウォークスルー： 画像データの読み書き
# + [markdown] colab_type="text" id="rjN2LFxFpcR9"
# 以下は、TFRecord を使って画像データを読み書きする方法の例です。この例の目的は、データ（この場合は画像）を入力し、そのデータを TFRecord ファイルに書き込んで、再びそのファイルを読み込み、画像を表示するという手順を最初から最後まで示すことです。
#
# これは、たとえば、おなじ入力データセットを使って複数のモデルを構築するといった場合に役立ちます。画像データをそのまま保存する代わりに、TFRecord 形式に前処理しておき、その後の処理やモデル構築に使用することができます。
#
# まずは、雪の中の猫の[画像](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg)と、ニューヨーク市にあるウイリアムズバーグ橋の [写真](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg)をダウンロードしましょう。
# + [markdown] colab_type="text" id="5Lk2qrKvN0yu"
# ### 画像の取得
# + colab_type="code" id="3a0fmwg8lHdF" colab={}
cat_in_snow = tf.keras.utils.get_file('320px-Felis_catus-cat_on_snow.jpg', 'https://storage.googleapis.com/download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg')
williamsburg_bridge = tf.keras.utils.get_file('194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg')
# + colab_type="code" id="7aJJh7vENeE4" colab={}
display.display(display.Image(filename=cat_in_snow))
display.display(display.HTML('Image cc-by: <a "href=https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg">Von.grzanka</a>'))
# + colab_type="code" id="KkW0uuhcXZqA" colab={}
display.display(display.Image(filename=williamsburg_bridge))
display.display(display.HTML('<a "href=https://commons.wikimedia.org/wiki/File:New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg">From Wikimedia</a>'))
# + [markdown] colab_type="text" id="VSOgJSwoN5TQ"
# ### TFRecord ファイルの書き出し
# + [markdown] colab_type="text" id="Azx83ryQEU6T"
# 上記で行ったように、この特徴量を `tf.Example` と互換のデータ型にエンコードできます。この場合には、生の画像文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための `label` 特徴量を付け加えます。猫の画像には `0` を、橋の画像には `1` を使うことにしましょう。
# + colab_type="code" id="kC4TS1ZEONHr" colab={}
image_labels = {
cat_in_snow : 0,
williamsburg_bridge : 1,
}
# + colab_type="code" id="c5njMSYNEhNZ" colab={}
# 猫の画像を使った例
image_string = open(cat_in_snow, 'rb').read()
label = image_labels[cat_in_snow]
# 関連する特徴量のディクショナリを作成
def image_example(image_string, label):
image_shape = tf.image.decode_jpeg(image_string).shape
feature = {
'height': _int64_feature(image_shape[0]),
'width': _int64_feature(image_shape[1]),
'depth': _int64_feature(image_shape[2]),
'label': _int64_feature(label),
'image_raw': _bytes_feature(image_string),
}
return tf.train.Example(features=tf.train.Features(feature=feature))
for line in str(image_example(image_string, label)).split('\n')[:15]:
print(line)
print('...')
# + [markdown] colab_type="text" id="2G_o3O9MN0Qx"
# ご覧のように、すべての特徴量が `tf.Example` メッセージに保存されました。上記のコードを関数化し、このサンプルメッセージを `images.tfrecords` ファイルに書き込みます。
# + colab_type="code" id="qcw06lQCOCZU" colab={}
# 生の画像を images.tfrecords ファイルに書き出す
# まず、2つの画像を tf.Example メッセージに変換し、
# 次に .tfrecords ファイルに書き出す
record_file = 'images.tfrecords'
with tf.io.TFRecordWriter(record_file) as writer:
for filename, label in image_labels.items():
image_string = open(filename, 'rb').read()
tf_example = image_example(image_string, label)
writer.write(tf_example.SerializeToString())
# + colab_type="code" id="yJrTe6tHPCfs" colab={}
# !du -sh {record_file}
# + [markdown] colab_type="text" id="jJSsCkZLPH6K"
# ### TFRecord ファイルの読み込み
#
# これで、`images.tfrecords` ファイルができました。このファイルの中のレコードをイテレートし、書き込んだものを読み出します。このユースケースでは、画像を復元するだけなので、必要なのは生画像の文字列だけです。上記のゲッター、すなわち、`example.features.feature['image_raw'].bytes_list.value[0]` を使って抽出することができます。猫と橋のどちらであるかを決めるため、ラベルも使用します。
# + colab_type="code" id="M6Cnfd3cTKHN" colab={}
raw_image_dataset = tf.data.TFRecordDataset('images.tfrecords')
# 特徴量を記述するディクショナリを作成
image_feature_description = {
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'depth': tf.io.FixedLenFeature([], tf.int64),
'label': tf.io.FixedLenFeature([], tf.int64),
'image_raw': tf.io.FixedLenFeature([], tf.string),
}
def _parse_image_function(example_proto):
# 入力の tf.Example のプロトコルバッファを上記のディクショナリを使って解釈
return tf.io.parse_single_example(example_proto, image_feature_description)
parsed_image_dataset = raw_image_dataset.map(_parse_image_function)
parsed_image_dataset
# + [markdown] colab_type="text" id="0PEEFPk4NEg1"
# TFRecord ファイルから画像を復元しましょう。
# + colab_type="code" id="yZf8jOyEIjSF" colab={}
for image_features in parsed_image_dataset:
image_raw = image_features['image_raw'].numpy()
display.display(display.Image(data=image_raw))
| 24,569 |
/notebooks/sdc_auto_mpg.ipynb | af21355773a9194329c6e074209225fe95e21f3f | [] | no_license | wajeehulhassanvii/computervision | https://github.com/wajeehulhassanvii/computervision | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 818,725 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: 'Python 3.7.7 64-bit (''mytf2'': conda)'
# language: python
# name: python37764bitmytf2condad0e6e6c9b9bc4440a94687b08616bf38
# ---
# # Import libraries
from __future__ import absolute_import, division, print_function, unicode_literals
import pathlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
gpus= tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
from tensorflow import keras
from tensorflow.keras import layers
# # Import dataset
# +
# dataset_path = keras.utils.get_file("auto-mpg.data", "https://archive.ics.uci.edu")
dataset_path = "../datasets/auto_mpg/auto-mpg.data"
# ../datasets/auto_mpg
column_names = ["MPG", "Cylinders", "Displacement", "Horsepower", "Weight",\
"Acceleration", "Model Year", "Origin"]
raw_dataset = pd.read_csv(dataset_path, names=column_names,
na_values= "?", comment="\t",
sep=" ", skipinitialspace=True)
dataset = raw_dataset.copy()
# -
dataset.head(-1)
# # Data manipulation
dataset.describe()
# ## fix missing values
dataset.Horsepower.isnull()
dataset.Horsepower.isnull().sum()
# get indexes of the missing values of a column (Horsepower)
dataset[dataset.Horsepower.isnull()]
dataset[dataset.Horsepower.isnull()].index
dataset[dataset.Horsepower.isnull()].index.to_list()
hp_mi = dataset[dataset.Horsepower.isnull()].index.to_list()
# ##### fill with mean
# +
# dataset.Horsepower.loc[hp_mi] = dataset.Horsepower.mean()
# -
dataset.Horsepower.loc[hp_mi]
# dataset.plot(y='Horsepower')
# plt.show()
# dataset.plot(y='Horsepower', x='Model Year', kind='scatter')
# plt.show()
# dataset.plot(y='Horsepower', x='Weight', kind='scatter')
# plt.show()
dataset.plot(y='Horsepower', x='Acceleration', kind='scatter')
plt.show()
dataset.loc[hp_mi]
# ### linear regression for missing value
# 
import numpy as np
dataset.dropna(subset=['Horsepower'])['Horsepower'].shape[0]
train_mv_y = np.array(dataset.dropna(subset=['Horsepower'])['Horsepower']).reshape(int(dataset.dropna(subset=['Horsepower'])['Horsepower'].shape[0]), 1)
train_mv_x = np.array(dataset.dropna(subset=['Horsepower'])['Weight']).reshape(int(dataset.dropna(subset=['Horsepower'])['Horsepower'].shape[0]), 1)
print(type(train_mv_x))
print(type(train_mv_y))
from sklearn import linear_model
regr_mv = linear_model.LinearRegression()
mv_model = regr_mv.fit(X=train_mv_x, y=train_mv_y)
score_r_square = regr_mv.score(X=train_mv_x, y=train_mv_y)
intercept_mv = regr_mv.intercept_
coef_mv = regr_mv.coef_
print(f"score_r_square: {score_r_square}")
print(f"intercept_mv: {intercept_mv}")
print(f"coef_mv: {coef_mv}")
regr_mv.predict(np.array(dataset.Weight.loc[hp_mi]).reshape(len(hp_mi),1))
dataset.Horsepower.loc[hp_mi] = regr_mv.predict(np.array(dataset.Weight.loc[hp_mi]).reshape(len(hp_mi),1)).reshape(6,)
dataset.Horsepower.describe()
# ## we do one_hot_encoding to categorical data
from sklearn.preprocessing import OneHotEncoder
dataset.Origin.value_counts()
# one_hot_encoder_origin = OneHotEncoder(handle_unknown='ignore')
# # dataset.Origin = one_hot_encoder_origin.fit_transform(dataset.Origin)
# dataset.Origin = one_hot_encoder_origin.fit_transform(dataset.Origin.values.reshape(-1,1)).toarray()
#
pd.get_dummies(dataset.Origin).iloc[:,0]
pd.get_dummies(dataset.Origin).iloc[:,1]
pd.get_dummies(dataset.Origin).iloc[:,2]
origin_col_names = []
for i in list(pd.get_dummies(dataset.Origin).columns):
print (f"origin_{i}")
origin_col_names.append(f"origin_{i}")
origin_col_names
dataset[origin_col_names[0]] = pd.get_dummies(dataset.Origin).iloc[:,0]
dataset[origin_col_names[1]] = pd.get_dummies(dataset.Origin).iloc[:,1]
dataset[origin_col_names[2]] = pd.get_dummies(dataset.Origin).iloc[:,2]
dataset.head()
dataset = dataset.drop(columns=['Origin'])
dataset
# ## Splitting the data
# ## CHECK DISTRIBUTIONS
sns.histplot(data=dataset, x=dataset.Displacement)
sns.histplot(data=dataset, x=dataset.MPG)
sns.histplot(data=dataset, x=dataset.MPG)
dataset.columns.to_list()
for i, col in enumerate(dataset.columns):
plt.figure(i)
sns.distplot(dataset[col])
import math
elements_each_row = 4
total_rows = math.ceil(dataset.shape[1] / elements_each_row)
last_row_elements = dataset.shape[1] % elements_each_row
dataset.hist
# %matplotlib inline
def show_distributions(df, fig_size_x, fig_size_y, kind='hist'):
#total_columns = df.shape[1]
# 4 columns in each row
import math
elements_each_row = 4
total_rows = math.ceil(df.shape[1] / elements_each_row)
last_row_elements = df.shape[1] % elements_each_row
fig, axes = plt.subplots(total_rows, elements_each_row)
fig = plt.figure(figsize=(fig_size_x,fig_size_y))
fig.suptitle('1 row x 2 columns axes with no data')
if kind == 'hist':
for i, feature in enumerate(df.columns):
axes = fig.add_subplot(total_rows, elements_each_row, i+1)
sns.histplot(data=df, ax=axes, x=df[feature])
if kind == 'dist':
for i, feature in enumerate(df.columns):
axes = fig.add_subplot(total_rows, elements_each_row, i+1)
sns.distplot(df[feature])
plt.show()
# +
# from visualization_utility_functions import show_distributions
# -
show_distributions(dataset, 20, 12, 'dist')
dataset.hist(bins=30, figsize=(12,12), density=True)
plt.show()
sns.boxplot(y=dataset.Displacement)
plt.ylim(top=int(dataset.Displacement.max() + (dataset.Displacement.max() * 0.15)),\
bottom=int(dataset.Displacement.min() - (dataset.Displacement.max() * 0.15)))
# +
# from visualization_utility_functions import plot_box
# -
sns.boxplot(y=dataset.Acceleration)
plt.ylim(top=int(dataset.Acceleration.max() + (dataset.Acceleration.max() * 0.15)),\
bottom=int(dataset.Acceleration.min() - (dataset.Acceleration.max() * 0.15)))
sns.scatterplot(y=dataset.Acceleration, x=dataset.index)
# ## Standardizing the data
# seperate one hot encoded data and normalize the rest
from sklearn.preprocessing import minmax_scale
col_names_x = ['Cylinders', 'Displacement', 'Horsepower', 'Weight','Acceleration', 'Model Year','origin_1', 'origin_2', 'origin_3']
y = dataset.drop(columns=col_names_x)
X = dataset[col_names_x]
X.columns
X_origins = X[['origin_1', 'origin_2', 'origin_3']]
X_origins
X = X.drop(columns=['origin_1', 'origin_2', 'origin_3'])
X
# X = minmax_scale(X)
def normalization(x):
return (x - x.mean()) / x.mean()
X = X.merge(X_origins, left_index=True, right_index=True)
X
X = normalization(X)
y = normalization(y)
# ## split into train and test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
# # Build and compile the model
def scratch_model(X_train):
model = keras.Sequential([
layers.Dense(64, activation=tf.nn.relu, input_shape=[len(X_train.keys())]),
layers.Dense(64, activation=tf.nn.relu),
layers.Dense(1)
])
optimizer = tf.keras.optimizers.RMSprop(0.001)
model.compile(loss='mean_squared_error',
optimizer=optimizer,
metrics=['mean_absolute_error', 'mean_squared_error'])
return model
model = scratch_model(X_train)
model.summary()
# # Train the model
# ## create call back for printing dot while model is training
class PrintDot(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs):
if epoch % 100 == 0:
print('')
print('.', end='')
EPOCHS = 1000
history = model.fit(X_train,
y_train,
epochs=EPOCHS,
validation_split=0.15,
verbose=0,
callbacks=[PrintDot()])
hist = pd.DataFrame(history.history)
hist
def plot_training_history(history):
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
plt.figure()
plt.xlabel('Epoch')
plt.ylabel('Mean Abs Error [MPG]')
plt.plot(hist['epoch'], hist['mean_absolute_error'],
label='Train Error')
plt.plot(hist['epoch'], hist['val_mean_absolute_error'],
label = 'Val Error')
plt.ylim([0,5])
plt.legend()
plt.figure()
plt.xlabel('Epoch')
plt.ylabel('Mean Square Error [$MPG^2$]')
plt.plot(hist['epoch'], hist['mean_squared_error'],
label='Train Error')
plt.plot(hist['epoch'], hist['val_mean_squared_error'],
label = 'Val Error')
plt.ylim([0,20])
plt.legend()
plt.show()
plot_training_history(history)
test_predictions = model.predict(X_test).flatten()
plt.scatter(y_test, test_predictions)
plt.xlabel('True Values [MPG]')
plt.ylabel('Predictions [MPG]')
plt.axis('equal')
plt.axis('square')
plt.xlim([0,plt.xlim()[1]])
plt.ylim([0,plt.ylim()[1]])
_ = plt.plot([-100, 100], [-100, 100])
plt.show()
| 9,233 |
/analyse-donnees-massives/.ipynb_checkpoints/analyse-donnees-massives-tp6-checkpoint.ipynb | 5c93d8b51b3be569729c2fdad93c2633115ed1f4 | [] | no_license | fxjollois/cours-2017-2018 | https://github.com/fxjollois/cours-2017-2018 | 3 | 4 | null | null | null | null | Jupyter Notebook | false | false | .py | 297,547 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] run_control={"frozen": false, "read_only": false}
# # TP6 - Analyse de données sous `Python`
#
# #### Analyse de Données Massives - Master 1ère année
#
# Nous utilisons dans ce TP le module [`scikit-learn`](http://scikit-learn.org/), dédié au *Machine Learning*. Pour mieux la découvrir, nous allons réaliser une étude de cas, avec les données `iris`.
#
# Dans cette étude, nous allons chercher à savoir s'il existe différentes sous-espèces d'iris. Pour cela, nous allons procéder par étapes ::
#
# 1. Visualisation des données, via une **ACP**
# 1. Classification non-supervisée interne à chaque espèce, via **DBSCAN**
# 1. Analyse des résultats
#
# Dans un premier temps, il va nous falloir importer les données (`iris`) que nous allons utiliser (via la librairie `pydataset`). Nous allons aussi utiliser d'autres librairies (telles que `seaborn`, `numpy`, `matplotlib` et `pandas`).
#
# Nous importerons les éléments de `scikit-learn` (module `sklearn`) au fur et à mesure.
# +
import numpy
import pandas
import matplotlib.pyplot as plt
import seaborn
seaborn.set_style("white")
# %matplotlib inline
# -
# Pour rappel, la table se présente comme ceci :
iris = pandas.read_csv("Iris.txt", sep="\t")
iris.head()
# ## ACP
#
# Dans le sous-module `decomposition`, nous allons importer la fonction [`PCA()`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html). Le fonctionnement de celle-ci est similaire à toutes les autres dans `scikit-learn`.
#
# 1. On créé d'abord un objet (nommé ici `pca`) qui va contenir le résultat de l'ACP. Dans la fonction `PCA()`, on pourra indiquer les paramètres tels que le nombre de composants à retenir (`n_components`) ou la méthode de calcul (`svd_solver`).
# 2. Ensuite, on ajuste (*fit* en anglais) sur des données avec la fonction `fit()` de l'objet précédemment créé. Dans cette fonction, nous devons donc passer les données à utiliser.
#
# Si l'on souhaite une ACP normée, il nous faut standardiser les données en amont, avec la fonction `scale()` du sous-module `preprocessing` (importée aussi ici). Bien évidemment, il ne faut passer que des variables quantitatives.
# +
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
pca = PCA()
pca.fit(scale(iris[iris.columns[:4]]))
# -
# L'objet `pca` comprend maintenant un certain nombre d'objets et de fonctions. Le premier objet est le tableau des variances expliquées (`explained_variance_`) par chaque dimension, et le ratio (proportion) de variance expliquée par dimension (`explained_variance_ratio_`).
print(pca.explained_variance_)
print(pca.explained_variance_ratio_)
# Bien évidemment, il est possible (et préférable) de faire un tableau récapitulatif, avec les valeurs propres, les proportions de variance expliquée simples et cumulées. Voici un petit code permettant de faire ceci.
eig = pandas.DataFrame(
{
"Dimension" : ["Dim" + str(x + 1) for x in range(4)],
"Valeur propre" : pca.explained_variance_,
"% variance expliquée" : numpy.round(pca.explained_variance_ratio_ * 100),
"% cum. var. expliquée" : numpy.round(numpy.cumsum(pca.explained_variance_ratio_) * 100)
},
columns = ["Dimension", "Valeur propre", "% variance expliquée", "% cum. var. expliquée"]
)
eig
# L'idée est de représenter graphiquement ces proportions de variances expliquées (qu'on passe en pourcentage par convenance).
g_eig = seaborn.barplot(x = "Dimension",
y = "% variance expliquée",
palette = ["lightseagreen"],
data = eig)
g_eig.set(ylabel = "Variance expliquée (%)")
g_eig.figure.suptitle("Variance expliquée par dimension")
plt.axhline(y = 25, linewidth = .5, color = "dimgray", linestyle = "--") # 25 = 100 / 4 (nb dimensions)
plt.text(3.25, 26, "25%")
# On remarque ici qu'avec seulement deux dimensions suffisent à expliquer 96 % de la variance des données. Nous allons maintenant calculer les coordonnées des iris sur les dimensions, avec la fonction `transform()` de l'objet `pca`.
iris_pca = pca.transform(iris[iris.columns[:4]])
# Afin de manipuler plus facilement l'objet obtenu par la suite, nous allons créer un `DataFrame` `pandas` en ne prenant que les deux premières dimensions, ainsi que les espèces.
# +
# Transformation en DataFrame pandas
iris_pca_df = pandas.DataFrame({
"Dim1" : iris_pca[:,0],
"Dim2" : iris_pca[:,1],
"Species" : iris.Species
})
# Résultat (premières lignes)
iris_pca_df.head()
# -
# Il est maintenant possible de représenter les données sur le premier plan factoriel, en ajoutant bien évidemment l'information sur les espèces.
g_pca = seaborn.lmplot("Dim1", "Dim2", hue = "Species", data = iris_pca_df, fit_reg = False)
g_pca.set(xlabel = "Dimension 1 (73%)", ylabel = "Dimension 2 (23 %)")
g_pca.fig.suptitle("Premier plan factoriel")
# Il est aussi possible de différencier l'affichage de ce premier plan par espèce, grâce à l'option `col` de `lmplot()`
g_pca2 = seaborn.lmplot("Dim1", "Dim2", hue = "Species", col = "Species", col_wrap = 2, data = iris_pca_df, fit_reg = False)
g_pca2.set(xlabel = "Dimension 1 (73%)", ylabel = "Dimension 2 (23 %)")
# ## DBSCAN
#
# [`DBSCAN`](https://fr.wikipedia.org/wiki/DBSCAN) est un algorithme de classification non supervisée, basé sur la densité. Il est intéressant car il ne nécessite pas de connaître le nombre de classes, mais une estimation de la densité (globale) des données. En effet, les points proches (distance inférieure à $\varepsilon$) sont consiédérés dans la même classe. Si toutefois cette classe comporte au moins un cetain nombre de points au final. Si ce n'est pas le cas, les points sont considérés comme *outliers* et mis à part.
#
# Nous utilisons ici la fonction [`DBSCAN()`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) du sous-module `cluster`. Celle-ci peut prendre en paramètre, entre autres, la valeur de $\varepsilon$ (`eps`, `.5` par défaut) et le nombre minimal d'objets dans une classe (`min_samples`, `5` par défaut).
#
# Puis nous ajustons les données standardisées (pour ne pas donner plus d'influences à une variable qu'à une autre).
# +
from sklearn.cluster import DBSCAN
db = DBSCAN(eps = .6, min_samples = 3)
db.fit(scale(iris[iris.columns[:4]]))
# -
# Les classes dont contenues dans l'objet `labels_` de `db`. On a pour chaque individu sa classe (de $0$ à $K-1$ pour $K$ classes obtenues). Les *outliers* sont étiquettés $-1$ .
db.labels_
# On peut faire un tableau récapitulatif des effectifs par classes.
eff = numpy.unique(db.labels_, return_counts = True)
pandas.DataFrame({
"Classe" : eff[0],
"Effectif" : eff[1]
})
# Pour la représentation des données, on ajoute ces labels de classe aux projections des iris sur le premier plan factoriel.
iris_pca_db = iris_pca_df.assign(Labels = db.labels_)
# On peut ainsi représenter les données en fonction des espèces et des classes obtenues.
seaborn.lmplot("Dim1", "Dim2", hue = "Labels", col = "Species", data = iris_pca_db, fit_reg = False)
# On peut aussi *splitter* le graphique en ligne pour chaque classe.
seaborn.lmplot("Dim1", "Dim2", hue = "Labels", col = "Species", row = "Labels", data = iris_pca_db, fit_reg = False)
# ## Exercice
#
# Nous allons utiliser les données [`pendigits`](https://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits) de l'*UCI Machine Learning Repository*. Ces données représentent le tracé des chiffres de 0 à 9 par plusieurs personnes. Pour chaque tracé, nous n'avons au final que les coordonnées $(X,Y)$ de 8 points et le chiffre tracé.
#
# Voici ci dessous comment importer les données directement.
pen_tes = pandas.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/pendigits/pendigits.tes",
header=None)
pen_tra = pandas.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/pendigits/pendigits.tra",
header=None)
pen = pen_tes.copy().append(pen_tra, ignore_index = True)
print(pen.shape)
pen.head()
# Comme vous le pouvez le remarquer, les noms des variables ne sont pas renseignés. Celles-ci sont $(x_j, y_j)_{j = 1,\ldots,8}$ et le chiffre. On va donc déjà créer le vecteur correspondant.
a = [c + n for c, n in zip(["x", "y"] * 8, [str(x) for x in range(1, 9) for i in range(2)])]
a.append("chiffre")
print(a)
# On va ensuite renommer les colonnes avec ce vecteur.
pen.columns = a
pen.head()
# Et pour la suite, nous allons créer une variable contenant les chiffres mais en tant que chaîne de caractère.
pen = pen.assign(chiffre_str = [str(c) for c in pen.chiffre])
# Par la suite, nous aurons besoin d'accéder aux $x_j$ uniquement, ou aux $y_j$, voire aux deux. Nous créons donc des vecteurs avec les noms de variables.
xN = ["x" + str(i + 1) for i in range(8)]
print(xN)
yN = ["y" + str(i + 1) for i in range(8)]
print(yN)
xyN = [a + b for a,b in zip(["x", "y"] * 8, [str(i + 1) for i in range(8) for j in range(2)])]
print(xyN)
# Ces données ont l'avantage d'être graphique. Nous allons donc représenter le premier tracé, qui est un $8$.
x = pen.loc[0, xN]
y = pen.loc[0, yN]
chiffre = pen.loc[0, "chiffre"]
plt.plot(x, y)
plt.title("Chiffre : " + str(chiffre))
# Nous allons régulièrement utiliser ce code, donc nous allons le stocker dans une fonction nommée `dessin()`. Dans celle-ci, nous allons mettre en paramètre les $x_j$ et les $y_j$, le chiffre, ainsi qu'un graphique dans lequel nous allons mettre le dessin. Ceci nous sera utile pour faire plusieurs représentations de chiffres.
# +
def dessin(p, x, y, chiffre):
p.plot(x, y)
p.set_title("Chiffre : " + str(chiffre))
p.axis("off")
p.set_xlim([-1, 101])
p.set_ylim([-1, 101])
fig, ax = plt.subplots()
dessin(ax, x, y, chiffre)
# -
# Ensuite, nous créons une liste de `DataFrame`, un pour chaque chiffre. La fonction `query()` permet donc de sélectionner des lignes d'un `DataFrame` en fonction d'une condition (ici, `chiffre` égal 0, 1, ..., 9). Pour éviter les problèmes d'index plus tard, nous devons les réinitialiser pour chaque `DataFrame`, avec la fonction `reset_index()`, en mettant `drop` à vrai. Ceci permet d'oublier les numéros de ligne du `DataFrame` global et que ceux-ci recommencent de 0 pour chaque sous-ensemble.
sub = [pen.query("chiffre == " + str(i)).reset_index(drop = True) for i in range(10)]
# Nous voulons maintenant représenter chaque premier exemple de chaque chiffre. Pour cela, nous recherchons la première ligne (`index = 0`) pour chaque sous-ensemble précédemment créé. Et pour simplifier le travail ensuite, nous renvoyons pour chaque chiffre, trois éléments : les $x_j$, les $y_j$ et le chiffre.
subxyc = [[s.loc[0, xN], s.loc[0, yN], s.loc[0, "chiffre"]] for s in sub]
# Puis, nous créons une figure (en spécifiant la taille). Et pour chaque chiffre, nous ajoutons un graphique à la figure avec la fonction `add_subplot()`. Celle-ci prend trois paramètres : le nombre de lignes, le nombre de colonnes et le numéro de placement du prochain graphique. Grâce à l'utilisation de la fonction `dessin()` et de l'objet `subxyc`, la réalisation est simple.
fig = plt.figure(figsize = (15, 5))
for i in range(10):
ax = fig.add_subplot(2, 5, i + 1)
dessin(ax, subxyc[i][0], subxyc[i][1], subxyc[i][2])
# Le but de ce TP va être de réfléchir à comment répondre à la question suivante :
#
# > Existe-t'il plusieurs façons d'écrire chaque chiffre ?
#
# Pour cela, nous allons dérouler les étapes suivantes :
#
# 1. Calculer la moyenne de chaque coordonnée $x_j$ et $y_j$, pour chaque chiffre
# 1. Représenter le tracé des *chiffres moyens* (i.e. en prenant les coordonnées moyennes donc)
# - Améliorer éventuellement la fonction `dessin()` pour ajouter, si demandé, les numéros des points
# 1. Réaliser une ACP sur les données (en comparant avec ou sans standardisation)
# 1. Représenter les chiffres sur le plan factoriel
# - sur un seul graphique
# - avec un graphique par chiffre, sur la même figure
# 1. Réaliser une classification via DBSCAN pour chaque chiffre
# - choisir un $\varepsilon$ et un nombre minimal de points
# - créer une fonction prenant en paramètre les valeurs ci-dessus à tester et qui réalise les opérations suivantes :
# - calcul de la partition
# - affichage de la répartition des classes
# - représentation des classes sur le plan factoriel (un graphique par classe éventuellement)
# - représentation des tracés moyens pour chaque classe afin de mieux comprendre les différences entre les classes
#
| 12,845 |
/projects/proj01/CarND-LaneLines-P1/P1.ipynb | 105436e612fc3a811e9ca418067faad83927ebb1 | [
"MIT"
] | permissive | raffaeleGrandi/SDCND | https://github.com/raffaeleGrandi/SDCND | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 1,765,122 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python [conda env:pytorch]
# language: python
# name: conda-env-pytorch-py
# ---
# + [markdown] tags=["pdf-title"]
# # Generative Adversarial Networks (GANs)
#
# So far in CS231N, all the applications of neural networks that we have explored have been **discriminative models** that take an input and are trained to produce a labeled output. This has ranged from straightforward classification of image categories to sentence generation (which was still phrased as a classification problem, our labels were in vocabulary space and we’d learned a recurrence to capture multi-word labels). In this notebook, we will expand our repetoire, and build **generative models** using neural networks. Specifically, we will learn how to build models which generate novel images that resemble a set of training images.
# 到目前为止，在CS231N中，我们所探索的所有神经网络应用都是**判别模型**，它接受输入并训练产生标记输出。这包括从图像类别的直接分类到句子生成（这仍然是一个分类问题，我们的标签在词汇空间中，我们已经学会了一种重现来捕获多词标签）。在本笔记本中，我们将扩大我们的重复，并建立**生成模型**使用神经网络。具体来说，我们将学习如何建立模型，生成类似于一组训练图像的新颖图像。
#
# ### What is a GAN?
#
# In 2014, [Goodfellow et al.](https://arxiv.org/abs/1406.2661) presented a method for training generative models called Generative Adversarial Networks (GANs for short). In a GAN, we build two different neural networks. Our first network is a traditional classification network, called the **discriminator**. We will train the discriminator to take images, and classify them as being real (belonging to the training set) or fake (not present in the training set). Our other network, called the **generator**, will take random noise as input and transform it using a neural network to produce images. The goal of the generator is to fool the discriminator into thinking the images it produced are real.
# 2014年，【古德费罗等人】(https://arxiv.org/abs/1406.2661)提出了一种生成模型的训练方法，简称生成对抗网络。在GAN中，我们建立了两个不同的神经网络。我们的第一个网络是传统的分类网络，称为**鉴别器**。我们将训练鉴别器拍摄图像，并将它们分类为真实（属于训练集）或虚假（不存在于训练集中）。我们的另一个网络，称为**生成器**，将随机噪声作为输入，并使用神经网络对其进行变换以生成图像。生成器的目标是欺骗鉴别器，使其认为生成的图像是真实的。
#
# We can think of this back and forth process of the generator ($G$) trying to fool the discriminator ($D$), and the discriminator trying to correctly classify real vs. fake as a minimax game:
# 我们可以把生成器（G）试图愚弄鉴别器（D）和鉴别器（D）的这个来回过程想象成一个极小极大的游戏：
#
# $$\underset{G}{\text{minimize}}\; \underset{D}{\text{maximize}}\; \mathbb{E}_{x \sim p_\text{data}}\left[\log D(x)\right] + \mathbb{E}_{z \sim p(z)}\left[\log \left(1-D(G(z))\right)\right]$$
# where $z \sim p(z)$ are the random noise samples, $G(z)$ are the generated images using the neural network generator $G$, and $D$ is the output of the discriminator, specifying the probability of an input being real. In [Goodfellow et al.](https://arxiv.org/abs/1406.2661), they analyze this minimax game and show how it relates to minimizing the Jensen-Shannon divergence between the training data distribution and the generated samples from $G$.
# 其中z∼p（z）是随机噪声样本，G（z）是使用神经网络生成器G生成的图像，D是鉴别器的输出，指定输入为实的概率。在Goodfellow等人中，他们分析了这个minimax博弈，并展示了它与最小化训练数据分布和从G。
#
# To optimize this minimax game, we will aternate between taking gradient *descent* steps on the objective for $G$, and gradient *ascent* steps on the objective for $D$:
# 1. update the **generator** ($G$) to minimize the probability of the __discriminator making the correct choice__.
# 2. update the **discriminator** ($D$) to maximize the probability of the __discriminator making the correct choice__.
# 为了优化这个极小极大博弈，我们将在$G$的目标上采取梯度*下降*步数，在$D$的目标上采取梯度*上升*步数：
# 1.更新**生成器**（$G$），以最大限度地降低鉴别器做出正确选择的概率。让鉴别器猜错
# 2.更新**鉴别器**（$D$），以最大化uu鉴别器做出正确选择的概率。让鉴别器猜对
#
# While these updates are useful for analysis, they do not perform well in practice. Instead, we will use a different objective when we update the generator: maximize the probability of the **discriminator making the incorrect choice**. This small change helps to allevaiate problems with the generator gradient vanishing when the discriminator is confident. This is the standard update used in most GAN papers, and was used in the original paper from [Goodfellow et al.](https://arxiv.org/abs/1406.2661).
# 虽然这些更新对分析很有用，但在实践中效果并不理想。相反，当我们更新生成器时，我们将使用一个不同的目标：最大化**鉴别器做出错误选择**的概率。当鉴别器有信心时，这个微小的变化有助于解决生成器梯度消失的问题。这是大多数GAN论文中使用的标准更新，并且在[Goodfellow等人]的原始论文中使用(https://arxiv.org/abs/1406.2661).
#
# In this assignment, we will alternate the following updates:
# 1. Update the generator ($G$) to maximize the probability of the discriminator making the incorrect choice on generated data:
# $$\underset{G}{\text{maximize}}\; \mathbb{E}_{z \sim p(z)}\left[\log D(G(z))\right]$$
# 2. Update the discriminator ($D$), to maximize the probability of the discriminator making the correct choice on real and generated data:
# $$\underset{D}{\text{maximize}}\; \mathbb{E}_{x \sim p_\text{data}}\left[\log D(x)\right] + \mathbb{E}_{z \sim p(z)}\left[\log \left(1-D(G(z))\right)\right]$$
#
# ### What else is there?
# Since 2014, GANs have exploded into a huge research area, with massive [workshops](https://sites.google.com/site/nips2016adversarial/), and [hundreds of new papers](https://github.com/hindupuravinash/the-gan-zoo). Compared to other approaches for generative models, they often produce the highest quality samples but are some of the most difficult and finicky models to train (see [this github repo](https://github.com/soumith/ganhacks) that contains a set of 17 hacks that are useful for getting models working). Improving the stabiilty and robustness of GAN training is an open research question, with new papers coming out every day! For a more recent tutorial on GANs, see [here](https://arxiv.org/abs/1701.00160). There is also some even more recent exciting work that changes the objective function to Wasserstein distance and yields much more stable results across model architectures: [WGAN](https://arxiv.org/abs/1701.07875), [WGAN-GP](https://arxiv.org/abs/1704.00028).
# 自2014年以来，GANs已经进入了一个巨大的研究领域，拥有大量的[研讨会](https://sites.google.com/site/nips2016adversarial/)，以及[数百篇新论文](https://github.com/hindupuravinash/the-gan-zoo). 与生成模型的其他方法相比，它们通常生成最高质量的样本，但却是最难训练和最挑剔的模型之一（参见[this github repo](https://github.com/soumith/ganhacks)它包含了一组17种对使模型工作有用的技巧）。提高训练的稳定性和鲁棒性是一个开放的研究问题，每天都有新的论文发表！有关GANs的最新教程，请参见[此处](https://arxiv.org/abs/1701.00160). 最近还有一些更令人兴奋的工作，将目标函数改为Wasserstein距离，并在模型架构中产生更稳定的结果：[WGAN](https://arxiv.org/abs/1701.07875)，[WGAN-GP](https://arxiv.org/abs/1704.00028).
#
# GANs are not the only way to train a generative model! For other approaches to generative modeling check out the [deep generative model chapter](http://www.deeplearningbook.org/contents/generative_models.html) of the Deep Learning [book](http://www.deeplearningbook.org). Another popular way of training neural networks as generative models is Variational Autoencoders (co-discovered [here](https://arxiv.org/abs/1312.6114) and [here](https://arxiv.org/abs/1401.4082)). Variatonal autoencoders combine neural networks with variationl inference to train deep generative models. These models tend to be far more stable and easier to train but currently don't produce samples that are as pretty as GANs.
# GAN不是训练生成型号的唯一途径！ 对于生成建模的其他方法，请查看[深度生成模型章节]（http://www.deeplearningbook.org/contents/generative_models.html）的深度学习[书]（http://www.deeplearningbook.org） 。 作为生成模型的培训神经网络的另一种流行方式是变形式自动化器（共同发现[这里]（https://arxiv.org/abs/1312.6114）和[这里]（https://arxiv.org/abs/1401.4082） ）。 VariAtonal AutoEncoders将神经网络与变形推断相结合，以培训深生成模型。 这些模型往往更加稳定，更容易训练，但目前不会产生像GAN一样漂亮的样本。
#
# Here's an example of what your outputs from the 3 different models you're going to train should look like... note that GANs are sometimes finicky, so your outputs might not look exactly like this... this is just meant to be a *rough* guideline of the kind of quality you can expect:
# 下面是一个例子，你将要训练的3个不同模型的输出应该是什么样的。。。请注意，GANs有时很挑剔，因此您的输出可能与此不完全相同。。。这只是一个粗略的指导方针，你可以期望：
# 
# + [markdown] tags=["pdf-ignore"]
# ## Setup
# + tags=["pdf-ignore"]
import torch
import torch.nn as nn
from torch.nn import init
import torchvision
import torchvision.transforms as T
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data import sampler
import torchvision.datasets as dset
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# %matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
# %load_ext autoreload
# %autoreload 2
def show_images(images):
images = np.reshape(images, [images.shape[0], -1]) # images reshape to (batch_size, D)
sqrtn = int(np.ceil(np.sqrt(images.shape[0])))
sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))
fig = plt.figure(figsize=(sqrtn, sqrtn))
gs = gridspec.GridSpec(sqrtn, sqrtn)
gs.update(wspace=0.05, hspace=0.05)
for i, img in enumerate(images):
ax = plt.subplot(gs[i])
plt.axis('off')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
plt.imshow(img.reshape([sqrtimg,sqrtimg]))
return
# -
# Colab users only
# %cd drive/My\ Drive/$FOLDERNAME/
# %cp -r gan-checks-tf.npz /content/
# %cd /content/
# +
from cs231n.gan_pytorch import preprocess_img, deprocess_img, rel_error, count_params, ChunkSampler
answers = dict(np.load('gan-checks-tf.npz'))
# + [markdown] tags=["pdf-ignore"]
# ## Dataset
# GANs are notoriously finicky with hyperparameters, and also require many training epochs. In order to make this assignment approachable without a GPU, we will be working on the MNIST dataset, which is 60,000 training and 10,000 test images. Each picture contains a centered image of white digit on black background (0 through 9). This was one of the first datasets used to train convolutional neural networks and it is fairly easy -- a standard CNN model can easily exceed 99% accuracy.
# 众所周知，GANs对超参数非常挑剔，而且需要很多训练时期。为了使这个任务在没有GPU的情况下变得容易，我们将在MNIST数据集上工作，它是60000个训练和10000个测试图像。每张图片包含一个黑色背景上的白色数字居中图像（0到9）。这是第一批用于训练卷积神经网络的数据集之一，而且相当简单——一个标准的CNN模型很容易超过99%的准确率。
#
# To simplify our code here, we will use the PyTorch MNIST wrapper, which downloads and loads the MNIST dataset. See the [documentation](https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py) for more information about the interface. The default parameters will take 5,000 of the training examples and place them into a validation dataset. The data will be saved into a folder called `MNIST_data`.
# + tags=["pdf-ignore"]
NUM_TRAIN = 50000
NUM_VAL = 5000
NOISE_DIM = 96
batch_size = 128
mnist_train = dset.MNIST('./cs231n/datasets/MNIST_data', train=True, download=True,
transform=T.ToTensor())
loader_train = DataLoader(mnist_train, batch_size=batch_size,
sampler=ChunkSampler(NUM_TRAIN, 0))
mnist_val = dset.MNIST('./cs231n/datasets/MNIST_data', train=True, download=True,
transform=T.ToTensor())
loader_val = DataLoader(mnist_val, batch_size=batch_size,
sampler=ChunkSampler(NUM_VAL, NUM_TRAIN))
imgs = loader_train.__iter__().next()[0].view(batch_size, 784).numpy().squeeze()
show_images(imgs)
# -
# ## Random Noise
# Generate uniform noise from -1 to 1 with shape `[batch_size, dim]`.
#
# Implement `sample_noise` in `cs231n/gan_pytorch.py`.
#
# Hint: use `torch.rand`.
#
# Make sure noise is the correct shape and type:
# + id="sample_noise_test"
from cs231n.gan_pytorch import sample_noise
def test_sample_noise():
batch_size = 3
dim = 4
torch.manual_seed(231)
z = sample_noise(batch_size, dim)
np_z = z.cpu().numpy()
assert np_z.shape == (batch_size, dim)
assert torch.is_tensor(z)
assert np.all(np_z >= -1.0) and np.all(np_z <= 1.0)
assert np.any(np_z < 0.0) and np.any(np_z > 0.0)
print('All tests passed!')
test_sample_noise()
# + [markdown] tags=["pdf-ignore"]
# ## Flatten
#
# Recall our Flatten operation from previous notebooks... this time we also provide an Unflatten, which you might want to use when implementing the convolutional generator. We also provide a weight initializer (and call it for you) that uses Xavier initialization instead of PyTorch's uniform default.
# 回想一下我们以前的笔记本中的展平操作。。。这一次我们还提供了一个Unflatten，在实现卷积生成器时可能需要使用它。我们还提供了一个权重初始值设定项（并为您调用它），它使用Xavier初始化，而不是PyTorch的统一默认值。
# + tags=["pdf-ignore"]
from cs231n.gan_pytorch import Flatten, Unflatten, initialize_weights
# + [markdown] tags=["pdf-ignore"]
# ## CPU / GPU
# By default all code will run on CPU. GPUs are not needed for this assignment, but will help you to train your models faster. If you do want to run the code on a GPU, then change the `dtype` variable in the following cell.
# **If you are a Colab user, it is recommeded to change colab runtime to GPU.**
# + tags=["pdf-ignore"]
dtype = torch.FloatTensor
#dtype = torch.cuda.FloatTensor
# -
# # Discriminator
# Our first step is to build a discriminator. Fill in the architecture as part of the `nn.Sequential` constructor in the function below. All fully connected layers should include bias terms. The architecture is:
# * Fully connected layer with input size 784 and output size 256
# * LeakyReLU with alpha 0.01
# * Fully connected layer with input_size 256 and output size 256
# * LeakyReLU with alpha 0.01
# * Fully connected layer with input size 256 and output size 1
#
# 我们的第一步是建立一个鉴别器。在下面的函数中，作为“nn.Sequential”构造函数的一部分填写体系结构。所有完全连接的层应包括偏置项。体系结构是：
# *全连接层，输入尺寸784，输出尺寸256
# *LeakyReLU，α0.01
# *完全连接层，输入大小为256，输出大小为256
# *LeakyReLU，α0.01
# *输入大小为256，输出大小为1的完全连接层
#
# Recall that the Leaky ReLU nonlinearity computes $f(x) = \max(\alpha x, x)$ for some fixed constant $\alpha$; for the LeakyReLU nonlinearities in the architecture above we set $\alpha=0.01$.
#
# The output of the discriminator should have shape `[batch_size, 1]`, and contain real numbers corresponding to the scores that each of the `batch_size` inputs is a real image.
#
# Implement `discriminator` in `cs231n/gan_pytorch.py`
# Test to make sure the number of parameters in the discriminator is correct:
# +
from cs231n.gan_pytorch import discriminator
def test_discriminator(true_count=267009):
model = discriminator()
cur_count = count_params(model)
if cur_count != true_count:
print('Incorrect number of parameters in discriminator. Check your achitecture.')
else:
print('Correct number of parameters in discriminator.')
test_discriminator()
# -
# # Generator
# Now to build the generator network:
# * Fully connected layer from noise_dim to 1024
# * `ReLU`
# * Fully connected layer with size 1024
# * `ReLU`
# * Fully connected layer with size 784
# * `TanH` (to clip the image to be in the range of [-1,1])
#
# Implement `generator` in `cs231n/gan_pytorch.py`
# Test to make sure the number of parameters in the generator is correct:
# +
from cs231n.gan_pytorch import generator
def test_generator(true_count=1858320):
model = generator(4)
cur_count = count_params(model)
if cur_count != true_count:
print('Incorrect number of parameters in generator. Check your achitecture.')
else:
print('Correct number of parameters in generator.')
test_generator()
# -
# # GAN Loss
#
# Compute the generator and discriminator loss. The generator loss is:
# $$\ell_G = -\mathbb{E}_{z \sim p(z)}\left[\log D(G(z))\right]$$
# and the discriminator loss is:
# $$ \ell_D = -\mathbb{E}_{x \sim p_\text{data}}\left[\log D(x)\right] - \mathbb{E}_{z \sim p(z)}\left[\log \left(1-D(G(z))\right)\right]$$
# Note that these are negated from the equations presented earlier as we will be *minimizing* these losses.
#
# **HINTS**: You should use the `bce_loss` function defined below to compute the binary cross entropy loss which is needed to compute the log probability of the true label given the logits output from the discriminator. Given a score $s\in\mathbb{R}$ and a label $y\in\{0, 1\}$, the binary cross entropy loss is
#
# $$ bce(s, y) = -y * \log(s) - (1 - y) * \log(1 - s) $$
#
# A naive implementation of this formula can be numerically unstable, so we have provided a numerically stable implementation for you below.
#
# You will also need to compute labels corresponding to real or fake and use the logit arguments to determine their size. Make sure you cast these labels to the correct data type using the global `dtype` variable, for example:
#
#
# `true_labels = torch.ones(size).type(dtype)`
#
# Instead of computing the expectation of $\log D(G(z))$, $\log D(x)$ and $\log \left(1-D(G(z))\right)$, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing.
#
# Implement `bce_loss`, `discriminator_loss`, `generator_loss` in `cs231n/gan_pytorch.py`
# Test your generator and discriminator loss. You should see errors < 1e-7.
# +
from cs231n.gan_pytorch import bce_loss, discriminator_loss, generator_loss
def test_discriminator_loss(logits_real, logits_fake, d_loss_true):
d_loss = discriminator_loss(torch.Tensor(logits_real).type(dtype),
torch.Tensor(logits_fake).type(dtype)).cpu().numpy()
print("Maximum error in d_loss: %g"%rel_error(d_loss_true, d_loss))
test_discriminator_loss(answers['logits_real'], answers['logits_fake'],
answers['d_loss_true'])
# +
def test_generator_loss(logits_fake, g_loss_true):
g_loss = generator_loss(torch.Tensor(logits_fake).type(dtype)).cpu().numpy()
print("Maximum error in g_loss: %g"%rel_error(g_loss_true, g_loss))
test_generator_loss(answers['logits_fake'], answers['g_loss_true'])
# -
# # Optimizing our loss
# Make a function that returns an `optim.Adam` optimizer for the given model with a 1e-3 learning rate, beta1=0.5, beta2=0.999. You'll use this to construct optimizers for the generators and discriminators for the rest of the notebook.
#
# Implement `get_optimizer` in `cs231n/gan_pytorch.py`
# # Training a GAN!
#
# We provide you the main training loop... you won't need to change `run_a_gan` in `cs231n/gan_pytorch.py`, but we encourage you to read through and understand it.
# + tags=["pdf-ignore"]
from cs231n.gan_pytorch import get_optimizer, run_a_gan
# +
# Make the discriminator
D = discriminator().type(dtype)
# Make the generator
G = generator().type(dtype)
# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator
D_solver = get_optimizer(D)
G_solver = get_optimizer(G)
# Run it!
images = run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss, loader_train)
# -
# Run the cell below to show the generated images.
numIter = 0
for img in images:
print("Iter: {}".format(numIter))
show_images(img)
plt.show()
numIter += 250
print()
# **Please tag the cell below on Gradescope while submitting.**
print("Vanilla GAN Fianl image:")
show_images(images[-1])
plt.show()
# + [markdown] tags=["pdf-ignore"]
# Well that wasn't so hard, was it? In the iterations in the low 100s you should see black backgrounds, fuzzy shapes as you approach iteration 1000, and decent shapes, about half of which will be sharp and clearly recognizable as we pass 3000.
# -
# # Least Squares GAN
# We'll now look at [Least Squares GAN](https://arxiv.org/abs/1611.04076), a newer, more stable alernative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement equation (9) in the paper, with the generator loss:
# $$\ell_G = \frac{1}{2}\mathbb{E}_{z \sim p(z)}\left[\left(D(G(z))-1\right)^2\right]$$
# and the discriminator loss:
# $$ \ell_D = \frac{1}{2}\mathbb{E}_{x \sim p_\text{data}}\left[\left(D(x)-1\right)^2\right] + \frac{1}{2}\mathbb{E}_{z \sim p(z)}\left[ \left(D(G(z))\right)^2\right]$$
#
#
# **HINTS**: Instead of computing the expectation, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing. When plugging in for $D(x)$ and $D(G(z))$ use the direct output from the discriminator (`scores_real` and `scores_fake`).
# 我们将对minibatch的元素进行平均，而不是计算期望值，因此确保通过平均而不是求和来合并损失。当插入D（x）和D（G（z））时，使用鉴别器的直接输出（scores\u real和scores\u fake）。
#
# Implement `ls_discriminator_loss`, `ls_generator_loss` in `cs231n/gan_pytorch.py`
# Before running a GAN with our new loss function, let's check it:
# +
from cs231n.gan_pytorch import ls_discriminator_loss, ls_generator_loss
def test_lsgan_loss(score_real, score_fake, d_loss_true, g_loss_true):
score_real = torch.Tensor(score_real).type(dtype)
score_fake = torch.Tensor(score_fake).type(dtype)
d_loss = ls_discriminator_loss(score_real, score_fake).cpu().numpy()
g_loss = ls_generator_loss(score_fake).cpu().numpy()
print("Maximum error in d_loss: %g"%rel_error(d_loss_true, d_loss))
print("Maximum error in g_loss: %g"%rel_error(g_loss_true, g_loss))
test_lsgan_loss(answers['logits_real'], answers['logits_fake'],
answers['d_loss_lsgan_true'], answers['g_loss_lsgan_true'])
# -
# Run the following cell to train your model!
# +
D_LS = discriminator().type(dtype)
G_LS = generator().type(dtype)
D_LS_solver = get_optimizer(D_LS)
G_LS_solver = get_optimizer(G_LS)
images = run_a_gan(D_LS, G_LS, D_LS_solver, G_LS_solver, ls_discriminator_loss, ls_generator_loss, loader_train)
# -
# Run the cell below to show generated images.
numIter = 0
for img in images:
print("Iter: {}".format(numIter))
show_images(img)
plt.show()
numIter += 250
print()
# **Please tag the cell below on Gradescope while submitting.**
print("LSGAN Fianl image:")
show_images(images[-1])
plt.show()
# # Deeply Convolutional GANs
# In the first part of the notebook, we implemented an almost direct copy of the original GAN network from Ian Goodfellow. However, this network architecture allows no real spatial reasoning. It is unable to reason about things like "sharp edges" in general because it lacks any convolutional layers. Thus, in this section, we will implement some of the ideas from [DCGAN](https://arxiv.org/abs/1511.06434), where we use convolutional networks
# 在笔记本的第一部分，我们从Ian Goodflow实施了原始GAN网络的几乎直接副本。 但是，该网络架构允许没有实际的空间推理。 它无法理解“尖锐的边缘”，因为它缺乏任何卷积层。 因此，在本节中，我们将从[DCGAN]（https://arxiv.org/abs/1511.06434）中实现一些想法，在那里我们使用卷积网络
#
# #### Discriminator
# We will use a discriminator inspired by the TensorFlow MNIST classification tutorial, which is able to get above 99% accuracy on the MNIST dataset fairly quickly.
# * Reshape into image tensor (Use Unflatten!)
# * Conv2D: 32 Filters, 5x5, Stride 1
# * Leaky ReLU(alpha=0.01)
# * Max Pool 2x2, Stride 2
# * Conv2D: 64 Filters, 5x5, Stride 1
# * Leaky ReLU(alpha=0.01)
# * Max Pool 2x2, Stride 2
# * Flatten
# * Fully Connected with output size 4 x 4 x 64
# * Leaky ReLU(alpha=0.01)
# * Fully Connected with output size 1
#
# Implement `build_dc_classifier` in `cs231n/gan_pytorch.py`
# +
from cs231n.gan_pytorch import build_dc_classifier
data = next(enumerate(loader_train))[-1][0].type(dtype)
b = build_dc_classifier(batch_size).type(dtype)
out = b(data)
print(out.size())
# -
# Check the number of parameters in your classifier as a sanity check:
# +
def test_dc_classifer(true_count=1102721):
model = build_dc_classifier(batch_size)
cur_count = count_params(model)
if cur_count != true_count:
print('Incorrect number of parameters in generator. Check your achitecture.')
else:
print('Correct number of parameters in generator.')
test_dc_classifer()
# -
# #### Generator
# For the generator, we will copy the architecture exactly from the [InfoGAN paper](https://arxiv.org/pdf/1606.03657.pdf). See Appendix C.1 MNIST. See the documentation for [tf.nn.conv2d_transpose](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose). We are always "training" in GAN mode.
# * Fully connected with output size 1024
# * `ReLU`
# * BatchNorm
# * Fully connected with output size 7 x 7 x 128
# * ReLU
# * BatchNorm
# * Reshape into Image Tensor of shape 7, 7, 128
# * Conv2D^T (Transpose): 64 filters of 4x4, stride 2, 'same' padding (use `padding=1`)
# * `ReLU`
# * BatchNorm
# * Conv2D^T (Transpose): 1 filter of 4x4, stride 2, 'same' padding (use `padding=1`)
# * `TanH`
# * Should have a 28x28x1 image, reshape back into 784 vector
#
# Implement `build_dc_generator` in `cs231n/gan_pytorch.py`
# +
from cs231n.gan_pytorch import build_dc_generator
test_g_gan = build_dc_generator().type(dtype)
test_g_gan.apply(initialize_weights)
fake_seed = torch.randn(batch_size, NOISE_DIM).type(dtype)
fake_images = test_g_gan.forward(fake_seed)
fake_images.size()
# -
# Check the number of parameters in your generator as a sanity check:
# +
def test_dc_generator(true_count=6580801):
model = build_dc_generator(4)
cur_count = count_params(model)
if cur_count != true_count:
print('Incorrect number of parameters in generator. Check your achitecture.')
else:
print('Correct number of parameters in generator.')
test_dc_generator()
# +
D_DC = build_dc_classifier(batch_size).type(dtype)
D_DC.apply(initialize_weights)
G_DC = build_dc_generator().type(dtype)
G_DC.apply(initialize_weights)
D_DC_solver = get_optimizer(D_DC)
G_DC_solver = get_optimizer(G_DC)
images = run_a_gan(D_DC, G_DC, D_DC_solver, G_DC_solver, discriminator_loss, generator_loss, loader_train, num_epochs=5)
# -
# Run the cell below to show generated images.
numIter = 0
for img in images:
print("Iter: {}".format(numIter))
show_images(img)
plt.show()
numIter += 250
print()
# **Please tag the cell below on Gradescope while submitting.**
print("DCGAN Fianl image:")
show_images(images[-1])
plt.show()
# + [markdown] tags=["pdf-inline"]
# ## INLINE QUESTION 1
#
# We will look at an example to see why alternating minimization of the same objective (like in a GAN) can be tricky business.
#
# Consider $f(x,y)=xy$. What does $\min_x\max_y f(x,y)$ evaluate to? (Hint: minmax tries to minimize the maximum value achievable.)
#
# Now try to evaluate this function numerically for 6 steps, starting at the point $(1,1)$,
# by using alternating gradient (first updating y, then updating x using that updated y) with step size $1$. **Here step size is the learning_rate, and steps will be learning_rate * gradient.**
# You'll find that writing out the update step in terms of $x_t,y_t,x_{t+1},y_{t+1}$ will be useful.
#
# Breifly explain what $\min_x\max_y f(x,y)$ evaluates to and record the six pairs of explicit values for $(x_t,y_t)$ in the table below.
#
# ### Your answer:
#
#
# $y_0$ | $y_1$ | $y_2$ | $y_3$ | $y_4$ | $y_5$ | $y_6$
# ----- | ----- | ----- | ----- | ----- | ----- | -----
# 1 | | | | | |
# $x_0$ | $x_1$ | $x_2$ | $x_3$ | $x_4$ | $x_5$ | $x_6$
# 1 | | | | | |
#
#
#
#
# + [markdown] tags=["pdf-inline"]
# ## INLINE QUESTION 2
# Using this method, will we ever reach the optimal value? Why or why not?
#
# ### Your answer:
#
# + [markdown] tags=["pdf-inline"]
# ## INLINE QUESTION 3
# If the generator loss decreases during training while the discriminator loss stays at a constant high value from the start, is this a good sign? Why or why not? A qualitative answer is sufficient.
#
# ### Your answer:
#
# + tags=["pdf-inline"]
| 27,672 |
/data-prework/1.-Python/6.-Rock–Paper–Scissors/rock-paper-scissors.ipynb | 91c9a2a527a5f9835f27ea0129ad86f1629113ef | [] | no_license | Alvaru89/data-prework | https://github.com/Alvaru89/data-prework | 0 | 0 | null | 2020-09-25T12:39:00 | 2020-09-10T23:59:24 | null | Jupyter Notebook | false | false | .py | 15,715 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# <img src="https://bit.ly/2VnXWr2" width="100" align="left">
# # Rock, Paper & Scissors
#
# Let's play the famous game against our computer. You can check the rules [here](https://en.wikipedia.org/wiki/Rock%E2%80%93paper%E2%80%93scissors).
#
# ## Task
# Create a program that imitates the playability of the well known game of rock, paper, scissors. Follow the guidelines provided.
#
# ## Tools
# 1. Loop: **for/while**
# 2. Functions: **input(), print()...**
# 3. Conditional statements: **if, elif, else**
# 4. Definition of functions. Modular programming
# 5. Import modules
#
# **To solve this challenge, the use of functions is recommended.**
#
# #### 1. Import the choice function of the random module.
import random
# #### 2. Create a list that includes the 3 possible gesture options of the game: 'rock', 'paper' or 'scissors'. Store the list in a variable called `gestures`.
gestures=('rock', 'paper','scissors')
# #### 3. Create a variable called `n_rounds` to store the maximum number of rounds to play in a game.
# Remember that the number of rounds must be odd: 1, 3, 5, ...
n_rounds=int(input())
while n_rounds%2==0:
print("Please introduce an odd number")
n_rounds=int(input())
# #### 4. Create a variable called `rounds_to_win` to store the number of rounds that a player must win to win the game.
# **Hint**: the value stored in `rounds_to_win` depends on the value of `n_rounds`.
rounds_to_win=round(n_rounds/2)+1
print(rounds_to_win)
# #### 5. Create two variables to store the number of rounds that the computer and the player have won. Call these variables `cpu_score` and `player_score`.
cpu_score=0
player_score=0
# #### 6. Define a function that randomly returns one of the 3 gesture options.
# You will use this function to simulate the gesture choice of the computer.
def computer_func():
i=random.randint(0,2)
return gestures[i]
# #### 7. Define a function that asks the player which is the gesture he or she wants to show: 'rock', 'paper' or 'scissors'.
# The player should only be allowed to choose one of the 3 gesture options. If the player's choice is not rock, paper or scissors, keep asking until it is.
def player():
print('Which gesture do you want to show? Choose rock, paper or scissors')
choice=input()
while choice!='rock' and choice!='paper' and choice!='scissors':
print('Please try again')
choice=input()
return choice
# #### 8. Define a function that checks who won a round.
# The function should return 0 if there is a tie, 1 if the computer wins and 2 if the player wins.
def checker(result):
if result=='tie':
return 0
if result=='computer':
return 1
if result=='player':
return 2
# #### 9. Define a function that prints the choice of the computer, the choice of the player and a message that announces who won the current round.
# You should also use this function to update the variables that count the number of rounds that the computer and the player have won. The score of the winner increases by one point. If there is a tie, the score does not increase.
def printer (player_choice,computer_choice,result,player_score,cpu_score):
print('The player choice is ', player_choice)
print('The computer choice is ', computer_choice)
if result=='computer':
cpu_score=cpu_score+1
print('The winner of this round is ',result)
elif result=='player':
player_score=player_score+1
print('The winner of this round is ',result)
elif result=='tie':
print('No winner this rounds. It is a tie.')
return player_score,cpu_score
# #### 10. Now it's time to code the execution of the game using the functions and variables you defined above.
#
# First, create a loop structure that repeats while no player reaches the minimum score necessary to win and the number of rounds is less than the maximum number of rounds to play in a game.
#
# Inside the loop, use the functions and variables above to create the execution of a round: ask for the player's choice, generate the random choice of the computer, show the round results, update the scores, etc.
# +
print('How many rounds do you want to play?')
n_rounds=int(input())
while n_rounds%2==0:
print("Please introduce an odd number")
n_rounds=int(input())
rounds_to_win=round(n_rounds/2)+1
cpu_score=0
player_score=0
while cpu_score<rounds_to_win and player_score<rounds_to_win:
player_choice=player()
computer_choice=computer_func()
if player_choice=='rock':
if computer_choice=='rock':
result='tie'
elif computer_choice=='paper':
result='computer'
elif computer_choice=='scissors':
result='player'
elif player_choice=='paper':
if computer_choice=='paper':
result='tie'
elif computer_choice=='scissors':
result='computer'
elif computer_choice=='rock':
result='player'
elif player_choice=='scissors':
if computer_choice=='scissors':
result='tie'
elif computer_choice=='rock':
result='computer'
elif computer_choice=='paper':
result='player'
winner=checker(result)
temp=printer(player_choice,computer_choice,result,player_score,cpu_score)
player_score=temp[0]
cpu_score=temp[1]
# -
# #### 11. Print the winner of the game based on who won more rounds.
# Remember that the game might be tied.
if cpu_score==rounds_to_win:
print('Computer wins')
elif player_score==rounds_to_win:
print('Player wins')
# # Bonus: Rock, Paper, Scissors, Lizard & Spock
# 
#
# In this challenge, you need to improve the previous game by adding two new options. To know more about the rules of the improved version of rock, paper, scissors, check this [link](http://www.samkass.com/theories/RPSSL.html).
#
# In addition, you will also need to improve how the game interacts with the player: the number of rounds to play, which must be an odd number, will be requested to the user until a valid number is entered. Define a new function to make that request.
#
# **Hint**: Try to reuse the code that you already coded in the previous challenge. If your code is efficient, this bonus will only consist of simple modifications to the original game.
# +
gestures2=('rock', 'paper','scissors','spock', 'lizard')
def player2():
print('Which gesture do you want to show? Choose rock, paper, scissors, spock or lizard')
choice=input()
while choice!='rock' and choice!='paper' and choice!='scissors' and choice!='spock' and choice!='lizard':
print('Please try again')
choice=input()
return choice
print('How many rounds do you want to play?')
n_rounds=int(input())
while n_rounds%2==0:
print("Please introduce an odd number")
n_rounds=int(input())
rounds_to_win=round(n_rounds/2)+1
cpu_score=0
player_score=0
while cpu_score<rounds_to_win and player_score<rounds_to_win:
player_choice=player2()
computer_choice=computer_func()
if player_choice=='rock':
if computer_choice=='rock':
result='tie'
elif computer_choice=='paper':
result='computer'
elif computer_choice=='scissors':
result='player'
elif computer_choice=='spock':
result='computer'
elif computer_choice=='lizard':
result='player'
elif player_choice=='paper':
if computer_choice=='paper':
result='tie'
elif computer_choice=='scissors':
result='computer'
elif computer_choice=='rock':
result='player'
elif computer_choice=='spock':
result='player'
elif computer_choice=='lizard':
result='computer'
elif player_choice=='scissors':
if computer_choice=='scissors':
result='tie'
elif computer_choice=='rock':
result='computer'
elif computer_choice=='paper':
result='player'
elif computer_choice=='spock':
result='computer'
elif computer_choice=='lizard':
result='player'
elif player_choice=='spock':
if computer_choice=='scissors':
result='player'
elif computer_choice=='rock':
result='player'
elif computer_choice=='paper':
result='computer'
elif computer_choice=='spock':
result='tie'
elif computer_choice=='lizard':
result='player'
elif player_choice=='lizard':
if computer_choice=='scissors':
result='computer'
elif computer_choice=='rock':
result='computer'
elif computer_choice=='paper':
result='player'
elif computer_choice=='spock':
result='player'
elif computer_choice=='lizard':
result='tie'
winner=checker(result)
temp=printer(player_choice,computer_choice,result,player_score,cpu_score)
player_score=temp[0]
cpu_score=temp[1]
if cpu_score==rounds_to_win:
print('Computer wins')
elif player_score==rounds_to_win:
print('Player wins')
# -
| 9,682 |
/tutorials/KDD16.ipynb | ac99c0326b3af9d7887bf6f28d98ea8b052edb4c | [
"Apache-2.0"
] | permissive | antinucleon/mxnet-notebooks | https://github.com/antinucleon/mxnet-notebooks | 0 | 0 | null | 2016-08-15T03:57:18 | 2016-08-15T03:57:17 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 837 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 2
# language: python
# name: python2
# ---
# # KDD 16 Hands-on Tutorials
#
# safd
#
#
# - [NDArray](../python/basic/ndarray.ipynb)
# - [Symbol](../python/basic/symbol.ipynb)
| 399 |
/ClassMaterial/Unit3/Class15/Class Notebook -- Messy.ipynb | 1a33bb57a4bfbd8d7888421ce509c7ef40e50949 | [] | no_license | Nhudgell/DAT-07-28 | https://github.com/Nhudgell/DAT-07-28 | 0 | 0 | null | 2020-08-11T17:16:15 | 2020-08-11T17:07:25 | null | Jupyter Notebook | false | false | .py | 61,471 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.pipeline import make_pipeline
from category_encoders import OrdinalEncoder
from sklearn.model_selection import train_test_split, cross_val_score
df = pd.read_csv('../data/bikeshare.csv', parse_dates=['datetime'])
df.head()
df['hour'] = df['datetime'].dt.hour
pipe = make_pipeline(OrdinalEncoder(), GradientBoostingRegressor())
X = df.drop(['count', 'datetime'], axis=1)
y = df['count']
X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=False, test_size=0.2)
X_test.tail()
X_train.head()
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, shuffle=False, test_size=0.2)
pipe.fit(X_train, y_train)
OrdinalEncoder().fit_transform(X_train)
pipe.score(X_val, y_val)
cross_val_score(estimator=pipe, X=X_train, y=y_train, cv=10)
pipe.steps[1][1].set_params()
# +
max_depth = [3, 4, 5]
num_trees = [100, 250, 500]
cv_scores = []
for depth in max_depth:
for tree in num_trees:
pipe.steps[1][1].set_params(n_estimators=tree, max_depth=depth)
pipe.fit(X_train, y_train)
val_score = pipe.score(X_val, y_val)
cv_dict = {'score': val_score, 'max_depth': depth, 'n_estimators': tree}
cv_scores.append(cv_dict)
max_params = max(cv_scores, key=lambda x: x['score'])
pipe.steps[1][1].set_params(max_depth=max_params['max_depth'], n_estimators=max_params['n_estimators'])
pipe.fit(X_train, y_train)
# -
X_train = pd.concat([X_train, X_val])
y_train = pd.concat([y_train, y_val])
pipe.fit(X_train, y_train)
max(cv_scores, key=lambda x: x['score'])
pipe.score(X_test, y_test)
df = pd.read_csv('../data/ks2.csv', encoding='utf-8', parse_dates=['launched', 'deadline'])
cat_avgs = df.groupby('category')[['goal']].mean().rename({'goal': 'cat_avg_goal'}, axis=1)
cat_avgs
df.head()
df = df.merge(cat_avgs, left_on='category', right_index=True)
df['cat_goal_pct'] = df['goal'] / df['cat_avg_goal']
df.head()
main_cats = df.groupby('main_category')[['goal']].mean().reset_index().rename({'goal': 'main_cat_goal_avg'}, axis=1)
df = df.merge(main_cats, on='main_category')
df['main_goal_pct'] = df['goal'] / df['main_cat_goal_avg']
df[['goal', 'main_cat_goal_avg', 'main_goal_pct']].head()
X = df.drop(['deadline', 'launched', 'state'], axis=1)
y = df['state']
df = extract_dates(df)
scores = get_val_scores(pipe, X, y, random_state=1985, stratify=True, use_kfold=False)
from utils import get_val_scores, extract_dates
pipe = make_pipeline(OrdinalEncoder(), xgb.XGBClassifier())
import xgboost as xgb
scores
df.head()
weekly_totals = df.groupby(['launched_year', 'launched_weekofyear'])[['ID']].count().reset_index().rename({'ID': 'Weekly_Total_Count'}, axis=1)
weekly_totals.head()
df.columns
df = df.merge(weekly_totals, on=['launched_year', 'launched_weekofyear'])
X = df.drop(['deadline', 'launched', 'state'], axis=1)
y = df['state']
HOT_ZONE_AREA if Center(C).
# +
def get_column_header_simple(str_orig_column_header, str_bad_column_header):
# as a warm up, create SHOT_ZONE_AREA_C
# use regex to get the value within the parentheses!
# then apply this function within the dictionary
#return '_'.join([str_orig_column_header, re.search(str_regex, str_bad_column_header)])
return '_'.join([
str_orig_column_header,
str_bad_column_header[str_bad_column_header.find("(")+1:str_bad_column_header.find(")")]])
get_column_header_simple("SHOT_ZONE_AREA", "Center(C)")
# +
# this solution will be more flexible than the one above
def get_column_header_regex(str_orig_column_header, str_bad_column_header, str_regex):
return '_'.join([str_orig_column_header, re.search(str_regex, str_bad_column_header).group(1)])
#get_column_header_regex("SHOT_ZONE_AREA", "Center(C)", "\((.*?)\)")
# +
# joining SHOT_ZONE_AREA encoded to df
df_onehotencode = pd.DataFrame(onehot_encoded) # this has the same index as df_shot_charts
# dict to rename columns
dict_columns = {
0: get_column_header_regex("SHOT_ZONE_AREA_A", label_encoder.inverse_transform([0])[0], "\((.*?)\)"),
1: get_column_header_regex("SHOT_ZONE_AREA_A", label_encoder.inverse_transform([1])[0], "\((.*?)\)"),
2: get_column_header_regex("SHOT_ZONE_AREA_A", label_encoder.inverse_transform([2])[0], "\((.*?)\)"),
3: get_column_header_regex("SHOT_ZONE_AREA_A", label_encoder.inverse_transform([3])[0], "\((.*?)\)"),
4: get_column_header_regex("SHOT_ZONE_AREA_A", label_encoder.inverse_transform([4])[0], "\((.*?)\)")
}
df_onehotencode.rename(columns=dict_columns, inplace=True)
#df_onehotencode
df_join = pd.merge(df_shot_charts, df_onehotencode, left_index=True, right_index=True)
# determine shots made by zone before agg
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_AREA_A_C'] == 1), 'SHOT_ZONE_AREA_M_C'] = 1
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_AREA_A_LC'] == 1), 'SHOT_ZONE_AREA_M_LC'] = 1
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_AREA_A_L'] == 1), 'SHOT_ZONE_AREA_M_L'] = 1
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_AREA_A_RC'] == 1), 'SHOT_ZONE_AREA_M_RC'] = 1
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_AREA_A_R'] == 1), 'SHOT_ZONE_AREA_M_R'] = 1
# https://stackoverflow.com/questions/21998354/pandas-wont-fillna-inplace
df_join.fillna({x: 0 for x in ['SHOT_ZONE_AREA_M_C', 'SHOT_ZONE_AREA_M_LC', 'SHOT_ZONE_AREA_M_L', 'SHOT_ZONE_AREA_M_RC', 'SHOT_ZONE_AREA_M_R']}, inplace=True)
#df_join.set_index(['PLAYER_ID', 'GAME_ID'], inplace=True)
df_join
# +
# SHOT_ZONE_BASIC
values = df_shot_charts['SHOT_ZONE_BASIC']
# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)
#integer_encoded
# binary encode
onehot_encoder = OneHotEncoder(sparse=False, categories='auto')
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
onehot_encoded
# inverted
#inverted = label_encoder.inverse_transform([np.argmax(onehot_encoded[1, :])]) # what does the enconding for the first row mean?
#inverted = label_encoder.inverse_transform([4])[0] # i.e., what does 4 mean? "Right Side(R)"
#inverted
# +
# joining SHOT_ZONE_BASIC encoded to df
df_onehotencode = pd.DataFrame(onehot_encoded) # this has the same index as df_shot_charts
# dict to rename columns
dict_columns = {
0: 'SHOT_ZONE_BASIC_A_MR',
1: 'SHOT_ZONE_BASIC_A_ATB3',
2: 'SHOT_ZONE_BASIC_A_RA',
3: 'SHOT_ZONE_BASIC_A_ITP',
4: 'SHOT_ZONE_BASIC_A_RC3',
5: 'SHOT_ZONE_BASIC_A_LC3'
}
df_onehotencode.rename(columns=dict_columns, inplace=True)
#df_onehotencode
#df_join = pd.merge(df_shot_charts, df_onehotencode, left_index=True, right_index=True)
df_join = pd.merge(df_join, df_onehotencode, left_index=True, right_index=True)
# determine shots made by zone before agg
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_BASIC_A_MR'] == 1), 'SHOT_ZONE_BASIC_M_MR'] = 1
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_BASIC_A_ATB3'] == 1), 'SHOT_ZONE_BASIC_M_ATB3'] = 1
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_BASIC_A_RA'] == 1), 'SHOT_ZONE_BASIC_M_RA'] = 1
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_BASIC_A_ITP'] == 1), 'SHOT_ZONE_BASIC_M_ITP'] = 1
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_BASIC_A_RC3'] == 1), 'SHOT_ZONE_BASIC_M_RC3'] = 1
df_join.loc[(df_join['SHOT_MADE_FLAG'] == 1) & (df_join['SHOT_ZONE_BASIC_A_LC3'] == 1), 'SHOT_ZONE_BASIC_M_LC3'] = 1
# https://stackoverflow.com/questions/21998354/pandas-wont-fillna-inplace
df_join.fillna({x: 0 for x in ['SHOT_ZONE_BASIC_M_MR', 'SHOT_ZONE_BASIC_M_ATB3', 'SHOT_ZONE_BASIC_M_RA', 'SHOT_ZONE_BASIC_M_ITP', 'SHOT_ZONE_BASIC_M_RC3', 'SHOT_ZONE_BASIC_M_LC3']}, inplace=True)
df_join.set_index(['PLAYER_ID', 'GAME_ID'], inplace=True)
#df_join.dtypes
#df_join['SHOT_ZONE_BASIC'].value_counts()
df_join.dtypes
# +
# dummy for SHOT_TYPE
# in the script, create a function for the onehotencoder
# +
# aggregate shot chart data by player-game
# THIS IS NOT UPDATED YET
list_agg = [np.sum, np.min, np.max, np.mean, np.std]
dict_agg = {
'MIN': list_agg,
'FGM': list_agg,
'FGA': list_agg,
'FG_PCT': list_agg,
'FG3M': list_agg,
'FG3A': list_agg,
'FG3_PCT': list_agg,
'FTM': list_agg,
'FTA': list_agg,
'FT_PCT': list_agg,
'OREB': list_agg,
'DREB': list_agg,
'REB': list_agg,
'AST': list_agg,
'STL': list_agg,
'BLK': list_agg,
'TOV': list_agg,
'PF': list_agg,
'PTS': list_agg
}
df_game_log_agg = df_game_log.groupby(['Player_ID', 'SEASON_ID']).agg(dict_agg) # this makes 'Player_ID' the index
# df_game_log_agg.loc[76001] # get all column data for the row index
# df_game_log_agg['MIN']['sum'][76001] # for the MIN col, get the sum col's value for 76001
df_game_log_agg
# -
# After we aggregate the shot chart data by player and game, join to the game log data via player and game.
| 9,350 |
/Lecture_01/.ipynb_checkpoints/Files-checkpoint.ipynb | 19df088602661c45a88eee4a8bf1b70edee42974 | [] | no_license | Namangarg007/Python-training | https://github.com/Namangarg007/Python-training | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 210,422 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python [conda env:Python3]
# language: python
# name: conda-env-Python3-py
# ---
from pathlib import Path
import os
import pandas as pd
import nccid_cleaning.etl as etl
from nccid_cleaning import clean_data_df, patient_df_pipeline
# This notebook can be used to generate CSV files containing patient clinical data, and image metadata for each patient and image file within the NCCID data.
# To use these tools you need to provide a `BASE_PATH` that points to the location of the data that has been pulled from the NCCID S3 bucket, where your local directory structure should match the original S3 structure. If you have split the data into training/test/validation sets, each subdirectory should have the same structure as the original S3 bucket and the below pipeline should be run separately for each of the dataset splits.
# You can set the local path to your NCCID data below by changing the `DEFAULT_PATH` variable or alternatively set as an environment variable, `NCCID_DATA_DIR` in e.g., `.bashrc`.
# Edit this to update your local NCCID data path
DEFAULT_PATH = "/project/data/training"
BASE_PATH = Path(os.getenv("NCCID_DATA_DIR", DEFAULT_PATH))
print(BASE_PATH)
# ## Imaging Metadata
# For the imaging metadata, a separate CSV is generated for each imaging modality: X-ray, CT, MRI. Three steps are performed:
# <l>
# <li> `select_image_files` - traverses the directory tree finding all files of the imaging modality. For X-ray is it recommended to set `select_all = True` to process all available X-ray files. Whereas, for 3D modalities, CT, and MRI, `select_first = True` is recommened to select only the first file of each imaging volume, to speed up run time and reduce redundancy of information. </li>
# <li> `ingest_dicom_jsons` - reads the DICOM header information for each file. </li>
# <li> `pydicom_to_df` - converts the DICOM metadata into a pandas DataFrame where the rows are images and columns are the DICOM attributes.
# </l> <br>
#
# The resulting DataFrames are saved as CSV files in `data/`
# subdirectories
XRAY_SUBDIR = "xray-metadata"
CT_SUBDIR = "ct-metadata"
MRI_SUBDIR = "mri-metadata"
# 1. finding image file lists within the subdirs
xray_files = etl.select_image_files(BASE_PATH / XRAY_SUBDIR, select_all=True)
ct_files = etl.select_image_files(BASE_PATH / CT_SUBDIR, select_first=True)
mri_files = etl.select_image_files(BASE_PATH / MRI_SUBDIR, select_first=True)
# 2. process image metadata
xray_datasets = etl.ingest_dicom_jsons(xray_files)
ct_datasets = etl.ingest_dicom_jsons(ct_files)
mri_datasets = etl.ingest_dicom_jsons(mri_files)
# 3. converting to DataFrame
xrays = etl.pydicom_to_df(xray_datasets)
cts = etl.pydicom_to_df(ct_datasets)
mris = etl.pydicom_to_df(mri_datasets)
# check structure of DFs
xrays.head()
# Save as csv
xrays.to_csv("data/xrays.csv")
cts.to_csv("data/cts.csv")
mris.to_csv("data/mris.csv")
# ## Patient Clinical Data
# For patient clinical data, the most recent <b>data</b> file (for COVID-positive) or <b>status</b> file (for COVID-negative) is parsed for each patient in the directory tree. The resulting DataFrame is generated using `patient_jsons_to_df`, where rows are patients and columns are data fields. <br>
#
# Three fields that are not in the original jsons files are included in the DataFrame:
# <l>
# <li> `filename_earliest_date` - earlist data/status file present for the patient. </li>
# <li> `filename_latest_date` - latest data/status file present for the patient. This is the file from which the rest of the patient's data has been pulled. </li>
# <li> `filename_covid_status` - indicates it the patient is in the COVID-postive or COVID-negative cohort, based on whether they have every been submitted with a <b>data</b> file (which are only present for positive patients. </li>
# </l>
PATIENT_SUBDIR = "data"
# process patient clinical data
patient_files = list(os.walk(BASE_PATH / PATIENT_SUBDIR))
patients = etl.patient_jsons_to_df(patient_files)
patients.head()
# ### Clean and enrich
# The cleaning pipeline can be run on the resulting patients DataFrame to improve quality. In addition, missing values in the patient DataFrame for Sex and Age, can be filled using the DICOM image headers. This step generates two new columns `sex_update` and `age_update`, from the cleaned columns `sex`, `age`.
# +
# cleaning
patients = clean_data_df(patients, patient_df_pipeline)
# enriching
images = [xrays, cts, mris] # list all image DFs
patients = etl.patient_data_dicom_update(patients, images)
patients.head()
# -
print(f"Sex Unknowns before merging with dicom: {(patients['sex']=='Unknown').sum()}")
print(f"Sex Unknowns after merging with dicom: {(patients['sex_update']=='Unknown').sum()}")
print("------")
print(f"Age NaNs before merging with dicom: {patients['age'].isnull().sum()}")
print(f"Age New after merging with dicom: {patients['age_update'].isnull().sum()}")
# save to csv
patients.to_csv("data/patients.csv")
of that class. Multiple classes can also be checked at once.
print (isinstance(1, int))
print (isinstance(1.0,int))
print (isinstance(1.0,(int,float)))
# ==> **pow(x,y,z)** can be used to find the power $x^y$ also the mod of the resulting value with the third specified number can be found i.e. : ($x^y$ % z).
print (pow(3,3))
print (pow(3,3,5))
print(27%5)
# The value of (3**3) % 5 is : 1
# ==> **range( )** function outputs the integers of the specified range. It can also be used to generate a series by specifying the difference between the two numbers within a particular range. The elements are returned in a list (will be discussing in detail later.)
# ==> range(start, stop, step)
print("==> start == Optional. An integer number specifying at which position to start. Default is 0")
print("==> stop == Optional. An integer number specifying at which position to end.")
print("==> step == Optional. An integer number specifying the incrementation. Default is 1")
print (range(3))
print (range(2,9))
print (range(2,27,8))
for i in range(0,10,1):
print(i)
# # Accepting User Inputs
# ==> **raw_input( )** accepts input and stores it as a string. Hence, if the user inputs a integer, the code should convert the string to an integer and then proceed.
abc = input("Type something here and it will be stored in variable abc \t")
type(abc)
# ==> **input( )**, this is used only for accepting only integer inputs.
abc1 = input("Only integer can be stored in variable abc \t")
type(abc1)
# ==> Note that **type( )** returns the format or the type of a variable or a number
# **RAJKUMAR ZALAVADIA - Mo: 7041645834 Email : [email protected]**
| 6,857 |
/06_데이터수집/01_다음영화정보수집.ipynb | 0a16fc54693f3f904e082d327b8a40bff06e58b1 | [] | no_license | dawoonyoon/mini_project | https://github.com/dawoonyoon/mini_project | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 10,863 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
from selenium import webdriver
import requests
import time
import pandas as pd
from IPython.display import clear_output
# webdriver 실행
driver = webdriver.Chrome('chromedriver')
# 다음 영화 사이트 접속
site = 'https://movie.daum.net/premovie/theater'
time.sleep(1)
driver.get(site)
# 스크롤:
for idx in range(10):
time.sleep(1)
script = 'window.scrollTo(0, document.body.scrollHeight)'
driver.execute_script(script)
# 영화 전체 목록 가져오기
a1 = driver.find_elements_by_css_selector('#mainContent > div > div.box_movie > ol')
a1
# 영화 전체 목록에서 li tag 가져오기
li_list = a1[0].find_elements_by_css_selector('li')
li_list
# +
# 데이터를 담을 딕셔너리
data_dict = {
'제목' : [],
'예매율' : [],
'평점' : [],
'개봉일' : []
}
# 영화의 수 만큼 반복
for movie_tag in li_list:
# 영화 제목 가져오기
title_tag = movie_tag.find_element_by_css_selector('div > div.thumb_cont > strong > a')
title = title_tag.text.strip()
# 평점
rating_tag = movie_tag.find_element_by_css_selector('div > div.thumb_cont > span.txt_append > span:nth-child(1) > span')
rating = rating_tag.text.strip()
# 예매율
ticket_tag = movie_tag.find_element_by_css_selector('div > div.thumb_cont > span.txt_append > span:nth-child(2) > span')
ticket = ticket_tag.text.strip()
# 개봉일
open_date_tag = movie_tag.find_element_by_css_selector('div > div.thumb_cont > span.txt_info > span')
open_date = open_date_tag.text.strip()
# 영화 포스터
poster_tag = movie_tag.find_element_by_css_selector('div > div.thumb_item > div.poster_movie > img')
# src 속성의 값 가져오기
src_attr = poster_tag.get_attribute('src')
# print(src_attr)
# 영화의 제목을 파일명으로 사용할 것이기 때문에
# os에서 거부하는 파일명을 정제한다
char_list = ['\\', '/', ':', '*', '?', '"', '<', '>', '|']
file_name = title
for c1 in char_list:
file_name = file_name.replace(c1, ' ')
# 이미지 데이터 내려받기
img_res = requests.get(src_attr)
# 저장!
with open(f'poster/{file_name}.jpg', 'wb') as fp:
fp.write(img_res.content)
# print(title)
# print(rating)
# print(ticket[:-1])
# print(open_date)
# print('------------------------')
# 데이터 담기
data_dict['제목'].append(title)
data_dict['예매율'].append(ticket[:-1])
data_dict['평점'].append(rating)
data_dict['개봉일'].append(open_date)
# DataFrame 생성
df1 = pd.DataFrame(data_dict)
# 저장
df1.to_csv('daum_movie.csv', encoding='utf-8-sig', index=False)
print('저장완료')
# -
| 2,716 |
/PY0101EN-5-2-Numpy2D (1).ipynb | 89e79ad9dfe21fcb626e37606ac026be1dbfe7f7 | [] | no_license | davemayes/Data-Science-Labs | https://github.com/davemayes/Data-Science-Labs | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 24,822 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %matplotlib notebook
import hyperspy.api as hs
import pyxem as pxm
import atomap.api as am
import ParticleSpy.api as ps
# !pwd
data_path = '/home/jovyan/data'
test_data = data_path + 'edx/EDS Spectrum Image.dm4'
s = hs.load(test_data)
s.plot()
# Estimated time needed: **20** minutes
#
# ## Objectives
#
# After completing this lab you will be able to:
#
# * Operate comfortably with `numpy`
# * Perform complex operations with `numpy`
#
# <h2>Table of Contents</h2>
# <div class="alert alert-block alert-info" style="margin-top: 20px">
# <ul>
# <li><a href="create">Create a 2D Numpy Array</a></li>
# <li><a href="access">Accessing different elements of a Numpy Array</a></li>
# <li><a href="op">Basic Operations</a></li>
# </ul>
#
# </div>
#
# <hr>
#
# <h2 id="create">Create a 2D Numpy Array</h2>
#
# +
# Import the libraries
import numpy as np
import matplotlib.pyplot as plt
# -
# Consider the list <code>a</code>, which contains three nested lists **each of equal size**.
#
# +
# Create a list
a = [[11, 12, 13], [21, 22, 23], [31, 32, 33]]
a
# -
# We can cast the list to a Numpy Array as follows:
#
# +
# Convert list to Numpy Array
# Every element is the same type
A = np.array(a)
A
# -
# We can use the attribute <code>ndim</code> to obtain the number of axes or dimensions, referred to as the rank.
#
# +
# Show the numpy array dimensions
A.ndim
# -
# Attribute <code>shape</code> returns a tuple corresponding to the size or number of each dimension.
#
# +
# Show the numpy array shape
A.shape
# -
# The total number of elements in the array is given by the attribute <code>size</code>.
#
# +
# Show the numpy array size
A.size
# -
# <hr>
#
# <h2 id="access">Accessing different elements of a Numpy Array</h2>
#
# We can use rectangular brackets to access the different elements of the array. The correspondence between the rectangular brackets and the list and the rectangular representation is shown in the following figure for a 3x3 array:
#
# <img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoEg.png" width="500" />
#
# We can access the 2nd-row, 3rd column as shown in the following figure:
#
# <img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoFT.png" width="400" />
#
# We simply use the square brackets and the indices corresponding to the element we would like:
#
# +
# Access the element on the second row and third column
A[1, 2]
# -
# We can also use the following notation to obtain the elements:
#
# +
# Access the element on the second row and third column
A[1][2]
# -
# Consider the elements shown in the following figure
#
# <img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoFF.png" width="400" />
#
# We can access the element as follows:
#
# +
# Access the element on the first row and first column
A[0][0]
# -
# We can also use slicing in numpy arrays. Consider the following figure. We would like to obtain the first two columns in the first row
#
# <img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoFSF.png" width="400" />
#
# This can be done with the following syntax:
#
# +
# Access the element on the first row and first and second columns
A[0][0:2]
# -
# Similarly, we can obtain the first two rows of the 3rd column as follows:
#
# +
# Access the element on the first and second rows and third column
A[0:2, 2]
# -
# Corresponding to the following figure:
#
# <img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/2D_numpy.png" width="550"><br />
#
# <h2 id="op">Basic Operations</h2>
#
# We can also add arrays. The process is identical to matrix addition. Matrix addition of <code>X</code> and <code>Y</code> is shown in the following figure:
#
# <img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoAdd.png" width="500" />
#
# The numpy array is given by <code>X</code> and <code>Y</code>
#
# +
# Create a numpy array X
X = np.array([[1, 0], [0, 1]])
X
# +
# Create a numpy array Y
Y = np.array([[2, 1], [1, 2]])
Y
# -
# We can add the numpy arrays as follows.
#
# +
# Add X and Y
Z = X + Y
Z
# -
# Multiplying a numpy array by a scaler is identical to multiplying a matrix by a scaler. If we multiply the matrix <code>Y</code> by the scaler 2, we simply multiply every element in the matrix by 2, as shown in the figure.
#
# <img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoDb.png" width="500" />
#
# We can perform the same operation in numpy as follows
#
# +
# Create a numpy array Y
Y = np.array([[2, 1], [1, 2]])
Y
# +
# Multiply Y with 2
Z = 2 * Y
Z
# -
# Multiplication of two arrays corresponds to an element-wise product or <em>Hadamard product</em>. Consider matrix <code>X</code> and <code>Y</code>. The Hadamard product corresponds to multiplying each of the elements in the same position, i.e. multiplying elements contained in the same color boxes together. The result is a new matrix that is the same size as matrix <code>Y</code> or <code>X</code>, as shown in the following figure.
#
# <img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoMul.png" width="500" />
#
# We can perform element-wise product of the array <code>X</code> and <code>Y</code> as follows:
#
# +
# Create a numpy array Y
Y = np.array([[2, 1], [1, 2]])
Y
# +
# Create a numpy array X
X = np.array([[1, 0], [0, 1]])
X
# +
# Multiply X with Y
Z = X * Y
Z
# -
# We can also perform matrix multiplication with the numpy arrays <code>A</code> and <code>B</code> as follows:
#
# First, we define matrix <code>A</code> and <code>B</code>:
#
# +
# Create a matrix A
A = np.array([[0, 1, 1], [1, 0, 1]])
A
# +
# Create a matrix B
B = np.array([[1, 1], [1, 1], [-1, 1]])
B
# -
# We use the numpy function <code>dot</code> to multiply the arrays together.
#
# +
# Calculate the dot product
Z = np.dot(A,B)
Z
# +
# Calculate the sine of Z
np.sin(Z)
# -
# We use the numpy attribute <code>T</code> to calculate the transposed matrix
#
# +
# Create a matrix C
C = np.array([[1,1],[2,2],[3,3]])
C
# +
# Get the transposed of C
C.T
# -
# <h2>Quiz on 2D Numpy Array</h2>
#
# Consider the following list <code>a</code>, convert it to Numpy Array.
#
# +
# Write your code below and press Shift+Enter to execute
a = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
A = np.array(a)
A
# -
# <details><summary>Click here for the solution</summary>
#
# ```python
# A = np.array(a)
# A
# ```
#
# </details>
#
# <details><summary>Click here for the solution</summary>
#
# ```python
# A = np.array(a)
# A
# ```
#
# </details>
#
# Calculate the numpy array size.
#
# Write your code below and press Shift+Enter to execute
A.size
# <details><summary>Click here for the solution</summary>
#
# ```python
# A.size
# ```
#
# </details>
#
# Access the element on the first row and first and second columns.
#
# Write your code below and press Shift+Enter to execute
A[0][0:2]
# <details><summary>Click here for the solution</summary>
#
# ```python
# A[0][0:2]
# ```
#
# </details>
#
# Perform matrix multiplication with the numpy arrays <code>A</code> and <code>B</code>.
#
# +
# Write your code below and press Shift+Enter to execute
B = np.array([[0, 1], [1, 0], [1, 1], [-1, 0]])
X = np.dot(A,B)
X
# -
# <details><summary>Click here for the solution</summary>
#
# ```python
# X = np.dot(A,B)
# X
# ```
#
# </details>
#
# <hr>
# <h2>The last exercise!</h2>
# <p>Congratulations, you have completed your first lesson and hands-on lab in Python. However, there is one more thing you need to do. The Data Science community encourages sharing work. The best way to share and showcase your work is to share it on GitHub. By sharing your notebook on GitHub you are not only building your reputation with fellow data scientists, but you can also show it off when applying for a job. Even though this was your first piece of work, it is never too early to start building good habits. So, please read and follow <a href="https://cognitiveclass.ai/blog/data-scientists-stand-out-by-sharing-your-notebooks/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01" target="_blank">this article</a> to learn how to share your work.
# <hr>
#
# ## Author
#
# <a href="https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01" target="_blank">Joseph Santarcangelo</a>
#
# ## Other contributors
#
# <a href="www.linkedin.com/in/jiahui-mavis-zhou-a4537814a">Mavis Zhou</a>
#
# ## Change Log
#
# | Date (YYYY-MM-DD) | Version | Changed By | Change Description |
# |---|---|---|---|
# | 2021-01-05 | 2.2 | Malika | Updated the solution for dot multiplication |
# | 2020-09-09 | 2.1 | Malika | Updated the screenshot for first two rows of the 3rd column |
# | 2020-08-26 | 2.0 | Lavanya | Moved lab to course repo in GitLab |
# | | | | |
# | | | | |
#
# <hr/>
#
# ## <h3 align="center"> © IBM Corporation 2020. All rights reserved. <h3/>
#
| 10,228 |
/Python programs for interview.ipynb | 0dd1ae2ab4f03793493e94a687657a279278dd0c | [] | no_license | NitinShelke/Interview-Questions | https://github.com/NitinShelke/Interview-Questions | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 10,893 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 2
# language: python
# name: python2
# ---
# # Batch Normalization
# One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].
#
# The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.
#
# The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.
#
# It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.
#
# [3] Sergey Ioffe and Christian Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing
# Internal Covariate Shift", ICML 2015.
# +
# As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
# %matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
# %load_ext autoreload
# %autoreload 2
def rel_error(x, y):
""" returns relative error """
return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))
# +
# Load the (preprocessed) CIFAR10 data.
data = get_CIFAR10_data()
for k, v in data.iteritems():
print '%s: ' % k, v.shape
# -
# ## Batch normalization: Forward
# In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.
# +
# Check the training-time forward pass by checking means and variances
# of features both before and after batch normalization
# Simulate the forward pass for a two-layer network
N, D1, D2, D3 = 200, 50, 60, 3
X = np.random.randn(N, D1)
W1 = np.random.randn(D1, D2)
W2 = np.random.randn(D2, D3)
a = np.maximum(0, X.dot(W1)).dot(W2)
print 'Before batch normalization:'
print ' means: ', a.mean(axis=0)
print ' stds: ', a.std(axis=0)
# Means should be close to zero and stds close to one
print 'After batch normalization (gamma=1, beta=0)'
a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})
print ' mean: ', a_norm.mean(axis=0)
print ' std: ', a_norm.std(axis=0)
# Now means should be close to beta and stds close to gamma
gamma = np.asarray([1.0, 2.0, 3.0])
beta = np.asarray([11.0, 12.0, 13.0])
a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})
print 'After batch normalization (nontrivial gamma, beta)'
print ' means: ', a_norm.mean(axis=0)
print ' stds: ', a_norm.std(axis=0)
# +
# Check the test-time forward pass by running the training-time
# forward pass many times to warm up the running averages, and then
# checking the means and variances of activations after a test-time
# forward pass.
N, D1, D2, D3 = 200, 50, 60, 3
W1 = np.random.randn(D1, D2)
W2 = np.random.randn(D2, D3)
bn_param = {'mode': 'train'}
gamma = np.ones(D3)
beta = np.zeros(D3)
for t in xrange(50):
X = np.random.randn(N, D1)
a = np.maximum(0, X.dot(W1)).dot(W2)
batchnorm_forward(a, gamma, beta, bn_param)
bn_param['mode'] = 'test'
X = np.random.randn(N, D1)
a = np.maximum(0, X.dot(W1)).dot(W2)
a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)
# Means should be close to zero and stds close to one, but will be
# noisier than training-time forward passes.
print 'After batch normalization (test-time):'
print ' means: ', a_norm.mean(axis=0)
print ' stds: ', a_norm.std(axis=0)
# -
# ## Batch Normalization: backward
# Now implement the backward pass for batch normalization in the function `batchnorm_backward`.
#
# To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.
#
# Once you have finished, run the following to numerically check your backward pass.
# +
# Gradient check batchnorm backward pass
N, D = 4, 5
x = 5 * np.random.randn(N, D) + 12
gamma = np.random.randn(D)
beta = np.random.randn(D)
dout = np.random.randn(N, D)
bn_param = {'mode': 'train'}
fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]
fg = lambda a: batchnorm_forward(x, gamma, beta, bn_param)[0]
fb = lambda b: batchnorm_forward(x, gamma, beta, bn_param)[0]
dx_num = eval_numerical_gradient_array(fx, x, dout)
da_num = eval_numerical_gradient_array(fg, gamma, dout)
db_num = eval_numerical_gradient_array(fb, beta, dout)
_, cache = batchnorm_forward(x, gamma, beta, bn_param)
dx, dgamma, dbeta = batchnorm_backward(dout, cache)
print 'dx error: ', rel_error(dx_num, dx)
print 'dgamma error: ', rel_error(da_num, dgamma)
print 'dbeta error: ', rel_error(db_num, dbeta)
# -
# ## Batch Normalization: alternative backward
# In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.
#
# Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.
#
# NOTE: You can still complete the rest of the assignment if you don't figure this part out, so don't worry too much if you can't get it.
# +
N, D = 100, 500
x = 5 * np.random.randn(N, D) + 12
gamma = np.random.randn(D)
beta = np.random.randn(D)
dout = np.random.randn(N, D)
bn_param = {'mode': 'train'}
out, cache = batchnorm_forward(x, gamma, beta, bn_param)
t1 = time.time()
dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)
t2 = time.time()
dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)
t3 = time.time()
print 'dx difference: ', rel_error(dx1, dx2)
print 'dgamma difference: ', rel_error(dgamma1, dgamma2)
print 'dbeta difference: ', rel_error(dbeta1, dbeta2)
print 'speedup: %.2fx' % ((t2 - t1) / (t3 - t2))
# -
# ## Fully Connected Nets with Batch Normalization
# Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.
#
# Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.
#
# HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`.
# +
N, D, H1, H2, C = 2, 15, 20, 30, 10
X = np.random.randn(N, D)
y = np.random.randint(C, size=(N,))
for reg in [0, 3.14]:
print 'Running check with reg = ', reg
model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,
reg=reg, weight_scale=5e-2, dtype=np.float64,
use_batchnorm=True)
loss, grads = model.loss(X, y)
print 'Initial loss: ', loss
for name in sorted(grads):
f = lambda _: model.loss(X, y)[0]
grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)
print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))
if reg == 0: print
# -
# # Batchnorm for deep networks
# Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization.
# +
# Try training a very deep net with batchnorm
hidden_dims = [100, 100, 100, 100, 100]
num_train = 1000
small_data = {
'X_train': data['X_train'][:num_train],
'y_train': data['y_train'][:num_train],
'X_val': data['X_val'],
'y_val': data['y_val'],
}
weight_scale = 2e-2
bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)
model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)
bn_solver = Solver(bn_model, small_data,
num_epochs=10, batch_size=50,
update_rule='adam',
optim_config={
'learning_rate': 1e-3,
},
verbose=True, print_every=200)
bn_solver.train()
solver = Solver(model, small_data,
num_epochs=10, batch_size=50,
update_rule='adam',
optim_config={
'learning_rate': 1e-3,
},
verbose=True, print_every=200)
solver.train()
# -
# Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster.
# +
plt.subplot(3, 1, 1)
plt.title('Training loss')
plt.xlabel('Iteration')
plt.subplot(3, 1, 2)
plt.title('Training accuracy')
plt.xlabel('Epoch')
plt.subplot(3, 1, 3)
plt.title('Validation accuracy')
plt.xlabel('Epoch')
plt.subplot(3, 1, 1)
plt.plot(solver.loss_history, 'o', label='baseline')
plt.plot(bn_solver.loss_history, 'o', label='batchnorm')
plt.subplot(3, 1, 2)
plt.plot(solver.train_acc_history, '-o', label='baseline')
plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')
plt.subplot(3, 1, 3)
plt.plot(solver.val_acc_history, '-o', label='baseline')
plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')
for i in [1, 2, 3]:
plt.subplot(3, 1, i)
plt.legend(loc='upper center', ncol=4)
plt.gcf().set_size_inches(15, 15)
plt.show()
# -
# # Batch normalization and initialization
# We will now run a small experiment to study the interaction of batch normalization and weight initialization.
#
# The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale.
# +
# Try training a very deep net with batchnorm
hidden_dims = [50, 50, 50, 50, 50, 50, 50]
num_train = 1000
small_data = {
'X_train': data['X_train'][:num_train],
'y_train': data['y_train'][:num_train],
'X_val': data['X_val'],
'y_val': data['y_val'],
}
bn_solvers = {}
solvers = {}
weight_scales = np.logspace(-4, 0, num=20)
for i, weight_scale in enumerate(weight_scales):
print 'Running weight scale %d / %d' % (i + 1, len(weight_scales))
bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)
model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)
bn_solver = Solver(bn_model, small_data,
num_epochs=10, batch_size=50,
update_rule='adam',
optim_config={
'learning_rate': 1e-3,
},
verbose=False, print_every=200)
bn_solver.train()
bn_solvers[weight_scale] = bn_solver
solver = Solver(model, small_data,
num_epochs=10, batch_size=50,
update_rule='adam',
optim_config={
'learning_rate': 1e-3,
},
verbose=False, print_every=200)
solver.train()
solvers[weight_scale] = solver
# +
# Plot results of weight scale experiment
best_train_accs, bn_best_train_accs = [], []
best_val_accs, bn_best_val_accs = [], []
final_train_loss, bn_final_train_loss = [], []
for ws in weight_scales:
best_train_accs.append(max(solvers[ws].train_acc_history))
bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))
best_val_accs.append(max(solvers[ws].val_acc_history))
bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))
final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))
bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))
plt.subplot(3, 1, 1)
plt.title('Best val accuracy vs weight initialization scale')
plt.xlabel('Weight initialization scale')
plt.ylabel('Best val accuracy')
plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')
plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')
plt.legend(ncol=2, loc='lower right')
plt.subplot(3, 1, 2)
plt.title('Best train accuracy vs weight initialization scale')
plt.xlabel('Weight initialization scale')
plt.ylabel('Best training accuracy')
plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')
plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')
plt.legend()
plt.subplot(3, 1, 3)
plt.title('Final training loss vs weight initialization scale')
plt.xlabel('Weight initialization scale')
plt.ylabel('Final training loss')
plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')
plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')
plt.legend()
plt.gcf().set_size_inches(10, 15)
plt.show()
# -
# # Question:
# Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did.
# # Answer:
#
| 15,662 |
/Analyze_Results.ipynb | e0115ff0df20ee36912a59a6ba40a04c5b09e3fd | [] | no_license | yuvaljacoby/seinfeld_laugh_prediction | https://github.com/yuvaljacoby/seinfeld_laugh_prediction | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 309,253 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Analyzing the Results
# First thing is of course the imports...
# +
import numpy as np
import pandas as pd
import os
from sklearn.metrics import confusion_matrix
from compare_models import plot_confusion_matrix, calc_best_threshold, compare_models_roc_curve
import matplotlib.pyplot as plt
# %load_ext autoreload
# %autoreload 2
# -
# ### Loading the Data & Predictions
#
# We trained our different models and outputted their results to a directory named 'results'.
# We also dumped the train/test data so we can analyze the results that were not seen during training.
#
# ##### Note: This notebook will not run as is, it needs the directory 'results' containing data & predictions.
# If you want to train the models and generate the prediction by yourself, you can run the following command:
# python3 run_training.py --run_everything
DIRCTORY_PATH = 'results'
MODELS_NAMES = ['MLP', 'logistic regression', 'CNN_no_ftrs', 'LSTM_no_ftrs', 'CNN', 'LSTM', 'LSTM_MULTI']
MODELS_CSVs = [os.path.join(DIRCTORY_PATH, 'model_predictions', '{}_predictions.csv'.format(model)) for model in MODELS_NAMES]
# Load the train/test data and the different predictions.
# +
df_train = pd.read_csv(os.path.join(DIRCTORY_PATH, "df_train.csv"))
df_test = pd.read_csv(os.path.join(DIRCTORY_PATH, "df_test.csv"))
thresholds = dict()
for model_name, model_csv in zip(MODELS_NAMES, MODELS_CSVs):
y_hat = pd.read_csv(model_csv, header=None).iloc[:, 0]
thresholds[model_name] = calc_best_threshold([df_test.is_funny], [y_hat], [model_name])[model_name]
df_test['{}_score'.format(model_name)] = y_hat
df_test['{}_pred'.format(model_name)] = y_hat > thresholds[model_name]
df_test['{}_score_minus_thres'.format(model_name)] = y_hat - thresholds[model_name]
# -
# ### Visualizing the Results
#
# Let's see some of the lines in the test data, including the different predictions.
# Note that the number mentioned is the difference between the score and the threshold chosen to separate funny and not-funny.
# Positive value = funny.
# Negative value = not-funny.
# We think about this value as "confidence", the higher it gets (in absolute value) the more "sure" the model with its prediction.
cols_to_view = ['character', 'txt', 'start', 'end', 'is_funny'] + ['{}_score_minus_thres'.format(model_name) for model_name in MODELS_NAMES]
df_test[cols_to_view].head(3)
# For example, the first sentence in the test data is Jerry opening a stand-up scene with
# 'Have you ever called someone and were disappointed when they answered?'
# and the prediction of the stroger models are correct (such as CNN and LSTM including addional features).
# ### Scores' plots
# Let's try to see how well our data was separated using the different models...
# +
def plot_scores(model_name):
y_hat = df_test['{}_score'.format(model_name)]
y_hat_positive = y_hat[df_test['is_funny']]
y_hat_negative = y_hat[~df_test['is_funny']]
plt.hist(y_hat_negative, bins=100, color='red', alpha=0.6, label='not funny', density=1)
plt.hist(y_hat_positive, bins=100, color='green', alpha=0.6, label='funny', density=1)
plt.axvline(x=thresholds[model_name], label='threshold', linewidth=3)
plt.legend()
plt.title("{} scores".format(model_name))
plt.show()
for model_name in MODELS_NAMES:
plot_scores(model_name)
# -
# ### ROC Curves
# Let's look at the ROC-curve of the different models:
auc = compare_models_roc_curve([df_test['is_funny'] for model in MODELS_NAMES],
[df_test['{}_score'.format(model)] for model in MODELS_NAMES],
MODELS_NAMES, out_dir=None)
# ### Confusion Matrices
# Let's see the confusion-matrices for the different models:
plot_confusion_matrix([df_test['is_funny'] for model in MODELS_NAMES[:4]],
[df_test['{}_pred'.format(model_name)] for model_name in MODELS_NAMES[:4]],
MODELS_NAMES[:4], out_dir=None)
plot_confusion_matrix([df_test['is_funny'] for model in MODELS_NAMES[4:]],
[df_test['{}_pred'.format(model_name)] for model_name in MODELS_NAMES[4:]],
MODELS_NAMES[4:], out_dir=None)
# ### Conclusion
# It's not perfect, but it definitely learned something :)
# We must remember that our data is quite noisy, so no-one can do it perfectly.
# Note that the additional features helped the models greatly.
# ### Characters Separation
# Let's compare how a good model (e.g. LSTM with multi-sentences) perform on different characters.
# Maybe it performs poorly on some character but worse on others?
model = 'LSTM_MULTI'
for char in ['JERRY', 'GEORGE', 'ELAINE', 'KRAMER']:
print("Confusion-matrix for {}".format(char))
mask = df_test['character'] == char
plot_confusion_matrix([df_test[mask]['is_funny']],
[df_test[mask]['{}_pred'.format(model)]],
[model], out_dir=None)
# We can see that the performance is quite the same between the 4 main characters.
# There are minor changes (1-2%) that are not really significant...
# ## Examining the Mistakes
# Let's look at the "most severe" False-Positive (i.e. the model thought it's funny but it doesn't).
# We also add some context (5 sentences before the allegedly 'funny' sentence).
model_name = 'LSTM'
y_hat = df_test['{}_score'.format(model_name)]
y_hat_neg = y_hat[~df_test['is_funny']]
worst_fp_idx = y_hat_neg.iloc[np.argsort(y_hat_neg)[::-1][:1].values].index
for i in worst_fp_idx:
print('\n{} score is {:.2}'.format(model_name, y_hat.iloc[i]))
print(df_test.iloc[(i-5):(i+1)][['character', 'txt', 'is_funny']].to_string(index=False))
# It doesn't seem so severe.
# By looking at it (without checking the label) one can think it's supposed to be funny...
# Let's look at some False-Negatives (i.e. the model thought it's not-funny but it is).
model_name = 'LSTM'
y_hat = df_test['{}_score'.format(model_name)]
y_hat_pos = y_hat[df_test['is_funny']]
worst_fn_idx = y_hat_pos.iloc[np.argsort(y_hat_pos)[3:5].values].index
for i in worst_fn_idx:
print('\n{} score is {:.2}'.format(model_name, y_hat.iloc[i]))
print(df_test.iloc[(i-5):(i+1)][['character', 'txt', 'is_funny']].to_string(index=False))
# These examples are quite difficult, it's not obvious that they're funny (just from the text).
# This shows us how difficult this task is...
| 6,700 |
/Untitled1.ipynb | 7061bc5df74b28df39e2d9e1662d7832d15b1290 | [] | no_license | Rohanbagulwar/car_price_predictor | https://github.com/Rohanbagulwar/car_price_predictor | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 176,394 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# !pip install numpy
# !pip install sklearn
# !pip install seaborn
# !pip install pandas
# !pip install pickle
# !pip install matplotlib
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import os
os.listdir()
df=pd.read_csv('car data.csv')
df.head(5)
df['Fuel_Type'].unique()
df.isnull().sum()
df['Car_Name'].dtype
df['Car_Name'].value_counts()
df['Owner'].unique()
for i in ['Transmission','Owner','Seller_Type']:
print(df[i].unique())
for i in df['Year']:
df['Year_old']=2020-i
final_dataset=df.copy()
final_dataset.drop('Car_Name',axis=1,inplace=True)
final_dataset
final_dataset.drop(['Year'],axis=1,inplace=True)
final_dataset
# for i in
final_dataset=pd.get_dummies(final_dataset,drop_first=True)
final_dataset
plt.figure(figsize=(10,10))
sns.heatmap(final_dataset.corr(),annot=True)
plt.show()
x=final_dataset.iloc[:,1:]
y=final_dataset.iloc[:,0]
from sklearn.ensemble import ExtraTreesRegressor
model=ExtraTreesRegressor()
model.fit(x,y)
pd.Series(model.feature_importances_,index=x.columns).nlargest(5).plot(kind='bar')
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)
from sklearn.ensemble import RandomForestRegressor
r_reg=RandomForestRegressor()
n_estimator=[int(x) for x in np.linspace(start=100,stop=1200,num=12)]
max_features=['auto','sqrt']
min_samples_split=[2,5,10,15,100]
max_depth=[int(i) for i in np.linspace(start=30,stop=120,num=10)]
from sklearn.model_selection import RandomizedSearchCV
random_grid={
'n_estimators':n_estimator,
'max_features':max_features,
'min_samples_leaf':min_samples_split,
'max_depth':max_depth
}
rf=RandomForestRegressor()
rfa=RandomizedSearchCV(rf,param_distributions=random_grid,scoring='neg_mean_squared_error',n_iter=10,random_state=42,cv=10,verbose=2,n_jobs=1)
rfa.fit(x_train,y_train)
rfa.best_params_
rfa.best_score_
prediction=rfa.predict(x_test)
sns.distplot(y_test-prediction)
plt.scatter(y_test,prediction)
import pickle
file=open('random_forest.pkl','wb')
pickle.dump(rfa,file)
file.close()
# !pip freeze > requirement.txt
filename = 'random_forest.pkl'
with open(filename, 'rb') as f:
model = pickle.load(f)
model.predict([])
certifi==2020.6.20
chardet==3.0.4
click==7.1.2
Flask==1.1.2
idna==2.10
itsdangerous==1.1.0
Jinja2==2.11.2
joblib==0.15.1
jsonify==0.5
MarkupSafe==1.1.1
numpy==1.19.0
requests==2.24.0
scikit-learn==0.23.1
scipy==1.5.0
sklearn==0.0
threadpoolctl==2.1.0
urllib3==1.25.9
Werkzeug==1.0.1
wincertstore==0.2
gunicorn
# !pip install flask
# !pip list
# !pip install jsonify
# !pip install requests
# !pip freeze > requirements.txt
# +
from flask import Flask, render_template, request
import jsonify
import requests
import pickle
import numpy as np
import sklearn
from sklearn.preprocessing import StandardScaler
app = Flask(__name__)
model = pickle.load(open('random_forest.pkl', 'rb'))
@app.route('/',methods=['GET'])
def Home():
return render_template('index.html')
standard_to = StandardScaler()
@app.route("/predict", methods=['POST'])
def predict():
Fuel_Type_Diesel=0
if request.method == 'POST':
Year = int(request.form['Year'])
Present_Price=float(request.form['Present_Price'])
Kms_Driven=int(request.form['Kms_Driven'])
Kms_Driven2=np.log(Kms_Driven)
Owner=int(request.form['Owner'])
Fuel_Type_Petrol=request.form['Fuel_Type_Petrol']
if(Fuel_Type_Petrol=='Petrol'):
Fuel_Type_Petrol=1
Fuel_Type_Diesel=0
else:
Fuel_Type_Petrol=0
Fuel_Type_Diesel=1
Year=2020-Year
Seller_Type_Individual=request.form['Seller_Type_Individual']
if(Seller_Type_Individual=='Individual'):
Seller_Type_Individual=1
else:
Seller_Type_Individual=0
Transmission_Mannual=request.form['Transmission_Mannual']
if(Transmission_Mannual=='Mannual'):
Transmission_Mannual=1
else:
Transmission_Mannual=0
prediction=model.predict([[Present_Price,Kms_Driven2,Owner,Year,Fuel_Type_Diesel,Fuel_Type_Petrol,Seller_Type_Individual,Transmission_Mannual]])
output=round(prediction[0],2)
if output<0:
return render_template('index.html',prediction_texts="Sorry you cannot sell this car")
else:
return render_template('index.html',prediction_text="You Can Sell The Car at {}".format(output))
else:
return render_template('index.html')
if __name__=="__main__":
app.run(debug=True)
# -
| 4,916 |
/3.3-work-on-xml/sliderule_dsi_xml_exercise.ipynb | 519b23f7589a9ef4be2b000d23c08308585dc1c6 | [] | no_license | oldrabbitramen/datascienceintensive | https://github.com/oldrabbitramen/datascienceintensive | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 19,271 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # 简易NN实现性别预测
# ##### Copyright © 2020 by Wangchuwen，2018202114. All rights reserved.
# ## 一.构造神经网络
# +
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.preprocessing import normalize
def sigmoid(x):
# Sigmoid activation function: f(x) = 1 / (1 + e^(-x))
return 1 / (1 + np.exp(-x))
def deriv_sigmoid(x):
# Derivative of sigmoid: f'(x) = f(x) * (1 - f(x))
fx = sigmoid(x)
return fx * (1 - fx)
def mse_loss(y_true, y_pred):
# y_true and y_pred are numpy arrays of the same length.
return ((y_true - y_pred) ** 2).mean()
class OurNeuralNetwork:
'''
A neural network with:
- 2 inputs
- a hidden layer with 2 neurons (h1, h2)
- an output layer with 1 neuron (o1)
*** DISCLAIMER ***:
The code below is intended to be simple and educational, NOT optimal.
Real neural net code looks nothing like this. DO NOT use this code.
Instead, read/run it to understand how this specific network works.
'''
def __init__(self):
# 权重，Weights
self.w1 = 0.5734666654181668
self.w2 = -0.7347844458644603
self.w3 = 0.35871598754027156
self.w4 = 0.17227684753124822
self.w5 = -0.05344509263243259
self.w6 = 1.3818657559335772
# 截距项，Biases
self.b1 = -0.013258754892991331
self.b2 = -0.8288385779821897
self.b3 = -0.5300464985969835
def feedforward(self, x):
# x is a numpy array with 2 elements.
h1 = sigmoid(self.w1 * x[0] + self.w2 * x[1] + self.b1)
h2 = sigmoid(self.w3 * x[0] + self.w4 * x[1] + self.b2)
o1 = sigmoid(self.w5 * h1 + self.w6 * h2 + self.b3)
return o1
def train(self, data, all_y_trues,r,e,k):
'''
- data is a (n x 2) numpy array, n = # of samples in the dataset.
- all_y_trues is a numpy array with n elements.
Elements in all_y_trues correspond to those in data.
'''
learn_rate = r
epochs = e # number of times to loop through the entire dataset
error=[]
for epoch in range(epochs):
for x, y_true in zip(data, all_y_trues):
# --- Do a feedforward (we'll need these values later)
sum_h1 = self.w1 * x[0] + self.w2 * x[1] + self.b1
h1 = sigmoid(sum_h1)
sum_h2 = self.w3 * x[0] + self.w4 * x[1] + self.b2
h2 = sigmoid(sum_h2)
sum_o1 = self.w5 * h1 + self.w6 * h2 + self.b3
o1 = sigmoid(sum_o1)
y_pred = o1
# --- Calculate partial derivatives.
# --- Naming: d_L_d_w1 represents "partial L / partial w1"
d_L_d_ypred = -2 * (y_true - round(y_pred))
# Neuron o1
d_ypred_d_w5 = h1 * deriv_sigmoid(sum_o1)
d_ypred_d_w6 = h2 * deriv_sigmoid(sum_o1)
d_ypred_d_b3 = deriv_sigmoid(sum_o1)
d_ypred_d_h1 = self.w5 * deriv_sigmoid(sum_o1)
d_ypred_d_h2 = self.w6 * deriv_sigmoid(sum_o1)
# Neuron h1
d_h1_d_w1 = x[0] * deriv_sigmoid(sum_h1)
d_h1_d_w2 = x[1] * deriv_sigmoid(sum_h1)
d_h1_d_b1 = deriv_sigmoid(sum_h1)
# Neuron h2
d_h2_d_w3 = x[0] * deriv_sigmoid(sum_h2)
d_h2_d_w4 = x[1] * deriv_sigmoid(sum_h2)
d_h2_d_b2 = deriv_sigmoid(sum_h2)
# --- Update weights and biases
# Neuron h1
self.w1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w1
self.w2 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w2
self.b1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_b1
# Neuron h2
self.w3 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w3
self.w4 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w4
self.b2 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_b2
# Neuron o1
self.w5 -= learn_rate * d_L_d_ypred * d_ypred_d_w5
self.w6 -= learn_rate * d_L_d_ypred * d_ypred_d_w6
self.b3 -= learn_rate * d_L_d_ypred * d_ypred_d_b3
# --- Calculate total loss at the end of each epoch
if epoch % 1 == 0:
#y_preds = np.rint(np.apply_along_axis(self.feedforward, 1, data))
y_preds = np.apply_along_axis(self.feedforward, 1, data)
loss = mse_loss(all_y_trues, y_preds)
#print("Epoch %d loss: %.3f" % (epoch, loss))
#print(y_preds)
error.append(loss)
if(k==1):
if epoch == e-1:
plt.figure(figsize=(12, 6))
plt.plot(np.arange(0,e), error, color='red', linestyle='dashed', marker='o', markerfacecolor='blue', markersize=10)
plt.title('Learning Rate=%1.3f'%learn_rate)
plt.xlabel('Epoch')
plt.ylabel('Error')
plt.show()
def predict(self,X):
#y_pre = np.rint(np.apply_along_axis(self.feedforward, 1, X))
y_pre = np.apply_along_axis(self.feedforward, 1, X)
return y_pre
# -
# ## 二.装载数据集
url='classdata.csv'
dataframe=pd.read_csv(url)
# ## 三.数据预处理
def judge(x):
if '男' == x:
return 1
elif '女'== x:
return 0
dataframe['MW'] = dataframe.S.apply(lambda x: judge(x))
# ## 四.分割训练集和测试集
# +
data=dataframe.values
X=data[:,1:3]
Y=data[:,4]
X_train=np.array(X)[1::2,].astype(float)
X_test=np.array(X)[0::2,].astype(float)
Y_train=np.array(Y)[1::2,].astype(float)
Y_test=np.array(Y)[0::2,].astype(float)
X_train= normalize(X_train, axis=0, norm='max')
#X_train[:,1]=X_train[:,1]
#X_train[:,0]=X_train[:,0]
X_test= normalize(X_test, axis=0, norm='max')
#X_test[:,1]=X_test[:,1]
#X_train[:,0]=X_train[:,0]
# -
# ## 五.训练神经网络并输出损失收敛图
# +
# Train our neural network!
network = OurNeuralNetwork()
network.train(X_train,Y_train,0.02,10,1)
# -
# ## 六.测试集上的分类效果
# +
Y_pred=network.predict(X_test)
fig=plt.figure()
ax1 = Axes3D(fig)
z = np.array(Y_test).astype(float)
zp = np.rint(np.array(Y_pred).astype(float))
x = np.array(X_test[:,0]).astype(float)
y = np.array(X_test[:,1]).astype(float)
print('预测值为：')
print(zp)
print('真实值为：')
print(z)
ax1.scatter3D(x,y,z,c='b') #真实值
ax1.scatter3D(x,y,zp,c='r',marker = '^') #预测值
ax1.set_xlabel('height')
ax1.set_ylabel('weight')
ax1.set_zlabel('sex')
ax1.set_title("red is pred,blue is real")
plt.show()
# -
hisCountry is not None:
lakeCountryList += thisCountry.find('name').text + ' | '
# get the highest elevated airport
for airports in document.iterfind('airport'):
# compare the highest elevation using elevation. make sure all airports have elevation and is a digit
thisElev = airports.find('elevation').text
if thisElev is not None and thisElev.isdigit() and float(thisElev) > float(a_elev):
a_name = airports.find('name').text
a_elev = airports.find('elevation').text
a_loc = airports.attrib['country'].split()
airCountryList = 'Country: '
for cty in a_loc:
thisCountry = document.find("country[@car_code='"+cty+"']")
if thisCountry is not None:
airCountryList += thisCountry.find('name').text
print r_name + '-' + r_length + 'KM ' + riverCountryList
print l_name + '-' + l_area + 'KM ' + lakeCountryList
print a_name + '-' + a_elev + 'M ' + airCountryList
# -
| 7,334 |
/Plus Minus Hacker Rank.ipynb | df5bb6333aa58a7f8e38adbf93ada3be9967f333 | [] | no_license | vishal254/LockCodedown-5 | https://github.com/vishal254/LockCodedown-5 | 0 | 1 | null | 2021-01-20T15:02:17 | 2020-10-03T17:00:36 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 1,450 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: conda_python3
# language: python
# name: conda_python3
# ---
# # Customer Churn Prediction with XGBoost
# _**Using Gradient Boosted Trees to Predict Mobile Customer Departure**_
#
# ---
#
# ---
#
# ## Contents
#
# 1. [Background](#Background)
# 1. [Setup](#Setup)
# 1. [Data](#Data)
# 1. [Train](#Train)
# 1. [Inference Pipeline](#Inference)
#
#
# ---
#
# ## Background
#
# _This notebook has been adapted from an [AWS blog post](https://aws.amazon.com/blogs/ai/predicting-customer-churn-with-amazon-machine-learning/)_
#
# Losing customers is costly for any business. Identifying unhappy customers early on gives you a chance to offer them incentives to stay. This notebook describes using machine learning (ML) for the automated identification of unhappy customers, also known as customer churn prediction. ML models rarely give perfect predictions though, so this notebook is also about how to incorporate the relative costs of prediction mistakes when determining the financial outcome of using ML.
#
# We use an example of churn that is familiar to all of us–leaving a mobile phone operator. Seems like I can always find fault with my provider du jour! And if my provider knows that I’m thinking of leaving, it can offer timely incentives–I can always use a phone upgrade or perhaps have a new feature activated–and I might just stick around. Incentives are often much more cost effective than losing and reacquiring a customer.
#
# ---
#
# ## Setup
#
# _This notebook was created and tested on an ml.m4.xlarge notebook instance._
#
# Let's start by specifying:
#
# - The S3 bucket and prefix that you want to use for training and model data. You need to create an S3 bucket to store your datasets and the model you build. Follow this [instruction](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/create-bucket.html) to create an S3 bucket. This should be within the same region as the Notebook Instance, training, and hosting.
# - The IAM role arn used to give training and hosting access to your data. See the documentation for how to create these. Note, if more than one role is required for notebook instances, training, and/or hosting, please replace the boto regexp with a the appropriate full IAM role arn string(s).
# + isConfigCell=true tags=["parameters"]
bucket = 'QS-SM-customer-churn'
prefix = 'sagemaker/QS-xgboost-churn'
# Define IAM role
import boto3
import re
from sagemaker import get_execution_role
role = get_execution_role()
# -
# Next, we'll import the Python libraries we'll need for the remainder of the exercise.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import io
import os
import sys
import time
import json
from IPython.display import display
from time import strftime, gmtime
import sagemaker
from sagemaker.predictor import csv_serializer
# ---
# ## Data
#
# Mobile operators have historical records on which customers ultimately ended up churning and which continued using the service. We can use this historical information to construct an ML model of one mobile operator’s churn using a process called training. After training the model, we can pass the profile information of an arbitrary customer (the same profile information that we used to train the model) to the model, and have the model predict whether this customer is going to churn. Of course, we expect the model to make mistakes–after all, predicting the future is tricky business! But I’ll also show how to deal with prediction errors.
#
# The dataset we use is publicly available and was mentioned in the book [Discovering Knowledge in Data](https://www.amazon.com/dp/0470908742/) by Daniel T. Larose. It is attributed by the author to the University of California Irvine Repository of Machine Learning Datasets. Let's download and read that dataset in now:
# !wget http://dataminingconsultant.com/DKD2e_data_sets.zo = number_1 + number_2
print('the sum of the numbers you entered is', sumo)
answer = input('Do you wanna preform the operation again?')
x = int(input('Enter a number.'))
x += 1
print(x)
x *= 2
print(x)
x /= 10
print(x)
x -=100
print(x)
# +
number = int(input('Enter a positive number.'))
if number <= 0:
print('Error')
number = int(input('Please enter a valid number'))
print(number)
else:
print(number)
# +
number = int(input('Enter a number between 1 and 100.'))
if number < 1 or number > 100:
print('Error')
number = int(input('Please enter a valid number.'))
else:
print(number)
# -
# # Chapter Five Exercises - Functions
# ### Please submit your answers to the following Review Questions.
# #### Refer the book for the question prompt.
# ### Multiple choice:
# #### 1-20
# +
print('Oppgave 1: c, a group of statements that exsit whitin a program for the purpose of performing a specific task is an function.')
print('Oppgave 2: a, a design thechnique that helps to reduce the duplication of code whitin a program and is a benefit of using functions is code reuse.')
print('Oppgave 3: d, the first line of a function definition is known as the header.')
print('Oppgave 4: b, you call a function to execute it.')
print('Oppgave 5: a, A design techniue that progrmmers use to break down an algorithm into functions is known as top-down design.')
print('Oppgave 6: d, A hierarchy chart is a diagram that gives a visual representation of the relationships between functions in a program.')
print('Oppgave 7: b, A local variable is a variable that is created inside a function.')
print('Oppgave 8: c, A scope is the part of a program in which a variable may be accessed.')
print('Oppgave 9: a, An agrument is a piece of data that is sent into a function.')
print('Oppgave 10: b, A parameter is a special variable that receives a piece of data when a function is called.')
print('Oppgave 11: d, a variable that is visible to every function in a program file is a global variable.')
print('Oppgave 12: b, when possible, you should avoid usin global varables in a program.')
print('Oppgave 13: b, Library function is a prewritten function that is built into a programming language.')
print('Oppgave 14: b, randint is a standard library function that returns a random integer within a specified range of values.')
print('Oppgave 15: ')
print('Oppgave 16:')
print('Oppgave 17: d, the return statement causes a function to end and sends a value back to the part of the program that called the function.')
print('Oppgave 18: b, the IPO chart is a design tool that describes the input, porcessing, and output of a function.')
print('Oppgave 19: c, the Boolean type of function returns either True or False.')
print('Oppgave 20: c, sqrt is a math module function.')
# -
# ### True or False:
# #### 1-15
# +
print('Oppgave 1: false')
print('Oppgave 2: true')
print('Oppgave 3: true')
print('Oppgave 4: false')
print('Oppgave 5: false')
print('Oppgave 6: true')
print('Oppgave 7: false')
print('Oppgave 8: false')
print('Oppgave 9: true')
print('Oppgave 10: false')
print('Oppgave 11: true')
print('Oppgave 12: false')
print('Oppgave 13: true')
print('Oppgave 14: true')
print('Oppgave 15: false')
# -
# #### Algorithm workbench:
# #### 1, 3-5, 10
# +
def shout(string):
print(string.upper() + '!')
shout('I hate phyton')
# +
def my_function(a, b, c):
print(my_function)
my_function(3, 2, 1)
# +
4
def main():
x = 1
y = 3.4
print(x, y)
change_us(x, y)
print(x, y)
def change_us(a, b):
a = 0
b = 0
print(a, b)
main()
# +
def my_function(x, y):
return x[y]
x = 'testing'
y = 2
print(my_function)
# +
def is_valid_length(int, str):
if len(str) > len(int):
return 'false'
else:
return 'true'
str = 'hello'
int = 6
print(is_valid_length)
# -
ing these feature pairs in some machine learning algorithms can create catastrophic problems, while in others it will only introduce minor redundancy and bias. We should remove one feature from each of the highly correlated pairs.
#
# We will use Amazon SageMaker built-in Scikit-learn library for preprocessing (and also postprocessing), and then use the Amazon SageMaker built-in XGboost algorithm for predictions. We’ll deploy both the library and the algorithm on the same endpoint using the Amazon SageMaker Inference Pipelines feature so you can pass raw input data directly to Amazon SageMaker. We’ll also reuse the preprocessing code between training and inference to reduce development overhead and errors.
#
# To run Scikit-learn on Sagemaker `SKLearn` Estimator with a script as an entry point. The training script is very similar to a training script you might run outside of SageMaker. Also, as this data set is pretty small in term of size, we use the 'local' mode for preprocessing and upload the transformer and transformed data into S3.
# +
from sagemaker.sklearn.estimator import SKLearn
sagemaker_session = sagemaker.Session()
git_config = {'repo': 'https://github.com/aws-samples/quicksight-sagemaker-integration-blog.git','branch': 'master'}
script_path = 'quicksight-sagemaker-integration/preprocessing.py'
sklearn_preprocessor = SKLearn(
entry_point=script_path,
git_config=git_config,
role=role,
instance_type="local",
framework_version="0.20.0")
sklearn_preprocessor.fit({'train': s3_input_train})
# -
# ### Preparing the training and validation dataset <a class="anchor" id="preprocess_train_data"></a>
# Now that our proprocessor is properly fitted, let's go ahead and preprocess our training and validation data. Let's use batch transform to directly preprocess the raw data and store right back into s3.
# +
# Define a SKLearn Transformer from the trained SKLearn Estimator
transform_train_output_path = 's3://{}/{}/{}/'.format(bucket, prefix, 'transformtrain-train-output')
scikit_learn_inferencee_model = sklearn_preprocessor.create_model(env={'TRANSFORM_MODE': 'feature-transform'})
transformer_train = scikit_learn_inferencee_model.transformer(
instance_count=1,
instance_type='local',
assemble_with = 'Line',
output_path = transform_train_output_path,
accept = 'text/csv')
# Preprocess training input
transformer_train.transform(s3_input_train.config['DataSource']['S3DataSource']['S3Uri'], content_type='text/csv')
print('Waiting for transform job: ' + transformer_train.latest_transform_job.job_name)
transformer_train.wait()
preprocessed_train_path = transformer_train.output_path + transformer_train.latest_transform_job.job_name
print(preprocessed_train_path)
# -
# Define a SKLearn Transformer from the trained SKLearn Estimator
transform_validation_output_path = 's3://{}/{}/{}/'.format(bucket, prefix, 'transformtrain-validation-output')
transformer_validation = scikit_learn_inferencee_model.transformer(
instance_count=1,
instance_type='local',
assemble_with = 'Line',
output_path = transform_validation_output_path,
accept = 'text/csv')
# Preprocess validation input
transformer_validation.transform(s3_input_validation.config['DataSource']['S3DataSource']['S3Uri'], content_type='text/csv')
print('Waiting for transform job: ' + transformer_validation.latest_transform_job.job_name)
transformer_validation.wait()
preprocessed_validation_path = transformer_validation.output_path+transformer_validation.latest_transform_job.job_name
print(preprocessed_validation_path)
# ---
# ## Train
#
# Moving onto training, first we'll need to specify the locations of the XGBoost algorithm containers.
from sagemaker.amazon.amazon_estimator import get_image_uri
container = sagemaker.image_uris.retrieve("xgboost", boto3.Session().region_name, "1.2-1")
# Then, because we're training with the CSV file format, we'll create `s3_input`s that our training function can use as a pointer to the files in S3.
s3_input_train_processed = sagemaker.session.TrainingInput(
preprocessed_train_path,
distribution='FullyReplicated',
content_type='text/csv',
s3_data_type='S3Prefix')
print(s3_input_train_processed.config)
s3_input_validation_processed = sagemaker.session.TrainingInput(
preprocessed_validation_path,
distribution='FullyReplicated',
content_type='text/csv',
s3_data_type='S3Prefix')
print(s3_input_validation_processed.config)
# Now, we can specify a few parameters like what type of training instances we'd like to use and how many, as well as our XGBoost hyperparameters. A few key hyperparameters are:
# - `max_depth` controls how deep each tree within the algorithm can be built. Deeper trees can lead to better fit, but are more computationally expensive and can lead to overfitting. There is typically some trade-off in model performance that needs to be explored between a large number of shallow trees and a smaller number of deeper trees.
# - `subsample` controls sampling of the training data. This technique can help reduce overfitting, but setting it too low can also starve the model of data.
# - `num_round` controls the number of boosting rounds. This is essentially the subsequent models that are trained using the residuals of previous iterations. Again, more rounds should produce a better fit on the training data, but can be computationally expensive or lead to overfitting.
# - `eta` controls how aggressive each round of boosting is. Larger values lead to more conservative boosting.
# - `gamma` controls how aggressively trees are grown. Larger values lead to more conservative models.
#
# More detail on XGBoost's hyperparmeters can be found on their GitHub [page](https://github.com/dmlc/xgboost/blob/master/doc/parameter.md).
# +
sess = sagemaker.Session()
xgb = sagemaker.estimator.Estimator(container,
role,
instance_count=1,
instance_type='ml.m4.xlarge',
output_path='s3://{}/{}/output'.format(bucket, prefix),
sagemaker_session=sess)
xgb.set_hyperparameters(max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
objective='binary:logistic',
num_round=100)
xgb.fit({'train': s3_input_train_processed, 'validation': s3_input_validation_processed})
# -
# ## Post-processing
# Define a SKLearn Transformer from the trained SKLearn Estimator
transform_postprocessor_path = 's3://{}/{}/{}/'.format(bucket, prefix, 'transformtrain-postprocessing-output')
scikit_learn_post_process_model = sklearn_preprocessor.create_model(env={'TRANSFORM_MODE': 'inverse-label-transform'})
transformer_post_processing = scikit_learn_post_process_model.transformer(
instance_count=1,
instance_type='local',
assemble_with = 'Line',
output_path = transform_postprocessor_path,
accept = 'text/csv')
# ## Inference Pipeline <a class="anchor" id="pipeline_setup"></a>
# Setting up a Machine Learning pipeline can be done with the create_model(). In this example, we configure our pipeline model with the fitted Scikit-learn inference model, the fitted Xgboost model and the psotprocessing model.
timestamp_prefix = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
model_name = 'QS-inference-pipeline-' + timestamp_prefix
client = boto3.client('sagemaker')
response = client.create_model(
ModelName=model_name,
Containers=[
{
'Image': sklearn_preprocessor.image_uri,
'ModelDataUrl': sklearn_preprocessor.model_data,
'Environment': {
"SAGEMAKER_SUBMIT_DIRECTORY": sklearn_preprocessor.uploaded_code.s3_prefix,
"TRANSFORM_MODE": "feature-transform",
"SAGEMAKER_CONTAINER_LOG_LEVEL": str(sklearn_preprocessor.container_log_level),
"SAGEMAKER_REGION": sklearn_preprocessor.sagemaker_session.boto_region_name,
"SAGEMAKER_PROGRAM": sklearn_preprocessor.uploaded_code.script_name
}
},
{
'Image': xgb.image_uri,
'ModelDataUrl': xgb.model_data,
"Environment": {}
},
{
'Image': scikit_learn_post_process_model.image_uri,
'ModelDataUrl': scikit_learn_post_process_model.model_data,
'Environment': {
"SAGEMAKER_SUBMIT_DIRECTORY": sklearn_preprocessor.uploaded_code.s3_prefix,
"TRANSFORM_MODE": "inverse-label-transform",
"SAGEMAKER_CONTAINER_LOG_LEVEL": str(sklearn_preprocessor.container_log_level),
"SAGEMAKER_REGION": sklearn_preprocessor.sagemaker_session.boto_region_name,
"SAGEMAKER_PROGRAM": sklearn_preprocessor.uploaded_code.script_name
}
},
],
ExecutionRoleArn = role,
)
model_name
| 17,085 |
/Deep-Learning/Trials/Simple Neural Network.ipynb | b6b4596bd610ce1d5ff125e9af396fbb7d12d3ca | [] | no_license | teodora-petkova/Artificial-Intelligence-Course | https://github.com/teodora-petkova/Artificial-Intelligence-Course | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 111,428 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import functools
np.random.seed(42)
# # Simple Neural Network
# ## Description ...
class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.W1 = np.random.rand(input_size, hidden_size)
self.W2 = np.random.rand(hidden_size, output_size)
self.b1 = np.zeros((1, hidden_size))
self.b2 = np.zeros((1, output_size))
def feedforward(self, X, sigma, outsigma):
A0 = X
self.Z1 = np.dot(A0, self.W1) + self.b1
self.A1 = sigma(self.Z1)
self.Z2 = np.dot(self.A1, self.W2) + self.b2
self.A2 = outsigma(self.Z2)
return self.A2
def backprop(self, X, y, d_sigma, d_outsigma, d_loss, alpha=1):
A0 = X
m = X.shape[0]
d_Z2 = d_loss(self.A2, y) * d_outsigma(self.Z2)
self.d_W2 = (1/m) * np.dot(self.A1.T, d_Z2)
self.d_b2 = (1/m) * np.sum(d_Z2, axis=0, keepdims=True)
d_Z1 = np.dot(d_Z2, self.W2.T) * d_sigma(self.Z1)
self.d_W1 = (1/m) * np.dot(A0.T, d_Z1)
self.d_b1 = (1/m) * np.sum(d_Z1, axis=0, keepdims=True)
self.W1 -= alpha * self.d_W1
self.W2 -= alpha * self.d_W2
self.b1 -= alpha * self.d_b1
self.b2 -= alpha * self.d_b2
def train(self, X, y, sigma, d_sigma, outsigma, d_outsigma, cost, d_cost):
y_hat = self.feedforward(X, sigma, outsigma)
loss_error = cost(y_hat, y)
self.backprop(X, y, d_sigma, d_outsigma, d_cost)
return y_hat, loss_error
# gradient check (in process...)
def gradient_check_W1(self, X, y, sigma, outsigma, cost, epsilon = 1e-7):
gradapprox = np.zeros(self.W1.shape)
saved_W1 = self.W1
for i in range(self.W1.shape[0]):
for j in range(self.W1.shape[1]):
self.W1[i][j] = self.W1[i][j] + epsilon
y_hat = self.feedforward(X, sigma, outsigma)
J_plus = cost(y_hat, y)
self.W1 = saved_W1
self.W1[i][j] = self.W1[i][j] - epsilon
y_hat = self.feedforward(X, sigma, outsigma)
J_minus = cost(y_hat, y)
gradapprox[i][j] = (J_plus - J_minus) / (2 * epsilon)
self.W1 = saved_W1
rel_error = lambda x, y: np.max(np.abs(x - y) / (np.maximum(epsilon, np.abs(x) + np.abs(y))))
diff = rel_error(self.d_W1, gradapprox)
return diff
# ## Activation Function
#
# Example of an activation function.
# Sigmoid is used for classification problems.
#
# $$ S(x)= \frac{1}{1+e^{-x}} $$
# +
def sigmoid(x):
return 1/(1 + np.exp(-x))
def sigmoid_derivative(x):
return sigmoid(x) * (1 - sigmoid(x))
# -
x = np.arange(-10, 11, 0.1)
y = sigmoid(x)
y_prim = sigmoid_derivative(x)
plt.plot(x, y)
plt.plot(x, y_prim)
plt.show()
# ## Loss Function
# +
def mse(y_hat, y):
return np.mean(np.square(y_hat - y))
def mse_derivative(y_hat, y):
return 2*(y_hat - y)
# +
X=np.array(([0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]), dtype=float)
y=np.array(([0],[1],[1],[0]), dtype=float)
input_size = X.shape[1]
hidden_size = 4
output_size = 1
nn = NeuralNetwork(input_size, hidden_size, output_size)
for i in range(2001):
y_hat, loss = nn.train(X, y,
sigmoid, sigmoid_derivative,
sigmoid, sigmoid_derivative,
mse, mse_derivative)
if i % 100 == 0:
print (f"for iteration #{str(i)}: {str(loss)}")
print ("Final predicted Output: \n" + str(y_hat))
# gradient check ...still in process...
diff = nn.gradient_check_W1(X, y, sigmoid, sigmoid, mse)
print("diff gradients W1:", diff)
# +
N = 100 # the number of points per class
D = 2 # the dimensionality (x, y coordinates)
C = 3 # the number of classes
X = np.zeros((N*C, D)) # data matrix (each row = single example)
y = np.zeros((N*C, C), dtype='uint8') # class labels
for j in range(C):
ix = range(N*j, N*(j+1))
r = np.linspace(0, 1, N) # radius
t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
y[ix] = np.zeros(C) # y should be one-hot encoded
y[ix, j] = 1
# lets visualize the data:
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.show()
# +
def relu(x):
return np.maximum(0, x)
def relu_derivative(x):
dx = lambda t: 1 if t > 0 else 0
return np.vectorize(dx)(x)
def softmax(a, y, N):
s = np.exp(a) / np.sum(np.exp(a), axis=1, keepdims=True)
return s
def cross_entropy(p, q, y, N):
#pp = p[range(N),y]
return - p * np.log(q+0.001) + 0.001
def softmax_derivative(x):
return 1
def cross_entropy_derivative(y_hat, y, N):
#yhat = y_hat[range(N), y]
return y_hat - y
# -
h = 100
nn2 = NeuralNetwork(D, h, 3)
for i in range(3001):
y_hat, loss = nn2.train(X, y,
relu, relu_derivative,
functools.partial(softmax, y = y, N = X.shape[0]), softmax_derivative,
functools.partial(cross_entropy, y = y, N = X.shape[0]),
functools.partial(cross_entropy_derivative, N = X.shape[0]))
if i % 100 == 0:
print (f"for iteration #{str(i)}: {str(np.sum(loss))}")
scores = nn2.Z2
predicted_classes = np.argmax(scores, axis=1)
y_classes = np.argmax(np.array(y), axis=1)
print (f"training accuracy: {np.mean(predicted_classes == y_classes)}")
# plot the resulting classifier
h = 0.02
x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1
y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
input_dots = np.c_[xx.ravel(), yy.ravel()]
Z = np.dot(np.maximum(0, np.dot(input_dots, nn2.W1) + nn2.b1), nn2.W2) + nn2.b2
Z = np.argmax(Z, axis=1)
Z = Z.reshape(xx.shape)
fig = plt.figure()
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
# Bibliography:
#
# 1. Softmax
# * https://deepnotes.io/softmax-crossentropy
# * https://aimatters.wordpress.com/2019/06/17/the-softmax-function-derivative/
# * https://aimatters.wordpress.com/2020/06/14/derivative-of-softmax-layer/
#
# 2. Entropy/ Cross-Entropy
# * https://www.youtube.com/watch?v=ErfnhcEV1O8
# * https://machinelearningmastery.com/cross-entropy-for-machine-learning/
# * https://datascience.stackexchange.com/questions/20296/cross-entropy-loss-explanation
#
# 3. Neural Network:
# * https://cs231n.github.io/neural-networks-case-study/
# * https://cs231n.github.io/
# * https://github.com/tyz910
# * http://cs231n.stanford.edu/handouts/linear-backprop.pdf
# * https://datascience-enthusiast.com/DL/Improving_DeepNeural_Networks_Gradient_Checking.html
# * https://peterroelants.github.io/
#
# 4. Numpy axis:
# * https://i.stack.imgur.com/Z29Nn.jpg
| 7,506 |
/Python Assignment-1.ipynb | fc051c5a70cf90b4ecef6d218e9989b5f8fe5d66 | [] | no_license | Dwarakamai-Bathula/assignment1 | https://github.com/Dwarakamai-Bathula/assignment1 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,521 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
nl=[]
for x in range(2000,3200):
if (x%7==0) and (x%5!=0):
nl.append(str(x))
print(','.join(nl))
print("Enter First Name:")
first=str(input())
print("Enter Last Name:")
last=str(input())
print(last+' '+first)
pi=3.142
d=12
r=d/2
volume=4/3*(3.14*r*r*r)
print("volume of sphere with diameter 12cm is:",volume)
| 591 |
/notebooks/Golden_Pass_2/Pass_2_Excitatory_4_Auto_Classifier_Whole_Neuron_Run_2/Testing_Datajoint_Adapted_Classifier.ipynb | 99bf2eda0ca107e85af6121cf8f8b4af0cf462c3 | [] | no_license | celiibrendan/Complete_Pinky100_Pipeline | https://github.com/celiibrendan/Complete_Pinky100_Pipeline | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 7,971 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
from whole_neuron_classifier_datajoint_adapted import extract_branches_whole_neuron
import datajoint as dj
import numpy as np
import datajoint as dj
import trimesh
import time
# +
dj.config['database.host'] = '10.28.0.34'
dj.config['database.user'] = 'celiib'
dj.config['database.password'] = 'newceliipass'
#schema = dj.schema('microns_ta3p100')
#ta3p100 = dj.create_virtual_module('ta3p100', 'microns_ta3p100')
schema = dj.schema("microns_pinky")
pinky = dj.create_virtual_module("pinky","microns_pinky")
# -
"""
Base definition:
def extract_branches_whole_neuron(import_Off_Flag,
**kwargs):
All options for functions
global_start = time.time()
# Step 0: Where to import from
if import_Off_Flag == True:
#if loading from an off file
mesh_file_location = kwargs.pop('mesh_file_location', "")
file_name = kwargs.pop('file_name', "")
else:
#if loading from datajoint
vertices = kwargs.pop('vertices', -1)
triangles = kwargs.pop('triangles', -1)
segment_id = kwargs.pop("segment_id",-1)
#Step 1: Mesh importing and Pymeshfix parameters
pymeshfix_Flag = kwargs.pop('pymeshfix_Flag', True)
joincomp = kwargs.pop('joincomp', False)
remove_smallest_components = kwargs.pop('remove_smallest_components', True)
#Step 2: CGAL segmentation parameters
import_CGAL_Flag = kwargs.pop('import_CGAL_Flag', False)
import_CGAL_paths = kwargs.pop('import_CGAL_paths', [[""],[""]])
clusters = kwargs.pop('clusters', 4)
smoothness = kwargs.pop('smoothness', 0.30)
#step 3: Soma identification parameters
size_multiplier = kwargs.pop('size_multiplier', 1)
soma_size_threshold = kwargs.pop("soma_size_threshold",3000)
#step 4: finding soma extensions parameters
soma_cap_min_width= kwargs.pop('soma_cap_min_width', 0.23)
soma_cap_max_faces= kwargs.pop('soma_cap_max_faces', 6000)
soma_cap_max_n_connections= kwargs.pop('soma_cap_max_n_connections', 6)
large_extension_size = kwargs.pop('large_extension_size', 1500)
large_extension_convex_max= kwargs.pop('soma_cap_conex_threshold', 3)
#Step 5: Apical Identifying Parameters
apical_mesh_threshold= kwargs.pop('apical_mesh_threshold', 2000)
apical_height_threshold= kwargs.pop('apical_height_threshold', 5000)
apical_sdf_threshold = kwargs.pop('apical_sdf_threshold', 0.09)
#Step 6: Classifying Entire Mesh parameters
classifier_cilia_threshold=kwargs.pop('classifier_cilia_threshold', 1000) #maximum size of cilia
classifier_stub_threshold=kwargs.pop('classifier_stub_threshold', 200) # minimum size of appndage of soma to not be considered stub and merged with the soma
classifier_non_dendrite_convex_threshold = kwargs.pop('classifier_non_dendrite_convex_threshold', 27.5) #must be above this value to be axon, cilia or error
classifier_axon_std_dev_threshold = kwargs.pop('classifier_axon_std_dev_threshold', 69) #standard deviation of convex measurements for which axon branches are under this threshold
classifier_stub_threshold_apical = kwargs.pop('classifier_stub_threshold_apical', 700) #the minimum size threshold for apical appendage not to be merged with apical
#Step 9: Output Configuration Parameters
return_Only_Labels = kwargs.pop("return_Only_Labels",False)
return_cilia=kwargs.pop('return_cilia', False)
return_soma=kwargs.pop('return_soma', False)
return_axon=kwargs.pop('return_axon', False)
return_error=kwargs.pop('return_error', False)
return_size_threshold=kwargs.pop('return_size_threshold', 200)
clean_temp_files=kwargs.pop('clean_temp_files', True)
"""
print("done")
# +
segment_id = 648518346349495660
#get the vertices and faces from datajoint
# get the newly stitched mesh
# get the original mesh
key = dict(segmentation=3,segment_id = segment_id)
verts,faces = (pinky.PymeshfixDecimatedExcitatoryStitchedMesh() & key).fetch1("vertices","triangles")
# +
#run the whole algorithm on the neuron to test
verts_labels, faces_labels = extract_branches_whole_neuron(import_Off_Flag=False,segment_id=segment_id,vertices=verts,
triangles=faces,pymeshfix_Flag=False,
import_CGAL_Flag=False,
return_Only_Labels=True,
clusters=3,
smoothness=0.20)
# +
#save the labels
# file_location = "/Users/brendancelii/Google Drive/Xaq Lab/Datajoint Project/2_Stitching_Meshes/test_meshes/"
# labels_file = "child_mesh_faces.npz"
# child_faces = np.load(file_location + labels_file)
# labels_list = child_faces["faces_list"]
np.savez("./test_labels/" + str(segment_id) + "_test_labels.npz",faces_list=faces_labels)
# +
# #read in the labels
# import csv
# triangles_labels=[]
# labels_file = "./temp/648518346349482020_fixed-cgal_4_0.30_revised.csv"
# with open(labels_file) as csvfile:
# for i,row in enumerate(csv.reader(csvfile)):
# triangles_labels.append(int(row[0]))
# print(triangles_labels)
# -
til.copyfile(source,destination)
# + id="OjIxsMORmj-j" colab_type="code" colab={}
#paper train and validation
for f in paper_files[:560]:
source= os.path.join(paper_dir,f)
destination = os.path.join(train_paper_dir,f)
shutil.copyfile(source,destination)
for f in paper_files[-280:]:
source= os.path.join(paper_dir,f)
destination = os.path.join(validation_paper_dir,f)
shutil.copyfile(source,destination)
# + id="PuHdHUq6ocV9" colab_type="code" colab={}
#scissors train and validation
for f in scissors_files[:560]:
source= os.path.join(scissors_dir,f)
destination = os.path.join(train_scissors_dir,f)
shutil.copyfile(source,destination)
for f in scissors_files[-280:]:
source= os.path.join(scissors_dir,f)
destination = os.path.join(validation_scissors_dir,f)
shutil.copyfile(source,destination)
# + id="aFX6O-QKopST" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 68} outputId="a8a1c0e0-133b-4b00-c92e-c09bb581d710"
print('total training rock images:', len(os.listdir(train_rock_dir)))
print('total training paper images:', len(os.listdir(train_paper_dir)))
print('total training scissors images:', len(os.listdir(train_scissors_dir)))
# + id="exbZc8eOpD8u" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 68} outputId="d453f597-ac34-4e44-a48b-677ad1939d32"
print('total validation rock images:', len(os.listdir(validation_rock_dir)))
print('total validation paper images:', len(os.listdir(validation_paper_dir)))
print('total validation scissors images:', len(os.listdir(validation_scissors_dir)))
# + id="5ttGIOv6pEwr" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 68} outputId="a56791e1-f698-4b60-d2e0-03c9db1624a8"
print('total test rock images:', len(os.listdir(test_rock_dir)))
print('total test paper images:', len(os.listdir(test_rock_dir)))
print('total test scissors images:', len(os.listdir(test_rock_dir)))
# + [markdown] id="qV__GN11yO2P" colab_type="text"
# ## TASK 1: Build a fully connect Neural Network
# First, let's try what we've learned from the previous lecture. We will build a FULLY connect neural networks to classify the gesture images.You are free to experiment with different structure of the network, data augmentation, dropout, different optimizer, and etc, to try to achieve the best performance on the TEST data in terms of accuracy. Watch out for overfitting.
#
# Note that you should set test aside when you train your model. In the end, please report your model accuracy on the test set.
# + id="LWTisYLQM1aM" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 340} outputId="b3feaa84-7fe3-4e3b-f6cf-16d8e5cf823e"
# TODO
from keras import models
from keras import layers
network = models.Sequential()
network.add(layers.Dense(64, activation='relu', input_shape=(150,150,3)))
network.add(layers.Dense(32, activation='relu'))
network.add(layers.Dense(32, activation='relu'))
network.add(layers.Flatten())
network.add(layers.Dense(3, activation='softmax'))
network.summary()
# + id="6aaiyuDep20F" colab_type="code" colab={}
from keras import optimizers
network.compile(loss='categorical_crossentropy',
optimizer=optimizers.RMSprop(lr=0.001),
metrics=['acc'])
# + id="T1ZPd7zNqCEy" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 68} outputId="2631b0cf-ac84-4b21-a050-1cca504e77fc"
from keras.preprocessing.image import ImageDataGenerator
# Rescale to 1./255
train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
# All images will be resized to 150x150
target_size=(150, 150),
batch_size=20,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(
valid_dir,
target_size=(150, 150),
batch_size=20,
class_mode='categorical')
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(150, 150),
batch_size=20,
class_mode='categorical')
# + id="b93_yR48rCqX" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 649} outputId="395dec20-b191-4d7c-e485-7680b2dccc59"
history = network.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=18,
validation_data=validation_generator,
validation_steps=50)
# + id="kY9VwuHIrIVo" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 545} outputId="3dc0fade-1e36-4bb1-d0f9-b5d9f3a40b3c"
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
# + id="KOVyDyn0rbh8" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 51} outputId="85b36d7d-7e74-498c-cf61-ae7c7d6483a1"
model_score = network.evaluate_generator(test_generator)
print("Model Test Loss:",model_score[0])
print("Model Test Accuracy:",model_score[1])
# + [markdown] id="40cMjm9K06YE" colab_type="text"
# ## TASK 2: Build Convolution Neural Network
# Now, let's try a convolution neural network (CNN) and see if we can achieve better performance. Similarly you are free to experiment with different structure of the network, techniques to avoid overfitting, different optimizer, and etc, to try to achieve the best performance on the TEST data in terms of accuracy.
#
# Note that you should set test aside when you train your model. In the end, please report your model accuracy on the test set.
# + id="lcxNZCS_090n" colab_type="code" colab={}
# TODO
# + id="EeLB3pT7rjLA" colab_type="code" colab={}
#using input shape 128 x128
model = models.Sequential()
model.add(layers.Conv2D(64, (3, 3), activation='relu',
input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
#use softmax
model.add(layers.Dense(3, activation='softmax'))
# + id="Jn2MrLsYrvow" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 595} outputId="9ee91053-e00d-4825-8270-9db2a161586a"
model.summary()
# + id="vv-IKT1xr69o" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 68} outputId="b2985278-2c69-495e-f949-7a38e67f1614"
from keras.preprocessing.image import ImageDataGenerator
# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
# All images will be resized to 150x150
target_size=(150, 150),
batch_size=20,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=20,
class_mode='categorical')
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(150, 150),
batch_size=20,
class_mode='categorical')
# + id="OFufRiH3sQ4s" colab_type="code" colab={}
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.RMSprop(lr=0.001),
metrics=['acc'])
# + id="OsaNABHPsR4-" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 377} outputId="3550c0fd-3111-4eda-e83e-6dd56079cc60"
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=10,
validation_data=validation_generator,
validation_steps=50)
# + id="ErNEBfBCsZR3" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 545} outputId="a3d2b781-9668-491d-d797-c32fa08582f6"
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
# + id="cgZXmNpvsjUt" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 51} outputId="6786a77d-cf60-48f4-d9c6-461dc8b478af"
model_score = model.evaluate_generator(test_generator)
print("Model Test Loss:",model_score[0])
print("Model Test Accuracy:",model_score[1])
# + [markdown] id="pUuqO-p-yrMV" colab_type="text"
# ## Use the best model to classify gestures
# You can now run the following code and use the model you trained to classify images uploaded from your laptop. Let us know how your model performs on the new unseen images.
# + id="ZABJp7T3VLCU" colab_type="code" colab={"resources": {"http://localhost:8080/nbextensions/google.colab/files.js": {"data": "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", "ok": true, "headers": [["content-type", "application/javascript"]], "status": 200, "status_text": ""}}, "base_uri": "https://localhost:8080/", "height": 91} outputId="80dbfe4b-33f8-41a7-c05d-aa455daa49fa"
import numpy as np
from google.colab import files
from keras.preprocessing import image
uploaded = files.upload()
for fn in uploaded.keys():
# predicting images
path = fn
img = image.load_img(path, target_size=(150, 150))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict(images, batch_size=10)
# + id="1D6xepkEnJfd" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 51} outputId="23819243-6ba6-4852-a355-1776fd91a392"
print(fn)
print(classes)
# + [markdown] id="VW_VDwNTnlyC" colab_type="text"
# I selected a hand scissor in a white background, and the result shows it belongs to the 3rd category (Scissor). The result is accurate.
| 23,741 |
/A1/hcds-a1-data-curation.ipynb | d746293e8323f563ecb055867e4f4b962ee852c6 | [
"MIT"
] | permissive | AaronJacobson/DATA512 | https://github.com/AaronJacobson/DATA512 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 155,935 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import xarray as xr
from saveCore_standalone_v2 import run_powerBlobs, powerBlob_utils as utils, util as wavelet
import matplotlib.pyplot as plt
from utils import constants as cnst
import pandas as pd
import cartopy
import cartopy.crs as ccrs
from utils import u_grid, u_interpolate as u_int, u_darrays as uda
from ccores import cores
# cd ..
#tag = 'MFG'
tag = 'MSG'
testfile = '/media/ck/Elements/Africa/WestAfrica/NFLICS/MCS_TIR/real_time_wavelet/2021/09/13/IR_108_BT_20210913_2100.nc'
data = xr.open_dataset(testfile, decode_cf=False)#.sel(lat=slice(6,8), lon=slice(6,9.5))'/media/ck/Elements/Africa/WestAfrica/NFLICS/MCS_TIR/real_time_wavelet/2020/06/25/IR_wavelet_BT_20200625_1300.nc'
data = data.isel(time=0)
data['IR108_BT'].values = (data['IR108_BT'])[:,::-1]
data
data['IR108_BT'].plot.contourf()
cut=data.sel(x=slice(100,1250), y=slice(0,500))#.sel(x=slice(0,500), y=slice(200,400))#.sel(x=slice(500,1000), y=slice(80,350))
f = plt.figure(figsize=(13,9), dpi=200)
plt.pcolormesh(cut['IR108_BT'].T,vmin=-90, vmax=-40)
### Create a wavelet decomposition object first. In this example, we initialise the NFLICS nowcasting 5km setup. The setups can be defined in ccores.constants
wObj = cores.dataset('METEOSAT3K_veraLS')
# +
grid_lons, grid_lats = np.meshgrid(cut.x.values, cut.y.values)
### The next step prepares the tir image for the wavelet routine. 'Perfect image' example, WITH edge smoothing.
wObj.read_img(cut['IR108_BT'].T.values, grid_lons, grid_lats, edge_smoothing=False)
# -
### The object saves the filtered image
f = plt.figure(figsize=(6,5), dpi=100)
ax = f.add_subplot(111)
plt.pcolormesh(wObj.image)
wObj.applyWavelet()
# +
### Same powers as above but as contours plotted onto the thermal-infrared image.
f = plt.figure(figsize=(13,11), dpi=200)
ax = f.add_subplot(221)
plt.pcolormesh(wObj.image, vmax=-50, vmin=-85, cmap='jet')
scale_id = 0
plt.contour(wObj.power[scale_id,:,:], levels=[0,1], cmap='jet_r', linewidths=0.6)
plt.title('Contours: '+str(wObj.scales[scale_id])+' km scale cores')
ax = f.add_subplot(222)
scale_id = 2
plt.pcolormesh(wObj.image, vmax=-50, vmin=-85, cmap='jet')
plt.contour(wObj.power[scale_id,:,:], levels=[0,1], cmap='jet_r', linewidths=0.6)
plt.title('Contours: '+str(wObj.scales[scale_id])+' km scale cores')
# -
filtered_power = wObj.scaleWeighting(wtype='nflicsv2')
# +
### For test purposes, we introduce some NaNs to create a second image
f = plt.figure(figsize=(8,5), dpi=200)
ax = f.add_subplot(111)
plt.pcolormesh(wObj.image, vmax=-50, vmin=-85, cmap='jet')
plt.contour(filtered_power, levels=[0,1], colors='k', linewidths=0.6)
plt.title('2021-09-13 | 21Z, Contours: NflicsV2 - cores from power values')
f.savefig('/home/ck/DIR/cornkle/figs/NFLICS/cores_nflicsV2_3km.jpg')
# -
| 3,039 |
/Wisconsin Breast cancer dataset.ipynb | 9f2836660133f26d21a35d292547eefd8fcfa5c9 | [] | no_license | ckkchinar/Classification-Problems | https://github.com/ckkchinar/Classification-Problems | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 151,493 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Case-Study 1 (Decision Tree,Randome Forest,KNN,NB)
# Consider The Wisconsin Breast Cancer Database.
#
# This dataset consists of 10 continuous attributes and 1 target class attribute.
#
# Class attribute shows the observation result, whether the patient is suffering from the benign tumor or malignant tumor.
#
# Benign tumors do not spread to other parts while the malignant tumor is cancerous.
# Breast Cancer Data Set Attribute Information:
# 1. Sample code number: id number
# 2. Clump Thickness: 1 – 10
# 3. Uniformity of Cell Size: 1 – 10
# 4. Uniformity of Cell Shape: 1 – 10
# 5. Marginal Adhesion: 1 – 10
# 6. Single Epithelial Cell Size: 1 – 10
# 7. Bare Nuclei: 1 – 10
# 8. Bland Chromatin: 1 – 10
# 9. Normal Nucleoli: 1 – 10
# 10. Mitoses: 1 – 10
# 11. Class: (2 for benign, 4 for malignant)
# ### 1.2 Import the Libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# %matplotlib inline
import warnings
warnings.filterwarnings('ignore')
# ### 1.3 Load the dataset
df=pd.read_csv('C:\\Python_Training\\Python_Labs\\6.SLC\\Take Home\\Day 3\\wisc_bc_data.csv')
df.head()
df.shape
# ### 1.4 Check the data type for dataset?
df.info()
# +
#Conclusion: except our label, all features are either float or int
# -
# ### 1.5 Check the data.describe for dataset?
df.describe()
# ### 1.6 Check the data.groupby count for diagnosis?
df.groupby(df['diagnosis']).count()
# ### 1.7 drop the first column from the data frame. This is Id column which is not used in modeling?
df=df.drop('id',axis=1)
fig,ax=plt.subplots(8,4,figsize=(15,30))
ax=ax.flatten()
for i in range(len(num_columns)):
sns.boxplot(x=df[num_columns].iloc[:,i],ax=ax[i])
plt.show()
num_columns=[]
for i in list(df.columns):
if df[i].dtype!='object':
num_columns.append(i)
print(num_columns)
#imputing the outliers in one go
for i in num_columns:
q3=df[i].quantile(0.75)
q1=df[i].quantile(0.25)
iqr=q3-q1
ll=q1-1.5*iqr
ul=q3+1.5*iqr
df.loc[df[i]>ul,i]=ul
df.loc[df[i]<ll,i]=ll
df.loc[54,'concavity_worst']
# ### 1.8 Create a separate dataframe consisting only of the features i.e independent attributes
features=df.drop('diagnosis',axis=1)
features.head()
# ### 1.9 convert the features into z scores as we do not know what units / scales were used and store them in new dataframe
#
# It is always adviced to scale numeric attributes in models that calculate distances.
from sklearn.preprocessing import StandardScaler
ss=StandardScaler()
features=pd.DataFrame(ss.fit_transform(features),columns=features.columns)
features.head()
# ### 1.10 Capture the class values from the 'diagnosis' colum.
df['diagnosis'].unique()
df['diagnosis'].value_counts()
from sklearn.preprocessing import LabelEncoder
ll=LabelEncoder()
df['diagnosis']=ll.fit_transform(df['diagnosis'])
df['diagnosis']
# ### 1.11 Extract the independent variable X and dependent variable Y?
X=features
y=df['diagnosis']
X.shape
# ### 1.12 Split the data into train and test set:(70/30)
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=3,test_size=0.3)
# ## 1.13 Import all the algorithms we want to test
# #### 1.13a) Prepare an array with all the algorithms
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
lr=LogisticRegression()
dt=DecisionTreeClassifier()
rf=RandomForestClassifier(**rf_search.best_params_)
ada=AdaBoostClassifier()
gbm=GradientBoostingClassifier()
knn=KNeighborsClassifier()
gaus=GaussianNB()
# +
from sklearn.model_selection import cross_val_score,KFold
names=['Logistic Regression','Decision Tree','RandomForest','Adaboost','GradientBoost','KNN','GaussianNB']
models=[lr,dt,rf,ada,gbm,knn,gaus]
result=[]
for name,model in zip(names,models):
kfold=KFold(n_splits=5,random_state=1)
score=cross_val_score(model,X,y,cv=kfold,scoring='roc_auc')
result.append(score)
print('Mean ROC_AUC for',name,':',np.mean(result))
# -
from sklearn.ensemble import VotingClassifier
from sklearn.metrics import roc_auc_score
vc=VotingClassifier(estimators=[(i,j) for i,j in zip(names,models)],voting='soft')
vc.fit(X,y)
y_pred=vc.predict(X)
print('roc auc score:',roc_auc_score(y,y_pred))
# +
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import randint as rint
rf=RandomForestClassifier()
params={'n_estimators':rint(1,200),
'max_depth':rint(2,10)}
rf_search=RandomizedSearchCV(rf,param_distributions=params,n_iter=200,n_jobs=-1,scoring='roc_auc',cv=5,
return_train_score=True,random_state=1,verbose=2)
rf_search.fit(X,y)
# -
rf_search.best_params_
from sklearn.model_selection import cross_val_score,KFold
kfold=KFold()
score=cross_val_score()
# #### 1.13 b) Prepare the configuration to run the (X,Y),seed=7
# +
# Decision Tree
# +
from sklearn.tree import DecisionTreeClassifier
DT=DecisionTreeClassifier(random_state=7)
DT.fit(X_train,y_train)
y_train_pred_DT=DT.predict(X_train)
y_train_prob_DT=DT.predict_proba(X_train)[:,1]
y_test_pred_DT=DT.predict(X_test)
y_test_prob_DT=DT.predict_proba(X_test)[:,1]
# +
# Random Forrest
# +
from sklearn.ensemble import RandomForestClassifier
RF=RandomForestClassifier(random_state=7)
RF.fit(X_train,y_train)
y_train_pred_RF=RF.predict(X_train)
y_train_prob_RF=RF.predict_proba(X_train)[:,1]
y_test_pred_RF=RF.predict(X_test)
y_test_prob_RF=RF.predict_proba(X_test)[:,1]
# +
# KNN
# +
from sklearn.neighbors import KNeighborsClassifier
KN=KNeighborsClassifier()
KN.fit(X_train,y_train)
y_train_pred_KN=KN.predict(X_train)
y_train_prob_KN=KN.predict_proba(X_train)[:,1]
y_test_pred_KN=KN.predict(X_test)
y_test_prob_KN=KN.predict_proba(X_test)[:,1]
# +
#Naive Bayes
# +
from sklearn.naive_bayes import GaussianNB
NB=GaussianNB()
NB.fit(X_train,y_train)
y_train_pred_NB=NB.predict(X_train)
y_train_prob_NB=NB.predict_proba(X_train)[:,1]
y_test_pred_NB=NB.predict(X_test)
y_test_prob_NB=NB.predict_proba(X_test)[:,1]
# -
# #### 1.13 c) Every algorithm is tested and results are collected and printed?
#
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,roc_auc_score,roc_curve
# Decision Tree
print('Accuracy of training data of Decision Tree:',accuracy_score(y_train,y_train_pred_DT))
print('Accuracy of testing of Decision Tree:',accuracy_score(y_test,y_test_pred_DT))
print('AUC score of training data of Decision Tree:',roc_auc_score(y_train,y_train_prob_DT))
print('AUC score of testing data of Decision Tree:',roc_auc_score(y_test,y_test_prob_DT))
# Random Forrest
print('Accuracy of training data of Random Forrest:',accuracy_score(y_train,y_train_pred_RF))
print('Accuracy of testing of Random Forrest:',accuracy_score(y_test,y_test_pred_RF))
print('AUC score of training data of Random Forrest:',roc_auc_score(y_train,y_train_prob_RF))
print('AUC score of testing data of Random Forrest:',roc_auc_score(y_test,y_test_prob_RF))
#KNN
print('Accuracy of training data of KNN:',accuracy_score(y_train,y_train_pred_KN))
print('Accuracy of testing data of KNN:',accuracy_score(y_test,y_test_pred_KN))
print('AUC score of training data of KNN:',roc_auc_score(y_train,y_train_prob_KN))
print('AUC score of testing data of KNN:',roc_auc_score(y_test,y_test_prob_KN))
#NB
print('Accuracy of training data of NB:',accuracy_score(y_train,y_train_pred_NB))
print('Accuracy of testing data of NB:',accuracy_score(y_test,y_test_pred_NB))
print('AUC score of training data of NB:',roc_auc_score(y_train,y_train_prob_NB))
print('AUC score of testing data of NB:',roc_auc_score(y_test,y_test_prob_NB))
# #### 1.13 d) boxplot algorithm comparison
from sklearn.linear_model import LogisticRegression
# +
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
models = []
models.append(('LR', LogisticRegression()))
models.append(('DT', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('RF', RandomForestClassifier()))
models.append(('NB', GaussianNB()))
results = []
names = []
scoring = 'roc_auc'
for name, model in models:
kfold = KFold(n_splits=10, random_state=7)
cv_results = cross_val_score(model, X, y, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
print('Mean AUC score for:',name,':',cv_results.mean())
# -
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
| 9,061 |
/Regressão_rede_neural_com_numpy.ipynb | d788d55a043a22990561f1c905109b17d625f055 | [
"MIT"
] | permissive | otavioaugusto1/deep-learning | https://github.com/otavioaugusto1/deep-learning | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 203,371 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/otavioaugusto1/deep-learning/blob/main/Regress%C3%A3o_rede_neural_com_numpy.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] id="aFHWWLT3ybbI"
# # A base de dados
# + [markdown] id="SAWQRvKPybbK"
# ### Carregando o dataset
# + colab={"resources": {"http://localhost:8080/nbextensions/google.colab/files.js": {"data": "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", "ok": true, "headers": [["content-type", "application/javascript"]], "status": 200, "status_text": ""}}, "base_uri": "https://localhost:8080/", "height": 109} id="EMLBaJItybbM" outputId="78220b84-4d79-43ad-91aa-6d1abf4f2ca5"
from google.colab import files
files.upload()
# + id="MluOa_0_ybbM"
import pandas as pd
dados = pd.read_csv('Bicicletas.csv')
# + [markdown] id="tekNLcSZybbN"
# ### Conhecendo a base de dados
# + colab={"base_uri": "https://localhost:8080/", "height": 204} id="_VFilMIpybbO" outputId="ad519456-f03d-4b80-c0d6-511730456e37"
dados.head()
# + colab={"base_uri": "https://localhost:8080/"} id="0qhn24z5ybbP" outputId="b6cc650a-0356-4b16-fbab-8672059326be"
dados.shape
# + id="I1b4pCyqybbQ"
import matplotlib.pyplot as plt
# + colab={"base_uri": "https://localhost:8080/", "height": 304} id="T1ear3y7ybbQ" outputId="102f6275-8ef2-4797-b83a-4e18d72148c8"
plt.rcParams.update({'font.size': 14})
plt.scatter(dados['temperatura'],dados['bicicletas_alugadas'])
plt.ylabel('bicicletas_alugadas')
plt.xlabel('temperatura')
# + colab={"base_uri": "https://localhost:8080/", "height": 355} id="2zU_l-m-ybbR" outputId="efa88d6a-3dac-4c72-f5ab-126ca88a6bed"
plt.scatter(dados['clima'],dados['bicicletas_alugadas'])
plt.ylabel('bicicletas_alugadas')
plt.xlabel('clima')
plt.rcParams.update({'font.size': 22})
indice=[1,2,3]
plt.xticks(indice, fontsize=14)
# + [markdown] id="57TklQELybbS"
# ### Normalizando a base de dados
# + id="_nXBp4LxybbT"
import numpy as np
# + id="mGOLV_dHybbT"
y = dados['bicicletas_alugadas'].values
# + colab={"base_uri": "https://localhost:8080/"} id="1KcObe0YybbU" outputId="e854c742-0887-41a7-cafb-0e0946b1908a"
X = dados[['clima','temperatura']].values
print(X)
# + colab={"base_uri": "https://localhost:8080/"} id="bKXISrV4ybbU" outputId="494f534b-f2e9-4ccd-de69-9e8692742587"
X = X/np.amax(X,axis=0)
print(X)
# + colab={"base_uri": "https://localhost:8080/"} id="m21Jke8EybbV" outputId="eb55c881-2944-480e-f338-f640cb996a8e"
ymax=np.amax(y)
y = y/ymax
print(y[0:10])
# + [markdown] id="vOgNCtGDybbW"
# ### Funções de ativação
# + id="SG69UUujybbX"
def sigmoid(Soma):
return 1/(1+np.exp(-Soma))
def relu(Soma):
return np.maximum(0,Soma)
# + [markdown] id="SfJhFqy9ybbX"
# ### Criando a estrutura da rede
# + id="K6toC-KBybbY"
arquitetura = [
{"dim_entrada": 2, "dim_saida": 50, "ativacao": "relu"},
{"dim_entrada": 50, "dim_saida": 1, "ativacao": "sigmoid"},
]
# + [markdown] id="GsTr4tL8ybbY"
# ### Pesos e viés
# + id="M-oI6ROdybbZ"
def inicia_camadas(arquitetura, seed = 99):
# inicia os valores aleatórios
np.random.seed(seed)
# numero de camadas da rede neural
numero_de_camadas = len(arquitetura)
# inicia armazenamento de parametros
valores_parametros = {}
# itera nas camadas da rede
for indice, camada in enumerate(arquitetura):
indice_camada = indice + 1
# extrai o numero de nodos nas camadas
tamanho_camada_entrada = camada["dim_entrada"]
tamanho_camada_saida = camada["dim_saida"]
# inicia os valores na matriz de pesos P
# e o vetor de viés ou bias b
valores_parametros['P' + str(indice_camada)] = np.random.randn(
tamanho_camada_saida, tamanho_camada_entrada) * 0.1
valores_parametros['b' + str(indice_camada)] = np.random.randn(
tamanho_camada_saida, 1) * 0.1
return valores_parametros
# + [markdown] id="9QJDOigBybba"
# ### Propagação da rede
# + id="Ev_hpRklybba"
def propaga_uma_camada(Ativado_anterior, Pesos_atual, b_atual, ativacao="relu"):
# cálculo da entrada para a função de ativação
Saida_atual = np.dot(Pesos_atual, Ativado_anterior) + b_atual
# selecção da função de ativação
if ativacao is "relu":
func_ativacao = relu
elif ativacao is "sigmoid":
func_ativacao = sigmoid
else:
raise Exception('Ainda não implementamos essa funcao')
# retorna a ativação calculada Ativado_atual e a matriz intermediária Saida
return func_ativacao(Saida_atual), Saida_atual
# + id="GDJPil-5ybbb"
def propaga_total(X, valores_parametros, arquitetura):
# memoria temporaria para a retropropagacao
memoria = {}
# O vetor X é a ativação para a camada 0
Ativado_atual = X
# iterações para as camadas
for indice, camada in enumerate(arquitetura):
# a numeração das camadas começa de 1
indice_camada = indice + 1
# utiliza a ativação da iteração anterior
Ativado_anterior = Ativado_atual
# extrai a função de ativação para a camada atual
func_ativacao_atual = camada["ativacao"]
# extrai os pesos da camada atual
Pesos_atual = valores_parametros["P" + str(indice_camada)]
# extrai o bias para a camada atual
b_atual = valores_parametros["b" + str(indice_camada)]
# cálculo da ativação para a camada atual
Ativado_atual, Saida_atual = propaga_uma_camada(Ativado_anterior, Pesos_atual, b_atual, func_ativacao_atual)
# salca os valores calculados na memória
memoria["A" + str(indice)] = Ativado_anterior
memoria["Z" + str(indice_camada)] = Saida_atual
# retorna o vetor predito e um dicionário contendo os valores intermediários
return Ativado_atual, memoria
# + [markdown] id="J-9CB1zgybbc"
# ### Testando a rede
# + id="e6LAcrr7ybbd"
valores_parametros = inicia_camadas(arquitetura, seed = 99)
y_estimado, memoria = propaga_total(np.transpose(X), valores_parametros, arquitetura)
# + colab={"base_uri": "https://localhost:8080/"} id="NdWIPs4Yybbd" outputId="56d9d708-ac58-4a5e-9f17-60054ac6eece"
y_estimado[0,0]*ymax
# + colab={"base_uri": "https://localhost:8080/"} id="OcHOI0cvybbe" outputId="99a4edf8-07a1-4437-ce12-d0b6708b4ecb"
y[0]*ymax
# + [markdown] id="zQ-JfV-bybbf"
# ### Atualização dos pesos
# + id="9HetY38mybbf"
def atualiza(valores_parametros, gradidentes, arquitetura, taxa_aprendizagem):
# iterações pelas camadas
for indice_camada, camada in enumerate(arquitetura, 1):
valores_parametros["P" + str(indice_camada)] -= taxa_aprendizagem * gradidentes["dP" + str(indice_camada)]
valores_parametros["b" + str(indice_camada)] -= taxa_aprendizagem * gradidentes["db" + str(indice_camada)]
return valores_parametros;
# + [markdown] id="DBUZIvbtybbf"
# ### Função de custo
# + id="Sv5yBKocybbg"
def valor_de_custo(Y_predito, Y):
# numero_de_exemplos
m = Y_predito.shape[1]
custo = -1 / m * (np.dot(Y, np.log(Y_predito).T) + np.dot(1 - Y, np.log(1 - Y_predito).T))
return np.squeeze(custo)
# + [markdown] id="aazff6pQybbg"
# ### Retropropagação
# + id="2BV4IEkfybbh"
def retropropagacao_total(Y_predito, Y, memoria, valores_parametros, arquitetura):
gradientes = {}
# numero de exemplos
#m = Y.shape[1]
# para garantir que os dois vetores tenham a mesma dimensão
Y = Y.reshape(Y_predito.shape)
# inicia o algoritmo de gradiente descendente
dAtivado_anterior = - (np.divide(Y, Y_predito) - np.divide(1 - Y, 1 - Y_predito));
for indice_camada_anterior, camada in reversed(list(enumerate(arquitetura))):
indice_camada_atual = indice_camada_anterior + 1
# Função de ativação para a camada atual
funcao_ativao_atual = camada["ativacao"]
dAtivado_atual = dAtivado_anterior
Ativado_anterior = memoria["A" + str(indice_camada_anterior)]
Saida_atual = memoria["Z" + str(indice_camada_atual)]
Pesos_atual = valores_parametros["P" + str(indice_camada_atual)]
b_atual = valores_parametros["b" + str(indice_camada_atual)]
dAtivado_anterior, dPesos_atual, db_atual = retropropagacao_uma_camada(
dAtivado_atual, Pesos_atual, b_atual, Saida_atual, Ativado_anterior, funcao_ativao_atual)
gradientes["dP" + str(indice_camada_atual)] = dPesos_atual
gradientes["db" + str(indice_camada_atual)] = db_atual
return gradientes
# + id="nHPEZtU8ybbi"
def sigmoid_retro(dAtivado, Saida):
sig = sigmoid(Saida)
return dAtivado * sig * (1 - sig)
def relu_retro(dAtivado, Saida):
dSaida = np.array(dAtivado, copy = True)
dSaida[Saida <= 0] = 0;
return dSaida;
# + id="NCQVLG9hybbi"
def retropropagacao_uma_camada(dAtivado_atual, Pesos_atual, b_atual, Saida_atual, Ativado_anterior, ativacao="relu"):
# número de exemplos
m = Ativado_anterior.shape[1]
# seleção função de ativação
if ativacao is "relu":
func_ativacao_retro = relu_retro
elif ativacao is "sigmoid":
func_ativacao_retro = sigmoid_retro
else:
raise Exception('Ainda não implementamos essa funcao')
# derivada da função de ativação
dSaida_atual = func_ativacao_retro(dAtivado_atual, Saida_atual)
# derivada da matriz de Pesos
dPesos_atual = np.dot(dSaida_atual, Ativado_anterior.T) / m
# derivada do vetor b
db_atual = np.sum(dSaida_atual, axis=1, keepdims=True) / m
# derivada da matriz A_anterior
dAtivado_anterior = np.dot(Pesos_atual.T, dSaida_atual)
return dAtivado_anterior, dPesos_atual, db_atual
# + [markdown] id="Lu31Sy_lybbj"
# ### Treinamento
# + id="9-qafsVmybbj"
def treino(X, Y,X_teste,Y_teste, arquitetura, epocas, taxa_aprendizagem):
# Inicia os parâmetros da rede neural
valores_parametros = inicia_camadas(arquitetura, 2)
# Listas que vão guardar o progresso da aprendizagem da rede
historia_custo = []
historia_custo_teste = []
# Atualiza a cada época
for i in range(epocas):
# Propaga a rede - Foward propagation
Y_predito, memoria = propaga_total(X, valores_parametros, arquitetura)
Y_predito_teste, memoria2 = propaga_total(X_teste, valores_parametros,
arquitetura)
# calcula as métricas e salva nas listas de história
custo = valor_de_custo(Y_predito, Y)
historia_custo.append(custo)
custo_teste = valor_de_custo(Y_predito_teste, Y_teste)
historia_custo_teste.append(custo_teste)
# Retropropagação - Backpropagation
gradientes = retropropagacao_total(Y_predito, Y, memoria,
valores_parametros, arquitetura)
# Atualiza os pesos
valores_parametros = atualiza(valores_parametros, gradientes,
arquitetura, taxa_aprendizagem)
if(i % 50 == 0):
print("Iteração: {:05} - custo: {:.5f} ".format(i, custo))
return valores_parametros, historia_custo, historia_custo_teste
# + id="6hZxyxtXybbk"
from sklearn.model_selection import train_test_split
# + id="C5EyOfoxybbk"
X_treino, X_teste, y_treino, y_teste = train_test_split( X, y, test_size=0.43, random_state=42)
# + colab={"base_uri": "https://localhost:8080/"} id="VC6y950Sybbl" outputId="678d29ea-24e9-4621-d738-3d30d3d40ad0"
# Treinamento
valores_parametros, historia_custo, historia_custo_teste = treino(np.transpose(X_treino), np.transpose(y_treino.reshape((y_treino.shape[0], 1))),
np.transpose(X_teste), np.transpose(y_teste.reshape((y_teste.shape[0], 1))),
arquitetura, 20000, 0.01)
# + colab={"base_uri": "https://localhost:8080/", "height": 303} id="SA6pi44Dybbl" outputId="61e756cc-ade0-4952-c02e-41ee026227ea"
plt.plot(historia_custo)
plt.plot(historia_custo_teste, 'r')
plt.legend(['Treinamento','Teste'])
plt.ylabel('Custo')
plt.xlabel('Épocas')
plt.show()
# + [markdown] id="hpwrxeKjybbn"
# ### Fazendo Previsões
# + id="nhKVHwzNybbn"
# Previsão
Y_pred, _ = propaga_total(np.transpose(X_teste), valores_parametros, arquitetura)
# + colab={"base_uri": "https://localhost:8080/", "height": 299} id="nwPufD2Hybbo" outputId="34270d18-72a2-4cc5-d2e6-c71748fda6c4"
plt.plot(np.transpose(X_teste)[1],ymax*y_teste,'.')
plt.plot(np.transpose(X_teste)[1],ymax*Y_pred.reshape([-1,1]),'.r')
plt.legend(['Reais','Preditos'])
plt.ylabel('bicicletas_alugadas')
plt.xlabel('temperatura')
plt.show()
# + colab={"base_uri": "https://localhost:8080/", "height": 293} id="kJNjjc5Dybbp" outputId="c8ea96eb-5466-4cca-f777-048db1cc033b"
plt.plot(3*np.transpose(X_teste)[0],ymax*y_teste,'.')
plt.plot(3*np.transpose(X_teste)[0],ymax*Y_pred.reshape([-1,1]),'.r')
plt.legend(['Reais','Preditos'])
plt.ylabel('bicicletas_alugadas')
plt.xlabel('clima')
plt.rcParams.update({'font.size': 22})
indice=[1,2,3]
plt.xticks(indice, fontsize=14)
plt.show()
# + id="WNwdnZ63ybbq"
# + id="S16gYB8_ybbq"
| 20,533 |
/DXF2020_1113.ipynb | 36906a605d85d898d14efe35225e8e6d0ea4f189 | [] | no_license | abenben/DXF2020 | https://github.com/abenben/DXF2020 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 26,349 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/abenben/DXF2020/blob/main/DXF2020_1113.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] id="O_KAKpWbxsRt"
# ## Agenda
# - 次元削減とクラスタリング（教師なし学習）
# - 回帰（教師あり、目的変数が連続値）
# - 分類（教師あり、目的変数が離散値）
# + id="RbZiZtgXxsRy"
# 可視化のための外部モジュールの読み込み
import matplotlib.pyplot as plt
# ノートブックの中に画を埋め込むための指示
# %matplotlib inline
# データサイエンスによく使うライブラリも読み込んでおく
import numpy as np
import pandas as pd
# + id="-N95v2xWxsR4"
# 手書き数字サンプルデータの読み込み準備
from sklearn.datasets import load_digits
# + id="nJdVlS-ZxsR7"
# 関数を呼び出してサンプルデータを読み込み変数（digits_data）で受け取る
# 詳しくは、以下の公式ドキュメントを参照
# https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits
digits_data = load_digits()
# + id="_xTVgt-zxsR_"
# 簡単な説明書の表示
# （実は間違っている。1797個しがデータが無い）
print(digits_data.DESCR)
# + id="PK_RzzNcxsSF"
#試しに1つ表示して見る
# 表示するデータのインデックスを指定
i = 10 # 0〜1798までで指定できる
# 変数で受け取る
image = digits_data['images'][i]
num = digits_data['target'][i]
print(f'ラベルは{num}')
# 画像の表示
_ = plt.imshow(image, cmap=plt.cm.binary) #またはgray_rでもOK
# 試してみよう
# iの値を変更してみる
# カラーマップを変更してみる
# https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
# + id="hcck4jmwxsSI"
# 1つの画像は8行8列の行列データ
digits_data['images'][i]
# + id="GSC1kaX4xsSL"
# ベクトルになっているデータもある
digits_data['data'][i]
# + id="eM_2-zyfxsSO"
# 全体をまとめて1つの表型のデータを作る
# 通常は行と列に名前を付ける
digits_df = pd.DataFrame(digits_data['data'])
# + id="1x9ZOOzXxsSS"
# 1797行、64列のデータ
# 1行が1つのサンプル。それぞれの列が説明変数
digits_df
# + id="Dl-qlh0NxsSW"
# サンプルを2次元平面にプロットするための便利関数を作る
def plot_2D(X, y, file_name=None):
plt.figure()
ax = plt.subplot(111)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet')
plt.colorbar()
#ax.set_aspect('equal')
if file_name:
plt.savefig(file_name)
plt.close()
# しかし、64次元のデータをどうやって、2次元へ・・・？
# + id="747P9YSPxsSZ"
# ここで使われるのが次元削減の手法
# まずはもっとも古典的なPCA（主成分分析）から
from sklearn.decomposition import PCA
# 2次元データを出力するPCAのインスタンスを用意
pca = PCA(n_components=2)
# digits_data['data']でもOK
digits_pca = pca.fit_transform(digits_df)
# digits_data['target']に正解（0〜9までの数字）が入っているので色が付く
plot_2D(digits_pca, digits_data['target'])
# + id="iyQJ1XVpxsSb"
# 次元削減にはいろいろな方法がある
# 最近は、ｔ−SNE（t-distributed Stochastic Neighbor Embedding）がよく使われる
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2)
digits_tsne = tsne.fit_transform(digits_df)
plot_2D(digits_tsne, digits_data['target'])
# + [markdown] id="4L8q1CYsxsSe"
# 次元削減を実行するときのパラメータが手法ごとにいろいろある
#
# perplexityfloat, optional (default: 30)
#
# The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50. Different values can result in significanlty different results.
# + id="XrNUXHXUxsSf"
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, perplexity=10)
digits_tsne = tsne.fit_transform(digits_df)
plot_2D(digits_tsne, digits_data['target'])
# 試してみよう
# perplexityを適当に設定してみよう
# 同じパラメータ設定でも違う結果がでます。どうしたら固定できるでしょうか？
# + id="mUN2FdHOxsSi"
from sklearn.manifold import TSNE
# random_stateを固定することで、結果を再現できる
# 詳しくは、t-SNEの中身を知る必要がある
tsne = TSNE(n_components=2, perplexity=30, random_state=0)
digits_tsne = tsne.fit_transform(digits_df)
plot_2D(digits_tsne, digits_data['target'])
# + id="q-wwcgh-xsSm"
# scikit-learnのサイトからK-meansクラスタリングのイメージ
from IPython.core.display import Image, display
display(Image("https://scikit-learn.org/stable/_images/sphx_glr_plot_kmeans_digits_001.png"))
# + id="lZXsg0d8xsSp"
from sklearn.cluster import KMeans
# 入力データを10クラスに分ける
kmeans = KMeans(n_clusters=10, random_state=0)
kmeans.fit(digits_df)
# サンプルが属するクラスター
kmeans.labels_
# + id="NWmAlDTAxsSs"
from sklearn.metrics import silhouette_score
for n_cluster in range(5, 16):
clusterer = KMeans(n_clusters=n_cluster, random_state=10)
cluster_labels = clusterer.fit_predict(digits_df)
silhouette_avg = silhouette_score(digits_df, cluster_labels)
print("For n_clusters =", n_cluster,
"The average silhouette_score is :", silhouette_avg)
# 詳しくは
# https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py
# + id="myovW9mJxsSu"
# パラメータにクラス数を指定しなくてもよいクラスタリング手法もある
from sklearn.cluster import AffinityPropagation
# random_stateの設定は、バージョン0．23から
clustering = AffinityPropagation().fit(digits_df)
# 何クラスに分かれたか？
len(set(clustering.labels_))
# + id="B-XF659CxsSx"
import seaborn as sns
sns.clustermap(digits_df)
# + id="JUKycOCPxsS0"
# 説明変数の分散のヒストグラム
plt.hist(digits_df.std())
# + id="63D5Qz90xsS3"
# 前処理の一例
# 分散が小さい（サンプル間でほとんどばらつきがない）説明変数を削除
idx = digits_df.std() > 2
filtered = digits_df[digits_df.columns[idx]]
sns.clustermap(filtered)
# 試してみよう
# 分散での前処理を調整して、いくつか階層的クラスタリングの図を描いてみよう
# + id="CcI2yuqNxsS8"
# + id="e_nPPGaVxsS_"
from sklearn.datasets import load_boston
boston_data = load_boston()
# + id="-i-ySLthxsTC"
print(boston_data.DESCR)
# + id="f9lT7fnCxsTE"
# 住宅の価格（＄1，000）
y = boston_data['target']
# 説明変数を準備
X = boston_data['data']
# + id="QwQTLTqpxsTH"
# DataFrameを作ります。
boston_df = pd.DataFrame(boston_data.data)
# 列名をつけます。
boston_df.columns = boston_data.feature_names
# 便利のために、価格列を追加します。
boston_df['PRICE'] = y
boston_df.head()
# + id="I_PWYOBLxsTL"
# 横軸に部屋数、縦軸に価格
boston_df.plot.scatter('RM', 'PRICE')
# 試してみよう
# X軸を変更してみてください。以下が比較的意味をとりやすいかも。
# 犯罪率 CRIM
# 窒素酸化物濃度 NOX
# 生徒と先生の費 PTRATIO
# + id="TVsTxDXPxsTO"
# seabornを使うと簡単に回帰直線を描けます
sns.lmplot('RM', 'PRICE', data = boston_df)
# + id="128eQZ0BxsTR"
# 便宜的に、訓練データと、目的変数を知らないことにするテストデータに分ける
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
# + id="OzAIVh1vxsTT"
# 全部で506サンプルあったが、訓練用とテスト用に分けられた。
print(X_train.shape, X_test.shape)
# + id="MkGNsYAsxsTW"
# 線形重回帰を使ったモデルを作る
from sklearn.linear_model import LinearRegression
# インスタンスを作って、訓練データからモデルを作成
reg = LinearRegression().fit(X_train, y_train)
# + id="zVa4h_rCxsTa"
# 線形重回帰なので、各変数の係数がわかる
print(reg.coef_)
print(reg.intercept_)
# + id="u25Ryrn1xsTc"
# 予測と当てはまりの良さを計算
from sklearn.metrics import mean_squared_error, r2_score
# 未知のサンプルの価格を予測
y_pred = reg.predict(X_test)
# The mean squared error
print('平均2乗誤差: ', mean_squared_error(y_test, y_pred))
# The coefficient of determination: 1 is perfect prediction
print('決定係数: ', r2_score(y_test, y_pred))
# + id="eBHrrFDbxsTf"
# 正解データと予測結果を図示
plt.scatter(y_test, y_pred)
plt.xlabel('test')
plt.ylabel('pred')
# + id="DQm99SrYxsTi"
from sklearn.ensemble import RandomForestRegressor
rf_reg = RandomForestRegressor(n_estimators=10, max_depth=3, random_state=0)
rf_reg.fit(X_train, y_train)
# + id="PyQlW-ONxsTl"
rf_reg_pred = rf_reg.predict(X_test)
print('平均2乗誤差: ', mean_squared_error(y_test, rf_reg_pred))
print('決定係数: ', r2_score(y_test, rf_reg_pred))
# 試してみよう
# n_estimatorsやmax_depthを変更するとどうなるでしょう？
# + id="45y9ZriQxsTn"
from sklearn.model_selection import GridSearchCV
parameters = {'n_estimators':[50, 100, 200], 'max_depth': [2, 4, 8, 16]}
rf_reg = RandomForestRegressor()
clf = GridSearchCV(rf_reg, parameters)
_ = clf.fit(X_train, y_train)
# + id="GYZ6V8EIxsTr"
# 最も性能がよいモデルで予測
rf_reg_pred = clf.best_estimator_.predict(X_test)
print('平均2乗誤差: ', mean_squared_error(y_test, rf_reg_pred))
print('決定係数: ', r2_score(y_test, rf_reg_pred))
# + id="ykdHD8rgxsTt"
# 使われたパラメータを表示
clf.best_estimator_.get_params
# + id="vEiCv_4lxsTw"
# 勾配ブースティングという方法が性能が良いので最近はよく使われている。
from sklearn.ensemble import GradientBoostingRegressor
gbr = GradientBoostingRegressor().fit(X_train, y_train)
gbr_pred = gbr.predict(X_test)
print('平均2乗誤差: ', mean_squared_error(y_test, gbr_pred))
print('決定係数: ', r2_score(y_test, gbr_pred))
# + id="57UJmOh9xsTy"
# + id="EfEeY_uQxsT2"
# ワインサンプルデータの準備
from sklearn.datasets import load_wine
wine_data = load_wine()
# + id="7TZItFG-xsT5"
# データの説明
print(wine_data.DESCR)
# + id="7eRlHFDUxsT8"
# 分類わけは数字で入っている
wine_data['target']
# + id="yQPx0mR2xsT-"
# PCAで2次元に落としこむ
pca = PCA(n_components=2)
wine_pca = pca.fit_transform(wine_data['data'])
plot_2D(wine_pca, wine_data['target'])
# + id="WYsNRKnnxsUC"
# データの正規化（規格化）
# 変数ごとに平均を引いて標準偏差で割るという処理をする。
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
ss_data = ss.fit_transform(wine_data['data'])
# PCAで2次元へ
pca = PCA(n_components=2)
ss_pca = pca.fit_transform(ss_data)
plot_2D(ss_pca, wine_data['target'])
# + id="gKhcTyjZxsUE"
# 2クラスの分類とROCによる評価
# SVM(support vector machine)を準備
from sklearn.svm import SVC
# ついでにRandom Forestsも準備
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import plot_roc_curve
X = wine_data['data']
# 0と1を0に、2を1に変換
y = wine_data['target'].copy()
y[y == 1] = 0
y[y == 2] = 1
# + id="_zU3G75sxsUJ"
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
svc = SVC(random_state=42)
svc.fit(X_train, y_train)
# + id="D5OJReO3xsUM"
# 作ったモデルで予測
svc_pred = svc.predict(X_test)
# + id="BP6qJ-QIxsUO"
# 全部0になってしまっている
svc_pred
# + id="fUnmM3F3xsUQ"
# ほんとは1のサンプルもある
y_test
# + id="ydwmPRUGxsUT"
# より詳しい結果を計算できる
from sklearn.metrics import classification_report
print(classification_report(y_test , svc_pred))
# + id="1vh0Z0n3xsUX"
# サンプルごとに予測の自信度は異なる
svc.decision_function(X_test)
# + id="EIsBDpqkxsUb"
# 受信者操作特性 Receiver operating characteristic
svc_disp = plot_roc_curve(svc, X_test, y_test)
plt.show()
# + id="yMgRrNIAxsUd"
# RandomForestsを使ってみる
# 直前に作ったSVMのROCに追加するためのコードが入っているので、ちょっとわかりにくい
rfc = RandomForestClassifier(n_estimators=10, random_state=42)
rfc.fit(X_train, y_train)
ax = plt.gca()
rfc_disp = plot_roc_curve(rfc, X_test, y_test, ax=ax, alpha=0.8)
svc_disp.plot(ax=ax, alpha=0.8)
plt.show()
# + id="frj2XLVpxsUf"
# もともとの3クラスの分類
# もう一度ｙを代入しなおす
y = wine_data['target'].copy()
# 訓練用とテスト用に分ける
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# + id="nv1izLbVxsUi"
# SVMは2クラス用だが、マルチクラスにもそのまま使える
svc = SVC(random_state=42)
svc.fit(X_train, y_train)
svc_pred = svc.predict(X_test)
svc_pred
# + id="KUu2Va77xsUk"
print(classification_report(y_test , svc_pred))
# + id="yo_LIcNYxsUo"
# 混合行列を使った可視化
from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, svc_pred)
conf_mat
# + id="set2b8BwxsUr"
# 行方向が正解、列方向が予測
# クラス1のrecall（再現率）は、
print(13/(13+5))
# クラス1のpredision（適合率）は
print(13/(13+8))
# 全体のaccuracy（正解率）は、
print((15+13+4)/45)
# + id="inpDW-DVxsUt"
# ヒートマップを使った可視化がわかりやすい
sns.heatmap(conf_mat, annot=True)
# + id="z6dNHYOqxsUv"
# RandomForestsがいい
rfc = RandomForestClassifier(n_estimators=10, random_state=42)
rfc.fit(X_train, y_train)
rfc_pred = rfc.predict(X_test)
print(classification_report(y_test , rfc_pred))
# + id="EBBWYgcvxsUx"
# どの変数が分類に効いているかがわかる。
fi = rfc.feature_importances_
# ただの棒グラフを描くのがちょっと面倒だったりする・・・。
plt.bar(range(len(fi)), fi, tick_label=wine_data['feature_names'])
plt.xticks(rotation=90)
# やってみよう
# 1つ前のセルで作ったRandomForestsのインスタンスで、random_stateの数字を変えると、feature importanceがどうなるか試してみてください。
# + id="wsvc8fO-xsUz"
# 卒業試験
# 以下のGradientBoostingClassifierを使って、予測モデルを作り、その精度を計算してください。feature importanceの棒グラフも描いてください。
from sklearn.ensemble import GradientBoostingClassifier
# + [markdown] id="1mUr4TL5xsU2"
# ## Appendix
#
# [決定係数の説明(Wikipedia)](https://ja.wikipedia.org/wiki/%E6%B1%BA%E5%AE%9A%E4%BF%82%E6%95%B0)
#
# [ROCの説明(Wikipedia)](https://ja.wikipedia.org/wiki/%E5%8F%97%E4%BF%A1%E8%80%85%E6%93%8D%E4%BD%9C%E7%89%B9%E6%80%A7)
#
# [決定木、RandomForests、勾配ブースティングのわかりやすいページ](https://www.codexa.net/lightgbm-beginner/)
| 11,887 |
/pytorch_practice.ipynb | d6b69e6ac79f10e37ea3759e6eccb473ebed9275 | [] | no_license | OlgaBelitskaya/colab_notebooks | https://github.com/OlgaBelitskaya/colab_notebooks | 1 | 1 | null | null | null | null | Jupyter Notebook | false | false | .py | 141,136 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernel_info:
# name: python3
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# ### Note
# * Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.
# +
# Dependencies and Setup
import pandas as pd
# File to Load (Remember to Change These)
file_to_load = "purchase_data.csv"
# Read Purchasing File and store into Pandas data frame
purchase_df = pd.read_csv(file_to_load)
purchase_df.head(5)
# -
# ## Player Count
# * Display the total number of players
#
#Get total number of players
v_tot_play= purchase_df["SN"].nunique()
print("Total Players:",v_tot_play )
# ## Purchasing Analysis (Total)
# * Run basic calculations to obtain number of unique items, average price, etc.
#
#
# * Create a summary data frame to hold the results
#
#
# * Optional: give the displayed data cleaner formatting
#
#
# * Display the summary data frame
#
# +
#Get basic calculations
#Get number of unique items
v_tot_items = purchase_df['Item Name'].nunique()
# Get the average price
v_avg_price= purchase_df['Price'].mean()
# Get number of purchases
v_tot_po= purchase_df["Purchase ID"].nunique()
# Get total revenue
v_tot_rev= purchase_df["Price"].sum()
# Create a list
data_list = {'Unique Items':[v_tot_items], 'Average Price':[v_avg_price],
'Number of Purchases':v_tot_po,'Total Revenue':v_tot_rev }
# Create a DataFrame
summary_df = pd.DataFrame(data_list)
# print dataframe.
summary_df.head(10).style.format({"Average Price":"${:20,.2f}","Total Revenue":"${:20,.2f}"})
# -
# ## Gender Demographics
# * Percentage and Count of Male Players
#
#
# * Percentage and Count of Female Players
#
#
# * Percentage and Count of Other / Non-Disclosed
#
#
#
# Group our original df based on Gender
v_gen_g_df= purchase_df.groupby(purchase_df["Gender"])
# Get unique players
v_gen_df=pd.DataFrame(v_gen_g_df["SN"].nunique())
#v_gen_df
# get the percentage by gender
v_gen_pert_df= pd.DataFrame((v_gen_g_df["SN"].count()*100)/v_tot_po)
#v_gen_pert_df
# Create the result table
v_sumt_gen_df= pd.merge(v_gen_df ,v_gen_pert_df, on="Gender")
v_sumt_gen_df
v_sumt_gen_df.rename(columns = {'SN_x':'Total Count', 'SN_y':'Percentage of Players',
}, inplace = True)
# sort & format the final table
v_sumt_gen_df.sort_values(by=['Percentage of Players'],ascending=False).style.format({ "Percentage of Players":"%{:20,.2f}"})
#
# ## Purchasing Analysis (Gender)
# * Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. by gender
#
#
#
#
# * Create a summary data frame to hold the results
#
#
# * Optional: give the displayed data cleaner formatting
#
#
# * Display the summary data frame
# +
# Create a df count base on grouped df on Gender
v_gen_grl_df = purchase_df.groupby(["Gender"])
#v_gen_grl_df.count().head()
#Get the purchase count
v_gen_count_df =pd.DataFrame(v_gen_grl_df["Purchase ID"].count())
#v_gen_count_df
# Get the avg po price
v_gen_df3 =pd.DataFrame(v_gen_grl_df["Price"].mean())
#v_gen_df3
#Get the total po value
v_gen_df4 =pd.DataFrame(v_gen_grl_df["Price"].sum())
#v_gen_df4
# calculate avg total purchase per person
v_gen_grl_df2x = purchase_df.groupby(["Gender","SN"])
v_gen_df4x = pd.DataFrame(v_gen_grl_df2x["Price"].mean())
v_gen_mean_df=v_gen_df4x.groupby("Gender").mean()
#creating a summary table results
v_po_sumtable1 = pd.merge(v_gen_count_df ,v_gen_df3, on=["Gender"])
v_po_sumtable2 = pd.merge(v_po_sumtable1 ,v_gen_df4, on=["Gender"])
v_po_sumtable3 = pd.merge(v_po_sumtable2 ,v_gen_mean_df, on=["Gender"])
v_po_sumtable3
v_po_sumtable3.rename(columns = {'Purchase ID':'Purchase Count', 'Price_x':'Average Purchase Price',
'Price_y':'Total Purchase Value', 'Price':'Avg Total Purchase per Person'
}, inplace = True)
v_po_sumtable3.head(3).style.format({"Average Purchase Price":"${:20,.2f}",
"Total Purchase Value":"${:20,.2f}",
"Avg Total Purchase per Person":"${:20,.2f}" })
# -
# ## Age Demographics
# * Establish bins for ages
#
# * Categorize the existing players using the age bins. Hint: use pd.cut()
#
#
# * Calculate the numbers and percentages by age group
#
#
# * Create a summary data frame to hold the results
#
#
# * Optional: round the percentage column to two decimal points
#
#
# * Display Age Demographics Table
#
# +
# Create bins
bins = [0, 9, 14, 19, 24, 29, 34, 39, 50]
group_names = [ "<10", "10-14", "15-19", "20-24","25-29", "30-34","35-39", "40+"]
purchase_df["Edge Grp"] = pd.cut(purchase_df["Age"], bins, labels=group_names,include_lowest=True)
#purchase_df.head(5)
v_edge_b_df= purchase_df.groupby("Edge Grp")
#v_edge_b_df.count().head()
v_gen_gpo_df =pd.DataFrame(v_edge_b_df["SN"].nunique())
#v_gen_gpo_df
v_gen_percent_df = pd.DataFrame((v_edge_b_df["SN"].count()*100)/v_tot_po)
#v_gen_percent_df
# Create summary df table results
v_sum_bin_df = pd.merge(v_gen_gpo_df ,v_gen_percent_df, on="Edge Grp")
#v_sum_bin_df
v_sum_bin_df.rename(columns = {'SN_x':'Total Count', 'SN_y':'Percentage of Players',
}, inplace = True)
v_sum_bin_df.head(10).style.format({"Percentage of Players":"%{:20,.2f}"})
# -
# ## Purchasing Analysis (Age)
# * Bin the purchase_data data frame by age
#
#
# * Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. in the table below
#
#
# * Create a summary data frame to hold the results
#
#
# * Optional: give the displayed data cleaner formatting
#
#
# * Display the summary data frame
# get the metrics
v_b_count_df =pd.DataFrame(v_edge_b_df["Gender"].count())
v_b_pavg_df =pd.DataFrame(v_edge_b_df["Price"].mean())
v_b_po_sum_df =pd.DataFrame(v_edge_b_df["Price"].sum())
# Create summary df table results
v_sum_po_df = pd.merge(v_b_count_df ,v_b_pavg_df, on="Edge Grp")
v_sum_po2_df = pd.merge(v_sum_po_df ,v_b_po_sum_df, on="Edge Grp")
#v_sum_po2_df
v_sum_po2_df.rename(columns = {'Gender':'Purchase Count', 'Price_x':'Average Purchase Price',
'Price_y':'Total Purchase Value',
}, inplace = True)
v_sum_po2_df.head(10).style.format({"Average Purchase Price":"${:20,.2f}",
"Total Purchase Value":"${:20,.2f}"})
#v_sum_po2_df
# ## Top Spenders
# * Run basic calculations to obtain the results in the table below
#
#
# * Create a summary data frame to hold the results
#
#
# * Sort the total purchase value column in descending order
#
#
# * Optional: give the displayed data cleaner formatting
#
#
# * Display a preview of the summary data frame
#
#
# +
# Group the original df by player
v_sn_gr_df = purchase_df.groupby(["SN"])
#v_sn_gr_df.count().head()
#get the metrics
v_sp_count_df = pd.DataFrame(v_sn_gr_df["Purchase ID"].count())
#v_sp_count_df.head(10)
v_sp_pavg_df = pd.DataFrame(v_sn_gr_df["Price"].mean())
#v_sp_pavg_df.head(10)
v_sp_psum_df = pd.DataFrame(v_sn_gr_df["Price"].sum())
#v_sp_psum_df.head(10)
# Create the results table
v_sum_sp_df = pd.merge(v_sp_count_df ,v_sp_pavg_df, on="SN")
v_sum_spf_df= pd.merge(v_sum_sp_df ,v_sp_psum_df, on="SN")
v_sum_spf_df.rename(columns = {'Purchase ID':'Purchase Count', 'Price_x':'Average Purchase Price',
'Price_y':'Total Purchase Value',
}, inplace = True)
v_sum_spf_df.sort_values(by=['Total Purchase Value'],ascending=False).style.format({"Average Purchase Price":"${:20,.2f}",
"Total Purchase Value":"${:20,.2f}"})
# -
# ## Most Popular Items
# * Retrieve the Item ID, Item Name, and Item Price columns
#
#
# * Group by Item ID and Item Name. Perform calculations to obtain purchase count, item price, and total purchase value
#
#
# * Create a summary data frame to hold the results
#
#
# * Sort the purchase count column in descending order
#
#
# * Optional: give the displayed data cleaner formatting
#
#
# * Display a preview of the summary data frame
#
#
# +
# Group the original df by item id and name
v_pitem_gr_df = purchase_df.groupby(["Item ID", "Item Name"])
#v_pitem_gr_df.count().head()
v_pitem_count_df = pd.DataFrame(v_pitem_gr_df ["Purchase ID"].count())
#v_pitem_count_df.head(10)
v_pitem_count_dfx = pd.DataFrame(v_pitem_gr_df ["Price"].mean())
#v_pitem_count_dfx.head(10)
v_sum_sp_dfad = pd.merge(v_pitem_count_df ,v_pitem_count_dfx , on=["Item ID","Item Name"])
#v_sum_sp_dfad
v_sum_sp_dfad["Total Purchase Value"] =v_sum_sp_dfad["Purchase ID"] * v_sum_sp_dfad["Price"]
v_sum_sp_dfad
v_sum_sp_dfad.rename(columns = {'Purchase ID':'Purchase Count', 'Price':'Item Price',
'Total Purchase Value':'Total Purchase Value',
}, inplace = True)
v_sum_sp_dfad.sort_values(by=['Purchase Count'],ascending=False).style.format({"Item Price":"${:20,.2f}",
"Total Purchase Value":"${:20,.2f}"})
# -
# ## Most Profitable Items
# * Sort the above table by total purchase value in descending order
#
#
# * Optional: give the displayed data cleaner formatting
#
#
# * Display a preview of the data frame
#
#
# +
#sort the table
v_sum_sp_dfad.sort_values(by=['Total Purchase Value'],ascending=False).style.format({"Item Price":"${:20,.2f}",
"Total Purchase Value":"${:20,.2f}"})
: 1590858267167, "user_tz": -180, "elapsed": 1020, "user": {"displayName": "Olga Safu", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhITqyZukwHZ9teMEwtxrx1LXmu7BQL_S_bK8qJFLU=s64", "userId": "13149748190150435632"}}
random_seed=23; batch_size=128
train=tmnist(root='data',train=True,download=True,
transform=transforms.ToTensor())
test=tmnist(root='data',train=False,
transform=transforms.ToTensor())
train_loader=tdl(dataset=train,shuffle=True,
batch_size=batch_size)
test_loader=tdl(dataset=test,shuffle=False,
batch_size=batch_size)
for images,labels in train_loader:
print('Image dimensions: %s'%str(images.shape))
print('Label dimensions: %s'%str(labels.shape))
break
# + id="grpd8febimc0" colab_type="code" colab={}
learning_rate=.1; epochs=15
num_features=784; num_classes=10
class SoftmaxRegression(torch.nn.Module):
def __init__(self,num_features,num_classes):
super(SoftmaxRegression,self).__init__()
self.linear=torch.nn.Linear(num_features,num_classes)
self.linear.weight.detach().zero_()
self.linear.bias.detach().zero_()
def forward(self,x):
logits=self.linear(x)
probs=tnnf.softmax(logits,dim=1)
return logits,probs
model=SoftmaxRegression(num_features=num_features,
num_classes=num_classes)
model.to(dev)
optimizer=torch.optim.SGD(model.parameters(),
lr=learning_rate)
# + id="O-0ol52si3MQ" colab_type="code" colab={}
def model_acc(model,data_loader,num_features):
correct_preds,num_examples=0,0
for features,targets in data_loader:
features=features.view(-1,num_features).to(dev)
targets=targets.to(dev)
logits,probs=model(features)
_,pred_labels=torch.max(probs,1)
num_examples+=targets.size(0)
correct_preds+=(pred_labels==targets).sum()
return correct_preds.float()/num_examples*100
# + id="P15sp2oyi7bb" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="a306d7c0-9486-461e-8054-6e159aa8e3eb" executionInfo={"status": "ok", "timestamp": 1590858475921, "user_tz": -180, "elapsed": 125180, "user": {"displayName": "Olga Safu", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhITqyZukwHZ9teMEwtxrx1LXmu7BQL_S_bK8qJFLU=s64", "userId": "13149748190150435632"}}
for epoch in range(epochs):
for batch_ids,(features,targets) in enumerate(train_loader):
features=features.view(-1,num_features).to(dev)
targets=targets.to(dev)
logits,probs=model(features)
cost=tnnf.cross_entropy(logits,targets)
optimizer.zero_grad(); cost.backward()
optimizer.step()
if not batch_ids%200:
print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f'
%(epoch+1,epochs,batch_ids,
len(train)//batch_size,cost))
with torch.set_grad_enabled(False):
print('Epoch: %03d/%03d train accuracy: %.2f%%'%\
(epoch+1,epochs,model_acc(model,train_loader,num_features)))
# + id="TJO3GwKsjDhv" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="17dfbbb0-5c0f-4545-f9cf-8ffd9f5e941b" executionInfo={"status": "ok", "timestamp": 1590858477684, "user_tz": -180, "elapsed": 1733, "user": {"displayName": "Olga Safu", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhITqyZukwHZ9teMEwtxrx1LXmu7BQL_S_bK8qJFLU=s64", "userId": "13149748190150435632"}}
print('Test accuracy: %.2f%%'%(model_acc(model,test_loader,num_features)))
# + [markdown] id="9hoNJRQgjHqV" colab_type="text"
# ## Applying to Color Images
# + id="bs1qEcGNjNK3" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 51} outputId="58321f38-d623-4638-ad41-3283601abee6" executionInfo={"status": "ok", "timestamp": 1590858707279, "user_tz": -180, "elapsed": 5117, "user": {"displayName": "Olga Safu", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhITqyZukwHZ9teMEwtxrx1LXmu7BQL_S_bK8qJFLU=s64", "userId": "13149748190150435632"}}
fpath='https://olgabelitskaya.github.io/'
zf='FlowerColorImages.h5.zip'
input_file=urllib.request.urlopen(fpath+zf)
output_file=open(zf,'wb');
output_file.write(input_file.read())
output_file.close(); input_file.close()
zipf=zipfile.ZipFile(zf,'r')
zipf.extractall(''); zipf.close()
f=h5py.File(zf[:-4],'r')
keys=list(f.keys()); print(keys)
X=np.array(f[keys[0]],dtype='float32')/255
y=np.array(f[keys[1]],dtype='int32')
N=len(y); n=int(.2*N); batch_size=16
shuffle_ids=np.arange(N)
np.random.RandomState(23).shuffle(shuffle_ids)
X,y=X[shuffle_ids],y[shuffle_ids]
X_test,X_train=X[:n],X[n:]
y_test,y_train=y[:n],y[n:]
X_train.shape,y_train.shape
# + id="PP1Og30jjgT8" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 51} outputId="59911eac-203e-4c0a-ad33-d70ee6c6d374" executionInfo={"status": "ok", "timestamp": 1590858710833, "user_tz": -180, "elapsed": 1088, "user": {"displayName": "Olga Safu", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhITqyZukwHZ9teMEwtxrx1LXmu7BQL_S_bK8qJFLU=s64", "userId": "13149748190150435632"}}
class TData(tds):
def __init__(self,X,y):
self.X=torch.tensor(X,dtype=torch.float32)
self.y=torch.tensor(y,dtype=torch.int32)
def __getitem__(self,index):
train_img,train_lbl=self.X[index],self.y[index]
return train_img,train_lbl
def __len__(self):
return self.y.shape[0]
train=TData(X_train,y_train)
test=TData(X_test,y_test)
train_loader=tdl(dataset=train,batch_size=batch_size,shuffle=True)
test_loader=tdl(dataset=test,batch_size=batch_size,shuffle=False)
for images,labels in train_loader:
print('Image dimensions: %s'%str(images.shape))
print('Label dimensions: %s'%str(labels.shape))
break
# + id="w_yaMorokY2W" colab_type="code" colab={}
learning_rate=.01; epochs=25
num_features=49152; num_classes=10
torch.manual_seed(random_seed)
model=SoftmaxRegression(num_features=num_features,
num_classes=num_classes)
model.to(dev)
optimizer=torch.optim.Adam(model.parameters(),lr=learning_rate)
# + id="l7yJDxX6kgVg" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="fb4a016b-726c-43b7-b4c3-884205ea7d67" executionInfo={"status": "ok", "timestamp": 1590858765475, "user_tz": -180, "elapsed": 4025, "user": {"displayName": "Olga Safu", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhITqyZukwHZ9teMEwtxrx1LXmu7BQL_S_bK8qJFLU=s64", "userId": "13149748190150435632"}}
for epoch in range(epochs):
for batch_ids,(features,targets) in enumerate(train_loader):
features=features.view(-1,num_features).to(dev)
targets=targets.to(dev)
logits,probs=model(features)
cost=tnnf.cross_entropy(logits,targets.long())
optimizer.zero_grad(); cost.backward()
optimizer.step()
if not batch_ids%10:
print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f'
%(epoch+1,epochs,batch_ids,
len(train)//batch_size,cost))
with torch.set_grad_enabled(False):
print('Epoch: %03d/%03d train accuracy: %.2f%%'%\
(epoch+1,epochs,model_acc(model,train_loader,num_features)))
# + id="jPlM9-YWkmcf" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="cee42e7d-091b-409f-9f8d-9a9d47e4d4e1" executionInfo={"status": "ok", "timestamp": 1590858786543, "user_tz": -180, "elapsed": 1302, "user": {"displayName": "Olga Safu", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhITqyZukwHZ9teMEwtxrx1LXmu7BQL_S_bK8qJFLU=s64", "userId": "13149748190150435632"}}
print('Test accuracy: %.2f%%'%(model_acc(model,test_loader,num_features)))
| 17,512 |
/Hamlet.ipynb | df2ebbfcac50eda363ed10482946bea289c5920b | [] | no_license | desertnaut/LIterature_Networks | https://github.com/desertnaut/LIterature_Networks | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 289,111 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %matplotlib inline
#
# # Path with L1- Logistic Regression
#
#
# Computes path on IRIS dataset.
#
#
#
# +
print(__doc__)
# Author: Alexandre Gramfort <[email protected]>
# License: BSD 3 clause
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn import datasets
from sklearn.svm import l1_min_c
iris = datasets.load_iris()
X = iris.data
y = iris.target
X = X[y != 2]
y = y[y != 2]
X -= np.mean(X, 0)
# #############################################################################
# Demo path functions
cs = l1_min_c(X, y, loss='log') * np.logspace(0, 3)
print("Computing regularization path ...")
start = datetime.now()
clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)
coefs_ = []
for c in cs:
clf.set_params(C=c)
clf.fit(X, y)
coefs_.append(clf.coef_.ravel().copy())
print("This took ", datetime.now() - start)
coefs_ = np.array(coefs_)
plt.plot(np.log10(cs), coefs_)
ymin, ymax = plt.ylim()
plt.xlabel('log(C)')
plt.ylabel('Coefficients')
plt.title('Logistic Regression Path')
plt.axis('tight')
plt.show()
tor in pers_l:
print actor
print
print 'The number of actors in Hamlet is', len(pers_l)
# -
# Ως "διαλογική σχέση" ("conversational relationship") μεταξύ δυο χαρακτήρων (actors) ορίζουμε την εμφάνιση των δυο χαρακτήρων σε ένα (κοινό) διάλογο. Οι μονάδες ή τα "κομμάτια" διαλόγου (conversational chunks) παρουσιάζονται στο κείμενο ως γραμμές κειμένου που περιέχονται μεταξύ δυο κενών γραμμών.
# +
graph_dic,ract_dic,pernode_dict,nodper_dic,cnum=create_graph_dict(act_dict,pers_l,pers_dict,u)
G, list_of_Graphs_final, Gagr, edgeList ,nmap ,mapping,k,n=synthetic_multi_level_dict(graph_dic,pernode_dict,nodper_dic,ract_dic,No_isolates=True)
conver_rel = 0
for k,v in graph_dic.items():
print nx.info(v)
conver_rel += len(v.edges())
print
print 'Actors appearing in Hamlet in all conversational relationships in all Acts:'
for i in pernode_dict:
print i
print
print 'The total number of actors appearing in Hamlet in all conversational relationships in all Acts is', len(pernode_dict.keys())
print 'The total number of conversational relationships (edges) appearing in all Acts of Hamlet is', conver_rel
# +
GI = graph_dic[ract_dic[cnum[0]]]
print "The number of actors in Hamlet's Act I is", len(GI.nodes())
print "The number of conversational relationships in Hamlet's Act I is", len(GI.edges())
GI.remove_nodes_from(nx.isolates(GI))
labels={i:v for v,i in pernode_dict.items() if i in GI.nodes()}
weights={(i[0],i[1]):i[2]['weight'] for i in GI.edges(data=True) }#if all((i[0],i[1])) in G.nodes() }
# print weights
# print GI.nodes()
plt.figure(figsize=(12,12))
pos=nx.spring_layout(GI)
nx.draw_networkx(GI,pos=pos,with_labels=False,alpha=0.4)
labe=nx.draw_networkx_labels(GI,pos=pos,labels=labels)
# nx.draw_networkx_labels(GI,pos=pos,labels=weights)
plt.title("Hamlet Act I")
kk=plt.axis('off')
# +
GI = graph_dic[ract_dic[cnum[1]]]
print "The number of actors in Hamlet's Act II is", len(GI.nodes())
print "The number of conversational relationships in Hamlet's Act II is", len(GI.edges())
GI.remove_nodes_from(nx.isolates(GI))
labels={i:v for v,i in pernode_dict.items() if i in GI.nodes()}
weights={(i[0],i[1]):i[2]['weight'] for i in GI.edges(data=True) }#if all((i[0],i[1])) in G.nodes() }
# print weights
# print GI.nodes()
plt.figure(figsize=(12,12))
pos=nx.spring_layout(GI)
nx.draw_networkx(GI,pos=pos,with_labels=False,alpha=0.4)
labe=nx.draw_networkx_labels(GI,pos=pos,labels=labels)
# nx.draw_networkx_labels(GI,pos=pos,labels=weights)
plt.title("Hamlet Act II")
kk=plt.axis('off')
# +
GI = graph_dic[ract_dic[cnum[2]]]
print "The number of actors in Hamlet's Act III is", len(GI.nodes())
print "The number of conversational relationships in Hamlet's Act III is", len(GI.edges())
GI.remove_nodes_from(nx.isolates(GI))
labels={i:v for v,i in pernode_dict.items() if i in GI.nodes()}
weights={(i[0],i[1]):i[2]['weight'] for i in GI.edges(data=True) }#if all((i[0],i[1])) in G.nodes() }
# print weights
# print GI.nodes()
plt.figure(figsize=(12,12))
pos=nx.spring_layout(GI)
nx.draw_networkx(GI,pos=pos,with_labels=False,alpha=0.4)
labe=nx.draw_networkx_labels(GI,pos=pos,labels=labels)
# nx.draw_networkx_labels(GI,pos=pos,labels=weights)
plt.title("Hamlet Act III")
kk=plt.axis('off')
# +
GI = graph_dic[ract_dic[cnum[3]]]
print "The number of actors in Hamlet's Act IV is", len(GI.nodes())
print "The number of conversational relationships in Hamlet's Act IV is", len(GI.edges())
GI.remove_nodes_from(nx.isolates(GI))
labels={i:v for v,i in pernode_dict.items() if i in GI.nodes()}
weights={(i[0],i[1]):i[2]['weight'] for i in GI.edges(data=True) }#if all((i[0],i[1])) in G.nodes() }
# print weights
# print GI.nodes()
plt.figure(figsize=(12,12))
pos=nx.spring_layout(GI)
nx.draw_networkx(GI,pos=pos,with_labels=False,alpha=0.4)
labe=nx.draw_networkx_labels(GI,pos=pos,labels=labels)
# nx.draw_networkx_labels(GI,pos=pos,labels=weights)
plt.title("Hamlet Act IV")
kk=plt.axis('off')
# +
GI = graph_dic[ract_dic[cnum[4]]]
print "The number of actors in Hamlet's Act V is", len(GI.nodes())
print "The number of conversational relationships in Hamlet's Act V is", len(GI.edges())
GI.remove_nodes_from(nx.isolates(GI))
labels={i:v for v,i in pernode_dict.items() if i in GI.nodes()}
weights={(i[0],i[1]):i[2]['weight'] for i in GI.edges(data=True) }#if all((i[0],i[1])) in G.nodes() }
# print weights
# print GI.nodes()
plt.figure(figsize=(12,12))
pos=nx.spring_layout(GI)
nx.draw_networkx(GI,pos=pos,with_labels=False,alpha=0.4)
labe=nx.draw_networkx_labels(GI,pos=pos,labels=labels)
# nx.draw_networkx_labels(GI,pos=pos,labels=weights)
plt.title("Hamlet Act V")
kk=plt.axis('off')
# +
G=plot_total_graph_with_weights(graph_dic,nodper_dic)
weights={(nd[0],nd[1]):str(nd[2]['weight']) for nd in G.edges(data=True)}
labels={i:v for v,i in pernode_dict.items() if i in G.nodes()}
print "The number of actors in Hamlet Network (all Acts) is", len(G.nodes())
print "The number of conversational relationships in Hamlet Network (all Acts) is", len(G.edges())
# print labels
plt.figure(figsize=(12,12))
pos=nx.spring_layout(G)
nx.draw_networkx(G,pos=pos,with_labels=False,alpha=0.4)
labe=nx.draw_networkx_labels(G,pos=pos,labels=labels)
nx.draw_networkx_edge_labels(G,pos=pos,edge_labels=weights)
plt.title("Hamlet Network")
kk=plt.axis('off')
# +
# G=nx.Graph()
# for k,v in graph_dic.items():
# for ed in v.edges(data=True):
# ww=ed[2]['weight']
# if G.has_edge(ed[0],ed[1]):
# wei=G[ed[0]][ed[1]]['weight']
# else:
# wei=0
# G.add_edge(ed[0],ed[1],weight=wei+ww)
# G.add_nodes_from(v.nodes())
# # GI = graph_dic[ract_dic[cnum[3]]]
# print "The number of actors in Hamlet Network (all Acts) is", len(G.nodes())
# print "The number of conversational relationships in Hamlet Network (all Acts) is", len(G.edges())
# G.remove_nodes_from(nx.isolates(G))
# labels={i:v for v,i in pernode_dict.items() if i in G.nodes()}
# plt.figure(figsize=(12,12))
# pos=nx.spring_layout(G)
# nx.draw_networkx(G,pos=pos,with_labels=False,alpha=0.4)
# labe=nx.draw_networkx_labels(G,pos=pos,labels=labels)
# # # nx.draw_networkx_labels(GI,pos=pos,labels=weights)
# plt.title("Hamlet Network")
# kk=plt.axis('off')
# -
| 7,619 |
/pytorch_tutorial/basic/linear_regression.ipynb | 44e9ab531b812c733b6a45d6a8358acb0bc251f0 | [] | no_license | kmrtanmay/pytorch-tutorial | https://github.com/kmrtanmay/pytorch-tutorial | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 19,011 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/kmrtanmay/pytorch-tutorial/blob/master/pytorch_tutorial/basic/linear_regression.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + id="C7wwOCKvJDuj" colab_type="code" colab={}
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
# + id="VXRATzY7JuI3" colab_type="code" colab={}
# Hyper-parameters
input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001
# + id="ikJhGDFrJ0NN" colab_type="code" colab={}
# Toy dataset
x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],
[9.779], [6.182], [7.59], [2.167], [7.042],
[10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
# + id="cV7b-roWKDyg" colab_type="code" colab={}
y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573],
[3.366], [2.596], [2.53], [1.221], [2.827],
[3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
# + id="u7pbjd_yKJRD" colab_type="code" colab={}
# Linear regression model
model = nn.Linear(input_size, output_size)
# + id="FKoVE5MIKRHv" colab_type="code" colab={}
# Loss and optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# + id="JBb59HewKhZ2" colab_type="code" outputId="e67869a0-6184-4ad3-8500-7314861af353" colab={"base_uri": "https://localhost:8080/", "height": 225}
# Train the model
for epoch in range(num_epochs):
# Convert numpy arrays to torch tensors
inputs = torch.from_numpy(x_train)
targets = torch.from_numpy(y_train)
# Forward pass
outputs = model(inputs)
loss = criterion(outputs, targets)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch+1) % 5 == 0:
print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
# + id="g6DQPI5MK6LA" colab_type="code" outputId="2c9834e8-f235-4de5-ae8f-3d86281f93fa" colab={"base_uri": "https://localhost:8080/", "height": 265}
# Plot the graph
predicted = model(torch.from_numpy(x_train)).detach().numpy()
plt.plot(x_train, y_train, 'ro', label='Original data')
plt.plot(x_train, predicted, label='Fitted line')
plt.legend()
plt.show()
# + id="qJlj2SdONUi4" colab_type="code" colab={}
# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')
# + id="H4STSYc0NZe3" colab_type="code" colab={}
| 2,792 |
/parse_pdf.ipynb | 88cef76508855b151c664b971303ff99180a9c9b | [] | no_license | kb-open/parse_pdf | https://github.com/kb-open/parse_pdf | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 380,422 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 2
# language: python
# name: python2
# ---
import oletools.thirdparty.olefile as olefile
from pprint import pprint
from binascii import unhexlify, hexlify
import os
import re
import string
import numpy as np
import struct
ole = olefile.OleFileIO("BX.smd")
myStream = ole.openstream(['EventLogStorage', 'SymbolTableStream'])
data = myStream.read()
ole.close()
def listToStr(listToConv): return reduce(lambda x,y:x+y, listToConv)
def grabNulls(myData):
myNulls = re.findall(r'[\x00]+', myData)
return [(len(myNull), myNull, len(filter(lambda x:x==myNull, myNulls))) for myNull in set(myNulls)]
def stringGrabber(myString):
mySplits = re.split(r'[\x00]{2,}.[\x00]{2,}', myString)
myPrintables = [filter(lambda x:x in string.printable, myEntry) for myEntry in mySplits]
myChars = [map(lambda x:chr(int(hexlify(x), 16)), myEntry) for myEntry in myPrintables]
#myPrintables = [filter(lambda x:ord(x) < 128 and x in string.printable, myEntry) for myEntry in mySplits]
#myPrintables = [filter(lambda x:x in string.printable and len(x)<=110, myEntry) for myEntry in mySplits]
myNonEmpties = filter(lambda x:len(x)>0, myChars)
#mySmallEnoughs = filter(lambda x:len(x)<=110, myNonEmpties)
stringsAgain = [listToStr(x) for x in myNonEmpties]
return [x.strip() for x in stringsAgain]
def createLexicon(myString):
return set(stringGrabber(myString))
ole = olefile.OleFileIO("BX.smd")
storageStream = ole.openstream(['EventLogStorage', 'SymbolTableStream']).read()
ole.close()
storageStreamLexicon = createLexicon(storageStream)
storageStreamLexicon
[x for x in storageStreamLexicon if re.match(r"L APB", x)]
mode10Strings = dict([(x, stringGrabber(mode10Streams[x])) for x in mode10Streams.keys()])
[(x, len(mode10Strings[x])) for x in mode10Strings.keys()]
mode10Strings.keys()
mode10Lexicons = dict([(x, createLexicon(mode10Streams[x])) for x in mode10Streams.keys()])
[(x, len(createLexicon(mode10Streams[x]))) for x in mode10Lexicons.keys()]
# +
ole = olefile.OleFileIO("BX.smd")
recorderSymbolTables = [x for x in ole.listdir() if len(x)==4 and x[0]=="RecorderStorage" and x[3]=="SymbolTableStream"]
recorderSymbolTablesStreams = dict([(x[1], ole.openstream(x).read()) for x in recorderSymbolTables])
ole.close()
# -
parameterStreamLexicons = dict([(x, createLexicon(recorderSymbolTablesStreams[x])) for x in recorderSymbolTablesStreams.keys()])
[(x, len(parameterStreamLexicons[x])) for x in parameterStreamLexicons.keys()]
parameterStreamLexicons['Mode 10']
parameterStreamLexicons['Mode 4']
parameterStreamLexicons
# +
ole = olefile.OleFileIO("BX.smd")
allLexicons = [createLexicon(ole.openstream(x).read()) for x in ole.listdir()]
ole.close()
# -
#Not as clean as I would have liked
masterLexicon = list(reduce(lambda x,y:x|y, allLexicons))
# +
ole = olefile.OleFileIO("BX.smd")
recorderParameterStreams = [x for x in ole.listdir() if len(x)==4 and x[0]=="RecorderStorage" and x[3]=="ParametersStream"]
recorderParameterStreamsDict = dict([(x[1], ole.openstream(x).read()) for x in recorderParameterStreams])
ole.close()
# -
recorderParameterStreamsDict.keys()
recorderParameterStreamsDict
filter(lambda x:x in string.printable, '!\xb9\r\x07H\xef\xd3\x11\xa1\x06\x00\x10K\xd7\xd5$d')
'!\rHK$d' in masterLexicon
# +
ole = olefile.OleFileIO("BX.smd")
allLexiconDict = dict([(x, createLexicon(ole.openstream(x).read())) for x in ole.listdir()])
ole.close()
# +
ole = olefile.OleFileIO("BX.smd")
allStreams = [ole.openstream(x).read() for x in ole.listdir()]
ole.close()
# +
ole = olefile.OleFileIO("BX.smd")
myDirs = ole.listdir()
streamAndTitle = [(myEntry, ole.openstream(myEntry).read()) for myEntry in myDirs]
noEmptyStreams = filter(lambda x: len(x[1])!=0, streamAndTitle)
titlesAndNulls = [(myEntry[0], grabNulls(myEntry[1])) for myEntry in noEmptyStreams]
ole.close()
# -
allNulls = [(myEntry[0], grabNulls(myEntry[1])) for myEntry in noEmptyStreams]
[(y[0], y[2]) for y in allNulls[0][1]]
#LAST TIME
#Trying to display nullcounts along with titles.
[(y[1][0], y[2]) for y in allNulls[0][1]]
(allNulls[0][0], allNulls[0][1][0][0], allNulls[0][1][0][2])
pprint([allNulls[0][0], [(y[0], y[2]) for y in allNulls[0][1]]])
[(x[0], x[1][0][0], x[1][0][2]) for x in allNulls][0]
#THE GOOD ONE
nullsForAll = [(x[0], [(y[0], y[2]) for y in x[1]]) for x in allNulls]
[y[[0,2]] for y in [np.array(x) for x in allNulls[0][1]]]
# +
ole = olefile.OleFileIO("BX.smd")
allLexicons = [(x, createLexicon(ole.openstream(x).read())) for x in ole.listdir()]
ole.close()
# -
allStreams[2][1]
| 4,824 |
/assignment-2/XOR.ipynb | fad9ea8a924411e0695947a8bf708dc111d9bab6 | [] | no_license | cmaspi/AI1104 | https://github.com/cmaspi/AI1104 | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 153,648 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
#importing librarier
import matplotlib.pyplot as plt
import numpy as np
import math
# +
#defining sigmoid function and its derivative
def sigmoid (x):
return 1/(1+np.exp(-x))
def sigmoid_der (x):
return sigmoid(x)*(1-sigmoid(x))
#mean , standard deviation for noise
mean=0
std=0.03
# +
# The follwoing is for XOR
# number of test cases
#initialisation of weight vectors
#alpha=np.random.uniform(-4,4,6)
#alpha=alpha.reshape(3,2)
#beta=np.array([np.random.uniform(-4,4,3)]).T
#print(alpha,beta)
alpha=np.random.uniform(-4,4,6)
alpha=alpha.reshape(3,2)
beta=np.array([np.random.uniform(-4,4,3)]).T
# The follwoing is for AND
# number of test cases
T=1000
N=int(0.8*T)
M=int(0.2*T)
y=np.ones(T)
# each node in input layer is either 0 or 1 (with some noise)
x1=np.random.randint(0,2,T)
x2=np.random.randint(0,2,T)
#converting to float
x1=x1.astype(float)
x2=x2.astype(float)
#if both nodes are 1 then output node should be 1
for i in range(T):
if x1[i]==0 and x2[i]==0:
y[i]=0
elif x1[i] and x2[i]:
y[i]=0
#adding noise
x1 += np.random.normal(mean, std, T)
x2 += np.random.normal(mean, std, T)
# appending a row of ones, reshaping the matrix
X=np.array([np.ones(T),x1,x2])
X=X.reshape(3,T)
#plotting the input data points
plt.plot(X[1,:],X[2,:],'o',label='pts')
plt.legend()
plt.grid(1)
plt.xlabel('x1')
plt.ylabel('x2')
plt.show()
#size of mini batch
m=50
#number of epochs
epoch =100
#learning rate
gamma=0.05
#The vector for mse values of training data
R_train=np.empty([1])
#The vector for mse values of testing data
R_test=np.empty([1])
# looping over epoch
for i in range(epoch):
#looping over the mini batches
for j in range(N//m):
#z is sgm(alpha.T * X)
z=np.matmul(alpha.T,X[:,j*m:(j+1)*m])
z=z.reshape(1,2*m)
for idx in range(2*m):
z[0][idx]=sigmoid(z[0][idx])
z=np.append(np.ones(m),z)
z=z.reshape(3,m)
#y_cap is sigmoid of beta.T * z
y_cap=np.matmul(beta.T,z)
for idx in range(m):
y_cap[0][idx]=sigmoid(y_cap[0][idx])
#print(beta,y_cap)
delta=-2*(y[j*m:(j+1)*m]-y_cap)
#making the delta vector( 1 cross m)
for idx in range(m):
delta[0][idx]=delta[0][idx]*sigmoid_der(np.dot(beta.T,z[:,idx]))
# making the S matrix (3 cross m)
S=np.matmul(beta,delta)
for idx in range(3):
for itr in range(m):
S[idx][itr]=S[idx][itr]*(z[idx][itr])*(1-(z[idx][itr]))
#updating the weight vector
beta-=gamma*np.matmul(z,delta.T)
alpha-=gamma*np.matmul(S,X[1:,j*m:(j+1)*m].T)
# finding z after every epoch
z=np.matmul(alpha.T,X)
z=z.reshape(1,2*T)
for idx in range(2*T):
z[0][idx]=sigmoid(z[0][idx])
z=np.append(np.ones(T),z)
z=z.reshape(3,T)
#finding the y cap after every epoch
y_cap=np.matmul(beta.T,z)
for idx in range(T):
y_cap[0][idx]=sigmoid(y_cap[0][idx])
# storing the errors in an array
print("epoch:",i+1)
R_train=np.append(R_train,np.sum((y[:N]-y_cap[0,:N])**2)/N)
R_test=np.append(R_test,np.sum((y[N:T]-y_cap[0,N:T])**2)/M)
print("Training error",np.sum((y[:N]-y_cap[0,:N])**2)/N)
print("Testing error",np.sum((y[N:T]-y_cap[0,N:T])**2)/M)
print(alpha,beta)
#print("Observation: Saddle point is reached")
#plotting the results
plt.plot(np.linspace(1,epoch,epoch),R_train[1:], 'r',label='training error')
plt.plot(np.linspace(1,epoch,epoch),R_test[1:],label='testing error')
plt.legend()
plt.grid()
plt.show()
# +
# The follwoing is for XOR
# number of test cases
#initialisation of weight vectors
alpha=np.random.uniform(-4,4,6)
alpha=alpha.reshape(3,2)
beta=np.array([np.random.uniform(-4,4,3)]).T
# The follwoing is for AND
# number of test cases
T=2500
N=int(0.8*T)
M=int(0.2*T)
y=np.ones(T)
# each node in input layer is either 0 or 1 (with some noise)
x1=np.random.randint(0,2,T)
x2=np.random.randint(0,2,T)
#converting to float
x1=x1.astype(float)
x2=x2.astype(float)
#if both nodes are 1 then output node should be 1
for i in range(T):
if x1[i]==0 and x2[i]==0:
y[i]=0
elif x1[i] and x2[i]:
y[i]=0
#adding noise
x1 += np.random.normal(mean, std, T)
x2 += np.random.normal(mean, std, T)
# appending a row of ones, reshaping the matrix
X=np.array([np.ones(T),x1,x2])
X=X.reshape(3,T)
#plotting the input data points
plt.plot(X[1,:],X[2,:],'o',label='pts')
plt.legend()
plt.grid(1)
plt.xlabel('x1')
plt.ylabel('x2')
plt.show()
#size of mini batch
m=50
#number of epochs
epoch =100
#learning rate
gamma=0.05
#The vector for mse values of training data
R_train=np.empty([1])
#The vector for mse values of testing data
R_test=np.empty([1])
# looping over epoch
for i in range(epoch):
#looping over the mini batches
for j in range(N//m):
#z is sgm(alpha.T * X)
z=np.matmul(alpha.T,X[:,j*m:(j+1)*m])
z=z.reshape(1,2*m)
for idx in range(2*m):
z[0][idx]=sigmoid(z[0][idx])
z=np.append(np.ones(m),z)
z=z.reshape(3,m)
#y_cap is sigmoid of beta.T * z
y_cap=np.matmul(beta.T,z)
for idx in range(m):
y_cap[0][idx]=sigmoid(y_cap[0][idx])
#print(beta,y_cap)
delta=-2*(y[j*m:(j+1)*m]-y_cap)
#making the delta vector( 1 cross m)
for idx in range(m):
delta[0][idx]=delta[0][idx]*sigmoid_der(np.dot(beta.T,z[:,idx]))
# making the S matrix (3 cross m)
S=np.matmul(beta,delta)
for idx in range(3):
for itr in range(m):
S[idx][itr]=S[idx][itr]*sigmoid(z[idx][itr])*(1-sigmoid(z[idx][itr]))
#updating the weight vector
beta-=gamma*np.matmul(z,delta.T)
alpha-=gamma*np.matmul(S,X[1:,j*m:(j+1)*m].T)
# finding z after every epoch
z=np.matmul(alpha.T,X)
z=z.reshape(1,2*T)
for idx in range(2*T):
z[0][idx]=sigmoid(z[0][idx])
z=np.append(np.ones(T),z)
z=z.reshape(3,T)
#finding the y cap after every epoch
y_cap=np.matmul(beta.T,z)
for idx in range(T):
y_cap[0][idx]=sigmoid(y_cap[0][idx])
# storing the errors in an array
print("epoch:",i+1)
R_train=np.append(R_train,np.sum((y[:N]-y_cap[0,:N])**2)/N)
R_test=np.append(R_test,np.sum((y[N:T]-y_cap[0,N:T])**2)/M)
print("Training error",np.sum((y[:N]-y_cap[0,:N])**2)/N)
print("Testing error",np.sum((y[N:T]-y_cap[0,N:T])**2)/M)
print(alpha,beta)
#plotting the results
plt.plot(np.linspace(1,epoch,epoch),R_train[1:], 'r',label='training error')
plt.plot(np.linspace(1,epoch,epoch),R_test[1:],label='testing error')
plt.legend()
plt.grid()
plt.show()
# +
# The follwoing is for XOR
# number of test cases
#initialisation of weight vectors
alpha=np.random.uniform(-4,4,6)
alpha=alpha.reshape(3,2)
beta=np.array([np.random.uniform(-4,4,3)]).T
# The follwoing is for AND
# number of test cases
T=5000
N=int(0.8*T)
M=int(0.2*T)
y=np.ones(T)
# each node in input layer is either 0 or 1 (with some noise)
x1=np.random.randint(0,2,T)
x2=np.random.randint(0,2,T)
#converting to float
x1=x1.astype(float)
x2=x2.astype(float)
#if both nodes are 1 then output node should be 1
for i in range(T):
if x1[i]==0 and x2[i]==0:
y[i]=0
elif x1[i] and x2[i]:
y[i]=0
#adding noise
x1 += np.random.normal(mean, std, T)
x2 += np.random.normal(mean, std, T)
# appending a row of ones, reshaping the matrix
X=np.array([np.ones(T),x1,x2])
X=X.reshape(3,T)
#plotting the input data points
plt.plot(X[1,:],X[2,:],'o',label='pts')
plt.legend()
plt.grid(1)
plt.xlabel('x1')
plt.ylabel('x2')
plt.show()
#size of mini batch
m=50
#number of epochs
epoch =100
#learning rate
gamma=0.05
#The vector for mse values of training data
R_train=np.empty([1])
#The vector for mse values of testing data
R_test=np.empty([1])
# looping over epoch
for i in range(epoch):
#looping over the mini batches
for j in range(N//m):
#z is sgm(alpha.T * X)
z=np.matmul(alpha.T,X[:,j*m:(j+1)*m])
z=z.reshape(1,2*m)
for idx in range(2*m):
z[0][idx]=sigmoid(z[0][idx])
z=np.append(np.ones(m),z)
z=z.reshape(3,m)
#y_cap is sigmoid of beta.T * z
y_cap=np.matmul(beta.T,z)
for idx in range(m):
y_cap[0][idx]=sigmoid(y_cap[0][idx])
#print(beta,y_cap)
delta=-2*(y[j*m:(j+1)*m]-y_cap)
#making the delta vector( 1 cross m)
for idx in range(m):
delta[0][idx]=delta[0][idx]*sigmoid_der(np.dot(beta.T,z[:,idx]))
# making the S matrix (3 cross m)
S=np.matmul(beta,delta)
for idx in range(3):
for itr in range(m):
S[idx][itr]=S[idx][itr]*sigmoid(z[idx][itr])*(1-sigmoid(z[idx][itr]))
#updating the weight vector
beta-=gamma*np.matmul(z,delta.T)
alpha-=gamma*np.matmul(S,X[1:,j*m:(j+1)*m].T)
# finding z after every epoch
z=np.matmul(alpha.T,X)
z=z.reshape(1,2*T)
for idx in range(2*T):
z[0][idx]=sigmoid(z[0][idx])
z=np.append(np.ones(T),z)
z=z.reshape(3,T)
#finding the y cap after every epoch
y_cap=np.matmul(beta.T,z)
for idx in range(T):
y_cap[0][idx]=sigmoid(y_cap[0][idx])
# storing the errors in an array
print("epoch:",i+1)
R_train=np.append(R_train,np.sum((y[:N]-y_cap[0,:N])**2)/N)
R_test=np.append(R_test,np.sum((y[N:T]-y_cap[0,N:T])**2)/M)
print("Training error",np.sum((y[:N]-y_cap[0,:N])**2)/N)
print("Testing error",np.sum((y[N:T]-y_cap[0,N:T])**2)/M)
#plotting the results
plt.plot(np.linspace(1,epoch,epoch),R_train[1:], 'r',label='training error')
plt.plot(np.linspace(1,epoch,epoch),R_test[1:],label='testing error')
plt.legend()
plt.grid()
plt.show()
# -
| 10,329 |
/code/neural_networks/TensorFlow_1.0.ipynb | 7fe625e6bc26ddedb5fd69e99147ed52618cdb8a | [] | no_license | AntoBrandi/Robotics-ND | https://github.com/AntoBrandi/Robotics-ND | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 3,219 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# ### Hello World TensorFlow
# +
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
# Create TensorFlow object called hello_constant
hello_constant = tf.constant('Hello World!')
with tf.Session() as sess:
# Run the tf.constant operation in the session
output = sess.run(hello_constant)
print(output)
# -
# ### Softmax Function
#
# 
# 
# +
def run():
output = None
logit_data = [2.0, 1.0, 0.1]
logits = tf.placeholder(tf.float32)
# Calculate the softmax of the logits
softmax = tf.nn.softmax(logits)
with tf.Session() as sess:
# Feed in the logit data
output = sess.run(softmax, feed_dict={logits: logit_data})
return output
run()
# -
# ### Cross-Entropy Function
#
# 
# 
# +
softmax_data = [0.7, 0.2, 0.1]
one_hot_data = [1.0, 0.0, 0.0]
softmax = tf.placeholder(tf.float32)
one_hot = tf.placeholder(tf.float32)
cross_entropy = -tf.reduce_sum(tf.multiply(one_hot, tf.log(softmax)))
# Print cross entropy from session
with tf.Session() as sess:
# Feed in the logit data
output = sess.run(cross_entropy, feed_dict={softmax: softmax_data, one_hot: one_hot_data})
print(output)
# -
| 1,537 |
/Lab 7/Lab 7.ipynb | 5282ddc07528431fd4e48a1e0f6bc3a655d85695 | [] | no_license | connortou/BioE131 | https://github.com/connortou/BioE131 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 16,895 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Connor Tou | BioE131 Lab 7
import numpy as np
import string
from os import listdir
from os.path import isfile, join
import matplotlib.pyplot as plt
# %matplotlib inline
# ## SIMULATING THE DATA
#
# ### Binary Data
# +
percentages = [i for i in range(50,101,10)]
for p in percentages:
binary_data = np.packbits(np.random.choice([0, 1], size=8*1000000*100, replace=True, p = [p/100, 1-p/100]))
open('Data/zeros_%sp' %p, 'wb').write(binary_data)
# -
# ### DNA Data
# +
# generate DNA sequence 100 million letters long with equal probability and save it into a file
dna = np.random.choice(['A', 'T', 'G', 'C'], size=100000000, replace=True);
open('Data/dna.fa', 'w').write(''.join(dna));
# -
# ### Protein Data
# +
# generate protein sequence 100 million letters long with equal probability and save it into a file
protein = np.random.choice(list(string.ascii_uppercase), size=100000000, replace=True);
open('Data/protein.fa', 'w').write(''.join(protein));
# -
# ## COMPRESSING THE DATA
# ### zeros_50p
#
# [5367152@ip-172-30-0-105 ~]$ time gzip -c Data/zeros_50p > Data/zeros_50p.gz
#
# real 0m3.412s
# user 0m3.351s
# sys 0m0.060s
#
# [5367152@ip-172-30-0-105 ~]$ time bzip2 -k Data/zeros_50p
#
# real 0m12.817s
# user 0m12.701s
# sys 0m0.116s
#
# [5367152@ip-172-30-0-105 ~]$ time pbzip2 -k Data/zeros_50p
#
# real 0m1.806s
# user 0m27.213s
# sys 0m0.654s
# ### zeros_60p
#
# [5367152@ip-172-30-0-105 ~]$ time gzip -c Data/zeros_60p > Data/zeros_60p.gz
#
# real 0m4.206s
# user 0m4.146s
# sys 0m0.060s
#
# [5367152@ip-172-30-0-105 ~]$ time bzip2 -k Data/zeros_60p
#
# real 0m11.928s
# user 0m11.835s
# sys 0m0.092s
#
# [5367152@ip-172-30-0-105 ~]$ time pbzip2 -k Data/zeros_60p
#
# real 0m1.670s
# user 0m25.209s
# sys 0m0.623s
# ### zeros_70p
#
# [5367152@ip-172-30-0-105 ~]$ time gzip -c Data/zeros_70p > Data/zeros_70p.gz
#
# real 0m6.779s
# user 0m6.727s
# sys 0m0.052s
#
# [5367152@ip-172-30-0-105 ~]$ time bzip2 -k Data/zeros_70p
#
# real 0m10.811s
# user 0m10.711s
# sys 0m0.100s
#
# [5367152@ip-172-30-0-105 ~]$ time pbzip2 -k Data/zeros_70p
#
# real 0m1.405s
# user 0m21.014s
# sys 0m0.648s
# ### zeros_80p
#
# [5367152@ip-172-30-0-105 ~]$ time gzip -c Data/zeros_80p > Data/zeros_80p.gz
#
# real 0m16.598s
# user 0m16.546s
# sys 0m0.052s
#
# [5367152@ip-172-30-0-105 ~]$ time bzip2 -k Data/zeros_80p
#
# real 0m9.991s
# user 0m9.934s
# sys 0m0.056s
#
# [5367152@ip-172-30-0-105 ~]$ time pbzip2 -k Data/zeros_80p
#
# real 0m1.173s
# user 0m17.472s
# sys 0m0.562s
# ### zeros_90p
#
# [5367152@ip-172-30-0-105 ~]$ time gzip -c Data/zeros_90p > Data/zeros_90p.gz
#
# real 0m25.732s
# user 0m25.679s
# sys 0m0.052s
#
# [5367152@ip-172-30-0-105 ~]$ time bzip2 -k Data/zeros_90p
#
# real 0m9.504s
# user 0m9.436s
# sys 0m0.068s
#
# [5367152@ip-172-30-0-105 ~]$ time pbzip2 -k Data/zeros_90p
#
# real 0m1.007s
# user 0m14.964s
# sys 0m0.495s
# ### zeros_100p
#
# [5367152@ip-172-30-0-105 ~]$ time gzip -c Data/zeros_100p > Data/zeros_100p.gz
#
# real 0m0.577s
# user 0m0.569s
# sys 0m0.008s
#
# [5367152@ip-172-30-0-105 ~]$ time bzip2 -k Data/zeros_100p
#
# real 0m0.855s
# user 0m0.823s
# sys 0m0.032s
#
#
# [5367152@ip-172-30-0-105 ~]$ time pbzip2 -k Data/zeros_100p
#
# real 0m0.142s
# user 0m1.746s
# sys 0m0.058s
# ### dna.fa
#
# [5367152@ip-172-30-0-105 ~]$ time gzip -c Data/dna.fa > dna.fa.gz
#
# real 0m22.989s
# user 0m22.956s
# sys 0m0.032s
#
#
# [5367152@ip-172-30-0-105 ~]$ time bzip2 -k Data/dna.fa
#
# real 0m9.279s
# user 0m9.238s
# sys 0m0.040s
#
# [5367152@ip-172-30-0-105 ~]$ time pbzip2 -k Data/dna.fa
#
# real 0m0.986s
# user 0m14.357s
# sys 0m0.538s
# ### protein.fa
#
# [5367152@ip-172-30-0-105 ~]$ time gzip -c Data/protein.fa > Data/protein.fa.gz
#
# real 0m3.746s
# user 0m3.710s
# sys 0m0.036s
#
# [5367152@ip-172-30-0-105 ~]$ time bzip2 -k Data/protein.fa
#
# real 0m9.338s
# user 0m9.242s
# sys 0m0.096s
#
# [5367152@ip-172-30-0-105 ~]$ time pbzip2 -k Data/protein.fa
#
# real 0m1.017s
# user 0m15.085s
# sys 0m0.516s
# ### Summary Table
# |original file|command|input file size|output file size|time elapsed|
# |------|------|------|------|------|
# |zeros_50p|gzip|100 MB|100 MB|0:03.412s|
# |zeros_50p|bzip2|100 MB|100 MB|0:12.817s|
# |zeros_50p|pbzip2|100 MB|100 MB|0:1.806s|
# |zeros_60p|gzip|100 MB|97.7 MB|0:04.206s|
# |zeros_60p|bzip2|100 MB|100 MB|0:11.928s|
# |zeros_60p|pbzip2|100 MB|100 MB|0:01.670s|
# |zeros_70p|gzip|100 MB|89.3 MB|0:06.779s|
# |zeros_70p|bzip2|100 MB|95.1 MB|0:10.811s|
# |zeros_70p|pbzip2|100 MB|95.1 MB|0:01.405s|
# |zeros_80p|gzip|100 MB|77.4 MB|0:16.598s|
# |zeros_80p|bzip2|100 MB|82.6 MB|0:09.991s|
# |zeros_80p|pbzip2|100 MB|82.6 MB|0:01.173s|
# |zeros_90p|gzip|100 MB|56.0 MB|0:25.732s|
# |zeros_90p|bzip2|100 MB|58.3 MB|0:09.504s|
# |zeros_90p|pbzip2|100 MB|58.4 MB|0:01.007s|
# |zeros_100p|gzip|100 MB|97.1 kB|0:00.577s|
# |zeros_100p|bzip2|100 MB|113 B|0:00.855s|
# |zeros_100p|pbzip2|100 MB|5.38 kB|0:00.142s|
# |dna.fa|gzip|100 MB|29.2 MB|0:22.989s|
# |dna.fa|bzip2|100 MB|27.3 MB|0:09.279s|
# |dna.fa|pbzip2|100 MB|27.3 MB|0:00.986s|
# |protein.fa|gzip|100 MB|63.5 MB|0:03.746s|
# |protein.fa|bzip2|100 MB|59.8 MB|0:09.338s|
# |protein.fa|pbzip2|100 MB|59.8 MB|0:01.017s|
# ## QUESTIONS:
# **Which algorithm achieves the best level of compression on each file type?**
# * For binary data, gzip attains the best level of compression (except for the 100% zeros data, where bzip had the highest compression). For dna and protein data, bzip2 and pbzip2 had higher compression than gzip.
#
# **Which algorithm is the fastest?**
# * pbzip2 is the fastest
#
# **What is the difference between bzip2 and pbzip2? Do you expect one to be faster and why?**
# * pbzip2 is a modified version of bzip2. One major difference is that pbzip2 supports multi-threading. Thus, on multi-CPU/ multi-core computers, linear speed improvements can be achieved. Because of this, pbzip2 is faster.
#
# **How does the level of compression change as the percentage of zeros increases? Why does this happen?**
# * Compression improves significantly with increasing percentage of zeros. This happens because there is less variation in the data.
#
# **What is the minimum number of bits required to store a single DNA base?**
# * The minimum number of bits required is log2(4)=2 since there are four different nucleotide possibilities.
#
# **What is the minimum number of bits required to store an amino acid letter?**
# * Since there are 20 amino acids, the minimum number of bits required is log2(20)=4.3. As this must be an integer number, 5 bits is required.
#
# **In your tests, how many bits did gzip and bzip2 actually require to store your random DNA and protein sequences?**
# * DNA: gzip takes 29.2MB. Thus, 29.2MB * (1e6 * 8)/100,000,000 = 2.34 bits per nucleotide
# * DNA: bzip2 takes 27.3MB. Thus, 27.3MB * (1e6 * 8)/100,000,000 = 2.18 bits per nucleotide
# * Proteins: gzip takes 63.5MB. Thus, 63.5MB * (1e6 * 8)/100,000,000 = 5.08 bits per amino acid letter
# * Proteins: bzip2 takes 59.8MB. Thus, 59.8MB * (1e6 * 8)/100,000,000 = 4.78 bits per amino acid letter
#
# **Are gzip and bzip2 performing well on DNA and proteins?**
# * bzip2 has a relatively good performance when looking at compression ratio; gzip is the worst among the three algorithms. Looking at speed, gzip and bzip2 are moderate - pbzip2 is much faster than the two. In summary, they have mediocre performance on DNA and protein data.
# +
from Bio import Entrez
from Bio import SeqIO
import sys
Entrez.email = "[email protected]"
list_seq = []
list_name = []
handle = Entrez.esearch(db = 'nucleotide',
term = 'gp120 and HIV',
sort = 'relevance',
idtype = 'acc',
retmax = 10)
for i in Entrez.read(handle)['IdList']:
handle = Entrez.efetch(db = 'nucleotide', id = i, rettype = 'gb', retmode = 'text')
record = SeqIO.read(handle, "genbank")
list_name.append(str(record.name))
list_seq.append(str(record.seq))
handle.close()
# +
dict_gp120 = dict(zip(list_name, list_seq))
ofile = open("multi_fasta.fa", "w")
for i in range(len(list_seq)):
ofile.write(">" + list_name[i] + "\n" +list_seq[i] + "\n")
ofile.close()
# -
# ### multi_fasta.fa
#
# [5367152@ip-172-30-0-105 ~]$ time gzip -c multi_fasta.fa > multi_fasta.fa.gz
#
# real 0m0.001s
# user 0m0.000s
# sys 0m0.001s
#
#
# [5367152@ip-172-30-0-105 ~]$ time bzip2 -k multi_fasta.fa
#
# real 0m0.002s
# user 0m0.002s
# sys 0m0.000s
#
# [5367152@ip-172-30-0-105 ~]$ time pbzip2 -k multi_fasta.fa
#
# real 0m0.003s
# user 0m0.003s
# sys 0m0.000s
# #### A priori, do you expect to achieve better or worse compression here than random data? Why?
# * There are more patterns in natural data (sequences from nature), so compression should be better.
# |original file|command type|input file size|output file size|compression ratio|
# |------|------|------|------|------|
# |multi_fasta.fa|gzip|5.46 kB|1.25 kB|22.89%|
# |multi_fasta.fa|bzip2|5.46 kB|1.33 kB|24.36%|
# #### How does the compression ratio of this file compare to random data?
# * For random dna sequences, the compression ratio is 29.2% for gzip and 27.3% for bzip. The comrpession ratio is lower for this file, meaning that compression is better for this file as expected.
# ## ESTIMATING COMPRESSION OF 1000 TERABYTES
# **Given the benchmarking data you obtained in this lab, which algorithm do you propose to use for each type of data? Provide an estimate for the fraction of space you can save using your compression scheme.**
#
# pbzip2 should be used for all three types of data. At the fastest, we have 0.1 seconds for real-life DNA data, 1.02 for protein data, and 1.81 seconds for random binary data (zeros_50p). These times will be used in the following calculations along with the respective compression ratios.
#
# 80% = resequencing of genomes and plasmids --> Using pbzip, a single computer can theoretically compress in a day (86400 seconds)*(100MB/0.1 seconds) = 86.4TB. In 24 hours, the reduction is: (86400 seconds/0.1 second)*100MB*(1-0.2436%) = 65.4TB. Thus, each day, a singel computer can take 86.4TB of dna data and compress it to 65.4TB of dna data.
#
# 10% = protein sequences --> use pbzip2. a single computer can theoretically compress in a day (86400 seconds)*(100MB/1.02 seconds) = 8.5TB. The reduction in data in 24 hours: (86400 seconds)*(100MB/1.02 seconds)*(1-0.598) = 3.4TB
#
# 10% = random binary data --> assuming this is a random binary with 50% zeros (using the time and compression ratio for this). a single computer can theoretically compress in a day (86400 seconds)*(100MB/1.81 seconds) = 4.77TB. The reduction in data in 24 hours: (86400 seconds/1.5 second)*100MB*(1-1) = 0TB. There is no compression.
#
# The company generates 1000TB of data each day. 800TB is dna sequences, 100TB is protein sequences, and 100TB is random binary data. Each day, 86.4TB of the dna sequence 800TB can be compressed to 65.4TB of data and 8.5TB of the protein sequence 100TB can be compressed to 3.4TB of data.
#
# Therefore, the total reduction is 21.0TB + 5.1TB + 0.0TB = 26.1TB.
#
# **How much of a bonus do you anticipate receiving this year?**
#
# 50 dollars/TB * 26.1TB = 1305 dollars. Therefore, per year, 1305 dollars/day*365days/year = 476,325 dollars
# # END OF LAB 7
ict(X_test)
mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)
std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])
mean_acc
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false}
# #### Plot model accuracy for Different number of Neighbors
# + button=false deletable=true new_sheet=false run_control={"read_only": false}
plt.plot(range(1,Ks),mean_acc,'g')
plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)
plt.legend(('Accuracy ', '+/- 3xstd'))
plt.ylabel('Accuracy ')
plt.xlabel('Number of Nabors (K)')
plt.tight_layout()
plt.show()
# + button=false deletable=true new_sheet=false run_control={"read_only": false}
print( "The best accuracy was with", mean_acc.max(), "with k=", mean_acc.argmax()+1)
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false}
# <h2>Want to learn more?</h2>
#
# IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href="http://cocl.us/ML0101EN-SPSSModeler">SPSS Modeler</a>
#
# Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href="https://cocl.us/ML0101EN_DSX">Watson Studio</a>
#
# <h3>Thanks for completing this lesson!</h3>
#
# <h4>Author: <a href="https://ca.linkedin.com/in/saeedaghabozorgi">Saeed Aghabozorgi</a></h4>
# <p><a href="https://ca.linkedin.com/in/saeedaghabozorgi">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>
#
# <hr>
#
# <p>Copyright © 2018 <a href="https://cocl.us/DX0108EN_CC">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href="https://bigdatauniversity.com/mit-license/">MIT License</a>.</p>
tps://localhost:8080/", "height": 1000}
import random
for i in range(10):
random.seed(i)
a = np.random.randint(0,10000)
img1 = torch.cat((true_test_foreground_data[i],test_foreground_data[i]),2)
imshow(img1)
# + id="wo78BztGTwwL"
def plot_vectors(u1,u2,u3):
img = np.reshape(u1,(3,32,32))
img = img / 2 + 0.5 # unnormalize
npimg = img#.numpy()
print("vector u1 norm",LA.norm(img))
plt.figure(1)
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.title("vector u1")
img = np.reshape(u2,(3,32,32))
img = img / 2 + 0.5 # unnormalize
npimg = img#.numpy()
print("vector u2 norm",LA.norm(img))
plt.figure(2)
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.title("vector u2")
img = np.reshape(u3,(3,32,32))
img = img / 2 + 0.5 # unnormalize
npimg = img#.numpy()
print("vector u3 norm",LA.norm(img))
plt.figure(3)
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.title("vector u3")
plt.show()
# + id="72zcYiJsTPEr" outputId="a667096d-e291-42e1-d095-98d2d4177405" colab={"base_uri": "https://localhost:8080/", "height": 865}
plot_vectors(u1,u2,u3)
# + id="wFpwvWrzYJQi"
class MosaicDataset(Dataset):
"""MosaicDataset dataset."""
def __init__(self, mosaic_list_of_images, mosaic_label, fore_idx):
"""
Args:
csv_file (string): Path to the csv file with annotations.
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.mosaic = mosaic_list_of_images
self.label = mosaic_label
self.fore_idx = fore_idx
def __len__(self):
return len(self.label)
def __getitem__(self, idx):
return self.mosaic[idx] , self.label[idx], self.fore_idx[idx]
# + id="DxW0w8_BXsih"
def create_mosaic_img(background_data, foreground_data, foreground_label, bg_idx,fg_idx,fg,fg1):
"""
bg_idx : list of indexes of background_data[] to be used as background images in mosaic
fg_idx : index of image to be used as foreground image from foreground data
fg : at what position/index foreground image has to be stored out of 0-8
"""
image_list=[]
j=0
for i in range(9):
if i != fg:
image_list.append(background_data[bg_idx[j]].type("torch.DoubleTensor"))
j+=1
else:
image_list.append(foreground_data[fg_idx].type("torch.DoubleTensor"))
label = foreground_label[fg_idx] -fg1 #-7 # minus 7 because our fore ground classes are 7,8,9 but we have to store it as 0,1,2
#image_list = np.concatenate(image_list ,axis=0)
image_list = torch.stack(image_list)
return image_list,label
# + id="jTpidLeLVyyK"
def init_mosaic_creation(bg_size, fg_size, desired_num, background_data, foreground_data, foreground_label,fg1):
# bg_size = 35000
# fg_size = 15000
# desired_num = 30000
mosaic_list_of_images =[] # list of mosaic images, each mosaic image is saved as list of 9 images
fore_idx =[] # list of indexes at which foreground image is present in a mosaic image i.e from 0 to 9
mosaic_label=[] # label of mosaic image = foreground class present in that mosaic
for i in range(desired_num):
np.random.seed(i+ bg_size + desired_num)
bg_idx = np.random.randint(0,bg_size,8)
# print(bg_idx)
np.random.seed(i+ fg_size + desired_num)
fg_idx = np.random.randint(0,fg_size)
# print(fg_idx)
fg = np.random.randint(0,9)
fore_idx.append(fg)
image_list,label = create_mosaic_img(background_data, foreground_data, foreground_label ,bg_idx,fg_idx,fg, fg1)
mosaic_list_of_images.append(image_list)
mosaic_label.append(label)
return mosaic_list_of_images, mosaic_label, fore_idx
# + id="WuIMxXjgV1sB"
train_mosaic_list_of_images, train_mosaic_label, train_fore_idx = init_mosaic_creation(bg_size = 35000,
fg_size = 15000,
desired_num = 30000,
background_data = train_background_data,
foreground_data = train_foreground_data,
foreground_label = train_foreground_label,
fg1 = fg1
)
# + id="jNw9xEHdYLRQ"
batch = 250
msd_1 = MosaicDataset(train_mosaic_list_of_images, train_mosaic_label , train_fore_idx)
train_loader_from_noise_train_mosaic_30k = DataLoader( msd_1,batch_size= batch ,shuffle=True)
# + id="uy9iem2zYT-p"
test_mosaic_list_of_images, test_mosaic_label, test_fore_idx = init_mosaic_creation(bg_size = 35000,
fg_size = 15000,
desired_num = 10000,
background_data = train_background_data,
foreground_data = train_foreground_data,
foreground_label = train_foreground_label,
fg1 = fg1
)
# + id="ek_hNOGfY_Rg"
batch = 250
msd_2 = MosaicDataset(test_mosaic_list_of_images, test_mosaic_label , test_fore_idx)
test_loader_from_noise_train_mosaic_30k = DataLoader( msd_2, batch_size= batch ,shuffle=True)
# + id="k9Fb3xqvZXgY"
test_mosaic_list_of_images_1, test_mosaic_label_1, test_fore_idx_1 = init_mosaic_creation(bg_size = 7000,
fg_size = 3000,
desired_num = 10000,
background_data = test_background_data,
foreground_data = test_foreground_data,
foreground_label = test_foreground_label,
fg1 = fg1
)
# + id="D491Dr2eZxXo"
batch = 250
msd_3 = MosaicDataset(test_mosaic_list_of_images_1, test_mosaic_label_1 , test_fore_idx_1)
test_loader_from_noise_test_mosaic_10k = DataLoader( msd_3, batch_size= batch ,shuffle=True)
# + id="vfEaNoxVaTEp"
test_mosaic_list_of_images_2, test_mosaic_label_2, test_fore_idx_2 = init_mosaic_creation(bg_size = 35000,
fg_size = 15000,
desired_num = 10000,
background_data = true_train_background_data,
foreground_data = true_train_foreground_data,
foreground_label = true_train_foreground_label,
fg1 = fg1
)
# + id="ytvVuHTgaTEu"
batch = 250
msd_4 = MosaicDataset(test_mosaic_list_of_images_2, test_mosaic_label_2, test_fore_idx_2)
test_loader_from_true_train_mosaic_30k = DataLoader( msd_4, batch_size= batch , shuffle=True)
# + id="cbN6OQzxaTEy"
test_mosaic_list_of_images_3, test_mosaic_label_3, test_fore_idx_3 = init_mosaic_creation(bg_size = 7000,
fg_size = 3000,
desired_num = 10000,
background_data = true_test_background_data,
foreground_data = true_test_foreground_data,
foreground_label = true_test_foreground_label,
fg1 = fg1
)
# + id="Mu890cyTaTE2"
batch = 250
msd_5 = MosaicDataset(test_mosaic_list_of_images_3, test_mosaic_label_3, test_fore_idx_3)
test_loader_from_true_train_mosaic_10k = DataLoader( msd_5, batch_size= batch ,shuffle=True)
# + id="dgQ0htWqkqzo"
class Module1(nn.Module):
def __init__(self):
super(Module1, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
self.fc4 = nn.Linear(10,1)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = self.fc4(x)
return x
# + id="XElkdct-kvQB"
class Module2(nn.Module):
def __init__(self):
super(Module2, self).__init__()
self.module1 = Module1().double()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
self.fc4 = nn.Linear(10,3)
def forward(self,z): #z batch of list of 9 images
y = torch.zeros([batch,3, 32,32], dtype=torch.float64)
x = torch.zeros([batch,9],dtype=torch.float64)
x = x.to("cuda")
y = y.to("cuda")
for i in range(9):
x[:,i] = self.module1.forward(z[:,i])[:,0]
x = F.softmax(x,dim=1)
x1 = x[:,0]
torch.mul(x1[:,None,None,None],z[:,0])
for i in range(9):
x1 = x[:,i]
y = y + torch.mul(x1[:,None,None,None],z[:,i])
y = y.contiguous()
y1 = self.pool(F.relu(self.conv1(y)))
y1 = self.pool(F.relu(self.conv2(y1)))
y1 = y1.contiguous()
y1 = y1.reshape(-1, 16 * 5 * 5)
y1 = F.relu(self.fc1(y1))
y1 = F.relu(self.fc2(y1))
y1 = F.relu(self.fc3(y1))
y1 = self.fc4(y1)
return y1 , x, y
# + id="Nus7AK1xRX7W"
def training(trainloader, fore_net, epochs=600):
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(fore_net.parameters(), lr=0.01, momentum=0.9)
nos_epochs = epochs
for epoch in range(nos_epochs): # loop over the dataset multiple times
running_loss = 0.0
cnt=0
mini_loss = []
iteration = 30000 // batch
for i, data in enumerate(train_loader_from_noise_train_mosaic_30k):
inputs , labels , fore_idx = data
inputs, labels, fore_idx = inputs.to("cuda"),labels.to("cuda"), fore_idx.to("cuda")
optimizer.zero_grad()
outputs, alphas, avg_images = fore_net(inputs)
_, predicted = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
mini = 40
if cnt % mini == mini - 1: # print every 40 mini-batches
print('[%d, %5d] loss: %.3f' %(epoch + 1, cnt + 1, running_loss / mini))
mini_loss.append(running_loss / mini)
running_loss = 0.0
cnt=cnt+1
if(np.average(mini_loss) <= 0.05):
break
print('Finished Training')
return fore_net, epoch
# + id="17GMe4WKSNji"
def testing(loader, fore_net):
correct = 0
total = 0
count = 0
flag = 1
focus_true_pred_true =0
focus_false_pred_true =0
focus_true_pred_false =0
focus_false_pred_false =0
argmax_more_than_half = 0
argmax_less_than_half =0
with torch.no_grad():
for data in loader:
inputs, labels , fore_idx = data
inputs, labels , fore_idx = inputs.to("cuda"),labels.to("cuda"), fore_idx.to("cuda")
outputs, alphas, avg_images = fore_net(inputs)
_, predicted = torch.max(outputs.data, 1)
for j in range(labels.size(0)):
count += 1
focus = torch.argmax(alphas[j])
if alphas[j][focus] >= 0.5 :
argmax_more_than_half += 1
else:
argmax_less_than_half += 1
if(focus == fore_idx[j] and predicted[j] == labels[j]):
focus_true_pred_true += 1
elif(focus != fore_idx[j] and predicted[j] == labels[j]):
focus_false_pred_true += 1
elif(focus == fore_idx[j] and predicted[j] != labels[j]):
focus_true_pred_false += 1
elif(focus != fore_idx[j] and predicted[j] != labels[j]):
focus_false_pred_false += 1
total += labels.size(0)
correct += (predicted == labels).sum().item()
return correct, total, focus_true_pred_true, focus_false_pred_true, focus_true_pred_false, focus_false_pred_false, argmax_more_than_half
# + id="lp0cGt63YuUc"
def enter_into(table, sno, correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half , fg, bg, epoch = "NA"):
entry = []
entry = [sno,'fg = '+ str(fg),'bg = '+str(bg), epoch, total, correct,]
entry.append((100.0*correct/total))
entry.append((100 * ftpt / total))
entry.append( (100 * ffpt / total))
entry.append( ( 100 * ftpf / total))
entry.append( ( 100 * ffpf / total))
entry.append( alpha_more_half)
table.append(entry)
print(" ")
print("="*160)
print(tabulate(table, headers=['S.No.', 'fg_class','bg_class','Epoch used','total_points', 'correct','accuracy','FTPT', 'FFPT', 'FTPF', 'FFPF', 'avg_img > 0.5'] ) )
print(" ")
print("="*160)
return table
# + id="uS6Gq-4VfX89"
def add_average_entry(table):
entry =[]
entry = ['Avg', "","" ,"" ,"" , "",]
entry.append( np.mean(np.array(table)[:,6].astype(np.float)) )
entry.append( np.mean(np.array(table)[:,7].astype(np.float)) )
entry.append( np.mean(np.array(table)[:,8].astype(np.float)) )
entry.append( np.mean(np.array(table)[:,9].astype(np.float)) )
entry.append( np.mean(np.array(table)[:,10].astype(np.float)) )
entry.append( np.mean(np.array(table)[:,11].astype(np.float)) )
table.append(entry)
print(" ")
print("="*160)
print(tabulate(table, headers=['S.No.', 'fg_class','bg_class','Epoch used','total_points', 'correct','accuracy','FTPT', 'FFPT', 'FTPF', 'FFPF', 'avg_img > 0.5'] ) )
print(" ")
print("="*160)
return table
# + id="M8ClgTOAbUQu"
train_table=[]
test_table1=[]
test_table2=[]
test_table3=[]
test_table4=[]
fg = [fg1,fg2,fg3]
bg = list(set([0,1,2,3,4,5,6,7,8,9])-set(fg))
# + id="TuIb2Y29kxWT" outputId="ad11f100-ef70-4574-e64a-cff438527a92" colab={"base_uri": "https://localhost:8080/", "height": 1000}
number_runs = 10
for i in range(number_runs):
fore_net = Module2().double()
fore_net = fore_net.to("cuda")
fore_net, epoch = training(train_loader_from_noise_train_mosaic_30k, fore_net)
correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half = testing(train_loader_from_noise_train_mosaic_30k, fore_net)
train_table = enter_into(train_table, i+1, correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half, fg, bg, str(epoch) )
correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half = testing(test_loader_from_noise_train_mosaic_30k, fore_net)
test_table1 = enter_into(test_table1, i+1, correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half , fg, bg )
correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half = testing(test_loader_from_noise_test_mosaic_10k, fore_net)
test_table2 = enter_into(test_table2, i+1, correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half, fg, bg )
correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half = testing(test_loader_from_true_train_mosaic_30k, fore_net)
test_table3 = enter_into(test_table3, i+1, correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half , fg, bg)
correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half = testing(test_loader_from_true_train_mosaic_10k, fore_net)
test_table4 = enter_into(test_table4, i+1, correct, total, ftpt, ffpt, ftpf, ffpf, alpha_more_half, fg, bg )
# + id="kloPmAalgpIz" outputId="d0992cae-0ecb-431f-f57a-c66ff1cb30a4" colab={"base_uri": "https://localhost:8080/", "height": 335}
train_table = add_average_entry(train_table)
# + id="00KPkU7EhPJj" outputId="ffe2a2a7-5df5-4b51-c56e-70458f302a90" colab={"base_uri": "https://localhost:8080/", "height": 335}
test_table1 = add_average_entry(test_table1)
# + id="pW_kUqi3hR6u" outputId="af3eb94c-f94f-4dc5-97a2-13bba29a37de" colab={"base_uri": "https://localhost:8080/", "height": 335}
test_table2 = add_average_entry(test_table2)
# + id="_ZlV6qErhUUL" outputId="d1af9a8e-a1f4-46b6-b184-ced299934314" colab={"base_uri": "https://localhost:8080/", "height": 335}
test_table3 = add_average_entry(test_table3)
# + id="BOvl6fUChV5j" outputId="014b1056-e727-4227-e9df-c2728083da41" colab={"base_uri": "https://localhost:8080/", "height": 335}
test_table4 = add_average_entry(test_table4)
# + id="nkyMi1VBpq9a"
# torch.save(fore_net.state_dict(),"/content/drive/My Drive/Research/mosaic_from_CIFAR_involving_bottop_eigen_vectors/fore_net_epoch"+str(epoch)+"_fg_used"+str(fg_used)+".pt")
| 31,984 |
/datasets/tiles/create_json.ipynb | 4e8aa003d3e536abe98c3a3665c0baa8993ce343 | [] | no_license | phananh1010/tile-super-resolution | https://github.com/phananh1010/tile-super-resolution | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,805 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python (env_pytorch_python3)
# language: python
# name: env_pytorch_python3
# ---
# +
import glob
import numpy as np
import os
import zipfile
import contextlib
import json
def get_filecount_from_zip(filepath):
with contextlib.closing(zipfile.ZipFile(filepath)) as archive:
count = len(archive.infolist())
return count
# +
filepath_list = glob.glob('./JPEGImages/*.zip')
filename_list = [item.split('/')[-1].replace('.zip', '') for item in filepath_list]
idx_list = list(range(len(filename_list)))
np.random.shuffle(idx_list)
filepath_list = [filepath_list[idx] for idx in idx_list]
filename_list = [filename_list[idx] for idx in idx_list]
# -
result = {filename_list[idx]:get_filecount_from_zip(filepath_list[idx])-3 for idx in list(range(len(filepath_list)))}
# +
PARTITION = 0
key_list = list(result.keys())
result_test = {item:result[item] for item in key_list[:PARTITION ]}
result_train = {item:result[item] for item in key_list[PARTITION :]}
print ('total samples: ', len(key_list))
# -
result_train_str = json.dumps(result_train)
with open('./train.json', 'w') as file:
file.write(result_train_str)
result_test_str = json.dumps(result_test)
with open('./test.json', 'w') as file:
file.write(result_train_str)
D and f not in modas:
modas.append(f)
print("\nThe mode is:\n>", modas)
print("\nHey: After performing your first operation it is necessary to restart the program or your results will be wrong!!")
if B == 3: #Standard derivation
Sample_mean = (sum_x) / (n-1)
R = Sample_mean**2
cuadrados = []
for dato in ACD:
r = (dato - Sample_mean)**2
cuadrados.append(r)
desviacion = ((sum(cuadrados)-R)/(n-2))**0.5
print("\nThe standard derivation is:\n>", desviacion)
print("\nHey: After performing your first operation it is necessary to restart the program or your results will be wrong!!")
# -
| 2,309 |
/fenics-notebook/tests/notebooks/fenics.ipynb | bce556fb3ddbff059a0208e669fd63ca66fd6d2f | [
"MIT"
] | permissive | ucphhpc/nbi-jupyter-docker-stacks | https://github.com/ucphhpc/nbi-jupyter-docker-stacks | 6 | 2 | MIT | 2023-03-27T07:08:42 | 2023-02-28T21:48:20 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 36,476 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
import os
import cv2
import json
import numpy as np
from detectron2.structures import BoxMode
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog
import random
from matplotlib import pyplot as plt
# +
def get_balloon_dicts(img_dir):
json_file = os.path.join(img_dir, "via_region_data.json")
with open(json_file) as f:
imgs_anns = json.load(f)
dataset_dicts = []
for idx, v in enumerate(imgs_anns.values()):
record = {}
filename = os.path.join(img_dir, v["filename"])
height, width = cv2.imread(filename).shape[:2]
record["file_name"] = filename
record["image_id"] = idx
record["height"] = height
record["width"] = width
annos = v["regions"]
objs = []
for _, anno in annos.items():
assert not anno["region_attributes"]
anno = anno["shape_attributes"]
px = anno["all_points_x"]
py = anno["all_points_y"]
poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)]
poly = [p for x in poly for p in x]
obj = {
"bbox": [np.min(px), np.min(py), np.max(px), np.max(py)],
"bbox_mode": BoxMode.XYXY_ABS,
"segmentation": [poly],
"category_id": 0,
}
objs.append(obj)
record["annotations"] = objs
dataset_dicts.append(record)
return dataset_dicts
dataset_path = "/opt/infilect/dev/datasets/balloon_dataset/balloon"
for d in ["train", "val"]:
DatasetCatalog.register("balloon_" + d, lambda d=d: get_balloon_dicts(dataset_path+"/"+ d))
MetadataCatalog.get("balloon_" + d).set(thing_classes=["balloon"], evaluator_type="coco")
balloon_metadata = MetadataCatalog.get("balloon_train")
# +
dataset_dicts = DatasetCatalog.get('balloon_train')
for d in random.sample(dataset_dicts, 3):
img = cv2.imread(d["file_name"])
v = Visualizer(img[:, :, ::-1], metadata=balloon_metadata, scale=0.5)
v = v.draw_dataset_dict(d)
plt.figure(figsize = (14, 10))
plt.imshow(cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))
plt.show()
# -
ex_col = 'Date').dropna()
benchmark_data = pd.read_csv('datasets/benchmark_data.csv', parse_dates= True, index_col = 'Date').dropna()
# + dc={"key": "11"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 2. A first glance at the data
# <p>Let's take a look the data to find out how many observations and variables we have at our disposal.</p>
# + dc={"key": "11"} tags=["sample_code"]
# Display summary for stock_data
print('Stocks\n')
stock_data.info()
stock_data.head()
# Display summary for benchmark_data
print('\nBenchmarks\n')
benchmark_data.info()
benchmark_data.head()
# + dc={"key": "18"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 3. Plot & summarize daily prices for Amazon and Facebook
# <p>Before we compare an investment in either Facebook or Amazon with the index of the 500 largest companies in the US, let's visualize the data, so we better understand what we're dealing with.</p>
# + dc={"key": "18"} tags=["sample_code"]
# visualize the stock_data
stock_data.plot(subplots = True, title = 'Stock Data')
# summarize the stock_data
stock_data.describe()
# + dc={"key": "25"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 4. Visualize & summarize daily values for the S&P 500
# <p>Let's also take a closer look at the value of the S&P 500, our benchmark.</p>
# + dc={"key": "25"} tags=["sample_code"]
# plot the benchmark_data
benchmark_data.plot(title = 'S&P 500')
# summarize the benchmark_data
benchmark_data.describe()
# + dc={"key": "32"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 5. The inputs for the Sharpe Ratio: Starting with Daily Stock Returns
# <p>The Sharpe Ratio uses the difference in returns between the two investment opportunities under consideration.</p>
# <p>However, our data show the historical value of each investment, not the return. To calculate the return, we need to calculate the percentage change in value from one day to the next. We'll also take a look at the summary statistics because these will become our inputs as we calculate the Sharpe Ratio. Can you already guess the result?</p>
# + dc={"key": "32"} tags=["sample_code"]
# calculate daily stock_data returns
stock_returns = stock_data.pct_change()
# plot the daily returns
stock_returns.plot()
# summarize the daily returns
stock_returns.describe()
# + dc={"key": "39"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 6. Daily S&P 500 returns
# <p>For the S&P 500, calculating daily returns works just the same way, we just need to make sure we select it as a <code>Series</code> using single brackets <code>[]</code> and not as a <code>DataFrame</code> to facilitate the calculations in the next step.</p>
# + dc={"key": "39"} tags=["sample_code"]
# calculate daily benchmark_data returns
# ... YOUR CODE FOR TASK 6 HERE ...
sp_returns = benchmark_data['S&P 500'].pct_change()
# plot the daily returns
sp_returns.plot()
# summarize the daily returns
sp_returns.describe()
# + dc={"key": "46"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 7. Calculating Excess Returns for Amazon and Facebook vs. S&P 500
# <p>Next, we need to calculate the relative performance of stocks vs. the S&P 500 benchmark. This is calculated as the difference in returns between <code>stock_returns</code> and <code>sp_returns</code> for each day.</p>
# + dc={"key": "46"}
print(sp_returns.head())
stock_returns.head()
# + dc={"key": "46"} tags=["sample_code"]
# calculate the difference in daily returns
excess_returns = stock_returns.sub(sp_returns, axis = 0)
# plot the excess_returns
excess_returns.plot()
# summarize the excess_returns
excess_returns.describe()
# + dc={"key": "53"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 8. The Sharpe Ratio, Step 1: The Average Difference in Daily Returns Stocks vs S&P 500
# <p>Now we can finally start computing the Sharpe Ratio. First we need to calculate the average of the <code>excess_returns</code>. This tells us how much more or less the investment yields per day compared to the benchmark.</p>
# + dc={"key": "53"} tags=["sample_code"]
# calculate the mean of excess_returns
avg_excess_return = excess_returns.mean()
# plot avg_excess_returns
avg_excess_return.plot.bar(title = 'Mean of the Return Difference')
# + dc={"key": "60"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 9. The Sharpe Ratio, Step 2: Standard Deviation of the Return Difference
# <p>It looks like there was quite a bit of a difference between average daily returns for Amazon and Facebook.</p>
# <p>Next, we calculate the standard deviation of the <code>excess_returns</code>. This shows us the amount of risk an investment in the stocks implies as compared to an investment in the S&P 500.</p>
# + dc={"key": "60"} tags=["sample_code"]
# calculate the standard deviations
sd_excess_return = excess_returns.std()
# plot the standard deviations
sd_excess_return.plot.bar(title = 'Standard Deviation of the Return Difference')
# + dc={"key": "67"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 10. Putting it all together
# <p>Now we just need to compute the ratio of <code>avg_excess_returns</code> and <code>sd_excess_returns</code>. The result is now finally the <em>Sharpe ratio</em> and indicates how much more (or less) return the investment opportunity under consideration yields per unit of risk.</p>
# <p>The Sharpe Ratio is often <em>annualized</em> by multiplying it by the square root of the number of periods. We have used daily data as input, so we'll use the square root of the number of trading days (5 days, 52 weeks, minus a few holidays): √252</p>
# + dc={"key": "67"} tags=["sample_code"]
# calculate the daily sharpe ratio
daily_sharpe_ratio = avg_excess_return.div(sd_excess_return)
# annualize the sharpe ratio
annual_factor = np.sqrt(252)
annual_sharpe_ratio = daily_sharpe_ratio.mul(annual_factor)
# plot the annualized sharpe ratio
annual_sharpe_ratio.plot.bar(title = 'Annualized Sharpe Ratio: Stocks vs S&P 500')
# + dc={"key": "74"} tags=["context"] run_control={"frozen": true} editable=false deletable=false
# ## 11. Conclusion
# <p>Given the two Sharpe ratios, which investment should we go for? In 2016, Amazon had a Sharpe ratio twice as high as Facebook. This means that an investment in Amazon returned twice as much compared to the S&P 500 for each unit of risk an investor would have assumed. In other words, in risk-adjusted terms, the investment in Amazon would have been more attractive.</p>
# <p>This difference was mostly driven by differences in return rather than risk between Amazon and Facebook. The risk of choosing Amazon over FB (as measured by the standard deviation) was only slightly higher so that the higher Sharpe ratio for Amazon ends up higher mainly due to the higher average daily returns for Amazon. </p>
# <p>When faced with investment alternatives that offer both different returns and risks, the Sharpe Ratio helps to make a decision by adjusting the returns by the differences in risk and allows an investor to compare investment opportunities on equal terms, that is, on an 'apples-to-apples' basis.</p>
# + dc={"key": "74"} tags=["sample_code"]
# Uncomment your choice.
buy_amazon = True
le.com/drive/15yMxBf1ltxrIaHCZUqqUWZBm6cqdcErF), i.e., the ${\rm TFIDF}$ term we consider for a movie $m$ in the set of movies $M$ and word $w$ is given by
#
# $$
# {\rm TFIDF}(m,w) = {\rm TF}(m,w) \:\times \:{\rm IDF}(w,M)
# $$
#
# Only TF_IDF values equal or higher than the __MIN\_TF_\_IDF__ threshold should be written to the output files. The standard value to use for __MIN\_TF_\_IDF__ is __0.1__ but you may adjust this value for testing purposes.
#
#
# __Important note__: you should __ONLY__ use the [Spark Data Frame API](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame) and [Spark SQL](https://docs.databricks.com/spark/latest/spark-sql/language-manual/select.html) in your code (__NOT__ Pandas, any other Spark or generic Python libraries).
#
#
#
# + id="eXBvfoQD1S4-" colab_type="code" colab={}
class Constants:
f = 'f'
f_max = 'f_max'
tf = 'TF'
n = 'n'
idf = 'IDF'
tf_idf = 'tfidf'
nr = 'nr'
def show(debug, dataframe, message=None, rows=10):
if debug:
if message:
print("> %s (%d rows)" % (message, dataframe.count()))
dataframe.show(rows)
def get_idf(data, word='word', doc='doc', nr=None, debug=False):
"""Calculates the Inverse Document Frequency (IDF) of a DataFrame
Args:
data: A DataFrame instance.
word: Column word for 'word'
doc: Column name for 'documents'
nr: number of documents in which 'word' appears
Returns:
DataFrame ('word', 'IDF, [nr])
"""
n_w_D = data\
.groupBy(word)\
.agg(F.countDistinct(doc).alias('n_w_D'))
show(debug, n_w_D.orderBy('n_w_D',ascending=False))
size_of_D = data.select(doc).distinct().count()
if debug: print("|D| = %d" % size_of_D)
IDF = n_w_D.withColumn(Constants.idf, F.log2(size_of_D / F.col('n_W_D')))
return IDF.withColumnRenamed('n_w_D', Constants.nr) if nr else IDF.drop('n_w_D')
def tf_idf(data, word, doc, debug):
# f - nr of times word has been associated with doc by user
# result -> (word, doc, Constants.f)
f = data.groupBy(word, doc)\
.agg(F.count(doc).alias(Constants.f))
show(debug, f, "group by ($word, $doc) count frequency done")
# data.orderBy(word).show()
# f.orderBy(word).show()
# f_max - maximum absolute frequency of any word used for doc
# result -> (doc, Constants.f_max)
f_max = f.groupBy(doc)\
.agg(F.max(Constants.f).alias(Constants.f_max))
show(debug, f_max, "Max frequency per movie done")
# call external function to calculate IDF
idf = get_idf(data, word, doc, Constants.nr, debug)
show(debug, idf, "IDF done")
# join Constants.f_max on doc, calculate TF, join with IDF on word
df = f.join(f_max, doc)\
.withColumn(Constants.tf, F.col(Constants.f) / F.col(Constants.f_max))\
.join(idf, word)
show(debug, df, "TF done")
# return dataframe with TF_IDF
return df.withColumn(Constants.tf_idf, df.TF * df.IDF)
# + id="SZjZBwN3jS-8" colab_type="code" outputId="18c31a8e-4a60-4ed5-f1fa-71301841b2af" colab={"base_uri": "https://localhost:8080/", "height": 295}
# Get TF-IDF for tags
word = 'tag'
wordFinal = 'word'
doc = 'movieId'
tfidf = tf_idf(tags, word, doc, False)
tfidf.cache()
# guarantee all columns are present
tfidf = tfidf.drop(Constants.f)\
.drop(Constants.f_max)\
.drop(Constants.tf)\
.drop(Constants.nr)\
.drop(Constants.idf)\
.withColumnRenamed(word, wordFinal)
# assert tfidf.columns == [word, doc, Constants.f, Constants.f_max, Constants.tf, Constants.nr, Constants.idf, Constants.tf_idf],\
assert tfidf.columns == [wordFinal, doc, Constants.tf_idf],\
"Columns do not match expected values for tfidfTags"
# preview the dataframe
tfidf.orderBy([Constants.f,Constants.tf_idf, doc,word], ascending=[0,0,1,1]).show()
# + [markdown] id="ZQpzAE9K9l9-" colab_type="text"
# ## Movie similarity based on the Jaccard index (TODO for bonus grading)
#
# For every pair of movies $m_1$ and $m_2$ compute a similary ratio based on the __Jaccard index__:
#
# $$
# {\rm JI}(m_1, m_2) = \frac{| {\rm urt}(m_1) \cap {\rm urt}(m_2)|}{ |{\rm urt}(m_1) \cup {\rm urt}(m_2)|}
# $$
#
# where ${\rm urt}(m)$ is defined as the set of users who have tagged or rated a movie $m$.
#
# For further reference on the Jaccard index metric see:
#
# - [Mining of Massive Data Sets, sec. 3.3.1](http://infolab.stanford.edu/%7Eullman/mmds/book.pdf)
# - [Wikipedia page for the Jaccard Index](https://en.wikipedia.org/wiki/Jaccard_index)
#
#
#
#
# + [markdown] id="7xLGy7TCW_hz" colab_type="text"
# Calculates the Jaccard index to measure similarity between movies based on user ratings.
#
# Linking a movie means rating >= 4.0
# + id="UdvDfhozHnMx" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 469} outputId="dc788cfb-c73b-4f62-8cd4-1cf3de1128c6"
def jaccard_index(df1, df2, column="user", sort="movie"):
# sort - prefix of the columns to sort
# column - prefix of the columns to use as sets
# 1. product - cross join to get movie1, movie2
# 2. count intersect set
# 3. count union set
# 4. calculate Jaccard Index
# 5. remove unwanted columns
users = [column + "1", column + "2"]
sorts = ["%s1" % (sort), "%s2" % (sort)]
df1 = df1.crossJoin(df2)\
.filter("%s < %s" % (sorts[0], sorts[1]))
df1 = df1.withColumn("user", F.size(F.array_union(users[0], users[1])))\
.withColumn("index", F.size(F.array_intersect(users[0], users[1])))\
.withColumn("jaccard_index", F.col("index")/F.col("user"))
return df1.drop(users[0], users[1])
def movieSimilarity(ratings, minRatings=10, threshold=4.0, debug=False):
ratings = ratings.filter("rating >= %i" % (threshold))
ratings=ratings.drop("rating")
show(debug, ratings, "like's dataframe")
# filter movies with less than minRatings ratings
# obtain set of users that LIKED a given movie
df_m1 = ratings.groupBy("movieId")\
.agg(F.collect_set(ratings.userId).alias("user1"))\
.withColumnRenamed("movieId", "movie1")\
.filter(minRatings < F.size("user1"))
show(debug, df_m1, "movie with users that liked it")
# duplicate dataframe for cross join
df_m2 = df_m1.withColumnRenamed("user1", "user2")\
.withColumnRenamed("movie1", "movie2")
show(debug, df_m2, "movie with users that liked it - copy renamed")
return jaccard_index(df_m1, df_m2)
ji = movieSimilarity(ratings).orderBy(['index','jaccard_index','movie1','movie2'], ascending=[0,0,1,1])
assert ji.columns == ["movie1", "movie2", "user", "index", "jaccard_index"], "unexpected column value"
ji.show()
# + [markdown] id="2L6Z9EdPbjkv" colab_type="text"
# ## Write output data to Parquet files and generate ZIP file
# + id="vyST7eVyVlXt" colab_type="code" outputId="75bf2097-e2cf-4549-8864-c09b583c0b05" colab={"base_uri": "https://localhost:8080/", "height": 451}
# Clean up first
# !rm -fr "$DATASET"/output
# !rm -f "$DATASET"/"$OUTPUT_ZIP_FILE"
if DEBUG:
# !ls -l $DATASET
writeParquet(movies_agg, DATASET + '/output/' + 'movies_agg.parquet')
writeParquet(tfidf, DATASET + '/output/' + 'tfidf.parquet')
# bonus
writeParquet(ji, DATASET + '/output/' + 'jaccardIndex.parquet')
if DEBUG:
print('Creating ZIP file ...')
# !cd "$DATASET"/output && zip -9qr ../"$OUTPUT_ZIP_FILE" .
if DEBUG:
# !ls -l $DATASET "$DATASET"/output
# + [markdown] id="0BZ3e-2m1G4k" colab_type="text"
# ## Copy output ZIP file to output bucket
# + id="oG2umQ870TZ3" colab_type="code" outputId="4698ba3d-f03c-4495-92e4-8ff27f8950be" colab={"base_uri": "https://localhost:8080/", "height": 69}
# ! gsutil cp $DATASET/output.zip gs://"$OUTPUT_BUCKET"/"$DATASET"/output.zip
# + [markdown] id="rcCm38U9w8_h" colab_type="text"
# ## Copy Parquet files to output bucket (optional)
# + id="-Mk_2_abxKTm" colab_type="code" outputId="acb858a5-3310-4a5a-ca5a-8b88f9be06d3" colab={"base_uri": "https://localhost:8080/", "height": 1000}
if COPY_PARQUET_FILES_TO_OUTPUT_BUCKET:
# ! gsutil -m cp -r $DATASET/output/movies_agg.parquet gs://"$OUTPUT_BUCKET"/"$DATASET"/
# ! gsutil -m cp -r $DATASET/output/tfidf.parquet gs://"$OUTPUT_BUCKET"/"$DATASET"/
# ! gsutil -m cp -r $DATASET/output/jaccardIndex.parquet gs://"$OUTPUT_BUCKET"/"$DATASET"/
# + [markdown] id="dJbP3yLDCTgx" colab_type="text"
# ## Send PubSub cloud message
# + [markdown] id="WDJ1dt_5CdJh" colab_type="text"
# This will trigger the LCF cloud function.
# + id="n5GwFGmV5QF4" colab_type="code" outputId="991cf1be-d58d-4099-c7d0-2703f30dc8bf" colab={"base_uri": "https://localhost:8080/", "height": 52}
if SEND_PUBSUB_MESSAGE:
# !gcloud pubsub topics publish $PUBSUB_TOPIC --message $DATASET
| 18,925 |
/exercise-feature-selection.ipynb | 998aae66bed3001e1a6a697bab3025ac1ca5c04f | [] | no_license | bhu-v/Feature-Engineering | https://github.com/bhu-v/Feature-Engineering | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 18,817 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# **This notebook is an exercise in the [Feature Engineering](https://www.kaggle.com/learn/feature-engineering) course. You can reference the tutorial at [this link](https://www.kaggle.com/matleonard/feature-selection).**
#
# ---
#
# # Introduction
#
# In this exercise you'll use some feature selection algorithms to improve your model. Some methods take a while to run, so you'll write functions and verify they work on small samples.
#
# To begin, run the code cell below to set up the exercise.
# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.feature_engineering.ex4 import *
# Then run the following cell. It takes a minute or so to run.
# +
import numpy as np
import pandas as pd
from sklearn import preprocessing, metrics
import lightgbm as lgb
import os
clicks = pd.read_parquet('../input/feature-engineering-data/baseline_data.pqt')
data_files = ['count_encodings.pqt',
'catboost_encodings.pqt',
'interactions.pqt',
'past_6hr_events.pqt',
'downloads.pqt',
'time_deltas.pqt',
'svd_encodings.pqt']
data_root = '../input/feature-engineering-data'
for file in data_files:
features = pd.read_parquet(os.path.join(data_root, file))
clicks = clicks.join(features)
def get_data_splits(dataframe, valid_fraction=0.1):
dataframe = dataframe.sort_values('click_time')
valid_rows = int(len(dataframe) * valid_fraction)
train = dataframe[:-valid_rows * 2]
# valid size == test size, last two sections of the data
valid = dataframe[-valid_rows * 2:-valid_rows]
test = dataframe[-valid_rows:]
return train, valid, test
def train_model(train, valid, test=None, feature_cols=None):
if feature_cols is None:
feature_cols = train.columns.drop(['click_time', 'attributed_time',
'is_attributed'])
dtrain = lgb.Dataset(train[feature_cols], label=train['is_attributed'])
dvalid = lgb.Dataset(valid[feature_cols], label=valid['is_attributed'])
param = {'num_leaves': 64, 'objective': 'binary',
'metric': 'auc', 'seed': 7}
num_round = 1000
print("Training model!")
bst = lgb.train(param, dtrain, num_round, valid_sets=[dvalid],
early_stopping_rounds=20, verbose_eval=False)
valid_pred = bst.predict(valid[feature_cols])
valid_score = metrics.roc_auc_score(valid['is_attributed'], valid_pred)
print(f"Validation AUC score: {valid_score}")
if test is not None:
test_pred = bst.predict(test[feature_cols])
test_score = metrics.roc_auc_score(test['is_attributed'], test_pred)
return bst, valid_score, test_score
else:
return bst, valid_score
# -
# ## Baseline Score
#
# Let's look at the baseline score for all the features we've made so far.
train, valid, test = get_data_splits(clicks)
_, baseline_score = train_model(train, valid)
# ### 1) Which data to use for feature selection?
#
# Since many feature selection methods require calculating statistics from the dataset, should you use all the data for feature selection?
#
# Run the following line after you've decided your answer.
# Check your answer (Run this code cell to receive credit!)
q_1.solution()
# Now we have 91 features we're using for predictions. With all these features, there is a good chance the model is overfitting the data. We might be able to reduce the overfitting by removing some features. Of course, the model's performance might decrease. But at least we'd be making the model smaller and faster without losing much performance.
# ### 2) Univariate Feature Selection
#
# Below, use `SelectKBest` with the `f_classif` scoring function to choose 40 features from the 91 features in the data.
# +
from sklearn.feature_selection import SelectKBest, f_classif
feature_cols = clicks.columns.drop(['click_time', 'attributed_time', 'is_attributed'])
train, valid, test = get_data_splits(clicks)
# Create the selector, keeping 40 features
selector = SelectKBest(f_classif, k=40)
X_new = selector.fit_transform(train[feature_cols], train['is_attributed'])
# Get back the features we've kept, zero out all other features
selected_features = pd.DataFrame(selector.inverse_transform(X_new),
index=train.index,
columns=feature_cols)
# Dropped columns have values of all 0s, so var is 0, drop them
dropped_columns = selected_features.columns[selected_features.var() == 0]
# Check your answer
q_2.check()
# -
# Uncomment these lines if you need some guidance
# q_2.hint()
q_2.solution()
_ = train_model(train.drop(dropped_columns, axis=1),
valid.drop(dropped_columns, axis=1))
# ### 3) The best value of K
#
# With this method we can choose the best K features, but we still have to choose K ourselves. How would you find the "best" value of K? That is, you want it to be small so you're keeping the best features, but not so small that it's degrading the model's performance.
#
# Run the following line after you've decided your answer.
# Check your answer (Run this code cell to receive credit!)
q_3.solution()
# ### 4) Use L1 regularization for feature selection
#
# Now try a more powerful approach using L1 regularization. Implement a function `select_features_l1` that returns a list of features to keep.
#
# Use a `LogisticRegression` classifier model with an L1 penalty to select the features. For the model, set:
# - the random state to 7,
# - the regularization parameter to 0.1,
# - and the solver to `'liblinear'`.
#
# Fit the model then use `SelectFromModel` to return a model with the selected features.
#
# The checking code will run your function on a sample from the dataset to provide more immediate feedback.
# +
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import SelectFromModel
def select_features_l1(X, y):
logistic = LogisticRegression(C=0.1, penalty="l1", random_state=7, solver='liblinear').fit(X, y)
model = SelectFromModel(logistic, prefit=True)
X_new = model.transform(X)
# Get back the kept features as a DataFrame with dropped columns as all 0s
selected_features = pd.DataFrame(model.inverse_transform(X_new),
index=X.index,
columns=X.columns)
# Dropped columns have values of all 0s, keep other columns
cols_to_keep = selected_features.columns[selected_features.var() != 0]
return cols_to_keep
# Check your answer
q_4.check()
# -
# Uncomment these if you're feeling stuck
#q_4.hint()
q_4.solution()
# +
n_samples = 10000
X, y = train[feature_cols][:n_samples], train['is_attributed'][:n_samples]
selected = select_features_l1(X, y)
dropped_columns = feature_cols.drop(selected)
_ = train_model(train.drop(dropped_columns, axis=1),
valid.drop(dropped_columns, axis=1))
# -
# ### 5) Feature Selection with Trees
#
# Since we're using a tree-based model, using another tree-based model for feature selection might produce better results. What would you do different to select the features using a trees classifier?
#
# Run the following line after you've decided your answer.
# Check your answer (Run this code cell to receive credit!)
q_5.solution()
# ### 6) Top K features with L1 regularization
#
# Here you've set the regularization parameter `C=0.1` which led to some number of features being dropped. However, by setting `C` you aren't able to choose a certain number of features to keep. What would you do to keep the top K important features using L1 regularization?
#
# Run the following line after you've decided your answer.
# Check your answer (Run this code cell to receive credit!)
q_6.solution()
# Congratulations on finishing this course! To keep learning, check out the rest of [our courses](https://www.kaggle.com/learn/overview). The machine learning explainability and deep learning courses are great next skills to learn!
# ---
#
#
#
#
# *Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum/161443) to chat with other Learners.*
| 8,483 |
/Pipeline.ipynb | 7b2d08a30e04281a230f479fc004e2c3372906ee | [] | no_license | annabaydina/ieee-fraud | https://github.com/annabaydina/ieee-fraud | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 17,999 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
# %load_ext autoreload
# %autoreload 2
main_path = r'lib'
data_path = main_path+'/data'
import sys
sys.path.append(main_path)
from lib import *
# from lib.ieee_fraud_nodes import *
from typing import List, Set, Dict, Optional, Any, Tuple, Type, Union
from lib.io import *
import os
main_dir = main_path
data_dir = f'{main_dir}/Data'
# -
# ## Processing raw, add NanCounts and reduce mem usage
# +
p = Pipeline(working_folder=f'{main_dir}/Snapshots/1/01-JoinReduceMem')
p.add_node(IEEEFraudTransactionLoaderNode, None, 'transactions',
params={
'input_directory': data_dir
})
p.add_node(IEEEFraudIdentityLoaderNode, None, 'identity',
params={
'input_directory': data_dir
})
p.add_node(AddNaNCountNode, 'transactions', 'transactions',
params={
'name': 'NanTransactionCount'
})
p.add_node(AddNaNCountNode, 'identity', 'identity',
params={
'name': 'NanIdentityCount'
})
p.add_node(JoinNode,
('transactions', 'identity'),
'data',
params={
'on': 'TransactionID'
})
p.add_node(EraserNode, params={
'remove_keys': ['transactions', 'identity']
})
p.add_node(ReduceMemoryUsageNode,
'data', 'data',
params={
'verbose': True
})
p.run(verbose=True)
print(p.data['data'].columns)
p.save_data('pickle')
# -
# ## Add basic feats
# +
data_dir =f'{main_dir}/Snapshots/1/01-JoinReduceMem'
p = Pipeline(working_folder=f'{main_dir}/Snapshots/1/full-fe')
p.add_node(LoaderNode, None, 'data',
params={
'input_directory': data_dir,
'file': 'data.pkl'
})
p.add_node(TimeTransformNode, 'data')
p.add_node(SomeAggregatesFromAnyaNode, 'data')
p.add_node(EmailTransformNode, 'data')
numerical_cols = ['id_%02d' % i for i in range(1,12)] + ["V%d"%i for i in range(1,340)] + ["D%d"%i for i in range(1,16)] + ["C%d"%i for i in range(1,15)] + ['dist1','TransactionAmt', 'NanIdentityCount', 'NanTransactionCount', '_Weekdays', '_Hours', '_Days', 'Date', 'dist2']
label_cols = ['M1', 'M2', 'M3','M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'card4', 'card6', 'ProductCD'] + ['id_%02d'%i for i in (12,15,16,28,29,32,34,35,36,37,38)]
label_cols += ['id_13', 'id_14', 'id_17', 'id_18', 'id_19', 'id_20', 'id_21', 'id_22', 'id_23', 'id_24', 'id_25', 'id_26', 'id_27',
'id_30', 'id_31', 'id_33', 'DeviceType', 'DeviceInfo', 'P_emaildomain',
'R_emaildomain', 'card1', 'card2', 'card3', 'card5', 'addr1', 'addr2',
'P_emaildomain_bin', 'P_emaildomain_suffix', 'R_emaildomain_bin', 'R_emaildomain_suffix']
#add_from Anya Features
numerical_cols+=['TransactionDT', 'Weekdays', 'Hours', 'Days','DT_M', 'DT_W', 'DT_D','id_02_to_mean_card1', 'id_02_to_mean_card4', 'id_02_to_std_card1', 'id_02_to_std_card4', 'D15_to_mean_card1', 'D15_to_mean_card4', 'D15_to_std_card1', 'D15_to_std_card4', 'D15_to_mean_addr1', 'D15_to_std_addr1', 'TransactionAmt_to_mean_card1', 'TransactionAmt_to_mean_card4', 'TransactionAmt_to_std_card1', 'TransactionAmt_to_std_card4', 'TransactionAmt_decimal','nulls1','screen_width', 'screen_height','TransactionAmt_Log', 'card1_count_full', 'Transaction_day_of_week', 'Transaction_hour', 'id_01_count_dist', 'id_31_count_dist', 'id_33_count_dist', 'id_36_count_dist', 'card2_count_full', 'card3_count_full', 'card4_count_full', 'card5_count_full', 'card6_count_full', 'addr1_count_full', 'addr2_count_full', 'id_36_count_full', 'M_sum', 'M_na', 'ProductCD_target_mean', 'M4_target_mean', 'card1_TransactionAmt_mean', 'card1_TransactionAmt_std', 'card2_TransactionAmt_mean', 'card2_TransactionAmt_std', 'card3_TransactionAmt_mean', 'card3_TransactionAmt_std', 'card5_TransactionAmt_mean', 'card5_TransactionAmt_std', 'uid_TransactionAmt_mean', 'uid_TransactionAmt_std', 'uid2_TransactionAmt_mean', 'uid2_TransactionAmt_std', 'uid3_TransactionAmt_mean', 'uid3_TransactionAmt_std', 'card1_fq_enc', 'card2_fq_enc', 'card3_fq_enc', 'card5_fq_enc', 'C1_fq_enc', 'C2_fq_enc', 'C3_fq_enc', 'C4_fq_enc', 'C5_fq_enc', 'C6_fq_enc', 'C7_fq_enc', 'C8_fq_enc', 'C9_fq_enc', 'C10_fq_enc', 'C11_fq_enc', 'C12_fq_enc', 'C13_fq_enc', 'C14_fq_enc', 'D1_fq_enc', 'D2_fq_enc', 'D3_fq_enc', 'D4_fq_enc', 'D5_fq_enc', 'D6_fq_enc', 'D7_fq_enc', 'D8_fq_enc', 'addr1_fq_enc', 'addr2_fq_enc', 'dist1_fq_enc', 'dist2_fq_enc', 'P_emaildomain_fq_enc', 'R_emaildomain_fq_enc', 'DeviceInfo_fq_enc', 'id_30_fq_enc', 'version_id_30_fq_enc', 'version_id_31_fq_enc', 'id_33_fq_enc', 'uid_fq_enc', 'uid2_fq_enc', 'uid3_fq_enc', 'DT_M_total', 'DT_W_total', 'DT_D_total', 'card1_DT_M', 'card2_DT_M', 'card3_DT_M', 'card5_DT_M', 'uid_DT_M', 'uid2_DT_M', 'uid3_DT_M', 'card1_DT_W', 'card2_DT_W', 'card3_DT_W', 'card5_DT_W', 'uid_DT_W', 'uid2_DT_W', 'uid3_DT_W', 'card1_DT_D', 'card2_DT_D', 'card3_DT_D', 'card5_DT_D', 'uid_DT_D', 'uid2_DT_D', 'uid3_DT_D']
label_cols+=['isNight','lastest_browser', 'device_name', 'device_version', 'OS_id_30', 'version_id_30', 'browser_id_31', 'version_id_31', 'had_id', 'id_02__id_20', 'id_02__D8', 'D11__DeviceInfo', 'DeviceInfo__P_emaildomain', 'P_emaildomain__C2', 'card2__dist1', 'card1__card5', 'card2__id_20', 'card5__P_emaildomain', 'addr1__card1','uid', 'uid2', 'uid3']
strange_cols = ['Transaction_day_of_week', 'Transaction_hour']
p.data['numerical_columns'] = numerical_cols
p.data['categorical_columns'] = label_cols
p.data['useless_columns'] = strange_cols
p.add_node(AddDeviceOSInfoNode, ('data', 'numerical_columns', 'categorical_columns'))
p.add_node(AddCardIdNode, ('data', 'numerical_columns', 'categorical_columns'))
p.add_node(AddNewCardIdNode, ('data', 'numerical_columns', 'categorical_columns'))
p.add_node(AddTemporalAggregates, input_key=('data', 'numerical_columns', 'categorical_columns'),
params={
'features':['TransactionAmt', 'C5', 'C8'],
'group_by': 'new_card_id'
})
p.save()
p.run()
p.save_data()
# del p
# gc.collect()
# -
# ## Device info node
add = p.data['data'][['new_card_id', 'start_date']]
add.to_pickle(f'{p.working_folder}/new_card_id.pkl')
# +
data_dir =f'{main_dir}/Snapshots/1/02-FeatureEngineering'
p = Pipeline(working_folder=f'{main_dir}/Snapshots/1/03-AddBrowser')
p.add_node(LoaderNode, None, 'data',
params={
'input_directory': data_dir,
'file': 'data.pkl'
})
p.add_node(LoaderNode, None, 'numerical_columns',
params={
'input_directory': data_dir,
'file': 'numerical_columns.yaml'
})
p.add_node(LoaderNode, None, 'categorical_columns',
params={
'input_directory': data_dir,
'file': 'categorical_columns.yaml'
})
p.add_node(AddDeviceOSInfoNode, ('data', 'numerical_columns', 'categorical_columns'))
p.save()
p.run(verbose=True)
p.save_data()
# -
# ## Add temporal aggregates
# +
data_dir =f'{main_dir}/Snapshots/1/99'
p = Pipeline(working_folder=f'{main_dir}/Snapshots/1/04-Temporal')
p.add_node(LoaderNode, None, 'data',
params={
'input_directory': data_dir,
'file': 'data.pkl'
})
p.add_node(LoaderNode, None, 'numerical_columns',
params={
'input_directory': data_dir,
'file': 'numerical_columns.yaml'
})
p.add_node(LoaderNode, None, 'categorical_columns',
params={
'input_directory': data_dir,
'file': 'categorical_columns.yaml'
})
p.add_node(AddTemporalAggregates, input_key=('data', 'numerical_columns', 'categorical_columns'),
params={
'features':['TransactionAmt']
}
)
p.save()
p.run(verbose=True)
p.save_data()
# -
# ## Encode categorial features
# +
data_dir =f'{main_dir}/Snapshots/1/99'
p = Pipeline(working_folder=f'{main_dir}/Snapshots/1/05-LabelEncoded')
p.add_node(LoaderNode, None, 'data',
params={
'input_directory': data_dir,
'file': 'data.pkl'
})
p.add_node(LoaderNode, None, 'numerical_columns',
params={
'input_directory': data_dir,
'file': 'numerical_columns.yaml'
})
p.add_node(LoaderNode, None, 'categorical_columns',
params={
'input_directory': data_dir,
'file': 'categorical_columns.yaml'
})
p.add_node(LabelEncoderNode, input_key=('data', 'numerical_columns', 'categorical_columns'),
output_key="label_encoded_data"
)
p.add_node(EraserNode, params={
'remove_keys': ['data']
})
p.save()
p.run(verbose=True)
p.save_data()
# -
p.save_data()
| 9,025 |
/Python Assignment/Python Programming Basic Assignment/Programming Assingment_19.ipynb | ed0b70b09f443fc26f2e42fd36933901ee2bc71d | [] | no_license | calkikhunt/iNeuron_ai | https://github.com/calkikhunt/iNeuron_ai | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 3,769 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
#Q1. Create a function that takes a string and returns a string in which each character is repeated once.
def double_char(a):
x = ""
for i in a:
x += i+i
return x
double_char("Hello World!")
#Q2. Create a function that reverses a boolean value and returns the string "boolean expected"
# if another variable type is given.
def reverse(num):
if type(num) != bool:
return 'boolean expected'
if num is True:
return False
return True
reverse(True)
#Q3. Create a function that returns the thickness (in meters) of a piece of paper after folding
# it n number of times. The paper starts off with a thickness of 0.5mm.
def num_layers(num):
a = 0.5
for i in range(21):
a = a * 2
return a/1000
num_layers(21)
#Q4. Create a function that takes a single string as argument and returns an ordered list
# containing the indices of all capital letters in the string.
def index_of_caps(x):
a = []
for i in range(len(x)):
if x[i].isupper():
a.append(i)
return a
index_of_caps("eQuINoX")
#Q5. Using list comprehensions, create a function that finds all even numbers from 1 to the given number.
def find_even_nums(n):
return [i for i in range(1,n+1) if i % 2 ==0]
find_even_nums(8)
| 1,565 |
/class material/Unit2/prompts/Class 6 - pd.merge().ipynb | 5f63eb6ab3e1e5abe41d0d6c3c2660c74083f19d | [] | no_license | JonathanBechtel/DAT-10-14 | https://github.com/JonathanBechtel/DAT-10-14 | 1 | 3 | null | 2019-11-04T21:27:43 | 2019-11-03T18:11:20 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 43,989 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# ## NumPy
#
# ### Arrays NumPy - Objetos multidimensionais com N dimesões
# ### Matrizes NumPy - Objetos estritamente com 2 dimensões
#
# A principal vantagem de utilizar matrizes com NumPy, é que este tipo de objeto possui convenientes notações para multiplicação de matrizes.
# Arrays e Matrizes NumPy possuem o atributo T para retornar a transposta da matriz, enquanto objetos do tipo matriz possuem adicionalmente os atributos I (Inversa) e H (Transposta Conjugada).
#
# Existem diferenças em operações de álgebra linear entre arrays e matrizes.
# Muitas funções NumPy retornam arrays e não matrizes como objetos resultante.
import sys
import numpy as np
print(sys.version)
np.__version__
# ## Criando Matrizes
mat1 = np.matrix('1, 2, 3; 4, 5, 6')
print(mat1)
type(mat1)
mat2 = np.matrix([[1, 2, 3], [4, 5, 6]])
print(mat2)
mat3 = np.matrix([ [0, 10, 0, 0, 0], [0, 0, 20, 0, 0], [0, 0, 0, 30, 0], [0, 0, 0, 0, 40], [0, 0, 0, 0, 0] ])
print(mat3)
mat3[2, 3]
# ## Matriz Esparsa
# Uma matriz esparsa possui uma grande quantidade de elementos que valem zero (ou não presentes, ou não necessários).
# Matrizes esparsas tem aplicações em problemas de engenharia, física (por exemplo, o método das malhas para resolução de circulos elétricos ou sistemas de equações lineares).
# Também tem aplicações em computação, como por exemplo em tecnologias de armazenamento de dados.
#
# A matriz esparsa é implementada através de um conjunto de listas ligadas que apontam para elementos diferentes de zero.
# De forma que os elementos que possuem valor zero não são armazenados.
import scipy.sparse
linhas = np.array([0, 1, 2 , 3])
colunas = np.array([1, 2, 3, 4])
valores = np.array([10, 20, 30, 40])
mat4 = scipy.sparse.coo_matrix((valores, (linhas, colunas)))
print(mat4)
print(mat4.todense())
scipy.sparse.isspmatrix_coo(mat4)
# ## Operações com Arrays e Matrizes
a = np.array([[1, 2], [3, 4]])
a
a * a
A = np.mat(a)
A
A * A
# $$\boxed{
# \begin{align}
# \begin{pmatrix} 1 & 2 \\ 3 & 4 \end{pmatrix} &
# \begin{pmatrix} 1 & 2 \\ 3 & 4 \end{pmatrix} =
# \begin{pmatrix} 7 & 10 \\ 15 & 22 \end{pmatrix}
# \end{align}
# }$$
from IPython.display import Image
Image('./images/Matriz.png')
np.dot(a, a)
# Convertendo um Array para Matriz
mat5 = np.asmatrix(a)
mat5
mat5 * mat5
# Convertendo uma Matriz para um Array
array2 = np.array(mat5)
array2
array2 * array2
means W1's shape was (2,2), b1 was (1,2), W2 was (2,1) and b2 was (1,1). Now you have to generalize it!
- In the for loop, use parameters['W' + str(l)] to access Wl, where l is the iterative integer.
"""
np.random.seed(3)
parameters = {}
L = len(layer_dims) # number of layers in the network
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) / np.sqrt(layer_dims[l-1])
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
assert(parameters['W' + str(l)].shape == layer_dims[l], layer_dims[l-1])
assert(parameters['W' + str(l)].shape == layer_dims[l], 1)
return parameters
def forward_propagation(X, parameters):
"""
Implements the forward propagation (and computes the loss) presented in Figure 2.
Arguments:
X -- input dataset, of shape (input size, number of examples)
parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3":
W1 -- weight matrix of shape ()
b1 -- bias vector of shape ()
W2 -- weight matrix of shape ()
b2 -- bias vector of shape ()
W3 -- weight matrix of shape ()
b3 -- bias vector of shape ()
Returns:
loss -- the loss function (vanilla logistic loss)
"""
# retrieve parameters
W1 = parameters["W1"]
b1 = parameters["b1"]
W2 = parameters["W2"]
b2 = parameters["b2"]
W3 = parameters["W3"]
b3 = parameters["b3"]
# LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID
Z1 = np.dot(W1, X) + b1
A1 = relu(Z1)
Z2 = np.dot(W2, A1) + b2
A2 = relu(Z2)
Z3 = np.dot(W3, A2) + b3
A3 = sigmoid(Z3)
cache = (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3)
return A3, cache
def backward_propagation(X, Y, cache):
"""
Implement the backward propagation presented in figure 2.
Arguments:
X -- input dataset, of shape (input size, number of examples)
Y -- true "label" vector (containing 0 if cat, 1 if non-cat)
cache -- cache output from forward_propagation()
Returns:
gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables
"""
m = X.shape[1]
(Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache
dZ3 = A3 - Y
dW3 = 1./m * np.dot(dZ3, A2.T)
db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)
dA2 = np.dot(W3.T, dZ3)
dZ2 = np.multiply(dA2, np.int64(A2 > 0))
dW2 = 1./m * np.dot(dZ2, A1.T)
db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)
dA1 = np.dot(W2.T, dZ2)
dZ1 = np.multiply(dA1, np.int64(A1 > 0))
dW1 = 1./m * np.dot(dZ1, X.T)
db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)
gradients = {"dZ3": dZ3, "dW3": dW3, "db3": db3,
"dA2": dA2, "dZ2": dZ2, "dW2": dW2, "db2": db2,
"dA1": dA1, "dZ1": dZ1, "dW1": dW1, "db1": db1}
return gradients
def update_parameters(parameters, grads, learning_rate):
"""
Update parameters using gradient descent
Arguments:
parameters -- python dictionary containing your parameters:
parameters['W' + str(i)] = Wi
parameters['b' + str(i)] = bi
grads -- python dictionary containing your gradients for each parameters:
grads['dW' + str(i)] = dWi
grads['db' + str(i)] = dbi
learning_rate -- the learning rate, scalar.
Returns:
parameters -- python dictionary containing your updated parameters
"""
n = len(parameters) // 2 # number of layers in the neural networks
# Update rule for each parameter
for k in range(n):
parameters["W" + str(k+1)] = parameters["W" + str(k+1)] - learning_rate * grads["dW" + str(k+1)]
parameters["b" + str(k+1)] = parameters["b" + str(k+1)] - learning_rate * grads["db" + str(k+1)]
return parameters
def predict(X, y, parameters):
"""
This function is used to predict the results of a n-layer neural network.
Arguments:
X -- data set of examples you would like to label
parameters -- parameters of the trained model
Returns:
p -- predictions for the given dataset X
"""
m = X.shape[1]
p = np.zeros((1,m), dtype = np.int)
# Forward propagation
a3, caches = forward_propagation(X, parameters)
# convert probas to 0/1 predictions
for i in range(0, a3.shape[1]):
if a3[0,i] > 0.5:
p[0,i] = 1
else:
p[0,i] = 0
# print results
#print ("predictions: " + str(p[0,:]))
#print ("true labels: " + str(y[0,:]))
print("Accuracy: " + str(np.mean((p[0,:] == y[0,:]))))
return p
def compute_cost(a3, Y):
"""
Implement the cost function
Arguments:
a3 -- post-activation, output of forward propagation
Y -- "true" labels vector, same shape as a3
Returns:
cost - value of the cost function
"""
m = Y.shape[1]
logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)
cost = 1./m * np.nansum(logprobs)
return cost
def load_dataset():
train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
train_set_x_orig = np.array(train_datas | 8,155 |
/BikeSharing_FE.ipynb | c49f5c5d65bd67358a40c00ae0a55375415e6291 | [] | no_license | TianYu-hpu/machine_learning | https://github.com/TianYu-hpu/machine_learning | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 913,320 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Input, Dense, Lambda
from keras.models import Model
from keras import backend as K
from keras import objectives
from keras.datasets import fashion_mnist
batch_size = 100
original_dim = 784
intermediate_dim = 256
latent_dim = 2
epochs = 50
x = Input(shape=(original_dim,))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
z_log_var = Dense(latent_dim)(h)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0.)
return z_mean + K.exp(z_log_var / 2) * epsilon
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
def vae_loss(x, x_decoded_mean):
xent_loss = original_dim * objectives.binary_crossentropy(x, x_decoded_mean)
kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return xent_loss + kl_loss
vae = Model(x, x_decoded_mean)
vae.compile(optimizer='rmsprop', loss=vae_loss)
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
vae.fit(x_train, x_train,
shuffle=True,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test, x_test))
# +
encoder = Model(x, z_mean)
x_test_encoded = encoder.predict(x_test, batch_size=batch_size)
plt.figure(figsize=(6, 6))
plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)
plt.colorbar()
plt.show()
# +
decoder_input = Input(shape=(latent_dim,))
_h_decoded = decoder_h(decoder_input)
_x_decoded_mean = decoder_mean(_h_decoded)
generator = Model(decoder_input, _x_decoded_mean)
n = 20
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
grid_x = np.linspace(-3, 3, n)
grid_y = np.linspace(-3, 3, n)
for i, xi in enumerate(grid_x):
for j, yi in enumerate(grid_y):
z_sample = np.array([[yi, xi]])
x_decoded = generator.predict(z_sample)
digit = x_decoded[0].reshape(digit_size, digit_size)
figure[(n - i - 1) * digit_size: (n - i) * digit_size,
j * digit_size: (j + 1) * digit_size] = digit
plt.figure(figsize=(10, 10))
plt.imshow(figure)
plt.show()
d = model.predict(X)
erro_sklearn = compute_error_for_line_given_points(b0, b1, x, y)
print("b0 = {}, b1 = {}, error = {}".format(b0, b1, compute_error_for_line_given_points(b0, b1, x, y)))
error = erro_manual-erro_sklearn
print(error)
# ### Questão 4 - A)
# %%time
x = points[:, 0]
y = points[:, 1]
learning_rate = 0.01
initial_b0 = 0 # y-intercept inicial
initial_b1 = 0 # inclinação inicial
num_iterations = 390000
erro_manual = run(x, y, initial_b0, initial_b1, learning_rate, num_iterations)
# ###### R: retorna valores 'não numéricos'
# ### Questão 4 - B)
# %%time
x = points[:, 0]
y = points[:, 1]
learning_rate = 0.01
initial_b0 = 0 # y-intercept inicial
initial_b1 = 0 # inclinação inicial
num_iterations = 70
erro_manual = run(x, y, initial_b0, initial_b1, learning_rate, num_iterations)
# ## Questão 5
x = np.array([1400,1600,1700,1875,1100,1550,2350,2450,1425,1700])
y = np.array([245000,312000,279000,308000,199000,219000,405000,324000,319000,255000])
conversor = 10.7639
learning_rate = 0.0000001
initial_b0 = 0 # y-intercept inicial
initial_b1 = 0 # inclinação inicial
num_iterations = 100000
erro_manual = run(x, y, initial_b0, initial_b1, learning_rate, num_iterations)
plt.grid()
plt.scatter(x,y, color="green")
dimensao_casa = 100 * conversor
print(dimensao_casa)
print(b0 + (b1 * dimensao_casa))
份与骑行量的关系
fig, ax = plt.subplots()
sn.barplot(data = train[['mnth', 'cnt']], x='mnth', y = 'cnt')
ax.set(title = "monthly distribution of counts")
#天气与骑行量的关系
fig, ax = plt.subplots()
sn.barplot(data = train[['weathersit', 'cnt']], x='weathersit', y = 'cnt')
ax.set(title = "weathersit distribution of counts")
#工作日与节假日的分布
fig, (ax1, ax2) = plt.subplots(ncols = 2)
sn.barplot(data = train, x = 'holiday', y = 'cnt', ax = ax1)
sn.barplot(data = train, x = 'workingday', y = 'cnt', ax = ax2)
# +
#数值型特征与y之间的相关性
corrMatt = train[['temp', 'atemp', 'hum', 'windspeed', 'casual', 'registered', 'cnt']].corr()
mask = np.array(corrMatt)
mask[np.tril_indices_from(mask)] = False
sn.heatmap(corrMatt, mask = mask, vmax = 8, square = True, annot = True)
# -
#类别性变量特征编码
#对类别性变量进行独热编码
#前面部分已经对离散型特征进行了转换为object的操作，这里就不用了
'''
categorical_features = ['season', 'mnth', 'weathersit', 'weekday']
for col in categorical_features:
print("{0}属性不同取值和出现次数".format(col))
print(train[col].value_counts())
#将类别性特征转化为object
train[col] = train[col].astype('object')
'''
X_train_cat = train[categorical_features]
#独热编码
X_train_cat = pd.get_dummies(X_train_cat)
X_train_cat.head()
# +
#数值型特征
#对数值型特征进行标准化/MinMaxScaler,去量纲
#数值型变量预处理
#感觉数据已经做过处理，取值都在0-1之间，这里用MinMaxScaler再处理一次
from sklearn.preprocessing import MinMaxScaler
mn_x = MinMaxScaler()
numerical_features = ['temp', 'atemp', 'hum', 'windspeed']
temp = mn_x.fit_transform(train[numerical_features])
X_train_num = pd.DataFrame(data = temp, columns = numerical_features, index = train.index)
X_train_num.head()
# -
#将数值型特征和类别性特征合并到一起
X_train = pd.concat([X_train_cat, X_train_num, train['holiday'], train['workingday']], axis = 1, ignore_index = False)
X_train.head()
FE_train = pd.concat([train['instant'], X_train, train['yr'], train['cnt']], axis = 1)
FE_train.to_csv('FE_BikeSharing.csv', index = False)
FE_train.head()
FE_train.info()
| 6,099 |
/nb.ipynb | 011cf50cb2673abea1409867c2ed20a95f17d1f8 | [
"MIT"
] | permissive | tlkh/ships-imagery-dataset | https://github.com/tlkh/ships-imagery-dataset | 0 | 0 | MIT | 2019-11-02T03:21:55 | 2019-11-02T03:21:28 | null | Jupyter Notebook | false | false | .py | 1,118,073 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Import Libraries
# + colab={"base_uri": "https://localhost:8080/", "height": 0} colab_type="code" executionInfo={"elapsed": 7175, "status": "ok", "timestamp": 1570473634312, "user": {"displayName": "Timothy Liu SG", "photoUrl": "", "userId": "04327513636844080478"}, "user_tz": -480} id="zNbGLsDSUe3W" outputId="4e551e00-c5a8-4d41-f6c2-7269f7941a68"
import pathlib
import numpy as np
import multiprocessing
import matplotlib.pyplot as plt
import tensorflow
print("TensorFlow version:", tensorflow.__version__)
import tensorflow.compat.v2 as tf
# -
# # Notebook Parameters
classes = ["not_ship", "ship"]
N_THREADS = multiprocessing.cpu_count()
BATCH_SIZE = 16
# # Loading Data
# ## Loading our Imagery Dataset
# +
dataset_path = pathlib.Path("./ships-imagery-dataset/")
image_count = len(list(dataset_path.glob('*/*.jpg')))
print("Total images:", image_count)
list_ds = tf.data.Dataset.list_files(str(dataset_path/'*/*.jpg'),
shuffle=True)
# -
# ### Functions to Preprocess Images
# +
@tf.function
def load_train_example(file_path):
label = tf.strings.split(file_path, '/')[-2]
if label == "ship":
_label = 1
else:
_label = 0
img = tf.io.read_file(file_path)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.cast(img, tf.float32)
img = img/127.5 - 1
img = tf.image.resize(img, [224, 224])
img = tf.image.random_flip_left_right(img)
img = tf.image.random_flip_up_down(img)
rot_k = np.random.randint(0, 4, size=1)[0]
img = tf.image.rot90(img, rot_k)
return img, tf.one_hot(_label, 2)
@tf.function
def load_test_example(file_path):
label = tf.strings.split(file_path, '/')[-2]
if label == "ship":
_label = 1
else:
_label = 0
img = tf.io.read_file(file_path)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.cast(img, tf.float32)
img = img/127.5 - 1
img = tf.image.resize(img, [224, 224])
return img, tf.one_hot(_label, 2)
# -
# ### Building Input Pipelines
# +
train_dataset = list_ds.shard(3, 0)
train_dataset = train_dataset.map(load_train_example, num_parallel_calls=N_THREADS)
train_dataset = train_dataset.repeat(-1)
train_dataset = train_dataset.shuffle(image_count//3)
train_dataset = train_dataset.batch(BATCH_SIZE)
train_dataset = train_dataset.prefetch(8)
val_dataset = list_ds.shard(3, 1)
val_dataset = val_dataset.map(load_test_example, num_parallel_calls=N_THREADS)
val_dataset = val_dataset.repeat(-1)
val_dataset = val_dataset.batch(BATCH_SIZE)
val_dataset = val_dataset.prefetch(8)
inf_dataset = list_ds.shard(3, 2)
inf_dataset = inf_dataset.map(load_test_example, num_parallel_calls=N_THREADS)
inf_dataset = inf_dataset.repeat(-1)
inf_dataset = inf_dataset.batch(1)
inf_dataset = inf_dataset.prefetch(32)
# run pipeline once
_, _, _ = train_dataset.take(1), val_dataset.take(1), inf_dataset.take(1)
# -
# ## Build Model
# + colab={} colab_type="code" id="BCK57jlvNpOO"
import tensorflow.keras.layers as layers
import tensorflow.keras.applications as models
def create_model(img_size=(224,224), num_class=2, train_base=True):
input_layer = layers.Input(shape=(img_size[0],img_size[1],3))
base = models.densenet.DenseNet121(input_tensor=input_layer,
include_top=False,
weights="imagenet")
base.trainable = train_base
x = base.output
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dropout(rate=0.2)(x)
preds = layers.Dense(num_class, activation="softmax")(x)
return tf.keras.models.Model(inputs=input_layer, outputs=preds)
# + colab={"base_uri": "https://localhost:8080/", "height": 51} colab_type="code" executionInfo={"elapsed": 24373, "status": "ok", "timestamp": 1570473651568, "user": {"displayName": "Timothy Liu SG", "photoUrl": "", "userId": "04327513636844080478"}, "user_tz": -480} id="YAwj90pGOIAy" outputId="8dfaabf3-1eab-402b-9897-42a0a45235a8"
model = create_model((224, 224), len(classes), train_base=True)
opt = tf.keras.optimizers.SGD()
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["acc"])
# -
# ## Train Model
# + colab={"base_uri": "https://localhost:8080/", "height": 153} colab_type="code" executionInfo={"elapsed": 132772, "status": "ok", "timestamp": 1570473759978, "user": {"displayName": "Timothy Liu SG", "photoUrl": "", "userId": "04327513636844080478"}, "user_tz": -480} id="7T8VVrn4Q12B" outputId="d5b7a7c7-0970-45ef-898e-283abfe787a6"
train_steps = int(image_count/3/BATCH_SIZE)
early_stop = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=10, restore_best_weights=True)
history = model.fit(train_dataset, steps_per_epoch=train_steps,
validation_data=val_dataset, validation_steps=train_steps,
callbacks=[early_stop], epochs=20, verbose=2)
# -
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper left')
plt.show()
loss, acc = model.evaluate(inf_dataset, steps=image_count//2)
print("Model test accuracy:", round(acc*100, 1), "%")
for _ in range(2):
plt.figure(figsize=(10,10))
i = 0
for batch in inf_dataset.take(9):
plt.subplot(3,3,i+1)
image, label = batch
pred = model.predict(image)
title = "predicted: " + classes[np.argmax(pred)]
img = (tf.cast(image[0], tf.float32).numpy() + 1) * 127.5
img = img.astype("int")
if np.argmax(label.numpy()) == np.argmax(pred):
img[:30, :30, 1] = 255
else:
img[:30, :30, 0] = 255
plt.imshow(img)
plt.title(title)
plt.axis("off")
i += 1
plt.show()
validation_loss[-1]}')
# Plot training and validation loss
epoch = np.arange(len(training_loss))
plt.figure()
plt.plot(epoch, training_loss, 'r', label='Training loss',)
plt.plot(epoch, validation_loss, 'b', label='Validation loss')
plt.legend()
plt.xlabel('Epoch'), plt.ylabel('NLL')
plt.show()
# + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="n9aSyKl5OtQN" outputId="338810b0-0955-4ef4-8f88-d2f80dd9687f"
# test case
plotIDs = [1,100]
test_loss = 0
net.eval()
id = 0
for inputs, targets in iter(test_loader):
id += 1
# Forward pass
outputs = net(inputs)
# Compute loss
loss = criterion(outputs, targets)
# Update loss
test_loss += loss.detach().cpu()
plt.figure(figsize=(30,3))
for i in range(input_dim):
stat_out = [x[i] for x in outputs[0]]
stat_tar = [x[i] for x in targets[0]]
plt.subplot(1, 9, i+1)
plt.plot(stat_out)
plt.plot(stat_tar)
plt.title(stops[i] + " -> " + stops[i+1])
plt.legend(["Prediction","Target"], loc='upper right')
print("Test Loss: ", (test_loss/len(test_loader)))
# + colab={"base_uri": "https://localhost:8080/", "height": 513} id="jC86wx0PJahF" outputId="0cd8dc02-34e6-475c-f380-27c1ce550a30"
def predict(startID, iterations):
inputs = torch.FloatTensor([rnn_data[(startID):(startID+seq_len-1)]]).cuda()
targets = torch.FloatTensor(rnn_data[(startID+seq_len):(startID+seq_len+iterations)])
results = []
batch_size = 1
for i in range(iterations):
outputs = net(inputs)
inputs = torch.cat((inputs[0][1:],outputs[0][-1:])).reshape(1,71,9)
results.append(outputs[0][-1])
plt.figure(figsize=(7,7))
for i in range(input_dim):
stat_out = [x[i] for x in results]
stat_tar = [x[i] for x in targets]
plt.subplot(3, 3, i+1)
plt.plot(stat_out)
plt.plot(stat_tar)
plt.title(stops[i] + " -> " + stops[i+1])
plt.legend(["Prediction","Target"], loc='upper right')
# predict(110, 24)
predict(160, 24)
# predict(270, 24)
plt.tight_layout()
plt.savefig(f'{path_to_project}/Plots/approach1.png',dpi=300)
| 8,184 |
/Python_fn.ipynb | 964911f807b91b31f0083e7cdfebb91131fc6d3a | [] | no_license | AnilKumar3/python_learning | https://github.com/AnilKumar3/python_learning | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 3,263 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python [conda env:anaconda3]
# language: python
# name: conda-env-anaconda3-py
# ---
# ## Temp converture
# +
def c2f (cel):
value=32.+cel*9./5
print ('Temp in FarH:',value)
def f2c (far):
value1=(far-32.)*5./9
print ('Temp in Cel:', value1)
print ('Welcome to Tempreture Converter')
ans='Y'
while (ans=='Y'):
value=input('what you want to do? (C)el --> Far OR (F)ar --> Cel')
print ('value:', value)
if value.upper()=='C':
cel=int(input('Enter Value in Cel:'))
print('Value in Cel:',cel)
c2f(cel)
elif value.upper()=='F':
far=int(input('Enter value in FarH:'))
print('Value in FarH:',far)
f2c(far)
else:
print ('Wrong value')
choice=input('Want to Continue (Y/N):')
if choice.upper()!='Y':
break
print ('Have a good day')
# -
f_to_c(60
)
f_to_c(110)
| 1,089 |
/01_the_machine_learning_landscape.ipynb | e16b3ce02505eb8cab83804abb627cdb4654340d | [
"Apache-2.0"
] | permissive | verystrongjoe/handson-ml | https://github.com/verystrongjoe/handson-ml | 0 | 0 | null | 2017-07-24T20:56:39 | 2017-07-24T17:12:13 | null | Jupyter Notebook | false | false | .py | 263,323 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python [default]
# language: python
# name: python3
# ---
# + [markdown] deletable=true editable=true
# **Chapter 1 – The Machine Learning landscape**
#
# _This is the code used to generate some of the figures in chapter 1._
# + [markdown] deletable=true editable=true
# # Setup
# + [markdown] deletable=true editable=true
# First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:
# + deletable=true editable=true slideshow={"slide_type": "-"}
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import numpy.random as rnd
import os
# to make this notebook's output stable across runs
rnd.seed(42)
# To plot pretty figures
# %matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
# Where to save the figures
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "fundamentals"
def save_fig(fig_id, tight_layout=True):
path = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID, fig_id + ".png")
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format='png', dpi=300)
# + [markdown] deletable=true editable=true
# # Load and prepare Life satisfaction data
# + deletable=true editable=true
import pandas as pd
# Download CSV from http://stats.oecd.org/index.aspx?DataSetCode=BLI
datapath = "datasets/lifesat/"
oecd_bli = pd.read_csv(datapath+"oecd_bli_2015.csv", thousands=',')
# -
oecd_bli = oecd_bli[oecd_bli["INEQUALITY"]=="TOT"]
oecd_bli = oecd_bli.pivot(index="Country", columns="Indicator", values="Value")
oecd_bli.head(2)
# + deletable=true editable=true
oecd_bli["Life satisfaction"].head()
# + [markdown] deletable=true editable=true
# # Load and prepare GDP per capita data
# + deletable=true editable=true
# Download data from http://goo.gl/j1MSKe (=> imf.org)
gdp_per_capita = pd.read_csv(datapath+"gdp_per_capita.csv", thousands=',', delimiter='\t',
encoding='latin1', na_values="n/a")
gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True)
gdp_per_capita.set_index("Country", inplace=True)
gdp_per_capita.head(2)
# + deletable=true editable=true
full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True)
full_country_stats.sort_values(by="GDP per capita", inplace=True)
full_country_stats
# + deletable=true editable=true
full_country_stats[["GDP per capita", 'Life satisfaction']].loc["United States"]
# + deletable=true editable=true
remove_indices = [0, 1, 6, 8, 33, 34, 35]
keep_indices = list(set(range(36)) - set(remove_indices))
sample_data = full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[keep_indices]
missing_data = full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[remove_indices]
# + deletable=true editable=true
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(5,3))
plt.axis([0, 60000, 0, 10])
position_text = {
"Hungary": (5000, 1),
"Korea": (18000, 1.7),
"France": (29000, 2.4),
"Australia": (40000, 3.0),
"United States": (52000, 3.8),
}
for country, pos_text in position_text.items():
pos_data_x, pos_data_y = sample_data.loc[country]
country = "U.S." if country == "United States" else country
plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,
arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))
plt.plot(pos_data_x, pos_data_y, "ro")
save_fig('money_happy_scatterplot')
plt.show()
# + deletable=true editable=true
sample_data.to_csv("life_satisfaction_vs_gdp_per_capita.csv")
# + deletable=true editable=true
sample_data.loc[list(position_text.keys())]
# + deletable=true editable=true
import numpy as np
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(5,3))
plt.axis([0, 60000, 0, 10])
X=np.linspace(0, 60000, 1000)
plt.plot(X, 2*X/100000, "r")
plt.text(40000, 2.7, r"$\theta_0 = 0$", fontsize=14, color="r")
plt.text(40000, 1.8, r"$\theta_1 = 2 \times 10^{-5}$", fontsize=14, color="r")
plt.plot(X, 8 - 5*X/100000, "g")
plt.text(5000, 9.1, r"$\theta_0 = 8$", fontsize=14, color="g")
plt.text(5000, 8.2, r"$\theta_1 = -5 \times 10^{-5}$", fontsize=14, color="g")
plt.plot(X, 4 + 5*X/100000, "b")
plt.text(5000, 3.5, r"$\theta_0 = 4$", fontsize=14, color="b")
plt.text(5000, 2.6, r"$\theta_1 = 5 \times 10^{-5}$", fontsize=14, color="b")
save_fig('tweaking_model_params_plot')
plt.show()
# + deletable=true editable=true
from sklearn import linear_model
lin1 = linear_model.LinearRegression()
Xsample = np.c_[sample_data["GDP per capita"]]
ysample = np.c_[sample_data["Life satisfaction"]]
lin1.fit(Xsample, ysample)
t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]
t0, t1
# + deletable=true editable=true
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(5,3))
plt.axis([0, 60000, 0, 10])
X=np.linspace(0, 60000, 1000)
plt.plot(X, t0 + t1*X, "b")
plt.text(5000, 3.1, r"$\theta_0 = 4.85$", fontsize=14, color="b")
plt.text(5000, 2.2, r"$\theta_1 = 4.91 \times 10^{-5}$", fontsize=14, color="b")
save_fig('best_fit_model_plot')
plt.show()
# + deletable=true editable=true
cyprus_gdp_per_capita = gdp_per_capita.loc["Cyprus"]["GDP per capita"]
print(cyprus_gdp_per_capita)
cyprus_predicted_life_satisfaction = lin1.predict(cyprus_gdp_per_capita)[0][0]
cyprus_predicted_life_satisfaction
# + deletable=true editable=true
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(5,3), s=1)
X=np.linspace(0, 60000, 1000)
plt.plot(X, t0 + t1*X, "b")
plt.axis([0, 60000, 0, 10])
plt.text(5000, 7.5, r"$\theta_0 = 4.85$", fontsize=14, color="b")
plt.text(5000, 6.6, r"$\theta_1 = 4.91 \times 10^{-5}$", fontsize=14, color="b")
plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita], [0, cyprus_predicted_life_satisfaction], "r--")
plt.text(25000, 5.0, r"Prediction = 5.96", fontsize=14, color="b")
plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, "ro")
save_fig('cyprus_prediction_plot')
plt.show()
# + deletable=true editable=true
sample_data[7:10]
# + deletable=true editable=true
(5.1+5.7+6.5)/3
# + deletable=true editable=true
backup = oecd_bli, gdp_per_capita
def prepare_country_stats(oecd_bli, gdp_per_capita):
return sample_data
# + deletable=true editable=true
# Code example
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
# Load the data
oecd_bli = pd.read_csv(datapath + "oecd_bli_2015.csv", thousands=',')
gdp_per_capita = pd.read_csv(datapath + "gdp_per_capita.csv",thousands=',',delimiter='\t',
encoding='latin1', na_values="n/a")
# Prepare the data
country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)
X = np.c_[country_stats["GDP per capita"]]
y = np.c_[country_stats["Life satisfaction"]]
# Visualize the data
country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction')
plt.show()
# Select a linear model
model = sklearn.linear_model.LinearRegression()
# Train the model
model.fit(X, y)
# Make a prediction for Cyprus
X_new = [[22587]] # Cyprus' GDP per capita
print(model.predict(X_new)) # outputs [[ 5.96242338]]
# + deletable=true editable=true
oecd_bli, gdp_per_capita = backup
# + deletable=true editable=true
missing_data
# + deletable=true editable=true
position_text2 = {
"Brazil": (1000, 9.0),
"Mexico": (11000, 9.0),
"Chile": (25000, 9.0),
"Czech Republic": (35000, 9.0),
"Norway": (60000, 3),
"Switzerland": (72000, 3.0),
"Luxembourg": (90000, 3.0),
}
# + deletable=true editable=true
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(8,3))
plt.axis([0, 110000, 0, 10])
for country, pos_text in position_text2.items():
pos_data_x, pos_data_y = missing_data.loc[country]
plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,
arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))
plt.plot(pos_data_x, pos_data_y, "rs")
X=np.linspace(0, 110000, 1000)
plt.plot(X, t0 + t1*X, "b:")
lin_reg_full = linear_model.LinearRegression()
Xfull = np.c_[full_country_stats["GDP per capita"]]
yfull = np.c_[full_country_stats["Life satisfaction"]]
lin_reg_full.fit(Xfull, yfull)
t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]
X = np.linspace(0, 110000, 1000)
plt.plot(X, t0full + t1full * X, "k")
save_fig('representative_training_data_scatterplot')
plt.show()
# + deletable=true editable=true
full_country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(8,3))
plt.axis([0, 110000, 0, 10])
from sklearn import preprocessing
from sklearn import pipeline
poly = preprocessing.PolynomialFeatures(degree=60, include_bias=False)
scaler = preprocessing.StandardScaler()
lin_reg2 = linear_model.LinearRegression()
pipeline_reg = pipeline.Pipeline([('poly', poly), ('scal', scaler), ('lin', lin_reg2)])
pipeline_reg.fit(Xfull, yfull)
curve = pipeline_reg.predict(X[:, np.newaxis])
plt.plot(X, curve)
save_fig('overfitting_model_plot')
plt.show()
# + deletable=true editable=true
full_country_stats.loc[[c for c in full_country_stats.index if "W" in c.upper()]]["Life satisfaction"]
# + deletable=true editable=true
gdp_per_capita.loc[[c for c in gdp_per_capita.index if "W" in c.upper()]].head()
# + deletable=true editable=true
plt.figure(figsize=(8,3))
plt.xlabel("GDP per capita")
plt.ylabel('Life satisfaction')
plt.plot(list(sample_data["GDP per capita"]), list(sample_data["Life satisfaction"]), "bo")
plt.plot(list(missing_data["GDP per capita"]), list(missing_data["Life satisfaction"]), "rs")
X = np.linspace(0, 110000, 1000)
plt.plot(X, t0full + t1full * X, "r--", label="Linear model on all data")
plt.plot(X, t0 + t1*X, "b:", label="Linear model on partial data")
ridge = linear_model.Ridge(alpha=10**9.5)
Xsample = np.c_[sample_data["GDP per capita"]]
ysample = np.c_[sample_data["Life satisfaction"]]
ridge.fit(Xsample, ysample)
t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]
plt.plot(X, t0ridge + t1ridge * X, "b", label="Regularized linear model on partial data")
plt.legend(loc="lower right")
plt.axis([0, 110000, 0, 10])
save_fig('ridge_model_plot')
plt.show()
# + deletable=true editable=true
backup = oecd_bli, gdp_per_capita
def prepare_country_stats(oecd_bli, gdp_per_capita):
return sample_data
# + deletable=true editable=true
# Replace this linear model:
model = sklearn.linear_model.LinearRegression()
# + deletable=true editable=true
# with this k-neighbors regression model:
model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)
# + deletable=true editable=true
X = np.c_[country_stats["GDP per capita"]]
y = np.c_[country_stats["Life satisfaction"]]
# Train the model
model.fit(X, y)
# Make a prediction for Cyprus
X_new = np.array([[22587.0]]) # Cyprus' GDP per capita
print(model.predict(X_new)) # outputs [[ 5.76666667]]
# + deletable=true editable=true
| 11,466 |
/Part_1_webscraping_ncaa_beautifulsoup.ipynb | 6d35a28101cec08030cc0fde03f335737e50a90b | [] | no_license | michaelkim9/nba_predictor_project | https://github.com/michaelkim9/nba_predictor_project | 0 | 1 | null | null | null | null | Jupyter Notebook | false | false | .py | 26,257 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
import re
# +
def SoupFromURL(url, suppressOutput=True):
if not suppressOutput:
print(url)
try:
r = requests.get(url)
except:
return None
return BeautifulSoup(r.text, "html5lib")
def remove_values_from_list(the_list, val):
return [value for value in the_list if value != val]
# -
# ### Scraping player page for stats
#
# The function below scrapes any given player url page for stats
def ncaa_stats_dict(url):
player_page = SoupFromURL(str(url))
#player profile info
a = player_page.find_all('div', attrs={'class':'nothumb'})[0]
a_soup = BeautifulSoup(str(a),"html5lib")
n = a_soup.text.split('\n')
n = remove_values_from_list(n,'')
n = remove_values_from_list(n,' ')
removal = ['(',' Hometown',' High School', ' More',' Position']
for remove in removal:
n = [x for x in n if not x.startswith(remove)]
try:
name = n[0]
name = name.replace('\t','')
except:
name = None
try:
position = n[2]
position = position.replace(' ','')
except:
position=None
try:
college = n[4]
college = college.replace(' School: ','')
except:
college = None
try:
ft = n[3][:3]
ft = int(ft.replace(' ',''))*12
inch = n[3][3:6]
inch = int(re.sub('[^0-9]','',inch))
height = ft + inch
except:
height = None
try:
weight = n[3][7:11]
weight = int(re.sub('[^0-9]','',weight))
except:
weight=None
#player stats
stat_list = []
try:
p = player_page.find_all('div', attrs={'class':'stats_pullout'})[0]
p_soup = BeautifulSoup(str(p),"html5lib")
stat = p_soup.text.split('\n')[1::4][2:]
#stat_list = []
for s in stat:
try:
if s == str(""):
stat_list.append(None)
else:
stat_list.append(float(s))
except:
stat_list.append(None)
except:
stat_list.extend([None,None,None,None,None,None,None,None,None,None])
#season count and draft year
season_count = player_page.find_all('div', attrs={'class':'overthrow table_container'})[0]
season_count_soup = BeautifulSoup(str(season_count),"html5lib")
season_count = season_count_soup.text.split('\n')
season_count = season_count[20:37]
season_count = [x for x in season_count if not 'Career' in x]
season_count = [x for x in season_count if not ' ' in x]
final_season_count = len(list(filter(None, season_count)))
draft_year_list = list(filter(None, season_count))
if len(draft_year_list) == 4:
final_year = str(draft_year_list[3])[5:7]
elif len(draft_year_list) == 3:
final_year = str(draft_year_list[2])[5:7]
elif len(draft_year_list) == 2:
final_year = str(draft_year_list[1])[5:7]
elif len(draft_year_list) == 1:
final_year = str(draft_year_list[0])[5:7]
if int(final_year[0]) in [0,1]:
final_draft_year = int(final_year) + 2000
elif int(final_year[0]) in [9,8,7,6,5,4,3,2]:
final_draft_year = int(final_year) + 1900
#steals, rebounds, turnovers and mins played per game
additional_stats = player_page.find_all('td', attrs={'class':'right'})
additional_soup = BeautifulSoup(str(additional_stats),"html5lib")
try:
steal_stat = additional_soup.text.split(',')[-5]
steals = float(steal_stat)
except:
steals = None
try:
block_stat = additional_soup.text.split(',')[-4]
blocks = float(block_stat)
except:
blocks = None
try:
turnover_stat = additional_soup.text.split(',')[-3]
turnovers = float(turnover_stat)
except:
turnovers = None
try:
minutes_stat = additional_soup.text.split(',')[-20]
minutes = float(minutes_stat)
except:
minutes=None
#creating player dictionary
player_dict = {
'player_name':name,
'position':position,
'height_inches':height,
'weight_lbs':weight,
'college':college,
'draft_year':final_draft_year,
'years_in_college':final_season_count,
'games':stat_list[0],
'minutes_per_game':minutes,
'points':stat_list[1],
'rebounds':stat_list[2],
'assists':stat_list[3],
'steals':steals,
'blocks':blocks,
'turnovers':turnovers,
'fg_percent':stat_list[4],
'3_fg_percent':stat_list[5],
'free_throw_percent':stat_list[6],
'effective_fg_percent':stat_list[7],
'player_efficiency_rating':stat_list[8],
'win_shares':stat_list[9]
}
return player_dict
ncaa_stats_dict('https://www.sports-reference.com/cbb/players/larry-bird-1.html')
# ### Get index page urls
#
# I noticed that there is a pattern to the player index page url's on sports-reference. I followed the pattern to get a list of url's.
# +
import string
index_urls = []
for letter in string.ascii_lowercase:
letter_page = 'https://www.sports-reference.com/cbb/players/{}-index.html'.format(letter)
index_urls.append(letter_page)
index_urls
# -
# ### Get player urls
#
# Within each of the index pages, there was a pattern within the HTML code that references all the links on the page. I scraped all the links and discarded the ones that weren't relevant such as the links to schools or other parts of the site that weren't player pages.
def get_player_urls(index_url_link):
player_urls = []
index_page = SoupFromURL(index_url_link)
index_names = index_page.find_all('p')
index_soup = BeautifulSoup(str(index_names),"html5lib")
links = index_soup('a', href=True)
for l in links:
try:
player_urls.append('https://www.sports-reference.com' + l.attrs['href'])
except:
pass
player_urls = [x for x in player_urls if not 'schools' in x]
player_urls.pop()
player_urls.pop()
player_urls.pop()
player_urls.pop()
return player_urls
get_player_urls('https://www.sports-reference.com/cbb/players/x-index.html')
# ### Generate Stats Dataframe Of All Players On Index Page
#
# Now that I have a function to scrape player pages for their stats and a list of index_urls with a function to get all the page urls, I decided to create another function below where you pass in the index url to generate a dataframe with player stats for all players listed on that page.
def generate_player_df(first_letter):
index_url_link = 'https://www.sports-reference.com/cbb/players/{}-index.html'.format(first_letter)
player_urls = []
index_page = SoupFromURL(index_url_link)
index_names = index_page.find_all('p')
index_soup = BeautifulSoup(str(index_names),"html5lib")
links = index_soup('a', href=True)
for l in links:
try:
player_urls.append('https://www.sports-reference.com' + l.attrs['href'])
except:
pass
player_urls = [x for x in player_urls if not 'schools' in x]
player_urls.pop()
player_urls.pop()
player_urls.pop()
player_urls.pop()
player_stats_list=[]
for url in player_urls:
try:
player_stats_list.append(ncaa_stats_dict(url))
print(url)
except:
pass
df = pd.DataFrame(player_stats_list)
df = df[[
'player_name',
'position',
'height_inches',
'weight_lbs',
'college',
'draft_year',
'years_in_college',
'games',
'minutes_per_game',
'points',
'rebounds',
'assists',
'steals',
'blocks',
'turnovers',
'fg_percent',
'3_fg_percent',
'free_throw_percent',
'effective_fg_percent',
'player_efficiency_rating',
'win_shares'
]]
return df
x_df = generate_player_df('x')
x_df
# ### Versions utilized for scraping
import sys
import bs4
import re
print('Python version:', sys.version_info)
print('BeautifulSoup version:', bs4.__version__)
print('Pandas version:', pd.__version__)
print('Numpy version:',np.__version__)
print('RegEx version:',re.__version__)
| 8,709 |
/sara/Image_normalization.ipynb | 4206756f183134d937897bef98d0bf6724ab0f7d | [] | no_license | dvp-tran/skin_cancer | https://github.com/dvp-tran/skin_cancer | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 343,150 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 2
# language: python
# name: python2
# ---
# +
from PIL import Image
from PIL import ImageStat
import math
import os
# function to return average brightness of an image
# Source: https://stackoverflow.com/questions/3490727/what-are-some-methods-to-analyze-image-brightness-using-python
def brightness(im_file):
im = Image.open(im_file)
stat = ImageStat.Stat(im)
r,g,b = stat.mean
return math.sqrt(0.241*(r**2) + 0.691*(g**2) + 0.068*(b**2)) #this is a way of averaging the r g b values to derive "human-visible" brightness
myList = [0.0]
deltaList = [0.0]
b = 0.0
num_images = 8581 # number of images
# loop to auto-generate image names and run prior function
for im in os.listdir('/home/sara_rabhi/skin_cancer/data/resized_train/'): # for loop runs from image number 1 thru 20
a = str(im)
image_name = im
myList.append(brightness('/home/sara_rabhi/skin_cancer/data/resized_train/'+image_name))
avg_brightness = sum(myList[1:])/num_images
print (myList)
print (avg_brightness)
# +
for i in range(1, num_images + 1):
deltaList.append(i)
deltaList[i] = avg_brightness - myList[i]
print (deltaList)
# -
k=1
for im in os.listdir('resized_train/'):
img_file_ = Image.open('resized_train/'+im)
img_file_ = img_file_.convert('RGB')
pixels = img_file_.load()
for i in range (img_file_.size[0]):
for j in range (img_file_.size[1]):
r, g, b = img_file_.getpixel((i,j)) # extracts r g b values for the i x j th pixel
pixels[i,j] = (r+int(deltaList[k]), g+int(deltaList[k]), b+int(deltaList[k]))
img_file_.save('norm_dir/'+im)
k+=1
o_bin(d // 2)
print(d % 2, end = '')
print("Binary equivalent of", a, "is", end = " ")
dec_to_bin(a)
print()
elif b == 2:
break
else:
print("wrong choice")
# +
a = b = c = 0
print("Enter -1 to exit")
while b != -1:
b = int(input("Enter integer: "))
if b != -1:
print(b, end = ", ")
elif b == -1:
print(b)
if b == 1:
c = c + 1
if b % 2 != 0:
a = a + 1
c = c + b
print()
if a > 1:
print("Total odd numbers:", a - 1)
print("Sum of odd numbers:", c)
print("Average of odd numbers: {:.3}".format(c / (a - 1)))
else:
print("No numbers are entered except -1")
# +
def power(n):
if n == 0:
return False
while n != 1:
if n % 2 != 0:
return False
n = n // 2
return True
while True:
print("Enter -1 to exit")
a = int(input("Enter a whole number: "))
if a == -1:
break
else:
if power(a):
print(a, "is a power of 2")
else:
print(a, "is not a power of 2")
# -
lst = list()
for i in range(3):
a = int(input("Enter integers: "))
lst.append(a)
print("Max:", max(lst))
main_drivers.groupby(['constructorRef_mapped', 'year']).apply(lambda x: x.nlargest(2,'race_count', keep='all')).reset_index(drop=True)
main_drivers.head()
# -
df=lap_speeds.merge(races[['date','year', 'raceId', 'circuitId']], on ='raceId', how='left')
df=df.merge(teams[['raceId', 'driverId', 'constructorRef', 'constructorRef_mapped']], on=['raceId', 'driverId'], how='left')
df=df.merge(circuits[['circuitId', 'circuitRef']], on='circuitId', how='left')
df['date'] = pd.to_datetime(df['date'])
df.head()
# +
avg_lap_speed = pd.DataFrame({'avg_lap_speed' : df.groupby(['year', 'constructorRef_mapped'])['km_per_min']
.mean()}).sort_values(['constructorRef_mapped', 'year']).reset_index()
avg_lap_speed = avg_lap_speed.sort_values(["constructorRef_mapped","year"])
avg_lap_speed = avg_lap_speed.merge(main_drivers, on = ['year', 'constructorRef_mapped'], how='left')
avg_lap_speed['drivers'] = avg_lap_speed .groupby(['year', 'constructorRef_mapped'])['surname'].transform(lambda x : ', '.join(x))
avg_lap_speed = avg_lap_speed[['year', 'constructorRef_mapped', 'avg_lap_speed', 'drivers']].drop_duplicates()
# -
avg_lap_speed['constructor_name'] = avg_lap_speed['constructorRef_mapped'].map(constructor_names)
avg_lap_speed.head()
avg_lap_speed.to_csv(r"graph_data\avg_lap_speed.csv", index=False)
# +
constructor_names = {'alfa': 'Alfa Romeo',
'alphatauri': 'AlphaTauri',
'mclaren': 'McLaren',
'mercedes': 'Mercedes',
'racing_point': 'Racing Point',
'red_bull': 'Red Bull',
'renault': 'Renault',
'ferrari': 'Ferrari',
'haas': 'Haas',
'williams': 'Williams'}
col_map = {'mercedes': 'mediumturquoise',
'red_bull': 'blue',
'racing_point': 'hotpink',
'mclaren': 'darkorange',
'renault': 'gold',
'ferrari': 'red',
'alphatauri': 'black',
'alfa': 'darkred',
'haas': 'darkgrey',
'williams': 'dodgerblue'}
line_map = {'mercedes': 'solid',
'red_bull': 'solid',
'racing_point': 'dashed',
'mclaren': 'solid',
'renault': 'solid',
'ferrari': 'solid',
'alphatauri': 'dotted',
'alfa': 'dotted',
'haas': 'dashed',
'williams': 'dotted'}
col_map = {'mercedes': 'mediumturquoise',
'red_bull': 'blue',
'racing_point': 'mediumturquoise',
'mclaren': 'darkorange',
'renault': 'gold',
'ferrari': 'red',
'alphatauri': 'blue',
'alfa': 'red',
'haas': 'red',
'williams': 'mediumturquoise'}
# +
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
sns.set(style="whitegrid")
plt.rcParams["font.weight"] = "bold"
plt.rcParams["axes.labelweight"] = "bold"
#plt.rcParams['xtick.major.size'] = 20
plt.rcParams['xtick.major.width'] = 2
plt.rcParams['ytick.major.width'] = 2
plt.rcParams['xtick.bottom'] = True
plt.rcParams['ytick.left'] = True
years = avg_lap_speed['year'].unique()
fig = plt.figure(figsize=(15,10))
ax1 = plt.gca()
ax1.set_xlim(1995,2021)
ax1.set_ylim(2.9,3.58)
for key, grp in avg_lap_speed.groupby(['constructorRef_mapped']):
ax1.plot(grp['year'], grp['avg_lap_speed'], label = constructor_names[key],
color=col_map[key], linestyle = line_map[key])
plt.xticks(np.arange(min(years), max(years)+1, 2.0))
leg = ax1.legend(loc='center left', bbox_to_anchor=(1, .8), fontsize=15)
leg.set_title('Constructors', prop = {'size':'x-large'})
leg.get_frame().set_linewidth(0.0)
leg._legend_box.sep = 20
for legobj in leg.legendHandles:
legobj.set_linewidth(4.0)
ax1.axvspan(1996, 2005, facecolor='azure', alpha=0.5)
ax1.axvspan(2006, 2013, facecolor='cornsilk', alpha=0.5)
ax1.axvspan(2014, 2020, facecolor='honeydew', alpha=0.5)
ax1.annotate('', xy=(1996, 3.53), xytext=(2005, 3.53), xycoords='data', textcoords='data',
arrowprops=dict(arrowstyle= '|-|', color='teal', lw=2, ls='--'))
ax1.annotate('V10/12 Engines', xy=(2000.5, 3.55), ha='center', va='center', color = 'teal', fontsize=16)
ax1.annotate('', xy=(2006, 3.53), xytext=(2013, 3.53), xycoords='data', textcoords='data',
arrowprops=dict(arrowstyle= '|-|', color='darkgoldenrod', lw=2, ls='--'))
ax1.annotate('V8 Engines', xy=(2009.5, 3.55), ha='center', va='center', color='darkgoldenrod', fontsize=16)
ax1.annotate('', xy=(2014, 3.53), xytext=(2020, 3.53), xycoords='data', textcoords='data',
arrowprops=dict(arrowstyle= '|-|', color='darkgreen', lw=2, ls='--'))
ax1.annotate('V6 - Hybrid Engines', xy=(2017, 3.55), ha='center', va='center', color='darkgreen', fontsize=16)
plt.title("Average Yearly Km/min by Constructor", fontsize=20, fontweight='bold', pad=20)
plt.xlabel("Year", fontsize=18, fontweight='bold', labelpad=20)
plt.ylabel("Km/min", fontsize=18, fontweight='bold', labelpad=20)
for tick in ax1.xaxis.get_major_ticks():
tick.label1.set_fontsize(15)
tick.label1.set_fontweight('bold')
for tick in ax1.yaxis.get_major_ticks():
tick.label1.set_fontsize(15)
tick.label1.set_fontweight('bold')
plt.setp(ax1.spines.values(), linewidth=3, color='black')
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
plt.savefig(r"figs\avgLapYear_V2.png", bbox_inches='tight')
plt.show()
# -
import plotly.graph_objects as go
from ipywidgets import widgets
# +
constructor_names = {'alfa': 'Alfa Romeo',
'alphatauri': 'AlphaTauri',
'mclaren': 'McLaren',
'mercedes': 'Mercedes',
'racing_point': 'Racing Point',
'red_bull': 'Red Bull',
'renault': 'Renault',
'ferrari': 'Ferrari',
'haas': 'Haas',
'williams': 'Williams'}
line_map = {'mercedes': 'solid',
'red_bull': 'solid',
'racing_point': 'dash',
'mclaren': 'solid',
'renault': 'solid',
'ferrari': 'solid',
'alphatauri': 'dot',
'alfa': 'dot',
'haas': 'dash',
'williams': 'dot'}
col_map = {'mercedes': 'mediumturquoise',
'red_bull': 'blue',
'racing_point': 'mediumturquoise',
'mclaren': 'darkorange',
'renault': 'gold',
'ferrari': 'red',
'alphatauri': 'blue',
'alfa': 'red',
'haas': 'red',
'williams': 'mediumturquoise'}
# +
fig = go.Figure()
df = avg_lap_speed#.loc[avg_lap_speed.constructorRef_mapped == 'mercedes', :]
for key, grp in df.groupby(['constructorRef_mapped']):
fig.add_trace(go.Scatter(x=grp['year'], y=grp['avg_lap_speed'],
mode='lines',
line=dict(color=col_map[key], dash=line_map[key]),
name=df.loc[df.constructorRef_mapped == key, 'constructor_name'].unique()[0], #constructor_names[key],
hovertemplate =
f'<b>Constructor:</b> {constructor_names[key]} <br>' +
'<b>Drivers:</b> %{text}<extra></extra>',
text = df.loc[(df.constructorRef_mapped == key), "drivers"],))
fig.add_vrect(
x0="1996", x1="2005",
fillcolor="azure", #opacity=0.5,
layer="below", line_width=0,
),
fig.add_vrect(
x0="2006", x1="2013",
fillcolor="cornsilk", #opacity=0.5,
layer="below", line_width=0,
),
fig.add_vrect(
x0="2014", x1="2020",
fillcolor="honeydew", #opacity=0.5,
layer="below", line_width=0,
),
fig.add_annotation(x=2005, y=3.53,
xref = "x", yref = "y", ax=1996, ay=3.53,
axref = "x", ayref = "y",
arrowhead = 5, arrowside="end+start", arrowsize=1.5, arrowcolor='teal')
fig.add_annotation(x=2000.5, y=3.55, text="<b>V10/12 Engines</b>",
showarrow=False, font=dict(size=18, color='teal'))
fig.add_annotation(x=2006, y=3.53,
xref = "x", yref = "y", ax=2013, ay=3.53,
axref = "x", ayref = "y",
arrowhead = 5, arrowside="end+start", arrowsize=1.5, arrowcolor='IndianRed')
fig.add_annotation(x=2009.5, y=3.55, text="<b>V8 Engines</b>",
showarrow=False, font=dict(size=18, color='IndianRed'))
fig.add_annotation(x=2020, y=3.53,
xref = "x", yref = "y", ax=2014, ay=3.53,
axref = "x", ayref = "y",
arrowhead = 5, arrowside="end+start", arrowsize=1.5, arrowcolor='darkgreen')
fig.add_annotation(x=2017, y=3.55, text="<b>V6 Hybrid Engines</b>",
showarrow=False, font=dict(size=18, color='darkgreen'))
fig.update_layout(
autosize=False,
width=1000,
height=800,
plot_bgcolor='rgba(0,0,0,0)',
title = dict(
text="<b>Average Yearly Km/min by Constructor</b>",
xanchor='left',
yanchor='top',
y=0.92,
#x=0.1
),
legend=dict(
title_text="<b>Constructors</b>",
),
yaxis=dict(
range=[2.9,3.58],
title_text="<b>Km/min</b>",
tickwidth=2,
ticks="outside",
ticklen=10,
tickprefix="<b>",ticksuffix ="</b>",
tickfont=dict(size=15),
showline=True,
linewidth=2,
linecolor='black',
gridcolor='lightGray'
),
xaxis=dict(
range=[1995,2021],
tick0=1996,
dtick=2,
title_text="<b>Year</b>",
tickwidth=2,
ticks="outside",
tickprefix="<b>",ticksuffix ="</b>",
ticklen=10,
showline=True,
linewidth=2,
linecolor='black',
gridcolor='lightGray'
),
font=dict(
size=15,
color='black')
)
fig.show()
# -
freq_hat= b2_frequencies
#ax2.plot(times_k, b2_frequencies,'k', linewidth = 0.75)
#ax2.plot(times, freq_hat[:-3],'k', linewidth = 0.75)
ax2.plot(times, b2_frequencies,'k', linewidth = 0.75)
#peristimulus time historgram
#and then compute the Fourier transform as
#plt.title(r'$\mathrm{Histogram\ of\ B2 Muscle Activity:}\ \mu=100,\ \sigma=15$', size=12, horizontalalignment='center', y=1.08) #verticalalignment='top')
ax1.set_ylabel('Crimson Stimulus', size=10)
ax1.set_title(r'$\mathrm{Time\ Course\ of\ Crimson\ Modulated\ B2\ Muscle\ Activity:}\ \mu=100,\ \sigma=15$', size=12, horizontalalignment='center', y=1.08) #verticalalignment='top')
ax2.set_ylabel('Frequency of B2 Firing', size=10)
plt.xlabel('Power Muscle Activity $\phi$', size=10)
import numpy.fft as fft
spectrum = fft.fft(data1)
#You can then plot the magnitudes of the FFT as
# -
# The last few cells are probably the ones you're after. So here you can see (vs the other notebook I sent you) the limitations of the direct data period/freq analysis. This will be ameliorated a bit by increasing the sample size. However, the other approach I tried before (that I can also try and recreate) was running these raw traces through filters to try get slightly smoother.
f1 = pd.read_csv('power_muscle_09_frequencies_df.csv')
f2 = pd.read_csv('power_muscle_09_periods_df.csv')
f3 = pd.read_csv('chrimson_09_df.csv')
f4 = pd.read_csv('times_09_df.csv')
# +
df = pd.DataFrame()
# Add a new columns with impoact force in units of Newtons
df['b2 frequencies'] = f1
df['b2 periods'] = f2
df['chrimson stimulus'] = f3
df['times'] = f4
# Take a look
df.head()
# +
max_chrimson_stim = df['chrimson stimulus'] >= 9.9
#max_chrimson_stim.head()
max_inds = df.loc[max_chrimson_stim]
max_inds.head()
# +
#for ind in range(len(max_inds)-1):
# if times[ind]-times[ind -1] >= 1:
# chrimson_on =
# +
crimson_on = []
crimson_off = []
for f in range(len(f3)-1):
if crimson[f]-crimson[f-1]>= 1.05: #1.55
crimson_on.append(f)
if crimson[f]-crimson[f+1] >= 1.05: #<= - 0.75:
crimson_off.append(f)
# +
for i in range(len(crimson_on)-1):
if crimson_on[i] - crimson_on[i+1] >= - 100:
del crimson_on[i+1]
for i in range(len(crimson_off)-1):
if crimson_off[i] - crimson_off[i+1] >= - 100:
del crimson_off[i+1]
# +
fig =plt.figure(14)
ax1 = plt.subplot(211)
ax1.plot(times, crimson, 'r', linewidth =1.0)
ax2 = plt.subplot(212, sharex=ax1)
ax2.set_ylim(0, 20)
#ax2.set_xlim(20, 80)
#savitzky_golay
'''freq_hat = savgol_filter(b2_frequencies, 7, 5) # window size 51, polynomial order 3'''
#freq_hat= b2_frequencies
#ax2.plot(times_k, b2_frequencies,'k', linewidth = 0.75)
for i in range(len(crimson_on)):
ax1.axvspan(times[crimson_on[i]], times[crimson_off[i]], facecolor = 'r', edgecolor = 'none', alpha = 0.3)
#ax2.axvspan(times[crimson_on[i]], times[crimson_off[i]], facecolor = 'r', edgecolor = 'none', alpha = 0.3)
# -
print(len(crimson_on), (crimson_on))
print(len(crimson_off), (crimson_off))
'''
df_freq_upon_signal = pd.DataFrame()
for i in range(len(crimson_on)):
on_signal = crimson_on[i]
f = b2_frequencies[on_signal-5000: on_signal+10000]
f2 = signal.resample(y2, )
xnew = np.linspace(0, times[len(y2)-1], 180000000, endpoint=False)
df_freq_upon_signal[i]=f
'''
# **** switch here to make the fixed time on the chrimson time scale and then
# upsample in the lower frequency area
# +
#***duplicate older version
#df_freq_upon_signal = pd.DataFrame()
#for i in range(len(crimson_on)):
# on_signal = crimson_on[i]
# off_signal = crimson_off[i]
# f = b2_frequencies[on_signal-5000: off_signal+1000]
# df_freq_upon_signal[i]=f
# -
crimson_off[0] + 1000 - crimson_on[0] + 2500
# +
#df_freq_upon_signal_2 = pd.DataFrame()
df_freq_upon_signal_3 = pd.DataFrame()
for i in range(len(crimson_on)-1):
on_signal = crimson_on[i] - 2500
off_signal = crimson_off[i] + 1000
#prior_off_signal = crimson_off[i-1]
if i != 0:
if i !=len(crimson_on):
next_off_signal = crimson_off[i+1]
next_on_signal = crimson_on[i+1]
prior_off_signal = crimson_off[i-1]
alt_f = b2_frequencies[prior_off_signal: next_on_signal]
f3 = signal.resample(alt_f, 1000000)
xnew3 = np.linspace(prior_off_signal, next_on_signal, 1000000, endpoint=False)
df_freq_upon_signal_3[i]=f3
#f = b2_frequencies[on_signal: off_signal]
#f3 = signal.resample(alt_f, 1000000)
#xnew3 = np.linspace(prior_off_signal, prior_off_signal, 1000000, endpoint=False)
#x = np.linspace (prior_off_signal,next_on_signal, next_on_signal-prior_off_signal, endpoint = False)
#f2 = signal.resample(f, 500000)
#xnew = np.linspace(on_signal, off_signal, 500000, endpoint=False)
#x = np.linspace (on_signal, off_signal, off_signal-on_signal, endpoint = False)
#df_freq_upon_signal_2[i]=f2
# -
# d={}
# for x in range(1,10):
# d["crimson{0}".format(i)]="Hello"
CHANGE FLY NUM
# +
#df_freq_upon_signal_2 = pd.DataFrame()
df_freq_upon_signal_4 = pd.DataFrame()
d={}
for i in range(len(crimson_on)-1):
on_signal = crimson_on[i] - 2500
off_signal = crimson_off[i] + 1000
#prior_off_signal = crimson_off[i-1]
if i != 0:
if i !=len(crimson_on):
next_off_signal = crimson_off[i+1]
next_on_signal = crimson_on[i+1]
prior_off_signal = crimson_off[i-1]
alt_f = b2_frequencies[prior_off_signal: next_on_signal]
f3 = signal.resample(alt_f, 1000000)
xnew3 = np.linspace(prior_off_signal, next_on_signal, 1000000, endpoint=False)
df_freq_upon_signal_4[i]=f3
resampled_crim = signal.resample(crimson[prior_off_signal: next_on_signal],1000000)
resampled_times = signal.resample(times[prior_off_signal: next_on_signal],1000000)
d["fly03_b2_df{0}".format(i)]=f3
d["fly03_crimson{0}".format(i)]=resampled_crim
d["fly03_times{0}".format(i)]= resampled_times
#f = b2_frequencies[on_signal: off_signal]
#f3 = signal.resample(alt_f, 1000000)
#xnew3 = np.linspace(prior_off_signal, prior_off_signal, 1000000, endpoint=False)
#x = np.linspace (prior_off_signal,next_on_signal, next_on_signal-prior_off_signal, endpoint = False)
#f2 = signal.resample(f, 500000)
#xnew = np.linspace(on_signal, off_signal, 500000, endpoint=False)
#x = np.linspace (on_signal, off_signal, off_signal-on_signal, endpoint = False)
#df_freq_upon_signal_2[i]=f2
# -
d
resampled_df = pd.DataFrame(d)
resampled_df.head()
from scipy import stats
fly_03_b2_mean_freq_change = df_freq_upon_signal_4.mean(axis=1)
fly_03_b2_freq_error = stats.sem(df_freq_upon_signal_4,axis = 1)
#fly_01_b2_mean_freq_change.iloc[0]
fly_03_b2_mean_freq_change_series = fly_03_b2_mean_freq_change.T
#type(_)
type(fly_03_b2_mean_freq_change_series)
# +
#fly_01_b2_mean_freq_change_series
# -
type(fly_03_b2_mean_freq_change)
type(fly_03_b2_freq_error)
#fly_01_b2_freq_error
#fly_01_b2_freq_error = pd.Series(fly_01_b2_freq_error)
#shape(fly_01_b2_freq_error)
#fly_01_b2_mean_freq_change = df.values(fly_01_b2_mean_freq_change)
# +
#fly_01_b2_mean_freq_change.head()
#fly_01_b2_freq_error = pd.DataFrame(fly_01_b2_freq_error)
# -
d["fly_03_b2_mean_freq_change_series"]=fly_03_b2_mean_freq_change_series
d["fly_03_b2_freq_error"]=fly_03_b2_freq_error
resampled_df = pd.DataFrame(d)
resampled_df.head()
# +
resampled_df.to_csv('/home/alysha/analysis_files/S81/s81_b2_df_fly_03.csv', index=False)
# -
# +
df_freq_upon_signal_2 = pd.DataFrame()
for i in range(len(crimson_on)):
on_signal = crimson_on[i] - 2500
off_signal = crimson_off[i] + 1000
#prior_off_signal = crimson_off[i-1]
f = b2_frequencies[on_signal: off_signal]
#f3 = signal.resample(alt_f, 1000000)
#xnew3 = np.linspace(prior_off_signal, prior_off_signal, 1000000, endpoint=False)
#x = np.linspace (prior_off_signal,next_on_signal, next_on_signal-prior_off_signal, endpoint = False)
f2 = signal.resample(f, 500000)
xnew = np.linspace(on_signal, off_signal, 500000, endpoint=False)
x = np.linspace (on_signal, off_signal, off_signal-on_signal, endpoint = False)
df_freq_upon_signal_2[i]=f2
# -
# +
x = np.arange(on_signal, off_signal)
xnew = np.linspace(on_signal, off_signal, 500000, endpoint=False)
import matplotlib.pyplot as plt
plt.plot(x, f, 'go-', xnew, f2, '.-', on_signal, f[12], 'ro')
plt.legend(['data', 'resampled'], loc='best')
plt.show()
# +
x = np.arange(on_signal, off_signal)
xnew = np.linspace(on_signal, off_signal, 500000, endpoint=False)
import matplotlib.pyplot as plt
plt.plot(x, f, 'go-', xnew, f2, '.-', off_signal, f[12], 'ro')
plt.legend(['data', 'resampled'], loc='best')
plt.show()
# -
# +
fig =plt.figure(18)
x = np.arange(prior_off_signal, next_on_signal)
xnew = np.linspace(prior_off_signal, next_on_signal, 1000000, endpoint=False)
ax2 = fig.add_subplot(212)
import matplotlib.pyplot as plt
plt.plot(x, alt_f, 'go-', xnew, f3, '.-', next_on_signal, alt_f[11], 'ro')
ax2.axvspan(on_signal, off_signal, facecolor = 'r', edgecolor = 'none', alpha = 0.2)
#ax2.axvspan(prior_on_signal, prior_off_signal, facecolor = 'r', edgecolor = 'none', alpha = 0.2)
plt.legend(['data', 'resampled'], loc='best')
plt.show()
# -
df_freq_upon_signal_3.head()
df_freq_upon_signal_2.head()
# +
outliers =[]
'''
for i in df_freq_upon_signal:
#on_signal = crimson_on[i]
for j in range(len(df_freq_upon_signal[i])):
if df_freq_upon_signal[i][j]>= 150:
outliers.append(i)
'''
# +
#print(outliers)
#new_df = df_freq_upon_signal.drop(17, axis=1)#.head()
# -
#new_df = df_freq_upon_signal_2
new_df = df_freq_upon_signal_3
# +
#fig =plt.figure(18)
#x = np.arange(prior_off_signal, next_on_signal)
#xnew = np.linspace(prior_off_signal, next_on_signal, 1000000, endpoint=False)
#ax2 = fig.add_subplot(212)
#import matplotlib.pyplot as plt
#plt.plot(x, alt_f, 'go-', xnew, f3, '.-', next_on_signal, alt_f[11], 'ro')
#ax2.axvspan(on_signal, off_signal, facecolor = 'r', edgecolor = 'none', alpha = 0.2)
##ax2.axvspan(prior_on_signal, prior_off_signal, facecolor = 'r', edgecolor = 'none', alpha = 0.2)
#plt.legend(['data', 'resampled'], loc='best')
#plt.show()
# +
fig =plt.figure(15)
ax1 = plt.subplot(211)
for i in new_df:
ax1.plot(xnew, new_df[i], 'b', linewidth =0.5, alpha = 0.2)
ax1.set_ylim((-10,500))
#ax1.set_ylimit(-0.1, 100)
#ax1.set_xlim((1200000,1300000))
mean_freq_change = new_df.mean(axis=1)
ax1.plot(xnew, mean_freq_change, 'b', linewidth =2.0)
ax1.axvspan(on_signal, off_signal, facecolor = 'r', edgecolor = 'none', alpha = 0.2)
'''
ax1.plot(times, crimson, 'r', linewidth =1.0)
ax2 = plt.subplot(212, sharex=ax1)
ax2.set_ylim(0, 20)
#ax2.set_xlim(20, 80)
#savitzky_golay
'''
'''freq_hat = savgol_filter(b2_frequencies, 7, 5) # window size 51, polynomial order 3'''
#freq_hat= b2_frequencies
#ax2.plot(times_k, b2_frequencies,'k', linewidth = 0.75)
#for i in range(len(crimson_on)):
# ax1.axvspan(times[crimson_on[i]], times[crimson_off[i]], facecolor = 'r', edgecolor = 'none', alpha = 0.3)
# #ax2.axvspan(times[crimson_on[i]], times[crimson_off[i]], facecolor = 'r', edgecolor = 'none', alpha = 0.3)
# -
from scipy import stats
mean_freq_change = new_df.mean(axis=1)
freq_error = stats.sem(new_df,axis = 1)
# +
fig =plt.figure(16)
#wbf_means = wbf_means[0:len(wbf_means):10]
#wbf_error = stats.sem(all_wbf,axis = 0)
#wbf_error = wbf_error[0:len(wbf_error):10]
ax2 = fig.add_subplot(212)
ax2.set_ylim((-30,150))
#ax1.set_ylimit(-0.1, 100)
#ax2.set_xlim((1220000,1300000))
#ax2.set_xlim((1340000,1400000))
#ax2.axvspan(0.25, .75, facecolor = 'r', edgecolor = 'none', alpha = 0.3)
ax2.axvspan(on_signal, off_signal, facecolor = 'r', edgecolor = 'none', alpha = 0.2)
ax2.plot(xnew,mean_freq_change, 'b')
ax2.fill_between(xnew,mean_freq_change+freq_error, mean_freq_change-freq_error,color='k', alpha = 0.3, edgecolor = 'none')
ax2.set_title(r'$\mathrm{Activity\ of\ Power \ Muscle:}$', size=12, horizontalalignment='center', y=1.08)# \ \mu=100,\ \sigma=15$', size=12, horizontalalignment='center', y=1.08) #verticalalignment='top')
ax2.set_ylabel('Frequency of Power Muscle Firing', size=10)
plt.xlabel('Time (s)', size=10) # $\phi$', size=10)
#ax2.title('B2 spike frequency')
# -
### Then plot this over time
### Update on git -- new analysis file for each genotype
### Develop a pattern of dataframe organization
### blue card
### bills
### analysis
### tutor
### cv website
### laudry
# +
#altair and bokeh plotting
new_df.to_csv('s81_b2_06_df.csv', index=False)
# -
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 | 32,768 |
/deepLizard.ipynb | 9ae37b34108bbca1d883e65dd041c4f7335ac0b1 | [] | no_license | codushlaine/ml | https://github.com/codushlaine/ml | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 54,835 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="f8NMnwZ6u1Iy"
# ## 유튜브 랭킹 시각화
# + id="5vZsc3j3uwru"
# !apt-get install -y fonts-nanum > /dev/null
# !fc-cache -fv > /dev/null
# !rm -rf ~/.cache/matplotlib > /dev/null
# + [markdown] id="sCY_q-7dvJB9"
# - 런타임 다시 시작
# + id="f5EfkFi5vDYQ"
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['axes.unicode_minus'] = False
plt.rc('font', family='NanumBarunGothic')
# + colab={"resources": {"http://localhost:8080/nbextensions/google.colab/files.js": {"data": "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", "ok": true, "headers": [["content-type", "application/javascript"]], "status": 200, "status_text": ""}}, "base_uri": "https://localhost:8080/", "height": 76} id="TxB7JIeyvVKZ" outputId="6a2eb73a-5fe1-4211-a287-f7e4bf7fc436"
# 파일 업로드
from google.colab import files
uploaded = files.upload()
filename = list(uploaded.keys())[0]
# + colab={"base_uri": "https://localhost:8080/", "height": 204} id="mfbozVzQvysB" outputId="d40b9f28-71ce-404f-b5ca-971e79473786"
df = pd.read_csv(filename)
df.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 306} id="xj__FlF1v8Oy" outputId="0a0d3503-4956-42bc-a2cd-cd1aec0c0d97"
# 구독자수(문자열) --> 구독자수2(정수) : 한번만 쓸거면 람다
df['구독자수2'] = df.구독자수.apply(lambda x: int(x.replace(',','')))
df.head()
# + id="5vnesEbqwu1N"
# 여러개 쓸 때는 함수로 만들어서 사용
def str2int(x):
return int(x.replace(',',''))
# + colab={"base_uri": "https://localhost:8080/", "height": 306} id="aT5tf7Wuxdqj" outputId="3424c1cd-c449-4bfd-875f-519cb4176101"
df['비디오수2'] = df.비디오수.apply(str2int)
df.head()
# + [markdown] id="tmWbIYMKyfSW"
# - 비디오수 Top20 (채널명, 비디오수) 막대 그래프
# + colab={"base_uri": "https://localhost:8080/", "height": 359} id="b0cu_qaxyJ_g" outputId="a5c2d2ed-5b7a-437d-c869-54ae0b852877"
df.sort_values(by='비디오수2', ascending=False).head(10)
# + colab={"base_uri": "https://localhost:8080/", "height": 450} id="Fsn9gUYy2gmi" outputId="3a9fe84c-3418-41c6-933c-47d0b2ed6fbf"
df2 = df[['채널명', '비디오수2']].sort_values(by='비디오수2', ascending=False)
df2.set_index('채널명', inplace=True)
df2.head(20).sort_values(by='비디오수2').plot(kind='barh', grid=True, figsize=(12,8))
plt.title('비디오수 Top20 채널')
plt.show()
# + colab={"base_uri": "https://localhost:8080/", "height": 463} id="ozWioiWP3K6y" outputId="1804d792-f91c-425e-d224-3df3a5fb582f"
import seaborn as sns
df2 = df[['채널명', '비디오수2']].sort_values(by='비디오수2', ascending=False)
plt.figure(figsize=(12,8))
sns.barplot(y='채널명', x='비디오수2', data=df2.head(20))
plt.title('비디오수 Top20 채널')
plt.show()
# + [markdown] id="bFioNFFL8jqC"
# - 조회수 기준 Top20 채널 시각화
# + colab={"base_uri": "https://localhost:8080/", "height": 426} id="rPQSiAjG45Jo" outputId="c41db3d6-a5ad-447d-e364-6aae6788628b"
df['조회수2'] = df.조회수.apply(str2int)
plt.figure(figsize=(12,8))
sns.barplot(y='채널명', x='조회수2', data=df.sort_values(by='조회수2', ascending=False).head(20))
plt.title('조회수 Top20 채널')
plt.show()
# + [markdown] id="I1S2abFa-vh6"
# - 카테고리별 채널수 분포
# + colab={"base_uri": "https://localhost:8080/", "height": 700} id="w676RUu9HMPz" outputId="242f4827-44b3-4e01-f462-4d36f5b9d759"
df.카테고리.value_counts().to_frame()
# + colab={"base_uri": "https://localhost:8080/", "height": 481} id="3FGGJlA1HfIW" outputId="16925900-846f-41ee-c366-f0160e6bc3c3"
df3 = df['카테고리'].value_counts().to_frame()
plt.figure(figsize=(12,8))
plt.pie('카테고리', labels=df3.index, data=df3, autopct='%.1f%%')
plt.title('카테고리별 채널 수')
plt.show()
# + [markdown] id="SEh8kMdUHipO"
# - 카테고리별 구독자수 합계 시각화
# + colab={"base_uri": "https://localhost:8080/", "height": 731} id="JSq5rvRzHh9P" outputId="bd9d6ccf-76dc-4765-f223-efa94cfb51a0"
df4 = df[['카테고리','구독자수2']].groupby('카테고리').agg(['count', 'sum'])
df4.columns = ['채널수','구독자수_합계']
df4.sort_values(by='구독자수_합계', ascending=False, inplace=True)
df4
# + colab={"base_uri": "https://localhost:8080/", "height": 471} id="MFuPcrkLHmF4" outputId="1c15ce62-7657-4a0f-d9bf-e58889dfcf27"
plt.figure(figsize=(12,8))
sns.barplot(x='구독자수_합계', y=df4.index, data=df4)
plt.title('카테고리별 구독자수 합계')
plt.grid()
plt.show()
# + colab={"base_uri": "https://localhost:8080/", "height": 474} id="TNdQiq4dHpCe" outputId="bcbfedb9-f45d-45c5-9b58-25204fdf4596"
df_new = df[['카테고리', '구독자수2']].groupby('카테고리').agg(['count','sum']) \
.reset_index().sort_values(by=('구독자수2', 'sum'), ascending=False)
plt.figure(figsize=(12, 8))
sns.barplot(x= ('구독자수2', 'sum'), y='카테고리', data=df_new)
plt.title('카테고리별 구독자수 합계', size=15)
plt.xlabel('구독자수(단위:억)', size=12)
plt.ylabel('카테고리', size=12)
plt.show()
# + colab={"base_uri": "https://localhost:8080/", "height": 471} id="IguF00s2Hrdj" outputId="f026786a-71e7-4758-d545-a5c5027a1a26"
df4 = df.groupby('카테고리').sum().sort_values(by='구독자수2',ascending=False)
plt.figure(figsize=(12,8))
sns.barplot(x='구독자수2', y=df4.index, data=df4)
plt.xlabel('구독자 합계')
plt.title('카테고리별 구독자 수')
plt.show()
# + id="9FZzp-n3Htcd"
| 12,108 |
/ML/recommend.ipynb | 831578c1add8bec7e1b22eea202c9a91590da436 | [
"MIT"
] | permissive | rajshah16/food-ordering-system-with-ML | https://github.com/rajshah16/food-ordering-system-with-ML | 3 | 1 | MIT | 2020-12-29T16:59:49 | 2020-12-29T16:53:53 | HTML | Jupyter Notebook | false | false | .py | 3,160 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
import numpy as np
import math
import json
import time
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
#from sklearn.externals import joblib
import scipy.sparse
from scipy.sparse import csr_matrix
import warnings; warnings.simplefilter('ignore')
from sklearn.feature_extraction.text import CountVectorizer
import joblib
ds = pd.read_csv("C:/Users/Ranveer/Desktop/proramming/ML/test/content.csv")
ds.head()
ds.tail()
tf = TfidfVectorizer(analyzer='word', ngram_range=(1, 3), min_df=0, stop_words='english')
tfidf_matrix = tf.fit_transform(ds['description'])
cosine_similarities = linear_kernel(tfidf_matrix, tfidf_matrix)
results = {}
for idx, row in ds.iterrows():
similar_indices = cosine_similarities[idx].argsort()[:-100:-1]
similar_items = [(cosine_similarities[idx][i], ds['id'][i]) for i in similar_indices]
results[row['id']] = similar_items[1:]
print('done!')
def item(id):
return ds.loc[ds['id'] == id]['description'].tolist()[0].split(' - ')[0]
# Just reads the results out of the dictionary.
def recommend(item_id, num):
print("Recommending " + str(num) + " products similar to " + item(item_id) + "...")
print("-------")
recs = results[item_id][:num]
for rec in recs:
print("Recommended: " + item(rec[1]) + " (score:" + str(rec[0]) + ")")
recommend(item_id=25, num=3)
# -
| 1,913 |
/Intro2MachineLearning/2_FirstMachineLearningModel.ipynb | 377616c9f1c010220d6772d80126fd41009599c4 | [] | no_license | tubademir23/globalaihub.kaggle-master | https://github.com/tubademir23/globalaihub.kaggle-master | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 10,820 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# **[Introduction to Machine Learning Home Page](https://www.kaggle.com/learn/intro-to-machine-learning)**
#
# ---
#
# ## Recap
# So far, you have loaded your data and reviewed it with the following code. Run this cell to set up your coding environment where the previous step left off.
# +
# Code you have previously used to load data
import pandas as pd
# Path of the file to read
iowa_file_path = '../input/train.csv'
home_data = pd.read_csv(iowa_file_path)
# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.machine_learning.ex3 import *
print("Setup Complete")
# -
# # Exercises
#
# ## Step 1: Specify Prediction Target
# Select the target variable, which corresponds to the sales price. Save this to a new variable called `y`. You'll need to print a list of the columns to find the name of the column you need.
#
# print the list of columns in the dataset to find the name of the prediction target
# +
#print(home_data.head())
y = home_data["SalePrice"]
# Check your answer
step_1.check()
# +
# The lines below will show you a hint or the solution.
# step_1.hint()
# step_1.solution()
# -
# ## Step 2: Create X
# Now you will create a DataFrame called `X` holding the predictive features.
#
# Since you want only some columns from the original data, you'll first create a list with the names of the columns you want in `X`.
#
# You'll use just the following columns in the list (you can copy and paste the whole list to save some typing, though you'll still need to add quotes):
# * LotArea
# * YearBuilt
# * 1stFlrSF
# * 2ndFlrSF
# * FullBath
# * BedroomAbvGr
# * TotRmsAbvGrd
#
# After you've created that list of features, use it to create the DataFrame that you'll use to fit the model.
# +
# Create the list of features below
feature_names = ['LotArea','YearBuilt','1stFlrSF','2ndFlrSF','FullBath','BedroomAbvGr','TotRmsAbvGrd']
# Select data corresponding to features in feature_names
X = home_data[feature_names]
# Check your answer
step_2.check()
# +
# step_2.hint()
# step_2.solution()
# -
# ## Review Data
# Before building a model, take a quick look at **X** to verify it looks sensible
# +
# Review data
# print description or statistics from X
print(X.describe())
# print the top few lines
print(X.head())
# -
# ## Step 3: Specify and Fit Model
# Create a `DecisionTreeRegressor` and save it iowa_model. Ensure you've done the relevant import from sklearn to run this command.
#
# Then fit the model you just created using the data in `X` and `y` that you saved above.
# +
from sklearn.tree import DecisionTreeRegressor
#specify the model.
#For model reproducibility, set a numeric value for random_state when specifying the model
iowa_model = DecisionTreeRegressor(random_state=1)
# Fit the model
iowa_model.fit(X,y)
# Check your answer
step_3.check()
# +
# step_3.hint()
# step_3.solution()
# -
# ## Step 4: Make Predictions
# Make predictions with the model's `predict` command using `X` as the data. Save the results to a variable called `predictions`.
# +
predictions = iowa_model.predict(X)
print(predictions)
# Check your answer
step_4.check()
# +
# step_4.hint()
# step_4.solution()
# -
# ## Think About Your Results
#
# Use the `head` method to compare the top few predictions to the actual home values (in `y`) for those same homes. Anything surprising?
#
# You can write code in this cell
print(predictions)
print(y)
# It's natural to ask how accurate the model's predictions will be and how you can improve that. That will be you're next step.
#
# # Keep Going
#
# You are ready for **[Model Validation](https://www.kaggle.com/dansbecker/model-validation).**
#
# ---
# **[Introduction to Machine Learning Home Page](https://www.kaggle.com/learn/intro-to-machine-learning)**
#
#
#
#
#
# *Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum/161285) to chat with other Learners.*
st, clf.predict_proba(X_test))
_ = plt.title('ROC Curves of Random Forest')
# ### Model: AdaBoost
adb = AdaBoostClassifier()
parameters = {'n_estimators': range(30, 60), 'learning_rate': [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1], 'algorithm': ['SAMME', 'SAMME.R']}
clf = GridSearchCV(adb, parameters, cv = 5)
clf.fit(X_train, y_train)
train_score_adb = clf.score(X_train, y_train)
test_score_adb = clf.score(X_test, y_test)
print("Training score: {:.5f}".format(train_score_adb))
print("Test score: {:.5f}".format(test_score_adb))
# #### Plot ROC Curves of AdaBoost
_ = plot_roc_curve(y_test, clf.predict_proba(X_test))
_ = plt.title('ROC Curves of AdaBoost')
# ### Model: Bagging
bgc = BaggingClassifier()
parameters = {'n_estimators': range(5,15),'bootstrap': [True, False], 'warm_start': [True, False]}
clf = GridSearchCV(bgc, parameters, cv = 5)
clf.fit(X_train, y_train)
train_score_bgc = clf.score(X_train, y_train)
test_score_bgc = clf.score(X_test, y_test)
print("Training score: {:.5f}".format(train_score_bgc))
print("Test score: {:.5f}".format(test_score_bgc))
# #### Plot ROC Curves of Bagging
_ = plot_roc_curve(y_test, clf.predict_proba(X_test))
_ = plt.title('ROC Curves of Bagging')
# ### Combine all the results together in one chart
models = pd.DataFrame({
'Model' : ['Logistic Regression', 'SVM', 'kNN', 'Decision Tree', 'Random Forest', 'AdaBoost', 'Bagging'],
'Training_Score' : [train_score_lgr, train_score_svm, train_score_knn, train_score_dt, train_score_rfc, train_score_adb, train_score_bgc],
'Testing_Score' : [test_score_lgr, test_score_svm, test_score_knn, test_score_dt, test_score_rfc, test_score_adb, test_score_bgc]
})
models.sort_values(by='Testing_Score', ascending=False)
# ## Final Thoughts:
#
# * Logistic Regression is one of the basic classification models that usually be used first to see the results;
# * Random Forest performs best among all the classification models;
# * Bagging model has really close score to random forest model (both training and test)
# * The scores and ROC curves indicate that all the models (except logistic regression) did a excellent job in predicting.
| 6,402 |
/notebooks/sidekick-varying-hyperparameters.ipynb | ead5bccf7541b9bd2df3803d5b620bd41eee9fb9 | [] | no_license | victorkristof/sidekick-prediction | https://github.com/victorkristof/sidekick-prediction | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 183,296 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 2
# language: python
# name: python2
# ---
# # Sidekick - Varying hyperparameters
# We try in this notebook to perform the regression over a project by varying the hyperparameters accross time.
# +
# %matplotlib inline
import os
import sys
sys.path.insert(0, os.path.abspath('../utils/')) # Add sibling to Python path
sys.path.insert(0, os.path.abspath('../src/')) # Add sibling to Python path
sys.stdout.flush() # Print output on the fly in Notebook
import matplotlib
matplotlib.rcParams['figure.figsize'] = (18,8)
matplotlib.rcParams['font.size'] = 16
matplotlib.rcParams['legend.fontsize'] = 16
from IPython.display import display
import numpy as np
import gptools
import cPickle as cp
import matplotlib.pyplot as plt
from math import floor
from dataset import Sidekick
from misc_utils import progress
DATA_DIR = "../data/sidekick"
# -
# ## Prepare data
sk = Sidekick()
project = sk['14035777']
threshold = 0.8
money = project.money
time, money = project.resample(money, N=100)
x_train, y_train, x_test, y_test = project.split(time, money, threshold)
# ## Train model
# ### Squared Exponential
k = gptools.SquaredExponentialKernel(param_bounds=[(0, 1), (1, 400)])
gp = gptools.GaussianProcess(k)
gp.add_data(x_train, y_train)
gp.optimize_hyperparameters(random_starts=20)
gp.plot()
# ### Varying lengthscale
k = gptools.GibbsKernel1dGaussArb(param_bounds=[(0, 1), (0, 10), (0, 10), (0, 5), (0, len(x_train))])
gp = gptools.GaussianProcess(k)
gp.add_data(x_train, y_train)
gp.optimize_hyperparameters(random_starts=20)
sigmaf = gp.params[0]
l1 = gp.params[1]
l2 = gp.params[2]
lw = gp.params[3]
x0 = gp.params[4]
gp.plot()
# ## Plot lengthscale
# +
def l_tanh(x, l1, l2, lw, x0):
return 0.5 * (l1+l2) - 0.5 * (l1 - l2)* np.tanh((x - x0) / lw)
def l_gauss(x, l1, l2, lw, x0):
return l1 - (l1 - l2) * np.exp(-4*np.log(2 * ((x - x0)**2) / (lw**2)))
x = np.arange(0, 1000)
plt.plot(x_train, l_gauss(x_train, l1, l2, lw, x0))
# -
# ### Plot prediction
y_star, err_y_star = gp.predict(x_test)
plt.plot(x_test, y_test, 'xb')
plt.plot(x_test, y_star, 'xr')
plt.legend(('Actual', 'Predicted'), loc=4)
| 2,340 |
/apps/ray/parameter_server/sharded_parameter_server.ipynb | 6c25925c2fc6c93f58ec175a02c4a92d1ab47067 | [
"Apache-2.0"
] | permissive | intel-analytics/analytics-zoo | https://github.com/intel-analytics/analytics-zoo | 3,104 | 996 | Apache-2.0 | 2023-09-06T01:51:18 | 2023-09-03T14:54:03 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 15,408 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python (ray_train)
# language: python
# name: ray_train
# ---
# # This notebook is adapted from:
# https://github.com/ray-project/tutorial/tree/master/examples/sharded_parameter_server.ipynb
#
# # Sharded Parameter Servers
#
# **GOAL:** The goal of this exercise is to use actor handles to implement a sharded parameter server example for **distributed asynchronous stochastic gradient descent**.
#
# Before doing this exercise, make sure you understand the concepts from the exercise on **Actor Handles**.
#
# ### Parameter Servers
#
# A parameter server is simply an object that stores the parameters (or "weights") of a machine learning model (this could be a neural network, a linear model, or something else). It exposes two methods: one for getting the parameters and one for updating the parameters.
#
# In a typical machine learning training application, worker processes will run in an infinite loop that does the following:
# 1. Get the latest parameters from the parameter server.
# 2. Compute an update to the parameters (using the current parameters and some data).
# 3. Send the update to the parameter server.
#
# The workers can operate synchronously (that is, in lock step), in which case distributed training with multiple workers is algorithmically equivalent to serial training with a larger batch of data. Alternatively, workers can operate independently and apply their updates asynchronously. The main benefit of asynchronous training is that a single slow worker will not slow down the other workers. The benefit of synchronous training is that the algorithm behavior is more predictable and reproducible.
# +
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import ray
import time
# -
# # Init SparkContext
# +
from zoo.common.nncontext import init_spark_on_local, init_spark_on_yarn
import numpy as np
import os
hadoop_conf_dir = os.environ.get('HADOOP_CONF_DIR')
if hadoop_conf_dir:
sc = init_spark_on_yarn(
hadoop_conf=hadoop_conf_dir,
conda_name=os.environ.get("ZOO_CONDA_NAME", "zoo"), # The name of the created conda-env
num_executors=2,
executor_cores=4,
executor_memory="2g",
driver_memory="2g",
driver_cores=1,
extra_executor_memory_for_ray="3g")
else:
sc = init_spark_on_local(cores = 8, conf = {"spark.driver.memory": "2g"})
# -
# It may take a while to ditribute the local environment including python and java to cluster
import ray
from zoo.ray import RayContext
ray_ctx = RayContext(sc=sc, object_store_memory="4g")
ray_ctx.init()
#ray.init(num_cpus=30, include_webui=False, ignore_reinit_error=True)
# A simple parameter server can be implemented as a Python class in a few lines of code.
#
# **EXERCISE:** Make the `ParameterServer` class an actor.
# +
dim = 10
@ray.remote
class ParameterServer(object):
def __init__(self, dim):
self.parameters = np.zeros(dim)
def get_parameters(self):
return self.parameters
def update_parameters(self, update):
self.parameters += update
ps = ParameterServer.remote(dim)
# -
# A worker can be implemented as a simple Python function that repeatedly gets the latest parameters, computes an update to the parameters, and sends the update to the parameter server.
# +
@ray.remote
def worker(ps, dim, num_iters):
for _ in range(num_iters):
# Get the latest parameters.
parameters = ray.get(ps.get_parameters.remote())
# Compute an update.
update = 1e-3 * parameters + np.ones(dim)
# Update the parameters.
ps.update_parameters.remote(update)
# Sleep a little to simulate a real workload.
time.sleep(0.5)
# Test that worker is implemented correctly. You do not need to change this line.
ray.get(worker.remote(ps, dim, 1))
# -
# Start two workers.
worker_results = [worker.remote(ps, dim, 100) for _ in range(2)]
# As the worker tasks are executing, you can query the parameter server from the driver and see the parameters changing in the background.
print(ray.get(ps.get_parameters.remote()))
# ## Sharding a Parameter Server
#
# As the number of workers increases, the volume of updates being sent to the parameter server will increase. At some point, the network bandwidth into the parameter server machine or the computation down by the parameter server may be a bottleneck.
#
# Suppose you have $N$ workers and $1$ parameter server, and suppose each of these is an actor that lives on its own machine. Furthermore, suppose the model size is $M$ bytes. Then sending all of the parameters from the workers to the parameter server will mean that $N * M$ bytes in total are sent to the parameter server. If $N = 100$ and $M = 10^8$, then the parameter server must receive ten gigabytes, which, assuming a network bandwidth of 10 giga*bits* per second, would take 8 seconds. This would be prohibitive.
#
# On the other hand, if the parameters are sharded (that is, split) across `K` parameter servers, `K` is `100`, and each parameter server lives on a separate machine, then each parameter server needs to receive only 100 megabytes, which can be done in 80 milliseconds. This is much better.
#
# **EXERCISE:** The code below defines a parameter server shard class. Modify this class to make `ParameterServerShard` an actor. We will need to revisit this code soon and increase `num_shards`.
# +
@ray.remote
class ParameterServerShard(object):
def __init__(self, sharded_dim):
self.parameters = np.zeros(sharded_dim)
def get_parameters(self):
return self.parameters
def update_parameters(self, update):
self.parameters += update
total_dim = (10 ** 8) // 8 # This works out to 100MB (we have 25 million
# float64 values, which are each 8 bytes).
num_shards = 2 # The number of parameter server shards.
assert total_dim % num_shards == 0, ('In this exercise, the number of shards must '
'perfectly divide the total dimension.')
# Start some parameter servers.
ps_shards = [ParameterServerShard.remote(total_dim // num_shards) for _ in range(num_shards)]
assert hasattr(ParameterServerShard, 'remote'), ('You need to turn ParameterServerShard into an '
'actor (by using the ray.remote keyword).')
# -
# The code below implements a worker that does the following.
# 1. Gets the latest parameters from all of the parameter server shards.
# 2. Concatenates the parameters together to form the full parameter vector.
# 3. Computes an update to the parameters.
# 4. Partitions the update into one piece for each parameter server.
# 5. Applies the right update to each parameter server shard.
# +
@ray.remote
def worker_task(total_dim, num_iters, *ps_shards):
# Note that ps_shards are passed in using Python's variable number
# of arguments feature. We do this because currently actor handles
# cannot be passed to tasks inside of lists or other objects.
for _ in range(num_iters):
# Get the current parameters from each parameter server.
parameter_shards = [ray.get(ps.get_parameters.remote()) for ps in ps_shards]
assert all([isinstance(shard, np.ndarray) for shard in parameter_shards]), (
'The parameter shards must be numpy arrays. Did you forget to call ray.get?')
# Concatenate them to form the full parameter vector.
parameters = np.concatenate(parameter_shards)
assert parameters.shape == (total_dim,)
# Compute an update.
update = np.ones(total_dim)
# Shard the update.
update_shards = np.split(update, len(ps_shards))
# Apply the updates to the relevant parameter server shards.
for ps, update_shard in zip(ps_shards, update_shards):
ps.update_parameters.remote(update_shard)
# Test that worker_task is implemented correctly. You do not need to change this line.
ray.get(worker_task.remote(total_dim, 1, *ps_shards))
# -
# **EXERCISE:** Experiment by changing the number of parameter server shards, the number of workers, and the size of the data.
#
# **NOTE:** Because these processes are all running on the same machine, network bandwidth will not be a limitation and sharding the parameter server will not help. To see the difference, you would need to run the application on multiple machines. There are still regimes where sharding a parameter server can help speed up computation on the same machine (by parallelizing the computation that the parameter server processes have to do). If you want to see this effect, you should implement a synchronous training application. In the asynchronous setting, the computation is staggered and so speeding up the parameter server usually does not matter.
# +
num_workers = 4
# Start some workers. Try changing various quantities and see how the
# duration changes.
start = time.time()
ray.get([worker_task.remote(total_dim, 5, *ps_shards) for _ in range(num_workers)])
print('This took {} seconds.'.format(time.time() - start))
# -
| 9,350 |
/Machine Learning Practice/traffic.ipynb | 8fb687180d16983fbff1568ddf4178ea02eb1161 | [] | no_license | xgenvn/Data-Science-Skills-Practice | https://github.com/xgenvn/Data-Science-Skills-Practice | 1 | 0 | null | 2020-08-21T03:39:46 | 2020-08-21T03:39:43 | null | Jupyter Notebook | false | false | .py | 9,621 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
#
#
# # <div> <center><b style="color:OrangeRed"> Flight Ticket Price Prediction </b> </center></div>
#
#
import numpy as np
import pandas as pd
import seaborn as sns
from pandas_profiling import ProfileReport
# <h2>Importing the data sets</h2>
df = pd.read_excel("Data_Flight.xlsx")
# # EDA
df.head()
# ## Below is the complete report of the dataset , toggle trough the widgets to get the information.
profile=ProfileReport(df, title='Pandas Profiling Report', explorative=True)
profile.to_widgets()
# +
sns.distplot(df['Price'])
# -
df['Airline'].value_counts().plot(kind='bar');
df['Total_Stops'].value_counts().plot(kind='bar');
df['Source'].value_counts().plot(kind='bar');
df['Destination'].value_counts().plot(kind='bar');
# +
print(df.shape)
# -
# <h2>Calculating some statistical data</h2>
df.describe()
# <h2>Checking the data type of the columns</h2>
df.info()
# ## Checking for null values
#
df.isnull().sum()
# ## Finding the rows that contain these values
print(df[df["Total_Stops"].isnull()])
print(df[df["Route"].isnull()])
df.dropna(inplace = True)
df.shape
df.isnull().sum() # No Null values now
# # Feature Engineering
# ### The airports in New Delhi and Delhi are the same.
df.replace({"New Delhi": "Delhi"}, inplace = True)
# ### Extracting Date and Month from Date_of_Journey
df["Journey_Month"] = pd.to_datetime(df["Date_of_Journey"],format = "%d/%m/%Y").dt.month
df["Journey_Date"] = pd.to_datetime(df["Date_of_Journey"],format="%d/%m/%Y").dt.day
df.head()
df.drop(["Date_of_Journey"],axis='columns',inplace=True) # Dropping the column "Date_of_Journey"
df.head()
# ### Extracting Hour and Minutes from Arrival_Time
df["Arrival_hour"] = pd.to_datetime(df['Arrival_Time']).dt.hour
df["Arrival_min"] = pd.to_datetime(df['Arrival_Time']).dt.minute
df.drop(["Arrival_Time"],axis=1,inplace=True) #Dropping the "Arrival_Time" Column
df.head()
# ### Extracting Hour and Minutes from Dep_Time
df['Dep_hour'] = pd.to_datetime(df["Dep_Time"]).dt.hour
df["Dep_min"] = pd.to_datetime(df["Dep_Time"]).dt.minute
df.drop(['Dep_Time'],axis=1,inplace=True) # #Dropping the "Dep_Time" Column
df.head()
# ### Exatracting a new column from 'Duration' column which show Total duration in Minutes
# +
df['Duration_in_Min']=(pd.to_timedelta(df['Duration']).dt.seconds // 60).astype(int)
# -
df.drop(['Duration'],axis=1,inplace=True) # #Dropping the "Duration" Column
df.head()
# ### Airline, Source & Destination are the nominal categorical variables, So converting those to dummy variables.
df.nunique()
Airline = pd.get_dummies(df[["Airline"]],drop_first=True)
Source = pd.get_dummies(df[["Source"]],drop_first=True)
Destination = pd.get_dummies(df[["Destination"]],drop_first=True)
Airline.head()
Source.head()
Destination.head()
# ### <font color="red"> Note - Originally Airlines, Source & Destination had 12, 6 & 5 unique values but data frame we are getting have 1 less column for each of them because "drop_first=True" and that is used to avoid "Dummy Trap"/ multicollinearity. </font>
# ## Merging all the data frames
#
df= pd.concat([df, Airline,Source,Destination],axis=1)
df.shape
df
df.drop(['Airline',"Source","Destination"],axis=1,inplace=True)
df
# ## Manipulating the values of the "Total_Stops" Column
df.replace({"non-stop": 0, "1 stop": 1, "2 stops": 2, "3 stops": 3, "4 stops": 4}, inplace = True)
df
df.drop(['Route','Additional_Info'],axis=1,inplace=True) # We see that routes and Total_stops do the same thing
df
sorted(df)
# +
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize = (29,29))
sns.heatmap(df.corr(), annot = True)
plt.show()
# +
X = df.drop(["Price"],axis=1)
y = df["Price"]
# -
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 7)
# +
from sklearn.ensemble import RandomForestRegressor
reg_rf = RandomForestRegressor()
# -
reg_rf.fit(X_train, y_train)
y_pred = reg_rf.predict(X_test)
y_pred
print(y_test, y_pred)
reg_rf.score(X_test,y_test)
# # Hyper Parameter Tuning
from sklearn.model_selection import RandomizedSearchCV
# Number of trees in random forest
n_estimators = [int(x) for x in np.linspace(start = 100, stop = 2000, num = 20)]
# Number of features to consider at every split
max_features = ['auto', 'sqrt']
# Maximum number of levels in tree
max_depth = [int(x) for x in np.linspace(5, 30, num = 10)]
# Minimum number of samples required to split a node
min_samples_split = [2, 5, 10, 15, 1000]
# Minimum number of samples required at each leaf node
min_samples_leaf = [1, 2, 5, 10]
random_grid = {'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf}
rf_random = RandomizedSearchCV(estimator = reg_rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)
rf_random.fit(X_train,y_train)
rf_random.best_params_
# +
from sklearn.metrics import accuracy_score
model = RandomForestRegressor(n_estimators = 1500,
min_samples_split = 5,
min_samples_leaf = 2,
max_features = 'auto',
max_depth = 24)
model.fit(X_train,y_train)
model.score(X_test,y_test)
# -
# # Creating a Joblib file for the model
import joblib
joblib.dump(model,"Flight_Ticket_Prediction_Model")
model=joblib.load('Flight_Ticket_Prediction_Model')
Airline_Air_India=0
Airline_GoAir=0
Airline_IndiGo=0
Airline_Jet_Airways=0
Airline_Jet_Airways_Business=0
Airline_Multiple_carriers=1
Airline_Multiple_carriers_Premium_economy=0
Airline_SpiceJet=0
Airline_Trujet=0
Airline_Vistara=0
Airline_Vistara_Premium_economy=0
Arrival_hour=19
Arrival_min=10
Dep_hour=9
Dep_min=50
Destination_Cochin=1
Destination_Delhi=0
Destination_Hyderabad=0
Destination_Kolkata=0
Duration_in_Min=560
Journey_Date=6
Journey_Month=3
Source_Chennai=0
Source_Delhi=1
Source_Kolkata=0
Source_Mumbai=0
Total_Stops=1
y_pred_single = model.predict([[Total_Stops,
Journey_Month,
Journey_Date,
Arrival_hour,
Arrival_min,
Dep_hour,
Dep_min,
Duration_in_Min,
Airline_Air_India,
Airline_GoAir,
Airline_IndiGo,
Airline_Jet_Airways,
Airline_Jet_Airways_Business,
Airline_Multiple_carriers,
Airline_Multiple_carriers_Premium_economy,
Airline_SpiceJet,
Airline_Trujet,
Airline_Vistara,
Airline_Vistara_Premium_economy,
Source_Chennai,
Source_Delhi,
Source_Kolkata,
Source_Mumbai,
Destination_Cochin,
Destination_Delhi,
Destination_Hyderabad,
Destination_Kolkata]])
y_pred_single
| 7,327 |
/homework6.ipynb | d1131d14c5213edea26b833c6ad968a2d4f92408 | [] | no_license | gugurt/ds4ph-bme | https://github.com/gugurt/ds4ph-bme | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 91,328 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/agu3/ds4ph-bme/blob/master/homework6.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] id="u7MrsS1vpSgN" colab_type="text"
# Consider the shhs.txt datset distributed over slack. Use linear regression with bmi to predict log(rdi4p + 1). Report the coefficients and a scatterplot with the fitted line.
# + id="axn_EP0TpMFR" colab_type="code" outputId="33b587a0-3045-46e7-8baa-872f21ac9342" colab={"base_uri": "https://localhost:8080/", "height": 332}
import pandas as pd
import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
dat = pd.read_csv('/content/shhs1.txt', delimiter = '\t')
dat
bmi = dat['bmi_s1']
lg = np.log(dat['rdi4p'] + 1)
lg = lg[~np.isnan(bmi)]
bmi = bmi[~np.isnan(bmi)]
beta1 = stats.pearsonr(bmi, lg)[0] * (np.std(lg) / np.std(bmi))
beta0 = np.mean(lg) - (beta1 * np.mean(bmi))
print(beta1)
print(beta0)
sns.scatterplot(bmi, lg)
sns.lineplot(bmi, beta0 + (beta1 * bmi))
plt.ylabel("log(rdi4p + 1)")
plt.xlabel('BMI')
# + [markdown] id="StPxQZOYHrqe" colab_type="text"
# Beta1 is 0.07648599151230158 and beta0 is -0.46304140227710433
# + [markdown] id="So0Miam5FPTu" colab_type="text"
# Using your formula from the previous question, predict rdi4p for a person with a bmi = 30.
# + id="HpkPc4CpFRmB" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 35} outputId="b72d1756-8c87-465b-ac72-943b08b81a5a"
import math
lg_30 = beta0 + (beta1 * 30)
lg_30
rdi4p_30 = math.exp(lg_30) - 1
print(rdi4p_30)
| 1,923 |
/homework/Day_014_correlation_example_Ans.ipynb | ada2e56043b6ef1a6e7fb18c0d316cb07fa7505d | [] | no_license | herocwhsu/100Day-ML-Marathon- | https://github.com/herocwhsu/100Day-ML-Marathon- | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 68,834 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # [作業目標]
# - 以下程式碼將示範在 python 如何利用 numpy 計算出兩組數據之間的相關係數，並觀察散佈圖
# - 藉由觀察相關矩陣與散佈圖的關係, 希望同學對 負相關 的變數分布情形也有比較直覺的理解
# # [作業重點]
# - 仿照 In[4], In[5] 的語法, 寫出負相關的變數, 並觀察相關矩陣以及分布圖
# +
# 載入基礎套件
import numpy as np
np.random.seed(1)
import matplotlib
import matplotlib.pyplot as plt
# %matplotlib inline
# -
# ### 弱相關
# +
# 隨機生成兩組 1000 個介於 0~50 的數的整數 x, y, 看看相關矩陣如何
x = np.random.randint(0, 50, 1000)
y = np.random.randint(0, 50, 1000)
# 呼叫 numpy 裡的相關矩陣函數 (corrcoef)
np.corrcoef(x, y)
# -
# 將分布畫出來看看吧
plt.scatter(x, y)
# ### 正相關
# +
# 隨機生成 1000 個介於 0~50 的數 x
x = np.random.randint(0, 50, 1000)
# 這次讓 y 與 x 正相關，再增加一些雜訊
y = x + np.random.normal(0, 10, 1000)
# 再次用 numpy 裡的函數來計算相關係數
np.corrcoef(x, y)
# -
# 再看看正相關的 x,y 分布
plt.scatter(x, y)
# # 作業
# - 參考範例程式碼，模擬一組負相關的資料，並計算出相關係數以及畫出 scatter plot
# +
# 隨機生成 1000 個介於 0~50 的數 x
x = np.random.randint(0, 50, 1000)
# 這次讓 y 與 x 正相關，再增加一些雜訊
y = 100 - x + np.random.normal(0, 5, 1000)
# 再次用 numpy 裡的函數來計算相關係數
np.corrcoef(x, y)
# -
# 再看看負相關的 x,y 分布
plt.scatter(x, y)
# 學員可以自行嘗試修改程式碼中模擬 y 的方式來觀察相關係數以及 scatter plot 的變化
tions, bins=np.arange(a, b + binwidth_h, binwidth_h))
# plt.hist(noOfobservations, bins=np.arange(a, b, binwidth_h))
plt.title("Histogram")
plt.xlabel("Values of X")
plt.ylabel("Frequency")
plt.show()
# +
#h = 1, minimum = a and maximum = b
# plt.hist(noOfobservations)
print("1(f)")
binwidth_h=1
plt.hist(noOfobservations, bins=np.arange(a, b + binwidth_h, binwidth_h))
plt.title("Histogram")
plt.xlabel("Values of X")
plt.ylabel("Frequency")
plt.show()
# +
#h = 2, minimum = a and maximum = b
# plt.hist(noOfobservations)
print("1(g)")
binwidth_h=2
plt.hist(noOfobservations, bins=np.arange(a, b + binwidth_h, binwidth_h))
plt.title("Histogram")
plt.xlabel("Values of X")
plt.ylabel("Frequency")
# plt.grid(axis="x")
# plt.grid(axis="y")
plt.show()
# +
#Five number summary of the box plot
Median=np.median(noOfobservations)
LowerQuartile=np.percentile(noOfobservations,25)
Q1=LowerQuartile
UpperQuartile=np.percentile(noOfobservations,75)
Q3=UpperQuartile
MaxValue=max(noOfobservations)
MinValue=min(noOfobservations)
InterQuartile=UpperQuartile-LowerQuartile
IQR=InterQuartile
print("2(a)")
print("MinValue ",MinValue)
print("LowerQuartile ",LowerQuartile)
print("Median ",Median)
print("UpperQuartile ",UpperQuartile)
print("MaxValue ",MaxValue)
# +
# Values of the 1.5 IQR whiskers
Lowerwhisker=Q1-1.5*IQR
Upperwhisker=Q3+1.5*IQR
print("2(a)")
print("InterQuartile ",IQR)
print("Lowerwhisker ",Lowerwhisker)
print("Upperwhisker ",Upperwhisker)
# +
#Five-number summary of x for category one of the group
isOne=(df.group==1)
# groupOne = data[isOne]['x']
groupOne = df[isOne]['x']
# print(One.head(20))
MinValueOne=min(groupOne)
LowerQuartileOne=np.percentile(groupOne,25)
MedianOne=np.median(groupOne)
UpperQuartileOne=np.percentile(groupOne,75)
MaxValueOne=max(groupOne)
InterQuartileOne=UpperQuartileOne-LowerQuartileOne
LowerwhiskerOne = LowerQuartileOne-1.5*InterQuartileOne
UpperwhiskerOne = UpperQuartileOne+1.5*InterQuartileOne
print("2(b)")
print("Min Value of group One ",MinValueOne)
print("Lower Quartile of group One ",LowerQuartileOne)
print("Median of group One ",Medianone)
print("Upper Quartile of group One ",UpperQuartileOne)
print("Max Value of group One ",MaxValueOne)
print("Lower whisker of group One ",LowerwhiskerOne)
print("Upper whisker of group One ",UpperwhiskerOne)
# -
groupOne.describe()
# +
#Five-number summary of x for category Zero of the group
isZero=(df.group!=1)
groupZero = data[isZero]['x']
MinValueZero=min(groupZero)
LowerQuartileZero=np.percentile(groupZero,25)
MedianZero=np.median(groupZero)
UpperQuartileZero=np.percentile(groupZero,75)
MaxValueZero=max(groupZero)
InterQuartileZero=UpperQuartileZero-LowerQuartileZero
LowerwhiskerZero = LowerQuartileZero-1.5*InterQuartileZero
UpperwhiskerZero = UpperQuartileZero+1.5*InterQuartileZero
print("2(b)")
print("Min Value of group Zero ",MinValueZero)
print("Lower Quartile of group Zero ",LowerQuartileZero)
print("Median of group Zero",Medianzero)
print("Upper Quartile of group Zero ",UpperQuartileZero)
print("Max Value of group Zero ",MaxValueZero)
print("Lower whisker of group Zero ",LowerwhiskerZero)
print("Upper whisker of group Zero ",UpperwhiskerZero)
# -
groupZero.describe()
# +
# Visualization values of x using the boxplot
print("2(c)")
plt.boxplot(noOfobservations,vert=False)
plt.title("Boxplot for Values of X")
plt.xlabel("Values of X")
plt.grid(axis="x")
plt.show()
# +
#Five number summary of x for each category of the group
isOne=(df.group==1)
isZero=(df.group!=1)
One = data[isOne]['x']
Zero = data[isZero]['x']
print("2(d)")
fig = plt.figure()
ax = fig.add_subplot(111)
# ax.boxplot(noOfobservations,vert=False)
ax.boxplot([noOfobservations,Zero,One],labels=['All x','0', '1'],vert=False)
# ax.boxplot([One,Zero], labels=['1', '0'],vert=False)
plt.xlabel("Values of X")
plt.ylabel("Values of groups")
plt.show()
# +
#Outliers for the entire data
# Lowerwhisker 27.4
# Upperwhisker 35.4
print("2(d)")
outliersBelowLowerwhisker=noOfobservations[noOfobservations<Lowerwhisker]
print(outliersBelowLowerwhisker)
outliersAboveUpperwhisker=noOfobservations[noOfobservations>Upperwhisker]
print(outliersAboveUpperwhisker)
# +
#Outliers for the group one
#Lower whisker of group One 29.449999999999992
#Upper whisker of group One 34.650000000000006
print("2(d)")
outliersLowerwhiskerone=groupOne[groupOne<LowerwhiskerOne]
print("Outliers of Lower Whisker for group one \n",outliersLowerwhiskerone)
outliersUpperwhiskerone=groupOne[groupOne>UpperwhiskerOne]
print("Outliers of Upper Whisker for group one \n",outliersUpperwhiskerone)
# +
#Outliers for the group Zero
#Lower whisker of group Zero 27.599999999999994
#Upper whisker of group Zero 32.400000000000006
print("2(d)")
outliersLowerwhiskerzero=groupZero[groupZero<LowerwhiskerZero]
print("Outliers of Lower Whisker for group zero \n",outliersLowerwhiskerzero)
outliersUpperwhiskerzero=groupZero[groupZero>UpperwhiskerZero]
print("Outliers of Upper Whisker for group zero \n",outliersUpperwhiskerzero)
# +
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
fraudData = pd.read_csv("E:\\Local Disk D\\IIT-C\\Sem 4\\CS 584 Machine Learning\\Homeworks\\Homework 1\\Fraud.csv")
df = pd.DataFrame(fraudData)
# print(df.head(10))
# -
df.head()
# +
#Percent of the fradulant data
totalData=df.FRAUD.count()
FraudData=(df.FRAUD == 1).sum()
# fraudData=df[df['FRAUD'] == 1].count()
percentFraud=(FraudData/totalData)*100
print("3(a)")
print("Percentage of fradulant data ",round(percentFraud,4))
# +
# Visualization of total spend using boxplot
print("3(b)")
isFradulantData=(df.FRAUD==1)
isOtherwiseData=(df.FRAUD!=1)
FradulantData = fraudData[isFradulantData]
OtherwiseData=fraudData[isOtherwiseData]
FradulantData = fraudData[isFradulantData]['TOTAL_SPEND']
OtherwiseData = fraudData[isOtherwiseData]['TOTAL_SPEND']
fig = plt.figure()
ax = fig.add_subplot(111)
ax.boxplot([OtherwiseData,FradulantData], labels=['0', '1'],vert=False)
plt.ylabel("Values of Fraud")
plt.xlabel("Total Amount of Claims")
plt.show()
# +
# Visualization of doctor visits using boxplot
print("3(b)")
isFradulantData=(df.FRAUD==1)
isOtherwiseData=(df.FRAUD!=1)
FradulantData = fraudData[isFradulantData]
OtherwiseData=fraudData[isOtherwiseData]
FradulantData = fraudData[isFradulantData]['DOCTOR_VISITS']
OtherwiseData = fraudData[isOtherwiseData]['DOCTOR_VISITS']
fig = plt.figure()
ax = fig.add_subplot(111)
ax.boxplot([OtherwiseData,FradulantData], labels=['0', '1'],vert=False)
plt.ylabel("Values of Fraud")
plt.xlabel("Doctor Visits")
plt.show()
# +
# Visualization of number of claims using boxplot
print("3(b)")
isFradulantData=(df.FRAUD==1)
isOtherwiseData=(df.FRAUD!=1)
FradulantData = fraudData[isFradulantData]
OtherwiseData=fraudData[isOtherwiseData]
FradulantData = fraudData[isFradulantData]['NUM_CLAIMS']
OtherwiseData = fraudData[isOtherwiseData]['NUM_CLAIMS']
fig = plt.figure()
ax = fig.add_subplot(111)
ax.boxplot([OtherwiseData,FradulantData], labels=['0', '1'],vert=False)
plt.ylabel("Values of Fraud")
plt.xlabel("Number of Claims Made")
plt.show()
# +
# Visualization of membership duration using boxplot
print("3(b)")
isFradulantData=(df.FRAUD==1)
isOtherwiseData=(df.FRAUD!=1)
FradulantData = fraudData[isFradulantData]
OtherwiseData=fraudData[isOtherwiseData]
FradulantData = fraudData[isFradulantData]['MEMBER_DURATION']
OtherwiseData = fraudData[isOtherwiseData]['MEMBER_DURATION']
fig = plt.figure()
ax = fig.add_subplot(111)
ax.boxplot([OtherwiseData,FradulantData], labels=['0', '1'],vert=False)
plt.ylabel("Values of Fraud")
plt.xlabel("Membership Duration")
plt.show()
# +
# Visualization of optical examination using boxplot
print("3(b)")
isFradulantData=(df.FRAUD==1)
isOtherwiseData=(df.FRAUD!=1)
FradulantData = fraudData[isFradulantData]
OtherwiseData=fraudData[isOtherwiseData]
FradulantData = fraudData[isFradulantData]['OPTOM_PRESC']
OtherwiseData = fraudData[isOtherwiseData]['OPTOM_PRESC']
fig = plt.figure()
ax = fig.add_subplot(111)
ax.boxplot([OtherwiseData,FradulantData], labels=['0', '1'],vert=False)
plt.ylabel("Values of Fraud")
plt.xlabel("Number of Optical Examination")
plt.show()
# +
# Visualization of number of members using boxplot
print("3(b)")
isFradulantData=(df.FRAUD==1)
isOtherwiseData=(df.FRAUD!=1)
FradulantData = fraudData[isFradulantData]
OtherwiseData=fraudData[isOtherwiseData]
FradulantData = fraudData[isFradulantData]['NUM_MEMBERS']
OtherwiseData = fraudData[isOtherwiseData]['NUM_MEMBERS']
fig = plt.figure()
ax = fig.add_subplot(111)
ax.boxplot([OtherwiseData,FradulantData], labels=['0', '1'],vert=False)
plt.ylabel("Values of Fraud")
plt.xlabel("Number of Members Covered ")
plt.show()
# -
#Orthonormalize interval variables
df.head()
# +
import scipy as sp
from scipy import linalg as la
from numpy import linalg as la2
intervalMatrix=np.array(fraudData.iloc[:,2:8].values)
orthonormalize=la.orth(intervalMatrix)
print("The orthonormalize matrix = \n", orthonormalize)
Varifiy = orthonormalize.transpose().dot(orthonormalize)
print("Identity Matrix = \n", Varifiy)
print (intervalMatrix.ndim)
# +
#Orthonormalizing interval variables
import scipy as sp
from scipy import linalg as la
from numpy import linalg as la2
intervalMatrix=np.matrix(fraudData.iloc[:,2:8].values)
print (intervalMatrix)
#Creating Transpose Matrix
transposeMatrix=intervalMatrix.transpose()*intervalMatrix
print("Multiplication of Transpose Matrix and original Matrix \n\n",transposeMatrix)
# -
#Eigen values and Eigenvectors
evals, evecs = la2.eigh(transposeMatrix)
print("3(c)(i)")
print("Eigenvalues of transposeMatrix = \n\n", evals)
print("Eigenvectors of transposeMatrix = \n\n",evecs)
# +
#Transformation matrix
print("3(c)(ii)")
transformationMatrix = evecs * la2.inv(np.sqrt(np.diagflat(evals)))
print("Transformation Matrix = \n\n", transformationMatrix)
# -
transf_im.shape
intervalMatrix.shape
transformationMatrix.shape
# +
#Transformation of intervalMatrix
transf_im=intervalMatrix*transformationMatrix
print("The Transformed Interval Matrix = \n\n", transf_im)
# +
# Identity Matrix to prove the the matrix is orthonormalization
print("3(c)(ii)")
xtx = transf_im.transpose()*transf_im
# print(np.shape(xtx))
print("Expect an Identity Matrix = \n\n", xtx)
# +
# Nearest Neighbors module
from sklearn.neighbors import KNeighborsClassifier
#Transform data as traindata
trainData = transf_im
targetData = df['FRAUD']
KNeighbor = KNeighborsClassifier(n_neighbors=5 , algorithm = 'brute', metric = 'euclidean')
nbrs = KNeighbor.fit(trainData, targetData)
print(nbrs)
# +
score=nbrs.score(trainData,targetData)
print("3(d)(i)")
print(score)
# +
# Observation of input variables
inputVariables = pd.DataFrame(columns=["TOTAL_SPEND", "DOCTOR_VISITS", "NUM_CLAIMS", "MEMBER_DURATION", "OPTOM_PRESC", "OPTOM_PRESC"],
data=[[7500,15,3,127,2,2]])
print("3(e)")
inputMatrix=np.matrix(inputVariables)
print(inputMatrix)
transInputMatrix = inputMatrix * transformationMatrix;
print(transInputMatrix)
myNeighbors = nbrs.kneighbors(transInputMatrix, return_distance = False)
print("Nearest Neighbors = \n\n", myNeighbors)
# -
#Values of all the target values
#Since the index starts from 0 so we subtract 1 from each neighbour
print("3(e)")
targetData[[588-1, 2897-1 ,1199-1, 1246-1 , 886-1]]
#Values of all the input
#Since the index starts from 0 so we subtract 1 from each neighbour
print("3(e)")
print(intervalMatrix[588-1])
print(intervalMatrix[2897-1])
print(intervalMatrix[1199-1])
print(intervalMatrix[1246-1])
print(intervalMatrix[886-1])
# +
nbrs.predict(transInputMatrix)
prediction=nbrs.predict(transInputMatrix)
print("3(f)")
print(prediction)
# -
class_proba=nbrs.predict_proba(inputMatrix)
print("3(f)")
print(class_proba)
| 13,261 |
/B/羊不能识别组群.ipynb | c73e5ccde4d08b7e0f2a18ac56494d058909cbd2 | [] | no_license | SYaoJun/Shepherd | https://github.com/SYaoJun/Shepherd | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 8,811 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
# %matplotlib inline
import matplotlib.pyplot as plt
from tkinter import *
from sklearn.cluster import MeanShift,estimate_bandwidth,KMeans
import pandas as pd
import numpy as np
import time
import math
tk =Tk()
tk.title('shepherd')
tk.wm_attributes("-topmost",1)
Width=600
Height = 600
canvas =Canvas(tk,width=Width,height=Height,bg='white',highlightthickness=0)
canvas.pack()
canvas.create_line(Width/2,0,Width/2,Height)
canvas.create_line(0,Height/2,Height,Height/2)
canvas.create_line(Height-100,Height-100,Height-100,Height,Height-100,Height-100,Height,Height-100)
tk.update()
Rs = 500
Ra = 12
Fn = 50
k=30
X=[]
n_sheeps=50
speed_of_sheep=5
speed_of_shepherd=1.2*speed_of_sheep
distance_of_approach=80
class Sheep:
def __init__(self,canvas,x,y,u,v,color):
'''对目标对象进行初始化'''
self.canvas =canvas
self.color=color
self.id =self.canvas.create_oval(x,y,u,v,fill =self.color)
self.x = np.random.uniform(-1,1)
self.y = np.random.uniform(-1,1)
self.canvas_height = self.canvas.winfo_height()
self.canvas_width = self.canvas.winfo_width()
self.tag=True
def position(self):
'''返回目标当前的位置'''
pos = self.canvas.coords(self.id)
return pos
def draw(self):
'''绘制目标对象的运动状态'''
if(self.tag==True):
pos = self.canvas.coords(self.id)
if pos[0] <=0:
self.x = 10
if pos[1] <=0:
self.y = 10
if pos[2] >self.canvas_width:
self.x = -10
if pos[3] > self.canvas_height:
self.y= -10
else:
self.x=0
self.y=0
self.canvas.move(self.id ,self.x,self.y)
def position2point(self):
'''把目标的两个坐标转换为中心的一个坐标'''
pos=self.position()
point=np.zeros((2),np.float32)
point[0]=(pos[0]+pos[2])/2
point[1]=(pos[1]+pos[3])/2
return point
def delete(self):
'''删除目标对象'''
self.canvas.delete(self.id)
def stop(self):
'''当目标对象到达指定区域则停止运动'''
self.tag=False
sheeps={}
colors=['green','blue','yellow','orange','pink','purple']
for i in range(n_sheeps):
np.random.seed(i)
x=np.random.randint(35,Height-100)
y=np.random.randint(35,Height-100)
X.append([x,y])
sheeps['sheep'+str(i)]=Sheep(canvas,x,y,x+10,y+10,colors[0])
X=np.array(X)
shepherd=Sheep(canvas,560,560,560+10,560+10,'red')
def position_point(pos):
'''将两个坐标点取平均值后集中为1个点'''
point=np.zeros((2),np.float32)
point[0]=(pos[0]+pos[2])/2
point[1]=(pos[1]+pos[3])/2
return point
def knn(x,others,k):
'''根据给出的坐标，计算出与该坐标最近的k个点,并返回局部中心点和羊内部作用力的合力方向'''
d =[math.sqrt(np.sum((x_ - x)**2)) for x_ in others]
near=np.argsort(d)
top=[others[i] for i in near[1:k+1]]
t=np.array(top)
local_m=[np.mean(t[:,0]),np.mean(t[:,1])]
ra=np.zeros(2,dtype=np.float32)
for p in near[1:k+1]:
if d[p] <= Ra:
ra+=(x - others[p])/(math.sqrt(np.sum((x-others[p])**2)))
return np.array(local_m,np.float32),ra
def check(sheep_lst,g_mean):
'''对所有的羊检查是否都在全局中心点的Fn半径范围内'''
n=len(sheep_lst)
dist = [math.sqrt(np.sum(x - g_mean)**2) for x in sheep_lst]
nearest_dist=np.sort(dist)
for i in range(n-1,-1,-1):
if nearest_dist[i] >Fn:
return False
return True
def find_farest(arrv,g_m):
'''找到离中心最远的羊'''
dist=[math.sqrt(np.sum(x - g_m)**2) for x in arrv]
nearr = np.argsort(dist)
return arrv[nearr[-1]]
def sheeps_move(herd,array,g_mean,k):
n = len(array)
last=np.zeros((n,2),dtype=np.float32)
for i in range(n):
#羊的当前位置
position= sheeps['sheep'+str(i)].position()
point = position_point(position)
#羊与牧羊犬之间的距离
ps_dist =math.sqrt(np.sum((point-herd)**2))
if ps_dist > Rs/5:
H=np.random.uniform(-2,2,size=2) #H为-1到1随机运动的大小
#H=H/math.sqrt(H[0]*H[0]+H[1]*H[1]) #把数据归一化
#last[i]=H
else:
rs=(point-herd)/ps_dist
l_mean,ra =knn(point,array,k)
C=(l_mean - point)/math.sqrt(np.sum((l_mean-point)**2))
if ps_dist < 3*Ra:
rs=0
H =0.5*last[i]+1.2*C + rs + 2*ra
H=H/math.sqrt(H[0]*H[0]+H[1]*H[1])
last[i]=H
H=H*speed_of_sheep
sheeps['sheep'+str(i)].x=H[0]
sheeps['sheep'+str(i)].y=H[1]
array[i]=last[i] + point
sheeps['sheep'+str(i)].draw()
def driving(herd,target,array,g_mean,k):
'''把羊往目标点驱赶'''
sheeps_move(herd,array,g_mean,k) #羊的移动状况
gt_dist =math.sqrt(np.sum((target-g_mean)**2))
Pd=(g_mean - target)/gt_dist*distance_of_approach + g_mean
rd=(Pd-herd)/math.sqrt(np.sum((Pd-herd)**2))*10
shepherd.x=rd[0]
shepherd.y=rd[1]
rd=rd+herd
g_mean=np.array([np.mean(array[:,0]),np.mean(array[:,1])])
shepherd.draw()
return array,g_mean,rd
def collecting(herd,array,g_mean,k):
'''把远离中心的羊聚集起来'''
far = find_farest(array,g_mean) #返回最远的羊的位置
sheeps_move(herd,array,g_mean,k) #羊的移动状况
gt_dist =math.sqrt(np.sum((far-g_mean)**2)) #最远的羊与中心的距离
Pc=(far - g_mean)/gt_dist*distance_of_approach + far
rd=(Pc-herd)/math.sqrt(np.sum((Pc-herd)**2))*speed_of_shepherd
shepherd.x=rd[0]
shepherd.y=rd[1]
rd=rd+herd
g_mean=np.array([np.mean(array[:,0]),np.mean(array[:,1])])
shepherd.draw()
return array,g_mean,rd
def all_sheeps_in(arrx):
'''判断是否所有羊都到达了目标范围'''
for p in arrx:
if(p[0]<Height-95 or p[1]<Height-95):
return False
return True
'''准备数据补充'''
target=np.array([Height,Height])
global_mean = np.array([np.mean(X[:,0]),np.mean(X[:,1])])
shepherd_point= position_point(shepherd.position())
'''集群和驱赶交替进行'''
while True:
if check(X, global_mean)==True:
X,global_mean,shepherd_point = driving(shepherd_point,target,X,global_mean,k)
else:
X,global_mean,shepherd_point = collecting(shepherd_point,X,global_mean,k)
if all_sheeps_in(X):
break
tk.update()
time.sleep(0.01)
label = Label(tk,text="游戏结束!",font=('楷体',40),fg='red')
label.place(x=180,y=280)
tk.mainloop()
| 6,546 |
/97/97_inference_sum.ipynb | b560f2cec8ad0e6b136ce0a4a8b2be18662c428c | [] | no_license | root4kaido/Rainforest-Connection | https://github.com/root4kaido/Rainforest-Connection | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 28,185 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# !pip install torch==1.6.0
# !pip install opencv-python
# !pip install torchvision==0.2.2
# !pip install albumentations
# !pip install tensorflow
# !pip install pytorch-lightning
# +
from pathlib import Path
import numpy as np
import pandas as pd
import typing as tp
import yaml
import random
import os
import sys
import soundfile as sf
import librosa
import cv2
import matplotlib.pyplot as plt
import time
import glob
from tqdm import tqdm
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import pytorch_lightning as pl
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
# import resnest.torch as resnest_torch
from torchvision import models
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import f1_score
# from resnet import ResNet, Bottleneck
from albumentations.core.transforms_interface import DualTransform, BasicTransform
import albumentations as albu
from sklearn.model_selection import StratifiedKFold
pd.options.display.max_rows = 500
pd.options.display.max_columns = 500
# -
# ## util
config_set = {
'dataset': {
'name': 'SpectrogramDataset',
'params': {
'img_size': 224,
'melspectrogram_parameters': {
'n_mels': 128,
'fmin': 50,
'fmax': 15000,
}
}
},
'loader': {
'train': {
'batch_size': 6,
'shuffle': True,
'num_workers': 2,
'pin_memory': True,
'drop_last': True,
},
'valid': {
'batch_size': 2,
'shuffle': False,
'num_workers': 2,
'pin_memory': True,
'drop_last': True,
}
}
}
SEED=100
PERIOD = 5
SPECIES_NUM = 24
EPOCH = 50
HOP_LEN = 512
SR = 48000
config = config_set
def set_seed(seed: int = 42):
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed) # type: ignore
set_seed(SEED)
INPUT_ROOT = Path("/home/knikaido/work/Rainforest-Connection/data")
RAW_DATA = INPUT_ROOT / "rfcx-species-audio-detection"
TRAIN_AUDIO_DIR = RAW_DATA / "train"
# TRAIN_RESAMPLED_AUDIO_DIRS = [
# INPUT_ROOT / "birdsong-resampled-train-audio-{:0>2}".format(i) for i in range(5)
# ]
TEST_AUDIO_DIR = RAW_DATA / "test"
OUTPUT_DIR = './output/'
pred_pathes = sorted(glob.glob(OUTPUT_DIR + '*.csv'))
pred_pathes
df_pred = np.zeros([1992, 24])
for path in pred_pathes:
df_pred += pd.read_csv(path).iloc[:, 1:].values
df_pred /= 5
sub = pd.read_csv(str(RAW_DATA / 'sample_submission.csv'))
sub.loc[:, 's0':'s23'] = df_pred
sub.to_csv(OUTPUT_DIR + '97sub.csv', index=False)
train_gby = pd.read_pickle(RAW_DATA / "train_gby_mel.pkl")
train_gby.head()
def mono_to_color(
X: np.ndarray, mean=None, std=None,
norm_max=None, norm_min=None, eps=1e-6
):
# Stack X as [X,X,X]
X = np.stack([X, X, X], axis=-1)
# Standardize
mean = mean or X.mean()
X = X - mean
std = std or X.std()
Xstd = X / (std + eps)
_min, _max = Xstd.min(), Xstd.max()
norm_max = norm_max or _max
norm_min = norm_min or _min
if (_max - _min) > eps:
# Normalize to [0, 255]
V = Xstd
V[V < norm_min] = norm_min
V[V > norm_max] = norm_max
V = 255 * (V - norm_min) / (norm_max - norm_min)
V = V.astype(np.uint8)
else:
# Just zero
V = np.zeros_like(Xstd, dtype=np.uint8)
return V
def get_criterion():
pos_weights = torch.ones(SPECIES_NUM)
pos_weights = pos_weights * SPECIES_NUM
loss_function = nn.BCEWithLogitsLoss(pos_weight=pos_weights)
return loss_function
class LitModule(pl.LightningModule):
def __init__(self):
super().__init__()
self.encoder = torch.hub.load('zhanghang1989/ResNeSt', 'resnest50', pretrained=True)
self.encoder.fc = nn.Sequential(
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Dropout(p=0.2),
nn.Linear(1024, 1024),
nn.ReLU(),
nn.Dropout(p=0.2),
nn.Linear(1024, SPECIES_NUM)
)
self.criterion = get_criterion()
def forward(self, x):
x_out = self.encoder(x)
return x_out
def configure_optimizers(self):
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, weight_decay=0.0001, momentum=0.9)
return optimizer
def training_step(self, train_batch, batch_idx):
x, y = train_batch
y_pred = self.encoder(x)
loss = self.criterion(y_pred, y)
self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
return loss
def validation_step(self, val_batch, batch_idx):
x, y = val_batch
y_pred = self.encoder(x)
loss = self.criterion(y_pred, y)
self.log('val_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
return loss
def validation_epoch_end(self, validation_step_outputs):
mean_loss = torch.stack([x for x in validation_step_outputs]).mean()
print('valid_epoch_loss = ', mean_loss)
self.log('valid_epoch_loss', mean_loss, prog_bar=True, logger=True)
# tqdm.write('Dice: \t%.3f' % mean_loss)
return mean_loss
def signal_to_mel(y, sr, mel_params):
len_y = len(y)
effective_length = int(SR * PERIOD)
start = 0
end = start + effective_length
images = []
while(start < len_y):
if(end > len_y):
break
y_ele = y[start:end]
melspec = librosa.feature.melspectrogram(y_ele, sr=sr, **mel_params['melspectrogram_parameters'])
melspec = librosa.power_to_db(melspec).astype(np.float32)
image = mono_to_color(melspec)
height, width, _ = image.shape
image = cv2.resize(image, (int(width * mel_params['img_size'] / height), mel_params['img_size']))
image = np.moveaxis(image, 2, 0)
image = (image / 255.0).astype(np.float32)
# image = torch.from_numpy(image).clone()
images.append(image)
start = end
end += effective_length
return np.array(images)
test_wav_pathes = sorted(glob.glob(str(TEST_AUDIO_DIR / '*.flac')))
len(test_wav_pathes)
device = torch.device("cuda")
model_pathes = sorted(glob.glob('./output/model*'))
model_pathes
for i, model_path in enumerate(model_pathes):
model = LitModule()
model.load_state_dict(torch.load(model_path))
model.eval().to(device)
preds = []
for path in tqdm(test_wav_pathes):
y, sr = sf.read(path)
mel_img = signal_to_mel(y, sr, config["dataset"]["params"])
mel_img = torch.from_numpy(mel_img).clone().to(device)
pred = model(mel_img)
pred = nn.Softmax()(pred)
pred = torch.mean(pred, 0)
pred = pred.to('cpu').detach().numpy().copy()
preds.append(pred)
preds = np.array(preds)
sub = pd.read_csv(str(RAW_DATA / 'sample_submission.csv'))
sub.loc[:, 's0':'s23'] = preds
sub.to_csv(OUTPUT_DIR + '37sub' + str(i) + '.csv', index=False)
# break
| 7,534 |
/DoublyLinkedLists.ipynb | 1881b4ebd4d1b30d6942fd7c5b4f0c7f80afa0f6 | [
"MIT"
] | permissive | rambasnet/CS2Notebooks | https://github.com/rambasnet/CS2Notebooks | 4 | 0 | null | null | null | null | Jupyter Notebook | false | false | .cpp | 19,333 | // ---
// jupyter:
// jupytext:
// text_representation:
// extension: .cpp
// format_name: light
// format_version: '1.5'
// jupytext_version: 1.15.2
// kernelspec:
// display_name: C++14
// language: C++14
// name: xeus-cling-cpp14
// ---
// # Doubly Linked Lists
// - https://opendsa-server.cs.vt.edu/ODSA/Books/CS2/html/ListDouble.html
// - https://en.cppreference.com/w/cpp/container/list
//
// ### Table of Contents
// - **[Introduction](#intro)**<br>
// - **[Implementation of Node](#node)**<br>
// - **[Operations on Doubly Linked List](#operations)**<br>
// - **[Doubly Linked List as ADT](#adt)**<br>
//
// ## Introduction
// - **Singly Linked List** allows for direct access from a list node only to the next node in forward direction
// - **Doubly Linked List** allows access in both directions -- forward and backward
// - giving easy access to next node and previous node
// ## Doubly Linked List
// - also called two-way list
// - each node is depicted with three boxes (members) each holding:
// 1. data (middle box)
// 2. address/pointer to the next node (right box)
// 3. address/pointer to the previous node (left box)
//
// <img src="./resources/DoublyLinkedList1.png">
//
// - diagonal slash (see last and first node) represents NULL pointer meaning it's not pointing to another node
// - head or first is a special pointer pointing to the first (header) node
// - tail or last is a special pointer pointing to the last (trailer) node
// - use pointer to traverse through the linked list (unlike index in array-based list)
//
//
// ## Common Operations
// - inserting and deleting nodes are common operations but need to deal with many cases.
// - if header and trailer nodes are used without actually storing the data, simplifies many special cases
// - see visualization at: https://opendsa-server.cs.vt.edu/ODSA/Books/CS2/html/ListDouble.html
// ## Implemenation of Node
// - since a node is a complex type with data (of various type) and pointers, we use struct or class to implement it
#include <iostream>
using namespace std;
struct Int_Node {
int data; // int data
Int_Node * next; // address of the next node
Int_Node * prev; // address of the previous node
};
// better implementation
template <class T>
struct Node {
T data; // data of some type T
Node<T> * next;
Node<T> * prev;
};
// ## Creating a Doubly Linked List
// - add elements 10, 20, 30, etc.
// - doubly linked list of: 10 <-> 20 <-> 30
Int_Node *head, *tail, *temp;
// +
// create empty header and trailer nodes as shown in figure above
temp = new Int_Node;
temp->data = 0;
temp->prev = NULL;
temp->next = NULL;
head = temp; // head points to header node
temp = new Int_Node;
temp->data = 0;
temp->prev = head; // trailer points to header
temp->next = NULL;
tail = temp;
head->next = tail; // header points to trailer
// -
// ## Push Back Element
// - inserting element at the end of the doubly linked list
// - algorithm steps:
// 1. create a new node with data
// - make new node's next point to trailer node
// - make new node's prev point to trailer's prev node
// - make trailer node's prev next point to the new node
// - make trailer node's prev point to the new node
// create and add the new node with 10 at the end
temp = new Int_Node;
temp->data = 10;
temp->next = tail;
temp->prev = tail->prev;
tail->prev->next = temp;
tail->prev = temp;
// create and add the new node with 20 at the end
temp = new Int_Node;
temp->data = 20;
temp->next = tail;
temp->prev = tail->prev;
tail->prev->next = temp;
tail->prev = temp;
// create and add the new node with 20 at the end
temp = new Int_Node;
temp->data = 30;
temp->next = tail;
temp->prev = tail->prev;
tail->prev->next = temp;
tail->prev = temp;
// ## Traversing Doubly Linked List
// - visiting every node of the linked list
// - access data, check and or update data
// - can be traversed both in forward and backward directions
void traverseForward(Int_Node *head) {
// start from header's next and go through every node
// stop before trailer
Int_Node * curr = head->next;
cout << "[";
while (curr != tail) {
cout << " " << curr->data;
curr = curr->next;
}
cout << " ]";
}
traverseForward(head);
void traverseBackward(Int_Node *tail) {
// start from trailers's prev and go through every node
// stop before header
Int_Node * curr = tail->prev;
cout << "[";
while (curr != head) {
cout << " " << curr->data;
curr = curr->prev;
}
cout << " ]";
}
traverseBackward(tail);
// ## Push Front Element
// - inserting element at the beginning of the doubly linked list
// - similar to push back operation
// - algorithm steps:
// 1. create a new node with data
// - make new node->next point to the head->next
// - make new node->prev point to the head
// - make head->next point to the new node
// - make new node->next->prev point to the new node
// insert a new node at the beginning (push_front)
temp = new Int_Node;
temp->data = 100;
temp->next = head->next;
temp->prev = head;
head->next = temp;
temp->next->prev = temp;
traverseForward(head);
traverseBackward(tail);
// insert a new node at the beginning (push_front)
temp = new Int_Node;
temp->data = 200;
temp->next = head->next;
temp->prev = head;
head->next = temp;
temp->next->prev = temp;
traverseForward(head);
// ## Doubly Linked List Remove
// - remove an element/node from the linked list
// - algorithm steps:
// 1. use a pointer, current
// - current is the node that needs to be deleted if found
// 2. if node is found delete it
// - update the doubly linked list
Int_Node * curr;
// delete 2nd node from the list
// NOTE: header is not an actual node!
curr = head->next->next;
curr->prev->next = curr->next;
curr->next->prev = curr->prev;
delete curr;
traverseForward(head);
// ## Doubly Linked List Insert
// - insert an element/node after certain node in the linked list
// - similar to push front operation
// - algorithm steps:
// 1. create a new node with the data
// - find the location where the new node needs to be inserted after, say curr
// - insert the new node at that location
// - update doubly linked list
// insert element as the 2nd node (after the first node) with key value 100
// NOTE: header node is not an actual node!
curr = head->next;
temp = new Int_Node;
temp->data = 100;
temp->next = curr->next;
temp->prev = curr;
curr->next = temp;
temp->next->prev = temp;
traverseForward(head);
// ## Doubly Linked List Implementation as ADT
// - following Doulby Linked list as ADT works for integer data
// - it can be easily converted into a template class
// - this is left as an exercise
#include <iostream>
using namespace std;
struct Int_Node {
int data; // int data
Int_Node * next; // address of the next node
Int_Node * prev; // address of the previous node
};
class IntDoublyList {
private:
Int_Node * head;
Int_Node * tail;
size_t count;
// removes curr node
void remove(Int_Node* curr) {
curr->prev->next = curr->next;
curr->next->prev = curr->prev;
delete curr;
this->count--;
}
public:
IntDoublyList() {
this->count = 0;
// create empty header and trailer nodes as shown in figure above
Int_Node * temp = new Int_Node; //create header node
temp->data = 0;
temp->prev = NULL;
temp->next = NULL;
head = temp; // head points to header node
temp = new Int_Node; // create trailer node
temp->data = 0;
temp->prev = head; // trailer points to header
temp->next = NULL;
tail = temp;
head->next = tail; // header points to trailer
}
bool empty() const {
return this->count == 0;
}
// adds an element to the end
void push_back(int data) {
Int_Node * node = new Int_Node;
node->data = data;
node->next = tail;
node->prev = tail->prev;
tail->prev->next = node;
tail->prev = node;
this->count++;
}
// inserts an element to the beginning
void push_front(int data) {
// FIXME
}
// access the last element
int back() {
return tail->prev->data;
}
// return the size of the list
size_t size() {
return this->count;
}
// access the first element
// FIXME - implement method to access the data in first node
// removes the last element
void pop_back() {
// nothing to do in an empty list
if (empty()) return;
this->remove(tail->prev);
}
// removes the first element
// FIXME - implement a method to remove the first node
// visits every node and prints the data
// traverse in forward direction
void traverseForward() {
cout << "[";
Int_Node * curr = head->next;
while (curr != tail) {
cout << " " << curr->data;
curr = curr->next;
}
cout << " ]";
}
// traverseBackward
// visits every node and prints the data in backward direction
void traverseBackward() {
// FIXME...
}
// insert a node with a given data after the node with the after_key value
// if the element with after_key not found, insert data at the end
void insert_after(int after_key, int data) {
// FIXME:
}
// clears the linked list deleting all the nodes
// except for the header and trailer nodes
void clear() {
// FIXME...
}
};
// test IntDoublyList with some data
IntDoublyList ilist;
ilist.traverseForward();
ilist.push_back(10);
ilist.traverseForward();
ilist.push_back(20);
ilist.push_back(30);
ilist.traverseForward();
ilist.pop_back();
ilist.traverseForward();
// ### Exercises
// 1. Linked lists are better than array-based lists when the final size of the list is known in advance.
// 1. True
// - False
//
// 2. Fix all the FIXMEs and test the fixes of doubly linked list ADT.
// 3. Convert Doubly Linked List ADT as a template class to store data of any type in the node.
| 10,656 |
/examples/02-plot/labels.ipynb | abdfe2a587f4f5964323d2cdf2a19659fe2f03b2 | [
"MIT"
] | permissive | pyvista/pyvista-examples | https://github.com/pyvista/pyvista-examples | 8 | 2 | MIT | 2022-01-12T06:32:22 | 2021-11-28T20:27:56 | Shell | Jupyter Notebook | false | false | .py | 4,751 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# **Assignment 2: Evaluating Regression Models**
#
# Group 3: Laura Fanelli, Mark Schieble, John Vaughan, Katie Zink
# ## Data Preparation, Exploration & Visualization
#import packages
import pandas as pd
import numpy as np
#pip install altair vega_datasets
import altair as alt
import matplotlib.pyplot as plt
# %matplotlib inline
# +
#data path
path_test = "C:/Users/ly580d/Desktop/Northwestern/7_Practical Machine Learning/Week 2/test.csv"
path_train = "C:/Users/ly580d/Desktop/Northwestern/7_Practical Machine Learning/Week 2/train.csv"
#read data
df_test = pd.read_csv(path_test)
df_train = pd.read_csv(path_train)
# -
#lets take a look at the data
df_train.head(3)
#Ensure that the test data is similar to the training set
df_test.head(3)
# +
#the data ID will be used by Keggle to scare the final submission. To retain the ID, we will set the ID as the index
df_test = df_test.set_index('Id') # Set ID as index on the test data
df_train = df_train.set_index('Id') # Set ID as index on the training data
# -
#histogram of each numerical attribute
# %matplotlib inline
import matplotlib.pyplot as plt
df_train.hist(bins=50, figsize=(20,15))
plt.show()
#feature creation - Total Floor SF
df_train['TotalFloorSF']=df_train['1stFlrSF']+df_train['2ndFlrSF']
df_test['TotalFloorSF']=df_test['1stFlrSF']+df_test['2ndFlrSF']
#feature creation - Quality Index
df_train['QualityIndex']=df_train['OverallQual']*df_train['OverallCond']
df_test['QualityIndex']=df_test['OverallQual']*df_test['OverallCond']
# To fix the large number of catagorical variables, we can use SKlearn's label encoder.
#This takes each catagory and replaces it with a numerical reference. This enables the Algorithm to read the data easier.
def encode_cat_var(df):
from sklearn.preprocessing import LabelEncoder
Label_Encoder = LabelEncoder()
cat_list = df.select_dtypes(include=['object']).columns.tolist()
for column in cat_list:
df[column] = Label_Encoder.fit_transform(df[column].astype('str'))
return df
df_train = encode_cat_var(df_train) #apply to train
df_test = encode_cat_var(df_test) #apply to test
#correlation between each attribute and our response variable SalePrice
corr_matrix = df_train.corr()
corr_matrix["SalePrice"].sort_values(ascending=False)
# function to fill missing values from list. Note, this was retired in the final version of the notebook
column_list = ['GarageCars']
def fill_missing_values(column_list, df):
for column in column_list:
df[column] = df[column].fillna(value=0,inplace=True)
return df
#clean dataframe function
def clean_df(df):
assert isinstance(df, pd.DataFrame)
df.fillna(value=0,inplace=True)
indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)
return df[indices_to_keep].astype(np.float64)
df_train = clean_df(df_train) #apply to train
df_test = clean_df(df_test) #apply to test
# ## Review research design and modeling methods
# +
#split the data into a test and train datasets. SalePrice is the y variable.
from sklearn.model_selection import train_test_split
X = df_train.drop(['SalePrice'], axis=1)
y = df_train['SalePrice'].values
X_train, X_val, y_train, y_val = train_test_split(X, y)
# +
#To Refine the model, we can adjust the number of features used. However, in adjusting, we only want the most important features.
#We can use a lasso model to rank features, and take only the variables most likely to predict sale price
from sklearn.linear_model import Lasso
model = Lasso(alpha=0.01)
model.fit(X_train,y_train)
feature_importance_test = pd.DataFrame(data=model.coef_,
columns=['feature_importance'],
index = X_train.columns).sort_values(by='feature_importance',
ascending=False)
feature_importance_test = feature_importance_test[feature_importance_test['feature_importance'] != 0] #Note, this can be change to greater or less than
#feature_importance_test.index.to_list()[:10] # unhide to see top features
# +
#Change the the value as needed - this will directly impact the model features.
# top_features = feature_importance_test.index.to_list()[:30]
#original syntax failed for me -- had to convert to list this way
top_features = feature_importance_test
top_features = top_features.index.tolist()
# top_features
# -
X = df_train[top_features] #Filter out all non top features
#random number generator - to generate the same shuffled indices
np.random.seed(42)
#test train split for the model
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
# +
#Build metrics to evaluate model performance - We will use RSME and R2 Suared
from sklearn import metrics
def model_evaluation(true, predicted):
rmse = np.sqrt(metrics.mean_squared_error(true, predicted))
r2_square = metrics.r2_score(true, predicted)
print('RMSE:', rmse)
print('R2 Square', r2_square)
# -
# ## Review Results & Evaluate Models
# ### Linear Regression
# +
#Linear Regression
from sklearn.linear_model import LinearRegression
print('Linear Regression Model')
lin_reg = LinearRegression(normalize=True)
lin_reg.fit(X_train,y_train)
pred = lin_reg.predict(X_test)
model_evaluation(y_test, pred)
# -
lin_reg.intercept_, lin_reg.coef_
# ### Linear Regression with Regularization
#
# It is almost preferable to have at least a little bit of regularization. Ridge is a good default, if you suspect that only a few features are actually useful, you should prefer Lasso or Elastic Net since they tend to reduce the useless features weight down to zero. In general Elastic Net is preferred over Lasso since Lasso may behave erratically when the number of features is greater than the number of training instances or when several features are strongle correlated.
# +
#Ridge using a matrix factorization technique by André-Louis Cholesky
print('Ridge Regression Model with Cholesky technique Alpha = 1')
model_Ridge = Ridge(alpha=1, solver="cholesky")
model_Ridge.fit(X_train, y_train)
pred = model_Ridge.predict(X_test)
model_evaluation(y_test, pred)
# +
print('Ridge Regression Model with Cholesky technique Alpha = 15')
model_Ridge = Ridge(alpha=15, solver="cholesky")
model_Ridge.fit(X_train, y_train)
pred = model_Ridge.predict(X_test)
model_evaluation(y_test, pred)
# +
#Ridge Lasso (Least Absolute Shrinkage and Selection Operator)
#eliminates the weights of the least important features (sets them to 0)
#automatically performs feature selection and outputs sparse model
from sklearn.linear_model import Lasso
print('Lasso Model')
model_Lasso = Lasso(alpha=0.1)
model_Lasso.fit(X_train, y_train)
pred = model_Lasso.predict(X_test)
model_evaluation(y_test, pred)
# -
model_Lasso.intercept_, model_Lasso.coef_
| 7,168 |
/Trabalho 2/src/Filtro passa-alta Butterworth.ipynb | 5b613c7a7f9c35f661b43080b42fbd9db7750f22 | [] | no_license | joaovicmendes/pdi-trabalho | https://github.com/joaovicmendes/pdi-trabalho | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 936,644 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Filtro passa-alta Butterworth
#
# Relembrando a fórmula do filtro:
# $$ H_{alta}(\mu, \nu) = \frac{1}{1 + {(\frac{D_0}{\sqrt{\mu^2 + \nu^2}})}^{2n}} $$
import numpy as np
import matplotlib.pyplot as plt
from scipy.fftpack import fft2, ifft2, fftfreq, fftshift
# +
def generate_frequencies(num_rows, num_cols):
'''Gera frequências do sinal tal que a frequência zero
esteja no centro dos arrays'''
freq_r = fftfreq(num_rows)
freq_c = fftfreq(num_cols)
freq_r = fftshift(freq_r)
freq_c = fftshift(freq_c)
return freq_r, freq_c
def filtro_passa_alta_butterworth(img, d0, n):
'''Cria um filtro passa-alta Butterworth de mesma dimensão que img. d0 e n são
utilizados para definir a mínima frequência que será mantida na imagem'''
num_rows, num_cols = img.shape
freq_r, freq_c = generate_frequencies(num_rows, num_cols)
high_pass_butterworth = np.zeros([num_rows, num_cols])
for row in range(num_rows):
for col in range(num_cols):
dist = np.sqrt(freq_r[row]**2 + freq_c[col]**2)
if dist == 0:
dist = 1
H = 1/(1+(d0/dist)**(2*n))
high_pass_butterworth[row,col] = H
return high_pass_butterworth
# +
# Leitura da Imagem
img = plt.imread('test_image.tiff')
num_rows, num_cols = img.shape
# Criando imagem aumentada para evitar interferência
# com as diversas cópias (virtuais) da imagem
img_padded = np.pad(img, ((0, num_rows), (0, num_cols)),
mode='symmetric')
# Cálculo da Transformada de Fourier e das frequências da imagem
Fimg = fft2(img_padded)
freq_r, freq_c = generate_frequencies(2*num_rows, 2*num_cols)
Fimg = fftshift(Fimg)
plt.figure(figsize=[14,7])
plt.subplot(1, 2, 1)
plt.imshow(img, 'gray')
plt.title("Imagem Original")
plt.subplot(1, 2, 2)
plt.pcolormesh(freq_c, freq_r, np.log(np.abs(Fimg)+1), cmap='gray', shading='auto')
plt.title("Imagem no domínio da frequência")
# +
# Calculando o filtro
lp_filter = filtro_passa_alta_butterworth(img_padded, d0=0.01, n=1)
# Aplicando o filtro na frequencia
Fimg_filtered = lp_filter*Fimg
plt.figure(figsize=[14,7])
plt.subplot(1, 2, 1)
plt.imshow(lp_filter, 'gray')
plt.title("Filtro passa-alta Butterworth")
plt.subplot(1, 2, 2)
plt.pcolormesh(freq_c, freq_r, np.log(np.abs(Fimg_filtered)+1), cmap='gray', shading='auto')
plt.title("Imagem no domínio da frequência com filtro aplicado")
# +
# Recuperando a imagem no domínio espacial usando a transformada inversa
Fimg_filtered = fftshift(Fimg_filtered)
img_filtered = np.real(ifft2(Fimg_filtered))
img_filtered = img_filtered[:num_rows, :num_cols]
plt.figure(figsize=[8,8])
plt.imshow(img_filtered, 'gray')
acc'])
plt.plot(history.history['val_acc'])
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['acc', 'val_acc', 'loss', 'val_loss'],loc=10)
plt.ylim((0,1.1))
# + deletable=true editable=true
| 3,211 |
/Untitled.ipynb | 779cb750722717f41a6bd0ac4aae15950f67b455 | [] | no_license | suhyuuk/Pokemon-ice-cave-puzzle-generator | https://github.com/suhyuuk/Pokemon-ice-cave-puzzle-generator | 0 | 1 | null | null | null | null | Jupyter Notebook | false | false | .py | 24,292 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
# Libraries
import numpy as np
import matplotlib.pyplot as plt
import ice_cave_library as ice
from random import *
from numpy.core.records import array
from numpy.lib.stride_tricks import _broadcast_to_dispatcher
#import pygame as pg
######################################################################
########################### Set-ups ##################################
width = 6
length = 6
startpoint = np.array([[0, 2], [0, 3]]) # same as (1, 3), (1, 4)
endpoint = np.array([[3, 7], [4, 7]]) # same as (4, 8), (5, 8)
initial_rocks = np.array([[4, 5]]) # same as (5, 6)
odds_of_rocks = 0.1 # 10% odds of rocks
#######################################################################
#######################################################################
## randomly set startpoint
startpoint_save = startpoint
startpoint = startpoint[randint(1, startpoint.shape[0]) - 1]
## check if the start / end move is vertical / horizontal
if startpoint[0] == 0 or startpoint[0] == width + 1:
vertical_s = 1
horizontal_s = 0
else:
vertical_s = 0
horizontal_s = 1
if endpoint[0][0] == 0 or endpoint[0][0] == length + 1:
vertical_e = 1
horizontal_e = 0
else:
vertical_e = 0
horizontal_e = 1
#######################################################################
#######################################################################
### restart point ###
problemo = 1
while problemo == 1:
problemo = 0
# Plot setups
maps = np.zeros((length + 2, width + 2))
## set rocks
maps[:, 0] = 1
maps[:, width + 1] = 1
maps[length + 1, :] = 1
maps[0, :] = 1
## set initial setups
keep = 1
row = startpoint[0]
column = startpoint[1]
horizontal = horizontal_s
vertical = vertical_s
now = startpoint
### path set
path = np.zeros([(length + 2) * (width + 2), 2])
num_path = 0
### rock set
rocks = np.zeros([(length + 2) * (width + 2), 2])
num_rocks = 0
for i in range(0, initial_rocks.shape[0]):
rocks[i] = initial_rocks[i]
num_rocks = num_rocks + 1
#######################################################################
#######################################################################
joints = 0
maps[startpoint] = 0.001
while keep == 1:
#find stuckpoint
maps = ice.imstuck(maps)
#update now_latest
now_latest = now
#decide next tile
now, maps, horizontal, rock_now, problemo = ice.nextile(now, maps, horizontal)
#update maps, path, rocks
maps, path, rocks, num_path, num_rocks = ice.writemap(maps, now, path, rocks, rock_now, num_path, num_rocks, horizontal, now_latest)
#swap
horizontal, vertical = vertical, horizontal
if joints > 4:
#check if the path can end
keep, maps, path, num_path = ice.endcheck(keep, maps, path, num_path, now, endpoint, horizontal, horizontal_e)
joints = joints + 1
#######################################################################
#######################################################################
# redraw maps
maps = ice.imnotstuck(maps)
rocks = rocks[0 : num_rocks, :]
path = path[0 : num_path, :]
path, num_path = ice.redraw(path, num_path)
## set startpoint as 2, endpoint as 3
for i in range(0, initial_rocks.shape[0]):
maps[initial_rocks[i][0], initial_rocks[i][1]] = 1
for i in range(0, startpoint.shape[0]):
maps[startpoint_save[i][0], startpoint_save[i][1]] = 2
for i in range(0, endpoint.shape[0]):
maps[endpoint[i][0], endpoint[i][1]] = 3
#######################################################################
#######################################################################
# plot
print(num_path)
print(path)
print(rocks)
print(now)
with np.printoptions(precision=3, suppress=True):
print(maps)
plt.plot(path[:, 0], path[:, 1], 'ro-')
# plt.plot(startpoint_save, 'r*')
# plt.plot(endpoint, 'r*')
plt.axis([0, length + 1, 0, width + 1])
plt.plot(rocks[:, 0], rocks[:, 1], 'bo')
plt.show()
# -
| 4,432 |
/Featureselection1.ipynb | 6436b45c27083bb89cf6a9ae3aa1bd3bcc1ff9f1 | [] | no_license | gabibu/unsupervisedLearning | https://github.com/gabibu/unsupervisedLearning | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 247,907 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Secret-key or symmetric cryptography
#
# ## 1 DES S-box $S_3$
#
# The input to the DES S-box $S_3$ is $110111$. What’s the output? Use Wikipedia, google, a book or some other source to find the table for $S_3$.
# Source: http://celan.informatik.uni-oldenburg.de/kryptos/info/des/sbox/
# 
#
# Output: 0011
# ## 2 3DES
#
# What is the effective key size of 3DES and why is it not 168 bits?
# + active=""
# it's 112 bits, not 168 due to meet-in-the-middle attack threat.
# -
# ## 3 Differences between AES and Rijndeal
#
# What are the differences between the AES candidate Rijndeal and AES with respect to block size, key size and number of rounds?
# As described in "[The Design of Rijandel](https://www.springer.com/us/book/9783540425809)": "The _only_ difference between Rijandel and the AES is the range of supported values for the block length and cipher key length".
#
# Rijndael is a block cipher with both a variable block length and a variable key length. The block length and the key length can be independently specified to any multiple of 32 bits, with a minimum of 128 bits and a maximum of 256 bits. It would be possible to define versions of Rijndael with a higher block length or key length, but currently there seems no need for it.
#
# The AES fixes the block length to 128 bits, and supports key lengths of 128, 192 or 256 bits only. The extra block and key lengths in Rijndael were not evaluated in the AES selection process, and consequently they are not adopted in the current FIPS standard.
# ## 4 AES S-box
#
# If we input the byte $11011101$ into the AES S-box, what’s the output? Use the table in slides!
# $1101 -> D -> row$
#
# $1101 -> D -> column$
#
# $11011101 -> C1 -> 11000001$
#
# 
# ## 5 Other Block ciphers
#
# Compare DES, 3DES and AES with other block ciphers like IDEA, Blowfish, Twofisch, RC5, RC6, Serpent and three more of Your choice. Make a table that shows key size, effective key size, block size, number of rounds, relative velocity of a hard- or software implementation.
# - https://pdfs.semanticscholar.org/e684/4c748d38997bf0de71cd7d05e58b09e310f6.pdf
# - https://www.cse.wustl.edu/~jain/cse567-06/ftp/encryption_perf/
# - http://www.ijcseonline.org/pub_paper/IJCSE-00187.pdf
#
# |Ciphers|key size| effective keysize|block size| number of rounds| relative velocity|
# |:--- |:--- |:--- |:--- |:--- |:--- |
# |DES|56 bits||64bits|16|1|
# |3DES| 112 bits ||64bits|48|0.3-0.5|
# |AES|128,192 or 256||128, 192 or 256|10, 12 or 14|0.6|
# |IDEA|128 bits||64 bits|8.5
# |Blowfish|32-448 bits||64 bits|16|1.2-3|
# |Twofish|
# |RC5|
# |RC6|128,192 or 256||128 bits|20|
# ## 6 Modes of operation
#
# You should be able to produce sketches of the 5 modes of operation and You should be able to write down the equations, relating, IVs (if any), plaintext block, key, ciphertext block, encryption and decryption, XOR.
# You should also understand the influence of a one-bit error in the ciphertext block.
# | Modes of Operation | Long Name | Cipher Type |
# |:--- |:--- |:--- |
# | ECB | Electronic Code Book Mode | Block |
# | CBC | Chained Block Cipher Mode | Block |
# | CFB | Cipher FeedBack Mode | Stream |
# | OFB | Output FeedBack Mode| Stream |
# | CTR | Counter Mode | Stream |
# ### ECB
#
# 
#
# #### Encryption
# $c_k = E(k, m_k),\ k=1,2,3,...$
#
# #### Decryption
# $m_k = D(k, c_k),\ k=1,2,3,...$
#
# #### Error Propagation
# An error in the ciphertext produces garbage output but does not propagate.
# ### CBC
#
# 
#
# #### Encryption
# $c_0 = IV$<br/>
# $c_k = E(k,m_k\oplus c_{k-1}),\ k = 1,2,3,...$
#
# #### Decryption
# $c_0 = IV$<br/>
# $m_k = D(k, c_k)\oplus c_{k-1},\ k = 1,2,3,...$
#
# #### Error Propagation
# An error in the ciphertext $c_k$ affects all bits of the corresponding plaintext $m_k$ and the one bit of $m_{k+1}$ with which the erroneous bit in $c_k$ is XOR-ed
# ### CFB
#
# 
#
# #### Encryption
# $c_0 = IV$<br/>
# $c_i = m_i \oplus E(k, c_{i-1},\ i=1,2,3...$
#
# #### Decryption
# $c_0 = IV$<br/>
# $m_i = c_i \oplus E(k, c_{i-1},\ i=1,2,3...$
#
# #### Error Propagation
# An error in the cipher block $c_k$ produces one error in the plaintext block $m_k$ at the bit position where the error has occured (as it is XOR-ed), and produces garbage in the next plaintext block $m_{k+1}$ as $E(k,c_{k_{faulty}})$ should produce a completely different output than $E(k, c_k)$, and therefore $c_{k+1}\oplus E(k,c_{k_{faulty}})$ should be complete gibberish.
# ### OFB
#
# 
#
# #### Encryption
# $z_0 = IV$<br/>
# $z_i = E_k(z_{i-1}),\ i=1,2,3,...$<br/>
# $c_i = m_i\oplus z_i,\ i=1,2,3,...$
#
# #### Decryption
# $z_0 = IV$<br/>
# $z_i = E_k(z_{i-1}),\ i=1,2,3,...$<br/>
# $m_i = c_i\oplus z_i,\ i=1,2,3,...$
#
# #### Error Propagation
# An error in cipher bit $c_i$ leads to an erroneous bit $m_i$ but does not propagate.
# ### CTR
#
# 
#
# #### Encryption
# $z_0 = IV$<br/>
# $z_i = IV\oplus i,\ i=1,2,3,...$<br/>
# $y_i = x_i\oplus E_k(z_i),\ i=1,2,3,...$
#
# #### Decryption
# $z_0 = IV$<br/>
# $z_i = IV\oplus i,\ i=1,2,3,...$<br/>
# $y_i = x_i\oplus E_k(z_i),\ i=1,2,3,...$
#
# #### Note on the IV
# The IV should be a nonce, but same nonce can be used throughout the session. It's main goal is to offset the counter startpoint, so that using the same key and first message does not generate the same ciphertext (think of handshakes/authentication).
#
# #### Error Propagation
# An error in $y_0$ generates one error in the decrypted $x_0$, but does not propagate.
# ## 7 RC4
#
# Use python in Jupyter Notebook to programm RC4. Do some research on RC4 and find out, why it should not be used any more!
# Siehe auch [Webbrowser: Endgültig Schluss mit RC4](https://www.heise.de/security/meldung/Webbrowser-Endgueltig-Schluss-mit-RC4-2805770.html) und [Der Lange Abschied von RC4](https://www.golem.de/news/verschluesselung-der-lange-abschied-von-rc4-1507-114877.html).
# +
def KSA(key):
keylength = len(key)
S = list(range(256))
j = 0
for i in range(256):
j = (j + S[i] + key[i % keylength]) % 256
S[i], S[j] = S[j], S[i]
return S
def PRGA(S):
i = 0
j = 0
while True:
i = (i + 1) % 256
j = (j + S[i]) % 256
S[i], S[j] = S[j], S[i]
yield S[(S[i] + S[j]) % 256]
def RC4(key):
S = KSA(key)
return PRGA(S)
def convert_key(s):
return [ord(c) for c in s]
# +
key = "Key"
plaintext = "Plaintext"
# ciphertext should be BBF316E8D940AF0AD3
key = convert_key(key)
keystream = RC4(key)
import sys
for c in plaintext:
sys.stdout.write("%02X" % (ord(c) ^ next(keystream)))
# -
# Vulnerabilities:
#
# - Pseudo Random Number Generator PRNG has higher probabilities for some numbers to appear.<br/>
# This lets an attacker analyse some input/output-pairs and find out the key
# - No nonce as input therefore it needs a new key for each stream.<br/>
# Since most applications just concatenate the nonce and the key, this is a problem because "over all possible RC4 keys, the statistics for the first few bytes of output keystream are strongly non-random, leaking information about the key."
# ## 8 Trivium
#
# Use python in Jupyter Notebook to programm Trivium. This is not an easy task: do it in groups of two!
#
# Use $0x00000000000000000000000000000000$ for the key, IV, and plaintext for initial testing.
#
# The expected ciphertext for this should be $0xFBE0BF265859051B517A2E4E239FC97F$.
#
# In the algorithm on slide “_Trivium — Initialization_”, the $+$ represents XOR (which in python is “^”), ·
# represents logical AND (which in python is “&”). The key-stream is
#
# $z_i = t_1 + t_2 + t_3$
#
# and the $i$th byte of the ciphertext $c_i$ of the plaintext $m_i$ is
#
# $c_i = z_i \oplus m_i$
#
# The following [site](https://asecuritysite.com/encryption/trivium) might be of some help!
# +
from collections import deque
from itertools import repeat
from sys import version_info
class Trivium:
def __init__(self, key, iv):
"""in the beginning we need to transform the key as well as the IV.
Afterwards we initialize the state."""
self.state = None
self.counter = 0
self.key = key # self._setLength(key)
self.iv = iv # self._setLength(iv)
# Initialize state
# len 93
init_list = list(map(int, list(self.key)))
init_list += list(repeat(0, 13))
# len 84
init_list += list(map(int, list(self.iv)))
init_list += list(repeat(0, 4))
# len 111
init_list += list(repeat(0, 108))
init_list += list([1, 1, 1])
self.state = deque(init_list)
# Do 4 full cycles, drop output
for i in range(4*288):
self._gen_keystream()
def encrypt(self, message):
"""To be implemented"""
pass
def decrypt(self, cipher):
"""To be implemented"""
#maybe with code from here https://github.com/mortasoft/Trivium/blob/master/trivium.py
# Line 119
pass
def keystream(self):
"""output keystream
only use this when you know what you are doing!!"""
while self.counter < 2**64:
self.counter += 1
yield self._gen_keystream()
def _setLength(self, input_data):
"""we cut off after 80 bits, alternatively we pad these with zeros."""
input_data = "{0:080b}".format(input_data)
if len(input_data) > 80:
input_data = input_data[:(len(input_data)-81):-1]
else:
input_data = input_data[::-1]
return input_data
def _gen_keystream(self):
"""this method generates triviums keystream"""
t_1 = self.state[65] ^ self.state[92]
t_2 = self.state[161] ^ self.state[176]
t_3 = self.state[242] ^ self.state[287]
out = t_1 ^ t_2 ^ t_3
u_1 = t_1 ^ self.state[90] & self.state[91] ^ self.state[170]
u_2 = t_2 ^ self.state[174] & self.state[175] ^ self.state[263]
u_3 = t_3 ^ self.state[285] & self.state[286] ^ self.state[68]
self.state.rotate(1)
self.state[0] = u_3
self.state[93] = u_1
self.state[177] = u_2
return out
import sys
k1="00000000000000000000"
i1="00000000000000000000"
print ("Key: "+k1)
print ("IV: "+i1)
def main():
KEY = hex_to_bits(k1)[::-1]
IV = hex_to_bits(i1)[::-1]
trivium = Trivium(KEY, IV)
next_key_bit = trivium.keystream().__next__
for i in range(1):
keystream = []
for j in range(128):
keystream.append(next_key_bit())
print ("Stream: "+bits_to_hex(keystream))
# Convert strings of hex to strings of bytes and back, little-endian style
_allbytes = dict([("%02X" % i, i) for i in range(256)])
def _hex_to_bytes(s):
return [_allbytes[s[i:i+2].upper()] for i in range(0, len(s), 2)]
def hex_to_bits(s):
return [(b >> i) & 1 for b in _hex_to_bytes(s)
for i in range(8)]
def bits_to_hex(b):
return "".join(["%02X" % sum([b[i + j] << j for j in range(8)])
for i in range(0, len(b), 8)])
if __name__ == "__main__":
main()
# -
# ## 9 OTP
#
# Make your own example with one-time pad. Why is it perfectly secure? Make sure, the key is truly random not used more than once and kept secret from adversaries.
# $m = 0110100001100101011011000110110001101111001000000111011101101111011100100110110001100100$<br />
# $k = 0110110111011101100100110001101100000001010001110010110111101010101110010001101100011100$
# +
m = '0110100001100101011011000110110001101111001000000111011101101111011100100110110001100100'
k = '0110110111011101100100110001101100000001010001110010110111101010101110010001101100011100'
c = int(m,2)^int(k,2)
print('m: ' + m)
print('k: ' + k)
print('c: ' + bin(c)[2:].zfill(len(m)))
print('d: ' + bin(c^int(k,2))[2:].zfill(len(m)))
print('m: ' + m)
| 12,425 |
/single_word.ipynb | 49d76c66d855b343c435ed6f9815909c09b6833b | [] | no_license | rammohanbethi/word-cloud | https://github.com/rammohanbethi/word-cloud | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 69,009 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %matplotlib inline
#
# Single Word
# ===========
#
# Make a word cloud with a single word that's repeated.
#
#
# +
import numpy as np
import matplotlib.pyplot as plt
from wordcloud import WordCloud
text = "Hello Rammohan Bethi is the summary of your career profile for freshers generally focus on skills, education, & internship"
x, y = np.ogrid[:300, :300]
mask = (x - 150) ** 2 + (y - 150) ** 2 > 130 ** 2
mask = 255 * mask.astype(int)
wc = WordCloud(background_color="white", repeat=True, mask=mask)
wc.generate(text)
plt.axis("off")
plt.imshow(wc, interpolation="bilinear")
plt.show()
# -
A" outputId="47133c1b-c3fa-4e12-965d-b84fea434b18"
import pandas as pd
url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'
base_dados = pd.read_csv(url_dados, compression = 'zip')
base_dados
# + colab={"base_uri": "https://localhost:8080/", "height": 253} id="FpZBoRuywBzZ" outputId="884f47b1-3632-4d18-fdc5-1bac6ccbc36a"
base_dados.head()
# + colab={"base_uri": "https://localhost:8080/"} id="GghM47XVwa03" outputId="e1ae792a-f1c9-4cce-f281-31bd5491f45f"
base_dados.shape
# + id="OMTuCYmpwwVS"
#cada linha é um experimento
# com droga e com controle,
# cada linha é uma cultura de celula que foi submetida a uma droga
# + colab={"base_uri": "https://localhost:8080/"} id="kRDMZN_lyMzX" outputId="32517a3b-132f-49f4-930b-67da15051e36"
base_dados.columns
# + colab={"base_uri": "https://localhost:8080/"} id="Fm9yBd768-U4" outputId="816d8398-0388-4f97-dd4b-e5bd43a21789"
base_dados['tratamento'] ## serie: conjunto de dados de uma coluna do dataframe
# + colab={"base_uri": "https://localhost:8080/"} id="Ny7B4QwV9Fx1" outputId="6386cad9-4121-428c-8ec0-57fe04c135f5"
base_dados['tratamento'].unique() ## identifica os valores unicos que aparecem em uma seia em especifico
# + colab={"base_uri": "https://localhost:8080/"} id="JFgq5772-S65" outputId="2c73171c-604f-4362-af8f-baaffb69c551"
base_dados['tempo'].unique() # o tempo do qual o sujeito ficou exposto ao fármaco
# + colab={"base_uri": "https://localhost:8080/"} id="Yjw5l9So-hvg" outputId="38ca08c1-0964-4c04-c2ac-9bcdc6bcde6c"
base_dados['dose'].unique() # as doses que foram utilizadas
# + colab={"base_uri": "https://localhost:8080/"} id="0Jm5UcV5-ufp" outputId="d6cd9e3d-dc28-4731-fc50-04ac816c88ab"
drogas = base_dados['droga'].unique() ## a nomenclatura é lista, mas o objeto é um array.
drogas
# + colab={"base_uri": "https://localhost:8080/"} id="gP955FG9AW6x" outputId="e1ec0505-b2f5-4cd2-b79a-a0d3fabbd3cb"
base_dados['tempo'].value_counts() ## conta os valores que se repetem na série
# + colab={"base_uri": "https://localhost:8080/"} id="3XsRyUhkHQr2" outputId="6354c8d3-543c-47b6-d08d-b09736770d68"
base_dados['droga'].value_counts()
# + colab={"base_uri": "https://localhost:8080/"} id="r5LFrhYdIKAU" outputId="cacdd5e8-ffbc-40fe-b128-7267466ce745"
base_dados['tratamento'].value_counts() ## aspas simples ou duplas, não tem diferença no python
# + colab={"base_uri": "https://localhost:8080/"} id="wzoFPNB_MXP7" outputId="1caf3c18-f601-4e12-fcef-74664fedd717"
base_dados['tratamento'].value_counts(normalize = True)
# + colab={"base_uri": "https://localhost:8080/"} id="0FmhsIgfMnGl" outputId="fadf23f3-7175-4c57-cb42-1316cd770310"
base_dados['dose'].value_counts(normalize = True)
# + colab={"base_uri": "https://localhost:8080/", "height": 265} id="hACeE3H0MuTO" outputId="8b70bfc5-0a61-4859-a7b2-84197b8d293a"
base_dados["tratamento"].value_counts().plot.pie()
# + colab={"base_uri": "https://localhost:8080/", "height": 265} id="6g2yUlBSNAcz" outputId="9523ad3c-8d4f-4624-baed-abc14f549167"
base_dados['tempo'].value_counts().plot.pie()
# + colab={"base_uri": "https://localhost:8080/"} id="H7_tsDxRNN8X" outputId="620d3210-cf68-4a26-e7b4-3c9d05a1e7a9"
base_dados['tempo'].value_counts(normalize = True) ## Sempre evitar gráficos de pizza, o que for de comer,
## normalmente não é o melhor para analisar
# + colab={"base_uri": "https://localhost:8080/", "height": 285} id="UYM2691-Nn6N" outputId="98cdfc0d-5bb2-4d49-9d7f-7e23c3d2c4a1"
base_dados['tempo'].value_counts().plot.bar()
# + colab={"base_uri": "https://localhost:8080/"} id="OeLLEMj0O61Y" outputId="97ab7577-77f0-4417-eca9-68f91f93d980"
base_dados['g-0'] > 0
# + colab={"base_uri": "https://localhost:8080/", "height": 253} id="7H7gN-TBPHXq" outputId="4dd6d342-c21a-4f0d-9c1b-2fca716826f5"
dados_maior_zero = base_dados[base_dados['g-0'] > 0 ]
dados_maior_zero.head()
# + colab={"base_uri": "https://localhost:8080/"} id="t_fdilFKPeKG" outputId="4011d550-b2b4-4e2a-8ef7-1b5b3fe25c89"
# + [markdown] id="wvXK5I5dKwip"
# ### Desafio 01: Investigar o por que a classe tratamento é tão desbalanceada
# + [markdown] id="A0B5kuwGVU6G"
# É visto que há um desbalanceamento na série tratamento pelo falo que de as amostras com_controle são amostras de controle, que servem com o parâmetro de comparação com a reação das outras amostras.
# + [markdown] id="ckMTEZK0K7BK"
# ### Desafio 02: Plotar as 5 útimas linhas da tabela
# + colab={"base_uri": "https://localhost:8080/", "height": 253} id="NME_kZ5CWMvX" outputId="e01691e3-ffc8-47a2-e171-a0a7fd54363a"
base_dados.tail() ##tail retorna as ultimas 5 linhas
# + [markdown] id="B0PJqohNK-Z-"
# ### Desafio 03: Proporção das classes tratamento
# + colab={"base_uri": "https://localhost:8080/"} id="x3DyrQbOWlzI" outputId="40ff6dc3-cfe9-41ab-cb32-3a1624106c98"
base_dados['tratamento'].value_counts(normalize = True)
# + [markdown] id="0BzCJmrBLP_a"
# ### Desafio 04 : Quantos Tipos de Drogas foram investigadas?
# + colab={"base_uri": "https://localhost:8080/"} id="0rgAMbLNLVwW" outputId="0c075e1b-77a5-4736-f157-1638b774ebf4"
drogas_count = base_dados["droga"].value_counts()
drogas_count
# + colab={"base_uri": "https://localhost:8080/"} id="E9gpbbPULfeY" outputId="609f03b4-c89d-4558-915d-f8fd0eb98859"
drogas_count.count()
# + id="bl6Rj8weKsms"
# + [markdown] id="6FiX87KeP39M"
# ### Desafio 05: Procurar na documentação o método query do pandas
# + colab={"base_uri": "https://localhost:8080/", "height": 439} id="sCslPbM9ZGab" outputId="1f868940-ba6a-4884-920e-b584ad24c4dc"
base_dados.query('dose == "D1" and tempo == 72 and tratamento == "com_controle"')
## o método query recebe expressoes que devem retornar boleanos, onde o true dessa expressão irá montar o DF resultante
# + id="c4yVP4togg3Q"
# ?pd.DataFrame.query
# + [markdown] id="-B9OLacuQGdr"
# ### Desafio 06: Procurar na documentação como deixar os gráficos melhores (matplotlib)
# + colab={"base_uri": "https://localhost:8080/", "height": 298} id="sRdcHaEwbBxH" outputId="5991d1c5-8715-4906-c556-1bcf344f296d"
base_dados['tempo'].value_counts().plot.bar( title = "Amostras por Tempo ", rot = 0, ylabel = 'Quantidade Amostrastop')
# + [markdown] id="eW664PmCQgjM"
# ### Desafio 07: Renomear as Colunas, retirando o hifen
# + colab={"base_uri": "https://localhost:8080/", "height": 253} id="7L1fOF7TYc0o" outputId="025ffdab-be93-4449-d66c-552e41d5661b"
base_dados.columns = base_dados.columns.str.replace('-','')
base_dados.tail()
# + [markdown] id="LlYTezsxQ_iz"
# ###Desafio 08: Resumo do que você aprendeu com os dados
# + [markdown] id="wOHpw1nJy36F"
# - É visto que a análise dos dados depende e muito do conhecimento do negócio, sem isso, ainda falta um pouco de direção para onde investigar.
#
# - Visto que 3289 drogas foram testadas nessa base de dados.
#
# - Suspeito que cada droga tenha uma relação diferente com o gene no tempo.
#
# - Ansioso para entender melhor o matplotlib, apresentação é o que encanta.
| 7,900 |
/GTFS Melbourne Metro Train Network.ipynb | 47bb89e8884cc1d106d8f09bdb7ec7bb4b29e387 | [] | no_license | joshuawebb1988/gtfs | https://github.com/joshuawebb1988/gtfs | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 313,494 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.model_selection as skl
from sklearn.linear_model import LinearRegression
import seaborn as sns
import statsmodels.formula.api as smf
from statsmodels.tools.eval_measures import rmse
# %matplotlib inline
sns.set(color_codes=True)#Set seaborn color coding
# -
supermarket_till_df = pd.read_csv("supermarket_till_transactions.csv")
supermarket_till_df.sample(10)
# Observe if basket size will determine the amount to be spend
basketSize_Spend_df = supermarket_till_df[["BASKET_SIZE","SPEND","CUST_LIFESTAGE"]]
basketSize_Spend_df.sample(10)
basketSize_Spend_df.nunique()
basketSize_Spend_df.CUST_LIFESTAGE.unique()
basketSize_Spend_df.CUST_LIFESTAGE.fillna("OT",inplace=True)
basketSize_Spend_df.nunique()
# Plot regression against actual data
plt.figure(figsize=(12, 6))
plt.plot(basketSize_Spend_df.BASKET_SIZE, basketSize_Spend_df.SPEND, 'ro') # scatter plot showing actual data
plt.title('Basket Size vs Spend')
plt.xlabel('Basket Size')
plt.ylabel('Spend')
plt.show()
sns.scatterplot(x = "BASKET_SIZE", y = "SPEND", data = basketSize_Spend_df)
basketSize_Spend_df = pd.get_dummies(data=basketSize_Spend_df, columns=["BASKET_SIZE"])
basketSize_Spend_df.sample(10)
# +
x = basketSize_Spend_df[['BASKET_SIZE_L', 'BASKET_SIZE_M', 'BASKET_SIZE_S']]
y = basketSize_Spend_df.SPEND
x_train, x_test, y_train, y_test = skl.train_test_split(x, y, train_size = 0.70, random_state = 10)
print(x_train.shape,x_test.shape, y_train.shape,y_test.shape)
# -
# Using stats_model to do a linear regression
stats_model = smf.ols('SPEND ~ BASKET_SIZE_L + BASKET_SIZE_M + BASKET_SIZE_S', data=basketSize_Spend_df)
stats_model = stats_model.fit()
stats_model.params
stats_model.summary()
ypred = stats_model.predict(x_test)
stats_rmse = rmse(y_test, ypred)
stats_rmse
# Using SKlearn Linear regression
lm = LinearRegression()
scikit_model = lm.fit(x_train,y_train)
scikit_model.coef_
scikit_model.intercept_
scikit_ypred = scikit_model.predict(x_test)
sci_rmse = rmse(y_test, scikit_ypred)
sci_rmse
x['tuesday'] + x['wednesday'] + x['thursday'] +\
x['friday'] + x['saturday'] + x['sunday'])), axis=1)
perweek.name = 'perweek'
regular_services = pd.concat([regular_services, perweek], axis=1)
regular_services_lookup = regular_services[['service_id','perweek']].set_index('service_id')
busy_date = regular_services.loc[regular_services['perweek'].idxmax()]['start_date']
print(busy_date)
fts = gt.compute_feed_time_series(feed, trip_stats, busy_date, freq='1H')
gt.downsample(fts, freq='4H')
gt.plot_feed_time_series(fts)
# +
#compute services, train-km, and train-seconds per week for each route
route_df = pd.merge(left=trip_stats, right=feed.trips[['service_id', 'trip_id']], how='left', on='trip_id')
route_grouping = route_df.groupby(by=['service_id','route_short_name'])
route_group_agg = route_grouping.agg({'duration':np.sum, 'distance':np.sum, 'trip_id': (lambda x: x.nunique())})
route_group_agg = route_group_agg.join(regular_services_lookup)
for idx, row in route_group_agg.iterrows():
route_group_agg.loc[idx, 'services_perweek'] = row['perweek'] * row['trip_id']
route_group_agg.loc[idx, 'distance_perweek'] = row['perweek'] * row['distance']
route_group_agg.loc[idx, 'time_perweek'] = row['perweek'] * row['duration'] * 3600
route_util = route_group_agg[['services_perweek','distance_perweek','time_perweek']].groupby(level=1).sum()
# +
#debug checks
#metro_codes[metro_codes['stop_id'] == '19855']
#feed.stop_times[feed.stop_times['trip_id'] == '8368.T0.2-FKN-H-mjp-1.1.H']
#feed.trips[feed.trips.trip_headsign == 'Frankston'].head(2)
# -
#show weekly stats per route
route_util
# +
#prepare stop_times for regular services into segments
#i.e. each row is a segment from station - to station
stop_times_df = pd.merge(left=feed.stop_times.sort_values(by=['trip_id','stop_sequence']), \
right=feed.trips[['service_id', 'trip_id', 'direction_id']], \
how='left', on='trip_id')
stop_times_df_regular = stop_times_df.join(regular_services_lookup, on='service_id', how='inner')
stop_times_df_regular['prev_distance'] = stop_times_df_regular.groupby(by=['trip_id'])['shape_dist_traveled'].shift(1)
stop_times_df_regular['next_distance'] = stop_times_df_regular.groupby(by=['trip_id'])['shape_dist_traveled'].shift(-1)
stop_times_df_regular['stop_distance'] = 0.0
stop_times_df_regular['stop_distance'] += ((stop_times_df_regular['shape_dist_traveled'] \
- stop_times_df_regular['prev_distance']) / 2).fillna(0.0)
stop_times_df_regular['stop_distance'] += ((stop_times_df_regular['next_distance'] \
- stop_times_df_regular['shape_dist_traveled']) / 2).fillna(0.0)
stop_times_df_regular['next_stop_id'] = stop_times_df_regular.groupby(by=['trip_id'])['stop_id'].shift(-1)
stop_times_df_regular['next_arrival_time'] = stop_times_df_regular.groupby(by=['trip_id'])['arrival_time'].shift(-1)
stop_times_df_regular['segment_time'] = (stop_times_df_regular['next_arrival_time'].apply(pd.to_timedelta) - \
stop_times_df_regular['departure_time'].apply(pd.to_timedelta))
stop_times_df_regular['segment_time'] = stop_times_df_regular['segment_time'].apply(lambda x: x.total_seconds())
stop_times_df_regular['segment_distance'] = (stop_times_df_regular['next_distance'] - \
stop_times_df_regular['shape_dist_traveled'])
stop_times_df_regular['next_seq_diff'] = stop_times_df_regular.groupby(by=['trip_id'])['stop_sequence'].diff(-1)
# -
#define segments
segment_df = (stop_times_df_regular[stop_times_df_regular['next_stop_id'].notnull()]\
[['stop_id', 'next_stop_id', 'trip_id', 'service_id', 'perweek', 'segment_time', \
'segment_distance', 'next_seq_diff', 'direction_id']])
#define express segment, skips a station, if from sequence - next sequence is greater than 1
segment_df['express'] = (segment_df['next_seq_diff'] < -1.1)
segment_df['time_perweek'] = segment_df['segment_time'] * segment_df['perweek']
segment_df['distance_perweek'] = segment_df['segment_distance'] * segment_df['perweek']
segment_df['segment_stop_list'] = segment_df.apply(lambda x: [x['stop_id'], x['next_stop_id']], axis=1)
segment_df['segment'] = segment_df['segment_stop_list'].apply(lambda x: str(x[0]) + ' ' + str(x[1]))
segment_df['segment_stop_1'] = segment_df['segment_stop_list'].apply(lambda x: x[0])
segment_df['segment_stop_2'] = segment_df['segment_stop_list'].apply(lambda x: x[1])
# +
#debug checks
#segment_df.head(2)
# -
#prepare segments to generate edges in networkx graph
segment_allstops = segment_df[segment_df['express'] == False]\
.groupby(by=['segment', 'segment_stop_1', 'segment_stop_2'])\
['segment_time', 'segment_distance'].agg(
{'segment_time':np.mean,
'segment_distance':np.mean
})
segment_allstops.reset_index(level=[1,2], inplace=True)
# +
#debug checks
#segment_allstops.head(2)
# +
#debug checks
#print('BBN {0}'.format(segment_df[segment_df['stop_id'] =='19898']['trip_id'].nunique()))
#print('NWG {0}'.format(segment_df[segment_df['stop_id'] =='19899']['trip_id'].nunique()))
#print('MCH {0}'.format(segment_df[segment_df['stop_id'] =='19900']['trip_id'].nunique()))
#print('HTD {0}'.format(segment_df[segment_df['stop_id'] =='19901']['trip_id'].nunique()))
#print('RWD {0}'.format(segment_df[segment_df['stop_id'] =='19902']['trip_id'].nunique()))
#print('\n')
#print('BBN {0}'.format(segment_df[segment_df['next_stop_id'] =='19898']['trip_id'].nunique()))
#print('NWG {0}'.format(segment_df[segment_df['next_stop_id'] =='19899']['trip_id'].nunique()))
#print('MCH {0}'.format(segment_df[segment_df['next_stop_id'] =='19900']['trip_id'].nunique()))
#print('HTD {0}'.format(segment_df[segment_df['next_stop_id'] =='19901']['trip_id'].nunique()))
#print('RWD {0}'.format(segment_df[segment_df['next_stop_id'] =='19902']['trip_id'].nunique()))
# -
#prepare stops to generate nodes in networkx graph
master_stops = feed.stops.set_index('stop_id')
master_stops = master_stops.join(metro_codes.set_index('stop_id')[['name', 'code_vic', 'code_aust']], how='left')
master_stops_pos = master_stops.apply(lambda x: (x['stop_lon'], x['stop_lat']), axis=1).to_dict()
# +
#INITIAL graph, used to calculate between stops visited by express segments
#using shortest path
#create directional graph from edges
trainG = nx.from_pandas_dataframe(segment_allstops, 'segment_stop_1', 'segment_stop_2',
edge_attr=['segment_distance', 'segment_time'],
create_using=nx.DiGraph())
#add nodes without routes e.g. not regular services
trainG.add_nodes_from(master_stops.index)
#add node attributes
stops_dic = master_stops.to_dict()
for k, v in stops_dic.items():
nx.set_node_attributes(trainG, k, v)
nx.set_node_attributes(trainG, 'pos', master_stops_pos)
# -
#function to generate [0,1],[1,2] segments from list of stops
def group(lst, n):
for i in range(0, len(lst), 1):
val = lst[i:i+n]
if len(val) == n:
yield list(val)
#INITIAL calculate utilisation stats for segments
segment_util = segment_df.groupby(by=['segment', 'segment_stop_1', 'segment_stop_2'])\
['perweek', 'segment_time', 'segment_distance', 'time_perweek', 'distance_perweek',
'express'].agg(
{'perweek':np.sum,
'segment_time':np.mean,
'segment_distance':np.mean,
'time_perweek':np.sum,
'distance_perweek':np.sum,
'express':any
})
segment_util.reset_index(level=[1,2], inplace=True)
# +
#debug check
#segment_util.head(2)
# -
#define stops visited by express segments
express_segment_util = segment_util[segment_util['express'] == True]
express_stops = {}
for idx, x in express_segment_util[['segment_stop_1','segment_stop_2']].iterrows():
stops = list(group(nx.shortest_path(trainG, source=x['segment_stop_1'], target=x['segment_stop_2']), 2))
express_stops.update({idx:stops})
express_stops = pd.Series(express_stops)
express_stops.index.rename(['segment'], inplace=True)
express_stops.name = 'stops'
# +
#debug check
#express_segment_util.loc['19973 20025']
# +
#define segments utilised by express segments i.e. components of an express segment
new_seg_util = {}
for idx, row in express_segment_util.join(express_stops).iterrows():
for i in row['stops']:
index = (str(i[0]) + ' ' + str(i[1]))
value = row['perweek']
new_seg_util.update({index:value})
new_seg_util = pd.Series(new_seg_util)
new_seg_util.index.rename('segment', inplace=True)
new_seg_util.name = 'perweek'
new_seg_util = pd.DataFrame(new_seg_util).join(segment_allstops)
new_seg_util = new_seg_util.join(
new_seg_util[['segment_time','segment_distance']].\
multiply(new_seg_util['perweek'], axis='index').\
rename(columns={'segment_time':'time_perweek', 'segment_distance':'distance_perweek'}))
new_seg_util['express'] = False
# +
#debug check
#print(new_seg_util.index.isin(segment_util.index))
# +
#debug check
#new_seg_util.head(2)
# -
#calculate utilisation stats inclusive of components of express segments
segment_util.update(
segment_util[['perweek','distance_perweek','time_perweek']].\
add(new_seg_util[['perweek','distance_perweek','time_perweek','express']], axis='columns', fill_value=0.0)
)
segment_util['express'] = segment_util['express'] == True
segment_util = segment_util[segment_util['express'] != True]
#debug check
segment_util[(segment_util['segment_stop_1'] == '22180') | (segment_util['segment_stop_2'] == '22180') ]
#calculate utilisation stats outbound from station
stop1_util = segment_util[['segment_stop_1', 'perweek', 'distance_perweek', 'time_perweek']].\
reset_index(level=0, drop=True).set_index('segment_stop_1', append=True)
stop1_util['distance_perweek'] = stop1_util['distance_perweek'] / 2
stop1_util['time_perweek'] = stop1_util['time_perweek'] / 2
stop1_util.index.names = ['direction_id','stop_id']
stop1_util = stop1_util.swaplevel(i='stop_id', j='direction_id')
stop_out_util = stop1_util.groupby(level=[0]).agg({'perweek':np.sum, 'distance_perweek':np.sum, 'time_perweek':np.sum})
stop_out_util['lines'] = stop1_util.groupby(level=[0]).size()
stop_out_util.rename(columns={'perweek':'out_perweek',
'time_perweek':'out_time_perweek',
'distance_perweek':'out_distance_perweek',
'lines':'out_lines'}, inplace=True)
#calculate utilisation stats inbound to station
stop2_util = segment_util[['segment_stop_2', 'perweek', 'distance_perweek', 'time_perweek']].\
reset_index(level=0, drop=True).set_index('segment_stop_2', append=True)
stop2_util['distance_perweek'] = stop2_util['distance_perweek'] / 2
stop2_util['time_perweek'] = stop2_util['time_perweek'] / 2
stop2_util.index.names = ['direction_id','stop_id']
stop2_util = stop2_util.swaplevel(i='stop_id', j='direction_id')
stop_in_util = stop2_util.groupby(level=[0]).agg({'perweek':np.sum, 'distance_perweek':np.sum, 'time_perweek':np.sum})
stop_in_util['lines'] = stop2_util.groupby(level=[0]).size()
stop_in_util.rename(columns={'perweek':'in_perweek',
'time_perweek':'in_time_perweek',
'distance_perweek':'in_distance_perweek',
'lines':'in_lines'}, inplace=True)
#merge together inbound and outbound station stats
master_stops_util = pd.merge(master_stops,
pd.merge(stop_in_util, stop_out_util, how='outer', left_index=True, right_index=True),
how='outer', left_index=True, right_index=True)
#debug check
master_stops_util.head(2)
# +
#create directed graph from edges including utilisation stats as attributes
trainG = nx.from_pandas_dataframe(segment_util, 'segment_stop_1', 'segment_stop_2',
edge_attr=['segment_distance', 'segment_time', 'perweek',
'distance_perweek', 'time_perweek'],
create_using=nx.DiGraph())
#add nodes without routes e.g. not regular services
trainG.add_nodes_from(master_stops_util.index)
#add node attributes including utilisation stats
stops_dic = master_stops_util.to_dict()
for k, v in stops_dic.items():
nx.set_node_attributes(trainG, k, v)
nx.set_node_attributes(trainG, 'pos', master_stops_pos)
# -
#draw using matplotlib
plt.rcParams['figure.figsize'] = (16.0, 12.0)
nx.draw_networkx(trainG, pos=master_stops_pos, arrows=False, node_size=20, node_color='b', \
with_labels=False, labels=master_stops[['code_vic']].to_dict()['code_vic'])
plt.show()
#function defines layout of edges for a bokeh plot of graph
def get_edges_specs(_network, _layout, weight_name=None):
d = dict(xs=[], ys=[], alphas=[])
if weight_name is not None:
weights = [d[weight_name] for u, v, d in _network.edges(data=True)]
max_weight = max(weights)
calc_alpha = lambda h: 0.1 + 0.6 * (h / max_weight)
for u, v, data in _network.edges(data=True):
d['xs'].append([_layout[u][0], _layout[v][0]])
d['ys'].append([_layout[u][1], _layout[v][1]])
if weight_name is not None:
d['alphas'].append(calc_alpha(data[weight_name]))
else:
d['alphas'].append(0.7)
return d
# +
#prepare bokeh plot of graph
layout = nx.spring_layout(trainG, pos=master_stops_pos, iterations=100, fixed=trainG.nodes())
nodes_source = ColumnDataSource(master_stops_util)
hover = HoverTool(tooltips=[('code', "@code_vic"), ('stop_id', '@stop_id'), ('name', '@name'),
('lat', '@stop_lat'), ('lon', '@stop_lon'), ('serv_perweek_in', '@in_perweek'),
('trainkm_perweek_in', '@in_distance_perweek'), ('serv_perweek_out', '@out_perweek'),
('trainkm_perweek_out', '@out_distance_perweek')])
plot = figure(plot_width=900, plot_height=600,
tools=['tap', hover, 'box_zoom', 'reset', 'pan', 'wheel_zoom'])
r_circles = plot.circle('stop_lon', 'stop_lat', source=nodes_source, size=5,
color='blue', level = 'overlay')
lines_source = ColumnDataSource(get_edges_specs(trainG, layout, 'perweek'))
r_lines = plot.multi_line('xs', 'ys', line_width=1.5,
alpha='alphas', color='navy',
source=lines_source)
# -
#generate script, plot data for embedded HTML
script, div = components(plot)
#html for embedded plot
plot_html = """
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Plots</title>
<link rel="stylesheet" href="http://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css" type="text/css" />
<script type="text/javascript" src="http://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.js"></script>
{0}
</head>
<body>
{1}
</body>
</html>
""".format(script, div)
#show html plot
HTML(plot_html)
# +
#show(plot)
# +
#write trainG to .yaml
#nx.readwrite.write_yaml(trainG, 'trainG.yaml')
#write segment_util to .csv
#segment_util.to_csv('segment_util.csv')
#write master_stops_util to .csv
#master_stops_util.to_csv('master_stops_util.csv')
#write plot_html to .html
#with open('plot_html.html', 'w') as outfile:
# outfile.write(plot_html)
# +
#read trainG from .yaml
#trainG = nx.readwrite.read_yaml('trainG.yaml')
#read segment_util from .csv
#segment_util = pd.DataFrame.from_csv('segment_util.csv')
#read master_stops_util from .csv
#master_stops_util = pd.DataFrame.from_csv('master_stops_util.csv')
#read plot_html from .html
#with open('plot_html.html', 'r') as infile:
# plot_html = infile.read()
# -
| 18,404 |
/SqlLite Database.ipynb | 117e6b62fc408e3a45a2d01a6b4a43709b0339fe | [] | no_license | mohamedsamir3/SqlLite-Database | https://github.com/mohamedsamir3/SqlLite-Database | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 144,808 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Chapter 5 오차역전파법
# 수치 미분은 시간이 오래 걸리는 단점이 있다. 오차역전파법(backpropagation)은 효율적 계산이 가능하다.
#
# - 수식을 통한 이해
# - 계산 그래프를 통한 이해 ★
#
# 참고
# - http://karpathy.github.io/
# - Stanford CS231n
# ## 5.1 계산 그래프
# **계산 그래프(computational graph)**는 계산 과정을 그래프로 나타낸 것이다. 그래프는 **노드(node)**와 **에지(edge)**로 표현된다.
# ### 5.1.1 계산 그래프로 풀다
# <img src="https://t1.daumcdn.net/cfile/tistory/997ED34B5B98F5F235">
# <center><small>▲ 간단한 계산 그래프</small></center>
#
# 계산 그래프 문제 흐름
# 1. 계산 그래프를 구성한다.
# 2. 그래프에서 계산을 왼쪽에서 오른쪽으로 진행한다.
#
# 계산을 왼쪽에서 오른쪽으로 진행하는 단계를 **순전파(forward propagation)**이라고 하고 반대 방향을 **역전파(backward propagation)**이라고 한다.
# ### 5.1.2 국소적 계산
# 계산 그래프는 국소적 계산을 전파해서 최종 결과를 얻을 수 있다는 특징이 있다. 즉, 다른 부분은 상관하지 않고 자신과 관계된 정보만 출력할 수 있다. 이러한 특징에 따라 각 노드는 자신과 관계된 계산에만 집중하면 된다.
#
# <img src="https://t1.daumcdn.net/cfile/tistory/991C9E495B98F60F1D">
# <center><small>▲ 국소적 계산의 예</small></center>
# ### 5.1.3 왜 계산 그래프로 푸는가?
# 계산 그래프의 이점
# - 국소적 계산으로 복잡한 문제를 단순화할 수 있다.
# - 역전파를 통해 (다수의) 미분을 효율적으로 계산할 수 있다.
#
# <img src="https://t1.daumcdn.net/cfile/tistory/997E914D5B98F62826">
# <center><small>▲ 역전파를 통한 미분</small></center>
# ## 5.2 연쇄법칙(chain rule)
# ### 5.2.1 계산 그래프의 역전파
# <img src="https://t1.daumcdn.net/cfile/tistory/999FD3425B98F63F1A">
#
# 국소적 미분은 상류에서 전달된 값과 곱해져서 앞쪽 노드로 전달된다.
# ### 5.2.2 연쇄법칙이란?
# 연쇄법칙은 합성 함수의 미분이 각 구성 함수의 미분의 곱으로 나타낸다는 성질을 이용한다.
#
# 예를 들어, $z = (x + y)^2$가 있을때 $x$에 대한 $z$의 미분은 다음과 같이 나타낼 수 있다.
#
# $$\frac {\partial z} {\partial x} = \frac {\partial z} {\partial t} \frac {\partial t} {\partial x}$$
#
# $$\frac {\partial z} {\partial t} = 2t$$
#
# $$\frac {\partial t} {\partial x} = 1$$
#
# $$\frac {\partial z} {\partial x} = \frac {\partial z} {\partial t} \frac {\partial t} {\partial x} = 2t \cdot 1 = 2(x + y)$$
# ### 5.2.3 연쇄법칙과 계산 그래프
# <img src="https://t1.daumcdn.net/cfile/tistory/997387465B98F65D13">
# ## 5.3 역전파
# ### 5.3.1 덧셈 노드의 역전파
# 덧셈 노드의 역전파는 입력된 값을 그대로 다음 노드로 보낸다.
#
# <img src="https://t1.daumcdn.net/cfile/tistory/99FB57455B98F67407">
# ### 5.3.2 곱셈 노드의 역전파
# 곱셈 노드의 역전파는 순전파 때의 입력 신호들을 서로 바꾼 값을 곱해서 하류로 보낸다. 그래서 곱셈 노드를 구현할 때는 순전파의 입력 신호를 변수에 저장한다.
#
# <img src="https://t1.daumcdn.net/cfile/tistory/99E3EF435B98F69309">
# ### 5.3.3 사과 쇼핑의 예
# <img src="https://t1.daumcdn.net/cfile/tistory/99AACA445B98F6A61E">
# <center><small>▲ 사과 쇼핑의 역전파 예</small></center>
#
# <img src="https://t1.daumcdn.net/cfile/tistory/99499E4E5B98F6C10E">
# <center><small>▲ 사과와 귤 쇼핑의 역전파 예</small></center>
# ## 5.4 단순한 계층 구현하기
# ### 5.4.1 곱셈 계층
class MulLayer:
def __init__(self):
self.x = None
self.y = None
def forward(self, x, y):
self.x = x
self.y = y
out = x * y
return out
def backward(self, dout):
dx = dout * self.y
dy = dout * self.x
return dx, dy
# - 사과 쇼핑 구현
# +
apple = 100
apple_num = 2
tax = 1.1
# 계층들
mul_apple_layer = MulLayer()
mul_tax_layer = MulLayer()
# 순전파
apple_price = mul_apple_layer.forward(apple, apple_num)
price = mul_tax_layer.forward(apple_price, tax)
print(price) # 다들 오차 나는지?
# +
# 역전파
dprice = 1
dapple_price, dtax = mul_tax_layer.backward(dprice)
dapple, dapple_num = mul_apple_layer.backward(dapple_price)
print(dapple, dapple_num, dtax)
# -
# ### 5.4.2 덧셈 계층
class AddLayer:
def __init__(self):
pass
def forward(self, x, y):
out = x + y
return out
def backward(self, dout):
dx = dout * 1
dy = dout * 1
return dx, dy
# 덧셈 계층은 그저 상류에서 내려온 미분을 하류로 흘러보내기만 하면 되기 때문에 따로 초기화할 필요가 없다.
# - 사과 2개와 귤 3개를 사는 상황
# +
apple_num = 2
apple = 100
mandarin_num = 3
mandarin = 150
tax = 1.1
mul_apple_layer = MulLayer()
mul_mandarin_layer = MulLayer()
add_fruit_layer = AddLayer()
mul_tax_layer = MulLayer()
apple_price = mul_apple_layer.forward(apple, apple_num)
mandarin_price = mul_mandarin_layer.forward(mandarin, mandarin_num)
fruit_price = add_fruit_layer.forward(apple_price, mandarin_price)
total_price = mul_tax_layer.forward(fruit_price, tax)
print(total_price)
dtotal_price = 1
dfruit_price, dtax = mul_tax_layer.backward(dprice)
dapple_price, dmandarin_price = add_fruit_layer.backward(dfruit_price)
dapple, dapple_num = mul_apple_layer.backward(dapple_price)
dmandarin, dmandarin_num = mul_mandarin_layer.backward(dmandarin_price)
print(dapple, dapple_num, dmandarin, dmandarin_num)
# -
# ## 5.5 활성화 함수 계층 구현하기
# ### 5.5.1 ReLU 계층
# - ReLU 수식
#
# $$y =
# \begin{cases}
# x \ (x > 0) \\
# 0 \ (x \leq 0)
# \end{cases}$$
#
# - ReLU 미분
#
# $$\frac {\partial y}{\partial x} =
# \begin{cases}
# 1 \ (x > 0) \\
# 0 \ (x \leq 0)
# \end{cases}$$
#
# <img src="https://t1.daumcdn.net/cfile/tistory/99E517485B98F6E504">
# <center><small>▲ ReLU 계산 그래프</small></center>
import numpy as np
class Relu:
def __init__(self):
self,mask = None # 입력 원소가 0 이하인 인덱스는 True, 0보다 큰 경우 False 유지
def forward(self, x):
self.mask = (x <= 0)
out = x.copy()
out[self.mask] = 0
return 0
def backward(self, dout):
dout[self.mask] = 0
dx = dout
return dx
x = np.array([[1.0, -0.5], [-2.0, 3.0]])
print(x)
mask = (x <= 0)
print(mask)
# mask 인스턴스 변수를 써서 mask의 원소가 True인 곳은 상류에서 전파된 미분값을 0으로 바꾼다.
# ### 5.5.2 Sigmoid 계층
# - 시그모이드 수식
#
# $$y = \frac 1 {1+exp(-x)}$$
#
# - '/' 노드, $y = \frac 1 x$ 미분
#
# $$\begin{align}
# \frac {\partial y} {\partial x} & = -\frac 1 {x^2} \\
# & = -y^2 \\
# \end{align}$$
#
# - exp 노드 미분
#
# $$\frac {\partial y} {\partial x} = exp(x)$$
#
#
# <img src="https://t1.daumcdn.net/cfile/tistory/999E3B4B5B98F72021">
# <center><small>▲ 시그모이드 순전파/역전파</small></center>
#
# - sigmoid 미분
#
# $$\begin{align}
# \frac {\partial y} {\partial x} & = y^2exp(-x) \\
# & = \frac 1 {(1 + exp(-x))^2} exp(-x) \\
# & = \frac 1 {1 + exp(-x)} \frac {exp(-x)} {1+exp(-x)} \\
# & = y(1-y)
# \end{align}$$
#
# 시그모이드 계층의 역전파는 순전파의 출력만으로 계산할 수 있다.
class Sigmoid:
def __init__(self):
self.out = None
def forward(self, x):
out = 1 / (1 + np.exp(-x))
self.out = out
return out
def backward(self, dout):
dx = dout * (1.0 - self.out) * self.out
return dx
# 구현에서 순전파의 출력을 out 인스턴스 변수에 저장해 놓고 역전파 계산할 때 사용한다.
# ## 5.6 Affine/Softmax 계층 구현하기
# ### 5.6.1 Affine 계층
# 행렬의 곱을 기하학에서는 **어파인 변환(affine transformation)**이라고 한다.
#
# $$\frac {\partial L} {\partial X} = \frac {\partial L} {\partial Y} \cdot W^T$$
#
# $$\frac {\partial L} {\partial W} = X^T \cdot \frac {\partial L} {\partial Y}$$
#
# <img src="https://t1.daumcdn.net/cfile/tistory/994002375B98F73E05">
#
# 계산 그래프에서 각 원소의 형상에 주의해야 한다.
# ### 5.6.2 배치용 Affine 계층
# <img src="https://t1.daumcdn.net/cfile/tistory/994510365B98F75122">
#
# 편향의 경우에는 순전파에서 각각의 데이터에 더해진다. 그래서 역전파 때는 편향의 원소에 역전파 값이 편향에 모여야 한다.
class Affine:
def __init__(self, W, b):
self.W = W
self.b = b
self.x = None
self.dW = None
self.db = None
def forward(self, x):
self.x = x
out = np.dot(x, self.W) + self.b
return out
def backward(self, dout):
dx = np.dot(dout, self.W.T)
self.dW = np.dot(self.x.T, dout)
self.db = np.sum(dout, axis=0)
return dx
# ### 5.6.3 Softmax-with-Loss 계층
# Softmax 계층은 출력의 합이 1이 되도록 정규화하여 출력한다.
#
# <img src="https://t1.daumcdn.net/cfile/tistory/995A16395B98F76820">
#
# <img src="https://t1.daumcdn.net/cfile/tistory/99EBF5395B98F7792B">
# <center><small>▲ Softmax-with-Loss 계층의 계산 그래프</small></center>
#
# <img src="https://camo.qiitausercontent.com/c879a9a466f923c0978973590908d2b1c0725592/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f3139373530382f66353935633337652d323562312d383666392d356438632d3937343532343461626633372e706e67" width=450>
# <center><small>▲ Softmax-with-Loss 계층 계산 그래프 간소화</small></center>
#
# Softmax 계층의 역전파 결과에서 중요한 점은 Softmax 계층의 출력과 정답 레이블의 차이로, 신경망의 현재 출력과 정답 레이블의 오차를 그래로 드러낸다는 것이다.
#
# 참고로 항등 함수의 손실 함수로 평균 제곱 오차를 사용하는데 이 때의 역전파 값도 위와 동일하다.
# 소프트맥스 오버플로 개선 버전
def softmax(a):
c = np.max(a)
exp_a = np.exp(a - c)
sum_exp_a = np.sum(exp_a)
y = exp_a / sum_exp_a
return y
# 데이터가 1개나 그 이상의 배치로 주어지는 경우
def cross_entropy_error(y, t):
# y가 1차원, 즉 하나의 데이터일 경우 shape을 바꿔준다.
if y.ndim == 1:
t = t.reshape(1, t.size)
y = y.reshape(1, y.size)
batch_size = y.shape[0]
return -np.sum(t * np.log(y + 1e-7)) / batch_size
class SoftmaxWithLoss:
def __init__(self):
self.loss = None # 손실
self.y = None # softmax 출력
self.t = None # 정답 레이블(원-핫 벡터)
def forward(self, x, t):
self.t = t
self.y = softmax(x)
self.loss = cross_entropy_error(self.y, self.t)
return self.loss
def backward(self, dout=1):
batch_size = self.t.shape[0]
dx = (self.y - self.t) / batch_size
return dx
# ## 5.7 오차역전파법 구현하기
# ### 5.7.1 신경망 학습의 전체 그림 (생략)
# ### 5.7.2 오차역전파법을 적용한 신경망 구현하기
import sys, os
sys.path.append(os.pardir)
import numpy as np
from common.layers import *
from common.gradient import numerical_gradient
from collections import OrderedDict
class TwoLayerNet:
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
# 가중치 초기화
self.params = {}
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
self.params['b2'] = np.zeros(output_size)
# 계층 생성
self.layers = OrderedDict()
self.layers['Affine1'] = Affine(self.params["W1"], self.params['b1'])
self.layers['Relu1'] = Relu()
self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])
self.lastLayer = SoftmaxWithLoss()
def predict(self, x):
for layer in self.layers.values():
x = layer.forward(x)
return x
# x: 입력 데이터, t: 정답 레이블
def loss(self, x, t):
y = self.predict(x)
return self.lastLayer.forward(y, t)
def accuracy(self, x, t):
y = self.predict(x)
y = np.argmax(y, axis=1)
if t.ndim != 1 :
t = np.argmax(t, axis=1)
accuracy = np.sum(y == t) / float(x.shape[0])
return accuracy
# x: 입력 데이터, t: 정답 레이블
def numerical_gradient(self, x, t):
loss_W = lambda W: self.loss(x, t)
grads = {}
grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
return grads
def gradient(self, x, t):
# 순전파
self.loss(x, t)
# 역전파
dout = 1
dout = self.lastLayer.backward(dout)
layers = list(self.layers.values())
layers.reverse()
for layer in layers:
dout = layer.backward(dout)
# 결과 저장
grads = {}
grads['W1'] = self.layers['Affine1'].dW
grads['b1'] = self.layers['Affine1'].db
grads['W2'] = self.layers['Affine2'].dW
grads['b2'] = self.layers['Affine2'].db
return grads
# ### 5.7.3 오차역전파법으로 구한 기울기 검증하기
# 기울기 구하는 방법
# - 수치 미분: 구현은 간단하지만 느리다
# - 해석적 방법: 오차역전파법 이용해서 매개변수 많아도 빠르게 계산 가능
#
# 실제 학습을 할 땐 계산이 빠른 오차역전파법을 이용하고 수치 미분은 오차역전파법을 정확하게 구현했는지 확인하는 용도로 사용한다. 두 방식으로 기울기가 일치하는 것을 확인하는 작업을 **기울기 확인(gradient check)**라고 한다.
import sys, os
sys.path.append(os.pardir)
import numpy as np
from dataset.mnist import load_mnist
from two_layer_net import TwoLayerNet
# +
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
x_batch = x_train[:3]
t_batch = t_train[:3]
grad_numerical = network.numerical_gradient(x_batch, t_batch)
grad_backprop = network.gradient(x_batch, t_batch)
# 가 가중치의 차이의 절댓값을 구한 후, 그 절댓값들의 평균을 낸다.
for key in grad_numerical.keys():
diff = np.average(np.abs(grad_backprop[key] - grad_numerical[key]))
print(key + ": " + str(diff))
# -
# ### 5.7.4 오차역전파법을 사용한 학습 구현하기
import sys, os
sys.path.append(os.pardir)
import numpy as np
from dataset.mnist import load_mnist
from two_layer_net import TwoLayerNet
# +
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
iters_num = 10000
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1
train_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
for i in range(iters_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
# 오차역전파법으로 기울기를 구한다.
grad = network.gradient(x_batch, t_batch)
# 갱신
for key in ('W1', 'b1', 'W2', 'b2'):
network.params[key] -= learning_rate * grad[key]
loss = network.loss(x_batch, t_batch)
train_loss_list.append(loss)
if i % iter_per_epoch == 0:
train_acc = network.accuracy(x_train, t_train)
test_acc = network.accuracy(x_test, t_test)
train_acc_list.append(train_acc)
test_acc_list.append(test_acc)
print(train_acc, test_acc)
# -
put_scale = np.asarray(norm_uniform(dataset,2)).squeeze()
print(input_scale.shape)
# Standardization (normalize by mean and standard deviation)
# +
def normalize(tensor, coordinates=1, std=None):
moments = []
std_centroids = []
for n_vid in range(tensor.shape[0]):
coord_moments = []
mean_value = [np.nanmean(tensor[n_vid, :,i::coordinates]) for i in range(coordinates)]
std_value = [np.nanstd(tensor[n_vid, :,i::coordinates]) for i in range(coordinates)]
centroids = np.ndarray((tensor.shape[1],coordinates))
for n_frame in range(tensor.shape[1]):
centroid = [np.nanmean(tensor[n_vid, n_frame, i::coordinates]) for i in range(coordinates)]
centroids[n_frame] = np.asarray(centroid)
std_centroid = [np.nanstd(centroids[:,i]) for i in range(coordinates)]
if std is not None:
std_value = [std[n_vid]]
for j in range(coordinates):
subtensor = tensor[:, :, j::coordinates]
subtensor[:] = np.subtract(subtensor, mean_value[j])
subtensor[:] = np.divide(subtensor, std_value[j])
coord_moments.append((mean_value[j], std_value[j]))
moments.append(coord_moments)
std_centroids.append(std_centroid)
return moments, std_centroids
moments, std_centroids = normalize(dataset, 2)
# -
# Shuffle to test different splits in every run.
# +
# Randomly shuffle videos
permutation = np.random.permutation(dataset.shape[0])
dataset, groundtruth, lengths = dataset[permutation], groundtruth[permutation], lengths[permutation]
input_scale = input_scale[permutation]
print(dataset.shape, groundtruth.shape, lengths.shape)
print(input_scale.shape)
# +
l1, l2 = len(dataset), len(groundtruth)
p1, p2 = 0.8, 0.9
# Split in train, validation and test
training_kp, val_kp, test_kp = dataset[:round(p1*l1)], dataset[round(p1*l1):round(p2*l1)], dataset[round(p2*l1):]
training_lbl, val_lbl, test_lbl = groundtruth[:round(p1*l2)], groundtruth[round(p1*l2):round(p2*l2)], groundtruth[round(p2*l2):]
training_lengths, val_lengths, test_lengths = lengths[:round(p1*l1)], lengths[round(p1*l1):round(p2*l1)], lengths[round(p2*l1):]
training_inpscale, val_inpscale, test_inpscale = input_scale[:round(p1*l1)], input_scale[round(p1*l1):round(p2*l1)], input_scale[round(p2*l1):]
print(training_kp.shape, val_kp.shape, test_kp.shape)
print(training_lbl.shape, val_lbl.shape, test_lbl.shape)
print(training_lengths.shape, val_lengths.shape, test_lengths.shape)
# -
# True scaling factors for z.
training_outscale = np.asarray(norm_uniform(training_lbl)).squeeze()
val_outscale_t = np.asarray(norm_uniform(val_lbl)).squeeze()
test_outscale_t = np.asarray(norm_uniform(test_lbl)).squeeze()
print(training_outscale.shape, val_outscale_t.shape)
# Inferred scaling factors for z.
alpha = LinearRegression(fit_intercept=True)#make_pipeline(PolynomialFeatures(9), LinearRegression(fit_intercept=True))
alpha.fit(training_inpscale, training_outscale)
val_outscale = alpha.predict(val_inpscale)
test_outscale = alpha.predict(test_inpscale)
print(val_outscale.shape, test_outscale.shape)
# +
val_outscale = np.concatenate((val_outscale[:,np.newaxis], val_outscale_t[:,np.newaxis]), axis=1)
test_outscale = np.concatenate((test_outscale[:,np.newaxis], test_outscale_t[:,np.newaxis]), axis=1)
print(val_outscale.shape)
# -
# Convert z-coordinates to bin encoding (label of the class it corresponds to). FOr that the z-axis is split in several "bins" each given an int as label.
def convert_to_bins(tensor):
max_z = np.nanmax(tensor)
min_z = np.nanmin(tensor)
z_to_bins = [min_z+i*(max_z-min_z)/21 for i in range(1, 22)]
with np.nditer(tensor, op_flags=['readwrite']) as it:
for x in it:
for z in z_to_bins:
if x < z:
x[...] = z_to_bins.index(z)
break
return z_to_bins
z_to_bins_tr = convert_to_bins(training_lbl)
z_to_bins_val = convert_to_bins(val_lbl)
z_to_bins_test = convert_to_bins(test_lbl)
print(z_to_bins_tr, groundtruth.shape)
print(training_lbl[2,3])
# +
# From python lists to pytorch tensors.
training_kp, val_kp, test_kp = torch.tensor(np.nan_to_num(training_kp), dtype=torch.float32), torch.tensor(np.nan_to_num(val_kp), dtype=torch.float32), torch.tensor(np.nan_to_num(test_kp), dtype=torch.float32)
training_lbl, val_lbl, test_lbl = torch.tensor(np.nan_to_num(training_lbl), dtype=torch.long), torch.tensor(np.nan_to_num(val_lbl), dtype=torch.long), torch.tensor(np.nan_to_num(test_lbl), dtype=torch.long)
training_lengths, val_lengths, test_lengths = torch.tensor(np.nan_to_num(training_lengths), dtype=torch.float32), torch.tensor(np.nan_to_num(val_lengths), dtype=torch.float32), torch.tensor(np.nan_to_num(test_lengths), dtype=torch.float32)
training_inpscale, val_inpscale, test_inpscale = torch.tensor(np.nan_to_num(training_inpscale), dtype=torch.float32), torch.tensor(np.nan_to_num(val_inpscale), dtype=torch.float32), torch.tensor(np.nan_to_num(test_inpscale), dtype=torch.float32)
training_outscale, val_outscale, test_outscale = torch.tensor(np.nan_to_num(training_outscale), dtype=torch.float32), torch.tensor(np.nan_to_num(val_outscale), dtype=torch.float32), torch.tensor(np.nan_to_num(test_outscale), dtype=torch.float32)
print(training_kp.shape, val_lbl.shape, test_lengths.shape)
print(training_inpscale.shape, training_outscale.shape)
# -
# Finally we define the batch_size and put the datasets in DataLoaders.
# +
train_data = TensorDataset(training_kp, training_lbl, training_lengths,
training_inpscale, training_outscale)
val_data = TensorDataset(val_kp, val_lbl, val_lengths,
val_inpscale, val_outscale)
test_data = TensorDataset(test_kp, test_lbl, test_lengths,
test_inpscale, test_outscale)
batch_size = 32
train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size, drop_last=True)
val_loader = DataLoader(val_data, shuffle=True, batch_size=batch_size, drop_last=True)
test_loader = DataLoader(test_data, shuffle=True, batch_size=batch_size, drop_last=True)
print(train_loader)
# -
# If we have a GPU available we set our device to GPU.
# +
# torch.cuda.is_available() checks and returns a Boolean True if a GPU is available, else it'll return False
is_cuda = torch.cuda.is_available()
# If we have a GPU available, we'll set our device to GPU. We'll use this device variable later in our code.
if is_cuda:
device = torch.device("cuda")
print("GPU is available")
else:
device = torch.device("cpu")
print("GPU not available, CPU used")
# -
# Let's print some examples to see whether it is loaded correctly or not.
# +
dataiter = iter(train_loader)
sample_x, sample_y, sample_len, iscale, oscale = dataiter.next()
print(sample_x.shape, sample_y.shape, sample_len.shape)
# -
# ## Model building
# It is time to build the model for this approach. It will consist on a single/double layer LSTM followed by a Linear layer with output size the number of keypoints we want to estimate. I also define a method to initialize the hidden_state of the cell.
class LSTM_2D3D(nn.Module):
def __init__(self, input_size, output_size, hidden_dim, n_layers, bidirectional, bins, dropout=0.):
super().__init__()
# Save the model parameters
self.output_size = output_size
self.n_layers = n_layers
self.hidden_dim = hidden_dim
self.bi = bidirectional
self.bins = bins
# Define the architecture
self.lstm = nn.LSTM(input_size, hidden_dim, n_layers, batch_first=True, bidirectional=bidirectional, dropout=dropout)
self.fc = nn.Sequential(
nn.Linear(hidden_dim*(2 if self.bi else 1), 256),
nn.Linear(256, output_size)
)
self.softmax=nn.LogSoftmax(dim=2)
def forward(self, x, state, lengths):
# Describe the forward step
batch_size, seq_len = x.size(0), x.size(1) # We save the batch size and the (maximum) sequence length
# Need to pack a tensor containing padded sequences of variable length
packed = nn.utils.rnn.pack_padded_sequence(x, lengths=lengths, batch_first=True, enforce_sorted=False)
ht, hidden_state = self.lstm(packed, state) # ht will be a PackedSequence
# Need to flatten and reshape the output to feed it to the Linear layer
ht = ht.data.contiguous() # ht will be of shape [sum(lengths), hidden_dim]
ot = self.fc(ht) # ot will be of shape [sum(lengths), ouput_size]
ot = ot.view(-1, self.output_size//self.bins, self.bins) #shape [sum(lengths), kp, bins]
ot = self.softmax(ot)
ot = torch.transpose(ot, 1, 2) # Transpose 'cause NLLLoss need the classes dimension as the second
l_ot = [ot[:int(length)] for length in lengths] # list of batch elements, each shape [lengths[i], bins, kp]
packed_ot = nn.utils.rnn.pack_sequence(l_ot, enforce_sorted=False) # PackedSequence
# Finally return to shape [batch_size, seq_len, bins, kp]
ot, _ = nn.utils.rnn.pad_packed_sequence(packed_ot, batch_first=True, total_length=seq_len)
return ot, hidden_state
def init_hidden(self, batch_size):
weight = next(self.parameters()).data
hidden = (weight.new(self.n_layers*(2 if self.bi else 1), batch_size, self.hidden_dim).zero_().to(device),
weight.new(self.n_layers*(2 if self.bi else 1), batch_size, self.hidden_dim).zero_().to(device))
return hidden
# +
# Define some model parameters
BINS = 21
INPUT_SIZE = sample_x.size(2)
OUTPUT_SIZE = sample_y.size(2)*BINS
HIDDEN_DIM = 512
N_LAYERS = 3
BIDIRECTIONAL = False
# Instantiate the model
model = LSTM_2D3D(INPUT_SIZE, OUTPUT_SIZE, HIDDEN_DIM, N_LAYERS, BIDIRECTIONAL, BINS, dropout=0.)
model.to(device)
print(model)
print(sum(p.numel() for p in model.parameters() if p.requires_grad))
# -
# ## Training
# Now we will proceed with the training. The first cell will define the learning rate, the loss function and the selected optimizer for the training process. Then we will proceed with a training over a number of epochs in which we will print it's training loss and validation loss. I also will be using Tensorboard to have a much nicer view of the results.
# +
def thresholded_output_transform(output):
y_pred, y = output
for i in range(y_pred.shape[2]):
indices = y_pred[:,:,i].max(dim=1)[1]
y_pred[:,:,i] = 0
for j in range(len(indices)):
y_pred[j,indices[j],i] = 1
return y_pred, y
accuracy = Accuracy(thresholded_output_transform)
BINS = 21
OUTPUT_SIZE = 26*BINS
# -
NUM_EPOCHS = 40
lr = 4e-6
loss_function = nn.NLLLoss()
one_cycle = True
optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=0.0)
if one_cycle:
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=lr,
steps_per_epoch=len(train_loader), epochs=NUM_EPOCHS,
div_factor=20.0, final_div_factor=1000.0)
from datetime import datetime
name = 'lot_body_class'
writer = SummaryWriter(log_dir=f'/deeplearning/logs/{name}{datetime.now()}_lr-{lr}_{NUM_EPOCHS}')
# +
timer_beg = timer()
tr_losses = []
val_losses = []
model.train()
for i in range(NUM_EPOCHS):
# Init the hidden state (ht, ct)
h = model.init_hidden(batch_size)
batch_losses = []
if i+1 == NUM_EPOCHS:
preds, inps, labls, lens = [], [], [], []
val_preds, val_inps, val_labls, val_lens = [], [], [], []
iscale, oscale, val_iscale, val_oscale = [], [], [], []
for inputs, labels, lengths, i_s, o_s in train_loader:
h = tuple([e.data for e in h])
inputs, labels, lengths = inputs.to(device), labels.to(device), lengths.to(device)
# Clear the gradients
optimizer.zero_grad()
# Forward step
output, h = model(inputs, h, lengths)
if i+1 == NUM_EPOCHS:
e = [preds, inps, labls, lens, iscale, oscale]
b = [output, inputs, labels, lengths, i_s, o_s]
for k in range(len(e)):
e[k].append(b[k])
# Loss calculation and backward step
loss = loss_function(nn.utils.rnn.pack_padded_sequence(output, lengths=lengths, batch_first=True,
enforce_sorted=False).data,
nn.utils.rnn.pack_padded_sequence(labels, lengths=lengths, batch_first=True,
enforce_sorted=False).data)
loss.backward()
# Weight update
optimizer.step()
# One cycle policy step
if one_cycle:
scheduler.step()
# Output data collection for showing
batch_losses.append(loss.item())
timer_end = timer()
tr_losses.append(np.mean(batch_losses))
writer.add_scalar('Loss/train', tr_losses[-1], i)
# Validation at the end of an epoch
val_h = model.init_hidden(batch_size)
model.eval()
val_loss = []
for inp, lab, lns, vis, vos in val_loader:
val_h = tuple([each.data for each in val_h])
inp, lab, lns = inp.to(device), lab.to(device), lns.to(device)
out, val_h = model(inp, val_h, lns)
if i+1 == NUM_EPOCHS:
e = [val_preds, val_inps, val_labls, val_lens, val_iscale, val_oscale]
b = [out, inp, lab, lns, vis, vos]
for k in range(len(e)):
e[k].append(b[k])
loss = loss_function(nn.utils.rnn.pack_padded_sequence(out, lengths=lns, batch_first=True,
enforce_sorted=False).data,
nn.utils.rnn.pack_padded_sequence(lab, lengths=lns, batch_first=True,
enforce_sorted=False).data)
val_loss.append(loss.item())
val_losses.append(np.mean(val_loss))
writer.add_scalar('Loss/validation', val_losses[-1], i)
model.train()
# Output loss and training time.
print(f"Finished epoch {i+1}/{NUM_EPOCHS} in {(timer_end-timer_beg):.2f}s.\n",
f"Loss: {np.mean(tr_losses[-1]):.4f}",
f" Val Loss: {val_losses[-1]:.4f}")
timer_beg = timer()
plt.figure()
plt.plot(tr_losses, label='train')
plt.plot(val_losses, label='validation')
plt.xlabel('Epoch')
plt.ylabel('NLLLoss')
plt.legend()
# -
# Save the predictions for training and validation.
# +
tr_predictions = torch.cat(tuple(preds), dim=0)
tr_inputs = torch.cat(tuple(inps), dim=0)
tr_groundtruth = torch.cat(tuple(labls), dim=0)
tr_lengths = torch.cat(tuple(lens), dim=0)
tr_inp_scale, tr_out_scale = torch.cat(tuple(iscale), dim=0), torch.cat(tuple(oscale), dim=0)
print(tr_inp_scale.shape, tr_inputs.shape)
val_predictions = torch.cat(tuple(val_preds), dim=0)
val_inputs = torch.cat(tuple(val_inps), dim=0)
val_groundtruth = torch.cat(tuple(val_labls), dim=0)
val_length = torch.cat(tuple(val_lens), dim=0)
val_inp_scale, val_out_scale = torch.cat(tuple(val_iscale), dim=0), torch.cat(tuple(val_oscale), dim=0)
# -
# Accuracy calculation.
accuracy.update((nn.utils.rnn.pack_padded_sequence(tr_predictions, lengths=tr_lengths, batch_first=True,
enforce_sorted=False).data,
nn.utils.rnn.pack_padded_sequence(tr_groundtruth, lengths=tr_lengths, batch_first=True,
enforce_sorted=False).data))
train_acc = accuracy.compute()
accuracy.update((nn.utils.rnn.pack_padded_sequence(val_predictions, lengths=val_length, batch_first=True,
enforce_sorted=False).data,
nn.utils.rnn.pack_padded_sequence(val_groundtruth, lengths=val_length, batch_first=True,
enforce_sorted=False).data))
val_acc = accuracy.compute()
print(f"Training accuracy: {train_acc*100:.3f} Validation accuracy: {val_acc*100:.3f}")
torch.save(model.state_dict(), f'./{name}.pt')
model.load_state_dict(torch.load(f'./{name}.pt'))
# ## Testing
#
# +
test_losses = []
MPJPE = []
h = model.init_hidden(batch_size)
preds, inps, labls, lengs = [], [], [], []
iscal, oscal = [], []
test_CK = [0,0]
model.eval()
for inputs_test, labels_test, lengths_test, is_test, os_test in test_loader:
h = tuple([each.data for each in h])
inputs_test, labels_test, lengths_test = inputs_test.to(device), labels_test.to(device), lengths_test.to(device)
output_test, h = model(inputs_test, h, lengths_test)
e = [preds, inps, labls, lengs, iscal, oscal]
b = [output_test, inputs_test, labels_test, lengths_test, is_test, os_test]
for k in range(len(e)):
e[k].append(b[k])
test_loss = loss_function(nn.utils.rnn.pack_padded_sequence(output_test, lengths=lengths_test, batch_first=True,
enforce_sorted=False).data,
nn.utils.rnn.pack_padded_sequence(labels_test, lengths=lengths_test, batch_first=True,
enforce_sorted=False).data)
test_losses.append(test_loss.item())
test_predictions = torch.cat(tuple(preds), dim=0)
test_inputs = torch.cat(tuple(inps), dim=0)
test_groundtruth = torch.cat(tuple(labls), dim=0)
test_lengths = torch.cat(tuple(lengs), dim=0)
test_inp_scale, test_out_scale = torch.cat(tuple(iscal), dim=0), torch.cat(tuple(oscal), dim=0)
accuracy.update((nn.utils.rnn.pack_padded_sequence(test_predictions, lengths=test_lengths, batch_first=True,
enforce_sorted=False).data,
nn.utils.rnn.pack_padded_sequence(test_groundtruth, lengths=test_lengths, batch_first=True,
enforce_sorted=False).data))
test_acc = accuracy.compute()
# -
print(f"Test loss: {np.mean(test_losses):.4f}", f"\nTest accuracy: {test_acc*100:.3f}")
# ### Save the results into a json
results = {'train':{'inputs':tr_inputs.tolist(), 'predictions':tr_predictions.tolist(), 'labels':tr_groundtruth.tolist(), 'lengths':tr_lengths.tolist(),
'is':tr_inp_scale.tolist(), 'os':tr_out_scale.tolist()},
'validation':{'inputs':val_inputs.tolist(), 'predictions':val_predictions.tolist(), 'labels':val_groundtruth.tolist(), 'lengths':val_length.tolist(),
'is':val_inp_scale.tolist(), 'os':val_out_scale.tolist()},
'test':{'inputs':test_inputs.tolist(), 'predictions':test_predictions.tolist(), 'labels':test_groundtruth.tolist(), 'lengths':test_lengths.tolist(),
'is':test_inp_scale.tolist(), 'os':test_out_scale.tolist()}}
with open('../../../results/clas_small_body.json', 'w') as fp:
json.dump(results, fp)
# ### Load results from json
with open('../../../results/clas_small_body.json', 'r') as j:
jd = json.load(j)
tr, val, test = jd['train'], jd['validation'], jd['test']
tr_inputs, tr_predictions, tr_groundtruth, tr_lengths, tr_inp_scale, tr_out_scale = tuple(torch.tensor(tr[n]) for n in ['inputs', 'predictions',
'labels', 'lengths',
'is', 'os'])
val_inputs, val_predictions, val_groundtruth, val_length, val_inp_scale, val_out_scale = tuple(torch.tensor(val[n]) for n in ['inputs', 'predictions',
'labels', 'lengths',
'is', 'os'])
test_inputs, test_predictions, test_groundtruth, test_lengths, test_inp_scale, test_out_scale = tuple(torch.tensor(test[n]) for n in ['inputs', 'predictions',
'labels', 'lengths',
'is', 'os'])
tr_inputs.shape, val_predictions.shape, test_lengths.shape
# ## Interpreation
# Now to better understanding of the results, I will plot some of the frames from the last batches on the training and validation, and also from testing.
def plot_and_rotate(c_inputs, c_z, frames, frame):
c_inputs[:,::2].mul_(mom_x[1])
c_inputs[:,1::2].mul_(mom_y[1])
c_z.mul_(stdz)
bodiesXY = torch.chunk(c_inputs[frames, :], len(frames), dim=0)
bodiesZ = torch.chunk(c_z[frames, :], len(frames), dim=0)
x = bodiesXY[frame].squeeze()[::2]
y = bodiesXY[frame].squeeze()[1::2]
z = bodiesZ[frame].squeeze()
r_eyebrow = [[c[i] for i in range(17, 22)] for c in [x,y,z]]
l_eyebrow = [[c[i] for i in range(22, 27)] for c in [x,y,z]]
l_eye = [[c[i] for i in range(42, 48)] for c in [x,y,z]]
r_eye = [[c[i] for i in range(36, 42)] for c in [x,y,z]]
nose1 = [[c[i] for i in range(27, 31)] for c in [x,y,z]]
nose2 = [[c[i] for i in range(31, 36)] for c in [x,y,z]]
ext_mouth = [[c[i] for i in range(48, 60)] for c in [x,y,z]]
int_mouth = [[c[i] for i in range(60, 68)] for c in [x,y,z]]
contour = [[c[i] for i in range(0, 17)] for c in [x,y,z]]
l_arm = [[c[i+112] for i in [1, 0, 9, 10, 11]] for c in [x,y,z]]
r_arm = [[c[i+112] for i in [0, 3, 4, 5]] for c in [x,y,z]]
l_leg = [[c[i+112] for i in [0, 2, 12, 13, 14, 22, 23, 24]] for c in [x,y,z]]
r_leg = [[c[i+112] for i in [2, 6, 7, 8, 19, 20, 21]] for c in [x,y,z]]
head = [[c[i+112] for i in [18, 17, 1, 15, 16]] for c in [x,y,z]]
rh0 = [[c[i+70] for i in [0, 1, 2, 3, 4]] for c in [x,y,z]]
rh1 = [[c[i+70] for i in [0, 5, 6, 7, 8]] for c in [x,y,z]]
rh2 = [[c[i+70] for i in [0, 9, 10, 11, 12]] for c in [x,y,z]]
rh3 = [[c[i+70] for i in [0, 13, 14, 15, 16]] for c in [x,y,z]]
rh4 = [[c[i+70] for i in [0, 17, 18, 19, 20]] for c in [x,y,z]]
lh0 = [[c[i+91] for i in [0, 1, 2, 3, 4]] for c in [x,y,z]]
lh1 = [[c[i+91] for i in [0, 5, 6, 7, 8]] for c in [x,y,z]]
lh2 = [[c[i+91] for i in [0, 9, 10, 11, 12]] for c in [x,y,z]]
lh3 = [[c[i+91] for i in [0, 13, 14, 15, 16]] for c in [x,y,z]]
lh4 = [[c[i+91] for i in [0, 17, 18, 19, 20]] for c in [x,y,z]]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
def init():
ax.plot(r_eyebrow[0], r_eyebrow[2], r_eyebrow[1])
ax.plot(l_eyebrow[0], l_eyebrow[2], l_eyebrow[1])
ax.plot(l_eye[0], l_eye[2], l_eye[1])
ax.plot(r_eye[0], r_eye[2], r_eye[1])
ax.plot(nose1[0], nose1[2], nose1[1])
ax.plot(nose2[0], nose2[2], nose2[1])
ax.plot(ext_mouth[0], ext_mouth[2], ext_mouth[1])
ax.plot(int_mouth[0], int_mouth[2], int_mouth[1])
ax.plot(rh0[0], rh0[2], rh0[1])
ax.plot(rh1[0], rh1[2], rh1[1])
ax.plot(rh2[0], rh2[2], rh2[1])
ax.plot(rh3[0], rh3[2], rh3[1])
ax.plot(rh4[0], rh4[2], rh4[1])
ax.plot(lh0[0], lh0[2], lh0[1])
ax.plot(lh1[0], lh1[2], lh1[1])
ax.plot(lh2[0], lh2[2], lh2[1])
ax.plot(lh3[0], lh3[2], lh3[1])
ax.plot(lh4[0], lh4[2], lh4[1])
ax.plot(r_arm[0], r_arm[2], r_arm[1])
ax.plot(l_arm[0], l_arm[2], l_arm[1])
ax.plot(r_leg[0], r_leg[2], r_leg[1])
ax.plot(l_leg[0], l_leg[2], l_leg[1])
ax.plot(head[0], head[2], head[1])
lims = ax.get_xlim(), ax.get_ylim(), ax.get_zlim()
spans = lims[0][1]-lims[0][0], lims[1][1]-lims[1][0], lims[2][1]-lims[2][0]
span = max(spans)
margins = [(span-s)/2 for s in spans]
ax.set_xlim(lims[0][0]-margins[0], lims[0][1]+margins[0])
ax.set_ylim(lims[1][0]-margins[1], lims[1][1]+margins[1])
ax.set_zlim(lims[2][0]-margins[2], lims[2][1]+margins[2])
return fig,
def animate(i):
ax.view_init(elev=220., azim=3.6*i)
return fig,
# Animate
ani = animation.FuncAnimation(fig, animate, init_func=init,
frames=100, interval=100, blit=True)
return ani
def plot_frames(predictions, groundtruth, inputs, video_n, frames, rot, train=False):
inp = inputs.clone()
preds = predictions.clone()
grtr = groundtruth.clone()
bodiesXY = torch.chunk(inp[video_n, frames, :], len(frames), dim=0)
pred_bodiesZ = torch.chunk(preds[video_n, frames, :], len(frames), dim=0)
true_bodiesZ = torch.chunk(grtr[video_n, frames, :], len(frames), dim=0)
nrows = np.ceil(len(frames)/2)
fig = plt.figure(figsize=(15, 6*nrows))
fig2 = plt.figure(figsize=(15, 6*nrows))
for frame in range(len(frames)):
x = bodiesXY[frame].squeeze()[::2].tolist()
y = bodiesXY[frame].squeeze()[1::2].tolist()
pred_z = pred_bodiesZ[frame].squeeze().tolist()
true_z = true_bodiesZ[frame].squeeze().tolist()
r = R.from_euler('y', rot, degrees=True)
xyz1, xyz2 = np.asarray([c for c in zip(x, y, pred_z)]), np.asarray([c for c in zip(x, y, true_z)])
xyz1, xyz2 = r.apply(xyz1), r.apply(xyz2)
x1, x2 = xyz1[:,0], xyz2[:,0]
y1, y2 = xyz1[:,1], xyz2[:,1]
pred_z, true_z = xyz1[:,2], xyz2[:,2]
if not train:
print((x2.max()-x2.min())/(x1.max()-x1.min()))
x1 = x1*((x2.max()-x2.min())/(x1.max()-x1.min()))
r_arm = tuple([[c[i] for i in [1, 0, 9, 10, 11]] for c in l] for l in [[x1, y1, pred_z], [x2, y2, true_z]])
l_arm = tuple([[c[i] for i in [0, 3, 4, 5]] for c in l] for l in [[x1, y1, pred_z], [x2, y2, true_z]])
r_leg = tuple([[c[i] for i in [0, 2, 12, 13, 14, 22, 23, 24]] for c in l] for l in [[x1, y1, pred_z], [x2, y2, true_z]])
l_leg = tuple([[c[i] for i in [2, 6, 7, 8, 19, 20, 21]] for c in l] for l in [[x1, y1, pred_z], [x2, y2, true_z]])
head = tuple([[c[i] for i in [18, 17, 1, 15, 16]] for c in l] for l in [[x1, y1, pred_z], [x2, y2, true_z]])
ax = fig.add_subplot(nrows, 2, frame+1, projection='3d')
ax.plot(r_arm[0][0], r_arm[0][1], r_arm[0][2])
ax.plot(l_arm[0][0], l_arm[0][1], l_arm[0][2])
ax.plot(r_leg[0][0], r_leg[0][1], r_leg[0][2])
ax.plot(l_leg[0][0], l_leg[0][1], l_leg[0][2])
ax.plot(head[0][0], head[0][1], head[0][2])
ax2 = fig2.add_subplot(nrows, 2, frame+1, projection='3d')
ax2.plot(r_arm[1][0], r_arm[1][1], r_arm[1][2])
ax2.plot(l_arm[1][0], l_arm[1][1], l_arm[1][2])
ax2.plot(r_leg[1][0], r_leg[1][1], r_leg[1][2])
ax2.plot(l_leg[1][0], l_leg[1][1], l_leg[1][2])
ax2.plot(head[1][0], head[1][1], head[1][2])
lims = ax.get_xlim(), ax.get_ylim(), ax.get_zlim()
spans = lims[0][1]-lims[0][0], lims[1][1]-lims[1][0], lims[2][1]-lims[2][0]
span = max(spans)
margins = [(span-s)/2 for s in spans]
ax.set_xlim(lims[0][0]-margins[0], lims[0][1]+margins[0])
ax.set_ylim(lims[1][0]-margins[1], lims[1][1]+margins[1])
ax.set_zlim(lims[2][0]-margins[2], lims[2][1]+margins[2])
lims2 = ax2.get_xlim(), ax2.get_ylim(), ax2.get_zlim()
spans2 = lims2[0][1]-lims2[0][0], lims2[1][1]-lims2[1][0], lims2[2][1]-lims2[2][0]
span2 = max(spans2)
margins2 = [(span2-s)/2 for s in spans2]
ax2.set_xlim(lims2[0][0]-margins2[0], lims2[0][1]+margins2[0])
ax2.set_ylim(lims2[1][0]-margins2[1], lims2[1][1]+margins2[1])
ax2.set_zlim(lims2[2][0]-margins2[2], lims2[2][1]+margins2[2])
ax.view_init(elev=-65., azim=-90.)
ax2.view_init(elev=-65., azim=-90.)
# ### Slice of frames
# Now let's plot a sequence of frames of the selected video. We will plot both the groundtruth and the predicted. But first, the output from the network needs to be converted from one-hot encoding to just the label of the predicted bin.
# +
# Last batches of training -output, inputs, labels-.
vid = 1
frames = [100]
c_inputs = training_kp[vid].clone()
c_labels = training_lbl[vid].clone()
c_inputs[:,::2].mul_(training_inpscale[vid, 0])
c_inputs[:,1::2].mul_(training_inpscale[vid, 1])
c_labels.mul_(training_outscale[vid])
# -
HTML(plot_and_rotate(c_inputs, c_output, frames, 0).to_html5_video())
c_inputs = tr_inputs[vid].clone()
HTML(plot_and_rotate(c_inputs, c_labels, frames, 0).to_html5_video())
# We repeat the same process for the last test batch.
# ### Slice of frames
# Now let's plot a sequence of frames of the selected video. We will plot both the groundtruth and the predicted.
y_pred, y = thresholded_output_transform((tr_predictions.view(-1,BINS,OUTPUT_SIZE//BINS), tr_groundtruth))
y_pred = y_pred.view(tr_predictions.shape)
y_pred = torch.transpose(y_pred, 2, 3)
y_pred_new = torch.zeros(y_pred.shape[:-1], dtype=torch.long)
print(y_pred[0,0])
print(y_pred_new.shape)
for i in range(len(y_pred)):
for j in range(len(y_pred[i])):
for k in range(len(y_pred[i,j])):
y_pred_new[i,j,k] = torch.where(y_pred[i,j,k]==1.)[0].item()
print(y_pred_new[0,0])
# +
frames = [i for i in range(1,9,2)]
video_n = 73
c_inputs = tr_inputs.clone()
c_output = y_pred_new.clone().float()
c_labels = tr_groundtruth.clone().float()
print(c_inputs.dtype, c_output.dtype, c_labels.dtype)
for vid in range(c_labels.shape[0]):
c_inputs[vid,:,::2].mul_(tr_inp_scale[vid, 0]/(c_inputs[vid,:,::2].max()-c_inputs[vid,:,::2].min()))
c_inputs[vid,:,1::2].mul_(tr_inp_scale[vid, 1]/(c_inputs[vid,:,1::2].max()-c_inputs[vid,:,1::2].min()))
c_output[vid].mul_(tr_out_scale[vid]/21)
c_labels[vid].mul_(tr_out_scale[vid]/21)
# -
plot_frames(c_output, c_labels, c_inputs, video_n, frames, -90, True)
y_pred, y = thresholded_output_transform((test_predictions.view(-1,BINS,OUTPUT_SIZE//BINS), test_groundtruth))
y_pred = y_pred.view(test_predictions.shape)
y_pred = torch.transpose(y_pred, 2, 3)
y_pred_new = torch.zeros(y_pred.shape[:-1], dtype=torch.long)
print(y_pred[0,0])
print(y_pred_new.shape)
for i in range(len(y_pred)):
for j in range(len(y_pred[i])):
for k in range(len(y_pred[i,j])):
y_pred_new[i,j,k] = torch.where(y_pred[i,j,k]==1.)[0].item()
print(y_pred_new[0,0])
# +
frames = [i for i in range(1,9,2)]
video_n = 17
c_inputs = test_inputs.clone()
c_output = y_pred_new.clone().float()
c_labels = test_groundtruth.clone().float()
for vid in range(c_labels.shape[0]):
c_inputs[vid,:,::2].mul_(test_inp_scale[vid, 0]/(c_inputs[vid,:,::2].max()-c_inputs[vid,:,::2].min()))
c_inputs[vid,:,1::2].mul_(test_inp_scale[vid, 1]/(c_inputs[vid,:,1::2].max()-c_inputs[vid,:,1::2].min()))
c_output[vid].mul_(test_out_scale[vid,0]/21)
c_labels[vid].mul_(test_out_scale[vid,1]/21)
# -
plot_frames(c_output, c_labels, c_inputs, video_n, frames, -90, True)
plt.close('all')
# +
frames = [i for i in range(1,9,2)]
video_n = 17
c_inputs = val_inputs.clone()
c_output = val_predictions.clone()
c_labels = val_groundtruth.clone()
for vid in range(c_labels.shape[0]):
c_inputs[vid,:,::2].mul_(val_inp_scale[vid, 0])
c_inputs[vid,:,1::2].mul_(val_inp_scale[vid, 1])
c_output[vid].mul_(val_out_scale[vid])
c_labels[vid].mul_(val_out_scale[vid])
c_inputs[vid,:,::2].mul_(val_mom_x[vid, 1])
c_inputs[:,:,1::2].mul_(val_mom_y[vid, 1])
c_output[vid].mul_(val_mom_z[vid])
c_labels[vid].mul_(val_mom_z[vid])
# -
plot_frames(c_output, c_labels, c_inputs, video_n, frames, -90)
| 46,035 |
/main.ipynb | b95b136a68a72bbab03b557bc8ff7ef2af79bf85 | [] | no_license | yanfengliu/CSCE990 | https://github.com/yanfengliu/CSCE990 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 228,228 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Recursion
# ---
#
# **Recursion:** a method to solve computational problem by relying on smaller instances of a solution to a given problem.
#
# The logic is that if we have a base case that helps to solves the smaller instance, then the base case solution helps to solve the bigger version of the solution.
#
# **Example of a common recursive function: Fibonacci Number*
#
# $fib(n)= fib(n-1) + fib(n-2)$
#
# $fib(0) = 0$
#
# $fib(1) = 1$
#
# | n | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
# | - | - | - | - | - | - | - | - | - | - | - | - |
# | fib(n) | 0 | 1 | 1 | 2 | 3 | 5 | 8 | 13 | 21 | 34 | 55 |
#
# Fibonacci numbers are derived from its past instances. The next value in the fibonacci number sequence is always the sum of the last two values of the sequence. Hence, the fibonacci number sequence is a _recurrence relation_.
#
# In the function statements above, the n value determines the location of the sequence. Some groups ignore 0th fibonacci number all together; however, we will be keeping it for our definition sake.
#
# Here is the fibonacci number function in Python:
# +
# Fibonacci Function
def fibonacci(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return fibonacci(n-1) + fibonacci(n-2)
print('n=0, fib:', fibonacci(0))
print('n=1, fib:', fibonacci(1))
print('n=2, fib:', fibonacci(2))
print('n=4, fib:', fibonacci(4))
print('n=7, fib:', fibonacci(7))
# -
# **Note**
# ```
# - This should be our very first time seeing an instance where we return a *function call*
# - The original fibonacci(n) is determined by the calculation of fibonacci(n-1) and fibonacci(n-2)
# - This will continously occur until we meet the condition of either n == 0 or n == 1
# ```
# ## Basic Idea of Recursion
#
# Let P be a problem:
# - Divide P into two or more subproblems (smaller instances)
# - Divide until the subproblems are simple enough to be solved
# - All the subproblem solutions are then combined to give a solution to the original problem
# - This is a basic program solving approach called: **“[Divide and Conquer Algorithms](https://en.wikipedia.org/wiki/Divide-and-conquer_algorithm)”**
#
# _This also leads to the basis of “[Dynamic Programming](https://en.wikipedia.org/wiki/Dynamic_programming)”_
#
#
#
# ## How to Design a Recursive Function
#
# **Recipe**: All recursive algorithms must have the following:
# 1. **Base Case** (i.e., when to stop; the simplest solution of the problem)
# 2. **Work toward Base Case:** where we make the problem simpler/smaller towards the base case
# 3. Recursive Call (i.e., call ourselves)
#
# **How does it work?**
# - In a recursive algorithm, the computer "remembers" every previous state of the problem.
# - This information is "held" by the computer on the "activation stack" (i.e., inside of each functions workspace).
# - Every function has its own workspace PER CALL of the function.
# - Once all the recursive calls are complete, we get our first function call's answer/result
#
# **Importance of a basecase**
#
# The base case should hold the simplest solution for the simplest, smallest instance of the problem.
#
# _Base Case:_ In a recursion algorithm, the problem is broken down to subproblem until we reach the base case.
# - Recursion Algorithms can have multiple base cases
# - Base cases are considered “end conditions”
#
# ## Example Problem: Adding all values from N to 1.
#
# - Let N be an integer value greater than 1
# - recursive_sum(n) will add all values from N to 1
#
# **Base Case:**
# ```
# N of 0: 0, no calculation needed
# N of 1: 1, no calculation needed
# ```
#
# **For all other N**
# ```
# The sum of all numbers below N is N + the recursive_sum of N-1; therefore:
#
# recursive_sum(n) = n + recursive_sum(n-1)
#
# This solution is classified as O(n).
# ```
# +
# Recursive Sum
def recursive_sum(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return n + recursive_sum(n-1)
# end of recursive_sum
print('n=1, result:', recursive_sum(1))
print('n=2, result:', recursive_sum(2))
print('n=4, result:', recursive_sum(4))
print('n=5, result:', recursive_sum(5))
print('n=7, result:', recursive_sum(7))
print('n=11, result:', recursive_sum(11))
# -
# ```
# Recursive Sum Execution Summary
#
# recursive_sum(5) → 5 + recursive_sum(4)
# recursive_sum(4) → 4 + recursive_sum(3)
# recursive_sum(3) → 3 + recursive_sum(2)
# recursive_sum(2) → 2 + recursive_sum(1)
# recursive_sum(1) → returns 1
#
# recursive_sum(2) → 2 + 1 = 3
# recursive_sum(3) → 3 + 3 = 6
# recursive_sum(4) → 4 + 6
# recursive_sum(5) → 5 + 10
#
# recursive_sum(5) returns 15
# ```
# ## Different Types/Properties of Recursion
#
# ### Single vs Multiple Recursion
#
# **Single**: It only calls itself once … only invokes one recursion to occur
#
# recursive_sum(n) = n + recursive_sum(n-1)
#
# **Multiple**: It can invoke multiple recursion to solve the answer
#
# fibonacci(n) = fibonacci(n-2) + fibonacci(n-1)
#
# ### Direct vs Indirect Recursion
#
# let f and g both be functions
#
# **Direct**: function f only calls f
#
# f(x) = f(x-1)
#
# **Indirect**: function f calls g and function g calls function f; hence, indirectly recursive calls
# - If there are more than 2 functions, we can create longer indirect chains
# - Some texts will define indirect recursion as mutal recursions
#
# ```
# f(x) = g(x) ; g(x) = f(x)
# ```
#
# ## Non-Tail Recursion vs Tail Recursion
#
# **Non-Tail Recursion:** For this recursion to finish, it must wait for all its recursive function calls to finish their activation stack's execution.
#
# **Tail Recursion:** The function call is the last thing that a function does. For this recursion to finish, we return a tail because we are affecting the value of the tail at every recursion.
#
# Tail Recursion is more efficient in terms of memory because it only does calculations at the very last recursive call.
#
# If you have the chance to write a tail recursion, you should write a tail recursion.
# +
# Tail Recursive N to 1 summation
def tail_recursive_sum(n, tail=0):
if n == 0:
return tail # we will be modifying this argument with our recursive calls
else:
return tail_recursive_sum(n-1, tail+n)
print('n=1, result:', tail_recursive_sum(1))
print('n=2, result:', tail_recursive_sum(2))
print('n=4, result:', tail_recursive_sum(4))
print('n=5, result:', tail_recursive_sum(5))
print('n=7, result:', tail_recursive_sum(7))
print('n=11, result:', tail_recursive_sum(11))
# -
# ```
# Tail Recursive Sum Execution Summary
#
# tail_recursive_sum(5) → tail_recursive_sum(4, 0+5)
# tail_recursive_sum(4,5) → tail_recursive_sum(3, 5+4)
# tail_recursive_sum(3,9) → 3 + tail_recursive_sum(2, 9+3)
# tail_recursive_sum(2,12) → 2 + recursive_sum(1, 12+2)
# tail_recursive_sum(1, 14) → recursive_sum(0, 14+1)
# tail_recursive_sum(0, 15) → returns 15 end of recursive call
#
# ```
| 7,345 |
/docs/notebooks/AnomalousSequence.ipynb | 468315c2a478ee72f15078321bb39fc33463a032 | [
"MIT",
"LicenseRef-scancode-generic-cla"
] | permissive | dg2kjb/msticpy | https://github.com/dg2kjb/msticpy | 0 | 1 | MIT | 2020-08-06T17:56:53 | 2020-08-06T14:56:34 | null | Jupyter Notebook | false | false | .py | 2,283,533 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Table of Contents
# * [msticpy - anomalous_sequence](#msticpy)
# * [Sessions explained](#create_sessions)
# * [Create sessions using msticpy](#msticpy_ses)
# * [Using the sessionize_data function](#sessionize_data)
# * [Explain the modelling approach](#explain_model)
# * [Using the score_sessions function](#model_function)
# * [Advanced: access Model class directly](#model_class)
# * [Visualise the modelled sessions](#visualize_function)
# * [Using the visualise_scored_sessions function](#visualize_function)
# * [Model and visualise sessions in one go](#score_and_visualise_sessions)
# * [Using the score_and_visualise_sessions function](#score_and_visualise_sessions)
# * [Sessionize other log types using KQL](#other_sessions)
# * [Authenticate Log Analytics](#la_auth)
# * [Office Activity Logs](#office_sessions)
# * [Sessionize using KQL](#office_sessions)
# * [Convert sessions into an allowed format for the modelling](#clean_exchange)
# * [AWS Cloud Trail Logs](#aws_sessions)
# * [Sessionize using KQL](#aws_sessions)
# * [Convert sessions into an allowed format for the modelling](#clean_aws)
# * [VM Process Logs](#vm_sessions)
# * [Sessionize using KQL](#vm_sessions)
# * [Convert sessions into an allowed format for the modelling](#clean_vm)
# # msticpy - anomalous_sequence subpackage <a id='msticpy'></a>
#
# Various types of security logs can be broken up into sessions/sequences where each session can be thought of as an ordered sequence of events. It can be useful to model these sessions in order to understand what the usual activity is like so that we can highlight anomalous sequences of events.
#
# A new subpackage called anomalous_sequence has been released to [msticpy](https://github.com/microsoft/msticpy/tree/master/msticpy/analysis/anomalous_sequence) recently. This library allows the user to sessionize, model and visualize their data via a high level interface.
#
# This notebook demonstrates the sessionizing, modelling and visualisation on some Office Exchange Admin logs from one of our demo tenants. However there is a section at the end which demonstrates how some other log types can be sessionized as well.
# +
# Imports
from msticpy.nbtools.utility import check_py_version
MIN_REQ_PYTHON = (3, 6)
check_py_version(MIN_REQ_PYTHON)
from typing import List, Dict, Union
# setting pandas display options for dataframe
import pandas as pd
pd.set_option("display.max_rows", 100)
pd.set_option("display.max_columns", 50)
pd.set_option("display.max_colwidth", 100)
# msticpy imports
from msticpy.analysis.anomalous_sequence import sessionize
from msticpy.analysis.anomalous_sequence.utils.data_structures import Cmd
from msticpy.analysis.anomalous_sequence import anomalous
from msticpy.analysis.anomalous_sequence.model import Model
from msticpy.data import QueryProvider
from msticpy.nbtools.wsconfig import WorkspaceConfig
# %env KQLMAGIC_LOAD_MODE=silent
print('finished the imports')
# -
# # What is a Session? <a id='create_sessions'></a>
#
# <b>In this context, a session is an ordered sequence of events/commands. The anomalous_sequence subpackage can handle 3 different formats for each of the sessions:</b>
#
# 1. sequence of just events/commands.\
# e.g. \["Set-User", "Set-Mailbox"\] <br/><br/>
# 2. sequence of events/commands with accompanying parameters.\
# \[Cmd(name="Set-User", params=\{"Identity', "Force"\}), Cmd(name="Set-Mailbox", params=\{"Identity", "AuditEnabled"\})\] <br/><br/>
# 3. sequence of events/commands with accompanying parameters and their corresponding values.\
# \[Cmd(name="Set-User", params=\{"Identity": "blahblah", "Force": 'true'\}), Cmd(name="Set-Mailbox", params=\{"Identity": "blahblah", "AuditEnabled": "false"\})\]
#
# The Cmd datatype can be accessed from <i>msticpy.analysis.anomalous_sequence.utils.data_structures</i>
# # Use the sessionize module from msticpy <a id='msticpy_ses'></a>
#
# In this section, we demonstrate how you can use msticpy to create sessions from your data.
#
# We read in some office exchange events from one of our demo tenants as a csv.
exchange = pd.read_csv('data/demo_exchange_data.csv')
exchange.head()
# ## If you are only interested in modelling the commands (without the accompanying parameters), then you could skip the next three cells and go straight to the [sessionizing](#sessionize_data).
#
# The reason for this is because each session is allowed to be either a list of strings, or a list of the Cmd datatype. The "Operation" column is a string already.
#
# However, if you are interested in including the parameters (and possibly the values), then you need the next two cells.
#
# We need to define a custom cleaning function which will combine the "Operation" and "Parameters" columns and convert them into one of the [allowed types](#create_sessions). This cleaning function is specific to the format of the exchange demo data which we have read in. Therefore, you may need to tweak it before you can use it on other data sets.
# +
# let's define a helper function for creating columns which have the Cmd datatype
def clean_exchange_params(operation: str, parameters: Union[str, Dict], include_vals: bool):
params = parameters
if isinstance(parameters, str):
params = eval(params)
new = dict()
for dic in params:
new[dic['Name']] = dic['Value']
if include_vals:
return Cmd(name=operation, params=new)
return Cmd(name=operation, params=set(new.keys()))
# +
# let's apply the helper function we defined to create columns which have the Cmd datatype
exchange['cmd_param'] = exchange.\
apply(lambda x:
clean_exchange_params(operation=x['Operation'], parameters=x['Parameters'], include_vals=False), axis=1)
exchange['cmd_param_val'] = exchange.\
apply(lambda x:
clean_exchange_params(operation=x['Operation'], parameters=x['Parameters'], include_vals=True), axis=1)
# -
exchange.head()
# ## Use the sessionize_data function <a id='sessionize_data'></a>
#
# We will do this for the first session type (with just commands).
#
# But because we created columns for all three session types, you can set the "event_col" parameter in the "sessionize_data" function below to any of the following:
#
# 1. Operation
# 2. cmd_param
# 3. cmd_param_val
#
#
# <b>Here are some details about the arguments for the sessionize_data function:</b>
#
# ```
# Help on function sessionize_data in module msticpy.analysis.anomalous_sequence.sessionize:
#
# sessionize_data(data: pd.DataFrame, user_identifier_cols: List[str], time_col: str, max_session_time_mins: int, max_event_separation_mins: int, event_col: str) -> pd.DataFrame
#
# Sessionize the input data.
#
# In particular, the resulting dataframe will have 1 row per session. It will contain the
# following columns: the user_identifier_cols, <time_col>_min, <time_col>_max,
# <event_col>_list, duration (<time_col>_max - <time_col>_min), number_events (length of the
# <event_col>_list value)
#
# Parameters
# ----------
# data: pd.DataFrame
# This dataframe should contain at least the following columns:
# - time stamp column
# - columns related to user name and/or computer name and/or ip address etc
# - column containing an event
# user_identifier_cols: List[str]
# Name of the columns which contain username and/or computer name and/or ip address etc.
# Each time the value of one of these columns changes, a new session will be started.
# time_col: str
# Name of the column which contains a time stamp.
# If this column is not already in datetime64[ns, UTC] format, it will be casted to it.
# max_session_time_mins: int
# The maximum length of a session in minutes. If a sequence of events for the same
# user_identifier_cols values exceeds this length, then a new session will be started.
# max_event_separation_mins: int
# The maximum length in minutes between two events in a session. If we have 2 events for
# the same user_identifier_cols values, and if those two events are more than
# `max_event_separation_mins` apart, then a new session will be started.
# event_col: str
# Name of the column which contains the event of interest.
# For example, if we are interested in sessionizing exchange admin commands,
# the "event_col" could contain values like: "Set-Mailbox" or "Set-User" etc.
#
# Returns
# -------
# pd.DataFrame containing the sessionized data. 1 row per session.
#
# ```
#
#
# +
# sessionize the data
sessions_df = sessionize.sessionize_data(
data=exchange,
user_identifier_cols=['UserId', 'ClientIP'],
time_col='TimeGenerated',
max_session_time_mins=20,
max_event_separation_mins=2,
event_col='Operation'
)
# -
sessions_df.shape
sessions_df.head()
# # Model the sessions <a id='explain_model'></a>
#
# We will give a brief description of how the modelling works under the hood for each of the three session types.
#
# * <b>Commands only</b>
# - We treat the sessions as an ordered sequence of commands.
# - We apply the Markov assumption where we assume each command depends only on the command immediately before it.
# - This means the likelihood of each session can be computed by multiplying a sequence of transition probabilities together.
# - We use a sliding window (e.g. of length 3) throughout each session and then use the likelihood of the rarest window as the score for the session.<br/><br/>
# * <b>Commands with Parameters</b>
# - All of the above ("commands only" case) except for one difference.
# - This time, we include the parameters in the modelling.
# - We make the assumption that the presence of each parameter is independent conditional on the command.
# - We therefore model the presence of the parameters as independent Bernoulli random variables (conditional on the command)
# - So to compute the likelihood of a session, each transition probability (of the commands) will be accompanied by a product of probabilities (for the parameters).
# - A subtlety to note, is that we take the geometric mean of the product of parameter probabilities. This is so we don't penalise commands which happen to have more parameters set than on average.
# - We use the same sliding window approach used with the "commands only" case. <br/><br/>
# * <b>Commands with Parameters and their Values</b>
# - All of the above ("commands with parameters" case) except for one difference.
# - This time, we include the values in the modelling.
# - Some rough heuristics are used to determine which parameters have values which are categorical (e.g. "true" and "false" or "high", "medium" and "low") vs values which are arbitrary strings (such as email addresses). There is the option to override the "modellable_params" directly in the Model class.
# - We also make the assumption that the values depend only on the parameters and not on the command.
# - So to compute the likelihood of a session, each transition probability (of the commands) will be accompanied by a product of probabilities (for the parameters and categorical values).
# - We use the same sliding window approach used with the "commands only" case.
#
#
# #### Important note:
# If you set the window length to be k, then only sessions which have at least k-1 commands will have a valid (not np.nan) score. The reason for the -1 is because we append an end token to each session by default, so a session of length k-1 gets treated as length k during the scoring.
#
#
# ## There are 3 high level functions available in this library
#
# 1. score_sessions
# 2. visualize_scored_sessions
# 3. score_and_visualize_sessions
# ## We will first demonstrate the high level function for modelling the sessions. <a id='model_function'></a>
#
# We will do this for the "Commands Only" session type.
#
# But depending on which column you chose as the event_col in the [sessionize_data function](#sessionize_data),
# you could set the "session_column" parameter in the "score_sessions" function below to any of the following:
#
# 1. Operation_list
# 2. cmd_param_list
# 3. cmd_param_val_list
#
# <b>Here are some details about the arguments for the score_sessions function:</b>
#
# ```
# Help on function score_sessions in module msticpy.analysis.anomalous_sequence.anomalous:
#
# score_sessions(data: pd.DataFrame, session_column: str, window_length: int) -> pd.DataFrame
#
# Model sessions using a sliding window approach within a markov model.
#
# Parameters
# ----------
# data: pd.DataFrame
# Dataframe which contains at least a column for sessions
# session_column: str
# name of the column which contains the sessions
# The values in the session column should take one of the following formats:
# examples formats of a session:
# 1) ['Set-User', 'Set-Mailbox']
# 2) [Cmd(name='Set-User', params={'Identity', 'Force'}),
# Cmd(name='Set-Mailbox', params={'Identity', 'AuditEnabled'})]
# 3) [Cmd(
# name='Set-User',
# params={'Identity': 'blahblah', 'Force': 'true'}
# ),
# Cmd(
# name='Set-Mailbox',
# params={'Identity': 'blahblah', 'AuditEnabled': 'false'}
# )]
# The Cmd datatype can be accessed from
# anomalous_sequence.utils.data_structures.Cmd
# window_length: int
# length of the sliding window to use when computing the likelihood
# metrics for each session.
# This should be set to an integer >= 2. Note that sessions which have
# fewer commands than the chosen window_length + 1 will end up with a
# np.nan score. (The + 1 is because we append a dummy `end_token` to each
# session before starting the sliding window, so a session of length 2,
# would be treated as length 3)
#
# Returns
# -------
# input dataframe with two additional columns appended.
#
# ```
#
# +
# This function will return a dataframe with two additonal columns appended:
# "rarest_window3_likelihood" and "rarest_window3"
modelled_df = anomalous.score_sessions(
data=sessions_df,
session_column='Operation_list',
window_length=3
)
# +
# Let's view the resulting dataframe in ascending order of the computed likelihood metric
modelled_df.sort_values('rarest_window3_likelihood').head()
# +
# we can view individual sessions in more detail
modelled_df.sort_values('rarest_window3_likelihood').rarest_window3.iloc[0]
# -
# # Now we demonstrate the visualization component of the library <a id='visualize_function'></a>
#
# We do this using the "visualise_scored_sessions" function. This function returns an interactive timeline plot which allows you to zoom into different sections etc.
#
# * The time of the session will be on the x-axis.
# * The computed likelihood metric will be on the y-axis.
# * lower likelihoods correspond to rarer sessions.
#
# <b>Important note</b>:
#
# During the scoring/modelling stage, if you set the window length to be k, then only sessions which have at least k-1 commands will appear in the interactive timeline plot. This is because sessions with fewer than k-1 commands will have a score of np.nan. The reason for the -1 is because we append an end token to each session by default, so a session of length k-1 gets treated as length k during the scoring.
#
# <b>Here are some details about the arguments for the visualise_scored_sessions function:</b>
#
# ```
# Help on function visualise_scored_sessions in module msticpy.analysis.anomalous_sequence.anomalous:
#
# visualise_scored_sessions(data_with_scores: pandas.core.frame.DataFrame, time_column: str, score_column: str, window_column: str, score_upper_bound: float = None, source_columns: list = None)
#
# Visualise the scored sessions on an interactive timeline.
#
# Parameters
# ----------
# data_with_scores: pd.DataFrame
# Dataframe which contains at least columns for time,
# session score, window representing the session
# time_column: str
# name of the column which contains a timestamp
# score_column: str
# name of the column which contains a numerical score for each
# of the sessions
# window_column: str
# name of the column which contains a representation of each of the sessions.
# This representation will appear in the tooltips in the figure.
# For example, it could be the rarest window of the session,
# or the full session etc.
# score_upper_bound: float, optional
# an optional upper bound on the score for the visualisation figure.
# This can help to zoom in on the more anomalous sessions
# source_columns: list, optional
# an optional list of source columns to include in the tooltips
# in the visualisation.
# Note, the content of each of these columns should be json serializable
# in order to be compatible with the figure
#
# Returns
# -------
# figure
# ```
#
# +
# visualise the scored sessions in an interactive timeline plot.
anomalous.visualise_scored_sessions(
data_with_scores=modelled_df,
time_column='TimeGenerated_min', # this will appear in the x-axis
score_column='rarest_window3_likelihood', # this will appear on the y-axis
window_column='rarest_window3', # this will represent the session in the tool-tips
source_columns=['UserId', 'ClientIP'] # specify any additonal columns to appear in the tool-tips
)
# -
# ## Now we demonstrate how you can score and visualise your sessions in one go. <a id='score_and_visualise_sessions'></a>
#
# We will do this for the "Commands only" session type.
#
# But depending on which column you chose as the event_col in the [sessionize_data function](#sessionize_data),
# you could set the "session_column" parameter in the "score_and_visualise_sessions" function below to any of the following:
#
# 1. Operation_list
# 2. cmd_param_list
# 3. cmd_param_val_list
#
# <b>Here are some details about the arguments for the score_and_visualise_sessions function:</b>
#
# ```
# Help on function score_and_visualise_sessions in module msticpy.analysis.anomalous_sequence.anomalous:
#
# score_and_visualise_sessions(data: pandas.core.frame.DataFrame, session_column: str, window_length: int, time_column: str, likelihood_upper_bound: float = None, source_columns: list = None)
#
# Model sessions and then produce an interactive timeline visualisation plot.
#
# In particular, the sessions are modelled using a sliding window approach
# within a markov model. The visualisation plot has time on the x-axis and
# the modelled session likelihood metric on the y-axis.
#
# Parameters
# ----------
# data: pd.DataFrame
# Dataframe which contains at least columns for time and sessions
# session_column: str
# name of the column which contains the sessions
# The values in the session column should take one of the following formats:
# examples formats of a session:
# 1) ['Set-User', 'Set-Mailbox']
# 2) [Cmd(name='Set-User', params={'Identity', 'Force'}),
# Cmd(name='Set-Mailbox', params={'Identity', 'AuditEnabled'})]
# 3) [Cmd(
# name='Set-User',
# params={'Identity': 'blahblah', 'Force': 'true'}
# ),
# Cmd(
# name='Set-Mailbox',
# params={'Identity': 'blahblah', 'AuditEnabled': 'false'}
# )]
# The Cmd datatype can be accessed from
# seqeunce.utils.data_structures.Cmd
# window_length: int
# length of the sliding window to use when computing the
# likelihood metrics for each session.
#
# This should be set to an integer >= 2.
# Note that sessions which have fewer commands than the chosen
# window_length + 1 will not appear in the visualisation. (The + 1 is
# because we append a dummy `end_token` to each session before starting
# the sliding window, so a session of length 2, would be treated as length
# 3)
# time_column: str
# name of the column which contains a timestamp
# likelihood_upper_bound: float, optional
# an optional upper bound on the likelihood metrics for the visualisation
# plot. This can help to zoom in on the more anomalous sessions
# source_columns: list, optional
# An optional list of source columns to include in the tooltips
# in the visualisation.
# Note, the content of each of these columns should be json
# serializable in order to be compatible with the figure
#
# Returns
# -------
# figure
# ```
# +
# let's model and visualise these sessions in one go
anomalous.score_and_visualise_sessions(
data=sessions_df,
session_column='Operation_list',
window_length=3,
time_column='TimeGenerated_min',
source_columns=['UserId', 'ClientIP']
)
# -
# # Advanced Users: Access the Model Class Directly <a id='model_class'></a>
#
# Users who would like to configure arguments related to whether start and end tokens are used or whether the geometric mean is computed, can access the Model class directly.
#
# There is also the option to specify the modellable_params argument if you do not wish for rough heuristics to be used to determine which parameters take categorical values and are hence suitable for modelling. If you wish to experiment with modelling the values of all the parameters (categorical + arbitrary strings), then you can use this argument to do so.
#
# <b>Here are some details about the methods available for the Model class:</b>
#
# ```
# Help on class Model in module msticpy.analysis.anomalous_sequence.model:
#
# class Model(builtins.object)
# | Model(sessions: List[List[Union[str, msticpy.analysis.anomalous_sequence.utils.data_structures.Cmd]]], modellable_params: set = None)
# |
# | Class for modelling sessions data.
# |
# | Methods defined here:
# |
# | __init__(self, sessions: List[List[Union[str, msticpy.analysis.anomalous_sequence.utils.data_structures.Cmd]]], modellable_params: set = None)
# | Instantiate the Model class.
# |
# | This Model class can be used to model sessions, where each
# | session is a sequence of commands. We use a sliding window
# | approach to calculate the rarest part of each session. We
# | can view the sessions in ascending order of this metric to
# | see if the top sessions are anomalous/malicious.
# |
# | Parameters
# | ----------
# | sessions: List[List[Union[str, Cmd]]]
# | list of sessions, where each session is a list of either
# | strings or a list of the Cmd datatype.
# |
# | The Cmd datatype should have "name" and "params" as attributes
# | where "name" is the name of the command (string) and "params"
# | is either a set of accompanying params or a dict of
# | accompanying params and values.
# |
# | examples formats of a session:
# | 1) ['Set-User', 'Set-Mailbox']
# | 2) [Cmd(name='Set-User', params={'Identity', 'Force'}),
# | Cmd(name='Set-Mailbox', params={'Identity', 'AuditEnabled'})]
# | 3) [Cmd(
# | name='Set-User',
# | params={'Identity': 'blahblah', 'Force': 'true'}
# | ),
# | Cmd(name='Set-Mailbox',
# | params={'Identity': 'blahblah', 'AuditEnabled': 'false'})]
# | modellable_params: set, optional
# | set of params which you deem to have categorical values which are suitable
# | for modelling.
# | Note this argument will only have an effect if your sessions include commands,
# | params and values. If your sessions include commands, params and values and
# | this argument is not set, then some rough heuristics will be used to determine
# | which params have values which are suitable for modelling.
# |
# | compute_geomean_lik_of_sessions(self)
# | Compute the geometric mean of the likelihood for each of the sessions.
# |
# | This is done by raising the likelihood of the session to the power of
# | (1 / k) where k is the length of the session.
# |
# | Note: If the lengths (number of commands) of the sessions vary a lot,
# | then you may not be able to fairly compare the likelihoods between a
# | long session and a short session. This is because longer sessions
# | involve multiplying more numbers together which are between 0 and 1.
# | Therefore the length of the session will be negatively correlated with
# | the likelihoods. If you take the geometric mean of the likelihood, then
# | you can compare the likelihoods more fairly across different session
# | lengths.
# |
# | compute_likelihoods_of_sessions(self, use_start_end_tokens: bool = True)
# | Compute the likelihoods for each of the sessions.
# |
# | Note: If the lengths (number of commands) of the sessions vary a lot,
# | then you may not be able to fairly compare the likelihoods between a
# | long session and a short session. This is because longer sessions
# | involve multiplying more numbers together which are between 0 and 1.
# | Therefore the length of the session will be negatively correlated with
# | the likelihoods. If you take the geometric mean of the likelihood, then
# | you can compare the likelihoods more fairly across different session
# | lengths
# |
# | Parameters
# | ----------
# | use_start_end_tokens: bool
# | if True, then `start_token` and `end_token` will be prepended
# | and appended to the session respectively before the calculations
# | are done
# |
# | compute_rarest_windows(self, window_len: int, use_start_end_tokens: bool = True, use_geo_mean: bool = False)
# | Find the rarest window and corresponding likelihood for each session.
# |
# | In particular, uses a sliding window approach to find the rarest window
# | and corresponding likelihood for that window for each session.
# |
# | If we have a long session filled with benign activity except for a small
# | window of suspicious behaviour, then this approach should be able to
# | identity the session as anomalous. This approach should be more
# | effective than simply taking the geometric mean of the full session
# | likelihood. This is because the small window of suspicious behaviour
# | might get averaged out by the majority benign behaviour in the session
# | when using the geometric mean approach.
# |
# | Note that if we have a session of length k, and we use a sliding window
# | of length k+1, then we will end up with np.nan for the rarest window
# | likelihood metric for that session. However, if `use_start_end_tokens`
# | is set to True, then because we will be appending self.end_token to the
# | session, the session will be treated as a session of length k+1,
# | therefore, we will end up with a non np.nan value.
# |
# | Parameters
# | ----------
# | window_len: int
# | length of sliding window for likelihood calculations
# | use_start_end_tokens: bool
# | if True, then `start_token` and `end_token` will be prepended
# | and appended to each
# | session respectively before the calculations are done
# | use_geo_mean: bool
# | if True, then each of the likelihoods of the sliding windows
# | will be raised to the power
# | of (1/`window_len`)
# |
# | compute_scores(self, use_start_end_tokens: bool)
# | Compute some likelihood based scores/metrics for each of the sessions.
# |
# | In particular, computes the likelihoods and geometric mean of
# | the likelihoods for each of the sessions. Also, uses the sliding
# | window approach to compute the rarest window likelihoods for each
# | of the sessions. It does this for windows of length 2 and 3.
# |
# | Note that if we have a session of length k, and we use a sliding
# | window of length k+1, then we will end up with np.nan for the
# | rarest window likelihood metric for that session.
# | However, if `use_start_end_tokens` is set to True, then
# | because we will be appending self.end_token to the session,
# | the session will be treated as a session of length k+1,
# | therefore, we will end up with a non np.nan value for that session.
# |
# | Parameters
# | ----------
# | use_start_end_tokens: bool
# | if True, then self.start_token and self.end_token will be
# | prepended and appended to each
# | of the sessions respectively before the calculations are done.
# |
# | compute_setof_params_cond_cmd(self, use_geo_mean: bool)
# | Compute likelihood of combinations of params conditional on the cmd.
# |
# | In particular, go through each command from each session and
# | compute the probability of that set of params (and values if provided)
# | appearing conditional on the command.
# |
# | This can help us to identify unlikely combinations of params
# | (and values if provided) for each distinct command.
# |
# | Note, this method is only available if each session is a list
# | of the Cmd datatype. It will result in an Exception if you
# | try and use it when each session is a list of strings.
# |
# | Parameters
# | ----------
# | use_geo_mean: bool
# | if True, then the probabilities will be raised to
# | the power of (1/K)
# | case1: we have only params:
# | Then K is the number of distinct params which appeared
# | for the given cmd across all the sessions.
# | case2: we have params and values:
# | Then K is the number of distinct params which appeared
# | for the given cmd across all the sessions + the number
# | of values which we included in the modelling for this cmd.
# |
# | train(self)
# | Train the model by computing counts and probabilities.
# |
# | In particular, computes the counts and probabilities of the commands
# | (and possibly the params if provided, and possibly the values if provided)
# |
# ```
#
model = Model(sessions=sessions_df.Operation_list.values.tolist())
model.train()
model.compute_rarest_windows(window_len=2)
model.rare_window_likelihoods[2][:5]
# # Sessionize Some Other Types of Logs using KQL <a id='other_sessions'></a>
#
# The aim of this section is to provide some starter guidance on how one might start to sessionize + model some other types of logs.
#
# In order to do the sessionizing using KQL, we use the [row_window_session](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/row-window-session-function) function.
#
#
#
# <b>Important note</b>: Throughout this section, the decisions made about which columns should be interpreted as commands/events and parameters are entirely subjective and alternative approaches may also be valid.
#
# ## Using LogAnalytics Query Provider <a id='la_auth'></a>
#
# msticpy has a QueryProvider class which you can use to connect to your Log Analytics data environment.
# Try to read workspace configuration from msticpyconfig.yaml, and then authenticate
try:
ws_config = WorkspaceConfig(workspace='Default')
qry_prov = QueryProvider(data_environment="LogAnalytics")
qry_prov.connect(connection_str=ws_config.code_connect_str)
except:
print('There is an issue with reading in the config file. Please fill in the following manually.')
tenant_id = input("Please enter your Log Analytics tenant id:")
workspace_id = input("Please enter your Log Analytics workspace id:")
la_connection_string = 'loganalytics://code().tenant("{}").workspace("{}")'.format(tenant_id, workspace_id)
qry_prov = QueryProvider(data_environment="LogAnalytics")
qry_prov.connect(connection_str=la_connection_string)
# ## Sessionize Office Activity Logs <a id='office_sessions'></a>
#
# The cell below contains a kusto query which queries the OfficeActivity table in Log Analytics. In this example, we wish for the sessions to be on a per UserId - ClientIP basis. In addition, we require that each session be no longer than 20 minutes in total, with each command no more than 2 minutes apart from each other. (These requirements can be adjusted for different data-sets/use-cases etc).
#
#
# <b>Here are some high level steps to the query:</b>
#
# - Add a time filter which goes back far enough so you have enough data to train the model.
# - Filter to the desired type of logs.
# - Exclude some known automated users (optional)
# - Sort the rows by UserId, ClientIp, TimeGenerated in ascending order
# - Use the native KQL function row_window_session to create an additional "begin" column to aid creating the sessions
# - Summarize the commands (and optionally parameters) by UserId, ClientIp, begin
# - Optionally exclude sessions which have only 1 command
#
# Note that in KQL, comments are made using //
# write kql query
query = """
let time_back = 60d;
OfficeActivity
| where TimeGenerated >= ago(time_back)
//
// filter to the event type of interest
| where RecordType == 'ExchangeAdmin'
//
// exclude some known automated users
| where UserId !startswith "NT AUTHORITY" and UserId !contains "prod.outlook.com"
//
// create new dynamic variable with the command as the key, and the parameters as the values
| extend params = todynamic(strcat('{"', Operation, '" : ', tostring(Parameters), '}'))
| project TimeGenerated, UserId, ClientIP, Operation, params
//
// sort by the user related columns and the timestamp column in ascending order
| sort by UserId asc, ClientIP asc, TimeGenerated asc
//
// calculate the start time of each session into the "begin" variable
// With each session max 20 mins in length with each event at most 2 mins apart.
// A new session is created each time one of the user related columns change.
| extend begin = row_window_session(TimeGenerated, 20m, 2m, UserId != prev(UserId) or ClientIP != prev(ClientIP))
//
// summarize the operations and the params by the user related variables and the "begin" variable
| summarize cmds=makelist(Operation), end=max(TimeGenerated), nCmds=count(), nDistinctCmds=dcount(Operation),
params=makelist(params) by UserId, ClientIP, begin
//
//optionally specify an order to the final columns
| project UserId, ClientIP, nCmds, nDistinctCmds, begin, end, duration=end-begin, cmds, params
//
// optionally filter out sessions which contain only one event
//| where nCmds > 1
"""
# execute the queryl
exchange_df = qry_prov.exec_query(query=query)
# I comment out this cell and run it again once it has run to prevent the notebook from slowing down
try:
print(exchange_df.shape)
except AttributeError as e:
exchange_df = _kql_raw_result_.to_dataframe()
print(exchange_df.shape)
exchange_df.head()
# ### Convert Exchange Sessions to Correct Format for the Model <a id='clean_exchange'></a>
#
# Recall the allowed session types [here](#create_sessions)
#
# <b>So let's see what needs to be done to the exchange_df</b>
#
# - The "cmds" column is already in a suitable format of type (1). This is because it is a list of strings.
# - If we wish to also include the parameters (and optionally the corresponding values) to the model, then we need to transform the "params" column slightly
# +
# define a helper function for converting the sessions with params (and values) into a suitable format
def process_exchange_session(session_with_params: [List[Dict[str, List[Dict[str, str]]]]], include_vals: bool) -> List[Cmd]:
"""
Converts an exchange session with params to an allowed format.
param session_with_params: example format:
[
{'Set-Mailbox': [{'Name': 'MessageCopyForSentAsEnabled', 'Value': 'True'},
{'Name': 'Identity', 'Value': '[email protected]'}]}
]
param include_vals: if True, then it will be transformed to a format which includes the values,
else the output will just contain the parameters
return: list of the Cmd data type which includes either just the parameters, or also the corresponding values
"""
new_ses = []
for cmd in session_with_params:
c = list(cmd.keys())[0]
par = list(cmd.values())[0]
new_pars = set()
if include_vals:
new_pars = dict()
for p in par:
if include_vals:
new_pars[p['Name']] = p['Value']
else:
new_pars.add(p['Name'])
new_ses.append(Cmd(name=c, params=new_pars))
return new_ses
# +
# let's create suitable sessions for params, and suitable sessions for params + values
sessions = exchange_df.cmds.values.tolist()
param_sessions = []
param_value_sessions = []
for ses in exchange_df.params.values.tolist():
new_ses_set = process_exchange_session(session_with_params=ses, include_vals=False)
new_ses_dict = process_exchange_session(session_with_params=ses, include_vals=True)
param_sessions.append(new_ses_set)
param_value_sessions.append(new_ses_dict)
# +
# let's see the differences between the three types of sessions
ind = 0
print(sessions[ind][:3])
print(param_sessions[ind][:3])
print(param_value_sessions[ind][:3])
# -
# let's add these reformatted sessions as columns to a dataframe
data = exchange_df
data['session'] = sessions
data['param_session'] = param_sessions
data['param_value_session'] = param_value_sessions
# ### Now we will model and visualise these sessions in one go.
#
# We do this using the <b>score_and_visualise_sessions</b> function.
#
# Since we created columns for all 3 session types, the session_column argument can be set to any of the following:
#
# - session
# - param_session
# - param_value_session
# +
# let's model and visualise these sessions in one go
anomalous.score_and_visualise_sessions(
data=data,
session_column='param_session',
window_length=3,
time_column='begin',
source_columns=['UserId', 'ClientIP']
)
# -
# ## Sessionize AWS Cloud Trail Logs <a id='aws_sessions'></a>
#
# The cell below contains a kusto query which queries the AWSCloudTrail table in Log Analytics. In this example, we wish for the sessions to be on a per UserId - ClientIP - UserAgent - role basis. In addition, we require that each session be no longer than 20 minutes in total, with each command no more than 2 minutes apart from each other. (These requirements can be adjusted for different data-sets/use-cases etc).
#
# Note we choose a much shorter time_back in this KQL query. This is just because the AWS Cloud Trail logs have a lot more data when compared with the exchange admin logs for this demo tenant. We therefore choose a shorter time back purely to prevent this demo notebook from slowing down.
query = """
let time_back = 1d;
AWSCloudTrail
| where TimeGenerated >= ago(time_back)
//
// filter to the event type of interest
| where EventTypeName == 'AwsApiCall'
//
// optionally exclude some rows which are not suitable for your use case
| where UserIdentityPrincipalid != '' and SessionIssuerUserName != ''
//
// create dynamic param variable which has the EventName as the key and the RequestParameters as the values
| extend par = iff(RequestParameters == '', '{}', RequestParameters)
| extend param = todynamic(strcat('{"', EventName, '": ', tostring(par), '}'))
//
// rename some columns
| project TimeGenerated, Operation=EventName, UserId=UserIdentityPrincipalid, ClientIP=SourceIpAddress, UserAgent, role=SessionIssuerUserName, param
//
// sort by the user related columns and the timestamp column in ascending order
| order by UserId asc, ClientIP asc, UserAgent asc, role asc, TimeGenerated asc
//
// calculate the start time of each session into the "begin" variable
// With each session max 20 mins in length with each event at most 2 mins apart.
// A new session is created each time one of the user related columns change.
| extend begin = row_window_session(TimeGenerated, 20m, 2m, UserId != prev(UserId) or ClientIP != prev(ClientIP) or UserAgent != prev(UserAgent) or role != prev(role))
//
// summarize the operations and the params by the user related variables and the "begin" variable
| summarize cmds=makelist(Operation), end=max(TimeGenerated), nCmds=count(), nDistinctCmds=dcount(Operation), UserAgent=any(UserAgent), role=any(role), params=makelist(param) by UserId, ClientIP, begin
//
// optionally specify an order to the final columns
| project UserId, ClientIP, nCmds, nDistinctCmds, begin, end, duration=end-begin, role, UserAgent, cmds, params
//
//optionally filter out sessions which contain only one event
| where nCmds > 1
"""
# execute the query
aws_df = qry_prov.exec_query(query=query)
# I comment out this cell and run it again once it has run to prevent the notebook from slowing down
try:
print(aws_df.shape)
except AttributeError as e:
aws_df = _kql_raw_result_.to_dataframe()
print(aws_df.shape)
aws_df.head()
# ### Convert AWS sessions to the correct format for the model <a id='clean_aws'></a>
#
# Recall the allowed session types [here](#create_sessions)
#
# <b>So let's see what needs to be done to the aws_df</b>
#
# The "cmds" column is already in a suitable format of type (1). This is because it is a list of strings.
# If we wish to also include the parameters (and optionally the corresponding values) to the model, then we need to transform the "params" column slightly
# +
# define a helper function for converting the sessions with params (and values) into a suitable format
def process_aws_session(session_with_params: List[Dict[str, Dict[str, any]]], include_vals: bool) -> List[Cmd]:
"""
Converts an aws session with params to an allowed format.
param session_with_params: example format:
[
{'GetAuthorizationToken': {'registryIds': ['123456']}},
{'GetAuthorizationToken': {'registryIds': ['123456', '654321']}}
]
Note that the accompanying values for the parameters can take dynamic types like dict, list etc.
However, when we transform the aws session into an allowed format, the value will be cast into a string type.
param include_vals: if True, then it will be transformed to a format which includes the values,
else the output will just contain the parameters
return: list of the Cmd data type which includes either just the parameters, or also the corresponding values
"""
new_ses = []
for cmd in session_with_params:
c = list(cmd.keys())[0]
par = list(cmd.values())[0]
new_pars = set()
if include_vals:
new_pars = dict()
for p, v in par.items():
if include_vals:
new_pars[p] = str(v)
else:
new_pars.add(p)
new_ses.append(Cmd(name=c, params=new_pars))
return new_ses
# +
# let's create suitable sessions for params, and suitable sessions for params + values
sessions = aws_df.cmds.values.tolist()
param_sessions = []
param_value_sessions = []
for ses in aws_df.params.values.tolist():
new_ses_set = process_aws_session(session_with_params=ses, include_vals=False)
new_ses_dict = process_aws_session(session_with_params=ses, include_vals=True)
param_sessions.append(new_ses_set)
param_value_sessions.append(new_ses_dict)
# +
# let's see the differences between the three types of sessions
ind = 0
print(sessions[ind][:3])
print(param_sessions[ind][:3])
print(param_value_sessions[ind][:3])
# -
# let's add these reformatted sessions as columns to a dataframe
data = aws_df
data['session'] = sessions
data['param_session'] = param_sessions
data['param_value_session'] = param_value_sessions
# ### Now we will model and visualise these sessions in one go.
#
# We do this using the <b>score_and_visualise_sessions</b> function.
#
# As before, since we created columns for all 3 session types, the session_column argument can be set to any of the following:
#
# - session
# - param_session
# - param_value_session
# +
# let's model and visualise these sessions in one go
anomalous.score_and_visualise_sessions(
data=data,
session_column='param_session',
window_length=3,
time_column='begin',
source_columns=['UserId', 'ClientIP']
)
# -
# ## Sessionize VM Process Logs <a id='vm_sessions'></a>
#
# The cell below contains a kusto query which queries the VMProcess table in Log Analytics. In this example, we wish for the sessions to be on a per UserId - Computer basis. In addition, we require that each session be no longer than 20 minutes in total, with each command no more than 2 minutes apart from each other. (These requirements can be adjusted for different data-sets/use-cases etc).
#
# Note that in the examples for [Office Activity](#office_sessions) and [AWS Cloud Trail](#aws_sessions) logs, it was fairly clear cut from the data what we could use as parameters for each of the events/commands. However, for the VM Process Logs, it is less clear.
#
# Some possible approaches:
#
# 1. The command line entries are provided. So a possible approach could be to parse the command line logs into the commands used and their accompanying parameters.
# 2. The executable name could be used as the event/command <br>
# a) The services associated with the executable could be used as the parameters <br>
# b) Or we could use a combination of some other columns as the parameters
#
# In this example, we apply approach (2b). In particular, we use "ExecutableName" as the event/command, and the following columns as parameters: "DisplayName", "ProductName", "Group", "ProductVersion", "ExecutablePath".
#
# <b>Important note:</b><br>
# Some modelling assumptions are made in the anomalous_sequence subpackage of msticpy.
#
# In particular, when we model the third session type (command + params + values), we make the assumption that the values depend only on the parameter and not on the command.
#
# This means if we were to treat the parameters as a dictionary for example:
#
# Cmd(name="miiserver", params={"ProductVersion": "123542", "ExecutablePath": "a/path"})
#
# Then the value "123542" will be conditioned only on param "ProductVersion" and value "a/path" will be conditioned only on param "ExecutablePath". But since ProductVersion, and ExecutablePath parameters will be present for all the events, this is not useful. We want the values to be conditioned on the executable.
#
# Therefore, for this approach, we will use the second session type (command + params). For example:
#
# Cmd(name="miiserver", params={"123542", "a/path"})
#
# Now, the presence of "123542" and "a/path" will be modelled independently conditional on the executable "miiserver"
#
# (note, this modification is still not perfect, since "123542" and "a/path" will each be modelled as Bernoulli instead of categorical. But this approach should hopefully still be affective at downscoring the likelihood of the rarer param settings conditional on the executable.)
#
#
#
query = """
let time_back = 7d;
VMProcess
| where TimeGenerated >= ago(time_back)
//
// exclude some known automated users
| where UserDomain != 'NT AUTHORITY'
| extend UserId = strcat(UserName, '--', UserDomain)
| where UserId != "--"
//
// replace backwards slash with forward slash in ExecutablePath and make it lower case
| extend path = replace(@'\\\\', @'/',tolower(ExecutablePath))
//
// create dynamic params variable which has the ExecutableName as the key and some other columns as the values
| extend params = todynamic(strcat('{"', ExecutableName, '": ["', DisplayName, '", "', ProductName, '", "', Group,'", "', ProductVersion, '", "', path, '"]}'))
//
// keep only the needed columns
| project TimeGenerated, Computer, UserId, ExecutableName, params
//
// sort by the user related columns and the timestamp column in ascending order
| sort by UserId asc, Computer asc, TimeGenerated asc
//
// calculate the start time of each session into the "begin" variable
// With each session max 20 mins in length with each event at most 2 mins apart.
// A new session is created each time one of the user related columns change.
| extend begin = row_window_session(TimeGenerated, 20m, 2m, UserId != prev(UserId) or Computer != prev(Computer))
//
// summarize the executables and the params by the user related variables and the "begin" variable
| summarize executables=makelist(ExecutableName), end=max(TimeGenerated), nExecutables=count(),
nDistinctExecutables=dcount(ExecutableName), params=makelist(params) by UserId, Computer, begin
//
// optionally specify an order to the final columns
| project UserId, Computer, nExecutables, nDistinctExecutables ,begin, end, duration=end-begin, executables, params
//
//optionally filter out sessions which contain only one event
//| where nExecutables > 1
"""
# execute the query
vm_df = qry_prov.exec_query(query=query)
# I comment out this cell and run it again once it has run to prevent the notebook from slowing down
try:
print(vm_df.shape)
except AttributeError as e:
vm_df = _kql_raw_result_.to_dataframe()
print(vm_df.shape)
vm_df.head()
# ### Convert VM Process sessions to the correct format for the model <a id='clean_vm'></a>
#
# Recall the allowed session types [here](#create_sessions)
#
# <b>So let's see what needs to be done to the vm_df</b>
#
# The "executables" column is already in a suitable format of type (1). This is because it is a list of strings.
# If we wish to also include the parameters to the model, then we need to transform the "params" column slightly.
# +
# define a helper function for converting the sessions with params into a suitable format
def process_vm_session(session_with_params: List[Dict[str, Dict[str, any]]]) -> List[Cmd]:
"""
Converts a vm session with params to an allowed format.
param session_with_params: example format:
[{'Explorer': ['Explorer','Microsoft® Windows® Operating System',
'Microsoft® Windows® Operating System', '10.0.14393.0', 'c:/windows/explorer.exe']}]
return: list of the Cmd data type which includes the parameters
"""
new_ses = []
for cmd in session_with_params:
c = list(cmd.keys())[0]
par = list(cmd.values())[0]
new_pars = set(par)
new_ses.append(Cmd(name=c, params=new_pars))
return new_ses
# +
# let's create suitable sessions for params
sessions = vm_df.executables.values.tolist()
param_sessions = []
for ses in vm_df.params.values.tolist():
new_ses_set = process_vm_session(session_with_params=ses)
param_sessions.append(new_ses_set)
# +
# let's see the differences between the two types of sessions
ind = 0
print(sessions[ind])
print(param_sessions[ind])
# -
# let's add these reformatted sessions as columns to a dataframe
data = vm_df
data['session'] = sessions
data['param_session'] = param_sessions
# ### Now we will model and visualise these sessions in one go.
#
# We do this using the <b>score_and_visualise_sessions</b> function.
#
# As before, since we created columns for 2 of the 3 session types, the session_column argument can be set to any of the following:
#
# - session
# - param_session
# +
# let's model and visualise these sessions in one go
anomalous.score_and_visualise_sessions(
data=data,
session_column='param_session',
window_length=3,
time_column='begin',
source_columns=['UserId', 'Computer']
)
| 52,768 |
/Probelm Set 2/Problem Set 2.ipynb | caee3c381ac0883a7d99644af32709feb11d4f0d | [] | no_license | Mgallese97/Data-Science-in-Practice | https://github.com/Mgallese97/Data-Science-in-Practice | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 53,738 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # <center style="font-size:140%;"> Report - Problem Set #*2*
# <center> Group Members: Giacomo Martiriggiano, Mattia Gallese, Sophie De Becker, Yao Di
# This Jupyter Notebook will outline the data cleaning process and binary classification for the churn factor of customers.csv for the assignment
# ## Data loading
#import modules needed for data analysis
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.model_selection import train_test_split, KFold, GridSearchCV
from sklearn.metrics import recall_score, accuracy_score, precision_score, confusion_matrix, classification_report
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
#read the csv file to transfer all the data into "data"
data = pd.read_csv('customers.csv')
# ## Data Cleaning
# We keep the data cleaning process as what we did for the Problem Set 1.
# The first step is to remove the empty value in the TotalCharges column
data['TotalCharges'] = data['TotalCharges'].replace(" ", np.nan).astype('float32')
data["SeniorCitizen"]=data["SeniorCitizen"].astype("object")
data = data[data["TotalCharges"].notnull()]
data = data.reset_index()[data.columns]
#now we forced the 0 and 1 to be objects and we know we can drop the empty values in Total charger
# Finally a number may be non-empty but still unrealistic for example negative tenure. Let's now check that tenure and charges are non negative
print((data['tenure'] <0).any())
print((data['TotalCharges']<0).any())
print((data['MonthlyCharges']<0).any())
# We also need to encode the inputs from enumerates to numbers
# ### Logistic regression MonthlyCharges
# +
data_origin = data.copy()
y=data_origin.Churn.replace({'No': 0, 'Yes': 1}).values
X_LR = data.iloc[:, 1:20].values
X_train, X_test, y_train, y_test = train_test_split(X_LR, y,stratify=y, test_size=0.2)
X_train_1D = X_train[:, 17, np.newaxis]
X_test_1D = X_test[:, 17, np.newaxis]
classifier = LogisticRegression().fit(X_train_1D, y_train)
accuracy = classifier.score(X_test_1D, y_test)
print('Classification accuracy: {:.2f}%'.format(accuracy * 100))
# -
proba = classifier.predict_proba(X_LR[:,17,np.newaxis])
plt.plot(X_LR[:,17], proba)
plt.title('Predicted probability of each class')
plt.xlabel('Data X')
plt.show()
# ### Logistic regression tenure
# +
X_train_1D = X_train[:, 4, np.newaxis]
X_test_1D = X_test[:, 4, np.newaxis]
classifier = LogisticRegression().fit(X_train_1D, y_train)
accuracy = classifier.score(X_test_1D, y_test)
print('Classification accuracy: {:.2f}%'.format(accuracy * 100))
# -
proba = classifier.predict_proba(X_LR[:,17,np.newaxis])
plt.plot(X_LR[:,17], proba, '*')
plt.title('Predicted probability of each class')
plt.xlabel('Data X')
plt.show()
# ### Random Forest
y_pred
# +
X_RF = data.iloc[:, 1:20]
dfX= pd.get_dummies(X_RF, columns= [i for i in X_RF.columns if X_RF[i].dtypes=='object'],drop_first=True)
y=data['Churn'].values
X_train, X_test, y_train, y_test = train_test_split(dfX, y,stratify=y, test_size=0.2)
classifier= RandomForestClassifier()
classifier.fit(X_train,y_train)
y_pred= classifier.predict(X_test)
print("The accuracy score for the classifier is :",accuracy_score(y_test,y_pred))
mat = confusion_matrix(y_test, y_pred)
s1=sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,
xticklabels=['No','Yes'],
yticklabels=['No','Yes'] )
plt.xlabel('true label')
plt.ylabel('predicted label')
bottom, top = s1.get_ylim()
s1.set_ylim(bottom + 0.5, top - 0.5)
# -
# ## Data numerize and resampling
# The key of the problem set is to develop a method to predict the performance of the customer. The prediction problem could be easily transfered to asup
# The key output of our supervised learning model is the "Churn" parameter. The first step is to explore it.
data_E2N = data[['gender', 'SeniorCitizen', 'Partner', 'Dependents',
'PhoneService', 'MultipleLines', 'InternetService',
'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
'PaymentMethod','Churn']].copy()
data_E2N = data_E2N.apply(lambda x: pd.factorize(x)[0])
scaler = MinMaxScaler()
data_E2N = pd.DataFrame(scaler.fit_transform(data_E2N),columns=data_E2N.columns)
data_N = data[['tenure', 'MonthlyCharges', 'TotalCharges']].copy()
data_N['TotalCharges'] = pd.to_numeric(data_N['TotalCharges'])
scaler2 = StandardScaler()
data_N = pd.DataFrame(scaler2.fit_transform(data_N),columns=data_N.columns)
data_final = pd.concat([data_E2N, data_N], axis=1)
data_final.Churn.value_counts()
# As we could observe, the dataset is not balanced based on the churn parameter. In order to get a reliable result, we need to downsampling the No Churn set of customer.
# +
x_train, x_test, y_train, y_test = train_test_split(data_final, data_final["Churn"], stratify=data_final["Churn"],test_size=0.3)
count_nochurn, count_churn = x_train["Churn"].value_counts()
x_train_nochurn = x_train[x_train['Churn'] == 0.0]
x_train_churn = x_train[x_train['Churn'] == 1.0]
x_train_nochurn_resample = x_train_nochurn.sample(count_churn)
x_train_resample = pd.concat([x_train_nochurn_resample, x_train_churn], axis=0)
# -
x_train_resample.Churn.value_counts()
# ## Problem Solving
#
# For the binary classification problem with multiple dimensions, support vector machine is a common solution to find a robust classifier
classifier = svm.SVC(gamma='auto')
classifier.fit(x_train_resample.drop(columns="Churn"), x_train_resample["Churn"])
y_predict=classifier.predict(x_test.drop(columns="Churn"))
print("The accuracy score for the classifier is :",accuracy_score(y_test,y_predict))
print("The recall score for the classifier is :",recall_score(y_test,y_predict,labels=[1,0]))
print("The precision score for the classifier is ::",precision_score(y_test,y_predict,labels=[1,0]))
pd.DataFrame(confusion_matrix(y_test,y_predict,labels=[1,0]), ["Churn_true","No Churn_true"], ["Churn_pred","No Churn_pred"])
# ### Cross Validation
# We could add cross validation to ensure that our model is not overfitting
# +
Accuracy_scores = []
Recall_scores = []
Precision_scores = []
k = 4
classifier = svm.SVC(gamma='auto')
cv = KFold(n_splits=k, random_state=42, shuffle=False)
for train_index, test_index in cv.split(data_final):
x_train, x_test, y_train, y_test = data_final.iloc[train_index], data_final.iloc[test_index], data_final.Churn.iloc[train_index], data_final.Churn.iloc[test_index]
x_train_nochurn = x_train[x_train['Churn'] == 0.0]
x_train_churn = x_train[x_train['Churn'] == 1.0]
x_train_nochurn_resample = x_train_nochurn.sample(count_churn)
x_train_resample = pd.concat([x_train_nochurn_resample, x_train_churn], axis=0)
classifier.fit(x_train_resample.drop(columns="Churn"), x_train_resample["Churn"])
y_predict=classifier.predict(x_test.drop(columns="Churn"))
Accuracy_scores.append(accuracy_score(y_test,y_predict))
Recall_scores.append(recall_score(y_test,y_predict,labels=[1,0]))
Precision_scores.append(precision_score(y_test,y_predict,labels=[1,0]))
print(pd.DataFrame(confusion_matrix(y_test,y_predict,labels=[1,0]), ["Churn_true","No Churn_true"], ["Churn_pred","No Churn_pred"]))
print("The accuracy scores for the ",k, "-fold classifier is :",Accuracy_scores)
print("The recall scores for the ",k, "-fold classifier is :",Recall_scores)
print("The precision scores for the ",k, "-fold classifier is :",Precision_scores)
# -
# ### Grid Search
# +
param_grid = [
{'C': [1, 3, 10, 30, 100], 'kernel': ['linear']},
{'C': [1, 3, 10, 30, 100], 'kernel': ['rbf'], 'gamma': [0.0001, 0.0003, 0.001]},
]
svc = svm.SVC()
classifier=GridSearchCV(svc,param_grid,cv=k)
x_train, x_test, y_train, y_test = train_test_split(data_final, data_final["Churn"], stratify=data_final["Churn"],test_size=0.3)
count_nochurn, count_churn = x_train["Churn"].value_counts()
x_train_nochurn = x_train[x_train['Churn'] == 0.0]
x_train_churn = x_train[x_train['Churn'] == 1.0]
x_train_nochurn_resample = x_train_nochurn.sample(count_churn)
x_train_resample = pd.concat([x_train_nochurn_resample, x_train_churn], axis=0)
classifier.fit(x_train_resample.drop(columns="Churn"), x_train_resample["Churn"])
y_predict=classifier.predict(x_test.drop(columns="Churn"))
print("The accuracy score for the best classifier is :",accuracy_score(y_test,y_predict))
print("The recall score for the best classifier is :",recall_score(y_test,y_predict,labels=[1,0]))
print("The precision score for the best classifier is ::",precision_score(y_test,y_predict,labels=[1,0]))
pd.DataFrame(confusion_matrix(y_test,y_predict,labels=[1,0]), ["Churn_true","No Churn_true"], ["Churn_pred","No Churn_pred"])
|
# | ----------------- | ------- | ------------- | ------------------------------------------------------------------- |
# | 2020-09-09 | 2.1 | Malika Singla | Updated the variable soundtrack_dict to soundtrack_dic in Questions |
# | 2020-08-26 | 2.0 | Lavanya | Moved lab to course repo in GitLab |
# | | | | |
# | | | | |
#
# ## <h3 align="center"> © IBM Corporation 2020. All rights reserved. <h3/>
#
| 9,881 |
/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb | 1f8ad67e13f8d70559992f9c0033979baeece57c | [
"MS-PL",
"MIT"
] | permissive | nemasobhani/MachineLearningNotebooks | https://github.com/nemasobhani/MachineLearningNotebooks | 0 | 0 | MIT | 2020-02-06T22:37:35 | 2020-02-05T19:34:53 | null | Jupyter Notebook | false | false | .py | 46,962 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3.6
# language: python
# name: python36
# ---
# Copyright (c) Microsoft Corporation. All rights reserved.
#
# Licensed under the MIT License.
# 
# + [markdown] nbpresent={"id": "bf74d2e9-2708-49b1-934b-e0ede342f475"}
# # Training, hyperparameter tune, and deploy with Keras
#
# ## Introduction
# This tutorial shows how to train a simple deep neural network using the MNIST dataset and Keras on Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of `28x28` pixels, representing number from 0 to 9. The goal is to create a multi-class classifier to identify the digit each image represents, and deploy it as a web service in Azure.
#
# For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/).
#
# ## Prerequisite:
# * Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning
# * If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:
# * install the AML SDK
# * create a workspace and its configuration file (`config.json`)
# * For local scoring test, you will also need to have `tensorflow` and `keras` installed in the current Jupyter kernel.
# -
# Let's get started. First let's import some Python libraries.
# + nbpresent={"id": "c377ea0c-0cd9-4345-9be2-e20fb29c94c3"}
# %matplotlib inline
import numpy as np
import os
import matplotlib.pyplot as plt
# + nbpresent={"id": "edaa7f2f-2439-4148-b57a-8c794c0945ec"}
import azureml
from azureml.core import Workspace
# check core SDK version number
print("Azure ML SDK Version: ", azureml.core.VERSION)
# -
# ## Initialize workspace
# Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`.
ws = Workspace.from_config()
print('Workspace name: ' + ws.name,
'Azure region: ' + ws.location,
'Subscription id: ' + ws.subscription_id,
'Resource group: ' + ws.resource_group, sep='\n')
# + [markdown] nbpresent={"id": "59f52294-4a25-4c92-bab8-3b07f0f44d15"}
# ## Create an Azure ML experiment
# Let's create an experiment named "keras-mnist" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.
# + nbpresent={"id": "bc70f780-c240-4779-96f3-bc5ef9a37d59"}
from azureml.core import Experiment
script_folder = './keras-mnist'
os.makedirs(script_folder, exist_ok=True)
exp = Experiment(workspace=ws, name='keras-mnist')
# -
# ## Explore data
#
# Before you train a model, you need to understand the data that you are using to train it. In this section you learn how to:
#
# * Download the MNIST dataset
# * Display some sample images
#
# ### Download the MNIST dataset
#
# Download the MNIST dataset and save the files into a `data` directory locally. Images and labels for both training and testing are downloaded.
# +
import urllib.request
data_folder = os.path.join(os.getcwd(), 'data')
os.makedirs(data_folder, exist_ok=True)
urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename=os.path.join(data_folder, 'train-images.gz'))
urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename=os.path.join(data_folder, 'train-labels.gz'))
urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename=os.path.join(data_folder, 'test-images.gz'))
urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename=os.path.join(data_folder, 'test-labels.gz'))
# -
# ### Display some sample images
#
# Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `utils.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compressed files into numpy arrays.
# +
# make sure utils.py is in the same directory as this code
from utils import load_data, one_hot_encode
# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.
X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0
X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)
y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)
# now let's show some randomly chosen images from the training set.
count = 0
sample_size = 30
plt.figure(figsize = (16, 6))
for i in np.random.permutation(X_train.shape[0])[:sample_size]:
count = count + 1
plt.subplot(1, sample_size, count)
plt.axhline('')
plt.axvline('')
plt.text(x=10, y=-10, s=y_train[i], fontsize=18)
plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)
plt.show()
# -
# Now you have an idea of what these images look like and the expected prediction outcome.
# + [markdown] nbpresent={"id": "defe921f-8097-44c3-8336-8af6700804a7"}
# ## Create a FileDataset
# A FileDataset references one or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)
# +
from azureml.core.dataset import Dataset
web_paths = [
'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
]
dataset = Dataset.File.from_files(path = web_paths)
# -
# Use the `register()` method to register datasets to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script.
dataset = dataset.register(workspace = ws,
name = 'mnist dataset',
description='training and test dataset',
create_new_version=True)
# ## Create or Attach existing AmlCompute
# You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.
# If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:
# 1. create the configuration (this step is local and only takes a second)
# 2. create the cluster (this step will take about **20 seconds**)
# 3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell
# +
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
# choose a name for your cluster
cluster_name = "gpu-cluster"
try:
compute_target = ComputeTarget(workspace=ws, name=cluster_name)
print('Found existing compute target')
except ComputeTargetException:
print('Creating a new compute target...')
compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',
max_nodes=4)
# create the cluster
compute_target = ComputeTarget.create(ws, cluster_name, compute_config)
# can poll for a minimum number of nodes and for a specific timeout.
# if no min node count is provided it uses the scale settings for the cluster
compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)
# use get_status() to get a detailed status for the current cluster.
print(compute_target.get_status().serialize())
# -
# Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named "gpu-cluster" of type `AmlCompute`.
compute_targets = ws.compute_targets
for name, ct in compute_targets.items():
print(name, ct.type, ct.provisioning_state)
# ## Copy the training files into the script folder
# The Keras training script is already created for you. You can simply copy it into the script folder, together with the utility library used to load compressed data file into numpy array.
# +
import shutil
# the training logic is in the keras_mnist.py file.
shutil.copy('./keras_mnist.py', script_folder)
# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.
shutil.copy('./utils.py', script_folder)
# + [markdown] nbpresent={"id": "2039d2d5-aca6-4f25-a12f-df9ae6529cae"}
# ## Construct neural network in Keras
# In the training script `keras_mnist.py`, it creates a very simple DNN (deep neural network), with just 2 hidden layers. The input layer has 28 * 28 = 784 neurons, each representing a pixel in an image. The first hidden layer has 300 neurons, and the second hidden layer has 100 neurons. The output layer has 10 neurons, each representing a targeted label from 0 to 9.
#
# 
# -
# ### Azure ML concepts
# Please note the following three things in the code below:
# 1. The script accepts arguments using the argparse package. In this case there is one argument `--data_folder` which specifies the FileDataset in which the script can find the MNIST data
# ```
# parser = argparse.ArgumentParser()
# parser.add_argument('--data_folder')
# ```
# 2. The script is accessing the Azure ML `Run` object by executing `run = Run.get_context()`. Further down the script is using the `run` to report the loss and accuracy at the end of each epoch via callback.
# ```
# run.log('Loss', log['loss'])
# run.log('Accuracy', log['acc'])
# ```
# 3. When running the script on Azure ML, you can write files out to a folder `./outputs` that is relative to the root directory. This folder is specially tracked by Azure ML in the sense that any files written to that folder during script execution on the remote target will be picked up by Run History; these files (known as artifacts) will be available as part of the run history record.
# The next cell will print out the training code for you to inspect.
with open(os.path.join(script_folder, './keras_mnist.py'), 'r') as f:
print(f.read())
# ## Create TensorFlow estimator & add Keras
# Next, we construct an `azureml.train.dnn.TensorFlow` estimator object, use the `gpu-cluster` as compute target, and pass the mount-point of the datastore to the training code as a parameter.
# The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed. In this case, we add `keras` package (for the Keras framework obviously), and `matplotlib` package for plotting a "Loss vs. Accuracy" chart and record it in run history.
# +
dataset = Dataset.get_by_name(ws, 'mnist dataset')
# list the files referenced by mnist dataset
dataset.to_path()
# +
from azureml.train.dnn import TensorFlow
script_params = {
'--data-folder': dataset.as_named_input('mnist').as_mount(),
'--batch-size': 50,
'--first-layer-neurons': 300,
'--second-layer-neurons': 100,
'--learning-rate': 0.001
}
est = TensorFlow(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
entry_script='keras_mnist.py',
pip_packages=['keras==2.2.5','azureml-dataprep[pandas,fuse]','matplotlib'])
# -
# ## Submit job to run
# Submit the estimator to the Azure ML experiment to kick off the execution.
run = exp.submit(est)
# ### Monitor the Run
# As the Run is executed, it will go through the following stages:
# 1. Preparing: A docker image is created matching the Python environment specified by the TensorFlow estimator and it will be uploaded to the workspace's Azure Container Registry. This step will only happen once for each Python environment -- the container will then be cached for subsequent runs. Creating and uploading the image takes about **5 minutes**. While the job is preparing, logs are streamed to the run history and can be viewed to monitor the progress of the image creation.
#
# 2. Scaling: If the compute needs to be scaled up (i.e. the AmlCompute cluster requires more nodes to execute the run than currently available), the cluster will attempt to scale up in order to make the required amount of nodes available. Scaling typically takes about **5 minutes**.
#
# 3. Running: All scripts in the script folder are uploaded to the compute target, data stores are mounted/copied and the `entry_script` is executed. While the job is running, stdout and the `./logs` folder are streamed to the run history and can be viewed to monitor the progress of the run.
#
# 4. Post-Processing: The `./outputs` folder of the run is copied over to the run history
#
# There are multiple ways to check the progress of a running job. We can use a Jupyter notebook widget.
#
# **Note: The widget will automatically update ever 10-15 seconds, always showing you the most up-to-date information about the run**
from azureml.widgets import RunDetails
RunDetails(run).show()
# We can also periodically check the status of the run object, and navigate to Azure portal to monitor the run.
run
run.wait_for_completion(show_output=True)
# In the outputs of the training script, it prints out the Keras version number. Please make a note of it.
# ### The Run object
# The Run object provides the interface to the run history -- both to the job and to the control plane (this notebook), and both while the job is running and after it has completed. It provides a number of interesting features for instance:
# * `run.get_details()`: Provides a rich set of properties of the run
# * `run.get_metrics()`: Provides a dictionary with all the metrics that were reported for the Run
# * `run.get_file_names()`: List all the files that were uploaded to the run history for this Run. This will include the `outputs` and `logs` folder, azureml-logs and other logs, as well as files that were explicitly uploaded to the run using `run.upload_file()`
#
# Below are some examples -- please run through them and inspect their output.
run.get_details()
run.get_metrics()
run.get_file_names()
# ## Download the saved model
# In the training script, the Keras model is saved into two files, `model.json` and `model.h5`, in the `outputs/models` folder on the gpu-cluster AmlCompute node. Azure ML automatically uploaded anything written in the `./outputs` folder into run history file store. Subsequently, we can use the `run` object to download the model files. They are under the the `outputs/model` folder in the run history file store, and are downloaded into a local folder named `model`.
# +
# create a model folder in the current directory
os.makedirs('./model', exist_ok=True)
for f in run.get_file_names():
if f.startswith('outputs/model'):
output_file_path = os.path.join('./model', f.split('/')[-1])
print('Downloading from {} to {} ...'.format(f, output_file_path))
run.download_file(name=f, output_file_path=output_file_path)
# -
# ## Predict on the test set
# Let's check the version of the local Keras. Make sure it matches with the version number printed out in the training script. Otherwise you might not be able to load the model properly.
# +
import keras
import tensorflow as tf
print("Keras version:", keras.__version__)
print("Tensorflow version:", tf.__version__)
# -
# Now let's load the downloaded model.
# +
from keras.models import model_from_json
# load json and create model
json_file = open('model/model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model/model.h5")
print("Model loaded from disk.")
# -
# Feed test dataset to the persisted model to get predictions.
# +
# evaluate loaded model on test data
loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
y_test_ohe = one_hot_encode(y_test, 10)
y_hat = np.argmax(loaded_model.predict(X_test), axis=1)
# print the first 30 labels and predictions
print('labels: \t', y_test[:30])
print('predictions:\t', y_hat[:30])
# -
# Calculate the overall accuracy by comparing the predicted value against the test set.
print("Accuracy on the test set:", np.average(y_hat == y_test))
# ## Intelligent hyperparameter tuning
# We have trained the model with one set of hyperparameters, now let's how we can do hyperparameter tuning by launching multiple runs on the cluster. First let's define the parameter space using random sampling.
# +
from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal
from azureml.train.hyperdrive import choice, loguniform
ps = RandomParameterSampling(
{
'--batch-size': choice(25, 50, 100),
'--first-layer-neurons': choice(10, 50, 200, 300, 500),
'--second-layer-neurons': choice(10, 50, 200, 500),
'--learning-rate': loguniform(-6, -1)
}
)
# -
# Next, we will create a new estimator without the above parameters since they will be passed in later by Hyperdrive configuration. Note we still need to keep the `data-folder` parameter since that's not a hyperparamter we will sweep.
est = TensorFlow(source_directory=script_folder,
script_params={'--data-folder': dataset.as_named_input('mnist').as_mount()},
compute_target=compute_target,
entry_script='keras_mnist.py',
pip_packages=['keras==2.2.5','azureml-dataprep[pandas,fuse]','matplotlib'])
# Now we will define an early termnination policy. The `BanditPolicy` basically states to check the job every 2 iterations. If the primary metric (defined later) falls outside of the top 10% range, Azure ML terminate the job. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric.
policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)
# Now we are ready to configure a run configuration object, and specify the primary metric `Accuracy` that's recorded in your training runs. If you go back to visit the training script, you will notice that this value is being logged after every epoch (a full batch set). We also want to tell the service that we are looking to maximizing this value. We also set the number of samples to 20, and maximal concurrent job to 4, which is the same as the number of nodes in our computer cluster.
hdc = HyperDriveConfig(estimator=est,
hyperparameter_sampling=ps,
policy=policy,
primary_metric_name='Accuracy',
primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,
max_total_runs=20,
max_concurrent_runs=4)
# Finally, let's launch the hyperparameter tuning job.
hdr = exp.submit(config=hdc)
# We can use a run history widget to show the progress. Be patient as this might take a while to complete.
RunDetails(hdr).show()
hdr.wait_for_completion(show_output=True)
# ### Warm start a Hyperparameter Tuning experiment and resuming child runs
# Often times, finding the best hyperparameter values for your model can be an iterative process, needing multiple tuning runs that learn from previous hyperparameter tuning runs. Reusing knowledge from these previous runs will accelerate the hyperparameter tuning process, thereby reducing the cost of tuning the model and will potentially improve the primary metric of the resulting model. When warm starting a hyperparameter tuning experiment with Bayesian sampling, trials from the previous run will be used as prior knowledge to intelligently pick new samples, so as to improve the primary metric. Additionally, when using Random or Grid sampling, any early termination decisions will leverage metrics from the previous runs to determine poorly performing training runs.
#
# Azure Machine Learning allows you to warm start your hyperparameter tuning run by leveraging knowledge from up to 5 previously completed hyperparameter tuning parent runs.
#
# Additionally, there might be occasions when individual training runs of a hyperparameter tuning experiment are cancelled due to budget constraints or fail due to other reasons. It is now possible to resume such individual training runs from the last checkpoint (assuming your training script handles checkpoints). Resuming an individual training run will use the same hyperparameter configuration and mount the storage used for that run. The training script should accept the "--resume-from" argument, which contains the checkpoint or model files from which to resume the training run. You can also resume individual runs as part of an experiment that spends additional budget on hyperparameter tuning. Any additional budget, after resuming the specified training runs is used for exploring additional configurations.
#
# For more information on warm starting and resuming hyperparameter tuning runs, please refer to the [Hyperparameter Tuning for Azure Machine Learning documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters)
#
# ## Find and register best model
# When all the jobs finish, we can find out the one that has the highest accuracy.
best_run = hdr.get_best_run_by_primary_metric()
print(best_run.get_details()['runDefinition']['arguments'])
# Now let's list the model files uploaded during the run.
print(best_run.get_file_names())
# We can then register the folder (and all files in it) as a model named `keras-dnn-mnist` under the workspace for deployment.
model = best_run.register_model(model_name='keras-mlp-mnist', model_path='outputs/model')
# ## Deploy the model in ACI
# Now we are ready to deploy the model as a web service running in Azure Container Instance [ACI](https://azure.microsoft.com/en-us/services/container-instances/). Azure Machine Learning accomplishes this by constructing a Docker image with the scoring logic and model baked in.
# ### Create score.py
# First, we will create a scoring script that will be invoked by the web service call.
#
# * Note that the scoring script must have two required functions, `init()` and `run(input_data)`.
# * In `init()` function, you typically load the model into a global object. This function is executed only once when the Docker container is started.
# * In `run(input_data)` function, the model is used to predict a value based on the input data. The input and output to `run` typically use JSON as serialization and de-serialization format but you are not limited to that.
# +
# %%writefile score.py
import json
import numpy as np
import os
from keras.models import model_from_json
from azureml.core.model import Model
def init():
global model
model_root = Model.get_model_path('keras-mlp-mnist')
# load json and create model
json_file = open(os.path.join(model_root, 'model.json'), 'r')
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
# load weights into new model
model.load_weights(os.path.join(model_root, "model.h5"))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
def run(raw_data):
data = np.array(json.loads(raw_data)['data'])
# make prediction
y_hat = np.argmax(model.predict(data), axis=1)
return y_hat.tolist()
# -
# ### Create myenv.yml
# We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify conda packages `tensorflow` and `keras`.
# +
from azureml.core.conda_dependencies import CondaDependencies
cd = CondaDependencies.create()
cd.add_tensorflow_conda_package()
cd.add_conda_package('keras==2.2.5')
cd.add_pip_package("azureml-defaults")
cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')
print(cd.serialize_to_string())
# -
# ### Deploy to ACI
# We are almost ready to deploy. Create the inference configuration and deployment configuration and deploy to ACI. This cell will run for about 7-8 minutes.
# +
from azureml.core.webservice import AciWebservice
from azureml.core.model import InferenceConfig
from azureml.core.model import Model
from azureml.core.environment import Environment
myenv = Environment.from_conda_specification(name="myenv", file_path="myenv.yml")
inference_config = InferenceConfig(entry_script="score.py", environment=myenv)
aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,
auth_enabled=True, # this flag generates API keys to secure access
memory_gb=1,
tags={'name': 'mnist', 'framework': 'Keras'},
description='Keras MLP on MNIST')
service = Model.deploy(workspace=ws,
name='keras-mnist-svc',
models=[model],
inference_config=inference_config,
deployment_config=aciconfig)
service.wait_for_deployment(True)
print(service.state)
# -
# **Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:** `print(service.get_logs())`
# This is the scoring web service endpoint:
print(service.scoring_uri)
# ### Test the deployed model
# Let's test the deployed model. Pick 30 random samples from the test set, and send it to the web service hosted in ACI. Note here we are using the `run` API in the SDK to invoke the service. You can also make raw HTTP calls using any HTTP tool such as curl.
#
# After the invocation, we print the returned predictions and plot them along with the input images. Use red font color and inversed image (white on black) to highlight the misclassified samples. Note since the model accuracy is pretty high, you might have to run the below cell a few times before you can see a misclassified sample.
# +
import json
# find 30 random samples from test set
n = 30
sample_indices = np.random.permutation(X_test.shape[0])[0:n]
test_samples = json.dumps({"data": X_test[sample_indices].tolist()})
test_samples = bytes(test_samples, encoding='utf8')
# predict using the deployed model
result = service.run(input_data=test_samples)
# compare actual value vs. the predicted values:
i = 0
plt.figure(figsize = (20, 1))
for s in sample_indices:
plt.subplot(1, n, i + 1)
plt.axhline('')
plt.axvline('')
# use different color for misclassified sample
font_color = 'red' if y_test[s] != result[i] else 'black'
clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys
plt.text(x=10, y=-10, s=y_test[s], fontsize=18, color=font_color)
plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)
i = i + 1
plt.show()
# -
# We can retrieve the API keys used for accessing the HTTP endpoint.
# Retrieve the API keys. Two keys were generated.
key1, Key2 = service.get_keys()
print(key1)
# We can now send construct raw HTTP request and send to the service. Don't forget to add key to the HTTP header.
# +
import requests
# send a random row from the test set to score
random_index = np.random.randint(0, len(X_test)-1)
input_data = "{\"data\": [" + str(list(X_test[random_index])) + "]}"
headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}
resp = requests.post(service.scoring_uri, input_data, headers=headers)
print("POST to url", service.scoring_uri)
#print("input data:", input_data)
print("label:", y_test[random_index])
print("prediction:", resp.text)
# -
# Let's look at the workspace after the web service was deployed. You should see
# * a registered model named 'keras-mlp-mnist' and with the id 'model:1'
# * a webservice called 'keras-mnist-svc' with some scoring URL
# +
models = ws.models
for name, model in models.items():
print("Model: {}, ID: {}".format(name, model.id))
webservices = ws.webservices
for name, webservice in webservices.items():
print("Webservice: {}, scoring URI: {}".format(name, webservice.scoring_uri))
# -
# ## Clean up
# You can delete the ACI deployment with a simple delete API call.
service.delete()
| 30,158 |
/notebooks/multiobjective_optermisation.ipynb | bfe72d19a49ac8ea0d7b8a83eba384f5cea8048c | [] | no_license | a2i2/threshy | https://github.com/a2i2/threshy | 0 | 0 | null | 2023-05-22T21:47:50 | 2020-10-21T02:18:48 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 55,188 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import pandas as pd
import numpy as np
import autograd.numpy as anp
from pymoo.model.evaluator import Evaluator
from pymoo.model.problem import Problem
from pymoo.optimize import minimize
from pymoo.algorithms.nsga2 import NSGA2
from pymoo.algorithms.rnsga3 import RNSGA3
from pymoo.algorithms.so_gradient_descent import GradientDescent
from pymoo.model.repair import Repair
from pymoo.factory import *
from sklearn import preprocessing
from sklearn.preprocessing import LabelEncoder
from sklearn.calibration import calibration_curve, CalibratedClassifierCV
from sklearn.metrics import confusion_matrix, brier_score_loss, log_loss
# -
# # TODO:
# - Find an example for multi-class classification (not multi-label)
# - Decide if we support REJECTS in the ground truth
# +
df = pd.read_csv("../input/mailguard-labeled-results.csv")
inputs = {
"id_column": "mid",
"ground_truth_column": "ground_truth",
"reject_label": "REJECT",
"min": 0,
"max": 1,
"probability_column" : "probabilities", # Optional???
"target_label" : "spam", # Optional???
}
# -
df = pd.read_csv("../input/predictions-email-classifier-percept_20191014-161825.csv")
inputs = {
"id_column": "email_id",
"ground_truth_column": "work_type",
"reject_label": "REJECT",
"min": 0,
"max": 100
}
# +
def ids_are_unique(df, inputs):
return len(df[inputs["id_column"]].unique()) == len(df[inputs["id_column"]])
def retrieve_labels(df, inputs):
return df[inputs["ground_truth_column"]].str.strip().sort_values().unique()
def rejects_are_present(df, inputs):
return df[inputs["ground_truth_column"]].str.strip().str.contains(inputs["reject_label"]).any()
def check_labels_have_columns(df, labels):
return len(set(df.columns) & set(labels)) == len(labels)
def normalise_probs_in_place(df, inputs, labels):
if inputs["min"] == 0 and inputs["max"] == 100:
for label in labels:
if label == inputs["reject_label"]:
continue
if (df[label] < 1).any() and (df[label] >= 0).any():
return
df[label] = df[label] / 100
elif inputs["min"] == 0 and inputs["max"] == 1:
# TODO: Check that the provided constraints are not violated
return
else:
raise ValueError("Normalisation rule not specified")
def prepare_labels(df, inputs):
labels = retrieve_labels(df, inputs)
if "target_label" in inputs:
labels = list(filter(lambda x: x == inputs["target_label"], labels))
if not check_labels_have_columns(df, labels) and not "probability_column" in inputs:
raise ValueError("Labels do not have column names for probabilities")
return sorted(labels)
def prepare_ground_truth(df, inputs, mapping):
ids = []
truth_columns = inputs["ground_truth_column"]
if not "target_label" in inputs:
all_labels = []
for a_id in df[inputs["id_column"]].unique():
ground_truth_labels = df[df[inputs["id_column"]] == a_id][truth_columns]
indexes = [mapping[s.strip()] for s in list(ground_truth_labels)]
ground_truth = np.zeros(len(mapping), dtype=int)
ground_truth[indexes] = 1
ids.append(a_id)
all_labels.append(ground_truth)
columns = list(mapping.keys())
ground_truth = pd.DataFrame(all_labels)
ground_truth.columns = columns
else:
ids = df[inputs["id_column"]].unique()
ground_truth = pd.DataFrame()
ground_truth[inputs["target_label"]] = (df[truth_columns] == inputs["target_label"]) * 1
ground_truth["id"] = ids
return ground_truth
def derive_probabilities(df, inputs):
if "probability_column" in inputs:
probabilities = pd.DataFrame()
probabilities[inputs["target_label"]] = df[inputs["probability_column"]]
probabilities["id"] = df[inputs["id_column"]]
else:
labels = list(retrieve_labels(df, inputs))
labels.insert(0, inputs["id_column"])
probabilities = df[labels]
probabilities = probabilities.rename(columns={inputs["id_column"]: "id"})
return probabilities.drop_duplicates("id")
def thres(x, lower, upper):
not_match = np.less(x, lower)
match = np.greater_equal(x, upper)
rejects = ~np.logical_xor(not_match, match)
return np.stack([not_match, match, rejects])
def matches(x):
return np.where(x == True)
def calculate_thresholds(labels, probabilities, thresholds):
predictions = pd.DataFrame()
for l in labels:
probs = probabilities[l]
lower = thresholds[l]["lower"]
upper = thresholds[l]["upper"]
if lower > upper:
raise ValueError("Lower %f should be less than %f" %(lower, upper))
results = matches(thres(probs, lower, upper))
sorted_index = np.argsort(results)[1]
predictions[l] = results[0][sorted_index]
predictions["id"] = df[inputs["id_column"]]
return predictions
def calculate_all_confusion_matricies(ground_truth, thresholded, labels):
all_matrices = []
for l in labels:
matrix = confusion_matrix(ground_truth[l], thresholded[l])
if np.shape(matrix) < (3,3):
matrix = np.c_[matrix, np.zeros(2)]
matrix = np.r_[matrix, [np.zeros(3)]]
all_matrices.append(matrix)
results = np.array(all_matrices, dtype=np.int)
# Column order: matches, not match, rejects
results[:,:,[0,1]] = results[:,:,[1, 0]]
# Row order: matches, not match, rejects
results[:,[0,1], :] = results[:,[1, 0], :]
return results
# +
labels = prepare_labels(df, inputs)
mapping = {label: i for i, label in enumerate(labels)}
normalise_probs_in_place(df, inputs, labels)
ground_truth = prepare_ground_truth(df, inputs, mapping)
probabilities = derive_probabilities(df, inputs)
# +
thresholds = {}
for l in labels:
thresholds[l] = {"lower": 0.60, "upper": 0.70}
thresholded = calculate_thresholds(labels, probabilities, thresholds)
len(thresholded), len(probabilities), len(ground_truth)
# -
all_matrices = calculate_all_confusion_matricies(ground_truth, thresholded, labels)
all_matrices
#thresholded[labels[0]][thresholded[labels[0]] == 0][ground_truth[labels[0]] == 0]
# +
threshold_matches = thresholded[thresholded[labels] == 1][labels].count()
ground_truth_matches = ground_truth[ground_truth[labels] == 1][labels].count()
results = pd.DataFrame()
results["label_bias"] = 1- threshold_matches / ground_truth_matches
results["reject_count"] = all_matrices[:,:,2].sum(axis=1, dtype=np.int)
results["ground_truth_counts"] = ground_truth_matches
results["prediction_counts"] = threshold_matches
results
# +
summary_results = {
"true_matches" : all_matrices[:,0][:,0].sum(),
"false_matches": all_matrices[:,1][:,0].sum(),
"missed_matches": all_matrices[:,0][:,1].sum(),
"rejects": all_matrices[:,:,2].sum(),
"totals": len(ground_truth)
}
summary_results
# -
# # Optimisation of the thresholds
# +
cost = {
"true_matches": 1000,
"false_matches": 3000,
"missed_matches": 500,
"rejects": 500,
"portion_size" : 1000,
"estimate_quantity":10000
}
labels = prepare_labels(df, inputs)
mapping = {label: i for i, label in enumerate(labels)}
normalise_probs_in_place(df, inputs, labels)
ground_truth = prepare_ground_truth(df, inputs, mapping)
probabilities = derive_probabilities(df, inputs)
ground_truth_matches = ground_truth[ground_truth[labels] == 1][labels].count()
# +
def calculate_matches(x, lower, upper):
not_match = np.less(x, lower)
match = np.greater_equal(x, upper)
rejects = ~np.logical_xor(not_match, match)
return np.stack([not_match, match, rejects])
def apply_thresholds(probabilities, lower, upper):
temp = calculate_matches(probabilities, lower, upper)
def value(x):
return np.where(x == True)[0][0]
return np.apply_along_axis(value, 0, temp)
def calculate_confusion_matrices(gt, pred):
result = np.zeros((gt.shape[1],3,3), dtype=np.int)
for i,(x,y) in enumerate(zip(gt.T,pred.T)):
matrix = confusion_matrix(x,y)
if np.shape(matrix) < (3,3):
matrix = np.c_[matrix, np.zeros(2)]
matrix = np.r_[matrix, [np.zeros(3)]]
elif np.shape(matrix) > (3, 3):
raise ValueError("Matrix should be 3x3, error in input labels")
# Column order: matches, not match, rejects
matrix[:,[0,1]] = matrix[:,[1, 0]]
# Row order: matches, not match, rejects
matrix[[0,1], :] = matrix[[1, 0], :]
result[i] = matrix
return result
def get_objective(probabilities, ground_truth, lower_thresholds, upper_thresholds):
thres = apply_thresholds(probabilities, lower_thresholds, upper_thresholds)
all_results = calculate_confusion_matrices(ground_truth, thres)
return np.array([all_results[:,0][:,0].sum(),
all_results[:,1][:,0].sum(),
all_results[:,0][:,1].sum(),
all_results[:,:,2].sum()])
np_probs = probabilities[labels].to_numpy()
gt = ground_truth[labels].to_numpy()
full_range = np.arange(0,1,0.01)
threshold_values = np.full((100,10), 0.51, dtype=np.int)
# TODO: Finish implementing optmisation
#threshold_values[:,8] = full_range
# r = np.zeros((100, 4), np.int)
# iterations = 100
# history = []
# best = ()
# new_threshold = anp.full(10, 0.51, dtype=np.int)
# while iterations:
# iterations += -1
# new_score = get_objective(np_probs, gt, new_threshold, new_threshold)
# new_score[:,1:] = new_score[:,1:] * -1
# score = np.sum(r,1)
# if best[0] < score:
# best = (score, new_threshold)
# history.append((score, new_threshold))
#https://gpflowopt.readthedocs.io/en/latest/notebooks/multiobjective.html#
# get_objective(np_probs, gt, threshold_values[i], threshold_values[i])
# -
pd.DataFrame(r).plot()
# # Pymoo optimisation
# +
class MyRepair(Repair):
def _do(self, problem, pop, **kwargs):
for i in range(len(pop)):
x = pop[i].X
if len(x) % 2 == 0:
for j in range(0, len(x), 2):
if x[j] > x[j + 1]:
x[j], x[j + 1] = x[j + 1], x[j]
return pop
class FindThresholds(Problem):
def __init__(self, number_of_thresholds, labels):
if not len(labels) == number_of_thresholds and not len(labels) * 2 == number_of_thresholds:
raise ValueError("Number of thresholds must be same size as labels or twice the size")
self.labels = labels
self.number_of_thresholds = number_of_thresholds
super().__init__(n_var=number_of_thresholds,
n_constr=0,
n_obj=4,
xl=anp.zeros((number_of_thresholds,), dtype=anp.double),
xu=anp.ones((number_of_thresholds,), dtype=anp.double),
type_var=anp.double,
elementwise_evaluation=True)
def _evaluate(self, X, out, *args, **kwargs):
f0 = []
f1 = []
f2 = []
f3 = []
if self.number_of_thresholds == 1:
thresholds = {l: {"lower": X[0], "upper":X[0]} for l in self.labels}
elif self.number_of_thresholds == len(self.labels) ** 2:
thresholds = {}
for label_index, i in enumerate(range(0, self.number_of_thresholds, 2)):
thresholds[self.labels[label_index]] = {"lower": X[i], "upper":X[i+1]}
else:
thresholds = {}
for i in range(0, self.number_of_thresholds):
thresholds[self.labels[i]] = {"lower": X[i], "upper":X[i]}
thresholded = calculate_thresholds(self.labels, probabilities, thresholds)
all_matrices = calculate_all_confusion_matricies(ground_truth, thresholded, self.labels)
# true matches
f0.append(all_matrices[:,0][:,0].sum() * -1) # Maximise the # of true matches
# false matches
f1.append(all_matrices[:,1][:,0].sum())
# missed matches
f2.append(all_matrices[:,0][:,1].sum())
# rejects
f3.append(all_matrices[:,:,2].sum())
out["F"] = anp.column_stack([f0, f1, f2, f3]).astype(anp.double)
# +
algorithm = NSGA2(
pop_size=40,
n_offsprings=10,
repair=MyRepair(),
sampling=get_sampling("real_random"),
crossover=get_crossover("real_sbx", prob=0.9, eta=15),
mutation=get_mutation("real_pm", eta=20),
eliminate_duplicates=True
)
ref_points = np.array([[0.5, 0.5, 0.5, 0.5]])
algorithm = RNSGA3(
ref_points=ref_points,
pop_per_ref_point=56,
mu=0.1)
problem = FindThresholds(10, labels)
res = minimize(problem,
algorithm,
("n_gen", 20),
seed=1,
save_history=True,
verbose=True)
# +
F = res.F
weights = np.array([0.7,0.1,0.1,0.1])
I = get_decomposition("weighted-sum").do(F, weights).argmin()
print("Best regarding decomposition: Point %s - %s, %s" % (I, F[I], res.X[I]))
# Best regarding decomposition: Point 0 - [-32. 1. 19. 0.], [0.67100345]
# +
X = anp.array(anp.matrix(anp.arange(0,1,0.01)).T)
gd = GradientDescent(anp.array([0.3]), termination=get_termination("n_eval", 200))
gd.evaluator = Evaluator()
gd.problem = FindThresholds(1, labels)
#gd.solve()
res = minimize(FindThresholds(1, labels),
gd,
("n_gen", 300),
seed=1,
save_history=True,
verbose=True)
# -
class GradientDescent2(GradientBasedAlgorithm):
def __init__(self, X, learning_rate=0.005, **kwargs) -> None:
super().__init__(X, **kwargs)
self.learning_rate = learning_rate
def restart(self):
self.learning_rate /= 2
def apply(self):
self.X = self.X - self.learning_rate * self.dX
| 14,303 |
/nn_labeler/scripts/.ipynb_checkpoints/preprocess-checkpoint.ipynb | 5632f6a971b336485173b3e0867550fcc39b735b | [] | no_license | hsaeidi1363/cnn_breath_tracker | https://github.com/hsaeidi1363/cnn_breath_tracker | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 16,838 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 2
# language: python
# name: python2
# ---
# +
import tensorflow as tf
print(tf.__version__)
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
device_name = tf.test.gpu_device_name()
print(device_name)
gpus = tf.config.experimental.list_physical_devices('GPU')
config = [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)]
if gpus:
# Restrict TensorFlow to only allocate 1*X GB of memory on the first GPU
try:
tf.config.experimental.set_virtual_device_configuration(gpus[0], config)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)
#gpus = tf.config.experimental.list_physical_devices('GPU')
#if gpus:
# try:
# Currently, memory growth needs to be the same across GPUs
# for gpu in gpus:
# tf.config.experimental.set_memory_growth(gpu, True)
# logical_gpus = tf.config.experimental.list_logical_devices('GPU')
# print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
# except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
# print(e)
import cv2 as cv
import matplotlib.pyplot as plt
#import matplotlib.image as mpimg
from PIL import Image
import csv
print(cv.__version__)
import glob
import numpy as np
# %matplotlib inline
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
# +
# Read the csv file
print('Step 1: reading images and labels')
samples = []
images = []
data_set_no = 1
data_set_len = np.zeros(data_set_no, dtype = np.int16)
for i in range(data_set_no):
label_file_name = '/home/hsaeidi/nn_data_for_breathing_tracker/set' + str((i+1))+'/labels/lebels.csv'
with open(label_file_name) as csvfile:
reader = csv.reader(csvfile)
for line in reader:
samples.append(line)
if i == 0:
data_set_len[i] = len(samples)
else:
data_set_len[i] = len(samples) - data_set_len[i-1]
address_name = '/home/hsaeidi/nn_data_for_breathing_tracker/set' + str((i+1))+'/images/img'
for j in range(data_set_len[i]):
tmp = address_name + str((j+1)) +'.png'
images.append(tmp)
#read images # read the image addresses
print(data_set_len)
sample_size = len(samples)
print('Total number of collected samples: ',sample_size)
images_no = len(images)
print('Total number of image addresses: ', images_no)
tt = np.cumsum(data_set_len)
print(tt)
ttt = np.insert(tt, 0, 0)
print(ttt)
# +
# data exploration
# read some sample images for a quick test
img1 = Image.open(str(images[0]))
img2 = Image.open(str(images[63]))
print('The sample image size is')
print(img1.size)
print(' and it contains this type of data')
print(np.asarray(img1))
print('when normalized it gets like this')
print(np.asarray(img1)/float(255))
#plot 2 of them
fig = plt.figure(figsize=(12,4))
plt.subplot(131)
plt.imshow(img1)
plt.title('img 1')
plt.subplot(132)
plt.imshow(img2)
plt.title('img 2')
# see how the summation looks like
plt.subplot(133)
img_last = Image.open(str(images[86]))
plt.imshow(img_last)
plt.title('half a cycle away frame')
fig.tight_layout()
# preparing some quick training data
x_raw = []
y_raw = []
moving_no = 0
stopped_no = 0
# calculate: f(n) - sum_(i = n-13)^(i = n-1) f(i), summations and f(n) - f(n-13)
data_offset = np.cumsum(data_set_len)
print(data_offset)
data_offset = np.insert(data_offset, 0, 0)
print(data_offset)
t_h = 14
t_h_1 = t_h -1
for k in range(data_set_no):
for i in range(data_offset[k] + t_h_1 , data_offset[k+1]):
x_d_all = np.zeros(img1.size) + np.asarray(Image.open(str(images[i])))/float(255)
x_sum_all = x_d_all
x_last2first = x_d_all - np.asarray(Image.open(str(images[i-t_h_1])) )/float(255)
for j in range(1,t_h):
img_tmp = Image.open(str(images[i-j]))
x_d_all -= np.asarray(img_tmp)/float(255)
x_sum_all += np.asarray(img_tmp)/float(255)
img_combined = cv.merge((x_d_all, x_last2first, x_sum_all))
#x_raw.append(img_combined)
# put the corresponding label: 1 when stopped breathing and 0 when breathing
if samples[i] == ['1']:
y_label = [0.0, 1.0]
x_raw.append(img_combined)
y_raw.append(y_label)
stopped_no += 1
#print('a stopped breathing sample')
else:
y_label = [1.0, 0.0]
if (i%3) == 0:
x_raw.append(img_combined)
y_raw.append(y_label)
moving_no += 1
# some info about the new processed data for training
#print(y_raw)
print('size of train data after calculations ',len(x_raw))
#print(' which is the total of ', sample_size, ' minus ', data_set_no,'x', '= ', data_set_no*13 )
print(' and totally ', moving_no, ' breathing samples vs', stopped_no, ' stopped breathing samples')
print('---------')
stopped_index = 0
print(len(y_raw))
y_tmp = np.array(y_raw)
for i in range(0,len(y_tmp)):
#print(i)
#print(y_tmp[i])
if y_tmp[i][1] == '1.0':
stopped_index = i
break
print(stopped_index)
print('---------')
fig = plt.figure(figsize=(12,4))
plt.subplot(131)
plt.imshow(x_raw[stopped_index+t_h][:,:,0])
plt.title('moving (x_d_all)')
plt.subplot(132)
plt.imshow(x_raw[stopped_index+t_h][:,:,1])
plt.title('moving (last - first)')
plt.subplot(133)
plt.imshow(x_raw[stopped_index+t_h][:,:,2])
plt.title('moving (sum all)')
fig = plt.figure(figsize=(12,4))
plt.subplot(131)
plt.imshow(x_raw[stopped_index][:,:,0])
plt.title('stopped (x_d_all)')
plt.subplot(132)
plt.imshow(x_raw[stopped_index][:,:,1])
plt.title('stopped (last - first)')
plt.subplot(133)
plt.imshow(x_raw[stopped_index][:,:,2])
plt.title('stopped (sum all)')
x_train, x_val, y_train, y_val = train_test_split(x_raw, y_raw, test_size=0.2, random_state=1, shuffle= True)
print('train data size and type:')
print(len(x_train))
print(type(x_train[0]))
print(x_train[0])
print(y_train[0])
#normalized the inputs between 0-1
x_train_normalized = []
x_val_normalized = []
scale = float(1)/1
print(scale)
for i in range(0,len(x_train)):
x_train_normalized.append(x_train[i]*scale)
for i in range(0,len(x_val)):
x_val_normalized.append(x_val[i]*scale)
# test the normalized data
print('an example of the normalized inputs')
print(x_train_normalized[0])
fig = plt.figure(figsize=(12,4))
plt.subplot(131)
plt.imshow(x_train[0])
plt.title('before normalization')
plt.subplot(132)
plt.imshow(x_train_normalized[0])
plt.title('normalized')
x_train_normalized = np.array(x_train_normalized)
print('shape of normalized train inputs')
print(x_train_normalized.shape)
x_train_normalized = x_train_normalized.reshape(x_train_normalized.shape[0], 128, 128, 3)
print('reshaped normalized train input dimensions')
print(x_train_normalized.shape)
y_train = np.array(y_train)
x_val_normalized = np.array(x_val_normalized)
print('shape of normalized validation inputs')
print(x_val_normalized.shape)
x_val_normalized = x_val_normalized.reshape(x_val_normalized.shape[0], 128, 128, 3)
print('reshaped normalized validation input dimensions')
print(x_val_normalized.shape)
y_val = np.array(y_val)
#print(y_val)
# +
# importing other useful packages for training
#import keras
#from keras.models import Sequential
#from keras.layers import Dense, Dropout, Flatten
#from keras.layers import Conv2D, MaxPooling2D
#from keras.optimizers import SGD
# are the following still working in tf 2.1?
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import SGD
# From the filtered and resized image (160x80x2), crop some less useful pixels from top an bottom for faster processing
crop_top = 0
crop_bottom = 0
# the format of final image that goes to the first convolution layer
ch, row, col = 3, 128-crop_top-crop_bottom, 128 # Trimmed image format
# Neural network architecture
model = Sequential()
# convolution layers that gradually become deeper
model.add(Conv2D(24, kernel_size = (5, 5), strides =(2,2), padding='same', activation='relu', input_shape=(row, col, ch)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(36, kernel_size = (5, 5), strides =(2,2), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(48, kernel_size = (5, 5), strides =(2,2), padding='same', activation='relu'))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size = (3, 3), strides =(2,2), padding='same', activation='relu'))
model.add(Dropout(0.25))
#removed temp:model.add(Conv2D(64, kernel_size = (3, 3), strides =(2,2), padding='same', activation='relu'))
#removed temp:model.add(Dropout(0.25))
# flattening the outputs and using dense layers up to the final output (i.e. steering angle)
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(8, activation='relu'))
# outputting the moving/stopped
model.add(Dense(2, activation='softmax'))
print('model defined')
model.summary()
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer='adam', # Optimizer
# Loss function to minimize
#loss='binary_crossentropy',
loss='categorical_crossentropy',
# List of metrics to monitor
metrics=["accuracy"])
print('compiled the model')
# -
print('training started')
history = model.fit(x_train_normalized, y_train,
batch_size=512,
epochs=100,
shuffle=True,
# We pass some validation for
# monitoring validation loss and metrics
# at the end of each epoch
validation_data=(x_val_normalized, y_val))
model.save('../saved_models/my_model')
test_ind = 37
yy = model.predict(x_train_normalized[test_ind].reshape(1, 128, 128, 3), batch_size=1)
print(yy)
print(y_train[test_ind])
# +
# some psuedo image inputs for quick test
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD
# Generate dummy data
x_train = np.random.random((100, 100, 100, 1))
y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
x_test = np.random.random((20, 100, 100, 1))
y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)
print(x_train.shape)
print(type(x_train))
print(x_train[0].shape)
model = Sequential()
# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.
# this applies 32 convolution filters of size 3x3 each.
#model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 1)))
model.add(Conv2D(32, kernel_size = (3, 3), strides =(2,2), padding='same', activation='relu', input_shape=(100, 100, 1)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
model.fit(x_train, y_train, batch_size=32, epochs=1)
score = model.evaluate(x_test, y_test, batch_size=32)
# +
bce = tf.keras.losses.BinaryCrossentropy()
loss = bce([0., 0., 1., 1.], [0., 0., 1., 0.])
print('Loss: ', loss.numpy())
test_a = np.zeros((3, 2, 2))
print(test_a)
print(test_a.shape)
img_combined = cv.merge((test_a[0], test_a[1], test_a[2]))
print(img_combined.shape)
for j in range(1,14):
print(j)
print(y_train)
| 12,452 |