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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
# adapted from http://www.cl.cam.ac.uk/~mgk25/ucs/wcwidth.c
# -thepaul
# This is an implementation of wcwidth() and wcswidth() (defined in
# IEEE Std 1002.1-2001) for Unicode.
#
# http://www.opengroup.org/onlinepubs/007904975/functions/wcwidth.html
# http://www.opengroup.org/onlinepubs/007904975/functions/wcswidth.html
#
# In fixed-width output devices, Latin characters all occupy a single
# "cell" position of equal width, whereas ideographic CJK characters
# occupy two such cells. Interoperability between terminal-line
# applications and (teletype-style) character terminals using the
# UTF-8 encoding requires agreement on which character should advance
# the cursor by how many cell positions. No established formal
# standards exist at present on which Unicode character shall occupy
# how many cell positions on character terminals. These routines are
# a first attempt of defining such behavior based on simple rules
# applied to data provided by the Unicode Consortium.
#
# For some graphical characters, the Unicode standard explicitly
# defines a character-cell width via the definition of the East Asian
# FullWidth (F), Wide (W), Half-width (H), and Narrow (Na) classes.
# In all these cases, there is no ambiguity about which width a
# terminal shall use. For characters in the East Asian Ambiguous (A)
# class, the width choice depends purely on a preference of backward
# compatibility with either historic CJK or Western practice.
# Choosing single-width for these characters is easy to justify as
# the appropriate long-term solution, as the CJK practice of
# displaying these characters as double-width comes from historic
# implementation simplicity (8-bit encoded characters were displayed
# single-width and 16-bit ones double-width, even for Greek,
# Cyrillic, etc.) and not any typographic considerations.
#
# Much less clear is the choice of width for the Not East Asian
# (Neutral) class. Existing practice does not dictate a width for any
# of these characters. It would nevertheless make sense
# typographically to allocate two character cells to characters such
# as for instance EM SPACE or VOLUME INTEGRAL, which cannot be
# represented adequately with a single-width glyph. The following
# routines at present merely assign a single-cell width to all
# neutral characters, in the interest of simplicity. This is not
# entirely satisfactory and should be reconsidered before
# establishing a formal standard in this area. At the moment, the
# decision which Not East Asian (Neutral) characters should be
# represented by double-width glyphs cannot yet be answered by
# applying a simple rule from the Unicode database content. Setting
# up a proper standard for the behavior of UTF-8 character terminals
# will require a careful analysis not only of each Unicode character,
# but also of each presentation form, something the author of these
# routines has avoided to do so far.
#
# http://www.unicode.org/unicode/reports/tr11/
#
# NAME -- 2007-05-26 (Unicode 5.0)
#
# Permission to use, copy, modify, and distribute this software
# for any purpose and without fee is hereby granted. The author
# disclaims all warranties with regard to this software.
#
# Latest C version: http://www.cl.cam.ac.uk/~mgk25/ucs/wcwidth.c
# auxiliary function for binary search in interval table
|
"""
Objects for dealing with Chebyshev series.
This module provides a number of objects (mostly functions) useful for
dealing with Chebyshev series, including a `Chebyshev` class that
encapsulates the usual arithmetic operations. (General information
on how this module represents and works with such polynomials is in the
docstring for its "parent" sub-package, `numpy.polynomial`).
Constants
---------
- `chebdomain` -- Chebyshev series default domain, [-1,1].
- `chebzero` -- (Coefficients of the) Chebyshev series that evaluates
identically to 0.
- `chebone` -- (Coefficients of the) Chebyshev series that evaluates
identically to 1.
- `chebx` -- (Coefficients of the) Chebyshev series for the identity map,
``f(x) = x``.
Arithmetic
----------
- `chebadd` -- add two Chebyshev series.
- `chebsub` -- subtract one Chebyshev series from another.
- `chebmul` -- multiply two Chebyshev series.
- `chebdiv` -- divide one Chebyshev series by another.
- `chebpow` -- raise a Chebyshev series to an positive integer power
- `chebval` -- evaluate a Chebyshev series at given points.
- `chebval2d` -- evaluate a 2D Chebyshev series at given points.
- `chebval3d` -- evaluate a 3D Chebyshev series at given points.
- `chebgrid2d` -- evaluate a 2D Chebyshev series on a Cartesian product.
- `chebgrid3d` -- evaluate a 3D Chebyshev series on a Cartesian product.
Calculus
--------
- `chebder` -- differentiate a Chebyshev series.
- `chebint` -- integrate a Chebyshev series.
Misc Functions
--------------
- `chebfromroots` -- create a Chebyshev series with specified roots.
- `chebroots` -- find the roots of a Chebyshev series.
- `chebvander` -- Vandermonde-like matrix for Chebyshev polynomials.
- `chebvander2d` -- Vandermonde-like matrix for 2D power series.
- `chebvander3d` -- Vandermonde-like matrix for 3D power series.
- `chebgauss` -- Gauss-Chebyshev quadrature, points and weights.
- `chebweight` -- Chebyshev weight function.
- `chebcompanion` -- symmetrized companion matrix in Chebyshev form.
- `chebfit` -- least-squares fit returning a Chebyshev series.
- `chebpts1` -- Chebyshev points of the first kind.
- `chebpts2` -- Chebyshev points of the second kind.
- `chebtrim` -- trim leading coefficients from a Chebyshev series.
- `chebline` -- Chebyshev series representing given straight line.
- `cheb2poly` -- convert a Chebyshev series to a polynomial.
- `poly2cheb` -- convert a polynomial to a Chebyshev series.
Classes
-------
- `Chebyshev` -- A Chebyshev series class.
See also
--------
`numpy.polynomial`
Notes
-----
The implementations of multiplication, division, integration, and
differentiation use the algebraic identities [1]_:
.. math ::
T_n(x) = \\frac{z^n + z^{-n}}{2} \\\\
z\\frac{dx}{dz} = \\frac{z - z^{-1}}{2}.
where
.. math :: x = \\frac{z + z^{-1}}{2}.
These identities allow a Chebyshev series to be expressed as a finite,
symmetric Laurent series. In this module, this sort of Laurent series
is referred to as a "z-series."
References
----------
.. [1] NAME et al., "Combinatorial Trigonometry with Chebyshev
Polynomials," *Journal of Statistical Planning and Inference 14*, 2008
(preprint: http://www.math.hmc.edu/~benjamin/papers/CombTrig.pdf, pg. 4)
""" |
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
"""
Beta diversity measures (:mod:`skbio.diversity.beta`)
=====================================================
.. currentmodule:: skbio.diversity.beta
This package contains helper functions for working with scipy's pairwise
distance (``pdist``) functions in scikit-bio, and will eventually be expanded
to contain pairwise distance/dissimilarity methods that are not implemented
(or planned to be implemented) in scipy.
The functions in this package currently support applying ``pdist`` functions
to all pairs of samples in a sample by observation count or abundance matrix
and returning an ``skbio.DistanceMatrix`` object. This application is
illustrated below for a few different forms of input.
Functions
---------
.. autosummary::
:toctree: generated/
pw_distances
pw_distances_from_table
Examples
--------
Create a table containing 7 OTUs and 6 samples:
.. plot::
:context:
>>> from skbio.diversity.beta import pw_distances
>>> import numpy as np
>>> data = [[23, 64, 14, 0, 0, 3, 1],
... [0, 3, 35, 42, 0, 12, 1],
... [0, 5, 5, 0, 40, 40, 0],
... [44, 35, 9, 0, 1, 0, 0],
... [0, 2, 8, 0, 35, 45, 1],
... [0, 0, 25, 35, 0, 19, 0]]
>>> ids = list('ABCDEF')
Compute Bray-Curtis distances between all pairs of samples and return a
``DistanceMatrix`` object:
>>> bc_dm = pw_distances(data, ids, "braycurtis")
>>> print(bc_dm)
6x6 distance matrix
IDs:
'A', 'B', 'C', 'D', 'E', 'F'
Data:
[[ 0. 0.78787879 0.86666667 0.30927835 0.85714286 0.81521739]
[ 0.78787879 0. 0.78142077 0.86813187 0.75 0.1627907 ]
[ 0.86666667 0.78142077 0. 0.87709497 0.09392265 0.71597633]
[ 0.30927835 0.86813187 0.87709497 0. 0.87777778 0.89285714]
[ 0.85714286 0.75 0.09392265 0.87777778 0. 0.68235294]
[ 0.81521739 0.1627907 0.71597633 0.89285714 0.68235294 0. ]]
Compute Jaccard distances between all pairs of samples and return a
``DistanceMatrix`` object:
>>> j_dm = pw_distances(data, ids, "jaccard")
>>> print(j_dm)
6x6 distance matrix
IDs:
'A', 'B', 'C', 'D', 'E', 'F'
Data:
[[ 0. 0.83333333 1. 1. 0.83333333 1. ]
[ 0.83333333 0. 1. 1. 0.83333333 1. ]
[ 1. 1. 0. 1. 1. 1. ]
[ 1. 1. 1. 0. 1. 1. ]
[ 0.83333333 0.83333333 1. 1. 0. 1. ]
[ 1. 1. 1. 1. 1. 0. ]]
Determine if the resulting distance matrices are significantly correlated
by computing the Mantel correlation between them. Then determine if the
p-value is significant based on an alpha of 0.05:
>>> from skbio.stats.distance import mantel
>>> r, p_value, n = mantel(j_dm, bc_dm)
>>> print(r)
-0.209362157621
>>> print(p_value < 0.05)
False
Compute PCoA for both distance matrices, and then find the Procrustes
M-squared value that results from comparing the coordinate matrices.
>>> from skbio.stats.ordination import PCoA
>>> bc_pc = PCoA(bc_dm).scores()
>>> j_pc = PCoA(j_dm).scores()
>>> from skbio.stats.spatial import procrustes
>>> print(procrustes(bc_pc.site, j_pc.site)[2])
0.466134984787
All of this only gets interesting in the context of sample metadata, so
let's define some:
>>> import pandas as pd
>>> sample_md = {
... 'A': {'body_site': 'gut', 'subject': 's1'},
... 'B': {'body_site': 'skin', 'subject': 's1'},
... 'C': {'body_site': 'tongue', 'subject': 's1'},
... 'D': {'body_site': 'gut', 'subject': 's2'},
... 'E': {'body_site': 'tongue', 'subject': 's2'},
... 'F': {'body_site': 'skin', 'subject': 's2'}}
>>> sample_md = pd.DataFrame.from_dict(sample_md, orient='index')
>>> sample_md
subject body_site
A s1 gut
B s1 skin
C s1 tongue
D s2 gut
E s2 tongue
F s2 skin
Now let's plot our PCoA results, coloring each sample by the subject it
was taken from:
>>> fig = bc_pc.plot(sample_md, 'subject',
... axis_labels=('PC 1', 'PC 2', 'PC 3'),
... title='Samples colored by subject', cmap='jet', s=50)
.. plot::
:context:
We don't see any clustering/grouping of samples. If we were to instead color
the samples by the body site they were taken from, we see that the samples
form three separate groups:
>>> import matplotlib.pyplot as plt
>>> plt.close('all') # not necessary for normal use
>>> fig = bc_pc.plot(sample_md, 'body_site',
... axis_labels=('PC 1', 'PC 2', 'PC 3'),
... title='Samples colored by body site', cmap='jet', s=50)
Ordination techniques, such as PCoA, are useful for exploratory analysis. The
next step is to quantify the strength of the grouping/clustering that we see in
ordination plots. There are many statistical methods available to accomplish
this; many operate on distance matrices. Let's use ANOSIM to quantify the
strength of the clustering we see in the ordination plots above, using our
Bray-Curtis distance matrix and sample metadata.
First test the grouping of samples by subject:
>>> from skbio.stats.distance import anosim
>>> results = anosim(bc_dm, sample_md, column='subject', permutations=999)
>>> results['test statistic']
-0.4074074074074075
>>> results['p-value'] < 0.1
False
The negative value of ANOSIM's R statistic indicates anti-clustering and the
p-value is insignificant at an alpha of 0.1.
Now let's test the grouping of samples by body site:
>>> results = anosim(bc_dm, sample_md, column='body_site', permutations=999)
>>> results['test statistic']
1.0
>>> results['p-value'] < 0.1
True
The R statistic of 1.0 indicates strong separation of samples based on body
site. The p-value is significant at an alpha of 0.1.
References
----------
.. [1] http://matplotlib.org/examples/mplot3d/scatter3d_demo.html
""" |
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to read all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <EMAIL>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
"""
A multi-dimensional ``Vector`` class, take 7: operator ``*``
A ``Vector`` is built from an iterable of numbers::
>>> Vector([3.1, 4.2])
Vector([3.1, 4.2])
>>> Vector((3, 4, 5))
Vector([3.0, 4.0, 5.0])
>>> Vector(range(10))
Vector([0.0, 1.0, 2.0, 3.0, 4.0, ...])
Tests with 2-dimensions (same results as ``vector2d_v1.py``)::
>>> v1 = Vector([3, 4])
>>> x, y = v1
>>> x, y
(3.0, 4.0)
>>> v1
Vector([3.0, 4.0])
>>> v1_clone = eval(repr(v1))
>>> v1 == v1_clone
True
>>> print(v1)
(3.0, 4.0)
>>> octets = bytes(v1)
>>> octets
b'd\\x00\\x00\\x00\\x00\\x00\\x00\\x08@\\x00\\x00\\x00\\x00\\x00\\x00\\x10@'
>>> abs(v1)
5.0
>>> bool(v1), bool(Vector([0, 0]))
(True, False)
Test of ``.frombytes()`` class method:
>>> v1_clone = Vector.frombytes(bytes(v1))
>>> v1_clone
Vector([3.0, 4.0])
>>> v1 == v1_clone
True
Tests with 3-dimensions::
>>> v1 = Vector([3, 4, 5])
>>> x, y, z = v1
>>> x, y, z
(3.0, 4.0, 5.0)
>>> v1
Vector([3.0, 4.0, 5.0])
>>> v1_clone = eval(repr(v1))
>>> v1 == v1_clone
True
>>> print(v1)
(3.0, 4.0, 5.0)
>>> abs(v1) # doctest:+ELLIPSIS
7.071067811...
>>> bool(v1), bool(Vector([0, 0, 0]))
(True, False)
Tests with many dimensions::
>>> v7 = Vector(range(7))
>>> v7
Vector([0.0, 1.0, 2.0, 3.0, 4.0, ...])
>>> abs(v7) # doctest:+ELLIPSIS
9.53939201...
Test of ``.__bytes__`` and ``.frombytes()`` methods::
>>> v1 = Vector([3, 4, 5])
>>> v1_clone = Vector.frombytes(bytes(v1))
>>> v1_clone
Vector([3.0, 4.0, 5.0])
>>> v1 == v1_clone
True
Tests of sequence behavior::
>>> v1 = Vector([3, 4, 5])
>>> len(v1)
3
>>> v1[0], v1[len(v1)-1], v1[-1]
(3.0, 5.0, 5.0)
Test of slicing::
>>> v7 = Vector(range(7))
>>> v7[-1]
6.0
>>> v7[1:4]
Vector([1.0, 2.0, 3.0])
>>> v7[-1:]
Vector([6.0])
>>> v7[1,2]
Traceback (most recent call last):
...
TypeError: Vector indices must be integers
Tests of dynamic attribute access::
>>> v7 = Vector(range(10))
>>> v7.x
0.0
>>> v7.y, v7.z, v7.t
(1.0, 2.0, 3.0)
Dynamic attribute lookup failures::
>>> v7.k
Traceback (most recent call last):
...
AttributeError: 'Vector' object has no attribute 'k'
>>> v3 = Vector(range(3))
>>> v3.t
Traceback (most recent call last):
...
AttributeError: 'Vector' object has no attribute 't'
>>> v3.spam
Traceback (most recent call last):
...
AttributeError: 'Vector' object has no attribute 'spam'
Tests of hashing::
>>> v1 = Vector([3, 4])
>>> v2 = Vector([3.1, 4.2])
>>> v3 = Vector([3, 4, 5])
>>> v6 = Vector(range(6))
>>> hash(v1), hash(v3), hash(v6)
(7, 2, 1)
Most hash values of non-integers vary from a 32-bit to 64-bit Python build::
>>> import sys
>>> hash(v2) == (384307168202284039 if sys.maxsize > 2**32 else 357915986)
True
Tests of ``format()`` with Cartesian coordinates in 2D::
>>> v1 = Vector([3, 4])
>>> format(v1)
'(3.0, 4.0)'
>>> format(v1, '.2f')
'(3.00, 4.00)'
>>> format(v1, '.3e')
'(3.000e+00, 4.000e+00)'
Tests of ``format()`` with Cartesian coordinates in 3D and 7D::
>>> v3 = Vector([3, 4, 5])
>>> format(v3)
'(3.0, 4.0, 5.0)'
>>> format(Vector(range(7)))
'(0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0)'
Tests of ``format()`` with spherical coordinates in 2D, 3D and 4D::
>>> format(Vector([1, 1]), 'h') # doctest:+ELLIPSIS
'<1.414213..., 0.785398...>'
>>> format(Vector([1, 1]), '.3eh')
'<1.414e+00, 7.854e-01>'
>>> format(Vector([1, 1]), '0.5fh')
'<1.41421, 0.78540>'
>>> format(Vector([1, 1, 1]), 'h') # doctest:+ELLIPSIS
'<1.73205..., 0.95531..., 0.78539...>'
>>> format(Vector([2, 2, 2]), '.3eh')
'<3.464e+00, 9.553e-01, 7.854e-01>'
>>> format(Vector([0, 0, 0]), '0.5fh')
'<0.00000, 0.00000, 0.00000>'
>>> format(Vector([-1, -1, -1, -1]), 'h') # doctest:+ELLIPSIS
'<2.0, 2.09439..., 2.18627..., 3.92699...>'
>>> format(Vector([2, 2, 2, 2]), '.3eh')
'<4.000e+00, 1.047e+00, 9.553e-01, 7.854e-01>'
>>> format(Vector([0, 1, 0, 0]), '0.5fh')
'<1.00000, 1.57080, 0.00000, 0.00000>'
Unary operator tests::
>>> v1 = Vector([3, 4])
>>> abs(v1)
5.0
>>> -v1
Vector([-3.0, -4.0])
>>> +v1
Vector([3.0, 4.0])
Basic tests of operator ``+``::
>>> v1 = Vector([3, 4, 5])
>>> v2 = Vector([6, 7, 8])
>>> v1 + v2
Vector([9.0, 11.0, 13.0])
>>> v1 + v2 == Vector([3+6, 4+7, 5+8])
True
>>> v3 = Vector([1, 2])
>>> v1 + v3 # short vectors are filled with 0.0 on addition
Vector([4.0, 6.0, 5.0])
Tests of ``+`` with mixed types::
>>> v1 + (10, 20, 30)
Vector([13.0, 24.0, 35.0])
>>> from vector2d_v3 import Vector2d
>>> v2d = Vector2d(1, 2)
>>> v1 + v2d
Vector([4.0, 6.0, 5.0])
Tests of ``+`` with mixed types, swapped operands::
>>> (10, 20, 30) + v1
Vector([13.0, 24.0, 35.0])
>>> from vector2d_v3 import Vector2d
>>> v2d = Vector2d(1, 2)
>>> v2d + v1
Vector([4.0, 6.0, 5.0])
Tests of ``+`` with an unsuitable operand:
>>> v1 + 1
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for +: 'Vector' and 'int'
>>> v1 + 'ABC'
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for +: 'Vector' and 'str'
Basic tests of operator ``*``::
>>> v1 = Vector([1, 2, 3])
>>> v1 * 10
Vector([10.0, 20.0, 30.0])
>>> 10 * v1
Vector([10.0, 20.0, 30.0])
Tests of ``*`` with unusual but valid operands::
>>> v1 * True
Vector([1.0, 2.0, 3.0])
>>> from fractions import Fraction
>>> v1 * Fraction(1, 3) # doctest:+ELLIPSIS
Vector([0.3333..., 0.6666..., 1.0])
Tests of ``*`` with unsuitable operands::
>>> v1 * (1, 2)
Traceback (most recent call last):
...
TypeError: can't multiply sequence by non-int of type 'Vector'
""" |
"""
Discrete Fourier Transform (:mod:`numpy.fft`)
=============================================
.. currentmodule:: numpy.fft
Standard FFTs
-------------
.. autosummary::
:toctree: generated/
fft Discrete Fourier transform.
ifft Inverse discrete Fourier transform.
fft2 Discrete Fourier transform in two dimensions.
ifft2 Inverse discrete Fourier transform in two dimensions.
fftn Discrete Fourier transform in N-dimensions.
ifftn Inverse discrete Fourier transform in N dimensions.
Real FFTs
---------
.. autosummary::
:toctree: generated/
rfft Real discrete Fourier transform.
irfft Inverse real discrete Fourier transform.
rfft2 Real discrete Fourier transform in two dimensions.
irfft2 Inverse real discrete Fourier transform in two dimensions.
rfftn Real discrete Fourier transform in N dimensions.
irfftn Inverse real discrete Fourier transform in N dimensions.
Hermitian FFTs
--------------
.. autosummary::
:toctree: generated/
hfft Hermitian discrete Fourier transform.
ihfft Inverse Hermitian discrete Fourier transform.
Helper routines
---------------
.. autosummary::
:toctree: generated/
fftfreq Discrete Fourier Transform sample frequencies.
rfftfreq DFT sample frequencies (for usage with rfft, irfft).
fftshift Shift zero-frequency component to center of spectrum.
ifftshift Inverse of fftshift.
Background information
----------------------
Fourier analysis is fundamentally a method for expressing a function as a
sum of periodic components, and for recovering the function from those
components. When both the function and its Fourier transform are
replaced with discretized counterparts, it is called the discrete Fourier
transform (DFT). The DFT has become a mainstay of numerical computing in
part because of a very fast algorithm for computing it, called the Fast
Fourier Transform (FFT), which was known to Gauss (1805) and was brought
to light in its current form by NAME and NAME [CT]_. Press et al. [NR]_
provide an accessible introduction to Fourier analysis and its
applications.
Because the discrete Fourier transform separates its input into
components that contribute at discrete frequencies, it has a great number
of applications in digital signal processing, e.g., for filtering, and in
this context the discretized input to the transform is customarily
referred to as a *signal*, which exists in the *time domain*. The output
is called a *spectrum* or *transform* and exists in the *frequency
domain*.
Implementation details
----------------------
There are many ways to define the DFT, varying in the sign of the
exponent, normalization, etc. In this implementation, the DFT is defined
as
.. math::
A_k = \\sum_{m=0}^{n-1} a_m \\exp\\left\\{-2\\pi i{mk \\over n}\\right\\}
\\qquad k = 0,\\ldots,n-1.
The DFT is in general defined for complex inputs and outputs, and a
single-frequency component at linear frequency :math:`f` is
represented by a complex exponential
:math:`a_m = \\exp\\{2\\pi i\\,f m\\Delta t\\}`, where :math:`\\Delta t`
is the sampling interval.
The values in the result follow so-called "standard" order: If ``A =
fft(a, n)``, then ``A[0]`` contains the zero-frequency term (the mean of
the signal), which is always purely real for real inputs. Then ``A[1:n/2]``
contains the positive-frequency terms, and ``A[n/2+1:]`` contains the
negative-frequency terms, in order of decreasingly negative frequency.
For an even number of input points, ``A[n/2]`` represents both positive and
negative Nyquist frequency, and is also purely real for real input. For
an odd number of input points, ``A[(n-1)/2]`` contains the largest positive
frequency, while ``A[(n+1)/2]`` contains the largest negative frequency.
The routine ``np.fft.fftfreq(n)`` returns an array giving the frequencies
of corresponding elements in the output. The routine
``np.fft.fftshift(A)`` shifts transforms and their frequencies to put the
zero-frequency components in the middle, and ``np.fft.ifftshift(A)`` undoes
that shift.
When the input `a` is a time-domain signal and ``A = fft(a)``, ``np.abs(A)``
is its amplitude spectrum and ``np.abs(A)**2`` is its power spectrum.
The phase spectrum is obtained by ``np.angle(A)``.
The inverse DFT is defined as
.. math::
a_m = \\frac{1}{n}\\sum_{k=0}^{n-1}A_k\\exp\\left\\{2\\pi i{mk\\over n}\\right\\}
\\qquad m = 0,\\ldots,n-1.
It differs from the forward transform by the sign of the exponential
argument and the normalization by :math:`1/n`.
Real and Hermitian transforms
-----------------------------
When the input is purely real, its transform is Hermitian, i.e., the
component at frequency :math:`f_k` is the complex conjugate of the
component at frequency :math:`-f_k`, which means that for real
inputs there is no information in the negative frequency components that
is not already available from the positive frequency components.
The family of `rfft` functions is
designed to operate on real inputs, and exploits this symmetry by
computing only the positive frequency components, up to and including the
Nyquist frequency. Thus, ``n`` input points produce ``n/2+1`` complex
output points. The inverses of this family assumes the same symmetry of
its input, and for an output of ``n`` points uses ``n/2+1`` input points.
Correspondingly, when the spectrum is purely real, the signal is
Hermitian. The `hfft` family of functions exploits this symmetry by
using ``n/2+1`` complex points in the input (time) domain for ``n`` real
points in the frequency domain.
In higher dimensions, FFTs are used, e.g., for image analysis and
filtering. The computational efficiency of the FFT means that it can
also be a faster way to compute large convolutions, using the property
that a convolution in the time domain is equivalent to a point-by-point
multiplication in the frequency domain.
Higher dimensions
-----------------
In two dimensions, the DFT is defined as
.. math::
A_{kl} = \\sum_{m=0}^{M-1} \\sum_{n=0}^{N-1}
a_{mn}\\exp\\left\\{-2\\pi i \\left({mk\\over M}+{nl\\over N}\\right)\\right\\}
\\qquad k = 0, \\ldots, M-1;\\quad l = 0, \\ldots, N-1,
which extends in the obvious way to higher dimensions, and the inverses
in higher dimensions also extend in the same way.
References
----------
.. [CT] NAME, NAME and John W. NAME, 1965, "An algorithm for the
machine calculation of complex Fourier series," *Math. Comput.*
19: 297-301.
.. [NR] NAME NAME NAME and NAME
2007, *Numerical Recipes: The Art of Scientific Computing*, ch.
12-13. Cambridge Univ. Press, Cambridge, UK.
Examples
--------
For examples, see the various functions.
""" |
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to read all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use a selector to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <EMAIL>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
{
'name': 'Web',
'category': 'Hidden',
'version': 'IP_ADDRESS',
'description':
"""
OpenERP Web core module.
========================
This module provides the core of the OpenERP Web Client.
""",
'depends': [],
'auto_install': True,
'post_load': 'wsgi_postload',
'js' : [
"static/src/fixbind.js",
"static/lib/datejs/globalization/en-US.js",
"static/lib/datejs/core.js",
"static/lib/datejs/parser.js",
"static/lib/datejs/sugarpak.js",
"static/lib/datejs/extras.js",
"static/lib/jquery/jquery-1.8.3.js",
"static/lib/jquery.MD5/jquery.md5.js",
"static/lib/jquery.form/jquery.form.js",
"static/lib/jquery.validate/jquery.validate.js",
"static/lib/jquery.ba-bbq/jquery.ba-bbq.js",
"static/lib/spinjs/spin.js",
"static/lib/jquery.autosize/jquery.autosize.js",
"static/lib/jquery.blockUI/jquery.blockUI.js",
"static/lib/jquery.placeholder/jquery.placeholder.js",
"static/lib/jquery.ui/js/jquery-ui-1.9.1.custom.js",
"static/lib/jquery.ui.timepicker/js/jquery-ui-timepicker-addon.js",
"static/lib/jquery.ui.notify/js/jquery.notify.js",
"static/lib/jquery.deferred-queue/jquery.deferred-queue.js",
"static/lib/jquery.scrollTo/jquery.scrollTo-min.js",
"static/lib/jquery.tipsy/jquery.tipsy.js",
"static/lib/jquery.textext/jquery.textext.js",
"static/lib/jquery.timeago/jquery.timeago.js",
"static/lib/qweb/qweb2.js",
"static/lib/underscore/underscore.js",
"static/lib/underscore/underscore.string.js",
"static/lib/backbone/backbone.js",
"static/lib/cleditor/jquery.cleditor.js",
"static/lib/py.js/lib/py.js",
"static/src/js/boot.js",
"static/src/js/testing.js",
"static/src/js/pyeval.js",
"static/src/js/corelib.js",
"static/src/js/coresetup.js",
"static/src/js/dates.js",
"static/src/js/formats.js",
"static/src/js/chrome.js",
"static/src/js/views.js",
"static/src/js/data.js",
"static/src/js/data_export.js",
"static/src/js/search.js",
"static/src/js/view_form.js",
"static/src/js/view_list.js",
"static/src/js/view_list_editable.js",
"static/src/js/view_tree.js",
],
'css' : [
"static/lib/jquery.ui.bootstrap/css/custom-theme/jquery-ui-1.9.0.custom.css",
"static/lib/jquery.ui.timepicker/css/jquery-ui-timepicker-addon.css",
"static/lib/jquery.ui.notify/css/ui.notify.css",
"static/lib/jquery.tipsy/tipsy.css",
"static/lib/jquery.textext/jquery.textext.css",
"static/src/css/base.css",
"static/src/css/data_export.css",
"static/lib/cleditor/jquery.cleditor.css",
],
'qweb' : [
"static/src/xml/*.xml",
],
'test': [
"static/test/testing.js",
"static/test/class.js",
"static/test/registry.js",
"static/test/form.js",
"static/test/data.js",
"static/test/list-utils.js",
"static/test/formats.js",
"static/test/rpc.js",
"static/test/evals.js",
"static/test/search.js",
"static/test/Widget.js",
"static/test/list.js",
"static/test/list-editable.js",
"static/test/mutex.js"
],
'bootstrap': True,
} |
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
# 2014-12-02 ch/doko Add workaround for gzip bomb vulnerability
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME Lundh.
#
# EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
#
# things to look into some day:
# TODO: sort out True/False/boolean issues for Python 2.3
|
"""
Basic functions used by several sub-packages and
useful to have in the main name-space.
Type Handling
-------------
================ ===================
iscomplexobj Test for complex object, scalar result
isrealobj Test for real object, scalar result
iscomplex Test for complex elements, array result
isreal Test for real elements, array result
imag Imaginary part
real Real part
real_if_close Turns complex number with tiny imaginary part to real
isneginf Tests for negative infinity, array result
isposinf Tests for positive infinity, array result
isnan Tests for nans, array result
isinf Tests for infinity, array result
isfinite Tests for finite numbers, array result
isscalar True if argument is a scalar
nan_to_num Replaces NaN's with 0 and infinities with large numbers
cast Dictionary of functions to force cast to each type
common_type Determine the minimum common type code for a group
of arrays
mintypecode Return minimal allowed common typecode.
================ ===================
Index Tricks
------------
================ ===================
mgrid Method which allows easy construction of N-d
'mesh-grids'
``r_`` Append and construct arrays: turns slice objects into
ranges and concatenates them, for 2d arrays appends rows.
index_exp Konrad Hinsen's index_expression class instance which
can be useful for building complicated slicing syntax.
================ ===================
Useful Functions
----------------
================ ===================
select Extension of where to multiple conditions and choices
extract Extract 1d array from flattened array according to mask
insert Insert 1d array of values into Nd array according to mask
linspace Evenly spaced samples in linear space
logspace Evenly spaced samples in logarithmic space
fix Round x to nearest integer towards zero
mod Modulo mod(x,y) = x % y except keeps sign of y
amax Array maximum along axis
amin Array minimum along axis
ptp Array max-min along axis
cumsum Cumulative sum along axis
prod Product of elements along axis
cumprod Cumluative product along axis
diff Discrete differences along axis
angle Returns angle of complex argument
unwrap Unwrap phase along given axis (1-d algorithm)
sort_complex Sort a complex-array (based on real, then imaginary)
trim_zeros Trim the leading and trailing zeros from 1D array.
vectorize A class that wraps a Python function taking scalar
arguments into a generalized function which can handle
arrays of arguments using the broadcast rules of
numerix Python.
================ ===================
Shape Manipulation
------------------
================ ===================
squeeze Return a with length-one dimensions removed.
atleast_1d Force arrays to be > 1D
atleast_2d Force arrays to be > 2D
atleast_3d Force arrays to be > 3D
vstack Stack arrays vertically (row on row)
hstack Stack arrays horizontally (column on column)
column_stack Stack 1D arrays as columns into 2D array
dstack Stack arrays depthwise (along third dimension)
split Divide array into a list of sub-arrays
hsplit Split into columns
vsplit Split into rows
dsplit Split along third dimension
================ ===================
Matrix (2D Array) Manipulations
-------------------------------
================ ===================
fliplr 2D array with columns flipped
flipud 2D array with rows flipped
rot90 Rotate a 2D array a multiple of 90 degrees
eye Return a 2D array with ones down a given diagonal
diag Construct a 2D array from a vector, or return a given
diagonal from a 2D array.
mat Construct a Matrix
bmat Build a Matrix from blocks
================ ===================
Polynomials
-----------
================ ===================
poly1d A one-dimensional polynomial class
poly Return polynomial coefficients from roots
roots Find roots of polynomial given coefficients
polyint Integrate polynomial
polyder Differentiate polynomial
polyadd Add polynomials
polysub Substract polynomials
polymul Multiply polynomials
polydiv Divide polynomials
polyval Evaluate polynomial at given argument
================ ===================
Import Tricks
-------------
================ ===================
ppimport Postpone module import until trying to use it
ppimport_attr Postpone module import until trying to use its attribute
ppresolve Import postponed module and return it.
================ ===================
Machine Arithmetics
-------------------
================ ===================
machar_single Single precision floating point arithmetic parameters
machar_double Double precision floating point arithmetic parameters
================ ===================
Threading Tricks
----------------
================ ===================
ParallelExec Execute commands in parallel thread.
================ ===================
1D Array Set Operations
-----------------------
Set operations for 1D numeric arrays based on sort() function.
================ ===================
ediff1d Array difference (auxiliary function).
unique Unique elements of an array.
intersect1d Intersection of 1D arrays with unique elements.
setxor1d Set exclusive-or of 1D arrays with unique elements.
in1d Test whether elements in a 1D array are also present in
another array.
union1d Union of 1D arrays with unique elements.
setdiff1d Set difference of 1D arrays with unique elements.
================ ===================
""" |
"""
========================================
Special functions (:mod:`scipy.special`)
========================================
.. module:: scipy.special
Nearly all of the functions below are universal functions and follow
broadcasting and automatic array-looping rules. Exceptions are noted.
Error handling
==============
Errors are handled by returning nans, or other appropriate values.
Some of the special function routines will emit warnings when an error
occurs. By default this is disabled. To enable such messages use
``errprint(1)``, and to disable such messages use ``errprint(0)``.
Example:
>>> print scipy.special.bdtr(-1,10,0.3)
>>> scipy.special.errprint(1)
>>> print scipy.special.bdtr(-1,10,0.3)
.. autosummary::
:toctree: generated/
errprint
SpecialFunctionWarning -- Warning that can be issued with ``errprint(True)``
Available functions
===================
Airy functions
--------------
.. autosummary::
:toctree: generated/
airy -- Airy functions and their derivatives.
airye -- Exponentially scaled Airy functions
ai_zeros -- [+]Zeros of Airy functions Ai(x) and Ai'(x)
bi_zeros -- [+]Zeros of Airy functions Bi(x) and Bi'(x)
itairy --
Elliptic Functions and Integrals
--------------------------------
.. autosummary::
:toctree: generated/
ellipj -- Jacobian elliptic functions
ellipk -- Complete elliptic integral of the first kind.
ellipkm1 -- ellipkm1(x) == ellipk(1 - x)
ellipkinc -- Incomplete elliptic integral of the first kind.
ellipe -- Complete elliptic integral of the second kind.
ellipeinc -- Incomplete elliptic integral of the second kind.
Bessel Functions
----------------
.. autosummary::
:toctree: generated/
jv -- Bessel function of real-valued order and complex argument.
jve -- Exponentially scaled Bessel function.
yn -- Bessel function of second kind (integer order).
yv -- Bessel function of the second kind (real-valued order).
yve -- Exponentially scaled Bessel function of the second kind.
kn -- Modified Bessel function of the second kind (integer order).
kv -- Modified Bessel function of the second kind (real order).
kve -- Exponentially scaled modified Bessel function of the second kind.
iv -- Modified Bessel function.
ive -- Exponentially scaled modified Bessel function.
hankel1 -- Hankel function of the first kind.
hankel1e -- Exponentially scaled Hankel function of the first kind.
hankel2 -- Hankel function of the second kind.
hankel2e -- Exponentially scaled Hankel function of the second kind.
The following is not an universal function:
.. autosummary::
:toctree: generated/
lmbda -- [+]Sequence of lambda functions with arbitrary order v.
Zeros of Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^
These are not universal functions:
.. autosummary::
:toctree: generated/
jnjnp_zeros -- [+]Zeros of integer-order Bessel functions and derivatives sorted in order.
jnyn_zeros -- [+]Zeros of integer-order Bessel functions and derivatives as separate arrays.
jn_zeros -- [+]Zeros of Jn(x)
jnp_zeros -- [+]Zeros of Jn'(x)
yn_zeros -- [+]Zeros of Yn(x)
ynp_zeros -- [+]Zeros of Yn'(x)
y0_zeros -- [+]Complex zeros: Y0(z0)=0 and values of Y0'(z0)
y1_zeros -- [+]Complex zeros: Y1(z1)=0 and values of Y1'(z1)
y1p_zeros -- [+]Complex zeros of Y1'(z1')=0 and values of Y1(z1')
Faster versions of common Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
j0 -- Bessel function of order 0.
j1 -- Bessel function of order 1.
y0 -- Bessel function of second kind of order 0.
y1 -- Bessel function of second kind of order 1.
i0 -- Modified Bessel function of order 0.
i0e -- Exponentially scaled modified Bessel function of order 0.
i1 -- Modified Bessel function of order 1.
i1e -- Exponentially scaled modified Bessel function of order 1.
k0 -- Modified Bessel function of the second kind of order 0.
k0e -- Exponentially scaled modified Bessel function of the second kind of order 0.
k1 -- Modified Bessel function of the second kind of order 1.
k1e -- Exponentially scaled modified Bessel function of the second kind of order 1.
Integrals of Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
itj0y0 -- Basic integrals of j0 and y0 from 0 to x.
it2j0y0 -- Integrals of (1-j0(t))/t from 0 to x and y0(t)/t from x to inf.
iti0k0 -- Basic integrals of i0 and k0 from 0 to x.
it2i0k0 -- Integrals of (i0(t)-1)/t from 0 to x and k0(t)/t from x to inf.
besselpoly -- Integral of a Bessel function: Jv(2* a* x) * x[+]lambda from x=0 to 1.
Derivatives of Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
jvp -- Nth derivative of Jv(v,z)
yvp -- Nth derivative of Yv(v,z)
kvp -- Nth derivative of Kv(v,z)
ivp -- Nth derivative of Iv(v,z)
h1vp -- Nth derivative of H1v(v,z)
h2vp -- Nth derivative of H2v(v,z)
Spherical Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^
These are not universal functions:
.. autosummary::
:toctree: generated/
sph_jn -- [+]Sequence of spherical Bessel functions, jn(z)
sph_yn -- [+]Sequence of spherical Bessel functions, yn(z)
sph_jnyn -- [+]Sequence of spherical Bessel functions, jn(z) and yn(z)
sph_in -- [+]Sequence of spherical Bessel functions, in(z)
sph_kn -- [+]Sequence of spherical Bessel functions, kn(z)
sph_inkn -- [+]Sequence of spherical Bessel functions, in(z) and kn(z)
Riccati-Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^
These are not universal functions:
.. autosummary::
:toctree: generated/
riccati_jn -- [+]Sequence of Ricatti-Bessel functions of first kind.
riccati_yn -- [+]Sequence of Ricatti-Bessel functions of second kind.
Struve Functions
----------------
.. autosummary::
:toctree: generated/
struve -- Struve function --- Hv(x)
modstruve -- Modified Struve function --- Lv(x)
itstruve0 -- Integral of H0(t) from 0 to x
it2struve0 -- Integral of H0(t)/t from x to Inf.
itmodstruve0 -- Integral of L0(t) from 0 to x.
Raw Statistical Functions
-------------------------
.. seealso:: :mod:`scipy.stats`: Friendly versions of these functions.
.. autosummary::
:toctree: generated/
bdtr -- Sum of terms 0 through k of the binomial pdf.
bdtrc -- Sum of terms k+1 through n of the binomial pdf.
bdtri -- Inverse of bdtr
bdtrik --
bdtrin --
btdtr -- Integral from 0 to x of beta pdf.
btdtri -- Quantiles of beta distribution
btdtria --
btdtrib --
fdtr -- Integral from 0 to x of F pdf.
fdtrc -- Integral from x to infinity under F pdf.
fdtri -- Inverse of fdtrc
gdtr -- Integral from 0 to x of gamma pdf.
gdtrc -- Integral from x to infinity under gamma pdf.
gdtria -- Inverse with respect to `a` of gdtr.
gdtrib -- Inverse with respect to `b` of gdtr.
gdtrix -- Inverse with respect to `x` of gdtr.
nbdtr -- Sum of terms 0 through k of the negative binomial pdf.
nbdtrc -- Sum of terms k+1 to infinity under negative binomial pdf.
nbdtri -- Inverse of nbdtr
nbdtrik --
nbdtrin --
ncfdtr -- CDF of non-central t distribution.
ncfdtridfd -- Find degrees of freedom (denominator) of noncentral F distribution.
ncfdtridfn -- Find degrees of freedom (numerator) of noncentral F distribution.
ncfdtri -- Inverse CDF of noncentral F distribution.
ncfdtrinc -- Find noncentrality parameter of noncentral F distribution.
nctdtr -- CDF of noncentral t distribution.
nctdtridf -- Find degrees of freedom of noncentral t distribution.
nctdtrit -- Inverse CDF of noncentral t distribution.
nctdtrinc -- Find noncentrality parameter of noncentral t distribution.
nrdtrimn -- Find mean of normal distribution from cdf and std.
nrdtrisd -- Find std of normal distribution from cdf and mean.
pdtr -- Sum of terms 0 through k of the Poisson pdf.
pdtrc -- Sum of terms k+1 to infinity of the Poisson pdf.
pdtri -- Inverse of pdtr
pdtrik --
stdtr -- Integral from -infinity to t of the Student-t pdf.
stdtridf --
stdtrit --
chdtr -- Integral from 0 to x of the Chi-square pdf.
chdtrc -- Integral from x to infnity of Chi-square pdf.
chdtri -- Inverse of chdtrc.
chdtriv --
ndtr -- Integral from -infinity to x of standard normal pdf
log_ndtr -- Logarithm of integral from -infinity to x of standard normal pdf
ndtri -- Inverse of ndtr (quantiles)
chndtr --
chndtridf --
chndtrinc --
chndtrix --
smirnov -- Kolmogorov-Smirnov complementary CDF for one-sided test statistic (Dn+ or Dn-)
smirnovi -- Inverse of smirnov.
kolmogorov -- The complementary CDF of the (scaled) two-sided test statistic (Kn*) valid for large n.
kolmogi -- Inverse of kolmogorov
tklmbda -- Tukey-Lambda CDF
logit --
expit --
boxcox -- Compute the Box-Cox transformation.
boxcox1p -- Compute the Box-Cox transformation.
Information Theory Functions
----------------------------
.. autosummary::
:toctree: generated/
entr -- entr(x) = -x*log(x)
rel_entr -- rel_entr(x, y) = x*log(x/y)
kl_div -- kl_div(x, y) = x*log(x/y) - x + y
huber -- Huber loss function.
pseudo_huber -- Pseudo-Huber loss function.
Gamma and Related Functions
---------------------------
.. autosummary::
:toctree: generated/
gamma -- Gamma function.
gammaln -- Log of the absolute value of the gamma function.
gammasgn -- Sign of the gamma function.
gammainc -- Incomplete gamma integral.
gammaincinv -- Inverse of gammainc.
gammaincc -- Complemented incomplete gamma integral.
gammainccinv -- Inverse of gammaincc.
beta -- Beta function.
betaln -- Log of the absolute value of the beta function.
betainc -- Incomplete beta integral.
betaincinv -- Inverse of betainc.
psi -- Logarithmic derivative of the gamma function.
rgamma -- One divided by the gamma function.
polygamma -- Nth derivative of psi function.
multigammaln -- Log of the multivariate gamma.
digamma -- Digamma function (derivative of the logarithm of gamma).
poch -- The Pochhammer symbol (rising factorial).
Error Function and Fresnel Integrals
------------------------------------
.. autosummary::
:toctree: generated/
erf -- Error function.
erfc -- Complemented error function (1- erf(x))
erfcx -- Scaled complemented error function exp(x**2)*erfc(x)
erfi -- Imaginary error function, -i erf(i x)
erfinv -- Inverse of error function
erfcinv -- Inverse of erfc
wofz -- Fadeeva function.
dawsn -- Dawson's integral.
fresnel -- Fresnel sine and cosine integrals.
fresnel_zeros -- Complex zeros of both Fresnel integrals
modfresnelp -- Modified Fresnel integrals F_+(x) and K_+(x)
modfresnelm -- Modified Fresnel integrals F_-(x) and K_-(x)
These are not universal functions:
.. autosummary::
:toctree: generated/
erf_zeros -- [+]Complex zeros of erf(z)
fresnelc_zeros -- [+]Complex zeros of Fresnel cosine integrals
fresnels_zeros -- [+]Complex zeros of Fresnel sine integrals
Legendre Functions
------------------
.. autosummary::
:toctree: generated/
lpmv -- Associated Legendre Function of arbitrary non-negative degree v.
sph_harm -- Spherical Harmonics (complex-valued) Y^m_n(theta,phi)
These are not universal functions:
.. autosummary::
:toctree: generated/
clpmn -- [+]Associated Legendre Function of the first kind for complex arguments.
lpn -- [+]Legendre Functions (polynomials) of the first kind
lqn -- [+]Legendre Functions of the second kind.
lpmn -- [+]Associated Legendre Function of the first kind for real arguments.
lqmn -- [+]Associated Legendre Function of the second kind.
Ellipsoidal Harmonics
---------------------
.. autosummary::
:toctree: generated/
ellip_harm -- Ellipsoidal harmonic E
ellip_harm_2 -- Ellipsoidal harmonic F
ellip_normal -- Ellipsoidal normalization constant
Orthogonal polynomials
----------------------
The following functions evaluate values of orthogonal polynomials:
.. autosummary::
:toctree: generated/
assoc_laguerre
eval_legendre
eval_chebyt
eval_chebyu
eval_chebyc
eval_chebys
eval_jacobi
eval_laguerre
eval_genlaguerre
eval_hermite
eval_hermitenorm
eval_gegenbauer
eval_sh_legendre
eval_sh_chebyt
eval_sh_chebyu
eval_sh_jacobi
The functions below, in turn, return the polynomial coefficients in
:class:`~.orthopoly1d` objects, which function similarly as :ref:`numpy.poly1d`.
The :class:`~.orthopoly1d` class also has an attribute ``weights`` which returns
the roots, weights, and total weights for the appropriate form of Gaussian
quadrature. These are returned in an ``n x 3`` array with roots in the first
column, weights in the second column, and total weights in the final column.
Note that :class:`~.orthopoly1d` objects are converted to ``poly1d`` when doing
arithmetic, and lose information of the original orthogonal polynomial.
.. autosummary::
:toctree: generated/
legendre -- [+]Legendre polynomial P_n(x) (lpn -- for function).
chebyt -- [+]Chebyshev polynomial T_n(x)
chebyu -- [+]Chebyshev polynomial U_n(x)
chebyc -- [+]Chebyshev polynomial C_n(x)
chebys -- [+]Chebyshev polynomial S_n(x)
jacobi -- [+]Jacobi polynomial P^(alpha,beta)_n(x)
laguerre -- [+]Laguerre polynomial, L_n(x)
genlaguerre -- [+]Generalized (Associated) Laguerre polynomial, L^alpha_n(x)
hermite -- [+]Hermite polynomial H_n(x)
hermitenorm -- [+]Normalized Hermite polynomial, He_n(x)
gegenbauer -- [+]Gegenbauer (Ultraspherical) polynomials, C^(alpha)_n(x)
sh_legendre -- [+]shifted Legendre polynomial, P*_n(x)
sh_chebyt -- [+]shifted Chebyshev polynomial, T*_n(x)
sh_chebyu -- [+]shifted Chebyshev polynomial, U*_n(x)
sh_jacobi -- [+]shifted Jacobi polynomial, J*_n(x) = G^(p,q)_n(x)
.. warning::
Computing values of high-order polynomials (around ``order > 20``) using
polynomial coefficients is numerically unstable. To evaluate polynomial
values, the ``eval_*`` functions should be used instead.
Hypergeometric Functions
------------------------
.. autosummary::
:toctree: generated/
hyp2f1 -- Gauss hypergeometric function (2F1)
hyp1f1 -- Confluent hypergeometric function (1F1)
hyperu -- Confluent hypergeometric function (U)
hyp0f1 -- Confluent hypergeometric limit function (0F1)
hyp2f0 -- Hypergeometric function (2F0)
hyp1f2 -- Hypergeometric function (1F2)
hyp3f0 -- Hypergeometric function (3F0)
Parabolic Cylinder Functions
----------------------------
.. autosummary::
:toctree: generated/
pbdv -- Parabolic cylinder function Dv(x) and derivative.
pbvv -- Parabolic cylinder function Vv(x) and derivative.
pbwa -- Parabolic cylinder function W(a,x) and derivative.
These are not universal functions:
.. autosummary::
:toctree: generated/
pbdv_seq -- [+]Sequence of parabolic cylinder functions Dv(x)
pbvv_seq -- [+]Sequence of parabolic cylinder functions Vv(x)
pbdn_seq -- [+]Sequence of parabolic cylinder functions Dn(z), complex z
Mathieu and Related Functions
-----------------------------
.. autosummary::
:toctree: generated/
mathieu_a -- Characteristic values for even solution (ce_m)
mathieu_b -- Characteristic values for odd solution (se_m)
These are not universal functions:
.. autosummary::
:toctree: generated/
mathieu_even_coef -- [+]sequence of expansion coefficients for even solution
mathieu_odd_coef -- [+]sequence of expansion coefficients for odd solution
The following return both function and first derivative:
.. autosummary::
:toctree: generated/
mathieu_cem -- Even Mathieu function
mathieu_sem -- Odd Mathieu function
mathieu_modcem1 -- Even modified Mathieu function of the first kind
mathieu_modcem2 -- Even modified Mathieu function of the second kind
mathieu_modsem1 -- Odd modified Mathieu function of the first kind
mathieu_modsem2 -- Odd modified Mathieu function of the second kind
Spheroidal Wave Functions
-------------------------
.. autosummary::
:toctree: generated/
pro_ang1 -- Prolate spheroidal angular function of the first kind
pro_rad1 -- Prolate spheroidal radial function of the first kind
pro_rad2 -- Prolate spheroidal radial function of the second kind
obl_ang1 -- Oblate spheroidal angular function of the first kind
obl_rad1 -- Oblate spheroidal radial function of the first kind
obl_rad2 -- Oblate spheroidal radial function of the second kind
pro_cv -- Compute characteristic value for prolate functions
obl_cv -- Compute characteristic value for oblate functions
pro_cv_seq -- Compute sequence of prolate characteristic values
obl_cv_seq -- Compute sequence of oblate characteristic values
The following functions require pre-computed characteristic value:
.. autosummary::
:toctree: generated/
pro_ang1_cv -- Prolate spheroidal angular function of the first kind
pro_rad1_cv -- Prolate spheroidal radial function of the first kind
pro_rad2_cv -- Prolate spheroidal radial function of the second kind
obl_ang1_cv -- Oblate spheroidal angular function of the first kind
obl_rad1_cv -- Oblate spheroidal radial function of the first kind
obl_rad2_cv -- Oblate spheroidal radial function of the second kind
Kelvin Functions
----------------
.. autosummary::
:toctree: generated/
kelvin -- All Kelvin functions (order 0) and derivatives.
kelvin_zeros -- [+]Zeros of All Kelvin functions (order 0) and derivatives
ber -- Kelvin function ber x
bei -- Kelvin function bei x
berp -- Derivative of Kelvin function ber x
beip -- Derivative of Kelvin function bei x
ker -- Kelvin function ker x
kei -- Kelvin function kei x
kerp -- Derivative of Kelvin function ker x
keip -- Derivative of Kelvin function kei x
These are not universal functions:
.. autosummary::
:toctree: generated/
ber_zeros -- [+]Zeros of Kelvin function bei x
bei_zeros -- [+]Zeros of Kelvin function ber x
berp_zeros -- [+]Zeros of derivative of Kelvin function ber x
beip_zeros -- [+]Zeros of derivative of Kelvin function bei x
ker_zeros -- [+]Zeros of Kelvin function kei x
kei_zeros -- [+]Zeros of Kelvin function ker x
kerp_zeros -- [+]Zeros of derivative of Kelvin function ker x
keip_zeros -- [+]Zeros of derivative of Kelvin function kei x
Combinatorics
-------------
.. autosummary::
:toctree: generated/
comb -- [+]Combinations of N things taken k at a time, "N choose k"
perm -- [+]Permutations of N things taken k at a time, "k-permutations of N"
Other Special Functions
-----------------------
.. autosummary::
:toctree: generated/
agm -- Arithmetic-Geometric Mean
bernoulli -- Bernoulli numbers
binom -- Binomial coefficient.
diric -- Dirichlet function (periodic sinc)
euler -- Euler numbers
expn -- Exponential integral.
exp1 -- Exponential integral of order 1 (for complex argument)
expi -- Another exponential integral -- Ei(x)
factorial -- The factorial function, n! = special.gamma(n+1)
factorial2 -- Double factorial, (n!)!
factorialk -- [+](...((n!)!)!...)! where there are k '!'
shichi -- Hyperbolic sine and cosine integrals.
sici -- Integral of the sinc and "cosinc" functions.
spence -- Dilogarithm integral.
lambertw -- Lambert W function
zeta -- Riemann zeta function of two arguments.
zetac -- Standard Riemann zeta function minus 1.
Convenience Functions
---------------------
.. autosummary::
:toctree: generated/
cbrt -- Cube root.
exp10 -- 10 raised to the x power.
exp2 -- 2 raised to the x power.
radian -- radian angle given degrees, minutes, and seconds.
cosdg -- cosine of the angle given in degrees.
sindg -- sine of the angle given in degrees.
tandg -- tangent of the angle given in degrees.
cotdg -- cotangent of the angle given in degrees.
log1p -- log(1+x)
expm1 -- exp(x)-1
cosm1 -- cos(x)-1
round -- round the argument to the nearest integer. If argument ends in 0.5 exactly, pick the nearest even integer.
xlogy -- x*log(y)
xlog1py -- x*log1p(y)
.. [+] in the description indicates a function which is not a universal
.. function and does not follow broadcasting and automatic
.. array-looping rules.
""" |
"""
Basic functions used by several sub-packages and
useful to have in the main name-space.
Type Handling
-------------
================ ===================
iscomplexobj Test for complex object, scalar result
isrealobj Test for real object, scalar result
iscomplex Test for complex elements, array result
isreal Test for real elements, array result
imag Imaginary part
real Real part
real_if_close Turns complex number with tiny imaginary part to real
isneginf Tests for negative infinity, array result
isposinf Tests for positive infinity, array result
isnan Tests for nans, array result
isinf Tests for infinity, array result
isfinite Tests for finite numbers, array result
isscalar True if argument is a scalar
nan_to_num Replaces NaN's with 0 and infinities with large numbers
cast Dictionary of functions to force cast to each type
common_type Determine the minimum common type code for a group
of arrays
mintypecode Return minimal allowed common typecode.
================ ===================
Index Tricks
------------
================ ===================
mgrid Method which allows easy construction of N-d
'mesh-grids'
``r_`` Append and construct arrays: turns slice objects into
ranges and concatenates them, for 2d arrays appends rows.
index_exp Konrad Hinsen's index_expression class instance which
can be useful for building complicated slicing syntax.
================ ===================
Useful Functions
----------------
================ ===================
select Extension of where to multiple conditions and choices
extract Extract 1d array from flattened array according to mask
insert Insert 1d array of values into Nd array according to mask
linspace Evenly spaced samples in linear space
logspace Evenly spaced samples in logarithmic space
fix Round x to nearest integer towards zero
mod Modulo mod(x,y) = x % y except keeps sign of y
amax Array maximum along axis
amin Array minimum along axis
ptp Array max-min along axis
cumsum Cumulative sum along axis
prod Product of elements along axis
cumprod Cumluative product along axis
diff Discrete differences along axis
angle Returns angle of complex argument
unwrap Unwrap phase along given axis (1-d algorithm)
sort_complex Sort a complex-array (based on real, then imaginary)
trim_zeros Trim the leading and trailing zeros from 1D array.
vectorize A class that wraps a Python function taking scalar
arguments into a generalized function which can handle
arrays of arguments using the broadcast rules of
numerix Python.
================ ===================
Shape Manipulation
------------------
================ ===================
squeeze Return a with length-one dimensions removed.
atleast_1d Force arrays to be >= 1D
atleast_2d Force arrays to be >= 2D
atleast_3d Force arrays to be >= 3D
vstack Stack arrays vertically (row on row)
hstack Stack arrays horizontally (column on column)
column_stack Stack 1D arrays as columns into 2D array
dstack Stack arrays depthwise (along third dimension)
stack Stack arrays along a new axis
split Divide array into a list of sub-arrays
hsplit Split into columns
vsplit Split into rows
dsplit Split along third dimension
================ ===================
Matrix (2D Array) Manipulations
-------------------------------
================ ===================
fliplr 2D array with columns flipped
flipud 2D array with rows flipped
rot90 Rotate a 2D array a multiple of 90 degrees
eye Return a 2D array with ones down a given diagonal
diag Construct a 2D array from a vector, or return a given
diagonal from a 2D array.
mat Construct a Matrix
bmat Build a Matrix from blocks
================ ===================
Polynomials
-----------
================ ===================
poly1d A one-dimensional polynomial class
poly Return polynomial coefficients from roots
roots Find roots of polynomial given coefficients
polyint Integrate polynomial
polyder Differentiate polynomial
polyadd Add polynomials
polysub Substract polynomials
polymul Multiply polynomials
polydiv Divide polynomials
polyval Evaluate polynomial at given argument
================ ===================
Iterators
---------
================ ===================
Arrayterator A buffered iterator for big arrays.
================ ===================
Import Tricks
-------------
================ ===================
ppimport Postpone module import until trying to use it
ppimport_attr Postpone module import until trying to use its attribute
ppresolve Import postponed module and return it.
================ ===================
Machine Arithmetics
-------------------
================ ===================
machar_single Single precision floating point arithmetic parameters
machar_double Double precision floating point arithmetic parameters
================ ===================
Threading Tricks
----------------
================ ===================
ParallelExec Execute commands in parallel thread.
================ ===================
1D Array Set Operations
-----------------------
Set operations for 1D numeric arrays based on sort() function.
================ ===================
ediff1d Array difference (auxiliary function).
unique Unique elements of an array.
intersect1d Intersection of 1D arrays with unique elements.
setxor1d Set exclusive-or of 1D arrays with unique elements.
in1d Test whether elements in a 1D array are also present in
another array.
union1d Union of 1D arrays with unique elements.
setdiff1d Set difference of 1D arrays with unique elements.
================ ===================
""" |
#import unittest
#import sys
#
#from DIRAC.Core.Base import Script
#Script.parseCommandLine()
#
#from DIRAC.ResourceStatusSystem.Utilities.mock import Mock
#
#from DIRAC.ResourceStatusSystem.Utilities.Exceptions import *
#from DIRAC.ResourceStatusSystem.PolicySystem.Status import *
#
#import DIRAC.ResourceStatusSystem.test.fake_Logger
#import DIRAC.ResourceStatusSystem.test.fake_Admin
#import DIRAC.ResourceStatusSystem.test.fake_NotificationClient
#
#from DIRAC.ResourceStatusSystem.PolicySystem.PEP import PEP
#from DIRAC.ResourceStatusSystem.PolicySystem.PDP import PDP
#from DIRAC.ResourceStatusSystem.PolicySystem.PolicyCaller import PolicyCaller
#
##############################################################################
#
#class PolicySystemTestCase(unittest.TestCase):
# """ Base class for the PDP - PEP test cases
# """
##############################################################################
#
# def setUp(self):
# sys.modules["DIRAC"] = DIRAC.ResourceStatusSystem.test.fake_Logger
# sys.modules["DIRAC.ResourceStatusSystem.Utilities.CS"] = DIRAC.ResourceStatusSystem.test.fake_Logger
# sys.modules["DIRAC.Core.Utilities.SiteCEMapping"] = DIRAC.ResourceStatusSystem.test.fake_Logger
# sys.modules["DIRAC.Core.Utilities.SiteSEMapping"] = DIRAC.ResourceStatusSystem.test.fake_Logger
# sys.modules["DIRAC.Core.Utilities.SitesDIRACGOCDBmapping"] = DIRAC.ResourceStatusSystem.test.fake_Logger
# sys.modules["DIRAC.Interfaces.API.DiracAdmin"] = DIRAC.ResourceStatusSystem.test.fake_Admin
# sys.modules["DIRAC.FrameworkSystem.Client.NotificationClient"] = DIRAC.ResourceStatusSystem.test.fake_NotificationClient
#
# from DIRAC.ResourceStatusSystem.Utilities.InfoGetter import InfoGetter
# from DIRAC.ResourceStatusSystem.PolicySystem.PolicyBase import PolicyBase
## from DIRAC.ResourceStatusSystem.PolicySystem.PolicyInvoker import PolicyInvoker
#
# from DIRAC import gConfig
# self.VO = gConfig.getValue("DIRAC/Extensions")
# if 'LHCb' in self.VO:
# self.VO = 'LHCb'
#
# self.mock_command = Mock()
# self.mock_policy = Mock()
# self.mock_p = Mock()
# self.mock_args = Mock()
# self.pb = PolicyBase()
## self.pi = PolicyInvoker()
# self.mock_pdp = Mock()
# self.mock_rsDB = Mock()
# self.mock_rmDB = Mock()
# self.mock_nc = Mock()
# self.mock_da = Mock()
# self.mock_da.getBannedSites.return_value = {'OK': True,
# 'Value': ['LCG.APC.fr', 'LCG.Bari.it', 'LCG.Catania.it']}
# self.mock_da.addSiteInMask.return_value = {'OK': True, 'Value': ''}
# self.mock_da.banSiteFromMask.return_value = {'OK': True, 'Value': ''}
# self.mock_da.sendMail.return_value = {'OK': True, 'Value': ''}
# self.mock_csAPI = Mock()
# self.mock_csAPI.setOption.return_value = {'OK': True, 'Value': ''}
# self.mock_csAPI.commit.return_value = {'OK': True, 'Value': ''}
# self.ig = InfoGetter(self.VO)
#
##############################################################################
#
#class PEPSuccess(PolicySystemTestCase):
#
##############################################################################
#
# def test_enforce(self):
#
# for policyType in PolicyTypes:
# for granularity in ValidRes:
# for status in ValidStatus:
# oldStatus = status # oldStatus never used by anything, let's reduce by 2 the duration of this lengthy test!!
# for newPolicyType in PolicyTypes:
# if policyType == newPolicyType:
# continue
## for newGranularity in ValidRes:
# for siteType in ValidSiteType:
# for serviceType in ValidServiceType:
# for resourceType in ValidResourceType:
# for user in ("RS_SVC", "Federico"):
# for setup in ("LHCb-Production", "LHCb-Development", "LHCb-Certification"):
#
# self.mock_pdp.takeDecision.return_value = {'PolicyCombinedResult': {'PolicyType':[policyType, newPolicyType],
# 'Action':True, 'Status':status,
# 'Reason':'testReason'},
# 'SinglePolicyResults': [{'Status': 'Active',
# 'PolicyName': 'SAM_CE_Policy',
# 'Reason': 'SAM:ok'},
# {'Status': 'Banned',
# 'PolicyName': 'DT_Policy_Scheduled',
# 'Reason': 'DT:OUTAGE in 1 hours',
# 'EndDate': '2010-02-16 15:00:00'}]}
# # pep = PEP(granularity, 'XX', status, oldStatus, 'XX', 'T1', 'Computing', 'CE', {'PolicyType':newPolicyType, 'Granularity':newGranularity})
#
#
# pep = PEP(self.VO, granularity, 'XX', status, oldStatus, 'XX', siteType,
# serviceType, resourceType, user)
#
#
# # self.mock_pdp.takeDecision.return_value = {'PolicyCombinedResult': [{'PolicyType':[policyType, newPolicyType],
# # 'Action':True, 'Status':status,
# # 'Reason':'testReason'},
# # {'PolicyType':[policyType, newPolicyType],
# # 'Action':True, 'Status':status,
# # 'Reason':'testReason'}],
# # 'SinglePolicyResults': [{'Status': 'Active',
# # 'PolicyName': 'SAM_CE_Policy',
# # 'Reason': 'SAM:ok',
# # 'SAT': True},
# # {'Status': 'Banned',
# # 'PolicyName': 'DT_Policy_Scheduled',
# # 'Reason': 'DT:OUTAGE in 1 hours',
# # 'EndDate': '2010-02-16 15:00:00',
# # 'SAT': True}] }
# # pep = PEP(granularity, 'XX', status, oldStatus, 'XX', 'T1', 'Computing', 'CE', {'PolicyType':newPolicyType, 'Granularity':newGranularity})
# # pep = PEP(granularity, 'XX', status, oldStatus, 'XX', 'T1', 'Computing', 'CE', user)
# self.mock_rsDB.getMonitoredsHistory.return_value = ('Active', 'Reason', '2010-04-09 09:54:52')
# res = pep.enforce(pdpIn = self.mock_pdp, rsDBIn = self.mock_rsDB, rmDBIn = self.mock_rmDB, ncIn = self.mock_nc,
# setupIn = setup, daIn = self.mock_da, csAPIIn = self.mock_csAPI)
# self.assertEqual(res, None)
# self.mock_pdp.takeDecision.return_value = {'PolicyCombinedResult': {'PolicyType':[policyType, newPolicyType],
# 'Action':False, 'Reason':'testReason'},
# 'SinglePolicyResults': [{'Status': 'Active',
# 'PolicyName': 'SAM_CE_Policy',
# 'Reason': 'SAM:ok'},
# {'Status': 'Banned',
# 'PolicyName': 'DT_Policy_Scheduled',
# 'Reason': 'DT:OUTAGE in 1 hours',
# 'EndDate': '2010-02-16 15:00:00'}]}
# res = pep.enforce(pdpIn = self.mock_pdp, rsDBIn = self.mock_rsDB, rmDBIn = self.mock_rmDB, ncIn = self.mock_nc,
# setupIn = setup, daIn = self.mock_da, csAPIIn = self.mock_csAPI)
# self.assertEqual(res, None)
#
#
# # self.mock_pdp.takeDecision.return_value = {'PolicyCombinedResult': [{'PolicyType':[policyType, newPolicyType],
# # 'Action':True, 'Status':status, 'Reason':'testReason'}],
# # 'SinglePolicyResults': [{'Status': 'Active',
# # 'PolicyName': 'SAM_CE_Policy',
# # 'Reason': 'SAM:ok',
# # 'SAT': True},
# # {'Status': 'Banned',
# # 'PolicyName': 'DT_Policy_Scheduled',
# # 'Reason': 'DT:OUTAGE in 1 hours',
# # 'EndDate': '2010-02-16 15:00:00',
# # 'SAT': True}] }
# # pep = PEP(granularity, 'XX', status, oldStatus, 'XX', 'T1', 'Computing', 'CE')
# # res = pep.enforce(pdpIn = self.mock_pdp, rsDBIn = self.mock_rsDB, ncIn = self.mock_nc)
# # self.assertEqual(res, None)
# # self.mock_pdp.takeDecision.return_value = {'PolicyCombinedResult': [{'PolicyType':[policyType, newPolicyType],
# # 'Action':False, 'Reason':'testReason'}],
# # 'SinglePolicyResults': [{'Status': 'Active',
# # 'PolicyName': 'SAM_CE_Policy',
# # 'Reason': 'SAM:ok',
# # 'SAT': True},
# # {'Status': 'Banned',
# # 'PolicyName': 'DT_Policy_Scheduled',
# # 'Reason': 'DT:OUTAGE in 1 hours',
# # 'EndDate': '2010-02-16 15:00:00',
# # 'SAT': True}] }
# # res = pep.enforce(pdpIn = self.mock_pdp, rsDBIn = self.mock_rsDB, ncIn = self.mock_nc)
# # self.assertEqual(res, None)
#
#
# # self.mock_pdp.takeDecision.return_value = {'PolicyCombinedResult': [{'PolicyType':[policyType, newPolicyType],
# # 'Action':True, 'Status':status, 'Reason':'testReason'}],
# # 'SinglePolicyResults': [{'Status': 'Active',
# # 'PolicyName': 'SAM_CE_Policy',
# # 'Reason': 'SAM:ok',
# # 'SAT': True},
# # {'Status': 'Banned',
# # 'PolicyName': 'DT_Policy_Scheduled',
# # 'Reason': 'DT:OUTAGE in 1 hours',
# # 'EndDate': '2010-02-16 15:00:00',
# # 'SAT': True}] }
# # pep = PEP(granularity, 'XX')
# # res = pep.enforce(pdpIn = self.mock_pdp, rsDBIn = self.mock_rsDB, ncIn = self.mock_nc)
# # self.assertEqual(res, None)
# # pep = PEP(granularity, 'XX')
# # res = pep.enforce(pdpIn = self.mock_pdp, rsDBIn = self.mock_rsDB, ncIn = self.mock_nc)
# # self.assertEqual(res, None)
# # self.mock_pdp.takeDecision.return_value = {'PolicyCombinedResult': [{'PolicyType':[policyType, newPolicyType],
# # 'Action':False, 'Reason':'testReason'}],
# # 'SinglePolicyResults': [{'Status': 'Active',
# # 'PolicyName': 'SAM_CE_Policy',
# # 'Reason': 'SAM:ok',
# # 'SAT': True},
# # {'Status': 'Banned',
# # 'PolicyName': 'DT_Policy_Scheduled',
# # 'Reason': 'DT:OUTAGE in 1 hours',
# # 'EndDate': '2010-02-16 15:00:00',
# # 'SAT': True}] }
# # res = pep.enforce(pdpIn = self.mock_pdp, rsDBIn = self.mock_rsDB, ncIn = self.mock_nc)
# # self.assertEqual(res, None)
#
##############################################################################
#
#class PEPFailure(PolicySystemTestCase):
#
##############################################################################
#
# def test_PEPFail(self):
#
# for policyType in PolicyTypes:
# for granularity in ValidRes:
# for status in ValidStatus:
# for oldStatus in ValidStatus:
# if status == oldStatus:
# continue
# for newPolicyType in PolicyTypes:
# if policyType == newPolicyType:
# continue
## for newGranularity in ValidRes:
# for siteType in ValidSiteType:
# for serviceType in ValidServiceType:
# for resourceType in ValidResourceType:
## pep = PEP(granularity, 'XX', status, oldStatus, 'XX', siteType, serviceType, resourceType, {'PolicyType':newPolicyType, 'Granularity':newGranularity})
# pep = PEP(self.VO, granularity, 'XX', status, oldStatus, 'XX', siteType, serviceType, resourceType)
# self.failUnlessRaises(Exception, pep.enforce, self.mock_pdp, self.mock_rsDB, ncIn = self.mock_nc,
# setupIn = 'LHCb-Development', daIn = self.mock_da, csAPIIn = self.mock_csAPI )
# self.failUnlessRaises(Exception, pep.enforce, self.mock_pdp, self.mock_rsDB, knownInfo={'DT':'AT_RISK'},
# ncIn = self.mock_nc, setupIn = 'LHCb-Development', daIn = self.mock_da,
# csAPIIn = self.mock_csAPI )
#
#
##############################################################################
#
# def test_PEPBadInputs(self):
# for policyType in PolicyTypes:
# for status in ValidStatus:
# for oldStatus in ValidStatus:
# if status == oldStatus:
# continue
# for policyType in PolicyTypes:
# for granularity in ValidRes:
# for status in ValidStatus:
# for oldStatus in ValidStatus:
# if status == oldStatus:
# continue
#
#
##############################################################################
#
#class PDPSuccess(PolicySystemTestCase):
#
##############################################################################
#
#
# def test_takeDecision(self):
#
# for granularity in ValidRes:
# for status in ValidStatus:
# for oldStatus in ValidStatus:
# if status == oldStatus: continue
# self.mock_p.evaluate.return_value = [{'Status': status, 'Reason': 'testReason', 'PolicyName': 'test_P'}]
# pdp = PDP(self.VO, granularity, 'XX', oldStatus, None, 'XX')
# res = pdp.takeDecision(policyIn = self.mock_p)
# res = res['PolicyCombinedResult']
# self.assert_(res['Action'])
#
# res = pdp.takeDecision(policyIn = self.mock_p, argsIn = ())
# res = res['PolicyCombinedResult']
# self.assert_(res['Action'])
#
# res = pdp.takeDecision(policyIn = self.mock_p, knownInfo={})
# res = res['PolicyCombinedResult']
# self.assert_(res['Action'])
#
# def test__policyCombination(self):
#
# for granularity in ValidRes:
# for status in ValidStatus:
# for oldStatus in ValidStatus:
# if status == oldStatus:
# continue
#
# for newStatus1 in ValidStatus:
# for newStatus2 in ValidStatus:
# pdp = PDP(self.VO, granularity, 'XX', status, oldStatus, 'XX')
# polRes = {'Status':newStatus1, 'Reason':'-Reason1-'}
# polRes2 = {'Status':newStatus2, 'Reason':'-Reason2-'}
#
#
# # 0 policies
# res = pdp._policyCombination([])
# self.assertEqual(res, {})
#
# # 1 policy
# res = pdp._policyCombination([polRes])
#
# if status == 'Banned':
# self.assertTrue(value_of_status(res['Status']) <= 1)
#
# if status == 'Banned' and newStatus1 in ['Active','Bad','Probing']:
# self.assertEqual(res['Status'], 'Probing')
# else:
# self.assertEqual(res['Status'], newStatus1)
#
#
# # 2 policies
# res = pdp._policyCombination([polRes, polRes2])
#
# if status == 'Banned':
# self.assertTrue(value_of_status(res['Status']) <= 1)
#
# if status == 'Banned' and newStatus1 in ['Active','Bad','Probing'] and newStatus2 in ['Active','Bad','Probing']:
# self.assertEqual(res['Status'], 'Probing')
#
# if status != 'Banned' and value_of_status(newStatus1) < value_of_status(newStatus1):
# self.assertEqual(res['Status'], newStatus1)
# if status != 'Banned' and value_of_status(newStatus2) < value_of_status(newStatus1):
# self.assertEqual(res['Status'], newStatus2)
#
# # all different policies
# def make_polres(status):
# return { 'Status': status, 'Reason': 'Because of ' + status }
# all_polres = [make_polres(s) for s in ValidStatus]
#
# res = pdp._policyCombination(all_polres)
# self.assertEqual(res['Status'], 'Banned')
#
##############################################################################
#
#class PDPFailure(PolicySystemTestCase):
#
##############################################################################
#
# def test_PolicyFail(self):
# for granularity in ValidRes:
# for status in ValidStatus:
# for oldStatus in ValidStatus:
# if status == oldStatus:
# continue
# pdp = PDP(self.VO, granularity, 'XX', status, oldStatus, 'XX')
# self.failUnlessRaises(Exception, pdp.takeDecision, self.mock_pdp, self.mock_rsDB)
# self.failUnlessRaises(Exception, pdp.takeDecision, self.mock_pdp, self.mock_rsDB, knownInfo={'DT':'AT_RISK'})
#
##############################################################################
#
# def test_PDPBadInputs(self):
# for status in ValidStatus:
# for oldStatus in ValidStatus:
# if status == oldStatus:
# continue
# for granularity in ValidRes:
# for oldStatus in ValidStatus:
# for status in ValidStatus:
# if status == oldStatus:
# continue
#
##############################################################################
#
#class PolicyCallerSuccess(PolicySystemTestCase):
#
# def test_policyInvocation(self):
# cc = Mock()
#
# policies_modules = {'Site':['DT_Policy', 'GGUSTickets_Policy'],
# 'Service': ['PilotsEfficiency_Simple_Policy', 'JobsEfficiency_Simple_Policy'],
# 'Resource':['SAMResults_Policy', 'DT_Policy'],
# 'StorageElementRead':['SEOccupancy_Policy', 'TransferQuality_Policy'],
# 'StorageElementWrite':['SEOccupancy_Policy', 'TransferQuality_Policy']
# }
#
# for g in ValidRes:
# for status in ValidStatus:
# self.mock_p.evaluate.return_value = {'Status':status,
# 'Reason':'testReason',
# 'PolicyName': 'test_P'}
# pc = PolicyCaller(commandCallerIn = cc)
#
# for pol_mod in policies_modules[g]:
# res = pc.policyInvocation(self.VO, g, 'XX', status, self.mock_p,
# (g, 'XX'), None, pol_mod)
# self.assertEqual(res['Status'], status)
#
# res = pc.policyInvocation(self.VO, g, 'XX', status, self.mock_p,
# None, None, pol_mod)
# self.assertEqual(res['Status'], status)
#
# for extraArgs in ((g, 'XX'), [(g, 'XX'), (g, 'XX')]):
# res = pc.policyInvocation(self.VO, g, 'XX', status, self.mock_p,
# None, None, pol_mod, extraArgs)
# self.assertEqual(res['Status'], status)
#
##############################################################################
#
#class PolicyBaseSuccess(PolicySystemTestCase):
#
# def test_setArgs(self):
# for g in ValidRes:
# for a in [(g, 'XX')]:
# self.pb.setArgs(a)
# self.assertEqual(self.pb.args, a)
#
# def test_evaluate(self):
# for g in ValidRes:
# for a in [(g, 'XX')]:
# self.pb.setArgs(a)
# self.mock_command.doCommand.return_value = {'Result':'aRes'}
# self.pb.setCommand(self.mock_command)
# res = self.pb.evaluate()
# self.assertEqual(res, 'aRes')
#
##############################################################################
#
#class PolicyBaseFailure(PolicySystemTestCase):
#
# def test_setBadArgs(self):
#
#
# # 6 arguments should be handled with no problem: why the limitation to 5 ?! (removing this test)
# # self.pb.setArgs(('Site', 'XX', 'Active', 'BOH', 'BOH', 'BOH'))
# # self.mock_command.doCommand.return_value = {'Result':'aRes'}
# # self.pb.setCommand(self.mock_command)
# # Lists are unsupported by Command for now, useless to test.
# # self.pb.setArgs([('Site', 'XX', 'Active', 'BOH', 'BOH', 'BOH'), ('Site', 'XX', 'Active', 'BOH', 'BOH', 'BOH')])
#
##############################################################################
#
## class PolicyInvokerSuccess(PolicySystemTestCase):
#
## def test_setPolicy(self):
## self.pi.setPolicy(self.mock_policy)
## self.assertEqual(self.pi.policy, self.mock_policy)
#
## def test_evaluatePolicy(self):
#
## self.mock_policy.evaluate.return_value = {'Result':'Satisfied', 'Status':'Banned', 'Reason':"reason"}
## self.pi.setPolicy(self.mock_policy)
## for granularity in ValidRes:
## res = self.pi.evaluatePolicy()
## self.assertEqual(res['Result'], 'Satisfied')
## self.mock_policy.evaluate.return_value = {'Result':'Un-Satisfied'}
## self.pi.setPolicy(self.mock_policy)
## for granularity in ValidRes:
## res = self.pi.evaluatePolicy()
## self.assertEqual(res['Result'], 'Un-Satisfied')
#
## #############################################################################
#
## class PolicyInvokerFailure(PolicySystemTestCase):
#
## def test_policyFail(self):
## for granularity in ValidRes:
## self.failUnlessRaises(Exception, self.pi.evaluatePolicy)
#
##############################################################################
#
#if __name__ == '__main__':
# suite = unittest.defaultTestLoader.loadTestsFromTestCase(PolicySystemTestCase)
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(PolicyBaseSuccess))
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(PolicyBaseFailure))
## suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(PolicyInvokerSuccess))
## suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(PolicyInvokerFailure))
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(PEPSuccess))
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(PEPFailure))
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(PDPSuccess))
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(PDPFailure))
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(PolicyCallerSuccess))
# testResult = unittest.TextTestRunner(verbosity=2).run(suite)
#
##############################################################################
|
# -*- encoding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# Copyright (C) 2004-2009 Tiny SPRL (<http://tiny.be>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
# SKR03
# =====
# Dieses Modul bietet Ihnen einen deutschen Kontenplan basierend auf dem SKR03.
# Gemäss der aktuellen Einstellungen ist die Firma nicht Umsatzsteuerpflichtig.
# Diese Grundeinstellung ist sehr einfach zu ändern und bedarf in der Regel
# grundsätzlich eine initiale Zuweisung von Steuerkonten zu Produkten und / oder
# Sachkonten oder zu Partnern.
# Die Umsatzsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten bei den Produktstammdaten hinterlegt werden (in Abhängigkeit der
# Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter Finanzbuchhaltung
# (Kategorie: Umsatzsteuer).
# Die Vorsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten ebenso bei den Produktstammdaten hinterlegt werden (in Abhängigkeit
# der Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter
# Finanzbuchhaltung (Kategorie: Vorsteuer).
# Die Zuordnung der Steuern für Ein- und Ausfuhren aus EU Ländern, sowie auch
# für den Ein- und Verkauf aus und in Drittländer sollten beim Partner
# (Lieferant/Kunde)hinterlegt werden (in Anhängigkeit vom Herkunftsland
# des Lieferanten/Kunden). Die Zuordnung beim Kunden ist 'höherwertig' als
# die Zuordnung bei Produkten und überschreibt diese im Einzelfall.
#
# Zur Vereinfachung der Steuerausweise und Buchung bei Auslandsgeschäften
# erlaubt OpenERP ein generelles Mapping von Steuerausweis und Steuerkonten
# (z.B. Zuordnung 'Umsatzsteuer 19%' zu 'steuerfreie Einfuhren aus der EU')
# zwecks Zuordnung dieses Mappings zum ausländischen Partner (Kunde/Lieferant).
# Die Rechnungsbuchung beim Einkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Vorsteuer Steuermessbetrag (z.B. Vorsteuer
# Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Vorsteuern' (z.B. Vorsteuer
# 19%). Durch multidimensionale Hierachien können verschiedene Positionen
# zusammengefasst werden und dann in Form eines Reports ausgegeben werden.
#
# Die Rechnungsbuchung beim Verkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Umsatzsteuer Steuermessbetrag
# (z.B. Umsatzsteuer Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Umsatzsteuer'
# (z.B. Umsatzsteuer 19%). Durch multidimensionale Hierachien können
# verschiedene Positionen zusammengefasst werden.
# Die zugewiesenen Steuerausweise können auf Ebene der einzelnen
# Rechnung (Eingangs- und Ausgangsrechnung) nachvollzogen werden,
# und dort gegebenenfalls angepasst werden.
# Rechnungsgutschriften führen zu einer Korrektur (Gegenposition)
# der Steuerbuchung, in Form einer spiegelbildlichen Buchung.
# SKR04
# =====
# Dieses Modul bietet Ihnen einen deutschen Kontenplan basierend auf dem SKR04.
# Gemäss der aktuellen Einstellungen ist die Firma nicht Umsatzsteuerpflichtig,
# d.h. im Standard existiert keine Zuordnung von Produkten und Sachkonten zu
# Steuerschlüsseln.
# Diese Grundeinstellung ist sehr einfach zu ändern und bedarf in der Regel
# grundsätzlich eine initiale Zuweisung von Steuerschlüsseln zu Produkten und / oder
# Sachkonten oder zu Partnern.
# Die Umsatzsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten bei den Produktstammdaten hinterlegt werden (in Abhängigkeit der
# Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter Finanzbuchhaltung
# (Kategorie: Umsatzsteuer).
# Die Vorsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten ebenso bei den Produktstammdaten hinterlegt werden (in Abhängigkeit
# der Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter
# Finanzbuchhaltung (Kategorie: Vorsteuer).
# Die Zuordnung der Steuern für Ein- und Ausfuhren aus EU Ländern, sowie auch
# für den Ein- und Verkauf aus und in Drittländer sollten beim Partner
# (Lieferant/Kunde) hinterlegt werden (in Anhängigkeit vom Herkunftsland
# des Lieferanten/Kunden). Die Zuordnung beim Kunden ist 'höherwertig' als
# die Zuordnung bei Produkten und überschreibt diese im Einzelfall.
#
# Zur Vereinfachung der Steuerausweise und Buchung bei Auslandsgeschäften
# erlaubt OpenERP ein generelles Mapping von Steuerausweis und Steuerkonten
# (z.B. Zuordnung 'Umsatzsteuer 19%' zu 'steuerfreie Einfuhren aus der EU')
# zwecks Zuordnung dieses Mappings zum ausländischen Partner (Kunde/Lieferant).
# Die Rechnungsbuchung beim Einkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Vorsteuer Steuermessbetrag (z.B. Vorsteuer
# Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Vorsteuern' (z.B. Vorsteuer
# 19%). Durch multidimensionale Hierachien können verschiedene Positionen
# zusammengefasst werden und dann in Form eines Reports ausgegeben werden.
#
# Die Rechnungsbuchung beim Verkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Umsatzsteuer Steuermessbetrag
# (z.B. Umsatzsteuer Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Umsatzsteuer'
# (z.B. Umsatzsteuer 19%). Durch multidimensionale Hierachien können
# verschiedene Positionen zusammengefasst werden.
# Die zugewiesenen Steuerausweise können auf Ebene der einzelnen
# Rechnung (Eingangs- und Ausgangsrechnung) nachvollzogen werden,
# und dort gegebenenfalls angepasst werden.
# Rechnungsgutschriften führen zu einer Korrektur (Gegenposition)
# der Steuerbuchung, in Form einer spiegelbildlichen Buchung.
|
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
match_stdout = default_match_stdout_function,
match_stderr = default_match_stderr_function,
diff = default_diff_stderr_function,
diff_stdout = default_diff_stdout_function,
diff_stderr = default_diff_stderr_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.diff_stderr(actual, expected)
test.diff_stdout(actual, expected)
test.match(actual, expected)
test.match_stderr(actual, expected)
test.match_stdout(actual, expected)
test.set_match_function(match, stdout, stderr)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound global functions that handle
matching in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
These functions are also available as static methods:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.TestCmd.match_re_dotall)
These static methods can be accessed by a string naming the method:
import TestCmd
test = TestCmd.TestCmd(match = 'match_exact')
test = TestCmd.TestCmd(match = 'match_re')
test = TestCmd.TestCmd(match = 'match_re_dotall')
The TestCmd module provides unbound global functions that can be used
for the "diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
test = TestCmd.TestCmd(diff = TestCmd.context_diff)
test = TestCmd.TestCmd(diff = TestCmd.unified_diff)
These functions are also available as static methods:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.TestCmd.match_re,
diff = TestCmd.TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.TestCmd.simple_diff)
test = TestCmd.TestCmd(diff = TestCmd.TestCmd.context_diff)
test = TestCmd.TestCmd(diff = TestCmd.TestCmd.unified_diff)
These static methods can be accessed by a string naming the method:
import TestCmd
test = TestCmd.TestCmd(match = 'match_re', diff = 'diff_re')
test = TestCmd.TestCmd(diff = 'simple_diff')
test = TestCmd.TestCmd(diff = 'context_diff')
test = TestCmd.TestCmd(diff = 'unified_diff')
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
#!/usr/bin/env python
# Try to determine how much RAM is currently being used per program.
# Note per _program_, not per process. So for example this script
# will report RAM used by all httpd process together. In detail it reports:
# sum(private RAM for program processes) + sum(Shared RAM for program processes)
# The shared RAM is problematic to calculate, and this script automatically
# selects the most accurate method available for your kernel.
# Licence: LGPLv2
# Author: EMAIL Source: http://www.pixelbeat.org/scripts/ps_mem.py
# V1.0 06 Jul 2005 Initial release
# V1.1 11 Aug 2006 root permission required for accuracy
# V1.2 08 Nov 2006 Add total to output
# Use KiB,MiB,... for units rather than K,M,...
# V1.3 22 Nov 2006 Ignore shared col from /proc/$pid/statm for
# 2.6 kernels up to and including 2.6.9.
# There it represented the total file backed extent
# V1.4 23 Nov 2006 Remove total from output as it's meaningless
# (the shared values overlap with other programs).
# Display the shared column. This extra info is
# useful, especially as it overlaps between programs.
# V1.5 26 Mar 2007 Remove redundant recursion from human()
# V1.6 05 Jun 2007 Also report number of processes with a given name.
# Patch from EMAIL V1.7 20 Sep 2007 Use PSS from /proc/$pid/smaps if available, which
# fixes some over-estimation and allows totalling.
# Enumerate the PIDs directly rather than using ps,
# which fixes the possible race between reading
# RSS with ps, and shared memory with this program.
# Also we can show non truncated command names.
# V1.8 28 Sep 2007 More accurate matching for stats in /proc/$pid/smaps
# as otherwise could match libraries causing a crash.
# Patch from EMAIL V1.9 20 Feb 2008 Fix invalid values reported when PSS is available.
# Reported by NAME <EMAIL>
# V3.1 10 May 2013
# http://github.com/pixelb/scripts/commits/master/scripts/ps_mem.py
# Notes:
#
# All interpreted programs where the interpreter is started
# by the shell or with env, will be merged to the interpreter
# (as that's what's given to exec). For e.g. all python programs
# starting with "#!/usr/bin/env python" will be grouped under python.
# You can change this by using the full command line but that will
# have the undesirable affect of splitting up programs started with
# differing parameters (for e.g. mingetty tty[1-6]).
#
# For 2.6 kernels up to and including 2.6.13 and later 2.4 redhat kernels
# (rmap vm without smaps) it can not be accurately determined how many pages
# are shared between processes in general or within a program in our case:
# http://lkml.org/lkml/2005/7/6/250
# A warning is printed if overestimation is possible.
# In addition for 2.6 kernels up to 2.6.9 inclusive, the shared
# value in /proc/$pid/statm is the total file-backed extent of a process.
# We ignore that, introducing more overestimation, again printing a warning.
# Since kernel 2.6.23-rc8-mm1 PSS is available in smaps, which allows
# us to calculate a more accurate value for the total RAM used by programs.
#
# Programs that use CLONE_VM without CLONE_THREAD are discounted by assuming
# they're the only programs that have the same /proc/$PID/smaps file for
# each instance. This will fail if there are multiple real instances of a
# program that then use CLONE_VM without CLONE_THREAD, or if a clone changes
# its memory map while we're checksumming each /proc/$PID/smaps.
#
# I don't take account of memory allocated for a program
# by other programs. For e.g. memory used in the X server for
# a program could be determined, but is not.
#
# FreeBSD is supported if linprocfs is mounted at /compat/linux/proc/
# FreeBSD 8.0 supports up to a level of Linux 2.6.16
# TODO/FIXME: The script currently requires root permission to gather
# memory usage details about all the processes. This restriction
# has to be relaxed --- when running without root only the user's
# processes details should be displayed
|
# import sys
# import scipy as SP
# import scipy.linalg as LA
# sys.path.insert(0,'/Users/casale/Documents/limix/limix')
# from limix.core.covar import freeform
# from limix.core.gp.gp3kronSumApprox import gp3kronSumApprox
# import limix.core.optimize.optimize_bfgs as OPT
# sys.path.append('./../../../build/release.darwin/interfaces/python/limix/modules')
# import varianceDecomposition as VAR
#
# import h5py
# import pylab as PL
# PL.ion()
#
# def genPheno(G=None,X=None,var_g=None,var_x=None,P=None):
# var_n = 1-var_g-var_x
# Yg = SP.dot(G,SP.randn(G.shape[1],P))
# Yx = SP.dot(X,SP.randn(X.shape[1],P))
# Yn = SP.randn(X.shape[0],P)
# Yg*= SP.sqrt(var_g/Yg.var(0).mean())
# Yx*= SP.sqrt(var_x/Yx.var(0).mean())
# Yn*= SP.sqrt(var_n/Yn.var(0).mean())
# RV = Yg+Yx+Yn
# RV-= RV.mean(0); RV/= RV.std(0)
# return RV
#
# if __name__=='__main__':
#
# P = 4
#
# seed = int(sys.argv[1])
# SP.random.seed(seed)
#
# # import data
# fname = 'data/arab107_preprocessed.hdf5'
# f = h5py.File(fname,'r')
# X = f['genotype'][:]
# X-= X.mean(0); X/=X.std(0)
# G = X[:,0:200]
#
# Y = genPheno(G=G,X=X,var_g=0.10,var_x=0.40,P=P)
# XX = SP.dot(X,X.T)
# XX/= XX.diagonal().mean()
# XX+= 1e-4*SP.eye(XX.shape[0])
# GG = SP.dot(G,G.T)
# GG/= GG.diagonal().mean()
# GG+= 1e-4*SP.eye(GG.shape[0])
#
# Cr = freeform(P)
# Cg = freeform(P)
# Cn = freeform(P)
# gp = gp3kronSumApprox(Y=Y,Cr=Cr,Cg=Cg,Cn=Cn,XX=XX,GG=GG)
# n_rips = 10
# for rip in range(n_rips):
#
# Cr.setRandomParams()
# Cg.setRandomParams()
# Cn.setRandomParams()
# params = gp.getParams()
# gp.setParams(params)
#
# conv,info = OPT.opt_hyper(gp,params,factr=1e3)
# print(conv)
#
# print('Cr')
# print((Cr.K()))
# print('Cg')
# print((Cg.K()))
# print('Cn')
# print((Cn.K()))
#
# ipdb.set_trace()
#
# gp.setBound(0)
# conv,info = OPT.opt_hyper(gp,params,factr=1e3)
# print(conv)
#
# print('Cr')
# print((Cr.K()))
# print('Cg')
# print((Cg.K()))
# print('Cn')
# print((Cn.K()))
#
# gp.setBound(1)
# conv,info = OPT.opt_hyper(gp,params,factr=1e3)
# print(conv)
#
# print('Cr')
# print((Cr.K()))
# print('Cg')
# print((Cg.K()))
# print('Cn')
# print((Cn.K()))
#
# ipdb.set_trace()
#
|
# -*- coding: utf-8 -*-
# Spearmint
#
# Academic and Non-Commercial Research Use Software License and Terms
# of Use
#
# Spearmint is a software package to perform Bayesian optimization
# according to specific algorithms (the “Software”). The Software is
# designed to automatically run experiments (thus the code name
# 'spearmint') in a manner that iteratively adjusts a number of
# parameters so as to minimize some objective in as few runs as
# possible.
#
# The Software was developed by NAME NAME and
# NAME at Harvard University, NAME at the
# University of Toronto (“Toronto”), and NAME at the
# Université de Sherbrooke (“Sherbrooke”), which assigned its rights
# in the Software to Socpra Sciences et Génie
# S.E.C. (“Socpra”). Pursuant to an inter-institutional agreement
# between the parties, it is distributed for free academic and
# non-commercial research use by the President and Fellows of Harvard
# College (“Harvard”).
#
# Using the Software indicates your agreement to be bound by the terms
# of this Software Use Agreement (“Agreement”). Absent your agreement
# to the terms below, you (the “End User”) have no rights to hold or
# use the Software whatsoever.
#
# Harvard agrees to grant hereunder the limited non-exclusive license
# to End User for the use of the Software in the performance of End
# User’s internal, non-commercial research and academic use at End
# User’s academic or not-for-profit research institution
# (“Institution”) on the following terms and conditions:
#
# 1. NO REDISTRIBUTION. The Software remains the property Harvard,
# Toronto and Socpra, and except as set forth in Section 4, End User
# shall not publish, distribute, or otherwise transfer or make
# available the Software to any other party.
#
# 2. NO COMMERCIAL USE. End User shall not use the Software for
# commercial purposes and any such use of the Software is expressly
# prohibited. This includes, but is not limited to, use of the
# Software in fee-for-service arrangements, core facilities or
# laboratories or to provide research services to (or in collaboration
# with) third parties for a fee, and in industry-sponsored
# collaborative research projects where any commercial rights are
# granted to the sponsor. If End User wishes to use the Software for
# commercial purposes or for any other restricted purpose, End User
# must execute a separate license agreement with Harvard.
#
# Requests for use of the Software for commercial purposes, please
# contact:
#
# Office of Technology Development
# Harvard University
# Smith Campus Center, Suite 727E
# 1350 Massachusetts Avenue
# Cambridge, MA 02138 USA
# Telephone: (617) 495-3067
# Facsimile: (617) 495-9568
# E-mail: EMAIL 3. OWNERSHIP AND COPYRIGHT NOTICE. Harvard, Toronto and Socpra own
# all intellectual property in the Software. End User shall gain no
# ownership to the Software. End User shall not remove or delete and
# shall retain in the Software, in any modifications to Software and
# in any Derivative Works, the copyright, trademark, or other notices
# pertaining to Software as provided with the Software.
#
# 4. DERIVATIVE WORKS. End User may create and use Derivative Works,
# as such term is defined under U.S. copyright laws, provided that any
# such Derivative Works shall be restricted to non-commercial,
# internal research and academic use at End User’s Institution. End
# User may distribute Derivative Works to other Institutions solely
# for the performance of non-commercial, internal research and
# academic use on terms substantially similar to this License and
# Terms of Use.
#
# 5. FEEDBACK. In order to improve the Software, comments from End
# Users may be useful. End User agrees to provide Harvard with
# feedback on the End User’s use of the Software (e.g., any bugs in
# the Software, the user experience, etc.). Harvard is permitted to
# use such information provided by End User in making changes and
# improvements to the Software without compensation or an accounting
# to End User.
#
# 6. NON ASSERT. End User acknowledges that Harvard, Toronto and/or
# Sherbrooke or Socpra may develop modifications to the Software that
# may be based on the feedback provided by End User under Section 5
# above. Harvard, Toronto and Sherbrooke/Socpra shall not be
# restricted in any way by End User regarding their use of such
# information. End User acknowledges the right of Harvard, Toronto
# and Sherbrooke/Socpra to prepare, publish, display, reproduce,
# transmit and or use modifications to the Software that may be
# substantially similar or functionally equivalent to End User’s
# modifications and/or improvements if any. In the event that End
# User obtains patent protection for any modification or improvement
# to Software, End User agrees not to allege or enjoin infringement of
# End User’s patent against Harvard, Toronto or Sherbrooke or Socpra,
# or any of the researchers, medical or research staff, officers,
# directors and employees of those institutions.
#
# 7. PUBLICATION & ATTRIBUTION. End User has the right to publish,
# present, or share results from the use of the Software. In
# accordance with customary academic practice, End User will
# acknowledge Harvard, Toronto and Sherbrooke/Socpra as the providers
# of the Software and may cite the relevant reference(s) from the
# following list of publications:
#
# Practical Bayesian Optimization of Machine Learning Algorithms
# NAME, NAME and NAME Neural Information Processing Systems, 2012
#
# Multi-Task Bayesian Optimization
# NAME, NAME and NAME Advances in Neural Information Processing Systems, 2013
#
# Input Warping for Bayesian Optimization of Non-stationary Functions
# NAME, NAME, NAME and NAME Preprint, arXiv:1402.0929, http://arxiv.org/abs/1402.0929, 2013
#
# Bayesian Optimization and Semiparametric Models with Applications to
# Assistive Technology NAME, PhD Thesis, University of
# Toronto, 2013
#
# 8. NO WARRANTIES. THE SOFTWARE IS PROVIDED "AS IS." TO THE FULLEST
# EXTENT PERMITTED BY LAW, HARVARD, TORONTO AND SHERBROOKE AND SOCPRA
# HEREBY DISCLAIM ALL WARRANTIES OF ANY KIND (EXPRESS, IMPLIED OR
# OTHERWISE) REGARDING THE SOFTWARE, INCLUDING BUT NOT LIMITED TO ANY
# IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
# PURPOSE, OWNERSHIP, AND NON-INFRINGEMENT. HARVARD, TORONTO AND
# SHERBROOKE AND SOCPRA MAKE NO WARRANTY ABOUT THE ACCURACY,
# RELIABILITY, COMPLETENESS, TIMELINESS, SUFFICIENCY OR QUALITY OF THE
# SOFTWARE. HARVARD, TORONTO AND SHERBROOKE AND SOCPRA DO NOT WARRANT
# THAT THE SOFTWARE WILL OPERATE WITHOUT ERROR OR INTERRUPTION.
#
# 9. LIMITATIONS OF LIABILITY AND REMEDIES. USE OF THE SOFTWARE IS AT
# END USER’S OWN RISK. IF END USER IS DISSATISFIED WITH THE SOFTWARE,
# ITS EXCLUSIVE REMEDY IS TO STOP USING IT. IN NO EVENT SHALL
# HARVARD, TORONTO OR SHERBROOKE OR SOCPRA BE LIABLE TO END USER OR
# ITS INSTITUTION, IN CONTRACT, TORT OR OTHERWISE, FOR ANY DIRECT,
# INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL, PUNITIVE OR OTHER
# DAMAGES OF ANY KIND WHATSOEVER ARISING OUT OF OR IN CONNECTION WITH
# THE SOFTWARE, EVEN IF HARVARD, TORONTO OR SHERBROOKE OR SOCPRA IS
# NEGLIGENT OR OTHERWISE AT FAULT, AND REGARDLESS OF WHETHER HARVARD,
# TORONTO OR SHERBROOKE OR SOCPRA IS ADVISED OF THE POSSIBILITY OF
# SUCH DAMAGES.
#
# 10. INDEMNIFICATION. To the extent permitted by law, End User shall
# indemnify, defend and hold harmless Harvard, Toronto and Sherbrooke
# and Socpra, their corporate affiliates, current or future directors,
# trustees, officers, faculty, medical and professional staff,
# employees, students and agents and their respective successors,
# heirs and assigns (the "Indemnitees"), against any liability,
# damage, loss or expense (including reasonable attorney's fees and
# expenses of litigation) incurred by or imposed upon the Indemnitees
# or any one of them in connection with any claims, suits, actions,
# demands or judgments arising from End User’s breach of this
# Agreement or its Institution’s use of the Software except to the
# extent caused by the gross negligence or willful misconduct of
# Harvard, Toronto or Sherbrooke or Socpra. This indemnification
# provision shall survive expiration or termination of this Agreement.
#
# 11. GOVERNING LAW. This Agreement shall be construed and governed by
# the laws of the Commonwealth of Massachusetts regardless of
# otherwise applicable choice of law standards.
#
# 12. NON-USE OF NAME. Nothing in this License and Terms of Use shall
# be construed as granting End Users or their Institutions any rights
# or licenses to use any trademarks, service marks or logos associated
# with the Software. You may not use the terms “Harvard” or
# “University of Toronto” or “Université de Sherbrooke” or “Socpra
# Sciences et Génie S.E.C.” (or a substantially similar term) in any
# way that is inconsistent with the permitted uses described
# herein. You agree not to use any name or emblem of Harvard, Toronto
# or Sherbrooke, or any of their subdivisions for any purpose, or to
# falsely suggest any relationship between End User (or its
# Institution) and Harvard, Toronto and/or Sherbrooke, or in any
# manner that would infringe or violate any of their rights.
#
# 13. End User represents and warrants that it has the legal authority
# to enter into this License and Terms of Use on behalf of itself and
# its Institution.
|
"""Stuff to parse Sun and NeXT audio files.
An audio file consists of a header followed by the data. The structure
of the header is as follows.
+---------------+
| magic word |
+---------------+
| header size |
+---------------+
| data size |
+---------------+
| encoding |
+---------------+
| sample rate |
+---------------+
| # of channels |
+---------------+
| info |
| |
+---------------+
The magic word consists of the 4 characters '.snd'. Apart from the
info field, all header fields are 4 bytes in size. They are all
32-bit unsigned integers encoded in big-endian byte order.
The header size really gives the start of the data.
The data size is the physical size of the data. From the other
parameters the number of frames can be calculated.
The encoding gives the way in which audio samples are encoded.
Possible values are listed below.
The info field currently consists of an ASCII string giving a
human-readable description of the audio file. The info field is
padded with NUL bytes to the header size.
Usage.
Reading audio files:
f = sunau.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
When the setpos() and rewind() methods are not used, the seek()
method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' or 'ULAW')
getcompname() -- returns human-readable version of
compression type ('not compressed' matches 'NONE')
getparams() -- returns a namedtuple consisting of all of the
above in the above order
getmarkers() -- returns None (for compatibility with the
aifc module)
getmark(id) -- raises an error since the mark does not
exist (for compatibility with the aifc module)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell() and the position given to setpos()
are compatible and have nothing to do with the actual position in the
file.
The close() method is called automatically when the class instance
is destroyed.
Writing audio files:
f = sunau.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)-- set all parameters at once
tell() -- return current position in output file
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes(b'') or
close() to patch up the sizes in the header.
The close() method is called automatically when the class instance
is destroyed.
""" |
# -*- encoding: utf-8 -*-
##############################################################################
#
# Copyright (c) 2009 Veritos - NAME - www.veritos.nl
#
# WARNING: This program as such is intended to be used by professional
# programmers who take the whole responsability of assessing all potential
# consequences resulting from its eventual inadequacies and bugs.
# End users who are looking for a ready-to-use solution with commercial
# garantees and support are strongly adviced to contract a Free Software
# Service Company like Veritos.
#
# This program is Free Software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#
##############################################################################
#
# Deze module werkt in OpenERP 5.0.0 (en waarschijnlijk hoger).
# Deze module werkt niet in OpenERP versie 4 en lager.
#
# Status 1.0 - getest op OpenERP 5.0.3
#
# Versie IP_ADDRESS
# account.account.type
# Basis gelegd voor alle account type
#
# account.account.template
# Basis gelegd met alle benodigde grootboekrekeningen welke via een menu-
# structuur gelinkt zijn aan rubrieken 1 t/m 9.
# De grootboekrekeningen gelinkt aan de account.account.type
# Deze links moeten nog eens goed nagelopen worden.
#
# account.chart.template
# Basis gelegd voor het koppelen van rekeningen aan debiteuren, crediteuren,
# bank, inkoop en verkoop boeken en de BTW configuratie.
#
# Versie IP_ADDRESS
# account.tax.code.template
# Basis gelegd voor de BTW configuratie (structuur)
# Heb als basis het BTW aangifte formulier gebruikt. Of dit werkt?
#
# account.tax.template
# De BTW rekeningen aangemaakt en deze gekoppeld aan de betreffende
# grootboekrekeningen
#
# Versie IP_ADDRESS
# Opschonen van de code en verwijderen van niet gebruikte componenten.
# Versie IP_ADDRESS
# Aanpassen a_expense van 3000 -> 7000
# record id='btw_code_5b' op negatieve waarde gezet
# Versie IP_ADDRESS
# BTW rekeningen hebben typeaanduiding gekregen t.b.v. purchase of sale
# Versie IP_ADDRESS
# Opschonen van module.
# Versie IP_ADDRESS
# Opschonen van module.
# Versie IP_ADDRESS
# Foutje in l10n_nl_wizard.xml gecorrigeerd waardoor de module niet volledig installeerde.
# Versie IP_ADDRESS
# Account Receivable en Payable goed gedefinieerd.
# Versie IP_ADDRESS
# Alle user_type_xxx velden goed gedefinieerd.
# Specifieke bouw en garage gerelateerde grootboeken verwijderd om een standaard module te creeeren.
# Deze module kan dan als basis worden gebruikt voor specifieke doelgroep modules te creeeren.
# Versie IP_ADDRESS
# Correctie van rekening 7010 (stond dubbel met 7014 waardoor installatie verkeerd ging)
# versie IP_ADDRESS
# Correctie op diverse rekening types van user_type_asset -> user_type_liability en user_type_equity
# versie IP_ADDRESS
# Kleine correctie op BTW te vorderen hoog, id was hetzelfde voor beide, waardoor hoog werd overschreven door # overig. Verduidelijking van omschrijvingen in belastingcodes t.b.v. aangifte overzicht.
# versie IP_ADDRESS
# BTW omschrijvingen aangepast, zodat rapporten er beter uitzien. 2a en 5b e.d. verwijderd en enkele omschrijvingen toegevoegd.
# versie IP_ADDRESS - Switch to English
# Added properties_stock_xxx accounts for correct stock valuation, changed 7000-accounts from type cash to type expense
# Changed naming of 7020 and 7030 to Kostprijs omzet xxxx
|
"""Exception classes for CherryPy.
CherryPy provides (and uses) exceptions for declaring that the HTTP response
should be a status other than the default "200 OK". You can ``raise`` them like
normal Python exceptions. You can also call them and they will raise
themselves; this means you can set an
:class:`HTTPError<cherrypy._cperror.HTTPError>`
or :class:`HTTPRedirect<cherrypy._cperror.HTTPRedirect>` as the
:attr:`request.handler<cherrypy._cprequest.Request.handler>`.
.. _redirectingpost:
Redirecting POST
================
When you GET a resource and are redirected by the server to another Location,
there's generally no problem since GET is both a "safe method" (there should
be no side-effects) and an "idempotent method" (multiple calls are no different
than a single call).
POST, however, is neither safe nor idempotent--if you
charge a credit card, you don't want to be charged twice by a redirect!
For this reason, *none* of the 3xx responses permit a user-agent (browser) to
resubmit a POST on redirection without first confirming the action with the
user:
===== ================================= ===========
300 Multiple Choices Confirm with the user
301 Moved Permanently Confirm with the user
302 Found (Object moved temporarily) Confirm with the user
303 See Other GET the new URI--no confirmation
304 Not modified (for conditional GET only--POST should not raise this error)
305 Use Proxy Confirm with the user
307 Temporary Redirect Confirm with the user
===== ================================= ===========
However, browsers have historically implemented these restrictions poorly;
in particular, many browsers do not force the user to confirm 301, 302
or 307 when redirecting POST. For this reason, CherryPy defaults to 303,
which most user-agents appear to have implemented correctly. Therefore, if
you raise HTTPRedirect for a POST request, the user-agent will most likely
attempt to GET the new URI (without asking for confirmation from the user).
We realize this is confusing for developers, but it's the safest thing we
could do. You are of course free to raise ``HTTPRedirect(uri, status=302)``
or any other 3xx status if you know what you're doing, but given the
environment, we couldn't let any of those be the default.
Custom Error Handling
=====================
.. image:: /refman/cperrors.gif
Anticipated HTTP responses
--------------------------
The 'error_page' config namespace can be used to provide custom HTML output for
expected responses (like 404 Not Found). Supply a filename from which the
output will be read. The contents will be interpolated with the values
%(status)s, %(message)s, %(traceback)s, and %(version)s using plain old Python
`string formatting <http://docs.python.org/2/library/stdtypes.html#string-formatting-operations>`_.
::
_cp_config = {
'error_page.404': os.path.join(localDir, "static/index.html")
}
Beginning in version 3.1, you may also provide a function or other callable as
an error_page entry. It will be passed the same status, message, traceback and
version arguments that are interpolated into templates::
def error_page_402(status, message, traceback, version):
return "Error %s - Well, I'm very sorry but you haven't paid!" % status
cherrypy.config.update({'error_page.402': error_page_402})
Also in 3.1, in addition to the numbered error codes, you may also supply
"error_page.default" to handle all codes which do not have their own error_page
entry.
Unanticipated errors
--------------------
CherryPy also has a generic error handling mechanism: whenever an unanticipated
error occurs in your code, it will call
:func:`Request.error_response<cherrypy._cprequest.Request.error_response>` to
set the response status, headers, and body. By default, this is the same
output as
:class:`HTTPError(500) <cherrypy._cperror.HTTPError>`. If you want to provide
some other behavior, you generally replace "request.error_response".
Here is some sample code that shows how to display a custom error message and
send an e-mail containing the error::
from cherrypy import _cperror
def handle_error():
cherrypy.response.status = 500
cherrypy.response.body = [
"<html><body>Sorry, an error occured</body></html>"
]
sendMail('EMAIL',
'Error in your web app',
_cperror.format_exc())
@cherrypy.config(**{'request.error_response': handle_error})
class Root:
pass
Note that you have to explicitly set
:attr:`response.body <cherrypy._cprequest.Response.body>`
and not simply return an error message as a result.
""" |
# Test 64-bit COMPARE LOGICAL AND BRANCH in cases where the sheer number of
# instructions causes some branches to be out of range.
# RUN: python %s | llc -mtriple=s390x-linux-gnu | FileCheck %s
# Construct:
#
# before0:
# conditional branch to after0
# ...
# beforeN:
# conditional branch to after0
# main:
# 0xffcc bytes, from MVIY instructions
# conditional branch to main
# after0:
# ...
# conditional branch to main
# afterN:
#
# Each conditional branch sequence occupies 12 bytes if it uses a short
# branch and 16 if it uses a long one. The ones before "main:" have to
# take the branch length into account, which is 6 for short branches,
# so the final (0x34 - 6) / 12 == 3 blocks can use short branches.
# The ones after "main:" do not, so the first 0x34 / 12 == 4 blocks
# can use short branches. The conservative algorithm we use makes
# one of the forward branches unnecessarily long, as noted in the
# check output below.
#
# CHECK: lgb [[REG:%r[0-5]]], 0(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 1(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 2(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 3(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 4(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# ...as mentioned above, the next one could be a CLGRJL instead...
# CHECK: lgb [[REG:%r[0-5]]], 5(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 6(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 7(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# ...main goes here...
# CHECK: lgb [[REG:%r[0-5]]], 25(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 26(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 27(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 28(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 29(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 30(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 31(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 32(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
|
"""
# urljoin tests
>>> UrlRewriter.urljoin('http://example.com/test/', '../file.html')
'http://example.com/file.html'
>>> UrlRewriter.urljoin('http://example.com/test/', '../path/../../../file.html')
'http://example.com/file.html'
>>> UrlRewriter.urljoin('http://example.com/test/', '/../file.html')
'http://example.com/file.html'
>>> UrlRewriter.urljoin('http://example.com/', '/abc/../../file.html')
'http://example.com/file.html'
>>> UrlRewriter.urljoin('http://example.com/path/more/', 'abc/../../file.html')
'http://example.com/path/file.html'
>>> UrlRewriter.urljoin('http://example.com/test/', 'file.html')
'http://example.com/test/file.html'
# UrlRewriter tests
>>> do_rewrite('other.html', '20131010/http://example.com/path/page.html', 'https://web.archive.org/web/')
'/web/20131010/http://example.com/path/other.html'
>>> do_rewrite('file.js', '20131010/http://example.com/path/page.html', 'https://web.archive.org/web/', 'js_')
'/web/20131010js_/http://example.com/path/file.js'
>>> do_rewrite('file.js', '20131010/http://example.com/', '/coll/')
'/coll/20131010/http://example.com/file.js'
>>> do_rewrite('file.js', '20131010/http://example.com', '/coll/', 'js_')
'/coll/20131010js_/http://example.com/file.js'
>>> do_rewrite('file.js', '20131010/http://example.com', '/coll/', '')
'/coll/20131010/http://example.com/file.js'
>>> do_rewrite('/other.html', '20130907*/http://example.com/path/page.html', 'http://localhost:8080/coll/')
'/coll/20130907*/http://example.com/other.html'
>>> do_rewrite('/other.html', '20130907*/http://example.com/path/page.html', '/coll/')
'/coll/20130907*/http://example.com/other.html'
>>> do_rewrite('./other.html', '20130907*/http://example.com/path/page.html', '/coll/')
'/coll/20130907*/http://example.com/path/other.html'
>>> do_rewrite('../other.html', '20131112im_/http://example.com/path/page.html', '/coll/')
'/coll/20131112im_/http://example.com/other.html'
>>> do_rewrite('../../other.html', '*/http://example.com/index.html', 'localhost:8080/')
'localhost:8080/*/http://example.com/other.html'
>>> do_rewrite('path/../../other.html', '*/http://example.com/index.html', 'localhost:8080/')
'localhost:8080/*/http://example.com/other.html'
>>> do_rewrite('http://some-other-site.com', '20101226101112/http://example.com/index.html', 'localhost:8080/')
'localhost:8080/20101226101112/http://some-other-site.com'
>>> do_rewrite('http://localhost:8080/web/2014im_/http://some-other-site.com', 'http://example.com/index.html', '/web/', full_prefix='http://localhost:8080/web/')
'http://localhost:8080/web/2014im_/http://some-other-site.com'
>>> do_rewrite('/web/http://some-other-site.com', 'http://example.com/index.html', '/web/', full_prefix='http://localhost:8080/web/')
'/web/http://some-other-site.com'
>>> do_rewrite(r'http:\/\/some-other-site.com', '20101226101112/http://example.com/index.html', 'https://localhost:8080/')
'https://localhost:8080/20101226101112/http:\\\\/\\\\/some-other-site.com'
>>> do_rewrite(r'//some-other-site.com', '20101226101112/http://example.com/index.html', 'http://localhost:8080/')
'//localhost:8080/20101226101112///some-other-site.com'
>>> do_rewrite(r'\/\/some-other-site.com', '20101226101112/http://example.com/index.html', 'http://localhost:8080/')
'//localhost:8080/20101226101112/\\\\/\\\\/some-other-site.com'
>>> do_rewrite(r'\\/\\/some-other-site.com', '20101226101112/http://example.com/index.html', 'https://localhost:8080/')
'//localhost:8080/20101226101112/\\\\/\\\\/some-other-site.com'
>>> do_rewrite(r'http:\/\/some-other-site.com', '20101226101112/http://example.com/index.html', 'https://localhost:8080/')
'https://localhost:8080/20101226101112/http:\\\\/\\\\/some-other-site.com'
>>> do_rewrite(r'http:\/\/some-other-site.com', '20101226101112/http://example.com/index.html', 'http://localhost:8080/')
'http://localhost:8080/20101226101112/http:\\\\/\\\\/some-other-site.com'
>>> do_rewrite('../../other.html', '2020/http://example.com/index.html', '/')
'/2020/http://example.com/other.html'
>>> do_rewrite('../../other.html', '2020/http://example.com/index.html', '')
'2020/http://example.com/other.html'
>>> do_rewrite('', '20131010010203/http://example.com/file.html', '/web/')
'/web/20131010010203/http://example.com/file.html'
>>> do_rewrite('#anchor', '20131010/http://example.com/path/page.html', 'https://web.archive.org/web/')
'#anchor'
>>> do_rewrite('mailto:EMAIL', '20131010/http://example.com/path/page.html', 'https://web.archive.org/web/')
'mailto:EMAIL'
>>> do_rewrite('file:///some/path/', '20131010/http://example.com/path/page.html', 'https://web.archive.org/web/')
'file:///some/path/'
>>> UrlRewriter('19960708im_/http://domain.example.com/path.txt', '/abc/').get_new_url(url='')
'/abc/19960708im_/'
>>> UrlRewriter('2013id_/example.com/file/path/blah.html', '/123/').get_new_url(timestamp='20131024')
'/123/20131024id_/http://example.com/file/path/blah.html'
# deprefix tests
>>> do_deprefix('2013id_/http://example.com/file/path/blah.html?param=http://localhost:8080/pywb/20141226/http://example.com/', '/pywb/', 'http://localhost:8080/pywb/')
'http://example.com/file/path/blah.html?param=http://example.com/'
>>> do_deprefix('2013id_/http://example.com/file/path/blah.html?param=http://localhost:8080/pywb/if_/https://example.com/filename.html', '/pywb/', 'http://localhost:8080/pywb/')
'http://example.com/file/path/blah.html?param=https://example.com/filename.html'
>>> do_deprefix('2013id_/http://example.com/file/path/blah.html?param=http://localhost:8080/pywb/https://example.com/filename.html', '/pywb/', 'http://localhost:8080/pywb/')
'http://example.com/file/path/blah.html?param=https://example.com/filename.html'
>>> do_deprefix('http://example.com/file.html?param=http://localhost:8080/pywb/https%3A//example.com/filename.html&other=value&a=b¶m2=http://localhost:8080/pywb/http://test.example.com', '/pywb/', 'http://localhost:8080/pywb/')
'http://example.com/file.html?param=https://example.com/filename.html&other=value&a=b¶m2=http://test.example.com'
# urlencoded
>>> do_deprefix('http://example.com/file.html?foo=bar&url=' + quote_plus('http://localhost:8080/pywb/http://example.com/filename.html') + '&foo2=bar2', '/pywb/', 'http://localhost:8080/pywb/')
'http://example.com/file.html?foo=bar&url=http://example.com/filename.html&foo2=bar2'
# with extra path
>>> do_deprefix('http://example.com/file.html?foo=bar&url=' + quote_plus('http://localhost:8080/pywb/extra/path/http://example.com/filename.html') + '&foo2=bar2', '/pywb/', 'http://localhost:8080/pywb/')
'http://example.com/file.html?foo=bar&url=http://example.com/filename.html&foo2=bar2'
# SchemeOnlyUrlRewriter tests
>>> SchemeOnlyUrlRewriter('http://example.com/').rewrite('https://example.com/abc')
'http://example.com/abc'
>>> SchemeOnlyUrlRewriter('http://example.com/abc').rewrite('http://example.com/abc')
'http://example.com/abc'
>>> SchemeOnlyUrlRewriter('https://example.com/abc').rewrite('http://example.com/abc')
'https://example.com/abc'
>>> SchemeOnlyUrlRewriter('https://example.com/abc').rewrite('https://example.com/abc')
'https://example.com/abc'
>>> SchemeOnlyUrlRewriter('http://example.com/abc').rewrite('//example.com/abc')
'//example.com/abc'
>>> SchemeOnlyUrlRewriter('https://example.com/abc').rewrite('//example.com/abc')
'//example.com/abc'
# rebase is identity
>>> x = SchemeOnlyUrlRewriter('http://example.com'); x.rebase_rewriter('https://example.com/') == x
True
""" |
"""
==============
Array indexing
==============
Array indexing refers to any use of the square brackets ([]) to index
array values. There are many options to indexing, which give numpy
indexing great power, but with power comes some complexity and the
potential for confusion. This section is just an overview of the
various options and issues related to indexing. Aside from single
element indexing, the details on most of these options are to be
found in related sections.
Assignment vs referencing
=========================
Most of the following examples show the use of indexing when
referencing data in an array. The examples work just as well
when assigning to an array. See the section at the end for
specific examples and explanations on how assignments work.
Single element indexing
=======================
Single element indexing for a 1-D array is what one expects. It work
exactly like that for other standard Python sequences. It is 0-based,
and accepts negative indices for indexing from the end of the array. ::
>>> x = np.arange(10)
>>> x[2]
2
>>> x[-2]
8
Unlike lists and tuples, numpy arrays support multidimensional indexing
for multidimensional arrays. That means that it is not necessary to
separate each dimension's index into its own set of square brackets. ::
>>> x.shape = (2,5) # now x is 2-dimensional
>>> x[1,3]
8
>>> x[1,-1]
9
Note that if one indexes a multidimensional array with fewer indices
than dimensions, one gets a subdimensional array. For example: ::
>>> x[0]
array([0, 1, 2, 3, 4])
That is, each index specified selects the array corresponding to the
rest of the dimensions selected. In the above example, choosing 0
means that remaining dimension of lenth 5 is being left unspecified,
and that what is returned is an array of that dimensionality and size.
It must be noted that the returned array is not a copy of the original,
but points to the same values in memory as does the original array.
In this case, the 1-D array at the first position (0) is returned.
So using a single index on the returned array, results in a single
element being returned. That is: ::
>>> x[0][2]
2
So note that ``x[0,2] = x[0][2]`` though the second case is more
inefficient a new temporary array is created after the first index
that is subsequently indexed by 2.
Note to those used to IDL or Fortran memory order as it relates to
indexing. Numpy uses C-order indexing. That means that the last
index usually represents the most rapidly changing memory location,
unlike Fortran or IDL, where the first index represents the most
rapidly changing location in memory. This difference represents a
great potential for confusion.
Other indexing options
======================
It is possible to slice and stride arrays to extract arrays of the
same number of dimensions, but of different sizes than the original.
The slicing and striding works exactly the same way it does for lists
and tuples except that they can be applied to multiple dimensions as
well. A few examples illustrates best: ::
>>> x = np.arange(10)
>>> x[2:5]
array([2, 3, 4])
>>> x[:-7]
array([0, 1, 2])
>>> x[1:7:2]
array([1, 3, 5])
>>> y = np.arange(35).reshape(5,7)
>>> y[1:5:2,::3]
array([[ 7, 10, 13],
[21, 24, 27]])
Note that slices of arrays do not copy the internal array data but
also produce new views of the original data.
It is possible to index arrays with other arrays for the purposes of
selecting lists of values out of arrays into new arrays. There are
two different ways of accomplishing this. One uses one or more arrays
of index values. The other involves giving a boolean array of the proper
shape to indicate the values to be selected. Index arrays are a very
powerful tool that allow one to avoid looping over individual elements in
arrays and thus greatly improve performance.
It is possible to use special features to effectively increase the
number of dimensions in an array through indexing so the resulting
array aquires the shape needed for use in an expression or with a
specific function.
Index arrays
============
Numpy arrays may be indexed with other arrays (or any other sequence-
like object that can be converted to an array, such as lists, with the
exception of tuples; see the end of this document for why this is). The
use of index arrays ranges from simple, straightforward cases to
complex, hard-to-understand cases. For all cases of index arrays, what
is returned is a copy of the original data, not a view as one gets for
slices.
Index arrays must be of integer type. Each value in the array indicates
which value in the array to use in place of the index. To illustrate: ::
>>> x = np.arange(10,1,-1)
>>> x
array([10, 9, 8, 7, 6, 5, 4, 3, 2])
>>> x[np.array([3, 3, 1, 8])]
array([7, 7, 9, 2])
The index array consisting of the values 3, 3, 1 and 8 correspondingly
create an array of length 4 (same as the index array) where each index
is replaced by the value the index array has in the array being indexed.
Negative values are permitted and work as they do with single indices
or slices: ::
>>> x[np.array([3,3,-3,8])]
array([7, 7, 4, 2])
It is an error to have index values out of bounds: ::
>>> x[np.array([3, 3, 20, 8])]
<type 'exceptions.IndexError'>: index 20 out of bounds 0<=index<9
Generally speaking, what is returned when index arrays are used is
an array with the same shape as the index array, but with the type
and values of the array being indexed. As an example, we can use a
multidimensional index array instead: ::
>>> x[np.array([[1,1],[2,3]])]
array([[9, 9],
[8, 7]])
Indexing Multi-dimensional arrays
=================================
Things become more complex when multidimensional arrays are indexed,
particularly with multidimensional index arrays. These tend to be
more unusal uses, but theyare permitted, and they are useful for some
problems. We'll start with thesimplest multidimensional case (using
the array y from the previous examples): ::
>>> y[np.array([0,2,4]), np.array([0,1,2])]
array([ 0, 15, 30])
In this case, if the index arrays have a matching shape, and there is
an index array for each dimension of the array being indexed, the
resultant array has the same shape as the index arrays, and the values
correspond to the index set for each position in the index arrays. In
this example, the first index value is 0 for both index arrays, and
thus the first value of the resultant array is y[0,0]. The next value
is y[2,1], and the last is y[4,2].
If the index arrays do not have the same shape, there is an attempt to
broadcast them to the same shape. If they cannot be broadcast to the
same shape, an exception is raised: ::
>>> y[np.array([0,2,4]), np.array([0,1])]
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be
broadcast to a single shape
The broadcasting mechanism permits index arrays to be combined with
scalars for other indices. The effect is that the scalar value is used
for all the corresponding values of the index arrays: ::
>>> y[np.array([0,2,4]), 1]
array([ 1, 15, 29])
Jumping to the next level of complexity, it is possible to only
partially index an array with index arrays. It takes a bit of thought
to understand what happens in such cases. For example if we just use
one index array with y: ::
>>> y[np.array([0,2,4])]
array([[ 0, 1, 2, 3, 4, 5, 6],
[14, 15, 16, 17, 18, 19, 20],
[28, 29, 30, 31, 32, 33, 34]])
What results is the construction of a new array where each value of
the index array selects one row from the array being indexed and the
resultant array has the resulting shape (size of row, number index
elements).
An example of where this may be useful is for a color lookup table
where we want to map the values of an image into RGB triples for
display. The lookup table could have a shape (nlookup, 3). Indexing
such an array with an image with shape (ny, nx) with dtype=np.uint8
(or any integer type so long as values are with the bounds of the
lookup table) will result in an array of shape (ny, nx, 3) where a
triple of RGB values is associated with each pixel location.
In general, the shape of the resulant array will be the concatenation
of the shape of the index array (or the shape that all the index arrays
were broadcast to) with the shape of any unused dimensions (those not
indexed) in the array being indexed.
Boolean or "mask" index arrays
==============================
Boolean arrays used as indices are treated in a different manner
entirely than index arrays. Boolean arrays must be of the same shape
as the array being indexed, or broadcastable to the same shape. In the
most straightforward case, the boolean array has the same shape: ::
>>> b = y>20
>>> y[b]
array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])
The result is a 1-D array containing all the elements in the indexed
array corresponding to all the true elements in the boolean array. As
with index arrays, what is returned is a copy of the data, not a view
as one gets with slices.
With broadcasting, multidimensional arrays may be the result. For
example: ::
>>> b[:,5] # use a 1-D boolean that broadcasts with y
array([False, False, False, True, True], dtype=bool)
>>> y[b[:,5]]
array([[21, 22, 23, 24, 25, 26, 27],
[28, 29, 30, 31, 32, 33, 34]])
Here the 4th and 5th rows are selected from the indexed array and
combined to make a 2-D array.
Combining index arrays with slices
==================================
Index arrays may be combined with slices. For example: ::
>>> y[np.array([0,2,4]),1:3]
array([[ 1, 2],
[15, 16],
[29, 30]])
In effect, the slice is converted to an index array
np.array([[1,2]]) (shape (1,2)) that is broadcast with the index array
to produce a resultant array of shape (3,2).
Likewise, slicing can be combined with broadcasted boolean indices: ::
>>> y[b[:,5],1:3]
array([[22, 23],
[29, 30]])
Structural indexing tools
=========================
To facilitate easy matching of array shapes with expressions and in
assignments, the np.newaxis object can be used within array indices
to add new dimensions with a size of 1. For example: ::
>>> y.shape
(5, 7)
>>> y[:,np.newaxis,:].shape
(5, 1, 7)
Note that there are no new elements in the array, just that the
dimensionality is increased. This can be handy to combine two
arrays in a way that otherwise would require explicitly reshaping
operations. For example: ::
>>> x = np.arange(5)
>>> x[:,np.newaxis] + x[np.newaxis,:]
array([[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
The ellipsis syntax maybe used to indicate selecting in full any
remaining unspecified dimensions. For example: ::
>>> z = np.arange(81).reshape(3,3,3,3)
>>> z[1,...,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
This is equivalent to: ::
>>> z[1,:,:,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
Assigning values to indexed arrays
==================================
As mentioned, one can select a subset of an array to assign to using
a single index, slices, and index and mask arrays. The value being
assigned to the indexed array must be shape consistent (the same shape
or broadcastable to the shape the index produces). For example, it is
permitted to assign a constant to a slice: ::
>>> x = np.arange(10)
>>> x[2:7] = 1
or an array of the right size: ::
>>> x[2:7] = np.arange(5)
Note that assignments may result in changes if assigning
higher types to lower types (like floats to ints) or even
exceptions (assigning complex to floats or ints): ::
>>> x[1] = 1.2
>>> x[1]
1
>>> x[1] = 1.2j
<type 'exceptions.TypeError'>: can't convert complex to long; use
long(abs(z))
Unlike some of the references (such as array and mask indices)
assignments are always made to the original data in the array
(indeed, nothing else would make sense!). Note though, that some
actions may not work as one may naively expect. This particular
example is often surprising to people: ::
>>> x = np.arange(0, 50, 10)
>>> x
array([ 0, 10, 20, 30, 40])
>>> x[np.array([1, 1, 3, 1])] += 1
>>> x
array([ 0, 11, 20, 31, 40])
Where people expect that the 1st location will be incremented by 3.
In fact, it will only be incremented by 1. The reason is because
a new array is extracted from the original (as a temporary) containing
the values at 1, 1, 3, 1, then the value 1 is added to the temporary,
and then the temporary is assigned back to the original array. Thus
the value of the array at x[1]+1 is assigned to x[1] three times,
rather than being incremented 3 times.
Dealing with variable numbers of indices within programs
========================================================
The index syntax is very powerful but limiting when dealing with
a variable number of indices. For example, if you want to write
a function that can handle arguments with various numbers of
dimensions without having to write special case code for each
number of possible dimensions, how can that be done? If one
supplies to the index a tuple, the tuple will be interpreted
as a list of indices. For example (using the previous definition
for the array z): ::
>>> indices = (1,1,1,1)
>>> z[indices]
40
So one can use code to construct tuples of any number of indices
and then use these within an index.
Slices can be specified within programs by using the slice() function
in Python. For example: ::
>>> indices = (1,1,1,slice(0,2)) # same as [1,1,1,0:2]
>>> z[indices]
array([39, 40])
Likewise, ellipsis can be specified by code by using the Ellipsis
object: ::
>>> indices = (1, Ellipsis, 1) # same as [1,...,1]
>>> z[indices]
array([[28, 31, 34],
[37, 40, 43],
[46, 49, 52]])
For this reason it is possible to use the output from the np.where()
function directly as an index since it always returns a tuple of index
arrays.
Because the special treatment of tuples, they are not automatically
converted to an array as a list would be. As an example: ::
>>> z[[1,1,1,1]] # produces a large array
array([[[[27, 28, 29],
[30, 31, 32], ...
>>> z[(1,1,1,1)] # returns a single value
40
""" |
# -*- coding: utf-8 -*-
#--------------------------------------------------------------------------------------------------------------------------------------------
# Scraper para pelisalacarta, palco y otros plugin de XBMC/Kodi basado en el Api de https://www.themoviedb.org/
# version 1.3:
# - Corregido error al devolver None el path_poster y el backdrop_path
# - Corregido error que hacia que en el listado de generos se fueran acumulando de una llamada a otra
# - Añadido metodo get_generos()
# - Añadido parametros opcional idioma_alternativo al metodo get_sinopsis()
#
#
# Uso:
# Metodos constructores:
# Tmdb(texto_buscado, tipo)
# Parametros:
# texto_buscado:(str) Texto o parte del texto a buscar
# tipo: ("movie" o "tv") Tipo de resultado buscado peliculas o series. Por defecto "movie"
# (opcional) idioma_busqueda: (str) codigo del idioma segun ISO 639-1
# (opcional) include_adult: (bool) Se incluyen contenidos para adultos en la busqueda o no. Por defecto 'False'
# (opcional) year: (str) Año de lanzamiento.
# (opcional) page: (int) Cuando hay muchos resultados para una busqueda estos se organizan por paginas.
# Podemos cargar la pagina que deseemos aunque por defecto siempre es la primera.
# Return:
# Esta llamada devuelve un objeto Tmdb que contiene la primera pagina del resultado de buscar 'texto_buscado'
# en la web themoviedb.org. Cuantos mas parametros opcionales se incluyan mas precisa sera la busqueda.
# Ademas el objeto esta inicializado con el primer resultado de la primera pagina de resultados.
# Tmdb(id_Tmdb,tipo)
# Parametros:
# id_Tmdb: (str) Codigo identificador de una determinada pelicula o serie en themoviedb.org
# tipo: ("movie" o "tv") Tipo de resultado buscado peliculas o series. Por defecto "movie"
# (opcional) idioma_busqueda: (str) codigo del idioma segun ISO 639-1
# Return:
# Esta llamada devuelve un objeto Tmdb que contiene el resultado de buscar una pelicula o serie con el identificador id_Tmd
# en la web themoviedb.org.
# Tmdb(external_id, external_source, tipo)
# Parametros:
# external_id: (str) Codigo identificador de una determinada pelicula o serie en la web referenciada por 'external_source'.
# external_source: (Para series:"imdb_id","freebase_mid","freebase_id","tvdb_id","tvrage_id"; Para peliculas:"imdb_id")
# tipo: ("movie" o "tv") Tipo de resultado buscado peliculas o series. Por defecto "movie"
# (opcional) idioma_busqueda: (str) codigo del idioma segun ISO 639-1
# Return:
# Esta llamada devuelve un objeto Tmdb que contiene el resultado de buscar una pelicula o serie con el identificador 'external_id' de
# la web referenciada por 'external_source' en la web themoviedb.org.
#
# Metodos principales:
# get_id(): Retorna un str con el identificador Tmdb de la pelicula o serie cargada o una cadena vacia si no hubiese nada cargado.
# get_sinopsis(idioma_alternativo): Retorna un str con la sinopsis de la serie o pelicula cargada.
# get_poster (tipo_respuesta,size): Obtiene el poster o un listado de posters.
# get_backdrop (tipo_respuesta,size): Obtiene una imagen de fondo o un listado de imagenes de fondo.
# get_fanart (tipo,idioma,temporada): Obtiene un listado de imagenes del tipo especificado de la web Fanart.tv
# get_episodio (temporada, capitulo): Obtiene un diccionario con datos especificos del episodio.
# get_generos(): Retorna un str con la lista de generos a los que pertenece la pelicula o serie.
#
#
# Otros metodos:
# load_resultado(resultado, page): Cuando la busqueda devuelve varios resultados podemos seleccionar que resultado concreto y de que pagina cargar los datos.
#
# Informacion sobre la api : http://docs.themoviedb.apiary.io
#--------------------------------------------------------------------------------------------------------------------------------------------
|
# Copyright 2011,2012 NAME Copyright 2008 (C) Nicira, Inc.
#
# 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 is derived from the packet library in NOX, which was
# developed by Nicira, Inc.
#======================================================================
#
# DNS Message Format
#
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | ID |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# |QR| Opcode |AA|TC|RD|RA|Z |AD|CD| RCODE |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Questions |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Answerrs |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Authority RRs |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Additional RRs |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Questions ... |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Answer RRs ... |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Authority RRs.. |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Additional RRs. |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
#
# Question format:
#
# 1 1 1 1 1 1
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | |
# / QNAME /
# / /
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | QTYPE |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | QCLASS |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
#
#
#
# All RRs have the following format:
# 1 1 1 1 1 1
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | |
# / /
# / NAME /
# | |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | TYPE |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | CLASS |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | TTL |
# | |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | RDLENGTH |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--|
# / RDATA /
# / /
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
#
#
#======================================================================
# TODO:
# SOA data
# General cleaup/rewrite (code is/has gotten pretty bad)
|
## @file
# @brief Documentation file for GnuCashs python bindings, input file for doxygen.
#
# This file holds the more explanatory parts of the doxygen-source-documentation.
# You will find the contents at @ref python_bindings_page.
#
# @par To-Do:
# @li Work out the relation of scheme/guile and python-bindings
# @li maybe join python_bindings_page and group
# @li work on the structure of the documentation to make it more clear
# @li try to make SWIG include the documentation of the c-source
# @li make function-links in SWIG-generated files work.
# @li some words to the tests
#
# @author NAME @date December 2010
# @ingroup python_bindings
## @defgroup python_bindings Python Bindings Module
# Also have a look at the page @ref python_bindings_page.
## @defgroup python_bindings_examples Python Bindings Examples Module
# @ingroup python_bindings
# The python-bindings come with quite a lot of example scripts.
## @page python_bindings_page Python bindings
# Also have a look at group @ref python_bindings.
#
# They can be found in src/optional/python-bindings.
#
# To enable them in the compilation process you have to add --enable-python-bindings
# to the call of ./configure.
#
# As a starting point have a look at the \link python_bindings_examples example-scripts\endlink.
#
# @section possibilities What are the Python bindings good for ?
#
# The python bindings supply the ability to access a wide range of the core functions of GnuCash. You
# can read and write Transactions, Commodities, Lots, access the business stuff... You gain the ability
# to manipulate your financial data with a flexible scripting language.
#
# Not everything GnuCash can is possible to access though. The bindings focus on basic accounting functions.
# Have a look at the examples to get an impression.
#
# @section python_bindings_section Principles
# The python-bindings are generated using SWIG from parts of the source-files of GnuCash.
#
# @note Python-scripts should not be executed while GnuCash runs. GnuCash is designed as
# a single user application with only one program accessing the data at one time. You can force your
# access but that may corrupt data. Maybe one day that may change but for the moment there is no active development on that.
#
# @subsection swigworks What SWIG does
#
# SWIG extracts informations from the c-sources and provides access to the structures
# to python. It's work is controlled by interface files :
#
# @li gnucash_core.i
# @li timespec.i
# @li glib.i
# @li @link base-typemaps.i src/base-typemaps.i @endlink This file is shared with Guile.
#
# it outputs:
#
# @li gnucash_core.c
# @li gnucash_core_c.py
#
# If you have generated your own local doxygen documentation (by "make doc") after having compiled the python-bindings, doxygen
# will include SWIGs output-files.
# It's actually quite interesting to have a look at them through doxygen, because they contain all that you can
# access from python.
#
# This c-style-api is the bottom layer. It is a quite raw extract and close to the original source. Some more details are described further down.
#
# For some parts there is a second layer of a nice pythonic interface. It is declared
# in
# @li gnucash_core.py and
# @li gnucash_business.py.
# @li function_class.py contains helper functions for that.
#
# @section howto How to use the Python bindings
# @subsection highlevel High level python wrapper classes
# If you
#
# @code >> import gnucash @endcode
#
# You can access the structures of the high level api. For Example you get a Session object by
#
# @code >> session=gnucash.Session() @endcode
#
# Here you will find easy to use things. But sometimes - and at the current level rather sooner than
# later - you may be forced to search for solutions at the :
#
# @subsection c_style_api C-style-api
#
# If you
#
# @code >> import gnucash @endcode
#
# The c-style-api can be accessed via gnucash.gnucash_core_c. You can have a look at all the possibilities
# at gnucash_core_c.py.
#
# You will find a lot of pointers here which you can just ignore if input and output of the function have the
# same type.
#
# For example you could start a session by gnucash.gnucash_core_c.qof_session_begin(). But if you just try
#
# @code session=gnucash.gnucash_core_c.qof_session_begin() @endcode
#
# you will get an error message and realize the lack of convenience for you have to add the correct function parameters.
#
# Not all of the available structures will work. SWIG just takes everything from the sources that it is fed with and translates it. Not everything
# is a working translation, because not everything has been worked through. At this point you are getting closer to the developers who you can
# contact at the mailing-list EMAIL There may be a workaround. Maybe the problem can only be fixed by changing SWIGs input
# files to correctly translate the c-source. Feel free to post a question at the developers list. It may awaken the interest of someone who creates
# some more beautiful python-interfaces.
#
# @section Thisorthat When to use which api ?
#
# The start would surely be the high-level api for you can be quite sure to have something working and you will maybe find
# explanations in the example-scripts. If you search for something that is not yet implemented in that way you will have to
# take your way to the c-style-api.
#
# @section pydoc (Further) documentation
#
# @li The documentation you just read uses doxygen. It collects documentation in GnuCash's sources. Besides that there is
# @li the classic python-documentation using help() and docstrings. Have a look at both.
# @li There is a page in the GnuCash wiki at http://wiki.gnucash.org/wiki/Python
# @li You may also have a look into the archives of EMAIL
# @li On Bugzilla there is also some interesting talk regarding the development process.
# @li Then you can use the abilities of svn to see the history of the code by @code svn log @endcode done in the directory of the python-bindings.
#
|
"""
Define a simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
The ``.npz`` format is the standard format for persisting *multiple* NumPy
arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
files, one for each array.
Capabilities
------------
- Can represent all NumPy arrays including nested record arrays and
object arrays.
- Represents the data in its native binary form.
- Supports Fortran-contiguous arrays directly.
- Stores all of the necessary information to reconstruct the array
including shape and dtype on a machine of a different
architecture. Both little-endian and big-endian arrays are
supported, and a file with little-endian numbers will yield
a little-endian array on any machine reading the file. The
types are described in terms of their actual sizes. For example,
if a machine with a 64-bit C "long int" writes out an array with
"long ints", a reading machine with 32-bit C "long ints" will yield
an array with 64-bit integers.
- Is straightforward to reverse engineer. Datasets often live longer than
the programs that created them. A competent developer should be
able to create a solution in his preferred programming language to
read most ``.npy`` files that he has been given without much
documentation.
- Allows memory-mapping of the data. See `open_memmep`.
- Can be read from a filelike stream object instead of an actual file.
- Stores object arrays, i.e. arrays containing elements that are arbitrary
Python objects. Files with object arrays are not to be mmapable, but
can be read and written to disk.
Limitations
-----------
- Arbitrary subclasses of numpy.ndarray are not completely preserved.
Subclasses will be accepted for writing, but only the array data will
be written out. A regular numpy.ndarray object will be created
upon reading the file.
.. warning::
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by 'f0', 'f1',
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
``loadedarray.view(correct_dtype)`` method.
File extensions
---------------
We recommend using the ``.npy`` and ``.npz`` extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using ``.npy`` and ``.npz``.
Version numbering
-----------------
The version numbering of these formats is independent of NumPy version
numbering. If the format is upgraded, the code in `numpy.io` will still
be able to read and write Version 1.0 files.
Format Version 1.0
------------------
The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
The next 1 byte is an unsigned byte: the major version number of the file
format, e.g. ``\\x01``.
The next 1 byte is an unsigned byte: the minor version number of the file
format, e.g. ``\\x00``. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of
the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array's
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (``\\n``) and padded with
spaces (``\\x20``) to make the total length of
``magic string + 4 + HEADER_LEN`` be evenly divisible by 16 for alignment
purposes.
The dictionary contains three keys:
"descr" : dtype.descr
An object that can be passed as an argument to the `numpy.dtype`
constructor to create the array's dtype.
"fortran_order" : bool
Whether the array data is Fortran-contiguous or not. Since
Fortran-contiguous arrays are a common form of non-C-contiguity,
we allow them to be written directly to disk for efficiency.
"shape" : tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in
alphabetic order. This is for convenience only. A writer SHOULD implement
this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
objects (i.e. ``dtype.hasobject is True``), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on ``fortran_order``) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that ``shape=()`` means there is
1 element) by ``dtype.itemsize``.
Notes
-----
The ``.npy`` format, including reasons for creating it and a comparison of
alternatives, is described fully in the "npy-format" NEP.
""" |
# -*- coding: utf-8 -*-
# routers are dictionaries of URL routing parameters.
#
# For each request, the effective router is:
# the built-in default base router (shown below),
# updated by the BASE router in routes.py routers,
# updated by the app-specific router in routes.py routers (if any),
# updated by the app-specific router from applications/app/routes.py routers (if any)
#
#
# Router members:
#
# default_application: default application name
# applications: list of all recognized applications, or 'ALL' to use all currently installed applications
# Names in applications are always treated as an application names when they appear first in an incoming URL.
# Set applications=None to disable the removal of application names from outgoing URLs.
# domains: optional dict mapping domain names to application names
# The domain name can include a port number: domain.com:8080
# The application name can include a controller: appx/ctlrx
# or a controller and a function: appx/ctlrx/fcnx
# Example:
# domains = { "domain.com" : "app",
# "x.domain.com" : "appx",
# },
# path_prefix: a path fragment that is prefixed to all outgoing URLs and stripped from all incoming URLs
#
# Note: default_application, applications, domains & path_prefix are permitted only in the BASE router,
# and domain makes sense only in an application-specific router.
# The remaining members can appear in the BASE router (as defaults for all applications)
# or in application-specific routers.
#
# default_controller: name of default controller
# default_function: name of default function (in all controllers) or dictionary of default functions
# by controller
# controllers: list of valid controllers in selected app
# or "DEFAULT" to use all controllers in the selected app plus 'static'
# or None to disable controller-name removal.
# Names in controllers are always treated as controller names when they appear in an incoming URL after
# the (optional) application and language names.
# functions: list of valid functions in the default controller (default None) or dictionary of valid
# functions by controller.
# If present, the default function name will be omitted when the controller is the default controller
# and the first arg does not create an ambiguity.
# languages: list of all supported languages
# Names in languages are always treated as language names when they appear in an incoming URL after
# the (optional) application name.
# default_language
# The language code (for example: en, it-it) optionally appears in the URL following
# the application (which may be omitted). For incoming URLs, the code is copied to
# request.language; for outgoing URLs it is taken from request.language.
# If languages=None, language support is disabled.
# The default_language, if any, is omitted from the URL.
# root_static: list of static files accessed from root (by default, favicon.ico & robots.txt)
# (mapped to the default application's static/ directory)
# Each default (including domain-mapped) application has its own root-static files.
# domain: the domain that maps to this application (alternative to using domains in the BASE router)
# exclusive_domain: If True (default is False), an exception is raised if an attempt is made to generate
# an outgoing URL with a different application without providing an explicit host.
# map_hyphen: If True (default is False), hyphens in incoming /a/c/f fields are converted
# to underscores, and back to hyphens in outgoing URLs.
# Language, args and the query string are not affected.
# map_static: By default (None), the default application is not stripped from static URLs.
# Set map_static=True to override this policy.
# Set map_static=False to map lang/static/file to static/lang/file
# acfe_match: regex for valid application, controller, function, extension /a/c/f.e
# file_match: regex for valid subpath (used for static file paths)
# if file_match does not contain '/', it is uses to validate each element of a static file subpath,
# rather than the entire subpath.
# args_match: regex for valid args
# This validation provides a measure of security.
# If it is changed, the application perform its own validation.
#
#
# The built-in default router supplies default values (undefined members are None):
#
# default_router = dict(
# default_application = 'init',
# applications = 'ALL',
# default_controller = 'default',
# controllers = 'DEFAULT',
# default_function = 'index',
# functions = None,
# default_language = None,
# languages = None,
# root_static = ['favicon.ico', 'robots.txt'],
# map_static = None,
# domains = None,
# map_hyphen = False,
# acfe_match = r'\w+$', # legal app/ctlr/fcn/ext
# file_match = r'([-+=@$%\w]|(?<=[-+=@$%\w])[./])*$', # legal static subpath
# args_match = r'([\w@ -]|(?<=[\w@ -])[.=])*$', # legal arg in args
# )
#
# See rewrite.map_url_in() and rewrite.map_url_out() for implementation details.
# This simple router set overrides only the default application name,
# but provides full rewrite functionality.
|
"""
============
Array basics
============
Array types and conversions between types
=========================================
NumPy supports a much greater variety of numerical types than Python does.
This section shows which are available, and how to modify an array's data-type.
============ ==========================================================
Data type Description
============ ==========================================================
``bool_`` Boolean (True or False) stored as a byte
``int_`` Default integer type (same as C ``long``; normally either
``int64`` or ``int32``)
intc Identical to C ``int`` (normally ``int32`` or ``int64``)
intp Integer used for indexing (same as C ``ssize_t``; normally
either ``int32`` or ``int64``)
int8 Byte (-128 to 127)
int16 Integer (-32768 to 32767)
int32 Integer (-2147483648 to 2147483647)
int64 Integer (-9223372036854775808 to 9223372036854775807)
uint8 Unsigned integer (0 to 255)
uint16 Unsigned integer (0 to 65535)
uint32 Unsigned integer (0 to 4294967295)
uint64 Unsigned integer (0 to 18446744073709551615)
``float_`` Shorthand for ``float64``.
float16 Half precision float: sign bit, 5 bits exponent,
10 bits mantissa
float32 Single precision float: sign bit, 8 bits exponent,
23 bits mantissa
float64 Double precision float: sign bit, 11 bits exponent,
52 bits mantissa
``complex_`` Shorthand for ``complex128``.
complex64 Complex number, represented by two 32-bit floats (real
and imaginary components)
complex128 Complex number, represented by two 64-bit floats (real
and imaginary components)
============ ==========================================================
Additionally to ``intc`` the platform dependent C integer types ``short``,
``long``, ``longlong`` and their unsigned versions are defined.
NumPy numerical types are instances of ``dtype`` (data-type) objects, each
having unique characteristics. Once you have imported NumPy using
::
>>> import numpy as np
the dtypes are available as ``np.bool_``, ``np.float32``, etc.
Advanced types, not listed in the table above, are explored in
section :ref:`structured_arrays`.
There are 5 basic numerical types representing booleans (bool), integers (int),
unsigned integers (uint) floating point (float) and complex. Those with numbers
in their name indicate the bitsize of the type (i.e. how many bits are needed
to represent a single value in memory). Some types, such as ``int`` and
``intp``, have differing bitsizes, dependent on the platforms (e.g. 32-bit
vs. 64-bit machines). This should be taken into account when interfacing
with low-level code (such as C or Fortran) where the raw memory is addressed.
Data-types can be used as functions to convert python numbers to array scalars
(see the array scalar section for an explanation), python sequences of numbers
to arrays of that type, or as arguments to the dtype keyword that many numpy
functions or methods accept. Some examples::
>>> import numpy as np
>>> x = np.float32(1.0)
>>> x
1.0
>>> y = np.int_([1,2,4])
>>> y
array([1, 2, 4])
>>> z = np.arange(3, dtype=np.uint8)
>>> z
array([0, 1, 2], dtype=uint8)
Array types can also be referred to by character codes, mostly to retain
backward compatibility with older packages such as Numeric. Some
documentation may still refer to these, for example::
>>> np.array([1, 2, 3], dtype='f')
array([ 1., 2., 3.], dtype=float32)
We recommend using dtype objects instead.
To convert the type of an array, use the .astype() method (preferred) or
the type itself as a function. For example: ::
>>> z.astype(float) #doctest: +NORMALIZE_WHITESPACE
array([ 0., 1., 2.])
>>> np.int8(z)
array([0, 1, 2], dtype=int8)
Note that, above, we use the *Python* float object as a dtype. NumPy knows
that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool_``,
that ``float`` is ``np.float_`` and ``complex`` is ``np.complex_``.
The other data-types do not have Python equivalents.
To determine the type of an array, look at the dtype attribute::
>>> z.dtype
dtype('uint8')
dtype objects also contain information about the type, such as its bit-width
and its byte-order. The data type can also be used indirectly to query
properties of the type, such as whether it is an integer::
>>> d = np.dtype(int)
>>> d
dtype('int32')
>>> np.issubdtype(d, np.integer)
True
>>> np.issubdtype(d, np.floating)
False
Array Scalars
=============
NumPy generally returns elements of arrays as array scalars (a scalar
with an associated dtype). Array scalars differ from Python scalars, but
for the most part they can be used interchangeably (the primary
exception is for versions of Python older than v2.x, where integer array
scalars cannot act as indices for lists and tuples). There are some
exceptions, such as when code requires very specific attributes of a scalar
or when it checks specifically whether a value is a Python scalar. Generally,
problems are easily fixed by explicitly converting array scalars
to Python scalars, using the corresponding Python type function
(e.g., ``int``, ``float``, ``complex``, ``str``, ``unicode``).
The primary advantage of using array scalars is that
they preserve the array type (Python may not have a matching scalar type
available, e.g. ``int16``). Therefore, the use of array scalars ensures
identical behaviour between arrays and scalars, irrespective of whether the
value is inside an array or not. NumPy scalars also have many of the same
methods arrays do.
Extended Precision
==================
Python's floating-point numbers are usually 64-bit floating-point numbers,
nearly equivalent to ``np.float64``. In some unusual situations it may be
useful to use floating-point numbers with more precision. Whether this
is possible in numpy depends on the hardware and on the development
environment: specifically, x86 machines provide hardware floating-point
with 80-bit precision, and while most C compilers provide this as their
``long double`` type, MSVC (standard for Windows builds) makes
``long double`` identical to ``double`` (64 bits). NumPy makes the
compiler's ``long double`` available as ``np.longdouble`` (and
``np.clongdouble`` for the complex numbers). You can find out what your
numpy provides with ``np.finfo(np.longdouble)``.
NumPy does not provide a dtype with more precision than C
``long double``\\s; in particular, the 128-bit IEEE quad precision
data type (FORTRAN's ``REAL*16``\\) is not available.
For efficient memory alignment, ``np.longdouble`` is usually stored
padded with zero bits, either to 96 or 128 bits. Which is more efficient
depends on hardware and development environment; typically on 32-bit
systems they are padded to 96 bits, while on 64-bit systems they are
typically padded to 128 bits. ``np.longdouble`` is padded to the system
default; ``np.float96`` and ``np.float128`` are provided for users who
want specific padding. In spite of the names, ``np.float96`` and
``np.float128`` provide only as much precision as ``np.longdouble``,
that is, 80 bits on most x86 machines and 64 bits in standard
Windows builds.
Be warned that even if ``np.longdouble`` offers more precision than
python ``float``, it is easy to lose that extra precision, since
python often forces values to pass through ``float``. For example,
the ``%`` formatting operator requires its arguments to be converted
to standard python types, and it is therefore impossible to preserve
extended precision even if many decimal places are requested. It can
be useful to test your code with the value
``1 + np.finfo(np.longdouble).eps``.
""" |
"""Stuff to parse AIFF-C and AIFF files.
Unless explicitly stated otherwise, the description below is true
both for AIFF-C files and AIFF files.
An AIFF-C file has the following structure.
+-----------------+
| FORM |
+-----------------+
| <size> |
+----+------------+
| | AIFC |
| +------------+
| | <chunks> |
| | . |
| | . |
| | . |
+----+------------+
An AIFF file has the string "AIFF" instead of "AIFC".
A chunk consists of an identifier (4 bytes) followed by a size (4 bytes,
big endian order), followed by the data. The size field does not include
the size of the 8 byte header.
The following chunk types are recognized.
FVER
<version number of AIFF-C defining document> (AIFF-C only).
MARK
<# of markers> (2 bytes)
list of markers:
<marker ID> (2 bytes, must be > 0)
<position> (4 bytes)
<marker name> ("pstring")
COMM
<# of channels> (2 bytes)
<# of sound frames> (4 bytes)
<size of the samples> (2 bytes)
<sampling frequency> (10 bytes, IEEE 80-bit extended
floating point)
in AIFF-C files only:
<compression type> (4 bytes)
<human-readable version of compression type> ("pstring")
SSND
<offset> (4 bytes, not used by this program)
<blocksize> (4 bytes, not used by this program)
<sound data>
A pstring consists of 1 byte length, a string of characters, and 0 or 1
byte pad to make the total length even.
Usage.
Reading AIFF files:
f = aifc.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
In some types of audio files, if the setpos() method is not used,
the seek() method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' for AIFF files)
getcompname() -- returns human-readable version of
compression type ('not compressed' for AIFF files)
getparams() -- returns a tuple consisting of all of the
above in the above order
getmarkers() -- get the list of marks in the audio file or None
if there are no marks
getmark(id) -- get mark with the specified id (raises an error
if the mark does not exist)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell(), the position given to setpos() and
the position of marks are all compatible and have nothing to do with
the actual position in the file.
The close() method is called automatically when the class instance
is destroyed.
Writing AIFF files:
f = aifc.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
aiff() -- create an AIFF file (AIFF-C default)
aifc() -- create an AIFF-C file
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)
-- set all parameters at once
setmark(id, pos, name)
-- add specified mark to the list of marks
tell() -- return current position in output file (useful
in combination with setmark())
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes('') or
close() to patch up the sizes in the header.
Marks can be added anytime. If there are any marks, ypu must call
close() after all frames have been written.
The close() method is called automatically when the class instance
is destroyed.
When a file is opened with the extension '.aiff', an AIFF file is
written, otherwise an AIFF-C file is written. This default can be
changed by calling aiff() or aifc() before the first writeframes or
writeframesraw.
""" |
# from django.core.exceptions import ObjectDoesNotExist
# from models import User, Project, Cluster, Server, ClusterProjectConnection, PrivateNetwork
#
#
# """
# User
# """
#
#
# def get_User(user_id):
# """
# :returns: User with this id,returns None if User does not exists.
# """
# try:
# user = User.objects.get(id=user_id)
# return user
# except ObjectDoesNotExist:
# return None
#
#
# def add_User(user_id):
# """
# Add a new User to the DB.
# :param id: the id provided by ~okeanos for this user.
# """
# user = User(id=user_id)
# user.save()
#
#
# def delete_User(user_id):
# """
# Deletes the user with this id.
# :returns: True if successfull,otherwise False.
# """
# user = get_User(user_id)
# if user is not None:
# user.delete()
# return True
# return False
#
# """
# Project
# """
#
#
# def get_Project(project_id):
# """
# :returns: Project with this id,returns None if Project does not exists.
# """
# try:
# project = Project.objects.get(id=project_id)
# return project
# except DoesNotExist:
# return None
#
#
# def add_Project(project_id, description=""):
# """
# Add a new Project to the DB.
# :param id: the id provided by ~okeanos for the project.
# """
# project = Project(id=project_id, description=description)
# project.save()
#
#
# def update_Project(project_id, description=""):
# """
# Update project to the DB.
# :param id: the id of the project.
# :param description: the new description of the project.
# """
# project = get_Project(project_id)
# project.description = description
# project.save()
#
#
# def get_Projects():
# """
# :returns: All the projects in DB.
# """
# return Project.objects.all()
#
#
# def get_Project_By_Cluster(cluster_id):
# """
# :returns: All the projects in DB that were used for this cluster.
# """
# return ClusterProjectConnection.objects.filter(cluster_id=cluster_id)
#
#
# def delete_Project(project_id):
# """
# Deletes the project with this id.
# :returns: True if successfull,otherwise False.
# """
# project = get_Project(project_id)
# if project is not None:
# project.delete()
# return True
# return False
#
#
# """
# Cluster
# """
#
#
# def get_Cluster(cluster_id):
# """
# :returns: Cluster with this id,returns None if Cluster does not exists.
# """
# try:
# cluster = Cluster.objects.get(id=cluster_id)
# return cluster
# except DoesNotExist:
# return None
#
#
# def add_Cluster(cluster_info=""):
# """
# Add a new Cluster to the DB.
# :param cluster_info: the content of the xml file containing information about
# the servers and services.
# """
# cluster = Cluster(cluster_info=cluster_info)
# cluster.save()
#
#
# def update_Cluster(cluster_id, cluster_info=""):
# """
# Update cluster to the DB.
# :param id: the id of the cluster.
# :param cluster_info: the content of the xml file containing information about
# the servers and services.
# """
# cluster = get_Cluster(cluster_id)
# cluster.description = description
# cluster.save()
#
#
# def get_Clusters():
# """
# :returns: All clusters from the DB.
# """
# return Cluster.objects.all()
#
#
# def get_Project_By_Project(project_id):
# """
# :returns: All the clusters in DB that were used for project.
# """
# return ClusterProjectConnection.objects.filter(project_id=project_id)
#
#
# def delete_Cluster(cluster_id):
# """
# Deletes the cluster with this id.
# :returns: True if successfull,otherwise False.
# """
# cluster = get_Cluster(cluster_id)
# if cluster is not None:
# cluster.delete()
# return True
# return False
#
#
# """
# Server
# """
#
#
# def get_Server(server_id=1):
# """
# :returns: Server with this id,returns None if Server does not exists.
# """
# try:
# server = Server.objects.get(id=server_id)
# return server
# except DoesNotExist:
# return None
#
#
# def get_Servers_by_Cluster(cluster_id=1):
# """
# :returns: Servers that belong to this cluster,returns None if no Servers are found.
# """
# cluster = Cluster.objects.get(id=cluster_id)
# return Server.objects.filter(cluster=cluster)
#
#
# def add_Server(server_id=1, cpus=1, disk=20, ram=2, pub_ip=None, priv_ip=None,
# Cluster=None, hostname=""):
# """
# Add a new Server to the DB.
# :param server_id: the id provided by ~okeanos when the server is created.
# :param cpus: number of cpus.
# :param ram: amount of ram.
# :param disk: amount of disk.
# :param pub-ip: public ip of server.
# :param priv_ip: private ip of server.
# :param hostname: hostname of server.
#
# """
# server = Server(id=server_id, cpus=cpus, disk=disk, ram=ram, pub_ip=pub_ip,
# priv_ip=priv_ip, Cluster=Cluster, hostname=hostname)
# server.save()
#
#
# def update_Server(server_id, cluster=None):
# """
# Update server to the DB.
# :param id: the id of the server.
# :parma cluster: the new cluster that the server belongs to.
# """
# server = get_Server(server_id)
# server.cluster = cluster
# server.save()
#
#
# def delete_Server(server_id):
# """
# Deletes the server with this id.
# :returns: True if successfull,otherwise False.
# """
# server = get_Server(server_id)
# if server is not None:
# server.delete()
# return True
# return False
#
#
# """
# PrivateNetwork
# """
#
#
# def get_PrivateNetwork(pn_id):
# """
# :returns: PrivateNetwork with this id,returns None if PrivateNetwork does not exists.
# """
# try:
# pn = PrivateNetwork.objects.get(id=pn_id)
# return pn
# except DoesNotExist:
# return None
#
#
# def add_PrivateNetwork(pn_id, subnet='', gateway=None, cluster=None):
# """
# Add a new private network to the DB.
# :param pn_id: the id provided by ~okeanos when the private network is created.
# :param subnet: the subnet of the network.
# :param gateway: the gateway of the network.
# :param cluster: the cluster the network belongs to.
# """
# pn = PrivateNetwork(id=pn_id, subnet=subnet, gateway=gateway, cluster=cluster)
# pn.save()
#
#
# def update_PrivateNetwork(pn_id, cluster=None):
# """
# Update private network to the DB.
# :param id: the id of the private network.
# :parma cluster: the new cluster that the network belongs to.
# """
# pn = get_PrivateNetwork(pn_id)
# pn.cluster = cluster
# pn.save()
#
#
# def get_PrivateNetwork_by_Cluster(pn_id):
# """
# :returns: PrivateNetwork that belong to this cluster,returns None
# if no PrivateNetwork is found.
# """
# cluster = Cluster.objects.get(id=pn_id)
# return PrivateNetwork.objects.filter(cluster=cluster)
#
#
# def delete_PrivateNetwork(pn_id):
# """
# Deletes the Private Network with this id.
# :returns: True if successfull,otherwise False.
# """
# pn = get_PrivateNetwork(pn_id)
# if pn is not None:
# pn.delete()
# return True
# return False
#
#
# """
# ClusterProjectConnection
# """
#
#
# def add_ClusterProjectConnection(project, cluster):
# """
# Add a new cluster project connection to the DB.
# :param cluster: the project.
# :param project: the cluster.
# """
# con = ClusterProjectConnection(project=project, cluster=cluster)
# con.save()
|
"""
====================
account management
====================
Forgot Password
===============
ensure you're logged out first::
>>> self.logout()
the forgotten password view::
>>> view = portal.restrictedTraverse("@@forgot")
>>> view
<...SimpleViewClass ...forgot.pt...>
>>> view.request.form['send'] = True
Getting a userid for an existing user::
>>> view.request.form['__ac_name'] = 'test_user_1_'
>>> view.brain_for_userid_or_email('test_user_1_').getId
'test_user_1_'
This should be the case even if the user forgets correct capitalization::
>>> view.brain_for_userid_or_email('Test_User_1_').getId
'test_user_1_'
The member needs to have a 'legitimate' email address::
>>> member = portal.membrane_tool(getUserName='test_user_1_')[0].getObject()
>>> member
<OpenMember at /plone/portal_memberdata/test_user_1_>
>>> member.setEmail('EMAIL')
>>> member.reindexObject(idxs=['getEmail'])
We can lookup the member by email too, now that he has one::
>>> view.request.form['__ac_name'] = 'EMAIL'
>>> view.brain_for_userid_or_email('EMAIL').getId
'test_user_1_'
Running handle request does all this, and sends the email::
>>> view.request.environ["REQUEST_METHOD"] = "POST"
>>> view.handle_request()
True
>>> view.request.environ["REQUEST_METHOD"] = "GET"
Now we should be able to get a string for later matching::
>>> randomstring = view.randomstring('test_user_1_')
>>> randomstring
'...'
Password Reset
==============
>>> view = portal.restrictedTraverse("@@reset-password")
>>> view
<...SimpleViewClass ...reset-password.pt...>
If no key is set, we taunt you craxorz::
>>> view.key
Traceback (innermost last):
...
Forbidden: Your password reset key is invalid. Please verify that it is identical to the email and try again.
But if a key is set, we can use it::
>>> view.request.form['key']=randomstring
>>> view.key == randomstring
True
To do the reset, we'll need to submit the form::
>>> view.request.environ["REQUEST_METHOD"] = "POST"
>>> view.request.form["set"]=True
>>> view.request.form["password"]='word'
>>> view.request.form["password2"]='word'
>>> view.request.form["userid"]='test_user_1_'
>>> view.handle_reset()
False
Why is this?
>>> view.portal_status_message[-1]
u'Passwords must contain at least 5 characters.'
Ensure that validate_password_form has the same functionality:
>>> view.validate_password_form('word', 'word', 'test_user_1_')
False
>>> view.portal_status_message
[u'Passwords must contain at least 5 characters.']
Now try non-matching passwords:
>>> view.validate_password_form('wordy', 'werdy', 'test_user_1_')
False
>>> view.portal_status_message
[u'Please make sure that both password fields are the same.']
Test doing the reset:
First, ensure there is no portal status message:
>>> clear_status_messages(view)
Next, ensure that we're using a valid password:
>>> view.validate_password_form('wordy', 'wordy', 'test_user_1_')
<OpenMember at ...>
This should work even with wrong capitalization and leading space:
>>> view.validate_password_form('wordy', 'wordy', ' tESt_uSEr_1_')
<OpenMember at ...>
Finally, handle the reset:
>>> view.request.form["password"] = 'wordy'
>>> view.request.form["password2"] = 'wordy'
>>> view.handle_reset()
True
>>> expected = '/'.join((view.context.portal_url(), 'people', 'test_user_1_', 'account'))
>>> expected == view.request.response.getHeader('location')
True
# XXX TODO: login with the new password [maybe in the login section]
Get Account Confirmation Code
=============================
Get a user so that we can try to get a user's confirmation code for manual registration::
>>> from Products.CMFCore.utils import getToolByName
>>> mt = getToolByName(portal, "portal_memberdata")
>>> user = mt.restrictedTraverse('m1')
>>> user
<OpenMember at ...>
>>> user.setUserConfirmationCode()
The getUserConfirmationCode method should only be available to site managers::
>>> m = user.restrictedTraverse("getUserConfirmationCode")
Traceback (most recent call last):
...
Unauthorized: You are not allowed to access 'getUserConfirmationCode' in this context
>>> self.login("m1")
>>> m = user.restrictedTraverse("getUserConfirmationCode")
Traceback (most recent call last):
...
Unauthorized: You are not allowed to access 'getUserConfirmationCode' in this context
When the method is accessible, it should return a string code for the user::
>>> self.loginAsPortalOwner()
>>> m = user.restrictedTraverse("getUserConfirmationCode")
>>> isinstance(m(), basestring)
True
Join
====
If you're already logged in, the join view redirects to the site root,
regardless of came_from::
>>> self.login()
>>> view = portal.restrictedTraverse("@@join")
>>> view.request.form['came_from'] = 'http://foo.com'
>>> view.handle_request()
'http://nohost/plone'
>>> view.request.RESPONSE.getStatus()
302
Test the join view by adding a member to the site.
Log out and fill in the form::
>>> self.logout()
>>> view = portal.restrictedTraverse("@@join")
>>> request = view.request
>>> request.environ["REQUEST_METHOD"] = "GET"
>>> form = dict(id='foobar',
... email='EMAIL',
... password= 'testy',
... confirm_password='testy')
>>> request.form.update(form)
The view has a validate() method which returns an error dict::
>>> view.ajax_validate()
{}
>>> # Make sure the password does not match the password confirmation
>>> request.form['confirm_password'] = 'mesty'
>>> # Set an invalid email address
>>> request.form['email'] = 'fakeemail'
>>> sorted(view.ajax_validate().keys())
['oc-confirm_password-error', 'oc-email-error', 'oc-password-error']
Test what happens when password is "password"
>>> request.form = dict(id='foouser3',
... fullname='foo user',
... email='EMAIL',
... password='password',
... confirm_password='password',
... )
>>> view.create_member()
{'password': u'"password" PASSWORD
>>> view.errors
{'password': u'"password" PASSWORD
Test what happens when both passwords are blank
>>> request.form = dict(id='foouser',
... fullname='foo user',
... email='EMAIL',
... )
>>> view.create_member()
{'password': u'no_password'}
>>> view.errors
{'password': u'no_password'}
Now let's finally create a member without errors::
>>> request.form.update(password='freddy',
... confirm_password='freddy',
... )
>>> view.create_member()
<OpenMember at /plone/portal_memberdata/foouser>
>>> pprint(view.errors)
{}
>>> request.form = form
If you add 'task|validate' to the request before submitting the form the
ajax_validate() method will be triggered. We set the mode to 'async' so we get
the response we send out for AJAX requests::
>>> request.form['task|validate'] = 'Foo'
>>> request.form['mode'] = 'async'
>>> view()
'<html><head><meta http-equiv="x-deliverance-no-theme" content="1"/></head><body> {...} </body></html>'
>>> del request.form['mode'], request.form['task|validate']
Submit the form for real now; we need to add 'task|join' to the request::
>>> request.form['confirm_password'] = 'testy'
>>> request.form['email'] = 'EMAIL'
>>> request.form['task|join'] = 'Foo'
>>> view = portal.restrictedTraverse("@@join")
>>> view.ajax_validate()
{}
Verify that the proper events gets sent out when a member gets created::
XXX (is this really necessary?)
>>> self.listen_for_object_events()
We need to make the request a POST::
>>> request.environ["REQUEST_METHOD"] = "POST"
>>> view.membertool.getMemberById('foobar')
>>> rendered = unicode(view())
>>> view.membertool.getMemberById('foobar')
<OpenMember at /plone/portal_memberdata/foobar...>
>>> from zope.app.event.interfaces import IObjectCreatedEvent
>>> self.event_fired(IObjectCreatedEvent)
True
We SHOULD be cleaning up our event handler here, but there's no way to unregister
an event handler in Z2.9, that API landed in Z2.10.
Ensure that you can't join the site with another foobar::
>>> clear_status_messages(view)
>>> view()
u'...The login name you selected is already in use. Please choose another...'
You also shouldn't be able to join with case-variants::
>>> clear_status_messages(view)
>>> form = dict(id='FooBar',
... email='EMAIL',
... password='testy',
... confirm_password='testy')
>>> view.request.form.update(form)
>>> view()
u'...The login name you selected is already in use. Please choose another...'
Email address are also unique::
>>> form = dict(id='sevenofnine',
... email='EMAIL',
... password='testy',
... confirm_password='testy')
>>> view.request.form.update(form)
>>> view()
u'...That email address is already in use. Please choose another...'
But we do allow appending to existing logins:: XXX appending what?
>>> form = dict(id='foobar3',
... email='EMAIL',
... password='testy',
... confirm_password='testy')
>>> view.request.form.update(form)
>>> rendered = view()
>>> 'Please choose another' not in rendered #@@ brittle
True
>>> view.membertool.getMemberById('foobar3')
<OpenMember at /plone/portal_memberdata/foobar3...>
Confirm
=======
See confirm.txt. But for testing other features below, we want to
confirm one member. (We could stand to do some redesigning for better
testability. This is cargo-culted from other tests, not sure if
there's an easier way...)
>>> user = mt.restrictedTraverse('foobar')
>>> self.loginAsPortalOwner()
>>> getcode = user.restrictedTraverse("getUserConfirmationCode")
>>> key = getcode()
>>> view = portal.restrictedTraverse("@@confirm-account")
>>> view.request.form.clear()
>>> view.request.form['key'] = key
>>> view.handle_confirmation()
'http://nohost/plone/init-login'
Login
=====
Logout first
>>> self.logout()
>>> portal.portal_membership.getAuthenticatedMember()
<SpecialUser 'Anonymous User'>
Get the login view
>>> view = portal.restrictedTraverse('@@login')
Clear the portal status messages and form
>>> clear_status_messages(view)
>>> view.request.form.clear()
Login [to be done]
>>> view.request.form['__ac_name'] = 'foobar'
>>> view.request.form['__ac_password'] = 'testy'
>>> output = view()
[Output should really be the user's homepage. but it isn't
due to the fact that PAS isn't called. Deal with this later]
Verify initial login converts email invites to mship invites
============================================================
Retrieve any member object for use in our test
>>> mtool = getToolByName(portal, 'portal_membership')
>>> tmtool = getToolByName(portal, 'portal_teams')
>>> wftool = getToolByName(portal, 'portal_workflow')
>>> mem_id = 'm1'
>>> proj_id = 'p4'
>>> mem = mtool.getMemberById(mem_id)
>>> team = tmtool.getTeamById(proj_id) # <- m1 isn't a member
>>> team._getOb(mem_id, None) is None
True
Artificially insert an email invite for the user (sacrificing a
dead chicken or two in the process)
>>> from zope.component import getUtility
>>> from opencore.interfaces.membership import IEmailInvites
>>> email_invites = getUtility(IEmailInvites)
>>> isinstance(email_invites.addInvitation(mem.getEmail(), proj_id), int)
True
Login as the member and trigger the 'init-login' view
>>> self.login(mem_id)
>>> view = portal.restrictedTraverse('init-login')
>>> view()
'http://...m1/tour'
We should have a pending membership, last workflow actor is not
the member himself
>>> mship = team._getOb(mem_id, None)
>>> mship is None
False
>>> wftool.getInfoFor(mship, 'review_state')
'pending'
>>> wf_id = wftool.getChainFor(mship)[0]
>>> history = wftool.getHistoryOf(wf_id, mship)
>>> history[-1]['actor'] != mem_id
True
Log out so we don't interfere w/ later tests
>>> self.logout()
Verify portal status messages aren't being swallowed
====================================================
First, let's get an instance of a view that returns a portal
status message, and redirects
>>> view = portal.restrictedTraverse('@@login')
Reset the portal status message
>>> clear_status_messages(view)
Now setup a pseudo post
>>> request = view.request
>>> request.form = dict(__ac_name='m1', login=True)
>>> request.environ['REQUEST_METHOD'] = 'POST'
Monkey patch some methods for easier testing
>>> old_membertool_isanon = view.membertool.isAnonymousUser
>>> old_update = view.update_credentials
>>> view.membertool.isAnonymousUser = lambda *a:True
>>> view.update_credentials = lambda *a:None
Now we simulate the call to login
>>> view.handle_login()
The portal status message should have some data in it now
>>> len(view.portal_status_message) > 0
True
Now restore the original methods
>>> view.membertool.isAnonymousUser = old_membertool_isanon
>>> view.update_credentials = old_update
Verify authentication challenges do the right thing
===================================================
Clear portal status messages and clear the form
>>> clear_status_messages(view)
>>> view.request.form.clear()
>>> view.request.form
{}
>>> oldview = view
Now go to the require_login location
>>> view = portal.restrictedTraverse('require_login')
This is not the view
>>> view
<FSPythonScript at /plone/require_login>
>>> output = view()
This is the old skin which redirects to the login page.
>>> 'Please sign in' in output
True
Remove test_user_1_
===================
Ensure test atomicity by removing the created user:
>>> self.logout()
>>> portal.portal_memberdata.manage_delObjects('test_user_1_')
Is the member still in the catalog?
Creation email message
=======================
Bug #1711. Member creation message should use the portal title.
>>> view = portal.restrictedTraverse("@@join")
>>> mh = view.get_tool('MailHost')
>>> mh
<...MailHostMock ...>
>>> view._send_mail_to_pending_user('unused id', 'EMAIL',
... 'http://confirm-url.com')
>>> emailtext = mh.messages[-1].get('msg')
>>> view.portal_title() in emailtext
True
""" |
#!/usr/bin/env python
# ***** BEGIN LICENSE BLOCK *****
# Version: MPL 1.1/GPL 2.0/LGPL 2.1
#
# The contents of this file are subject to the Mozilla Public License Version
# 1.1 (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.mozilla.org/MPL/
#
# Software distributed under the License is distributed on an "AS IS" basis,
# WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
# for the specific language governing rights and limitations under the
# License.
#
# The Original Code is font utility code.
#
# The Initial Developer of the Original Code is Mozilla Corporation.
# Portions created by the Initial Developer are Copyright (C) 2009
# the Initial Developer. All Rights Reserved.
#
# Contributor(s):
# NAME <EMAIL>
#
# Alternatively, the contents of this file may be used under the terms of
# either the GNU General Public License Version 2 or later (the "GPL"), or
# the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
# in which case the provisions of the GPL or the LGPL are applicable instead
# of those above. If you wish to allow use of your version of this file only
# under the terms of either the GPL or the LGPL, and not to allow others to
# use your version of this file under the terms of the MPL, indicate your
# decision by deleting the provisions above and replace them with the notice
# and other provisions required by the GPL or the LGPL. If you do not delete
# the provisions above, a recipient may use your version of this file under
# the terms of any one of the MPL, the GPL or the LGPL.
#
# ***** END LICENSE BLOCK ***** */
# eotlitetool.py - create EOT version of OpenType font for use with IE
#
# Usage: eotlitetool.py [-o output-filename] font1 [font2 ...]
#
# OpenType file structure
# http://www.microsoft.com/typography/otspec/otff.htm
#
# Types:
#
# BYTE 8-bit unsigned integer.
# CHAR 8-bit signed integer.
# USHORT 16-bit unsigned integer.
# SHORT 16-bit signed integer.
# ULONG 32-bit unsigned integer.
# Fixed 32-bit signed fixed-point number (16.16)
# LONGDATETIME Date represented in number of seconds since 12:00 midnight, January 1, 1904. The value is represented as a signed 64-bit integer.
#
# SFNT Header
#
# Fixed sfnt version // 0x00010000 for version 1.0.
# USHORT numTables // Number of tables.
# USHORT searchRange // (Maximum power of 2 <= numTables) x 16.
# USHORT entrySelector // Log2(maximum power of 2 <= numTables).
# USHORT rangeShift // NumTables x 16-searchRange.
#
# Table Directory
#
# ULONG tag // 4-byte identifier.
# ULONG checkSum // CheckSum for this table.
# ULONG offset // Offset from beginning of TrueType font file.
# ULONG length // Length of this table.
#
# OS/2 Table (Version 4)
#
# USHORT version // 0x0004
# SHORT xAvgCharWidth
# USHORT usWeightClass
# USHORT usWidthClass
# USHORT fsType
# SHORT ySubscriptXSize
# SHORT ySubscriptYSize
# SHORT ySubscriptXOffset
# SHORT ySubscriptYOffset
# SHORT ySuperscriptXSize
# SHORT ySuperscriptYSize
# SHORT ySuperscriptXOffset
# SHORT ySuperscriptYOffset
# SHORT yStrikeoutSize
# SHORT yStrikeoutPosition
# SHORT sFamilyClass
# BYTE panose[10]
# ULONG ulUnicodeRange1 // Bits 0-31
# ULONG ulUnicodeRange2 // Bits 32-63
# ULONG ulUnicodeRange3 // Bits 64-95
# ULONG ulUnicodeRange4 // Bits 96-127
# CHAR achVendID[4]
# USHORT fsSelection
# USHORT usFirstCharIndex
# USHORT usLastCharIndex
# SHORT sTypoAscender
# SHORT sTypoDescender
# SHORT sTypoLineGap
# USHORT usWinAscent
# USHORT usWinDescent
# ULONG ulCodePageRange1 // Bits 0-31
# ULONG ulCodePageRange2 // Bits 32-63
# SHORT sxHeight
# SHORT sCapHeight
# USHORT usDefaultChar
# USHORT usBreakChar
# USHORT usMaxContext
#
#
# The Naming Table is organized as follows:
#
# [name table header]
# [name records]
# [string data]
#
# Name Table Header
#
# USHORT format // Format selector (=0).
# USHORT count // Number of name records.
# USHORT stringOffset // Offset to start of string storage (from start of table).
#
# Name Record
#
# USHORT platformID // Platform ID.
# USHORT encodingID // Platform-specific encoding ID.
# USHORT languageID // Language ID.
# USHORT nameID // Name ID.
# USHORT length // String length (in bytes).
# USHORT offset // String offset from start of storage area (in bytes).
#
# head Table
#
# Fixed tableVersion // Table version number 0x00010000 for version 1.0.
# Fixed fontRevision // Set by font manufacturer.
# ULONG checkSumAdjustment // To compute: set it to 0, sum the entire font as ULONG, then store 0xB1B0AFBA - sum.
# ULONG magicNumber // Set to 0x5F0F3CF5.
# USHORT flags
# USHORT unitsPerEm // Valid range is from 16 to 16384. This value should be a power of 2 for fonts that have TrueType outlines.
# LONGDATETIME created // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# LONGDATETIME modified // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# SHORT xMin // For all glyph bounding boxes.
# SHORT yMin
# SHORT xMax
# SHORT yMax
# USHORT macStyle
# USHORT lowestRecPPEM // Smallest readable size in pixels.
# SHORT fontDirectionHint
# SHORT indexToLocFormat // 0 for short offsets, 1 for long.
# SHORT glyphDataFormat // 0 for current format.
#
#
#
# Embedded OpenType (EOT) file format
# http://www.w3.org/Submission/EOT/
#
# EOT version 0x00020001
#
# An EOT font consists of a header with the original OpenType font
# appended at the end. Most of the data in the EOT header is simply a
# copy of data from specific tables within the font data. The exceptions
# are the 'Flags' field and the root string name field. The root string
# is a set of names indicating domains for which the font data can be
# used. A null root string implies the font data can be used anywhere.
# The EOT header is in little-endian byte order but the font data remains
# in big-endian order as specified by the OpenType spec.
#
# Overall structure:
#
# [EOT header]
# [EOT name records]
# [font data]
#
# EOT header
#
# ULONG eotSize // Total structure length in bytes (including string and font data)
# ULONG fontDataSize // Length of the OpenType font (FontData) in bytes
# ULONG version // Version number of this format - 0x00020001
# ULONG flags // Processing Flags (0 == no special processing)
# BYTE fontPANOSE[10] // OS/2 Table panose
# BYTE charset // DEFAULT_CHARSET (0x01)
# BYTE italic // 0x01 if ITALIC in OS/2 Table fsSelection is set, 0 otherwise
# ULONG weight // OS/2 Table usWeightClass
# USHORT fsType // OS/2 Table fsType (specifies embedding permission flags)
# USHORT magicNumber // Magic number for EOT file - 0x504C.
# ULONG unicodeRange1 // OS/2 Table ulUnicodeRange1
# ULONG unicodeRange2 // OS/2 Table ulUnicodeRange2
# ULONG unicodeRange3 // OS/2 Table ulUnicodeRange3
# ULONG unicodeRange4 // OS/2 Table ulUnicodeRange4
# ULONG codePageRange1 // OS/2 Table ulCodePageRange1
# ULONG codePageRange2 // OS/2 Table ulCodePageRange2
# ULONG checkSumAdjustment // head Table CheckSumAdjustment
# ULONG reserved[4] // Reserved - must be 0
# USHORT padding1 // Padding - must be 0
#
# EOT name records
#
# USHORT FamilyNameSize // Font family name size in bytes
# BYTE FamilyName[FamilyNameSize] // Font family name (name ID = 1), little-endian UTF-16
# USHORT Padding2 // Padding - must be 0
#
# USHORT StyleNameSize // Style name size in bytes
# BYTE StyleName[StyleNameSize] // Style name (name ID = 2), little-endian UTF-16
# USHORT Padding3 // Padding - must be 0
#
# USHORT VersionNameSize // Version name size in bytes
# bytes VersionName[VersionNameSize] // Version name (name ID = 5), little-endian UTF-16
# USHORT Padding4 // Padding - must be 0
#
# USHORT FullNameSize // Full name size in bytes
# BYTE FullName[FullNameSize] // Full name (name ID = 4), little-endian UTF-16
# USHORT Padding5 // Padding - must be 0
#
# USHORT RootStringSize // Root string size in bytes
# BYTE RootString[RootStringSize] // Root string, little-endian UTF-16
|
"""
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from standard or custom formats
4) Creating arrays from raw bytes through the use of strings or buffers
5) Use of special library functions (e.g., random)
This section will not cover means of replicating, joining, or otherwise
expanding or mutating existing arrays. Nor will it cover creating object
arrays or record arrays. Both of those are covered in their own sections.
Converting Python array_like Objects to Numpy Arrays
====================================================
In general, numerical data arranged in an array-like structure in Python can
be converted to arrays through the use of the array() function. The most
obvious examples are lists and tuples. See the documentation for array() for
details for its use. Some objects may support the array-protocol and allow
conversion to arrays this way. A simple way to find out if the object can be
converted to a numpy array using array() is simply to try it interactively and
see if it works! (The Python Way).
Examples: ::
>>> x = np.array([2,3,1,0])
>>> x = np.array([2, 3, 1, 0])
>>> x = np.array([[1,2.0],[0,0],(1+1j,3.)]) # note mix of tuple and lists,
and types
>>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]])
Intrinsic Numpy Array Creation
==============================
Numpy has built-in functions for creating arrays from scratch:
zeros(shape) will create an array filled with 0 values with the specified
shape. The default dtype is float64.
``>>> np.zeros((2, 3))
array([[ 0., 0., 0.], [ 0., 0., 0.]])``
ones(shape) will create an array filled with 1 values. It is identical to
zeros in all other respects.
arange() will create arrays with regularly incrementing values. Check the
docstring for complete information on the various ways it can be used. A few
examples will be given here: ::
>>> np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.arange(2, 10, dtype=np.float)
array([ 2., 3., 4., 5., 6., 7., 8., 9.])
>>> np.arange(2, 3, 0.1)
array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])
Note that there are some subtleties regarding the last usage that the user
should be aware of that are described in the arange docstring.
linspace() will create arrays with a specified number of elements, and
spaced equally between the specified beginning and end values. For
example: ::
>>> np.linspace(1., 4., 6)
array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])
The advantage of this creation function is that one can guarantee the
number of elements and the starting and end point, which arange()
generally will not do for arbitrary start, stop, and step values.
indices() will create a set of arrays (stacked as a one-higher dimensioned
array), one per dimension with each representing variation in that dimension.
An example illustrates much better than a verbal description: ::
>>> np.indices((3,3))
array([[[0, 0, 0], [1, 1, 1], [2, 2, 2]], [[0, 1, 2], [0, 1, 2], [0, 1, 2]]])
This is particularly useful for evaluating functions of multiple dimensions on
a regular grid.
Reading Arrays From Disk
========================
This is presumably the most common case of large array creation. The details,
of course, depend greatly on the format of data on disk and so this section
can only give general pointers on how to handle various formats.
Standard Binary Formats
-----------------------
Various fields have standard formats for array data. The following lists the
ones with known python libraries to read them and return numpy arrays (there
may be others for which it is possible to read and convert to numpy arrays so
check the last section as well)
::
HDF5: PyTables
FITS: PyFITS
Examples of formats that cannot be read directly but for which it is not hard
to convert are libraries like PIL (able to read and write many image formats
such as jpg, png, etc).
Common ASCII Formats
------------------------
Comma Separated Value files (CSV) are widely used (and an export and import
option for programs like Excel). There are a number of ways of reading these
files in Python. There are CSV functions in Python and functions in pylab
(part of matplotlib).
More generic ascii files can be read using the io package in scipy.
Custom Binary Formats
---------------------
There are a variety of approaches one can use. If the file has a relatively
simple format then one can write a simple I/O library and use the numpy
fromfile() function and .tofile() method to read and write numpy arrays
directly (mind your byteorder though!) If a good C or C++ library exists that
read the data, one can wrap that library with a variety of techniques though
that certainly is much more work and requires significantly more advanced
knowledge to interface with C or C++.
Use of Special Libraries
------------------------
There are libraries that can be used to generate arrays for special purposes
and it isn't possible to enumerate all of them. The most common uses are use
of the many array generation functions in random that can generate arrays of
random values, and some utility functions to generate special matrices (e.g.
diagonal).
""" |
"""
=======================================
Signal processing (:mod:`scipy.signal`)
=======================================
Convolution
===========
.. autosummary::
:toctree: generated/
convolve -- N-dimensional convolution.
correlate -- N-dimensional correlation.
fftconvolve -- N-dimensional convolution using the FFT.
convolve2d -- 2-dimensional convolution (more options).
correlate2d -- 2-dimensional correlation (more options).
sepfir2d -- Convolve with a 2-D separable FIR filter.
choose_conv_method -- Chooses faster of FFT and direct convolution methods.
B-splines
=========
.. autosummary::
:toctree: generated/
bspline -- B-spline basis function of order n.
cubic -- B-spline basis function of order 3.
quadratic -- B-spline basis function of order 2.
gauss_spline -- Gaussian approximation to the B-spline basis function.
cspline1d -- Coefficients for 1-D cubic (3rd order) B-spline.
qspline1d -- Coefficients for 1-D quadratic (2nd order) B-spline.
cspline2d -- Coefficients for 2-D cubic (3rd order) B-spline.
qspline2d -- Coefficients for 2-D quadratic (2nd order) B-spline.
cspline1d_eval -- Evaluate a cubic spline at the given points.
qspline1d_eval -- Evaluate a quadratic spline at the given points.
spline_filter -- Smoothing spline (cubic) filtering of a rank-2 array.
Filtering
=========
.. autosummary::
:toctree: generated/
order_filter -- N-dimensional order filter.
medfilt -- N-dimensional median filter.
medfilt2d -- 2-dimensional median filter (faster).
wiener -- N-dimensional wiener filter.
symiirorder1 -- 2nd-order IIR filter (cascade of first-order systems).
symiirorder2 -- 4th-order IIR filter (cascade of second-order systems).
lfilter -- 1-dimensional FIR and IIR digital linear filtering.
lfiltic -- Construct initial conditions for `lfilter`.
lfilter_zi -- Compute an initial state zi for the lfilter function that
-- corresponds to the steady state of the step response.
filtfilt -- A forward-backward filter.
savgol_filter -- Filter a signal using the Savitzky-Golay filter.
deconvolve -- 1-d deconvolution using lfilter.
sosfilt -- 1-dimensional IIR digital linear filtering using
-- a second-order sections filter representation.
sosfilt_zi -- Compute an initial state zi for the sosfilt function that
-- corresponds to the steady state of the step response.
sosfiltfilt -- A forward-backward filter for second-order sections.
hilbert -- Compute 1-D analytic signal, using the Hilbert transform.
hilbert2 -- Compute 2-D analytic signal, using the Hilbert transform.
decimate -- Downsample a signal.
detrend -- Remove linear and/or constant trends from data.
resample -- Resample using Fourier method.
resample_poly -- Resample using polyphase filtering method.
upfirdn -- Upsample, apply FIR filter, downsample.
Filter design
=============
.. autosummary::
:toctree: generated/
bilinear -- Digital filter from an analog filter using
-- the bilinear transform.
bilinear_zpk -- Digital filter from an analog filter using
-- the bilinear transform.
findfreqs -- Find array of frequencies for computing filter response.
firls -- FIR filter design using least-squares error minimization.
firwin -- Windowed FIR filter design, with frequency response
-- defined as pass and stop bands.
firwin2 -- Windowed FIR filter design, with arbitrary frequency
-- response.
freqs -- Analog filter frequency response from TF coefficients.
freqs_zpk -- Analog filter frequency response from ZPK coefficients.
freqz -- Digital filter frequency response from TF coefficients.
freqz_zpk -- Digital filter frequency response from ZPK coefficients.
sosfreqz -- Digital filter frequency response for SOS format filter.
group_delay -- Digital filter group delay.
iirdesign -- IIR filter design given bands and gains.
iirfilter -- IIR filter design given order and critical frequencies.
kaiser_atten -- Compute the attenuation of a Kaiser FIR filter, given
-- the number of taps and the transition width at
-- discontinuities in the frequency response.
kaiser_beta -- Compute the Kaiser parameter beta, given the desired
-- FIR filter attenuation.
kaiserord -- Design a Kaiser window to limit ripple and width of
-- transition region.
minimum_phase -- Convert a linear phase FIR filter to minimum phase.
savgol_coeffs -- Compute the FIR filter coefficients for a Savitzky-Golay
-- filter.
remez -- Optimal FIR filter design.
unique_roots -- Unique roots and their multiplicities.
residue -- Partial fraction expansion of b(s) / a(s).
residuez -- Partial fraction expansion of b(z) / a(z).
invres -- Inverse partial fraction expansion for analog filter.
invresz -- Inverse partial fraction expansion for digital filter.
BadCoefficients -- Warning on badly conditioned filter coefficients
Lower-level filter design functions:
.. autosummary::
:toctree: generated/
abcd_normalize -- Check state-space matrices and ensure they are rank-2.
band_stop_obj -- Band Stop Objective Function for order minimization.
besselap -- Return (z,p,k) for analog prototype of Bessel filter.
buttap -- Return (z,p,k) for analog prototype of Butterworth filter.
cheb1ap -- Return (z,p,k) for type I Chebyshev filter.
cheb2ap -- Return (z,p,k) for type II Chebyshev filter.
cmplx_sort -- Sort roots based on magnitude.
ellipap -- Return (z,p,k) for analog prototype of elliptic filter.
lp2bp -- Transform a lowpass filter prototype to a bandpass filter.
lp2bp_zpk -- Transform a lowpass filter prototype to a bandpass filter.
lp2bs -- Transform a lowpass filter prototype to a bandstop filter.
lp2bs_zpk -- Transform a lowpass filter prototype to a bandstop filter.
lp2hp -- Transform a lowpass filter prototype to a highpass filter.
lp2hp_zpk -- Transform a lowpass filter prototype to a highpass filter.
lp2lp -- Transform a lowpass filter prototype to a lowpass filter.
lp2lp_zpk -- Transform a lowpass filter prototype to a lowpass filter.
normalize -- Normalize polynomial representation of a transfer function.
Matlab-style IIR filter design
==============================
.. autosummary::
:toctree: generated/
butter -- Butterworth
buttord
cheby1 -- Chebyshev Type I
cheb1ord
cheby2 -- Chebyshev Type II
cheb2ord
ellip -- Elliptic (Cauer)
ellipord
bessel -- Bessel (no order selection available -- try butterod)
iirnotch -- Design second-order IIR notch digital filter.
iirpeak -- Design second-order IIR peak (resonant) digital filter.
Continuous-Time Linear Systems
==============================
.. autosummary::
:toctree: generated/
lti -- Continuous-time linear time invariant system base class.
StateSpace -- Linear time invariant system in state space form.
TransferFunction -- Linear time invariant system in transfer function form.
ZerosPolesGain -- Linear time invariant system in zeros, poles, gain form.
lsim -- continuous-time simulation of output to linear system.
lsim2 -- like lsim, but `scipy.integrate.odeint` is used.
impulse -- impulse response of linear, time-invariant (LTI) system.
impulse2 -- like impulse, but `scipy.integrate.odeint` is used.
step -- step response of continuous-time LTI system.
step2 -- like step, but `scipy.integrate.odeint` is used.
freqresp -- frequency response of a continuous-time LTI system.
bode -- Bode magnitude and phase data (continuous-time LTI).
Discrete-Time Linear Systems
============================
.. autosummary::
:toctree: generated/
dlti -- Discrete-time linear time invariant system base class.
StateSpace -- Linear time invariant system in state space form.
TransferFunction -- Linear time invariant system in transfer function form.
ZerosPolesGain -- Linear time invariant system in zeros, poles, gain form.
dlsim -- simulation of output to a discrete-time linear system.
dimpulse -- impulse response of a discrete-time LTI system.
dstep -- step response of a discrete-time LTI system.
dfreqresp -- frequency response of a discrete-time LTI system.
dbode -- Bode magnitude and phase data (discrete-time LTI).
LTI Representations
===================
.. autosummary::
:toctree: generated/
tf2zpk -- transfer function to zero-pole-gain.
tf2sos -- transfer function to second-order sections.
tf2ss -- transfer function to state-space.
zpk2tf -- zero-pole-gain to transfer function.
zpk2sos -- zero-pole-gain to second-order sections.
zpk2ss -- zero-pole-gain to state-space.
ss2tf -- state-pace to transfer function.
ss2zpk -- state-space to pole-zero-gain.
sos2zpk -- second-order sections to zero-pole-gain.
sos2tf -- second-order sections to transfer function.
cont2discrete -- continuous-time to discrete-time LTI conversion.
place_poles -- pole placement.
Waveforms
=========
.. autosummary::
:toctree: generated/
chirp -- Frequency swept cosine signal, with several freq functions.
gausspulse -- Gaussian modulated sinusoid
max_len_seq -- Maximum length sequence
sawtooth -- Periodic sawtooth
square -- Square wave
sweep_poly -- Frequency swept cosine signal; freq is arbitrary polynomial
unit_impulse -- Discrete unit impulse
Window functions
================
For window functions, see the `scipy.signal.windows` namespace.
In the `scipy.signal` namespace, there is a convenience function to
obtain these windows by name:
.. autosummary::
:toctree: generated/
get_window -- Return a window of a given length and type.
Wavelets
========
.. autosummary::
:toctree: generated/
cascade -- compute scaling function and wavelet from coefficients
daub -- return low-pass
morlet -- Complex Morlet wavelet.
qmf -- return quadrature mirror filter from low-pass
ricker -- return ricker wavelet
cwt -- perform continuous wavelet transform
Peak finding
============
.. autosummary::
:toctree: generated/
argrelmin -- Calculate the relative minima of data
argrelmax -- Calculate the relative maxima of data
argrelextrema -- Calculate the relative extrema of data
find_peaks -- Find a subset of peaks inside a signal.
find_peaks_cwt -- Find peaks in a 1-D array with wavelet transformation.
peak_prominences -- Calculate the prominence of each peak in a signal.
peak_widths -- Calculate the width of each peak in a signal.
Spectral Analysis
=================
.. autosummary::
:toctree: generated/
periodogram -- Compute a (modified) periodogram
welch -- Compute a periodogram using Welch's method
csd -- Compute the cross spectral density, using Welch's method
coherence -- Compute the magnitude squared coherence, using Welch's method
spectrogram -- Compute the spectrogram
lombscargle -- Computes the Lomb-Scargle periodogram
vectorstrength -- Computes the vector strength
stft -- Compute the Short Time Fourier Transform
istft -- Compute the Inverse Short Time Fourier Transform
check_COLA -- Check the COLA constraint for iSTFT reconstruction
check_NOLA -- Check the NOLA constraint for iSTFT reconstruction
""" |
"""
============
Array basics
============
Array types and conversions between types
=========================================
Numpy supports a much greater variety of numerical types than Python does.
This section shows which are available, and how to modify an array's data-type.
========== ==========================================================
Data type Description
========== ==========================================================
bool_ Boolean (True or False) stored as a byte
int_ Default integer type (same as C ``long``; normally either
``int64`` or ``int32``)
intc Identical to C ``int`` (normally ``int32`` or ``int64``)
intp Integer used for indexing (same as C ``ssize_t``; normally
either ``int32`` or ``int64``)
int8 Byte (-128 to 127)
int16 Integer (-32768 to 32767)
int32 Integer (-2147483648 to 2147483647)
int64 Integer (-9223372036854775808 to 9223372036854775807)
uint8 Unsigned integer (0 to 255)
uint16 Unsigned integer (0 to 65535)
uint32 Unsigned integer (0 to 4294967295)
uint64 Unsigned integer (0 to 18446744073709551615)
float_ Shorthand for ``float64``.
float16 Half precision float: sign bit, 5 bits exponent,
10 bits mantissa
float32 Single precision float: sign bit, 8 bits exponent,
23 bits mantissa
float64 Double precision float: sign bit, 11 bits exponent,
52 bits mantissa
complex_ Shorthand for ``complex128``.
complex64 Complex number, represented by two 32-bit floats (real
and imaginary components)
complex128 Complex number, represented by two 64-bit floats (real
and imaginary components)
========== ==========================================================
Additionally to ``intc`` the platform dependent C integer types ``short``,
``long``, ``longlong`` and their unsigned versions are defined.
Numpy numerical types are instances of ``dtype`` (data-type) objects, each
having unique characteristics. Once you have imported NumPy using
::
>>> import numpy as np
the dtypes are available as ``np.bool_``, ``np.float32``, etc.
Advanced types, not listed in the table above, are explored in
section :ref:`structured_arrays`.
There are 5 basic numerical types representing booleans (bool), integers (int),
unsigned integers (uint) floating point (float) and complex. Those with numbers
in their name indicate the bitsize of the type (i.e. how many bits are needed
to represent a single value in memory). Some types, such as ``int`` and
``intp``, have differing bitsizes, dependent on the platforms (e.g. 32-bit
vs. 64-bit machines). This should be taken into account when interfacing
with low-level code (such as C or Fortran) where the raw memory is addressed.
Data-types can be used as functions to convert python numbers to array scalars
(see the array scalar section for an explanation), python sequences of numbers
to arrays of that type, or as arguments to the dtype keyword that many numpy
functions or methods accept. Some examples::
>>> import numpy as np
>>> x = np.float32(1.0)
>>> x
1.0
>>> y = np.int_([1,2,4])
>>> y
array([1, 2, 4])
>>> z = np.arange(3, dtype=np.uint8)
>>> z
array([0, 1, 2], dtype=uint8)
Array types can also be referred to by character codes, mostly to retain
backward compatibility with older packages such as Numeric. Some
documentation may still refer to these, for example::
>>> np.array([1, 2, 3], dtype='f')
array([ 1., 2., 3.], dtype=float32)
We recommend using dtype objects instead.
To convert the type of an array, use the .astype() method (preferred) or
the type itself as a function. For example: ::
>>> z.astype(float) #doctest: +NORMALIZE_WHITESPACE
array([ 0., 1., 2.])
>>> np.int8(z)
array([0, 1, 2], dtype=int8)
Note that, above, we use the *Python* float object as a dtype. NumPy knows
that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool_``,
that ``float`` is ``np.float_`` and ``complex`` is ``np.complex_``.
The other data-types do not have Python equivalents.
To determine the type of an array, look at the dtype attribute::
>>> z.dtype
dtype('uint8')
dtype objects also contain information about the type, such as its bit-width
and its byte-order. The data type can also be used indirectly to query
properties of the type, such as whether it is an integer::
>>> d = np.dtype(int)
>>> d
dtype('int32')
>>> np.issubdtype(d, int)
True
>>> np.issubdtype(d, float)
False
Array Scalars
=============
Numpy generally returns elements of arrays as array scalars (a scalar
with an associated dtype). Array scalars differ from Python scalars, but
for the most part they can be used interchangeably (the primary
exception is for versions of Python older than v2.x, where integer array
scalars cannot act as indices for lists and tuples). There are some
exceptions, such as when code requires very specific attributes of a scalar
or when it checks specifically whether a value is a Python scalar. Generally,
problems are easily fixed by explicitly converting array scalars
to Python scalars, using the corresponding Python type function
(e.g., ``int``, ``float``, ``complex``, ``str``, ``unicode``).
The primary advantage of using array scalars is that
they preserve the array type (Python may not have a matching scalar type
available, e.g. ``int16``). Therefore, the use of array scalars ensures
identical behaviour between arrays and scalars, irrespective of whether the
value is inside an array or not. NumPy scalars also have many of the same
methods arrays do.
""" |
"""
===================
Universal Functions
===================
Ufuncs are, generally speaking, mathematical functions or operations that are
applied element-by-element to the contents of an array. That is, the result
in each output array element only depends on the value in the corresponding
input array (or arrays) and on no other array elements. NumPy comes with a
large suite of ufuncs, and scipy extends that suite substantially. The simplest
example is the addition operator: ::
>>> np.array([0,2,3,4]) + np.array([1,1,-1,2])
array([1, 3, 2, 6])
The unfunc module lists all the available ufuncs in numpy. Documentation on
the specific ufuncs may be found in those modules. This documentation is
intended to address the more general aspects of unfuncs common to most of
them. All of the ufuncs that make use of Python operators (e.g., +, -, etc.)
have equivalent functions defined (e.g. add() for +)
Type coercion
=============
What happens when a binary operator (e.g., +,-,\\*,/, etc) deals with arrays of
two different types? What is the type of the result? Typically, the result is
the higher of the two types. For example: ::
float32 + float64 -> float64
int8 + int32 -> int32
int16 + float32 -> float32
float32 + complex64 -> complex64
There are some less obvious cases generally involving mixes of types
(e.g. uints, ints and floats) where equal bit sizes for each are not
capable of saving all the information in a different type of equivalent
bit size. Some examples are int32 vs float32 or uint32 vs int32.
Generally, the result is the higher type of larger size than both
(if available). So: ::
int32 + float32 -> float64
uint32 + int32 -> int64
Finally, the type coercion behavior when expressions involve Python
scalars is different than that seen for arrays. Since Python has a
limited number of types, combining a Python int with a dtype=np.int8
array does not coerce to the higher type but instead, the type of the
array prevails. So the rules for Python scalars combined with arrays is
that the result will be that of the array equivalent the Python scalar
if the Python scalar is of a higher 'kind' than the array (e.g., float
vs. int), otherwise the resultant type will be that of the array.
For example: ::
Python int + int8 -> int8
Python float + int8 -> float64
ufunc methods
=============
Binary ufuncs support 4 methods.
**.reduce(arr)** applies the binary operator to elements of the array in
sequence. For example: ::
>>> np.add.reduce(np.arange(10)) # adds all elements of array
45
For multidimensional arrays, the first dimension is reduced by default: ::
>>> np.add.reduce(np.arange(10).reshape(2,5))
array([ 5, 7, 9, 11, 13])
The axis keyword can be used to specify different axes to reduce: ::
>>> np.add.reduce(np.arange(10).reshape(2,5),axis=1)
array([10, 35])
**.accumulate(arr)** applies the binary operator and generates an an
equivalently shaped array that includes the accumulated amount for each
element of the array. A couple examples: ::
>>> np.add.accumulate(np.arange(10))
array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])
>>> np.multiply.accumulate(np.arange(1,9))
array([ 1, 2, 6, 24, 120, 720, 5040, 40320])
The behavior for multidimensional arrays is the same as for .reduce(),
as is the use of the axis keyword).
**.reduceat(arr,indices)** allows one to apply reduce to selected parts
of an array. It is a difficult method to understand. See the documentation
at:
**.outer(arr1,arr2)** generates an outer operation on the two arrays arr1 and
arr2. It will work on multidimensional arrays (the shape of the result is
the concatenation of the two input shapes.: ::
>>> np.multiply.outer(np.arange(3),np.arange(4))
array([[0, 0, 0, 0],
[0, 1, 2, 3],
[0, 2, 4, 6]])
Output arguments
================
All ufuncs accept an optional output array. The array must be of the expected
output shape. Beware that if the type of the output array is of a different
(and lower) type than the output result, the results may be silently truncated
or otherwise corrupted in the downcast to the lower type. This usage is useful
when one wants to avoid creating large temporary arrays and instead allows one
to reuse the same array memory repeatedly (at the expense of not being able to
use more convenient operator notation in expressions). Note that when the
output argument is used, the ufunc still returns a reference to the result.
>>> x = np.arange(2)
>>> np.add(np.arange(2),np.arange(2.),x)
array([0, 2])
>>> x
array([0, 2])
and & or as ufuncs
==================
Invariably people try to use the python 'and' and 'or' as logical operators
(and quite understandably). But these operators do not behave as normal
operators since Python treats these quite differently. They cannot be
overloaded with array equivalents. Thus using 'and' or 'or' with an array
results in an error. There are two alternatives:
1) use the ufunc functions logical_and() and logical_or().
2) use the bitwise operators & and \\|. The drawback of these is that if
the arguments to these operators are not boolean arrays, the result is
likely incorrect. On the other hand, most usages of logical_and and
logical_or are with boolean arrays. As long as one is careful, this is
a convenient way to apply these operators.
""" |
"""Landlab component that simulates potential evapotranspiration rate.
Potential Evapotranspiration Component calculates spatially distributed
potential evapotranspiration based on input radiation factor (spatial
distribution of incoming radiation) using chosen method such as constant
or Priestley Taylor. Ref: ASCE-EWRI Task Committee Report Jan 2005.
.. codeauthor:: NAME and NAME import numpy as np
>>> from landlab import RasterModelGrid
>>> from landlab.components.pet import PotentialEvapotranspiration
Create a grid on which to calculate potential evapotranspiration rate.
>>> grid = RasterModelGrid((5, 4), spacing=(0.2, 0.2))
The grid will need some input data. To check the names of the fields
that provide the input to this component, use the *input_var_names*
class property.
>>> PotentialEvapotranspiration.input_var_names
('radiation__ratio_to_flat_surface',)
Check the units for the fields.
>>> PotentialEvapotranspiration.var_units('radiation__ratio_to_flat_surface')
'None'
Create the input fields.
>>> grid['cell']['radiation__ratio_to_flat_surface'] = np.array([
... 0.38488566, 0.38488566,
... 0.33309785, 0.33309785,
... 0.37381705, 0.37381705])
If you are not sure about one of the input or output variables, you can
get help for specific variables.
>>> PotentialEvapotranspiration.var_help('radiation__ratio_to_flat_surface')
name: radiation__ratio_to_flat_surface
description:
ratio of total incident shortwave radiation on sloped surface
to flat surface
units: None
at: cell
intent: in
Check the output variable names
>>> sorted(PotentialEvapotranspiration.output_var_names)
['radiation__incoming_shortwave_flux',
'radiation__net_flux',
'radiation__net_longwave_flux',
'radiation__net_shortwave_flux',
'surface__potential_evapotranspiration_rate']
Instantiate the 'PotentialEvapotranspiration' component to work on this grid,
and run it.
>>> PET = PotentialEvapotranspiration(grid, method='PriestleyTaylor')
Run the *update* method to update output variables with current time
>>> current_time = 0.5
>>> PET.update(current_time)
>>> PET.grid.at_cell['radiation__incoming_shortwave_flux']
array([ 33.09968448, 33.09968448, 28.64599771, 28.64599771,
32.14779789, 32.14779789])
>>> PET.grid.at_cell['radiation__net_flux']
array([ 13.9764353 , 13.9764353 , 12.09585347, 12.09585347,
13.57449849, 13.57449849])
>>> PET.grid.at_cell['radiation__net_shortwave_flux']
array([ 13.23987379, 13.23987379, 11.45839908, 11.45839908,
12.85911915, 12.85911915])
>>> PET.grid.at_cell['surface__potential_evapotranspiration_rate']
array([ 0.25488065, 0.25488065, 0.22058551, 0.22058551, 0.24755075,
0.24755075])
""" |
# -*- encoding: utf-8 -*-
##############################################################################
#
# Copyright (c) 2009 Veritos - NAME - www.veritos.nl
#
# WARNING: This program as such is intended to be used by professional
# programmers who take the whole responsability of assessing all potential
# consequences resulting from its eventual inadequacies and bugs.
# End users who are looking for a ready-to-use solution with commercial
# garantees and support are strongly adviced to contract a Free Software
# Service Company like Veritos.
#
# This program is Free Software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#
##############################################################################
#
# Deze module werkt in OpenERP 5.0.0 (en waarschijnlijk hoger).
# Deze module werkt niet in OpenERP versie 4 en lager.
#
# Status 1.0 - getest op OpenERP 5.0.3
#
# Versie IP_ADDRESS
# account.account.type
# Basis gelegd voor alle account type
#
# account.account.template
# Basis gelegd met alle benodigde grootboekrekeningen welke via een menu-
# structuur gelinkt zijn aan rubrieken 1 t/m 9.
# De grootboekrekeningen gelinkt aan de account.account.type
# Deze links moeten nog eens goed nagelopen worden.
#
# account.chart.template
# Basis gelegd voor het koppelen van rekeningen aan debiteuren, crediteuren,
# bank, inkoop en verkoop boeken en de BTW configuratie.
#
# Versie IP_ADDRESS
# account.tax.code.template
# Basis gelegd voor de BTW configuratie (structuur)
# Heb als basis het BTW aangifte formulier gebruikt. Of dit werkt?
#
# account.tax.template
# De BTW rekeningen aangemaakt en deze gekoppeld aan de betreffende
# grootboekrekeningen
#
# Versie IP_ADDRESS
# Opschonen van de code en verwijderen van niet gebruikte componenten.
# Versie IP_ADDRESS
# Aanpassen a_expense van 3000 -> 7000
# record id='btw_code_5b' op negatieve waarde gezet
# Versie IP_ADDRESS
# BTW rekeningen hebben typeaanduiding gekregen t.b.v. purchase of sale
# Versie IP_ADDRESS
# Opschonen van module.
# Versie IP_ADDRESS
# Opschonen van module.
# Versie IP_ADDRESS
# Foutje in l10n_nl_wizard.xml gecorrigeerd waardoor de module niet volledig installeerde.
# Versie IP_ADDRESS
# Account Receivable en Payable goed gedefinieerd.
# Versie IP_ADDRESS
# Alle user_type_xxx velden goed gedefinieerd.
# Specifieke bouw en garage gerelateerde grootboeken verwijderd om een standaard module te creeeren.
# Deze module kan dan als basis worden gebruikt voor specifieke doelgroep modules te creeeren.
# Versie IP_ADDRESS
# Correctie van rekening 7010 (stond dubbel met 7014 waardoor installatie verkeerd ging)
# versie IP_ADDRESS
# Correctie op diverse rekening types van user_type_asset -> user_type_liability en user_type_equity
# versie IP_ADDRESS
# Kleine correctie op BTW te vorderen hoog, id was hetzelfde voor beide, waardoor hoog werd overschreven door # overig. Verduidelijking van omschrijvingen in belastingcodes t.b.v. aangifte overzicht.
# versie IP_ADDRESS
# BTW omschrijvingen aangepast, zodat rapporten er beter uitzien. 2a en 5b e.d. verwijderd en enkele omschrijvingen toegevoegd.
# versie IP_ADDRESS - Switch to English
# Added properties_stock_xxx accounts for correct stock valuation, changed 7000-accounts from type cash to type expense
# Changed naming of 7020 and 7030 to Kostprijs omzet xxxx
|
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
diff = default_diff_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.match(actual, expected)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound functions that handle matching
in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
The TestCmd module provides unbound functions that can be used for the
"diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
# ABANDONWARE ### ABANDONWARE ### ABANDONWARE ### ABANDONWARE ### ABANDONWARE ### ABANDONWARE ###
# ABANDONWARE ### ABANDONWARE ### ABANDONWARE ### ABANDONWARE ### ABANDONWARE ### ABANDONWARE ###
#
# At the moment no one is writing a "integrity" Requests.
# It can be resurrected once we'll write those requests again.
# In any case, to be used it should be re-written in the spirit of the new RMS system,
# e.g. like /DataManagement/Agent/RequestOperations object
# """ LFCvsSEAgent takes data integrity checks from the RequestDB and verifies the integrity of the supplied directory.
# """
# from DIRAC import gLogger, gConfig, gMonitor, S_OK, S_ERROR
# from DIRAC.Core.Base.AgentModule import AgentModule
# from DIRAC.Core.Utilities.Pfn import pfnparse, pfnunparse
# from DIRAC.Core.DISET.RPCClient import RPCClient
# from DIRAC.RequestManagementSystem.Client.RequestClient import RequestClient
# from DIRAC.RequestManagementSystem.Client.RequestContainer import RequestContainer
# from DIRAC.DataManagementSystem.Client.ReplicaManager import ReplicaManager
# from DIRAC.DataManagementSystem.Agent.NamespaceBrowser import NamespaceBrowser
#
# import time, os
# from types import *
#
# AGENT_NAME = "DataManagement/LFCvsSEAgent"
#
# __RCSID__ = "$Id$"
#
# class LFCvsSEAgent( AgentModule ):
#
# def initialize( self ):
#
# self.RequestDBClient = RequestClient()
# self.ReplicaManager = ReplicaManager()
# # This sets the Default Proxy to used as that defined under
# # /Operations/Shifter/DataManager
# # the shifterProxy option in the Configuration can be used to change this default.
# self.am_setOption( 'shifterProxy', 'DataManager' )
#
# return S_OK()
#
# def execute( self ):
#
# res = self.RequestDBClient.getRequest( 'integrity' )
# if not res['OK']:
# gLogger.info( "LFCvsSEAgent.execute: Failed to get request from database." )
# return S_OK()
# elif not res['Value']:
# gLogger.info( "LFCvsSEAgent.execute: No requests to be executed found." )
# return S_OK()
# requestString = res['Value']['RequestString']
# requestName = res['Value']['RequestName']
# sourceServer = res['Value']['Server']
# gLogger.info( "LFCvsSEAgent.execute: Obtained request %s" % requestName )
# oRequest = RequestContainer( request = requestString )
#
# ################################################
# # Find the number of sub-requests from the request
# res = oRequest.getNumSubRequests( 'integrity' )
# if not res['OK']:
# errStr = "LFCvsSEAgent.execute: Failed to obtain number of integrity subrequests."
# gLogger.error( errStr, res['Message'] )
# return S_OK()
# gLogger.info( "LFCvsSEAgent.execute: Found %s sub requests." % res['Value'] )
#
# ################################################
# # For all the sub-requests in the request
# for ind in range( res['Value'] ):
# gLogger.info( "LFCvsSEAgent.execute: Processing sub-request %s." % ind )
# subRequestAttributes = oRequest.getSubRequestAttributes( ind, 'integrity' )['Value']
# if subRequestAttributes['Status'] == 'Waiting':
# subRequestFiles = oRequest.getSubRequestFiles( ind, 'integrity' )['Value']
# operation = subRequestAttributes['Operation']
#
# ################################################
# # If the sub-request is a lfcvsse operation
# if operation == 'LFCvsSE':
# gLogger.info( "LFCvsSEAgent.execute: Attempting to execute %s sub-request." % operation )
# for subRequestFile in subRequestFiles:
# if subRequestFile['Status'] == 'Waiting':
# lfn = subRequestFile['LFN']
# oNamespaceBrowser = NamespaceBrowser( lfn )
#
# # Loop over all the directories and sub-directories
# while ( oNamespaceBrowser.isActive() ):
# currentDir = oNamespaceBrowser.getActiveDir()
# gLogger.info( "LFCvsSEAgent.execute: Attempting to get contents of %s." % currentDir )
# res = self.ReplicaManager.getCatalogDirectoryContents( currentDir )
# if not res['OK']:
# subDirs = [currentDir]
# elif res['Value']['Failed'].has_key( currentDir ):
# subDirs = [currentDir]
# else:
# subDirs = res['Value']['Successful'][currentDir]['SubDirs']
# files = res['Value']['Successful'][currentDir]['Files']
#
# lfnSizeDict = {}
# pfnLfnDict = {}
# pfnStatusDict = {}
# sePfnDict = {}
# for lfn, lfnDict in files.items():
# lfnSizeDict[lfn] = lfnDict['MetaData']['Size']
# for se in lfnDict['Replicas'].keys():
# pfn = lfnDict['Replicas'][se]['PFN']
# status = lfnDict['Replicas'][se]['Status']
# pfnStatusDict[pfn] = status
# pfnLfnDict[pfn] = lfn
# if not sePfnDict.has_key( se ):
# sePfnDict[se] = []
# sePfnDict[se].append( pfn )
#
# for storageElementName, physicalFiles in sePfnDict.items():
# gLogger.info( "LFCvsSEAgent.execute: Attempting to get metadata for files on %s." % storageElementName )
# res = self.ReplicaManager.getStorageFileMetadata( physicalFiles, storageElementName )
# if not res['OK']:
# gLogger.error( "LFCvsSEAgent.execute: Completely failed to get physical file metadata.", res['Message'] )
# else:
# for pfn in res['Value']['Failed'].keys():
# gLogger.error( "LFCvsSEAgent.execute: Failed to get metadata.", "%s %s" % ( pfn, res['Value']['Failed'][pfn] ) )
# lfn = pfnLfnDict[pfn]
# fileMetadata = {'Prognosis':'MissingSEPfn', 'LFN':lfn, 'PFN':pfn, 'StorageElement':storageElementName, 'Size':lfnSizeDict[lfn]}
# IntegrityDB = RPCClient( 'DataManagement/DataIntegrity' )
# resInsert = IntegrityDB.insertProblematic( AGENT_NAME, fileMetadata )
# if resInsert['OK']:
# gLogger.info( "LFCvsSEAgent.execute: Successfully added to IntegrityDB." )
# gLogger.error( "Change the status in the LFC,ProcDB...." )
# else:
# gLogger.error( "Shit, fuck, bugger. Add the failover." )
# for pfn, pfnDict in res['Value']['Successful'].items():
# lfn = pfnLfnDict[pfn]
# catalogSize = int( lfnSizeDict[lfn] )
# storageSize = int( pfnDict['Size'] )
# if int( catalogSize ) == int( storageSize ):
# gLogger.info( "LFCvsSEAgent.execute: Catalog and storage sizes match.", "%s %s" % ( pfn, storageElementName ) )
# gLogger.info( "Change the status in the LFC" )
# else:
# gLogger.error( "LFCvsSEAgent.execute: Catalog and storage size mis-match.", "%s %s" % ( pfn, storageElementName ) )
# fileMetadata = {'Prognosis':'PfnSizeMismatch', 'LFN':lfn, 'PFN':pfn, 'StorageElement':storageElementName}
# IntegrityDB = RPCClient( 'DataManagement/DataIntegrity' )
# resInsert = IntegrityDB.insertProblematic( AGENT_NAME, fileMetadata )
# if resInsert['OK']:
# gLogger.info( "LFCvsSEAgent.execute: Successfully added to IntegrityDB." )
# gLogger.error( "Change the status in the LFC,ProcDB...." )
# else:
# gLogger.error( "Shit, fuck, bugger. Add the failover." )
# oNamespaceBrowser.updateDirs( subDirs )
# oRequest.setSubRequestFileAttributeValue( ind, 'integrity', lfn, 'Status', 'Done' )
#
# ################################################
# # If the sub-request is none of the above types
# else:
# gLogger.info( "LFCvsSEAgent.execute: Operation not supported.", operation )
#
# ################################################
# # Determine whether there are any active files
# if oRequest.isSubRequestEmpty( ind, 'integrity' )['Value']:
# oRequest.setSubRequestStatus( ind, 'integrity', 'Done' )
#
# ################################################
# # If the sub-request is already in terminal state
# else:
# gLogger.info( "LFCvsSEAgent.execute: Sub-request %s is status '%s' and not to be executed." % ( ind, subRequestAttributes['Status'] ) )
#
# ################################################
# # Generate the new request string after operation
# requestString = oRequest.toXML()['Value']
# res = self.RequestDBClient.updateRequest( requestName, requestString, sourceServer )
#
# return S_OK()
|
"""
[2017-09-26] Challenge #333 [Easy] Packet Assembler
https://www.reddit.com/r/dailyprogrammer/comments/72ivih/20170926_challenge_333_easy_packet_assembler/
#Description
When a message is transmitted over the internet, it is split into multiple packets, each packet is transferred
individually, and the packets are reassembled into the original message by the receiver. Because the internet exists in
the real world, and because the real world can be messy, packets do not always arrive in the order in which they are
sent. For today's challenge, your program must collect packets from stdin, assemble them in the correct order, and
print the completed messages to stdout.
The point of reading from stdin is to simulate incoming packets. For the purposes of this challenge, assume there is a
potentially unlimited number of packets. Your program should not depend on knowing how many packets there are in total.
Simply sorting the input in its entirety would technically work, but defeats the purpose of this exercise.
#Input description
Each line of input represents a single packet. Each line will be formatted as `X Y Z some_text`, where X Y and Z are
positive integer and some_text is an arbitrary string. X represents the message ID (ie which message this packet is a
part of). Y represents the packet ID (ie the index of this packet in the message) (packets are zero-indexed, so the
first packet in a message will have Y=0, the last packet in a message will have Y=Z-1). Z represents the total number
of packets in the message.
It is guaranteed that there will be no duplicate packets or message IDs.
##Example input
6220 1 10 Because he's the hero Gotham deserves,
6220 9 10
5181 5 7 in time, like tears in rain. Time to die.
6220 3 10 So we'll hunt him.
6220 5 10 Because he's not a hero.
5181 6 7
5181 2 7 shoulder of Orion. I watched C-beams
5181 4 7 Gate. All those moments will be lost
6220 6 10 He's a silent guardian.
5181 3 7 glitter in the dark near the Tannhäuser
6220 7 10 A watchful protector.
5181 1 7 believe. Attack ships on fire off the
6220 0 10 We have to chase him.
5181 0 7 I've seen things you people wouldn't
6220 4 10 Because he can take it.
6220 2 10 but not the one it needs right now.
6220 8 10 A Dark Knight.
#Output description
Output each completed message, one line per packet. Messages should be outputted in the order in which they are
completed.
##Example output
5181 0 7 I've seen things you people wouldn't
5181 1 7 believe. Attack ships on fire off the
5181 2 7 shoulder of Orion. I watched C-beams
5181 3 7 glitter in the dark near the Tannhäuser
5181 4 7 Gate. All those moments will be lost
5181 5 7 in time, like tears in rain. Time to die.
5181 6 7
6220 0 10 We have to chase him.
6220 1 10 Because he's the hero Gotham deserves,
6220 2 10 but not the one it needs right now.
6220 3 10 So we'll hunt him.
6220 4 10 Because he can take it.
6220 5 10 Because he's not a hero.
6220 6 10 He's a silent guardian.
6220 7 10 A watchful protector.
6220 8 10 A Dark Knight.
6220 9 10
#Challenge input
7469 1 7 believe. Attack ships on fire off the
9949 6 10 He's a silent guardian.
2997 9 19 Force is a pathway to many abilities some
6450 2 11 is a vestige of the vox populi, now vacant, vanished. However, this valorous
6450 10 11
6450 8 11 veers most verbose, so let me simply add that it's my very good honour to meet
6450 5 11 and voracious violation of volition! The only verdict is vengeance; a vendetta
9949 1 10 Because he's the hero Gotham deserves,
6450 1 11 and villain by the vicissitudes of fate. This visage, no mere veneer of vanity,
2997 13 19 he did. Unfortunately, he taught his
9949 8 10 A Dark Knight.
1938 4 17 by the iniquities of the selfish and the
1938 0 17 You read the Bible, NAME Well there's
2997 0 19 Did you ever hear the tragedy of Darth
2997 1 19 Plagueis the Wise? I thought not. It's not a
1938 8 17 of darkness, for he is truly is brother's
2997 14 19 apprentice everything he knew, then his
6450 3 11 visitation of a bygone vexation stands vivified, and has vowed to vanquish these
1938 12 17 who attempt to poison and destroy my
6450 9 11 you and you may call me V.
7469 2 7 shoulder of Orion. I watched C-beams
2997 10 19 consider to be unnatural. He became so
1938 1 17 this passage I got memorized, sorta fits
2997 5 19 Force to influence the midichlorians to
1938 6 17 in the name of charity and good will,
7469 0 7 I've seen things you people wouldn't
9949 4 10 Because he can take it.
6450 7 11 vindicate the vigilant and the virtuous. Verily, this vichyssoise of verbiage
9949 0 10 We have to chase him.
9949 7 10 A watchful protector.
2997 3 19 legend. NAME was a Dark Lord of the
6450 6 11 held as a votive, not in vain, for the value and veracity of such shall one day
2997 8 19 cared about from dying. The dark side of the
1938 10 17 And I will strike down upon thee with
1938 11 17 great vengeance and furious anger those
1938 7 17 shepherds the weak through the valley
1938 2 17 this occasion. Ezekiel 25:17? "The path
2997 18 19
9949 9 10
1938 14 17 the Lord when I lay my vengeance upon
1938 15 17 thee."
1938 9 17 keeper and the finder of lost children.
1938 13 17 brothers. And you will know my name is
9949 2 10 but not the one it needs right now.
2997 16 19 he could have others from death, but not
2997 7 19 dark side that he could even keep the once he
1938 5 17 tyranny of evil men. Blessed is he who,
2997 17 19 himself.
2997 6 19 create life...He had such a knowledge of the
2997 12 19 losing his power. Which eventually, of course,
7469 4 7 Gate. All those moments will be lost
2997 2 19 story the Jedi would tell you. It's a Sith
1938 16 17
2997 4 19 Sith so powerful and so wise, he could use the
1938 3 17 of the righteous man is beset on all sides
2997 11 19 powerful...The only thing he was afraid of was
7469 6 7
2997 15 19 apprentice killed him in his sleep. Ironic,
7469 5 7 in time, like tears in rain. Time to die.
9949 3 10 So we'll hunt him.
7469 3 7 glitter in the dark near the Tannhäuser
6450 4 11 venal and virulent vermin vanguarding vice and vouchsafing the violently vicious
6450 0 11 Voilà! In view, a humble vaudevillian veteran, cast vicariously as both victim
9949 5 10 Because he's not a hero.
#Finally
Have a good challenge idea?
Consider submitting it to /r/dailyprogrammer_ideas
""" |
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
|
"""
========================
Broadcasting over arrays
========================
The term broadcasting describes how numpy treats arrays with different
shapes during arithmetic operations. Subject to certain constraints,
the smaller array is "broadcast" across the larger array so that they
have compatible shapes. Broadcasting provides a means of vectorizing
array operations so that looping occurs in C instead of Python. It does
this without making needless copies of data and usually leads to
efficient algorithm implementations. There are, however, cases where
broadcasting is a bad idea because it leads to inefficient use of memory
that slows computation.
NumPy operations are usually done on pairs of arrays on an
element-by-element basis. In the simplest case, the two arrays must
have exactly the same shape, as in the following example:
>>> a = np.array([1.0, 2.0, 3.0])
>>> b = np.array([2.0, 2.0, 2.0])
>>> a * b
array([ 2., 4., 6.])
NumPy's broadcasting rule relaxes this constraint when the arrays'
shapes meet certain constraints. The simplest broadcasting example occurs
when an array and a scalar value are combined in an operation:
>>> a = np.array([1.0, 2.0, 3.0])
>>> b = 2.0
>>> a * b
array([ 2., 4., 6.])
The result is equivalent to the previous example where ``b`` was an array.
We can think of the scalar ``b`` being *stretched* during the arithmetic
operation into an array with the same shape as ``a``. The new elements in
``b`` are simply copies of the original scalar. The stretching analogy is
only conceptual. NumPy is smart enough to use the original scalar value
without actually making copies, so that broadcasting operations are as
memory and computationally efficient as possible.
The code in the second example is more efficient than that in the first
because broadcasting moves less memory around during the multiplication
(``b`` is a scalar rather than an array).
General Broadcasting Rules
==========================
When operating on two arrays, NumPy compares their shapes element-wise.
It starts with the trailing dimensions, and works its way forward. Two
dimensions are compatible when
1) they are equal, or
2) one of them is 1
If these conditions are not met, a
``ValueError: frames are not aligned`` exception is thrown, indicating that
the arrays have incompatible shapes. The size of the resulting array
is the maximum size along each dimension of the input arrays.
Arrays do not need to have the same *number* of dimensions. For example,
if you have a ``256x256x3`` array of RGB values, and you want to scale
each color in the image by a different value, you can multiply the image
by a one-dimensional array with 3 values. Lining up the sizes of the
trailing axes of these arrays according to the broadcast rules, shows that
they are compatible::
Image (3d array): 256 x 256 x 3
Scale (1d array): 3
Result (3d array): 256 x 256 x 3
When either of the dimensions compared is one, the other is
used. In other words, dimensions with size 1 are stretched or "copied"
to match the other.
In the following example, both the ``A`` and ``B`` arrays have axes with
length one that are expanded to a larger size during the broadcast
operation::
A (4d array): 8 x 1 x 6 x 1
B (3d array): 7 x 1 x 5
Result (4d array): 8 x 7 x 6 x 5
Here are some more examples::
A (2d array): 5 x 4
B (1d array): 1
Result (2d array): 5 x 4
A (2d array): 5 x 4
B (1d array): 4
Result (2d array): 5 x 4
A (3d array): 15 x 3 x 5
B (3d array): 15 x 1 x 5
Result (3d array): 15 x 3 x 5
A (3d array): 15 x 3 x 5
B (2d array): 3 x 5
Result (3d array): 15 x 3 x 5
A (3d array): 15 x 3 x 5
B (2d array): 3 x 1
Result (3d array): 15 x 3 x 5
Here are examples of shapes that do not broadcast::
A (1d array): 3
B (1d array): 4 # trailing dimensions do not match
A (2d array): 2 x 1
B (3d array): 8 x 4 x 3 # second from last dimensions mismatched
An example of broadcasting in practice::
>>> x = np.arange(4)
>>> xx = x.reshape(4,1)
>>> y = np.ones(5)
>>> z = np.ones((3,4))
>>> x.shape
(4,)
>>> y.shape
(5,)
>>> x + y
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be broadcast to a single shape
>>> xx.shape
(4, 1)
>>> y.shape
(5,)
>>> (xx + y).shape
(4, 5)
>>> xx + y
array([[ 1., 1., 1., 1., 1.],
[ 2., 2., 2., 2., 2.],
[ 3., 3., 3., 3., 3.],
[ 4., 4., 4., 4., 4.]])
>>> x.shape
(4,)
>>> z.shape
(3, 4)
>>> (x + z).shape
(3, 4)
>>> x + z
array([[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.]])
Broadcasting provides a convenient way of taking the outer product (or
any other outer operation) of two arrays. The following example shows an
outer addition operation of two 1-d arrays::
>>> a = np.array([0.0, 10.0, 20.0, 30.0])
>>> b = np.array([1.0, 2.0, 3.0])
>>> a[:, np.newaxis] + b
array([[ 1., 2., 3.],
[ 11., 12., 13.],
[ 21., 22., 23.],
[ 31., 32., 33.]])
Here the ``newaxis`` index operator inserts a new axis into ``a``,
making it a two-dimensional ``4x1`` array. Combining the ``4x1`` array
with ``b``, which has shape ``(3,)``, yields a ``4x3`` array.
See `this article <http://wiki.scipy.org/EricsBroadcastingDoc>`_
for illustrations of broadcasting concepts.
""" |
#!/usr/bin/env python
# (c) 2013, NAME <EMAIL>
#
# This file is part of Ansible.
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
#
#
# Author: NAME <EMAIL>
#
# Description:
# This module queries local or remote Docker daemons and generates
# inventory information.
#
# This plugin does not support targeting of specific hosts using the --host
# flag. Instead, it queries the Docker API for each container, running
# or not, and returns this data all once.
#
# The plugin returns the following custom attributes on Docker containers:
# docker_args
# docker_config
# docker_created
# docker_driver
# docker_exec_driver
# docker_host_config
# docker_hostname_path
# docker_hosts_path
# docker_id
# docker_image
# docker_name
# docker_network_settings
# docker_path
# docker_resolv_conf_path
# docker_state
# docker_volumes
# docker_volumes_rw
#
# Requirements:
# The docker-py module: https://github.com/dotcloud/docker-py
#
# Notes:
# A config file can be used to configure this inventory module, and there
# are several environment variables that can be set to modify the behavior
# of the plugin at runtime:
# DOCKER_CONFIG_FILE
# DOCKER_HOST
# DOCKER_VERSION
# DOCKER_TIMEOUT
# DOCKER_PRIVATE_SSH_PORT
# DOCKER_DEFAULT_IP
#
# Environment Variables:
# environment variable: DOCKER_CONFIG_FILE
# description:
# - A path to a Docker inventory hosts/defaults file in YAML format
# - A sample file has been provided, colocated with the inventory
# file called 'docker.yml'
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_HOST
# description:
# - The socket on which to connect to a Docker daemon API
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_VERSION
# description:
# - Version of the Docker API to use
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_TIMEOUT
# description:
# - Timeout in seconds for connections to Docker daemon API
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_PRIVATE_SSH_PORT
# description:
# - The private port (container port) on which SSH is listening
# for connections
# default: 22
# required: false
# environment variable: DOCKER_DEFAULT_IP
# description:
# - This environment variable overrides the container SSH connection
# IP address (aka, 'ansible_ssh_host')
#
# This option allows one to override the ansible_ssh_host whenever
# Docker has exercised its default behavior of binding private ports
# to all interfaces of the Docker host. This behavior, when dealing
# with remote Docker hosts, does not allow Ansible to determine
# a proper host IP address on which to connect via SSH to containers.
# By default, this inventory module assumes all IP_ADDRESS-exposed
# ports to be bound to localhost:<port>. To override this
# behavior, for example, to bind a container's SSH port to the public
# interface of its host, one must manually set this IP.
#
# It is preferable to begin to launch Docker containers with
# ports exposed on publicly accessible IP addresses, particularly
# if the containers are to be targeted by Ansible for remote
# configuration, not accessible via localhost SSH connections.
#
# Docker containers can be explicitly exposed on IP addresses by
# a) starting the daemon with the --ip argument
# b) running containers with the -P/--publish ip::containerPort
# argument
# default: IP_ADDRESS if port exposed on IP_ADDRESS by Docker
# required: false
#
# Examples:
# Use the config file:
# DOCKER_CONFIG_FILE=./docker.yml docker.py --list
#
# Connect to docker instance on localhost port 4243
# DOCKER_HOST=tcp://localhost:4243 docker.py --list
#
# Any container's ssh port exposed on IP_ADDRESS will mapped to
# another IP address (where Ansible will attempt to connect via SSH)
# DOCKER_DEFAULT_IP=IP_ADDRESS docker.py --list
|
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
diff = default_diff_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.match(actual, expected)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound functions that handle matching
in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
The TestCmd module provides unbound functions that can be used for the
"diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
#!/usr/bin/python3
#
# This script acts as a HTTP/HTTPS reverse-proxy with several restrictions imposed upon which
# requests and from whom it should process, similarly to the .htaccess file in Apache2's mod_rewrite.
#
# malleable_redirector was created to resolve the problem of effective IR/AV/EDRs/Sandboxes evasion on the
# C2 redirector's backyard.
#
# The proxy along with this plugin can both act as a CobaltStrike Teamserver C2 redirector, given Malleable C2
# profile used during the campaign and teamserver's hostname:port. The plugin will parse supplied malleable profile
# in order to understand which inbound requests may possibly come from the compatible Beacon or are not compliant with
# the profile and therefore should be misdirected. Sections such as http-stager, http-get, http-post and their corresponding
# uris, headers, prepend/append patterns, User-Agent are all used to distinguish between legitimate beacon's request
# and some Internet noise or IR/AV/EDRs out of bound inquiries.
#
# The plugin was also equipped with marvelous known bad IP ranges coming from:
# USERNAME and the others:
# https://gist.github.com/USERNAME/971385e8334e189d93a6cb4671238b10
#
# Using a IP addresses blacklist along with known to be bad keywords lookup on Reverse-IP DNS queries and HTTP headers,
# is considerably increasing plugin's resiliency to the unauthorized peers wanting to examine protected infrastructure.
#
# Use wisely, stay safe.
#
# Example usage:
# $ python3 proxy2.py -P 80/http -P 443/https -p plugins/malleable_redirector.py --config malleable-redir-config.yml
#
# [INFO] 19:21:42: Loading 1 plugin...
# [INFO] 19:21:42: Plugin "malleable_redirector" has been installed.
# [INFO] 19:21:42: Preparing SSL certificates and keys for https traffic interception...
# [INFO] 19:21:42: Using provided CA key file: ca-cert/ca.key
# [INFO] 19:21:42: Using provided CA certificate file: ca-cert/ca.crt
# [INFO] 19:21:42: Using provided Certificate key: ca-cert/cert.key
# [INFO] 19:21:42: Serving http proxy on: IP_ADDRESS, port: 80...
# [INFO] 19:21:42: Serving https proxy on: IP_ADDRESS, port: 443...
# [INFO] 19:21:42: [REQUEST] GET /jquery-3.3.1.min.js
# [INFO] 19:21:42: == Valid malleable http-get request inbound.
# [INFO] 19:21:42: Plugin redirected request from [code.jquery.com] to [IP_ADDRESS:8080]
# [INFO] 19:21:42: [RESPONSE] HTTP 200 OK, length: 5543
# [INFO] 19:21:45: [REQUEST] GET /jquery-3.3.1.min.js
# [INFO] 19:21:45: == Valid malleable http-get request inbound.
# [INFO] 19:21:45: Plugin redirected request from [code.jquery.com] to [IP_ADDRESS:8080]
# [INFO] 19:21:45: [RESPONSE] HTTP 200 OK, length: 5543
# [INFO] 19:21:46: [REQUEST] GET /
# [ERROR] 19:21:46: [DROP, reason:1] inbound User-Agent differs from the one defined in C2 profile.
# [INFO] 19:21:46: [RESPONSE] HTTP 301 Moved Permanently, length: 212
# [INFO] 19:21:48: [REQUEST] GET /jquery-3.3.1.min.js
# [INFO] 19:21:48: == Valid malleable http-get request inbound.
# [INFO] 19:21:48: Plugin redirected request from [code.jquery.com] to [IP_ADDRESS:8080]
#
# The above output contains a line pointing out that there has been an unauthorized, not compliant with our C2
# profile inbound request, which got dropped due to incompatible User-Agent string presented:
# [...]
# [DROP, reason:1] inbound User-Agent differs from the one defined in C2 profile.
# [...]
#
# Requirements:
# - brotli
# - yaml
#
# Author:
# NAME / USERNAME '20
# <EMAIL>
#
|
"""
Perm Store
author: NAME EMAIL updated: 12/31/2014
Persistent storage solution. Database hierarchy is organized as: ::\n
PermStore[database_id][bucket_id][doc_uid] = {key1:value1,key2:value2,...}
Dependencies: next.constants to determine location of mongoDB server
Some common functions
###############################
Initialization::\n
db = PermStore()
Database functions::\n
exists,didSucceed,message = db.exists(database_id,bucket_id,doc_uid,key)
value,didSucceed,message = db.get(database_id,bucket_id,doc_uid,key)
doc,didSucceed,message = db.getDoc(database_id,bucket_id,doc_uid)
docs,didSucceed,message = db.getDocsByPattern(database_id,bucket_id,filter_dict)
didSucceed,message = db.set(database_id,bucket_id,doc_uid,key,value)
didSucceed,message = db.setDoc(database_id,bucket_id,doc_uid,doc)
didSucceed,message = db.delete(database_id,bucket_id,doc_uid,key)
didSucceed,message = db.deleteDoc(database_id,bucket_id,doc_uid)
didSucceed,message = db.deleteDocsByPattern(database_id,bucket_id,filter_dict)
didSucceed,message = db.deleteBucket(database_id,bucket_id)
didSucceed,message = db.deleteDatabase(database_id)
didSucceed,message = db.deleteAll()
Database inspection ::\n
docNames,didSucceed,message = db.getDocNames(database_id,bucket_id)
bucketNames,didSucceed,message = db.getBucketNames(database_id)
databaseNames,didSucceed,message = db.getDatabaseNames()
Some example usage
###############################
Let's first inititlize the database ::\n
from next.database.PermStore import PermStore
db = PermStore()
And let's assume that the database is empty, which we can enforce by deleting everything ::\n
didSucceed,message = db.deleteAll()
Building up a document one key at a time ::\n
database_id = 'things'
bucket_id = 'animals'
doc_uid = 'cat'
didSucceed,message = db.set(database_id,bucket_id,doc_uid,'color','black')
didSucceed,message = db.set(database_id,bucket_id,doc_uid,'num_legs',4)
didSucceed,message = db.set(database_id,bucket_id,doc_uid,'age',7.5)
Inserting a document ::\n
database_id = 'things'
bucket_id = 'animals'
doc_uid = 'dog'
doc = {'color':'brown','num_legs':4,'age':9.5}
didSucceed,message = db.setDoc(database_id,bucket_id,doc_uid,doc)
doc_uid = 'human'
doc = {'color':'tan','num_legs':2,'age':28}
didSucceed,message = db.setDoc(database_id,bucket_id,doc_uid,doc)
Retrieving values ::\n
value,didSucceed,message = db.get('things','animals','dog','age')
print value
>>> 9.5
Retrieving docs ::\n
doc,didSucceed,message = db.getDoc('things','animals','cat')
print doc
>>> {u'color': u'black', u'age': 7.5, u'_id': u'cat', u'num_legs': 4}
doc,didSucceed,message = db.getDoc('things','animals','dog')
print doc
>>> {u'color': u'brown', u'age': 9.5, u'_id': u'dog', u'num_legs': 4}
Advanced doc retrieval ::\n
docs,didSucceed,message = db.getDocsByPattern('things','animals',{})
print docs
>>> [{u'color': u'black', u'age': 7.5, u'_id': 'cat', u'num_legs': 4}, {u'color': u'brown', u'age': 9.5, u'_id': 'dog', u'num_legs': 4}, {u'color': u'tan', u'age': 28, u'_id': 'human', u'num_legs': 2}]
docs,didSucceed,message = db.getDocsByPattern('things','animals',{'num_legs':4})
>>> [{u'color': u'black', u'age': 7.5, u'_id': 'cat', u'num_legs': 4}, {u'color': u'brown', u'age': 9.5, u'_id': 'dog', u'num_legs': 4}]
docs,didSucceed,message = db.getDocsByPattern('things','animals',{'age':{ '$gte':8,'$lt':10} })
>>> [{u'color': u'brown', u'age': 9.5, u'_id': 'dog', u'num_legs': 4}]
docs,didSucceed,message = db.getDocsByPattern('things','animals',{'age':{ '$gte':8 }, 'num_legs':2 })
>>> [{u'color': u'tan', u'age': 28, u'_id': 'human', u'num_legs': 2}]
Doc retrival with time ::\n
from datetime import datetime,timedelta
t_0 = datetime.now()
t_1 = t_0 + timedelta(0,30)
t_2 = t_1 + timedelta(0,15)
t_3 = t_0 + timedelta(0,55)
# (if doc_uid=None, one is automatically generated)
didSucceed,message = db.setDoc('users','keys',None,{'user_id':'sd89w3hr292r','key':'a0jd103b2r','timestamp':t_0})
didSucceed,message = db.setDoc('users','keys',None,{'user_id':'sd89w3hr292r','key':'w8dh28232f','timestamp':t_1})
didSucceed,message = db.setDoc('users','keys',None,{'user_id':'sd89w3hr292r','key':'89yf9hgfwe','timestamp':t_2})
didSucceed,message = db.setDoc('users','keys',None,{'user_id':'sd89w3hr292r','key':'edhe2dqw9d','timestamp':t_3})
ts = t_1 - timedelta(0,1)
te = t_2 + timedelta(0,1)
docs,didSucceed,message = db.getDocsByPattern('users','keys',{'timestamp':{ '$gte':ts,'$lte':te } })
print docs
>>> [{u'timestamp': '2015-01-23 10:57:14.779000', u'_id': '54c2996c319da682ebb17576', u'user_id': u'sd89w3hr292r', u'key': u'w8dh28232f'}, {u'timestamp': '2015-01-23 10:57:29.779000', u'_id': '54c2996c319da682ebb17577', u'user_id': u'sd89w3hr292r', u'key': u'89yf9hgfwe'}]
""" |
"""
=====================================================
Optimization and root finding (:mod:`scipy.optimize`)
=====================================================
.. currentmodule:: scipy.optimize
Optimization
============
Local Optimization
------------------
.. autosummary::
:toctree: generated/
minimize - Unified interface for minimizers of multivariate functions
minimize_scalar - Unified interface for minimizers of univariate functions
OptimizeResult - The optimization result returned by some optimizers
OptimizeWarning - The optimization encountered problems
The `minimize` function supports the following methods:
.. toctree::
optimize.minimize-neldermead
optimize.minimize-powell
optimize.minimize-cg
optimize.minimize-bfgs
optimize.minimize-newtoncg
optimize.minimize-lbfgsb
optimize.minimize-tnc
optimize.minimize-cobyla
optimize.minimize-slsqp
optimize.minimize-dogleg
optimize.minimize-trustncg
optimize.minimize-trustexact
The `minimize_scalar` function supports the following methods:
.. toctree::
optimize.minimize_scalar-brent
optimize.minimize_scalar-bounded
optimize.minimize_scalar-golden
The specific optimization method interfaces below in this subsection are
not recommended for use in new scripts; all of these methods are accessible
via a newer, more consistent interface provided by the functions above.
General-purpose multivariate methods:
.. autosummary::
:toctree: generated/
fmin - Nelder-Mead Simplex algorithm
fmin_powell - Powell's (modified) level set method
fmin_cg - Non-linear (Polak-Ribiere) conjugate gradient algorithm
fmin_bfgs - Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno)
fmin_ncg - Line-search Newton Conjugate Gradient
Constrained multivariate methods:
.. autosummary::
:toctree: generated/
fmin_l_bfgs_b - Zhu, Byrd, and Nocedal's constrained optimizer
fmin_tnc - Truncated Newton code
fmin_cobyla - Constrained optimization by linear approximation
fmin_slsqp - Minimization using sequential least-squares programming
differential_evolution - stochastic minimization using differential evolution
Univariate (scalar) minimization methods:
.. autosummary::
:toctree: generated/
fminbound - Bounded minimization of a scalar function
brent - 1-D function minimization using Brent method
golden - 1-D function minimization using Golden Section method
Equation (Local) Minimizers
---------------------------
.. autosummary::
:toctree: generated/
leastsq - Minimize the sum of squares of M equations in N unknowns
least_squares - Feature-rich least-squares minimization.
nnls - Linear least-squares problem with non-negativity constraint
lsq_linear - Linear least-squares problem with bound constraints
Global Optimization
-------------------
.. autosummary::
:toctree: generated/
basinhopping - Basinhopping stochastic optimizer
brute - Brute force searching optimizer
differential_evolution - stochastic minimization using differential evolution
Rosenbrock function
-------------------
.. autosummary::
:toctree: generated/
rosen - The Rosenbrock function.
rosen_der - The derivative of the Rosenbrock function.
rosen_hess - The Hessian matrix of the Rosenbrock function.
rosen_hess_prod - Product of the Rosenbrock Hessian with a vector.
Fitting
=======
.. autosummary::
:toctree: generated/
curve_fit -- Fit curve to a set of points
Root finding
============
Scalar functions
----------------
.. autosummary::
:toctree: generated/
brentq - quadratic interpolation Brent method
brenth - Brent method, modified by NAME with hyperbolic extrapolation
ridder - Ridder's method
bisect - Bisection method
newton - Secant method or Newton's method
Fixed point finding:
.. autosummary::
:toctree: generated/
fixed_point - Single-variable fixed-point solver
Multidimensional
----------------
General nonlinear solvers:
.. autosummary::
:toctree: generated/
root - Unified interface for nonlinear solvers of multivariate functions
fsolve - Non-linear multi-variable equation solver
broyden1 - Broyden's first method
broyden2 - Broyden's second method
The `root` function supports the following methods:
.. toctree::
optimize.root-hybr
optimize.root-lm
optimize.root-broyden1
optimize.root-broyden2
optimize.root-anderson
optimize.root-linearmixing
optimize.root-diagbroyden
optimize.root-excitingmixing
optimize.root-krylov
optimize.root-dfsane
Large-scale nonlinear solvers:
.. autosummary::
:toctree: generated/
newton_krylov
anderson
Simple iterations:
.. autosummary::
:toctree: generated/
excitingmixing
linearmixing
diagbroyden
:mod:`Additional information on the nonlinear solvers <scipy.optimize.nonlin>`
Linear Programming
==================
Simplex Algorithm:
.. autosummary::
:toctree: generated/
linprog -- Linear programming using the simplex algorithm
linprog_verbose_callback -- Sample callback function for linprog
The `linprog` function supports the following methods:
.. toctree::
optimize.linprog-simplex
Assignment problems:
.. autosummary::
:toctree: generated/
linear_sum_assignment -- Solves the linear-sum assignment problem
Utilities
=========
.. autosummary::
:toctree: generated/
approx_fprime - Approximate the gradient of a scalar function
bracket - Bracket a minimum, given two starting points
check_grad - Check the supplied derivative using finite differences
line_search - Return a step that satisfies the strong Wolfe conditions
show_options - Show specific options optimization solvers
LbfgsInvHessProduct - Linear operator for L-BFGS approximate inverse Hessian
""" |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# ***********************IMPORTANT NMAP LICENSE TERMS************************
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|
"""
==================================
Constants (:mod:`scipy.constants`)
==================================
.. currentmodule:: scipy.constants
Physical and mathematical constants and units.
Mathematical constants
======================
================ =================================================================
``pi`` Pi
``golden`` Golden ratio
``golden_ratio`` Golden ratio
================ =================================================================
Physical constants
==================
=========================== =================================================================
``c`` speed of light in vacuum
``speed_of_light`` speed of light in vacuum
``mu_0`` the magnetic constant :math:`\mu_0`
``epsilon_0`` the electric constant (vacuum permittivity), :math:`\epsilon_0`
``h`` the Planck constant :math:`h`
``Planck`` the Planck constant :math:`h`
``hbar`` :math:`\hbar = h/(2\pi)`
``G`` Newtonian constant of gravitation
``gravitational_constant`` Newtonian constant of gravitation
``g`` standard acceleration of gravity
``e`` elementary charge
``elementary_charge`` elementary charge
``R`` molar gas constant
``gas_constant`` molar gas constant
``alpha`` fine-structure constant
``fine_structure`` fine-structure constant
``N_A`` Avogadro constant
``Avogadro`` Avogadro constant
``k`` Boltzmann constant
``Boltzmann`` Boltzmann constant
``sigma`` Stefan-Boltzmann constant :math:`\sigma`
``Stefan_Boltzmann`` Stefan-Boltzmann constant :math:`\sigma`
``Wien`` Wien displacement law constant
``Rydberg`` Rydberg constant
``m_e`` electron mass
``electron_mass`` electron mass
``m_p`` proton mass
``proton_mass`` proton mass
``m_n`` neutron mass
``neutron_mass`` neutron mass
=========================== =================================================================
Constants database
------------------
In addition to the above variables, :mod:`scipy.constants` also contains the
2014 CODATA recommended values [CODATA2014]_ database containing more physical
constants.
.. autosummary::
:toctree: generated/
value -- Value in physical_constants indexed by key
unit -- Unit in physical_constants indexed by key
precision -- Relative precision in physical_constants indexed by key
find -- Return list of physical_constant keys with a given string
ConstantWarning -- Constant sought not in newest CODATA data set
.. data:: physical_constants
Dictionary of physical constants, of the format
``physical_constants[name] = (value, unit, uncertainty)``.
Available constants:
====================================================================== ====
%(constant_names)s
====================================================================== ====
Units
=====
SI prefixes
-----------
============ =================================================================
``yotta`` :math:`10^{24}`
``zetta`` :math:`10^{21}`
``exa`` :math:`10^{18}`
``peta`` :math:`10^{15}`
``tera`` :math:`10^{12}`
``giga`` :math:`10^{9}`
``mega`` :math:`10^{6}`
``kilo`` :math:`10^{3}`
``hecto`` :math:`10^{2}`
``deka`` :math:`10^{1}`
``deci`` :math:`10^{-1}`
``centi`` :math:`10^{-2}`
``milli`` :math:`10^{-3}`
``micro`` :math:`10^{-6}`
``nano`` :math:`10^{-9}`
``pico`` :math:`10^{-12}`
``femto`` :math:`10^{-15}`
``atto`` :math:`10^{-18}`
``zepto`` :math:`10^{-21}`
============ =================================================================
Binary prefixes
---------------
============ =================================================================
``kibi`` :math:`2^{10}`
``mebi`` :math:`2^{20}`
``gibi`` :math:`2^{30}`
``tebi`` :math:`2^{40}`
``pebi`` :math:`2^{50}`
``exbi`` :math:`2^{60}`
``zebi`` :math:`2^{70}`
``yobi`` :math:`2^{80}`
============ =================================================================
Weight
------
================= ============================================================
``gram`` :math:`10^{-3}` kg
``metric_ton`` :math:`10^{3}` kg
``grain`` one grain in kg
``lb`` one pound (avoirdupous) in kg
``pound`` one pound (avoirdupous) in kg
``oz`` one ounce in kg
``ounce`` one ounce in kg
``stone`` one stone in kg
``grain`` one grain in kg
``long_ton`` one long ton in kg
``short_ton`` one short ton in kg
``troy_ounce`` one Troy ounce in kg
``troy_pound`` one Troy pound in kg
``carat`` one carat in kg
``m_u`` atomic mass constant (in kg)
``u`` atomic mass constant (in kg)
``atomic_mass`` atomic mass constant (in kg)
================= ============================================================
Angle
-----
================= ============================================================
``degree`` degree in radians
``arcmin`` arc minute in radians
``arcminute`` arc minute in radians
``arcsec`` arc second in radians
``arcsecond`` arc second in radians
================= ============================================================
Time
----
================= ============================================================
``minute`` one minute in seconds
``hour`` one hour in seconds
``day`` one day in seconds
``week`` one week in seconds
``year`` one year (365 days) in seconds
``Julian_year`` one Julian year (365.25 days) in seconds
================= ============================================================
Length
------
===================== ============================================================
``inch`` one inch in meters
``foot`` one foot in meters
``yard`` one yard in meters
``mile`` one mile in meters
``mil`` one mil in meters
``pt`` one point in meters
``point`` one point in meters
``survey_foot`` one survey foot in meters
``survey_mile`` one survey mile in meters
``nautical_mile`` one nautical mile in meters
``fermi`` one Fermi in meters
``angstrom`` one Angstrom in meters
``micron`` one micron in meters
``au`` one astronomical unit in meters
``astronomical_unit`` one astronomical unit in meters
``light_year`` one light year in meters
``parsec`` one parsec in meters
===================== ============================================================
Pressure
--------
================= ============================================================
``atm`` standard atmosphere in pascals
``atmosphere`` standard atmosphere in pascals
``bar`` one bar in pascals
``torr`` one torr (mmHg) in pascals
``mmHg`` one torr (mmHg) in pascals
``psi`` one psi in pascals
================= ============================================================
Area
----
================= ============================================================
``hectare`` one hectare in square meters
``acre`` one acre in square meters
================= ============================================================
Volume
------
=================== ========================================================
``liter`` one liter in cubic meters
``litre`` one liter in cubic meters
``gallon`` one gallon (US) in cubic meters
``gallon_US`` one gallon (US) in cubic meters
``gallon_imp`` one gallon (UK) in cubic meters
``fluid_ounce`` one fluid ounce (US) in cubic meters
``fluid_ounce_US`` one fluid ounce (US) in cubic meters
``fluid_ounce_imp`` one fluid ounce (UK) in cubic meters
``bbl`` one barrel in cubic meters
``barrel`` one barrel in cubic meters
=================== ========================================================
Speed
-----
================== ==========================================================
``kmh`` kilometers per hour in meters per second
``mph`` miles per hour in meters per second
``mach`` one Mach (approx., at 15 C, 1 atm) in meters per second
``speed_of_sound`` one Mach (approx., at 15 C, 1 atm) in meters per second
``knot`` one knot in meters per second
================== ==========================================================
Temperature
-----------
===================== =======================================================
``zero_Celsius`` zero of Celsius scale in Kelvin
``degree_Fahrenheit`` one Fahrenheit (only differences) in Kelvins
===================== =======================================================
.. autosummary::
:toctree: generated/
convert_temperature
C2K
K2C
F2C
C2F
F2K
K2F
Energy
------
==================== =======================================================
``eV`` one electron volt in Joules
``electron_volt`` one electron volt in Joules
``calorie`` one calorie (thermochemical) in Joules
``calorie_th`` one calorie (thermochemical) in Joules
``calorie_IT`` one calorie (International Steam Table calorie, 1956) in Joules
``erg`` one erg in Joules
``Btu`` one British thermal unit (International Steam Table) in Joules
``Btu_IT`` one British thermal unit (International Steam Table) in Joules
``Btu_th`` one British thermal unit (thermochemical) in Joules
``ton_TNT`` one ton of TNT in Joules
==================== =======================================================
Power
-----
==================== =======================================================
``hp`` one horsepower in watts
``horsepower`` one horsepower in watts
==================== =======================================================
Force
-----
==================== =======================================================
``dyn`` one dyne in newtons
``dyne`` one dyne in newtons
``lbf`` one pound force in newtons
``pound_force`` one pound force in newtons
``kgf`` one kilogram force in newtons
``kilogram_force`` one kilogram force in newtons
==================== =======================================================
Optics
------
.. autosummary::
:toctree: generated/
lambda2nu
nu2lambda
References
==========
.. [CODATA2014] CODATA Recommended Values of the Fundamental
Physical Constants 2014.
http://physics.nist.gov/cuu/Constants/index.html
""" |
"""
Statements represent mechanistic relationships between biological agents.
Statement classes follow an inheritance hierarchy, with all Statement types
inheriting from the parent class :py:class:`Statement`. At
the next level in the hierarchy are the following classes:
- :py:class:`Complex`
- :py:class:`Modification`
- :py:class:`SelfModification`
- :py:class:`RegulateActivity`
- :py:class:`RegulateAmount`
- :py:class:`ActiveForm`
- :py:class:`Translocation`
- :py:class:`Gef`
- :py:class:`Gap`
- :py:class:`Conversion`
There are several types of Statements representing post-translational
modifications that further inherit from
:py:class:`Modification`:
- :py:class:`Phosphorylation`
- :py:class:`Dephosphorylation`
- :py:class:`Ubiquitination`
- :py:class:`Deubiquitination`
- :py:class:`Sumoylation`
- :py:class:`Desumoylation`
- :py:class:`Hydroxylation`
- :py:class:`Dehydroxylation`
- :py:class:`Acetylation`
- :py:class:`Deacetylation`
- :py:class:`Glycosylation`
- :py:class:`Deglycosylation`
- :py:class:`Farnesylation`
- :py:class:`Defarnesylation`
- :py:class:`Geranylgeranylation`
- :py:class:`Degeranylgeranylation`
- :py:class:`Palmitoylation`
- :py:class:`Depalmitoylation`
- :py:class:`Myristoylation`
- :py:class:`Demyristoylation`
- :py:class:`Ribosylation`
- :py:class:`Deribosylation`
- :py:class:`Methylation`
- :py:class:`Demethylation`
There are additional subtypes of :py:class:`SelfModification`:
- :py:class:`Autophosphorylation`
- :py:class:`Transphosphorylation`
Interactions between proteins are often described simply in terms of their
effect on a protein's "activity", e.g., "Active MEK activates ERK", or "DUSP6
inactives ERK". These types of relationships are indicated by the
:py:class:`RegulateActivity` abstract base class which has subtypes
- :py:class:`Activation`
- :py:class:`Inhibition`
while the :py:class:`RegulateAmount` abstract base class has subtypes
- :py:class:`IncreaseAmount`
- :py:class:`DecreaseAmount`
Statements involve one or more *Concepts*, which, depending on the
semantics of the Statement, are typically biological *Agents*,
such as proteins, represented by the class :py:class:`Agent`.
Agents can have several types of context specified on them including
- a specific post-translational modification state (indicated by one or
more instances of :py:class:`ModCondition`),
- other bound Agents (:py:class:`BoundCondition`),
- mutations (:py:class:`MutCondition`),
- an activity state (:py:class:`ActivityCondition`), and
- cellular location
The *active* form of an agent (in terms of its post-translational modifications
or bound state) is indicated by an instance of the class
:py:class:`ActiveForm`.
Agents also carry grounding information which links them to database entries.
These database references are represented as a dictionary in the `db_refs`
attribute of each Agent. The dictionary can have multiple entries. For
instance, INDRA's input Processors produce genes and proteins that carry both
UniProt and HGNC IDs in db_refs, whenever possible. FamPlex provides a name
space for protein families that are typically used in the literature. More
information about FamPlex can be found here:
https://github.com/sorgerlab/famplex
+------------------------+------------------+--------------------------+
| Type | Database | Example |
+========================+==================+==========================+
| Gene/Protein | HGNC | {'HGNC': '11998'} |
+------------------------+------------------+--------------------------+
| Gene/Protein | UniProt | {'UP': 'P04637'} |
+------------------------+------------------+--------------------------+
| Gene/Protein | Entrez | {'EGID': '5583'} |
+------------------------+------------------+--------------------------+
| Gene/Protein family | FamPlex | {'FPLX': 'ERK'} |
+------------------------+------------------+--------------------------+
| Gene/Protein family | InterPro | {'IP': 'IPR000308'} |
+------------------------+------------------+--------------------------+
| Gene/Protein family | Pfam | {'PF': 'PF00071'} |
+------------------------+------------------+--------------------------+
| Gene/Protein family | NextProt family | {'NXPFA': '03114'} |
+------------------------+------------------+--------------------------+
| Chemical | ChEBI | {'CHEBI': 'CHEBI:63637'} |
+------------------------+------------------+--------------------------+
| Chemical | PubChem | {'PUBCHEM': '42611257'} |
+------------------------+------------------+--------------------------+
| Chemical | LINCS / HMS-LINCS| {'LINCS': '42611257'} |
+------------------------+------------------+--------------------------+
| Metabolite | HMDB | {'HMDB': 'HMDB00122'} |
+------------------------+------------------+--------------------------+
| Process, location, etc.| GO | {'GO': 'GO:0006915'} |
+------------------------+------------------+--------------------------+
| Process, disease, etc. | MeSH | {'MESH': 'D008113'} |
+------------------------+------------------+--------------------------+
| General terms | NCIT | {'NCIT': 'C28597'} |
+------------------------+------------------+--------------------------+
| Raw text | TEXT | {'TEXT': 'Nf-kappaB'} |
+------------------------+------------------+--------------------------+
The evidence for a given Statement, which could include relevant citations,
database identifiers, and passages of text from the scientific literature, is
contained in one or more :py:class:`Evidence` objects associated with the
Statement.
JSON serialization of INDRA Statements
--------------------------------------
Statements can be serialized into JSON and deserialized from JSON to allow
their exchange in a platform-independent way. We also provide a JSON
schema (see http://json-schema.org to learn about schemas) in
https://raw.githubusercontent.com/sorgerlab/indra/master/indra/resources/statements_schema.json
which can be used to validate INDRA Statements JSONs.
Some validation tools include:
- jsonschema
a Python package to validate JSON content with respect to
a schema
- ajv-cli
Available at https://www.npmjs.com/package/ajv-cli
Install with "npm install -g ajv-cli" and then validate with:
ajv -s statements_schema.json -d file_to_validate.json. This tool
provides more sophisticated and better interpretable output than
jsonschema.
- Web based tools
There are a variety of web-based tools for validation with JSON schemas,
including https://www.jsonschemavalidator.net
""" |
"""
=====================================================
Optimization and root finding (:mod:`scipy.optimize`)
=====================================================
.. currentmodule:: scipy.optimize
Optimization
============
Local Optimization
------------------
.. autosummary::
:toctree: generated/
minimize - Unified interface for minimizers of multivariate functions
minimize_scalar - Unified interface for minimizers of univariate functions
OptimizeResult - The optimization result returned by some optimizers
OptimizeWarning - The optimization encountered problems
The `minimize` function supports the following methods:
.. toctree::
optimize.minimize-neldermead
optimize.minimize-powell
optimize.minimize-cg
optimize.minimize-bfgs
optimize.minimize-newtoncg
optimize.minimize-lbfgsb
optimize.minimize-tnc
optimize.minimize-cobyla
optimize.minimize-slsqp
optimize.minimize-dogleg
optimize.minimize-trustncg
The `minimize_scalar` function supports the following methods:
.. toctree::
optimize.minimize_scalar-brent
optimize.minimize_scalar-bounded
optimize.minimize_scalar-golden
The specific optimization method interfaces below in this subsection are
not recommended for use in new scripts; all of these methods are accessible
via a newer, more consistent interface provided by the functions above.
General-purpose multivariate methods:
.. autosummary::
:toctree: generated/
fmin - Nelder-Mead Simplex algorithm
fmin_powell - Powell's (modified) level set method
fmin_cg - Non-linear (Polak-Ribiere) conjugate gradient algorithm
fmin_bfgs - Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno)
fmin_ncg - Line-search Newton Conjugate Gradient
Constrained multivariate methods:
.. autosummary::
:toctree: generated/
fmin_l_bfgs_b - Zhu, Byrd, and Nocedal's constrained optimizer
fmin_tnc - Truncated Newton code
fmin_cobyla - Constrained optimization by linear approximation
fmin_slsqp - Minimization using sequential least-squares programming
differential_evolution - stochastic minimization using differential evolution
Univariate (scalar) minimization methods:
.. autosummary::
:toctree: generated/
fminbound - Bounded minimization of a scalar function
brent - 1-D function minimization using Brent method
golden - 1-D function minimization using Golden Section method
Equation (Local) Minimizers
---------------------------
.. autosummary::
:toctree: generated/
leastsq - Minimize the sum of squares of M equations in N unknowns
least_squares - Feature-rich least-squares minimization.
nnls - Linear least-squares problem with non-negativity constraint
lsq_linear - Linear least-squares problem with bound constraints
Global Optimization
-------------------
.. autosummary::
:toctree: generated/
basinhopping - Basinhopping stochastic optimizer
brute - Brute force searching optimizer
differential_evolution - stochastic minimization using differential evolution
Rosenbrock function
-------------------
.. autosummary::
:toctree: generated/
rosen - The Rosenbrock function.
rosen_der - The derivative of the Rosenbrock function.
rosen_hess - The Hessian matrix of the Rosenbrock function.
rosen_hess_prod - Product of the Rosenbrock Hessian with a vector.
Fitting
=======
.. autosummary::
:toctree: generated/
curve_fit -- Fit curve to a set of points
Root finding
============
Scalar functions
----------------
.. autosummary::
:toctree: generated/
brentq - quadratic interpolation Brent method
brenth - Brent method, modified by Harris with hyperbolic extrapolation
ridder - Ridder's method
bisect - Bisection method
newton - Secant method or Newton's method
Fixed point finding:
.. autosummary::
:toctree: generated/
fixed_point - Single-variable fixed-point solver
Multidimensional
----------------
General nonlinear solvers:
.. autosummary::
:toctree: generated/
root - Unified interface for nonlinear solvers of multivariate functions
fsolve - Non-linear multi-variable equation solver
broyden1 - Broyden's first method
broyden2 - Broyden's second method
The `root` function supports the following methods:
.. toctree::
optimize.root-hybr
optimize.root-lm
optimize.root-broyden1
optimize.root-broyden2
optimize.root-anderson
optimize.root-linearmixing
optimize.root-diagbroyden
optimize.root-excitingmixing
optimize.root-krylov
optimize.root-dfsane
Large-scale nonlinear solvers:
.. autosummary::
:toctree: generated/
newton_krylov
anderson
Simple iterations:
.. autosummary::
:toctree: generated/
excitingmixing
linearmixing
diagbroyden
:mod:`Additional information on the nonlinear solvers <scipy.optimize.nonlin>`
Linear Programming
==================
Simplex Algorithm:
.. autosummary::
:toctree: generated/
linprog -- Linear programming using the simplex algorithm
linprog_verbose_callback -- Sample callback function for linprog
The `linprog` function supports the following methods:
.. toctree::
optimize.linprog-simplex
Assignment problems:
.. autosummary::
:toctree: generated/
linear_sum_assignment -- Solves the linear-sum assignment problem
Utilities
=========
.. autosummary::
:toctree: generated/
approx_fprime - Approximate the gradient of a scalar function
bracket - Bracket a minimum, given two starting points
check_grad - Check the supplied derivative using finite differences
line_search - Return a step that satisfies the strong Wolfe conditions
show_options - Show specific options optimization solvers
LbfgsInvHessProduct - Linear operator for L-BFGS approximate inverse Hessian
""" |
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
diff = default_diff_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.match(actual, expected)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound functions that handle matching
in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
The TestCmd module provides unbound functions that can be used for the
"diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
###########################################################################################
# Implementation of the stochastic depth algorithm described in the paper
#
# NAME et al. "Deep networks with stochastic depth." arXiv preprint arXiv:1603.09382 (2016).
#
# Reference torch implementation can be found at https://github.com/yueatsprograms/Stochastic_Depth
#
# There are some differences in the implementation:
# - A BN->ReLU->Conv is used for skip connection when input and output shapes are different,
# as oppose to a padding layer.
# - The residual block is different: we use BN->ReLU->Conv->BN->ReLU->Conv, as oppose to
# Conv->BN->ReLU->Conv->BN (->ReLU also applied to skip connection).
# - We did not try to match with the same initialization, learning rate scheduling, etc.
#
#--------------------------------------------------------------------------------
# A sample from the running log (We achieved ~9.4% error after 500 epochs, some
# more careful tuning of the hyper parameters and maybe also the arch is needed
# to achieve the reported numbers in the paper):
#
# INFO:root:Epoch[80] Batch [50] Speed: 1020.95 samples/sec Train-accuracy=0.910080
# INFO:root:Epoch[80] Batch [100] Speed: 1013.41 samples/sec Train-accuracy=0.912031
# INFO:root:Epoch[80] Batch [150] Speed: 1035.48 samples/sec Train-accuracy=0.913438
# INFO:root:Epoch[80] Batch [200] Speed: 1045.00 samples/sec Train-accuracy=0.907344
# INFO:root:Epoch[80] Batch [250] Speed: 1055.32 samples/sec Train-accuracy=0.905937
# INFO:root:Epoch[80] Batch [300] Speed: 1071.71 samples/sec Train-accuracy=0.912500
# INFO:root:Epoch[80] Batch [350] Speed: 1033.73 samples/sec Train-accuracy=0.910937
# INFO:root:Epoch[80] Train-accuracy=0.919922
# INFO:root:Epoch[80] Time cost=48.348
# INFO:root:Saved checkpoint to "sd-110-0081.params"
# INFO:root:Epoch[80] Validation-accuracy=0.880142
# ...
# INFO:root:Epoch[115] Batch [50] Speed: 1037.04 samples/sec Train-accuracy=0.937040
# INFO:root:Epoch[115] Batch [100] Speed: 1041.12 samples/sec Train-accuracy=0.934219
# INFO:root:Epoch[115] Batch [150] Speed: 1036.02 samples/sec Train-accuracy=0.933125
# INFO:root:Epoch[115] Batch [200] Speed: 1057.49 samples/sec Train-accuracy=0.938125
# INFO:root:Epoch[115] Batch [250] Speed: 1060.56 samples/sec Train-accuracy=0.933438
# INFO:root:Epoch[115] Batch [300] Speed: 1046.25 samples/sec Train-accuracy=0.935625
# INFO:root:Epoch[115] Batch [350] Speed: 1043.83 samples/sec Train-accuracy=0.927188
# INFO:root:Epoch[115] Train-accuracy=0.938477
# INFO:root:Epoch[115] Time cost=47.815
# INFO:root:Saved checkpoint to "sd-110-0116.params"
# INFO:root:Epoch[115] Validation-accuracy=0.884415
# ...
# INFO:root:Saved checkpoint to "sd-110-0499.params"
# INFO:root:Epoch[498] Validation-accuracy=0.908554
# INFO:root:Epoch[499] Batch [50] Speed: 1068.28 samples/sec Train-accuracy=0.991422
# INFO:root:Epoch[499] Batch [100] Speed: 1053.10 samples/sec Train-accuracy=0.991094
# INFO:root:Epoch[499] Batch [150] Speed: 1042.89 samples/sec Train-accuracy=0.995156
# INFO:root:Epoch[499] Batch [200] Speed: 1066.22 samples/sec Train-accuracy=0.991406
# INFO:root:Epoch[499] Batch [250] Speed: 1050.56 samples/sec Train-accuracy=0.990781
# INFO:root:Epoch[499] Batch [300] Speed: 1032.02 samples/sec Train-accuracy=0.992500
# INFO:root:Epoch[499] Batch [350] Speed: 1062.16 samples/sec Train-accuracy=0.992969
# INFO:root:Epoch[499] Train-accuracy=0.994141
# INFO:root:Epoch[499] Time cost=47.401
# INFO:root:Saved checkpoint to "sd-110-0500.params"
# INFO:root:Epoch[499] Validation-accuracy=0.906050
# ###########################################################################################
|
#!/usr/bin/env python3
# versatiletrainer.py
#
# Based on logisticpredict, which was based on (!)
# logisticleave1out.py which was based on (!!)
# parallel_crossvalidate.py from the paceofchange repo.
#
# The goal of the module is to construct predictive
# models of corpus *subsets*, in a very flexible way.
# This is necessary because my literary-historical
# interest in modeling is very rarely just
# "model everything and see what we get." I usually
# want to model chronological subsets, or experiment
# with different definitions of classes, or apply a
# model trained on subset A to subset B.
#
# Three problems in particular have to be solved:
#
# a) multilabel modeling
#
# I'm likely to have a corpus where each text bears
# several different class tags. The groups of texts
# identified by class tags will often overlap, and this
# can make it tricky to define positive and negative classes
# for a given modeling pass. Our goal is to ensure
# that no volumes with a positive tag are present in
# the negative class. At the same time,
#
# b) balancing distributions across time
#
# It is vital to ensure that the positive and negative
# classes have similar distributions across
# the timeline. Otherwise you will *definitely* get
# a model that is partly a model of language change.
# Other metadata categories (nationality and gender)
# might also need to be balanced across the positive
# and negative classes, if/when possible.
#
# finally, c) holding out authors
#
# If you just treat volumes as individuals and select a
# test set as a random sample, information about authors can
# leak from test into training, and give you unrealistically
# high accuracy. (You're learning to recognize Radcliffe, not
# learning to recognize the Gothic.) To avoid this, we make sure
# that groups of volumes by the same author are always in the same
# "fold" of crossvalidation.
#
# (Note that the success of this strategy depends on a previous
# fuzzy-matching pass across the corpus to make sure that authors
# have precisely the same name in every row, without extra initials
# or commas, etc).
#
# Much of the work I've just described is handled by
# the function get_data_for_model(), in this module, and inside
# the module *metafilter*, which it calls.
#
# Because we want to be very versatile, there are unfortunately
# a lot of arguments for get_data_for_model(). We pass in three tuples,
# each of which unpacks into a bunch of arguments.
#
# paths unpacks into
# sourcefolder, extension, metadatapath, outputpath, vocabpath
# where
# sourcefolder is the directory with data files
# extension is the extension those files end with
# metadatapath is the path to a metadata csv
# outputpath is the path to a csv of results to be written
# and vocabpath is the path to a file of words to be used
# as features for all models
#
# exclusions unpacks into
# excludeif, excludeifnot, excludebelow, excludeabove, sizecap
# where
# all the "excludes" are dictionaries pairing a key (the name of a metadata
# column) with a value that should be excluded -- if it's present,
# absent, lower than this, or higher than this.
# sizecap limits the number of vols in the positive class; randomly
# sampled if greater.
#
# classifyconditions unpacks into:
# positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions
# where
# positive_tags is a list of tags to be included in the positive class
# negative_tags is a list of tags to be selected for the negative class
# (unless volume also has a positive_tag, and note that the negative class
# is always selected to match the chronological distribution of the positive
# as closely as possible)
# datetype is the date column to be used for chronological distribution
# numfeatures can be used to limit the features in this model to top N;
# it is in practice not functional right now because I'm using all
# features in the vocab file -- originally selected by doc frequency in
# the whole corpus
# regularization is a constant to be handed to scikit-learn (I'm using one
# established in previous experiments on a different corpus)
# and testconditions ... is complex.
#
# The variable testconditions will be a set of tags. It may contain tags for classes
# that are to be treated as a test set. Positive volumes will be assigned to
# this set if they have no positive tags that are *not* in testconditions.
# A corresponding group of negative volumes will at the same time
# be assigned. It can also contain two integers to be interpreted as dates, a
# pastthreshold and futurethreshold. Dates outside these thresholds will not
# be used for training. If date thresholds are provided they must be provided
# as a pair to clarify which one is the pastthreshold and which the future.
# If you're only wanting to exclude volumes in the future, provide a past
# threshold like "1."
# All of these conditions exclude volumes from the training set, and place them
# in a set that is used only for testing. But also note that these
# exclusions are always IN ADDITION TO holding-out-authors.
# In other words, if an author w/ multiple volumes has only some of them excluded
# from training by testconditions, it is *still* the case that the author will never
# be in a training set when her own volumes are being predicted.
|
"""
Sparse Matrices
===============
SciPy 2-D sparse matrix package.
Original code by NAME and extended by NAME NAME and NAME are seven available sparse matrix types:
1. csc_matrix: Compressed Sparse Column format
2. csr_matrix: Compressed Sparse Row format
3. bsr_matrix: Block Sparse Row format
4. lil_matrix: List of Lists format
5. dok_matrix: Dictionary of Keys format
6. coo_matrix: COOrdinate format (aka IJV, triplet format)
7. dia_matrix: DIAgonal format
To construct a matrix efficiently, use either lil_matrix (recommended) or
dok_matrix. The lil_matrix class supports basic slicing and fancy
indexing with a similar syntax to NumPy arrays. As illustrated below,
the COO format may also be used to efficiently construct matrices.
To perform manipulations such as multiplication or inversion, first
convert the matrix to either CSC or CSR format. The lil_matrix format is
row-based, so conversion to CSR is efficient, whereas conversion to CSC
is less so.
All conversions among the CSR, CSC, and COO formats are efficient,
linear-time operations.
Example 1
---------
Construct a 1000x1000 lil_matrix and add some values to it:
>>> from scipy.sparse import lil_matrix
>>> from scipy.sparse.linalg import spsolve
>>> from numpy.linalg import solve, norm
>>> from numpy.random import rand
>>> A = lil_matrix((1000, 1000))
>>> A[0, :100] = rand(100)
>>> A[1, 100:200] = A[0, :100]
>>> A.setdiag(rand(1000))
Now convert it to CSR format and solve A x = b for x:
>>> A = A.tocsr()
>>> b = rand(1000)
>>> x = spsolve(A, b)
Convert it to a dense matrix and solve, and check that the result
is the same:
>>> x_ = solve(A.todense(), b)
Now we can compute norm of the error with:
>>> err = norm(x-x_)
>>> err < 1e-10
True
It should be small :)
Example 2
---------
Construct a matrix in COO format:
>>> from scipy import sparse
>>> from numpy import array
>>> I = array([0,3,1,0])
>>> J = array([0,3,1,2])
>>> V = array([4,5,7,9])
>>> A = sparse.coo_matrix((V,(I,J)),shape=(4,4))
Notice that the indices do not need to be sorted.
Duplicate (i,j) entries are summed when converting to CSR or CSC.
>>> I = array([0,0,1,3,1,0,0])
>>> J = array([0,2,1,3,1,0,0])
>>> V = array([1,1,1,1,1,1,1])
>>> B = sparse.coo_matrix((V,(I,J)),shape=(4,4)).tocsr()
This is useful for constructing finite-element stiffness and mass matrices.
Further Details
---------------
CSR column indices are not necessarily sorted. Likewise for CSC row
indices. Use the .sorted_indices() and .sort_indices() methods when
sorted indices are required (e.g. when passing data to other libraries).
Package Contents
================
Modules
-------
.. autosummary::
:toctree: generated/
base - Base class for sparse matrices
bsr - Compressed Block Sparse Row matrix format
compressed - Sparse matrix base class using compressed storage
construct - Functions to construct sparse matrices
coo - A sparse matrix in COOrdinate or 'triplet' format
csc - Compressed Sparse Column matrix format
csgraph - Compressed Sparse graph algorithms
csr - Compressed Sparse Row matrix format
data - Base class for sparse matrice with a .data attribute
dia - Sparse DIAgonal format
dok - Dictionary Of Keys based matrix
extract - Functions to extract parts of sparse matrices
lil - LInked List sparse matrix class
linalg -
sparsetools - A collection of routines for sparse matrix operations
spfuncs - Functions that operate on sparse matrices
sputils - Utility functions for sparse matrix module
Classes
-------
.. autosummary::
:toctree: generated/
SparseEfficiencyWarning -
SparseWarning -
bsr_matrix - Block Sparse Row matrix
coo_matrix - A sparse matrix in COOrdinate format
csc_matrix - Compressed Sparse Column matrix
csr_matrix - Compressed Sparse Row matrix
dia_matrix - Sparse matrix with DIAgonal storage
dok_matrix - Dictionary Of Keys based sparse matrix
lil_matrix - Row-based linked list sparse matrix
Functions
---------
.. autosummary::
:toctree: generated/
bmat - Build a sparse matrix from sparse sub-blocks
cs_graph_components -
eye - Sparse MxN matrix whose k-th diagonal is all ones
find -
hstack - Stack sparse matrices horizontally (column wise)
identity - Identity matrix in sparse format
issparse -
isspmatrix -
isspmatrix_bsr -
isspmatrix_coo -
isspmatrix_csc -
isspmatrix_csr -
isspmatrix_dia -
isspmatrix_dok -
isspmatrix_lil -
kron - kronecker product of two sparse matrices
kronsum - kronecker sum of sparse matrices
lil_diags - Generate a lil_matrix with the given diagonals
lil_eye - RxC lil_matrix whose k-th diagonal set to one
rand - Random values in a given shape
spdiags - Return a sparse matrix from diagonals
tril - Lower triangular portion of a matrix in sparse format
triu - Upper triangular portion of a matrix in sparse format
vstack - Stack sparse matrices vertically (row wise)
""" |
"""
=======================================
Signal processing (:mod:`scipy.signal`)
=======================================
Convolution
===========
.. autosummary::
:toctree: generated/
convolve -- N-dimensional convolution.
correlate -- N-dimensional correlation.
fftconvolve -- N-dimensional convolution using the FFT.
convolve2d -- 2-dimensional convolution (more options).
correlate2d -- 2-dimensional correlation (more options).
sepfir2d -- Convolve with a 2-D separable FIR filter.
choose_conv_method -- Chooses faster of FFT and direct convolution methods.
B-splines
=========
.. autosummary::
:toctree: generated/
bspline -- B-spline basis function of order n.
cubic -- B-spline basis function of order 3.
quadratic -- B-spline basis function of order 2.
gauss_spline -- Gaussian approximation to the B-spline basis function.
cspline1d -- Coefficients for 1-D cubic (3rd order) B-spline.
qspline1d -- Coefficients for 1-D quadratic (2nd order) B-spline.
cspline2d -- Coefficients for 2-D cubic (3rd order) B-spline.
qspline2d -- Coefficients for 2-D quadratic (2nd order) B-spline.
cspline1d_eval -- Evaluate a cubic spline at the given points.
qspline1d_eval -- Evaluate a quadratic spline at the given points.
spline_filter -- Smoothing spline (cubic) filtering of a rank-2 array.
Filtering
=========
.. autosummary::
:toctree: generated/
order_filter -- N-dimensional order filter.
medfilt -- N-dimensional median filter.
medfilt2d -- 2-dimensional median filter (faster).
wiener -- N-dimensional wiener filter.
symiirorder1 -- 2nd-order IIR filter (cascade of first-order systems).
symiirorder2 -- 4th-order IIR filter (cascade of second-order systems).
lfilter -- 1-dimensional FIR and IIR digital linear filtering.
lfiltic -- Construct initial conditions for `lfilter`.
lfilter_zi -- Compute an initial state zi for the lfilter function that
-- corresponds to the steady state of the step response.
filtfilt -- A forward-backward filter.
savgol_filter -- Filter a signal using the Savitzky-Golay filter.
deconvolve -- 1-d deconvolution using lfilter.
sosfilt -- 1-dimensional IIR digital linear filtering using
-- a second-order sections filter representation.
sosfilt_zi -- Compute an initial state zi for the sosfilt function that
-- corresponds to the steady state of the step response.
sosfiltfilt -- A forward-backward filter for second-order sections.
hilbert -- Compute 1-D analytic signal, using the Hilbert transform.
hilbert2 -- Compute 2-D analytic signal, using the Hilbert transform.
decimate -- Downsample a signal.
detrend -- Remove linear and/or constant trends from data.
resample -- Resample using Fourier method.
resample_poly -- Resample using polyphase filtering method.
upfirdn -- Upsample, apply FIR filter, downsample.
Filter design
=============
.. autosummary::
:toctree: generated/
bilinear -- Digital filter from an analog filter using
-- the bilinear transform.
findfreqs -- Find array of frequencies for computing filter response.
firls -- FIR filter design using least-squares error minimization.
firwin -- Windowed FIR filter design, with frequency response
-- defined as pass and stop bands.
firwin2 -- Windowed FIR filter design, with arbitrary frequency
-- response.
freqs -- Analog filter frequency response from TF coefficients.
freqs_zpk -- Analog filter frequency response from ZPK coefficients.
freqz -- Digital filter frequency response from TF coefficients.
freqz_zpk -- Digital filter frequency response from ZPK coefficients.
sosfreqz -- Digital filter frequency response for SOS format filter.
group_delay -- Digital filter group delay.
iirdesign -- IIR filter design given bands and gains.
iirfilter -- IIR filter design given order and critical frequencies.
kaiser_atten -- Compute the attenuation of a Kaiser FIR filter, given
-- the number of taps and the transition width at
-- discontinuities in the frequency response.
kaiser_beta -- Compute the Kaiser parameter beta, given the desired
-- FIR filter attenuation.
kaiserord -- Design a Kaiser window to limit ripple and width of
-- transition region.
minimum_phase -- Convert a linear phase FIR filter to minimum phase.
savgol_coeffs -- Compute the FIR filter coefficients for a Savitzky-Golay
-- filter.
remez -- Optimal FIR filter design.
unique_roots -- Unique roots and their multiplicities.
residue -- Partial fraction expansion of b(s) / a(s).
residuez -- Partial fraction expansion of b(z) / a(z).
invres -- Inverse partial fraction expansion for analog filter.
invresz -- Inverse partial fraction expansion for digital filter.
BadCoefficients -- Warning on badly conditioned filter coefficients
Lower-level filter design functions:
.. autosummary::
:toctree: generated/
abcd_normalize -- Check state-space matrices and ensure they are rank-2.
band_stop_obj -- Band Stop Objective Function for order minimization.
besselap -- Return (z,p,k) for analog prototype of Bessel filter.
buttap -- Return (z,p,k) for analog prototype of Butterworth filter.
cheb1ap -- Return (z,p,k) for type I Chebyshev filter.
cheb2ap -- Return (z,p,k) for type II Chebyshev filter.
cmplx_sort -- Sort roots based on magnitude.
ellipap -- Return (z,p,k) for analog prototype of elliptic filter.
lp2bp -- Transform a lowpass filter prototype to a bandpass filter.
lp2bs -- Transform a lowpass filter prototype to a bandstop filter.
lp2hp -- Transform a lowpass filter prototype to a highpass filter.
lp2lp -- Transform a lowpass filter prototype to a lowpass filter.
normalize -- Normalize polynomial representation of a transfer function.
Matlab-style IIR filter design
==============================
.. autosummary::
:toctree: generated/
butter -- Butterworth
buttord
cheby1 -- Chebyshev Type I
cheb1ord
cheby2 -- Chebyshev Type II
cheb2ord
ellip -- Elliptic (Cauer)
ellipord
bessel -- Bessel (no order selection available -- try butterod)
iirnotch -- Design second-order IIR notch digital filter.
iirpeak -- Design second-order IIR peak (resonant) digital filter.
Continuous-Time Linear Systems
==============================
.. autosummary::
:toctree: generated/
lti -- Continuous-time linear time invariant system base class.
StateSpace -- Linear time invariant system in state space form.
TransferFunction -- Linear time invariant system in transfer function form.
ZerosPolesGain -- Linear time invariant system in zeros, poles, gain form.
lsim -- continuous-time simulation of output to linear system.
lsim2 -- like lsim, but `scipy.integrate.odeint` is used.
impulse -- impulse response of linear, time-invariant (LTI) system.
impulse2 -- like impulse, but `scipy.integrate.odeint` is used.
step -- step response of continous-time LTI system.
step2 -- like step, but `scipy.integrate.odeint` is used.
freqresp -- frequency response of a continuous-time LTI system.
bode -- Bode magnitude and phase data (continuous-time LTI).
Discrete-Time Linear Systems
============================
.. autosummary::
:toctree: generated/
dlti -- Discrete-time linear time invariant system base class.
StateSpace -- Linear time invariant system in state space form.
TransferFunction -- Linear time invariant system in transfer function form.
ZerosPolesGain -- Linear time invariant system in zeros, poles, gain form.
dlsim -- simulation of output to a discrete-time linear system.
dimpulse -- impulse response of a discrete-time LTI system.
dstep -- step response of a discrete-time LTI system.
dfreqresp -- frequency response of a discrete-time LTI system.
dbode -- Bode magnitude and phase data (discrete-time LTI).
LTI Representations
===================
.. autosummary::
:toctree: generated/
tf2zpk -- transfer function to zero-pole-gain.
tf2sos -- transfer function to second-order sections.
tf2ss -- transfer function to state-space.
zpk2tf -- zero-pole-gain to transfer function.
zpk2sos -- zero-pole-gain to second-order sections.
zpk2ss -- zero-pole-gain to state-space.
ss2tf -- state-pace to transfer function.
ss2zpk -- state-space to pole-zero-gain.
sos2zpk -- second-order sections to zero-pole-gain.
sos2tf -- second-order sections to transfer function.
cont2discrete -- continuous-time to discrete-time LTI conversion.
place_poles -- pole placement.
Waveforms
=========
.. autosummary::
:toctree: generated/
chirp -- Frequency swept cosine signal, with several freq functions.
gausspulse -- Gaussian modulated sinusoid
max_len_seq -- Maximum length sequence
sawtooth -- Periodic sawtooth
square -- Square wave
sweep_poly -- Frequency swept cosine signal; freq is arbitrary polynomial
unit_impulse -- Discrete unit impulse
Window functions
================
.. autosummary::
:toctree: generated/
get_window -- Return a window of a given length and type.
barthann -- Bartlett-Hann window
bartlett -- Bartlett window
blackman -- Blackman window
blackmanharris -- Minimum 4-term Blackman-Harris window
bohman -- Bohman window
boxcar -- Boxcar window
chebwin -- Dolph-Chebyshev window
cosine -- Cosine window
exponential -- Exponential window
flattop -- Flat top window
gaussian -- Gaussian window
general_gaussian -- Generalized Gaussian window
hamming -- Hamming window
hann -- Hann window
hanning -- Hann window
kaiser -- Kaiser window
nuttall -- Nuttall's minimum 4-term Blackman-Harris window
parzen -- Parzen window
slepian -- Slepian window
triang -- Triangular window
tukey -- Tukey window
Wavelets
========
.. autosummary::
:toctree: generated/
cascade -- compute scaling function and wavelet from coefficients
daub -- return low-pass
morlet -- Complex Morlet wavelet.
qmf -- return quadrature mirror filter from low-pass
ricker -- return ricker wavelet
cwt -- perform continuous wavelet transform
Peak finding
============
.. autosummary::
:toctree: generated/
find_peaks_cwt -- Attempt to find the peaks in the given 1-D array
argrelmin -- Calculate the relative minima of data
argrelmax -- Calculate the relative maxima of data
argrelextrema -- Calculate the relative extrema of data
Spectral Analysis
=================
.. autosummary::
:toctree: generated/
periodogram -- Compute a (modified) periodogram
welch -- Compute a periodogram using Welch's method
csd -- Compute the cross spectral density, using Welch's method
coherence -- Compute the magnitude squared coherence, using Welch's method
spectrogram -- Compute the spectrogram
lombscargle -- Computes the Lomb-Scargle periodogram
vectorstrength -- Computes the vector strength
stft -- Compute the Short Time Fourier Transform
istft -- Compute the Inverse Short Time Fourier Transform
check_COLA -- Check the COLA constraint for iSTFT reconstruction
""" |
# Check the basic discovery process, including a sub-suite.
#
# RUN: %{lit} %{inputs}/discovery \
# RUN: -j 1 --debug --show-tests --show-suites \
# RUN: -v > %t.out 2> %t.err
# RUN: FileCheck --check-prefix=CHECK-BASIC-OUT < %t.out %s
# RUN: FileCheck --check-prefix=CHECK-BASIC-ERR < %t.err %s
#
# CHECK-BASIC-ERR: loading suite config '{{.*}}/discovery/lit.cfg'
# CHECK-BASIC-ERR-DAG: loading suite config '{{.*}}/discovery/subsuite/lit.cfg'
# CHECK-BASIC-ERR-DAG: loading local config '{{.*}}/discovery/subdir/lit.local.cfg'
#
# CHECK-BASIC-OUT: -- Test Suites --
# CHECK-BASIC-OUT: sub-suite - 2 tests
# CHECK-BASIC-OUT: Source Root: {{.*/discovery/subsuite$}}
# CHECK-BASIC-OUT: Exec Root : {{.*/discovery/subsuite$}}
# CHECK-BASIC-OUT: top-level-suite - 3 tests
# CHECK-BASIC-OUT: Source Root: {{.*/discovery$}}
# CHECK-BASIC-OUT: Exec Root : {{.*/discovery$}}
#
# CHECK-BASIC-OUT: -- Available Tests --
# CHECK-BASIC-OUT: sub-suite :: test-one
# CHECK-BASIC-OUT: sub-suite :: test-two
# CHECK-BASIC-OUT: top-level-suite :: subdir/test-three
# CHECK-BASIC-OUT: top-level-suite :: test-one
# CHECK-BASIC-OUT: top-level-suite :: test-two
# Check discovery when exact test names are given.
#
# RUN: %{lit} \
# RUN: %{inputs}/discovery/subdir/test-three.py \
# RUN: %{inputs}/discovery/subsuite/test-one.txt \
# RUN: -j 1 --show-tests --show-suites -v > %t.out
# RUN: FileCheck --check-prefix=CHECK-EXACT-TEST < %t.out %s
#
# CHECK-EXACT-TEST: -- Available Tests --
# CHECK-EXACT-TEST: sub-suite :: test-one
# CHECK-EXACT-TEST: top-level-suite :: subdir/test-three
# Check discovery when using an exec path.
#
# RUN: %{lit} %{inputs}/exec-discovery \
# RUN: -j 1 --debug --show-tests --show-suites \
# RUN: -v > %t.out 2> %t.err
# RUN: FileCheck --check-prefix=CHECK-ASEXEC-OUT < %t.out %s
# RUN: FileCheck --check-prefix=CHECK-ASEXEC-ERR < %t.err %s
#
# CHECK-ASEXEC-ERR: loading suite config '{{.*}}/exec-discovery/lit.site.cfg'
# CHECK-ASEXEC-ERR: load_config from '{{.*}}/discovery/lit.cfg'
# CHECK-ASEXEC-ERR: loaded config '{{.*}}/discovery/lit.cfg'
# CHECK-ASEXEC-ERR: loaded config '{{.*}}/exec-discovery/lit.site.cfg'
# CHECK-ASEXEC-ERR-DAG: loading suite config '{{.*}}/discovery/subsuite/lit.cfg'
# CHECK-ASEXEC-ERR-DAG: loading local config '{{.*}}/discovery/subdir/lit.local.cfg'
#
# CHECK-ASEXEC-OUT: -- Test Suites --
# CHECK-ASEXEC-OUT: sub-suite - 2 tests
# CHECK-ASEXEC-OUT: Source Root: {{.*/discovery/subsuite$}}
# CHECK-ASEXEC-OUT: Exec Root : {{.*/discovery/subsuite$}}
# CHECK-ASEXEC-OUT: top-level-suite - 3 tests
# CHECK-ASEXEC-OUT: Source Root: {{.*/discovery$}}
# CHECK-ASEXEC-OUT: Exec Root : {{.*/exec-discovery$}}
#
# CHECK-ASEXEC-OUT: -- Available Tests --
# CHECK-ASEXEC-OUT: sub-suite :: test-one
# CHECK-ASEXEC-OUT: sub-suite :: test-two
# CHECK-ASEXEC-OUT: top-level-suite :: subdir/test-three
# CHECK-ASEXEC-OUT: top-level-suite :: test-one
# CHECK-ASEXEC-OUT: top-level-suite :: test-two
# Check discovery when exact test names are given.
#
# FIXME: Note that using a path into a subsuite doesn't work correctly here.
#
# RUN: %{lit} \
# RUN: %{inputs}/exec-discovery/subdir/test-three.py \
# RUN: -j 1 --show-tests --show-suites -v > %t.out
# RUN: FileCheck --check-prefix=CHECK-ASEXEC-EXACT-TEST < %t.out %s
#
# CHECK-ASEXEC-EXACT-TEST: -- Available Tests --
# CHECK-ASEXEC-EXACT-TEST: top-level-suite :: subdir/test-three
# Check that we don't recurse infinitely when loading an site specific test
# suite located inside the test source root.
#
# RUN: %{lit} \
# RUN: %{inputs}/exec-discovery-in-tree/obj/ \
# RUN: -j 1 --show-tests --show-suites -v > %t.out
# RUN: FileCheck --check-prefix=CHECK-ASEXEC-INTREE < %t.out %s
#
# CHECK-ASEXEC-INTREE: exec-discovery-in-tree-suite - 1 tests
# CHECK-ASEXEC-INTREE-NEXT: Source Root: {{.*/exec-discovery-in-tree$}}
# CHECK-ASEXEC-INTREE-NEXT: Exec Root : {{.*/exec-discovery-in-tree/obj$}}
# CHECK-ASEXEC-INTREE-NEXT: -- Available Tests --
# CHECK-ASEXEC-INTREE-NEXT: exec-discovery-in-tree-suite :: test-one
|
#!/bin/env python
# Script that uses RHN API to clone RHN Errata to Satellite
# or Spacewalk server.
# Copyright (c) 2008--2011 Red Hat, Inc.
#
# Author: NAME (EMAIL)
#
# This script is an extension of the original "rhn-clone-errata.py"
# script written by: NAME (EMAIL)
#
# (THANKS!)
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#
# Version Information:
#
# 0.1 - 2009-09-01 - NAME
#
# Initial release. Lots of problems. Oof.
#
# 0.2 - 2009-09-11 - NAME
#
# Updated methodology for handling errata. Breaking up individual
# errata appended with a channel identifier to better automate publishing
# of errata.
#
# Some code reworking. I still suck at python. Removed deprecated "sets"
# module.
#
# 0.3 - 2009-09-17 - NAME
#
# Fixed a rather glaring bug in the logic regarding relevant channel
# for package selection. Ugh.
#
# 0.4 - 2009-10-01 - NAME
#
# Modified how the publish happens. Now it creates the errata and THEN
# calls the separate errata.publish() function. I was having some
# intermittent time-outs doing the two together in the errata.create()
# function.
#
# 0.5 - 2010-03-17 - NAME
#
# Moved servers, users and passwords to a config file of your choice.
# Many config options changed as a result. Options on the command line
# override config file options.
#
# Merged proxy support code from NAME <EMAIL> (THANKS!)
#
# Modified some of the formatting for logfile output.
#
# I continue to suck at Python.
#
# 0.6 - 2010-03-18 - NAME
#
# Corrected a grievous bug in the new Proxy code.
#
# Moved Channel and ChannelSuffix maps to the config file.
#
# 0.7 - 2010-11-10 - NAME
#
# Minor bugfixes a/o cosmetic changes.
#
# 0.8.1 - 2011-06-06 - NAME
#
# Testing out new proxy code for handling authenticated proxies also.
# NOT PRODUCTION CODE
#
# 0.8.2 - 2011-06-06 - NAME
#
# Update to new proxy code.
#
# 0.8.3 - 2011-06-06 - NAME
#
# Add selector for which server connections need proxy. This is crude, will cleanup later.
#
# 0.8.4 - 2011-06-06 - NAME
#
# Add some code to handle transparent proxies.
#
# 0.9.0 - 2011-11-17 - NAME
#
# Included patch from NAME <EMAIL> that gives an option for a
# full sync of all channels listed in the configuration file.
#
# Thanks, NAME Additionally, changed the default behaviour of how the script handles errata that are
# missing packages on the system. The script now skips any errata that is missing one
# or more packages on the system. However, I've added an option to allow the script
# to ignore missing packages so that the old behaviour remains.
#
# 0.9.1
#
# Whitspace cleanup and additon of CVE handling.
#
# 0.9.2 - 2012-02-14 - NAME
#
# Rewrite of package searching and handling.
# Fix some problems with CVE handling.
#
|
"""
Basic functions used by several sub-packages and
useful to have in the main name-space.
Type Handling
-------------
================ ===================
iscomplexobj Test for complex object, scalar result
isrealobj Test for real object, scalar result
iscomplex Test for complex elements, array result
isreal Test for real elements, array result
imag Imaginary part
real Real part
real_if_close Turns complex number with tiny imaginary part to real
isneginf Tests for negative infinity, array result
isposinf Tests for positive infinity, array result
isnan Tests for nans, array result
isinf Tests for infinity, array result
isfinite Tests for finite numbers, array result
isscalar True if argument is a scalar
nan_to_num Replaces NaN's with 0 and infinities with large numbers
cast Dictionary of functions to force cast to each type
common_type Determine the minimum common type code for a group
of arrays
mintypecode Return minimal allowed common typecode.
================ ===================
Index Tricks
------------
================ ===================
mgrid Method which allows easy construction of N-d
'mesh-grids'
``r_`` Append and construct arrays: turns slice objects into
ranges and concatenates them, for 2d arrays appends rows.
index_exp Konrad Hinsen's index_expression class instance which
can be useful for building complicated slicing syntax.
================ ===================
Useful Functions
----------------
================ ===================
select Extension of where to multiple conditions and choices
extract Extract 1d array from flattened array according to mask
insert Insert 1d array of values into Nd array according to mask
linspace Evenly spaced samples in linear space
logspace Evenly spaced samples in logarithmic space
fix Round x to nearest integer towards zero
mod Modulo mod(x,y) = x % y except keeps sign of y
amax Array maximum along axis
amin Array minimum along axis
ptp Array max-min along axis
cumsum Cumulative sum along axis
prod Product of elements along axis
cumprod Cumluative product along axis
diff Discrete differences along axis
angle Returns angle of complex argument
unwrap Unwrap phase along given axis (1-d algorithm)
sort_complex Sort a complex-array (based on real, then imaginary)
trim_zeros Trim the leading and trailing zeros from 1D array.
vectorize A class that wraps a Python function taking scalar
arguments into a generalized function which can handle
arrays of arguments using the broadcast rules of
numerix Python.
================ ===================
Shape Manipulation
------------------
================ ===================
squeeze Return a with length-one dimensions removed.
atleast_1d Force arrays to be > 1D
atleast_2d Force arrays to be > 2D
atleast_3d Force arrays to be > 3D
vstack Stack arrays vertically (row on row)
hstack Stack arrays horizontally (column on column)
column_stack Stack 1D arrays as columns into 2D array
dstack Stack arrays depthwise (along third dimension)
split Divide array into a list of sub-arrays
hsplit Split into columns
vsplit Split into rows
dsplit Split along third dimension
================ ===================
Matrix (2D Array) Manipulations
-------------------------------
================ ===================
fliplr 2D array with columns flipped
flipud 2D array with rows flipped
rot90 Rotate a 2D array a multiple of 90 degrees
eye Return a 2D array with ones down a given diagonal
diag Construct a 2D array from a vector, or return a given
diagonal from a 2D array.
mat Construct a Matrix
bmat Build a Matrix from blocks
================ ===================
Polynomials
-----------
================ ===================
poly1d A one-dimensional polynomial class
poly Return polynomial coefficients from roots
roots Find roots of polynomial given coefficients
polyint Integrate polynomial
polyder Differentiate polynomial
polyadd Add polynomials
polysub Substract polynomials
polymul Multiply polynomials
polydiv Divide polynomials
polyval Evaluate polynomial at given argument
================ ===================
Import Tricks
-------------
================ ===================
ppimport Postpone module import until trying to use it
ppimport_attr Postpone module import until trying to use its attribute
ppresolve Import postponed module and return it.
================ ===================
Machine Arithmetics
-------------------
================ ===================
machar_single Single precision floating point arithmetic parameters
machar_double Double precision floating point arithmetic parameters
================ ===================
Threading Tricks
----------------
================ ===================
ParallelExec Execute commands in parallel thread.
================ ===================
1D Array Set Operations
-----------------------
Set operations for 1D numeric arrays based on sort() function.
================ ===================
ediff1d Array difference (auxiliary function).
unique Unique elements of an array.
intersect1d Intersection of 1D arrays with unique elements.
setxor1d Set exclusive-or of 1D arrays with unique elements.
in1d Test whether elements in a 1D array are also present in
another array.
union1d Union of 1D arrays with unique elements.
setdiff1d Set difference of 1D arrays with unique elements.
================ ===================
""" |
"""Drag-and-drop support for Tkinter.
This is very preliminary. I currently only support dnd *within* one
application, between different windows (or within the same window).
I an trying to make this as generic as possible -- not dependent on
the use of a particular widget or icon type, etc. I also hope that
this will work with Pmw.
To enable an object to be dragged, you must create an event binding
for it that starts the drag-and-drop process. Typically, you should
bind <ButtonPress> to a callback function that you write. The function
should call Tkdnd.dnd_start(source, event), where 'source' is the
object to be dragged, and 'event' is the event that invoked the call
(the argument to your callback function). Even though this is a class
instantiation, the returned instance should not be stored -- it will
be kept alive automatically for the duration of the drag-and-drop.
When a drag-and-drop is already in process for the Tk interpreter, the
call is *ignored*; this normally averts starting multiple simultaneous
dnd processes, e.g. because different button callbacks all
dnd_start().
The object is *not* necessarily a widget -- it can be any
application-specific object that is meaningful to potential
drag-and-drop targets.
Potential drag-and-drop targets are discovered as follows. Whenever
the mouse moves, and at the start and end of a drag-and-drop move, the
Tk widget directly under the mouse is inspected. This is the target
widget (not to be confused with the target object, yet to be
determined). If there is no target widget, there is no dnd target
object. If there is a target widget, and it has an attribute
dnd_accept, this should be a function (or any callable object). The
function is called as dnd_accept(source, event), where 'source' is the
object being dragged (the object passed to dnd_start() above), and
'event' is the most recent event object (generally a <Motion> event;
it can also be <ButtonPress> or <ButtonRelease>). If the dnd_accept()
function returns something other than None, this is the new dnd target
object. If dnd_accept() returns None, or if the target widget has no
dnd_accept attribute, the target widget's parent is considered as the
target widget, and the search for a target object is repeated from
there. If necessary, the search is repeated all the way up to the
root widget. If none of the target widgets can produce a target
object, there is no target object (the target object is None).
The target object thus produced, if any, is called the new target
object. It is compared with the old target object (or None, if there
was no old target widget). There are several cases ('source' is the
source object, and 'event' is the most recent event object):
- Both the old and new target objects are None. Nothing happens.
- The old and new target objects are the same object. Its method
dnd_motion(source, event) is called.
- The old target object was None, and the new target object is not
None. The new target object's method dnd_enter(source, event) is
called.
- The new target object is None, and the old target object is not
None. The old target object's method dnd_leave(source, event) is
called.
- The old and new target objects differ and neither is None. The old
target object's method dnd_leave(source, event), and then the new
target object's method dnd_enter(source, event) is called.
Once this is done, the new target object replaces the old one, and the
Tk mainloop proceeds. The return value of the methods mentioned above
is ignored; if they raise an exception, the normal exception handling
mechanisms take over.
The drag-and-drop processes can end in two ways: a final target object
is selected, or no final target object is selected. When a final
target object is selected, it will always have been notified of the
potential drop by a call to its dnd_enter() method, as described
above, and possibly one or more calls to its dnd_motion() method; its
dnd_leave() method has not been called since the last call to
dnd_enter(). The target is notified of the drop by a call to its
method dnd_commit(source, event).
If no final target object is selected, and there was an old target
object, its dnd_leave(source, event) method is called to complete the
dnd sequence.
Finally, the source object is notified that the drag-and-drop process
is over, by a call to source.dnd_end(target, event), specifying either
the selected target object, or None if no target object was selected.
The source object can use this to implement the commit action; this is
sometimes simpler than to do it in the target's dnd_commit(). The
target's dnd_commit() method could then simply be aliased to
dnd_leave().
At any time during a dnd sequence, the application can cancel the
sequence by calling the cancel() method on the object returned by
dnd_start(). This will call dnd_leave() if a target is currently
active; it will never call dnd_commit().
""" |
#!/usr/bin/env python3
# -*- coding, utf-8 -*-
# Íåïàðàìåòðè÷åñêèå êðèòåðèè äëÿ ñëîæíûõ ãèïîòåç î ãàììà-ðàñïðåäåëåíèè
# ñ ÎÌÏ îáîèõ ïàðàìåòðîâ
# Non-parametric tests for composite hypotheses about gamma distribution
# with ML estimation of both parameters
# Copyright © 2014 Âàñèëèé Ãîðîõîâ-Àïåëüñèíîâ
# This file is part of code for my bachelor's thesis.
#
# Code for my bachelor's thesis is free software: you can redistribute
# it and/or modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# Code for my bachelor's thesis is distributed in the hope that it will
# be useful, but WITHOUT ANY WARRANTY; without even the implied warranty
# of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with code for my bachelor's thesis. If not, see
# <http://www.gnu.org/licenses/>.
# Requirements: Python 3 (works with 3.3), NumPy, SciPy
# Èñòî÷íèêè:
# Îðèãèíàëüíûå êðèòè÷åñêèå çíà÷åíèÿ:
# 1. Ðåêîìåíäàöèè ïî ñòàíäàðòèçàöèè Ð 50.1.037-2002. Ïðèêëàäíàÿ
# ñòàòèñòèêà. Ïðàâèëà ïðîâåðêè ñîãëàñèÿ îïûòíîãî ðàñïðåäåëåíèÿ ñ
# òåîðåòè÷åñêèì. ×àñòü II. Íåïàðàìåòðè÷åñêèå êðèòåðèè. Ì.,
# Ãîññòàíäàðò Ðîññèè.
# Ñêîððåêòèðîâàííûå êðèòè÷åñêèå çíà÷åíèÿ:
# 2. Ëåìåøêî Á. Þ., Ëåìåøêî Ñ. Á. Ìîäåëè ðàñïðåäåëåíèé ñòàòèñòèê
# íåïàðàìåòðè÷åñêèõ êðèòåðèåâ ñîãëàñèÿ ïðè ïðîâåðêå ñëîæíûõ ãèïîòåç
# ñ èñïîëüçîâàíèåì îöåíîê ìàêñèìàëüíîãî ïðàâäîïîäîáèÿ. ×àñòü II //
# Èçìåðèòåëüíàÿ òåõíèêà. 2009. ¹8. Ñ. 1726.
# 3. Ëåìåøêî Á. Þ. [è äð.] Ñòàòèñòè÷åñêèé àíàëèç äàííûõ, ìîäåëèðîâàíèå
# è èññëåäîâàíèå âåðîÿòíîñòíûõ çàêîíîìåðíîñòåé. Êîìïüþòåðíûé
# ïîäõîä. Íîâîñèáèðñê, Èçä-âî ÍÃÒÓ, 2011. 888 ñ.
# ISBN 978-5-7782-1590-0
# Data
|
"""
Implementation of the trigsimp algorithm by Fu et al.
The idea behind the ``fu`` algorithm is to use a sequence of rules, applied
in what is heuristically known to be a smart order, to select a simpler
expression that is equivalent to the input.
There are transform rules in which a single rule is applied to the
expression tree. The following are just mnemonic in nature; see the
docstrings for examples.
TR0 - simplify expression
TR1 - sec-csc to cos-sin
TR2 - tan-cot to sin-cos ratio
TR2i - sin-cos ratio to tan
TR3 - angle canonicalization
TR4 - functions at special angles
TR5 - powers of sin to powers of cos
TR6 - powers of cos to powers of sin
TR7 - reduce cos power (increase angle)
TR8 - expand products of sin-cos to sums
TR9 - contract sums of sin-cos to products
TR10 - separate sin-cos arguments
TR10i - collect sin-cos arguments
TR11 - reduce double angles
TR12 - separate tan arguments
TR12i - collect tan arguments
TR13 - expand product of tan-cot
TRmorrie - prod(cos(x*2**i), (i, 0, k - 1)) -> sin(2**k*x)/(2**k*sin(x))
TR14 - factored powers of sin or cos to cos or sin power
TR15 - negative powers of sin to cot power
TR16 - negative powers of cos to tan power
TR22 - tan-cot powers to negative powers of sec-csc functions
TR111 - negative sin-cos-tan powers to csc-sec-cot
There are 4 combination transforms (CTR1 - CTR4) in which a sequence of
transformations are applied and the simplest expression is selected from
a few options.
Finally, there are the 2 rule lists (RL1 and RL2), which apply a
sequence of transformations and combined transformations, and the ``fu``
algorithm itself, which applies rules and rule lists and selects the
best expressions. There is also a function ``L`` which counts the number
of trigonometric funcions that appear in the expression.
Other than TR0, re-writing of expressions is not done by the transformations.
e.g. TR10i finds pairs of terms in a sum that are in the form like
``cos(x)*cos(y) + sin(x)*sin(y)``. Such expression are targeted in a bottom-up
traversal of the expression, but no manipulation to make them appear is
attempted. For example,
Set-up for examples below:
>>> from sympy.simplify.fu import fu, L, TR9, TR10i, TR11
>>> from sympy import factor, sin, cos, powsimp
>>> from sympy.abc import x, y, z, a
>>> from time import time
>>> eq = cos(x + y)/cos(x)
>>> TR10i(eq.expand(trig=True))
-sin(x)*sin(y)/cos(x) + cos(y)
If the expression is put in "normal" form (with a common denominator) then
the transformation is successful:
>>> TR10i(_.normal())
cos(x + y)/cos(x)
TR11's behavior is similar. It rewrites double angles as smaller angles but
doesn't do any simplification of the result.
>>> TR11(sin(2)**a*cos(1)**(-a), 1)
(2*sin(1)*cos(1))**a*cos(1)**(-a)
>>> powsimp(_)
(2*sin(1))**a
The temptation is to try make these TR rules "smarter" but that should really
be done at a higher level; the TR rules should try maintain the "do one thing
well" principle. There is one exception, however. In TR10i and TR9 terms are
recognized even when they are each multiplied by a common factor:
>>> fu(a*cos(x)*cos(y) + a*sin(x)*sin(y))
a*cos(x - y)
Factoring with ``factor_terms`` is used but it it "JIT"-like, being delayed
until it is deemed necessary. Furthermore, if the factoring does not
help with the simplification, it is not retained, so
``a*cos(x)*cos(y) + a*sin(x)*sin(z)`` does not become the factored
(but unsimplified in the trigonometric sense) expression:
>>> fu(a*cos(x)*cos(y) + a*sin(x)*sin(z))
a*sin(x)*sin(z) + a*cos(x)*cos(y)
In some cases factoring might be a good idea, but the user is left
to make that decision. For example:
>>> expr=((15*sin(2*x) + 19*sin(x + y) + 17*sin(x + z) + 19*cos(x - z) +
... 25)*(20*sin(2*x) + 15*sin(x + y) + sin(y + z) + 14*cos(x - z) +
... 14*cos(y - z))*(9*sin(2*y) + 12*sin(y + z) + 10*cos(x - y) + 2*cos(y -
... z) + 18)).expand(trig=True).expand()
In the expanded state, there are nearly 1000 trig functions:
>>> L(expr)
932
If the expression where factored first, this would take time but the
resulting expression would be transformed very quickly:
>>> def clock(f, n=2):
... t=time(); f(); return round(time()-t, n)
...
>>> clock(lambda: factor(expr)) # doctest: +SKIP
0.86
>>> clock(lambda: TR10i(expr), 3) # doctest: +SKIP
0.016
If the unexpanded expression is used, the transformation takes longer but
not as long as it took to factor it and then transform it:
>>> clock(lambda: TR10i(expr), 2) # doctest: +SKIP
0.28
So neither expansion nor factoring is used in ``TR10i``: if the
expression is already factored (or partially factored) then expansion
with ``trig=True`` would destroy what is already known and take
longer; if the expression is expanded, factoring may take longer than
simply applying the transformation itself.
Although the algorithms should be canonical, always giving the same
result, they may not yield the best result. This, in general, is
the nature of simplification where searching all possible transformation
paths is very expensive. Here is a simple example. There are 6 terms
in the following sum:
>>> expr = (sin(x)**2*cos(y)*cos(z) + sin(x)*sin(y)*cos(x)*cos(z) +
... sin(x)*sin(z)*cos(x)*cos(y) + sin(y)*sin(z)*cos(x)**2 + sin(y)*sin(z) +
... cos(y)*cos(z))
>>> args = expr.args
Serendipitously, fu gives the best result:
>>> fu(expr)
3*cos(y - z)/2 - cos(2*x + y + z)/2
But if different terms were combined, a less-optimal result might be
obtained, requiring some additional work to get better simplification,
but still less than optimal. The following shows an alternative form
of ``expr`` that resists optimal simplification once a given step
is taken since it leads to a dead end:
>>> TR9(-cos(x)**2*cos(y + z) + 3*cos(y - z)/2 +
... cos(y + z)/2 + cos(-2*x + y + z)/4 - cos(2*x + y + z)/4)
sin(2*x)*sin(y + z)/2 - cos(x)**2*cos(y + z) + 3*cos(y - z)/2 + cos(y + z)/2
Here is a smaller expression that exhibits the same behavior:
>>> a = sin(x)*sin(z)*cos(x)*cos(y) + sin(x)*sin(y)*cos(x)*cos(z)
>>> TR10i(a)
sin(x)*sin(y + z)*cos(x)
>>> newa = _
>>> TR10i(expr - a) # this combines two more of the remaining terms
sin(x)**2*cos(y)*cos(z) + sin(y)*sin(z)*cos(x)**2 + cos(y - z)
>>> TR10i(_ + newa) == _ + newa # but now there is no more simplification
True
Without getting lucky or trying all possible pairings of arguments, the
final result may be less than optimal and impossible to find without
better heuristics or brute force trial of all possibilities.
Notes
=====
This work was started by NAME at the Technological School
"Electronic systems" (30.11.2011).
References
==========
http://rfdz.ph-noe.ac.at/fileadmin/Mathematik_Uploads/ACDCA/
DESTIME2006/DES_contribs/Fu/simplification.pdf
http://www.sosmath.com/trig/Trig5/trig5/pdf/pdf.html gives a formula sheet.
""" |
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
diff = default_diff_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.match(actual, expected)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound functions that handle matching
in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
The TestCmd module provides unbound functions that can be used for the
"diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
"""
Wrappers to LAPACK library
==========================
flapack -- wrappers for Fortran [*] LAPACK routines
clapack -- wrappers for ATLAS LAPACK routines
calc_lwork -- calculate optimal lwork parameters
get_lapack_funcs -- query for wrapper functions.
[*] If ATLAS libraries are available then Fortran routines
actually use ATLAS routines and should perform equally
well to ATLAS routines.
Module flapack
++++++++++++++
In the following all function names are shown without
type prefix (s,d,c,z). Optimal values for lwork can
be computed using calc_lwork module.
Linear Equations
----------------
Drivers::
lu,piv,x,info = gesv(a,b,overwrite_a=0,overwrite_b=0)
lub,piv,x,info = gbsv(kl,ku,ab,b,overwrite_ab=0,overwrite_b=0)
c,x,info = posv(a,b,lower=0,overwrite_a=0,overwrite_b=0)
Computational routines::
lu,piv,info = getrf(a,overwrite_a=0)
x,info = getrs(lu,piv,b,trans=0,overwrite_b=0)
inv_a,info = getri(lu,piv,lwork=min_lwork,overwrite_lu=0)
c,info = potrf(a,lower=0,clean=1,overwrite_a=0)
x,info = potrs(c,b,lower=0,overwrite_b=0)
inv_a,info = potri(c,lower=0,overwrite_c=0)
inv_c,info = trtri(c,lower=0,unitdiag=0,overwrite_c=0)
Linear Least Squares (LLS) Problems
-----------------------------------
Drivers::
v,x,s,rank,info = gelss(a,b,cond=-1.0,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
Computational routines::
qr,tau,info = geqrf(a,lwork=min_lwork,overwrite_a=0)
q,info = orgqr|ungqr(qr,tau,lwork=min_lwork,overwrite_qr=0,overwrite_tau=1)
Generalized Linear Least Squares (LSE and GLM) Problems
-------------------------------------------------------
Standard Eigenvalue and Singular Value Problems
-----------------------------------------------
Drivers::
w,v,info = syev|heev(a,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0)
w,v,info = syevd|heevd(a,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0)
w,v,info = syevr|heevr(a,compute_v=1,lower=0,vrange=,irange=,atol=-1.0,lwork=min_lwork,overwrite_a=0)
t,sdim,(wr,wi|w),vs,info = gees(select,a,compute_v=1,sort_t=0,lwork=min_lwork,select_extra_args=(),overwrite_a=0)
wr,(wi,vl|w),vr,info = geev(a,compute_vl=1,compute_vr=1,lwork=min_lwork,overwrite_a=0)
u,s,vt,info = gesdd(a,compute_uv=1,lwork=min_lwork,overwrite_a=0)
Computational routines::
ht,tau,info = gehrd(a,lo=0,hi=n-1,lwork=min_lwork,overwrite_a=0)
ba,lo,hi,pivscale,info = gebal(a,scale=0,permute=0,overwrite_a=0)
Generalized Eigenvalue and Singular Value Problems
--------------------------------------------------
Drivers::
w,v,info = sygv|hegv(a,b,itype=1,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
w,v,info = sygvd|hegvd(a,b,itype=1,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
(alphar,alphai|alpha),beta,vl,vr,info = ggev(a,b,compute_vl=1,compute_vr=1,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
Auxiliary routines
------------------
a,info = lauum(c,lower=0,overwrite_c=0)
a = laswp(a,piv,k1=0,k2=len(piv)-1,off=0,inc=1,overwrite_a=0)
Module clapack
++++++++++++++
Linear Equations
----------------
Drivers::
lu,piv,x,info = gesv(a,b,rowmajor=1,overwrite_a=0,overwrite_b=0)
c,x,info = posv(a,b,lower=0,rowmajor=1,overwrite_a=0,overwrite_b=0)
Computational routines::
lu,piv,info = getrf(a,rowmajor=1,overwrite_a=0)
x,info = getrs(lu,piv,b,trans=0,rowmajor=1,overwrite_b=0)
inv_a,info = getri(lu,piv,rowmajor=1,overwrite_lu=0)
c,info = potrf(a,lower=0,clean=1,rowmajor=1,overwrite_a=0)
x,info = potrs(c,b,lower=0,rowmajor=1,overwrite_b=0)
inv_a,info = potri(c,lower=0,rowmajor=1,overwrite_c=0)
inv_c,info = trtri(c,lower=0,unitdiag=0,rowmajor=1,overwrite_c=0)
Auxiliary routines
------------------
a,info = lauum(c,lower=0,rowmajor=1,overwrite_c=0)
Module calc_lwork
+++++++++++++++++
Optimal lwork is maxwrk. Default is minwrk.
minwrk,maxwrk = gehrd(prefix,n,lo=0,hi=n-1)
minwrk,maxwrk = gesdd(prefix,m,n,compute_uv=1)
minwrk,maxwrk = gelss(prefix,m,n,nrhs)
minwrk,maxwrk = getri(prefix,n)
minwrk,maxwrk = geev(prefix,n,compute_vl=1,compute_vr=1)
minwrk,maxwrk = heev(prefix,n,lower=0)
minwrk,maxwrk = syev(prefix,n,lower=0)
minwrk,maxwrk = gees(prefix,n,compute_v=1)
minwrk,maxwrk = geqrf(prefix,m,n)
minwrk,maxwrk = gqr(prefix,m,n)
""" |
"""CPStats, a package for collecting and reporting on program statistics.
Overview
========
Statistics about program operation are an invaluable monitoring and debugging
tool. Unfortunately, the gathering and reporting of these critical values is
usually ad-hoc. This package aims to add a centralized place for gathering
statistical performance data, a structure for recording that data which
provides for extrapolation of that data into more useful information,
and a method of serving that data to both human investigators and
monitoring software. Let's examine each of those in more detail.
Data Gathering
--------------
Just as Python's `logging` module provides a common importable for gathering
and sending messages, performance statistics would benefit from a similar
common mechanism, and one that does *not* require each package which wishes
to collect stats to import a third-party module. Therefore, we choose to
re-use the `logging` module by adding a `statistics` object to it.
That `logging.statistics` object is a nested dict. It is not a custom class,
because that would 1) require libraries and applications to import a third-
party module in order to participate, 2) inhibit innovation in extrapolation
approaches and in reporting tools, and 3) be slow. There are, however, some
specifications regarding the structure of the dict.
{
+----"SQLAlchemy": {
| "Inserts": 4389745,
| "Inserts per Second":
| lambda s: s["Inserts"] / (time() - s["Start"]),
| C +---"Table Statistics": {
| o | "widgets": {-----------+
N | l | "Rows": 1.3M, | Record
a | l | "Inserts": 400, |
m | e | },---------------------+
e | c | "froobles": {
s | t | "Rows": 7845,
p | i | "Inserts": 0,
a | o | },
c | n +---},
e | "Slow Queries":
| [{"Query": "SELECT * FROM widgets;",
| "Processing Time": 47.840923343,
| },
| ],
+----},
}
The `logging.statistics` dict has four levels. The topmost level is nothing
more than a set of names to introduce modularity, usually along the lines of
package names. If the SQLAlchemy project wanted to participate, for example,
it might populate the item `logging.statistics['SQLAlchemy']`, whose value
would be a second-layer dict we call a "namespace". Namespaces help multiple
packages to avoid collisions over key names, and make reports easier to read,
to boot. The maintainers of SQLAlchemy should feel free to use more than one
namespace if needed (such as 'SQLAlchemy ORM'). Note that there are no case
or other syntax constraints on the namespace names; they should be chosen
to be maximally readable by humans (neither too short nor too long).
Each namespace, then, is a dict of named statistical values, such as
'Requests/sec' or 'Uptime'. You should choose names which will look
good on a report: spaces and capitalization are just fine.
In addition to scalars, values in a namespace MAY be a (third-layer)
dict, or a list, called a "collection". For example, the CherryPy StatsTool
keeps track of what each request is doing (or has most recently done)
in a 'Requests' collection, where each key is a thread ID; each
value in the subdict MUST be a fourth dict (whew!) of statistical data about
each thread. We call each subdict in the collection a "record". Similarly,
the StatsTool also keeps a list of slow queries, where each record contains
data about each slow query, in order.
Values in a namespace or record may also be functions, which brings us to:
Extrapolation
-------------
The collection of statistical data needs to be fast, as close to unnoticeable
as possible to the host program. That requires us to minimize I/O, for example,
but in Python it also means we need to minimize function calls. So when you
are designing your namespace and record values, try to insert the most basic
scalar values you already have on hand.
When it comes time to report on the gathered data, however, we usually have
much more freedom in what we can calculate. Therefore, whenever reporting
tools (like the provided StatsPage CherryPy class) fetch the contents of
`logging.statistics` for reporting, they first call `extrapolate_statistics`
(passing the whole `statistics` dict as the only argument). This makes a
deep copy of the statistics dict so that the reporting tool can both iterate
over it and even change it without harming the original. But it also expands
any functions in the dict by calling them. For example, you might have a
'Current Time' entry in the namespace with the value "lambda scope: time.time()".
The "scope" parameter is the current namespace dict (or record, if we're
currently expanding one of those instead), allowing you access to existing
static entries. If you're truly evil, you can even modify more than one entry
at a time.
However, don't try to calculate an entry and then use its value in further
extrapolations; the order in which the functions are called is not guaranteed.
This can lead to a certain amount of duplicated work (or a redesign of your
schema), but that's better than complicating the spec.
After the whole thing has been extrapolated, it's time for:
Reporting
---------
The StatsPage class grabs the `logging.statistics` dict, extrapolates it all,
and then transforms it to HTML for easy viewing. Each namespace gets its own
header and attribute table, plus an extra table for each collection. This is
NOT part of the statistics specification; other tools can format how they like.
You can control which columns are output and how they are formatted by updating
StatsPage.formatting, which is a dict that mirrors the keys and nesting of
`logging.statistics`. The difference is that, instead of data values, it has
formatting values. Use None for a given key to indicate to the StatsPage that a
given column should not be output. Use a string with formatting (such as '%.3f')
to interpolate the value(s), or use a callable (such as lambda v: v.isoformat())
for more advanced formatting. Any entry which is not mentioned in the formatting
dict is output unchanged.
Monitoring
----------
Although the HTML output takes pains to assign unique id's to each <td> with
statistical data, you're probably better off fetching /cpstats/data, which
outputs the whole (extrapolated) `logging.statistics` dict in JSON format.
That is probably easier to parse, and doesn't have any formatting controls,
so you get the "original" data in a consistently-serialized format.
Note: there's no treatment yet for datetime objects. Try time.time() instead
for now if you can. Nagios will probably thank you.
Turning Collection Off
----------------------
It is recommended each namespace have an "Enabled" item which, if False,
stops collection (but not reporting) of statistical data. Applications
SHOULD provide controls to pause and resume collection by setting these
entries to False or True, if present.
Usage
=====
To collect statistics on CherryPy applications:
from cherrypy.lib import cpstats
appconfig['/']['tools.cpstats.on'] = True
To collect statistics on your own code:
import logging
# Initialize the repository
if not hasattr(logging, 'statistics'): logging.statistics = {}
# Initialize my namespace
mystats = logging.statistics.setdefault('My Stuff', {})
# Initialize my namespace's scalars and collections
mystats.update({
'Enabled': True,
'Start Time': time.time(),
'Important Events': 0,
'Events/Second': lambda s: (
(s['Important Events'] / (time.time() - s['Start Time']))),
})
...
for event in events:
...
# Collect stats
if mystats.get('Enabled', False):
mystats['Important Events'] += 1
To report statistics:
root.cpstats = cpstats.StatsPage()
To format statistics reports:
See 'Reporting', above.
""" |
#!/usr/bin/env python
# txt2tags - generic text conversion tool
# http://txt2tags.sf.net
#
# Copyright 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008 NAME This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 2.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You have received a copy of the GNU General Public License along
# with this program, on the COPYING file.
#
########################################################################
#
# BORING CODE EXPLANATION AHEAD
#
# Just read it if you wish to understand how the txt2tags code works.
#
########################################################################
#
# The code that [1] parses the marked text is separated from the
# code that [2] insert the target tags.
#
# [1] made by: def convert()
# [2] made by: class BlockMaster
#
# The structures of the marked text are identified and its contents are
# extracted into a data holder (Python lists and dictionaries).
#
# When parsing the source file, the blocks (para, lists, quote, table)
# are opened with BlockMaster, right when found. Then its contents,
# which spans on several lines, are feeded into a special holder on the
# BlockMaster instance. Just when the block is closed, the target tags
# are inserted for the full block as a whole, in one pass. This way, we
# have a better control on blocks. Much better than the previous line by
# line approach.
#
# In other words, whenever inside a block, the parser *holds* the tag
# insertion process, waiting until the full block is read. That was
# needed primary to close paragraphs for the XHTML target, but
# proved to be a very good adding, improving many other processing.
#
# -------------------------------------------------------------------
#
# These important classes are all documented:
# CommandLine, SourceDocument, ConfigMaster, ConfigLines.
#
# There is a RAW Config format and all kind of configuration is first
# converted to this format. Then a generic method parses it.
#
# These functions get information about the input file(s) and take
# care of the init processing:
# get_infiles_config(), process_source_file() and convert_this_files()
#
########################################################################
#XXX Python coding warning
# Avoid common mistakes:
# - do NOT use newlist=list instead newlist=list[:]
# - do NOT use newdic=dic instead newdic=dic.copy()
# - do NOT use dic[key] instead dic.get(key)
# - do NOT use del dic[key] without has_key() before
#XXX Smart Image Align don't work if the image is a link
# Can't fix that because the image is expanded together with the
# link, at the linkbank filling moment. Only the image is passed
# to parse_images(), not the full line, so it is always 'middle'.
#XXX Paragraph separation not valid inside Quote
# Quote will not have <p></p> inside, instead will close and open
# again the <blockquote>. This really sux in CSS, when defining a
# different background color. Still don't know how to fix it.
#XXX TODO (maybe)
# New mark or macro which expands to an anchor full title.
# It is necessary to parse the full document in this order:
# DONE 1st scan: HEAD: get all settings, including %!includeconf
# DONE 2nd scan: BODY: expand includes & apply %!preproc
# 3rd scan: BODY: read titles and compose TOC info
# 4th scan: BODY: full parsing, expanding [#anchor] 1st
# Steps 2 and 3 can be made together, with no tag adding.
# Two complete body scans will be *slow*, don't know if it worths.
# One solution may be add the titles as postproc rules
##############################################################################
# User config (1=ON, 0=OFF)
|
"""Drag-and-drop support for Tkinter.
This is very preliminary. I currently only support dnd *within* one
application, between different windows (or within the same window).
I an trying to make this as generic as possible -- not dependent on
the use of a particular widget or icon type, etc. I also hope that
this will work with Pmw.
To enable an object to be dragged, you must create an event binding
for it that starts the drag-and-drop process. Typically, you should
bind <ButtonPress> to a callback function that you write. The function
should call Tkdnd.dnd_start(source, event), where 'source' is the
object to be dragged, and 'event' is the event that invoked the call
(the argument to your callback function). Even though this is a class
instantiation, the returned instance should not be stored -- it will
be kept alive automatically for the duration of the drag-and-drop.
When a drag-and-drop is already in process for the Tk interpreter, the
call is *ignored*; this normally averts starting multiple simultaneous
dnd processes, e.g. because different button callbacks all
dnd_start().
The object is *not* necessarily a widget -- it can be any
application-specific object that is meaningful to potential
drag-and-drop targets.
Potential drag-and-drop targets are discovered as follows. Whenever
the mouse moves, and at the start and end of a drag-and-drop move, the
Tk widget directly under the mouse is inspected. This is the target
widget (not to be confused with the target object, yet to be
determined). If there is no target widget, there is no dnd target
object. If there is a target widget, and it has an attribute
dnd_accept, this should be a function (or any callable object). The
function is called as dnd_accept(source, event), where 'source' is the
object being dragged (the object passed to dnd_start() above), and
'event' is the most recent event object (generally a <Motion> event;
it can also be <ButtonPress> or <ButtonRelease>). If the dnd_accept()
function returns something other than None, this is the new dnd target
object. If dnd_accept() returns None, or if the target widget has no
dnd_accept attribute, the target widget's parent is considered as the
target widget, and the search for a target object is repeated from
there. If necessary, the search is repeated all the way up to the
root widget. If none of the target widgets can produce a target
object, there is no target object (the target object is None).
The target object thus produced, if any, is called the new target
object. It is compared with the old target object (or None, if there
was no old target widget). There are several cases ('source' is the
source object, and 'event' is the most recent event object):
- Both the old and new target objects are None. Nothing happens.
- The old and new target objects are the same object. Its method
dnd_motion(source, event) is called.
- The old target object was None, and the new target object is not
None. The new target object's method dnd_enter(source, event) is
called.
- The new target object is None, and the old target object is not
None. The old target object's method dnd_leave(source, event) is
called.
- The old and new target objects differ and neither is None. The old
target object's method dnd_leave(source, event), and then the new
target object's method dnd_enter(source, event) is called.
Once this is done, the new target object replaces the old one, and the
Tk mainloop proceeds. The return value of the methods mentioned above
is ignored; if they raise an exception, the normal exception handling
mechanisms take over.
The drag-and-drop processes can end in two ways: a final target object
is selected, or no final target object is selected. When a final
target object is selected, it will always have been notified of the
potential drop by a call to its dnd_enter() method, as described
above, and possibly one or more calls to its dnd_motion() method; its
dnd_leave() method has not been called since the last call to
dnd_enter(). The target is notified of the drop by a call to its
method dnd_commit(source, event).
If no final target object is selected, and there was an old target
object, its dnd_leave(source, event) method is called to complete the
dnd sequence.
Finally, the source object is notified that the drag-and-drop process
is over, by a call to source.dnd_end(target, event), specifying either
the selected target object, or None if no target object was selected.
The source object can use this to implement the commit action; this is
sometimes simpler than to do it in the target's dnd_commit(). The
target's dnd_commit() method could then simply be aliased to
dnd_leave().
At any time during a dnd sequence, the application can cancel the
sequence by calling the cancel() method on the object returned by
dnd_start(). This will call dnd_leave() if a target is currently
active; it will never call dnd_commit().
""" |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
# adapted from http://www.cl.cam.ac.uk/~mgk25/ucs/wcwidth.c
# -thepaul
# This is an implementation of wcwidth() and wcswidth() (defined in
# IEEE Std 1002.1-2001) for Unicode.
#
# http://www.opengroup.org/onlinepubs/007904975/functions/wcwidth.html
# http://www.opengroup.org/onlinepubs/007904975/functions/wcswidth.html
#
# In fixed-width output devices, Latin characters all occupy a single
# "cell" position of equal width, whereas ideographic CJK characters
# occupy two such cells. Interoperability between terminal-line
# applications and (teletype-style) character terminals using the
# UTF-8 encoding requires agreement on which character should advance
# the cursor by how many cell positions. No established formal
# standards exist at present on which Unicode character shall occupy
# how many cell positions on character terminals. These routines are
# a first attempt of defining such behavior based on simple rules
# applied to data provided by the Unicode Consortium.
#
# For some graphical characters, the Unicode standard explicitly
# defines a character-cell width via the definition of the East Asian
# FullWidth (F), Wide (W), Half-width (H), and Narrow (Na) classes.
# In all these cases, there is no ambiguity about which width a
# terminal shall use. For characters in the East Asian Ambiguous (A)
# class, the width choice depends purely on a preference of backward
# compatibility with either historic CJK or Western practice.
# Choosing single-width for these characters is easy to justify as
# the appropriate long-term solution, as the CJK practice of
# displaying these characters as double-width comes from historic
# implementation simplicity (8-bit encoded characters were displayed
# single-width and 16-bit ones double-width, even for Greek,
# Cyrillic, etc.) and not any typographic considerations.
#
# Much less clear is the choice of width for the Not East Asian
# (Neutral) class. Existing practice does not dictate a width for any
# of these characters. It would nevertheless make sense
# typographically to allocate two character cells to characters such
# as for instance EM SPACE or VOLUME INTEGRAL, which cannot be
# represented adequately with a single-width glyph. The following
# routines at present merely assign a single-cell width to all
# neutral characters, in the interest of simplicity. This is not
# entirely satisfactory and should be reconsidered before
# establishing a formal standard in this area. At the moment, the
# decision which Not East Asian (Neutral) characters should be
# represented by double-width glyphs cannot yet be answered by
# applying a simple rule from the Unicode database content. Setting
# up a proper standard for the behavior of UTF-8 character terminals
# will require a careful analysis not only of each Unicode character,
# but also of each presentation form, something the author of these
# routines has avoided to do so far.
#
# http://www.unicode.org/unicode/reports/tr11/
#
# NAME -- 2007-05-26 (Unicode 5.0)
#
# Permission to use, copy, modify, and distribute this software
# for any purpose and without fee is hereby granted. The author
# disclaims all warranties with regard to this software.
#
# Latest C version: http://www.cl.cam.ac.uk/~mgk25/ucs/wcwidth.c
# auxiliary function for binary search in interval table
|
#!/usr/bin/env python
# (c) 2013, NAME <EMAIL>
#
# This file is part of Ansible.
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
#
#
# Author: NAME <EMAIL>
#
# Description:
# This module queries local or remote Docker daemons and generates
# inventory information.
#
# This plugin does not support targeting of specific hosts using the --host
# flag. Instead, it queries the Docker API for each container, running
# or not, and returns this data all once.
#
# The plugin returns the following custom attributes on Docker containers:
# docker_args
# docker_config
# docker_created
# docker_driver
# docker_exec_driver
# docker_host_config
# docker_hostname_path
# docker_hosts_path
# docker_id
# docker_image
# docker_name
# docker_network_settings
# docker_path
# docker_resolv_conf_path
# docker_state
# docker_volumes
# docker_volumes_rw
#
# Requirements:
# The docker-py module: https://github.com/dotcloud/docker-py
#
# Notes:
# A config file can be used to configure this inventory module, and there
# are several environment variables that can be set to modify the behavior
# of the plugin at runtime:
# DOCKER_CONFIG_FILE
# DOCKER_HOST
# DOCKER_VERSION
# DOCKER_TIMEOUT
# DOCKER_PRIVATE_SSH_PORT
# DOCKER_DEFAULT_IP
#
# Environment Variables:
# environment variable: DOCKER_CONFIG_FILE
# description:
# - A path to a Docker inventory hosts/defaults file in YAML format
# - A sample file has been provided, colocated with the inventory
# file called 'docker.yml'
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_HOST
# description:
# - The socket on which to connect to a Docker daemon API
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_VERSION
# description:
# - Version of the Docker API to use
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_TIMEOUT
# description:
# - Timeout in seconds for connections to Docker daemon API
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_PRIVATE_SSH_PORT
# description:
# - The private port (container port) on which SSH is listening
# for connections
# default: 22
# required: false
# environment variable: DOCKER_DEFAULT_IP
# description:
# - This environment variable overrides the container SSH connection
# IP address (aka, 'ansible_ssh_host')
#
# This option allows one to override the ansible_ssh_host whenever
# Docker has exercised its default behavior of binding private ports
# to all interfaces of the Docker host. This behavior, when dealing
# with remote Docker hosts, does not allow Ansible to determine
# a proper host IP address on which to connect via SSH to containers.
# By default, this inventory module assumes all IP_ADDRESS-exposed
# ports to be bound to localhost:<port>. To override this
# behavior, for example, to bind a container's SSH port to the public
# interface of its host, one must manually set this IP.
#
# It is preferable to begin to launch Docker containers with
# ports exposed on publicly accessible IP addresses, particularly
# if the containers are to be targeted by Ansible for remote
# configuration, not accessible via localhost SSH connections.
#
# Docker containers can be explicitly exposed on IP addresses by
# a) starting the daemon with the --ip argument
# b) running containers with the -P/--publish ip::containerPort
# argument
# default: IP_ADDRESS if port exposed on IP_ADDRESS by Docker
# required: false
#
# Examples:
# Use the config file:
# DOCKER_CONFIG_FILE=./docker.yml docker.py --list
#
# Connect to docker instance on localhost port 4243
# DOCKER_HOST=tcp://localhost:4243 docker.py --list
#
# Any container's ssh port exposed on IP_ADDRESS will mapped to
# another IP address (where Ansible will attempt to connect via SSH)
# DOCKER_DEFAULT_IP=IP_ADDRESS docker.py --list
|
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
# 2014-12-02 ch/doko Add workaround for gzip bomb vulnerability
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME Lundh.
#
# EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
|
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
#
# XML-RPC CLIENT LIBRARY
# $Id: xmlrpclib.py 41594 2005-12-04 19:11:17Z USERNAME $
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
#
# things to look into some day:
# TODO: sort out True/False/boolean issues for Python 2.3
|
# -*- coding: utf-8 -*-
# -- Dual Licence ----------------------------------------------------------
############################################################################
# GPL License #
# #
# This file is a SCons (http://www.scons.org/) builder #
# Copyright (c) 2012-14, NAME <EMAIL> #
# This program is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as #
# published by the Free Software Foundation, either version 3 of the #
# License, or (at your option) any later version. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with this program. If not, see <http://www.gnu.org/licenses/>. #
############################################################################
# --------------------------------------------------------------------------
############################################################################
# BSD 3-Clause License #
# #
# This file is a SCons (http://www.scons.org/) builder #
# Copyright (c) 2012-14, NAME <EMAIL> #
# All rights reserved. #
# #
# Redistribution and use in source and binary forms, with or without #
# modification, are permitted provided that the following conditions are #
# met: #
# #
# 1. Redistributions of source code must retain the above copyright #
# notice, this list of conditions and the following disclaimer. #
# #
# 2. Redistributions in binary form must reproduce the above copyright #
# notice, this list of conditions and the following disclaimer in the #
# documentation and/or other materials provided with the distribution. #
# #
# 3. Neither the name of the copyright holder nor the names of its #
# contributors may be used to endorse or promote products derived from #
# this software without specific prior written permission. #
# #
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS #
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT #
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A #
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT #
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, #
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED #
# TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR #
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF #
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING #
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS #
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #
############################################################################
# The Unpack Builder can be used for unpacking archives (eg Zip, TGZ, BZ, ... ).
# The emitter of the Builder reads the archive data and creates a returning file list
# the builder extract the archive. The environment variable stores a dictionary "UNPACK"
# for set different extractions (subdict "EXTRACTOR"):
# {
# PRIORITY => a value for setting the extractor order (lower numbers = extractor is used earlier)
# SUFFIX => defines a list with file suffixes, which should be handled with this extractor
# EXTRACTSUFFIX => suffix of the extract command
# EXTRACTFLAGS => a string parameter for the RUN command for extracting the data
# EXTRACTCMD => full extract command of the builder
# RUN => the main program which will be started (if the parameter is empty, the extractor will be ignored)
# LISTCMD => the listing command for the emitter
# LISTFLAGS => the string options for the RUN command for showing a list of files
# LISTSUFFIX => suffix of the list command
# LISTEXTRACTOR => a optional Python function, that is called on each output line of the
# LISTCMD for extracting file & dir names, the function need two parameters (first line number,
# second line content) and must return a string with the file / dir path (other value types
# will be ignored)
# }
# Other options in the UNPACK dictionary are:
# STOPONEMPTYFILE => bool variable for stoping if the file has empty size (default True)
# VIWEXTRACTOUTPUT => shows the output messages of the extraction command (default False)
# EXTRACTDIR => path in that the data will be extracted (default #)
#
# The file which is handled by the first suffix match of the extractor, the extractor list can be append for other files.
# The order of the extractor dictionary creates the listing & extractor command eg file extension .tar.gz should be
# before .gz, because the tar.gz is extract in one shoot.
#
# Under *nix system these tools are supported: tar, bzip2, gzip, unzip
# Under Windows only 7-Zip (http://www.7-zip.org/) is supported
|
"""
Matplotlib provides sophisticated date plotting capabilities, standing on the
shoulders of python :mod:`datetime`, the add-on modules :mod:`pytz` and
:mod:`dateutil`. :class:`datetime` objects are converted to floating point
numbers which represent time in days since 0001-01-01 UTC, plus 1. For
example, 0001-01-01, 06:00 is 1.25, not 0.25. The helper functions
:func:`date2num`, :func:`num2date` and :func:`drange` are used to facilitate
easy conversion to and from :mod:`datetime` and numeric ranges.
.. note::
Like Python's datetime, mpl uses the Gregorian calendar for all
conversions between dates and floating point numbers. This practice
is not universal, and calendar differences can cause confusing
differences between what Python and mpl give as the number of days
since 0001-01-01 and what other software and databases yield. For
example, the US Naval Observatory uses a calendar that switches
from Julian to Gregorian in October, 1582. Hence, using their
calculator, the number of days between 0001-01-01 and 2006-04-01 is
732403, whereas using the Gregorian calendar via the datetime
module we find::
In [31]:date(2006,4,1).toordinal() - date(1,1,1).toordinal()
Out[31]:732401
A wide range of specific and general purpose date tick locators and
formatters are provided in this module. See
:mod:`matplotlib.ticker` for general information on tick locators
and formatters. These are described below.
All the matplotlib date converters, tickers and formatters are
timezone aware, and the default timezone is given by the timezone
parameter in your :file:`matplotlibrc` file. If you leave out a
:class:`tz` timezone instance, the default from your rc file will be
assumed. If you want to use a custom time zone, pass a
:class:`pytz.timezone` instance with the tz keyword argument to
:func:`num2date`, :func:`plot_date`, and any custom date tickers or
locators you create. See `pytz <http://pythonhosted.org/pytz/>`_ for
information on :mod:`pytz` and timezone handling.
The `dateutil module <https://dateutil.readthedocs.io/en/stable/>`_ provides
additional code to handle date ticking, making it easy to place ticks
on any kinds of dates. See examples below.
Date tickers
------------
Most of the date tickers can locate single or multiple values. For
example::
# import constants for the days of the week
from matplotlib.dates import MO, TU, WE, TH, FR, SA, SU
# tick on mondays every week
loc = WeekdayLocator(byweekday=MO, tz=tz)
# tick on mondays and saturdays
loc = WeekdayLocator(byweekday=(MO, SA))
In addition, most of the constructors take an interval argument::
# tick on mondays every second week
loc = WeekdayLocator(byweekday=MO, interval=2)
The rrule locator allows completely general date ticking::
# tick every 5th easter
rule = rrulewrapper(YEARLY, byeaster=1, interval=5)
loc = RRuleLocator(rule)
Here are all the date tickers:
* :class:`MinuteLocator`: locate minutes
* :class:`HourLocator`: locate hours
* :class:`DayLocator`: locate specifed days of the month
* :class:`WeekdayLocator`: Locate days of the week, e.g., MO, TU
* :class:`MonthLocator`: locate months, e.g., 7 for july
* :class:`YearLocator`: locate years that are multiples of base
* :class:`RRuleLocator`: locate using a
:class:`matplotlib.dates.rrulewrapper`. The
:class:`rrulewrapper` is a simple wrapper around a
:class:`dateutil.rrule` (`dateutil
<https://dateutil.readthedocs.io/en/stable/>`_) which allow almost
arbitrary date tick specifications. See `rrule example
<../examples/pylab_examples/date_demo_rrule.html>`_.
* :class:`AutoDateLocator`: On autoscale, this class picks the best
:class:`MultipleDateLocator` to set the view limits and the tick
locations.
Date formatters
---------------
Here all all the date formatters:
* :class:`AutoDateFormatter`: attempts to figure out the best format
to use. This is most useful when used with the :class:`AutoDateLocator`.
* :class:`DateFormatter`: use :func:`strftime` format strings
* :class:`IndexDateFormatter`: date plots with implicit *x*
indexing.
""" |
# (c) 2013, NAME <EMAIL> red hat, inc
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# take a list of files and (optionally) a list of paths
# return the first existing file found in the paths
# [file1, file2, file3], [path1, path2, path3]
# search order is:
# path1/file1
# path1/file2
# path1/file3
# path2/file1
# path2/file2
# path2/file3
# path3/file1
# path3/file2
# path3/file3
# first file found with os.path.exists() is returned
# no file matches raises ansibleerror
# EXAMPLES
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: foo ${inventory_hostname} bar
# paths: /tmp/production /tmp/staging
# that will look for files in this order:
# /tmp/production/foo
# ${inventory_hostname}
# bar
# /tmp/staging/foo
# ${inventory_hostname}
# bar
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: /some/place/foo ${inventory_hostname} /some/place/else
# that will look for files in this order:
# /some/place/foo
# $relative_path/${inventory_hostname}
# /some/place/else
# example - including tasks:
# tasks:
# - include: $item
# with_first_found:
# - files: generic
# paths: tasks/staging tasks/production
# this will include the tasks in the file generic where it is found first (staging or production)
# example simple file lists
#tasks:
#- name: first found file
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname} foo
# example skipping if no matched files
# First_found also offers the ability to control whether or not failing
# to find a file returns an error or not
#
#- name: first found file - or skip
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname}
# skip: true
# example a role with default configuration and configuration per host
# you can set multiple terms with their own files and paths to look through.
# consider a role that sets some configuration per host falling back on a default config.
#
#- name: some configuration template
# template: src={{ item }} dest=/etc/file.cfg mode=0444 owner=root group=root
# with_first_found:
# - files:
# - ${inventory_hostname}/etc/file.cfg
# paths:
# - ../../../templates.overwrites
# - ../../../templates
# - files:
# - etc/file.cfg
# paths:
# - templates
# the above will return an empty list if the files cannot be found at all
# if skip is unspecificed or if it is set to false then it will return a list
# error which can be caught bye ignore_errors: true for that action.
# finally - if you want you can use it, in place to replace first_available_file:
# you simply cannot use the - files, path or skip options. simply replace
# first_available_file with with_first_found and leave the file listing in place
#
#
# - name: with_first_found like first_available_file
# action: copy src=$item dest=/tmp/faftest
# with_first_found:
# - ../files/foo
# - ../files/bar
# - ../files/baz
# ignore_errors: true
|
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to read all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <EMAIL>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
# -*- Mode: python -*-
#
# $Id: MP3Info.py,v 1.12 2004/05/19 16:07:09 vivake Exp $
#
# Copyright (c) 2002-2004 NAME (vivakeATlab49.com). All rights reserved.
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
# USA
#
# This software is maintained by Vivake (vivakeATlab49.com) and is available at:
# http://www.lab49.com/~vivake/python/MP3Info.py
#
# ( 7/2003) - Incorporated various changes from Stan Seibert
# <volsungATxiph.org> for more robust ID3 detection. Includes
# looking for all 11 sync bits and limits on how far to look
# for sync bits depending on presence of ID3v2 headers.
# (11/2003) - Incorporated various changes from Stan Seibert
# <volsungATxiph.org> for must robust ID3 detection. Includes
# fixes to VBR detection and better finding of frame headers.
# 1.2 ( 4/2004) - Integrated a fix from NAME <frnknstnATiafrica.com>
# for the function ID3v2Frame. I was determining the size of
# the frame using struct unpacking for signed 8-bit integers,
# but should have been getting unsigned 8-bit integers.
# 1.3 ( 4/2004) - Added a proper CVS Id comment.
# 1.4 ( 4/2004) - Added an 'is_vbr' flag to denote that a bitrate from a
# VBR-encoded file is an approximate (average) bitrate.
# Suggested by NAME <willemATpastelhorn.com>
# 1.5 ( 5/2004) - Protected contributor e-mail addresses from spamming.
# 1.6 ( 5/2004) - Changed 'False' to '0' and 'True' to '1' globally, to work
# with older versions of Python.
# 1.7 ( 5/2004) - Fixed a mistake in the main call to _parse_xing() where the
# values for seekstart and seeklimit are inverted. This causes
# MP3Info to rarely find the Xing header and report invalid
# lengths for VBR mp3s. Thanks to NAME
# <cpelteATnoos.fr> for this patch.
# 1.8 ( 5/2004) - Backported the 'filesize2' attribute from edna, which shows
# the filesize in megabytes.
# 1.9 ( 5/2004) - Increased amount of information printed out from command-line
# use.
# 1.10 ( 5/2004) - Added the 'length_minutes' and 'length_seconds' attributes,
# which are used by edna. Whoever added them to edna had done
# so incorrectly.
# 1.11 ( 5/2004) - Added the 'total_time' attribute, which is just a synonym for
# 'length,' since it was used by edna. This allows the current
# MP3Info.py to be a drop-in replacement for the old one in
# edna.
# 1.12 ( 5/2004) - The program MP3Ext inserts illegal frames, which cause MP3Info
# to break. These are now ignored. Thanks to NAME <thomas_roweATpsuaslum.com> for reporting this bug.
|
#
# ElementTree
# $Id: ElementTree.py 2326 2005-03-17 07:45:21Z USERNAME $
#
# light-weight XML support for Python 1.5.2 and later.
#
# history:
# 2001-10-20 fl created (from various sources)
# 2001-11-01 fl return root from parse method
# 2002-02-16 fl sort attributes in lexical order
# 2002-04-06 fl TreeBuilder refactoring, added PythonDoc markup
# 2002-05-01 fl finished TreeBuilder refactoring
# 2002-07-14 fl added basic namespace support to ElementTree.write
# 2002-07-25 fl added QName attribute support
# 2002-10-20 fl fixed encoding in write
# 2002-11-24 fl changed default encoding to ascii; fixed attribute encoding
# 2002-11-27 fl accept file objects or file names for parse/write
# 2002-12-04 fl moved XMLTreeBuilder back to this module
# 2003-01-11 fl fixed entity encoding glitch for us-ascii
# 2003-02-13 fl added XML literal factory
# 2003-02-21 fl added ProcessingInstruction/PI factory
# 2003-05-11 fl added tostring/fromstring helpers
# 2003-05-26 fl added ElementPath support
# 2003-07-05 fl added makeelement factory method
# 2003-07-28 fl added more well-known namespace prefixes
# 2003-08-15 fl fixed typo in ElementTree.findtext (Thomas NAME 2003-09-04 fl fall back on emulator if ElementPath is not installed
# 2003-10-31 fl markup updates
# 2003-11-15 fl fixed nested namespace bug
# 2004-03-28 fl added XMLID helper
# 2004-06-02 fl added default support to findtext
# 2004-06-08 fl fixed encoding of non-ascii element/attribute names
# 2004-08-23 fl take advantage of post-2.1 expat features
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
#
# Copyright (c) 1999-2005 by NAME All rights reserved.
#
# EMAIL
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2005 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
# Licensed to PSF under a Contributor Agreement.
# See http://www.python.org/2.4/license for licensing details.
|
"""Stuff to parse AIFF-C and AIFF files.
Unless explicitly stated otherwise, the description below is true
both for AIFF-C files and AIFF files.
An AIFF-C file has the following structure.
+-----------------+
| FORM |
+-----------------+
| <size> |
+----+------------+
| | AIFC |
| +------------+
| | <chunks> |
| | . |
| | . |
| | . |
+----+------------+
An AIFF file has the string "AIFF" instead of "AIFC".
A chunk consists of an identifier (4 bytes) followed by a size (4 bytes,
big endian order), followed by the data. The size field does not include
the size of the 8 byte header.
The following chunk types are recognized.
FVER
<version number of AIFF-C defining document> (AIFF-C only).
MARK
<# of markers> (2 bytes)
list of markers:
<marker ID> (2 bytes, must be > 0)
<position> (4 bytes)
<marker name> ("pstring")
COMM
<# of channels> (2 bytes)
<# of sound frames> (4 bytes)
<size of the samples> (2 bytes)
<sampling frequency> (10 bytes, IEEE 80-bit extended
floating point)
in AIFF-C files only:
<compression type> (4 bytes)
<human-readable version of compression type> ("pstring")
SSND
<offset> (4 bytes, not used by this program)
<blocksize> (4 bytes, not used by this program)
<sound data>
A pstring consists of 1 byte length, a string of characters, and 0 or 1
byte pad to make the total length even.
Usage.
Reading AIFF files:
f = aifc.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
In some types of audio files, if the setpos() method is not used,
the seek() method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' for AIFF files)
getcompname() -- returns human-readable version of
compression type ('not compressed' for AIFF files)
getparams() -- returns a tuple consisting of all of the
above in the above order
getmarkers() -- get the list of marks in the audio file or None
if there are no marks
getmark(id) -- get mark with the specified id (raises an error
if the mark does not exist)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell(), the position given to setpos() and
the position of marks are all compatible and have nothing to do with
the actual position in the file.
The close() method is called automatically when the class instance
is destroyed.
Writing AIFF files:
f = aifc.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
aiff() -- create an AIFF file (AIFF-C default)
aifc() -- create an AIFF-C file
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)
-- set all parameters at once
setmark(id, pos, name)
-- add specified mark to the list of marks
tell() -- return current position in output file (useful
in combination with setmark())
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes('') or
close() to patch up the sizes in the header.
Marks can be added anytime. If there are any marks, ypu must call
close() after all frames have been written.
The close() method is called automatically when the class instance
is destroyed.
When a file is opened with the extension '.aiff', an AIFF file is
written, otherwise an AIFF-C file is written. This default can be
changed by calling aiff() or aifc() before the first writeframes or
writeframesraw.
""" |
"""Exception classes for CherryPy.
CherryPy provides (and uses) exceptions for declaring that the HTTP response
should be a status other than the default "200 OK". You can ``raise`` them like
normal Python exceptions. You can also call them and they will raise
themselves; this means you can set an
:class:`HTTPError<cherrypy._cperror.HTTPError>`
or :class:`HTTPRedirect<cherrypy._cperror.HTTPRedirect>` as the
:attr:`request.handler<cherrypy._cprequest.Request.handler>`.
.. _redirectingpost:
Redirecting POST
================
When you GET a resource and are redirected by the server to another Location,
there's generally no problem since GET is both a "safe method" (there should
be no side-effects) and an "idempotent method" (multiple calls are no different
than a single call).
POST, however, is neither safe nor idempotent--if you
charge a credit card, you don't want to be charged twice by a redirect!
For this reason, *none* of the 3xx responses permit a user-agent (browser) to
resubmit a POST on redirection without first confirming the action with the
user:
===== ================================= ===========
300 Multiple Choices Confirm with the user
301 Moved Permanently Confirm with the user
302 Found (Object moved temporarily) Confirm with the user
303 See Other GET the new URI--no confirmation
304 Not modified (for conditional GET only--POST should not raise this error)
305 Use Proxy Confirm with the user
307 Temporary Redirect Confirm with the user
===== ================================= ===========
However, browsers have historically implemented these restrictions poorly;
in particular, many browsers do not force the user to confirm 301, 302
or 307 when redirecting POST. For this reason, CherryPy defaults to 303,
which most user-agents appear to have implemented correctly. Therefore, if
you raise HTTPRedirect for a POST request, the user-agent will most likely
attempt to GET the new URI (without asking for confirmation from the user).
We realize this is confusing for developers, but it's the safest thing we
could do. You are of course free to raise ``HTTPRedirect(uri, status=302)``
or any other 3xx status if you know what you're doing, but given the
environment, we couldn't let any of those be the default.
Custom Error Handling
=====================
.. image:: /refman/cperrors.gif
Anticipated HTTP responses
--------------------------
The 'error_page' config namespace can be used to provide custom HTML output for
expected responses (like 404 Not Found). Supply a filename from which the
output will be read. The contents will be interpolated with the values
%(status)s, %(message)s, %(traceback)s, and %(version)s using plain old Python
`string formatting <http://docs.python.org/2/library/stdtypes.html#string-formatting-operations>`_.
::
_cp_config = {
'error_page.404': os.path.join(localDir, "static/index.html")
}
Beginning in version 3.1, you may also provide a function or other callable as
an error_page entry. It will be passed the same status, message, traceback and
version arguments that are interpolated into templates::
def error_page_402(status, message, traceback, version):
return "Error %s - Well, I'm very sorry but you haven't paid!" % status
cherrypy.config.update({'error_page.402': error_page_402})
Also in 3.1, in addition to the numbered error codes, you may also supply
"error_page.default" to handle all codes which do not have their own error_page
entry.
Unanticipated errors
--------------------
CherryPy also has a generic error handling mechanism: whenever an unanticipated
error occurs in your code, it will call
:func:`Request.error_response<cherrypy._cprequest.Request.error_response>` to
set the response status, headers, and body. By default, this is the same
output as
:class:`HTTPError(500) <cherrypy._cperror.HTTPError>`. If you want to provide
some other behavior, you generally replace "request.error_response".
Here is some sample code that shows how to display a custom error message and
send an e-mail containing the error::
from cherrypy import _cperror
def handle_error():
cherrypy.response.status = 500
cherrypy.response.body = [
"<html><body>Sorry, an error occured</body></html>"
]
sendMail('EMAIL',
'Error in your web app',
_cperror.format_exc())
class Root:
_cp_config = {'request.error_response': handle_error}
Note that you have to explicitly set
:attr:`response.body <cherrypy._cprequest.Response.body>`
and not simply return an error message as a result.
""" |
"""Stuff to parse Sun and NeXT audio files.
An audio file consists of a header followed by the data. The structure
of the header is as follows.
+---------------+
| magic word |
+---------------+
| header size |
+---------------+
| data size |
+---------------+
| encoding |
+---------------+
| sample rate |
+---------------+
| # of channels |
+---------------+
| info |
| |
+---------------+
The magic word consists of the 4 characters '.snd'. Apart from the
info field, all header fields are 4 bytes in size. They are all
32-bit unsigned integers encoded in big-endian byte order.
The header size really gives the start of the data.
The data size is the physical size of the data. From the other
parameters the number of frames can be calculated.
The encoding gives the way in which audio samples are encoded.
Possible values are listed below.
The info field currently consists of an ASCII string giving a
human-readable description of the audio file. The info field is
padded with NUL bytes to the header size.
Usage.
Reading audio files:
f = sunau.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
When the setpos() and rewind() methods are not used, the seek()
method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' or 'ULAW')
getcompname() -- returns human-readable version of
compression type ('not compressed' matches 'NONE')
getparams() -- returns a tuple consisting of all of the
above in the above order
getmarkers() -- returns None (for compatibility with the
aifc module)
getmark(id) -- raises an error since the mark does not
exist (for compatibility with the aifc module)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell() and the position given to setpos()
are compatible and have nothing to do with the actual position in the
file.
The close() method is called automatically when the class instance
is destroyed.
Writing audio files:
f = sunau.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)-- set all parameters at once
tell() -- return current position in output file
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes('') or
close() to patch up the sizes in the header.
The close() method is called automatically when the class instance
is destroyed.
""" |
"""
==================================
Constants (:mod:`scipy.constants`)
==================================
.. module:: scipy.constants
Physical and mathematical constants and units.
Mathematical constants
======================
============ =================================================================
``pi`` Pi
``golden`` Golden ratio
============ =================================================================
Physical constants
==================
============= =================================================================
``c`` speed of light in vacuum
``mu_0`` the magnetic constant :math:`\mu_0`
``epsilon_0`` the electric constant (vacuum permittivity), :math:`\epsilon_0`
``h`` the Planck constant :math:`h`
``hbar`` :math:`\hbar = h/(2\pi)`
``G`` Newtonian constant of gravitation
``g`` standard acceleration of gravity
``e`` elementary charge
``R`` molar gas constant
``alpha`` fine-structure constant
``N_A`` Avogadro constant
``k`` Boltzmann constant
``sigma`` Stefan-Boltzmann constant :math:`\sigma`
``Wien`` Wien displacement law constant
``Rydberg`` Rydberg constant
``m_e`` electron mass
``m_p`` proton mass
``m_n`` neutron mass
============= =================================================================
Constants database
------------------
In addition to the above variables, :mod:`scipy.constants` also contains the
2010 CODATA recommended values [CODATA2010]_ database containing more physical
constants.
.. autosummary::
:toctree: generated/
value -- Value in physical_constants indexed by key
unit -- Unit in physical_constants indexed by key
precision -- Relative precision in physical_constants indexed by key
find -- Return list of physical_constant keys with a given string
ConstantWarning -- Constant sought not in newest CODATA data set
.. data:: physical_constants
Dictionary of physical constants, of the format
``physical_constants[name] = (value, unit, uncertainty)``.
Available constants:
====================================================================== ====
%(constant_names)s
====================================================================== ====
Units
=====
SI prefixes
-----------
============ =================================================================
``yotta`` :math:`10^{24}`
``zetta`` :math:`10^{21}`
``exa`` :math:`10^{18}`
``peta`` :math:`10^{15}`
``tera`` :math:`10^{12}`
``giga`` :math:`10^{9}`
``mega`` :math:`10^{6}`
``kilo`` :math:`10^{3}`
``hecto`` :math:`10^{2}`
``deka`` :math:`10^{1}`
``deci`` :math:`10^{-1}`
``centi`` :math:`10^{-2}`
``milli`` :math:`10^{-3}`
``micro`` :math:`10^{-6}`
``nano`` :math:`10^{-9}`
``pico`` :math:`10^{-12}`
``femto`` :math:`10^{-15}`
``atto`` :math:`10^{-18}`
``zepto`` :math:`10^{-21}`
============ =================================================================
Binary prefixes
---------------
============ =================================================================
``kibi`` :math:`2^{10}`
``mebi`` :math:`2^{20}`
``gibi`` :math:`2^{30}`
``tebi`` :math:`2^{40}`
``pebi`` :math:`2^{50}`
``exbi`` :math:`2^{60}`
``zebi`` :math:`2^{70}`
``yobi`` :math:`2^{80}`
============ =================================================================
Weight
------
================= ============================================================
``gram`` :math:`10^{-3}` kg
``metric_ton`` :math:`10^{3}` kg
``grain`` one grain in kg
``lb`` one pound (avoirdupous) in kg
``oz`` one ounce in kg
``stone`` one stone in kg
``grain`` one grain in kg
``long_ton`` one long ton in kg
``short_ton`` one short ton in kg
``troy_ounce`` one Troy ounce in kg
``troy_pound`` one Troy pound in kg
``carat`` one carat in kg
``m_u`` atomic mass constant (in kg)
================= ============================================================
Angle
-----
================= ============================================================
``degree`` degree in radians
``arcmin`` arc minute in radians
``arcsec`` arc second in radians
================= ============================================================
Time
----
================= ============================================================
``minute`` one minute in seconds
``hour`` one hour in seconds
``day`` one day in seconds
``week`` one week in seconds
``year`` one year (365 days) in seconds
``Julian_year`` one Julian year (365.25 days) in seconds
================= ============================================================
Length
------
================= ============================================================
``inch`` one inch in meters
``foot`` one foot in meters
``yard`` one yard in meters
``mile`` one mile in meters
``mil`` one mil in meters
``pt`` one point in meters
``survey_foot`` one survey foot in meters
``survey_mile`` one survey mile in meters
``nautical_mile`` one nautical mile in meters
``fermi`` one Fermi in meters
``angstrom`` one Angstrom in meters
``micron`` one micron in meters
``au`` one astronomical unit in meters
``light_year`` one light year in meters
``parsec`` one parsec in meters
================= ============================================================
Pressure
--------
================= ============================================================
``atm`` standard atmosphere in pascals
``bar`` one bar in pascals
``torr`` one torr (mmHg) in pascals
``psi`` one psi in pascals
================= ============================================================
Area
----
================= ============================================================
``hectare`` one hectare in square meters
``acre`` one acre in square meters
================= ============================================================
Volume
------
=================== ========================================================
``liter`` one liter in cubic meters
``gallon`` one gallon (US) in cubic meters
``gallon_imp`` one gallon (UK) in cubic meters
``fluid_ounce`` one fluid ounce (US) in cubic meters
``fluid_ounce_imp`` one fluid ounce (UK) in cubic meters
``bbl`` one barrel in cubic meters
=================== ========================================================
Speed
-----
================= ==========================================================
``kmh`` kilometers per hour in meters per second
``mph`` miles per hour in meters per second
``mach`` one Mach (approx., at 15 C, 1 atm) in meters per second
``knot`` one knot in meters per second
================= ==========================================================
Temperature
-----------
===================== =======================================================
``zero_Celsius`` zero of Celsius scale in Kelvin
``degree_Fahrenheit`` one Fahrenheit (only differences) in Kelvins
===================== =======================================================
.. autosummary::
:toctree: generated/
C2K
K2C
F2C
C2F
F2K
K2F
Energy
------
==================== =======================================================
``eV`` one electron volt in Joules
``calorie`` one calorie (thermochemical) in Joules
``calorie_IT`` one calorie (International Steam Table calorie, 1956) in Joules
``erg`` one erg in Joules
``Btu`` one British thermal unit (International Steam Table) in Joules
``Btu_th`` one British thermal unit (thermochemical) in Joules
``ton_TNT`` one ton of TNT in Joules
==================== =======================================================
Power
-----
==================== =======================================================
``hp`` one horsepower in watts
==================== =======================================================
Force
-----
==================== =======================================================
``dyn`` one dyne in newtons
``lbf`` one pound force in newtons
``kgf`` one kilogram force in newtons
==================== =======================================================
Optics
------
.. autosummary::
:toctree: generated/
lambda2nu
nu2lambda
References
==========
.. [CODATA2010] CODATA Recommended Values of the Fundamental
Physical Constants 2010.
http://physics.nist.gov/cuu/Constants/index.html
""" |
# RUN: %{lit} %{inputs}/discovery | FileCheck --check-prefix=CHECK-BASIC %s
# CHECK-BASIC: Testing: 5 tests
# Check that regex-filtering works
#
# RUN: %{lit} --filter 'o[a-z]e' %{inputs}/discovery | FileCheck --check-prefix=CHECK-FILTER %s
# CHECK-FILTER: Testing: 2 of 5 tests
# Check that maximum counts work
#
# RUN: %{lit} --max-tests 3 %{inputs}/discovery | FileCheck --check-prefix=CHECK-MAX %s
# CHECK-MAX: Testing: 3 of 5 tests
# Check that sharding partitions the testsuite in a way that distributes the
# rounding error nicely (i.e. 5/3 => 2 2 1, not 1 1 3 or whatever)
#
# RUN: %{lit} --num-shards 3 --run-shard 1 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD0-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD0-OUT < %t.out %s
# CHECK-SHARD0-ERR: note: Selecting shard 1/3 = size 2/5 = tests #(3*k)+1 = [1, 4]
# CHECK-SHARD0-OUT: Testing: 2 of 5 tests
#
# RUN: %{lit} --num-shards 3 --run-shard 2 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD1-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD1-OUT < %t.out %s
# CHECK-SHARD1-ERR: note: Selecting shard 2/3 = size 2/5 = tests #(3*k)+2 = [2, 5]
# CHECK-SHARD1-OUT: Testing: 2 of 5 tests
#
# RUN: %{lit} --num-shards 3 --run-shard 3 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD2-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD2-OUT < %t.out %s
# CHECK-SHARD2-ERR: note: Selecting shard 3/3 = size 1/5 = tests #(3*k)+3 = [3]
# CHECK-SHARD2-OUT: Testing: 1 of 5 tests
# Check that sharding via env vars works.
#
# RUN: env LIT_NUM_SHARDS=3 LIT_RUN_SHARD=1 %{lit} %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD0-ENV-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD0-ENV-OUT < %t.out %s
# CHECK-SHARD0-ENV-ERR: note: Selecting shard 1/3 = size 2/5 = tests #(3*k)+1 = [1, 4]
# CHECK-SHARD0-ENV-OUT: Testing: 2 of 5 tests
#
# RUN: env LIT_NUM_SHARDS=3 LIT_RUN_SHARD=2 %{lit} %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD1-ENV-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD1-ENV-OUT < %t.out %s
# CHECK-SHARD1-ENV-ERR: note: Selecting shard 2/3 = size 2/5 = tests #(3*k)+2 = [2, 5]
# CHECK-SHARD1-ENV-OUT: Testing: 2 of 5 tests
#
# RUN: env LIT_NUM_SHARDS=3 LIT_RUN_SHARD=3 %{lit} %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD2-ENV-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD2-ENV-OUT < %t.out %s
# CHECK-SHARD2-ENV-ERR: note: Selecting shard 3/3 = size 1/5 = tests #(3*k)+3 = [3]
# CHECK-SHARD2-ENV-OUT: Testing: 1 of 5 tests
# Check that providing more shards than tests results in 1 test per shard
# until we run out, then 0.
#
# RUN: %{lit} --num-shards 100 --run-shard 2 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-ERR1 < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-OUT1 < %t.out %s
# CHECK-SHARD-BIG-ERR1: note: Selecting shard 2/100 = size 1/5 = tests #(100*k)+2 = [2]
# CHECK-SHARD-BIG-OUT1: Testing: 1 of 5 tests
#
# RUN: %{lit} --num-shards 100 --run-shard 6 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-ERR2 < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-OUT2 < %t.out %s
# CHECK-SHARD-BIG-ERR2: note: Selecting shard 6/100 = size 0/5 = tests #(100*k)+6 = []
# CHECK-SHARD-BIG-OUT2: Testing: 0 of 5 tests
#
# RUN: %{lit} --num-shards 100 --run-shard 50 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-ERR3 < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-OUT3 < %t.out %s
# CHECK-SHARD-BIG-ERR3: note: Selecting shard 50/100 = size 0/5 = tests #(100*k)+50 = []
# CHECK-SHARD-BIG-OUT3: Testing: 0 of 5 tests
# Check that range constraints are enforced
#
# RUN: not %{lit} --num-shards 0 --run-shard 2 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-ERR < %t.err %s
# CHECK-SHARD-ERR: error: --num-shards must be positive
#
# RUN: not %{lit} --num-shards 3 --run-shard 4 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-ERR2 < %t.err %s
# CHECK-SHARD-ERR2: error: --run-shard must be between 1 and --num-shards (inclusive)
|
# (c) 2013, NAME <EMAIL> red hat, inc
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# take a list of files and (optionally) a list of paths
# return the first existing file found in the paths
# [file1, file2, file3], [path1, path2, path3]
# search order is:
# path1/file1
# path1/file2
# path1/file3
# path2/file1
# path2/file2
# path2/file3
# path3/file1
# path3/file2
# path3/file3
# first file found with os.path.exists() is returned
# no file matches raises ansibleerror
# EXAMPLES
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: foo ${inventory_hostname} bar
# paths: /tmp/production /tmp/staging
# that will look for files in this order:
# /tmp/production/foo
# ${inventory_hostname}
# bar
# /tmp/staging/foo
# ${inventory_hostname}
# bar
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: /some/place/foo ${inventory_hostname} /some/place/else
# that will look for files in this order:
# /some/place/foo
# $relative_path/${inventory_hostname}
# /some/place/else
# example - including tasks:
# tasks:
# - include: $item
# with_first_found:
# - files: generic
# paths: tasks/staging tasks/production
# this will include the tasks in the file generic where it is found first (staging or production)
# example simple file lists
#tasks:
#- name: first found file
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname} foo
# example skipping if no matched files
# First_found also offers the ability to control whether or not failing
# to find a file returns an error or not
#
#- name: first found file - or skip
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname}
# skip: true
# example a role with default configuration and configuration per host
# you can set multiple terms with their own files and paths to look through.
# consider a role that sets some configuration per host falling back on a default config.
#
#- name: some configuration template
# template: src={{ item }} dest=/etc/file.cfg mode=0444 owner=root group=root
# with_first_found:
# - files:
# - ${inventory_hostname}/etc/file.cfg
# paths:
# - ../../../templates.overwrites
# - ../../../templates
# - files:
# - etc/file.cfg
# paths:
# - templates
# the above will return an empty list if the files cannot be found at all
# if skip is unspecificed or if it is set to false then it will return a list
# error which can be caught bye ignore_errors: true for that action.
# finally - if you want you can use it, in place to replace first_available_file:
# you simply cannot use the - files, path or skip options. simply replace
# first_available_file with with_first_found and leave the file listing in place
#
#
# - name: with_first_found like first_available_file
# action: copy src=$item dest=/tmp/faftest
# with_first_found:
# - ../files/foo
# - ../files/bar
# - ../files/baz
# ignore_errors: true
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# ***********************IMPORTANT NMAP LICENSE TERMS************************
# * *
# * The Nmap Security Scanner is (C) 1996-2013 Insecure.Com LLC. Nmap is *
# * also a registered trademark of Insecure.Com LLC. This program is free *
# * software; you may redistribute and/or modify it under the terms of the *
# * GNU General Public License as published by the Free Software *
# * Foundation; Version 2 ("GPL"), BUT ONLY WITH ALL OF THE CLARIFICATIONS *
# * AND EXCEPTIONS DESCRIBED HEREIN. This guarantees your right to use, *
# * modify, and redistribute this software under certain conditions. If *
# * you wish to embed Nmap technology into proprietary software, we sell *
# * alternative licenses (contact EMAIL Dozens of software *
# * vendors already license Nmap technology such as host discovery, port *
# * scanning, OS detection, version detection, and the Nmap Scripting *
# * Engine. *
# * *
# * Note that the GPL places important restrictions on "derivative works", *
# * yet it does not provide a detailed definition of that term. To avoid *
# * misunderstandings, we interpret that term as broadly as copyright law *
# * allows. For example, we consider an application to constitute a *
# * derivative work for the purpose of this license if it does any of the *
# * following with any software or content covered by this license *
# * ("Covered Software"): *
# * *
# * o Integrates source code from Covered Software. *
# * *
# * o Reads or includes copyrighted data files, such as Nmap's nmap-os-db *
# * or nmap-service-probes. *
# * *
# * o Is designed specifically to execute Covered Software and parse the *
# * results (as opposed to typical shell or execution-menu apps, which will *
# * execute anything you tell them to). *
# * *
# * o Includes Covered Software in a proprietary executable installer. The *
# * installers produced by InstallShield are an example of this. Including *
# * Nmap with other software in compressed or archival form does not *
# * trigger this provision, provided appropriate open source decompression *
# * or de-archiving software is widely available for no charge. For the *
# * purposes of this license, an installer is considered to include Covered *
# * Software even if it actually retrieves a copy of Covered Software from *
# * another source during runtime (such as by downloading it from the *
# * Internet). *
# * *
# * o Links (statically or dynamically) to a library which does any of the *
# * above. *
# * *
# * o Executes a helper program, module, or script to do any of the above. *
# * *
# * This list is not exclusive, but is meant to clarify our interpretation *
# * of derived works with some common examples. Other people may interpret *
# * the plain GPL differently, so we consider this a special exception to *
# * the GPL that we apply to Covered Software. Works which meet any of *
# * these conditions must conform to all of the terms of this license, *
# * particularly including the GPL Section 3 requirements of providing *
# * source code and allowing free redistribution of the work as a whole. *
# * *
# * As another special exception to the GPL terms, Insecure.Com LLC grants *
# * permission to link the code of this program with any version of the *
# * OpenSSL library which is distributed under a license identical to that *
# * listed in the included docs/licenses/OpenSSL.txt file, and distribute *
# * linked combinations including the two. *
# * *
# * Any redistribution of Covered Software, including any derived works, *
# * must obey and carry forward all of the terms of this license, including *
# * obeying all GPL rules and restrictions. For example, source code of *
# * the whole work must be provided and free redistribution must be *
# * allowed. All GPL references to "this License", are to be treated as *
# * including the terms and conditions of this license text as well. *
# * *
# * Because this license imposes special exceptions to the GPL, Covered *
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# * software. The terms, conditions, and exceptions of this license must *
# * be included as well. This license is incompatible with some other open *
# * source licenses as well. In some cases we can relicense portions of *
# * Nmap or grant special permissions to use it in other open source *
# * software. Please contact EMAIL with any such requests. *
# * Similarly, we don't incorporate incompatible open source software into *
# * Covered Software without special permission from the copyright holders. *
# * *
# * If you have any questions about the licensing restrictions on using *
# * Nmap in other works, are happy to help. As mentioned above, we also *
# * offer alternative license to integrate Nmap into proprietary *
# * applications and appliances. These contracts have been sold to dozens *
# * of software vendors, and generally include a perpetual license as well *
# * as providing for priority support and updates. They also fund the *
# * continued development of Nmap. Please email EMAIL for further *
# * information. *
# * *
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# * *
# * Source is provided to this software because we believe users have a *
# * right to know exactly what a program is going to do before they run it. *
# * This also allows you to audit the software for security holes (none *
# * have been found so far). *
# * *
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# * and add new features. You are highly encouraged to send your changes *
# * to the EMAIL mailing list for possible incorporation into the *
# * main distribution. By sending these changes to Fyodor or one of the *
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# * license file for more details (it's in a COPYING file included with *
# * Nmap, and also available from https://svn.nmap.org/nmap/COPYING *
# * *
# ***************************************************************************/
|
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to reqd all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <EMAIL>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
"""
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from standard or custom formats
4) Creating arrays from raw bytes through the use of strings or buffers
5) Use of special library functions (e.g., random)
This section will not cover means of replicating, joining, or otherwise
expanding or mutating existing arrays. Nor will it cover creating object
arrays or record arrays. Both of those are covered in their own sections.
Converting Python array_like Objects to Numpy Arrays
====================================================
In general, numerical data arranged in an array-like structure in Python can
be converted to arrays through the use of the array() function. The most
obvious examples are lists and tuples. See the documentation for array() for
details for its use. Some objects may support the array-protocol and allow
conversion to arrays this way. A simple way to find out if the object can be
converted to a numpy array using array() is simply to try it interactively and
see if it works! (The Python Way).
Examples: ::
>>> x = np.array([2,3,1,0])
>>> x = np.array([2, 3, 1, 0])
>>> x = np.array([[1,2.0],[0,0],(1+1j,3.)]) # note mix of tuple and lists,
and types
>>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]])
Intrinsic Numpy Array Creation
==============================
Numpy has built-in functions for creating arrays from scratch:
zeros(shape) will create an array filled with 0 values with the specified
shape. The default dtype is float64.
``>>> np.zeros((2, 3))
array([[ 0., 0., 0.], [ 0., 0., 0.]])``
ones(shape) will create an array filled with 1 values. It is identical to
zeros in all other respects.
arange() will create arrays with regularly incrementing values. Check the
docstring for complete information on the various ways it can be used. A few
examples will be given here: ::
>>> np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.arange(2, 10, dtype=np.float)
array([ 2., 3., 4., 5., 6., 7., 8., 9.])
>>> np.arange(2, 3, 0.1)
array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])
Note that there are some subtleties regarding the last usage that the user
should be aware of that are described in the arange docstring.
linspace() will create arrays with a specified number of elements, and
spaced equally between the specified beginning and end values. For
example: ::
>>> np.linspace(1., 4., 6)
array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])
The advantage of this creation function is that one can guarantee the
number of elements and the starting and end point, which arange()
generally will not do for arbitrary start, stop, and step values.
indices() will create a set of arrays (stacked as a one-higher dimensioned
array), one per dimension with each representing variation in that dimension.
An example illustrates much better than a verbal description: ::
>>> np.indices((3,3))
array([[[0, 0, 0], [1, 1, 1], [2, 2, 2]], [[0, 1, 2], [0, 1, 2], [0, 1, 2]]])
This is particularly useful for evaluating functions of multiple dimensions on
a regular grid.
Reading Arrays From Disk
========================
This is presumably the most common case of large array creation. The details,
of course, depend greatly on the format of data on disk and so this section
can only give general pointers on how to handle various formats.
Standard Binary Formats
-----------------------
Various fields have standard formats for array data. The following lists the
ones with known python libraries to read them and return numpy arrays (there
may be others for which it is possible to read and convert to numpy arrays so
check the last section as well)
::
HDF5: PyTables
FITS: PyFITS
Examples of formats that cannot be read directly but for which it is not hard
to convert are libraries like PIL (able to read and write many image formats
such as jpg, png, etc).
Common ASCII Formats
------------------------
Comma Separated Value files (CSV) are widely used (and an export and import
option for programs like Excel). There are a number of ways of reading these
files in Python. There are CSV functions in Python and functions in pylab
(part of matplotlib).
More generic ascii files can be read using the io package in scipy.
Custom Binary Formats
---------------------
There are a variety of approaches one can use. If the file has a relatively
simple format then one can write a simple I/O library and use the numpy
fromfile() function and .tofile() method to read and write numpy arrays
directly (mind your byteorder though!) If a good C or C++ library exists that
read the data, one can wrap that library with a variety of techniques though
that certainly is much more work and requires significantly more advanced
knowledge to interface with C or C++.
Use of Special Libraries
------------------------
There are libraries that can be used to generate arrays for special purposes
and it isn't possible to enumerate all of them. The most common uses are use
of the many array generation functions in random that can generate arrays of
random values, and some utility functions to generate special matrices (e.g.
diagonal).
""" |
"""CPStats, a package for collecting and reporting on program statistics.
Overview
========
Statistics about program operation are an invaluable monitoring and debugging
tool. Unfortunately, the gathering and reporting of these critical values is
usually ad-hoc. This package aims to add a centralized place for gathering
statistical performance data, a structure for recording that data which
provides for extrapolation of that data into more useful information,
and a method of serving that data to both human investigators and
monitoring software. Let's examine each of those in more detail.
Data Gathering
--------------
Just as Python's `logging` module provides a common importable for gathering
and sending messages, performance statistics would benefit from a similar
common mechanism, and one that does *not* require each package which wishes
to collect stats to import a third-party module. Therefore, we choose to
re-use the `logging` module by adding a `statistics` object to it.
That `logging.statistics` object is a nested dict. It is not a custom class,
because that would:
1. require libraries and applications to import a third-party module in
order to participate
2. inhibit innovation in extrapolation approaches and in reporting tools, and
3. be slow.
There are, however, some specifications regarding the structure of the dict.::
{
+----"SQLAlchemy": {
| "Inserts": 4389745,
| "Inserts per Second":
| lambda s: s["Inserts"] / (time() - s["Start"]),
| C +---"Table Statistics": {
| o | "widgets": {-----------+
N | l | "Rows": 1.3M, | Record
a | l | "Inserts": 400, |
m | e | },---------------------+
e | c | "froobles": {
s | t | "Rows": 7845,
p | i | "Inserts": 0,
a | o | },
c | n +---},
e | "Slow Queries":
| [{"Query": "SELECT * FROM widgets;",
| "Processing Time": 47.840923343,
| },
| ],
+----},
}
The `logging.statistics` dict has four levels. The topmost level is nothing
more than a set of names to introduce modularity, usually along the lines of
package names. If the SQLAlchemy project wanted to participate, for example,
it might populate the item `logging.statistics['SQLAlchemy']`, whose value
would be a second-layer dict we call a "namespace". Namespaces help multiple
packages to avoid collisions over key names, and make reports easier to read,
to boot. The maintainers of SQLAlchemy should feel free to use more than one
namespace if needed (such as 'SQLAlchemy ORM'). Note that there are no case
or other syntax constraints on the namespace names; they should be chosen
to be maximally readable by humans (neither too short nor too long).
Each namespace, then, is a dict of named statistical values, such as
'Requests/sec' or 'Uptime'. You should choose names which will look
good on a report: spaces and capitalization are just fine.
In addition to scalars, values in a namespace MAY be a (third-layer)
dict, or a list, called a "collection". For example, the CherryPy
:class:`StatsTool` keeps track of what each request is doing (or has most
recently done) in a 'Requests' collection, where each key is a thread ID; each
value in the subdict MUST be a fourth dict (whew!) of statistical data about
each thread. We call each subdict in the collection a "record". Similarly,
the :class:`StatsTool` also keeps a list of slow queries, where each record
contains data about each slow query, in order.
Values in a namespace or record may also be functions, which brings us to:
Extrapolation
-------------
The collection of statistical data needs to be fast, as close to unnoticeable
as possible to the host program. That requires us to minimize I/O, for example,
but in Python it also means we need to minimize function calls. So when you
are designing your namespace and record values, try to insert the most basic
scalar values you already have on hand.
When it comes time to report on the gathered data, however, we usually have
much more freedom in what we can calculate. Therefore, whenever reporting
tools (like the provided :class:`StatsPage` CherryPy class) fetch the contents
of `logging.statistics` for reporting, they first call
`extrapolate_statistics` (passing the whole `statistics` dict as the only
argument). This makes a deep copy of the statistics dict so that the
reporting tool can both iterate over it and even change it without harming
the original. But it also expands any functions in the dict by calling them.
For example, you might have a 'Current Time' entry in the namespace with the
value "lambda scope: time.time()". The "scope" parameter is the current
namespace dict (or record, if we're currently expanding one of those
instead), allowing you access to existing static entries. If you're truly
evil, you can even modify more than one entry at a time.
However, don't try to calculate an entry and then use its value in further
extrapolations; the order in which the functions are called is not guaranteed.
This can lead to a certain amount of duplicated work (or a redesign of your
schema), but that's better than complicating the spec.
After the whole thing has been extrapolated, it's time for:
Reporting
---------
The :class:`StatsPage` class grabs the `logging.statistics` dict, extrapolates
it all, and then transforms it to HTML for easy viewing. Each namespace gets
its own header and attribute table, plus an extra table for each collection.
This is NOT part of the statistics specification; other tools can format how
they like.
You can control which columns are output and how they are formatted by updating
StatsPage.formatting, which is a dict that mirrors the keys and nesting of
`logging.statistics`. The difference is that, instead of data values, it has
formatting values. Use None for a given key to indicate to the StatsPage that a
given column should not be output. Use a string with formatting
(such as '%.3f') to interpolate the value(s), or use a callable (such as
lambda v: v.isoformat()) for more advanced formatting. Any entry which is not
mentioned in the formatting dict is output unchanged.
Monitoring
----------
Although the HTML output takes pains to assign unique id's to each <td> with
statistical data, you're probably better off fetching /cpstats/data, which
outputs the whole (extrapolated) `logging.statistics` dict in JSON format.
That is probably easier to parse, and doesn't have any formatting controls,
so you get the "original" data in a consistently-serialized format.
Note: there's no treatment yet for datetime objects. Try time.time() instead
for now if you can. Nagios will probably thank you.
Turning Collection Off
----------------------
It is recommended each namespace have an "Enabled" item which, if False,
stops collection (but not reporting) of statistical data. Applications
SHOULD provide controls to pause and resume collection by setting these
entries to False or True, if present.
Usage
=====
To collect statistics on CherryPy applications::
from cherrypy.lib import cpstats
appconfig['/']['tools.cpstats.on'] = True
To collect statistics on your own code::
import logging
# Initialize the repository
if not hasattr(logging, 'statistics'): logging.statistics = {}
# Initialize my namespace
mystats = logging.statistics.setdefault('My Stuff', {})
# Initialize my namespace's scalars and collections
mystats.update({
'Enabled': True,
'Start Time': time.time(),
'Important Events': 0,
'Events/Second': lambda s: (
(s['Important Events'] / (time.time() - s['Start Time']))),
})
...
for event in events:
...
# Collect stats
if mystats.get('Enabled', False):
mystats['Important Events'] += 1
To report statistics::
root.cpstats = cpstats.StatsPage()
To format statistics reports::
See 'Reporting', above.
""" |
# -*- encoding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# Copyright (C) 2004-2009 Tiny SPRL (<http://tiny.be>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
# SKR03
# =====
# Dieses Modul bietet Ihnen einen deutschen Kontenplan basierend auf dem SKR03.
# Gemäss der aktuellen Einstellungen ist die Firma nicht Umsatzsteuerpflichtig.
# Diese Grundeinstellung ist sehr einfach zu ändern und bedarf in der Regel
# grundsätzlich eine initiale Zuweisung von Steuerkonten zu Produkten und / oder
# Sachkonten oder zu Partnern.
# Die Umsatzsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten bei den Produktstammdaten hinterlegt werden (in Abhängigkeit der
# Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter Finanzbuchhaltung
# (Kategorie: Umsatzsteuer).
# Die Vorsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten ebenso bei den Produktstammdaten hinterlegt werden (in Abhängigkeit
# der Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter
# Finanzbuchhaltung (Kategorie: Vorsteuer).
# Die Zuordnung der Steuern für Ein- und Ausfuhren aus EU Ländern, sowie auch
# für den Ein- und Verkauf aus und in Drittländer sollten beim Partner
# (Lieferant/Kunde)hinterlegt werden (in Anhängigkeit vom Herkunftsland
# des Lieferanten/Kunden). Die Zuordnung beim Kunden ist 'höherwertig' als
# die Zuordnung bei Produkten und überschreibt diese im Einzelfall.
#
# Zur Vereinfachung der Steuerausweise und Buchung bei Auslandsgeschäften
# erlaubt OpenERP ein generelles Mapping von Steuerausweis und Steuerkonten
# (z.B. Zuordnung 'Umsatzsteuer 19%' zu 'steuerfreie Einfuhren aus der EU')
# zwecks Zuordnung dieses Mappings zum ausländischen Partner (Kunde/Lieferant).
# Die Rechnungsbuchung beim Einkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Vorsteuer Steuermessbetrag (z.B. Vorsteuer
# Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Vorsteuern' (z.B. Vorsteuer
# 19%). Durch multidimensionale Hierachien können verschiedene Positionen
# zusammengefasst werden und dann in Form eines Reports ausgegeben werden.
#
# Die Rechnungsbuchung beim Verkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Umsatzsteuer Steuermessbetrag
# (z.B. Umsatzsteuer Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Umsatzsteuer'
# (z.B. Umsatzsteuer 19%). Durch multidimensionale Hierachien können
# verschiedene Positionen zusammengefasst werden.
# Die zugewiesenen Steuerausweise können auf Ebene der einzelnen
# Rechnung (Eingangs- und Ausgangsrechnung) nachvollzogen werden,
# und dort gegebenenfalls angepasst werden.
# Rechnungsgutschriften führen zu einer Korrektur (Gegenposition)
# der Steuerbuchung, in Form einer spiegelbildlichen Buchung.
# SKR04
# =====
# Dieses Modul bietet Ihnen einen deutschen Kontenplan basierend auf dem SKR04.
# Gemäss der aktuellen Einstellungen ist die Firma nicht Umsatzsteuerpflichtig,
# d.h. im Standard existiert keine Zuordnung von Produkten und Sachkonten zu
# Steuerschlüsseln.
# Diese Grundeinstellung ist sehr einfach zu ändern und bedarf in der Regel
# grundsätzlich eine initiale Zuweisung von Steuerschlüsseln zu Produkten und / oder
# Sachkonten oder zu Partnern.
# Die Umsatzsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten bei den Produktstammdaten hinterlegt werden (in Abhängigkeit der
# Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter Finanzbuchhaltung
# (Kategorie: Umsatzsteuer).
# Die Vorsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten ebenso bei den Produktstammdaten hinterlegt werden (in Abhängigkeit
# der Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter
# Finanzbuchhaltung (Kategorie: Vorsteuer).
# Die Zuordnung der Steuern für Ein- und Ausfuhren aus EU Ländern, sowie auch
# für den Ein- und Verkauf aus und in Drittländer sollten beim Partner
# (Lieferant/Kunde) hinterlegt werden (in Anhängigkeit vom Herkunftsland
# des Lieferanten/Kunden). Die Zuordnung beim Kunden ist 'höherwertig' als
# die Zuordnung bei Produkten und überschreibt diese im Einzelfall.
#
# Zur Vereinfachung der Steuerausweise und Buchung bei Auslandsgeschäften
# erlaubt OpenERP ein generelles Mapping von Steuerausweis und Steuerkonten
# (z.B. Zuordnung 'Umsatzsteuer 19%' zu 'steuerfreie Einfuhren aus der EU')
# zwecks Zuordnung dieses Mappings zum ausländischen Partner (Kunde/Lieferant).
# Die Rechnungsbuchung beim Einkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Vorsteuer Steuermessbetrag (z.B. Vorsteuer
# Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Vorsteuern' (z.B. Vorsteuer
# 19%). Durch multidimensionale Hierachien können verschiedene Positionen
# zusammengefasst werden und dann in Form eines Reports ausgegeben werden.
#
# Die Rechnungsbuchung beim Verkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Umsatzsteuer Steuermessbetrag
# (z.B. Umsatzsteuer Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Umsatzsteuer'
# (z.B. Umsatzsteuer 19%). Durch multidimensionale Hierachien können
# verschiedene Positionen zusammengefasst werden.
# Die zugewiesenen Steuerausweise können auf Ebene der einzelnen
# Rechnung (Eingangs- und Ausgangsrechnung) nachvollzogen werden,
# und dort gegebenenfalls angepasst werden.
# Rechnungsgutschriften führen zu einer Korrektur (Gegenposition)
# der Steuerbuchung, in Form einer spiegelbildlichen Buchung.
|
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to read all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <EMAIL>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
"""
This module processes Python exceptions that relate to HTTP exceptions
by defining a set of exceptions, all subclasses of HTTPException.
Each exception, in addition to being a Python exception that can be
raised and caught, is also a WSGI application and ``webob.Response``
object.
This module defines exceptions according to RFC 2068 [1]_ : codes with
100-300 are not really errors; 400's are client errors, and 500's are
server errors. According to the WSGI specification [2]_ , the application
can call ``start_response`` more then once only under two conditions:
(a) the response has not yet been sent, or (b) if the second and
subsequent invocations of ``start_response`` have a valid ``exc_info``
argument obtained from ``sys.exc_info()``. The WSGI specification then
requires the server or gateway to handle the case where content has been
sent and then an exception was encountered.
Exception
HTTPException
HTTPOk
* 200 - :class:`HTTPOk`
* 201 - :class:`HTTPCreated`
* 202 - :class:`HTTPAccepted`
* 203 - :class:`HTTPNonAuthoritativeInformation`
* 204 - :class:`HTTPNoContent`
* 205 - :class:`HTTPResetContent`
* 206 - :class:`HTTPPartialContent`
HTTPRedirection
* 300 - :class:`HTTPMultipleChoices`
* 301 - :class:`HTTPMovedPermanently`
* 302 - :class:`HTTPFound`
* 303 - :class:`HTTPSeeOther`
* 304 - :class:`HTTPNotModified`
* 305 - :class:`HTTPUseProxy`
* 307 - :class:`HTTPTemporaryRedirect`
HTTPError
HTTPClientError
* 400 - :class:`HTTPBadRequest`
* 401 - :class:`HTTPUnauthorized`
* 402 - :class:`HTTPPaymentRequired`
* 403 - :class:`HTTPForbidden`
* 404 - :class:`HTTPNotFound`
* 405 - :class:`HTTPMethodNotAllowed`
* 406 - :class:`HTTPNotAcceptable`
* 407 - :class:`HTTPProxyAuthenticationRequired`
* 408 - :class:`HTTPRequestTimeout`
* 409 - :class:`HTTPConflict`
* 410 - :class:`HTTPGone`
* 411 - :class:`HTTPLengthRequired`
* 412 - :class:`HTTPPreconditionFailed`
* 413 - :class:`HTTPRequestEntityTooLarge`
* 414 - :class:`HTTPRequestURITooLong`
* 415 - :class:`HTTPUnsupportedMediaType`
* 416 - :class:`HTTPRequestRangeNotSatisfiable`
* 417 - :class:`HTTPExpectationFailed`
* 422 - :class:`HTTPUnprocessableEntity`
* 423 - :class:`HTTPLocked`
* 424 - :class:`HTTPFailedDependency`
* 428 - :class:`HTTPPreconditionRequired`
* 429 - :class:`HTTPTooManyRequests`
* 431 - :class:`HTTPRequestHeaderFieldsTooLarge`
* 451 - :class:`HTTPUnavailableForLegalReasons`
HTTPServerError
* 500 - :class:`HTTPInternalServerError`
* 501 - :class:`HTTPNotImplemented`
* 502 - :class:`HTTPBadGateway`
* 503 - :class:`HTTPServiceUnavailable`
* 504 - :class:`HTTPGatewayTimeout`
* 505 - :class:`HTTPVersionNotSupported`
* 511 - :class:`HTTPNetworkAuthenticationRequired`
Usage notes
-----------
The HTTPException class is complicated by 4 factors:
1. The content given to the exception may either be plain-text or
as html-text.
2. The template may want to have string-substitutions taken from
the current ``environ`` or values from incoming headers. This
is especially troublesome due to case sensitivity.
3. The final output may either be text/plain or text/html
mime-type as requested by the client application.
4. Each exception has a default explanation, but those who
raise exceptions may want to provide additional detail.
Subclass attributes and call parameters are designed to provide an easier path
through the complications.
Attributes:
``code``
the HTTP status code for the exception
``title``
remainder of the status line (stuff after the code)
``explanation``
a plain-text explanation of the error message that is
not subject to environment or header substitutions;
it is accessible in the template via %(explanation)s
``detail``
a plain-text message customization that is not subject
to environment or header substitutions; accessible in
the template via %(detail)s
``body_template``
a content fragment (in HTML) used for environment and
header substitution; the default template includes both
the explanation and further detail provided in the
message
Parameters:
``detail``
a plain-text override of the default ``detail``
``headers``
a list of (k,v) header pairs
``comment``
a plain-text additional information which is
usually stripped/hidden for end-users
``body_template``
a string.Template object containing a content fragment in HTML
that frames the explanation and further detail
To override the template (which is HTML content) or the plain-text
explanation, one must subclass the given exception; or customize it
after it has been created. This particular breakdown of a message
into explanation, detail and template allows both the creation of
plain-text and html messages for various clients as well as
error-free substitution of environment variables and headers.
The subclasses of :class:`~_HTTPMove`
(:class:`~HTTPMultipleChoices`, :class:`~HTTPMovedPermanently`,
:class:`~HTTPFound`, :class:`~HTTPSeeOther`, :class:`~HTTPUseProxy` and
:class:`~HTTPTemporaryRedirect`) are redirections that require a ``Location``
field. Reflecting this, these subclasses have two additional keyword arguments:
``location`` and ``add_slash``.
Parameters:
``location``
to set the location immediately
``add_slash``
set to True to redirect to the same URL as the request, except with a
``/`` appended
Relative URLs in the location will be resolved to absolute.
References:
.. [1] http://www.python.org/peps/pep-0333.html#error-handling
.. [2] http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html#sec10.5
""" |
"""The tests for the MQTT light platform.
Configuration for RGB Version with brightness:
light:
platform: mqtt
name: "Office Light RGB"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
rgb_state_topic: "office/rgb1/rgb/status"
rgb_command_topic: "office/rgb1/rgb/set"
qos: 0
payload_on: "on"
payload_off: "off"
Configuration for XY Version with brightness:
light:
platform: mqtt
name: "Office Light XY"
state_topic: "office/xy1/light/status"
command_topic: "office/xy1/light/switch"
brightness_state_topic: "office/xy1/brightness/status"
brightness_command_topic: "office/xy1/brightness/set"
xy_state_topic: "office/xy1/xy/status"
xy_command_topic: "office/xy1/xy/set"
qos: 0
payload_on: "on"
payload_off: "off"
config without RGB:
light:
platform: mqtt
name: "Office Light"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
qos: 0
payload_on: "on"
payload_off: "off"
config without RGB and brightness:
light:
platform: mqtt
name: "Office Light"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
qos: 0
payload_on: "on"
payload_off: "off"
config for RGB Version with brightness and scale:
light:
platform: mqtt
name: "Office Light RGB"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
brightness_scale: 99
rgb_state_topic: "office/rgb1/rgb/status"
rgb_command_topic: "office/rgb1/rgb/set"
rgb_scale: 99
qos: 0
payload_on: "on"
payload_off: "off"
config with brightness and color temp
light:
platform: mqtt
name: "Office Light Color Temp"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
brightness_scale: 99
color_temp_state_topic: "office/rgb1/color_temp/status"
color_temp_command_topic: "office/rgb1/color_temp/set"
qos: 0
payload_on: "on"
payload_off: "off"
config with brightness and effect
light:
platform: mqtt
name: "Office Light Color Temp"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
brightness_scale: 99
effect_state_topic: "office/rgb1/effect/status"
effect_command_topic: "office/rgb1/effect/set"
effect_list:
- rainbow
- colorloop
qos: 0
payload_on: "on"
payload_off: "off"
config for RGB Version with white value and scale:
light:
platform: mqtt
name: "Office Light RGB"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
white_value_state_topic: "office/rgb1/white_value/status"
white_value_command_topic: "office/rgb1/white_value/set"
white_value_scale: 99
rgb_state_topic: "office/rgb1/rgb/status"
rgb_command_topic: "office/rgb1/rgb/set"
rgb_scale: 99
qos: 0
payload_on: "on"
payload_off: "off"
config for RGB Version with RGB command template:
light:
platform: mqtt
name: "Office Light RGB"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
rgb_state_topic: "office/rgb1/rgb/status"
rgb_command_topic: "office/rgb1/rgb/set"
rgb_command_template: "{{ '#%02x%02x%02x' | format(red, green, blue)}}"
qos: 0
payload_on: "on"
payload_off: "off"
Configuration for HS Version with brightness:
light:
platform: mqtt
name: "Office Light HS"
state_topic: "office/hs1/light/status"
command_topic: "office/hs1/light/switch"
brightness_state_topic: "office/hs1/brightness/status"
brightness_command_topic: "office/hs1/brightness/set"
hs_state_topic: "office/hs1/hs/status"
hs_command_topic: "office/hs1/hs/set"
qos: 0
payload_on: "on"
payload_off: "off"
""" |
#!/usr/bin/python2.7
# coding=utf-8
#
# Copyright (c) NAME This program is free software: you can redistribute
# it and/or modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation, either version 2 of the
# License, or any newer version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
# more details.
#
# You should have received a copy of the GNU General Public License along with
# this program. If not, see https://www.gnu.org/licenses/gpl-2.0.txt
#
#
# author : NAME email : EMAIL date : Saturday, December 31 20:43:50 CET 2016
# version : 0.4.4
#
# - 0.0.0 Friday, December 19 10:18:02 KST 2014
# Created.
# - 0.1.1 Monday, December 22 19:19:27 KST 2014
# Real Working Script...
# - 0.2.0 Monday, March 30 09:36:52 KST 2015, USERNAME - Export the selected PV lists based on the input PV list (as input file)
# - Clean up some lines in code, such as the argument default values, unused variables,
# - 0.3.0 Tuesday, January 5 11:18:13 CET 2016, USERNAME - introduce src and target location of the extracted file,
# - 0.4.0 Thursday, November 17 12:30:40 CET 2016
# - clean up and fix the pattern
# - 0.4.1 Wednesday, November 23 13:50:36 CET 2016,
# - fix the hard-coded local time according to UTC
# - add "archived" the entire target directory in target path
# - remove the intermediate path
# - 0.4.2 Thursday, November 24 12:17:39 CET 2016
# - use ftime as the directory and zip file names
# - add the remove function in valid with zip option.
# - 0.4.3 Friday, November 25 09:34:01 CET 2016
# - add import errno
# - 0.4.4 Saturday, December 31 20:40:34 CET 2016
# - remove the leading zero in the directory name
# - clean up old codes and comments
#
#
# An example in cronjob (crontab -e) in every 5 mins
#
# AA ip : IP_ADDRESS
# Target : /var/www/data
# Days : 1
# PV list : test_ioc_pv_list
# Mean : no
# */5 * * * * export DISPLAY=:0.0 && /usr/bin/python /home/aauser/epicsarchiverap-sites/aa_scripts/getData.py -i IP_ADDRESS -d 1 -t /var/www/data/ -f ics_pv_list -z >/dev/null 2>&1
# Get date from Archiver Appliance to put them in ${HOME}/Archappl_ipaddress/CURRENT_TIME
# Compress that directory and put them in ${HOME}/Archappl_ipaddress/
# /usr/bin/python /home/aauser/epicsarchiverap-sites/aa_scripts/getData.py -i IP_ADDRESS -d 1 -t ${HOME} -z -f test_ioc_pv_list
# /usr/bin/python /home/aauser/epicsarchiverap-sites/aa_scripts/getData.py -i IP_ADDRESS -d 1 -f test_ioc_pv_list -t ${HOME} -z
# /usr/bin/python /home/aauser/epicsarchiverap-sites/aa_scripts/getData.py -i IP_ADDRESS -d 1 -f test_ioc_pv_list -t ${HOME} -z -rm
|
#import numpy as np
#import subprocess, sys, os.path
#from itertools import *
#import pandas as pd
#import logging
#from snpreader import SnpReader
#from pysnptools.standardizer import Unit
#from pysnptools.standardizer import Identity
#from pysnptools.pstreader import PstData
#import warnings
#import time
#def _iidset(reader):
# if reader is None:
# return set()
# return {tuple(item) for item in reader.iid}
#def _in_other(i_little,iidset_list):
# little = iidset_list[i_little]
# for i_big,big in enumerate(iidset_list):
# #Set 'a' is in set 'b' if
# # 'a' is a proper subset of 'b'
# # 'a' equals 'b' but 'b' is listed first (this also stops a set from being a subset of itself)
# if little < big or (i_big < i_little and little == big):
# return True
# return False
##!!!this is unused and untested. Also, since it only works with SnpReader a better name would be MergeByIid
## should we first confirm that all col_poperty values match across the items? (if so, do NaN, right too)
#class _MergeRows(SnpReader):
# @staticmethod
# def factory(*readerlist):
# #Remove any readers for which another reader has all the same row ids
# iidset_list = [_iidset(reader) for reader in readerlist]
# readerlist = [reader for index, reader in enumerate(readerlist) if not _in_other(index,iidset_list)]
# if len(readerlist) == 0:
# return None
# if len(readerlist) == 1:
# return readerlist[0]
# return _MergeRows(*readerlist)
# def __init__(self, *readerlist):
# self.readerlist = readerlist
# def __repr__(self):
# return "{0}({1})".format(self.__class__.__name__,",".join([repr(reader) for reader in self.readerlist]))
# @property
# def row(self):
# if not hasattr(self,"_row"):
# self._row = np.concatenate([reader.row for reader in self.readerlist])
# return self._row
# @property
# def col(self):
# if not hasattr(self,"_col"):
# self._col = self.readerlist[0].col
# for i in range(1,len(self.readerlist)):
# assert np.array_equal(self._col,self.readerlist[i].col), "all col's must be the same"
# return self._col
# @property
# def col_property(self):
# return self.readerlist[0].col_property
# def _find_one(self,iid_index_or_none,sid_index_or_none):
# assert sid_index_or_none is None, "Expect sid_index_or_none to be None"
# assert iid_index_or_none is not None, "Expect iid_index_or_none to be not None"
# result = None
# iid_goal = self.iid[iid_index_or_none]
# for i, reader in enumerate(self.readerlist):
# try:
# iididx = reader.iid_to_index(iid_goal)
# except:
# continue # leave the loop
# assert result is None or len(result[1])==0, "for now code assumes all values will be read from one part of merged SnpReader"
# result = i, iididx
# assert result is not None and len(result[1]) == len(iid_goal), "Could not find all indexes."
# return result
# def _read(self, iid_index_or_none, sid_index_or_none, order, dtype, force_python_only, view_ok):
# i, iididx = self._find_one(iid_index_or_none, sid_index_or_none)
# result = readerlist[i]._read(iididx, sid_index_or_none, order, dtype, force_python_only, view_ok)
# return result
# def __getitem__(self, iid_indexer_and_snp_indexer):
# if isinstance(iid_indexer_and_snp_indexer,tuple): # similar code elsewhere
# iid0_indexer, iid1_indexer = iid_indexer_and_snp_indexer
# else:
# iid0_indexer = iid_indexer_and_snp_indexer
# iid1_indexer = iid0_indexer
# i, iididx = self._find_one(iid0_indexer, None)
# result = self.readerlist[i][iididx,iid1_indexer]
# return result
|
#Python 3+ Script for converting ban table format as of 2018-10-28 made by USERNAME starting ensure you have installed the mysqlclient package https://github.com/PyMySQL/mysqlclient-python
#It can be downloaded from command line with pip:
#pip install mysqlclient
#
#You will also have to create a new ban table for inserting converted data to per the schema:
#CREATE TABLE `ban` (
# `id` INT(11) UNSIGNED NOT NULL AUTO_INCREMENT,
# `bantime` DATETIME NOT NULL,
# `server_ip` INT(10) UNSIGNED NOT NULL,
# `server_port` SMALLINT(5) UNSIGNED NOT NULL,
# `round_id` INT(11) UNSIGNED NOT NULL,
# `role` VARCHAR(32) NULL DEFAULT NULL,
# `expiration_time` DATETIME NULL DEFAULT NULL,
# `applies_to_admins` TINYINT(1) UNSIGNED NOT NULL DEFAULT '0',
# `reason` VARCHAR(2048) NOT NULL,
# `ckey` VARCHAR(32) NULL DEFAULT NULL,
# `ip` INT(10) UNSIGNED NULL DEFAULT NULL,
# `computerid` VARCHAR(32) NULL DEFAULT NULL,
# `a_ckey` VARCHAR(32) NOT NULL,
# `a_ip` INT(10) UNSIGNED NOT NULL,
# `a_computerid` VARCHAR(32) NOT NULL,
# `who` VARCHAR(2048) NOT NULL,
# `adminwho` VARCHAR(2048) NOT NULL,
# `edits` TEXT NULL DEFAULT NULL,
# `unbanned_datetime` DATETIME NULL DEFAULT NULL,
# `unbanned_ckey` VARCHAR(32) NULL DEFAULT NULL,
# `unbanned_ip` INT(10) UNSIGNED NULL DEFAULT NULL,
# `unbanned_computerid` VARCHAR(32) NULL DEFAULT NULL,
# `unbanned_round_id` INT(11) UNSIGNED NULL DEFAULT NULL,
# PRIMARY KEY (`id`),
# KEY `idx_ban_isbanned` (`ckey`,`role`,`unbanned_datetime`,`expiration_time`),
# KEY `idx_ban_isbanned_details` (`ckey`,`ip`,`computerid`,`role`,`unbanned_datetime`,`expiration_time`),
# KEY `idx_ban_count` (`bantime`,`a_ckey`,`applies_to_admins`,`unbanned_datetime`,`expiration_time`)
#) ENGINE=InnoDB DEFAULT CHARSET=latin1;
#This is to prevent the destruction of existing data and allow rollbacks to be performed in the event of an error during conversion
#Once conversion is complete remember to rename the old and new ban tables; it's up to you if you want to keep the old table
#
#To view the parameters for this script, execute it with the argument --help
#All the positional arguments are required, remember to include prefixes in your table names if you use them
#An example of the command used to execute this script from powershell:
#python ban_conversion_2018-10-28.py "localhost" "root" "password" "feedback" "SS13_ban" "SS13_ban_new"
#I found that this script would complete conversion of 35000 rows in approximately 20 seconds, results will depend on the size of your ban table and computer used
#
#The script has been tested to complete with tgstation's ban table as of 2018-09-02 02:19:56
#In the event of an error the new ban table is automatically truncated
#The source table is never modified so you don't have to worry about losing any data due to errors
#Some additional error correction is performed to fix problems specific to legacy and invalid data in tgstation's ban table, these operations are tagged with a 'TG:' comment
#Even if you don't have any of these specific problems in your ban table the operations won't have matter as they have an insignificant effect on runtime
#
#While this script is safe to run with your game server(s) active, any bans created after the script has started won't be converted
#You will also have to ensure that the code and table names are updated between rounds as neither will be compatible
|
# The following CSI codes supported by xcode are not tested.
# Query ReGIS/Sixel attributes: CSI ? Pi ; Pa ; P vS
# Initiate highlight mouse tracking: CSI Ps ; Ps ; Ps ; Ps ; Ps T
# Media Copy (MC): CSI Pm i
# Media Copy (MC, DEC-specific): CSI ? Pm i
# Character Attributes (SGR): CSI Pm m
# Disable modifiers: CSI > Ps n
# Set pointer mode: CSI > Ps p
# Load LEDs (DECLL): CSI Ps q
# Set cursor style (DECSCUSR): CIS Ps SP q
# Select character protection attribute (DECSCA): CSI Ps " q [This is already tested by DECSED and DECSEL]
# Window manipulation: CSI Ps; Ps; Ps t
# Reverse Attributes in Rectangular Area (DECRARA): CSI Pt ; Pl ; Pb ; Pr ; Ps $ t
# Set warning bell volume (DECSWBV): CSI Ps SP t
# Set margin-bell volume (DECSMBV): CSI Ps SP u
# Enable Filter Rectangle (DECEFR): CSI Pt ; Pl ; Pb ; Pr ' w
# Request Terminal Parameters (DECREQTPARM): CSI Ps x
# Select Attribute Change Extent (DECSACE): CSI Ps * x
# Request Checksum of Rectangular Area (DECRQCRA): CSI Pi ; Pg ; Pt ; Pl ; Pb ; Pr * y
# Select Locator Events (DECSLE): CSI Pm ' {
# Request Locator Position (DECRQLP): CSI PS ' |
# ESC SP L Set ANSI conformance level 1 (dpANS X3.134.1).
# ESC SP M Set ANSI conformance level 2 (dpANS X3.134.1).
# ESC SP N Set ANSI conformance level 3 (dpANS X3.134.1).
# In xterm, all these do is fiddle with character sets, which are not testable.
# ESC # 3 DEC double-height line, top half (DECDHL).
# ESC # 4 DEC double-height line, bottom half (DECDHL).
# ESC # 5 DEC single-width line (DECSWL).
# ESC # 6 DEC double-width line (DECDWL).
# Double-width affects display only and is generally not introspectable. Wrap
# doesn't work so there's no way to tell where the cursor is visually.
# ESC % @ Select default character set. That is ISO 8859-1 (ISO 2022).
# ESC % G Select UTF-8 character set (ISO 2022).
# ESC ( C Designate G0 Character Set (ISO 2022, VT100).
# ESC ) C Designate G1 Character Set (ISO 2022, VT100).
# ESC * C Designate G2 Character Set (ISO 2022, VT220).
# ESC + C Designate G3 Character Set (ISO 2022, VT220).
# ESC - C Designate G1 Character Set (VT300).
# ESC . C Designate G2 Character Set (VT300).
# ESC / C Designate G3 Character Set (VT300).
# Character set stuff is not introspectable.
# Shift in (SI): ^O
# Shift out (SO): ^N
# Space (SP): 0x20
# Tab (TAB): 0x09 [tested in HTS]
# ESC = Application Keypad (DECKPAM).
# ESC > Normal Keypad (DECKPNM).
# ESC F Cursor to lower left corner of screen. This is enabled by the
# hpLowerleftBugCompat resource. (Not worth testing as it's off by
# default, and silly regardless)
# ESC l Memory Lock (per HP terminals). Locks memory above the cursor.
# ESC m Memory Unlock (per HP terminals).
# ESC n Invoke the G2 Character Set as GL (LS2).
# ESC o Invoke the G3 Character Set as GL (LS3).
# ESC | Invoke the G3 Character Set as GR (LS3R).
# ESC } Invoke the G2 Character Set as GR (LS2R).
# ESC ~ Invoke the G1 Character Set as GR (LS1R).
# DCS + p Pt ST Set Termcap/Terminfo Data
# DCS + q Pt ST Request Termcap/Terminfo String
# The following OSC commands are tested in xterm_winops and don't have their own test:
# Ps = 0 -> Change Icon Name and Window Title to Pt.
# Ps = 1 -> Change Icon Name to Pt.
# Ps = 2 -> Change Window Title to Pt.
# This test is too ill-defined and X-specific, and is not tested:
# Ps = 3 -> Set X property on top-level window. Pt should be
# in the form "prop=value", or just "prop" to delete the prop-
# erty
# No introspection for whether special color are enabled/disabled:
# Ps = 6 ; c; f -> Enable/disable Special Color Number c. The
# second parameter tells xterm to enable the corresponding color
# mode if nonzero, disable it if zero.
# Off by default, obvious security issues:
# Ps = 4 6 -> Change Log File to Pt. (This is normally dis-
# abled by a compile-time option).
# No introspection for fonts:
# Ps = 5 0 -> Set Font to Pt.
# No-op:
# Ps = 5 1 -> reserved for Emacs shell.
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