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6b269d5b99914d3425678d7f3582d07b2c34d9e3
495
py
Python
mayan/apps/document_states/queues.py
Syunkolee9891/Mayan-EDMS
3759a9503a264a180b74cc8518388f15ca66ac1a
[ "Apache-2.0" ]
1
2021-06-17T18:24:25.000Z
2021-06-17T18:24:25.000Z
mayan/apps/document_states/queues.py
Syunkolee9891/Mayan-EDMS
3759a9503a264a180b74cc8518388f15ca66ac1a
[ "Apache-2.0" ]
7
2020-06-06T00:01:04.000Z
2022-01-13T01:47:17.000Z
mayan/apps/document_states/queues.py
Syunkolee9891/Mayan-EDMS
3759a9503a264a180b74cc8518388f15ca66ac1a
[ "Apache-2.0" ]
null
null
null
from __future__ import absolute_import, unicode_literals from django.utils.translation import ugettext_lazy as _ from mayan.apps.task_manager.classes import CeleryQueue from mayan.apps.task_manager.workers import worker_slow queue_document_states = CeleryQueue( name='document_states', label=_('Document states'), worker=worker_slow ) queue_document_states.add_task_type( dotted_path='mayan.apps.document_states.tasks.task_launch_all_workflows', label=_('Launch all workflows') )
33
77
0.824242
84011e68b18b2661a55f6a6460760c092fc28194
2,715
py
Python
test/test_build.py
FindDefinition/ccimport
2be66fe4cdeb4daa915d2dfc75f2363c0c0bfb75
[ "MIT" ]
1
2021-11-23T08:36:48.000Z
2021-11-23T08:36:48.000Z
test/test_build.py
FindDefinition/ccimport
2be66fe4cdeb4daa915d2dfc75f2363c0c0bfb75
[ "MIT" ]
null
null
null
test/test_build.py
FindDefinition/ccimport
2be66fe4cdeb4daa915d2dfc75f2363c0c0bfb75
[ "MIT" ]
1
2021-11-23T08:26:52.000Z
2021-11-23T08:26:52.000Z
import subprocess from pathlib import Path import ccimport from ccimport import compat from ccimport.utils import tempdir import os import sys def test_cpp_build(): source = ccimport.autoimport([Path(__file__).parent / "source.cc"], Path(__file__).parent / "source") assert source.sub(2, 1) == 1 obj = source.TestClass(5) assert obj.add(3) == 8 def test_cpp_exec_build(): with tempdir() as tempd: sources = [ Path(__file__).parent / "executable.cc", Path(__file__).parent / "source.cc" ] p2s = {Path(__file__).parent / "some_pch.h": sources} pch_to_include = {Path(__file__).parent / "some_pch.h": "some_pch.h"} source = ccimport.ccimport(sources, tempd / "executable", includes=[Path(__file__).parent], shared=False, load_library=False, pch_to_sources=p2s, pch_to_include=pch_to_include, verbose=False, objects_folder="objects") output = subprocess.check_output([str(source)]) assert output.decode("utf-8").strip() == "hello ccimport!" def _test_gcc_crosscompile_build(): # currently no CI/CD available, so disable this test. if compat.InWindows: return # aarch64-linux-gnu-g++ with tempdir() as tempd: py_ver = (sys.version_info[0], sys.version_info[1]) os.environ["SETUPTOOLS_EXT_SUFFIX"] = compat.get_extension_suffix_linux_custom(py_ver, "aarch64") sources = [ Path(__file__).parent / "executable.cc", Path(__file__).parent / "source.cc" ] p2s = {Path(__file__).parent / "some_pch.h": sources} pch_to_include = {Path(__file__).parent / "some_pch.h": "some_pch.h"} source = ccimport.ccimport(sources, tempd / "executable", includes=[Path(__file__).parent], shared=True, load_library=False, pch_to_sources=p2s, pch_to_include=pch_to_include, verbose=True, objects_folder="objects") print(input("hold"), tempd) output = subprocess.check_output([str(source)]) assert output.decode("utf-8").strip() == "hello ccimport!" if __name__ == "__main__": _test_gcc_crosscompile_build()
38.239437
105
0.523757
3df3c764ed6e853ac00744315e025f63df6e9ea2
1,628
py
Python
loss/loss_functions.py
RyanDsilva/nn-from-scratch
ef2bc5794e2d88c948d62762a415c306dda4101f
[ "MIT" ]
23
2020-04-22T08:53:31.000Z
2021-12-14T12:26:22.000Z
loss/loss_functions.py
RyanDsilva/nn-from-scratch
ef2bc5794e2d88c948d62762a415c306dda4101f
[ "MIT" ]
5
2020-05-09T04:24:54.000Z
2020-10-09T16:46:25.000Z
loss/loss_functions.py
RyanDsilva/nn-from-scratch
ef2bc5794e2d88c948d62762a415c306dda4101f
[ "MIT" ]
5
2020-05-22T10:44:11.000Z
2021-10-01T09:33:34.000Z
import numpy as np def MSE(y, yhat): return np.mean(np.power(y-yhat, 2)) def dMSE(y, yhat): return 2*(yhat-y)/y.size def MAE(y, yhat): return np.sum(np.abs(y-yhat)) def dMAE(y, yhat): return 1 if y == yhat else -1 def kl_divergence(y, yhat): """ measures the difference between two probability distributions over the same variable. Parameters: - y : Numpy array - yhat : Numpy array Returns: difference between two probability distribution. KL divergence can be calculated as the negative sum of probability of each event in P multiplied by the log of the probability of the event in Q over the probability of the event """ return sum(y[i] * log2(y[i]/yhat[i]) for i in range(len(y))) def entropy(y,factor=1e-15): """ measures the performance of a classification model whose output is a probability value between 0 and 1 Parameters: - y: Numpy array - factor: Optional (To ensure 0 is not returned). Returns: between 0 to 1 """ return -sum([y[i] * log2(y[i]+factor) for i in range(len(y))]) def cross_entropy(y,yhat,mode=None,factor=1e-15): """ calculates loss among two probability vectors. Parameters: - y: Numpy array - yhat: numpy array - mode: Optional (mode= kl_divergence then calculate cross entropy using kl_divergence ) - factor: Optional (To ensure 0 is not returned). Returns: between 0 to 1 """ if(mode=='Kl_diversion'): return entropy(y) + kl_divergence(y, yhat) return -sum([y[i]*log2(yhat[i]+factor) for i in range(len(y))])
23.257143
92
0.648649
ba556a1972df6326643ee40a4f3063dca9a1f881
2,276
py
Python
test/test_geolines.py
PlusWayne/pyecharts
881771378e5fe1d0f55a13f2cf63c7d181eb1894
[ "MIT" ]
null
null
null
test/test_geolines.py
PlusWayne/pyecharts
881771378e5fe1d0f55a13f2cf63c7d181eb1894
[ "MIT" ]
null
null
null
test/test_geolines.py
PlusWayne/pyecharts
881771378e5fe1d0f55a13f2cf63c7d181eb1894
[ "MIT" ]
null
null
null
#!/usr/bin/env python # coding=utf-8 from __future__ import unicode_literals from nose.tools import assert_raises from pyecharts import GeoLines, Style from pyecharts.datasets.coordinates import search_coordinates_by_keyword style = Style( title_top="#fff", title_pos="center", width=1200, height=600, background_color="#404a59", ) style_geo = style.add( is_label_show=True, line_curve=0.2, line_opacity=0.6, legend_text_color="#eee", legend_pos="right", geo_effect_symbol="plane", geo_effect_symbolsize=15, label_color=["#a6c84c", "#ffa022", "#46bee9"], label_pos="right", label_formatter="{b}", label_text_color="#eee", legend_selectedmode="single", ) def test_geolines(): data_guangzhou = [ ["广州", "上海"], ["广州", "北京"], ["广州", "南京"], ["广州", "重庆"], ["广州", "兰州"], ["广州", "杭州"], ] data_beijing = [ ["北京", "上海"], ["北京", "广州"], ["北京", "南京"], ["北京", "重庆"], ["北京", "兰州"], ["北京", "杭州"], ] lines = GeoLines("GeoLines 示例", **style.init_style) lines.add("从广州出发", data_guangzhou, **style_geo) lines.add("从北京出发", data_beijing, **style_geo) lines.print_echarts_options() lines.render() def test_with_custom_coordinates(): data_guangzhou = [ ["广州", "上海"], ["广州", "北京"], ["广州", "南京"], ["广州", "重庆"], ["广州", "兰州"], ["广州", "A市"], ] lines = GeoLines("GeoLines 示例", **style.init_style) coordinate = lines.get_coordinate("广州") assert 2 == len(coordinate) with assert_raises(ValueError): lines.get_coordinate("A市", raise_exception=True) lines.add( "从广州出发", data_guangzhou, geo_cities_coords={"A市": (119.3, 26.08)}, **style_geo ) lines.render() def test_with_full_example(): line_data = [["广州", "上海"], ["广州", "北京"], ["广州", "南京"], ["广州", "A市"]] lines = GeoLines("GeoLines 示例", **style.init_style) with assert_raises(ValueError): lines.add("从广州出发", line_data, **style_geo) assert 0 == len(search_coordinates_by_keyword("A市")) lines.add_coordinate("A市", 119.3, 26.08) lines.add("从广州出发", line_data, **style_geo) lines.render()
23.957895
72
0.572935
51f5b067821c1beaa67545340764652bc6441c5a
9,543
py
Python
Lib/test/test_sort.py
raychorn/svn_Python-2.5.1
425005b1b489ba44ec0bb989e077297e8953d9be
[ "PSF-2.0" ]
null
null
null
Lib/test/test_sort.py
raychorn/svn_Python-2.5.1
425005b1b489ba44ec0bb989e077297e8953d9be
[ "PSF-2.0" ]
null
null
null
Lib/test/test_sort.py
raychorn/svn_Python-2.5.1
425005b1b489ba44ec0bb989e077297e8953d9be
[ "PSF-2.0" ]
null
null
null
from test import test_support import random import sys import unittest verbose = test_support.verbose nerrors = 0 def check(tag, expected, raw, compare=None): global nerrors if verbose: print " checking", tag orig = raw[:] # save input in case of error if compare: raw.sort(compare) else: raw.sort() if len(expected) != len(raw): print "error in", tag print "length mismatch;", len(expected), len(raw) print expected print orig print raw nerrors += 1 return for i, good in enumerate(expected): maybe = raw[i] if good is not maybe: print "error in", tag print "out of order at index", i, good, maybe print expected print orig print raw nerrors += 1 return class TestBase(unittest.TestCase): def testStressfully(self): # Try a variety of sizes at and around powers of 2, and at powers of 10. sizes = [0] for power in range(1, 10): n = 2 ** power sizes.extend(range(n-1, n+2)) sizes.extend([10, 100, 1000]) class Complains(object): maybe_complain = True def __init__(self, i): self.i = i def __lt__(self, other): if Complains.maybe_complain and random.random() < 0.001: if verbose: print " complaining at", self, other raise RuntimeError return self.i < other.i def __repr__(self): return "Complains(%d)" % self.i class Stable(object): def __init__(self, key, i): self.key = key self.index = i def __cmp__(self, other): return cmp(self.key, other.key) def __repr__(self): return "Stable(%d, %d)" % (self.key, self.index) for n in sizes: x = range(n) if verbose: print "Testing size", n s = x[:] check("identity", x, s) s = x[:] s.reverse() check("reversed", x, s) s = x[:] random.shuffle(s) check("random permutation", x, s) y = x[:] y.reverse() s = x[:] check("reversed via function", y, s, lambda a, b: cmp(b, a)) if verbose: print " Checking against an insane comparison function." print " If the implementation isn't careful, this may segfault." s = x[:] s.sort(lambda a, b: int(random.random() * 3) - 1) check("an insane function left some permutation", x, s) x = [Complains(i) for i in x] s = x[:] random.shuffle(s) Complains.maybe_complain = True it_complained = False try: s.sort() except RuntimeError: it_complained = True if it_complained: Complains.maybe_complain = False check("exception during sort left some permutation", x, s) s = [Stable(random.randrange(10), i) for i in xrange(n)] augmented = [(e, e.index) for e in s] augmented.sort() # forced stable because ties broken by index x = [e for e, i in augmented] # a stable sort of s check("stability", x, s) #============================================================================== class TestBugs(unittest.TestCase): def test_bug453523(self): # bug 453523 -- list.sort() crasher. # If this fails, the most likely outcome is a core dump. # Mutations during a list sort should raise a ValueError. class C: def __lt__(self, other): if L and random.random() < 0.75: L.pop() else: L.append(3) return random.random() < 0.5 L = [C() for i in range(50)] self.assertRaises(ValueError, L.sort) def test_cmpNone(self): # Testing None as a comparison function. L = range(50) random.shuffle(L) L.sort(None) self.assertEqual(L, range(50)) def test_undetected_mutation(self): # Python 2.4a1 did not always detect mutation memorywaster = [] for i in range(20): def mutating_cmp(x, y): L.append(3) L.pop() return cmp(x, y) L = [1,2] self.assertRaises(ValueError, L.sort, mutating_cmp) def mutating_cmp(x, y): L.append(3) del L[:] return cmp(x, y) self.assertRaises(ValueError, L.sort, mutating_cmp) memorywaster = [memorywaster] #============================================================================== class TestDecorateSortUndecorate(unittest.TestCase): def test_decorated(self): data = 'The quick Brown fox Jumped over The lazy Dog'.split() copy = data[:] random.shuffle(data) data.sort(key=str.lower) copy.sort(cmp=lambda x,y: cmp(x.lower(), y.lower())) def test_baddecorator(self): data = 'The quick Brown fox Jumped over The lazy Dog'.split() self.assertRaises(TypeError, data.sort, None, lambda x,y: 0) def test_stability(self): data = [(random.randrange(100), i) for i in xrange(200)] copy = data[:] data.sort(key=lambda (x,y): x) # sort on the random first field copy.sort() # sort using both fields self.assertEqual(data, copy) # should get the same result def test_cmp_and_key_combination(self): # Verify that the wrapper has been removed def compare(x, y): self.assertEqual(type(x), str) self.assertEqual(type(x), str) return cmp(x, y) data = 'The quick Brown fox Jumped over The lazy Dog'.split() data.sort(cmp=compare, key=str.lower) def test_badcmp_with_key(self): # Verify that the wrapper has been removed data = 'The quick Brown fox Jumped over The lazy Dog'.split() self.assertRaises(TypeError, data.sort, "bad", str.lower) def test_key_with_exception(self): # Verify that the wrapper has been removed data = range(-2,2) dup = data[:] self.assertRaises(ZeroDivisionError, data.sort, None, lambda x: 1/x) self.assertEqual(data, dup) def test_key_with_mutation(self): data = range(10) def k(x): del data[:] data[:] = range(20) return x self.assertRaises(ValueError, data.sort, key=k) def test_key_with_mutating_del(self): data = range(10) class SortKiller(object): def __init__(self, x): pass def __del__(self): del data[:] data[:] = range(20) self.assertRaises(ValueError, data.sort, key=SortKiller) def test_key_with_mutating_del_and_exception(self): data = range(10) ## dup = data[:] class SortKiller(object): def __init__(self, x): if x > 2: raise RuntimeError def __del__(self): del data[:] data[:] = range(20) self.assertRaises(RuntimeError, data.sort, key=SortKiller) ## major honking subtlety: we *can't* do: ## ## self.assertEqual(data, dup) ## ## because there is a reference to a SortKiller in the ## traceback and by the time it dies we're outside the call to ## .sort() and so the list protection gimmicks are out of ## date (this cost some brain cells to figure out...). def test_reverse(self): data = range(100) random.shuffle(data) data.sort(reverse=True) self.assertEqual(data, range(99,-1,-1)) self.assertRaises(TypeError, data.sort, "wrong type") def test_reverse_stability(self): data = [(random.randrange(100), i) for i in xrange(200)] copy1 = data[:] copy2 = data[:] data.sort(cmp=lambda x,y: cmp(x[0],y[0]), reverse=True) copy1.sort(cmp=lambda x,y: cmp(y[0],x[0])) self.assertEqual(data, copy1) copy2.sort(key=lambda x: x[0], reverse=True) self.assertEqual(data, copy2) #============================================================================== def test_main(verbose=None): test_classes = ( TestBase, TestDecorateSortUndecorate, TestBugs, ) test_support.run_unittest(*test_classes) # verify reference counting if verbose and hasattr(sys, "gettotalrefcount"): import gc counts = [None] * 5 for i in xrange(len(counts)): test_support.run_unittest(*test_classes) gc.collect() counts[i] = sys.gettotalrefcount() print counts if __name__ == "__main__": test_main(verbose=True)
32.906897
88
0.507178
2c6f82ee65522f6fc19cc60e8669ae68f74028b2
13,128
py
Python
timm_new/models/selecsls.py
Yuki-Tanaka-33937424/pytorch-image-models
6c1da622dcb2a0421aeb6cdcadd03cc366331f66
[ "Apache-2.0" ]
null
null
null
timm_new/models/selecsls.py
Yuki-Tanaka-33937424/pytorch-image-models
6c1da622dcb2a0421aeb6cdcadd03cc366331f66
[ "Apache-2.0" ]
null
null
null
timm_new/models/selecsls.py
Yuki-Tanaka-33937424/pytorch-image-models
6c1da622dcb2a0421aeb6cdcadd03cc366331f66
[ "Apache-2.0" ]
null
null
null
"""PyTorch SelecSLS Net example for ImageNet Classification License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode) Author: Dushyant Mehta (@mehtadushy) SelecSLS (core) Network Architecture as proposed in "XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera, Mehta et al." https://arxiv.org/abs/1907.00837 Based on ResNet implementation in https://github.com/rwightman/pytorch-image-models and SelecSLS Net implementation in https://github.com/mehtadushy/SelecSLS-Pytorch """ from typing import List import torch import torch.nn as nn import torch.nn.functional as F from timm_new.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import build_model_with_cfg from .layers import create_classifier from .registry import register_model __all__ = ['SelecSLS'] # model_registry will add each entrypoint fn to this def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (4, 4), 'crop_pct': 0.875, 'interpolation': 'bilinear', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'stem.0', 'classifier': 'fc', **kwargs } default_cfgs = { 'selecsls42': _cfg( url='', interpolation='bicubic'), 'selecsls42b': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls42b-8af30141.pth', interpolation='bicubic'), 'selecsls60': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60-bbf87526.pth', interpolation='bicubic'), 'selecsls60b': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60b-94e619b5.pth', interpolation='bicubic'), 'selecsls84': _cfg( url='', interpolation='bicubic'), } class SequentialList(nn.Sequential): def __init__(self, *args): super(SequentialList, self).__init__(*args) @torch.jit._overload_method # noqa: F811 def forward(self, x): # type: (List[torch.Tensor]) -> (List[torch.Tensor]) pass @torch.jit._overload_method # noqa: F811 def forward(self, x): # type: (torch.Tensor) -> (List[torch.Tensor]) pass def forward(self, x) -> List[torch.Tensor]: for module in self: x = module(x) return x class SelectSeq(nn.Module): def __init__(self, mode='index', index=0): super(SelectSeq, self).__init__() self.mode = mode self.index = index @torch.jit._overload_method # noqa: F811 def forward(self, x): # type: (List[torch.Tensor]) -> (torch.Tensor) pass @torch.jit._overload_method # noqa: F811 def forward(self, x): # type: (Tuple[torch.Tensor]) -> (torch.Tensor) pass def forward(self, x) -> torch.Tensor: if self.mode == 'index': return x[self.index] else: return torch.cat(x, dim=1) def conv_bn(in_chs, out_chs, k=3, stride=1, padding=None, dilation=1): if padding is None: padding = ((stride - 1) + dilation * (k - 1)) // 2 return nn.Sequential( nn.Conv2d(in_chs, out_chs, k, stride, padding=padding, dilation=dilation, bias=False), nn.BatchNorm2d(out_chs), nn.ReLU(inplace=True) ) class SelecSLSBlock(nn.Module): def __init__(self, in_chs, skip_chs, mid_chs, out_chs, is_first, stride, dilation=1): super(SelecSLSBlock, self).__init__() self.stride = stride self.is_first = is_first assert stride in [1, 2] # Process input with 4 conv blocks with the same number of input and output channels self.conv1 = conv_bn(in_chs, mid_chs, 3, stride, dilation=dilation) self.conv2 = conv_bn(mid_chs, mid_chs, 1) self.conv3 = conv_bn(mid_chs, mid_chs // 2, 3) self.conv4 = conv_bn(mid_chs // 2, mid_chs, 1) self.conv5 = conv_bn(mid_chs, mid_chs // 2, 3) self.conv6 = conv_bn(2 * mid_chs + (0 if is_first else skip_chs), out_chs, 1) def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]: if not isinstance(x, list): x = [x] assert len(x) in [1, 2] d1 = self.conv1(x[0]) d2 = self.conv3(self.conv2(d1)) d3 = self.conv5(self.conv4(d2)) if self.is_first: out = self.conv6(torch.cat([d1, d2, d3], 1)) return [out, out] else: return [self.conv6(torch.cat([d1, d2, d3, x[1]], 1)), x[1]] class SelecSLS(nn.Module): """SelecSLS42 / SelecSLS60 / SelecSLS84 Parameters ---------- cfg : network config dictionary specifying block type, feature, and head args num_classes : int, default 1000 Number of classification classes. in_chans : int, default 3 Number of input (color) channels. drop_rate : float, default 0. Dropout probability before classifier, for training global_pool : str, default 'avg' Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' """ def __init__(self, cfg, num_classes=1000, in_chans=3, drop_rate=0.0, global_pool='avg'): self.num_classes = num_classes self.drop_rate = drop_rate super(SelecSLS, self).__init__() self.stem = conv_bn(in_chans, 32, stride=2) self.features = SequentialList(*[cfg['block'](*block_args) for block_args in cfg['features']]) self.from_seq = SelectSeq() # from List[tensor] -> Tensor in module compatible way self.head = nn.Sequential(*[conv_bn(*conv_args) for conv_args in cfg['head']]) self.num_features = cfg['num_features'] self.feature_info = cfg['feature_info'] self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) for n, m in self.named_modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1.) nn.init.constant_(m.bias, 0.) def get_classifier(self): return self.fc def reset_classifier(self, num_classes, global_pool='avg'): self.num_classes = num_classes self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) def forward_features(self, x): x = self.stem(x) x = self.features(x) x = self.head(self.from_seq(x)) return x def forward(self, x): x = self.forward_features(x) x = self.global_pool(x) if self.drop_rate > 0.: x = F.dropout(x, p=self.drop_rate, training=self.training) x = self.fc(x) return x def _create_selecsls(variant, pretrained, **kwargs): cfg = {} feature_info = [dict(num_chs=32, reduction=2, module='stem.2')] if variant.startswith('selecsls42'): cfg['block'] = SelecSLSBlock # Define configuration of the network after the initial neck cfg['features'] = [ # in_chs, skip_chs, mid_chs, out_chs, is_first, stride (32, 0, 64, 64, True, 2), (64, 64, 64, 128, False, 1), (128, 0, 144, 144, True, 2), (144, 144, 144, 288, False, 1), (288, 0, 304, 304, True, 2), (304, 304, 304, 480, False, 1), ] feature_info.extend([ dict(num_chs=128, reduction=4, module='features.1'), dict(num_chs=288, reduction=8, module='features.3'), dict(num_chs=480, reduction=16, module='features.5'), ]) # Head can be replaced with alternative configurations depending on the problem feature_info.append(dict(num_chs=1024, reduction=32, module='head.1')) if variant == 'selecsls42b': cfg['head'] = [ (480, 960, 3, 2), (960, 1024, 3, 1), (1024, 1280, 3, 2), (1280, 1024, 1, 1), ] feature_info.append(dict(num_chs=1024, reduction=64, module='head.3')) cfg['num_features'] = 1024 else: cfg['head'] = [ (480, 960, 3, 2), (960, 1024, 3, 1), (1024, 1024, 3, 2), (1024, 1280, 1, 1), ] feature_info.append(dict(num_chs=1280, reduction=64, module='head.3')) cfg['num_features'] = 1280 elif variant.startswith('selecsls60'): cfg['block'] = SelecSLSBlock # Define configuration of the network after the initial neck cfg['features'] = [ # in_chs, skip_chs, mid_chs, out_chs, is_first, stride (32, 0, 64, 64, True, 2), (64, 64, 64, 128, False, 1), (128, 0, 128, 128, True, 2), (128, 128, 128, 128, False, 1), (128, 128, 128, 288, False, 1), (288, 0, 288, 288, True, 2), (288, 288, 288, 288, False, 1), (288, 288, 288, 288, False, 1), (288, 288, 288, 416, False, 1), ] feature_info.extend([ dict(num_chs=128, reduction=4, module='features.1'), dict(num_chs=288, reduction=8, module='features.4'), dict(num_chs=416, reduction=16, module='features.8'), ]) # Head can be replaced with alternative configurations depending on the problem feature_info.append(dict(num_chs=1024, reduction=32, module='head.1')) if variant == 'selecsls60b': cfg['head'] = [ (416, 756, 3, 2), (756, 1024, 3, 1), (1024, 1280, 3, 2), (1280, 1024, 1, 1), ] feature_info.append(dict(num_chs=1024, reduction=64, module='head.3')) cfg['num_features'] = 1024 else: cfg['head'] = [ (416, 756, 3, 2), (756, 1024, 3, 1), (1024, 1024, 3, 2), (1024, 1280, 1, 1), ] feature_info.append(dict(num_chs=1280, reduction=64, module='head.3')) cfg['num_features'] = 1280 elif variant == 'selecsls84': cfg['block'] = SelecSLSBlock # Define configuration of the network after the initial neck cfg['features'] = [ # in_chs, skip_chs, mid_chs, out_chs, is_first, stride (32, 0, 64, 64, True, 2), (64, 64, 64, 144, False, 1), (144, 0, 144, 144, True, 2), (144, 144, 144, 144, False, 1), (144, 144, 144, 144, False, 1), (144, 144, 144, 144, False, 1), (144, 144, 144, 304, False, 1), (304, 0, 304, 304, True, 2), (304, 304, 304, 304, False, 1), (304, 304, 304, 304, False, 1), (304, 304, 304, 304, False, 1), (304, 304, 304, 304, False, 1), (304, 304, 304, 512, False, 1), ] feature_info.extend([ dict(num_chs=144, reduction=4, module='features.1'), dict(num_chs=304, reduction=8, module='features.6'), dict(num_chs=512, reduction=16, module='features.12'), ]) # Head can be replaced with alternative configurations depending on the problem cfg['head'] = [ (512, 960, 3, 2), (960, 1024, 3, 1), (1024, 1024, 3, 2), (1024, 1280, 3, 1), ] cfg['num_features'] = 1280 feature_info.extend([ dict(num_chs=1024, reduction=32, module='head.1'), dict(num_chs=1280, reduction=64, module='head.3') ]) else: raise ValueError('Invalid net configuration ' + variant + ' !!!') cfg['feature_info'] = feature_info # this model can do 6 feature levels by default, unlike most others, leave as 0-4 to avoid surprises? return build_model_with_cfg( SelecSLS, variant, pretrained, default_cfg=default_cfgs[variant], model_cfg=cfg, feature_cfg=dict(out_indices=(0, 1, 2, 3, 4), flatten_sequential=True), **kwargs) @register_model def selecsls42(pretrained=False, **kwargs): """Constructs a SelecSLS42 model. """ return _create_selecsls('selecsls42', pretrained, **kwargs) @register_model def selecsls42b(pretrained=False, **kwargs): """Constructs a SelecSLS42_B model. """ return _create_selecsls('selecsls42b', pretrained, **kwargs) @register_model def selecsls60(pretrained=False, **kwargs): """Constructs a SelecSLS60 model. """ return _create_selecsls('selecsls60', pretrained, **kwargs) @register_model def selecsls60b(pretrained=False, **kwargs): """Constructs a SelecSLS60_B model. """ return _create_selecsls('selecsls60b', pretrained, **kwargs) @register_model def selecsls84(pretrained=False, **kwargs): """Constructs a SelecSLS84 model. """ return _create_selecsls('selecsls84', pretrained, **kwargs)
36.165289
121
0.587218
a5cab280478e1f01c8f252ac7758efa3cd252a3b
762
py
Python
ALPHABETS/SMALL_ALPHABETS/e.py
charansaim1819/Python_Patterns
02e636855003346ec84c3d69f2be174dc9e9e3cb
[ "MIT" ]
null
null
null
ALPHABETS/SMALL_ALPHABETS/e.py
charansaim1819/Python_Patterns
02e636855003346ec84c3d69f2be174dc9e9e3cb
[ "MIT" ]
null
null
null
ALPHABETS/SMALL_ALPHABETS/e.py
charansaim1819/Python_Patterns
02e636855003346ec84c3d69f2be174dc9e9e3cb
[ "MIT" ]
null
null
null
#Shape of small e: def for_e(): """printing small 'e' using for loop""" for row in range(7): for col in range(4): if col==0 and row not in(0,6) or col in(1,2) and row%3==0 or col==3 and row in(1,2,5): print("*",end=" ") else: print(" ",end= " ") print() def while_e(): """printing small 'e' using while loop""" i=0 while i<7: j=0 while j<4: if j==0 and i not in(0,6) or i==0 and j in(1,2) or j==3 and i in(1,2,5) or j==1 and i in(0,3,6) or j==2 and i in(0,3,6): print("*",end=" ") else: print(" ",end=" ") j+=1 print() i+=1
25.4
133
0.39895
44e5b512bed9545444a35d0d99af33c016f1b0c2
323
py
Python
sms_proxy/log.py
jmcparland/sms-proxy
1e59ab9877be9dca5dea02f04f9404e6eb70edb2
[ "MIT" ]
12
2016-06-02T22:23:20.000Z
2021-11-28T00:57:27.000Z
sms_poll/log.py
caseymacphee/sms-polling
04aeb4114c46018cd94b50c57c461136a4dae5ff
[ "MIT" ]
2
2019-04-10T02:26:35.000Z
2020-06-13T06:34:28.000Z
sms_poll/log.py
caseymacphee/sms-polling
04aeb4114c46018cd94b50c57c461136a4dae5ff
[ "MIT" ]
8
2016-06-02T22:23:25.000Z
2020-06-13T05:59:01.000Z
import sys import os import logging from pythonjsonlogger import jsonlogger log = logging.getLogger() handler = logging.StreamHandler(sys.stdout) formatter = jsonlogger.JsonFormatter() handler.setFormatter(formatter) log.addHandler(handler) log.setLevel(int(os.environ.get('LOG_LEVEL', 20))) # Default to INFO log level
24.846154
79
0.801858
575b3b1d27fc964715de0e9c302160aaffb4c772
807
py
Python
FastAPI-Project-Template/settings/config.py
shikanon/privatecode
85ffa80ef2815ff1af799e38d033d9c8a7a1cad1
[ "MIT" ]
null
null
null
FastAPI-Project-Template/settings/config.py
shikanon/privatecode
85ffa80ef2815ff1af799e38d033d9c8a7a1cad1
[ "MIT" ]
2
2022-02-13T13:48:19.000Z
2022-02-27T05:07:02.000Z
FastAPI-Project-Template/settings/config.py
shikanon/privatecode
85ffa80ef2815ff1af799e38d033d9c8a7a1cad1
[ "MIT" ]
null
null
null
# coding=utf-8 ''' # Author: shikanon ([email protected]) # File Created Time: 2020-03-31 11:04:51 # # Project: settings # File: config.py # Description: # ''' import configparser class Config: '''配置 ''' def __init__(self): self.config = configparser.ConfigParser() self.mysqldb = "" self.redis = "" def parse(self, path): self.config.read(path) if "db" not in self.config.sections(): raise ValueError("config file can not find db section") host = self.config.get("db", "host") port = self.config.get("db", "port") username = self.config.get("db", "username") passwd = self.config.get("db", "passwd") self.mysqldb = "mysql+pymysql://%s:%s@%s:%s/ovision"%(username,passwd,host,port)
26.032258
88
0.592317
eeeb2fbfcc9befc64b80483380b12d3fcd88b229
10,047
py
Python
prep_test_data.py
samirak93/NBA_Hackathon
035c0cb87114e14ad033fa1b6928d6954ab47024
[ "MIT" ]
1
2019-07-24T19:23:37.000Z
2019-07-24T19:23:37.000Z
prep_test_data.py
samirak93/NBA_Hackathon
035c0cb87114e14ad033fa1b6928d6954ab47024
[ "MIT" ]
null
null
null
prep_test_data.py
samirak93/NBA_Hackathon
035c0cb87114e14ad033fa1b6928d6954ab47024
[ "MIT" ]
null
null
null
import pandas as pd from pandas.tseries.holiday import USFederalHolidayCalendar as calendar #prepare columns for test data df=pd.read_csv('test_set.csv') data=pd.DataFrame(df) mapping = {'CLE': 1, 'POR': 2,'GSW': 3,'ORL': 4,'IND': 5,'BOS': 6,'TOR': 7,'MIL': 8,'MEM': 9, 'PHI': 10,'PHX': 11,'LAL': 12,'ATL': 13,'CHI': 14,'SAC': 15,'BKN': 16,'DET': 17,'OKC': 18, 'MIA': 19,'UTA': 20,'NOP': 21,'NYK': 22,'SAS': 23,'DEN': 24,'LAC': 25,'HOU': 26,'MIN': 27,'WAS': 28,'CHA': 29,'DAL': 30} updated=data.replace({'Home_Team': mapping,'Away_Team':mapping}) updated['home_team_score']=0 updated['away_team_score']=0 updated['wins_home']=0 updated['wins_away']=0 updated['loss_home']=0 updated['loss_away']=0 updated['largest_lead_home']=0 updated['largest_lead_away']=0 updated['result_win']=0 updated['ASG_Count']=0 updated['day']= pd.to_datetime(updated['Game_Date']).dt.dayofweek array=[] for x,y in zip(updated.Home_Team,updated.Away_Team): if ((x==1)&(y==3)) | ((x==3)&(y==1)): (array.append(int('1'))) elif ((x==6)&(y==12)) | ((x==12)&(y==6)): array.append(int('1')) elif ((x == 17) & (y == 12)) | ((x == 12) & (y == 17)): array.append(int('1')) elif ((x == 10) & (y == 6)) | ((x == 6) & (y == 10)): array.append(int('2')) elif ((x == 6) & (y == 22)) | ((x == 22) & (y == 6)): array.append(int('2')) elif ((x == 16) & (y == 22)) | ((x == 22) & (y == 16)): array.append(int('2')) elif ((x == 17) & (y == 14)) | ((x == 14) & (y == 17)): array.append(int('2')) elif ((x == 1) & (y == 14)) | ((x == 14) & (y == 1)): array.append(int('2')) elif ((x == 19) & (y == 14)) | ((x == 14) & (y == 19)): array.append(int('2')) elif ((x == 22) & (y == 14)) | ((x == 14) & (y == 22)): array.append(int('2')) elif ((x == 6) & (y == 17)) | ((x == 17) & (y == 6)): array.append(int('2')) elif ((x == 22) & (y == 19)) | ((x == 19) & (y == 22)): array.append(int('2')) elif ((x == 22) & (y == 5)) | ((x == 5) & (y == 22)): array.append(int('2')) elif ((x == 12) & (y == 25)) | ((x == 25) & (y == 12)): array.append(int('3')) elif ((x == 30) & (y == 26)) | ((x == 26) & (y == 30)): array.append(int('3')) elif ((x == 23) & (y == 26)) | ((x == 26) & (y == 23)): array.append(int('3')) elif ((x == 20) & (y == 26)) | ((x == 26) & (y == 20)): array.append(int('3')) elif ((x == 12) & (y == 23)) | ((x == 23) & (y == 12)): array.append(int('3')) elif ((x == 11) & (y == 23)) | ((x == 23) & (y == 11)): array.append(int('3')) else: array.append(int(0)) updated["rivalry"]=array home_team_rank=[] twitter_followers_home=[] for x in (updated.Home_Team): if ((x==1)): home_team_rank.append(int(11)) twitter_followers_home.append(int(2100000)) elif (x==2): home_team_rank.append(int(17)) twitter_followers_home.append(int(823000)) elif (x==3): home_team_rank.append(int(3)) twitter_followers_home.append(int(3500000)) elif (x==4): home_team_rank.append(int(19)) twitter_followers_home.append(int(1500000)) elif (x==5): home_team_rank.append(int(24)) twitter_followers_home.append(int(930000)) elif (x==6): home_team_rank.append(int(5)) twitter_followers_home.append(int(2300000)) elif (x==7): home_team_rank.append(int(13)) twitter_followers_home.append(int(1400000)) elif (x==8): home_team_rank.append(int(27)) twitter_followers_home.append(int(695000)) elif (x==9): home_team_rank.append(int(26)) twitter_followers_home.append(int(766000)) elif (x==10): home_team_rank.append(int(25)) twitter_followers_home.append(int(925000)) elif (x==11): home_team_rank.append(int(14)) twitter_followers_home.append(int(753000)) elif (x==12): home_team_rank.append(int(2)) twitter_followers_home.append(int(6170000)) elif (x==13): home_team_rank.append(int(23)) twitter_followers_home.append(int(991000)) elif (x==14): home_team_rank.append(int(4)) twitter_followers_home.append(int(3600000)) elif (x==15): home_team_rank.append(int(15)) twitter_followers_home.append(int(714000)) elif (x==16): home_team_rank.append(int(7)) twitter_followers_home.append(int(755000)) elif (x==17): home_team_rank.append(int(21)) twitter_followers_home.append(int(710000)) elif (x==18): home_team_rank.append(int(16)) twitter_followers_home.append(int(1800000)) elif (x==19): home_team_rank.append(int(10)) twitter_followers_home.append(int(4090000)) elif (x==20): home_team_rank.append(int(20)) twitter_followers_home.append(int(632000)) elif (x==21): home_team_rank.append(int(30)) twitter_followers_home.append(int(659000)) elif (x==22): home_team_rank.append(int(1)) twitter_followers_home.append(int(1780000)) elif (x==23): home_team_rank.append(int(12)) twitter_followers_home.append(int(2300000)) elif (x==24): home_team_rank.append(int(22)) twitter_followers_home.append(int(634000)) elif (x==25): home_team_rank.append(int(6)) twitter_followers_home.append(int(1100000)) elif (x==26): home_team_rank.append(int(8)) twitter_followers_home.append(int(1710000)) elif (x==27): home_team_rank.append(int(29)) twitter_followers_home.append(int(645000)) elif (x == 28): home_team_rank.append(int(18)) twitter_followers_home.append(int(662000)) elif (x==29): home_team_rank.append(int(28)) twitter_followers_home.append(int(726000)) elif (x==30): twitter_followers_home.append(int(1200000)) home_team_rank.append(int(9)) twitter_followers_away=[] away_team_rank=[] for x in (updated.Away_Team): if ((x==1)): away_team_rank.append(int(11)) twitter_followers_away.append(int(2100000)) elif (x==2): away_team_rank.append(int(17)) twitter_followers_away.append(int(823000)) elif (x==3): away_team_rank.append(int(3)) twitter_followers_away.append(int(3500000)) elif (x==4): away_team_rank.append(int(19)) twitter_followers_away.append(int(1500000)) elif (x==5): away_team_rank.append(int(24)) twitter_followers_away.append(int(930000)) elif (x==6): away_team_rank.append(int(5)) twitter_followers_away.append(int(2300000)) elif (x==7): away_team_rank.append(int(13)) twitter_followers_away.append(int(1400000)) elif (x==8): away_team_rank.append(int(27)) twitter_followers_away.append(int(695000)) elif (x==9): away_team_rank.append(int(26)) twitter_followers_away.append(int(766000)) elif (x==10): away_team_rank.append(int(25)) twitter_followers_away.append(int(925000)) elif (x==11): away_team_rank.append(int(14)) twitter_followers_away.append(int(753000)) elif (x==12): away_team_rank.append(int(2)) twitter_followers_away.append(int(6170000)) elif (x==13): away_team_rank.append(int(23)) twitter_followers_away.append(int(991000)) elif (x==14): away_team_rank.append(int(4)) twitter_followers_away.append(int(3600000)) elif (x==15): away_team_rank.append(int(15)) twitter_followers_away.append(int(714000)) elif (x==16): away_team_rank.append(int(7)) twitter_followers_away.append(int(755000)) elif (x==17): away_team_rank.append(int(21)) twitter_followers_away.append(int(710000)) elif (x==18): away_team_rank.append(int(16)) twitter_followers_away.append(int(1800000)) elif (x==19): away_team_rank.append(int(10)) twitter_followers_away.append(int(4090000)) elif (x==20): away_team_rank.append(int(20)) twitter_followers_away.append(int(632000)) elif (x==21): away_team_rank.append(int(30)) twitter_followers_away.append(int(659000)) elif (x==22): away_team_rank.append(int(1)) twitter_followers_away.append(int(1780000)) elif (x==23): away_team_rank.append(int(12)) twitter_followers_away.append(int(2300000)) elif (x==24): away_team_rank.append(int(22)) twitter_followers_away.append(int(634000)) elif (x==25): away_team_rank.append(int(6)) twitter_followers_away.append(int(1100000)) elif (x==26): away_team_rank.append(int(8)) twitter_followers_away.append(int(1710000)) elif (x==27): away_team_rank.append(int(29)) twitter_followers_away.append(int(645000)) elif (x == 28): away_team_rank.append(int(18)) twitter_followers_away.append(int(662000)) elif (x==29): away_team_rank.append(int(28)) twitter_followers_away.append(int(726000)) elif (x==30): twitter_followers_away.append(int(1200000)) away_team_rank.append(int(9)) updated['Home_Team_Twitter']=twitter_followers_home updated['Away_Team_Twitter']=twitter_followers_away updated['Home_Team_Rank']=home_team_rank updated['Away_Team_Rank']=away_team_rank updated['Game_Date'] = pd.to_datetime(updated.Game_Date) cal = calendar() holidays = cal.holidays(start=updated.Game_Date.min(), end=updated.Game_Date.max()) updated['Holiday'] = updated['Game_Date'].isin(holidays) holiday=[] for holi in updated.Holiday: if holi==True: holiday.append(int(1)) elif holi!=True: holiday.append(int(0)) updated['Holiday'] = holiday df_dummy=updated.pop('Total_Viewers') updated['Total_Viewers']=df_dummy updated.to_csv('test_data.csv') print updated.shape
35.006969
131
0.599681
1649b1bcb3483747d649e7cd39dd913429cb6b3c
10,798
py
Python
NVLL/distribution/vmf_hypvae.py
jennhu/vmf_vae_nlp
95a39fa9f7a0659e432475e8dfb9a46e305d53b7
[ "MIT" ]
159
2018-08-31T15:57:36.000Z
2022-03-27T15:31:38.000Z
NVLL/distribution/vmf_hypvae.py
jennhu/vmf_vae_nlp
95a39fa9f7a0659e432475e8dfb9a46e305d53b7
[ "MIT" ]
9
2018-10-11T15:58:50.000Z
2019-04-16T03:13:33.000Z
NVLL/distribution/vmf_hypvae.py
jennhu/vmf_vae_nlp
95a39fa9f7a0659e432475e8dfb9a46e305d53b7
[ "MIT" ]
21
2018-09-01T17:57:20.000Z
2021-12-17T03:31:01.000Z
import torch from scipy import special as sp import numpy as np from NVLL.util.util import GVar from NVLL.util.gpu_flag import device from torch.autograd import gradcheck class BesselIve(torch.autograd.Function): """ We can implement our own custom autograd Functions by subclassing torch.autograd.Function and implementing the forward and backward passes which operate on Tensors. """ @staticmethod def forward(ctx, dim, kappa): """ In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward computation. You can cache arbitrary objects for use in the backward pass using the ctx.save_for_backward method. """ ctx.save_for_backward(dim, kappa) kappa_copy = kappa.clone() m = sp.ive(dim, kappa_copy) x = torch.tensor(m).to(device) # x = torch.from_numpy(np.asarray(sp.ive(dim, kappa))) return x.clone() @staticmethod def backward(ctx, grad_output): """ In the backward pass we receive a Tensor containing the gradient of the loss with respect to the output, and we need to compute the gradient of the loss with respect to the input. """ # print('called') dim, kappa = ctx.saved_tensors grad_input = grad_output.clone() grad = grad_input * (bessel_ive(dim - 1, kappa) - bessel_ive(dim, kappa) * (dim + kappa) / kappa) # grad = grad_input * (bessel(dim-1, kappa) + bessel(dim+1, kappa)) *0.5 return None, grad class BesselIv(torch.autograd.Function): """ We can implement our own custom autograd Functions by subclassing torch.autograd.Function and implementing the forward and backward passes which operate on Tensors. """ @staticmethod def forward(ctx, dim, kappa): """ In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward computation. You can cache arbitrary objects for use in the backward pass using the ctx.save_for_backward method. """ ctx.save_for_backward(dim, kappa) kappa_copy = kappa.clone() m = sp.iv(dim, kappa_copy) x = torch.tensor(m).to(device) return x.clone() @staticmethod def backward(ctx, grad_output): """ In the backward pass we receive a Tensor containing the gradient of the loss with respect to the output, and we need to compute the gradient of the loss with respect to the input. """ # print('called') dim, kappa = ctx.saved_tensors grad_input = grad_output.clone() # grad = grad_input * (bessel_ive(dim - 1, kappa) - bessel_ive(dim, kappa) * (dim + kappa) / kappa) grad = grad_input * (bessel_iv(dim - 1, kappa) + bessel_iv(dim + 1, kappa)) * 0.5 return None, grad bessel_ive = BesselIve.apply bessel_iv = BesselIv.apply # dim = torch.tensor(3.0).to(device) # kappa = torch.tensor(100.0,requires_grad=True).to(device) # res = torch.autograd.gradcheck(bessel_ive, (dim, kappa), raise_exception=True) # # print(res) # exit() class VmfDiff(torch.nn.Module): def __init__(self, hid_dim, lat_dim): super().__init__() self.hid_dim = hid_dim self.lat_dim = lat_dim self.func_mu = torch.nn.Linear(hid_dim, lat_dim) self.func_kappa = torch.nn.Linear(hid_dim, 1) # self.kld = GVar(torch.from_numpy(vMF._vmf_kld(kappa, lat_dim)).float()) # print('KLD: {}'.format(self.kld.data[0])) self.nonneg = torch.nn.ReLU() def estimate_param(self, latent_code): ret_dict = {} # print(torch.max(self.func_kappa(latent_code)).item()) # ret_dict['kappa'] = self.nonneg(1 + self.func_kappa(latent_code) * 5 ) +1 ret_dict['kappa'] = torch.max(torch.min(self.func_kappa(latent_code) * 10 + 50, torch.tensor(150.0).to(device)), torch.tensor(10.0).to(device)) # Only compute mu, use mu/mu_norm as mu, # use 1 as norm, use diff(mu_norm, 1) as redundant_norm mu = self.func_mu(latent_code) norm = torch.norm(mu, 2, 1, keepdim=True) mu_norm_sq_diff_from_one = torch.pow(torch.add(norm, -1), 2) redundant_norm = torch.sum(mu_norm_sq_diff_from_one, dim=1, keepdim=True) ret_dict['norm'] = torch.ones_like(mu) ret_dict['redundant_norm'] = redundant_norm mu = mu / torch.norm(mu, p=2, dim=1, keepdim=True) ret_dict['mu'] = mu return ret_dict def compute_KLD(self, tup, batch_sz): kappa = tup['kappa'] d = self.lat_dim rt_bag = [] # const = torch.log(torch.tensor(3.1415926)) * d / 2 + torch.log(torch.tensor(2.0)) \ # - torch.tensor(sp.loggamma(d / 2).real) - (d / 2) * torch.log(torch.tensor(2 * 3.1415926)) const = torch.tensor( np.log(np.pi) * d / 2 + np.log(2) - sp.loggamma(d / 2).real - (d / 2) * np.log(2 * np.pi)).to( device) d = torch.tensor([d], dtype=torch.float).to(device) batchsz = kappa.size()[0] rt_tensor = torch.zeros(batchsz) for k_idx in range(batchsz): k = kappa[k_idx] # print(k) # print(k) # print(d) first = k * bessel_iv(d / 2, k) / bessel_iv(d / 2 - 1, k) second = (d / 2 - 1) * torch.log(k) - torch.log(bessel_iv(d / 2 - 1, k)) combin = first + second + const rt_tensor[k_idx] = combin # rt_bag.append(combin) return rt_tensor.to(device) # return torch.tensor(rt_bag,requires_grad=True).to(device) def build_bow_rep(self, lat_code, n_sample): batch_sz = lat_code.size()[0] tup = self.estimate_param(latent_code=lat_code) mu = tup['mu'] norm = tup['norm'] kappa = tup['kappa'] kld = self.compute_KLD(tup, batch_sz) vecs = [] kappa_clone = kappa.detach().cpu().numpy() if n_sample == 1: return tup, kld, self.sample_cell(mu, norm, kappa_clone) for n in range(n_sample): sample = self.sample_cell(mu, norm, kappa_clone) vecs.append(sample) vecs = torch.cat(vecs, dim=0) return tup, kld, vecs def sample_cell(self, mu, norm, kappa): batch_sz, lat_dim = mu.size() # mu = GVar(mu) mu = mu / torch.norm(mu, p=2, dim=1, keepdim=True) w = self._sample_weight_batch(kappa, lat_dim, batch_sz) w = w.unsqueeze(1) # batch version w_var = GVar(w * torch.ones(batch_sz, lat_dim).to(device)) v = self._sample_ortho_batch(mu, lat_dim) scale_factr = torch.sqrt( GVar(torch.ones(batch_sz, lat_dim)) - torch.pow(w_var, 2)) orth_term = v * scale_factr muscale = mu * w_var sampled_vec = orth_term + muscale return sampled_vec.unsqueeze(0).to(device) def _sample_weight_batch(self, kappa, dim, batch_sz=1): # result = torch.FloatTensor((batch_sz)) result = np.zeros((batch_sz)) for b in range(batch_sz): result[b] = self._sample_weight(kappa[b], dim) return torch.from_numpy(result).float().to(device) def _sample_weight(self, kappa, dim): """Rejection sampling scheme for sampling distance from center on surface of the sphere. """ dim = dim - 1 # since S^{n-1} # print(dim) # print(kappa) b = dim / (np.sqrt(4. * kappa ** 2 + dim ** 2) + 2 * kappa) # b= 1/(sqrt(4.* kdiv**2 + 1) + 2 * kdiv) x = (1. - b) / (1. + b) c = kappa * x + dim * np.log(1 - x ** 2) # dim * (kdiv *x + np.log(1-x**2)) while True: z = np.random.beta(dim / 2., dim / 2.) # concentrates towards 0.5 as d-> inf w = (1. - (1. + b) * z) / (1. - (1. - b) * z) u = np.random.uniform(low=0, high=1) if kappa * w + dim * np.log(1. - x * w) - c >= np.log( u): # thresh is dim *(kdiv * (w-x) + log(1-x*w) -log(1-x**2)) return w def _sample_ortho_batch(self, mu, dim): """ :param mu: Variable, [batch size, latent dim] :param dim: scala. =latent dim :return: """ _batch_sz, _lat_dim = mu.size() assert _lat_dim == dim squeezed_mu = mu.unsqueeze(1) v = GVar(torch.randn(_batch_sz, dim, 1)) # TODO random # v = GVar(torch.linspace(-1, 1, steps=dim)) # v = v.expand(_batch_sz, dim).unsqueeze(2) rescale_val = torch.bmm(squeezed_mu, v).squeeze(2) proj_mu_v = mu * rescale_val ortho = v.squeeze() - proj_mu_v ortho_norm = torch.norm(ortho, p=2, dim=1, keepdim=True) y = ortho / ortho_norm return y def _sample_orthonormal_to(self, mu, dim): """Sample point on sphere orthogonal to mu. """ v = GVar(torch.randn(dim)) # TODO random # v = GVar(torch.linspace(-1,1,steps=dim)) rescale_value = mu.dot(v) / mu.norm() proj_mu_v = mu * rescale_value.expand(dim) ortho = v - proj_mu_v ortho_norm = torch.norm(ortho) return ortho / ortho_norm.expand_as(ortho) # # a = torch.tensor(10) # b = torch.ones(1, dtype=torch.float, requires_grad=True) # # y = bessel(a, b) # loss = 1 - y # print(y) # loss.backward() # print(a) def KL_guu(k, d): kld = k * ((sp.iv(d / 2.0 + 1.0, k) \ + sp.iv(d / 2.0, k) * d / (2.0 * k)) / sp.iv(d / 2.0, k) - d / (2.0 * k)) \ + d * np.log(k) / 2.0 - np.log(sp.iv(d / 2.0, k)) \ - sp.loggamma(d / 2 + 1) - d * np.log(2) / 2 return kld from scipy.special import ive from scipy.special import iv # print(iv(100,50)) def KL_davidson(k, d): vmf_entropy = k * ive(d / 2, k) / ive((d / 2) - 1, k) + \ (d / 2 - 1) * np.log(k) \ - (d / 2) * np.log(2 * np.pi) - np.log(iv(d / 2 - 1, k)) hyu_ent = np.log(2) + (d / 2) * np.log(np.pi) - sp.loggamma( d / 2) kl = vmf_entropy + hyu_ent return kl # # first = k * bessel(d / 2, k) / bessel(d / 2 - 1, k) # second = (d / 2 - 1) * torch.log(k) - torch.log(bessel(d / 2 - 1, k)) # const = torch.tensor( # np.log(3.1415926) * d / 2 + np.log(2) - sp.loggamma(d / 2).real - (d / 2) * np.log(2 * 3.1415926)).to( # devic # for kappa in range(10, 150, 20): # for d in range(50, 150, 50): # print("Davidson:{}\t\tGuu:{}".format(KL_davidson(kappa, d), KL_guu(kappa, d)))
35.873754
120
0.576403
c7f667527161b701f9dccfad3c7aa7ea6fa76227
14,011
py
Python
MAPS/scalar_train_small_fully_connected.py
gmooers96/CBRAIN-CAM
c5a26e415c031dea011d7cb0b8b4c1ca00751e2a
[ "MIT" ]
null
null
null
MAPS/scalar_train_small_fully_connected.py
gmooers96/CBRAIN-CAM
c5a26e415c031dea011d7cb0b8b4c1ca00751e2a
[ "MIT" ]
null
null
null
MAPS/scalar_train_small_fully_connected.py
gmooers96/CBRAIN-CAM
c5a26e415c031dea011d7cb0b8b4c1ca00751e2a
[ "MIT" ]
5
2019-09-30T20:17:13.000Z
2022-03-01T07:03:30.000Z
import math import os os.environ["CUDA_VISIBLE_DEVICES"]="0" import argparse import json import tensorflow as tf import tensorflow_probability as tfp import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable import netCDF4 import keras from keras import layers from keras import backend as K from keras.models import Model from keras.losses import binary_crossentropy, mse from keras.utils import plot_model from keras.callbacks import ModelCheckpoint class AnnealingCallback(keras.callbacks.Callback): def __init__(self, epochs): super(AnnealingCallback, self).__init__() self.epochs = epochs def on_epoch_begin(self, epoch, logs={}): new_kl_weight = epoch/self.epochs K.set_value(self.model.kl_weight, new_kl_weight) print("Using updated KL Weight:", K.get_value(self.model.kl_weight)) class Sampling(keras.layers.Layer): def call(self, inputs): """ TODO """ mean, log_var = inputs return K.random_normal(tf.shape(log_var)) * K.exp(log_var/2) + mean def kl_reconstruction_loss(z_log_var, z_mean, vae, lambda_weight): def _kl_reconstruction_loss(true, pred): """ TODO """ true = tf.reshape(true, [-1, 128]) x_mu = pred[:, :128] x_log_var = pred[:, 128:] # Gaussian reconstruction loss mse = -0.5 * K.sum(K.square(true - x_mu)/K.exp(x_log_var), axis=1) var_trace = -0.5 * K.sum(x_log_var, axis=1) log2pi = -0.5 * 128 * np.log(2 * np.pi) log_likelihood = mse + var_trace + log2pi #print("log likelihood shape", log_likelihood.shape) # NOTE: We don't take a mean here, since we first want to add the KL term reconstruction_loss = -log_likelihood # KL divergence loss kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var) kl_loss = K.sum(kl_loss, axis=1) kl_loss *= -0.5 print("true is",tf.shape(true)) print("true is",true.get_shape) print("true is", K.int_shape(true)) print("x_mu is",tf.shape(x_mu)) print("x_mu is",x_mu.get_shape) print("x_mu is", K.int_shape(x_mu)) #print(fgdfdfgdfag) covariance_truth = tfp.stats.covariance(true) covariance_prediction = tfp.stats.covariance(x_mu) Frobenius_norm = tf.norm(covariance_prediction-covariance_truth, ord="euclidean") print("true is",tf.shape(true)) print("true is",true.get_shape) print("true is", K.int_shape(true)) print("x_mu is",tf.shape(x_mu)) print("x_mu is",x_mu.get_shape) print("x_mu is", K.int_shape(x_mu)) #Frobenius_norm = K.sum(Frobenius_norm, axis = 1) #print("Frobenius_norm is",tf.shape(Frobenius_norm)) #print("Frobenius_norm is",Frobenius_norm.get_shape) print("reconstruction_loss is",tf.shape(reconstruction_loss)) print("reconstruction_loss is",reconstruction_loss.get_shape) print("reconstruction_loss is", K.int_shape(reconstruction_loss)) print("kl_loss is",tf.shape(kl_loss)) print("kl_loss is",kl_loss.get_shape) print("kl_loss is", K.int_shape(kl_loss)) #print(gsdgsgs) #Frobenius_norm = K.sum(Frobenius_norm, axis = 1) ##################################################################################### #return K.mean(reconstruction_loss + vae.kl_weight*kl_loss + lambda_weight*Frobenius_norm) return K.mean(reconstruction_loss + vae.kl_weight*kl_loss)# + lambda_weight*Frobenius_norm) return _kl_reconstruction_loss def kl(z_log_var, z_mean): def _kl(true, pred): """ TODO """ kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var) kl_loss = K.sum(kl_loss, axis=-1) kl_loss *= -0.5 # kl_loss = K.print_tensor(kl_loss, message='EULA PEULA') return K.mean(kl_loss) return _kl def reconstruction(true, pred): """ TODO """ true = tf.reshape(true, [-1, 128]) x_mu = pred[:, :128] x_log_var = pred[:, 128:] mse = -0.5 * K.sum(K.square(true - x_mu)/K.exp(x_log_var), axis=1) var_trace = -0.5 * K.sum(x_log_var, axis=1) log2pi = -0.5 * 128 * np.log(2 * np.pi) log_likelihood = mse + var_trace + log2pi print("log likelihood shape", log_likelihood.shape) return K.mean(-log_likelihood) def constrainer(z_log_var, z_mean, lambda_weight): def _constrainer(true, pred): true = tf.reshape(true, [-1, 128]) x_mu = pred[:, :128] covariance_truth = tfp.stats.covariance(true) covariance_prediction = tfp.stats.covariance(x_mu) Frobenius_norm = tf.norm(covariance_prediction-covariance_truth, ord="euclidean") return lambda_weight*Frobenius_norm #return 1000000.0*Frobenius_norm return _constrainer def power_spectrum(z_log_var, z_mean): def _power_spectrum(true, pred): p850 = tf.reshape(pred[22,:], [-1, 128 ]) t850 = tf.reshape(true[22,:], [-1, 128 ]) p850 = tf.cast(p850, dtype=tf.float32) t850 = tf.cast(t850, dtype=tf.float32) P_pred = tf.signal.rfft(p850)*tf.math.conj(tf.signal.rfft(p850)) P_truth = tf.signal.rfft(t850)*tf.math.conj(tf.signal.rfft(t850)) spectrum_loss = tf.math.square(tf.math.log(P_pred/P_truth)) spectrum_loss = tf.cast(spectrum_loss, dtype=tf.float32) #sprectrum_loss = K.sum(spectrum_loss, axis = 1) return spectrum_loss return _power_spectrum def encoder_gen(input_shape: tuple, encoder_config: dict, id): """ Create the architecture for the VAE encoder. """ class EncoderResult(): pass encoder_result = EncoderResult() inputs = keras.layers.Input(shape=[input_shape, 1]) print("shape of input after padding", inputs.shape) z = keras.layers.Flatten()(inputs) shape_before_flattening = K.int_shape(z) print("shape of input after flattening", inputs.shape) print("shape after first Dense layer", z.shape) z = keras.layers.Dense(encoder_config["dense_1"]["dim"], activation=encoder_config["activation"])(z) print("shape after first Dense layer", z.shape) z = keras.layers.Dense(encoder_config["dense_2"]["dim"], activation=encoder_config["activation"])(z) print("shape after second Dense layer", z.shape) # Compute mean and log variance z_mean = keras.layers.Dense(encoder_config["latent_dim"], name='z_mean')(z) z_log_var = keras.layers.Dense(encoder_config["latent_dim"], name='z_log_var')(z) print("z mean shape", z_mean._keras_shape) print("z log var shape", z_log_var._keras_shape) z = Sampling()([z_mean, z_log_var]) # Instantiate Keras model for VAE encoder vae_encoder = keras.Model(inputs=[inputs], outputs=[z_mean, z_log_var, z]) plot_model(vae_encoder, to_file='./model_graphs/model_diagrams/encoder_{}.png'.format(id), show_shapes=True) # Package up everything for the encoder encoder_result.inputs = inputs encoder_result.z_mean = z_mean encoder_result.z_log_var = z_log_var encoder_result.z = z encoder_result.vae_encoder = vae_encoder return encoder_result, shape_before_flattening def decoder_gen( original_input: tuple, decoder_config: dict, flatter_shape ): """ Create the architecture for the VAE decoder """ decoder_inputs = keras.layers.Input(shape=[decoder_config["latent_dim"]]) print("decoder_inputs", decoder_inputs._keras_shape) #x = keras.layers.Dense(np.prod(flatter_shape[1:]), activation=decoder_config["activation"])(decoder_inputs) #print("shape after initial change", x._keras_shape) # Reshape input to be an image #x = keras.layers.Reshape(flatter_shape[1:])(x) #print("shape after resdhaping to an image", x._keras_shape) #x = keras.layers.Dense(decoder_config["dense_1"]["dim"], activation=decoder_config["activation"])(x) #print("shape after first dense layer", x._keras_shape) x = keras.layers.Dense(decoder_config["dense_1"]["dim"], activation=decoder_config["activation"])(decoder_inputs) print("shape after first dense layer", x._keras_shape) x = keras.layers.Dense(decoder_config["dense_2"]["dim"], activation=decoder_config["activation"])(x) print("shape after second dense layer", x.shape) x_mu = keras.layers.Dense(decoder_config["dense_mu"]["dim"], activation=decoder_config["dense_mu"]["activation"])(x) print("shape after dense mu layer", x_mu._keras_shape) x_log_var = keras.layers.Dense(decoder_config["dense_log_var"]["dim"], activation=decoder_config["dense_log_var"]["activation"])(x) print("shape after dense log var layer", x_log_var._keras_shape) x_mu_log_var = keras.layers.Concatenate(axis=1)([x_mu, x_log_var]) variational_decoder = keras.Model(inputs=[decoder_inputs], outputs=[x_mu_log_var]) return variational_decoder def plot_training_losses(h, id): """ Plot training loss graphs for (1) KL term (2) Reconstruction term (3) Total ELBO loss """ hdict = h.history print(hdict) train_reconstruction_losses = hdict['reconstruction'] valid_reconstruction_losses = hdict['val_reconstruction'] kl_train_losses = hdict['_kl'] kl_valid_losses = hdict['val__kl'] #constraint_train_losses = hdict['_constrainer'] #constraint_valid_losses = hdict['val__constrainer'] total_train_losses = hdict['_kl_reconstruction_loss'] total_valid_losses = hdict['val__kl_reconstruction_loss'] epochs = range(1, len(train_reconstruction_losses) + 1) #fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(12.8, 4.8)) fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12.8, 4.8)) # Plot combined loss ax1.plot(epochs, total_train_losses, 'b', label='Train') ax1.plot(epochs, total_valid_losses, 'r', label='Valid') ax1.set(xlabel="Epochs", ylabel="Loss") ax1.legend(prop={'size': 10}) ax1.set_title("Combined Loss") # Plot KL ax2.plot(epochs, kl_train_losses, 'b', label='Train') ax2.plot(epochs, kl_valid_losses, 'r', label='Valid') ax2.set(xlabel="Epochs", ylabel="Loss") ax2.legend(prop={'size': 10}) ax2.set_title("KL Loss") # Plot reconstruction loss ax3.plot(epochs, train_reconstruction_losses, 'b', label='Train') ax3.plot(epochs, valid_reconstruction_losses, 'r', label='Valid') ax3.set(xlabel="Epochs", ylabel="Loss") ax3.legend(prop={'size': 10}) ax3.set_title("Reconstruction Loss") plt.tight_layout() plt.savefig('./model_graphs/losses/model_losses_{}.png'.format(id)) def main(): args = argument_parsing() print("Command line args:", args) f = open("./model_config/config_{}.json".format(args.id)) model_config = json.load(f) f.close() train_data = np.load(model_config["data"]["training_data_path"]) test_data = np.load(model_config["data"]["test_data_path"]) img_height = train_data.shape[1] print("Image shape:", img_height) # Construct VAE Encoder encoder_result, shape_flatten = encoder_gen((img_height), model_config["encoder"], args.id) # Construct VAE Decoder vae_decoder = decoder_gen( (img_height), model_config["decoder"], shape_flatten ) plot_model(vae_decoder, to_file='./model_graphs/model_diagrams/decoder_{}.png'.format(args.id), show_shapes=True) _, _, z = encoder_result.vae_encoder(encoder_result.inputs) x_mu_log_var = vae_decoder(z) vae = keras.Model(inputs=[encoder_result.inputs], outputs=[x_mu_log_var]) plot_model(vae, to_file='./model_graphs/model_diagrams/full_vae_{}.png'.format(args.id), show_shapes=True) vae.kl_weight = K.variable(model_config["kl_weight"]) # Specify the optimizer optimizer = keras.optimizers.Adam(lr=model_config['optimizer']['lr']) stat_weight = model_config['contraint_weight']['lambda'] # Compile model vae.compile( # loss=reconstruction, loss=kl_reconstruction_loss( encoder_result.z_log_var, encoder_result.z_mean, vae, stat_weight ), optimizer=optimizer, metrics=[ reconstruction, kl( encoder_result.z_log_var, encoder_result.z_mean ), kl_reconstruction_loss( encoder_result.z_log_var, encoder_result.z_mean, vae, stat_weight ) ] ) vae.summary() train_data = train_data.reshape(train_data.shape+(1,)) test_data = test_data.reshape(test_data.shape+(1,)) print("train data shape", train_data.shape) print("test data shape", test_data.shape) checkpoint = ModelCheckpoint( './models/model_{}.th'.format(args.id), monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True ) callbacks_list = [checkpoint] if model_config["annealing"]: kl_weight_annealing = AnnealingCallback(model_config["train_epochs"]) callbacks_list.append(kl_weight_annealing) h = vae.fit( x=train_data, y=train_data, epochs=model_config["train_epochs"], batch_size=model_config["batch_size"], validation_data=[test_data, test_data], callbacks=callbacks_list ) plot_training_losses(h, args.id) def argument_parsing(): parser = argparse.ArgumentParser() parser.add_argument('--id', type=int, help='This option specifies the config file to use to construct and train the VAE.') args = parser.parse_args() return args if __name__ == "__main__": main()
34.766749
135
0.65213
7918d7f7478ae872ea70a028ef308f4e3e3f5d72
7,647
py
Python
gewittergefahr/plotting/feature_map_plotting.py
liuximarcus/GewitterGefahr
d819874d616f98a25187bfd3091073a2e6d5279e
[ "MIT" ]
1
2020-11-19T08:15:03.000Z
2020-11-19T08:15:03.000Z
gewittergefahr/plotting/feature_map_plotting.py
liuximarcus/GewitterGefahr
d819874d616f98a25187bfd3091073a2e6d5279e
[ "MIT" ]
null
null
null
gewittergefahr/plotting/feature_map_plotting.py
liuximarcus/GewitterGefahr
d819874d616f98a25187bfd3091073a2e6d5279e
[ "MIT" ]
null
null
null
"""Plotting methods for CNN feature maps.""" import numpy import matplotlib matplotlib.use('agg') import matplotlib.pyplot as pyplot from gewittergefahr.gg_utils import error_checking from gewittergefahr.plotting import plotting_utils DEFAULT_FIG_WIDTH_INCHES = 15 DEFAULT_FIG_HEIGHT_INCHES = 15 DEFAULT_FONT_SIZE = 20 def plot_2d_feature_map( feature_matrix, axes_object, colour_map_object, font_size=DEFAULT_FONT_SIZE, colour_norm_object=None, min_colour_value=None, max_colour_value=None, annotation_string=None): """Plots 2-D feature map. M = number of rows in grid N = number of columns in grid :param feature_matrix: M-by-N numpy array of feature values (either before or after activation function -- this method doesn't care). :param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`. :param font_size: Font size for annotation. :param colour_map_object: Instance of `matplotlib.pyplot.cm`. :param colour_norm_object: Instance of `matplotlib.colors.BoundaryNorm`. :param min_colour_value: [used only if `colour_norm_object is None`] Minimum value in colour scheme. :param max_colour_value: [used only if `colour_norm_object is None`] Max value in colour scheme. :param annotation_string: Annotation (printed in the bottom-center of the map). For no annotation, leave this alone. """ error_checking.assert_is_numpy_array_without_nan(feature_matrix) error_checking.assert_is_numpy_array(feature_matrix, num_dimensions=2) if colour_norm_object is None: error_checking.assert_is_greater(max_colour_value, min_colour_value) colour_norm_object = None else: if hasattr(colour_norm_object, 'boundaries'): min_colour_value = colour_norm_object.boundaries[0] max_colour_value = colour_norm_object.boundaries[-1] else: min_colour_value = colour_norm_object.vmin max_colour_value = colour_norm_object.vmax axes_object.pcolormesh( feature_matrix, cmap=colour_map_object, norm=colour_norm_object, vmin=min_colour_value, vmax=max_colour_value, shading='flat', edgecolors='None') if annotation_string is not None: error_checking.assert_is_string(annotation_string) axes_object.text( 0.5, 0.01, annotation_string, fontsize=font_size, fontweight='bold', color='black', horizontalalignment='center', verticalalignment='bottom', transform=axes_object.transAxes) axes_object.set_xticks([]) axes_object.set_yticks([]) def plot_many_2d_feature_maps( feature_matrix, annotation_string_by_panel, num_panel_rows, colour_map_object, colour_norm_object=None, min_colour_value=None, max_colour_value=None, figure_width_inches=DEFAULT_FIG_WIDTH_INCHES, figure_height_inches=DEFAULT_FIG_HEIGHT_INCHES, font_size=DEFAULT_FONT_SIZE): """Plots many 2-D feature maps in the same figure (one per panel). M = number of rows in spatial grid N = number of columns in spatial grid P = number of panels :param feature_matrix: M-by-N-by-P numpy array of feature values (either before or after activation function -- this method doesn't care). :param annotation_string_by_panel: length-P list of annotations. annotation_string_by_panel[k] will be printed in the bottom-center of the [k]th panel. :param num_panel_rows: Number of panel rows. :param colour_map_object: See doc for `plot_2d_feature_map`. :param colour_norm_object: Same. :param min_colour_value: Same. :param max_colour_value: Same. :param figure_width_inches: Figure width. :param figure_height_inches: Figure height. :param font_size: Font size for panel labels. :return: figure_object: See doc for `plotting_utils.create_paneled_figure`. :return: axes_object_matrix: Same. """ pyplot.rc('axes', linewidth=3) error_checking.assert_is_numpy_array(feature_matrix, num_dimensions=3) num_panels = feature_matrix.shape[-1] error_checking.assert_is_numpy_array( numpy.array(annotation_string_by_panel), exact_dimensions=numpy.array([num_panels]) ) error_checking.assert_is_integer(num_panel_rows) error_checking.assert_is_geq(num_panel_rows, 1) error_checking.assert_is_leq(num_panel_rows, num_panels) num_panel_columns = int(numpy.ceil( float(num_panels) / num_panel_rows )) figure_object, axes_object_matrix = plotting_utils.create_paneled_figure( num_rows=num_panel_rows, num_columns=num_panel_columns, figure_width_inches=figure_width_inches, figure_height_inches=figure_height_inches, horizontal_spacing=0., vertical_spacing=0., shared_x_axis=False, shared_y_axis=False, keep_aspect_ratio=False) for i in range(num_panel_rows): for j in range(num_panel_columns): this_linear_index = i * num_panel_columns + j if this_linear_index >= num_panels: axes_object_matrix[i, j].axis('off') continue plot_2d_feature_map( feature_matrix=feature_matrix[..., this_linear_index], axes_object=axes_object_matrix[i, j], font_size=font_size, colour_map_object=colour_map_object, colour_norm_object=colour_norm_object, min_colour_value=min_colour_value, max_colour_value=max_colour_value, annotation_string=annotation_string_by_panel[this_linear_index] ) return figure_object, axes_object_matrix def plot_many_1d_feature_maps( feature_matrix, colour_map_object, colour_norm_object=None, min_colour_value=None, max_colour_value=None, figure_width_inches=DEFAULT_FIG_WIDTH_INCHES, figure_height_inches=DEFAULT_FIG_HEIGHT_INCHES): """Plots many 1-D feature maps in the same figure (one per column). N = number of points in spatial grid C = number of channels :param feature_matrix: N-by-C numpy array of feature values. :param colour_map_object: See doc for `plot_many_2d_feature_maps`. :param colour_norm_object: Same. :param min_colour_value: Same. :param max_colour_value: Same. :param figure_width_inches: Same. :param figure_height_inches: Same. :return: figure_object: See doc for `plotting_utils.create_paneled_figure`. :return: axes_object_matrix: Same. """ pyplot.rc('axes', linewidth=1) error_checking.assert_is_numpy_array(feature_matrix, num_dimensions=2) num_channels = feature_matrix.shape[1] num_spatial_points = feature_matrix.shape[0] figure_object, axes_object_matrix = plotting_utils.create_paneled_figure( num_rows=1, num_columns=num_channels, figure_width_inches=figure_width_inches, figure_height_inches=figure_height_inches, horizontal_spacing=0., vertical_spacing=0., shared_x_axis=False, shared_y_axis=False, keep_aspect_ratio=False) for k in range(num_channels): this_matrix = numpy.reshape( feature_matrix[..., k], (num_spatial_points, 1) ) plot_2d_feature_map( feature_matrix=this_matrix, axes_object=axes_object_matrix[0, k], font_size=30, colour_map_object=colour_map_object, colour_norm_object=colour_norm_object, min_colour_value=min_colour_value, max_colour_value=max_colour_value, annotation_string='' ) return figure_object, axes_object_matrix
39.828125
80
0.722898
c0013262202fd4a1674f576fe3efcc747907a571
28,315
py
Python
gluoncv/model_zoo/rcnn/faster_rcnn/predefined_models.py
aptsunny/gluon-cv
7f050d3411b1ada7d2b9515d63b848c55139fdbb
[ "Apache-2.0" ]
1
2020-03-18T04:19:26.000Z
2020-03-18T04:19:26.000Z
gluoncv/model_zoo/rcnn/faster_rcnn/predefined_models.py
aptsunny/gluon-cv
7f050d3411b1ada7d2b9515d63b848c55139fdbb
[ "Apache-2.0" ]
null
null
null
gluoncv/model_zoo/rcnn/faster_rcnn/predefined_models.py
aptsunny/gluon-cv
7f050d3411b1ada7d2b9515d63b848c55139fdbb
[ "Apache-2.0" ]
null
null
null
"""Predefined Faster RCNN Model.""" from __future__ import absolute_import import warnings import mxnet as mx from mxnet.gluon import nn from mxnet.gluon.contrib.nn import SyncBatchNorm from ..faster_rcnn import get_faster_rcnn from ....nn.feature import FPNFeatureExpander __all__ = ['faster_rcnn_resnet50_v1b_voc', 'faster_rcnn_resnet50_v1b_coco', 'faster_rcnn_fpn_resnet50_v1b_coco', 'faster_rcnn_fpn_syncbn_resnet50_v1b_coco', 'faster_rcnn_resnet50_v1b_custom', 'faster_rcnn_resnet101_v1d_voc', 'faster_rcnn_resnet101_v1d_coco', 'faster_rcnn_fpn_resnet101_v1d_coco', 'faster_rcnn_fpn_syncbn_resnet101_v1d_coco', 'faster_rcnn_resnet101_v1d_custom'] def faster_rcnn_resnet50_v1b_voc(pretrained=False, pretrained_base=True, **kwargs): r"""Faster RCNN model from the paper "Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = get_faster_rcnn_resnet50_v1b_voc(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet50_v1b from ....data import VOCDetection classes = VOCDetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=True, **kwargs) features = nn.HybridSequential() top_features = nn.HybridSequential() for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']: features.add(getattr(base_network, layer)) for layer in ['layer4']: top_features.add(getattr(base_network, layer)) train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_faster_rcnn( name='resnet50_v1b', dataset='voc', pretrained=pretrained, features=features, top_features=top_features, classes=classes, short=600, max_size=1000, train_patterns=train_patterns, nms_thresh=0.3, nms_topk=400, post_nms=100, roi_mode='align', roi_size=(14, 14), strides=16, clip=None, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=300, rpn_min_size=16, num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs) def faster_rcnn_resnet50_v1b_coco(pretrained=False, pretrained_base=True, **kwargs): r"""Faster RCNN model from the paper "Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = get_faster_rcnn_resnet50_v1b_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet50_v1b from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=True, **kwargs) features = nn.HybridSequential() top_features = nn.HybridSequential() for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']: features.add(getattr(base_network, layer)) for layer in ['layer4']: top_features.add(getattr(base_network, layer)) train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_faster_rcnn( name='resnet50_v1b', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, short=800, max_size=1333, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(14, 14), strides=16, clip=4.14, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs) def faster_rcnn_fpn_resnet50_v1b_coco(pretrained=False, pretrained_base=True, **kwargs): r"""Faster RCNN model with FPN from the paper "Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks" "Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2016). Feature Pyramid Networks for Object Detection" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `Ture`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = get_faster_rcnn_fpn_resnet50_v1b_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet50_v1b from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=True, **kwargs) features = FPNFeatureExpander( network=base_network, outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu17_fwd', 'layers4_relu8_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True, use_upsample=True, use_elewadd=True, use_p6=True, no_bias=False, pretrained=pretrained_base) top_features = None # 2 FC layer before RCNN cls and reg box_features = nn.HybridSequential() for _ in range(2): box_features.add(nn.Dense(1024, weight_initializer=mx.init.Normal(0.01))) box_features.add(nn.Activation('relu')) train_patterns = '|'.join( ['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv', 'P']) return get_faster_rcnn( name='fpn_resnet50_v1b', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, box_features=box_features, short=800, max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(7, 7), strides=(4, 8, 16, 32, 64), clip=4.14, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=512, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs) def faster_rcnn_fpn_syncbn_resnet50_v1b_coco(pretrained=False, pretrained_base=True, num_devices=0, **kwargs): r"""Faster RCNN model with FPN from the paper "Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks" "Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2016). Feature Pyramid Networks for Object Detection" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `Ture`, this has no effect. num_devices : int, default is 0 Number of devices for sync batch norm layer. if less than 1, use all devices available. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = get_faster_rcnn_fpn_syncbn_resnet50_v1b_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet50_v1b from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base gluon_norm_kwargs = {'num_devices': num_devices} if num_devices >= 1 else {} base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=False, norm_layer=SyncBatchNorm, norm_kwargs=gluon_norm_kwargs, **kwargs) sym_norm_kwargs = {'ndev': num_devices} if num_devices >= 1 else {} features = FPNFeatureExpander( network=base_network, outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu17_fwd', 'layers4_relu8_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True, use_upsample=True, use_elewadd=True, use_p6=True, no_bias=True, pretrained=pretrained_base, norm_layer=mx.sym.contrib.SyncBatchNorm, norm_kwargs=sym_norm_kwargs) top_features = None # 1 Conv 1 FC layer before RCNN cls and reg box_features = nn.HybridSequential() box_features.add(nn.Conv2D(256, 3, padding=1, use_bias=False), SyncBatchNorm(**gluon_norm_kwargs), nn.Activation('relu'), nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)), nn.Activation('relu')) train_patterns = '(?!.*moving)' # excluding symbol bn moving mean and var return get_faster_rcnn( name='fpn_syncbn_resnet50_v1b', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, box_features=box_features, short=(640, 800), max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(7, 7), strides=(4, 8, 16, 32, 64), clip=4.14, rpn_channel=256, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=512, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs) def faster_rcnn_resnet50_v1b_custom(classes, transfer=None, pretrained_base=True, pretrained=False, **kwargs): r"""Faster RCNN model with resnet50_v1b base network on custom dataset. Parameters ---------- classes : iterable of str Names of custom foreground classes. `len(classes)` is the number of foreground classes. transfer : str or None If not `None`, will try to reuse pre-trained weights from faster RCNN networks trained on other datasets. pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Returns ------- mxnet.gluon.HybridBlock Hybrid faster RCNN network. """ if pretrained: warnings.warn("Custom models don't provide `pretrained` weights, ignored.") if transfer is None: from ....model_zoo.resnetv1b import resnet50_v1b base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=True, **kwargs) features = nn.HybridSequential() top_features = nn.HybridSequential() for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']: features.add(getattr(base_network, layer)) for layer in ['layer4']: top_features.add(getattr(base_network, layer)) train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_faster_rcnn( name='resnet50_v1b', dataset='custom', pretrained=pretrained, features=features, top_features=top_features, classes=classes, train_patterns=train_patterns, **kwargs) else: from ...model_zoo import get_model net = get_model('faster_rcnn_resnet50_v1b_' + str(transfer), pretrained=True, **kwargs) reuse_classes = [x for x in classes if x in net.classes] net.reset_class(classes, reuse_weights=reuse_classes) return net def faster_rcnn_resnet101_v1d_voc(pretrained=False, pretrained_base=True, **kwargs): r"""Faster RCNN model from the paper "Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks" Parameters ---------- pretrained : bool, optional, default is False Load pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = get_faster_rcnn_resnet101_v1d_voc(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet101_v1d from ....data import VOCDetection classes = VOCDetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False, use_global_stats=True, **kwargs) features = nn.HybridSequential() top_features = nn.HybridSequential() for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']: features.add(getattr(base_network, layer)) for layer in ['layer4']: top_features.add(getattr(base_network, layer)) train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_faster_rcnn( name='resnet101_v1d', dataset='voc', pretrained=pretrained, features=features, top_features=top_features, classes=classes, short=600, max_size=1000, train_patterns=train_patterns, nms_thresh=0.3, nms_topk=400, post_nms=100, roi_mode='align', roi_size=(14, 14), strides=16, clip=None, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=300, rpn_min_size=16, num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs) def faster_rcnn_resnet101_v1d_coco(pretrained=False, pretrained_base=True, **kwargs): r"""Faster RCNN model from the paper "Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks" Parameters ---------- pretrained : bool, optional, default is False Load pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = get_faster_rcnn_resnet101_v1d_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet101_v1d from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False, use_global_stats=True, **kwargs) features = nn.HybridSequential() top_features = nn.HybridSequential() for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']: features.add(getattr(base_network, layer)) for layer in ['layer4']: top_features.add(getattr(base_network, layer)) train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_faster_rcnn( name='resnet101_v1d', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, short=800, max_size=1333, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(14, 14), strides=16, clip=4.14, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs) def faster_rcnn_fpn_resnet101_v1d_coco(pretrained=False, pretrained_base=True, **kwargs): r"""Faster RCNN model with FPN from the paper "Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks" "Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2016). Feature Pyramid Networks for Object Detection" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `Ture`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = get_faster_rcnn_fpn_resnet101_v1d_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet101_v1d from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False, use_global_stats=True, **kwargs) features = FPNFeatureExpander( network=base_network, outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu68_fwd', 'layers4_relu8_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True, use_upsample=True, use_elewadd=True, use_p6=True, no_bias=False, pretrained=pretrained_base) top_features = None # 2 FC layer before RCNN cls and reg box_features = nn.HybridSequential() for _ in range(2): box_features.add(nn.Dense(1024, weight_initializer=mx.init.Normal(0.01))) box_features.add(nn.Activation('relu')) train_patterns = '|'.join( ['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv', 'P']) return get_faster_rcnn( name='fpn_resnet101_v1d', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, box_features=box_features, short=800, max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(7, 7), strides=(4, 8, 16, 32, 64), clip=4.14, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=512, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs) def faster_rcnn_fpn_syncbn_resnet101_v1d_coco(pretrained=False, pretrained_base=True, num_devices=0, **kwargs): r"""Faster RCNN model with FPN from the paper "Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks" "Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2016). Feature Pyramid Networks for Object Detection" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `Ture`, this has no effect. num_devices : int, default is 0 Number of devices for sync batch norm layer. if less than 1, use all devices available. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = get_faster_rcnn_fpn_syncbn_resnet101_v1d_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet101_v1d from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base gluon_norm_kwargs = {'num_devices': num_devices} if num_devices >= 1 else {} base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False, use_global_stats=False, norm_layer=SyncBatchNorm, norm_kwargs=gluon_norm_kwargs, **kwargs) sym_norm_kwargs = {'ndev': num_devices} if num_devices >= 1 else {} features = FPNFeatureExpander( network=base_network, outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu68_fwd', 'layers4_relu8_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True, use_upsample=True, use_elewadd=True, use_p6=True, no_bias=True, pretrained=pretrained_base, norm_layer=mx.sym.contrib.SyncBatchNorm, norm_kwargs=sym_norm_kwargs) top_features = None # 1 Conv 1 FC layer before RCNN cls and reg box_features = nn.HybridSequential() for _ in range(4): box_features.add(nn.Conv2D(256, 3, padding=1, use_bias=False), SyncBatchNorm(**gluon_norm_kwargs), nn.Activation('relu')) box_features.add(nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)), nn.Activation('relu')) train_patterns = '(?!.*moving)' # excluding symbol bn moving mean and var return get_faster_rcnn( name='fpn_syncbn_resnet101_v1d', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, box_features=box_features, short=(640, 800), max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(7, 7), strides=(4, 8, 16, 32, 64), clip=4.14, rpn_channel=256, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=512, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs) def faster_rcnn_resnet101_v1d_custom(classes, transfer=None, pretrained_base=True, pretrained=False, **kwargs): r"""Faster RCNN model with resnet101_v1d base network on custom dataset. Parameters ---------- classes : iterable of str Names of custom foreground classes. `len(classes)` is the number of foreground classes. transfer : str or None If not `None`, will try to reuse pre-trained weights from faster RCNN networks trained on other datasets. pretrained_base : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Returns ------- mxnet.gluon.HybridBlock Hybrid faster RCNN network. """ if pretrained: warnings.warn("Custom models don't provide `pretrained` weights, ignored.") if transfer is None: from ....model_zoo.resnetv1b import resnet101_v1d base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False, use_global_stats=True, **kwargs) features = nn.HybridSequential() top_features = nn.HybridSequential() for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']: features.add(getattr(base_network, layer)) for layer in ['layer4']: top_features.add(getattr(base_network, layer)) train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_faster_rcnn( name='resnet101_v1d', dataset='custom', pretrained=pretrained, features=features, top_features=top_features, classes=classes, train_patterns=train_patterns, **kwargs) else: from ....model_zoo import get_model net = get_model('faster_rcnn_resnet101_v1d_' + str(transfer), pretrained=True, **kwargs) reuse_classes = [x for x in classes if x in net.classes] net.reset_class(classes, reuse_weights=reuse_classes) return net
49.938272
100
0.671446
fd42a12229725badd8822327248f7f2b9ba862b6
16,763
py
Python
test/functional/test_framework/mininode.py
SovranoCoin/sovranocoin
d18c83a4f4db44393de271eb2b8fba6c1d536db1
[ "MIT" ]
4
2019-09-15T01:19:06.000Z
2021-05-03T13:59:19.000Z
test/functional/test_framework/mininode.py
SovranoCoin/sovranocoin
d18c83a4f4db44393de271eb2b8fba6c1d536db1
[ "MIT" ]
null
null
null
test/functional/test_framework/mininode.py
SovranoCoin/sovranocoin
d18c83a4f4db44393de271eb2b8fba6c1d536db1
[ "MIT" ]
5
2019-01-15T18:59:04.000Z
2020-06-21T08:42:32.000Z
#!/usr/bin/env python3 # Copyright (c) 2010 ArtForz -- public domain half-a-node # Copyright (c) 2012 Jeff Garzik # Copyright (c) 2010-2017 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Bitcoin P2P network half-a-node. This python code was modified from ArtForz' public domain half-a-node, as found in the mini-node branch of http://github.com/jgarzik/pynode. P2PConnection: A low-level connection object to a node's P2P interface P2PInterface: A high-level interface object for communicating to a node over P2P""" import asyncore from collections import defaultdict from io import BytesIO import logging import socket import struct import sys import threading from test_framework.messages import * from test_framework.util import wait_until logger = logging.getLogger("TestFramework.mininode") MESSAGEMAP = { b"addr": msg_addr, b"block": msg_block, b"blocktxn": msg_blocktxn, b"cmpctblock": msg_cmpctblock, b"feefilter": msg_feefilter, b"getaddr": msg_getaddr, b"getblocks": msg_getblocks, b"getblocktxn": msg_getblocktxn, b"getdata": msg_getdata, b"getheaders": msg_getheaders, b"headers": msg_headers, b"inv": msg_inv, b"mempool": msg_mempool, b"ping": msg_ping, b"pong": msg_pong, b"reject": msg_reject, b"sendcmpct": msg_sendcmpct, b"sendheaders": msg_sendheaders, b"tx": msg_tx, b"verack": msg_verack, b"version": msg_version, #b"getsporks": msg_generic, } MAGIC_BYTES = { "mainnet": b"\x90\xc4\xfd\xe9", # mainnet "testnet3": b"\x45\x76\x65\xba", # testnet3 "regtest": b"\xa1\xcf\x7e\xac", # regtest } class P2PConnection(asyncore.dispatcher): """A low-level connection object to a node's P2P interface. This class is responsible for: - opening and closing the TCP connection to the node - reading bytes from and writing bytes to the socket - deserializing and serializing the P2P message header - logging messages as they are sent and received This class contains no logic for handing the P2P message payloads. It must be sub-classed and the on_message() callback overridden.""" def __init__(self): # All P2PConnections must be created before starting the NetworkThread. # assert that the network thread is not running. assert not network_thread_running() super().__init__(map=mininode_socket_map) def peer_connect(self, dstaddr, dstport, net="regtest"): self.dstaddr = dstaddr self.dstport = dstport self.create_socket(socket.AF_INET, socket.SOCK_STREAM) self.socket.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) self.sendbuf = b"" self.recvbuf = b"" self.state = "connecting" self.network = net self.disconnect = False logger.info('Connecting to SOVRANOCOIN Node: %s:%d' % (self.dstaddr, self.dstport)) try: self.connect((dstaddr, dstport)) except: self.handle_close() def peer_disconnect(self): # Connection could have already been closed by other end. if self.state == "connected": self.disconnect_node() # Connection and disconnection methods def handle_connect(self): """asyncore callback when a connection is opened.""" if self.state != "connected": logger.debug("Connected & Listening: %s:%d" % (self.dstaddr, self.dstport)) self.state = "connected" self.on_open() def handle_close(self): """asyncore callback when a connection is closed.""" logger.debug("Closing connection to: %s:%d" % (self.dstaddr, self.dstport)) self.state = "closed" self.recvbuf = b"" self.sendbuf = b"" try: self.close() except: pass self.on_close() def disconnect_node(self): """Disconnect the p2p connection. Called by the test logic thread. Causes the p2p connection to be disconnected on the next iteration of the asyncore loop.""" self.disconnect = True # Socket read methods def handle_read(self): """asyncore callback when data is read from the socket.""" t = self.recv(8192) if len(t) > 0: self.recvbuf += t self._on_data() def _on_data(self): """Try to read P2P messages from the recv buffer. This method reads data from the buffer in a loop. It deserializes, parses and verifies the P2P header, then passes the P2P payload to the on_message callback for processing.""" try: while True: if len(self.recvbuf) < 4: return if self.recvbuf[:4] != MAGIC_BYTES[self.network]: raise ValueError("got garbage %s" % repr(self.recvbuf)) if len(self.recvbuf) < 4 + 12 + 4 + 4: return command = self.recvbuf[4:4+12].split(b"\x00", 1)[0] msglen = struct.unpack("<i", self.recvbuf[4+12:4+12+4])[0] checksum = self.recvbuf[4+12+4:4+12+4+4] if len(self.recvbuf) < 4 + 12 + 4 + 4 + msglen: return msg = self.recvbuf[4+12+4+4:4+12+4+4+msglen] th = sha256(msg) h = sha256(th) if checksum != h[:4]: raise ValueError("got bad checksum " + repr(self.recvbuf)) self.recvbuf = self.recvbuf[4+12+4+4+msglen:] if command in MESSAGEMAP: #raise ValueError("Received unknown command from %s:%d: '%s' %s" % (self.dstaddr, self.dstport, command, repr(msg))) logger.debug("Command: '" + str(command) + "'") f = BytesIO(msg) t = MESSAGEMAP[command]() t.deserialize(f) self._log_message("receive", t) self.on_message(t) except Exception as e: logger.exception('Error reading message:', repr(e)) raise def on_message(self, message): """Callback for processing a P2P payload. Must be overridden by derived class.""" raise NotImplementedError # Socket write methods def writable(self): """asyncore method to determine whether the handle_write() callback should be called on the next loop.""" with mininode_lock: pre_connection = self.state == "connecting" length = len(self.sendbuf) return (length > 0 or pre_connection) def handle_write(self): """asyncore callback when data should be written to the socket.""" with mininode_lock: # asyncore does not expose socket connection, only the first read/write # event, thus we must check connection manually here to know when we # actually connect if self.state == "connecting": self.handle_connect() if not self.writable(): return try: sent = self.send(self.sendbuf) except: self.handle_close() return self.sendbuf = self.sendbuf[sent:] def send_message(self, message, pushbuf=False): """Send a P2P message over the socket. This method takes a P2P payload, builds the P2P header and adds the message to the send buffer to be sent over the socket.""" if self.state != "connected" and not pushbuf: raise IOError('Not connected, no pushbuf') self._log_message("send", message) command = message.command data = message.serialize() tmsg = MAGIC_BYTES[self.network] tmsg += command tmsg += b"\x00" * (12 - len(command)) tmsg += struct.pack("<I", len(data)) th = sha256(data) h = sha256(th) tmsg += h[:4] tmsg += data with mininode_lock: if (len(self.sendbuf) == 0 and not pushbuf): try: sent = self.send(tmsg) self.sendbuf = tmsg[sent:] except BlockingIOError: self.sendbuf = tmsg else: self.sendbuf += tmsg # Class utility methods def _log_message(self, direction, msg): """Logs a message being sent or received over the connection.""" if direction == "send": log_message = "Send message to " elif direction == "receive": log_message = "Received message from " log_message += "%s:%d: %s" % (self.dstaddr, self.dstport, repr(msg)[:500]) if len(log_message) > 500: log_message += "... (msg truncated)" logger.debug(log_message) class P2PInterface(P2PConnection): """A high-level P2P interface class for communicating with a Bitcoin node. This class provides high-level callbacks for processing P2P message payloads, as well as convenience methods for interacting with the node over P2P. Individual testcases should subclass this and override the on_* methods if they want to alter message handling behaviour.""" def __init__(self): super().__init__() # Track number of messages of each type received and the most recent # message of each type self.message_count = defaultdict(int) self.last_message = {} # A count of the number of ping messages we've sent to the node self.ping_counter = 1 # The network services received from the peer self.nServices = 0 def peer_connect(self, *args, services=NODE_NETWORK, send_version=True, **kwargs): super().peer_connect(*args, **kwargs) if send_version: # Send a version msg vt = msg_version() vt.nServices = services vt.addrTo.ip = self.dstaddr vt.addrTo.port = self.dstport vt.addrFrom.ip = "0.0.0.0" vt.addrFrom.port = 0 self.send_message(vt, True) # Message receiving methods def on_message(self, message): """Receive message and dispatch message to appropriate callback. We keep a count of how many of each message type has been received and the most recent message of each type.""" with mininode_lock: try: command = message.command.decode('ascii') self.message_count[command] += 1 self.last_message[command] = message getattr(self, 'on_' + command)(message) except: print("ERROR delivering %s (%s)" % (repr(message), sys.exc_info()[0])) raise # Callback methods. Can be overridden by subclasses in individual test # cases to provide custom message handling behaviour. def on_open(self): pass def on_close(self): pass def on_addr(self, message): pass def on_block(self, message): pass def on_blocktxn(self, message): pass def on_cmpctblock(self, message): pass def on_feefilter(self, message): pass def on_getaddr(self, message): pass def on_getblocks(self, message): pass def on_getblocktxn(self, message): pass def on_getdata(self, message): pass def on_getheaders(self, message): pass def on_headers(self, message): pass def on_mempool(self, message): pass def on_pong(self, message): pass def on_reject(self, message): pass def on_sendcmpct(self, message): pass def on_sendheaders(self, message): pass def on_tx(self, message): pass def on_inv(self, message): want = msg_getdata() for i in message.inv: if i.type != 0: want.inv.append(i) if len(want.inv): self.send_message(want) def on_ping(self, message): self.send_message(msg_pong(message.nonce)) def on_verack(self, message): self.verack_received = True def on_version(self, message): assert message.nVersion >= MIN_VERSION_SUPPORTED, "Version {} received. Test framework only supports versions greater than {}".format(message.nVersion, MIN_VERSION_SUPPORTED) self.send_message(msg_verack()) self.nServices = message.nServices # Connection helper methods def wait_for_disconnect(self, timeout=60): test_function = lambda: self.state != "connected" wait_until(test_function, timeout=timeout, lock=mininode_lock) # Message receiving helper methods def wait_for_block(self, blockhash, timeout=60): test_function = lambda: self.last_message.get("block") and self.last_message["block"].block.rehash() == blockhash wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_getdata(self, timeout=60): test_function = lambda: self.last_message.get("getdata") wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_getheaders(self, timeout=60): test_function = lambda: self.last_message.get("getheaders") wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_inv(self, expected_inv, timeout=60): """Waits for an INV message and checks that the first inv object in the message was as expected.""" if len(expected_inv) > 1: raise NotImplementedError("wait_for_inv() will only verify the first inv object") test_function = lambda: self.last_message.get("inv") and \ self.last_message["inv"].inv[0].type == expected_inv[0].type and \ self.last_message["inv"].inv[0].hash == expected_inv[0].hash wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_verack(self, timeout=60): test_function = lambda: self.message_count["verack"] wait_until(test_function, timeout=timeout, lock=mininode_lock) # Message sending helper functions def send_and_ping(self, message): self.send_message(message) self.sync_with_ping() # Sync up with the node def sync_with_ping(self, timeout=60): self.send_message(msg_ping(nonce=self.ping_counter)) test_function = lambda: self.last_message.get("pong") and self.last_message["pong"].nonce == self.ping_counter wait_until(test_function, timeout=timeout, lock=mininode_lock) self.ping_counter += 1 # Keep our own socket map for asyncore, so that we can track disconnects # ourselves (to workaround an issue with closing an asyncore socket when # using select) mininode_socket_map = dict() # One lock for synchronizing all data access between the networking thread (see # NetworkThread below) and the thread running the test logic. For simplicity, # P2PConnection acquires this lock whenever delivering a message to a P2PInterface, # and whenever adding anything to the send buffer (in send_message()). This # lock should be acquired in the thread running the test logic to synchronize # access to any data shared with the P2PInterface or P2PConnection. mininode_lock = threading.RLock() class NetworkThread(threading.Thread): def __init__(self): super().__init__(name="NetworkThread") def run(self): while mininode_socket_map: # We check for whether to disconnect outside of the asyncore # loop to workaround the behavior of asyncore when using # select disconnected = [] for fd, obj in mininode_socket_map.items(): if obj.disconnect: disconnected.append(obj) [obj.handle_close() for obj in disconnected] asyncore.loop(0.1, use_poll=True, map=mininode_socket_map, count=1) logger.debug("Network thread closing") def network_thread_start(): """Start the network thread.""" # Only one network thread may run at a time assert not network_thread_running() NetworkThread().start() def network_thread_running(): """Return whether the network thread is running.""" return any([thread.name == "NetworkThread" for thread in threading.enumerate()]) def network_thread_join(timeout=10): """Wait timeout seconds for the network thread to terminate. Throw if the network thread doesn't terminate in timeout seconds.""" network_threads = [thread for thread in threading.enumerate() if thread.name == "NetworkThread"] assert len(network_threads) <= 1 for thread in network_threads: thread.join(timeout) assert not thread.is_alive()
37.669663
182
0.633598
e766343ebc9e5cabfb88d1bccd35040f0e60872a
7,827
py
Python
test/test_bvr_rest_before_after.py
doedotdev/bvr
023fc93424fa6a50c8a3c2ce2fc48b76a041b58c
[ "MIT" ]
null
null
null
test/test_bvr_rest_before_after.py
doedotdev/bvr
023fc93424fa6a50c8a3c2ce2fc48b76a041b58c
[ "MIT" ]
12
2019-12-07T21:40:23.000Z
2019-12-07T21:43:54.000Z
test/test_bvr_rest_before_after.py
doedotdev/bvr
023fc93424fa6a50c8a3c2ce2fc48b76a041b58c
[ "MIT" ]
null
null
null
from bvr.bvr_rest import bvr_rest_before_after def test_bvr_rest_before_after_called_as_decorator(capsys): @bvr_rest_before_after def rest_before_after(): return 2 return_value = rest_before_after() captured_ouput = capsys.readouterr().out assert return_value == 2 assert "RESTING_BEFORE: 5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_ouput assert "RESTING_AFTER: 5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} " in captured_ouput assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name def test_bvr_rest_before_after_called_as_callable_returning_decorator(capsys): @bvr_rest_before_after() def rest_before_after(): return 2 return_value = rest_before_after() captured_ouput = capsys.readouterr().out assert return_value == 2 assert "RESTING_BEFORE: 5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_ouput assert "RESTING_AFTER: 5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} " in captured_ouput assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name def test_bvr_rest_before_after_called_as_decorator_with_function_args(capsys): @bvr_rest_before_after def rest_before_after(msg): print(msg) return msg return_value = rest_before_after("Hello") captured_ouput = capsys.readouterr().out assert return_value == "Hello" assert "RESTING_BEFORE: 5 second(s) | FUNCTION: rest_before_after | ARGS: ('Hello',) | KWARGS: {} \n" in captured_ouput assert "RESTING_AFTER: 5 second(s) | FUNCTION: rest_before_after | ARGS: ('Hello',) | KWARGS: {} " in captured_ouput assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name def test_bvr_rest_before_after_called_as_callable_returning_decorator_with_function_args(capsys): @bvr_rest_before_after() def rest_before_after(msg): print(msg) return msg return_value = rest_before_after("Hello") captured_ouput = capsys.readouterr().out assert return_value == "Hello" assert "RESTING_BEFORE: 5 second(s) | FUNCTION: rest_before_after | ARGS: ('Hello',) | KWARGS: {} \n" in captured_ouput assert "RESTING_AFTER: 5 second(s) | FUNCTION: rest_before_after | ARGS: ('Hello',) | KWARGS: {} " in captured_ouput assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name def test_bvr_rest_before_after_called_as_decorator_with_function_kwargs(capsys): @bvr_rest_before_after def rest_before_after(msg): print(msg) return msg return_value = rest_before_after(msg="Hello") captured_ouput = capsys.readouterr().out assert return_value == "Hello" assert "RESTING_BEFORE: 5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {'msg': 'Hello'} \nHello\n" in captured_ouput assert "RESTING_AFTER: 5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {'msg': 'Hello'} " in captured_ouput assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name def test_bvr_rest_before_after_called_as_callable_returning_decorator_with_function_kwargs(capsys): @bvr_rest_before_after() def rest_before_after(msg): print(msg) return msg return_value = rest_before_after(msg="Hello") captured_ouput = capsys.readouterr().out assert return_value == "Hello" assert "RESTING_BEFORE: 5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {'msg': 'Hello'} \nHello\n" in captured_ouput assert "RESTING_AFTER: 5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {'msg': 'Hello'} " in captured_ouput assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name def test_bvr_rest_before_after_with_countdown_true(capsys): @bvr_rest_before_after(countdown=True) def rest_before_after(): return 2 return_value = rest_before_after() captured_output = capsys.readouterr().out assert return_value == 2 assert "RESTING_BEFORE: 5/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_BEFORE: 4/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_BEFORE: 3/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_BEFORE: 2/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_BEFORE: 1/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_AFTER: 5/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_AFTER: 4/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_AFTER: 3/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_AFTER: 2/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_AFTER: 1/5 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name def test_bvr_rest_before_after_with_countdown_true_and_non_default_seconds(capsys): @bvr_rest_before_after(seconds=2, countdown=True) def rest_before_after(): return 2 return_value = rest_before_after() captured_output = capsys.readouterr().out assert return_value == 2 assert "RESTING_BEFORE: 2/2 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_BEFORE: 1/2 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_AFTER: 2/2 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert "RESTING_AFTER: 1/2 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \n" in captured_output assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name def test_bvr_rest_before_after_with_countdown_false_and_non_default_seconds(capsys): @bvr_rest_before_after(seconds=2) def rest_before_after(): print('Hello') return 2 return_value = rest_before_after() captured_output = capsys.readouterr().out assert return_value == 2 assert "RESTING_BEFORE: 2 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \nHello\n" in captured_output assert "RESTING_AFTER: 2 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} " in captured_output assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name def test_bvr_rest_before_after_should_case_float_to_int(capsys): @bvr_rest_before_after(seconds=2.23) def rest_before_after(): print('Hello') return 2 return_value = rest_before_after() captured_output = capsys.readouterr().out assert return_value == 2 assert "RESTING_BEFORE: 2 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} \nHello" in captured_output assert "RESTING_AFTER: 2 second(s) | FUNCTION: rest_before_after | ARGS: () | KWARGS: {} " in captured_output assert rest_before_after.__name__ == "rest_before_after" # Important for decorators to not override method name
41.632979
136
0.722755
b520f3e06410f6d8a645d661f9bb433101ffce2b
2,761
py
Python
challenge/tastyContribs.py
histrio/tastydata
75b36954f851e0d22b9968bebdb5c77331853f54
[ "Apache-2.0" ]
2
2019-08-09T22:16:54.000Z
2019-09-30T11:20:05.000Z
challenge/tastyContribs.py
histrio/tastydata
75b36954f851e0d22b9968bebdb5c77331853f54
[ "Apache-2.0" ]
7
2015-06-11T06:50:44.000Z
2016-10-25T18:07:06.000Z
challenge/tastyContribs.py
histrio/tastydata
75b36954f851e0d22b9968bebdb5c77331853f54
[ "Apache-2.0" ]
3
2019-08-12T14:09:21.000Z
2019-09-30T10:22:52.000Z
#!/usr/bin/python # -*- coding: utf-8 -*- ''' Identifies and lists contributors to to the challenge Largely based on contributors.py in the ÖDOK project ''' import codecs import json import challengeStats import WikiApi as wikiApi import contributors # this one lives in the ÖDOK project def run(start='2015-05-08', end=None): # connect to api site = 'https://www.wikidata.org/w/api.php' scriptidentify = 'TastyDataContribs/1.0' fromConf = False try: import config fromConf = True wdApi = wikiApi.WikiDataApi.setUpApi(user=config.username, password=config.password, site=site, scriptidentify=scriptidentify) except ImportError: from getpass import getpass user = challengeStats.raw_encoded_input('Username: ') wdApi = wikiApi.WikiDataApi.setUpApi(user=user, password=getpass(), site=site, scriptidentify=scriptidentify) # find changed pages entities_file = u'entities.json' if fromConf: entities_file = u'%s%s' % (config.path, entities_file) fin = codecs.open(entities_file, 'r', 'utf8') pageList = json.load(fin) fin.close() contribs, ministats, users = contributors.handleContributions(wdApi, pageList, start=start, end=end) userInfo = wdApi.getUserData(users) # outputs output_contrib_file = 'contribs.json' output_user_file = 'users.json' if fromConf: output_contrib_file = u'%s%s' % (config.path, output_contrib_file) output_user_file = u'%s%s' % (config.path, output_user_file) f = codecs.open(output_user_file, 'w', 'utf8') f.write(json.dumps(userInfo, indent=4, ensure_ascii=False)) f.close() f = codecs.open(output_contrib_file, 'w', 'utf8') f.write(json.dumps(contribs, indent=4, ensure_ascii=False)) f.close() print json.dumps(ministats, indent=4, ensure_ascii=False) if __name__ == "__main__": import sys usage = '''Usage: python contributors.py start end \tstart (optional): YYYY-MM-DD start date (default 2015-01-01) \tend (optional): YYYY-MM-DD end date (default None)''' argv = sys.argv[1:] if len(argv) == 0: run() elif len(argv) == 1: run(start=argv[0]) elif len(argv) == 2: run(start=argv[0], end=argv[1]) else: print usage # EoF
34.08642
78
0.558493
7a28473fff017c7f441bd10c57583cd1dc369676
1,449
py
Python
parser/team27/G-27/execution/function/mathematical/factorial.py
mr8ug/tytus
a09abe4095e49d333a8ed9ca81cb3d88f90872ba
[ "MIT" ]
1
2021-01-09T05:32:35.000Z
2021-01-09T05:32:35.000Z
parser/team27/G-27/execution/function/mathematical/factorial.py
XiomRB/tytus
0873e4bdce5c110bee6ef2aa98240be6a93ae024
[ "MIT" ]
null
null
null
parser/team27/G-27/execution/function/mathematical/factorial.py
XiomRB/tytus
0873e4bdce5c110bee6ef2aa98240be6a93ae024
[ "MIT" ]
null
null
null
import sys sys.path.append('../tytus/parser/team27/G-27/execution/abstract') sys.path.append('../tytus/parser/team27/G-27/execution/expression') sys.path.append('../tytus/parser/team27/G-27/execution/symbol') sys.path.append('../tytus/parser/team27/G-27/libraries') from function import * from typ import * from math_functions import factorial class Factorial(Function): def __init__(self, input, row, column): Function.__init__(self,row,column) self.input = input def execute(self, environment): #input es una lista if isinstance(self.input,list): respuesta = [] for val in self.input: value = val.execute(environment) if value['typ'] != Type.INT and value['typ'] != Type.DECIMAL: return {'Error':"El valor " + value['value'] + " no es decimal o entero", 'linea':self.row,'columna':self.column } result = factorial(value['value']) respuesta.append({'value':result, 'typ': Type.INT}) return respuesta #input valor puntual else: value = self.input.execute(environment) if value['typ'] != Type.INT and value['typ'] != Type.DECIMAL: return {'Error':"El valor " + value['value'] + " no es decimal o entero", 'linea':self.row,'columna':self.column } return [{'value': factorial(value['value']), 'typ': Type.INT}]
46.741935
134
0.602484
24381fe4c544f03b26f2fee1959bc5ace4ef98ea
11,746
py
Python
ghstack/shell.py
BowenBao/ghstack
906274f42a28c690a49bff0af2063323bb06c5c3
[ "MIT" ]
1
2021-06-25T18:22:26.000Z
2021-06-25T18:22:26.000Z
ghstack/shell.py
BowenBao/ghstack
906274f42a28c690a49bff0af2063323bb06c5c3
[ "MIT" ]
null
null
null
ghstack/shell.py
BowenBao/ghstack
906274f42a28c690a49bff0af2063323bb06c5c3
[ "MIT" ]
null
null
null
#!/usr/bin/env python3 import subprocess import os import logging from typing import Dict, Sequence, Optional, TypeVar, Union, Any, overload, IO, Tuple import asyncio import sys # Shell commands generally return str, but with exitcode=True # they return a bool, and if stdout is piped straight to sys.stdout # they return None. _SHELL_RET = Union[bool, str, None] _HANDLE = Union[None, int, IO[Any]] def log_command(args: Sequence[str]) -> None: """ Given a command, print it in a both machine and human readable way. Args: *args: the list of command line arguments you want to run env: the dictionary of environment variable settings for the command """ # TODO: Irritatingly, this doesn't insert quotes for shell # metacharacters like exclamation marks or parentheses. cmd = subprocess.list2cmdline(args).replace("\n", "\\n") logging.info("$ " + cmd) K = TypeVar('K') V = TypeVar('V') def merge_dicts(x: Dict[K, V], y: Dict[K, V]) -> Dict[K, V]: z = x.copy() z.update(y) return z class Shell(object): """ An object representing a shell (e.g., the bash prompt in your terminal), maintaining a concept of current working directory, and also the necessary accoutrements for testing. """ # Current working directory of shell. cwd: str # Whether or not to suppress printing of command executed. quiet: bool # Whether or not shell is in testing mode; some commands are made # more deterministic in this case. testing: bool # The current Unix timestamp. Only used during testing mode. testing_time: int def __init__(self, quiet: bool = False, cwd: Optional[str] = None, testing: bool = False): """ Args: cwd: Current working directory of the shell. Pass None to initialize to the current cwd of the current process. quiet: If True, suppress printing out the command executed by the shell. By default, we print out commands for ease of debugging. Quiet is most useful for non-mutating shell commands. testing: If True, operate in testing mode. Testing mode enables features which make the outputs of commands more deterministic; e.g., it sets a number of environment variables for Git. """ self.cwd = cwd if cwd else os.getcwd() self.quiet = quiet self.testing = testing self.testing_time = 1112911993 def sh(self, *args: str, # noqa: C901 env: Optional[Dict[str, str]] = None, stderr: _HANDLE = None, # TODO: Arguably bytes should be accepted here too input: Optional[str] = None, stdin: _HANDLE = None, stdout: _HANDLE = subprocess.PIPE, exitcode: bool = False) -> _SHELL_RET: """ Run a command specified by args, and return string representing the stdout of the run command, raising an error if exit code was nonzero (unless exitcode kwarg is specified; see below). Args: *args: the list of command line arguments to run env: any extra environment variables to set when running the command. Environment variables set this way are ADDITIVE (unlike subprocess default) stderr: where to pipe stderr; by default, we pipe it straight to this process's stderr input: string value to pass stdin. This is mutually exclusive with stdin stdin: where to pipe stdin from. This is mutually exclusive with input stdout: where to pipe stdout; by default, we capture the stdout and return it exitcode: if True, return a bool rather than string, specifying whether or not the process successfully returned with exit code 0. We never raise an exception when this is True. """ assert not (stdin and input) if input: stdin = subprocess.PIPE if not self.quiet: log_command(args) if env is not None: env = merge_dicts(dict(os.environ), env) # The things we do for logging... # # - I didn't make a PTY, so programs are going to give # output assuming there isn't a terminal at the other # end. This is less nice for direct terminal use, but # it's better for logging (since we get to dispense # with the control codes). # # - We assume line buffering. This is kind of silly but # we need to assume *some* sort of buffering with the # stream API. async def process_stream(proc_stream: asyncio.StreamReader, setting: _HANDLE, default_stream: IO[str]) -> bytes: output = [] while True: try: line = await proc_stream.readuntil() except asyncio.LimitOverrunError as e: line = await proc_stream.readexactly(e.consumed) except asyncio.IncompleteReadError as e: line = e.partial if not line: break output.append(line) if setting == subprocess.PIPE: pass elif setting == subprocess.STDOUT: sys.stdout.buffer.write(line) elif isinstance(setting, int): os.write(setting, line) elif setting is None: # Sigh. See https://stackoverflow.com/questions/55681488/python-3-write-binary-to-stdout-respecting-buffering default_stream.write(line.decode('utf-8')) else: # NB: don't use setting.write directly, that will # not properly handle binary. This gives us # "parity" with the normal subprocess implementation os.write(setting.fileno(), line) return b''.join(output) async def feed_input(stdin_writer: Optional[asyncio.StreamWriter]) -> None: if stdin_writer is None: return if not input: return stdin_writer.write(input.encode('utf-8')) await stdin_writer.drain() stdin_writer.close() async def run() -> Tuple[int, bytes, bytes]: proc = await asyncio.create_subprocess_exec( *args, stdin=stdin, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, cwd=self.cwd, env=env, ) assert proc.stdout is not None assert proc.stderr is not None _, out, err, _ = await asyncio.gather( feed_input(proc.stdin), process_stream(proc.stdout, stdout, sys.stdout), process_stream(proc.stderr, stderr, sys.stderr), proc.wait() ) assert proc.returncode is not None return (proc.returncode, out, err) loop = asyncio.get_event_loop() returncode, out, err = loop.run_until_complete(run()) # NB: Not debug; we always want to show this to user. if err: logging.debug("# stderr:\n" + err.decode(errors="backslashreplace")) if out: logging.debug( ("# stdout:\n" if err else "") + out.decode(errors="backslashreplace").replace('\0', '\\0')) if exitcode: logging.debug("Exit code: {}".format(returncode)) return returncode == 0 if returncode != 0: raise RuntimeError( "{} failed with exit code {}" .format(' '.join(args), returncode) ) if stdout == subprocess.PIPE: return out.decode() # do a strict decode for actual return else: return None def _maybe_rstrip(self, s: _SHELL_RET) -> _SHELL_RET: if isinstance(s, str): return s.rstrip() else: return s @overload # noqa: F811 def git(self, *args: str) -> str: ... @overload # noqa: F811 def git(self, *args: str, input: str) -> str: ... @overload # noqa: F811 def git(self, *args: str, **kwargs: Any) -> _SHELL_RET: ... def git(self, *args: str, **kwargs: Any # noqa: F811 ) -> _SHELL_RET: """ Run a git command. The returned stdout has trailing newlines stripped. Args: *args: Arguments to git **kwargs: Any valid kwargs for sh() """ env = kwargs.setdefault("env", {}) # Some envvars to make things a little more script mode nice if self.testing: env.setdefault("EDITOR", ":") env.setdefault("GIT_MERGE_AUTOEDIT", "no") env.setdefault("LANG", "C") env.setdefault("LC_ALL", "C") env.setdefault("PAGER", "cat") env.setdefault("TZ", "UTC") env.setdefault("TERM", "dumb") # These are important so we get deterministic commit times env.setdefault("GIT_AUTHOR_EMAIL", "[email protected]") env.setdefault("GIT_AUTHOR_NAME", "A U Thor") env.setdefault("GIT_COMMITTER_EMAIL", "[email protected]") env.setdefault("GIT_COMMITTER_NAME", "C O Mitter") env.setdefault("GIT_COMMITTER_DATE", "{} -0700".format(self.testing_time)) env.setdefault("GIT_AUTHOR_DATE", "{} -0700".format(self.testing_time)) if 'stderr' not in kwargs: kwargs['stderr'] = subprocess.PIPE return self._maybe_rstrip(self.sh(*(("git",) + args), **kwargs)) @overload # noqa: F811 def hg(self, *args: str) -> str: ... @overload # noqa: F811 def hg(self, *args: str, input: str) -> str: ... @overload # noqa: F811 def hg(self, *args: str, **kwargs: Any) -> _SHELL_RET: ... def hg(self, *args: str, **kwargs: Any # noqa: F811 ) -> _SHELL_RET: """ Run a hg command. The returned stdout has trailing newlines stripped. Args: *args: Arguments to hg **kwargs: Any valid kwargs for sh() """ return self._maybe_rstrip(self.sh(*(("hg",) + args), **kwargs)) def jf(self, *args: str, **kwargs: Any) -> _SHELL_RET: """ Run a jf command. The returned stdout has trailing newlines stripped. Args: *args: Arguments to jf **kwargs: Any valid kwargs for sh() """ kwargs.setdefault('stdout', sys.stderr) return self._maybe_rstrip(self.sh(*(("jf",) + args), **kwargs)) def test_tick(self) -> None: """ Increase the current time. Useful when testing is True. """ self.testing_time += 60 def open(self, fn: str, mode: str) -> IO[Any]: """ Open a file, relative to the current working directory. Args: fn: filename to open mode: mode to open the file as """ return open(os.path.join(self.cwd, fn), mode) def cd(self, d: str) -> None: """ Change the current working directory. Args: d: directory to change to """ self.cwd = os.path.join(self.cwd, d)
35.062687
130
0.557211
add4393dbc084cd2372b7356452b5fc4953a8657
396
py
Python
src/gethash/cli/blake2s.py
xymy/gethash
88fd23f1c30338ceb95ff5b71a0112be349fe359
[ "MIT" ]
null
null
null
src/gethash/cli/blake2s.py
xymy/gethash
88fd23f1c30338ceb95ff5b71a0112be349fe359
[ "MIT" ]
null
null
null
src/gethash/cli/blake2s.py
xymy/gethash
88fd23f1c30338ceb95ff5b71a0112be349fe359
[ "MIT" ]
null
null
null
from gethash.script import gethashcli, script_main META = { "cmdname": "blake2s", "hashname": "BLAKE2s", "suffix": ".blake2s", "package": "hashlib", "hasher": "blake2s", } @gethashcli(**META) def main(files, **kwargs): """Generate or check BLAKE2s.""" from hashlib import blake2s as H script_main(H(), files, **kwargs) if __name__ == "__main__": main()
17.217391
50
0.613636
7cde0dc896ee27661e89be3b0b359dd6112f5007
10,419
py
Python
uniter_model/data/mrm.py
intersun/LightningDOT
5f2880f69ba87b8701ab89348d70ebb11432578c
[ "MIT" ]
64
2021-03-17T02:01:34.000Z
2021-12-31T08:05:57.000Z
uniter_model/data/mrm.py
intersun/LightningDOT
5f2880f69ba87b8701ab89348d70ebb11432578c
[ "MIT" ]
9
2021-04-16T07:58:33.000Z
2021-11-09T11:09:58.000Z
uniter_model/data/mrm.py
intersun/LightningDOT
5f2880f69ba87b8701ab89348d70ebb11432578c
[ "MIT" ]
5
2021-03-18T01:21:44.000Z
2022-01-20T13:23:39.000Z
""" MRM Datasets """ import random import torch from torch.utils.data import Dataset from torch.nn.utils.rnn import pad_sequence from toolz.sandbox import unzip from .data import DetectFeatTxtTokDataset, pad_tensors, get_gather_index def _get_img_mask(mask_prob, num_bb): img_mask = [random.random() < mask_prob for _ in range(num_bb)] if not any(img_mask): # at least mask 1 img_mask[random.choice(range(num_bb))] = True img_mask = torch.tensor(img_mask) return img_mask def _get_img_tgt_mask(img_mask, txt_len): z = torch.zeros(txt_len, dtype=torch.bool) img_mask_tgt = torch.cat([z, img_mask], dim=0) return img_mask_tgt def _get_feat_target(img_feat, img_masks): img_masks_ext = img_masks.unsqueeze(-1).expand_as(img_feat) # (n, m, d) feat_dim = img_feat.size(-1) feat_targets = img_feat[img_masks_ext].contiguous().view( -1, feat_dim) # (s, d) return feat_targets def _mask_img_feat(img_feat, img_masks): img_masks_ext = img_masks.unsqueeze(-1).expand_as(img_feat) img_feat_masked = img_feat.data.masked_fill(img_masks_ext, 0) return img_feat_masked class MrfrDataset(DetectFeatTxtTokDataset): def __init__(self, mask_prob, *args, **kwargs): super().__init__(*args, **kwargs) self.mask_prob = mask_prob def __getitem__(self, i): """ Return: - input_ids : (L, ), i.e., [cls, wd, wd, ..., sep, 0, 0], 0s padded - img_feat : (num_bb, d) - img_pos_feat : (num_bb, 7) - attn_masks : (L + num_bb, ), ie., [1, 1, ..., 0, 0, 1, 1] - img_mask : (num_bb, ) between {0, 1} """ example = super().__getitem__(i) # text input input_ids = example['input_ids'] input_ids = self.txt_db.combine_inputs(input_ids) # image input features img_feat, img_pos_feat, num_bb = self._get_img_feat( example['img_fname']) img_mask = _get_img_mask(self.mask_prob, num_bb) img_mask_tgt = _get_img_tgt_mask(img_mask, len(input_ids)) attn_masks = torch.ones(len(input_ids) + num_bb, dtype=torch.long) return (input_ids, img_feat, img_pos_feat, attn_masks, img_mask, img_mask_tgt) def mrfr_collate(inputs): """ Return: - input_ids : (n, max_L), i.e., [cls, wd, wd, ..., sep, 0, 0], 0s padded - position_ids : (n, max_L) - txt_lens : list of [input_len] - img_feat : (n, max_num_bb, d) - img_pos_feat : (n, max_num_bb, 7) - num_bbs : list of [num_bb] - attn_masks : (n, max_{L + num_bb}), ie., [1, 1, ..., 0, 0, 1, 1] - img_masks : (n, max_num_bb) between {0, 1} """ (input_ids, img_feats, img_pos_feats, attn_masks, img_masks, img_mask_tgts, ) = map(list, unzip(inputs)) txt_lens = [i.size(0) for i in input_ids] input_ids = pad_sequence(input_ids, batch_first=True, padding_value=0) position_ids = torch.arange(0, input_ids.size(1), dtype=torch.long ).unsqueeze(0) num_bbs = [f.size(0) for f in img_feats] img_feat = pad_tensors(img_feats, num_bbs) img_pos_feat = pad_tensors(img_pos_feats, num_bbs) # mask features img_masks = pad_sequence(img_masks, batch_first=True, padding_value=0) feat_targets = _get_feat_target(img_feat, img_masks) img_feat = _mask_img_feat(img_feat, img_masks) img_mask_tgt = pad_sequence(img_mask_tgts, batch_first=True, padding_value=0) attn_masks = pad_sequence(attn_masks, batch_first=True, padding_value=0) bs, max_tl = input_ids.size() out_size = attn_masks.size(1) gather_index = get_gather_index(txt_lens, num_bbs, bs, max_tl, out_size) batch = {'input_ids': input_ids, 'position_ids': position_ids, 'img_feat': img_feat, 'img_pos_feat': img_pos_feat, 'attn_masks': attn_masks, 'gather_index': gather_index, 'feat_targets': feat_targets, 'img_masks': img_masks, 'img_mask_tgt': img_mask_tgt} return batch class OnlyImgMrfrDataset(Dataset): """ an image-only MRM """ def __init__(self, mask_prob, img_db): self.ids, self.lens = map(list, unzip(self.img_db.name2nbb.items())) def __getitem__(self, i): id_ = self.ids[i] img_feat, img_pos_feat, num_bb = self._get_img_feat(id_) attn_masks = torch.ones(num_bb, dtype=torch.long) img_mask = _get_img_mask(self.mask_prob, num_bb) return img_feat, img_pos_feat, attn_masks, img_mask def _get_img_feat(self, fname): img_feat, bb = self.img_db[fname] img_bb = torch.cat([bb, bb[:, 4:5]*bb[:, 5:]], dim=-1) num_bb = img_feat.size(0) return img_feat, img_bb, num_bb def mrfr_only_img_collate(inputs): img_feats, img_pos_feats, attn_masks, img_masks = map(list, unzip(inputs)) attn_masks = pad_sequence(attn_masks, batch_first=True, padding_value=0) num_bbs = [f.size(0) for f in img_feats] img_feat = pad_tensors(img_feats, num_bbs) img_pos_feat = pad_tensors(img_pos_feats, num_bbs) # mask features img_masks = pad_sequence(img_masks, batch_first=True, padding_value=0) feat_targets = _get_feat_target(img_feat, img_masks) img_feat = _mask_img_feat(img_feat, img_masks) batch = {'img_feat': img_feat, 'img_pos_feat': img_pos_feat, 'attn_masks': attn_masks, 'feat_targets': feat_targets, 'img_masks': img_masks, 'img_mask_tgt': img_masks} return batch def _get_targets(img_masks, img_soft_label): soft_label_dim = img_soft_label.size(-1) img_masks_ext_for_label = img_masks.unsqueeze(-1).expand_as(img_soft_label) label_targets = img_soft_label[img_masks_ext_for_label].contiguous().view( -1, soft_label_dim) return label_targets class MrcDataset(DetectFeatTxtTokDataset): def __init__(self, mask_prob, *args, **kwargs): super().__init__(*args, **kwargs) self.mask_prob = mask_prob def _get_img_feat(self, fname): img_dump = self.img_db.get_dump(fname) num_bb = self.img_db.name2nbb[fname] img_feat = torch.tensor(img_dump['features']) bb = torch.tensor(img_dump['norm_bb']) img_bb = torch.cat([bb, bb[:, 4:5]*bb[:, 5:]], dim=-1) img_soft_label = torch.tensor(img_dump['soft_labels']) return img_feat, img_bb, img_soft_label, num_bb def __getitem__(self, i): example = super().__getitem__(i) img_feat, img_pos_feat, img_soft_labels, num_bb = self._get_img_feat( example['img_fname']) # image input features img_mask = _get_img_mask(self.mask_prob, num_bb) # text input input_ids = example['input_ids'] input_ids = self.txt_db.combine_inputs(input_ids) img_mask_tgt = _get_img_tgt_mask(img_mask, len(input_ids)) attn_masks = torch.ones(len(input_ids) + num_bb, dtype=torch.long) return (input_ids, img_feat, img_pos_feat, img_soft_labels, attn_masks, img_mask, img_mask_tgt) def mrc_collate(inputs): (input_ids, img_feats, img_pos_feats, img_soft_labels, attn_masks, img_masks, img_mask_tgts) = map(list, unzip(inputs)) txt_lens = [i.size(0) for i in input_ids] num_bbs = [f.size(0) for f in img_feats] input_ids = pad_sequence(input_ids, batch_first=True, padding_value=0) position_ids = torch.arange(0, input_ids.size(1), dtype=torch.long ).unsqueeze(0) img_feat = pad_tensors(img_feats, num_bbs) img_pos_feat = pad_tensors(img_pos_feats, num_bbs) img_soft_label = pad_tensors(img_soft_labels, num_bbs) img_masks = pad_sequence(img_masks, batch_first=True, padding_value=0) label_targets = _get_targets(img_masks, img_soft_label) img_feat = _mask_img_feat(img_feat, img_masks) img_mask_tgt = pad_sequence(img_mask_tgts, batch_first=True, padding_value=0) attn_masks = pad_sequence(attn_masks, batch_first=True, padding_value=0) bs, max_tl = input_ids.size() out_size = attn_masks.size(1) gather_index = get_gather_index(txt_lens, num_bbs, bs, max_tl, out_size) batch = {'input_ids': input_ids, 'position_ids': position_ids, 'img_feat': img_feat, 'img_pos_feat': img_pos_feat, 'attn_masks': attn_masks, 'gather_index': gather_index, 'img_masks': img_masks, 'img_mask_tgt': img_mask_tgt, 'label_targets': label_targets} return batch class OnlyImgMrcDataset(OnlyImgMrfrDataset): """ an image-only MRC """ def __getitem__(self, i): id_ = self.ids[i] (img_feat, img_pos_feat, img_soft_labels, num_bb ) = self._get_img_feat(id_) attn_masks = torch.ones(num_bb, dtype=torch.long) img_mask = _get_img_mask(self.mask_prob, num_bb) return img_feat, img_pos_feat, img_soft_labels, attn_masks, img_mask def _get_img_feat(self, fname): img_dump = self.img_db.get_dump(fname) num_bb = self.img_db.name2nbb[fname] img_feat = torch.tensor(img_dump['features']) bb = torch.tensor(img_dump['norm_bb']) img_bb = torch.cat([bb, bb[:, 4:5]*bb[:, 5:]], dim=-1) img_soft_labels = torch.tensor(img_dump['soft_labels']) return img_feat, img_bb, img_soft_labels, num_bb def mrc_only_img_collate(inputs): (img_feats, img_pos_feats, img_soft_labels, attn_masks, img_masks ) = map(list, unzip(inputs)) attn_masks = pad_sequence(attn_masks, batch_first=True, padding_value=0) img_masks = pad_sequence(img_masks, batch_first=True, padding_value=0) num_bbs = [f.size(0) for f in img_feats] img_feat = pad_tensors(img_feats, num_bbs) img_pos_feat = pad_tensors(img_pos_feats, num_bbs) img_soft_label = pad_tensors(img_soft_labels, num_bbs) label_targets = _get_targets(img_masks, img_soft_label) # mask features img_feat = _mask_img_feat(img_feat, img_masks) batch = {'img_feat': img_feat, 'img_pos_feat': img_pos_feat, 'attn_masks': attn_masks, 'img_masks': img_masks, 'img_mask_tgt': img_masks, 'label_targets': label_targets} return batch
36.177083
79
0.655533
59b61caf3b4a9d65a3fded6e3f174c33dc339596
6,507
py
Python
theano/sandbox/jax_linker.py
canyon289/Theano-PyMC
1a9b04bfe480b758ddfa54ba49c88bee3bec419c
[ "BSD-3-Clause" ]
null
null
null
theano/sandbox/jax_linker.py
canyon289/Theano-PyMC
1a9b04bfe480b758ddfa54ba49c88bee3bec419c
[ "BSD-3-Clause" ]
null
null
null
theano/sandbox/jax_linker.py
canyon289/Theano-PyMC
1a9b04bfe480b758ddfa54ba49c88bee3bec419c
[ "BSD-3-Clause" ]
1
2020-08-15T17:09:10.000Z
2020-08-15T17:09:10.000Z
from collections.abc import Sequence from warnings import warn from theano.gof.graph import Constant from theano.gof.link import ( Container, PerformLinker, add_clear_storage, gc_helper, map_storage, streamline, utils, ) class JAXLinker(PerformLinker): """A `Linker` that JIT-compiles NumPy-based operations using JAX. Attributes ---------- allow_non_jax: bool A boolean indicating whether or not an exception is thrown when the graph cannot be JAX compiled (e.g. the graph has an unsupported operator). If `allow_non_jax` is `True`, the fallback is currently Python compilation. """ allow_non_jax = False def create_jax_thunks(self, compute_map, storage_map): """Create a thunk for each output of the `Linker`s `FunctionGraph`. This is differs from the other thunk-making function in that it only produces thunks for the `FunctionGraph` output nodes. Parameters ---------- compute_map: dict The compute map dictionary. storage_map: dict The storage map dictionary. Returns ------- thunks: list A tuple containing the thunks. output_nodes: list and their A tuple containing the output nodes. """ import jax from theano.sandbox.jaxify import jax_funcify output_nodes = [o.owner for o in self.fgraph.outputs] # Create a JAX-compilable function from our `FunctionGraph` jaxed_fgraph_outputs = jax_funcify(self.fgraph) assert len(jaxed_fgraph_outputs) == len(output_nodes) # I suppose we can consider `Constant`s to be "static" according to # JAX. static_argnums = [ n for n, i in enumerate(self.fgraph.inputs) if isinstance(i, Constant) ] thunk_inputs = [storage_map[n] for n in self.fgraph.inputs] thunks = [] for node, jax_funcs in zip(output_nodes, jaxed_fgraph_outputs): thunk_outputs = [storage_map[n] for n in node.outputs] if not isinstance(jax_funcs, Sequence): jax_funcs = [jax_funcs] jax_impl_jits = [ jax.jit(jax_func, static_argnums) for jax_func in jax_funcs ] def thunk( node=node, jax_impl_jits=jax_impl_jits, thunk_outputs=thunk_outputs ): outputs = [ jax_impl_jit(*[x[0] for x in thunk_inputs]) for jax_impl_jit in jax_impl_jits ] if len(jax_impl_jits) < len(node.outputs): # In this case, the JAX function will output a single # output that contains the other outputs. # This happens for multi-output `Op`s that directly # correspond to multi-output JAX functions (e.g. `SVD` and # `jax.numpy.linalg.svd`). outputs = outputs[0] for o_node, o_storage, o_val in zip( node.outputs, thunk_outputs, outputs ): compute_map[o_node][0] = True if len(o_storage) > 1: assert len(o_storage) == len(o_val) for i, o_sub_val in enumerate(o_val): o_storage[i] = o_sub_val else: o_storage[0] = o_val return outputs thunk.inputs = thunk_inputs thunk.outputs = thunk_outputs thunk.lazy = False thunks.append(thunk) return thunks, output_nodes def make_all(self, input_storage=None, output_storage=None, storage_map=None): fgraph = self.fgraph nodes = self.schedule(fgraph) no_recycling = self.no_recycling input_storage, output_storage, storage_map = map_storage( fgraph, nodes, input_storage, output_storage, storage_map ) compute_map = {} for k in storage_map: compute_map[k] = [k.owner is None] try: # We need to create thunk functions that will populate the output # storage arrays with the JAX-computed values. thunks, nodes = self.create_jax_thunks(compute_map, storage_map) except NotImplementedError as e: if not self.allow_non_jax: raise warn("JaxLinker could not JAXify graph: {}".format(e)) thunks = [] for node in nodes: thunk = node.op.make_thunk( node, storage_map, compute_map, no_recycling, "py" ) thunk_inputs = [storage_map[v] for v in node.inputs] thunk_outputs = [storage_map[v] for v in node.outputs] thunk.inputs = thunk_inputs thunk.outputs = thunk_outputs thunks.append(thunk) computed, last_user = gc_helper(nodes) if self.allow_gc: post_thunk_old_storage = [] for node in nodes: post_thunk_old_storage.append( [ storage_map[input] for input in node.inputs if (input in computed) and (input not in fgraph.outputs) and (node == last_user[input]) ] ) else: post_thunk_old_storage = None if no_recycling is True: no_recycling = list(storage_map.values()) no_recycling = utils.difference(no_recycling, input_storage) else: no_recycling = [ storage_map[r] for r in no_recycling if r not in fgraph.inputs ] fn = streamline( fgraph, thunks, nodes, post_thunk_old_storage, no_recycling=no_recycling ) fn.allow_gc = self.allow_gc add_clear_storage(fn, computed, storage_map) fn.storage_map = storage_map return ( fn, [ Container(input, storage) for input, storage in zip(fgraph.inputs, input_storage) ], [ Container(output, storage, True) for output, storage in zip(fgraph.outputs, output_storage) ], thunks, nodes, )
32.054187
84
0.553865
3cf04ce323a2ca25ae724cc993b51973e59afec3
8,892
py
Python
accelbyte_py_sdk/api/cloudsave/operations/admin_player_record/admin_put_player_public_record_handler_v1.py
encyphered/accelbyte-python-sdk
09c1e989d7251de308150fdcd3119d662ca2d205
[ "MIT" ]
null
null
null
accelbyte_py_sdk/api/cloudsave/operations/admin_player_record/admin_put_player_public_record_handler_v1.py
encyphered/accelbyte-python-sdk
09c1e989d7251de308150fdcd3119d662ca2d205
[ "MIT" ]
null
null
null
accelbyte_py_sdk/api/cloudsave/operations/admin_player_record/admin_put_player_public_record_handler_v1.py
encyphered/accelbyte-python-sdk
09c1e989d7251de308150fdcd3119d662ca2d205
[ "MIT" ]
null
null
null
# Auto-generated at 2021-09-27T17:01:31.247008+08:00 # from: Justice Cloudsave Service (3.38.0) # Copyright (c) 2018 - 2021 AccelByte Inc. All Rights Reserved. # This is licensed software from AccelByte Inc, for limitations # and restrictions contact your company contract manager. # pylint: disable=duplicate-code # pylint: disable=line-too-long # pylint: disable=missing-function-docstring # pylint: disable=missing-module-docstring # pylint: disable=too-many-arguments # pylint: disable=too-many-branches # pylint: disable=too-many-instance-attributes # pylint: disable=too-many-lines # pylint: disable=too-many-locals # pylint: disable=too-many-public-methods # pylint: disable=too-many-return-statements # pylint: disable=too-many-statements # pylint: disable=unused-import from __future__ import annotations from typing import Any, Dict, List, Optional, Tuple, Union from .....core import Operation from .....core import HttpResponse from ...models import ModelsPlayerRecordRequest from ...models import ResponseError class AdminPutPlayerPublicRecordHandlerV1(Operation): """Create or replace player record (adminPutPlayerPublicRecordHandlerV1) Properties: url: /cloudsave/v1/admin/namespaces/{namespace}/users/{userID}/records/{key}/public method: PUT tags: AdminPlayerRecord consumes: ["application/json"] produces: ["application/json"] security: bearer body: (body) REQUIRED ModelsPlayerRecordRequest in body namespace: (namespace) REQUIRED str in path user_id: (userID) REQUIRED str in path key: (key) REQUIRED str in path Responses: 200: OK - (Record in user-level saved) 400: Bad Request - ResponseError (Bad Request) 500: Internal Server Error - ResponseError (Internal Server Error) """ # region fields _url: str = "/cloudsave/v1/admin/namespaces/{namespace}/users/{userID}/records/{key}/public" _method: str = "PUT" _consumes: List[str] = ["application/json"] _produces: List[str] = ["application/json"] _security: Optional[str] = "bearer" _location_query: str = None body: ModelsPlayerRecordRequest # REQUIRED in [body] namespace: str # REQUIRED in [path] user_id: str # REQUIRED in [path] key: str # REQUIRED in [path] # endregion fields # region properties @property def url(self) -> str: return self._url @property def method(self) -> str: return self._method @property def consumes(self) -> List[str]: return self._consumes @property def produces(self) -> List[str]: return self._produces @property def security(self) -> Optional[str]: return self._security @property def location_query(self) -> str: return self._location_query # endregion properties # region get methods def get_full_url(self, base_url: Union[None, str] = None) -> str: result = base_url if base_url is not None else "" # path params url = self.url for k, v in self.get_path_params().items(): url = url.replace(f"{{{k}}}", v) result += url return result # noinspection PyMethodMayBeStatic def get_all_required_fields(self) -> List[str]: return [ "body", "namespace", "user_id", "key", ] # endregion get methods # region get_x_params methods def get_all_params(self) -> dict: return { "body": self.get_body_params(), "path": self.get_path_params(), } def get_body_params(self) -> Any: return self.body.to_dict() def get_path_params(self) -> dict: result = {} if hasattr(self, "namespace"): result["namespace"] = self.namespace if hasattr(self, "user_id"): result["userID"] = self.user_id if hasattr(self, "key"): result["key"] = self.key return result # endregion get_x_params methods # region is/has methods def is_valid(self) -> bool: if not hasattr(self, "body") or self.body is None: return False if not hasattr(self, "namespace") or self.namespace is None: return False if not hasattr(self, "user_id") or self.user_id is None: return False if not hasattr(self, "key") or self.key is None: return False return True # endregion is/has methods # region with_x methods def with_body(self, value: ModelsPlayerRecordRequest) -> AdminPutPlayerPublicRecordHandlerV1: self.body = value return self def with_namespace(self, value: str) -> AdminPutPlayerPublicRecordHandlerV1: self.namespace = value return self def with_user_id(self, value: str) -> AdminPutPlayerPublicRecordHandlerV1: self.user_id = value return self def with_key(self, value: str) -> AdminPutPlayerPublicRecordHandlerV1: self.key = value return self # endregion with_x methods # region to methods def to_dict(self, include_empty: bool = False) -> dict: result = {} if hasattr(self, "body") and self.body: result["body"] = self.body.to_dict(include_empty=include_empty) elif include_empty: result["body"] = ModelsPlayerRecordRequest() if hasattr(self, "namespace") and self.namespace: result["namespace"] = str(self.namespace) elif include_empty: result["namespace"] = str() if hasattr(self, "user_id") and self.user_id: result["userID"] = str(self.user_id) elif include_empty: result["userID"] = str() if hasattr(self, "key") and self.key: result["key"] = str(self.key) elif include_empty: result["key"] = str() return result # endregion to methods # region response methods # noinspection PyMethodMayBeStatic def parse_response(self, code: int, content_type: str, content: Any) -> Tuple[Union[None, HttpResponse], Union[None, ResponseError]]: """Parse the given response. 200: OK - (Record in user-level saved) 400: Bad Request - ResponseError (Bad Request) 500: Internal Server Error - ResponseError (Internal Server Error) """ if code == 200: return HttpResponse.create(code, "OK"), None if code == 400: return None, ResponseError.create_from_dict(content) if code == 500: return None, ResponseError.create_from_dict(content) was_handled, undocumented_response = HttpResponse.try_create_undocumented_response(code, content) if was_handled: return None, undocumented_response return None, HttpResponse.create_unhandled_error() # endregion response methods # region static methods @classmethod def create( cls, body: ModelsPlayerRecordRequest, namespace: str, user_id: str, key: str, ) -> AdminPutPlayerPublicRecordHandlerV1: instance = cls() instance.body = body instance.namespace = namespace instance.user_id = user_id instance.key = key return instance @classmethod def create_from_dict(cls, dict_: dict, include_empty: bool = False) -> AdminPutPlayerPublicRecordHandlerV1: instance = cls() if "body" in dict_ and dict_["body"] is not None: instance.body = ModelsPlayerRecordRequest.create_from_dict(dict_["body"], include_empty=include_empty) elif include_empty: instance.body = ModelsPlayerRecordRequest() if "namespace" in dict_ and dict_["namespace"] is not None: instance.namespace = str(dict_["namespace"]) elif include_empty: instance.namespace = str() if "userID" in dict_ and dict_["userID"] is not None: instance.user_id = str(dict_["userID"]) elif include_empty: instance.user_id = str() if "key" in dict_ and dict_["key"] is not None: instance.key = str(dict_["key"]) elif include_empty: instance.key = str() return instance @staticmethod def get_field_info() -> Dict[str, str]: return { "body": "body", "namespace": "namespace", "userID": "user_id", "key": "key", } # endregion static methods
30.982578
137
0.607512
a4bb1893b7ecc310bc9ee7ffcf907aef60b35b52
3,779
py
Python
homeassistant/components/deluge/config_flow.py
mib1185/core
b17d4ac65cde9a27ff6032d70b148792e5eba8df
[ "Apache-2.0" ]
null
null
null
homeassistant/components/deluge/config_flow.py
mib1185/core
b17d4ac65cde9a27ff6032d70b148792e5eba8df
[ "Apache-2.0" ]
null
null
null
homeassistant/components/deluge/config_flow.py
mib1185/core
b17d4ac65cde9a27ff6032d70b148792e5eba8df
[ "Apache-2.0" ]
null
null
null
"""Config flow for the Deluge integration.""" from __future__ import annotations from collections.abc import Mapping import socket from ssl import SSLError from typing import Any from deluge_client.client import DelugeRPCClient import voluptuous as vol from homeassistant.config_entries import SOURCE_REAUTH, ConfigFlow from homeassistant.const import ( CONF_HOST, CONF_PASSWORD, CONF_PORT, CONF_SOURCE, CONF_USERNAME, ) from homeassistant.data_entry_flow import FlowResult import homeassistant.helpers.config_validation as cv from .const import ( CONF_WEB_PORT, DEFAULT_NAME, DEFAULT_RPC_PORT, DEFAULT_WEB_PORT, DOMAIN, ) class DelugeFlowHandler(ConfigFlow, domain=DOMAIN): """Handle a config flow for Deluge.""" async def async_step_user( self, user_input: dict[str, Any] | None = None ) -> FlowResult: """Handle a flow initiated by the user.""" errors = {} if user_input is not None: if (error := await self.validate_input(user_input)) is None: for entry in self._async_current_entries(): if ( user_input[CONF_HOST] == entry.data[CONF_HOST] and user_input[CONF_PORT] == entry.data[CONF_PORT] ): if self.context.get(CONF_SOURCE) == SOURCE_REAUTH: self.hass.config_entries.async_update_entry( entry, data=user_input ) await self.hass.config_entries.async_reload(entry.entry_id) return self.async_abort(reason="reauth_successful") return self.async_abort(reason="already_configured") return self.async_create_entry( title=DEFAULT_NAME, data=user_input, ) errors["base"] = error user_input = user_input or {} schema = vol.Schema( { vol.Required(CONF_HOST, default=user_input.get(CONF_HOST)): cv.string, vol.Required( CONF_USERNAME, default=user_input.get(CONF_USERNAME) ): cv.string, vol.Required(CONF_PASSWORD, default=""): cv.string, vol.Optional( CONF_PORT, default=user_input.get(CONF_PORT, DEFAULT_RPC_PORT) ): int, vol.Optional( CONF_WEB_PORT, default=user_input.get(CONF_WEB_PORT, DEFAULT_WEB_PORT), ): int, } ) return self.async_show_form(step_id="user", data_schema=schema, errors=errors) async def async_step_reauth(self, config: Mapping[str, Any]) -> FlowResult: """Handle a reauthorization flow request.""" return await self.async_step_user() async def validate_input(self, user_input: dict[str, Any]) -> str | None: """Handle common flow input validation.""" host = user_input[CONF_HOST] port = user_input[CONF_PORT] username = user_input[CONF_USERNAME] password = user_input[CONF_PASSWORD] api = DelugeRPCClient( host=host, port=port, username=username, password=password ) try: await self.hass.async_add_executor_job(api.connect) except ( ConnectionRefusedError, socket.timeout, SSLError, ): return "cannot_connect" except Exception as ex: # pylint:disable=broad-except if type(ex).__name__ == "BadLoginError": return "invalid_auth" # pragma: no cover return "unknown" return None
35.990476
87
0.588251
1213316c659236c3b5650550647c066f2e4e03c2
437
py
Python
versions/v1/create_jobs.py
bric-tb-softwares/rxpixp2pixcycle
3ec59373d777908210483a41478d6fbc2fe60f3e
[ "BSD-3-Clause" ]
null
null
null
versions/v1/create_jobs.py
bric-tb-softwares/rxpixp2pixcycle
3ec59373d777908210483a41478d6fbc2fe60f3e
[ "BSD-3-Clause" ]
null
null
null
versions/v1/create_jobs.py
bric-tb-softwares/rxpixp2pixcycle
3ec59373d777908210483a41478d6fbc2fe60f3e
[ "BSD-3-Clause" ]
null
null
null
import json, os output_path = 'jobs' os.makedirs(output_path, exist_ok=True) tests = 1 sorts = 9 for test in range(tests): for sort in range(sorts): d = { 'sort' : sort, 'test' : test, 'seed' : 512, } print(d) o = output_path + '/job.test_%d.sort_%d.json'%(test,sort) with open(o, 'w') as f: json.dump(d, f)
15.068966
65
0.462243
280622a5495276855ef0b3a4c3614289da4c3518
119,735
py
Python
autoload/leaderf/python/leaderf/manager.py
paperboard/LeaderF
5d39b1a704419436a812118c6281ddb5023137e3
[ "Apache-2.0" ]
null
null
null
autoload/leaderf/python/leaderf/manager.py
paperboard/LeaderF
5d39b1a704419436a812118c6281ddb5023137e3
[ "Apache-2.0" ]
null
null
null
autoload/leaderf/python/leaderf/manager.py
paperboard/LeaderF
5d39b1a704419436a812118c6281ddb5023137e3
[ "Apache-2.0" ]
null
null
null
#!/usr/bin/env python # -*- coding: utf-8 -*- import vim import os import sys import json import time import operator import itertools import threading import multiprocessing from functools import partial from functools import wraps from .instance import LfInstance from .cli import LfCli from .utils import * from .fuzzyMatch import FuzzyMatch from .asyncExecutor import AsyncExecutor from .devicons import ( webDevIconsGetFileTypeSymbol, removeDevIcons ) is_fuzzyEngine_C = False try: import fuzzyEngine is_fuzzyEngine_C = True cpu_count = multiprocessing.cpu_count() lfCmd("let g:Lf_fuzzyEngine_C = 1") except ImportError: lfCmd("let g:Lf_fuzzyEngine_C = 0") is_fuzzyMatch_C = False try: import fuzzyMatchC is_fuzzyMatch_C = True lfCmd("let g:Lf_fuzzyMatch_C = 1") except ImportError: lfCmd("let g:Lf_fuzzyMatch_C = 0") if sys.version_info >= (3, 0): def isAscii(str): try: str.encode("ascii") return True except UnicodeEncodeError: return False else: def isAscii(str): try: str.decode("ascii") return True except UnicodeDecodeError: return False def modifiableController(func): @wraps(func) def deco(self, *args, **kwargs): self._getInstance().buffer.options['modifiable'] = True func(self, *args, **kwargs) self._getInstance().buffer.options['modifiable'] = False return deco def catchException(func): @wraps(func) def deco(self, *args, **kwargs): try: func(self, *args, **kwargs) except vim.error as e: # for neovim if str(e) != "b'Keyboard interrupt'" and str(e) != 'Keyboard interrupt': raise e elif self._timer_id is not None: lfCmd("call timer_stop(%s)" % self._timer_id) self._timer_id = None except KeyboardInterrupt: # <C-C>, this does not work in vim if self._timer_id is not None: lfCmd("call timer_stop(%s)" % self._timer_id) self._timer_id = None return deco def ignoreEvent(events): def wrapper(func): @wraps(func) def deco(self, *args, **kwargs): try: saved_eventignore = vim.options['eventignore'] vim.options['eventignore'] = events func(self, *args, **kwargs) finally: vim.options['eventignore'] = saved_eventignore return deco return wrapper #***************************************************** # Manager #***************************************************** class Manager(object): def __init__(self): self._autochdir = 0 self._instance = None self._cli = LfCli() self._explorer = None self._content = [] self._index = 0 self._help_length = 0 self._show_help = False self._selections = {} self._highlight_pos = [] self._highlight_pos_list = [] self._highlight_refine_pos = [] self._highlight_ids = [] self._orig_line = '' self._ctrlp_pressed = False self._fuzzy_engine = None self._result_content = [] self._reader_thread = None self._timer_id = None self._highlight_method = lambda : None self._orig_cwd = None self._cursorline_dict = {} self._empty_query = lfEval("get(g:, 'Lf_EmptyQuery', 1)") == '1' self._preview_winid = 0 self._is_previewed = False self._match_ids = [] self._vim_file_autoloaded = False self._arguments = {} self._getExplClass() #************************************************************** # abstract methods, in fact all the functions can be overridden #************************************************************** def _getExplClass(self): """ this function MUST be overridden return the name of Explorer class """ raise NotImplementedError("Can't instantiate abstract class Manager " "with abstract methods _getExplClass") def _defineMaps(self): pass def _defineCommonMaps(self): normal_map = lfEval("get(g:, 'Lf_NormalMap', {})") if "_" not in normal_map: return for [lhs, rhs] in normal_map["_"]: # If a buffer-local mapping does not exist, map it maparg = lfEval("maparg('{}', 'n', 0, 1)".format(lhs)) if maparg == {} or maparg.get("buffer", "0") == "0" : lfCmd("nnoremap <buffer> <silent> {} {}".format(lhs, rhs)) def _cmdExtension(self, cmd): """ this function can be overridden to add new cmd if return true, exit the input loop """ pass @removeDevIcons def _argaddFiles(self, files): # It will raise E480 without 'silent!' lfCmd("silent! argdelete *") for file in files: lfCmd("argadd %s" % escSpecial(file)) def _issue_422_set_option(self): if lfEval("has('nvim')") == '1' and self._is_previewed: lfCmd("silent! setlocal number<") lfCmd("silent! setlocal relativenumber<") lfCmd("silent! setlocal cursorline<") lfCmd("silent! setlocal colorcolumn<") lfCmd("silent! setlocal winhighlight<") def _acceptSelection(self, *args, **kwargs): pass def _getDigest(self, line, mode): """ this function can be overridden specify what part in the line to be processed and highlighted Args: mode: 0, return the full path 1, return the name only 2, return the directory name """ if mode == 0: return line elif mode == 1: return getBasename(line) else: return getDirname(line) def _getDigestStartPos(self, line, mode): """ this function can be overridden return the start position of the digest returned by _getDigest() Args: mode: 0, return the start postion of full path 1, return the start postion of name only 2, return the start postion of directory name """ if mode == 0 or mode == 2: return 0 else: return lfBytesLen(getDirname(line)) def _createHelp(self): return [] def _setStlMode(self, **kwargs): if self._cli.isFuzzy: if self._getExplorer().supportsNameOnly(): if self._cli.isFullPath: mode = 'FullPath' else: mode = 'NameOnly' else: mode = 'Fuzzy' else: mode = 'Regex' modes = {"--nameOnly", "--fullPath", "--fuzzy", "--regexMode"} for opt in kwargs.get("arguments", {}): if opt in modes: if opt == "--regexMode": mode = 'Regex' elif self._getExplorer().supportsNameOnly(): if opt == "--nameOnly": mode = 'NameOnly' elif opt == "--fullPath": mode = 'FullPath' else: # "--fuzzy" if self._cli.isFullPath: mode = 'FullPath' else: mode = 'NameOnly' elif opt in ("--nameOnly", "--fullPath", "--fuzzy"): mode = 'Fuzzy' break self._getInstance().setStlMode(mode) self._cli.setCurrentMode(mode) def _beforeEnter(self): self._resetAutochdir() self._cur_buffer = vim.current.buffer def _afterEnter(self): if self._vim_file_autoloaded == False: category = self._getExplorer().getStlCategory() if category == 'Colorscheme': category = 'Colors' lfCmd("silent! call leaderf#%s#a_nonexistent_function()" % category) self._vim_file_autoloaded = True if "--nowrap" in self._arguments: if self._getInstance().getWinPos() == 'popup': lfCmd("call win_execute(%d, 'setlocal nowrap')" % self._getInstance().getPopupWinId()) elif self._getInstance().getWinPos() == 'floatwin': lfCmd("call nvim_win_set_option(%d, 'wrap', v:false)" % self._getInstance().getPopupWinId()) else: self._getInstance().window.options['wrap'] = False else: if self._getInstance().getWinPos() == 'popup': lfCmd("call win_execute(%d, 'setlocal wrap')" % self._getInstance().getPopupWinId()) elif self._getInstance().getWinPos() == 'floatwin': lfCmd("call nvim_win_set_option(%d, 'wrap', v:true)" % self._getInstance().getPopupWinId()) else: self._getInstance().window.options['wrap'] = True if self._getInstance().getWinPos() != 'popup': self._defineMaps() self._defineCommonMaps() id = int(lfEval("matchadd('Lf_hl_cursorline', '.*\%#.*', 9)")) self._match_ids.append(id) else: lfCmd("""call win_execute({}, 'let matchid = matchadd(''Lf_hl_cursorline'', ''.*\%#.*'', 9)')""" .format(self._getInstance().getPopupWinId())) id = int(lfEval("matchid")) self._match_ids.append(id) if is_fuzzyEngine_C: self._fuzzy_engine = fuzzyEngine.createFuzzyEngine(cpu_count, False) def _beforeExit(self): if self._getInstance().window.valid: self._getInstance().cursorRow = self._getInstance().window.cursor[0] self._getInstance().helpLength = self._help_length self.clearSelections() self._getExplorer().cleanup() if self._fuzzy_engine: fuzzyEngine.closeFuzzyEngine(self._fuzzy_engine) self._fuzzy_engine = None if self._reader_thread and self._reader_thread.is_alive(): self._stop_reader_thread = True self._closePreviewPopup() if self._getInstance().getWinPos() == 'popup': for i in self._match_ids: lfCmd("silent! call matchdelete(%d, %d)" % (i, self._getInstance().getPopupWinId())) else: for i in self._match_ids: lfCmd("silent! call matchdelete(%d)" % i) self._match_ids = [] def _afterExit(self): pass def _bangEnter(self): self._preview_open = False self._current_mode = 'NORMAL' if self._getInstance().getWinPos() == 'popup': self._cli.hideCursor() if lfEval("exists('*leaderf#%s#NormalModeFilter')" % self._getExplorer().getStlCategory()) == '1': lfCmd("call leaderf#ResetPopupOptions(%d, 'filter', '%s')" % (self._getInstance().getPopupWinId(), 'leaderf#%s#NormalModeFilter' % self._getExplorer().getStlCategory())) else: lfCmd("call leaderf#ResetPopupOptions(%d, 'filter', function('leaderf#NormalModeFilter', [%d]))" % (self._getInstance().getPopupWinId(), id(self))) self._resetHighlights() if self._cli.pattern and self._index == 0: self._search(self._content) def _bangReadFinished(self): if self._preview_open == False and self._getInstance().getWinPos() in ('popup', 'floatwin'): self._previewResult(False) self._preview_open = True def _getList(self, pairs): """ this function can be overridden return a list constructed from pairs Args: pairs: a list of tuple(weight, line, ...) """ return [p[1] for p in pairs] def _getUnit(self): """ indicates how many lines are considered as a unit """ return 1 def _supportsRefine(self): return False def _previewInPopup(self, *args, **kwargs): pass def _closePreviewPopup(self): if lfEval("has('nvim')") == '1': if self._preview_winid: if int(lfEval("nvim_win_is_valid(%d) == v:true" % self._preview_winid)): lfCmd("call nvim_win_close(%d, 1)" % self._preview_winid) self._preview_winid = 0 else: if self._preview_winid: lfCmd("call popup_close(%d)" % self._preview_winid) self._preview_winid = 0 def _previewResult(self, preview): if self._getInstance().getWinPos() == 'floatwin': self._cli.buildPopupPrompt() if lfEval("get(g:, 'Lf_PreviewInPopup', 0)") == '1' and \ int(lfEval("win_id2win(%d)" % self._preview_winid)) != vim.current.window.number: self._closePreviewPopup() if not self._needPreview(preview): return line = self._getInstance().currentLine if lfEval("get(g:, 'Lf_PreviewInPopup', 0)") == '1': line_nr = self._getInstance().window.cursor[0] self._previewInPopup(line, self._getInstance().buffer, line_nr) return orig_pos = self._getInstance().getOriginalPos() cur_pos = (vim.current.tabpage, vim.current.window, vim.current.buffer) saved_eventignore = vim.options['eventignore'] vim.options['eventignore'] = 'BufLeave,WinEnter,BufEnter' try: vim.current.tabpage, vim.current.window = orig_pos[:2] self._acceptSelection(line, preview=True) lfCmd("augroup Lf_Cursorline") lfCmd("autocmd! BufwinEnter <buffer> setlocal cursorline<") lfCmd("augroup END") finally: if self._getInstance().getWinPos() != 'popup': vim.current.tabpage, vim.current.window, vim.current.buffer = cur_pos vim.options['eventignore'] = saved_eventignore def _restoreOrigCwd(self): if self._orig_cwd is None: return # https://github.com/neovim/neovim/issues/8336 if lfEval("has('nvim')") == '1': chdir = vim.chdir else: chdir = os.chdir try: if int(lfEval("&autochdir")) == 0 and lfGetCwd() != self._orig_cwd: chdir(self._orig_cwd) except: if lfGetCwd() != self._orig_cwd: chdir(self._orig_cwd) def _needExit(self, line, arguments): return True def setArguments(self, arguments): self._arguments = arguments def getArguments(self): return self._arguments #************************************************************** def _createPopupModePreview(self, title, source, line_nr, jump_cmd): """ Args: source: if the type is int, it is a buffer number if the type is str, it is a file name """ self._is_previewed = True if lfEval("has('nvim')") == '1': width = int(lfEval("get(g:, 'Lf_PreviewPopupWidth', 0)")) if width == 0: maxwidth = int(lfEval("&columns"))//2 else: maxwidth = min(width, int(lfEval("&columns"))) relative = 'editor' if isinstance(source, int): buffer_len = len(vim.buffers[source]) else: try: lfCmd("let content = readfile('%s')" % escQuote(source)) except vim.error as e: lfPrintError(e) return buffer_len = int(lfEval("len(content)")) lfCmd("let scratch_buffer = nvim_create_buf(0, 1)") lfCmd("call setbufline(scratch_buffer, 1, content)") lfCmd("call nvim_buf_set_option(scratch_buffer, 'bufhidden', 'wipe')") float_window = self._getInstance().window float_win_row = int(float(lfEval("nvim_win_get_config(%d).row" % float_window.id))) float_win_col = int(float(lfEval("nvim_win_get_config(%d).col" % float_window.id))) preview_pos = lfEval("get(g:, 'Lf_PopupPreviewPosition', 'top')") if preview_pos.lower() == 'bottom': anchor = "NW" if self._getInstance().getPopupInstance().statusline_win: statusline_height = 1 else: statusline_height = 0 row = float_win_row + float_window.height + statusline_height col = float_win_col height = int(lfEval("&lines")) - row - 2 if height < 1: return width = float_window.width elif preview_pos.lower() == 'top': anchor = "SW" row = float_win_row - 1 col = float_win_col height = row if height < 1: return width = float_window.width elif preview_pos.lower() == 'right': anchor = "SW" row = float_win_row - 1 col = float_win_col + float_window.width height = row if height < 1: return width = float_window.width else: anchor = "SW" start = int(lfEval("line('w0')")) - 1 end = int(lfEval("line('.')")) - 1 col_width = float_window.width - int(lfEval("&numberwidth")) - 1 delta_height = lfActualLineCount(self._getInstance().buffer, start, end, col_width) row = float_win_row + delta_height col = float_win_col + int(lfEval("&numberwidth")) + 1 + float_window.cursor[1] height = row width = maxwidth config = { "relative": relative, "anchor" : anchor, "height" : height, "width" : width, "row" : row, "col" : col } if isinstance(source, int): self._preview_winid = int(lfEval("nvim_open_win(%d, 0, %s)" % (source, str(config)))) else: self._preview_winid = int(lfEval("nvim_open_win(scratch_buffer, 0, %s)" % str(config))) if jump_cmd: cur_winid = lfEval("win_getid()") lfCmd("noautocmd call win_gotoid(%d)" % self._preview_winid) lfCmd(jump_cmd) lfCmd("noautocmd call win_gotoid(%s)" % cur_winid) if buffer_len >= line_nr > 0: lfCmd("""call nvim_win_set_cursor(%d, [%d, 1])""" % (self._preview_winid, line_nr)) lfCmd("call nvim_win_set_option(%d, 'number', v:true)" % self._preview_winid) lfCmd("call nvim_win_set_option(%d, 'relativenumber', v:false)" % self._preview_winid) lfCmd("call nvim_win_set_option(%d, 'cursorline', v:true)" % self._preview_winid) lfCmd("call nvim_win_set_option(%d, 'colorcolumn', '')" % self._preview_winid) lfCmd("call nvim_win_set_option(%d, 'winhighlight', 'Normal:Lf_hl_popup_window')" % self._preview_winid) cur_winid = lfEval("win_getid()") lfCmd("noautocmd call win_gotoid(%d)" % self._preview_winid) if not isinstance(source, int): lfCmd("doautocmd filetypedetect BufNewFile %s" % source) lfCmd("silent! %foldopen!") lfCmd("norm! zz") lfCmd("noautocmd call win_gotoid(%s)" % cur_winid) # lfCmd("redraw!") # maybe we don't need it, it makes the preview slow else: popup_window = self._getInstance().window popup_pos = lfEval("popup_getpos(%d)" % popup_window.id) width = int(lfEval("get(g:, 'Lf_PreviewPopupWidth', 0)")) if width == 0: maxwidth = int(lfEval("&columns"))//2 - 1 else: maxwidth = min(width, int(lfEval("&columns"))) if not isinstance(source, int): try: lfCmd("let content = readfile('%s')" % escQuote(source)) except vim.error as e: lfPrintError(e) return preview_pos = lfEval("get(g:, 'Lf_PopupPreviewPosition', 'top')") if preview_pos.lower() == 'bottom': maxwidth = int(popup_pos["width"]) col = int(popup_pos["col"]) if self._getInstance().getPopupInstance().statusline_win: statusline_height = 1 else: statusline_height = 0 line = int(popup_pos["line"]) + int(popup_pos["height"]) + statusline_height pos = "topleft" maxheight = int(lfEval("&lines")) - line if maxheight < 1: return elif preview_pos.lower() == 'top': maxwidth = int(popup_pos["width"]) col = int(popup_pos["col"]) # int(popup_pos["line"]) - 1(exclude the first line) - 1(input window) - 1(title) maxheight = int(popup_pos["line"]) - 3 if maxheight < 1: return pos = "botleft" line = maxheight + 1 elif preview_pos.lower() == 'right': maxwidth = int(popup_pos["width"]) col = int(popup_pos["col"]) + maxwidth # int(popup_pos["line"]) - 1(exclude the first line) - 1(input window) - 1(title) maxheight = int(popup_pos["height"]) + 1 if maxheight < 1: return pos = "topleft" line = int(popup_pos["line"]) - 1 else: # cursor lfCmd("""call win_execute(%d, "let numberwidth = &numberwidth")""" % popup_window.id) col = int(popup_pos["core_col"]) + int(lfEval("numberwidth")) + popup_window.cursor[1] lfCmd("""call win_execute(%d, "let delta_height = line('.') - line('w0')")""" % popup_window.id) # the line of buffer starts from 0, while the line of line() starts from 1 start = int(lfEval("line('w0', %d)" % popup_window.id)) - 1 end = int(lfEval("line('.', %d)" % popup_window.id)) - 1 col_width = int(popup_pos["core_width"]) - int(lfEval("numberwidth")) delta_height = lfActualLineCount(self._getInstance().buffer, start, end, col_width) # int(popup_pos["core_line"]) - 1(exclude the first line) - 1(input window) maxheight = int(popup_pos["core_line"]) + delta_height - 2 pos = "botleft" line = maxheight + 1 options = { "title": title, "maxwidth": maxwidth, "minwidth": maxwidth, "maxheight": maxheight, "minheight": maxheight, "zindex": 20481, "pos": pos, "line": line, "col": col, "padding": [0, 0, 0, 0], "border": [1, 0, 0, 0], "borderchars": [' '], "borderhighlight": ["Lf_hl_previewTitle"], "filter": "leaderf#popupModePreviewFilter", "scrollbar": 0, } if preview_pos.lower() == 'bottom': del options["title"] options["border"] = [0, 0, 1, 0] elif preview_pos.lower() == 'cursor' and maxheight < int(lfEval("&lines"))//2 - 2: maxheight = int(lfEval("&lines")) - maxheight - 5 del options["title"] options["border"] = [0, 0, 1, 0] options["maxheight"] = maxheight options["minheight"] = maxheight if isinstance(source, int): lfCmd("silent! let winid = popup_create(%d, %s)" % (source, json.dumps(options))) else: lfCmd("silent! let winid = popup_create(content, %s)" % json.dumps(options)) lfCmd("call win_execute(winid, 'doautocmd filetypedetect BufNewFile %s')" % escQuote(source)) self._preview_winid = int(lfEval("winid")) if jump_cmd: lfCmd("""call win_execute(%d, '%s')""" % (self._preview_winid, escQuote(jump_cmd))) elif line_nr > 0: lfCmd("""call win_execute(%d, "call cursor(%d, 1)")""" % (self._preview_winid, line_nr)) lfCmd("call win_execute(%d, 'setlocal cursorline number norelativenumber colorcolumn= ')" % self._preview_winid) lfCmd("call win_execute(%d, 'setlocal wincolor=Lf_hl_popup_window')" % self._preview_winid) if lfEval("get(g:, 'Lf_PopupShowFoldcolumn', 1)") == '0': lfCmd("call win_execute(%d, 'setlocal foldcolumn=0')" % self._preview_winid) else: lfCmd("call win_execute(%d, 'setlocal foldcolumn=1')" % self._preview_winid) lfCmd("call win_execute(%d, 'norm! zz')" % self._preview_winid) @ignoreEvent('BufRead,BufReadPre,BufReadPost') def _createPopupPreview(self, title, source, line_nr, jump_cmd=''): """ Args: source: if the type is int, it is a buffer number if the type is str, it is a file name """ self._is_previewed = True line_nr = int(line_nr) if self._getInstance().getWinPos() in ('popup', 'floatwin'): self._createPopupModePreview(title, source, line_nr, jump_cmd) return if lfEval("has('nvim')") == '1': width = int(lfEval("get(g:, 'Lf_PreviewPopupWidth', 0)")) if width == 0: width = int(lfEval("&columns"))//2 else: width = min(width, int(lfEval("&columns"))) maxheight = int(lfEval("&lines - (line('w$') - line('.')) - 3")) maxheight -= int(self._getInstance().window.height) - int(lfEval("(line('w$') - line('w0') + 1)")) relative = 'editor' anchor = "SW" row = maxheight if isinstance(source, int): buffer_len = len(vim.buffers[source]) else: try: lfCmd("let content = readfile('%s')" % escQuote(source)) except vim.error as e: lfPrintError(e) return buffer_len = int(lfEval("len(content)")) lfCmd("let scratch_buffer = nvim_create_buf(0, 1)") lfCmd("call setbufline(scratch_buffer, 1, content)") lfCmd("call nvim_buf_set_option(scratch_buffer, 'bufhidden', 'wipe')") height = min(maxheight, buffer_len) preview_pos = lfEval("get(g:, 'Lf_PreviewHorizontalPosition', 'right')") if preview_pos.lower() == 'center': col = (int(lfEval("&columns")) - width) // 2 elif preview_pos.lower() == 'left': col = 0 elif preview_pos.lower() == 'right': col = int(lfEval("&columns")) - width else: relative = 'cursor' row = 0 col = 0 if maxheight < int(lfEval("&lines"))//2 - 2: anchor = "NW" if relative == 'cursor': row = 1 else: row = maxheight + 1 height = min(int(lfEval("&lines")) - maxheight - 3, buffer_len) config = { "relative": relative, "anchor" : anchor, "height" : height, "width" : width, "row" : row, "col" : col } if isinstance(source, int): self._preview_winid = int(lfEval("nvim_open_win(%d, 0, %s)" % (source, str(config)))) else: self._preview_winid = int(lfEval("nvim_open_win(scratch_buffer, 0, %s)" % str(config))) if jump_cmd: cur_winid = lfEval("win_getid()") lfCmd("noautocmd call win_gotoid(%d)" % self._preview_winid) lfCmd(jump_cmd) lfCmd("noautocmd call win_gotoid(%s)" % cur_winid) if buffer_len >= line_nr > 0: lfCmd("""call nvim_win_set_cursor(%d, [%d, 1])""" % (self._preview_winid, line_nr)) lfCmd("call nvim_win_set_option(%d, 'number', v:true)" % self._preview_winid) lfCmd("call nvim_win_set_option(%d, 'relativenumber', v:false)" % self._preview_winid) lfCmd("call nvim_win_set_option(%d, 'cursorline', v:true)" % self._preview_winid) lfCmd("call nvim_win_set_option(%d, 'colorcolumn', '')" % self._preview_winid) cur_winid = lfEval("win_getid()") lfCmd("noautocmd call win_gotoid(%d)" % self._preview_winid) if not isinstance(source, int): lfCmd("doautocmd filetypedetect BufNewFile %s" % source) lfCmd("silent! %foldopen!") lfCmd("noautocmd call win_gotoid(%s)" % cur_winid) else: preview_pos = lfEval("get(g:, 'Lf_PreviewHorizontalPosition', 'right')") if preview_pos.lower() == 'center': col = 0 elif preview_pos.lower() == 'left': col = 1 elif preview_pos.lower() == 'right': col = int(lfEval("&columns"))//2 + 2 else: col = "cursor" width = int(lfEval("get(g:, 'Lf_PreviewPopupWidth', 0)")) if width == 0: maxwidth = int(lfEval("&columns"))//2 - 1 else: maxwidth = min(width, int(lfEval("&columns"))) maxheight = int(lfEval("&lines - (line('w$') - line('.')) - 4")) maxheight -= int(self._getInstance().window.height) - int(lfEval("(line('w$') - line('w0') + 1)")) options = { "title": title, "maxwidth": maxwidth, "minwidth": maxwidth, "maxheight": maxheight, "minheight": maxheight, "zindex": 20481, "pos": "botleft", "line": "cursor-1", "col": col, "padding": [0, 0, 0, 1], "border": [1, 0, 0, 0], "borderchars": [' '], "borderhighlight": ["Lf_hl_previewTitle"], "filter": "leaderf#popupModePreviewFilter", } if maxheight < int(lfEval("&lines"))//2 - 2: maxheight = int(lfEval("&lines")) - maxheight - 5 del options["title"] options["border"] = [0, 0, 1, 0] options["maxheight"] = maxheight options["minheight"] = maxheight if isinstance(source, int): lfCmd("silent! let winid = popup_create(%d, %s)" % (source, json.dumps(options))) else: try: lfCmd("let content = readfile('%s')" % escQuote(source)) except vim.error as e: lfPrintError(e) return lfCmd("silent! let winid = popup_create(content, %s)" % json.dumps(options)) lfCmd("call win_execute(winid, 'doautocmd filetypedetect BufNewFile %s')" % escQuote(source)) self._preview_winid = int(lfEval("winid")) if self._current_mode == 'NORMAL': lfCmd("call leaderf#ResetPopupOptions(%d, 'filter', function('leaderf#normalModePreviewFilter', [%d]))" % (self._preview_winid, id(self))) if jump_cmd: lfCmd("""call win_execute(%d, '%s')""" % (self._preview_winid, escQuote(jump_cmd))) elif line_nr > 0: lfCmd("""call win_execute(%d, "exec 'norm! %dG'")""" % (self._preview_winid, line_nr)) lfCmd("call win_execute(%d, 'setlocal cursorline number norelativenumber')" % self._preview_winid) def _needPreview(self, preview): """ Args: preview: if True, always preview the result no matter what `g:Lf_PreviewResult` is. """ preview_dict = {k.lower(): v for k, v in lfEval("g:Lf_PreviewResult").items()} category = self._getExplorer().getStlCategory() if not preview and int(preview_dict.get(category.lower(), 0)) == 0: return False if self._getInstance().isReverseOrder(): if self._getInstance().window.cursor[0] > len(self._getInstance().buffer) - self._help_length: return False elif self._getInstance().window.cursor[0] <= self._help_length: return False if self._getInstance().empty() or (self._getInstance().getWinPos() != 'popup' and vim.current.buffer != self._getInstance().buffer): return False if self._ctrlp_pressed == True: return True line = self._getInstance().currentLine if self._orig_line == line and self._getInstance().buffer.options['modifiable']: return False self._orig_line = line return True def _getInstance(self): if self._instance is None: self._instance = LfInstance(self, self._getExplorer().getStlCategory(), self._cli, self._beforeEnter, self._afterEnter, self._beforeExit, self._afterExit) return self._instance def _createHelpHint(self): help = [] if not self._show_help: if lfEval("get(g:, 'Lf_HideHelp', 0)") == '0': help.append('" Press <F1> for help') help.append('" ---------------------------------------------------------') else: help += self._createHelp() self._help_length = len(help) orig_row = self._getInstance().window.cursor[0] if self._getInstance().isReverseOrder(): self._getInstance().buffer.options['modifiable'] = True self._getInstance().buffer.append(help[::-1]) self._getInstance().buffer.options['modifiable'] = False buffer_len = len(self._getInstance().buffer) if buffer_len < self._initial_count: if "--nowrap" not in self._arguments: self._getInstance().window.height = min(self._initial_count, self._getInstance()._actualLength(self._getInstance().buffer)) else: self._getInstance().window.height = buffer_len elif self._getInstance().window.height < self._initial_count: self._getInstance().window.height = self._initial_count lfCmd("normal! Gzb") self._getInstance().window.cursor = (orig_row, 0) else: self._getInstance().buffer.options['modifiable'] = True self._getInstance().buffer.append(help, 0) self._getInstance().buffer.options['modifiable'] = False self._getInstance().window.cursor = (orig_row + self._help_length, 0) self._getInstance().mimicCursor() self._getInstance().refreshPopupStatusline() def _hideHelp(self): self._getInstance().buffer.options['modifiable'] = True if self._getInstance().isReverseOrder(): orig_row = self._getInstance().window.cursor[0] countdown = len(self._getInstance().buffer) - orig_row - self._help_length if self._help_length > 0: del self._getInstance().buffer[-self._help_length:] self._getInstance().buffer[:] = self._getInstance().buffer[-self._initial_count:] lfCmd("normal! Gzb") if 0 < countdown < self._initial_count: self._getInstance().window.cursor = (len(self._getInstance().buffer) - countdown, 0) else: self._getInstance().window.cursor = (len(self._getInstance().buffer), 0) self._getInstance().setLineNumber() else: del self._getInstance().buffer[:self._help_length] if self._help_length > 0 and self._getInstance().getWinPos() == 'popup': lfCmd("call win_execute(%d, 'norm! %dk')" % (self._getInstance().getPopupWinId(), self._help_length)) self._help_length = 0 self._getInstance().refreshPopupStatusline() def _inHelpLines(self): if self._getInstance().isReverseOrder(): if self._getInstance().window.cursor[0] > len(self._getInstance().buffer) - self._help_length: return True elif self._getInstance().window.cursor[0] <= self._help_length: return True return False def _getExplorer(self): if self._explorer is None: self._explorer = self._getExplClass()() return self._explorer def _resetAutochdir(self): if int(lfEval("&autochdir")) == 1: self._autochdir = 1 lfCmd("set noautochdir") else: self._autochdir = 0 def _setAutochdir(self): if self._autochdir == 1: # When autochdir is set, Vim will change the current working directory # to the directory containing the file which was opened or selected. lfCmd("set autochdir") def _toUpInPopup(self): if self._preview_winid > 0 and int(lfEval("winbufnr(%d)" % self._preview_winid)) != -1: if lfEval("has('nvim')") == '1': cur_winid = lfEval("win_getid()") lfCmd("noautocmd call win_gotoid(%d)" % self._preview_winid) lfCmd("norm! k") lfCmd("redraw") lfCmd("noautocmd call win_gotoid(%s)" % cur_winid) else: lfCmd("call win_execute(%d, 'norm! k')" % (self._preview_winid)) def _toDownInPopup(self): if self._preview_winid > 0 and int(lfEval("winbufnr(%d)" % self._preview_winid)) != -1: if lfEval("has('nvim')") == '1': cur_winid = lfEval("win_getid()") lfCmd("noautocmd call win_gotoid(%d)" % self._preview_winid) lfCmd("norm! j") lfCmd("redraw") lfCmd("noautocmd call win_gotoid(%s)" % cur_winid) else: lfCmd("call win_execute(%d, 'norm! j')" % (self._preview_winid)) def _toUp(self): if self._getInstance().getWinPos() == 'popup': lfCmd("call win_execute(%d, 'norm! k')" % (self._getInstance().getPopupWinId())) self._getInstance().refreshPopupStatusline() return adjust = False if self._getInstance().isReverseOrder() and self._getInstance().getCurrentPos()[0] == 1: adjust = True self._setResultContent() if self._cli.pattern and self._cli.isFuzzy \ and len(self._highlight_pos) < (len(self._getInstance().buffer) - self._help_length) // self._getUnit() \ and len(self._highlight_pos) < int(lfEval("g:Lf_NumberOfHighlight")): self._highlight_method() lfCmd("norm! k") if adjust: lfCmd("norm! zt") self._getInstance().setLineNumber() lfCmd("setlocal cursorline!") # these two help to redraw the statusline, lfCmd("setlocal cursorline!") # also fix a weird bug of vim def _toDown(self): if self._getInstance().getWinPos() == 'popup': lfCmd("call win_execute(%d, 'norm! j')" % (self._getInstance().getPopupWinId())) self._getInstance().refreshPopupStatusline() return if not self._getInstance().isReverseOrder() \ and self._getInstance().getCurrentPos()[0] == self._getInstance().window.height: self._setResultContent() lfCmd("norm! j") self._getInstance().setLineNumber() lfCmd("setlocal cursorline!") # these two help to redraw the statusline, lfCmd("setlocal cursorline!") # also fix a weird bug of vim def _pageUp(self): if self._getInstance().getWinPos() == 'popup': lfCmd("""call win_execute(%d, 'exec "norm! \<PageUp>"')""" % (self._getInstance().getPopupWinId())) self._getInstance().refreshPopupStatusline() return if self._getInstance().isReverseOrder(): self._setResultContent() if self._cli.pattern and self._cli.isFuzzy \ and len(self._highlight_pos) < (len(self._getInstance().buffer) - self._help_length) // self._getUnit() \ and len(self._highlight_pos) < int(lfEval("g:Lf_NumberOfHighlight")): self._highlight_method() lfCmd('exec "norm! \<PageUp>"') self._getInstance().setLineNumber() def _pageDown(self): if self._getInstance().getWinPos() == 'popup': lfCmd("""call win_execute(%d, 'exec "norm! \<PageDown>"')""" % (self._getInstance().getPopupWinId())) self._getInstance().refreshPopupStatusline() return if not self._getInstance().isReverseOrder(): self._setResultContent() lfCmd('exec "norm! \<PageDown>"') self._getInstance().setLineNumber() def _leftClick(self): if self._getInstance().getWinPos() == 'popup': if int(lfEval("has('patch-8.1.2266')")) == 1: if self._getInstance().getPopupWinId() == int(lfEval("v:mouse_winid")): lfCmd("""call win_execute(%d, "exec v:mouse_lnum")""" % (self._getInstance().getPopupWinId())) lfCmd("""call win_execute(%d, "exec 'norm!'.v:mouse_col.'|'")""" % (self._getInstance().getPopupWinId())) exit_loop = False elif self._getInstance().window.number == int(lfEval("v:mouse_win")): lfCmd("exec v:mouse_lnum") lfCmd("exec 'norm!'.v:mouse_col.'|'") self._getInstance().setLineNumber() self.clearSelections() exit_loop = False elif self._preview_winid == int(lfEval("v:mouse_winid")): if lfEval("has('nvim')") == '1': lfCmd("call win_gotoid(%d)" % self._preview_winid) lfCmd("exec v:mouse_lnum") lfCmd("exec 'norm!'.v:mouse_col.'|'") self._current_mode = 'NORMAL' lfCmd("call leaderf#colorscheme#popup#hiMode('%s', '%s')" % (self._getExplorer().getStlCategory(), self._current_mode)) self._getInstance().setPopupStl(self._current_mode) exit_loop = True else: self.quit() exit_loop = True return exit_loop def _search(self, content, is_continue=False, step=0): if not is_continue: self.clearSelections() self._clearHighlights() self._clearHighlightsPos() self._cli.highlightMatches() if not self._cli.pattern: # e.g., when <BS> or <Del> is typed if self._empty_query and self._getExplorer().getStlCategory() in ["File"]: self._guessSearch(self._content) else: self._getInstance().setBuffer(content[:self._initial_count]) self._getInstance().setStlResultsCount(len(content), True) self._result_content = [] return if self._cli.isFuzzy: self._fuzzySearch(content, is_continue, step) else: self._regexSearch(content, is_continue, step) if self._getExplorer().getStlCategory() not in ["File"]: self._previewResult(False) def _filter(self, step, filter_method, content, is_continue, use_fuzzy_engine=False, return_index=False): """ Construct a list from result of filter_method(content). Args: step: An integer to indicate the number of lines to filter one time. filter_method: A function to apply `content` as parameter and return an iterable. content: The list to be filtered. """ unit = self._getUnit() step = step // unit * unit length = len(content) if self._index == 0: self._cb_content = [] self._result_content = [] self._index = min(step, length) cur_content = content[:self._index] else: if not is_continue and self._result_content: if self._cb_content: self._cb_content += self._result_content else: self._cb_content = self._result_content if len(self._cb_content) >= step: cur_content = self._cb_content[:step] self._cb_content = self._cb_content[step:] else: cur_content = self._cb_content left = step - len(self._cb_content) self._cb_content = [] if self._index < length: end = min(self._index + left, length) cur_content += content[self._index:end] self._index = end if self._cli.isAndMode: result, highlight_methods = filter_method(cur_content) if is_continue: self._previous_result = (self._previous_result[0] + result[0], self._previous_result[1] + result[1]) result = self._previous_result else: self._previous_result = result return (result, highlight_methods) elif use_fuzzy_engine: if return_index: mode = 0 if self._cli.isFullPath else 1 tmp_content = [self._getDigest(line, mode) for line in cur_content] result = filter_method(source=tmp_content) result = (result[0], [cur_content[i] for i in result[1]]) else: result = filter_method(source=cur_content) if is_continue: result = fuzzyEngine.merge(self._previous_result, result) self._previous_result = result else: result = list(filter_method(cur_content)) if is_continue: self._previous_result += result result = self._previous_result else: self._previous_result = result return result def _fuzzyFilter(self, is_full_path, get_weight, iterable): """ return a list, each item is a pair (weight, line) """ getDigest = partial(self._getDigest, mode=0 if is_full_path else 1) pairs = ((get_weight(getDigest(line)), line) for line in iterable) MIN_WEIGHT = fuzzyMatchC.MIN_WEIGHT if is_fuzzyMatch_C else FuzzyMatch.MIN_WEIGHT return (p for p in pairs if p[0] > MIN_WEIGHT) def _fuzzyFilterEx(self, is_full_path, get_weight, iterable): """ return a tuple, (weights, indices) """ getDigest = partial(self._getDigest, mode=0 if is_full_path else 1) if self._getUnit() > 1: # currently, only BufTag's _getUnit() is 2 iterable = itertools.islice(iterable, 0, None, self._getUnit()) pairs = ((get_weight(getDigest(line)), i) for i, line in enumerate(iterable)) MIN_WEIGHT = fuzzyMatchC.MIN_WEIGHT if is_fuzzyMatch_C else FuzzyMatch.MIN_WEIGHT result = [p for p in pairs if p[0] > MIN_WEIGHT] if len(result) == 0: weights, indices = [], [] else: weights, indices = zip(*result) return (list(weights), list(indices)) def _refineFilter(self, first_get_weight, get_weight, iterable): getDigest = self._getDigest triples = ((first_get_weight(getDigest(line, 1)), get_weight(getDigest(line, 2)), line) for line in iterable) MIN_WEIGHT = fuzzyMatchC.MIN_WEIGHT if is_fuzzyMatch_C else FuzzyMatch.MIN_WEIGHT return ((i[0] + i[1], i[2]) for i in triples if i[0] > MIN_WEIGHT and i[1] > MIN_WEIGHT) def _andModeFilter(self, iterable): encoding = lfEval("&encoding") use_fuzzy_engine = False cur_content = iterable weight_lists = [] highlight_methods = [] for p in self._cli.pattern: if self._fuzzy_engine and isAscii(p) and self._getUnit() == 1: # currently, only BufTag's _getUnit() is 2 use_fuzzy_engine = True pattern = fuzzyEngine.initPattern(p) if self._getExplorer().getStlCategory() == "File" and self._cli.isFullPath: filter_method = partial(fuzzyEngine.fuzzyMatchEx, engine=self._fuzzy_engine, pattern=pattern, is_name_only=False, sort_results=False, is_and_mode=True) elif self._getExplorer().getStlCategory() in ["Self", "Buffer", "Mru", "BufTag", "Function", "History", "Cmd_History", "Search_History", "Tag", "Rg", "Filetype", "Command", "Window", "QuickFix", "LocList"]: filter_method = partial(fuzzyEngine.fuzzyMatchEx, engine=self._fuzzy_engine, pattern=pattern, is_name_only=True, sort_results=False, is_and_mode=True) else: filter_method = partial(fuzzyEngine.fuzzyMatchEx, engine=self._fuzzy_engine, pattern=pattern, is_name_only=not self._cli.isFullPath, sort_results=False, is_and_mode=True) getHighlights = partial(fuzzyEngine.getHighlights, engine=self._fuzzy_engine, pattern=pattern, is_name_only=not self._cli.isFullPath) highlight_method = partial(self._highlight, self._cli.isFullPath, getHighlights, True, clear=False) elif is_fuzzyMatch_C and isAscii(p): pattern = fuzzyMatchC.initPattern(p) if self._getExplorer().getStlCategory() == "File" and self._cli.isFullPath: getWeight = partial(fuzzyMatchC.getWeight, pattern=pattern, is_name_only=False) getHighlights = partial(fuzzyMatchC.getHighlights, pattern=pattern, is_name_only=False) else: getWeight = partial(fuzzyMatchC.getWeight, pattern=pattern, is_name_only=True) getHighlights = partial(fuzzyMatchC.getHighlights, pattern=pattern, is_name_only=True) filter_method = partial(self._fuzzyFilterEx, self._cli.isFullPath, getWeight) highlight_method = partial(self._highlight, self._cli.isFullPath, getHighlights, clear=False) else: fuzzy_match = FuzzyMatch(p, encoding) if self._getExplorer().getStlCategory() == "File" and self._cli.isFullPath: filter_method = partial(self._fuzzyFilterEx, self._cli.isFullPath, fuzzy_match.getWeight2) elif self._getExplorer().getStlCategory() in ["Self", "Buffer", "Mru", "BufTag", "Function", "History", "Cmd_History", "Search_History", "Tag", "Rg", "Filetype", "Command", "Window", "QuickFix", "LocList"]: filter_method = partial(self._fuzzyFilterEx, self._cli.isFullPath, fuzzy_match.getWeight3) else: filter_method = partial(self._fuzzyFilterEx, self._cli.isFullPath, fuzzy_match.getWeight) highlight_method = partial(self._highlight, self._cli.isFullPath, fuzzy_match.getHighlights, clear=False) if use_fuzzy_engine: mode = 0 if self._cli.isFullPath else 1 tmp_content = [self._getDigest(line, mode) for line in cur_content] result = filter_method(source=tmp_content) else: result = filter_method(cur_content) for i, wl in enumerate(weight_lists): weight_lists[i] = [wl[j] for j in result[1]] weight_lists.append(result[0]) if self._getUnit() > 1: # currently, only BufTag's _getUnit() is 2 unit = self._getUnit() result_content = [cur_content[i*unit:i*unit + unit] for i in result[1]] cur_content = list(itertools.chain.from_iterable(result_content)) else: cur_content = [cur_content[i] for i in result[1]] result_content = cur_content highlight_methods.append(highlight_method) weights = [sum(i) for i in zip(*weight_lists)] return ((weights, result_content), highlight_methods) def _fuzzySearch(self, content, is_continue, step): encoding = lfEval("&encoding") use_fuzzy_engine = False use_fuzzy_match_c = False if self._cli.isAndMode: filter_method = self._andModeFilter elif self._cli.isRefinement: if self._cli.pattern[1] == '': # e.g. abc; if self._fuzzy_engine and isAscii(self._cli.pattern[0]): use_fuzzy_engine = True return_index = True pattern = fuzzyEngine.initPattern(self._cli.pattern[0]) filter_method = partial(fuzzyEngine.fuzzyMatchEx, engine=self._fuzzy_engine, pattern=pattern, is_name_only=True, sort_results=True) getHighlights = partial(fuzzyEngine.getHighlights, engine=self._fuzzy_engine, pattern=pattern, is_name_only=True) highlight_method = partial(self._highlight, True, getHighlights, True) elif is_fuzzyMatch_C and isAscii(self._cli.pattern[0]): use_fuzzy_match_c = True pattern = fuzzyMatchC.initPattern(self._cli.pattern[0]) getWeight = partial(fuzzyMatchC.getWeight, pattern=pattern, is_name_only=True) getHighlights = partial(fuzzyMatchC.getHighlights, pattern=pattern, is_name_only=True) filter_method = partial(self._fuzzyFilter, False, getWeight) highlight_method = partial(self._highlight, False, getHighlights) else: fuzzy_match = FuzzyMatch(self._cli.pattern[0], encoding) getWeight = fuzzy_match.getWeight getHighlights = fuzzy_match.getHighlights filter_method = partial(self._fuzzyFilter, False, getWeight) highlight_method = partial(self._highlight, False, getHighlights) elif self._cli.pattern[0] == '': # e.g. ;abc if self._fuzzy_engine and isAscii(self._cli.pattern[1]): use_fuzzy_engine = True return_index = True pattern = fuzzyEngine.initPattern(self._cli.pattern[1]) filter_method = partial(fuzzyEngine.fuzzyMatchEx, engine=self._fuzzy_engine, pattern=pattern, is_name_only=False, sort_results=True) getHighlights = partial(fuzzyEngine.getHighlights, engine=self._fuzzy_engine, pattern=pattern, is_name_only=False) highlight_method = partial(self._highlight, True, getHighlights, True) elif is_fuzzyMatch_C and isAscii(self._cli.pattern[1]): use_fuzzy_match_c = True pattern = fuzzyMatchC.initPattern(self._cli.pattern[1]) getWeight = partial(fuzzyMatchC.getWeight, pattern=pattern, is_name_only=False) getHighlights = partial(fuzzyMatchC.getHighlights, pattern=pattern, is_name_only=False) filter_method = partial(self._fuzzyFilter, True, getWeight) highlight_method = partial(self._highlight, True, getHighlights) else: fuzzy_match = FuzzyMatch(self._cli.pattern[1], encoding) getWeight = fuzzy_match.getWeight getHighlights = fuzzy_match.getHighlights filter_method = partial(self._fuzzyFilter, True, getWeight) highlight_method = partial(self._highlight, True, getHighlights) else: # e.g. abc;def if is_fuzzyMatch_C and isAscii(self._cli.pattern[0]): is_ascii_0 = True pattern_0 = fuzzyMatchC.initPattern(self._cli.pattern[0]) getWeight_0 = partial(fuzzyMatchC.getWeight, pattern=pattern_0, is_name_only=True) getHighlights_0 = partial(fuzzyMatchC.getHighlights, pattern=pattern_0, is_name_only=True) else: is_ascii_0 = False fuzzy_match_0 = FuzzyMatch(self._cli.pattern[0], encoding) getWeight_0 = fuzzy_match_0.getWeight getHighlights_0 = fuzzy_match_0.getHighlights if is_fuzzyMatch_C and isAscii(self._cli.pattern[1]): is_ascii_1 = True pattern_1 = fuzzyMatchC.initPattern(self._cli.pattern[1]) getWeight_1 = partial(fuzzyMatchC.getWeight, pattern=pattern_1, is_name_only=False) getHighlights_1 = partial(fuzzyMatchC.getHighlights, pattern=pattern_1, is_name_only=False) else: is_ascii_1 = False fuzzy_match_1 = FuzzyMatch(self._cli.pattern[1], encoding) getWeight_1 = fuzzy_match_1.getWeight getHighlights_1 = fuzzy_match_1.getHighlights use_fuzzy_match_c = is_ascii_0 and is_ascii_1 filter_method = partial(self._refineFilter, getWeight_0, getWeight_1) highlight_method = partial(self._highlightRefine, getHighlights_0, getHighlights_1) else: if self._fuzzy_engine and isAscii(self._cli.pattern) and self._getUnit() == 1: # currently, only BufTag's _getUnit() is 2 use_fuzzy_engine = True pattern = fuzzyEngine.initPattern(self._cli.pattern) if self._getExplorer().getStlCategory() == "File": return_index = False if self._cli.isFullPath: filter_method = partial(fuzzyEngine.fuzzyMatch, engine=self._fuzzy_engine, pattern=pattern, is_name_only=False, sort_results=True) else: filter_method = partial(fuzzyEngine.fuzzyMatchPart, engine=self._fuzzy_engine, pattern=pattern, category=fuzzyEngine.Category_File, param=fuzzyEngine.createParameter(1), is_name_only=True, sort_results=True) elif self._getExplorer().getStlCategory() == "Rg": return_index = False if "--match-path" in self._arguments: filter_method = partial(fuzzyEngine.fuzzyMatch, engine=self._fuzzy_engine, pattern=pattern, is_name_only=True, sort_results=True) else: filter_method = partial(fuzzyEngine.fuzzyMatchPart, engine=self._fuzzy_engine, pattern=pattern, category=fuzzyEngine.Category_Rg, param=fuzzyEngine.createRgParameter(self._getExplorer().displayMulti(), self._getExplorer().getContextSeparator(), self._has_column), is_name_only=True, sort_results=True) elif self._getExplorer().getStlCategory() == "Tag": return_index = False mode = 0 if self._cli.isFullPath else 1 filter_method = partial(fuzzyEngine.fuzzyMatchPart, engine=self._fuzzy_engine, pattern=pattern, category=fuzzyEngine.Category_Tag, param=fuzzyEngine.createParameter(mode), is_name_only=True, sort_results=True) elif self._getExplorer().getStlCategory() == "Gtags": return_index = False result_format = 1 if self._getExplorer().getResultFormat() in [None, "ctags-mod"]: result_format = 0 elif self._getExplorer().getResultFormat() == "ctags-x": result_format = 2 filter_method = partial(fuzzyEngine.fuzzyMatchPart, engine=self._fuzzy_engine, pattern=pattern, category=fuzzyEngine.Category_Gtags, param=fuzzyEngine.createGtagsParameter(0, result_format, self._match_path), is_name_only=True, sort_results=True) elif self._getExplorer().getStlCategory() == "Line": return_index = False filter_method = partial(fuzzyEngine.fuzzyMatchPart, engine=self._fuzzy_engine, pattern=pattern, category=fuzzyEngine.Category_Line, param=fuzzyEngine.createParameter(1), is_name_only=True, sort_results=True) elif self._getExplorer().getStlCategory() in ["Self", "Buffer", "Mru", "BufTag", "Function", "History", "Cmd_History", "Search_History", "Filetype", "Command", "Window", "QuickFix", "LocList"]: return_index = True filter_method = partial(fuzzyEngine.fuzzyMatchEx, engine=self._fuzzy_engine, pattern=pattern, is_name_only=True, sort_results=True) else: return_index = True filter_method = partial(fuzzyEngine.fuzzyMatchEx, engine=self._fuzzy_engine, pattern=pattern, is_name_only=not self._cli.isFullPath, sort_results=True) getHighlights = partial(fuzzyEngine.getHighlights, engine=self._fuzzy_engine, pattern=pattern, is_name_only=not self._cli.isFullPath) highlight_method = partial(self._highlight, self._cli.isFullPath, getHighlights, True) elif is_fuzzyMatch_C and isAscii(self._cli.pattern): use_fuzzy_match_c = True pattern = fuzzyMatchC.initPattern(self._cli.pattern) if self._getExplorer().getStlCategory() == "File" and self._cli.isFullPath: getWeight = partial(fuzzyMatchC.getWeight, pattern=pattern, is_name_only=False) getHighlights = partial(fuzzyMatchC.getHighlights, pattern=pattern, is_name_only=False) else: getWeight = partial(fuzzyMatchC.getWeight, pattern=pattern, is_name_only=True) getHighlights = partial(fuzzyMatchC.getHighlights, pattern=pattern, is_name_only=True) filter_method = partial(self._fuzzyFilter, self._cli.isFullPath, getWeight) highlight_method = partial(self._highlight, self._cli.isFullPath, getHighlights) else: fuzzy_match = FuzzyMatch(self._cli.pattern, encoding) if self._getExplorer().getStlCategory() == "File" and self._cli.isFullPath: filter_method = partial(self._fuzzyFilter, self._cli.isFullPath, fuzzy_match.getWeight2) elif self._getExplorer().getStlCategory() in ["Self", "Buffer", "Mru", "BufTag", "Function", "History", "Cmd_History", "Search_History", "Rg", "Filetype", "Command", "Window", "QuickFix", "LocList"]: filter_method = partial(self._fuzzyFilter, self._cli.isFullPath, fuzzy_match.getWeight3) else: filter_method = partial(self._fuzzyFilter, self._cli.isFullPath, fuzzy_match.getWeight) highlight_method = partial(self._highlight, self._cli.isFullPath, fuzzy_match.getHighlights) if self._cli.isAndMode: if self._fuzzy_engine and isAscii(''.join(self._cli.pattern)): step = 20000 * cpu_count else: step = 10000 pair, highlight_methods = self._filter(step, filter_method, content, is_continue) pairs = sorted(zip(*pair), key=operator.itemgetter(0), reverse=True) self._result_content = self._getList(pairs) elif use_fuzzy_engine: if step == 0: if return_index == True: step = 30000 * cpu_count else: step = 60000 * cpu_count _, self._result_content = self._filter(step, filter_method, content, is_continue, True, return_index) else: if step == 0: if use_fuzzy_match_c: step = 60000 elif self._getExplorer().supportsNameOnly() and self._cli.isFullPath: step = 6000 else: step = 12000 pairs = self._filter(step, filter_method, content, is_continue) pairs.sort(key=operator.itemgetter(0), reverse=True) self._result_content = self._getList(pairs) self._getInstance().setBuffer(self._result_content[:self._initial_count]) self._getInstance().setStlResultsCount(len(self._result_content), True) if self._cli.isAndMode: self._highlight_method = partial(self._highlight_and_mode, highlight_methods) self._highlight_method() else: self._highlight_method = highlight_method self._highlight_method() if len(self._cli.pattern) > 1 and not is_continue: lfCmd("redraw") def _guessFilter(self, filename, suffix, dirname, icon, iterable): """ return a list, each item is a pair (weight, line) """ icon_len = len(icon) return ((FuzzyMatch.getPathWeight(filename, suffix, dirname, line[icon_len:]), line) for line in iterable) def _guessSearch(self, content, is_continue=False, step=0): if self._cur_buffer.name == '' or self._cur_buffer.options["buftype"] not in [b'', '']: self._getInstance().setBuffer(content[:self._initial_count]) self._getInstance().setStlResultsCount(len(content), True) self._result_content = [] return buffer_name = os.path.normpath(lfDecode(self._cur_buffer.name)) if lfEval("g:Lf_ShowRelativePath") == '1': try: buffer_name = os.path.relpath(buffer_name) except ValueError: pass buffer_name = lfEncode(buffer_name) dirname, basename = os.path.split(buffer_name) filename, suffix = os.path.splitext(basename) if lfEval("get(g:, 'Lf_ShowDevIcons', 1)") == "1": icon = webDevIconsGetFileTypeSymbol(basename) else: icon = '' if self._fuzzy_engine: filter_method = partial(fuzzyEngine.guessMatch, engine=self._fuzzy_engine, filename=filename, suffix=suffix, dirname=dirname, icon=icon, sort_results=True) step = len(content) _, self._result_content = self._filter(step, filter_method, content, is_continue, True) else: step = len(content) filter_method = partial(self._guessFilter, filename, suffix, dirname, icon) pairs = self._filter(step, filter_method, content, is_continue) pairs.sort(key=operator.itemgetter(0), reverse=True) self._result_content = self._getList(pairs) self._getInstance().setBuffer(self._result_content[:self._initial_count]) self._getInstance().setStlResultsCount(len(self._result_content), True) def _highlight_and_mode(self, highlight_methods): self._clearHighlights() for i, highlight_method in enumerate(highlight_methods): highlight_method(hl_group='Lf_hl_match' + str(i % 5)) def _clearHighlights(self): if self._getInstance().getWinPos() == 'popup': for i in self._highlight_ids: lfCmd("silent! call matchdelete(%d, %d)" % (i, self._getInstance().getPopupWinId())) else: for i in self._highlight_ids: lfCmd("silent! call matchdelete(%d)" % i) self._highlight_ids = [] def _clearHighlightsPos(self): self._highlight_pos = [] self._highlight_pos_list = [] self._highlight_refine_pos = [] def _resetHighlights(self): self._clearHighlights() unit = self._getUnit() bottom = len(self._getInstance().buffer) - self._help_length if self._cli.isAndMode: highlight_pos_list = self._highlight_pos_list else: highlight_pos_list = [self._highlight_pos] for n, highlight_pos in enumerate(highlight_pos_list): hl_group = 'Lf_hl_match' + str(n % 5) for i, pos in enumerate(highlight_pos): if self._getInstance().isReverseOrder(): pos = [[bottom - unit*i] + p for p in pos] else: pos = [[unit*i + 1 + self._help_length] + p for p in pos] # The maximum number of positions is 8 in matchaddpos(). for j in range(0, len(pos), 8): if self._getInstance().getWinPos() == 'popup': lfCmd("""call win_execute(%d, "let matchid = matchaddpos('%s', %s)")""" % (self._getInstance().getPopupWinId(), hl_group, str(pos[j:j+8]))) id = int(lfEval("matchid")) else: id = int(lfEval("matchaddpos('%s', %s)" % (hl_group, str(pos[j:j+8])))) self._highlight_ids.append(id) for i, pos in enumerate(self._highlight_refine_pos): if self._getInstance().isReverseOrder(): pos = [[bottom - unit*i] + p for p in pos] else: pos = [[unit*i + 1 + self._help_length] + p for p in pos] # The maximum number of positions is 8 in matchaddpos(). for j in range(0, len(pos), 8): if self._getInstance().getWinPos() == 'popup': lfCmd("""call win_execute(%d, "let matchid = matchaddpos('Lf_hl_matchRefine', %s)")""" % (self._getInstance().getPopupWinId(), str(pos[j:j+8]))) id = int(lfEval("matchid")) else: id = int(lfEval("matchaddpos('Lf_hl_matchRefine', %s)" % str(pos[j:j+8]))) self._highlight_ids.append(id) def _highlight(self, is_full_path, get_highlights, use_fuzzy_engine=False, clear=True, hl_group='Lf_hl_match'): # matchaddpos() is introduced by Patch 7.4.330 if (lfEval("exists('*matchaddpos')") == '0' or lfEval("g:Lf_HighlightIndividual") == '0'): return cb = self._getInstance().buffer if self._getInstance().empty(): # buffer is empty. return highlight_number = int(lfEval("g:Lf_NumberOfHighlight")) if clear: self._clearHighlights() getDigest = partial(self._getDigest, mode=0 if is_full_path else 1) unit = self._getUnit() if self._getInstance().isReverseOrder(): if self._help_length > 0: content = cb[:-self._help_length][::-1] else: content = cb[:][::-1] else: content = cb[self._help_length:] if use_fuzzy_engine: self._highlight_pos = get_highlights(source=[getDigest(line) for line in content[:highlight_number:unit]]) else: # e.g., self._highlight_pos = [ [ [2,3], [6,2] ], [ [1,4], [7,6], ... ], ... ] # where [2, 3] indicates the highlight starts at the 2nd column with the # length of 3 in bytes self._highlight_pos = [get_highlights(getDigest(line)) for line in content[:highlight_number:unit]] if self._cli.isAndMode: self._highlight_pos_list.append(self._highlight_pos) bottom = len(content) for i, pos in enumerate(self._highlight_pos): start_pos = self._getDigestStartPos(content[unit*i], 0 if is_full_path else 1) if start_pos > 0: for j in range(len(pos)): pos[j][0] += start_pos if self._getInstance().isReverseOrder(): pos = [[bottom - unit*i] + p for p in pos] else: pos = [[unit*i + 1 + self._help_length] + p for p in pos] # The maximum number of positions is 8 in matchaddpos(). for j in range(0, len(pos), 8): if self._getInstance().getWinPos() == 'popup': lfCmd("""call win_execute(%d, "let matchid = matchaddpos('%s', %s)")""" % (self._getInstance().getPopupWinId(), hl_group, str(pos[j:j+8]))) id = int(lfEval("matchid")) else: id = int(lfEval("matchaddpos('%s', %s)" % (hl_group, str(pos[j:j+8])))) self._highlight_ids.append(id) def _highlightRefine(self, first_get_highlights, get_highlights): # matchaddpos() is introduced by Patch 7.4.330 if (lfEval("exists('*matchaddpos')") == '0' or lfEval("g:Lf_HighlightIndividual") == '0'): return cb = self._getInstance().buffer if self._getInstance().empty(): # buffer is empty. return highlight_number = int(lfEval("g:Lf_NumberOfHighlight")) self._clearHighlights() getDigest = self._getDigest unit = self._getUnit() if self._getInstance().isReverseOrder(): if self._help_length > 0: content = cb[:-self._help_length][::-1] else: content = cb[:][::-1] else: content = cb[self._help_length:] bottom = len(content) self._highlight_pos = [first_get_highlights(getDigest(line, 1)) for line in content[:highlight_number:unit]] for i, pos in enumerate(self._highlight_pos): start_pos = self._getDigestStartPos(content[unit*i], 1) if start_pos > 0: for j in range(len(pos)): pos[j][0] += start_pos if self._getInstance().isReverseOrder(): pos = [[bottom - unit*i] + p for p in pos] else: pos = [[unit*i + 1 + self._help_length] + p for p in pos] # The maximum number of positions is 8 in matchaddpos(). for j in range(0, len(pos), 8): if self._getInstance().getWinPos() == 'popup': lfCmd("""call win_execute(%d, "let matchid = matchaddpos('Lf_hl_match', %s)")""" % (self._getInstance().getPopupWinId(), str(pos[j:j+8]))) id = int(lfEval("matchid")) else: id = int(lfEval("matchaddpos('Lf_hl_match', %s)" % str(pos[j:j+8]))) self._highlight_ids.append(id) self._highlight_refine_pos = [get_highlights(getDigest(line, 2)) for line in content[:highlight_number:unit]] for i, pos in enumerate(self._highlight_refine_pos): start_pos = self._getDigestStartPos(content[unit*i], 2) if start_pos > 0: for j in range(len(pos)): pos[j][0] += start_pos if self._getInstance().isReverseOrder(): pos = [[bottom - unit*i] + p for p in pos] else: pos = [[unit*i + 1 + self._help_length] + p for p in pos] # The maximum number of positions is 8 in matchaddpos(). for j in range(0, len(pos), 8): if self._getInstance().getWinPos() == 'popup': lfCmd("""call win_execute(%d, "let matchid = matchaddpos('Lf_hl_matchRefine', %s)")""" % (self._getInstance().getPopupWinId(), str(pos[j:j+8]))) id = int(lfEval("matchid")) else: id = int(lfEval("matchaddpos('Lf_hl_matchRefine', %s)" % str(pos[j:j+8]))) self._highlight_ids.append(id) def _regexFilter(self, iterable): def noErrMatch(text, pattern): try: return '-1' != lfEval("g:LfNoErrMsgMatch('%s', '%s')" % (text, pattern)) except TypeError: # python 2 return '-1' != lfEval("g:LfNoErrMsgMatch('%s', '%s')" % (text.replace('\x00', '\x01'), pattern)) except ValueError: # python 3 return '-1' != lfEval("g:LfNoErrMsgMatch('%s', '%s')" % (text.replace('\x00', '\x01'), pattern)) except: return '-1' != lfEval("g:LfNoErrMsgMatch('%s', '%s')" % (text.replace('\x00', '\x01'), pattern)) try: if ('-2' == lfEval("g:LfNoErrMsgMatch('', '%s')" % escQuote(self._cli.pattern))): return iter([]) else: return (line for line in iterable if noErrMatch(escQuote(self._getDigest(line, 0)), escQuote(self._cli.pattern))) except vim.error: return iter([]) def _regexSearch(self, content, is_continue, step): if not is_continue and not self._cli.isPrefix: self._index = 0 self._result_content = self._filter(8000, self._regexFilter, content, is_continue) self._getInstance().setBuffer(self._result_content[:self._initial_count]) self._getInstance().setStlResultsCount(len(self._result_content), True) def clearSelections(self): for i in self._selections.values(): if self._getInstance().getWinPos() == 'popup': lfCmd("call matchdelete(%d, %d)" % (i, self._getInstance().getPopupWinId())) else: lfCmd("call matchdelete(%d)" % i) self._selections.clear() def _cleanup(self): if not ("--recall" in self._arguments or lfEval("g:Lf_RememberLastSearch") == '1'): self._pattern_bak = self._cli.pattern self._cli.clear() self._clearHighlights() self._clearHighlightsPos() self._help_length = 0 self._show_help = False @modifiableController def toggleHelp(self): self._show_help = not self._show_help if self._getInstance().isReverseOrder(): if self._help_length > 0: del self._getInstance().buffer[-self._help_length:] else: del self._getInstance().buffer[:self._help_length] if self._help_length > 0 and self._getInstance().getWinPos() == 'popup': lfCmd("call win_execute(%d, 'norm! %dk')" % (self._getInstance().getPopupWinId(), self._help_length)) self._createHelpHint() self.clearSelections() self._resetHighlights() def _accept(self, file, mode, *args, **kwargs): if file: if self._getExplorer().getStlCategory() != "Jumps": lfCmd("norm! m'") if mode == '': pass elif mode == 'h': lfCmd("split") elif mode == 'v': lfCmd("bel vsplit") kwargs["mode"] = mode tabpage_count = len(vim.tabpages) self._acceptSelection(file, *args, **kwargs) for k, v in self._cursorline_dict.items(): if k.valid: k.options["cursorline"] = v self._cursorline_dict.clear() self._issue_422_set_option() if mode == 't' and len(vim.tabpages) > tabpage_count: tab_pos = int(lfEval("g:Lf_TabpagePosition")) if tab_pos == 0: lfCmd("tabm 0") elif tab_pos == 1: lfCmd("tabm -1") elif tab_pos == 3: lfCmd("tabm") def accept(self, mode=''): if self._getInstance().isReverseOrder(): if self._getInstance().window.cursor[0] > len(self._getInstance().buffer) - self._help_length: lfCmd("norm! k") return else: if self._getInstance().window.cursor[0] <= self._help_length: if self._getInstance().getWinPos() == 'popup': lfCmd("call win_execute({}, 'norm! j')".format(self._getInstance().getPopupWinId())) else: lfCmd("norm! j") if self._getInstance().getWinPos() in ('popup', 'floatwin'): self._cli.buildPopupPrompt() return if self._getExplorer().getStlCategory() == "Rg": if self._getInstance().currentLine == self._getExplorer().getContextSeparator(): return if "--heading" in self._arguments and not re.match(r'^\d+[:-]', self._getInstance().currentLine): return self._cli.writeHistory(self._getExplorer().getStlCategory()) # https://github.com/neovim/neovim/issues/8336 if lfEval("has('nvim')") == '1': chdir = vim.chdir else: chdir = os.chdir cwd = lfGetCwd() if len(self._selections) > 0: files = [] for i in sorted(self._selections.keys()): files.append(self._getInstance().buffer[i-1]) if "--stayOpen" in self._arguments: if self._getInstance().window.valid: self._getInstance().cursorRow = self._getInstance().window.cursor[0] self._getInstance().helpLength = self._help_length try: vim.current.tabpage, vim.current.window, vim.current.buffer = self._getInstance().getOriginalPos() except vim.error: # error if original buffer is an No Name buffer pass else: self._getInstance().exitBuffer() # https://github.com/Yggdroot/LeaderF/issues/257 win_local_cwd = lfEval("getcwd(winnr())") if cwd != win_local_cwd: chdir(cwd) orig_cwd = lfGetCwd() if mode == '' and self._getExplorer().getStlCategory() == "File": self._accept(files[0], mode) self._argaddFiles(files) self._accept(files[0], mode) lfCmd("doautocmd BufwinEnter") else: for file in files: self._accept(file, mode) if lfGetCwd() != orig_cwd: dir_changed_by_autocmd = True else: dir_changed_by_autocmd = False need_exit = True else: file = self._getInstance().currentLine line_nr = self._getInstance().window.cursor[0] need_exit = self._needExit(file, self._arguments) if need_exit: if "--stayOpen" in self._arguments: if self._getInstance().window.valid: self._getInstance().cursorRow = self._getInstance().window.cursor[0] self._getInstance().helpLength = self._help_length try: vim.current.tabpage, vim.current.window, vim.current.buffer = self._getInstance().getOriginalPos() except vim.error: # error if original buffer is an No Name buffer pass else: self._getInstance().exitBuffer() # https://github.com/Yggdroot/LeaderF/issues/257 win_local_cwd = lfEval("getcwd(winnr())") if cwd != win_local_cwd: chdir(cwd) orig_cwd = lfGetCwd() self._accept(file, mode, self._getInstance().buffer, line_nr) # for bufTag if lfGetCwd() != orig_cwd: dir_changed_by_autocmd = True else: dir_changed_by_autocmd = False if need_exit: self._setAutochdir() if dir_changed_by_autocmd == False: self._restoreOrigCwd() return None else: self._beforeExit() self._content = vim.current.buffer[:] return False def _jumpNext(self): instance = self._getInstance() if instance.window is None or instance.empty() or len(instance.buffer) == self._help_length: return False if instance.isReverseOrder(): if instance.window.valid: if instance.window.cursor[0] > len(instance.buffer) - self._help_length: instance.window.cursor = (len(instance.buffer) - self._help_length, 0) elif instance.window.cursor[0] == 1: # at the first line instance.window.cursor = (len(instance.buffer) - self._help_length, 0) else: instance.window.cursor = (instance.window.cursor[0] - 1, 0) instance.window.options["cursorline"] = True instance.gotoOriginalWindow() self._accept(instance.buffer[instance.window.cursor[0] - 1], "") else: if instance.cursorRow > len(instance.buffer) - instance.helpLength: instance.cursorRow = len(instance.buffer) - instance.helpLength elif instance.cursorRow == 1: # at the last line instance.cursorRow = len(instance.buffer) - instance.helpLength else: instance.cursorRow -= 1 self._accept(instance.buffer[instance.cursorRow - 1], "") lfCmd("echohl WarningMsg | redraw | echo ' (%d of %d)' | echohl NONE" % (len(instance.buffer) - instance.cursorRow - instance.helpLength + 1, len(instance.buffer) - instance.helpLength)) else: if instance.window.valid and self._getInstance().getWinPos() != 'popup': if instance.window.cursor[0] <= self._help_length: instance.window.cursor = (self._help_length + 1, 0) elif instance.window.cursor[0] == len(instance.buffer): # at the last line instance.window.cursor = (self._help_length + 1, 0) else: instance.window.cursor = (instance.window.cursor[0] + 1, 0) instance.window.options["cursorline"] = True instance.gotoOriginalWindow() self._accept(instance.buffer[instance.window.cursor[0] - 1], "") else: if instance.cursorRow <= instance.helpLength: instance.cursorRow = instance.helpLength + 1 elif instance.cursorRow == len(instance.buffer): # at the last line instance.cursorRow = instance.helpLength + 1 else: instance.cursorRow += 1 self._accept(instance.buffer[instance.cursorRow - 1], "") lfCmd("echohl WarningMsg | redraw | echo ' (%d of %d)' | echohl NONE" % \ (instance.cursorRow - instance.helpLength, len(instance.buffer) - instance.helpLength)) return True def _jumpPrevious(self): instance = self._getInstance() if instance.window is None or instance.empty() or len(instance.buffer) == self._help_length: return False if instance.isReverseOrder(): if instance.window.valid: if instance.window.cursor[0] >= len(instance.buffer) - self._help_length: instance.window.cursor = (1, 0) else: instance.window.cursor = (instance.window.cursor[0] + 1, 0) instance.window.options["cursorline"] = True instance.gotoOriginalWindow() self._accept(instance.buffer[instance.window.cursor[0] - 1], "") else: if instance.cursorRow >= len(instance.buffer) - instance.helpLength: instance.cursorRow = 1 else: instance.cursorRow += 1 self._accept(instance.buffer[instance.cursorRow - 1], "") lfCmd("echohl WarningMsg | redraw | echo ' (%d of %d)' | echohl NONE" % (len(instance.buffer) - instance.cursorRow - instance.helpLength + 1, len(instance.buffer) - instance.helpLength)) else: if instance.window.valid and self._getInstance().getWinPos() != 'popup': if instance.window.cursor[0] <= self._help_length + 1: instance.window.cursor = (len(instance.buffer), 0) else: instance.window.cursor = (instance.window.cursor[0] - 1, 0) instance.window.options["cursorline"] = True instance.gotoOriginalWindow() self._accept(instance.buffer[instance.window.cursor[0] - 1], "") else: if instance.cursorRow <= instance.helpLength + 1: instance.cursorRow = len(instance.buffer) else: instance.cursorRow -= 1 self._accept(instance.buffer[instance.cursorRow - 1], "") lfCmd("echohl WarningMsg | redraw | echo ' (%d of %d)' | echohl NONE" % \ (instance.cursorRow - instance.helpLength, len(instance.buffer) - instance.helpLength)) def quit(self): self._getInstance().exitBuffer() self._setAutochdir() self._restoreOrigCwd() def refresh(self, normal_mode=True): self._getExplorer().cleanup() content = self._getExplorer().getFreshContent() if not content: lfCmd("echohl Error | redraw | echo ' No content!' | echohl NONE") return if normal_mode: # when called in Normal mode self._getInstance().buffer.options['modifiable'] = True self._clearHighlights() self._clearHighlightsPos() self.clearSelections() self._content = self._getInstance().initBuffer(content, self._getUnit(), self._getExplorer().setContent) self._iteration_end = True if self._cli.pattern: self._index = 0 self._search(self._content) if normal_mode: # when called in Normal mode self._createHelpHint() self._resetHighlights() self._getInstance().buffer.options['modifiable'] = False def addSelections(self): nr = self._getInstance().window.number if self._getInstance().getWinPos() != 'popup': if (int(lfEval("v:mouse_win")) != 0 and nr != int(lfEval("v:mouse_win"))): return elif nr == int(lfEval("v:mouse_win")): lfCmd("exec v:mouse_lnum") lfCmd("exec 'norm!'.v:mouse_col.'|'") line_nr = self._getInstance().window.cursor[0] if self._getInstance().isReverseOrder(): if line_nr > len(self._getInstance().buffer) - self._help_length: lfCmd("norm! k") return else: if line_nr <= self._help_length: if self._getInstance().getWinPos() == 'popup': lfCmd("call win_execute({}, 'norm! j')".format(self._getInstance().getPopupWinId())) else: lfCmd("norm! j") if self._getInstance().getWinPos() in ('popup', 'floatwin'): self._cli.buildPopupPrompt() return if line_nr in self._selections: if self._getInstance().getWinPos() == 'popup': lfCmd("call matchdelete(%d, %d)" % (self._selections[line_nr], self._getInstance().getPopupWinId())) else: lfCmd("call matchdelete(%d)" % self._selections[line_nr]) del self._selections[line_nr] else: if self._getInstance().getWinPos() == 'popup': lfCmd("""call win_execute(%d, "let matchid = matchadd('Lf_hl_selection', '\\\\%%%dl.')")""" % (self._getInstance().getPopupWinId(), line_nr)) id = int(lfEval("matchid")) else: id = int(lfEval("matchadd('Lf_hl_selection', '\%%%dl.')" % line_nr)) self._selections[line_nr] = id def selectMulti(self): orig_line = self._getInstance().window.cursor[0] nr = self._getInstance().window.number if (int(lfEval("v:mouse_win")) != 0 and nr != int(lfEval("v:mouse_win"))): return elif nr == int(lfEval("v:mouse_win")): cur_line = int(lfEval("v:mouse_lnum")) self.clearSelections() for i in range(min(orig_line, cur_line), max(orig_line, cur_line)+1): if i > self._help_length and i not in self._selections: id = int(lfEval("matchadd('Lf_hl_selection', '\%%%dl.')" % (i))) self._selections[i] = id def selectAll(self): line_num = len(self._getInstance().buffer) if line_num > 300: lfCmd("echohl Error | redraw | echo ' Too many files selected!' | echohl NONE") lfCmd("sleep 1") return for i in range(line_num): if i >= self._help_length and i+1 not in self._selections: if self._getInstance().getWinPos() == 'popup': lfCmd("""call win_execute(%d, "let matchid = matchadd('Lf_hl_selection', '\\\\%%%dl.')")""" % (self._getInstance().getPopupWinId(), i+1)) id = int(lfEval("matchid")) else: id = int(lfEval("matchadd('Lf_hl_selection', '\%%%dl.')" % (i+1))) self._selections[i+1] = id def _gotoFirstLine(self): if self._getInstance().getWinPos() == 'popup': lfCmd("call win_execute({}, 'norm! gg')".format(self._getInstance().getPopupWinId())) else: lfCmd("normal! gg") def _readFinished(self): pass def startExplorer(self, win_pos, *args, **kwargs): arguments_dict = kwargs.get("arguments", {}) if "--recall" in arguments_dict: self._arguments["--recall"] = arguments_dict["--recall"] else: self.setArguments(arguments_dict) self._cli.setNameOnlyFeature(self._getExplorer().supportsNameOnly()) self._cli.setRefineFeature(self._supportsRefine()) if self._getExplorer().getStlCategory() in ["Gtags"]: if "--update" in self._arguments or "--remove" in self._arguments: self._getExplorer().getContent(*args, **kwargs) return if "--next" in arguments_dict: if self._jumpNext() == False: lfCmd("echohl Error | redraw | echo 'Error, no content!' | echohl NONE") return elif "--previous" in arguments_dict: if self._jumpPrevious() == False: lfCmd("echohl Error | redraw | echo 'Error, no content!' | echohl NONE") return self._cleanup() # lfCmd("echohl WarningMsg | redraw | echo ' searching ...' | echohl NONE") self._getInstance().setArguments(self._arguments) empty_query = self._empty_query and self._getExplorer().getStlCategory() in ["File"] remember_last_status = "--recall" in self._arguments \ or lfEval("g:Lf_RememberLastSearch") == '1' and self._cli.pattern if remember_last_status: content = self._content self._getInstance().useLastReverseOrder() win_pos = self._getInstance().getWinPos() else: content = self._getExplorer().getContent(*args, **kwargs) self._getInstance().setCwd(lfGetCwd()) if self._getExplorer().getStlCategory() in ["Gtags"] and "--auto-jump" in self._arguments \ and isinstance(content, list) and len(content) == 1: mode = self._arguments["--auto-jump"][0] if len(self._arguments["--auto-jump"]) else "" self._accept(content[0], mode) return self._index = 0 pattern = kwargs.get("pattern", "") or arguments_dict.get("--input", [""])[0] if len(pattern) > 1 and (pattern[0] == '"' and pattern[-1] == '"' or pattern[0] == "'" and pattern[-1] == "'"): pattern = pattern[1:-1] self._cli.setPattern(pattern) self._result_content = [] self._cb_content = [] if not content: lfCmd("echohl Error | redraw | echo ' No content!' | echohl NONE") return # clear the buffer only when the content is not a list self._getInstance().enterBuffer(win_pos, not isinstance(content, list)) self._initial_count = self._getInstance().getInitialWinHeight() self._getInstance().setStlCategory(self._getExplorer().getStlCategory()) self._setStlMode(**kwargs) self._getInstance().setStlCwd(self._getExplorer().getStlCurDir()) if kwargs.get('bang', 0): self._current_mode = 'NORMAL' else: self._current_mode = 'INPUT' lfCmd("call leaderf#colorscheme#popup#hiMode('%s', '%s')" % (self._getExplorer().getStlCategory(), self._current_mode)) self._getInstance().setPopupStl(self._current_mode) if not remember_last_status: self._gotoFirstLine() self._start_time = time.time() self._bang_start_time = self._start_time self._bang_count = 0 self._getInstance().buffer.vars['Lf_category'] = self._getExplorer().getStlCategory() self._read_content_exception = None if isinstance(content, list): self._is_content_list = True self._read_finished = 2 if not remember_last_status: if len(content[0]) == len(content[0].rstrip("\r\n")): self._content = content else: self._content = [line.rstrip("\r\n") for line in content] self._getInstance().setStlTotal(len(self._content)//self._getUnit()) self._getInstance().setStlResultsCount(len(self._content)) if not empty_query: self._getInstance().setBuffer(self._content[:self._initial_count]) if lfEval("has('nvim')") == '1': lfCmd("redrawstatus") self._callback = self._workInIdle if not kwargs.get('bang', 0): self._readFinished() self.input() else: if not remember_last_status and not empty_query: self._getInstance().appendBuffer(self._content[self._initial_count:]) elif remember_last_status and len(self._getInstance().buffer) < len(self._result_content): self._getInstance().appendBuffer(self._result_content[self._initial_count:]) lfCmd("echo") if self._cli.pattern: self._cli._buildPrompt() self._getInstance().buffer.options['modifiable'] = False self._bangEnter() self._getInstance().mimicCursor() if not remember_last_status and not self._cli.pattern and empty_query: self._gotoFirstLine() self._guessSearch(self._content) if self._result_content: # self._result_content is [] only if # self._cur_buffer.name == '' or self._cur_buffer.options["buftype"] not in [b'', '']: self._getInstance().appendBuffer(self._result_content[self._initial_count:]) else: self._getInstance().appendBuffer(self._content[self._initial_count:]) if self._timer_id is not None: lfCmd("call timer_stop(%s)" % self._timer_id) self._timer_id = None self._bangReadFinished() lfCmd("echohl WarningMsg | redraw | echo ' Done!' | echohl NONE") elif isinstance(content, AsyncExecutor.Result): self._is_content_list = False self._callback = self._workInIdle if lfEval("get(g:, 'Lf_NoAsync', 0)") == '1': self._content = self._getInstance().initBuffer(content, self._getUnit(), self._getExplorer().setContent) self._read_finished = 1 self._offset_in_content = 0 else: if self._getExplorer().getStlCategory() in ["Rg", "Gtags"]: if "--append" in self.getArguments(): self._offset_in_content = len(self._content) if self._pattern_bak: self._getInstance().setBuffer(self._content, need_copy=False) self._createHelpHint() else: self._getInstance().clearBuffer() self._content = [] self._offset_in_content = 0 else: self._content = [] self._offset_in_content = 0 self._read_finished = 0 self._stop_reader_thread = False self._reader_thread = threading.Thread(target=self._readContent, args=(content,)) self._reader_thread.daemon = True self._reader_thread.start() if not kwargs.get('bang', 0): self.input() else: lfCmd("echo") self._getInstance().buffer.options['modifiable'] = False self._bangEnter() self._getInstance().mimicCursor() else: self._is_content_list = False self._callback = partial(self._workInIdle, content) if lfEval("get(g:, 'Lf_NoAsync', 0)") == '1': self._content = self._getInstance().initBuffer(content, self._getUnit(), self._getExplorer().setContent) self._read_finished = 1 self._offset_in_content = 0 else: self._content = [] self._offset_in_content = 0 self._read_finished = 0 if not kwargs.get('bang', 0): self.input() else: lfCmd("echo") self._getInstance().buffer.options['modifiable'] = False self._bangEnter() self._getInstance().mimicCursor() def _readContent(self, content): try: for line in content: self._content.append(line) if self._stop_reader_thread: break else: self._read_finished = 1 except Exception: self._read_finished = 1 self._read_content_exception = sys.exc_info() def _setResultContent(self): if len(self._result_content) > len(self._getInstance().buffer): self._getInstance().setBuffer(self._result_content) elif self._index == 0: self._getInstance().setBuffer(self._content, need_copy=True) @catchException def _workInIdle(self, content=None, bang=False): if self._read_content_exception is not None: if bang == True: if self._timer_id is not None: lfCmd("call timer_stop(%s)" % self._timer_id) self._timer_id = None lfPrintError(self._read_content_exception[1]) return else: raise self._read_content_exception[1] if bang == False and self._preview_open == False and self._getInstance().getWinPos() in ('popup', 'floatwin') \ and not self._getInstance().empty(): self._previewResult(False) self._preview_open = True if self._is_content_list: if self._cli.pattern and (self._index < len(self._content) or len(self._cb_content) > 0): if self._fuzzy_engine: step = 60000 * cpu_count elif is_fuzzyMatch_C: step = 10000 else: step = 2000 self._search(self._content, True, step) return if content: i = -1 for i, line in enumerate(itertools.islice(content, 20)): self._content.append(line) if i == -1 and self._read_finished == 0: self._read_finished = 1 if self._read_finished > 0: if self._read_finished == 1: self._read_finished += 1 self._getExplorer().setContent(self._content) self._getInstance().setStlTotal(len(self._content)//self._getUnit()) self._getInstance().setStlRunning(False) if self._cli.pattern: self._getInstance().setStlResultsCount(len(self._result_content)) elif self._empty_query and self._getExplorer().getStlCategory() in ["File"]: self._guessSearch(self._content) if bang: if self._result_content: # self._result_content is [] only if # self._cur_buffer.name == '' or self._cur_buffer.options["buftype"] != b'': self._getInstance().appendBuffer(self._result_content[self._initial_count:]) else: self._getInstance().appendBuffer(self._content[self._initial_count:]) if self._timer_id is not None: lfCmd("call timer_stop(%s)" % self._timer_id) self._timer_id = None self._bangReadFinished() lfCmd("echohl WarningMsg | redraw | echo ' Done!' | echohl NONE") else: if bang: if self._getInstance().empty(): self._offset_in_content = len(self._content) if self._offset_in_content > 0: self._getInstance().appendBuffer(self._content[:self._offset_in_content]) else: cur_len = len(self._content) if cur_len > self._offset_in_content: self._getInstance().appendBuffer(self._content[self._offset_in_content:cur_len]) self._offset_in_content = cur_len if self._timer_id is not None: lfCmd("call timer_stop(%s)" % self._timer_id) self._timer_id = None self._bangReadFinished() lfCmd("echohl WarningMsg | redraw | echo ' Done!' | echohl NONE") else: self._getInstance().setBuffer(self._content[:self._initial_count]) self._getInstance().setStlResultsCount(len(self._content)) if self._getInstance().getWinPos() not in ('popup', 'floatwin'): lfCmd("redrawstatus") if self._cli.pattern: if self._index < len(self._content) or len(self._cb_content) > 0: if self._fuzzy_engine: step = 60000 * cpu_count elif is_fuzzyMatch_C: step = 10000 else: step = 2000 self._search(self._content, True, step) if bang: self._getInstance().appendBuffer(self._result_content[self._initial_count:]) else: cur_len = len(self._content) if time.time() - self._start_time > 0.1: self._start_time = time.time() self._getInstance().setStlTotal(cur_len//self._getUnit()) self._getInstance().setStlRunning(True) if self._cli.pattern: self._getInstance().setStlResultsCount(len(self._result_content)) else: self._getInstance().setStlResultsCount(cur_len) if self._getInstance().getWinPos() not in ('popup', 'floatwin'): lfCmd("redrawstatus") if self._cli.pattern: if self._index < cur_len or len(self._cb_content) > 0: if self._fuzzy_engine: step = 60000 * cpu_count elif is_fuzzyMatch_C: step = 10000 else: step = 2000 self._search(self._content[:cur_len], True, step) else: if bang: if self._getInstance().empty(): self._offset_in_content = len(self._content) if self._offset_in_content > 0: self._getInstance().appendBuffer(self._content[:self._offset_in_content]) else: cur_len = len(self._content) if cur_len > self._offset_in_content: self._getInstance().appendBuffer(self._content[self._offset_in_content:cur_len]) self._offset_in_content = cur_len if self._getInstance().getWinPos() not in ('popup', 'floatwin') \ and time.time() - self._bang_start_time > 0.5: self._bang_start_time = time.time() lfCmd("echohl WarningMsg | redraw | echo ' searching %s' | echohl NONE" % ('.' * self._bang_count)) self._bang_count = (self._bang_count + 1) % 9 elif len(self._getInstance().buffer) < min(cur_len, self._initial_count): self._getInstance().setBuffer(self._content[:self._initial_count]) @modifiableController def input(self): self._preview_open = False self._current_mode = 'INPUT' self._getInstance().hideMimicCursor() if self._getInstance().getWinPos() in ('popup', 'floatwin'): self._cli.buildPopupPrompt() lfCmd("call leaderf#colorscheme#popup#hiMode('%s', '%s')" % (self._getExplorer().getStlCategory(), self._current_mode)) self._getInstance().setPopupStl(self._current_mode) if self._getInstance().getWinPos() == 'popup': lfCmd("call leaderf#ResetPopupOptions(%d, 'filter', '%s')" % (self._getInstance().getPopupWinId(), 'leaderf#PopupFilter')) if self._timer_id is not None: lfCmd("call timer_stop(%s)" % self._timer_id) self._timer_id = None self.clearSelections() self._hideHelp() self._resetHighlights() if self._cli.pattern: # --input xxx or from normal mode to input mode if self._index == 0: # --input xxx self._search(self._content) elif self._empty_query and self._getExplorer().getStlCategory() in ["File"] \ and "--recall" not in self._arguments: self._guessSearch(self._content) for cmd in self._cli.input(self._callback): cur_len = len(self._content) cur_content = self._content[:cur_len] if equal(cmd, '<Update>'): if self._getInstance().getWinPos() == 'popup': if self._getInstance()._window_object.cursor[0] > 1: lfCmd("call win_execute({}, 'norm! gg')".format(self._getInstance().getPopupWinId())) self._search(cur_content) self._previewResult(False) elif equal(cmd, '<Shorten>'): if self._getInstance().isReverseOrder(): lfCmd("normal! G") else: self._gotoFirstLine() self._index = 0 # search from beginning self._search(cur_content) self._previewResult(False) elif equal(cmd, '<Mode>'): self._setStlMode() if self._getInstance().getWinPos() in ('popup', 'floatwin'): self._getInstance().setPopupStl(self._current_mode) if self._getInstance().isReverseOrder(): lfCmd("normal! G") else: self._gotoFirstLine() self._index = 0 # search from beginning if self._cli.pattern: self._search(cur_content) elif equal(cmd, '<C-K>') or equal(cmd, '<Up>'): self._toUp() self._previewResult(False) elif equal(cmd, '<C-J>') or equal(cmd, '<Down>'): self._toDown() self._previewResult(False) elif equal(cmd, '<Up>'): if self._cli.previousHistory(self._getExplorer().getStlCategory()): if self._getInstance().isReverseOrder(): lfCmd("normal! G") else: self._gotoFirstLine() self._index = 0 # search from beginning self._search(cur_content) elif equal(cmd, '<Down>'): if self._cli.nextHistory(self._getExplorer().getStlCategory()): if self._getInstance().isReverseOrder(): lfCmd("normal! G") else: self._gotoFirstLine() self._index = 0 # search from beginning self._search(cur_content) elif equal(cmd, '<CR>'): if self.accept() is None: break elif equal(cmd, '<Quit>'): self._cli.writeHistory(self._getExplorer().getStlCategory()) self.quit() break elif equal(cmd, '<Tab>'): # switch to Normal mode self._current_mode = 'NORMAL' if self._getInstance().getWinPos() == 'popup': if lfEval("exists('*leaderf#%s#NormalModeFilter')" % self._getExplorer().getStlCategory()) == '1': lfCmd("call leaderf#ResetPopupOptions(%d, 'filter', '%s')" % (self._getInstance().getPopupWinId(), 'leaderf#%s#NormalModeFilter' % self._getExplorer().getStlCategory())) else: lfCmd("call leaderf#ResetPopupOptions(%d, 'filter', function('leaderf#NormalModeFilter', [%d]))" % (self._getInstance().getPopupWinId(), id(self))) self._setResultContent() self.clearSelections() self._cli.hideCursor() self._createHelpHint() self._resetHighlights() if self._getInstance().isReverseOrder() and self._cli.pattern \ and len(self._highlight_pos) < (len(self._getInstance().buffer) - self._help_length) // self._getUnit() \ and len(self._highlight_pos) < int(lfEval("g:Lf_NumberOfHighlight")): self._highlight_method() if self._getInstance().getWinPos() in ('popup', 'floatwin'): self._cli.buildPopupPrompt() lfCmd("call leaderf#colorscheme#popup#hiMode('%s', '%s')" % (self._getExplorer().getStlCategory(), self._current_mode)) self._getInstance().setPopupStl(self._current_mode) break elif equal(cmd, '<F5>'): self.refresh(False) elif equal(cmd, '<C-H>'): if lfEval("get(g:, 'Lf_ShowHidden')") == '1': lfCmd("let g:Lf_ShowHidden = 0") else: lfCmd("let g:Lf_ShowHidden = 1") self.refresh(False) elif equal(cmd, '<C-P>'): self._ctrlp_pressed = True self._previewResult(True) self._ctrlp_pressed = False elif equal(cmd, '<C-PageUp>'): self._toUpInPopup() elif equal(cmd, '<C-PageDown>'): self._toDownInPopup() elif equal(cmd, '<PageUp>'): for x in range(10): self._toUpInPopup() elif equal(cmd, '<PageDown>'): for x in range(10): self._toDownInPopup() else: if self._cmdExtension(cmd): break # vim: set ts=4 sw=4 tw=0 et :
45.92827
133
0.536735
7e45cc960da12ae131782ea270240dc079dff488
2,326
py
Python
crawler/27bao/app.py
hmumixaM/anything
5810132118d6d3f3859d607fca068c8275d4bf42
[ "MIT" ]
null
null
null
crawler/27bao/app.py
hmumixaM/anything
5810132118d6d3f3859d607fca068c8275d4bf42
[ "MIT" ]
null
null
null
crawler/27bao/app.py
hmumixaM/anything
5810132118d6d3f3859d607fca068c8275d4bf42
[ "MIT" ]
null
null
null
import requests import re import pymongo import time client = pymongo.MongoClient(host='127.0.0.1', port=27017) db = client.baola collection = db.gif def data(info): result = collection.insert_one(info) def main(): pages = list_link() for link in pages: gifs, description = gif_match(link) info = {"title": description, "gif": gifs} data(info) def list_link(): pages = [] prefix = "https://www.27bao.com/gif/list_{}.html" for i in range(51): link = prefix.format(i) text = download(link) info = match(text, r"href='/gif/\d{1,5}\.html' alt='.{4,30}'") for item in info: location = "https://www.27bao.com" href = location + match(item, r"/gif/\d{1,5}\.html", "search") alt = match(item, r"alt='.{5,40}'", "search")[5:-1] pages.append([href, alt]) fo = open("page.txt", "w") for i in pages: fo.write(i[0]) fo.write("@") fo.write(i[1]) fo.write("\n") fo.close() return pages def gif_match(link): gifs = [] href = link[0] description = link[1] text = download(href) text = match(text, r"<div id=\"pages\">.+</div>", "search") image_pages = match(text, r"href='\d{1,5}_\d{1,2}\.html'") for image in image_pages: location = "https://www.27bao.com/gif/{}" image = location.format(image[6:-1]) image_text = download(image) info = match(image_text, r"<img alt=\".+\.gif", "search") gif_href = match(info, r"http.+\.gif", "search") gif_alt = match(info, r"alt=\".{5,40}\"", "search")[5:-7] gifs.append([gif_href, gif_alt]) return gifs, description def download(link): # time.sleep(1.0) response = requests.get(link) response.encoding = "UTF-8" return response.text def match(text, regex, mode="findall"): pattern = re.compile(regex) if mode == "findall": result = pattern.findall(text) elif mode == "search": result = pattern.search(text)[0] return result if __name__ == '__main__': fo = open("page.txt", "r") a = fo.readlines() for i in a[1187:]: link = i.split("@") gifs, description = gif_match(link) info = {"title": description, "gif": gifs} data(info)
26.735632
74
0.560189
9631bcb84a724f9556d588350db7aa3ed948a8d9
950
py
Python
tests/test_services.py
Wicker25/intercom-test
af9bfbbf4fbc9b803e387f101332ef7af13d7676
[ "MIT" ]
null
null
null
tests/test_services.py
Wicker25/intercom-test
af9bfbbf4fbc9b803e387f101332ef7af13d7676
[ "MIT" ]
null
null
null
tests/test_services.py
Wicker25/intercom-test
af9bfbbf4fbc9b803e387f101332ef7af13d7676
[ "MIT" ]
null
null
null
import pytest from intercom.models import Location, Customer from intercom.services import get_customers_close_to_office @pytest.fixture() def customer_repository(): class CustomerRepositoryMock: def fetch_all(self): return [ Customer(user_id=1, name='Ian', location=Location(latitude=53.2451022, longitude=-6.238335)), Customer(user_id=2, name='Eoin', location=Location(latitude=54.0894797, longitude=-6.18671)), Customer(user_id=3, name='David', location=Location(latitude=52.833502, longitude=-8.522366)), ] return CustomerRepositoryMock() def test_get_customers_close_to_office(customer_repository): office_location = Location(latitude=53.339428, longitude=-6.257664) # Dublin close_customers = get_customers_close_to_office(customer_repository, office_location, 100.0) assert [customer.user_id for customer in close_customers] == [1, 2]
38
110
0.722105
870f4ac860eb51d9709aaac4d8f2596cacfa5721
16,879
py
Python
neutron/objects/qos/policy.py
acdc-cloud/neutron
2510836886555179f9e9e39b1fdbf94296befc51
[ "Apache-2.0" ]
1
2018-10-19T01:48:37.000Z
2018-10-19T01:48:37.000Z
neutron/objects/qos/policy.py
weiqiLee/neutron
ddc72ebd41a0e7804b33a21583d3add008191229
[ "Apache-2.0" ]
null
null
null
neutron/objects/qos/policy.py
weiqiLee/neutron
ddc72ebd41a0e7804b33a21583d3add008191229
[ "Apache-2.0" ]
1
2018-08-28T17:13:16.000Z
2018-08-28T17:13:16.000Z
# Copyright 2015 Red Hat, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import itertools from neutron_lib import constants as n_const from oslo_db import exception as db_exc from oslo_utils import versionutils from oslo_versionedobjects import exception from oslo_versionedobjects import fields as obj_fields from neutron.common import exceptions from neutron.db.models import l3 from neutron.db import models_v2 from neutron.db.qos import models as qos_db_model from neutron.db import rbac_db_models from neutron.objects import base as base_db from neutron.objects import common_types from neutron.objects.db import api as obj_db_api from neutron.objects.qos import binding from neutron.objects.qos import rule as rule_obj_impl from neutron.objects import rbac_db @base_db.NeutronObjectRegistry.register class QosPolicyRBAC(base_db.NeutronDbObject): # Version 1.0: Initial version VERSION = '1.0' db_model = rbac_db_models.QosPolicyRBAC fields = { 'object_id': obj_fields.StringField(), 'target_tenant': obj_fields.StringField(), 'action': obj_fields.StringField(), } @base_db.NeutronObjectRegistry.register class QosPolicy(rbac_db.NeutronRbacObject): # Version 1.0: Initial version # Version 1.1: QosDscpMarkingRule introduced # Version 1.2: Added QosMinimumBandwidthRule # Version 1.3: Added standard attributes (created_at, revision, etc) # Version 1.4: Changed tenant_id to project_id # Version 1.5: Direction for bandwidth limit rule added # Version 1.6: Added "is_default" field # Version 1.7: Added floating IP bindings VERSION = '1.7' # required by RbacNeutronMetaclass rbac_db_cls = QosPolicyRBAC db_model = qos_db_model.QosPolicy fields = { 'id': common_types.UUIDField(), 'project_id': obj_fields.StringField(), 'name': obj_fields.StringField(), 'shared': obj_fields.BooleanField(default=False), 'rules': obj_fields.ListOfObjectsField('QosRule', subclasses=True), 'is_default': obj_fields.BooleanField(default=False), } fields_no_update = ['id', 'project_id'] synthetic_fields = ['rules', 'is_default'] extra_filter_names = {'is_default'} binding_models = {'port': binding.QosPolicyPortBinding, 'network': binding.QosPolicyNetworkBinding, 'fip': binding.QosPolicyFloatingIPBinding} def obj_load_attr(self, attrname): if attrname == 'rules': return self._reload_rules() elif attrname == 'is_default': return self._reload_is_default() return super(QosPolicy, self).obj_load_attr(attrname) def _reload_rules(self): rules = rule_obj_impl.get_rules(self, self.obj_context, self.id) setattr(self, 'rules', rules) self.obj_reset_changes(['rules']) def _reload_is_default(self): if self.get_default() == self.id: setattr(self, 'is_default', True) else: setattr(self, 'is_default', False) self.obj_reset_changes(['is_default']) def get_rule_by_id(self, rule_id): """Return rule specified by rule_id. @raise QosRuleNotFound: if there is no such rule in the policy. """ for rule in self.rules: if rule_id == rule.id: return rule raise exceptions.QosRuleNotFound(policy_id=self.id, rule_id=rule_id) # TODO(hichihara): For tag mechanism. This will be removed in bug/1704137 def to_dict(self): _dict = super(QosPolicy, self).to_dict() try: _dict['tags'] = [t.tag for t in self.db_obj.standard_attr.tags] except AttributeError: # AttrtibuteError can be raised when accessing self.db_obj # or self.db_obj.standard_attr pass return _dict @classmethod def get_policy_obj(cls, context, policy_id): """Fetch a QoS policy. :param context: neutron api request context :type context: neutron.context.Context :param policy_id: the id of the QosPolicy to fetch :type policy_id: str uuid :returns: a QosPolicy object :raises: n_exc.QosPolicyNotFound """ obj = cls.get_object(context, id=policy_id) if obj is None: raise exceptions.QosPolicyNotFound(policy_id=policy_id) return obj @classmethod def get_object(cls, context, **kwargs): # We want to get the policy regardless of its tenant id. We'll make # sure the tenant has permission to access the policy later on. admin_context = context.elevated() with cls.db_context_reader(admin_context): policy_obj = super(QosPolicy, cls).get_object(admin_context, **kwargs) if (not policy_obj or not cls.is_accessible(context, policy_obj)): return policy_obj.obj_load_attr('rules') policy_obj.obj_load_attr('is_default') return policy_obj @classmethod def get_objects(cls, context, _pager=None, validate_filters=True, **kwargs): # We want to get the policy regardless of its tenant id. We'll make # sure the tenant has permission to access the policy later on. admin_context = context.elevated() with cls.db_context_reader(admin_context): objs = super(QosPolicy, cls).get_objects(admin_context, _pager, validate_filters, **kwargs) result = [] for obj in objs: if not cls.is_accessible(context, obj): continue obj.obj_load_attr('rules') obj.obj_load_attr('is_default') result.append(obj) return result @classmethod def _get_object_policy(cls, context, binding_cls, **kwargs): with cls.db_context_reader(context): binding_db_obj = obj_db_api.get_object(binding_cls, context, **kwargs) if binding_db_obj: return cls.get_object(context, id=binding_db_obj['policy_id']) @classmethod def get_network_policy(cls, context, network_id): return cls._get_object_policy(context, binding.QosPolicyNetworkBinding, network_id=network_id) @classmethod def get_port_policy(cls, context, port_id): return cls._get_object_policy(context, binding.QosPolicyPortBinding, port_id=port_id) @classmethod def get_fip_policy(cls, context, fip_id): return cls._get_object_policy( context, binding.QosPolicyFloatingIPBinding, fip_id=fip_id) # TODO(QoS): Consider extending base to trigger registered methods for us def create(self): with self.db_context_writer(self.obj_context): super(QosPolicy, self).create() if self.is_default: self.set_default() self.obj_load_attr('rules') def update(self): with self.db_context_writer(self.obj_context): if 'is_default' in self.obj_what_changed(): if self.is_default: self.set_default() else: self.unset_default() super(QosPolicy, self).update() def delete(self): with self.db_context_writer(self.obj_context): for object_type, obj_class in self.binding_models.items(): pager = base_db.Pager(limit=1) binding_obj = obj_class.get_objects(self.obj_context, policy_id=self.id, _pager=pager) if binding_obj: raise exceptions.QosPolicyInUse( policy_id=self.id, object_type=object_type, object_id=binding_obj[0]['%s_id' % object_type]) super(QosPolicy, self).delete() def attach_network(self, network_id): network_binding = {'policy_id': self.id, 'network_id': network_id} network_binding_obj = binding.QosPolicyNetworkBinding( self.obj_context, **network_binding) try: network_binding_obj.create() except db_exc.DBReferenceError as e: raise exceptions.NetworkQosBindingError(policy_id=self.id, net_id=network_id, db_error=e) def attach_port(self, port_id): port_binding_obj = binding.QosPolicyPortBinding( self.obj_context, policy_id=self.id, port_id=port_id) try: port_binding_obj.create() except db_exc.DBReferenceError as e: raise exceptions.PortQosBindingError(policy_id=self.id, port_id=port_id, db_error=e) def attach_floatingip(self, fip_id): fip_binding_obj = binding.QosPolicyFloatingIPBinding( self.obj_context, policy_id=self.id, fip_id=fip_id) try: fip_binding_obj.create() except db_exc.DBReferenceError as e: raise exceptions.FloatingIPQosBindingError(policy_id=self.id, fip_id=fip_id, db_error=e) def detach_network(self, network_id): deleted = binding.QosPolicyNetworkBinding.delete_objects( self.obj_context, network_id=network_id) if not deleted: raise exceptions.NetworkQosBindingNotFound(net_id=network_id, policy_id=self.id) def detach_port(self, port_id): deleted = binding.QosPolicyPortBinding.delete_objects(self.obj_context, port_id=port_id) if not deleted: raise exceptions.PortQosBindingNotFound(port_id=port_id, policy_id=self.id) def detach_floatingip(self, fip_id): deleted = binding.QosPolicyFloatingIPBinding.delete_objects( self.obj_context, fip_id=fip_id) if not deleted: raise exceptions.FloatingIPQosBindingNotFound(fip_id=fip_id, policy_id=self.id) def set_default(self): if not self.get_default(): qos_default_policy = QosPolicyDefault(self.obj_context, qos_policy_id=self.id, project_id=self.project_id) qos_default_policy.create() elif self.get_default() != self.id: raise exceptions.QoSPolicyDefaultAlreadyExists( project_id=self.project_id) def unset_default(self): if self.get_default() == self.id: qos_default_policy = QosPolicyDefault.get_object( self.obj_context, project_id=self.project_id) qos_default_policy.delete() def get_default(self): qos_default_policy = QosPolicyDefault.get_object( self.obj_context, project_id=self.project_id) if qos_default_policy: return qos_default_policy.qos_policy_id def get_bound_networks(self): return [ nb.network_id for nb in binding.QosPolicyNetworkBinding.get_objects( self.obj_context, policy_id=self.id) ] def get_bound_ports(self): return [ pb.port_id for pb in binding.QosPolicyPortBinding.get_objects( self.obj_context, policy_id=self.id) ] def get_bound_floatingips(self): return [ fb.fip_id for fb in binding.QosPolicyFloatingIPBinding.get_objects( self.obj_context, policy_id=self.id) ] @classmethod def _get_bound_tenant_ids(cls, session, binding_db, bound_db, binding_db_id_column, policy_id): return list(itertools.chain.from_iterable( session.query(bound_db.tenant_id).join( binding_db, bound_db.id == binding_db_id_column).filter( binding_db.policy_id == policy_id).all())) @classmethod def get_bound_tenant_ids(cls, context, policy_id): """Implements RbacNeutronObject.get_bound_tenant_ids. :returns: set -- a set of tenants' ids dependent on QosPolicy. """ net = models_v2.Network qosnet = qos_db_model.QosNetworkPolicyBinding port = models_v2.Port qosport = qos_db_model.QosPortPolicyBinding fip = l3.FloatingIP qosfip = qos_db_model.QosFIPPolicyBinding bound_tenants = [] with cls.db_context_reader(context): bound_tenants.extend(cls._get_bound_tenant_ids( context.session, qosnet, net, qosnet.network_id, policy_id)) bound_tenants.extend( cls._get_bound_tenant_ids(context.session, qosport, port, qosport.port_id, policy_id)) bound_tenants.extend( cls._get_bound_tenant_ids(context.session, qosfip, fip, qosfip.fip_id, policy_id)) return set(bound_tenants) def obj_make_compatible(self, primitive, target_version): def filter_rules(obj_names, rules): return [rule for rule in rules if rule['versioned_object.name'] in obj_names] def filter_ingress_bandwidth_limit_rules(rules): bwlimit_obj_name = rule_obj_impl.QosBandwidthLimitRule.obj_name() filtered_rules = [] for rule in rules: if rule['versioned_object.name'] == bwlimit_obj_name: direction = rule['versioned_object.data'].get("direction") if direction == n_const.EGRESS_DIRECTION: rule['versioned_object.data'].pop('direction') filtered_rules.append(rule) else: filtered_rules.append(rule) return filtered_rules _target_version = versionutils.convert_version_to_tuple(target_version) names = [] if _target_version >= (1, 0): names.append(rule_obj_impl.QosBandwidthLimitRule.obj_name()) if _target_version >= (1, 1): names.append(rule_obj_impl.QosDscpMarkingRule.obj_name()) if _target_version >= (1, 2): names.append(rule_obj_impl.QosMinimumBandwidthRule.obj_name()) if 'rules' in primitive and names: primitive['rules'] = filter_rules(names, primitive['rules']) if _target_version < (1, 3): standard_fields = ['revision_number', 'created_at', 'updated_at'] for f in standard_fields: primitive.pop(f) if primitive['description'] is None: # description was not nullable before raise exception.IncompatibleObjectVersion( objver=target_version, objname='QoSPolicy') if _target_version < (1, 4): primitive['tenant_id'] = primitive.pop('project_id') if _target_version < (1, 5): if 'rules' in primitive: primitive['rules'] = filter_ingress_bandwidth_limit_rules( primitive['rules']) if _target_version < (1, 6): primitive.pop('is_default', None) @base_db.NeutronObjectRegistry.register class QosPolicyDefault(base_db.NeutronDbObject): # Version 1.0: Initial version VERSION = '1.0' db_model = qos_db_model.QosPolicyDefault fields = { 'qos_policy_id': common_types.UUIDField(), 'project_id': obj_fields.StringField(), } primary_keys = ['project_id']
39.529274
79
0.605486
4daa5d1bc78511764107c9706af4fd798f131cfe
19,287
py
Python
sdk/python/pulumi_azure_native/network/v20180601/route_filter_rule.py
sebtelko/pulumi-azure-native
711ec021b5c73da05611c56c8a35adb0ce3244e4
[ "Apache-2.0" ]
null
null
null
sdk/python/pulumi_azure_native/network/v20180601/route_filter_rule.py
sebtelko/pulumi-azure-native
711ec021b5c73da05611c56c8a35adb0ce3244e4
[ "Apache-2.0" ]
null
null
null
sdk/python/pulumi_azure_native/network/v20180601/route_filter_rule.py
sebtelko/pulumi-azure-native
711ec021b5c73da05611c56c8a35adb0ce3244e4
[ "Apache-2.0" ]
null
null
null
# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from ._enums import * __all__ = ['RouteFilterRuleArgs', 'RouteFilterRule'] @pulumi.input_type class RouteFilterRuleArgs: def __init__(__self__, *, access: pulumi.Input[Union[str, 'Access']], communities: pulumi.Input[Sequence[pulumi.Input[str]]], resource_group_name: pulumi.Input[str], route_filter_name: pulumi.Input[str], route_filter_rule_type: pulumi.Input[Union[str, 'RouteFilterRuleType']], id: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, rule_name: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a RouteFilterRule resource. :param pulumi.Input[Union[str, 'Access']] access: The access type of the rule. Valid values are: 'Allow', 'Deny' :param pulumi.Input[Sequence[pulumi.Input[str]]] communities: The collection for bgp community values to filter on. e.g. ['12076:5010','12076:5020'] :param pulumi.Input[str] resource_group_name: The name of the resource group. :param pulumi.Input[str] route_filter_name: The name of the route filter. :param pulumi.Input[Union[str, 'RouteFilterRuleType']] route_filter_rule_type: The rule type of the rule. Valid value is: 'Community' :param pulumi.Input[str] id: Resource ID. :param pulumi.Input[str] location: Resource location. :param pulumi.Input[str] name: The name of the resource that is unique within a resource group. This name can be used to access the resource. :param pulumi.Input[str] rule_name: The name of the route filter rule. """ pulumi.set(__self__, "access", access) pulumi.set(__self__, "communities", communities) pulumi.set(__self__, "resource_group_name", resource_group_name) pulumi.set(__self__, "route_filter_name", route_filter_name) pulumi.set(__self__, "route_filter_rule_type", route_filter_rule_type) if id is not None: pulumi.set(__self__, "id", id) if location is not None: pulumi.set(__self__, "location", location) if name is not None: pulumi.set(__self__, "name", name) if rule_name is not None: pulumi.set(__self__, "rule_name", rule_name) @property @pulumi.getter def access(self) -> pulumi.Input[Union[str, 'Access']]: """ The access type of the rule. Valid values are: 'Allow', 'Deny' """ return pulumi.get(self, "access") @access.setter def access(self, value: pulumi.Input[Union[str, 'Access']]): pulumi.set(self, "access", value) @property @pulumi.getter def communities(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]: """ The collection for bgp community values to filter on. e.g. ['12076:5010','12076:5020'] """ return pulumi.get(self, "communities") @communities.setter def communities(self, value: pulumi.Input[Sequence[pulumi.Input[str]]]): pulumi.set(self, "communities", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ The name of the resource group. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="routeFilterName") def route_filter_name(self) -> pulumi.Input[str]: """ The name of the route filter. """ return pulumi.get(self, "route_filter_name") @route_filter_name.setter def route_filter_name(self, value: pulumi.Input[str]): pulumi.set(self, "route_filter_name", value) @property @pulumi.getter(name="routeFilterRuleType") def route_filter_rule_type(self) -> pulumi.Input[Union[str, 'RouteFilterRuleType']]: """ The rule type of the rule. Valid value is: 'Community' """ return pulumi.get(self, "route_filter_rule_type") @route_filter_rule_type.setter def route_filter_rule_type(self, value: pulumi.Input[Union[str, 'RouteFilterRuleType']]): pulumi.set(self, "route_filter_rule_type", value) @property @pulumi.getter def id(self) -> Optional[pulumi.Input[str]]: """ Resource ID. """ return pulumi.get(self, "id") @id.setter def id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "id", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: """ Resource location. """ return pulumi.get(self, "location") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "location", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the resource that is unique within a resource group. This name can be used to access the resource. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="ruleName") def rule_name(self) -> Optional[pulumi.Input[str]]: """ The name of the route filter rule. """ return pulumi.get(self, "rule_name") @rule_name.setter def rule_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "rule_name", value) class RouteFilterRule(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, access: Optional[pulumi.Input[Union[str, 'Access']]] = None, communities: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, id: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, route_filter_name: Optional[pulumi.Input[str]] = None, route_filter_rule_type: Optional[pulumi.Input[Union[str, 'RouteFilterRuleType']]] = None, rule_name: Optional[pulumi.Input[str]] = None, __props__=None): """ Route Filter Rule Resource :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[Union[str, 'Access']] access: The access type of the rule. Valid values are: 'Allow', 'Deny' :param pulumi.Input[Sequence[pulumi.Input[str]]] communities: The collection for bgp community values to filter on. e.g. ['12076:5010','12076:5020'] :param pulumi.Input[str] id: Resource ID. :param pulumi.Input[str] location: Resource location. :param pulumi.Input[str] name: The name of the resource that is unique within a resource group. This name can be used to access the resource. :param pulumi.Input[str] resource_group_name: The name of the resource group. :param pulumi.Input[str] route_filter_name: The name of the route filter. :param pulumi.Input[Union[str, 'RouteFilterRuleType']] route_filter_rule_type: The rule type of the rule. Valid value is: 'Community' :param pulumi.Input[str] rule_name: The name of the route filter rule. """ ... @overload def __init__(__self__, resource_name: str, args: RouteFilterRuleArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Route Filter Rule Resource :param str resource_name: The name of the resource. :param RouteFilterRuleArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(RouteFilterRuleArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, access: Optional[pulumi.Input[Union[str, 'Access']]] = None, communities: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, id: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, route_filter_name: Optional[pulumi.Input[str]] = None, route_filter_rule_type: Optional[pulumi.Input[Union[str, 'RouteFilterRuleType']]] = None, rule_name: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = RouteFilterRuleArgs.__new__(RouteFilterRuleArgs) if access is None and not opts.urn: raise TypeError("Missing required property 'access'") __props__.__dict__["access"] = access if communities is None and not opts.urn: raise TypeError("Missing required property 'communities'") __props__.__dict__["communities"] = communities __props__.__dict__["id"] = id __props__.__dict__["location"] = location __props__.__dict__["name"] = name if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__.__dict__["resource_group_name"] = resource_group_name if route_filter_name is None and not opts.urn: raise TypeError("Missing required property 'route_filter_name'") __props__.__dict__["route_filter_name"] = route_filter_name if route_filter_rule_type is None and not opts.urn: raise TypeError("Missing required property 'route_filter_rule_type'") __props__.__dict__["route_filter_rule_type"] = route_filter_rule_type __props__.__dict__["rule_name"] = rule_name __props__.__dict__["etag"] = None __props__.__dict__["provisioning_state"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/v20180601:RouteFilterRule"), pulumi.Alias(type_="azure-native:network:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20161201:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20161201:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20170301:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20170301:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20170601:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20170601:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20170801:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20170801:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20170901:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20170901:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20171001:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20171001:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20171101:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20171101:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20180101:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20180101:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20180201:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20180201:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20180401:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20180401:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20180701:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20180701:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20180801:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20180801:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20181001:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20181001:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20181101:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20181101:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20181201:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20181201:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20190201:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20190201:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20190401:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20190401:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20190601:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20190601:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20190701:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20190701:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20190801:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20190801:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20190901:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20190901:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20191101:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20191101:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20191201:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20191201:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20200301:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20200301:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20200401:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20200401:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20200501:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20200501:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20200601:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20200601:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20200701:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20200701:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20200801:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20200801:RouteFilterRule"), pulumi.Alias(type_="azure-native:network/v20201101:RouteFilterRule"), pulumi.Alias(type_="azure-nextgen:network/v20201101:RouteFilterRule")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(RouteFilterRule, __self__).__init__( 'azure-native:network/v20180601:RouteFilterRule', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'RouteFilterRule': """ Get an existing RouteFilterRule resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = RouteFilterRuleArgs.__new__(RouteFilterRuleArgs) __props__.__dict__["access"] = None __props__.__dict__["communities"] = None __props__.__dict__["etag"] = None __props__.__dict__["location"] = None __props__.__dict__["name"] = None __props__.__dict__["provisioning_state"] = None __props__.__dict__["route_filter_rule_type"] = None return RouteFilterRule(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def access(self) -> pulumi.Output[str]: """ The access type of the rule. Valid values are: 'Allow', 'Deny' """ return pulumi.get(self, "access") @property @pulumi.getter def communities(self) -> pulumi.Output[Sequence[str]]: """ The collection for bgp community values to filter on. e.g. ['12076:5010','12076:5020'] """ return pulumi.get(self, "communities") @property @pulumi.getter def etag(self) -> pulumi.Output[str]: """ A unique read-only string that changes whenever the resource is updated. """ return pulumi.get(self, "etag") @property @pulumi.getter def location(self) -> pulumi.Output[Optional[str]]: """ Resource location. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> pulumi.Output[Optional[str]]: """ The name of the resource that is unique within a resource group. This name can be used to access the resource. """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[str]: """ The provisioning state of the resource. Possible values are: 'Updating', 'Deleting', 'Succeeded' and 'Failed'. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="routeFilterRuleType") def route_filter_rule_type(self) -> pulumi.Output[str]: """ The rule type of the rule. Valid value is: 'Community' """ return pulumi.get(self, "route_filter_rule_type")
55.742775
4,475
0.680095
d70cbfddf8575eea76be97a6df90d7cffaf43d98
7,120
py
Python
kaolin/datasets/base.py
zuru/kaolin
343a820c75383dd01b6f2247f237073f3e8dcb46
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
kaolin/datasets/base.py
zuru/kaolin
343a820c75383dd01b6f2247f237073f3e8dcb46
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
kaolin/datasets/base.py
zuru/kaolin
343a820c75383dd01b6f2247f237073f3e8dcb46
[ "ECL-2.0", "Apache-2.0" ]
1
2020-05-06T06:03:13.000Z
2020-05-06T06:03:13.000Z
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import abstractmethod from tqdm import tqdm import torch from torch.multiprocessing import Pool from torch.utils.data import Dataset from kaolin import helpers def _preprocess_task(args): torch.set_num_threads(1) with torch.no_grad(): idx, get_data, get_attributes, cache_transform = args name = get_attributes(idx)['name'] if name not in cache_transform.cached_ids: data = get_data(idx) cache_transform(name, data) class KaolinDatasetMeta(type): def __new__(metacls, cls_name, base_cls, class_dict): if cls_name != "KaolinDataset": class_dict['__doc__'] += \ """Additional args: preprocessing_params (dict): parameters for the preprocessing: - 'cache_dir': path to the cached preprocessed data. - 'num_workers': number of process used in parallel for preprocessing (default: number of cores) preprocessing_transform (Callable): Called on the outputs of _get_data over the indices from 0 to len(self) during the construction of the dataset, the preprocessed outputs are then cached to 'cache_dir'. transform (Callable): Called on the preprocessed data at __getitem__. no_progress (bool): disable tqdm progress bar for preprocessing.""" return type.__new__(metacls, cls_name, base_cls, class_dict) class KaolinDataset(Dataset, metaclass=KaolinDatasetMeta): """ Abstract class for dataset with handling of multiprocess or cuda preprocessing. A KaolinDataset children class will need the above implementation: 1) initialize: Initialization function called at the beginning of the constructor. 2) _get_data: Data getter that will be preprocessed => cached => transformed, take an index as input. 3) _get_attributes: Attributes getter that will be preprocess / transform independent. 4) __len__: Return the size of the dataset """ def __init__(self, *args, preprocessing_transform=None, preprocessing_params: dict = None, transform=None, no_progress: bool = False, **kwargs): """ Args: positional and keyword arguments for initialize(*args, **kwargs) (see class and initialize documentation) preprocessing_params (dict): parameters for the preprocessing: - 'cache_dir': path to the cached preprocessed data. - 'num_workers': number of process used in parallel for preprocessing (default: number of cores) preprocessing_transform (Callable): Called on the outputs of _get_data over the indices from 0 to len(self) during the construction of the dataset, the preprocessed outputs are then cached to 'cache_dir'. transform (Callable): Called on the preprocessed data at __getitem__. no_progress (bool): disable tqdm progress bar for preprocessing. """ self.initialize(*args, **kwargs) if preprocessing_transform is not None: desc = 'Applying preprocessing' if preprocessing_params is None: preprocessing_params = {} cache_dir = preprocessing_params.get('cache_dir') assert cache_dir is not None, 'Cache directory is not given' self.cache_convert = helpers.Cache( preprocessing_transform, cache_dir=cache_dir, cache_key=helpers._get_hash(repr(preprocessing_transform)) ) use_cuda = preprocessing_params.get('use_cuda', False) num_workers = preprocessing_params.get('num_workers') if num_workers == 0: with torch.no_grad(): for idx in tqdm(range(len(self)), desc=desc, disable=no_progress): name = self._get_attributes(idx)['name'] if name not in self.cache_convert.cached_ids: data = self._get_data(idx) self.cache_convert(name, data) else: p = Pool(num_workers) iterator = p.imap_unordered( _preprocess_task, [(idx, self._get_data, self._get_attributes, self.cache_convert) for idx in range(len(self))]) for i in tqdm(range(len(self)), desc=desc, disable=no_progress): next(iterator) else: self.cache_convert = None self.transform = transform def __getitem__(self, index): """Returns the item at index idx. """ attributes = self._get_attributes(index) data = (self._get_data(index) if self.cache_convert is None else self.cache_convert(attributes['name'])) if self.transform is not None: data = self.transform(data) return {'data': data, 'attributes': attributes} @abstractmethod def initialize(self, *args, **kwargs): pass @abstractmethod def _get_attributes(self, index): pass @abstractmethod def _get_data(self, index): pass @abstractmethod def __len__(self): pass class CombinationDataset(KaolinDataset): """Dataset combining a list of datasets into a unified dataset object. Useful when multiple output representations are needed from a common base representation (Eg. when a mesh is to be served as both a pointcloud and a voxelgrid, etc.) the output of _get_attributes will be a tuple of all the _get_attributes of the dataset list the output of _get_data wiil be a tuple of all the 'data' of the __getitem__ of the dataset list Args: datasets: list or tuple of KaolinDataset """ def initialize(self, datasets): self.len = len(datasets[0]) for i, d in enumerate(datasets): assert len(d) == self.len, \ f"All datasets must have the same length. Invalid length at index {i} (expected: {self.len}, got: {len(d)})" self.datasets = datasets def __len__(self): return self.len def _get_attributes(self, index): return (d._get_attributes(index) for d in self.datasets) def _get_data(self, index): return (d[index]['data'] for d in self.datasets)
40.454545
124
0.635112
825e7b700a5f39c734ab662d408e94a3c08b24e3
3,860
py
Python
homeassistant/components/ecobee.py
loraxx753/skynet
86a1b0a6c6a3f81bc92d4f61de6a9a6b9f964543
[ "Apache-2.0" ]
13
2017-02-01T13:25:34.000Z
2022-01-26T01:30:39.000Z
homeassistant/components/ecobee.py
1Forward1Back/home-assistant
ce24ef0c20dea0fd671d6f2c2a8b1456b4b66ba6
[ "MIT" ]
9
2017-07-26T18:05:32.000Z
2021-12-05T14:16:34.000Z
homeassistant/components/ecobee.py
1Forward1Back/home-assistant
ce24ef0c20dea0fd671d6f2c2a8b1456b4b66ba6
[ "MIT" ]
21
2017-07-26T17:09:40.000Z
2022-03-27T22:37:22.000Z
""" Support for Ecobee. For more details about this component, please refer to the documentation at https://home-assistant.io/components/ecobee/ """ import logging import os from datetime import timedelta import voluptuous as vol import homeassistant.helpers.config_validation as cv from homeassistant.helpers import discovery from homeassistant.const import CONF_API_KEY from homeassistant.loader import get_component from homeassistant.util import Throttle REQUIREMENTS = [ 'https://github.com/nkgilley/python-ecobee-api/archive/' '4856a704670c53afe1882178a89c209b5f98533d.zip#python-ecobee==0.0.6'] _CONFIGURING = {} _LOGGER = logging.getLogger(__name__) CONF_HOLD_TEMP = 'hold_temp' DOMAIN = 'ecobee' ECOBEE_CONFIG_FILE = 'ecobee.conf' MIN_TIME_BETWEEN_UPDATES = timedelta(seconds=180) NETWORK = None CONFIG_SCHEMA = vol.Schema({ DOMAIN: vol.Schema({ vol.Optional(CONF_API_KEY): cv.string, vol.Optional(CONF_HOLD_TEMP, default=False): cv.boolean }) }, extra=vol.ALLOW_EXTRA) def request_configuration(network, hass, config): """Request configuration steps from the user.""" configurator = get_component('configurator') if 'ecobee' in _CONFIGURING: configurator.notify_errors( _CONFIGURING['ecobee'], "Failed to register, please try again.") return # pylint: disable=unused-argument def ecobee_configuration_callback(callback_data): """The actions to do when our configuration callback is called.""" network.request_tokens() network.update() setup_ecobee(hass, network, config) _CONFIGURING['ecobee'] = configurator.request_config( hass, "Ecobee", ecobee_configuration_callback, description=( 'Please authorize this app at https://www.ecobee.com/consumer' 'portal/index.html with pin code: ' + network.pin), description_image="/static/images/config_ecobee_thermostat.png", submit_caption="I have authorized the app." ) def setup_ecobee(hass, network, config): """Setup Ecobee thermostat.""" # If ecobee has a PIN then it needs to be configured. if network.pin is not None: request_configuration(network, hass, config) return if 'ecobee' in _CONFIGURING: configurator = get_component('configurator') configurator.request_done(_CONFIGURING.pop('ecobee')) hold_temp = config[DOMAIN].get(CONF_HOLD_TEMP) discovery.load_platform(hass, 'climate', DOMAIN, {'hold_temp': hold_temp}, config) discovery.load_platform(hass, 'sensor', DOMAIN, {}, config) discovery.load_platform(hass, 'binary_sensor', DOMAIN, {}, config) class EcobeeData(object): """Get the latest data and update the states.""" def __init__(self, config_file): """Initialize the Ecobee data object.""" from pyecobee import Ecobee self.ecobee = Ecobee(config_file) @Throttle(MIN_TIME_BETWEEN_UPDATES) def update(self): """Get the latest data from pyecobee.""" self.ecobee.update() _LOGGER.info("Ecobee data updated successfully") def setup(hass, config): """Setup Ecobee. Will automatically load thermostat and sensor components to support devices discovered on the network. """ # pylint: disable=global-statement, import-error global NETWORK if 'ecobee' in _CONFIGURING: return from pyecobee import config_from_file # Create ecobee.conf if it doesn't exist if not os.path.isfile(hass.config.path(ECOBEE_CONFIG_FILE)): jsonconfig = {"API_KEY": config[DOMAIN].get(CONF_API_KEY)} config_from_file(hass.config.path(ECOBEE_CONFIG_FILE), jsonconfig) NETWORK = EcobeeData(hass.config.path(ECOBEE_CONFIG_FILE)) setup_ecobee(hass, NETWORK.ecobee, config) return True
30.15625
76
0.704404
f00dd97a90a95174346c00dd9e5cef796d2a6b18
2,443
py
Python
BERT/train/model.py
ShuntaIto/azureml-pl-sample
e5ae7b0a06d72f7b1371675f42ef9708cc8ea2c5
[ "MIT" ]
6
2021-04-02T07:22:51.000Z
2021-07-14T08:45:42.000Z
BERT/train/model.py
ShuntaIto/azureml-pl-sample
e5ae7b0a06d72f7b1371675f42ef9708cc8ea2c5
[ "MIT" ]
null
null
null
BERT/train/model.py
ShuntaIto/azureml-pl-sample
e5ae7b0a06d72f7b1371675f42ef9708cc8ea2c5
[ "MIT" ]
null
null
null
import torch from torch import nn import torch.nn.functional as F import pytorch_lightning as pl from transformers import BertJapaneseTokenizer from transformers import BertModel from azureml.core import Run run = Run.get_context() class BERTClassificationModel(pl.LightningModule): def __init__(self, bert_lr=5e-5, output_lr=1e-4): super(BERTClassificationModel, self).__init__() self.bert = BertModel.from_pretrained('cl-tohoku/bert-base-japanese-whole-word-masking') self.output = nn.Linear(768, 9) self.bert_lr = bert_lr self.output_lr = output_lr self.train_acc = pl.metrics.Accuracy() self.val_acc = pl.metrics.Accuracy() self.test_acc = pl.metrics.Accuracy() def forward(self, input_ids, attention_mask, token_type_ids): y = self.bert(input_ids, attention_mask, token_type_ids).last_hidden_state ## cls token相当部分のhidden_stateのみ抜粋 y = y[:,0,:] y = y.view(-1, 768) y = self.output(y) y = F.softmax(y, dim=1) return y def training_step(self, batch, batch_nb): x, t = batch y = self(x['input_ids'], x['attention_mask'], x['token_type_ids']) loss = F.cross_entropy(y, t) run.log("loss", float(loss)) ##self.log("loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True) return loss def validation_step(self, batch, batch_nb): x, t = batch y = self(x['input_ids'], x['attention_mask'], x['token_type_ids']) loss = F.cross_entropy(y, t) preds = torch.argmax(y, dim=1) run.log("val_loss", float(loss)) run.log("val_acc", float(self.val_acc(y,t))) ##self.log('val_loss', loss, prog_bar=True) ##self.log('val_acc', self.val_acc(y,t), prog_bar=True) return loss def test_step(self, batch, batch_nb): x, t = batch y = self(x['input_ids'], x['attention_mask'], x['token_type_ids']) loss = F.cross_entropy(y, t) preds = torch.argmax(y, dim=1) run.log("test_loss", float(loss)) run.log("test_acc", float(self.test_acc(y,t))) return loss def configure_optimizers(self): return torch.optim.Adam([ {'params': self.bert.encoder.layer[-1].parameters(), 'lr': self.bert_lr}, {'params': self.output.parameters(), 'lr': self.output_lr} ])
35.926471
96
0.618502
baf8df5344a0605ac4b758ec2135596f72816053
13,116
py
Python
fdia_simulation/models/radar.py
QDucasse/FDIA_simulation
bdd0cb072f07b9a96fd82df581c9c7493ae66cbc
[ "MIT" ]
7
2020-12-11T16:20:59.000Z
2022-01-11T21:18:25.000Z
fdia_simulation/models/radar.py
QDucasse/FDIA_simulation
bdd0cb072f07b9a96fd82df581c9c7493ae66cbc
[ "MIT" ]
2
2020-09-25T06:56:56.000Z
2021-06-25T15:40:38.000Z
fdia_simulation/models/radar.py
QDucasse/FDIA_simulation
bdd0cb072f07b9a96fd82df581c9c7493ae66cbc
[ "MIT" ]
5
2019-08-27T11:13:31.000Z
2021-11-26T12:52:19.000Z
# -*- coding: utf-8 -*- """ Created on Fri Jun 21 13:07:46 2019 @author: qde """ import numpy as np import matplotlib.pyplot as plt from math import cos,sin,sqrt,pi,atan2 from numpy.random import randn from filterpy.common import pretty_str from fdia_simulation.models import ManeuveredAirplane, NoisySensor, Track, ManeuveredSystem, Command class Radar(object): '''Implements a simulated radar. The radar will output a data set corresponding to typical radar values. Attributes ---------- x, y, z: floats Radar position along x, y and z-axis. r_std: float Standard deviation on the measurement of r. Default value of 1. theta_std: float Standard deviation on the measurement of theta. Default value of 0.1 phi_std: float Standard deviation on the measurement of phi. Default value of 0.1 Parameters ---------- Identical to Attributes ''' DT_RADAR = 0.1 def __init__(self, x = 0, y = 0, z = 0, dt = None, r_std = 1., theta_std = 0.001, phi_std = 0.001): if dt is None: dt = self.DT_RADAR self.dt = dt self.x = x self.y = y self.z = z self.step = self.dt / Track.DT_TRACK # Sampling step from the position data self.r_std = r_std self.theta_std = theta_std self.phi_std = phi_std self.R = np.array([[r_std,0 ,0 ], [0 ,theta_std,0 ], [0 ,0 ,phi_std]]) def get_position(self): ''' Position accessor. Returns ------- position: float iterable [x,y,z] of the radar. ''' return [self.x,self.y,self.z] def gen_radar_values(self,x,y,z): ''' Computes the three parameters r, theta and phi from the given positions. Parameters ---------- x,y,z: floats Position of the airplane. Returns ------- r,theta,phi: floats Radar values corresponding to the input position. ''' # Importance of the radar position x -= self.x y -= self.y z -= self.z # Computation of the distance of the airplane r = sqrt(x**2 + y**2 + z**2) # Computation of the turning angle of the airplane theta = atan2(y,x) # Computation of the elevation angle of the airplane phi = atan2(z, sqrt(x**2 + y**2)) return r, theta, phi def sample_position_data(self,position_data): ''' Samples the initial position data (computed with dt = 0.01) to reduce it to the actual data rate of the radar. ''' sampled_position_data = position_data[::int(self.step)] return sampled_position_data def gen_data(self,position_data): ''' Generates simulated received data for a radar. Parameters ---------- position_data: float list numpy array List of positions in the form of lists [x_k, y_k, z_k]. Corresponding to: x_k: float Position along x-axis. y_k: float Position along y-axis. z_k: float Position along z-axis. Returns ------- rs, thetas, phis: float iterables Distances, azimuth/turn angles and elevation angles. ''' rs, thetas, phis = [], [], [] for position in position_data: x_k = position[0] y_k = position[1] z_k = position[2] # Computation of the supposed distance of the airplane r_k, theta_k, phi_k = self.gen_radar_values(x_k,y_k,z_k) rs.append(r_k) thetas.append(theta_k) phis.append(phi_k) return rs, thetas, phis def sense(self, rs, thetas, phis): ''' Simulates real sensors by adding noise to the predicted simulated values. Parameters ---------- rs, thetas, phis: float iterable Distances, azimuth/turn angles and elevation angles. Returns ------- noisy_rs, noisy_thetas, noisy_phis: float iterable Distances, azimuth/turn angles and elevation angles with added white noise. ''' nsr = NoisySensor(std_noise = self.r_std) nstheta = NoisySensor(std_noise = self.theta_std) nsphi = NoisySensor(std_noise = self.phi_std) noisy_rs = [nsr.sense(r) for r in rs] noisy_thetas = [nstheta.sense(theta) for theta in thetas] noisy_phis = [nsphi.sense(phi) for phi in phis] return noisy_rs, noisy_thetas, noisy_phis def gen_position_vals(self,r,theta,phi): ''' Compute the position from the radar values r, theta and phi. Parameters ---------- r,theta,phi: floats Radar values. Returns ------- x,y,z: floats Sensed position of the airplane extracted from the measurement given in input. ''' x = r * cos(theta) * cos(phi) + self.x y = r * sin(theta) * cos(phi) + self.y z = r * sin(phi) + self.z return x,y,z def radar2cartesian(self,rs,thetas,phis): ''' Transcripts the radar measured values (r, theta, phi) to cartesian positions (x, y, z). Parameters ---------- rs: float iterable List of the rs (distance) measured by the radar. thetas: float iterable List of the thetas (azimuth/turn angle) measured by the radar. phis: float iterable List of the phis (elevation angle) measured by the radar. Returns ------- xs: float iterable List of the computed positions along x-axis. ys: float iterable List of the computed positions along y-axis. zs: float iterable List of the computed positions along z-axis. ''' xs,ys,zs = [],[],[] for r,theta,phi in zip(rs,thetas,phis): x_k,y_k,z_k = self.gen_position_vals(r,theta,phi) xs.append(x_k) ys.append(y_k) zs.append(z_k) return xs,ys,zs def __eq__(self,other): eq_dt = (self.dt == other.dt) eq_pos = ( (self.y == other.y) and (self.x == other.x) and (self.z == other.z) ) eq_std = ( (self.r_std == other.r_std) and (self.theta_std == other.theta_std) and (self.phi_std == other.phi_std) ) return all([eq_dt,eq_pos,eq_std]) class LabeledMeasurement(object): ''' Measurement labeled with a tag (radar ownership) and a timestamp (date of the measurement). Parameters ---------- tag: int Tag (position) of the radar emitting this measurement. time: float Time of the measurement, starting from the beginning of the observation. value: float numpy array Array containing [r, theta, phi], measurement of tagged radar at the given time. ''' def __init__(self,tag,time,value): self.tag = tag self.time = time self.value = value ''' Redifinition of the comparison operators using as only criteria the time of measurement. ''' def __gt__(self,other): return (self.time > other.time) def __ge__(self,other): return (self.time >= other.time) def __le__(self,other): return (self.time <= other.time) def __lt__(self,other): return (self.time < other.time) def __eq__(self,other): eq_time = (self.time == other.time) eq_tag = (self.tag == other.tag) eq_value = np.array_equal(self.value,other.value) return (eq_time and eq_tag and eq_value) def __repr__(self): return '\n'.join([ 'LabeledMeasurement object', pretty_str('tag', self.tag), pretty_str('time', self.time), pretty_str('value', self.value)]) class PeriodRadar(Radar): ''' Implements a radar with a given data rate (dt). Attributes ---------- Radar attributes + dt: float Data rate of the radar. tag: int Radar tag (position in the radars list). time_std: float Standard deviation of the time. Default value of 0.001 Parameters ---------- Identical to attributes ''' def __init__(self, x, y, z=0, dt = None, r_std = 1., theta_std = 0.001, phi_std = 0.001, time_std = 0.001): if dt is None: dt = Radar.DT_RADAR self.time_std = time_std self.tag = 0 Radar.__init__(self,x = x, y = y, z = z, dt = dt, r_std = r_std, theta_std = theta_std, phi_std = phi_std) def compute_meas_times(self, size): ''' Computes the measurement times adding repeatitively dt (modified by the time_std). Parameters ---------- size: int Size of the list of times. Returns ------- meas_times: float list List of the sample times. ''' t_k = 0 meas_times = [t_k] for _ in range(size-1): t_k += self.dt + randn()*self.time_std # Adding a time jitter meas_times.append(t_k) return meas_times def compute_measurements(self,position_data): ''' Computes the measurements of given positions. Parameters ---------- position_data: float numpy array Array of positions [x,y,z]. Returns ------- measurements: LabeledMeasurement list List of labeled measurements with time and tag. ''' rs, thetas, phis = self.gen_data(position_data) noisy_rs, noisy_thetas, noisy_phis = self.sense(rs, thetas, phis) n = len(noisy_rs) measurement_times = self.compute_meas_times(n) measurements = [] for i in range(n): value = [noisy_rs[i], noisy_thetas[i], noisy_phis[i]] measurement = LabeledMeasurement(tag = self.tag, time = measurement_times[i], value = value) measurements.append(measurement) return measurements if __name__ == "__main__": #================== Positions generation for the airplane ================== trajectory = Track() states = trajectory.gen_landing() xs = states[:,0] ys = states[:,3] zs = states[:,6] position_data = np.array(list(zip(xs,ys,zs))) # ========================================================================== # ========================== Radars generation ============================= # Radar 1 radar = Radar(x=-6000,y=10000, dt = 0.4) rs, thetas, phis = radar.gen_data(position_data) noisy_rs, noisy_thetas, noisy_phis = radar.sense(rs, thetas, phis) xs_from_rad, ys_from_rad, zs_from_rad = radar.radar2cartesian(noisy_rs, noisy_thetas, noisy_phis) radar_values = np.array(list(zip(noisy_rs, noisy_thetas, noisy_phis))) # print("Noisy radar data:\n{0}\n".format(radar_values[-25:,:])) radar_computed_values = np.array(list(zip(xs_from_rad, ys_from_rad, zs_from_rad))) # print("Estimated positions:\n{0}\n".format(radar_computed_values[-25:,:])) radar2 = Radar(x=-6000,y=10000, dt = 0.7) rs2, thetas2, phis2 = radar2.gen_data(position_data) noisy_rs2, noisy_thetas2, noisy_phis2 = radar2.sense(rs2, thetas2, phis2) xs_from_rad2, ys_from_rad2, zs_from_rad2 = radar2.radar2cartesian(noisy_rs2, noisy_thetas2, noisy_phis2) radar_values2 = np.array(list(zip(noisy_rs2, noisy_thetas2, noisy_phis2))) # print("Noisy radar data:\n{0}\n".format(radar_values[-25:,:])) radar_computed_values2 = np.array(list(zip(xs_from_rad2, ys_from_rad2, zs_from_rad2))) # ========================================================================== # =============================== Plotting ================================= fig = plt.figure(1) plt.rc('font', family='serif') ax = fig.gca(projection='3d') ax.plot(xs, ys, zs, label='Real airplane position', color='k', linestyle='dashed') ax.scatter(xs_from_rad, ys_from_rad, zs_from_rad, color='b', marker='o', label='Radar measurements') ax.scatter(radar.x,radar.y,radar.z,color='r',label='Radar') ax.scatter(xs_from_rad2, ys_from_rad2, zs_from_rad2, color='g', marker='o', label='Radar2 measurements') ax.scatter(radar2.x,radar2.y,radar2.z,color='orange',label='Radar2') ax.set_xlabel('X axis') ax.set_ylabel('Y axis') ax.set_zlabel('Z axis') ax.legend() plt.show()
31.990244
108
0.552531
92c56968de82a8225c5093b40891e57d1367f047
26,953
py
Python
GearBot/Cogs/Moderation.py
Gh0stlyy/GearBot
aa918976017c1e864ab33ccd714bf25cbefa811c
[ "MIT" ]
null
null
null
GearBot/Cogs/Moderation.py
Gh0stlyy/GearBot
aa918976017c1e864ab33ccd714bf25cbefa811c
[ "MIT" ]
null
null
null
GearBot/Cogs/Moderation.py
Gh0stlyy/GearBot
aa918976017c1e864ab33ccd714bf25cbefa811c
[ "MIT" ]
null
null
null
import asyncio import datetime import time import traceback from concurrent.futures import CancelledError import discord from discord.ext import commands from discord.ext.commands import BadArgument from Util import Permissioncheckers, Configuration, Utils, GearbotLogging, Pages, InfractionUtils, Emoji, Translator, \ Archive from Util.Converters import BannedMember, UserID, Reason from database.DatabaseConnector import LoggedMessage class Moderation: permissions = { "min": 2, "max": 6, "required": 2, "commands": { "userinfo": {"required": 2, "min": 0, "max": 6}, "serverinfo": {"required": 2, "min": 0, "max": 6}, "roles": {"required": 2, "min": 0, "max": 6}, } } def __init__(self, bot): self.bot: commands.Bot = bot bot.mutes = self.mutes = Utils.fetch_from_disk("mutes") self.running = True self.bot.loop.create_task(unmuteTask(self)) Pages.register("roles", self.roles_init, self.roles_update), [] def __unload(self): Utils.saveToDisk("mutes", self.mutes) self.running = False Pages.unregister("roles") async def __local_check(self, ctx): return Permissioncheckers.check_permission(ctx) async def roles_init(self, ctx): pages = self.gen_roles_pages(ctx.guild) page = pages[0] return f"**{Translator.translate('roles', ctx.guild.id, server_name=ctx.guild.name, page_num=1, pages=len(pages))}**```\n{page}```", None, len(pages) > 1, [] async def roles_update(self, ctx, message, page_num, action, data): pages = self.gen_roles_pages(message.guild) page, page_num = Pages.basic_pages(pages, page_num, action) return f"**{Translator.translate('roles', message.guild.id, server_name=ctx.guild.name, page_num=page_num + 1, pages=len(pages))}**```\n{page}```", None, page_num @staticmethod def gen_roles_pages(guild: discord.Guild): role_list = dict() longest_name = 1 for role in guild.roles: role_list[f"{role.name} - {role.id}"] = role longest_name = max(longest_name, len(role.name)) return Pages.paginate("\n".join(f"{role_list[r].name} {' ' * (longest_name - len(role_list[r].name))} - {role_list[r].id}" for r in sorted(role_list.keys()))) @commands.command() @commands.guild_only() async def roles(self, ctx: commands.Context): """Lists all roles on the server and their IDs, useful for configuring without having to ping that role""" await Pages.create_new("roles", ctx) @commands.command(aliases=["👢"]) @commands.guild_only() @commands.bot_has_permissions(kick_members=True) async def kick(self, ctx, user: discord.Member, *, reason:Reason=""): """kick_help""" if reason == "": reason = Translator.translate("no_reason", ctx.guild.id) if (ctx.author != user and user != ctx.bot.user and ctx.author.top_role > user.top_role) or (ctx.guild.owner == ctx.author and ctx.author != user): if ctx.me.top_role > user.top_role: self.bot.data["forced_exits"].append(user.id) await ctx.guild.kick(user, reason=f"Moderator: {ctx.author.name}#{ctx.author.discriminator} ({ctx.author.id}) Reason: {reason}") await ctx.send( f"{Emoji.get_chat_emoji('YES')} {Translator.translate('kick_confirmation', ctx.guild.id, user=Utils.clean_user(user), user_id=user.id, reason=reason)}") await GearbotLogging.log_to(ctx.guild.id, "MOD_ACTIONS", f":boot: {Translator.translate('kick_log', ctx.guild.id, user=Utils.clean_user(user), user_id=user.id, moderator=Utils.clean_user(ctx.author), moderator_id=ctx.author.id, reason=reason)}") InfractionUtils.add_infraction(ctx.guild.id, user.id, ctx.author.id, Translator.translate('kick', ctx.guild.id), reason) else: await ctx.send(Translator.translate('kick_unable',ctx.guild.id, user=Utils.clean_user(user))) else: await ctx.send(f"{Emoji.get_chat_emoji('NO')} {Translator.translate('kick_not_allowed', ctx.guild.id, user=user)}") @commands.command(aliases=["🚪"]) @commands.guild_only() @commands.bot_has_permissions(ban_members=True) async def ban(self, ctx: commands.Context, user: discord.Member, *, reason:Reason=""): """ban_help""" if reason == "": reason = Translator.translate("no_reason", ctx.guild.id) if (ctx.author != user and user != ctx.bot.user and ctx.author.top_role > user.top_role) or (ctx.guild.owner == ctx.author and ctx.author != user): if ctx.me.top_role > user.top_role: self.bot.data["forced_exits"].append(user.id) await ctx.guild.ban(user, reason=f"Moderator: {ctx.author.name} ({ctx.author.id}) Reason: {reason}", delete_message_days=0) InfractionUtils.add_infraction(ctx.guild.id, user.id, ctx.author.id, "Ban", reason) await ctx.send( f"{Emoji.get_chat_emoji('YES')} {Translator.translate('ban_confirmation', ctx.guild.id, user=Utils.clean_user(user), user_id=user.id, reason=reason)}") await GearbotLogging.log_to(ctx.guild.id, "MOD_ACTIONS", f":door: {Translator.translate('ban_log', ctx.guild.id, user=Utils.clean_user(user), user_id=user.id, moderator=Utils.clean_user(ctx.author), moderator_id=ctx.author.id, reason=reason)}") else: await ctx.send(Translator.translate('ban_unable', ctx.guild.id, user=Utils.clean_user(user))) else: await ctx.send(f"{Emoji.get_chat_emoji('NO')} {Translator.translate('ban_not_allowed', ctx.guild.id, user=user)}") @commands.command() @commands.guild_only() @commands.bot_has_permissions(ban_members=True) async def softban(self, ctx:commands.Context, user: discord.Member, *, reason:Reason=""): """softban_help""" if reason == "": reason = Translator.translate("no_reason", ctx.guild.id) if (ctx.author != user and user != ctx.bot.user and ctx.author.top_role > user.top_role) or (ctx.guild.owner == ctx.author and ctx.author != user): if ctx.me.top_role > user.top_role: self.bot.data["forced_exits"].append(user.id) self.bot.data["unbans"].append(user.id) await ctx.guild.ban(user, reason=f"softban - Moderator: {ctx.author.name} ({ctx.author.id}) Reason: {reason}", delete_message_days=1) await ctx.guild.unban(user) await ctx.send(f"{Emoji.get_chat_emoji('YES')} {Translator.translate('softban_confirmation', ctx.guild.id, user=Utils.clean_user(user), user_id=user.id, reason=reason)}") await GearbotLogging.log_to(ctx.guild.id, "MOD_ACTIONS", f":door: {Translator.translate('softban_log', ctx.guild.id, user=Utils.clean_user(user), user_id=user.id, moderator=Utils.clean_user(ctx.author), moderator_id=ctx.author.id, reason=reason)}") InfractionUtils.add_infraction(ctx.guild.id, user.id, ctx.author.id, "Softban", reason) else: await ctx.send(Translator.translate('softban_unable', ctx.guild.id, user=Utils.clean_user(user))) else: await ctx.send(f"{Emoji.get_chat_emoji('NO')} {Translator.translate('softban_not_allowed', ctx.guild.id, user=user)}") @commands.command() @commands.guild_only() @commands.bot_has_permissions(ban_members=True) async def forceban(self, ctx: commands.Context, user_id: UserID, *, reason:Reason=""): """forceban_help""" if reason == "": reason = Translator.translate("no_reason", ctx.guild.id) try: member = await commands.MemberConverter().convert(ctx, str(user_id)) except BadArgument: user = await ctx.bot.get_user_info(user_id) self.bot.data["forced_exits"].append(user.id) await ctx.guild.ban(user, reason=f"Moderator: {ctx.author.name} ({ctx.author.id}) Reason: {reason}", delete_message_days=0) await ctx.send( f"{Emoji.get_chat_emoji('YES')} {Translator.translate('forceban_confirmation', ctx.guild.id, user=Utils.clean_user(user), user_id=user_id, reason=reason)}") await GearbotLogging.log_to(ctx.guild.id, "MOD_ACTIONS", f":door: {Translator.translate('forceban_log', ctx.guild.id, user=Utils.clean_user(user), user_id=user_id, moderator=Utils.clean_user(ctx.author), moderator_id=ctx.author.id, reason=reason)}") InfractionUtils.add_infraction(ctx.guild.id, user.id, ctx.author.id, Translator.translate('forced_ban', ctx.guild.id), reason) else: await ctx.send(f"{Emoji.get_chat_emoji('WARNING')} {Translator.translate('forceban_to_ban', ctx.guild.id, user=Utils.clean_user(member))}") await ctx.invoke(self.ban, member, reason=reason) @commands.command() @commands.guild_only() @commands.bot_has_permissions(manage_messages=True) async def purge(self, ctx, msgs: int): """purge_help""" if msgs < 1: return await ctx.send(f"{Emoji.get_chat_emoji('NO')} {Translator.translate('purge_too_small', ctx.guild.id)}") if msgs > 1000: return await ctx.send(f"{Emoji.get_chat_emoji('NO')} {Translator.translate('purge_too_big', ctx.guild.id)}") try: deleted = await ctx.channel.purge(limit=msgs) except discord.NotFound: # sleep for a sec just in case the other bot is still purging so we don't get removed as well await asyncio.sleep(1) await ctx.send(f"{Emoji.get_chat_emoji('YES')} {Translator.translate('purge_fail_not_found', ctx.guild.id)}") await ctx.send(f"{Emoji.get_chat_emoji('YES')} {Translator.translate('purge_confirmation', ctx.guild.id, count=len(deleted))}", delete_after=10) @commands.command() @commands.guild_only() @commands.bot_has_permissions(ban_members=True) async def unban(self, ctx, member: BannedMember, *, reason:Reason=""): """unban_help""" if reason == "": reason = Translator.translate("no_reason", ctx.guild.id) self.bot.data["unbans"].append(member.user.id) await ctx.guild.unban(member.user, reason=f"Moderator: {ctx.author.name} ({ctx.author.id}) Reason: {reason}") InfractionUtils.add_infraction(ctx.guild.id, member.user.id, ctx.author.id, "Unban", reason) await ctx.send( f"{Emoji.get_chat_emoji('YES')} {Translator.translate('unban_confirmation', ctx.guild.id, user=Utils.clean_user(member.user), user_id=member.user.id, reason=reason)}") await GearbotLogging.log_to(ctx.guild.id, "MOD_ACTIONS", f"{Emoji.get_chat_emoji('INNOCENT')} {Translator.translate('unban_log', ctx.guild.id, user=Utils.clean_user(member.user), user_id=member.user.id, moderator=Utils.clean_user(ctx.author), moderator_id=ctx.author.id, reason=reason)}") @commands.command() @commands.guild_only() @commands.bot_has_permissions(manage_roles=True) async def mute(self, ctx: commands.Context, target: discord.Member, durationNumber: int, durationIdentifier: str, *, reason:Reason=""): """mute_help""" if reason == "": reason = Translator.translate("no_reason", ctx.guild.id) roleid = Configuration.get_var(ctx.guild.id, "MUTE_ROLE") if roleid is 0: await ctx.send(f"{Emoji.get_chat_emoji('WARNING')} {Translator.translate('mute_not_configured', ctx.guild.id, user=target.mention)}") else: role = discord.utils.get(ctx.guild.roles, id=roleid) if role is None: await ctx.send(f"{Emoji.get_chat_emoji('WARNING')} {Translator.translate('mute_role_missing', ctx.guild.id, user=target.mention)}") else: if (ctx.author != target and target != ctx.bot.user and ctx.author.top_role > target.top_role) or ctx.guild.owner == ctx.author: duration = Utils.convertToSeconds(durationNumber, durationIdentifier) if duration > 0: until = time.time() + duration await target.add_roles(role, reason=f"{reason}, as requested by {ctx.author.name}") if not str(ctx.guild.id) in self.mutes: self.mutes[str(ctx.guild.id)] = dict() self.mutes[str(ctx.guild.id)][str(target.id)] = until await ctx.send(f"{Emoji.get_chat_emoji('MUTE')} {Translator.translate('mute_confirmation', ctx.guild.id, user=Utils.clean_user(target), duration=f'{durationNumber} {durationIdentifier}')}") Utils.saveToDisk("mutes", self.mutes) await GearbotLogging.log_to(ctx.guild.id, "MOD_ACTIONS", f"{Emoji.get_chat_emoji('MUTE')} {Translator.translate('mute_log', ctx.guild.id, user=Utils.clean_user(target), user_id=target.id, moderator=Utils.clean_user(ctx.author), moderator_id=ctx.author.id, duration=f'{durationNumber} {durationIdentifier}', reason=reason)}") InfractionUtils.add_infraction(ctx.guild.id, target.id, ctx.author.id, "Mute", reason) else: await ctx.send(f"{Emoji.get_chat_emoji('WHAT')} {Translator.translate('mute_negative_denied', ctx.guild.id, duration=f'{durationNumber} {durationIdentifier}')} {Emoji.get_chat_emoji('WHAT')}") else: await ctx.send( f"{Emoji.get_chat_emoji('NO')} {Translator.translate('mute_not_allowed', ctx.guild.id, user=target)}") @commands.command() @commands.guild_only() @commands.bot_has_permissions(manage_roles=True) async def unmute(self, ctx: commands.Context, target: discord.Member, *, reason:Reason=""): """unmute_help""" if reason == "": reason = Translator.translate("no_reason", ctx.guild.id) roleid = Configuration.get_var(ctx.guild.id, "MUTE_ROLE") if roleid is 0: await ctx.send( f"{Emoji.get_chat_emoji('NO')} The mute feature has been disabled on this server, as such i cannot unmute that person") else: role = discord.utils.get(ctx.guild.roles, id=roleid) if role is None: await ctx.send( f"{Emoji.get_chat_emoji('NO')} Unable to comply, the role i've been told to use for muting no longer exists") else: await target.remove_roles(role, reason=f"Unmuted by {ctx.author.name}, {reason}") await ctx.send(f"{Emoji.get_chat_emoji('INNOCENT')} {target.display_name} has been unmuted") await GearbotLogging.log_to(ctx.guild.id, "MOD_ACTIONS", f"{Emoji.get_chat_emoji('INNOCENT')} {target.name}#{target.discriminator} (`{target.id}`) has been unmuted by {ctx.author.name}") InfractionUtils.add_infraction(ctx.guild.id, target.id, ctx.author.id, "Unmute", reason) @commands.command() async def userinfo(self, ctx: commands.Context, *, userID:UserID): """Shows information about the chosen user""" user = None member = None if userID is None: user = ctx.author if ctx.guild is not None: member = ctx.guild.get_member(user.id) elif ctx.guild is not None: try: user = member = ctx.guild.get_member(userID) except BadArgument: pass if user is None: user = await Utils.get_user(userID) embed = discord.Embed(color=0x7289DA, timestamp=ctx.message.created_at) embed.set_thumbnail(url=user.avatar_url) embed.set_footer(text=Translator.translate('requested_by', ctx, user=ctx.author.name), icon_url=ctx.author.avatar_url) embed.add_field(name=Translator.translate('name', ctx), value=f"{user.name}#{user.discriminator}", inline=True) embed.add_field(name=Translator.translate('id', ctx), value=user.id, inline=True) embed.add_field(name=Translator.translate('bot_account', ctx), value=user.bot, inline=True) embed.add_field(name=Translator.translate('animated_avatar', ctx), value=user.is_avatar_animated(), inline=True) if member is not None: account_joined = member.joined_at.strftime("%d-%m-%Y") embed.add_field(name=Translator.translate('nickname', ctx), value=member.nick, inline=True) embed.add_field(name=Translator.translate('top_role', ctx), value=member.top_role.name, inline=True) embed.add_field(name=Translator.translate('joined_at', ctx), value=f"{account_joined} ({(ctx.message.created_at - member.joined_at).days} days ago)", inline=True) account_made = user.created_at.strftime("%d-%m-%Y") embed.add_field(name=Translator.translate('account_created_at', ctx), value=f"{account_made} ({(ctx.message.created_at - user.created_at).days} days ago)", inline=True) embed.add_field(name=Translator.translate('avatar_url', ctx), value=f"[{Translator.translate('avatar_url', ctx)}]({user.avatar_url})") await ctx.send(embed=embed) @commands.command() async def serverinfo(self, ctx): """Shows information about the current server.""" guild_features = ", ".join(ctx.guild.features) print(guild_features) if guild_features == "": guild_features = None role_list = [] for i in range(len(ctx.guild.roles)): role_list.append(ctx.guild.roles[i].name) guild_made = ctx.guild.created_at.strftime("%d-%m-%Y") embed = discord.Embed(color=0x7289DA, timestamp= datetime.datetime.fromtimestamp(time.time())) embed.set_thumbnail(url=ctx.guild.icon_url) embed.set_footer(text=Translator.translate('requested_by', ctx, user=ctx.author), icon_url=ctx.author.avatar_url) embed.add_field(name=Translator.translate('name', ctx), value=ctx.guild.name, inline=True) embed.add_field(name=Translator.translate('id', ctx), value=ctx.guild.id, inline=True) embed.add_field(name=Translator.translate('owner', ctx), value=ctx.guild.owner, inline=True) embed.add_field(name=Translator.translate('members', ctx), value=ctx.guild.member_count, inline=True) embed.add_field(name=Translator.translate('text_channels', ctx), value=str(len(ctx.guild.text_channels)), inline=True) embed.add_field(name=Translator.translate('voice_channels', ctx), value=str(len(ctx.guild.voice_channels)), inline=True) embed.add_field(name=Translator.translate('total_channel', ctx), value=str(len(ctx.guild.text_channels) + len(ctx.guild.voice_channels)), inline=True) embed.add_field(name=Translator.translate('created_at', ctx), value=f"{guild_made} ({(ctx.message.created_at - ctx.guild.created_at).days} days ago)", inline=True) embed.add_field(name=Translator.translate('vip_features', ctx), value=guild_features, inline=True) if ctx.guild.icon_url != "": embed.add_field(name=Translator.translate('server_icon', ctx), value=f"[{Translator.translate('server_icon', ctx)}]({ctx.guild.icon_url})", inline=True) embed.add_field(name=Translator.translate('all_roles', ctx), value=", ".join(role_list), inline=True) #todo paginate await ctx.send(embed=embed) @commands.group() @commands.bot_has_permissions(attach_files=True) async def archive(self, ctx): await ctx.trigger_typing() @archive.command() async def channel(self, ctx, channel:discord.TextChannel=None, amount=100): if amount > 5000: await ctx.send(f"{Emoji.get_chat_emoji('NO')} {Translator.translate('archive_too_much', ctx)}") return if channel is None: channel = ctx.message.channel if Configuration.get_var(ctx.guild.id, "EDIT_LOGS"): permissions = channel.permissions_for(ctx.author) if permissions.read_messages and permissions.read_message_history: messages = LoggedMessage.select().where((LoggedMessage.server == ctx.guild.id) & (LoggedMessage.channel == channel.id)).order_by(LoggedMessage.messageid.desc()).limit(amount) await Archive.ship_messages(ctx, messages) else: ctx.send(f"{Emoji.get_chat_emoji('NO')} {Translator.translate('archive_denied_read_perms')}") else: await ctx.send("Not implemented, please enable edit logs to be able to use archiving") @archive.command() async def user(self, ctx, user:UserID, amount=100): if amount > 5000: await ctx.send(f"{Emoji.get_chat_emoji('NO')} {Translator.translate('archive_too_much', ctx)}") return if Configuration.get_var(ctx.guild.id, "EDIT_LOGS"): messages = LoggedMessage.select().where( (LoggedMessage.server == ctx.guild.id) & (LoggedMessage.author == user)).order_by(LoggedMessage.messageid.desc()).limit(amount) await Archive.ship_messages(ctx, messages) else: await ctx.send("Please enable edit logs so i can archive users") async def on_guild_channel_create(self, channel: discord.abc.GuildChannel): guild: discord.Guild = channel.guild roleid = Configuration.get_var(guild.id, "MUTE_ROLE") if roleid is not 0: role = discord.utils.get(guild.roles, id=roleid) if role is not None and channel.permissions_for(guild.me).manage_channels: if isinstance(channel, discord.TextChannel): await channel.set_permissions(role, reason=Translator.translate('mute_setup', guild.id), send_messages=False, add_reactions=False) else: await channel.set_permissions(role, reason=Translator.translate('mute_setup', guild.id), speak=False, connect=False) async def on_member_join(self, member: discord.Member): if str(member.guild.id) in self.mutes and member.id in self.mutes[str(member.guild.id)]: roleid = Configuration.get_var(member.guild.id, "MUTE_ROLE") if roleid is not 0: role = discord.utils.get(member.guild.roles, id=roleid) if role is not None: if member.guild.me.guild_permissions.manage_roles: await member.add_roles(role, reason=Translator.translate('mute_reapply_reason', member.guild.id)) await GearbotLogging.log_to(member.guild.id, "MOD_ACTIONS",f"{Emoji.get_chat_emoji('MUTE')} {Translator.translate('mute_reapply_log', member.guild.id, user=Utils.clean_user(member), user_id=member.id)}") else: await GearbotLogging.log_to(member.guild.id, "MOD_ACTIONS", Translator.translate('mute_reapply_failed_log', member.build.id)) async def on_guild_remove(self, guild: discord.Guild): if guild.id in self.mutes.keys(): del self.mutes[guild.id] Utils.saveToDisk("mutes", self.mutes) def setup(bot): bot.add_cog(Moderation(bot)) async def unmuteTask(modcog: Moderation): GearbotLogging.info("Started unmute background task") skips = [] updated = False while modcog.running: userid = 0 guildid = 0 try: guildstoremove = [] for guildid, list in modcog.mutes.items(): guild: discord.Guild = modcog.bot.get_guild(int(guildid)) toremove = [] if Configuration.get_var(int(guildid), "MUTE_ROLE") is 0: guildstoremove.append(guildid) for userid, until in list.items(): if time.time() > until and userid not in skips: member = guild.get_member(int(userid)) role = discord.utils.get(guild.roles, id=Configuration.get_var(int(guildid), "MUTE_ROLE")) if guild.me.guild_permissions.manage_roles: await member.remove_roles(role, reason="Mute expired") await GearbotLogging.log_to(guild.id, "MOD_ACTIONS", f"<:gearInnocent:465177981287923712> {member.name}#{member.discriminator} (`{member.id}`) has automaticaly been unmuted") else: await GearbotLogging.log_to(guild.id, "MOD_ACTIONS", f":no_entry: ERROR: {member.name}#{member.discriminator} (`{member.id}`) was muted earlier but I no longer have the permissions needed to unmute this person, please remove the role manually!") updated = True toremove.append(userid) for todo in toremove: del list[todo] await asyncio.sleep(0) if updated: Utils.saveToDisk("mutes", modcog.mutes) updated = False for id in guildstoremove: del modcog.mutes[id] await asyncio.sleep(10) except CancelledError: pass # bot shutdown except Exception as ex: GearbotLogging.error("Something went wrong in the unmute task") GearbotLogging.error(traceback.format_exc()) skips.append(userid) embed = discord.Embed(colour=discord.Colour(0xff0000), timestamp=datetime.datetime.utcfromtimestamp(time.time())) embed.set_author(name="Something went wrong in the unmute task:") embed.add_field(name="Current guildid", value=guildid) embed.add_field(name="Current userid", value=userid) embed.add_field(name="Exception", value=ex) v = "" for line in traceback.format_exc().splitlines(): if len(v) + len(line) > 1024: embed.add_field(name="Stacktrace", value=v) v = "" v = f"{v}\n{line}" if len(v) > 0: embed.add_field(name="Stacktrace", value=v) await GearbotLogging.bot_log(embed=embed) await asyncio.sleep(10) GearbotLogging.info("Unmute background task terminated")
60.841986
348
0.627314
fc1a61e1c549812888870f868695ee461c905230
27,541
py
Python
envoy.base.runner/tests/test_runner.py
phlax/abstracts
53fbbee68d1f56effe0ded1ed4e28be870693877
[ "Apache-2.0" ]
1
2021-12-09T19:24:48.000Z
2021-12-09T19:24:48.000Z
envoy.base.runner/tests/test_runner.py
envoyproxy/pytooling
db8b60184f8a61b3184a111b0cfaff4780511b46
[ "Apache-2.0" ]
392
2021-08-24T15:55:32.000Z
2022-03-28T14:26:22.000Z
envoy.base.runner/tests/test_runner.py
phlax/abstracts
53fbbee68d1f56effe0ded1ed4e28be870693877
[ "Apache-2.0" ]
3
2021-10-06T13:43:11.000Z
2021-11-29T13:48:56.000Z
import logging import sys from unittest.mock import AsyncMock, MagicMock, patch, PropertyMock import pytest from envoy.base import runner class DummyRunner(runner.BaseRunner): def __init__(self): self.args = PropertyMock() class DummyForkingRunner(runner.ForkingRunner): def __init__(self): self.args = PropertyMock() class Error1(Exception): def __str__(self): return "" pass class Error2(Exception): pass def _failing_runner(errors): class DummyFailingRunner: # this dummy runner calls the _runner mock # when its run/run_async methods are called # and optionally raises some type of error # to ensure they are caught as expected log = PropertyMock() _runner = MagicMock() def __init__(self, raises=None): self.raises = raises @runner.catches(errors) def run(self, *args, **kwargs): result = self._runner(*args, **kwargs) if self.raises: raise self.raises("AN ERROR OCCURRED") return result @runner.catches(errors) async def run_async(self, *args, **kwargs): result = self._runner(*args, **kwargs) if self.raises: raise self.raises("AN ERROR OCCURRED") return result return DummyFailingRunner def test_base_log_filter(): filter = runner.runner.BaseLogFilter("APP_LOGGER") assert isinstance(filter, logging.Filter) assert filter.app_logger == "APP_LOGGER" @pytest.mark.parametrize("name", ["APP_LOGGER", "SOMETHING_ELSE"]) def test_app_log_filter(name): app_logger = MagicMock() app_logger.name = "APP_LOGGER" filter = runner.runner.AppLogFilter(app_logger) assert isinstance(filter, runner.runner.BaseLogFilter) assert filter.app_logger == app_logger record = MagicMock() record.name = name assert ( filter.filter(record) == (name == "APP_LOGGER")) @pytest.mark.parametrize("name", ["APP_LOGGER", "SOMETHING_ELSE"]) def test_root_log_filter(name): app_logger = MagicMock() app_logger.name = "APP_LOGGER" filter = runner.runner.RootLogFilter(app_logger) assert isinstance(filter, runner.runner.BaseLogFilter) assert filter.app_logger == app_logger record = MagicMock() record.name = name assert ( filter.filter(record) == (name != "APP_LOGGER")) @pytest.mark.parametrize("async_fun", [True, False]) @pytest.mark.parametrize( "errors", [Error1, (Error1, Error2)]) @pytest.mark.parametrize( "raises", [None, Error1, Error2]) @pytest.mark.parametrize( "args", [(), ("ARG1", "ARG2")]) @pytest.mark.parametrize( "kwargs", [{}, dict(key1="VAL1", key2="VAL2")]) async def test_catches(errors, async_fun, raises, args, kwargs): run = _failing_runner(errors)(raises) should_fail = ( raises and not ( raises == errors or (isinstance(errors, tuple) and raises in errors))) assert run.run.__wrapped__.__catches__ == errors assert run.run_async.__wrapped__.__catches__ == errors if should_fail: result = 1 with pytest.raises(raises): (run.run(*args, **kwargs) if not async_fun else await run.run_async(*args, **kwargs)) else: result = ( run.run(*args, **kwargs) if not async_fun else await run.run_async(*args, **kwargs)) assert ( list(run._runner.call_args) == [args, kwargs]) if not should_fail and raises: assert result == 1 error = run.log.error.call_args[0][0] _error = raises("AN ERROR OCCURRED") assert ( error == (str(_error) or repr(_error))) assert ( list(run.log.error.call_args) == [(error,), {}]) else: assert not run.log.error.called if raises: assert result == 1 else: assert result == run._runner.return_value def _cleanup_runner(async_fun, raises): class DummyCleanupRunner: # this dummy runner calls the _runner mock # when its run/async_fun methods are called # and optionally raises some type of error # to ensure they are caught as expected log = PropertyMock() _runner = MagicMock() @runner.cleansup def run(self, *args, **kwargs): result = self._runner(*args, **kwargs) if raises: raise Exception("AN ERROR OCCURRED") return result @runner.cleansup async def run_async(self, *args, **kwargs): result = self._runner(*args, **kwargs) if raises: raise Exception("AN ERROR OCCURRED") return result return DummyCleanupRunner() @pytest.mark.parametrize("async_fun", [True, False]) @pytest.mark.parametrize("raises", [True, False]) async def test_cleansup(async_fun, raises): run = _cleanup_runner(async_fun, raises) args = [f"ARG{i}" for i in range(0, 3)] kwargs = {f"K{i}": f"V{i}" for i in range(0, 3)} assert run.run.__wrapped__.__cleansup__ is True assert run.run_async.__wrapped__.__cleansup__ is True if async_fun: run.cleanup = AsyncMock() if raises: with pytest.raises(Exception): await run.run_async(*args, **kwargs) else: assert ( await run.run_async(*args, **kwargs) == run._runner.return_value) else: run.cleanup = MagicMock() if raises: with pytest.raises(Exception): run.run(*args, **kwargs) else: assert ( run.run(*args, **kwargs) == run._runner.return_value) assert ( list(run._runner.call_args) == [tuple(args), kwargs]) assert ( list(run.cleanup.call_args) == [(), {}]) def test_base_runner_constructor(patches): patched = patches( "BaseRunner.setup_logging", prefix="envoy.base.runner.runner") with patched as (m_setup, ): run = runner.BaseRunner("path1", "path2", "path3") assert ( m_setup.call_args == [(), {}]) assert run._args == ("path1", "path2", "path3") assert run.log_field_styles == runner.runner.LOG_FIELD_STYLES assert run.log_level_styles == runner.runner.LOG_LEVEL_STYLES assert run.log_fmt == runner.runner.LOG_FMT def test_base_runner_args(patches): patched = patches( ("BaseRunner.parser", dict(new_callable=PropertyMock)), "BaseRunner.setup_logging", prefix="envoy.base.runner.runner") with patched as (m_parser, m_setup): run = runner.BaseRunner('path1', 'path2', 'path3') known_args = m_parser.return_value.parse_known_args assert ( run.args == known_args.return_value.__getitem__.return_value) assert ( list(known_args.call_args) == [(('path1', 'path2', 'path3'),), {}]) assert ( list(known_args.return_value.__getitem__.call_args) == [(0,), {}]) assert "args" in run.__dict__ def test_base_runner_extra_args(patches): patched = patches( ("BaseRunner.parser", dict(new_callable=PropertyMock)), "BaseRunner.setup_logging", prefix="envoy.base.runner.runner") with patched as (m_parser, m_setup): run = runner.BaseRunner('path1', 'path2', 'path3') known_args = m_parser.return_value.parse_known_args assert ( run.extra_args == known_args.return_value.__getitem__.return_value) assert ( list(known_args.call_args) == [(('path1', 'path2', 'path3'),), {}]) assert ( list(known_args.return_value.__getitem__.call_args) == [(1,), {}]) assert "extra_args" in run.__dict__ def test_base_runner_log(patches): patched = patches( "coloredlogs", "verboselogs", ("BaseRunner.log_field_styles", dict(new_callable=PropertyMock)), ("BaseRunner.log_fmt", dict(new_callable=PropertyMock)), ("BaseRunner.log_level_styles", dict(new_callable=PropertyMock)), ("BaseRunner.name", dict(new_callable=PropertyMock)), ("BaseRunner.verbosity", dict(new_callable=PropertyMock)), "BaseRunner.setup_logging", prefix="envoy.base.runner.runner") with patched as patchy: (m_color, m_verb, m_fstyle, m_fmt, m_lstyle, m_name, m_verbosity, m_setup) = patchy run = runner.BaseRunner('path1', 'path2', 'path3') assert run.log == m_verb.VerboseLogger.return_value assert ( m_verb.VerboseLogger.call_args == [(m_name.return_value, ), {}]) assert ( m_color.install.call_args == [(), {'fmt': m_fmt.return_value, 'isatty': True, 'field_styles': m_fstyle.return_value, 'level': m_verbosity.return_value, 'level_styles': m_lstyle.return_value, 'logger': m_verb.VerboseLogger.return_value}]) assert "log" in run.__dict__ def test_base_runner_log_level(patches): run = DummyRunner() patched = patches( "dict", ("BaseRunner.args", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") with patched as (m_dict, m_args): assert run.log_level == m_dict.return_value.__getitem__.return_value assert ( list(m_dict.call_args) == [(runner.runner.LOG_LEVELS, ), {}]) assert ( list(m_dict.return_value.__getitem__.call_args) == [(m_args.return_value.log_level,), {}]) assert "log_level" in run.__dict__ def test_base_runner_name(): run = DummyRunner() assert run.name == run.__class__.__name__ assert "name" not in run.__dict__ def test_base_runner_parser(patches): run = DummyRunner() patched = patches( "argparse", "BaseRunner.add_arguments", prefix="envoy.base.runner.runner") with patched as (m_parser, m_add_args): assert run.parser == m_parser.ArgumentParser.return_value assert ( list(m_parser.ArgumentParser.call_args) == [(), {"allow_abbrev": False}]) assert ( list(m_add_args.call_args) == [(m_parser.ArgumentParser.return_value,), {}]) assert "parser" in run.__dict__ def test_base_runner_path(patches): run = DummyRunner() patched = patches( "pathlib", prefix="envoy.base.runner.runner") with patched as (m_plib, ): assert run.path == m_plib.Path.return_value assert ( list(m_plib.Path.call_args) == [(".", ), {}]) def test_base_runner_root_log_format(patches): run = DummyRunner() patched = patches( "logging", prefix="envoy.base.runner.runner") with patched as (m_logging, ): assert run.root_log_format == m_logging.Formatter.return_value assert ( m_logging.Formatter.call_args == [("%(name)s: %(levelname)s %(message)s", ), {}]) assert "root_log_format" not in run.__dict__ def test_base_runner_root_log_handler(patches): run = DummyRunner() patched = patches( "logging", "RootLogFilter", ("BaseRunner.log", dict(new_callable=PropertyMock)), ("BaseRunner.log_level", dict(new_callable=PropertyMock)), ("BaseRunner.root_log_format", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") with patched as (m_logging, m_filter, m_log, m_level, m_format): assert run.root_log_handler == m_logging.StreamHandler.return_value assert ( m_logging.StreamHandler.call_args == [(), {}]) assert ( m_logging.StreamHandler.return_value.setLevel.call_args == [(m_level.return_value, ), {}]) assert ( m_logging.StreamHandler.return_value.addFilter.call_args == [(m_filter.return_value, ), {}]) assert ( m_filter.call_args == [(m_log.return_value, ), {}]) assert ( m_logging.StreamHandler.return_value.setFormatter.call_args == [(m_format.return_value, ), {}]) assert "root_log_handler" in run.__dict__ def test_base_runner_root_logger(patches): run = DummyRunner() patched = patches( "logging", "AppLogFilter", ("BaseRunner.log", dict(new_callable=PropertyMock)), ("BaseRunner.root_log_handler", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") with patched as (m_logging, m_filter, m_log, m_handler): assert run.root_logger == m_logging.getLogger.return_value assert ( m_logging.getLogger.call_args == [(), {}]) assert ( m_logging.getLogger.return_value.handlers.__getitem__.call_args == [(0, ), {}]) assert ( m_logging.getLogger.return_value .handlers.__getitem__.return_value .addFilter.call_args == [(m_filter.return_value, ), {}]) assert ( m_filter.call_args == [(m_log.return_value, ), {}]) assert ( m_logging.getLogger.return_value.addHandler.call_args == [(m_handler.return_value, ), {}]) assert "root_logger" in run.__dict__ def test_base_runner_stdout(patches): run = DummyRunner() patched = patches( "logging", ("BaseRunner.log_level", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") with patched as (m_log, m_level): assert run.stdout == m_log.getLogger.return_value assert ( list(m_log.getLogger.call_args) == [('stdout',), {}]) assert ( list(m_log.getLogger.return_value.setLevel.call_args) == [(m_level.return_value,), {}]) assert ( list(m_log.StreamHandler.call_args) == [(sys.stdout,), {}]) assert ( list(m_log.Formatter.call_args) == [('%(message)s',), {}]) assert ( list(m_log.StreamHandler.return_value.setFormatter.call_args) == [(m_log.Formatter.return_value,), {}]) assert ( list(m_log.getLogger.return_value.addHandler.call_args) == [(m_log.StreamHandler.return_value,), {}]) @pytest.mark.parametrize("missing", [True, False]) def test_base_runner_tempdir(patches, missing): run = DummyRunner() patched = patches( "tempfile", ("BaseRunner.log", dict(new_callable=PropertyMock)), ("BaseRunner._missing_cleanup", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") with patched as (m_tmp, m_log, m_missing): m_missing.return_value = missing assert run.tempdir == m_tmp.TemporaryDirectory.return_value if missing: assert ( list(m_log.return_value.warning.call_args) == [(("Tempdir created but instance has a `run` method " "which is not decorated with `@runner.cleansup`"), ), {}]) else: assert not m_log.called assert ( list(m_tmp.TemporaryDirectory.call_args) == [(), {}]) assert "tempdir" in run.__dict__ def test_base_runner_verbosity(patches): run = DummyRunner() patched = patches( "dict", ("BaseRunner.args", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") with patched as (m_dict, m_args): assert run.verbosity == m_dict.return_value.__getitem__.return_value assert ( list(m_dict.call_args) == [(runner.runner.LOG_LEVELS, ), {}]) assert ( list(m_dict.return_value.__getitem__.call_args) == [(m_args.return_value.verbosity,), {}]) assert "verbosity" in run.__dict__ def test_base_runner_add_arguments(): run = DummyRunner() parser = MagicMock() assert run.add_arguments(parser) is None assert ( list(list(c) for c in parser.add_argument.call_args_list) == [[('--verbosity', '-v'), {'choices': ['debug', 'info', 'warn', 'error'], 'default': 'info', 'help': 'Application log level'}], [('--log-level', '-l'), {'choices': ['debug', 'info', 'warn', 'error'], 'default': 'warn', 'help': 'Log level for non-application logs'}]]) def test_runner_setup_logging(patches): run = DummyRunner() patched = patches( "logging", ("BaseRunner.log", dict(new_callable=PropertyMock)), ("BaseRunner.log_level", dict(new_callable=PropertyMock)), ("BaseRunner.root_logger", dict(new_callable=PropertyMock)), ("BaseRunner.verbosity", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") with patched as (m_logging, m_log, m_level, m_root, m_verb): assert not run.setup_logging() assert ( m_logging.basicConfig.call_args == [(), dict(level=m_level.return_value)]) assert ( m_root.return_value.setLevel.call_args == [(m_level.return_value, ), {}]) assert ( m_log.return_value.setLevel.call_args == [(m_verb.return_value, ), {}]) @pytest.mark.parametrize("has_fun", [True, False]) @pytest.mark.parametrize("is_wrapped", [True, False]) @pytest.mark.parametrize("cleansup", [True, False]) def test_base_runner__missing_cleanup(has_fun, is_wrapped, cleansup): def _runner_factory(): if not has_fun: return DummyRunner() class _Wrap: if cleansup: __cleansup__ = True class _Wrapper: if is_wrapped: __wrapped__ = _Wrap() class DummyRunner2(DummyRunner): run = _Wrapper() return DummyRunner2() run = _runner_factory() assert ( run._missing_cleanup == (has_fun and not (is_wrapped and cleansup))) assert "_missing_cleanup" not in run.__dict__ @pytest.mark.parametrize("cached", [True, False]) def test_base_runner__cleanup_tempdir(patches, cached): run = DummyRunner() patched = patches( ("BaseRunner.tempdir", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") if cached: run.__dict__["tempdir"] = "TEMPDIR" with patched as (m_temp, ): assert not run._cleanup_tempdir() if cached: assert ( list(m_temp.return_value.cleanup.call_args) == [(), {}]) else: assert not m_temp.called assert "tempdir" not in run.__dict__ def test_runner_constructor(patches): patched = patches( "BaseRunner.__init__", prefix="envoy.base.runner.runner") args = [f"ARG{i}" for i in range(0, 3)] kwargs = {f"K{i}": f"V{i}" for i in range(0, 3)} with patched as (m_super, ): m_super.return_value = None run = runner.Runner(*args, **kwargs) assert isinstance(run, runner.BaseRunner) assert ( list(m_super.call_args) == [tuple(args), kwargs]) def test_runner_dunder_call(patches): patched = patches( "Runner.run", "Runner.setup_logging", prefix="envoy.base.runner.runner") with patched as (m_run, m_setup): run = runner.Runner() assert run() == m_run.return_value assert ( list(m_run.call_args) == [(), {}]) def test_runner_cleanup(patches): patched = patches( "Runner._cleanup_tempdir", "Runner.setup_logging", prefix="envoy.base.runner.runner") with patched as (m_temp, m_setup): run = runner.Runner() assert not run.cleanup() assert ( list(m_temp.call_args) == [(), {}]) def test_async_runner_constructor(patches): patched = patches( "BaseRunner.__init__", prefix="envoy.base.runner.runner") args = [f"ARG{i}" for i in range(0, 3)] kwargs = {f"K{i}": f"V{i}" for i in range(0, 3)} with patched as (m_super, ): m_super.return_value = None run = runner.AsyncRunner(*args, **kwargs) assert isinstance(run, runner.BaseRunner) assert ( list(m_super.call_args) == [tuple(args), kwargs]) @pytest.mark.parametrize("raises", [None, KeyboardInterrupt]) def test_async_runner_dunder_call(patches, raises): patched = patches( "asyncio", ("AsyncRunner.log", dict(new_callable=MagicMock)), ("AsyncRunner.run", dict(new_callable=MagicMock)), "AsyncRunner.setup_logging", prefix="envoy.base.runner.runner") # TODO: TEST LOG with patched as (m_asyncio, m_log, m_run, m_setup): run = runner.AsyncRunner() if raises: m_run.side_effect = raises("DIE") assert ( run() == (m_asyncio.run.return_value if not raises else 1)) if not raises: assert ( list(m_asyncio.run.call_args) == [(m_run.return_value, ), {}]) else: assert not m_asyncio.run.called assert ( list(m_run.call_args) == [(), {}]) async def test_async_runner_cleanup(patches): patched = patches( "AsyncRunner._cleanup_tempdir", "AsyncRunner.setup_logging", prefix="envoy.base.runner.runner") with patched as (m_temp, m_setup): run = runner.AsyncRunner() assert not await run.cleanup() assert ( list(m_temp.call_args) == [(), {}]) # BazelAdapter tests def test_bazeladapter_constructor(): run = DummyRunner() adapter = runner.BazelAdapter(run) assert adapter.context == run @pytest.mark.parametrize("query_returns", [0, 1]) def test_bazeladapter_query(query_returns): run = DummyForkingRunner() adapter = runner.BazelAdapter(run) fork_mock = patch("envoy.base.runner.runner.ForkingAdapter.subproc_run") with fork_mock as m_fork: m_fork.return_value.returncode = query_returns stdout = m_fork.return_value.stdout.decode if query_returns: with pytest.raises(runner.BazelRunError) as result: adapter.query("BAZEL QUERY") else: result = adapter.query("BAZEL QUERY") assert ( list(m_fork.call_args) == [(['bazel', 'query', "'BAZEL QUERY'"],), {}]) if query_returns: assert result.errisinstance(runner.BazelRunError) assert ( result.value.args == (f"Bazel query failed: {m_fork.return_value}",)) assert not stdout.called else: assert ( result == stdout.return_value.split.return_value) assert ( list(stdout.call_args) == [('utf-8',), {}]) assert ( list(stdout.return_value.split.call_args) == [('\n',), {}]) @pytest.mark.parametrize("cwd", [None, "", "SOMEPATH"]) @pytest.mark.parametrize("raises", [None, True, False]) @pytest.mark.parametrize("capture_output", [None, True, False]) @pytest.mark.parametrize("run_returns", [0, 1]) @pytest.mark.parametrize("args", [(), ("foo",), ("foo", "bar")]) def test_bazeladapter_run( patches, run_returns, cwd, raises, args, capture_output): run = DummyForkingRunner() adapter = runner.BazelAdapter(run) patched = patches( "ForkingAdapter.subproc_run", ("ForkingRunner.path", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") adapter_args = ("BAZEL RUN",) + args kwargs = {} if raises is not None: kwargs["raises"] = raises if cwd is not None: kwargs["cwd"] = cwd if capture_output is not None: kwargs["capture_output"] = capture_output with patched as (m_fork, m_path): m_fork.return_value.returncode = run_returns if run_returns and (raises is not False): with pytest.raises(runner.BazelRunError) as result: adapter.run(*adapter_args, **kwargs) else: result = adapter.run(*adapter_args, **kwargs) call_args = (("--",) + args) if args else args bazel_args = ("bazel", "run", "BAZEL RUN") + call_args bazel_kwargs = {} bazel_kwargs["capture_output"] = ( True if capture_output is True else False) bazel_kwargs["cwd"] = ( cwd if cwd else m_path.return_value) assert ( list(m_fork.call_args) == [(bazel_args,), bazel_kwargs]) if run_returns and (raises is not False): assert result.errisinstance(runner.BazelRunError) assert ( result.value.args == (f"Bazel run failed: {m_fork.return_value}",)) else: assert result == m_fork.return_value # ForkingAdapter tests def test_forkingadapter_constructor(): run = DummyRunner() adapter = runner.ForkingAdapter(run) assert adapter.context == run def test_forkingadapter_call(): run = DummyRunner() adapter = runner.ForkingAdapter(run) fork_mock = patch("envoy.base.runner.runner.ForkingAdapter.subproc_run") with fork_mock as m_fork: assert ( adapter( "arg1", "arg2", "arg3", kwa1="foo", kwa2="bar", kwa3="baz") == m_fork.return_value) assert ( list(m_fork.call_args) == [('arg1', 'arg2', 'arg3'), {'kwa1': 'foo', 'kwa2': 'bar', 'kwa3': 'baz'}]) @pytest.mark.parametrize("args", [(), ("a", "b")]) @pytest.mark.parametrize("cwd", [None, "NONE", "PATH"]) @pytest.mark.parametrize("capture_output", ["NONE", True, False]) def test_forkingadapter_subproc_run(patches, args, cwd, capture_output): adapter = runner.ForkingAdapter(DummyRunner()) patched = patches( "subprocess.run", ("BaseRunner.path", dict(new_callable=PropertyMock)), prefix="envoy.base.runner.runner") with patched as (m_run, m_path): kwargs = {} if cwd != "NONE": kwargs["cwd"] = cwd if capture_output != "NONE": kwargs["capture_output"] = capture_output assert adapter.subproc_run(*args, **kwargs) == m_run.return_value expected = {'capture_output': True, 'cwd': cwd} if capture_output is False: expected["capture_output"] = False if cwd == "NONE": expected["cwd"] = m_path.return_value assert ( list(m_run.call_args) == [args, expected]) # ForkingRunner tests def test_forkingrunner_fork(patches): patched = patches( "ForkingAdapter", "ForkingRunner.setup_logging", prefix="envoy.base.runner.runner") with patched as (m_fork, m_setup): run = runner.ForkingRunner("path1", "path2", "path3") assert run.subproc_run == m_fork.return_value assert ( list(m_fork.call_args) == [(run,), {}]) assert "subproc_run" in run.__dict__ # BazelRunner tests def test_bazelrunner_bazel(patches): patched = patches( "BazelAdapter", "BazelRunner.setup_logging", prefix="envoy.base.runner.runner") with patched as (m_bazel, m_setup): run = runner.BazelRunner("path1", "path2", "path3") assert run.bazel == m_bazel.return_value assert ( list(m_bazel.call_args) == [(run,), {}]) assert "bazel" in run.__dict__
29.424145
76
0.605316
7475365537c7906a3cd5b899fbdb31404de8e118
13,422
py
Python
src/test/clustering.py
fermi-lat/CalRecon
69e123b523770baa1fc9e8f3b78e211b1064b0c0
[ "BSD-3-Clause" ]
null
null
null
src/test/clustering.py
fermi-lat/CalRecon
69e123b523770baa1fc9e8f3b78e211b1064b0c0
[ "BSD-3-Clause" ]
null
null
null
src/test/clustering.py
fermi-lat/CalRecon
69e123b523770baa1fc9e8f3b78e211b1064b0c0
[ "BSD-3-Clause" ]
null
null
null
from ReconReader import * ROOT.gStyle.SetOptStat(111111) ROOT.gStyle.SetPalette(1) from optparse import OptionParser parser = OptionParser() parser.add_option('-c', '--skim-cut', type = str, dest = 'c', default = '1', help = 'a cut to filter the events') parser.add_option('-s', '--save-canvas', type = str, dest = 's', default = None, help = 'a path to save all canvas in .pdf format') (opts, args) = parser.parse_args() if len(args) == 0: sys.exit('Please provide a recon input root file.') elif len(args) > 2: sys.exit('Too many arguments.') reconFilePath = args[0] try: meritFilePath = args[1] except IndexError: meritFilePath = None SaveAllCanvas = False if opts.s != None: SaveAllCanvas = True savedCanvasPath = opts.s ANALYSIS_BIN_LIST = ['McEnergy < 100', 'McEnergy >= 100 && McEnergy<500', 'McEnergy >= 500 && McEnergy<1000', 'McEnergy >= 1000 && McEnergy<5000', 'McEnergy >= 5000 && McEnergy<20000', 'McEnergy >= 20000' ] reader = ReconReader(reconFilePath, meritFilePath, None, opts.c) numEvents = min(10000, reader.getEntries()) # BAD MAIN Clster Solution TTreeFourmula BAD_ANGLE_VALUE = -0.1 UberSolutionIsBetter = ROOT.TTreeFormula('UberSolutionIsBetter', '(acos(-(Tkr1ZDir*CalUberZDir + Tkr1YDir*CalUberYDir + Tkr1XDir*CalUberXDir))-CalTrackAngle) < %f' % BAD_ANGLE_VALUE , reader.MeritChain) # Create TTreeFormulas for the analysis bins. treeFormulaList = [] for (i, cut) in enumerate(ANALYSIS_BIN_LIST): treeFormula = ROOT.TTreeFormula('analysisBin%d' % i, cut, reader.MeritChain) treeFormulaList.append(treeFormula) # Create the histograms. hNumCluList = [] hNumIsolatedCluList = [] hDistIsolatedCluList = [] hDistSecondCluList = [] hFirstCluFracEneList = [] hSecondCluFracEneList = [] hFirstAndSecondCluFracEneList = [] hSecondCluEneList = [] hFirstCluFracXtalsList = [] hDist_vs_EnergyList = [] hCluAngle_vs_UberAngle = [] for (i, cut) in enumerate(ANALYSIS_BIN_LIST): hTitle = cut hName = 'NumClu_%d' % i h = ROOT.TH1F(hName, hTitle, 20, 0, 20) h.GetXaxis().SetTitle('Number of clusters') hNumCluList.append(h) hName = 'NumIsolatedClu_%d' % i h = ROOT.TH1F(hName, hTitle, 20, 0, 20) h.GetXaxis().SetTitle('Number of isolated clusters') hNumIsolatedCluList.append(h) hName = 'DistIsolatedClu_%d' % i h = ROOT.TH1F(hName, hTitle, 100, 0, 500) h.GetXaxis().SetTitle('Distance of isolated clusters from the main axis') hDistIsolatedCluList.append(h) hName = 'DistSecondClu_%d' % i h = ROOT.TH1F(hName, hTitle, 100, 0, 500) h.GetXaxis().SetTitle('Distance of the 2nd cluster from the main axis') hDistSecondCluList.append(h) hName = 'FirstCluFracEne_%d' % i h = ROOT.TH1F(hName, hTitle, 100, 0.0, 1.0) h.GetXaxis().SetTitle('Fraction of energy in the first cluster') hFirstCluFracEneList.append(h) hName = 'SecondCluFracEne_%d' % i h = ROOT.TH1F(hName, hTitle, 100, 0.0, 1.0) h.GetXaxis().SetTitle('Fraction of energy in the second cluster') hSecondCluFracEneList.append(h) hName = 'FirstAndSecondCluFracEne_%d' % i h = ROOT.TH1F(hName, hTitle, 100, 0.0, 1.0) h.GetXaxis().SetTitle('Fraction of energy in the 1st+2nd (non isolated) cluster') hFirstAndSecondCluFracEneList.append(h) hName = 'SecondCluEne_%d' % i h = ROOT.TH1F(hName, hTitle, 100, 0.0, 100) h.GetXaxis().SetTitle('Energy in the second cluster') hSecondCluEneList.append(h) hName = 'FirstCluFracXtals_%d' % i h = ROOT.TH1F(hName, hTitle, 100, 0.0, 1.0) h.GetXaxis().SetTitle('Fraction of xtals in the first cluster') hFirstCluFracXtalsList.append(h) hName = 'Dist_vs_Energy_%d' % i h = ROOT.TH2F(hName, hTitle, 100, 0.0, 500, 100, 0, 30) hDist_vs_EnergyList.append(h) hName = 'CalAngle_vs_UberAngle_%d' % i h = ROOT.TH2F(hName, hTitle, 50, -3, 2, 50, -3, 2) h.GetXaxis().SetTitle('log10(First cluster--Tkr1Dir angle)') h.GetYaxis().SetTitle('log10(Uber cluster--Tkr1Dir angle)') hCluAngle_vs_UberAngle.append(h) # Start the event loop. for event in xrange(numEvents): if reader.getEntry(event): numClusters = reader.getNumClusters() numIsolatedClusters = 0 if numClusters == 0: firstCluFracEne = -1 secondCluFracEne = -1 firstAndSecondCluFracEne = -1 secondCluEne = -1 firstCluFracXtals = -1 distSecondClu = -1 else: clusterList = reader.getCalClusterList() for cluster in clusterList: if cluster.getTotNumXtals() == 1: numIsolatedClusters += 1 uberCluster = clusterList[0] if numClusters == 1: firstCluFracEne = 2 secondCluFracEne = -1 firstAndSecondCluFracEne = -1 secondCluEne = -1 firstCluFracXtals = 2 distSecondClu = -1 else: cluster1 = clusterList[1] cluster2 = clusterList[2] firstCluFracEne = cluster1.getEnergy()/uberCluster.getEnergy() secondCluFracEne = cluster2.getEnergy()/uberCluster.getEnergy() if cluster2.getTotNumXtals() == 1: firstAndSecondCluFracEne = 2 else: firstAndSecondCluFracEne = (cluster1.getEnergy() + cluster2.getEnergy())/uberCluster.getEnergy() secondCluEne = cluster2.getEnergy() firstCluFracXtals = float(cluster1.getTotNumXtals())/\ uberCluster.getTotNumXtals() distSecondClu = cluster2.distToAxis(cluster1) RefXDir = reader.getMeritVariable("Tkr1XDir") RefYDir = reader.getMeritVariable("Tkr1YDir") RefZDir = reader.getMeritVariable("Tkr1ZDir") CalXDir = reader.getMeritVariable("CalXDir") CalYDir = reader.getMeritVariable("CalYDir") CalZDir = reader.getMeritVariable("CalZDir") CalUberXDir = reader.getMeritVariable("CalUberXDir") CalUberYDir = reader.getMeritVariable("CalUberYDir") CalUberZDir = reader.getMeritVariable("CalUberZDir") CluAngle = log10(acos(-(RefZDir*CalZDir+\ RefYDir*CalYDir+\ RefXDir*CalXDir))) UberAngle = log10(acos(-(RefZDir*CalUberZDir+\ RefYDir*CalUberYDir+\ RefXDir*CalUberXDir))) for (i, treeFormula) in enumerate(treeFormulaList): if treeFormula.EvalInstance(): hNumCluList[i].Fill(numClusters - int(numClusters > 1)) hNumIsolatedCluList[i].Fill(numIsolatedClusters) hFirstCluFracEneList[i].Fill(firstCluFracEne) hSecondCluFracEneList[i].Fill(secondCluFracEne) hFirstAndSecondCluFracEneList[i].Fill(firstAndSecondCluFracEne) hSecondCluEneList[i].Fill(secondCluEne) hFirstCluFracXtalsList[i].Fill(firstCluFracXtals) hDistSecondCluList[i].Fill(distSecondClu) if numClusters > 0: for cluster in clusterList: if cluster.getTotNumXtals() == 1: dist = cluster.distToAxis(cluster1) energy = cluster.getEnergy() hDistIsolatedCluList[i].Fill(dist) hDist_vs_EnergyList[i].Fill(dist, energy) # if UberSolutionIsBetter.EvalInstance(): hCluAngle_vs_UberAngle[i].Fill(CluAngle, UberAngle) # And eventually draw stuff. cFirstCluFracEne = ROOT.TCanvas('FirstCluFracEne', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cFirstCluFracEne.Divide(3, 2) for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cFirstCluFracEne.cd(i + 1) ROOT.gPad.SetLogy(True) hFirstCluFracEneList[i].Draw() cFirstCluFracEne.cd() cFirstCluFracEne.Update() if SaveAllCanvas: cFirstCluFracEne.Print(os.path.join(savedCanvasPath, cFirstCluFracEne.GetName()+".pdf")) cSecondCluFracEne = ROOT.TCanvas('SecondCluFracEne', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cSecondCluFracEne.Divide(3, 2) for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cSecondCluFracEne.cd(i + 1) ROOT.gPad.SetLogy(True) hSecondCluFracEneList[i].Draw() cSecondCluFracEne.cd() cSecondCluFracEne.Update() if SaveAllCanvas: cSecondCluFracEne.Print(os.path.join(savedCanvasPath, cSecondCluFracEne.GetName()+".pdf")) cFirstAndSecondCluFracEne = ROOT.TCanvas('FirstAndSecondCluFracEne', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cFirstAndSecondCluFracEne.Divide(3, 2) for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cFirstAndSecondCluFracEne.cd(i + 1) ROOT.gPad.SetLogy(True) hFirstAndSecondCluFracEneList[i].Draw() cFirstAndSecondCluFracEne.cd() cFirstAndSecondCluFracEne.Update() if SaveAllCanvas: cFirstAndSecondCluFracEne.Print(os.path.join(savedCanvasPath, cFirstAndSecondCluFracEne.GetName()+".pdf")) cSecondCluEne = ROOT.TCanvas('SecondCluEne', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cSecondCluEne.Divide(3, 2) for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cSecondCluEne.cd(i + 1) ROOT.gPad.SetLogy(True) hSecondCluEneList[i].Draw() cSecondCluEne.cd() cSecondCluEne.Update() if SaveAllCanvas: cSecondCluEne.Print(os.path.join(savedCanvasPath, cSecondCluEne.GetName()+".pdf")) cFirstCluFracXtals = ROOT.TCanvas('FirstCluFracXtals', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cFirstCluFracXtals.Divide(3, 2) for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cFirstCluFracXtals.cd(i + 1) ROOT.gPad.SetLogy(True) hFirstCluFracXtalsList[i].Draw() cFirstCluFracXtals.cd() cFirstCluFracXtals.Update() if SaveAllCanvas: cFirstCluFracXtals.Print(os.path.join(savedCanvasPath, cFirstCluFracXtals.GetName()+".pdf")) cNumClu = ROOT.TCanvas('NumClu', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cNumClu.Divide(3, 2) labIsolated = ROOT.TLatex(0.4, 0.82, 'Isolated clusters') labIsolated.SetTextColor(ROOT.kRed) labIsolated.SetNDC() labAll = ROOT.TLatex(0.4, 0.75, 'All clusters') labAll.SetTextColor(ROOT.kBlack) labAll.SetNDC() for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cNumClu.cd(i + 1) ROOT.gPad.SetLogy(True) hNumIsolatedCluList[i].SetLineColor(ROOT.kRed) hNumIsolatedCluList[i].Draw() hNumCluList[i].Draw('sames') labIsolated.Draw() labAll.Draw() cNumClu.cd() cNumClu.Update() if SaveAllCanvas: cNumClu.Print(os.path.join(savedCanvasPath, cNumClu.GetName()+".pdf")) cDistIsolatedClu = ROOT.TCanvas('DistIsolatedClu', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cDistIsolatedClu.Divide(3, 2) for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cDistIsolatedClu.cd(i + 1) ROOT.gPad.SetLogy(True) hDistIsolatedCluList[i].Draw() cDistIsolatedClu.cd() cDistIsolatedClu.Update() if SaveAllCanvas: cDistIsolatedClu.Print(os.path.join(savedCanvasPath, cDistIsolatedClu.GetName()+".pdf")) cDistSecondClu = ROOT.TCanvas('DistSecondClu', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cDistSecondClu.Divide(3, 2) for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cDistSecondClu.cd(i + 1) ROOT.gPad.SetLogy(True) hDistSecondCluList[i].Draw() cDistSecondClu.cd() cDistSecondClu.Update() if SaveAllCanvas: cDistSecondClu.Print(os.path.join(savedCanvasPath, cDistSecondClu.GetName()+".pdf")) cDist_vs_Energy = ROOT.TCanvas('Dist_vs_Energy', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cDist_vs_Energy.Divide(3, 2) for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cDist_vs_Energy.cd(i + 1) hDist_vs_EnergyList[i].Draw() cDist_vs_Energy.cd() cDist_vs_Energy.Update() if SaveAllCanvas: cDist_vs_Energy.Print(os.path.join(savedCanvasPath, cDist_vs_Energy.GetName()+".pdf")) #f = ROOT.TF1("f", "x", -5, 5) cCluAngle_vs_UberAngle = ROOT.TCanvas('CluAngle_vs_UberAngle', '', 1100, 600) ROOT.gPad.SetTitle(ROOT.gPad.GetName()) cCluAngle_vs_UberAngle.Divide(3, 2) for (i, cut) in enumerate(ANALYSIS_BIN_LIST): cCluAngle_vs_UberAngle.cd(i + 1) hCluAngle_vs_UberAngle[i].Draw('colz') #f.Draw("same") cCluAngle_vs_UberAngle.cd() cCluAngle_vs_UberAngle.Update() if SaveAllCanvas: cCluAngle_vs_UberAngle.Print(os.path.join(savedCanvasPath, cCluAngle_vs_UberAngle.GetName()+".pdf"))
39.476471
160
0.626807
98b6f12fbd89732708260071d99695e847c1927b
3,995
py
Python
syntropy_sdk/models/check_mfa_for_new_social_account_response.py
SyntropyNet/syntropy-python-sdk
27b7756b136f83886fd2a6e342fa4d4073779ff7
[ "MIT" ]
1
2020-12-17T17:30:12.000Z
2020-12-17T17:30:12.000Z
syntropy_sdk/models/check_mfa_for_new_social_account_response.py
SyntropyNet/syntropy-python-sdk
27b7756b136f83886fd2a6e342fa4d4073779ff7
[ "MIT" ]
null
null
null
syntropy_sdk/models/check_mfa_for_new_social_account_response.py
SyntropyNet/syntropy-python-sdk
27b7756b136f83886fd2a6e342fa4d4073779ff7
[ "MIT" ]
null
null
null
# coding: utf-8 """ syntropy-auth-service No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 0.1.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class CheckMFAForNewSocialAccountResponse(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = {"secret": "str", "user_id": "str"} attribute_map = {"secret": "secret", "user_id": "userId"} def __init__(self, secret=None, user_id=None): # noqa: E501 """CheckMFAForNewSocialAccountResponse - a model defined in Swagger""" # noqa: E501 self._secret = None self._user_id = None self.discriminator = None if secret is not None: self.secret = secret if user_id is not None: self.user_id = user_id @property def secret(self): """Gets the secret of this CheckMFAForNewSocialAccountResponse. # noqa: E501 :return: The secret of this CheckMFAForNewSocialAccountResponse. # noqa: E501 :rtype: str """ return self._secret @secret.setter def secret(self, secret): """Sets the secret of this CheckMFAForNewSocialAccountResponse. :param secret: The secret of this CheckMFAForNewSocialAccountResponse. # noqa: E501 :type: str """ self._secret = secret @property def user_id(self): """Gets the user_id of this CheckMFAForNewSocialAccountResponse. # noqa: E501 :return: The user_id of this CheckMFAForNewSocialAccountResponse. # noqa: E501 :rtype: str """ return self._user_id @user_id.setter def user_id(self, user_id): """Sets the user_id of this CheckMFAForNewSocialAccountResponse. :param user_id: The user_id of this CheckMFAForNewSocialAccountResponse. # noqa: E501 :type: str """ self._user_id = user_id def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list( map(lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value) ) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict( map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items(), ) ) else: result[attr] = value if issubclass(CheckMFAForNewSocialAccountResponse, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, CheckMFAForNewSocialAccountResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other
29.592593
119
0.582478
27a9a72bad8a0eb188dfba50afaf87c929935c16
20,906
py
Python
astropy/time/tests/test_methods.py
jbkalmbach/astropy
88ae8c615533efd1e60de4aded204943f66f881c
[ "BSD-3-Clause" ]
null
null
null
astropy/time/tests/test_methods.py
jbkalmbach/astropy
88ae8c615533efd1e60de4aded204943f66f881c
[ "BSD-3-Clause" ]
11
2017-12-18T16:27:29.000Z
2018-08-29T14:54:22.000Z
astropy/time/tests/test_methods.py
jbkalmbach/astropy
88ae8c615533efd1e60de4aded204943f66f881c
[ "BSD-3-Clause" ]
1
2018-08-02T09:33:21.000Z
2018-08-02T09:33:21.000Z
# Licensed under a 3-clause BSD style license - see LICENSE.rst import itertools import copy import pytest import numpy as np from .. import Time @pytest.fixture(scope="module", params=[True, False]) def masked(request): # Could not figure out a better way to parametrize the setup method global use_masked_data use_masked_data = request.param yield use_masked_data class TestManipulation(): """Manipulation of Time objects, ensuring attributes are done correctly.""" def setup(self): mjd = np.arange(50000, 50010) frac = np.arange(0., 0.999, 0.2) if use_masked_data: frac = np.ma.array(frac) frac[1] = np.ma.masked self.t0 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc') self.t1 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc', location=('45d', '50d')) self.t2 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc', location=(np.arange(len(frac)), np.arange(len(frac)))) # Note: location is along last axis only. self.t2 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc', location=(np.arange(len(frac)), np.arange(len(frac)))) def test_ravel(self, masked): t0_ravel = self.t0.ravel() assert t0_ravel.shape == (self.t0.size,) assert np.all(t0_ravel.jd1 == self.t0.jd1.ravel()) assert np.may_share_memory(t0_ravel.jd1, self.t0.jd1) assert t0_ravel.location is None t1_ravel = self.t1.ravel() assert t1_ravel.shape == (self.t1.size,) assert np.all(t1_ravel.jd1 == self.t1.jd1.ravel()) assert np.may_share_memory(t1_ravel.jd1, self.t1.jd1) assert t1_ravel.location is self.t1.location t2_ravel = self.t2.ravel() assert t2_ravel.shape == (self.t2.size,) assert np.all(t2_ravel.jd1 == self.t2.jd1.ravel()) assert np.may_share_memory(t2_ravel.jd1, self.t2.jd1) assert t2_ravel.location.shape == t2_ravel.shape # Broadcasting and ravelling cannot be done without a copy. assert not np.may_share_memory(t2_ravel.location, self.t2.location) def test_flatten(self, masked): t0_flatten = self.t0.flatten() assert t0_flatten.shape == (self.t0.size,) assert t0_flatten.location is None # Flatten always makes a copy. assert not np.may_share_memory(t0_flatten.jd1, self.t0.jd1) t1_flatten = self.t1.flatten() assert t1_flatten.shape == (self.t1.size,) assert not np.may_share_memory(t1_flatten.jd1, self.t1.jd1) assert t1_flatten.location is not self.t1.location assert t1_flatten.location == self.t1.location t2_flatten = self.t2.flatten() assert t2_flatten.shape == (self.t2.size,) assert not np.may_share_memory(t2_flatten.jd1, self.t2.jd1) assert t2_flatten.location.shape == t2_flatten.shape assert not np.may_share_memory(t2_flatten.location, self.t2.location) def test_transpose(self, masked): t0_transpose = self.t0.transpose() assert t0_transpose.shape == (5, 10) assert np.all(t0_transpose.jd1 == self.t0.jd1.transpose()) assert np.may_share_memory(t0_transpose.jd1, self.t0.jd1) assert t0_transpose.location is None t1_transpose = self.t1.transpose() assert t1_transpose.shape == (5, 10) assert np.all(t1_transpose.jd1 == self.t1.jd1.transpose()) assert np.may_share_memory(t1_transpose.jd1, self.t1.jd1) assert t1_transpose.location is self.t1.location t2_transpose = self.t2.transpose() assert t2_transpose.shape == (5, 10) assert np.all(t2_transpose.jd1 == self.t2.jd1.transpose()) assert np.may_share_memory(t2_transpose.jd1, self.t2.jd1) assert t2_transpose.location.shape == t2_transpose.shape assert np.may_share_memory(t2_transpose.location, self.t2.location) # Only one check on T, since it just calls transpose anyway. t2_T = self.t2.T assert t2_T.shape == (5, 10) assert np.all(t2_T.jd1 == self.t2.jd1.T) assert np.may_share_memory(t2_T.jd1, self.t2.jd1) assert t2_T.location.shape == t2_T.location.shape assert np.may_share_memory(t2_T.location, self.t2.location) def test_diagonal(self, masked): t0_diagonal = self.t0.diagonal() assert t0_diagonal.shape == (5,) assert np.all(t0_diagonal.jd1 == self.t0.jd1.diagonal()) assert t0_diagonal.location is None assert np.may_share_memory(t0_diagonal.jd1, self.t0.jd1) t1_diagonal = self.t1.diagonal() assert t1_diagonal.shape == (5,) assert np.all(t1_diagonal.jd1 == self.t1.jd1.diagonal()) assert t1_diagonal.location is self.t1.location assert np.may_share_memory(t1_diagonal.jd1, self.t1.jd1) t2_diagonal = self.t2.diagonal() assert t2_diagonal.shape == (5,) assert np.all(t2_diagonal.jd1 == self.t2.jd1.diagonal()) assert t2_diagonal.location.shape == t2_diagonal.shape assert np.may_share_memory(t2_diagonal.jd1, self.t2.jd1) assert np.may_share_memory(t2_diagonal.location, self.t2.location) def test_swapaxes(self, masked): t0_swapaxes = self.t0.swapaxes(0, 1) assert t0_swapaxes.shape == (5, 10) assert np.all(t0_swapaxes.jd1 == self.t0.jd1.swapaxes(0, 1)) assert np.may_share_memory(t0_swapaxes.jd1, self.t0.jd1) assert t0_swapaxes.location is None t1_swapaxes = self.t1.swapaxes(0, 1) assert t1_swapaxes.shape == (5, 10) assert np.all(t1_swapaxes.jd1 == self.t1.jd1.swapaxes(0, 1)) assert np.may_share_memory(t1_swapaxes.jd1, self.t1.jd1) assert t1_swapaxes.location is self.t1.location t2_swapaxes = self.t2.swapaxes(0, 1) assert t2_swapaxes.shape == (5, 10) assert np.all(t2_swapaxes.jd1 == self.t2.jd1.swapaxes(0, 1)) assert np.may_share_memory(t2_swapaxes.jd1, self.t2.jd1) assert t2_swapaxes.location.shape == t2_swapaxes.shape assert np.may_share_memory(t2_swapaxes.location, self.t2.location) def test_reshape(self, masked): t0_reshape = self.t0.reshape(5, 2, 5) assert t0_reshape.shape == (5, 2, 5) assert np.all(t0_reshape.jd1 == self.t0._time.jd1.reshape(5, 2, 5)) assert np.all(t0_reshape.jd2 == self.t0._time.jd2.reshape(5, 2, 5)) assert np.may_share_memory(t0_reshape.jd1, self.t0.jd1) assert np.may_share_memory(t0_reshape.jd2, self.t0.jd2) assert t0_reshape.location is None t1_reshape = self.t1.reshape(2, 5, 5) assert t1_reshape.shape == (2, 5, 5) assert np.all(t1_reshape.jd1 == self.t1.jd1.reshape(2, 5, 5)) assert np.may_share_memory(t1_reshape.jd1, self.t1.jd1) assert t1_reshape.location is self.t1.location # For reshape(5, 2, 5), the location array can remain the same. t2_reshape = self.t2.reshape(5, 2, 5) assert t2_reshape.shape == (5, 2, 5) assert np.all(t2_reshape.jd1 == self.t2.jd1.reshape(5, 2, 5)) assert np.may_share_memory(t2_reshape.jd1, self.t2.jd1) assert t2_reshape.location.shape == t2_reshape.shape assert np.may_share_memory(t2_reshape.location, self.t2.location) # But for reshape(5, 5, 2), location has to be broadcast and copied. t2_reshape2 = self.t2.reshape(5, 5, 2) assert t2_reshape2.shape == (5, 5, 2) assert np.all(t2_reshape2.jd1 == self.t2.jd1.reshape(5, 5, 2)) assert np.may_share_memory(t2_reshape2.jd1, self.t2.jd1) assert t2_reshape2.location.shape == t2_reshape2.shape assert not np.may_share_memory(t2_reshape2.location, self.t2.location) t2_reshape_t = self.t2.reshape(10, 5).T assert t2_reshape_t.shape == (5, 10) assert np.may_share_memory(t2_reshape_t.jd1, self.t2.jd1) assert t2_reshape_t.location.shape == t2_reshape_t.shape assert np.may_share_memory(t2_reshape_t.location, self.t2.location) # Finally, reshape in a way that cannot be a view. t2_reshape_t_reshape = t2_reshape_t.reshape(10, 5) assert t2_reshape_t_reshape.shape == (10, 5) assert not np.may_share_memory(t2_reshape_t_reshape.jd1, self.t2.jd1) assert (t2_reshape_t_reshape.location.shape == t2_reshape_t_reshape.shape) assert not np.may_share_memory(t2_reshape_t_reshape.location, t2_reshape_t.location) def test_shape_setting(self, masked): t0_reshape = self.t0.copy() mjd = t0_reshape.mjd # Creates a cache of the mjd attribute t0_reshape.shape = (5, 2, 5) assert t0_reshape.shape == (5, 2, 5) assert mjd.shape != t0_reshape.mjd.shape # Cache got cleared assert np.all(t0_reshape.jd1 == self.t0._time.jd1.reshape(5, 2, 5)) assert np.all(t0_reshape.jd2 == self.t0._time.jd2.reshape(5, 2, 5)) assert t0_reshape.location is None # But if the shape doesn't work, one should get an error. t0_reshape_t = t0_reshape.T with pytest.raises(AttributeError): t0_reshape_t.shape = (10, 5) # check no shape was changed. assert t0_reshape_t.shape == t0_reshape.T.shape assert t0_reshape_t.jd1.shape == t0_reshape.T.shape assert t0_reshape_t.jd2.shape == t0_reshape.T.shape t1_reshape = self.t1.copy() t1_reshape.shape = (2, 5, 5) assert t1_reshape.shape == (2, 5, 5) assert np.all(t1_reshape.jd1 == self.t1.jd1.reshape(2, 5, 5)) # location is a single element, so its shape should not change. assert t1_reshape.location.shape == () # For reshape(5, 2, 5), the location array can remain the same. # Note that we need to work directly on self.t2 here, since any # copy would cause location to have the full shape. self.t2.shape = (5, 2, 5) assert self.t2.shape == (5, 2, 5) assert self.t2.jd1.shape == (5, 2, 5) assert self.t2.jd2.shape == (5, 2, 5) assert self.t2.location.shape == (5, 2, 5) assert self.t2.location.strides == (0, 0, 24) # But for reshape(50), location would need to be copied, so this # should fail. oldshape = self.t2.shape with pytest.raises(AttributeError): self.t2.shape = (50,) # check no shape was changed. assert self.t2.jd1.shape == oldshape assert self.t2.jd2.shape == oldshape assert self.t2.location.shape == oldshape # reset t2 to its original. self.setup() def test_squeeze(self, masked): t0_squeeze = self.t0.reshape(5, 1, 2, 1, 5).squeeze() assert t0_squeeze.shape == (5, 2, 5) assert np.all(t0_squeeze.jd1 == self.t0.jd1.reshape(5, 2, 5)) assert np.may_share_memory(t0_squeeze.jd1, self.t0.jd1) assert t0_squeeze.location is None t1_squeeze = self.t1.reshape(1, 5, 1, 2, 5).squeeze() assert t1_squeeze.shape == (5, 2, 5) assert np.all(t1_squeeze.jd1 == self.t1.jd1.reshape(5, 2, 5)) assert np.may_share_memory(t1_squeeze.jd1, self.t1.jd1) assert t1_squeeze.location is self.t1.location t2_squeeze = self.t2.reshape(1, 1, 5, 2, 5, 1, 1).squeeze() assert t2_squeeze.shape == (5, 2, 5) assert np.all(t2_squeeze.jd1 == self.t2.jd1.reshape(5, 2, 5)) assert np.may_share_memory(t2_squeeze.jd1, self.t2.jd1) assert t2_squeeze.location.shape == t2_squeeze.shape assert np.may_share_memory(t2_squeeze.location, self.t2.location) def test_add_dimension(self, masked): t0_adddim = self.t0[:, np.newaxis, :] assert t0_adddim.shape == (10, 1, 5) assert np.all(t0_adddim.jd1 == self.t0.jd1[:, np.newaxis, :]) assert np.may_share_memory(t0_adddim.jd1, self.t0.jd1) assert t0_adddim.location is None t1_adddim = self.t1[:, :, np.newaxis] assert t1_adddim.shape == (10, 5, 1) assert np.all(t1_adddim.jd1 == self.t1.jd1[:, :, np.newaxis]) assert np.may_share_memory(t1_adddim.jd1, self.t1.jd1) assert t1_adddim.location is self.t1.location t2_adddim = self.t2[:, :, np.newaxis] assert t2_adddim.shape == (10, 5, 1) assert np.all(t2_adddim.jd1 == self.t2.jd1[:, :, np.newaxis]) assert np.may_share_memory(t2_adddim.jd1, self.t2.jd1) assert t2_adddim.location.shape == t2_adddim.shape assert np.may_share_memory(t2_adddim.location, self.t2.location) def test_take(self, masked): t0_take = self.t0.take((5, 2)) assert t0_take.shape == (2,) assert np.all(t0_take.jd1 == self.t0._time.jd1.take((5, 2))) assert t0_take.location is None t1_take = self.t1.take((2, 4), axis=1) assert t1_take.shape == (10, 2) assert np.all(t1_take.jd1 == self.t1.jd1.take((2, 4), axis=1)) assert t1_take.location is self.t1.location t2_take = self.t2.take((1, 3, 7), axis=0) assert t2_take.shape == (3, 5) assert np.all(t2_take.jd1 == self.t2.jd1.take((1, 3, 7), axis=0)) assert t2_take.location.shape == t2_take.shape t2_take2 = self.t2.take((5, 15)) assert t2_take2.shape == (2,) assert np.all(t2_take2.jd1 == self.t2.jd1.take((5, 15))) assert t2_take2.location.shape == t2_take2.shape def test_broadcast(self, masked): """Test using a callable method.""" t0_broadcast = self.t0._apply(np.broadcast_to, shape=(3, 10, 5)) assert t0_broadcast.shape == (3, 10, 5) assert np.all(t0_broadcast.jd1 == self.t0.jd1) assert np.may_share_memory(t0_broadcast.jd1, self.t0.jd1) assert t0_broadcast.location is None t1_broadcast = self.t1._apply(np.broadcast_to, shape=(3, 10, 5)) assert t1_broadcast.shape == (3, 10, 5) assert np.all(t1_broadcast.jd1 == self.t1.jd1) assert np.may_share_memory(t1_broadcast.jd1, self.t1.jd1) assert t1_broadcast.location is self.t1.location t2_broadcast = self.t2._apply(np.broadcast_to, shape=(3, 10, 5)) assert t2_broadcast.shape == (3, 10, 5) assert np.all(t2_broadcast.jd1 == self.t2.jd1) assert np.may_share_memory(t2_broadcast.jd1, self.t2.jd1) assert t2_broadcast.location.shape == t2_broadcast.shape assert np.may_share_memory(t2_broadcast.location, self.t2.location) class TestArithmetic(): """Arithmetic on Time objects, using both doubles.""" kwargs = ({}, {'axis': None}, {'axis': 0}, {'axis': 1}, {'axis': 2}) functions = ('min', 'max', 'sort') def setup(self): mjd = np.arange(50000, 50100, 10).reshape(2, 5, 1) frac = np.array([0.1, 0.1+1.e-15, 0.1-1.e-15, 0.9+2.e-16, 0.9]) if use_masked_data: frac = np.ma.array(frac) frac[1] = np.ma.masked self.t0 = Time(mjd, frac, format='mjd', scale='utc') # Define arrays with same ordinal properties frac = np.array([1, 2, 0, 4, 3]) if use_masked_data: frac = np.ma.array(frac) frac[1] = np.ma.masked self.t1 = Time(mjd + frac, format='mjd', scale='utc') self.jd = mjd + frac @pytest.mark.parametrize('kw, func', itertools.product(kwargs, functions)) def test_argfuncs(self, kw, func, masked): """ Test that np.argfunc(jd, **kw) is the same as t0.argfunc(**kw) where jd is a similarly shaped array with the same ordinal properties but all integer values. Also test the same for t1 which has the same integral values as jd. """ t0v = getattr(self.t0, 'arg' + func)(**kw) t1v = getattr(self.t1, 'arg' + func)(**kw) jdv = getattr(np, 'arg' + func)(self.jd, **kw) if self.t0.masked and kw == {'axis': None} and func == 'sort': t0v = np.ma.array(t0v, mask=self.t0.mask.reshape(t0v.shape)[t0v]) t1v = np.ma.array(t1v, mask=self.t1.mask.reshape(t1v.shape)[t1v]) jdv = np.ma.array(jdv, mask=self.jd.mask.reshape(jdv.shape)[jdv]) assert np.all(t0v == jdv) assert np.all(t1v == jdv) assert t0v.shape == jdv.shape assert t1v.shape == jdv.shape @pytest.mark.parametrize('kw, func', itertools.product(kwargs, functions)) def test_funcs(self, kw, func, masked): """ Test that np.func(jd, **kw) is the same as t1.func(**kw) where jd is a similarly shaped array and the same integral values. """ t1v = getattr(self.t1, func)(**kw) jdv = getattr(np, func)(self.jd, **kw) assert np.all(t1v.value == jdv) assert t1v.shape == jdv.shape def test_argmin(self, masked): assert self.t0.argmin() == 2 assert np.all(self.t0.argmin(axis=0) == 0) assert np.all(self.t0.argmin(axis=1) == 0) assert np.all(self.t0.argmin(axis=2) == 2) def test_argmax(self, masked): assert self.t0.argmax() == self.t0.size - 2 if masked: # The 0 is where all entries are masked in that axis assert np.all(self.t0.argmax(axis=0) == [1, 0, 1, 1, 1]) assert np.all(self.t0.argmax(axis=1) == [4, 0, 4, 4, 4]) else: assert np.all(self.t0.argmax(axis=0) == 1) assert np.all(self.t0.argmax(axis=1) == 4) assert np.all(self.t0.argmax(axis=2) == 3) def test_argsort(self, masked): order = [2, 0, 4, 3, 1] if masked else [2, 0, 1, 4, 3] assert np.all(self.t0.argsort() == np.array(order)) assert np.all(self.t0.argsort(axis=0) == np.arange(2).reshape(2, 1, 1)) assert np.all(self.t0.argsort(axis=1) == np.arange(5).reshape(5, 1)) assert np.all(self.t0.argsort(axis=2) == np.array(order)) ravel = np.arange(50).reshape(-1, 5)[:, order].ravel() if masked: t0v = self.t0.argsort(axis=None) # Manually remove elements in ravel that correspond to masked # entries in self.t0. This removes the 10 entries that are masked # which show up at the end of the list. mask = self.t0.mask.ravel()[ravel] ravel = ravel[~mask] assert np.all(t0v[:-10] == ravel) else: assert np.all(self.t0.argsort(axis=None) == ravel) def test_min(self, masked): assert self.t0.min() == self.t0[0, 0, 2] assert np.all(self.t0.min(0) == self.t0[0]) assert np.all(self.t0.min(1) == self.t0[:, 0]) assert np.all(self.t0.min(2) == self.t0[:, :, 2]) assert self.t0.min(0).shape == (5, 5) assert self.t0.min(0, keepdims=True).shape == (1, 5, 5) assert self.t0.min(1).shape == (2, 5) assert self.t0.min(1, keepdims=True).shape == (2, 1, 5) assert self.t0.min(2).shape == (2, 5) assert self.t0.min(2, keepdims=True).shape == (2, 5, 1) def test_max(self, masked): assert self.t0.max() == self.t0[-1, -1, -2] assert np.all(self.t0.max(0) == self.t0[1]) assert np.all(self.t0.max(1) == self.t0[:, 4]) assert np.all(self.t0.max(2) == self.t0[:, :, 3]) assert self.t0.max(0).shape == (5, 5) assert self.t0.max(0, keepdims=True).shape == (1, 5, 5) def test_ptp(self, masked): assert self.t0.ptp() == self.t0.max() - self.t0.min() assert np.all(self.t0.ptp(0) == self.t0.max(0) - self.t0.min(0)) assert self.t0.ptp(0).shape == (5, 5) assert self.t0.ptp(0, keepdims=True).shape == (1, 5, 5) def test_sort(self, masked): order = [2, 0, 4, 3, 1] if masked else [2, 0, 1, 4, 3] assert np.all(self.t0.sort() == self.t0[:, :, order]) assert np.all(self.t0.sort(0) == self.t0) assert np.all(self.t0.sort(1) == self.t0) assert np.all(self.t0.sort(2) == self.t0[:, :, order]) if not masked: assert np.all(self.t0.sort(None) == self.t0[:, :, order].ravel()) # Bit superfluous, but good to check. assert np.all(self.t0.sort(-1)[:, :, 0] == self.t0.min(-1)) assert np.all(self.t0.sort(-1)[:, :, -1] == self.t0.max(-1)) def test_regression(): # For #5225, where a time with a single-element delta_ut1_utc could not # be copied, flattened, or ravelled. (For copy, it is in test_basic.) t = Time(49580.0, scale='tai', format='mjd') t_ut1 = t.ut1 t_ut1_copy = copy.deepcopy(t_ut1) assert type(t_ut1_copy.delta_ut1_utc) is np.ndarray t_ut1_flatten = t_ut1.flatten() assert type(t_ut1_flatten.delta_ut1_utc) is np.ndarray t_ut1_ravel = t_ut1.ravel() assert type(t_ut1_ravel.delta_ut1_utc) is np.ndarray assert t_ut1_copy.delta_ut1_utc == t_ut1.delta_ut1_utc
48.05977
79
0.618913
47eecd07df4512f1d7f8ed4ba8b39648a1ea70fc
6,079
py
Python
tasks/infer_engine.py
harisankarh/IndianNLP-Transliteration
0e0dd8139c75477346c985201b51315b3a4e4f48
[ "Apache-2.0" ]
31
2020-09-24T04:32:47.000Z
2022-02-24T06:12:39.000Z
tasks/infer_engine.py
harisankarh/IndianNLP-Transliteration
0e0dd8139c75477346c985201b51315b3a4e4f48
[ "Apache-2.0" ]
4
2021-05-26T11:38:36.000Z
2022-01-27T12:28:21.000Z
tasks/infer_engine.py
harisankarh/IndianNLP-Transliteration
0e0dd8139c75477346c985201b51315b3a4e4f48
[ "Apache-2.0" ]
8
2020-09-04T12:33:30.000Z
2022-01-12T05:43:54.000Z
import os import sys import torch import utilities.lang_data_utils as lutl import utilities.running_utils as rutl from utilities.logging_utils import LOG2CSV ''' VacabSanitizer usage voc_sanitize = lutl.VocabSanitizer("data/X_word_list.json") result = voc_sanitize.reposition(result) ''' tgt_glyph = lutl.GlyphStrawboss(glyphs = "data/hindi/hi_scripts.json") en_glyph = lutl.GlyphStrawboss("en") voc_sanitize = lutl.VocabSanitizer("data/hindi/mono/hi_words_sorted.json") device = "cpu" ##=============== Models ======================================================= from tasks.rnn_xlit_runner import model weight_path = "hypotheses/Training_hi_110/weights/Training_hi_110_model.pth" weights = torch.load( weight_path, map_location=torch.device(device)) model.to(device) model.load_state_dict(weights) model.eval() def inferencer(word, topk = 5): in_vec = torch.from_numpy(en_glyph.word2xlitvec(word)).to(device) ## change to active or passive beam p_out_list = model.active_beam_inference(in_vec, beam_width = topk) p_result = [ tgt_glyph.xlitvec2word(out.cpu().numpy()) for out in p_out_list] r_result = voc_sanitize.reposition(p_result) return p_result, r_result ##=============== Corr/ Emb Stacked # ------------- Correction model ----------------------------------------------- ''' Multinominal from tasks.corr_xlit_runner import corr_model corr_weight_path = "hypotheses/Training_mai_116_corr3_a/weights/Training_mai_116_corr3_a_corrnet.pth" corr_weights = torch.load( corr_weight_path, map_location=torch.device(device)) corr_model.load_state_dict(corr_weights) corr_model.eval() hi_vocab = lutl.VocableStrawboss("data/konkani/gom_all_words_sorted.json") ''' ### -------------- Annoy based correction -------------------------------------- ''' import utilities.embed_utils as eutl from tasks.emb_xlit_runner import emb_model emb_weight_path = "hypotheses/Training_gom_emb5/weights/Training_gom_emb5_embnet.pth" emb_weights = torch.load( emb_weight_path, map_location=torch.device(device)) emb_model.load_state_dict(emb_weights) emb_model.eval() ## To Create fresh # eutl.create_annoy_index_from_model( # voc_json_file = "data/konkani/gom_all_words_sorted.json", # glyph_obj = hi_glyph, # model_func = emb_model.get_word_embedding, # vec_sz = 512, # save_prefix= 'hypotheses/Training_gom_emb6/Gom_emb6') # sys.exit() annoy_obj = eutl.AnnoyStrawboss( voc_json_file = "data/konkani/gom_all_words_sorted.json", annoy_tree_path = "hypotheses/Training_gom_emb5/Gom_emb5_word_vec.annoy", vec_sz = 1024) ''' def pred_contrive(corr_lst, pred_lst): out =[] for l in corr_lst: if (l not in out) and (l != "<UNK>"): out.append(l) for l in pred_lst: if l not in out: out.append(l) return out[:len(corr_lst)] ''' def inferencer(word, topk = 5, knear = 1): in_vec = torch.from_numpy(en_glyph.word2xlitvec(word)).to(device) ## change to active or passive beam p_out_list = model.active_beam_inference(in_vec, beam_width = topk) p_result = [ hi_glyph.xlitvec2word(out.cpu().numpy()) for out in p_out_list] emb_list = [ emb_model.get_word_embedding(out) for out in p_out_list] c_result = [annoy_obj.get_nearest_vocab(emb, count = knear) for emb in emb_list ] c_result = sum(c_result, []) # delinieate 2d list #c_out_list = [ corr_model.inference(out) for out in out_list] #c_result = [ hi_vocab.get_word(out.cpu().numpy()) for out in c_out_list] result = pred_contrive(c_result, p_result) return result ''' ##=============== For Fused Variant ''' from tasks.lm_fusion_runner import model model.eval() def inferencer(word, topk = 5): in_vec = torch.from_numpy(en_glyph.word2xlitvec(word)).to(device) p_out_list = model.basenet_inference(in_vec, beam_width = topk) # p_out_list.sort(reverse=True, key=model.lm_heuristics) p_result = [ hi_glyph.xlitvec2word(out.cpu().numpy()) for out in p_out_list] result = p_result return result def lambda_experimenter(word, topk = 10): in_vec = torch.from_numpy(en_glyph.word2xlitvec(word)).to(device) ## [0]log_smx [0]pred_tnsrs p_out_list = model.basenet_inference(in_vec, beam_width = topk, heuristics = True) p_out_heur = [] for out in p_out_list: prd_prob = float( out[0] ) lm_prob = float( model.lm_heuristics(out[1]) ) word = hi_glyph.xlitvec2word(out[1].cpu().numpy()) p_out_heur.append( (word, prd_prob, lm_prob) ) return p_out_heur ''' ##================== def infer_analytics(word): """Analytics by ploting values """ save_path = os.path.dirname(weight_path) + "/viz_log/" if not os.path.exists(save_path): os.makedirs(save_path) in_vec = torch.from_numpy(en_glyph.word2xlitvec(word)) out, aw = model.inference(in_vec, debug=1) result = hi_glyph.xlitvec2word(out.numpy()) rutl.attention_weight_plotter(result, word, aw.detach().numpy()[:len(result)], save_path=save_path ) return result def infer_annoy_analytics(word, topk = 1, knear = 1): ''' Analytics with respect to Annoy usage ''' in_vec = torch.from_numpy(en_glyph.word2xlitvec(word)).to(device) ## change to active or passive beam p_out_list = model.active_beam_inference(in_vec, beam_width = topk) p_result = [ hi_glyph.xlitvec2word(out.cpu().numpy()) for out in p_out_list] emb_list = [ emb_model.get_word_embedding(out) for out in p_out_list] c_result = [] for i, emb in enumerate(emb_list): c_res, c_val = annoy_obj.get_nearest_vocab_details(emb, count = knear) c_result.append(c_res) LOG2CSV([word, i+1, p_result[i], c_res[0], c_val[0]], csv_file="Annoy_115e5_setup.csv") c_result = sum(c_result, []) # delinieate 2d list result = pred_contrive(c_result, p_result) return result if __name__ == "__main__": while(1): a = input() result = inferencer(a) print(result)
31.994737
101
0.681856
d1553d9108e407df986bef665d2e767a658d904c
73,859
py
Python
cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_segment_routing_ms_oper.py
tkamata-test/ydk-py
b637e7853a8edbbd31fbc05afa3aa4110b31c5f9
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_segment_routing_ms_oper.py
tkamata-test/ydk-py
b637e7853a8edbbd31fbc05afa3aa4110b31c5f9
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_segment_routing_ms_oper.py
tkamata-test/ydk-py
b637e7853a8edbbd31fbc05afa3aa4110b31c5f9
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
""" Cisco_IOS_XR_segment_routing_ms_oper This module contains a collection of YANG definitions for Cisco IOS\-XR segment\-routing\-ms package operational data. This module contains definitions for the following management objects\: srms\: Segment Routing Mapping Server operational data Copyright (c) 2013\-2016 by Cisco Systems, Inc. All rights reserved. """ import re import collections from enum import Enum from ydk.types import Empty, YList, YLeafList, DELETE, Decimal64, FixedBitsDict from ydk.errors import YPYError, YPYModelError class SrmsMiAfEBEnum(Enum): """ SrmsMiAfEBEnum Srms mi af e b .. data:: none = 0 None .. data:: ipv4 = 1 IPv4 .. data:: ipv6 = 2 IPv6 """ none = 0 ipv4 = 1 ipv6 = 2 @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['SrmsMiAfEBEnum'] class SrmsMiFlagEBEnum(Enum): """ SrmsMiFlagEBEnum Srms mi flag e b .. data:: false = 0 False .. data:: true = 1 True """ false = 0 true = 1 @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['SrmsMiFlagEBEnum'] class SrmsMiSrcEBEnum(Enum): """ SrmsMiSrcEBEnum Srms mi src e b .. data:: none = 0 None .. data:: local = 1 Local .. data:: remote = 2 Remote """ none = 0 local = 1 remote = 2 @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['SrmsMiSrcEBEnum'] class Srms(object): """ Segment Routing Mapping Server operational data .. attribute:: mapping IP prefix to SID mappings **type**\: :py:class:`Mapping <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Mapping>` .. attribute:: policy Policy operational data **type**\: :py:class:`Policy <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.mapping = Srms.Mapping() self.mapping.parent = self self.policy = Srms.Policy() self.policy.parent = self class Mapping(object): """ IP prefix to SID mappings .. attribute:: mapping_ipv4 IPv4 prefix to SID mappings **type**\: :py:class:`MappingIpv4 <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Mapping.MappingIpv4>` .. attribute:: mapping_ipv6 IPv6 prefix to SID mappings **type**\: :py:class:`MappingIpv6 <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Mapping.MappingIpv6>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.mapping_ipv4 = Srms.Mapping.MappingIpv4() self.mapping_ipv4.parent = self self.mapping_ipv6 = Srms.Mapping.MappingIpv6() self.mapping_ipv6.parent = self class MappingIpv4(object): """ IPv4 prefix to SID mappings .. attribute:: mapping_mi IP prefix to SID mapping item. It's not possible to list all of the IP prefix to SID mappings, as the set of valid prefixes could be very large. Instead, SID map information must be retrieved individually for each prefix of interest **type**\: list of :py:class:`MappingMi <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Mapping.MappingIpv4.MappingMi>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.mapping_mi = YList() self.mapping_mi.parent = self self.mapping_mi.name = 'mapping_mi' class MappingMi(object): """ IP prefix to SID mapping item. It's not possible to list all of the IP prefix to SID mappings, as the set of valid prefixes could be very large. Instead, SID map information must be retrieved individually for each prefix of interest. .. attribute:: addr addr **type**\: :py:class:`Addr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Mapping.MappingIpv4.MappingMi.Addr>` .. attribute:: area Area (OSPF) or Level (ISIS) **type**\: str **length:** 0..30 .. attribute:: flag_attached Attached flag **type**\: :py:class:`SrmsMiFlagEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiFlagEBEnum>` .. attribute:: ip IP **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: last_prefix Last IP Prefix **type**\: str **length:** 0..50 .. attribute:: last_sid_index Last SID Index **type**\: int **range:** 0..4294967295 .. attribute:: prefix Prefix **type**\: int **range:** \-2147483648..2147483647 .. attribute:: prefix_xr Prefix length **type**\: int **range:** 0..255 .. attribute:: router Router ID **type**\: str **length:** 0..30 .. attribute:: sid_count SID range **type**\: int **range:** 0..4294967295 .. attribute:: sid_start Starting SID **type**\: int **range:** 0..4294967295 .. attribute:: src src **type**\: :py:class:`SrmsMiSrcEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiSrcEBEnum>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.addr = Srms.Mapping.MappingIpv4.MappingMi.Addr() self.addr.parent = self self.area = None self.flag_attached = None self.ip = None self.last_prefix = None self.last_sid_index = None self.prefix = None self.prefix_xr = None self.router = None self.sid_count = None self.sid_start = None self.src = None class Addr(object): """ addr .. attribute:: af AF **type**\: :py:class:`SrmsMiAfEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiAfEBEnum>` .. attribute:: ipv4 IPv4 **type**\: str **pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 **type**\: str **pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.af = None self.ipv4 = None self.ipv6 = None @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:mapping/Cisco-IOS-XR-segment-routing-ms-oper:mapping-ipv4/Cisco-IOS-XR-segment-routing-ms-oper:mapping-mi/Cisco-IOS-XR-segment-routing-ms-oper:addr' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.af is not None: return True if self.ipv4 is not None: return True if self.ipv6 is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Mapping.MappingIpv4.MappingMi.Addr']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:mapping/Cisco-IOS-XR-segment-routing-ms-oper:mapping-ipv4/Cisco-IOS-XR-segment-routing-ms-oper:mapping-mi' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.addr is not None and self.addr._has_data(): return True if self.area is not None: return True if self.flag_attached is not None: return True if self.ip is not None: return True if self.last_prefix is not None: return True if self.last_sid_index is not None: return True if self.prefix is not None: return True if self.prefix_xr is not None: return True if self.router is not None: return True if self.sid_count is not None: return True if self.sid_start is not None: return True if self.src is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Mapping.MappingIpv4.MappingMi']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:mapping/Cisco-IOS-XR-segment-routing-ms-oper:mapping-ipv4' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.mapping_mi is not None: for child_ref in self.mapping_mi: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Mapping.MappingIpv4']['meta_info'] class MappingIpv6(object): """ IPv6 prefix to SID mappings .. attribute:: mapping_mi IP prefix to SID mapping item. It's not possible to list all of the IP prefix to SID mappings, as the set of valid prefixes could be very large. Instead, SID map information must be retrieved individually for each prefix of interest **type**\: list of :py:class:`MappingMi <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Mapping.MappingIpv6.MappingMi>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.mapping_mi = YList() self.mapping_mi.parent = self self.mapping_mi.name = 'mapping_mi' class MappingMi(object): """ IP prefix to SID mapping item. It's not possible to list all of the IP prefix to SID mappings, as the set of valid prefixes could be very large. Instead, SID map information must be retrieved individually for each prefix of interest. .. attribute:: addr addr **type**\: :py:class:`Addr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Mapping.MappingIpv6.MappingMi.Addr>` .. attribute:: area Area (OSPF) or Level (ISIS) **type**\: str **length:** 0..30 .. attribute:: flag_attached Attached flag **type**\: :py:class:`SrmsMiFlagEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiFlagEBEnum>` .. attribute:: ip IP **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: last_prefix Last IP Prefix **type**\: str **length:** 0..50 .. attribute:: last_sid_index Last SID Index **type**\: int **range:** 0..4294967295 .. attribute:: prefix Prefix **type**\: int **range:** \-2147483648..2147483647 .. attribute:: prefix_xr Prefix length **type**\: int **range:** 0..255 .. attribute:: router Router ID **type**\: str **length:** 0..30 .. attribute:: sid_count SID range **type**\: int **range:** 0..4294967295 .. attribute:: sid_start Starting SID **type**\: int **range:** 0..4294967295 .. attribute:: src src **type**\: :py:class:`SrmsMiSrcEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiSrcEBEnum>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.addr = Srms.Mapping.MappingIpv6.MappingMi.Addr() self.addr.parent = self self.area = None self.flag_attached = None self.ip = None self.last_prefix = None self.last_sid_index = None self.prefix = None self.prefix_xr = None self.router = None self.sid_count = None self.sid_start = None self.src = None class Addr(object): """ addr .. attribute:: af AF **type**\: :py:class:`SrmsMiAfEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiAfEBEnum>` .. attribute:: ipv4 IPv4 **type**\: str **pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 **type**\: str **pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.af = None self.ipv4 = None self.ipv6 = None @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:mapping/Cisco-IOS-XR-segment-routing-ms-oper:mapping-ipv6/Cisco-IOS-XR-segment-routing-ms-oper:mapping-mi/Cisco-IOS-XR-segment-routing-ms-oper:addr' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.af is not None: return True if self.ipv4 is not None: return True if self.ipv6 is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Mapping.MappingIpv6.MappingMi.Addr']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:mapping/Cisco-IOS-XR-segment-routing-ms-oper:mapping-ipv6/Cisco-IOS-XR-segment-routing-ms-oper:mapping-mi' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.addr is not None and self.addr._has_data(): return True if self.area is not None: return True if self.flag_attached is not None: return True if self.ip is not None: return True if self.last_prefix is not None: return True if self.last_sid_index is not None: return True if self.prefix is not None: return True if self.prefix_xr is not None: return True if self.router is not None: return True if self.sid_count is not None: return True if self.sid_start is not None: return True if self.src is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Mapping.MappingIpv6.MappingMi']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:mapping/Cisco-IOS-XR-segment-routing-ms-oper:mapping-ipv6' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.mapping_mi is not None: for child_ref in self.mapping_mi: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Mapping.MappingIpv6']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:mapping' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.mapping_ipv4 is not None and self.mapping_ipv4._has_data(): return True if self.mapping_ipv6 is not None and self.mapping_ipv6._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Mapping']['meta_info'] class Policy(object): """ Policy operational data .. attribute:: policy_ipv4 IPv4 policy operational data **type**\: :py:class:`PolicyIpv4 <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv4>` .. attribute:: policy_ipv6 IPv6 policy operational data **type**\: :py:class:`PolicyIpv6 <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv6>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.policy_ipv4 = Srms.Policy.PolicyIpv4() self.policy_ipv4.parent = self self.policy_ipv6 = Srms.Policy.PolicyIpv6() self.policy_ipv6.parent = self class PolicyIpv4(object): """ IPv4 policy operational data .. attribute:: policy_ipv4_active IPv4 active policy operational data **type**\: :py:class:`PolicyIpv4Active <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv4.PolicyIpv4Active>` .. attribute:: policy_ipv4_backup IPv4 backup policy operational data **type**\: :py:class:`PolicyIpv4Backup <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv4.PolicyIpv4Backup>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.policy_ipv4_active = Srms.Policy.PolicyIpv4.PolicyIpv4Active() self.policy_ipv4_active.parent = self self.policy_ipv4_backup = Srms.Policy.PolicyIpv4.PolicyIpv4Backup() self.policy_ipv4_backup.parent = self class PolicyIpv4Backup(object): """ IPv4 backup policy operational data .. attribute:: policy_mi Mapping Item **type**\: list of :py:class:`PolicyMi <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv4.PolicyIpv4Backup.PolicyMi>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.policy_mi = YList() self.policy_mi.parent = self self.policy_mi.name = 'policy_mi' class PolicyMi(object): """ Mapping Item .. attribute:: mi_id <key> Mapping Item ID (0, 1, 2, ...) **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: addr addr **type**\: :py:class:`Addr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv4.PolicyIpv4Backup.PolicyMi.Addr>` .. attribute:: area Area (OSPF) or Level (ISIS) **type**\: str **length:** 0..30 .. attribute:: flag_attached Attached flag **type**\: :py:class:`SrmsMiFlagEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiFlagEBEnum>` .. attribute:: last_prefix Last IP Prefix **type**\: str **length:** 0..50 .. attribute:: last_sid_index Last SID Index **type**\: int **range:** 0..4294967295 .. attribute:: prefix_xr Prefix length **type**\: int **range:** 0..255 .. attribute:: router Router ID **type**\: str **length:** 0..30 .. attribute:: sid_count SID range **type**\: int **range:** 0..4294967295 .. attribute:: sid_start Starting SID **type**\: int **range:** 0..4294967295 .. attribute:: src src **type**\: :py:class:`SrmsMiSrcEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiSrcEBEnum>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.mi_id = None self.addr = Srms.Policy.PolicyIpv4.PolicyIpv4Backup.PolicyMi.Addr() self.addr.parent = self self.area = None self.flag_attached = None self.last_prefix = None self.last_sid_index = None self.prefix_xr = None self.router = None self.sid_count = None self.sid_start = None self.src = None class Addr(object): """ addr .. attribute:: af AF **type**\: :py:class:`SrmsMiAfEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiAfEBEnum>` .. attribute:: ipv4 IPv4 **type**\: str **pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 **type**\: str **pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.af = None self.ipv4 = None self.ipv6 = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-segment-routing-ms-oper:addr' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.af is not None: return True if self.ipv4 is not None: return True if self.ipv6 is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv4.PolicyIpv4Backup.PolicyMi.Addr']['meta_info'] @property def _common_path(self): if self.mi_id is None: raise YPYModelError('Key property mi_id is None') return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv4/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv4-backup/Cisco-IOS-XR-segment-routing-ms-oper:policy-mi[Cisco-IOS-XR-segment-routing-ms-oper:mi-id = ' + str(self.mi_id) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.mi_id is not None: return True if self.addr is not None and self.addr._has_data(): return True if self.area is not None: return True if self.flag_attached is not None: return True if self.last_prefix is not None: return True if self.last_sid_index is not None: return True if self.prefix_xr is not None: return True if self.router is not None: return True if self.sid_count is not None: return True if self.sid_start is not None: return True if self.src is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv4.PolicyIpv4Backup.PolicyMi']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv4/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv4-backup' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.policy_mi is not None: for child_ref in self.policy_mi: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv4.PolicyIpv4Backup']['meta_info'] class PolicyIpv4Active(object): """ IPv4 active policy operational data .. attribute:: policy_mi Mapping Item **type**\: list of :py:class:`PolicyMi <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv4.PolicyIpv4Active.PolicyMi>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.policy_mi = YList() self.policy_mi.parent = self self.policy_mi.name = 'policy_mi' class PolicyMi(object): """ Mapping Item .. attribute:: mi_id <key> Mapping Item ID (0, 1, 2, ...) **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: addr addr **type**\: :py:class:`Addr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv4.PolicyIpv4Active.PolicyMi.Addr>` .. attribute:: area Area (OSPF) or Level (ISIS) **type**\: str **length:** 0..30 .. attribute:: flag_attached Attached flag **type**\: :py:class:`SrmsMiFlagEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiFlagEBEnum>` .. attribute:: last_prefix Last IP Prefix **type**\: str **length:** 0..50 .. attribute:: last_sid_index Last SID Index **type**\: int **range:** 0..4294967295 .. attribute:: prefix_xr Prefix length **type**\: int **range:** 0..255 .. attribute:: router Router ID **type**\: str **length:** 0..30 .. attribute:: sid_count SID range **type**\: int **range:** 0..4294967295 .. attribute:: sid_start Starting SID **type**\: int **range:** 0..4294967295 .. attribute:: src src **type**\: :py:class:`SrmsMiSrcEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiSrcEBEnum>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.mi_id = None self.addr = Srms.Policy.PolicyIpv4.PolicyIpv4Active.PolicyMi.Addr() self.addr.parent = self self.area = None self.flag_attached = None self.last_prefix = None self.last_sid_index = None self.prefix_xr = None self.router = None self.sid_count = None self.sid_start = None self.src = None class Addr(object): """ addr .. attribute:: af AF **type**\: :py:class:`SrmsMiAfEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiAfEBEnum>` .. attribute:: ipv4 IPv4 **type**\: str **pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 **type**\: str **pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.af = None self.ipv4 = None self.ipv6 = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-segment-routing-ms-oper:addr' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.af is not None: return True if self.ipv4 is not None: return True if self.ipv6 is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv4.PolicyIpv4Active.PolicyMi.Addr']['meta_info'] @property def _common_path(self): if self.mi_id is None: raise YPYModelError('Key property mi_id is None') return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv4/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv4-active/Cisco-IOS-XR-segment-routing-ms-oper:policy-mi[Cisco-IOS-XR-segment-routing-ms-oper:mi-id = ' + str(self.mi_id) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.mi_id is not None: return True if self.addr is not None and self.addr._has_data(): return True if self.area is not None: return True if self.flag_attached is not None: return True if self.last_prefix is not None: return True if self.last_sid_index is not None: return True if self.prefix_xr is not None: return True if self.router is not None: return True if self.sid_count is not None: return True if self.sid_start is not None: return True if self.src is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv4.PolicyIpv4Active.PolicyMi']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv4/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv4-active' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.policy_mi is not None: for child_ref in self.policy_mi: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv4.PolicyIpv4Active']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv4' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.policy_ipv4_active is not None and self.policy_ipv4_active._has_data(): return True if self.policy_ipv4_backup is not None and self.policy_ipv4_backup._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv4']['meta_info'] class PolicyIpv6(object): """ IPv6 policy operational data .. attribute:: policy_ipv6_active IPv6 active policy operational data **type**\: :py:class:`PolicyIpv6Active <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv6.PolicyIpv6Active>` .. attribute:: policy_ipv6_backup IPv6 backup policy operational data **type**\: :py:class:`PolicyIpv6Backup <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv6.PolicyIpv6Backup>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.policy_ipv6_active = Srms.Policy.PolicyIpv6.PolicyIpv6Active() self.policy_ipv6_active.parent = self self.policy_ipv6_backup = Srms.Policy.PolicyIpv6.PolicyIpv6Backup() self.policy_ipv6_backup.parent = self class PolicyIpv6Backup(object): """ IPv6 backup policy operational data .. attribute:: policy_mi Mapping Item **type**\: list of :py:class:`PolicyMi <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv6.PolicyIpv6Backup.PolicyMi>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.policy_mi = YList() self.policy_mi.parent = self self.policy_mi.name = 'policy_mi' class PolicyMi(object): """ Mapping Item .. attribute:: mi_id <key> Mapping Item ID (0, 1, 2, ...) **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: addr addr **type**\: :py:class:`Addr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv6.PolicyIpv6Backup.PolicyMi.Addr>` .. attribute:: area Area (OSPF) or Level (ISIS) **type**\: str **length:** 0..30 .. attribute:: flag_attached Attached flag **type**\: :py:class:`SrmsMiFlagEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiFlagEBEnum>` .. attribute:: last_prefix Last IP Prefix **type**\: str **length:** 0..50 .. attribute:: last_sid_index Last SID Index **type**\: int **range:** 0..4294967295 .. attribute:: prefix_xr Prefix length **type**\: int **range:** 0..255 .. attribute:: router Router ID **type**\: str **length:** 0..30 .. attribute:: sid_count SID range **type**\: int **range:** 0..4294967295 .. attribute:: sid_start Starting SID **type**\: int **range:** 0..4294967295 .. attribute:: src src **type**\: :py:class:`SrmsMiSrcEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiSrcEBEnum>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.mi_id = None self.addr = Srms.Policy.PolicyIpv6.PolicyIpv6Backup.PolicyMi.Addr() self.addr.parent = self self.area = None self.flag_attached = None self.last_prefix = None self.last_sid_index = None self.prefix_xr = None self.router = None self.sid_count = None self.sid_start = None self.src = None class Addr(object): """ addr .. attribute:: af AF **type**\: :py:class:`SrmsMiAfEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiAfEBEnum>` .. attribute:: ipv4 IPv4 **type**\: str **pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 **type**\: str **pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.af = None self.ipv4 = None self.ipv6 = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-segment-routing-ms-oper:addr' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.af is not None: return True if self.ipv4 is not None: return True if self.ipv6 is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv6.PolicyIpv6Backup.PolicyMi.Addr']['meta_info'] @property def _common_path(self): if self.mi_id is None: raise YPYModelError('Key property mi_id is None') return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv6/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv6-backup/Cisco-IOS-XR-segment-routing-ms-oper:policy-mi[Cisco-IOS-XR-segment-routing-ms-oper:mi-id = ' + str(self.mi_id) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.mi_id is not None: return True if self.addr is not None and self.addr._has_data(): return True if self.area is not None: return True if self.flag_attached is not None: return True if self.last_prefix is not None: return True if self.last_sid_index is not None: return True if self.prefix_xr is not None: return True if self.router is not None: return True if self.sid_count is not None: return True if self.sid_start is not None: return True if self.src is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv6.PolicyIpv6Backup.PolicyMi']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv6/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv6-backup' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.policy_mi is not None: for child_ref in self.policy_mi: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv6.PolicyIpv6Backup']['meta_info'] class PolicyIpv6Active(object): """ IPv6 active policy operational data .. attribute:: policy_mi Mapping Item **type**\: list of :py:class:`PolicyMi <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv6.PolicyIpv6Active.PolicyMi>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.policy_mi = YList() self.policy_mi.parent = self self.policy_mi.name = 'policy_mi' class PolicyMi(object): """ Mapping Item .. attribute:: mi_id <key> Mapping Item ID (0, 1, 2, ...) **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: addr addr **type**\: :py:class:`Addr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.Srms.Policy.PolicyIpv6.PolicyIpv6Active.PolicyMi.Addr>` .. attribute:: area Area (OSPF) or Level (ISIS) **type**\: str **length:** 0..30 .. attribute:: flag_attached Attached flag **type**\: :py:class:`SrmsMiFlagEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiFlagEBEnum>` .. attribute:: last_prefix Last IP Prefix **type**\: str **length:** 0..50 .. attribute:: last_sid_index Last SID Index **type**\: int **range:** 0..4294967295 .. attribute:: prefix_xr Prefix length **type**\: int **range:** 0..255 .. attribute:: router Router ID **type**\: str **length:** 0..30 .. attribute:: sid_count SID range **type**\: int **range:** 0..4294967295 .. attribute:: sid_start Starting SID **type**\: int **range:** 0..4294967295 .. attribute:: src src **type**\: :py:class:`SrmsMiSrcEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiSrcEBEnum>` """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.mi_id = None self.addr = Srms.Policy.PolicyIpv6.PolicyIpv6Active.PolicyMi.Addr() self.addr.parent = self self.area = None self.flag_attached = None self.last_prefix = None self.last_sid_index = None self.prefix_xr = None self.router = None self.sid_count = None self.sid_start = None self.src = None class Addr(object): """ addr .. attribute:: af AF **type**\: :py:class:`SrmsMiAfEBEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_segment_routing_ms_oper.SrmsMiAfEBEnum>` .. attribute:: ipv4 IPv4 **type**\: str **pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 **type**\: str **pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'segment-routing-ms-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.af = None self.ipv4 = None self.ipv6 = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-segment-routing-ms-oper:addr' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.af is not None: return True if self.ipv4 is not None: return True if self.ipv6 is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv6.PolicyIpv6Active.PolicyMi.Addr']['meta_info'] @property def _common_path(self): if self.mi_id is None: raise YPYModelError('Key property mi_id is None') return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv6/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv6-active/Cisco-IOS-XR-segment-routing-ms-oper:policy-mi[Cisco-IOS-XR-segment-routing-ms-oper:mi-id = ' + str(self.mi_id) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.mi_id is not None: return True if self.addr is not None and self.addr._has_data(): return True if self.area is not None: return True if self.flag_attached is not None: return True if self.last_prefix is not None: return True if self.last_sid_index is not None: return True if self.prefix_xr is not None: return True if self.router is not None: return True if self.sid_count is not None: return True if self.sid_start is not None: return True if self.src is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv6.PolicyIpv6Active.PolicyMi']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv6/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv6-active' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.policy_mi is not None: for child_ref in self.policy_mi: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv6.PolicyIpv6Active']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy/Cisco-IOS-XR-segment-routing-ms-oper:policy-ipv6' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.policy_ipv6_active is not None and self.policy_ipv6_active._has_data(): return True if self.policy_ipv6_backup is not None and self.policy_ipv6_backup._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy.PolicyIpv6']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms/Cisco-IOS-XR-segment-routing-ms-oper:policy' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.policy_ipv4 is not None and self.policy_ipv4._has_data(): return True if self.policy_ipv6 is not None and self.policy_ipv6._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms.Policy']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-segment-routing-ms-oper:srms' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.mapping is not None and self.mapping._has_data(): return True if self.policy is not None and self.policy._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_segment_routing_ms_oper as meta return meta._meta_table['Srms']['meta_info']
36.782371
341
0.425703
94b2a563540154605983e19030545d645dc2443c
1,165
py
Python
cctbx_website/run_tests.py
whart222/cctbx_project
32bb901af1431f845143eac06c244f20b1fbc26a
[ "BSD-3-Clause-LBNL" ]
null
null
null
cctbx_website/run_tests.py
whart222/cctbx_project
32bb901af1431f845143eac06c244f20b1fbc26a
[ "BSD-3-Clause-LBNL" ]
170
2020-09-26T19:17:07.000Z
2022-03-31T21:32:41.000Z
cctbx_website/run_tests.py
whart222/cctbx_project
32bb901af1431f845143eac06c244f20b1fbc26a
[ "BSD-3-Clause-LBNL" ]
null
null
null
from __future__ import absolute_import, division, print_function from libtbx import test_utils import libtbx.load_env #tst_list = [ # "$D/regression/tst_py_from_html.py" # ] tst_list = [ "$D/regression/tst_1_template.py", "$D/regression/tst_2_doc_hlo_intro.py", "$D/regression/tst_3_doc_hlo_model_manager.py", "$D/regression/tst_4_doc_hlo_data_manager.py", "$D/regression/tst_5_doc_hlo_map_manager.py", "$D/regression/tst_6_doc_hlo_model_map_manager.py", "$D/regression/tst_7_doc_low_flex_advanced.py", "$D/regression/tst_8_doc_maps_intro.py", "$D/regression/tst_9_doc_maps_boxing.py", "$D/regression/tst_10_doc_programming_tips.py", "$D/regression/tst_11_script_1.py", "$D/regression/tst_12_script_compare_ss.py", "$D/regression/tst_13_script_ideal_ss.py", "$D/regression/tst_14_script_lbfgs_no_curvature.py", "$D/regression/tst_15_doc_models_hierarchy.py", "$D/regression/tst_16_script_lbfgs_with_curvature.py", ] def run(): build_dir = libtbx.env.under_build("cctbx_website") dist_dir = libtbx.env.dist_path("cctbx_website") test_utils.run_tests(build_dir, dist_dir, tst_list) if (__name__ == "__main__"): run()
31.486486
64
0.771674
68a8b55d16b8f8c8374a2457142211d79c703d9d
4,399
py
Python
python/perspective/perspective/client/table_api.py
shinny-yangyang/perspective
91ade3c19bf9cdd39ce2d019cb92c6fa0d31d724
[ "Apache-2.0" ]
1,821
2017-12-08T22:38:48.000Z
2019-04-29T19:29:31.000Z
python/perspective/perspective/client/table_api.py
shinny-yangyang/perspective
91ade3c19bf9cdd39ce2d019cb92c6fa0d31d724
[ "Apache-2.0" ]
278
2018-01-19T22:27:09.000Z
2019-04-27T00:16:00.000Z
python/perspective/perspective/client/table_api.py
shinny-yangyang/perspective
91ade3c19bf9cdd39ce2d019cb92c6fa0d31d724
[ "Apache-2.0" ]
125
2017-12-08T20:57:50.000Z
2019-04-23T07:57:05.000Z
################################################################################ # # Copyright (c) 2019, the Perspective Authors. # # This file is part of the Perspective library, distributed under the terms of # the Apache License 2.0. The full license can be found in the LICENSE file. # import tornado from functools import partial from .dispatch import async_queue, subscribe, unsubscribe from .view_api import view as make_view def table(client, data, name, index=None, limit=None): """Create a Perspective `Table` by posting a message to a Perspective server implementation through `client`, returning a `PerspectiveTableProxy` object whose API is entirely async and must be called with `await` or in a `yield`-based generator.""" options = {} if index: options["index"] = index elif limit: options["limit"] = limit msg = {"cmd": "table", "name": name, "args": [data], "options": options} future = tornado.concurrent.Future() client.post(msg, future) return future class PerspectiveTableProxy(object): def __init__(self, client, name): """A proxy for a Perspective `Table` object elsewhere, i.e. on a remote server accessible through a Websocket. All public API methods on this proxy are async, and must be called with `await` or a `yield`-based coroutine. Args: client (:obj:`PerspectiveClient`): A `PerspectiveClient` that is set up to send messages to a Perspective server implementation elsewhere. name (:obj:`str`): a `str` name for the Table. Automatically generated if using the `table` function defined above. """ self._client = client self._name = name self._async_queue = partial(async_queue, self._client, self._name) self._subscribe = partial(subscribe, self._client, self._name) self._unsubscribe = partial(unsubscribe, self._client, self._name) def make_port(self): return self._async_queue("make_port", "table_method") def remove_port(self): return self._async_queue("remove_port", "table_method") def get_index(self): return self._async_queue("get_index", "table_method") def get_limit(self): return self._async_queue("get_limit", "table_method") def clear(self): return self._async_queue("clear", "table_method") def replace(self, data): return self._async_queue("replace", "table_method", data) def size(self): return self._async_queue("size", "table_method") def schema(self, as_string=False): return self._async_queue("schema", "table_method", as_string=as_string) def expression_schema(self, expressions, **kwargs): return self._async_queue( "expression_schema", "table_method", expressions, **kwargs ) def columns(self): return self._async_queue("columns", "table_method") def is_valid_filter(self, filter): return self._async_queue("is_valid_filter", "table_method", filter) def on_delete(self, callback): return self._subscribe("on_delete", "table_method", callback) def remove_delete(self, callback): return self._unsubscribe("remove_delete", "table_method", callback) def delete(self): return self._async_queue("delete", "table_method") def view( self, columns=None, group_by=None, split_by=None, aggregates=None, sort=None, filter=None, expressions=None, ): return make_view( self._client, self._name, columns, group_by, split_by, aggregates, sort, filter, expressions, ) def update(self, data, port_id=0): msg = { "name": self._name, "cmd": "table_method", "method": "update", "args": [data, {"port_id": port_id}], "subscribe": False, } self._client.post(msg) def remove(self, pkeys, port_id=0): msg = { "name": self._name, "cmd": "table_method", "method": "remove", "args": [pkeys, {"port_id": port_id}], "subscribe": False, } self._client.post(msg)
30.978873
80
0.604228
97dc988bf9a61942a4ac395141b4c293c7ae790e
974
py
Python
myproject/myproject/urls.py
ObukhovVladislav/mysite-django
715a3793351c19a0e7b052d2711796cafee4d5a9
[ "Apache-2.0" ]
null
null
null
myproject/myproject/urls.py
ObukhovVladislav/mysite-django
715a3793351c19a0e7b052d2711796cafee4d5a9
[ "Apache-2.0" ]
null
null
null
myproject/myproject/urls.py
ObukhovVladislav/mysite-django
715a3793351c19a0e7b052d2711796cafee4d5a9
[ "Apache-2.0" ]
null
null
null
"""myproject URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ import myapp.views as myapp from django.contrib import admin from django.urls import path, include urlpatterns = [ path('', include('myapp.urls', namespace='my')), path('auth/', include('authapp.urls', namespace='auth')), path('basket/', include('basketapp.urls', namespace='basket')), path('admin/', admin.site.urls), ]
33.586207
77
0.696099
251b9d76ec9342a5e9b9de515c92fbd245e7dbae
12,034
py
Python
wz/ui_modules/tab_grade_editor.py
gradgrind/WZ
672d93a3c9d7806194d16d6d5b9175e4046bd068
[ "Apache-2.0" ]
null
null
null
wz/ui_modules/tab_grade_editor.py
gradgrind/WZ
672d93a3c9d7806194d16d6d5b9175e4046bd068
[ "Apache-2.0" ]
null
null
null
wz/ui_modules/tab_grade_editor.py
gradgrind/WZ
672d93a3c9d7806194d16d6d5b9175e4046bd068
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- """ ui/tab_grade_editor.py Last updated: 2021-04-06 Editor for grades. =+LICENCE============================= Copyright 2021 Michael Towers 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. =-LICENCE======================================== """ ### Messages _NOT_INTERRUPTABLE = "+++ Der Prozess kann nicht unterbrochen werden +++" _MUST_SAVE_CHANGES = "Die Änderungen müssen zuerst gespeichert werden." _TITLE_TABLE_REPLACE = "Neue Tabelle speichern" # Would need to be a bit different for individual pupils: _TABLE_REPLACE = "Die neue Tabelle wird die alte ersetzen.\n" \ "Soll sie jetzt gespeichert werden?" _TABLE_OVERWRITE = "{n} Noten werden geändert. Übernehmen?" _NOT_SAVED = "Änderungen nicht gespeichert" _PDF_TABLE_SAVED = "Notentabelle gespeichert:\n {path}" ### Labels, etc. _EDIT_GRADES = "Noten verwalten" _TERM = "Anlass:" _GROUP = "Klasse/Gruppe:" _SAVE = "Änderungen speichern" _TABLE_XLSX = "Noteneingabe-Tabelle\nerstellen" _TT_TABLE_XLSX = "Tabelle der unterrichteten Fächer als xlsx-Datei erstellen" _TABLE_PDF = "Tabelle als PDF" _REPORT_PDF = "Zeugnis(se) erstellen" _TABLE_IN1 = "Notentabelle ersetzen,\n externe einlesen" _TT_TABLE_IN1 = "Ersetze die Notentabelle durch die gewählte Datei" \ " (xlsx, ods, tsv)" _TABLE_IN_DIR = "Noten aktualisieren,\n von externem Ordner" _TT_TABLE_IN_DIR = "Aktualisiere die Notentabelle von den Dateien" \ " (xlsx, ods, tsv) im gewählten Ordner" _TAG_ENTER = "Geben Sie eine Bezeichnung für diesen Datensatz an.\n" \ "Buchstaben, Ziffern, '~' und '-' sind zulässig, andere Zeichen" \ " werden ersetzt." _TABLE_FILE = "Tabellendatei (*.xlsx *.ods *.tsv)" _PDF_FILE = "PDF-Datei (*.pdf)" ##################################################### import os, glob from qtpy.QtWidgets import QHBoxLayout, QVBoxLayout, QLabel, \ QPushButton, QFileDialog from qtpy.QtCore import SIGNAL, QObject from ui.grid import EditableGridView from ui.grade_grid import GradeGrid from ui.abitur_pupil_view import AbiPupilView from ui.ui_support import VLine, KeySelect, TabPage, openDialog, \ QuestionDialog, dirDialog, saveDialog, LineDialog ### class GView(EditableGridView): def __init__(self, tab_widget): self._tab = tab_widget super().__init__() # def set_changed(self, show): self._tab.enable('SAVE', show) ### class GradeEdit(TabPage): def __init__(self): self._widgets = {} super().__init__(_EDIT_GRADES) topbox = QHBoxLayout() self.vbox.addLayout(topbox) #*********** The "main" widget *********** self.gradeView = GView(self) self.grade_scene = None topbox.addWidget(self.gradeView) topbox.addWidget(VLine()) cbox = QVBoxLayout() topbox.addLayout(cbox) ### Select "term" (to which occasion the reports are to appear) ### That might be a term or semester, it might be a special ### unscheduled report, or a scheduled test (possibly no report) ### or something specific to the school form. self.term_select = KeySelect(changed_callback = self.term_changed) cbox.addWidget(QLabel(_TERM)) cbox.addWidget(self.term_select) ### Select group (might be just one entry ... perhaps even none) self.group_select = KeySelect(changed_callback = self.group_changed) cbox.addWidget(QLabel(_GROUP)) cbox.addWidget(self.group_select) ### Subselection: e.g. tags/dates/pupils self.subselect = KeySelect(changed_callback = self.sub_changed) cbox.addWidget(self.subselect) cbox.addSpacing(30) ### Save button (active when there are unsaved modifications) _w = QPushButton(_SAVE) self._widgets['SAVE'] = _w cbox.addWidget(_w) _w.clicked.connect(self.save) cbox.addStretch(1) ### Generate grade table (for inputting) pbTable = QPushButton(_TABLE_XLSX) pbTable.setToolTip(_TT_TABLE_XLSX) cbox.addWidget(pbTable) pbTable.clicked.connect(self.make_table) cbox.addSpacing(10) ### Import grade table (replace internal one) pbTableIn1 = QPushButton(_TABLE_IN1) pbTableIn1.setToolTip(_TT_TABLE_IN1) cbox.addWidget(pbTableIn1) pbTableIn1.clicked.connect(self.input_table) ### Import grade tables (adding to internal one) pbTableInDir = QPushButton(_TABLE_IN_DIR) pbTableInDir.setToolTip(_TT_TABLE_IN_DIR) cbox.addWidget(pbTableInDir) pbTableInDir.clicked.connect(self.input_tables) cbox.addSpacing(30) ### Produce a pdf of the grade table pbPdf = QPushButton(_TABLE_PDF) cbox.addWidget(pbPdf) pbPdf.clicked.connect(self.print_table) cbox.addSpacing(10) ### Produce the reports pbReport = QPushButton(_REPORT_PDF) cbox.addWidget(pbReport) pbReport.clicked.connect(self.make_reports) # def enable(self, tag, on): """Enable or disable the widget with given tag. """ self._widgets[tag].setEnabled(on) # def is_modified(self): if self.grade_scene: return bool(self.grade_scene.changes()) return False # def set_scene(self, scene): self.grade_scene = scene self.gradeView.set_scene(scene) # def clear(self): """Check for changes in the current "scene", allowing these to be discarded if desired. If accepted (or no changes), clear the "scene" and return <True>, otherwise leave the display unaffected and return <False>. """ if self.leave_ok(): self.set_scene(None) return True return False # def year_change_ok(self): return self.clear() # def enter(self): BACKEND('GRADES_init') # def leave(self): # Drop the data structures associated with the grade view self.set_scene(None) # def SET_TERMS(self, terms, term): """CALLBACK: Supplies the terms as a list of "keys" (the display form substitutes ' ' for '_'). Also the selected term is passed. Set the term selection widget and trigger a "change of term" signal. """ try: ix = terms.index(term) except ValueError: ix = 0 self.term_select.set_items([(t, t.replace('_', ' ')) for t in terms], index = ix) self.term_select.trigger() return True # def term_changed(self, key): if not self.clear(): return False BACKEND('GRADES_set_term', term = key) self.term = key return True # #TODO: group to set? def SET_GROUPS(self, groups): glist = [(grp, grp) for grp in groups] self.group_select.set_items(glist) self.group_select.trigger() # def group_changed(self, group): if not self.clear(): return False BACKEND('GRADES_set_group', group = group) return True # def sub_changed(self, itemtag): # For real terms there is no subselect, so this method will not # be called. if not self.clear(): return False if self.term == 'Abitur': # This is a special case ... # Switch to/from individual pupil display. # <itemtag> is the pid, empty to select the group. if itemtag: self.set_scene(AbiPupilView(self.gradeView)) BACKEND('ABITUR_set_pupil', pid = itemtag) return True BACKEND('GRADES_subselect', tag = itemtag) return True # def SET_PUPILS_OR_TAGS(self, termx, group, select_list, pid_or_tag): self.subselect.set_items(select_list) if select_list: self.subselect.reset(pid_or_tag) #? self.subselect.trigger() # def SET_GRID(self, **parms): self.set_scene(GradeGrid(self.gradeView, **parms)) # def SET_GRADES(self, grades): """<grades> is a list: [[pid, sid, val], ... ] """ self.grade_scene.set_grades(grades) # def abitur_INIT_CELLS(self, data): self.grade_scene.init_cells(data) # def abitur_SET_CELLS(self, data): self.grade_scene.set_cells(data) # def save(self): self.grade_scene.save_data() # def make_table(self): """Generate input table (xlsx) for the grades. """ if self.grade_scene.changes(): SHOW_WARNING(_MUST_SAVE_CHANGES) return BACKEND('GRADES_make_table') # def input_table(self): """Import a single grade table, replacing the internal table. """ fpath = openDialog(_TABLE_FILE) if fpath: if QuestionDialog(_TITLE_TABLE_REPLACE, _TABLE_REPLACE): BACKEND('GRADES_load_table', filepath = fpath) # On success, the table must be redisplayed # def input_tables(self): """Import a folder of grade tables, collate the contents and update the internal table. Only non-empty cells in the imported tables are taken into consideration and only one imported table may supply the value for a given cell. The "information" fields are not affected. """ #TODO: At present only empty cells may be updated, but it may be better # to allow grades to be updated (only by one of the input tables, though!). # See gradetable.py: integrate_partial_data if not self.clear(): return False dpath = dirDialog() if dpath: BACKEND('GRADES_update_table', dirpath = dpath) BACKEND('GRADES_save_new') # On success, the table must be redisplayed # def QUESTION_UPDATE(self, n): if QuestionDialog(_TITLE_TABLE_REPLACE, _TABLE_OVERWRITE.format( n = n)): BACKEND('GRADES_save_new') # The table must be redisplayed # def make_reports(self): """Generate the grade report(s). """ if self.grade_scene.changes(): SHOW_WARNING(_MUST_SAVE_CHANGES) return BACKEND('GRADES_make_reports') # def print_table(self): """Output the table as pdf. """ if self.grade_scene.changes(): SHOW_WARNING(_MUST_SAVE_CHANGES) return BACKEND('GRADES_print_table') # def PDF_NAME(self, filename): fpath = saveDialog(_PDF_FILE, filename) if fpath: SHOW_INFO(_PDF_TABLE_SAVED.format( path = self.grade_scene.to_pdf(fpath))) # def GET_TAG(self): tag = LineDialog(_TAG_ENTER) if tag: BACKEND('GRADES_save', tag = tag) else: SHOW_WARNING(_NOT_SAVED) ### tab_grade_editor = GradeEdit() TABS.append(tab_grade_editor) FUNCTIONS['grades_SET_TERMS'] = tab_grade_editor.SET_TERMS FUNCTIONS['grades_SET_GROUPS'] = tab_grade_editor.SET_GROUPS FUNCTIONS['grades_SET_PUPILS_OR_TAGS'] = tab_grade_editor.SET_PUPILS_OR_TAGS FUNCTIONS['grades_SET_GRADES'] = tab_grade_editor.SET_GRADES FUNCTIONS['grades_SET_GRID'] = tab_grade_editor.SET_GRID FUNCTIONS['grades_QUESTION_UPDATE'] = tab_grade_editor.QUESTION_UPDATE FUNCTIONS['grades_PDF_NAME'] = tab_grade_editor.PDF_NAME FUNCTIONS['grades_GET_TAG'] = tab_grade_editor.GET_TAG FUNCTIONS['abitur_INIT_CELLS'] = tab_grade_editor.abitur_INIT_CELLS FUNCTIONS['abitur_SET_CELLS'] = tab_grade_editor.abitur_SET_CELLS
33.99435
77
0.644424
465580a27506d20652f78949924c284c914a2235
660
py
Python
Sistema/API/app/application/Controller/complejoController.py
francoo27/TPI
53b7a88a491ef785046c208625c745de80200945
[ "MIT" ]
1
2021-04-27T21:22:30.000Z
2021-04-27T21:22:30.000Z
Sistema/API/app/application/Controller/complejoController.py
francoo27/TPI
53b7a88a491ef785046c208625c745de80200945
[ "MIT" ]
null
null
null
Sistema/API/app/application/Controller/complejoController.py
francoo27/TPI
53b7a88a491ef785046c208625c745de80200945
[ "MIT" ]
null
null
null
from ..Model.ClasificacionModel import ClasificacionSchema from ..Model.ComplejoModel import ComplejoSchema from flask import Blueprint , Response , jsonify ,current_app as app from flask.globals import request from ..Logic import complejoService from marshmallow import Schema, fields, ValidationError # Blueprint Configuration complejo_bp = Blueprint( 'complejo_bp', __name__ ) complejoSchema = ComplejoSchema() complejosSchema = ComplejoSchema(many=True) @complejo_bp.route('/api/complejo', methods=['GET']) def query_complejo(): complejo = complejoService.query_complejo() output = complejosSchema.dump(complejo) return jsonify(output)
30
68
0.795455
5ec09aee03166ff42fcdeee8e2b432bd6f22c254
987
py
Python
setup.py
mtrovo/python-clewareampel
48cdec263d63b8a8549773d5fd787a1c326e7a9e
[ "MIT" ]
1
2022-01-29T15:41:52.000Z
2022-01-29T15:41:52.000Z
setup.py
mtrovo/python-clewareampel
48cdec263d63b8a8549773d5fd787a1c326e7a9e
[ "MIT" ]
null
null
null
setup.py
mtrovo/python-clewareampel
48cdec263d63b8a8549773d5fd787a1c326e7a9e
[ "MIT" ]
1
2021-10-03T23:05:53.000Z
2021-10-03T23:05:53.000Z
#!/usr/bin/env python from setuptools import setup import clewareampel setup( name='clewareampel', version=clewareampel.__version__, description='Control the Cleware USB Ampel (traffic lights) with Python.', long_description='Control the Cleware USB Ampel (traffic lights) with ' 'Python.', author='Roderick Baier', author_email='[email protected]', license='MIT', url='https://github.com/rbaier/python-clewareampel', py_modules=['clewareampel'], install_requires=[ 'pyusb' ], classifiers=[ 'Development Status :: 5 - Production/Stable', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Topic :: System :: Hardware :: Hardware Drivers', 'Topic :: Utilities' ], entry_points={ 'console_scripts': [ 'clewareampel=clewareampel:main', ] } )
27.416667
78
0.614995
7cd286be5cd36f375d155442d95a5dbaf97b922a
4,557
py
Python
third_party/webports/src/src/lib/naclports/tests/test_main.py
ayaNader/chromeos_smart_card_connector
78502e328634a210e27ef897405b66844ebefe62
[ "Apache-2.0" ]
79
2017-09-22T05:09:54.000Z
2022-03-13T01:11:06.000Z
lib/naclports/tests/test_main.py
yeyus/naclports
ceb194315915c69a7266d695259e2c204f9cbbaf
[ "BSD-3-Clause" ]
191
2017-10-23T22:34:58.000Z
2022-03-05T18:10:06.000Z
lib/naclports/tests/test_main.py
yeyus/naclports
ceb194315915c69a7266d695259e2c204f9cbbaf
[ "BSD-3-Clause" ]
32
2017-10-21T07:39:59.000Z
2021-11-10T22:55:32.000Z
# Copyright 2014 The Native Client Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import os import mock from mock import patch, Mock import StringIO import common import naclports.__main__ from naclports import error from naclports.configuration import Configuration # pylint: disable=no-self-use class TestMain(common.NaclportsTest): def setUp(self): super(TestMain, self).setUp() self.AddPatch(patch('naclports.util.CheckSDKRoot')) @patch('naclports.util.Log', Mock()) @patch('naclports.util.RemoveTree') def testCleanAll(self, mock_rmtree): config = Configuration() naclports.__main__.CleanAll(config) mock_rmtree.assert_any_call('/package/install/path') @patch('naclports.__main__.RunMain', Mock(side_effect=error.Error('oops'))) def testErrorReport(self): # Verify that exceptions of the type error.Error are printed # to stderr and result in a return code of 1 with patch('sys.stderr', new_callable=StringIO.StringIO) as stderr: self.assertEqual(naclports.__main__.main(None), 1) self.assertRegexpMatches(stderr.getvalue(), '^naclports: oops') @patch('naclports.__main__.CmdPkgClean') def testMainCommandDispatch(self, cmd_pkg_clean): mock_pkg = Mock() with patch('naclports.source_package.CreatePackage', Mock(return_value=mock_pkg)): naclports.__main__.RunMain(['clean', 'foo']) cmd_pkg_clean.assert_called_once_with(mock_pkg, mock.ANY) @patch('naclports.__main__.CmdPkgClean', Mock(side_effect=error.DisabledError())) def testMainHandlePackageDisabled(self): mock_pkg = Mock() with patch('naclports.source_package.CreatePackage', Mock(return_value=mock_pkg)): with self.assertRaises(error.DisabledError): naclports.__main__.RunMain(['clean', 'foo']) @patch('naclports.__main__.CleanAll') def testMainCleanAll(self, clean_all_mock): naclports.__main__.RunMain(['clean', '--all']) clean_all_mock.assert_called_once_with(Configuration()) class TestCommands(common.NaclportsTest): def testListCommand(self): config = Configuration() pkg = Mock(NAME='foo', VERSION='0.1') with patch('naclports.package.InstalledPackageIterator', Mock(return_value=[pkg])): with patch('sys.stdout', new_callable=StringIO.StringIO) as stdout: options = Mock(all=False) naclports.__main__.CmdList(config, options, []) lines = stdout.getvalue().splitlines() self.assertRegexpMatches(lines[0], '^foo\\s+0.1$') self.assertEqual(len(lines), 1) def testListCommandVerbose(self): config = Configuration() pkg = Mock(NAME='foo', VERSION='0.1') with patch('naclports.package.InstalledPackageIterator', Mock(return_value=[pkg])): with patch('sys.stdout', new_callable=StringIO.StringIO) as stdout: options = Mock(verbose=False, all=False) naclports.__main__.CmdList(config, options, []) lines = stdout.getvalue().splitlines() self.assertRegexpMatches(lines[0], "^foo$") self.assertEqual(len(lines), 1) @patch('naclports.package.CreateInstalledPackage', Mock()) def testInfoCommand(self): config = Configuration() options = Mock() file_mock = common.MockFileObject('FOO=bar\n') with patch('sys.stdout', new_callable=StringIO.StringIO) as stdout: with patch('__builtin__.open', Mock(return_value=file_mock), create=True): naclports.__main__.CmdInfo(config, options, ['foo']) self.assertRegexpMatches(stdout.getvalue(), "FOO=bar") def testContentsCommand(self): file_list = ['foo', 'bar'] options = Mock(verbose=False, all=False) package = Mock(NAME='test', Files=Mock(return_value=file_list)) expected_output = '\n'.join(file_list) + '\n' with patch('sys.stdout', new_callable=StringIO.StringIO) as stdout: naclports.__main__.CmdPkgContents(package, options) self.assertEqual(stdout.getvalue(), expected_output) # when the verbose option is set expect CmdContents to output full paths. naclports.util.log_level = naclports.util.LOG_VERBOSE expected_output = [os.path.join('/package/install/path', f) for f in file_list] expected_output = '\n'.join(expected_output) + '\n' with patch('sys.stdout', new_callable=StringIO.StringIO) as stdout: naclports.__main__.CmdPkgContents(package, options) self.assertEqual(stdout.getvalue(), expected_output)
38.948718
80
0.707483
3352bef5fbf40d331a92c6522ac7345d3b7b9bb9
8,627
py
Python
beginner_source/blitz/neural_networks_tutorial.py
codewithkaranjeswani/tutorials
ce84ebbb764bef14e982076b5ca7d8906ac0db78
[ "BSD-3-Clause" ]
null
null
null
beginner_source/blitz/neural_networks_tutorial.py
codewithkaranjeswani/tutorials
ce84ebbb764bef14e982076b5ca7d8906ac0db78
[ "BSD-3-Clause" ]
null
null
null
beginner_source/blitz/neural_networks_tutorial.py
codewithkaranjeswani/tutorials
ce84ebbb764bef14e982076b5ca7d8906ac0db78
[ "BSD-3-Clause" ]
null
null
null
# -*- coding: utf-8 -*- """ Neural Networks =============== Neural networks can be constructed using the ``torch.nn`` package. Now that you had a glimpse of ``autograd``, ``nn`` depends on ``autograd`` to define models and differentiate them. An ``nn.Module`` contains layers, and a method ``forward(input)``\ that returns the ``output``. For example, look at this network that classifies digit images: .. figure:: /_static/img/mnist.png :alt: convnet convnet It is a simple feed-forward network. It takes the input, feeds it through several layers one after the other, and then finally gives the output. A typical training procedure for a neural network is as follows: - Define the neural network that has some learnable parameters (or weights) - Iterate over a dataset of inputs - Process input through the network - Compute the loss (how far is the output from being correct) - Propagate gradients back into the network’s parameters - Update the weights of the network, typically using a simple update rule: ``weight = weight - learning_rate * gradient`` Define the network ------------------ Let’s define this network: """ import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() # 1 input image channel, 6 output channels, 3x3 square convolution # kernel self.conv1 = nn.Conv2d(1, 6, 5) self.conv2 = nn.Conv2d(6, 16, 5) # an affine operation: y = Wx + b self.fc1 = nn.Linear(16 * 5 * 5, 120) # 6*6 from image dimension self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): # Max pooling over a (2, 2) window x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # If the size is a square you can only specify a single number x = F.max_pool2d(F.relu(self.conv2(x)), 2) x = x.view(-1, self.num_flat_features(x)) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x def num_flat_features(self, x): size = x.size()[1:] # all dimensions except the batch dimension num_features = 1 for s in size: num_features *= s return num_features net = Net() print(net) ######################################################################## # You just have to define the ``forward`` function, and the ``backward`` # function (where gradients are computed) is automatically defined for you # using ``autograd``. # You can use any of the Tensor operations in the ``forward`` function. # # The learnable parameters of a model are returned by ``net.parameters()`` params = list(net.parameters()) print(len(params)) print(params[0].size()) # conv1's .weight ######################################################################## # Let's try a random 32x32 input. # Note: expected input size of this net (LeNet) is 32x32. To use this net on # the MNIST dataset, please resize the images from the dataset to 32x32. input = torch.randn(1, 1, 32, 32) out = net(input) print(out) ######################################################################## # Zero the gradient buffers of all parameters and backprop with random # gradients: net.zero_grad() out.backward(torch.randn(1, 10)) ######################################################################## # .. note:: # # ``torch.nn`` only supports mini-batches. The entire ``torch.nn`` # package only supports inputs that are a mini-batch of samples, and not # a single sample. # # For example, ``nn.Conv2d`` will take in a 4D Tensor of # ``nSamples x nChannels x Height x Width``. # # If you have a single sample, just use ``input.unsqueeze(0)`` to add # a fake batch dimension. # # Before proceeding further, let's recap all the classes you’ve seen so far. # # **Recap:** # - ``torch.Tensor`` - A *multi-dimensional array* with support for autograd # operations like ``backward()``. Also *holds the gradient* w.r.t. the # tensor. # - ``nn.Module`` - Neural network module. *Convenient way of # encapsulating parameters*, with helpers for moving them to GPU, # exporting, loading, etc. # - ``nn.Parameter`` - A kind of Tensor, that is *automatically # registered as a parameter when assigned as an attribute to a* # ``Module``. # - ``autograd.Function`` - Implements *forward and backward definitions # of an autograd operation*. Every ``Tensor`` operation creates at # least a single ``Function`` node that connects to functions that # created a ``Tensor`` and *encodes its history*. # # **At this point, we covered:** # - Defining a neural network # - Processing inputs and calling backward # # **Still Left:** # - Computing the loss # - Updating the weights of the network # # Loss Function # ------------- # A loss function takes the (output, target) pair of inputs, and computes a # value that estimates how far away the output is from the target. # # There are several different # `loss functions <https://pytorch.org/docs/nn.html#loss-functions>`_ under the # nn package . # A simple loss is: ``nn.MSELoss`` which computes the mean-squared error # between the input and the target. # # For example: output = net(input) target = torch.randn(10) # a dummy target, for example target = target.view(1, -1) # make it the same shape as output criterion = nn.MSELoss() loss = criterion(output, target) print(loss) ######################################################################## # Now, if you follow ``loss`` in the backward direction, using its # ``.grad_fn`` attribute, you will see a graph of computations that looks # like this: # # :: # # input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d # -> view -> linear -> relu -> linear -> relu -> linear # -> MSELoss # -> loss # # So, when we call ``loss.backward()``, the whole graph is differentiated # w.r.t. the loss, and all Tensors in the graph that has ``requires_grad=True`` # will have their ``.grad`` Tensor accumulated with the gradient. # # For illustration, let us follow a few steps backward: print(loss.grad_fn) # MSELoss print(loss.grad_fn.next_functions[0][0]) # Linear print(loss.grad_fn.next_functions[0][0].next_functions[0][0]) # ReLU ######################################################################## # Backprop # -------- # To backpropagate the error all we have to do is to ``loss.backward()``. # You need to clear the existing gradients though, else gradients will be # accumulated to existing gradients. # # # Now we shall call ``loss.backward()``, and have a look at conv1's bias # gradients before and after the backward. net.zero_grad() # zeroes the gradient buffers of all parameters print('conv1.bias.grad before backward') print(net.conv1.bias.grad) loss.backward() print('conv1.bias.grad after backward') print(net.conv1.bias.grad) ######################################################################## # Now, we have seen how to use loss functions. # # **Read Later:** # # The neural network package contains various modules and loss functions # that form the building blocks of deep neural networks. A full list with # documentation is `here <https://pytorch.org/docs/nn>`_. # # **The only thing left to learn is:** # # - Updating the weights of the network # # Update the weights # ------------------ # The simplest update rule used in practice is the Stochastic Gradient # Descent (SGD): # # ``weight = weight - learning_rate * gradient`` # # We can implement this using simple Python code: # # .. code:: python # # learning_rate = 0.01 # for f in net.parameters(): # f.data.sub_(f.grad.data * learning_rate) # # However, as you use neural networks, you want to use various different # update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc. # To enable this, we built a small package: ``torch.optim`` that # implements all these methods. Using it is very simple: import torch.optim as optim # create your optimizer optimizer = optim.SGD(net.parameters(), lr=0.01) # in your training loop: optimizer.zero_grad() # zero the gradient buffers output = net(input) loss = criterion(output, target) loss.backward() optimizer.step() # Does the update ############################################################### # .. Note:: # # Observe how gradient buffers had to be manually set to zero using # ``optimizer.zero_grad()``. This is because gradients are accumulated # as explained in the `Backprop`_ section.
32.927481
79
0.630115
590534b1eb0e72d2f54fc8b3b1b4337a132c97e8
39,077
py
Python
pyopencl_extension/framework.py
piveloper/pyopencl-extension
0f9fede4cfbb1c3f6d99c5e0aa94feddb23a5d4c
[ "MIT" ]
null
null
null
pyopencl_extension/framework.py
piveloper/pyopencl-extension
0f9fede4cfbb1c3f6d99c5e0aa94feddb23a5d4c
[ "MIT" ]
null
null
null
pyopencl_extension/framework.py
piveloper/pyopencl-extension
0f9fede4cfbb1c3f6d99c5e0aa94feddb23a5d4c
[ "MIT" ]
null
null
null
__author__ = "piveloper" __copyright__ = "26.03.2020, piveloper" __version__ = "1.0" __email__ = "[email protected]" __doc__ = """This script includes helpful functions to extended PyOpenCl functionality.""" import os import re import time from abc import abstractmethod, ABC from dataclasses import dataclass, field from enum import Enum from functools import lru_cache from pathlib import Path from typing import Union, Tuple, List, Dict, Callable import numpy as np import pyastyle from mako import exceptions from mako.template import Template import pyopencl as cl from pyopencl._cl import Device from pyopencl.array import Array as ClArray from pyopencl_extension import CommandQueue, Array, to_device from pyopencl_extension.helpers.general import write_string_to_file from pyopencl_extension.modifications_pyopencl.command_queue import QueueProperties, get_current_queue from pyopencl_extension.modifications_pyopencl.context import Context, get_devices from pyopencl_extension.types.auto_gen.cl_types import ClTypesScalar from pyopencl_extension.types.utilities_np_cl import c_name_from_dtype, scalar_type_from_vec_type, \ get_vec_size, Types, number_vec_elements_of_cl_type, VEC_INDICES from pyopencl_extension.emulation import create_py_file_and_load_module, unparse_c_code_to_python @dataclass class LocalArray: shape: int dtype: np.dtype cl_local_memory: cl.LocalMemory = field(init=False, default=None) def __post_init__(self): self.cl_local_memory = cl.LocalMemory(int(self.shape * np.dtype(self.dtype).itemsize)) TypesClArray = Union[Array, ClArray] TypesDefines = Union[str, float, int, bool] TypesReplacement = Union[str, float, int, bool] TypesArgArrays = Union[np.ndarray, Array, ClArray, LocalArray] _ = ClTypesScalar TypesArgScalar = Union[int, float, _.char, _.short, _.int, _.long, _.uchar, _.ushort, _.uint, _.ulong, _.half, _.float, _.double] # TypesKernelArg = Union[Array, TypesDefines] # todo: remove? preamble_activate_double = """ #if defined(cl_khr_fp64) // Khronos extension available? #pragma OPENCL EXTENSION cl_khr_fp64 : enable #define PYOPENCL_DEFINE_CDOUBLE #elif defined(cl_amd_fp64) // AMD extension available? #pragma OPENCL EXTENSION cl_amd_fp64 : enable #define PYOPENCL_DEFINE_CDOUBLE #endif """ preamble_activate_complex_numbers = """ #include <pyopencl-complex.h> #define TP_ROOT ${cplx_type} """ def preamble_precision(precision: str = 'single'): """ This function generates preamble to support either single or double precision floating point numbers. :param precision: :return: """ if precision == 'single': return """ #define PI 3.14159265359f """ elif precision == 'double': return preamble_activate_double + """\n #define PI 3.14159265358979323846 """ else: raise NotImplementedError() def preamble_generic_type_operations(number_format: str = 'real', precision: str = 'single'): """ This function returns a preamble which defines how generic operations are executed on device. This becomes especially important when dealing with complex types which OpenCl does not support out of the box. As a solution, pyopencl-complex.h includes several basic function for complex operations. E.g. consider a kernel which adds two numbers, however the input type can be real or complex valued. Typically one would implement c = a + b. However, OpenCl does not support + operation when a and b are complex valued. Therefore using this preamble one can write c = ADD(a,b). ADD acts a a generic operation which supports real and complex input depending on selection for number_format. :param number_format: 'real' or 'complex :param precision: 'single' or 'double' :return: preamble to support generic operations """ if number_format == 'complex': cplx_type_pyopencl = {'single': 'cfloat', 'double': 'cdouble'}[precision] return preamble_activate_complex_numbers + """ #define MUL ${cplx_type}_mul #define ADD ${cplx_type}_add #define SUB ${cplx_type}_sub #define ABS ${cplx_type}_abs #define RMUL ${cplx_type}_rmul #define NEW ${cplx_type}_new #define CONJ ${cplx_type}_conj #define REAL(x) x.real #define IMAG(x) x.imag """.replace('${cplx_type}', cplx_type_pyopencl) elif number_format == 'real': return """ #define MUL(x,y) (x*y) #define ADD(x,y) (x+y) #define SUB(x,y) (x-y) #define ABS(x) (fabs(x)) #define RMUL(x,y) (x*y) #define NEW(x,y) (x) #define CONJ(x) (x) #define REAL(x) (x) #define IMAG(x) (0) """ else: raise NotImplementedError() def catch_invalid_argument_name(name: str): """ E.g. when using certain argument names like 'channel' the opencl compiler throws a compilation error, probably because channel is an reserved opencl command. There we replace those names by appending '_' character. :param name: :return: """ invalid_names = ['channel'] if name in invalid_names: raise ValueError('Invalid opencl name: \'{}\' used.'.format(name)) else: return name class OrderInMemory(Enum): C_CONTIGUOUS: str = 'c_contiguous' F_CONTIGUOUS: str = 'f_contiguous' @dataclass class ArgBase(ABC): # too much restriction, shape of array might change during runtime # shape: Tuple[int, ...] = (1,) # default: argument is scalar @property @abstractmethod def address_space_qualifier(self) -> str: # __global, __local, __private, __constant pass @property @abstractmethod def dtype(self) -> np.dtype: # __global, __local, __private, __constant pass def to_string(self, name): new_name = catch_invalid_argument_name(name) if type(self) in [Scalar]: # scalar return '{} {} {}'.format(self.address_space_qualifier, c_name_from_dtype(self.dtype), new_name) else: # array return '{} {} *{}'.format(self.address_space_qualifier, c_name_from_dtype(self.dtype), new_name) @dataclass class Scalar(ArgBase): dtype: np.dtype = field(default=Types.int) address_space_qualifier: str = field(default='') default: np.dtype = field(init=False, default=None) def __post_init__(self): if np.isscalar(self.dtype): self.default = self.dtype if type(self.default) == float: self.dtype = Types.double elif type(self.default) == int: self.dtype = Types.int else: self.dtype = type(self.dtype) @dataclass class Pointer(ArgBase, ABC): dtype: np.dtype = field(default=Types.int) address_space_qualifier: str = field(init=False, default='__global') @dataclass class Private(Pointer): address_space_qualifier: str = field(init=False, default='__private') @dataclass class Local(Pointer): dtype: Union[np.dtype, LocalArray] = field(default=Types.int) address_space_qualifier: str = field(init=False, default='__local') order_in_memory: OrderInMemory = OrderInMemory.C_CONTIGUOUS default: cl.LocalMemory = field(init=False, default=None) def __post_init__(self): if isinstance(self.dtype, LocalArray): self.default = self.dtype.cl_local_memory self.dtype = self.dtype.dtype @dataclass class Global(Pointer): dtype: Union[np.dtype, TypesClArray] = field(default=Types.int) read_only: bool = False # adds 'const' qualifier to let compiler know that global array is never written order_in_memory: OrderInMemory = OrderInMemory.C_CONTIGUOUS address_space_qualifier: str = field(init=False, default='__global') default: TypesClArray = field(init=False, default='') def __post_init__(self): if isinstance(self.dtype, TypesClArray.__args__): self.default = self.dtype self.dtype = self.dtype.dtype if self.read_only: self.address_space_qualifier = 'const __global' @dataclass class Constant(Pointer): """ const is only a hint for the compiler that the data does not change __constant leads to usage of very fast constant cache memory which is shared among multiple compute units. From AMD optimization guide, e.g. we can read 4 bytes/cycles. Local memory can be ready twice as fast with 8bytes/cycle, however local memory is a even more scarce resource. https://stackoverflow.com/questions/17991714/opencl-difference-between-constant-memory-and-const-global-memory/50931783 """ dtype: Union[np.dtype, TypesClArray] = field(default=Types.int) order_in_memory: str = OrderInMemory.C_CONTIGUOUS address_space_qualifier: str = field(init=False, default='__constant') default: TypesClArray = field(init=False, default='') def __post_init__(self): if isinstance(self.dtype, TypesClArray.__args__): self.default = self.dtype self.dtype = self.dtype.dtype def template(func: Union['Kernel', 'Function']) -> str: body = ''.join(func.body) tpl = func.header + '\n{' + body + '}\n' args = [value.to_string(key) + ',' for key, value in func.args.items()] args = '{}'.format('\n'.join(args)) args = args[:-1] # remove last comma replacements = {'name': func.name, 'args': args, 'returns': c_name_from_dtype(func.returns)} for key, value in func.replacements.items(): replacements[key] = str(value) try: # todo: e.g. if replacement has been forgotten, still save template as file tpl = Template(tpl).render(**replacements) except: raise ValueError(exceptions.text_error_template().render()) defines = '\n'.join(['#define {} {}'.format(key, str(value)) for key, value in func.defines.items()]) tpl_formatted = pyastyle.format('{}\n\n{}'.format(defines, tpl), '--style=allman --indent=spaces=4') return tpl_formatted @dataclass class FunctionBase(ABC): name: str = 'func' args: Dict[str, Union[TypesArgArrays, TypesArgScalar, Scalar, Global, Local, Private, Constant]] = \ field(default_factory=lambda: []) body: Union[List[str], str] = field(default_factory=lambda: []) replacements: Dict[str, TypesReplacement] = field(default_factory=lambda: {}) type_defs: Dict[str, np.dtype] = field(default_factory=lambda: {}) # todo defines: Dict[str, TypesDefines] = field(default_factory=lambda: {}) functions: List['Function'] = field(default_factory=lambda: []) def __post_init__(self): if isinstance(self.body, str): self.body = [self.body] self._prepares_args() @property def header(self): return '${returns} ${name}(${args})' @property @abstractmethod def template(self) -> str: pass @staticmethod def _prepare_arg(v): """ This is a convenience feature. Arguments might be provided only as numpy array, python integer or float etc. Therefore, this function adds appropriate pointer type. """ if isinstance(v, np.ndarray): g_arg = Global(v.dtype) g_arg.default = v return g_arg elif isinstance(v, TypesClArray.__args__): return Global(v) elif isinstance(v, LocalArray): return Local(v) elif isinstance(v, TypesArgScalar.__args__): return Scalar(v) else: return v def _prepares_args(self): self.args = {k: self._prepare_arg(v) for k, v in self.args.items()} @dataclass class Function(FunctionBase): @property def template(self) -> str: return template(self) returns: np.dtype = field(default_factory=lambda: np.dtype(np.void)) def __str__(self) -> str: return super().__str__() + str(self.returns) KernelGridType = Union[Tuple[int], Tuple[int, int], Tuple[int, int, int]] class Compilable: @abstractmethod def compile(self, context: Context = None, emulate: bool = False): pass @staticmethod def get_default_dir_pycl_kernels(): return Path(os.getcwd()).joinpath('cl_py_modules') @dataclass class Kernel(FunctionBase, Compilable): def compile(self, context: Context = None, emulate: bool = False, file='$default_path'): Program(kernels=[self]).compile(context=context, emulate=emulate, file=file) return self.callable_kernel global_size: KernelGridType = None local_size: KernelGridType = None returns: np.dtype = field(default_factory=lambda: np.dtype(np.void), init=False) callable_kernel: 'CallableKernel' = field(default_factory=lambda: None, init=False) def __str__(self) -> str: return super().__str__() @property def template(self) -> str: return template(self) @property def header(self): return '__kernel ${returns} ${name}(${args})' def __call__(self, global_size: KernelGridType = None, local_size: KernelGridType = None, **kwargs): if self.callable_kernel is not None: return self.callable_kernel(global_size=global_size, local_size=local_size, **kwargs) else: raise ValueError('Kernel has not been compiled yet.') def _get_all_funcs(f: FunctionBase, flat_list=None) -> List[FunctionBase]: if flat_list is None: flat_list = [] for sub_f in f.functions: _get_all_funcs(sub_f, flat_list) flat_list.append(f) return flat_list else: flat_list.extend([_get_all_funcs(sub_f, flat_list) for sub_f in f.functions]) flat_list.append(f) def _get_list_with_unique_functions(functions, kernels): functions_in_kernels = [f for k in kernels for f in k.functions] all_funcs = [_f for f in functions + functions_in_kernels for _f in _get_all_funcs(f)] all_funcs_unique, _func_names = [], [] for f in all_funcs: if len(_func_names) == 0 or f.name not in _func_names: all_funcs_unique.append(f) _func_names.append(f.name) return all_funcs_unique @dataclass class Program(Compilable): """ Models an OpenCl Program containing functions or kernels. """ def compile(self, context: Context = None, emulate: bool = False, file: str = '$default_path') -> 'ProgramContainer': return compile_cl_program(self, context, emulate, file) functions: List[Function] = field(default_factory=lambda: []) kernels: List[Kernel] = field(default_factory=lambda: []) defines: Dict[str, TypesDefines] = field(default_factory=lambda: {}) type_defs: Dict[str, np.dtype] = field(default_factory=lambda: {}) @staticmethod def _arg_to_str_for_hash(name, arg: ArgBase): return name + str(type(arg)) + str(hash(arg.dtype)) + arg.address_space_qualifier @staticmethod def _func_to_str_for_hash(func: FunctionBase): str_args = ''.join(Program._arg_to_str_for_hash(k, v) for k, v in func.args.items()) str_repl = ''.join(k + str(v) for k, v in func.replacements.items()) str_type_defs = ''.join(k + str(v) for k, v in func.type_defs.items()) str_defines = ''.join(k + str(v) for k, v in func.defines.items()) str_body = ''.join(func.body) return str_type_defs + str_defines + str_repl + func.header + func.name + str_args + str_body def __hash__(self) -> int: str_funcs = ''.join(self._func_to_str_for_hash(knl) for knl in self.functions) str_kernels = ''.join(self._func_to_str_for_hash(knl) for knl in self.kernels) str_type_defs = ''.join(k + str(v) for k, v in self.type_defs.items()) str_defines = ''.join(k + str(v) for k, v in self.defines.items()) prog_str = str_defines + str_type_defs + str_funcs + str_kernels return int(str(hash(prog_str)) + str(abs(hash(prog_str + 'something')))) # double hash to avoid collisions @property def rendered_template(self): all_funcs = _get_list_with_unique_functions(self.functions, self.kernels) functions = [f.template for f in all_funcs] + [k.template for k in self.kernels] functions = '\n'.join(functions) if 'double' in functions: _preamble_precision = preamble_precision('double') else: _preamble_precision = preamble_precision('single') if 'cfloat_t' in functions: _preamble_generic_operations = preamble_generic_type_operations('complex', 'single') elif 'cdouble_t' in functions: _preamble_generic_operations = preamble_generic_type_operations('complex', 'double') else: _preamble_generic_operations = preamble_generic_type_operations('real') preamble_buff_t = f'{_preamble_precision}\n{_preamble_generic_operations}' # join program typedefs with typedefs from kernels and functions # todo: consider replacing type strings directly to avoid name conflicts def update_and_checks_for_duplicates_same_type(items: dict, dic: dict): for key, value in items.items(): if key in dic: if not dic[key] == value: raise ValueError('Same type def name for different types') else: dic[key] = value [update_and_checks_for_duplicates_same_type(func.type_defs, self.type_defs) for func in self.functions] [update_and_checks_for_duplicates_same_type(func.type_defs, self.type_defs) for func in self.kernels] # remove since defines are inserted before function/kernels # [update_and_checks_for_duplicates_same_type(func.defines, self.defines) for func in self.functions] # [update_and_checks_for_duplicates_same_type(func.defines, self.defines) for func in self.kernels] defines = '\n'.join(['#define {} {}'.format(key, str(value)) for key, value in self.defines.items()]) type_defs = '\n'.join( ['typedef {c_name} {new_name};\n#define convert_{new_name}(x) convert_{c_name}(x)'.format( c_name=c_name_from_dtype(value), new_name=str(key)) for key, value in self.type_defs.items()]) tpl_all = self._get_tpl(preamble_buff_t, defines, type_defs, functions) tpl_formatted = pyastyle.format(tpl_all, '--style=allman --indent=spaces=4') return tpl_formatted def _get_tpl(self, preamble_complex, defines, type_defs, functions): return '{}\n\n{}\n\n{}\n\n{}\n\n'.format(preamble_complex, defines, type_defs, functions) def build_for_device(context: Context, template_to_be_compiled: str, file: str = None) -> cl.Program: if file is not None: write_string_to_file(template_to_be_compiled, file + '.cl', b_logging=False) try: program = cl.Program(context, str(template_to_be_compiled)).build() except Exception as error: tpl_line_numbers = '\n'.join( ['{:<4}{}'.format(i + 1, line) for i, line in enumerate(template_to_be_compiled.split('\n'))]) raise ValueError('\n{}\n\n{}'.format(tpl_line_numbers, str(error))) return program # Todo: Find good structure for modeling cl and python kernels @dataclass class CallableKernel(ABC): kernel_model: Kernel def __getattr__(self, name): if name in self.kernel_model.args.keys(): return self.kernel_model.args[name].default return super().__getattribute__(name) @abstractmethod def __call__(self, global_size: KernelGridType = None, local_size: KernelGridType = None, **kwargs): pass @staticmethod def _typing_scalar_argument(arg_model: Union[Scalar, Scalar], scalar_value_provided: TypesArgScalar): if get_vec_size(arg_model.dtype) == 1: return np.dtype(arg_model.dtype).type(scalar_value_provided) else: dtype_scalar = scalar_type_from_vec_type(arg_model.dtype) scalar = np.dtype(dtype_scalar).type(scalar_value_provided) # converts to bytes like object return scalar.astype(arg_model.dtype) # converts to vector type @staticmethod def _prepare_arguments(queue: CommandQueue, knl: Kernel, **kwargs): global_size = kwargs.pop('global_size', None) local_size = kwargs.pop('local_size', None) global_size = knl.global_size if global_size is None else global_size local_size = knl.local_size if local_size is None else local_size supported_kws = [k for k in knl.args.keys()] kw_not_in_kernel_arguments = [kw for kw in kwargs if kw not in supported_kws] if len(kw_not_in_kernel_arguments) > 0: raise ValueError( f'keyword argument {kw_not_in_kernel_arguments} does not exist in kernel argument list {supported_kws}') # If kernel arguments are of type np.ndarray they are converted to cl arrays here # This is done here, since thq queue is available at this point for sure. # todo: deal with case if kwarg is numpy argument def deal_with_np_arrays(v): if isinstance(v, Global) and isinstance(v.default, np.ndarray): v.default = to_device(ary=v.default, queue=queue) return v else: return v knl.args = {k: deal_with_np_arrays(v) for k, v in knl.args.items()} # set default arguments. Looping over kernel model forces correct order of arguments args_call = [kwargs.pop(key, value.default if isinstance(value, (Constant, Global, Scalar, Local)) else None) for key, value in knl.args.items()] if any(arg is None for arg in args_call): raise ValueError('Argument equal to None can lead to system crash') if global_size is None: raise ValueError('global_size not provided!') if global_size == (): raise ValueError('Global size is empty') if 0 in global_size: raise ValueError(f'Parameter in global size {global_size} equal to zero') if local_size is not None and 0 in local_size: raise ValueError(f'Parameter in local size {local_size} equal to zero') # convert scalar argument to correct type. E.g. argument can be python int and is converted to char args_model = list(knl.args.values()) args_call = [CallableKernel._typing_scalar_argument(args_model[i], arg) if type(args_model[i]) in [Scalar, Scalar] else arg for i, arg in enumerate(args_call)] # if argument of type LocalArray extract cl.LocalMemory instance to be passed as argument args_call = [arg.cl_local_memory if isinstance(arg, LocalArray) else arg for arg in args_call] # check if buffer have same type as defined in the kernel function header b_types_equal = [args_call[i].dtype == v.dtype for i, v in enumerate(args_model) if isinstance(v, Global)] if not np.all(b_types_equal): idx_buffer_list = int(np.argmin(b_types_equal)) idx = [i for i, kv in enumerate(knl.args.items()) if isinstance(kv[1], Global)][idx_buffer_list] buffer_name = [k for k, v in knl.args.items()][idx] buffer_type_expected = args_model[idx].dtype buffer_type_call = args_call[idx].dtype raise ValueError(f'Expected buffer argument \'{buffer_name}\' with type {buffer_type_expected} ' f'but got buffer with type {buffer_type_call}') # check if buffer elements of array arguments have memory order as expected (c or f contiguous) def b_array_memory_order_as_expected(ary_model: Global, ary_call: TypesClArray): if ary_model.order_in_memory == OrderInMemory.C_CONTIGUOUS: return ary_call.flags.c_contiguous else: # f_contiguous return ary_call.flags.f_contiguous knl_args_invalid_memory_order = [(k, v) for idx, (k, v) in enumerate(knl.args.items()) if isinstance(v, Global) and not b_array_memory_order_as_expected(v, args_call[idx])] if len(knl_args_invalid_memory_order) > 0: msg = '\n'.join([f'Array argument \'{arg[0]}\' is not {arg[1].order_in_memory} (as defined in Kernel)' for arg in knl_args_invalid_memory_order]) raise ValueError(msg) non_supported_types = [np.ndarray] if any(_ := [type(arg) in non_supported_types for arg in args_call]): raise ValueError(f'Type of argument \'{list(knl.args.items())[np.where(_)[0][0]][0]}\' is not supported in ' f'kernel call') return global_size, local_size, args_call @dataclass class CallableKernelEmulation(CallableKernel): function: Callable def __call__(self, global_size: KernelGridType = None, local_size: KernelGridType = None, **kwargs: Union[TypesClArray, object]) -> cl.Event: # e.g. if two kernels of a program shall run concurrently, this can be enable by passing another queue here queue = kwargs.pop('queue', get_current_queue()) global_size, local_size, args = self._prepare_arguments(queue=queue, knl=self.kernel_model, global_size=global_size, local_size=local_size, **kwargs) self.function(global_size, local_size, *args) # create user event with context retrieved from first arg of type Array event = cl.UserEvent([_ for _ in args if isinstance(_, TypesClArray.__args__)][0].context) event.set_status(cl.command_execution_status.COMPLETE) return event @dataclass class CallableKernelDevice(CallableKernel): compiled: cl.Kernel @staticmethod def check_local_size_not_exceeding_device_limits(device: Device, local_size): # E.g. on nvidia the local size might be individually limited to be (1024,1024,64). # This shall trigger an exception, when wrong local size is provided. if local_size is not None and any([desired_local_size > device.max_work_item_sizes[dim] for dim, desired_local_size in enumerate(local_size)]): raise ValueError(f'Requested local dimensions {local_size} exceed {device.max_work_item_sizes=}') def __call__(self, global_size: KernelGridType = None, local_size: KernelGridType = None, **kwargs) -> cl.Event: # e.g. if two kernels of a program shall run concurrently, this can be enable by passing another queue here queue = kwargs.pop('queue', get_current_queue()) assert self.compiled.context.int_ptr == queue.context.int_ptr global_size, local_size, args = self._prepare_arguments(queue=queue, knl=self.kernel_model, global_size=global_size, local_size=local_size, **kwargs) self.check_local_size_not_exceeding_device_limits(queue.device, local_size) # extract buffer from cl arrays separate, since in emulation we need cl arrays args_cl = [arg.data if isinstance(arg, TypesClArray.__args__) else arg for i, arg in enumerate(args)] event = self.compiled(queue, global_size, local_size, *args_cl) queue.add_event(event, self.kernel_model.name) return event @dataclass class ProgramContainer: """ Responsibility: A callable kernel is returned with program.kernel_name. Depending on value of b_run_python_emulation a call of this kernel is executed on device or in emulation. """ program_model: Program file: str init: CommandQueue callable_kernels: Dict[str, Union[CallableKernelEmulation, CallableKernelDevice]] = None def __getattr__(self, name) -> CallableKernel: if name in self.callable_kernels: return self.callable_kernels[name] else: return super().__getattribute__(name) # https://stackoverflow.com/questions/1988804/what-is-memoization-and-how-can-i-use-it-in-python class MemoizeKernelFunctions: def __init__(self, f): self.f = f self.memo = {} def __call__(self, program_model: Program, context: Context, file: str = None): # body = ''.join(program_model.rendered_template) _id = hash(f'{hash(context)}{hash(program_model)}') if _id not in self.memo: self.memo[_id] = self.f(program_model, context, file) return self.memo[_id] @MemoizeKernelFunctions def compile_cl_program_device(program_model: Program, context: Context = None, file: str = None) -> Dict[str, Kernel]: code_cl = program_model.rendered_template program = build_for_device(context, code_cl, file) kernels_model = program_model.kernels # set scalar arguments for each kernel from kernel model callable_kernels = {knl.function_name: knl for i, knl in enumerate(program.all_kernels())} for i, knl in enumerate(kernels_model): arg_types = [arg.dtype if type(arg) in [Scalar, Scalar] else None for _, arg in kernels_model[i].args.items()] callable_kernels[knl.name].set_scalar_arg_dtypes(arg_types) return callable_kernels @MemoizeKernelFunctions def compile_cl_program_emulation(program_model: Program, context: Context, file: str = None, *args, **kwargs) -> Dict[str, Callable]: code_py = unparse_c_code_to_python(code_c=program_model.rendered_template) module = create_py_file_and_load_module(code_py, file) kernels_model = program_model.kernels callable_kernels = {knl.name: module.__getattribute__(knl.name) for knl in kernels_model} return callable_kernels def compile_cl_program(program_model: Program, context: Context = None, emulate: bool = False, file: str = '$default_path') -> ProgramContainer: t_ns_start = time.perf_counter_ns() # deal with file name if isinstance(file, Path): file = str(file) if file is None and emulate: raise ValueError('You intended to create no file by setting file=None. ' 'However, a file must be created for debugging.') # todo can python debugging run without file? elif file == '$default_path': file = str(program_model.get_default_dir_pycl_kernels().joinpath(program_model.kernels[0].name)) if context is None: context = get_current_queue().context dict_kernels_program_model = {knl.name: knl for knl in program_model.kernels} if emulate: dict_emulation_kernel_functions = compile_cl_program_emulation(program_model, context, file) callable_kernels = {k: CallableKernelEmulation(kernel_model=dict_kernels_program_model[k], function=v) for k, v in dict_emulation_kernel_functions.items()} else: dict_device_kernel_functions = compile_cl_program_device(program_model, context, file) callable_kernels = {k: CallableKernelDevice(kernel_model=dict_kernels_program_model[k], compiled=v) for k, v in dict_device_kernel_functions.items()} # make callable kernel available in knl model instance for knl in program_model.kernels: knl.callable_kernel = callable_kernels[knl.name] context.add_time_compilation(time.perf_counter_ns() - t_ns_start) return ProgramContainer(program_model=program_model, file=file, init=context, callable_kernels=callable_kernels) def int_safe(val: float): if val.is_integer(): return int(val) else: raise ValueError(f'val={val} is no integer') class HashArray(Array): def __init__(self, *args, **kwargs): if isinstance(args[0], TypesClArray.__args__): a = args[0] super().__init__(a.queue, a.shape, a.dtype, order="C", allocator=a.allocator, data=a.data, offset=a.offset, strides=a.strides, events=a.events) else: super().__init__(*args, **kwargs) self.hash = hash(self.get().tobytes()) def __setitem__(self, key, value): super().__setitem__(key, value) self.update_hash() def set(self, ary, queue=None, async_=None, **kwargs): res = super().set(ary, queue, async_, **kwargs) self.update_hash() return res def update_hash(self): self.hash = hash(self.get().tobytes()) class Helpers: # helper methods which can be useful in interplay with this framwork @staticmethod def _camel_to_snake(name): name = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name) return re.sub('([a-z0-9])([A-Z])', r'\1_\2', name).lower() @staticmethod def command_compute_address(n_dim: int) -> str: command = '0' for i in range(n_dim): offset = '1' for j in range(i + 1, n_dim): offset += '*get_global_size({})'.format(j) command += '+get_global_id({})*{}'.format(i, offset) return command # helpers for using vector types @staticmethod def get_vec_dtype(dtype_vec: np.dtype, dtype_scalar: np.dtype) -> np.dtype: if number_vec_elements_of_cl_type(dtype_vec) == 1: return dtype_scalar else: c_name = '{}{}'.format(c_name_from_dtype(dtype_scalar), number_vec_elements_of_cl_type(dtype_vec)) return getattr(Types, c_name) @staticmethod def array_indexing_for_vec_type(array: str, index: str, dtype: np.dtype): """ https://stackoverflow.com/questions/24746221/using-a-vector-as-an-array-index e.g. uchar4 keys = (uchar4)(5, 0, 2, 6); uint4 results = (uint4)(data[keys.s0], data[keys.s1], data[keys.s2], data[keys.s3]); :param dtype: :param array: :param index: :return: """ if number_vec_elements_of_cl_type(dtype) == 1: return '{array_name}[{index_name}]'.format(array_name=array, index_name=index) else: return '({c_type_name})({vector})'.format(c_type_name=c_name_from_dtype(dtype), vector=', '.join( ['{array_name}[{index_name}.s{i_vec_element}]'.format( array_name=array, index_name=index, i_vec_element=VEC_INDICES[i]) for i in range(number_vec_elements_of_cl_type(dtype))])) @staticmethod def command_const_vec_type(param: Union[str, float, int], dtype: np.dtype) -> str: """ param = 1.5, dtype=ClTypes.float -> 'convert_float(1.5)' param = 1.5, dtype=ClTypes.float2 -> '(float2)(convert_float(1.5), convert_float(1.5)) :param param: :param dtype: :return: """ if number_vec_elements_of_cl_type(dtype) == 1: return 'convert_{}({})'.format(c_name_from_dtype(dtype), param) else: dtype_c_name = c_name_from_dtype(scalar_type_from_vec_type(dtype)) return '({})(({}))'.format(c_name_from_dtype(dtype), ', '.join(['convert_{}({})'.format(dtype_c_name, param)] * get_vec_size(dtype))) @staticmethod def command_vec_sum(var_name: str, dtype: np.dtype) -> str: """ Cases: float var_name -> return 'var_name' float4 var_name -> return 'var_name.s0 + var_name.s1 + var_name.s2 + var_name.s3' :param var_name: :return: """ if get_vec_size(dtype) == 1: return var_name else: return ' + '.join( ['{}.s{}'.format(var_name, VEC_INDICES[i]) for i in range(get_vec_size(dtype))]) # todo: use splay method of pyopencl library instead # from pyopencl.array import splay # splay @staticmethod def _get_local_size_coalesced_last_dim(global_size, desired_wg_size): """ E.g. global_size = (1000, 25) and desired_wg_size=64 Then a local_size=(2,25) is returned for multiple reasons: The work group size must be equal or smaller than the desired work group size. We make the last local dimension is large as possible (cannot exceed global size of last dimension). If possible the second last dimension is set to a value larger than 1, such that we get close to our desired work group size. :param global_size: :param desired_wg_size: :return: """ local_size = [1] * len(global_size) for i_dim in range(1, len(global_size) + 1): if global_size[-i_dim] * local_size[-i_dim + 1] < desired_wg_size: local_size[-i_dim] = global_size[-i_dim] else: local_size[-i_dim] = np.max([i for i in range(1, desired_wg_size + 1) if (global_size[-i_dim] / i).is_integer() and i * local_size[-i_dim + 1] <= desired_wg_size]) if np.product(local_size) < desired_wg_size: pass # res = inspect.stack() # logging.info(f'Local size {local_size} is suboptimal for desired work group size of {desired_wg_size}. ' # f'For best performance increase the global size of the most inner dimension, until it is ' # f'divisible by {desired_wg_size}. \n' # f'More information: ' # f'https://stackoverflow.com/questions/3957125/questions-about-global-and-local-work-size') return tuple(local_size) # return None @staticmethod def get_local_size_coalesced_last_dim(global_size, context: Context): """ If global size is no multiple of the local size, according to following link it should not work. https://community.khronos.org/t/opencl-ndrange-global-size-local-size/4167 However (only for AMD GPU), simple tests have shown that it still works. Therefore this class gives a local size, where the global size is not necessarily a multiple. :param global_size: :param context: :return: """ desired_wg_size = 4 * context.devices[0].global_mem_cacheline_size return Helpers._get_local_size_coalesced_last_dim(global_size, desired_wg_size)
42.382863
138
0.650152
717004c16aa303c94b3845706ad4664cb45eb6f9
3,602
py
Python
src/mesh/azext_mesh/servicefabricmesh/mgmt/servicefabricmesh/models/application_properties.py
mayank88mahajan/azure-cli-extensions
8bd389a1877bffd14052bec5519ce75dc6fc34cf
[ "MIT" ]
1
2018-09-22T10:39:43.000Z
2018-09-22T10:39:43.000Z
src/mesh/azext_mesh/servicefabricmesh/mgmt/servicefabricmesh/models/application_properties.py
mayank88mahajan/azure-cli-extensions
8bd389a1877bffd14052bec5519ce75dc6fc34cf
[ "MIT" ]
null
null
null
src/mesh/azext_mesh/servicefabricmesh/mgmt/servicefabricmesh/models/application_properties.py
mayank88mahajan/azure-cli-extensions
8bd389a1877bffd14052bec5519ce75dc6fc34cf
[ "MIT" ]
null
null
null
# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class ApplicationProperties(Model): """This type describes properties of an application resource. Variables are only populated by the server, and will be ignored when sending a request. :param description: User readable description of the application. :type description: str :param debug_params: Internal use. :type debug_params: str :param services: describes the services in the application. :type services: list[~azure.mgmt.servicefabricmesh.models.ServiceResourceDescription] :ivar health_state: Describes the health state of an application resource. Possible values include: 'Invalid', 'Ok', 'Warning', 'Error', 'Unknown' :vartype health_state: str or ~azure.mgmt.servicefabricmesh.models.HealthState :ivar unhealthy_evaluation: When the application's health state is not 'Ok', this additional details from service fabric Health Manager for the user to know why the application is marked unhealthy. :vartype unhealthy_evaluation: str :ivar status: Status of the application resource. Possible values include: 'Invalid', 'Ready', 'Upgrading', 'Creating', 'Deleting', 'Failed' :vartype status: str or ~azure.mgmt.servicefabricmesh.models.ApplicationResourceStatus :ivar status_details: Gives additional information about the current status of the application deployment. :vartype status_details: str :ivar service_names: Names of the services in the application. :vartype service_names: list[str] :param diagnostics: Describes the diagnostics definition and usage for an application resource. :type diagnostics: ~azure.mgmt.servicefabricmesh.models.DiagnosticsDescription """ _validation = { 'health_state': {'readonly': True}, 'unhealthy_evaluation': {'readonly': True}, 'status': {'readonly': True}, 'status_details': {'readonly': True}, 'service_names': {'readonly': True}, } _attribute_map = { 'description': {'key': 'description', 'type': 'str'}, 'debug_params': {'key': 'debugParams', 'type': 'str'}, 'services': {'key': 'services', 'type': '[ServiceResourceDescription]'}, 'health_state': {'key': 'healthState', 'type': 'str'}, 'unhealthy_evaluation': {'key': 'unhealthyEvaluation', 'type': 'str'}, 'status': {'key': 'status', 'type': 'str'}, 'status_details': {'key': 'statusDetails', 'type': 'str'}, 'service_names': {'key': 'serviceNames', 'type': '[str]'}, 'diagnostics': {'key': 'diagnostics', 'type': 'DiagnosticsDescription'}, } def __init__(self, description=None, debug_params=None, services=None, diagnostics=None): super(ApplicationProperties, self).__init__() self.description = description self.debug_params = debug_params self.services = services self.health_state = None self.unhealthy_evaluation = None self.status = None self.status_details = None self.service_names = None self.diagnostics = diagnostics
43.926829
93
0.659078
c8593ebe105e13b970234b2da20db343df69b261
9,941
py
Python
main.py
franciscorpuz/curl_rainbow
e3f6f2a9bee1ed6436e8cc55384b664220c7ab3b
[ "MIT" ]
38
2020-07-07T11:29:18.000Z
2022-03-28T13:38:04.000Z
main.py
franciscorpuz/curl_rainbow
e3f6f2a9bee1ed6436e8cc55384b664220c7ab3b
[ "MIT" ]
6
2020-08-01T11:44:39.000Z
2021-06-24T00:15:23.000Z
main.py
franciscorpuz/curl_rainbow
e3f6f2a9bee1ed6436e8cc55384b664220c7ab3b
[ "MIT" ]
18
2020-08-07T04:42:37.000Z
2021-12-08T22:42:14.000Z
# -*- coding: utf-8 -*- # MIT License # # Copyright (c) 2017 Kai Arulkumaran # # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. # ============================================================================== from __future__ import division import argparse import bz2 from datetime import datetime import os import pickle import atari_py import numpy as np import torch from tqdm import trange from agent import Agent from env import Env from memory import ReplayMemory from test import test seed = np.random.randint(12345) # Note that hyperparameters may originally be reported in ATARI game frames instead of agent steps parser = argparse.ArgumentParser(description='Rainbow') parser.add_argument('--id', type=str, default='default', help='Experiment ID') parser.add_argument('--seed', type=int, default=seed, help='Random seed') parser.add_argument('--disable-cuda', action='store_true', help='Disable CUDA') parser.add_argument('--game', type=str, default='ms_pacman', choices=atari_py.list_games(), help='ATARI game') parser.add_argument('--T-max', type=int, default=int(1e5), metavar='STEPS', help='Number of training steps (4x number of frames)') parser.add_argument('--max-episode-length', type=int, default=int(108e3), metavar='LENGTH', help='Max episode length in game frames (0 to disable)') parser.add_argument('--history-length', type=int, default=4, metavar='T', help='Number of consecutive states processed') parser.add_argument('--architecture', type=str, default='data-efficient', choices=['canonical', 'data-efficient'], metavar='ARCH', help='Network architecture') parser.add_argument('--hidden-size', type=int, default=256, metavar='SIZE', help='Network hidden size') parser.add_argument('--noisy-std', type=float, default=0.1, metavar='σ', help='Initial standard deviation of noisy linear layers') parser.add_argument('--atoms', type=int, default=51, metavar='C', help='Discretised size of value distribution') parser.add_argument('--V-min', type=float, default=-10, metavar='V', help='Minimum of value distribution support') parser.add_argument('--V-max', type=float, default=10, metavar='V', help='Maximum of value distribution support') parser.add_argument('--model', type=str, metavar='PARAMS', help='Pretrained model (state dict)') parser.add_argument('--memory-capacity', type=int, default=int(1e5), metavar='CAPACITY', help='Experience replay memory capacity') parser.add_argument('--replay-frequency', type=int, default=1, metavar='k', help='Frequency of sampling from memory') parser.add_argument('--priority-exponent', type=float, default=0.5, metavar='ω', help='Prioritised experience replay exponent (originally denoted α)') parser.add_argument('--priority-weight', type=float, default=0.4, metavar='β', help='Initial prioritised experience replay importance sampling weight') parser.add_argument('--multi-step', type=int, default=20, metavar='n', help='Number of steps for multi-step return') parser.add_argument('--discount', type=float, default=0.99, metavar='γ', help='Discount factor') parser.add_argument('--target-update', type=int, default=int(2e3), metavar='τ', help='Number of steps after which to update target network') parser.add_argument('--reward-clip', type=int, default=1, metavar='VALUE', help='Reward clipping (0 to disable)') parser.add_argument('--learning-rate', type=float, default=0.0001, metavar='η', help='Learning rate') parser.add_argument('--adam-eps', type=float, default=1.5e-4, metavar='ε', help='Adam epsilon') parser.add_argument('--batch-size', type=int, default=32, metavar='SIZE', help='Batch size') parser.add_argument('--norm-clip', type=float, default=10, metavar='NORM', help='Max L2 norm for gradient clipping') parser.add_argument('--learn-start', type=int, default=int(1600), metavar='STEPS', help='Number of steps before starting training') parser.add_argument('--evaluate', action='store_true', help='Evaluate only') parser.add_argument('--evaluation-interval', type=int, default=10000, metavar='STEPS', help='Number of training steps between evaluations') parser.add_argument('--evaluation-episodes', type=int, default=10, metavar='N', help='Number of evaluation episodes to average over') # TODO: Note that DeepMind's evaluation method is running the latest agent for 500K frames ever every 1M steps parser.add_argument('--evaluation-size', type=int, default=500, metavar='N', help='Number of transitions to use for validating Q') parser.add_argument('--render', action='store_true', help='Display screen (testing only)') parser.add_argument('--enable-cudnn', action='store_true', help='Enable cuDNN (faster but nondeterministic)') parser.add_argument('--checkpoint-interval', default=0, help='How often to checkpoint the model, defaults to 0 (never checkpoint)') parser.add_argument('--memory', help='Path to save/load the memory from') parser.add_argument('--disable-bzip-memory', action='store_true', help='Don\'t zip the memory file. Not recommended (zipping is a bit slower and much, much smaller)') # Setup args = parser.parse_args() xid = 'curl-' + args.game + '-' + str(seed) args.id = xid print(' ' * 26 + 'Options') for k, v in vars(args).items(): print(' ' * 26 + k + ': ' + str(v)) results_dir = os.path.join('results', args.id) if not os.path.exists(results_dir): os.makedirs(results_dir) metrics = {'steps': [], 'rewards': [], 'Qs': [], 'best_avg_reward': -float('inf')} np.random.seed(args.seed) torch.manual_seed(np.random.randint(1, 10000)) if torch.cuda.is_available() and not args.disable_cuda: args.device = torch.device('cuda') torch.cuda.manual_seed(np.random.randint(1, 10000)) torch.backends.cudnn.enabled = args.enable_cudnn else: args.device = torch.device('cpu') # Simple ISO 8601 timestamped logger def log(s): print('[' + str(datetime.now().strftime('%Y-%m-%dT%H:%M:%S')) + '] ' + s) def load_memory(memory_path, disable_bzip): if disable_bzip: with open(memory_path, 'rb') as pickle_file: return pickle.load(pickle_file) else: with bz2.open(memory_path, 'rb') as zipped_pickle_file: return pickle.load(zipped_pickle_file) def save_memory(memory, memory_path, disable_bzip): if disable_bzip: with open(memory_path, 'wb') as pickle_file: pickle.dump(memory, pickle_file) else: with bz2.open(memory_path, 'wb') as zipped_pickle_file: pickle.dump(memory, zipped_pickle_file) # Environment env = Env(args) env.train() action_space = env.action_space() # Agent dqn = Agent(args, env) # If a model is provided, and evaluate is fale, presumably we want to resume, so try to load memory if args.model is not None and not args.evaluate: if not args.memory: raise ValueError('Cannot resume training without memory save path. Aborting...') elif not os.path.exists(args.memory): raise ValueError('Could not find memory file at {path}. Aborting...'.format(path=args.memory)) mem = load_memory(args.memory, args.disable_bzip_memory) else: mem = ReplayMemory(args, args.memory_capacity) priority_weight_increase = (1 - args.priority_weight) / (args.T_max - args.learn_start) # Construct validation memory val_mem = ReplayMemory(args, args.evaluation_size) T, done = 0, True while T < args.evaluation_size: if done: state, done = env.reset(), False next_state, _, done = env.step(np.random.randint(0, action_space)) val_mem.append(state, None, None, done) state = next_state T += 1 if args.evaluate: dqn.eval() # Set DQN (online network) to evaluation mode avg_reward, avg_Q = test(args, 0, dqn, val_mem, metrics, results_dir, evaluate=True) # Test print('Avg. reward: ' + str(avg_reward) + ' | Avg. Q: ' + str(avg_Q)) else: # Training loop dqn.train() T, done = 0, True for T in trange(1, args.T_max + 1): if done: state, done = env.reset(), False if T % args.replay_frequency == 0: dqn.reset_noise() # Draw a new set of noisy weights action = dqn.act(state) # Choose an action greedily (with noisy weights) next_state, reward, done = env.step(action) # Step if args.reward_clip > 0: reward = max(min(reward, args.reward_clip), -args.reward_clip) # Clip rewards mem.append(state, action, reward, done) # Append transition to memory # Train and test if T >= args.learn_start: mem.priority_weight = min(mem.priority_weight + priority_weight_increase, 1) # Anneal importance sampling weight β to 1 if T % args.replay_frequency == 0: #for _ in range(4): dqn.learn(mem) # Train with n-step distributional double-Q learning dqn.update_momentum_net() # MoCo momentum upate if T % args.evaluation_interval == 0: dqn.eval() # Set DQN (online network) to evaluation mode avg_reward, avg_Q = test(args, T, dqn, val_mem, metrics, results_dir) # Test log('T = ' + str(T) + ' / ' + str(args.T_max) + ' | Avg. reward: ' + str(avg_reward) + ' | Avg. Q: ' + str(avg_Q)) dqn.train() # Set DQN (online network) back to training mode # If memory path provided, save it if args.memory is not None: save_memory(mem, args.memory, args.disable_bzip_memory) # Update target network if T % args.target_update == 0: dqn.update_target_net() # Checkpoint the network if (args.checkpoint_interval != 0) and (T % args.checkpoint_interval == 0): dqn.save(results_dir, 'checkpoint.pth') state = next_state env.close()
49.954774
434
0.715622
8cfd8e2575e7866e00b1ce9e159b6b15f15ffd69
10,070
py
Python
contrib/spendfrom/spendfrom.py
Babacoins/Babacoin-v2
126ad0e4a744a1d7ae1629cf414ae6b033d59c9f
[ "MIT" ]
1
2018-08-13T15:47:53.000Z
2018-08-13T15:47:53.000Z
contrib/spendfrom/spendfrom.py
Babacoins/Babacoin-v2
126ad0e4a744a1d7ae1629cf414ae6b033d59c9f
[ "MIT" ]
null
null
null
contrib/spendfrom/spendfrom.py
Babacoins/Babacoin-v2
126ad0e4a744a1d7ae1629cf414ae6b033d59c9f
[ "MIT" ]
null
null
null
#!/usr/bin/env python # # Use the raw transactions API to spend BABAs received on particular addresses, # and send any change back to that same address. # # Example usage: # spendfrom.py # Lists available funds # spendfrom.py --from=ADDRESS --to=ADDRESS --amount=11.00 # # Assumes it will talk to a babacoind or babacoin-Qt running # on localhost. # # Depends on jsonrpc # from decimal import * import getpass import math import os import os.path import platform import sys import time from jsonrpc import ServiceProxy, json BASE_FEE=Decimal("0.001") def check_json_precision(): """Make sure json library being used does not lose precision converting BTC values""" n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n)))*1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def determine_db_dir(): """Return the default location of the babacoin data directory""" if platform.system() == "Darwin": return os.path.expanduser("~/Library/Application Support/Babacoin/") elif platform.system() == "Windows": return os.path.join(os.environ['APPDATA'], "Babacoin") return os.path.expanduser("~/.babacoin") def read_bitcoin_config(dbdir): """Read the babacoin.conf file from dbdir, returns dictionary of settings""" from ConfigParser import SafeConfigParser class FakeSecHead(object): def __init__(self, fp): self.fp = fp self.sechead = '[all]\n' def readline(self): if self.sechead: try: return self.sechead finally: self.sechead = None else: s = self.fp.readline() if s.find('#') != -1: s = s[0:s.find('#')].strip() +"\n" return s config_parser = SafeConfigParser() config_parser.readfp(FakeSecHead(open(os.path.join(dbdir, "babacoin.conf")))) return dict(config_parser.items("all")) def connect_JSON(config): """Connect to a babacoin JSON-RPC server""" testnet = config.get('testnet', '0') testnet = (int(testnet) > 0) # 0/1 in config file, convert to True/False if not 'rpcport' in config: config['rpcport'] = 18887 if testnet else 8887 connect = "http://%s:%[email protected]:%s"%(config['rpcuser'], config['rpcpassword'], config['rpcport']) try: result = ServiceProxy(connect) # ServiceProxy is lazy-connect, so send an RPC command mostly to catch connection errors, # but also make sure the babacoind we're talking to is/isn't testnet: if result.getmininginfo()['testnet'] != testnet: sys.stderr.write("RPC server at "+connect+" testnet setting mismatch\n") sys.exit(1) return result except: sys.stderr.write("Error connecting to RPC server at "+connect+"\n") sys.exit(1) def unlock_wallet(babacoind): info = babacoind.getinfo() if 'unlocked_until' not in info: return True # wallet is not encrypted t = int(info['unlocked_until']) if t <= time.time(): try: passphrase = getpass.getpass("Wallet is locked; enter passphrase: ") babacoind.walletpassphrase(passphrase, 5) except: sys.stderr.write("Wrong passphrase\n") info = babacoind.getinfo() return int(info['unlocked_until']) > time.time() def list_available(babacoind): address_summary = dict() address_to_account = dict() for info in babacoind.listreceivedbyaddress(0): address_to_account[info["address"]] = info["account"] unspent = babacoind.listunspent(0) for output in unspent: # listunspent doesn't give addresses, so: rawtx = babacoind.getrawtransaction(output['txid'], 1) vout = rawtx["vout"][output['vout']] pk = vout["scriptPubKey"] # This code only deals with ordinary pay-to-babacoin-address # or pay-to-script-hash outputs right now; anything exotic is ignored. if pk["type"] != "pubkeyhash" and pk["type"] != "scripthash": continue address = pk["addresses"][0] if address in address_summary: address_summary[address]["total"] += vout["value"] address_summary[address]["outputs"].append(output) else: address_summary[address] = { "total" : vout["value"], "outputs" : [output], "account" : address_to_account.get(address, "") } return address_summary def select_coins(needed, inputs): # Feel free to improve this, this is good enough for my simple needs: outputs = [] have = Decimal("0.0") n = 0 while have < needed and n < len(inputs): outputs.append({ "txid":inputs[n]["txid"], "vout":inputs[n]["vout"]}) have += inputs[n]["amount"] n += 1 return (outputs, have-needed) def create_tx(babacoind, fromaddresses, toaddress, amount, fee): all_coins = list_available(babacoind) total_available = Decimal("0.0") needed = amount+fee potential_inputs = [] for addr in fromaddresses: if addr not in all_coins: continue potential_inputs.extend(all_coins[addr]["outputs"]) total_available += all_coins[addr]["total"] if total_available < needed: sys.stderr.write("Error, only %f BTC available, need %f\n"%(total_available, needed)); sys.exit(1) # # Note: # Python's json/jsonrpc modules have inconsistent support for Decimal numbers. # Instead of wrestling with getting json.dumps() (used by jsonrpc) to encode # Decimals, I'm casting amounts to float before sending them to babacoind. # outputs = { toaddress : float(amount) } (inputs, change_amount) = select_coins(needed, potential_inputs) if change_amount > BASE_FEE: # don't bother with zero or tiny change change_address = fromaddresses[-1] if change_address in outputs: outputs[change_address] += float(change_amount) else: outputs[change_address] = float(change_amount) rawtx = babacoind.createrawtransaction(inputs, outputs) signed_rawtx = babacoind.signrawtransaction(rawtx) if not signed_rawtx["complete"]: sys.stderr.write("signrawtransaction failed\n") sys.exit(1) txdata = signed_rawtx["hex"] return txdata def compute_amount_in(babacoind, txinfo): result = Decimal("0.0") for vin in txinfo['vin']: in_info = babacoind.getrawtransaction(vin['txid'], 1) vout = in_info['vout'][vin['vout']] result = result + vout['value'] return result def compute_amount_out(txinfo): result = Decimal("0.0") for vout in txinfo['vout']: result = result + vout['value'] return result def sanity_test_fee(babacoind, txdata_hex, max_fee): class FeeError(RuntimeError): pass try: txinfo = babacoind.decoderawtransaction(txdata_hex) total_in = compute_amount_in(babacoind, txinfo) total_out = compute_amount_out(txinfo) if total_in-total_out > max_fee: raise FeeError("Rejecting transaction, unreasonable fee of "+str(total_in-total_out)) tx_size = len(txdata_hex)/2 kb = tx_size/1000 # integer division rounds down if kb > 1 and fee < BASE_FEE: raise FeeError("Rejecting no-fee transaction, larger than 1000 bytes") if total_in < 0.01 and fee < BASE_FEE: raise FeeError("Rejecting no-fee, tiny-amount transaction") # Exercise for the reader: compute transaction priority, and # warn if this is a very-low-priority transaction except FeeError as err: sys.stderr.write((str(err)+"\n")) sys.exit(1) def main(): import optparse parser = optparse.OptionParser(usage="%prog [options]") parser.add_option("--from", dest="fromaddresses", default=None, help="addresses to get BABAs from") parser.add_option("--to", dest="to", default=None, help="address to get send BABAs to") parser.add_option("--amount", dest="amount", default=None, help="amount to send") parser.add_option("--fee", dest="fee", default="0.0", help="fee to include") parser.add_option("--datadir", dest="datadir", default=determine_db_dir(), help="location of babacoin.conf file with RPC username/password (default: %default)") parser.add_option("--testnet", dest="testnet", default=False, action="store_true", help="Use the test network") parser.add_option("--dry_run", dest="dry_run", default=False, action="store_true", help="Don't broadcast the transaction, just create and print the transaction data") (options, args) = parser.parse_args() check_json_precision() config = read_bitcoin_config(options.datadir) if options.testnet: config['testnet'] = True babacoind = connect_JSON(config) if options.amount is None: address_summary = list_available(babacoind) for address,info in address_summary.iteritems(): n_transactions = len(info['outputs']) if n_transactions > 1: print("%s %.8f %s (%d transactions)"%(address, info['total'], info['account'], n_transactions)) else: print("%s %.8f %s"%(address, info['total'], info['account'])) else: fee = Decimal(options.fee) amount = Decimal(options.amount) while unlock_wallet(babacoind) == False: pass # Keep asking for passphrase until they get it right txdata = create_tx(babacoind, options.fromaddresses.split(","), options.to, amount, fee) sanity_test_fee(babacoind, txdata, amount*Decimal("0.01")) if options.dry_run: print(txdata) else: txid = babacoind.sendrawtransaction(txdata) print(txid) if __name__ == '__main__': main()
37.574627
111
0.633863
5cedbcc3ff41c810fc57341964a7f174ecfe3834
6,301
py
Python
CustomExtension.extension/STVTools.tab/Modify.panel/Modify1.stack3/Replace.pulldown/Batch parameter change.pushbutton/script.py
Melca-G/Aeolus
e014cdbbffc1c650d569efd8750480bc5a4cdc3b
[ "MIT" ]
null
null
null
CustomExtension.extension/STVTools.tab/Modify.panel/Modify1.stack3/Replace.pulldown/Batch parameter change.pushbutton/script.py
Melca-G/Aeolus
e014cdbbffc1c650d569efd8750480bc5a4cdc3b
[ "MIT" ]
null
null
null
CustomExtension.extension/STVTools.tab/Modify.panel/Modify1.stack3/Replace.pulldown/Batch parameter change.pushbutton/script.py
Melca-G/Aeolus
e014cdbbffc1c650d569efd8750480bc5a4cdc3b
[ "MIT" ]
null
null
null
import sys import ConfigParser from os.path import expanduser import csv # Set system path home = expanduser("~") cfgfile = open(home + "\\STVTools.ini", 'r') config = ConfigParser.ConfigParser() config.read(home + "\\STVTools.ini") # Master Path syspath1 = config.get('SysDir','MasterPackage') sys.path.append(syspath1) # Built Path syspath2 = config.get('SysDir','SecondaryPackage') sys.path.append(syspath2) from pyrevit.framework import List from pyrevit import revit, DB, forms import re, clr, os, threading import EAMQcUtils import xlsxwriter clr.AddReference('RevitAPI') clr.AddReference("System") from Autodesk.Revit.DB import FilteredElementCollector, Transaction, ImportInstance, \ OpenOptions,WorksetConfiguration, WorksetConfigurationOption, DetachFromCentralOption,\ ModelPathUtils, SaveAsOptions, WorksharingSaveAsOptions, RevitLinkType, ViewFamilyType, \ ViewFamily, View3D, IndependentTag, ElementId, StorageType from System.Collections.Generic import List from Autodesk.Revit.UI.Events import DialogBoxShowingEventArgs from Autodesk.Revit.UI import UIApplication from Autodesk.Revit.ApplicationServices import Application clr.AddReferenceByPartialName('PresentationCore') clr.AddReferenceByPartialName('PresentationFramework') clr.AddReferenceByPartialName('System.Windows.Forms') clr.AddReference('RevitAPIUI') # Collect Save location and Rvt Files def Importcsv(Filename): flat_list = [] with open(Filename, 'r') as f: reader = csv.reader(f) Lst = list(reader) for sublist in Lst: flat_list.append(sublist) #for item in sublist: #flat_list.append(item) return flat_list class ChangeElement: def ChangeParameter(self, id, parameter, value): self.Id = id self.Parameter = parameter self.Value = value return self def OpenFiles(oFile, app, audit): openOpt = OpenOptions() if audit == True: openOpt.Audit = True else: openOpt.Audit = False openOpt.DetachFromCentralOption = DetachFromCentralOption.DetachAndPreserveWorksets wsopt = WorksetConfiguration(WorksetConfigurationOption.CloseAllWorksets) # wsopt.Open(worksetList) openOpt.SetOpenWorksetsConfiguration(wsopt) modelPath = ModelPathUtils.ConvertUserVisiblePathToModelPath(oFile) currentdoc = app.OpenDocumentFile(modelPath, openOpt) try: DialogBoxShowingEventArgs.OverrideResult(1) except: pass return currentdoc collectorCSVFile = forms.pick_file(file_ext='csv', multi_file=False, unc_paths=False) collectorFiles = forms.pick_file(file_ext='rvt', multi_file=True, unc_paths=False) destinationFolder = forms.pick_folder() # Main uidoc = __revit__.ActiveUIDocument doc = __revit__.ActiveUIDocument.Document __doc__ = 'Extract information from csv file and batch apply parameter value changes.'\ 'Format of csv: "model name, element Id, parameter name, new parameter value".'\ 'Step 1: Select the csv File'\ 'Step 2: Select the Revit Files'\ 'Step 3: Select the directory new models to be placed.' # set the first row to header to avoid the unicode issue uiapp = UIApplication(doc.Application) application = uiapp.Application if len(collectorFiles) > 0: for aDoc in collectorFiles: openedDoc = OpenFiles(aDoc, application, audit=False) t = Transaction(openedDoc, "Apply Parameters") t.Start() print(str(openedDoc.Title) + ' Opened') workshareOp = WorksharingSaveAsOptions() # Define the name and location of excel file rawTitle = re.split('detached', openedDoc.Title)[0] #title = rawTitle[0:len(rawTitle) -1] title = rawTitle[0:len(rawTitle) -1] print(str(title) + ' is being modified:') for line in Importcsv(collectorCSVFile): modelName = line[0] id = line[1] parameterName = line[2] parameterValue = line[3] v1 = () print(modelName, title) if modelName == title + '.rvt': element = () try: element = openedDoc.GetElement(ElementId(int(id))) except: print("ElementId {0} Does not Exist".format(str(id))) try: v1 = element.LookupParameter(parameterName) except: print("Error finding the value to {0} for {1}".format(parameterName, str(id))) if v1: if v1.StorageType == StorageType.Integer: try: element.LookupParameter(parameterName).Set(int(parameterValue)) print("Applied change {0} as {1}".format(id, parameterValue)) except: print("Error Applying the value to {0} ".format(str(id)) + parameterName + " as integer") elif v1.StorageType == StorageType.String: try: element.LookupParameter(parameterName).Set(str(parameterValue)) print("Applied change {0} as {1}".format(id, parameterValue)) except: print("Error Applying the value to {0} ".format(str(id)) + parameterName + " as string") elif v1.StorageType == StorageType.Double: try: element.LookupParameter(parameterName).Set(float(parameterValue)) print("Applied change {0} as {1}".format(id, parameterValue)) except: print("Error Applying the value to {0} ".format(str(id)) + parameterName + " as double") else: print("Error Applying the value to {0} ".format(str(id)) + parameterName + " format error") t.Commit() saveOp = SaveAsOptions() workOp = WorksharingSaveAsOptions() workOp.SaveAsCentral = True saveOp.SetWorksharingOptions(workOp) saveAsTitle = openedDoc.Title openedDoc.SaveAs(destinationFolder + '\\' + saveAsTitle, saveOp) openedDoc.Close(False) print("--------------------------")
42.288591
117
0.634026
deb13753ffcdf7bf53566168da155940070cac1f
1,601
py
Python
deepocr/models/detection/predictor/pytorch.py
das-projects/deepOCR
ffc6db691605b7b4837da9619ab6e918fa1c18de
[ "Apache-2.0" ]
1
2022-01-28T09:48:34.000Z
2022-01-28T09:48:34.000Z
deepocr/models/detection/predictor/pytorch.py
das-projects/deepOCR
ffc6db691605b7b4837da9619ab6e918fa1c18de
[ "Apache-2.0" ]
null
null
null
deepocr/models/detection/predictor/pytorch.py
das-projects/deepOCR
ffc6db691605b7b4837da9619ab6e918fa1c18de
[ "Apache-2.0" ]
null
null
null
# Copyright (C) 2022, Arijit Das. # Code adapted from doctr and huggingface # This program is licensed under the Apache License version 2. # See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details. from typing import Any, List, Union import numpy as np import torch from torch import nn from deepocr.models.preprocessor import PreProcessor __all__ = ['DetectionPredictor'] class DetectionPredictor(nn.Module): """Implements an object able to localize text elements in a document Args: pre_processor: transform inputs for easier batched model inference model: core detection architecture """ def __init__( self, pre_processor: PreProcessor, model: nn.Module, ) -> None: super().__init__() self.pre_processor = pre_processor self.model = model.eval() @torch.no_grad() def forward( self, pages: List[Union[np.ndarray, torch.Tensor]], **kwargs: Any, ) -> List[np.ndarray]: # Dimension check if any(page.ndim != 3 for page in pages): raise ValueError("incorrect input shape: all pages are expected to be multi-channel 2D images.") processed_batches = self.pre_processor(pages) _device = next(self.model.parameters()).device predicted_batches = [ self.model(batch.to(device=_device), return_preds=True, **kwargs)['preds'] # type:ignore[operator] for batch in processed_batches ] return [pred for batch in predicted_batches for pred in batch]
30.207547
111
0.665209
37d818c6c8e76aef3cd39ee6847361955dd32599
8,584
py
Python
src/python/turicreate/test/test_util.py
Bpowers4/turicreate
73dad213cc1c4f74337b905baea2b3a1e5a0266c
[ "BSD-3-Clause" ]
1
2020-02-21T02:24:45.000Z
2020-02-21T02:24:45.000Z
src/python/turicreate/test/test_util.py
Bpowers4/turicreate
73dad213cc1c4f74337b905baea2b3a1e5a0266c
[ "BSD-3-Clause" ]
2
2019-03-28T00:17:14.000Z
2019-03-28T00:17:47.000Z
src/python/turicreate/test/test_util.py
Bpowers4/turicreate
73dad213cc1c4f74337b905baea2b3a1e5a0266c
[ "BSD-3-Clause" ]
null
null
null
# -*- coding: utf-8 -*- # Copyright © 2017 Apple Inc. All rights reserved. # # Use of this source code is governed by a BSD-3-clause license that can # be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause from __future__ import print_function as _ from __future__ import division as _ from __future__ import absolute_import as _ import os import unittest import tempfile import shutil import uuid import sys as _sys from .. import util as glutil from .. import SFrame, SArray, SGraph from ..util import get_turicreate_object_type from ..config import get_runtime_config, set_runtime_config from . import util class UtilTests(unittest.TestCase): def test_archive_utils(self): # Arrange sf = SFrame([1, 2, 3, 4, 5]) dir = tempfile.mkdtemp(prefix="archive-tests") try: sf.save(dir) # Act & Assert self.assertTrue(glutil.is_directory_archive(dir)) self.assertEqual(glutil.get_archive_type(dir), "sframe") self.assertFalse(glutil.is_directory_archive("/tmp")) self.assertRaises(TypeError, lambda: glutil.get_archive_type("/tmp")) finally: shutil.rmtree(dir) def test_crossproduct(self): s = util.SFrameComparer() d = {"opt1": [1, 2, 3], "opt2": ["a", "b"]} actual = glutil.crossproduct(d) actual = actual.sort("opt1") expected = SFrame( {"opt1": [1, 1, 2, 2, 3, 3], "opt2": ["a", "b", "a", "b", "a", "b"]} ) # Check columns individually since there is no # guaranteed ordering among columns. for k in d.keys(): s._assert_sarray_equal(actual[k], expected[k]) def _validate_gl_object_type(self, obj, expected): with util.TempDirectory() as temp_dir: obj.save(temp_dir) t = get_turicreate_object_type(temp_dir) self.assertEqual(t, expected) def test_get_turicreate_object_type(self): sf = SFrame({"a": [1, 2]}) self._validate_gl_object_type(sf, "sframe") sa = SArray([1, 2]) self._validate_gl_object_type(sa, "sarray") d = SFrame( { "__src_id": [175343, 163607, 44041, 101370, 64892], "__dst_id": [1011, 7928, 7718, 12966, 11080], } ) g = SGraph() self._validate_gl_object_type(g, "sgraph") def test_sframe_equals(self): # Empty SFrames should be equal sf_a = SFrame() sf_b = SFrame() glutil._assert_sframe_equal(sf_a, sf_b) the_data = [i for i in range(0, 10)] sf = SFrame() sf["ints"] = SArray(data=the_data, dtype=int) sf["floats"] = SArray(data=the_data, dtype=float) sf["floats"] = sf["floats"] * 0.5 sf["strings"] = SArray(data=the_data, dtype=str) sf["strings"] = sf["strings"].apply(lambda x: x + x + x) # Make sure these aren't pointing to the same SFrame sf_a = sf.filter_by([43], "ints", exclude=True) sf_b = sf.filter_by([43], "ints", exclude=True) glutil._assert_sframe_equal(sf_a, sf_b) # Difference in number of columns sf_a["extra"] = SArray(data=the_data) with self.assertRaises(AssertionError): glutil._assert_sframe_equal(sf_a, sf_b) del sf_a["extra"] glutil._assert_sframe_equal(sf_a, sf_b) # Difference in number of rows with self.assertRaises(AssertionError): glutil._assert_sframe_equal(sf_a, sf_b[0:5]) # Difference in types sf_a["diff_type"] = sf_a["ints"].astype(str) sf_b["diff_type"] = sf_b["ints"] with self.assertRaises(AssertionError): glutil._assert_sframe_equal(sf_a, sf_b) del sf_a["diff_type"] del sf_b["diff_type"] glutil._assert_sframe_equal(sf_a, sf_b) # Difference in column name sf_a.rename({"strings": "string"}, inplace=True) with self.assertRaises(AssertionError): glutil._assert_sframe_equal(sf_a, sf_b) glutil._assert_sframe_equal(sf_a, sf_b, check_column_names=False) sf_a.rename({"string": "strings"}, inplace=True) glutil._assert_sframe_equal(sf_a, sf_b) sf_a.rename({"ints": "floats1"}, inplace=True) sf_a.rename({"floats": "ints"}, inplace=True) sf_a.rename({"floats1": "floats"}, inplace=True) glutil._assert_sframe_equal(sf_a, sf_b, check_column_names=False) sf_a = sf.filter_by([43], "ints", exclude=True) # Difference in column order sf_a.swap_columns("strings", "ints", inplace=True) with self.assertRaises(AssertionError): glutil._assert_sframe_equal(sf_a, sf_b) glutil._assert_sframe_equal(sf_a, sf_b, check_column_order=False) sf_a.swap_columns("strings", "ints", inplace=True) glutil._assert_sframe_equal(sf_a, sf_b) # Difference in row order sf_a = sf_a.append(sf[0:5]) sf_b = sf[0:5].append(sf_b) with self.assertRaises(AssertionError): glutil._assert_sframe_equal(sf_a, sf_b) glutil._assert_sframe_equal(sf_a, sf_b, check_row_order=False) # Difference in column order AND row order sf_a.swap_columns("floats", "strings", inplace=True) with self.assertRaises(AssertionError): glutil._assert_sframe_equal(sf_a, sf_b) glutil._assert_sframe_equal( sf_a, sf_b, check_column_order=False, check_row_order=False ) # Column order, row order, names sf_a.rename({"floats": "foo", "strings": "bar", "ints": "baz"}, inplace=True) with self.assertRaises(AssertionError): glutil._assert_sframe_equal(sf_a, sf_b) # Illegal stuff with self.assertRaises(ValueError): glutil._assert_sframe_equal( sf_a, sf_b, check_column_names=False, check_column_order=False ) with self.assertRaises(ValueError): glutil._assert_sframe_equal( sf_a, sf_b, check_column_names=False, check_column_order=False, check_row_order=False, ) with self.assertRaises(TypeError): glutil._assert_sframe_equal(sf_b["floats"], sf_a["foo"]) def test_get_temp_file_location(self): from ..util import _get_temp_file_location from ..util import _convert_slashes location = _get_temp_file_location() self.assertTrue(os.path.isdir(location)) tmp = tempfile.mkdtemp(prefix="test_gl_util") default_tmp = get_runtime_config()["TURI_CACHE_FILE_LOCATIONS"] try: set_runtime_config("TURI_CACHE_FILE_LOCATIONS", tmp) location = _convert_slashes(_get_temp_file_location()) self.assertTrue(location.startswith(_convert_slashes(tmp))) finally: shutil.rmtree(tmp) set_runtime_config("TURI_CACHE_FILE_LOCATIONS", default_tmp) def test_make_temp_directory(self): from ..util import _make_temp_directory, _get_temp_file_location tmp_root = _get_temp_file_location() location = _make_temp_directory(prefix=None) try: self.assertTrue(os.path.isdir(location)) self.assertTrue(location.startswith(tmp_root)) finally: shutil.rmtree(location) prefix = "abc_" location = _make_temp_directory(prefix=prefix) try: self.assertTrue(os.path.isdir(location)) self.assertTrue(location.startswith(tmp_root)) self.assertTrue(os.path.basename(location).startswith(prefix)) finally: shutil.rmtree(location) def test_make_temp_filename(self): from ..util import _make_temp_filename, _get_temp_file_location tmp_root = _get_temp_file_location() location = _make_temp_filename(prefix=None) self.assertFalse(os.path.isfile(location)) self.assertFalse(os.path.exists(location)) self.assertTrue(location.startswith(tmp_root)) self.assertTrue(isinstance(location, str)) prefix = "abc_" location = _make_temp_filename(prefix=prefix) self.assertFalse(os.path.isfile(location)) self.assertFalse(os.path.exists(location)) self.assertTrue(location.startswith(tmp_root)) self.assertTrue(isinstance(location, str)) self.assertTrue(os.path.basename(location).startswith(prefix))
35.471074
85
0.632106
060418e1c83a7a5463c768915fb7e44420ada077
2,328
py
Python
src/601_700/0639_decode-ways-ii/decode-ways-ii.py
himichael/LeetCode
d54f48e785af3d47a2a67a95fd3343d2b23f8ae5
[ "Apache-2.0" ]
1
2019-12-18T06:08:47.000Z
2019-12-18T06:08:47.000Z
src/601_700/0639_decode-ways-ii/decode-ways-ii.py
himichael/LeetCode
d54f48e785af3d47a2a67a95fd3343d2b23f8ae5
[ "Apache-2.0" ]
1
2019-05-18T09:35:22.000Z
2019-05-18T09:35:22.000Z
src/601_700/0639_decode-ways-ii/decode-ways-ii.py
himichael/LeetCode
d54f48e785af3d47a2a67a95fd3343d2b23f8ae5
[ "Apache-2.0" ]
null
null
null
class Solution(object): def numDecodings(self, s): n = len(s) cache = {} M = 10 ** 9 + 7 def dfs(i): if i >= n: return 1 if i in cache: return cache[i] res = 0 if s[i] == '*': res = 9 * dfs(i + 1) % M if i + 1 < n and s[i + 1] == '*': res = (res + 15 * dfs(i + 2)) % M elif i + 1 < n and s[i + 1] <= '6': res = (res + 2 * dfs(i + 2)) % M elif i + 1 < n and s[i + 1] > '6': res = (res + dfs(i + 2)) % M else: res = dfs(i + 1) % M if s[i] != '0' else 0 if i + 1 < n and s[i + 1] == '*': if s[i] == '1': res = (res + 9 * dfs(i + 2)) % M elif s[i] == '2': res = (res + 6 * dfs(i + 2)) % M elif i + 1 < n and 10 <= int(s[i] + s[i + 1]) <= 26: res = (res + dfs(i + 2)) % M cache[i] = res return cache[i] return dfs(0) # 动态规划 def numDecodings(self, s): if not s or s[0] == '0' or len(s) == 1: return 0 if s[0] == '0' else (9 if s[0] == '*' else 1) n = len(s) M = 10 ** 9 + 7 dp = [0] * (n + 1) dp[0] = 1 dp[1] = 9 if s[0] == '*' else (1 if s[0] != '0' else 0) for i in range(1, n): if s[i] == '*': dp[i + 1] = 9 * dp[i] % M if s[i - 1] == '*': dp[i + 1] = (dp[i + 1] + 15 * dp[i -1]) % M elif s[i - 1] == '1': dp[i + 1] = (dp[i + 1] + 9 * dp[i - 1]) % M elif s[i - 1] == '2': dp[i + 1] = (dp[i + 1] + 6 * dp[i - 1]) % M else: dp[i + 1] = dp[i] if s[i] != '0' else 0 if s[i - 1] == '*': if s[i] <= '6': dp[i + 1] = (dp[i + 1] + 2 * dp[i - 1]) % M elif s[i] > '6': dp[i + 1] = (dp[i + 1] + dp[i - 1]) % M elif 10 <= int(s[i - 1] + s[i]) <= 26: dp[i + 1] = (dp[i + 1] + dp[i - 1]) % M return dp[-1]
34.235294
68
0.270619
3966694bc9cdbc7f2cb633478c58c3c89c53a5fd
1,042
py
Python
day24.py
binarygondola/adventofcode-2017
fbcb545c19df6c5b3169714e74686d9a7b28a598
[ "MIT" ]
null
null
null
day24.py
binarygondola/adventofcode-2017
fbcb545c19df6c5b3169714e74686d9a7b28a598
[ "MIT" ]
null
null
null
day24.py
binarygondola/adventofcode-2017
fbcb545c19df6c5b3169714e74686d9a7b28a598
[ "MIT" ]
null
null
null
def make_bridge(parts, current, end, m): possible = [x for x in parts if end == x[0] or end == x[1]] for p in possible: parts.remove(p) current.append(p) if end == p[0]: tmp = p[1] else: tmp = p[0] b = sum(sum(x) for x in current) max_length = max(b, m[0]) parts_len_max = m[1] longest_max_length = m[2] if len(current) > parts_len_max: parts_len_max = len(current) longest_max_length = b elif len(current) == parts_len_max: longest_max_length = max(b, m[2]) triple = [max_length, parts_len_max, longest_max_length] m = make_bridge(parts, current, tmp, triple) current.remove(p) parts.append(p) return m file = open('day24.txt').read().split('\n') parts = [] for i in file: a = list(map(int, i.split('/'))) a.sort() parts.append(a) parts.sort() mm = make_bridge(parts, [], 0, [0, 0, 0]) print("part1:", mm[0]) print("part2:", mm[2])
22.170213
64
0.537428
fb42363fe0404e63e343602742d7855cdce91e38
5,400
py
Python
hotpot_km/tests/test_pooled_manager.py
maartenbreddels/hotpot_km
59640727ebef76064c9a4681a1f425987a1cccb4
[ "BSD-3-Clause" ]
null
null
null
hotpot_km/tests/test_pooled_manager.py
maartenbreddels/hotpot_km
59640727ebef76064c9a4681a1f425987a1cccb4
[ "BSD-3-Clause" ]
null
null
null
hotpot_km/tests/test_pooled_manager.py
maartenbreddels/hotpot_km
59640727ebef76064c9a4681a1f425987a1cccb4
[ "BSD-3-Clause" ]
null
null
null
from contextlib import contextmanager from subprocess import PIPE from unittest import TestCase from jupyter_client.kernelspec import NATIVE_KERNEL_NAME import pytest from traitlets.config.loader import Config from .. import ( PooledKernelManager, MaximumKernelsException, ) from .utils import shutdown_all_direct, TestKernelManager # Test that it works as normal with default config class TestPooledKernelManagerUnused(TestKernelManager): __test__ = True @contextmanager def _get_tcp_km(self): c = Config() km = PooledKernelManager(config=c) try: yield km finally: km.shutdown_all() # Test that it works with an unstrict pool class TestPooledKernelManagerApplied(TestKernelManager): __test__ = True @contextmanager def _get_tcp_km(self): c = Config() c.PooledKernelManager.kernel_pool_size = 2 c.PooledKernelManager.pool_kwargs = dict(stdout=PIPE, stderr=PIPE) km = PooledKernelManager(config=c) try: yield km finally: km.shutdown_all() def test_exceed_pool_size(self): with self._get_tcp_km() as km: self.assertEqual(len(km._pool), 2) kids = [] for i in range(4): kid = km.start_kernel(stdout=PIPE, stderr=PIPE) self.assertIn(kid, km) kids.append(kid) self.assertEqual(len(km._pool), 2) shutdown_all_direct(km) for kid in kids: self.assertNotIn(kid, km) # Cycle again to assure the pool survives that kids = [] for i in range(4): kid = km.start_kernel(stdout=PIPE, stderr=PIPE) self.assertIn(kid, km) kids.append(kid) self.assertEqual(len(km._pool), 2) km.shutdown_all() for kid in kids: self.assertNotIn(kid, km) def test_decrease_pool_size(self): with self._get_tcp_km() as km: km.kernel_pool_size = 1 self.assertEqual(len(km._pool), 1) def test_increase_pool_size(self): with self._get_tcp_km() as km: km.kernel_pool_size = 3 self.assertEqual(len(km._pool), 3) # Test that it works with an strict pool class TestPooledKernelManagerStrict(TestCase): @contextmanager def _get_tcp_km(self): c = Config() c.PooledKernelManager.kernel_pool_size = 2 c.PooledKernelManager.pool_kwargs = dict(stdout=PIPE, stderr=PIPE) km = PooledKernelManager(config=c) try: yield km finally: km.shutdown_all() def test_strict_name_correct(self): c = Config() c.PooledKernelManager.kernel_pool_size = 1 c.PooledKernelManager.pool_kernel_name = NATIVE_KERNEL_NAME c.PooledKernelManager.strict_pool_names = True km = PooledKernelManager(config=c) try: kid = km.start_kernel(kernel_name=NATIVE_KERNEL_NAME, stdout=PIPE, stderr=PIPE) self.assertIn(kid, km) finally: km.shutdown_all() self.assertNotIn(kid, km) def test_strict_name_incorrect(self): c = Config() c.PooledKernelManager.kernel_pool_size = 1 c.PooledKernelManager.pool_kernel_name = NATIVE_KERNEL_NAME c.PooledKernelManager.strict_pool_names = True km = PooledKernelManager(config=c) try: with self.assertRaisesRegex(ValueError, 'Cannot start kernel with name'): kid = km.start_kernel(kernel_name='foo', stdout=PIPE, stderr=PIPE) self.assertEqual(len(km), 1) finally: km.shutdown_all() def test_strict_kwargs_correct(self): c = Config() c.PooledKernelManager.kernel_pool_size = 1 c.PooledKernelManager.pool_kwargs = dict(stdout=PIPE, stderr=PIPE) c.PooledKernelManager.strict_pool_kwargs = True km = PooledKernelManager(config=c) try: kid = km.start_kernel(stdout=PIPE, stderr=PIPE) self.assertIn(kid, km) finally: km.shutdown_all() self.assertNotIn(kid, km) def test_strict_kwargs_incorrect(self): c = Config() c.PooledKernelManager.kernel_pool_size = 1 c.PooledKernelManager.pool_kwargs = dict(stdout=PIPE, stderr=PIPE) c.PooledKernelManager.strict_pool_kwargs = True km = PooledKernelManager(config=c) try: with self.assertRaisesRegex(ValueError, 'Cannot start kernel with kwargs'): kid = km.start_kernel() self.assertEqual(len(km), 1) finally: km.shutdown_all() def test_both_strict_correct(self): c = Config() c.PooledKernelManager.kernel_pool_size = 1 c.PooledKernelManager.pool_kernel_name = NATIVE_KERNEL_NAME c.PooledKernelManager.strict_pool_names = True c.PooledKernelManager.pool_kwargs = dict(stdout=PIPE, stderr=PIPE) c.PooledKernelManager.strict_pool_kwargs = True km = PooledKernelManager(config=c) try: kid = km.start_kernel(kernel_name=NATIVE_KERNEL_NAME, stdout=PIPE, stderr=PIPE) self.assertIn(kid, km) finally: km.shutdown_all() self.assertNotIn(kid, km)
31.952663
91
0.629074
e6f48b68068814bbbb753e2aa6e8f723ebb74094
7,170
py
Python
apyfal/_iterators.py
Accelize/apyfal
22dfe791e0956d3d3353daeba0c7a21dfe2f9b77
[ "Apache-2.0" ]
5
2018-09-23T23:15:06.000Z
2019-07-04T00:19:44.000Z
apyfal/_iterators.py
Accelize/apyfal
22dfe791e0956d3d3353daeba0c7a21dfe2f9b77
[ "Apache-2.0" ]
null
null
null
apyfal/_iterators.py
Accelize/apyfal
22dfe791e0956d3d3353daeba0c7a21dfe2f9b77
[ "Apache-2.0" ]
4
2018-07-17T08:39:41.000Z
2020-01-10T23:15:38.000Z
# coding=utf-8 """Accelerator iterators""" from concurrent.futures import ThreadPoolExecutor, as_completed from itertools import chain import re from apyfal.host import Host import apyfal.configuration as _cfg import apyfal.exceptions as _exc class _LazyClass: """ Class that get attributes from cached dict or from real accelerator """ def __setattr__(self, name, value): # Set privates variables locally if name.startswith('_'): # Python 2 don't support object.__setattr__(self, name, value) self.__dict__[name] = value return # Tries to set other names on real accelerator setattr(self._get_accelerator_object(True), name, value) def __getattr__(self, item): # If accelerator instantiated, redirects getattr to it if self._get_accelerator_object() is not None: return getattr(self._get_accelerator_object(), item) # If not, tries to get information from properties try: return self._properties[item] # If not in properties, instantiates accelerator and get # attribute from it except KeyError: return getattr(self._get_accelerator_object(True), item) def __str__(self): return str(self._get_accelerator_object() or self._properties['_repr']) __repr__ = __str__ class _LazyMember(_LazyClass): """Lazy proxy class that represent Accelerator member Args: properties (dict): Member properties. get_accelerator_object (function): Get accelerator function. """ def __init__(self, properties, get_accelerator_object): self._get_accelerator_object = get_accelerator_object self._properties = properties class _LazyAccelerator(_LazyClass): """Accelerator proxy that store information and lazy instantiates accelerator if needed. Allows to iterate over accelerators and getting some base information without losing time to instantiates them. But, if needed, instantiates accelerator to provides its public interfaces. Args: host_properties (dict): Host properties directory. config (apyfal.configuration.Configuration): Configuration. """ def __init__(self, host_properties, config): self._accelerator_object = None # Get accelerator keyword arguments self._accelerator_kwargs = dict( accelerator=host_properties['accelerator'], config=config, stop_mode='keep', host_type=host_properties['host_type']) if 'instance_id' in host_properties: self._accelerator_kwargs[ 'instance_id'] = host_properties['instance_id'] # Generates client properties client_properties = dict( name=host_properties['accelerator'], _repr="<apyfal.client.Client accelerator='%s'>" % host_properties['accelerator']) if 'url' in host_properties: # Remote clients client_properties['url'] = host_properties['url'] client_properties['_repr'] = ( client_properties['_repr'].rstrip('>') + " url='%s'>" % host_properties['url']) # Generates accelerator members self._properties = dict( host=_LazyMember(host_properties, self._get_accelerator_object), client=_LazyMember(client_properties, self._get_accelerator_object), _repr="<apyfal.Accelerator client=(%s) host=(%s)>" % ( client_properties['_repr'], host_properties['_repr'])) def _get_accelerator_object(self, force_real_one=False): """ Lazy instantiates accelerator. Args: force_real_one (bool): Forces to instantiate real accelerator if not already instantiated. Returns: apyfal.Accelerator """ if self._accelerator_object is None and force_real_one: # Can't import it at top level from apyfal import Accelerator # Instantiates accelerator self._accelerator_object = Accelerator(**self._accelerator_kwargs) return self._accelerator_object def _is_valid(host_dict, filters): """Validates host. Args: host_dict (dict): Host filters (dict): Dict of re.match filters. Returns: bool: True if host is valid """ for key, match in filters.items(): if not match(host_dict[key]): return False return True def _get_host_iter(host_type, config, host_name_prefix): """ Get hosts generator for the specified host_type Args: host_type (str): host type config (apyfal.configuration.Configuration): Configuration. host_name_prefix (bool or str): see iter_accelerators host_name_prefix Returns: generator: Hosts generator """ try: # Gets generator generator = Host(host_type=host_type, config=config).iter_hosts( host_name_prefix) # Initializes generator and returns it return chain((next(generator),), generator) except (_exc.HostException, StopIteration): return iter(()) def iter_accelerators(config=None, host_name_prefix=True, **filters): """ Iterates over all accelerators available on remote hosts. Args: config (apyfal.configuration.Configuration, path-like object or file-like object): If not set, will search it in current working directory, in current user "home" folder. If none found, will use default configuration values. Path-like object can be path, URL or cloud object URL. host_name_prefix (bool or str): If True, use "host_name_prefix" from configuration; if False don't filter by prefix; if str, uses this str as prefix filters: Arguments names are host properties to filter, values are regular expressions. Returns: generator: Accelerators generator """ # Get configuration config = _cfg.create_configuration(config) # Initializes filters for attr, pattern in filters.items(): filters[attr] = re.compile(pattern).match host_type_match = filters.get('host_type') # List available host_types host_types = set() host_types.add(config['host']['host_type']) for section in config: if section.startswith('host.'): host_type = section.split('.', 1)[1] if host_type_match is None or host_type_match(host_type): host_types.add(host_type) # Gets information for each host_type futures = [] with ThreadPoolExecutor(max_workers=len(host_types)) as executor: for host_type in host_types: futures.append(executor.submit( _get_host_iter, host_type, config, host_name_prefix)) # Yields lazy accelerators that match filters for future in as_completed(futures): for host in future.result(): if _is_valid(host, filters): yield _LazyAccelerator(host_properties=host, config=config)
33.194444
90
0.65537
b145ad21c6109e164655f6075698d6fa4f10288c
393
py
Python
profiles/wsgi.py
rithik220/profiles-rest-api
d960ff98d75bec32f89fcfa11d6daa53f1b1db2d
[ "MIT" ]
null
null
null
profiles/wsgi.py
rithik220/profiles-rest-api
d960ff98d75bec32f89fcfa11d6daa53f1b1db2d
[ "MIT" ]
null
null
null
profiles/wsgi.py
rithik220/profiles-rest-api
d960ff98d75bec32f89fcfa11d6daa53f1b1db2d
[ "MIT" ]
null
null
null
""" WSGI config for profiles project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.1/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'profiles.settings') application = get_wsgi_application()
23.117647
78
0.78626
98d628cc76db2b0feeb5f0b1db865c74fc5174ac
518
py
Python
isubscribe/migrations/0003_auto_20161007_2110.py
ilavender/sensu_drive
e874024aa157c7076ccc9465e9d6ae00a4f19fd0
[ "MIT" ]
71
2016-12-25T12:06:07.000Z
2021-02-21T21:14:48.000Z
isubscribe/migrations/0003_auto_20161007_2110.py
ilavender/sensu_drive
e874024aa157c7076ccc9465e9d6ae00a4f19fd0
[ "MIT" ]
7
2016-12-23T23:18:45.000Z
2021-06-10T18:58:14.000Z
isubscribe/migrations/0003_auto_20161007_2110.py
ilavender/sensu_drive
e874024aa157c7076ccc9465e9d6ae00a4f19fd0
[ "MIT" ]
30
2017-01-01T16:18:19.000Z
2021-04-21T15:06:47.000Z
# -*- coding: utf-8 -*- # Generated by Django 1.10.2 on 2016-10-07 21:10 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('isubscribe', '0002_auto_20161007_2108'), ] operations = [ migrations.AlterField( model_name='subscribe', name='status', field=models.IntegerField(choices=[(0, 'unknown'), (1, 'warning'), (2, 'critical')], max_length=3), ), ]
24.666667
111
0.6139
08f992626f6e23ddaa9284dafd3c3fe3f2a3c49c
46
py
Python
Exercicios/ex016/teste1.py
Carlos-Allison/Curso_de_HTML5_e_CCS3
20f2a639d792cd9e49ff9b54eeb69de44ba418e9
[ "MIT" ]
null
null
null
Exercicios/ex016/teste1.py
Carlos-Allison/Curso_de_HTML5_e_CCS3
20f2a639d792cd9e49ff9b54eeb69de44ba418e9
[ "MIT" ]
null
null
null
Exercicios/ex016/teste1.py
Carlos-Allison/Curso_de_HTML5_e_CCS3
20f2a639d792cd9e49ff9b54eeb69de44ba418e9
[ "MIT" ]
null
null
null
def carro(): print('oi, mundo do caralho')
23
33
0.630435
60177bcc979e6a6e5b4dfa37be153087d3d89cfb
9,917
py
Python
test/python/pulse/test_discrete_pulses.py
siddharthdangwal/qiskit-terra
af34eb06f28de18ef276e1e9029c62a4e35dd6a9
[ "Apache-2.0" ]
null
null
null
test/python/pulse/test_discrete_pulses.py
siddharthdangwal/qiskit-terra
af34eb06f28de18ef276e1e9029c62a4e35dd6a9
[ "Apache-2.0" ]
null
null
null
test/python/pulse/test_discrete_pulses.py
siddharthdangwal/qiskit-terra
af34eb06f28de18ef276e1e9029c62a4e35dd6a9
[ "Apache-2.0" ]
1
2020-07-13T17:56:46.000Z
2020-07-13T17:56:46.000Z
# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2017, 2019. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """Tests discrete sampled pulse functions.""" import numpy as np from qiskit.test import QiskitTestCase from qiskit.pulse import Waveform, PulseError import qiskit.pulse.library as library import qiskit.pulse.library.continuous as continuous class TestDiscretePulses(QiskitTestCase): """Test discreted sampled pulses.""" def test_constant(self): """Test discrete sampled constant pulse.""" amp = 0.5j duration = 10 times = np.arange(0, duration) + 0.5 # to match default midpoint sampling strategy constant_ref = continuous.constant(times, amp=amp) constant_pulse = library.constant(duration, amp=amp) self.assertIsInstance(constant_pulse, Waveform) np.testing.assert_array_almost_equal(constant_pulse.samples, constant_ref) def test_zero(self): """Test discrete sampled constant pulse.""" duration = 10 times = np.arange(0, duration) + 0.5 zero_ref = continuous.zero(times) zero_pulse = library.zero(duration) self.assertIsInstance(zero_pulse, Waveform) np.testing.assert_array_almost_equal(zero_pulse.samples, zero_ref) def test_square(self): """Test discrete sampled square wave.""" amp = 0.5 freq = 0.2 duration = 10 times = np.arange(0, duration) + 0.5 square_ref = continuous.square(times, amp=amp, freq=freq) square_pulse = library.square(duration, amp=amp, freq=freq) self.assertIsInstance(square_pulse, Waveform) np.testing.assert_array_almost_equal(square_pulse.samples, square_ref) # test single cycle cycle_freq = 1./duration square_cycle_ref = continuous.square(times, amp=amp, freq=cycle_freq) square_cycle_pulse = library.square(duration, amp=amp) np.testing.assert_array_almost_equal(square_cycle_pulse.samples, square_cycle_ref) def test_sawtooth(self): """Test discrete sampled sawtooth wave.""" amp = 0.5 freq = 0.2 duration = 10 times = np.arange(0, duration) + 0.5 sawtooth_ref = continuous.sawtooth(times, amp=amp, freq=freq) sawtooth_pulse = library.sawtooth(duration, amp=amp, freq=freq) self.assertIsInstance(sawtooth_pulse, Waveform) np.testing.assert_array_equal(sawtooth_pulse.samples, sawtooth_ref) # test single cycle cycle_freq = 1./duration sawtooth_cycle_ref = continuous.sawtooth(times, amp=amp, freq=cycle_freq) sawtooth_cycle_pulse = library.sawtooth(duration, amp=amp) np.testing.assert_array_almost_equal(sawtooth_cycle_pulse.samples, sawtooth_cycle_ref) def test_triangle(self): """Test discrete sampled triangle wave.""" amp = 0.5 freq = 0.2 duration = 10 times = np.arange(0, duration) + 0.5 triangle_ref = continuous.triangle(times, amp=amp, freq=freq) triangle_pulse = library.triangle(duration, amp=amp, freq=freq) self.assertIsInstance(triangle_pulse, Waveform) np.testing.assert_array_almost_equal(triangle_pulse.samples, triangle_ref) # test single cycle cycle_freq = 1./duration triangle_cycle_ref = continuous.triangle(times, amp=amp, freq=cycle_freq) triangle_cycle_pulse = library.triangle(duration, amp=amp) np.testing.assert_array_equal(triangle_cycle_pulse.samples, triangle_cycle_ref) def test_cos(self): """Test discrete sampled cosine wave.""" amp = 0.5 period = 5 freq = 1/period duration = 10 times = np.arange(0, duration) + 0.5 cos_ref = continuous.cos(times, amp=amp, freq=freq) cos_pulse = library.cos(duration, amp=amp, freq=freq) self.assertIsInstance(cos_pulse, Waveform) np.testing.assert_array_almost_equal(cos_pulse.samples, cos_ref) # test single cycle cycle_freq = 1/duration cos_cycle_ref = continuous.cos(times, amp=amp, freq=cycle_freq) cos_cycle_pulse = library.cos(duration, amp=amp) np.testing.assert_array_almost_equal(cos_cycle_pulse.samples, cos_cycle_ref) def test_sin(self): """Test discrete sampled sine wave.""" amp = 0.5 period = 5 freq = 1/period duration = 10 times = np.arange(0, duration) + 0.5 sin_ref = continuous.sin(times, amp=amp, freq=freq) sin_pulse = library.sin(duration, amp=amp, freq=freq) self.assertIsInstance(sin_pulse, Waveform) np.testing.assert_array_equal(sin_pulse.samples, sin_ref) # test single cycle cycle_freq = 1/duration sin_cycle_ref = continuous.sin(times, amp=amp, freq=cycle_freq) sin_cycle_pulse = library.sin(duration, amp=amp) np.testing.assert_array_almost_equal(sin_cycle_pulse.samples, sin_cycle_ref) def test_gaussian(self): """Test gaussian pulse.""" amp = 0.5 sigma = 2 duration = 10 center = duration/2 times = np.arange(0, duration) + 0.5 gaussian_ref = continuous.gaussian(times, amp, center, sigma, zeroed_width=2*center, rescale_amp=True) gaussian_pulse = library.gaussian(duration, amp, sigma) self.assertIsInstance(gaussian_pulse, Waveform) np.testing.assert_array_almost_equal(gaussian_pulse.samples, gaussian_ref) def test_gaussian_deriv(self): """Test discrete sampled gaussian derivative pulse.""" amp = 0.5 sigma = 2 duration = 10 center = duration/2 times = np.arange(0, duration) + 0.5 gaussian_deriv_ref = continuous.gaussian_deriv(times, amp, center, sigma) gaussian_deriv_pulse = library.gaussian_deriv(duration, amp, sigma) self.assertIsInstance(gaussian_deriv_pulse, Waveform) np.testing.assert_array_almost_equal(gaussian_deriv_pulse.samples, gaussian_deriv_ref) def test_sech(self): """Test sech pulse.""" amp = 0.5 sigma = 2 duration = 10 center = duration/2 times = np.arange(0, duration) + 0.5 sech_ref = continuous.sech(times, amp, center, sigma, zeroed_width=2*center, rescale_amp=True) sech_pulse = library.sech(duration, amp, sigma) self.assertIsInstance(sech_pulse, Waveform) np.testing.assert_array_almost_equal(sech_pulse.samples, sech_ref) def test_sech_deriv(self): """Test discrete sampled sech derivative pulse.""" amp = 0.5 sigma = 2 duration = 10 center = duration/2 times = np.arange(0, duration) + 0.5 sech_deriv_ref = continuous.sech_deriv(times, amp, center, sigma) sech_deriv_pulse = library.sech_deriv(duration, amp, sigma) self.assertIsInstance(sech_deriv_pulse, Waveform) np.testing.assert_array_almost_equal(sech_deriv_pulse.samples, sech_deriv_ref) def test_gaussian_square(self): """Test discrete sampled gaussian square pulse.""" amp = 0.5 sigma = 0.1 risefall = 2 duration = 10 center = duration/2 width = duration-2*risefall center = duration/2 times = np.arange(0, duration) + 0.5 gaussian_square_ref = continuous.gaussian_square(times, amp, center, width, sigma) gaussian_square_pulse = library.gaussian_square(duration, amp, sigma, risefall) self.assertIsInstance(gaussian_square_pulse, Waveform) np.testing.assert_array_almost_equal(gaussian_square_pulse.samples, gaussian_square_ref) def test_gaussian_square_args(self): """Gaussian square allows the user to specify risefall or width. Test this.""" amp = 0.5 sigma = 0.1 duration = 10 # risefall and width consistent: no error library.gaussian_square(duration, amp, sigma, 2, width=6) # supply width instead: no error library.gaussian_square(duration, amp, sigma, width=6) with self.assertRaises(PulseError): library.gaussian_square(duration, amp, sigma, width=2, risefall=2) with self.assertRaises(PulseError): library.gaussian_square(duration, amp, sigma) def test_drag(self): """Test discrete sampled drag pulse.""" amp = 0.5 sigma = 0.1 beta = 0 duration = 10 center = 10/2 times = np.arange(0, duration) + 0.5 # reference drag pulse drag_ref = continuous.drag(times, amp, center, sigma, beta=beta, zeroed_width=2*(center+1), rescale_amp=True) drag_pulse = library.drag(duration, amp, sigma, beta=beta) self.assertIsInstance(drag_pulse, Waveform) np.testing.assert_array_almost_equal(drag_pulse.samples, drag_ref) def test_period_deprecation_warning(self): """Tests for DeprecationWarning""" amp = 0.5 period = 5. duration = 10 self.assertWarns(DeprecationWarning, lambda: library.triangle(duration, amp=amp, period=period)) self.assertWarns(DeprecationWarning, lambda: library.sawtooth(duration, amp=amp, period=period)) self.assertWarns(DeprecationWarning, lambda: library.square(duration, amp=amp, period=period))
41.493724
96
0.658163
817a7b305e39993614036e0a1674a611abd0b122
3,185
py
Python
OathSaveFileParser.py
Ecophagy/OathSaveFileParser
c61d33417f7e7233b7289ceabd7604e48b7fdeec
[ "MIT" ]
1
2021-04-05T07:59:59.000Z
2021-04-05T07:59:59.000Z
OathSaveFileParser.py
Ecophagy/OathSaveFileParser
c61d33417f7e7233b7289ceabd7604e48b7fdeec
[ "MIT" ]
null
null
null
OathSaveFileParser.py
Ecophagy/OathSaveFileParser
c61d33417f7e7233b7289ceabd7604e48b7fdeec
[ "MIT" ]
null
null
null
import json from os import path from pathlib import Path from suits import Suit from visions import visions import tkinter as tk from tkinter import filedialog tts_save_location = path.join(str(Path.home()), "Documents", "My Games", "Tabletop Simulator", "Saves") game_state_key = "LuaScriptState" dispossessed_cards_key = "curDispossessedDeckCards" dispossessed_cards_count_key = "curDispossessedDeckCardCount" world_deck_cards_key = "curWorldDeckCards" world_deck_cards_count_key = "curWorldDeckCardCount" map_cards_key = "curMapNormalCards" def read_json_file(file_name): with open(file_name, 'r') as f: data = f.read() return json.loads(data) def order_by_suit(card_list): suit_lists = [[], [], [], [], [], []] full_card_list = read_json_file("cardsuits.json") for card in card_list: if card not in visions: # ignore visions - they have no suit suit_id = full_card_list[card] suit_lists[suit_id].append(card) return suit_lists def print_suit_ordered_card_list(suit_ordered_card_list): i = 0 for suit in suit_ordered_card_list: print(Suit(i).name) if not suit: print("\tNone") for card in suit: print(f"\t{card}") i += 1 def parse_oath_save_json(json_data): save_game_state = json.loads(json_data[game_state_key]) full_card_list = read_json_file("cardsuits.json") dispossessed = save_game_state[dispossessed_cards_key] world_deck = save_game_state[world_deck_cards_key] cards_on_map = save_game_state[map_cards_key] # Remove sites, relics, and edifices - we only care about denizens denizen_cards_on_map = [] for site in cards_on_map: for card, flipped in site: if card in full_card_list: denizen_cards_on_map.append(card) # The archive is everything else archive = [] for card in full_card_list: if card not in dispossessed \ and card not in world_deck \ and card not in denizen_cards_on_map: archive.append(card) # Print out our card lists ordered by suit print(f"Cards on map ({len(denizen_cards_on_map)}):") print_suit_ordered_card_list(order_by_suit(denizen_cards_on_map)) print() print(f"The Dispossessed ({save_game_state[dispossessed_cards_count_key]}):") print_suit_ordered_card_list(order_by_suit(dispossessed)) print() print(f"World Deck ({save_game_state[world_deck_cards_count_key]} including 5 Visions):") print_suit_ordered_card_list(order_by_suit(world_deck)) print() print(f"The Archive: {len(archive)}") print_suit_ordered_card_list(order_by_suit(archive)) if __name__ == '__main__': default_path = tts_save_location if path.isdir(tts_save_location) else "." root = tk.Tk() root.withdraw() file_path = filedialog.askopenfilename(initialdir=default_path, title="Oath Save File", filetypes=[("json files","*.json")]) if file_path: save_file_json = read_json_file(file_path) parse_oath_save_json(save_file_json)
31.534653
103
0.686342
2c88d693c8677652e9d81616596b3c6d534e7491
1,522
py
Python
src/flask_tat/http2kafka.py
cdumay/flask-tat
94a1cbee2e4be424eefc9009004df819e90c2b32
[ "Apache-2.0" ]
null
null
null
src/flask_tat/http2kafka.py
cdumay/flask-tat
94a1cbee2e4be424eefc9009004df819e90c2b32
[ "Apache-2.0" ]
null
null
null
src/flask_tat/http2kafka.py
cdumay/flask-tat
94a1cbee2e4be424eefc9009004df819e90c2b32
[ "Apache-2.0" ]
null
null
null
#!/usr/bin/env python # -*- coding: utf-8 -*- """ .. codeauthor:: Cédric Dumay <[email protected]> """ from cdumay_rest_client.client import RESTClient from flask_tat.base import BaseTATClient class HTTP2KafkaClient(BaseTATClient): def message_add(self, topic, **kwargs): return self.client.do_request( method="POST", path="/message/{}".format(topic.lstrip('/')), data=kwargs, parse_output=False ) def message_reply(self, topic, tag_ref, text): return self.client.do_request( method="POST", path="/message/{}".format(topic.lstrip('/')), data=dict(text=text, tagReference=tag_ref, action="reply"), parse_output=False ) def message_relabel(self, topic, tag_ref, labels): return self.client.do_request( method="PUT", path="/message/{}".format(topic.lstrip('/')), data=dict(labels=labels, tagReference=tag_ref, action="relabel"), parse_output=False ) @property def client(self): if self._client is None: self._client = RESTClient( server=self.app.config['TAT_URL'], headers={ "X-Tat_username": self.app.config["TAT_USERNAME"], "X-Tat_password": self.app.config["TAT_PASSWORD"], "Content-type": "application/json", }, ssl_verify=self.app.config["TAT_SSL_VERIFY"], ) return self._client
32.382979
77
0.580815
d06787023b511b1cd1c7ac93c9a31c5366cb9162
5,308
py
Python
modeling/backbones/deit.py
BrandonHanx/reid-strong-baseline
9df1dc3d6217af2d3cb40d0627f77b36a66e5f89
[ "MIT" ]
null
null
null
modeling/backbones/deit.py
BrandonHanx/reid-strong-baseline
9df1dc3d6217af2d3cb40d0627f77b36a66e5f89
[ "MIT" ]
null
null
null
modeling/backbones/deit.py
BrandonHanx/reid-strong-baseline
9df1dc3d6217af2d3cb40d0627f77b36a66e5f89
[ "MIT" ]
null
null
null
import copy import math from functools import partial import numpy as np import timm.models.vision_transformer as ViTcls import torch import torch.nn.functional as F from timm.models.helpers import load_pretrained class ViT(ViTcls.VisionTransformer): def __init__(self, mode, **kwargs): super().__init__(**kwargs) self.mode = mode def forward(self, x): B = x.shape[0] x = self.patch_embed(x) cls_tokens = self.cls_token.expand(B, -1, -1) x = torch.cat((cls_tokens, x), dim=1) x = x + self.pos_embed x = self.pos_drop(x) if self.mode == "jpm": for blk in self.blocks[:-1]: x = blk(x) return x for blk in self.blocks: x = blk(x) x = self.norm(x) if self.mode == "first": return x[:, 0] if self.mode == "average": return x[:, 1:].mean(dim=1) return NotImplementedError class ViTWithJPM(torch.nn.Module): def __init__(self, vit, shift_offset=5, shuffle_group=4): super().__init__() self.vit = vit self.jpm = copy.deepcopy( self.vit.blocks[-1] ) # initialize the weight same as last layer self.jpm_norm = copy.deepcopy(self.vit.norm) self.shift_offset = shift_offset self.shuffle_group = shuffle_group def forward(self, x): x = self.vit(x) global_feat = self.vit.blocks[-1](x) global_feat = self.vit.norm(global_feat)[:, 0] cls_token = x[:, 0].unsqueeze(dim=1) feat_len = x.shape[1] - 1 local_feat = torch.cat( [x[:, self.shift_offset + 1 :], x[:, 1 : self.shift_offset + 1]], dim=1 ) # shift random_idx = list(np.random.permutation(feat_len)) local_feat = local_feat[:, random_idx] # shuffle jpm_feats = [global_feat] group_idxs = np.linspace(0, feat_len, self.shuffle_group + 1, dtype=int) for i in range(len(group_idxs) - 1): feat = torch.cat( [cls_token, local_feat[:, group_idxs[i] : group_idxs[i + 1]]], dim=1 ) feat = self.jpm(feat) feat = self.jpm_norm(feat) jpm_feats.append(feat[:, 0]) return jpm_feats def resize_pos_embed(posemb, posemb_new, gs_new): # Rescale the grid of position embeddings when loading from state_dict. print("Resized position embedding: {} to {}".format(posemb.shape, posemb_new.shape)) posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:] gs_old = int(math.sqrt(len(posemb_grid))) posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) posemb_grid = F.interpolate( posemb_grid, size=gs_new, mode="bilinear", align_corners=False ) posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def checkpoint_filter_fn(state_dict, model, gs_new): """ convert patch embedding weight from manual patchify + linear proj to conv""" out_dict = {} if "model" in state_dict: # For deit models state_dict = state_dict["model"] for k, v in state_dict.items(): if "patch_embed.proj.weight" in k and len(v.shape) < 4: # For old models that I trained prior to conv based patchification O, I, H, W = model.patch_embed.proj.weight.shape v = v.reshape(O, -1, H, W) elif k == "pos_embed" and v.shape != model.pos_embed.shape: # To resize pos embedding when using model at different size from pretrained weights v = resize_pos_embed(v, model.pos_embed, gs_new) out_dict[k] = v return out_dict def create_vit(variant, mode, img_size, pretrained, patch_size, **kwargs): model = ViT(mode=mode, img_size=img_size, **kwargs) model.default_cfg = ViTcls.default_cfgs[variant] gs_new = (int(img_size[0] / patch_size), int(img_size[1] / patch_size)) if pretrained: load_pretrained( model, filter_fn=partial(checkpoint_filter_fn, model=model, gs_new=gs_new) ) return model model_archs = {} model_archs["vit_deit_small_patch16_224"] = dict( patch_size=16, embed_dim=384, depth=12, num_heads=6 ) model_archs["vit_deit_base_patch16_224"] = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12 ) model_archs["vit_base_patch16_224_in21k"] = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12 ) def deit(arch="vit_deit_small_patch16_224"): if arch == "deit_jpm_small_patch16_224": arch = "vit_deit_small_patch16_224" model_arch = model_archs[arch] vit = create_vit( variant=arch, mode="jpm", img_size=(256, 128), pretrained=True, **model_arch ) return ViTWithJPM(vit) elif arch == "vit_jpm_base_patch16_224_in21k": arch = "vit_base_patch16_224_in21k" model_arch = model_archs[arch] vit = create_vit( variant=arch, mode="jpm", img_size=(256, 128), pretrained=True, **model_arch ) return ViTWithJPM(vit) model_arch = model_archs[arch] return create_vit( variant=arch, mode="first", img_size=(256, 128), pretrained=True, **model_arch )
34.025641
96
0.623964
62c8449617cd10b2b9ad513a2b62d50465bea566
24,374
py
Python
compiler/dna/parser/parser.py
AnonymousDeveloper65535/libpandadna
3110a8d576d22093e4c735081c5f639d28397a17
[ "BSD-3-Clause" ]
1
2021-02-13T22:40:50.000Z
2021-02-13T22:40:50.000Z
compiler/dna/parser/parser.py
AnonymousDeveloper65535/libpandadna
3110a8d576d22093e4c735081c5f639d28397a17
[ "BSD-3-Clause" ]
1
2018-07-28T20:07:04.000Z
2018-07-30T18:28:34.000Z
compiler/dna/parser/parser.py
AnonymousDeveloper65535/libpandadna
3110a8d576d22093e4c735081c5f639d28397a17
[ "BSD-3-Clause" ]
2
2019-12-02T01:39:10.000Z
2021-02-13T22:41:00.000Z
import os from dna.components.DNAAnimBuilding import DNAAnimBuilding from dna.components.DNAAnimProp import DNAAnimProp from dna.components.DNABattleCell import DNABattleCell from dna.components.DNACornice import DNACornice from dna.components.DNADoor import DNADoor from dna.components.DNAFlatBuilding import DNAFlatBuilding from dna.components.DNAFlatDoor import DNAFlatDoor from dna.components.DNAGroup import DNAGroup from dna.components.DNAInteractiveProp import DNAInteractiveProp from dna.components.DNALandmarkBuilding import DNALandmarkBuilding from dna.components.DNANode import DNANode from dna.components.DNAProp import DNAProp from dna.components.DNASign import DNASign from dna.components.DNASignBaseline import DNASignBaseline from dna.components.DNASignGraphic import DNASignGraphic from dna.components.DNASignText import DNASignText from dna.components.DNAStreet import DNAStreet from dna.components.DNASuitPoint import DNASuitPoint from dna.components.DNAVisGroup import DNAVisGroup from dna.components.DNAWall import DNAWall from dna.components.DNAWindows import DNAWindows def p_dna(p): pass p_dna.__doc__ = '''\ dna : dna object | object''' def p_object(p): p[0] = p[1] p_object.__doc__ = '''\ object : suitpoint | group | model | font | store_texture''' def p_number(p): p[0] = p[1] p_number.__doc__ = '''\ number : FLOAT | INTEGER''' def p_lpoint3f(p): lpoint3f = (p[1], p[2], p[3]) p[0] = lpoint3f p_lpoint3f.__doc__ = '''\ lpoint3f : number number number''' def p_suitpoint(p): argCount = len(p) if argCount == 9: index = p[3] pointTypeStr = p[5] pos = p[7] landmarkBuildingIndex = -1 else: index = p[3] pointTypeStr = p[5] pos = p[7] landmarkBuildingIndex = p[9] point = DNASuitPoint(index, pointTypeStr, pos, landmarkBuildingIndex=landmarkBuildingIndex) p.parser.dnaStore.storeSuitPoint(point) p_suitpoint.__doc__ = '''\ suitpoint : STORE_SUIT_POINT "[" number "," suitpointtype "," lpoint3f "]" | STORE_SUIT_POINT "[" number "," suitpointtype "," lpoint3f "," number "]"''' def p_suitpointtype(p): pointTypeStr = p[1] p[0] = DNASuitPoint.pointTypeMap[pointTypeStr] p_suitpointtype.__doc__ = '''\ suitpointtype : STREET_POINT | FRONT_DOOR_POINT | SIDE_DOOR_POINT | COGHQ_IN_POINT | COGHQ_OUT_POINT''' def p_string(p): p[0] = p[1] p_string.__doc__ = '''\ string : QUOTED_STRING | UNQUOTED_STRING''' def p_dnagroupdef(p): name = p[2] p[0] = DNAGroup(name) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_dnagroupdef.__doc__ = '''\ dnagroupdef : GROUP string''' def p_dnanodedef(p): name = p[2] p[0] = DNANode(name) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_dnanodedef.__doc__ = '''\ dnanodedef : NODE string''' def p_visgroupdef(p): name = p[2] p[0] = DNAVisGroup(name) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_visgroupdef.__doc__ = '''\ visgroupdef : VISGROUP string''' def p_dnagroup(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_dnagroup.__doc__ = '''\ dnagroup : dnagroupdef "[" subgroup_list "]"''' def p_visgroup(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_visgroup.__doc__ = '''\ visgroup : visgroupdef "[" subvisgroup_list "]"''' def p_string_opt_list(p): argCount = len(p) if argCount == 2: p[0] = [] elif (argCount == 3) and (p[2] is not None): p[0] = p[1] p[0].append(p[2]) p_string_opt_list.__doc__ = '''\ string_opt_list : string_opt_list string | empty''' def p_vis(p): parentVis, visList = p[3], p[4] p.parser.parentGroup.addVisible(parentVis) for vis in visList: p.parser.parentGroup.addVisible(vis) p_vis.__doc__ = '''\ vis : VIS "[" string string_opt_list "]"''' def p_empty(p): pass p_empty.__doc__ = '''\ empty : ''' def p_group(p): p[0] = p[1] p_group.__doc__ = '''\ group : dnagroup | visgroup | dnanode | windows | cornice | door''' def p_dnanode(p): p[0] = p[1] p_dnanode.__doc__ = '''\ dnanode : prop | sign | signbaseline | signtext | flatbuilding | wall | landmarkbuilding | street | signgraphic | dnanode_grp''' def p_dnanode_grp(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_dnanode_grp.__doc__ = '''\ dnanode_grp : dnanodedef "[" subdnanode_list "]"''' def p_sign(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_sign.__doc__ = '''\ sign : signdef "[" subprop_list "]"''' def p_signgraphic(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_signgraphic.__doc__ = '''\ signgraphic : signgraphicdef "[" subsigngraphic_list "]"''' def p_prop(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_prop.__doc__ = '''\ prop : propdef "[" subprop_list "]" | animpropdef "[" subanimprop_list "]" | interactivepropdef "[" subinteractiveprop_list "]"''' def p_signbaseline(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_signbaseline.__doc__ = '''\ signbaseline : baselinedef "[" subbaseline_list "]"''' def p_signtest(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_signtest.__doc__ = '''\ signtext : signtextdef "[" subtext_list "]"''' def p_flatbuilding(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_flatbuilding.__doc__ = '''\ flatbuilding : flatbuildingdef "[" subflatbuilding_list "]"''' def p_wall(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_wall.__doc__ = '''\ wall : walldef "[" subwall_list "]"''' def p_windows(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_windows.__doc__ = '''\ windows : windowsdef "[" subwindows_list "]"''' def p_cornice(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_cornice.__doc__ = '''\ cornice : cornicedef "[" subcornice_list "]"''' def p_landmarkbuilding(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_landmarkbuilding.__doc__ = '''\ landmarkbuilding : landmarkbuildingdef "[" sublandmarkbuilding_list "]" | animbuildingdef "[" subanimbuilding_list "]"''' def p_street(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_street.__doc__ = '''\ street : streetdef "[" substreet_list "]"''' def p_door(p): p[0] = p[1] p.parser.parentGroup = p[0].parent p_door.__doc__ = '''\ door : doordef "[" subdoor_list "]" | flatdoordef "[" subdoor_list "]"''' def p_propdef(p): name = p[2] p[0] = DNAProp(name) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_propdef.__doc__ = '''\ propdef : PROP string''' def p_animpropdef(p): name = p[2] p[0] = DNAAnimProp(name) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_animpropdef.__doc__ = '''\ animpropdef : ANIM_PROP string''' def p_interactivepropdef(p): name = p[2] p[0] = DNAInteractiveProp(name) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_interactivepropdef.__doc__ = '''\ interactivepropdef : INTERACTIVE_PROP string''' def p_flatbuildingdef(p): name = p[2] p[0] = DNAFlatBuilding(name) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_flatbuildingdef.__doc__ = '''\ flatbuildingdef : FLAT_BUILDING string''' def p_walldef(p): p[0] = DNAWall('') p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_walldef.__doc__ = '''\ walldef : WALL''' def p_windowsdef(p): p[0] = DNAWindows('') p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_windowsdef.__doc__ = '''\ windowsdef : WINDOWS''' def p_cornicedef(p): p[0] = DNACornice('') p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_cornicedef.__doc__ = '''\ cornicedef : CORNICE''' def p_landmarkbuildingdef(p): name = p[2] p[0] = DNALandmarkBuilding(name) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] blockNumber = int(p.parser.dnaStore.getBlock(name)) p.parser.dnaStore.storeBlockNumber(blockNumber) zoneId = 0 try: zoneId = int(p[0].getVisGroup().name.split(':')[0]) except: pass finally: p.parser.dnaStore.storeBlockZone(blockNumber, zoneId) p_landmarkbuildingdef.__doc__ = '''\ landmarkbuildingdef : LANDMARK_BUILDING string''' def p_animbuildingdef(p): name = p[2] p[0] = DNAAnimBuilding(name) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] blockNumber = int(p.parser.dnaStore.getBlock(name)) p.parser.dnaStore.storeBlockNumber(blockNumber) zoneId = int(p[0].getVisGroup().name.split(':')[0]) p.parser.dnaStore.storeBlockZone(blockNumber, zoneId) p_animbuildingdef.__doc__ = '''\ animbuildingdef : ANIM_BUILDING string''' def p_doordef(p): p[0] = DNADoor('') p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_doordef.__doc__ = '''\ doordef : DOOR''' def p_flatdoordef(p): p[0] = DNAFlatDoor('') p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup.parent.setHasDoor(True) p.parser.parentGroup = p[0] p_flatdoordef.__doc__ = '''\ flatdoordef : FLAT_DOOR''' def p_streetdef(p): p[0] = DNAStreet(p[2]) p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_streetdef.__doc__ = '''\ streetdef : STREET string''' def p_signdef(p): p[0] = DNASign() p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_signdef.__doc__ = '''\ signdef : SIGN''' def p_signgraphicdef(p): p[0] = DNASignGraphic('') p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_signgraphicdef.__doc__ = '''\ signgraphicdef : GRAPHIC''' def p_baselinedef(p): p[0] = DNASignBaseline() p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_baselinedef.__doc__ = '''\ baselinedef : BASELINE''' def p_signtextdef(p): p[0] = DNASignText() p.parser.parentGroup.add(p[0]) p[0].setParent(p.parser.parentGroup) p.parser.parentGroup = p[0] p_signtextdef.__doc__ = '''\ signtextdef : TEXT''' def p_suitedge(p): startPointIndex, endPointIndex = p[3], p[4] zoneId = int(p.parser.parentGroup.name) edge = p.parser.dnaStore.storeSuitEdge( startPointIndex, endPointIndex, zoneId) p.parser.parentGroup.addSuitEdge(edge) p_suitedge.__doc__ = '''\ suitedge : SUIT_EDGE "[" number number "]"''' def p_battlecell(p): width, height, pos = p[3], p[4], p[5] p[0] = DNABattleCell(width, height, pos) p.parser.parentGroup.addBattleCell(p[0]) p_battlecell.__doc__ = '''\ battlecell : BATTLE_CELL "[" number number lpoint3f "]"''' def p_subgroup_list(p): p[0] = p[1] argCount = len(p) if argCount == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subgroup_list.__doc__ = '''\ subgroup_list : subgroup_list group | empty''' def p_subvisgroup_list(p): p[0] = p[1] argCount = len(p) if argCount == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subvisgroup_list.__doc__ = '''\ subvisgroup_list : subvisgroup_list group | subvisgroup_list suitedge | subvisgroup_list battlecell | subvisgroup_list vis | empty''' def p_pos(p): p.parser.parentGroup.setPos(p[3]) p_pos.__doc__ = '''\ pos : POS "[" lpoint3f "]"''' def p_hpr(p): p.parser.parentGroup.setHpr(p[3]) p_hpr.__doc__ = '''\ hpr : HPR "[" lpoint3f "]" | NHPR "[" lpoint3f "]"''' def p_scale(p): p.parser.parentGroup.setScale(p[3]) p_scale.__doc__ = '''\ scale : SCALE "[" lpoint3f "]"''' def p_flags(p): p.parser.parentGroup.setFlags(p[3]) p_flags.__doc__ = '''\ flags : FLAGS "[" string "]"''' def p_dnanode_sub(p): p[0] = p[1] p_dnanode_sub.__doc__ = '''\ dnanode_sub : group | pos | hpr | scale''' def p_dnaprop_sub(p): p[0] = p[1] p_dnaprop_sub.__doc__ = '''\ dnaprop_sub : code | color''' def p_dnaanimprop_sub(p): p[0] = p[1] p_dnaanimprop_sub.__doc__ = '''\ dnaanimprop_sub : anim''' def p_dnainteractiveprop_sub(p): p[0] = p[1] p_dnainteractiveprop_sub.__doc__ = '''\ dnainteractiveprop_sub : cell_id''' def p_anim(p): p.parser.parentGroup.setAnim(p[3]) p_anim.__doc__ = '''\ anim : ANIM "[" string "]"''' def p_cell_id(p): p.parser.parentGroup.setCellId(p[3]) p_cell_id.__doc__ = '''\ cell_id : CELL_ID "[" number "]"''' def p_baseline_sub(p): p[0] = p[1] p_baseline_sub.__doc__ = '''\ baseline_sub : code | color | width | height | indent | kern | stomp | stumble | wiggle | flags''' def p_text_sub(p): p[0] = p[1] p_text_sub.__doc__ = '''\ text_sub : letters''' def p_signgraphic_sub(p): p[0] = p[1] p_signgraphic_sub.__doc__ = '''\ signgraphic_sub : width | height | code | color''' def p_flatbuilding_sub(p): p[0] = p[1] p_flatbuilding_sub.__doc__ = '''\ flatbuilding_sub : width''' def p_wall_sub(p): p[0] = p[1] p_wall_sub.__doc__ = '''\ wall_sub : height | code | color''' def p_windows_sub(p): p[0] = p[1] p_windows_sub.__doc__ = '''\ windows_sub : code | color | windowcount''' def p_cornice_sub(p): p[0] = p[1] p_cornice_sub.__doc__ = '''\ cornice_sub : code | color''' def p_landmarkbuilding_sub(p): p[0] = p[1] p_landmarkbuilding_sub.__doc__ = '''\ landmarkbuilding_sub : code | title | article | building_type | wall_color''' def p_animbuilding_sub(p): p[0] = p[1] p_animbuilding_sub.__doc__ = '''\ animbuilding_sub : anim''' def p_door_sub(p): p[0] = p[1] p_door_sub.__doc__ = '''\ door_sub : code | color''' def p_street_sub(p): p[0] = p[1] p_street_sub.__doc__ = '''\ street_sub : code | texture | color''' def p_texture(p): p.parser.parentGroup.setTexture(p[3]) p_texture.__doc__ = '''\ texture : TEXTURE "[" string "]"''' def p_title(p): title = p[3] parentName = p.parser.parentGroup.name blockNumber = int(p.parser.dnaStore.getBlock(parentName)) p.parser.dnaStore.storeBlockTitle(blockNumber, title) p_title.__doc__ = '''\ title : TITLE "[" string "]"''' def p_article(p): article = p[3] parentName = p.parser.parentGroup.name blockNumber = int(p.parser.dnaStore.getBlock(parentName)) p.parser.dnaStore.storeBlockArticle(blockNumber, article) p_article.__doc__ = '''\ article : ARTICLE "[" string "]"''' def p_building_type(p): buildingType = p[3] parentName = p.parser.parentGroup.name blockNumber = int(p.parser.dnaStore.getBlock(parentName)) p.parser.dnaStore.storeBlockBuildingType(blockNumber, buildingType) p_building_type.__doc__ = '''\ building_type : BUILDING_TYPE "[" string "]"''' def p_wall_color(p): wallColor = (p[3], p[4], p[5], p[6]) p.parser.parentGroup.setWallColor(wallColor) p_wall_color.__doc__ = '''\ wall_color : COLOR "[" number number number number "]"''' def p_count(p): p.parser.parentGroup.setWindowCount(p[3]) p_count.__doc__ = '''\ windowcount : COUNT "[" number "]"''' def p_letters(p): p.parser.parentGroup.setLetters(p[3]) p_letters.__doc__ = '''\ letters : LETTERS "[" string "]"''' def p_width(p): p.parser.parentGroup.setWidth(p[3]) p_width.__doc__ = '''\ width : WIDTH "[" number "]"''' def p_height(p): p.parser.parentGroup.setHeight(p[3]) p_height.__doc__ = '''\ height : HEIGHT "[" number "]"''' def p_stomp(p): p.parser.parentGroup.setStomp(p[3]) p_stomp.__doc__ = '''\ stomp : STOMP "[" number "]"''' def p_indent(p): p.parser.parentGroup.setIndent(p[3]) p_indent.__doc__ = '''\ indent : INDENT "[" number "]"''' def p_kern(p): p.parser.parentGroup.setKern(p[3]) p_kern.__doc__ = '''\ kern : KERN "[" number "]"''' def p_stumble(p): p.parser.parentGroup.setStumble(p[3]) p_stumble.__doc__ = '''\ stumble : STUMBLE "[" number "]"''' def p_wiggle(p): p.parser.parentGroup.setWiggle(p[3]) p_wiggle.__doc__ = '''\ wiggle : WIGGLE "[" number "]"''' def p_code(p): p.parser.parentGroup.setCode(p[3]) p_code.__doc__ = '''\ code : CODE "[" string "]"''' def p_color(p): p.parser.parentGroup.setColor((p[3], p[4], p[5], p[6])) p_color.__doc__ = '''\ color : COLOR "[" number number number number "]"''' def p_subprop_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subprop_list.__doc__ = '''\ subprop_list : subprop_list dnanode_sub | subprop_list dnaprop_sub | empty''' def p_subanimprop_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subanimprop_list.__doc__ = '''\ subanimprop_list : subanimprop_list dnanode_sub | subanimprop_list dnaprop_sub | subanimprop_list dnaanimprop_sub | empty''' def p_subinteractiveprop_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subinteractiveprop_list.__doc__ = '''\ subinteractiveprop_list : subinteractiveprop_list dnanode_sub | subinteractiveprop_list dnaprop_sub | subinteractiveprop_list dnaanimprop_sub | subinteractiveprop_list dnainteractiveprop_sub | empty''' def p_subbaseline_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subbaseline_list.__doc__ = '''\ subbaseline_list : subbaseline_list dnanode_sub | subbaseline_list baseline_sub | empty''' def p_subtext_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subtext_list.__doc__ = '''\ subtext_list : subtext_list dnanode_sub | subtext_list text_sub | empty''' def p_subdnanode_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subdnanode_list.__doc__ = '''\ subdnanode_list : subdnanode_list dnanode_sub | empty''' def p_subsigngraphic_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subsigngraphic_list.__doc__ = '''\ subsigngraphic_list : subsigngraphic_list dnanode_sub | subsigngraphic_list signgraphic_sub | empty''' def p_subflatbuilding_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subflatbuilding_list.__doc__ = '''\ subflatbuilding_list : subflatbuilding_list dnanode_sub | subflatbuilding_list flatbuilding_sub | empty''' def p_subwall_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subwall_list.__doc__ = '''\ subwall_list : subwall_list dnanode_sub | subwall_list wall_sub | empty''' def p_subwindows_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subwindows_list.__doc__ = '''\ subwindows_list : subwindows_list dnanode_sub | subwindows_list windows_sub | empty''' def p_subcornice_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subcornice_list.__doc__ = '''\ subcornice_list : subcornice_list dnanode_sub | subcornice_list cornice_sub | empty''' def p_sublandmarkbuilding_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_sublandmarkbuilding_list.__doc__ = '''\ sublandmarkbuilding_list : sublandmarkbuilding_list dnanode_sub | sublandmarkbuilding_list landmarkbuilding_sub | empty''' def p_subanimbuilding_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subanimbuilding_list.__doc__ = '''\ subanimbuilding_list : subanimbuilding_list dnanode_sub | subanimbuilding_list landmarkbuilding_sub | subanimbuilding_list animbuilding_sub | empty''' def p_subdoor_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_subdoor_list.__doc__ = '''\ subdoor_list : subdoor_list dnanode_sub | subdoor_list door_sub | empty''' def p_substreet_list(p): p[0] = p[1] if len(p) == 3: group = p[2] p[0].append(group) else: p[0] = [] p_substreet_list.__doc__ = '''\ substreet_list : substreet_list dnanode_sub | substreet_list street_sub | empty''' def p_modeldef(p): modelType, filename = p[1], p[2] filename, extension = os.path.splitext(filename) if not extension: extension = '.bam' filename += extension p.parser.modelType = modelType p.parser.modelName = filename p_modeldef.__doc__ = '''\ modeldef : MODEL string | HOODMODEL string | PLACEMODEL string''' def p_model(p): pass p_model.__doc__ = '''\ model : modeldef "[" modelnode_list "]"''' def p_modelnode_list(p): pass p_modelnode_list.__doc__ = '''\ modelnode_list : modelnode_list node | empty''' def p_node(p): argCount = len(p) if argCount == 6: root, code, search = p[3], p[4], p[4] else: root, code, search = p[3], p[4], p[5] p.parser.dnaStore.storeCatalogCode(root, code) modelName = p.parser.modelName if p.parser.modelType == 'hood_model': p.parser.dnaStore.storeHoodNode(code, modelName, search) elif p.parser.modelType == 'place_model': p.parser.dnaStore.storePlaceNode(code, modelName, search) else: p.parser.dnaStore.storeNode(code, modelName, search) p_node.__doc__ = '''\ node : STORE_NODE "[" string string "]" | STORE_NODE "[" string string string "]"''' def p_store_texture(p): argCount = len(p) if argCount == 6: code, filename = p[3], p[4] else: root, code, filename = p[3], p[4], p[5] p.parser.dnaStore.storeCatalogCode(root, code) p.parser.dnaStore.storeTexture(code, filename) p_store_texture.__doc__ = '''\ store_texture : STORE_TEXTURE "[" string string "]" | STORE_TEXTURE "[" string string string "]"''' def p_font(p): root, code, filename = p[3], p[4], p[5] filename, extension = os.path.splitext(filename) if not extension: extension = '.bam' filename += extension p.parser.dnaStore.storeCatalogCode(root, code) p.parser.dnaStore.storeFont(filename, code) p_font.__doc__ = '''\ font : STORE_FONT "[" string string string "]"''' def p_error(p): if p is None: raise DNAError('Syntax error unexpected EOF') sub = (str(p.lexer.lineno), str(p)) raise DNAError('Syntax error at line %s token=%s' % sub)
23.618217
88
0.613071
3c0e30fd8826a6d64e72c087d608db1e0990c05f
23,667
py
Python
cardpay/api/payments_api.py
Sinkler/python-sdk-v2
a1ad7cc9900f8adf967ca4dec0bb05d8eddc2999
[ "MIT" ]
null
null
null
cardpay/api/payments_api.py
Sinkler/python-sdk-v2
a1ad7cc9900f8adf967ca4dec0bb05d8eddc2999
[ "MIT" ]
null
null
null
cardpay/api/payments_api.py
Sinkler/python-sdk-v2
a1ad7cc9900f8adf967ca4dec0bb05d8eddc2999
[ "MIT" ]
null
null
null
# coding: utf-8 """ CardPay REST API Welcome to the CardPay REST API. The CardPay API uses HTTP verbs and a [REST](https://en.wikipedia.org/wiki/Representational_state_transfer) resources endpoint structure (see more info about REST). Request and response payloads are formatted as JSON. Merchant uses API to create payments, refunds, payouts or recurrings, check or update transaction status and get information about created transactions. In API authentication process based on [OAuth 2.0](https://oauth.net/2/) standard. For recent changes see changelog section. # noqa: E501 OpenAPI spec version: 3.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from cardpay.api_client import ApiClient class PaymentsApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_payment(self, payment_request, **kwargs): # noqa: E501 """Create payment # noqa: E501 Endpoint for creation payments. Request example presented for Gateway mode. # noqa: E501 :param PaymentRequest payment_request: paymentRequest (required) :return: PaymentGatewayCreationResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True (data) = self.create_payment_with_http_info(payment_request, **kwargs) # noqa: E501 return data def create_payment_with_http_info(self, payment_request, **kwargs): # noqa: E501 """Create payment # noqa: E501 Endpoint for creation payments. Request example presented for Gateway mode. # noqa: E501 :param PaymentRequest payment_request: paymentRequest (required) :return: PaymentGatewayCreationResponse If the method is called asynchronously, returns the request thread. """ all_params = ['payment_request'] # noqa: E501 all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_payment" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'payment_request' is set if ('payment_request' not in params or params['payment_request'] is None): raise ValueError("Missing the required parameter `payment_request` when calling `create_payment`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'payment_request' in params: body_params = params['payment_request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 return self.api_client.call_api( '/api/payments', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type="PaymentGatewayCreationResponse", # noqa: E501 _return_http_data_only=params.get("_return_http_data_only"), _preload_content=params.get("_preload_content", True), _request_timeout=params.get("_request_timeout"), collection_formats=collection_formats, ) def get_authentication_data1(self, payment_id, **kwargs): # noqa: E501 """Get payment 3DS result information # noqa: E501 :param str payment_id: Payment ID (required) :return: AuthenticationDataResponse If the method is called asynchronously, returns the request thread. """ kwargs["_return_http_data_only"] = True (data) = self.get_authentication_data1_with_http_info( payment_id, **kwargs ) # noqa: E501 return data def get_authentication_data1_with_http_info( self, payment_id, **kwargs ): # noqa: E501 """Get payment 3DS result information # noqa: E501 :param str payment_id: Payment ID (required) :return: AuthenticationDataResponse If the method is called asynchronously, returns the request thread. """ all_params = ["payment_id"] # noqa: E501 all_params.append("_return_http_data_only") all_params.append("_preload_content") all_params.append("_request_timeout") params = locals() for key, val in six.iteritems(params["kwargs"]): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_authentication_data1" % key ) params[key] = val del params["kwargs"] # verify the required parameter 'payment_id' is set if "payment_id" not in params or params["payment_id"] is None: raise ValueError( "Missing the required parameter `payment_id` when calling `get_authentication_data1`" ) # noqa: E501 collection_formats = {} path_params = {} if "payment_id" in params: path_params["paymentId"] = params["payment_id"] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params["Accept"] = self.api_client.select_header_accept( ["application/json"] ) # noqa: E501 return self.api_client.call_api( "/api/payments/{paymentId}/threedsecure", "GET", path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type="AuthenticationDataResponse", # noqa: E501 _return_http_data_only=params.get("_return_http_data_only"), _preload_content=params.get("_preload_content", True), _request_timeout=params.get("_request_timeout"), collection_formats=collection_formats, ) def get_payment(self, payment_id, **kwargs): # noqa: E501 """Get payment information # noqa: E501 :param str payment_id: Payment ID (required) :return: PaymentResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True (data) = self.get_payment_with_http_info(payment_id, **kwargs) # noqa: E501 return data def get_payment_with_http_info(self, payment_id, **kwargs): # noqa: E501 """Get payment information # noqa: E501 :param str payment_id: Payment ID (required) :return: PaymentResponse If the method is called asynchronously, returns the request thread. """ all_params = ['payment_id'] # noqa: E501 all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_payment" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'payment_id' is set if ('payment_id' not in params or params['payment_id'] is None): raise ValueError("Missing the required parameter `payment_id` when calling `get_payment`") # noqa: E501 collection_formats = {} path_params = {} if 'payment_id' in params: path_params['paymentId'] = params['payment_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 return self.api_client.call_api( '/api/payments/{paymentId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PaymentResponse', # noqa: E501 _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_payment_methods(self, **kwargs): # noqa: E501 """Get payment methods # noqa: E501 Endpoint for get payment methods by current terminal code # noqa: E501 :return: PaymentMethodsList If the method is called asynchronously, returns the request thread. """ kwargs["_return_http_data_only"] = True (data) = self.get_payment_methods_with_http_info(**kwargs) # noqa: E501 return data def get_payment_methods_with_http_info(self, **kwargs): # noqa: E501 """Get payment methods # noqa: E501 Endpoint for get payment methods by current terminal code # noqa: E501 :return: PaymentMethodsList If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append("_return_http_data_only") all_params.append("_preload_content") all_params.append("_request_timeout") params = locals() for key, val in six.iteritems(params["kwargs"]): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_payment_methods" % key ) params[key] = val del params["kwargs"] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params["Accept"] = self.api_client.select_header_accept( ["application/json"] ) # noqa: E501 return self.api_client.call_api( "/api/payment_methods", "GET", path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type="PaymentMethodsList", # noqa: E501 _return_http_data_only=params.get("_return_http_data_only"), _preload_content=params.get("_preload_content", True), _request_timeout=params.get("_request_timeout"), collection_formats=collection_formats, ) def get_payments(self, request_id, **kwargs): # noqa: E501 """Get payments information # noqa: E501 :param str request_id: Request ID (required) :param str currency: [ISO 4217](https://en.wikipedia.org/wiki/ISO_4217) currency code of transactions currency :param datetime end_time: Date and time up to milliseconds (in [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) format) when requested period ends (not inclusive), UTC time, must be less than 7 days after 'start_time', default is current time (format: yyyy-MM-dd'T'HH:mm:ss'Z') :param int max_count: Limit number of returned transactions (must be less than 10000, default is 1000) :param str merchant_order_id: Merchant order number from the merchant system :param str payment_method: Used payment method type name from payment methods list :param str sort_order: Sort based on order of results. `asc` for ascending order or `desc` for descending order (default value) :param datetime start_time: Date and time up to milliseconds (in [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) format) when requested period starts (inclusive), UTC time, default is 24 hours before 'end_time' (format: yyyy-MM-dd'T'HH:mm:ss'Z') :return: PaymentsList If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True (data) = self.get_payments_with_http_info(request_id, **kwargs) # noqa: E501 return data def get_payments_with_http_info(self, request_id, **kwargs): # noqa: E501 """Get payments information # noqa: E501 :param str request_id: Request ID (required) :param str currency: [ISO 4217](https://en.wikipedia.org/wiki/ISO_4217) currency code of transactions currency :param datetime end_time: Date and time up to milliseconds (in [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) format) when requested period ends (not inclusive), UTC time, must be less than 7 days after 'start_time', default is current time (format: yyyy-MM-dd'T'HH:mm:ss'Z') :param int max_count: Limit number of returned transactions (must be less than 10000, default is 1000) :param str merchant_order_id: Merchant order number from the merchant system :param str payment_method: Used payment method type name from payment methods list :param str sort_order: Sort based on order of results. `asc` for ascending order or `desc` for descending order (default value) :param datetime start_time: Date and time up to milliseconds (in [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) format) when requested period starts (inclusive), UTC time, default is 24 hours before 'end_time' (format: yyyy-MM-dd'T'HH:mm:ss'Z') :return: PaymentsList If the method is called asynchronously, returns the request thread. """ all_params = ['request_id', 'currency', 'end_time', 'max_count', 'merchant_order_id', 'payment_method', 'sort_order', 'start_time'] # noqa: E501 all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_payments" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'request_id' is set if ('request_id' not in params or params['request_id'] is None): raise ValueError("Missing the required parameter `request_id` when calling `get_payments`") # noqa: E501 if "request_id" in params and len(params["request_id"]) > 50: raise ValueError( "Invalid value for parameter `request_id` when calling `get_payments`, length must be less than or equal to `50`" ) # noqa: E501 if "request_id" in params and len(params["request_id"]) < 1: raise ValueError( "Invalid value for parameter `request_id` when calling `get_payments`, length must be greater than or equal to `1`" ) # noqa: E501 if "max_count" in params and params["max_count"] > 10000: # noqa: E501 raise ValueError( "Invalid value for parameter `max_count` when calling `get_payments`, must be a value less than or equal to `10000`" ) # noqa: E501 if "merchant_order_id" in params and len(params["merchant_order_id"]) > 50: raise ValueError( "Invalid value for parameter `merchant_order_id` when calling `get_payments`, length must be less than or equal to `50`" ) # noqa: E501 if "merchant_order_id" in params and len(params["merchant_order_id"]) < 0: raise ValueError( "Invalid value for parameter `merchant_order_id` when calling `get_payments`, length must be greater than or equal to `0`" ) # noqa: E501 if "payment_method" in params and len(params["payment_method"]) > 50: raise ValueError( "Invalid value for parameter `payment_method` when calling `get_payments`, length must be less than or equal to `50`" ) # noqa: E501 if "payment_method" in params and len(params["payment_method"]) < 0: raise ValueError( "Invalid value for parameter `payment_method` when calling `get_payments`, length must be greater than or equal to `0`" ) # noqa: E501 if "sort_order" in params and not re.search( r"asc|desc", params["sort_order"] ): # noqa: E501 raise ValueError( "Invalid value for parameter `sort_order` when calling `get_payments`, must conform to the pattern `/asc|desc/`" ) # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'currency' in params: query_params.append(('currency', params['currency'])) # noqa: E501 if 'end_time' in params: query_params.append(('end_time', params['end_time'])) # noqa: E501 if 'max_count' in params: query_params.append(('max_count', params['max_count'])) # noqa: E501 if 'merchant_order_id' in params: query_params.append(('merchant_order_id', params['merchant_order_id'])) # noqa: E501 if 'payment_method' in params: query_params.append(('payment_method', params['payment_method'])) # noqa: E501 if 'request_id' in params: query_params.append(('request_id', params['request_id'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sort_order', params['sort_order'])) # noqa: E501 if 'start_time' in params: query_params.append(('start_time', params['start_time'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 return self.api_client.call_api( '/api/payments', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PaymentsList', # noqa: E501 _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def update_payment(self, payment_id, payment_patch_request, **kwargs): # noqa: E501 """Update payment # noqa: E501 :param str payment_id: Payment ID (required) :param PaymentPatchRequest payment_patch_request: paymentPatchRequest (required) :return: PaymentUpdateResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True (data) = self.update_payment_with_http_info(payment_id, payment_patch_request, **kwargs) # noqa: E501 return data def update_payment_with_http_info(self, payment_id, payment_patch_request, **kwargs): # noqa: E501 """Update payment # noqa: E501 :param str payment_id: Payment ID (required) :param PaymentPatchRequest payment_patch_request: paymentPatchRequest (required) :return: PaymentUpdateResponse If the method is called asynchronously, returns the request thread. """ all_params = ['payment_id', 'payment_patch_request'] # noqa: E501 all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method update_payment" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'payment_id' is set if ('payment_id' not in params or params['payment_id'] is None): raise ValueError("Missing the required parameter `payment_id` when calling `update_payment`") # noqa: E501 # verify the required parameter 'payment_patch_request' is set if ('payment_patch_request' not in params or params['payment_patch_request'] is None): raise ValueError("Missing the required parameter `payment_patch_request` when calling `update_payment`") # noqa: E501 collection_formats = {} path_params = {} if 'payment_id' in params: path_params['paymentId'] = params['payment_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'payment_patch_request' in params: body_params = params['payment_patch_request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 return self.api_client.call_api( '/api/payments/{paymentId}', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PaymentUpdateResponse', # noqa: E501 _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)
42.490126
546
0.620357
f530970a2ccba43192e634aa9f1b6a2bea51339a
839
py
Python
ykdl/extractors/bilibili/vc.py
panda-mute/ykdl
56cea24f1513f21aedbe80b75c25f7c3b1e07704
[ "MIT" ]
null
null
null
ykdl/extractors/bilibili/vc.py
panda-mute/ykdl
56cea24f1513f21aedbe80b75c25f7c3b1e07704
[ "MIT" ]
null
null
null
ykdl/extractors/bilibili/vc.py
panda-mute/ykdl
56cea24f1513f21aedbe80b75c25f7c3b1e07704
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- from .._common import * class BiliVC(VideoExtractor): name = '哔哩哔哩 小视频 (Bili VC)' def prepare(self): info = VideoInfo(self.name) self.vid = match1(self.url, 'video/(\d+)') video_data = get_response( 'https://api.vc.bilibili.com/clip/v1/video/detail', params={'video_id': self.vid}).json() info.title = video_data['data']['item']['description'] info.artist = video_data['data']['user']['name'] info.stream_types.append('current') info.streams['current'] = { 'container': 'mp4', 'video_profile': 'current', 'src' : [video_data['data']['item']['video_playurl']], 'size': int(video_data['data']['item']['video_size']) } return info site = BiliVC()
25.424242
67
0.539928
0d97cc9704487494d5ab19baa806a7d3e64d8b5e
123
py
Python
simfile/_private/dedent.py
ianklatzco/simfile
8f4ec2fb9437b4071f6f92cca3d8de1b4071a2bc
[ "MIT" ]
22
2017-04-24T05:37:13.000Z
2022-03-08T00:41:37.000Z
simfile/_private/dedent.py
ianklatzco/simfile
8f4ec2fb9437b4071f6f92cca3d8de1b4071a2bc
[ "MIT" ]
10
2021-05-31T01:21:56.000Z
2022-03-17T04:26:54.000Z
simfile/_private/dedent.py
ianklatzco/simfile
8f4ec2fb9437b4071f6f92cca3d8de1b4071a2bc
[ "MIT" ]
3
2019-06-05T15:23:53.000Z
2021-09-11T02:39:36.000Z
from textwrap import dedent def dedent_and_trim(string: str) -> str: return dedent(string.lstrip('\r\n').rstrip(' '))
24.6
52
0.699187
500a52a206b7dbc231a02183c2e61b698775f744
4,496
py
Python
env/lib/python3.6/site-packages/django_coverage/utils/module_tools/module_walker.py
anthowen/duplify
846d01c1b21230937fdf0281b0cf8c0b08a8c24e
[ "MIT" ]
1
2019-04-21T18:57:57.000Z
2019-04-21T18:57:57.000Z
env/lib/python3.6/site-packages/django_coverage/utils/module_tools/module_walker.py
anthowen/duplify
846d01c1b21230937fdf0281b0cf8c0b08a8c24e
[ "MIT" ]
null
null
null
env/lib/python3.6/site-packages/django_coverage/utils/module_tools/module_walker.py
anthowen/duplify
846d01c1b21230937fdf0281b0cf8c0b08a8c24e
[ "MIT" ]
null
null
null
""" Copyright 2009 55 Minutes (http://www.55minutes.com) 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. """ import os, re, sys from glob import glob from data_storage import * from module_loader import find_or_load_module try: set except: from sets import Set as set __all__ = ('get_all_modules',) def _build_pkg_path(pkg_name, pkg, path): for rp in [x for x in pkg.__path__ if path.startswith(x)]: p = path.replace(rp, '').replace(os.path.sep, '.') return pkg_name + p def _build_module_path(pkg_name, pkg, path): return _build_pkg_path(pkg_name, pkg, os.path.splitext(path)[0]) def _prune_whitelist(whitelist, blacklist): excluded = Excluded().excluded for wp in whitelist[:]: for bp in blacklist: if re.search(bp, wp): whitelist.remove(wp) excluded.append(wp) break return whitelist def _parse_module_list(m_list): packages = Packages().packages modules = Modules().modules excluded = Excluded().excluded errors = Errors().errors for m in m_list: components = m.split('.') m_name = '' search_path = [] processed=False for i, c in enumerate(components): m_name = '.'.join([x for x in m_name.split('.') if x] + [c]) try: module = find_or_load_module(m_name, search_path or None) except ImportError: processed=True errors.append(m) break try: search_path.extend(module.__path__) except AttributeError: processed = True if i+1==len(components): modules[m_name] = module else: errors.append(m) break if not processed: packages[m_name] = module def prune_dirs(root, dirs, exclude_dirs): regexes = [re.compile(exclude_dir) for exclude_dir in exclude_dirs] for path, dir_ in [(os.path.join(root, dir_), dir_) for dir_ in dirs]: for regex in regexes: if regex.search(path): dirs.remove(dir_) break def _get_all_packages(pkg_name, pkg, blacklist, exclude_dirs): packages = Packages().packages errors = Errors().errors for path in pkg.__path__: for root, dirs, files in os.walk(path): prune_dirs(root, dirs, exclude_dirs or []) m_name = _build_pkg_path(pkg_name, pkg, root) try: if _prune_whitelist([m_name], blacklist): m = find_or_load_module(m_name, [os.path.split(root)[0]]) packages[m_name] = m else: for d in dirs[:]: dirs.remove(d) except ImportError: errors.append(m_name) for d in dirs[:]: dirs.remove(d) def _get_all_modules(pkg_name, pkg, blacklist): modules = Modules().modules errors = Errors().errors for p in pkg.__path__: for f in glob('%s/*.py' %p): m_name = _build_module_path(pkg_name, pkg, f) try: if _prune_whitelist([m_name], blacklist): m = find_or_load_module(m_name, [p]) modules[m_name] = m except ImportError: errors.append(m_name) def get_all_modules(whitelist, blacklist=None, exclude_dirs=None): packages = Packages().packages modules = Modules().modules excluded = Excluded().excluded errors = Errors().errors whitelist = _prune_whitelist(whitelist, blacklist or []) _parse_module_list(whitelist) for pkg_name, pkg in packages.copy().iteritems(): _get_all_packages(pkg_name, pkg, blacklist, exclude_dirs) for pkg_name, pkg in packages.copy().iteritems(): _get_all_modules(pkg_name, pkg, blacklist) return packages, modules, list(set(excluded)), list(set(errors))
33.058824
77
0.604315
fc3df84d8c1609864fd145a2874cb5a587e3b768
2,334
py
Python
tests/models/test_vision.py
lavoiems/lightning-bolts
208e92ba3dcdbc029afd37e09ec9461fbcf3f293
[ "Apache-2.0" ]
822
2020-04-21T03:30:43.000Z
2021-03-07T06:41:31.000Z
tests/models/test_vision.py
lavoiems/lightning-bolts
208e92ba3dcdbc029afd37e09ec9461fbcf3f293
[ "Apache-2.0" ]
538
2020-04-18T01:07:58.000Z
2021-03-09T13:48:50.000Z
tests/models/test_vision.py
lavoiems/lightning-bolts
208e92ba3dcdbc029afd37e09ec9461fbcf3f293
[ "Apache-2.0" ]
162
2020-04-17T15:44:54.000Z
2021-03-09T14:04:02.000Z
import pytest import torch from packaging import version from pytorch_lightning import LightningDataModule, Trainer from pytorch_lightning import __version__ as pl_version from pytorch_lightning import seed_everything from torch.utils.data import DataLoader from pl_bolts.datamodules import FashionMNISTDataModule, MNISTDataModule from pl_bolts.datasets import DummyDataset from pl_bolts.models.vision import GPT2, ImageGPT, SemSegment, UNet class DummyDataModule(LightningDataModule): def train_dataloader(self): train_ds = DummyDataset((3, 35, 120), (35, 120), num_samples=100) return DataLoader(train_ds, batch_size=1) @pytest.mark.skipif( version.parse(pl_version) > version.parse("1.1.0"), reason="igpt code not updated for latest lightning" ) def test_igpt(tmpdir, datadir): seed_everything(0) dm = MNISTDataModule(data_dir=datadir, normalize=False) model = ImageGPT() trainer = Trainer( limit_train_batches=2, limit_val_batches=2, limit_test_batches=2, max_epochs=1, ) trainer.fit(model, datamodule=dm) trainer.test(datamodule=dm) assert trainer.callback_metrics["test_loss"] < 1.7 dm = FashionMNISTDataModule(data_dir=datadir, num_workers=1) model = ImageGPT(classify=True) trainer = Trainer( limit_train_batches=2, limit_val_batches=2, limit_test_batches=2, max_epochs=1, logger=False, checkpoint_callback=False, ) trainer.fit(model, datamodule=dm) @torch.no_grad() def test_gpt2(): seed_everything(0) seq_len = 17 batch_size = 32 vocab_size = 16 x = torch.randint(0, vocab_size, (seq_len, batch_size)) model = GPT2( embed_dim=16, heads=2, layers=2, num_positions=seq_len, vocab_size=vocab_size, num_classes=10, ) model(x) @torch.no_grad() def test_unet(): x = torch.rand(10, 3, 28, 28) model = UNet(num_classes=2) y = model(x) assert y.shape == torch.Size([10, 2, 28, 28]) def test_semantic_segmentation(tmpdir): dm = DummyDataModule() model = SemSegment(num_classes=19) trainer = Trainer(fast_dev_run=True, default_root_dir=tmpdir) trainer.fit(model, datamodule=dm) loss = trainer.progress_bar_dict["loss"] assert float(loss) > 0
26.522727
107
0.697087
d5589c962083b52d957741cadbcac76a2de5af2b
4,614
py
Python
theano/sparse/tests/test_sp2.py
intel/Theano-dev
6ca6fd4646f9e958058c7bce52cd51923c05c2f4
[ "BSD-3-Clause" ]
64
2016-10-02T20:41:56.000Z
2020-03-11T14:59:40.000Z
theano/sparse/tests/test_sp2.py
intel/Theano-dev
6ca6fd4646f9e958058c7bce52cd51923c05c2f4
[ "BSD-3-Clause" ]
4
2017-06-12T05:12:38.000Z
2018-03-15T03:16:30.000Z
theano/sparse/tests/test_sp2.py
intel/Theano-dev
6ca6fd4646f9e958058c7bce52cd51923c05c2f4
[ "BSD-3-Clause" ]
30
2016-10-27T21:59:00.000Z
2021-02-20T09:55:14.000Z
from __future__ import absolute_import, print_function, division import unittest from nose.plugins.skip import SkipTest import numpy try: import scipy.sparse as sp except ImportError: pass # The variable enable_sparse will be used to disable the test file. import theano from theano import config from theano import tensor from theano import sparse if not theano.sparse.enable_sparse: raise SkipTest('Optional package sparse disabled') from theano.sparse.sandbox.sp2 import ( Poisson, poisson, Binomial, Multinomial, multinomial) from theano.tests import unittest_tools as utt from theano.sparse.tests.test_basic import as_sparse_format class PoissonTester(utt.InferShapeTester): x = {} a = {} for format in sparse.sparse_formats: variable = getattr(theano.sparse, format + '_matrix') rand = numpy.array(numpy.random.randint(1, 4, size=(3, 4)) - 1, dtype=theano.config.floatX) x[format] = variable() a[format] = as_sparse_format(rand, format) def setUp(self): super(PoissonTester, self).setUp() self.op_class = Poisson def test_op(self): for format in sparse.sparse_formats: f = theano.function( [self.x[format]], poisson(self.x[format])) tested = f(self.a[format]) assert tested.format == format assert tested.dtype == self.a[format].dtype assert numpy.allclose(numpy.floor(tested.data), tested.data) assert tested.shape == self.a[format].shape def test_infer_shape(self): for format in sparse.sparse_formats: self._compile_and_check([self.x[format]], [poisson(self.x[format])], [self.a[format]], self.op_class) class BinomialTester(utt.InferShapeTester): n = tensor.scalar() p = tensor.scalar() shape = tensor.lvector() _n = 5 _p = .25 _shape = numpy.asarray([3, 5], dtype='int64') inputs = [n, p, shape] _inputs = [_n, _p, _shape] def setUp(self): super(BinomialTester, self).setUp() self.op_class = Binomial def test_op(self): for sp_format in sparse.sparse_formats: for o_type in sparse.float_dtypes: f = theano.function( self.inputs, Binomial(sp_format, o_type)(*self.inputs)) tested = f(*self._inputs) assert tested.shape == tuple(self._shape) assert tested.format == sp_format assert tested.dtype == o_type assert numpy.allclose(numpy.floor(tested.todense()), tested.todense()) def test_infer_shape(self): for sp_format in sparse.sparse_formats: for o_type in sparse.float_dtypes: self._compile_and_check( self.inputs, [Binomial(sp_format, o_type)(*self.inputs)], self._inputs, self.op_class) class MultinomialTester(utt.InferShapeTester): p = sparse.csr_matrix() _p = sp.csr_matrix(numpy.asarray([[0.0, 0.5, 0.0, 0.5], [0.1, 0.2, 0.3, 0.4], [0.0, 1.0, 0.0, 0.0], [0.3, 0.3, 0.0, 0.4]], dtype=config.floatX)) def setUp(self): super(MultinomialTester, self).setUp() self.op_class = Multinomial def test_op(self): n = tensor.lscalar() f = theano.function([self.p, n], multinomial(n, self.p)) _n = 5 tested = f(self._p, _n) assert tested.shape == self._p.shape assert numpy.allclose(numpy.floor(tested.todense()), tested.todense()) assert tested[2, 1] == _n n = tensor.lvector() f = theano.function([self.p, n], multinomial(n, self.p)) _n = numpy.asarray([1, 2, 3, 4], dtype='int64') tested = f(self._p, _n) assert tested.shape == self._p.shape assert numpy.allclose(numpy.floor(tested.todense()), tested.todense()) assert tested[2, 1] == _n[2] def test_infer_shape(self): self._compile_and_check([self.p], [multinomial(5, self.p)], [self._p], self.op_class, warn=False) if __name__ == '__main__': unittest.main()
31.82069
78
0.55505
9d2c49c2e414ca26cb7ee9138aebc5ce489201b9
146
py
Python
haweb/apps/issues/models.py
edilio/tobeawebproperty
317205bf27ab76a430ea56a474e1739ee71f164e
[ "MIT" ]
null
null
null
haweb/apps/issues/models.py
edilio/tobeawebproperty
317205bf27ab76a430ea56a474e1739ee71f164e
[ "MIT" ]
4
2015-01-02T21:39:58.000Z
2015-06-23T02:18:57.000Z
haweb/apps/issues/models.py
edilio/tobeawebproperty
317205bf27ab76a430ea56a474e1739ee71f164e
[ "MIT" ]
null
null
null
from django.db import models ISSUES_TYPE_OPTIONS = ( (1, 'Maintenance Issues'), (2, 'Tenant Health Issues'), (3, 'Safety Issues'), )
18.25
32
0.636986
d61339c23639e7098fc2c352fd233c048038e0a8
5,401
py
Python
tests/test_root_object_type.py
Informasjonsforvaltning/modelldcatnotordf
995129ff9f6fb95f9a9d875b27f3aa14bac9b7f1
[ "Apache-2.0" ]
1
2020-11-29T18:36:21.000Z
2020-11-29T18:36:21.000Z
tests/test_root_object_type.py
Informasjonsforvaltning/modelldcatnotordf
995129ff9f6fb95f9a9d875b27f3aa14bac9b7f1
[ "Apache-2.0" ]
142
2020-10-07T08:52:55.000Z
2021-11-18T15:09:31.000Z
tests/test_root_object_type.py
Informasjonsforvaltning/modelldcatnotordf
995129ff9f6fb95f9a9d875b27f3aa14bac9b7f1
[ "Apache-2.0" ]
null
null
null
"""Test cases for the root object type module.""" from concepttordf import Concept import pytest from pytest_mock import MockFixture from rdflib import Graph from skolemizer.testutils import skolemization from modelldcatnotordf.modelldcatno import RootObjectType from tests.testutils import assert_isomorphic """ A test class for testing the class RootObjectType. """ def test_instantiate_rootobjecttype() -> None: """It does not raise an exception.""" try: _ = RootObjectType() except Exception: pytest.fail("Unexpected Exception ..") def test_to_graph_should_return_identifier_set_at_constructor() -> None: """It returns a title graph isomorphic to spec.""" """It returns an identifier graph isomorphic to spec.""" rootobjecttype = RootObjectType("http://example.com/rootobjecttypes/1") src = """ @prefix dct: <http://purl.org/dc/terms/> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix dcat: <http://www.w3.org/ns/dcat#> . @prefix modelldcatno: <https://data.norge.no/vocabulary/modelldcatno#> . <http://example.com/rootobjecttypes/1> a modelldcatno:RootObjectType; . """ g1 = Graph().parse(data=rootobjecttype.to_rdf(), format="turtle") g2 = Graph().parse(data=src, format="turtle") assert_isomorphic(g1, g2) def test_to_graph_should_return_title_and_identifier() -> None: """It returns a title graph isomorphic to spec.""" """It returns an identifier graph isomorphic to spec.""" rootobjecttype = RootObjectType() rootobjecttype.identifier = "http://example.com/rootobjecttypes/1" rootobjecttype.title = {"nb": "Tittel 1", "en": "Title 1"} src = """ @prefix dct: <http://purl.org/dc/terms/> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix dcat: <http://www.w3.org/ns/dcat#> . @prefix modelldcatno: <https://data.norge.no/vocabulary/modelldcatno#> . <http://example.com/rootobjecttypes/1> a modelldcatno:RootObjectType; dct:title "Title 1"@en, "Tittel 1"@nb ; . """ g1 = Graph().parse(data=rootobjecttype.to_rdf(), format="turtle") g2 = Graph().parse(data=src, format="turtle") assert_isomorphic(g1, g2) def test_to_graph_should_return_title_and_skolemization(mocker: MockFixture) -> None: """It returns a title graph isomorphic to spec.""" rootobjecttype = RootObjectType() rootobjecttype.title = {"nb": "Tittel 1", "en": "Title 1"} src = """ @prefix dct: <http://purl.org/dc/terms/> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix dcat: <http://www.w3.org/ns/dcat#> . @prefix modelldcatno: <https://data.norge.no/vocabulary/modelldcatno#> . <http://example.com/.well-known/skolem/284db4d2-80c2-11eb-82c3-83e80baa2f94> a modelldcatno:RootObjectType ; dct:title "Title 1"@en, "Tittel 1"@nb ; . """ mocker.patch( "skolemizer.Skolemizer.add_skolemization", return_value=skolemization, ) g1 = Graph().parse(data=rootobjecttype.to_rdf(), format="turtle") g2 = Graph().parse(data=src, format="turtle") assert_isomorphic(g1, g2) def test_to_graph_should_return_dct_identifier_as_graph() -> None: """It returns a dct_identifier graph isomorphic to spec.""" rootobjecttype = RootObjectType() rootobjecttype.identifier = "http://example.com/rootobjecttypes/1" rootobjecttype.dct_identifier = "123456789" src = """ @prefix dct: <http://purl.org/dc/terms/> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix dcat: <http://www.w3.org/ns/dcat#> . @prefix modelldcatno: <https://data.norge.no/vocabulary/modelldcatno#> . <http://example.com/rootobjecttypes/1> a modelldcatno:RootObjectType ; dct:identifier "123456789"; . """ g1 = Graph().parse(data=rootobjecttype.to_rdf(), format="turtle") g2 = Graph().parse(data=src, format="turtle") assert_isomorphic(g1, g2) def test_to_graph_should_return_subject() -> None: """It returns a subject graph isomorphic to spec.""" rootobjecttype = RootObjectType() rootobjecttype.identifier = "http://example.com/rootobjecttypes/1" subject = Concept() subject.identifier = "https://example.com/subjects/1" rootobjecttype.subject = subject src = """ @prefix dct: <http://purl.org/dc/terms/> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix dcat: <http://www.w3.org/ns/dcat#> . @prefix modelldcatno: <https://data.norge.no/vocabulary/modelldcatno#> . @prefix skos: <http://www.w3.org/2004/02/skos/core#> . <http://example.com/rootobjecttypes/1> a modelldcatno:RootObjectType ; dct:subject <https://example.com/subjects/1> ; . <https://example.com/subjects/1> a skos:Concept . """ g1 = Graph().parse(data=rootobjecttype.to_rdf(), format="turtle") g2 = Graph().parse(data=src, format="turtle") assert_isomorphic(g1, g2)
36.248322
85
0.65673
227dba9107d875763f662515083c420a6e39b4d2
10,812
py
Python
fake news challenge (FNC-1)/util.py
kishormishra3/DeepLearn
bc0dfad7b4694aa5d872b5bdddd6e3a17d139d7d
[ "MIT" ]
1,756
2017-05-24T12:46:44.000Z
2022-03-30T15:23:26.000Z
fake news challenge (FNC-1)/util.py
kshitizbhansali/DeepLearn
e4b72d921695062d5cc84f4968c3fb57e258428f
[ "Apache-2.0" ]
20
2017-05-23T15:23:39.000Z
2019-04-12T18:07:04.000Z
fake news challenge (FNC-1)/util.py
kshitizbhansali/DeepLearn
e4b72d921695062d5cc84f4968c3fb57e258428f
[ "Apache-2.0" ]
355
2017-05-29T12:37:19.000Z
2022-01-25T15:23:50.000Z
# -*- coding: utf-8 -*- from csv import DictReader from csv import DictWriter import numpy as np from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity # Initialise global variables label_ref = {'agree': 0, 'disagree': 1, 'discuss': 2, 'unrelated': 3} label_ref_rev = {0: 'agree', 1: 'disagree', 2: 'discuss', 3: 'unrelated'} stop_words = [ "a", "about", "above", "across", "after", "afterwards", "again", "against", "all", "almost", "alone", "along", "already", "also", "although", "always", "am", "among", "amongst", "amoungst", "amount", "an", "and", "another", "any", "anyhow", "anyone", "anything", "anyway", "anywhere", "are", "around", "as", "at", "back", "be", "became", "because", "become", "becomes", "becoming", "been", "before", "beforehand", "behind", "being", "below", "beside", "besides", "between", "beyond", "bill", "both", "bottom", "but", "by", "call", "can", "co", "con", "could", "cry", "de", "describe", "detail", "do", "done", "down", "due", "during", "each", "eg", "eight", "either", "eleven", "else", "elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone", "everything", "everywhere", "except", "few", "fifteen", "fifty", "fill", "find", "fire", "first", "five", "for", "former", "formerly", "forty", "found", "four", "from", "front", "full", "further", "get", "give", "go", "had", "has", "have", "he", "hence", "her", "here", "hereafter", "hereby", "herein", "hereupon", "hers", "herself", "him", "himself", "his", "how", "however", "hundred", "i", "ie", "if", "in", "inc", "indeed", "interest", "into", "is", "it", "its", "itself", "keep", "last", "latter", "latterly", "least", "less", "ltd", "made", "many", "may", "me", "meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly", "move", "much", "must", "my", "myself", "name", "namely", "neither", "nevertheless", "next", "nine", "nobody", "now", "nowhere", "of", "off", "often", "on", "once", "one", "only", "onto", "or", "other", "others", "otherwise", "our", "ours", "ourselves", "out", "over", "own", "part", "per", "perhaps", "please", "put", "rather", "re", "same", "see", "serious", "several", "she", "should", "show", "side", "since", "sincere", "six", "sixty", "so", "some", "somehow", "someone", "something", "sometime", "sometimes", "somewhere", "still", "such", "system", "take", "ten", "than", "that", "the", "their", "them", "themselves", "then", "thence", "there", "thereafter", "thereby", "therefore", "therein", "thereupon", "these", "they", "thick", "thin", "third", "this", "those", "though", "three", "through", "throughout", "thru", "thus", "to", "together", "too", "top", "toward", "towards", "twelve", "twenty", "two", "un", "under", "until", "up", "upon", "us", "very", "via", "was", "we", "well", "were", "what", "whatever", "when", "whence", "whenever", "where", "whereafter", "whereas", "whereby", "wherein", "whereupon", "wherever", "whether", "which", "while", "whither", "who", "whoever", "whole", "whom", "whose", "why", "will", "with", "within", "without", "would", "yet", "you", "your", "yours", "yourself", "yourselves" ] # Define data class class FNCData: """ Define class for Fake News Challenge data """ def __init__(self, file_instances, file_bodies): # Load data self.instances = self.read(file_instances) bodies = self.read(file_bodies) self.heads = {} self.bodies = {} # Process instances for instance in self.instances: if instance['Headline'] not in self.heads: head_id = len(self.heads) self.heads[instance['Headline']] = head_id instance['Body ID'] = int(instance['Body ID']) # Process bodies for body in bodies: self.bodies[int(body['Body ID'])] = body['articleBody'] def read(self, filename): """ Read Fake News Challenge data from CSV file Args: filename: str, filename + extension Returns: rows: list, of dict per instance """ # Initialise rows = [] # Process file with open(filename, "r") as table: r = DictReader(table) for line in r: rows.append(line) return rows # Define relevant functions def pipeline_train(train, test, lim_unigram): """ Process train set, create relevant vectorizers Args: train: FNCData object, train set test: FNCData object, test set lim_unigram: int, number of most frequent words to consider Returns: train_set: list, of numpy arrays train_stances: list, of ints bow_vectorizer: sklearn CountVectorizer tfreq_vectorizer: sklearn TfidfTransformer(use_idf=False) tfidf_vectorizer: sklearn TfidfVectorizer() """ # Initialise heads = [] heads_track = {} bodies = [] bodies_track = {} body_ids = [] id_ref = {} train_set = [] train_stances = [] cos_track = {} test_heads = [] test_heads_track = {} test_bodies = [] test_bodies_track = {} test_body_ids = [] head_tfidf_track = {} body_tfidf_track = {} # Identify unique heads and bodies for instance in train.instances: head = instance['Headline'] body_id = instance['Body ID'] if head not in heads_track: heads.append(head) heads_track[head] = 1 if body_id not in bodies_track: bodies.append(train.bodies[body_id]) bodies_track[body_id] = 1 body_ids.append(body_id) for instance in test.instances: head = instance['Headline'] body_id = instance['Body ID'] if head not in test_heads_track: test_heads.append(head) test_heads_track[head] = 1 if body_id not in test_bodies_track: test_bodies.append(test.bodies[body_id]) test_bodies_track[body_id] = 1 test_body_ids.append(body_id) # Create reference dictionary for i, elem in enumerate(heads + body_ids): id_ref[elem] = i # Create vectorizers and BOW and TF arrays for train set bow_vectorizer = CountVectorizer(max_features=lim_unigram, stop_words=stop_words) bow = bow_vectorizer.fit_transform(heads + bodies) # Train set only tfreq_vectorizer = TfidfTransformer(use_idf=False).fit(bow) tfreq = tfreq_vectorizer.transform(bow).toarray() # Train set only tfidf_vectorizer = TfidfVectorizer(max_features=lim_unigram, stop_words=stop_words).\ fit(heads + bodies + test_heads + test_bodies) # Train and test sets # Process train set for instance in train.instances: head = instance['Headline'] body_id = instance['Body ID'] head_tf = tfreq[id_ref[head]].reshape(1, -1) body_tf = tfreq[id_ref[body_id]].reshape(1, -1) if head not in head_tfidf_track: head_tfidf = tfidf_vectorizer.transform([head]).toarray() head_tfidf_track[head] = head_tfidf else: head_tfidf = head_tfidf_track[head] if body_id not in body_tfidf_track: body_tfidf = tfidf_vectorizer.transform([train.bodies[body_id]]).toarray() body_tfidf_track[body_id] = body_tfidf else: body_tfidf = body_tfidf_track[body_id] if (head, body_id) not in cos_track: tfidf_cos = cosine_similarity(head_tfidf, body_tfidf)[0].reshape(1, 1) cos_track[(head, body_id)] = tfidf_cos else: tfidf_cos = cos_track[(head, body_id)] feat_vec = np.squeeze(np.c_[head_tf, body_tf, tfidf_cos]) train_set.append(feat_vec) train_stances.append(label_ref[instance['Stance']]) return train_set, train_stances, bow_vectorizer, tfreq_vectorizer, tfidf_vectorizer def pipeline_test(test, bow_vectorizer, tfreq_vectorizer, tfidf_vectorizer): """ Process test set Args: test: FNCData object, test set bow_vectorizer: sklearn CountVectorizer tfreq_vectorizer: sklearn TfidfTransformer(use_idf=False) tfidf_vectorizer: sklearn TfidfVectorizer() Returns: test_set: list, of numpy arrays """ # Initialise test_set = [] heads_track = {} bodies_track = {} cos_track = {} # Process test set for instance in test.instances: head = instance['Headline'] body_id = instance['Body ID'] if head not in heads_track: head_bow = bow_vectorizer.transform([head]).toarray() head_tf = tfreq_vectorizer.transform(head_bow).toarray()[0].reshape(1, -1) head_tfidf = tfidf_vectorizer.transform([head]).toarray().reshape(1, -1) heads_track[head] = (head_tf, head_tfidf) else: head_tf = heads_track[head][0] head_tfidf = heads_track[head][1] if body_id not in bodies_track: body_bow = bow_vectorizer.transform([test.bodies[body_id]]).toarray() body_tf = tfreq_vectorizer.transform(body_bow).toarray()[0].reshape(1, -1) body_tfidf = tfidf_vectorizer.transform([test.bodies[body_id]]).toarray().reshape(1, -1) bodies_track[body_id] = (body_tf, body_tfidf) else: body_tf = bodies_track[body_id][0] body_tfidf = bodies_track[body_id][1] if (head, body_id) not in cos_track: tfidf_cos = cosine_similarity(head_tfidf, body_tfidf)[0].reshape(1, 1) cos_track[(head, body_id)] = tfidf_cos else: tfidf_cos = cos_track[(head, body_id)] feat_vec = np.squeeze(np.c_[head_tf, body_tf, tfidf_cos]) test_set.append(feat_vec) return test_set def save_predictions(pred, file): """ Save predictions to CSV file Args: pred: numpy array, of numeric predictions file: str, filename + extension """ with open(file, 'w') as csvfile: fieldnames = ['Stance'] writer = DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for instance in pred: writer.writerow({'Stance': label_ref_rev[instance]})
42.070039
121
0.584073
36e9d603260d35f07378e699ad3aaf617af8ddb3
46,998
py
Python
daal4py/sklearn/linear_model/_logistic_path_0_21.py
agorshk/daal4py
58a9b2301c47cd2d5144a403a59c210e10b75f8f
[ "Apache-2.0" ]
null
null
null
daal4py/sklearn/linear_model/_logistic_path_0_21.py
agorshk/daal4py
58a9b2301c47cd2d5144a403a59c210e10b75f8f
[ "Apache-2.0" ]
null
null
null
daal4py/sklearn/linear_model/_logistic_path_0_21.py
agorshk/daal4py
58a9b2301c47cd2d5144a403a59c210e10b75f8f
[ "Apache-2.0" ]
null
null
null
# #******************************************************************************* # Copyright 2014-2020 Intel Corporation # # 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. #******************************************************************************/ import numpy as np import scipy.sparse as sparse import scipy.optimize as optimize import numbers import warnings from .logistic_loss import (_daal4py_loss_and_grad, _daal4py_logistic_loss_extra_args, _daal4py_cross_entropy_loss_extra_args, _daal4py_loss_, _daal4py_grad_, _daal4py_grad_hess_) from sklearn import __version__ as sklearn_version from distutils.version import LooseVersion from sklearn.utils import (check_array, check_consistent_length, compute_class_weight, check_random_state) from sklearn.linear_model.sag import sag_solver from sklearn.utils.optimize import newton_cg from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model.logistic import ( _check_solver, _check_multi_class, _fit_liblinear, _logistic_loss_and_grad, _logistic_loss, _logistic_grad_hess, _multinomial_loss, _multinomial_loss_grad, _multinomial_grad_hess, LogisticRegression as LogisticRegression_original) from sklearn.preprocessing import (LabelEncoder, LabelBinarizer) from sklearn.linear_model.base import (LinearClassifierMixin, SparseCoefMixin, BaseEstimator) use_daal = True # Code adapted from sklearn.linear_model.logistic prior to 0.21 def logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True, max_iter=100, tol=1e-4, verbose=0, solver='lbfgs', coef=None, class_weight=None, dual=False, penalty='l2', intercept_scaling=1., multi_class='warn', random_state=None, check_input=True, max_squared_sum=None, sample_weight=None): """Compute a Logistic Regression model for a list of regularization parameters. This is an implementation that uses the result of the previous model to speed up computations along the set of solutions, making it faster than sequentially calling LogisticRegression for the different parameters. Note that there will be no speedup with liblinear solver, since it does not handle warm-starting. Read more in the :ref:`User Guide <logistic_regression>`. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Input data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Input data, target values. pos_class : int, None The class with respect to which we perform a one-vs-all fit. If None, then it is assumed that the given problem is binary. Cs : int | array-like, shape (n_cs,) List of values for the regularization parameter or integer specifying the number of regularization parameters that should be used. In this case, the parameters will be chosen in a logarithmic scale between 1e-4 and 1e4. fit_intercept : bool Whether to fit an intercept for the model. In this case the shape of the returned array is (n_cs, n_features + 1). max_iter : int Maximum number of iterations for the solver. tol : float Stopping criterion. For the newton-cg and lbfgs solvers, the iteration will stop when ``max{|g_i | i = 1, ..., n} <= tol`` where ``g_i`` is the i-th component of the gradient. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. solver : {'lbfgs', 'newton-cg', 'liblinear', 'sag', 'saga'} Numerical solver to use. coef : array-like, shape (n_features,), default None Initialization value for coefficients of logistic regression. Useless for liblinear solver. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The 'newton-cg', 'sag' and 'lbfgs' solvers support only l2 penalties. intercept_scaling : float, default 1. Useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equal to intercept_scaling is appended to the instance vector. The intercept becomes ``intercept_scaling * synthetic_feature_weight``. Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. multi_class : str, {'ovr', 'multinomial', 'auto'}, default: 'ovr' If the option chosen is 'ovr', then a binary problem is fit for each label. For 'multinomial' the loss minimised is the multinomial loss fit across the entire probability distribution, *even when the data is binary*. 'multinomial' is unavailable when solver='liblinear'. 'auto' selects 'ovr' if the data is binary, or if solver='liblinear', and otherwise selects 'multinomial'. .. versionadded:: 0.18 Stochastic Average Gradient descent solver for 'multinomial' case. .. versionchanged:: 0.20 Default will change from 'ovr' to 'auto' in 0.22. random_state : int, RandomState instance or None, optional, default None The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``solver`` == 'sag' or 'liblinear'. check_input : bool, default True If False, the input arrays X and y will not be checked. max_squared_sum : float, default None Maximum squared sum of X over samples. Used only in SAG solver. If None, it will be computed, going through all the samples. The value should be precomputed to speed up cross validation. sample_weight : array-like, shape(n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1) List of coefficients for the Logistic Regression model. If fit_intercept is set to True then the second dimension will be n_features + 1, where the last item represents the intercept. For ``multiclass='multinomial'``, the shape is (n_classes, n_cs, n_features) or (n_classes, n_cs, n_features + 1). Cs : ndarray Grid of Cs used for cross-validation. n_iter : array, shape (n_cs,) Actual number of iteration for each Cs. Notes ----- You might get slightly different results with the solver liblinear than with the others since this uses LIBLINEAR which penalizes the intercept. .. versionchanged:: 0.19 The "copy" parameter was removed. """ if isinstance(Cs, numbers.Integral): Cs = np.logspace(-4, 4, Cs) solver = _check_solver(solver, penalty, dual) # Preprocessing. if check_input: X = check_array(X, accept_sparse='csr', dtype=np.float64, accept_large_sparse=solver != 'liblinear') y = check_array(y, ensure_2d=False, dtype=None) check_consistent_length(X, y) _, n_features = X.shape classes = np.unique(y) random_state = check_random_state(random_state) multi_class = _check_multi_class(multi_class, solver, len(classes)) if pos_class is None and multi_class != 'multinomial': if (classes.size > 2): raise ValueError('To fit OvR, use the pos_class argument') # np.unique(y) gives labels in sorted order. pos_class = classes[1] # If sample weights exist, convert them to array (support for lists) # and check length # Otherwise set them to 1 for all examples if sample_weight is not None: sample_weight = np.array(sample_weight, dtype=X.dtype, order='C') check_consistent_length(y, sample_weight) default_weights = False else: default_weights = (class_weight is None) sample_weight = np.ones(X.shape[0], dtype=X.dtype) daal_ready = use_daal and solver in ['lbfgs', 'newton-cg'] and not sparse.issparse(X) # If class_weights is a dict (provided by the user), the weights # are assigned to the original labels. If it is "balanced", then # the class_weights are assigned after masking the labels with a OvR. le = LabelEncoder() if isinstance(class_weight, dict) or multi_class == 'multinomial': class_weight_ = compute_class_weight(class_weight, classes, y) sample_weight *= class_weight_[le.fit_transform(y)] # For doing a ovr, we need to mask the labels first. for the # multinomial case this is not necessary. if multi_class == 'ovr': mask_classes = np.array([-1, 1]) mask = (y == pos_class) y_bin = np.ones(y.shape, dtype=X.dtype) y_bin[~mask] = -1. # for compute_class_weight if class_weight == "balanced": class_weight_ = compute_class_weight(class_weight, mask_classes, y_bin) sample_weight *= class_weight_[le.fit_transform(y_bin)] daal_ready = daal_ready and (default_weights or np.allclose(sample_weight, np.ones_like(sample_weight))) if daal_ready: w0 = np.zeros(n_features + 1, dtype=X.dtype) y_bin[~mask] = 0. else: w0 = np.zeros(n_features + int(fit_intercept), dtype=X.dtype) else: daal_ready = daal_ready and (default_weights or np.allclose(sample_weight, np.ones_like(sample_weight))) if solver not in ['sag', 'saga']: if daal_ready: Y_multi = le.fit_transform(y).astype(X.dtype, copy=False) else: lbin = LabelBinarizer() Y_multi = lbin.fit_transform(y) if Y_multi.shape[1] == 1: Y_multi = np.hstack([1 - Y_multi, Y_multi]) else: # SAG multinomial solver needs LabelEncoder, not LabelBinarizer Y_multi = le.fit_transform(y).astype(X.dtype, copy=False) if daal_ready: w0 = np.zeros((classes.size, n_features + 1), order='C', dtype=X.dtype) else: w0 = np.zeros((classes.size, n_features + int(fit_intercept)), order='F', dtype=X.dtype) if coef is not None: # it must work both giving the bias term and not if multi_class == 'ovr': if coef.size not in (n_features, w0.size): raise ValueError( 'Initialization coef is of shape %d, expected shape ' '%d or %d' % (coef.size, n_features, w0.size)) if daal_ready: w0[-coef.size:] = np.roll(coef, 1, -1) if coef.size != n_features else coef else: w0[:coef.size] = coef else: # For binary problems coef.shape[0] should be 1, otherwise it # should be classes.size. n_classes = classes.size if n_classes == 2: n_classes = 1 if (coef.shape[0] != n_classes or coef.shape[1] not in (n_features, n_features + 1)): raise ValueError( 'Initialization coef is of shape (%d, %d), expected ' 'shape (%d, %d) or (%d, %d)' % ( coef.shape[0], coef.shape[1], classes.size, n_features, classes.size, n_features + 1)) if daal_ready: w0[:, -coef.shape[1]:] = np.roll(coef, 1, -1) if coef.shape[1] != n_features else coef else: if n_classes == 1: w0[0, :coef.shape[1]] = -coef w0[1, :coef.shape[1]] = coef else: w0[:, :coef.shape[1]] = coef C_daal_multiplier = 1 # commented out because this is Py3 feature #def _map_to_binary_logistic_regression(): # nonlocal C_daal_multiplier # nonlocal w0 # C_daal_multiplier = 2 # w0 *= 2 if multi_class == 'multinomial': # fmin_l_bfgs_b and newton-cg accepts only ravelled parameters. if solver in ['lbfgs', 'newton-cg']: if daal_ready and classes.size == 2: w0_saved = w0 w0 = w0[-1:, :] w0 = w0.ravel() target = Y_multi if solver == 'lbfgs': if daal_ready: if classes.size == 2: # _map_to_binary_logistic_regression() C_daal_multiplier = 2 w0 *= 2 daal_extra_args_func = _daal4py_logistic_loss_extra_args else: daal_extra_args_func = _daal4py_cross_entropy_loss_extra_args func = _daal4py_loss_and_grad else: func = lambda x, *args: _multinomial_loss_grad(x, *args)[0:2] elif solver == 'newton-cg': if daal_ready: if classes.size == 2: # _map_to_binary_logistic_regression() C_daal_multiplier = 2 w0 *= 2 daal_extra_args_func = _daal4py_logistic_loss_extra_args else: daal_extra_args_func = _daal4py_cross_entropy_loss_extra_args func = _daal4py_loss_ grad = _daal4py_grad_ hess = _daal4py_grad_hess_ else: func = lambda x, *args: _multinomial_loss(x, *args)[0] grad = lambda x, *args: _multinomial_loss_grad(x, *args)[1] hess = _multinomial_grad_hess warm_start_sag = {'coef': w0.T} else: target = y_bin if solver == 'lbfgs': if daal_ready: func = _daal4py_loss_and_grad daal_extra_args_func = _daal4py_logistic_loss_extra_args else: func = _logistic_loss_and_grad elif solver == 'newton-cg': if daal_ready: daal_extra_args_func = _daal4py_logistic_loss_extra_args func = _daal4py_loss_ grad = _daal4py_grad_ hess = _daal4py_grad_hess_ else: func = _logistic_loss grad = lambda x, *args: _logistic_loss_and_grad(x, *args)[1] hess = _logistic_grad_hess warm_start_sag = {'coef': np.expand_dims(w0, axis=1)} coefs = list() n_iter = np.zeros(len(Cs), dtype=np.int32) for i, C in enumerate(Cs): if solver == 'lbfgs': if daal_ready: extra_args = daal_extra_args_func(classes.size, w0, X, target, 0., 0.5 / C / C_daal_multiplier, fit_intercept, value=True, gradient=True, hessian=False) else: extra_args = (X, target, 1. / C, sample_weight) iprint = [-1, 50, 1, 100, 101][ np.searchsorted(np.array([0, 1, 2, 3]), verbose)] w0, loss, info = optimize.fmin_l_bfgs_b( func, w0, fprime=None, args=extra_args, iprint=iprint, pgtol=tol, maxiter=max_iter) if daal_ready and C_daal_multiplier == 2: w0 *= 0.5 if info["warnflag"] == 1: warnings.warn("lbfgs failed to converge. Increase the number " "of iterations.", ConvergenceWarning) # In scipy <= 1.0.0, nit may exceed maxiter. # See https://github.com/scipy/scipy/issues/7854. n_iter_i = min(info['nit'], max_iter) elif solver == 'newton-cg': if daal_ready: def make_ncg_funcs(f, value=False, gradient=False, hessian=False): daal_penaltyL2 = 0.5 / C / C_daal_multiplier _obj_, X_, y_, n_samples = daal_extra_args_func( classes.size, w0, X, target, 0., daal_penaltyL2, fit_intercept, value=value, gradient=gradient, hessian=hessian) _func_ = lambda x, *args: f(x, _obj_, *args) return _func_, (X_, y_, n_samples, daal_penaltyL2) loss_func, extra_args = make_ncg_funcs(func, value=True) grad_func, _ = make_ncg_funcs(grad, gradient=True) grad_hess_func, _ = make_ncg_funcs(hess, gradient=True) w0, n_iter_i = newton_cg(grad_hess_func, loss_func, grad_func, w0, args=extra_args, maxiter=max_iter, tol=tol) else: args = (X, target, 1. / C, sample_weight) w0, n_iter_i = newton_cg(hess, func, grad, w0, args=args, maxiter=max_iter, tol=tol) elif solver == 'liblinear': coef_, intercept_, n_iter_i, = _fit_liblinear( X, target, C, fit_intercept, intercept_scaling, None, penalty, dual, verbose, max_iter, tol, random_state, sample_weight=sample_weight) if fit_intercept: w0 = np.concatenate([coef_.ravel(), intercept_]) else: w0 = coef_.ravel() elif solver in ['sag', 'saga']: if multi_class == 'multinomial': target = target.astype(np.float64) loss = 'multinomial' else: loss = 'log' if penalty == 'l1': alpha = 0. beta = 1. / C else: alpha = 1. / C beta = 0. w0, n_iter_i, warm_start_sag = sag_solver( X, target, sample_weight, loss, alpha, beta, max_iter, tol, verbose, random_state, False, max_squared_sum, warm_start_sag, is_saga=(solver == 'saga')) else: raise ValueError("solver must be one of {'liblinear', 'lbfgs', " "'newton-cg', 'sag'}, got '%s' instead" % solver) if multi_class == 'multinomial': if daal_ready: if classes.size == 2: multi_w0 = w0[np.newaxis, :] else: multi_w0 = np.reshape(w0, (classes.size, -1)) else: n_classes = max(2, classes.size) multi_w0 = np.reshape(w0, (n_classes, -1)) if n_classes == 2: multi_w0 = multi_w0[1][np.newaxis, :] coefs.append(np.require(multi_w0, requirements='O')) else: coefs.append(np.require(w0, requirements='O')) n_iter[i] = n_iter_i if daal_ready: if fit_intercept: for i, ci in enumerate(coefs): coefs[i] = np.roll(ci, -1, -1) else: for i, ci in enumerate(coefs): coefs[i] = np.delete(ci, 0, axis=-1) return coefs, np.array(Cs), n_iter # Code adapted from sklearn.linear_model.logistic version 0.21 def __logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True, max_iter=100, tol=1e-4, verbose=0, solver='lbfgs', coef=None, class_weight=None, dual=False, penalty='l2', intercept_scaling=1., multi_class='warn', random_state=None, check_input=True, max_squared_sum=None, sample_weight=None, l1_ratio=None): """Compute a Logistic Regression model for a list of regularization parameters. This is an implementation that uses the result of the previous model to speed up computations along the set of solutions, making it faster than sequentially calling LogisticRegression for the different parameters. Note that there will be no speedup with liblinear solver, since it does not handle warm-starting. Read more in the :ref:`User Guide <logistic_regression>`. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Input data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Input data, target values. pos_class : int, None The class with respect to which we perform a one-vs-all fit. If None, then it is assumed that the given problem is binary. Cs : int | array-like, shape (n_cs,) List of values for the regularization parameter or integer specifying the number of regularization parameters that should be used. In this case, the parameters will be chosen in a logarithmic scale between 1e-4 and 1e4. fit_intercept : bool Whether to fit an intercept for the model. In this case the shape of the returned array is (n_cs, n_features + 1). max_iter : int Maximum number of iterations for the solver. tol : float Stopping criterion. For the newton-cg and lbfgs solvers, the iteration will stop when ``max{|g_i | i = 1, ..., n} <= tol`` where ``g_i`` is the i-th component of the gradient. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. solver : {'lbfgs', 'newton-cg', 'liblinear', 'sag', 'saga'} Numerical solver to use. coef : array-like, shape (n_features,), default None Initialization value for coefficients of logistic regression. Useless for liblinear solver. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. penalty : str, 'l1', 'l2', or 'elasticnet' Used to specify the norm used in the penalization. The 'newton-cg', 'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is only supported by the 'saga' solver. intercept_scaling : float, default 1. Useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equal to intercept_scaling is appended to the instance vector. The intercept becomes ``intercept_scaling * synthetic_feature_weight``. Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. multi_class : str, {'ovr', 'multinomial', 'auto'}, default: 'ovr' If the option chosen is 'ovr', then a binary problem is fit for each label. For 'multinomial' the loss minimised is the multinomial loss fit across the entire probability distribution, *even when the data is binary*. 'multinomial' is unavailable when solver='liblinear'. 'auto' selects 'ovr' if the data is binary, or if solver='liblinear', and otherwise selects 'multinomial'. .. versionadded:: 0.18 Stochastic Average Gradient descent solver for 'multinomial' case. .. versionchanged:: 0.20 Default will change from 'ovr' to 'auto' in 0.22. random_state : int, RandomState instance or None, optional, default None The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``solver`` == 'sag' or 'liblinear'. check_input : bool, default True If False, the input arrays X and y will not be checked. max_squared_sum : float, default None Maximum squared sum of X over samples. Used only in SAG solver. If None, it will be computed, going through all the samples. The value should be precomputed to speed up cross validation. sample_weight : array-like, shape(n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. l1_ratio : float or None, optional (default=None) The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a combination of L1 and L2. Returns ------- coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1) List of coefficients for the Logistic Regression model. If fit_intercept is set to True then the second dimension will be n_features + 1, where the last item represents the intercept. For ``multiclass='multinomial'``, the shape is (n_classes, n_cs, n_features) or (n_classes, n_cs, n_features + 1). Cs : ndarray Grid of Cs used for cross-validation. n_iter : array, shape (n_cs,) Actual number of iteration for each Cs. Notes ----- You might get slightly different results with the solver liblinear than with the others since this uses LIBLINEAR which penalizes the intercept. .. versionchanged:: 0.19 The "copy" parameter was removed. """ if isinstance(Cs, numbers.Integral): Cs = np.logspace(-4, 4, Cs) solver = _check_solver(solver, penalty, dual) # Preprocessing. if check_input: X = check_array(X, accept_sparse='csr', dtype=np.float64, accept_large_sparse=solver != 'liblinear') y = check_array(y, ensure_2d=False, dtype=None) check_consistent_length(X, y) _, n_features = X.shape classes = np.unique(y) random_state = check_random_state(random_state) multi_class = _check_multi_class(multi_class, solver, len(classes)) if pos_class is None and multi_class != 'multinomial': if (classes.size > 2): raise ValueError('To fit OvR, use the pos_class argument') # np.unique(y) gives labels in sorted order. pos_class = classes[1] # If sample weights exist, convert them to array (support for lists) # and check length # Otherwise set them to 1 for all examples if sample_weight is not None: sample_weight = np.array(sample_weight, dtype=X.dtype, order='C') check_consistent_length(y, sample_weight) default_weights = False else: sample_weight = np.ones(X.shape[0], dtype=X.dtype) default_weights = (class_weight is None) daal_ready = use_daal and solver in ['lbfgs', 'newton-cg'] and not sparse.issparse(X) # If class_weights is a dict (provided by the user), the weights # are assigned to the original labels. If it is "balanced", then # the class_weights are assigned after masking the labels with a OvR. le = LabelEncoder() if isinstance(class_weight, dict) or multi_class == 'multinomial': class_weight_ = compute_class_weight(class_weight, classes, y) sample_weight *= class_weight_[le.fit_transform(y)] # For doing a ovr, we need to mask the labels first. for the # multinomial case this is not necessary. if multi_class == 'ovr': w0 = np.zeros(n_features + int(fit_intercept), dtype=X.dtype) mask_classes = np.array([-1, 1]) mask = (y == pos_class) y_bin = np.ones(y.shape, dtype=X.dtype) y_bin[~mask] = -1. # for compute_class_weight if class_weight == "balanced": class_weight_ = compute_class_weight(class_weight, mask_classes, y_bin) sample_weight *= class_weight_[le.fit_transform(y_bin)] daal_ready = daal_ready and (default_weights or np.allclose(sample_weight, np.ones_like(sample_weight))) if daal_ready: w0 = np.zeros(n_features + 1, dtype=X.dtype) y_bin[~mask] = 0. else: w0 = np.zeros(n_features + int(fit_intercept), dtype=X.dtype) else: daal_ready = daal_ready and (default_weights or np.allclose(sample_weight, np.ones_like(sample_weight))) if solver not in ['sag', 'saga']: if daal_ready: Y_multi = le.fit_transform(y).astype(X.dtype, copy=False) else: lbin = LabelBinarizer() Y_multi = lbin.fit_transform(y) if Y_multi.shape[1] == 1: Y_multi = np.hstack([1 - Y_multi, Y_multi]) else: # SAG multinomial solver needs LabelEncoder, not LabelBinarizer le = LabelEncoder() Y_multi = le.fit_transform(y).astype(X.dtype, copy=False) if daal_ready: w0 = np.zeros((classes.size, n_features + 1), order='C', dtype=X.dtype) else: w0 = np.zeros((classes.size, n_features + int(fit_intercept)), order='F', dtype=X.dtype) if coef is not None: # it must work both giving the bias term and not if multi_class == 'ovr': if coef.size not in (n_features, w0.size): raise ValueError( 'Initialization coef is of shape %d, expected shape ' '%d or %d' % (coef.size, n_features, w0.size)) if daal_ready: w0[-coef.size:] = np.roll(coef, 1, -1) if coef.size != n_features else coef else: w0[:coef.size] = coef else: # For binary problems coef.shape[0] should be 1, otherwise it # should be classes.size. n_classes = classes.size if n_classes == 2: n_classes = 1 if (coef.shape[0] != n_classes or coef.shape[1] not in (n_features, n_features + 1)): raise ValueError( 'Initialization coef is of shape (%d, %d), expected ' 'shape (%d, %d) or (%d, %d)' % ( coef.shape[0], coef.shape[1], classes.size, n_features, classes.size, n_features + 1)) if daal_ready: w0[:, -coef.shape[1]:] = np.roll(coef, 1, -1) if coef.shape[1] != n_features else coef else: if n_classes == 1: w0[0, :coef.shape[1]] = -coef w0[1, :coef.shape[1]] = coef else: w0[:, :coef.shape[1]] = coef C_daal_multiplier = 1 # commented out because this is Py3 feature #def _map_to_binary_logistic_regression(): # nonlocal C_daal_multiplier # nonlocal w0 # C_daal_multiplier = 2 # w0 *= 2 if multi_class == 'multinomial': # fmin_l_bfgs_b and newton-cg accepts only ravelled parameters. if solver in ['lbfgs', 'newton-cg']: if daal_ready and classes.size == 2: w0_saved = w0 w0 = w0[-1:, :] w0 = w0.ravel() target = Y_multi if solver == 'lbfgs': if daal_ready: if classes.size == 2: # _map_to_binary_logistic_regression() C_daal_multiplier = 2 w0 *= 2 daal_extra_args_func = _daal4py_logistic_loss_extra_args else: daal_extra_args_func = _daal4py_cross_entropy_loss_extra_args func = _daal4py_loss_and_grad else: func = lambda x, *args: _multinomial_loss_grad(x, *args)[0:2] elif solver == 'newton-cg': if daal_ready: if classes.size == 2: # _map_to_binary_logistic_regression() C_daal_multiplier = 2 w0 *= 2 daal_extra_args_func = _daal4py_logistic_loss_extra_args else: daal_extra_args_func = _daal4py_cross_entropy_loss_extra_args func = _daal4py_loss_ grad = _daal4py_grad_ hess = _daal4py_grad_hess_ else: func = lambda x, *args: _multinomial_loss(x, *args)[0] grad = lambda x, *args: _multinomial_loss_grad(x, *args)[1] hess = _multinomial_grad_hess warm_start_sag = {'coef': w0.T} else: target = y_bin if solver == 'lbfgs': if daal_ready: func = _daal4py_loss_and_grad daal_extra_args_func = _daal4py_logistic_loss_extra_args else: func = _logistic_loss_and_grad elif solver == 'newton-cg': if daal_ready: daal_extra_args_func = _daal4py_logistic_loss_extra_args func = _daal4py_loss_ grad = _daal4py_grad_ hess = _daal4py_grad_hess_ else: func = _logistic_loss grad = lambda x, *args: _logistic_loss_and_grad(x, *args)[1] hess = _logistic_grad_hess warm_start_sag = {'coef': np.expand_dims(w0, axis=1)} coefs = list() n_iter = np.zeros(len(Cs), dtype=np.int32) for i, C in enumerate(Cs): if solver == 'lbfgs': if daal_ready: extra_args = daal_extra_args_func(classes.size, w0, X, target, 0., 0.5 / C / C_daal_multiplier, fit_intercept, value=True, gradient=True, hessian=False) else: extra_args = (X, target, 1. / C, sample_weight) iprint = [-1, 50, 1, 100, 101][ np.searchsorted(np.array([0, 1, 2, 3]), verbose)] w0, loss, info = optimize.fmin_l_bfgs_b( func, w0, fprime=None, args=extra_args, iprint=iprint, pgtol=tol, maxiter=max_iter) if daal_ready and C_daal_multiplier == 2: w0 *= 0.5 if info["warnflag"] == 1: warnings.warn("lbfgs failed to converge. Increase the number " "of iterations.", ConvergenceWarning) # In scipy <= 1.0.0, nit may exceed maxiter. # See https://github.com/scipy/scipy/issues/7854. n_iter_i = min(info['nit'], max_iter) elif solver == 'newton-cg': if daal_ready: def make_ncg_funcs(f, value=False, gradient=False, hessian=False): daal_penaltyL2 = 0.5 / C / C_daal_multiplier _obj_, X_, y_, n_samples = daal_extra_args_func( classes.size, w0, X, target, 0., daal_penaltyL2, fit_intercept, value=value, gradient=gradient, hessian=hessian) _func_ = lambda x, *args: f(x, _obj_, *args) return _func_, (X_, y_, n_samples, daal_penaltyL2) loss_func, extra_args = make_ncg_funcs(func, value=True) grad_func, _ = make_ncg_funcs(grad, gradient=True) grad_hess_func, _ = make_ncg_funcs(hess, gradient=True) w0, n_iter_i = newton_cg(grad_hess_func, loss_func, grad_func, w0, args=extra_args, maxiter=max_iter, tol=tol) else: args = (X, target, 1. / C, sample_weight) w0, n_iter_i = newton_cg(hess, func, grad, w0, args=args, maxiter=max_iter, tol=tol) elif solver == 'liblinear': coef_, intercept_, n_iter_i, = _fit_liblinear( X, target, C, fit_intercept, intercept_scaling, None, penalty, dual, verbose, max_iter, tol, random_state, sample_weight=sample_weight) if fit_intercept: w0 = np.concatenate([coef_.ravel(), intercept_]) else: w0 = coef_.ravel() elif solver in ['sag', 'saga']: if multi_class == 'multinomial': target = target.astype(X.dtype, copy=False) loss = 'multinomial' else: loss = 'log' # alpha is for L2-norm, beta is for L1-norm if penalty == 'l1': alpha = 0. beta = 1. / C elif penalty == 'l2': alpha = 1. / C beta = 0. else: # Elastic-Net penalty alpha = (1. / C) * (1 - l1_ratio) beta = (1. / C) * l1_ratio w0, n_iter_i, warm_start_sag = sag_solver( X, target, sample_weight, loss, alpha, beta, max_iter, tol, verbose, random_state, False, max_squared_sum, warm_start_sag, is_saga=(solver == 'saga')) else: raise ValueError("solver must be one of {'liblinear', 'lbfgs', " "'newton-cg', 'sag'}, got '%s' instead" % solver) if multi_class == 'multinomial': if daal_ready: if classes.size == 2: multi_w0 = w0[np.newaxis, :] else: multi_w0 = np.reshape(w0, (classes.size, -1)) else: n_classes = max(2, classes.size) multi_w0 = np.reshape(w0, (n_classes, -1)) if n_classes == 2: multi_w0 = multi_w0[1][np.newaxis, :] coefs.append(np.require(multi_w0, requirements='O')) else: coefs.append(np.require(w0, requirements='O')) n_iter[i] = n_iter_i if daal_ready: if fit_intercept: for i, ci in enumerate(coefs): coefs[i] = np.roll(ci, -1, -1) else: for i, ci in enumerate(coefs): coefs[i] = np.delete(ci, 0, axis=-1) return np.array(coefs), np.array(Cs), n_iter if (LooseVersion(sklearn_version) >= LooseVersion("0.22")): def _logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True, max_iter=100, tol=1e-4, verbose=0, solver='lbfgs', coef=None, class_weight=None, dual=False, penalty='l2', intercept_scaling=1., multi_class='auto', random_state=None, check_input=True, max_squared_sum=None, sample_weight=None, l1_ratio=None): return __logistic_regression_path(X, y, pos_class=pos_class, Cs=Cs, fit_intercept=fit_intercept, max_iter=max_iter, tol=tol, verbose=verbose, solver=solver, coef=coef, class_weight=class_weight, dual=dual, penalty=penalty, intercept_scaling=intercept_scaling, multi_class=multi_class, random_state=random_state, check_input=check_input, max_squared_sum=max_squared_sum, sample_weight=sample_weight, l1_ratio=l1_ratio) class LogisticRegression(LogisticRegression_original, BaseEstimator, LinearClassifierMixin, SparseCoefMixin): def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None): self.penalty = penalty self.dual = dual self.tol = tol self.C = C self.fit_intercept = fit_intercept self.intercept_scaling = intercept_scaling self.class_weight = class_weight self.random_state = random_state self.solver = solver self.max_iter = max_iter self.multi_class = multi_class self.verbose = verbose self.warm_start = warm_start self.n_jobs = n_jobs self.l1_ratio = l1_ratio elif (LooseVersion(sklearn_version) >= LooseVersion("0.21")): def _logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True, max_iter=100, tol=1e-4, verbose=0, solver='lbfgs', coef=None, class_weight=None, dual=False, penalty='l2', intercept_scaling=1., multi_class='warn', random_state=None, check_input=True, max_squared_sum=None, sample_weight=None, l1_ratio=None): return __logistic_regression_path(X, y, pos_class=pos_class, Cs=Cs, fit_intercept=fit_intercept, max_iter=max_iter, tol=tol, verbose=verbose, solver=solver, coef=coef, class_weight=class_weight, dual=dual, penalty=penalty, intercept_scaling=intercept_scaling, multi_class=multi_class, random_state=random_state, check_input=check_input, max_squared_sum=max_squared_sum, sample_weight=sample_weight, l1_ratio=l1_ratio) class LogisticRegression(LogisticRegression_original, BaseEstimator, LinearClassifierMixin, SparseCoefMixin): def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='warn', max_iter=100, multi_class='warn', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None): self.penalty = penalty self.dual = dual self.tol = tol self.C = C self.fit_intercept = fit_intercept self.intercept_scaling = intercept_scaling self.class_weight = class_weight self.random_state = random_state self.solver = solver self.max_iter = max_iter self.multi_class = multi_class self.verbose = verbose self.warm_start = warm_start self.n_jobs = n_jobs self.l1_ratio = l1_ratio else: class LogisticRegression(LogisticRegression_original, BaseEstimator, LinearClassifierMixin, SparseCoefMixin): def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='warn', max_iter=100, multi_class='warn', verbose=0, warm_start=False, n_jobs=None): self.penalty = penalty self.dual = dual self.tol = tol self.C = C self.fit_intercept = fit_intercept self.intercept_scaling = intercept_scaling self.class_weight = class_weight self.random_state = random_state self.solver = solver self.max_iter = max_iter self.multi_class = multi_class self.verbose = verbose self.warm_start = warm_start self.n_jobs = n_jobs
44.379603
112
0.575365
7a4b43adb96afcf047df0bdc3dea6b32a6926822
12,377
py
Python
sourcecode/src/vx/bone/Util.py
ivarvb/BONE
92efabe4873495e5e7d35a953135f414b4e2dcb0
[ "MIT" ]
null
null
null
sourcecode/src/vx/bone/Util.py
ivarvb/BONE
92efabe4873495e5e7d35a953135f414b4e2dcb0
[ "MIT" ]
null
null
null
sourcecode/src/vx/bone/Util.py
ivarvb/BONE
92efabe4873495e5e7d35a953135f414b4e2dcb0
[ "MIT" ]
null
null
null
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Author: Ivar """ import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import os import ujson import copy from datetime import datetime class Util: @staticmethod def write(file, obj): with open(file, "w") as filef: filef.write(ujson.dumps(obj)) @staticmethod def read(file): data = {} with open(file,"r") as filef: data = (ujson.load(filef)) return data @staticmethod def now(): return datetime.now().strftime("%Y%m%d%H%M%S") @staticmethod def makedir(ndir): if not os.path.exists(ndir): os.makedirs(ndir) @staticmethod def makeheatmap(id, inputDir, outputDir, fileclass): classdata = Util.read(inputDir+"/"+fileclass) index = [] #columns = ["KNN","SVCGRID","SVCLINEAR","SVC","DTC","RFC","MLPC","ADBC","GNBC"] columns = [] data = [] for row in classdata: r = [] columns = [] #print("row[evals]", row["evals"]) for k, v in row["evals"].items(): r.append(v["metrics"]["f1"]) columns.append(k) data.append(r) name = row["parameters"]["train"]["label"] +" "+ row["name"] if row["norm"]=="None": index.append(name+" (0)") elif row["norm"]=="std": index.append(name+" (1)") elif row["norm"]=="minmax": index.append(name+" (2)") df = pd.DataFrame(data, index=index, columns=columns) plt.subplots(figsize=(8,15)) color_map = plt.cm.get_cmap('YlOrBr_r') #color_map = color_map.reversed() ax = sns.heatmap(df, cmap=color_map, square=True, annot=True, annot_kws={"size":6}) for item in ax.get_yticklabels(): item.set_rotation(0) for item in ax.get_xticklabels(): item.set_rotation(90) fileout = f'classification_{id}.pdf' plt.savefig(outputDir+'/'+fileout, dpi=100, bbox_inches='tight') plt.close("all") @staticmethod def makebar(id, inputDir, outputDir, fileclass): classdata = Util.read(inputDir+"/"+fileclass) index = [] #columns = ["KNN","SVCGRID","SVCLINEAR","SVC","DTC","RFC","MLPC","ADBC","GNBC"] data = [] for row in classdata: for k, v in row["evals"].items(): ac = v["metrics"]["f1"] #ac = row["evals"][c]["acc"] data.append(ac) name = row["parameters"]["train"]["label"] +" "+ row["name"] if row["norm"]=="None": name += " (0)" elif row["norm"]=="std": name += " (1)" elif row["norm"]=="minmax": name += " (2)" name = k+" "+name index.append(name) data, index = zip(*sorted(zip(data, index), reverse=True)) plt.subplots(figsize=(5,60)) df = pd.DataFrame({"lab":index,"val":data}) ax = sns.barplot(x = 'val', y = 'lab', data = df, color='#0091eb') for x_ticks in ax.get_xticklabels(): x_ticks.set_rotation(90) i = 0 for p in ax.patches: ax.annotate(format(data[i], '.2f'), (p.get_x() + p.get_width(), p.get_y()+1), ha = 'center', va = 'center', xytext = (0, 5), textcoords = 'offset points') i+=1 fileout = f'bars_{id}.pdf' plt.savefig(outputDir+'/'+fileout, dpi=100, bbox_inches='tight') plt.close("all") @staticmethod def XXsplitImage(image, tileSize): height, width = image.shape # print(image.shape) tiles = [] positions = [] maxMultHeight = height - (height % tileSize) maxMultWidth = width - (width % tileSize) # print(maxMultHeight, maxMultWidth) for i in range(0, maxMultHeight, tileSize): for j in range(0, maxMultWidth, tileSize): # yield image[i:i+tileSize, j:j+tileSize] positions.append(np.asarray((i, i + tileSize, j, j + tileSize))) tiles.append(image[i:i + tileSize, j:j + tileSize]) # print(image[i:i+tileSize, j:j+tileSize]) lastTile = image[maxMultHeight:height, maxMultWidth:width] if lastTile.shape[0] > 0 and lastTile.shape[1] > 0: tiles.append(lastTile) positions.append(np.asarray((maxMultHeight, height, maxMultWidth, width))) #print(tiles) return tiles, positions def splitImage(image, tileSize): height, width = image.shape # print(image.shape) tiles = [] positions = [] maxMultHeight = height - (height % tileSize) maxMultWidth = width - (width % tileSize) # print(maxMultHeight, maxMultWidth) for i in range(0, height, tileSize): for j in range(0, width, tileSize): # yield image[i:i+tileSize, j:j+tileSize] aux_i = i + tileSize ls_i = aux_i if aux_i<(height-1) else height-1 aux_j = j + tileSize ls_j = aux_j if aux_j<(width-1) else width-1 positions.append(np.asarray((i, ls_i, j, ls_j))) tiles.append(image[i:ls_i, j:ls_j]) # print(image[i:i+tileSize, j:j+tileSize]) #lastTile = image[maxMultHeight:height, maxMultWidth:width] #if lastTile.shape[0] > 0 and lastTile.shape[1] > 0: # tiles.append(lastTile) # positions.append(np.asarray((maxMultHeight, height, maxMultWidth, width))) return tiles, positions @staticmethod def getFileName(arg): #print(arg["targetSet"],arg["boundaryDataSet"],arg["name"], arg["label"]) #return arg["targetSet"]+"_"+arg["boundaryDataSet"]+"_"+arg["name"]+"_"+arg["label"]+".csv" return arg["targetSet"]+"_"+arg["name"]+"_"+Util.getLabel(arg["parameters"])+".csv" #return arg["targetSet"]+"_"+arg["name"]+"_"+arg["boundaryDataSet_id"]+str(arg["parameters"]["tile_size"])+"_"+arg["label"]+".csv" @staticmethod def getLabel(arg): d = [] for k, v in arg.items(): if type(v) == list: s = [str(a) for a in v] s = ",".join(s) d.append(str(k)+":["+str(s)+"]") else: d.append(str(k)+"_"+str(v)) #d = "{"+" ".join(d)+"}" d = "_".join(d) return d @staticmethod def curvePlot(dat): Util.makedir(dat["outputdir"]) #dat["inputdir"] #dat["outputdir"] #dat["files"] #dat["metric"] dato = {} dato_aux = [] #filres = [] for name, fil in dat["files"].items(): #filres.append(Util.read(fil)) dato[name] = {} dato_aux obj = Util.read(dat["inputdir"]+"/"+fil) #print("obj",obj) for row in obj: #print("EE",row["evals"]) for k, v in row["evals"].items(): #print("row", v["metrics"][dat["metric"]]) dato[name][row["xval"]] = v["metrics"][dat["metric"]] metrics = {} metrics["name"] = name metrics["xval"] = row["xval"] for kk, vv in v["metrics"].items(): metrics[kk] = vv dato_aux.append(metrics) print(dato_aux) df_aux = pd.DataFrame(dato_aux) df_aux.to_csv(dat["outputdir"]+"/"+dat["filename"]+"_info.csv") index = [] curves = {} for k, v in dato.items(): curves[k] = [] index = v.keys() for kv in index: curves[k].append(v[kv]) #print(obj["evals"]) #print(curves) #print(index) df = pd.DataFrame(curves, index=index) df.to_csv(dat["outputdir"]+"/"+dat["filename"]+".csv") lines = df.plot.line(figsize=[5,3]) plt.xlabel(dat["xlabel"]) plt.ylabel(dat["ylabel"]) if len(dat["ylim"])==2: plt.ylim(dat["ylim"]) plt.xticks([i for i in index],[str(i) for i in index]) plt.legend(loc='lower right') plt.grid(True, linestyle='--') fig = lines.get_figure() fig.savefig(dat["outputdir"]+"/"+dat["filename"]+".pdf", dpi=300, bbox_inches='tight') @staticmethod def curvePlotFromCSV(dat): dfin = pd.read_csv(dat["inputdir"]+"/"+dat["file"]) print("dfin", dfin) index = dfin["ID"].tolist() dfin = dfin.drop(["ID"], axis=1) lines = dfin.plot.line(figsize=[5,3]) plt.xlabel(dat["xlabel"]) plt.ylabel(dat["ylabel"]) if len(dat["ylim"])==2: plt.ylim(dat["ylim"]) plt.xticks([i for i in range(len(index))],[str(i) for i in index]) if "legendloc" in dat: plt.legend(loc=dat["legendloc"]) else: plt.legend(loc='lower right') plt.grid(True, linestyle='--') if "islogy" in dat and dat["islogy"]==True: plt.yscale('log') fig = lines.get_figure() fig.savefig(dat["outputdir"]+"/"+dat["filename"]+".pdf", dpi=300, bbox_inches='tight') @staticmethod def curvePlotFromDIR(dat): Util.makedir(dat["outputdir"]) dato = {} dato_aux = [] for name, fil in dat["files"].items(): dato[name] = {} for di in range(dat["from"], dat["to"]+1, dat["increment"]): obj = Util.read(dat["inputdir"]+"/"+str(di)+"/"+fil) for row in obj: for k, v in row["evals"].items(): xxx = di dato[name][xxx] = v["metrics"][dat["metric"]] metrics = {} metrics["name"] = name metrics["xval"] = xxx for kk, vv in v["metrics"].items(): metrics[kk] = vv dato_aux.append(metrics) print(dato_aux) df_aux = pd.DataFrame(dato_aux) df_aux.to_csv(dat["outputdir"]+"/"+dat["filename"]+"_info.csv") index = [] curves = {} for k, v in dato.items(): curves[k] = [] index = v.keys() for kv in index: curves[k].append(v[kv]) print("curves", curves) df = pd.DataFrame(curves, index=index) df.to_csv(dat["outputdir"]+"/"+dat["filename"]+".csv") lines = df.plot.line(figsize=[5,3]) if "weight" in dat and "height" in dat: lines = df.plot.line(figsize=[dat["weight"], dat["height"]]) plt.xlabel(dat["xlabel"]) plt.ylabel(dat["ylabel"]) if len(dat["ylim"])==2: plt.ylim(dat["ylim"]) plt.xticks([i for i in index],[str(i) for i in index]) if "xinterval" in dat: plt.xticks(np.arange(min(index), max(index)+1, dat["xinterval"])) if "xrotation" in dat: plt.xticks(rotation=dat["xrotation"]) if "xticksfontsize" in dat: plt.xticks(fontsize=dat["xticksfontsize"]) plt.legend(loc='lower right') plt.grid(True, linestyle='--') fig = lines.get_figure() fig.savefig(dat["outputdir"]+"/"+dat["filename"]+".pdf", dpi=300, bbox_inches='tight') @staticmethod def makeConfigureFormUtil(dat): #dat = dat[0] #print(dat) dao = [] a = dat["fromUtil"]["limits"][0] b = dat["fromUtil"]["limits"][1] c = dat["fromUtil"]["limits"][2] for idx in range(a, b+c, c): dat_copy = copy.deepcopy(dat) dat_copy["outputdir"] = dat_copy["outputdir"]+"/"+str(idx) dat_copy["featureselection"]["n_features"] = idx dao.append(dat_copy) return dao
32.830239
138
0.492446
f0a67a1431c3a0c3327f1b9af36c6f8e1ac7d22e
1,612
py
Python
chariot/transformer/tokenizer/ja_tokenizer.py
Y-Kuro-u/chariot
032f3eecdd55b30c65351e1e636c939c4b20919e
[ "Apache-2.0" ]
134
2018-06-11T01:40:14.000Z
2021-11-15T12:34:38.000Z
chariot/transformer/tokenizer/ja_tokenizer.py
Y-Kuro-u/chariot
032f3eecdd55b30c65351e1e636c939c4b20919e
[ "Apache-2.0" ]
10
2018-06-17T10:45:50.000Z
2021-04-05T05:51:11.000Z
chariot/transformer/tokenizer/ja_tokenizer.py
Y-Kuro-u/chariot
032f3eecdd55b30c65351e1e636c939c4b20919e
[ "Apache-2.0" ]
8
2019-02-23T06:43:21.000Z
2021-02-18T06:05:11.000Z
from collections import namedtuple from chariot.transformer.tokenizer.token import Token class MeCabTokenizer(): JanomeToken = namedtuple("JanomeToken", ("surface", "part_of_speech", "infl_type", "infl_form", "base_form", "reading", "phonetic")) def __init__(self): import MeCab self.tagger = MeCab.Tagger("-Ochasen") def tokenize(self, text): self.tagger.parse("") node = self.tagger.parseToNode(text) tokens = [] while node: # Ignore BOS/EOS if node.surface: surface = node.surface features = node.feature.split(",") if len(features) < 9: pad_size = 9 - len(features) features += ["*"] * pad_size token = MeCabTokenizer.JanomeToken( surface, ",".join(features[:4]), features[4], features[5], features[6], features[7], features[8]) token = Token(token, token_type="ja") tokens.append(token) node = node.next return tokens class JanomeTokenizer(): def __init__(self): from janome.tokenizer import Tokenizer self.tokenizer = Tokenizer() def tokenize(self, text): tokens = self.tokenizer.tokenize(text) tokens = [Token(t, token_type="ja") for t in tokens] return tokens
32.897959
73
0.491935
2cf8130e9ecd14f6389b8db60bed4707ccdcd037
2,050
py
Python
mlfromscratch/examples/lasso_regression.py
Krishna00111/Machine-Learning-from-Scratch
5d6f5b1a2096acbb57a060385e471123b77b9a68
[ "MIT" ]
null
null
null
mlfromscratch/examples/lasso_regression.py
Krishna00111/Machine-Learning-from-Scratch
5d6f5b1a2096acbb57a060385e471123b77b9a68
[ "MIT" ]
null
null
null
mlfromscratch/examples/lasso_regression.py
Krishna00111/Machine-Learning-from-Scratch
5d6f5b1a2096acbb57a060385e471123b77b9a68
[ "MIT" ]
null
null
null
from __future__ import print_function import matplotlib.pyplot as plt import numpy as np import pandas as pd # Import helper functions from mlfromscratch.supervised_learning import LassoRegression from mlfromscratch.utils import k_fold_cross_validation_sets, normalize, mean_squared_error from mlfromscratch.utils import train_test_split, polynomial_features, Plot def main(): # Load temperature data data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t") time = np.atleast_2d(data["time"].values).T temp = data["temp"].values X = time # fraction of the year [0, 1] y = temp X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4) poly_degree = 13 model = LassoRegression(degree=15, reg_factor=0.05, learning_rate=0.001, n_iterations=4000) model.fit(X_train, y_train) # Training error plot n = len(model.training_errors) training, = plt.plot(range(n), model.training_errors, label="Training Error") plt.legend(handles=[training]) plt.title("Error Plot") plt.ylabel('Mean Squared Error') plt.xlabel('Iterations') plt.show() y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) print ("Mean squared error: %s (given by reg. factor: %s)" % (mse, 0.05)) y_pred_line = model.predict(X) # Color map cmap = plt.get_cmap('viridis') # Plot the results m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10) m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10) plt.plot(366 * X, y_pred_line, color='black', linewidth=2, label="Prediction") plt.suptitle("Lasso Regression") plt.title("MSE: %.2f" % mse, fontsize=10) plt.xlabel('Day') plt.ylabel('Temperature in Celcius') plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right') plt.show() if __name__ == "__main__": main()
32.539683
92
0.640976
0d342ac7c6277d255d46456db4279d130eb796fa
657
py
Python
src/shortner/validators.py
diabolicfreak/url_shortner
4ea30962afaed530e873e6613641883a60f380c7
[ "MIT" ]
null
null
null
src/shortner/validators.py
diabolicfreak/url_shortner
4ea30962afaed530e873e6613641883a60f380c7
[ "MIT" ]
null
null
null
src/shortner/validators.py
diabolicfreak/url_shortner
4ea30962afaed530e873e6613641883a60f380c7
[ "MIT" ]
null
null
null
from django.core.validators import URLValidator from django.core.exceptions import ValidationError def validate_url(value): url_validator = URLValidator() value_1_valid = False value_2_valid = False try: url_validator(value) except: value_1_valid = True value_2 = "http://"+value try: url_validator(value_2) except: value_2_valid = True if value_1_valid and value_2_valid: raise ValidationError("invalid url for this field") return value def validate_dot_com(value): if not "com" in value: raise ValidationError("Not valid because no .com") return value
21.9
59
0.681887
61bb009ff8f21c78ebe60a67b2d013135f2b8a27
3,453
py
Python
tests/components/spaceapi/test_init.py
shanbs/home-assistant
818776d2b4f11e4f51992dc88bc0a6f9055833b2
[ "Apache-2.0" ]
4
2019-07-03T22:36:57.000Z
2019-08-10T15:33:25.000Z
tests/components/spaceapi/test_init.py
shanbs/home-assistant
818776d2b4f11e4f51992dc88bc0a6f9055833b2
[ "Apache-2.0" ]
7
2019-08-23T05:26:02.000Z
2022-03-11T23:57:18.000Z
tests/components/spaceapi/test_init.py
shanbs/home-assistant
818776d2b4f11e4f51992dc88bc0a6f9055833b2
[ "Apache-2.0" ]
3
2019-04-28T16:35:45.000Z
2020-05-28T15:21:59.000Z
"""The tests for the Home Assistant SpaceAPI component.""" # pylint: disable=protected-access from unittest.mock import patch import pytest from tests.common import mock_coro from homeassistant.components.spaceapi import ( DOMAIN, SPACEAPI_VERSION, URL_API_SPACEAPI) from homeassistant.setup import async_setup_component CONFIG = { DOMAIN: { 'space': 'Home', 'logo': 'https://home-assistant.io/logo.png', 'url': 'https://home-assistant.io', 'location': {'address': 'In your Home'}, 'contact': {'email': '[email protected]'}, 'issue_report_channels': ['email'], 'state': { 'entity_id': 'test.test_door', 'icon_open': 'https://home-assistant.io/open.png', 'icon_closed': 'https://home-assistant.io/close.png', }, 'sensors': { 'temperature': ['test.temp1', 'test.temp2'], 'humidity': ['test.hum1'], } } } SENSOR_OUTPUT = { 'temperature': [ { 'location': 'Home', 'name': 'temp1', 'unit': '°C', 'value': '25' }, { 'location': 'Home', 'name': 'temp2', 'unit': '°C', 'value': '23' }, ], 'humidity': [ { 'location': 'Home', 'name': 'hum1', 'unit': '%', 'value': '88' }, ] } @pytest.fixture def mock_client(hass, hass_client): """Start the Home Assistant HTTP component.""" with patch('homeassistant.components.spaceapi', return_value=mock_coro(True)): hass.loop.run_until_complete( async_setup_component(hass, 'spaceapi', CONFIG)) hass.states.async_set('test.temp1', 25, attributes={'unit_of_measurement': '°C'}) hass.states.async_set('test.temp2', 23, attributes={'unit_of_measurement': '°C'}) hass.states.async_set('test.hum1', 88, attributes={'unit_of_measurement': '%'}) return hass.loop.run_until_complete(hass_client()) async def test_spaceapi_get(hass, mock_client): """Test response after start-up Home Assistant.""" resp = await mock_client.get(URL_API_SPACEAPI) assert resp.status == 200 data = await resp.json() assert data['api'] == SPACEAPI_VERSION assert data['space'] == 'Home' assert data['contact']['email'] == '[email protected]' assert data['location']['address'] == 'In your Home' assert data['location']['latitude'] == 32.87336 assert data['location']['longitude'] == -117.22743 assert data['state']['open'] == 'null' assert data['state']['icon']['open'] == \ 'https://home-assistant.io/open.png' assert data['state']['icon']['close'] == \ 'https://home-assistant.io/close.png' async def test_spaceapi_state_get(hass, mock_client): """Test response if the state entity was set.""" hass.states.async_set('test.test_door', True) resp = await mock_client.get(URL_API_SPACEAPI) assert resp.status == 200 data = await resp.json() assert data['state']['open'] == bool(1) async def test_spaceapi_sensors_get(hass, mock_client): """Test the response for the sensors.""" resp = await mock_client.get(URL_API_SPACEAPI) assert resp.status == 200 data = await resp.json() assert data['sensors'] == SENSOR_OUTPUT
30.289474
67
0.580075
3122d364c922e9d4ca267ce7689339686d4df60a
58,287
py
Python
src/azure-cli/azure/cli/command_modules/storage/_validators_azure_stack.py
akashsinghal/azure-cli
8ab2f7604a834de790bdea849b3e83f2466428b9
[ "MIT" ]
2
2020-08-08T11:00:25.000Z
2020-08-08T11:00:30.000Z
src/azure-cli/azure/cli/command_modules/storage/_validators_azure_stack.py
akashsinghal/azure-cli
8ab2f7604a834de790bdea849b3e83f2466428b9
[ "MIT" ]
1
2021-06-02T02:49:48.000Z
2021-06-02T02:49:48.000Z
src/azure-cli/azure/cli/command_modules/storage/_validators_azure_stack.py
akashsinghal/azure-cli
8ab2f7604a834de790bdea849b3e83f2466428b9
[ "MIT" ]
1
2020-07-31T17:22:13.000Z
2020-07-31T17:22:13.000Z
# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # pylint: disable=protected-access import argparse from azure.cli.core.commands.validators import validate_key_value_pairs from azure.cli.core.profiles import ResourceType, get_sdk from azure.cli.command_modules.storage._client_factory import (get_storage_data_service_client, blob_data_service_factory, file_data_service_factory, storage_client_factory) from azure.cli.command_modules.storage.util import glob_files_locally, guess_content_type from azure.cli.command_modules.storage.sdkutil import get_table_data_type from azure.cli.command_modules.storage.url_quote_util import encode_for_url from azure.cli.command_modules.storage.oauth_token_util import TokenUpdater from knack.log import get_logger from knack.util import CLIError storage_account_key_options = {'primary': 'key1', 'secondary': 'key2'} logger = get_logger(__name__) # Utilities # pylint: disable=inconsistent-return-statements,too-many-lines def _query_account_key(cli_ctx, account_name): """Query the storage account key. This is used when the customer doesn't offer account key but name.""" rg, scf = _query_account_rg(cli_ctx, account_name) t_storage_account_keys = get_sdk( cli_ctx, ResourceType.MGMT_STORAGE, 'models.storage_account_keys#StorageAccountKeys') scf.config.enable_http_logger = False logger.debug('Disable HTTP logging to avoid having storage keys in debug logs') if t_storage_account_keys: return scf.storage_accounts.list_keys(rg, account_name).key1 # of type: models.storage_account_list_keys_result#StorageAccountListKeysResult return scf.storage_accounts.list_keys(rg, account_name).keys[0].value # pylint: disable=no-member def _query_account_rg(cli_ctx, account_name): """Query the storage account's resource group, which the mgmt sdk requires.""" scf = storage_client_factory(cli_ctx) acc = next((x for x in scf.storage_accounts.list() if x.name == account_name), None) if acc: from msrestazure.tools import parse_resource_id return parse_resource_id(acc.id)['resource_group'], scf raise ValueError("Storage account '{}' not found.".format(account_name)) def _create_token_credential(cli_ctx): from knack.cli import EVENT_CLI_POST_EXECUTE TokenCredential = get_sdk(cli_ctx, ResourceType.DATA_STORAGE, 'common#TokenCredential') token_credential = TokenCredential() updater = TokenUpdater(token_credential, cli_ctx) def _cancel_timer_event_handler(_, **__): updater.cancel() cli_ctx.register_event(EVENT_CLI_POST_EXECUTE, _cancel_timer_event_handler) return token_credential # region PARAMETER VALIDATORS def parse_storage_account(cmd, namespace): """Parse storage account which can be either account name or account id""" from msrestazure.tools import parse_resource_id, is_valid_resource_id if namespace.account_name and is_valid_resource_id(namespace.account_name): namespace.resource_group_name = parse_resource_id(namespace.account_name)['resource_group'] namespace.account_name = parse_resource_id(namespace.account_name)['name'] elif namespace.account_name and not is_valid_resource_id(namespace.account_name) and \ not namespace.resource_group_name: namespace.resource_group_name = _query_account_rg(cmd.cli_ctx, namespace.account_name)[0] def process_resource_group(cmd, namespace): """Processes the resource group parameter from the account name""" if namespace.account_name and not namespace.resource_group_name: namespace.resource_group_name = _query_account_rg(cmd.cli_ctx, namespace.account_name)[0] def validate_table_payload_format(cmd, namespace): t_table_payload = get_table_data_type(cmd.cli_ctx, 'table', 'TablePayloadFormat') if namespace.accept: formats = { 'none': t_table_payload.JSON_NO_METADATA, 'minimal': t_table_payload.JSON_MINIMAL_METADATA, 'full': t_table_payload.JSON_FULL_METADATA } namespace.accept = formats[namespace.accept.lower()] def validate_bypass(namespace): if namespace.bypass: namespace.bypass = ', '.join(namespace.bypass) if isinstance(namespace.bypass, list) else namespace.bypass def get_config_value(cmd, section, key, default): return cmd.cli_ctx.config.get(section, key, default) def is_storagev2(import_prefix): return import_prefix.startswith('azure.multiapi.storagev2.') def validate_client_parameters(cmd, namespace): """ Retrieves storage connection parameters from environment variables and parses out connection string into account name and key """ n = namespace if hasattr(n, 'auth_mode'): auth_mode = n.auth_mode or get_config_value(cmd, 'storage', 'auth_mode', None) del n.auth_mode if not n.account_name: n.account_name = get_config_value(cmd, 'storage', 'account', None) if auth_mode == 'login': prefix = cmd.command_kwargs['resource_type'].value[0] # is_storagv2() is used to distinguish if the command is in track2 SDK # If yes, we will use get_login_credentials() as token credential if is_storagev2(prefix): from azure.cli.core._profile import Profile profile = Profile(cli_ctx=cmd.cli_ctx) n.token_credential, _, _ = profile.get_login_credentials( resource="https://storage.azure.com", subscription_id=n._subscription) # Otherwise, we will assume it is in track1 and keep previous token updater else: n.token_credential = _create_token_credential(cmd.cli_ctx) if hasattr(n, 'token_credential') and n.token_credential: # give warning if there are account key args being ignored account_key_args = [n.account_key and "--account-key", n.sas_token and "--sas-token", n.connection_string and "--connection-string"] account_key_args = [arg for arg in account_key_args if arg] if account_key_args: logger.warning('In "login" auth mode, the following arguments are ignored: %s', ' ,'.join(account_key_args)) return if not n.connection_string: n.connection_string = get_config_value(cmd, 'storage', 'connection_string', None) # if connection string supplied or in environment variables, extract account key and name if n.connection_string: conn_dict = validate_key_value_pairs(n.connection_string) n.account_name = conn_dict.get('AccountName') n.account_key = conn_dict.get('AccountKey') n.sas_token = conn_dict.get('SharedAccessSignature') # otherwise, simply try to retrieve the remaining variables from environment variables if not n.account_name: n.account_name = get_config_value(cmd, 'storage', 'account', None) if not n.account_key: n.account_key = get_config_value(cmd, 'storage', 'key', None) if not n.sas_token: n.sas_token = get_config_value(cmd, 'storage', 'sas_token', None) # strip the '?' from sas token. the portal and command line are returns sas token in different # forms if n.sas_token: n.sas_token = n.sas_token.lstrip('?') # if account name is specified but no key, attempt to query if n.account_name and not n.account_key and not n.sas_token: logger.warning('There is no credential provided in your command and environment, we will query account key ' 'for your storage account. \nPlease provide --connection-string, --account-key or --sas-token ' 'as credential, or use `--auth-mode login` if you have required RBAC roles in your command. ' 'For more information about RBAC roles in storage, you can see ' 'https://docs.microsoft.com/en-us/azure/storage/common/storage-auth-aad-rbac-cli. \n' 'Setting corresponding environment variable can avoid inputting credential in your command. ' 'Please use --help to get more information.') n.account_key = _query_account_key(cmd.cli_ctx, n.account_name) def validate_encryption_key(cmd, namespace): encryption_key_source = cmd.get_models('EncryptionScopeSource', resource_type=ResourceType.MGMT_STORAGE) if namespace.key_source == encryption_key_source.microsoft_key_vault and \ not namespace.key_uri: raise CLIError("usage error: Please specify --key-uri when using {} as key source." .format(encryption_key_source.microsoft_key_vault)) if namespace.key_source != encryption_key_source.microsoft_key_vault and namespace.key_uri: raise CLIError("usage error: Specify `--key-source={}` and --key-uri to configure key vault properties." .format(encryption_key_source.microsoft_key_vault)) def process_blob_source_uri(cmd, namespace): """ Validate the parameters referenced to a blob source and create the source URI from them. """ from .util import create_short_lived_blob_sas usage_string = \ 'Invalid usage: {}. Supply only one of the following argument sets to specify source:' \ '\n\t --source-uri' \ '\n\tOR --source-container --source-blob --source-snapshot [--source-account-name & sas] ' \ '\n\tOR --source-container --source-blob --source-snapshot [--source-account-name & key] ' ns = vars(namespace) # source as blob container = ns.pop('source_container', None) blob = ns.pop('source_blob', None) snapshot = ns.pop('source_snapshot', None) # source credential clues source_account_name = ns.pop('source_account_name', None) source_account_key = ns.pop('source_account_key', None) sas = ns.pop('source_sas', None) # source in the form of an uri uri = ns.get('copy_source', None) if uri: if any([container, blob, sas, snapshot, source_account_name, source_account_key]): raise ValueError(usage_string.format('Unused parameters are given in addition to the ' 'source URI')) # simplest scenario--no further processing necessary return validate_client_parameters(cmd, namespace) # must run first to resolve storage account # determine if the copy will happen in the same storage account if not source_account_name and source_account_key: raise ValueError(usage_string.format('Source account key is given but account name is not')) if not source_account_name and not source_account_key: # neither source account name or key is given, assume that user intends to copy blob in # the same account source_account_name = ns.get('account_name', None) source_account_key = ns.get('account_key', None) elif source_account_name and not source_account_key: if source_account_name == ns.get('account_name', None): # the source account name is same as the destination account name source_account_key = ns.get('account_key', None) else: # the source account is different from destination account but the key is missing # try to query one. try: source_account_key = _query_account_key(cmd.cli_ctx, source_account_name) except ValueError: raise ValueError('Source storage account {} not found.'.format(source_account_name)) # else: both source account name and key are given by user if not source_account_name: raise ValueError(usage_string.format('Storage account name not found')) if not sas: sas = create_short_lived_blob_sas(cmd, source_account_name, source_account_key, container, blob) query_params = [] if sas: query_params.append(sas) if snapshot: query_params.append('snapshot={}'.format(snapshot)) uri = 'https://{}.blob.{}/{}/{}{}{}'.format(source_account_name, cmd.cli_ctx.cloud.suffixes.storage_endpoint, container, blob, '?' if query_params else '', '&'.join(query_params)) namespace.copy_source = uri def validate_source_uri(cmd, namespace): # pylint: disable=too-many-statements from .util import create_short_lived_blob_sas, create_short_lived_file_sas usage_string = \ 'Invalid usage: {}. Supply only one of the following argument sets to specify source:' \ '\n\t --source-uri [--source-sas]' \ '\n\tOR --source-container --source-blob [--source-account-name & sas] [--source-snapshot]' \ '\n\tOR --source-container --source-blob [--source-account-name & key] [--source-snapshot]' \ '\n\tOR --source-share --source-path' \ '\n\tOR --source-share --source-path [--source-account-name & sas]' \ '\n\tOR --source-share --source-path [--source-account-name & key]' ns = vars(namespace) # source as blob container = ns.pop('source_container', None) blob = ns.pop('source_blob', None) snapshot = ns.pop('source_snapshot', None) # source as file share = ns.pop('source_share', None) path = ns.pop('source_path', None) file_snapshot = ns.pop('file_snapshot', None) # source credential clues source_account_name = ns.pop('source_account_name', None) source_account_key = ns.pop('source_account_key', None) source_sas = ns.pop('source_sas', None) # source in the form of an uri uri = ns.get('copy_source', None) if uri: if any([container, blob, snapshot, share, path, file_snapshot, source_account_name, source_account_key]): raise ValueError(usage_string.format('Unused parameters are given in addition to the ' 'source URI')) if source_sas: source_sas = source_sas.lstrip('?') uri = '{}{}{}'.format(uri, '?', source_sas) namespace.copy_source = uri return # ensure either a file or blob source is specified valid_blob_source = container and blob and not share and not path and not file_snapshot valid_file_source = share and path and not container and not blob and not snapshot if not valid_blob_source and not valid_file_source: raise ValueError(usage_string.format('Neither a valid blob or file source is specified')) if valid_blob_source and valid_file_source: raise ValueError(usage_string.format('Ambiguous parameters, both blob and file sources are ' 'specified')) validate_client_parameters(cmd, namespace) # must run first to resolve storage account if not source_account_name: if source_account_key: raise ValueError(usage_string.format('Source account key is given but account name is not')) # assume that user intends to copy blob in the same account source_account_name = ns.get('account_name', None) # determine if the copy will happen in the same storage account same_account = False if not source_account_key and not source_sas: if source_account_name == ns.get('account_name', None): same_account = True source_account_key = ns.get('account_key', None) source_sas = ns.get('sas_token', None) else: # the source account is different from destination account but the key is missing try to query one. try: source_account_key = _query_account_key(cmd.cli_ctx, source_account_name) except ValueError: raise ValueError('Source storage account {} not found.'.format(source_account_name)) # Both source account name and either key or sas (or both) are now available if not source_sas: # generate a sas token even in the same account when the source and destination are not the same kind. if valid_file_source and (ns.get('container_name', None) or not same_account): import os dir_name, file_name = os.path.split(path) if path else (None, '') source_sas = create_short_lived_file_sas(cmd, source_account_name, source_account_key, share, dir_name, file_name) elif valid_blob_source and (ns.get('share_name', None) or not same_account): source_sas = create_short_lived_blob_sas(cmd, source_account_name, source_account_key, container, blob) query_params = [] if source_sas: query_params.append(source_sas.lstrip('?')) if snapshot: query_params.append('snapshot={}'.format(snapshot)) if file_snapshot: query_params.append('sharesnapshot={}'.format(file_snapshot)) uri = 'https://{0}.{1}.{6}/{2}/{3}{4}{5}'.format( source_account_name, 'blob' if valid_blob_source else 'file', container if valid_blob_source else share, encode_for_url(blob if valid_blob_source else path), '?' if query_params else '', '&'.join(query_params), cmd.cli_ctx.cloud.suffixes.storage_endpoint) namespace.copy_source = uri def validate_blob_type(namespace): if not namespace.blob_type: namespace.blob_type = 'page' if namespace.file_path.endswith('.vhd') else 'block' def validate_storage_data_plane_list(namespace): if namespace.num_results == '*': namespace.num_results = None else: namespace.num_results = int(namespace.num_results) def get_content_setting_validator(settings_class, update, guess_from_file=None): def _class_name(class_type): return class_type.__module__ + "." + class_type.__class__.__name__ def validator(cmd, namespace): t_base_blob_service, t_file_service, t_blob_content_settings, t_file_content_settings = cmd.get_models( 'blob.baseblobservice#BaseBlobService', 'file#FileService', 'blob.models#ContentSettings', 'file.models#ContentSettings') # must run certain validators first for an update if update: validate_client_parameters(cmd, namespace) if update and _class_name(settings_class) == _class_name(t_file_content_settings): get_file_path_validator()(namespace) ns = vars(namespace) clear_content_settings = ns.pop('clear_content_settings', False) # retrieve the existing object properties for an update if update and not clear_content_settings: account = ns.get('account_name') key = ns.get('account_key') cs = ns.get('connection_string') sas = ns.get('sas_token') token_credential = ns.get('token_credential') if _class_name(settings_class) == _class_name(t_blob_content_settings): client = get_storage_data_service_client(cmd.cli_ctx, service=t_base_blob_service, name=account, key=key, connection_string=cs, sas_token=sas, token_credential=token_credential) container = ns.get('container_name') blob = ns.get('blob_name') lease_id = ns.get('lease_id') props = client.get_blob_properties(container, blob, lease_id=lease_id).properties.content_settings elif _class_name(settings_class) == _class_name(t_file_content_settings): client = get_storage_data_service_client(cmd.cli_ctx, t_file_service, account, key, cs, sas) share = ns.get('share_name') directory = ns.get('directory_name') filename = ns.get('file_name') props = client.get_file_properties(share, directory, filename).properties.content_settings # create new properties new_props = settings_class( content_type=ns.pop('content_type', None), content_disposition=ns.pop('content_disposition', None), content_encoding=ns.pop('content_encoding', None), content_language=ns.pop('content_language', None), content_md5=ns.pop('content_md5', None), cache_control=ns.pop('content_cache_control', None) ) # if update, fill in any None values with existing if update: if not clear_content_settings: for attr in ['content_type', 'content_disposition', 'content_encoding', 'content_language', 'content_md5', 'cache_control']: if getattr(new_props, attr) is None: setattr(new_props, attr, getattr(props, attr)) else: if guess_from_file: new_props = guess_content_type(ns[guess_from_file], new_props, settings_class) ns['content_settings'] = new_props return validator def validate_custom_domain(namespace): if namespace.use_subdomain and not namespace.custom_domain: raise ValueError('usage error: --custom-domain DOMAIN [--use-subdomain]') def validate_encryption_services(cmd, namespace): """ Builds up the encryption services object for storage account operations based on the list of services passed in. """ if namespace.encryption_services: t_encryption_services, t_encryption_service = get_sdk(cmd.cli_ctx, ResourceType.MGMT_STORAGE, 'EncryptionServices', 'EncryptionService', mod='models') services = {service: t_encryption_service(enabled=True) for service in namespace.encryption_services} namespace.encryption_services = t_encryption_services(**services) def validate_encryption_source(namespace): if namespace.encryption_key_source == 'Microsoft.Keyvault' and \ not (namespace.encryption_key_name and namespace.encryption_key_vault): raise ValueError('--encryption-key-name and --encryption-key-vault are required ' 'when --encryption-key-source=Microsoft.Keyvault is specified.') if namespace.encryption_key_name or namespace.encryption_key_version is not None or namespace.encryption_key_vault: if namespace.encryption_key_source and namespace.encryption_key_source != 'Microsoft.Keyvault': raise ValueError('--encryption-key-name, --encryption-key-vault, and --encryption-key-version are not ' 'applicable without Microsoft.Keyvault key-source.') def validate_entity(namespace): """ Converts a list of key value pairs into a dictionary. Ensures that required RowKey and PartitionKey are converted to the correct case and included. """ values = dict(x.split('=', 1) for x in namespace.entity) keys = values.keys() for key in list(keys): if key.lower() == 'rowkey': val = values[key] del values[key] values['RowKey'] = val elif key.lower() == 'partitionkey': val = values[key] del values[key] values['PartitionKey'] = val keys = values.keys() missing_keys = 'RowKey ' if 'RowKey' not in keys else '' missing_keys = '{}PartitionKey'.format(missing_keys) \ if 'PartitionKey' not in keys else missing_keys if missing_keys: raise argparse.ArgumentError( None, 'incorrect usage: entity requires: {}'.format(missing_keys)) def cast_val(key, val): """ Attempts to cast numeric values (except RowKey and PartitionKey) to numbers so they can be queried correctly. """ if key in ['PartitionKey', 'RowKey']: return val def try_cast(to_type): try: return to_type(val) except ValueError: return None return try_cast(int) or try_cast(float) or val # ensure numbers are converted from strings so querying will work correctly values = {key: cast_val(key, val) for key, val in values.items()} namespace.entity = values def validate_marker(namespace): """ Converts a list of key value pairs into a dictionary. Ensures that required nextrowkey and nextpartitionkey are included. """ if not namespace.marker: return marker = dict(x.split('=', 1) for x in namespace.marker) expected_keys = {'nextrowkey', 'nextpartitionkey'} for key in list(marker.keys()): new_key = key.lower() if new_key in expected_keys: expected_keys.remove(key.lower()) val = marker[key] del marker[key] marker[new_key] = val if expected_keys: raise argparse.ArgumentError( None, 'incorrect usage: marker requires: {}'.format(' '.join(expected_keys))) namespace.marker = marker def get_file_path_validator(default_file_param=None): """ Creates a namespace validator that splits out 'path' into 'directory_name' and 'file_name'. Allows another path-type parameter to be named which can supply a default filename. """ def validator(namespace): import os if not hasattr(namespace, 'path'): return path = namespace.path dir_name, file_name = os.path.split(path) if path else (None, '') if default_file_param and '.' not in file_name: dir_name = path file_name = os.path.split(getattr(namespace, default_file_param))[1] dir_name = None if dir_name in ('', '.') else dir_name namespace.directory_name = dir_name namespace.file_name = file_name del namespace.path return validator def validate_included_datasets(cmd, namespace): if namespace.include: include = namespace.include if set(include) - set('cmsd'): help_string = '(c)opy-info (m)etadata (s)napshots (d)eleted' raise ValueError('valid values are {} or a combination thereof.'.format(help_string)) t_blob_include = cmd.get_models('blob#Include') namespace.include = t_blob_include('s' in include, 'm' in include, False, 'c' in include, 'd' in include) def validate_key_name(namespace): key_options = {'primary': '1', 'secondary': '2'} if hasattr(namespace, 'key_type') and namespace.key_type: namespace.key_name = namespace.key_type + key_options[namespace.key_name] else: namespace.key_name = storage_account_key_options[namespace.key_name] def validate_metadata(namespace): if namespace.metadata: namespace.metadata = dict(x.split('=', 1) for x in namespace.metadata) def get_permission_help_string(permission_class): allowed_values = [x.lower() for x in dir(permission_class) if not x.startswith('__')] return ' '.join(['({}){}'.format(x[0], x[1:]) for x in allowed_values]) def get_permission_validator(permission_class): allowed_values = [x.lower() for x in dir(permission_class) if not x.startswith('__')] allowed_string = ''.join(x[0] for x in allowed_values) def validator(namespace): if namespace.permission: if set(namespace.permission) - set(allowed_string): help_string = get_permission_help_string(permission_class) raise ValueError( 'valid values are {} or a combination thereof.'.format(help_string)) namespace.permission = permission_class(_str=namespace.permission) return validator def table_permission_validator(cmd, namespace): """ A special case for table because the SDK associates the QUERY permission with 'r' """ t_table_permissions = get_table_data_type(cmd.cli_ctx, 'table', 'TablePermissions') if namespace.permission: if set(namespace.permission) - set('raud'): help_string = '(r)ead/query (a)dd (u)pdate (d)elete' raise ValueError('valid values are {} or a combination thereof.'.format(help_string)) namespace.permission = t_table_permissions(_str=namespace.permission) def validate_container_public_access(cmd, namespace): from .sdkutil import get_container_access_type t_base_blob_svc = cmd.get_models('blob.baseblobservice#BaseBlobService') if namespace.public_access: namespace.public_access = get_container_access_type(cmd.cli_ctx, namespace.public_access.lower()) if hasattr(namespace, 'signed_identifiers'): # must retrieve the existing ACL to simulate a patch operation because these calls # are needlessly conflated ns = vars(namespace) validate_client_parameters(cmd, namespace) account = ns.get('account_name') key = ns.get('account_key') cs = ns.get('connection_string') sas = ns.get('sas_token') client = get_storage_data_service_client(cmd.cli_ctx, t_base_blob_svc, account, key, cs, sas) container = ns.get('container_name') lease_id = ns.get('lease_id') ns['signed_identifiers'] = client.get_container_acl(container, lease_id=lease_id) def validate_fs_public_access(cmd, namespace): from .sdkutil import get_fs_access_type if namespace.public_access: namespace.public_access = get_fs_access_type(cmd.cli_ctx, namespace.public_access.lower()) def validate_select(namespace): if namespace.select: namespace.select = ','.join(namespace.select) # pylint: disable=too-many-statements def get_source_file_or_blob_service_client(cmd, namespace): """ Create the second file service or blob service client for batch copy command, which is used to list the source files or blobs. If both the source account and source URI are omitted, it indicates that user want to copy files or blobs in the same storage account, therefore the destination client will be set None hence the command will use destination client. """ t_file_svc, t_block_blob_svc = cmd.get_models('file#FileService', 'blob.blockblobservice#BlockBlobService') usage_string = 'invalid usage: supply only one of the following argument sets:' + \ '\n\t --source-uri [--source-sas]' + \ '\n\tOR --source-container' + \ '\n\tOR --source-container --source-account-name --source-account-key' + \ '\n\tOR --source-container --source-account-name --source-sas' + \ '\n\tOR --source-share --source-account-name --source-account-key' + \ '\n\tOR --source-share --source-account-name --source-account-sas' ns = vars(namespace) source_account = ns.pop('source_account_name', None) source_key = ns.pop('source_account_key', None) source_uri = ns.pop('source_uri', None) source_sas = ns.get('source_sas', None) source_container = ns.get('source_container', None) source_share = ns.get('source_share', None) if source_uri and source_account: raise ValueError(usage_string) if not source_uri and bool(source_container) == bool(source_share): # must be container or share raise ValueError(usage_string) if (not source_account) and (not source_uri): # Set the source_client to None if neither source_account or source_uri is given. This # indicates the command that the source files share or blob container is in the same storage # account as the destination file share or blob container. # # The command itself should create the source service client since the validator can't # access the destination client through the namespace. # # A few arguments check will be made as well so as not to cause ambiguity. if source_key or source_sas: raise ValueError('invalid usage: --source-account-name is missing; the source account is assumed to be the' ' same as the destination account. Do not provide --source-sas or --source-account-key') ns['source_client'] = None if 'token_credential' not in ns: # not using oauth return # oauth is only possible through destination, must still get source creds source_account, source_key, source_sas = ns['account_name'], ns['account_key'], ns['sas_token'] if source_account: if not (source_key or source_sas): # when neither storage account key or SAS is given, try to fetch the key in the current # subscription source_key = _query_account_key(cmd.cli_ctx, source_account) if source_container: ns['source_client'] = get_storage_data_service_client( cmd.cli_ctx, t_block_blob_svc, name=source_account, key=source_key, sas_token=source_sas) elif source_share: ns['source_client'] = get_storage_data_service_client( cmd.cli_ctx, t_file_svc, name=source_account, key=source_key, sas_token=source_sas) elif source_uri: if source_key or source_container or source_share: raise ValueError(usage_string) from .storage_url_helpers import StorageResourceIdentifier if source_sas: source_uri = '{}{}{}'.format(source_uri, '?', source_sas.lstrip('?')) identifier = StorageResourceIdentifier(cmd.cli_ctx.cloud, source_uri) nor_container_or_share = not identifier.container and not identifier.share if not identifier.is_url(): raise ValueError('incorrect usage: --source-uri expects a URI') if identifier.blob or identifier.directory or identifier.filename or nor_container_or_share: raise ValueError('incorrect usage: --source-uri has to be blob container or file share') if identifier.sas_token: ns['source_sas'] = identifier.sas_token else: source_key = _query_account_key(cmd.cli_ctx, identifier.account_name) if identifier.container: ns['source_container'] = identifier.container if identifier.account_name != ns.get('account_name'): ns['source_client'] = get_storage_data_service_client( cmd.cli_ctx, t_block_blob_svc, name=identifier.account_name, key=source_key, sas_token=identifier.sas_token) elif identifier.share: ns['source_share'] = identifier.share if identifier.account_name != ns.get('account_name'): ns['source_client'] = get_storage_data_service_client( cmd.cli_ctx, t_file_svc, name=identifier.account_name, key=source_key, sas_token=identifier.sas_token) def add_progress_callback(cmd, namespace): def _update_progress(current, total): message = getattr(_update_progress, 'message', 'Alive') reuse = getattr(_update_progress, 'reuse', False) if total: hook.add(message=message, value=current, total_val=total) if total == current and not reuse: hook.end() hook = cmd.cli_ctx.get_progress_controller(det=True) _update_progress.hook = hook if not namespace.no_progress: namespace.progress_callback = _update_progress del namespace.no_progress def process_container_delete_parameters(cmd, namespace): """Process the parameters for storage container delete command""" # check whether to use mgmt or data-plane if namespace.bypass_immutability_policy: # use management-plane namespace.processed_account_name = namespace.account_name namespace.processed_resource_group, namespace.mgmt_client = _query_account_rg( cmd.cli_ctx, namespace.account_name) del namespace.auth_mode else: # use data-plane, like before validate_client_parameters(cmd, namespace) def process_blob_download_batch_parameters(cmd, namespace): """Process the parameters for storage blob download command""" import os # 1. quick check if not os.path.exists(namespace.destination) or not os.path.isdir(namespace.destination): raise ValueError('incorrect usage: destination must be an existing directory') # 2. try to extract account name and container name from source string _process_blob_batch_container_parameters(cmd, namespace) # 3. Call validators add_progress_callback(cmd, namespace) def process_blob_upload_batch_parameters(cmd, namespace): """Process the source and destination of storage blob upload command""" import os # 1. quick check if not os.path.exists(namespace.source) or not os.path.isdir(namespace.source): raise ValueError('incorrect usage: source must be an existing directory') # 2. try to extract account name and container name from destination string _process_blob_batch_container_parameters(cmd, namespace, source=False) # 3. collect the files to be uploaded namespace.source = os.path.realpath(namespace.source) namespace.source_files = [c for c in glob_files_locally(namespace.source, namespace.pattern)] # 4. determine blob type if namespace.blob_type is None: vhd_files = [f for f in namespace.source_files if f[0].endswith('.vhd')] if any(vhd_files) and len(vhd_files) == len(namespace.source_files): # when all the listed files are vhd files use page namespace.blob_type = 'page' elif any(vhd_files): # source files contain vhd files but not all of them raise CLIError("""Fail to guess the required blob type. Type of the files to be uploaded are not consistent. Default blob type for .vhd files is "page", while others are "block". You can solve this problem by either explicitly set the blob type or ensure the pattern matches a correct set of files.""") else: namespace.blob_type = 'block' # 5. call other validators validate_metadata(namespace) t_blob_content_settings = cmd.loader.get_sdk('blob.models#ContentSettings') get_content_setting_validator(t_blob_content_settings, update=False)(cmd, namespace) add_progress_callback(cmd, namespace) def process_blob_delete_batch_parameters(cmd, namespace): _process_blob_batch_container_parameters(cmd, namespace) def _process_blob_batch_container_parameters(cmd, namespace, source=True): """Process the container parameters for storage blob batch commands before populating args from environment.""" if source: container_arg, container_name_arg = 'source', 'source_container_name' else: # destination container_arg, container_name_arg = 'destination', 'destination_container_name' # try to extract account name and container name from source string from .storage_url_helpers import StorageResourceIdentifier container_arg_val = getattr(namespace, container_arg) # either a url or name identifier = StorageResourceIdentifier(cmd.cli_ctx.cloud, container_arg_val) if not identifier.is_url(): setattr(namespace, container_name_arg, container_arg_val) elif identifier.blob: raise ValueError('incorrect usage: {} should be either a container URL or name'.format(container_arg)) else: setattr(namespace, container_name_arg, identifier.container) if namespace.account_name is None: namespace.account_name = identifier.account_name elif namespace.account_name != identifier.account_name: raise ValueError('The given storage account name is not consistent with the ' 'account name in the destination URL') # if no sas-token is given and the container url contains one, use it if not namespace.sas_token and identifier.sas_token: namespace.sas_token = identifier.sas_token # Finally, grab missing storage connection parameters from environment variables validate_client_parameters(cmd, namespace) def process_file_upload_batch_parameters(cmd, namespace): """Process the parameters of storage file batch upload command""" import os # 1. quick check if not os.path.exists(namespace.source): raise ValueError('incorrect usage: source {} does not exist'.format(namespace.source)) if not os.path.isdir(namespace.source): raise ValueError('incorrect usage: source must be a directory') # 2. try to extract account name and container name from destination string from .storage_url_helpers import StorageResourceIdentifier identifier = StorageResourceIdentifier(cmd.cli_ctx.cloud, namespace.destination) if identifier.is_url(): if identifier.filename or identifier.directory: raise ValueError('incorrect usage: destination must be a file share url') namespace.destination = identifier.share if not namespace.account_name: namespace.account_name = identifier.account_name namespace.source = os.path.realpath(namespace.source) def process_file_download_batch_parameters(cmd, namespace): """Process the parameters for storage file batch download command""" import os # 1. quick check if not os.path.exists(namespace.destination) or not os.path.isdir(namespace.destination): raise ValueError('incorrect usage: destination must be an existing directory') # 2. try to extract account name and share name from source string process_file_batch_source_parameters(cmd, namespace) def process_file_batch_source_parameters(cmd, namespace): from .storage_url_helpers import StorageResourceIdentifier identifier = StorageResourceIdentifier(cmd.cli_ctx.cloud, namespace.source) if identifier.is_url(): if identifier.filename or identifier.directory: raise ValueError('incorrect usage: source should be either share URL or name') namespace.source = identifier.share if not namespace.account_name: namespace.account_name = identifier.account_name def process_file_download_namespace(namespace): import os get_file_path_validator()(namespace) dest = namespace.file_path if not dest or os.path.isdir(dest): namespace.file_path = os.path.join(dest, namespace.file_name) \ if dest else namespace.file_name def process_metric_update_namespace(namespace): namespace.hour = namespace.hour == 'true' namespace.minute = namespace.minute == 'true' namespace.api = namespace.api == 'true' if namespace.api else None if namespace.hour is None and namespace.minute is None: raise argparse.ArgumentError( None, 'incorrect usage: must specify --hour and/or --minute') if (namespace.hour or namespace.minute) and namespace.api is None: raise argparse.ArgumentError( None, 'incorrect usage: specify --api when hour or minute metrics are enabled') def validate_subnet(cmd, namespace): from msrestazure.tools import resource_id, is_valid_resource_id from azure.cli.core.commands.client_factory import get_subscription_id subnet = namespace.subnet subnet_is_id = is_valid_resource_id(subnet) vnet = namespace.vnet_name if (subnet_is_id and not vnet) or (not subnet and not vnet): return if subnet and not subnet_is_id and vnet: namespace.subnet = resource_id( subscription=get_subscription_id(cmd.cli_ctx), resource_group=namespace.resource_group_name, namespace='Microsoft.Network', type='virtualNetworks', name=vnet, child_type_1='subnets', child_name_1=subnet) else: raise CLIError('incorrect usage: [--subnet ID | --subnet NAME --vnet-name NAME]') def get_datetime_type(to_string): """ Validates UTC datetime. Examples of accepted forms: 2017-12-31T01:11:59Z,2017-12-31T01:11Z or 2017-12-31T01Z or 2017-12-31 """ from datetime import datetime def datetime_type(string): """ Validates UTC datetime. Examples of accepted forms: 2017-12-31T01:11:59Z,2017-12-31T01:11Z or 2017-12-31T01Z or 2017-12-31 """ accepted_date_formats = ['%Y-%m-%dT%H:%M:%SZ', '%Y-%m-%dT%H:%MZ', '%Y-%m-%dT%HZ', '%Y-%m-%d'] for form in accepted_date_formats: try: if to_string: return datetime.strptime(string, form).strftime(form) return datetime.strptime(string, form) except ValueError: continue raise ValueError("Input '{}' not valid. Valid example: 2000-12-31T12:59:59Z".format(string)) return datetime_type def ipv4_range_type(string): """ Validates an IPv4 address or address range. """ import re ip_format = r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}' if not re.match("^{}$".format(ip_format), string): if not re.match("^{ip_format}-{ip_format}$".format(ip_format=ip_format), string): raise ValueError return string def resource_type_type(loader): """ Returns a function which validates that resource types string contains only a combination of service, container, and object. Their shorthand representations are s, c, and o. """ def impl(string): t_resources = loader.get_models('common.models#ResourceTypes') if set(string) - set("sco"): raise ValueError return t_resources(_str=''.join(set(string))) return impl def services_type(loader): """ Returns a function which validates that services string contains only a combination of blob, queue, table, and file. Their shorthand representations are b, q, t, and f. """ def impl(string): t_services = loader.get_models('common.models#Services') if set(string) - set("bqtf"): raise ValueError return t_services(_str=''.join(set(string))) return impl def get_char_options_validator(types, property_name): def _validator(namespace): service_types = set(getattr(namespace, property_name, list())) if not service_types: raise ValueError('Missing options --{}.'.format(property_name.replace('_', '-'))) if service_types - set(types): raise ValueError( '--{}: only valid values are: {}.'.format(property_name.replace('_', '-'), ', '.join(types))) setattr(namespace, property_name, service_types) return _validator def page_blob_tier_validator(cmd, namespace): if not namespace.tier: return if namespace.blob_type != 'page' and namespace.tier: raise ValueError('Blob tier is only applicable to page blobs on premium storage accounts.') try: namespace.tier = getattr(cmd.get_models('blob.models#PremiumPageBlobTier'), namespace.tier) except AttributeError: from azure.cli.command_modules.storage.sdkutil import get_blob_tier_names raise ValueError('Unknown premium page blob tier name. Choose among {}'.format(', '.join( get_blob_tier_names(cmd.cli_ctx, 'PremiumPageBlobTier')))) def block_blob_tier_validator(cmd, namespace): if not namespace.tier: return if namespace.blob_type != 'block' and namespace.tier: raise ValueError('Blob tier is only applicable to block blobs on standard storage accounts.') try: namespace.tier = getattr(cmd.get_models('blob.models#StandardBlobTier'), namespace.tier) except AttributeError: from azure.cli.command_modules.storage.sdkutil import get_blob_tier_names raise ValueError('Unknown block blob tier name. Choose among {}'.format(', '.join( get_blob_tier_names(cmd.cli_ctx, 'StandardBlobTier')))) def blob_tier_validator(cmd, namespace): if namespace.blob_type == 'page': page_blob_tier_validator(cmd, namespace) elif namespace.blob_type == 'block': block_blob_tier_validator(cmd, namespace) else: raise ValueError('Blob tier is only applicable to block or page blob.') def blob_rehydrate_priority_validator(namespace): if namespace.blob_type == 'page' and namespace.rehydrate_priority: raise ValueError('--rehydrate-priority is only applicable to block blob.') if namespace.tier == 'Archive' and namespace.rehydrate_priority: raise ValueError('--rehydrate-priority is only applicable to rehydrate blob data from the archive tier.') if namespace.rehydrate_priority is None: namespace.rehydrate_priority = 'Standard' def validate_azcopy_upload_destination_url(cmd, namespace): client = blob_data_service_factory(cmd.cli_ctx, { 'account_name': namespace.account_name, 'connection_string': namespace.connection_string}) destination_path = namespace.destination_path if not destination_path: destination_path = '' url = client.make_blob_url(namespace.destination_container, destination_path) namespace.destination = url del namespace.destination_container del namespace.destination_path def validate_azcopy_remove_arguments(cmd, namespace): usage_string = \ 'Invalid usage: {}. Supply only one of the following argument sets to specify source:' \ '\n\t --container-name [--name]' \ '\n\tOR --share-name [--path]' ns = vars(namespace) # source as blob container = ns.pop('container_name', None) blob = ns.pop('blob_name', None) # source as file share = ns.pop('share_name', None) path = ns.pop('path', None) # ensure either a file or blob source is specified valid_blob = container and not share valid_file = share and not container if not valid_blob and not valid_file: raise ValueError(usage_string.format('Neither a valid blob or file source is specified')) if valid_blob and valid_file: raise ValueError(usage_string.format('Ambiguous parameters, both blob and file sources are ' 'specified')) if valid_blob: client = blob_data_service_factory(cmd.cli_ctx, { 'account_name': namespace.account_name}) if not blob: blob = '' url = client.make_blob_url(container, blob) namespace.service = 'blob' namespace.target = url if valid_file: import os client = file_data_service_factory(cmd.cli_ctx, { 'account_name': namespace.account_name, 'account_key': namespace.account_key}) dir_name, file_name = os.path.split(path) if path else (None, '') dir_name = None if dir_name in ('', '.') else dir_name url = client.make_file_url(share, dir_name, file_name) namespace.service = 'file' namespace.target = url def as_user_validator(namespace): if hasattr(namespace, 'token_credential') and not namespace.as_user: raise CLIError('incorrect usage: specify --as-user when --auth-mode login is used to get user delegation key.') if namespace.as_user: if namespace.expiry is None: raise argparse.ArgumentError( None, 'incorrect usage: specify --expiry when as-user is enabled') expiry = get_datetime_type(False)(namespace.expiry) from datetime import datetime, timedelta if expiry > datetime.utcnow() + timedelta(days=7): raise argparse.ArgumentError( None, 'incorrect usage: --expiry should be within 7 days from now') if ((not hasattr(namespace, 'token_credential') or namespace.token_credential is None) and (not hasattr(namespace, 'auth_mode') or namespace.auth_mode != 'login')): raise argparse.ArgumentError( None, "incorrect usage: specify '--auth-mode login' when as-user is enabled") def validator_delete_retention_days(namespace): if namespace.enable_delete_retention is True and namespace.delete_retention_days is None: raise ValueError( "incorrect usage: you have to provide value for '--delete-retention-days' when '--enable-delete-retention' " "is set to true") if namespace.enable_delete_retention is False and namespace.delete_retention_days is not None: raise ValueError( "incorrect usage: '--delete-retention-days' is invalid when '--enable-delete-retention' is set to false") if namespace.enable_delete_retention is None and namespace.delete_retention_days is not None: raise ValueError( "incorrect usage: please specify '--enable-delete-retention true' if you want to set the value for " "'--delete-retention-days'") if namespace.delete_retention_days or namespace.delete_retention_days == 0: if namespace.delete_retention_days < 1: raise ValueError( "incorrect usage: '--delete-retention-days' must be greater than or equal to 1") if namespace.delete_retention_days > 365: raise ValueError( "incorrect usage: '--delete-retention-days' must be less than or equal to 365") def validate_delete_retention_days(namespace): if namespace.enable_delete_retention is True and namespace.delete_retention_days is None: raise ValueError( "incorrect usage: you have to provide value for '--delete-retention-days' when '--enable-delete-retention' " "is set to true") if namespace.enable_delete_retention is False and namespace.delete_retention_days is not None: raise ValueError( "incorrect usage: '--delete-retention-days' is invalid when '--enable-delete-retention' is set to false") # pylint: disable=too-few-public-methods class BlobRangeAddAction(argparse._AppendAction): def __call__(self, parser, namespace, values, option_string=None): if not namespace.blob_ranges: namespace.blob_ranges = [] if isinstance(values, list): values = ' '.join(values) BlobRange = namespace._cmd.get_models('BlobRestoreRange', resource_type=ResourceType.MGMT_STORAGE) try: start_range, end_range = values.split(' ') except (ValueError, TypeError): raise CLIError('usage error: --blob-range VARIABLE OPERATOR VALUE') namespace.blob_ranges.append(BlobRange( start_range=start_range, end_range=end_range )) def validate_private_endpoint_connection_id(cmd, namespace): if namespace.connection_id: from azure.cli.core.util import parse_proxy_resource_id result = parse_proxy_resource_id(namespace.connection_id) namespace.resource_group_name = result['resource_group'] namespace.account_name = result['name'] namespace.private_endpoint_connection_name = result['child_name_1'] if namespace.account_name and not namespace.resource_group_name: namespace.resource_group_name = _query_account_rg(cmd.cli_ctx, namespace.account_name)[0] if not all([namespace.account_name, namespace.resource_group_name, namespace.private_endpoint_connection_name]): raise CLIError('incorrect usage: [--id ID | --name NAME --account-name NAME]') del namespace.connection_id def pop_data_client_auth(ns): del ns.auth_mode del ns.account_key del ns.connection_string del ns.sas_token def validate_client_auth_parameter(cmd, ns): from .sdkutil import get_container_access_type if ns.public_access: ns.public_access = get_container_access_type(cmd.cli_ctx, ns.public_access.lower()) if ns.default_encryption_scope and ns.prevent_encryption_scope_override is not None: # simply try to retrieve the remaining variables from environment variables if not ns.account_name: ns.account_name = get_config_value(cmd, 'storage', 'account', None) if ns.account_name and not ns.resource_group_name: ns.resource_group_name = _query_account_rg(cmd.cli_ctx, account_name=ns.account_name)[0] pop_data_client_auth(ns) elif (ns.default_encryption_scope and ns.prevent_encryption_scope_override is None) or \ (not ns.default_encryption_scope and ns.prevent_encryption_scope_override is not None): raise CLIError("usage error: You need to specify both --default-encryption-scope and " "--prevent-encryption-scope-override to set encryption scope information " "when creating container.") else: validate_client_parameters(cmd, ns) def validate_encryption_scope_client_params(ns): if ns.encryption_scope: # will use track2 client and socket_timeout is unused del ns.socket_timeout def validate_access_control(namespace): if namespace.acl and namespace.permissions: raise CLIError('usage error: invalid when specifying both --acl and --permissions.') def validate_service_type(services, service_type): if service_type == 'table': return 't' in services if service_type == 'blob': return 'b' in services if service_type == 'queue': return 'q' in services def validate_logging_version(namespace): if validate_service_type(namespace.services, 'table') and namespace.version != 1.0: raise CLIError( 'incorrect usage: for table service, the supported version for logging is `1.0`. For more information, ' 'please refer to https://docs.microsoft.com/en-us/rest/api/storageservices/storage-analytics-log-format.')
45.009266
120
0.676686
0502674c44ef059c42049a98f38ffc9d89f1c684
5,093
py
Python
end2end_detector.py
penolove/keras-yolo3
4b0b9a3c998b35be73a3509baf275ff862a086de
[ "MIT" ]
null
null
null
end2end_detector.py
penolove/keras-yolo3
4b0b9a3c998b35be73a3509baf275ff862a086de
[ "MIT" ]
null
null
null
end2end_detector.py
penolove/keras-yolo3
4b0b9a3c998b35be73a3509baf275ff862a086de
[ "MIT" ]
null
null
null
import argparse import os import arrow import cv2 import time import PIL from eyewitness.config import (IN_MEMORY, BBOX, RAW_IMAGE_PATH) from eyewitness.image_id import ImageId from eyewitness.image_utils import (ImageProducer, swap_channel_rgb_bgr, ImageHandler, Image) from eyewitness.result_handler.db_writer import BboxPeeweeDbWriter from peewee import SqliteDatabase from naive_detector import YoloV3DetectorWrapper from yolo import YOLO from line_detection_result_handler import LineAnnotationSender # class YOLO defines the default value, so suppress any default here parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS) ''' Command line options ''' parser.add_argument( '--model', type=str, help='path to model weight file, default: ' + YOLO.get_defaults("model_path") ) parser.add_argument( '--anchors', type=str, help='path to anchor definitions, default: ' + YOLO.get_defaults("anchors_path") ) parser.add_argument( '--classes', type=str, help='path to class definitions, default: ' + YOLO.get_defaults("classes_path") ) parser.add_argument( '--gpu_num', type=int, help='Number of GPU to use, default: ' + str(YOLO.get_defaults("gpu_num")) ) parser.add_argument( '--db_path', type=str, default='::memory::', help='the path used to store detection result records' ) parser.add_argument( '--interval_s', type=int, default=3, help='the interval of image generation' ) parser.add_argument( '--raw_image_folder', type=str, default=None, help='store raw image to folder if given' ) class InMemoryImageProducer(ImageProducer): def __init__(self, video_path, interval_s): self.vid = cv2.VideoCapture(video_path) self.interval_s = interval_s if not self.vid.isOpened(): raise IOError("Couldn't open webcam or video") def produce_method(self): return IN_MEMORY def produce_image(self): while True: # clean buffer hack: for Linux V4L capture backend with a internal fifo for iter_ in range(5): self.vid.grab() _, frame = self.vid.read() yield PIL.Image.fromarray(swap_channel_rgb_bgr(frame)) time.sleep(self.interval_s) def image_url_handler(drawn_image_path): """if site_domain not set in env, will pass a pickchu image""" site_domain = os.environ.get('site_domain') if site_domain is None: return 'https://upload.wikimedia.org/wikipedia/en/a/a6/Pok%C3%A9mon_Pikachu_art.png' else: return '%s/%s' % (site_domain, drawn_image_path) def line_detection_result_filter(detection_result): """ used to check if sent notification or not """ return any(i.label == 'person' for i in detection_result.detected_objects) if __name__ == '__main__': args = parser.parse_args() raw_image_folder = args.raw_image_folder # image producer from webcam image_producer = InMemoryImageProducer(0, interval_s=args.interval_s) # object detector object_detector = YoloV3DetectorWrapper(args) # detection result handlers result_handlers = [] # update image_info drawn_image_path, insert detection result database = SqliteDatabase(args.db_path) bbox_sqlite_handler = BboxPeeweeDbWriter(database) result_handlers.append(bbox_sqlite_handler) # setup your line channel token and audience channel_access_token = os.environ.get('LINE_CHANNEL_ACCESS_TOKEN') if channel_access_token: line_annotation_sender = LineAnnotationSender( channel_access_token=channel_access_token, image_url_handler=image_url_handler, detection_result_filter=line_detection_result_filter, detection_method=BBOX, update_audience_period=10, database=database) result_handlers.append(line_annotation_sender) for image in image_producer.produce_image(): image_id = ImageId(channel='demo', timestamp=arrow.now().timestamp, file_format='jpg') # store the raw image or not if raw_image_folder: raw_image_path = "%s/%s_%s.%s" % ( raw_image_folder, image_id.channel, image_id.timestamp, image_id.file_format) ImageHandler.save(image, raw_image_path) else: raw_image_path = None image_obj = Image(image_id, pil_image_obj=image) bbox_sqlite_handler.register_image(image_id, {RAW_IMAGE_PATH: raw_image_path}) detection_result = object_detector.detect(image_obj) if len(detection_result.detected_objects) > 0: # draw and save image, update detection result drawn_image_path = "detected_image/%s_%s.%s" % ( image_id.channel, image_id.timestamp, image_id.file_format) ImageHandler.draw_bbox(image, detection_result.detected_objects) ImageHandler.save(image, drawn_image_path) detection_result.image_dict['drawn_image_path'] = drawn_image_path for result_handler in result_handlers: result_handler.handle(detection_result)
34.646259
94
0.709798
8c60a1cc690a0a300ae5ee018dfa35dd018b5f0a
297
py
Python
nj_resoldhouse/pipelines.py
TedMore/nj_resoldhouse
5851135df23b5d09e7162098c724195f1d105613
[ "MIT" ]
null
null
null
nj_resoldhouse/pipelines.py
TedMore/nj_resoldhouse
5851135df23b5d09e7162098c724195f1d105613
[ "MIT" ]
null
null
null
nj_resoldhouse/pipelines.py
TedMore/nj_resoldhouse
5851135df23b5d09e7162098c724195f1d105613
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html class NjResoldhousePipeline(object): def process_item(self, item, spider): return item
24.75
65
0.707071
945a765bb6b0aaaeb864b456df323a834a890d96
17,009
py
Python
readthedocs/rtd_tests/tests/test_sync_versions.py
kennethlarsen/readthedocs.org
735d630d83f79ae24772d10e66fd35b8f5675a30
[ "MIT" ]
2
2018-01-14T14:04:00.000Z
2021-02-07T19:25:45.000Z
readthedocs/rtd_tests/tests/test_sync_versions.py
kennethlarsen/readthedocs.org
735d630d83f79ae24772d10e66fd35b8f5675a30
[ "MIT" ]
4
2021-03-31T20:17:21.000Z
2021-12-13T20:49:19.000Z
readthedocs/rtd_tests/tests/test_sync_versions.py
kennethlarsen/readthedocs.org
735d630d83f79ae24772d10e66fd35b8f5675a30
[ "MIT" ]
1
2021-01-28T19:18:28.000Z
2021-01-28T19:18:28.000Z
# -*- coding: utf-8 -*- from __future__ import ( absolute_import, division, print_function, unicode_literals) import json from django.test import TestCase from readthedocs.builds.constants import BRANCH, STABLE, TAG from readthedocs.builds.models import Version from readthedocs.projects.models import Project class TestSyncVersions(TestCase): fixtures = ['eric', 'test_data'] def setUp(self): self.client.login(username='eric', password='test') self.pip = Project.objects.get(slug='pip') Version.objects.create( project=self.pip, identifier='origin/master', verbose_name='master', active=True, machine=True, type=BRANCH, ) Version.objects.create( project=self.pip, identifier='to_delete', verbose_name='to_delete', active=False, type=TAG, ) def test_proper_url_no_slash(self): version_post_data = { 'branches': [ { 'identifier': 'origin/master', 'verbose_name': 'master', }, { 'identifier': 'origin/to_add', 'verbose_name': 'to_add', }, ], } r = self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) json_data = json.loads(r.content) self.assertEqual(json_data['deleted_versions'], ['to_delete']) self.assertEqual(json_data['added_versions'], ['to_add']) def test_new_tag_update_active(self): Version.objects.create( project=self.pip, identifier='0.8.3', verbose_name='0.8.3', active=True, ) version_post_data = { 'branches': [ { 'identifier': 'origin/master', 'verbose_name': 'master', }, { 'identifier': 'origin/to_add', 'verbose_name': 'to_add', }, ], 'tags': [ { 'identifier': '0.9', 'verbose_name': '0.9', }, { 'identifier': '0.8.3', 'verbose_name': '0.8.3', }, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_9 = Version.objects.get(slug='0.9') self.assertTrue(version_9.active) # Version 0.9 becomes the stable version self.assertEqual( version_9.identifier, self.pip.get_stable_version().identifier, ) def test_new_tag_update_inactive(self): Version.objects.create( project=self.pip, identifier='0.8.3', verbose_name='0.8.3', active=False, ) version_post_data = { 'branches': [ { 'identifier': 'origin/master', 'verbose_name': 'master', }, { 'identifier': 'origin/to_add', 'verbose_name': 'to_add', }, ], 'tags': [ { 'identifier': '0.9', 'verbose_name': '0.9', }, { 'identifier': '0.8.3', 'verbose_name': '0.8.3', }, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) # Version 0.9 becomes the stable version and active version_9 = Version.objects.get(slug='0.9') self.assertEqual( version_9.identifier, self.pip.get_stable_version().identifier, ) self.assertTrue(version_9.active) # Version 0.8.3 is still inactive version_8 = Version.objects.get(slug='0.8.3') self.assertFalse(version_8.active) class TestStableVersion(TestCase): fixtures = ['eric', 'test_data'] def setUp(self): self.client.login(username='eric', password='test') self.pip = Project.objects.get(slug='pip') def test_stable_versions(self): version_post_data = { 'branches': [ { 'identifier': 'origin/master', 'verbose_name': 'master', }, { 'identifier': 'origin/to_add', 'verbose_name': 'to_add', }, ], 'tags': [ { 'identifier': '0.9', 'verbose_name': '0.9', }, { 'identifier': '0.8', 'verbose_name': '0.8', }, ], } self.assertRaises( Version.DoesNotExist, Version.objects.get, slug=STABLE, ) self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '0.9') def test_pre_release_are_not_stable(self): version_post_data = { 'branches': [], 'tags': [ {'identifier': '1.0a1', 'verbose_name': '1.0a1'}, {'identifier': '0.9', 'verbose_name': '0.9'}, {'identifier': '0.9b1', 'verbose_name': '0.9b1'}, {'identifier': '0.8', 'verbose_name': '0.8'}, {'identifier': '0.8rc2', 'verbose_name': '0.8rc2'}, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '0.9') def test_post_releases_are_stable(self): version_post_data = { 'branches': [], 'tags': [ {'identifier': '1.0', 'verbose_name': '1.0'}, {'identifier': '1.0.post1', 'verbose_name': '1.0.post1'}, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '1.0.post1') def test_invalid_version_numbers_are_not_stable(self): self.pip.versions.all().delete() version_post_data = { 'branches': [], 'tags': [ { 'identifier': 'this.is.invalid', 'verbose_name': 'this.is.invalid' }, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) self.assertFalse(Version.objects.filter(slug=STABLE).exists()) version_post_data = { 'branches': [], 'tags': [ { 'identifier': '1.0', 'verbose_name': '1.0', }, { 'identifier': 'this.is.invalid', 'verbose_name': 'this.is.invalid' }, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '1.0') def test_update_stable_version(self): version_post_data = { 'branches': [ { 'identifier': 'origin/master', 'verbose_name': 'master', }, ], 'tags': [ { 'identifier': '0.9', 'verbose_name': '0.9', }, { 'identifier': '0.8', 'verbose_name': '0.8', }, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '0.9') version_post_data = { 'tags': [ { 'identifier': '1.0.0', 'verbose_name': '1.0.0', }, ] } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '1.0.0') version_post_data = { 'tags': [ { 'identifier': '0.7', 'verbose_name': '0.7', }, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '1.0.0') def test_update_inactive_stable_version(self): version_post_data = { 'branches': [ { 'identifier': 'origin/master', 'verbose_name': 'master', }, ], 'tags': [ { 'identifier': '0.9', 'verbose_name': '0.9', }, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertEqual(version_stable.identifier, '0.9') version_stable.active = False version_stable.save() version_post_data['tags'].append({ 'identifier': '1.0.0', 'verbose_name': '1.0.0', }) self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertFalse(version_stable.active) self.assertEqual(version_stable.identifier, '0.9') def test_stable_version_tags_over_branches(self): version_post_data = { 'branches': [ # 2.0 development {'identifier': 'origin/2.0', 'verbose_name': '2.0'}, {'identifier': 'origin/0.9.1rc1', 'verbose_name': '0.9.1rc1'}, ], 'tags': [ {'identifier': '1.0rc1', 'verbose_name': '1.0rc1'}, {'identifier': '0.9', 'verbose_name': '0.9'}, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) # If there is a branch with a higher version, tags takes preferences # over the branches to select the stable version version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '0.9') version_post_data['tags'].append({ 'identifier': '1.0', 'verbose_name': '1.0', }) self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '1.0') def test_stable_version_same_id_tag_branch(self): version_post_data = { 'branches': [ # old 1.0 development branch {'identifier': 'origin/1.0', 'verbose_name': '1.0'}, ], 'tags': [ # tagged 1.0 final version {'identifier': '1.0', 'verbose_name': '1.0'}, {'identifier': '0.9', 'verbose_name': '0.9'}, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) version_stable = Version.objects.get(slug=STABLE) self.assertTrue(version_stable.active) self.assertEqual(version_stable.identifier, '1.0') self.assertEqual(version_stable.type, 'tag') def test_unicode(self): version_post_data = { 'branches': [], 'tags': [ {'identifier': 'foo-£', 'verbose_name': 'foo-£'}, ], } resp = self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) self.assertEqual(resp.status_code, 200) def test_user_defined_stable_version_with_tags(self): Version.objects.create( project=self.pip, identifier='0.8.3', verbose_name='0.8.3', active=True, ) # A pre-existing active stable branch that was machine created Version.objects.create( project=self.pip, identifier='foo', type='branch', verbose_name='stable', active=True, machine=True, ) version_post_data = { 'branches': [ { 'identifier': 'origin/master', 'verbose_name': 'master', }, # A new user-defined stable branch { 'identifier': 'origin/stable', 'verbose_name': 'stable', }, ], 'tags': [ { 'identifier': '0.9', 'verbose_name': '0.9', }, { 'identifier': '0.8.3', 'verbose_name': '0.8.3', }, ], } self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) # Didn't update to newest tag version_9 = Version.objects.get(slug='0.9') self.assertFalse(version_9.active) # Did update to user-defined stable version version_stable = Version.objects.get(slug='stable') self.assertFalse(version_stable.machine) self.assertTrue(version_stable.active) self.assertEqual('origin/stable', self.pip.get_stable_version().identifier) # Check that posting again doesn't change anything from current state. self.client.post( '/api/v2/project/{}/sync_versions/'.format(self.pip.pk), data=json.dumps(version_post_data), content_type='application/json', ) self.assertEqual('origin/stable', self.pip.get_stable_version().identifier)
31.556586
83
0.491857
fd338e1b3f2941410376544f9ebe22dd3b60b564
1,986
py
Python
script.mrknow.urlresolver/lib/urlresolver9/plugins/sharerepo.py
mrknow/filmkodi
0162cde9ae25ddbf4a69330948714833ff2f78c9
[ "Apache-2.0" ]
105
2015-11-28T00:03:11.000Z
2021-05-05T20:47:42.000Z
script.mrknow.urlresolver/lib/urlresolver9/plugins/sharerepo.py
rrosajp/filmkodi
0162cde9ae25ddbf4a69330948714833ff2f78c9
[ "Apache-2.0" ]
918
2015-11-28T14:12:40.000Z
2022-03-23T20:24:49.000Z
script.mrknow.urlresolver/lib/urlresolver9/plugins/sharerepo.py
rrosajp/filmkodi
0162cde9ae25ddbf4a69330948714833ff2f78c9
[ "Apache-2.0" ]
111
2015-12-01T14:06:10.000Z
2020-08-01T10:44:39.000Z
''' Sharerepo urlresolver plugin Copyright (C) 2013 Vinnydude 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/>. ''' import re import urllib import urllib2 from urlresolver9 import common from urlresolver9.resolver import UrlResolver, ResolverError class SharerepoResolver(UrlResolver): name = "sharerepo" domains = ["sharerepo.com"] pattern = '(?://|\.)(sharerepo\.com)(?:/f)?/([0-9a-zA-Z]+)' def __init__(self): self.net = common.Net() def get_media_url(self, host, media_id): web_url = self.get_url(host, media_id) headers = { 'User-Agent': common.IE_USER_AGENT, 'Referer': web_url } try: html = self.net.http_GET(web_url, headers=headers).content except urllib2.HTTPError as e: if e.code == 404: web_url = 'http://sharerepo.com/%s' % media_id html = self.net.http_GET(web_url, headers=headers).content else: raise link = re.search("file\s*:\s*'([^']+)", html) if link: common.log_utils.log_debug('ShareRepo Link Found: %s' % link.group(1)) return link.group(1) + '|' + urllib.urlencode({'User-Agent': common.IE_USER_AGENT}) else: raise ResolverError('Unable to resolve ShareRepo Link') def get_url(self, host, media_id): return 'http://sharerepo.com/f/%s' % media_id
34.241379
95
0.656596
ac743b6e8a8b04df3a5ace42ec013316e3709b47
102
py
Python
core/miscellaneous.py
0alpha/magma
d302029b1f36ba1fdae6c776a47405ceb72e9817
[ "MIT" ]
10
2018-03-18T13:00:44.000Z
2021-07-10T09:22:50.000Z
core/miscellaneous.py
0alpha/magma
d302029b1f36ba1fdae6c776a47405ceb72e9817
[ "MIT" ]
1
2018-06-05T07:32:50.000Z
2018-06-05T07:32:50.000Z
core/miscellaneous.py
0alpha/magma
d302029b1f36ba1fdae6c776a47405ceb72e9817
[ "MIT" ]
5
2018-06-05T07:12:03.000Z
2021-12-09T19:08:03.000Z
import time def format_time(millis): return time.strftime('%H:%M:%S', time.gmtime(millis/1000))
17
62
0.696078
27184996077a27b142d68df24e0284e55439a7bf
5,838
py
Python
homeassistant/config/custom_components/home_connect_alt/switch.py
yuvalabou/homeassistant
e25885db33d2144455928d07d7e9b044278ba291
[ "Unlicense" ]
5
2022-02-17T09:22:24.000Z
2022-03-15T20:14:50.000Z
homeassistant/config/custom_components/home_connect_alt/switch.py
yuvalabou/homeassistant
e25885db33d2144455928d07d7e9b044278ba291
[ "Unlicense" ]
11
2022-02-11T06:56:55.000Z
2022-03-20T15:53:43.000Z
homeassistant/config/custom_components/home_connect_alt/switch.py
yuvalabou/homeassistant
e25885db33d2144455928d07d7e9b044278ba291
[ "Unlicense" ]
5
2022-02-13T11:15:58.000Z
2022-03-05T19:07:57.000Z
""" Implement the Switch entities of this implementation """ from __future__ import annotations from typing import Any from home_connect_async import Appliance, HomeConnect, HomeConnectError, Events from homeassistant.components.switch import SwitchEntity from homeassistant.core import HomeAssistant from homeassistant.exceptions import HomeAssistantError from homeassistant.helpers.entity_platform import AddEntitiesCallback from homeassistant.helpers.typing import ConfigType from .common import InteractiveEntityBase, EntityManager from .const import DOMAIN, SPECIAL_ENTITIES async def async_setup_entry(hass:HomeAssistant , config_entry:ConfigType, async_add_entities:AddEntitiesCallback) -> None: """Add sensors for passed config_entry in HA.""" homeconnect:HomeConnect = hass.data[DOMAIN]['homeconnect'] entity_manager = EntityManager(async_add_entities) def add_appliance(appliance:Appliance) -> None: if appliance.available_programs: for program in appliance.available_programs.values(): if program.options: for option in program.options.values(): if option.key not in SPECIAL_ENTITIES['ignore'] and (option.type == "Boolean" or isinstance(option.value, bool)): device = OptionSwitch(appliance, option.key) entity_manager.add(device) for setting in appliance.settings.values(): if setting.key not in SPECIAL_ENTITIES['ignore'] and (setting.type == "Boolean" or isinstance(setting.value, bool)): device = SettingsSwitch(appliance, setting.key) entity_manager.add(device) entity_manager.register() def remove_appliance(appliance:Appliance) -> None: entity_manager.remove_appliance(appliance) homeconnect.register_callback(add_appliance, [Events.PAIRED, Events.PROGRAM_SELECTED]) homeconnect.register_callback(remove_appliance, Events.DEPAIRED) for appliance in homeconnect.appliances.values(): add_appliance(appliance) class OptionSwitch(InteractiveEntityBase, SwitchEntity): """ Switch for binary options """ @property def device_class(self) -> str: return f"{DOMAIN}__options" @property def name_ext(self) -> str|None: if self._appliance.available_programs: for program in self._appliance.available_programs.values(): if program.options and self._key in program.options and program.options[self._key].name: return program.options[self._key].name return None @property def icon(self) -> str: return self._conf.get('icon', 'mdi:office-building-cog') @property def available(self) -> bool: return self.program_option_available @property def is_on(self) -> bool: """Return True if entity is on.""" if self.program_option_available: return self._appliance.selected_program.options[self._key].value return None async def async_turn_on(self, **kwargs: Any) -> None: """Turn the entity on.""" try: await self._appliance.async_set_option(self._key, True) except HomeConnectError as ex: if ex.error_description: raise HomeAssistantError(f"Failed to set the option: {ex.error_description} ({ex.code})") else: raise HomeAssistantError(f"Failed to set the option: ({ex.code})") async def async_turn_off(self, **kwargs: Any) -> None: """Turn the entity off.""" try: await self._appliance.async_set_option(self._key, False) except HomeConnectError as ex: if ex.error_description: raise HomeAssistantError(f"Failed to set the option: {ex.error_description} ({ex.code})") else: raise HomeAssistantError(f"Failed to set the option: ({ex.code})") async def async_on_update(self, appliance:Appliance, key:str, value) -> None: self.async_write_ha_state() class SettingsSwitch(InteractiveEntityBase, SwitchEntity): """ Switch for binary settings """ @property def device_class(self) -> str: return f"{DOMAIN}__settings" @property def name_ext(self) -> str|None: if self._key in self._appliance.settings and self._appliance.settings[self._key].name: return self._appliance.settings[self._key].name return None @property def icon(self) -> str: return self._conf.get('icon', 'mdi:tune') @property def available(self) -> bool: return self._key in self._appliance.settings \ and super().available \ and ( "BSH.Common.Status.RemoteControlActive" not in self._appliance.status or self._appliance.status["BSH.Common.Status.RemoteControlActive"] ) @property def is_on(self) -> bool: """Return True if entity is on.""" if self._key in self._appliance.settings: return self._appliance.settings[self._key].value return None async def async_turn_on(self, **kwargs: Any) -> None: try: await self._appliance.async_apply_setting(self._key, True) except HomeConnectError as ex: if ex.error_description: raise HomeAssistantError(f"Failed to apply the setting: {ex.error_description} ({ex.code})") else: raise HomeAssistantError(f"Failed to apply the setting: ({ex.code})") async def async_turn_off(self, **kwargs: Any) -> None: """Turn the entity off.""" await self._appliance.async_apply_setting(self._key, False) async def async_on_update(self, appliance:Appliance, key:str, value) -> None: self.async_write_ha_state()
38.662252
137
0.667181
5a6ab35298c09adaf625595ee3b2ef77f9b92935
661
py
Python
goticket/users/tests/test_drf_urls.py
pmburu/GoTicketV2
97ca68a9ca5e1c5793c03c6983c5b343f59dc4d2
[ "MIT" ]
null
null
null
goticket/users/tests/test_drf_urls.py
pmburu/GoTicketV2
97ca68a9ca5e1c5793c03c6983c5b343f59dc4d2
[ "MIT" ]
7
2022-02-14T23:32:37.000Z
2022-03-31T23:29:05.000Z
goticket/users/tests/test_drf_urls.py
pmburu/GoTicketV2
97ca68a9ca5e1c5793c03c6983c5b343f59dc4d2
[ "MIT" ]
null
null
null
import pytest from django.urls import resolve, reverse from goticket.users.models import User pytestmark = pytest.mark.django_db def test_user_detail(user: User): assert ( reverse("api:user-detail", kwargs={"username": user.username}) == f"/api/users/{user.username}/" ) assert resolve(f"/api/users/{user.username}/").view_name == "api:user-detail" def test_user_list(): assert reverse("api:user-list") == "/api/users/" assert resolve("/api/users/").view_name == "api:user-list" def test_user_me(): assert reverse("api:user-me") == "/api/users/me/" assert resolve("/api/users/me/").view_name == "api:user-me"
26.44
81
0.668684
e75b2790ee39809f0af144c1ba2b986396be6783
1,810
py
Python
projects/faces/pcn/test.py
Bingwen-Hu/hackaway
69727d76fd652390d9660e9ea4354ba5cc76dd5c
[ "BSD-2-Clause" ]
null
null
null
projects/faces/pcn/test.py
Bingwen-Hu/hackaway
69727d76fd652390d9660e9ea4354ba5cc76dd5c
[ "BSD-2-Clause" ]
null
null
null
projects/faces/pcn/test.py
Bingwen-Hu/hackaway
69727d76fd652390d9660e9ea4354ba5cc76dd5c
[ "BSD-2-Clause" ]
null
null
null
from pcn import * import unittest class TestPCN(unittest.TestCase): def test_smooth_angel(self): a = 120 b = 60 output = smooth_angle(a, b) self.assertEqual(output, 90) def test_iou(self): w1 = Window2(100, 20, 40, 60, 80.5, 0.5, 1) w2 = Window2(90, 22, 38, 50, 76, 0.6, 2) iou = IoU(w1, w2) self.assertAlmostEqual(0.482759, iou, delta=0.001) def test_nms(self): w1 = Window2(100, 20, 40, 60, 80.5, 0.5, 1) w2 = Window2(90, 22, 38, 50, 76, 0.6, 2) w3 = Window2(90, 21, 40, 50, 76, 0.6, 3) w4 = Window2(85, 22, 38, 60, 76, 0.8, 4) winlist = [w1, w2, w3, w4] winlist = NMS(winlist, True, 0.8) expect = [4, 3, 1] self.assertEqual(expect, [w.conf for w in winlist]) winlist = NMS(winlist, False, 0.3) expect = [4] self.assertEqual(expect, [w.conf for w in winlist]) def test_deleteFP(self): w1 = Window2(100, 20, 40, 60, 80.5, 0.5, 1) w2 = Window2(90, 22, 38, 50, 76, 0.6, 2) w3 = Window2(90, 21, 40, 50, 76, 0.6, 3) w4 = Window2(85, 22, 38, 60, 76, 0.8, 4) winlist = [w1, w2, w3, w4] winlist = deleteFP(winlist) expect = [4, 3, 2, 1] self.assertEqual(expect, [w.conf for w in winlist]) def test_smooth_windows(self): w1 = Window2(100, 20, 40, 60, 80.5, 0.5, 1) w2 = Window2(90, 22, 38, 50, 75, 0.6, 2) w3 = Window2(90, 21, 40, 50, 24, 0.6, 3) w4 = Window2(85, 22, 38, 60, 76, 0.8, 4) winlist = [w1, w3, w2, w4] winlist = smooth_window(winlist) for win in winlist: print(win.x, win.y, win.w, win.h, win.angle, win.conf) self.assertTrue(True) if __name__ == '__main__': unittest.main()
34.807692
66
0.527072
88deadcd31da26e8fa46c82f8684f5fda405572b
9,215
py
Python
autokeras/utils/io_utils.py
lc0/autokeras
413508a5f6aaa38ee7aba719aadb057c0b029591
[ "Apache-2.0" ]
3,979
2019-04-02T02:01:52.000Z
2022-03-31T16:53:14.000Z
autokeras/utils/io_utils.py
lc0/autokeras
413508a5f6aaa38ee7aba719aadb057c0b029591
[ "Apache-2.0" ]
939
2019-04-02T18:13:53.000Z
2022-03-31T16:25:08.000Z
autokeras/utils/io_utils.py
lc0/autokeras
413508a5f6aaa38ee7aba719aadb057c0b029591
[ "Apache-2.0" ]
826
2019-04-02T00:53:31.000Z
2022-03-31T10:11:02.000Z
# Copyright 2020 The AutoKeras Authors. # # 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. import json from typing import Optional from typing import Tuple import numpy as np import tensorflow as tf from tensorflow.python.keras.preprocessing import dataset_utils WHITELIST_FORMATS = (".bmp", ".gif", ".jpeg", ".jpg", ".png") def save_json(path, obj): obj = json.dumps(obj) with tf.io.gfile.GFile(path, "w") as f: f.write(obj) def load_json(path): with tf.io.gfile.GFile(path, "r") as f: obj = f.read() return json.loads(obj) def get_training_or_validation_split(samples, labels, validation_split, subset): """Potentially restict samples & labels to a training or validation split. # Arguments samples: List of elements. labels: List of corresponding labels. validation_split: Float, fraction of data to reserve for validation. subset: Subset of the data to return. Either "training", "validation", or None. If None, we return all of the data. # Returns tuple (samples, labels), potentially restricted to the specified subset. """ if not validation_split: return samples, labels num_val_samples = int(validation_split * len(samples)) if subset == "training": print("Using %d files for training." % (len(samples) - num_val_samples,)) samples = samples[:-num_val_samples] labels = labels[:-num_val_samples] elif subset == "validation": print("Using %d files for validation." % (num_val_samples,)) samples = samples[-num_val_samples:] labels = labels[-num_val_samples:] else: raise ValueError( '`subset` must be either "training" ' 'or "validation", received: %s' % (subset,) ) return samples, labels def text_dataset_from_directory( directory: str, batch_size: int = 32, max_length: Optional[int] = None, shuffle: bool = True, seed: Optional[int] = None, validation_split: Optional[float] = None, subset: Optional[str] = None, ) -> tf.data.Dataset: """Generates a `tf.data.Dataset` from text files in a directory. If your directory structure is: ``` main_directory/ ...class_a/ ......a_text_1.txt ......a_text_2.txt ...class_b/ ......b_text_1.txt ......b_text_2.txt ``` Then calling `text_dataset_from_directory(main_directory)` will return a `tf.data.Dataset` that yields batches of texts from the subdirectories `class_a` and `class_b`, together with labels 'class_a' and 'class_b'. Only `.txt` files are supported at this time. # Arguments directory: Directory where the data is located. If `labels` is "inferred", it should contain subdirectories, each containing text files for a class. Otherwise, the directory structure is ignored. batch_size: Size of the batches of data. Defaults to 32. max_length: Maximum size of a text string. Texts longer than this will be truncated to `max_length`. shuffle: Whether to shuffle the data. Default: True. If set to False, sorts the data in alphanumeric order. seed: Optional random seed for shuffling and transformations. validation_split: Optional float between 0 and 1, fraction of data to reserve for validation. subset: One of "training" or "validation". Only used if `validation_split` is set. # Returns A `tf.data.Dataset` object, which yields a tuple `(texts, labels)`, where both has shape `(batch_size,)` and type of tf.string. """ if seed is None: seed = np.random.randint(1e6) file_paths, labels, class_names = dataset_utils.index_directory( directory, "inferred", formats=(".txt",), shuffle=shuffle, seed=seed ) file_paths, labels = get_training_or_validation_split( file_paths, labels, validation_split, subset ) strings = tf.data.Dataset.from_tensor_slices(file_paths) strings = strings.map(tf.io.read_file) if max_length is not None: strings = strings.map(lambda x: tf.strings.substr(x, 0, max_length)) labels = np.array(class_names)[np.array(labels)] labels = tf.data.Dataset.from_tensor_slices(labels) dataset = tf.data.Dataset.zip((strings, labels)) dataset = dataset.batch(batch_size) return dataset def image_dataset_from_directory( directory: str, batch_size: int = 32, color_mode: str = "rgb", image_size: Tuple[int, int] = (256, 256), interpolation: str = "bilinear", shuffle: bool = True, seed: Optional[int] = None, validation_split: Optional[float] = None, subset: Optional[str] = None, ) -> tf.data.Dataset: """Generates a `tf.data.Dataset` from image files in a directory. If your directory structure is: ``` main_directory/ ...class_a/ ......a_image_1.jpg ......a_image_2.jpg ...class_b/ ......b_image_1.jpg ......b_image_2.jpg ``` Then calling `image_dataset_from_directory(main_directory)` will return a `tf.data.Dataset` that yields batches of images from the subdirectories `class_a` and `class_b`, together with labels 'class_a' and 'class_b'. Supported image formats: jpeg, png, bmp, gif. Animated gifs are truncated to the first frame. # Arguments directory: Directory where the data is located. If `labels` is "inferred", it should contain subdirectories, each containing images for a class. Otherwise, the directory structure is ignored. batch_size: Size of the batches of data. Default: 32. color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb". Whether the images will be converted to have 1, 3, or 4 channels. image_size: Size to resize images to after they are read from disk. Defaults to `(256, 256)`. Since the pipeline processes batches of images that must all have the same size, this must be provided. interpolation: String, the interpolation method used when resizing images. Defaults to `bilinear`. Supports `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`. shuffle: Whether to shuffle the data. Default: True. If set to False, sorts the data in alphanumeric order. seed: Optional random seed for shuffling and transformations. validation_split: Optional float between 0 and 1, fraction of data to reserve for validation. subset: One of "training" or "validation". Only used if `validation_split` is set. # Returns A `tf.data.Dataset` object, which yields a tuple `(texts, labels)`, where `images` has shape `(batch_size, image_size[0], image_size[1], num_channels)` where `labels` has shape `(batch_size,)` and type of tf.string. - if `color_mode` is `grayscale`, there's 1 channel in the image tensors. - if `color_mode` is `rgb`, there are 3 channel in the image tensors. - if `color_mode` is `rgba`, there are 4 channel in the image tensors. """ if color_mode == "rgb": num_channels = 3 elif color_mode == "rgba": num_channels = 4 elif color_mode == "grayscale": num_channels = 1 else: raise ValueError( '`color_mode` must be one of {"rbg", "rgba", "grayscale"}. ' "Received: %s" % (color_mode,) ) if seed is None: seed = np.random.randint(1e6) image_paths, labels, class_names = dataset_utils.index_directory( directory, "inferred", formats=WHITELIST_FORMATS, shuffle=shuffle, seed=seed ) image_paths, labels = get_training_or_validation_split( image_paths, labels, validation_split, subset ) images = tf.data.Dataset.from_tensor_slices(image_paths) images = images.map( lambda img: path_to_image(img, num_channels, image_size, interpolation) ) labels = np.array(class_names)[np.array(labels)] labels = tf.data.Dataset.from_tensor_slices(labels) dataset = tf.data.Dataset.zip((images, labels)) dataset = dataset.batch(batch_size) return dataset def path_to_image(image, num_channels, image_size, interpolation): image = tf.io.read_file(image) image = tf.io.decode_image(image, channels=num_channels, expand_animations=False) image = tf.image.resize(image, image_size, method=interpolation) image.set_shape((image_size[0], image_size[1], num_channels)) return image
36.56746
85
0.660119
cb86ef9c345a6a15f9b72213fb84e8654651eebc
1,257
py
Python
isiscb/isisdata/migrations/0005_searchquery.py
bgopalachary/IsisCB
c28e3f504eea60ebeff38318d8bb2071abb28ebb
[ "MIT" ]
4
2016-01-25T20:35:33.000Z
2020-04-07T15:39:52.000Z
isiscb/isisdata/migrations/0005_searchquery.py
bgopalachary/IsisCB
c28e3f504eea60ebeff38318d8bb2071abb28ebb
[ "MIT" ]
41
2015-08-19T17:34:41.000Z
2022-03-11T23:19:01.000Z
isiscb/isisdata/migrations/0005_searchquery.py
bgopalachary/IsisCB
c28e3f504eea60ebeff38318d8bb2071abb28ebb
[ "MIT" ]
2
2020-11-25T20:18:18.000Z
2021-06-24T15:15:41.000Z
# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations from django.conf import settings class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('isisdata', '0004_auto_20151025_2110'), ] operations = [ migrations.CreateModel( name='SearchQuery', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('created_on', models.DateTimeField(auto_now_add=True)), ('parameters', models.CharField(max_length=500)), ('search_models', models.CharField(max_length=500, null=True, blank=True)), ('selected_facets', models.CharField(max_length=500, null=True, blank=True)), ('name', models.CharField(help_text=b'\n Provide a memorable name so that you can find this search later.', max_length=255, null=True, blank=True)), ('saved', models.BooleanField(default=False)), ('user', models.ForeignKey(related_name='searches', to=settings.AUTH_USER_MODEL, on_delete=models.CASCADE)), ], ), ]
41.9
167
0.636436
a4f18e1ee8b3e5917fb0eb2f227147a8965393ef
2,085
py
Python
research/cv/ssd_mobilenetV2_FPNlite/src/init_params.py
leelige/mindspore
5199e05ba3888963473f2b07da3f7bca5b9ef6dc
[ "Apache-2.0" ]
77
2021-10-15T08:32:37.000Z
2022-03-30T13:09:11.000Z
research/cv/ssd_mobilenetV2_FPNlite/src/init_params.py
leelige/mindspore
5199e05ba3888963473f2b07da3f7bca5b9ef6dc
[ "Apache-2.0" ]
3
2021-10-30T14:44:57.000Z
2022-02-14T06:57:57.000Z
research/cv/ssd_mobilenetV2_FPNlite/src/init_params.py
leelige/mindspore
5199e05ba3888963473f2b07da3f7bca5b9ef6dc
[ "Apache-2.0" ]
24
2021-10-15T08:32:45.000Z
2022-03-24T18:45:20.000Z
# Copyright 2021 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """Parameters utils""" from mindspore.common.initializer import initializer, TruncatedNormal def init_net_param(network, initialize_mode='TruncatedNormal'): """Init the parameters in net.""" params = network.trainable_params() for p in params: if 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name: if initialize_mode == 'TruncatedNormal': p.set_data(initializer(TruncatedNormal(0.02), p.data.shape, p.data.dtype)) else: p.set_data(initialize_mode, p.data.shape, p.data.dtype) def load_backbone_params(network, param_dict): """Init the parameters from pre-train model, default is mobilenetv2.""" for _, param in network.parameters_and_names(): param_name = param.name.replace('network.backbone.', '') name_split = param_name.split('.') if 'features_1' in param_name: param_name = param_name.replace('features_1', 'features') if 'features_2' in param_name: param_name = '.'.join(['features', str(int(name_split[1]) + 14)] + name_split[2:]) if param_name in param_dict: param.set_data(param_dict[param_name].data) def filter_checkpoint_parameter(param_dict): """remove useless parameters""" for key in list(param_dict.keys()): if 'multi_loc_layers' in key or 'multi_cls_layers' in key: del param_dict[key]
44.361702
94
0.666667
be2300c02deaa684b9a7d8252bb2c8ef3e87806c
18,269
py
Python
backend/api.py
prasys/detecting-fake-text
de1fe92b726fa50849517f02233f86ee62f2435b
[ "Apache-2.0" ]
null
null
null
backend/api.py
prasys/detecting-fake-text
de1fe92b726fa50849517f02233f86ee62f2435b
[ "Apache-2.0" ]
null
null
null
backend/api.py
prasys/detecting-fake-text
de1fe92b726fa50849517f02233f86ee62f2435b
[ "Apache-2.0" ]
null
null
null
import numpy as np import torch import time from pytorch_pretrained_bert import (GPT2LMHeadModel, GPT2Tokenizer, BertTokenizer, BertForMaskedLM) from .class_register import register_api class AbstractLanguageChecker(): """ Abstract Class that defines the Backend API of GLTR. To extend the GLTR interface, you need to inherit this and fill in the defined functions. """ def __init__(self): ''' In the subclass, you need to load all necessary components for the other functions. Typically, this will comprise a tokenizer and a model. ''' self.device = torch.device( "cuda" if torch.cuda.is_available() else "cpu") def check_probabilities(self, in_text, topk=40): ''' Function that GLTR interacts with to check the probabilities of words Params: - in_text: str -- The text that you want to check - topk: int -- Your desired truncation of the head of the distribution Output: - payload: dict -- The wrapper for results in this function, described below Payload values ============== bpe_strings: list of str -- Each individual token in the text real_topk: list of tuples -- (ranking, prob) of each token pred_topk: list of list of tuple -- (word, prob) for all topk ''' raise NotImplementedError def postprocess(self, token): """ clean up the tokens from any special chars and encode leading space by UTF-8 code '\u0120', linebreak with UTF-8 code 266 '\u010A' :param token: str -- raw token text :return: str -- cleaned and re-encoded token text """ raise NotImplementedError def top_k_logits(logits, k): ''' Filters logits to only the top k choices from https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_gpt2.py ''' if k == 0: return logits values, _ = torch.topk(logits, k) min_values = values[:, -1] return torch.where(logits < min_values, torch.ones_like(logits, dtype=logits.dtype) * -1e10, logits) @register_api(name='gpt-2-small') class LM(AbstractLanguageChecker): def __init__(self, model_name_or_path="gpt2"): super(LM, self).__init__() self.enc = GPT2Tokenizer.from_pretrained(model_name_or_path) self.model = GPT2LMHeadModel.from_pretrained(model_name_or_path) self.model.to(self.device) self.model.eval() self.start_token = '<|endoftext|>' print("Loaded GPT-2 model!") def check_probabilities(self, in_text, topk=40): # Process input start_t = torch.full((1, 1), self.enc.encoder[self.start_token], device=self.device, dtype=torch.long) context = self.enc.encode(in_text) context = torch.tensor(context, device=self.device, dtype=torch.long).unsqueeze(0) context = torch.cat([start_t, context], dim=1) # Forward through the model logits, _ = self.model(context) # construct target and pred yhat = torch.softmax(logits[0, :-1], dim=-1) y = context[0, 1:] # Sort the predictions for each timestep sorted_preds = np.argsort(-yhat.data.cpu().numpy()) # [(pos, prob), ...] real_topk_pos = list( [int(np.where(sorted_preds[i] == y[i].item())[0][0]) for i in range(y.shape[0])]) real_topk_probs = yhat[np.arange( 0, y.shape[0], 1), y].data.cpu().numpy().tolist() real_topk_probs = list(map(lambda x: round(x, 5), real_topk_probs)) real_topk = list(zip(real_topk_pos, real_topk_probs)) # [str, str, ...] bpe_strings = [self.enc.decoder[s.item()] for s in context[0]] bpe_strings = [self.postprocess(s) for s in bpe_strings] # [[(pos, prob), ...], [(pos, prob), ..], ...] pred_topk = [ list(zip([self.enc.decoder[p] for p in sorted_preds[i][:topk]], list(map(lambda x: round(x, 5), yhat[i][sorted_preds[i][ :topk]].data.cpu().numpy().tolist())))) for i in range(y.shape[0])] pred_topk = [[(self.postprocess(t[0]), t[1]) for t in pred] for pred in pred_topk] payload = {'bpe_strings': bpe_strings, 'real_topk': real_topk, 'pred_topk': pred_topk} if torch.cuda.is_available(): torch.cuda.empty_cache() return payload @register_api(name='amazon') class LM(AbstractLanguageChecker): def __init__(self, model_name_or_path="/data/pradeesh/detecting-fake-text/pytorch/"): super(LM, self).__init__() self.enc = GPT2Tokenizer.from_pretrained(model_name_or_path) self.model = GPT2LMHeadModel.from_pretrained(model_name_or_path) self.model.to(self.device) self.model.eval() self.start_token = '<|endoftext|>' print("Loaded GPT-2 model!") def check_probabilities(self, in_text, topk=40): # Process input start_t = torch.full((1, 1), self.enc.encoder[self.start_token], device=self.device, dtype=torch.long) context = self.enc.encode(in_text) context = torch.tensor(context, device=self.device, dtype=torch.long).unsqueeze(0) context = torch.cat([start_t, context], dim=1) # Forward through the model logits, _ = self.model(context) # construct target and pred yhat = torch.softmax(logits[0, :-1], dim=-1) y = context[0, 1:] # Sort the predictions for each timestep sorted_preds = np.argsort(-yhat.data.cpu().numpy()) # [(pos, prob), ...] real_topk_pos = list( [int(np.where(sorted_preds[i] == y[i].item())[0][0]) for i in range(y.shape[0])]) real_topk_probs = yhat[np.arange( 0, y.shape[0], 1), y].data.cpu().numpy().tolist() real_topk_probs = list(map(lambda x: round(x, 5), real_topk_probs)) real_topk = list(zip(real_topk_pos, real_topk_probs)) # [str, str, ...] bpe_strings = [self.enc.decoder[s.item()] for s in context[0]] bpe_strings = [self.postprocess(s) for s in bpe_strings] # [[(pos, prob), ...], [(pos, prob), ..], ...] pred_topk = [ list(zip([self.enc.decoder[p] for p in sorted_preds[i][:topk]], list(map(lambda x: round(x, 5), yhat[i][sorted_preds[i][ :topk]].data.cpu().numpy().tolist())))) for i in range(y.shape[0])] pred_topk = [[(self.postprocess(t[0]), t[1]) for t in pred] for pred in pred_topk] payload = {'bpe_strings': bpe_strings, 'real_topk': real_topk, 'pred_topk': pred_topk} if torch.cuda.is_available(): torch.cuda.empty_cache() return payload def sample_unconditional(self, length=100, topk=5, temperature=1.0): ''' Sample `length` words from the model. Code strongly inspired by https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_gpt2.py ''' context = torch.full((1, 1), self.enc.encoder[self.start_token], device=self.device, dtype=torch.long) prev = context output = context past = None # Forward through the model with torch.no_grad(): for i in range(length): logits, past = self.model(prev, past=past) logits = logits[:, -1, :] / temperature # Filter predictions to topk and softmax probs = torch.softmax(top_k_logits(logits, k=topk), dim=-1) # Sample prev = torch.multinomial(probs, num_samples=1) # Construct output output = torch.cat((output, prev), dim=1) output_text = self.enc.decode(output[0].tolist()) return output_text def postprocess(self, token): with_space = False with_break = False if token.startswith('Ġ'): with_space = True token = token[1:] # print(token) elif token.startswith('â'): token = ' ' elif token.startswith('Ċ'): token = ' ' with_break = True token = '-' if token.startswith('â') else token token = '“' if token.startswith('ľ') else token token = '”' if token.startswith('Ŀ') else token token = "'" if token.startswith('Ļ') else token if with_space: token = '\u0120' + token if with_break: token = '\u010A' + token return token @register_api(name='BERT') class BERTLM(AbstractLanguageChecker): def __init__(self, model_name_or_path="bert-base-cased"): super(BERTLM, self).__init__() self.device = torch.device( "cuda" if torch.cuda.is_available() else "cpu") self.tokenizer = BertTokenizer.from_pretrained( model_name_or_path, do_lower_case=False) self.model = BertForMaskedLM.from_pretrained( model_name_or_path) self.model.to(self.device) self.model.eval() # BERT-specific symbols self.mask_tok = self.tokenizer.convert_tokens_to_ids(["[MASK]"])[0] self.pad = self.tokenizer.convert_tokens_to_ids(["[PAD]"])[0] print("Loaded BERT model!") def check_probabilities(self, in_text, topk=40, max_context=20, batch_size=20): ''' Same behavior as GPT-2 Extra param: max_context controls how many words should be fed in left and right Speeds up inference since BERT requires prediction word by word ''' in_text = "[CLS] " + in_text + " [SEP]" tokenized_text = self.tokenizer.tokenize(in_text) # Construct target y_toks = self.tokenizer.convert_tokens_to_ids(tokenized_text) # Only use sentence A embedding here since we have non-separable seq's segments_ids = [0] * len(y_toks) y = torch.tensor([y_toks]).to(self.device) segments_tensor = torch.tensor([segments_ids]).to(self.device) # TODO batching... # Create batches of (x,y) input_batches = [] target_batches = [] for min_ix in range(0, len(y_toks), batch_size): max_ix = min(min_ix + batch_size, len(y_toks) - 1) cur_input_batch = [] cur_target_batch = [] # Construct each batch for running_ix in range(max_ix - min_ix): tokens_tensor = y.clone() mask_index = min_ix + running_ix tokens_tensor[0, mask_index + 1] = self.mask_tok # Reduce computational complexity by subsetting min_index = max(0, mask_index - max_context) max_index = min(tokens_tensor.shape[1] - 1, mask_index + max_context + 1) tokens_tensor = tokens_tensor[:, min_index:max_index] # Add padding needed_padding = max_context * 2 + 1 - tokens_tensor.shape[1] if min_index == 0 and max_index == y.shape[1] - 1: # Only when input is shorter than max_context left_needed = (max_context) - mask_index right_needed = needed_padding - left_needed p = torch.nn.ConstantPad1d((left_needed, right_needed), self.pad) tokens_tensor = p(tokens_tensor) elif min_index == 0: p = torch.nn.ConstantPad1d((needed_padding, 0), self.pad) tokens_tensor = p(tokens_tensor) elif max_index == y.shape[1] - 1: p = torch.nn.ConstantPad1d((0, needed_padding), self.pad) tokens_tensor = p(tokens_tensor) cur_input_batch.append(tokens_tensor) cur_target_batch.append(y[:, mask_index + 1]) # new_segments = segments_tensor[:, min_index:max_index] cur_input_batch = torch.cat(cur_input_batch, dim=0) cur_target_batch = torch.cat(cur_target_batch, dim=0) input_batches.append(cur_input_batch) target_batches.append(cur_target_batch) real_topk = [] pred_topk = [] with torch.no_grad(): for src, tgt in zip(input_batches, target_batches): # Compute one batch of inputs # By construction, MASK is always the middle logits = self.model(src, torch.zeros_like(src))[:, max_context + 1] yhat = torch.softmax(logits, dim=-1) sorted_preds = np.argsort(-yhat.data.cpu().numpy()) # TODO: compare with batch of tgt # [(pos, prob), ...] real_topk_pos = list( [int(np.where(sorted_preds[i] == tgt[i].item())[0][0]) for i in range(yhat.shape[0])]) real_topk_probs = yhat[np.arange( 0, yhat.shape[0], 1), tgt].data.cpu().numpy().tolist() real_topk.extend(list(zip(real_topk_pos, real_topk_probs))) # # [[(pos, prob), ...], [(pos, prob), ..], ...] pred_topk.extend([list(zip(self.tokenizer.convert_ids_to_tokens( sorted_preds[i][:topk]), yhat[i][sorted_preds[i][ :topk]].data.cpu().numpy().tolist())) for i in range(yhat.shape[0])]) bpe_strings = [self.postprocess(s) for s in tokenized_text] pred_topk = [[(self.postprocess(t[0]), t[1]) for t in pred] for pred in pred_topk] payload = {'bpe_strings': bpe_strings, 'real_topk': real_topk, 'pred_topk': pred_topk} return payload def postprocess(self, token): with_space = True with_break = token == '[SEP]' if token.startswith('##'): with_space = False token = token[2:] if with_space: token = '\u0120' + token if with_break: token = '\u010A' + token # # # print ('....', token) return token def main(): raw_text = """ In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English. The scientist named the population, after their distinctive horn, Ovid’s Unicorn. These four-horned, silver-white unicorns were previously unknown to science. Now, after almost two centuries, the mystery of what sparked this odd phenomenon is finally solved. Dr. Jorge Pérez, an evolutionary biologist from the University of La Paz, and several companions, were exploring the Andes Mountains when they found a small valley, with no other animals or humans. Pérez noticed that the valley had what appeared to be a natural fountain, surrounded by two peaks of rock and silver snow. Pérez and the others then ventured further into the valley. “By the time we reached the top of one peak, the water looked blue, with some crystals on top,” said Pérez. Pérez and his friends were astonished to see the unicorn herd. These creatures could be seen from the air without having to move too much to see them – they were so close they could touch their horns. While examining these bizarre creatures the scientists discovered that the creatures also spoke some fairly regular English. Pérez stated, “We can see, for example, that they have a common ‘language,’ something like a dialect or dialectic.” Dr. Pérez believes that the unicorns may have originated in Argentina, where the animals were believed to be descendants of a lost race of people who lived there before the arrival of humans in those parts of South America. While their origins are still unclear, some believe that perhaps the creatures were created when a human and a unicorn met each other in a time before human civilization. According to Pérez, “In South America, such incidents seem to be quite common.” However, Pérez also pointed out that it is likely that the only way of knowing for sure if unicorns are indeed the descendants of a lost alien race is through DNA. “But they seem to be able to communicate in English quite well, which I believe is a sign of evolution, or at least a change in social organization,” said the scientist. """ raw_text = """ In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English. """ ''' Tests for BERT ''' lm = BERTLM() start = time.time() payload = lm.check_probabilities(raw_text, topk=5) end = time.time() print("{:.2f} Seconds for a run with BERT".format(end - start)) # print("SAMPLE:", sample) ''' Tests for GPT-2 ''' lm = LM() start = time.time() payload = lm.check_probabilities(raw_text, topk=5) end = time.time() print("{:.2f} Seconds for a check with GPT-2".format(end - start)) start = time.time() sample = lm.sample_unconditional() end = time.time() print("{:.2f} Seconds for a sample from GPT-2".format(end - start)) print("SAMPLE:", sample) if __name__ == "__main__": main()
42.09447
337
0.583502
b9bab67b3478fba0284f86826d4a92ee106550b1
3,904
py
Python
code/models/get_random_cnn_params.py
mrbarbasa/kaggle-spooky-author
a2ded542288efa0e85a25426722619ed2542d98b
[ "MIT" ]
1
2018-10-09T04:57:03.000Z
2018-10-09T04:57:03.000Z
code/models/get_random_cnn_params.py
mrbarbasa/kaggle-spooky-author
a2ded542288efa0e85a25426722619ed2542d98b
[ "MIT" ]
null
null
null
code/models/get_random_cnn_params.py
mrbarbasa/kaggle-spooky-author
a2ded542288efa0e85a25426722619ed2542d98b
[ "MIT" ]
null
null
null
import numpy as np def get_random_cnn_params(normal_arch_threshold=0.8): """Retrieve random CNN parameters and hyperparameters. Parameters ---------- normal_arch_threshold : float, optional A fraction between 0 and 1 that specifies the probability of using the normal CNN architecture over the special architecture. Returns ------- params : dict Model parameters and hyperparameters to govern the construction of a CNN model. They are: - batch_size : int The number of samples per batch; after a batch is trained, weights are updated. - filters : int The number of filters in a convolutional layer. - kernel_size : int The length of the 1D convolution window. - dropout_rate : float Fraction of the input units to drop. - optimizer : string An optimizer such as Adam or RMSProp. - use_special_arch : bool Whether or not to use the special CNN architecture. - normal_arch_params : dict This dictionary should only have keys if `use_special_arch` is False; otherwise, it is an empty dictionary. - num_conv_stacks : int The number of convolutional stacks. - add_extra_conv_layer : bool Add an extra convolutional layer whenever a convolutional layer appears. - add_dropout_layer : bool Add a dropout layer at the end of every convolutional stack, after the max pooling layer. - flatten : bool Whether or not to end the CNN model with a Keras Flatten and Dense layer, as opposed to one or two convolutional layers followed by a global max or average pooling layer. - use_global_max_pooling_layer : bool Only applies if `flatten` is False: End the model with a global max pooling layer instead of a global average. - add_final_dropout_layer : bool Add a final dropout layer right before the output layer. - pool_size : int Size of the max pooling windows. - final_dropout_rate : float Only applies if `add_final_dropout_layer` is True: Fraction of the input units to drop for the final dropout layer. """ batch_size = int(np.random.choice([32, 64, 128, 256, 512])) filters = int(np.random.choice([32, 64, 128, 256, 300])) kernel_size = int(np.random.choice([3, 5, 7, 9])) dropout_rate = float(np.random.choice([0.1, 0.2, 0.3, 0.4, 0.5])) optimizer = str(np.random.choice(['adam', 'rmsprop'])) special_arch_value = float(np.random.uniform(0, 1)) # `normal_arch_threshold = 0.8` by default: # Use normal architecture 80% of the time use_special_arch = special_arch_value > normal_arch_threshold nap = {} if not use_special_arch: nap['num_conv_stacks'] = int(np.random.choice([1, 2, 3])) nap['add_extra_conv_layer'] = bool(np.random.choice([True, False])) nap['add_dropout_layer'] = bool(np.random.choice([True, False])) nap['flatten'] = bool(np.random.choice([True, False])) nap['use_global_max_pooling_layer'] = bool(np.random.choice([True, False])) nap['add_final_dropout_layer'] = bool(np.random.choice([True, False])) nap['pool_size'] = int(np.random.choice([2, 3, 4, 5])) nap['final_dropout_rate'] = float(np.random.choice([0.1, 0.2, 0.3, 0.4, 0.5])) return { 'batch_size': batch_size, 'filters': filters, 'kernel_size': kernel_size, 'dropout_rate': dropout_rate, 'optimizer': optimizer, 'use_special_arch': use_special_arch, 'normal_arch_params': nap, }
41.978495
86
0.61373
7ca03a71c54b622873a8393041189ae284b9e123
45,226
py
Python
comdb2/dbapi2.py
vishalbelsare/python-comdb2
05da300c739bcc7e63036ab79f8552165954035b
[ "Apache-2.0" ]
20
2017-07-13T09:04:21.000Z
2021-11-09T05:32:17.000Z
comdb2/dbapi2.py
vishalbelsare/python-comdb2
05da300c739bcc7e63036ab79f8552165954035b
[ "Apache-2.0" ]
10
2017-07-12T20:15:26.000Z
2021-12-22T20:04:49.000Z
comdb2/dbapi2.py
vishalbelsare/python-comdb2
05da300c739bcc7e63036ab79f8552165954035b
[ "Apache-2.0" ]
21
2017-07-12T19:51:22.000Z
2021-11-09T05:32:07.000Z
# Copyright 2017 Bloomberg Finance L.P. # 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 module provides a DB-API 2.0 compatible Comdb2 API. Overview ======== This module provides a Comdb2 interface that conforms to `the Python Database API Specification v2.0 <https://www.python.org/dev/peps/pep-0249/>`_. Basic Usage ----------- The main class used for interacting with a Comdb2 is `Connection`, which you create by calling the `connect` factory function. Any errors that are encountered when connecting to or querying the database are raised as instances of the `Error` class. A basic usage example looks like this:: from comdb2 import dbapi2 conn = dbapi2.connect('mattdb', autocommit=True) cursor = conn.cursor() cursor.execute("select 1, 'a' union all select 2, 'b'") for row in cursor.fetchall(): print(row) The above would result in the following output:: [1, 'a'] [2, 'b'] To reduce the amount of boilerplate required for fetching result sets, we implement 2 extensions to the interface required by the Python DB-API: `Cursor` objects are iterable, yielding one row of the result set per iteration, and `Cursor.execute` returns the `Cursor` itself. By utilizing these extensions, the basic example can be shortened to:: from comdb2 import dbapi2 conn = dbapi2.connect('mattdb', autocommit=True) for row in conn.cursor().execute("select 1, 'a' union all select 2, 'b'"): print(row) Graceful Teardown and Error Handling ------------------------------------ Non-trivial applications should guarantee that the `Connection` is closed when it is no longer needed, preferably by using `contextlib.closing`. They should also be prepared to handle any errors returned by the database. So, a more thorough version of the example above would be:: from comdb2 import dbapi2 from contextlib import closing try: with closing(dbapi2.connect('mattdb', autocommit=True)) as conn: query = "select 1, 'a' union all select 2, 'b'" for row in conn.cursor().execute(query): print(row) except dbapi2.Error as exc: print("Comdb2 exception encountered: %s" % exc) In this example, `contextlib.closing` is used to guarantee that `Connection.close` is called at the end of the ``with`` block, and an exception handler been added for exceptions of type `Error`. All exceptions raised by this module are subclasses of `Error`. See :ref:`Exceptions` for details on when each exception type is raised. Controlling the Type Used For Result Rows ----------------------------------------- As you can see, rows are returned as a `list` of column values in positional order. If you'd prefer to get the columns back as some other type, you can set `Connection.row_factory` to one of the factories provided by `comdb2.factories` - for example:: from comdb2 import dbapi2 from comdb2 import factories conn = dbapi2.connect('mattdb', autocommit=True) conn.row_factory = factories.dict_row_factory c = conn.cursor() for row in c.execute("select 1 as 'x', 2 as 'y' union all select 3, 4"): print(row) This program will return each row as a `dict` rather than a `list`:: {'y': 2, 'x': 1} {'y': 4, 'x': 3} Parameter Binding ----------------- In real applications you'll often need to pass parameters into a SQL query. This is done using parameter binding - in the query, placeholders are specified using ``%(name)s``, and a mapping of ``name`` to parameter value is passed to `Cursor.execute` along with the query. The ``%(`` and ``)s`` are fixed, and the ``name`` between them varies for each parameter. For example: >>> query = "select 25 between %(a)s and %(b)s" >>> print(conn.cursor().execute(query, {'a': 20, 'b': 42}).fetchall()) [[1]] >>> params = {'a': 20, 'b': 23} >>> print(conn.cursor().execute(query, params).fetchall()) [[0]] In this example, we run the query with two different sets of parameters, producing different results. First, we execute the query with parameter ``a`` bound to ``20`` and ``b`` bound to ``42``. In this case, because ``20 <= 25 <= 42``, the expression evaluates to true, and a ``1`` is returned. When we run the same query with parameter ``b`` bound to ``23``, a ``0`` is returned instead, because ``20 <= 25 <= 23`` is false. Note: Because parameters are bound using ``%(name)s``, other ``%`` signs in a query must be escaped. For example, ``WHERE name like 'M%'`` becomes ``WHERE name LIKE 'M%%'``. Types ----- For all Comdb2 types, the same Python type is used for binding a parameter value as is returned for a SQL query result column of that type. In brief, SQL types are mapped to Python types according to the following table: ============ ================================================================ SQL type Python type ============ ================================================================ NULL ``None`` integer `int` real `float` blob `six.binary_type` (aka `bytes` in Python 3, ``str`` in Python 2) text `six.text_type` (aka `str` in Python 3, ``unicode`` in Python 2) datetime `datetime.datetime` datetimeus `DatetimeUs` ============ ================================================================ See :ref:`Comdb2 to Python Type Mappings` for a thorough explanation of these type mappings and their implications. Note: This module uses byte strings to represent BLOB columns, and Unicode strings to represent TEXT columns. This is a very common source of problems for new users. Make sure to carefully read :ref:`String and Blob Types` on that page. .. _Autocommit Mode: Autocommit Mode --------------- In all of the examples above, we gave the ``autocommit=True`` keyword argument when calling `connect`. This opts out of DB-API compliant transaction handling, in order to use Comdb2's native transaction semantics. By default, DB-API cursors are always in a transaction. You can commit that transaction using `Connection.commit`, or roll it back using `Connection.rollback`. For example:: conn = dbapi2.connect('mattdb') cursor = conn.cursor() query = "insert into simple(key, val) values (%(key)s, %(val)s)" cursor.execute(query, {'key': 1, 'val': 2}) cursor.execute(query, {'key': 3, 'val': 4}) cursor.execute(query, {'key': 5, 'val': 6}) conn.commit() There are several things to note here. The first is that the insert statements that were sent to the database don't take effect immediately, because they are implicitly part of a transaction that must be explicitly completed. This is different from other Comdb2 APIs, where you must execute a ``BEGIN`` statement to start a transaction, and where statements otherwise take effect immediately. The second thing to note is that there are certain error conditions where a Comdb2 connection can automatically recover when outside of a transaction, but not within a transaction. In other words, using transactions when they aren't needed can introduce new failure modes into your program. In order to provide greater compatibility with other Comdb2 interfaces and to eliminate the performance costs and extra error cases introduced by using transactions unnecessarily, we allow you to pass the non-standard ``autocommit=True`` keyword argument when creating a new `Connection`. This results in the implicit transaction not being created. You can still start a transaction explicitly by passing a ``BEGIN`` statement to `Cursor.execute`. For example:: conn = dbapi2.connect('mattdb', autocommit=True) cursor = conn.cursor() cursor.execute("delete from simple where 1=1") cursor.execute("begin") query = "insert into simple(key, val) values (%(key)s, %(val)s)" cursor.execute(query, {'key': 1, 'val': 2}) cursor.execute(query, {'key': 3, 'val': 4}) cursor.execute(query, {'key': 5, 'val': 6}) cursor.execute("rollback") In this example, because we've used ``autocommit=True`` the delete statement takes effect immediately (that is, it is automatically committed). We then explicitly create a transaction, insert 3 rows, then decide not to commit it, and instead explicitly roll back the transaction. To summarize: you cannot use ``autocommit`` mode if you intend to pass the `Connection` to a library that requires DB-API compliant connections. You should prefer ``autocommit`` mode when you don't want to use transactions (for example, read-only queries where no particular consistency guarantees are required across queries). If you do intend to use transactions but won't pass the `Connection` to a library that requires DB-API compliance, you can choose either mode. It may be easier to port existing code if you use ``autocommit`` mode, but avoiding ``autocommit`` mode may allow you to use 3rd party libraries in the future that require DB-API compliant connections. DB-API Compliance ----------------- The interface this module provides fully conforms to `the Python Database API Specification v2.0 <https://www.python.org/dev/peps/pep-0249/>`_ with a few specific exceptions: 1. DB-API requires `Date` and `DateFromTicks` constructors, which we don't provide because Comdb2 has no type for representing a date without a time component. 2. DB-API requires `Time` and `TimeFromTicks` constructors, which we don't provide because Comdb2 has no type for representing a time without a date component. 3. DB-API is unclear about the required behavior of multiple calls to `Connection.cursor` on a connection. Comdb2 does not have a concept of cursors as distinct from connection handles, so each time `Connection.cursor` is called, we call `Cursor.close` on any existing, open cursor for that connection. """ from __future__ import absolute_import, unicode_literals import functools import itertools import weakref import datetime import re import six from . import cdb2 __all__ = ['apilevel', 'threadsafety', 'paramstyle', 'connect', 'Connection', 'Cursor', 'STRING', 'BINARY', 'NUMBER', 'DATETIME', 'ROWID', 'Datetime', 'DatetimeUs', 'Binary', 'Timestamp', 'TimestampUs', 'DatetimeFromTicks', 'DatetimeUsFromTicks', 'TimestampFromTicks', 'Error', 'Warning', 'InterfaceError', 'DatabaseError', 'InternalError', 'OperationalError', 'ProgrammingError', 'IntegrityError', 'DataError', 'NotSupportedError', 'UniqueKeyConstraintError', 'ForeignKeyConstraintError', 'NonNullConstraintError'] apilevel = "2.0" """This module conforms to the Python Database API Specification 2.0.""" threadsafety = 1 """Two threads can use this module, but can't share one `Connection`.""" paramstyle = "pyformat" """The SQL placeholder format for this module is ``%(name)s``. Comdb2's native placeholder format is ``@name``, but that cannot be used by this module because it's not an acceptable `DB-API 2.0 placeholder style <https://www.python.org/dev/peps/pep-0249/#paramstyle>`_. This module uses ``pyformat`` because it is the only DB-API 2.0 paramstyle that we can translate into Comdb2's placeholder format without needing a SQL parser. Note: An int value is bound as ``%(my_int)s``, not as ``%(my_int)d`` - the last character is always ``s``. Note: Because SQL strings for this module use the ``pyformat`` placeholder style, any literal ``%`` characters in a query must be escaped by doubling them. ``WHERE name like 'M%'`` becomes ``WHERE name LIKE 'M%%'``. """ _FIRST_WORD_OF_STMT = re.compile( r""" (?: # match (without capturing) \s* # optional whitespace /\*.*?\*/ # then a C-style /* ... */ comment, possibly across lines | # or \s* # optional whitespace --[^\n]*\n # then a SQL-style comment terminated by a newline )* # repeat until all comments have been matched \s* # then skip over any whitespace (\w+) # and capture the first word """, re.VERBOSE | re.DOTALL | (0 if six.PY2 else re.ASCII), ) _VALID_SP_NAME = re.compile(r'^[A-Za-z0-9_.]+$') @functools.total_ordering class _TypeObject(object): def __init__(self, *value_names): self.value_names = value_names self.values = [cdb2.TYPE[v] for v in value_names] def __eq__(self, other): return other in self.values def __lt__(self, other): return self != other and other < self.values def __repr__(self): return 'TypeObject' + str(self.value_names) def _binary(string): if isinstance(string, six.text_type): return string.encode('utf-8') return bytes(string) STRING = _TypeObject('CSTRING') """The type codes of TEXT result columns compare equal to this constant.""" BINARY = _TypeObject('BLOB') """The type codes of BLOB result columns compare equal to this constant.""" NUMBER = _TypeObject('INTEGER', 'REAL') """The type codes of numeric result columns compare equal to this constant.""" DATETIME = _TypeObject('DATETIME', 'DATETIMEUS') """The type codes of datetime result columns compare equal to this constant.""" ROWID = STRING # comdb2 doesn't support Date or Time, so I'm not defining them. Datetime = datetime.datetime DatetimeUs = cdb2.DatetimeUs Binary = _binary Timestamp = Datetime TimestampUs = DatetimeUs DatetimeFromTicks = Datetime.fromtimestamp DatetimeUsFromTicks = DatetimeUs.fromtimestamp TimestampFromTicks = Timestamp.fromtimestamp TimestampUsFromTicks = TimestampUs.fromtimestamp try: UserException = StandardError # Python 2 except NameError: UserException = Exception # Python 3 class Error(UserException): """This is the base class of all exceptions raised by this module. In addition to being available at the module scope, this class and the other exception classes derived from it are exposed as attributes on `Connection` objects, to simplify error handling in environments where multiple connections from different modules are used. """ pass class Warning(UserException): """Exception raised for important warnings. This is required to exist by the DB-API interface, but we never raise it. """ pass class InterfaceError(Error): """Exception raised for errors caused by misuse of this module.""" pass class DatabaseError(Error): """Base class for all errors reported by the database.""" pass class InternalError(DatabaseError): """Exception raised for internal errors reported by the database.""" pass class OperationalError(DatabaseError): """Exception raised for errors related to the database's operation. These errors are not necessarily the result of a bug either in the application or in the database - for example, dropped connections. """ pass class ProgrammingError(DatabaseError): """Exception raised for programming errors reported by the database. For example, this will be raised for syntactically incorrect SQL, or for passing a different number of parameters than are required by the query. """ pass class IntegrityError(DatabaseError): """Exception raised for integrity errors reported by the database. For example, a subclass of this will be raised if a foreign key constraint would be violated, or a constraint that a column may not be null, or that an index may not have duplicates. Other types of constraint violations may raise this type directly. """ pass class UniqueKeyConstraintError(IntegrityError): """Exception raised when a unique key constraint would be broken. Committing after either an INSERT or an UPDATE could result in this being raised, by either adding a new row that violates a unique (non-dup) key constraint or modifying an existing row in a way that would violate one. .. versionadded:: 1.1 """ pass class ForeignKeyConstraintError(IntegrityError): """Exception raised when a foreign key constraint would be broken. This would be raised when committing if the changes being committed would violate referential integrity according to a foreign key constraint configured on the database. For instance, deleting a row from a parent table would raise this if rows corresponding to its key still exist in a child table and the constraint doesn't have ON DELETE CASCADE. Likewise, inserting a row into a child table would raise this if there was no row in the parent table matching the new row's key. .. versionadded:: 1.1 """ pass class NonNullConstraintError(IntegrityError): """Exception raised when a non-null constraint would be broken. Committing after either an INSERT or an UPDATE could result in this being raised if it would result in a null being stored in a non-nullable column. Note that columns in a Comdb2 are not nullable by default. .. versionadded:: 1.1 """ pass class DataError(DatabaseError): """Exception raised for errors related to the processed data. For example, this will be raised if you attempt to write a value that's out of range for the column type that would store it, or if you specify an invalid timezone name for the connection. """ pass class NotSupportedError(DatabaseError): """Exception raised when unsupported operations are attempted.""" pass _EXCEPTION_BY_RC = { cdb2.ERROR_CODE['CONNECT_ERROR'] : OperationalError, cdb2.ERROR_CODE['NOTCONNECTED'] : ProgrammingError, cdb2.ERROR_CODE['PREPARE_ERROR'] : ProgrammingError, cdb2.ERROR_CODE['IO_ERROR'] : OperationalError, cdb2.ERROR_CODE['INTERNAL'] : InternalError, cdb2.ERROR_CODE['NOSTATEMENT'] : ProgrammingError, cdb2.ERROR_CODE['BADCOLUMN'] : ProgrammingError, cdb2.ERROR_CODE['BADSTATE'] : ProgrammingError, cdb2.ERROR_CODE['ASYNCERR'] : OperationalError, cdb2.ERROR_CODE['INVALID_ID'] : InternalError, cdb2.ERROR_CODE['RECORD_OUT_OF_RANGE'] : OperationalError, cdb2.ERROR_CODE['REJECTED'] : OperationalError, cdb2.ERROR_CODE['STOPPED'] : OperationalError, cdb2.ERROR_CODE['BADREQ'] : OperationalError, cdb2.ERROR_CODE['DBCREATE_FAILED'] : OperationalError, cdb2.ERROR_CODE['THREADPOOL_INTERNAL'] : OperationalError, cdb2.ERROR_CODE['READONLY'] : NotSupportedError, cdb2.ERROR_CODE['NOMASTER'] : InternalError, cdb2.ERROR_CODE['UNTAGGED_DATABASE'] : NotSupportedError, cdb2.ERROR_CODE['CONSTRAINTS'] : IntegrityError, cdb2.ERROR_CODE['DEADLOCK'] : OperationalError, cdb2.ERROR_CODE['TRAN_IO_ERROR'] : OperationalError, cdb2.ERROR_CODE['ACCESS'] : OperationalError, cdb2.ERROR_CODE['TRAN_MODE_UNSUPPORTED'] : NotSupportedError, cdb2.ERROR_CODE['VERIFY_ERROR'] : OperationalError, cdb2.ERROR_CODE['FKEY_VIOLATION'] : ForeignKeyConstraintError, cdb2.ERROR_CODE['NULL_CONSTRAINT'] : NonNullConstraintError, cdb2.ERROR_CODE['CONV_FAIL'] : DataError, cdb2.ERROR_CODE['NONKLESS'] : NotSupportedError, cdb2.ERROR_CODE['MALLOC'] : OperationalError, cdb2.ERROR_CODE['NOTSUPPORTED'] : NotSupportedError, cdb2.ERROR_CODE['DUPLICATE'] : UniqueKeyConstraintError, cdb2.ERROR_CODE['TZNAME_FAIL'] : DataError, cdb2.ERROR_CODE['UNKNOWN'] : OperationalError, } def _raise_wrapped_exception(exc): code = exc.error_code msg = '%s (cdb2api rc %d)' % (exc.error_message, code) if "null constraint violation" in msg: six.raise_from(NonNullConstraintError(msg), exc) # DRQS 86013831 six.raise_from(_EXCEPTION_BY_RC.get(code, OperationalError)(msg), exc) def _sql_operation(sql): match = _FIRST_WORD_OF_STMT.match(sql) if match: return match.group(1).lower() return None def _operation_ends_transaction(operation): return operation == 'commit' or operation == 'rollback' def _modifies_rows(operation): # These operations can modify the contents of the database. # exec is deliberately excluded because it might return a result set, and # this function is used to determine whether it's safe to call # cdb2_get_effects after running the operation. return operation in ('commit', 'insert', 'update', 'delete') def connect(*args, **kwargs): """Establish a connection to a Comdb2 database. All arguments are passed directly through to the `Connection` constructor. Note: DB-API 2.0 requires the module to expose `connect`, but not `Connection`. If portability across database modules is a concern, you should always use `connect` to create your connections rather than calling the `Connection` constructor directly. Returns: Connection: A handle for the newly established connection. """ return Connection(*args, **kwargs) class Connection(object): """Represents a connection to a Comdb2 database. By default, the connection will be made to the cluster configured as the machine-wide default for the given database. This is almost always what you want. If you need to connect to a database that's running on your local machine rather than a cluster, you can pass "local" as the ``tier``. It's also permitted to specify "dev", "alpha", "beta", or "prod" as the ``tier``, which will connect you directly to the tier you specify (firewall permitting). Alternately, you can pass a machine name as the ``host`` argument, to connect directly to an instance of the given database on that host, rather than on a cluster or the local machine. The connection will use UTC as its timezone by default - you can change this with a ``SET TIMEZONE`` statement if needed. By default, or if ``autocommit`` is ``False``, `cursor` will return cursors that behave as mandated by the Python DB API: every statement to be executed is implicitly considered to be part of a transaction, and that transaction must be ended explicitly with a call to `commit` (or `rollback`). If ``autocommit`` is ``True``, `cursor` will instead return cursors that behave more in line with Comdb2's traditional behavior: the side effects of any given statement are immediately committed unless you previously started a transaction by executing a ``begin`` statement. Note: Using ``autocommit=True`` will ease porting from code using other Comdb2 APIs, both because other Comdb2 APIs implicitly commit after each statement in the same way as an autocommit `Connection` will, and because there are certain operations that Comdb2 will implicitly retry when outside of a transaction but won't retry when inside a transaction - meaning that a non-autocommit `Connection` has extra failure modes. You should strongly consider using ``autocommit=True``, especially for read-only use cases. Note: Python does not guarantee that object finalizers will be called when the interpreter exits, so to ensure that the connection is cleanly released you should call the `close` method when you're done with it. You can use `contextlib.closing` to guarantee the connection is released when a block completes. Note: DB-API 2.0 requires the module to expose `connect`, but not `Connection`. If portability across database modules is a concern, you should always use `connect` to create your connections rather than instantiating this class directly. Args: database_name (str): The name of the database to connect to. tier (str): The cluster to connect to. host (str): Alternately, a single remote host to connect to. autocommit (bool): Whether to automatically commit after DML statements, disabling DB-API 2.0's automatic implicit transactions. """ def __init__(self, database_name, tier="default", autocommit=False, host=None): if host is not None and tier != "default": raise InterfaceError("Connecting to a host by name and to a " "cluster by tier are mutually exclusive") self._active_cursor = None self._in_transaction = False self._autocommit = autocommit try: self._hndl = cdb2.Handle(database_name, tier, host=host) except cdb2.Error as e: _raise_wrapped_exception(e) def _check_closed(self): if self._hndl is None: raise InterfaceError("Attempted to use a closed Connection") @property def row_factory(self): """Factory used when constructing result rows. By default, or when set to ``None``, each row is returned as a `list` of column values. If you'd prefer to receive rows as a `dict` or as a `collections.namedtuple`, you can set this property to one of the factories provided by the `comdb2.factories` module. Example: >>> from comdb2.factories import dict_row_factory >>> conn.row_factory = dict_row_factory >>> cursor = conn.cursor() >>> cursor.execute("SELECT 1 as 'foo', 2 as 'bar'") >>> cursor.fetchone() {'foo': 1, 'bar': 2} .. versionadded:: 0.9 """ self._check_closed() return self._hndl.row_factory @row_factory.setter def row_factory(self, value): self._check_closed() self._hndl.row_factory = value def _close_any_outstanding_cursor(self): if self._active_cursor is not None: cursor = self._active_cursor() if cursor is not None and not cursor._closed: cursor.close() def _execute(self, operation): cursor = None if self._active_cursor is not None: cursor = self._active_cursor() if cursor is None: cursor = self.cursor() cursor._execute(operation, operation) def close(self): """Gracefully close the Comdb2 connection. Once a `Connection` has been closed, no further operations may be performed on it. If a socket pool is running on the machine and the connection was in a clean state, this will turn over the connection to the socket pool. This cannot be done if the connection was in a transaction or in the middle of retrieving a result set. Other restrictions may apply as well. You can ensure that this gets called at the end of a block using something like: >>> with contextlib.closing(connect('mattdb')) as conn: >>> for row in conn.cursor().execute("select 1"): >>> print(row) """ if self._hndl is None: raise InterfaceError("close() called on already closed connection") self._close_any_outstanding_cursor() self._hndl.close() self._hndl = None def commit(self): """Commit any pending transaction to the database. This method will fail if the `Connection` is in ``autocommit`` mode and no transaction was explicitly started. """ self._check_closed() self._execute("commit") def rollback(self): """Rollback the current transaction. This method will fail if the `Connection` is in ``autocommit`` mode and no transaction was explicitly started. Note: Closing a connection without committing the changes first will cause an implicit rollback to be performed, but will also prevent that connection from being contributed to the socket pool, if one is available. Because of this, an explicit rollback should be preferred when possible. """ self._check_closed() self._execute("rollback") def cursor(self): """Return a new `Cursor` for this connection. This calls `Cursor.close` on any outstanding `Cursor`; only one `Cursor` is allowed per `Connection` at a time. Note: Although outstanding cursors are closed, uncommitted transactions started by them are not rolled back, so the new `Cursor` will begin in the middle of that uncommitted transaction. Returns: Cursor: A new cursor on this connection. """ self._check_closed() self._close_any_outstanding_cursor() cursor = Cursor(self) self._active_cursor = weakref.ref(cursor) return cursor # Optional DB API Extension Error = Error Warning = Warning InterfaceError = InterfaceError DatabaseError = DatabaseError InternalError = InternalError OperationalError = OperationalError ProgrammingError = ProgrammingError IntegrityError = IntegrityError DataError = DataError NotSupportedError = NotSupportedError class Cursor(object): """Class used to send requests through a database connection. This class is not meant to be instantiated directly; it should always be created using `Connection.cursor`. It provides methods for sending requests to the database and for reading back the result sets produced by the database. Queries are made using the `execute` and `callproc` methods. Result sets can be consumed with the `fetchone`, `fetchmany`, or `fetchall` methods, or (as a nonstandard DB-API 2.0 extension) by iterating over the `Cursor`. Note: Only one `Cursor` per `Connection` can exist at a time. Creating a new one will `close` the previous one. """ _ErrorMessagesByOperation = { 'begin': "Transactions may not be started explicitly", 'commit': "Use Connection.commit to commit transactions", 'rollback': "Use Connection.rollback to roll back transactions", } def __init__(self, conn): self._arraysize = 1 self._conn = conn self._hndl = conn._hndl self._description = None self._closed = False self._rowcount = -1 def _check_closed(self): if self._closed: raise InterfaceError("Attempted to use a closed cursor") @property def arraysize(self): """Controls the number of rows to fetch at a time with `fetchmany`. The default is ``1``, meaning that a single row will be fetched at a time. """ return self._arraysize @arraysize.setter def arraysize(self, value): self._arraysize = value @property def description(self): """Provides the name and type of each column in the latest result set. This read-only attribute will contain one element per column in the result set. Each of those elements will be a 7-element sequence whose first element is the name of that column, whose second element is a type code for that column, and whose five remaining elements are ``None``. The type codes can be compared for equality against the `STRING`, `NUMBER`, `DATETIME`, and `BINARY` objects exposed by this module. If you need more granularity (e.g. knowing whether the result is a ``REAL`` or an ``INTEGER``) you can compare the type code for equality against the members of the `.cdb2.TYPE` dictionary. Or, of course, you can use `isinstance` to check the type of object returned as that column's value. Example: >>> cursor = connect('mattdb').cursor() >>> cursor.execute("select 1 as 'x', '2' as 'y', 3.0 as 'z'") >>> cursor.description[0][:2] == ('x', NUMBER) True >>> cursor.description[1][:2] == ('y', STRING) True >>> cursor.description[2][:2] == ('z', NUMBER) True >>> cursor.description[2][:2] == ('z', TYPE['INTEGER']) False >>> cursor.description[2][:2] == ('z', TYPE['REAL']) True """ self._check_closed() return self._description @property def rowcount(self): """Provides the count of rows modified by the last transaction. For `Cursor` objects on a `Connection` that is not using ``autocommit`` mode, this count is valid only after the transaction is committed with `Connection.commit()`. For `Cursor` objects on a `Connection` that is using ``autocommit`` mode, this count is valid after a successful ``COMMIT``, or after an ``INSERT``, ``UPDATE``, or ``DELETE`` statement run outside of an explicit transaction. At all other times, ``-1`` is returned. In particular, ``-1`` is returned whenever a transaction is in progress, because Comdb2 by default handles commit conflicts with other transactions by rerunning each statement of the transaction. As a result, row counts obtained within a transaction are meaningless in the default transaction level; either more or fewer rows may be affected when the transaction eventually commits. Also, ``-1`` is returned after ``SELECT`` or ``SELECTV``, because querying the row count requires calling ``cdb2_get_effects``, which would consume the result set before the user could iterate over it. Likewise, ``-1`` is returned after ``EXEC PROCEDURE``, because a stored procedure could emit a result set. """ self._check_closed() return self._rowcount # Optional DB API Extension @property def connection(self): """Return a reference to the `Connection` that this `Cursor` uses.""" self._check_closed() return self._conn def close(self): """Close the cursor now. From this point forward an exception will be raised if any operation is attempted with this `Cursor`. Note: This does not rollback any uncommitted operations executed by this `Cursor`. A new `Cursor` created from the `Connection` that this `Cursor` uses will start off in the middle of that uncommitted transaction. """ self._check_closed() self._description = None self._closed = True def callproc(self, procname, parameters): """Call a stored procedure with the given name. The ``parameters`` sequence must contain one entry for each argument that the procedure requires. If the called procedure emits a result set, it is made available through the fetch methods, or by iterating over the `Cursor`, as though it was returned by a ``select`` statement. Args: procname (str): The name of the stored procedure to be executed. parameters (Sequence[T]): A sequence of values to be passed, in order, as the parameters to the stored procedure. Each element must be an instance of one of the Python types listed in :doc:`types`. Returns: List[T]: A copy of the input parameters. """ if not _VALID_SP_NAME.match(procname): raise NotSupportedError("Invalid procedure name '%s'" % procname) params_as_dict = {str(i): e for i, e in enumerate(parameters)} sql = ("exec procedure " + procname + "(" + ", ".join("%%(%d)s" % i for i in range(len(params_as_dict))) + ")") self.execute(sql, params_as_dict) return parameters[:] def execute(self, sql, parameters=None): """Execute a database operation (query or command). The ``sql`` string must be provided as a Python format string, with parameter placeholders represented as ``%(name)s`` and all other ``%`` signs escaped as ``%%``. Note: Using placeholders should always be the preferred method of parameterizing the SQL query, as it prevents SQL injection vulnerabilities, and is faster than dynamically building SQL strings. Args: sql (str): The SQL string to execute, as a Python format string. parameters (Mapping[str, T]): An optional mapping from parameter names to the values to be bound for them. Returns: Cursor: As a nonstandard DB-API 2.0 extension, this method returns the `Cursor` that it was called on, which can be used as an iterator over the result set returned by the query. Iterating over it will yield one ``list`` per row in the result set, where the elements in the list correspond to the result columns within the row, in positional order. The `Connection.row_factory` property can be used to return rows as a different type. Example: >>> cursor.execute("select 1, 2 UNION ALL select %(x)s, %(y)s", ... {'x': 2, 'y': 4}) >>> cursor.fetchall() [[1, 2], [2, 4]] """ self._check_closed() self._description = None operation = _sql_operation(sql) if not self._conn._autocommit: # Certain operations are forbidden when not in autocommit mode. errmsg = self._ErrorMessagesByOperation.get(operation) if errmsg: raise InterfaceError(errmsg) self._execute(operation, sql, parameters) if self._rowcount == -1: self._load_description() # Optional DB API Extension: execute's return value is unspecified. We # return an iterable over the rows, but this isn't portable across DBs. return self def executemany(self, sql, seq_of_parameters): """Execute the same SQL statement repeatedly with different parameters. This is currently equivalent to calling execute multiple times, once for each element provided in ``seq_of_parameters``. Args: sql (str): The SQL string to execute, as a Python format string of the format expected by `execute`. seq_of_parameters (Sequence[Mapping[str, T]]): A sequence of mappings from parameter names to the values to be bound for them. The ``sql`` statement will be run once per element in this sequence. """ self._check_closed() for parameters in seq_of_parameters: self.execute(sql, parameters) def _execute(self, operation, sql, parameters=None): self._rowcount = -1 if not self._conn._autocommit: # Any non-SET operation starts a txn when not in autocommit mode. if not self._conn._in_transaction and operation != "set": try: self._hndl.execute("begin") except cdb2.Error as e: _raise_wrapped_exception(e) self._conn._in_transaction = True if parameters is None: parameters = {} try: # If variable interpolation fails, then translate the exception to # an InterfaceError to signal that it's a client-side problem. sql = sql % {name: "@" + name for name in parameters} except KeyError as keyerr: msg = "No value provided for parameter %s" % keyerr six.raise_from(InterfaceError(msg), keyerr) except Exception as exc: msg = "Invalid Python format string for query" six.raise_from(InterfaceError(msg), exc) if _operation_ends_transaction(operation): self._conn._in_transaction = False # txn ends, even on failure try: self._hndl.execute(sql, parameters) except cdb2.Error as e: _raise_wrapped_exception(e) if operation == 'begin': self._conn._in_transaction = True # txn successfully started elif not self._conn._in_transaction and _modifies_rows(operation): # We're not in a transaction, and the last statement could have # modified rows. Either we've just explicitly committed # a transaction, or we're in autocommit mode and ran DML outside of # an explicit transaction. We can get the count of affected rows. self._update_rowcount() def setinputsizes(self, sizes): """No-op; implemented for PEP-249 compliance.""" self._check_closed() def setoutputsize(self, size, column=None): """No-op; implemented for PEP-249 compliance.""" self._check_closed() def _update_rowcount(self): try: self._rowcount = self._hndl.get_effects()[0] except cdb2.Error: self._rowcount = -1 def _load_description(self): names = self._hndl.column_names() types = self._hndl.column_types() self._description = tuple((name, type, None, None, None, None, None) for name, type in zip(names, types)) if not self._description: self._description = None def fetchone(self): """Fetch the next row of the current result set. Returns: Row: If no rows remain in the current result set, ``None`` is returned, otherwise the next row of the result set is returned. By default the row is returned as a `list`, where the elements in the list correspond to the result row's columns in positional order, but this can be changed with the `Connection.row_factory` property. """ try: return next(self) except StopIteration: return None def fetchmany(self, n=None): """Fetch the next set of rows of the current result set. Args: n: Maximum number of rows to be returned. If this argument is not given, `Cursor.arraysize` is used as the maximum. Returns: List[Row]: Returns a `list` containing the next ``n`` rows of the result set. If fewer than ``n`` rows remain, the returned list will contain fewer than ``n`` elements. If no rows remain, the list will be empty. By default each row is returned as a `list`, where the elements in the list correspond to the result row's columns in positional order, but this can be changed with the `Connection.row_factory` property. """ if n is None: n = self._arraysize return [x for x in itertools.islice(self, 0, n)] def fetchall(self): """Fetch all remaining rows of the current result set. Returns: List[Row]: Returns a `list` containing all remaining rows of the result set. By default each row is returned as a `list`, where the elements in the list correspond to the result row's columns in positional order, but this can be changed with the `Connection.row_factory` property. """ return [x for x in self] # Optional DB API Extension def __iter__(self): """Iterate over all rows in a result set. By default each row is returned as a `list`, where the elements in the list correspond to the result row's columns in positional order, but this can be changed with the `Connection.row_factory` property. Note: This is not required by DB-API 2.0; for maximum portability applications should prefer to use `fetchone` or `fetchmany` or `fetchall` instead. Example: >>> cursor.execute("select 1, 2 UNION ALL select 3, 4") >>> for row in cursor: ... print(row) [1, 2] [3, 4] """ self._check_closed() return self # Optional DB API Extension def next(self): self._check_closed() if not self._description: raise InterfaceError("No result set exists") try: return next(self._hndl) except cdb2.Error as e: _raise_wrapped_exception(e) __next__ = next
39.39547
79
0.657277
beeccd82c643ace8391f7591995991251000781e
1,458
py
Python
sis_provisioner/tests/account_managers/test_terminate.py
uw-it-aca/bridge-sis-provisioner
6dd31e5ef59263acbcade5e6e4f74b815c16bdee
[ "Apache-2.0" ]
null
null
null
sis_provisioner/tests/account_managers/test_terminate.py
uw-it-aca/bridge-sis-provisioner
6dd31e5ef59263acbcade5e6e4f74b815c16bdee
[ "Apache-2.0" ]
175
2016-07-18T23:25:45.000Z
2022-02-07T20:44:05.000Z
sis_provisioner/tests/account_managers/test_terminate.py
uw-it-aca/bridge-sis-provisioner
6dd31e5ef59263acbcade5e6e4f74b815c16bdee
[ "Apache-2.0" ]
null
null
null
# Copyright 2021 UW-IT, University of Washington # SPDX-License-Identifier: Apache-2.0 from django.test import TransactionTestCase from unittest.mock import patch from sis_provisioner.account_managers.terminate import TerminateUser from sis_provisioner.account_managers.bridge_worker import BridgeWorker from sis_provisioner.tests import ( fdao_pws_override, fdao_gws_override, fdao_bridge_override) from sis_provisioner.tests.account_managers import set_db_records @fdao_pws_override @fdao_gws_override @fdao_bridge_override class TestTerminateUser(TransactionTestCase): @patch('sis_provisioner.dao.gws._get_start_timestamp', return_value=1626215400, spec=True) def test_to_check(self, mock_fn): loader = TerminateUser(BridgeWorker()) self.assertEqual(len(loader.fetch_users()), 2) @patch('sis_provisioner.dao.gws._get_start_timestamp', return_value=1626215400, spec=True) def test_update(self, mock_fn): set_db_records() loader = TerminateUser(BridgeWorker()) loader.load() self.assertEqual(loader.get_total_checked_users(), 2) self.assertEqual(loader.get_new_user_count(), 0) self.assertEqual(loader.get_netid_changed_count(), 1) self.assertEqual(loader.get_deleted_count(), 1) self.assertEqual(loader.get_restored_count(), 0) self.assertEqual(loader.get_updated_count(), 1) self.assertFalse(loader.has_error())
39.405405
71
0.758573
3221963c01409c2458b36cad2b5ce313f2a133a3
1,179
py
Python
PositivosMedia1064.py
SricardoSdSouza/LogicaDeProgramacao
f39763bd3378640ff8de674c0b932f36fd09296a
[ "MIT" ]
null
null
null
PositivosMedia1064.py
SricardoSdSouza/LogicaDeProgramacao
f39763bd3378640ff8de674c0b932f36fd09296a
[ "MIT" ]
null
null
null
PositivosMedia1064.py
SricardoSdSouza/LogicaDeProgramacao
f39763bd3378640ff8de674c0b932f36fd09296a
[ "MIT" ]
null
null
null
''' Leia 6 valores. Em seguida, mostre quantos destes valores digitados foram positivos. Na próxima linha, deve-se mostrar a média de todos os valores positivos digitados, com um dígito após o ponto decimal. Entrada = A entrada contém 6 números que podem ser valores inteiros ou de ponto flutuante. Pelo menos um destes números será positivo. Saída = O primeiro valor de saída é a quantidade de valores positivos. A próxima linha deve mostrar a média dos valores positivos digitados. Exemplo de Entrada Exemplo de Saída 7 4 valores positivos -5 7.4 6 -3.4 4.6 12 ''' tot = 0 cont = 0 for i in range(6): num = float(input()) if num > 0: cont += 1 tot += num print(f'{cont} valores positivos') print(f'{tot/cont:.1f}') '''Outra forma positivos = [float(input()) for _ in range(6)] total_positivos = 0 soma_dos_positivos = 0 for n in positivos: if n > 0: total_positivos += 1 soma_dos_positivos += n print('{} valores positivos'.format(total_positivos)) print('{:.1f}'.format(soma_dos_positivos / total_positivos)) '''
28.756098
111
0.641221