xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

c7eeb6ad96b62ee9ed39e894eafdc5891f6f7f42

28

py

Python

mozumder/management/writers/__init__.py

mozumder/django-mozumder

887ce303249eac2d77de062fd57023dbc4b782dd

[ "MIT" ]

1

2020-06-13T06:12:16.000Z

mozumder/management/writers/__init__.py

mozumder/django-mozumder

887ce303249eac2d77de062fd57023dbc4b782dd

[ "MIT" ]

4

2020-06-18T03:53:29.000Z

2021-06-09T17:56:12.000Z

mozumder/management/writers/__init__.py

mozumder/django-mozumder

887ce303249eac2d77de062fd57023dbc4b782dd

[ "MIT" ]

null

from .app import write_app

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1bf3c26f75d5263e015cbfa1925edca979266e2f

106

py

Python

globals.py

srdjanko/CarND-LaneLines-P1

ea09c92b81b983d73317b334b19995d641ad314a

[ "MIT" ]

null

globals.py

srdjanko/CarND-LaneLines-P1

ea09c92b81b983d73317b334b19995d641ad314a

[ "MIT" ]

null

globals.py

srdjanko/CarND-LaneLines-P1

ea09c92b81b983d73317b334b19995d641ad314a

[ "MIT" ]

null

import numpy as np glob_previous_lanes = [np.array([0.0, 0.0, 0.0, 0.0]), np.array([0.0, 0.0, 0.0, 0.0])]

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Python

LearningAlgorithm/pain/ANN (New).py

Mirotivo/biovid

4cc4b1d2afd3f37224c74fe982d67aee99b81dc0

[ "BSD-2-Clause" ]

null

LearningAlgorithm/pain/ANN (New).py

Mirotivo/biovid

4cc4b1d2afd3f37224c74fe982d67aee99b81dc0

[ "BSD-2-Clause" ]

null

LearningAlgorithm/pain/ANN (New).py

Mirotivo/biovid

4cc4b1d2afd3f37224c74fe982d67aee99b81dc0

[ "BSD-2-Clause" ]

null

import ClassifierModel import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import confusion_matrix # Importing the dataset dataset = pd.read_csv('features/Table_Step2_159Features-85Subs-5Levels-z.csv') Combined = dataset.iloc[:,7:].values # Imputing the missing features and replacing it with the mean value excluding the first column # axis 0 is columnwise && axis 1 is rowwise. Combined[:,1:] = ClassifierModel.ImputeDataSet(Combined) # Separating each level # 0.1 level_zero_one, 1 level_one, 2 level_two,3 level_three,4 level_four level_zero_one,level_one,level_two,level_three,level_four=ClassifierModel.SeparateEachLevel(Combined) # Classify between the baseline and pain threshold # axis 0 is columnwise && axis 1 is rowwise. Combined = np.concatenate((level_zero_one, level_one), axis=0) # Encoding classes to integer levels X,y = ClassifierModel.features_lables_split(Combined) # Splitting the dataset into the Training set and Test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 7) # Feature Scaling X_train = ClassifierModel.NormalizeFeatures(X_train) X_test = ClassifierModel.NormalizeFeatures(X_test) # Part 2 - Now let's make the ANN! # Importing the Keras libraries and packages import keras from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras import metrics from sklearn.decomposition import PCA pca = PCA(n_components=70)# adjust yourself pca.fit(X_train) X_train = pca.transform(X_train) X_test = pca.transform(X_test) # Initialising the ANN classifier = Sequential() # Adding the input layer and the first hidden layer classifier.add(Dense(units = 35, activation = 'relu',kernel_initializer="uniform", input_dim = 70)) # Adding the second hidden layer #.add(Dense(output_dim = 80, init = 'uniform', activation = 'relu')) #classifier.add(Dense(units = 40, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(units = 50, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(output_dim = 20, init = 'uniform', activation = 'relu')) classifier.add(Dense(units = 10, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(units = 15, activation = 'relu',kernel_initializer="uniform")) # Adding the output layer classifier.add(Dense(output_dim = 1, kernel_initializer = 'uniform', activation = 'sigmoid')) # Compiling the ANN classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) # Fitting the ANN to the Training set classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100) # Predicting the Test set results y_pred = classifier.predict(X_test) y_pred = (y_pred > 0.5) # Making the Confusion Matrix cm = confusion_matrix(y_test, y_pred) ClassifierModel.Visualize_CM(cm,[0,1]) # Evaluation accuracy,precision,recall,sensitivity,specificity = ClassifierModel.EvaluateClassifier(y_test, y_pred) print('Accuracy (BvsT1): '+str(accuracy)) print('Precision (BvsT1): '+str(precision)) print('Recall (BvsT1): '+str(recall)) print('Sensitivity (BvsT1): '+str(sensitivity)) print('Specificity (BvsT1): '+str(specificity)) # Classify between the baseline and pain threshold # axis 0 is columnwise && axis 1 is rowwise. Combined = np.concatenate((level_zero_one, level_two), axis=0) # Encoding classes to integer levels X,y = ClassifierModel.features_lables_split(Combined) # Splitting the dataset into the Training set and Test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 7) # Feature Scaling X_train = ClassifierModel.NormalizeFeatures(X_train) X_test = ClassifierModel.NormalizeFeatures(X_test) # Part 2 - Now let's make the ANN! # Importing the Keras libraries and packages import keras from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras import metrics # Initialising the ANN classifier = Sequential() # Adding the input layer and the first hidden layer classifier.add(Dense(units = 120, activation = 'relu',kernel_initializer="uniform", input_dim = 159)) # Adding the second hidden layer #.add(Dense(output_dim = 80, init = 'uniform', activation = 'relu')) classifier.add(Dense(units = 90, activation = 'relu',kernel_initializer="uniform")) classifier.add(Dense(units = 40, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(units = 50, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(output_dim = 20, init = 'uniform', activation = 'relu')) classifier.add(Dense(units = 30, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(units = 15, activation = 'relu',kernel_initializer="uniform")) # Adding the output layer classifier.add(Dense(output_dim = 1, kernel_initializer = 'uniform', activation = 'sigmoid')) # Compiling the ANN classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) # Fitting the ANN to the Training set classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix cm = confusion_matrix(y_test, y_pred) ClassifierModel.Visualize_CM(cm,[0,1]) # Evaluation accuracy,precision,recall,sensitivity,specificity = ClassifierModel.EvaluateClassifier(y_test, y_pred) print('Accuracy (BvsT2): '+str(accuracy)) print('Precision (BvsT2): '+str(precision)) print('Recall (BvsT2): '+str(recall)) print('Sensitivity (BvsT2): '+str(sensitivity)) print('Specificity (BvsT2): '+str(specificity)) # Classify between the baseline and pain threshold # axis 0 is columnwise && axis 1 is rowwise. Combined = np.concatenate((level_zero_one, level_three), axis=0) # Encoding classes to integer levels X,y = ClassifierModel.features_lables_split(Combined) # Splitting the dataset into the Training set and Test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 7) # Feature Scaling X_train = ClassifierModel.NormalizeFeatures(X_train) X_test = ClassifierModel.NormalizeFeatures(X_test) # Part 2 - Now let's make the ANN! # Importing the Keras libraries and packages import keras from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras import metrics # Initialising the ANN classifier = Sequential() # Adding the input layer and the first hidden layer classifier.add(Dense(units = 120, activation = 'relu',kernel_initializer="uniform", input_dim = 159)) # Adding the second hidden layer #.add(Dense(output_dim = 80, init = 'uniform', activation = 'relu')) classifier.add(Dense(units = 90, activation = 'relu',kernel_initializer="uniform")) classifier.add(Dense(units = 40, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(units = 50, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(output_dim = 20, init = 'uniform', activation = 'relu')) classifier.add(Dense(units = 30, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(units = 15, activation = 'relu',kernel_initializer="uniform")) # Adding the output layer classifier.add(Dense(output_dim = 1, kernel_initializer = 'uniform', activation = 'sigmoid')) # Compiling the ANN classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) # Fitting the ANN to the Training set classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix cm = confusion_matrix(y_test, y_pred) ClassifierModel.Visualize_CM(cm,[0,1]) # Evaluation accuracy,precision,recall,sensitivity,specificity = ClassifierModel.EvaluateClassifier(y_test, y_pred) print('Accuracy (BvsT3): '+str(accuracy)) print('Precision (BvsT3): '+str(precision)) print('Recall (BvsT3): '+str(recall)) print('Sensitivity (BvsT3): '+str(sensitivity)) print('Specificity (BvsT3): '+str(specificity)) # Classify between the baseline and pain threshold # axis 0 is columnwise && axis 1 is rowwise. Combined = np.concatenate((level_zero_one, level_four), axis=0) # Encoding classes to integer levels X,y = ClassifierModel.features_lables_split(Combined) # Splitting the dataset into the Training set and Test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 7) # Part 2 - Now let's make the ANN! # Importing the Keras libraries and packages import keras from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras import metrics # Initialising the ANN classifier = Sequential() # Adding the input layer and the first hidden layer classifier.add(Dense(units = 120, activation = 'relu',kernel_initializer="uniform", input_dim = 159)) # Adding the second hidden layer #.add(Dense(output_dim = 80, init = 'uniform', activation = 'relu')) classifier.add(Dense(units = 90, activation = 'relu',kernel_initializer="uniform")) classifier.add(Dense(units = 40, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(units = 50, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(output_dim = 20, init = 'uniform', activation = 'relu')) classifier.add(Dense(units = 30, activation = 'relu',kernel_initializer="uniform")) #classifier.add(Dense(units = 15, activation = 'relu',kernel_initializer="uniform")) # Adding the output layer classifier.add(Dense(output_dim = 1, kernel_initializer = 'uniform', activation = 'sigmoid')) # Compiling the ANN classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) # Fitting the ANN to the Training set classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100) # Fitting Kernel SVM to the Training set classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix cm = confusion_matrix(y_test, y_pred) ClassifierModel.Visualize_CM(cm,[0,1]) # Evaluation accuracy,precision,recall,sensitivity,specificity = ClassifierModel.EvaluateClassifier(y_test, y_pred) print('Accuracy (BvsT4): '+str(accuracy)) print('Precision (BvsT4): '+str(precision)) print('Recall (BvsT4): '+str(recall)) print('Sensitivity (BvsT4): '+str(sensitivity)) print('Specificity (BvsT4): '+str(specificity)) # Classify between the baseline and pain threshold # axis 0 is columnwise && axis 1 is rowwise. Combined = np.concatenate((level_zero_one, level_one), axis=0) Combined = np.concatenate((Combined, level_four), axis=0) # Encoding classes to integer levels X,y = ClassifierModel.features_lables_split(Combined) # Splitting the dataset into the Training set and Test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 7) # Feature Scaling X_train = ClassifierModel.NormalizeFeatures(X_train) X_test = ClassifierModel.NormalizeFeatures(X_test) # Fitting Kernel SVM to the Training set classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix cm = confusion_matrix(y_test, y_pred) ClassifierModel.Visualize_CM(cm,[0,1,2]) # Evaluation accuracy,precision,recall,sensitivity,specificity = ClassifierModel.EvaluateClassifier(y_test, y_pred) print('Accuracy (BvsT1vsT4): '+str(accuracy)) print('Precision (BvsT1vsT4): '+str(precision)) print('Recall (BvsT1vsT4): '+str(recall)) print('Sensitivity (BvsT1vsT4): '+str(sensitivity)) print('Specificity (BvsT1vsT4): '+str(specificity)) # Classify between the baseline and pain threshold # axis 0 is columnwise && axis 1 is rowwise. Combined = np.concatenate((level_zero_one, level_one), axis=0) Combined = np.concatenate((Combined, level_two), axis=0) Combined = np.concatenate((Combined, level_three), axis=0) Combined = np.concatenate((Combined, level_four), axis=0) # Encoding classes to integer levels X,y = ClassifierModel.features_lables_split(Combined) # Splitting the dataset into the Training set and Test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 7) # Feature Scaling X_train = ClassifierModel.NormalizeFeatures(X_train) X_test = ClassifierModel.NormalizeFeatures(X_test) # Fitting Kernel SVM to the Training set classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix cm = confusion_matrix(y_test, y_pred) ClassifierModel.Visualize_CM(cm,[0,1,2,3,4]) # Evaluation accuracy,precision,recall,sensitivity,specificity = ClassifierModel.EvaluateClassifier(y_test, y_pred) print('Accuracy (BvsT1vsT2vsT3vsT4): '+str(accuracy)) print('Precision (BvsT1vsT2vsT3vsT4): '+str(precision)) print('Recall (BvsT1vsT2vsT3vsT4): '+str(recall)) print('Sensitivity (BvsT1vsT2vsT3vsT4): '+str(sensitivity)) print('Specificity (BvsT1vsT2vsT3vsT4): '+str(specificity))

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404f31179de619313ef31f7c965ce5b6e1d50d1a

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py

Python

d3rlpy_addons/models/torch/q_functions.py

jamartinh/d3rlpy-addons

9561432062cfe83150d17c0908b2013d15d71ee3

[ "MIT" ]

1

2021-08-28T14:32:15.000Z

d3rlpy_addons/models/torch/q_functions.py

jamartinh/d3rlpy-addons

9561432062cfe83150d17c0908b2013d15d71ee3

[ "MIT" ]

null

d3rlpy_addons/models/torch/q_functions.py

jamartinh/d3rlpy-addons

9561432062cfe83150d17c0908b2013d15d71ee3

[ "MIT" ]

null

from typing import cast import torch from torch import nn from d3rlpy.models.torch import Encoder, EncoderWithAction from d3rlpy.models.torch.q_functions.mean_q_function import ( ContinuousMeanQFunction, DiscreteMeanQFunction, ) from d3rlpy.models.torch.q_functions.qr_q_function import ( ContinuousQRQFunction, DiscreteQRQFunction, ) class DiscreteDQRQFunction(DiscreteQRQFunction): _fc0: nn.Linear _q_value_offset: float def __init__( self, encoder: Encoder, action_size: int, n_quantiles: int, q_value_offset: float = 0.0, ): super().__init__(encoder, action_size, n_quantiles) # initial q_values for approximation self._q_value_offset = q_value_offset # get a new instance or clone a frozen copy self._fc0 = type(self._fc)(encoder.get_feature_size(), n_quantiles) # copy weights and stuff self._fc0.load_state_dict(self._fc.state_dict()) # freeze model by freezing parameters for param in self._fc0.parameters(): param.requires_grad = False # set fc0 in eval mode only self._fc0.eval() def _compute_quantiles( self, h: torch.Tensor, taus: torch.Tensor ) -> torch.Tensor: h = cast( torch.Tensor, (self._fc(h) - self._fc0(h)) + self._q_value_offset ) return h.view(-1, self._action_size, self._n_quantiles) class ContinuousDQRQFunction(ContinuousQRQFunction): _fc0: nn.Linear _q_value_offset: float def __init__( self, encoder: EncoderWithAction, n_quantiles: int, q_value_offset: float = 0.0, ): super().__init__(encoder, n_quantiles) # initial q_values for approximation self._q_value_offset = q_value_offset # get a new instance or clone a frozen copy self._fc0 = type(self._fc)( encoder.get_feature_size(), self._n_quantiles ) # copy weights and stuff self._fc0.load_state_dict(self._fc.state_dict()) # freeze model by freezing parameters for param in self._fc0.parameters(): param.requires_grad = False # set fc0 in eval mode only self._fc0.eval() def _compute_quantiles( self, h: torch.Tensor, taus: torch.Tensor ) -> torch.Tensor: return cast( torch.Tensor, (self._fc(h) - self._fc0(h)) + self._q_value_offset ) class DiscreteDMeanQFunction(DiscreteMeanQFunction): _fc0: nn.Linear _q_value_offset: float def __init__( self, encoder: Encoder, action_size: int, q_value_offset: float = 0.0 ): super().__init__(encoder=encoder, action_size=action_size) # initial q_values for approximation self._q_value_offset = q_value_offset # get a new instance or clone a frozen copy self._fc0 = type(self._fc)(encoder.get_feature_size(), 1) # copy weights and stuff self._fc0.load_state_dict(self._fc.state_dict()) # freeze model by freezing parameters for param in self._fc0.parameters(): param.requires_grad = False # set fc0 in eval mode only self._fc0.eval() def forward(self, x: torch.Tensor) -> torch.Tensor: return cast( torch.Tensor, self._fc(self._encoder(x)) - self._fc0(self._encoder(x)) + self._q_value_offset, ) class ContinuousDMeanQFunction(ContinuousMeanQFunction): _fc0: nn.Linear _q_value_offset: float def __init__(self, encoder: EncoderWithAction, q_value_offset: float = 0.0): super().__init__(encoder=encoder) # initial q_values for approximation self._q_value_offset = q_value_offset # get a new instance or clone a frozen copy self._fc0 = type(self._fc)(encoder.get_feature_size(), 1) # copy weights and stuff self._fc0.load_state_dict(self._fc.state_dict()) # freeze model by freezing parameters for param in self._fc0.parameters(): param.requires_grad = False # set fc0 in eval mode only self._fc0.eval() def forward(self, x: torch.Tensor, action: torch.Tensor) -> torch.Tensor: return cast( torch.Tensor, self._fc(self._encoder(x, action)) - self._fc0(self._encoder(x, action)) + self._q_value_offset, )

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py

Python

modules/routing.py

ZikeYan/RoutedFusion

866699ff1eba48cdad20bbde9cc498c17848ac50

[ "BSD-3-Clause" ]

100

2020-07-10T11:54:46.000Z

2022-03-16T08:22:22.000Z

modules/routing.py

ZikeYan/RoutedFusion

866699ff1eba48cdad20bbde9cc498c17848ac50

[ "BSD-3-Clause" ]

19

2020-09-20T14:32:23.000Z

2022-01-17T00:39:24.000Z

modules/routing.py

ZikeYan/RoutedFusion

866699ff1eba48cdad20bbde9cc498c17848ac50

[ "BSD-3-Clause" ]

15

2020-09-21T14:15:23.000Z

2022-01-12T23:09:41.000Z

import torch class UNet(torch.nn.Module): """ Basic UNet building block, calling itself recursively. Note that the final output does not have a ReLU applied. """ def __init__(self, Cin, F, Cout, depth, batchnorms=True): super().__init__() self.F = F self.depth = depth if batchnorms: self.pre = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(Cin, F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(F), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(F), torch.nn.ReLU(), ) self.post = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(3 * F, F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(F), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, Cout, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(Cout), ) else: self.pre = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(Cin, F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), ) self.post = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(3 * F, F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, Cout, kernel_size=3, stride=1, padding=0), torch.nn.ReLU() ) if depth > 1: self.process = UNet(F, 2 * F, 2 * F, depth - 1, batchnorms=batchnorms) else: if batchnorms: self.process = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, 2 * F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(2 * F), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(2 * F, 2 * F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(2 * F), torch.nn.ReLU(), ) else: self.process = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, 2 * F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(2 * F, 2 * F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), ) self.maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def forward(self, data): features = self.pre(data) lower_scale = self.maxpool(features) lower_features = self.process(lower_scale) upsampled = torch.nn.functional.interpolate(lower_features, scale_factor=2, mode="bilinear", align_corners=False) H = data.shape[2] W = data.shape[3] upsampled = upsampled[:, :, :H, :W] output = self.post(torch.cat((features, upsampled), dim=1)) return output class ConfidenceRouting(torch.nn.Module): """ Network for confidence routing in RoutedFusion. """ def __init__(self, Cin, F, Cout, depth, batchnorms=True): super().__init__() self.F = F self.depth = depth if batchnorms: self.pre = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(Cin, F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(F), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(F), torch.nn.ReLU(), ) self.post = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(3 * F, F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(F), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, Cout, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(Cout), ) else: self.pre = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(Cin, F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), ) self.post = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(3 * F, F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, Cout, kernel_size=3, stride=1, padding=0), torch.nn.ReLU() ) if depth > 1: self.process = UNet(F, 2 * F, 2 * F, depth - 1, batchnorms=batchnorms) else: if batchnorms: self.process = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, 2 * F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(2 * F), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(2 * F, 2 * F, kernel_size=3, stride=1, padding=0), torch.nn.BatchNorm2d(2 * F), torch.nn.ReLU(), ) else: self.process = torch.nn.Sequential( torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, 2 * F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(2 * F, 2 * F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), ) self.uncertainty = torch.nn.Sequential(torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(3 * F, F, kernel_size=3, stride=1, padding=0), torch.nn.ReLU(), torch.nn.ReflectionPad2d(1), torch.nn.Conv2d(F, Cout, kernel_size=3, stride=1, padding=0), torch.nn.ReLU()) self.maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def forward(self, data): features = self.pre(data) lower_scale = self.maxpool(features) lower_features = self.process(lower_scale) upsampled = torch.nn.functional.interpolate(lower_features, scale_factor=2, mode="bilinear", align_corners=False) H = data.shape[2] W = data.shape[3] upsampled = upsampled[:, :, :H, :W] output = self.post(torch.cat((features, upsampled), dim=1)) uncertainty = self.uncertainty(torch.cat((features, upsampled), dim=1)) return torch.cat((output, uncertainty), dim=1) def get_influence_percentages(self): """ This function is intended to return a matrix of influences. I.e. for each output channel it returns the percentage it is controlled by each input channel. Very very roughly speaking, as all this does is iteratively calculate these percentages based on fractional absolute weighting. Output: percentages -- C_out x C_in matrix giving the weights """ if isinstance(self.pre[1], torch.nn.BatchNorm2d): print("BatchNorm UNets not supported for influence percentages") return None pre1 = self.pre[1].weight.abs().sum(dim=3).sum(dim=2) pre1 = pre1 / pre1.sum(dim=1, keepdim=True) pre2 = self.pre[4].weight.abs().sum(dim=3).sum(dim=2) pre2 = pre2 / pre2.sum(dim=1, keepdim=True) pre2 = torch.matmul(pre2, pre1) if isinstance(self.process, UNet): process2 = torch.matmul(self.process.get_influence_percentages(), pre2) else: process1 = self.process[1].weight.abs().sum(dim=3).sum(dim=2) process1 = process1 / process1.sum(dim=1, keepdim=True) process1 = torch.matmul(process1, pre2) process2 = self.process[4].weight.abs().sum(dim=3).sum(dim=2) process2 = process2 / process2.sum(dim=1, keepdim=True) process2 = torch.matmul(process2, process1) post1 = self.post[1].weight.abs().sum(dim=3).sum(dim=2) post1 = post1 / post1.sum(dim=1, keepdim=True) post1 = torch.matmul(post1, torch.cat((pre2, process2), dim=0)) post2 = self.post[4].weight.abs().sum(dim=3).sum(dim=2) post2 = post2 / post2.sum(dim=1, keepdim=True) post2 = torch.matmul(post2, post1) return post2 return final_layer

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null

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6

dc0c08243e4a84c07bf42df1f008b3d55ddce1d3

11,599

py

Python

source/tests/py_tests/unsafe_test.py

Panzerschrek/U-00DC-Sprache

eb677a66d178985433a62eb6b8a50ce2cdb14b1a

[ "BSD-3-Clause" ]

45

2016-06-21T22:28:43.000Z

2022-03-26T12:21:46.000Z

source/tests/py_tests/unsafe_test.py

Panzerschrek/U-00DC-Sprache

eb677a66d178985433a62eb6b8a50ce2cdb14b1a

[ "BSD-3-Clause" ]

6

2020-07-12T18:00:10.000Z

2021-11-30T11:20:14.000Z

source/tests/py_tests/unsafe_test.py

Panzerschrek/U-00DC-Sprache

eb677a66d178985433a62eb6b8a50ce2cdb14b1a

[ "BSD-3-Clause" ]

5

2019-09-03T17:20:34.000Z

2022-01-30T15:10:21.000Z

from py_tests_common import * def UnsafeBlockDeclaration_Test0(): c_program_text= """ fn Foo() { unsafe{} } """ tests_lib.build_program( c_program_text ) def UnsafeFunctionDeclaration_Test0(): c_program_text= """ fn Foo() unsafe; """ tests_lib.build_program( c_program_text ) def UnsafeFunctionDeclaration_Test1(): c_program_text= """ fn Foo() unsafe : i32; """ tests_lib.build_program( c_program_text ) def UnsafeFunctionCallOutsideUnsafeBlock_Test0(): c_program_text= """ fn Bar() unsafe; fn Foo() { Bar(); // Regular function call } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 5 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test1(): c_program_text= """ fn Bar( i32 &imut x ); fn Bar( i32 & mut x ) unsafe; fn Foo() { Bar(42); // Ok, safe function selected. } """ tests_lib.build_program( c_program_text ) def UnsafeFunctionCallOutsideUnsafeBlock_Test2(): c_program_text= """ fn Bar( i32 &imut x ); fn Bar( i32 & mut x ) unsafe; fn Foo() { var i32 mut x= 0; Bar(x); // Error, unsafe function selected. } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 7 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test3(): c_program_text= """ struct S { fn constructor() unsafe {} } fn Foo() { var S s; // Error, implicitly calling unsafe constructor } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 8 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test4(): c_program_text= """ struct S { fn constructor( i32 x ) unsafe {} } fn Foo() { var S s( 666 ); // Error, explicitly calling unsafe constructor } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 8 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test5(): c_program_text= """ struct S { fn constructor( S& other ) unsafe {} // unsafe copy constructor } fn Foo() { var S s0; var S s1= s0; // Error, calling unsafe copy-constructor } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 9 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test6(): c_program_text= """ struct S { op= ( mut this, S& other ) unsafe {} } fn Foo() { var S s0, mut s1; s1= s0; // Error, calling unsafe copy assignment operator } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 9 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test7(): c_program_text= """ struct S { op++( mut this ) unsafe {} } fn Foo() { var S mut s; ++s; } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 9 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test8(): c_program_text= """ struct S { op[]( mut this, i32 x ) unsafe {} } fn Foo() { var S mut s; s[0]; } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 9 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test9(): c_program_text= """ struct S { op()( this ) unsafe {} } fn Foo() { var S mut s; s(); } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 9 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test10(): c_program_text= """ fn Bar() unsafe; fn Foo() unsafe { Bar(); // Even in unsafe function we needs unsafe block to call other unsafe functions. } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 5 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test11(): c_program_text= """ struct S { fn destructor() unsafe {} } fn Foo() { var S s; } // Error, calling unsafe destructor here """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 9 ) def UnsafeFunctionCallOutsideUnsafeBlock_Test12(): c_program_text= """ struct S { fn destructor() unsafe {} } struct B // Error, while generating default-destructor. Currently, classes with unsafe destructor can not be members of other classes. { S s; } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 6 ) def UnsafeFunctionCallInsideUnsafeBlock_Test0(): c_program_text= """ fn Bar() unsafe; fn Foo() { unsafe { Bar(); // Ok, we are inside unsafe block } } """ tests_lib.build_program( c_program_text ) def UnsafeFunctionCallInsideUnsafeBlock_Test1(): c_program_text= """ struct S { fn constructor() unsafe {} } fn Foo() { unsafe { var S s; // Ok, implicitly call here unsafe constructor } } """ tests_lib.build_program( c_program_text ) def UnsafeFunctionCallInsideUnsafeBlock_Test2(): c_program_text= """ fn Bar() unsafe; fn Foo() { unsafe { { Bar(); // Ok, we are inside unsafe block } } } """ tests_lib.build_program( c_program_text ) def UnsafeFunctionCallInsideUnsafeBlock_Test3(): c_program_text= """ fn Bar() unsafe; fn Foo() { unsafe { if(true) { Bar(); // Ok, we are inside unsafe block } } } """ tests_lib.build_program( c_program_text ) def UnsafeFunctionCallInsideUnsafeBlock_Test4(): c_program_text= """ fn Bar() unsafe; fn Foo() { unsafe { while(true) { Bar(); // Ok, we are inside unsafe block break; } } } """ tests_lib.build_program( c_program_text ) def UnsafeFunctionCallInsideUnsafeBlock_Test5(): c_program_text= """ struct S { fn destructor() unsafe {} } fn Foo() { unsafe { var S s; } // Ok, call unsafe destructor at end of unsafe block. } """ tests_lib.build_program( c_program_text ) def CouldNotOverloadFunction_ForUnsafe_Test0(): c_program_text= """ fn Foo(); fn Foo() unsafe; """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "CouldNotOverloadFunction" ) assert( errors_list[0].src_loc.line == 2 or errors_list[0].src_loc.line == 3 ) def CouldNotOverloadFunction_ForUnsafe_Test1(): c_program_text= """ fn Foo() unsafe; fn Foo() {} // Trying to create body without 'unsafe' """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "CouldNotOverloadFunction" ) assert( errors_list[0].src_loc.line == 2 or errors_list[0].src_loc.line == 3 ) def FunctionDoesNotOverride_ForUnsafe_Test0(): c_program_text= """ class A polymorph { fn virtual Foo( this ); } class B : A { fn virtual override Foo( this ) unsafe; // 'unsafe' breaks 'override' here } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "FunctionDoesNotOverride" ) assert( errors_list[0].src_loc.line == 9 ) def ExplicitAccessToSpecialMethodsIsUnsafe_Test0(): c_program_text= """ struct S {} // have generated default-constructor fn Foo() { var S mut s; s.constructor; } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "ExplicitAccessToThisMethodIsUnsafe" ) assert( errors_list[0].src_loc.line == 6 ) def ExplicitAccessToSpecialMethodsIsUnsafe_Test1(): c_program_text= """ struct S {} // have generated default-constructor fn Foo() { S::constructor; } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "ExplicitAccessToThisMethodIsUnsafe" ) assert( errors_list[0].src_loc.line == 5 ) def ExplicitAccessToSpecialMethodsIsUnsafe_Test2(): c_program_text= """ struct S { fn destructor(){} } fn Foo() { var S mut s; s.destructor(); } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "ExplicitAccessToThisMethodIsUnsafe" ) assert( errors_list[0].src_loc.line == 6 ) def ExplicitAccessToSpecialMethodsIsUnsafe_Test3(): c_program_text= """ struct S { fn destructor(){} } fn Foo() { ::S::destructor; } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "ExplicitAccessToThisMethodIsUnsafe" ) assert( errors_list[0].src_loc.line == 5 ) def ExplicitAccessToSpecialMethodsIsUnsafe_Test4(): c_program_text= """ struct S { fn constructor( i32 x ){} } fn Foo() { var S mut s(0); unsafe{ s.constructor(42); } // ok, can access constructor in unsafe block } """ tests_lib.build_program( c_program_text ) def ExplicitAccessToSpecialMethodsIsUnsafe_Test5(): c_program_text= """ struct S { fn destructor(){} } fn Foo() { var S mut s; unsafe{ s.destructor(); } // ok, can access destructor in unsafe block } """ tests_lib.build_program( c_program_text ) def SafeBlockResetsUnsafe_Test(): c_program_text= """ fn Bar() unsafe; fn Foo() { unsafe { safe { Bar(); } } } """ errors_list= ConvertErrors( tests_lib.build_program_with_errors( c_program_text ) ) assert( len(errors_list) > 0 ) assert( errors_list[0].error_code == "UnsafeFunctionCallOutsideUnsafeBlock" ) assert( errors_list[0].src_loc.line == 9 ) def UnsafeInsideUnsafe_Test(): c_program_text= """ fn Bar() unsafe; fn Foo() { unsafe { { unsafe { Bar(); } } } } """ tests_lib.build_program( c_program_text )

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6

dc13f3dc88fc46000cab8adaba79cc069ecf9805

28,659

py

Python

data_preprocess/data_pretreat.py

TAN-OpenLab/TCSE-net

fc6ecf704a9c128a9b5b6853cffa8486ee0f54e8

[ "Apache-2.0" ]

null

data_preprocess/data_pretreat.py

TAN-OpenLab/TCSE-net

fc6ecf704a9c128a9b5b6853cffa8486ee0f54e8

[ "Apache-2.0" ]

null

data_preprocess/data_pretreat.py

TAN-OpenLab/TCSE-net

fc6ecf704a9c128a9b5b6853cffa8486ee0f54e8

[ "Apache-2.0" ]

null

# import numpy as np # import six.moves.cPickle as pickle # from data_preprocess import config # import networkx as nx # from data_preprocess import Laplacian # import scipy.sparse # from collections import Counter # import gc # LABEL_NUM = 0 # import math # # # trans the original ids to 1~n # class IndexDict: # def __init__(self, original_ids): # self.original_to_new = {} # self.new_to_original = [] # cnt = 0 # for i in original_ids: # new = self.original_to_new.get(i, cnt) #get（key,default）, # if new == cnt: #节点i为未加入o_t_n，为节点i编号放入o_t_n # self.original_to_new[i] = cnt # cnt += 1 # self.new_to_original.append(i) #去重后的original，对应new_to_original # # def new(self, original): # if type(original) is int: # return self.original_to_new[original] # else: # if type(original[0]) is int: # return [self.original_to_new[i] for i in original] # else: # return [[self.original_to_new[i] for i in l] for l in original] # # def original(self, new): # if type(new) is int: # return self.new_to_original[new] # else: # if type(new[0]) is int: # return [self.new_to_original[i] for i in new] # else: # return [[self.new_to_original[i] for i in l] for l in new] # # def length(self): # return len(self.new_to_original) # # #trainsform the sequence to list # #graphs：字典{级联id：【【种子】，时间】，【【节点1，节点2】，时间】，【节点3，节点4】，时间】】} # def sequence2list(flename): # graphs = {} # with open(flename, 'r') as f: # for line in f: # walks = line.strip().split('\t') # graphs[walks[0]] = [] #walk[0] = cascadeID # for i in range(1, len(walks)): # s = walks[i].split(":")[0] #node # t = walks[i].split(":")[1] #time # graphs[walks[0]].append([[str(xx) for xx in s.split(",")],int(t)]) # return graphs # # #read label and size from cascade file # # label：字典{级联id:label} 级联在三小时后的增量 # # sizes：字典{级联id：级联的边数量} 级联在三小时内的转发量 # # 二者相加为级联的总转发量 # def read_labelANDsize(filename): # labels = {} # sizes = {} # with open(filename, 'r') as f: # for line in f: # profile = line.split('\t') # labels[profile[0]] = profile[-1] # sizes[profile[0]] = int(profile[3]) # return labels,sizes # # #original_ids:每个图的ID # def get_original_ids(graphs): # original_ids = set() # for graph in graphs.keys(): # for walk in graphs[graph]: # for i in walk[0]: # original_ids.add(i) # print ("length of original ids:",len(original_ids)) # return original_ids # # def get_nodes(graph): # nodes = {} # j = 0 # for walk in graph: # for i in walk[0]: # if i not in nodes.keys(): # nodes[i] = j # j = j+1 # return nodes # # def get_max_deepth(graphs): # node_deepth = {} # max_deepth = 0 # #max_leaf = 0 # for graph in graphs.values(): # for walk in graph: # if walk[1] == 0: # node_deepth[walk[0][0]] = 1 # else: # for i in range(len(walk[0]) - 1): # if walk[0][i] in node_deepth.keys(): # node_deepth[walk[0][i + 1]] = node_deepth.get(walk[0][i]) + 1 # else: # node_deepth[walk[0][i]] = 0 # node_deepth[walk[0][i + 1]] = node_deepth.get(walk[0][i]) + 1 # deepth = max(node_deepth.values()) # max_deepth = max(max_deepth,deepth) # #leafs = max(Counter(node_deepth.values()).values()) # #max_leaf = max(max_leaf,leafs) # return max_deepth#,max_leaf # # def BFS_label(G,root): # G_bfs_node = {} # G_BFS = nx.DiGraph() # node_ith_no = {} # i = 0 # G_bfs_node[root] = i # node_ith_no[root] = 0 # this_root= set() # this_root.add(root) # has_visit = set() # #has_visit.add(root) # G_BFS.add_node(G_bfs_node.get(root)) # isolates = list(nx.isolates(G)) # G.remove_nodes_from(list(nx.isolates(G))) # while len(has_visit) != nx.number_of_nodes(G): # next_root = set() # for n in this_root: # node = set() # if n not in has_visit: # has_visit.add(n) # for s in G.successors(n): # node.add(s) # next_root.add(s) # node = sorted(node, key=lambda d:G.nodes[d]['time']) # j = 0 # for s in node: # if s not in G_bfs_node.keys(): # i += 1 # G_bfs_node[s] = i # node_ith_no[s] = j # j += 1 # G_BFS.add_edge(G_bfs_node.get(n), G_bfs_node.get(s)) # this_root = next_root # j = 0 # for s in isolates: # i += 1 # G_BFS[s] = i # G_BFS.add_node(G_bfs_node.get(s)) # node_ith_no[s] = j # j += 1 # return G_BFS, G_bfs_node,node_ith_no # # def G_time_ordered(G): # nodes = list(G.nodes) # nodes = sorted(nodes, key=lambda d: G.nodes[d]['time']) # nodes_times ={} # G_time = nx.DiGraph() # for (m,n) in G.edges(): # nodes_times[m] = nodes.index(m) # nodes_times[n] = nodes.index(n) # G_time.add_node(nodes_times.get(m),time = G.nodes[m]['time']) # G_time.add_node(nodes_times.get(n), time=G.nodes[n]['time']) # G_time.add_edge(nodes_times.get(m),nodes_times.get(n)) # # return G_time, nodes_times # # #处理数据将其转化为输入格式，节点的embedding X【级联总数，num—sequence，max-num，max-num】，级联所在局部网络的拉普拉斯矩阵L，log（Y），每个级联内连接的发生时间 # def write_XYSIZE_data(graphs,labels,sizes,LEN_SEQUENCE,NUM_SEQUENCE,index,max_num, n_time_interval,filename): # #get the x,y,and size data # id_data = [] # x_data = [] # y_data = [] # sz_data = [] # time_data = [] # trend_data = [] # Laplacian_data = [] # l=0 # maxdeep =0 # # for key,graph in graphs.items(): # print(l) # l+=1 # id = key # label = labels[key].split() # y = int(label[LABEL_NUM]) #label # temp = [] # temp_time = np.zeros([NUM_SEQUENCE, n_time_interval],int)#store time # temp_trend = np.zeros([1,NUM_SEQUENCE],int) # size_temp = len(graph) # if size_temp != sizes[key]: # print (size_temp,sizes[key]) # # #nodes_items = get_nodes(graph) #级联的所有节点{节点id：节点编号} # #nodes_list = nodes_items.values() # nx_G = nx.DiGraph() # #nx_G.add_nodes_from(nodes_list) # #将每个级联内部节点间的邻接矩阵（带有自环） # temp_dict = {} # node_deepth = {} # for walk in graph: # if walk[1] == 0: # node_deepth[walk[0][0]] = 0 # nx_G.add_node(walk[0][0], time=walk[1]) # root = walk[0][0] # for i in range(len(walk[0]) - 1): # nx_G.add_node(walk[0][i + 1], time=walk[1]) # nx_G.add_edge(walk[0][i], walk[0][i + 1]) # if walk[0][i] in node_deepth.keys(): # node_deepth[walk[0][i + 1]] = node_deepth.get(walk[0][i]) + 1 # else: # node_deepth[walk[0][i]] = 0 # node_deepth[walk[0][i + 1]] = node_deepth.get(walk[0][i]) + 1 # # # walk_time = math.floor(walk[1] / ((config.observation+1)/ NUM_SEQUENCE)) # 3*60 *60/180 # time_interval = math.floor(walk[1] / ((config.observation+1)/ n_time_interval))#观察时长内划分六个时间间隔 # temp_time[walk_time, time_interval] = 1 # temp_trend[0, walk_time] +=1 # if not temp_dict.get(walk_time): # temp_dict[walk_time] = [] # temp_dict[walk_time].append(walk[0]) # # #temp_dict = sorted(temp_dict.items(), key=lambda a: a[0]) # #G_BFS, G_bfs_node, node_ith_no = BFS_label(nx_G, root) # G_time, node_times_orders = G_time_ordered(nx_G) # temp_emb = np.zeros(shape=(max_num, 50)) # for i in range(NUM_SEQUENCE): # if i in temp_dict.keys(): # for value in temp_dict.get(i): # for p in range(len(value)): # d1 = node_deepth[value[p]] // 100 % 10 # d2 = node_deepth[value[p]] //10 % 10 # d3 = node_deepth[value[p]] // 1 % 10 # n1 = node_times_orders[value[p]] //10 % 10 # n2 = node_times_orders[value[p]] //1 % 10 # temp_emb[node_times_orders[value[p]]][10-1-d1] = 1 # temp_emb[node_times_orders[value[p]]][10*2 - 1 - d2] = 1 # temp_emb[node_times_orders[value[p]]][10*3 - 1 - d3] = 1 # temp_emb[node_times_orders[value[p]]][10*4 - 1 - n1] = 1 # temp_emb[node_times_orders[value[p]]][10*5 -1 - n2] = 1 # temp_s = scipy.sparse.coo_matrix(temp_emb, dtype=np.float32) # temp.append(temp_s) # else: # temp_s = temp[i-1] # temp.append(temp_s) # # deep = max(list(node_deepth.values())) # maxdeep = max(deep,maxdeep) # #caculate laplacian # L = Laplacian.calculate_scaled_laplacian_dir(G_time, kind_of_laplacin= 'caslaplacian', lambda_max=None) # M, M = L.shape # M = int(M) # L = L.todense() # if M < max_num: # col_padding_L = np.zeros(shape=(M, max_num - M)) # L_col_padding = np.column_stack((L, col_padding_L)) # row_padding = np.zeros(shape=(max_num - M, max_num)) # L_col_row_padding = np.row_stack((L_col_padding, row_padding)) # Lapla = scipy.sparse.coo_matrix(L_col_row_padding, dtype=np.float32) # else: # Lapla = scipy.sparse.coo_matrix(L, dtype=np.float32) # # time_data.append(temp_time) # trend_data.append((np.log(temp_trend+1.0)/np.log(2.0)).tolist()) # id_data.append(id) # x_data.append(temp) # y_data.append(np.log(y+1.0)/np.log(2.0)) # Laplacian_data.append(Lapla) # sz_data.append(size_temp) # gc.collect() # print('maxdeepth',maxdeep) # pickle.dump((id_data,x_data,Laplacian_data, y_data, sz_data, time_data, trend_data,index.length()), open(filename,'wb')) # # def get_maxsize(sizes): # max_size = 0 # for cascadeID in sizes: # max_size = max(max_size,sizes[cascadeID]) # gc.collect() # return max_size # # #级联的最大长度（级联中边的数量） # def get_max_length(graphs): # len_sequence = 0 # max_num = 0 # for cascadeID in graphs: # max_num = max(max_num,len(graphs[cascadeID])) # for sequence in graphs[cascadeID]: # len_sequence = max(len_sequence,len(sequence[0])) # gc.collect() # return len_sequence # # def get_max_node_num(graphs): # max_num = 0 # for key,graph in graphs.items(): # nodes = get_nodes(graph) # max_num = max(max_num,len(nodes)) # return max_num # # if __name__ == "__main__": # # ### data set 数据转换，输入为list### # graphs_train = sequence2list(config.shortestpath_train) # # graphs_val = sequence2list(config.shortestpath_val) # graphs_test = sequence2list(config.shortestpath_test) # # # train_depth = get_max_deepth(graphs_train) # # test_depth = get_max_deepth(graphs_test) # # val_depth = get_max_deepth(graphs_val) # # max_depth = max(test_depth,train_depth,val_depth) # # #max_leaf = max(train_leafs,test_leafs,val_leafs) # # print('max_depth',max_depth) # #print('max_leaf',max_leaf) # # # get Laplacian ## # cascade_train = config.cascade_train # cascade_test = config.cascade_test # cascade_val = config.cascade_val # # ### get labels ### # labels_train, sizes_train = read_labelANDsize(config.cascade_train) # labels是{id：观测时间后的转发量}以及sizes级联长度{id：级联总的转发数量} # labels_val, sizes_val = read_labelANDsize(config.cascade_val) # labels_test, sizes_test = read_labelANDsize(config.cascade_test) # # NUM_SEQUENCE = max(get_maxsize(sizes_train),get_maxsize(sizes_val),get_maxsize(sizes_test)) #三小时内，转发最多的级联的大小 884 # NUM_SEQUENCE =config.num_squ # print(NUM_SEQUENCE) # # # LEN_SEQUENCE_train = get_max_length(graphs_train) #每个数据集内，级联内某一传播链的最大长度 # # LEN_SEQUENCE_val = get_max_length(graphs_val) # # LEN_SEQUENCE_test = get_max_length(graphs_test) # # LEN_SEQUENCE = max(LEN_SEQUENCE_train,LEN_SEQUENCE_val,LEN_SEQUENCE_test) #26 # # print(LEN_SEQUENCE) # LEN_SEQUENCE =0 # # # max_num_train = get_max_node_num(graphs_train) #参与级联的最大节点数 # max_num_test = get_max_node_num(graphs_test) # max_num_val = get_max_node_num(graphs_val) # max_num = max(max_num_train, max_num_test, max_num_val) # print(max_num) #100 # # # # get the total original_ids and tranform the index from 0 ~n-1 # original_ids = get_original_ids(graphs_train)\ # .union(get_original_ids(graphs_val))\ # .union(get_original_ids(graphs_test)) # # original_ids.add(-1) # ## index is new index # index = IndexDict(original_ids) #字典{节点对id：节点对} # # print("create train") # write_XYSIZE_data(graphs_train, labels_train,sizes_train,LEN_SEQUENCE,NUM_SEQUENCE,index,max_num,config.n_time_interval, config.train_pkl) # print("create val an test") # write_XYSIZE_data(graphs_val, labels_val, sizes_val,LEN_SEQUENCE,NUM_SEQUENCE,index,max_num,config.n_time_interval, config.val_pkl) # write_XYSIZE_data(graphs_test, labels_test, sizes_test,LEN_SEQUENCE,NUM_SEQUENCE,index,max_num,config.n_time_interval, config.test_pkl) # pickle.dump((len(original_ids),NUM_SEQUENCE,LEN_SEQUENCE), open(config.information,'wb')) # print("Finish!!!") #teittwr import numpy as np import six.moves.cPickle as pickle from data_preprocess import config import networkx as nx from data_preprocess import Laplacian import scipy.sparse from collections import Counter import gc LABEL_NUM = 0 import math # trans the original ids to 1~n class IndexDict: def __init__(self, original_ids): self.original_to_new = {} self.new_to_original = [] cnt = 0 for i in original_ids: new = self.original_to_new.get(i, cnt) #get（key,default）, if new == cnt: #节点i为未加入o_t_n，为节点i编号放入o_t_n self.original_to_new[i] = cnt cnt += 1 self.new_to_original.append(i) #去重后的original，对应new_to_original def new(self, original): if type(original) is int: return self.original_to_new[original] else: if type(original[0]) is int: return [self.original_to_new[i] for i in original] else: return [[self.original_to_new[i] for i in l] for l in original] def original(self, new): if type(new) is int: return self.new_to_original[new] else: if type(new[0]) is int: return [self.new_to_original[i] for i in new] else: return [[self.new_to_original[i] for i in l] for l in new] def length(self): return len(self.new_to_original) #trainsform the sequence to list #graphs：字典{级联id：【【种子】，时间】，【【节点1，节点2】，时间】，【节点3，节点4】，时间】】} def sequence2list(flename): graphs = {} with open(flename, 'r') as f: for line in f: walks = line.strip().split('\t') graphs[walks[0]] = [] #walk[0] = cascadeID for i in range(1, len(walks)): s = walks[i].split(":")[0] #node t = walks[i].split(":")[1] #time graphs[walks[0]].append([[str(xx) for xx in s.split(",")],int(t)]) return graphs #read label and size from cascade file # label：字典{级联id:label} 级联在三小时后的增量 # sizes：字典{级联id：级联的边数量} 级联在三小时内的转发量 # 二者相加为级联的总转发量 def read_labelANDsize(filename): labels = {} sizes = {} with open(filename, 'r') as f: for line in f: profile = line.split('\t') labels[profile[0]] = profile[-1] sizes[profile[0]] = int(profile[3]) return labels,sizes #original_ids:每个图的ID def get_original_ids(graphs): original_ids = set() for graph in graphs.keys(): for walk in graphs[graph]: for i in walk[0]: original_ids.add(i) print ("length of original ids:",len(original_ids)) return original_ids def get_nodes(graph): nodes = {} j = 0 for walk in graph: for i in walk[0]: if i not in nodes.keys(): nodes[i] = j j = j+1 return nodes def get_max_deepth(graphs): node_deepth = {} max_deepth = 0 #max_leaf = 0 for graph in graphs.values(): for walk in graph: if walk[1] == 0: node_deepth[walk[0][0]] = 0 else: for i in range(len(walk[0]) - 1): if walk[0][i] in node_deepth.keys(): node_deepth[walk[0][i + 1]] = node_deepth.get(walk[0][i]) + 1 else: node_deepth[walk[0][i]] = 0 node_deepth[walk[0][i + 1]] = node_deepth.get(walk[0][i]) + 1 deepth = max(node_deepth.values()) max_deepth = max(max_deepth,deepth) #leafs = max(Counter(node_deepth.values()).values()) #max_leaf = max(max_leaf,leafs) return max_deepth#,max_leaf def BFS_label(G,root): G_bfs_node = {} G_BFS = nx.DiGraph() node_ith_no = {} i = 0 G_bfs_node[root] = i node_ith_no[root] = 0 this_root= set() this_root.add(root) has_visit = set() #has_visit.add(root) G_BFS.add_node(G_bfs_node.get(root)) isolates = list(nx.isolates(G)) G.remove_nodes_from(list(nx.isolates(G))) while len(has_visit) != nx.number_of_nodes(G): next_root = set() for n in this_root: node = set() if n not in has_visit: has_visit.add(n) for s in G.successors(n): node.add(s) next_root.add(s) node = sorted(node, key=lambda d:G.nodes[d]['time']) j = 0 for s in node: if s not in G_bfs_node.keys(): i += 1 G_bfs_node[s] = i node_ith_no[s] = j j += 1 G_BFS.add_edge(G_bfs_node.get(n), G_bfs_node.get(s)) this_root = next_root j = 0 for s in isolates: i += 1 G_BFS[s] = i G_BFS.add_node(G_bfs_node.get(s)) node_ith_no[s] = j j += 1 return G_BFS, G_bfs_node,node_ith_no def G_time_ordered(G): nodes = list(G.nodes) nodes = sorted(nodes, key=lambda d: G.nodes[d]['time']) nodes_times ={} G_time = nx.DiGraph() for (m,n) in G.edges(): nodes_times[m] = nodes.index(m) nodes_times[n] = nodes.index(n) G_time.add_node(nodes_times.get(m),time = G.nodes[m]['time']) G_time.add_node(nodes_times.get(n), time=G.nodes[n]['time']) G_time.add_edge(nodes_times.get(m),nodes_times.get(n)) return G_time, nodes_times #处理数据将其转化为输入格式，节点的embedding X【级联总数，num—sequence，max-num，max-num】，级联所在局部网络的拉普拉斯矩阵L，log（Y），每个级联内连接的发生时间 def write_XYSIZE_data(graphs,labels,sizes,LEN_SEQUENCE,NUM_SEQUENCE,index,max_num, n_time_interval,filename): #get the x,y,and size data id_data = [] x_data = [] y_data = [] sz_data = [] time_data = [] trend_data = [] Laplacian_data = [] l=0 maxdeep =0 for key,graph in graphs.items(): print(l) l+=1 id = key label = labels[key].split() y = int(label[LABEL_NUM]) #label temp = [] temp_time = np.zeros([NUM_SEQUENCE, n_time_interval],int)#store time temp_trend = np.zeros([1,NUM_SEQUENCE],int) size_temp = len(graph) if size_temp != sizes[key]: print (size_temp,sizes[key]) #nodes_items = get_nodes(graph) #级联的所有节点{节点id：节点编号} #nodes_list = nodes_items.values() nx_G = nx.DiGraph() #nx_G.add_nodes_from(nodes_list) #将每个级联内部节点间的邻接矩阵（带有自环） temp_dict = {} node_deepth = {} for walk in graph: if walk[1] == 0: node_deepth[walk[0][0]] = 0 nx_G.add_node(walk[0][0], time=walk[1]) root = walk[0][0] for i in range(len(walk[0]) - 1): if walk[0][i] not in nx_G.nodes(): node_deepth[walk[0][0]] = 0 nx_G.add_node(walk[0][i], time=0) nx_G.add_edge(walk[0][i], walk[0][i + 1]) nx_G.add_node(walk[0][i + 1], time=walk[1]) nx_G.add_edge(walk[0][i], walk[0][i + 1]) if walk[0][i] in node_deepth.keys(): node_deepth[walk[0][i + 1]] = node_deepth.get(walk[0][i]) + 1 else: node_deepth[walk[0][i]] = 0 node_deepth[walk[0][i + 1]] = node_deepth.get(walk[0][i]) + 1 walk_time = math.floor(walk[1] / ((config.observation+1)/ NUM_SEQUENCE)) # 3*60 *60/180 time_interval = math.floor(walk[1] / ((config.observation+1)/ n_time_interval))#观察时长内划分六个时间间隔 temp_time[walk_time, time_interval] = 1 temp_trend[0, walk_time] +=1 if not temp_dict.get(walk_time): temp_dict[walk_time] = [] temp_dict[walk_time].append(walk[0]) #temp_dict = sorted(temp_dict.items(), key=lambda a: a[0]) #G_BFS, G_bfs_node, node_ith_no = BFS_label(nx_G, root) G_time, node_times_orders = G_time_ordered(nx_G) temp_emb = np.zeros(shape=(max_num, 50)) for i in range(NUM_SEQUENCE): if i in temp_dict.keys(): for value in temp_dict.get(i): for p in range(len(value)): d1 = node_deepth[value[p]] // 100 % 10 d2 = node_deepth[value[p]] //10 % 10 d3 = node_deepth[value[p]] // 1 % 10 n1 = node_times_orders[value[p]] //10 % 10 n2 = node_times_orders[value[p]] //1 % 10 temp_emb[node_times_orders[value[p]]][10-1-d1] = 1 temp_emb[node_times_orders[value[p]]][10*2 - 1 - d2] = 1 temp_emb[node_times_orders[value[p]]][10*3 - 1 - d3] = 1 temp_emb[node_times_orders[value[p]]][10*4 - 1 - n1] = 1 temp_emb[node_times_orders[value[p]]][10*5 -1 - n2] = 1 temp_s = scipy.sparse.coo_matrix(temp_emb, dtype=np.float32) temp.append(temp_s) else: temp_s = temp[i-1] temp.append(temp_s) deep = max(list(node_deepth.values())) maxdeep = max(deep,maxdeep) #caculate laplacian L = Laplacian.calculate_scaled_laplacian_dir(G_time, kind_of_laplacin= 'caslaplacian', lambda_max=None) M, M = L.shape M = int(M) L = L.todense() if M < max_num: col_padding_L = np.zeros(shape=(M, max_num - M)) L_col_padding = np.column_stack((L, col_padding_L)) row_padding = np.zeros(shape=(max_num - M, max_num)) L_col_row_padding = np.row_stack((L_col_padding, row_padding)) Lapla = scipy.sparse.coo_matrix(L_col_row_padding, dtype=np.float32) else: Lapla = scipy.sparse.coo_matrix(L, dtype=np.float32) time_data.append(temp_time) trend_data.append((np.log(temp_trend+1.0)/np.log(2.0)).tolist()) id_data.append(id) x_data.append(temp) y_data.append(np.log(y+1.0)/np.log(2.0)) Laplacian_data.append(Lapla) sz_data.append(size_temp) gc.collect() print('maxdeepth',maxdeep) pickle.dump((id_data,x_data,Laplacian_data, y_data, sz_data, time_data, trend_data,index.length()), open(filename,'wb')) def get_maxsize(sizes): max_size = 0 for cascadeID in sizes: max_size = max(max_size,sizes[cascadeID]) gc.collect() return max_size #级联的最大长度（级联中边的数量） def get_max_length(graphs): len_sequence = 0 max_num = 0 for cascadeID in graphs: max_num = max(max_num,len(graphs[cascadeID])) for sequence in graphs[cascadeID]: len_sequence = max(len_sequence,len(sequence[0])) gc.collect() return len_sequence def get_max_node_num(graphs): max_num = 0 for key,graph in graphs.items(): nodes = get_nodes(graph) max_num = max(max_num,len(nodes)) return max_num if __name__ == "__main__": ### data set 数据转换，输入为list### graphs_train = sequence2list(config.shortestpath_train) # graphs_val = sequence2list(config.shortestpath_val) graphs_test = sequence2list(config.shortestpath_test) train_depth = get_max_deepth(graphs_train) test_depth = get_max_deepth(graphs_test) val_depth = get_max_deepth(graphs_val) max_depth = max(test_depth,train_depth,val_depth) #max_leaf = max(train_leafs,test_leafs,val_leafs) print('max_depth',max_depth) #print('max_leaf',max_leaf) # get Laplacian ## cascade_train = config.cascade_train cascade_test = config.cascade_test cascade_val = config.cascade_val ### get labels ### labels_train, sizes_train = read_labelANDsize(config.cascade_train) # labels是{id：观测时间后的转发量}以及sizes级联长度{id：级联总的转发数量} labels_val, sizes_val = read_labelANDsize(config.cascade_val) labels_test, sizes_test = read_labelANDsize(config.cascade_test) NUM_SEQUENCE = max(get_maxsize(sizes_train),get_maxsize(sizes_val),get_maxsize(sizes_test)) #三小时内，转发最多的级联的大小 884 #NUM_SEQUENCE =config.num_squ print('numsequence') print(get_maxsize(sizes_train)) print(get_maxsize(sizes_val)) print(get_maxsize(sizes_test)) LEN_SEQUENCE_train = get_max_length(graphs_train) #每个数据集内，级联内某一传播链的最大长度 LEN_SEQUENCE_val = get_max_length(graphs_val) LEN_SEQUENCE_test = get_max_length(graphs_test) LEN_SEQUENCE = max(LEN_SEQUENCE_train,LEN_SEQUENCE_val,LEN_SEQUENCE_test) #26 print('LEN_SEQUENCE') print(LEN_SEQUENCE_train) print(LEN_SEQUENCE_val) print(LEN_SEQUENCE_test) #LEN_SEQUENCE =0 # max_num_train = get_max_node_num(graphs_train) #参与级联的最大节点数 max_num_test = get_max_node_num(graphs_test) max_num_val = get_max_node_num(graphs_val) max_num = max(max_num_train, max_num_test, max_num_val) print(max_num) #100 # # get the total original_ids and tranform the index from 0 ~n-1 original_ids = get_original_ids(graphs_train)\ .union(get_original_ids(graphs_val))\ .union(get_original_ids(graphs_test)) original_ids.add(-1) ## index is new index index = IndexDict(original_ids) #字典{节点对id：节点对} # print("create train") # write_XYSIZE_data(graphs_train, labels_train,sizes_train,LEN_SEQUENCE,NUM_SEQUENCE,index,max_num,config.n_time_interval, config.train_pkl) # print("create val an test") # write_XYSIZE_data(graphs_val, labels_val, sizes_val,LEN_SEQUENCE,NUM_SEQUENCE,index,max_num,config.n_time_interval, config.val_pkl) # write_XYSIZE_data(graphs_test, labels_test, sizes_test,LEN_SEQUENCE,NUM_SEQUENCE,index,max_num,config.n_time_interval, config.test_pkl) # pickle.dump((len(original_ids),NUM_SEQUENCE,LEN_SEQUENCE), open(config.information,'wb')) # print("Finish!!!")

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90742a5e4260d68b0d437394227e4d4a691cb2be

12,635

py

Python

tests/quick/engine/test_project_q_engine.py

WrathfulSpatula/SimulaQron

eaa5548df2f992e187ee70ccd81f192a1ce93e14

[ "BSD-3-Clause" ]

25

2017-11-20T08:50:12.000Z

2018-07-31T19:02:19.000Z

tests/quick/engine/test_project_q_engine.py

WrathfulSpatula/SimulaQron

eaa5548df2f992e187ee70ccd81f192a1ce93e14

[ "BSD-3-Clause" ]

23

2017-11-21T21:47:28.000Z

2018-10-03T08:28:41.000Z

tests/quick/engine/test_project_q_engine.py

WrathfulSpatula/SimulaQron

eaa5548df2f992e187ee70ccd81f192a1ce93e14

[ "BSD-3-Clause" ]

13

2017-11-20T08:50:14.000Z

2018-09-01T21:44:00.000Z

import unittest import numpy as np from simulaqron.toolbox import has_module from simulaqron.settings import SimBackend if has_module.main(SimBackend.PROJECTQ.value): from simulaqron.virtual_node.project_q_simulator import projectQEngine from simulaqron.virtual_node.basics import noQubitError, quantumError from projectq.types._qubit import Qubit _has_module = True else: _has_module = False def if_has_module(test): def new_test(self): if _has_module: test(self) return new_test class TestProjectQEngine_init(unittest.TestCase): @if_has_module def test_init(self): eng = projectQEngine("Alice", 0) self.assertEqual(eng.maxQubits, 10) self.assertEqual(eng.activeQubits, 0) self.assertEqual(len(eng.qubitReg), 0) eng = projectQEngine("Alice", 0, 5) self.assertEqual(eng.maxQubits, 5) self.assertEqual(eng.activeQubits, 0) self.assertEqual(len(eng.qubitReg), 0) class TestProjectQEngine(unittest.TestCase): @if_has_module def setUp(self): self.eng = projectQEngine("Alice", 0) @staticmethod def abs_inner_product(state, ref): comb_state = np.array(state[0]) + 1j * np.array(state[1]) inner = np.dot(comb_state, np.array(ref).conj()) return np.abs(inner) @if_has_module def test_add_fresh_qubit(self): num = self.eng.add_fresh_qubit() self.assertEqual(num, 0) self.assertEqual(self.eng.activeQubits, 1) self.assertEqual(len(self.eng.qubitReg), 1) self.assertTrue(isinstance(self.eng.qubitReg[num], Qubit)) @if_has_module def test_add_to_many_fresh_qubits(self): for _ in range(10): self.eng.add_fresh_qubit() with self.assertRaises(noQubitError): self.eng.add_fresh_qubit() @if_has_module def test_add_qubit(self): new_state = [1, 0] num = self.eng.add_qubit(new_state) self.assertEqual(num, 0) self.assertEqual(self.eng.activeQubits, 1) self.assertEqual(len(self.eng.qubitReg), 1) state = self.eng.get_register_RI()[1] self.assertAlmostEqual(self.abs_inner_product(state, new_state), 1) @if_has_module def test_add_qubit_H(self): new_state = [1 / np.sqrt(2), 1 / np.sqrt(2)] num = self.eng.add_qubit(new_state) self.assertEqual(num, 0) self.assertEqual(self.eng.activeQubits, 1) self.assertEqual(len(self.eng.qubitReg), 1) state = self.eng.get_register_RI()[1] self.assertAlmostEqual(self.abs_inner_product(state, new_state), 1) @if_has_module def test_add_unphysical_qubit(self): new_state = [1, 1] with self.assertRaises(quantumError): self.eng.add_qubit(new_state) @if_has_module def test_remove_qubit(self): num = self.eng.add_fresh_qubit() self.eng.remove_qubit(num) self.assertEqual(self.eng.activeQubits, 0) self.assertEqual(len(self.eng.qubitReg), 0) with self.assertRaises(quantumError): self.eng.remove_qubit(num) @if_has_module def test_get_register_RI(self): self.eng.add_fresh_qubit() self.eng.add_fresh_qubit() state = self.eng.get_register_RI()[1] self.assertAlmostEqual(self.abs_inner_product(state, [1, 0, 0, 0]), 1) @if_has_module def test_H(self): num = self.eng.add_fresh_qubit() self.eng.apply_H(num) state = self.eng.get_register_RI()[1] self.assertAlmostEqual(self.abs_inner_product(state, [1 / np.sqrt(2), 1 / np.sqrt(2)]), 1) @if_has_module def test_K(self): num = self.eng.add_fresh_qubit() self.eng.apply_K(num) state = self.eng.get_register_RI()[1] self.assertAlmostEqual(self.abs_inner_product(state, [1 / np.sqrt(2), 1j / np.sqrt(2)]), 1) @if_has_module def test_X(self): num = self.eng.add_fresh_qubit() self.eng.apply_X(num) state = self.eng.get_register_RI()[1] self.assertAlmostEqual(self.abs_inner_product(state, [0, 1]), 1) @if_has_module def test_Y(self): num = self.eng.add_fresh_qubit() self.eng.apply_H(num) self.eng.apply_Y(num) state = self.eng.get_register_RI()[1] ref = [-1j / np.sqrt(2), 1j / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_Z(self): num = self.eng.add_fresh_qubit() self.eng.apply_H(num) self.eng.apply_Z(num) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), -1 / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_Rx(self): num = self.eng.add_fresh_qubit() self.eng.apply_rotation(num, (1, 0, 0), np.pi / 2) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), -1j / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_Ry(self): num = self.eng.add_fresh_qubit() self.eng.apply_rotation(num, (0, 1, 0), np.pi / 2) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), 1 / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_Rz(self): num = self.eng.add_fresh_qubit() self.eng.apply_H(num) self.eng.apply_rotation(num, (0, 0, 1), np.pi / 2) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), 1j / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_faulty_rot(self): num = self.eng.add_fresh_qubit() self.eng.apply_H(num) with self.assertRaises(NotImplementedError): self.eng.apply_rotation(num, (1, 0, 1), np.pi / 2) @if_has_module def test_cnot(self): num1 = self.eng.add_fresh_qubit() num2 = self.eng.add_fresh_qubit() self.eng.apply_H(num1) self.eng.apply_CNOT(num1, num2) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_cz(self): num1 = self.eng.add_fresh_qubit() num2 = self.eng.add_fresh_qubit() self.eng.apply_H(num1) self.eng.apply_H(num2) self.eng.apply_CPHASE(num1, num2) state = self.eng.get_register_RI()[1] ref = [1 / 2, 1 / 2, 1 / 2, -1 / 2] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_measure0(self): num = self.eng.add_fresh_qubit() m = self.eng.measure_qubit(num) self.assertEqual(m, 0) self.assertEqual(self.eng.activeQubits, 0) @if_has_module def test_measure1(self): num = self.eng.add_fresh_qubit() self.eng.apply_X(num) m = self.eng.measure_qubit(num) self.assertEqual(m, 1) self.assertEqual(self.eng.activeQubits, 0) @if_has_module def test_measure_inplace(self): num = self.eng.add_fresh_qubit() m = self.eng.measure_qubit_inplace(num) self.assertEqual(m, 0) self.assertEqual(self.eng.activeQubits, 1) @if_has_module def test_absorb_both_empty(self): eng2 = projectQEngine("Alice", 0) self.eng.absorb(eng2) self.assertEqual(self.eng.activeQubits, 0) self.assertEqual(len(self.eng.qubitReg), 0) @if_has_module def test_absorb_other_empty(self): num = self.eng.add_fresh_qubit() self.eng.apply_H(num) eng2 = projectQEngine("Alice", 0) self.eng.absorb(eng2) self.assertEqual(self.eng.activeQubits, 1) self.assertEqual(len(self.eng.qubitReg), 1) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), 1 / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_absorb_this_empty_H(self): eng2 = projectQEngine("Alice", 0) num = eng2.add_fresh_qubit() eng2.apply_H(num) self.eng.absorb(eng2) self.assertEqual(self.eng.activeQubits, 1) self.assertEqual(len(self.eng.qubitReg), 1) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), 1 / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_absorb_this_empty_CNOT(self): eng2 = projectQEngine("Alice", 0) num1 = eng2.add_fresh_qubit() num2 = eng2.add_fresh_qubit() eng2.apply_H(num1) eng2.apply_CNOT(num1, num2) self.eng.absorb(eng2) self.assertEqual(self.eng.activeQubits, 2) self.assertEqual(len(self.eng.qubitReg), 2) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_absorb_this_empty_GHZ(self): n = 5 eng2 = projectQEngine("Alice", 0) qubits = [eng2.add_fresh_qubit() for _ in range(n)] eng2.apply_H(qubits[0]) for i in range(1, n): eng2.apply_CNOT(qubits[0], qubits[i]) self.eng.absorb(eng2) self.assertEqual(self.eng.activeQubits, n) self.assertEqual(len(self.eng.qubitReg), n) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2)] + [0] * (2 ** n - 2) + [1 / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_absorb_2GHZ(self): n = 5 eng2 = projectQEngine("Alice", 0) for eng in [self.eng, eng2]: qubits = [eng.add_fresh_qubit() for _ in range(n)] eng.apply_H(qubits[0]) for i in range(1, n): eng.apply_CNOT(qubits[0], qubits[i]) self.eng.absorb(eng2) self.assertEqual(self.eng.activeQubits, 2 * n) self.assertEqual(len(self.eng.qubitReg), 2 * n) @if_has_module def test_absorb_to_big_this_empty(self): eng2 = projectQEngine("Alice", 0, 11) for _ in range(11): eng2.add_fresh_qubit() with self.assertRaises(quantumError): self.eng.absorb(eng2) @if_has_module def test_absorb_to_big(self): self.eng.add_fresh_qubit() eng2 = projectQEngine("Alice", 0) for _ in range(10): eng2.add_fresh_qubit() with self.assertRaises(quantumError): self.eng.absorb(eng2) @if_has_module def test_absorb_parts_both_empty(self): eng2 = projectQEngine("Alice", 0) self.eng.absorb_parts(*eng2.get_register_RI(), eng2.activeQubits) self.assertEqual(self.eng.activeQubits, 0) self.assertEqual(len(self.eng.qubitReg), 0) @if_has_module def test_absorb_parts(self): self.eng.add_fresh_qubit() eng2 = projectQEngine("Alice", 0) eng2.add_fresh_qubit() self.eng.absorb_parts(*eng2.get_register_RI(), eng2.activeQubits) self.assertEqual(self.eng.activeQubits, 2) self.assertEqual(len(self.eng.qubitReg), 2) state = self.eng.get_register_RI()[1] ref = [1, 0, 0, 0] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_absorb_parts_EPR(self): eng2 = projectQEngine("Alice", 0) num1 = eng2.add_fresh_qubit() num2 = eng2.add_fresh_qubit() eng2.apply_H(num1) eng2.apply_CNOT(num1, num2) self.eng.absorb_parts(*eng2.get_register_RI(), eng2.activeQubits) self.assertEqual(self.eng.activeQubits, 2) self.assertEqual(len(self.eng.qubitReg), 2) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) @if_has_module def test_absorb_parts_other_empty(self): num = self.eng.add_fresh_qubit() self.eng.apply_H(num) eng2 = projectQEngine("Alice", 0) self.eng.absorb_parts(*eng2.get_register_RI(), eng2.activeQubits) self.assertEqual(self.eng.activeQubits, 1) self.assertEqual(len(self.eng.qubitReg), 1) state = self.eng.get_register_RI()[1] ref = [1 / np.sqrt(2), 1 / np.sqrt(2)] self.assertAlmostEqual(self.abs_inner_product(state, ref), 1) if __name__ == "__main__": if _has_module: unittest.main()

34.711538

99

0.630233

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4.231544

0.068233

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null

0

1

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null

0

6

907eef92babb79ecb7a048beb308da9513218409

99

py

Python

heron/bots/helpers/utils.py

thetomcraig/HERON

11dc5e3c4bcffde200866c108574a950cef7f944

[ "MIT" ]

3

2018-02-24T22:17:40.000Z

2021-05-18T21:29:17.000Z

heron/bots/helpers/utils.py

thetomcraig/HERON

11dc5e3c4bcffde200866c108574a950cef7f944

[ "MIT" ]

16

2020-06-05T17:29:38.000Z

2021-09-19T19:54:54.000Z

heron/bots/helpers/utils.py

thetomcraig/HERON

11dc5e3c4bcffde200866c108574a950cef7f944

[ "MIT" ]

null

import random import logging def clear_set(set_to_clear): [x.delete() for x in set_to_clear]

14.142857

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18

99

3.833333

0.611111

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99

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1

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6

90a4cdeb9a0d87cb839c802b110d0222d779c222

1,860

py

Python

Microsoft/unit_aera.py

jsourabh1/6Days6Company

65899c7c9d4734bd2151a9b54de633b1709ce583

[ "MIT" ]

1

2022-01-11T02:04:57.000Z

Microsoft/unit_aera.py

jsourabh1/6Days6Company

65899c7c9d4734bd2151a9b54de633b1709ce583

[ "MIT" ]

null

Microsoft/unit_aera.py

jsourabh1/6Days6Company

65899c7c9d4734bd2151a9b54de633b1709ce583

[ "MIT" ]

null

class Solution: #Function to find unit area of the largest region of 1s. def findMaxArea(self, grid): #Code here def dfs(i,j,grid): if i >=0 and j >=0 and i < len(grid) and j <len(grid[i]) and grid[i][j] == 1: # return 0 grid[i][j] = 0 up = dfs(i,j+1,grid) down = dfs(i,j-1,grid) left = dfs(i-1,j,grid) right = dfs(i+1,j,grid) first=dfs(i-1,j-1,grid) second=dfs(i-1,j+1,grid) third=dfs(i+1,j+1,grid) fourth=dfs(i+1,j-1,grid) return up + down + left + right + 1+first+second+third+fourth return 0 # [[0, 0, -1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, -1, 1, 1, 0, 0, 0], # [0, -1, -1, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0], # [0, -1, 0, 0, -1, -1, 0, 0, 1, 0, 1, 0, 0], # [0, -1, 0, 0, -1, -1, 0, 0, 1, 1, 1, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], # [0, 0, 0, 0, 0, 0, 0, -1, 1, 1, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0]] count = 0 for i in range(len(grid)): for j in range(len(grid[0])): if grid[i][j] == 1: count = max(dfs(i,j,grid), count) return count T=1 for i in range(T): grid=[[0,0,1,0,0,0,0,1,0,0,0,0,0],[0,0,0,0,0,0,0,1,1,1,0,0,0],[0,1,1,0,1,0,0,0,0,0,0,0,0],[0,1,0,0,1,1,0,0,1,0,1,0,0],[0,1,0,0,1,1,0,0,1,1,1,0,0],[0,0,0,0,0,0,0,0,0,0,1,0,0],[0,0,0,0,0,0,0,1,1,1,0,0,0],[0,0,0,0,0,0,0,1,1,0,0,0,0]] obj = Solution() ans = obj.findMaxArea(grid) print(ans) # } Driver Code Ends

33.818182

234

0.372043

370

1,860

1.87027

0.124324

0.367052

0.429191

0.462428

0.42341

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0.302023

0.300578

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0.211293

0.409677

1,860

55

235

33.818182

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0.222222

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null

1

0

1

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1

0

null

0

6

90ac0e0abccb1e62d904481e9a2d6d2945871409

99

py

Python

tests/app/models.py

Stranger6667/djoffers

3aff8953f56dea88326ffc294c9551b5ef7458ab

[ "MIT" ]

1

2018-05-19T15:58:29.000Z

tests/app/models.py

Stranger6667/djoffers

3aff8953f56dea88326ffc294c9551b5ef7458ab

[ "MIT" ]

5

2016-09-14T09:00:30.000Z

2018-05-12T09:54:35.000Z

tests/app/models.py

Stranger6667/djoffers

3aff8953f56dea88326ffc294c9551b5ef7458ab

[ "MIT" ]

1

2018-02-21T12:54:18.000Z

# coding: utf-8 from djoffers.models import HasOffersModel class Offer(HasOffersModel): pass

14.142857

42

0.767677

12

99

6.333333

0.916667

0

0.012048

0.161616

99

6

43

16.5

0.903614

0.131313

0

1

0

true

0.333333

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0.666667

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1

0

null

0

1

0

1

0

6

90b9be0376bda238c41829e6eb19e8065f66b34c

7,046

py

Python

tests/unit-tests/test_rst_footnotes.py

bob-schumaker/confluencebuilder

4e767395e4abbedb1955c7b1b1449244886ecb24

[ "BSD-2-Clause" ]

158

2019-03-18T13:42:40.000Z

2022-03-25T09:46:59.000Z

tests/unit-tests/test_rst_footnotes.py

bob-schumaker/confluencebuilder

4e767395e4abbedb1955c7b1b1449244886ecb24

[ "BSD-2-Clause" ]

192

2019-03-15T14:12:25.000Z

2022-03-27T18:35:48.000Z

tests/unit-tests/test_rst_footnotes.py

bob-schumaker/confluencebuilder

4e767395e4abbedb1955c7b1b1449244886ecb24

[ "BSD-2-Clause" ]

54

2019-03-22T14:14:31.000Z

2022-03-08T06:54:28.000Z

# -*- coding: utf-8 -*- """ :copyright: Copyright 2020 Sphinx Confluence Builder Contributors (AUTHORS) :license: BSD-2-Clause (LICENSE) """ from tests.lib import build_sphinx from tests.lib import parse from tests.lib import prepare_conf import os import unittest class TestConfluenceRstFootnotes(unittest.TestCase): @classmethod def setUpClass(self): self.config = prepare_conf() test_dir = os.path.dirname(os.path.realpath(__file__)) self.dataset = os.path.join(test_dir, 'datasets', 'common') self.filenames = [ 'footnotes', ] def test_storage_rst_footnotes(self): out_dir = build_sphinx(self.dataset, config=self.config, filenames=self.filenames) with parse('footnotes', out_dir) as data: # ########################################################## # footnotes # ########################################################## footnote_link_containers = data.find_all('sup') self.assertEqual(len(footnote_link_containers), 3) # footnote a container = footnote_link_containers.pop(0) ac_link = container.find('ac:link') self.assertIsNotNone(ac_link) self.assertTrue(ac_link.has_attr('ac:anchor')) self.assertEqual(ac_link['ac:anchor'], 'id5') link_body = ac_link.find('ac:plain-text-link-body') self.assertIsNotNone(link_body) self.assertEqual(link_body.text, '[1]') # leader anchor back to this footnote a anchor_tag = container.find_previous_sibling() self.assertIsNotNone(anchor_tag) self.assertEqual(anchor_tag.name, 'ac:structured-macro') self.assertTrue(anchor_tag.has_attr('ac:name')) self.assertEqual(anchor_tag['ac:name'], 'anchor') anchor_param = anchor_tag.find('ac:parameter', recursive=False) self.assertIsNotNone(anchor_param) self.assertEqual(anchor_param.text, 'id1') # footnote b container = footnote_link_containers.pop(0) ac_link = container.find('ac:link') self.assertIsNotNone(ac_link) self.assertTrue(ac_link.has_attr('ac:anchor')) self.assertEqual(ac_link['ac:anchor'], 'note') link_body = ac_link.find('ac:plain-text-link-body') self.assertIsNotNone(link_body) self.assertEqual(link_body.text, '[3]') # 3 since 2 was pre-reserved # leader anchor back to this footnote b anchor_tag = container.find_previous_sibling() self.assertIsNotNone(anchor_tag) self.assertEqual(anchor_tag.name, 'ac:structured-macro') self.assertTrue(anchor_tag.has_attr('ac:name')) self.assertEqual(anchor_tag['ac:name'], 'anchor') anchor_param = anchor_tag.find('ac:parameter', recursive=False) self.assertIsNotNone(anchor_param) self.assertEqual(anchor_param.text, 'id2') # footnote c container = footnote_link_containers.pop(0) ac_link = container.find('ac:link') self.assertIsNotNone(ac_link) self.assertTrue(ac_link.has_attr('ac:anchor')) self.assertEqual(ac_link['ac:anchor'], 'id4') link_body = ac_link.find('ac:plain-text-link-body') self.assertIsNotNone(link_body) self.assertEqual(link_body.text, '[2]') # leader anchor back to this footnote 3 anchor_tag = container.find_previous_sibling() self.assertIsNotNone(anchor_tag) self.assertEqual(anchor_tag.name, 'ac:structured-macro') self.assertTrue(anchor_tag.has_attr('ac:name')) self.assertEqual(anchor_tag['ac:name'], 'anchor') anchor_param = anchor_tag.find('ac:parameter', recursive=False) self.assertIsNotNone(anchor_param) self.assertEqual(anchor_param.text, 'id3') # ########################################################## # footnote table # ########################################################## footnote_table = data.find('table') self.assertIsNotNone(footnote_table) footnote_rows = footnote_table.find_all('tr') self.assertEqual(len(footnote_rows), 3) # footnote a tds = footnote_rows[0].find_all('td', recursive=False) self.assertEqual(len(tds), 2) anchor_tag = tds[0].find('ac:structured-macro', {'ac:name': 'anchor'}) self.assertIsNotNone(anchor_tag) anchor_param = anchor_tag.find('ac:parameter') self.assertIsNotNone(anchor_param) self.assertEqual(anchor_param.text, 'id4') ac_link = tds[0].find('ac:link') self.assertIsNotNone(ac_link) self.assertTrue(ac_link.has_attr('ac:anchor')) self.assertEqual(ac_link['ac:anchor'], 'id3') link_body = ac_link.find('ac:plain-text-link-body') self.assertIsNotNone(link_body) self.assertEqual(link_body.text, '2') self.assertEqual(tds[1].text.strip(), 'footnote 2') # footnote b tds = footnote_rows[1].find_all('td', recursive=False) self.assertEqual(len(tds), 2) anchor_tag = tds[0].find('ac:structured-macro', {'ac:name': 'anchor'}) self.assertIsNotNone(anchor_tag) anchor_param = anchor_tag.find('ac:parameter') self.assertIsNotNone(anchor_param) self.assertEqual(anchor_param.text, 'id5') ac_link = tds[0].find('ac:link') self.assertIsNotNone(ac_link) self.assertTrue(ac_link.has_attr('ac:anchor')) self.assertEqual(ac_link['ac:anchor'], 'id1') link_body = ac_link.find('ac:plain-text-link-body') self.assertIsNotNone(link_body) self.assertEqual(link_body.text, '1') self.assertEqual(tds[1].text.strip(), 'footnote num') # footnote c tds = footnote_rows[2].find_all('td', recursive=False) self.assertEqual(len(tds), 2) anchor_tag = tds[0].find('ac:structured-macro', {'ac:name': 'anchor'}) self.assertIsNotNone(anchor_tag) anchor_param = anchor_tag.find('ac:parameter') self.assertIsNotNone(anchor_param) self.assertEqual(anchor_param.text, 'note') ac_link = tds[0].find('ac:link') self.assertIsNotNone(ac_link) self.assertTrue(ac_link.has_attr('ac:anchor')) self.assertEqual(ac_link['ac:anchor'], 'id2') link_body = ac_link.find('ac:plain-text-link-body') self.assertIsNotNone(link_body) self.assertEqual(link_body.text, '3') self.assertEqual(tds[1].text.strip(), 'footnote note')

40.034091

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782

7,046

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0

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7,046

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0

null

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1

0

null

0

1

0

6

90e51b7ae376b3f11ca35636ff6cdbbd5e076d2b

40

py

Python

tools/targets/models_list.py

JeremyRubin/tornado-trails

68b58e0b8dd455df016cf4b9f62d0f50b692c69c

[ "MIT" ]

1

2017-01-28T14:15:55.000Z

tools/targets/models_list.py

JeremyRubin/tornado-trails

68b58e0b8dd455df016cf4b9f62d0f50b692c69c

[ "MIT" ]

null

tools/targets/models_list.py

JeremyRubin/tornado-trails

68b58e0b8dd455df016cf4b9f62d0f50b692c69c

[ "MIT" ]

null

from models.BaseHandler import BaseModel

40

0.9

5

40

7.2

1

0

0.075

40

1

40

0.972973

0

1

0

true

0

1

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1

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1

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null

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null

0

1

0

1

0

1

0

6

291652a616932e7684d3cae54ad5b82cb4350b00

77

py

Python

freight/api/exceptions.py

rob-opsi/freight

9b8e58ce80f6a2ad21769806bdb7f32e68713ce2

[ "Apache-2.0" ]

null

freight/api/exceptions.py

rob-opsi/freight

9b8e58ce80f6a2ad21769806bdb7f32e68713ce2

[ "Apache-2.0" ]

null

freight/api/exceptions.py

rob-opsi/freight

9b8e58ce80f6a2ad21769806bdb7f32e68713ce2

[ "Apache-2.0" ]

1

2020-07-03T00:52:08.000Z

from __future__ import absolute_import class ApiError(Exception): pass

12.833333

38

0.792208

9

77

6.222222

0.888889

0

0.168831

77

5

39

15.4

0.875

0

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0

true

0.333333

0

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null

0

1

0

null

0

1

0

1

0

6

2920efb9463a60d493a1eb855b7178e5b1125b30

40

py

Python

helloGoogle.py

simonczhang/google-cloud

d3dfc2e959a8aa97feb21fc6f4902fc8b50e4857

[ "MIT" ]

null

helloGoogle.py

simonczhang/google-cloud

d3dfc2e959a8aa97feb21fc6f4902fc8b50e4857

[ "MIT" ]

null

helloGoogle.py

simonczhang/google-cloud

d3dfc2e959a8aa97feb21fc6f4902fc8b50e4857

[ "MIT" ]

null

print('Hello World Google Cloud!!!!!')

20

39

0.65

5

40

5.2

1

0

0.125

40

1

40

0.742857

0

0.74359

0

1

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true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

292ac6eab5d45da8aa42c146f26a367e0d811af2

85

py

Python

test/test_pytest_direnv.py

brent-moffit/pytest-direnv

cdd44a19386c25d5b464d517f39f692b6fee46ae

[ "MIT" ]

null

test/test_pytest_direnv.py

brent-moffit/pytest-direnv

cdd44a19386c25d5b464d517f39f692b6fee46ae

[ "MIT" ]

null

test/test_pytest_direnv.py

brent-moffit/pytest-direnv

cdd44a19386c25d5b464d517f39f692b6fee46ae

[ "MIT" ]

null

import os def test_direnv_load(): assert os.getenv("TEST_VAR") == "test value"

14.166667

48

0.682353

13

85

4.230769

0.769231

0

0.176471

85

5

49

17

0.785714

0

0.211765

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

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0

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0

null

0

1

0

1

0

1

0

6

29300de506112f0c4d5bb4761fc0f023f2ebf6bc

8,260

py

Python

src/garage/tf/models/cnn.py

bainro/garage

c5afbb19524792d9bbad9b9741f45e1d48ddca3d

[ "MIT" ]

null

src/garage/tf/models/cnn.py

bainro/garage

c5afbb19524792d9bbad9b9741f45e1d48ddca3d

[ "MIT" ]

null

src/garage/tf/models/cnn.py

bainro/garage

c5afbb19524792d9bbad9b9741f45e1d48ddca3d

[ "MIT" ]

null

"""CNN in TensorFlow.""" import tensorflow as tf def cnn(input_var, filter_dims, num_filters, strides, name, padding, hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(), hidden_b_init=tf.zeros_initializer()): """Convolutional neural network (CNN). Note: Based on 'NHWC' data format: [batch, height, width, channel]. Args: input_var (tf.Tensor): Input tf.Tensor to the CNN. filter_dims (tuple[int]): Dimension of the filters. For example, (3, 5) means there are two convolutional layers. The filter for first layer is of dimension (3 x 3) and the second one is of dimension (5 x 5). num_filters (tuple[int]): Number of filters. For example, (3, 32) means there are two convolutional layers. The filter for the first layer has 3 channels and the second one with 32 channels. strides (tuple[int]): The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2. name (str): Network name, also the variable scope. padding (str): The type of padding algorithm to use, either 'SAME' or 'VALID'. hidden_nonlinearity (callable): Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation. hidden_w_init (callable): Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor. hidden_b_init (callable): Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor. Return: tf.Tensor: The output tf.Tensor of the CNN. """ with tf.compat.v1.variable_scope(name): h = input_var for index, (filter_dim, num_filter, stride) in enumerate(zip(filter_dims, num_filters, strides)): _stride = [1, stride, stride, 1] h = _conv(h, 'h{}'.format(index), filter_dim, num_filter, _stride, hidden_w_init, hidden_b_init, padding) if hidden_nonlinearity is not None: h = hidden_nonlinearity(h) # flatten dim = tf.reduce_prod(h.get_shape()[1:].as_list()) return tf.reshape(h, [-1, dim]) def cnn_with_max_pooling(input_var, filter_dims, num_filters, strides, name, pool_shapes, pool_strides, padding, hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(), hidden_b_init=tf.zeros_initializer()): """Convolutional neural network (CNN) with max-pooling. Note: Based on 'NHWC' data format: [batch, height, width, channel]. Args: input_var (tf.Tensor): Input tf.Tensor to the CNN. filter_dims (tuple[int]): Dimension of the filters. For example, (3, 5) means there are two convolutional layers. The filter for first layer is of dimension (3 x 3) and the second one is of dimension (5 x 5). num_filters (tuple[int]): Number of filters. For example, (3, 32) means there are two convolutional layers. The filter for the first layer has 3 channels and the second one with 32 channels. strides (tuple[int]): The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2. name (str): Model name, also the variable scope of the cnn. pool_shapes (tuple[int]): Dimension of the pooling layer(s). For example, (2, 2) means that all the pooling layers have shape (2, 2). pool_strides (tuple[int]): The strides of the pooling layer(s). For example, (2, 2) means that all the pooling layers have strides (2, 2). padding (str): The type of padding algorithm to use, either 'SAME' or 'VALID'. hidden_nonlinearity (callable): Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation. hidden_w_init (callable): Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor. hidden_b_init (callable): Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor. Return: tf.Tensor: The output tf.Tensor of the CNN. """ pool_strides = [1, pool_strides[0], pool_strides[1], 1] pool_shapes = [1, pool_shapes[0], pool_shapes[1], 1] with tf.compat.v1.variable_scope(name): h = input_var for index, (filter_dim, num_filter, stride) in enumerate(zip(filter_dims, num_filters, strides)): _stride = [1, stride, stride, 1] h = _conv(h, 'h{}'.format(index), filter_dim, num_filter, _stride, hidden_w_init, hidden_b_init, padding) if hidden_nonlinearity is not None: h = hidden_nonlinearity(h) h = tf.nn.max_pool2d(h, ksize=pool_shapes, strides=pool_strides, padding=padding) # flatten dim = tf.reduce_prod(h.get_shape()[1:].as_list()) return tf.reshape(h, [-1, dim]) def _conv(input_var, name, filter_size, num_filter, strides, hidden_w_init, hidden_b_init, padding): """Helper function for performing convolution. Args: input_var (tf.Tensor): Input tf.Tensor to the CNN. name (str): Variable scope of the convolution Op. filter_size (tuple[int]): Dimension of the filters. For example, (3, 5) means there are two convolutional layers. The filter for first layer is of dimension (3 x 3) and the second one is of dimension (5 x 5). num_filter (tuple[int]): Number of filters. For example, (3, 32) means there are two convolutional layers. The filter for the first layer has 3 channels and the second one with 32 channels. strides (tuple[int]): The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2. hidden_w_init (callable): Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor. hidden_b_init (callable): Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor. padding (str): The type of padding algorithm to use, either 'SAME' or 'VALID'. Return: tf.Tensor: The output of the convolution. """ # channel from input input_shape = input_var.get_shape()[-1] # [filter_height, filter_width, in_channels, out_channels] w_shape = [filter_size, filter_size, input_shape, num_filter] b_shape = [1, 1, 1, num_filter] with tf.compat.v1.variable_scope(name): weight = tf.compat.v1.get_variable('weight', w_shape, initializer=hidden_w_init) bias = tf.compat.v1.get_variable('bias', b_shape, initializer=hidden_b_init) return tf.nn.conv2d( input_var, weight, strides=strides, padding=padding) + bias

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katas/kyu_7/find_the_stray_number.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

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40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/kyu_7/find_the_stray_number.py

akalynych/CodeWars

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[ "MIT" ]

null

katas/kyu_7/find_the_stray_number.py

akalynych/CodeWars

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def stray(arr): return reduce(lambda prev, curr: prev ^ curr, arr)

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awesome_panel_extensions/assets/svg_icons.py

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awesome_panel_extensions/assets/svg_icons.py

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awesome_panel_extensions/assets/svg_icons.py

MarcSkovMadsen/panel-extensions-template

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"""This module provides a collection of SVG Icons""" # pylint: disable=line-too-long GIF_SVG = """ <svg class="pnx-icon" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg"> <path fill-rule="evenodd" d="M12.002 4h-10a1 1 0 0 0-1 1v8l2.256-2.354a.5.5 0 0 1 .63-.062l2.66 1.773 3.71-3.71a.5.5 0 0 1 .577-.094l1.777 1.947V5a1 1 0 0 0-1-1zm-10-1a2 2 0 0 0-2 2v8a2 2 0 0 0 2 2h10a2 2 0 0 0 2-2V5a2 2 0 0 0-2-2h-10zm4 4.5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0z"/> <path fill-rule="evenodd" d="M4 2h10a1 1 0 0 1 1 1v8a1 1 0 0 1-1 1v1a2 2 0 0 0 2-2V3a2 2 0 0 0-2-2H4a2 2 0 0 0-2 2h1a1 1 0 0 1 1-1z"/> </svg>""" MP4_SVG = """ <svg class="pnx-icon" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg"> <path fill-rule="evenodd" d="M0 1a1 1 0 0 1 1-1h14a1 1 0 0 1 1 1v14a1 1 0 0 1-1 1H1a1 1 0 0 1-1-1V1zm4 0h8v6H4V1zm8 8H4v6h8V9zM1 1h2v2H1V1zm2 3H1v2h2V4zM1 7h2v2H1V7zm2 3H1v2h2v-2zm-2 3h2v2H1v-2zM15 1h-2v2h2V1zm-2 3h2v2h-2V4zm2 3h-2v2h2V7zm-2 3h2v2h-2v-2zm2 3h-2v2h2v-2z"/> </svg>""" YOUTUBE_SVG = """ <svg class="pnx-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" role="img" viewBox="0 0 576 512"> <path d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 25 288 25 288 25S117.22 25 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.251-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.308V175.185l142.739 81.205-142.739 81.201z"/> </svg> """ DOC_SVG = """ <svg class="pnx-icon" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg"> <path fill-rule="evenodd" d="M1 2.828v9.923c.918-.35 2.107-.692 3.287-.81 1.094-.111 2.278-.039 3.213.492V2.687c-.654-.689-1.782-.886-3.112-.752-1.234.124-2.303.523-3.388.893zm7.5-.141v9.746c.935-.53 2.12-.603 3.213-.493 1.18.12 2.25.461 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BINDER = """<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" class="pnx-icon pnx-icon-binder" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 212.118 65.883" enable-background="new 0 0 212.118 65.883" xml:space="preserve"> <switch> <g> <g> <path fill="#545454" d="M50.751,48.727V12.472h7.251v17.547c1.885-2.32,4.544-3.094,7.299-3.094 c6.042,0,10.586,5.269,10.586,11.167S71.344,49.21,65.302,49.21c-2.755,0-5.849-0.87-7.299-3.046v2.562H50.751z M63.078,43.409 c2.9,0,5.076-2.514,5.076-5.317s-2.175-5.317-5.076-5.317s-5.076,2.514-5.076,5.317S60.178,43.409,63.078,43.409z"/> <path fill="#545454" d="M84.35,15.855c2.32,0,4.254,1.885,4.254,4.254c0,2.32-1.934,4.254-4.254,4.254 c-2.369,0-4.254-1.934-4.254-4.254C80.096,17.741,81.981,15.855,84.35,15.855z M80.724,48.727V27.409h7.251v21.318H80.724z"/> <path fill="#545454" d="M115.819,48.727h-7.25V36.642c0-2.514-0.967-3.867-3.239-3.867s-4.061,2.417-4.061,5.317v10.635h-7.251 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c0.629,0,1.596,0.193,2.176,0.483v6.574h-0.098c-0.725-0.87-1.788-1.208-2.9-1.208C187.61,32.774,185.532,35.53,185.532,38.092z" /> </g> <circle fill="none" stroke="#F5A252" stroke-width="4.8342" stroke-miterlimit="10" cx="27.879" cy="23.939" r="9.542"/> <circle fill="none" stroke="#579ACA" stroke-width="4.8342" stroke-miterlimit="10" cx="27.879" cy="42.499" r="9.543"/> <circle fill="none" stroke="#E66581" stroke-width="4.8342" stroke-miterlimit="10" cx="18.551" cy="33.289" r="9.543"/> <path fill="none" stroke="#579ACA" stroke-width="4.8342" stroke-miterlimit="10" d="M20.196,36.836 c0.759-1.031,1.74-1.927,2.921-2.607c4.566-2.63,10.401-1.06,13.031,3.507"/> <path fill="none" stroke="#F5A252" stroke-width="4.8342" stroke-miterlimit="10" d="M19.61,28.701 c-2.63-4.566-1.061-10.401,3.507-13.032c4.567-2.63,10.401-1.059,13.031,3.508"/> </g> </switch> </svg>""" FAST_COLLAPSED_ICON = """ <svg style="stroke: #E62F63" width="18" height="18" viewBox="0 0 18 18" fill="none" xmlns="http://www.w3.org/2000/svg" slot="collapsed-icon"> <path d="M15.2222 1H2.77778C1.79594 1 1 1.79594 1 2.77778V15.2222C1 16.2041 1.79594 17 2.77778 17H15.2222C16.2041 17 17 16.2041 17 15.2222V2.77778C17 1.79594 16.2041 1 15.2222 1Z" stroke-linecap="round" stroke-linejoin="round"></path> <path d="M9 5.44446V12.5556" stroke-linecap="round" stroke-linejoin="round"></path> <path d="M5.44446 9H12.5556" stroke-linecap="round" stroke-linejoin="round"></path> </svg> """ FAST_EXPANDED_ICON = """ <svg style="stroke: #E62F63" width="18" height="18" viewBox="0 0 18 18" fill="none" xmlns="http://www.w3.org/2000/svg" slot="expanded-icon"> <path d="M15.2222 1H2.77778C1.79594 1 1 1.79594 1 2.77778V15.2222C1 16.2041 1.79594 17 2.77778 17H15.2222C16.2041 17 17 16.2041 17 15.2222V2.77778C17 1.79594 16.2041 1 15.2222 1Z" stroke-linecap="round" stroke-linejoin="round"></path> <path d="M5.44446 9H12.5556" stroke-linecap="round" stroke-linejoin="round"></path> </svg> """ ICONS = { "binder": BINDER, "code": CODE_SVG, "doc": DOC_SVG, "envelope": ENVELOPE, "external_link": EXTERNAL_LINK, "facebook": FACEBOOK, "gif": GIF_SVG, "linked_in": LINKED_IN, "mp4": MP4_SVG, "reddit": REDDIT, "twitter": TWITTER, "youtube": YOUTUBE_SVG, "collapsed": FAST_COLLAPSED_ICON, "expanded": FAST_EXPANDED_ICON, }

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null

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6

462be090cc5a7e40770e0321bc8bd623feadf8bb

8,867

py

Python

Ago-Dic-2019/Ricardo_Romero_Medina/Ordinario/database.py

Arbupa/DAS_Sistemas

52263ab91436b2e5a24ce6f8493aaa2e2fe92fb1

[ "MIT" ]

41

2017-09-26T09:36:32.000Z

2022-03-19T18:05:25.000Z

Ago-Dic-2019/Ricardo_Romero_Medina/Ordinario/database.py

Arbupa/DAS_Sistemas

52263ab91436b2e5a24ce6f8493aaa2e2fe92fb1

[ "MIT" ]

67

2017-09-11T05:06:12.000Z

2022-02-14T04:44:04.000Z

Ago-Dic-2019/Ricardo_Romero_Medina/Ordinario/database.py

Arbupa/DAS_Sistemas

52263ab91436b2e5a24ce6f8493aaa2e2fe92fb1

[ "MIT" ]

210

2017-09-01T00:10:08.000Z

2022-03-19T18:05:12.000Z

import sqlite3 class basedatos(): def __init__(self): self.crear_tabla_personajes() self.crear_tabla_locaciones() self.crear_tabla_Episodios() def crear_tabla_personajes(self): try: conexion = sqlite3.connect('Ordinario/BaseDatos/rickandmorty.db') cursor = conexion.cursor() print('Conectado a SQLite') query = ''' CREATE TABLE IF NOT EXISTS personajes( Id TEXT PRIMARY KEY, Nombre TEXT, Status TEXT, Species TEXT, Type TEXT, Origin TEXT, Location TEXT, id_locacion TEXT, Episode TEXT, id_episodio TEXT, Url TEXT, foreign key (id_locacion) references locaciones(Id), foreign key (id_episodio) references episodios(Id) ); ''' cursor.execute(query) row = cursor.fetchall() print('Tabla creada correctamente', row) cursor.close() except sqlite3.Error as error: print('Error con la conexion', error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def insertar_personajes(self,per): try: conexion = sqlite3.connect('Ordinario/BaseDatos/rickandmorty.db') cursor = conexion.cursor() print('Conectado') query = """INSERT INTO personajes VALUES ("{}","{}","{}","{}","{}","{}","{}","{}","{}","{}","{}")""".format(per._Id, per._Nombre, per._Status, per._Species, per._Origen, per._Tipo, per._Location, per._Id_Locacion, per._Episode, per._Id_Episodio, per._Url) resultado = cursor.execute(query) conexion.commit() print('Valor Insertado Correctamente', resultado) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def ver_personajes(self): try: conexion = sqlite3.connect('Ordinario/BaseDatos/rickandmorty.db') cursor = conexion.cursor() print('Conectado') query = 'SELECT * FROM personajes;' cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') for row in rows: print('Id: {}\nNombre: {}\nStatus: {}\nSpecies: {}\nOrigen: {}\nTipo: {}\nLocacion: {}\nNumero Locacion: {}\nEpisodio: {}\nNumero Episodio: {}\nURL: {}'.format(*row)) return 'Id: {}\nNombre: {}\nStatus: {}\nSpecies: {}\nOrigen: {}\nTipo: {}\nLocacion: {}\nNumero Locacion: {}\nEpisodio: {}\nNumero Episodio: {}\nURL: {}'.format(*row) print('Total de registros: ', len(rows)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def crear_tabla_locaciones(self): try: conexion = sqlite3.connect('Ordinario/BaseDatos/rickandmorty.db') cursor = conexion.cursor() print('Conectado a SQLite') query = ''' CREATE TABLE IF NOT EXISTS locaciones( Id TEXT PRIMARY KEY, Nombre TEXT, Type TEXT, Dimension TEXT, Residents TEXT, id_personaje TEXT, Url TEXT, foreign key (id_personaje) references personajes (Id) ); ''' cursor.execute(query) row = cursor.fetchall() print('Tabla creada correctamente', row) cursor.close() except sqlite3.Error as error: print('Error con la conexion', error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def insertar_locaciones(self,loc): try: conexion = sqlite3.connect('Ordinario/BaseDatos/rickandmorty.db') cursor = conexion.cursor() print('Conectado') query = """INSERT INTO locaciones VALUES ("{}","{}","{}","{}","{}","{}","{}")""".format(loc._Id, loc._Nombre, loc._Tipo, loc._Dimension, loc._Residentes, loc._Id_Residente, loc._Url) resultado = cursor.execute(query) conexion.commit() print('Valor Insertado Correctamente', resultado) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def ver_locaciones(self): try: conexion = sqlite3.connect('Ordinario/BaseDatos/rickandmorty.db') cursor = conexion.cursor() print('Conectado') query = 'SELECT * FROM locaciones;' cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') for row in rows: print('Id: {}\nNombre: {}\nTipo: {}\nDimension: {}\nResidente: {}\nNumero Residente: {}\nURL: {}'.format(*row)) print('Total de registros: ', len(rows)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def crear_tabla_Episodios(self): try: conexion = sqlite3.connect('Ordinario/BaseDatos/rickandmorty.db') cursor = conexion.cursor() print('Conectado a SQLite') query = ''' CREATE TABLE IF NOT EXISTS episodios( Id TEXT PRIMARY KEY, Nombre TEXT, air_date TEXT, Episode TEXT, Character TEXT, id_personaje TEXT, Url TEXT, foreign key (id_personaje) references personajes (Id) ); ''' cursor.execute(query) row = cursor.fetchall() print('Tabla creada correctamente', row) cursor.close() except sqlite3.Error as error: print('Error con la conexion', error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def insertar_episodios(self,epi): try: conexion = sqlite3.connect('Ordinario/BaseDatos/rickandmorty.db') cursor = conexion.cursor() print('Conectado') query = """INSERT INTO episodios VALUES ("{}","{}","{}","{}","{}","{}","{}")""".format(epi._Id, epi._Nombre, epi._Al_Aire, epi._Episodio, epi._Personaje, epi._Id_Personaje, epi._Url) resultado = cursor.execute(query) conexion.commit() print('Valor Insertado Correctamente', resultado) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def ver_episodios(self): try: conexion = sqlite3.connect('Ordinario/BaseDatos/rickandmorty.db') cursor = conexion.cursor() print('Conectado') query = 'SELECT * FROM episodios;' cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') for row in rows: print('Id: {}\nNombre: {}\nPrimera Transmision: {}\nEpisodio: {}\nPersonaje: {}\nNumero Personaje: {}\nURL: {}'.format(*row)) print('Total de registros: ', len(rows)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') if __name__ == '__main__': db = basedatos() #db.ver_personajes() #db.ver_locaciones() #db.ver_episodios()

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null

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465443438003613058727e8573e00546439040db

165

py

Python

classified/probe/all.py

tehmaze/classified

479768675cdff0156510741d0c0ea37b9b10c099

[ "MIT" ]

6

2015-03-17T21:37:58.000Z

2020-05-20T12:45:57.000Z

classified/probe/all.py

tehmaze/classified

479768675cdff0156510741d0c0ea37b9b10c099

[ "MIT" ]

2

2016-01-14T12:42:17.000Z

2020-05-19T09:38:31.000Z

classified/probe/all.py

tehmaze/classified

479768675cdff0156510741d0c0ea37b9b10c099

[ "MIT" ]

1

2016-06-10T12:23:03.000Z

# Project imports from classified.probe.pan import PAN from classified.probe.pcap import PCAP from classified.probe.ssl import SSL __all__ = ['PAN', 'PCAP', 'SSL']

23.571429

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null

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1

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6

4655246d86fd27b7f96852b412c0d2094dbac383

44

py

Python

scorebot/__init__.py

bombsimon/hltv-python

6b882fea88245c239fc034892f75898d65adcb0c

[ "MIT" ]

10

2019-10-14T00:40:50.000Z

2022-03-30T21:46:35.000Z

scorebot/__init__.py

bombsimon/hltv-python

6b882fea88245c239fc034892f75898d65adcb0c

[ "MIT" ]

6

2020-07-24T14:21:05.000Z

2022-03-10T07:32:52.000Z

scorebot/__init__.py

bombsimon/hltv-python

6b882fea88245c239fc034892f75898d65adcb0c

[ "MIT" ]

4

2020-04-25T08:47:12.000Z

2022-03-20T14:38:13.000Z

from .game import * from .scorebot import *

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6

d3f0197e49bc897998f568d9536fdaec3fa92f54

129

py

Python

siesta_utils/__init__.py

semodi/siesta_utils

3bca36440e31cdda7784a16a50b93f2b88e1550f

[ "BSD-3-Clause" ]

null

siesta_utils/__init__.py

semodi/siesta_utils

3bca36440e31cdda7784a16a50b93f2b88e1550f

[ "BSD-3-Clause" ]

null

siesta_utils/__init__.py

semodi/siesta_utils

3bca36440e31cdda7784a16a50b93f2b88e1550f

[ "BSD-3-Clause" ]

null

""" This is the base file of siesta_utils """ from . import grid from . import mat from . import conversions from . import xyz

14.333333

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null

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1

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1

0

1

0

6

310202927a7c280ed1e91bd9898e32f149ac0a57

1,726

py

Python

exercisemodule1.py

shyed2001/Python_Programming

93ef958e3d8aa77f9191b550972235ce4fe4a6cb

[ "bzip2-1.0.6" ]

2

2019-05-01T04:32:14.000Z

2019-05-04T11:28:18.000Z

exercisemodule1.py

shyed2001/python-learning-basics

93ef958e3d8aa77f9191b550972235ce4fe4a6cb

[ "bzip2-1.0.6" ]

null

exercisemodule1.py

shyed2001/python-learning-basics

93ef958e3d8aa77f9191b550972235ce4fe4a6cb

[ "bzip2-1.0.6" ]

null

#------------------------------------------------------------------------------- # Name: Return Function # Purpose: Exercise # # Author: Shyed Shahriar Housaini # # Created: 01/05/2019 # Copyright: (c) User 2019 # Licence: <your licence> #------------------------------------------------------------------------------- monthConv={ "jan": "January", "feb": "February", "mar": "March", "apr": "April", "may": "May", "jun": "June", "jul": "July", "aug": "August", "sep":"September", "oct":"October", "nov":"November", "dec":"December" } print(""" Assigned key value pairs are - monthConv={ "jan": "January", "feb": "February", "mar": "March", "apr": "April", "may": "May", "jun": "June", "jul": "July", "aug": "August", "sep":"September", "oct":"October", "nov":"November", "dec":"December" } """) print(""" print(monthConv["nov"]) print(monthConv.get("mar")) print(monthConv.get("ma"," Not a valid key")) """) print(monthConv["nov"]) print(monthConv.get("mar")) print(monthConv.get("mov")) print(monthConv.get("ma")) print(monthConv.get("ma"," Not a valid key")) monthConv2={ 1: "january", 2: "february", 3: "march", 4: "april", 5: "may", 6: "june" } print(""" monthConv2={ 1: "january", 2: "february", 3: "march", 4: "april", 5: "may", 6: "june" } """) print(""" print(monthConv2[6]) print(monthConv2.get(3)) print(monthConv2.get(7)) print(monthConv2.get(9," Not a valid key")) """) print(monthConv2[6]) print(monthConv2.get("january")) print(monthConv2.get(3)) print(monthConv2.get(7)) print(monthConv2.get(9," Not a valid key")) print(monthConv2.get["january"]) print(monthConv["mov"]) print(""" """)

18.76087

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null

0

1

0

1

0

1

0

null

0

6

3170c00771eea27b977e07210808ea67511be341

599

py

Python

app/dao/db.py

rbarbioni/python-flask-api

b245e167eae6482e80b8b69b33e418eea2b31aff

[ "Apache-2.0" ]

3

2020-07-01T19:42:42.000Z

2021-06-06T00:47:48.000Z

app/dao/db.py

rbarbioni/python-flask-api

b245e167eae6482e80b8b69b33e418eea2b31aff

[ "Apache-2.0" ]

1

2021-04-04T23:45:29.000Z

app/dao/db.py

rbarbioni/python-flask-api

b245e167eae6482e80b8b69b33e418eea2b31aff

[ "Apache-2.0" ]

null

def all(session, Model): return session.query(Model).all() def query(session, Model, filter): return session.query(Model).filter(filter) def query_first(session, Model, filter): return session.query(Model).filter(filter).first() def query_all(session, Model, filter): return session.query(Model).filter(filter).all() def insert(session, model): return session.add(model) def update(session, Model, filter, model): return session.query(Model).filter(filter).update(model) def delete(session, Model, filter): return session.query(Model).filter(filter).delete()

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1

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null

0

1

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1

0

6

31a03ed1a0689adc286790c1a7f849019c090a84

107

py

Python

agpy/__init__.py

bhanuponguru/ag_py

e43103ad2776505aa307abe2299a29c6ce5d9277

[ "MIT" ]

null

agpy/__init__.py

bhanuponguru/ag_py

e43103ad2776505aa307abe2299a29c6ce5d9277

[ "MIT" ]

null

agpy/__init__.py

bhanuponguru/ag_py

e43103ad2776505aa307abe2299a29c6ce5d9277

[ "MIT" ]

null

from . import * from .window import * from .keycodes import * from .keyboard import * from .timer import *

17.833333

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31af26efaf31e4646621c77194c27461260cc893

42

py

Python

__init__.py

PVSemk/segmentation_models.pytorch

8d9b033be918dfc1e6186d9ef404cc7d2c171e8d

[ "MIT" ]

null

__init__.py

PVSemk/segmentation_models.pytorch

8d9b033be918dfc1e6186d9ef404cc7d2c171e8d

[ "MIT" ]

null

__init__.py

PVSemk/segmentation_models.pytorch

8d9b033be918dfc1e6186d9ef404cc7d2c171e8d

[ "MIT" ]

null

from segmentation_models_pytorch import *

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41

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1

0

1

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6

9ee4406dc9bf92cfadac96d5e4789ff203d741d6

2,586

py

Python

tools/ci/test_autocomplete.py

iPlon-org/esp-idf

a5227db2a75102ca1a17860188c3c352a529a01b

[ "Apache-2.0" ]

8,747

2016-08-18T14:58:24.000Z

2022-03-31T20:58:55.000Z

tools/ci/test_autocomplete.py

iPlon-org/esp-idf

a5227db2a75102ca1a17860188c3c352a529a01b

[ "Apache-2.0" ]

8,603

2016-08-20T08:55:56.000Z

2022-03-31T23:04:01.000Z

tools/ci/test_autocomplete.py

iPlon-org/esp-idf

a5227db2a75102ca1a17860188c3c352a529a01b

[ "Apache-2.0" ]

6,380

2016-08-18T18:17:00.000Z

2022-03-31T22:25:57.000Z

#!/usr/bin/env python import os import sys import unittest import pexpect class Test(unittest.TestCase): def test_fish(self): os.environ['TERM'] = 'vt100' child = pexpect.spawn('fish -i') with open(os.environ['IDF_PATH'] + '/fish' + str(sys.version_info.major) + '.out', 'wb') as output: child.logfile = output child.sendline('. ./export.fish') result = child.expect( ['Go to the project directory and run.*idf\\.py build', pexpect.EOF, pexpect.TIMEOUT], timeout=40) self.assertEqual(result, 0, 'Export was not successful!') child.send('idf.py \t\t') result = child.expect(['all.*app.*app-flash.*bootloader.*', pexpect.EOF, pexpect.TIMEOUT], timeout=40) self.assertEqual(result, 0, 'Autocompletion for idf.py failed in fish!') def test_bash(self): os.environ['TERM'] = 'xterm-256color' child = pexpect.spawn('bash -i') with open(os.environ['IDF_PATH'] + '/bash' + str(sys.version_info.major) + '.out', 'wb') as output: child.logfile = output child.sendline('. ./export.sh') child.send('idf.py \t\t') result = child.expect( ['Go to the project directory and run.*idf\\.py build', pexpect.EOF, pexpect.TIMEOUT], timeout=40) self.assertEqual(result, 0, 'Export was not successful!') result = child.expect( ['all.*app.*app-flash.*bootloader.*bootloader-flash.*build-system-targets.*clean.*', pexpect.EOF, pexpect.TIMEOUT], timeout=40) self.assertEqual(result, 0, 'Autocompletion for idf.py failed in bash!') def test_zsh(self): child = pexpect.spawn('zsh -i') with open(os.environ['IDF_PATH'] + '/zsh' + str(sys.version_info.major) + '.out', 'wb') as output: child.logfile = output child.sendline('. ./export.sh') result = child.expect( ['Go to the project directory and run.*idf\\.py build', pexpect.EOF, pexpect.TIMEOUT], timeout=40) self.assertEqual(result, 0, 'Export was not successful!') child.send('idf.py \t\t') result = child.expect( ['all.*app.*app-flash.*bootloader.*bootloader-flash.*build-system-targets.*clean.*', pexpect.EOF, pexpect.TIMEOUT], timeout=40) self.assertEqual(result, 0, 'Autocompletion for idf.py failed in zsh!') if __name__ == '__main__': unittest.main()

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0.163265

0

null

0

1

0

null

0

6

9eeb15541b84931e72cdfc3031e7c3ea73a14774

331

py

Python

templates/python.flask/{{cookiecutter.project_safe_name}}/app/main/views/misc.py

by46/recipe

203abd2141a536b66b4e57d073169a49395be1f0

[ "MIT" ]

null

templates/python.flask/{{cookiecutter.project_safe_name}}/app/main/views/misc.py

by46/recipe

203abd2141a536b66b4e57d073169a49395be1f0

[ "MIT" ]

null

templates/python.flask/{{cookiecutter.project_safe_name}}/app/main/views/misc.py

by46/recipe

203abd2141a536b66b4e57d073169a49395be1f0

[ "MIT" ]

null

from flask import current_app from flask import render_template from app.main import main @main.route("/version", methods=['GET']) def version(): return render_template('main/version.html', version=current_app.config['VERSION']) @main.route("/faq.htm") def faq(): return render_template('main/faq.html')

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0.210526

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0

null

0

1

0

null

0

1

0

1

0

6

730092245bc786f464d599057fcb22425131b790

2,735

py

Python

transactions/models.py

Guilehm/expense-control-system

c0f8393497f54076cb15008f1bda9efc5081025b

[ "MIT" ]

1

2022-02-16T23:23:02.000Z

transactions/models.py

guilehm/expense-control-system

c0f8393497f54076cb15008f1bda9efc5081025b

[ "MIT" ]

40

2018-07-01T15:49:05.000Z

2018-09-06T02:37:24.000Z

transactions/models.py

Guilehm/Expense-Control-System

c0f8393497f54076cb15008f1bda9efc5081025b

[ "MIT" ]

1

2019-05-05T13:43:55.000Z

from django.contrib.auth.models import User from django.core.validators import MinValueValidator from django.db import models from django.db.models import Sum class ExpenseQuerySet(models.QuerySet): def total(self): return self.aggregate(Sum('total'))['total__sum'] or 0 class RevenueQuerySet(models.QuerySet): def total(self): return self.aggregate(Sum('total'))['total__sum'] or 0 # Create your models here. class Revenue(models.Model): account = models.ForeignKey('bank.BankAccount', on_delete=models.CASCADE, related_name='revenues') user = models.ForeignKey(User, on_delete=models.CASCADE) title = models.CharField(max_length=50) description = models.TextField(blank=True, null=True) total = models.DecimalField( max_digits=9, decimal_places=2, validators=[MinValueValidator(0), ] ) competition_date = models.DateField(db_index=True, blank=True, null=True) due_date = models.DateField(db_index=True) received_out = models.BooleanField(default=False) note = models.TextField(blank=True, null=True) category = models.ForeignKey( 'core.Category', related_name='revenues', on_delete=models.CASCADE, ) tags = models.ManyToManyField( 'core.Tag', related_name='revenues', blank=True, ) objects = RevenueQuerySet.as_manager() date_added = models.DateTimeField(auto_now_add=True, db_index=True) date_changed = models.DateTimeField(auto_now=True, db_index=True) def __str__(self): return self.title class Expense(models.Model): account = models.ForeignKey('bank.BankAccount', on_delete=models.CASCADE, related_name='expenses') user = models.ForeignKey(User, on_delete=models.CASCADE) title = models.CharField(max_length=50) description = models.TextField(blank=True, null=True) total = models.DecimalField( max_digits=9, decimal_places=2, validators=[MinValueValidator(0), ] ) competition_date = models.DateField(db_index=True, blank=True, null=True) due_date = models.DateField(db_index=True) paid_out = models.BooleanField(default=False) note = models.TextField(blank=True, null=True) category = models.ForeignKey( 'core.Category', related_name='expenses', on_delete=models.CASCADE, null=True, ) tags = models.ManyToManyField( 'core.Tag', related_name='expenses', blank=True, ) objects = ExpenseQuerySet.as_manager() date_added = models.DateTimeField(auto_now_add=True, db_index=True) date_changed = models.DateTimeField(auto_now=True, db_index=True) def __str__(self): return self.title

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null

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null

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6

73292cd965a33861268b4a580907fd3b067bff62

17,113

py

Python

insights/parsers/tests/test_multipath_v4_ll.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

121

2017-05-30T20:23:25.000Z

2022-03-23T12:52:15.000Z

insights/parsers/tests/test_multipath_v4_ll.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

1,977

2017-05-26T14:36:03.000Z

2022-03-31T10:38:53.000Z

insights/parsers/tests/test_multipath_v4_ll.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

244

2017-05-30T20:22:57.000Z

2022-03-26T10:09:39.000Z

from insights.parsers import multipath_v4_ll from insights.tests import context_wrap import doctest MULTIPATH_V4_LL_INFO = """ ===== paths list ===== uuid hcil dev dev_t pri dm_st chk_st vend/prod/rev dev_st 0:0:0:0 sda 8:0 -1 undef ready VMware,Virtual disk running 3:0:0:1 sdb 8:16 -1 undef ready IET,VIRTUAL-DISK running 4:0:0:1 sdc 8:32 -1 undef ready IET,VIRTUAL-DISK running Oct 28 14:02:44 | *word = 0, len = 1 Oct 28 14:02:44 | *word = E, len = 1 Oct 28 14:02:44 | *word = 1, len = 1 Oct 28 14:02:44 | *word = 0, len = 1 Oct 28 14:02:44 | *word = A, len = 1 Oct 28 14:02:44 | *word = 0, len = 1 mpathg (36f01faf000da360b0000033c528fea6d) dm-2 DELL,MD36xxi size=54T features='3 queue_if_no_path pg_init_retries 50' hwhandler='1 rdac' wp=rw |-+- policy='round-robin 0' prio=0 status=active | |- 12:0:0:1 sdc 8:32 active ready running | |- 11:0:0:1 sdi 8:128 active ready running | |- 15:0:0:1 sdo 8:224 active ready running | `- 17:0:0:1 sdv 65:80 active ready running `-+- policy='round-robin 0' prio=0 status=enabled |- 13:0:0:1 sdf 8:80 active ready running |- 14:0:0:1 sdl 8:176 active ready running |- 16:0:0:1 sdr 65:16 active ready running `- 18:0:0:1 sdx 65:112 active ready running mpathe (36f01faf000da3761000004323aa6fbce) dm-4 DELL,MD36xxi size=44T features='3 queue_if_no_path pg_init_retries 55' hwhandler='2 rdac' wp=rx |-+- policy='round-robin 0' prio=1 status=active | |- 12:0:0:2 sdc 8:32 active ready running | |- 11:0:0:2 sdi 8:128 active ready running | |- 15:0:0:2 sdo 8:224 active ready running | `- 17:0:0:2 sdv 65:80 active ready running `-+- policy='round-robin 0' prio=1 status=enabled |- 13:0:0:2 sdf 8:80 active ready running |- 14:0:0:2 sdl 8:176 active ready running |- 16:0:0:2 sdr 65:16 active ready running `- 18:0:0:2 sdx 65:112 active ready running 36001405b1629f80d52a4c898f8856e43 dm-5 LIO-ORG ,block0_sdb size=2.0G features='0' hwhandler='0' wp=rw |-+- policy='service-time 0' prio=1 status=active | `- 3:0:0:0 sdc 8:32 active ready running `-+- policy='service-time 0' prio=1 status=enabled `- 4:0:0:0 sdb 8:16 active ready running mpatha (1IET 00080001) dm-0 IET,VIRTUAL-DISK size=16G features='0' hwhandler='0' wp=rw |-+- policy='round-robin 0' prio=1 status=active | `- 3:0:0:1 sdb 8:16 active ready running `-+- policy='round-robin 0' prio=1 status=enabled `- 4:0:0:1 sdc 8:32 active ready running 1IET 00080001 dm-0 IET,VIRTUAL-DISK size=16G features='0' hwhandler='0' wp=rw |-+- policy='round-robin 0' prio=1 status=active | `- 3:0:0:1 sdb 8:16 active ready running `-+- policy='round-robin 0' prio=1 status=enabled `- 4:0:0:1 sdc 8:32 active ready running mpathb (1IET 00080002) dm-8 COMPELNT,Compellent Vol size=16G features='0' hwhandler='0' wp=rw |-+- policy='round-robin 0' prio=1 status=active | `- 3:0:0:1 sdb 8:16 active ready running `-+- policy='round-robin 0' prio=1 status=enabled `- 4:0:0:1 sdc 8:32 active ready running 1IET 00080007 dm-19 COMPELNT,Compellent Vol size=16G features='0' hwhandler='0' wp=rw |-+- policy='round-robin 0' prio=1 status=active | `- 3:0:0:1 sdb 8:16 active ready running `-+- policy='round-robin 0' prio=1 status=enabled `- 4:0:0:1 sdc 8:32 active ready running mpathc (test_with_no_devs) dm-1 uninitialized size=10G features='0' hwhandler='0' wp=rw """.strip() MULTIPATH_V4_LL_INFO_RHEL_5 = """ sdz: size = 293601280 sdz: vendor = DGC sdz: product = RAID 5 sdz: rev = 0430 sdz: h:b:t:l = 5:0:1:6 sdz: tgt_node_name = 0x50060160c4603569 sdz: path checker = emc_clariion (controller setting) sdz: checker timeout = 60000 ms (sysfs setting) sdz: state = 2 sr0: blacklisted sr1: blacklisted ===== paths list ===== uuid hcil dev dev_t pri dm_st chk_st vend/prod/rev 5:0:1:7 sdaa 65:160 0 [undef][ready] DGC,RAID 5 3:0:0:5 sdab 65:176 0 [undef][ready] DGC,RAID 5 3:0:0:6 sdac 65:192 0 [undef][ready] DGC,RAID 5 3:0:0:7 sdad 65:208 0 [undef][ready] DGC,RAID 5 3:0:1:5 sdae 65:224 0 [undef][ready] DGC,RAID 5 3:0:1:6 sdaf 65:240 0 [undef][ready] DGC,RAID 5 3:0:1:7 sdag 66:0 0 [undef][ready] DGC,RAID 5 0:2:0:0 sda 8:0 0 [undef][ready] DELL,PERC 6/i 3:0:0:0 sdb 8:16 0 [undef][ready] DGC,RAID 5 3:0:0:1 sdc 8:32 0 [undef][ready] DGC,RAID 5 3:0:0:2 sdd 8:48 0 [undef][ready] DGC,RAID 5 3:0:0:3 sde 8:64 0 [undef][ready] DGC,RAID 5 3:0:0:4 sdf 8:80 0 [undef][ready] DGC,RAID 5 3:0:1:0 sdg 8:96 0 [undef][ready] DGC,RAID 5 3:0:1:1 sdh 8:112 0 [undef][ready] DGC,RAID 5 3:0:1:2 sdi 8:128 0 [undef][ready] DGC,RAID 5 3:0:1:3 sdj 8:144 0 [undef][ready] DGC,RAID 5 3:0:1:4 sdk 8:160 0 [undef][ready] DGC,RAID 5 5:0:0:0 sdl 8:176 0 [undef][ready] DGC,RAID 5 5:0:0:1 sdm 8:192 0 [undef][ready] DGC,RAID 5 5:0:0:2 sdn 8:208 0 [undef][ready] DGC,RAID 5 5:0:0:3 sdo 8:224 0 [undef][ready] DGC,RAID 5 5:0:0:4 sdp 8:240 0 [undef][ready] DGC,RAID 5 5:0:1:0 sdq 65:0 0 [undef][ready] DGC,RAID 5 5:0:1:1 sdr 65:16 0 [undef][ready] DGC,RAID 5 5:0:1:2 sds 65:32 0 [undef][ready] DGC,RAID 5 5:0:1:3 sdt 65:48 0 [undef][ready] DGC,RAID 5 5:0:1:4 sdu 65:64 0 [undef][ready] DGC,RAID 5 5:0:0:5 sdv 65:80 0 [undef][ready] DGC,RAID 5 5:0:0:6 sdw 65:96 0 [undef][ready] DGC,RAID 5 5:0:0:7 sdx 65:112 0 [undef][ready] DGC,RAID 5 5:0:1:5 sdy 65:128 0 [undef][ready] DGC,RAID 5 5:0:1:6 sdz 65:144 0 [undef][ready] DGC,RAID 5 params = 1 queue_if_no_path 1 emc 2 1 round-robin 0 2 1 8:160 1000 8:240 1000 round-robin 0 2 1 8:80 1000 65:64 1000 status = 2 0 1 0 2 1 A 0 2 0 8:160 A 0 8:240 A 0 E 0 2 0 8:80 A 0 65:64 A 0 *word = 1, len = 1 *word = queue_if_no_path, len = 16 *word = 1, len = 1 *word = emc, len = 3 sdu: getprio = /sbin/mpath_prio_emc /dev/%n (controller setting) process 31154 forking to exec '/sbin/mpath_prio_emc /dev/sdu' ((nil)) forked 31158 sdu: prio = 0 *word = 2, len = 1 *word = 1, len = 1 L004 (360060160ade32800f2e3baf47665e211) dm-9 DGC,RAID 5 [size=100G][features=1 queue_if_no_path][hwhandler=1 emc][rw] \_ round-robin 0 [prio=1][active] \_ 3:0:1:4 sdk 8:160 [active][ready] \_ 5:0:0:4 sdp 8:240 [active][ready] \_ round-robin 0 [prio=0][enabled] \_ 3:0:0:4 sdf 8:80 [active][ready] \_ 5:0:1:4 sdu 65:64 [active][ready] params = 1 queue_if_no_path 1 emc 2 1 round-robin 0 2 1 8:64 1000 65:48 1000 round-robin 0 2 1 8:144 1000 8:224 1000 status = 2 0 1 0 2 1 A 0 2 0 8:64 A 0 65:48 A 0 E 0 2 0 8:144 A 0 8:224 A 0 *word = 1, len = 1 *word = queue_if_no_path, len = 16 """ def test_class_RHEL6(): mp = multipath_v4_ll.MultipathDevices(context_wrap(MULTIPATH_V4_LL_INFO)) assert len(mp.devices) == 7 assert mp.devices[0] == { "alias": "mpathg", "wwid": "36f01faf000da360b0000033c528fea6d", "dm_name": "dm-2", "venprod": "DELL,MD36xxi", "size": "54T", "features": "3 queue_if_no_path pg_init_retries 50", "hwhandler": "1 rdac", "wp": "rw", "path_group": [{ "policy": "round-robin 0", "prio": "0", "status": "active", "path": [ ['12:0:0:1', 'sdc', '8:32', 'active', 'ready', 'running'], ['11:0:0:1', 'sdi', '8:128', 'active', 'ready', 'running'], ['15:0:0:1', 'sdo', '8:224', 'active', 'ready', 'running'], ['17:0:0:1', 'sdv', '65:80', 'active', 'ready', 'running'] ] }, { "policy": "round-robin 0", "prio": "0", "status": "enabled", "path": [ ['13:0:0:1', 'sdf', '8:80', 'active', 'ready', 'running'], ['14:0:0:1', 'sdl', '8:176', 'active', 'ready', 'running'], ['16:0:0:1', 'sdr', '65:16', 'active', 'ready', 'running'], ['18:0:0:1', 'sdx', '65:112', 'active', 'ready', 'running'] ] }] } assert mp.devices[0].get('size') == '54T' assert mp.devices[1].get('path_group') == [{ "policy": "round-robin 0", "prio": "1", "status": "active", "path": [ ['12:0:0:2', 'sdc', '8:32', 'active', 'ready', 'running'], ['11:0:0:2', 'sdi', '8:128', 'active', 'ready', 'running'], ['15:0:0:2', 'sdo', '8:224', 'active', 'ready', 'running'], ['17:0:0:2', 'sdv', '65:80', 'active', 'ready', 'running'] ] }, { "policy": "round-robin 0", "prio": "1", "status": "enabled", "path": [ ['13:0:0:2', 'sdf', '8:80', 'active', 'ready', 'running'], ['14:0:0:2', 'sdl', '8:176', 'active', 'ready', 'running'], ['16:0:0:2', 'sdr', '65:16', 'active', 'ready', 'running'], ['18:0:0:2', 'sdx', '65:112', 'active', 'ready', 'running'] ] }] assert mp.devices[2].get('hwhandler') == "0" assert mp.devices[3].get('alias') == "mpatha" assert mp.devices[4].get('wwid') == "1IET 00080001" assert mp.devices[5].get('venprod') == "COMPELNT,Compellent Vol" assert mp.devices[5].get('dm_name') == "dm-8" assert mp.devices[6].get('venprod') == "COMPELNT,Compellent Vol" assert mp.devices[6].get('dm_name') == "dm-19" # Note that there's no data for the made-up 'mpathc', since there's no # path group information and only devices with path group information # get saved. assert mp.dms == ['dm-2', 'dm-4', 'dm-5', 'dm-0', 'dm-0', 'dm-8', 'dm-19'] assert mp.by_dm['dm-2'] == mp.devices[0] assert mp.aliases == ['mpathg', 'mpathe', 'mpatha', 'mpathb'] assert mp.by_alias['mpathg'] == mp.devices[0] assert mp.wwids == [ '36f01faf000da360b0000033c528fea6d', '36f01faf000da3761000004323aa6fbce', '36001405b1629f80d52a4c898f8856e43', '1IET 00080001', '1IET 00080001', '1IET 00080002', '1IET 00080007' ] assert mp.by_wwid['1IET 00080001'] == mp.devices[4] # Pseudo list accessors assert len(mp) == 7 for i, item in enumerate(mp): assert item == mp.devices[i] assert item == mp[i] assert len(mp.raw_info_lines) == 11 assert "===== paths list =====" in mp.raw_info_lines def test_get_multipath_v4_ll(): multipath_v4_ll_list = multipath_v4_ll.get_multipath_v4_ll(context_wrap(MULTIPATH_V4_LL_INFO)) assert len(multipath_v4_ll_list) == 7 assert multipath_v4_ll_list[0] == { "alias": "mpathg", "wwid": "36f01faf000da360b0000033c528fea6d", "dm_name": "dm-2", "venprod": "DELL,MD36xxi", "size": "54T", "features": "3 queue_if_no_path pg_init_retries 50", "hwhandler": "1 rdac", "wp": "rw", "path_group": [{ "policy": "round-robin 0", "prio": "0", "status": "active", "path": [ ['12:0:0:1', 'sdc', '8:32', 'active', 'ready', 'running'], ['11:0:0:1', 'sdi', '8:128', 'active', 'ready', 'running'], ['15:0:0:1', 'sdo', '8:224', 'active', 'ready', 'running'], ['17:0:0:1', 'sdv', '65:80', 'active', 'ready', 'running'] ] }, { "policy": "round-robin 0", "prio": "0", "status": "enabled", "path": [ ['13:0:0:1', 'sdf', '8:80', 'active', 'ready', 'running'], ['14:0:0:1', 'sdl', '8:176', 'active', 'ready', 'running'], ['16:0:0:1', 'sdr', '65:16', 'active', 'ready', 'running'], ['18:0:0:1', 'sdx', '65:112', 'active', 'ready', 'running'] ] }] } assert multipath_v4_ll_list[0].get('size') == '54T' assert multipath_v4_ll_list[1].get('path_group') == [{ "policy": "round-robin 0", "prio": "1", "status": "active", "path": [ ['12:0:0:2', 'sdc', '8:32', 'active', 'ready', 'running'], ['11:0:0:2', 'sdi', '8:128', 'active', 'ready', 'running'], ['15:0:0:2', 'sdo', '8:224', 'active', 'ready', 'running'], ['17:0:0:2', 'sdv', '65:80', 'active', 'ready', 'running'] ] }, { "policy": "round-robin 0", "prio": "1", "status": "enabled", "path": [ ['13:0:0:2', 'sdf', '8:80', 'active', 'ready', 'running'], ['14:0:0:2', 'sdl', '8:176', 'active', 'ready', 'running'], ['16:0:0:2', 'sdr', '65:16', 'active', 'ready', 'running'], ['18:0:0:2', 'sdx', '65:112', 'active', 'ready', 'running'] ] }] assert multipath_v4_ll_list[2].get('hwhandler') == "0" assert multipath_v4_ll_list[3].get('alias') == "mpatha" assert multipath_v4_ll_list[4].get('wwid') == "1IET 00080001" assert multipath_v4_ll_list[5].get('venprod') == "COMPELNT,Compellent Vol" assert multipath_v4_ll_list[5].get('dm_name') == "dm-8" assert multipath_v4_ll_list[6].get('venprod') == "COMPELNT,Compellent Vol" assert multipath_v4_ll_list[6].get('dm_name') == "dm-19" # Note that there's no data for the made-up 'mpathc', since there's no # path group information and only devices with path group information # get saved. def test_get_multipath_v4_ll_RHEL_5(): """ Test alternate device line prefixes, and ignoring extra clutter in input. """ multipath_v4_ll_list = multipath_v4_ll.get_multipath_v4_ll(context_wrap(MULTIPATH_V4_LL_INFO_RHEL_5)) assert len(multipath_v4_ll_list) == 1 """ L004 (360060160ade32800f2e3baf47665e211) dm-9 DGC,RAID 5 [size=100G][features=1 queue_if_no_path][hwhandler=1 emc][rw] \_ round-robin 0 [prio=1][active] \_ 3:0:1:4 sdk 8:160 [active][ready] \_ 5:0:0:4 sdp 8:240 [active][ready] \_ round-robin 0 [prio=0][enabled] \_ 3:0:0:4 sdf 8:80 [active][ready] \_ 5:0:1:4 sdu 65:64 [active][ready] """ path_dev = multipath_v4_ll_list[0] assert path_dev['alias'] == 'L004' assert path_dev['wwid'] == '360060160ade32800f2e3baf47665e211' assert path_dev['dm_name'] == 'dm-9' assert path_dev['venprod'] == 'DGC,RAID 5' assert path_dev['size'] == '100G' assert path_dev['features'] == '1 queue_if_no_path' assert path_dev['hwhandler'] == '1 emc' assert path_dev['wp'] == 'rw' assert path_dev['path_group'][0]['policy'] == 'round-robin 0' assert path_dev['path_group'][0]['prio'] == '1' assert path_dev['path_group'][0]['status'] == 'active' assert len(path_dev['path_group'][0]['path']) == 2 paths = path_dev['path_group'][0]['path'] assert len(paths) == 2 assert paths[0] == ['3:0:1:4', 'sdk', '8:160', 'active', 'ready'] MULTIPATH_V4_LL_DOC = """ ===== paths list ===== uuid hcil dev dev_t pri dm_st chk_st vend/prod/rev dev_st 0:0:0:0 sda 8:0 -1 undef ready VMware,Virtual disk running 3:0:0:1 sdb 8:16 -1 undef ready IET,VIRTUAL-DISK running 4:0:0:1 sdc 8:32 -1 undef ready IET,VIRTUAL-DISK running Oct 28 14:02:44 | *word = 0, len = 1 Oct 28 14:02:44 | *word = E, len = 1 Oct 28 14:02:44 | *word = 1, len = 1 Oct 28 14:02:44 | *word = 0, len = 1 Oct 28 14:02:44 | *word = A, len = 1 Oct 28 14:02:44 | *word = 0, len = 1 mpathg (36f01faf000da360b0000033c528fea6d) dm-2 DELL,MD36xxi size=54T features='3 queue_if_no_path pg_init_retries 50' hwhandler='1 rdac' wp=rw |-+- policy='round-robin 0' prio=0 status=active | |- 12:0:0:1 sdc 8:32 active ready running | |- 11:0:0:1 sdi 8:128 active ready running | |- 15:0:0:1 sdo 8:224 active ready running | `- 17:0:0:1 sdv 65:80 active ready running `-+- policy='round-robin 0' prio=0 status=enabled |- 13:0:0:1 sdf 8:80 active ready running |- 14:0:0:1 sdl 8:176 active ready running |- 16:0:0:1 sdr 65:16 active ready running `- 18:0:0:1 sdx 65:112 active ready running mpathe (36f01faf000da3761000004323aa6fbce) dm-4 DELL,MD36xxi size=54T features='3 queue_if_no_path pg_init_retries 55' hwhandler='1 rdac' wp=rw |-+- policy='round-robin 0' prio=0 status=active | |- 13:0:0:2 sdg 8:96 active faulty running | |- 14:0:0:2 sdm 8:192 active faulty running | |- 16:0:0:2 sds 65:32 active faulty running | `- 18:0:0:2 sdy 65:128 active faulty running `-+- policy='round-robin 0' prio=0 status=enabled |- 12:0:0:2 sdd 8:48 active faulty running |- 11:0:0:2 sdj 8:144 active faulty running |- 15:0:0:2 sdp 8:240 active faulty running `- 17:0:0:2 sdw 65:96 active faulty running 36001405b1629f80d52a4c898f8856e43 dm-5 LIO-ORG ,block0_sdb size=2.0G features='0' hwhandler='0' wp=rw |-+- policy='service-time 0' prio=1 status=active | `- 3:0:0:0 sdc 8:32 active ready running `-+- policy='service-time 0' prio=1 status=enabled `- 4:0:0:0 sdb 8:16 active ready running """ def test_doc_examples(): env = { 'MultipathDevices': multipath_v4_ll.MultipathDevices, 'mpaths': multipath_v4_ll.MultipathDevices(context_wrap(MULTIPATH_V4_LL_DOC)), } failed, total = doctest.testmod(multipath_v4_ll, globs=env) assert failed == 0

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733c5c6ca77da668bc1c3e90e95aa23118657e58

25

py

Python

crabs/api_caller/__init__.py

jonathanshuai/crabs

2177f9d829a35a670619e1141d10a0442df25aa8

[ "BSD-3-Clause" ]

null

crabs/api_caller/__init__.py

jonathanshuai/crabs

2177f9d829a35a670619e1141d10a0442df25aa8

[ "BSD-3-Clause" ]

6

2021-03-18T20:50:52.000Z

2022-03-11T23:28:02.000Z

crabs/api_caller/__init__.py

jonathanshuai/crabs

2177f9d829a35a670619e1141d10a0442df25aa8

[ "BSD-3-Clause" ]

null

from .crabcaller import *

25

0.8

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734216486f12fde78fd5abd1f947568c07390014

13,843

py

Python

bot.py

Shikib/reddit_chat

db3effbd8b74835d6ee824d90884f47c2efcf589

[ "MIT" ]

null

bot.py

Shikib/reddit_chat

db3effbd8b74835d6ee824d90884f47c2efcf589

[ "MIT" ]

null

bot.py

Shikib/reddit_chat

db3effbd8b74835d6ee824d90884f47c2efcf589

[ "MIT" ]

null

import string import json import random from nltk import word_tokenize from nltk import pos_tag class ContextAwareMarkovBot(): def __init__(self, ngram_len=5, size_reweight=True, punctuation_dataset=None, style_dataset=None, subreddit=None): self.ngram_len = ngram_len self.size_reweight = int(size_reweight) self.punctuation_dataset = punctuation_dataset self.style_dataset = style_dataset self.subreddit = subreddit def generate_message(self, prompt): def _add_to_back(chain): potential_next_words = {} for depth in range(1, min(self.ngram_len, len(chain))+1): # import pdb; pdb.set_trace() try: gram = chain[-depth:] except: break # Turn the gram into a hashable tuple to read from the style graph words = " ".join([t[0] for t in gram]) tags = " ".join([t[1] for t in gram]) gram_tuple = (words,tags) # If this chain of words has never occurred before, continue if gram_tuple not in self.style_graph: continue # Potential next words. Take the top twenty. all_word_scores = self.style_graph[gram_tuple] all_words = all_word_scores.keys() top_words = \ sorted(all_words, key=lambda w: -all_word_scores[w])[:20] word_scores = {word: all_word_scores[word] for word in top_words} # Use the part of speech tag information to determine the next POS if tags not in self.punctuation_graph: continue all_pos_scores = self.punctuation_graph[tags] all_pos = all_pos_scores.keys() top_pos = sorted(all_pos, key=lambda p: -all_pos_scores[p]) pos_scores = {pos: all_pos_scores[pos] for pos in top_pos} word_scores = \ {word: 1.0*(word_scores[word]+pos_scores[word[1]])/2 for word in top_words if word[1] in pos_scores} # Update master word list for word,score in word_scores.items(): if word not in potential_next_words: potential_next_words[word] = 0 potential_next_words[word] = \ potential_next_words[word] + \ score * (depth ** 2*self.size_reweight) # Only consider the top 50 words all_words = potential_next_words.keys() top_words = \ sorted(all_words, key=lambda w: -potential_next_words[w])[:50] potential_next_words = \ {word: potential_next_words[word] for word in top_words} # Choose the next word proportional to its score choice = random.random() scores_sum = sum(potential_next_words.values()) if len(potential_next_words.keys()) == 0: return None,0 for word,score in potential_next_words.items(): choice -= score*1.0/scores_sum if word[1] == '.' or choice <= 0: return word,score def _add_to_front(chain): potential_next_words = {} for depth in range(1, min(self.ngram_len, len(chain))+1): gram = chain[-depth:] # Turn the gram into a hashable tuple to read from the style graph words = " ".join([t[0] for t in gram]) tags = " ".join([t[1] for t in gram]) gram_tuple = (words,tags) # If this chain of words has never occurred before, continue if gram_tuple not in self.rstyle_graph: continue # Potential next words. Take the top twenty. all_word_scores = self.rstyle_graph[gram_tuple] all_words = all_word_scores.keys() top_words = \ sorted(all_words, key=lambda w: -all_word_scores[w])[:20] word_scores = {word: all_word_scores[word] for word in top_words} # Use the part of speech tag information to determine the next POS if tags not in self.rpunctuation_graph: continue all_pos_scores = self.rpunctuation_graph[tags] all_pos = all_pos_scores.keys() top_pos = sorted(all_pos, key=lambda p: -all_pos_scores[p]) pos_scores = {pos: all_pos_scores[pos] for pos in top_pos} word_scores = \ {word: 1.0*(word_scores[word]+pos_scores[word[1]])/2 for word in top_words if word[1] in pos_scores} # Update master word list for word,score in word_scores.items(): if word not in potential_next_words: potential_next_words[word] = 0 potential_next_words[word] = \ potential_next_words[word] + \ score * (depth ** 2*self.size_reweight) # Only consider the top 50 words all_words = potential_next_words.keys() top_words = \ sorted(all_words, key=lambda w: -potential_next_words[w])[:50] potential_next_words = \ {word: potential_next_words[word] for word in top_words} # Choose the next word proportional to its score choice = random.random() scores_sum = sum(potential_next_words.values()) if len(potential_next_words.keys()) == 0: return None,0 for word,score in potential_next_words.items(): choice -= score*1.0/scores_sum if word[1] == '.' or choice <= 0: return word,score prompt = prompt.lower() prompt = \ "".join([ch for ch in prompt if ch not in "'"]) words = word_tokenize(prompt) chain = pos_tag(words) delete_len = 0 while len(chain) < 30: back_word,bscore = _add_to_back(chain) front_word,fscore = _add_to_front(chain[::-1]) if bscore > fscore and chain[-1][1] != '.': chain.append(back_word) elif chain[0][1] != '.': chain = [front_word] + chain else: break return " ".join([word[0] for word in chain]) def train_punctuation(self): # Initialize POS graph self.punctuation_graph = {} def _add_message_to_punctuation(message): score = message[1] message = message[0] # Remove contractions and potentially other characters message = \ "".join([ch for ch in message if ch not in "'"]) words = word_tokenize(message) tagged_words = pos_tag(words) for gram_len in range(1, self.ngram_len+1): # The minus one is to ensure that we always have a word # right after the gram for i in range(len(tagged_words)-gram_len-1): gram = tagged_words[i:i+gram_len] # Turn the gram into a hashable string. tags = " ".join([t[1] for t in gram]) # Identify the type of the word that comes after the gram next_word = tagged_words[i+gram_len][1] if tags not in self.punctuation_graph: self.punctuation_graph[tags] = {} if next_word not in self.punctuation_graph[tags]: self.punctuation_graph[tags][next_word] = 0 self.punctuation_graph[tags][next_word] += score # Need to turn the text into the right format messages = self.extract_messages(self.punctuation_dataset) for message in messages: _add_message_to_punctuation(message) def reverse_train_punctuation(self): # Initialize POS graph self.rpunctuation_graph = {} def _add_message_to_punctuation(message): score = message[1] message = message[0] # Remove contractions and potentially other characters message = \ "".join([ch for ch in message if ch not in "'"]) words = ['.'] + word_tokenize(message)[::-1] tagged_words = pos_tag(words) for gram_len in range(1, self.ngram_len+1): # The minus one is to ensure that we always have a word # right after the gram for i in range(len(tagged_words)-gram_len-1): gram = tagged_words[i:i+gram_len] # Turn the gram into a hashable string. tags = " ".join([t[1] for t in gram]) # Identify the type of the word that comes after the gram next_word = tagged_words[i+gram_len][1] if tags not in self.rpunctuation_graph: self.rpunctuation_graph[tags] = {} if next_word not in self.rpunctuation_graph[tags]: self.rpunctuation_graph[tags][next_word] = 0 self.rpunctuation_graph[tags][next_word] += score # Need to turn the text into the right format messages = self.extract_messages(self.punctuation_dataset) for message in messages: _add_message_to_punctuation(message) def train_style(self): # Initialize POS graph self.style_graph = {} def _add_message_to_style(message): score = message[1] message = message[0] # Remove contractions and potentially other characters message = \ "".join([ch for ch in message if ch not in "'"]) words = word_tokenize(message) tagged_words = pos_tag(words) for gram_len in range(1, self.ngram_len): # The minus one is to ensure that we always have a word # right after the gram for i in range(len(tagged_words)-gram_len-1): gram = tagged_words[i:i+gram_len] # Turn the gram into a hashable tuple. words = " ".join([t[0] for t in gram]) tags = " ".join([t[1] for t in gram]) gram_tuple = (words,tags) # Identify the the word that comes after the gram next_word = tagged_words[i+gram_len] if gram_tuple not in self.style_graph: self.style_graph[gram_tuple] = {} if next_word not in self.style_graph[gram_tuple]: self.style_graph[gram_tuple][next_word] = 0 self.style_graph[gram_tuple][next_word] += score # Need to turn the text into the right format messages = self.extract_messages(self.style_dataset) for message in messages: _add_message_to_style(message) def reverse_train_style(self): # Initialize POS graph self.rstyle_graph = {} def _add_message_to_style(message): score = message[1] message = message[0] # Remove contractions and potentially other characters message = \ "".join([ch for ch in message if ch not in "'"]) words = ['.'] + word_tokenize(message)[::-1] tagged_words = pos_tag(words) for gram_len in range(1, self.ngram_len): # The minus one is to ensure that we always have a word # right after the gram for i in range(len(tagged_words)-gram_len-1): gram = tagged_words[i:i+gram_len] # Turn the gram into a hashable tuple. words = " ".join([t[0] for t in gram]) tags = " ".join([t[1] for t in gram]) gram_tuple = (words,tags) # Identify the the word that comes after the gram next_word = tagged_words[i+gram_len] if gram_tuple not in self.rstyle_graph: self.rstyle_graph[gram_tuple] = {} if next_word not in self.rstyle_graph[gram_tuple]: self.rstyle_graph[gram_tuple][next_word] = 0 self.rstyle_graph[gram_tuple][next_word] += score # Need to turn the text into the right format messages = self.extract_messages(self.style_dataset) for message in messages: _add_message_to_style(message) def extract_messages(self, filename): messages = [] with open(filename) as f: for i in range(10000): try: message = f.next() except: break message = json.loads(message) messages.append((message['body'].lower(), message['score'])) return messages if __name__ == '__main__': cmb = ContextAwareMarkovBot(ngram_len=10, punctuation_dataset='AskReddit', style_dataset='AskReddit', subreddit='AskReddit') cmb.train_style() cmb.train_punctuation() cmb.reverse_train_style() cmb.reverse_train_punctuation() import pdb; pdb.set_trace() cmb.generate_message("I wonder")

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1

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null

0

6

b402b6c30bd40cf69e25b007c5d9719fdf7d4842

183

py

Python

apps/employee/views/its_alive.py

victtorvpb/employee-manager

fa0ea41a80e38378feef1fcbc071d9615f1d5b54

[ "Apache-2.0" ]

null

apps/employee/views/its_alive.py

victtorvpb/employee-manager

fa0ea41a80e38378feef1fcbc071d9615f1d5b54

[ "Apache-2.0" ]

15

2019-08-18T17:20:23.000Z

2021-06-09T18:15:40.000Z

apps/employee/views/its_alive.py

victtorvpb/employee-manager

fa0ea41a80e38378feef1fcbc071d9615f1d5b54

[ "Apache-2.0" ]

1

2019-08-20T00:47:04.000Z

from rest_framework.decorators import api_view from rest_framework.response import Response @api_view(['GET']) def its_alive(request): return Response({'message': 'its_alive'})

22.875

46

0.775956

25

183

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0

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183

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6

b40b1558a9aa57bad311c7e28bca41b05079ec96

27,782

py

Python

azure-mgmt-batchai/tests/test_mgmt_batchai_jobs.py

Christina-Kang/azure-sdk-for-python

bbf982eb06aab04b8151f69f1d230b7f5fb96ebf

[ "MIT" ]

1

2022-03-30T22:39:15.000Z

azure-mgmt-batchai/tests/test_mgmt_batchai_jobs.py

Christina-Kang/azure-sdk-for-python

bbf982eb06aab04b8151f69f1d230b7f5fb96ebf

[ "MIT" ]

54

2016-03-25T17:25:01.000Z

2018-10-22T17:27:54.000Z

azure-mgmt-batchai/tests/test_mgmt_batchai_jobs.py

Christina-Kang/azure-sdk-for-python

bbf982eb06aab04b8151f69f1d230b7f5fb96ebf

[ "MIT" ]

2

2017-01-20T18:25:46.000Z

2017-05-12T21:31:47.000Z

# 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. # -------------------------------------------------------------------------- # pylint: disable=line-too-long import re from azure.storage.blob import BlockBlobService from azure.storage.file import FileService from devtools_testutils import AzureMgmtTestCase, StorageAccountPreparer from devtools_testutils import ResourceGroupPreparer from msrestazure.azure_exceptions import CloudError import azure.mgmt.batchai.models as models from azure.mgmt.batchai import BatchAIManagementClient from . import helpers class JobTestCase(AzureMgmtTestCase): def setUp(self): super(JobTestCase, self).setUp() self.client = helpers.create_batchai_client(self) # type: BatchAIManagementClient @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer() def test_job_creation_and_deletion(self, resource_group, location, cluster, storage_account, storage_account_key): """Tests simple scenario for a job - submit, check results, delete.""" job = helpers.create_custom_job(self.client, resource_group.name, location, cluster.id, 'job', 1, 'echo hi | tee {0}/hi.txt'.format(helpers.JOB_OUTPUT_DIRECTORY_PATH_ENV), container=models.ContainerSettings( image_source_registry=models.ImageSourceRegistry(image='ubuntu')) ) # type: models.Job self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.succeeded) # Check standard job output helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, helpers.STANDARD_OUTPUT_DIRECTORY_ID, {u'stdout.txt': u'hi\n', u'stderr.txt': u''}) # Check job's output helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, helpers.JOB_OUTPUT_DIRECTORY_ID, {u'hi.txt': u'hi\n'}) # Check that we can access the output files directly in storage using path segment returned by the server helpers.assert_file_in_file_share(self, storage_account.name, storage_account_key, job.job_output_directory_path_segment + '/' + helpers.STDOUTERR_FOLDER_NAME, 'stdout.txt', u'hi\n') self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer() def test_running_job_deletion(self, resource_group, location, cluster): """Tests deletion of a running job.""" job = helpers.create_custom_job(self.client, resource_group.name, location, cluster.id, 'job', 1, 'sleep 600') self.assertEqual( helpers.wait_for_job_start_running(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.running) self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer() def test_running_job_termination(self, resource_group, location, cluster): """Tests termination of a running job.""" job = helpers.create_custom_job(self.client, resource_group.name, location, cluster.id, 'longrunning', 1, 'sleep 600') self.assertEqual( helpers.wait_for_job_start_running(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.running) self.client.jobs.terminate(resource_group.name, job.name).result() self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.failed) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer(target_nodes=0, wait=False) def test_queued_job_termination(self, resource_group, location, cluster): """Tests termination of a job in queued state.""" # Create a job which will be in queued state because the cluster has no compute nodes. job = helpers.create_custom_job(self.client, resource_group.name, location, cluster.id, 'job', 1, 'true') self.client.jobs.terminate(resource_group.name, job.name).result() self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.failed) self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer() def test_completed_job_termination(self, resource_group, location, cluster): """Tests termination of completed job.""" job = helpers.create_custom_job(self.client, resource_group.name, location, cluster.id, 'job', 1, 'true') self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.succeeded) # termination of completed job is NOP and must not change the execution state. self.client.jobs.terminate(resource_group.name, job.name).result() self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.succeeded) self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer() def test_failed_job_reporting(self, resource_group, location, cluster): """Tests if job failure is reported correctly.""" job = helpers.create_custom_job(self.client, resource_group.name, location, cluster.id, 'job', 1, 'false') self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.failed) job = self.client.jobs.get(resource_group.name, job.name) self.assertEqual(job.execution_info.exit_code, 1) self.assertEqual(len(job.execution_info.errors), 1) self.assertEqual(job.execution_info.errors[0].code, 'JobFailed') self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer() def test_job_preparation_host(self, resource_group, location, cluster): """Tests job preparation execution for a job running on a host.""" # create a job with job preparation which populates input data in $AZ_BATCHAI_INPUT_INPUT/hi.txt job = helpers.create_custom_job( self.client, resource_group.name, location, cluster.id, 'job', 1, 'cat $AZ_BATCHAI_INPUT_INPUT/hi.txt', 'mkdir -p $AZ_BATCHAI_INPUT_INPUT && echo hello | tee $AZ_BATCHAI_INPUT_INPUT/hi.txt') self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.succeeded) helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, helpers.STANDARD_OUTPUT_DIRECTORY_ID, {u'stdout.txt': u'hello\n', u'stderr.txt': u'', u'stdout-job_prep.txt': u'hello\n', u'stderr-job_prep.txt': u''}) self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer() def test_job_preparation_container(self, resource_group, location, cluster): """Tests job preparation execution for a job running in a container.""" # create a job with job preparation which populates input data in $AZ_BATCHAI_INPUT_INPUT/hi.txt job = helpers.create_custom_job( self.client, resource_group.name, location, cluster.id, 'job', 1, 'cat $AZ_BATCHAI_INPUT_INPUT/hi.txt', 'mkdir -p $AZ_BATCHAI_INPUT_INPUT && echo hello | tee $AZ_BATCHAI_INPUT_INPUT/hi.txt', container=models.ContainerSettings( image_source_registry=models.ImageSourceRegistry(image='ubuntu'))) self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.succeeded) helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, helpers.STANDARD_OUTPUT_DIRECTORY_ID, {u'stdout.txt': u'hello\n', u'stderr.txt': u'', u'stdout-job_prep.txt': u'hello\n', u'stderr-job_prep.txt': u''}) self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer() def test_job_host_preparation_failure_reporting(self, resource_group, location, cluster): """Tests if job preparation failure is reported correctly.""" # create a job with failing job preparation job = helpers.create_custom_job( self.client, resource_group.name, location, cluster.id, 'job', 1, 'true', 'false') self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.failed) job = self.client.jobs.get(resource_group.name, job.name) self.assertEqual(job.execution_info.exit_code, 1) self.assertEqual(len(job.execution_info.errors), 1) self.assertEqual(job.execution_info.errors[0].code, 'JobPreparationFailed') print(job.serialize()) self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer() def test_job_container_preparation_failure_reporting(self, resource_group, location, cluster): """Tests if job preparation failure is reported correctly.""" # create a job with failing job preparation job = helpers.create_custom_job(self.client, resource_group.name, location, cluster.id, 'job', 1, 'true', 'false', container=models.ContainerSettings( image_source_registry=models.ImageSourceRegistry(image='ubuntu'))) self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.failed) job = self.client.jobs.get(resource_group.name, job.name) self.assertEqual(job.execution_info.exit_code, 1) self.assertEqual(len(job.execution_info.errors), 1) self.assertEqual(job.execution_info.errors[0].code, 'JobPreparationFailed') self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer(target_nodes=2) def test_password_less_ssh(self, resource_group, location, cluster): """Tests if password-less ssh is configured on hosts.""" job = helpers.create_custom_job(self.client, resource_group.name, location, cluster.id, 'job', 2, 'ssh 10.0.0.4 echo done && ssh 10.0.0.5 echo done') self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.succeeded) job = self.client.jobs.get(resource_group.name, job.name) helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, helpers.STANDARD_OUTPUT_DIRECTORY_ID, {u'stdout.txt': u'done\ndone\n', u'stderr.txt': re.compile('Permanently added.*')}) self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer(target_nodes=2) def test_password_less_ssh_in_container(self, resource_group, location, cluster): """Tests if password-less ssh is configured in containers.""" job = helpers.create_custom_job(self.client, resource_group.name, location, cluster.id, 'job', 2, 'ssh 10.0.0.5 echo done && ssh 10.0.0.5 echo done', container=models.ContainerSettings( image_source_registry=models.ImageSourceRegistry(image='ubuntu'))) self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.succeeded) job = self.client.jobs.get(resource_group.name, job.name) helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, helpers.STANDARD_OUTPUT_DIRECTORY_ID, {u'stdout.txt': u'done\ndone\n', u'stderr.txt': re.compile('Permanently added.*')}) self.client.jobs.delete(resource_group.name, job.name).result() self.assertRaises(CloudError, lambda: self.client.jobs.get(resource_group.name, job.name)) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer(target_nodes=1) def test_job_level_mounting(self, resource_group, location, cluster, storage_account, storage_account_key): """Tests if it's possible to mount external file systems for a job.""" job_name = 'job' # Create file share and container to mount on the job level if storage_account.name != helpers.FAKE_STORAGE.name: files = FileService(storage_account.name, storage_account_key) files.create_share('jobshare', fail_on_exist=False) blobs = BlockBlobService(storage_account.name, storage_account_key) blobs.create_container('jobcontainer', fail_on_exist=False) job = self.client.jobs.create( resource_group.name, job_name, parameters=models.JobCreateParameters( location=location, cluster=models.ResourceId(id=cluster.id), node_count=1, mount_volumes=models.MountVolumes( azure_file_shares=[ models.AzureFileShareReference( account_name=storage_account.name, azure_file_url='https://{0}.file.core.windows.net/{1}'.format( storage_account.name, 'jobshare'), relative_mount_path='job_afs', credentials=models.AzureStorageCredentialsInfo( account_key=storage_account_key ), ) ], azure_blob_file_systems=[ models.AzureBlobFileSystemReference( account_name=storage_account.name, container_name='jobcontainer', relative_mount_path='job_bfs', credentials=models.AzureStorageCredentialsInfo( account_key=storage_account_key ), ) ] ), # Put standard output on cluster level AFS to check that the job has access to it. std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format(helpers.AZURE_FILES_MOUNTING_PATH), # Create two output directories on job level AFS and blobfuse. output_directories=[ models.OutputDirectory(id='OUTPUT1', path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/job_afs'), models.OutputDirectory(id='OUTPUT2', path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/job_bfs') ], # Check that the job preparation has access to job level file systems. job_preparation=models.JobPreparation( command_line='echo afs > $AZ_BATCHAI_OUTPUT_OUTPUT1/prep_afs.txt; ' 'echo bfs > $AZ_BATCHAI_OUTPUT_OUTPUT2/prep_bfs.txt; ' 'echo done' ), # Check that the job has access to job custom_toolkit_settings=models.CustomToolkitSettings( command_line='echo afs > $AZ_BATCHAI_OUTPUT_OUTPUT1/job_afs.txt; ' 'echo bfs > $AZ_BATCHAI_OUTPUT_OUTPUT2/job_bfs.txt; ' 'mkdir $AZ_BATCHAI_OUTPUT_OUTPUT1/afs; ' 'echo afs > $AZ_BATCHAI_OUTPUT_OUTPUT1/afs/job_afs.txt; ' 'mkdir $AZ_BATCHAI_OUTPUT_OUTPUT2/bfs; ' 'echo bfs > $AZ_BATCHAI_OUTPUT_OUTPUT2/bfs/job_bfs.txt; ' 'echo done' ) ) ).result() self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.succeeded) job = self.client.jobs.get(resource_group.name, job.name) # Assert job and job prep standard output is populated on cluster level filesystem helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, helpers.STANDARD_OUTPUT_DIRECTORY_ID, {u'stdout.txt': u'done\n', u'stderr.txt': u'', u'stdout-job_prep.txt': u'done\n', u'stderr-job_prep.txt': u''}) # Assert files are generated on job level AFS helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, 'OUTPUT1', {u'job_afs.txt': u'afs\n', u'prep_afs.txt': u'afs\n', u'afs': None}) # Assert files are generated on job level blobfuse helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, 'OUTPUT2', {u'job_bfs.txt': u'bfs\n', u'prep_bfs.txt': u'bfs\n', u'bfs': None}) # Assert subfolders are available via API helpers.assert_job_files_in_path_are(self, self.client, resource_group.name, job.name, 'OUTPUT1', 'afs', {u'job_afs.txt': u'afs\n'}) helpers.assert_job_files_in_path_are(self, self.client, resource_group.name, job.name, 'OUTPUT2', 'bfs', {u'job_bfs.txt': u'bfs\n'}) # Assert that we can access the output files created on job level mount volumes directly in storage using path # segment returned by the server. if storage_account.name != helpers.FAKE_STORAGE.name: files = FileService(storage_account.name, storage_account_key) self.assertTrue( files.exists('jobshare', job.job_output_directory_path_segment + '/' + helpers.OUTPUT_DIRECTORIES_FOLDER_NAME, 'job_afs.txt')) blobs = BlockBlobService(storage_account.name, storage_account_key) self.assertTrue( blobs.exists('jobcontainer', job.job_output_directory_path_segment + '/' + helpers.OUTPUT_DIRECTORIES_FOLDER_NAME + '/job_bfs.txt')) # After the job is done the filesystems should be unmounted automatically, check this by submitting a new job. checker = self.client.jobs.create( resource_group.name, 'checker', parameters=models.JobCreateParameters( location=location, cluster=models.ResourceId(id=cluster.id), node_count=1, std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format(helpers.AZURE_FILES_MOUNTING_PATH), custom_toolkit_settings=models.CustomToolkitSettings( command_line='echo job; df | grep -E "job_bfs|job_afs"' ) ) ).result() # Check the job failed because there are not job level mount volumes anymore self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, checker.name, helpers.MINUTE), models.ExecutionState.failed) # Check that the cluster level AFS was still mounted helpers.assert_job_files_are(self, self.client, resource_group.name, checker.name, helpers.STANDARD_OUTPUT_DIRECTORY_ID, {u'stdout.txt': u'job\n', u'stderr.txt': u''}) @ResourceGroupPreparer(location=helpers.LOCATION) @StorageAccountPreparer(name_prefix='psdk', location=helpers.LOCATION, playback_fake_resource=helpers.FAKE_STORAGE) @helpers.ClusterPreparer(target_nodes=1) def test_job_environment_variables_and_secrets(self, resource_group, location, cluster): """Tests if it's possible to mount external file systems for a job.""" job_name = 'job' job = self.client.jobs.create( resource_group.name, job_name, parameters=models.JobCreateParameters( location=location, cluster=models.ResourceId(id=cluster.id), node_count=1, std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format(helpers.AZURE_FILES_MOUNTING_PATH), environment_variables=[ models.EnvironmentVariable(name='VARIABLE', value='VALUE') ], secrets=[ models.EnvironmentVariableWithSecretValue(name='SECRET_VARIABLE', value='SECRET') ], # Check that the job preparation has access to env variables and secrets. job_preparation=models.JobPreparation( command_line='echo $VARIABLE $SECRET_VARIABLE' ), # Check that the job has access to env variables and secrets. custom_toolkit_settings=models.CustomToolkitSettings( command_line='echo $VARIABLE $SECRET_VARIABLE' ) ) ).result() # type: models.Job self.assertEqual( helpers.wait_for_job_completion(self.is_live, self.client, resource_group.name, job.name, helpers.MINUTE), models.ExecutionState.succeeded) # Check that environment variables are reported by the server. self.assertEqual(len(job.environment_variables), 1) self.assertEqual(job.environment_variables[0].name, 'VARIABLE') self.assertEqual(job.environment_variables[0].value, 'VALUE') # Check that secrets are reported back by server, but value is not reported. self.assertEqual(len(job.secrets), 1) self.assertEqual(job.secrets[0].name, 'SECRET_VARIABLE') self.assertIsNone(job.secrets[0].value) # Check that job and job prep had access to the env variables and secrets. helpers.assert_job_files_are(self, self.client, resource_group.name, job.name, helpers.STANDARD_OUTPUT_DIRECTORY_ID, {u'stdout.txt': u'VALUE SECRET\n', u'stderr.txt': u'', u'stdout-job_prep.txt': u'VALUE SECRET\n', u'stderr-job_prep.txt': u''})

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0.076969

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0.831586

0.821518

0.801859

0.767544

0.721494

0.715596

0

0.004068

0.274458

27,782

449

120

61.875278

0.828695

0.106688

0

0.692308

0

0.005495

0.083893

0.02501

0

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1

0.041209

false

0.005495

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0.002747

0

null

0

1

0

null

0

6

b4242c170c7c228d8ac1f21e05664f506fce7d75

4,395

py

Python

python/data-visualization/bar-plot/bar-weights/bar.py

lijiansong/lang

e255709da2b12e09dea45f86d54f77a19b96f13b

[ "WTFPL" ]

1

2020-01-09T03:22:09.000Z

python/data-visualization/bar-plot/bar-weights/bar.py

lijiansong/lang

e255709da2b12e09dea45f86d54f77a19b96f13b

[ "WTFPL" ]

null

python/data-visualization/bar-plot/bar-weights/bar.py

lijiansong/lang

e255709da2b12e09dea45f86d54f77a19b96f13b

[ "WTFPL" ]

null

import matplotlib import matplotlib.pyplot as plt import numpy as np def autolabel(ax, rects): """Attach a text label above each bar in *rects*, displaying its height.""" for rect in rects: height = rect.get_height() if height > 0.0: ax.annotate('{}'.format(height), xy=(rect.get_x() + rect.get_width() / 2, height), xytext=(0, 3), # 3 points vertical offset textcoords="offset points", ha='center', va='bottom') else: ax.annotate('{}'.format(height), xy=(rect.get_x() + rect.get_width() / 2, height), xytext=(0, -15), # 15 points vertical offset textcoords="offset points", ha='center', va='bottom') def draw_e2e_lr(): labels = ['BS', 'DP', 'MP', 'TN'] mobilenet_lr = [17.0, -20.0, -10.1, 19.8] squeezenet_lr = [-9.8, -17.2, -7.4, 37.2] ssd_mobilenet_lr = [-10.6, -21.0, -13.9, 31.8] densenet121_lr = [-0.5, 16.8, -7.1, 55.9] resnet50_lr = [8.9, 2.4, -8.6, 47.4] ssd_vgg16_lr = [5.3, 24.4, -27.0, 51.2] net_num = 6 width = 0.35 x0 = [(1+(net_num+1)*i)*width for i in range(len(labels))] x1 = [(2+(net_num+1)*i)*width for i in range(len(labels))] x2 = [(3+(net_num+1)*i)*width for i in range(len(labels))] x3 = [(4+(net_num+1)*i)*width for i in range(len(labels))] x4 = [(5+(net_num+1)*i)*width for i in range(len(labels))] x5 = [(6+(net_num+1)*i)*width for i in range(len(labels))] fig, ax = plt.subplots() rects_mobilenet = ax.bar(x0, mobilenet_lr, width, label='MobileNet') rects_squeezenet = ax.bar(x1, squeezenet_lr, width, label='SqueezeNet') rects_ssd_mobilenet = ax.bar(x2, ssd_mobilenet_lr, width, label='SSD_MobileNetV1') rects_densenet121 = ax.bar(x3, densenet121_lr, width, label='DenseNet121') rects_resnet50 = ax.bar(x4, resnet50_lr, width, label='ResNet50') rects_ssd_vgg16 = ax.bar(x5, ssd_vgg16_lr, width, label='SSD_VGG16') # Add some text for labels, title and custom x-axis tick labels, etc. ax.set_ylabel('LR weights(%)') ax.set_title('LR weights of hyper-parameters over end-to-end throughput') ax.set_xticks(x3) ax.set_xticklabels(labels) ax.legend() autolabel(ax, rects_mobilenet) autolabel(ax, rects_squeezenet) autolabel(ax, rects_ssd_mobilenet) autolabel(ax, rects_densenet121) autolabel(ax, rects_resnet50) autolabel(ax, rects_ssd_vgg16) fig.tight_layout() plt.show() def draw_hw_lr(): labels = ['BS', 'DP', 'MP', 'TN'] mobilenet_lr = [-1.4, 16.9, 13.8, 29.0] squeezenet_lr = [-2.5, 21.5, 20.1, 29.7] ssd_mobilenet_lr = [-0.7, 20.1, 7.7, 23.3] densenet121_lr = [0.3, 27.4, 17.4, 51.0] resnet50_lr = [2.2, 22.7, 12.1, 38.0] ssd_vgg16_lr = [-0.3, 20.1, 5.0, 23.2] net_num = 6 width = 0.35 x0 = [(1+(net_num+1)*i)*width for i in range(len(labels))] x1 = [(2+(net_num+1)*i)*width for i in range(len(labels))] x2 = [(3+(net_num+1)*i)*width for i in range(len(labels))] x3 = [(4+(net_num+1)*i)*width for i in range(len(labels))] x4 = [(5+(net_num+1)*i)*width for i in range(len(labels))] x5 = [(6+(net_num+1)*i)*width for i in range(len(labels))] fig, ax = plt.subplots() rects_mobilenet = ax.bar(x0, mobilenet_lr, width, label='MobileNet') rects_squeezenet = ax.bar(x1, squeezenet_lr, width, label='SqueezeNet') rects_ssd_mobilenet = ax.bar(x2, ssd_mobilenet_lr, width, label='SSD_MobileNetV1') rects_densenet121 = ax.bar(x3, densenet121_lr, width, label='DenseNet121') rects_resnet50 = ax.bar(x4, resnet50_lr, width, label='ResNet50') rects_ssd_vgg16 = ax.bar(x5, ssd_vgg16_lr, width, label='SSD_VGG16') # Add some text for labels, title and custom x-axis tick labels, etc. ax.set_ylabel('LR weights(%)') ax.set_title('LR weights of hyper-parameters over hardware throughput') ax.set_xticks(x3) ax.set_xticklabels(labels) ax.legend() autolabel(ax, rects_mobilenet) autolabel(ax, rects_squeezenet) autolabel(ax, rects_ssd_mobilenet) autolabel(ax, rects_densenet121) autolabel(ax, rects_resnet50) autolabel(ax, rects_ssd_vgg16) fig.tight_layout() plt.show() if __name__ == '__main__': draw_e2e_lr() draw_hw_lr()

39.241071

86

0.615472

695

4,395

3.729496

0.184173

0.032407

0.080247

0.037037

0.810957

0.790123

0

0.079695

0.223436

4,395

111

87

39.594595

0.679754

0.058476

0

0.688172

0

0.082344

0

1

0.032258

false

0

0.032258

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0.064516

0

null

0

1

0

null

0

6

b4302d63e4350509e38eb74a1aa6b86644bafefb

6,379

py

Python

fireant/tests/dataset/operations/test_cumulative.py

mikeengland/fireant

63c12728c11f1fb252265459f8b8f384d20414b9

[ "Apache-2.0" ]

122

2016-08-05T13:34:52.000Z

2022-03-15T13:21:13.000Z

fireant/tests/dataset/operations/test_cumulative.py

mikeengland/fireant

63c12728c11f1fb252265459f8b8f384d20414b9

[ "Apache-2.0" ]

321

2016-08-10T08:48:15.000Z

2021-07-28T13:08:18.000Z

fireant/tests/dataset/operations/test_cumulative.py

mikeengland/fireant

63c12728c11f1fb252265459f8b8f384d20414b9

[ "Apache-2.0" ]

27

2016-08-10T08:11:08.000Z

2021-08-23T08:14:37.000Z

from unittest import TestCase from unittest.mock import MagicMock import pandas as pd import pandas.testing from fireant import CumMean, CumProd, CumSum, Field from fireant.dataset.references import Reference, WeekOverWeek from fireant.tests.dataset.mocks import ( ElectionOverElection, dimx1_date_df, dimx2_date_str_df, dimx2_date_str_ref_df, mock_dataset, ) class CumSumTests(TestCase): def test_apply_to_timeseries(self): cumsum = CumSum(mock_dataset.fields.wins) result = cumsum.apply(dimx1_date_df, None) expected = pd.Series([2, 4, 6, 8, 10, 12], name='$wins', index=dimx1_date_df.index) pandas.testing.assert_series_equal(expected, result) def test_apply_to_timeseries_with_uni_dim(self): cumsum = CumSum(mock_dataset.fields.wins) result = cumsum.apply(dimx2_date_str_df, None) expected = pd.Series([2, 0, 0, 2, 2, 2, 4, 4, 4, 6, 4, 6, 6], name='$wins', index=dimx2_date_str_df.index) pandas.testing.assert_series_equal(expected, result) def test_apply_to_timeseries_with_uni_dim_and_ref(self): cumsum = CumSum(mock_dataset.fields.wins) result = cumsum.apply(dimx2_date_str_ref_df, ElectionOverElection(mock_dataset.fields.timestamp)) expected = pd.Series( [2.0, 0.0, 2.0, 0.0, 4.0, 0.0, 6.0, 2.0, 6.0, 4.0, 6.0], name='$wins_eoe', index=dimx2_date_str_ref_df.index ) pandas.testing.assert_series_equal(expected, result) def test_apply_cummulative_for_delta_percent(self): dataset = MagicMock() dataset.table._table_name = "table" field = Field("value", None) cumsum = CumSum(field) reference = Reference(field, WeekOverWeek, delta=True, delta_percent=True) df = pd.DataFrame.from_dict( { "$value": [55, 60, 108], "$value_wow": [50, 50, 100], "$cumsum(value)": [55, 115, 223], "$value_wow_delta_percent": [10, 20, 8], } ) result = cumsum.apply(df, reference) pandas.testing.assert_series_equal(pd.Series([10.0, 15.0, 11.5]), result) class CumProdTests(TestCase): def test_apply_to_timeseries(self): cumprod = CumProd(mock_dataset.fields.wins) result = cumprod.apply(dimx1_date_df, None) expected = pd.Series([2, 4, 8, 16, 32, 64], name='$wins', index=dimx1_date_df.index) pandas.testing.assert_series_equal(expected, result) def test_apply_to_timeseries_with_uni_dim(self): cumprod = CumProd(mock_dataset.fields.wins) result = cumprod.apply(dimx2_date_str_df, None) expected = pd.Series([2] + [0] * 12, name='$wins', index=dimx2_date_str_df.index) pandas.testing.assert_series_equal(expected, result) def test_apply_to_timeseries_with_uni_dim_and_ref(self): cumprod = CumProd(mock_dataset.fields.wins) result = cumprod.apply(dimx2_date_str_ref_df, ElectionOverElection(mock_dataset.fields.timestamp)) expected = pd.Series([2.0] + [0.0] * 10, name='$wins_eoe', index=dimx2_date_str_ref_df.index) pandas.testing.assert_series_equal(expected, result) def test_apply_cummulative_for_delta_percent(self): dataset = MagicMock() dataset.table._table_name = "table" field = Field("value", None) cumsum = CumProd(field) reference = Reference(field, WeekOverWeek, delta=True, delta_percent=True) df = pd.DataFrame.from_dict( { "$value": [55, 60, 108], "$value_wow": [50, 50, 100], "$cumprod(value)": [55, 3300, 356400], "$value_wow_delta_percent": [10, 20, 8], } ) result = cumsum.apply(df, reference) pandas.testing.assert_series_equal(pd.Series([10.0, 32.0, 42.56]), result) class CumMeanTests(TestCase): def test_apply_to_timeseries(self): cummean = CumMean(mock_dataset.fields.votes) result = cummean.apply(dimx1_date_df, None) expected = dimx1_date_df['$votes'].astype(float).cumsum() / range(1, len(dimx1_date_df) + 1) pandas.testing.assert_series_equal(expected, result) def test_apply_to_timeseries_with_uni_dim(self): cummean = CumMean(mock_dataset.fields.votes) result = cummean.apply(dimx2_date_str_df, None) expected = pd.Series( [ 7579518.0, 1076384.0, 6564547.0, 7937233.5, 7465807.5, 8484218.666666666, 8322786.0, 9313940.5, 8614866.75, 9935978.0, 8521509.8, 9091928.0, 9341064.0, ], name='$votes', index=dimx2_date_str_df.index, ) pandas.testing.assert_series_equal(expected, result) def test_apply_to_timeseries_with_uni_dim_and_ref(self): cummean = CumMean(mock_dataset.fields.votes) result = cummean.apply(dimx2_date_str_ref_df, ElectionOverElection(mock_dataset.fields.timestamp)) expected = pd.Series( [ 7579518.0, 1076384.0, 7072032.5, 4685666.5, 7503711.0, 6316507.333333333, 8136969.0, 7688157.0, 8407797.0, 8635351.2, 8364511.166666667, ], name='$votes_eoe', index=dimx2_date_str_ref_df.index, ) pandas.testing.assert_series_equal(expected, result) def test_apply_cummulative_for_delta_percent(self): dataset = MagicMock() dataset.table._table_name = "table" field = Field("value", None) cumsum = CumMean(field) reference = Reference(field, WeekOverWeek, delta=True, delta_percent=True) df = pd.DataFrame.from_dict( { "$value": [55, 60, 108], "$value_wow": [50, 50, 100], "$cummean(value)": [55, 57.5, 74 + (1 / 3)], "$value_wow_delta_percent": [10, 20, 8], } ) result = cumsum.apply(df, reference) pandas.testing.assert_series_equal(pd.Series([10.0, 15.0, 11.5]), result)

35.837079

120

0.604797

780

6,379

4.696154

0.158974

0.034398

0.045864

0.0819

0.801256

0.796615

0.790063

0.755119

0.749113

0

0.091108

0.282489

6,379

177

121

36.039548

0.709198

0

0.468966

0

0.039818

0.011287

0

0.082759

1

0.082759

false

0

0.048276

0

0.151724

0

null

0

1

0

null

0

6

b444d45303e82e585a54f1edfd58ec87812630e1

91

py

Python

h5Nastran/__init__.py

mjredmond/mrNastran

4fa57c16e93622ad8be3fb2ed221415ed25c5635

[ "BSD-3-Clause" ]

3

2017-12-02T05:13:05.000Z

2017-12-07T04:34:13.000Z

h5Nastran/__init__.py

mjredmond/mrNastran

4fa57c16e93622ad8be3fb2ed221415ed25c5635

[ "BSD-3-Clause" ]

null

h5Nastran/__init__.py

mjredmond/mrNastran

4fa57c16e93622ad8be3fb2ed221415ed25c5635

[ "BSD-3-Clause" ]

null

from __future__ import print_function, absolute_import from .h5_nastran import H5Nastran

30.333333

55

0.857143

12

91

5.916667

0.75

0

0.025

0.120879

91

3

56

30.333333

0.8625

0

1

0

true

0

1

0

1

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b4656df643fc1944f75c2bfa06a3b14a366eacd2

297

py

Python

problems/303_range-sum-query-immutable.py

okuda-seminar/review_leetcode

9774dbb85b836c3ebab4b24d77774ed05abb7a32

[ "MIT" ]

null

problems/303_range-sum-query-immutable.py

okuda-seminar/review_leetcode

9774dbb85b836c3ebab4b24d77774ed05abb7a32

[ "MIT" ]

170

2021-05-11T14:03:05.000Z

2021-11-30T14:22:52.000Z

problems/303_range-sum-query-immutable.py

ryuji0123/review_leetcode

9774dbb85b836c3ebab4b24d77774ed05abb7a32

[ "MIT" ]

null

# n = nums.length # time = O(n) # space = O(1) class NumArray: def __init__(self, nums: List[int]): self.cumlative_sum = list(accumulate([0] + nums)) def sumRange(self, left: int, right: int) -> int: return self.cumlative_sum[right + 1] - self.cumlative_sum[left]

29.7

71

0.612795

42

297

4.166667

0.52381

0.222857

0.274286

0

0.013216

0.23569

297

9

72

33

0.757709

0.13468

0

1

0.4

false

0

0.2

0.8

0

null

1

0

1

0

null

0

1

0

1

0

6

b4658d9f43c54ae2ea2fc56353419388cc31bb9d

124

py

Python

grayscale/clang/math/max.py

KennethanCeyer/grayscale

646a11ea47f2120f317e554c736d8054aa55c4c4

[ "MIT" ]

null

grayscale/clang/math/max.py

KennethanCeyer/grayscale

646a11ea47f2120f317e554c736d8054aa55c4c4

[ "MIT" ]

null

grayscale/clang/math/max.py

KennethanCeyer/grayscale

646a11ea47f2120f317e554c736d8054aa55c4c4

[ "MIT" ]

null

from typing import List from grayscale.clang import dll def max(nums: List[float]) -> float: return dll.gs_max(nums)

15.5

36

0.725806

20

124

4.45

0.65

0.157303

0

0.177419

124

7

37

17.714286

0.872549

0

1

0.25

false

0

0.5

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

81eca17d648d69799b04a6eb883103a30bb9582e

552

py

Python

qlearning4k/games/game.py

AlexGreason/DeepQLearning

bc554946bf84644b3430aeab9ad27c2c1f08f689

[ "MIT" ]

null

qlearning4k/games/game.py

AlexGreason/DeepQLearning

bc554946bf84644b3430aeab9ad27c2c1f08f689

[ "MIT" ]

null

qlearning4k/games/game.py

AlexGreason/DeepQLearning

bc554946bf84644b3430aeab9ad27c2c1f08f689

[ "MIT" ]

null

class Game: def __init__(self): self.reset() @property def name(self): return "Game" @property def nb_actions(self): return 0 def reset(self): pass def play(self, action): pass def get_state(self): return None def get_score(self): return 0 def is_over(self): return False def is_won(self): return False def get_frame(self, player=None): if player is None: return self.get_state() else: return self.get_state(player) def draw(self): return self.get_state()

13.8

35

0.628623

80

552

4.175

0.35

0.209581

0.116766

0.161677

0

0.004975

0.271739

552

39

36

14.153846

0.825871

0

0.357143

0

0.007797

0

1

0.392857

false

0.071429

0

0.25

0.75

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

81f37e5d92a3cb9be77ba832f0b779004ed5796a

110

py

Python

mediafeed/core/views.py

rennerocha/youtube-organizer

a267b3281f183cc4bcf37b3543324084540bfc25

[ "MIT" ]

11

2020-06-17T18:00:04.000Z

2020-07-15T13:11:36.000Z

mediafeed/core/views.py

rennerocha/youtube-organizer

a267b3281f183cc4bcf37b3543324084540bfc25

[ "MIT" ]

12

2020-06-24T19:16:07.000Z

2020-07-21T13:33:14.000Z

mediafeed/core/views.py

rennerocha/youtube-organizer

a267b3281f183cc4bcf37b3543324084540bfc25

[ "MIT" ]

null

from django.shortcuts import redirect def user_profile(request): return redirect("/c/renne/computacao")

18.333333

42

0.772727

14

110

6

0.928571

0

0.127273

110

5

43

22

0.875

0

0.172727

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

81f6ef5dc045d6a8046b8d32045bc73e02891351

70

py

Python

package1/package2/__init__.py

leowindwave/YAPT

ee5ec568ed746f90a18dc514836624d435a7ccdb

[ "CC0-1.0" ]

4

2017-03-06T09:49:11.000Z

2019-10-16T00:09:38.000Z

package1/package2/__init__.py

leowindwave/YAPT

ee5ec568ed746f90a18dc514836624d435a7ccdb

[ "CC0-1.0" ]

null

package1/package2/__init__.py

leowindwave/YAPT

ee5ec568ed746f90a18dc514836624d435a7ccdb

[ "CC0-1.0" ]

7

2017-11-02T11:00:30.000Z

2020-01-31T22:41:27.000Z

print("package1/package2/__init__.py excuted") from . import module2

17.5

46

0.785714

9

70

5.666667

1

0

0.047619

0.1

70

3

47

23.333333

0.761905

0

0.536232

0.42029

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

1

null

0

1

0

1

0

1

0

6

c31858a31f560a11e9505ccb53e5f4bff21e18ae

14,558

py

Python

python-impl/serdesZ.py

Byeongjee/bls_sigs_ref-fork

ae912b8f701fa93d26886eadef02d5d9c76e99ed

[ "Apache-2.0" ]

2

2020-09-14T20:52:56.000Z

2021-07-11T14:58:14.000Z

python-impl/serdesZ.py

Byeongjee/bls_sigs_ref-fork

ae912b8f701fa93d26886eadef02d5d9c76e99ed

[ "Apache-2.0" ]

1

2019-10-23T15:06:57.000Z

python-impl/serdesZ.py

Byeongjee/bls_sigs_ref-fork

ae912b8f701fa93d26886eadef02d5d9c76e99ed

[ "Apache-2.0" ]

1

2020-02-10T01:00:33.000Z

#!/usr/bin/python # vim: syntax=python # # point serialization / deserialization # using the ZCash format # https://github.com/zkcrypto/pairing/blob/master/src/bls12_381/README.md # https://github.com/zcash/zcash/issues/2517 # (C) 2019 Riad S. Wahby <rsw@cs.stanford.edu> # # see the comment at the top of ../sage-impl/serdesZ.sage for more info import struct from consts import p from curve_ops import from_jacobian, point_eq from fields import Fq, Fq2, sgn0, sqrt_F2 from serdes import DeserError, SerError, _to_bytes_F1, _to_bytes_F2, \ _from_bytes_F1, _from_bytes_F2, _gx1, _gx2, \ F1_zero, F1_one, F2_zero, F2_one def serialize(P, compressed=True): if isinstance(P[0], Fq): return _serialize_help(P, compressed, _to_bytes_F1, 48, _gx1) if isinstance(P[0], Fq2): return _serialize_help(P, compressed, _to_bytes_F2, 96, _gx2) raise SerError("cannot serialize " + str(P)) def _serialize_help(P, compressed, to_bytes, clen, g): # point at infinity if P[2] == 0: if compressed: return b'\xc0' + b'\x00' * (clen - 1) return b'\x40' + b'\x00' * (2 * clen - 1) (x, y) = from_jacobian(P) if pow(y, 2) != g(x): raise SerError("cannot serialize invalid point") x_str = to_bytes(x) if not compressed: return struct.pack("=" + "B" * 2 * clen, *(x_str + to_bytes(y))) y_neg = sgn0(y) < 0 tag_bits = 0xa0 if y_neg else 0x80 x_str[0] = x_str[0] | tag_bits return struct.pack("=" + "B" * clen, *x_str) def deserialize(sp, is_ell2=False): if not is_ell2: return _deserialize_help(sp, _from_bytes_F1, 48, _gx1, lambda x: pow(x, (p + 1) // 4), F1_zero, F1_one) return _deserialize_help(sp, _from_bytes_F2, 96, _gx2, sqrt_F2, F2_zero, F2_one) def _deserialize_help(sp, from_bytes, clen, g, sqrt_fn, zero, one): data = list(struct.unpack("=" + "B" * len(sp), sp)) (tag, data[0]) = (data[0] >> 5, data[0] & 0x1f) if tag in (0b001, 0b011, 0b111): raise DeserError("cannot deserialize value with invalid tag: %d" % tag) if tag == 0b000: # uncompressed point if len(data) != 2 * clen: raise DeserError("invalid uncompresed point: length must be %d, got %d" % (2 * clen, len(data))) x = from_bytes(data[:clen]) y = from_bytes(data[clen:]) if pow(y, 2) != g(x): raise DeserError("invalid uncompressed point: not on curve") return (x, y, one) if tag in (0b010, 0b110): # point at infinity expected_len = 2 * clen if tag == 0b010 else clen if len(data) != expected_len: raise DeserError("invalid point at infinity: length must be %d, got %d" % (expected_len, len(data))) if any( d != 0 for d in data ): raise DeserError("invalid point at infinity: must be all 0s other than tag") return (zero, one, zero) if tag in (0b100, 0b101): # compressed point if len(data) != clen: raise DeserError("invalid compressed point: length must be %d, got %d" % (clen, len(data))) x = from_bytes(data) # recompute y gx = g(x) y = sqrt_fn(gx) if y is None or pow(y, 2) != gx: raise DeserError("invalid compressed point: g(x) is nonsquare") # fix sign of y y_neg = -1 if tag == 0b101 else 1 y = y_neg * sgn0(y) * y return (x, y, one) raise DeserError("invalid tag %d" % tag) if __name__ == "__main__": import binascii import random import sys from opt_swu_g1 import opt_swu_map from opt_swu_g2 import opt_swu2_map invalid_inputs_1 = [ # infinity points: too short "c000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "4000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", # infinity points: not all zeros "c00000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000", "400000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "400000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000", # bad tags "3a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa", "7a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa", "fa0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa", # wrong length for compresed point "9a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaa", "9a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaaaa", # wrong length for uncompressed point "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", # invalid x-coord "9a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa", # invalid elm of Fp --- equal to p (must be strictly less) "9a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaab", "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaab", # point not on curve "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa", ] invalid_inputs_2 = [ # infinity points: too short "c000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "4000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", # infinity points: not all zeros "c00000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "c00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000", "400000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "400000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "400000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "400000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000", # bad tags "3a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "7a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "fa0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", # wrong length for compressed point "9a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "9a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", # wrong length for uncompressed point "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", # invalid x-coord "9a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaa7", # invalid elm of Fp --- equal to p (must be strictly less) "9a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaab000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "9a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaab", "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaab000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaab", "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa3a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa", # point not on curve "1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaaa", ] def test_ell(P): Puc = deserialize(serialize(P, False), isinstance(P[0], Fq2)) Pc = deserialize(serialize(P, True), isinstance(P[0], Fq2)) assert point_eq(P, Puc) assert point_eq(P, Pc) def main(): for Pinf in ((F1_zero, F1_one, F1_zero), (F2_zero, F2_one, F2_zero)): test_ell(Pinf) sys.stdout.write('.') sys.stdout.flush() for _ in range(0, 32): sys.stdout.write('.') sys.stdout.flush() test_ell(opt_swu_map(Fq(p, random.getrandbits(380)))) test_ell(opt_swu2_map(Fq2(p, random.getrandbits(380), random.getrandbits(380)))) for (ell2, invals) in ((False, invalid_inputs_1), (True, invalid_inputs_2)): curve_name = "E2" if ell2 else "E1" for (idx, inval) in enumerate(invals): try: deserialize(binascii.unhexlify(inval), ell2) except DeserError: sys.stdout.write('*') sys.stdout.flush() else: raise DeserError("expected failed deserialization of #%d on %s" % (idx, curve_name)) sys.stdout.write('\n') main()

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1

0

1

0

1

null

1

0

6

c34243eb18839812cd504a19a6e986c5e0497445

124

py

Python

main.py

diazcal/data-analytics-spanish-parliament-seat-allocation

9131302c76edeb8b35ffa3b078232f9f6919ecd8

[ "MIT" ]

null

main.py

diazcal/data-analytics-spanish-parliament-seat-allocation

9131302c76edeb8b35ffa3b078232f9f6919ecd8

[ "MIT" ]

null

main.py

diazcal/data-analytics-spanish-parliament-seat-allocation

9131302c76edeb8b35ffa3b078232f9f6919ecd8

[ "MIT" ]

null

from datasets.parties.all import df_regions_votes_and_parties from datasets.regions.province import df_community_and_provice

62

0.91129

19

124

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0.631579

0.226415

0

0.056452

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0

1

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6

c37628f0ba44b8d21af74a0700b20d8e4066269d

4,292

py

Python

cmd/trainer/model/model.py

andrewcopp/coup

184629e546e0f190b6c16ca8b1d8c6a8c94ac578

[ "MIT" ]

1

2020-02-16T22:10:53.000Z

cmd/trainer/model/model.py

andrewcopp/coup

184629e546e0f190b6c16ca8b1d8c6a8c94ac578

[ "MIT" ]

null

cmd/trainer/model/model.py

andrewcopp/coup

184629e546e0f190b6c16ca8b1d8c6a8c94ac578

[ "MIT" ]

null

import tensorflow as tf import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # The model is responsible for all of the TensorFlow logic. class Model: def __init__(self): self.epsilon = 0.01 def initialize(self, outfile): tf.reset_default_graph() n_inputs = 301 n_outputs = 1 n_hidden_layer = 256 with tf.device('/cpu:0'): weights = { 'hidden_layer': tf.Variable(tf.truncated_normal([n_inputs, n_hidden_layer])), 'out': tf.Variable(tf.truncated_normal([n_hidden_layer, n_outputs])) } biases = { 'hidden_layer': tf.Variable(tf.zeros([n_hidden_layer])), 'out': tf.Variable(tf.zeros([n_outputs])) } init = tf.global_variables_initializer() saver = tf.train.Saver() with tf.Session() as sess: sess.run(init) saver.save(sess, outfile) def transfer(self, infile, outfile): n_inputs = 301 n_outputs = 1 n_hidden_layer = 256 with tf.device('/cpu:0'): weights = { 'hidden_layer': tf.Variable(tf.truncated_normal([n_inputs, n_hidden_layer])), 'out': tf.Variable(tf.truncated_normal([n_hidden_layer, n_outputs])) } biases = { 'hidden_layer': tf.Variable(tf.zeros([n_hidden_layer])), 'out': tf.Variable(tf.zeros([n_outputs])) } saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, infile) saver.save(sess, outfile) def train(self, infile, outfile, inputs, outputs): tf.reset_default_graph() learning_rate = 0.01 n_inputs = 301 n_outputs = 1 n_hidden_layer = 256 with tf.device('/cpu:0'): features = tf.placeholder(tf.float32, [None, n_inputs]) labels = tf.placeholder(tf.float32, [None, n_outputs]) weights = { 'hidden_layer': tf.Variable(tf.truncated_normal([n_inputs, n_hidden_layer])), 'out': tf.Variable(tf.truncated_normal([n_hidden_layer, n_outputs])) } biases = { 'hidden_layer': tf.Variable(tf.zeros([n_hidden_layer])), 'out': tf.Variable(tf.zeros([n_outputs])) } layer_1 = tf.add(tf.matmul(features, weights['hidden_layer']), biases['hidden_layer']) layer_1 = tf.nn.relu(layer_1) logits = tf.add(tf.matmul(layer_1, weights['out']), biases['out']) logits = tf.nn.tanh(logits) cost = tf.reduce_sum(tf.pow(logits-labels, 2))/(2*1) optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, infile) sess.run(optimizer, feed_dict={features: inputs, labels: outputs}) saver.save(sess, outfile) def fit(self, infile, inputs): tf.reset_default_graph() n_inputs = 301 n_outputs = 1 n_hidden_layer = 256 with tf.device('/cpu:0'): features = tf.placeholder(tf.float32, [None, n_inputs]) labels = tf.placeholder(tf.float32, [None, n_outputs]) weights = { 'hidden_layer': tf.Variable(tf.truncated_normal([n_inputs, n_hidden_layer])), 'out': tf.Variable(tf.truncated_normal([n_hidden_layer, n_outputs])) } biases = { 'hidden_layer': tf.Variable(tf.zeros([n_hidden_layer])), 'out': tf.Variable(tf.zeros([n_outputs])) } layer_1 = tf.add(tf.matmul(features, weights['hidden_layer']), biases['hidden_layer']) layer_1 = tf.nn.relu(layer_1) logits = tf.add(tf.matmul(layer_1, weights['out']), biases['out']) logits = tf.nn.tanh(logits) saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, infile) prediction = sess.run(logits, feed_dict={features: inputs}) return prediction # layout - check # communication # saving - check # data # model

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6

c382b8dfb0cfea4ff39a3c7b2f9370c0d9694512

66

py

Python

nntoolbox/vision/losses/__init__.py

nhatsmrt/nn-toolbox

689b9924d3c88a433f8f350b89c13a878ac7d7c3

[ "Apache-2.0" ]

16

2019-07-11T15:57:41.000Z

2020-09-08T13:52:45.000Z

nntoolbox/vision/losses/__init__.py

nhatsmrt/nn-toolbox

689b9924d3c88a433f8f350b89c13a878ac7d7c3

[ "Apache-2.0" ]

1

2022-01-18T22:21:57.000Z

nntoolbox/vision/losses/__init__.py

nhatsmrt/nn-toolbox

689b9924d3c88a433f8f350b89c13a878ac7d7c3

[ "Apache-2.0" ]

1

2019-08-07T10:07:09.000Z

from .style import * from .metrics import * from .robust import *

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8,526

py

Python

lino_book/projects/lydia/tests/dumps/18.12.0/finan_bankstatementitem.py

lino-framework/lino_book

4eab916832cd8f48ff1b9fc8c2789f0b437da0f8

[ "BSD-2-Clause" ]

3

2016-08-25T05:58:09.000Z

2019-12-05T11:13:45.000Z

lino_book/projects/lydia/tests/dumps/18.12.0/finan_bankstatementitem.py

lino-framework/lino_book

4eab916832cd8f48ff1b9fc8c2789f0b437da0f8

[ "BSD-2-Clause" ]

18

2016-11-12T21:38:58.000Z

2019-12-03T17:54:38.000Z

lino_book/projects/lydia/tests/dumps/18.12.0/finan_bankstatementitem.py

lino-framework/lino_book

4eab916832cd8f48ff1b9fc8c2789f0b437da0f8

[ "BSD-2-Clause" ]

9

2016-10-15T11:12:33.000Z

2021-09-22T04:37:37.000Z

# -*- coding: UTF-8 -*- logger.info("Loading 85 objects to table finan_bankstatementitem...") # fields: id, seqno, match, amount, dc, remark, account, partner, date, voucher loader.save(create_finan_bankstatementitem(1,1,u'SLS 25/2015','405.00',True,u'',2,113,None,129)) loader.save(create_finan_bankstatementitem(2,2,u'SLS 26/2015','350.00',True,u'',2,114,None,129)) loader.save(create_finan_bankstatementitem(3,3,u'SLS 41/2015','260.00',True,u'',2,115,None,129)) loader.save(create_finan_bankstatementitem(4,4,u'SLS 42/2015','260.00',True,u'',2,116,None,129)) loader.save(create_finan_bankstatementitem(5,5,u'SLS 43/2015','247.00',True,u'',2,117,None,129)) loader.save(create_finan_bankstatementitem(6,6,u'SLS 46/2015','150.00',True,u'',2,118,None,129)) loader.save(create_finan_bankstatementitem(7,7,u'SLS 45/2015','150.00',True,u'',2,119,None,129)) loader.save(create_finan_bankstatementitem(8,8,u'SLS 44/2015','150.00',True,u'',2,120,None,129)) loader.save(create_finan_bankstatementitem(10,10,u'SLS 47/2015','150.00',True,u'',2,123,None,129)) loader.save(create_finan_bankstatementitem(11,11,u'SLS 31/2015','85.00',True,u'',2,132,None,129)) loader.save(create_finan_bankstatementitem(12,12,u'SLS 32/2015','42.00',True,u'',2,134,None,129)) loader.save(create_finan_bankstatementitem(13,13,u'SLS 33/2015','117.30',True,u'',2,139,None,129)) loader.save(create_finan_bankstatementitem(14,14,u'SLS 34/2015','60.00',True,u'',2,141,None,129)) loader.save(create_finan_bankstatementitem(15,15,u'SLS 35/2015','120.00',True,u'',2,146,None,129)) loader.save(create_finan_bankstatementitem(16,16,u'SLS 29/2015','120.00',True,u'',2,150,None,129)) loader.save(create_finan_bankstatementitem(17,17,u'SLS 30/2015','90.00',True,u'',2,151,None,129)) loader.save(create_finan_bankstatementitem(18,18,u'SLS 37/2015','95.00',True,u'',2,156,None,129)) loader.save(create_finan_bankstatementitem(19,19,u'SLS 38/2015','60.00',True,u'',2,157,None,129)) loader.save(create_finan_bankstatementitem(20,20,u'SLS 40/2015','60.00',True,u'',2,165,None,129)) loader.save(create_finan_bankstatementitem(21,21,u'SLS 36/2015','60.00',True,u'',2,172,None,129)) loader.save(create_finan_bankstatementitem(23,23,u'SLS 28/2015','360.00',True,u'',2,180,None,129)) loader.save(create_finan_bankstatementitem(24,1,u'SLS 43/2015','13.00',True,u'',2,117,None,130)) loader.save(create_finan_bankstatementitem(25,2,u'SLS 27/2015','189.00',True,u'',2,122,None,130)) loader.save(create_finan_bankstatementitem(26,3,u'SLS 32/2015','18.36',True,u'',2,134,None,130)) loader.save(create_finan_bankstatementitem(27,4,u'SLS 33/2015','2.30',False,u'',2,139,None,130)) loader.save(create_finan_bankstatementitem(28,5,u'SLS 37/2015','5.00',True,u'',2,156,None,130)) loader.save(create_finan_bankstatementitem(29,6,u'SLS 39/2015','115.00',True,u'',2,173,None,130)) loader.save(create_finan_bankstatementitem(30,7,u'SLS 48/2015','180.00',True,u'',2,113,None,130)) loader.save(create_finan_bankstatementitem(31,8,u'SLS 49/2015','114.00',True,u'',2,114,None,130)) loader.save(create_finan_bankstatementitem(32,9,u'SLS 64/2015','60.00',True,u'',2,115,None,130)) loader.save(create_finan_bankstatementitem(33,10,u'SLS 65/2015','60.00',True,u'',2,116,None,130)) loader.save(create_finan_bankstatementitem(34,11,u'SLS 66/2015','60.00',True,u'',2,117,None,130)) loader.save(create_finan_bankstatementitem(36,13,u'SLS 68/2015','30.00',True,u'',2,119,None,130)) loader.save(create_finan_bankstatementitem(37,14,u'SLS 67/2015','30.00',True,u'',2,120,None,130)) loader.save(create_finan_bankstatementitem(38,15,u'SLS 50/2015','101.50',True,u'',2,122,None,130)) loader.save(create_finan_bankstatementitem(39,16,u'SLS 70/2015','30.60',True,u'',2,123,None,130)) loader.save(create_finan_bankstatementitem(40,17,u'SLS 54/2015','175.00',True,u'',2,132,None,130)) loader.save(create_finan_bankstatementitem(41,18,u'SLS 55/2015','60.00',True,u'',2,134,None,130)) loader.save(create_finan_bankstatementitem(42,19,u'SLS 56/2015','120.00',True,u'',2,139,None,130)) loader.save(create_finan_bankstatementitem(43,20,u'SLS 57/2015','60.00',True,u'',2,141,None,130)) loader.save(create_finan_bankstatementitem(44,21,u'SLS 58/2015','137.75',True,u'',2,146,None,130)) loader.save(create_finan_bankstatementitem(45,22,u'SLS 52/2015','85.00',True,u'',2,150,None,130)) loader.save(create_finan_bankstatementitem(46,23,u'SLS 53/2015','60.00',True,u'',2,151,None,130)) loader.save(create_finan_bankstatementitem(47,24,u'SLS 60/2015','115.00',True,u'',2,156,None,130)) loader.save(create_finan_bankstatementitem(49,26,u'SLS 63/2015','60.00',True,u'',2,165,None,130)) loader.save(create_finan_bankstatementitem(50,27,u'SLS 59/2015','90.00',True,u'',2,172,None,130)) loader.save(create_finan_bankstatementitem(51,28,u'SLS 62/2015','73.50',True,u'',2,173,None,130)) loader.save(create_finan_bankstatementitem(52,29,u'SLS 51/2015','122.40',True,u'',2,180,None,130)) loader.save(create_finan_bankstatementitem(53,1,u'SLS 27/2015','81.00',True,u'',2,122,None,131)) loader.save(create_finan_bankstatementitem(54,2,u'SLS 32/2015','0.36',False,u'',2,134,None,131)) loader.save(create_finan_bankstatementitem(55,3,u'SLS 49/2015','6.00',True,u'',2,114,None,131)) loader.save(create_finan_bankstatementitem(56,4,u'SLS 69/2015','30.00',True,u'',2,118,None,131)) loader.save(create_finan_bankstatementitem(57,5,u'SLS 50/2015','41.32',True,u'',2,122,None,131)) loader.save(create_finan_bankstatementitem(58,6,u'SLS 70/2015','0.60',False,u'',2,123,None,131)) loader.save(create_finan_bankstatementitem(59,7,u'SLS 58/2015','7.25',True,u'',2,146,None,131)) loader.save(create_finan_bankstatementitem(60,8,u'SLS 61/2015','60.00',True,u'',2,157,None,131)) loader.save(create_finan_bankstatementitem(62,10,u'SLS 51/2015','2.40',False,u'',2,180,None,131)) loader.save(create_finan_bankstatementitem(63,11,u'SLS 71/2015','115.00',True,u'',2,113,None,131)) loader.save(create_finan_bankstatementitem(64,12,u'SLS 72/2015','42.00',True,u'',2,114,None,131)) loader.save(create_finan_bankstatementitem(65,13,u'SLS 73/2015','107.10',True,u'',2,122,None,131)) loader.save(create_finan_bankstatementitem(66,14,u'SLS 77/2015','120.00',True,u'',2,132,None,131)) loader.save(create_finan_bankstatementitem(67,15,u'SLS 78/2015','60.00',True,u'',2,134,None,131)) loader.save(create_finan_bankstatementitem(68,16,u'SLS 79/2015','85.00',True,u'',2,139,None,131)) loader.save(create_finan_bankstatementitem(69,17,u'SLS 80/2015','60.00',True,u'',2,141,None,131)) loader.save(create_finan_bankstatementitem(70,18,u'SLS 81/2015','109.25',True,u'',2,146,None,131)) loader.save(create_finan_bankstatementitem(71,19,u'SLS 75/2015','120.00',True,u'',2,150,None,131)) loader.save(create_finan_bankstatementitem(72,20,u'SLS 76/2015','60.00',True,u'',2,151,None,131)) loader.save(create_finan_bankstatementitem(73,21,u'SLS 83/2015','120.00',True,u'',2,156,None,131)) loader.save(create_finan_bankstatementitem(75,23,u'SLS 86/2015','60.00',True,u'',2,165,None,131)) loader.save(create_finan_bankstatementitem(76,24,u'SLS 82/2015','60.00',True,u'',2,172,None,131)) loader.save(create_finan_bankstatementitem(77,25,u'SLS 85/2015','70.00',True,u'',2,173,None,131)) loader.save(create_finan_bankstatementitem(78,26,u'SLS 74/2015','61.20',True,u'',2,180,None,131)) loader.save(create_finan_bankstatementitem(79,1,u'SLS 50/2015','2.18',True,u'',2,122,None,132)) loader.save(create_finan_bankstatementitem(80,2,u'SLS 62/2015','31.50',True,u'',2,173,None,132)) loader.save(create_finan_bankstatementitem(81,3,u'SLS 72/2015','18.00',True,u'',2,114,None,132)) loader.save(create_finan_bankstatementitem(82,4,u'SLS 73/2015','2.10',False,u'',2,122,None,132)) loader.save(create_finan_bankstatementitem(83,5,u'SLS 81/2015','5.46',True,u'',2,146,None,132)) loader.save(create_finan_bankstatementitem(84,6,u'SLS 84/2015','60.00',True,u'',2,157,None,132)) loader.save(create_finan_bankstatementitem(85,7,u'SLS 85/2015','30.00',True,u'',2,173,None,132)) loader.save(create_finan_bankstatementitem(86,8,u'SLS 74/2015','1.20',False,u'',2,180,None,132)) loader.save(create_finan_bankstatementitem(88,10,u'SLS 14/2015','880.00',True,u'',2,114,None,132)) loader.save(create_finan_bankstatementitem(89,11,u'SLS 15/2015','1050.00',True,u'',2,115,None,132)) loader.save(create_finan_bankstatementitem(90,12,u'SLS 16/2015','196.00',True,u'',2,115,None,132)) loader.save(create_finan_bankstatementitem(91,13,u'SLS 17/2015','459.00',True,u'',2,116,None,132)) loader.save(create_finan_bankstatementitem(92,14,u'SLS 18/2015','880.00',True,u'',2,117,None,132)) loader.flush_deferred_objects()

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1

0

null

0

1

0

6

5ee9d3ad979c4e3739b43f8dfb84a47d62152a0d

5,417

py

Python

frontend/amundsen_application/api/preview/v0.py

defendercrypt/amundsen

83c728b646020f60cf2270c12e766fe4af8c9948

[ "Apache-2.0" ]

2,072

2020-08-11T20:16:48.000Z

2022-03-31T07:04:05.000Z

frontend/amundsen_application/api/preview/v0.py

defendercrypt/amundsen

83c728b646020f60cf2270c12e766fe4af8c9948

[ "Apache-2.0" ]

795

2020-08-11T15:24:39.000Z

2022-03-31T18:56:13.000Z

frontend/amundsen_application/api/preview/v0.py

defendercrypt/amundsen

83c728b646020f60cf2270c12e766fe4af8c9948

[ "Apache-2.0" ]

671

2020-08-11T20:39:56.000Z

2022-03-31T08:39:07.000Z

# Copyright Contributors to the Amundsen project. # SPDX-License-Identifier: Apache-2.0 import json import logging from pkg_resources import iter_entry_points from http import HTTPStatus from flask import Response, jsonify, make_response, request, current_app as app from flask.blueprints import Blueprint from marshmallow import ValidationError from werkzeug.utils import import_string from amundsen_application.models.preview_data import PreviewDataSchema LOGGER = logging.getLogger(__name__) PREVIEW_CLIENT_CLASS = None PREVIEW_CLIENT_INSTANCE = None for entry_point in iter_entry_points(group='preview_client', name='table_preview_client_class'): preview_client_class = entry_point.load() if preview_client_class is not None: PREVIEW_CLIENT_CLASS = preview_client_class preview_blueprint = Blueprint('preview', __name__, url_prefix='/api/preview/v0') @preview_blueprint.route('/', methods=['POST']) def get_table_preview() -> Response: global PREVIEW_CLIENT_INSTANCE global PREVIEW_CLIENT_CLASS try: if PREVIEW_CLIENT_INSTANCE is None: if PREVIEW_CLIENT_CLASS is not None: PREVIEW_CLIENT_INSTANCE = PREVIEW_CLIENT_CLASS() logging.warn('Setting preview_client via entry_point is DEPRECATED and ' 'will be removed in a future version') elif (app.config['PREVIEW_CLIENT_ENABLED'] and app.config['PREVIEW_CLIENT'] is not None): PREVIEW_CLIENT_CLASS = import_string(app.config['PREVIEW_CLIENT']) PREVIEW_CLIENT_INSTANCE = PREVIEW_CLIENT_CLASS() else: payload = jsonify({'previewData': {}, 'msg': 'A client for the preview feature must be configured'}) return make_response(payload, HTTPStatus.NOT_IMPLEMENTED) response = PREVIEW_CLIENT_INSTANCE.get_preview_data(params=request.get_json()) status_code = response.status_code preview_data = json.loads(response.data).get('preview_data') if status_code == HTTPStatus.OK: # validate the returned table preview data try: data = PreviewDataSchema().load(preview_data) payload = jsonify({'previewData': data, 'msg': 'Success'}) except ValidationError as err: logging.error('Preview data dump returned errors: ' + str(err.messages)) raise Exception('The preview client did not return a valid PreviewData object') else: message = 'Encountered error: Preview client request failed with code ' + str(status_code) logging.error(message) # only necessary to pass the error text payload = jsonify({'previewData': {'error_text': preview_data.get('error_text', '')}, 'msg': message}) return make_response(payload, status_code) except Exception as e: message = f'Encountered exception: {str(e)}' logging.exception(message) payload = jsonify({'previewData': {}, 'msg': message}) return make_response(payload, HTTPStatus.INTERNAL_SERVER_ERROR) @preview_blueprint.route('/feature_preview', methods=['POST']) def get_feature_preview() -> Response: global PREVIEW_CLIENT_INSTANCE global PREVIEW_CLIENT_CLASS try: if PREVIEW_CLIENT_INSTANCE is None: if PREVIEW_CLIENT_CLASS is not None: PREVIEW_CLIENT_INSTANCE = PREVIEW_CLIENT_CLASS() logging.warn('Setting preview_client via entry_point is DEPRECATED and ' 'will be removed in a future version') elif (app.config['PREVIEW_CLIENT_ENABLED'] and app.config['PREVIEW_CLIENT'] is not None): PREVIEW_CLIENT_CLASS = import_string(app.config['PREVIEW_CLIENT']) PREVIEW_CLIENT_INSTANCE = PREVIEW_CLIENT_CLASS() else: payload = jsonify({'previewData': {}, 'msg': 'A client for the preview feature must be configured'}) return make_response(payload, HTTPStatus.NOT_IMPLEMENTED) response = PREVIEW_CLIENT_INSTANCE.get_feature_preview_data(params=request.get_json()) status_code = response.status_code preview_data = json.loads(response.data).get('preview_data') if status_code == HTTPStatus.OK: # validate the returned feature preview data try: data = PreviewDataSchema().load(preview_data) payload = jsonify({'previewData': data, 'msg': 'Success'}) except ValidationError as err: logging.error('Preview data dump returned errors: ' + str(err.messages)) raise Exception('The preview client did not return a valid PreviewData object') else: message = 'Encountered error: Preview client request failed with code ' + str(status_code) logging.error(message) # only necessary to pass the error text payload = jsonify({'previewData': {'error_text': preview_data.get('error_text', '')}, 'msg': message}) return make_response(payload, status_code) except Exception as e: message = f'Encountered exception: {str(e)}' logging.exception(message) payload = jsonify({'previewData': {}, 'msg': message}) return make_response(payload, HTTPStatus.INTERNAL_SERVER_ERROR)

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116

0.669559

615

5,417

5.669919

0.203252

0.149125

0.082592

0.037855

0.79696

0.78004

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0.000733

0.2446

5,417

112

117

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0.021978

false

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0

null

0

1

0

1

0

null

0

6

6f43cd5b0453abbd73fe68134a618d48c431ddc6

95

py

Python

mineapy/__init__.py

vpandey-om/mineapy

a533196244d17aa69a5846eb6e197bd4899797b0

[ "Apache-2.0" ]

null

mineapy/__init__.py

vpandey-om/mineapy

a533196244d17aa69a5846eb6e197bd4899797b0

[ "Apache-2.0" ]

null

mineapy/__init__.py

vpandey-om/mineapy

a533196244d17aa69a5846eb6e197bd4899797b0

[ "Apache-2.0" ]

null

# __init__.py from .core.taskEnrich import TaskEnrichment from .core.rxnExp import ReactionExp

23.75

43

0.831579

12

95

6.25

0.75

0.213333

0

0.105263

95

3

44

31.666667

0.882353

0.115789

0

1

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true

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null

1

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1

0

null

0

1

0

1

0

1

0

6

6f7774960f769e54c568bdc74840f7065f2feedc

54

py

Python

conceptnet_rocks/__init__.py

ldtoolkit/conceptnet-rocks

4dda14c6a2a0fdd036a49ad20927a46bd8121848

[ "Apache-2.0" ]

9

2020-11-17T22:01:21.000Z

2022-02-06T14:38:59.000Z

conceptnet_rocks/__init__.py

ldtoolkit/conceptnet-rocks

4dda14c6a2a0fdd036a49ad20927a46bd8121848

[ "Apache-2.0" ]

null

conceptnet_rocks/__init__.py

ldtoolkit/conceptnet-rocks

4dda14c6a2a0fdd036a49ad20927a46bd8121848

[ "Apache-2.0" ]

null

from conceptnet_rocks.database import AssertionFinder

27

53

0.907407

6

54

8

1

0

0.074074

54

1

54

0.96

0

1

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true

0

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null

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null

0

1

0

1

0

1

0

1

0

6

488f5d67b7ac2fe657a5d2907e565b05c480e805

272

py

Python

src/facet/data/partition/__init__.py

skandupmanyu/facet

545ade531ecfa617bad346ebef12955afa876cca

[ "Apache-2.0" ]

null

src/facet/data/partition/__init__.py

skandupmanyu/facet

545ade531ecfa617bad346ebef12955afa876cca

[ "Apache-2.0" ]

null

src/facet/data/partition/__init__.py

skandupmanyu/facet

545ade531ecfa617bad346ebef12955afa876cca

[ "Apache-2.0" ]

null

""" Partitioners to generate series of numerical and categorical values to be used as inputs for simulations. - Numerical partitions are intervals, represented by their central value. - Categorical partitions are the categories themselves. """ from ._partition import *

27.2

78

0.797794

34

272

6.352941

0.852941

0.12037

0

0.150735

272

9

79

30.222222

0.935065

0.867647

0

1

0

1

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true

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null

0

1

0

1

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1

0

null

0

1

0

1

0

1

0

6

489e63234b22c8d38c2ca2b00af9632517b032e1

132

py

Python

test/run/t302.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

2,671

2015-01-03T08:23:25.000Z

2022-03-31T06:15:48.000Z

test/run/t302.py

csev/skulpt

9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f

[ "MIT" ]

972

2015-01-05T08:11:00.000Z

2022-03-29T13:47:15.000Z

test/run/t302.py

csev/skulpt

9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f

[ "MIT" ]

845

2015-01-03T19:53:36.000Z

2022-03-29T18:34:22.000Z

# Test that re-setting the value in a dict doesn't mess with its length d = {'foo':2} print len(d), d d['foo'] = 13 print len(d), d

22

71

0.651515

29

132

2.965517

0.724138

0.069767

0.209302

0.232558

0

0.028302

0.19697

132

5

72

26.4

0.783019

0.522727

0

0.5

0

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null

0

null

0.5

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

489f5606bf73e9310f93bc1a1bc449ed57c1169d

259

py

Python

Demo/Demo_gym/envs/unittest/__init__.py

Remosy/iceHocekeyIRL

1ffeaf8a9bd9585038629be41a2da552e0a4473b

[ "MIT" ]

null

Demo/Demo_gym/envs/unittest/__init__.py

Remosy/iceHocekeyIRL

1ffeaf8a9bd9585038629be41a2da552e0a4473b

[ "MIT" ]

3

2019-03-09T02:35:24.000Z

2019-09-27T11:05:01.000Z

Demo/Demo_gym/envs/unittest/__init__.py

Remosy/iceHocekeyIRL

1ffeaf8a9bd9585038629be41a2da552e0a4473b

[ "MIT" ]

null

from Demo_gym.envs.unittest.cube_crash import CubeCrash from Demo_gym.envs.unittest.cube_crash import CubeCrashSparse from Demo_gym.envs.unittest.cube_crash import CubeCrashScreenBecomesBlack from Demo_gym.envs.unittest.memorize_digits import MemorizeDigits

43.166667

73

0.888031

36

259

6.166667

0.388889

0.144144

0.198198

0.27027

0.617117

0.513514

0

0.065637

259

5

74

51.8

0.917355

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

48a43db6c315d7eabb9426087b90c38a51818939

60

py

Python

analogue_algorithm/__init__.py

tjwixtrom/analogue_algorithm

2627556b9c8282fa0872f66daecc35b28362fd82

[ "BSD-3-Clause" ]

2

2019-08-05T13:44:18.000Z

2022-02-16T14:06:54.000Z

analogue_algorithm/__init__.py

tjwixtrom/adaptive_WRF

2627556b9c8282fa0872f66daecc35b28362fd82

[ "BSD-3-Clause" ]

3

2018-07-25T16:33:09.000Z

2018-08-23T14:57:08.000Z

analogue_algorithm/__init__.py

tjwixtrom/analogue_algorithm

2627556b9c8282fa0872f66daecc35b28362fd82

[ "BSD-3-Clause" ]

null

from .calc import * from .plots import * from .wrf import *

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1

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6

48e54faa7e5016db1ebd55ddd6a9f5cbbc0d77e4

190

py

Python

tests/utils.py

yuezheng/Kado

ad26a7c3b90a6a956a799471dac1cbfd457cfab5

[ "MIT" ]

null

tests/utils.py

yuezheng/Kado

ad26a7c3b90a6a956a799471dac1cbfd457cfab5

[ "MIT" ]

null

tests/utils.py

yuezheng/Kado

ad26a7c3b90a6a956a799471dac1cbfd457cfab5

[ "MIT" ]

null

import asyncio loop = asyncio.get_event_loop() def async_testcase(coro): def wrapper(*args, **kwargs): return loop.run_until_complete(coro(*args, **kwargs)) return wrapper

21.111111

61

0.705263

25

190

5.16

0.64

0.155039

0.248062

0

0.173684

190

9

62

21.111111

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null

0

1

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1

0

6

5b10b3e85cfd54e39036714308d61eb420624c0e

353

py

Python

py_ms_cognitive/__init__.py

mfridson/msf-image-capstone

c338eaf6ef401a8c70000775d0bfafdc5ce3f79c

[ "MIT" ]

null

py_ms_cognitive/__init__.py

mfridson/msf-image-capstone

c338eaf6ef401a8c70000775d0bfafdc5ce3f79c

[ "MIT" ]

null

py_ms_cognitive/__init__.py

mfridson/msf-image-capstone

c338eaf6ef401a8c70000775d0bfafdc5ce3f79c

[ "MIT" ]

null

from .py_ms_cognitive_search.py_ms_cognitive_web_search import PyMsCognitiveWebSearch from .py_ms_cognitive_search.py_ms_cognitive_news_search import PyMsCognitiveNewsSearch from .py_ms_cognitive_search.py_ms_cognitive_video_search import PyMsCognitiveVideoSearch from .py_ms_cognitive_search.py_ms_cognitive_image_search import PyMsCognitiveImageSearch

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0.934844

48

353

6.291667

0.291667

0.10596

0.344371

0.225166

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0.042493

353

4

90

88.25

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null

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1

0

1

0

6

d2933015b557671510b72c295744f7e47ebfdf34

77

py

Python

FuzzyTree/__init__.py

juanmabelda/FuzzyClassifier

8020756e062ab4d2a36d2a6cb40640fbab69f801

[ "MIT" ]

1

2021-09-30T08:54:58.000Z

FuzzyTree/__init__.py

juanmabelda/FuzzyClassifier

8020756e062ab4d2a36d2a6cb40640fbab69f801

[ "MIT" ]

null

FuzzyTree/__init__.py

juanmabelda/FuzzyClassifier

8020756e062ab4d2a36d2a6cb40640fbab69f801

[ "MIT" ]

2

2018-07-17T03:05:42.000Z

2021-10-14T08:25:31.000Z

from .FT_optimize import * from .FuzzyVars import * from .FuzzyTree import *

19.25

26

0.766234

10

77

5.8

0.6

0.344828

0

0.155844

77

3

27

25.666667

0.892308

0

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0

true

0

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null

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1

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1

0

1

0

6

d2c61c5ee326af97cd94908abb0c0eb301f2d7ff

45

py

Python

gwrappy/gmail/__init__.py

hairizuanbinnoorazman/gwrappy

aae569eb87d0aeac6126ccceac8a208b8dfdcf51

[ "Apache-2.0" ]

5

2016-09-21T10:27:05.000Z

2017-03-13T11:37:16.000Z

gwrappy/gmail/__init__.py

hairizuanbinnoorazman/gwrappy

aae569eb87d0aeac6126ccceac8a208b8dfdcf51

[ "Apache-2.0" ]

1

2021-11-15T17:46:52.000Z

gwrappy/gmail/__init__.py

hairizuanbinnoorazman/gwrappy

aae569eb87d0aeac6126ccceac8a208b8dfdcf51

[ "Apache-2.0" ]

2

2016-09-21T10:34:59.000Z

2017-04-05T10:38:10.000Z

from gwrappy.gmail.gmail import GmailUtility

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1

45

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null

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0

null

0

1

0

1

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1

0

6

d2ca3de2589fd5ff18ebd273e770335616f66368

2,620

py

Python

crowdgezwitscher/twitter/migrations/0006_auto_20170408_2226.py

Strassengezwitscher/Crowdgezwitscher

afdd433acb35c1a554ba79464b744975de065151

[ "MIT" ]

4

2016-07-22T07:20:31.000Z

2016-11-13T18:13:34.000Z

crowdgezwitscher/twitter/migrations/0006_auto_20170408_2226.py

Strassengezwitscher/Strassengezwitscher

afdd433acb35c1a554ba79464b744975de065151

[ "MIT" ]

402

2016-04-26T08:38:17.000Z

2022-03-11T23:26:49.000Z

crowdgezwitscher/twitter/migrations/0006_auto_20170408_2226.py

Strassengezwitscher/Crowdgezwitscher

afdd433acb35c1a554ba79464b744975de065151

[ "MIT" ]

1

2018-01-14T16:58:57.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.10 on 2017-04-08 22:26 from django.db import migrations import base def forwards_func(apps, schema_editor): """ Convert Tweet's and TwitterAccount's fields storing ID values to fit UnsignedBigIntegerField. This applies to Tweet's tweet_id and TwitterAccount's account_id and last_known_tweet_id. Do so by substracting 2^63 to have full 64-bit value range. """ Tweet = apps.get_model('twitter', 'Tweet') TwitterAccount = apps.get_model('twitter', 'TwitterAccount') db_alias = schema_editor.connection.alias for tweet in Tweet.objects.using(db_alias).all(): tweet.tweet_id = int(tweet.tweet_id) - 2 ** 63 tweet.save() for account in TwitterAccount.objects.using(db_alias).all(): account.account_id = int(account.account_id) - 2 ** 63 if account.last_known_tweet_id == '': account.last_known_tweet_id = 0 - 2 ** 63 else: account.last_known_tweet_id = int(account.last_known_tweet_id) - 2 ** 63 account.save() def reverse_func(apps, schema_editor): """ Convert Tweet's and TwitterAccount's fields storing ID values to signed 64-bit value range again. This applies to Tweet's tweet_id and TwitterAccount's account_id and last_known_tweet_id. Do so by adding 2^63. """ Tweet = apps.get_model('twitter', 'Tweet') TwitterAccount = apps.get_model('twitter', 'TwitterAccount') db_alias = schema_editor.connection.alias for tweet in Tweet.objects.using(db_alias).all(): tweet.tweet_id = int(tweet.tweet_id) + 2 ** 63 tweet.save() for account in TwitterAccount.objects.using(db_alias).all(): account.account_id = int(account.account_id) + 2 ** 63 account.last_known_tweet_id = int(account.last_known_tweet_id) + 2 ** 63 account.save() class Migration(migrations.Migration): dependencies = [ ('twitter', '0005_auto_20170226_1348'), ] operations = [ migrations.RunPython(forwards_func, reverse_func), migrations.AlterField( model_name='tweet', name='tweet_id', field=base.fields.UnsignedBigIntegerField(unique=True), ), migrations.AlterField( model_name='twitteraccount', name='account_id', field=base.fields.UnsignedBigIntegerField(unique=True), ), migrations.AlterField( model_name='twitteraccount', name='last_known_tweet_id', field=base.fields.UnsignedBigIntegerField(default=0), ), ]

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0.731971

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0

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2,620

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0

1

0

1

0

null

0

6

d2e9df7f3bac1ad8904f4c3a13db92bd82b17e98

11,330

py

Python

Brevet_US_4661747_Edwin_Gray_Power_Tube/Version_3/assembly_v1.py

Jay4C/Python-Macros-For_FreeCAD

12ce5441a26731377fa43e86ccd2be675740d3a0

[ "MIT" ]

null

Brevet_US_4661747_Edwin_Gray_Power_Tube/Version_3/assembly_v1.py

Jay4C/Python-Macros-For_FreeCAD

12ce5441a26731377fa43e86ccd2be675740d3a0

[ "MIT" ]

null

Brevet_US_4661747_Edwin_Gray_Power_Tube/Version_3/assembly_v1.py

Jay4C/Python-Macros-For_FreeCAD

12ce5441a26731377fa43e86ccd2be675740d3a0

[ "MIT" ]

null

import FreeCAD, Part, Drawing, math, Mesh DOC = FreeCAD.activeDocument() DOC_NAME = "assembly_v1" def clear_doc(): # Clear the active document deleting all the objects for obj in DOC.Objects: DOC.removeObject(obj.Name) def setview(): # Rearrange View FreeCAD.Gui.SendMsgToActiveView("ViewFit") FreeCAD.Gui.activeDocument().activeView().viewAxometric() if DOC is None: FreeCAD.newDocument(DOC_NAME) FreeCAD.setActiveDocument(DOC_NAME) DOC = FreeCAD.activeDocument() else: clear_doc() # EPS= tolerance to use to cut the parts EPS = 0.10 EPS_C = EPS * -0.5 # part_tank Mesh.insert(u"part_tank.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_tank").ShapeColor = (0.10,0.10,0.10) FreeCAD.getDocument("assembly_v1").getObject("part_tank").Placement = App.Placement(App.Vector(0,0,0),App.Rotation(App.Vector(0,0,1),0)) FreeCADGui.getDocument("assembly_v1").getObject("part_tank").Transparency = 80 # part_support_laser_cutting _ 1 Mesh.insert(u"part_support_laser_cutting.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_support_laser_cutting").ShapeColor = (1.00,1.00,0.00) FreeCAD.getDocument("assembly_v1").getObject("part_support_laser_cutting").Placement = App.Placement(App.Vector(0,0,-2),App.Rotation(App.Vector(0,1,0),0)) # part_tige_filetee_m8_1000l for the cathode Mesh.insert(u"part_tige_filetee_m8_1000l.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_tige_filetee_m8_1000l").ShapeColor = (0.50,0.50,0.50) FreeCAD.getDocument("assembly_v1").getObject("part_tige_filetee_m8_1000l").Placement = App.Placement(App.Vector(50/2 - 4 - 4, 0, -20),App.Rotation(App.Vector(0,0,1),0)) # part_tige_filetee_m8_1000l for the anode Mesh.insert(u"part_tige_filetee_m8_1000l.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_tige_filetee_m8_1000l001").ShapeColor = (0.50,0.50,0.50) FreeCAD.getDocument("assembly_v1").getObject("part_tige_filetee_m8_1000l001").Placement = App.Placement(App.Vector(-50/2 + 4 + 4, 0, -20),App.Rotation(App.Vector(0,0,1),0)) # Rank 1 # part_rondelle_8m for part_tige_filetee_m8_1000l Mesh.insert(u"part_rondelle_8m.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_rondelle_8m").ShapeColor = (0.30,0.20,0.20) FreeCAD.getDocument("assembly_v1").getObject("part_rondelle_8m").Placement = App.Placement(App.Vector(50/2 - 4 - 4, 0, -3.5),App.Rotation(App.Vector(0,0,1),0)) # part_rondelle_8m for part_tige_filetee_m8_1000l001 Mesh.insert(u"part_rondelle_8m.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_rondelle_8m001").ShapeColor = (0.30,0.20,0.20) FreeCAD.getDocument("assembly_v1").getObject("part_rondelle_8m001").Placement = App.Placement(App.Vector(-50/2 + 4 + 4, 0, -3.5),App.Rotation(App.Vector(0,0,1),0)) # part_ecrou_8m for part_tige_filetee_m8_1000l Mesh.insert(u"part_ecrou_8m.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_ecrou_8m").ShapeColor = (0.25,0.25,0.20) FreeCAD.getDocument("assembly_v1").getObject("part_ecrou_8m").Placement = App.Placement(App.Vector(50/2 - 4 - 4, 0, -11.5),App.Rotation(App.Vector(0,0,1),0)) # part_ecrou_8m for part_tige_filetee_m8_1000l001 Mesh.insert(u"part_ecrou_8m.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_ecrou_8m001").ShapeColor = (0.25,0.25,0.20) FreeCAD.getDocument("assembly_v1").getObject("part_ecrou_8m001").Placement = App.Placement(App.Vector(-50/2 + 4 + 4, 0, -11.5),App.Rotation(App.Vector(0,0,1),0)) # Rank 2 # part_rondelle_8m for part_tige_filetee_m8_1000l Mesh.insert(u"part_rondelle_8m.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_rondelle_8m002").ShapeColor = (0.30,0.20,0.20) FreeCAD.getDocument("assembly_v1").getObject("part_rondelle_8m002").Placement = App.Placement(App.Vector(50/2 - 4 - 4, 0, 0),App.Rotation(App.Vector(0,0,1),0)) # part_rondelle_8m for part_tige_filetee_m8_1000l001 Mesh.insert(u"part_rondelle_8m.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_rondelle_8m003").ShapeColor = (0.30,0.20,0.20) FreeCAD.getDocument("assembly_v1").getObject("part_rondelle_8m003").Placement = App.Placement(App.Vector(-50/2 + 4 + 4, 0, 0),App.Rotation(App.Vector(0,0,1),0)) # part_ecrou_8m for part_tige_filetee_m8_1000l Mesh.insert(u"part_ecrou_8m.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_ecrou_8m002").ShapeColor = (0.25,0.25,0.20) FreeCAD.getDocument("assembly_v1").getObject("part_ecrou_8m002").Placement = App.Placement(App.Vector(50/2 - 4 - 4, 0, 1.5),App.Rotation(App.Vector(0,0,1),0)) # part_ecrou_8m for part_tige_filetee_m8_1000l001 Mesh.insert(u"part_ecrou_8m.stl","assembly_v1") FreeCADGui.getDocument("assembly_v1").getObject("part_ecrou_8m003").ShapeColor = (0.25,0.25,0.20) FreeCAD.getDocument("assembly_v1").getObject("part_ecrou_8m003").Placement = App.Placement(App.Vector(-50/2 + 4 + 4, 0, 1.5),App.Rotation(App.Vector(0,0,1),0)) number_of_steps_electrode = 180 number_of_steps = number_of_steps_electrode * 2 + 2 # insertion part_equerre_assemblage_laser_cutting for spacing the electrodes for i in range(0, number_of_steps): location = (2.5*i + 9.5) if i < 1: Mesh.insert(u"part_equerre_assemblage_laser_cutting.stl","assembly_v1") FreeCAD.getDocument("assembly_v1").getObject("part_equerre_assemblage_laser_cutting").Placement = App.Placement(App.Vector(50/2 - 4 - 4, 0, location), App.Rotation(App.Vector(0,0,1), 180)) FreeCADGui.getDocument("assembly_v1").getObject("part_equerre_assemblage_laser_cutting").ShapeColor = (0.30,0.20,0.20) elif 1 <= i < 10: Mesh.insert(u"part_equerre_assemblage_laser_cutting.stl","assembly_v1") FreeCAD.getDocument("assembly_v1").getObject("part_equerre_assemblage_laser_cutting00" + str(i)).Placement = App.Placement(App.Vector(50/2 - 4 - 4, 0, location), App.Rotation(App.Vector(0,0,1), 180)) FreeCADGui.getDocument("assembly_v1").getObject("part_equerre_assemblage_laser_cutting00" + str(i)).ShapeColor = (0.30,0.20,0.20) elif i < 100: Mesh.insert(u"part_equerre_assemblage_laser_cutting.stl","assembly_v1") FreeCAD.getDocument("assembly_v1").getObject("part_equerre_assemblage_laser_cutting0" + str(i)).Placement = App.Placement(App.Vector(50/2 - 4 - 4, 0, location), App.Rotation(App.Vector(0,0,1), 180)) FreeCADGui.getDocument("assembly_v1").getObject("part_equerre_assemblage_laser_cutting0" + str(i)).ShapeColor = (0.30,0.20,0.20) else: Mesh.insert(u"part_equerre_assemblage_laser_cutting.stl","assembly_v1") FreeCAD.getDocument("assembly_v1").getObject("part_equerre_assemblage_laser_cutting" + str(i)).Placement = App.Placement(App.Vector(50/2 - 4 - 4, 0, location), App.Rotation(App.Vector(0,0,1), 180)) FreeCADGui.getDocument("assembly_v1").getObject("part_equerre_assemblage_laser_cutting" + str(i)).ShapeColor = (0.30,0.20,0.20) # insertion part_electrode_laser_cutting for the cathode Mesh.insert(u"part_electrode_laser_cutting.stl","assembly_v1") FreeCAD.getDocument("assembly_v1").getObject("part_electrode_laser_cutting").Placement = App.Placement(App.Vector(0, 0, 11), App.Rotation(App.Vector(0, 0, 1), 0)) FreeCADGui.getDocument("assembly_v1").getObject("part_electrode_laser_cutting").ShapeColor = (0.60,0.40,0.20) for i in range(0, number_of_steps_electrode): location = 5*i + 16 if i < 9: Mesh.insert(u"part_electrode_laser_cutting.stl","assembly_v1") FreeCAD.getDocument("assembly_v1").getObject("part_electrode_laser_cutting00" + str(i+1)).Placement = App.Placement(App.Vector(0, 0, location), App.Rotation(App.Vector(0, 0, 1), 0)) FreeCADGui.getDocument("assembly_v1").getObject("part_electrode_laser_cutting00" + str(i+1)).ShapeColor = (0.60,0.40,0.20) elif i < 99: Mesh.insert(u"part_electrode_laser_cutting.stl","assembly_v1") FreeCAD.getDocument("assembly_v1").getObject("part_electrode_laser_cutting0" + str(i+1)).Placement = App.Placement(App.Vector(0, 0, location), App.Rotation(App.Vector(0, 0, 1), 0)) FreeCADGui.getDocument("assembly_v1").getObject("part_electrode_laser_cutting0" + str(i+1)).ShapeColor = (0.60,0.40,0.20) else: Mesh.insert(u"part_electrode_laser_cutting.stl","assembly_v1") FreeCAD.getDocument("assembly_v1").getObject("part_electrode_laser_cutting" + str(i+1)).Placement = App.Placement(App.Vector(0, 0, location), App.Rotation(App.Vector(0, 0, 1), 0)) FreeCADGui.getDocument("assembly_v1").getObject("part_electrode_laser_cutting" + str(i+1)).ShapeColor = (0.60,0.40,0.20) # insertion part_electrode_laser_cutting for the anode for i in range(0, number_of_steps_electrode): location = 5*i + 13.5 Mesh.insert(u"part_electrode_laser_cutting.stl","assembly_v1") FreeCAD.getDocument("assembly_v1").getObject("part_electrode_laser_cutting" + str(i + number_of_steps_electrode + 1)).Placement = App.Placement(App.Vector(0, 0, location), App.Rotation(App.Vector(0, 0, 1), 180)) FreeCADGui.getDocument("assembly_v1").getObject("part_electrode_laser_cutting" + str(i + number_of_steps_electrode + 1)).ShapeColor = (0.20,0.40,0.60) setview() # Generate PNG files file = 'assembly_v1_v3_' # Ombr� Gui.runCommand('Std_DrawStyle',5) i = 1 Gui.activeDocument().activeView().viewIsometric() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewFront() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewTop() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewRight() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewRear() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewBottom() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewLeft() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') # Filaire Gui.runCommand('Std_DrawStyle',2) i += 1 Gui.activeDocument().activeView().viewIsometric() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewFront() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewTop() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewRight() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewRear() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewBottom() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current') i += 1 Gui.activeDocument().activeView().viewLeft() Gui.activeDocument().activeView().saveImage(file + str(i) + '.png',1117,388,'Current')

53.696682

215

0.744837

1,697

11,330

4.769004

0.078963

0.081552

0.111578

0.159397

0.903497

0.890523

0.884097

0.870011

0.849623

0.804522

0.000088

0.078184

0.084466

11,330

210

216

53.952381

0.701918

0.074404

0

0.471831

0

0.242473

0.111249

0

1

0.014085

false

0

0.007042

0

0.021127

0

null

0

1

0

null

0

6

d2f05b8491060776946784bb978aea415af8a3b0

267

py

Python

commons/helm/exceptions.py

unikubehq/commons

d4e64ca400d4ffe388cb9470bfce004a301e4be1

[ "Apache-2.0" ]

2

2021-06-17T07:50:57.000Z

2021-08-08T11:53:40.000Z

commons/helm/exceptions.py

unikubehq/commons

d4e64ca400d4ffe388cb9470bfce004a301e4be1

[ "Apache-2.0" ]

34

2021-06-10T14:30:36.000Z

2022-02-21T08:23:51.000Z

commons/helm/exceptions.py

unikubehq/commons

d4e64ca400d4ffe388cb9470bfce004a301e4be1

[ "Apache-2.0" ]

null

class RepositoryBranchUnavailable(Exception): pass class RepositoryAuthenticationFailed(Exception): pass class RepositoryCloningFailed(Exception): pass class HelmDependencyError(Exception): pass class HelmChartRenderError(Exception): pass

14.052632

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null

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6

d2fd6f259b01f9da4b448b00fd8cb8c764c72a79

137

py

Python

backend/app/translation/components/__init__.py

griviala/garpix_page

55f1d9bc6d1de29d18e15369bebcbef18811b5a4

[ "MIT" ]

null

backend/app/translation/components/__init__.py

griviala/garpix_page

55f1d9bc6d1de29d18e15369bebcbef18811b5a4

[ "MIT" ]

null

backend/app/translation/components/__init__.py

griviala/garpix_page

55f1d9bc6d1de29d18e15369bebcbef18811b5a4

[ "MIT" ]

null

from .text import TextComponentTranslationOptions # noqa from .text_description import TextDescriptionComponentTranslationOptions # noqa

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0

6

96212ff19d59c1a6d97c48690333d22c2deef703

5,456

py

Python

numerik061.py

matzegltg/miau

89b580baccbd258fbfd81bc19b46603a07873f14

[ "MIT" ]

null

numerik061.py

matzegltg/miau

89b580baccbd258fbfd81bc19b46603a07873f14

[ "MIT" ]

1

2021-05-07T15:50:51.000Z

numerik061.py

matzegltg/miau

89b580baccbd258fbfd81bc19b46603a07873f14

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy as np import math as mth def explizitEuler(a, b, p0, h, n): t = [0] y = [p0] for i in range(0, n): t.append(t[i]+h) y.append(y[i]+h*(a*y[i]-b*y[i]**2)) return t,y def heun(a,b,p0,h,n): t = [0] y = [p0] for i in range(0,n): t.append(t[i]+h) ytilde = y[i] + h*(a*y[i]-b*y[i]**2) y.append(y[i] + h/2 * ((a*y[i]-b*y[i]**2) + (a*ytilde - b*ytilde**2))) return t, y def aufgabeA(): t1, y1 = explizitEuler(a=2, b=0.01, p0=1, h=0.01, n=1000) t20, y20 = explizitEuler(a=2, b=0.01, p0=20, h=0.01, n=1000) t100, y100 = explizitEuler(a=2, b=0.01, p0=100, h=0.01, n=1000) t200, y200 = explizitEuler(a=2, b=0.01, p0=200, h=0.01, n=1000) t400, y400 = explizitEuler(a=2, b=0.01, p0=400, h=0.01, n=1000) # Lösung mit p0 = 1 #t = np.linspace(0,10, 1000) #y = 2/(0.01+(2-0.01)*np.exp(-2*t)) #plt.plot(t, y, "--", color = "grey", label = "solution for p0 = 1") plt.plot(t1, y1, label = "p0 = 1") plt.plot(t20, y20, label = "p0 = 20") plt.plot(t100, y100, label = "p0 = 100") plt.plot(t200, y200, label = "p0 = 200") plt.plot(t400, y400, label = "p0 = 400") plt.title("Aufgabe 6a") plt.xlabel('t') plt.ylabel('y(t)') plt.legend(loc="lower right") plt.grid(True) plt.show() def aufgabeAHeun(): t1, y1 = heun(a=2, b=0.01, p0=1, h=0.01, n=1000) t20, y20 = heun(a=2, b=0.01, p0=20, h=0.01, n=1000) t100, y100 = heun(a=2, b=0.01, p0=100, h=0.01, n=1000) t200, y200 = heun(a=2, b=0.01, p0=200, h=0.01, n=1000) t400, y400 = heun(a=2, b=0.01, p0=400, h=0.01, n=1000) # Lösung mit p0 = 1 #t = np.linspace(0,10, 1000) #y = 2/(0.01+(2-0.01)*np.exp(-2*t)) #plt.plot(t, y, "--", color = "grey", label = "solution for p0 = 1") plt.plot(t1, y1, label = "p0 = 1") plt.plot(t20, y20, label = "p0 = 20") plt.plot(t100, y100, label = "p0 = 100") plt.plot(t200, y200, label = "p0 = 200") plt.plot(t400, y400, label = "p0 = 400") plt.title("Aufgabe 6a - Verfahren von Heun") plt.xlabel('t') plt.ylabel('p(t)') plt.legend(loc="lower right") plt.grid(True) plt.show() def aufgabeB(): t1, y1 = explizitEuler(a=2, b=0.01, p0=1, h=0.01, n=2000) t20, y20 = explizitEuler(a=2, b=0.01, p0=1, h=0.1, n=200) t100, y100 = explizitEuler(a=2, b=0.01, p0=1, h=0.5, n=40) t200, y200 = explizitEuler(a=2, b=0.01, p0=1, h=1, n=20) # Lösung mit p0 = 1 t = np.linspace(0,10, 1000) y = 2/(0.01+(2-0.01)*np.exp(-2*t)) plt.plot(t, y, "--", color = "grey", label = "solution for p0 = 1") plt.plot(t1, y1, label = "h = 0.01") plt.plot(t20, y20, label = "h = 0.1") plt.plot(t100, y100, label = "h = 0.5") plt.plot(t200, y200, label = "h = 1") plt.title("Aufgabe 6b") plt.xlabel('t') plt.ylabel('p(t)') plt.legend(loc="lower right") plt.grid(True) plt.show() def aufgabeBHeun(): t1, y1 = heun(a=2, b=0.01, p0=1, h=0.01, n=2000) t20, y20 = heun(a=2, b=0.01, p0=1, h=0.1, n=200) t100, y100 = heun(a=2, b=0.01, p0=1, h=0.5, n=40) t200, y200 = heun(a=2, b=0.01, p0=1, h=1, n=20) # Lösung mit p0 = 1 t = np.linspace(0,10, 1000) y = 2/(0.01+(2-0.01)*np.exp(-2*t)) plt.plot(t, y, "--", color = "grey", label = "solution for p0 = 1") plt.plot(t1, y1, label = "h = 0.01") plt.plot(t20, y20, label = "h = 0.1") plt.plot(t100, y100, label = "h = 0.5") plt.plot(t200, y200, label = "h = 1") plt.title("Aufgabe 6b - Verfahren von Heun") plt.xlabel('t') plt.ylabel('p(t)') plt.legend(loc="lower right") plt.grid(True) plt.show() def aufgabeC(): nachse = [] fehler = [] fehlerHeun = [] p3 = 2/(0.01+(2-0.01)*mth.exp(-2*3)) for i in range(5,21): n = 2**i h = 3/n nachse.append(n) p0 = 1 b = 0.01 a = 2 t1, y1 = explizitEuler(a, b, p0, h, n) err = abs(y1[2**i] - p3) fehler.append(err) nachse = [] for i in range(5,21): n = 2**i h = 3/n nachse.append(n) p0 = 1 b = 0.01 a = 2 t1, y1 = heun(a, b, p0, h, n) err = abs(y1[2**i] - p3) fehlerHeun.append(err) plt.plot(nachse, fehler, "o-", label = "expliziter Euler Fehler") plt.plot(nachse, fehlerHeun, "o-", label = "Heun Fehler") # Lösung mit p0 = 1 #t = np.linspace(0,10, 1000) # y = 2/(0.01+(2-0.01)*np.exp(-2*t)) plt.title("Aufgabe 6c - Fehlervergleich") plt.xlabel('n') plt.xscale('log') plt.ylabel('fehler') plt.yscale('log') plt.legend(loc="lower right") plt.grid(True) plt.show() def aufgabeCgraphs(): nachse = [] fehler = [] p3 = 2/(0.01+(2-0.01)*mth.exp(-2*3)) for i in range(5,21): n = 2**i h = 3/n nachse.append(n) p0 = 1 b = 0.01 a = 2 t1, y1 = explizitEuler(a, b, p0, h, n) err = abs(y1[2**i] - p3) fehler.append(err) plt.plot(t1, y1, label = f"n = {n}") # Lösung mit p0 = 1 #t = np.linspace(0,10, 1000) # y = 2/(0.01+(2-0.01)*np.exp(-2*t)) plt.title("Aufgabe 6c") plt.xlabel('n') #plt.xscale('log') plt.ylabel('fehler') #plt.yscale('log') plt.legend(loc="lower right") plt.grid(True) plt.show() aufgabeC()

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null

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6

824d5ddea51829f38ce291a0eb96fd3507c38844

48

py

Python

torchtracer/utils/__init__.py

OIdiotLin/torchtracer

ca85c414b27e3edabaa2fea17ab9943fa668f952

[ "MIT" ]

52

2018-11-14T22:14:53.000Z

2022-03-24T13:03:21.000Z

torchtracer/utils/__init__.py

OIdiotLin/torchtracer

ca85c414b27e3edabaa2fea17ab9943fa668f952

[ "MIT" ]

4

2018-11-14T08:46:45.000Z

2020-12-14T11:28:54.000Z

torchtracer/utils/__init__.py

OIdiotLin/torchtracer

ca85c414b27e3edabaa2fea17ab9943fa668f952

[ "MIT" ]

6

2019-06-05T07:17:06.000Z

2021-08-31T03:10:35.000Z

from torchtracer.utils.storeman import StoreMan

24

47

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0

6

82757b7f062a9d40e5d11f521661bd62ca0a2744

50,604

py

Python

pytracking-master/ltr/data/processing.py

wsumel/AMMC

ef101878b4a97f07984186ea09146348c0526fa6

[ "Apache-2.0" ]

3

2021-12-02T11:34:37.000Z

2021-12-19T09:30:10.000Z

pytracking-master/ltr/data/processing.py

wsumel/AMMC

ef101878b4a97f07984186ea09146348c0526fa6

[ "Apache-2.0" ]

null

pytracking-master/ltr/data/processing.py

wsumel/AMMC

ef101878b4a97f07984186ea09146348c0526fa6

[ "Apache-2.0" ]

null

import torch import math import numpy as np import torchvision.transforms as transforms from pytracking import TensorDict import ltr.data.processing_utils as prutils def stack_tensors(x): if isinstance(x, (list, tuple)) and isinstance(x[0], torch.Tensor): return torch.stack(x) return x class BaseProcessing: """ Base class for Processing. Processing class is used to process the data returned by a dataset, before passing it through the network. For example, it can be used to crop a search region around the object, apply various data augmentations, etc.""" def __init__(self, transform=transforms.ToTensor(), train_transform=None, test_transform=None, joint_transform=None): """ args: transform - The set of transformations to be applied on the images. Used only if train_transform or test_transform is None. train_transform - The set of transformations to be applied on the train images. If None, the 'transform' argument is used instead. test_transform - The set of transformations to be applied on the test images. If None, the 'transform' argument is used instead. joint_transform - The set of transformations to be applied 'jointly' on the train and test images. For example, it can be used to convert both test and train images to grayscale. """ self.transform = {'train': transform if train_transform is None else train_transform, 'test': transform if test_transform is None else test_transform, 'joint': joint_transform} def __call__(self, data: TensorDict): raise NotImplementedError class ATOMProcessing(BaseProcessing): """ The processing class used for training ATOM. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. A set of proposals are then generated for the test images by jittering the ground truth box. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generates proposals by adding noise to the input box args: box - input box returns: torch.Tensor - Array of shape (num_proposals, 4) containing proposals torch.Tensor - Array of shape (num_proposals,) containing IoU overlap of each proposal with the input box. The IoU is mapped to [-1, 1] """ # Generate proposals num_proposals = self.proposal_params['boxes_per_frame'] proposal_method = self.proposal_params.get('proposal_method', 'default') if proposal_method == 'default': proposals = torch.zeros((num_proposals, 4)) gt_iou = torch.zeros(num_proposals) for i in range(num_proposals): proposals[i, :], gt_iou[i] = prutils.perturb_box(box, min_iou=self.proposal_params['min_iou'], sigma_factor=self.proposal_params['sigma_factor']) elif proposal_method == 'gmm': proposals, _, _ = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], num_samples=num_proposals) gt_iou = prutils.iou(box.view(1,4), proposals.view(-1,4)) # Map to [-1, 1] gt_iou = gt_iou * 2 - 1 return proposals, gt_iou def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_iou' """ # Apply joint transforms if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes, _ = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals frame2_proposals, gt_iou = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = list(frame2_proposals) data['proposal_iou'] = list(gt_iou) # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class KLBBregProcessing(BaseProcessing): """ Based on ATOMProcessing. It supports training ATOM using the Maximum Likelihood or KL-divergence based learning introduced in [https://arxiv.org/abs/1909.12297] and in PrDiMP [https://arxiv.org/abs/2003.12565]. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ """ # Generate proposals proposals, proposal_density, gt_density = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], gt_sigma=self.proposal_params['gt_sigma'], num_samples=self.proposal_params[ 'boxes_per_frame'], add_mean_box=self.proposal_params.get( 'add_mean_box', False)) return proposals, proposal_density, gt_density def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_density', 'gt_density' """ # Apply joint transforms if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes, _ = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals proposals, proposal_density, gt_density = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = proposals data['proposal_density'] = proposal_density data['gt_density'] = gt_density # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class ATOMwKLProcessing(BaseProcessing): """Same as ATOMProcessing but using the GMM-based sampling of proposal boxes used in KLBBregProcessing.""" def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ """ # Generate proposals proposals, proposal_density, gt_density = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], self.proposal_params['gt_sigma'], self.proposal_params['boxes_per_frame']) iou = prutils.iou_gen(proposals, box.view(1, 4)) return proposals, proposal_density, gt_density, iou def __call__(self, data: TensorDict): # Apply joint transforms if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes, _ = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals proposals, proposal_density, gt_density, proposal_iou = zip( *[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = proposals data['proposal_density'] = proposal_density data['gt_density'] = gt_density data['proposal_iou'] = proposal_iou # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class DiMPProcessing(BaseProcessing): """ The processing class used for training DiMP. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. A Gaussian label centered at the target is generated for each image. These label functions are used for computing the loss of the predicted classification model on the test images. A set of proposals are also generated for the test images by jittering the ground truth box. These proposals are used to train the bounding box estimating branch. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, crop_type='replicate', max_scale_change=None, mode='pair', proposal_params=None, label_function_params=None, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. crop_type - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. label_function_params - Arguments for the label generation process. See _generate_label_function for details. """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.crop_type = crop_type self.mode = mode self.max_scale_change = max_scale_change self.proposal_params = proposal_params self.label_function_params = label_function_params def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generates proposals by adding noise to the input box args: box - input box returns: torch.Tensor - Array of shape (num_proposals, 4) containing proposals torch.Tensor - Array of shape (num_proposals,) containing IoU overlap of each proposal with the input box. The IoU is mapped to [-1, 1] """ # Generate proposals num_proposals = self.proposal_params['boxes_per_frame'] proposal_method = self.proposal_params.get('proposal_method', 'default') if proposal_method == 'default': proposals = torch.zeros((num_proposals, 4)) gt_iou = torch.zeros(num_proposals) for i in range(num_proposals): proposals[i, :], gt_iou[i] = prutils.perturb_box(box, min_iou=self.proposal_params['min_iou'], sigma_factor=self.proposal_params['sigma_factor']) elif proposal_method == 'gmm': proposals, _, _ = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], num_samples=num_proposals) gt_iou = prutils.iou(box.view(1, 4), proposals.view(-1, 4)) else: raise ValueError('Unknown proposal method.') # Map to [-1, 1] gt_iou = gt_iou * 2 - 1 return proposals, gt_iou def _generate_label_function(self, target_bb): """ Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_function_params['sigma_factor'], self.label_function_params['kernel_sz'], self.label_function_params['feature_sz'], self.output_sz, end_pad_if_even=self.label_function_params.get('end_pad_if_even', True)) return gauss_label def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_iou', 'test_label' (optional), 'train_label' (optional), 'test_label_density' (optional), 'train_label_density' (optional) """ if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] crops, boxes = prutils.target_image_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz, mode=self.crop_type, max_scale_change=self.max_scale_change) data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals if self.proposal_params: frame2_proposals, gt_iou = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = list(frame2_proposals) data['proposal_iou'] = list(gt_iou) # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) # Generate label functions if self.label_function_params is not None: data['train_label'] = self._generate_label_function(data['train_anno']) data['test_label'] = self._generate_label_function(data['test_anno']) return data class KLDiMPProcessing(BaseProcessing): """ The processing class used for training PrDiMP that additionally supports the probabilistic classifier and bounding box regressor. See DiMPProcessing for details. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, crop_type='replicate', max_scale_change=None, mode='pair', proposal_params=None, label_function_params=None, label_density_params=None, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. crop_type - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. label_function_params - Arguments for the label generation process. See _generate_label_function for details. label_density_params - Arguments for the label density generation process. See _generate_label_function for details. """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.crop_type = crop_type self.mode = mode self.max_scale_change = max_scale_change self.proposal_params = proposal_params self.label_function_params = label_function_params self.label_density_params = label_density_params def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generate proposal sample boxes from a GMM proposal distribution and compute their ground-truth density. This is used for ML and KL based regression learning of the bounding box regressor. args: box - input bounding box """ # Generate proposals proposals, proposal_density, gt_density = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], gt_sigma=self.proposal_params['gt_sigma'], num_samples=self.proposal_params['boxes_per_frame'], add_mean_box=self.proposal_params.get('add_mean_box', False)) return proposals, proposal_density, gt_density def _generate_label_function(self, target_bb): """ Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_function_params['sigma_factor'], self.label_function_params['kernel_sz'], self.label_function_params['feature_sz'], self.output_sz, end_pad_if_even=self.label_function_params.get('end_pad_if_even', True)) return gauss_label def _generate_label_density(self, target_bb): """ Generates the gaussian label density centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ feat_sz = self.label_density_params['feature_sz'] * self.label_density_params.get('interp_factor', 1) gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_density_params['sigma_factor'], self.label_density_params['kernel_sz'], feat_sz, self.output_sz, end_pad_if_even=self.label_density_params.get('end_pad_if_even', True), density=True, uni_bias=self.label_density_params.get('uni_weight', 0.0)) gauss_label *= (gauss_label > self.label_density_params.get('threshold', 0.0)).float() if self.label_density_params.get('normalize', False): g_sum = gauss_label.sum(dim=(-2,-1)) valid = g_sum>0.01 gauss_label[valid, :, :] /= g_sum[valid].view(-1, 1, 1) gauss_label[~valid, :, :] = 1.0 / (gauss_label.shape[-2] * gauss_label.shape[-1]) gauss_label *= 1.0 - self.label_density_params.get('shrink', 0.0) return gauss_label def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_density', 'gt_density', 'test_label' (optional), 'train_label' (optional), 'test_label_density' (optional), 'train_label_density' (optional) """ if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] crops, boxes = prutils.target_image_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz, mode=self.crop_type, max_scale_change=self.max_scale_change) data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals proposals, proposal_density, gt_density = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = proposals data['proposal_density'] = proposal_density data['gt_density'] = gt_density for s in ['train', 'test']: is_distractor = data.get('is_distractor_{}_frame'.format(s), None) if is_distractor is not None: for is_dist, box in zip(is_distractor, data[s+'_anno']): if is_dist: box[0] = 99999999.9 box[1] = 99999999.9 # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) # Generate label functions if self.label_function_params is not None: data['train_label'] = self._generate_label_function(data['train_anno']) data['test_label'] = self._generate_label_function(data['test_anno']) if self.label_density_params is not None: data['train_label_density'] = self._generate_label_density(data['train_anno']) data['test_label_density'] = self._generate_label_density(data['test_anno']) return data class LWLProcessing(BaseProcessing): """ The processing class used for training LWL. The images are processed in the following way. First, the target bounding box (computed using the segmentation mask)is jittered by adding some noise. Next, a rectangular region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. The argument 'crop_type' determines how out-of-frame regions are handled when cropping the search region. For instance, if crop_type == 'replicate', the boundary pixels are replicated in case the search region crop goes out of frame. If crop_type == 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, crop_type='replicate', max_scale_change=None, mode='pair', new_roll=False, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - The size (width, height) to which the search region is resized. The aspect ratio is always preserved when resizing the search region center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. crop_type - Determines how out-of-frame regions are handled when cropping the search region. If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when shrinking the search region to fit the image (only applicable to 'inside' and 'inside_major' cropping modes). In case the desired shrink factor exceeds the max_scale_change, the search region is only shrunk to the factor max_scale_change. Out-of-frame regions are then handled by replicating the boundary pixels. If max_scale_change is set to None, unbounded shrinking is allowed. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames new_roll - Whether to use the same random roll values for train and test frames when applying the joint transformation. If True, a new random roll is performed for the test frame transformations. Thus, if performing random flips, the set of train frames and the set of test frames will be flipped independently. """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.crop_type = crop_type self.mode = mode self.max_scale_change = max_scale_change self.new_roll = new_roll def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ if self.scale_jitter_factor.get('mode', 'gauss') == 'gauss': jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) elif self.scale_jitter_factor.get('mode', 'gauss') == 'uniform': jittered_size = box[2:4] * torch.exp(torch.FloatTensor(2).uniform_(-self.scale_jitter_factor[mode], self.scale_jitter_factor[mode])) else: raise Exception max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode])).float() jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def __call__(self, data: TensorDict): # Apply joint transformations. i.e. All train/test frames in a sequence are applied the transformation with the # same parameters if self.transform['joint'] is not None: data['train_images'], data['train_anno'], data['train_masks'] = self.transform['joint']( image=data['train_images'], bbox=data['train_anno'], mask=data['train_masks']) data['test_images'], data['test_anno'], data['test_masks'] = self.transform['joint']( image=data['test_images'], bbox=data['test_anno'], mask=data['test_masks'], new_roll=self.new_roll) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] orig_anno = data[s + '_anno'] # Extract a crop containing the target crops, boxes, mask_crops = prutils.target_image_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz, mode=self.crop_type, max_scale_change=self.max_scale_change, masks=data[s + '_masks']) # Apply independent transformations to each image data[s + '_images'], data[s + '_anno'], data[s + '_masks'] = self.transform[s](image=crops, bbox=boxes, mask=mask_crops, joint=False) # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class KYSProcessing(BaseProcessing): """ The processing class used for training KYS. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. A Gaussian label centered at the target is generated for each image. These label functions are used for computing the loss of the predicted classification model on the test images. A set of proposals are also generated for the test images by jittering the ground truth box. These proposals can be used to train the bounding box estimating branch. """ def __init__(self, search_area_factor, output_sz, center_jitter_param, scale_jitter_param, proposal_params=None, label_function_params=None, min_crop_inside_ratio=0, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _generate_synthetic_motion for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _generate_synthetic_motion for how the jittering is done. proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. label_function_params - Arguments for the label generation process. See _generate_label_function for details. min_crop_inside_ratio - Minimum amount of cropped search area which should be inside the image. See _check_if_crop_inside_image for details. """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_param = center_jitter_param self.scale_jitter_param = scale_jitter_param self.proposal_params = proposal_params self.label_function_params = label_function_params self.min_crop_inside_ratio = min_crop_inside_ratio def _check_if_crop_inside_image(self, box, im_shape): x, y, w, h = box.tolist() if w <= 0.0 or h <= 0.0: return False crop_sz = math.ceil(math.sqrt(w * h) * self.search_area_factor) x1 = x + 0.5 * w - crop_sz * 0.5 x2 = x1 + crop_sz y1 = y + 0.5 * h - crop_sz * 0.5 y2 = y1 + crop_sz w_inside = max(min(x2, im_shape[1]) - max(x1, 0), 0) h_inside = max(min(y2, im_shape[0]) - max(y1, 0), 0) crop_area = ((x2 - x1) * (y2 - y1)) if crop_area > 0: inside_ratio = w_inside * h_inside / crop_area return inside_ratio > self.min_crop_inside_ratio else: return False def _generate_synthetic_motion(self, boxes, images, mode): num_frames = len(boxes) out_boxes = [] for i in range(num_frames): jittered_box = None for _ in range(10): orig_box = boxes[i] jittered_size = orig_box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_param[mode + '_factor']) if self.center_jitter_param.get(mode + '_mode', 'uniform') == 'uniform': max_offset = (jittered_size.prod().sqrt() * self.center_jitter_param[mode + '_factor']).item() offset_factor = (torch.rand(2) - 0.5) jittered_center = orig_box[0:2] + 0.5 * orig_box[2:4] + max_offset * offset_factor if self.center_jitter_param.get(mode + '_limit_motion', False) and i > 0: prev_out_box_center = out_boxes[-1][:2] + 0.5 * out_boxes[-1][2:] if abs(jittered_center[0] - prev_out_box_center[0]) > out_boxes[-1][2:].prod().sqrt() * 2.5: jittered_center[0] = orig_box[0] + 0.5 * orig_box[2] + max_offset * offset_factor[0] * -1 if abs(jittered_center[1] - prev_out_box_center[1]) > out_boxes[-1][2:].prod().sqrt() * 2.5: jittered_center[1] = orig_box[1] + 0.5 * orig_box[3] + max_offset * offset_factor[1] * -1 jittered_box = torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) if self._check_if_crop_inside_image(jittered_box, images[i].shape): break else: jittered_box = torch.tensor([1, 1, 10, 10]).float() out_boxes.append(jittered_box) return out_boxes def _generate_proposals(self, frame2_gt_crop): # Generate proposals num_proposals = self.proposal_params['boxes_per_frame'] frame2_proposals = np.zeros((num_proposals, 4)) gt_iou = np.zeros(num_proposals) sample_p = np.zeros(num_proposals) for i in range(num_proposals): frame2_proposals[i, :], gt_iou[i], sample_p[i] = prutils.perturb_box( frame2_gt_crop, min_iou=self.proposal_params['min_iou'], sigma_factor=self.proposal_params['sigma_factor'] ) gt_iou = gt_iou * 2 - 1 return frame2_proposals, gt_iou def _generate_label_function(self, target_bb, target_absent=None): gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_function_params['sigma_factor'], self.label_function_params['kernel_sz'], self.label_function_params['feature_sz'], self.output_sz, end_pad_if_even=self.label_function_params.get( 'end_pad_if_even', True)) if target_absent is not None: gauss_label *= (1 - target_absent).view(-1, 1, 1).float() return gauss_label def __call__(self, data: TensorDict): if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: # Generate synthetic sequence jittered_anno = self._generate_synthetic_motion(data[s + '_anno'], data[s + '_images'], s) # Crop images crops, boxes, _ = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Add transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) if self.proposal_params: frame2_proposals, gt_iou = zip(*[self._generate_proposals(a.numpy()) for a in data['test_anno']]) data['test_proposals'] = [torch.tensor(p, dtype=torch.float32) for p in frame2_proposals] data['proposal_iou'] = [torch.tensor(gi, dtype=torch.float32) for gi in gt_iou] data = data.apply(stack_tensors) if self.label_function_params is not None: data['train_label'] = self._generate_label_function(data['train_anno']) test_target_absent = 1 - (data['test_visible'] * data['test_valid_anno']) data['test_label'] = self._generate_label_function(data['test_anno'], test_target_absent) return data

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null

0

6

82767a9d9793c1b713066c9b3c5518ba6c69dd27

63

py

Python

external/__init__.py

personalOS1234/Core-OS-2.0

12a933776fce246f5425faf479d7811af222f2af

[ "MIT" ]

null

external/__init__.py

personalOS1234/Core-OS-2.0

12a933776fce246f5425faf479d7811af222f2af

[ "MIT" ]

null

external/__init__.py

personalOS1234/Core-OS-2.0

12a933776fce246f5425faf479d7811af222f2af

[ "MIT" ]

null

#TODO replace all this libraries with ours from .pyaes import *

31.5

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1

0

1

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6

828ac2174a6739c989e8e1ea209b6e2ac2458536

82

py

Python

odp/job/publish/saeon.py

SAEON/Open-Data-Platform

8509c39c6f65ba18518e825e2359213ec4c67af5

[ "MIT" ]

null

odp/job/publish/saeon.py

SAEON/Open-Data-Platform

8509c39c6f65ba18518e825e2359213ec4c67af5

[ "MIT" ]

null

odp/job/publish/saeon.py

SAEON/Open-Data-Platform

8509c39c6f65ba18518e825e2359213ec4c67af5

[ "MIT" ]

null

from odp.job.publish import Publisher class SAEONPublisher(Publisher): pass

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null

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1

0

1

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6

7d994a3e0c083b02d29cc8b8b8f7c78301de92bc

4,372

py

Python

whython/values/value_number.py

NexInfinite/whython

0c4099ed27151e99ba63465acd0abb2e38bba8ad

[ "MIT" ]

44

2021-08-12T00:23:24.000Z

2022-02-22T08:33:02.000Z

whython/values/value_number.py

NexInfinite/whython

0c4099ed27151e99ba63465acd0abb2e38bba8ad

[ "MIT" ]

null

whython/values/value_number.py

NexInfinite/whython

0c4099ed27151e99ba63465acd0abb2e38bba8ad

[ "MIT" ]

4

2021-08-12T04:02:43.000Z

2021-08-25T08:58:19.000Z

# *################### # * IMPORTS # *################### from values.value_values import Value from errors import RTError import math # *################### # * NUMBER # *################### class Number(Value): def __init__(self, value): super().__init__() self.value = value def added_to(self, other): if isinstance(other, Number): return Number(self.value + other.value).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def subtracted_by(self, other): if isinstance(other, Number): return Number(self.value - other.value).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def multiplied_by(self, other): if isinstance(other, Number): return Number(self.value * other.value).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def exponent_by(self, other): if isinstance(other, Number): return Number(self.value ** other.value).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def get_comparison_eq(self, other): if isinstance(other, Number): return Number(int(self.value == other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def get_comparison_ne(self, other): if isinstance(other, Number): return Number(int(self.value != other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def get_comparison_lt(self, other): if isinstance(other, Number): return Number(int(self.value < other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def get_comparison_lte(self, other): if isinstance(other, Number): return Number(int(self.value <= other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def get_comparison_gt(self, other): if isinstance(other, Number): return Number(int(self.value > other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def get_comparison_gte(self, other): if isinstance(other, Number): return Number(int(self.value >= other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def anded_by(self, other): if isinstance(other, Number): return Number(int(self.value and other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def ored_by(self, other): if isinstance(other, Number): return Number(int(self.value or other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def notted(self): return Number(1 if self.value == 0 else 0).set_context(self.context), None def divided_by(self, other): if isinstance(other, Number): if other.value == 0: return None, RTError( other.pos_start, other.pos_end, "Division by zero.", self.context ) return Number(self.value / other.value).set_context(self.context), None else: return None, Value.illegal_operation(self.pos_start, other.pos_end) def copy(self): copy = Number(self.value) copy.set_pos(self.pos_start, self.pos_end) copy.set_context(self.context) return copy def is_true(self): return self.value != 0 def __repr__(self): return str(self.value) Number.null = Number(0) Number.ignore = Number(None) Number.false = Number(0) Number.true = Number(1) Number.pi = Number(math.pi)

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0.079245

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0

null

0

1

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null

0

1

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6

7dea385a46f18cb818a8c7e337ce410ee1b8020e

22

py

Python

my_module/my_module/__init__.py

Hekstra-Lab/nesm-python

5b13cebe0019a589ea234d1191148e120aa75114

[ "BSD-3-Clause" ]

1

2021-05-07T18:03:30.000Z

my_module/my_module/__init__.py

Hekstra-Lab/nesm-python

5b13cebe0019a589ea234d1191148e120aa75114

[ "BSD-3-Clause" ]

1

2021-05-13T14:54:27.000Z

my_module/my_module/__init__.py

Hekstra-Lab/nesm-python

5b13cebe0019a589ea234d1191148e120aa75114

[ "BSD-3-Clause" ]

null

from myfuncs import *

11

21

0.772727

3

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5.666667

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null

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1

0

1

0

1

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6

818ea9ab02a5c39743786bfc6ceccb71b87ebacb

49

py

Python

social/backends/email.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

1,987

2015-01-01T16:12:45.000Z

2022-03-29T14:24:25.000Z

social/backends/email.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

731

2015-01-01T22:55:25.000Z

2022-03-10T15:07:51.000Z

virtual/lib/python3.6/site-packages/social/backends/email.py

dennismwaniki67/awards

80ed10541f5f751aee5f8285ab1ad54cfecba95f

[ "MIT" ]

1,082

2015-01-01T16:27:26.000Z

2022-03-22T21:18:33.000Z

from social_core.backends.email import EmailAuth

24.5

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6

818f86c3e551dc02afd9f458a08b02841b948e64

2,512

py

Python

alpaca/resources/tutorials/ALPACA_atlas_to_ROI.py

C0C0AN/ALPACA

bfe6012ebc7f7df92ddda2eede3b1b41eb39db90

[ "BSD-3-Clause" ]

5

2018-12-14T14:17:44.000Z

2020-11-03T03:15:04.000Z

alpaca/resources/tutorials/ALPACA_atlas_to_ROI.py

PeerHerholz/ALPACA

39b037c38a122d4e8c3cf2cfe465d38a7c31fa99

[ "BSD-3-Clause" ]

9

2018-06-01T16:11:39.000Z

2020-03-21T01:37:35.000Z

alpaca/resources/tutorials/ALPACA_atlas_to_ROI.py

C0C0AN/ALPACA

bfe6012ebc7f7df92ddda2eede3b1b41eb39db90

[ "BSD-3-Clause" ]

5

2018-01-26T15:16:40.000Z

2020-11-03T03:15:05.000Z

# --- # jupyter: # jupytext: # formats: py,ipynb # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.3.3 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # #           Automatic Locazation and Parcellation of Auditory Cortex Areas ##                                                     (ALPACA) #                                                                        <img src="../img/ALPACA_logo.png" alt="alpaca logo" width="370" height="250" border="10"> # ##    extracting (auditory cortex) regions of interest (ROIs) from atlases of the human brain # ### This notebook will focus on how to extract regions of interest from atlases of the human. As ALPACA is all about the [auditory cortex](https://en.wikipedia.org/wiki/Auditory_cortex), all examples will be used to extract [regions of interest](https://en.wikipedia.org/wiki/Region_of_interest#Medical_imaging) within the auditory cortex . Given that most atlases of the human brain are in a [reference space, e.g. the mni space](http://www.lead-dbs.org/?p=1241), a section of the notebook will also show how to transform regions of interest from reference to a participants respective native space. Comparable to other notebooks of the ALPACA toolbox, the methods and analyses steps described here are easy to adapt for other, more general purposes than "just" auditory neuroscience related topics. # ### Around the brain in 80 atlases # You might ask yourself "What's with all that talking about atlases? What's actually an atlas of the human brain?". So, to enable the best possible understanding and to bring everyone (nearly) on the same page, the first section of this notebook will give a brief overview of atlases of the human brain.

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1

0

null

0

1

0

6

819ccf03ef12dc81905b777b7e6c0142e0e730bf

28

py

Python

sample_app/__init__.py

gutoxp/flask-vuejs

86f3eb196d174ccfa3fb62174de83f6101bf91ff

[ "BSD-3-Clause" ]

122

2021-06-21T17:30:29.000Z

2022-03-25T06:21:38.000Z

sample_app/__init__.py

gutoxp/flask-vuejs

86f3eb196d174ccfa3fb62174de83f6101bf91ff

[ "BSD-3-Clause" ]

125

2021-09-01T12:06:48.000Z

2022-03-30T11:32:57.000Z

app/frontend/__init__.py

openstate/coronalert

9aa24cc0ea75b85e9bda0cfcd6ff592a2c61c95e

[ "CC-BY-4.0" ]

21

2021-06-22T10:08:15.000Z

2022-03-18T08:57:02.000Z

from .app import create_app

14

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0.916667

0

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1

0

1

0

1

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6

81a73f999345b2e78f6d1435100ac35570598cf2

160

py

Python

CodingBat/Warmup-1/near_hundred.py

N-l1/dmoj

bbd55ab45731774385805eb31ea790454a3a6819

[ "MIT" ]

null

CodingBat/Warmup-1/near_hundred.py

N-l1/dmoj

bbd55ab45731774385805eb31ea790454a3a6819

[ "MIT" ]

null

CodingBat/Warmup-1/near_hundred.py

N-l1/dmoj

bbd55ab45731774385805eb31ea790454a3a6819

[ "MIT" ]

null

""" Warmup-1 > near_hundred Find this problem at: https://codingbat.com/prob/p124676 """ def near_hundred(n): return abs(100-n) <= 10 or abs(200-n) <= 10

16

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0.211538

0

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null

0

1

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1

0

6

81ccae0065c891a162b04752f381d4d01a04d59f

45,771

py

Python

Experiment.py

pitcheverlasting/wrr-AED-drivers

d6803bfd80756c8d5f899952940033d45fcf57c4

[ "Unlicense" ]

null

Experiment.py

pitcheverlasting/wrr-AED-drivers

d6803bfd80756c8d5f899952940033d45fcf57c4

[ "Unlicense" ]

null

Experiment.py

pitcheverlasting/wrr-AED-drivers

d6803bfd80756c8d5f899952940033d45fcf57c4

[ "Unlicense" ]

null

__author__ = 'lpeng' from pylab import * import pickle, itertools import scipy.io import scipy.stats from scipy.optimize import minimize from scipy import signal import statsmodels.tools.eval_measures as evaluate import pandas as pd import IO, FFT, Plotting from PET_Library import Data import gc from scipy.interpolate import interp1d gc.collect() # #=============================path================================ datadir = '../Data' workspace = '../workspace/201609' # figdir = '/home/water5/lpeng/Figure/pan_spectral/201609' figdir = '/home/water5/lpeng/Figure/pan_spectral/201705' ##=============================variable============================ vars = ['time', 'p', 'tavg', 'tmin', 'tmax', 'ea', 'rh', 'tc', 'lc', 'wind', 'ts', 'sun', 'rain', 'pan', 'vpd', 'estavg', 'rh_test'] variables = ['tavg', 'wind', 'ea', 'vpd'] vars_pan_var = ['pan', 'tavg', 'sun', 'wind', 'ea', 'vpd'] vars_penpan = ['tavg', 'tmax', 'tmin', 'p', 'ea', 'wind', 'sun', 'lat', 'elev'] # 'tc' basinlongs=['Songhuajiang', 'Liaohe', 'Northwestern', 'Haihe', 'Yellow', 'Yangtze', 'Huaihe', 'Southeastern', 'Southwestern', 'Pearl'] geoinfo = load('%s/station_geoinfo' %workspace) station_number = load('%s/basin_station_number' %workspace) ## Time styr = 1961 edyr = 2001 stdy = datetime.datetime(styr, 1, 1) eddy = datetime.datetime(edyr, 12, 31) dates = pd.date_range(stdy, eddy, freq='D') tstep = len(dates) dyears = dates.year dmonths = dates.month doys = dates.dayofyear # doys = vstack([dates[i].timetuple().tm_yday for i in xrange(0, tstep)]) # julian day ## quality check using data availability as criteria # the station list is for PET models station_flag = pickle.load(open('station_sunhours_80_flag','rb')) station_qc = [np.where(station_flag[ibasin][:, 0]==0)[0] for ibasin in xrange(0, 10)] station_pan_flag = pickle.load(open('station_pan_80_flag','rb')) station_pan_qc = [np.where(station_pan_flag[ibasin][:, 0]==0)[0] for ibasin in xrange(0, 10)] good_stations = [intersect1d(station_qc[i], station_pan_qc[i]) for i in xrange(0, 10)] ###======================Datetime Toolkits============================= def Gapfill(daily): "return the array, not pandas object" ts = pd.Series(daily, index=dates).fillna(method='pad').values return ts def daily2annual(daily): ts = pd.Series(daily, index=dates).resample('A', how='mean').values return ts def daily2monthly(daily): ts = pd.Series(daily, index=dates).resample('M', how='mean').values return ts def daily2monthly_df(array): arr = pd.DataFrame(array.T, index=dates).resample('M', how='mean').values return arr.T def daily2weekly_df(array): arr = pd.DataFrame(array.T, index=dates).resample('W', how='mean').values return arr.T def daily2annual_df(array): arr = pd.DataFrame(array.T, index=dates).resample('A', how='mean').values return arr.T ###==================================================================== def msc_groupby_DI(arid): # aridrange = [0, 1, 1.5, 2.5, 5, 250] aridrange = [0, 2, 4, 8, 250] index_DI = [] for igroup in xrange(0, len(aridrange)-1): low = aridrange[igroup] high = aridrange[igroup+1] index_DI.append(np.where((arid>=low) & (arid<high))[0]) return index_DI def msc_groupby_DI_more(arid): # aridrange = [0, 1, 1.5, 2.5, 5, 250] aridrange = [0, 1, 1.5, 2, 4, 8, 20, 40, 80, 160, 250] index_DI = [] for igroup in xrange(0, len(aridrange)-1): low = aridrange[igroup] high = aridrange[igroup+1] index_DI.append(np.where((arid>=low) & (arid<high))[0]) return index_DI ########################################################################## # for spectral coherece analysis ########################################################################## nf = 513 nbasin = 10 nvar = 8 sampling_frequency = 1/(24.0 * 3600.0) # unit: per day def Coherence_Frequency(): data = scipy.io.loadmat('%s/1_AP.mat' %(datadir)) input = data[variables[0]][0, 0][0:tstep].flatten() pan = data['pan'][0, 0][0:tstep].flatten() freq = FFT.Coherence(input, pan, sampling_frequency, 'linear')[0] return freq def cel2Kelvin(input): input['tmax'] = input['tmax']+273.16 input['tmin'] = input['tmin']+273.16 input['tavg'] = input['tavg']+273.16 return input def Kelvin2cel(input): input['tmax'] = input['tmax']-273.16 input['tmin'] = input['tmin']-273.16 input['tavg'] = input['tavg']-273.16 return input from matplotlib import rc rc('font', family='serif') #')'Times New Roman' ########################################################################## # Calculate penpan modelled PE ########################################################################## def chunk_st_ed(npts): if npts%2 == 0: st = npts/2; ed= npts/2-1 else: st = npts/2; ed = npts/2 return st, ed def Permute_each_month_station(input): input_rand = [] # Set for a month for mon in xrange(1, 13): # pool out all the data within a month into a Dataframe idx_mon = np.where((dyears >= styr) & (dyears <= edyr) & (dmonths == mon))[0] # input.loc[input.index.month==mon] df = input.iloc[idx_mon] # retrieve all the index in the original order days = df.index def shuffle(df): "each shuffle will produce a new dataframe with original order" # resample all the data by permutation df = df.sample(frac=1) # other method: np.random.permutation() # store the original time step, it looks like not very useful # df['dates'] = df.index # reset the time index with original order df.index = days # df = df.reset_index(drop=True) this is to remove the original index return df input_rand.append(shuffle(df)) # [shuffle(df) for i in xrange(10)] del df input_rand = pd.concat(input_rand).sort_index() # scale the mean to the original monthly mean # For temperature convert to kelvin input = cel2Kelvin(input) input_rand = cel2Kelvin(input_rand) month_mean_obs = input.resample('MS', how='mean') month_mean_exp = input_rand.resample('MS', how='mean') ratio = (month_mean_obs/month_mean_exp).reindex(dates, method='ffill') input_rand = input_rand * ratio # input = Kelvin2cel(input) input_rand = Kelvin2cel(input_rand) return input_rand.to_dict(orient='series') def Sample_each_week_station(input): input_rand = [] # select each day of year for d in xrange(1, 367): # For each single day, sample an ensemble with a 7-day window across multiple years wdays = np.arange(d-3, d+4) constant = ones((7)) * 366 mask1 = constant * (wdays>366) mask2 = constant * (wdays<1) wdays_crop = wdays - mask1 + mask2 idx_doy = np.where((doys == wdays[3]))[0] days = input.iloc[idx_doy].index # retrieve all the index in the original order N = len(days) # number of day of year across all years, especially for 366 idx_doy_wind = [np.where((doys == wdays_crop[i]))[0] for i in xrange(0, 7)] idx_doy_wind = list(itertools.chain.from_iterable(idx_doy_wind)) df = input.iloc[idx_doy_wind] def shuffle(df): "each shuffle will produce a new dataframe with original order" # resample all the data by permutation df = df.sample(n=N) # reset the time index with original order df.index = days return df input_rand.append(shuffle(df)) del df input_rand = pd.concat(input_rand).sort_index() # scale the mean to the original monthly mean # For temperature convert to kelvin input = cel2Kelvin(input) input_rand = cel2Kelvin(input_rand) month_mean_obs = input.resample('MS', how='mean') month_mean_exp = input_rand.resample('MS', how='mean') ratio = (month_mean_obs/month_mean_exp).reindex(dates, method='ffill') input_rand = input_rand * ratio # input = Kelvin2cel(input) input_rand = Kelvin2cel(input_rand) # plt.plot(input_rand['wind']) # plt.plot(input['wind']) return input_rand.to_dict(orient='series') def Sample_station_window(input, wind): input_rand = [] for d in xrange(1, 367): # For each single day, sample an ensemble with a 7-day window across multiple years wdays = np.arange(d-wind/2, d+wind/2+1) constant = ones((wind)) * 366 mask1 = constant * (wdays>366) mask2 = constant * (wdays<1) wdays_crop = wdays - mask1 + mask2 idx_doy = np.where((doys == wdays[wind/2]))[0] days = input.iloc[idx_doy].index # retrieve all the index in the original order N = len(days) # number of day of year across all years, especially for 366 idx_doy_wind = [np.where((doys == wdays_crop[i]))[0] for i in xrange(0, wind)] idx_doy_wind = list(itertools.chain.from_iterable(idx_doy_wind)) df = input.iloc[idx_doy_wind] def shuffle(df): "each shuffle will produce a new dataframe with original order" # resample all the data by permutation df = df.sample(n=N) # reset the time index with original order df.index = days return df input_rand.append(shuffle(df)) del df input_rand = pd.concat(input_rand).sort_index() # scale the mean to the original monthly mean # For temperature convert to kelvin input = cel2Kelvin(input) input_rand = cel2Kelvin(input_rand) # month_mean_obs = input.resample('MS', how='mean') # month_mean_exp = input_rand.resample('MS', how='mean') # ratio = (month_mean_obs/month_mean_exp).reindex(dates, method='ffill') mean_obs = input.resample('%sD' %wind, how='mean') mean_exp = input_rand.resample('%sD' %wind, how='mean') ratio = (mean_obs/mean_exp).reindex(dates, method='ffill') input_rand = input_rand * ratio # input = Kelvin2cel(input) input_rand = Kelvin2cel(input_rand) return input_rand.to_dict(orient='series') def Remove_interannual_variability(input): # For temperature convert to kelvin input = cel2Kelvin(input) ann_mean_obs = input.resample('AS', how='mean') clim_obs = ann_mean_obs.mean() ratio = (clim_obs/ann_mean_obs).reindex(dates, method='ffill') input_niav = input * ratio # input = Kelvin2cel(input) input_niav = Kelvin2cel(input_niav) return input_niav.to_dict(orient='series') def Remove_shortterm_variability(input, vars, npts): # For temperature convert to kelvin output = input.copy() output = cel2Kelvin(output) def moving_average(y, npts): return np.convolve(y, np.ones(npts)/npts, mode='same') def running_mean(y, npts): return pd.rolling_mean(y, npts, center=True) # [npts-1:] for var in vars: # output[var] = moving_average(output[var], npts) output[var] = running_mean(output[var], npts) input_smooth = Kelvin2cel(output) return input_smooth.to_dict(orient='series') def Remove_variable_shortterm_variability(input, var, npts): # For temperature convert to kelvin output = input.copy() output = cel2Kelvin(output) def moving_average(y, npts): return pd.rolling_mean(y, npts)[npts-1:] output[var] = moving_average(output[var], npts) input_smooth = Kelvin2cel(output) return input_smooth.to_dict(orient='series') def Calculate_Ep_daily(): PENPAN = [] for ibasin in xrange(0, 10): data = scipy.io.loadmat('%s/%s_AP.mat' %(datadir, ibasin+1)) for istation in good_stations[ibasin]: print ibasin, istation index = np.where(geoinfo[:, 0]==data['station_name'][0, istation])[0] # Read all the necessary input into a dataframe input = {vars_penpan[i]: Gapfill(data[v][0, istation][0:tstep].flatten()) for i, v in enumerate(vars_penpan[:-2])} input = pd.DataFrame.from_dict(input) input.index = dates # Remove inter-annual variability # INPUT = Remove_interannual_variability(input) npts = 15 # INPUT = Remove_shortterm_variability(input, vars_penpan[:-2], npts) # Radiation # INPUT = Remove_variable_shortterm_variability(input, 'sun', npts) # ea # INPUT = Remove_variable_shortterm_variability(input, 'ea', npts) # wind # INPUT = Remove_variable_shortterm_variability(input, 'wind', npts) # tair INPUT = Remove_shortterm_variability(input, ['tmax', 'tmin', 'tavg'], npts) INPUT['doy'] = doys INPUT['lat'] = geoinfo[index, 1] INPUT['elev'] = geoinfo[index, 3] ### Calculate Epan res = Data(INPUT, 'sunhours') PENPAN.append(res.penpan) pe_model = array(PENPAN) # pe_model.dump('%s/pe_mod_penpan_removeiav_good_stations' %(workspace)) # pe_model.dump('%s/pe_mod_penpan_removeshort15_good_stations' %(workspace)) # pe_model.dump('%s/pe_mod_penpan_removeshort7_good_stations' %(workspace)) # pe_model.dump('%s/pe_mod_penpan_removeshort15_sun_good_stations' %(workspace)) # pe_model.dump('%s/pe_mod_penpan_removeshort15_ea_good_stations' %(workspace)) # pe_model.dump('%s/pe_mod_penpan_removeshort15_wind_good_stations' %(workspace)) # pe_model.dump('%s/pe_mod_penpan_removeshort15_tair_good_stations' %(workspace)) return # Calculate_Ep_daily() # exit() def Calculate_Ep_daily_smoothwindow(): "only take tair as example" for npts in (7, 15, 30): PENPAN = [] for ibasin in xrange(0, 10): data = scipy.io.loadmat('%s/%s_AP.mat' %(datadir, ibasin+1)) for istation in good_stations[ibasin]: print ibasin, istation index = np.where(geoinfo[:, 0]==data['station_name'][0, istation])[0] # Read all the necessary input into a dataframe input = {vars_penpan[i]: Gapfill(data[v][0, istation][0:tstep].flatten()) for i, v in enumerate(vars_penpan[:-2])} input = pd.DataFrame.from_dict(input) input.index = dates # tair # INPUT = Remove_shortterm_variability(input, ['tmax', 'tmin', 'tavg'], npts) # wind # INPUT = Remove_shortterm_variability(input, ['wind'], npts) # humidity INPUT = Remove_shortterm_variability(input, ['ea'], npts) # sun # INPUT = Remove_shortterm_variability(input, [], npts) INPUT['doy'] = doys INPUT['lat'] = geoinfo[index, 1] INPUT['elev'] = geoinfo[index, 3] ### Calculate Epan res = Data(INPUT, 'sunhours') #, npts) PENPAN.append(res.penpan) pe_model = array(PENPAN) # pe_model.dump('%s/pe_mod_penpan_removeshort%s_tair_good_stations' %(workspace, npts)) # pe_model.dump('%s/pe_mod_penpan_removeshort%s_rnet_good_stations' %(workspace, npts)) # pe_model.dump('%s/pe_mod_penpan_removeshort%s_wind_good_stations' %(workspace, npts)) # pe_model.dump('%s/pe_mod_penpan_removeshort%s_ea_good_stations' %(workspace, npts)) return # Calculate_Ep_daily_smoothwindow() # exit() def Calculate_Ep_daily_smoothvariable(): "for one period, four variables" npts = 7 PENPAN = [] for ibasin in xrange(0, 10): data = scipy.io.loadmat('%s/%s_AP.mat' %(datadir, ibasin+1)) for istation in good_stations[ibasin]: print ibasin, istation index = np.where(geoinfo[:, 0]==data['station_name'][0, istation])[0] # Read all the necessary input into a dataframe input = {vars_penpan[i]: Gapfill(data[v][0, istation][0:tstep].flatten()) for i, v in enumerate(vars_penpan[:-2])} input = pd.DataFrame.from_dict(input) input.index = dates # tair # INPUT = Remove_shortterm_variability(input, ['tmax', 'tmin', 'tavg'], npts) # wind # INPUT = Remove_shortterm_variability(input, ['wind'], npts) # humidity # INPUT = Remove_shortterm_variability(input, ['ea'], npts) # sun INPUT = Remove_shortterm_variability(input, [], npts) INPUT['doy'] = doys INPUT['lat'] = geoinfo[index, 1] INPUT['elev'] = geoinfo[index, 3] ### Calculate Epan # res = Data(INPUT, 'sunhours') res = Data(INPUT, 'sunhours', npts) # for rnet PENPAN.append(res.penpan) pe_model = array(PENPAN) pe_model.dump('%s/pe_mod_penpan_removeshort%s_rnet_good_stations' %(workspace, npts)) return # Calculate_Ep_daily_smoothvariable() # exit() def Calculate_Ep_daily_smooth_test(): for npts in (7, 15, 31, 61): PENPAN = [] for ibasin in xrange(0, 10): data = scipy.io.loadmat('%s/%s_AP.mat' %(datadir, ibasin+1)) for istation in good_stations[ibasin]: print ibasin, istation index = np.where(geoinfo[:, 0]==data['station_name'][0, istation])[0] # Read all the necessary input into a dataframe input = {vars_penpan[i]: Gapfill(data[v][0, istation][0:tstep].flatten()) for i, v in enumerate(vars_penpan[:-2])} input = pd.DataFrame.from_dict(input) input.index = dates # Remove inter-annual variability # INPUT = Remove_interannual_variability(input) # vars_penpan = ['tavg', 'tmax', 'tmin', 'p', 'ea', 'wind', 'sun', 'lat', 'elev'] # 'tc' # INPUT = Remove_shortterm_variability(input, vars_penpan[:-2], npts) # tair INPUT = Remove_shortterm_variability(input, ['tmax', 'tmin', 'tavg'], npts) # tair+wind # INPUT = Remove_shortterm_variability(input, ['tmax', 'tmin', 'tavg', 'wind'], npts) # tair+wind+humidity # INPUT = Remove_shortterm_variability(input, ['tmax', 'tmin', 'tavg', 'wind', 'ea'], npts) # tair+wind+humidity+pressure # INPUT = Remove_shortterm_variability(input, ['tmax', 'tmin', 'tavg', 'wind', 'ea', 'p'], npts) # tair+wind+humidity+pressure+sun # INPUT = Remove_shortterm_variability(input, ['tmax', 'tmin', 'tavg', 'wind', 'ea', 'p'], npts) # tair+sun # INPUT = Remove_shortterm_variability(input, ['tmax', 'tmin', 'tavg', 'wind', 'ea'], npts) INPUT['doy'] = doys INPUT['lat'] = geoinfo[index, 1] INPUT['elev'] = geoinfo[index, 3] ### Calculate Epan res = Data(INPUT, 'sunhours') #, npts) PENPAN.append(res.penpan) pe_model = array(PENPAN) return # Calculate_Ep_daily_smooth_test() # exit() def Calculate_Ep_daily_ensemble(): PENPAN = [] for ibasin in xrange(0, 10): data = scipy.io.loadmat('%s/%s_AP.mat' %(datadir, ibasin+1)) for istation in good_stations[ibasin]: print ibasin, istation index = np.where(geoinfo[:, 0]==data['station_name'][0, istation])[0] # Read all the necessary input into a dataframe input = {vars_penpan[i]: Gapfill(data[v][0, istation][0:tstep].flatten()) for i, v in enumerate(vars_penpan[:-2])} input = pd.DataFrame.from_dict(input) input.index = dates # Run the permutation program with multi-ensemble penpan_ens = [] for i in xrange(10): # Set # of ensemble to 10 # Monthy permutation # INPUT = Permute_each_month_station(input) # Weekly permutation INPUT = Sample_each_week_station(input) INPUT['doy'] = doys INPUT['lat'] = geoinfo[index, 1] INPUT['elev'] = geoinfo[index, 3] ### Calculate Epan res = Data(INPUT, 'sunhours') penpan_ens.append(res.penpan) # Collect all the ensembles PENPAN.append(penpan_ens) del penpan_ens pe_model = array(PENPAN) # pe_model.dump('%s/pe_mod_penpan_monthpermute_ens10_good_stations' %(workspace)) pe_model.dump('%s/pe_mod_penpan_weeksample_ens10_good_stations' %(workspace)) return # Calculate_Ep_daily_ensemble() # exit() def Calculate_Ep_daily_samplewindow(): for window in (7, 31, 91): PENPAN = [] for ibasin in xrange(0, 10): data = scipy.io.loadmat('%s/%s_AP.mat' %(datadir, ibasin+1)) for istation in good_stations[ibasin]: print ibasin, istation index = np.where(geoinfo[:, 0]==data['station_name'][0, istation])[0] # Read all the necessary input into a dataframe input = {vars_penpan[i]: Gapfill(data[v][0, istation][0:tstep].flatten()) for i, v in enumerate(vars_penpan[:-2])} input = pd.DataFrame.from_dict(input) input.index = dates # Run the permutation program with multi-ensemble penpan_ens = [] for i in xrange(10): # Set # of ensemble to 10 # Monthy permutation INPUT = Sample_station_window(input, window) INPUT['doy'] = doys INPUT['lat'] = geoinfo[index, 1] INPUT['elev'] = geoinfo[index, 3] ### Calculate Epan res = Data(INPUT, 'sunhours') penpan_ens.append(res.penpan) # Collect all the ensembles PENPAN.append(penpan_ens) del penpan_ens pe_model = array(PENPAN) pe_model.dump('%s/pe_mod_penpan_sample%sd_ens10_good_stations' %(workspace, window)) return # Calculate_Ep_daily_samplewindow() # exit() def Coherence_obs_permute(): "Compare the observed pan with the modelled pan" obs = load('%s/pe_mod_penpan_good_stations' %(workspace)) # permute = load('%s/pe_mod_penpan_monthpermute_ens10_good_stations' %(workspace)) permute = load('%s/pe_mod_penpan_weeksample_ens10_good_stations' %(workspace)) cohere = [] for ist in xrange(0, 228): print ist cohere.append(array([FFT.Coherence(obs[ist, :], permute[ist, i, :], sampling_frequency, 'linear')[1] for i in xrange(10)])) #.reshape(1, 5, nf)) cohere = array(cohere) # cohere.dump('%s/coherence_penpan_monthpermute_ens10_good_stations' %(workspace)) cohere.dump('%s/coherence_penpan_weeksample_ens10_good_stations' %(workspace)) return # Coherence_obs_permute() # exit() def Coherence_obs_remove_variability_variable(): "Compare the effects of removing shortterm variability in each variable" obs = load('%s/pe_mod_penpan_good_stations' %(workspace)) # exp = load('%s/pe_mod_penpan_removeiav_good_stations' %(workspace)) npt = 7 st, ed = chunk_st_ed(npt) vars = ['tair', 'ea', 'rnet', 'wind'] for var in vars[1:]: exp = load('%s/pe_mod_penpan_removeshort%s_%s_good_stations' %(workspace, npt, var)) cohere = [] for ist in xrange(0, 228): print ist # cohere.append(FFT.Coherence(obs[ist, npt:-npt], exp[ist, npt:-npt], sampling_frequency, 'linear')[1]) # for removeshort cohere.append(FFT.Coherence(obs[ist, st:-ed], exp[ist, st:-ed], sampling_frequency, 'linear')[1]) # for removeshort cohere = array(cohere) cohere.dump('%s/coherence_penpan_removeshort%sd_%s_good_stations' %(workspace, npt, var)) return # Coherence_obs_remove_variability_variable() # exit() def Coherence_obs_remove_variability_window(): "Test how the window of moving averaging affect the results" obs = load('%s/pe_mod_penpan_good_stations' %(workspace)) for npt in (7, 15, 30): st, ed = chunk_st_ed(npt) # exp = load('%s/pe_mod_penpan_removeiav_good_stations' %(workspace)) # exp = load('%s/pe_mod_penpan_removeshort%s_tair_good_stations' %(workspace, npt)) # exp = load('%s/pe_mod_penpan_removeshort%s_wind_good_stations' %(workspace, npt)) # exp = load('%s/pe_mod_penpan_removeshort%s_rnet_good_stations' %(workspace, npt)) # exp = load('%s/pe_mod_penpan_removeshort%s_ea_good_stations' %(workspace, npt)) cohere = [] for ist in xrange(0, 228): print ist cohere.append(FFT.Coherence(obs[ist, st:-ed], exp[ist, st:-ed], sampling_frequency, 'linear')[1]) # for removeshort cohere = array(cohere) # cohere.dump('%s/coherence_penpan_removeshort%sd_tair_good_stations' %(workspace, npt)) # cohere.dump('%s/coherence_penpan_removeshort%sd_wind_good_stations' %(workspace, npt)) # cohere.dump('%s/coherence_penpan_removeshort%sd_rnet_good_stations' %(workspace, npt)) # cohere.dump('%s/coherence_penpan_removeshort%sd_ea_good_stations' %(workspace, npt)) # for npt in (7, 31, 91): # exp = load('%s/pe_mod_penpan_sample%sd_ens10_good_stations' %(workspace, npt)) # cohere = [] # for ist in xrange(0, 228): # print ist # cohere.append(array([FFT.Coherence(obs[ist, :], exp[ist, i, :], sampling_frequency, 'linear')[1] for i in xrange(10)])) # cohere = array(cohere) # cohere.dump('%s/coherence_penpan_sample%sd_good_stations' %(workspace, npt)) return # Coherence_obs_remove_variability_window() # exit() def Plot_PSD_obs_remove_variability_window(): "Compare the observed pan with the modelled pan" freq = Coherence_Frequency() fig, ax = plt.subplots(figsize=(8, 4)) data = [] for npt in (7, 15, 30): # exp = load('%s/pe_mod_penpan_removeiav_good_stations' %(workspace)) exp = load('%s/pe_mod_penpan_removeshort%s_tair_good_stations' %(workspace, npt)) psd = [] for ist in xrange(0, 228): print ist psd.append(FFT.Power_Spectrum(exp[ist, npt-1:], sampling_frequency, 'linear')[1]) # psd.append(FFT.Power_Spectrum(obs[ist, npt:-npt], sampling_frequency, 'linear')[1]) psd = array(psd) data.append(mean(psd, axis=0)) data = array(data) Plotting.CoherenceWindowPlot(ax, data, sampling_frequency, freq) plt.show() return # Plot_PSD_obs_remove_variability_window() # exit() def Plot_crossspectrum_obs_remove_variability_window(): "Compare the observed pan with the modelled pan" freq = Coherence_Frequency() obs = load('%s/pe_mod_penpan_good_stations' %(workspace)) fig, ax = plt.subplots(figsize=(8, 4)) data = [] for npt in (7, 15, 30): # exp = load('%s/pe_mod_penpan_removeiav_good_stations' %(workspace)) exp = load('%s/pe_mod_penpan_removeshort%s_tair_good_stations' %(workspace, npt)) cross = [] for ist in xrange(0, 228): print ist cross.append(FFT.CrossPowerSpectrum(obs[ist, npt-1:], exp[ist, npt-1:], sampling_frequency, 'linear')[1]) # cross.append(FFT.CrossPowerSpectrum(obs[ist, npt:-npt], exp[ist, npt:-npt], sampling_frequency, 'linear')[1]) cross = array(cross) data.append(mean(cross, axis=0)) data = array(data) Plotting.CoherenceWindowPlot(ax, data, sampling_frequency, freq) plt.show() return # Plot_crossspectrum_obs_remove_variability_window() # exit() def Plot_Coherence_Average(): fig = plt.figure(figsize=(9, 5)) freq = Coherence_Frequency() # cohere = load('%s/coherence_penpan_monthpermute_ens10_good_stations' %(workspace)) # cohere = load('%s/coherence_penpan_removeiav_good_stations' %(workspace)) # cohere = load('%s/coherence_penpan_removeshort15_good_stations' %(workspace)) # cohere = load('%s/coherence_penpan_removeshort7_good_stations' %(workspace)) # cohere = load('%s/coherence_penpan_removeshort15_sun_good_stations' %(workspace)) # cohere = load('%s/coherence_penpan_removeshort15_ea_good_stations' %(workspace)) # cohere = load('%s/coherence_penpan_removeshort15_wind_good_stations' %(workspace)) cohere = load('%s/coherence_penpan_removeshort15_tair_good_stations' %(workspace)) # cohere = load('%s/coherence_penpan_weeksample_ens10_good_stations' %(workspace)) # for all stations average ax = fig.add_subplot(1, 1, 1) # plot all ensemble: found they can be averaged # Plotting.CoherenceEnsemblePlot(ax, mean(cohere, axis=0), sampling_frequency, freq, 'Average') # plot ensemble mean and then all aridity # cohere_avg = mean(cohere, axis=1) arid = [] for ibasin in xrange(0, 10): arid.append(load('%s/aridity_station_%s' %(workspace, basinlongs[ibasin]))) arid = array(list(itertools.chain(*arid))) index_DI = msc_groupby_DI(arid) Plotting.CoherenceAridityPlot(ax, cohere, index_DI, sampling_frequency, freq, '', '') # for ensemble: cohere_avg # ax.legend(loc=2, fontsize=15) plt.show() # savefig('%s/coh_penpan_removeiav_good_stations.tif' %(figdir), dpi=200) # savefig('%s/coh_penpan_removeshort15_good_stations.tif' %(figdir), dpi=200) # savefig('%s/coh_penpan_removeshort7_good_stations.tif' %(figdir), dpi=200) # savefig('%s/coh_penpan_weeksample_ens10_good_stations.tif' %(figdir), dpi=200) return # Plot_Coherence_Average() # exit() def Plot_Coherence_multiple(): freq = Coherence_Frequency() rnet = load('%s/coherence_penpan_removeshort30d_rnet_good_stations' %(workspace)) wind = load('%s/coherence_penpan_removeshort30d_wind_good_stations' %(workspace)) tair = load('%s/coherence_penpan_removeshort30d_tair_good_stations' %(workspace)) ea = load('%s/coherence_penpan_removeshort30d_ea_good_stations' %(workspace)) all = load('%s/coherence_penpan_removeshort15_good_stations' %(workspace)) sample = load('%s/coherence_penpan_weeksample_ens10_good_stations' %(workspace)) # plot ensemble mean and then all aridity sample_avg = mean(sample, axis=1) vars = [tair, rnet, ea, wind, all, sample_avg] labels = ('a', 'b', 'c', 'd', 'e', 'f') varnames = ['Tair', 'Solar', r'$e_a$', 'Wind', 'All', 'Randomized'] arid = [] for ibasin in xrange(0, 10): arid.append(load('%s/aridity_station_%s' %(workspace, basinlongs[ibasin]))) arid = array(list(itertools.chain(*arid))) index_DI = msc_groupby_DI(arid) fig, axes = plt.subplots(3, 2, figsize=(11, 10)) for i, var in enumerate(vars): Plotting.CoherenceAridityPlot(axes[i/2, i%2], var, index_DI, sampling_frequency, freq, labels[i], varnames[i]) # for ensemble: cohere_avg if i == 5: axes[i/2, i%2].legend(loc=2, fontsize=13) fig.tight_layout() plt.show() # savefig('%s/coh_penpan_removeshort15_multi_good_stations.tif' %(figdir), dpi=200) return # Plot_Coherence_multiple() # exit() def Plot_Coherence_multiwindow(): freq = Coherence_Frequency() fig, ax = plt.subplots(2, 2, figsize=(9, 6), sharey=True, sharex=True) varnames = ['tair', 'rnet', 'wind', 'ea'] nums = ['(a) ', '(b) ', '(c) ', '(d) '] labels = [r'$T_a$', r'$R_n$', r'$u_2$', r'$e_a$'] for i in range(0,4): vars = [load('%s/coherence_penpan_removeshort%sd_%s_good_stations' %(workspace, npts, varnames[i])) for npts in (7, 15, 30)] coh = array([mean(var, axis=0) for var in vars]) # vars = [load('%s/coherence_penpan_sample%sd_good_stations' %(workspace, npt)) for npt in (7, 31, 91)] # coh = array([mean(mean(var, axis=1), axis=0) for var in vars]) Plotting.CoherenceWindowPlot(ax[i/2, i%2], 1-coh, nums[i]+labels[i], sampling_frequency, freq) ax[1,1].legend(loc='upper right', frameon=False, fontsize=14) fig.tight_layout() plt.show() # savefig('%s/figS1_coh_penpan_removeshort7-30_4var_good_stations.tif' %(figdir), dpi=300) return # Plot_Coherence_multiwindow() # exit() def Plot_Coherence_samplewindow(): freq = Coherence_Frequency() names = ['(a) Sampling window', '(b) Dryness'] fig, axes = plt.subplots(1, 2, figsize=(10.5, 3.5)) # The first figure vars = [load('%s/coherence_penpan_sample%sd_good_stations' %(workspace, npt)) for npt in (91, 31, 7)] coh = array([mean(mean(var, axis=1), axis=0) for var in vars]) i = 0 Plotting.CoherenceWindow2Plot(axes[i], coh, sampling_frequency, freq, names[i]) axes[i].legend(loc=2, fontsize=14) sample = load('%s/coherence_penpan_sample7d_good_stations' %(workspace)) # plot ensemble mean and then all aridity sample_avg = mean(sample, axis=1) arid = [] for ibasin in xrange(0, 10): arid.append(load('%s/aridity_station_%s' %(workspace, basinlongs[ibasin]))) arid = array(list(itertools.chain(*arid))) index_DI = msc_groupby_DI(arid) # for i, var in enumerate(vars): i = 1 Plotting.CoherenceAridityPlot(axes[i], sample_avg, index_DI, sampling_frequency, freq, names[i], '') # for ensemble: cohere_avg axes[i].legend(loc=2, fontsize=12) fig.tight_layout() plt.show() # savefig('%s/coh_penpan_sample_window_dryness_good_stations.tif' %(figdir), dpi=300) return # Plot_Coherence_samplewindow() # exit() def Plot_Coherence_loss_samplewindow(): freq = Coherence_Frequency() freq_ts = sampling_frequency/freq names = ['(a) Sampling window', '(b) Dryness'] fig, axes = plt.subplots(1, 2, figsize=(10.5, 3.5)) # The first figure vars = [load('%s/coherence_penpan_sample%sd_good_stations' %(workspace, npt)) for npt in (91, 31, 7)] coh = array([mean(mean(var, axis=1), axis=0) for var in vars]) coh_point = array([interp1d(freq_ts[:], 1-coh)(day)[()] for day in [7, 30, 90, 120, 180, 365]]) i = 0 Plotting.CoherenceWindowPointPlot(axes[i], coh_point, names[i]) axes[i].legend(loc=3, frameon=False, fontsize=14) # The second figure sample = load('%s/coherence_penpan_sample7d_good_stations' %(workspace)) sample_avg = mean(sample, axis=1) # plot ensemble mean and then all aridity sample_point = array([interp1d(freq_ts[:], 1-sample_avg)(day)[()] for day in [7, 30, 90, 120, 180, 365]]) arid = [] for ibasin in xrange(0, 10): arid.append(load('%s/aridity_station_%s' %(workspace, basinlongs[ibasin]))) arid = array(list(itertools.chain(*arid))) index_DI = msc_groupby_DI(arid) i = 1 Plotting.CoherenceWindowAridityPointPlot(axes[i], sample_point, index_DI, names[i]) # for ensemble: cohere_avg axes[i].legend(loc=3, frameon=False, fontsize=12) fig.tight_layout() plt.show() # savefig('%s/fig9_coh_penpan_sample_window_dryness_point_good_stations.tif' %(figdir), dpi=300) return # Plot_Coherence_loss_samplewindow() # exit() def Plot_Coherence_loss_samplewindow_extra(): freq = Coherence_Frequency() freq_ts = sampling_frequency/freq names = ['(a) Sampling window', '(b) Dryness (7d window)', '(c) Dryness (30d window)', '(d) Dryness (90d window)'] fig, axes = plt.subplots(2, 2, figsize=(10.5, 6.5)) arid = [] for ibasin in xrange(0, 10): arid.append(load('%s/aridity_station_%s' %(workspace, basinlongs[ibasin]))) arid = array(list(itertools.chain(*arid))) index_DI = msc_groupby_DI(arid) # # The first figure vars = [load('%s/coherence_penpan_sample%sd_good_stations' %(workspace, npt)) for npt in (91, 31, 7)] coh = array([mean(mean(var, axis=1), axis=0) for var in vars]) coh_point = array([interp1d(freq_ts[:], 1-coh)(day)[()] for day in [7, 30, 90, 120, 180, 365]]) i = 0 Plotting.CoherenceWindowPointPlot(axes[0,0], coh_point, names[i]) axes[0,0].legend(loc=3, frameon=False, fontsize=14) # The second figure sample = load('%s/coherence_penpan_sample7d_good_stations' %(workspace)) sample_avg = mean(sample, axis=1) # plot ensemble mean and then all aridity sample_point = array([interp1d(freq_ts[:], 1-sample_avg)(day)[()] for day in [7, 30, 90, 120, 180, 365]]) i = 1 Plotting.CoherenceWindowAridityPointPlot(axes[0,1], sample_point, index_DI, names[i]) # for ensemble: cohere_avg # The 3rd figure sample = load('%s/coherence_penpan_sample31d_good_stations' %(workspace)) sample_avg = mean(sample, axis=1) # plot ensemble mean and then all aridity sample_30 = array([interp1d(freq_ts[:], 1-sample_avg)(day)[()] for day in [7, 30, 90, 120, 180, 365]]) i = 2 Plotting.CoherenceWindowAridityPointPlot(axes[1,0], sample_30, index_DI, names[i]) # The 4th figure sample = load('%s/coherence_penpan_sample91d_good_stations' %(workspace)) sample_avg = mean(sample, axis=1) # plot ensemble mean and then all aridity sample_90 = array([interp1d(freq_ts[:], 1-sample_avg)(day)[()] for day in [7, 30, 90, 120, 180, 365]]) i = 3 Plotting.CoherenceWindowAridityPointPlot(axes[1,1], sample_90, index_DI, names[i]) # for ensemble: cohere_avg axes[1,1].legend(loc=3, frameon=False, fontsize=12) fig.tight_layout() plt.show() # savefig('%s/fig10_coh_penpan_sample_window_dryness_point_good_stations_all.tif' %(figdir), dpi=300) return # Plot_Coherence_loss_samplewindow_extra() # exit() def Plot_Coherence_grid_test(): freq = Coherence_Frequency() rnet = load('%s/coherence_penpan_removeshort30d_rnet_good_stations' %(workspace)) wind = load('%s/coherence_penpan_removeshort30d_wind_good_stations' %(workspace)) tair = load('%s/coherence_penpan_removeshort30d_tair_good_stations' %(workspace)) ea = load('%s/coherence_penpan_removeshort30d_ea_good_stations' %(workspace)) vars = [tair, rnet, wind, ea] arid = [] for ibasin in xrange(0, 10): arid.append(load('%s/aridity_station_%s' %(workspace, basinlongs[ibasin]))) arid = array(list(itertools.chain(*arid))) index_DI = msc_groupby_DI(arid) matrix = np.zeros((4, 2, 3)) freq_ts = sampling_frequency/freq for iv, var in enumerate(vars): res = array([[interp1d(freq_ts[:], var[istation, :])(day)[()] for day in [7, 15, 30]] for istation in xrange(0, var.shape[0])]) wet = vstack((res[index_DI[0], :], res[index_DI[1], :])) dry = vstack((res[index_DI[2], :], res[index_DI[3], :])) matrix[iv, 0, :] = mean(wet, axis=0) matrix[iv, 1, :] = mean(dry, axis=0) # rearrange the matrix into table imshow_data = np.zeros((4, 6)) for id in range(0,2): for it in range(0,3): for iv in range(0,4): row = id*2 + iv/2 col = it*2 + iv%2 print row, col, matrix[iv, id, it] imshow_data[row, col] = 1 - matrix[iv, id, it] plt.imshow(imshow_data, cmap='hot_r', interpolation='nearest') plt.show() # savefig('%s/coh_penpan_removeshort15_multi_good_stations.tif' %(figdir), dpi=200) return # Plot_Coherence_grid_test() # exit() def Plot_Coherence_grid_average(fig, ax, npt, title): "try my best to extract the information" rnet = load('%s/coherence_penpan_removeshort%sd_rnet_good_stations' %(workspace, npt)) wind = load('%s/coherence_penpan_removeshort%sd_wind_good_stations' %(workspace, npt)) tair = load('%s/coherence_penpan_removeshort%sd_tair_good_stations' %(workspace, npt)) ea = load('%s/coherence_penpan_removeshort%sd_ea_good_stations' %(workspace, npt)) vars = [tair, rnet, wind, ea] varnames = [r'$T_a$', r'$R_n$', r'$u_2$', r'$e_a$'] arid = [] for ibasin in xrange(0, 10): arid.append(load('%s/aridity_station_%s' %(workspace, basinlongs[ibasin]))) arid = array(list(itertools.chain(*arid))) index_DI = msc_groupby_DI(arid) # make a 3D matrix for summary table matrix = np.zeros((4, 2, 2)) freq = Coherence_Frequency() freq_ts = sampling_frequency/freq for iv, var in enumerate(vars): index_week = (freq_ts>=2) & (freq_ts<=7) index_month = (freq_ts>7) & (freq_ts<=30) res = vstack((mean(var[:, index_week], axis=1), mean(var[:, index_month], axis=1))) wet = hstack((res[:, index_DI[0]], res[:, index_DI[1]])) dry = hstack((res[:, index_DI[2]], res[:, index_DI[3]])) matrix[iv, 0, :] = mean(wet, axis=1) matrix[iv, 1, :] = mean(dry, axis=1) # rearrange the matrix into table imshow_data = np.zeros((4, 4)); imshow_label = np.empty((4, 4), dtype=int) for id in range(0,2): for it in range(0,2): for iv in range(0,4): row = id*2 + iv/2 col = it*2 + iv%2 print row, col imshow_data[row, col] = 1 - matrix[iv, id, it] # This is the influence 1-MSC imshow_label[row, col] = iv im = ax.imshow(imshow_data, vmax=0.45, vmin=0.0, cmap='YlOrRd', interpolation='nearest') # colorbar if npt == 30: # set up the axis cax = fig.add_axes([0.91, 0.12, 0.02, 0.78]) cb = fig.colorbar(im, cax) # adjust the size # cb = ax.colorbar(im) #, fraction=0.046, pad=0.04) # magic number!!!!! cb.ax.tick_params(labelsize=14) # change the colorbar fontsize # Text portion ind_array = np.arange(0, 4, 1) x, y = meshgrid(ind_array, ind_array) for xloc, yloc in zip(x.flatten(), y.flatten()): ax.text(xloc, yloc, varnames[imshow_label[yloc, xloc]], va='center', ha='center', fontsize=20) # Two separate lines ax.plot([1.5, 1.5], [-0.5, 3.5], c='black', linewidth=2) ax.plot([-0.5, 3.5],[1.5, 1.5], c='black', linewidth=2) # x y label fig.subplots_adjust(bottom=0.12) ax.set_xticks((0.5, 2.5)) ax.set_yticks((0.5, 2.5)) ax.set_xticklabels(['Weekly cycle\n(2-7d)', 'Monthly cycle\n(7-30d)'], fontsize=16) ax.set_yticklabels(["Wet" "\n" r"($\phi$<4)", "Dry" "\n" r"($\phi$>4)"], fontsize=16) # treat this as special string ax.set_title(title, fontsize=16) # savefig('%s/coh_grid_average_scale_climate_4var_removeshort30.tif' %(figdir), dpi=300) return def Plot_Coherence_grid_average_multiple(): fig, ax = plt.subplots(1, 2, figsize=(12, 5)) Plot_Coherence_grid_average(fig, ax[0], 7, '(a) Window = 7d') Plot_Coherence_grid_average(fig, ax[1], 30, '(b) Window = 30d') plt.show() # savefig('%s/coh_grid_average_scale_climate_4var_removeshort7-30.tif' %(figdir), dpi=300) return # Plot_Coherence_grid_average_multiple() # exit() def Plot_Coherence_grid_average_update(fig, ax, npt, title): "try my best to extract the information" rnet = load('%s/coherence_penpan_removeshort%sd_rnet_good_stations' %(workspace, npt)) wind = load('%s/coherence_penpan_removeshort%sd_wind_good_stations' %(workspace, npt)) tair = load('%s/coherence_penpan_removeshort%sd_tair_good_stations' %(workspace, npt)) ea = load('%s/coherence_penpan_removeshort%sd_ea_good_stations' %(workspace, npt)) vars = [tair, rnet, wind, ea] varnames = [r'$T_a$', r'$R_n$', r'$u_2$', r'$e_a$'] arid = [] for ibasin in xrange(0, 10): arid.append(load('%s/aridity_station_%s' %(workspace, basinlongs[ibasin]))) arid = array(list(itertools.chain(*arid))) index_DI = msc_groupby_DI(arid) # make a 3D matrix for summary table matrix = np.zeros((4, 2, 2)) freq = Coherence_Frequency() freq_ts = sampling_frequency/freq for iv, var in enumerate(vars): index_week = (freq_ts>=2) & (freq_ts<=7) index_month = (freq_ts>7) & (freq_ts<=30) res = vstack((mean(var[:, index_week], axis=1), mean(var[:, index_month], axis=1))) wet = hstack((res[:, index_DI[0]], res[:, index_DI[1]])) dry = hstack((res[:, index_DI[2]], res[:, index_DI[3]])) matrix[iv, 0, :] = mean(wet, axis=1) matrix[iv, 1, :] = mean(dry, axis=1) # rearrange the matrix into table imshow_data = np.zeros((4, 4)); imshow_label = np.empty((4, 4), dtype=int) for id in range(0,2): for it in range(0,2): for iv in range(0,4): row = id*2 + iv/2 col = it*2 + iv%2 print row, col imshow_data[row, col] = 1 - matrix[iv, id, it] # This is the influence 1-MSC imshow_label[row, col] = iv im = ax.imshow(imshow_data, vmax=0.45, vmin=0.0, cmap='YlOrRd', interpolation='nearest') # colorbar # set up the axis # cax = fig.add_axes([0.91, 0.12, 0.02, 0.78]) # cb = fig.colorbar(im, cax) # adjust the size cb = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04) # magic number!!!!! cb.ax.tick_params(labelsize=14) # change the colorbar fontsize # Text portion ind_array = np.arange(0, 4, 1) x, y = meshgrid(ind_array, ind_array) for xloc, yloc in zip(x.flatten(), y.flatten()): ax.text(xloc, yloc, varnames[imshow_label[yloc, xloc]], va='center', ha='center', fontsize=20) # Two separate lines ax.plot([1.5, 1.5], [-0.5, 3.5], c='black', linewidth=2) ax.plot([-0.5, 3.5],[1.5, 1.5], c='black', linewidth=2) # x y label fig.subplots_adjust(bottom=0.12) ax.set_xticks((0.5, 2.5)) ax.set_yticks((0.5, 2.5)) ax.set_xticklabels(['Weekly cycle\n(2-7d)', 'Monthly cycle\n(7-30d)'], fontsize=14) if npt==7: ax.set_yticklabels(["Wet" "\n" r"($\phi$<4)", "Dry" "\n" r"($\phi$>4)"], fontsize=16) # treat this as special string else: ax.set_yticklabels(["", ""], fontsize=16) # treat this as special string ax.set_title(title, fontsize=16) # savefig('%s/coh_grid_average_scale_climate_4var_removeshort30.tif' %(figdir), dpi=300) return def Coherence_grid_average(npt): "try my best to extract the information" rnet = load('%s/coherence_penpan_removeshort%sd_rnet_good_stations' %(workspace, npt)) wind = load('%s/coherence_penpan_removeshort%sd_wind_good_stations' %(workspace, npt)) tair = load('%s/coherence_penpan_removeshort%sd_tair_good_stations' %(workspace, npt)) ea = load('%s/coherence_penpan_removeshort%sd_ea_good_stations' %(workspace, npt)) vars = [tair, rnet, wind, ea] arid = [] for ibasin in xrange(0, 10): arid.append(load('%s/aridity_station_%s' %(workspace, basinlongs[ibasin]))) arid = array(list(itertools.chain(*arid))) index_DI = msc_groupby_DI(arid) # make a 3D matrix for summary table matrix = np.zeros((4, 2, 2)) freq = Coherence_Frequency() freq_ts = sampling_frequency/freq for iv, var in enumerate(vars): index_week = (freq_ts>=2) & (freq_ts<=7) index_month = (freq_ts>7) & (freq_ts<=30) res = vstack((mean(var[:, index_week], axis=1), mean(var[:, index_month], axis=1))) wet = hstack((res[:, index_DI[0]], res[:, index_DI[1]])) dry = hstack((res[:, index_DI[2]], res[:, index_DI[3]])) matrix[iv, 0, :] = mean(wet, axis=1) matrix[iv, 1, :] = mean(dry, axis=1) # rearrange the matrix into table imshow_data = np.zeros((4, 4)); imshow_label = np.empty((4, 4), dtype=int) for id in range(0,2): for it in range(0,2): for iv in range(0,4): row = id*2 + iv/2 col = it*2 + iv%2 print row, col imshow_data[row, col] = 1 - matrix[iv, id, it] # This is the influence 1-MSC imshow_label[row, col] = iv return imshow_data, imshow_label from matplotlib.colors import Normalize class MidpointNormalize(Normalize): def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False): self.midpoint = midpoint Normalize.__init__(self, vmin, vmax, clip) def __call__(self, value, clip=None): x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1] return np.ma.masked_array(np.interp(value, x, y)) def Plot_difference(fig, ax, label, data1, data2, title): varnames = [r'$T_a$', r'$R_n$', r'$u_2$', r'$e_a$'] # my_cmap = cm.YlOrRd # my_cmap.set_under('white') norm = MidpointNormalize(vmax=0.4, midpoint=0, vmin=-0.1) im = ax.imshow(data2-data1, cmap='bwr', norm=norm, interpolation='nearest') # colorbar # set up the axis cb = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04) # magic number!!!!! cb.ax.tick_params(labelsize=14) # change the colorbar fontsize # Text portion ind_array = np.arange(0, 4, 1) x, y = meshgrid(ind_array, ind_array) for xloc, yloc in zip(x.flatten(), y.flatten()): ax.text(xloc, yloc, varnames[label[yloc, xloc]], va='center', ha='center', fontsize=20) # Two separate lines ax.plot([1.5, 1.5], [-0.5, 3.5], c='black', linewidth=2) ax.plot([-0.5, 3.5],[1.5, 1.5], c='black', linewidth=2) # x y label fig.subplots_adjust(bottom=0.12) ax.set_xticks((0.5, 2.5)) ax.set_yticks((0.5, 2.5)) ax.set_xticklabels(['Weekly cycle\n(2-7d)', 'Monthly cycle\n(7-30d)'], fontsize=14) ax.set_yticklabels(["", ""], fontsize=16) # treat this as special string ax.set_title(title, fontsize=16) return def Plot_Coherence_grid_average_multiple_update(): fig, ax = plt.subplots(1, 3, figsize=(12, 4)) Plot_Coherence_grid_average_update(fig, ax[0], 7, '(a) Window = 7d') Plot_Coherence_grid_average_update(fig, ax[1], 30, '(b) Window = 30d') data7, labels = Coherence_grid_average(7) data30, labels = Coherence_grid_average(30) Plot_difference(fig, ax[2], labels, data7, data30, '(c) 30d - 7d') fig.tight_layout() plt.show() # savefig('%s/Fig9_coh_grid_average_scale_climate_4var_diff_removeshort7-30.tif' %(figdir), dpi=300) return # Plot_Coherence_grid_average_multiple_update() # exit() def Print_Coherence_Average(): cohere = [] freq = Coherence_Frequency() freq_ts = sampling_frequency/freq for ibasin in xrange(0, 10): cohere_basin = load('%s/coherence_obs_5model_good_station_%s' %(workspace, basinlongs[ibasin])) cohere.append(cohere_basin) # for all stations average res = [interp1d(freq_ts[:], mean(vstack(cohere), axis=0))(day)[()] for day in [250]] print res return # Print_Coherence_Average() # exit()

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c4b976326693bd6b5616d027c084ce393d9a07aa

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py

Python

lib/hachoir/wx/resource/__init__.py

0x20Man/Watcher3

4656b42bc5879a3741bb95f534b7c6612a25264d

[ "Apache-2.0" ]

320

2017-03-28T23:33:45.000Z

2022-02-17T08:45:01.000Z

lib/hachoir/wx/resource/__init__.py

0x20Man/Watcher3

4656b42bc5879a3741bb95f534b7c6612a25264d

[ "Apache-2.0" ]

300

2017-03-28T19:22:54.000Z

2021-12-01T01:11:55.000Z

lib/hachoir/wx/resource/__init__.py

0x20Man/Watcher3

4656b42bc5879a3741bb95f534b7c6612a25264d

[ "Apache-2.0" ]

90

2017-03-29T16:12:43.000Z

2022-03-01T06:23:48.000Z

from .resource import * # noqa

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py

Python

tests/api/v1/endpoints/test_privacy_request_endpoints.py

nathanawmk/fidesops

1ab840206a78e60673aebd5838ba567095512a58

[ "Apache-2.0" ]

null

tests/api/v1/endpoints/test_privacy_request_endpoints.py

nathanawmk/fidesops

1ab840206a78e60673aebd5838ba567095512a58

[ "Apache-2.0" ]

null

tests/api/v1/endpoints/test_privacy_request_endpoints.py

nathanawmk/fidesops

1ab840206a78e60673aebd5838ba567095512a58

[ "Apache-2.0" ]

null

import json from datetime import datetime from typing import List from unittest import mock from sqlalchemy import ( column, table, select, ) from fastapi_pagination import Params import pytest from starlette.testclient import TestClient from fidesops.api.v1.urn_registry import ( PRIVACY_REQUESTS, V1_URL_PREFIX, REQUEST_PREVIEW, ) from fidesops.api.v1.scope_registry import ( PRIVACY_REQUEST_CREATE, STORAGE_CREATE_OR_UPDATE, PRIVACY_REQUEST_READ, ) from fidesops.db.session import ( get_db_engine, get_db_session, ) from fidesops.models.client import ClientDetail from fidesops.models.privacy_request import PrivacyRequest from fidesops.models.policy import DataCategory from fidesops.schemas.dataset import DryRunDatasetResponse from fidesops.util.cache import get_identity_cache_key page_size = Params().size def stringify_date(log_date: datetime) -> str: return log_date.strftime("%Y-%m-%dT%H:%M:%S.%f+00:00") class TestCreatePrivacyRequest: @pytest.fixture(scope="function") def url(self, oauth_client: ClientDetail, policy) -> str: return V1_URL_PREFIX + PRIVACY_REQUESTS def test_privacy_request_unauthenticated(self, api_client: TestClient, url): resp = api_client.post(url) assert resp.status_code == 401 def test_privacy_request_wrong_scopes( self, api_client: TestClient, url, generate_auth_header ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) resp = api_client.post(url, json={}, headers=auth_header) assert resp.status_code == 403 @mock.patch("fidesops.task.graph_task.run_access_request") def test_create_privacy_request( self, run_access_request_mock, url, db, api_client: TestClient, generate_auth_header, policy, ): data = [ { "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": policy.key, "identities": [{"email": "test@example.com"}], } ] auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) resp = api_client.post(url, json=data, headers=auth_header) assert resp.status_code == 200 response_data = resp.json()["succeeded"] assert len(response_data) == 1 pr = PrivacyRequest.get(db=db, id=response_data[0]["id"]) pr.delete(db=db) assert run_access_request_mock.called @mock.patch("fidesops.task.graph_task.run_access_request") def test_create_privacy_request_limit_exceeded( self, _, url, db, api_client: TestClient, generate_auth_header, policy, ): payload = [] for i in range(0, 51): payload.append( { "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": policy.key, "identities": [{"email": "ftest{i}@example.com"}], }, ) auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) response = api_client.post(url, headers=auth_header, json=payload) assert 422 == response.status_code assert ( json.loads(response.text)["detail"][0]["msg"] == "ensure this value has at most 50 items" ) @mock.patch("fidesops.models.privacy_request.PrivacyRequest.start_processing") def test_create_privacy_request_starts_processing( self, start_processing_mock, url, api_client: TestClient, db, generate_auth_header, policy, ): data = [ { "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": policy.key, "identities": [{"email": "test@example.com"}], } ] auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) resp = api_client.post(url, json=data, headers=auth_header) assert resp.status_code == 200 assert start_processing_mock.called response_data = resp.json()["succeeded"] pr = PrivacyRequest.get(db=db, id=response_data[0]["id"]) pr.delete(db=db) @mock.patch("fidesops.task.graph_task.run_access_request") def test_create_privacy_request_with_external_id( self, run_access_request_mock, url, db, api_client: TestClient, generate_auth_header, policy, ): external_id = "ext_some-uuid-here-1234" data = [ { "external_id": external_id, "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": policy.key, "identities": [{"email": "test@example.com"}], } ] auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) resp = api_client.post( V1_URL_PREFIX + PRIVACY_REQUESTS, json=data, headers=auth_header ) assert resp.status_code == 200 response_data = resp.json()["succeeded"] assert len(response_data) == 1 assert response_data[0]["external_id"] == external_id pr = PrivacyRequest.get(db=db, id=response_data[0]["id"]) assert pr.external_id == external_id pr.delete(db=db) assert run_access_request_mock.called @mock.patch("fidesops.task.graph_task.run_access_request") def test_create_privacy_request_caches_identity( self, run_access_request_mock, url, db, api_client: TestClient, generate_auth_header, policy, cache, ): identity = {"email": "test@example.com"} data = [ { "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": policy.key, "identities": [identity], } ] auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) resp = api_client.post(url, json=data, headers=auth_header) assert resp.status_code == 200 response_data = resp.json()["succeeded"] assert len(response_data) == 1 pr = PrivacyRequest.get(db=db, id=response_data[0]["id"]) key = get_identity_cache_key( privacy_request_id=pr.id, identity_attribute=list(identity.keys())[0], ) assert cache.get(key) == list(identity.values())[0] pr.delete(db=db) assert run_access_request_mock.called def test_create_privacy_request_no_identities( self, url, api_client: TestClient, generate_auth_header, policy, ): data = [ { "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": policy.key, "identities": [], } ] auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) resp = api_client.post(url, json=data, headers=auth_header) assert resp.status_code == 200 response_data = resp.json()["succeeded"] assert len(response_data) == 0 response_data = resp.json()["failed"] assert len(response_data) == 1 @pytest.mark.integration def test_create_and_process_access_request( self, postgres_example_test_dataset_config, url, db, api_client: TestClient, generate_auth_header, policy, ): customer_email = "customer-1@example.com" data = [ { "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": policy.key, "identities": [{"email": customer_email}], } ] auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) resp = api_client.post(url, json=data, headers=auth_header) assert resp.status_code == 200 response_data = resp.json()["succeeded"] assert len(response_data) == 1 pr = PrivacyRequest.get(db=db, id=response_data[0]["id"]) results = pr.get_results() assert len(results.keys()) == 11 for key in results.keys(): assert results[key] is not None assert results[key] != {} result_key_prefix = f"EN_{pr.id}__access_request__postgres_example_test_dataset:" customer_key = result_key_prefix + "customer" assert results[customer_key][0]["email"] == customer_email visit_key = result_key_prefix + "visit" assert results[visit_key][0]["email"] == customer_email pr.delete(db=db) @pytest.mark.integration_erasure def test_create_and_process_erasure_request_specific_category( self, postgres_example_test_dataset_config, url, db, api_client: TestClient, generate_auth_header, erasure_policy, ): customer_email = "customer-1@example.com" customer_id = 1 data = [ { "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": erasure_policy.key, "identities": [{"email": customer_email}], } ] auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) resp = api_client.post(url, json=data, headers=auth_header) assert resp.status_code == 200 response_data = resp.json()["succeeded"] assert len(response_data) == 1 pr = PrivacyRequest.get(db=db, id=response_data[0]["id"]) pr.delete(db=db) example_postgres_uri = ( "postgresql://postgres:postgres@postgres_example/postgres_example" ) engine = get_db_engine(database_uri=example_postgres_uri) SessionLocal = get_db_session(engine=engine) integration_db = SessionLocal() stmt = select( column("id"), column("name"), ).select_from(table("customer")) res = integration_db.execute(stmt).all() customer_found = False for row in res: if customer_id in row: customer_found = True # Check that the `name` field is `None` assert row[1] is None assert customer_found @pytest.mark.integration_erasure def test_create_and_process_erasure_request_generic_category( self, postgres_example_test_dataset_config, url, db, api_client: TestClient, generate_auth_header, erasure_policy, ): # It's safe to change this here since the `erasure_policy` fixture is scoped # at function level target = erasure_policy.rules[0].targets[0] target.data_category = DataCategory("user.provided.identifiable.contact").value target.save(db=db) email = "customer-2@example.com" customer_id = 2 data = [ { "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": erasure_policy.key, "identities": [{"email": email}], } ] auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) resp = api_client.post(url, json=data, headers=auth_header) assert resp.status_code == 200 response_data = resp.json()["succeeded"] assert len(response_data) == 1 pr = PrivacyRequest.get(db=db, id=response_data[0]["id"]) pr.delete(db=db) example_postgres_uri = ( "postgresql://postgres:postgres@postgres_example/postgres_example" ) engine = get_db_engine(database_uri=example_postgres_uri) SessionLocal = get_db_session(engine=engine) integration_db = SessionLocal() stmt = select( column("id"), column("email"), ).select_from(table("customer")) res = integration_db.execute(stmt).all() customer_found = False for row in res: if customer_id in row: customer_found = True # Check that the `email` field is `None` and that its data category # ("user.provided.identifiable.contact.email") has been erased by the parent # category ("user.provided.identifiable.contact") assert row[1] is None else: # There are two rows other rows, and they should not have been erased assert row[1] in ["customer-1@example.com", "jane@example.com"] assert customer_found @pytest.mark.integration_erasure def test_create_and_process_erasure_request_with_table_joins( self, postgres_example_test_dataset_config, url, db, api_client: TestClient, generate_auth_header, erasure_policy, ): # It's safe to change this here since the `erasure_policy` fixture is scoped # at function level target = erasure_policy.rules[0].targets[0] target.data_category = DataCategory( "user.provided.identifiable.financial" ).value target.save(db=db) customer_email = "customer-1@example.com" customer_id = 1 data = [ { "requested_at": "2021-08-30T16:09:37.359Z", "policy_key": erasure_policy.key, "identities": [{"email": customer_email}], } ] auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_CREATE]) resp = api_client.post(url, json=data, headers=auth_header) assert resp.status_code == 200 response_data = resp.json()["succeeded"] assert len(response_data) == 1 pr = PrivacyRequest.get(db=db, id=response_data[0]["id"]) pr.delete(db=db) example_postgres_uri = ( "postgresql://postgres:postgres@postgres_example/postgres_example" ) engine = get_db_engine(database_uri=example_postgres_uri) SessionLocal = get_db_session(engine=engine) integration_db = SessionLocal() stmt = select( column("customer_id"), column("id"), column("ccn"), column("code"), column("name"), ).select_from(table("payment_card")) res = integration_db.execute(stmt).all() card_found = False for row in res: if row[0] == customer_id: card_found = True assert row[2] is None assert row[3] is None assert row[4] is None assert card_found == True class TestGetPrivacyRequests: @pytest.fixture(scope="function") def url(self, oauth_client: ClientDetail) -> str: return V1_URL_PREFIX + PRIVACY_REQUESTS def test_get_privacy_requests_unauthenticated(self, api_client: TestClient, url): response = api_client.get(url, headers={}) assert 401 == response.status_code def test_get_privacy_requests_wrong_scope( self, api_client: TestClient, generate_auth_header, url ): auth_header = generate_auth_header(scopes=[STORAGE_CREATE_OR_UPDATE]) response = api_client.get(url, headers=auth_header) assert 403 == response.status_code def test_conflicting_query_params( self, api_client: TestClient, generate_auth_header, url ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get( url + f"?completed_lt=2021-01-01&errored_gt=2021-01-02", headers=auth_header, ) assert 400 == response.status_code def test_get_privacy_requests_by_id( self, api_client: TestClient, url, generate_auth_header, privacy_request, postgres_execution_log, mongo_execution_log, ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get( url + f"?id={privacy_request.id}", headers=auth_header ) assert 200 == response.status_code expected_resp = { "items": [ { "id": privacy_request.id, "created_at": stringify_date(privacy_request.created_at), "started_processing_at": stringify_date( privacy_request.started_processing_at ), "finished_processing_at": None, "status": privacy_request.status.value, "external_id": privacy_request.external_id, } ], "total": 1, "page": 1, "size": page_size, } resp = response.json() assert resp == expected_resp def test_filter_privacy_requests_by_status( self, api_client: TestClient, url, generate_auth_header, privacy_request, succeeded_privacy_request, failed_privacy_request, ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get(url + f"?status=complete", headers=auth_header) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 1 assert resp["items"][0]["id"] == succeeded_privacy_request.id response = api_client.get(url + f"?status=error", headers=auth_header) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 1 assert resp["items"][0]["id"] == failed_privacy_request.id def test_filter_privacy_requests_by_external_id( self, db, api_client, url, generate_auth_header, privacy_request, succeeded_privacy_request, failed_privacy_request, ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get( url + f"?external_id={succeeded_privacy_request.id}", headers=auth_header ) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 0 privacy_request.external_id = "test_external_id_1" privacy_request.save(db) response = api_client.get( url + f"?external_id=test_external_id_1", headers=auth_header ) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 1 assert resp["items"][0]["id"] == privacy_request.id def test_filter_privacy_requests_by_created( self, api_client: TestClient, generate_auth_header, privacy_request, succeeded_privacy_request, failed_privacy_request, url, ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get(url + f"?created_lt=2019-01-01", headers=auth_header) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 0 response = api_client.get(url + f"?created_gt=2019-01-01", headers=auth_header) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 3 assert resp["items"][0]["id"] == privacy_request.id assert resp["items"][1]["id"] == succeeded_privacy_request.id assert resp["items"][2]["id"] == failed_privacy_request.id def test_filter_privacy_requests_by_started( self, api_client: TestClient, generate_auth_header, privacy_request, succeeded_privacy_request, failed_privacy_request, url, ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get(url + f"?started_lt=2021-05-01", headers=auth_header) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 2 assert resp["items"][0]["id"] == privacy_request.id assert resp["items"][1]["id"] == failed_privacy_request.id response = api_client.get(url + f"?started_gt=2021-05-01", headers=auth_header) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 1 assert resp["items"][0]["id"] == succeeded_privacy_request.id def test_filter_privacy_requests_by_completed( self, api_client: TestClient, generate_auth_header, privacy_request, succeeded_privacy_request, failed_privacy_request, url, ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get( url + f"?completed_lt=2021-10-01", headers=auth_header ) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 0 response = api_client.get( url + f"?completed_gt=2021-10-01", headers=auth_header ) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 1 assert resp["items"][0]["id"] == succeeded_privacy_request.id def test_filter_privacy_requests_by_errored( self, api_client: TestClient, generate_auth_header, privacy_request, succeeded_privacy_request, failed_privacy_request, url, ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get(url + f"?errored_lt=2021-01-01", headers=auth_header) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 0 response = api_client.get(url + f"?errored_gt=2021-01-01", headers=auth_header) assert 200 == response.status_code resp = response.json() assert len(resp["items"]) == 1 assert resp["items"][0]["id"] == failed_privacy_request.id def test_verbose_privacy_requests( self, api_client: TestClient, generate_auth_header, privacy_request: PrivacyRequest, postgres_execution_log, second_postgres_execution_log, mongo_execution_log, url, ): """Test privacy requests endpoint with verbose query param to show execution logs""" auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get(url + f"?verbose=True", headers=auth_header) assert 200 == response.status_code resp = response.json() assert ( postgres_execution_log.updated_at < second_postgres_execution_log.updated_at ) expected_resp = { "items": [ { "id": privacy_request.id, "created_at": stringify_date(privacy_request.created_at), "started_processing_at": stringify_date( privacy_request.started_processing_at ), "finished_processing_at": None, "status": privacy_request.status.value, "external_id": privacy_request.external_id, "results": { "my-mongo-db": [ { "collection_name": "orders", "fields_affected": [ { "path": "orders.name", "field_name": "name", "data_categories": [ "user.provided.identifiable.contact.name" ], } ], "message": None, "action_type": "access", "status": "in_processing", "updated_at": stringify_date( mongo_execution_log.updated_at ), } ], "my-postgres-db": [ { "collection_name": "user", "fields_affected": [ { "path": "user.email", "field_name": "email", "data_categories": [ "user.provided.identifiable.contact.email" ], } ], "message": None, "action_type": "access", "status": "pending", "updated_at": stringify_date( postgres_execution_log.updated_at ), }, { "collection_name": "address", "fields_affected": [ { "path": "address.street", "field_name": "street", "data_categories": [ "user.provided.identifiable.contact.street" ], }, { "path": "address.city", "field_name": "city", "data_categories": [ "user.provided.identifiable.contact.city" ], }, ], "message": "Database timed out.", "action_type": "access", "status": "error", "updated_at": stringify_date( second_postgres_execution_log.updated_at ), }, ], }, }, ], "total": 1, "page": 1, "size": page_size, } assert resp == expected_resp class TestGetExecutionLogs: @pytest.fixture(scope="function") def url(self, db, privacy_request): return V1_URL_PREFIX + PRIVACY_REQUESTS + f"/{privacy_request.id}/log" def test_get_execution_logs_unauthenticated( self, api_client: TestClient, privacy_request, url ): response = api_client.get(url + "/", headers={}) assert 401 == response.status_code def test_get_execution_logs_wrong_scope( self, api_client: TestClient, generate_auth_header, url ): auth_header = generate_auth_header(scopes=[STORAGE_CREATE_OR_UPDATE]) response = api_client.get(url, headers=auth_header) assert 403 == response.status_code def test_get_execution_logs_invalid_privacy_request_id( self, api_client: TestClient, generate_auth_header ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get( V1_URL_PREFIX + PRIVACY_REQUESTS + f"/invalid_privacy_request_id/log", headers=auth_header, ) assert 404 == response.status_code def test_get_execution_logs( self, api_client: TestClient, generate_auth_header, url, postgres_execution_log, mongo_execution_log, second_postgres_execution_log, ): auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.get( url, headers=auth_header, ) assert 200 == response.status_code resp = response.json() expected_resp = { "items": [ { "collection_name": "user", "fields_affected": [ { "path": "user.email", "field_name": "email", "data_categories": [ "user.provided.identifiable.contact.email" ], } ], "message": None, "action_type": "access", "status": "pending", "updated_at": stringify_date(postgres_execution_log.updated_at), "dataset_name": "my-postgres-db", }, { "collection_name": "orders", "fields_affected": [ { "path": "orders.name", "field_name": "name", "data_categories": [ "user.provided.identifiable.contact.name" ], } ], "message": None, "action_type": "access", "status": "in_processing", "updated_at": stringify_date(mongo_execution_log.updated_at), "dataset_name": "my-mongo-db", }, { "collection_name": "address", "fields_affected": [ { "path": "address.street", "field_name": "street", "data_categories": [ "user.provided.identifiable.contact.street" ], }, { "path": "address.city", "field_name": "city", "data_categories": [ "user.provided.identifiable.contact.city" ], }, ], "message": "Database timed out.", "action_type": "access", "status": "error", "updated_at": stringify_date( second_postgres_execution_log.updated_at ), "dataset_name": "my-postgres-db", }, ], "total": 3, "page": 1, "size": page_size, } assert resp == expected_resp class TestRequestPreview: @pytest.fixture(scope="function") def url(self, db, privacy_request): return V1_URL_PREFIX + REQUEST_PREVIEW def test_request_preview( self, dataset_config_preview, api_client: TestClient, url, generate_auth_header, ) -> None: auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) data = [dataset_config_preview.fides_key] response = api_client.put(url, headers=auth_header, json=data) assert response.status_code == 200 response_body: List[DryRunDatasetResponse] = json.loads(response.text) assert ( next( response["query"] for response in response_body if response["collectionAddress"]["dataset"] == "postgres" if response["collectionAddress"]["collection"] == "subscriptions" ) == "SELECT email,id FROM subscriptions WHERE email = ?" ) def test_request_preview_all( self, dataset_config_preview, api_client: TestClient, url, generate_auth_header, ) -> None: auth_header = generate_auth_header(scopes=[PRIVACY_REQUEST_READ]) response = api_client.put(url, headers=auth_header) assert response.status_code == 200 response_body: List[DryRunDatasetResponse] = json.loads(response.text) assert ( next( response["query"] for response in response_body if response["collectionAddress"]["dataset"] == "postgres" if response["collectionAddress"]["collection"] == "subscriptions" ) == "SELECT email,id FROM subscriptions WHERE email = ?" )

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480ec3917010a1744d09ab7f53ce32a734f03500

118

py

Python

rl/spaces/__init__.py

taylor1355/gym-agents

3ef5aa0d09b82a7ad63358222f5dae3839d6ca04

[ "MIT" ]

null

rl/spaces/__init__.py

taylor1355/gym-agents

3ef5aa0d09b82a7ad63358222f5dae3839d6ca04

[ "MIT" ]

null

rl/spaces/__init__.py

taylor1355/gym-agents

3ef5aa0d09b82a7ad63358222f5dae3839d6ca04

[ "MIT" ]

null

from rl.spaces.utils import is_discrete from rl.spaces.utils import cardinality from rl.spaces.utils import enumerate

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py

Python

arcpyext/toolbox/__init__.py

PeterReyne/arcpyext

9307115da8f0b6a30e2ca741fb6a7d09e54fd0f3

[ "BSD-3-Clause" ]

11

2015-05-01T04:08:30.000Z

2019-09-21T05:00:58.000Z

arcpyext/toolbox/__init__.py

PeterReyne/arcpyext

9307115da8f0b6a30e2ca741fb6a7d09e54fd0f3

[ "BSD-3-Clause" ]

14

2015-06-23T02:46:44.000Z

2019-10-11T00:46:11.000Z

arcpyext/toolbox/__init__.py

PeterReyne/arcpyext

9307115da8f0b6a30e2ca741fb6a7d09e54fd0f3

[ "BSD-3-Clause" ]

9

2015-02-27T05:25:42.000Z

2020-01-19T05:43:14.000Z

from .PythonToolbox import PythonToolbox

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py

Python

remodet_repository_LEE/Projects/Det_CATDOG/DetRelease_Net.py

UrwLee/Remo_experience

a59d5b9d6d009524672e415c77d056bc9dd88c72

[ "MIT" ]

null

remodet_repository_LEE/Projects/Det_CATDOG/DetRelease_Net.py

UrwLee/Remo_experience

a59d5b9d6d009524672e415c77d056bc9dd88c72

[ "MIT" ]

null

remodet_repository_LEE/Projects/Det_CATDOG/DetRelease_Net.py

UrwLee/Remo_experience

a59d5b9d6d009524672e415c77d056bc9dd88c72

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os import sys import math sys.dont_write_bytecode = True import caffe from caffe import layers as L from caffe import params as P from caffe.proto import caffe_pb2 sys.path.append('../') from PyLib.LayerParam.MultiBoxLossLayerParam import * from PyLib.NetLib.ConvBNLayer import * from PyLib.NetLib.InceptionLayer import * from PyLib.NetLib.MultiScaleLayer import * from PyLib.NetLib.VggNet import VGG16_BaseNet_ChangeChannel from PyLib.NetLib.YoloNet import YoloNetPart from AddC6 import * from TPDUtils import * from DetectorHeader import * from DetNet_Param import * from DetRelease_Data import * from DetRelease_General import * def Deconv(net,from_layer,num_output,group,kernel_size,stride,lr_mult,decay_mult,use_bn,use_scale,use_relu,add_str = "",deconv_name = "_Upsample"): deconv_param = { 'num_output': num_output, 'kernel_size': kernel_size, 'pad': 0, 'stride': stride, 'weight_filler': dict(type='gaussian', std=0.01), 'bias_filler': dict(type='constant', value=0.0), 'bias_term': True, 'group': group, } kwargs_deconv = { 'param': [dict(lr_mult=lr_mult, decay_mult=decay_mult)], 'convolution_param': deconv_param } out_layer = from_layer + deconv_name net[out_layer] = L.Deconvolution(net[from_layer + add_str], **kwargs_deconv) base_conv_name = out_layer from_layer = out_layer # parameters for batchnorm layer. bn_kwargs = { 'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)], 'eps': 0.001, } sb_kwargs = { 'bias_term': True, 'param': [dict(lr_mult=lr_mult, decay_mult=0), dict(lr_mult=lr_mult, decay_mult=0)], 'filler': dict(type='constant', value=1.0), 'bias_filler': dict(type='constant', value=0.2), } if use_bn: bn_name = '{}_bn'.format(base_conv_name) net[bn_name] = L.BatchNorm(net[from_layer], in_place=True, **bn_kwargs) from_layer = bn_name if use_scale: sb_name = '{}_scale'.format(base_conv_name) net[sb_name] = L.Scale(net[from_layer], in_place=True, **sb_kwargs) from_layer = sb_name if use_relu: relu_name = '{}_relu'.format(base_conv_name) net[relu_name] = L.ReLU(net[from_layer], in_place=True) from_layer = relu_name return out_layer def MultiScaleEltLayer(net,layers = [],kernels =[], strides = [],out_layer = "",num_channels = 128,lr=1.0,decay=1.0,add_str = "",use_bn = True,flag_withparamname=False): assert len(layers) == len(kernels) == len(strides) feat_layers = [] for i in xrange(len(layers)): f_layer = layers[i] o_layer = f_layer + "_adapfeat" + out_layer[-1] k = kernels[i] ConvBNUnitLayer(net, f_layer + add_str, o_layer, use_bn=use_bn, use_relu=False, num_output=num_channels, kernel_size=k, pad=(k-1)/2, stride=strides[i], use_scale=True, leaky=False, lr_mult=lr, decay_mult=decay,flag_withparamname=flag_withparamname,pose_string=add_str) feat_layers.append(net[o_layer + add_str]) net[out_layer + add_str] = L.Eltwise(*feat_layers, eltwise_param=dict(operation=P.Eltwise.SUM)) relu_name = out_layer + "_relu" + add_str net[relu_name] = L.ReLU(net[out_layer + add_str], in_place=True) def DetRelease_FirstBodyPartPoseNet(train=True): ##Step1: Create Data for Body_Part Detection of 16:9, 9:16 and Pose Estimation ##Step2: Create BaseNet for three subnets until conv5_5 ##Step3: Create Conv6 for Body_Part Detection for Detection subnets(16:9 and 9:16) ##Step4: Create featuremap1,featuremap2,featuremap3 for Detection subnet_16:9 ##Step5: Create featuremap1,featuremap2,featuremap3 for Detection subnet_9:16 ##Step6: Create Header and Body Loss for subnet_16:9 ##Step7: Create Header and Part Loss for subnet_16:9 ##Step8: Create Header and Body Loss for subnet_9:16 ##Step9: Create Header and Part Loss for subnet_9:16 ##Step10:Create Pose Estimation convf and stage loss net = caffe.NetSpec() ##Step1: Create Data for Body_Part Detection of 16:9, 9:16 and Pose Estimation net = get_DAPDataLayer(net, train=train, batchsize=batch_size,data_name = "data",label_name = "label",flag_169=flag_169_global) if train: net = get_poseDataLayer(net, train=train, batch_size=batch_size,data_name="data_pose", label_name="label_pose") net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \ L.Slice(net["label_pose"], ntop=4, slice_param=dict(slice_point=[34, 52, 86], axis=1)) net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD)) net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD)) ##Step2: Create BaseNet for three subnets until conv5_5 use_bn = False channels = ((32,), (64,), (128, 64, 128), (192, 96, 192, 96, 192), (256, 128, 256, 128, 256)) strides = (True, True, True, False, False) kernels = ((3,), (3,), (3, 1, 3), (3, 1, 3, 1, 3), (3, 1, 3, 1, 3)) pool_last = (False,False,False,True,True) net = VGG16_BaseNet_ChangeChannel(net, from_layer="data", channels=channels, strides=strides, kernels=kernels,freeze_layers=[], pool_last=pool_last,flag_withparamname=True,add_string='', use_bn=use_bn,lr_mult=lr_conv1_conv5,decay_mult=1.0,use_global_stats=None) if train: pool_last = (False, False, False, True, False) net = VGG16_BaseNet_ChangeChannel(net, from_layer="data_pose", channels=channels, strides=strides, kernels=kernels, freeze_layers=[], pool_last=pool_last, flag_withparamname=True, add_string='_pose', use_bn=use_bn, lr_mult=lr_conv1_conv5, decay_mult=1.0, use_global_stats=None) ##Step3: Create Conv6 for Body_Part Detection for Detection subnets(16:9 and 9:16) conv6_output = Conv6_Param.get('conv6_output',[]) conv6_kernal_size = Conv6_Param.get('conv6_kernal_size',[]) from_layer = "pool5" net = addconv6(net, from_layer=from_layer, use_bn=use_bn, conv6_output=conv6_output, \ conv6_kernal_size=conv6_kernal_size, pre_name="conv6", start_pool=False, lr_mult=lr_conv6_adap, decay_mult=1, n_group=1, flag_withparamname=False) ##Step4:Create featuremap1,featuremap2,featuremap3 for Detection subnet_16:9 #layers = ["conv3_3", "conv4_5"] #kernels = [3, 3] #strides = [1, 1] #out_layer = "featuremap1" #num_channels = 128 #add_str = "" #MultiScaleEltLayer(net, layers=layers, kernels=kernels, strides=strides, out_layer=out_layer, # num_channels=num_channels, lr=lr_conv6_adap, decay=1.0, use_bn=use_bn, add_str=add_str, # flag_withparamname=False) #layers = ["conv4_5", "conv5_5"] #kernels = [3, 3] #strides = [2, 1] #out_layer = "featuremap2" #num_channels = 128 #add_str = "" #MultiScaleEltLayer(net, layers=layers, kernels=kernels, strides=strides, out_layer=out_layer, # num_channels=num_channels, lr=lr_conv6_adap, decay=1.0, use_bn=use_bn, add_str=add_str, # flag_withparamname=False) #layers = ["conv5_5", "conv6_5"] #kernels = [3, 3] #strides = [2, 1] #out_layer = "featuremap3" #num_channels = 128 #add_str = "" #MultiScaleEltLayer(net, layers=layers, kernels=kernels, strides=strides, out_layer=out_layer, # num_channels=num_channels, lr=lr_conv6_adap, decay=1.0, use_bn=use_bn, add_str=add_str, # flag_withparamname=False) add_str = "" net['featuremap3'] = L.Concat(net['conv6_5']) net,topdown16 = tdm(net,'conv5_5','featuremap3',2,freeze = False) net,topdown8 = tdm(net,'conv4_5','featuremap2',1,freeze = False) ##Step6:Create Header and Body Loss for subnet_16:9 data_layer = "data" gt_label = "label" if flag_169_global: net_width = 368 net_height = 368 else: net_width = 368 net_height = 368 ssd_Param_1 = get_ssd_Param_1(flag_169=flag_169_global,bboxloss_loc_weight = bboxloss_loc_weight_body,bboxloss_conf_weight=bboxloss_conf_weight_body) mbox_1_layers = SsdDetectorHeaders(net, \ net_width=net_width, net_height=net_height, data_layer=data_layer, \ from_layers=ssd_Param_1.get('feature_layers', []), \ num_classes=ssd_Param_1.get("num_classes", 2), \ boxsizes=ssd_Param_1.get("anchor_boxsizes", []), \ aspect_ratios=ssd_Param_1.get("anchor_aspect_ratios", []), \ prior_variance=ssd_Param_1.get("anchor_prior_variance", [0.1, 0.1, 0.2, 0.2]), \ flip=ssd_Param_1.get("anchor_flip", True), \ clip=ssd_Param_1.get("anchor_clip", True), \ normalizations=ssd_Param_1.get("interlayers_normalizations", []), \ use_batchnorm=ssd_Param_1.get("interlayers_use_batchnorm", True), \ inter_layer_channels=ssd_Param_1.get("interlayers_channels_kernels", []), \ use_focus_loss=ssd_Param_1.get("bboxloss_using_focus_loss", False), \ use_dense_boxes=ssd_Param_1.get('bboxloss_use_dense_boxes', False), \ stage=1, lr_mult=lr_inter_loss,flag_withparamname=False,add_str=add_str, AnchorFixed = AnchorFixed) ##Step7:Create Header and Part Loss for subnet_16:9 ssd_Param_2 = get_ssd_Param_2(flag_169=flag_169_global, bboxloss_loc_weight=bboxloss_loc_weight_part,bboxloss_conf_weight=bboxloss_conf_weight_part) mbox_2_layers = SsdDetectorHeaders(net, \ net_width=net_width, net_height=net_height, data_layer=data_layer, \ from_layers=ssd_Param_2.get('feature_layers', []), \ num_classes=ssd_Param_2.get("num_classes", 2), \ boxsizes=ssd_Param_2.get("anchor_boxsizes", []), \ aspect_ratios=ssd_Param_2.get("anchor_aspect_ratios", []), \ prior_variance=ssd_Param_2.get("anchor_prior_variance", [0.1, 0.1, 0.2, 0.2]), \ flip=ssd_Param_2.get("anchor_flip", True), \ clip=ssd_Param_2.get("anchor_clip", True), \ normalizations=ssd_Param_2.get("interlayers_normalizations", []), \ use_batchnorm=ssd_Param_2.get("interlayers_use_batchnorm", True), \ inter_layer_channels=ssd_Param_2.get("interlayers_channels_kernels", []), \ use_focus_loss=ssd_Param_2.get("bboxloss_using_focus_loss", False), \ use_dense_boxes=ssd_Param_2.get('bboxloss_use_dense_boxes', False), \ stage=2, lr_mult=lr_inter_loss,flag_withparamname=False, add_str=add_str) if train: loss_param = get_loss_param(normalization=ssd_Param_1.get("bboxloss_normalization", P.Loss.VALID)) mbox_1_layers.append(net[gt_label]) bboxloss_param = { 'gt_labels': ssd_Param_1.get('gt_labels', []), 'target_labels': ssd_Param_1.get('target_labels', []), 'num_classes': ssd_Param_1.get("num_classes", 2), 'alias_id': ssd_Param_1.get("alias_id", 0), 'loc_loss_type': ssd_Param_1.get("bboxloss_loc_loss_type", P.MultiBoxLoss.SMOOTH_L1), 'conf_loss_type': ssd_Param_1.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX), 'loc_weight': ssd_Param_1.get("bboxloss_loc_weight", 1), 'conf_weight': ssd_Param_1.get("bboxloss_conf_weight", 1), 'overlap_threshold': ssd_Param_1.get("bboxloss_overlap_threshold", 0.5), 'neg_overlap': ssd_Param_1.get("bboxloss_neg_overlap", 0.5), 'size_threshold': ssd_Param_1.get("bboxloss_size_threshold", 0.0001), 'do_neg_mining': ssd_Param_1.get("bboxloss_do_neg_mining", True), 'neg_pos_ratio': ssd_Param_1.get("bboxloss_neg_pos_ratio", 3), 'using_focus_loss': ssd_Param_1.get("bboxloss_using_focus_loss", False), 'gama': ssd_Param_1.get("bboxloss_focus_gama", 2), 'use_difficult_gt': ssd_Param_1.get("bboxloss_use_difficult_gt", False), 'code_type': ssd_Param_1.get("bboxloss_code_type", P.PriorBox.CENTER_SIZE), 'flag_noperson':ssd_Param_1.get("flag_noperson", False), 'match_type': P.MultiBoxLoss.PER_PREDICTION, 'share_location': True, 'use_prior_for_matching': True, 'background_label_id': 0, 'encode_variance_in_target': False, 'map_object_to_agnostic': False, 'matchtype_anchorgt':ssd_Param_1.get("matchtype_anchorgt", "REMOVELARGMARGIN"), 'margin_ratio':ssd_Param_1.get("margin_ratio", 0.25), 'sigma_angtdist':ssd_Param_1.get("sigma_angtdist", 0.1), } if body_loss_type == "BBoxLoss": net["mbox_1_loss"] = L.BBoxLoss(*mbox_1_layers, bbox_loss_param=bboxloss_param, \ loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')), \ propagate_down=[True, True, False, False]) else: net["mbox_1_loss"] = L.BBoxLossWTIOUCKCOVER(*mbox_1_layers, bbox_loss_param=bboxloss_param, \ loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')), \ propagate_down=[True, True, False, False]) loss_param = get_loss_param(normalization=ssd_Param_2.get("bboxloss_normalization", P.Loss.VALID)) mbox_2_layers.append(net[gt_label]) bboxloss_param = { 'gt_labels': ssd_Param_2.get('gt_labels', []), 'target_labels': ssd_Param_2.get('target_labels', []), 'num_classes': ssd_Param_2.get("num_classes", 2), 'alias_id': ssd_Param_2.get("alias_id", 0), 'loc_loss_type': ssd_Param_2.get("bboxloss_loc_loss_type", P.MultiBoxLoss.SMOOTH_L1), 'conf_loss_type': ssd_Param_2.get("bboxloss_conf_loss_type", P.MultiBoxLoss.LOGISTIC), 'loc_weight': ssd_Param_2.get("bboxloss_loc_weight", 1), 'conf_weight': ssd_Param_2.get("bboxloss_conf_weight", 1), 'overlap_threshold': ssd_Param_2.get("bboxloss_overlap_threshold", 0.5), 'neg_overlap': ssd_Param_2.get("bboxloss_neg_overlap", 0.5), 'size_threshold': ssd_Param_2.get("bboxloss_size_threshold", 0.0001), 'do_neg_mining': ssd_Param_2.get("bboxloss_do_neg_mining", True), 'neg_pos_ratio': ssd_Param_2.get("bboxloss_neg_pos_ratio", 3), 'using_focus_loss': ssd_Param_2.get("bboxloss_using_focus_loss", False), 'gama': ssd_Param_2.get("bboxloss_focus_gama", 2), 'use_difficult_gt': ssd_Param_2.get("bboxloss_use_difficult_gt", False), 'code_type': ssd_Param_2.get("bboxloss_code_type", P.PriorBox.CENTER_SIZE), 'use_prior_for_matching': True, 'encode_variance_in_target': False, 'flag_noperson': ssd_Param_2.get('flag_noperson', False), } net["mbox_2_loss"] = L.DenseBBoxLoss(*mbox_2_layers, dense_bbox_loss_param=bboxloss_param, \ loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')), \ propagate_down=[True, True, False, False]) ##Step10:Create Pose Estimation convf and stage loss from_layer = "conv5_5" add_str = "_pose" num_output = 128 group = 1 kernel_size = 2 stride = 2 use_bn = False use_scale = False use_relu = False out_layer1 = Deconv(net, from_layer, num_output, group, kernel_size, stride, lr_pose, 1.0, use_bn, use_scale,use_relu,add_str) from_layer = "conv4_5" out_layer2 = from_layer + "_adap" kernel_size = 3 ConvBNUnitLayer(net, from_layer + add_str, out_layer2, use_bn=use_bn, use_relu=False, num_output=num_output, kernel_size=kernel_size, pad=(kernel_size - 1) / 2, stride=1, use_scale=use_scale, leaky=False, lr_mult=lr_pose,decay_mult=1.0) feat_layers = [] feat_layers.append(net[out_layer1]) feat_layers.append(net[out_layer2]) out_layer = "convf" net[out_layer] = L.Eltwise(*feat_layers, eltwise_param=dict(operation=P.Eltwise.SUM)) # relu_name = out_layer + "_relu" # net[relu_name] = L.ReLU(net[out_layer], in_place=True) use_stage = 3 use_3_layers = 5 use_1_layers = 0 n_channel = 64 kernel_size = 3 baselayer = "convf" flag_output_sigmoid = False for stage in range(use_stage): if stage == 0: from_layer = baselayer else: from_layer = "concat_stage{}".format(stage) outlayer = "concat_stage{}".format(stage + 1) if stage == use_stage - 1: short_cut = False else: short_cut = True net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage + 1, mask_vec="vec_mask", mask_heat="heat_mask", \ label_vec="vec_label", label_heat="heat_label", \ use_3_layers=use_3_layers, use_1_layers=use_1_layers, short_cut=short_cut, \ base_layer=baselayer, lr=lr_pose, decay=1.0, num_channels=n_channel, kernel_size=kernel_size, flag_sigmoid=flag_output_sigmoid,loss_weight=0.1) else: if ssd_Param_1.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.SOFTMAX: reshape_name = "mbox_1_conf_reshape" + add_str net[reshape_name] = L.Reshape(mbox_1_layers[1], \ shape=dict(dim=[0, -1, ssd_Param_1.get("num_classes", 2)])) softmax_name = "mbox_1_conf_softmax" + add_str net[softmax_name] = L.Softmax(net[reshape_name], axis=2) flatten_name = "mbox_1_conf_flatten" + add_str net[flatten_name] = L.Flatten(net[softmax_name], axis=1) mbox_1_layers[1] = net[flatten_name] elif ssd_Param_1.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.LOGISTIC: sigmoid_name = "mbox_1_conf_sigmoid" + add_str net[sigmoid_name] = L.Sigmoid(mbox_1_layers[1]) mbox_1_layers[1] = net[sigmoid_name] else: raise ValueError("Unknown conf loss type.") det_out_param = { 'num_classes': ssd_Param_1.get("num_classes", 2), 'target_labels': ssd_Param_1.get('detout_target_labels', []), 'alias_id': ssd_Param_1.get("alias_id", 0), 'conf_threshold': ssd_Param_1.get("detout_conf_threshold", 0.01), 'nms_threshold': ssd_Param_1.get("detout_nms_threshold", 0.45), 'size_threshold': ssd_Param_1.get("detout_size_threshold", 0.0001), 'top_k': ssd_Param_1.get("detout_top_k", 30), 'share_location': True, 'code_type': P.PriorBox.CENTER_SIZE, 'background_label_id': 0, 'variance_encoded_in_target': False, } net["detection_out_1" + add_str] = L.DetOut(*mbox_1_layers, \ detection_output_param=det_out_param, \ include=dict(phase=caffe_pb2.Phase.Value('TEST'))) ##Step7:Create Part Header and sigmoid conf for subnet_16:9 if ssd_Param_2.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.SOFTMAX: reshape_name = "mbox_2_conf_reshape" + add_str net[reshape_name] = L.Reshape(mbox_2_layers[1], \ shape=dict(dim=[0, -1, ssd_Param_2.get("num_classes", 2)])) softmax_name = "mbox_2_conf_softmax" + add_str net[softmax_name] = L.Softmax(net[reshape_name], axis=2) flatten_name = "mbox_2_conf_flatten" + add_str net[flatten_name] = L.Flatten(net[softmax_name], axis=1) mbox_2_layers[1] = net[flatten_name] elif ssd_Param_2.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.LOGISTIC: sigmoid_name = "mbox_2_conf_sigmoid" + add_str net[sigmoid_name] = L.Sigmoid(mbox_2_layers[1]) mbox_2_layers[1] = net[sigmoid_name] else: raise ValueError("Unknown conf loss type.") det_out_param = { 'num_classes': ssd_Param_2.get("num_classes", 2), 'target_labels': ssd_Param_2.get('detout_target_labels', []), 'alias_id': ssd_Param_2.get("alias_id", 0), 'conf_threshold': ssd_Param_2.get("detout_conf_threshold", 0.01), 'nms_threshold': ssd_Param_2.get("detout_nms_threshold", 0.45), 'size_threshold': ssd_Param_2.get("detout_size_threshold", 0.0001), 'top_k': ssd_Param_2.get("detout_top_k", 30), 'share_location': True, 'code_type': P.PriorBox.CENTER_SIZE, 'background_label_id': 0, 'variance_encoded_in_target': False, } net["detection_out_2" + add_str] = L.DenseDetOut(*mbox_2_layers, \ detection_output_param=det_out_param, \ include=dict(phase=caffe_pb2.Phase.Value('TEST'))) ##Step8:Create evaluation part for subnet_16:9 eval_Param = get_eval_Param([0, 1, 3]) det_eval_param = { 'gt_labels': eval_Param.get('eval_gt_labels', []), 'num_classes': eval_Param.get("eval_num_classes", 2), 'evaluate_difficult_gt': eval_Param.get("eval_difficult_gt", False), 'boxsize_threshold': eval_Param.get("eval_boxsize_threshold", [0, 0.01, 0.05, 0.1, 0.15, 0.2, 0.25]), 'iou_threshold': eval_Param.get("eval_iou_threshold", [0.9, 0.75, 0.5]), 'background_label_id': 0, } det_out_layers = [] det_out_layers.append(net['detection_out_1' + add_str]) det_out_layers.append(net['detection_out_2' + add_str]) name = 'det_out' + add_str net[name] = L.Concat(*det_out_layers, axis=2) net["det_accu" + add_str] = L.DetEval(net[name], net[gt_label], \ detection_evaluate_param=det_eval_param, \ include=dict(phase=caffe_pb2.Phase.Value('TEST'))) return net def DetRelease_SecondPartAllNet(train=True): net = caffe.NetSpec() if train: ##Step1: Create Data for Body_Part Detection of 16:9, 9:16 and Pose Estimation net = get_DAPDataLayer(net, train=train, batchsize=batch_size,data_name = "data",label_name = "label",flag_169=flag_169_global) net = get_MinihandDataLayer(net, train=train, data_name="data_minihand", label_name="label_minihand", flag_169=flag_169_global) else: net = get_DAPDataLayer(net, train=train, batchsize=batch_size, data_name="data", label_name="label",flag_169=flag_169_global) ##Step2: Create BaseNet for three subnets until conv5_5 lr_mult = 0.0 decay_mult = 1.0 use_bn = False channels = ((32,), (64,), (128, 64, 128), (192, 96, 192, 96, 192), (256, 128, 256, 128, 256)) strides = (True, True, True, False, False) kernels = ((3,), (3,), (3, 1, 3), (3, 1, 3, 1, 3), (3, 1, 3, 1, 3)) pool_last = (False,False,False,True,True) net = VGG16_BaseNet_ChangeChannel(net, from_layer="data", channels=channels, strides=strides, kernels=kernels,freeze_layers=[], pool_last=pool_last,flag_withparamname=True,add_string='', use_bn=use_bn,lr_mult=lr_conv1_conv5,decay_mult=1.0,use_global_stats=None) if train: channels = ((32,), (64,), (128, 64, 128), (192, 96, 192, 96, 192)) strides = (True, True, True, False) kernels = ((3,), (3,), (3, 1, 3), (3, 1, 3, 1, 3)) pool_last = (False, False, False, False) net = VGG16_BaseNet_ChangeChannel(net, from_layer="data_minihand", channels=channels, strides=strides, kernels=kernels, freeze_layers=[], pool_last=pool_last, flag_withparamname=True, add_string='_minihand',use_bn=use_bn, lr_mult=lr_conv1_conv5, decay_mult=1.0, use_global_stats=None) ##Step3: Create Conv6 for Body_Part Detection for Detection subnets(16:9 and 9:16) conv6_output = Conv6_Param.get('conv6_output',[]) conv6_kernal_size = Conv6_Param.get('conv6_kernal_size',[]) from_layer = "pool5" net = addconv6(net, from_layer=from_layer, use_bn=use_bn, conv6_output=conv6_output, \ conv6_kernal_size=conv6_kernal_size, pre_name="conv6", start_pool=False, lr_mult=lr_conv6_adap, decay_mult=1, n_group=1, flag_withparamname=False) ##Step4:Create featuremap1,featuremap2,featuremap3 for Detection subnet_16:9 layers = ["conv3_3", "conv4_5"] kernels = [3, 3] strides = [1, 1] out_layer = "featuremap1" num_channels = 128 add_str = "" MultiScaleEltLayer(net, layers=layers, kernels=kernels, strides=strides, out_layer=out_layer, num_channels=num_channels, lr=lr_conv6_adap, decay=1.0, use_bn=use_bn, add_str=add_str, flag_withparamname=True) layers = ["conv4_5", "conv5_5"] kernels = [3, 3] strides = [2, 1] out_layer = "featuremap2" num_channels = 128 add_str = "" MultiScaleEltLayer(net, layers=layers, kernels=kernels, strides=strides, out_layer=out_layer, num_channels=num_channels, lr=lr_conv6_adap, decay=1.0, use_bn=use_bn, add_str=add_str, flag_withparamname=True) layers = ["conv5_5", "conv6_5"] kernels = [3, 3] strides = [2, 1] out_layer = "featuremap3" num_channels = 128 add_str = "" MultiScaleEltLayer(net, layers=layers, kernels=kernels, strides=strides, out_layer=out_layer, num_channels=num_channels, lr=lr_conv6_adap, decay=1.0, use_bn=use_bn, add_str=add_str, flag_withparamname=True) ##Step6:Create Header and Body Loss for subnet_16:9 add_str = "" data_layer = "data" + add_str if flag_169_global: net_width = 512 net_height = 288 else: net_width = 512 net_height = 288 ##Step7:Create Header and Part Loss for subnet_16:9 ssd_Param_2 = get_ssd_Param_2(flag_169=flag_169_global, bboxloss_loc_weight=bboxloss_loc_weight_part, bboxloss_conf_weight=bboxloss_conf_weight_part) mbox_2_layers = SsdDetectorHeaders(net, \ net_width=net_width, net_height=net_height, data_layer=data_layer, \ from_layers=ssd_Param_2.get('feature_layers', []), \ num_classes=ssd_Param_2.get("num_classes", 2), \ boxsizes=ssd_Param_2.get("anchor_boxsizes", []), \ aspect_ratios=ssd_Param_2.get("anchor_aspect_ratios", []), \ prior_variance=ssd_Param_2.get("anchor_prior_variance", [0.1, 0.1, 0.2, 0.2]), \ flip=ssd_Param_2.get("anchor_flip", True), \ clip=ssd_Param_2.get("anchor_clip", True), \ normalizations=ssd_Param_2.get("interlayers_normalizations", []), \ use_batchnorm=ssd_Param_2.get("interlayers_use_batchnorm", True), \ inter_layer_channels=ssd_Param_2.get("interlayers_channels_kernels", []), \ use_focus_loss=ssd_Param_2.get("bboxloss_using_focus_loss", False), \ use_dense_boxes=ssd_Param_2.get('bboxloss_use_dense_boxes', False), \ stage=2,flag_withparamname=False,add_str=add_str,lr_mult=lr_inter_loss) use_bn = False init_xavier = False if train: add_str = "_minihand" else: add_str = "" from_layer = "conv1" + add_str out_layer = 'conv2_hand' ConvBNUnitLayer(net, from_layer, out_layer, use_bn=use_bn, use_relu=False, num_output=64, kernel_size=3, pad=1, stride=2, use_scale=False, leaky=False, lr_mult=1, decay_mult=1, init_xavier=init_xavier) from_layer = "conv4_5" Deconv(net, from_layer, num_output=64, group=1, kernel_size=2, stride=2, lr_mult=1.0, decay_mult=1.0, use_bn=use_bn, use_scale=use_bn, use_relu=False, add_str=add_str,deconv_name="_miniUpsample") out_layer = "mini_multiscale" net[out_layer] = L.Eltwise(net["conv2_hand"], net["conv4_5" + "_miniUpsample"], eltwise_param=dict(operation=P.Eltwise.SUM)) from_layer = out_layer out_layer = from_layer + "_relu" net[out_layer] = L.ReLU(net[from_layer], in_place=True) data_layer = "data" + add_str ssd_Param_3 = get_ssd_Param_3(flag_169_global, bboxloss_loc_weight=bboxloss_loc_weight_part, bboxloss_conf_weight=bboxloss_conf_weight_part) mbox_3_layers = SsdDetectorHeaders(net, \ net_width=net_width, net_height=net_height, data_layer=data_layer, \ from_layers=ssd_Param_3.get('feature_layers', []), \ num_classes=ssd_Param_3.get("num_classes", 2), \ boxsizes=ssd_Param_3.get("anchor_boxsizes", []), \ aspect_ratios=ssd_Param_3.get("anchor_aspect_ratios", []), \ prior_variance=ssd_Param_3.get("anchor_prior_variance", [0.1, 0.1, 0.2, 0.2]), \ flip=ssd_Param_3.get("anchor_flip", True), \ clip=ssd_Param_3.get("anchor_clip", True), \ normalizations=ssd_Param_3.get("interlayers_normalizations", []), \ use_batchnorm=ssd_Param_3.get("interlayers_use_batchnorm", True), \ inter_layer_channels=ssd_Param_3.get("interlayers_channels_kernels", []), \ use_focus_loss=ssd_Param_3.get("bboxloss_using_focus_loss", False), \ use_dense_boxes=ssd_Param_3.get('bboxloss_use_dense_boxes', False), \ stage=3,lr_mult=lr_inter_loss) if train: gt_label = "label" loss_param = get_loss_param(normalization=ssd_Param_2.get("bboxloss_normalization", P.Loss.VALID)) mbox_2_layers.append(net[gt_label]) bboxloss_param = { 'gt_labels': ssd_Param_2.get('gt_labels', []), 'target_labels': ssd_Param_2.get('target_labels', []), 'num_classes': ssd_Param_2.get("num_classes", 2), 'alias_id': ssd_Param_2.get("alias_id", 0), 'loc_loss_type': ssd_Param_2.get("bboxloss_loc_loss_type", P.MultiBoxLoss.SMOOTH_L1), 'conf_loss_type': ssd_Param_2.get("bboxloss_conf_loss_type", P.MultiBoxLoss.LOGISTIC), 'loc_weight': ssd_Param_2.get("bboxloss_loc_weight", 1), 'conf_weight': ssd_Param_2.get("bboxloss_conf_weight", 1), 'overlap_threshold': ssd_Param_2.get("bboxloss_overlap_threshold", 0.5), 'neg_overlap': ssd_Param_2.get("bboxloss_neg_overlap", 0.5), 'size_threshold': ssd_Param_2.get("bboxloss_size_threshold", 0.0001), 'do_neg_mining': ssd_Param_2.get("bboxloss_do_neg_mining", True), 'neg_pos_ratio': ssd_Param_2.get("bboxloss_neg_pos_ratio", 3), 'using_focus_loss': ssd_Param_2.get("bboxloss_using_focus_loss", False), 'gama': ssd_Param_2.get("bboxloss_focus_gama", 2), 'use_difficult_gt': ssd_Param_2.get("bboxloss_use_difficult_gt", False), 'code_type': ssd_Param_2.get("bboxloss_code_type", P.PriorBox.CENTER_SIZE), 'use_prior_for_matching': True, 'encode_variance_in_target': False, 'flag_noperson': ssd_Param_2.get('flag_noperson', False), } net["mbox_2_loss"] = L.DenseBBoxLoss(*mbox_2_layers, dense_bbox_loss_param=bboxloss_param, \ loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')), \ propagate_down=[True, True, False, False]) gt_label = "label_minihand" loss_param = get_loss_param(normalization=ssd_Param_3.get("bboxloss_normalization", P.Loss.VALID)) mbox_3_layers.append(net[gt_label]) bboxloss_param = { 'gt_labels': ssd_Param_3.get('gt_labels', []), 'target_labels': ssd_Param_3.get('target_labels', []), 'num_classes': ssd_Param_3.get("num_classes", 2), 'alias_id': ssd_Param_3.get("alias_id", 0), 'loc_loss_type': ssd_Param_3.get("bboxloss_loc_loss_type", P.MultiBoxLoss.SMOOTH_L1), 'conf_loss_type': ssd_Param_3.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX), 'loc_weight': ssd_Param_3.get("bboxloss_loc_weight", 1), 'conf_weight': ssd_Param_3.get("bboxloss_conf_weight", 1), 'overlap_threshold': ssd_Param_3.get("bboxloss_overlap_threshold", 0.5), 'neg_overlap': ssd_Param_3.get("bboxloss_neg_overlap", 0.5), 'size_threshold': ssd_Param_3.get("bboxloss_size_threshold", 0.0001), 'do_neg_mining': ssd_Param_3.get("bboxloss_do_neg_mining", True), 'neg_pos_ratio': ssd_Param_3.get("bboxloss_neg_pos_ratio", 3), 'using_focus_loss': ssd_Param_3.get("bboxloss_using_focus_loss", False), 'gama': ssd_Param_3.get("bboxloss_focus_gama", 2), 'use_difficult_gt': ssd_Param_3.get("bboxloss_use_difficult_gt", False), 'code_type': ssd_Param_3.get("bboxloss_code_type", P.PriorBox.CENTER_SIZE), 'flag_noperson': ssd_Param_3.get('flag_noperson', False), 'match_type': P.MultiBoxLoss.PER_PREDICTION, 'share_location': True, 'use_prior_for_matching': True, 'background_label_id': 0, 'encode_variance_in_target': False, 'map_object_to_agnostic': False, } net["mbox_3_loss"] = L.BBoxLoss(*mbox_3_layers, bbox_loss_param=bboxloss_param, \ loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')), \ propagate_down=[True, True, False, False]) else: if ssd_Param_2.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.SOFTMAX: reshape_name = "mbox_2_conf_reshape" + add_str net[reshape_name] = L.Reshape(mbox_2_layers[1], \ shape=dict(dim=[0, -1, ssd_Param_2.get("num_classes", 2)])) softmax_name = "mbox_2_conf_softmax" + add_str net[softmax_name] = L.Softmax(net[reshape_name], axis=2) flatten_name = "mbox_2_conf_flatten" + add_str net[flatten_name] = L.Flatten(net[softmax_name], axis=1) mbox_2_layers[1] = net[flatten_name] elif ssd_Param_2.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.LOGISTIC: sigmoid_name = "mbox_2_conf_sigmoid" + add_str net[sigmoid_name] = L.Sigmoid(mbox_2_layers[1]) mbox_2_layers[1] = net[sigmoid_name] else: raise ValueError("Unknown conf loss type.") det_out_param = { 'num_classes': ssd_Param_2.get("num_classes", 2), 'target_labels': ssd_Param_2.get('detout_target_labels', []), 'alias_id': ssd_Param_2.get("alias_id", 0), 'conf_threshold': ssd_Param_2.get("detout_conf_threshold", 0.01), 'nms_threshold': ssd_Param_2.get("detout_nms_threshold", 0.45), 'size_threshold': ssd_Param_2.get("detout_size_threshold", 0.0001), 'top_k': ssd_Param_2.get("detout_top_k", 30), 'share_location': True, 'code_type': P.PriorBox.CENTER_SIZE, 'background_label_id': 0, 'variance_encoded_in_target': False, } net["detection_out_2"] = L.DenseDetOut(*mbox_2_layers, \ detection_output_param=det_out_param, \ include=dict(phase=caffe_pb2.Phase.Value('TEST'))) if ssd_Param_3.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.SOFTMAX: reshape_name = "mbox_3_conf_reshape" + add_str net[reshape_name] = L.Reshape(mbox_3_layers[1], \ shape=dict(dim=[0, -1, ssd_Param_3.get("num_classes", 2)])) softmax_name = "mbox_3_conf_softmax" + add_str net[softmax_name] = L.Softmax(net[reshape_name], axis=2) flatten_name = "mbox_3_conf_flatten" + add_str net[flatten_name] = L.Flatten(net[softmax_name], axis=1) mbox_3_layers[1] = net[flatten_name] elif ssd_Param_3.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.LOGISTIC: sigmoid_name = "mbox_3_conf_sigmoid" + add_str net[sigmoid_name] = L.Sigmoid(mbox_3_layers[1]) mbox_3_layers[1] = net[sigmoid_name] else: raise ValueError("Unknown conf loss type.") det_out_param = { 'num_classes': ssd_Param_3.get("num_classes", 2), 'target_labels': ssd_Param_3.get('detout_target_labels', []), 'alias_id': ssd_Param_3.get("alias_id", 0), 'conf_threshold': ssd_Param_3.get("detout_conf_threshold", 0.01), 'nms_threshold': ssd_Param_3.get("detout_nms_threshold", 0.45), 'size_threshold': ssd_Param_3.get("detout_size_threshold", 0.0001), 'top_k': ssd_Param_3.get("detout_top_k", 30), 'share_location': True, 'code_type': P.PriorBox.CENTER_SIZE, 'background_label_id': 0, 'variance_encoded_in_target': False, } net["detection_out_3"] = L.DenseDetOut(*mbox_3_layers, \ detection_output_param=det_out_param, \ include=dict(phase=caffe_pb2.Phase.Value('TEST'))) ##Step8:Create evaluation part for subnet_16:9 eval_Param = get_eval_Param([1, 3]) det_eval_param = { 'gt_labels': eval_Param.get('eval_gt_labels', []), 'num_classes': eval_Param.get("eval_num_classes", 2), 'evaluate_difficult_gt': eval_Param.get("eval_difficult_gt", False), 'boxsize_threshold': eval_Param.get("eval_boxsize_threshold", [0, 0.01, 0.05, 0.1, 0.15, 0.2, 0.25]), 'iou_threshold': eval_Param.get("eval_iou_threshold", [0.9, 0.75, 0.5]), 'background_label_id': 0, } det_out_layers = [] det_out_layers.append(net['detection_out_2' + add_str]) det_out_layers.append(net['detection_out_3' + add_str]) name = 'det_out' + add_str net[name] = L.Concat(*det_out_layers, axis=2) net["det_accu" + add_str] = L.DetEval(net[name], net["label"], \ detection_evaluate_param=det_eval_param, \ include=dict(phase=caffe_pb2.Phase.Value('TEST'))) return net def DetRelease_SecondPartAllNetMiniHandFace(train=True): net = caffe.NetSpec() ##Step1: Create Data for Body_Part Detection of 16:9, 9:16 and Pose Estimation if train: net = get_DAPDataLayer(net, train=train, batchsize=batch_size, data_name="data_pd", label_name="label_pd", flag_169=flag_169_global) net = get_MinihandDataLayer(net, train=train, data_name="data_minihand", label_name="label_minihand", flag_169=flag_169_global) data = [] data.append(net["data_minihand"]) data.append(net["data_pd"]) net["data"] = L.Concat(*data, axis=0) label = [] label.append(net["label_minihand"]) label.append(net["label_pd"]) net["label"] = L.Concat(*label, axis=2) else: net = get_DAPDataLayer(net, train=train, batchsize=batch_size, data_name="data", label_name="label",flag_169=flag_169_global) ##Step2: Create BaseNet for three subnets until conv5_5 use_bn = False channels = ((32,), (64,), (128, 64, 128), (192, 96, 192, 96, 192), (256, 128, 256, 128, 256)) strides = (True, True, True, False, False) kernels = ((3,), (3,), (3, 1, 3), (3, 1, 3, 1, 3), (3, 1, 3, 1, 3)) pool_last = (False,False,False,True,True) net = VGG16_BaseNet_ChangeChannel(net, from_layer="data", channels=channels, strides=strides, kernels=kernels,freeze_layers=[], pool_last=pool_last,flag_withparamname=True,add_string='', use_bn=use_bn,lr_mult=lr_conv1_conv5,decay_mult=1.0,use_global_stats=None) ##Step3: Create Conv6 for Body_Part Detection for Detection subnets(16:9 and 9:16) conv6_output = Conv6_Param.get('conv6_output',[]) conv6_kernal_size = Conv6_Param.get('conv6_kernal_size',[]) from_layer = "pool5" net = addconv6(net, from_layer=from_layer, use_bn=use_bn, conv6_output=conv6_output, \ conv6_kernal_size=conv6_kernal_size, pre_name="conv6", start_pool=False, lr_mult=lr_conv6_adap, decay_mult=1, n_group=1, flag_withparamname=False) ##Step4:Create featuremap1,featuremap2,featuremap3 for Detection subnet_16:9 layers = ["conv3_3", "conv4_5"] kernels = [3, 3] strides = [1, 1] out_layer = "featuremap1" num_channels = 128 add_str = "" MultiScaleEltLayer(net, layers=layers, kernels=kernels, strides=strides, out_layer=out_layer, num_channels=num_channels, lr=lr_conv6_adap, decay=1.0, use_bn=use_bn, add_str=add_str, flag_withparamname=True) layers = ["conv4_5", "conv5_5"] kernels = [3, 3] strides = [2, 1] out_layer = "featuremap2" num_channels = 128 add_str = "" MultiScaleEltLayer(net, layers=layers, kernels=kernels, strides=strides, out_layer=out_layer, num_channels=num_channels, lr=lr_conv6_adap, decay=1.0, use_bn=use_bn, add_str=add_str, flag_withparamname=True) layers = ["conv5_5", "conv6_5"] kernels = [3, 3] strides = [2, 1] out_layer = "featuremap3" num_channels = 128 add_str = "" MultiScaleEltLayer(net, layers=layers, kernels=kernels, strides=strides, out_layer=out_layer, num_channels=num_channels, lr=lr_conv6_adap, decay=1.0, use_bn=use_bn, add_str=add_str, flag_withparamname=True) use_bn = False init_xavier = False from_layer = "conv1" out_layer = 'conv2_mini' ConvBNUnitLayer(net, from_layer, out_layer, use_bn=use_bn, use_relu=False, num_output=64, kernel_size=3, pad=1, stride=2, use_scale=False, leaky=False, lr_mult=1, decay_mult=1, init_xavier=init_xavier) from_layer = "conv4_5" Deconv(net, from_layer, num_output=64, group=1, kernel_size=2, stride=2, lr_mult=1.0, decay_mult=1.0, use_bn=use_bn, use_scale=use_bn, use_relu=False, add_str="", deconv_name="_miniUpsample") out_layer = "mini_multiscale" net[out_layer] = L.Eltwise(net["conv2_mini"], net["conv4_5" + "_miniUpsample"], eltwise_param=dict(operation=P.Eltwise.SUM)) from_layer = out_layer out_layer = from_layer + "_relu" net[out_layer] = L.ReLU(net[from_layer], in_place=True) ##Step6:Create Header and Body Loss for subnet_16:9 data_layer = "data" gt_label = "label" if flag_169_global: net_width = 512 net_height = 288 else: net_width = 512 net_height = 288 ##Step7:Create Header and Part Loss for subnet_16:9 ssd_Param_2 = get_ssd_Param_4(flag_169=flag_169_global, bboxloss_loc_weight=bboxloss_loc_weight_part, bboxloss_conf_weight=bboxloss_conf_weight_part) mbox_2_layers = SsdDetectorHeaders(net, \ net_width=net_width, net_height=net_height, data_layer=data_layer, \ from_layers=ssd_Param_2.get('feature_layers', []), \ num_classes=ssd_Param_2.get("num_classes", 2), \ boxsizes=ssd_Param_2.get("anchor_boxsizes", []), \ aspect_ratios=ssd_Param_2.get("anchor_aspect_ratios", []), \ prior_variance=ssd_Param_2.get("anchor_prior_variance", [0.1, 0.1, 0.2, 0.2]), \ flip=ssd_Param_2.get("anchor_flip", True), \ clip=ssd_Param_2.get("anchor_clip", True), \ normalizations=ssd_Param_2.get("interlayers_normalizations", []), \ use_batchnorm=ssd_Param_2.get("interlayers_use_batchnorm", True), \ inter_layer_channels=ssd_Param_2.get("interlayers_channels_kernels", []), \ use_focus_loss=ssd_Param_2.get("bboxloss_using_focus_loss", False), \ use_dense_boxes=ssd_Param_2.get('bboxloss_use_dense_boxes', False), \ stage=2,flag_withparamname=False,add_str=add_str,lr_mult=lr_inter_loss) if train: loss_param = get_loss_param(normalization=ssd_Param_2.get("bboxloss_normalization", P.Loss.VALID)) mbox_2_layers.append(net[gt_label]) bboxloss_param = { 'gt_labels': ssd_Param_2.get('gt_labels', []), 'target_labels': ssd_Param_2.get('target_labels', []), 'num_classes': ssd_Param_2.get("num_classes", 2), 'alias_id': ssd_Param_2.get("alias_id", 0), 'loc_loss_type': ssd_Param_2.get("bboxloss_loc_loss_type", P.MultiBoxLoss.SMOOTH_L1), 'conf_loss_type': ssd_Param_2.get("bboxloss_conf_loss_type", P.MultiBoxLoss.LOGISTIC), 'loc_weight': ssd_Param_2.get("bboxloss_loc_weight", 1), 'conf_weight': ssd_Param_2.get("bboxloss_conf_weight", 1), 'overlap_threshold': ssd_Param_2.get("bboxloss_overlap_threshold", 0.5), 'neg_overlap': ssd_Param_2.get("bboxloss_neg_overlap", 0.5), 'size_threshold': ssd_Param_2.get("bboxloss_size_threshold", 0.0001), 'do_neg_mining': ssd_Param_2.get("bboxloss_do_neg_mining", True), 'neg_pos_ratio': ssd_Param_2.get("bboxloss_neg_pos_ratio", 3), 'using_focus_loss': ssd_Param_2.get("bboxloss_using_focus_loss", False), 'gama': ssd_Param_2.get("bboxloss_focus_gama", 2), 'use_difficult_gt': ssd_Param_2.get("bboxloss_use_difficult_gt", False), 'code_type': ssd_Param_2.get("bboxloss_code_type", P.PriorBox.CENTER_SIZE), 'use_prior_for_matching': True, 'encode_variance_in_target': False, 'flag_noperson': ssd_Param_2.get('flag_noperson', False), } net["mbox_2_loss" + add_str] = L.DenseBBoxLoss(*mbox_2_layers, dense_bbox_loss_param=bboxloss_param, \ loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')), \ propagate_down=[True, True, False, False]) else: if ssd_Param_2.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.SOFTMAX: reshape_name = "mbox_2_conf_reshape" + add_str net[reshape_name] = L.Reshape(mbox_2_layers[1], \ shape=dict(dim=[0, -1, ssd_Param_2.get("num_classes", 2)])) softmax_name = "mbox_2_conf_softmax" + add_str net[softmax_name] = L.Softmax(net[reshape_name], axis=2) flatten_name = "mbox_2_conf_flatten" + add_str net[flatten_name] = L.Flatten(net[softmax_name], axis=1) mbox_2_layers[1] = net[flatten_name] elif ssd_Param_2.get("bboxloss_conf_loss_type", P.MultiBoxLoss.SOFTMAX) == P.MultiBoxLoss.LOGISTIC: sigmoid_name = "mbox_2_conf_sigmoid" + add_str net[sigmoid_name] = L.Sigmoid(mbox_2_layers[1]) mbox_2_layers[1] = net[sigmoid_name] else: raise ValueError("Unknown conf loss type.") det_out_param = { 'num_classes': ssd_Param_2.get("num_classes", 2), 'target_labels': ssd_Param_2.get('detout_target_labels', []), 'alias_id': ssd_Param_2.get("alias_id", 0), 'conf_threshold': ssd_Param_2.get("detout_conf_threshold", 0.01), 'nms_threshold': ssd_Param_2.get("detout_nms_threshold", 0.45), 'size_threshold': ssd_Param_2.get("detout_size_threshold", 0.0001), 'top_k': ssd_Param_2.get("detout_top_k", 30), 'share_location': True, 'code_type': P.PriorBox.CENTER_SIZE, 'background_label_id': 0, 'variance_encoded_in_target': False, } net["detection_out_2"] = L.DenseDetOut(*mbox_2_layers, \ detection_output_param=det_out_param, \ include=dict(phase=caffe_pb2.Phase.Value('TEST'))) ##Step8:Create evaluation part for subnet_16:9 eval_Param = get_eval_Param([1, 3]) det_eval_param = { 'gt_labels': eval_Param.get('eval_gt_labels', []), 'num_classes': eval_Param.get("eval_num_classes", 2), 'evaluate_difficult_gt': eval_Param.get("eval_difficult_gt", False), 'boxsize_threshold': eval_Param.get("eval_boxsize_threshold", [0, 0.01, 0.05, 0.1, 0.15, 0.2, 0.25]), 'iou_threshold': eval_Param.get("eval_iou_threshold", [0.9, 0.75, 0.5]), 'background_label_id': 0, } net["det_accu"] = L.DetEval(net["detection_out_2"], net[gt_label], \ detection_evaluate_param=det_eval_param, \ include=dict(phase=caffe_pb2.Phase.Value('TEST'))) return net def mPose_StageX_Train(net, from_layer="concat_stage1", out_layer="concat_stage2", stage=1, \ mask_vec="vec_mask", mask_heat="heat_mask",label_vec="vec_label", label_heat="heat_label", \ use_3_layers=5, use_1_layers=2, short_cut=True,base_layer="convf", lr=1.0, decay=1.0,num_channels = 128,flag_sigmoid = False, kernel_size=3,addstrs = '',flag_change_layer=False,flag_hasoutput=True,flag_hasloss=True,id_layer_until=0, relu_layer_until = False,loss_weight=1.0): kwargs = {'param': [dict(lr_mult=lr, decay_mult=decay), dict(lr_mult=2*lr, decay_mult=0)], 'weight_filler': dict(type='gaussian', std=0.01), 'bias_filler': dict(type='constant', value=0)} assert from_layer in net.keys() from1_layer = from_layer from2_layer = from_layer if use_1_layers > 0: numlayers = use_3_layers + 1 else: numlayers = use_3_layers for layer in range(1, numlayers): # vec if layer == numlayers - 1 and flag_change_layer: num_channels = 64 conv_vec = "stage{}_conv{}_vec".format(stage,layer) + addstrs net[conv_vec] = L.Convolution(net[from1_layer], num_output=num_channels, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs) # heat conv_heat = "stage{}_conv{}_heat".format(stage,layer) + addstrs net[conv_heat] = L.Convolution(net[from2_layer], num_output=num_channels, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs) if layer == id_layer_until: if relu_layer_until: relu_vec = "stage{}_relu{}_vec".format(stage, layer) net[relu_vec] = L.ReLU(net[conv_vec], in_place=True) relu_heat = "stage{}_relu{}_heat".format(stage,layer) net[relu_heat] = L.ReLU(net[conv_heat], in_place=True) return net else: return net else: relu_vec = "stage{}_relu{}_vec".format(stage, layer) net[relu_vec] = L.ReLU(net[conv_vec], in_place=True) from1_layer = relu_vec relu_heat = "stage{}_relu{}_heat".format(stage, layer) net[relu_heat] = L.ReLU(net[conv_heat], in_place=True) from2_layer = relu_heat if flag_hasoutput: if use_1_layers > 0: for layer in range(1, use_1_layers): # vec conv_vec = "stage{}_conv{}_vec".format(stage,use_3_layers+layer) + addstrs net[conv_vec] = L.Convolution(net[from1_layer], num_output=num_channels, pad=0, kernel_size=1, **kwargs) relu_vec = "stage{}_relu{}_vec".format(stage,use_3_layers+layer) + addstrs net[relu_vec] = L.ReLU(net[conv_vec], in_place=True) from1_layer = relu_vec # heat conv_heat = "stage{}_conv{}_heat".format(stage,use_3_layers+layer) + addstrs net[conv_heat] = L.Convolution(net[from2_layer], num_output=num_channels, pad=0, kernel_size=1, **kwargs) relu_heat = "stage{}_relu{}_heat".format(stage,use_3_layers+layer) + addstrs net[relu_heat] = L.ReLU(net[conv_heat], in_place=True) from2_layer = relu_heat # output conv_vec = "stage{}_conv{}_vec".format(stage,use_3_layers+use_1_layers) + addstrs net[conv_vec] = L.Convolution(net[from1_layer], num_output=34, pad=0, kernel_size=1, **kwargs) conv_heat = "stage{}_conv{}_heat".format(stage,use_3_layers+use_1_layers) + addstrs net[conv_heat] = L.Convolution(net[from2_layer], num_output=18, pad=0, kernel_size=1, **kwargs) else: # output by 3x3 if flag_change_layer: kernel_size = 3 conv_vec = "stage{}_conv{}_vec".format(stage,use_3_layers) + addstrs net[conv_vec] = L.Convolution(net[from1_layer], num_output=34, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs) if flag_sigmoid: conv_vec_sig = conv_vec + "_sig" net[conv_vec_sig] = L.Sigmoid(net[conv_vec]) conv_vec = conv_vec_sig conv_heat = "stage{}_conv{}_heat".format(stage,use_3_layers) + addstrs net[conv_heat] = L.Convolution(net[from2_layer], num_output=18, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs) if flag_sigmoid: conv_heat_sig = conv_heat + "_sig" net[conv_heat_sig] = L.Sigmoid(net[conv_heat]) conv_heat = conv_heat_sig if flag_hasloss: weight_vec = "weight_stage{}_vec".format(stage) weight_heat = "weight_stage{}_heat".format(stage) loss_vec = "loss_stage{}_vec".format(stage) loss_heat = "loss_stage{}_heat".format(stage) net[weight_vec] = L.Eltwise(net[conv_vec], net[mask_vec], eltwise_param=dict(operation=P.Eltwise.PROD)) net[loss_vec] = L.EuclideanLoss(net[weight_vec], net[label_vec], loss_weight=loss_weight) net[weight_heat] = L.Eltwise(net[conv_heat], net[mask_heat], eltwise_param=dict(operation=P.Eltwise.PROD)) net[loss_heat] = L.EuclideanLoss(net[weight_heat], net[label_heat], loss_weight=loss_weight) # 特征拼接 if short_cut: fea_layers = [] fea_layers.append(net[conv_vec]) fea_layers.append(net[conv_heat]) assert base_layer in net.keys() fea_layers.append(net[base_layer]) net[out_layer] = L.Concat(*fea_layers, axis=1) return net

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Python

agents/models.py

anindex/deepRL-projects

bed03d1f985c8340fc75f715028b632bdce40641

[ "MIT" ]

null

agents/models.py

anindex/deepRL-projects

bed03d1f985c8340fc75f715028b632bdce40641

[ "MIT" ]

null

agents/models.py

anindex/deepRL-projects

bed03d1f985c8340fc75f715028b632bdce40641

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F import numpy as np def hidden_init(layer): fan_in = layer.weight.data.size()[0] lim = 1. / np.sqrt(fan_in) return (-lim, lim) class FCQNetwork(nn.Module): """Fully connected DNN Q function which outputs array of action values""" def __init__(self, state_size, action_size, seed, fc1_units=64, fc2_units=64): """Initialize parameters and build model. Params ====== state_size (int): Dimension of each state action_size (int): Dimension of each action seed (int): Random seed fc1_units (int): Number of nodes in first hidden layer fc2_units (int): Number of nodes in second hidden layer """ super(FCQNetwork, self).__init__() self.seed = torch.manual_seed(seed) self.fc1 = nn.Linear(state_size, fc1_units) self.fc2 = nn.Linear(fc1_units, fc2_units) # action input from second fc layer self.fc3 = nn.Linear(fc2_units, action_size) def forward(self, state): """Build a network that maps state -> action values.""" x = F.relu(self.fc1(state)) x = F.relu(self.fc2(x)) return self.fc3(x) class CNNQNetwork(nn.Module): """CNN Q Function, which outputs array of action values""" def __init__(self, state_size, action_size, seed, conv1_filters=16, conv2_filters=16, conv3_filters=16, fc1_units=200, fc2_units=200): """Initialize parameters and build model. Params ====== state_size (list): Shape of each state image, e.g [3, 28, 28] action_size (int): Dimension of each action seed (int): Random seed conv1_filters (int): Number of filters for first CNN layer conv2_filters (int): Number of filters for second CNN layer conv3_filters (int): Number of filters for third CNN layer fc1_units (int): Number of nodes in first FC layer fc2_units (int): Number of nodes in second FC layer """ super(CNNQNetwork, self).__init__() self.seed = torch.manual_seed(seed) self.conv1 = nn.Conv2d(state_size[0], conv1_filters, 3, padding=1) self.conv2 = nn.Conv2d(conv1_filters, conv2_filters, 3, padding=1) self.conv3 = nn.Conv2d(conv2_filters, conv3_filters, 3, padding=1) self.fc1 = nn.Linear(conv3_filters*state_size[1]*state_size[2], fc1_units) # action input from first fc layer self.drop = nn.Dropout(p=0.4) self.fc2 = nn.Linear(fc1_units, fc2_units) self.fc3 = nn.Linear(fc2_units, action_size) def forward(self, state): """Build a network that maps state -> action values.""" x = F.relu(self.conv1(state)) x = F.relu(self.conv2(x)) x = F.relu(self.conv3(x)) x = x.view(x.size(0), -1) x = F.relu(self.fc1(x)) x = self.drop(x) x = F.relu(self.fc2(x)) return self.fc3(x) class FCCritic(nn.Module): """Fully connected DNN Critics Q Model.""" def __init__(self, state_size, action_size, seed, fc1_units=256, fc2_units=256, fc3_units=128): """Initialize parameters and build model. Params ====== state_size (int): Dimension of each state action_size (int): Dimension of each action seed (int): Random seed fc1_units (int): Number of nodes in first hidden layer fc2_units (int): Number of nodes in second hidden layer """ super(FCCritic, self).__init__() self.seed = torch.manual_seed(seed) self.fc1 = nn.Linear(state_size, fc1_units) self.fc2 = nn.Linear(fc1_units + action_size, fc2_units) # action input from second fc layer self.fc3 = nn.Linear(fc2_units, fc3_units) self.fc4 = nn.Linear(fc3_units, 1) self.reset_parameters() def reset_parameters(self): self.fc1.weight.data.uniform_(*hidden_init(self.fc1)) self.fc2.weight.data.uniform_(*hidden_init(self.fc2)) self.fc3.weight.data.uniform_(*hidden_init(self.fc3)) self.fc4.weight.data.uniform_(-3e-3, 3e-3) def forward(self, state, action): """Build a network that maps state -> action values.""" xs = F.leaky_relu(self.fc1(state)) x = torch.cat((xs, action), dim=1) x = F.leaky_relu(self.fc2(x)) x = F.leaky_relu(self.fc3(x)) return self.fc4(x) class CNNCritic(nn.Module): """CNN Critics Q Model. Implement based on DDPG paper 2016""" def __init__(self, state_size, action_size, seed, conv1_filters=32, conv2_filters=32, conv3_filters=32, fc1_units=256, fc2_units=256): """Initialize parameters and build model. Params ====== state_size (list): Shape of each state image, e.g [3, 28, 28] action_size (int): Dimension of each action seed (int): Random seed conv1_filters (int): Number of filters for first CNN layer conv2_filters (int): Number of filters for second CNN layer conv3_filters (int): Number of filters for third CNN layer fc1_units (int): Number of nodes in first FC layer fc2_units (int): Number of nodes in second FC layer """ super(CNNCritic, self).__init__() self.seed = torch.manual_seed(seed) self.conv1 = nn.Conv2d(state_size[0], conv1_filters, 3, padding=1) self.conv2 = nn.Conv2d(conv1_filters, conv2_filters, 3, padding=1) self.conv3 = nn.Conv2d(conv2_filters, conv3_filters, 3, padding=1) self.fc1 = nn.Linear(conv3_filters*state_size[1]*state_size[2] + action_size, fc1_units) # action input from first fc layer self.drop = nn.Dropout(p=0.4) self.fc2 = nn.Linear(fc1_units, fc2_units) self.fc3 = nn.Linear(fc2_units, 1) def forward(self, state, action): """Build a network that maps state -> action values.""" xs = F.relu(self.conv1(state)) xs = F.relu(self.conv2(xs)) xs = F.relu(self.conv3(xs)) xs = xs.view(x.size(0), -1) x = torch.cat((xs, action), dim=1) x = F.relu(self.fc1(x)) x = self.drop(x) x = F.relu(self.fc2(x)) return self.fc3(x) class FCPolicy(nn.Module): """Actor (Policy) Model.""" def __init__(self, state_size, action_size, seed, fc_units=256): """Initialize parameters and build model. Params ====== state_size (int): Dimension of each state action_size (int): Dimension of each action seed (int): Random seed fc1_units (int): Number of nodes in first hidden layer fc2_units (int): Number of nodes in second hidden layer """ super(FCPolicy, self).__init__() self.seed = torch.manual_seed(seed) self.fc1 = nn.Linear(state_size, fc_units) self.fc2 = nn.Linear(fc_units, action_size) self.reset_parameters() def reset_parameters(self): self.fc1.weight.data.uniform_(*hidden_init(self.fc1)) self.fc2.weight.data.uniform_(-3e-3, 3e-3) def forward(self, state): """Build an actor (policy) network that maps states -> actions.""" x = F.relu(self.fc1(state)) return F.tanh(self.fc2(x)) class CNNPolicy(nn.Module): """Actor (Policy) Model.""" def __init__(self, state_size, action_size, seed, conv1_filters=32, conv2_filters=32, conv3_filters=32, fc1_units=200, fc2_units=200): """Initialize parameters and build model. Params ====== state_size (list): Shape of each state image, e.g [3, 28, 28] action_size (int): Dimension of each action seed (int): Random seed conv1_filters (int): Number of filters for first CNN layer conv2_filters (int): Number of filters for second CNN layer conv3_filters (int): Number of filters for third CNN layer fc1_units (int): Number of nodes in first FC layer fc2_units (int): Number of nodes in second FC layer """ super(CNNPolicy, self).__init__() self.seed = torch.manual_seed(seed) self.conv1 = nn.Conv2d(state_size[0], conv1_filters, 3, padding=1) self.conv2 = nn.Conv2d(conv1_filters, conv2_filters, 3, padding=1) self.conv3 = nn.Conv2d(conv2_filters, conv3_filters, 3, padding=1) self.fc1 = nn.Linear(conv3_filters*state_size[1]*state_size[2], fc1_units) # action input from first fc layer self.drop = nn.Dropout(p=0.4) self.fc2 = nn.Linear(fc1_units, fc2_units) self.fc3 = nn.Linear(fc2_units, action_size) def forward(self, state): """Build an actor (policy) network that maps states -> actions.""" x = F.relu(self.conv1(state)) x = F.relu(self.conv2(x)) x = F.relu(self.conv3(x)) x = x.view(x.size(0), -1) x = F.relu(self.fc1(x)) x = self.drop(x) x = F.relu(self.fc2(x)) return F.softmax(self.fc3(x), dim=1) class MAFCPolicy(nn.Module) : r"""A simple deterministic policy network with batch norms Args: observationShape (tuple): shape of the observations given to the network actionShape (tuple): shape of the actions to be computed by the network """ def __init__(self, state_size, action_size, seed, fc1_units=256, fc2_units=128) : super(MAFCPolicy, self).__init__() self.seed = torch.manual_seed(seed) self.bn_input = nn.BatchNorm1d(state_size) self.fc1 = nn.Linear(state_size, fc1_units) self.bn_fc1 = nn.BatchNorm1d(fc1_units) self.fc2 = nn.Linear(fc1_units, fc2_units) self.bn_fc2 = nn.BatchNorm1d(fc2_units) self.fc3 = nn.Linear(fc2_units, action_size) self.reset_parameters() def reset_parameters(self) : self.fc1.weight.data.uniform_(*hidden_init(self.fc1)) self.fc2.weight.data.uniform_(*hidden_init(self.fc2)) self.fc3.weight.data.uniform_(-3e-3, 3e-3) def forward(self, state) : r"""Forward pass for this deterministic policy Args: state (torch.tensor): observation used to decide the action """ x = self.bn_input(state) x = F.relu(self.bn_fc1(self.fc1(x))) x = F.relu(self.bn_fc2(self.fc2(x))) return F.tanh(self.fc3(x)) class MAFCCritic(nn.Module) : r"""A simple critic Q-network with batch norm to be used for the centralized critics Args: joint_state_size (tuple): shape of the augmented state representation [o1,o2,...on] joint_action_size (tuple): shape of the augmented action representation [a1,a2,...,an] """ def __init__( self, joint_state_size, joint_action_size, seed, fc1_units=128, fc2_units=128) : super(MAFCCritic, self).__init__() self.seed = torch.manual_seed(seed) self.bn_input = nn.BatchNorm1d(joint_state_size) self.fc1 = nn.Linear(joint_state_size, fc1_units) self.fc2 = nn.Linear(fc1_units + joint_action_size, fc2_units) self.fc3 = nn.Linear(fc2_units, 1) self.reset_parameters() def reset_parameters(self) : self.fc1.weight.data.uniform_(*hidden_init(self.fc1)) self.fc2.weight.data.uniform_(*hidden_init(self.fc2)) self.fc3.weight.data.uniform_(-3e-3, 3e-3) def forward(self, joint_states, joint_actions) : r"""Forward pass for this critic at a given (x=[o1,...,an],a=[a1...an]) pair Args: joint_states (torch.tensor): augmented observation [o1,o2,...,on] joint_actions (torch.tensor): augmented action [a1,a2,...,an] """ xs = self.bn_input(joint_states) xs = F.relu(self.fc1(xs)) x = torch.cat([xs, joint_actions], dim=1) x = F.relu(self.fc2(x)) return self.fc3(x)

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6fc3e5508b4f0802cc3a81044d7f2b7c9afff5d9

2,613

py

Python

forge_api_client/projects.py

dmh126/forge-python-data-management-api

9c33f220021251a0340346065e3dd1998fc49a12

[ "MIT" ]

1

2019-07-02T08:32:22.000Z

forge_api_client/projects.py

dmh126/forge-python-data-management-api

9c33f220021251a0340346065e3dd1998fc49a12

[ "MIT" ]

null

forge_api_client/projects.py

dmh126/forge-python-data-management-api

9c33f220021251a0340346065e3dd1998fc49a12

[ "MIT" ]

2

2019-07-04T05:13:42.000Z

2020-05-09T22:15:05.000Z

from .utils import get_request, post_request, authorized class Projects: @authorized def getProjects(self, hub_id): url = self.api_url + '/project/v1/hubs/%s/projects' % hub_id headers = { 'Authorization': '%s %s' % (self.token_type, self.access_token) } return get_request(url, headers) @authorized def getProject(self, hub_id, project_id): url = self.api_url + '/project/v1/hubs/%s/projects/%s' % (hub_id, project_id) headers = { 'Authorization': '%s %s' % (self.token_type, self.access_token) } return get_request(url, headers) @authorized def getProjectHub(self, hub_id, project_id): url = self.api_url + '/project/v1/hubs/%s/projects/%s/hub' % (hub_id, project_id) headers = { 'Authorization': '%s %s' % (self.token_type, self.access_token) } return get_request(url, headers) @authorized def getTopFolders(self, hub_id, project_id): url = self.api_url + '/project/v1/hubs/%s/projects/%s/topFolders' % (hub_id, project_id) headers = { 'Authorization': '%s %s' % (self.token_type, self.access_token) } return get_request(url, headers) @authorized def getDownload(self, project_id, download_id): url = self.api_url + '/data/v1/projects/%s/downloads/%s' % (project_id, download_id) headers = { 'Authorization': '%s %s' % (self.token_type, self.access_token) } return get_request(url, headers) @authorized def getJobs(self, project_id, job_id): url = self.api_url + '/data/v1/projects/%s/jobs/%s' % (project_id, job_id) headers = { 'Authorization': '%s %s' % (self.token_type, self.access_token) } return get_request(url, headers) @authorized def postDownload(self, project_id, body): url = self.api_url + '/data/v1/projects/%s/downloads' % (project_id) headers = { 'Authorization': '%s %s' % (self.token_type, self.access_token), 'Content-Type': 'application/vnd.api+json' } data = body return post_request(url, data, headers) @authorized def postStorage(self, project_id, body): url = self.api_url + '/data/v1/projects/%s/storage' % (project_id) headers = { 'Authorization': '%s %s' % (self.token_type, self.access_token), 'Content-Type': 'application/vnd.api+json' } data = body return post_request(url, data, headers)

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6fcf9642fc5fe4ee0c1be8619cab8c162f326921

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py

Python

src/devstacks_devops_terraform/__init__.py

devstacks-dev/devops-terraform

440527c9b54a7709d57275983445efcbfa2f287f

[ "MIT" ]

null

src/devstacks_devops_terraform/__init__.py

devstacks-dev/devops-terraform

440527c9b54a7709d57275983445efcbfa2f287f

[ "MIT" ]

null

src/devstacks_devops_terraform/__init__.py

devstacks-dev/devops-terraform

440527c9b54a7709d57275983445efcbfa2f287f

[ "MIT" ]

null

from .generator import greetings

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0

1

0

1

0

6

d22d20c963b14b58d56a550692f47d82d0155e65

85

py

Python

myapi/tasks/example.py

trinanda/cookiecutter-flask-restful

126ebe8168c4a4c95eaf2bad81e6027ba2fe6b72

[ "MIT" ]

null

myapi/tasks/example.py

trinanda/cookiecutter-flask-restful

126ebe8168c4a4c95eaf2bad81e6027ba2fe6b72

[ "MIT" ]

1

2019-03-09T00:17:04.000Z

myapi/tasks/example.py

trinanda/cookiecutter-flask-restful

126ebe8168c4a4c95eaf2bad81e6027ba2fe6b72

[ "MIT" ]

null

from myapi.extensions import celery @celery.task def dummy_task(): return "OK"

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0

1

0

1

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6

d233dec1b30f4a8e85e6d54ea505355d0cdde812

35

py

Python

rankedlist/__init__.py

xingrongtech/RankedList

f2ffa427f689a2095752238feda49336c4b9fe2c

[ "MIT" ]

null

rankedlist/__init__.py

xingrongtech/RankedList

f2ffa427f689a2095752238feda49336c4b9fe2c

[ "MIT" ]

null

rankedlist/__init__.py

xingrongtech/RankedList

f2ffa427f689a2095752238feda49336c4b9fe2c

[ "MIT" ]

null

from .rankedlist import RankedList

17.5

34

0.857143

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7.5

0.75

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1

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6

d2587a986995ed912f1eed154d44a6518d16042d

44

py

Python

apps/oozie/src/oozie/views/__init__.py

digideskio/hortonworks-sandbox

dd8e95c91faee3daa094707baeb94c3953b41efa

[ "Apache-2.0" ]

19

2015-05-01T19:59:03.000Z

2021-12-09T08:03:16.000Z

apps/oozie/src/oozie/views/__init__.py

digideskio/hortonworks-sandbox

dd8e95c91faee3daa094707baeb94c3953b41efa

[ "Apache-2.0" ]

1

2018-01-03T15:26:49.000Z

apps/oozie/src/oozie/views/__init__.py

hortonworks/hortonworks-sandbox

dd8e95c91faee3daa094707baeb94c3953b41efa

[ "Apache-2.0" ]

30

2015-03-25T19:40:07.000Z

2021-05-28T22:59:26.000Z

from dashboard import * from editor import *

22

23

0.795455

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0.666667

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6

d25dc18cacc12af632c66ef562099866d2949cba

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py

Python

src/erwin/t1_map/__init__.py

lamyj/erwin

a2a7c945827a54c1e89dbedb31c82e34363bf7d1

[ "MIT" ]

2

2021-11-09T10:57:52.000Z

2022-02-18T09:55:42.000Z

src/erwin/t1_map/__init__.py

lamyj/erwin

a2a7c945827a54c1e89dbedb31c82e34363bf7d1

[ "MIT" ]

null

src/erwin/t1_map/__init__.py

lamyj/erwin

a2a7c945827a54c1e89dbedb31c82e34363bf7d1

[ "MIT" ]

null

""" T₁/R₁ mapping """ from .vfa import VFA

8.8

20

0.613636

7

44

3.857143

0.857143

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0.057143

0.204545

44

4

21

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0.714286

0.295455

0

1

0

true

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null

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0

1

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1

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6

d27af1dbe5a3ae87ff10c25d86fd49e8563d8e45

77

py

Python

deliravision/torch/models/gans/pixel_da/__init__.py

delira-dev/vision_torch

d944aa67d319bd63a2add5cb89e8308413943de6

[ "BSD-2-Clause" ]

4

2019-08-03T09:56:50.000Z

2019-09-05T09:32:06.000Z

deliravision/torch/models/gans/pixel_da/__init__.py

delira-dev/vision_torch

d944aa67d319bd63a2add5cb89e8308413943de6

[ "BSD-2-Clause" ]

23

2019-08-03T14:16:47.000Z

2019-10-22T10:15:10.000Z

deliravision/torch/models/gans/pixel_da/__init__.py

delira-dev/vision_torch

d944aa67d319bd63a2add5cb89e8308413943de6

[ "BSD-2-Clause" ]

null

from deliravision.models.gans.pixel_da.pixel_da import PixelDomainAdaptation

38.5

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1

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6

96351faa75c583ab0a7fda7f037b07c89953a0ab

48

py

Python

LibSerial7/__init__.py

jaemilton/LibSerial7

f483cd7474cd494281e384d2e74d65652ca581d6

[ "MIT" ]

null

LibSerial7/__init__.py

jaemilton/LibSerial7

f483cd7474cd494281e384d2e74d65652ca581d6

[ "MIT" ]

null

LibSerial7/__init__.py

jaemilton/LibSerial7

f483cd7474cd494281e384d2e74d65652ca581d6

[ "MIT" ]

null

from LibSerial7.LibSerial7 import Serial, Uart

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9653cf8a45a231964f80b1ed559a8192aeae2d9c

96

py

Python

venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_vars.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_vars.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_vars.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/22/40/84/e3c3548f23bbe01f30ab9e9cf756b205c886a9ecd1ffe0b6d8d16f3412

96

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96

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null

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968d4f71dbcb3a79e231f5cb9f7d498d449d09d8

124

py

Python

python/testData/deprecation/deprecatedImport.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/deprecation/deprecatedImport.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/deprecation/deprecatedImport.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

import <warning descr="the deprecated module is deprecated; use a non-deprecated module instead">deprecatedModule</warning>

62

123

0.822581

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0

1

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null

0

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1

0

6

969d634b194cdf37c7a28cd729f819bd809a449d

81,940

py

Python

sdk/python/pulumi_alicloud/cms/outputs.py

pulumi/pulumi-alicloud

9c34d84b4588a7c885c6bec1f03b5016e5a41683

[ "ECL-2.0", "Apache-2.0" ]

42

2019-03-18T06:34:37.000Z

2022-03-24T07:08:57.000Z

sdk/python/pulumi_alicloud/cms/outputs.py

pulumi/pulumi-alicloud

9c34d84b4588a7c885c6bec1f03b5016e5a41683

[ "ECL-2.0", "Apache-2.0" ]

152

2019-04-15T21:03:44.000Z

2022-03-29T18:00:57.000Z

sdk/python/pulumi_alicloud/cms/outputs.py

pulumi/pulumi-alicloud

9c34d84b4588a7c885c6bec1f03b5016e5a41683

[ "ECL-2.0", "Apache-2.0" ]

3

2020-08-26T17:30:07.000Z

2021-07-05T01:37:45.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** 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 . import outputs __all__ = [ 'AlarmEscalationsCritical', 'AlarmEscalationsInfo', 'AlarmEscalationsWarn', 'GroupMetricRuleEscalations', 'GroupMetricRuleEscalationsCritical', 'GroupMetricRuleEscalationsInfo', 'GroupMetricRuleEscalationsWarn', 'MetricRuleTemplateAlertTemplate', 'MetricRuleTemplateAlertTemplateEscalations', 'MetricRuleTemplateAlertTemplateEscalationsCritical', 'MetricRuleTemplateAlertTemplateEscalationsInfo', 'MetricRuleTemplateAlertTemplateEscalationsWarn', 'MonitorGroupInstancesInstance', 'SiteMonitorIspCity', 'GetAlarmContactGroupsGroupResult', 'GetAlarmContactsContactResult', 'GetGroupMetricRulesRuleResult', 'GetGroupMetricRulesRuleEscalationResult', 'GetGroupMetricRulesRuleEscalationCriticalResult', 'GetGroupMetricRulesRuleEscalationInfoResult', 'GetGroupMetricRulesRuleEscalationWarnResult', 'GetMetricRuleTemplatesTemplateResult', 'GetMetricRuleTemplatesTemplateAlertTemplateResult', 'GetMetricRuleTemplatesTemplateAlertTemplateEscalationResult', 'GetMetricRuleTemplatesTemplateAlertTemplateEscalationCriticalResult', 'GetMetricRuleTemplatesTemplateAlertTemplateEscalationInfoResult', 'GetMetricRuleTemplatesTemplateAlertTemplateEscalationWarnResult', 'GetMonitorGroupInstancesInstanceResult', 'GetMonitorGroupInstancesInstanceInstanceResult', 'GetMonitorGroupsGroupResult', ] @pulumi.output_type class AlarmEscalationsCritical(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "comparisonOperator": suggest = "comparison_operator" if suggest: pulumi.log.warn(f"Key '{key}' not found in AlarmEscalationsCritical. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: AlarmEscalationsCritical.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: AlarmEscalationsCritical.__key_warning(key) return super().get(key, default) def __init__(__self__, *, comparison_operator: Optional[str] = None, statistics: Optional[str] = None, threshold: Optional[str] = None, times: Optional[int] = None): """ :param str comparison_operator: Critical level alarm comparison operator. Valid values: ["<=", "<", ">", ">=", "==", "!="]. Default to "==". :param str statistics: Critical level alarm statistics method. It must be consistent with that defined for metrics. Valid values: ["Average", "Minimum", "Maximum", "Value", "ErrorCodeMaximum", "Sum", "Count"]. Default to "Average". :param str threshold: Critical level alarm threshold value, which must be a numeric value currently. :param int times: Critical level alarm retry times. Default to 3. """ if comparison_operator is not None: pulumi.set(__self__, "comparison_operator", comparison_operator) if statistics is not None: pulumi.set(__self__, "statistics", statistics) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if times is not None: pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> Optional[str]: """ Critical level alarm comparison operator. Valid values: ["<=", "<", ">", ">=", "==", "!="]. Default to "==". """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> Optional[str]: """ Critical level alarm statistics method. It must be consistent with that defined for metrics. Valid values: ["Average", "Minimum", "Maximum", "Value", "ErrorCodeMaximum", "Sum", "Count"]. Default to "Average". """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> Optional[str]: """ Critical level alarm threshold value, which must be a numeric value currently. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> Optional[int]: """ Critical level alarm retry times. Default to 3. """ return pulumi.get(self, "times") @pulumi.output_type class AlarmEscalationsInfo(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "comparisonOperator": suggest = "comparison_operator" if suggest: pulumi.log.warn(f"Key '{key}' not found in AlarmEscalationsInfo. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: AlarmEscalationsInfo.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: AlarmEscalationsInfo.__key_warning(key) return super().get(key, default) def __init__(__self__, *, comparison_operator: Optional[str] = None, statistics: Optional[str] = None, threshold: Optional[str] = None, times: Optional[int] = None): """ :param str comparison_operator: Critical level alarm comparison operator. Valid values: ["<=", "<", ">", ">=", "==", "!="]. Default to "==". :param str statistics: Critical level alarm statistics method. It must be consistent with that defined for metrics. Valid values: ["Average", "Minimum", "Maximum", "Value", "ErrorCodeMaximum", "Sum", "Count"]. Default to "Average". :param str threshold: Critical level alarm threshold value, which must be a numeric value currently. :param int times: Critical level alarm retry times. Default to 3. """ if comparison_operator is not None: pulumi.set(__self__, "comparison_operator", comparison_operator) if statistics is not None: pulumi.set(__self__, "statistics", statistics) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if times is not None: pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> Optional[str]: """ Critical level alarm comparison operator. Valid values: ["<=", "<", ">", ">=", "==", "!="]. Default to "==". """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> Optional[str]: """ Critical level alarm statistics method. It must be consistent with that defined for metrics. Valid values: ["Average", "Minimum", "Maximum", "Value", "ErrorCodeMaximum", "Sum", "Count"]. Default to "Average". """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> Optional[str]: """ Critical level alarm threshold value, which must be a numeric value currently. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> Optional[int]: """ Critical level alarm retry times. Default to 3. """ return pulumi.get(self, "times") @pulumi.output_type class AlarmEscalationsWarn(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "comparisonOperator": suggest = "comparison_operator" if suggest: pulumi.log.warn(f"Key '{key}' not found in AlarmEscalationsWarn. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: AlarmEscalationsWarn.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: AlarmEscalationsWarn.__key_warning(key) return super().get(key, default) def __init__(__self__, *, comparison_operator: Optional[str] = None, statistics: Optional[str] = None, threshold: Optional[str] = None, times: Optional[int] = None): """ :param str comparison_operator: Critical level alarm comparison operator. Valid values: ["<=", "<", ">", ">=", "==", "!="]. Default to "==". :param str statistics: Critical level alarm statistics method. It must be consistent with that defined for metrics. Valid values: ["Average", "Minimum", "Maximum", "Value", "ErrorCodeMaximum", "Sum", "Count"]. Default to "Average". :param str threshold: Critical level alarm threshold value, which must be a numeric value currently. :param int times: Critical level alarm retry times. Default to 3. """ if comparison_operator is not None: pulumi.set(__self__, "comparison_operator", comparison_operator) if statistics is not None: pulumi.set(__self__, "statistics", statistics) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if times is not None: pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> Optional[str]: """ Critical level alarm comparison operator. Valid values: ["<=", "<", ">", ">=", "==", "!="]. Default to "==". """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> Optional[str]: """ Critical level alarm statistics method. It must be consistent with that defined for metrics. Valid values: ["Average", "Minimum", "Maximum", "Value", "ErrorCodeMaximum", "Sum", "Count"]. Default to "Average". """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> Optional[str]: """ Critical level alarm threshold value, which must be a numeric value currently. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> Optional[int]: """ Critical level alarm retry times. Default to 3. """ return pulumi.get(self, "times") @pulumi.output_type class GroupMetricRuleEscalations(dict): def __init__(__self__, *, critical: Optional['outputs.GroupMetricRuleEscalationsCritical'] = None, info: Optional['outputs.GroupMetricRuleEscalationsInfo'] = None, warn: Optional['outputs.GroupMetricRuleEscalationsWarn'] = None): """ :param 'GroupMetricRuleEscalationsCriticalArgs' critical: The critical level. :param 'GroupMetricRuleEscalationsInfoArgs' info: The info level. :param 'GroupMetricRuleEscalationsWarnArgs' warn: The warn level. """ if critical is not None: pulumi.set(__self__, "critical", critical) if info is not None: pulumi.set(__self__, "info", info) if warn is not None: pulumi.set(__self__, "warn", warn) @property @pulumi.getter def critical(self) -> Optional['outputs.GroupMetricRuleEscalationsCritical']: """ The critical level. """ return pulumi.get(self, "critical") @property @pulumi.getter def info(self) -> Optional['outputs.GroupMetricRuleEscalationsInfo']: """ The info level. """ return pulumi.get(self, "info") @property @pulumi.getter def warn(self) -> Optional['outputs.GroupMetricRuleEscalationsWarn']: """ The warn level. """ return pulumi.get(self, "warn") @pulumi.output_type class GroupMetricRuleEscalationsCritical(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "comparisonOperator": suggest = "comparison_operator" if suggest: pulumi.log.warn(f"Key '{key}' not found in GroupMetricRuleEscalationsCritical. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: GroupMetricRuleEscalationsCritical.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: GroupMetricRuleEscalationsCritical.__key_warning(key) return super().get(key, default) def __init__(__self__, *, comparison_operator: Optional[str] = None, statistics: Optional[str] = None, threshold: Optional[str] = None, times: Optional[int] = None): """ :param str comparison_operator: The comparison operator of the threshold for warn-level alerts. :param str statistics: The statistical aggregation method for warn-level alerts. :param str threshold: The threshold for warn-level alerts. :param int times: The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ if comparison_operator is not None: pulumi.set(__self__, "comparison_operator", comparison_operator) if statistics is not None: pulumi.set(__self__, "statistics", statistics) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if times is not None: pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> Optional[str]: """ The comparison operator of the threshold for warn-level alerts. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> Optional[str]: """ The statistical aggregation method for warn-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> Optional[str]: """ The threshold for warn-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> Optional[int]: """ The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class GroupMetricRuleEscalationsInfo(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "comparisonOperator": suggest = "comparison_operator" if suggest: pulumi.log.warn(f"Key '{key}' not found in GroupMetricRuleEscalationsInfo. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: GroupMetricRuleEscalationsInfo.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: GroupMetricRuleEscalationsInfo.__key_warning(key) return super().get(key, default) def __init__(__self__, *, comparison_operator: Optional[str] = None, statistics: Optional[str] = None, threshold: Optional[str] = None, times: Optional[int] = None): """ :param str comparison_operator: The comparison operator of the threshold for warn-level alerts. :param str statistics: The statistical aggregation method for warn-level alerts. :param str threshold: The threshold for warn-level alerts. :param int times: The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ if comparison_operator is not None: pulumi.set(__self__, "comparison_operator", comparison_operator) if statistics is not None: pulumi.set(__self__, "statistics", statistics) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if times is not None: pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> Optional[str]: """ The comparison operator of the threshold for warn-level alerts. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> Optional[str]: """ The statistical aggregation method for warn-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> Optional[str]: """ The threshold for warn-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> Optional[int]: """ The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class GroupMetricRuleEscalationsWarn(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "comparisonOperator": suggest = "comparison_operator" if suggest: pulumi.log.warn(f"Key '{key}' not found in GroupMetricRuleEscalationsWarn. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: GroupMetricRuleEscalationsWarn.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: GroupMetricRuleEscalationsWarn.__key_warning(key) return super().get(key, default) def __init__(__self__, *, comparison_operator: Optional[str] = None, statistics: Optional[str] = None, threshold: Optional[str] = None, times: Optional[int] = None): """ :param str comparison_operator: The comparison operator of the threshold for warn-level alerts. :param str statistics: The statistical aggregation method for warn-level alerts. :param str threshold: The threshold for warn-level alerts. :param int times: The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ if comparison_operator is not None: pulumi.set(__self__, "comparison_operator", comparison_operator) if statistics is not None: pulumi.set(__self__, "statistics", statistics) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if times is not None: pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> Optional[str]: """ The comparison operator of the threshold for warn-level alerts. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> Optional[str]: """ The statistical aggregation method for warn-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> Optional[str]: """ The threshold for warn-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> Optional[int]: """ The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class MetricRuleTemplateAlertTemplate(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "metricName": suggest = "metric_name" elif key == "ruleName": suggest = "rule_name" if suggest: pulumi.log.warn(f"Key '{key}' not found in MetricRuleTemplateAlertTemplate. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: MetricRuleTemplateAlertTemplate.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: MetricRuleTemplateAlertTemplate.__key_warning(key) return super().get(key, default) def __init__(__self__, *, category: str, metric_name: str, namespace: str, rule_name: str, escalations: Optional['outputs.MetricRuleTemplateAlertTemplateEscalations'] = None, webhook: Optional[str] = None): """ :param str category: The abbreviation of the service name. Valid values: `ecs`, `rds`, `ads`, `slb`, `vpc`, `apigateway`, `cdn`, `cs`, `dcdn`, `ddos`, `eip`, `elasticsearch`, `emr`, `ess`, `hbase`, `iot_edge`, `kvstore_sharding`, `kvstore_splitrw`, `kvstore_standard`, `memcache`, `mns`, `mongodb`, `mongodb_cluster`, `mongodb_sharding`, `mq_topic`, `ocs`, `opensearch`, `oss`, `polardb`, `petadata`, `scdn`, `sharebandwidthpackages`, `sls`, `vpn`. :param str metric_name: The name of the metric. :param str namespace: The namespace of the service. :param str rule_name: The name of the alert rule. :param 'MetricRuleTemplateAlertTemplateEscalationsArgs' escalations: The information about the trigger condition based on the alert level. See the following `Block escalations`. :param str webhook: The callback URL to which a POST request is sent when an alert is triggered based on the alert rule. """ pulumi.set(__self__, "category", category) pulumi.set(__self__, "metric_name", metric_name) pulumi.set(__self__, "namespace", namespace) pulumi.set(__self__, "rule_name", rule_name) if escalations is not None: pulumi.set(__self__, "escalations", escalations) if webhook is not None: pulumi.set(__self__, "webhook", webhook) @property @pulumi.getter def category(self) -> str: """ The abbreviation of the service name. Valid values: `ecs`, `rds`, `ads`, `slb`, `vpc`, `apigateway`, `cdn`, `cs`, `dcdn`, `ddos`, `eip`, `elasticsearch`, `emr`, `ess`, `hbase`, `iot_edge`, `kvstore_sharding`, `kvstore_splitrw`, `kvstore_standard`, `memcache`, `mns`, `mongodb`, `mongodb_cluster`, `mongodb_sharding`, `mq_topic`, `ocs`, `opensearch`, `oss`, `polardb`, `petadata`, `scdn`, `sharebandwidthpackages`, `sls`, `vpn`. """ return pulumi.get(self, "category") @property @pulumi.getter(name="metricName") def metric_name(self) -> str: """ The name of the metric. """ return pulumi.get(self, "metric_name") @property @pulumi.getter def namespace(self) -> str: """ The namespace of the service. """ return pulumi.get(self, "namespace") @property @pulumi.getter(name="ruleName") def rule_name(self) -> str: """ The name of the alert rule. """ return pulumi.get(self, "rule_name") @property @pulumi.getter def escalations(self) -> Optional['outputs.MetricRuleTemplateAlertTemplateEscalations']: """ The information about the trigger condition based on the alert level. See the following `Block escalations`. """ return pulumi.get(self, "escalations") @property @pulumi.getter def webhook(self) -> Optional[str]: """ The callback URL to which a POST request is sent when an alert is triggered based on the alert rule. """ return pulumi.get(self, "webhook") @pulumi.output_type class MetricRuleTemplateAlertTemplateEscalations(dict): def __init__(__self__, *, critical: Optional['outputs.MetricRuleTemplateAlertTemplateEscalationsCritical'] = None, info: Optional['outputs.MetricRuleTemplateAlertTemplateEscalationsInfo'] = None, warn: Optional['outputs.MetricRuleTemplateAlertTemplateEscalationsWarn'] = None): """ :param 'MetricRuleTemplateAlertTemplateEscalationsCriticalArgs' critical: The condition for triggering critical-level alerts. See the following `Block critical`. :param 'MetricRuleTemplateAlertTemplateEscalationsInfoArgs' info: The condition for triggering info-level alerts. See the following `Block info`. :param 'MetricRuleTemplateAlertTemplateEscalationsWarnArgs' warn: The condition for triggering warn-level alerts. See the following `Block warn`. """ if critical is not None: pulumi.set(__self__, "critical", critical) if info is not None: pulumi.set(__self__, "info", info) if warn is not None: pulumi.set(__self__, "warn", warn) @property @pulumi.getter def critical(self) -> Optional['outputs.MetricRuleTemplateAlertTemplateEscalationsCritical']: """ The condition for triggering critical-level alerts. See the following `Block critical`. """ return pulumi.get(self, "critical") @property @pulumi.getter def info(self) -> Optional['outputs.MetricRuleTemplateAlertTemplateEscalationsInfo']: """ The condition for triggering info-level alerts. See the following `Block info`. """ return pulumi.get(self, "info") @property @pulumi.getter def warn(self) -> Optional['outputs.MetricRuleTemplateAlertTemplateEscalationsWarn']: """ The condition for triggering warn-level alerts. See the following `Block warn`. """ return pulumi.get(self, "warn") @pulumi.output_type class MetricRuleTemplateAlertTemplateEscalationsCritical(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "comparisonOperator": suggest = "comparison_operator" if suggest: pulumi.log.warn(f"Key '{key}' not found in MetricRuleTemplateAlertTemplateEscalationsCritical. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: MetricRuleTemplateAlertTemplateEscalationsCritical.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: MetricRuleTemplateAlertTemplateEscalationsCritical.__key_warning(key) return super().get(key, default) def __init__(__self__, *, comparison_operator: Optional[str] = None, statistics: Optional[str] = None, threshold: Optional[str] = None, times: Optional[str] = None): """ :param str comparison_operator: The comparison operator of the threshold for critical-level alerts. Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. :param str statistics: The statistical aggregation method for critical-level alerts. :param str threshold: The threshold for critical-level alerts. :param str times: The consecutive number of times for which the metric value is measured before a critical-level alert is triggered. """ if comparison_operator is not None: pulumi.set(__self__, "comparison_operator", comparison_operator) if statistics is not None: pulumi.set(__self__, "statistics", statistics) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if times is not None: pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> Optional[str]: """ The comparison operator of the threshold for critical-level alerts. Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> Optional[str]: """ The statistical aggregation method for critical-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> Optional[str]: """ The threshold for critical-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> Optional[str]: """ The consecutive number of times for which the metric value is measured before a critical-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class MetricRuleTemplateAlertTemplateEscalationsInfo(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "comparisonOperator": suggest = "comparison_operator" if suggest: pulumi.log.warn(f"Key '{key}' not found in MetricRuleTemplateAlertTemplateEscalationsInfo. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: MetricRuleTemplateAlertTemplateEscalationsInfo.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: MetricRuleTemplateAlertTemplateEscalationsInfo.__key_warning(key) return super().get(key, default) def __init__(__self__, *, comparison_operator: Optional[str] = None, statistics: Optional[str] = None, threshold: Optional[str] = None, times: Optional[str] = None): """ :param str comparison_operator: The comparison operator of the threshold for critical-level alerts. Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. :param str statistics: The statistical aggregation method for critical-level alerts. :param str threshold: The threshold for critical-level alerts. :param str times: The consecutive number of times for which the metric value is measured before a critical-level alert is triggered. """ if comparison_operator is not None: pulumi.set(__self__, "comparison_operator", comparison_operator) if statistics is not None: pulumi.set(__self__, "statistics", statistics) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if times is not None: pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> Optional[str]: """ The comparison operator of the threshold for critical-level alerts. Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> Optional[str]: """ The statistical aggregation method for critical-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> Optional[str]: """ The threshold for critical-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> Optional[str]: """ The consecutive number of times for which the metric value is measured before a critical-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class MetricRuleTemplateAlertTemplateEscalationsWarn(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "comparisonOperator": suggest = "comparison_operator" if suggest: pulumi.log.warn(f"Key '{key}' not found in MetricRuleTemplateAlertTemplateEscalationsWarn. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: MetricRuleTemplateAlertTemplateEscalationsWarn.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: MetricRuleTemplateAlertTemplateEscalationsWarn.__key_warning(key) return super().get(key, default) def __init__(__self__, *, comparison_operator: Optional[str] = None, statistics: Optional[str] = None, threshold: Optional[str] = None, times: Optional[str] = None): """ :param str comparison_operator: The comparison operator of the threshold for critical-level alerts. Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. :param str statistics: The statistical aggregation method for critical-level alerts. :param str threshold: The threshold for critical-level alerts. :param str times: The consecutive number of times for which the metric value is measured before a critical-level alert is triggered. """ if comparison_operator is not None: pulumi.set(__self__, "comparison_operator", comparison_operator) if statistics is not None: pulumi.set(__self__, "statistics", statistics) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if times is not None: pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> Optional[str]: """ The comparison operator of the threshold for critical-level alerts. Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> Optional[str]: """ The statistical aggregation method for critical-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> Optional[str]: """ The threshold for critical-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> Optional[str]: """ The consecutive number of times for which the metric value is measured before a critical-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class MonitorGroupInstancesInstance(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "instanceId": suggest = "instance_id" elif key == "instanceName": suggest = "instance_name" elif key == "regionId": suggest = "region_id" if suggest: pulumi.log.warn(f"Key '{key}' not found in MonitorGroupInstancesInstance. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: MonitorGroupInstancesInstance.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: MonitorGroupInstancesInstance.__key_warning(key) return super().get(key, default) def __init__(__self__, *, category: str, instance_id: str, instance_name: str, region_id: str): """ :param str category: The category of instance. :param str instance_id: The id of instance. :param str instance_name: The name of instance. :param str region_id: The region id of instance. """ pulumi.set(__self__, "category", category) pulumi.set(__self__, "instance_id", instance_id) pulumi.set(__self__, "instance_name", instance_name) pulumi.set(__self__, "region_id", region_id) @property @pulumi.getter def category(self) -> str: """ The category of instance. """ return pulumi.get(self, "category") @property @pulumi.getter(name="instanceId") def instance_id(self) -> str: """ The id of instance. """ return pulumi.get(self, "instance_id") @property @pulumi.getter(name="instanceName") def instance_name(self) -> str: """ The name of instance. """ return pulumi.get(self, "instance_name") @property @pulumi.getter(name="regionId") def region_id(self) -> str: """ The region id of instance. """ return pulumi.get(self, "region_id") @pulumi.output_type class SiteMonitorIspCity(dict): def __init__(__self__, *, city: str, isp: str): pulumi.set(__self__, "city", city) pulumi.set(__self__, "isp", isp) @property @pulumi.getter def city(self) -> str: return pulumi.get(self, "city") @property @pulumi.getter def isp(self) -> str: return pulumi.get(self, "isp") @pulumi.output_type class GetAlarmContactGroupsGroupResult(dict): def __init__(__self__, *, alarm_contact_group_name: str, contacts: Sequence[str], describe: str, enable_subscribed: bool, id: str): """ :param str alarm_contact_group_name: The name of Alarm Contact Group. :param Sequence[str] contacts: The alarm contacts in the alarm group. :param str describe: The description of the Alarm Group. :param bool enable_subscribed: Indicates whether the alarm group subscribes to weekly reports. :param str id: The ID of the CMS. """ pulumi.set(__self__, "alarm_contact_group_name", alarm_contact_group_name) pulumi.set(__self__, "contacts", contacts) pulumi.set(__self__, "describe", describe) pulumi.set(__self__, "enable_subscribed", enable_subscribed) pulumi.set(__self__, "id", id) @property @pulumi.getter(name="alarmContactGroupName") def alarm_contact_group_name(self) -> str: """ The name of Alarm Contact Group. """ return pulumi.get(self, "alarm_contact_group_name") @property @pulumi.getter def contacts(self) -> Sequence[str]: """ The alarm contacts in the alarm group. """ return pulumi.get(self, "contacts") @property @pulumi.getter def describe(self) -> str: """ The description of the Alarm Group. """ return pulumi.get(self, "describe") @property @pulumi.getter(name="enableSubscribed") def enable_subscribed(self) -> bool: """ Indicates whether the alarm group subscribes to weekly reports. """ return pulumi.get(self, "enable_subscribed") @property @pulumi.getter def id(self) -> str: """ The ID of the CMS. """ return pulumi.get(self, "id") @pulumi.output_type class GetAlarmContactsContactResult(dict): def __init__(__self__, *, alarm_contact_name: str, channels_aliim: str, channels_ding_web_hook: str, channels_mail: str, channels_sms: str, channels_state_aliim: str, channels_state_ding_web_hook: str, channels_state_mail: str, channels_status_sms: str, contact_groups: Sequence[str], describe: str, id: str, lang: str): """ :param str alarm_contact_name: The name of the alarm contact. :param str channels_aliim: The TradeManager ID of the alarm contact. :param str channels_ding_web_hook: The webhook URL of the DingTalk chatbot. :param str channels_mail: The email address of the alarm contact. :param str channels_sms: The phone number of the alarm contact. :param str channels_state_aliim: Indicates whether the TradeManager ID is valid. :param str channels_state_ding_web_hook: Indicates whether the DingTalk chatbot is normal. :param str channels_state_mail: The status of the email address. :param str channels_status_sms: The status of the phone number. :param Sequence[str] contact_groups: The alert groups to which the alarm contact is added. :param str describe: The description of the alarm contact. :param str id: The ID of the alarm contact. """ pulumi.set(__self__, "alarm_contact_name", alarm_contact_name) pulumi.set(__self__, "channels_aliim", channels_aliim) pulumi.set(__self__, "channels_ding_web_hook", channels_ding_web_hook) pulumi.set(__self__, "channels_mail", channels_mail) pulumi.set(__self__, "channels_sms", channels_sms) pulumi.set(__self__, "channels_state_aliim", channels_state_aliim) pulumi.set(__self__, "channels_state_ding_web_hook", channels_state_ding_web_hook) pulumi.set(__self__, "channels_state_mail", channels_state_mail) pulumi.set(__self__, "channels_status_sms", channels_status_sms) pulumi.set(__self__, "contact_groups", contact_groups) pulumi.set(__self__, "describe", describe) pulumi.set(__self__, "id", id) pulumi.set(__self__, "lang", lang) @property @pulumi.getter(name="alarmContactName") def alarm_contact_name(self) -> str: """ The name of the alarm contact. """ return pulumi.get(self, "alarm_contact_name") @property @pulumi.getter(name="channelsAliim") def channels_aliim(self) -> str: """ The TradeManager ID of the alarm contact. """ return pulumi.get(self, "channels_aliim") @property @pulumi.getter(name="channelsDingWebHook") def channels_ding_web_hook(self) -> str: """ The webhook URL of the DingTalk chatbot. """ return pulumi.get(self, "channels_ding_web_hook") @property @pulumi.getter(name="channelsMail") def channels_mail(self) -> str: """ The email address of the alarm contact. """ return pulumi.get(self, "channels_mail") @property @pulumi.getter(name="channelsSms") def channels_sms(self) -> str: """ The phone number of the alarm contact. """ return pulumi.get(self, "channels_sms") @property @pulumi.getter(name="channelsStateAliim") def channels_state_aliim(self) -> str: """ Indicates whether the TradeManager ID is valid. """ return pulumi.get(self, "channels_state_aliim") @property @pulumi.getter(name="channelsStateDingWebHook") def channels_state_ding_web_hook(self) -> str: """ Indicates whether the DingTalk chatbot is normal. """ return pulumi.get(self, "channels_state_ding_web_hook") @property @pulumi.getter(name="channelsStateMail") def channels_state_mail(self) -> str: """ The status of the email address. """ return pulumi.get(self, "channels_state_mail") @property @pulumi.getter(name="channelsStatusSms") def channels_status_sms(self) -> str: """ The status of the phone number. """ return pulumi.get(self, "channels_status_sms") @property @pulumi.getter(name="contactGroups") def contact_groups(self) -> Sequence[str]: """ The alert groups to which the alarm contact is added. """ return pulumi.get(self, "contact_groups") @property @pulumi.getter def describe(self) -> str: """ The description of the alarm contact. """ return pulumi.get(self, "describe") @property @pulumi.getter def id(self) -> str: """ The ID of the alarm contact. """ return pulumi.get(self, "id") @property @pulumi.getter def lang(self) -> str: return pulumi.get(self, "lang") @pulumi.output_type class GetGroupMetricRulesRuleResult(dict): def __init__(__self__, *, contact_groups: str, dimensions: str, effective_interval: str, email_subject: str, enable_state: bool, escalations: Sequence['outputs.GetGroupMetricRulesRuleEscalationResult'], group_id: str, group_metric_rule_name: str, id: str, metric_name: str, namespace: str, no_effective_interval: str, period: int, resources: str, rule_id: str, silence_time: int, source_type: str, status: str, webhook: str): """ :param str contact_groups: Alarm contact group. :param str dimensions: The dimensions that specify the resources to be associated with the alert rule. :param str effective_interval: The time period during which the alert rule is effective. :param str email_subject: The subject of the alert notification email. :param bool enable_state: Indicates whether the alert rule is enabled. :param Sequence['GetGroupMetricRulesRuleEscalationArgs'] escalations: Alarm level. :param str group_id: The ID of the application group. :param str group_metric_rule_name: The name of the alert rule. :param str id: The ID of the Group Metric Rule. :param str metric_name: The name of the metric. :param str namespace: The namespace of the service. :param str no_effective_interval: The time period during which the alert rule is ineffective. :param int period: The aggregation period of the monitoring data. Unit: seconds. The value is an integral multiple of 60. Default value: `300`. :param str resources: The resources that are associated with the alert rule. :param str rule_id: The ID of the alert rule. :param int silence_time: The mute period during which new alerts are not reported even if the alert trigger conditions are met. Unit: seconds. Default value: `86400`, which is equivalent to one day. :param str source_type: The type of the alert rule. The value is fixed to METRIC, indicating an alert rule for time series metrics. :param str status: The status of Group Metric Rule.. :param str webhook: The callback URL. """ pulumi.set(__self__, "contact_groups", contact_groups) pulumi.set(__self__, "dimensions", dimensions) pulumi.set(__self__, "effective_interval", effective_interval) pulumi.set(__self__, "email_subject", email_subject) pulumi.set(__self__, "enable_state", enable_state) pulumi.set(__self__, "escalations", escalations) pulumi.set(__self__, "group_id", group_id) pulumi.set(__self__, "group_metric_rule_name", group_metric_rule_name) pulumi.set(__self__, "id", id) pulumi.set(__self__, "metric_name", metric_name) pulumi.set(__self__, "namespace", namespace) pulumi.set(__self__, "no_effective_interval", no_effective_interval) pulumi.set(__self__, "period", period) pulumi.set(__self__, "resources", resources) pulumi.set(__self__, "rule_id", rule_id) pulumi.set(__self__, "silence_time", silence_time) pulumi.set(__self__, "source_type", source_type) pulumi.set(__self__, "status", status) pulumi.set(__self__, "webhook", webhook) @property @pulumi.getter(name="contactGroups") def contact_groups(self) -> str: """ Alarm contact group. """ return pulumi.get(self, "contact_groups") @property @pulumi.getter def dimensions(self) -> str: """ The dimensions that specify the resources to be associated with the alert rule. """ return pulumi.get(self, "dimensions") @property @pulumi.getter(name="effectiveInterval") def effective_interval(self) -> str: """ The time period during which the alert rule is effective. """ return pulumi.get(self, "effective_interval") @property @pulumi.getter(name="emailSubject") def email_subject(self) -> str: """ The subject of the alert notification email. """ return pulumi.get(self, "email_subject") @property @pulumi.getter(name="enableState") def enable_state(self) -> bool: """ Indicates whether the alert rule is enabled. """ return pulumi.get(self, "enable_state") @property @pulumi.getter def escalations(self) -> Sequence['outputs.GetGroupMetricRulesRuleEscalationResult']: """ Alarm level. """ return pulumi.get(self, "escalations") @property @pulumi.getter(name="groupId") def group_id(self) -> str: """ The ID of the application group. """ return pulumi.get(self, "group_id") @property @pulumi.getter(name="groupMetricRuleName") def group_metric_rule_name(self) -> str: """ The name of the alert rule. """ return pulumi.get(self, "group_metric_rule_name") @property @pulumi.getter def id(self) -> str: """ The ID of the Group Metric Rule. """ return pulumi.get(self, "id") @property @pulumi.getter(name="metricName") def metric_name(self) -> str: """ The name of the metric. """ return pulumi.get(self, "metric_name") @property @pulumi.getter def namespace(self) -> str: """ The namespace of the service. """ return pulumi.get(self, "namespace") @property @pulumi.getter(name="noEffectiveInterval") def no_effective_interval(self) -> str: """ The time period during which the alert rule is ineffective. """ return pulumi.get(self, "no_effective_interval") @property @pulumi.getter def period(self) -> int: """ The aggregation period of the monitoring data. Unit: seconds. The value is an integral multiple of 60. Default value: `300`. """ return pulumi.get(self, "period") @property @pulumi.getter def resources(self) -> str: """ The resources that are associated with the alert rule. """ return pulumi.get(self, "resources") @property @pulumi.getter(name="ruleId") def rule_id(self) -> str: """ The ID of the alert rule. """ return pulumi.get(self, "rule_id") @property @pulumi.getter(name="silenceTime") def silence_time(self) -> int: """ The mute period during which new alerts are not reported even if the alert trigger conditions are met. Unit: seconds. Default value: `86400`, which is equivalent to one day. """ return pulumi.get(self, "silence_time") @property @pulumi.getter(name="sourceType") def source_type(self) -> str: """ The type of the alert rule. The value is fixed to METRIC, indicating an alert rule for time series metrics. """ return pulumi.get(self, "source_type") @property @pulumi.getter def status(self) -> str: """ The status of Group Metric Rule.. """ return pulumi.get(self, "status") @property @pulumi.getter def webhook(self) -> str: """ The callback URL. """ return pulumi.get(self, "webhook") @pulumi.output_type class GetGroupMetricRulesRuleEscalationResult(dict): def __init__(__self__, *, criticals: Sequence['outputs.GetGroupMetricRulesRuleEscalationCriticalResult'], infos: Sequence['outputs.GetGroupMetricRulesRuleEscalationInfoResult'], warns: Sequence['outputs.GetGroupMetricRulesRuleEscalationWarnResult']): """ :param Sequence['GetGroupMetricRulesRuleEscalationCriticalArgs'] criticals: The critical level. :param Sequence['GetGroupMetricRulesRuleEscalationInfoArgs'] infos: The info level. :param Sequence['GetGroupMetricRulesRuleEscalationWarnArgs'] warns: The warn level. """ pulumi.set(__self__, "criticals", criticals) pulumi.set(__self__, "infos", infos) pulumi.set(__self__, "warns", warns) @property @pulumi.getter def criticals(self) -> Sequence['outputs.GetGroupMetricRulesRuleEscalationCriticalResult']: """ The critical level. """ return pulumi.get(self, "criticals") @property @pulumi.getter def infos(self) -> Sequence['outputs.GetGroupMetricRulesRuleEscalationInfoResult']: """ The info level. """ return pulumi.get(self, "infos") @property @pulumi.getter def warns(self) -> Sequence['outputs.GetGroupMetricRulesRuleEscalationWarnResult']: """ The warn level. """ return pulumi.get(self, "warns") @pulumi.output_type class GetGroupMetricRulesRuleEscalationCriticalResult(dict): def __init__(__self__, *, comparison_operator: str, statistics: str, threshold: str, times: int): """ :param str comparison_operator: The comparison operator of the threshold for warn-level alerts. :param str statistics: The statistical aggregation method for warn-level alerts. :param str threshold: The threshold for warn-level alerts. :param int times: The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ pulumi.set(__self__, "comparison_operator", comparison_operator) pulumi.set(__self__, "statistics", statistics) pulumi.set(__self__, "threshold", threshold) pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> str: """ The comparison operator of the threshold for warn-level alerts. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> str: """ The statistical aggregation method for warn-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> str: """ The threshold for warn-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> int: """ The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class GetGroupMetricRulesRuleEscalationInfoResult(dict): def __init__(__self__, *, comparison_operator: str, statistics: str, threshold: str, times: int): """ :param str comparison_operator: The comparison operator of the threshold for warn-level alerts. :param str statistics: The statistical aggregation method for warn-level alerts. :param str threshold: The threshold for warn-level alerts. :param int times: The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ pulumi.set(__self__, "comparison_operator", comparison_operator) pulumi.set(__self__, "statistics", statistics) pulumi.set(__self__, "threshold", threshold) pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> str: """ The comparison operator of the threshold for warn-level alerts. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> str: """ The statistical aggregation method for warn-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> str: """ The threshold for warn-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> int: """ The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class GetGroupMetricRulesRuleEscalationWarnResult(dict): def __init__(__self__, *, comparison_operator: str, statistics: str, threshold: str, times: int): """ :param str comparison_operator: The comparison operator of the threshold for warn-level alerts. :param str statistics: The statistical aggregation method for warn-level alerts. :param str threshold: The threshold for warn-level alerts. :param int times: The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ pulumi.set(__self__, "comparison_operator", comparison_operator) pulumi.set(__self__, "statistics", statistics) pulumi.set(__self__, "threshold", threshold) pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> str: """ The comparison operator of the threshold for warn-level alerts. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> str: """ The statistical aggregation method for warn-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> str: """ The threshold for warn-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> int: """ The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class GetMetricRuleTemplatesTemplateResult(dict): def __init__(__self__, *, alert_templates: Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateResult'], description: str, group_id: str, id: str, metric_rule_template_name: str, rest_version: str, template_id: str): """ :param Sequence['GetMetricRuleTemplatesTemplateAlertTemplateArgs'] alert_templates: The details of alert rules that are generated based on the alert template. :param str description: The description of the alert template. :param str group_id: GroupId. :param str id: The ID of the Metric Rule Template. :param str metric_rule_template_name: The name of the alert template. :param str rest_version: The version of the alert template. :param str template_id: The ID of the alert template. """ pulumi.set(__self__, "alert_templates", alert_templates) pulumi.set(__self__, "description", description) pulumi.set(__self__, "group_id", group_id) pulumi.set(__self__, "id", id) pulumi.set(__self__, "metric_rule_template_name", metric_rule_template_name) pulumi.set(__self__, "rest_version", rest_version) pulumi.set(__self__, "template_id", template_id) @property @pulumi.getter(name="alertTemplates") def alert_templates(self) -> Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateResult']: """ The details of alert rules that are generated based on the alert template. """ return pulumi.get(self, "alert_templates") @property @pulumi.getter def description(self) -> str: """ The description of the alert template. """ return pulumi.get(self, "description") @property @pulumi.getter(name="groupId") def group_id(self) -> str: """ GroupId. """ return pulumi.get(self, "group_id") @property @pulumi.getter def id(self) -> str: """ The ID of the Metric Rule Template. """ return pulumi.get(self, "id") @property @pulumi.getter(name="metricRuleTemplateName") def metric_rule_template_name(self) -> str: """ The name of the alert template. """ return pulumi.get(self, "metric_rule_template_name") @property @pulumi.getter(name="restVersion") def rest_version(self) -> str: """ The version of the alert template. """ return pulumi.get(self, "rest_version") @property @pulumi.getter(name="templateId") def template_id(self) -> str: """ The ID of the alert template. """ return pulumi.get(self, "template_id") @pulumi.output_type class GetMetricRuleTemplatesTemplateAlertTemplateResult(dict): def __init__(__self__, *, category: str, escalations: Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateEscalationResult'], metric_name: str, namespace: str, rule_name: str, selector: str, webhook: str): """ :param str category: The abbreviation of the service name. Valid values: `ecs`, `rds`, `ads`, `slb`, `vpc`, `apigateway`, `cdn`, `cs`, `dcdn`, `ddos`, `eip`, `elasticsearch`, `emr`, `ess`, `hbase`, `iot_edge`, `kvstore_sharding`, `kvstore_splitrw`, `kvstore_standard`, `memcache`, `mns`, `mongodb`, `mongodb_cluster`, `mongodb_sharding`, `mq_topic`, `ocs`, `opensearch`, `oss`, `polardb`, `petadata`, `scdn`, `sharebandwidthpackages`, `sls`, `vpn`. :param Sequence['GetMetricRuleTemplatesTemplateAlertTemplateEscalationArgs'] escalations: The information about the trigger condition based on the alert level. :param str metric_name: The name of the metric. :param str namespace: The namespace of the service. :param str rule_name: The name of the alert rule. :param str webhook: The callback URL to which a POST request is sent when an alert is triggered based on the alert rule. """ pulumi.set(__self__, "category", category) pulumi.set(__self__, "escalations", escalations) pulumi.set(__self__, "metric_name", metric_name) pulumi.set(__self__, "namespace", namespace) pulumi.set(__self__, "rule_name", rule_name) pulumi.set(__self__, "selector", selector) pulumi.set(__self__, "webhook", webhook) @property @pulumi.getter def category(self) -> str: """ The abbreviation of the service name. Valid values: `ecs`, `rds`, `ads`, `slb`, `vpc`, `apigateway`, `cdn`, `cs`, `dcdn`, `ddos`, `eip`, `elasticsearch`, `emr`, `ess`, `hbase`, `iot_edge`, `kvstore_sharding`, `kvstore_splitrw`, `kvstore_standard`, `memcache`, `mns`, `mongodb`, `mongodb_cluster`, `mongodb_sharding`, `mq_topic`, `ocs`, `opensearch`, `oss`, `polardb`, `petadata`, `scdn`, `sharebandwidthpackages`, `sls`, `vpn`. """ return pulumi.get(self, "category") @property @pulumi.getter def escalations(self) -> Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateEscalationResult']: """ The information about the trigger condition based on the alert level. """ return pulumi.get(self, "escalations") @property @pulumi.getter(name="metricName") def metric_name(self) -> str: """ The name of the metric. """ return pulumi.get(self, "metric_name") @property @pulumi.getter def namespace(self) -> str: """ The namespace of the service. """ return pulumi.get(self, "namespace") @property @pulumi.getter(name="ruleName") def rule_name(self) -> str: """ The name of the alert rule. """ return pulumi.get(self, "rule_name") @property @pulumi.getter def selector(self) -> str: return pulumi.get(self, "selector") @property @pulumi.getter def webhook(self) -> str: """ The callback URL to which a POST request is sent when an alert is triggered based on the alert rule. """ return pulumi.get(self, "webhook") @pulumi.output_type class GetMetricRuleTemplatesTemplateAlertTemplateEscalationResult(dict): def __init__(__self__, *, criticals: Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateEscalationCriticalResult'], infos: Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateEscalationInfoResult'], warns: Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateEscalationWarnResult']): """ :param Sequence['GetMetricRuleTemplatesTemplateAlertTemplateEscalationCriticalArgs'] criticals: The condition for triggering critical-level alerts. :param Sequence['GetMetricRuleTemplatesTemplateAlertTemplateEscalationInfoArgs'] infos: The condition for triggering info-level alerts. :param Sequence['GetMetricRuleTemplatesTemplateAlertTemplateEscalationWarnArgs'] warns: The condition for triggering warn-level alerts. """ pulumi.set(__self__, "criticals", criticals) pulumi.set(__self__, "infos", infos) pulumi.set(__self__, "warns", warns) @property @pulumi.getter def criticals(self) -> Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateEscalationCriticalResult']: """ The condition for triggering critical-level alerts. """ return pulumi.get(self, "criticals") @property @pulumi.getter def infos(self) -> Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateEscalationInfoResult']: """ The condition for triggering info-level alerts. """ return pulumi.get(self, "infos") @property @pulumi.getter def warns(self) -> Sequence['outputs.GetMetricRuleTemplatesTemplateAlertTemplateEscalationWarnResult']: """ The condition for triggering warn-level alerts. """ return pulumi.get(self, "warns") @pulumi.output_type class GetMetricRuleTemplatesTemplateAlertTemplateEscalationCriticalResult(dict): def __init__(__self__, *, comparison_operator: str, statistics: str, threshold: str, times: str): """ :param str comparison_operator: The comparison operator of the threshold for warn-level alerts.Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. :param str statistics: The statistical aggregation method for warn-level alerts. :param str threshold: The threshold for warn-level alerts. :param str times: The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ pulumi.set(__self__, "comparison_operator", comparison_operator) pulumi.set(__self__, "statistics", statistics) pulumi.set(__self__, "threshold", threshold) pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> str: """ The comparison operator of the threshold for warn-level alerts.Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> str: """ The statistical aggregation method for warn-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> str: """ The threshold for warn-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> str: """ The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class GetMetricRuleTemplatesTemplateAlertTemplateEscalationInfoResult(dict): def __init__(__self__, *, comparison_operator: str, statistics: str, threshold: str, times: str): """ :param str comparison_operator: The comparison operator of the threshold for warn-level alerts.Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. :param str statistics: The statistical aggregation method for warn-level alerts. :param str threshold: The threshold for warn-level alerts. :param str times: The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ pulumi.set(__self__, "comparison_operator", comparison_operator) pulumi.set(__self__, "statistics", statistics) pulumi.set(__self__, "threshold", threshold) pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> str: """ The comparison operator of the threshold for warn-level alerts.Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> str: """ The statistical aggregation method for warn-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> str: """ The threshold for warn-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> str: """ The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class GetMetricRuleTemplatesTemplateAlertTemplateEscalationWarnResult(dict): def __init__(__self__, *, comparison_operator: str, statistics: str, threshold: str, times: str): """ :param str comparison_operator: The comparison operator of the threshold for warn-level alerts.Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. :param str statistics: The statistical aggregation method for warn-level alerts. :param str threshold: The threshold for warn-level alerts. :param str times: The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ pulumi.set(__self__, "comparison_operator", comparison_operator) pulumi.set(__self__, "statistics", statistics) pulumi.set(__self__, "threshold", threshold) pulumi.set(__self__, "times", times) @property @pulumi.getter(name="comparisonOperator") def comparison_operator(self) -> str: """ The comparison operator of the threshold for warn-level alerts.Valid values: `GreaterThanOrEqualToThreshold`, `GreaterThanThreshold`, `LessThanOrEqualToThreshold`, `LessThanThreshold`, `NotEqualToThreshold`, `GreaterThanYesterday`, `LessThanYesterday`, `GreaterThanLastWeek`, `LessThanLastWeek`, `GreaterThanLastPeriod`, `LessThanLastPeriod`. """ return pulumi.get(self, "comparison_operator") @property @pulumi.getter def statistics(self) -> str: """ The statistical aggregation method for warn-level alerts. """ return pulumi.get(self, "statistics") @property @pulumi.getter def threshold(self) -> str: """ The threshold for warn-level alerts. """ return pulumi.get(self, "threshold") @property @pulumi.getter def times(self) -> str: """ The consecutive number of times for which the metric value is measured before a warn-level alert is triggered. """ return pulumi.get(self, "times") @pulumi.output_type class GetMonitorGroupInstancesInstanceResult(dict): def __init__(__self__, *, instances: Sequence['outputs.GetMonitorGroupInstancesInstanceInstanceResult']): pulumi.set(__self__, "instances", instances) @property @pulumi.getter def instances(self) -> Sequence['outputs.GetMonitorGroupInstancesInstanceInstanceResult']: return pulumi.get(self, "instances") @pulumi.output_type class GetMonitorGroupInstancesInstanceInstanceResult(dict): def __init__(__self__, *, category: str, instance_id: str, instance_name: str, region_id: str): pulumi.set(__self__, "category", category) pulumi.set(__self__, "instance_id", instance_id) pulumi.set(__self__, "instance_name", instance_name) pulumi.set(__self__, "region_id", region_id) @property @pulumi.getter def category(self) -> str: return pulumi.get(self, "category") @property @pulumi.getter(name="instanceId") def instance_id(self) -> str: return pulumi.get(self, "instance_id") @property @pulumi.getter(name="instanceName") def instance_name(self) -> str: return pulumi.get(self, "instance_name") @property @pulumi.getter(name="regionId") def region_id(self) -> str: return pulumi.get(self, "region_id") @pulumi.output_type class GetMonitorGroupsGroupResult(dict): def __init__(__self__, *, bind_url: str, contact_groups: Sequence[str], dynamic_tag_rule_id: str, gmt_create: int, gmt_modified: int, group_id: str, id: str, monitor_group_name: str, service_id: str, tags: Mapping[str, Any], template_ids: Sequence[str], type: str): """ :param str bind_url: The URL of the Kubernetes cluster from which the application group is synchronized. :param Sequence[str] contact_groups: The list of alert groups that receive alert notifications for the application group. :param str dynamic_tag_rule_id: The ID of the tag rule. :param int gmt_create: The time when the application group was created. :param int gmt_modified: The time when the application group was modified. :param str group_id: The ID of the application group. :param str id: The ID of the Monitor Group. :param str monitor_group_name: The name of the application group. :param str service_id: The ID of the Alibaba Cloud service. :param Mapping[str, Any] tags: A map of tags assigned to the Cms Monitor Group. :param Sequence[str] template_ids: The alert templates applied to the application group. :param str type: The type of the application group. """ pulumi.set(__self__, "bind_url", bind_url) pulumi.set(__self__, "contact_groups", contact_groups) pulumi.set(__self__, "dynamic_tag_rule_id", dynamic_tag_rule_id) pulumi.set(__self__, "gmt_create", gmt_create) pulumi.set(__self__, "gmt_modified", gmt_modified) pulumi.set(__self__, "group_id", group_id) pulumi.set(__self__, "id", id) pulumi.set(__self__, "monitor_group_name", monitor_group_name) pulumi.set(__self__, "service_id", service_id) pulumi.set(__self__, "tags", tags) pulumi.set(__self__, "template_ids", template_ids) pulumi.set(__self__, "type", type) @property @pulumi.getter(name="bindUrl") def bind_url(self) -> str: """ The URL of the Kubernetes cluster from which the application group is synchronized. """ return pulumi.get(self, "bind_url") @property @pulumi.getter(name="contactGroups") def contact_groups(self) -> Sequence[str]: """ The list of alert groups that receive alert notifications for the application group. """ return pulumi.get(self, "contact_groups") @property @pulumi.getter(name="dynamicTagRuleId") def dynamic_tag_rule_id(self) -> str: """ The ID of the tag rule. """ return pulumi.get(self, "dynamic_tag_rule_id") @property @pulumi.getter(name="gmtCreate") def gmt_create(self) -> int: """ The time when the application group was created. """ return pulumi.get(self, "gmt_create") @property @pulumi.getter(name="gmtModified") def gmt_modified(self) -> int: """ The time when the application group was modified. """ return pulumi.get(self, "gmt_modified") @property @pulumi.getter(name="groupId") def group_id(self) -> str: """ The ID of the application group. """ return pulumi.get(self, "group_id") @property @pulumi.getter def id(self) -> str: """ The ID of the Monitor Group. """ return pulumi.get(self, "id") @property @pulumi.getter(name="monitorGroupName") def monitor_group_name(self) -> str: """ The name of the application group. """ return pulumi.get(self, "monitor_group_name") @property @pulumi.getter(name="serviceId") def service_id(self) -> str: """ The ID of the Alibaba Cloud service. """ return pulumi.get(self, "service_id") @property @pulumi.getter def tags(self) -> Mapping[str, Any]: """ A map of tags assigned to the Cms Monitor Group. """ return pulumi.get(self, "tags") @property @pulumi.getter(name="templateIds") def template_ids(self) -> Sequence[str]: """ The alert templates applied to the application group. """ return pulumi.get(self, "template_ids") @property @pulumi.getter def type(self) -> str: """ The type of the application group. """ return pulumi.get(self, "type")

38.505639

456

0.640896

8,421

81,940

6.045719

0.045482

0.022412

0.038813

0.056726

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0.766239

0.743513

0.69374

0.67235

0.654004

0

0.000443

0.256322

81,940

2,127

457

38.523742

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false

0

0.004866

0.007299

0.344688

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1

0

1

0

1

0

null

0

6

7383605c0462c57365d6f53de4e69dc1d56d2a98

3,669

py

Python

scivision_plankton_models/model.py

acocac/scivision-plankton-torch

9d382b7ed95bd051fcf1cd90d82966202b26d659

[ "BSD-3-Clause" ]

null

scivision_plankton_models/model.py

acocac/scivision-plankton-torch

9d382b7ed95bd051fcf1cd90d82966202b26d659

[ "BSD-3-Clause" ]

null

scivision_plankton_models/model.py

acocac/scivision-plankton-torch

9d382b7ed95bd051fcf1cd90d82966202b26d659

[ "BSD-3-Clause" ]

null

import numpy as np import torch import torchvision import pickle class resnet50_label1: def __init__(self): # preload the pretrained model model = torchvision.models.resnet50(pretrained=True) num_ftrs = model.fc.in_features target_classes = 2 model.fc = torch.nn.Linear(num_ftrs, target_classes) # replace default weights by the fine-tune model model.load_state_dict(torch.load(f'/output/models/resnet50/resnet50_label1_001.pth', map_location=torch.device('cpu'))) # path of your weights #initialise the model in evaluation mode self.pretrained_model = model self.pretrained_model.eval() def predict(self, image: np.ndarray) -> np.ndarray: batch = torch.tensor(image) y = self.pretrained_model(batch) return y class resnet50_label2: def __init__(self): # preload the pretrained model model = torchvision.models.resnet50(pretrained=True) num_ftrs = model.fc.in_features target_classes = 3 model.fc = torch.nn.Linear(num_ftrs, target_classes) # replace default weights by the fine-tune model model.load_state_dict(torch.load(f'/output/models/resnet50/resnet50_label2_001.pth', map_location=torch.device('cpu'))) # path of your weights #initialise the model in evaluation mode self.pretrained_model = model self.pretrained_model.eval() def predict(self, image: np.ndarray) -> np.ndarray: batch = torch.tensor(image) y = self.pretrained_model(batch) return y class resnet50_label3: def __init__(self): # preload the pretrained model model = torchvision.models.resnet50(pretrained=True) num_ftrs = model.fc.in_features target_classes = 39 model.fc = torch.nn.Linear(num_ftrs, target_classes) # replace default weights by the fine-tune model model.load_state_dict(torch.load(f'/output/models/resnet50/resnet50_label3_001.pth', map_location=torch.device('cpu'))) # path of your weights #initialise the model in evaluation mode self.pretrained_model = model self.pretrained_model.eval() def predict(self, image: np.ndarray) -> np.ndarray: batch = torch.tensor(image) y = self.pretrained_model(batch) return y class randomforest_label1: def __init__(self): with open(f"/output/models/randomforest/rf-label1.pkl", 'rb') as f: rf_model = pickle.load(f) self.pretrained_model = rf_model # def features(self, image: np.ndarray) -> np.ndarray: # self.img_features = 'test' def predict(self, image: np.ndarray) -> np.ndarray: y = self.pretrained_model.predict(image) return y class randomforest_label2: def __init__(self): with open(f"/output/models/randomforest/rf-label2.pkl", 'rb') as f: rf_model = pickle.load(f) self.pretrained_model = rf_model # def features(self, image: np.ndarray) -> np.ndarray: # self.img_features = 'test' def predict(self, image: np.ndarray) -> np.ndarray: y = self.pretrained_model.predict(image) return y class randomforest_label3: def __init__(self): with open(f"/output/models/randomforest/rf-label3.pkl", 'rb') as f: rf_model = pickle.load(f) self.pretrained_model = rf_model # def features(self, image: np.ndarray) -> np.ndarray: # self.img_features = 'test' def predict(self, image: np.ndarray) -> np.ndarray: y = self.pretrained_model.predict(image) return y if __name__ == "__main__": pass

34.613208

151

0.663123

476

3,669

4.918067

0.159664

0.115335

0.121743

0.069201

0.927381

0

0.017475

0.235759

3,669

106

152

34.613208

0.817404

0.179613

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0.695652

0

0.095922

0.088235

0

1

0.173913

false

0.014493

0.057971

0

0.405797

0

null

0

1

0

null

0

6

7387850975897cf1ca8c2626ab811840531d9e95

37

py

Python

GmailWrapper_JE/je_gmail/__init__.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

2

2020-12-30T06:37:10.000Z

2020-12-30T07:27:45.000Z

GmailWrapper_JE/je_gmail/__init__.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

GmailWrapper_JE/je_gmail/__init__.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

from je_gmail.core import GmailCore

18.5

36

0.837838

6

37

5

1

0

0.135135

37

1

37

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

73986838dc338f097b909f6c861e928a3573dbd4

229

py

Python

Daraja/mpesa_integration/keys.py

DelivceNdegwa/Daraja-

6d46ae3b57c3fe3e91877578db7b975d2b0fd1a3

[ "MIT" ]

null

Daraja/mpesa_integration/keys.py

DelivceNdegwa/Daraja-

6d46ae3b57c3fe3e91877578db7b975d2b0fd1a3

[ "MIT" ]

null

Daraja/mpesa_integration/keys.py

DelivceNdegwa/Daraja-

6d46ae3b57c3fe3e91877578db7b975d2b0fd1a3

[ "MIT" ]

null

shortcode = "174379" lipa_na_mpesa_pass_key = "bfb279f9aa9bdbcf158e97dd71a467cd2e0c893059b10f78e6b72ada1ed2c91" phone_number = "254711994966" consumer_key = "aGd3WKTtLpL9AsvGkfi6MaLNqE5H1mCA" consumer_secret = "JBqhnOTuxftnTe2E"

38.166667

90

0.868996

17

229

11.294118

0.882353

0

0.271028

0.065502

229

5

91

45.8

0.626168

0

0.563319

0.414847

0

1

0

false

0.2

0

1

0

1

null

0

1

0

1

0

1

null

0

1

0

6

73b5506c8d4f24e33676da44a510e9e0c0a8215c

8,947

py

Python

testsuite/test_resolver.py

ronaldoussoren/objc_asyncio

89e573fd4c95592515ea8c8a4abfeebdd261fde2

[ "MIT-0" ]

2

2021-02-20T22:10:54.000Z

2021-03-26T21:45:06.000Z

testsuite/test_resolver.py

ronaldoussoren/objc_asyncio

89e573fd4c95592515ea8c8a4abfeebdd261fde2

[ "MIT-0" ]

null

testsuite/test_resolver.py

ronaldoussoren/objc_asyncio

89e573fd4c95592515ea8c8a4abfeebdd261fde2

[ "MIT-0" ]

null

import socket import unittest import unittest.mock from . import utils class TestResolver(utils.TestCase): def test_getaddrinfo(self): for dom, port in (("blog.ronaldoussoren.net", 80), ("www.python.org", "https")): with self.subTest(dom=dom, family="*"): infos = self.loop.run_until_complete(self.loop.getaddrinfo(dom, port)) self.assertEqual(set(infos), set(socket.getaddrinfo(dom, port))) for dom, port in (("blog.ronaldoussoren.net", 80), ("www.python.org", "https")): with self.subTest(dom=dom, family="IPv4"): infos = self.loop.run_until_complete( self.loop.getaddrinfo(dom, port, family=socket.AF_INET) ) self.assertEqual( set(infos), set(socket.getaddrinfo(dom, port, family=socket.AF_INET)), ) for dom, port in (("blog.ronaldoussoren.net", 80), ("www.python.org", "https")): with self.subTest(dom=dom, proto="STREAM"): infos = self.loop.run_until_complete( self.loop.getaddrinfo(dom, port, proto=socket.SOCK_STREAM) ) self.assertEqual( set(infos), set(socket.getaddrinfo(dom, port, proto=socket.SOCK_STREAM)), ) def test_getaddrinfo_debug(self): with utils.captured_log() as stream: self.loop.set_debug(True) for dom, port in ( ("blog.ronaldoussoren.net", 80), ("www.python.org", "https"), ): with self.subTest(dom=dom, family="*"): stream.seek(0) stream.truncate() infos = self.loop.run_until_complete( self.loop.getaddrinfo(dom, port) ) self.assertEqual(set(infos), set(socket.getaddrinfo(dom, port))) contents = stream.getvalue() self.assertIn("Get address info", contents) self.assertIn("Getting address info", contents) self.assertNotIn("family=", contents) self.assertNotIn("type=", contents) self.assertNotIn("proto=", contents) self.assertNotIn("flags=", contents) self.assertIn("DEBUG", contents) for dom, port in ( ("blog.ronaldoussoren.net", 80), ("www.python.org", "https"), ): with self.subTest(dom=dom, family="IPv4"): stream.seek(0) stream.truncate() infos = self.loop.run_until_complete( self.loop.getaddrinfo(dom, port, family=socket.AF_INET) ) self.assertEqual( set(infos), set(socket.getaddrinfo(dom, port, family=socket.AF_INET)), ) contents = stream.getvalue() self.assertIn("family=", contents) self.assertNotIn("type=", contents) self.assertNotIn("proto=", contents) self.assertNotIn("flags=", contents) for dom, port in ( ("blog.ronaldoussoren.net", 80), ("www.python.org", "https"), ): with self.subTest(dom=dom, type="STREAM"): stream.seek(0) stream.truncate() infos = self.loop.run_until_complete( self.loop.getaddrinfo(dom, port, type=socket.SOCK_STREAM) ) self.assertEqual( set(infos), set(socket.getaddrinfo(dom, port, type=socket.SOCK_STREAM)), ) contents = stream.getvalue() self.assertNotIn("family=", contents) self.assertIn("type=", contents) self.assertNotIn("proto=", contents) self.assertNotIn("flags=", contents) for dom, port in ( ("blog.ronaldoussoren.net", 80), ("www.python.org", "https"), ): with self.subTest(dom=dom, proto="TCP"): stream.seek(0) stream.truncate() infos = self.loop.run_until_complete( self.loop.getaddrinfo( dom, port, type=socket.SOCK_STREAM, proto=socket.IPPROTO_TCP ) ) self.assertEqual( set(infos), set( socket.getaddrinfo( dom, port, type=socket.SOCK_STREAM, proto=socket.IPPROTO_TCP, ) ), ) contents = stream.getvalue() self.assertNotIn("family=", contents) self.assertIn("type=", contents) self.assertIn("proto=", contents) self.assertNotIn("flags=", contents) for dom, port in ( ("blog.ronaldoussoren.net", 80), ("www.python.org", "https"), ): with self.subTest(dom=dom, flags="AI_CANONNAME"): stream.seek(0) stream.truncate() infos = self.loop.run_until_complete( self.loop.getaddrinfo(dom, port, flags=socket.AI_CANONNAME) ) self.assertEqual( set(infos), set(socket.getaddrinfo(dom, port, flags=socket.AI_CANONNAME)), ) contents = stream.getvalue() self.assertNotIn("family=", contents) self.assertNotIn("type=", contents) self.assertNotIn("proto=", contents) self.assertIn("flags=", contents) with self.subTest("Resolving error"): stream.seek(0) stream.truncate() awaitable = self.loop.getaddrinfo("nosuchhost.python.org", 443) with self.assertRaises(socket.error): self.loop.run_until_complete(awaitable) contents = stream.getvalue() self.assertIn("Getting address info", contents) self.assertIn("failed in", contents) self.assertIn("DEBUG", contents) # Check that slow queries get logged at INFO level by (crudely) # mocking a slow clock. with self.subTest("Slow resolver"): with unittest.mock.patch( "objc_asyncio.PyObjCEventLoop.time", side_effect=list(range(1000)) ): stream.seek(0) stream.truncate() awaitable = self.loop.getaddrinfo("www.python.org", 443) self.loop.run_until_complete(awaitable) contents = stream.getvalue() self.assertIn("INFO", contents) with self.subTest("Slow resolver"): with unittest.mock.patch( "objc_asyncio.PyObjCEventLoop.time", side_effect=list(range(1000)) ): stream.seek(0) stream.truncate() awaitable = self.loop.getaddrinfo("nosuchhost.python.org", 443) with self.assertRaises(socket.error): self.loop.run_until_complete(awaitable) contents = stream.getvalue() self.assertIn("INFO", contents) def test_getaddrinfo_no_such_addr(self): awaitable = self.loop.getaddrinfo("nosuchhost.python.org", 443) with self.assertRaises(socket.error): self.loop.run_until_complete(awaitable) self.loop.set_debug(True) awaitable = self.loop.getaddrinfo("nosuchhost.python.org", 443) with self.assertRaises(socket.error): self.loop.run_until_complete(awaitable) def test_getnameinfo(self): infos = socket.getaddrinfo( "blog.ronaldoussoren.net", 80, proto=socket.SOCK_STREAM ) self.assertNotEqual(infos, []) for flags in (0, socket.NI_NOFQDN): for info in infos: result = self.loop.run_until_complete( self.loop.getnameinfo(info[-1], flags) ) self.assertEqual(result, socket.getnameinfo(info[-1], flags))

41.613953

88

0.483067

791

8,947

5.388116

0.128951

0.056312

0.067574

0.052557

0.86321

0.813937

0.77405

0.768888

0.757156

0

0.010221

0.409523

8,947

214

89

41.808411

0.796517

0.009277

0

0.659459

0

0.090735

0.040289

0

0.227027

1

0.021622

false

0

0.021622

0

0.048649

0

null

0

1

0

null

0

6

fb50de40d27619dedc16d39d5566afd759239db8

26,107

py

Python

morepath/tests/test_directive.py

hugovk/morepath

5596f9ce43ee4e5cd73eaa2ab9ef37825f88ae28

[ "BSD-3-Clause" ]

null

morepath/tests/test_directive.py

hugovk/morepath

5596f9ce43ee4e5cd73eaa2ab9ef37825f88ae28

[ "BSD-3-Clause" ]

null

morepath/tests/test_directive.py

hugovk/morepath

5596f9ce43ee4e5cd73eaa2ab9ef37825f88ae28

[ "BSD-3-Clause" ]

null

import dectate from .fixtures import ( basic, nested, abbr, mapply_bug, method, conflict, noconverter, ) from dectate import ConflictError, DirectiveReportError from morepath.error import LinkError from morepath.view import render_html from morepath.converter import Converter import morepath import reg import pytest from webtest import TestApp as Client def test_basic(): c = Client(basic.app()) response = c.get("/foo") assert response.body == b"The view for model: foo" response = c.get("/foo/link") assert response.body == b"http://localhost/foo" def test_basic_json(): c = Client(basic.app()) response = c.get("/foo/json") assert response.body == b'{"id":"foo"}' def test_basic_root(): c = Client(basic.app()) response = c.get("/") assert response.body == b"The root: ROOT" # + is to make sure we get the view, not the sub-model as # the model is greedy response = c.get("/+link") assert response.body == b"http://localhost/" def test_nested(): c = Client(nested.outer_app()) response = c.get("/inner/foo") assert response.body == b"The view for model: foo" response = c.get("/inner/foo/link") assert response.body == b"http://localhost/inner/foo" def test_abbr(): c = Client(abbr.app()) response = c.get("/foo") assert response.body == b"Default view: foo" response = c.get("/foo/edit") assert response.body == b"Edit view: foo" def test_scanned_static_method(): c = Client(method.app()) response = c.get("/static") assert response.body == b"Static Method" root = method.Root() assert isinstance(root.static_method(), method.StaticMethod) def test_scanned_no_converter(): with pytest.raises(DirectiveReportError): noconverter.app.commit() def test_scanned_conflict(): with pytest.raises(ConflictError): conflict.app.commit() def test_basic_scenario(): class app(morepath.App): pass @app.path(path="") class Root(object): def __init__(self): self.value = "ROOT" class Model(object): def __init__(self, id): self.id = id @app.path(model=Model, path="{id}") def get_model(id): return Model(id) @app.view(model=Model) def default(self, request): return "The view for model: %s" % self.id @app.view(model=Model, name="link") def link(self, request): return request.link(self) @app.view(model=Model, name="json", render=morepath.render_json) def json(self, request): return {"id": self.id} @app.view(model=Root) def root_default(self, request): return "The root: %s" % self.value @app.view(model=Root, name="link") def root_link(self, request): return request.link(self) c = Client(app()) response = c.get("/foo") assert response.body == b"The view for model: foo" response = c.get("/foo/link") assert response.body == b"http://localhost/foo" response = c.get("/foo/json") assert response.body == b'{"id":"foo"}' response = c.get("/") assert response.body == b"The root: ROOT" # + is to make sure we get the view, not the sub-model response = c.get("/+link") assert response.body == b"http://localhost/" def test_link_to_unknown_model(): class app(morepath.App): pass @app.path(path="") class Root(object): def __init__(self): self.value = "ROOT" class Model(object): def __init__(self, id): self.id = id @app.view(model=Root) def root_link(self, request): try: return request.link(Model("foo")) except LinkError: return "Link error" @app.view(model=Root, name="default") def root_link_with_default(self, request): try: return request.link(Model("foo"), default="hey") except LinkError: return "Link Error" c = Client(app()) response = c.get("/") assert response.body == b"Link error" response = c.get("/default") assert response.body == b"Link Error" def test_link_to_none(): class app(morepath.App): pass @app.path(path="") class Root(object): def __init__(self): self.value = "ROOT" class Model(object): def __init__(self, id): self.id = id @app.view(model=Root) def root_link(self, request): return str(request.link(None) is None) @app.view(model=Root, name="default") def root_link_with_default(self, request): return request.link(None, default="unknown") c = Client(app()) response = c.get("/") assert response.body == b"True" response = c.get("/default") assert response.body == b"unknown" def test_link_with_parameters(): class app(morepath.App): pass @app.path(path="") class Root(object): def __init__(self): self.value = "ROOT" class Model(object): def __init__(self, id, param): self.id = id self.param = param @app.path(model=Model, path="{id}") def get_model(id, param=0): assert isinstance(param, int) return Model(id, param) @app.view(model=Model) def default(self, request): return "The view for model: %s %s" % (self.id, self.param) @app.view(model=Model, name="link") def link(self, request): return request.link(self) c = Client(app()) response = c.get("/foo") assert response.body == b"The view for model: foo 0" response = c.get("/foo/link") assert response.body == b"http://localhost/foo?param=0" response = c.get("/foo?param=1") assert response.body == b"The view for model: foo 1" response = c.get("/foo/link?param=1") assert response.body == b"http://localhost/foo?param=1" def test_root_link_with_parameters(): class app(morepath.App): pass @app.path(path="") class Root(object): def __init__(self, param=0): assert isinstance(param, int) self.param = param @app.view(model=Root) def default(self, request): return "The view for root: %s" % self.param @app.view(model=Root, name="link") def link(self, request): return request.link(self) c = Client(app()) response = c.get("/") assert response.body == b"The view for root: 0" response = c.get("/link") assert response.body == b"http://localhost/?param=0" response = c.get("/?param=1") assert response.body == b"The view for root: 1" response = c.get("/link?param=1") assert response.body == b"http://localhost/?param=1" def test_link_with_prefix(): class app(morepath.App): pass @app.path(path="") class Root(object): pass @app.view(model=Root, name="link") def link(self, request): return request.link(self) @app.link_prefix() def link_prefix(request): return request.headers["TESTPREFIX"] c = Client(app()) # we don't do anything with the prefix, so a slash at the end of the prefix # leads to a double prefix at the end response = c.get("/link", headers={"TESTPREFIX": "http://testhost/"}) assert response.body == b"http://testhost//" response = c.get("/link", headers={"TESTPREFIX": "http://testhost"}) assert response.body == b"http://testhost/" def test_link_with_prefix_app_arg(): class App(morepath.App): pass @App.path(path="") class Root(object): pass @App.view(model=Root, name="link") def link(self, request): return request.link(self) @App.link_prefix() def link_prefix(app, request): assert isinstance(app, App) return request.headers["TESTPREFIX"] c = Client(App()) # we don't do anything with the prefix, so a slash at the end of the prefix # leads to a double prefix at the end response = c.get("/link", headers={"TESTPREFIX": "http://testhost/"}) assert response.body == b"http://testhost//" response = c.get("/link", headers={"TESTPREFIX": "http://testhost"}) assert response.body == b"http://testhost/" def test_link_prefix_cache(): class app(morepath.App): pass @app.path(path="") class Root(object): pass @app.view(model=Root, name="link") def link(self, request): request.link(self) # make an extra call before returning return request.link(self) @app.link_prefix() def link_prefix(request): if not hasattr(request, "callnumber"): request.callnumber = 1 else: request.callnumber += 1 return str(request.callnumber) c = Client(app()) response = c.get("/link") assert response.body == b"1/" def test_link_with_invalid_prefix(): class app(morepath.App): pass @app.path(path="") class Root(object): pass @app.view(model=Root, name="link") def link(self, request): return request.link(self) @app.link_prefix() def link_prefix(request): return None c = Client(app()) with pytest.raises(TypeError): c.get("/link") def test_external_link_prefix(): class App(morepath.App): pass class ExternalApp(morepath.App): pass class InternalDoc(object): pass class ExternalDoc(object): pass @App.path(model=InternalDoc, path="") def internal_path(request): return InternalDoc() @ExternalApp.path(model=ExternalDoc, path="external") def external_path(request): return ExternalDoc() @App.defer_links(model=ExternalDoc) def defer_external_links(app, obj): return ExternalApp() @ExternalApp.link_prefix() def prefix_external_link(request): return "example.org" @App.json(model=InternalDoc) def main_view(self, request): return { "internal_link": request.link(InternalDoc()), "external_link_def": request.link(ExternalDoc()), "external_link_expl": request.link( ExternalDoc(), app=ExternalApp() ), } assert Client(App()).get("/").json == { "external_link_expl": "example.org/external", "external_link_def": "example.org/external", "internal_link": "http://localhost/", } def test_implicit_variables(): class app(morepath.App): pass @app.path(path="") class Root(object): pass class Model(object): def __init__(self, id): self.id = id @app.path(model=Model, path="{id}") def get_model(id): return Model(id) @app.view(model=Model) def default(self, request): return "The view for model: %s" % self.id @app.view(model=Model, name="link") def link(self, request): return request.link(self) c = Client(app()) response = c.get("/foo/link") assert response.body == b"http://localhost/foo" def test_implicit_parameters(): class app(morepath.App): pass @app.path(path="") class Root(object): pass class Model(object): def __init__(self, id): self.id = id @app.path(model=Model, path="foo") def get_model(id): return Model(id) @app.view(model=Model) def default(self, request): return "The view for model: %s" % self.id @app.view(model=Model, name="link") def link(self, request): return request.link(self) c = Client(app()) response = c.get("/foo") assert response.body == b"The view for model: None" response = c.get("/foo?id=bar") assert response.body == b"The view for model: bar" response = c.get("/foo/link") assert response.body == b"http://localhost/foo" response = c.get("/foo/link?id=bar") assert response.body == b"http://localhost/foo?id=bar" def test_implicit_parameters_default(): class app(morepath.App): pass @app.path(path="") class Root(object): pass class Model(object): def __init__(self, id): self.id = id @app.path(model=Model, path="foo") def get_model(id="default"): return Model(id) @app.view(model=Model) def default(self, request): return "The view for model: %s" % self.id @app.view(model=Model, name="link") def link(self, request): return request.link(self) c = Client(app()) response = c.get("/foo") assert response.body == b"The view for model: default" response = c.get("/foo?id=bar") assert response.body == b"The view for model: bar" response = c.get("/foo/link") assert response.body == b"http://localhost/foo?id=default" response = c.get("/foo/link?id=bar") assert response.body == b"http://localhost/foo?id=bar" def test_simple_root(): class app(morepath.App): pass class Hello(object): pass hello = Hello() @app.path(model=Hello, path="") def hello_model(): return hello @app.view(model=Hello) def hello_view(self, request): return "hello" c = Client(app()) response = c.get("/") assert response.body == b"hello" def test_json_directive(): class app(morepath.App): pass @app.path(path="{id}") class Model(object): def __init__(self, id): self.id = id @app.json(model=Model) def json(self, request): return {"id": self.id} c = Client(app()) response = c.get("/foo") assert response.body == b'{"id":"foo"}' def test_redirect(): class app(morepath.App): pass @app.path(path="") class Root(object): def __init__(self): pass @app.view(model=Root, render=render_html) def default(self, request): return morepath.redirect("/") c = Client(app()) c.get("/", status=302) def test_root_conflict(): class app(morepath.App): pass @app.path(path="") class Root(object): pass @app.path(path="") class Something(object): pass with pytest.raises(ConflictError): app.commit() def test_root_conflict2(): class app(morepath.App): pass @app.path(path="") class Root(object): pass @app.path(path="/") class Something(object): pass with pytest.raises(ConflictError): app.commit() def test_root_no_conflict_different_apps(): class app_a(morepath.App): pass class app_b(morepath.App): pass @app_a.path(path="") class Root(object): pass @app_b.path(path="") class Something(object): pass dectate.commit(app_a, app_b) def test_model_conflict(): class app(morepath.App): pass class A(object): pass @app.path(model=A, path="a") def get_a(): return A() @app.path(model=A, path="a") def get_a_again(): return A() with pytest.raises(ConflictError): app.commit() def test_path_conflict(): class app(morepath.App): pass class A(object): pass class B(object): pass @app.path(model=A, path="a") def get_a(): return A() @app.path(model=B, path="a") def get_b(): return B() with pytest.raises(ConflictError): app.commit() def test_path_conflict_with_variable(): class app(morepath.App): pass class A(object): pass class B(object): pass @app.path(model=A, path="a/{id}") def get_a(id): return A() @app.path(model=B, path="a/{id2}") def get_b(id): return B() with pytest.raises(ConflictError): app.commit() def test_path_conflict_with_variable_different_converters(): class app(morepath.App): pass class A(object): pass class B(object): pass @app.path(model=A, path="a/{id}", converters=Converter(decode=int)) def get_a(id): return A() @app.path(model=B, path="a/{id}") def get_b(id): return B() with pytest.raises(ConflictError): app.commit() def test_model_no_conflict_different_apps(): class app_a(morepath.App): pass class app_b(morepath.App): pass class A(object): pass @app_a.path(model=A, path="a") def get_a(): return A() @app_b.path(model=A, path="a") def get_a_again(): return A() dectate.commit(app_a, app_b) def test_view_conflict(): class app(morepath.App): pass class Model(object): pass @app.view(model=Model, name="a") def a_view(self, request): pass @app.view(model=Model, name="a") def a1_view(self, request): pass with pytest.raises(ConflictError): app.commit() def test_view_no_conflict_different_names(): class app(morepath.App): pass class Model(object): pass @app.view(model=Model, name="a") def a_view(self, request): pass @app.view(model=Model, name="b") def b_view(self, request): pass app.commit() def test_view_no_conflict_different_predicates(): class app(morepath.App): pass class Model(object): pass @app.view(model=Model, name="a", request_method="GET") def a_view(self, request): pass @app.view(model=Model, name="a", request_method="POST") def b_view(self, request): pass app.commit() def test_view_no_conflict_different_apps(): class app_a(morepath.App): pass class app_b(morepath.App): pass class Model(object): pass @app_a.view(model=Model, name="a") def a_view(self, request): pass @app_b.view(model=Model, name="a") def a1_view(self, request): pass dectate.commit(app_a, app_b) def test_view_conflict_with_json(): class app(morepath.App): pass class Model(object): pass @app.view(model=Model, name="a") def a_view(self, request): pass @app.json(model=Model, name="a") def a1_view(self, request): pass with pytest.raises(ConflictError): app.commit() def test_view_conflict_with_html(): class app(morepath.App): pass class Model(object): pass @app.view(model=Model, name="a") def a_view(self, request): pass @app.html(model=Model, name="a") def a1_view(self, request): pass with pytest.raises(ConflictError): app.commit() def test_function(): class App(morepath.App): @morepath.dispatch_method("a") def func(self, a): return "default" class A(object): pass @App.method(App.func, a=A) def a_func(app, request): return "A" app = App() assert app.func(A()) == "A" assert app.func(None) == "default" def test_method(): class App(morepath.App): @morepath.dispatch_method("a") def func(self, a): return "default" class A(object): pass @App.method(App.func, a=A) def a_func(app, request): assert isinstance(app, App) return "A" app = App() assert app.func(A()) == "A" assert app.func(None) == "default" def test_function_conflict(): class app(morepath.App): @morepath.dispatch_method("a") def func(self, a): pass class A(object): pass @app.method(app.func, a=A) def a_func(app, a, request): pass @app.method(app.func, a=A) def a1_func(app, a, request): pass with pytest.raises(ConflictError): app.commit() def test_function_no_conflict_different_apps(): class base(morepath.App): @morepath.dispatch_method("a") def func(self, a): pass class app_a(base): pass class app_b(base): pass class A(object): pass @app_a.method(base.func, a=A) def a_func(app, a): pass @app_b.method(base.func, a=A) def a1_func(app, a): pass dectate.commit(app_a, app_b) def test_run_app_with_context_without_it(): class app(morepath.App): pass def __init__(self, mount_id): self.mount_id = mount_id with pytest.raises(TypeError): app() def test_mapply_bug(): c = Client(mapply_bug.app()) response = c.get("/") assert response.body == b"the root" def test_abbr_imperative(): class app(morepath.App): pass class Model(object): pass @app.path(path="/", model=Model) def get_model(): return Model() with app.view(model=Model) as view: @view() def default(self, request): return "Default view" @view(name="edit") def edit(self, request): return "Edit view" c = Client(app()) response = c.get("/") assert response.body == b"Default view" response = c.get("/edit") assert response.body == b"Edit view" def test_abbr_exception(): class app(morepath.App): pass class Model(object): pass @app.path(path="/", model=Model) def get_model(): return Model() try: with app.view(model=Model) as view: @view() def default(self, request): return "Default view" 1 / 0 @view(name="edit") def edit(self, request): return "Edit view" except ZeroDivisionError: pass c = Client(app()) response = c.get("/") assert response.body == b"Default view" # an exception happened halfway, so this one is never registered c.get("/edit", status=404) def test_abbr_imperative2(): class app(morepath.App): pass class Model(object): pass @app.path(path="/", model=Model) def get_model(): return Model() with app.view(model=Model) as view: @view() def default(self, request): return "Default view" @view(name="edit") def edit(self, request): return "Edit view" c = Client(app()) response = c.get("/") assert response.body == b"Default view" response = c.get("/edit") assert response.body == b"Edit view" def test_abbr_nested(): class app(morepath.App): pass class Model(object): pass @app.path(path="/", model=Model) def get_model(): return Model() with app.view(model=Model) as view: @view() def default(self, request): return "Default" with view(name="extra") as view: @view() def get(self, request): return "Get" @view(request_method="POST") def post(self, request): return "Post" c = Client(app()) response = c.get("/") assert response.body == b"Default" response = c.get("/extra") assert response.body == b"Get" response = c.post("/extra") assert response.body == b"Post" def test_function_directive(): class app(morepath.App): @morepath.dispatch_method("o") def mygeneric(self, o): return "The object: %s" % o class Foo(object): def __init__(self, value): self.value = value def __repr__(self): return "<Foo with value: %s>" % self.value @app.method(app.mygeneric, o=Foo) def mygeneric_for_foo(app, o): return "The foo object: %s" % o a = app() assert a.mygeneric("blah") == "The object: blah" assert a.mygeneric(Foo(1)) == ("The foo object: <Foo with value: 1>") def test_classgeneric_function_directive(): class app(morepath.App): @morepath.dispatch_method(reg.match_class("o")) def mygeneric(self, o): return "The object" class Foo(object): pass @app.method(app.mygeneric, o=Foo) def mygeneric_for_foo(app, o): return "The foo object" a = app() assert a.mygeneric(object) == "The object" assert a.mygeneric(Foo) == "The foo object" def test_staticmethod(): class App(morepath.App): pass @App.path("/") class Root(object): pass class A(object): @staticmethod @App.view(model=Root) def root_default(self, request): assert isinstance(self, Root) return "Hello world" c = Client(App()) response = c.get("/") assert response.body == b"Hello world" def test_classmethod_equivalent_to_staticmethod(): class App(morepath.App): pass @App.path("/") class Root(object): pass class A(object): @classmethod @App.view(model=Root) def root_default(self, request): assert isinstance(self, Root) return "Hello world" c = Client(App()) response = c.get("/") assert response.body == b"Hello world" def test_classmethod_bound_outside(): class App(morepath.App): pass @App.path("/") class Root(object): pass class A(object): @classmethod def root_default(cls, self, request): assert isinstance(self, Root) return "Hello world" App.view(model=Root)(A.root_default) c = Client(App()) response = c.get("/") assert response.body == b"Hello world" def test_instantiation_before_config(): class App(morepath.App): pass # Typically, instantiating App would be done later, after the # decorators. Since this use case has been found in the wild, we # might as well make sure it works: app = App() @App.path(path="") class Hello(object): pass @App.view(model=Hello) def hello_view(self, request): return "hello" c = Client(app) response = c.get("/") assert response.body == b"hello"

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fb7810c86a4c1abe39e465a8de48615bc8895b39

147

py

Python

order.py

saykent/gitwork

5dc0734137b617428f5a8ee25ceb826b9c5cd2b4

[ "Apache-2.0" ]

null

order.py

saykent/gitwork

5dc0734137b617428f5a8ee25ceb826b9c5cd2b4

[ "Apache-2.0" ]

null

order.py

saykent/gitwork

5dc0734137b617428f5a8ee25ceb826b9c5cd2b4

[ "Apache-2.0" ]

null

def order_list(): pass def order_details(): pass def add_order(): pass def update_order(): pass def delete_order(): pass

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fb9640883de6f5ac8c553518167ea8c4e733e172

376

py

Python

Week 1 In-out, int and bool/Task08-09.py

retverd/python_hse

cb9bfb092c1cf68ae0c53b9919ca24a71a8cbf88

[ "MIT" ]

null

Week 1 In-out, int and bool/Task08-09.py

retverd/python_hse

cb9bfb092c1cf68ae0c53b9919ca24a71a8cbf88

[ "MIT" ]

null

Week 1 In-out, int and bool/Task08-09.py

retverd/python_hse

cb9bfb092c1cf68ae0c53b9919ca24a71a8cbf88

[ "MIT" ]

null

# #8 У 60 белочек было 38746298762973632324233242 орешков. Они решили разделить их поровну. Сколько орешков досталось # каждой белочке? В качестве ответа необходимо сдать целое число. # #9 Часы показывали полночь. Прошло 38746298762973632324233242 минут. Сколько полных часов прошло? В качестве ответа # необходимо сдать целое число. print(38746298762973632324233242 // 60)

47

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6

fba1a8f2931bbbd48e8964dee3d1819709bf50ec

85

py

Python

sympycore/algebras/__init__.py

radovankavicky/pymaclab

21da758f64ed0b62969c9289576f677e977cfd98

[ "Apache-2.0" ]

96

2015-01-25T05:59:56.000Z

2021-12-29T14:05:22.000Z

sympycore/algebras/__init__.py

1zinnur9/pymaclab

21da758f64ed0b62969c9289576f677e977cfd98

[ "Apache-2.0" ]

3

2015-12-17T19:25:46.000Z

2018-06-19T07:05:20.000Z

sympycore/algebras/__init__.py

1zinnur9/pymaclab

21da758f64ed0b62969c9289576f677e977cfd98

[ "Apache-2.0" ]

36

2016-01-31T15:22:01.000Z

2021-03-29T07:03:07.000Z

from .groups import Group, AdditiveGroup, AdditiveAbelianGroup, MultiplicativeGroup

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