xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

832c8761a575784e1b640474985a108f44948f54

780

py

Python

utils/system/load_carla.py

Czworldy/GP_traj

96261f39a5a322092e3a6be98938bb4601f0f746

[ "MIT" ]

1

2021-06-08T06:09:55.000Z

utils/system/load_carla.py

Czworldy/GP_traj

96261f39a5a322092e3a6be98938bb4601f0f746

[ "MIT" ]

null

utils/system/load_carla.py

Czworldy/GP_traj

96261f39a5a322092e3a6be98938bb4601f0f746

[ "MIT" ]

null

import os import sys import glob def load(path): try: # sys.path.append(path+'/PythonAPI') # sys.path.append(glob.glob(path+'/PythonAPI/carla/dist/carla-*%d.%d-%s.egg' % ( # sys.version_info.major, # sys.version_info.minor, # 'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0]) sys.path.append(path+'/PythonAPI') # sys.path.append(glob.glob(path+'/PythonAPI/carla/dist/carla-*%d.%d-%s.egg' % ( # sys.version_info.major, # sys.version_info.minor, # 'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0]) sys.path.append(glob.glob(path+'/PythonAPI/carla/dist/carla-0.9.10-py3.7-linux-x86_64.egg')[0]) except: print('Fail to load carla library')

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0.784753

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0.232051

780

21

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null

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null

0

1

0

6

8355d614004b17bfcb3f45e4442a8b6ec42659c2

183

py

Python

pizza_store/enums/__init__.py

astsu-dev/pizza-store-backend

902f6e5e2c88ba029b2bff61da8fc4684664ead9

[ "MIT" ]

2

2021-07-10T15:47:45.000Z

2021-12-13T18:09:30.000Z

pizza_store/enums/__init__.py

astsu-dev/pizza-store-backend

902f6e5e2c88ba029b2bff61da8fc4684664ead9

[ "MIT" ]

null

pizza_store/enums/__init__.py

astsu-dev/pizza-store-backend

902f6e5e2c88ba029b2bff61da8fc4684664ead9

[ "MIT" ]

null

from pizza_store.enums.permissions import CategoryPermission, ProductPermission from pizza_store.enums.role import Role __all__ = ["CategoryPermission", "ProductPermission", "Role"]

36.6

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0.830601

19

183

7.684211

0.526316

0.123288

0.191781

0.260274

0

0.081967

183

4

80

45.75

0.869048

0

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0

1

0

false

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null

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1

0

null

0

1

0

1

0

6

36a990cc6a6e3e5504c06bc7f2883f381f78b2f2

42

py

Python

lib/django-1.3/django/contrib/messages/__init__.py

MiCHiLU/google_appengine_sdk

3da9f20d7e65e26c4938d2c4054bc4f39cbc5522

[ "Apache-2.0" ]

790

2015-01-03T02:13:39.000Z

2020-05-10T19:53:57.000Z

django/contrib/messages/__init__.py

mradziej/django

5d38965743a369981c9a738a298f467f854a2919

[ "BSD-3-Clause" ]

1,361

2015-01-08T23:09:40.000Z

2020-04-14T00:03:04.000Z

django/contrib/messages/__init__.py

mradziej/django

5d38965743a369981c9a738a298f467f854a2919

[ "BSD-3-Clause" ]

155

2015-01-08T22:59:31.000Z

2020-04-08T08:01:53.000Z

from api import * from constants import *

14

23

0.761905

6

42

5.333333

0.666667

0

0.190476

42

2

24

21

0.941176

0

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true

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1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

6

36e973d4fc94162fdd1e28fa244ac8e1f2e1f0e6

25

py

Python

pylfi/priors/__init__.py

nicolossus/pylfi

7950aff5c36e7368cbe77b32ef348966b905f5cf

[ "MIT" ]

null

pylfi/priors/__init__.py

nicolossus/pylfi

7950aff5c36e7368cbe77b32ef348966b905f5cf

[ "MIT" ]

null

pylfi/priors/__init__.py

nicolossus/pylfi

7950aff5c36e7368cbe77b32ef348966b905f5cf

[ "MIT" ]

null

from .prior import Prior

12.5

24

0.8

4

25

5

0.75

0

0.16

25

1

25

0.952381

0

1

0

true

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null

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1

0

null

0

1

0

1

0

1

0

6

36e987de86625fb265967bed791c06ac9307414b

92

py

Python

altair/__init__.py

jakevdp/altair2

46d391034c5b72867c9e4d01f3a7c7c536533add

[ "BSD-3-Clause" ]

2

2018-02-03T05:35:52.000Z

2018-02-05T21:00:18.000Z

altair/__init__.py

jakevdp/altair2

46d391034c5b72867c9e4d01f3a7c7c536533add

[ "BSD-3-Clause" ]

null

altair/__init__.py

jakevdp/altair2

46d391034c5b72867c9e4d01f3a7c7c536533add

[ "BSD-3-Clause" ]

null

from .schema import vegalite_version from .api import Chart from .schema.channels import *

18.4

36

0.804348

13

92

5.615385

0.615385

0.273973

0

0.141304

92

4

37

23

0.924051

0

1

0

true

0

1

0

1

0

1

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

36ed2a423ccbe261f9ce0fa402dda9039931c5d3

20,707

py

Python

src/network_architecture.py

kms8527/rl_decision_making_with_uncertainty

7a655e218c432be1498ff19e4811fc7595bd1413

[ "MIT" ]

30

2020-04-27T13:01:58.000Z

2022-03-15T07:15:17.000Z

src/network_architecture.py

kms8527/rl_decision_making_with_uncertainty

7a655e218c432be1498ff19e4811fc7595bd1413

[ "MIT" ]

5

2020-04-23T08:29:36.000Z

2022-02-10T01:26:01.000Z

src/network_architecture.py

kms8527/rl_decision_making_with_uncertainty

7a655e218c432be1498ff19e4811fc7595bd1413

[ "MIT" ]

12

2020-06-11T04:19:53.000Z

2022-02-16T09:30:26.000Z

from keras.models import Sequential, Model from keras.layers import Dense, Activation, Flatten, Lambda, add, Input, Reshape, Conv1D, MaxPooling1D, concatenate import keras.backend as K class NetworkMLP(object): """ This class is used to build a neural network with an MLP structure. There are different functions that builds a standard MLP, w/wo dueling architecture, and w/wo additional untrainable prior network. Args: nb_inputs (int): Number of inputs to the network. nb_outputs (int): Number of outputs from the network. nb_hidden_layers (int): Number of hidden layers. nb_hidden_neurons (int): Number of neurons in the hidden layers. duel (bool): Use dueling architecture. prior (bool): Use an additional untrainable prior network. prior_scale_factor (float): Scale factor that balances trainable/untrainable contribution to the output. duel_type (str): 'avg', 'max', or 'naive' activation (str): Type of activation function, see Keras for definition window_length (int): How many historic states that are used as input. Set to 1 in this work. """ def __init__(self, nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, duel, prior, prior_scale_factor=10., duel_type='avg', activation='relu', window_length=1): self.model = None if not prior and not duel: self.build_mlp(nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, activation=activation, window_length=window_length) elif not prior and duel: self.build_mlp_dueling(nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, dueling_type=duel_type, activation=activation, window_length=window_length) elif prior and not duel: self.build_prior_plus_trainable(nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, activation=activation, prior_scale_factor=prior_scale_factor, window_length=window_length) elif prior and duel: self.build_prior_plus_trainable_dueling(nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, dueling_type=duel_type, activation=activation, prior_scale_factor=prior_scale_factor, window_length=window_length) else: raise Exception('Error in Network creation') def build_mlp(self, nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, activation='relu', window_length=1): self.model = Sequential() self.model.add(Flatten(input_shape=(window_length, nb_inputs))) for _ in range(nb_hidden_layers): self.model.add(Dense(nb_hidden_neurons)) self.model.add(Activation(activation)) self.model.add(Dense(nb_outputs, activation='linear')) def build_mlp_dueling(self, nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, dueling_type='avg', activation='relu', window_length=1): self.build_mlp(nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, activation=activation, window_length=window_length) layer = self.model.layers[-2] y = Dense(nb_outputs + 1, activation='linear')(layer.output) if dueling_type == 'avg': outputlayer = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.mean(a[:, 1:], keepdims=True), output_shape=(nb_outputs,))(y) elif dueling_type == 'max': outputlayer = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.max(a[:, 1:], keepdims=True), output_shape=(nb_outputs,))(y) elif dueling_type == 'naive': outputlayer = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:], output_shape=(nb_outputs,))(y) else: assert False, "dueling_type must be one of {'avg','max','naive'}" self.model = Model(inputs=self.model.input, outputs=outputlayer) def build_prior_plus_trainable(self, nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, activation='relu', prior_scale_factor=1., window_length=1): net_input = Input(shape=(window_length, nb_inputs), name='input') prior_net = Flatten()(net_input) for _ in range(nb_hidden_layers): prior_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal', trainable=False)(prior_net) prior_out = Dense(nb_outputs, activation='linear', trainable=False, name='prior_out')(prior_net) prior_scale = Lambda(lambda x: x * prior_scale_factor, name='prior_scale')(prior_out) trainable_net = Flatten(input_shape=(window_length, nb_inputs))(net_input) for _ in range(nb_hidden_layers): trainable_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal', trainable=True)(trainable_net) trainable_out = Dense(nb_outputs, activation='linear', trainable=True, name='trainable_out')(trainable_net) add_output = add([trainable_out, prior_scale], name='add') self.model = Model(inputs=net_input, outputs=add_output) def build_prior_plus_trainable_dueling(self, nb_inputs, nb_outputs, nb_hidden_layers, nb_hidden_neurons, activation='relu', prior_scale_factor=1., dueling_type='avg', window_length=1): net_input = Input(shape=(window_length, nb_inputs), name='input') prior_net = Flatten()(net_input) for _ in range(nb_hidden_layers): prior_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal', trainable=False)(prior_net) prior_out_wo_dueling = Dense(nb_outputs + 1, activation='linear', trainable=False, name='prior_out_wo_dueling')(prior_net) if dueling_type == 'avg': prior_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.mean(a[:, 1:], keepdims=True), output_shape=(nb_outputs,), name='prior_out')(prior_out_wo_dueling) elif dueling_type == 'max': prior_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.max(a[:, 1:], keepdims=True), output_shape=(nb_outputs,), name='prior_out')(prior_out_wo_dueling) elif dueling_type == 'naive': prior_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:], output_shape=(nb_outputs,), name='prior_out')(prior_out_wo_dueling) else: assert False, "dueling_type must be one of {'avg','max','naive'}" prior_scale = Lambda(lambda x: x * prior_scale_factor, name='prior_scale')(prior_out) trainable_net = Flatten(input_shape=(window_length, nb_inputs))(net_input) for _ in range(nb_hidden_layers): trainable_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal', trainable=True)(trainable_net) trainable_out_wo_dueling = Dense(nb_outputs + 1, activation='linear', trainable=True, name='trainable_out_wo_dueling')(trainable_net) if dueling_type == 'avg': trainable_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.mean(a[:, 1:], keepdims=True), output_shape=(nb_outputs,), name='trainable_out')(trainable_out_wo_dueling) elif dueling_type == 'max': trainable_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.max(a[:, 1:], keepdims=True), output_shape=(nb_outputs,), name='trainable_out')(trainable_out_wo_dueling) elif dueling_type == 'naive': trainable_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:], output_shape=(nb_outputs,), name='trainable_out')(trainable_out_wo_dueling) else: assert False, "dueling_type must be one of {'avg','max','naive'}" add_output = add([trainable_out, prior_scale], name='add') self.model = Model(inputs=net_input, outputs=add_output) class NetworkCNN(object): """ This class is used to build a neural network with a CNN structure. There are different functions that builds a standard CNN, w/wo dueling architecture, and w/wo additional untrainable prior network. Args: nb_ego_states (int): Number of states that describe the ego vehicle. nb_states_per_vehicle (int): Number of states that describe each of the surrounding vehicles. nb_vehicles (int): Maximum number of surrounding vehicles. nb_actions: (int): Number of outputs from the network. nb_conv_layers (int): Number of convolutional layers. nb_conv_filters (int): Number of convolutional filters. nb_hidden_fc_layers (int): Number of hidden layers. nb_hidden_neurons (int): Number of neurons in the hidden layers. duel (bool): Use dueling architecture. prior (bool): Use an additional untrainable prior network. prior_scale_factor (float): Scale factor that balances trainable/untrainable contribution to the output. duel_type (str): 'avg', 'max', or 'naive' activation (str): Type of activation function, see Keras for definition window_length (int): How many historic states that are used as input. Set to 1 in this work. """ def __init__(self, nb_ego_states, nb_states_per_vehicle, nb_vehicles, nb_actions, nb_conv_layers, nb_conv_filters, nb_hidden_fc_layers, nb_hidden_neurons, duel, prior, prior_scale_factor=10., duel_type='avg', activation='relu', window_length=1): self.model = None if not prior and not duel: self.build_cnn(nb_ego_states, nb_states_per_vehicle, nb_vehicles, nb_actions, nb_conv_layers, nb_conv_filters, nb_hidden_fc_layers, nb_hidden_neurons, activation=activation, window_length=window_length) elif not prior and duel: self.build_cnn_dueling(nb_ego_states, nb_states_per_vehicle, nb_vehicles, nb_actions, nb_conv_layers, nb_conv_filters, nb_hidden_fc_layers, nb_hidden_neurons, dueling_type=duel_type, activation=activation, window_length=window_length) elif prior and duel: self.build_cnn_dueling_prior(nb_ego_states, nb_states_per_vehicle, nb_vehicles, nb_actions, nb_conv_layers, nb_conv_filters, nb_hidden_fc_layers, nb_hidden_neurons, dueling_type=duel_type, activation=activation, prior_scale_factor=prior_scale_factor, window_length=window_length) else: raise Exception('Error in Network creation') def build_cnn(self, nb_ego_states, nb_states_per_vehicle, nb_vehicles, nb_actions, nb_conv_layers, nb_conv_filters, nb_hidden_fc_layers, nb_hidden_neurons, activation='relu', window_length=1): nb_inputs = nb_ego_states + nb_states_per_vehicle * nb_vehicles net_input = Input(shape=(window_length, nb_inputs), name='input') flat_input = Flatten()(net_input) input_ego = Lambda(lambda state: state[:, :nb_ego_states * window_length])(flat_input) input_others = Lambda(lambda state: state[:, nb_ego_states * window_length:])(flat_input) input_others_reshaped = Reshape((nb_vehicles * nb_states_per_vehicle * window_length, 1,), input_shape=(nb_vehicles * nb_states_per_vehicle * window_length,))(input_others) conv_net = Conv1D(nb_conv_filters, nb_states_per_vehicle*window_length, strides=nb_states_per_vehicle*window_length, activation=activation, kernel_initializer='glorot_normal')(input_others_reshaped) for _ in range(nb_conv_layers-1): conv_net = Conv1D(nb_conv_filters, 1, strides=1, activation=activation, kernel_initializer='glorot_normal')(conv_net) pool = MaxPooling1D(pool_size=nb_vehicles)(conv_net) conv_net_out = Reshape((nb_conv_filters,), input_shape=(1, nb_conv_filters,), name='convnet_out')(pool) merged = concatenate([input_ego, conv_net_out]) joint_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal')(merged) for _ in range(nb_hidden_fc_layers-1): joint_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal')(joint_net) output = Dense(nb_actions, activation='linear', name='output')(joint_net) self.model = Model(inputs=net_input, outputs=output) def build_cnn_dueling(self, nb_ego_states, nb_states_per_vehicle, nb_vehicles, nb_actions, nb_conv_layers, nb_conv_filters, nb_hidden_fc_layers, nb_hidden_neurons, activation='relu', window_length=1, dueling_type='avg'): self. build_cnn(nb_ego_states=nb_ego_states, nb_states_per_vehicle=nb_states_per_vehicle, nb_vehicles=nb_vehicles, nb_actions=nb_actions, nb_conv_layers=nb_conv_layers, nb_conv_filters=nb_conv_filters, nb_hidden_fc_layers=nb_hidden_fc_layers, nb_hidden_neurons=nb_hidden_neurons, activation=activation, window_length=window_length) layer = self.model.layers[-2] y = Dense(nb_actions + 1, activation='linear')(layer.output) if dueling_type == 'avg': outputlayer = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.mean(a[:, 1:], keepdims=True), output_shape=(nb_actions,))(y) elif dueling_type == 'max': outputlayer = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.max(a[:, 1:], keepdims=True), output_shape=(nb_actions,))(y) elif dueling_type == 'naive': outputlayer = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:], output_shape=(nb_actions,))(y) else: assert False, "dueling_type must be one of {'avg','max','naive'}" self.model = Model(inputs=self.model.input, outputs=outputlayer) def build_cnn_dueling_prior(self, nb_ego_states, nb_states_per_vehicle, nb_vehicles, nb_actions, nb_conv_layers, nb_conv_filters, nb_hidden_fc_layers, nb_hidden_neurons, activation='relu', window_length=1, dueling_type='avg', prior_scale_factor=1.): nb_inputs = nb_ego_states + nb_states_per_vehicle * nb_vehicles net_input = Input(shape=(window_length, nb_inputs), name='input') flat_input = Flatten()(net_input) input_ego = Lambda(lambda state: state[:, :nb_ego_states * window_length])(flat_input) input_others = Lambda(lambda state: state[:, nb_ego_states * window_length:])(flat_input) input_others_reshaped = Reshape((nb_vehicles * nb_states_per_vehicle * window_length, 1,), input_shape=(nb_vehicles * nb_states_per_vehicle * window_length,))(input_others) prior_conv_net = Conv1D(nb_conv_filters, nb_states_per_vehicle * window_length, strides=nb_states_per_vehicle * window_length, activation=activation, kernel_initializer='glorot_normal', trainable=False)(input_others_reshaped) for _ in range(nb_conv_layers - 1): prior_conv_net = Conv1D(nb_conv_filters, 1, strides=1, activation=activation, kernel_initializer='glorot_normal', trainable=False)(prior_conv_net) prior_pool = MaxPooling1D(pool_size=nb_vehicles)(prior_conv_net) prior_conv_net_out = Reshape((nb_conv_filters,), input_shape=(1, nb_conv_filters,), name='prior_convnet_out')(prior_pool) prior_merged = concatenate([input_ego, prior_conv_net_out]) prior_joint_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal', trainable=False)(prior_merged) for _ in range(nb_hidden_fc_layers-1): prior_joint_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal', trainable=False)(prior_joint_net) prior_out_wo_dueling = Dense(nb_actions+1, activation='linear', name='prior_out_wo_dueling', trainable=False)(prior_joint_net) if dueling_type == 'avg': prior_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.mean(a[:, 1:], keepdims=True), output_shape=(nb_actions,), name='prior_out')(prior_out_wo_dueling) elif dueling_type == 'max': prior_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.max(a[:, 1:], keepdims=True), output_shape=(nb_actions,), name='prior_out')(prior_out_wo_dueling) elif dueling_type == 'naive': prior_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:], output_shape=(nb_actions,), name='prior_out')(prior_out_wo_dueling) else: assert False, "dueling_type must be one of {'avg','max','naive'}" prior_scale = Lambda(lambda x: x * prior_scale_factor, name='prior_scale')(prior_out) trainable_conv_net = Conv1D(nb_conv_filters, nb_states_per_vehicle * window_length, strides=nb_states_per_vehicle * window_length, activation=activation, kernel_initializer='glorot_normal', trainable=True)(input_others_reshaped) for _ in range(nb_conv_layers - 1): trainable_conv_net = Conv1D(nb_conv_filters, 1, strides=1, activation=activation, kernel_initializer='glorot_normal', trainable=True)(trainable_conv_net) trainable_pool = MaxPooling1D(pool_size=nb_vehicles)(trainable_conv_net) trainable_conv_net_out = Reshape((nb_conv_filters,), input_shape=(1, nb_conv_filters,), name='trainable_convnet_out')(trainable_pool) trainable_merged = concatenate([input_ego, trainable_conv_net_out]) trainable_joint_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal', trainable=True)(trainable_merged) for _ in range(nb_hidden_fc_layers-1): trainable_joint_net = Dense(nb_hidden_neurons, activation=activation, kernel_initializer='glorot_normal', trainable=True)(trainable_joint_net) trainable_out_wo_dueling = Dense(nb_actions + 1, activation='linear', name='trainable_out_wo_dueling', trainable=True)(trainable_joint_net) if dueling_type == 'avg': trainable_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.mean(a[:, 1:], keepdims=True), output_shape=(nb_actions,), name='trainable_out')(trainable_out_wo_dueling) elif dueling_type == 'max': trainable_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.max(a[:, 1:], keepdims=True), output_shape=(nb_actions,), name='trainable_out')(trainable_out_wo_dueling) elif dueling_type == 'naive': trainable_out = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:], output_shape=(nb_actions,), name='trainable_out')(trainable_out_wo_dueling) else: assert False, "dueling_type must be one of {'avg','max','naive'}" add_output = add([trainable_out, prior_scale], name='final_output') self.model = Model(inputs=net_input, outputs=add_output)

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7fc96fdbbeba39e3dc3674c16474510f2cec35d7

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py

Python

src/aiocleverbot/__init__.py

johan-naizu/aiocleverbot

ca82dbc2eafdee87435903c9b1bace481927996d

[ "MIT" ]

3

2021-05-04T14:21:52.000Z

2021-06-23T15:41:35.000Z

src/aiocleverbot/__init__.py

johan-naizu/aiocleverbot

ca82dbc2eafdee87435903c9b1bace481927996d

[ "MIT" ]

1

2021-05-06T12:17:13.000Z

2022-03-04T10:28:18.000Z

src/aiocleverbot/__init__.py

johan-naizu/aiocleverbot

ca82dbc2eafdee87435903c9b1bace481927996d

[ "MIT" ]

null

from .aiocleverbot import cleverbot

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7fce90d6f92015e289905a4d20814ef61f94859e

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py

Python

gdbplotlib/__init__.py

X-Neon/gdbplotlib

80c4d59826f3f91275bb223c77ecabc49ae90578

[ "MIT" ]

20

2020-03-14T00:52:39.000Z

2022-01-18T22:33:19.000Z

gdbplotlib/__init__.py

X-Neon/gdbplotlib

80c4d59826f3f91275bb223c77ecabc49ae90578

[ "MIT" ]

1

2021-11-18T08:21:38.000Z

2021-11-20T21:24:58.000Z

gdbplotlib/__init__.py

X-Neon/gdbplotlib

80c4d59826f3f91275bb223c77ecabc49ae90578

[ "MIT" ]

1

2021-02-23T00:04:43.000Z

from . import plot, save

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6

3d14cc836ef8377ba4048d1c88819aa8f9e1d94d

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py

Python

OCRLibrary/utils/imagereading/text_locating.py

bendurston/robotframework-ocrlibrary

da310346a60260165118cba112995e58ebb3a103

[ "Apache-2.0" ]

5

2021-05-19T07:09:43.000Z

2022-02-10T16:33:05.000Z

OCRLibrary/utils/imagereading/text_locating.py

bendurston/robotframework-ocrlibrary

da310346a60260165118cba112995e58ebb3a103

[ "Apache-2.0" ]

3

2021-05-19T07:15:28.000Z

2021-05-23T20:12:41.000Z

OCRLibrary/utils/imagereading/text_locating.py

bendurston/robotframework-ocrlibrary

da310346a60260165118cba112995e58ebb3a103

[ "Apache-2.0" ]

1

2021-05-19T07:10:03.000Z

""" Text locating module. """ from pytesseract import image_to_data, Output def return_text_coordinates(img, text, pyt_conf, lang): """ This keyword is find the coordinates of text in an image. """ data = image_to_data(img, output_type=Output.DICT, config=pyt_conf, lang=lang) boxes = len(data['level']) for i in range(boxes): text_from_image = data['text'][i] if text_from_image == text: box_bounds = (int(data['left'][i]), int(data['top'][i]), int(data['width'][i]), int(data['height'][i])) x = box_bounds[0] + box_bounds[2]/2 y = box_bounds[1] + box_bounds[3]/2 return x, y return None def return_multiple_text_coordinates(img, text, pyt_conf, lang): """ To be used when there are multiple occurrences of the same text you wish to find. """ data = image_to_data(img, output_type=Output.DICT, config=pyt_conf, lang=lang) boxes = len(data['level']) list_of_coordinates = [] for i in range(boxes): text_from_image = data['text'][i] if text_from_image == text: box_bounds = (int(data['left'][i]), int(data['top'][i]), int(data['width'][i]), int(data['height'][i])) x = box_bounds[0] + box_bounds[2]/2 y = box_bounds[1] + box_bounds[3]/2 coordinates = (x, y) list_of_coordinates.append(coordinates) if not list_of_coordinates: return None return list_of_coordinates def return_text_bounds(img, text, pyt_conf, lang): """ This keyword is find the coordinates of text in an image. """ data = image_to_data(img, output_type=Output.DICT, config=pyt_conf, lang=lang) boxes = len(data['level']) for i in range(boxes): text_from_image = data['text'][i] if text_from_image == text: box_bounds = (int(data['left'][i]), int(data['top'][i]), int(data['width'][i]), int(data['height'][i])) return box_bounds return None def return_multiple_text_bounds(img, text, pyt_conf, lang): """ To be used when there are multiple occurrences of the same text you wish to find. """ data = image_to_data(img, output_type=Output.DICT, config=pyt_conf, lang=lang) boxes = len(data['level']) list_of_box_bounds = [] for i in range(boxes): text_from_image = data['text'][i] if text_from_image == text: box_bounds = (int(data['left'][i]), int(data['top'][i]), int(data['width'][i]), int(data['height'][i])) list_of_box_bounds.append(box_bounds) if not list_of_box_bounds: return None return list_of_box_bounds

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3d46c4af15c615220255c7edeb7f8324b1f6dce6

37

py

Python

vase/__init__.py

gantzgraf/vape

f939cb527d72d852cb0919a57332110c15c5fd4a

[ "MIT" ]

4

2020-03-25T06:09:39.000Z

2021-03-23T11:22:00.000Z

vase/__init__.py

gantzgraf/vape

f939cb527d72d852cb0919a57332110c15c5fd4a

[ "MIT" ]

1

2020-10-02T14:50:30.000Z

2020-10-12T15:24:24.000Z

vase/__init__.py

gantzgraf/vape

f939cb527d72d852cb0919a57332110c15c5fd4a

[ "MIT" ]

1

2021-02-20T11:32:34.000Z

from vase.version import __version__

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6

3d5db949fe9eba516f73d8617a97cf0aeaa0d4a9

108

py

Python

popcorn_gallery/notifications/context_processors.py

Koenkk/popcorn_maker

0978b9f98dacd4e8eb753404b24eb584f410aa11

[ "BSD-3-Clause" ]

15

2015-03-23T02:55:20.000Z

2021-01-12T12:42:30.000Z

popcorn_gallery/notifications/context_processors.py

Koenkk/popcorn_maker

0978b9f98dacd4e8eb753404b24eb584f410aa11

[ "BSD-3-Clause" ]

null

popcorn_gallery/notifications/context_processors.py

Koenkk/popcorn_maker

0978b9f98dacd4e8eb753404b24eb584f410aa11

[ "BSD-3-Clause" ]

16

2015-02-18T21:43:31.000Z

2021-11-09T22:50:03.000Z

from .models import Notice def notifications(request): return {'notice_list': Notice.live.all()[:5]}

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0

1

0

1

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6

e9f2f22fd79eef346224db15cb32a1f63571d160

40

py

Python

tools/bianjie.py

bianjie0618/marked_SiamMask

51d2aa832a74b76a5cd63aae274d1dea1dc16a2c

[ "MIT" ]

null

tools/bianjie.py

bianjie0618/marked_SiamMask

51d2aa832a74b76a5cd63aae274d1dea1dc16a2c

[ "MIT" ]

null

tools/bianjie.py

bianjie0618/marked_SiamMask

51d2aa832a74b76a5cd63aae274d1dea1dc16a2c

[ "MIT" ]

null

import torch # print(torch.__version__)

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6

18598366367edfcefb01654f69c2bd013d7ae463

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py

Python

tests/test_functions.py

theblackfly/protoflow

02a77e59f6afc8d462a738874d06eca810911166

[ "MIT" ]

3

2020-10-07T05:04:05.000Z

2021-02-10T15:04:55.000Z

tests/test_functions.py

theblackfly/protoflow

02a77e59f6afc8d462a738874d06eca810911166

[ "MIT" ]

5

2020-04-09T13:36:15.000Z

2020-12-17T16:30:50.000Z

tests/test_functions.py

theblackfly/protoflow

02a77e59f6afc8d462a738874d06eca810911166

[ "MIT" ]

2

2020-10-01T21:48:16.000Z

2021-04-10T18:20:25.000Z

"""ProtoFlow functions test suite.""" import unittest import numpy as np from protoflow.functions import distances class TestDistances(unittest.TestCase): def setUp(self): pass def test_lpnorm_p2_1d(self): # yapf: disable x = np.array([[0, 0]], dtype='float32') w = np.array([[1, 1]], dtype='float32') actual = distances.lpnorm_distance(x, w, p=2) desired = np.array([[1.4142]]) # yapf: enable self.assertIsNone( np.testing.assert_array_almost_equal(actual, desired, decimal=4)) def test_lpnorm_p2_2d(self): # yapf: disable x = np.array([[0, 0], [1, 1]], dtype='float32') w = np.array([[1, 1], [1, 1], [1, 1]], dtype='float32') actual = distances.lpnorm_distance(x, w, p=2) desired = np.array([[1.4142, 1.4142, 1.4142], [0.0000, 0.0000, 0.0000]]) # yapf: enable self.assertIsNone( np.testing.assert_array_almost_equal(actual, desired, decimal=4)) def test_omega(self): # yapf: disable x = np.array([[0, 0], [1, 1]], dtype='float32') w = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype='float32') omega = np.eye(w.shape[1], dtype='float32') actual = distances.omega_distance(x, w, omega) desired = np.array([[0.0000, 1.0000, 1.0000, 1.4142], [1.4142, 1.0000, 1.0000, 0.0000]]) ** 2 # yapf: enable self.assertIsNone( np.testing.assert_array_almost_equal(actual, desired, decimal=4)) def test_lomega_eye_omegas(self): # yapf: disable x = np.array([[0, 0], [1, 1]], dtype='float32') w = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype='float32') omegas = np.stack([np.eye(w.shape[1], dtype='float32')] * w.shape[0]) actual = distances.lomega_distance(x, w, omegas) desired = np.array([[0.0000, 1.0000, 1.0000, 1.4142], [1.4142, 1.0000, 1.0000, 0.0000]]) ** 2 # yapf: enable self.assertIsNone( np.testing.assert_array_almost_equal(actual, desired, decimal=4)) def test_lomega_zeros_omegas(self): # yapf: disable x = np.array([[0, 0], [1, 1]], dtype='float32') w = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype='float32') omegas = np.stack([np.zeros( (w.shape[1], w.shape[1]), dtype='float32')] * w.shape[0]) actual = distances.lomega_distance(x, w, omegas) desired = np.array([[0.000000, 0.000000, 0.000000, 0.000000], [0.000000, 0.000000, 0.000000, 0.000000]]) # yapf: enable self.assertIsNone( np.testing.assert_array_almost_equal(actual, desired, decimal=4)) def test_lomega_ones_omegas(self): # yapf: disable x = np.array([[0, 0], [1, 1]], dtype='float32') w = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype='float32') omegas = np.stack([np.ones( (w.shape[1], w.shape[1]), dtype='float32')] * w.shape[0]) actual = distances.lomega_distance(x, w, omegas) desired = np.array([[0.000000, 1.4142135, 1.4142135, 2.828427], [2.828427, 1.4142135, 1.4142135, 0.000000]]) ** 2 # yapf: enable self.assertIsNone( np.testing.assert_array_almost_equal(actual, desired, decimal=4)) if __name__ == '__main__': unittest.main()

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null

0

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null

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6

185bde9e31af09145e63b665a66f4c0b53a750fe

26

py

Python

pyopt_tools/__init__.py

Fus3n/pyopt-tools

ec30de59c885fc1f03b1256d931131b22e1cf5b7

[ "MIT" ]

2

2022-01-08T21:09:37.000Z

2022-01-12T16:09:04.000Z

pyopt_tools/__init__.py

Fus3n/pyopt-tools

ec30de59c885fc1f03b1256d931131b22e1cf5b7

[ "MIT" ]

null

pyopt_tools/__init__.py

Fus3n/pyopt-tools

ec30de59c885fc1f03b1256d931131b22e1cf5b7

[ "MIT" ]

null

from pyopt_tools import *

13

25

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4

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1

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1

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0.909091

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1

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null

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1

0

1

0

1

0

6

a14d4177b56d1da2699f6d71ae1b86091568fbe8

136

py

Python

myML/myml.py

bobbyisot/MachineLearning

522f2a832d457ea6d14f8f8c72fca44bc15f59db

[ "MIT" ]

null

myML/myml.py

bobbyisot/MachineLearning

522f2a832d457ea6d14f8f8c72fca44bc15f59db

[ "MIT" ]

null

myML/myml.py

bobbyisot/MachineLearning

522f2a832d457ea6d14f8f8c72fca44bc15f59db

[ "MIT" ]

null

class Test_2020(object): def __init__(self): self.a = 1 print(f'success') def add_a(self): self.a += 1

17

25

0.529412

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7

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19.428571

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null

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1

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null

0

1

0

6

a1aec7d801aea5ebf861295cb9900e84026ee22b

32

py

Python

cit-api/pipeline/tests/views/__init__.py

bcgov/CIT

b9db4f169b52e9a6293b3ee1e61935888074215a

[ "Apache-2.0" ]

10

2020-11-12T15:13:40.000Z

2022-03-05T22:33:08.000Z

cit-api/pipeline/tests/views/__init__.py

bcgov/CIT

b9db4f169b52e9a6293b3ee1e61935888074215a

[ "Apache-2.0" ]

28

2020-07-17T16:33:55.000Z

2022-03-21T16:24:25.000Z

cit-api/pipeline/tests/views/__init__.py

bcgov/CIT

b9db4f169b52e9a6293b3ee1e61935888074215a

[ "Apache-2.0" ]

5

2020-11-02T23:39:53.000Z

2022-03-01T19:09:45.000Z

from .test_opportunity import *

16

31

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0

1

0

6

b80ed33f2221b2a3732b6390e63d6a5350d7dd13

43

py

Python

pextant/analysis/kmlsupport.py

norheim/pextant

f4235719279c0e6f178ae1e0f8b1ea3346533915

[ "MIT" ]

null

pextant/analysis/kmlsupport.py

norheim/pextant

f4235719279c0e6f178ae1e0f8b1ea3346533915

[ "MIT" ]

1

2019-12-03T03:52:41.000Z

2019-12-04T14:50:36.000Z

pextant/analysis/kmlsupport.py

norheim/pextant

f4235719279c0e6f178ae1e0f8b1ea3346533915

[ "MIT" ]

1

2019-12-03T02:37:57.000Z

from pykml.factory import KML_ElementMaker

21.5

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6.166667

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1

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6

62ce75ed0bc80d716e869ec2ea08d0c3839e1ab4

354

py

Python

ensemble_boxes/__init__.py

Sergey-Zlobin/Weighted-Boxes-Fusion

773ed6f9513ade442c0f89885f3a36d95cf0629d

[ "MIT" ]

2

2021-07-19T05:19:00.000Z

2022-03-06T03:48:02.000Z

ensemble_boxes/__init__.py

Sergey-Zlobin/Weighted-Boxes-Fusion

773ed6f9513ade442c0f89885f3a36d95cf0629d

[ "MIT" ]

null

ensemble_boxes/__init__.py

Sergey-Zlobin/Weighted-Boxes-Fusion

773ed6f9513ade442c0f89885f3a36d95cf0629d

[ "MIT" ]

1

2021-09-15T21:26:39.000Z

# coding: utf-8 __author__ = 'ZFTurbo: https://kaggle.com/zfturbo' from .ensemble_boxes_wbf import weighted_boxes_fusion from .ensemble_boxes_nmw import non_maximum_weighted from .ensemble_boxes_nms import nms_method from .ensemble_boxes_nms import nms from .ensemble_boxes_nms import soft_nms from .ensemble_boxes_wbf_3d import weighted_boxes_fusion_3d

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6

a7fd14f6753ca35ce9c471c3412feedb1e337095

16,068

py

Python

models/backbones/resnet.py

crutcher/stylelens

8df3704f56fe6a30395eadcb1aee2e11563dfabb

[ "MIT" ]

null

models/backbones/resnet.py

crutcher/stylelens

8df3704f56fe6a30395eadcb1aee2e11563dfabb

[ "MIT" ]

null

models/backbones/resnet.py

crutcher/stylelens

8df3704f56fe6a30395eadcb1aee2e11563dfabb

[ "MIT" ]

null

# ------------------------------------------------------------------------------ # Libraries # ------------------------------------------------------------------------------ from collections import OrderedDict import torch.nn as nn from timm.models.resnet import BasicBlock, Bottleneck from timm.models.resnet import ResNet as BaseResNet from timm.models.resnet import default_cfgs, load_pretrained from base import BaseBackboneWrapper # ------------------------------------------------------------------------------ # ResNetBlock # ------------------------------------------------------------------------------ class ResNetBasicBlock(nn.Module): expansion = 1 def __init__(self, in_channels, out_channels): super(ResNetBasicBlock, self).__init__() downsample = nn.Sequential( OrderedDict( [ ( "conv", nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), ), ("bn", nn.BatchNorm2d(out_channels)), ] ) ) self.block = BasicBlock( in_channels, int(out_channels / BasicBlock.expansion), downsample=downsample, ) def forward(self, x): x = self.block(x) return x class ResNetBottleneckBlock(nn.Module): expansion = 4 def __init__(self, in_channels, out_channels): super(ResNetBottleneckBlock, self).__init__() downsample = nn.Sequential( OrderedDict( [ ( "conv", nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), ), ("bn", nn.BatchNorm2d(out_channels)), ] ) ) self.block = Bottleneck( in_channels, int(out_channels / Bottleneck.expansion), downsample=downsample, ) def forward(self, x): x = self.block(x) return x # ------------------------------------------------------------------------------ # ResNet # ------------------------------------------------------------------------------ class ResNet(BaseResNet, BaseBackboneWrapper): def __init__(self, block, layers, frozen_stages=-1, norm_eval=False, **kargs): super(ResNet, self).__init__(block=block, layers=layers, **kargs) self.frozen_stages = frozen_stages self.norm_eval = norm_eval def forward(self, input): # Stem x1 = self.conv1(input) x1 = self.bn1(x1) x1 = self.relu(x1) # Stage1 x2 = self.maxpool(x1) x2 = self.layer1(x2) # Stage2 x3 = self.layer2(x2) # Stage3 x4 = self.layer3(x3) # Stage4 x5 = self.layer4(x4) # Output return x1, x2, x3, x4, x5 def init_from_imagenet(self, archname): load_pretrained(self, default_cfgs[archname], self.num_classes) def _freeze_stages(self): # Freeze stem if self.frozen_stages >= 0: self.bn1.eval() for module in [self.conv1, self.bn1]: for param in module.parameters(): param.requires_grad = False # Chosen subsequent blocks are also frozen for stage_idx in range(1, self.frozen_stages + 1): for module in getattr(self, "layer%d" % (stage_idx)): module.eval() for param in module.parameters(): param.requires_grad = False # ------------------------------------------------------------------------------ # Versions of ResNet # ------------------------------------------------------------------------------ def resnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-18 model.""" default_cfg = default_cfgs["resnet18"] model = ResNet( BasicBlock, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnet34(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-34 model.""" default_cfg = default_cfgs["resnet34"] model = ResNet( BasicBlock, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnet26(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-26 model.""" default_cfg = default_cfgs["resnet26"] model = ResNet( Bottleneck, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnet26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-26 v1d model. This is technically a 28 layer ResNet, sticking with 'd' modifier from Gluon for now. """ default_cfg = default_cfgs["resnet26d"] model = ResNet( Bottleneck, [2, 2, 2, 2], stem_width=32, deep_stem=True, avg_down=True, num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-50 model.""" default_cfg = default_cfgs["resnet50"] model = ResNet( Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnet101(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-101 model.""" default_cfg = default_cfgs["resnet101"] model = ResNet( Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnet152(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-152 model.""" default_cfg = default_cfgs["resnet152"] model = ResNet( Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def tv_resnet34(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-34 model with original Torchvision weights.""" model = ResNet( BasicBlock, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfgs["tv_resnet34"] if pretrained: load_pretrained(model, model.default_cfg, num_classes, in_chans) return model def tv_resnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-50 model with original Torchvision weights.""" model = ResNet( Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfgs["tv_resnet50"] if pretrained: load_pretrained(model, model.default_cfg, num_classes, in_chans) return model def wide_resnet50_2(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a Wide ResNet-50-2 model. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. """ model = ResNet( Bottleneck, [3, 4, 6, 3], base_width=128, num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfgs["wide_resnet50_2"] if pretrained: load_pretrained(model, model.default_cfg, num_classes, in_chans) return model def wide_resnet101_2(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a Wide ResNet-101-2 model. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same. """ model = ResNet( Bottleneck, [3, 4, 23, 3], base_width=128, num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfgs["wide_resnet101_2"] if pretrained: load_pretrained(model, model.default_cfg, num_classes, in_chans) return model def resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt50-32x4d model.""" default_cfg = default_cfgs["resnext50_32x4d"] model = ResNet( Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnext50d_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt50d-32x4d model. ResNext50 w/ deep stem & avg pool downsample""" default_cfg = default_cfgs["resnext50d_32x4d"] model = ResNet( Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, stem_width=32, deep_stem=True, avg_down=True, num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt-101 32x4d model.""" default_cfg = default_cfgs["resnext101_32x4d"] model = ResNet( Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4, num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnext101_32x8d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt-101 32x8d model.""" default_cfg = default_cfgs["resnext101_32x8d"] model = ResNet( Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8, num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt101-64x4d model.""" default_cfg = default_cfgs["resnext101_32x4d"] model = ResNet( Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4, num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def tv_resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt50-32x4d model with original Torchvision weights.""" default_cfg = default_cfgs["tv_resnext50_32x4d"] model = ResNet( Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, num_classes=num_classes, in_chans=in_chans, **kwargs ) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def ig_resnext101_32x8d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt-101 32x8 model pre-trained on weakly-supervised data and finetuned on ImageNet from Figure 5 in `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ Args: pretrained (bool): load pretrained weights num_classes (int): number of classes for classifier (default: 1000 for pretrained) in_chans (int): number of input planes (default: 3 for pretrained / color) """ default_cfg = default_cfgs["ig_resnext101_32x8d"] model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def ig_resnext101_32x16d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt-101 32x16 model pre-trained on weakly-supervised data and finetuned on ImageNet from Figure 5 in `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ Args: pretrained (bool): load pretrained weights num_classes (int): number of classes for classifier (default: 1000 for pretrained) in_chans (int): number of input planes (default: 3 for pretrained / color) """ default_cfg = default_cfgs["ig_resnext101_32x16d"] model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=16, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def ig_resnext101_32x32d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt-101 32x32 model pre-trained on weakly-supervised data and finetuned on ImageNet from Figure 5 in `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ Args: pretrained (bool): load pretrained weights num_classes (int): number of classes for classifier (default: 1000 for pretrained) in_chans (int): number of input planes (default: 3 for pretrained / color) """ default_cfg = default_cfgs["ig_resnext101_32x32d"] model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=32, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model def ig_resnext101_32x48d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt-101 32x48 model pre-trained on weakly-supervised data and finetuned on ImageNet from Figure 5 in `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ Args: pretrained (bool): load pretrained weights num_classes (int): number of classes for classifier (default: 1000 for pretrained) in_chans (int): number of input planes (default: 3 for pretrained / color) """ default_cfg = default_cfgs["ig_resnext101_32x48d"] model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=48, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model

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6

c51382a38a7e8c406e1db5aa246ad6bdb7e3cd0b

45,129

py

Python

tests/test_filtering.py

SimonHurst/py-pdf-parser

4186115b64115e9916475d4a034542a64f57457b

[ "MIT" ]

null

tests/test_filtering.py

SimonHurst/py-pdf-parser

4186115b64115e9916475d4a034542a64f57457b

[ "MIT" ]

null

tests/test_filtering.py

SimonHurst/py-pdf-parser

4186115b64115e9916475d4a034542a64f57457b

[ "MIT" ]

null

import re from mock import patch, call from py_pdf_parser.components import PDFDocument, PDFElement from py_pdf_parser.common import BoundingBox from py_pdf_parser.exceptions import NoElementFoundError, MultipleElementsFoundError from py_pdf_parser.filtering import ElementList from py_pdf_parser.loaders import Page from .base import BaseTestCase from .utils import FakePDFMinerTextElement, create_pdf_document class TestFiltering(BaseTestCase): def setUp(self): self.elem1 = FakePDFMinerTextElement() self.elem2 = FakePDFMinerTextElement() self.elem3 = FakePDFMinerTextElement() self.elem4 = FakePDFMinerTextElement() self.elem5 = FakePDFMinerTextElement() self.elem6 = FakePDFMinerTextElement() self.doc = create_pdf_document( [self.elem1, self.elem2, self.elem3, self.elem4, self.elem5, self.elem6] ) self.elem_list = self.doc.elements def test_add_tag_to_elements(self): self.elem_list.add_tag_to_elements("foo") for elem in self.elem_list: self.assertIn("foo", elem.tags) def test_ignored_elements_are_excluded(self): self.assertEqual(len(self.doc.elements), len(self.elem_list)) self.elem_list[0].ignore() self.assertEqual(len(self.doc.elements), len(self.elem_list) - 1) self.assertNotIn(self.elem_list[0], self.doc.elements) def test_filter_by_tag(self): self.assertEqual(len(self.elem_list.filter_by_tag("foo")), 0) self.elem_list[0].add_tag("foo") self.assertEqual(len(self.elem_list.filter_by_tag("foo")), 1) self.assertIn(self.elem_list[0], self.elem_list.filter_by_tag("foo")) self.elem_list[1].add_tag("bar") self.assertEqual(len(self.elem_list.filter_by_tag("foo")), 1) self.assertIn(self.elem_list[0], self.elem_list.filter_by_tag("foo")) self.elem_list[2].add_tag("foo") self.assertEqual(len(self.elem_list.filter_by_tag("foo")), 2) self.assertIn(self.elem_list[0], self.elem_list.filter_by_tag("foo")) self.assertIn(self.elem_list[2], self.elem_list.filter_by_tag("foo")) def test_filter_by_tags(self): self.assertEqual(len(self.elem_list.filter_by_tags("foo", "bar")), 0) self.elem_list[0].add_tag("foo") self.assertEqual(len(self.elem_list.filter_by_tags("foo", "bar")), 1) self.assertIn(self.elem_list[0], self.elem_list.filter_by_tags("foo", "bar")) self.elem_list[1].add_tag("bar") self.assertEqual(len(self.elem_list.filter_by_tags("foo", "bar")), 2) self.assertIn(self.elem_list[0], self.elem_list.filter_by_tags("foo", "bar")) self.assertIn(self.elem_list[1], self.elem_list.filter_by_tags("foo", "bar")) self.elem_list[2].add_tag("foo") self.assertEqual(len(self.elem_list.filter_by_tags("foo", "bar")), 3) self.assertIn(self.elem_list[0], self.elem_list.filter_by_tags("foo", "bar")) self.assertIn(self.elem_list[1], self.elem_list.filter_by_tags("foo", "bar")) self.assertIn(self.elem_list[2], self.elem_list.filter_by_tags("foo", "bar")) self.elem_list[3].add_tag("baz") self.assertEqual(len(self.elem_list.filter_by_tags("foo", "bar")), 3) self.assertIn(self.elem_list[0], self.elem_list.filter_by_tags("foo", "bar")) self.assertIn(self.elem_list[1], self.elem_list.filter_by_tags("foo", "bar")) self.assertIn(self.elem_list[2], self.elem_list.filter_by_tags("foo", "bar")) def test_filter_by_text_equal(self): elem1 = FakePDFMinerTextElement(text="foo") elem2 = FakePDFMinerTextElement(text="bar") elem3 = FakePDFMinerTextElement(text="foobar") elem4 = FakePDFMinerTextElement(text="baz") doc = create_pdf_document([elem1, elem2, elem3, elem4]) self.assertEqual(len(doc.elements.filter_by_text_equal("hello")), 0) self.assertEqual(len(doc.elements.filter_by_text_equal("baz")), 1) self.assert_original_element_in(elem4, doc.elements.filter_by_text_equal("baz")) self.assertEqual(len(doc.elements.filter_by_text_equal("foo")), 1) self.assert_original_element_in(elem1, doc.elements.filter_by_text_equal("foo")) def test_filter_by_text_contains(self): elem1 = FakePDFMinerTextElement(text="foo") elem2 = FakePDFMinerTextElement(text="bar") elem3 = FakePDFMinerTextElement(text="foobar") elem4 = FakePDFMinerTextElement(text="baz") doc = create_pdf_document([elem1, elem2, elem3, elem4]) self.assertEqual(len(doc.elements.filter_by_text_contains("hello")), 0) self.assertEqual(len(doc.elements.filter_by_text_contains("baz")), 1) self.assert_original_element_in( elem4, doc.elements.filter_by_text_contains("baz") ) self.assertEqual(len(doc.elements.filter_by_text_contains("foo")), 2) self.assert_original_element_in( elem1, doc.elements.filter_by_text_contains("foo") ) self.assert_original_element_in( elem3, doc.elements.filter_by_text_contains("foo") ) def test_filter_by_regex(self): elem1 = FakePDFMinerTextElement(text="foo 1") elem2 = FakePDFMinerTextElement(text="foo") elem3 = FakePDFMinerTextElement(text="foo 987 ") elem4 = FakePDFMinerTextElement(text=" Foo 100") doc = create_pdf_document([elem1, elem2, elem3, elem4]) self.assertEqual(len(doc.elements.filter_by_regex(r"^\d+$")), 0) filter_result = doc.elements.filter_by_regex(r"^foo \d+$") self.assertEqual(len(filter_result), 2) self.assert_original_element_in(elem1, filter_result) self.assert_original_element_in(elem3, filter_result) # Test with a regex flag to ignore the case filter_result = doc.elements.filter_by_regex( r"^foo \d+$", regex_flags=re.IGNORECASE ) self.assertEqual(len(filter_result), 3) self.assert_original_element_in(elem1, filter_result) self.assert_original_element_in(elem3, filter_result) self.assert_original_element_in(elem4, filter_result) # Test with non stripped text filter_result = doc.elements.filter_by_regex(r"^foo \d+$", stripped=False) self.assertEqual(len(filter_result), 1) self.assert_original_element_in(elem1, filter_result) # Test with a regex flag to ignore the case and non stripped text, while giving # a regex with an empty space filter_result = doc.elements.filter_by_regex( r"^ foo \d+$", regex_flags=re.IGNORECASE, stripped=False ) self.assertEqual(len(filter_result), 1) self.assert_original_element_in(elem4, filter_result) def test_filter_by_font(self): elem1 = FakePDFMinerTextElement(font_name="foo", font_size=2) elem2 = FakePDFMinerTextElement(font_name="bar", font_size=3) doc = create_pdf_document([elem1, elem2]) self.assertEqual(len(doc.elements.filter_by_font("hello,1")), 0) self.assertEqual(len(doc.elements.filter_by_font("foo,2")), 1) # Check if "foo,2" has been added to cache self.assertEqual(doc._element_indexes_by_font, {"foo,2": set([0])}) self.assert_original_element_in(elem1, doc.elements.filter_by_font("foo,2")) # Check we can still filter for another font which is not in cache self.assertEqual(len(doc.elements.filter_by_font("bar,3")), 1) self.assertEqual( doc._element_indexes_by_font, {"foo,2": set([0]), "bar,3": set([1])} ) self.assert_original_element_in(elem2, doc.elements.filter_by_font("bar,3")) doc = create_pdf_document([elem1, elem2], font_mapping={"foo,2": "font_a"}) self.assertEqual(len(doc.elements.filter_by_font("hello,1")), 0) self.assertEqual(len(doc.elements.filter_by_font("foo,2")), 0) self.assertEqual(len(doc.elements.filter_by_font("font_a")), 1) # Check if "font_a" has been added to cache self.assertEqual(doc._element_indexes_by_font, {"font_a": set([0])}) self.assert_original_element_in(elem1, doc.elements.filter_by_font("font_a")) def test_filter_by_fonts(self): elem1 = FakePDFMinerTextElement(font_name="foo", font_size=2) elem2 = FakePDFMinerTextElement(font_name="bar", font_size=3) elem3 = FakePDFMinerTextElement(font_name="baz", font_size=3) doc = create_pdf_document([elem1, elem2, elem3]) self.assertEqual(len(doc.elements.filter_by_fonts("hello,1")), 0) self.assertEqual(len(doc.elements.filter_by_fonts("foo,2", "bar,3")), 2) # Check if "foo,2" and "bar,3" have been added to cache self.assertEqual( doc._element_indexes_by_font, {"foo,2": set([0]), "bar,3": set([1])} ) self.assert_original_element_in( elem1, doc.elements.filter_by_fonts("foo,2", "bar,3") ) self.assert_original_element_in( elem2, doc.elements.filter_by_fonts("foo,2", "bar,3") ) doc = create_pdf_document( [elem1, elem2, elem3], font_mapping={"foo,2": "font_a", "bar,3": "font_b", "baz,3": "font_c"}, ) self.assertEqual(len(doc.elements.filter_by_font("hello,1")), 0) self.assertEqual(len(doc.elements.filter_by_fonts("foo,2", "bar,3")), 0) self.assertEqual(len(doc.elements.filter_by_fonts("font_a", "font_b")), 2) # Check if "font_a" and "font_b" have been added to cache self.assertEqual( doc._element_indexes_by_font, {"font_a": set([0]), "font_b": set([1])} ) self.assert_original_element_in( elem1, doc.elements.filter_by_fonts("font_a", "font_b") ) self.assert_original_element_in( elem2, doc.elements.filter_by_fonts("font_a", "font_b") ) # Check we can still filter for another font which is not in cache self.assertEqual(len(doc.elements.filter_by_fonts("font_b", "font_c")), 2) self.assert_original_element_in( elem2, doc.elements.filter_by_fonts("font_b", "font_c") ) self.assert_original_element_in( elem3, doc.elements.filter_by_fonts("font_b", "font_c") ) self.assertEqual( doc._element_indexes_by_font, {"font_a": set([0]), "font_b": set([1]), "font_c": set([2])}, ) def test_filter_by_page(self): elem1 = FakePDFMinerTextElement() elem2 = FakePDFMinerTextElement() elem3 = FakePDFMinerTextElement() page1 = Page(width=100, height=100, elements=[elem1, elem2]) page2 = Page(width=100, height=100, elements=[elem3]) doc = PDFDocument({1: page1, 2: page2}) self.assertEqual(len(doc.elements.filter_by_page(1)), 2) self.assert_original_element_in(elem1, doc.elements.filter_by_page(1)) self.assert_original_element_in(elem2, doc.elements.filter_by_page(1)) def test_filter_by_pages(self): elem1 = FakePDFMinerTextElement() elem2 = FakePDFMinerTextElement() elem3 = FakePDFMinerTextElement() elem4 = FakePDFMinerTextElement() page1 = Page(width=100, height=100, elements=[elem1, elem2]) page2 = Page(width=100, height=100, elements=[elem3]) page3 = Page(width=100, height=100, elements=[elem4]) doc = PDFDocument({1: page1, 2: page2, 3: page3}) self.assertEqual(len(doc.elements.filter_by_pages(1, 2)), 3) self.assert_original_element_in(elem1, doc.elements.filter_by_pages(1, 2)) self.assert_original_element_in(elem2, doc.elements.filter_by_pages(1, 2)) self.assert_original_element_in(elem3, doc.elements.filter_by_pages(1, 2)) def test_filter_by_section_name(self): self.doc.sectioning.create_section("foo", self.elem_list[0], self.elem_list[1]) self.assertEqual(len(self.elem_list.filter_by_section_name("foo")), 2) self.assertIn(self.elem_list[0], self.elem_list.filter_by_section_name("foo")) self.assertIn(self.elem_list[1], self.elem_list.filter_by_section_name("foo")) self.doc.sectioning.create_section("foo", self.elem_list[3], self.elem_list[5]) self.assertEqual(len(self.elem_list.filter_by_section_name("foo")), 5) self.assertIn(self.elem_list[0], self.elem_list.filter_by_section_name("foo")) self.assertIn(self.elem_list[1], self.elem_list.filter_by_section_name("foo")) self.assertIn(self.elem_list[3], self.elem_list.filter_by_section_name("foo")) self.assertIn(self.elem_list[4], self.elem_list.filter_by_section_name("foo")) self.assertIn(self.elem_list[5], self.elem_list.filter_by_section_name("foo")) def test_filter_by_section_names(self): self.doc.sectioning.create_section("foo", self.elem_list[0], self.elem_list[1]) self.doc.sectioning.create_section("bar", self.elem_list[3], self.elem_list[5]) self.doc.sectioning.create_section("foo", self.elem_list[3], self.elem_list[5]) self.assertEqual(len(self.elem_list.filter_by_section_names("foo", "bar")), 5) self.assertIn( self.elem_list[0], self.elem_list.filter_by_section_names("foo", "bar") ) self.assertIn( self.elem_list[1], self.elem_list.filter_by_section_names("foo", "bar") ) self.assertIn( self.elem_list[3], self.elem_list.filter_by_section_names("foo", "bar") ) self.assertIn( self.elem_list[4], self.elem_list.filter_by_section_names("foo", "bar") ) self.assertIn( self.elem_list[5], self.elem_list.filter_by_section_names("foo", "bar") ) def test_filter_by_section(self): self.doc.sectioning.create_section("foo", self.elem_list[0], self.elem_list[1]) self.doc.sectioning.create_section("foo", self.elem_list[3], self.elem_list[5]) self.assertEqual(len(self.elem_list.filter_by_section("foo_0")), 2) self.assertIn(self.elem_list[0], self.elem_list.filter_by_section("foo_0")) self.assertIn(self.elem_list[1], self.elem_list.filter_by_section("foo_0")) # Filtering for non-existent section should return empty ElementList self.assertEqual(len(self.elem_list.filter_by_section("bar")), 0) def test_filter_by_sections(self): self.doc.sectioning.create_section("foo", self.elem_list[0], self.elem_list[1]) self.doc.sectioning.create_section("foo", self.elem_list[3], self.elem_list[5]) self.assertEqual(len(self.elem_list.filter_by_sections("foo_0", "foo_1")), 5) self.assertIn( self.elem_list[0], self.elem_list.filter_by_sections("foo_0", "foo_1") ) self.assertIn( self.elem_list[1], self.elem_list.filter_by_sections("foo_0", "foo_1") ) self.assertIn( self.elem_list[3], self.elem_list.filter_by_sections("foo_0", "foo_1") ) self.assertIn( self.elem_list[4], self.elem_list.filter_by_sections("foo_0", "foo_1") ) self.assertIn( self.elem_list[5], self.elem_list.filter_by_sections("foo_0", "foo_1") ) def test_ignore_elements(self): self.elem_list.ignore_elements() self.assertTrue(self.elem_list[0].ignored) self.assertTrue(self.elem_list[1].ignored) self.assertTrue(self.elem_list[2].ignored) self.assertTrue(self.elem_list[3].ignored) self.assertTrue(self.elem_list[4].ignored) self.assertTrue(self.elem_list[5].ignored) self.assertEqual(0, len(self.doc.elements)) self.assertEqual(self.doc._ignored_indexes, set([0, 1, 2, 3, 4, 5])) @patch.object(PDFElement, "partially_within", autospec=True) def test_to_the_right_of(self, partially_within_mock): partially_within_mock.side_effect = ( lambda self, bounding_box: self.text() == "within" ) elem1 = FakePDFMinerTextElement( text="within", bounding_box=BoundingBox(50, 51, 50, 51) ) elem2 = FakePDFMinerTextElement(text="within") elem3 = FakePDFMinerTextElement() elem4 = FakePDFMinerTextElement(text="within") elem5 = FakePDFMinerTextElement() elem6 = FakePDFMinerTextElement(text="within") page1 = Page(elements=[elem1, elem2, elem3, elem4], width=100, height=100) page2 = Page(elements=[elem5, elem6], width=100, height=100) doc = PDFDocument(pages={1: page1, 2: page2}) elem_list = doc.elements pdf_elem1 = self.extract_element_from_list(elem1, elem_list) pdf_elem2 = self.extract_element_from_list(elem2, elem_list) pdf_elem3 = self.extract_element_from_list(elem3, elem_list) pdf_elem4 = self.extract_element_from_list(elem4, elem_list) result = elem_list.to_the_right_of(pdf_elem1) # expected_bbox is from the right edge of elem1 to the right edge of the page expected_bbox = BoundingBox(51, 100, 50, 51) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 2) self.assertIn(pdf_elem2, result) self.assertIn(pdf_elem4, result) # Also test with inclusive=True partially_within_mock.reset_mock() result = elem_list.to_the_right_of(pdf_elem1, inclusive=True) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 3) self.assertIn(pdf_elem1, result) self.assertIn(pdf_elem2, result) self.assertIn(pdf_elem4, result) # Test specifying tolerance expected_bbox = BoundingBox(51, 100, 50.1, 50.9) partially_within_mock.reset_mock() result = elem_list.to_the_right_of(pdf_elem1, tolerance=0.1) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) @patch.object(PDFElement, "partially_within", autospec=True) def test_to_the_left_of(self, partially_within_mock): partially_within_mock.side_effect = ( lambda self, bounding_box: self.text() == "within" ) elem1 = FakePDFMinerTextElement( text="within", bounding_box=BoundingBox(50, 51, 50, 51) ) elem2 = FakePDFMinerTextElement(text="within") elem3 = FakePDFMinerTextElement() elem4 = FakePDFMinerTextElement(text="within") elem5 = FakePDFMinerTextElement() elem6 = FakePDFMinerTextElement(text="within") page1 = Page(elements=[elem1, elem2, elem3, elem4], width=100, height=100) page2 = Page(elements=[elem5, elem6], width=100, height=100) doc = PDFDocument(pages={1: page1, 2: page2}) elem_list = doc.elements pdf_elem1 = self.extract_element_from_list(elem1, elem_list) pdf_elem2 = self.extract_element_from_list(elem2, elem_list) pdf_elem3 = self.extract_element_from_list(elem3, elem_list) pdf_elem4 = self.extract_element_from_list(elem4, elem_list) result = elem_list.to_the_left_of(pdf_elem1) # expected_bbox is from the left edge of elem1 to the left edge of the page expected_bbox = BoundingBox(0, 50, 50, 51) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 2) self.assertIn(pdf_elem2, result) self.assertIn(pdf_elem4, result) # Also test with inclusive=True partially_within_mock.reset_mock() result = elem_list.to_the_left_of(pdf_elem1, inclusive=True) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 3) self.assertIn(pdf_elem1, result) self.assertIn(pdf_elem2, result) self.assertIn(pdf_elem4, result) # Test specifying tolerance expected_bbox = BoundingBox(0, 50, 50.1, 50.9) partially_within_mock.reset_mock() result = elem_list.to_the_left_of(pdf_elem1, tolerance=0.1) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) @patch.object(PDFElement, "partially_within", autospec=True) def test_below(self, partially_within_mock): partially_within_mock.side_effect = ( lambda self, bounding_box: self.text() == "within" ) elem1 = FakePDFMinerTextElement(text="within") elem2 = FakePDFMinerTextElement() elem3 = FakePDFMinerTextElement( text="within", bounding_box=BoundingBox(50, 51, 50, 51) ) elem4 = FakePDFMinerTextElement(text="within") elem5 = FakePDFMinerTextElement() elem6 = FakePDFMinerTextElement(text="within") elem7 = FakePDFMinerTextElement() elem8 = FakePDFMinerTextElement(text="within") page1 = Page(elements=[elem1, elem2], width=100, height=100) page2 = Page(elements=[elem3, elem4, elem5, elem6], width=100, height=100) page3 = Page(elements=[elem7, elem8], width=100, height=100) doc = PDFDocument(pages={1: page1, 2: page2, 3: page3}) elem_list = doc.elements pdf_elem3 = self.extract_element_from_list(elem3, elem_list) pdf_elem4 = self.extract_element_from_list(elem4, elem_list) pdf_elem5 = self.extract_element_from_list(elem5, elem_list) pdf_elem6 = self.extract_element_from_list(elem6, elem_list) pdf_elem7 = self.extract_element_from_list(elem7, elem_list) pdf_elem8 = self.extract_element_from_list(elem8, elem_list) result = elem_list.below(pdf_elem3) # expected_bbox is from the left edge of elem1 to the left edge of the page expected_bbox = BoundingBox(50, 51, 0, 50) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 2) self.assertIn(pdf_elem4, result) self.assertIn(pdf_elem6, result) # Also test with inclusive=True partially_within_mock.reset_mock() result = elem_list.below(pdf_elem3, inclusive=True) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 3) self.assertIn(pdf_elem3, result) self.assertIn(pdf_elem4, result) self.assertIn(pdf_elem6, result) # Also test with all_pages=True partially_within_mock.reset_mock() result = elem_list.below(pdf_elem3, all_pages=True) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), call(pdf_elem7, BoundingBox(50, 51, 0, 100)), call(pdf_elem8, BoundingBox(50, 51, 0, 100)), ], any_order=True, ) self.assertEqual(len(result), 3) self.assertIn(pdf_elem4, result) self.assertIn(pdf_elem6, result) self.assertIn(pdf_elem8, result) # Test specifying tolerance expected_bbox = BoundingBox(50.1, 50.9, 0, 50) partially_within_mock.reset_mock() result = elem_list.below(pdf_elem3, tolerance=0.1) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) @patch.object(PDFElement, "partially_within", autospec=True) def test_above(self, partially_within_mock): partially_within_mock.side_effect = ( lambda self, bounding_box: self.text() == "within" ) elem1 = FakePDFMinerTextElement(text="within") elem2 = FakePDFMinerTextElement() elem3 = FakePDFMinerTextElement( text="within", bounding_box=BoundingBox(50, 51, 50, 51) ) elem4 = FakePDFMinerTextElement(text="within") elem5 = FakePDFMinerTextElement() elem6 = FakePDFMinerTextElement(text="within") elem7 = FakePDFMinerTextElement() elem8 = FakePDFMinerTextElement(text="within") page1 = Page(elements=[elem1, elem2], width=100, height=100) page2 = Page(elements=[elem3, elem4, elem5, elem6], width=100, height=100) page3 = Page(elements=[elem7, elem8], width=100, height=100) doc = PDFDocument(pages={1: page1, 2: page2, 3: page3}) elem_list = doc.elements pdf_elem1 = self.extract_element_from_list(elem1, elem_list) pdf_elem2 = self.extract_element_from_list(elem2, elem_list) pdf_elem3 = self.extract_element_from_list(elem3, elem_list) pdf_elem4 = self.extract_element_from_list(elem4, elem_list) pdf_elem5 = self.extract_element_from_list(elem5, elem_list) pdf_elem6 = self.extract_element_from_list(elem6, elem_list) result = elem_list.above(pdf_elem3) # expected_bbox is from the left edge of elem1 to the left edge of the page expected_bbox = BoundingBox(50, 51, 51, 100) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 2) self.assertIn(pdf_elem4, result) self.assertIn(pdf_elem6, result) # Also test with inclusive=True partially_within_mock.reset_mock() result = elem_list.above(pdf_elem3, inclusive=True) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 3) self.assertIn(pdf_elem3, result) self.assertIn(pdf_elem4, result) self.assertIn(pdf_elem6, result) # Also test with all_pages=True partially_within_mock.reset_mock() result = elem_list.above(pdf_elem3, all_pages=True) partially_within_mock.assert_has_calls( [ call(pdf_elem1, BoundingBox(50, 51, 0, 100)), call(pdf_elem2, BoundingBox(50, 51, 0, 100)), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 3) self.assertIn(pdf_elem1, result) self.assertIn(pdf_elem4, result) self.assertIn(pdf_elem6, result) # Test specifying tolerance expected_bbox = BoundingBox(50.1, 50.9, 51, 100) partially_within_mock.reset_mock() result = elem_list.above(pdf_elem3, tolerance=0.1) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) @patch.object(PDFElement, "partially_within", autospec=True) def test_vertically_in_line_with(self, partially_within_mock): partially_within_mock.side_effect = ( lambda self, bounding_box: self.text() == "within" ) elem1 = FakePDFMinerTextElement(text="within") elem2 = FakePDFMinerTextElement() elem3 = FakePDFMinerTextElement( text="within", bounding_box=BoundingBox(50, 51, 50, 51) ) elem4 = FakePDFMinerTextElement(text="within") elem5 = FakePDFMinerTextElement() elem6 = FakePDFMinerTextElement(text="within") elem7 = FakePDFMinerTextElement() elem8 = FakePDFMinerTextElement(text="within") page1 = Page(elements=[elem1, elem2], width=100, height=100) page2 = Page(elements=[elem3, elem4, elem5, elem6], width=100, height=100) page3 = Page(elements=[elem7, elem8], width=100, height=100) doc = PDFDocument(pages={1: page1, 2: page2, 3: page3}) elem_list = doc.elements pdf_elem1 = self.extract_element_from_list(elem1, elem_list) pdf_elem2 = self.extract_element_from_list(elem2, elem_list) pdf_elem3 = self.extract_element_from_list(elem3, elem_list) pdf_elem4 = self.extract_element_from_list(elem4, elem_list) pdf_elem5 = self.extract_element_from_list(elem5, elem_list) pdf_elem6 = self.extract_element_from_list(elem6, elem_list) pdf_elem7 = self.extract_element_from_list(elem7, elem_list) pdf_elem8 = self.extract_element_from_list(elem8, elem_list) result = elem_list.vertically_in_line_with(pdf_elem3) # expected_bbox is from the left edge of elem1 to the left edge of the page expected_bbox = BoundingBox(50, 51, 0, 100) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 2) self.assertIn(pdf_elem4, result) self.assertIn(pdf_elem6, result) # Also test with inclusive=True partially_within_mock.reset_mock() result = elem_list.vertically_in_line_with(pdf_elem3, inclusive=True) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 3) self.assertIn(pdf_elem3, result) self.assertIn(pdf_elem4, result) self.assertIn(pdf_elem6, result) # Also test with all_pages=True partially_within_mock.reset_mock() result = elem_list.vertically_in_line_with(pdf_elem3, all_pages=True) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), call(pdf_elem7, expected_bbox), call(pdf_elem8, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 4) self.assertIn(pdf_elem1, result) self.assertIn(pdf_elem4, result) self.assertIn(pdf_elem6, result) self.assertIn(pdf_elem8, result) # Test specifying tolerance expected_bbox = BoundingBox(50.1, 50.9, 0, 100) partially_within_mock.reset_mock() result = elem_list.vertically_in_line_with(pdf_elem3, tolerance=0.1) partially_within_mock.assert_has_calls( [ call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), call(pdf_elem5, expected_bbox), call(pdf_elem6, expected_bbox), ], any_order=True, ) @patch.object(PDFElement, "partially_within", autospec=True) def test_horizontally_in_line_with(self, partially_within_mock): partially_within_mock.side_effect = ( lambda self, bounding_box: self.text() == "within" ) elem1 = FakePDFMinerTextElement( text="within", bounding_box=BoundingBox(50, 51, 50, 51) ) elem2 = FakePDFMinerTextElement(text="within") elem3 = FakePDFMinerTextElement() elem4 = FakePDFMinerTextElement(text="within") elem5 = FakePDFMinerTextElement() elem6 = FakePDFMinerTextElement(text="within") page1 = Page(elements=[elem1, elem2, elem3, elem4], width=100, height=100) page2 = Page(elements=[elem5, elem6], width=100, height=100) doc = PDFDocument(pages={1: page1, 2: page2}) elem_list = doc.elements pdf_elem1 = self.extract_element_from_list(elem1, elem_list) pdf_elem2 = self.extract_element_from_list(elem2, elem_list) pdf_elem3 = self.extract_element_from_list(elem3, elem_list) pdf_elem4 = self.extract_element_from_list(elem4, elem_list) result = elem_list.horizontally_in_line_with(pdf_elem1) # expected_bbox is from the left edge of elem1 to the left edge of the page expected_bbox = BoundingBox(0, 100, 50, 51) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 2) self.assertIn(pdf_elem2, result) self.assertIn(pdf_elem4, result) # Also test with inclusive=True partially_within_mock.reset_mock() result = elem_list.horizontally_in_line_with(pdf_elem1, inclusive=True) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 3) self.assertIn(pdf_elem1, result) self.assertIn(pdf_elem2, result) self.assertIn(pdf_elem4, result) # Test specifying tolerance expected_bbox = BoundingBox(0, 100, 50.1, 50.9) partially_within_mock.reset_mock() result = elem_list.horizontally_in_line_with(pdf_elem1, tolerance=0.1) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) @patch.object(PDFElement, "partially_within", autospec=True) def test_filter_partially_within_bounding_box(self, partially_within_mock): partially_within_mock.side_effect = ( lambda self, bounding_box: self.text() == "within" ) elem1 = FakePDFMinerTextElement(text="within") elem2 = FakePDFMinerTextElement(text="within") elem3 = FakePDFMinerTextElement() elem4 = FakePDFMinerTextElement(text="within") elem5 = FakePDFMinerTextElement() elem6 = FakePDFMinerTextElement(text="within") page1 = Page(elements=[elem1, elem2, elem3, elem4], width=100, height=100) page2 = Page(elements=[elem5, elem6], width=100, height=100) doc = PDFDocument(pages={1: page1, 2: page2}) elem_list = doc.elements pdf_elem1 = self.extract_element_from_list(elem1, elem_list) pdf_elem2 = self.extract_element_from_list(elem2, elem_list) pdf_elem3 = self.extract_element_from_list(elem3, elem_list) pdf_elem4 = self.extract_element_from_list(elem4, elem_list) result = elem_list.filter_partially_within_bounding_box( BoundingBox(0, 1, 0, 1), 1 ) # expected_bbox is from the left edge of elem1 to the left edge of the page expected_bbox = BoundingBox(0, 1, 0, 1) partially_within_mock.assert_has_calls( [ call(pdf_elem1, expected_bbox), call(pdf_elem2, expected_bbox), call(pdf_elem3, expected_bbox), call(pdf_elem4, expected_bbox), ], any_order=True, ) self.assertEqual(len(result), 3) self.assertIn(pdf_elem1, result) self.assertIn(pdf_elem2, result) self.assertIn(pdf_elem4, result) def test_before(self): result = self.elem_list.before(self.elem_list[2]) self.assertEqual(len(result), 2) self.assertIn(self.elem_list[0], result) self.assertIn(self.elem_list[1], result) result = self.elem_list.before(self.elem_list[2], inclusive=True) self.assertEqual(len(result), 3) self.assertIn(self.elem_list[0], result) self.assertIn(self.elem_list[1], result) self.assertIn(self.elem_list[2], result) def test_after(self): result = self.elem_list.after(self.elem_list[3]) self.assertEqual(len(result), 2) self.assertIn(self.elem_list[4], result) self.assertIn(self.elem_list[5], result) result = self.elem_list.after(self.elem_list[3], inclusive=True) self.assertEqual(len(result), 3) self.assertIn(self.elem_list[3], result) self.assertIn(self.elem_list[4], result) self.assertIn(self.elem_list[5], result) def test_between(self): result = self.elem_list.between(self.elem_list[2], self.elem_list[5]) self.assertEqual(len(result), 2) self.assertIn(self.elem_list[3], result) self.assertIn(self.elem_list[4], result) result = self.elem_list.between( self.elem_list[2], self.elem_list[5], inclusive=True ) self.assertEqual(len(result), 4) self.assertIn(self.elem_list[2], result) self.assertIn(self.elem_list[3], result) self.assertIn(self.elem_list[4], result) self.assertIn(self.elem_list[5], result) def test_extract_single_element(self): with self.assertRaises(MultipleElementsFoundError): self.elem_list.extract_single_element() with self.assertRaises(NoElementFoundError): self.elem_list.filter_by_tag("non_existent_tag").extract_single_element() elem1 = FakePDFMinerTextElement() page = Page(elements=[elem1], width=100, height=100) doc = PDFDocument(pages={1: page}) pdf_elem_1 = self.extract_element_from_list(elem1, doc.elements) result = doc.elements.extract_single_element() self.assertEqual(result, pdf_elem_1) def test_add_element(self): empty_elem_list = self.elem_list.filter_by_tag("non_existent_tag") result = empty_elem_list.add_element(self.elem_list[0]) self.assertEqual(len(result), 1) self.assertIn(self.elem_list[0], result) result = result.add_element(self.elem_list[0]) self.assertEqual(len(result), 1) self.assertIn(self.elem_list[0], result) result = result.add_element(self.elem_list[4]) self.assertEqual(len(result), 2) self.assertIn(self.elem_list[0], result) self.assertIn(self.elem_list[4], result) def test_add_elements(self): empty_elem_list = self.elem_list.filter_by_tag("non_existent_tag") result = empty_elem_list.add_elements(self.elem_list[0], self.elem_list[1]) self.assertEqual(len(result), 2) self.assertIn(self.elem_list[0], result) self.assertIn(self.elem_list[1], result) def test_remove_element(self): original_length = len(self.elem_list) result = self.elem_list.remove_element(self.elem_list[0]) self.assertEqual(len(result), original_length - 1) self.assertNotIn(self.elem_list[0], result) result = result.remove_element(self.elem_list[0]) self.assertEqual(len(result), original_length - 1) self.assertNotIn(self.elem_list[0], result) result = result.remove_element(self.elem_list[4]) self.assertEqual(len(result), original_length - 2) self.assertNotIn(self.elem_list[0], result) self.assertNotIn(self.elem_list[4], result) def test_remove_elements(self): original_length = len(self.elem_list) result = self.elem_list.remove_elements(self.elem_list[0], self.elem_list[1]) self.assertEqual(len(result), original_length - 2) self.assertNotIn(self.elem_list[0], result) self.assertNotIn(self.elem_list[1], result) def test_repr(self): self.assertEqual(repr(self.elem_list), "<ElementList of 6 elements>") def test_getitem(self): self.assert_original_element_equal(self.elem1, self.elem_list[0]) self.assertIsInstance(self.elem_list[1:3], ElementList) self.assertEqual(len(self.elem_list[1:3]), 2) self.assertIn(self.elem_list[1], self.elem_list[1:3]) self.assertIn(self.elem_list[2], self.elem_list[1:3]) def test_eq(self): with self.assertRaises(NotImplementedError): self.elem_list == "foo" second_elem_list = ElementList(self.doc, set([0, 1, 2, 3, 4, 5])) self.assertTrue(self.elem_list == second_elem_list) # Test with different indexes second_elem_list = ElementList(self.doc, set([0, 1, 2, 3, 4])) self.assertFalse(self.elem_list == second_elem_list) # Test with different document doc = PDFDocument( pages={ 1: Page( elements=[ FakePDFMinerTextElement(), FakePDFMinerTextElement(), FakePDFMinerTextElement(), FakePDFMinerTextElement(), FakePDFMinerTextElement(), FakePDFMinerTextElement(), ], width=100, height=100, ) } ) second_elem_list = ElementList(doc, set([0, 1, 2, 3, 4, 5])) self.assertFalse(self.elem_list == second_elem_list) def test_len(self): self.assertEqual(len(self.elem_list), 6) def test_sub(self): list_1 = ElementList(self.doc, set([0, 1, 2, 3, 4, 5])) list_2 = ElementList(self.doc, set([0, 2])) result = list_1 - list_2 self.assertEqual(result, ElementList(self.doc, set([1, 3, 4, 5]))) def test_or(self): list_1 = ElementList(self.doc, set([0, 2])) list_2 = ElementList(self.doc, set([2, 3, 4])) result = list_1 | list_2 self.assertEqual(result, ElementList(self.doc, set([0, 2, 3, 4]))) def test_xor(self): list_1 = ElementList(self.doc, set([0, 2])) list_2 = ElementList(self.doc, set([2, 3, 4])) result = list_1 ^ list_2 self.assertEqual(result, ElementList(self.doc, set([0, 3, 4]))) def test_and(self): list_1 = ElementList(self.doc, set([0, 2])) list_2 = ElementList(self.doc, set([2, 3, 4])) result = list_1 & list_2 self.assertEqual(result, ElementList(self.doc, set([2])))

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3d6fa83aaa7061e3a55d9c3e540e727756dc2c22

187

py

Python

venv/Lib/site-packages/dpyConsole/errors.py

oOperaho/Cruzeiro

57730376e8ac347531984ef49fc1349e084c2b5a

[ "MIT" ]

13

2020-10-07T04:21:24.000Z

2022-01-31T20:36:55.000Z

venv/Lib/site-packages/dpyConsole/errors.py

oOperaho/Cruzeiro

57730376e8ac347531984ef49fc1349e084c2b5a

[ "MIT" ]

null

venv/Lib/site-packages/dpyConsole/errors.py

oOperaho/Cruzeiro

57730376e8ac347531984ef49fc1349e084c2b5a

[ "MIT" ]

1

2021-06-17T00:35:41.000Z

class CommandNotFound(Exception): def __init__(self, command_name): self.name = command_name def __str__(self): return f"Command with name {self.name} not found"

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py

Python

openmc/stats/__init__.py

janmalec/openmc

4a4ac4c351d41fe153ca3341820cc507e484ce50

[ "MIT" ]

262

2018-08-09T21:27:03.000Z

2022-03-24T05:02:10.000Z

openmc/stats/__init__.py

janmalec/openmc

4a4ac4c351d41fe153ca3341820cc507e484ce50

[ "MIT" ]

753

2018-08-03T15:26:57.000Z

2022-03-29T23:54:48.000Z

openmc/stats/__init__.py

janmalec/openmc

4a4ac4c351d41fe153ca3341820cc507e484ce50

[ "MIT" ]

196

2018-08-06T13:41:14.000Z

2022-03-29T20:47:12.000Z

from openmc.stats.univariate import * from openmc.stats.multivariate import *

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py

Python

automix/transition/__init__.py

MZehren/Automix

dfaa00a9e7c5c0938c0a9d275c07f3a3e5f87e43

[ "MIT" ]

18

2020-07-20T01:51:40.000Z

2022-02-25T07:32:11.000Z

automix/transition/__init__.py

MZehren/Automix

dfaa00a9e7c5c0938c0a9d275c07f3a3e5f87e43

[ "MIT" ]

2

2021-03-23T03:26:02.000Z

2021-07-19T12:51:25.000Z

automix/transition/__init__.py

MZehren/Automix

dfaa00a9e7c5c0938c0a9d275c07f3a3e5f87e43

[ "MIT" ]

5

2021-01-03T15:34:28.000Z

2022-02-22T06:07:06.000Z

""" Package which generates the transitions. """

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34,473

py

Python

train_helper.py

ItGirls/event_extraction

b8b3bd9c5c34e7a1be086a7fcd608ef89335e4c1

[ "MIT" ]

203

2020-05-18T02:26:24.000Z

2022-03-30T09:49:42.000Z

train_helper.py

ItGirls/event_extraction

b8b3bd9c5c34e7a1be086a7fcd608ef89335e4c1

[ "MIT" ]

14

2020-05-19T02:34:16.000Z

2021-09-10T07:52:31.000Z

train_helper.py

djj-djj/event_extraction

dfecdbb7d0a905495065cc7a0a4afd10e6dd1130

[ "MIT" ]

34

2020-05-19T02:25:26.000Z

2022-03-30T09:49:34.000Z

import os import numpy as np import time import logging from common_utils import set_logger import tensorflow as tf from sklearn.metrics import f1_score from models.bert_mrc import bert_mrc_model_fn_builder from models.bert_event_type_classification import bert_classification_model_fn_builder from data_processing.data_utils import * from data_processing.event_prepare_data import EventRolePrepareMRC, EventTypeClassificationPrepare # from data_processing.event_prepare_data import EventRoleClassificationPrepare from data_processing.event_prepare_data import event_input_bert_mrc_mul_fn, event_index_class_input_bert_fn from data_processing.event_prepare_data import event_binclass_input_bert_fn from models.bert_event_type_classification import bert_binaryclassification_model_fn_builder from data_processing.event_prepare_data import event_input_verfify_mrc_fn from models.event_verify_av import event_verify_mrc_model_fn_builder from configs.event_config import event_config # import horovod.tensorflow as hvd os.environ["CUDA_VISIBLE_DEVICES"] = "0" logger = set_logger("[run training]") # logger = logging.getLogger('train') # logger.setLevel(logging.INFO) # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION']='1' def serving_input_receiver_fn(): """Serving input_fn that builds features from placeholders Returns ------- tf.estimator.export.ServingInputReceiver """ words = tf.placeholder(dtype=tf.int32, shape=[None, None], name='words') nwords = tf.placeholder(dtype=tf.int32, shape=[None], name='text_length') words_seq = tf.placeholder(dtype=tf.int32, shape=[None, None], name='words_seq') receiver_tensors = {'words': words, 'text_length': nwords, 'words_seq': words_seq} features = {'words': words, 'text_length': nwords, 'words_seq': words_seq} return tf.estimator.export.ServingInputReceiver(features, receiver_tensors) def bert_serving_input_receiver_fn(): """Serving input_fn that builds features from placeholders Returns ------- tf.estimator.export.ServingInputReceiver """ words = tf.placeholder(dtype=tf.int32, shape=[None, None], name='words') nwords = tf.placeholder(dtype=tf.int32, shape=[None], name='text_length') receiver_tensors = {'words': words, 'text_length': nwords} features = {'words': words, 'text_length': nwords} return tf.estimator.export.ServingInputReceiver(features, receiver_tensors) def bert_event_type_serving_input_receiver_fn(): words = tf.placeholder(dtype=tf.int32, shape=[None, None], name='words') nwords = tf.placeholder(dtype=tf.int32, shape=[None], name='text_length') token_type_ids = tf.placeholder(dtype=tf.int32, shape=[None, None], name="token_type_ids") type_index_ids = tf.placeholder(dtype=tf.int32, shape=[None, 65], name="type_index_in_ids_list") receiver_tensors = {'words': words, 'text_length': nwords, 'token_type_ids': token_type_ids, 'type_index_in_ids_list': type_index_ids} features = {'words': words, 'text_length': nwords, 'token_type_ids': token_type_ids, 'type_index_in_ids_list': type_index_ids} return tf.estimator.export.ServingInputReceiver(features, receiver_tensors) def bert_event_bin_serving_input_receiver_fn(): words = tf.placeholder(dtype=tf.int32, shape=[None, None], name='words') nwords = tf.placeholder(dtype=tf.int32, shape=[None], name='text_length') token_type_ids = tf.placeholder(dtype=tf.int32, shape=[None, None], name="token_type_ids") receiver_tensors = {'words': words, 'text_length': nwords, 'token_type_ids': token_type_ids} features = {'words': words, 'text_length': nwords, 'token_type_ids': token_type_ids} return tf.estimator.export.ServingInputReceiver(features, receiver_tensors) def bert_mrc_serving_input_receiver_fn(): # features['words'],features['text_length'],features['query_length'],features['token_type_ids'] words = tf.placeholder(dtype=tf.int32, shape=[None, None], name='words') nwords = tf.placeholder(dtype=tf.int32, shape=[None], name='text_length') query_lengths = tf.placeholder(dtype=tf.int32, shape=[None], name="query_length") token_type_ids = tf.placeholder(dtype=tf.int32, shape=[None, None], name="token_type_ids") receiver_tensors = {'words': words, 'text_length': nwords, 'query_length': query_lengths, 'token_type_ids': token_type_ids} features = {'words': words, 'text_length': nwords, 'query_length': query_lengths, 'token_type_ids': token_type_ids} return tf.estimator.export.ServingInputReceiver(features, receiver_tensors) def run_event_role_mrc(args): """ baseline 用mrc来做事件role抽取 :param args: :return: """ model_base_dir = event_config.get(args.model_checkpoint_dir).format(args.fold_index) pb_model_dir = event_config.get(args.model_pb_dir).format(args.fold_index) vocab_file_path = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("vocab_file")) bert_config_file = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("bert_config_path")) slot_file = os.path.join(event_config.get("slot_list_root_path"), event_config.get("bert_slot_complete_file_name_role")) schema_file = os.path.join(event_config.get("data_dir"), event_config.get("event_schema")) query_map_file = os.path.join(event_config.get("slot_list_root_path"), event_config.get("query_map_file")) data_loader = EventRolePrepareMRC(vocab_file_path, 512, slot_file, schema_file, query_map_file) # train_file = os.path.join(event_config.get("data_dir"), event_config.get("event_data_file_train")) # eval_file = os.path.join(event_config.get("data_dir"), event_config.get("event_data_file_eval")) # data_list,label_start_list,label_end_list,query_len_list,token_type_id_list # train_datas, train_labels_start,train_labels_end,train_query_lens,train_token_type_id_list,dev_datas, dev_labels_start,dev_labels_end,dev_query_lens,dev_token_type_id_list = data_loader._read_json_file(train_file,eval_file,True) # dev_datas, dev_labels_start,dev_labels_end,dev_query_lens,dev_token_type_id_list = data_loader._read_json_file(eval_file,None,False) # train_datas, train_labels_start,train_labels_end,train_query_lens,train_token_type_id_list,dev_datas, dev_labels_start,dev_labels_end,dev_query_lens,dev_token_type_id_list = data_loader._merge_ee_and_re_datas(train_file,eval_file,"relation_extraction/data/train_data.json","relation_extraction/data/dev_data.json") train_datas = np.load("data/neg_fold_data_{}/token_ids_train.npy".format(args.fold_index), allow_pickle=True) train_labels = np.load("data/neg_fold_data_{}/multi_labels_train.npy".format(args.fold_index), allow_pickle=True) train_query_lens = np.load("data/neg_fold_data_{}/query_lens_train.npy".format(args.fold_index), allow_pickle=True) train_token_type_id_list = np.load("data/neg_fold_data_{}/token_type_ids_train.npy".format(args.fold_index), allow_pickle=True) dev_datas = np.load("data/neg_fold_data_{}/token_ids_dev.npy".format(args.fold_index), allow_pickle=True) dev_labels = np.load("data/neg_fold_data_{}/multi_labels_dev.npy".format(args.fold_index), allow_pickle=True) dev_query_lens = np.load("data/neg_fold_data_{}/query_lens_dev.npy".format(args.fold_index), allow_pickle=True) dev_token_type_id_list = np.load("data/neg_fold_data_{}/token_type_ids_dev.npy".format(args.fold_index), allow_pickle=True) train_samples_nums = len(train_datas) dev_samples_nums = len(dev_datas) if train_samples_nums % args.train_batch_size != 0: each_epoch_steps = int(train_samples_nums / args.train_batch_size) + 1 else: each_epoch_steps = int(train_samples_nums / args.train_batch_size) # each_epoch_steps = int(data_loader.train_samples_nums/args.train_batch_size)+1 logger.info('*****train_set sample nums:{}'.format(train_samples_nums)) logger.info('*****dev_set sample nums:{}'.format(dev_samples_nums)) logger.info('*****train each epoch steps:{}'.format(each_epoch_steps)) train_steps_nums = each_epoch_steps * args.epochs # train_steps_nums = each_epoch_steps * args.epochs // hvd.size() logger.info('*****train_total_steps:{}'.format(train_steps_nums)) decay_steps = args.decay_epoch * each_epoch_steps logger.info('*****train decay steps:{}'.format(decay_steps)) # dropout_prob是丢弃概率 params = {"dropout_prob": args.dropout_prob, "num_labels": data_loader.labels_map_len, "rnn_size": args.rnn_units, "num_layers": args.num_layers, "hidden_units": args.hidden_units, "decay_steps": decay_steps, "train_steps": train_steps_nums, "num_warmup_steps": int(train_steps_nums * 0.1)} # dist_strategy = tf.contrib.distribute.MirroredStrategy(num_gpus=args.gpu_nums) config_tf = tf.ConfigProto() config_tf.gpu_options.allow_growth = True run_config = tf.estimator.RunConfig( model_dir=model_base_dir, save_summary_steps=each_epoch_steps, save_checkpoints_steps=each_epoch_steps, session_config=config_tf, keep_checkpoint_max=3, # train_distribute=dist_strategy ) bert_init_checkpoints = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("bert_init_checkpoints")) # init_checkpoints = "output/model/merge_usingtype_roberta_traindev_event_role_bert_mrc_model_desmodified_lowercase/checkpoint/model.ckpt-1218868" model_fn = bert_mrc_model_fn_builder(bert_config_file, bert_init_checkpoints, args) estimator = tf.estimator.Estimator( model_fn, params=params, config=run_config) if args.do_train: train_input_fn = lambda: event_input_bert_mrc_mul_fn( train_datas, train_labels, train_token_type_id_list, train_query_lens, is_training=True, is_testing=False, args=args) eval_input_fn = lambda: event_input_bert_mrc_mul_fn( dev_datas, dev_labels, dev_token_type_id_list, dev_query_lens, is_training=False, is_testing=False, args=args) train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=train_steps_nums ) exporter = tf.estimator.BestExporter(exports_to_keep=1, serving_input_receiver_fn=bert_mrc_serving_input_receiver_fn) eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, exporters=[exporter], throttle_secs=0) # for _ in range(args.epochs): tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) # "bert_ce_model_pb" estimator.export_saved_model(pb_model_dir, bert_mrc_serving_input_receiver_fn) def run_event_classification(args): """ 事件类型分析，多标签二分类问题，借鉴NL2SQL预测column的方法 :param args: :return: """ model_base_dir = event_config.get(args.model_checkpoint_dir).format(args.fold_index) pb_model_dir = event_config.get(args.model_pb_dir).format(args.fold_index) print(model_base_dir) print(pb_model_dir) vocab_file_path = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("vocab_file")) bert_config_file = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("bert_config_path")) event_type_file = os.path.join(event_config.get("slot_list_root_path"), event_config.get("event_type_file")) data_loader = EventTypeClassificationPrepare(vocab_file_path, 512, event_type_file) train_file = os.path.join(event_config.get("data_dir"), event_config.get("event_data_file_train")) eval_file = os.path.join(event_config.get("data_dir"), event_config.get("event_data_file_eval")) # train_data_list,train_label_list,train_token_type_id_list,dev_data_list,dev_label_list,dev_token_type_id_list = data_loader._read_json_file(train_file,eval_file,is_train=True) train_data_list = np.load("data/index_type_fold_data_{}/token_ids_train.npy".format(args.fold_index), allow_pickle=True) train_label_list = np.load("data/index_type_fold_data_{}/labels_train.npy".format(args.fold_index), allow_pickle=True) train_token_type_id_list = np.load("data/index_type_fold_data_{}/token_type_ids_train.npy".format(args.fold_index), allow_pickle=True) train_type_index_ids_list = np.load( "data/index_type_fold_data_{}/type_index_in_token_ids_train.npy".format(args.fold_index), allow_pickle=True) dev_data_list = np.load("data/index_type_fold_data_{}/token_ids_dev.npy".format(args.fold_index), allow_pickle=True) dev_label_list = np.load("data/index_type_fold_data_{}/labels_dev.npy".format(args.fold_index), allow_pickle=True) dev_token_type_id_list = np.load("data/index_type_fold_data_{}/token_type_ids_dev.npy".format(args.fold_index), allow_pickle=True) dev_type_index_ids_list = np.load( "data/index_type_fold_data_{}/type_index_in_token_ids_dev.npy".format(args.fold_index), allow_pickle=True) train_labels = np.array(train_label_list) # print(train_labels.shape) print(train_labels.shape) a = np.sum(train_labels, axis=0) a = [max(a) / ele for ele in a] class_weight = np.array(a) class_weight = np.reshape(class_weight, (1, 65)) print(class_weight) # dev_datas,dev_token_type_ids,dev_labels = data_loader._read_json_file(eval_file) train_samples_nums = len(train_data_list) dev_samples_nums = len(dev_data_list) if train_samples_nums % args.train_batch_size != 0: each_epoch_steps = int(train_samples_nums / args.train_batch_size) + 1 else: each_epoch_steps = int(train_samples_nums / args.train_batch_size) # each_epoch_steps = int(data_loader.train_samples_nums/args.train_batch_size)+1 logger.info('*****train_set sample nums:{}'.format(train_samples_nums)) logger.info('*****train each epoch steps:{}'.format(each_epoch_steps)) train_steps_nums = each_epoch_steps * args.epochs # train_steps_nums = each_epoch_steps * args.epochs // hvd.size() logger.info('*****train_total_steps:{}'.format(train_steps_nums)) decay_steps = args.decay_epoch * each_epoch_steps logger.info('*****train decay steps:{}'.format(decay_steps)) # dropout_prob是丢弃概率 params = {"dropout_prob": args.dropout_prob, "num_labels": data_loader.labels_map_len, "rnn_size": args.rnn_units, "num_layers": args.num_layers, "hidden_units": args.hidden_units, "decay_steps": decay_steps, "class_weight": class_weight} # dist_strategy = tf.contrib.distribute.MirroredStrategy(num_gpus=args.gpu_nums) config_tf = tf.ConfigProto() config_tf.gpu_options.allow_growth = True # "bert_ce_model_dir" # mirrored_strategy = tf.distribute.MirroredStrategy() # config_tf.gpu_options.visible_device_list = str(hvd.local_rank()) # checkpoint_path = os.path.join(bert_config.get(args.model_checkpoint_dir), str(hvd.rank())) run_config = tf.estimator.RunConfig( model_dir=model_base_dir, save_summary_steps=train_steps_nums + 10, save_checkpoints_steps=each_epoch_steps, session_config=config_tf, keep_checkpoint_max=1, # train_distribute=dist_strategy ) bert_init_checkpoints = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("bert_init_checkpoints")) model_fn = bert_classification_model_fn_builder(bert_config_file, bert_init_checkpoints, args) estimator = tf.estimator.Estimator( model_fn, params=params, config=run_config) if args.do_train: # train_input_fn = lambda: data_loader.create_dataset(is_training=True,is_testing=False, args=args) # eval_input_fn = lambda: data_loader.create_dataset(is_training=False,is_testing=False,args=args) # train_X,train_Y = np.load(data_loader.train_X_path,allow_pickle=True),np.load(data_loader.train_Y_path,allow_pickle=True) # train_input_fn = lambda :event_class_input_bert_fn(train_data_list,token_type_ids=train_token_type_id_list,label_map_len=data_loader.labels_map_len, # is_training=True,is_testing=False,args=args,input_Ys=train_label_list) train_input_fn = lambda: event_index_class_input_bert_fn(train_data_list, token_type_ids=train_token_type_id_list, type_index_ids_list=train_type_index_ids_list, label_map_len=data_loader.labels_map_len, is_training=True, is_testing=False, args=args, input_Ys=train_label_list) # eval_X,eval_Y = np.load(data_loader.valid_X_path,allow_pickle=True),np.load(data_loader.valid_Y_path,allow_pickle=True) # eval_input_fn = lambda: event_class_input_bert_fn(dev_data_list,token_type_ids=dev_token_type_id_list,label_map_len=data_loader.labels_map_len, # is_training=False,is_testing=False,args=args,input_Ys=dev_label_list) eval_input_fn = lambda: event_index_class_input_bert_fn(dev_data_list, token_type_ids=dev_token_type_id_list, type_index_ids_list=dev_type_index_ids_list, label_map_len=data_loader.labels_map_len, is_training=False, is_testing=False, args=args, input_Ys=dev_label_list) train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=train_steps_nums ) exporter = tf.estimator.BestExporter(exports_to_keep=1, serving_input_receiver_fn=bert_event_type_serving_input_receiver_fn) eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, throttle_secs=0, exporters=[exporter]) tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) # "bert_ce_model_pb" estimator.export_saved_model(pb_model_dir, bert_event_type_serving_input_receiver_fn) def run_event_binclassification(args): """ retroreader中的eav模块，即第一遍阅读模块，预测该问题是否有回答 :param args: :return: """ model_base_dir = event_config.get(args.model_checkpoint_dir).format(args.fold_index) pb_model_dir = event_config.get(args.model_pb_dir).format(args.fold_index) print(model_base_dir) print(pb_model_dir) vocab_file_path = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("vocab_file")) bert_config_file = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("bert_config_path")) event_type_file = os.path.join(event_config.get("slot_list_root_path"), event_config.get("event_type_file")) # data_loader =EventTypeClassificationPrepare(vocab_file_path,512,event_type_file) # train_file = os.path.join(event_config.get("data_dir"),event_config.get("event_data_file_train")) # eval_file = os.path.join(event_config.get("data_dir"),event_config.get("event_data_file_eval")) # train_data_list,train_label_list,train_token_type_id_list,dev_data_list,dev_label_list,dev_token_type_id_list = data_loader._read_json_file(train_file,eval_file,is_train=True) train_data_list = np.load("data/verify_neg_fold_data_{}/token_ids_train.npy".format(args.fold_index), allow_pickle=True) # train_label_list = np.load("data/verify_neg_fold_data_{}/has_answer_train.npy".format(args.fold_index),allow_pickle=True) train_label_list = [] train_start_labels = np.load("data/verify_neg_fold_data_{}/labels_start_train.npy".format(args.fold_index), allow_pickle=True) dev_start_labels = np.load("data/verify_neg_fold_data_{}/labels_start_dev.npy".format(args.fold_index), allow_pickle=True) train_token_type_id_list = np.load("data/verify_neg_fold_data_{}/token_type_ids_train.npy".format(args.fold_index), allow_pickle=True) dev_data_list = np.load("data/verify_neg_fold_data_{}/token_ids_dev.npy".format(args.fold_index), allow_pickle=True) # dev_label_list = np.load("data/verify_neg_fold_data_{}/has_answer_dev.npy".format(args.fold_index),allow_pickle=True) dev_label_list = [] dev_token_type_id_list = np.load("data/verify_neg_fold_data_{}/token_type_ids_dev.npy".format(args.fold_index), allow_pickle=True) # dev_datas,dev_token_type_ids,dev_labels = data_loader._read_json_file(eval_file) train_samples_nums = len(train_data_list) for i in range(train_samples_nums): if sum(train_start_labels[i]) == 0: train_label_list.append(0) else: train_label_list.append(1) train_label_list = np.array(train_label_list).reshape((train_samples_nums, 1)) dev_samples_nums = len(dev_data_list) for i in range(dev_samples_nums): if sum(dev_start_labels[i]) == 0: dev_label_list.append(0) else: dev_label_list.append(1) dev_label_list = np.array(dev_label_list).reshape((dev_samples_nums, 1)) if train_samples_nums % args.train_batch_size != 0: each_epoch_steps = int(train_samples_nums / args.train_batch_size) + 1 else: each_epoch_steps = int(train_samples_nums / args.train_batch_size) # each_epoch_steps = int(data_loader.train_samples_nums/args.train_batch_size)+1 logger.info('*****train_set sample nums:{}'.format(train_samples_nums)) logger.info('*****train each epoch steps:{}'.format(each_epoch_steps)) train_steps_nums = each_epoch_steps * args.epochs # train_steps_nums = each_epoch_steps * args.epochs // hvd.size() logger.info('*****train_total_steps:{}'.format(train_steps_nums)) decay_steps = args.decay_epoch * each_epoch_steps logger.info('*****train decay steps:{}'.format(decay_steps)) # dropout_prob是丢弃概率 params = {"dropout_prob": args.dropout_prob, "num_labels": 1, "rnn_size": args.rnn_units, "num_layers": args.num_layers, "hidden_units": args.hidden_units, "decay_steps": decay_steps, "class_weight": 1} # dist_strategy = tf.contrib.distribute.MirroredStrategy(num_gpus=args.gpu_nums) config_tf = tf.ConfigProto() config_tf.gpu_options.allow_growth = True # "bert_ce_model_dir" # mirrored_strategy = tf.distribute.MirroredStrategy() # config_tf.gpu_options.visible_device_list = str(hvd.local_rank()) # checkpoint_path = os.path.join(bert_config.get(args.model_checkpoint_dir), str(hvd.rank())) run_config = tf.estimator.RunConfig( model_dir=model_base_dir, save_summary_steps=train_steps_nums + 10, save_checkpoints_steps=each_epoch_steps, session_config=config_tf, keep_checkpoint_max=1, # train_distribute=dist_strategy ) bert_init_checkpoints = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("bert_init_checkpoints")) model_fn = bert_binaryclassification_model_fn_builder(bert_config_file, bert_init_checkpoints, args) estimator = tf.estimator.Estimator( model_fn, params=params, config=run_config) if args.do_train: # train_input_fn = lambda: data_loader.create_dataset(is_training=True,is_testing=False, args=args) # eval_input_fn = lambda: data_loader.create_dataset(is_training=False,is_testing=False,args=args) # train_X,train_Y = np.load(data_loader.train_X_path,allow_pickle=True),np.load(data_loader.train_Y_path,allow_pickle=True) # train_input_fn = lambda :event_class_input_bert_fn(train_data_list,token_type_ids=train_token_type_id_list,label_map_len=data_loader.labels_map_len, # is_training=True,is_testing=False,args=args,input_Ys=train_label_list) train_input_fn = lambda: event_binclass_input_bert_fn(train_data_list, token_type_ids=train_token_type_id_list, label_map_len=1, is_training=True, is_testing=False, args=args, input_Ys=train_label_list) # eval_X,eval_Y = np.load(data_loader.valid_X_path,allow_pickle=True),np.load(data_loader.valid_Y_path,allow_pickle=True) # eval_input_fn = lambda: event_class_input_bert_fn(dev_data_list,token_type_ids=dev_token_type_id_list,label_map_len=data_loader.labels_map_len, # is_training=False,is_testing=False,args=args,input_Ys=dev_label_list) eval_input_fn = lambda: event_binclass_input_bert_fn(dev_data_list, token_type_ids=dev_token_type_id_list, label_map_len=1, is_training=False, is_testing=False, args=args, input_Ys=dev_label_list) train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=train_steps_nums ) exporter = tf.estimator.BestExporter(exports_to_keep=1, serving_input_receiver_fn=bert_event_bin_serving_input_receiver_fn) eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, throttle_secs=0, exporters=[exporter]) tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) # "bert_ce_model_pb" estimator.export_saved_model(pb_model_dir, bert_event_bin_serving_input_receiver_fn) def run_event_verify_role_mrc(args): """ retro reader 第二阶段的精度模块，同时训练两个任务，role抽取和问题是否可以回答 :param args: :return: """ model_base_dir = event_config.get(args.model_checkpoint_dir).format(args.fold_index) pb_model_dir = event_config.get(args.model_pb_dir).format(args.fold_index) vocab_file_path = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("vocab_file")) bert_config_file = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("bert_config_path")) slot_file = os.path.join(event_config.get("slot_list_root_path"), event_config.get("bert_slot_complete_file_name_role")) schema_file = os.path.join(event_config.get("data_dir"), event_config.get("event_schema")) query_map_file = os.path.join(event_config.get("slot_list_root_path"), event_config.get("query_map_file")) data_loader = EventRolePrepareMRC(vocab_file_path, 512, slot_file, schema_file, query_map_file) # train_file = os.path.join(event_config.get("data_dir"), event_config.get("event_data_file_train")) # eval_file = os.path.join(event_config.get("data_dir"), event_config.get("event_data_file_eval")) # data_list,label_start_list,label_end_list,query_len_list,token_type_id_list # train_datas, train_labels_start,train_labels_end,train_query_lens,train_token_type_id_list,dev_datas, dev_labels_start,dev_labels_end,dev_query_lens,dev_token_type_id_list = data_loader._read_json_file(train_file,eval_file,True) # dev_datas, dev_labels_start,dev_labels_end,dev_query_lens,dev_token_type_id_list = data_loader._read_json_file(eval_file,None,False) # train_datas, train_labels_start,train_labels_end,train_query_lens,train_token_type_id_list,dev_datas, dev_labels_start,dev_labels_end,dev_query_lens,dev_token_type_id_list = data_loader._merge_ee_and_re_datas(train_file,eval_file,"relation_extraction/data/train_data.json","relation_extraction/data/dev_data.json") train_has_answer_label_list = [] dev_has_answer_label_list = [] train_datas = np.load("data/verify_neg_fold_data_{}/token_ids_train.npy".format(args.fold_index), allow_pickle=True) # train_has_answer_label_list = np.load("data/verify_neg_fold_data_{}/has_answer_train.npy".format(args.fold_index),allow_pickle=True) train_token_type_id_list = np.load("data/verify_neg_fold_data_{}/token_type_ids_train.npy".format(args.fold_index), allow_pickle=True) dev_datas = np.load("data/verify_neg_fold_data_{}/token_ids_dev.npy".format(args.fold_index), allow_pickle=True) # dev_has_answer_label_list = np.load("data/verify_neg_fold_data_{}/has_answer_dev.npy".format(args.fold_index),allow_pickle=True) dev_token_type_id_list = np.load("data/verify_neg_fold_data_{}/token_type_ids_dev.npy".format(args.fold_index), allow_pickle=True) train_query_lens = np.load("data/verify_neg_fold_data_{}/query_lens_train.npy".format(args.fold_index), allow_pickle=True) dev_query_lens = np.load("data/verify_neg_fold_data_{}/query_lens_dev.npy".format(args.fold_index), allow_pickle=True) train_start_labels = np.load("data/verify_neg_fold_data_{}/labels_start_train.npy".format(args.fold_index), allow_pickle=True) dev_start_labels = np.load("data/verify_neg_fold_data_{}/labels_start_dev.npy".format(args.fold_index), allow_pickle=True) train_end_labels = np.load("data/verify_neg_fold_data_{}/labels_end_train.npy".format(args.fold_index), allow_pickle=True) dev_end_labels = np.load("data/verify_neg_fold_data_{}/labels_end_dev.npy".format(args.fold_index), allow_pickle=True) train_samples_nums = len(train_datas) for i in range(train_samples_nums): if sum(train_start_labels[i]) == 0: train_has_answer_label_list.append(0) else: train_has_answer_label_list.append(1) train_has_answer_label_list = np.array(train_has_answer_label_list).reshape((train_samples_nums, 1)) dev_samples_nums = len(dev_datas) for i in range(dev_samples_nums): if sum(dev_start_labels[i]) == 0: dev_has_answer_label_list.append(0) else: dev_has_answer_label_list.append(1) dev_has_answer_label_list = np.array(dev_has_answer_label_list).reshape((dev_samples_nums, 1)) if train_samples_nums % args.train_batch_size != 0: each_epoch_steps = int(train_samples_nums / args.train_batch_size) + 1 else: each_epoch_steps = int(train_samples_nums / args.train_batch_size) # each_epoch_steps = int(data_loader.train_samples_nums/args.train_batch_size)+1 logger.info('*****train_set sample nums:{}'.format(train_samples_nums)) logger.info('*****dev_set sample nums:{}'.format(dev_samples_nums)) logger.info('*****train each epoch steps:{}'.format(each_epoch_steps)) train_steps_nums = each_epoch_steps * args.epochs # train_steps_nums = each_epoch_steps * args.epochs // hvd.size() logger.info('*****train_total_steps:{}'.format(train_steps_nums)) decay_steps = args.decay_epoch * each_epoch_steps logger.info('*****train decay steps:{}'.format(decay_steps)) # dropout_prob是丢弃概率 params = {"dropout_prob": args.dropout_prob, "num_labels": 2, "rnn_size": args.rnn_units, "num_layers": args.num_layers, "hidden_units": args.hidden_units, "decay_steps": decay_steps, "train_steps": train_steps_nums, "num_warmup_steps": int(train_steps_nums * 0.1)} # dist_strategy = tf.contrib.distribute.MirroredStrategy(num_gpus=args.gpu_nums) config_tf = tf.ConfigProto() config_tf.gpu_options.allow_growth = True run_config = tf.estimator.RunConfig( model_dir=model_base_dir, save_summary_steps=each_epoch_steps, save_checkpoints_steps=each_epoch_steps, session_config=config_tf, keep_checkpoint_max=3, # train_distribute=dist_strategy ) bert_init_checkpoints = os.path.join(event_config.get("bert_pretrained_model_path"), event_config.get("bert_init_checkpoints")) # init_checkpoints = "output/model/merge_usingtype_roberta_traindev_event_role_bert_mrc_model_desmodified_lowercase/checkpoint/model.ckpt-1218868" model_fn = event_verify_mrc_model_fn_builder(bert_config_file, bert_init_checkpoints, args) estimator = tf.estimator.Estimator( model_fn, params=params, config=run_config) if args.do_train: train_input_fn = lambda: event_input_verfify_mrc_fn( train_datas, train_start_labels, train_end_labels, train_token_type_id_list, train_query_lens, train_has_answer_label_list, is_training=True, is_testing=False, args=args) eval_input_fn = lambda: event_input_verfify_mrc_fn( dev_datas, dev_start_labels, dev_end_labels, dev_token_type_id_list, dev_query_lens, dev_has_answer_label_list, is_training=False, is_testing=False, args=args) train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=train_steps_nums ) exporter = tf.estimator.BestExporter(exports_to_keep=1, serving_input_receiver_fn=bert_mrc_serving_input_receiver_fn) eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, exporters=[exporter], throttle_secs=0) # for _ in range(args.epochs): tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) # "bert_ce_model_pb" estimator.export_saved_model(pb_model_dir, bert_mrc_serving_input_receiver_fn)

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34,473

4.641564

0.052216

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0.040158

0.037469

0.953164

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0

null

0

1

0

null

0

6

3daed6f58209c91edf2eaa9ed7eb396be2af00ff

4,451

py

Python

tests/test_process_listing.py

ProzorroUKR/prozorro_chronograph

f8a560322259b5bb07035b133f545a614130de73

[ "Apache-2.0" ]

null

tests/test_process_listing.py

ProzorroUKR/prozorro_chronograph

f8a560322259b5bb07035b133f545a614130de73

[ "Apache-2.0" ]

null

tests/test_process_listing.py

ProzorroUKR/prozorro_chronograph

f8a560322259b5bb07035b133f545a614130de73

[ "Apache-2.0" ]

null

from uuid import uuid4 from datetime import timedelta from unittest.mock import patch, Mock from freezegun import freeze_time from prozorro_chronograph.utils import get_now from prozorro_chronograph.scheduler import process_listing, push from prozorro_chronograph.settings import TZ from .base import BaseTenderTest class TestTenderProcessListing(BaseTenderTest): @freeze_time("2012-01-14") @patch("prozorro_chronograph.scheduler.check_auction") @patch("prozorro_chronograph.scheduler.randint", return_value=2) @patch("prozorro_chronograph.scheduler.asyncio.sleep") @patch("prozorro_chronograph.scheduler.scheduler.add_job") async def test_process_listing_without_next_check(self, mock_add_job, mock_sleep, _, __, caplog): tender = { "id": uuid4().hex, "submissionMethodDetails": {"quick": "value"}, "auctionPeriod": { "shouldStartAfter": (get_now() + timedelta(days=2)).isoformat(), "startDate": (get_now() + timedelta(days=1)).isoformat() } } server_id_cookie = "value" await process_listing(server_id_cookie, tender) mock_add_job.assert_called_once_with( push, "date", run_date=get_now() + timedelta(seconds=2), id=f'resync_{tender["id"]}', name=f'Resync {tender["id"]}', misfire_grace_time=60 * 60, args=["resync", tender["id"], server_id_cookie], replace_existing=True, ) assert f'Start processing tender: {tender["id"]}' in caplog.messages[0] assert f'Set resync job for tender {tender["id"]}' in caplog.messages[1] assert len(caplog.messages) == 2 mock_sleep.assert_called_once_with(1) @freeze_time("2012-01-14") @patch("prozorro_chronograph.scheduler.check_auction") @patch("prozorro_chronograph.scheduler.randint", Mock(return_value=2)) @patch("prozorro_chronograph.scheduler.asyncio.sleep") @patch("prozorro_chronograph.scheduler.scheduler.add_job") async def test_process_listing_with_next_check(self, mock_add_job, mock_sleep, _, caplog): next_check = get_now() - timedelta(days=2) tenant_id = uuid4().hex tender = { "id": tenant_id, "next_check": next_check.isoformat(), } server_id_cookie = "value" await process_listing(server_id_cookie, tender) mock_add_job.assert_called_once() mock_add_job.assert_called_with( push, "date", run_date=get_now() + timedelta(seconds=2), timezone=TZ, id=f'recheck_{tender["id"]}', name=f'Recheck {tender["id"]}', misfire_grace_time=60 * 60, replace_existing=True, args=["recheck", tender["id"], server_id_cookie], ) assert f'Start processing tender: {tender["id"]}' in caplog.messages[0] assert f"Tender {tenant_id} don't need to resync" in caplog.messages[1] assert len(caplog.messages) == 2 mock_sleep.assert_called_once_with(1) @freeze_time("2012-01-14") @patch("prozorro_chronograph.scheduler.check_auction") @patch("prozorro_chronograph.scheduler.randint", return_value=2) @patch("prozorro_chronograph.scheduler.asyncio.sleep") @patch("prozorro_chronograph.scheduler.scheduler.add_job") async def test_process_listing_with_next_check_without_recheck_job(self, mock_add_job, mock_sleep, _, __, caplog): next_check = get_now() + timedelta(days=2) tenant_id = uuid4().hex tender = { "id": tenant_id, "next_check": next_check.isoformat(), } server_id_cookie = "value" await process_listing(server_id_cookie, tender) mock_add_job.assert_called_once_with( push, "date", run_date=next_check + timedelta(seconds=2), timezone=TZ, id=f'recheck_{tender["id"]}', name=f'Recheck {tender["id"]}', misfire_grace_time=60 * 60, replace_existing=True, args=["recheck", tender["id"], server_id_cookie], ) assert f'Start processing tender: {tender["id"]}' in caplog.messages[0] assert f"Tender {tenant_id} don't need to resync" in caplog.messages[1] assert len(caplog.messages) == 2 mock_sleep.assert_called_once_with(1)

42.390476

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529

4,451

5.136106

0.175803

0.047111

0.133971

0.145749

0.802356

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0.773647

0.763342

0.749356

0

0.018294

0.238598

4,451

104

119

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0.783417

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0.091837

0

null

0

1

0

null

0

6

3db81911591cf7db62ea9a321497d44bb4cc2036

118

py

Python

examples/rietveld/rietveld_helper/views.py

NaN-tic/django-gae2django

ea3bc1e8aa9e072bf93b131816e0d20e1795c999

[ "Apache-2.0" ]

3

2015-05-04T13:49:41.000Z

2017-04-25T06:27:39.000Z

examples/rietveld/rietveld_helper/views.py

NaN-tic/django-gae2django

ea3bc1e8aa9e072bf93b131816e0d20e1795c999

[ "Apache-2.0" ]

1

2021-05-21T20:01:35.000Z

examples/rietveld/rietveld_helper/views.py

NaN-tic/django-gae2django

ea3bc1e8aa9e072bf93b131816e0d20e1795c999

[ "Apache-2.0" ]

2

2020-11-13T17:43:42.000Z

2021-05-21T20:01:32.000Z

from django.http import HttpResponseRedirect def admin_redirect(request): return HttpResponseRedirect('/admin/')

23.6

44

0.805085

12

118

7.833333

0.833333

0

0.110169

118

4

45

29.5

0.895238

0

0.059322

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

3ddbea75aa0c18cb6e05235979d67b9d5d5b30a2

81

py

Python

accounts/admin_inlines.py

biotech2021/uniTicket

8c441eac18e67a983e158326b1c4b82f00f1f1ef

[ "Apache-2.0" ]

15

2019-09-06T06:47:08.000Z

2022-01-17T06:39:54.000Z

accounts/admin_inlines.py

biotech2021/uniTicket

8c441eac18e67a983e158326b1c4b82f00f1f1ef

[ "Apache-2.0" ]

69

2019-09-06T12:03:19.000Z

2022-03-26T14:30:53.000Z

accounts/admin_inlines.py

biotech2021/uniTicket

8c441eac18e67a983e158326b1c4b82f00f1f1ef

[ "Apache-2.0" ]

13

2019-09-11T10:54:20.000Z

2021-11-23T09:09:19.000Z

from django import forms from django.contrib import admin from .models import *

16.2

32

0.802469

12

81

5.416667

0.583333

0.307692

0

0.160494

81

4

33

20.25

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0

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true

0

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1

0

null

0

1

0

1

0

1

0

6

9a780bff9e57996d58fffbbf3bd5e95b123ddaca

615

py

Python

biz/t9.py

relax-space/python-learning

22987e20a4b0a741e1c5ed8603a952a0fc8dd4bd

[ "Apache-2.0" ]

null

biz/t9.py

relax-space/python-learning

22987e20a4b0a741e1c5ed8603a952a0fc8dd4bd

[ "Apache-2.0" ]

null

biz/t9.py

relax-space/python-learning

22987e20a4b0a741e1c5ed8603a952a0fc8dd4bd

[ "Apache-2.0" ]

null

import pytest @pytest.fixture() def a1(tmp_path_factory, worker_id): return 11 # @pytest.mark.parametrize('common_arg1', [{}]) class TestParametrized: @pytest.mark.parametrize(('a','b'), [{0:[1],1:[22]}],ids=['12','23']) def test_1(self, a1, a,b): print(a1) pass # @pytest.mark.parametrize('b', [0, 1]) # def test_2(self, common_arg1, b): # common_arg1[2] = 4 # print(common_arg1) # pass # @pytest.mark.parametrize('x', [0, 1]) # def test_100(self, common_arg1, x): # common_arg1[1] = 5 # print(common_arg1) # pass

21.206897

73

0.560976

84

615

3.952381

0.416667

0.210843

0.253012

0.150602

0

0.073913

0.252033

615

28

74

21.964286

0.647826

0.489431

0

0.019868

0

1

0.222222

false

0.111111

0.555556

0.111111

0

null

1

0

null

0

1

0

1

0

1

0

6

9aa0cd2ac436f5c595fac6dd4b232f01a919c0bb

26

py

Python

build/lib/ajaira/__init__.py

amitrm/ajaira

aca6b4970cc134480f0e0b9b313dccb86558b738

[ "MIT" ]

null

build/lib/ajaira/__init__.py

amitrm/ajaira

aca6b4970cc134480f0e0b9b313dccb86558b738

[ "MIT" ]

null

build/lib/ajaira/__init__.py

amitrm/ajaira

aca6b4970cc134480f0e0b9b313dccb86558b738

[ "MIT" ]

null

from ajaira.jinis import *

26

0.807692

4

26

5.25

1

0

0.115385

26

1

26

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9aa695b63f4986f611994222d09b78d25bf4ca16

114

py

Python

python/speak/goodbye.py

kyle-cook/templates

f1047a8c31a42507acbd7a27e66db0825be811a6

[ "MIT" ]

null

python/speak/goodbye.py

kyle-cook/templates

f1047a8c31a42507acbd7a27e66db0825be811a6

[ "MIT" ]

null

python/speak/goodbye.py

kyle-cook/templates

f1047a8c31a42507acbd7a27e66db0825be811a6

[ "MIT" ]

null

def goodbyeworld(): """ Prints Goodbye World! to the STDOUT screen buffer """ return "Goodbye World!"

28.5

61

0.649123

13

114

5.692308

0.846154

0.324324

0

0.236842

114

4

62

28.5

0.850575

0.429825

0

0.241379

0

1

0.5

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

9ac2c5d0c20d5fbe9178b654996a021b76f88a41

13,612

py

Python

tests/test_priors.py

RobinAlgayres/beer

15ad0dad5a49f98e658e948724e05df347ffe3b8

[ "MIT" ]

46

2018-02-27T18:15:08.000Z

2022-02-16T22:10:55.000Z

tests/test_priors.py

RobinAlgayres/beer

15ad0dad5a49f98e658e948724e05df347ffe3b8

[ "MIT" ]

16

2018-01-26T14:18:51.000Z

2021-02-05T09:34:00.000Z

tests/test_priors.py

RobinAlgayres/beer

15ad0dad5a49f98e658e948724e05df347ffe3b8

[ "MIT" ]

26

2018-03-12T14:03:26.000Z

2021-05-24T21:15:01.000Z

'Test the priors package.' import sys sys.path.insert(0, './') sys.path.insert(0, './tests') import unittest import torch import beer from basetest import BaseTest class BaseTestPrior(BaseTest): def test_exp_sufficient_statistics(self): stats1 = self.prior.expected_sufficient_statistics() copied_tensor = torch.tensor(self.prior.natural_parameters, requires_grad=True) log_norm = self.prior.log_norm(copied_tensor) torch.autograd.backward(log_norm) stats2 = copied_tensor.grad self.assertArraysAlmostEqual(stats1.numpy(), stats2.numpy()) ######################################################################## # Dirichlet. ######################################################################## class TestDirichletPrior(BaseTestPrior): def setUp(self): dim = 10 self.std_parameters = 2 * torch.ones(dim) self.prior = beer.priors.DirichletPrior(self.std_parameters) def test_natural2std(self): std_params = self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual( std_params.numpy(), self.std_parameters.numpy() ) def test_std2natural(self): std_params = self.prior.to_std_parameters(self.prior.natural_parameters) nparams = self.prior.to_natural_parameters(std_params) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) ######################################################################## # Gamma. ######################################################################## class TestGammaPrior(BaseTestPrior): def setUp(self): dim = 10 self.shape = torch.tensor(2).type(self.type) self.rate = torch.tensor(.5).type(self.type) self.prior = beer.priors.GammaPrior(self.shape, self.rate) def test_natural2std(self): shape, rate = self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual(shape.numpy(), self.shape.numpy()) self.assertArraysAlmostEqual(rate.numpy(), self.rate.numpy()) def test_std2natural(self): shape, rate = self.prior.to_std_parameters(self.prior.natural_parameters) nparams = self.prior.to_natural_parameters(shape, rate) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) ######################################################################## # Wishart. ######################################################################## class TestWishartPrior(BaseTestPrior): def setUp(self): dim = 10 self.scale = torch.eye(dim).type(self.type) self.dof = torch.tensor(dim + 2).type(self.type) self.prior = beer.priors.WishartPrior(self.scale, self.dof) def test_natural2std(self): scale, dof = self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual(scale.numpy(), self.scale.numpy()) self.assertArraysAlmostEqual(dof.numpy(), self.dof.numpy()) def test_std2natural(self): scale, dof = self.prior.to_std_parameters(self.prior.natural_parameters) nparams = self.prior.to_natural_parameters(scale, dof) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) ######################################################################## # Normal Full covariance. ######################################################################## class TestNormalFullCovariancePrior(BaseTestPrior): def setUp(self): dim = 10 self.scale = torch.eye(dim).type(self.type) self.dof = torch.tensor(dim + 2).type(self.type) self.prior_precision = beer.priors.WishartPrior(self.scale, self.dof) self.mean = 3 * torch.ones(dim).type(self.type) self.scale = torch.tensor(1.5).type(self.type) self.prior = beer.priors.NormalFullCovariancePrior(self.mean, self.scale, self.prior_precision) def test_natural2std(self): mean, scale = self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual(mean.numpy(), self.mean.numpy()) self.assertArraysAlmostEqual(scale.numpy(), self.scale.numpy()) def test_std2natural(self): mean, scale = self.prior.to_std_parameters(self.prior.natural_parameters) nparams = self.prior.to_natural_parameters(mean, scale) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) ######################################################################## # Normal Wishart. ######################################################################## class TestNormalWishartPrior(BaseTestPrior): def setUp(self): dim = 10 self.mean = 3 + torch.zeros(dim).type(self.type) self.scale = torch.tensor(2.5).type(self.type) self.mean_precision = torch.eye(dim).type(self.type) \ + torch.ger(self.mean, self.mean) self.dof = torch.tensor(dim + 2).type(self.type) self.prior = beer.priors.NormalWishartPrior(self.mean, self.scale, self.mean_precision, self.dof) def test_natural2std(self): mean, scale, mean_precision, dof = \ self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual(mean.numpy(), self.mean.numpy()) self.assertArraysAlmostEqual(scale.numpy(), self.scale.numpy()) self.assertArraysAlmostEqual(mean_precision.numpy(), self.mean_precision.numpy()) self.assertArraysAlmostEqual(dof.numpy(), self.dof.numpy()) def test_std2natural(self): mean, scale, mean_precision, dof = self.prior.to_std_parameters() nparams = self.prior.to_natural_parameters(mean, scale, mean_precision, dof) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) ######################################################################## # Normal Gamma. ######################################################################## class TestNormalGammaPrior(BaseTestPrior): def setUp(self): dim = 10 self.mean = 3 + torch.zeros(dim).type(self.type) self.scale = torch.tensor(2.5).type(self.type) self.shape = torch.tensor(3).type(self.type) self.rates = 2* torch.ones(dim).type(self.type) self.prior = beer.priors.NormalGammaPrior(self.mean, self.scale, self.shape, self.rates) def test_natural2std(self): mean, scale, shape, rates = \ self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual(mean.numpy(), self.mean.numpy()) self.assertArraysAlmostEqual(scale.numpy(), self.scale.numpy()) self.assertArraysAlmostEqual(shape.numpy(), self.shape.numpy()) self.assertArraysAlmostEqual(rates.numpy(), self.rates.numpy()) def test_std2natural(self): mean, scale, shape, rates = self.prior.to_std_parameters() nparams = self.prior.to_natural_parameters(mean, scale, shape, rates) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) ######################################################################## # Isotropic Normal Gamma. ######################################################################## class TestIsotropicNormalGammaPrior(BaseTestPrior): def setUp(self): dim = 10 self.mean = 3 + torch.zeros(dim).type(self.type) self.scale = torch.tensor(2.5).type(self.type) self.shape = torch.tensor(3).type(self.type) self.rate = torch.tensor(2).type(self.type) self.prior = beer.priors.IsotropicNormalGammaPrior(self.mean, self.scale, self.shape, self.rate) def test_natural2std(self): mean, scale, shape, rate = \ self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual(mean.numpy(), self.mean.numpy()) self.assertArraysAlmostEqual(scale.numpy(), self.scale.numpy()) self.assertArraysAlmostEqual(shape.numpy(), self.shape.numpy()) self.assertArraysAlmostEqual(rate.numpy(), self.rate.numpy()) def test_std2natural(self): mean, scale, shape, rate = self.prior.to_std_parameters() nparams = self.prior.to_natural_parameters(mean, scale, shape, rate) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) ######################################################################## # Joint Isotropic Normal Gamma. ######################################################################## class TestJointIsotropicNormalGammaPrior(BaseTestPrior): def setUp(self): dim = 10 k = 3 self.means = 3 + torch.zeros(k, dim).type(self.type) self.scales = 2.5 * torch.ones(k).type(self.type) self.shape = torch.tensor(3).type(self.type) self.rate = torch.tensor(2).type(self.type) self.prior = beer.priors.JointIsotropicNormalGammaPrior(self.means, self.scales, self.shape, self.rate) def test_natural2std(self): means, scales, shape, rate = \ self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual(means.numpy(), self.means.numpy()) self.assertArraysAlmostEqual(scales.numpy(), self.scales.numpy()) self.assertArraysAlmostEqual(shape.numpy(), self.shape.numpy()) self.assertArraysAlmostEqual(rate.numpy(), self.rate.numpy()) def test_std2natural(self): means, scales, shape, rate = self.prior.to_std_parameters() nparams = self.prior.to_natural_parameters(means, scales, shape, rate) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) ######################################################################## # Joint Normal Gamma. ######################################################################## class TestJointNormalGammaPrior(BaseTestPrior): def setUp(self): dim = 10 k = 3 self.means = 3 + torch.zeros(k, dim).type(self.type) self.scales = 2.5 * torch.ones(k).type(self.type) self.shape = torch.tensor(3).type(self.type) self.rates = 2 * torch.ones(dim).type(self.type) self.prior = beer.priors.JointNormalGammaPrior(self.means, self.scales, self.shape, self.rates) def test_natural2std(self): means, scales, shape, rates = \ self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual(means.numpy(), self.means.numpy()) self.assertArraysAlmostEqual(scales.numpy(), self.scales.numpy()) self.assertArraysAlmostEqual(shape.numpy(), self.shape.numpy()) self.assertArraysAlmostEqual(rates.numpy(), self.rates.numpy()) def test_std2natural(self): means, scales, shape, rates = self.prior.to_std_parameters() nparams = self.prior.to_natural_parameters(means, scales, shape, rates) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) ######################################################################## # Joint Normal Wishart. ######################################################################## class TestJointNormalWishartPrior(BaseTestPrior): def setUp(self): dim = 10 k = 3 self.means = 3 + torch.zeros(k, dim).type(self.type) self.scales = 2.5 * torch.ones(k).type(self.type) self.mean_precision = torch.eye(dim).type(self.type) \ + .1 * torch.ger(self.means[0], self.means[0]) self.dof = torch.tensor(dim + 2).type(self.type) self.prior = beer.priors.JointNormalWishartPrior(self.means, self.scales, self.mean_precision, self.dof) def test_natural2std(self): means, scales, mean_precision, dof = \ self.prior.to_std_parameters(self.prior.natural_parameters) self.assertArraysAlmostEqual(means.numpy(), self.means.numpy()) self.assertArraysAlmostEqual(scales.numpy(), self.scales.numpy()) self.assertArraysAlmostEqual(mean_precision.numpy(), self.mean_precision.numpy()) self.assertArraysAlmostEqual(dof.numpy(), self.dof.numpy()) def test_std2natural(self): means, scales, mean_precision, dof = self.prior.to_std_parameters() nparams = self.prior.to_natural_parameters(means, scales, mean_precision, dof) self.assertArraysAlmostEqual(nparams.numpy(), self.prior.natural_parameters.numpy()) __all__ = [ 'TestDirichletPrior', 'TestGammaPrior', 'TestNormalFullCovariancePrior', 'TestIsotropicNormalGammaPrior', 'TestJointIsotropicNormalGammaPrior', 'TestJointNormalGammaPrior', 'TestJointNormalWishartPrior', 'TestNormalGammaPrior', 'TestNormalWishartPrior', 'TestWishartPrior' ]

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9ae0ad7d8e5c931eee5b2d6ddc09b58506666f70

173

py

Python

algorithm/__init__.py

zzhmark/insitu

6b0f51e9b92601ca80b94b4c1c6b82655fd1b68a

[ "MIT" ]

1

2020-09-07T08:28:56.000Z

algorithm/__init__.py

zzhmark/insitu

6b0f51e9b92601ca80b94b4c1c6b82655fd1b68a

[ "MIT" ]

null

algorithm/__init__.py

zzhmark/insitu

6b0f51e9b92601ca80b94b4c1c6b82655fd1b68a

[ "MIT" ]

null

from .batch import batch_apply from .preprocessing import extract, register from .GMM import global_gmm, local_gmm from .scoring import global_gmm_compare, local_gmm_compare

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6

9ae6c715427b06f449d9a8409cc86e92e851bb7e

36

py

Python

src/linkstation/__init__.py

iKaew/linkstation

098e145a00c447bb4cf480b60645ae383136c3d9

[ "MIT" ]

null

src/linkstation/__init__.py

iKaew/linkstation

098e145a00c447bb4cf480b60645ae383136c3d9

[ "MIT" ]

null

src/linkstation/__init__.py

iKaew/linkstation

098e145a00c447bb4cf480b60645ae383136c3d9

[ "MIT" ]

null

from .linkstation import LinkStation

36

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6

9aed91d742e76d2c41e4a10555e49d79b6f2f884

10,800

py

Python

tests/test_core/test_core.py

TomVollerthun1337/logsmith

f2ecab4dea295d5493a9a3e77a2837b13fa139e5

[ "Apache-2.0" ]

19

2020-01-18T00:25:43.000Z

2022-03-14T07:39:08.000Z

tests/test_core/test_core.py

TomVollerthun1337/logsmith

f2ecab4dea295d5493a9a3e77a2837b13fa139e5

[ "Apache-2.0" ]

85

2020-01-21T12:13:56.000Z

2022-03-31T04:01:03.000Z

tests/test_core/test_core.py

TomVollerthun1337/logsmith

f2ecab4dea295d5493a9a3e77a2837b13fa139e5

[ "Apache-2.0" ]

2

2020-06-25T06:15:19.000Z

2021-02-15T18:17:38.000Z

from unittest import TestCase, mock from unittest.mock import call, Mock from app.core.config import Config from app.core.result import Result from app.core.core import Core from tests.test_data.test_accounts import get_test_accounts from tests.test_data.test_results import get_success_result, get_error_result, get_failed_result class TestCore(TestCase): @mock.patch('app.core.core.Config.load_from_disk') def setUp(self, mock_config): self.core = Core() self.config = Config() self.config.set_accounts(get_test_accounts()) self.core.config = self.config self.success_result = get_success_result() self.fail_result = get_failed_result() self.error_result = get_error_result() @mock.patch('app.core.core.credentials') def test_login__no_access_key(self, mock_credentials): mock_credentials.check_access_key.return_value = self.error_result result = self.core.login(self.config.get_group('development'), None) expected = [call.check_access_key()] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual(self.error_result, result) @mock.patch('app.core.core.credentials') def test_login__session_token_error(self, mock_credentials): mock_credentials.check_access_key.return_value = self.success_result mock_credentials.check_session.return_value = self.error_result result = self.core.login(self.config.get_group('development'), None) expected = [call.check_access_key(), call.check_session()] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual(self.error_result, result) @mock.patch('app.core.core.credentials') def test_login__mfa_error(self, mock_credentials): mock_credentials.check_access_key.return_value = self.success_result mock_credentials.check_session.return_value = self.fail_result self.core._renew_session = Mock() self.core._renew_session.return_value = self.error_result result = self.core.login(self.config.get_group('development'), None) expected = [call.check_access_key(), call.check_session()] self.assertEqual(expected, mock_credentials.mock_calls) expected = [call(None)] self.assertEqual(expected, self.core._renew_session.mock_calls) self.assertEqual(self.error_result, result) @mock.patch('app.core.core.files') @mock.patch('app.core.core.credentials') def test_login__successful_login(self, mock_credentials, _): mock_credentials.check_access_key.return_value = self.success_result mock_credentials.check_session.return_value = self.success_result self.core._renew_session = Mock() self.core._renew_session.return_value = self.success_result mock_credentials.get_user_name.return_value = 'test-user' mock_credentials.fetch_role_credentials.return_value = self.success_result mock_credentials.write_profile_config.return_value = self.success_result self.core._handle_support_files = Mock() mock_mfa_callback = Mock() profile_group = self.config.get_group('development') result = self.core.login(profile_group, mock_mfa_callback) expected = [call.check_access_key(), call.check_session(), call.get_user_name(), call.fetch_role_credentials('test-user', profile_group), call.write_profile_config(profile_group, 'us-east-1')] self.assertEqual(expected, mock_credentials.mock_calls) expected = [call(profile_group)] self.assertEqual(expected, self.core._handle_support_files.mock_calls) self.assertEqual(profile_group, self.core.active_profile_group) self.assertEqual(None, self.core.region_override) self.assertEqual(True, result.was_success) self.assertEqual(False, result.was_error) @mock.patch('app.core.core.credentials') def test_login__logout(self, mock_credentials): mock_credentials.fetch_role_credentials.return_value = self.success_result mock_credentials.write_profile_config.return_value = self.success_result result = self.core.logout() expected = [call.fetch_role_credentials(user_name='none', profile_group=self.core.empty_profile_group), call.write_profile_config(profile_group=self.core.empty_profile_group, region='')] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual(True, result.was_success) self.assertEqual(False, result.was_error) @mock.patch('app.core.core.credentials') def test_login__logout_error(self, mock_credentials): mock_credentials.fetch_role_credentials.return_value = self.error_result result = self.core.logout() expected = [call.fetch_role_credentials(user_name='none', profile_group=self.core.empty_profile_group)] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual(self.error_result, result) @mock.patch('app.core.core.credentials') def test_rotate_access_key__no_access_key(self, mock_credentials): mock_credentials.check_access_key.return_value = self.error_result result = self.core.rotate_access_key() expected = [call.check_access_key()] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual(self.error_result, result) @mock.patch('app.core.core.iam') @mock.patch('app.core.core.credentials') def test_rotate_access_key__successful_rotate(self, mock_credentials, mock_iam): mock_credentials.check_access_key.return_value = self.success_result mock_credentials.check_session.return_value = self.success_result mock_credentials.get_user_name.return_value = 'test-user' mock_credentials.get_access_key_id.return_value = '12345' access_key_result = Result() access_key_result.add_payload({ 'AccessKeyId': 12345, 'SecretAccessKey': 67890 }) access_key_result.set_success() mock_iam.create_access_key.return_value = access_key_result result = self.core.rotate_access_key() expected = [call.check_access_key(), call.check_session(), call.get_user_name(), call.get_access_key_id(), call.set_access_key(key_id=12345, access_key=67890)] self.assertEqual(expected, mock_credentials.mock_calls) expected = [call.create_access_key('test-user'), call.delete_iam_access_key('test-user', '12345')] self.assertEqual(expected, mock_iam.mock_calls) self.assertEqual(True, result.was_success) self.assertEqual(False, result.was_error) def test_get_region__not_logged_in(self): region = self.core.get_region() self.assertEqual(None, region) def test_get_region__active_profile_group(self): self.core.active_profile_group = self.config.get_group('development') region = self.core.get_region() self.assertEqual('us-east-1', region) def test_get_region__region_overwrite(self): self.core.active_profile_group = self.config.get_group('development') self.core.region_override = 'eu-north-1' region = self.core.get_region() self.assertEqual('eu-north-1', region) @mock.patch('app.core.core.mfa') @mock.patch('app.core.core.credentials') def test__renew_session__token_from_shell(self, mock_credentials, mock_mfa_shell): mock_mfa_shell.fetch_mfa_token_from_shell.return_value = '12345' mock_credentials.fetch_session_token.return_value = self.success_result mock_mfa_callback = Mock() result = self.core._renew_session(mock_mfa_callback) self.assertEqual(0, mock_mfa_callback.call_count) expected = [call.fetch_session_token('12345')] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual(True, result.was_success) self.assertEqual(False, result.was_error) @mock.patch('app.core.core.mfa') @mock.patch('app.core.core.credentials') def test__renew_session__no_token_from_mfa_callback(self, mock_credentials, mock_mfa_shell): mock_mfa_shell.fetch_mfa_token_from_shell.return_value = None mock_credentials.fetch_session_token.return_value = self.success_result mock_mfa_callback = Mock() mock_mfa_callback.return_value = '' result = self.core._renew_session(mock_mfa_callback) self.assertEqual(1, mock_mfa_callback.call_count) expected = [] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual(False, result.was_success) self.assertEqual(True, result.was_error) @mock.patch('app.core.core.mfa') @mock.patch('app.core.core.credentials') def test__renew_session__token_from_mfa_callback(self, mock_credentials, mock_mfa_shell): mock_mfa_shell.fetch_mfa_token_from_shell.return_value = None mock_credentials.fetch_session_token.return_value = self.success_result mock_mfa_callback = Mock() mock_mfa_callback.return_value = '12345' result = self.core._renew_session(mock_mfa_callback) self.assertEqual(1, mock_mfa_callback.call_count) expected = [call.fetch_session_token('12345')] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual(True, result.was_success) self.assertEqual(False, result.was_error) @mock.patch('app.core.core.credentials') def test__set_region__not_logged_in(self, mock_credentials): mock_credentials.write_profile_config.return_value = self.success_result result = self.core.set_region('eu-north-1') expected = [] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual('eu-north-1', self.core.region_override) self.assertEqual(True, result.was_success) self.assertEqual(False, result.was_error) @mock.patch('app.core.core.credentials') def test__set_region__logged_in(self, mock_credentials): mock_credentials.write_profile_config.return_value = self.success_result self.core.active_profile_group = self.config.get_group('development') result = self.core.set_region('eu-north-1') expected = [call.write_profile_config(self.config.get_group('development'), 'eu-north-1')] self.assertEqual(expected, mock_credentials.mock_calls) self.assertEqual('eu-north-1', self.core.region_override) self.assertEqual(True, result.was_success) self.assertEqual(False, result.was_error)

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0.182685

10,800

252

112

42.857143

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0.089947

false

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0

null

0

1

0

null

0

6

b10c673c60ab16d18a47df85e8a0518a30dea1bb

11,931

py

Python

apis/nb/clients/identity_manager_client/V2ScalablegroupApi.py

CiscoDevNet/APIC-EM-Generic-Scripts-

74211d9488f1e77cf56ef86dba20ec8e8eb49cc1

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

45

2016-06-09T15:41:25.000Z

2019-08-06T17:13:11.000Z

apis/nb/clients/identity_manager_client/V2ScalablegroupApi.py

CiscoDevNet/APIC-EM-Generic-Scripts

74211d9488f1e77cf56ef86dba20ec8e8eb49cc1

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

36

2016-06-12T03:03:56.000Z

2017-03-13T18:20:11.000Z

apis/nb/clients/identity_manager_client/V2ScalablegroupApi.py

CiscoDevNet/APIC-EM-Generic-Scripts

74211d9488f1e77cf56ef86dba20ec8e8eb49cc1

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

15

2016-06-22T03:51:37.000Z

2019-07-10T10:06:02.000Z

#!/usr/bin/env python #pylint: skip-file # This source code is licensed under the Apache license found in the # LICENSE file in the root directory of this project. import sys import os import urllib.request, urllib.parse, urllib.error from .models import * class V2ScalablegroupApi(object): def __init__(self, apiClient): self.apiClient = apiClient def getScalableGroupByFilters(self, **kwargs): """Retrieves scalable group based on a given filter Args: name, str: Retrieve policies for a given name (required) offset, str: Starting index of the resources (1 based) (required) limit, str: Number of resources to return (required) Returns: ScalableGroupListResult """ allParams = ['name', 'offset', 'limit'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method getScalableGroupByFilters" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/scalable-group' resourcePath = resourcePath.replace('{format}', 'json') method = 'GET' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('name' in params): queryParams['name'] = self.apiClient.toPathValue(params['name']) if ('offset' in params): queryParams['offset'] = self.apiClient.toPathValue(params['offset']) if ('limit' in params): queryParams['limit'] = self.apiClient.toPathValue(params['limit']) postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'ScalableGroupListResult') return responseObject def updateScalableGroups(self, **kwargs): """Updates existing scalable group. Args: scalableGroupDtos, list[ScalableGroupDTO]: scalableGroupDtos (required) Returns: TaskIdResult """ allParams = ['scalableGroupDtos'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method updateScalableGroups" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/scalable-group' resourcePath = resourcePath.replace('{format}', 'json') method = 'PUT' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('scalableGroupDtos' in params): bodyParam = params['scalableGroupDtos'] postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'TaskIdResult') return responseObject def addScalableGroups(self, **kwargs): """Creates scalable group inheriting properties from an existing scalable group. Args: scalableGroupDtos, list[ScalableGroupDTO]: scalableGroupDtos (required) Returns: TaskIdResult """ allParams = ['scalableGroupDtos'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method addScalableGroups" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/scalable-group' resourcePath = resourcePath.replace('{format}', 'json') method = 'POST' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('scalableGroupDtos' in params): bodyParam = params['scalableGroupDtos'] postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'TaskIdResult') return responseObject def getCount(self, **kwargs): """getCount Args: Returns: CountResult """ allParams = [] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method getCount" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/scalable-group/count' resourcePath = resourcePath.replace('{format}', 'json') method = 'GET' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'CountResult') return responseObject def getEndPointGroupbyId(self, **kwargs): """List scalable group based on id Args: id, str: id (required) Returns: ScalableGroupResult """ allParams = ['id'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method getEndPointGroupbyId" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/scalable-group/{id}' resourcePath = resourcePath.replace('{format}', 'json') method = 'GET' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('id' in params): replacement = str(self.apiClient.toPathValue(params['id'])) replacement = urllib.parse.quote(replacement) resourcePath = resourcePath.replace('{' + 'id' + '}', replacement) postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'ScalableGroupResult') return responseObject def deleteScalableGroup(self, **kwargs): """Delete a scalable group by its id. Args: id, str: id (required) Returns: TaskIdResult """ allParams = ['id'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method deleteScalableGroup" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/scalable-group/{id}' resourcePath = resourcePath.replace('{format}', 'json') method = 'DELETE' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('id' in params): replacement = str(self.apiClient.toPathValue(params['id'])) replacement = urllib.parse.quote(replacement) resourcePath = resourcePath.replace('{' + 'id' + '}', replacement) postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'TaskIdResult') return responseObject def getUserByScalableGroupId(self, **kwargs): """List scalable group based on id Args: id, str: id (required) Returns: ApicEmUserListResult """ allParams = ['id'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method getUserByScalableGroupId" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/scalable-group/{id}/users' resourcePath = resourcePath.replace('{format}', 'json') method = 'GET' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('id' in params): replacement = str(self.apiClient.toPathValue(params['id'])) replacement = urllib.parse.quote(replacement) resourcePath = resourcePath.replace('{' + 'id' + '}', replacement) postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'ApicEmUserListResult') return responseObject

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945

11,931

6.637037

0.142857

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0.781091

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1

0

1

0

null

0

6

49082ded2358d451f3a446c6629c4569ee6c21fc

33

py

Python

utils/parsers/__init__.py

jvsn19/tcc

8ca9434deb50903ef23e712a079ed159dd7c28b6

[ "MIT" ]

null

utils/parsers/__init__.py

jvsn19/tcc

8ca9434deb50903ef23e712a079ed159dd7c28b6

[ "MIT" ]

1

2021-02-25T22:37:49.000Z

utils/parsers/__init__.py

jvsn19/tcc

8ca9434deb50903ef23e712a079ed159dd7c28b6

[ "MIT" ]

null

from .TCCParser import TCCParser

16.5

32

0.848485

4

33

7

0.75

0

0.121212

33

1

33

0.965517

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true

0

1

0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

49197d58f50419a076f1129a8ab1be883c2698d6

42

py

Python

runtime/musl-lkl/apps/python/helloworld.py

dme26/intravisor

9bf9c50aa14616bd9bd66eee47623e8b61514058

[ "MIT" ]

11

2022-02-05T12:12:43.000Z

2022-03-08T08:09:08.000Z

runtime/musl-lkl/apps/python/helloworld.py

dme26/intravisor

9bf9c50aa14616bd9bd66eee47623e8b61514058

[ "MIT" ]

null

runtime/musl-lkl/apps/python/helloworld.py

dme26/intravisor

9bf9c50aa14616bd9bd66eee47623e8b61514058

[ "MIT" ]

1

2022-02-22T20:32:22.000Z

print("Hello world from CHERI python\n");

21

41

0.738095

7

42

4.428571

1

0

0.119048

42

1

42

0.837838

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0.738095

0

1

0

true

0

1

0

null

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1

0

1

0

null

0

1

0

1

0

6

4953c7d4287407342e1c774902be0cc3504df9b9

26

py

Python

models/__init__.py

bolero2/vggnet-torch

912046be3f0581e0217c2cf5b596e6318aad241b

[ "Apache-2.0" ]

2

2021-04-23T03:49:30.000Z

2021-04-23T03:49:33.000Z

models/__init__.py

bolero2/vggnet-torch

912046be3f0581e0217c2cf5b596e6318aad241b

[ "Apache-2.0" ]

null

models/__init__.py

bolero2/vggnet-torch

912046be3f0581e0217c2cf5b596e6318aad241b

[ "Apache-2.0" ]

null

from .models import VGGNet

26

0.846154

4

26

5.5

1

0

0.115385

26

1

26

0.956522

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4967a3e2a09b3fe98ed0fc8d035d3e866c664dc5

70

py

Python

continual_learning/methods/task_incremental/multi_task/gg/super_mask_pruning/base/__init__.py

jaryP/ContinualAI

7d9b7614066d219ebd72049692da23ad6ec132b0

[ "MIT" ]

null

continual_learning/methods/task_incremental/multi_task/gg/super_mask_pruning/base/__init__.py

jaryP/ContinualAI

7d9b7614066d219ebd72049692da23ad6ec132b0

[ "MIT" ]

null

continual_learning/methods/task_incremental/multi_task/gg/super_mask_pruning/base/__init__.py

jaryP/ContinualAI

7d9b7614066d219ebd72049692da23ad6ec132b0

[ "MIT" ]

null

from .distributions import * from .layer import * from .utils import *

23.333333

28

0.757143

9

70

5.888889

0.555556

0.377358

0

0.157143

70

3

29

23.333333

0.898305

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

498d46e1852df10e7f414e05b90a9d8080e31f36

99

py

Python

override/__main__.py

davidhyman/override

e34bd3c8676233439de5c002367b3bff5c1b88d6

[ "MIT" ]

null

override/__main__.py

davidhyman/override

e34bd3c8676233439de5c002367b3bff5c1b88d6

[ "MIT" ]

1

2017-07-11T22:03:27.000Z

override/__main__.py

davidhyman/override

e34bd3c8676233439de5c002367b3bff5c1b88d6

[ "MIT" ]

null

from override.cli import init_from_command if __name__ == '__main__': init_from_command()

19.8

43

0.737374

13

99

4.692308

0.692308

0.262295

0.491803

0

0.181818

99

4

44

24.75

0.753086

0

0.084211

0

1

0

true

0

0.333333

0

0.333333

0

1

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null

1

0

1

0

null

0

1

0

1

0

6

77053c923f7a5dc49b4a3bfa5219f2cbffa44bce

178

py

Python

virl/generators/__init__.py

gve-vse-tim/virlutils

64687229ea8763509aca54b63144b61037e5228f

[ "MIT" ]

12

2018-03-27T14:02:22.000Z

2018-06-07T16:19:38.000Z

virl/generators/__init__.py

gve-vse-tim/virlutils

64687229ea8763509aca54b63144b61037e5228f

[ "MIT" ]

29

2017-12-14T16:38:12.000Z

2018-08-19T18:41:06.000Z

virl/generators/__init__.py

gve-vse-tim/virlutils

64687229ea8763509aca54b63144b61037e5228f

[ "MIT" ]

7

2018-03-02T15:42:22.000Z

2020-04-20T11:25:32.000Z

from .pyats_testbed import pyats_testbed_generator # noqa from .ansible_inventory import ansible_inventory_generator # noqa from .nso_payload import nso_payload_generator # noqa

44.5

65

0.865169

24

178

6.041667

0.416667

0.268966

0.234483

0

0.101124

178

3

66

59.333333

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true

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1

0

null

0

1

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1

0

6

773cfab9368b9d43a339a35fda07e8f99d190375

42,300

py

Python

pybind/slxos/v17r_2_00/isis_state/interface_detail/isis_intf/circ_metrics/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

null

pybind/slxos/v17r_2_00/isis_state/interface_detail/isis_intf/circ_metrics/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

null

pybind/slxos/v17r_2_00/isis_state/interface_detail/isis_intf/circ_metrics/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class circ_metrics(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-isis-operational - based on the path /isis-state/interface-detail/isis-intf/circ-metrics. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: ISIS circuit attributes """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__level','__auth_check','__auth_mode','__auth_key','__circ_metric','__ip6_circ_metric','__circ_priority','__hello_int','__hello_mult','__dis','__dis_ch','__next_hello','__active_adj',) _yang_name = 'circ-metrics' _rest_name = 'circ-metrics' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__hello_int = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="hello-int", rest_name="hello-int", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) self.__ip6_circ_metric = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="ip6-circ-metric", rest_name="ip6-circ-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) self.__auth_key = YANGDynClass(base=unicode, is_leaf=True, yang_name="auth-key", rest_name="auth-key", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='string', is_config=False) self.__circ_metric = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="circ-metric", rest_name="circ-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) self.__dis_ch = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="dis-ch", rest_name="dis-ch", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) self.__active_adj = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="active-adj", rest_name="active-adj", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) self.__level = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'isis-level1-2': {'value': 0}, u'isis-level1': {'value': 1}, u'isis-level2': {'value': 2}},), is_leaf=True, yang_name="level", rest_name="level", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-level', is_config=False) self.__auth_mode = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'none': {'value': 0}, u'cleartext': {'value': 1}, u'md5': {'value': 2}},), is_leaf=True, yang_name="auth-mode", rest_name="auth-mode", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='auth-mode', is_config=False) self.__circ_priority = YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), is_leaf=True, yang_name="circ-priority", rest_name="circ-priority", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint8', is_config=False) self.__dis = YANGDynClass(base=unicode, is_leaf=True, yang_name="dis", rest_name="dis", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='string', is_config=False) self.__next_hello = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="next-hello", rest_name="next-hello", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) self.__auth_check = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="auth-check", rest_name="auth-check", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) self.__hello_mult = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="hello-mult", rest_name="hello-mult", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'isis-state', u'interface-detail', u'isis-intf', u'circ-metrics'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'isis-state', u'interface-detail', u'isis-intf', u'circ-metrics'] def _get_level(self): """ Getter method for level, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/level (isis-level) YANG Description: ISIS operation mode """ return self.__level def _set_level(self, v, load=False): """ Setter method for level, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/level (isis-level) If this variable is read-only (config: false) in the source YANG file, then _set_level is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_level() directly. YANG Description: ISIS operation mode """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'isis-level1-2': {'value': 0}, u'isis-level1': {'value': 1}, u'isis-level2': {'value': 2}},), is_leaf=True, yang_name="level", rest_name="level", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-level', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """level must be of a type compatible with isis-level""", 'defined-type': "brocade-isis-operational:isis-level", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'isis-level1-2': {'value': 0}, u'isis-level1': {'value': 1}, u'isis-level2': {'value': 2}},), is_leaf=True, yang_name="level", rest_name="level", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-level', is_config=False)""", }) self.__level = t if hasattr(self, '_set'): self._set() def _unset_level(self): self.__level = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'isis-level1-2': {'value': 0}, u'isis-level1': {'value': 1}, u'isis-level2': {'value': 2}},), is_leaf=True, yang_name="level", rest_name="level", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-level', is_config=False) def _get_auth_check(self): """ Getter method for auth_check, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/auth_check (isis-status) YANG Description: If authentication enabled on incoming IS-IS PDUs """ return self.__auth_check def _set_auth_check(self, v, load=False): """ Setter method for auth_check, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/auth_check (isis-status) If this variable is read-only (config: false) in the source YANG file, then _set_auth_check is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_auth_check() directly. YANG Description: If authentication enabled on incoming IS-IS PDUs """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="auth-check", rest_name="auth-check", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """auth_check must be of a type compatible with isis-status""", 'defined-type': "brocade-isis-operational:isis-status", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="auth-check", rest_name="auth-check", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False)""", }) self.__auth_check = t if hasattr(self, '_set'): self._set() def _unset_auth_check(self): self.__auth_check = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="auth-check", rest_name="auth-check", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) def _get_auth_mode(self): """ Getter method for auth_mode, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/auth_mode (auth-mode) YANG Description: IS-IS authentication mode """ return self.__auth_mode def _set_auth_mode(self, v, load=False): """ Setter method for auth_mode, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/auth_mode (auth-mode) If this variable is read-only (config: false) in the source YANG file, then _set_auth_mode is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_auth_mode() directly. YANG Description: IS-IS authentication mode """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'none': {'value': 0}, u'cleartext': {'value': 1}, u'md5': {'value': 2}},), is_leaf=True, yang_name="auth-mode", rest_name="auth-mode", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='auth-mode', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """auth_mode must be of a type compatible with auth-mode""", 'defined-type': "brocade-isis-operational:auth-mode", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'none': {'value': 0}, u'cleartext': {'value': 1}, u'md5': {'value': 2}},), is_leaf=True, yang_name="auth-mode", rest_name="auth-mode", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='auth-mode', is_config=False)""", }) self.__auth_mode = t if hasattr(self, '_set'): self._set() def _unset_auth_mode(self): self.__auth_mode = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'none': {'value': 0}, u'cleartext': {'value': 1}, u'md5': {'value': 2}},), is_leaf=True, yang_name="auth-mode", rest_name="auth-mode", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='auth-mode', is_config=False) def _get_auth_key(self): """ Getter method for auth_key, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/auth_key (string) YANG Description: IS-IS authentication key """ return self.__auth_key def _set_auth_key(self, v, load=False): """ Setter method for auth_key, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/auth_key (string) If this variable is read-only (config: false) in the source YANG file, then _set_auth_key is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_auth_key() directly. YANG Description: IS-IS authentication key """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="auth-key", rest_name="auth-key", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='string', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """auth_key must be of a type compatible with string""", 'defined-type': "string", 'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="auth-key", rest_name="auth-key", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='string', is_config=False)""", }) self.__auth_key = t if hasattr(self, '_set'): self._set() def _unset_auth_key(self): self.__auth_key = YANGDynClass(base=unicode, is_leaf=True, yang_name="auth-key", rest_name="auth-key", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='string', is_config=False) def _get_circ_metric(self): """ Getter method for circ_metric, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/circ_metric (uint32) YANG Description: ISIS circuit Metric """ return self.__circ_metric def _set_circ_metric(self, v, load=False): """ Setter method for circ_metric, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/circ_metric (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_circ_metric is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_circ_metric() directly. YANG Description: ISIS circuit Metric """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="circ-metric", rest_name="circ-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """circ_metric must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="circ-metric", rest_name="circ-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__circ_metric = t if hasattr(self, '_set'): self._set() def _unset_circ_metric(self): self.__circ_metric = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="circ-metric", rest_name="circ-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) def _get_ip6_circ_metric(self): """ Getter method for ip6_circ_metric, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/ip6_circ_metric (uint32) YANG Description: ISISv6 circuit Metric """ return self.__ip6_circ_metric def _set_ip6_circ_metric(self, v, load=False): """ Setter method for ip6_circ_metric, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/ip6_circ_metric (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_ip6_circ_metric is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ip6_circ_metric() directly. YANG Description: ISISv6 circuit Metric """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="ip6-circ-metric", rest_name="ip6-circ-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """ip6_circ_metric must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="ip6-circ-metric", rest_name="ip6-circ-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__ip6_circ_metric = t if hasattr(self, '_set'): self._set() def _unset_ip6_circ_metric(self): self.__ip6_circ_metric = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="ip6-circ-metric", rest_name="ip6-circ-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) def _get_circ_priority(self): """ Getter method for circ_priority, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/circ_priority (uint8) YANG Description: Circuit Priority """ return self.__circ_priority def _set_circ_priority(self, v, load=False): """ Setter method for circ_priority, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/circ_priority (uint8) If this variable is read-only (config: false) in the source YANG file, then _set_circ_priority is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_circ_priority() directly. YANG Description: Circuit Priority """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), is_leaf=True, yang_name="circ-priority", rest_name="circ-priority", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint8', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """circ_priority must be of a type compatible with uint8""", 'defined-type': "uint8", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), is_leaf=True, yang_name="circ-priority", rest_name="circ-priority", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint8', is_config=False)""", }) self.__circ_priority = t if hasattr(self, '_set'): self._set() def _unset_circ_priority(self): self.__circ_priority = YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), is_leaf=True, yang_name="circ-priority", rest_name="circ-priority", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint8', is_config=False) def _get_hello_int(self): """ Getter method for hello_int, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/hello_int (uint32) YANG Description: Hello interval """ return self.__hello_int def _set_hello_int(self, v, load=False): """ Setter method for hello_int, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/hello_int (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_hello_int is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_hello_int() directly. YANG Description: Hello interval """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="hello-int", rest_name="hello-int", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """hello_int must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="hello-int", rest_name="hello-int", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__hello_int = t if hasattr(self, '_set'): self._set() def _unset_hello_int(self): self.__hello_int = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="hello-int", rest_name="hello-int", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) def _get_hello_mult(self): """ Getter method for hello_mult, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/hello_mult (uint32) YANG Description: Hello multiplier """ return self.__hello_mult def _set_hello_mult(self, v, load=False): """ Setter method for hello_mult, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/hello_mult (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_hello_mult is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_hello_mult() directly. YANG Description: Hello multiplier """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="hello-mult", rest_name="hello-mult", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """hello_mult must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="hello-mult", rest_name="hello-mult", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__hello_mult = t if hasattr(self, '_set'): self._set() def _unset_hello_mult(self): self.__hello_mult = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="hello-mult", rest_name="hello-mult", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) def _get_dis(self): """ Getter method for dis, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/dis (string) YANG Description: Designated IS """ return self.__dis def _set_dis(self, v, load=False): """ Setter method for dis, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/dis (string) If this variable is read-only (config: false) in the source YANG file, then _set_dis is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_dis() directly. YANG Description: Designated IS """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="dis", rest_name="dis", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='string', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """dis must be of a type compatible with string""", 'defined-type': "string", 'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="dis", rest_name="dis", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='string', is_config=False)""", }) self.__dis = t if hasattr(self, '_set'): self._set() def _unset_dis(self): self.__dis = YANGDynClass(base=unicode, is_leaf=True, yang_name="dis", rest_name="dis", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='string', is_config=False) def _get_dis_ch(self): """ Getter method for dis_ch, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/dis_ch (uint32) YANG Description: DIS changes """ return self.__dis_ch def _set_dis_ch(self, v, load=False): """ Setter method for dis_ch, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/dis_ch (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_dis_ch is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_dis_ch() directly. YANG Description: DIS changes """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="dis-ch", rest_name="dis-ch", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """dis_ch must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="dis-ch", rest_name="dis-ch", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__dis_ch = t if hasattr(self, '_set'): self._set() def _unset_dis_ch(self): self.__dis_ch = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="dis-ch", rest_name="dis-ch", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) def _get_next_hello(self): """ Getter method for next_hello, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/next_hello (uint32) YANG Description: Time remaining until next hello """ return self.__next_hello def _set_next_hello(self, v, load=False): """ Setter method for next_hello, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/next_hello (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_next_hello is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_next_hello() directly. YANG Description: Time remaining until next hello """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="next-hello", rest_name="next-hello", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """next_hello must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="next-hello", rest_name="next-hello", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__next_hello = t if hasattr(self, '_set'): self._set() def _unset_next_hello(self): self.__next_hello = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="next-hello", rest_name="next-hello", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) def _get_active_adj(self): """ Getter method for active_adj, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/active_adj (uint32) YANG Description: Number of active adjacencies """ return self.__active_adj def _set_active_adj(self, v, load=False): """ Setter method for active_adj, mapped from YANG variable /isis_state/interface_detail/isis_intf/circ_metrics/active_adj (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_active_adj is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_active_adj() directly. YANG Description: Number of active adjacencies """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="active-adj", rest_name="active-adj", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """active_adj must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="active-adj", rest_name="active-adj", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__active_adj = t if hasattr(self, '_set'): self._set() def _unset_active_adj(self): self.__active_adj = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="active-adj", rest_name="active-adj", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) level = __builtin__.property(_get_level) auth_check = __builtin__.property(_get_auth_check) auth_mode = __builtin__.property(_get_auth_mode) auth_key = __builtin__.property(_get_auth_key) circ_metric = __builtin__.property(_get_circ_metric) ip6_circ_metric = __builtin__.property(_get_ip6_circ_metric) circ_priority = __builtin__.property(_get_circ_priority) hello_int = __builtin__.property(_get_hello_int) hello_mult = __builtin__.property(_get_hello_mult) dis = __builtin__.property(_get_dis) dis_ch = __builtin__.property(_get_dis_ch) next_hello = __builtin__.property(_get_next_hello) active_adj = __builtin__.property(_get_active_adj) _pyangbind_elements = {'level': level, 'auth_check': auth_check, 'auth_mode': auth_mode, 'auth_key': auth_key, 'circ_metric': circ_metric, 'ip6_circ_metric': ip6_circ_metric, 'circ_priority': circ_priority, 'hello_int': hello_int, 'hello_mult': hello_mult, 'dis': dis, 'dis_ch': dis_ch, 'next_hello': next_hello, 'active_adj': active_adj, }

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null

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774192f08d5bb7ebb989858494927682c42cfedf

3,802

py

Python

pkgs/sdk-pkg/src/genie/libs/sdk/triggers/ha/reload/iosxe/reload.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

null

pkgs/sdk-pkg/src/genie/libs/sdk/triggers/ha/reload/iosxe/reload.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

null

pkgs/sdk-pkg/src/genie/libs/sdk/triggers/ha/reload/iosxe/reload.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

null

'''IOSXE implementation for Reload triggers''' # import ats from ats import aetest # Genie Libs from genie.libs.sdk.triggers.ha.ha import \ TriggerReload as CommonReload, \ TriggerReloadLc class TriggerReload(CommonReload): @aetest.setup def verify_prerequisite(self, uut, abstract, steps, timeout): '''Learn Ops object and verify the requirements. If the requirements are not satisfied, then skip to the next testcase. Args: uut (`obj`): Device object. abstract (`obj`): Abstract object. steps (`step obj`): aetest step object timeout (`timeout obj`): Timeout Object Returns: None Raises: pyATS Results ''' self.skipped('No implementation for generic iosxe HA reload', goto=['next_tc']) class TriggerReloadActiveRP(CommonReload): @aetest.setup def verify_prerequisite(self, uut, abstract, steps, timeout): '''Learn Ops object and verify the requirements. If the requirements are not satisfied, then skip to the next testcase. Args: uut (`obj`): Device object. abstract (`obj`): Abstract object. steps (`step obj`): aetest step object timeout (`timeout obj`): Timeout Object Returns: None Raises: pyATS Results ''' self.skipped('No implementation for generic iosxe HA reload', goto=['next_tc']) class TriggerReloadStandbyRP(CommonReload): @aetest.setup def verify_prerequisite(self, uut, abstract, steps, timeout): '''Learn Ops object and verify the requirements. If the requirements are not satisfied, then skip to the next testcase. Args: uut (`obj`): Device object. abstract (`obj`): Abstract object. steps (`step obj`): aetest step object timeout (`timeout obj`): Timeout Object Returns: None Raises: pyATS Results ''' self.skipped('No implementation for generic iosxe HA reload', goto=['next_tc']) class TriggerReloadMember(TriggerReloadLc): @aetest.setup def verify_prerequisite(self, uut, abstract, steps, timeout): '''Learn Ops object and verify the requirements. If the requirements are not satisfied, then skip to the next testcase. Args: uut (`obj`): Device object. abstract (`obj`): Abstract object. steps (`step obj`): aetest step object timeout (`timeout obj`): Timeout Object Returns: None Raises: pyATS Results ''' self.skipped('No implementation for generic iosxe HA reload', goto=['next_tc']) class TriggerReloadActiveFP(TriggerReloadLc): @aetest.setup def verify_prerequisite(self, uut, abstract, steps, timeout): '''Learn Ops object and verify the requirements. If the requirements are not satisfied, then skip to the next testcase. Args: uut (`obj`): Device object. abstract (`obj`): Abstract object. steps (`step obj`): aetest step object timeout (`timeout obj`): Timeout Object Returns: None Raises: pyATS Results ''' self.skipped('No implementation for generic iosxe HA reload', goto=['next_tc'])

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0.072254

0.033719

0.04817

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3,802

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null

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null

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6

774d460a44b1fd2cd9d90a55b8b07a67072600b3

276

py

Python

data-mining/cluster-analysis/assignment/clustering-data/python/evaluation.py

4979/courses

dd9efa0a6b60cead833f36a6bfa518dd4fece17f

[ "Apache-2.0" ]

null

data-mining/cluster-analysis/assignment/clustering-data/python/evaluation.py

4979/courses

dd9efa0a6b60cead833f36a6bfa518dd4fece17f

[ "Apache-2.0" ]

null

data-mining/cluster-analysis/assignment/clustering-data/python/evaluation.py

4979/courses

dd9efa0a6b60cead833f36a6bfa518dd4fece17f

[ "Apache-2.0" ]

null

from math import log, sqrt def purity(groundtruthAssignment, algorithmAssignment): purity = 0 # TODO # Compute the purity return purity def NMI(groundtruthAssignment, algorithmAssignment): NMI = 0 # TODO # Compute the NMI return NMI

15.333333

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11,302

py

Python

leap/datasets/sim_dataset.py

weirayao/leap

8d10b8413d02d3be49d5c02a13a0aa60a741d8da

[ "MIT" ]

7

2022-01-06T18:37:57.000Z

2022-03-20T17:11:30.000Z

leap/datasets/sim_dataset.py

weirayao/leap

8d10b8413d02d3be49d5c02a13a0aa60a741d8da

[ "MIT" ]

null

leap/datasets/sim_dataset.py

weirayao/leap

8d10b8413d02d3be49d5c02a13a0aa60a741d8da

[ "MIT" ]

null

import os import glob import torch import random import numpy as np from torch.utils.data import Dataset import ipdb as pdb class SimulationDataset(Dataset): def __init__(self, directory, transition="linear_nongaussian"): super().__init__() assert transition in ["linear_nongaussian", "post_nonlinear_gaussian", "post_nonlinear_nongaussian", "post_nonlinear_nongaussian"] self.path = os.path.join(directory, transition, "data.npz") self.npz = np.load(self.path) self.data = { } for key in ["yt", "xt", "yt_", "xt_"]: self.data[key] = self.npz[key] def __len__(self): return len(self.data["yt"]) def __getitem__(self, idx): yt = torch.from_numpy(self.data["yt"][idx].astype('float32')) xt = torch.from_numpy(self.data["xt"][idx].astype('float32')) yt_ = torch.from_numpy(self.data["yt_"][idx].astype('float32')).unsqueeze(0) xt_ = torch.from_numpy(np.expand_dims(self.data["xt_"][idx], axis=0).astype('float32')) sample = {"yt": yt, "yt_": yt_, "xt": xt, "xt_": xt_} return sample class SimulationDatasetTwoSample(Dataset): def __init__(self, transition="linear_nongaussian"): super().__init__() assert transition in ["linear_nongaussian", "post_nonlinear_gaussian", "post_nonlinear_nongaussian", "post_nonlinear_nongaussian"] self.path = os.path.join(DIR, transition, "data.npz") self.npz = np.load(self.path) self.data = { } for key in ["yt", "xt", "yt_", "xt_"]: self.data[key] = self.npz[key] self.min = np.min(self.data["xt_"], axis=0).reshape(1, -1) self.max = np.max(self.data["xt_"], axis=0).reshape(1, -1) def __len__(self): return len(self.data["yt"]) def __getitem__(self, idx): yt = torch.from_numpy(self.data["yt"][idx]) xt = torch.from_numpy((self.data["xt"][idx]-self.min / (self.max-self.min))) yt_ = torch.from_numpy(self.data["yt_"][idx]).unsqueeze(0) xt_ = torch.from_numpy((np.expand_dims(self.data["xt_"][idx], axis=0)-self.min) / (self.max-self.min)) sample1 = {"yt": yt, "yt_": yt_, "xt": xt, "xt_": xt_} idx_rnd = random.randint(0, len(self.data["yt"])-1) ytr = torch.from_numpy(self.data["yt"][idx_rnd]) xtr = torch.from_numpy((self.data["xt"][idx_rnd]-self.min / (self.max-self.min))) ytr_ = torch.from_numpy(self.data["yt_"][idx_rnd]).unsqueeze(0) xtr_ = torch.from_numpy((np.expand_dims(self.data["xt_"][idx_rnd], axis=0)-self.min) / (self.max-self.min)) sample2 = {"yt": ytr, "yt_": ytr_, "xt": xtr, "xt_": xtr_} return sample1, sample2 class TupleDataset(torch.utils.data.Dataset): def __init__(self, split: str = "train"): super().__init__() assert split in ("train", "val") with open(os.path.join(DIR, "%s.txt"%split), 'r') as f: self.datum_names = [datum_name.rstrip() for datum_name in f.readlines()] self.samples_per_datum = 64 def __len__(self): return len(self.datum_names) * self.samples_per_datum def __getitem__(self, idx): datum_idx = idx // self.samples_per_datum sample_idx = idx % self.samples_per_datum self.datum_names = [ele.replace('\\', '/') for ele in self.datum_names] datum = np.load(self.datum_names[datum_idx]) # latent factor # yt = y_t (batch_size, length, size) # yt_ = y_(t+1) (batch_size, 1, size) # observed variable # xt = x_t (batch_size, length, size) # xt_ = x_(t+1) (batch_size, 1, size) sample = {"yt": torch.from_numpy(datum["yt"][sample_idx]), "yt_": torch.from_numpy(datum["yt_"][sample_idx]), "xt": torch.from_numpy(datum["xt"][sample_idx]), "xt_": torch.from_numpy(datum["xt_"][sample_idx])} return sample class SimulationDatasetTS(Dataset): def __init__(self, directory, transition="linear_nongaussian_ts"): super().__init__() assert transition in ["linear_nongaussian_ts", "nonlinear_gaussian_ts", "nonlinear_nongaussian_ts", "pnl_nongaussian_ts"] self.path = os.path.join(directory, transition, "data.npz") self.npz = np.load(self.path) self.data = { } for key in ["yt", "xt"]: self.data[key] = self.npz[key] def __len__(self): return len(self.data["yt"]) def __getitem__(self, idx): yt = torch.from_numpy(self.data["yt"][idx].astype('float32')) xt = torch.from_numpy(self.data["xt"][idx].astype('float32')) sample = {"yt": yt, "xt": xt} return sample class SimulationDatasetTSTwoSample(Dataset): def __init__(self, directory, transition="linear_nongaussian_ts"): super().__init__() assert transition in ["linear_nongaussian_ts", "nonlinear_gaussian_ts", "nonlinear_gaussian_sparse_ts", "nonlinear_nongaussian_ts", "pnl_nongaussian_ts", "instan_temporal", "case1_dependency"] self.path = os.path.join(directory, transition, "data.npz") self.npz = np.load(self.path) self.data = { } for key in ["yt", "xt"]: self.data[key] = self.npz[key] def __len__(self): return len(self.data["yt"]) def __getitem__(self, idx): yt = torch.from_numpy(self.data["yt"][idx].astype('float32')) xt = torch.from_numpy(self.data["xt"][idx].astype('float32')) idx_rnd = random.randint(0, len(self.data["yt"])-1) ytr = torch.from_numpy(self.data["yt"][idx_rnd].astype('float32')) xtr = torch.from_numpy(self.data["xt"][idx_rnd].astype('float32')) sample = {"s1": {"yt": yt, "xt": xt}, "s2": {"yt": ytr, "xt": xtr} } return sample class SimulationDatasetTSTwoSampleNS(Dataset): def __init__(self, directory, transition="linear_nongaussian_ts"): super().__init__() self.path = os.path.join(directory, transition, "data.npz") self.npz = np.load(self.path) self.data = { } for key in ["yt", "xt", "ct"]: self.data[key] = self.npz[key] def __len__(self): return len(self.data["yt"]) def __getitem__(self, idx): yt = torch.from_numpy(self.data["yt"][idx].astype('float32')) xt = torch.from_numpy(self.data["xt"][idx].astype('float32')) ct = torch.from_numpy(self.data["ct"][idx].astype('float32')) idx_rnd = random.randint(0, len(self.data["yt"])-1) ytr = torch.from_numpy(self.data["yt"][idx_rnd].astype('float32')) xtr = torch.from_numpy(self.data["xt"][idx_rnd].astype('float32')) ctr = torch.from_numpy(self.data["ct"][idx_rnd].astype('float32')) sample = {"s1": {"yt": yt, "xt": xt, "ct": ct}, "s2": {"yt": ytr, "xt": xtr, "ct": ctr} } return sample class SimulationDatasetPCL(Dataset): def __init__(self, directory, transition="linear_nongaussian_ts", lags=2): super().__init__() assert transition in ["linear_nongaussian_ts", "nonlinear_gaussian_ts", "nonlinear_gaussian_sparse_ts", "nonlinear_gau_cins", "nonlinear_nongaussian_ts", "nonlinear_ns", "nonlinear_gau_ns", "nonlinear_gau_cins_sparse"] self.path = os.path.join(directory, transition, "data.npz") self.npz = np.load(self.path) self.L = lags self.data = { } for key in ["yt", "xt"]: self.data[key] = self.npz[key] def __len__(self): return len(self.data["yt"]) def __getitem__(self, idx): yt = torch.from_numpy(self.data["yt"][idx].astype('float32')) xt = torch.from_numpy(self.data["xt"][idx].astype('float32')) xt_cur, xt_his = self.seq_to_pairs(xt) idx_rnd = random.randint(0, len(self.data["yt"])-1) ytr = torch.from_numpy(self.data["yt"][idx_rnd].astype('float32')) xtr = torch.from_numpy(self.data["xt"][idx_rnd].astype('float32')) xtr_cur, xtr_his = self.seq_to_pairs(xtr) xt_cat = torch.cat((xt_cur, xt_his), dim=1) xtr_cat = torch.cat((xt_cur, xtr_his), dim=1) sample = {"s1": {"yt": yt, "xt": xt}, "s2": {"yt": ytr, "xt": xtr}, "pos": {"x": xt_cat, "y": 1}, "neg": {"x": xtr_cat, "y": 0} } return sample def seq_to_pairs(self, x): x = x.unfold(dimension = 0, size = self.L+1, step = 1) x = torch.swapaxes(x, 1, 2) xx, yy = x[:,-1:], x[:,:-1] return xx, yy class SimulationDatasetPCLNS(Dataset): def __init__(self, directory, transition="linear_nongaussian_ts", lags=2): super().__init__() assert transition in ["linear_nongaussian_ts", "nonlinear_gaussian_ts", "nonlinear_gaussian_sparse_ts", "nonlinear_gau_cins", "nonlinear_nongaussian_ts", "nonlinear_ns", "nonlinear_gau_ns", "nonlinear_gau_cins_sparse"] self.path = os.path.join(directory, transition, "data.npz") self.npz = np.load(self.path) self.L = lags self.data = { } for key in ["yt", "xt", "ct"]: self.data[key] = self.npz[key] def __len__(self): return len(self.data["yt"]) def __getitem__(self, idx): yt = torch.from_numpy(self.data["yt"][idx].astype('float32')) xt = torch.from_numpy(self.data["xt"][idx].astype('float32')) ct = torch.from_numpy(self.data["ct"][idx].astype('float32')) xt_cur, xt_his = self.seq_to_pairs(xt) idx_rnd = random.randint(0, len(self.data["yt"])-1) ytr = torch.from_numpy(self.data["yt"][idx_rnd].astype('float32')) xtr = torch.from_numpy(self.data["xt"][idx_rnd].astype('float32')) ctr = torch.from_numpy(self.data["ct"][idx_rnd].astype('float32')) xtr_cur, xtr_his = self.seq_to_pairs(xtr) xt_cat = torch.cat((xt_cur, xt_his), dim=1) xtr_cat = torch.cat((xt_cur, xtr_his), dim=1) sample = {"s1": {"yt": yt, "xt": xt, "ct": ct}, "s2": {"yt": ytr, "xt": xtr, "ct": ctr}, "pos": {"x": xt_cat, "y": 1}, "neg": {"x": xtr_cat, "y": 0} } return sample def seq_to_pairs(self, x): x = x.unfold(dimension = 0, size = self.L+1, step = 1) x = torch.swapaxes(x, 1, 2) xx, yy = x[:,-1:], x[:,:-1] return xx, yy class DANS(Dataset): def __init__(self, directory, dataset="da_10"): super().__init__() self.path = os.path.join(directory, dataset, "data.npz") self.npz = np.load(self.path) self.data = { } for key in ["y", "x", "c"]: self.data[key] = self.npz[key] def __len__(self): return len(self.data["y"]) def __getitem__(self, idx): y = torch.from_numpy(self.data["y"][idx].astype('float32')) x = torch.from_numpy(self.data["x"][idx].astype('float32')) c = torch.from_numpy(self.data["c"][idx, None].astype('float32')) sample = {"y": y, "x": x, "c": c} return sample

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6200e1b1ca6a347279d51d109eef180ba174fd5b

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py

Python

src/__init__.py

HaritzPuerto/Entity_Extractor

30abbbbb45eb2cbdc836ee00dd4e38137ef653a0

[ "Apache-2.0" ]

2

2022-03-14T12:46:06.000Z

2022-03-31T09:14:56.000Z

src/__init__.py

HaritzPuerto/Entity_Extractor

30abbbbb45eb2cbdc836ee00dd4e38137ef653a0

[ "Apache-2.0" ]

1

2022-03-20T14:18:04.000Z

src/__init__.py

HaritzPuerto/Entity_Extractor

30abbbbb45eb2cbdc836ee00dd4e38137ef653a0

[ "Apache-2.0" ]

null

from .SRL import SRL_model from .NER import Entity_model from .Flair_NER import Flair_NER

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6217e3d9ff366fbd5edc0aa942ec7886403f289c

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py

Python

201801/t3.py

xyFryan/python3

9c9b2e6be4e27fa7f78bb2b1c4c3146d3c174741

[ "MIT" ]

null

201801/t3.py

xyFryan/python3

9c9b2e6be4e27fa7f78bb2b1c4c3146d3c174741

[ "MIT" ]

null

201801/t3.py

xyFryan/python3

9c9b2e6be4e27fa7f78bb2b1c4c3146d3c174741

[ "MIT" ]

null

#!/usr/bin/python3 import test test.print_test();

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62270c545d771421ce9c6674abf357cbcd9be1a9

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py

Python

server/__init__.py

Jugbot/AWS-Mechanical-Turk-Audio-Classification

a65c8f258b6a8fd8dc55c75c688b08eb51907c60

[ "MIT" ]

1

2019-04-05T16:26:51.000Z

server/__init__.py

Jugbot/AWS-Mechanical-Turk-Audio-Classification

a65c8f258b6a8fd8dc55c75c688b08eb51907c60

[ "MIT" ]

null

server/__init__.py

Jugbot/AWS-Mechanical-Turk-Audio-Classification

a65c8f258b6a8fd8dc55c75c688b08eb51907c60

[ "MIT" ]

null

from server.main import app

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6230323b42853ba1481e85e248e497f79da3fe56

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py

Python

src/domain/system.py

gmdlba/simulation

d47b58417bf7380f2bbf552275f9b3e51253e1a5

[ "MIT" ]

null

src/domain/system.py

gmdlba/simulation

d47b58417bf7380f2bbf552275f9b3e51253e1a5

[ "MIT" ]

null

src/domain/system.py

gmdlba/simulation

d47b58417bf7380f2bbf552275f9b3e51253e1a5

[ "MIT" ]

null

class System_Status(): def __init__(self, plant_state=None): if plant_state is None: plant_state = [1, 1, 1] self.plant_state = plant_state def __str__(self): return f"El estado de la planta es {self.plant_state}"

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6

624eaff1c656c15da5c07040c0e7ff8f095597be

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py

Python

dl/models/vgg/vgg.py

jjjkkkjjj/pytorch.dl

d82aa1191c14f328c62de85e391ac6fa1b4c7ee3

[ "MIT" ]

2

2021-02-06T22:40:13.000Z

2021-03-26T09:15:34.000Z

dl/models/vgg/vgg.py

jjjkkkjjj/pytorch.dl

d82aa1191c14f328c62de85e391ac6fa1b4c7ee3

[ "MIT" ]

8

2020-07-11T07:10:51.000Z

2022-03-12T00:39:03.000Z

dl/models/vgg/vgg.py

jjjkkkjjj/pytorch.dl

d82aa1191c14f328c62de85e391ac6fa1b4c7ee3

[ "MIT" ]

2

2021-03-26T09:19:42.000Z

2021-07-27T02:38:09.000Z

from torch import nn from ...models.vgg.base import VGGBase from ..layers import Conv2d from collections import OrderedDict """ https://stackoverflow.com/questions/55140554/convolutional-encoder-error-runtimeerror-input-and-target-shapes-do-not-matc/55143487#55143487 Here is the formula; N --> Input Size, F --> Filter Size, stride-> Stride Size, pdg-> Padding size ConvTranspose2d; OutputSize = N*stride + F - stride - pdg*2 Conv2d; OutputSize = (N - F)/stride + 1 + pdg*2/stride [e.g. 32/3=10 it ignores after the comma] """ class VGGBase11_bn(VGGBase): def __init__(self, input_channels, **kwargs): Conv2d.batch_norm = True conv_layers = [ *Conv2d.block_relumpool('1', 1, input_channels, 64), *Conv2d.block_relumpool('2', 1, 64, 128), *Conv2d.block_relumpool('3', 2, 128, 256), *Conv2d.block_relumpool('4', 2, 256, 512), *Conv2d.block_relumpool('5', 2, 512, 512), ] super().__init__(model_name='vgg11_bn', conv_layers=nn.ModuleDict(OrderedDict(conv_layers)), **kwargs) class VGGBase11(VGGBase): def __init__(self, input_channels, **kwargs): Conv2d.batch_norm = False conv_layers = [ *Conv2d.block_relumpool('1', 1, input_channels, 64), *Conv2d.block_relumpool('2', 1, 64, 128), *Conv2d.block_relumpool('3', 2, 128, 256), *Conv2d.block_relumpool('4', 2, 256, 512), *Conv2d.block_relumpool('5', 2, 512, 512) ] super().__init__(model_name='vgg11', conv_layers=nn.ModuleDict(OrderedDict(conv_layers)), **kwargs) class VGGBase16_bn(VGGBase): def __init__(self, input_channels, **kwargs): Conv2d.batch_norm = True conv_layers = [ *Conv2d.block_relumpool('1', 2, input_channels, 64), *Conv2d.block_relumpool('2', 2, 64, 128), *Conv2d.block_relumpool('3', 3, 128, 256), *Conv2d.block_relumpool('4', 3, 256, 512), *Conv2d.block_relumpool('5', 3, 512, 512), ] super().__init__(model_name='vgg16_bn', conv_layers=nn.ModuleDict(OrderedDict(conv_layers)), **kwargs) class VGGBase16(VGGBase): def __init__(self, input_channels, **kwargs): Conv2d.batch_norm = False conv_layers = [ *Conv2d.block_relumpool('1', 2, input_channels, 64), *Conv2d.block_relumpool('2', 2, 64, 128), *Conv2d.block_relumpool('3', 3, 128, 256), *Conv2d.block_relumpool('4', 3, 256, 512), *Conv2d.block_relumpool('5', 3, 512, 512), ] super().__init__(model_name='vgg16', conv_layers=nn.ModuleDict(OrderedDict(conv_layers)), **kwargs)

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null

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658f841c46696234266c0bbf55da63fdc81b7dff

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py

Python

subdomains/admin.py

sjy5386/subshorts

d8170ee4a66989c3e852f86aa83bab6341e3aa10

[ "MIT" ]

3

2022-03-08T19:02:41.000Z

2022-03-16T23:04:37.000Z

subdomains/admin.py

sjy5386/subshorts

d8170ee4a66989c3e852f86aa83bab6341e3aa10

[ "MIT" ]

5

2022-03-17T02:16:52.000Z

2022-03-18T02:55:25.000Z

subdomains/admin.py

sjy5386/subshorts

d8170ee4a66989c3e852f86aa83bab6341e3aa10

[ "MIT" ]

null

from django.contrib import admin from .models import * admin.site.register(Subdomain) admin.site.register(ReservedName)

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65edf682b39992b397ab3a02ee656721d844a71d

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py

Python

pytorch_mask_rcnn/datasets/__init__.py

JinchengHeRyan/STATS402_Final_MaskRcnn

c8103751008afb2f969c7e321a7e843a50c0f681

[ "MIT" ]

null

pytorch_mask_rcnn/datasets/__init__.py

JinchengHeRyan/STATS402_Final_MaskRcnn

c8103751008afb2f969c7e321a7e843a50c0f681

[ "MIT" ]

null

pytorch_mask_rcnn/datasets/__init__.py

JinchengHeRyan/STATS402_Final_MaskRcnn

c8103751008afb2f969c7e321a7e843a50c0f681

[ "MIT" ]

null

from .utils import * try: from .coco_eval import CocoEvaluator, prepare_for_coco except ImportError: pass try: from .dali import DALICOCODataLoader except ImportError: pass

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1

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6

02945d19eebbe4dd9cc640f7077e8d492e5632ed

48

py

Python

pyglobe3d/core/icosalogic/__init__.py

ka-tet19/pyglobelib

d62f7636f5f971b897eba8fcf787fabb5ed181f1

[ "BSD-3-Clause" ]

null

pyglobe3d/core/icosalogic/__init__.py

ka-tet19/pyglobelib

d62f7636f5f971b897eba8fcf787fabb5ed181f1

[ "BSD-3-Clause" ]

null

pyglobe3d/core/icosalogic/__init__.py

ka-tet19/pyglobelib

d62f7636f5f971b897eba8fcf787fabb5ed181f1

[ "BSD-3-Clause" ]

null

from pyglobe3d.core.icosalogic.mesh import Mesh

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null

0

1

0

1

0

1

0

6

02b0b575523bd9ae71f87863129e7a6af9f09c87

4,419

py

Python

tests/test_rewards.py

GeorgianBadita/Dronem-gym-envirnoment

f3b488f6a4b55722c4b129051555a68d7775278c

[ "MIT" ]

5

2020-06-13T10:43:42.000Z

2022-01-25T10:37:32.000Z

tests/test_rewards.py

GeorgianBadita/Dronem-gym-envirnoment

f3b488f6a4b55722c4b129051555a68d7775278c

[ "MIT" ]

null

tests/test_rewards.py

GeorgianBadita/Dronem-gym-envirnoment

f3b488f6a4b55722c4b129051555a68d7775278c

[ "MIT" ]

null

""" @author: Badita Marin-Georgian @email: geo.badita@gmail.com @date: 24.03.2020 23:44 """ from env_interpretation import State, settings from env_interpretation.reward_utils import OneMinusOneRewardGiverAllowIllegal from env_interpretation.utils import n_from_prod def test_one_minus_one_reward_bad_env_3(env3_robots): env_data = env3_robots.get_env_metadata() action = [15, 20, 56] action = n_from_prod(env_data['sets'], action) interpreted_action = env3_robots.get_action_from_space(action) reward_giver = OneMinusOneRewardGiverAllowIllegal() assert reward_giver.give_reward(State([15, 20, 1], 5, None), interpreted_action, env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']) == 2 * settings.REWARD_FOR_INVALID_MEETING - 1 def test_one_minus_one_reward_good_env_3(env3_robots): env_data = env3_robots.get_env_metadata() action = [57, 0, 0] action = n_from_prod(env_data['sets'], action) interpreted_action = env3_robots.get_action_from_space(action) reward_giver = OneMinusOneRewardGiverAllowIllegal() assert reward_giver.give_reward(State([2, 2, 0], 10, None), interpreted_action, env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']) == 1 action = [58, 0, 0] action = n_from_prod(env_data['sets'], action) interpreted_action = env3_robots.get_action_from_space(action) assert reward_giver.give_reward(State([0, 0, 0], 10, None), interpreted_action, env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']) == settings.REWARD_FOR_INVALID_TRANSFER def test_one_minus_one_reward_bad_env_4(env4_robots): env_data = env4_robots.get_env_metadata() action = [1, 2, 20, 15, 13, 28] action = n_from_prod(env_data['sets'], action) # {(r0 -> r1: 1), (r0 -> r2: 2), (r3 -> r0: 5), # (r1 -> r2: 15), (r1 -> r3: 13), (r3 -> r2: 13)} interpreted_action = env4_robots.get_action_from_space(action) reward_giver = OneMinusOneRewardGiverAllowIllegal() assert reward_giver.give_reward( State([10, 10, 10, 10], 8, None), interpreted_action, env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']) == 5 * settings.REWARD_FOR_INVALID_MEETING + settings.REWARD_FOR_INVALID_TRANSFER assert reward_giver.give_reward( State([0, 0, 0, 0], 8, None), interpreted_action, env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']) == 5 * settings.REWARD_FOR_INVALID_MEETING + settings.REWARD_FOR_INVALID_TRANSFER action = [0] * (env_data['num_robots'] * (env_data['num_robots'] - 1) // 2) action = n_from_prod(env_data['sets'], action) interpreted_action = env4_robots.get_action_from_space(action) assert reward_giver.give_reward( State([0, 0, 0, 0], 8, None), interpreted_action, env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']) == 0 def test_one_minus_one_reward_good_env_4(env4_robots): env_data = env4_robots.get_env_metadata() action = [17, 0, 0, 0, 0, 13] action = n_from_prod(env_data['sets'], action) # {(r1 -> r0: 2), , # (r2 -> r3: 13)} interpreted_action = env4_robots.get_action_from_space(action) reward_giver = OneMinusOneRewardGiverAllowIllegal() assert reward_giver.give_reward( State([10, 10, 15, 0], 10, None), interpreted_action, env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']) == 0 assert reward_giver.give_reward( State([10, 10, 10, 10], 10, None), interpreted_action, env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']) == settings.REWARD_FOR_INVALID_TRANSFER + 1 action = [25, 0, 0, 0, 0, 13] action = n_from_prod(env_data['sets'], action) # {(r1 -> r0: 10), , # (r2 -> r3: 13)} interpreted_action = env4_robots.get_action_from_space(action) assert reward_giver.give_reward( State([10, 10, 15, 0], 10, None), interpreted_action, env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']) == 0

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null

0

1

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null

0

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02d0cb10c454325e6ea54791f5698f384b461991

186

py

Python

env/gym_nav/envs/__init__.py

liuandrew/training-rl-algo

ca56d65209de0bf88ac1e1db2269bb7daac4da47

[ "MIT" ]

null

env/gym_nav/envs/__init__.py

liuandrew/training-rl-algo

ca56d65209de0bf88ac1e1db2269bb7daac4da47

[ "MIT" ]

null

env/gym_nav/envs/__init__.py

liuandrew/training-rl-algo

ca56d65209de0bf88ac1e1db2269bb7daac4da47

[ "MIT" ]

null

from gym_nav.envs.nav_env import NavEnv from gym_nav.envs.nav_env_flat import NavEnvFlat from gym_nav.envs.morris_env import MorrisNav from gym_nav.envs.gridworld_env import GridworldNav

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1

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6

02e53fadc815c7b93be7c8aadcc291f00ebcd0a7

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py

Python

Lab/Project2/15/cubes.py

doubleliao/PyCrashCourse

d6a0b62f3b8ea9fee5efde9edd9006ca3961be25

[ "MIT" ]

null

Lab/Project2/15/cubes.py

doubleliao/PyCrashCourse

d6a0b62f3b8ea9fee5efde9edd9006ca3961be25

[ "MIT" ]

null

Lab/Project2/15/cubes.py

doubleliao/PyCrashCourse

d6a0b62f3b8ea9fee5efde9edd9006ca3961be25

[ "MIT" ]

null

""" 15-1. Cubes: A number raised to the third power is a cube. Plot the first five cubic numbers, and then plot the first 5000 cubic numbers. """ import matplotlib.pyplot as plt x_s = range(1, 6) y_s = [x**3 for x in x_s] plt.style.use('seaborn') fig, ax = plt.subplots() ax.scatter(x_s, y_s, edgecolor='red', s=40) # Set chart title and label axes. ax.set_title("Cubes Numbers", fontsize=24) ax.set_xlabel("Value", fontsize=14) ax.set_ylabel("Cube of Value", fontsize=14) # Set size of tick labels. ax.tick_params(axis='both', labelsize=14) plt.show() import matplotlib.pyplot as plt x_s = range(1, 5001) y_s = [x**3 for x in x_s] plt.style.use('seaborn') fig, ax = plt.subplots() ax.scatter(x_s, y_s, edgecolor='None', s=40) # Set chart title and label axes. ax.set_title("Cubes Numbers", fontsize=24) ax.set_xlabel("Value", fontsize=14) ax.set_ylabel("Cube of Value", fontsize=14) # Set size of tick labels. ax.tick_params(axis='both', labelsize=14) # Set the range for each axis. ax.axis([0, 5500, 0, 133500000000]) plt.show()

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f3231ec6848564972b88abca0761e8fdfb2e1e7a

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py

Python

en/062/python/main.py

franciscogomes2020/exercises

8b33c4b9349a9331e4002a8225adc2a482c70024

[ "MIT" ]

null

en/062/python/main.py

franciscogomes2020/exercises

8b33c4b9349a9331e4002a8225adc2a482c70024

[ "MIT" ]

null

en/062/python/main.py

franciscogomes2020/exercises

8b33c4b9349a9331e4002a8225adc2a482c70024

[ "MIT" ]

null

# Improve CHALLENGE 061 by asking the user if he wants to show some more terms. The program will exit when it says it wants to show 0 terms.

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b835c2f17babd04b2aadc3e55adf33f090e6e815

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py

Python

tests/layers/test_recurrent_layers.py

FGDBTKD/neupy

1f5e1ae9364e8c7816df79678a4648c689d2a5d1

[ "MIT" ]

null

tests/layers/test_recurrent_layers.py

FGDBTKD/neupy

1f5e1ae9364e8c7816df79678a4648c689d2a5d1

[ "MIT" ]

null

tests/layers/test_recurrent_layers.py

FGDBTKD/neupy

1f5e1ae9364e8c7816df79678a4648c689d2a5d1

[ "MIT" ]

null

import numpy as np from sklearn.model_selection import train_test_split from neupy.exceptions import LayerConnectionError from neupy.datasets import reber from neupy import layers, algorithms, init from base import BaseTestCase def add_padding(data): n_sampels = len(data) max_seq_length = max(map(len, data)) data_matrix = np.zeros((n_sampels, max_seq_length)) for i, sample in enumerate(data): data_matrix[i, -len(sample):] = sample return data_matrix class LSTMTestCase(BaseTestCase): def setUp(self): super(LSTMTestCase, self).setUp() data, labels = reber.make_reber_classification( n_samples=100, return_indeces=True) data = add_padding(data + 1) # +1 to shift indeces self.data = train_test_split(data, labels, test_size=0.2) self.n_categories = len(reber.avaliable_letters) + 1 self.n_time_steps = self.data[0].shape[1] def train_lstm(self, data, **lstm_options): x_train, x_test, y_train, y_test = data network = algorithms.RMSProp( [ layers.Input(self.n_time_steps), layers.Embedding(self.n_categories, 10), layers.LSTM(20, **lstm_options), layers.Sigmoid(1), ], step=0.05, verbose=False, batch_size=16, error='binary_crossentropy', ) network.train(x_train, y_train, x_test, y_test, epochs=20) y_predicted = network.predict(x_test).round() accuracy = (y_predicted.T == y_test).mean() return accuracy def test_simple_lstm_sequence_classification(self): accuracy = self.train_lstm(self.data) self.assertGreaterEqual(accuracy, 0.9) def test_simple_lstm_without_precomputed_input(self): accuracy = self.train_lstm(self.data, precompute_input=False) self.assertGreaterEqual(accuracy, 0.9) def test_lstm_with_gradient_clipping(self): accuracy = self.train_lstm(self.data, gradient_clipping=1) self.assertGreaterEqual(accuracy, 0.9) def test_lstm_with_enabled_peepholes_option(self): accuracy = self.train_lstm(self.data, peepholes=True) self.assertGreaterEqual(accuracy, 0.9) def test_lstm_with_enabled_unroll_scan_option(self): accuracy = self.train_lstm(self.data, unroll_scan=True) self.assertGreaterEqual(accuracy, 0.9) def test_lstm_with_enabled_backwards_option(self): x_train, x_test, y_train, y_test = self.data x_train = x_train[:, ::-1] x_test = x_test[:, ::-1] data = x_train, x_test, y_train, y_test accuracy = self.train_lstm(data, backwards=True) self.assertGreaterEqual(accuracy, 0.9) accuracy = self.train_lstm(data, backwards=True, unroll_scan=True) self.assertGreaterEqual(accuracy, 0.9) def test_lstm_output_shapes(self): network_1 = layers.join( layers.Input((10, 2)), layers.LSTM(20, only_return_final=True), ) self.assertEqual(network_1.output_shape, (20,)) network_2 = layers.join( layers.Input((10, 2)), layers.LSTM(20, only_return_final=False), ) self.assertEqual(network_2.output_shape, (10, 20)) def test_stacked_lstm(self): x_train, x_test, y_train, y_test = self.data network = algorithms.RMSProp( [ layers.Input(self.n_time_steps), layers.Embedding(self.n_categories, 10), layers.LSTM(10, only_return_final=False, weights=init.Normal(0.1)), layers.LSTM(2, weights=init.Normal(0.1)), layers.Sigmoid(1), ], step=0.05, verbose=False, batch_size=1, error='binary_crossentropy', ) network.train(x_train, y_train, x_test, y_test, epochs=10) y_predicted = network.predict(x_test).round() accuracy = (y_predicted.T == y_test).mean() self.assertGreaterEqual(accuracy, 0.9) def test_stacked_lstm_with_enabled_backwards_option(self): x_train, x_test, y_train, y_test = self.data x_train = x_train[:, ::-1] x_test = x_test[:, ::-1] network = algorithms.RMSProp( [ layers.Input(self.n_time_steps), layers.Embedding(self.n_categories, 10), layers.LSTM(10, only_return_final=False, backwards=True), layers.LSTM(2, backwards=True), layers.Sigmoid(1), ], step=0.1, verbose=False, batch_size=1, error='binary_crossentropy', ) network.train(x_train, y_train, x_test, y_test, epochs=20) y_predicted = network.predict(x_test).round() accuracy = (y_predicted.T == y_test).mean() self.assertGreaterEqual(accuracy, 0.9) def test_lstm_with_4d_input(self): x_train, x_test, y_train, y_test = self.data network = algorithms.RMSProp( [ layers.Input(self.n_time_steps), layers.Embedding(self.n_categories, 10), # Make 4D input layers.Reshape((self.n_time_steps, 5, 2), name='reshape'), layers.LSTM(10), layers.Sigmoid(1), ], step=0.1, verbose=False, batch_size=1, error='binary_crossentropy', ) network.train(x_train, y_train, x_test, y_test, epochs=2) reshape = network.connection.end('reshape') # +1 for batch size output_dimension = len(reshape.output_shape) + 1 self.assertEqual(4, output_dimension) def test_lstm_connection_exceptions(self): with self.assertRaises(LayerConnectionError): layers.Input(1) > layers.LSTM(10) def test_lstm_modify_only_one_weight_parameter(self): lstm_layer = layers.LSTM(2, weights=dict( weight_in_to_ingate=init.Constant(0) )) layers.join( layers.Input((5, 3)), lstm_layer, ) for key, value in lstm_layer.weights.items(): if key == 'weight_in_to_ingate': self.assertIsInstance(value, init.Constant) else: self.assertIsInstance(value, init.XavierUniform) def test_lstm_initialization_exceptions(self): with self.assertRaisesRegexp(ValueError, 'invalid key'): layers.LSTM(1, weights=dict(unknown_parameter=10)) with self.assertRaisesRegexp(ValueError, 'callable'): layers.LSTM(1, activation_functions=dict(ingate=10)) with self.assertRaises(TypeError): layers.LSTM(1, activation_functions=lambda x: x) class GRUTestCase(BaseTestCase): def setUp(self): super(GRUTestCase, self).setUp() data, labels = reber.make_reber_classification( n_samples=100, return_indeces=True) data = add_padding(data + 1) # +1 to shift indeces # self.data = x_train, x_test, y_train, y_test self.data = train_test_split(data, labels, test_size=0.2) self.n_categories = len(reber.avaliable_letters) + 1 self.n_time_steps = self.data[0].shape[1] def train_gru(self, data, **gru_options): x_train, x_test, y_train, y_test = data network = algorithms.RMSProp( [ layers.Input(self.n_time_steps), layers.Embedding(self.n_categories, 10), layers.GRU(20, **gru_options), layers.Sigmoid(1), ], step=0.05, verbose=False, batch_size=16, error='binary_crossentropy', ) network.train(x_train, y_train, x_test, y_test, epochs=20) y_predicted = network.predict(x_test).round() accuracy = (y_predicted.T == y_test).mean() return accuracy def test_simple_gru_sequence_classification(self): accuracy = self.train_gru(self.data) self.assertGreaterEqual(accuracy, 0.9) def test_simple_gru_without_precomputed_input(self): accuracy = self.train_gru(self.data, precompute_input=False) self.assertGreaterEqual(accuracy, 0.9) def test_gru_with_gradient_clipping(self): accuracy = self.train_gru(self.data, gradient_clipping=1) self.assertGreaterEqual(accuracy, 0.9) def test_gru_with_enabled_unroll_scan_option(self): accuracy = self.train_gru(self.data, unroll_scan=True) self.assertGreaterEqual(accuracy, 0.9) def test_gru_with_enabled_backwards_option(self): x_train, x_test, y_train, y_test = self.data x_train = x_train[:, ::-1] x_test = x_test[:, ::-1] data = x_train, x_test, y_train, y_test accuracy = self.train_gru(data, backwards=True) self.assertGreaterEqual(accuracy, 0.9) accuracy = self.train_gru(data, backwards=True, unroll_scan=True) self.assertGreaterEqual(accuracy, 0.9) def test_gru_output_shapes(self): network_1 = layers.join( layers.Input((10, 2)), layers.GRU(20, only_return_final=True), ) self.assertEqual(network_1.output_shape, (20,)) network_2 = layers.join( layers.Input((10, 2)), layers.GRU(20, only_return_final=False), ) self.assertEqual(network_2.output_shape, (10, 20)) def test_stacked_gru(self): x_train, x_test, y_train, y_test = self.data network = algorithms.RMSProp( [ layers.Input(self.n_time_steps), layers.Embedding(self.n_categories, 10), layers.GRU(10, only_return_final=False, weights=init.Normal(0.1)), layers.GRU(1, weights=init.Normal(0.1)), layers.Sigmoid(1), ], step=0.05, verbose=False, batch_size=1, error='binary_crossentropy', ) network.train(x_train, y_train, x_test, y_test, epochs=10) y_predicted = network.predict(x_test).round() accuracy = (y_predicted.T == y_test).mean() self.assertGreaterEqual(accuracy, 0.9) def test_stacked_gru_with_enabled_backwards_option(self): x_train, x_test, y_train, y_test = self.data x_train = x_train[:, ::-1] x_test = x_test[:, ::-1] network = algorithms.RMSProp( [ layers.Input(self.n_time_steps), layers.Embedding(self.n_categories, 10), layers.GRU(10, only_return_final=False, backwards=True), layers.GRU(2, backwards=True), layers.Sigmoid(1), ], step=0.02, verbose=False, batch_size=10, error='binary_crossentropy', ) network.train(x_train, y_train, x_test, y_test, epochs=20) y_predicted = network.predict(x_test).round() accuracy = (y_predicted.T == y_test).mean() self.assertGreaterEqual(accuracy, 0.9) def test_gru_with_4d_input(self): x_train, x_test, y_train, y_test = self.data network = algorithms.RMSProp( [ layers.Input(self.n_time_steps), layers.Embedding(self.n_categories, 10), # Make 4D input layers.Reshape((self.n_time_steps, 5, 2), name='reshape'), layers.GRU(10), layers.Sigmoid(1), ], step=0.1, verbose=False, batch_size=1, error='binary_crossentropy', ) network.train(x_train, y_train, x_test, y_test, epochs=2) reshape = network.connection.end('reshape') # +1 for batch size output_dimension = len(reshape.output_shape) + 1 self.assertEqual(4, output_dimension) def test_gru_connection_exceptions(self): with self.assertRaises(LayerConnectionError): layers.Input(1) > layers.GRU(10) def test_gru_modify_only_one_weight_parameter(self): gru_layer = layers.GRU(2, weights=dict( weight_in_to_updategate=init.Constant(0) )) layers.join( layers.Input((5, 3)), gru_layer, ) for key, value in gru_layer.weights.items(): if key == 'weight_in_to_updategate': self.assertIsInstance(value, init.Constant) else: self.assertIsInstance(value, init.XavierUniform) def test_gru_initialization_exceptions(self): with self.assertRaisesRegexp(ValueError, 'invalid key'): layers.GRU(1, weights=dict(unknown_parameter=10)) with self.assertRaisesRegexp(ValueError, 'callable'): layers.GRU(1, activation_functions=dict(ingate=10)) with self.assertRaises(TypeError): layers.GRU(1, activation_functions=lambda x: x)

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b885c62c90c7043f7b93c2fed73cd96a9a5b3e70

104

py

Python

labext/prelude.py

binh-vu/ipywidgets_extra

3ddf46445306b2aa158bf3f696ec33f8ddd499e7

[ "MIT" ]

3

2020-06-21T22:57:55.000Z

2021-06-03T23:36:39.000Z

labext/prelude.py

binh-vu/ipywidgets_extra

3ddf46445306b2aa158bf3f696ec33f8ddd499e7

[ "MIT" ]

null

labext/prelude.py

binh-vu/ipywidgets_extra

3ddf46445306b2aa158bf3f696ec33f8ddd499e7

[ "MIT" ]

1

2020-06-20T19:50:37.000Z

import labext.modules as M import labext.widgets as W import labext.apps as A from labext.tag import Tag

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6

b21f92eeaeb951819c01d10ed2515ea53ac09d75

34

py

Python

tudo/ex001.py

Ramon-Erik/Exercicios-Python

158a7f1846dd3d486aa0517fa337d46d73aab649

[ "MIT" ]

1

2021-07-08T00:35:57.000Z

tudo/ex001.py

Ramon-Erik/Exercicios-Python

158a7f1846dd3d486aa0517fa337d46d73aab649

[ "MIT" ]

null

tudo/ex001.py

Ramon-Erik/Exercicios-Python

158a7f1846dd3d486aa0517fa337d46d73aab649

[ "MIT" ]

null

print('Olá, mundo! Olá usuário!')

17

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null

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6

b221778ec011db0189ab1af3f784b1474b34b6cb

60

py

Python

PBO/PBO_18142/Latihan_6.3parameter2.py

rosnialabania/PBO

597c71d778483b97443a3944a03cea759771fecc

[ "MIT" ]

null

PBO/PBO_18142/Latihan_6.3parameter2.py

rosnialabania/PBO

597c71d778483b97443a3944a03cea759771fecc

[ "MIT" ]

null

PBO/PBO_18142/Latihan_6.3parameter2.py

rosnialabania/PBO

597c71d778483b97443a3944a03cea759771fecc

[ "MIT" ]

null

def hello(rosnia_labania): print("hello {rosnia_labania}")

15

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1

0

6

b243ed1a20a70fa0d6069e81221bb36a885f7d21

42

py

Python

lib/dao/rdbms/__init__.py

the-constant/fammelody

970fb3a4a5d5d0dffd19f22a8a75cbf226fd57c2

[ "Apache-2.0" ]

null

lib/dao/rdbms/__init__.py

the-constant/fammelody

970fb3a4a5d5d0dffd19f22a8a75cbf226fd57c2

[ "Apache-2.0" ]

null

lib/dao/rdbms/__init__.py

the-constant/fammelody

970fb3a4a5d5d0dffd19f22a8a75cbf226fd57c2

[ "Apache-2.0" ]

null

from .legal import * from .market import *

21

0.738095

6

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0.666667

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0.166667

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6

a2707bc4d9ab391f8afe81038cbbb0dd668cd0bc

221

py

Python

dataduct/etl/__init__.py

hillsdale18/ProjectX

4518d724eeb8ac73a6eae1d076d4846244e0944a

[ "Apache-2.0" ]

3

2017-12-29T11:26:15.000Z

2022-02-11T16:44:28.000Z

dataduct/etl/__init__.py

hillsdale18/ProjectX

4518d724eeb8ac73a6eae1d076d4846244e0944a

[ "Apache-2.0" ]

7

2017-09-21T23:25:24.000Z

2021-03-29T21:46:45.000Z

dataduct/etl/__init__.py

recurly/dataduct

29aec3526e170e5ad3b59a135780e72b69209f0b

[ "Apache-2.0" ]

1

2020-05-12T08:54:38.000Z

from .etl_actions import activate_pipeline from .etl_actions import create_pipeline from .etl_actions import read_pipeline_definition from .etl_actions import validate_pipeline from .etl_actions import visualize_pipeline

36.833333

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6

a283da01f94b6cd141c889c9bc8fdabba19ea5e8

39

py

Python

quiz_mill/__init__.py

eoas-ubc/quiz_mill

922989244964e2147ecac9186bdafae8d8f91813

[ "MIT", "BSD-3-Clause" ]

null

quiz_mill/__init__.py

eoas-ubc/quiz_mill

922989244964e2147ecac9186bdafae8d8f91813

[ "MIT", "BSD-3-Clause" ]

null

quiz_mill/__init__.py

eoas-ubc/quiz_mill

922989244964e2147ecac9186bdafae8d8f91813

[ "MIT", "BSD-3-Clause" ]

1

2021-06-01T23:03:17.000Z

from .solve_layers import do_two_matrix

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6

a2b7207bc1f4292b9acf62355ca2faac44b9dd87

16

py

Python

test/login.py

hongren798911/xuexigit

a0925a222db9468333e3e91aa6b76423f01326b7

[ "MIT" ]

null

test/login.py

hongren798911/xuexigit

a0925a222db9468333e3e91aa6b76423f01326b7

[ "MIT" ]

null

test/login.py

hongren798911/xuexigit

a0925a222db9468333e3e91aa6b76423f01326b7

[ "MIT" ]

null

a = 10 dev = 1

4

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1

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1

0

1

0

null

0

6

a2c0e4d57badbdb14df6f2cb71bc2e477f8a177e

112

py

Python

PokerRL/rl/base_cls/workers/__init__.py

MAWUT0R/PokerRL

95708a5f7a16cb151bc4253132bdfd22ea7a9b25

[ "MIT" ]

247

2019-06-20T16:41:36.000Z

2022-03-28T11:40:12.000Z

PokerRL/rl/base_cls/workers/__init__.py

MAWUT0R/PokerRL

95708a5f7a16cb151bc4253132bdfd22ea7a9b25

[ "MIT" ]

11

2019-08-23T09:20:31.000Z

2021-12-05T23:44:27.000Z

PokerRL/rl/base_cls/workers/__init__.py

MAWUT0R/PokerRL

95708a5f7a16cb151bc4253132bdfd22ea7a9b25

[ "MIT" ]

61

2019-06-17T06:06:11.000Z

2022-03-01T17:55:44.000Z

from .ChiefBase import * from .DriverBase import * from .ParameterServerBase import * from .WorkerBase import *

22.4

34

0.785714

12

112

7.333333

0.5

0.340909

0

0.142857

112

4

35

28

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

a2c373f29ed31ee9058b99d7c574372addb8c851

33

py

Python

goto_cloud/commander/public.py

jdepoix/goto_cloud

59bb9923026e1b1dc6e8e08fb6b21300c8e8854a

[ "MIT" ]

2

2018-02-04T23:22:17.000Z

2019-04-15T12:06:04.000Z

goto_cloud/commander/public.py

jdepoix/goto_cloud

59bb9923026e1b1dc6e8e08fb6b21300c8e8854a

[ "MIT" ]

null

goto_cloud/commander/public.py

jdepoix/goto_cloud

59bb9923026e1b1dc6e8e08fb6b21300c8e8854a

[ "MIT" ]

null

from .commander import Commander

16.5

32

0.848485

4

33

7

0.75

0

0.121212

33

1

33

0.965517

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a2d32c6029cfe13c7fc37f21be0adfcdc61e5002

38

py

Python

Ep. 1/gameengine/__init__.py

mandaw2014/gameengine_tutorial

9a2edb85e7885b36e04fdb9132508a0c13b5a42b

[ "MIT" ]

null

Ep. 1/gameengine/__init__.py

mandaw2014/gameengine_tutorial

9a2edb85e7885b36e04fdb9132508a0c13b5a42b

[ "MIT" ]

null

Ep. 1/gameengine/__init__.py

mandaw2014/gameengine_tutorial

9a2edb85e7885b36e04fdb9132508a0c13b5a42b

[ "MIT" ]

null

from gameengine.main import GameEngine

38

0.894737

5

38

6.8

0.8

0

0.078947

38

1

38

0.971429

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a2f066ba35848bffc041b1d139b4f42d9a1b1d93

54

py

Python

scripts/templates/fastApiCrud/partials/controller_init_property.py

sulthonzh/zaruba

ec9262f43da17d86330da2c593b7da451aabd60f

[ "Apache-2.0" ]

null

scripts/templates/fastApiCrud/partials/controller_init_property.py

sulthonzh/zaruba

ec9262f43da17d86330da2c593b7da451aabd60f

[ "Apache-2.0" ]

null

scripts/templates/fastApiCrud/partials/controller_init_property.py

sulthonzh/zaruba

ec9262f43da17d86330da2c593b7da451aabd60f

[ "Apache-2.0" ]

null

self.zaruba_entity_name_repo = zaruba_entity_name_repo

54

0.925926

9

54

4.888889

0.555556

0.545455

0.727273

0.909091

0

0.037037

54

1

54

0.846154

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

6

0c2c76f272fe46dc5497b0937a3efe7982ce8d65

192

py

Python

remind/admin.py

jscpeterson/reminders

f1ad78daff6314a697a32a0a52d5ac16aa54eeca

[ "FSFAP" ]

null

remind/admin.py

jscpeterson/reminders

f1ad78daff6314a697a32a0a52d5ac16aa54eeca

[ "FSFAP" ]

null

remind/admin.py

jscpeterson/reminders

f1ad78daff6314a697a32a0a52d5ac16aa54eeca

[ "FSFAP" ]

null

from django.contrib import admin from cases.models import Case, Motion from remind.models import Deadline admin.site.register(Case) admin.site.register(Deadline) admin.site.register(Motion)

21.333333

37

0.822917

28

192

5.642857

0.464286

0.170886

0.322785

0.316456

0

0.09375

192

8

38

24

0.908046

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

a75c2a14a99561b27bca85847bf349af8fad1f76

62

py

Python

test_impall.py

rec/import_all

2d864d016ce9b320521fcaa9f5206d5cbef19107

[ "MIT" ]

1

2019-05-26T15:09:32.000Z

test_impall.py

rec/import_all

2d864d016ce9b320521fcaa9f5206d5cbef19107

[ "MIT" ]

12

2019-05-13T12:56:13.000Z

2019-10-01T13:30:12.000Z

test_impall.py

rec/impall

2d864d016ce9b320521fcaa9f5206d5cbef19107

[ "MIT" ]

null

import impall class ImpAllTest(impall.ImpAllTest): pass

10.333333

36

0.758065

7

62

6.714286

0.714286

0

0.177419

62

5

37

12.4

0.921569

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

a75d277fba51dec40450eb94f734ae3f79b9b574

10,509

py

Python

lib/services/server/ncloud_server/__init__.py

KidongSohn/ncloud-sdk-py

1c62471a9bd320d77164ed3193a0ebb9f64229ff

[ "MIT" ]

null

lib/services/server/ncloud_server/__init__.py

KidongSohn/ncloud-sdk-py

1c62471a9bd320d77164ed3193a0ebb9f64229ff

[ "MIT" ]

null

lib/services/server/ncloud_server/__init__.py

KidongSohn/ncloud-sdk-py

1c62471a9bd320d77164ed3193a0ebb9f64229ff

[ "MIT" ]

null

# coding: utf-8 # flake8: noqa """ server OpenAPI spec version: 2018-06-22T02:34:44Z Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import # import apis into sdk package from ncloud_server.api.v2_api import V2Api # import ApiClient from ncloud_server.api_client import ApiClient from ncloud_server.configuration import Configuration # import models into sdk package from ncloud_server.model.access_control_group import AccessControlGroup from ncloud_server.model.access_control_rule import AccessControlRule from ncloud_server.model.add_nas_volume_access_control_request import AddNasVolumeAccessControlRequest from ncloud_server.model.add_nas_volume_access_control_response import AddNasVolumeAccessControlResponse from ncloud_server.model.add_port_forwarding_rules_request import AddPortForwardingRulesRequest from ncloud_server.model.add_port_forwarding_rules_response import AddPortForwardingRulesResponse from ncloud_server.model.associate_public_ip_with_server_instance_request import AssociatePublicIpWithServerInstanceRequest from ncloud_server.model.associate_public_ip_with_server_instance_response import AssociatePublicIpWithServerInstanceResponse from ncloud_server.model.block_storage_instance import BlockStorageInstance from ncloud_server.model.block_storage_snapshot_instance import BlockStorageSnapshotInstance from ncloud_server.model.change_nas_volume_size_request import ChangeNasVolumeSizeRequest from ncloud_server.model.change_nas_volume_size_response import ChangeNasVolumeSizeResponse from ncloud_server.model.change_server_instance_spec_request import ChangeServerInstanceSpecRequest from ncloud_server.model.change_server_instance_spec_response import ChangeServerInstanceSpecResponse from ncloud_server.model.common_code import CommonCode from ncloud_server.model.create_block_storage_instance_request import CreateBlockStorageInstanceRequest from ncloud_server.model.create_block_storage_instance_response import CreateBlockStorageInstanceResponse from ncloud_server.model.create_login_key_request import CreateLoginKeyRequest from ncloud_server.model.create_login_key_response import CreateLoginKeyResponse from ncloud_server.model.create_member_server_image_request import CreateMemberServerImageRequest from ncloud_server.model.create_member_server_image_response import CreateMemberServerImageResponse from ncloud_server.model.create_nas_volume_instance_request import CreateNasVolumeInstanceRequest from ncloud_server.model.create_nas_volume_instance_response import CreateNasVolumeInstanceResponse from ncloud_server.model.create_public_ip_instance_request import CreatePublicIpInstanceRequest from ncloud_server.model.create_public_ip_instance_response import CreatePublicIpInstanceResponse from ncloud_server.model.create_server_instances_request import CreateServerInstancesRequest from ncloud_server.model.create_server_instances_response import CreateServerInstancesResponse from ncloud_server.model.delete_block_storage_instances_request import DeleteBlockStorageInstancesRequest from ncloud_server.model.delete_block_storage_instances_response import DeleteBlockStorageInstancesResponse from ncloud_server.model.delete_login_key_request import DeleteLoginKeyRequest from ncloud_server.model.delete_login_key_response import DeleteLoginKeyResponse from ncloud_server.model.delete_member_server_images_request import DeleteMemberServerImagesRequest from ncloud_server.model.delete_member_server_images_response import DeleteMemberServerImagesResponse from ncloud_server.model.delete_nas_volume_instance_request import DeleteNasVolumeInstanceRequest from ncloud_server.model.delete_nas_volume_instance_response import DeleteNasVolumeInstanceResponse from ncloud_server.model.delete_port_forwarding_rules_request import DeletePortForwardingRulesRequest from ncloud_server.model.delete_port_forwarding_rules_response import DeletePortForwardingRulesResponse from ncloud_server.model.delete_public_ip_instances_request import DeletePublicIpInstancesRequest from ncloud_server.model.delete_public_ip_instances_response import DeletePublicIpInstancesResponse from ncloud_server.model.disassociate_public_ip_from_server_instance_request import DisassociatePublicIpFromServerInstanceRequest from ncloud_server.model.disassociate_public_ip_from_server_instance_response import DisassociatePublicIpFromServerInstanceResponse from ncloud_server.model.get_access_control_group_list_request import GetAccessControlGroupListRequest from ncloud_server.model.get_access_control_group_list_response import GetAccessControlGroupListResponse from ncloud_server.model.get_access_control_group_server_instance_list_request import GetAccessControlGroupServerInstanceListRequest from ncloud_server.model.get_access_control_group_server_instance_list_response import GetAccessControlGroupServerInstanceListResponse from ncloud_server.model.get_access_control_rule_list_request import GetAccessControlRuleListRequest from ncloud_server.model.get_access_control_rule_list_response import GetAccessControlRuleListResponse from ncloud_server.model.get_block_storage_instance_list_request import GetBlockStorageInstanceListRequest from ncloud_server.model.get_block_storage_instance_list_response import GetBlockStorageInstanceListResponse from ncloud_server.model.get_block_storage_snapshot_instance_list_request import GetBlockStorageSnapshotInstanceListRequest from ncloud_server.model.get_block_storage_snapshot_instance_list_response import GetBlockStorageSnapshotInstanceListResponse from ncloud_server.model.get_login_key_list_request import GetLoginKeyListRequest from ncloud_server.model.get_login_key_list_response import GetLoginKeyListResponse from ncloud_server.model.get_member_server_image_list_request import GetMemberServerImageListRequest from ncloud_server.model.get_member_server_image_list_response import GetMemberServerImageListResponse from ncloud_server.model.get_nas_volume_instance_list_request import GetNasVolumeInstanceListRequest from ncloud_server.model.get_nas_volume_instance_list_response import GetNasVolumeInstanceListResponse from ncloud_server.model.get_nas_volume_instance_rating_list_request import GetNasVolumeInstanceRatingListRequest from ncloud_server.model.get_nas_volume_instance_rating_list_response import GetNasVolumeInstanceRatingListResponse from ncloud_server.model.get_port_forwarding_rule_list_request import GetPortForwardingRuleListRequest from ncloud_server.model.get_port_forwarding_rule_list_response import GetPortForwardingRuleListResponse from ncloud_server.model.get_public_ip_instance_list_request import GetPublicIpInstanceListRequest from ncloud_server.model.get_public_ip_instance_list_response import GetPublicIpInstanceListResponse from ncloud_server.model.get_public_ip_target_server_instance_list_request import GetPublicIpTargetServerInstanceListRequest from ncloud_server.model.get_public_ip_target_server_instance_list_response import GetPublicIpTargetServerInstanceListResponse from ncloud_server.model.get_raid_list_request import GetRaidListRequest from ncloud_server.model.get_raid_list_response import GetRaidListResponse from ncloud_server.model.get_region_list_request import GetRegionListRequest from ncloud_server.model.get_region_list_response import GetRegionListResponse from ncloud_server.model.get_root_password_request import GetRootPasswordRequest from ncloud_server.model.get_root_password_response import GetRootPasswordResponse from ncloud_server.model.get_server_image_product_list_request import GetServerImageProductListRequest from ncloud_server.model.get_server_image_product_list_response import GetServerImageProductListResponse from ncloud_server.model.get_server_instance_list_request import GetServerInstanceListRequest from ncloud_server.model.get_server_instance_list_response import GetServerInstanceListResponse from ncloud_server.model.get_server_product_list_request import GetServerProductListRequest from ncloud_server.model.get_server_product_list_response import GetServerProductListResponse from ncloud_server.model.get_zone_list_request import GetZoneListRequest from ncloud_server.model.get_zone_list_response import GetZoneListResponse from ncloud_server.model.login_key import LoginKey from ncloud_server.model.member_server_image import MemberServerImage from ncloud_server.model.nas_volume_instance import NasVolumeInstance from ncloud_server.model.nas_volume_instance_custom_ip import NasVolumeInstanceCustomIp from ncloud_server.model.nas_volume_instance_rating import NasVolumeInstanceRating from ncloud_server.model.port_forwarding_rule import PortForwardingRule from ncloud_server.model.port_forwarding_rule_parameter import PortForwardingRuleParameter from ncloud_server.model.product import Product from ncloud_server.model.public_ip_instance import PublicIpInstance from ncloud_server.model.raid import Raid from ncloud_server.model.reboot_server_instances_request import RebootServerInstancesRequest from ncloud_server.model.reboot_server_instances_response import RebootServerInstancesResponse from ncloud_server.model.recreate_server_instance_request import RecreateServerInstanceRequest from ncloud_server.model.recreate_server_instance_response import RecreateServerInstanceResponse from ncloud_server.model.region import Region from ncloud_server.model.remove_nas_volume_access_control_request import RemoveNasVolumeAccessControlRequest from ncloud_server.model.remove_nas_volume_access_control_response import RemoveNasVolumeAccessControlResponse from ncloud_server.model.root_password import RootPassword from ncloud_server.model.server_instance import ServerInstance from ncloud_server.model.set_nas_volume_access_control_request import SetNasVolumeAccessControlRequest from ncloud_server.model.set_nas_volume_access_control_response import SetNasVolumeAccessControlResponse from ncloud_server.model.start_server_instances_request import StartServerInstancesRequest from ncloud_server.model.start_server_instances_response import StartServerInstancesResponse from ncloud_server.model.stop_server_instances_request import StopServerInstancesRequest from ncloud_server.model.stop_server_instances_response import StopServerInstancesResponse from ncloud_server.model.terminate_server_instances_request import TerminateServerInstancesRequest from ncloud_server.model.terminate_server_instances_response import TerminateServerInstancesResponse from ncloud_server.model.zone import Zone

80.838462

134

0.929013

1,220

10,509

7.571311

0.159016

0.119086

0.190538

0.243261

0.498863

0.47169

0.443651

0.323915

0.182743

0.073184

0

0.001798

0.047483

10,509

129

135

81.465116

0.920979

0.02103

0

1

0

1

0

true

0.027027

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

a7b77a219f5aefa517f37b7dd044873b918acf5c

2,804

py

Python

lib/status/commands/http_request.py

chrissimpkins/status

4e76d751c537d42dd5603779cbe893551be2b89e

[ "MIT", "Unlicense" ]

2

2017-04-10T20:17:44.000Z

2021-07-19T19:07:34.000Z

lib/status/commands/http_request.py

chrissimpkins/status

4e76d751c537d42dd5603779cbe893551be2b89e

[ "MIT", "Unlicense" ]

1

2021-07-19T19:09:30.000Z

2021-07-19T19:22:36.000Z

lib/status/commands/http_request.py

chrissimpkins/status

4e76d751c537d42dd5603779cbe893551be2b89e

[ "MIT", "Unlicense" ]

4

2016-12-20T19:57:07.000Z

2019-02-16T08:08:15.000Z

#!/usr/bin/env python # encoding: utf-8 import sys from Naked.toolshed.network import HTTP from Naked.toolshed.system import exit_success from Naked.toolshed.system import stderr from requests.exceptions import ConnectionError class Get: def __init__(self, url): self.url = url def get_response(self): try: the_url = prepare_url(self.url) http = HTTP(the_url) http.get() resp = http.response() # confirm that a response was returned, abort if not if resp == None and the_url.startswith('https://'): stderr("Unable to connect to the requested URL. This can happen if the secure HTTP protocol is not supported at the requested URL.") sys.exit(1) elif resp == None: stderr("Unable to connect to the requested URL. Please confirm your URL and try again.") sys.exit(1) if len(resp.history) > 0: count = len(resp.history) for i in range(count): print(str(resp.history[i].status_code) + " : " + str(resp.history[i].url)) print(str(http.res.status_code) + " : " + http.res.url) exit_success() except ConnectionError: error_string = "Unable to connect to the URL, " + self.url stderr(error_string, 1) except Exception as e: raise e class Post: def __init__(self, url): self.url = url def post_response(self): try: the_url = prepare_url(self.url) http = HTTP(the_url) http.post() resp = http.response() # confirm that a response was returned, abort if not if resp == None and the_url.startswith('https://'): stderr("Unable to connect to the requested URL. This can happen if the secure HTTP protocol is not supported at the requested URL.") sys.exit(1) elif resp == None: stderr("Unable to connect to the requested URL. Please confirm your URL and try again.") sys.exit(1) if len(resp.history) > 0: count = len(resp.history) for i in range(count): print(str(resp.history[i].status_code) + " : " + str(resp.history[i].url)) print(str(http.res.status_code) + " : " + the_url) exit_success() except ConnectionError as ce: error_string = "Unable to connect to the URL, " + self.url stderr(error_string, 1) except Exception as e: raise e def prepare_url(url): if url.startswith('http://') or url.startswith('https://'): return url else: return 'http://' + url

35.948718

148

0.564551

356

2,804

4.359551

0.241573

0.034794

0.03866

0.065722

0.835052

0.752577

0.717784

0

0.004852

0.338445

2,804

77

149

36.415584

0.831806

0.049215

0

0.677419

0

0.032258

0.191585

0

1

0.080645

false

0

0.080645

0

0.225806

0.064516

0

null

0

1

0

null

0

6

ac167b5b9c718ac4e9de726305e2afdc0ceccb9d

208

py

Python

flow_py_sdk/signer/__init__.py

nichandy/flow-py-sdk

716c1690f38eeb78f479d1cf860b974cc6a53b04

[ "MIT" ]

null

flow_py_sdk/signer/__init__.py

nichandy/flow-py-sdk

716c1690f38eeb78f479d1cf860b974cc6a53b04

[ "MIT" ]

null

flow_py_sdk/signer/__init__.py

nichandy/flow-py-sdk

716c1690f38eeb78f479d1cf860b974cc6a53b04

[ "MIT" ]

1

2021-09-15T10:29:00.000Z

from flow_py_sdk.signer.hash_algo import HashAlgo from flow_py_sdk.signer.sign_algo import SignAlgo from flow_py_sdk.signer.signer import Signer from flow_py_sdk.signer.in_memory_signer import InMemorySigner

41.6

62

0.884615

36

208

4.777778

0.388889

0.186047

0.232558

0.302326

0.44186

0

0.076923

208

4

63

52

0.895833

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

ac1b3e37b2c9396c347c1094b15d99fc28069ea5

164

py

Python

pages/context_procesor.py

mahanfarzaneh2000/Freelara

803cd0e75c5c03ee23ed6dea5202f3e6a7af4864

[ "Apache-2.0" ]

null

pages/context_procesor.py

mahanfarzaneh2000/Freelara

803cd0e75c5c03ee23ed6dea5202f3e6a7af4864

[ "Apache-2.0" ]

null

pages/context_procesor.py

mahanfarzaneh2000/Freelara

803cd0e75c5c03ee23ed6dea5202f3e6a7af4864

[ "Apache-2.0" ]

1

2021-04-11T09:59:54.000Z

import datetime from gigs.models import Category def context_procesor(request): return{'year':datetime.datetime.now().year,'categories':Category.objects.all()}

32.8

83

0.786585

21

164

6.095238

0.761905

0

0.079268

164

5

83

32.8

0.847682

0

0.084848

0

1

0.25

false

0

0.5

0.25

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ac3208f3bf096fc7be1d65ea7a50e5473c75efb0

588

py

Python

deedee_btc_data_process.py

justin-oxford/dee-dee-btc

0d6c97a819d6de48d1c846e2859bc0c4a7a50ebe

[ "MIT" ]

null

deedee_btc_data_process.py

justin-oxford/dee-dee-btc

0d6c97a819d6de48d1c846e2859bc0c4a7a50ebe

[ "MIT" ]

null

deedee_btc_data_process.py

justin-oxford/dee-dee-btc

0d6c97a819d6de48d1c846e2859bc0c4a7a50ebe

[ "MIT" ]

null

# # # # # #IMPORTS # ------------------------------------------------------------------------------------------------- from imports import * # ------------------------------------------------------------------------------------------------- #CONSTANTS # ------------------------------------------------------------------------------------------------- # ------------------------------------------------------------------------------------------------- # FUNCTIONS # -------------------------------------------------------------------------------------------------

29.4

100

0.071429

6

588

7

0.833333

0

0.079932

588

19

101

30.947368

0.077634

0.877551

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ac363f5486b65cdfe26061696dc8ef0b2d5c6f69

96

py

Python

venv/lib/python3.8/site-packages/future/moves/urllib/response.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/future/moves/urllib/response.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/future/moves/urllib/response.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/64/46/4c/2f41696c1fbe18878114fbd2cdbb65549e84b11e2d088e2a07b0fcb054

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ac69f6987b01935cec09f2d1c2672f3dd8df321c

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py

Python

graphein/molecule/features/__init__.py

avivko/graphein

0a2d5e39787cf002c06b03615d9dd3fe62e0171d

[ "MIT" ]

null

graphein/molecule/features/__init__.py

avivko/graphein

0a2d5e39787cf002c06b03615d9dd3fe62e0171d

[ "MIT" ]

null

graphein/molecule/features/__init__.py

avivko/graphein

0a2d5e39787cf002c06b03615d9dd3fe62e0171d

[ "MIT" ]

null

from .edges import * from .graph import * from .nodes import *

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py

Python

src/core/tests.py

resumme/resum.me

7fe8fdad2a3d946ab15cc91e6c6ea00fdd99d495

[ "MIT" ]

10

2018-10-11T06:47:00.000Z

2020-05-05T06:26:15.000Z

src/core/tests.py

resumme/resum.me

7fe8fdad2a3d946ab15cc91e6c6ea00fdd99d495

[ "MIT" ]

22

2018-10-15T13:56:30.000Z

2022-03-11T23:32:48.000Z

src/core/tests.py

resumme/resum.me

7fe8fdad2a3d946ab15cc91e6c6ea00fdd99d495

[ "MIT" ]

5

2018-10-16T19:12:49.000Z

2018-10-20T07:46:47.000Z

def test_test_are_working(): assert True

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py

Python

htcl_totals_test.py

NREL/scout

acf38df7ce877cbd8c1c10f4f61fdf1d088fd947

[ "Apache-2.0", "BSD-3-Clause" ]

null

htcl_totals_test.py

NREL/scout

acf38df7ce877cbd8c1c10f4f61fdf1d088fd947

[ "Apache-2.0", "BSD-3-Clause" ]

null

htcl_totals_test.py

NREL/scout

acf38df7ce877cbd8c1c10f4f61fdf1d088fd947

[ "Apache-2.0", "BSD-3-Clause" ]

null

#!/usr/bin/env python3 """ Tests for running the htcl_totals.py routine """ # Import code to be tested import htcl_totals # Import needed packages import unittest import itertools class CommonMethods(object): """Define common methods for use in all tests below.""" def dict_check(self, dict1, dict2): """Check the equality of two dicts. Args: dict1 (dict): First dictionary to be compared dict2 (dict): Second dictionary to be compared Raises: AssertionError: If dictionaries are not equal. """ # zip() and zip_longest() produce tuples for the items # identified, where in the case of a dict, the first item # in the tuple is the key and the second item is the value; # in the case where the dicts are not of identical size, # zip_longest() will use the fill value created below as a # substitute in the dict that has missing content; this # value is given as a tuple to be of comparable structure # to the normal output from zip_longest() fill_val = ('substituted entry', 5.2) # In this structure, k and k2 are the keys that correspond to # the dicts or unitary values that are found in i and i1, # respectively, at the current level of the recursive # exploration of dict1 and dict1, respectively for (k, i), (k2, i2) in itertools.zip_longest(sorted(dict1.items()), sorted(dict2.items()), fillvalue=fill_val): # Confirm that at the current location in the dict structure, # the keys are equal; this should fail if one of the dicts # is empty, is missing section(s), or has different key names self.assertEqual(k, k2) # If the recursion has not yet reached the terminal/leaf node if isinstance(i, dict): # Test that the dicts from the current keys are equal self.assertCountEqual(i, i2) # Continue to recursively traverse the dict self.dict_check(i, i2) else: # At the terminal/leaf node, formatted as a point value self.assertAlmostEqual(i, i2, places=2) class SumHtClEnergyTest(unittest.TestCase, CommonMethods): """Test operation of 'sum_htcl_energy' function. Verify that function properly sums all heating and cooling energy for a given climate zone, building type, and structure type combination, converting from site to source energy in the process. Attributes: aeo_years (list): Modeling time horizon. ss_conv (dict): Site-source conversion factors. ok_msegs_in (dict): Sample stock/energy data to use in developing sums. ok_out (dict): Sum totals that should be yielded by function given valid sample inputs. """ @classmethod def setUpClass(cls): """Define objects/variables for use across all class functions.""" cls.aeo_years = ["2009", "2010"] cls.ss_conv = { "electricity": {"2009": 3, "2010": 4}, "natural gas": {"2009": 1, "2010": 1}, "distillate": {"2009": 1, "2010": 1}, "other fuel": {"2009": 1, "2010": 1}} cls.ok_msegs_in = { "AIA_CZ1": { "single family home": { "new homes": {"2009": 1, "2010": 1}, "total homes": {"2009": 10, "2010": 10}, "total square footage": {"2009": 100, "2010": 100}, "electricity": { "lighting": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "secondary heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "cooling": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "water heating": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "natural gas": { "heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "secondary heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "water heating": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}}, "assembly": { "new square footage": {"2009": 1, "2010": 1}, "total square footage": {"2009": 5, "2010": 5}, "electricity": { "lighting": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "cooling": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "refrigeration": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}} }, "distillate": { "heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "water heating": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}}}, "AIA_CZ2": { "single family home": { "new homes": {"2009": 1, "2010": 1}, "total homes": {"2009": 100, "2010": 100}, "total square footage": {"2009": 1000, "2010": 1000}, "electricity": { "lighting": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "secondary heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "cooling": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "water heating": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "natural gas": { "heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "secondary heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "water heating": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}}, "assembly": { "new square footage": {"2009": 1, "2010": 1}, "total square footage": {"2009": 10, "2010": 10}, "electricity": { "lighting": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "cooling": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "refrigeration": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}} }, "distillate": { "heating": { "supply": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}, "demand": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}, "water heating": { "tech 1": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}, "tech 2": { "stock": {"2009": 1, "2010": 1}, "energy": {"2009": 1, "2010": 1}}}}}}} cls.ok_out = { "AIA_CZ1": { "single family home": { "new": {"2009": 2.2, "2010": 5.6}, "existing": {"2009": 19.8, "2010": 22.4} }, "assembly": { "new": {"2009": 2.8, "2010": 7.2}, "existing": {"2009": 11.2, "2010": 10.8} }}, "AIA_CZ2": { "single family home": { "new": {"2009": 0.22, "2010": 0.56}, "existing": {"2009": 21.78, "2010": 27.44} }, "assembly": { "new": {"2009": 1.4, "2010": 3.6}, "existing": {"2009": 12.6, "2010": 14.4} }}} def test_ok(self): """Test for correct function output given valid inputs.""" self.dict_check( htcl_totals.sum_htcl_energy( self.ok_msegs_in, self.aeo_years, self.ss_conv), self.ok_out) # Offer external code execution (include all lines below this point in all # test files) def main(): """Trigger default behavior of running all test fixtures in the file.""" unittest.main() if __name__ == "__main__": main()

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3bb507b77a6778e085bb52ca3348733be6a09dc2

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py

Python

jose/tests/test_content_encryption.py

bwhmather/jose

8bf1f08afd5b4bd21f20bf86f3474afb671d8992

[ "BSD-3-Clause" ]

null

jose/tests/test_content_encryption.py

bwhmather/jose

8bf1f08afd5b4bd21f20bf86f3474afb671d8992

[ "BSD-3-Clause" ]

null

jose/tests/test_content_encryption.py

bwhmather/jose

8bf1f08afd5b4bd21f20bf86f3474afb671d8992

[ "BSD-3-Clause" ]

null

import unittest from jose.algorithms.content_encryption import ( A128CBC_HS256, A192CBC_HS384, A256CBC_HS512, ) class AES_CBC_HMAC_SHA2_Base(unittest.TestCase): def test_encrypt(self): encrypter = self.algorithm(self.key) ciphertext, auth_token = encrypter.encrypt( self.plaintext, adata=self.adata, iv=self.iv ) self.assertEqual(self.ciphertext, ciphertext) self.assertEqual(self.auth_token, auth_token) def test_decrypt(self): decrypter = self.algorithm(self.key) plaintext = decrypter.decrypt( self.ciphertext, auth_token=self.auth_token, adata=self.adata, iv=self.iv ) self.assertEqual(self.plaintext, plaintext) class Test_A128CBC_HS256(AES_CBC_HMAC_SHA2_Base): algorithm = A128CBC_HS256 key = ( b'\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f' b'\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f' ) plaintext = ( b'\x41\x20\x63\x69\x70\x68\x65\x72\x20\x73\x79\x73\x74\x65\x6d\x20' b'\x6d\x75\x73\x74\x20\x6e\x6f\x74\x20\x62\x65\x20\x72\x65\x71\x75' b'\x69\x72\x65\x64\x20\x74\x6f\x20\x62\x65\x20\x73\x65\x63\x72\x65' b'\x74\x2c\x20\x61\x6e\x64\x20\x69\x74\x20\x6d\x75\x73\x74\x20\x62' b'\x65\x20\x61\x62\x6c\x65\x20\x74\x6f\x20\x66\x61\x6c\x6c\x20\x69' b'\x6e\x74\x6f\x20\x74\x68\x65\x20\x68\x61\x6e\x64\x73\x20\x6f\x66' b'\x20\x74\x68\x65\x20\x65\x6e\x65\x6d\x79\x20\x77\x69\x74\x68\x6f' b'\x75\x74\x20\x69\x6e\x63\x6f\x6e\x76\x65\x6e\x69\x65\x6e\x63\x65' ) iv = ( b'\x1a\xf3\x8c\x2d\xc2\xb9\x6f\xfd\xd8\x66\x94\x09\x23\x41\xbc\x04' ) adata = ( b'\x54\x68\x65\x20\x73\x65\x63\x6f\x6e\x64\x20\x70\x72\x69\x6e\x63' b'\x69\x70\x6c\x65\x20\x6f\x66\x20\x41\x75\x67\x75\x73\x74\x65\x20' b'\x4b\x65\x72\x63\x6b\x68\x6f\x66\x66\x73' ) ciphertext = ( b'\xc8\x0e\xdf\xa3\x2d\xdf\x39\xd5\xef\x00\xc0\xb4\x68\x83\x42\x79' b'\xa2\xe4\x6a\x1b\x80\x49\xf7\x92\xf7\x6b\xfe\x54\xb9\x03\xa9\xc9' b'\xa9\x4a\xc9\xb4\x7a\xd2\x65\x5c\x5f\x10\xf9\xae\xf7\x14\x27\xe2' b'\xfc\x6f\x9b\x3f\x39\x9a\x22\x14\x89\xf1\x63\x62\xc7\x03\x23\x36' b'\x09\xd4\x5a\xc6\x98\x64\xe3\x32\x1c\xf8\x29\x35\xac\x40\x96\xc8' b'\x6e\x13\x33\x14\xc5\x40\x19\xe8\xca\x79\x80\xdf\xa4\xb9\xcf\x1b' b'\x38\x4c\x48\x6f\x3a\x54\xc5\x10\x78\x15\x8e\xe5\xd7\x9d\xe5\x9f' b'\xbd\x34\xd8\x48\xb3\xd6\x95\x50\xa6\x76\x46\x34\x44\x27\xad\xe5' b'\x4b\x88\x51\xff\xb5\x98\xf7\xf8\x00\x74\xb9\x47\x3c\x82\xe2\xdb' ) auth_token = ( b'\x65\x2c\x3f\xa3\x6b\x0a\x7c\x5b\x32\x19\xfa\xb3\xa3\x0b\xc1\xc4' ) class Test_A192CBC_HS384(AES_CBC_HMAC_SHA2_Base): algorithm = A192CBC_HS384 key = ( b'\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f' b'\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f' b'\x20\x21\x22\x23\x24\x25\x26\x27\x28\x29\x2a\x2b\x2c\x2d\x2e\x2f' ) plaintext = ( b'\x41\x20\x63\x69\x70\x68\x65\x72\x20\x73\x79\x73\x74\x65\x6d\x20' b'\x6d\x75\x73\x74\x20\x6e\x6f\x74\x20\x62\x65\x20\x72\x65\x71\x75' b'\x69\x72\x65\x64\x20\x74\x6f\x20\x62\x65\x20\x73\x65\x63\x72\x65' b'\x74\x2c\x20\x61\x6e\x64\x20\x69\x74\x20\x6d\x75\x73\x74\x20\x62' b'\x65\x20\x61\x62\x6c\x65\x20\x74\x6f\x20\x66\x61\x6c\x6c\x20\x69' b'\x6e\x74\x6f\x20\x74\x68\x65\x20\x68\x61\x6e\x64\x73\x20\x6f\x66' b'\x20\x74\x68\x65\x20\x65\x6e\x65\x6d\x79\x20\x77\x69\x74\x68\x6f' b'\x75\x74\x20\x69\x6e\x63\x6f\x6e\x76\x65\x6e\x69\x65\x6e\x63\x65' ) iv = ( b'\x1a\xf3\x8c\x2d\xc2\xb9\x6f\xfd\xd8\x66\x94\x09\x23\x41\xbc\x04' ) adata = ( b'\x54\x68\x65\x20\x73\x65\x63\x6f\x6e\x64\x20\x70\x72\x69\x6e\x63' b'\x69\x70\x6c\x65\x20\x6f\x66\x20\x41\x75\x67\x75\x73\x74\x65\x20' b'\x4b\x65\x72\x63\x6b\x68\x6f\x66\x66\x73' ) ciphertext = ( b'\xea\x65\xda\x6b\x59\xe6\x1e\xdb\x41\x9b\xe6\x2d\x19\x71\x2a\xe5' b'\xd3\x03\xee\xb5\x00\x52\xd0\xdf\xd6\x69\x7f\x77\x22\x4c\x8e\xdb' b'\x00\x0d\x27\x9b\xdc\x14\xc1\x07\x26\x54\xbd\x30\x94\x42\x30\xc6' b'\x57\xbe\xd4\xca\x0c\x9f\x4a\x84\x66\xf2\x2b\x22\x6d\x17\x46\x21' b'\x4b\xf8\xcf\xc2\x40\x0a\xdd\x9f\x51\x26\xe4\x79\x66\x3f\xc9\x0b' b'\x3b\xed\x78\x7a\x2f\x0f\xfc\xbf\x39\x04\xbe\x2a\x64\x1d\x5c\x21' b'\x05\xbf\xe5\x91\xba\xe2\x3b\x1d\x74\x49\xe5\x32\xee\xf6\x0a\x9a' b'\xc8\xbb\x6c\x6b\x01\xd3\x5d\x49\x78\x7b\xcd\x57\xef\x48\x49\x27' b'\xf2\x80\xad\xc9\x1a\xc0\xc4\xe7\x9c\x7b\x11\xef\xc6\x00\x54\xe3' ) auth_token = ( b'\x84\x90\xac\x0e\x58\x94\x9b\xfe\x51\x87\x5d\x73\x3f\x93\xac\x20' b'\x75\x16\x80\x39\xcc\xc7\x33\xd7' ) class Test_A256CBC_HS512(AES_CBC_HMAC_SHA2_Base): algorithm = A256CBC_HS512 key = ( b'\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f' b'\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f' b'\x20\x21\x22\x23\x24\x25\x26\x27\x28\x29\x2a\x2b\x2c\x2d\x2e\x2f' b'\x30\x31\x32\x33\x34\x35\x36\x37\x38\x39\x3a\x3b\x3c\x3d\x3e\x3f' ) plaintext = ( b'\x41\x20\x63\x69\x70\x68\x65\x72\x20\x73\x79\x73\x74\x65\x6d\x20' b'\x6d\x75\x73\x74\x20\x6e\x6f\x74\x20\x62\x65\x20\x72\x65\x71\x75' b'\x69\x72\x65\x64\x20\x74\x6f\x20\x62\x65\x20\x73\x65\x63\x72\x65' b'\x74\x2c\x20\x61\x6e\x64\x20\x69\x74\x20\x6d\x75\x73\x74\x20\x62' b'\x65\x20\x61\x62\x6c\x65\x20\x74\x6f\x20\x66\x61\x6c\x6c\x20\x69' b'\x6e\x74\x6f\x20\x74\x68\x65\x20\x68\x61\x6e\x64\x73\x20\x6f\x66' b'\x20\x74\x68\x65\x20\x65\x6e\x65\x6d\x79\x20\x77\x69\x74\x68\x6f' b'\x75\x74\x20\x69\x6e\x63\x6f\x6e\x76\x65\x6e\x69\x65\x6e\x63\x65' ) iv = ( b'\x1a\xf3\x8c\x2d\xc2\xb9\x6f\xfd\xd8\x66\x94\x09\x23\x41\xbc\x04' ) adata = ( b'\x54\x68\x65\x20\x73\x65\x63\x6f\x6e\x64\x20\x70\x72\x69\x6e\x63' b'\x69\x70\x6c\x65\x20\x6f\x66\x20\x41\x75\x67\x75\x73\x74\x65\x20' b'\x4b\x65\x72\x63\x6b\x68\x6f\x66\x66\x73' ) ciphertext = ( b'\x4a\xff\xaa\xad\xb7\x8c\x31\xc5\xda\x4b\x1b\x59\x0d\x10\xff\xbd' b'\x3d\xd8\xd5\xd3\x02\x42\x35\x26\x91\x2d\xa0\x37\xec\xbc\xc7\xbd' b'\x82\x2c\x30\x1d\xd6\x7c\x37\x3b\xcc\xb5\x84\xad\x3e\x92\x79\xc2' b'\xe6\xd1\x2a\x13\x74\xb7\x7f\x07\x75\x53\xdf\x82\x94\x10\x44\x6b' b'\x36\xeb\xd9\x70\x66\x29\x6a\xe6\x42\x7e\xa7\x5c\x2e\x08\x46\xa1' b'\x1a\x09\xcc\xf5\x37\x0d\xc8\x0b\xfe\xcb\xad\x28\xc7\x3f\x09\xb3' b'\xa3\xb7\x5e\x66\x2a\x25\x94\x41\x0a\xe4\x96\xb2\xe2\xe6\x60\x9e' b'\x31\xe6\xe0\x2c\xc8\x37\xf0\x53\xd2\x1f\x37\xff\x4f\x51\x95\x0b' b'\xbe\x26\x38\xd0\x9d\xd7\xa4\x93\x09\x30\x80\x6d\x07\x03\xb1\xf6' ) auth_token = ( b'\x4d\xd3\xb4\xc0\x88\xa7\xf4\x5c\x21\x68\x39\x64\x5b\x20\x12\xbf' b'\x2e\x62\x69\xa8\xc5\x6a\x81\x6d\xbc\x1b\x26\x77\x61\x95\x5b\xc5' ) def load_tests(loader, standard_tests, pattern): return unittest.TestSuite(( loader.loadTestsFromTestCase(Test_A128CBC_HS256), loader.loadTestsFromTestCase(Test_A192CBC_HS384), loader.loadTestsFromTestCase(Test_A256CBC_HS512), ))

42.073864

75

0.636867

1,451

7,405

3.217781

0.189524

0.034697

0.017348

0.015421

0.508246

0.504391

0.487042

0.46948

0

0.310494

0.158406

7,405

175

76

42.314286

0.438703

0

0.430556

0

0.506944

0.651452

0

1

0

0.020833

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0.020833

false

0

0.013889

0.006944

0.215278

0

null

0

1

0

1

0

1

null

1

0

6

3bfada2366d53f055901db95b2e965098fc08271

35

py

Python

src/pwbus_http/__init__.py

fszostak/pwbus-web

1412ea9e7869f04fbfeccd212ac4f9ed28a6a17f

[ "MIT" ]

null

src/pwbus_http/__init__.py

fszostak/pwbus-web

1412ea9e7869f04fbfeccd212ac4f9ed28a6a17f

[ "MIT" ]

1

2021-04-16T00:43:09.000Z

src/pwbus_http/__init__.py

fszostak/pwbus-web

1412ea9e7869f04fbfeccd212ac4f9ed28a6a17f

[ "MIT" ]

null

# __init__.py from . import server

11.666667

20

0.742857

5

35

4.4

1

0

0.171429

35

2

21

17.5

0.758621

0.314286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

02238e7529201e6b8e5f434a65291956fc9af2a1

165

py

Python

autogl/module/__init__.py

dedsec-9/AutoGL

487f2b2f798b9b1363ad5dc100fb410b12222e06

[ "MIT" ]

824

2020-11-30T14:38:07.000Z

2022-03-19T10:14:04.000Z

autogl/module/__init__.py

dedsec-9/AutoGL

487f2b2f798b9b1363ad5dc100fb410b12222e06

[ "MIT" ]

38

2020-12-21T12:32:57.000Z

2022-01-31T02:32:05.000Z

autogl/module/__init__.py

dedsec-9/AutoGL

487f2b2f798b9b1363ad5dc100fb410b12222e06

[ "MIT" ]

85

2020-12-21T05:16:09.000Z

2022-03-28T08:44:22.000Z

from . import feature, model, train, hpo, nas, ensemble from .ensemble import * from .feature import * from .hpo import * from .model import * from .train import *

20.625

55

0.721212

23

165

5.173913

0.347826

0.336134

0

0.181818

165

7

56

23.571429

0.881481

0

1

0

true

0

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

65fd27df92475b5614da5ef867df169ec8ad55cd

187

py

Python

openapi_python_client/parser/__init__.py

Maistho/openapi-python-client

e123b966e8d31db173e09cabc8284a855d07e425

[ "MIT" ]

2

2020-10-15T07:25:50.000Z

2021-09-14T21:29:08.000Z

openapi_python_client/parser/__init__.py

Maistho/openapi-python-client

e123b966e8d31db173e09cabc8284a855d07e425

[ "MIT" ]

79

2020-09-10T00:47:21.000Z

2022-03-25T02:07:31.000Z

openapi_python_client/parser/__init__.py

Maistho/openapi-python-client

e123b966e8d31db173e09cabc8284a855d07e425

[ "MIT" ]

1

2020-11-03T00:11:57.000Z

""" Classes representing the data in the OpenAPI schema """ __all__ = ["GeneratorData", "import_string_from_reference"] from .openapi import GeneratorData, import_string_from_reference

31.166667

64

0.802139

22

187

6.363636

0.590909

0.271429

0.357143

0.414286

0.542857

0

0.112299

187

5

65

37.4

0.843373

0.272727

0

0.320313

0.21875

0

1

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false

0

1

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1

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1

0

null

1

0

1

0

null

0

1

0

6

5a00d7b477fcf498888514aef1011741f72965b5

33

py

Python

virtual-check.py

ZachZemo/testing-pack

d769ff5242660da9311b23059e64da11d6e991d4

[ "MIT" ]

null

virtual-check.py

ZachZemo/testing-pack

d769ff5242660da9311b23059e64da11d6e991d4

[ "MIT" ]

null

virtual-check.py

ZachZemo/testing-pack

d769ff5242660da9311b23059e64da11d6e991d4

[ "MIT" ]

null

import example_pkg print(1 + 1)

8.25

18

0.727273

6

33

3.833333

0.833333

0

0.074074

0.181818

33

3

19

11

0.777778

0

1

0

true

0

0.5

0

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null

0

1

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null

0

1

0

1

0

1

0

6

5a180c482b4f52e795c9fd0bfc7efbe15a1b85f5

2,933

py

Python

stock_portfolio/stock_portfolio/tests/test_models_stock.py

zarkle/pyramid-stocks

493ad5a5b77e99dcff8e8234bf0616db1fbb4c98

[ "MIT" ]

null

stock_portfolio/stock_portfolio/tests/test_models_stock.py

zarkle/pyramid-stocks

493ad5a5b77e99dcff8e8234bf0616db1fbb4c98

[ "MIT" ]

4

2019-12-26T16:42:42.000Z

2020-01-06T18:53:34.000Z

stock_portfolio/stock_portfolio/tests/test_models_stock.py

zarkle/pyramid-stocks

493ad5a5b77e99dcff8e8234bf0616db1fbb4c98

[ "MIT" ]

null

def test_stock_model(db_session): """test make a new stock""" from ..models import Stock assert len(db_session.query(Stock).all()) == 0 stock = Stock( symbol="MU", companyName="Micron Technology Inc.", exchange="Nasdaq Global Select", industry="Semiconductors", website="http://www.micron.com", description="Micron Technology Inc along with its subsidiaries provide memory and storage solutions. Its product portfolio consists of memory and storage technologies such as DRAM, NAND, NOR and 3D XPoint memory.", CEO="Michael Stewart", issueType="cs", sector="Technology" ) db_session.add(stock) assert len(db_session.query(Stock).all()) == 1 # def test_make_user_no_password(db_session): # """test can't make new user with no password""" # from ..models import Stock # import pytest # from sqlalchemy.exc import DBAPIError # assert len(db_session.query(Stock).all()) == 0 # user = Stock( # username='me', # password=None, # email='me@me.com', # ) # with pytest.raises(DBAPIError): # db_session.add(user) # assert len(db_session.query(Stock).all()) == 0 # assert db_session.query(Stock).one_or_none() is None def test_make_stock_no_ceo(db_session): """test can make new stock with no ceo""" from ..models import Stock assert len(db_session.query(Stock).all()) == 0 stock = Stock( symbol="MU", companyName="Micron Technology Inc.", exchange="Nasdaq Global Select", industry="Semiconductors", website="http://www.micron.com", description="Micron Technology Inc along with its subsidiaries provide memory and storage solutions. Its product portfolio consists of memory and storage technologies such as DRAM, NAND, NOR and 3D XPoint memory.", CEO="", issueType="cs", sector="Technology" ) db_session.add(stock) assert len(db_session.query(Stock).all()) == 1 def test_new_stock_in_database(db_session): """test new stock gets added to database""" from ..models import Stock assert len(db_session.query(Stock).all()) == 0 user = Stock( symbol="MU", companyName="Micron Technology Inc.", exchange="Nasdaq Global Select", industry="Semiconductors", website="http://www.micron.com", description="Micron Technology Inc along with its subsidiaries provide memory and storage solutions. Its product portfolio consists of memory and storage technologies such as DRAM, NAND, NOR and 3D XPoint memory.", CEO="Michael Stewart", issueType="cs", sector="Technology" ) db_session.add(user) query = db_session.query(Stock) stock = query.filter(Stock.symbol == 'MU').first() assert isinstance(stock, Stock) assert len(db_session.query(Stock).all()) == 1

36.209877

222

0.653256

371

2,933

5.072776

0.237197

0.086079

0.074389

0.100956

0.742827

0.725824

0.706164

0

0.004874

0.230481

2,933

80

223

36.6625

0.828977

0.20866

0

0.764706

0

0.058824

0.393886

0

0.137255

1

0.058824

false

0

0.058824

0

0.117647

0

null

0

1

0

1

0

null

0

6

5a51ee89b9eeac5958eb7116447a51a5dd4f50ea

99

py

Python

Lesson4/matr.py

shinkai-tester/python_beginner

a934328c9a50241cc3f02a423060e16aab53b425

[ "Apache-2.0" ]

2

2021-06-01T13:24:04.000Z

2021-06-01T13:27:47.000Z

Lesson4/matr.py

shinkai-tester/python_beginner

a934328c9a50241cc3f02a423060e16aab53b425

[ "Apache-2.0" ]

null

Lesson4/matr.py

shinkai-tester/python_beginner

a934328c9a50241cc3f02a423060e16aab53b425

[ "Apache-2.0" ]

null

for i in range(1, 10): for j in range(1, 10): print(i * j, end=' ') print(end='\n')

24.75

29

0.474747

19

99

2.473684

0.526316

0.297872

0.340426

0.425532

0

0.088235

0.313131

99

4

30

24.75

0.602941

0

0.03

0

1

0

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

6

ce66428f0c80237f3197f503efa4975a2a382a26

170

py

Python

esmonitor/views/index_view.py

cristianprice/pyesmonitor

df89968d7b2566a9e1c4afd89a1156a285580747

[ "Apache-2.0" ]

null

esmonitor/views/index_view.py

cristianprice/pyesmonitor

df89968d7b2566a9e1c4afd89a1156a285580747

[ "Apache-2.0" ]

null

esmonitor/views/index_view.py

cristianprice/pyesmonitor

df89968d7b2566a9e1c4afd89a1156a285580747

[ "Apache-2.0" ]

null

from django.http import HttpResponse from django.shortcuts import render def index(request): return render(request, 'esmonitor/index.html', {'none': 'none'})

24.285714

69

0.723529

21

170

5.857143

0.666667

0.162602

0

0.158824

170

6

70

28.333333

0.86014

0

0.170732

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

ce9283e286495a9bef3f7f62965af7424b69d4aa

10,370

py

Python

gerenciador/anuncios/tests/test_views.py

diogo-alves/gerenciador_anuncios

fc62a818d803594ba0c31c755cbb83c165c1488f

[ "MIT" ]

null

gerenciador/anuncios/tests/test_views.py

diogo-alves/gerenciador_anuncios

fc62a818d803594ba0c31c755cbb83c165c1488f

[ "MIT" ]

null

gerenciador/anuncios/tests/test_views.py

diogo-alves/gerenciador_anuncios

fc62a818d803594ba0c31c755cbb83c165c1488f

[ "MIT" ]

null

from http import HTTPStatus from django.urls import reverse from django.test import TestCase from ..models import Anuncio, Cliente class ClienteCreateViewTests(TestCase): def setUp(self): self.url = reverse('anuncios:cliente_create') def test_template_utilizado(self): response = self.client.get(self.url) self.assertTemplateUsed(response, 'anuncios/cliente_create.html') def test_resposta_get(self): response = self.client.get(self.url) self.assertEqual(response.status_code, HTTPStatus.OK) def test_redirecionamento_apos_criacao(self): response = self.client.post(self.url, data={'nome': 'Cliente1'}, follow=True) cliente = response.context.get('cliente') self.assertEqual(Cliente.objects.count(), 1) self.assertEqual(response.status_code, HTTPStatus.OK) self.assertRedirects(response, reverse('anuncios:cliente_detail', kwargs={'pk': cliente.pk})) def test_redirecionamento_apos_clicar_em_salvar_e_adicionar_outro(self): dados_cliente = { 'nome': 'Anúncio 1', 'btn_salvar_e_adicionar_outro': '' } response = self.client.post(self.url, data=dados_cliente) self.assertEqual(Cliente.objects.count(), 1) self.assertEqual(response.status_code, HTTPStatus.FOUND) self.assertRedirects(response, reverse('anuncios:cliente_create')) class ClienteDetailViewTests(TestCase): @classmethod def setUpTestData(cls): cls.cliente = Cliente.objects.create(nome='Cliente 1') def setUp(self): self.url = reverse('anuncios:cliente_detail', kwargs={'pk': self.cliente.pk}) def test_template_utilizado(self): response = self.client.get(self.url) self.assertTemplateUsed(response, 'anuncios/cliente_detail.html') def test_resposta_get(self): response = self.client.get(self.url) self.assertEqual(response.status_code, HTTPStatus.OK) class ClienteUpdateViewTests(TestCase): @classmethod def setUpTestData(cls): cls.cliente = Cliente.objects.create(nome='Cliente 1') def setUp(self): self.url = reverse('anuncios:cliente_update', kwargs={'pk': self.cliente.pk}) def test_template_utilizado(self): response = self.client.get(self.url) self.assertTemplateUsed(response, 'anuncios/cliente_update.html') def test_resposta_get(self): response = self.client.get(self.url) self.assertEqual(response.status_code, HTTPStatus.OK) def test_redirecionamento_apos_edicao(self): dados_cliente = {'nome': 'Novo nome do cliente'} response = self.client.post(self.url, data=dados_cliente) cliente_atualizado = Cliente.objects.get(pk=self.cliente.pk) self.assertEqual(cliente_atualizado.nome, dados_cliente['nome']) self.assertRedirects(response, reverse('anuncios:cliente_detail', kwargs={'pk': self.cliente.pk})) self.assertEqual(Cliente.objects.count(), 1) class ClienteDeleteViewTests(TestCase): @classmethod def setUpTestData(cls): cls.cliente = Cliente.objects.create(nome='Cliente 1') def setUp(self): self.url = reverse('anuncios:cliente_delete', kwargs={'pk': self.cliente.pk}) def test_exclusao_de_objeto(self): response = self.client.post(self.url) self.assertEqual(Cliente.objects.count(), 0) self.assertRedirects(response, reverse('anuncios:cliente_list')) class ClienteListViewTests(TestCase): @classmethod def setUpTestData(cls): Cliente.objects.bulk_create([ Cliente(nome='Cliente 1'), Cliente(nome='Cliente 2'), ]) def setUp(self): self.url = reverse('anuncios:cliente_list') def test_template_utilizado(self): response = self.client.get(self.url) self.assertTemplateUsed(response, 'anuncios/cliente_list.html') def test_resposta_get(self): response = self.client.get(self.url) self.assertEqual(response.status_code, HTTPStatus.OK) def test_quantidade_de_objetos_retornados(self): response = self.client.get(self.url) object_list = response.context.get('object_list') self.assertEqual(len(object_list), 2) class AnuncioCreateViewTests(TestCase): def setUp(self): self.url = reverse('anuncios:anuncio_create') def test_templates_utilizados(self): response = self.client.get(self.url) self.assertTemplateUsed(response, 'anuncios/anuncio_create.html') self.assertTemplateUsed(response, 'anuncios/_anuncio_form_fields.html') def test_resposta_get(self): response = self.client.get(self.url) self.assertEqual(response.status_code, HTTPStatus.OK) def test_redirecionamento_apos_criacao(self): cliente = Cliente.objects.create(nome='Cliente 1') dados_anuncio = { 'nome': 'Anúncio 1', 'cliente': cliente.pk, 'data_inicio': '2021-05-01', 'data_termino': '2021-05-31', 'investimento_diario': '5000' } response = self.client.post(self.url, data=dados_anuncio, follow=True) anuncio = response.context.get('anuncio') self.assertEqual(Anuncio.objects.count(), 1) self.assertEqual(response.status_code, HTTPStatus.OK) self.assertRedirects(response, reverse('anuncios:anuncio_detail', kwargs={'pk': anuncio.pk})) def test_redirecionamento_apos_clicar_em_salvar_e_adicionar_outro(self): cliente = Cliente.objects.create(nome='Cliente 1') dados_anuncio = { 'nome': 'Anúncio 1', 'cliente': cliente.pk, 'data_inicio': '2021-05-01', 'data_termino': '2021-05-31', 'investimento_diario': '5000', 'btn_salvar_e_adicionar_outro': '' } response = self.client.post(self.url, data=dados_anuncio) self.assertEqual(Anuncio.objects.count(), 1) self.assertEqual(response.status_code, HTTPStatus.FOUND) self.assertRedirects(response, reverse('anuncios:anuncio_create')) class AnuncioDetailViewTests(TestCase): @classmethod def setUpTestData(cls): cliente = Cliente.objects.create(nome='Cliente 1') cls.anuncio = Anuncio.objects.create( nome='Anúncio 1', cliente=cliente, data_inicio='2021-05-01', data_termino='2021-05-31', investimento_diario=5000.00 ) def setUp(self): self.url = reverse('anuncios:anuncio_detail', kwargs={'pk': self.anuncio.pk}) def test_template_utilizado(self): response = self.client.get(self.url) self.assertTemplateUsed(response, 'anuncios/anuncio_detail.html') def test_resposta_get(self): response = self.client.get(self.url) self.assertEqual(response.status_code, HTTPStatus.OK) class AnuncioUpdateViewTests(TestCase): @classmethod def setUpTestData(cls): cls.cliente = Cliente.objects.create(nome='Cliente 1') cls.anuncio = Anuncio.objects.create( nome='Anúncio 1', cliente=cls.cliente, data_inicio='2021-05-01', data_termino='2021-05-31', investimento_diario=5000.00 ) def setUp(self): self.url = reverse('anuncios:anuncio_update', kwargs={'pk': self.anuncio.pk}) def test_templates_utilizados(self): response = self.client.get(self.url) self.assertTemplateUsed(response, 'anuncios/anuncio_update.html') self.assertTemplateUsed(response, 'anuncios/_anuncio_form_fields.html') def test_resposta_get(self): response = self.client.get(self.url) self.assertEqual(response.status_code, HTTPStatus.OK) def test_redirecionamento_apos_edicao(self): dados_anuncio = { 'nome': 'Anúncio editado', 'cliente': self.cliente.pk, 'data_inicio': '2021-05-01', 'data_termino': '2021-05-31', 'investimento_diario': '5000', } response = self.client.post(self.url, data=dados_anuncio) anuncio_atualizado = Anuncio.objects.get(pk=self.anuncio.pk) self.assertEqual(anuncio_atualizado.nome, dados_anuncio['nome']) self.assertRedirects(response, reverse('anuncios:anuncio_detail', kwargs={'pk': self.anuncio.pk})) self.assertEqual(Anuncio.objects.count(), 1) class AnuncioDeleteViewTests(TestCase): @classmethod def setUpTestData(cls): cliente = Cliente.objects.create(nome='Cliente 1') cls.anuncio = Anuncio.objects.create( nome='Anúncio 1', cliente=cliente, data_inicio='2021-05-01', data_termino='2021-05-31', investimento_diario=5000.00 ) def setUp(self): self.url = reverse('anuncios:anuncio_delete', kwargs={'pk': self.anuncio.pk}) def test_exclusao_de_objeto(self): response = self.client.post(self.url) self.assertEqual(Anuncio.objects.count(), 0) self.assertRedirects(response, reverse('anuncios:anuncio_list')) class AnuncioListViewTests(TestCase): @classmethod def setUpTestData(cls): cliente = Cliente.objects.create(nome='Cliente') Anuncio.objects.bulk_create([ Anuncio( nome='Anúncio 1', cliente=cliente, data_inicio='2021-05-01', data_termino='2021-05-31', investimento_diario=5000.00 ), Anuncio( nome='Anúncio 2', cliente=cliente, data_inicio='2021-04-01', data_termino='2021-05-01', investimento_diario=1000.00 ) ]) def setUp(self): self.url = reverse('anuncios:anuncio_list') def test_template_utilizado(self): response = self.client.get(self.url) self.assertTemplateUsed(response, 'anuncios/anuncio_list.html') def test_resposta_get(self): response = self.client.get(self.url) self.assertEqual(response.status_code, HTTPStatus.OK) def test_quantidade_de_objetos_retornados(self): response = self.client.get(self.url) object_list = response.context.get('object_list') self.assertEqual(len(object_list), 2)

35.272109

106

0.657184

1,160

10,370

5.725

0.091379

0.037946

0.070471

0.069568

0.868092

0.858907

0.841741

0.810119

0.772625

0.725493

0

0.024727

0.223915

10,370

293

107

35.392491

0.800447

0

0.674009

0

0.132015

0.072324

0

0.185022

1

0.193833

false

0

0.017621

0

0.255507

0

null

0

1

0

null

0

6

0c7675e739a8c7fe8730ae95cbbd459121a09f22

9,211

py

Python

SR/model/UNetSR.py

AntonyYX/Super-Resolution

9a5a55169b08849be39a42f0ee955feb60527fbf

[ "MIT" ]

null

SR/model/UNetSR.py

AntonyYX/Super-Resolution

9a5a55169b08849be39a42f0ee955feb60527fbf

[ "MIT" ]

null

SR/model/UNetSR.py

AntonyYX/Super-Resolution

9a5a55169b08849be39a42f0ee955feb60527fbf

[ "MIT" ]

1

2021-10-02T11:03:49.000Z

""" @author: Huakun Shen, Abhishek Thakur @reference: https://youtu.be/u1loyDCoGbE """ import torch from torch import nn from PIL import Image from torch.nn.modules import padding from torchvision import transforms import torch.nn.functional as F def double_convolution(in_c, out_c, ksize=3): return nn.Sequential( nn.Conv2d(in_c, out_c, kernel_size=ksize, padding=ksize//2), nn.ReLU(inplace=True), nn.Conv2d(out_c, out_c, kernel_size=ksize, padding=ksize//2), nn.ReLU(inplace=True), ) class UNetSR(nn.Module): """ unetsr = UNetSR(in_c=3, out_c=3, output_paddings=[1, 1]).to(device) unet_config = { 'epochs': 150, 'save_period': 10, 'batch_size': 8, 'checkpoint_dir': RESULT_PATH / 'result/unetsr_100_300_perceptual_loss_w_seed', 'log_step': 10, 'start_epoch': 1, 'criterion': criterion, 'dataset_type': 'same_300', 'low_res': 100, 'high_res': 300, 'device': device, 'scheduler': { 'step_size': 5, 'gamma': 0.85 }, 'optimizer': optim.Adam(unetsr.parameters(), lr=0.002), 'train_set_percentage': 0.9, 'num_worker': multiprocessing.cpu_count(), 'test_all_multiprocess_cpu': 1, 'test_only': False } """ def __init__(self, in_c: int = 3, out_c: int = 3, ksize=3, output_paddings=[1, 1]): """output_paddings: second number is 0 when input size is 600, 1 if input size is 300""" super(UNetSR, self).__init__() self.MaxPool2d = nn.MaxPool2d(kernel_size=2, stride=2) # encoder part self.encoder_conv_1 = double_convolution(in_c, 64, ksize=ksize) self.encoder_conv_2 = double_convolution(64, 128, ksize=ksize) self.encoder_conv_3 = double_convolution(128, 256, ksize=ksize) self.encoder_conv_4 = double_convolution(256, 512, ksize=ksize) self.encoder_conv_5 = double_convolution(512, 1024, ksize=ksize) # decoder part self.ConvT2D_1 = nn.ConvTranspose2d( in_channels=1024, out_channels=512, kernel_size=2, stride=2, output_padding=output_paddings[0]) self.decoder_conv_1 = double_convolution(1024, 512, ksize=ksize) self.ConvT2D_2 = nn.ConvTranspose2d( in_channels=512, out_channels=256, kernel_size=2, stride=2, output_padding=output_paddings[1]) self.decoder_conv_2 = double_convolution(512, 256, ksize=ksize) self.ConvT2D_3 = nn.ConvTranspose2d( in_channels=256, out_channels=128, kernel_size=2, stride=2) self.decoder_conv_3 = double_convolution(256, 128, ksize=ksize) self.ConvT2D_4 = nn.ConvTranspose2d( in_channels=128, out_channels=64, kernel_size=2, stride=2) self.decoder_conv_4 = double_convolution(128, 64, ksize=ksize) # output layer to 3 channels self.final = nn.Conv2d(64, out_c, kernel_size=1) def forward(self, image): x1 = self.encoder_conv_1(image) # to be concatenated to decoder x2 = self.MaxPool2d(x1) # print(1, x2.shape) x3 = self.encoder_conv_2(x2) # to be concatenated to decoder x4 = self.MaxPool2d(x3) # print(2, x4.shape) x5 = self.encoder_conv_3(x4) # to be concatenated to decoder x6 = self.MaxPool2d(x5) # print(3, x6.shape) x7 = self.encoder_conv_4(x6) # to be concatenated to decoder x8 = self.MaxPool2d(x7) # print(4, x8.shape) x9 = self.encoder_conv_5(x8) # print(5, x9.shape) x = self.ConvT2D_1(x9) # print(6, x.shape) x = self.decoder_conv_1(torch.cat([x, x7], 1)) # print(7, x.shape) x = self.ConvT2D_2(x) # print(8, x.shape) x = self.decoder_conv_2(torch.cat([x, x5], 1)) # print(9, x.shape) x = self.ConvT2D_3(x) x = self.decoder_conv_3(torch.cat([x, x3], 1)) # print(10, x.shape) x = self.ConvT2D_4(x) # print(11, x.shape) x = self.decoder_conv_4(torch.cat([x, x1], 1)) # print(12, x.shape) x = self.final(x) # print(13, x.shape) return x class UNetNoTop(nn.Module): """ remove top layer skip connection """ def __init__(self, in_c: int = 3, out_c: int = 3, ksize=3): super(UNetNoTop, self).__init__() self.MaxPool2d = nn.MaxPool2d(kernel_size=2, stride=2) # encoder part self.encoder_conv_1 = double_convolution(in_c, 64, ksize=ksize) self.encoder_conv_2 = double_convolution(64, 128, ksize=ksize) self.encoder_conv_3 = double_convolution(128, 256, ksize=ksize) self.encoder_conv_4 = double_convolution(256, 512, ksize=ksize) self.encoder_conv_5 = double_convolution(512, 1024, ksize=ksize) # decoder part self.ConvT2D_1 = nn.ConvTranspose2d( in_channels=1024, out_channels=512, kernel_size=2, stride=2, output_padding=1) self.decoder_conv_1 = double_convolution(1024, 512, ksize=ksize) self.ConvT2D_2 = nn.ConvTranspose2d( in_channels=512, out_channels=256, kernel_size=2, stride=2, output_padding=1) self.decoder_conv_2 = double_convolution(512, 256, ksize=ksize) self.ConvT2D_3 = nn.ConvTranspose2d( in_channels=256, out_channels=128, kernel_size=2, stride=2) self.decoder_conv_3 = double_convolution(256, 128, ksize=ksize) self.ConvT2D_4 = nn.ConvTranspose2d( in_channels=128, out_channels=64, kernel_size=2, stride=2) self.decoder_conv_4 = double_convolution(64, 64, ksize=ksize) # output layer to 3 channels self.final = nn.Conv2d(64, out_c, kernel_size=1) def forward(self, image): x1 = self.encoder_conv_1(image) # to be concatenated to decoder x2 = self.MaxPool2d(x1) x3 = self.encoder_conv_2(x2) # to be concatenated to decoder x4 = self.MaxPool2d(x3) x5 = self.encoder_conv_3(x4) # to be concatenated to decoder x6 = self.MaxPool2d(x5) x7 = self.encoder_conv_4(x6) # to be concatenated to decoder x8 = self.MaxPool2d(x7) x9 = self.encoder_conv_5(x8) x = self.ConvT2D_1(x9) x = self.decoder_conv_1(torch.cat([x, x7], 1)) x = self.ConvT2D_2(x) x = self.decoder_conv_2(torch.cat([x, x5], 1)) x = self.ConvT2D_3(x) x = self.decoder_conv_3(torch.cat([x, x3], 1)) x = self.ConvT2D_4(x) # x = self.decoder_conv_4(torch.cat([x, x1], 1)) x = self.decoder_conv_4(x) x = self.final(x) return x class UNetD4(nn.Module): """ UNet depth=4, instead of original 5 layers """ def __init__(self, in_c: int = 3, out_c: int = 3, ksize=3): super(UNetD4, self).__init__() self.MaxPool2d = nn.MaxPool2d(kernel_size=2, stride=2) # encoder part self.encoder_conv_1 = double_convolution(in_c, 64, ksize=ksize) self.encoder_conv_2 = double_convolution(64, 128, ksize=ksize) self.encoder_conv_3 = double_convolution(128, 256, ksize=ksize) self.encoder_conv_4 = double_convolution(256, 512, ksize=ksize) self.encoder_conv_5 = double_convolution(512, 1024, ksize=ksize) # decoder part self.ConvT2D_1 = nn.ConvTranspose2d( in_channels=1024, out_channels=512, kernel_size=2, stride=2, output_padding=1) self.decoder_conv_1 = double_convolution(1024, 512, ksize=ksize) self.ConvT2D_2 = nn.ConvTranspose2d( in_channels=512, out_channels=256, kernel_size=2, stride=2, output_padding=1) self.decoder_conv_2 = double_convolution(512, 256, ksize=ksize) self.ConvT2D_3 = nn.ConvTranspose2d( in_channels=256, out_channels=128, kernel_size=2, stride=2) self.decoder_conv_3 = double_convolution(256, 128, ksize=ksize) self.ConvT2D_4 = nn.ConvTranspose2d( in_channels=128, out_channels=64, kernel_size=2, stride=2) self.decoder_conv_4 = double_convolution(128, 64, ksize=ksize) # output layer to 3 channels self.final = nn.Conv2d(64, out_c, kernel_size=1) def forward(self, image): x1 = self.encoder_conv_1(image) # to be concatenated to decoder x2 = self.MaxPool2d(x1) x3 = self.encoder_conv_2(x2) # to be concatenated to decoder x4 = self.MaxPool2d(x3) x5 = self.encoder_conv_3(x4) # to be concatenated to decoder x6 = self.MaxPool2d(x5) x7 = self.encoder_conv_4(x6) # to be concatenated to decoder x = self.ConvT2D_2(x7) x = self.decoder_conv_2(torch.cat([x, x5], 1)) x = self.ConvT2D_3(x) x = self.decoder_conv_3(torch.cat([x, x3], 1)) x = self.ConvT2D_4(x) x = self.decoder_conv_4(torch.cat([x, x1], 1)) x = self.final(x) return x if __name__ == "__main__": image = torch.rand((1, 3, 300, 300)) # print(image.size()) # model = UNetSR() # model = UNetNoTop() model = UNetD4() # model = UNetSR(output_paddings=[1, 0]) out = model(image) print(out.shape)

38.219917

107

0.625774

1,309

9,211

4.181054

0.126814

0.058286

0.079481

0.046592

0.789695

0.765028

0.748767

0.743651

0

0.083576

0.255673

9,211

240

108

38.379167

0.714702

0.196721

0

0.8

0

0.001109

0

1

0.05

false

0

0.042857

0.007143

0.142857

0.007143

0

null

0

1

0

1

0

null

0

6

0cb50033cd8564760f4f1d546b7f76337d99cb3e

115

py

Python

nn/activation_functions.py

nemoNoboru/GEN0

715385f5f243db04c86e6737a8ee93c9af786078

[ "MIT" ]

1

2017-11-27T09:19:59.000Z

nn/activation_functions.py

nemoNoboru/GEN0

715385f5f243db04c86e6737a8ee93c9af786078

[ "MIT" ]

null

nn/activation_functions.py

nemoNoboru/GEN0

715385f5f243db04c86e6737a8ee93c9af786078

[ "MIT" ]

null

import numpy as np def relu(i): return np.vectorize(np.maximum(i, 0, i)) def tanh(i): return np.tanh(i)

12.777778

44

0.634783

22

115

3.318182

0.545455

0.191781

0.246575

0

0.011111

0.217391

115

8

45

14.375

0.8

0

1

0.4

false

0

0.2

0.4

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

0cb8a98d40512dbb0223d6197d119055c3144770

33

py

Python

xecd_rates/__init__.py

doguskidik/xecd-rates-python

6584c96891f5b91b9e7ceff6883ea4144b6326eb

[ "MIT" ]

1

2020-07-07T20:58:36.000Z

xecd_rates/__init__.py

doguskidik/xecd-rates-python

6584c96891f5b91b9e7ceff6883ea4144b6326eb

[ "MIT" ]

1

2022-02-14T19:53:51.000Z

xecd_rates/__init__.py

doguskidik/xecd-rates-python

6584c96891f5b91b9e7ceff6883ea4144b6326eb

[ "MIT" ]

null

from xecd_rates.Xecd import Xecd

16.5

32

0.848485

6

33

4.5

0.666667

0

0.121212

33

1

33

0.931034

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0cb9afae15e4b8401ecdc98f48752463215e843b

33,485

py

Python

gw-odw_Day2_with_Solns/Tuto_2.2_Matched_Filtering_In_action with solutions.py

basuparth/grav_wave_workshop3

eb9e2ff066bb1928e5a1dbc8cd8d24344515aae4

[ "MIT" ]

null

gw-odw_Day2_with_Solns/Tuto_2.2_Matched_Filtering_In_action with solutions.py

basuparth/grav_wave_workshop3

eb9e2ff066bb1928e5a1dbc8cd8d24344515aae4

[ "MIT" ]

null

gw-odw_Day2_with_Solns/Tuto_2.2_Matched_Filtering_In_action with solutions.py

basuparth/grav_wave_workshop3

eb9e2ff066bb1928e5a1dbc8cd8d24344515aae4

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # <img style="float: left;padding: 1.3em" src="https://indico.in2p3.fr/event/18313/logo-786578160.png"> # # # Gravitational Wave Open Data Workshop #3 # # # ## Tutorial 2.2 PyCBC Tutorial, Matched Filtering in Action # # We will be using the [PyCBC](http://github.com/ligo-cbc/pycbc) library, which is used to study gravitational-wave data, find astrophysical sources due to compact binary mergers, and study their parameters. These are some of the same tools that the LIGO and Virgo collaborations use to find gravitational waves in LIGO/Virgo data # # In this tutorial we will walk through how find a specific signal in LIGO data. We present matched filtering in PyCBC, which is optimal in the case of Gaussian noise and a known signal model. In reality our noise is not entirely Guassian, and in practice we use a variety of techniques to separate signals from noise in addition to the use of the matched filter. # # Additional [examples](http://pycbc.org/pycbc/latest/html/#library-examples-and-interactive-tutorials) and module level documentation are [here](http://pycbc.org/pycbc/latest/html/py-modindex.html) # ## Installation (execute only if running on a cloud platform!) # In[1]: # -- Use the following for Google Colab get_ipython().system(" pip install -q 'lalsuite==6.66' 'PyCBC==1.15.3'") # **Important:** With Google Colab, you may need to restart the runtime after running the cell above. # ### Looking for a specific signal in the data # # If you know what signal you are looking for in the data, then matched filtering is known to be the optimal method in Gaussian noise to extract the siganl. Even when the parameters of the signal are unkown, one can test for each set of parameters one is interesting in finding. # #### preconditioning the data # # The purpose of this is to reduce the dynamic range of the data and supress low freqeuncy behavior which can introduce numerical artefacts. We may also wish to reduce the sample rate of the data if high frequency content is not important. PyCBC contains an interface to the GWOSC catalog, so you can easily access the data and parameters of the published gravitational-wave signals # In[2]: get_ipython().run_line_magic('matplotlib', 'inline') import pylab from pycbc.catalog import Merger from pycbc.filter import resample_to_delta_t, highpass # As an example we use the GW150914 data merger = Merger("GW150914") # Get the data from the Hanford detector strain = merger.strain('H1') # Remove the low frequency content and downsample the data to 2048Hz strain = highpass(strain, 15.0) strain = resample_to_delta_t(strain, 1.0/2048) pylab.plot(strain.sample_times, strain) pylab.xlabel('Time (s)') pylab.show() # _Note_: To read data from a local file instead of from the GWOSC server, we can use the [pycbc.frame.read_frame(file, channel_name)](https://github.com/gwastro/pycbc/blob/master/docs/frame.rst) method. # #### filter wraparound # # Note the spike in the data at the boundaries. This is caused by the highpass and resampling stages filtering the data. When the filter is applied to the boundaries, it wraps around to the beginning of the data. Since the data itself has a discontinuity (i.e. it is not cyclic) the filter itself will ring off for a time up to the length of the filter. # # Even if a visible transient is not seen, we want to avoid filters that act on times which are not causally connect. To avoid this we trim the ends of the data sufficiently to ensure that they do not wraparound the input. We will enforce this requirement in all steps of our filtering. # In[3]: # Remove 2 seconds of data from both the beginning and end conditioned = strain.crop(2, 2) pylab.plot(conditioned.sample_times, conditioned) pylab.xlabel('Time (s)') pylab.show() # #### calculate the power spectral density # # Optimal matched filtering requires weighting the frequency components of the potential signal and data by the noise amplitude. We can view this as filtering the data with the time series equivelant of 1 / PSD. To ensure that we can control the effective length of the filter, we window the time domain equivalent of the PSD to a specific length. This has the effect of losing some information about line behavior in the detector, however, since our signals span a large frequency range, and lines are narrow, this is a negligible effect. # # Important note: Computing a PSD from data that might contain signals, non-Gaussianities and non-stationarities is not trivial. In this example we use Welch's method to obtain a PSD estimate. PyCBC's PSD module contains tools for measuring PSDs, or directly using pre-generated PSDs. # In[4]: from pycbc.psd import interpolate, inverse_spectrum_truncation # Estimate the power spectral density # We use 4 second samples of our time series in Welch method. psd = conditioned.psd(4) # Now that we have the psd we need to interpolate it to match our data # and then limit the filter length of 1 / PSD. After this, we can # directly use this PSD to filter the data in a controlled manner psd = interpolate(psd, conditioned.delta_f) # 1/PSD will now act as a filter with an effective length of 4 seconds # Since the data has been highpassed above 15 Hz, and will have low values # below this we need to inform the function to not include frequencies # below this frequency. psd = inverse_spectrum_truncation(psd, 4 * conditioned.sample_rate, low_frequency_cutoff=15) # #### make your signal model # # Conceptually, matched filtering involves laying the potential signal over your data and integrating (after weighting frequencies correctly). If there is a signal in the data that aligns with your 'template', you will get a large value when integrated over. # In[5]: from pycbc.waveform import get_td_waveform # In this case we "know" what the signal parameters are. In a search # we would grid over the parameters and calculate the SNR time series # for each one # We'll assume equal masses, and non-rotating black holes which is within the posterior probability # of GW150914. m = 36 # Solar masses hp, hc = get_td_waveform(approximant="SEOBNRv4_opt", mass1=m, mass2=m, delta_t=conditioned.delta_t, f_lower=20) # We will resize the vector to match our data hp.resize(len(conditioned)) # The waveform begins at the start of the vector, so if we want the # SNR time series to correspond to the approximate merger location # we need to shift the data so that the merger is approximately at the # first bin of the data. # The cyclic_time_shift method shifts the timeseries by a given amount of time. # It treats the data as if it were on a ring so points shifted off the end # of the series reappear at the start. Note that time stamps are *not* in # general affected (as the start time of the full array is shifted), # but the index of each point in the vector is. # # By convention waveforms returned from `get_td_waveform` have their # merger stamped with time zero, so we can use the start time to # shift the merger into position pylab.figure() pylab.title('Before shifting') pylab.plot(hp.sample_times, hp) pylab.xlabel('Time (s)') pylab.ylabel('Strain') template = hp.cyclic_time_shift(hp.start_time) pylab.figure() pylab.title('After shifting') pylab.plot(template.sample_times, template) pylab.xlabel('Time (s)') pylab.ylabel('Strain') # #### calculating the signal-to-noise time series # # In this section we will now calculate the signal-to-noise time series for our template. We'll take care to handle issues of filter corruption / wraparound by truncating the output time series. We need to account for both the length of the template and 1 / PSD. # In[6]: from pycbc.filter import matched_filter import numpy snr = matched_filter(template, conditioned, psd=psd, low_frequency_cutoff=20) # Remove time corrupted by the template filter and the psd filter # We remove 4 seonds at the beginning and end for the PSD filtering # And we remove 4 additional seconds at the beginning to account for # the template length (this is somewhat generous for # so short a template). A longer signal such as from a BNS, would # require much more padding at the beginning of the vector. snr = snr.crop(4 + 4, 4) # Why are we taking an abs() here? # The `matched_filter` function actually returns a 'complex' SNR. # What that means is that the real portion correponds to the SNR # associated with directly filtering the template with the data. # The imaginary portion corresponds to filtering with a template that # is 90 degrees out of phase. Since the phase of a signal may be # anything, we choose to maximize over the phase of the signal. pylab.figure(figsize=[10, 4]) pylab.plot(snr.sample_times, abs(snr)) pylab.ylabel('Signal-to-noise') pylab.xlabel('Time (s)') pylab.show() peak = abs(snr).numpy().argmax() snrp = snr[peak] time = snr.sample_times[peak] print("We found a signal at {}s with SNR {}".format(time, abs(snrp))) # ### Aligning and Subtracting the Proposed Signal # # In the previous section we found a peak in the signal-to-noise for a proposed binary black hole merger. We can use this SNR peak to align our proposal to the data, and to also subtract our proposal from the data. # In[7]: from pycbc.filter import sigma # The time, amplitude, and phase of the SNR peak tell us how to align # our proposed signal with the data. # Shift the template to the peak time dt = time - conditioned.start_time aligned = template.cyclic_time_shift(dt) # scale the template so that it would have SNR 1 in this data aligned /= sigma(aligned, psd=psd, low_frequency_cutoff=20.0) # Scale the template amplitude and phase to the peak value aligned = (aligned.to_frequencyseries() * snrp).to_timeseries() aligned.start_time = conditioned.start_time # #### Visualize the overlap between the signal and data # # To compare the data an signal on equal footing, and to concentrate on the frequency range that is important. We will whiten both the template and the data, and then bandpass both the data and template between 30-300 Hz. In this way, any signal that is in the data is transformed in the same way that the template is. # In[8]: # We do it this way so that we can whiten both the template and the data white_data = (conditioned.to_frequencyseries() / psd**0.5).to_timeseries() white_template = (aligned.to_frequencyseries() / psd**0.5).to_timeseries() white_data = white_data.highpass_fir(30., 512).lowpass_fir(300, 512) white_template = white_template.highpass_fir(30, 512).lowpass_fir(300, 512) # Select the time around the merger white_data = white_data.time_slice(merger.time-.2, merger.time+.1) white_template = white_template.time_slice(merger.time-.2, merger.time+.1) pylab.figure(figsize=[15, 3]) pylab.plot(white_data.sample_times, white_data, label="Data") pylab.plot(white_template.sample_times, white_template, label="Template") pylab.legend() pylab.show() # #### Subtracting the signal from the data # # Now that we've aligned the template we can simply subtract it. Let's see below how that looks in the time-frequency plots! # In[9]: subtracted = conditioned - aligned # Plot the original data and the subtracted signal data for data, title in [(conditioned, 'Original H1 Data'), (subtracted, 'Signal Subtracted from H1 Data')]: t, f, p = data.whiten(4, 4).qtransform(.001, logfsteps=100, qrange=(8, 8), frange=(20, 512)) pylab.figure(figsize=[15, 3]) pylab.title(title) pylab.pcolormesh(t, f, p**0.5, vmin=1, vmax=6) pylab.yscale('log') pylab.xlabel('Time (s)') pylab.ylabel('Frequency (Hz)') pylab.xlim(merger.time - 2, merger.time + 1) pylab.show() # ## Challenge! # # Use the methods demonstrated above to see if you can calculate the SNR # time series in the following data sets. What is the SNR of each signal? # Which template matched best to which data? # # Information that may be useful: # # * Signals are all placed between 100 and 120 seconds into the frame file. # * You may assume mass1 = mass1 (equal mass) and that each component mass is one of 15, 30, or 45. # * Each file starts at gps time 0, and ends at gps time 128 # * The channel name in each file is "H1:TEST-STRAIN" # In[10]: # Download the challenge set files from pycbc.frame import read_frame import urllib def get_file(fname): url = "https://github.com/gw-odw/odw-2020/raw/master/Data/{}" url = url.format(fname) urllib.request.urlretrieve(url, fname) print('Getting : {}'.format(url)) files = ['PyCBC_T2_0.gwf', 'PyCBC_T2_1.gwf', 'PyCBC_T2_2.gwf'] for fname in files: get_file(fname) # An example of how to read the data from these files: file_name = "PyCBC_T2_0.gwf" # LOSC bulk data typically uses the same convention for internal channels names # Strain is typically IFO:LOSC-STRAIN, where IFO can be H1/L1/V1. channel_name = "H1:TEST-STRAIN" start = 0 end = start + 128 ts = read_frame(file_name, channel_name, start, end) # ### Analysis of PyCBC_T2_0.gwf # In[11]: get_ipython().run_line_magic('matplotlib', 'inline') import pylab from pycbc.catalog import Merger from pycbc.filter import resample_to_delta_t, highpass # As an example we use the GW150914 data file_name1 = "PyCBC_T2_0.gwf" channel_name1 = "H1:TEST-STRAIN" start = 0 end = start + 128 ts1 = read_frame(file_name1, channel_name1, start, end) # Remove the low frequency content and downsample the data to 2048Hz strain1 = highpass(ts1, 15.0) strain1 = resample_to_delta_t(strain1, 1.0/2048) pylab.plot(strain1.sample_times, strain1) pylab.xlabel('Time (s)') pylab.show() # In[12]: # Remove 2 seconds of data from both the beginning and end conditioned1 = strain1.crop(2, 2) pylab.plot(conditioned1.sample_times, conditioned1) pylab.xlabel('Time (s)') pylab.show() # In[13]: from pycbc.psd import interpolate, inverse_spectrum_truncation # Estimate the power spectral density # We use 4 second samples of our time series in Welch method. psd1 = conditioned1.psd(4) # Now that we have the psd we need to interpolate it to match our data # and then limit the filter length of 1 / PSD. After this, we can # directly use this PSD to filter the data in a controlled manner psd1 = interpolate(psd1, conditioned1.delta_f) # 1/PSD will now act as a filter with an effective length of 4 seconds # Since the data has been highpassed above 15 Hz, and will have low values # below this we need to inform the function to not include frequencies # below this frequency. psd1 = inverse_spectrum_truncation(psd1, 4 * conditioned1.sample_rate, low_frequency_cutoff=15) # In[14]: from pycbc.waveform import get_td_waveform # In this case we "know" what the signal parameters are. In a search # we would grid over the parameters and calculate the SNR time series # for each one # We'll assume equal masses, and non-rotating black holes which is within the posterior probability # of GW150914. m = 45 # Solar masses hp, hc = get_td_waveform(approximant="SEOBNRv4_opt", mass1=m, mass2=m, delta_t=conditioned1.delta_t, f_lower=20) # We will resize the vector to match our data hp.resize(len(conditioned1)) # The waveform begins at the start of the vector, so if we want the # SNR time series to correspond to the approximate merger location # we need to shift the data so that the merger is approximately at the # first bin of the data. # The cyclic_time_shift method shifts the timeseries by a given amount of time. # It treats the data as if it were on a ring so points shifted off the end # of the series reappear at the start. Note that time stamps are *not* in # general affected (as the start time of the full array is shifted), # but the index of each point in the vector is. # # By convention waveforms returned from `get_td_waveform` have their # merger stamped with time zero, so we can use the start time to # shift the merger into position pylab.figure() pylab.title('Before shifting') pylab.plot(hp.sample_times, hp) pylab.xlabel('Time (s)') pylab.ylabel('Strain') template = hp.cyclic_time_shift(hp.start_time) pylab.figure() pylab.title('After shifting') pylab.plot(template.sample_times, template) pylab.xlabel('Time (s)') pylab.ylabel('Strain') # In[15]: from pycbc.filter import matched_filter import numpy snr = matched_filter(template, conditioned1, psd=psd1, low_frequency_cutoff=20) # Remove time corrupted by the template filter and the psd filter # We remove 4 seonds at the beginning and end for the PSD filtering # And we remove 4 additional seconds at the beginning to account for # the template length (this is somewhat generous for # so short a template). A longer signal such as from a BNS, would # require much more padding at the beginning of the vector. snr = snr.crop(4 + 4, 4) # Why are we taking an abs() here? # The `matched_filter` function actually returns a 'complex' SNR. # What that means is that the real portion correponds to the SNR # associated with directly filtering the template with the data. # The imaginary portion corresponds to filtering with a template that # is 90 degrees out of phase. Since the phase of a signal may be # anything, we choose to maximize over the phase of the signal. pylab.figure(figsize=[10, 4]) pylab.plot(snr.sample_times, abs(snr)) pylab.ylabel('Signal-to-noise') pylab.xlabel('Time (s)') pylab.show() peak = abs(snr).numpy().argmax() snrp = snr[peak] time = snr.sample_times[peak] print("We found a signal at {}s with SNR {}".format(time, abs(snrp))) # In[16]: from pycbc.filter import sigma # The time, amplitude, and phase of the SNR peak tell us how to align # our proposed signal with the data. # Shift the template to the peak time dt = time - conditioned1.start_time aligned = template.cyclic_time_shift(dt) # scale the template so that it would have SNR 1 in this data aligned /= sigma(aligned, psd=psd1, low_frequency_cutoff=20.0) # Scale the template amplitude and phase to the peak value aligned = (aligned.to_frequencyseries() * snrp).to_timeseries() aligned.start_time = conditioned1.start_time # In[17]: # We do it this way so that we can whiten both the template and the data white_data = (conditioned1.to_frequencyseries() / psd1**0.5).to_timeseries() white_template = (aligned.to_frequencyseries() / psd1**0.5).to_timeseries() white_data = white_data.highpass_fir(30., 512).lowpass_fir(300, 512) white_template = white_template.highpass_fir(30, 512).lowpass_fir(300, 512) # Select the time around the merger white_data = white_data.time_slice(time-.2, time+.1) white_template = white_template.time_slice(time-.2, time+.1) pylab.figure(figsize=[15, 3]) pylab.plot(white_data.sample_times, white_data, label="Data") pylab.plot(white_template.sample_times, white_template, label="Template") pylab.legend() pylab.show() # In[19]: subtracted = conditioned1 - aligned # Plot the original data and the subtracted signal data for data, title in [(conditioned1, 'Original H1 Data'), (subtracted, 'Signal Subtracted from H1 Data')]: t, f, p = data.whiten(4, 4).qtransform(.001, logfsteps=100, qrange=(8, 8), frange=(20, 512)) pylab.figure(figsize=[15, 3]) pylab.title(title) pylab.pcolormesh(t, f, p**0.5, vmin=1, vmax=6) pylab.yscale('log') pylab.xlabel('Time (s)') pylab.ylabel('Frequency (Hz)') pylab.xlim(time - 2, time + 1) pylab.show() # ### Analysis of PyCBC_T2_1.gwf # In[20]: get_ipython().run_line_magic('matplotlib', 'inline') import pylab from pycbc.catalog import Merger from pycbc.filter import resample_to_delta_t, highpass # As an example we use the GW150914 data file_name1 = "PyCBC_T2_1.gwf" channel_name1 = "H1:TEST-STRAIN" start = 0 end = start + 128 ts1 = read_frame(file_name1, channel_name1, start, end) # Remove the low frequency content and downsample the data to 2048Hz strain1 = highpass(ts1, 15.0) strain1 = resample_to_delta_t(strain1, 1.0/2048) pylab.plot(strain1.sample_times, strain1) pylab.xlabel('Time (s)') pylab.show() # In[21]: # Remove 2 seconds of data from both the beginning and end conditioned1 = strain1.crop(2, 2) pylab.plot(conditioned1.sample_times, conditioned1) pylab.xlabel('Time (s)') pylab.show() # In[22]: from pycbc.psd import interpolate, inverse_spectrum_truncation # Estimate the power spectral density # We use 4 second samples of our time series in Welch method. psd1 = conditioned1.psd(4) # Now that we have the psd we need to interpolate it to match our data # and then limit the filter length of 1 / PSD. After this, we can # directly use this PSD to filter the data in a controlled manner psd1 = interpolate(psd1, conditioned1.delta_f) # 1/PSD will now act as a filter with an effective length of 4 seconds # Since the data has been highpassed above 15 Hz, and will have low values # below this we need to inform the function to not include frequencies # below this frequency. psd1 = inverse_spectrum_truncation(psd1, 4 * conditioned1.sample_rate, low_frequency_cutoff=15) # In[23]: from pycbc.waveform import get_td_waveform # In this case we "know" what the signal parameters are. In a search # we would grid over the parameters and calculate the SNR time series # for each one # We'll assume equal masses, and non-rotating black holes which is within the posterior probability # of GW150914. m = 30 # Solar masses hp, hc = get_td_waveform(approximant="SEOBNRv4_opt", mass1=m, mass2=m, delta_t=conditioned1.delta_t, f_lower=20) # We will resize the vector to match our data hp.resize(len(conditioned1)) # The waveform begins at the start of the vector, so if we want the # SNR time series to correspond to the approximate merger location # we need to shift the data so that the merger is approximately at the # first bin of the data. # The cyclic_time_shift method shifts the timeseries by a given amount of time. # It treats the data as if it were on a ring so points shifted off the end # of the series reappear at the start. Note that time stamps are *not* in # general affected (as the start time of the full array is shifted), # but the index of each point in the vector is. # # By convention waveforms returned from `get_td_waveform` have their # merger stamped with time zero, so we can use the start time to # shift the merger into position pylab.figure() pylab.title('Before shifting') pylab.plot(hp.sample_times, hp) pylab.xlabel('Time (s)') pylab.ylabel('Strain') template = hp.cyclic_time_shift(hp.start_time) pylab.figure() pylab.title('After shifting') pylab.plot(template.sample_times, template) pylab.xlabel('Time (s)') pylab.ylabel('Strain') # In[24]: from pycbc.filter import matched_filter import numpy snr = matched_filter(template, conditioned1, psd=psd1, low_frequency_cutoff=20) # Remove time corrupted by the template filter and the psd filter # We remove 4 seonds at the beginning and end for the PSD filtering # And we remove 4 additional seconds at the beginning to account for # the template length (this is somewhat generous for # so short a template). A longer signal such as from a BNS, would # require much more padding at the beginning of the vector. snr = snr.crop(4 + 4, 4) # Why are we taking an abs() here? # The `matched_filter` function actually returns a 'complex' SNR. # What that means is that the real portion correponds to the SNR # associated with directly filtering the template with the data. # The imaginary portion corresponds to filtering with a template that # is 90 degrees out of phase. Since the phase of a signal may be # anything, we choose to maximize over the phase of the signal. pylab.figure(figsize=[10, 4]) pylab.plot(snr.sample_times, abs(snr)) pylab.ylabel('Signal-to-noise') pylab.xlabel('Time (s)') pylab.show() peak = abs(snr).numpy().argmax() snrp = snr[peak] time = snr.sample_times[peak] print("We found a signal at {}s with SNR {}".format(time, abs(snrp))) # In[25]: from pycbc.filter import sigma # The time, amplitude, and phase of the SNR peak tell us how to align # our proposed signal with the data. # Shift the template to the peak time dt = time - conditioned1.start_time aligned = template.cyclic_time_shift(dt) # scale the template so that it would have SNR 1 in this data aligned /= sigma(aligned, psd=psd1, low_frequency_cutoff=20.0) # Scale the template amplitude and phase to the peak value aligned = (aligned.to_frequencyseries() * snrp).to_timeseries() aligned.start_time = conditioned1.start_time # In[26]: # We do it this way so that we can whiten both the template and the data white_data = (conditioned1.to_frequencyseries() / psd1**0.5).to_timeseries() white_template = (aligned.to_frequencyseries() / psd1**0.5).to_timeseries() white_data = white_data.highpass_fir(30., 512).lowpass_fir(300, 512) white_template = white_template.highpass_fir(30, 512).lowpass_fir(300, 512) # Select the time around the merger white_data = white_data.time_slice(time-.2, time+.1) white_template = white_template.time_slice(time-.2, time+.1) pylab.figure(figsize=[15, 3]) pylab.plot(white_data.sample_times, white_data, label="Data") pylab.plot(white_template.sample_times, white_template, label="Template") pylab.legend() pylab.show() # In[27]: subtracted = conditioned1 - aligned # Plot the original data and the subtracted signal data for data, title in [(conditioned1, 'Original H1 Data'), (subtracted, 'Signal Subtracted from H1 Data')]: t, f, p = data.whiten(4, 4).qtransform(.001, logfsteps=100, qrange=(8, 8), frange=(20, 512)) pylab.figure(figsize=[15, 3]) pylab.title(title) pylab.pcolormesh(t, f, p**0.5, vmin=1, vmax=6) pylab.yscale('log') pylab.xlabel('Time (s)') pylab.ylabel('Frequency (Hz)') pylab.xlim(time - 2, time + 1) pylab.show() # ### Analysis of PyCBC_T2_2.gwf # In[28]: get_ipython().run_line_magic('matplotlib', 'inline') import pylab from pycbc.catalog import Merger from pycbc.filter import resample_to_delta_t, highpass # As an example we use the GW150914 data file_name1 = "PyCBC_T2_2.gwf" channel_name1 = "H1:TEST-STRAIN" start = 0 end = start + 128 ts1 = read_frame(file_name1, channel_name1, start, end) # Remove the low frequency content and downsample the data to 2048Hz strain1 = highpass(ts1, 15.0) strain1 = resample_to_delta_t(strain1, 1.0/2048) pylab.plot(strain1.sample_times, strain1) pylab.xlabel('Time (s)') pylab.show() # In[29]: # Remove 2 seconds of data from both the beginning and end conditioned1 = strain1.crop(2, 2) pylab.plot(conditioned1.sample_times, conditioned1) pylab.xlabel('Time (s)') pylab.show() # In[30]: from pycbc.psd import interpolate, inverse_spectrum_truncation # Estimate the power spectral density # We use 4 second samples of our time series in Welch method. psd1 = conditioned1.psd(4) # Now that we have the psd we need to interpolate it to match our data # and then limit the filter length of 1 / PSD. After this, we can # directly use this PSD to filter the data in a controlled manner psd1 = interpolate(psd1, conditioned1.delta_f) # 1/PSD will now act as a filter with an effective length of 4 seconds # Since the data has been highpassed above 15 Hz, and will have low values # below this we need to inform the function to not include frequencies # below this frequency. psd1 = inverse_spectrum_truncation(psd1, 4 * conditioned1.sample_rate, low_frequency_cutoff=15) # In[31]: from pycbc.waveform import get_td_waveform # In this case we "know" what the signal parameters are. In a search # we would grid over the parameters and calculate the SNR time series # for each one # We'll assume equal masses, and non-rotating black holes which is within the posterior probability # of GW150914. m = 15 # Solar masses hp, hc = get_td_waveform(approximant="SEOBNRv4_opt", mass1=m, mass2=m, delta_t=conditioned1.delta_t, f_lower=20) # We will resize the vector to match our data hp.resize(len(conditioned1)) # The waveform begins at the start of the vector, so if we want the # SNR time series to correspond to the approximate merger location # we need to shift the data so that the merger is approximately at the # first bin of the data. # The cyclic_time_shift method shifts the timeseries by a given amount of time. # It treats the data as if it were on a ring so points shifted off the end # of the series reappear at the start. Note that time stamps are *not* in # general affected (as the start time of the full array is shifted), # but the index of each point in the vector is. # # By convention waveforms returned from `get_td_waveform` have their # merger stamped with time zero, so we can use the start time to # shift the merger into position pylab.figure() pylab.title('Before shifting') pylab.plot(hp.sample_times, hp) pylab.xlabel('Time (s)') pylab.ylabel('Strain') template = hp.cyclic_time_shift(hp.start_time) pylab.figure() pylab.title('After shifting') pylab.plot(template.sample_times, template) pylab.xlabel('Time (s)') pylab.ylabel('Strain') # In[32]: from pycbc.filter import matched_filter import numpy snr = matched_filter(template, conditioned1, psd=psd1, low_frequency_cutoff=20) # Remove time corrupted by the template filter and the psd filter # We remove 4 seonds at the beginning and end for the PSD filtering # And we remove 4 additional seconds at the beginning to account for # the template length (this is somewhat generous for # so short a template). A longer signal such as from a BNS, would # require much more padding at the beginning of the vector. snr = snr.crop(4 + 4, 4) # Why are we taking an abs() here? # The `matched_filter` function actually returns a 'complex' SNR. # What that means is that the real portion correponds to the SNR # associated with directly filtering the template with the data. # The imaginary portion corresponds to filtering with a template that # is 90 degrees out of phase. Since the phase of a signal may be # anything, we choose to maximize over the phase of the signal. pylab.figure(figsize=[10, 4]) pylab.plot(snr.sample_times, abs(snr)) pylab.ylabel('Signal-to-noise') pylab.xlabel('Time (s)') pylab.show() peak = abs(snr).numpy().argmax() snrp = snr[peak] time = snr.sample_times[peak] print("We found a signal at {}s with SNR {}".format(time, abs(snrp))) # In[33]: from pycbc.filter import sigma # The time, amplitude, and phase of the SNR peak tell us how to align # our proposed signal with the data. # Shift the template to the peak time dt = time - conditioned1.start_time aligned = template.cyclic_time_shift(dt) # scale the template so that it would have SNR 1 in this data aligned /= sigma(aligned, psd=psd1, low_frequency_cutoff=20.0) # Scale the template amplitude and phase to the peak value aligned = (aligned.to_frequencyseries() * snrp).to_timeseries() aligned.start_time = conditioned1.start_time # In[34]: # We do it this way so that we can whiten both the template and the data white_data = (conditioned1.to_frequencyseries() / psd1**0.5).to_timeseries() white_template = (aligned.to_frequencyseries() / psd1**0.5).to_timeseries() white_data = white_data.highpass_fir(30., 512).lowpass_fir(300, 512) white_template = white_template.highpass_fir(30, 512).lowpass_fir(300, 512) # Select the time around the merger white_data = white_data.time_slice(time-.2, time+.1) white_template = white_template.time_slice(time-.2, time+.1) pylab.figure(figsize=[15, 3]) pylab.plot(white_data.sample_times, white_data, label="Data") pylab.plot(white_template.sample_times, white_template, label="Template") pylab.legend() pylab.show() # In[35]: subtracted = conditioned1 - aligned # Plot the original data and the subtracted signal data for data, title in [(conditioned1, 'Original H1 Data'), (subtracted, 'Signal Subtracted from H1 Data')]: t, f, p = data.whiten(4, 4).qtransform(.001, logfsteps=100, qrange=(8, 8), frange=(20, 512)) pylab.figure(figsize=[15, 3]) pylab.title(title) pylab.pcolormesh(t, f, p**0.5, vmin=1, vmax=6) pylab.yscale('log') pylab.xlabel('Time (s)') pylab.ylabel('Frequency (Hz)') pylab.xlim(time - 2, time + 1) pylab.show()

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Python

src/HartreeParticleDSL/test/backends/C_AOS/test_C_AOS_op_precedence.py

stfc/HartreeParticleDSL

17990f1a85c9cbec3c4dfa0923e2c44cad6f381c

[ "MIT" ]

null

src/HartreeParticleDSL/test/backends/C_AOS/test_C_AOS_op_precedence.py

stfc/HartreeParticleDSL

17990f1a85c9cbec3c4dfa0923e2c44cad6f381c

[ "MIT" ]

47

2021-09-16T10:28:05.000Z

2022-03-15T14:24:33.000Z

src/HartreeParticleDSL/test/backends/C_AOS/test_C_AOS_op_precedence.py

stfc/HartreeParticleDSL

17990f1a85c9cbec3c4dfa0923e2c44cad6f381c

[ "MIT" ]

1

2021-09-27T15:20:01.000Z

from HartreeParticleDSL.backends.C_AOS.visitors import * from HartreeParticleDSL.backends.C_AOS.C_AOS import * import ast import inspect import textwrap import pytest from HartreeParticleDSL.HartreeParticleDSLExceptions import IllegalLoopError, UnsupportedCodeError, \ IllegalArgumentCountError import HartreeParticleDSL.HartreeParticleDSL as HartreeParticleDSL def test_plus_mul(): '''Test the plus_mul order for C_AOS''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = 1 + 2 * 3 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( 1 + ( 2 * 3 ) )" in out def test_plus_div(): '''Test the plus_div order for C_AOS''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = 1 + 2 / 3 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( 1 + ( 2 / 3 ) )" in out def test_mul_div(): '''Test the mul_div order for C_AOS''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = 1 * 2 / 3 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( ( 1 * 2 ) / 3 )" in out def a(): b = 1 / 2 * 3 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( ( 1 / 2 ) * 3 )" in out def test_bracket_plus_mul(): '''Test how brackets affect the plus_mul order for C_AOS''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = (1 + 2) * 3 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( ( 1 + 2 ) * 3 )" in out def test_bracket_plus_div(): '''Test how brackets affect the plus_div order for C_AOS''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = (1 + 2) / 3 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( ( 1 + 2 ) / 3 )" in out def test_gte_plus(): '''Test order of gte and plus for C_AOS''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = 3 >= 1 + 2 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( 3 >= ( 1 + 2 ) )" in out def test_gt_plus(): '''Test order of gt and plus for C_AOS''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = 3 > 1 + 2 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( 3 > ( 1 + 2 ) )" in out def test_lt_plus(): '''Test order of lt and plus for C_AOS''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = 3 < 1 + 2 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( 3 < ( 1 + 2 ) )" in out def test_lte_plus(): '''Test order of lte and plus for C_AOS''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = 3 <= 1 + 2 c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "( 3 <= ( 1 + 2 ) )" in out def test_not_land(): '''Test order of not and l_and works''' aos = C_AOS() aos.disable_variable_checks() HartreeParticleDSL.set_backend(aos) v = c_visitor(aos) def a(): b = not z and y c = ast.parse(textwrap.dedent(inspect.getsource(a))) out = v.visit(c) assert "!z && y" in out

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py

Python

pysal/spreg/tests/test_ml_lag.py

cubensys/pysal

8d50990f6e6603ba79ae1a887a20a1e3a0734e51

[ "MIT", "BSD-3-Clause" ]

null

pysal/spreg/tests/test_ml_lag.py

cubensys/pysal

8d50990f6e6603ba79ae1a887a20a1e3a0734e51

[ "MIT", "BSD-3-Clause" ]

null

pysal/spreg/tests/test_ml_lag.py

cubensys/pysal

8d50990f6e6603ba79ae1a887a20a1e3a0734e51

[ "MIT", "BSD-3-Clause" ]

1

2021-07-19T01:46:17.000Z

import unittest import pysal import scipy import numpy as np from pysal.spreg.ml_lag import ML_Lag from pysal.spreg import utils from pysal.common import RTOL from skip import SKIP @unittest.skipIf(SKIP, "Skipping MLLag Tests") class TestMLError(unittest.TestCase): def setUp(self): db = pysal.open(pysal.examples.get_path("baltim.dbf"),'r') self.ds_name = "baltim.dbf" self.y_name = "PRICE" self.y = np.array(db.by_col(self.y_name)).T self.y.shape = (len(self.y),1) self.x_names = ["NROOM","AGE","SQFT"] self.x = np.array([db.by_col(var) for var in self.x_names]).T ww = pysal.open(pysal.examples.get_path("baltim_q.gal")) self.w = ww.read() ww.close() self.w_name = "baltim_q.gal" self.w.transform = 'r' def _estimate_and_compare(self, **kwargs): reg = ML_Lag(self.y, self.x, w=self.w, name_y=self.y_name, name_x=self.x_names, name_w=self.w_name,name_ds=self.ds_name, **kwargs) betas = np.array([[-6.04040164], [ 3.48995114], [-0.20103955], [ 0.65462382], [ 0.62351143]]) np.testing.assert_allclose(reg.betas,betas,RTOL) u = np.array([ 47.51218398]) np.testing.assert_allclose(reg.u[0],u,RTOL) predy = np.array([-0.51218398]) np.testing.assert_allclose(reg.predy[0],predy,RTOL) n = 211 np.testing.assert_allclose(reg.n,n,RTOL) k = 5 np.testing.assert_allclose(reg.k,k,RTOL) y = np.array([ 47.]) np.testing.assert_allclose(reg.y[0],y,RTOL) x = np.array([ 1. , 4. , 148. , 11.25]) np.testing.assert_allclose(reg.x[0],x,RTOL) e = np.array([ 41.99251608]) np.testing.assert_allclose(reg.e_pred[0],e,RTOL) my = 44.307180094786695 np.testing.assert_allclose(reg.mean_y,my) sy = 23.606076835380495 np.testing.assert_allclose(reg.std_y,sy) vm = np.array([ 28.57288755, 1.42341656, 0.00288068, 0.02956392, 0.00332139]) np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL) sig2 = 216.27525647243797 np.testing.assert_allclose(reg.sig2,sig2,RTOL) pr2 = 0.6133020721559487 np.testing.assert_allclose(reg.pr2,pr2) std_err = np.array([ 5.34536131, 1.19307022, 0.05367198, 0.17194162, 0.05763147]) np.testing.assert_allclose(reg.std_err,std_err,RTOL) logll = -875.92771143484833 np.testing.assert_allclose(reg.logll,logll,RTOL) aic = 1761.8554228696967 np.testing.assert_allclose(reg.aic,aic,RTOL) schwarz = 1778.614713537077 np.testing.assert_allclose(reg.schwarz,schwarz,RTOL) def test_dense(self): self._estimate_and_compare(method='FULL') def test_ord(self): reg = ML_Lag(self.y, self.x, w=self.w, name_y=self.y_name, name_x=self.x_names, name_w=self.w_name,name_ds=self.ds_name, method='ORD') betas = np.array([[-6.04040164], [ 3.48995114], [-0.20103955], [ 0.65462382], [ 0.62351143]]) np.testing.assert_allclose(reg.betas,betas,RTOL) u = np.array([ 47.51218398]) np.testing.assert_allclose(reg.u[0],u,RTOL) predy = np.array([-0.51218398]) np.testing.assert_allclose(reg.predy[0],predy,RTOL) n = 211 np.testing.assert_allclose(reg.n,n,RTOL) k = 5 np.testing.assert_allclose(reg.k,k,RTOL) y = np.array([ 47.]) np.testing.assert_allclose(reg.y[0],y,RTOL) x = np.array([ 1. , 4. , 148. , 11.25]) np.testing.assert_allclose(reg.x[0],x,RTOL) e = np.array([ 41.99251608]) np.testing.assert_allclose(reg.e_pred[0],e,RTOL) my = 44.307180094786695 np.testing.assert_allclose(reg.mean_y,my) sy = 23.606076835380495 np.testing.assert_allclose(reg.std_y,sy) vm = np.array([ 28.63404, 1.423698, 0.002884738, 0.02957845, 0.003379166]) np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL) sig2 = 216.27525647243797 np.testing.assert_allclose(reg.sig2,sig2,RTOL) pr2 = 0.6133020721559487 np.testing.assert_allclose(reg.pr2,pr2) std_err = np.array([ 5.351078, 1.193188, 0.05371, 0.171984, 0.058131]) np.testing.assert_allclose(reg.std_err,std_err,RTOL) logll = -875.92771143484833 np.testing.assert_allclose(reg.logll,logll,RTOL) aic = 1761.8554228696967 np.testing.assert_allclose(reg.aic,aic,RTOL) schwarz = 1778.614713537077 np.testing.assert_allclose(reg.schwarz,schwarz,RTOL) def test_LU(self): self._estimate_and_compare(method='LU') if __name__ == '__main__': unittest.main()

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py

Python

Snowflake/__init__.py

leonardozcm/Point-Completion-Fig-AutoGenerator

109f5a414f51469fac82d0d23cde69efb9cf97e0

[ "Apache-2.0" ]

31

2021-08-22T15:01:58.000Z

2022-03-19T12:26:21.000Z

models/__init__.py

AllenXiangX/PMP-Net

c6a65da629f0faafd3b1e2dd060e84ab53b9379f

[ "MIT" ]

10

2021-09-06T09:07:38.000Z

2022-02-12T08:12:54.000Z

models/__init__.py

leonardozcm/SnowflakeNet

93e7151610765e7e2b41ace2d03c8750f0b6c80c

[ "MIT" ]

5

2021-08-30T00:53:17.000Z

2022-03-20T11:57:25.000Z

import sys sys.path.append('../pointnet2_ops_lib') sys.path.append('..')

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py

Python

testing/unit2_test.py

cerrno/swe-talk-materials

0f11e34a850959202d2e5fb2ecd71416195ddc7c

[ "MIT" ]

null

testing/unit2_test.py

cerrno/swe-talk-materials

0f11e34a850959202d2e5fb2ecd71416195ddc7c

[ "MIT" ]

null

testing/unit2_test.py

cerrno/swe-talk-materials

0f11e34a850959202d2e5fb2ecd71416195ddc7c

[ "MIT" ]

null

from unit2 import palindrome def test_palindrome(): assert palindrome('test') == 0 assert palindrome('kayak') == 1

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66

py

Python

acq4/analysis/modules/AtlasBuilder/__init__.py

aleonlein/acq4

4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555

[ "MIT" ]

47

2015-01-05T16:18:10.000Z

2022-03-16T13:09:30.000Z

acq4/analysis/modules/AtlasBuilder/__init__.py

aleonlein/acq4

4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555

[ "MIT" ]

48

2015-04-19T16:51:41.000Z

2022-03-31T14:48:16.000Z

acq4/analysis/modules/AtlasBuilder/__init__.py

sensapex/acq4

9561ba73caff42c609bd02270527858433862ad8

[ "MIT" ]

32

2015-01-15T14:11:49.000Z

2021-07-15T13:44:52.000Z

from __future__ import print_function from .AtlasBuilder import *

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py

Python

tensorflow/python/ops/ragged/ragged_to_tensor_op_test.py

MathMachado/tensorflow

56afda20b15f234c23e8393f7e337e7dd2659c2d

[ "Apache-2.0" ]

848

2019-12-03T00:16:17.000Z

2022-03-31T22:53:17.000Z

tensorflow/python/ops/ragged/ragged_to_tensor_op_test.py

MathMachado/tensorflow

56afda20b15f234c23e8393f7e337e7dd2659c2d

[ "Apache-2.0" ]

656

2019-12-03T00:48:46.000Z

2022-03-31T18:41:54.000Z

tensorflow/python/ops/ragged/ragged_to_tensor_op_test.py

MathMachado/tensorflow

56afda20b15f234c23e8393f7e337e7dd2659c2d

[ "Apache-2.0" ]

506

2019-12-03T00:46:26.000Z

2022-03-30T10:34:56.000Z

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for ragged.to_tensor.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops.ragged import ragged_conversion_ops from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor from tensorflow.python.platform import googletest @test_util.run_all_in_graph_and_eager_modes class RaggedTensorToTensorOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): def testDocStringExamples(self): """Example from ragged_to_tensor.__doc__.""" rt = ragged_factory_ops.constant([[9, 8, 7], [], [6, 5], [4]]) dt = rt.to_tensor() self.assertAllEqual(dt, [[9, 8, 7], [0, 0, 0], [6, 5, 0], [4, 0, 0]]) @parameterized.parameters( { 'rt_input': [], 'ragged_rank': 1, 'expected': [], 'expected_shape': [0, 0], }, { 'rt_input': [[1, 2, 3], [], [4], [5, 6]], 'expected': [[1, 2, 3], [0, 0, 0], [4, 0, 0], [5, 6, 0]] }, { 'rt_input': [[1, 2, 3], [], [4], [5, 6]], 'default': 9, 'expected': [[1, 2, 3], [9, 9, 9], [4, 9, 9], [5, 6, 9]] }, { 'rt_input': [[[1], [2], [3]], [], [[4]], [[5], [6]]], 'ragged_rank': 1, 'default': [9], 'expected': [[[1], [2], [3]], [[9], [9], [9]], [[4], [9], [9]], [[5], [6], [9]]] }, { 'rt_input': [[[1, 2], [], [3, 4]], [], [[5]], [[6, 7], [8]]], 'expected': [ [[1, 2], [0, 0], [3, 4]], # [[0, 0], [0, 0], [0, 0]], # [[5, 0], [0, 0], [0, 0]], # [[6, 7], [8, 0], [0, 0]], # ] }, { 'rt_input': [[[1, 2], [], [3, 4]], [], [[5]], [[6, 7], [8]]], 'default': 9, 'expected': [ [[1, 2], [9, 9], [3, 4]], # [[9, 9], [9, 9], [9, 9]], # [[5, 9], [9, 9], [9, 9]], # [[6, 7], [8, 9], [9, 9]], # ] }, { 'rt_input': [[[1], [2], [3]]], 'ragged_rank': 1, 'default': 0, 'expected': [[[1], [2], [3]]], }, { 'rt_input': [[[[1], [2]], [], [[3]]]], 'default': 9, 'expected': [[[[1], [2]], [[9], [9]], [[3], [9]]]], }, ) def testRaggedTensorToTensor(self, rt_input, expected, ragged_rank=None, default=None, expected_shape=None): rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank) dt = rt.to_tensor(default) self.assertIsInstance(dt, ops.Tensor) self.assertEqual(rt.dtype, dt.dtype) self.assertTrue(dt.shape.is_compatible_with(rt.shape)) if expected_shape is not None: expected = np.ndarray(expected_shape, buffer=np.array(expected)) self.assertAllEqual(dt, expected) @parameterized.parameters( { 'rt_input': [[1, 2, 3]], 'default': [0], 'error': (ValueError, r'Shape $1,$ must have rank at most 0'), }, { 'rt_input': [[[1, 2], [3, 4]], [[5, 6]]], 'ragged_rank': 1, 'default': [7, 8, 9], 'error': (ValueError, r'Shapes $3,$ and $2,$ are incompatible'), }, { 'rt_input': [[1, 2, 3]], 'default': 'a', 'error': (TypeError, '.*'), }, ) def testError(self, rt_input, default, error, ragged_rank=None): rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank) with self.assertRaisesRegexp(error[0], error[1]): rt.to_tensor(default) # This covers the tests above, but with the new implementation. @test_util.run_all_in_graph_and_eager_modes class RaggedTensorToTensorOpNewTest(test_util.TensorFlowTestCase, parameterized.TestCase): def testDocStringExamples(self): """Example from ragged_to_tensor.__doc__.""" rt = ragged_factory_ops.constant([[9, 8, 7], [], [6, 5], [4]]) dt = ragged_conversion_ops.ragged_to_dense(rt) self.assertAllEqual(dt, [[9, 8, 7], [0, 0, 0], [6, 5, 0], [4, 0, 0]]) @parameterized.parameters( { 'rt_input': [], 'ragged_rank': 1, 'expected': [], 'expected_shape': [0, 0], }, { 'rt_input': [[1, 2, 3], [], [4], [5, 6]], 'expected': [[1, 2, 3], [0, 0, 0], [4, 0, 0], [5, 6, 0]] }, { 'rt_input': [[1, 2, 3], [], [4], [5, 6]], 'default': 9, 'expected': [[1, 2, 3], [9, 9, 9], [4, 9, 9], [5, 6, 9]] }, { 'rt_input': [[[1], [2], [3]], [], [[4]], [[5], [6]]], 'ragged_rank': 1, 'default': [9], 'expected': [[[1], [2], [3]], [[9], [9], [9]], [[4], [9], [9]], [[5], [6], [9]]] }, { 'rt_input': [[[1, 2], [], [3, 4]], [], [[5]], [[6, 7], [8]]], 'expected': [ [[1, 2], [0, 0], [3, 4]], # [[0, 0], [0, 0], [0, 0]], # [[5, 0], [0, 0], [0, 0]], # [[6, 7], [8, 0], [0, 0]], # ] }, { 'rt_input': [[[1, 2], [], [3, 4]], [], [[5]], [[6, 7], [8]]], 'default': 9, 'expected': [ [[1, 2], [9, 9], [3, 4]], # [[9, 9], [9, 9], [9, 9]], # [[5, 9], [9, 9], [9, 9]], # [[6, 7], [8, 9], [9, 9]], # ] }, { 'rt_input': [[[1], [2], [3]]], 'ragged_rank': 1, 'default': 0, 'expected': [[[1], [2], [3]]], }, { 'rt_input': [[[[1], [2]], [], [[3]]]], 'default': 9, 'expected': [[[[1], [2]], [[9], [9]], [[3], [9]]]], }, ) def testRaggedTensorToTensor(self, rt_input, expected, ragged_rank=None, default=None, expected_shape=None): rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank) dt = ragged_conversion_ops.ragged_to_dense(rt, default_value=default) self.assertIsInstance(dt, ops.Tensor) self.assertEqual(rt.dtype, dt.dtype) self.assertTrue(dt.shape.is_compatible_with(rt.shape)) if expected_shape is not None: expected = np.ndarray(expected_shape, buffer=np.array(expected)) self.assertAllEqual(dt, expected) @parameterized.parameters( { 'rt_input': [[1, 2, 3]], 'default': 'a', 'error': (TypeError, '.*'), }, { 'rt_input': [[1, 2, 3]], 'default': 'b', 'error': (TypeError, '.*'), }) def testError(self, rt_input, default, error, ragged_rank=None): rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank) with self.assertRaisesRegexp(error[0], error[1]): ragged_conversion_ops.ragged_to_dense(rt, default_value=default) @test_util.run_all_in_graph_and_eager_modes class RaggedToTensorOpAdditionalTests(test_util.TensorFlowTestCase): def _compare_to_reference(self, ragged_tensor, expected=None, default_value=None): treatment = ragged_conversion_ops.ragged_to_dense( ragged_tensor, default_value=default_value) control = ragged_tensor.to_tensor(default_value=default_value) self.assertAllEqual(control, treatment) if expected is not None: self.assertAllEqual(expected, treatment) def test_already_dense_simple(self): """This studies a tensor initialized with value_rowids and nrows.""" input_data = RaggedTensor.from_value_rowids( values=constant_op.constant([6, 7, 8, 9, 10, 11], dtype=dtypes.int64), value_rowids=constant_op.constant([0, 0, 0, 1, 1, 1], dtype=dtypes.int64), nrows=constant_op.constant(2, dtype=dtypes.int64), validate=True) self._compare_to_reference(input_data, [[6, 7, 8], [9, 10, 11]]) def test_already_dense_with_dense_values_and_default(self): """This studies a tensor initialized with value_rowids and nrows.""" input_data = RaggedTensor.from_value_rowids( values=constant_op.constant( [[6, 7], [8, 9], [10, 11], [12, 13], [14, 15], [16, 17]], dtype=dtypes.int64), value_rowids=constant_op.constant([0, 0, 0, 1, 1, 1], dtype=dtypes.int64), nrows=constant_op.constant(2, dtype=dtypes.int64), validate=True) self._compare_to_reference( input_data, [[[6, 7], [8, 9], [10, 11]], [[12, 13], [14, 15], [16, 17]]], default_value=constant_op.constant([31, 32], dtype=dtypes.int64)) def test_already_dense_with_dense_values(self): """This studies a tensor initialized with value_rowids and nrows.""" input_data = RaggedTensor.from_value_rowids( values=constant_op.constant( [[6, 7], [8, 9], [10, 11], [12, 13], [14, 15], [16, 17]], dtype=dtypes.int64), value_rowids=constant_op.constant([0, 0, 0, 1, 1, 1], dtype=dtypes.int64), nrows=constant_op.constant(2, dtype=dtypes.int64), validate=True) self._compare_to_reference( input_data, [[[6, 7], [8, 9], [10, 11]], [[12, 13], [14, 15], [16, 17]]]) def test_ragged_with_dense_values_and_default(self): """This studies a tensor initialized with value_rowids and nrows.""" input_data = RaggedTensor.from_value_rowids( values=constant_op.constant( [[6, 7], [8, 9], [10, 11], [12, 13], [14, 15]], dtype=dtypes.int64), value_rowids=constant_op.constant([0, 0, 0, 1, 1], dtype=dtypes.int64), nrows=constant_op.constant(2, dtype=dtypes.int64), validate=True) self._compare_to_reference( input_data, [[[6, 7], [8, 9], [10, 11]], [[12, 13], [14, 15], [2, 3]]], default_value=[2, 3]) def test_ragged_with_dense_values_and_small_default(self): """This studies a tensor initialized with value_rowids and nrows.""" input_data = RaggedTensor.from_value_rowids( values=constant_op.constant( [[6, 7], [8, 9], [10, 11], [12, 13], [14, 15]], dtype=dtypes.int64), value_rowids=constant_op.constant([0, 0, 0, 1, 1], dtype=dtypes.int64), nrows=constant_op.constant(2, dtype=dtypes.int64), validate=True) self._compare_to_reference( input_data, [[[6, 7], [8, 9], [10, 11]], [[12, 13], [14, 15], [2, 2]]], default_value=2) def test_already_dense_with_dense_values_string(self): """This studies a tensor initialized with value_rowids and nrows.""" input_data = RaggedTensor.from_value_rowids( values=constant_op.constant( [[b'a', b'b'], [b'c', b'd'], [b'e', b'f'], [b'g', b'jalapeno'], [b'kangaroo', b'llama'], [b'manzana', b'nectar']], dtype=dtypes.string), value_rowids=constant_op.constant([0, 0, 0, 1, 1, 1], dtype=dtypes.int64), nrows=constant_op.constant(2, dtype=dtypes.int64), validate=True) self._compare_to_reference(input_data, [[[b'a', b'b'], [b'c', b'd'], [b'e', b'f']], [[b'g', b'jalapeno'], [b'kangaroo', b'llama'], [b'manzana', b'nectar']]]) def test_already_dense_with_string(self): """This studies a tensor initialized with value_rowids and nrows.""" input_data = RaggedTensor.from_value_rowids( values=constant_op.constant( ['a', 'b', 'c', 'd', 'e', 'antidisestablishmentarianism'], dtype=dtypes.string), value_rowids=constant_op.constant([0, 0, 0, 1, 1, 1], dtype=dtypes.int64), nrows=constant_op.constant(2, dtype=dtypes.int64), validate=True) self._compare_to_reference( input_data, [[b'a', b'b', b'c'], [b'd', b'e', b'antidisestablishmentarianism']]) def test_already_dense(self): input_data = ragged_factory_ops.constant([[0, 1, 2], [3, 4, 5]]) self._compare_to_reference(input_data, [[0, 1, 2], [3, 4, 5]]) def test_true_ragged(self): input_data = ragged_factory_ops.constant([[0, 1, 2], [], [3]]) self._compare_to_reference(input_data, [[0, 1, 2], [0, 0, 0], [3, 0, 0]]) def test_true_ragged_default_3(self): input_data = ragged_factory_ops.constant([[0, 1, 2], [], [3]]) self._compare_to_reference( input_data, [[0, 1, 2], [3, 3, 3], [3, 3, 3]], default_value=3) def test_three_dimensional_ragged(self): input_data = ragged_factory_ops.constant([[[0, 1, 2], []], [], [[3]]]) self._compare_to_reference( input_data, [[[0, 1, 2], [3, 3, 3]], [[3, 3, 3], [3, 3, 3]], [[3, 3, 3], [3, 3, 3]]], default_value=3) def test_empty_tensor(self): input_data = RaggedTensor.from_value_rowids( values=constant_op.constant([], dtype=dtypes.int64), value_rowids=constant_op.constant([], dtype=dtypes.int64), nrows=constant_op.constant(2, dtype=dtypes.int64), validate=True) self._compare_to_reference(input_data, [[], []], default_value=3) def test_empty_last(self): input_data = ragged_factory_ops.constant([[0, 1, 2], [], [3], []]) self._compare_to_reference(input_data, [[0, 1, 2], [0, 0, 0], [3, 0, 0], [0, 0, 0]]) def test_shape_limit(self): input_data = ragged_factory_ops.constant([[0, 1, 2, 3], [], [4], []]) actual = ragged_conversion_ops.ragged_to_dense(input_data, shape=[2, 3]) self.assertAllEqual(actual, [[0, 1, 2], [0, 0, 0]]) self.assertEqual(actual.shape.as_list(), [2, 3]) def test_shape_limit_tuple(self): input_data = ragged_factory_ops.constant([[0, 1, 2, 3], [], [4], []]) actual = ragged_conversion_ops.ragged_to_dense(input_data, shape=(2, 3)) self.assertAllEqual(actual, [[0, 1, 2], [0, 0, 0]]) self.assertEqual(actual.shape.as_list(), [2, 3]) def test_shape_limit_tensor_shape(self): input_data = ragged_factory_ops.constant([[0, 1, 2, 3], [], [4], []]) actual = ragged_conversion_ops.ragged_to_dense( input_data, shape=tensor_shape.TensorShape([2, 3])) self.assertAllEqual(actual, [[0, 1, 2], [0, 0, 0]]) self.assertEqual(actual.shape.as_list(), [2, 3]) def test_shape_half_limit_tensor_shape(self): input_data = ragged_factory_ops.constant([[0, 1, 2, 3], [], [4], []]) actual = ragged_conversion_ops.ragged_to_dense( input_data, shape=tensor_shape.TensorShape([2, None])) self.assertAllEqual(actual, [[0, 1, 2, 3], [0, 0, 0, 0]]) def test_skip_eager_shape_half_limit_tensor_shape(self): # Eager would produce a shape of [2, 4] input_data = ragged_factory_ops.constant([[0, 1, 2, 3], [], [4], []]) actual = ragged_conversion_ops.ragged_to_dense( input_data, shape=tensor_shape.TensorShape([2, None])) result = actual.shape.as_list() # This is equal to [2, 4] in eager, or [2, None] in non-eager. self.assertEqual(result[0], 2) def test_shape_limit_shape_is_tensor_int64(self): input_data = ragged_factory_ops.constant([[0, 1, 2, 3], [], [4], []]) actual = ragged_conversion_ops.ragged_to_dense( input_data, shape=constant_op.constant([2, 3], dtype=dtypes.int64)) self.assertAllEqual(actual, [[0, 1, 2], [0, 0, 0]]) self.assertEqual(actual.shape.as_list(), [2, 3]) def test_shape_limit_shape_is_tensor_int32(self): input_data = ragged_factory_ops.constant([[0, 1, 2, 3], [], [4], []]) actual = ragged_conversion_ops.ragged_to_dense( input_data, shape=constant_op.constant([2, 3], dtype=dtypes.int32)) self.assertAllEqual(actual, [[0, 1, 2], [0, 0, 0]]) self.assertEqual(actual.shape.as_list(), [2, 3]) def test_shape_expand_first_dim(self): input_data = ragged_factory_ops.constant([[0, 1, 2], [], [3]]) actual = ragged_conversion_ops.ragged_to_dense(input_data, shape=[4, 4]) self.assertAllEqual( actual, [[0, 1, 2, 0], [0, 0, 0, 0], [3, 0, 0, 0], [0, 0, 0, 0]]) self.assertEqual(actual.shape.as_list(), [4, 4]) def test_value_transposed(self): # This test tries to get a tensor in columnar format, where I am uncertain # as to whether the underlying op, which copies data in the raw format, # could fail. my_value = array_ops.transpose( constant_op.constant([[0, 1, 2, 3], [4, 5, 6, 7]])) input_data = RaggedTensor.from_value_rowids( values=my_value, value_rowids=constant_op.constant([0, 1, 2, 3], dtype=dtypes.int64), nrows=constant_op.constant(4, dtype=dtypes.int64), validate=True) self._compare_to_reference(input_data, [[[0, 4]], [[1, 5]], [[2, 6]], [[3, 7]]]) # This fails on the older version of to_tensor. def test_broadcast_default(self): # This test is commented out. The functionality here is not supported. # The dense dimension here is 2 x 2 input_data = ragged_factory_ops.constant([[[[1, 2], [3, 4]]], []], ragged_rank=1) # This placeholder has a 2 x 1 dimension. default_value = array_ops.placeholder_with_default([[5], [6]], shape=None) actual = ragged_conversion_ops.ragged_to_dense( input_data, default_value=default_value) expected = [[[[1, 2], [3, 4]]], [[[5, 5], [6, 6]]]] self.assertAllEqual(actual, expected) # This fails on the older version of to_tensor. def test_broadcast_default_no_placeholder(self): # Again, this functionality is not supported. It fails more gracefully # when creating the op. input_data = ragged_factory_ops.constant([[[[1, 2], [3, 4]]], []], ragged_rank=1) # default_value has a 2 x 1 dimension. default_value = constant_op.constant([[5], [6]], shape=None) actual = ragged_conversion_ops.ragged_to_dense( input_data, default_value=default_value) expected = [[[[1, 2], [3, 4]]], [[[5, 5], [6, 6]]]] self.assertAllEqual(actual, expected) def test_shape_expand_second_dim(self): input_data = ragged_factory_ops.constant([[0, 1, 2], [], [3], []]) actual = ragged_conversion_ops.ragged_to_dense(input_data, shape=[3, 4]) self.assertAllEqual(actual, [[0, 1, 2, 0], [0, 0, 0, 0], [3, 0, 0, 0]]) def test_empty_tensor_with_shape(self): input_data = RaggedTensor.from_value_rowids( values=constant_op.constant([], dtype=dtypes.int64), value_rowids=constant_op.constant([], dtype=dtypes.int64), nrows=constant_op.constant(2, dtype=dtypes.int64), validate=True) actual = ragged_conversion_ops.ragged_to_dense( input_data, default_value=3, shape=[2, 3]) self.assertAllEqual(actual, [[3, 3, 3], [3, 3, 3]]) if __name__ == '__main__': googletest.main()

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lichee/utils/tfrecord/torch/__init__.py

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lichee/utils/tfrecord/torch/__init__.py

zhaijunyu/Lichee

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2022-03-05T12:30:13.000Z

lichee/utils/tfrecord/torch/__init__.py

zhaijunyu/Lichee

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# -*- coding: utf-8 -*- """ tfrecord torch dataset实现 """ from . import dataset from .dataset import TFRecordDataset from .dataset import MultiTFRecordDataset

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