xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

8a89bff29c5f953408573b588357469aa0dc0348

425

py

Python

webapp/starter/config/settings/partials/AUTH.py

somacci/django-sample-docker

6033313a76f7444004143ce7d0143633dc12e09d

[ "MIT" ]

null

webapp/starter/config/settings/partials/AUTH.py

somacci/django-sample-docker

6033313a76f7444004143ce7d0143633dc12e09d

[ "MIT" ]

null

webapp/starter/config/settings/partials/AUTH.py

somacci/django-sample-docker

6033313a76f7444004143ce7d0143633dc12e09d

[ "MIT" ]

null

AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ]

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8ab413df49fe238447647dd743f2fd11591cd1af

337

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Python

solardatatools/algorithms/__init__.py

catzzz/solar-data-tools

dc173c1036bc2e3116b302f3fd442b1cb030e0b0

[ "BSD-2-Clause" ]

3

2019-02-26T18:06:12.000Z

2019-04-16T19:49:27.000Z

solardatatools/algorithms/__init__.py

catzzz/solar-data-tools

dc173c1036bc2e3116b302f3fd442b1cb030e0b0

[ "BSD-2-Clause" ]

1

2019-03-28T19:02:37.000Z

solardatatools/algorithms/__init__.py

catzzz/solar-data-tools

dc173c1036bc2e3116b302f3fd442b1cb030e0b0

[ "BSD-2-Clause" ]

1

2019-03-06T17:52:27.000Z

from solardatatools.algorithms.capacity_change import CapacityChange from solardatatools.algorithms.time_shifts import TimeShift from solardatatools.algorithms.sunrise_sunset_estimation import SunriseSunset from solardatatools.algorithms.soiling import soiling_seperation from solardatatools.algorithms.clipping import ClippingDetection

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6

0a18c4ab2cb7a99e784a0e26bbf3a3908a4fd997

15,683

py

Python

deepFilter/dl_models.py

fperdigon/DeepFilter_as_in_Arxiv

d340a71942aee29d9655d6298c745390fc501ddc

[ "MIT" ]

null

deepFilter/dl_models.py

fperdigon/DeepFilter_as_in_Arxiv

d340a71942aee29d9655d6298c745390fc501ddc

[ "MIT" ]

null

deepFilter/dl_models.py

fperdigon/DeepFilter_as_in_Arxiv

d340a71942aee29d9655d6298c745390fc501ddc

[ "MIT" ]

null

#============================================================ # # Deep Learning BLW Filtering # Deep Learning models # # author: Francisco Perdigon Romero # email: fperdigon88@gmail.com # github id: fperdigon # #=========================================================== import keras from keras.models import Sequential, Model from keras.layers import Dense, Conv1D, Flatten, Dropout, BatchNormalization,\ concatenate, Activation, Input, Conv2DTranspose, Lambda, LSTM, Reshape, Embedding import keras.backend as K def Conv1DTranspose(input_tensor, filters, kernel_size, strides=2, activation='relu', padding='same'): """ https://stackoverflow.com/a/45788699 input_tensor: tensor, with the shape (batch_size, time_steps, dims) filters: int, output dimension, i.e. the output tensor will have the shape of (batch_size, time_steps, filters) kernel_size: int, size of the convolution kernel strides: int, convolution step size padding: 'same' | 'valid' """ x = Lambda(lambda x: K.expand_dims(x, axis=2))(input_tensor) x = Conv2DTranspose(filters=filters, kernel_size=(kernel_size, 1), activation=activation, strides=(strides, 1), padding=padding)(x) x = Lambda(lambda x: K.squeeze(x, axis=2))(x) return x ########################################################################## ###### MODULES ####### def LFilter_module(x, layers): LB0 = Conv1D(filters=int(layers / 4), kernel_size=3, activation='linear', strides=1, padding='same')(x) LB1 = Conv1D(filters=int(layers / 4), kernel_size=5, activation='linear', strides=1, padding='same')(x) LB2 = Conv1D(filters=int(layers / 4), kernel_size=9, activation='linear', strides=1, padding='same')(x) LB3 = Conv1D(filters=int(layers / 4), kernel_size=15, activation='linear', strides=1, padding='same')(x) x = concatenate([LB0, LB1, LB2, LB3]) return x def NLFilter_module(x, layers): NLB0 = Conv1D(filters=int(layers / 4), kernel_size=3, activation='relu', strides=1, padding='same')(x) NLB1 = Conv1D(filters=int(layers / 4), kernel_size=5, activation='relu', strides=1, padding='same')(x) NLB2 = Conv1D(filters=int(layers / 4), kernel_size=9, activation='relu', strides=1, padding='same')(x) NLB3 = Conv1D(filters=int(layers / 4), kernel_size=15, activation='relu', strides=1, padding='same')(x) x = concatenate([NLB0, NLB1, NLB2, NLB3]) return x def LANLFilter_module(x, layers): LB0 = Conv1D(filters=int(layers / 8), kernel_size=3, activation='linear', strides=1, padding='same')(x) LB1 = Conv1D(filters=int(layers / 8), kernel_size=5, activation='linear', strides=1, padding='same')(x) LB2 = Conv1D(filters=int(layers / 8), kernel_size=9, activation='linear', strides=1, padding='same')(x) LB3 = Conv1D(filters=int(layers / 8), kernel_size=15, activation='linear', strides=1, padding='same')(x) NLB0 = Conv1D(filters=int(layers / 8), kernel_size=3, activation='relu', strides=1, padding='same')(x) NLB1 = Conv1D(filters=int(layers / 8), kernel_size=5, activation='relu', strides=1, padding='same')(x) NLB2 = Conv1D(filters=int(layers / 8), kernel_size=9, activation='relu', strides=1, padding='same')(x) NLB3 = Conv1D(filters=int(layers / 8), kernel_size=15, activation='relu', strides=1, padding='same')(x) x = concatenate([LB0, LB1, LB2, LB3, NLB0, NLB1, NLB2, NLB3]) return x def LANLFilter_module_dilated(x, layers): LB1 = Conv1D(filters=int(layers / 6), kernel_size=5, activation='linear', dilation_rate=3, padding='same')(x) LB2 = Conv1D(filters=int(layers / 6), kernel_size=9, activation='linear', dilation_rate=3, padding='same')(x) LB3 = Conv1D(filters=int(layers / 6), kernel_size=15, dilation_rate=3, activation='linear', padding='same')(x) NLB1 = Conv1D(filters=int(layers / 6), kernel_size=5, activation='relu', dilation_rate=3, padding='same')(x) NLB2 = Conv1D(filters=int(layers / 6), kernel_size=9, activation='relu', dilation_rate=3, padding='same')(x) NLB3 = Conv1D(filters=int(layers / 6), kernel_size=15, dilation_rate=3, activation='relu', padding='same')(x) x = concatenate([LB1, LB2, LB3, NLB1, NLB2, NLB3]) # x = BatchNormalization()(x) return x ###### MODELS ####### def deep_filter_vanilla_linear(): model = Sequential() model.add(Conv1D(filters=64, kernel_size=9, activation='linear', input_shape=(512, 1), strides=1, padding='same')) model.add(Conv1D(filters=64, kernel_size=9, activation='linear', strides=1, padding='same')) model.add(Conv1D(filters=32, kernel_size=9, activation='linear', strides=1, padding='same')) model.add(Conv1D(filters=32, kernel_size=9, activation='linear', strides=1, padding='same')) model.add(Conv1D(filters=16, kernel_size=9, activation='linear', strides=1, padding='same')) model.add(Conv1D(filters=16, kernel_size=9, activation='linear', strides=1, padding='same')) model.add(Conv1D(filters=1, kernel_size=9, activation='linear', strides=1, padding='same')) return model def deep_filter_vanilla_Nlinear(): model = Sequential() model.add(Conv1D(filters=64, kernel_size=9, activation='relu', input_shape=(512, 1), strides=1, padding='same')) model.add(Conv1D(filters=64, kernel_size=9, activation='relu', strides=1, padding='same')) model.add(Conv1D(filters=32, kernel_size=9, activation='relu', strides=1, padding='same')) model.add(Conv1D(filters=32, kernel_size=9, activation='relu', strides=1, padding='same')) model.add(Conv1D(filters=16, kernel_size=9, activation='relu', strides=1, padding='same')) model.add(Conv1D(filters=16, kernel_size=9, activation='relu', strides=1, padding='same')) model.add(Conv1D(filters=1, kernel_size=9, activation='linear', strides=1, padding='same')) return model def deep_filter_I_linear(): input_shape = (None, 1) input = Input(shape=input_shape) tensor = LFilter_module(input, 64) tensor = LFilter_module(tensor, 64) tensor = LFilter_module(tensor, 32) tensor = LFilter_module(tensor, 32) tensor = LFilter_module(tensor, 16) tensor = LFilter_module(tensor, 16) predictions = Conv1D(filters=1, kernel_size=9, activation='linear', strides=1, padding='same')(tensor) model = Model(inputs=[input], outputs=predictions) return model def deep_filter_I_Nlinear(): input_shape = (None, 1) input = Input(shape=input_shape) tensor = NLFilter_module(input, 64) tensor = NLFilter_module(tensor, 64) tensor = NLFilter_module(tensor, 32) tensor = NLFilter_module(tensor, 32) tensor = NLFilter_module(tensor, 16) tensor = NLFilter_module(tensor, 16) predictions = Conv1D(filters=1, kernel_size=9, activation='linear', strides=1, padding='same')(tensor) model = Model(inputs=[input], outputs=predictions) return model def deep_filter_I_LANL(): # TODO: Make the doc input_shape = (None, 1) input = Input(shape=input_shape) tensor = LANLFilter_module(input, 64) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module(tensor, 64) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module(tensor, 32) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module(tensor, 32) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module(tensor, 16) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module(tensor, 16) tensor = BatchNormalization()(tensor) predictions = Conv1D(filters=1, kernel_size=9, activation='linear', strides=1, padding='same')(tensor) model = Model(inputs=[input], outputs=predictions) return model def deep_filter_model_I_LANL_dilated(): # TODO: Make the doc input_shape = (None, 1) input = Input(shape=input_shape) tensor = LANLFilter_module(input, 64) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module_dilated(tensor, 64) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module(tensor, 32) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module_dilated(tensor, 32) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module(tensor, 16) tensor = BatchNormalization()(tensor) tensor = LANLFilter_module_dilated(tensor, 16) tensor = BatchNormalization()(tensor) predictions = Conv1D(filters=1, kernel_size=9, activation='linear', strides=1, padding='same')(tensor) model = Model(inputs=[input], outputs=predictions) return model def FCN_DAE(): # Implementation of FCN_DAE approach presented in # Chiang, H. T., Hsieh, Y. Y., Fu, S. W., Hung, K. H., Tsao, Y., & Chien, S. Y. (2019). # Noise reduction in ECG signals using fully convolutional denoising autoencoders. # IEEE Access, 7, 60806-60813. input_shape = (None, 1) input = Input(shape=input_shape) x = Conv1D(filters=40, input_shape=(512, 1), kernel_size=16, activation='elu', strides=2, padding='same')(input) x = BatchNormalization()(x) x = Conv1D(filters=20, kernel_size=16, activation='elu', strides=2, padding='same')(x) x = BatchNormalization()(x) x = Conv1D(filters=20, kernel_size=16, activation='elu', strides=2, padding='same')(x) x = BatchNormalization()(x) x = Conv1D(filters=20, kernel_size=16, activation='elu', strides=2, padding='same')(x) x = BatchNormalization()(x) x = Conv1D(filters=40, kernel_size=16, activation='elu', strides=2, padding='same')(x) x = BatchNormalization()(x) x = Conv1D(filters=1, kernel_size=16, activation='elu', strides=1, padding='same')(x) x = BatchNormalization()(x) # Keras has no 1D Traspose Convolution, instead we use Conv2DTranspose function # in a souch way taht is mathematically equivalent x = Conv1DTranspose(input_tensor=x, filters=1, kernel_size=16, activation='elu', strides=1, padding='same') x = BatchNormalization()(x) x = Conv1DTranspose(input_tensor=x, filters=40, kernel_size=16, activation='elu', strides=2, padding='same') x = BatchNormalization()(x) x = Conv1DTranspose(input_tensor=x, filters=20, kernel_size=16, activation='elu', strides=2, padding='same') x = BatchNormalization()(x) x = Conv1DTranspose(input_tensor=x, filters=20, kernel_size=16, activation='elu', strides=2, padding='same') x = BatchNormalization()(x) x = Conv1DTranspose(input_tensor=x, filters=20, kernel_size=16, activation='elu', strides=2, padding='same') x = BatchNormalization()(x) x = Conv1DTranspose(input_tensor=x, filters=40, kernel_size=16, activation='elu', strides=2, padding='same') x = BatchNormalization()(x) predictions = Conv1DTranspose(input_tensor=x, filters=1, kernel_size=16, activation='linear', strides=1, padding='same') model = Model(inputs=[input], outputs=predictions) return model def DRRN_denoising(): # Implementation of DRNN approach presented in # Antczak, K. (2018). Deep recurrent neural networks for ECG signal denoising. # arXiv preprint arXiv:1807.11551. model = Sequential() model.add(LSTM(64, input_shape=(None, 1), return_sequences=True)) model.add(Dense(64, activation='relu')) model.add(Dense(64, activation='relu')) model.add(Dense(1, activation='linear')) return model

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Python

tests/test_childclasses.py

basfora/milp_sim

963b816c79e992964b4fe65813453fd73f499f30

[ "MIT" ]

null

tests/test_childclasses.py

basfora/milp_sim

963b816c79e992964b4fe65813453fd73f499f30

[ "MIT" ]

null

tests/test_childclasses.py

basfora/milp_sim

963b816c79e992964b4fe65813453fd73f499f30

[ "MIT" ]

null

from milp_sim.risk.classes.child_mespp import MyInputs2, MySearcher2, MySolverData2 def get_specs(): specs = MyInputs2() specs.set_graph(4) # solver parameter: central x distributed specs.set_solver_type('distributed') # target motion specs.set_target_motion('static') # searchers' detection: capture range and false negatives m = 2 specs.set_capture_range(0) specs.set_size_team(m) # position v0 = [1, 1] specs.set_start_searchers(v0) b_0 = [0.0 for i in range(10)] b_0[8] = 0.5 b_0[6] = 0.5 specs.set_b0(b_0) # time-step stuff: deadline mission (tau), planning horizon (h), re-plan frequency (theta) h = 3 specs.set_all_times(h) specs.set_theta(1) # solver timeout (in sec) specs.set_timeout(10) # danger stuff specs.set_threshold([3, 4], 'kappa') eta_true = [1, 3, 3, 4, 5, 3, 4, 4, 1] eta_priori = eta_true specs.set_danger_data(eta_true, 'true') specs.set_danger_data(eta_priori, 'priori') return specs def get_specs3(): specs = MyInputs2() specs.set_graph(4) # solver parameter: central x distributed specs.set_solver_type('distributed') # target motion specs.set_target_motion('static') # searchers' detection: capture range and false negatives m = 2 specs.set_capture_range(0) specs.set_size_team(m) # position v0 = [1, 1] specs.set_start_searchers(v0) b_0 = [0.0 for i in range(10)] b_0[8] = 0.5 b_0[6] = 0.5 specs.set_b0(b_0) # time-step stuff: deadline mission (tau), planning horizon (h), re-plan frequency (theta) h = 3 specs.set_all_times(h) specs.set_theta(1) # solver timeout (in sec) specs.set_timeout(10) # danger stuff specs.set_threshold([3, 4], 'kappa') specs.set_threshold([0.95, 0.90], 'alpha') eta_true = [1, 3, 3, 4, 5, 3, 4, 4, 1] eta_priori = eta_true specs.set_danger_data(eta_true, 'true') specs.set_danger_data(eta_priori, 'priori') specs.set_danger_perception('prob') return specs def test_myinputs2(): specs = get_specs() assert len(specs.graph.vs) == 9 assert specs.b0 == [0, 0, 0, 0, 0, 0, 0.5, 0, 0.5, 0] assert specs.start_searcher_random is False assert specs.start_searcher_v == [1, 1] assert specs.horizon == 3 assert specs.kappa == [3, 4] assert specs.danger_true == [1, 3, 3, 4, 5, 3, 4, 4, 1] assert specs.danger_priori == [1, 3, 3, 4, 5, 3, 4, 4, 1] assert specs.perception == 'point' def test_specs_prob(): specs = get_specs3() assert specs.alpha == [0.95, 0.90] assert specs.kappa == [3, 4] assert specs.perception == 'prob'

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Python

src/CTL/tests/test_CTMRG.py

wistaria/CTL

ddb0f917369df4e7233b5ab097595c6ce254862e

[ "MIT" ]

11

2021-06-23T15:47:03.000Z

2022-03-17T03:29:55.000Z

src/CTL/tests/test_CTMRG.py

wistaria/CTL

ddb0f917369df4e7233b5ab097595c6ce254862e

[ "MIT" ]

1

2021-10-16T15:10:54.000Z

src/CTL/tests/test_CTMRG.py

wistaria/CTL

ddb0f917369df4e7233b5ab097595c6ce254862e

[ "MIT" ]

2

2021-06-23T09:11:20.000Z

2021-10-16T15:35:18.000Z

from CTL.tests.packedTest import PackedTest from CTL.models.Ising import plaquetteIsingTensor, IsingTNFromUndirectedGraph from CTL.examples.CTMRG import CTMRG from CTL.funcs.graphFuncs import doubleSquareLatticeFBC from CTL.tensor.contract.optimalContract import contractAndCostWithSequence from CTL.examples.MPS import contractWithMPS import CTL.funcs.funcs as funcs class TestCTMRG(PackedTest): def __init__(self, methodName = 'runTest'): super().__init__(methodName = methodName, name = 'CTMRG') def test_exactCTMRG(self): # test case for non-interacting Ising model weight = 0.0 doubleLatticeFBC = doubleSquareLatticeFBC(n = 3, m = 3, weight = weight) # 24 tensors tensorNetwork = IsingTNFromUndirectedGraph(doubleLatticeFBC) seq = [(2, 15), (14, 2), (5, 2), (9, 20), (21, 9), (6, 9), (16, 6), (11, 23), (22, 11), (8, 11), (19, 8), (10, 8), (6, 8), (7, 6), (18, 6), (2, 6), (17, 2), (4, 2), (1, 2), (13, 1), (3, 1), (12, 1), (0, 1)] Z, cost = contractAndCostWithSequence(tensorList = tensorNetwork, seq = seq) print('Z = {}, cost = {}'.format(Z.single(), cost)) # exactZ = 2694263494.5463686 # pre-calculated # print('exact Z = {}'.format(exactZ)) # ZMPS = contractWithMPS(tensorList = tensorNetwork, chi = 16) # print('Z from MPS = {}'.format(ZMPS.single())) a = plaquetteIsingTensor(weight = weight, diamondForm = True) ctmrg = CTMRG(a, chi = 16) # for i in range(1, 5): # print('CTMRG Z(L = {}) = {}'.format(i, ctmrg.getZ(L = i))) # with self.assertWarns(RuntimeWarning): ZCTMRG = ctmrg.getZ(L = 3) print('CTMRG Z = {}'.format(ZCTMRG)) self.assertTrue(funcs.floatRelativeEqual(ZCTMRG, Z.single(), eps = 1e-10)) # test case for Ising model weight = 0.5 for nn in range(1, 3): doubleLatticeFBC = doubleSquareLatticeFBC(n = nn, m = nn, weight = weight) # 24 tensors tensorNetwork = IsingTNFromUndirectedGraph(doubleLatticeFBC) Z, cost = contractAndCostWithSequence(tensorList = tensorNetwork) print('Z for L = {} is {}'.format(nn, Z.single())) doubleLatticeFBC = doubleSquareLatticeFBC(n = 3, m = 3, weight = weight) # 24 tensors tensorNetwork = IsingTNFromUndirectedGraph(doubleLatticeFBC) seq = [(2, 15), (14, 2), (5, 2), (9, 20), (21, 9), (6, 9), (16, 6), (11, 23), (22, 11), (8, 11), (19, 8), (10, 8), (6, 8), (7, 6), (18, 6), (2, 6), (17, 2), (4, 2), (1, 2), (13, 1), (3, 1), (12, 1), (0, 1)] Z, cost = contractAndCostWithSequence(tensorList = tensorNetwork, seq = seq) print('Z = {}, cost = {}'.format(Z.single(), cost)) # exactZ = 2694263494.5463686 # pre-calculated # print('exact Z = {}'.format(exactZ)) # ZMPS = contractWithMPS(tensorList = tensorNetwork, chi = 16) # print('Z from MPS = {}'.format(ZMPS.single())) a = plaquetteIsingTensor(weight = weight, diamondForm = True) # for i in range(1, 5): # print('CTMRG Z(L = {}) = {}'.format(i, ctmrg.getSingleZ(L = i))) ctmrg = CTMRG(a, chi = 16) # for i in range(1, 5): # print('CTMRG Z(L = {}) = {}'.format(i, ctmrg.getZ(L = i))) # with self.assertWarns(RuntimeWarning): ZCTMRG = ctmrg.getZ(L = 3) print('CTMRG Z = {}'.format(ZCTMRG)) self.assertTrue(funcs.floatRelativeEqual(ZCTMRG, Z.single(), eps = 1e-10)) weight = 0.5 doubleLatticeFBC = doubleSquareLatticeFBC(n = 5, m = 5, weight = weight) # 24 tensors tensorNetwork = IsingTNFromUndirectedGraph(doubleLatticeFBC) Z, cost = contractAndCostWithSequence(tensorList = tensorNetwork, seq = None, greedy = True) print('Z = {}, cost = {}'.format(Z.single(), cost)) # ZMPS = contractWithMPS(tensorList = tensorNetwork, chi = 16) # print('Z from MPS = {}'.format(ZMPS.single())) a = plaquetteIsingTensor(weight = weight, diamondForm = True) ctmrg = CTMRG(a, chi = 16) ZCTMRG = ctmrg.getZ(L = 5) print('CTMRG Z = {}'.format(ZCTMRG)) self.assertTrue(funcs.floatRelativeEqual(ZCTMRG, Z.single(), eps = 1e-10)) weight = 0.5 doubleLatticeFBC = doubleSquareLatticeFBC(n = 7, m = 7, weight = weight) # 24 tensors tensorNetwork = IsingTNFromUndirectedGraph(doubleLatticeFBC) # Z, cost = contractAndCostWithSequence(tensorList = tensorNetwork, seq = None, greedy = True) # print('Z = {}, cost = {}'.format(Z.single(), cost)) ZMPS = contractWithMPS(tensorList = tensorNetwork, chi = 16) print('Z from MPS = {}'.format(ZMPS.single())) a = plaquetteIsingTensor(weight = weight, diamondForm = True) ctmrg = CTMRG(a, chi = 16) ZCTMRG = ctmrg.getZ(L = 7) print('CTMRG Z = {}'.format(ZCTMRG)) self.assertTrue(funcs.floatRelativeEqual(ZCTMRG, ZMPS.single(), eps = 1e-10)) weight = 0.7 doubleLatticeFBC = doubleSquareLatticeFBC(n = 6, m = 6, weight = weight) # 24 tensors tensorNetwork = IsingTNFromUndirectedGraph(doubleLatticeFBC) # Z, cost = contractAndCostWithSequence(tensorList = tensorNetwork, seq = None, greedy = True) # print('Z = {}, cost = {}'.format(Z.single(), cost)) ZMPS = contractWithMPS(tensorList = tensorNetwork, chi = 16) print('Z from MPS = {}'.format(ZMPS.single())) a = plaquetteIsingTensor(weight = weight, diamondForm = True) ctmrg = CTMRG(a, chi = 16) ZCTMRG = ctmrg.getZ(L = 6) print('CTMRG Z = {}'.format(ZCTMRG)) self.assertTrue(funcs.floatRelativeEqual(ZCTMRG, ZMPS.single(), eps = 1e-10)) # test case for J1-J2 Ising model(not work for current CTMRG assuming symmetry) # weight = (0.3, 0.4) # doubleLatticeFBC = doubleSquareLatticeFBC(n = 3, m = 3, weight = weight) # 24 tensors # tensorNetwork = IsingTNFromUndirectedGraph(doubleLatticeFBC) # seq = [(2, 15), (14, 2), (5, 2), (9, 20), (21, 9), (6, 9), (16, 6), (11, 23), (22, 11), (8, 11), (19, 8), (10, 8), (6, 8), (7, 6), (18, 6), (2, 6), (17, 2), (4, 2), (1, 2), (13, 1), (3, 1), (12, 1), (0, 1)] # Z, cost = contractAndCostWithSequence(tensorList = tensorNetwork, seq = seq) # print('Z = {}, cost = {}'.format(Z.single(), cost)) # # exactZ = 2694263494.5463686 # pre-calculated # # print('exact Z = {}'.format(exactZ)) # # ZMPS = contractWithMPS(tensorList = tensorNetwork, chi = 16) # # print('Z from MPS = {}'.format(ZMPS.single())) # a = plaquetteIsingTensor(weight = weight, diamondForm = True) # # for i in range(1, 5): # # print('CTMRG Z(L = {}) = {}'.format(i, ctmrg.getSingleZ(L = i))) # ctmrg = CTMRG(a, chi = 16) # # with self.assertWarns(RuntimeWarning): # ZCTMRG = ctmrg.getZ(L = 3) # print('CTMRG Z = {}'.format(ZCTMRG)) # self.assertTrue(funcs.floatRelativeEqual(ZCTMRG, Z.single(), eps = 1e-10))

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6

0a5605a8d00c5785a20884753f4983402948650a

6,364

py

Python

rbac/rbac_flask/app/blueprints/extra/extra_bp.py

5GEVE/5G-EVE-PORTAL-BACKEND-rbac

33dbf612fd507f35372f850ac6d7e9669eed1bb0

[ "MIT" ]

null

rbac/rbac_flask/app/blueprints/extra/extra_bp.py

5GEVE/5G-EVE-PORTAL-BACKEND-rbac

33dbf612fd507f35372f850ac6d7e9669eed1bb0

[ "MIT" ]

2

2021-04-30T21:00:46.000Z

2021-06-02T00:47:09.000Z

rbac/rbac_flask/app/blueprints/extra/extra_bp.py

5GEVE/5G-EVE-PORTAL-BACKEND-rbac

33dbf612fd507f35372f850ac6d7e9669eed1bb0

[ "MIT" ]

null

from flask import ( Blueprint, jsonify, request ) from app import oidc, config #from flask_jwt_extended import ( jwt_optional, get_jwt_identity ) from app.keycloak.keycloak_client import Keycloak import requests, json, collections from requests.auth import HTTPBasicAuth # BLUEPRINT CREATION bp = Blueprint('extra', __name__, url_prefix='/portal/rbac/extra') # Keycloak adapter kc_client = Keycloak() # Bugzilla URL BZ_URL = config['bz_url'] # ROUTES DEFINITION """ Retrieves available roles """ @bp.route('/realmroles', methods=['GET']) def get_realm_roles(): status_code, msg = kc_client.get_available_roles() return jsonify({"details": msg}), status_code ########################## ## Use cases management ## ########################## @bp.route('/use-cases', methods=['GET']) @oidc.accept_token(require_token=True) def get_use_cases(): token = str(request.headers['authorization']).split(" ")[1] status_code, msg = kc_client.token_to_user(token) if status_code == requests.codes.ok: status_code, msg = kc_client.get_user_attributes(msg['id'], "use_cases") return jsonify({"details": msg}), status_code @bp.route('/use-cases', methods=['POST']) @oidc.accept_token(require_token=True) def add_use_cases(): if not request.is_json: return jsonify({"details": "No json provided"}), 400 data = request.get_json() try: if not data['use_cases']: return jsonify({"details": "No use cases provided"}), 400 except Exception as e: return jsonify({"details": "use_cases key not found at the provided JSON"}), 400 if not type(data['use_cases']) == list: return jsonify({"details": "Use cases must be provided using a list of elements"}), 400 token = str(request.headers['authorization']).split(" ")[1] status_code, msg = kc_client.token_to_user(token) if status_code == requests.codes.ok: status_code, msg = kc_client.add_user_attributes(msg['id'], "use_cases", data['use_cases']) return jsonify({"details": msg}), status_code @bp.route('/use-cases', methods=['DELETE']) @oidc.accept_token(require_token=True) def delete_use_cases(): if not request.is_json: return jsonify({"details": "No json provided"}), 400 data = request.get_json() if not data['use_cases']: return jsonify({"details": "No use cases provided"}), 400 if not type(data['use_cases']) == list: return jsonify({"details": "Use cases must be provided using a list of elements"}), 400 token = str(request.headers['authorization']).split(" ")[1] status_code, msg = kc_client.token_to_user(token) if status_code == requests.codes.ok: status_code, msg = kc_client.delete_user_attributes(msg['id'], "use_cases", data['use_cases']) return jsonify({"details": msg}), status_code ################### ## Managed sites ## ################### @bp.route('/managed-sites', methods=['GET']) @oidc.accept_token(require_token=True) def get_managed_sites(): token = str(request.headers['authorization']).split(" ")[1] status_code, msg = kc_client.token_to_user(token) if status_code == requests.codes.ok: if "SiteManager" in msg['roles']: status_code, msg = kc_client.get_user_attributes(msg['id'], "managed_sites") else: msg = {"managed_sites": []} status_code = 200 return jsonify({"details": msg}), status_code @bp.route('/managed-sites', methods=['POST']) @oidc.accept_token(require_token=True) def add_managed_sites(): if not request.is_json: return jsonify({"details": "No json provided"}), 400 data = request.get_json() try: if not data['managed_sites']: return jsonify({"details": "No use cases provided"}), 400 except Exception as e: return jsonify({"details": "managed_sites key not found at the provided JSON"}), 400 if not type(data['managed_sites']) == list: return jsonify({"details": "Use cases must be provided using a list of elements"}), 400 token = str(request.headers['authorization']).split(" ")[1] status_code, msg = kc_client.token_to_user(token) if status_code == requests.codes.ok: if "SiteManager" in msg['roles']: status_code, msg = kc_client.add_user_attributes(msg['id'], "managed_sites", data['managed_sites']) else: msg = {"managed_sites": []} status_code = 200 return jsonify({"details": msg}), status_code @bp.route('/managed-sites', methods=['DELETE']) @oidc.accept_token(require_token=True) def delete_managed_sites(): if not request.is_json: return jsonify({"details": "No json provided"}), 400 data = request.get_json() if not 'managed_sites' in data.keys(): return jsonify({"details": "No use cases provided"}), 400 if not type(data['managed_sites']) == list: return jsonify({"details": "Use cases must be provided using a list of elements"}), 400 token = str(request.headers['authorization']).split(" ")[1] status_code, msg = kc_client.token_to_user(token) if status_code == requests.codes.ok: if "SiteManager" in msg['roles']: status_code, msg = kc_client.delete_user_attributes(msg['id'], "managed_sites", data['managed_sites']) else: msg = {"managed_sites": []} status_code = 200 return jsonify({"details": msg}), status_code #### For testing purposes #### @bp.route('/services', methods=['GET']) @oidc.accept_token(require_token=True) def services(): token = str(request.headers['authorization']).split(" ")[1] status_code, msg = kc_client.token_to_user(token) if status_code == requests.codes.ok: if "5geve_admin" in msg['roles']: services = [{'name':'Experiments'}, {'name': 'VNF Storage'}, {'name': 'Services Catalogue'}, {'name': 'Tickets'}] elif "5geve_experimenter" in msg['roles']: services = [{'name':'Experiments'}, {'name': 'Services Catalogue'}, {'name': 'Tickets'}] elif "5geve_vnfdev" in msg['roles']: services = [{'name': 'VNF Storage'}, {'name': 'Tickets'}] else: services = [{}] return jsonify({'details': services}), status_code return msg, status_code

33.671958

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808

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0

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6,364

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33.851064

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0

null

0

1

0

null

0

6

0a6ffbb8ba7a5ff75b8ff96954c520a3df8a5e60

118

py

Python

cslib/__init__.py

Suresoft-GLaDOS/cxbuild

1eb568bc11ae8854b1a6025c969ec94c96d6a4a9

[ "MIT" ]

2

2021-11-01T02:11:59.000Z

2021-11-04T09:19:45.000Z

cslib/__init__.py

HansolChoe/cxbuild

c289e40efdf92f34e7781772b3b84e0a1c7d0af2

[ "MIT" ]

3

2021-11-04T06:23:38.000Z

2021-11-19T01:54:05.000Z

cslib/__init__.py

HansolChoe/cxbuild

c289e40efdf92f34e7781772b3b84e0a1c7d0af2

[ "MIT" ]

2

2021-11-01T03:01:28.000Z

2021-11-04T09:19:28.000Z

from .csutil import * from .zip import * from .lib import * from .filepattern import fnmatch from .windowsAPI import *

23.6

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16

118

5.625

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118

5

33

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null

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0

1

0

1

0

6

6a665d43dd0c7e99f641d87290bfbdab268de20c

7,413

py

Python

PythonCodeChallenge-04/linked-list/tests/test_linked_list.py

MusaabShalaldeh/401-data-structures-and-algorithms

9fc4bf2011062f9710daa2eb36568392e62f8ab5

[ "MIT" ]

null

PythonCodeChallenge-04/linked-list/tests/test_linked_list.py

MusaabShalaldeh/401-data-structures-and-algorithms

9fc4bf2011062f9710daa2eb36568392e62f8ab5

[ "MIT" ]

null

PythonCodeChallenge-04/linked-list/tests/test_linked_list.py

MusaabShalaldeh/401-data-structures-and-algorithms

9fc4bf2011062f9710daa2eb36568392e62f8ab5

[ "MIT" ]

null

from linked_list import __version__ from linked_list.linked_list import Node, LinkedList import pytest def test_version(): assert __version__ == '0.1.0' def test_node_has_int_data(): # Arrange any data that you need to run your test expected = 1 # Act on the subject of the test to produce some actual output node = Node(1) actual = node.data # Assert assert actual == expected def test_node_has_str_data(): # Arrange any data that you need to run your test expected = "a" # Act on the subject of the test to produce some actual output node = Node("a") actual = node.data # Assert assert actual == expected def test_node_is_a_Node(): # Arrange any data that you need to run your test expected = "Node" # Act on the subject of the test to produce some actual output node = Node(1) actual = type(node).__name__ # Assert assert actual == expected def test_node_without_value(): with pytest.raises(TypeError): node = Node() def test_new_linked_list_is_empty(): expected = None ll = LinkedList() actual = ll.head assert actual == expected def test_linked_list_insert(): # Arrange expected = 1 ll = LinkedList() # Act ll.insert(1) node = ll.head actual = node.data # Assert assert actual == expected def test_linked_list_insert_twice(): # Arrange expected = 0 ll = LinkedList() # Act ll.insert(1) ll.insert(0) node = ll.head actual = node.data # Assert assert actual == expected assert ll.head._next.data == 1 def test_linked_list_includes(): # Arrange expected = True ll = LinkedList() # Act ll.insert(0) ll.insert(5) ll.insert(9) actual = ll.includes(5) # Assert assert actual == expected def test_linked_list_includes_fail(): # Arrange expected = False ll = LinkedList() # Act ll.insert(0) ll.insert(5) ll.insert(9) actual = ll.includes(20) # Assert assert actual == expected def test_linked_list_includes_first_item(): # Arrange expected = True ll = LinkedList() # Act ll.insert(0) ll.insert(5) ll.insert(9) actual = ll.includes(0) # Assert assert actual == expected def test_linked_list_includes_last_item(): # Arrange expected = True ll = LinkedList() # Act ll.insert(0) ll.insert(5) ll.insert(9) actual = ll.includes(9) # Assert assert actual == expected def test_linked_list_to_string(): # Arrange expected = "{ 29 } -> { 9 } -> { 5 } -> { 0 } -> NULL" ll = LinkedList() # Act ll.insert(0) ll.insert(5) ll.insert(9) ll.insert(29) actual = str(ll) # Assert assert actual == expected def test_linked_empty_list_to_string(): # Arrange expected = "NULL" ll = LinkedList() # Act actual = str(ll) # Assert assert actual == expected def test_linked_list_append(): # Arrange expected = "{ 9 } -> { 5 } -> { 0 } -> { 29 } -> NULL" ll = LinkedList() # Act ll.insert(0) ll.insert(5) ll.insert(9) ll.append(29) actual = str(ll) # Assert assert actual == expected def test_linked_list_append_multi_values(): # Arrange expected = "{ 0 } -> { 5 } -> { 9 } -> { 29 } -> NULL" ll = LinkedList() # Act ll.append(0) ll.append(5) ll.append(9) ll.append(29) actual = str(ll) # Assert assert actual == expected def test_linked_list_append_empty(): # Arrange expected = "{ 29 } -> NULL" ll = LinkedList() # Act ll.append(29) actual = str(ll) # Assert assert actual == expected def test_linked_list_insert_before(): # Arrange expected = "{ 9 } -> { 29 } -> { 5 } -> { 0 } -> NULL" ll = LinkedList() # Act ll.insert(0) ll.insert(5) ll.insert(9) ll.insert_before(5,29) actual = str(ll) # Assert assert actual == expected def test_linked_list_insert_before_not_found(): # Arrange expected = "{ 9 } -> { 7 } -> { 0 } -> NULL" ll = LinkedList() # Act ll.insert(0) ll.insert(7) ll.insert(9) ll.insert_before(5,29) actual = str(ll) # Assert assert actual == expected def test_linked_list_insert_before_first(): # Arrange expected = "{ 29 } -> { 1 } -> NULL" ll = LinkedList() # Act ll.insert(1) ll.insert_before(1,29) actual = str(ll) # Assert assert actual == expected def test_linked_list_insert_after(): # Arrange expected = "{ 9 } -> { 5 } -> { 25 } -> { 0 } -> NULL" ll = LinkedList() # Act ll.insert(0) ll.insert(5) ll.insert(9) ll.insert_after(5,25) actual = str(ll) # Assert assert actual == expected def test_linked_list_insert_after_last(): # Arrange expected = "{ 9 } -> { 5 } -> { 0 } -> { 25 } -> NULL" ll = LinkedList() # Act ll.insert(0) ll.insert(5) ll.insert(9) ll.insert_after(0,25) actual = str(ll) # Assert assert actual == expected def test_linked_list_kth(): #Arrange expected = 8 ll = LinkedList() #Actual ll.insert(2) ll.insert(8) ll.insert(3) ll.insert(1) actual = ll.kthFromEnd(3) # Assert assert actual == expected def test_second_linked_list_kth(): #Arrange expected = 2 ll = LinkedList() #Actual ll.insert(2) ll.insert(8) ll.insert(3) ll.insert(1) actual = ll.kthFromEnd(4) # Assert assert actual == expected def test_third_linked_list_kth(): #Arrange expected = 10 ll = LinkedList() #Actual ll.insert(2) ll.insert(10) ll.insert(86) ll.insert(6) ll.insert(3) ll.insert(1) actual = ll.kthFromEnd(5) # Assert assert actual == expected def test_fourth_linked_list_kth(): #Arrange expected = 2 ll = LinkedList() #Actual ll.insert(2) ll.insert(10) ll.insert(86) ll.insert(6) ll.insert(3) ll.insert(1) actual = ll.kthFromEnd(0) # Assert assert actual == expected def test_linked_list_kth_greaterthan(): #Arrange expected = None ll = LinkedList() #Actual ll.insert(2) ll.insert(10) ll.insert(86) ll.insert(6) ll.insert(3) ll.insert(1) actual = ll.kthFromEnd(8) # Assert assert actual == expected def test_linked_list_kth_same_length(): #Arrange expected = 2 ll = LinkedList() #Actual ll.insert(2) ll.insert(10) ll.insert(86) ll.insert(6) ll.insert(3) ll.insert(1) actual = ll.kthFromEnd(6) # Assert assert actual == expected def test_linked_list_kth_negative_k(): #Arrange expected = None ll = LinkedList() #Actual ll.insert(2) ll.insert(10) ll.insert(86) ll.insert(6) ll.insert(3) ll.insert(1) actual = ll.kthFromEnd(-5) # Assert assert actual == expected def test_linked_list_size_1(): #Arrange expected = 10 ll = LinkedList() #Actual ll.insert(10) actual = ll.kthFromEnd(1) # Assert assert actual == expected def test_linked_list_kth_happy_path(): #Arrange expected = 86 ll = LinkedList() #Actual ll.insert(2) ll.insert(10) ll.insert(86) ll.insert(6) ll.insert(3) actual = ll.kthFromEnd(3) # Assert assert actual == expected def test_linked_list_zip(): #Arrange expected = "{ 1 } -> { 2 } -> { 3 } -> { 4 } -> { 5 } -> { 6 } -> NULL" first_ll = LinkedList() second_ll = LinkedList() #Actual first_ll.append(1) first_ll.append(2) first_ll.append(3) second_ll.append(4) second_ll.append(5) second_ll.append(6) newList = LinkedList.zipLists(first_ll,second_ll) actual = str(newList) # Assert assert actual == expected

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0

null

0

1

0

1

0

null

0

6

6a83ba2968e6e0e7097619aa81b3138aa1100508

174

py

Python

nighres/filtering/__init__.py

atsuch/nighres

eb6265befb0b65b99c858ecb1c328d4d63e5a293

[ "Apache-2.0" ]

null

nighres/filtering/__init__.py

atsuch/nighres

eb6265befb0b65b99c858ecb1c328d4d63e5a293

[ "Apache-2.0" ]

null

nighres/filtering/__init__.py

atsuch/nighres

eb6265befb0b65b99c858ecb1c328d4d63e5a293

[ "Apache-2.0" ]

1

2019-01-21T10:53:38.000Z

from filter_ridge_structures import filter_ridge_structures from bandpass_filtering import bandpass_filtering from recursive_ridge_diffusion import recursive_ridge_diffusion

43.5

63

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true

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0

1

0

null

0

1

0

6

6a845bedd74859215be0c2807e24d85c9123a020

25

py

Python

__init__.py

xiangxw/pyhook_py3k

3a0a1fe8fb190e10761dd80f55a4cf8efd0fb3e3

[ "MIT" ]

82

2015-01-18T12:28:33.000Z

2022-03-15T19:23:03.000Z

portcat/deploy/pyHook/__init__.py

Ghostik2005/smallprojects

eed57f8b706f810ab5eb7be7c1121cfd0e8f12e4

[ "MIT" ]

12

2017-07-23T22:47:13.000Z

2022-02-27T14:10:12.000Z

portcat/deploy/pyHook/__init__.py

Ghostik2005/smallprojects

eed57f8b706f810ab5eb7be7c1121cfd0e8f12e4

[ "MIT" ]

51

2015-01-17T08:37:40.000Z

2021-09-06T01:46:04.000Z

from HookManager import *

25

0.84

3

25

7

1

0

0.12

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1

25

0.954545

0

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true

0

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0

1

0

null

0

1

0

1

0

1

0

6

6a88eb26fb999cc50f1b80dbaab7e1aa5cfe660e

23

py

Python

tests/system/scripts/divide_with_zero.py

kmaork/madbg

9f6097d510897ddf56eb9d87d3ac82b3a177344a

[ "MIT" ]

48

2019-07-05T23:16:42.000Z

2022-03-17T09:18:13.000Z

tests/system/scripts/divide_with_zero.py

kmaork/madbg

9f6097d510897ddf56eb9d87d3ac82b3a177344a

[ "MIT" ]

30

2020-07-07T13:48:00.000Z

2022-03-24T09:19:39.000Z

tests/system/scripts/divide_with_zero.py

kmaork/madbg

9f6097d510897ddf56eb9d87d3ac82b3a177344a

[ "MIT" ]

2

2021-08-16T16:30:27.000Z

2022-01-27T11:32:20.000Z

yo = 1 if yo: 1 / 0

7.666667

9

0.391304

6

23

1.5

0.666667

0

0.25

0.478261

23

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1

0

null

0

6

6ac2b9a1da628138a7e6e46a2f9ee0e0bf659efc

21

py

Python

rl_with_videos/algorithms/__init__.py

simonr98/Reinforcement-Learning-with-Videos

e40cec6b8d817276375e940696b290fc4e1e8bc7

[ "MIT" ]

25

2020-12-02T23:13:29.000Z

2022-02-25T07:57:30.000Z

rl_with_videos/algorithms/__init__.py

simonr98/Reinforcement-Learning-with-Videos

e40cec6b8d817276375e940696b290fc4e1e8bc7

[ "MIT" ]

8

2020-12-12T14:11:58.000Z

2021-12-10T20:06:04.000Z

rl_with_videos/algorithms/__init__.py

simonr98/Reinforcement-Learning-with-Videos

e40cec6b8d817276375e940696b290fc4e1e8bc7

[ "MIT" ]

8

2020-12-25T19:43:15.000Z

2021-10-13T02:53:58.000Z

from .sac import SAC

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6

6ac7d8f6f94f5567228a6a56b4baa65270eff244

1,084

py

Python

magni/cs/reconstruction/__init__.py

SIP-AAU/Magni

6328dc98a273506f433af52e6bd394754a844550

[ "BSD-2-Clause" ]

42

2015-02-09T10:17:26.000Z

2021-12-21T09:38:04.000Z

magni/cs/reconstruction/__init__.py

SIP-AAU/Magni

6328dc98a273506f433af52e6bd394754a844550

[ "BSD-2-Clause" ]

3

2015-03-20T12:00:40.000Z

2015-03-20T12:01:16.000Z

magni/cs/reconstruction/__init__.py

SIP-AAU/Magni

6328dc98a273506f433af52e6bd394754a844550

[ "BSD-2-Clause" ]

14

2015-04-28T03:08:32.000Z

2021-07-24T13:29:24.000Z

""" .. Copyright (c) 2014-2017, Magni developers. All rights reserved. See LICENSE.rst for further information. Subpackage providing implementations of generic reconstruction algorithms. Each subpackage provides a family of generic reconstruction algorithms. Thus each subpackage has a config module and a run function which provide the interface of the given family of reconstruction algorithms. Routine listings ---------------- amp Subpackage providing implementations of Approximate Message Passing (AMP). gamp Subpackage providing implementations of Generalised Approximate Message Passing (GAMP). it Subpackage providing implementations of Iterative Thresholding (IT). iht Subpackage providing implementations of Iterative Hard Thresholding (IHT). (Deprecated) sl0 Subpackage providing implementations of Smoothed l0 Norm (SL0). """ from magni.cs.reconstruction import amp from magni.cs.reconstruction import gamp from magni.cs.reconstruction import it from magni.cs.reconstruction import iht from magni.cs.reconstruction import sl0

30.971429

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0.238876

0.252927

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1,084

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0

6

6ae4255a565f4acd1ead35ae4081f22ca73f2147

3,917

py

Python

pyspedas/stereo/__init__.py

MAVENSDC/pyspedas

05ede2603acb514bc7803be054016142c0851685

[ "MIT" ]

1

2020-07-07T19:52:40.000Z

pyspedas/stereo/__init__.py

MAVENSDC/pyspedas

05ede2603acb514bc7803be054016142c0851685

[ "MIT" ]

null

pyspedas/stereo/__init__.py

MAVENSDC/pyspedas

05ede2603acb514bc7803be054016142c0851685

[ "MIT" ]

null

from .load import load def mag(trange=['2013-11-5', '2013-11-6'], probe='a', datatype='8hz', suffix='', get_support_data=False, varformat=None, downloadonly=False, notplot=False, no_update=False, time_clip=False): """ This function loads data from the magnetometer Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] datatype: str Data type; Valid options: 8hz, 32hz suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. downloadonly: bool Set this flag to download the CDF files, but not load them into tplot variables notplot: bool Return the data in hash tables instead of creating tplot variables no_update: bool If set, only load data from your local cache time_clip: bool Time clip the variables to exactly the range specified in the trange keyword Returns: List of tplot variables created. """ return load(instrument='mag', trange=trange, probe=probe, datatype=datatype, suffix=suffix, get_support_data=get_support_data, varformat=varformat, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update) def plastic(trange=['2013-11-5', '2013-11-6'], probe='a', datatype='1min', level='l2', suffix='', get_support_data=False, varformat=None, downloadonly=False, notplot=False, no_update=False, time_clip=False): """ This function loads data from the PLASTIC instrument Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] datatype: str Data type; Valid options: 1min suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. downloadonly: bool Set this flag to download the CDF files, but not load them into tplot variables notplot: bool Return the data in hash tables instead of creating tplot variables no_update: bool If set, only load data from your local cache time_clip: bool Time clip the variables to exactly the range specified in the trange keyword Returns: List of tplot variables created. """ return load(instrument='plastic', trange=trange, probe=probe, level=level, datatype=datatype, suffix=suffix, get_support_data=get_support_data, varformat=varformat, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update)

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0.033389

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null

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6

0a727b7f8741a52e5727c2a36123dd5bc830103b

26

py

Python

ui/display/__init__.py

opdich/pidrop-thumbdrive

50fb02bb4354fc2cc52d65707c43e8b0bb62ae81

[ "MIT" ]

null

ui/display/__init__.py

opdich/pidrop-thumbdrive

50fb02bb4354fc2cc52d65707c43e8b0bb62ae81

[ "MIT" ]

null

ui/display/__init__.py

opdich/pidrop-thumbdrive

50fb02bb4354fc2cc52d65707c43e8b0bb62ae81

[ "MIT" ]

null

from .ws_display import *

13

25

0.769231

4

26

4.75

1

0

0.153846

26

1

26

0.863636

0

1

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true

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0

1

0

null

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1

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null

0

1

0

1

0

1

0

6

0aa3a6cb24a36cb953448a0bb6596ef70ee8da62

28,461

py

Python

cfgov/v1/migrations/0250_add_fields_to_simplechart_block.py

alexandersirris/consumerfinance.gov

611bd5d88188177759faa1fbc63ae57deb88cfbd

[ "CC0-1.0" ]

37

2020-08-18T19:52:39.000Z

2022-03-23T08:08:41.000Z

cfgov/v1/migrations/0250_add_fields_to_simplechart_block.py

alexandersirris/consumerfinance.gov

611bd5d88188177759faa1fbc63ae57deb88cfbd

[ "CC0-1.0" ]

338

2020-08-14T20:46:36.000Z

2022-03-31T20:49:32.000Z

cfgov/v1/migrations/0250_add_fields_to_simplechart_block.py

alexandersirris/consumerfinance.gov

611bd5d88188177759faa1fbc63ae57deb88cfbd

[ "CC0-1.0" ]

14

2020-10-21T15:27:03.000Z

2022-03-17T03:16:36.000Z

# Generated by Django 2.2.16 on 2021-03-16 15:27 import django.core.validators from django.db import migrations import jobmanager.blocks import v1.atomic_elements.organisms import v1.blocks import wagtail.core.blocks import wagtail.core.fields import wagtail.images.blocks class Migration(migrations.Migration): dependencies = [ ('v1', '0249_add_product_filter_to_enforcement_filter_control'), ] operations = [ migrations.AlterField( model_name='browsepage', name='content', field=wagtail.core.fields.StreamField([('full_width_text', wagtail.core.blocks.StreamBlock([('content', wagtail.core.blocks.RichTextBlock(icon='edit')), ('content_with_anchor', wagtail.core.blocks.StructBlock([('content_block', wagtail.core.blocks.RichTextBlock()), ('anchor_link', wagtail.core.blocks.StructBlock([('link_id', wagtail.core.blocks.CharBlock(help_text='\n ID will be auto-generated on save.\n However, you may enter some human-friendly text that\n will be incorporated to make it easier to read.\n ', label='ID for this content block', required=False))]))])), ('heading', wagtail.core.blocks.StructBlock([('text', v1.blocks.HeadingTextBlock(required=False)), ('level', wagtail.core.blocks.ChoiceBlock(choices=[('h2', 'H2'), ('h3', 'H3'), ('h4', 'H4')])), ('icon', v1.blocks.HeadingIconBlock(help_text='Input the name of an icon to appear to the left of the heading. E.g., approved, help-round, etc. <a href="https://cfpb.github.io/design-system/foundation/iconography">See full list of icons</a>', required=False))], required=False)), ('image', wagtail.core.blocks.StructBlock([('image', wagtail.core.blocks.StructBlock([('upload', wagtail.images.blocks.ImageChooserBlock(required=False)), ('alt', wagtail.core.blocks.CharBlock(help_text="If the image is decorative (i.e., if a screenreader wouldn't have anything useful to say about it), leave the Alt field blank.", required=False))])), ('image_width', wagtail.core.blocks.ChoiceBlock(choices=[('full', 'full'), (470, '470px'), (270, '270px'), (170, '170px')])), ('image_position', wagtail.core.blocks.ChoiceBlock(choices=[('right', 'right'), ('left', 'left')], help_text='Does not apply if the image is full-width')), ('text', wagtail.core.blocks.RichTextBlock(label='Caption', required=False)), ('is_bottom_rule', wagtail.core.blocks.BooleanBlock(default=True, help_text='Check to add a horizontal rule line to bottom of inset.', label='Has bottom rule line', required=False))])), ('table_block', v1.atomic_elements.organisms.AtomicTableBlock(table_options={'renderer': 'html'})), ('quote', wagtail.core.blocks.StructBlock([('body', wagtail.core.blocks.TextBlock()), ('citation', wagtail.core.blocks.TextBlock(required=False)), ('is_large', wagtail.core.blocks.BooleanBlock(required=False))])), ('cta', wagtail.core.blocks.StructBlock([('slug_text', wagtail.core.blocks.CharBlock(required=False)), ('paragraph_text', wagtail.core.blocks.RichTextBlock(required=False)), ('button', wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False)), ('size', wagtail.core.blocks.ChoiceBlock(choices=[('regular', 'Regular'), ('large', 'Large Primary')]))]))])), ('related_links', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(required=False)), ('paragraph', wagtail.core.blocks.RichTextBlock(required=False)), ('links', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False))])))])), ('reusable_text', v1.blocks.ReusableTextChooserBlock('v1.ReusableText')), ('email_signup', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(default='Stay informed', required=False)), ('default_heading', wagtail.core.blocks.BooleanBlock(default=True, help_text='If selected, heading will be styled as an H5 with green top rule. Deselect to style header as H3.', label='Default heading style', required=False)), ('text', wagtail.core.blocks.CharBlock(help_text='Write a sentence or two about what kinds of emails the user is signing up for, how frequently they will be sent, etc.', required=False)), ('gd_code', wagtail.core.blocks.CharBlock(help_text='Code for the topic (i.e., mailing list) you want people who submit this form to subscribe to. Format: USCFPB_###', label='GovDelivery code', required=False)), ('disclaimer_page', wagtail.core.blocks.PageChooserBlock(help_text='Choose the page that the "See Privacy Act statement" link should go to. If in doubt, use "Generic Email Sign-Up Privacy Act Statement".', label='Privacy Act statement', required=False))])), ('well', wagtail.core.blocks.StructBlock([('content', wagtail.core.blocks.RichTextBlock(label='Well', required=False))])), ('well_with_ask_search', wagtail.core.blocks.StructBlock([('content', wagtail.core.blocks.RichTextBlock(label='Well', required=False)), ('ask_search', wagtail.core.blocks.StructBlock([('show_label', wagtail.core.blocks.BooleanBlock(default=True, help_text='Whether to show form label.', required=False)), ('placeholder', wagtail.core.blocks.TextBlock(help_text='Text to show for the input placeholder text.', required=False))]))]))])), ('info_unit_group', wagtail.core.blocks.StructBlock([('format', wagtail.core.blocks.ChoiceBlock(choices=[('50-50', '50/50'), ('33-33-33', '33/33/33'), ('25-75', '25/75')], help_text='Choose the number and width of info unit columns.', label='Format')), ('heading', wagtail.core.blocks.StructBlock([('text', v1.blocks.HeadingTextBlock(required=False)), ('level', wagtail.core.blocks.ChoiceBlock(choices=[('h2', 'H2'), ('h3', 'H3'), ('h4', 'H4')])), ('icon', v1.blocks.HeadingIconBlock(help_text='Input the name of an icon to appear to the left of the heading. E.g., approved, help-round, etc. <a href="https://cfpb.github.io/design-system/foundation/iconography">See full list of icons</a>', required=False))], required=False)), ('intro', wagtail.core.blocks.RichTextBlock(help_text='If this field is not empty, the Heading field must also be set.', required=False)), ('link_image_and_heading', wagtail.core.blocks.BooleanBlock(default=True, help_text="Check this to link all images and headings to the URL of the first link in their unit's list, if there is a link.", required=False)), ('has_top_rule_line', wagtail.core.blocks.BooleanBlock(default=False, help_text='Check this to add a horizontal rule line to top of info unit group.', required=False)), ('lines_between_items', wagtail.core.blocks.BooleanBlock(default=False, help_text='Check this to show horizontal rule lines between info units.', label='Show rule lines between items', required=False)), ('info_units', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('image', wagtail.core.blocks.StructBlock([('upload', wagtail.images.blocks.ImageChooserBlock(required=False)), ('alt', wagtail.core.blocks.CharBlock(help_text="If the image is decorative (i.e., if a screenreader wouldn't have anything useful to say about it), leave the Alt field blank.", required=False))])), ('heading', wagtail.core.blocks.StructBlock([('text', v1.blocks.HeadingTextBlock(required=False)), ('level', wagtail.core.blocks.ChoiceBlock(choices=[('h2', 'H2'), ('h3', 'H3'), ('h4', 'H4')])), ('icon', v1.blocks.HeadingIconBlock(help_text='Input the name of an icon to appear to the left of the heading. E.g., approved, help-round, etc. <a href="https://cfpb.github.io/design-system/foundation/iconography">See full list of icons</a>', required=False))], default={'level': 'h3'}, required=False)), ('body', wagtail.core.blocks.RichTextBlock(blank=True, required=False)), ('links', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False))]), required=False))]))), ('sharing', wagtail.core.blocks.StructBlock([('shareable', wagtail.core.blocks.BooleanBlock(help_text='If checked, share links will be included below the items.', label='Include sharing links?', required=False)), ('share_blurb', wagtail.core.blocks.CharBlock(help_text='Sets the tweet text, email subject line, and LinkedIn post text.', required=False))]))])), ('simple_chart', wagtail.core.blocks.StructBlock([('title', wagtail.core.blocks.CharBlock(required=True)), ('subtitle', wagtail.core.blocks.CharBlock(required=False)), ('figure', wagtail.core.blocks.CharBlock(required=False)), ('chart_type', wagtail.core.blocks.ChoiceBlock(choices=[('bar', 'Bar'), ('datetime', 'Datetime'), ('line', 'Line'), ('tilemap', 'Tilemap')])), ('data_source', wagtail.core.blocks.TextBlock(help_text="URL of the chart's data source or an array of JSON data", required=True)), ('data_series', wagtail.core.blocks.TextBlock(help_text='A string or array of keys (JSON) or headers (CSV) to include as data in the chart. Labels may be included via: {"key": <key>, "label": <label>}', required=False)), ('x_axis_data', wagtail.core.blocks.TextBlock(help_text='A string for a key/column or data array to include as categories or x values, depending on chart type.', required=False)), ('description', wagtail.core.blocks.CharBlock(help_text='Accessible description of the chart content', required=True)), ('y_axis_label', wagtail.core.blocks.CharBlock(help_text='y-axis label', required=True)), ('x_axis_label', wagtail.core.blocks.CharBlock(help_text='x-axis label, if needed', required=False)), ('transform', wagtail.core.blocks.CharBlock(help_text='Name of the javascript function in chart-hooks.js to run on the provided data before handing it to the chart', required=False)), ('filters', wagtail.core.blocks.CharBlock(help_text='Array of JSON objects of the form {"key": <key>, "label": <label>} to filter the underlying chart data on', required=False)), ('style_overrides', wagtail.core.blocks.TextBlock(help_text='A JSON object with style overrides for the underlying Highcharts chart. No object merging is done, nested objects should be referenced with dot notation: {"tooltip.shape": "circle"}', required=False)), ('credits', wagtail.core.blocks.CharBlock(help_text='Attribution for the data source', required=False)), ('date_published', wagtail.core.blocks.CharBlock(help_text='When the underlying data was published', required=False)), ('download_file', wagtail.core.blocks.CharBlock(help_text='Location of a file to download, if different from the data source', required=False)), ('download_text', wagtail.core.blocks.CharBlock(help_text='Custom text for the chart download field', required=False)), ('notes', wagtail.core.blocks.TextBlock(help_text='General chart information', required=False))])), ('expandable_group', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(help_text='Added as an <code><h3></code> at the top of this block. Also adds a wrapping <code><div></code> whose <code>id</code> attribute comes from a slugified version of this heading, creating an anchor that can be used when linking to this part of the page.', required=False)), ('body', wagtail.core.blocks.RichTextBlock(required=False)), ('is_accordion', wagtail.core.blocks.BooleanBlock(required=False)), ('has_top_rule_line', wagtail.core.blocks.BooleanBlock(default=False, help_text='Check this to add a horizontal rule line to top of expandable group.', required=False)), ('expandables', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('label', wagtail.core.blocks.CharBlock(required=False)), ('is_bordered', wagtail.core.blocks.BooleanBlock(required=False)), ('is_midtone', wagtail.core.blocks.BooleanBlock(required=False)), ('is_expanded', wagtail.core.blocks.BooleanBlock(required=False)), ('content', wagtail.core.blocks.StreamBlock([('paragraph', wagtail.core.blocks.RichTextBlock(required=False)), ('well', wagtail.core.blocks.StructBlock([('content', wagtail.core.blocks.RichTextBlock(label='Well', required=False))])), ('links', wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False))])), ('email', wagtail.core.blocks.StructBlock([('emails', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('url', wagtail.core.blocks.EmailBlock(label='Email address')), ('text', wagtail.core.blocks.CharBlock(label='Link text (optional)', required=False))])))])), ('phone', wagtail.core.blocks.StructBlock([('fax', wagtail.core.blocks.BooleanBlock(default=False, label='Is this number a fax?', required=False)), ('phones', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('number', wagtail.core.blocks.CharBlock(help_text='Do not include spaces or dashes. Ex. 8554112372', max_length=15, validators=[django.core.validators.RegexValidator(message='Enter a numeric phone number, without punctuation.', regex='^\\d*$')])), ('extension', wagtail.core.blocks.CharBlock(max_length=4, required=False)), ('vanity', wagtail.core.blocks.CharBlock(help_text='A phoneword version of the above number. Include any formatting. Ex. (555) 222-CFPB', max_length=15, required=False)), ('tty', wagtail.core.blocks.CharBlock(help_text='Do not include spaces or dashes. Ex. 8554112372', label='TTY', max_length=15, required=False, validators=[django.core.validators.RegexValidator(message='Enter a numeric phone number, without punctuation.', regex='^\\d*$')])), ('tty_ext', wagtail.core.blocks.CharBlock(label='TTY Extension', max_length=4, required=False))])))])), ('address', wagtail.core.blocks.StructBlock([('label', wagtail.core.blocks.CharBlock(required=False)), ('title', wagtail.core.blocks.CharBlock(required=False)), ('street', wagtail.core.blocks.CharBlock(required=False)), ('city', wagtail.core.blocks.CharBlock(max_length=50, required=False)), ('state', wagtail.core.blocks.CharBlock(max_length=25, required=False)), ('zip_code', wagtail.core.blocks.CharBlock(max_length=15, required=False))]))], blank=True))])))])), ('expandable', wagtail.core.blocks.StructBlock([('label', wagtail.core.blocks.CharBlock(required=False)), ('is_bordered', wagtail.core.blocks.BooleanBlock(required=False)), ('is_midtone', wagtail.core.blocks.BooleanBlock(required=False)), ('is_expanded', wagtail.core.blocks.BooleanBlock(required=False)), ('content', wagtail.core.blocks.StreamBlock([('paragraph', wagtail.core.blocks.RichTextBlock(required=False)), ('well', wagtail.core.blocks.StructBlock([('content', wagtail.core.blocks.RichTextBlock(label='Well', required=False))])), ('links', wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False))])), ('email', wagtail.core.blocks.StructBlock([('emails', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('url', wagtail.core.blocks.EmailBlock(label='Email address')), ('text', wagtail.core.blocks.CharBlock(label='Link text (optional)', required=False))])))])), ('phone', wagtail.core.blocks.StructBlock([('fax', wagtail.core.blocks.BooleanBlock(default=False, label='Is this number a fax?', required=False)), ('phones', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('number', wagtail.core.blocks.CharBlock(help_text='Do not include spaces or dashes. Ex. 8554112372', max_length=15, validators=[django.core.validators.RegexValidator(message='Enter a numeric phone number, without punctuation.', regex='^\\d*$')])), ('extension', wagtail.core.blocks.CharBlock(max_length=4, required=False)), ('vanity', wagtail.core.blocks.CharBlock(help_text='A phoneword version of the above number. Include any formatting. Ex. (555) 222-CFPB', max_length=15, required=False)), ('tty', wagtail.core.blocks.CharBlock(help_text='Do not include spaces or dashes. Ex. 8554112372', label='TTY', max_length=15, required=False, validators=[django.core.validators.RegexValidator(message='Enter a numeric phone number, without punctuation.', regex='^\\d*$')])), ('tty_ext', wagtail.core.blocks.CharBlock(label='TTY Extension', max_length=4, required=False))])))])), ('address', wagtail.core.blocks.StructBlock([('label', wagtail.core.blocks.CharBlock(required=False)), ('title', wagtail.core.blocks.CharBlock(required=False)), ('street', wagtail.core.blocks.CharBlock(required=False)), ('city', wagtail.core.blocks.CharBlock(max_length=50, required=False)), ('state', wagtail.core.blocks.CharBlock(max_length=25, required=False)), ('zip_code', wagtail.core.blocks.CharBlock(max_length=15, required=False))]))], blank=True))])), ('well', wagtail.core.blocks.StructBlock([('content', wagtail.core.blocks.RichTextBlock(label='Well', required=False))])), ('video_player', wagtail.core.blocks.StructBlock([('video_id', wagtail.core.blocks.RegexBlock(error_messages={'invalid': 'The YouTube video ID is in the wrong format.'}, help_text='Enter the YouTube video ID, which is located at the end of the video URL, after "v=". For example, the video ID for https://www.youtube.com/watch?v=1V0Ax9OIc84 is 1V0Ax9OIc84.', label='YouTube video ID', regex='^[\\w-]{11}$', required=False)), ('thumbnail_image', wagtail.images.blocks.ImageChooserBlock(help_text='Optional thumbnail image to show before and after the video plays. If the thumbnail image is not set here, the video player will default to showing the thumbnail that was set in (or automatically chosen by) YouTube.', required=False))])), ('snippet_list', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(required=False)), ('body', wagtail.core.blocks.RichTextBlock(required=False)), ('has_top_rule_line', wagtail.core.blocks.BooleanBlock(default=False, help_text='Check this to add a horizontal rule line above this block.', required=False)), ('image', wagtail.core.blocks.StructBlock([('upload', wagtail.images.blocks.ImageChooserBlock(required=False)), ('alt', wagtail.core.blocks.CharBlock(help_text="If the image is decorative (i.e., if a screenreader wouldn't have anything useful to say about it), leave the Alt field blank.", required=False))])), ('actions_column_width', wagtail.core.blocks.ChoiceBlock(choices=[('70', '70%'), ('66', '66%'), ('60', '60%'), ('50', '50%'), ('40', '40%'), ('33', '33%'), ('30', '30%')], help_text='Choose the width in % that you wish to set the Actions column in a resource list.', label='Width of "Actions" column', required=False)), ('show_thumbnails', wagtail.core.blocks.BooleanBlock(help_text="If selected, each resource in the list will include a 150px-wide image from the resource's thumbnail field.", required=False)), ('actions', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('link_label', wagtail.core.blocks.CharBlock(help_text='E.g., "Download" or "Order free prints"')), ('snippet_field', wagtail.core.blocks.ChoiceBlock(choices=[('related_file', 'Related file'), ('alternate_file', 'Alternate file'), ('link', 'Link'), ('alternate_link', 'Alternate link')], help_text='The field that the action link should point to'))]))), ('tags', wagtail.core.blocks.ListBlock(wagtail.core.blocks.CharBlock(label='Tag'), help_text='Enter tag names to filter the snippets. For a snippet to match and be output in the list, it must have been tagged with all of the tag names listed here. The tag names are case-insensitive.'))])), ('table_block', v1.atomic_elements.organisms.AtomicTableBlock(table_options={'renderer': 'html'})), ('feedback', wagtail.core.blocks.StructBlock([('was_it_helpful_text', wagtail.core.blocks.CharBlock(default='Was this page helpful to you?', help_text='Use this field only for feedback forms that use "Was this helpful?" radio buttons.', required=False)), ('intro_text', wagtail.core.blocks.CharBlock(help_text='Optional feedback intro', required=False)), ('question_text', wagtail.core.blocks.CharBlock(help_text='Optional expansion on intro', required=False)), ('radio_intro', wagtail.core.blocks.CharBlock(help_text='Leave blank unless you are building a feedback form with extra radio-button prompts, as in /owning-a-home/help-us-improve/.', required=False)), ('radio_text', wagtail.core.blocks.CharBlock(default='This information helps us understand your question better.', required=False)), ('radio_question_1', wagtail.core.blocks.CharBlock(default='How soon do you expect to buy a home?', required=False)), ('radio_question_2', wagtail.core.blocks.CharBlock(default='Do you currently own a home?', required=False)), ('button_text', wagtail.core.blocks.CharBlock(default='Submit')), ('contact_advisory', wagtail.core.blocks.RichTextBlock(help_text='Use only for feedback forms that ask for a contact email', required=False))])), ('raw_html_block', wagtail.core.blocks.RawHTMLBlock(label='Raw HTML block')), ('conference_registration_form', wagtail.core.blocks.StructBlock([('govdelivery_code', wagtail.core.blocks.CharBlock(help_text='Conference registrants will be subscribed to this GovDelivery topic.', label='GovDelivery code')), ('govdelivery_question_id', wagtail.core.blocks.RegexBlock(error_messages={'invalid': 'GovDelivery question ID must be 5 digits.'}, help_text='Enter the ID of the question in GovDelivery that is being used to track registration for this conference. It is the number in the question URL, e.g., the <code>12345</code> in <code>https://admin.govdelivery.com/questions/12345/edit</code>.', label='GovDelivery question ID', regex='^\\d{5,}$', required=False)), ('govdelivery_answer_id', wagtail.core.blocks.RegexBlock(error_messages={'invalid': 'GovDelivery answer ID must be 5 digits.'}, help_text='Enter the ID of the affirmative answer for the above question. To find it, right-click on the answer in the listing on a page like <code>https://admin.govdelivery.com/questions/12345/answers</code>, inspect the element, and look around in the source for a five-digit ID associated with that answer. <strong>Required if Govdelivery question ID is set.</strong>', label='GovDelivery answer ID', regex='^\\d{5,}$', required=False)), ('capacity', wagtail.core.blocks.IntegerBlock(help_text='Enter the (physical) conference attendance limit as a number.')), ('success_message', wagtail.core.blocks.RichTextBlock(help_text='Enter a message that will be shown on successful registration.')), ('at_capacity_message', wagtail.core.blocks.RichTextBlock(help_text='Enter a message that will be shown when the event is at capacity.')), ('failure_message', wagtail.core.blocks.RichTextBlock(help_text='Enter a message that will be shown if the GovDelivery subscription fails.'))])), ('chart_block', wagtail.core.blocks.StructBlock([('title', wagtail.core.blocks.CharBlock(required=True)), ('chart_type', wagtail.core.blocks.ChoiceBlock(choices=[('bar', 'Bar | % y-axis values'), ('line', 'Line | millions/billions y-axis values'), ('line-index', 'Line-Index | integer y-axis values'), ('tile_map', 'Tile Map | grid-like USA map')])), ('color_scheme', wagtail.core.blocks.ChoiceBlock(choices=[('blue', 'Blue'), ('gold', 'Gold'), ('green', 'Green'), ('navy', 'Navy'), ('neutral', 'Neutral'), ('purple', 'Purple'), ('teal', 'Teal')], help_text='Chart\'s color scheme. See "https://github.com/cfpb/cfpb-chart-builder#createchart-options-".', required=False)), ('data_source', wagtail.core.blocks.CharBlock(help_text='Location of the chart\'s data source relative to "https://files.consumerfinance.gov/data/". For example,"consumer-credit-trends/auto-loans/num_data_AUT.csv".', required=True)), ('date_published', wagtail.core.blocks.DateBlock(help_text='Automatically generated when CCT cron job runs')), ('description', wagtail.core.blocks.CharBlock(help_text='Briefly summarize the chart for visually impaired users.', required=True)), ('has_top_rule_line', wagtail.core.blocks.BooleanBlock(default=False, help_text='Check this to add a horizontal rule line to top of chart block.', required=False)), ('last_updated_projected_data', wagtail.core.blocks.DateBlock(help_text='Month of latest entry in dataset')), ('metadata', wagtail.core.blocks.CharBlock(help_text='Optional metadata for the chart to use. For example, with CCT this would be the chart\'s "group".', required=False)), ('note', wagtail.core.blocks.CharBlock(help_text='Text to display as a footnote. For example, "Data from the last six months are not final."', required=False)), ('y_axis_label', wagtail.core.blocks.CharBlock(help_text='Custom y-axis label. NOTE: Line-Index chart y-axis is not overridable with this field!', required=False))])), ('mortgage_chart_block', wagtail.core.blocks.StructBlock([('content_block', wagtail.core.blocks.RichTextBlock()), ('title', wagtail.core.blocks.CharBlock(form_classname='title', required=True)), ('description', wagtail.core.blocks.CharBlock(help_text='Chart summary for visually impaired users.', required=False)), ('note', wagtail.core.blocks.CharBlock(help_text='Text for "Note" section of footnotes.', required=False)), ('has_top_rule_line', wagtail.core.blocks.BooleanBlock(default=False, help_text='Check this to add a horizontal rule line to top of chart block.', required=False))])), ('mortgage_map_block', wagtail.core.blocks.StructBlock([('content_block', wagtail.core.blocks.RichTextBlock()), ('title', wagtail.core.blocks.CharBlock(form_classname='title', required=True)), ('description', wagtail.core.blocks.CharBlock(help_text='Chart summary for visually impaired users.', required=False)), ('note', wagtail.core.blocks.CharBlock(help_text='Text for "Note" section of footnotes.', required=False)), ('has_top_rule_line', wagtail.core.blocks.BooleanBlock(default=False, help_text='Check this to add a horizontal rule line to top of chart block.', required=False))])), ('mortgage_downloads_block', wagtail.core.blocks.StructBlock([('show_archives', wagtail.core.blocks.BooleanBlock(default=False, help_text='Check this box to allow the archival section to display. No section will appear if there are no archival downloads.', required=False))])), ('data_snapshot', wagtail.core.blocks.StructBlock([('market_key', wagtail.core.blocks.CharBlock(help_text='Market identifier, e.g. AUT', max_length=20, required=True)), ('num_originations', wagtail.core.blocks.CharBlock(help_text='Number of originations, e.g. 1.2 million', max_length=20)), ('value_originations', wagtail.core.blocks.CharBlock(help_text='Total dollar value of originations, e.g. $3.4 billion', max_length=20)), ('year_over_year_change', wagtail.core.blocks.CharBlock(help_text='Percentage change, e.g. 5.6% increase', max_length=20)), ('last_updated_projected_data', wagtail.core.blocks.DateBlock(help_text='Month of latest entry in dataset')), ('num_originations_text', wagtail.core.blocks.CharBlock(help_text='Descriptive sentence, e.g. Auto loans originated', max_length=100)), ('value_originations_text', wagtail.core.blocks.CharBlock(help_text='Descriptive sentence, e.g. Dollar volume of new loans', max_length=100)), ('year_over_year_change_text', wagtail.core.blocks.CharBlock(help_text='Descriptive sentence, e.g. In year-over-year originations', max_length=100)), ('inquiry_month', wagtail.core.blocks.DateBlock(help_text='Month of latest entry in dataset for inquiry data', max_length=20, required=False)), ('inquiry_year_over_year_change', wagtail.core.blocks.CharBlock(help_text='Percentage change, e.g. 5.6% increase', max_length=20, required=False)), ('inquiry_year_over_year_change_text', wagtail.core.blocks.CharBlock(help_text='Descriptive sentence, e.g. In year-over-year inquiries', max_length=100, required=False)), ('tightness_month', wagtail.core.blocks.DateBlock(help_text='Month of latest entry in dataset for credit tightness data', max_length=20, required=False)), ('tightness_year_over_year_change', wagtail.core.blocks.CharBlock(help_text='Percentage change, e.g. 5.6% increase', max_length=20, required=False)), ('tightness_year_over_year_change_text', wagtail.core.blocks.CharBlock(help_text='Descriptive sentence, e.g. In year-over-year credit tightness', max_length=100, required=False)), ('image', wagtail.images.blocks.ImageChooserBlock(icon='image', required=False))])), ('job_listing_table', jobmanager.blocks.JobListingTable()), ('yes_checklist', wagtail.core.blocks.StructBlock([('checklist', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('item', wagtail.core.blocks.CharBlock(help_text='Short description for a checkbox item')), ('details', wagtail.core.blocks.RichTextBlock(help_text='Deeper explanation of the item', required=False))])))]))], blank=True), ), ]

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0aa68315c1c0a24b069c75fd053530f5560e09eb

5,398

py

Python

pubsub/google/cloud/pubsub_v1/gapic/subscriber_client_config.py

DaveCheez/google-cloud-python

fc03d4d41f13e9d13db7206438163b3a471fdabd

[ "Apache-2.0" ]

2

2021-11-26T07:08:43.000Z

2022-03-07T20:20:04.000Z

pubsub/google/cloud/pubsub_v1/gapic/subscriber_client_config.py

DaveCheez/google-cloud-python

fc03d4d41f13e9d13db7206438163b3a471fdabd

[ "Apache-2.0" ]

null

pubsub/google/cloud/pubsub_v1/gapic/subscriber_client_config.py

DaveCheez/google-cloud-python

fc03d4d41f13e9d13db7206438163b3a471fdabd

[ "Apache-2.0" ]

1

2020-04-14T10:47:41.000Z

config = { "interfaces": { "google.pubsub.v1.Subscriber": { "retry_codes": { "idempotent": ["ABORTED", "UNAVAILABLE", "UNKNOWN"], "non_idempotent": ["UNAVAILABLE"], "none": [], }, "retry_params": { "default": { "initial_retry_delay_millis": 100, "retry_delay_multiplier": 1.3, "max_retry_delay_millis": 60000, "initial_rpc_timeout_millis": 60000, "rpc_timeout_multiplier": 1.0, "max_rpc_timeout_millis": 60000, "total_timeout_millis": 600000, }, "messaging": { "initial_retry_delay_millis": 100, "retry_delay_multiplier": 1.3, "max_retry_delay_millis": 60000, "initial_rpc_timeout_millis": 25000, "rpc_timeout_multiplier": 1.0, "max_rpc_timeout_millis": 25000, "total_timeout_millis": 600000, }, "streaming_messaging": { "initial_retry_delay_millis": 100, "retry_delay_multiplier": 1.3, "max_retry_delay_millis": 60000, "initial_rpc_timeout_millis": 600000, "rpc_timeout_multiplier": 1.0, "max_rpc_timeout_millis": 600000, "total_timeout_millis": 600000, }, }, "methods": { "CreateSubscription": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "GetSubscription": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "UpdateSubscription": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ListSubscriptions": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "DeleteSubscription": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ModifyAckDeadline": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "Acknowledge": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "messaging", }, "Pull": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "messaging", }, "StreamingPull": { "timeout_millis": 900000, "retry_codes_name": "none", "retry_params_name": "streaming_messaging", }, "ModifyPushConfig": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ListSnapshots": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "CreateSnapshot": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "UpdateSnapshot": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "DeleteSnapshot": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "Seek": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "SetIamPolicy": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "GetIamPolicy": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "TestIamPermissions": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, }, } } }

40.586466

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5,398

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null

0

1

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1

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null

0

6

0ab27da3f1b8ec67fcf976c9a3cffba870946ea5

3,285

py

Python

econtools/metrics/tests/test_ols.py

fqueiro/econtools

cae1cfc82e02cb9081c247b530b10dc68ee18820

[ "BSD-3-Clause" ]

93

2018-02-12T17:21:39.000Z

2022-03-11T23:14:18.000Z

econtools/metrics/tests/test_ols.py

fqueiro/econtools

cae1cfc82e02cb9081c247b530b10dc68ee18820

[ "BSD-3-Clause" ]

5

2018-09-05T02:10:05.000Z

2022-01-07T17:07:23.000Z

econtools/metrics/tests/test_ols.py

fqueiro/econtools

cae1cfc82e02cb9081c247b530b10dc68ee18820

[ "BSD-3-Clause" ]

25

2018-06-06T07:35:03.000Z

2021-12-10T06:59:06.000Z

from os import path import pandas as pd from econtools.metrics.util.testing import RegCompare from econtools.metrics.api import reg from econtools.metrics.tests.data.src_ols import (ols_std, ols_robust, ols_hc2, ols_hc3, ols_cluster) class TestOLS_std(RegCompare): @classmethod def setup_class(cls): """Stata reg output from `sysuse auto; reg price mpg`""" cls.init(cls) cls.precision['vce'] = 6 test_path = path.split(path.relpath(__file__))[0] auto_path = path.join(test_path, 'data', 'auto.dta') autodata = pd.read_stata(auto_path) y = 'price' x = ['mpg', 'length'] cls.result = reg(autodata, y, x, addcons=True) cls.expected = ols_std class TestOLS_std_y_list(RegCompare): @classmethod def setup_class(cls): """Stata reg output from `sysuse auto; reg price mpg`""" cls.init(cls) cls.precision['vce'] = 6 test_path = path.split(path.relpath(__file__))[0] auto_path = path.join(test_path, 'data', 'auto.dta') autodata = pd.read_stata(auto_path) y = ['price'] x = ['mpg', 'length'] cls.result = reg(autodata, y, x, addcons=True) cls.expected = ols_std class TestOLS_hc1(RegCompare): @classmethod def setup_class(cls): """Stata reg output from `sysuse auto; reg price mpg`""" cls.init(cls) test_path = path.split(path.relpath(__file__))[0] auto_path = path.join(test_path, 'data', 'auto.dta') autodata = pd.read_stata(auto_path) y = 'price' x = ['mpg', 'length'] cls.result = reg(autodata, y, x, vce_type='hc1', addcons=True) cls.expected = ols_robust class TestOLS_hc2(RegCompare): @classmethod def setup_class(cls): """Stata reg output from `sysuse auto; reg price mpg`""" cls.init(cls) test_path = path.split(path.relpath(__file__))[0] auto_path = path.join(test_path, 'data', 'auto.dta') autodata = pd.read_stata(auto_path) y = 'price' x = ['mpg', 'length'] cls.result = reg(autodata, y, x, vce_type='hc2', addcons=True) cls.expected = ols_hc2 class TestOLS_hc3(RegCompare): @classmethod def setup_class(cls): """Stata reg output from `sysuse auto; reg price mpg`""" cls.init(cls) test_path = path.split(path.relpath(__file__))[0] auto_path = path.join(test_path, 'data', 'auto.dta') autodata = pd.read_stata(auto_path) y = 'price' x = ['mpg', 'length'] cls.result = reg(autodata, y, x, vce_type='hc3', addcons=True) cls.expected = ols_hc3 class TestOLS_cluster(RegCompare): @classmethod def setup_class(cls): """Stata reg output from `sysuse auto; reg price mpg`""" cls.init(cls) test_path = path.split(path.relpath(__file__))[0] auto_path = path.join(test_path, 'data', 'auto.dta') autodata = pd.read_stata(auto_path) y = 'price' x = ['mpg', 'length'] cls.result = reg(autodata, y, x, cluster='gear_ratio', addcons=True) cls.expected = ols_cluster if __name__ == '__main__': import pytest pytest.main()

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3,285

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0.81982

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0

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0.264536

3,285

105

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31.285714

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0

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1

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0

0.077922

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0.233766

0

null

0

1

0

null

0

6

0ad9a66f32e1c97d292f41da047191a0b12596eb

67

py

Python

__init__.py

18F/codetalker

1c665de99a804d5abda1ddbccb055e05e3efa5e5

[ "CC0-1.0" ]

1

2017-12-27T21:21:09.000Z

__init__.py

18F/codetalker

1c665de99a804d5abda1ddbccb055e05e3efa5e5

[ "CC0-1.0" ]

null

__init__.py

18F/codetalker

1c665de99a804d5abda1ddbccb055e05e3efa5e5

[ "CC0-1.0" ]

2

2019-05-21T18:53:21.000Z

2021-02-18T11:11:28.000Z

from codetalker.main.api import app def runserver(): app.run()

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145

py

Python

j1708/__init__.py

grimm-co/stm32-j1708

5ca0b1961752ba49f816ce22cbc6aaefb4ae95dc

[ "BSD-3-Clause" ]

3

2021-11-05T21:09:56.000Z

2021-11-19T03:16:41.000Z

j1708/__init__.py

grimm-co/stm32-j1708

5ca0b1961752ba49f816ce22cbc6aaefb4ae95dc

[ "BSD-3-Clause" ]

null

j1708/__init__.py

grimm-co/stm32-j1708

5ca0b1961752ba49f816ce22cbc6aaefb4ae95dc

[ "BSD-3-Clause" ]

null

from .iface import * from .msg import * from .pids import J1708PID from .mids import J1708MID from .pid_types import * from .exceptions import *

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6

0ae4e4c1a6372b4cf31e4efbb1321fefb0ad204d

333

py

Python

python_modules/dagster/dagster/core/storage/event_log/__init__.py

bitdotioinc/dagster

4fe395a37b206b1a48b956fa5dd72bf698104cca

[ "Apache-2.0" ]

1

2020-08-10T23:03:37.000Z

python_modules/dagster/dagster/core/storage/event_log/__init__.py

bitdotioinc/dagster

4fe395a37b206b1a48b956fa5dd72bf698104cca

[ "Apache-2.0" ]

7

2022-03-16T06:55:04.000Z

2022-03-18T07:03:25.000Z

python_modules/dagster/dagster/core/storage/event_log/__init__.py

bitdotioinc/dagster

4fe395a37b206b1a48b956fa5dd72bf698104cca

[ "Apache-2.0" ]

1

2020-08-20T14:20:31.000Z

from .base import AssetAwareEventLogStorage, EventLogStorage from .in_memory import InMemoryEventLogStorage from .schema import SqlEventLogStorageMetadata, SqlEventLogStorageTable from .sql_event_log import AssetAwareSqlEventLogStorage, SqlEventLogStorage from .sqlite import ConsolidatedSqliteEventLogStorage, SqliteEventLogStorage

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6

e40a7d69d316cb7515e268b4763f820bb7d33bd5

47

py

Python

vanirio/__init__.py

vaniriovanhalteren/sdk-python

947b08fbe046d46275bf39bc95984fbf3edc0e6c

[ "MIT" ]

null

vanirio/__init__.py

vaniriovanhalteren/sdk-python

947b08fbe046d46275bf39bc95984fbf3edc0e6c

[ "MIT" ]

null

vanirio/__init__.py

vaniriovanhalteren/sdk-python

947b08fbe046d46275bf39bc95984fbf3edc0e6c

[ "MIT" ]

1

2022-02-08T08:15:07.000Z

from vanirio.module.interface import Interface

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6

7c3c76ea23eb59c959fa2075932d96ee456288c9

158

py

Python

app/models/__init__.py

Jotasenpai/DigitalMediaStoreRESTfull

bb776d398e1756b1ff2fd4f392b80479ae29847d

[ "MIT" ]

null

app/models/__init__.py

Jotasenpai/DigitalMediaStoreRESTfull

bb776d398e1756b1ff2fd4f392b80479ae29847d

[ "MIT" ]

null

app/models/__init__.py

Jotasenpai/DigitalMediaStoreRESTfull

bb776d398e1756b1ff2fd4f392b80479ae29847d

[ "MIT" ]

null

from .albums import Album # noqa:F401 from .artists import Artist # noqa:F401 from .genres import Genre # noqa:F401 from .tracks import Track # noqa:F401

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7c5d9a3bfc798cd28299893025ba6e5dedc7b045

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py

Python

ecogdata/datasource/tests/test_mapped_source.py

miketrumpis/ecogdata

ff65820198e69608634c12686a86b97ac3a77558

[ "BSD-3-Clause" ]

null

ecogdata/datasource/tests/test_mapped_source.py

miketrumpis/ecogdata

ff65820198e69608634c12686a86b97ac3a77558

[ "BSD-3-Clause" ]

null

ecogdata/datasource/tests/test_mapped_source.py

miketrumpis/ecogdata

ff65820198e69608634c12686a86b97ac3a77558

[ "BSD-3-Clause" ]

null

import os import pytest import numpy as np from ecogdata.datasource.array_abstractions import HDF5Buffer from ecogdata.datasource.memmap import MappedSource, MemoryBlowOutError from ecogdata.datasource.basic import PlainArraySource from .test_array_abstractions import _create_hdf5, _create_buffer, _create_binder def test_basic_construction(): aux_arrays = ('test1', 'test2') buffer, data = _create_buffer(aux_arrays=aux_arrays) # hacky way to get h5py.File object... hdf = buffer.file_array.file aligned = dict([(k, HDF5Buffer(hdf[k])) for k in aux_arrays]) map_source = MappedSource(buffer, aligned_arrays=aligned) shape = data.shape assert map_source.shape == shape, 'Shape wrong' assert map_source.binary_channel_mask.sum() == shape[0], 'Wrong number of active channels' for field in aux_arrays: assert hasattr(map_source, field), 'Aux field {} not preserved'.format(field) assert getattr(map_source, field).shape[1] == shape[1], 'aligned field {} wrong length'.format(field) # repeat for transpose map_source = MappedSource(buffer, aligned_arrays=aligned, transpose=True) assert map_source.shape == shape[::-1], 'Shape wrong in transpose' assert map_source.binary_channel_mask.sum() == shape[1], 'Wrong number of active channels in transpose' def test_basic_construction_binder(): buffer, data = _create_binder(axis=1) map_source = MappedSource(buffer) shape = data.shape assert map_source.shape == shape, 'Shape wrong' assert map_source.binary_channel_mask.sum() == shape[0], 'Wrong number of active channels' # repeat for transpose map_source = MappedSource(buffer, transpose=True) assert map_source.shape == shape[::-1], 'Shape wrong in transpose' assert map_source.binary_channel_mask.sum() == shape[1], 'Wrong number of active channels in transpose' def test_construction_from_single_source(): aux_arrays = ('test1', 'test2') f = _create_hdf5(aux_arrays=aux_arrays) shape = f['data'].shape map_source = MappedSource.from_hdf_sources(f, 'data', aligned_arrays=aux_arrays) assert map_source.shape == shape, 'Shape wrong' assert map_source.binary_channel_mask.sum() == shape[0], 'Wrong number of active channels' for field in aux_arrays: assert hasattr(map_source, field), 'Aux field {} not preserved'.format(field) assert getattr(map_source, field).shape[1] == shape[1], 'aligned field {} wrong length'.format(field) # repeat for transpose map_source = MappedSource.from_hdf_sources(f, 'data', aligned_arrays=aux_arrays, transpose=True) assert map_source.shape == shape[::-1], 'Shape wrong in transpose' assert map_source.binary_channel_mask.sum() == shape[1], 'Wrong number of active channels in transpose' def test_construction_from_sources(): aux_arrays = ('test1', 'test2') files = [_create_hdf5(aux_arrays=aux_arrays) for i in range(3)] shape = files[0]['data'].shape shape = (shape[0], 3 * shape[1]) map_source = MappedSource.from_hdf_sources(files, 'data', aligned_arrays=aux_arrays) assert map_source.shape == shape, 'Shape wrong' assert map_source.binary_channel_mask.sum() == shape[0], 'Wrong number of active channels' for field in aux_arrays: assert hasattr(map_source, field), 'Aux field {} not preserved'.format(field) assert getattr(map_source, field).shape[1] == shape[1], 'aligned field {} wrong length'.format(field) # repeat for transpose: now sources are stacked on axis=0, but the resulting shape is transposed per vector # timeseries convention (channels X samples) shape = files[0]['data'].shape shape = (shape[0] * 3, shape[1]) map_source = MappedSource.from_hdf_sources(files, 'data', aligned_arrays=aux_arrays, transpose=True) assert map_source.shape == shape[::-1], 'Shape wrong in transpose' assert map_source.binary_channel_mask.sum() == shape[1], 'Wrong number of active channels in transpose' for field in aux_arrays: assert hasattr(map_source, field), 'Aux field {} not preserved'.format(field) assert getattr(map_source, field).shape[0] == shape[0], 'aligned field {} wrong length'.format(field) def test_joining(): aux_arrays = ('test1', 'test2') files = [_create_hdf5(aux_arrays=aux_arrays) for i in range(3)] map_source1 = MappedSource.from_hdf_sources(files, 'data', aligned_arrays=aux_arrays) next_file = _create_hdf5(aux_arrays=aux_arrays) map_source2 = MappedSource.from_hdf_sources(next_file, 'data', aligned_arrays=aux_arrays) full_map = map_source1.join(map_source2) assert full_map.shape == (len(map_source1), map_source1.shape[1] + map_source2.shape[1]), 'binder to buffer appending failed' full_map = map_source2.join(map_source1) assert full_map.shape == (len(map_source1), map_source1.shape[1] + map_source2.shape[1]), 'buffer to binder appending failed' def test_joiningT(): aux_arrays = ('test1', 'test2') files = [_create_hdf5(aux_arrays=aux_arrays) for i in range(3)] map_source1 = MappedSource.from_hdf_sources(files, 'data', aligned_arrays=aux_arrays, transpose=True) next_file = _create_hdf5(aux_arrays=aux_arrays) map_source2 = MappedSource.from_hdf_sources(next_file, 'data', aligned_arrays=aux_arrays, transpose=True) full_map = map_source1.join(map_source2) assert full_map.shape == (len(map_source1), map_source1.shape[1] + map_source2.shape[1]), 'binder to buffer appending failed' full_map = map_source2.join(map_source1) assert full_map.shape == (len(map_source1), map_source1.shape[1] + map_source2.shape[1]), 'buffer to binder appending failed' def test_direct_mapped(): f = _create_hdf5() mapped_source = MappedSource.from_hdf_sources(f, 'data') assert mapped_source.is_direct_map, 'direct map should be true' mapped_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=range(4)) assert not mapped_source.is_direct_map, 'direct map should be false' # for transposed disk arrays f = _create_hdf5(transpose=True) mapped_source = MappedSource.from_hdf_sources(f, 'data', transpose=True) assert mapped_source.is_direct_map, 'direct map should be true' mapped_source = MappedSource.from_hdf_sources(f, 'data', transpose=True, electrode_channels=range(4)) assert not mapped_source.is_direct_map, 'direct map should be false' def test_scaling(): f = _create_hdf5() float_data = f['data'][:, 500:1000].astype('d') map_source = MappedSource.from_hdf_sources(f, 'data', units_scale=2.0) assert np.all(map_source[:, 500:1000] == float_data * 2).all(), 'scalar scaling wrong' map_source = MappedSource.from_hdf_sources(f, 'data', units_scale=(-100, 2.0)) assert np.all(map_source[:, 500:1000] == (float_data - 100) * 2).all(), 'affine scaling wrong' def test_electrode_subset(): f = _create_hdf5() electrode_channels = [2, 4, 6, 8] map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels) data = f['data'][:, :][electrode_channels] assert np.all(data[:, 100:200] == map_source[:, 100:200]), 'electrode subset failed' def test_electrode_subsetT(): f = _create_hdf5(transpose=True) electrode_channels = [2, 4, 6, 8] map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels, transpose=True) data = f['data'][:, :][:, electrode_channels].T assert np.all(data[:, 100:200] == map_source[:, 100:200]), 'electrode subset failed in transpose' def test_channel_map(): f = _create_hdf5() electrode_channels = list(range(10)) binary_mask = np.ones(10, '?') binary_mask[:5] = False # so channels 5, 6, 7, 8, 9 should be active map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels) map_source.set_channel_mask(binary_mask) assert (map_source.binary_channel_mask == binary_mask).all(), 'binary mask wrong' data = f['data'][:, :][electrode_channels, :] assert np.all(data[5:, 100:200] == map_source[:, 100:200]), 'channel masking failed' # unmask map_source.set_channel_mask(None) binary_mask[:] = True assert (map_source.binary_channel_mask == binary_mask).all(), 'binary mask wrong' data = f['data'][:, :][electrode_channels, :] assert np.all(data[:, 100:200] == map_source[:, 100:200]), 'channel masking failed' def test_channel_mapT(): f = _create_hdf5(transpose=True) electrode_channels = list(range(10)) binary_mask = np.ones(10, '?') binary_mask[:5] = False # so channels 5, 6, 7, 8, 9 should be active map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels, transpose=True) map_source.set_channel_mask(binary_mask) assert (map_source.binary_channel_mask == binary_mask).all(), 'binary mask wrong in transpose' data = f['data'][:, :][:, electrode_channels].T assert np.all(data[5:, 100:200] == map_source[:, 100:200]), 'channel masking failed in transpose' # unmask map_source.set_channel_mask(None) binary_mask[:] = True assert (map_source.binary_channel_mask == binary_mask).all(), 'binary mask wrong' data = f['data'][:, :][:, electrode_channels].T assert np.all(data[:, 100:200] == map_source[:, 100:200]), 'channel masking failed' def test_channel_slicing(): f = _create_hdf5() electrode_channels = list(range(6, 17)) map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels, units_scale=5.0) data_first_channels = map_source[:3, :] with map_source.channels_are_maps(True): first_channels = map_source[:3] assert isinstance(first_channels, MappedSource), 'slice did not return new map' assert np.array_equal(data_first_channels, first_channels[:, :]), 'new map data mis-mapped' first_channels = map_source[:3] assert isinstance(first_channels, np.ndarray), 'slice-as-array failed' assert np.array_equal(data_first_channels, first_channels), 'slice-as-array wrong data' def test_channel_slicingT(): f = _create_hdf5(transpose=True) electrode_channels = list(range(6, 17)) map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels, transpose=True, units_scale=5.0) data_first_channels = map_source[:3, :] with map_source.channels_are_maps(True): first_channels = map_source[:3] assert isinstance(first_channels, MappedSource), 'slice did not return new map' assert np.array_equal(data_first_channels, first_channels[:, :]), 'new map data mis-mapped' first_channels = map_source[:3] assert isinstance(first_channels, np.ndarray), 'slice-as-array failed' assert np.array_equal(data_first_channels, first_channels), 'slice-as-array wrong data' def test_channel_slicing_with_mask(): f = _create_hdf5() electrode_channels = list(range(6, 17)) map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels) mask = map_source.binary_channel_mask mask[:5] = False map_source.set_channel_mask(mask) data_first_channels = map_source[:3, :] with map_source.channels_are_maps(True): first_channels = map_source[:3] assert isinstance(first_channels, MappedSource), 'slice did not return new map' assert np.array_equal(data_first_channels, first_channels[:, :]), 'new map data mis-mapped' first_channels = map_source[:3] assert isinstance(first_channels, np.ndarray), 'slice-as-array failed' assert np.array_equal(data_first_channels, first_channels), 'slice-as-array wrong data' def test_big_slicing_exception(): import ecogdata.expconfig.global_config as globalconfig f = _create_hdf5() data = f['data'] globalconfig.OVERRIDE['memory_limit'] = data.size * data.dtype.itemsize / 2.0 map_source = MappedSource.from_hdf_sources(f, 'data') with pytest.raises(MemoryBlowOutError): try: map_source[:, :] except Exception as e: raise e finally: globalconfig.OVERRIDE.pop('memory_limit') def test_big_slicing_allowed(): import ecogdata.expconfig.global_config as globalconfig f = _create_hdf5() data = f['data'] globalconfig.OVERRIDE['memory_limit'] = data.size * data.dtype.itemsize / 2.0 map_source = MappedSource.from_hdf_sources(f, 'data') try: with map_source.big_slices(True): _ = map_source[:, :] except MemoryBlowOutError as e: assert False, 'Big slicing context failed' finally: globalconfig.OVERRIDE.pop('memory_limit') def test_big_slicing_allowed_always(): import ecogdata.expconfig.global_config as globalconfig f = _create_hdf5() data = f['data'] globalconfig.OVERRIDE['memory_limit'] = data.size * data.dtype.itemsize / 2.0 map_source = MappedSource.from_hdf_sources(f, 'data', raise_on_big_slice=False) try: _ = map_source[:, :] except MemoryBlowOutError as e: assert False, 'Big slicing context failed' finally: globalconfig.OVERRIDE.pop('memory_limit') def test_write(): f = _create_hdf5() electrode_channels = list(range(10)) binary_mask = np.ones(10, '?') binary_mask[:5] = False # so channels 5, 6, 7, 8, 9 should be active map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels) shp = map_source.shape rand_pattern = np.random.randint(0, 100, size=(2, shp[1])) map_source[:2] = rand_pattern # use full-slice syntax to get data assert np.array_equal(map_source[:2, :], rand_pattern), 'write failed (map subset)' map_source.set_channel_mask(binary_mask) # write again map_source[:2] = rand_pattern assert np.array_equal(map_source[:2, :], rand_pattern), 'write failed (map subset and mask)' def test_write_to_binder(): files = [_create_hdf5() for i in range(3)] electrode_channels = list(range(10)) binary_mask = np.ones(10, '?') binary_mask[:5] = False # so channels 5, 6, 7, 8, 9 should be active map_source = MappedSource.from_hdf_sources(files, 'data', electrode_channels=electrode_channels) # make a write that spans buffers single_length = files[0]['data'].shape[1] rand_pattern = np.random.randint(0, 100, size=(2, 205)) sl = np.s_[:2, single_length - 100: single_length + 105] map_source[sl] = rand_pattern # use full-slice syntax to get data assert np.array_equal(map_source[sl], rand_pattern), 'write failed to binder (map subset)' map_source.set_channel_mask(binary_mask) # write again map_source[sl] = rand_pattern assert np.array_equal(map_source[sl], rand_pattern), 'write failed to binder (map subset and mask)' def test_iter(): f = _create_hdf5() electrode_channels = [2, 4, 6, 8] data = f['data'][:] block_size = data.shape[1] // 2 + 100 map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels) blocks = list(map_source.iter_blocks(block_size)) assert (data[electrode_channels][:, :block_size] == blocks[0]).all(), 'first block wrong' assert (data[electrode_channels][:, block_size:] == blocks[1]).all(), 'second block wrong' blocks = list(map_source.iter_blocks(block_size, reverse=True)) assert (data[electrode_channels][:, block_size:][:, ::-1] == blocks[0]).all(), 'first rev block wrong' assert (data[electrode_channels][:, :block_size][:, ::-1] == blocks[1]).all(), 'second rev block wrong' def test_iter_binder(): files = [_create_hdf5(n_cols=100) for i in range(3)] electrode_channels = [2, 4, 6, 8] data = np.concatenate([f['data'][:] for f in files], axis=1) block_size = data.shape[1] // 2 + 20 map_source = MappedSource.from_hdf_sources(files, 'data', electrode_channels=electrode_channels) blocks = list(map_source.iter_blocks(block_size)) assert (data[electrode_channels][:, :block_size] == blocks[0]).all(), 'first block wrong' assert (data[electrode_channels][:, block_size:] == blocks[1]).all(), 'second block wrong' blocks = list(map_source.iter_blocks(block_size, reverse=True)) assert (data[electrode_channels][:, block_size:][:, ::-1] == blocks[0]).all(), 'first rev block wrong' assert (data[electrode_channels][:, :block_size][:, ::-1] == blocks[1]).all(), 'second rev block wrong' def test_iter_overlap(): f = _create_hdf5(n_cols=100) data = f['data'][:] block_size = 20 overlap = 10 map_source = MappedSource.from_hdf_sources(f, 'data') blocks = list(map_source.iter_blocks(block_size, overlap=overlap)) assert (data[:, :block_size] == blocks[0]).all(), 'first block wrong' assert (data[:, (block_size - overlap):(2 * block_size - overlap)] == blocks[1]).all(), 'second block wrong' # last block is a partial, starting at index 90 assert (data[:, -10:] == blocks[-1]).all(), 'last block wrong' blocks = list(map_source.iter_blocks(block_size, reverse=True, overlap=overlap)) assert (data[:, :block_size] == blocks[-1][:, ::-1]).all(), 'first block wrong' assert (data[:, (block_size - overlap):(2 * block_size - overlap)] == blocks[-2][:, ::-1]).all(), 'second block wrong' assert (data[:, -10:] == blocks[0][:, ::-1]).all(), 'last block wrong' def test_iter_overlap_binder(): files = [_create_hdf5(n_cols=100) for i in range(3)] data = np.concatenate([f['data'][:] for f in files], axis=1) block_size = 20 overlap = 10 map_source = MappedSource.from_hdf_sources(files, 'data') blocks = list(map_source.iter_blocks(block_size, overlap=overlap)) assert (data[:, :block_size] == blocks[0]).all(), 'first block wrong' assert (data[:, (block_size - overlap):(2 * block_size - overlap)] == blocks[1]).all(), 'second block wrong' # last block is a partial, starting at index 90 assert (data[:, -10:] == blocks[-1]).all(), 'last block wrong' blocks = list(map_source.iter_blocks(block_size, reverse=True, overlap=overlap)) assert (data[:, :block_size] == blocks[-1][:, ::-1]).all(), 'first block wrong' assert (data[:, (block_size - overlap):(2 * block_size - overlap)] == blocks[-2][:, ::-1]).all(), 'second block wrong' assert (data[:, -10:] == blocks[0][:, ::-1]).all(), 'last block wrong' def test_iterT(): f = _create_hdf5(transpose=True) electrode_channels = [2, 4, 6, 8] data = f['data'][:].T block_size = data.shape[1] // 2 + 100 map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels, transpose=True) blocks = list(map_source.iter_blocks(block_size)) assert (data[electrode_channels][:, :block_size] == blocks[0]).all(), 'first block wrong in transpose' assert (data[electrode_channels][:, block_size:] == blocks[1]).all(), 'second block wrong in transpose' def test_iterT_binder(): files = [_create_hdf5(transpose=True, n_cols=100) for i in range(3)] data = np.concatenate([f['data'][:] for f in files], axis=0).T electrode_channels = [2, 4, 6, 8] block_size = data.shape[1] // 2 + 20 map_source = MappedSource.from_hdf_sources(files, 'data', electrode_channels=electrode_channels, transpose=True) blocks = list(map_source.iter_blocks(block_size)) assert (data[electrode_channels][:, :block_size] == blocks[0]).all(), 'first block wrong in transpose' assert (data[electrode_channels][:, block_size:] == blocks[1]).all(), 'second block wrong in transpose' def test_iter_channels(): f = _create_hdf5(n_rows=10, n_cols=100) map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=[2, 4, 6, 8, 9]) data = f['data'][:] channel_blocks = [] for chans in map_source.iter_channels(chans_per_block=2): channel_blocks.append(chans) for n, chans in enumerate(np.array_split(data[[2, 4, 6, 8, 9]], 3)): assert np.array_equal(channel_blocks[n], chans), 'channel block {} not equal'.format(n) def test_iter_channelsT(): f = _create_hdf5(n_rows=10, n_cols=100, transpose=True) map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=[2, 4, 6, 8, 9], transpose=True) data = f['data'][:].T channel_blocks = [] for chans in map_source.iter_channels(chans_per_block=2): channel_blocks.append(chans) for n, chans in enumerate(np.array_split(data[[2, 4, 6, 8, 9]], 3)): assert np.array_equal(channel_blocks[n], chans), 'channel block {} not equal'.format(n) def _clean_up_hdf_files(temp_files): for f in temp_files: name = f.filename f.close() if os.path.exists(name): os.unlink(name) def test_basic_mirror(): try: f = _create_hdf5(n_rows=25, n_cols=500) electrode_channels = [2, 4, 6, 8] map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels) temp_files = [] clone1 = map_source.mirror(new_rate_ratio=None, writeable=True, mapped=True, channel_compatible=False, filename='foo.h5') temp_files.append(clone1.data_buffer._array.file) assert clone1.shape == (len(electrode_channels), 500), 'wrong # of channels' assert clone1.writeable, 'Should be writeable' assert isinstance(clone1, MappedSource), 'Clone is not a MappedSource' clone2 = map_source.mirror(new_rate_ratio=None, mapped=False, channel_compatible=False) assert isinstance(clone2, PlainArraySource), 'Not-mapped file should be PlainArraySource' except Exception as e: raise e finally: _clean_up_hdf_files(temp_files) def test_mirror_modes(): f = _create_hdf5(n_rows=25, n_cols=500) electrode_channels = [2, 4, 6, 8] map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels) clone1 = map_source.mirror(writeable=True, mapped=True, channel_compatible=False) assert clone1.shape == (len(electrode_channels), 500), 'wrong # of samples' clone2 = map_source.mirror(writeable=True, mapped=True, channel_compatible=True) assert clone2.data_buffer.shape == (25, 500), 'wrong # of channels for channel-compat' f = _create_hdf5(n_rows=25, n_cols=500, transpose=True) map_source = MappedSource.from_hdf_sources(f, 'data', electrode_channels=electrode_channels, transpose=True) clone3 = map_source.mirror(mapped=True, channel_compatible=True) assert clone3.data_buffer.shape == (25, 500), 'mapped mirror did not reverse the source transpose'

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Python

tests/e2e/test_api_assembly_strategy_combinations_book_then_lang.py

danparisd/InterleavedResourcesGenerator

150e6c223e2eb9f63ebb41c6ba4b7c5d3337e1dc

[ "MIT" ]

null

tests/e2e/test_api_assembly_strategy_combinations_book_then_lang.py

danparisd/InterleavedResourcesGenerator

150e6c223e2eb9f63ebb41c6ba4b7c5d3337e1dc

[ "MIT" ]

null

tests/e2e/test_api_assembly_strategy_combinations_book_then_lang.py

danparisd/InterleavedResourcesGenerator

150e6c223e2eb9f63ebb41c6ba4b7c5d3337e1dc

[ "MIT" ]

1

2021-09-10T20:37:07.000Z

import os import pathlib import re import bs4 import pytest import requests from document.config import settings from document.entrypoints.app import app from fastapi.testclient import TestClient ################################################## ## Tests for assembly strategy book -hen-language def check_finished_document_with_verses_success( response: requests.Response, finished_document_path: str ) -> None: """ Helper to keep tests DRY. Check that the finished_document_path exists and also check that the HTML file associated with it exists and includes verses_html. """ finished_document_path = os.path.join(settings.output_dir(), finished_document_path) assert os.path.isfile(finished_document_path) html_file = "{}.html".format(finished_document_path.split(".")[0]) assert os.path.isfile(html_file) assert response.json() == { "finished_document_request_key": pathlib.Path(finished_document_path).stem, "message": settings.SUCCESS_MESSAGE, } with open(html_file, "r") as fin: html = fin.read() parser = bs4.BeautifulSoup(html, "html.parser") body: bs4.elements.ResultSet = parser.find_all("body") assert body verses_html: bs4.elements.ResultSet = parser.find_all( "span", attrs={"class": "v-num"} ) assert verses_html assert response.ok def check_finished_document_with_body_success( response: requests.Response, finished_document_path: str ) -> None: """ Helper to keep tests DRY. Check that the finished_document_path exists and also check that the HTML file associated with it exists and includes body. """ finished_document_path = os.path.join(settings.output_dir(), finished_document_path) assert os.path.isfile(finished_document_path) html_file = "{}.html".format(finished_document_path.split(".")[0]) assert os.path.isfile(html_file) assert response.json() == { "finished_document_request_key": pathlib.Path(finished_document_path).stem, "message": settings.SUCCESS_MESSAGE, } with open(html_file, "r") as fin: html = fin.read() parser = bs4.BeautifulSoup(html, "html.parser") body: bs4.elements.ResultSet = parser.find_all("body") assert body assert response.ok def check_finished_document_without_verses_success( response: requests.Response, finished_document_path: str ) -> None: """ Helper to keep tests DRY. Check that the finished_document_path exists and also check that the HTML file associated with it exists and includes body but not verses_html. """ finished_document_path = os.path.join(settings.output_dir(), finished_document_path) assert os.path.exists(finished_document_path) html_file = "{}.html".format(finished_document_path.split(".")[0]) assert os.path.exists(html_file) with open(html_file, "r") as fin: html = fin.read() parser = bs4.BeautifulSoup(html, "html.parser") body: bs4.elements.ResultSet = parser.find_all("body") assert body verses_html: bs4.elements.ResultSet = parser.find_all( "span", attrs={"class": "v-num"} ) # reg is malformed and udb does not exist, thus there is # no html generated assert not verses_html assert response.ok def test_en_ulb_wa_col_en_tn_wa_col_en_tq_wa_col_en_tw_wa_col_fr_f10_col_fr_tn_col_fr_tq_col_fr_tw_col_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "col", }, { "lang_code": "fr", "resource_type": "f10", "resource_code": "col", }, { "lang_code": "fr", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "fr", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "fr", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = "en-ulb-wa-col_en-tn-wa-col_en-tq-wa-col_en-tw-wa-col_fr-f10-col_fr-tn-col_fr-tq-col_fr-tw-col_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_en_ulb_wa_col_en_tn_wa_col_en_tq_wa_col_en_tw_wa_col_pt_br_ulb_col_pt_br_tn_col_pt_br_tq_col_pt_br_tw_col_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "col", }, { "lang_code": "pt-br", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "pt-br", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "pt-br", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "pt-br", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = "en-ulb-wa-col_en-tn-wa-col_en-tq-wa-col_en-tw-wa-col_pt-br-ulb-col_pt-br-tn-col_pt-br-tq-col_pt-br-tw-col_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_pt_br_ulb_col_pt_br_tn_col_pt_br_tq_col_pt_br_tw_col_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "pt-br", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "pt-br", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "pt-br", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "pt-br", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = "pt-br-ulb-col_pt-br-tn-col_pt-br-tq-col_pt-br-tw-col_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_fr_f10_col_fr_tn_col_fr_tq_col_fr_tw_col_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "fr", "resource_type": "f10", "resource_code": "col", }, { "lang_code": "fr", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "fr", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "fr", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = ( "fr-f10-col_fr-tn-col_fr-tq-col_fr-tw-col_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_en_ulb_wa_col_en_tn_wa_col_en_tq_wa_col_en_tw_wa_col_tl_ulb_col_tl_tn_col_tl_tq_col_tl_tw_col_tl_udb_col_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "col", }, { "lang_code": "tl", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "tl", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "tl", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "tl", "resource_type": "tw", "resource_code": "col", }, { "lang_code": "tl", "resource_type": "udb", "resource_code": "col", }, ], }, ) finished_document_path = "en-ulb-wa-col_en-tn-wa-col_en-tq-wa-col_en-tw-wa-col_tl-ulb-col_tl-tn-col_tl-tq-col_tl-tw-col_tl-udb-col_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_en_ulb_wa_tit_en_tn_wa_tit_book_language_order() -> None: "English ulb-wa and tn-wa for book of Timothy." with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "tit", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "tit", }, ], }, ) finished_document_path = "en-ulb-wa-tit_en-tn-wa-tit_book_language_order.pdf" finished_document_path = os.path.join( settings.output_dir(), finished_document_path ) assert os.path.isfile(finished_document_path) assert response.json() == { "finished_document_request_key": pathlib.Path(finished_document_path).stem, "message": settings.SUCCESS_MESSAGE, } def test_sw_ulb_col_sw_tn_col_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "sw", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "col", }, ], }, ) finished_document_path = "sw-ulb-col_sw-tn-col_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_sw_ulb_col_sw_tn_col_sw_ulb_tit_sw_tn_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "sw", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "ulb", "resource_code": "tit", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "tit", }, ], }, ) finished_document_path = ( "sw-ulb-col_sw-tn-col_sw-ulb-tit_sw-tn-tit_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_en_ulb_wa_col_en_tn_wa_col_sw_ulb_col_sw_tn_col_sw_ulb_tit_sw_tn_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "ulb", "resource_code": "tit", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "tit", }, ], }, ) finished_document_path = "en-ulb-wa-col_en-tn-wa-col_sw-ulb-col_sw-tn-col_sw-ulb-tit_sw-tn-tit_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_en_ulb_wa_col_en_tn_wa_col_en_tq_wa_col_sw_ulb_col_sw_tn_col_sw_tq_col_sw_ulb_tit_sw_tn_tit_sw_tq_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "ulb", "resource_code": "tit", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "tit", }, { "lang_code": "sw", "resource_type": "tq", "resource_code": "tit", }, ], }, ) finished_document_path = "en-ulb-wa-col_en-tn-wa-col_en-tq-wa-col_sw-ulb-col_sw-tn-col_sw-tq-col_sw-ulb-tit_sw-tn-tit_sw-tq-tit_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_en_ulb_wa_col_en_tq_wa_col_sw_ulb_col_sw_tq_col_sw_ulb_tit_sw_tq_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "ulb", "resource_code": "tit", }, { "lang_code": "sw", "resource_type": "tq", "resource_code": "tit", }, ], }, ) finished_document_path = "en-ulb-wa-col_en-tq-wa-col_sw-ulb-col_sw-tq-col_sw-ulb-tit_sw-tq-tit_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_en_tn_wa_col_en_tq_wa_col_en_tw_wa_col_sw_tn_col_sw_tq_col_sw_tw_col_sw_tn_tit_sw_tq_tit_sw_tw_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tw", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "tit", }, { "lang_code": "sw", "resource_type": "tq", "resource_code": "tit", }, { "lang_code": "sw", "resource_type": "tw", "resource_code": "tit", }, ], }, ) finished_document_path = "en-tn-wa-col_en-tq-wa-col_en-tw-wa-col_sw-tn-col_sw-tq-col_sw-tw-col_sw-tn-tit_sw-tq-tit_sw-tw-tit_book_language_order.pdf" check_finished_document_with_body_success(response, finished_document_path) def test_en_tn_wa_col_en_tw_wa_col_sw_tn_col_sw_tw_col_sw_tn_tit_sw_tw_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tw", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "tit", }, { "lang_code": "sw", "resource_type": "tw", "resource_code": "tit", }, ], }, ) finished_document_path = "en-tn-wa-col_en-tw-wa-col_sw-tn-col_sw-tw-col_sw-tn-tit_sw-tw-tit_book_language_order.pdf" check_finished_document_with_body_success(response, finished_document_path) def test_en_tq_wa_col_en_tw_wa_col_sw_tq_col_sw_tw_col_sw_tq_tit_sw_tw_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = ( "en-tq-wa-col_en-tw-wa-col_sw-tq-col_sw-tw-col_book_language_order.pdf" ) check_finished_document_with_body_success(response, finished_document_path) def test_en_tw_wa_col_sw_tw_col_sw_tw_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = "en-tw-wa-col_sw-tw-col_book_language_order.pdf" check_finished_document_with_body_success(response, finished_document_path) def test_en_tn_wa_col_en_tq_wa_col_sw_tn_col_sw_tq_col_sw_tn_tit_sw_tq_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tq", "resource_code": "col", }, ], }, ) finished_document_path = ( "en-tn-wa-col_en-tq-wa-col_sw-tn-col_sw-tq-col_book_language_order.pdf" ) check_finished_document_with_body_success(response, finished_document_path) def test_en_tq_wa_col_sw_tq_col_sw_tq_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tq", "resource_code": "col", }, ], }, ) finished_document_path = "en-tq-wa-col_sw-tq-col_book_language_order.pdf" check_finished_document_with_body_success(response, finished_document_path) def test_en_tn_wa_col_sw_tn_col_sw_tn_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "tn", "resource_code": "tit", }, ], }, ) finished_document_path = ( "en-tn-wa-col_sw-tn-col_sw-tn-tit_book_language_order.pdf" ) check_finished_document_with_body_success(response, finished_document_path) def test_en_ulb_wa_col_sw_ulb_col_sw_ulb_tit_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "sw", "resource_type": "ulb", "resource_code": "tit", }, ], }, ) finished_document_path = ( "en-ulb-wa-col_sw-ulb-col_sw-ulb-tit_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_gu_ulb_mrk_gu_tn_mrk_gu_tq_mrk_gu_tw_mrk_gu_udb_mrk_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "gu", "resource_type": "ulb", "resource_code": "mrk", }, { "lang_code": "gu", "resource_type": "tn", "resource_code": "mrk", }, { "lang_code": "gu", "resource_type": "tq", "resource_code": "mrk", }, { "lang_code": "gu", "resource_type": "tw", "resource_code": "mrk", }, { "lang_code": "gu", "resource_type": "udb", "resource_code": "mrk", }, ], }, ) finished_document_path = "gu-ulb-mrk_gu-tn-mrk_gu-tq-mrk_gu-tw-mrk_gu-udb-mrk_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_mr_ulb_mrk_mr_tn_mrk_mr_tq_mrk_mr_tw_mrk_mr_udb_mrk_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "mr", "resource_type": "ulb", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "tn", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "tq", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "tw", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "udb", "resource_code": "mrk", }, ], }, ) finished_document_path = "mr-ulb-mrk_mr-tn-mrk_mr-tq-mrk_mr-tw-mrk_mr-udb-mrk_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_mr_ulb_mrk_mr_tn_mrk_mr_tq_mrk_mr_udb_mrk_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "mr", "resource_type": "ulb", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "tn", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "tq", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "udb", "resource_code": "mrk", }, ], }, ) finished_document_path = ( "mr-ulb-mrk_mr-tn-mrk_mr-tq-mrk_mr-udb-mrk_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_mr_ulb_mrk_mr_tn_mrk_mr_tw_mrk_mr_udb_mrk_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "mr", "resource_type": "ulb", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "tn", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "tw", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "udb", "resource_code": "mrk", }, ], }, ) finished_document_path = ( "mr-ulb-mrk_mr-tn-mrk_mr-tw-mrk_mr-udb-mrk_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_mr_ulb_mrk_mr_tn_mrk_mr_udb_mrk_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "mr", "resource_type": "ulb", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "tn", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "udb", "resource_code": "mrk", }, ], }, ) finished_document_path = ( "mr-ulb-mrk_mr-tn-mrk_mr-udb-mrk_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_mr_ulb_mrk_mr_tq_mrk_mr_udb_mrk_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "mr", "resource_type": "ulb", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "tq", "resource_code": "mrk", }, { "lang_code": "mr", "resource_type": "udb", "resource_code": "mrk", }, ], }, ) finished_document_path = ( "mr-ulb-mrk_mr-tq-mrk_mr-udb-mrk_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) @pytest.mark.skip def test_gu_ulb_mic_gu_tn_mic_gu_tq_mic_gu_tw_mic_gu_ta_mic_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "gu", "resource_type": "ulb", "resource_code": "mic", }, { "lang_code": "gu", "resource_type": "tn", "resource_code": "mic", }, { "lang_code": "gu", "resource_type": "tq", "resource_code": "mic", }, { "lang_code": "gu", "resource_type": "tw", "resource_code": "mic", }, { "lang_code": "gu", "resource_type": "ta", "resource_code": "mic", }, ], }, ) finished_document_path = ( "gu-ulb-mic_gu-tn-mic_gu-tq-mic_gu-tw-mic_gu-ta-mic_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_tl_ulb_gen_tl_udb_gen_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "tl", "resource_type": "ulb", "resource_code": "gen", }, { "lang_code": "tl", "resource_type": "udb", "resource_code": "gen", }, ], }, ) finished_document_path = "tl-ulb-gen_tl-udb-gen_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_gu_tn_mat_gu_tq_mat_gu_tw_mat_gu_udb_mat_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "gu", "resource_type": "tn", "resource_code": "mat", }, { "lang_code": "gu", "resource_type": "tq", "resource_code": "mat", }, { "lang_code": "gu", "resource_type": "tw", "resource_code": "mat", }, { "lang_code": "gu", "resource_type": "udb", "resource_code": "mat", }, ], }, ) finished_document_path = ( "gu-tn-mat_gu-tq-mat_gu-tw-mat_gu-udb-mat_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_gu_tn_mat_gu_tq_mat_gu_udb_mat_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "gu", "resource_type": "tn", "resource_code": "mat", }, { "lang_code": "gu", "resource_type": "tq", "resource_code": "mat", }, { "lang_code": "gu", "resource_type": "udb", "resource_code": "mat", }, ], }, ) finished_document_path = ( "gu-tn-mat_gu-tq-mat_gu-udb-mat_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_tl_tn_gen_tl_tw_gen_tl_udb_gen_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "tl", "resource_type": "tn", "resource_code": "gen", }, { "lang_code": "tl", "resource_type": "tw", "resource_code": "gen", }, { "lang_code": "tl", "resource_type": "udb", "resource_code": "gen", }, ], }, ) finished_document_path = ( "tl-tn-gen_tl-tw-gen_tl-udb-gen_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_tl_tq_gen_tl_udb_gen_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "tl", "resource_type": "tq", "resource_code": "gen", }, { "lang_code": "tl", "resource_type": "udb", "resource_code": "gen", }, ], }, ) finished_document_path = "tl-tq-gen_tl-udb-gen_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_tl_tw_gen_tl_udb_gen_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "tl", "resource_type": "tw", "resource_code": "gen", }, { "lang_code": "tl", "resource_type": "udb", "resource_code": "gen", }, ], }, ) finished_document_path = "tl-tw-gen_tl-udb-gen_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_tl_udb_gen_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "tl", "resource_type": "udb", "resource_code": "gen", }, ], }, ) finished_document_path = "tl-udb-gen_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_fr_ulb_rev_fr_tn_rev_fr_tq_rev_fr_tw_rev_fr_udb_rev_book_language_order() -> None: """Demonstrate listing unfound resources, in this case fr-udb-rev""" with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "fr", "resource_type": "ulb", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "tn", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "tq", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "tw", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "udb", "resource_code": "rev", }, ], }, ) finished_document_path = "fr-ulb-rev_fr-tn-rev_fr-tq-rev_fr-tw-rev_fr-udb-rev_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_fr_ulb_rev_fr_tn_rev_fr_tq_rev_fr_tw_rev_fr_f10_rev_book_language_order() -> None: """ Demonstrate two USFM resources, French, and use of a special USFM resource: f10. """ with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "fr", "resource_type": "ulb", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "tn", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "tq", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "tw", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "f10", "resource_code": "rev", }, ], }, ) finished_document_path = "fr-ulb-rev_fr-tn-rev_fr-tq-rev_fr-tw-rev_fr-f10-rev_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_fr_ulb_rev_fr_tq_rev_fr_tw_rev_fr_f10_rev_book_language_order() -> None: """ Demonstrate two USFM resources, French, and use of a special USFM resource: f10. """ with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "fr", "resource_type": "ulb", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "tq", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "tw", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "f10", "resource_code": "rev", }, ], }, ) finished_document_path = ( "fr-ulb-rev_fr-tq-rev_fr-tw-rev_fr-f10-rev_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_fr_ulb_rev_fr_tw_rev_fr_udb_rev_book_language_order() -> None: """Demonstrate listing unfound resources, in this case fr-udb-rev""" with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "fr", "resource_type": "ulb", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "tw", "resource_code": "rev", }, { "lang_code": "fr", "resource_type": "f10", "resource_code": "rev", }, ], }, ) finished_document_path = ( "fr-ulb-rev_fr-tw-rev_fr-f10-rev_book_language_order.pdf" ) check_finished_document_with_verses_success(response, finished_document_path) def test_ndh_x_chindali_reg_mat_ndh_x_chindali_tn_mat_ndh_x_chindali_tq_mat_ndh_x_chindali_tw_mat_ndh_x_chindali_udb_mat_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "ndh-x-chindali", "resource_type": "reg", "resource_code": "mat", }, { "lang_code": "ndh-x-chindali", "resource_type": "tn", "resource_code": "mat", }, { "lang_code": "ndh-x-chindali", "resource_type": "tq", "resource_code": "mat", }, { "lang_code": "ndh-x-chindali", "resource_type": "tw", "resource_code": "mat", }, { "lang_code": "ndh-x-chindali", "resource_type": "udb", "resource_code": "mat", }, ], }, ) finished_document_path = "ndh-x-chindali-reg-mat_ndh-x-chindali-tn-mat_ndh-x-chindali-tq-mat_ndh-x-chindali-tw-mat_ndh-x-chindali-udb-mat_book_language_order.pdf" with pytest.raises(Exception): check_finished_document_without_verses_success( response, finished_document_path ) def test_en_ulb_wa_col_en_tn_wa_col_en_tq_wa_col_en_tw_wa_col_es_419_ulb_col_es_419_tn_col_es_419_tq_col_es_419_tw_col_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "col", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "col", }, { "lang_code": "es-419", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "es-419", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "es-419", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "es-419", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = "en-ulb-wa-col_en-tn-wa-col_en-tq-wa-col_en-tw-wa-col_es-419-ulb-col_es-419-tn-col_es-419-tq-col_es-419-tw-col_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_es_ulb_col_es_tn_col_en_tq_col_es_tw_col_book_language_order() -> None: """ Ask for a combination of available and unavailable resources. """ with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "es", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "es", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "es", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "es", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = ( "es-ulb-col_es-tn-col_es-tq-col_es-tw-col_book_language_order.pdf" ) check_finished_document_without_verses_success(response, finished_document_path) def test_llx_ulb_col_llx_tn_col_en_tq_col_llx_tw_col_book_language_order() -> None: """ Ask for an unavailable resource and assert that the verses_html is not generated. """ with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "llx", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "llx", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "llx", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "llx", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = ( "llx-ulb-col_llx-tn-col_llx-tq-col_llx-tw-col_book_language_order.pdf" ) check_finished_document_without_verses_success(response, finished_document_path) def test_llx_reg_col_llx_tn_col_en_tq_col_llx_tw_col_book_language_order() -> None: """ Ask for an unavailable resource and assert that the verses_html is not generated. """ with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "llx", "resource_type": "reg", "resource_code": "col", }, { "lang_code": "llx", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "llx", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "llx", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = ( "llx-reg-col_llx-tn-col_llx-tq-col_llx-tw-col_book_language_order.pdf" ) finished_document_path = os.path.join( settings.output_dir(), finished_document_path ) html_file = "{}.html".format(finished_document_path.split(".")[0]) assert os.path.exists(finished_document_path) assert os.path.exists(html_file) assert response.ok with open(html_file, "r") as fin: html = fin.read() parser = bs4.BeautifulSoup(html, "html.parser") body: bs4.elements.ResultSet = parser.find_all("body") assert body verses_html: bs4.elements.ResultSet = parser.find_all( "span", attrs={"class": "v-num"} ) # Resource requested doesn't exist or isn't available so # we assert that the verses_html was not generated and # thus not present in the document. assert not verses_html def test_es_419_ulb_col_es_419_tn_col_en_tq_col_es_419_tw_col_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "es-419", "resource_type": "ulb", "resource_code": "col", }, { "lang_code": "es-419", "resource_type": "tn", "resource_code": "col", }, { "lang_code": "es-419", "resource_type": "tq", "resource_code": "col", }, { "lang_code": "es-419", "resource_type": "tw", "resource_code": "col", }, ], }, ) finished_document_path = "es-419-ulb-col_es-419-tn-col_es-419-tq-col_es-419-tw-col_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_es_419_ulb_rom_es_419_tn_rom_en_tq_rom_es_419_tw_rom_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "es-419", "resource_type": "ulb", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "tn", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "tq", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "tw", "resource_code": "rom", }, ], }, ) finished_document_path = "es-419-ulb-rom_es-419-tn-rom_es-419-tq-rom_es-419-tw-rom_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_en_ulb_wa_rom_en_tn_wa_rom_en_tq_wa_rom_en_tw_wa_rom_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "rom", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "rom", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "rom", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "rom", }, ], }, ) finished_document_path = "en-ulb-wa-rom_en-tn-wa-rom_en-tq-wa-rom_en-tw-wa-rom_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) # BUG See output in ~/.ghq/bitbucket.org/foobar77/timesheets/worklog3.org [[id:6F839365-1C34-4F36-B056-A91B8E5E92B5][Logs]] # @pytest.mark.skip def test_en_ulb_wa_rom_en_tn_wa_rom_en_tq_wa_rom_en_tw_wa_rom_es_419_ulb_rom_es_419_tn_rom_en_tq_rom_es_419_tw_rom_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "rom", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "rom", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "rom", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "ulb", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "tn", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "tq", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "tw", "resource_code": "rom", }, ], }, ) finished_document_path = "en-ulb-wa-rom_en-tn-wa-rom_en-tq-wa-rom_en-tw-wa-rom_es-419-ulb-rom_es-419-tn-rom_es-419-tq-rom_es-419-tw-rom_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_en_ulb_wa_jon_en_tn_wa_jon_en_tq_wa_jon_en_tw_wa_jon_es_419_ulb_rom_es_419_tn_rom_en_tq_rom_es_419_tw_rom_book_language_order() -> None: with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "en", "resource_type": "ulb-wa", "resource_code": "jon", }, { "lang_code": "en", "resource_type": "tn-wa", "resource_code": "jon", }, { "lang_code": "en", "resource_type": "tq-wa", "resource_code": "jon", }, { "lang_code": "en", "resource_type": "tw-wa", "resource_code": "jon", }, { "lang_code": "es-419", "resource_type": "ulb", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "tn", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "tq", "resource_code": "rom", }, { "lang_code": "es-419", "resource_type": "tw", "resource_code": "rom", }, ], }, ) finished_document_path = "en-ulb-wa-jon_en-tn-wa-jon_en-tq-wa-jon_en-tw-wa-jon_es-419-ulb-rom_es-419-tn-rom_es-419-tq-rom_es-419-tw-rom_book_language_order.pdf" check_finished_document_with_verses_success(response, finished_document_path) def test_invalid_document_request() -> None: with pytest.raises(Exception): with TestClient(app=app, base_url=settings.api_test_url()) as client: response: requests.Response = client.post( "/documents", json={ "email_address": settings.TO_EMAIL_ADDRESS, "assembly_strategy_kind": "book_language_order", "resource_requests": [ { "lang_code": "", "resource_type": "xxx", "resource_code": "blah", }, ], }, ) finished_document_path = "invalid_file_that_doesnt_exist.pdf" check_finished_document_with_verses_success( response, finished_document_path )

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null

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null

0

6

7c8c6883fff5d98216807c1edf9e2f1f7e73ae23

4,870

py

Python

src/xhorizon/shell_junction/reg_corner_masks.py

xh-diagrams/xhorizon

20b3f2f0f621ca2a31c9f6a1d5fcd06692a700ce

[ "MIT" ]

1

2020-04-01T16:14:00.000Z

src/xhorizon/shell_junction/reg_corner_masks.py

xh-diagrams/xhorizon

20b3f2f0f621ca2a31c9f6a1d5fcd06692a700ce

[ "MIT" ]

1

2020-04-26T14:41:31.000Z

src/xhorizon/shell_junction/reg_corner_masks.py

xh-diagrams/xhorizon

20b3f2f0f621ca2a31c9f6a1d5fcd06692a700ce

[ "MIT" ]

1

2021-04-15T09:23:29.000Z

""" This module provides functions for applying uvbounds masks to correct blocks in a given region. These functions should directly edit and return the input region object. """ import numpy as np def EFreg(reg, abcd=None, u0=None, v0=None): """ """ ## sort if (len(reg.blocks)-1)==1: if reg.metfunc.sgnf(0)==-1.0: out = EFreg1a(reg, abcd=abcd, u0=u0, v0=v0) if (len(reg.blocks)-1)==2: if reg.metfunc.sgnf(0)==1.0: out = EFreg2a(reg, abcd=abcd, u0=u0, v0=v0) ## return return out def EFreg2a(reg, abcd=None, u0=None, v0=None): """ For Hayward-like regions (aka N=2 and f(0)>0). Assumes slice in outermost block. reg = The region to mask. abcd = Which corner junction label is this piece, with standard label scheme. Should be a single letter string 'a' 'b' 'c' or 'd'. u0, v0 = Location to slice. Returns the same region that was input, after editing. Region is masked by removing unwanted blocks and setting uvbounds on remaining blocks. Block order is [inner, middle, outer] (this is arbitrary, just happens to be how EFreg2a is generated). Setting any of the uvbounds to np.nan causes all inequalities to fail, which causes an error unless block is removed. """ ## initialize uvb = [dict(), dict(), dict()] ## set proper uvbounds for each block based on abcd type ## case a if abcd=='a': uvb = [dict(vmin=v0), dict(vmin=v0), dict(vmin=v0, umin=u0)] ## case b if abcd=='b': uvb = [dict(vmin=np.nan), dict(vmin=np.nan), dict(vmax=v0,umax=u0)] ## case c if abcd=='c': uvb = [dict(vmin=np.nan), dict(vmin=np.nan), dict(vmin=v0,umax=u0)] ## case d if abcd=='d': uvb = [dict(vmax=v0), dict(vmax=v0), dict(vmax=v0, umin=u0)] ## update blocks uvbounds for i in range(len(reg.blocks)): reg.blocks[i].uvbounds.update(uvb[i]) ## keep blocks only if no nan in uvbounds values keep = [] for b in reg.blocks: if not np.nan in b.uvbounds.values(): keep += [b] reg.blocks = keep ## return return reg def EFreg1a(reg, abcd=None, u0=None, v0=None): """ For Schwarzschild-like regions (aka N=1 and f(0)<0). Assumes slice in outermost block. reg = The region to mask. abcd = Which corner junction label is this piece, with standard label scheme. Should be a single letter string 'a' 'b' 'c' or 'd'. u0, v0 = Location to slice. Returns the same region that was input, after editing. Region is masked by removing unwanted blocks and setting uvbounds on remaining blocks. Block order is [inner, outer] (this is arbitrary, just happens to be how EFreg1a is generated). Setting any of the uvbounds to np.nan causes all inequalities to fail, which causes an error unless block is removed. """ ## initialize uvb = [dict(), dict()] ## set proper uvbounds for each block based on abcd type ## case a if abcd=='a': uvb = [dict(vmin=v0), dict(vmin=v0, umin=u0)] ## case b if abcd=='b': uvb = [dict(vmin=np.nan), dict(vmax=v0,umax=u0)] ## case c if abcd=='c': uvb = [dict(vmin=np.nan), dict(vmin=v0,umax=u0)] ## case d if abcd=='d': uvb = [dict(vmax=v0), dict(vmax=v0, umin=u0)] ## update blocks uvbounds for i in range(len(reg.blocks)): reg.blocks[i].uvbounds.update(uvb[i]) ## keep blocks only if no nan in uvbounds values keep = [] for b in reg.blocks: if not np.nan in b.uvbounds.values(): keep += [b] reg.blocks = keep ## return return reg def MAXreg2a(reg, abcd=None, u0=None, v0=None): """ For Schwarzschild-like regions (aka N=2 and f(0)>0). reg = The region to mask. abcd = Which corner junction label is this piece, with standard label scheme. Should be a single letter string 'a' 'b' 'c' or 'd'. u0, v0 = Location to slice. Returns the same region that was input, after editing. Region is masked by removing unwanted blocks and setting uvbounds on remaining blocks. Block order is [top, right, bottom, left] (this is arbitrary, just happens to be how MAXreg2a is generated). Setting any of the uvbounds to np.nan causes all inequalities to fail, which causes an error unless block is removed. """ ## initialize uvb = [dict(), dict(), dict(), dict()] ## set proper uvbounds for each block based on abcd type ## case a if abcd=='a': uvb = [dict(vmin=v0), dict(vmin=v0,umin=u0), dict(vmin=np.nan), dict(vmin=np.nan)] ## case b if abcd=='b': uvb = [dict(vmin=np.nan), dict(vmax=v0,umax=u0), dict(umax=u0), dict(vmin=np.nan)] ## case c if abcd=='c': uvb = [dict(vmin=np.nan), dict(vmin=v0,umax=u0), dict(vmin=np.nan), dict(vmin=np.nan)] ## case d if abcd=='d': uvb = [dict(vmax=v0), dict(vmax=v0,umin=u0), dict(umin=u0), dict()] ## update blocks uvbounds for i in range(len(reg.blocks)): reg.blocks[i].uvbounds.update(uvb[i]) ## keep blocks only if no nan in uvbounds values keep = [] for b in reg.blocks: if not np.nan in b.uvbounds.values(): keep += [b] reg.blocks = keep ## return return reg

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0

null

0

1

0

null

0

6

7cbbae958f35ae19b481b8ed05d1b2e7648e8b54

4,501

py

Python

tests/test_middleware.py

miracle2k/hypercorn

b062659a476e7508e8c4ea5cb329a13b0e24074b

[ "MIT" ]

null

tests/test_middleware.py

miracle2k/hypercorn

b062659a476e7508e8c4ea5cb329a13b0e24074b

[ "MIT" ]

null

tests/test_middleware.py

miracle2k/hypercorn

b062659a476e7508e8c4ea5cb329a13b0e24074b

[ "MIT" ]

null

from typing import Callable import pytest from hypercorn.middleware import DispatcherMiddleware, HTTPToHTTPSRedirectMiddleware from .helpers import empty_framework @pytest.mark.asyncio async def test_http_to_https_redirect_middleware_http() -> None: app = HTTPToHTTPSRedirectMiddleware(empty_framework, "localhost") sent_events = [] async def send(message: dict) -> None: nonlocal sent_events sent_events.append(message) scope = {"type": "http", "scheme": "http", "path": "/abc", "query_string": b"a=b"} await app(scope, None, send) assert sent_events == [ { "type": "http.response.start", "status": 307, "headers": [(b"location", b"https://localhost/abc?a=b")], }, {"type": "http.response.body"}, ] @pytest.mark.asyncio async def test_http_to_https_redirect_middleware_websocket() -> None: app = HTTPToHTTPSRedirectMiddleware(empty_framework, "localhost") sent_events = [] async def send(message: dict) -> None: nonlocal sent_events sent_events.append(message) scope = { "type": "websocket", "scheme": "ws", "path": "/abc", "query_string": b"a=b", "extensions": {"websocket.http.response": {}}, } await app(scope, None, send) assert sent_events == [ { "type": "websocket.http.response.start", "status": 307, "headers": [(b"location", b"wss://localhost/abc?a=b")], }, {"type": "websocket.http.response.body"}, ] @pytest.mark.asyncio async def test_http_to_https_redirect_middleware_websocket_http2() -> None: app = HTTPToHTTPSRedirectMiddleware(empty_framework, "localhost") sent_events = [] async def send(message: dict) -> None: nonlocal sent_events sent_events.append(message) scope = { "type": "websocket", "http_version": "2", "scheme": "ws", "path": "/abc", "query_string": b"a=b", "extensions": {"websocket.http.response": {}}, } await app(scope, None, send) assert sent_events == [ { "type": "websocket.http.response.start", "status": 307, "headers": [(b"location", b"https://localhost/abc?a=b")], }, {"type": "websocket.http.response.body"}, ] @pytest.mark.asyncio async def test_http_to_https_redirect_middleware_websocket_no_rejection() -> None: app = HTTPToHTTPSRedirectMiddleware(empty_framework, "localhost") sent_events = [] async def send(message: dict) -> None: nonlocal sent_events sent_events.append(message) scope = { "type": "websocket", "http_version": "2", "scheme": "ws", "path": "/abc", "query_string": b"a=b", } await app(scope, None, send) assert sent_events == [{"type": "websocket.close"}] @pytest.mark.asyncio async def test_dispatcher_middleware() -> None: class EchoFramework: def __init__(self, name: str) -> None: self.name = name async def __call__(self, scope: dict, receive: Callable, send: Callable) -> None: response = f"{self.name}-{scope['path']}" await send( { "type": "http.response.start", "status": 200, "headers": [(b"content-length", b"%d" % len(response))], } ) await send({"type": "http.response.body", "body": response.encode()}) app = DispatcherMiddleware({"/api/x": EchoFramework("apix"), "/api": EchoFramework("api")}) sent_events = [] async def send(message: dict) -> None: nonlocal sent_events sent_events.append(message) scope = {"type": "http", "asgi": {"version": "3.0"}} await app(dict(path="/api/x/b", **scope), None, send) await app(dict(path="/api/b", **scope), None, send) await app(dict(path="/", **scope), None, send) assert sent_events == [ {"type": "http.response.start", "status": 200, "headers": [(b"content-length", b"7")]}, {"type": "http.response.body", "body": b"apix-/b"}, {"type": "http.response.start", "status": 200, "headers": [(b"content-length", b"6")]}, {"type": "http.response.body", "body": b"api-/b"}, {"type": "http.response.start", "status": 404, "headers": [(b"content-length", b"0")]}, {"type": "http.response.body"}, ]

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null

0

6

7cd823cda65cdbb90961858e3a1d6bd003e6ef53

101

py

Python

tests/exog/random/random_exog_75_40.py

shaido987/pyaf

b9afd089557bed6b90b246d3712c481ae26a1957

[ "BSD-3-Clause" ]

377

2016-10-13T20:52:44.000Z

2022-03-29T18:04:14.000Z

tests/exog/random/random_exog_75_40.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

160

2016-10-13T16:11:53.000Z

2022-03-28T04:21:34.000Z

tests/exog/random/random_exog_75_40.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

63

2017-03-09T14:51:18.000Z

2022-03-27T20:52:57.000Z

import tests.exog.test_random_exogenous as testrandexog testrandexog.test_random_exogenous( 75,40);

25.25

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101

5.928571

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6

86212b3a7a3032c0947ed6072ef915dfc197880e

467

py

Python

Jupyter/imports4PyMOLjupyter.py

MooersLab/jupyterlabpymolpysnipsplus

b886750d63372434df53d4d6d7cdad6cb02ae4e7

[ "MIT" ]

null

Jupyter/imports4PyMOLjupyter.py

MooersLab/jupyterlabpymolpysnipsplus

b886750d63372434df53d4d6d7cdad6cb02ae4e7

[ "MIT" ]

null

Jupyter/imports4PyMOLjupyter.py

MooersLab/jupyterlabpymolpysnipsplus

b886750d63372434df53d4d6d7cdad6cb02ae4e7

[ "MIT" ]

null

# Description: Imports needed for most uses of pymol in Jupyter. Combination of importPyMOL and importPythonDisplay. # Source: placeHolder """ cmd.do('from pymol import cmd') cmd.do('from IPython.display import Image') cmd.do('from IPython.core.display import HTML') cmd.do('PATH = "/Users/blaine/"') """ cmd.do('from pymol import cmd') cmd.do('from IPython.display import Image') cmd.do('from IPython.core.display import HTML') cmd.do('PATH = "/Users/blaine/"')

31.133333

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467

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118

33.357143

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0

null

0

1

0

1

0

1

0

6

8645ec4f1149539697a4f57bff38e8f75ae06368

27

py

Python

devel/lib/python2.7/dist-packages/plutodrone/srv/__init__.py

EveVengerov/Gesture-Controlling-Drone

8fe38dbfdc496472e13e76bcdb55b471f51b42ea

[ "MIT" ]

2

2021-09-22T19:06:19.000Z

2021-09-22T20:22:40.000Z

devel/lib/python2.7/dist-packages/plutodrone/srv/__init__.py

EveVengerov/Gesture-Controlling-Drone

8fe38dbfdc496472e13e76bcdb55b471f51b42ea

[ "MIT" ]

null

devel/lib/python2.7/dist-packages/plutodrone/srv/__init__.py

EveVengerov/Gesture-Controlling-Drone

8fe38dbfdc496472e13e76bcdb55b471f51b42ea

[ "MIT" ]

null

from ._PlutoPilot import *

13.5

26

0.777778

3

27

6.666667

1

0

0.148148

27

1

27

0.869565

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true

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null

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1

0

1

0

6

868557ee807d80b9e8d4eb597572bfb9efb9c2f1

345

py

Python

core/serializers/__init__.py

decosterkevin/foodtrack-back

c459b7f30854e6d114ffb0ff04b1ae7f36b73cd8

[ "MIT" ]

null

core/serializers/__init__.py

decosterkevin/foodtrack-back

c459b7f30854e6d114ffb0ff04b1ae7f36b73cd8

[ "MIT" ]

4

2021-04-08T21:59:06.000Z

2021-06-10T20:42:55.000Z

core/serializers/__init__.py

decosterkevin/foodtrack-back

c459b7f30854e6d114ffb0ff04b1ae7f36b73cd8

[ "MIT" ]

null

from .address import AddressSerializer, ExploitationSerializer from .product import ProductSerializer from .cart import CartSerializer, CartItemSerializer from .profile import ProductorProfileSerializer, UserProfileSerializer, SimpleProductorProfileSerializer # ProductorProfileFullSerializer, from .product_profile import FullProductSerializer

57.5

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0.892754

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345

11.807692

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0

0.075362

345

5

139

69

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null

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1

0

1

0

1

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6

868b2b62a8277f827af964fc21cbf6fe424b4257

39

py

Python

_main_train/_model_builder/__init__.py

oxquantum/CVAE

0352ddc51fbfd8d57b155e6de66b4c34e010beac

[ "MIT" ]

null

_main_train/_model_builder/__init__.py

oxquantum/CVAE

0352ddc51fbfd8d57b155e6de66b4c34e010beac

[ "MIT" ]

null

_main_train/_model_builder/__init__.py

oxquantum/CVAE

0352ddc51fbfd8d57b155e6de66b4c34e010beac

[ "MIT" ]

null

from .model_builder import build_model

19.5

38

0.871795

6

39

5.333333

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0

0.102564

39

1

39

0.914286

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1

0

null

0

1

0

1

0

1

0

6

86977aa569f51218759bfe9641c2b16086343b7b

173

py

Python

produto/meus_produtos/admin.py

guilhon/aplicacao-basica-python-django

e5fd9c5a227428047cae50a38cb9bd4b74f3860b

[ "MIT" ]

null

produto/meus_produtos/admin.py

guilhon/aplicacao-basica-python-django

e5fd9c5a227428047cae50a38cb9bd4b74f3860b

[ "MIT" ]

null

produto/meus_produtos/admin.py

guilhon/aplicacao-basica-python-django

e5fd9c5a227428047cae50a38cb9bd4b74f3860b

[ "MIT" ]

null

from django.contrib import admin from meus_produtos.models import Produto class ProdutoModelAdmin(admin.ModelAdmin): pass admin.site.register(Produto, ProdutoModelAdmin)

21.625

47

0.843931

21

173

6.904762

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0

0.092486

173

7

48

24.714286

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true

0.2

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null

0

1

0

null

0

1

0

1

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6

869ebb0ce11286078960ce6087033256e269154b

204

py

Python

mhw/__init__.py

reireias/mhw

1e988f5e2cf019ff486af256ef323e49bb5af671

[ "MIT" ]

null

mhw/__init__.py

reireias/mhw

1e988f5e2cf019ff486af256ef323e49bb5af671

[ "MIT" ]

null

mhw/__init__.py

reireias/mhw

1e988f5e2cf019ff486af256ef323e49bb5af671

[ "MIT" ]

null

""" MHW utility tools package """ from .damage import Condition, calculate from . import motionlist from . import monster from .util import generate_skill_patterns, generate_targets, skill_rank, to_label

25.5

81

0.803922

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204

5.888889

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0

0.127451

204

7

82

29.142857

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true

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null

0

1

0

1

0

1

0

6

86e35754c4f172d31af661e8762ebddddd72bc9c

5,919

py

Python

src/service/service.py

LeapSunrise/Z-Moves_bot

ac2f0a47f769166976896e80b356ba6968ed7f02

[ "MIT" ]

3

2021-03-15T14:06:38.000Z

2021-05-28T17:37:34.000Z

src/service/service.py

LeapSunrise/z-moves-bot

ac2f0a47f769166976896e80b356ba6968ed7f02

[ "MIT" ]

null

src/service/service.py

LeapSunrise/z-moves-bot

ac2f0a47f769166976896e80b356ba6968ed7f02

[ "MIT" ]

null

# -*- coding: utf-8 -*- # !/usr/bin/python3.8.5 import random import string from src.schedule_parser.schedule_parser import * from src.service.buttons import * separator = '_' * 35 def dynamic_menu_links_inline_keyboard_generator(chat_id): """ Generates dynamic main_menu/links inline keyboard. :param chat_id: :return: """ user_group = db.get_user_info(chat_id)[2] keyboard = telebot.types.InlineKeyboardMarkup() keyboard.add(inline_add_link_button) if db.get_links(chat_id, user_group) is not None: keyboard.add(inline_change_link_button) keyboard.add(inline_remove_link_button) return keyboard def dynamic_menu_hotlines_inline_keyboard_generator(chat_id): """ Generates dynamic main_menu/hotlines inline keyboard. :param chat_id: :return: """ user_group = db.get_user_info(chat_id)[2] keyboard = telebot.types.InlineKeyboardMarkup() keyboard.add(inline_add_hotline_button) if db.get_hotlines(chat_id, user_group) is not None: keyboard.add(inline_change_hotline_button) keyboard.add(inline_remove_hotline_button) return keyboard def generate_inline_subjects_to_add_link(chat_id): """ Generates subjects inline keyboard to add links. Button text creates as "(subject name)". Callback_data creates as "(first 10 symbols of subject name)". :param chat_id: :return: """ list_subjects = tuple(Schedule.get_lessons(chat_id)) keyboard = telebot.types.InlineKeyboardMarkup() for item in list_subjects: keyboard.add(telebot.types.InlineKeyboardButton(text=item, callback_data=f"link_add_{item[:25]}")) keyboard.add(inline_links_first_back_button) return keyboard def generate_inline_subjects_to_add_hotline(chat_id): """ Generates subjects inline keyboard to add hotlines. Button text creates as "(subject name)". Callback_data creates as "hl_(first 10 symbols of subject name)". :param chat_id: :return: """ list_subjects = tuple(Schedule.get_lessons(chat_id)) keyboard = telebot.types.InlineKeyboardMarkup() for item in list_subjects: keyboard.add(telebot.types.InlineKeyboardButton(text=item, callback_data=f"hotline_add_{item[:25]}")) keyboard.add(inline_first_back_button_hotlines) return keyboard def generate_inline_linked_subjects_to_change(chat_id): """ Generates subjects inline keyboard to change links. Button text creates as "(subject type) - (subject name)". Callback_data creates as "lch_(link addition date)". :param chat_id: :return: """ user_group = db.get_user_info(chat_id)[2] keyboard = telebot.types.InlineKeyboardMarkup() if db.get_links(chat_id, user_group) is not None: for item in db.get_links(chat_id, user_group): keyboard.add(telebot.types.InlineKeyboardButton(text=f"{item[2]} - {item[1]}", callback_data=f"link_ch_{item[6]}")) keyboard.add(inline_links_first_back_button) return keyboard else: return '' def generate_inline_hotlined_subjects_to_change(chat_id): """ Generates subjects inline keyboard to change hotlines. Button text creates as "(hotline date) - (subject name)". Callback_data creates as "hlch_(hotline addition date)". :param chat_id: :return: """ user_group = db.get_user_info(chat_id)[2] keyboard = telebot.types.InlineKeyboardMarkup() if db.get_hotlines(chat_id, user_group) is not None: for item in db.get_hotlines(chat_id, user_group): keyboard.add(telebot.types.InlineKeyboardButton(text=f"{item[3].strftime('%d.%m')} - {item[1]}", callback_data=f"hotline_ch_{item[5]}")) keyboard.add(inline_first_back_button_hotlines) return keyboard else: return '' def generate_inline_linked_subjects_to_remove(chat_id): """ Generates subjects inline keyboard to remove links. Button text creates as "(subject type) - (subject name)". Callback_data creates as "lrm_(link addition date)". :param chat_id: :return: """ user_group = db.get_user_info(chat_id)[2] keyboard = telebot.types.InlineKeyboardMarkup() if db.get_links(chat_id, user_group) is not None: for item in db.get_links(chat_id, user_group): keyboard.add(telebot.types.InlineKeyboardButton(text=f"{item[2]} - {item[1]}", callback_data=f"link_rm_{item[6]}")) keyboard.add(inline_links_first_back_button) return keyboard else: return '' def generate_inline_hotlined_subjects_to_remove(chat_id): """ Generates subjects inline keyboard to remove hotlines. Button text creates as "(hotline date) - (subject name)". Callback_data creates as "hlrm_(hotline addition date)". :param chat_id: :return: """ user_group = db.get_user_info(chat_id)[2] keyboard = telebot.types.InlineKeyboardMarkup() if db.get_hotlines(chat_id, user_group) is not None: for item in db.get_hotlines(chat_id, user_group): keyboard.add(telebot.types.InlineKeyboardButton(text=f"{item[3].strftime('%d.%m')} - {item[1]}", callback_data=f"hotline_rm_{item[5]}")) keyboard.add(inline_first_back_button_hotlines) return keyboard else: return '' def rozklad_api_work_checker(): """ Simple rozklad API accessibility checker. :return: """ try: requests.get('https://api.rozklad.org.ua/', timeout=3) except: return False def token_generator(length): ra = '0123456789' + string.ascii_letters + '0123456789' return ''.join(random.choice(ra) for i in range(length))

31.994595

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0.673087

753

5,919

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0.156707

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0.03963

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0.72893

0

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0.224362

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184

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0

1

0.114943

false

0

0.045977

0

0.321839

0

null

0

1

0

null

0

6

86f31af3cdd4d02394795da5a728222683ef5121

146

py

Python

Domain/Python/if_else.py

bhansa/Hack

dd312af4446fa86da5e4740f6efc3c1ba50e53de

[ "Apache-2.0" ]

null

Domain/Python/if_else.py

bhansa/Hack

dd312af4446fa86da5e4740f6efc3c1ba50e53de

[ "Apache-2.0" ]

null

Domain/Python/if_else.py

bhansa/Hack

dd312af4446fa86da5e4740f6efc3c1ba50e53de

[ "Apache-2.0" ]

null

n=int(raw_input()) if n%2!=0: print "Weird" elif n>2 and n<5: print "Not Weird" elif n>6 and n<20: print "Weird" elif n>20: print "Not Weird"

14.6

18

0.643836

33

146

2.818182

0.454545

0.290323

0.322581

0

0.07563

0.184932

146

9

19

16.222222

0.705882

0

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0

0.191781

0

null

0

null

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1

0

1

0

null

0

1

0

1

0

6

811a0cea2aad461710a4790da7c00b08558defd7

38

py

Python

image_classification/SwinTransformer/augmentation.py

chuliuT/PaddleViT

282e5013f0460fa9f9b010775ff4d2607e7370ef

[ "Apache-2.0" ]

null

image_classification/SwinTransformer/augmentation.py

chuliuT/PaddleViT

282e5013f0460fa9f9b010775ff4d2607e7370ef

[ "Apache-2.0" ]

null

image_classification/SwinTransformer/augmentation.py

chuliuT/PaddleViT

282e5013f0460fa9f9b010775ff4d2607e7370ef

[ "Apache-2.0" ]

null

import paddle import paddle.nn as nn

9.5

22

0.789474

7

38

4.285714

0.571429

0.8

0

0.184211

38

3

23

12.666667

0.967742

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d4df8b5b86a7dcfb6b73f51e985891eb3a98cdd3

75

py

Python

jacdac/power_supply/__init__.py

microsoft/jacdac-python

712ad5559e29065f5eccb5dbfe029c039132df5a

[ "MIT" ]

1

2022-02-15T21:30:36.000Z

jacdac/power_supply/__init__.py

microsoft/jacdac-python

712ad5559e29065f5eccb5dbfe029c039132df5a

[ "MIT" ]

null

jacdac/power_supply/__init__.py

microsoft/jacdac-python

712ad5559e29065f5eccb5dbfe029c039132df5a

[ "MIT" ]

1

2022-02-08T19:32:45.000Z

# Autogenerated file. from .client import PowerSupplyClient # type: ignore

25

52

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true

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1

0

null

0

1

0

1

0

1

0

6

be0b31613218ea2d46744aae47249304600611a6

7,667

py

Python

deploy/virenv/lib/python2.7/site-packages/haystack/reverse/cli.py

wangvictor2012/liuwei

0a06f8fd56d78162f81f1e7e7def7bfdeb4472e1

[ "BSD-3-Clause" ]

null

deploy/virenv/lib/python2.7/site-packages/haystack/reverse/cli.py

wangvictor2012/liuwei

0a06f8fd56d78162f81f1e7e7def7bfdeb4472e1

[ "BSD-3-Clause" ]

null

deploy/virenv/lib/python2.7/site-packages/haystack/reverse/cli.py

wangvictor2012/liuwei

0a06f8fd56d78162f81f1e7e7def7bfdeb4472e1

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """Entry points related to reverse. """ import os import sys from haystack import argparse_utils from haystack import cli from haystack.reverse import api # the description of the function REVERSE_DESC = 'Reverse the data structure from the process memory' REVERSE_SHOW_DESC = 'Show the record at a specific address' REVERSE_PARENT_DESC = 'List the predecessors pointing to the record at this address' REVERSE_HEX_DESC = 'Show the Hex values for the record at that address.' def reverse_argparser(reverse_parser): reverse_parser.set_defaults(func=reverse_cmdline) return reverse_parser def reverse_show_argparser(show_parser): """ Show function options argument parser """ show_parser.add_argument('address', type=argparse_utils.int16, help='Record memory address in hex') show_parser.set_defaults(func=reverse_show_cmdline) return show_parser def reverse_parents_argparser(parents_parser): parents_parser.add_argument('address', type=argparse_utils.int16, action='store', default=None, help='Hex address of the child structure') parents_parser.set_defaults(func=show_predecessors_cmdline) return parents_parser def reverse_hex_argparser(hex_parser): hex_parser.add_argument('address', type=argparse_utils.int16, action='store', default=None, help='Specify the address of the record, or encompassed by the record') hex_parser.set_defaults(func=show_hex) return hex_parser def show_hex(args): """ Show the Hex values for the record at that address. """ memory_handler = cli.get_memory_handler(args) process_context = memory_handler.get_reverse_context() ctx = process_context.get_context_for_address(args.address) try: st = ctx.get_record_at_address(args.address) print repr(st.bytes) except ValueError as e: print None return def show_predecessors_cmdline(args): """ Show the predecessors that point to a record at a particular address. :param opt: :return: """ memory_handler = cli.get_memory_handler(args) process_context = memory_handler.get_reverse_context() ctx = process_context.get_context_for_address(args.address) try: child_record = ctx.get_record_at_address(args.address) except ValueError as e: print None return records = api.get_record_predecessors(memory_handler, child_record) if len(records) == 0: print None else: for p_record in records: print '#0x%x\n%s\n' % (p_record.address, p_record.to_string()) return def reverse_show_cmdline(args): """ Show the record at a specific address. """ memory_handler = cli.get_memory_handler(args) process_context = memory_handler.get_reverse_context() ctx = process_context.get_context_for_address(args.address) try: st = ctx.get_record_at_address(args.address) print st.to_string() except ValueError: print None return def reverse_cmdline(args): """ Reverse """ from haystack.reverse import api as rapi # get the memory handler adequate for the type requested memory_handler = cli.get_memory_handler(args) # do the search rapi.reverse_instances(memory_handler) return def main_reverse(): argv = sys.argv[1:] desc = REVERSE_DESC + cli.DUMPTYPE_BASE_DESC rootparser = cli.base_argparser(program_name=os.path.basename(sys.argv[0]), description=desc) rootparser.add_argument('dump_folder_name', type=argparse_utils.readable, help='Use this memory dump folder') reverse_argparser(rootparser) opts = rootparser.parse_args(argv) opts.dumptype = cli.DUMPTYPE_BASE # apply verbosity cli.set_logging_level(opts) # execute function opts.func(opts) return def minidump_reverse(): argv = sys.argv[1:] desc = REVERSE_DESC + cli.DUMPTYPE_MINIDUMP_DESC rootparser = cli.base_argparser(program_name=os.path.basename(sys.argv[0]), description=desc) rootparser.add_argument('dump_filename', type=argparse_utils.readable, help='Use this memory dump file') reverse_argparser(rootparser) opts = rootparser.parse_args(argv) opts.dumptype = cli.DUMPTYPE_MINIDUMP # apply verbosity cli.set_logging_level(opts) # execute function opts.func(opts) return def main_reverse_show(): argv = sys.argv[1:] desc = REVERSE_SHOW_DESC + cli.DUMPTYPE_BASE_DESC rootparser = cli.base_argparser(program_name=os.path.basename(sys.argv[0]), description=desc) rootparser.add_argument('dump_folder_name', type=argparse_utils.readable, help='Use this memory dump folder') reverse_show_argparser(rootparser) opts = rootparser.parse_args(argv) opts.dumptype = cli.DUMPTYPE_BASE # apply verbosity cli.set_logging_level(opts) # execute function opts.func(opts) return def minidump_reverse_show(): argv = sys.argv[1:] desc = REVERSE_SHOW_DESC + cli.DUMPTYPE_MINIDUMP_DESC rootparser = cli.base_argparser(program_name=os.path.basename(sys.argv[0]), description=desc) rootparser.add_argument('dump_filename', type=argparse_utils.readable, help='Use this memory dump file') reverse_show_argparser(rootparser) opts = rootparser.parse_args(argv) opts.dumptype = cli.DUMPTYPE_MINIDUMP # apply verbosity cli.set_logging_level(opts) # execute function opts.func(opts) return def main_reverse_parents(): argv = sys.argv[1:] desc = REVERSE_PARENT_DESC + cli.DUMPTYPE_BASE_DESC rootparser = cli.base_argparser(program_name=os.path.basename(sys.argv[0]), description=desc) rootparser.add_argument('dump_folder_name', type=argparse_utils.readable, help='Use this memory dump folder') reverse_parents_argparser(rootparser) opts = rootparser.parse_args(argv) opts.dumptype = cli.DUMPTYPE_BASE # apply verbosity cli.set_logging_level(opts) # execute function opts.func(opts) return def minidump_reverse_parents(): argv = sys.argv[1:] desc = REVERSE_PARENT_DESC + cli.DUMPTYPE_MINIDUMP_DESC rootparser = cli.base_argparser(program_name=os.path.basename(sys.argv[0]), description=desc) rootparser.add_argument('dump_filename', type=argparse_utils.readable, help='Use this memory dump file') reverse_parents_argparser(rootparser) opts = rootparser.parse_args(argv) opts.dumptype = cli.DUMPTYPE_MINIDUMP # apply verbosity cli.set_logging_level(opts) # execute function opts.func(opts) return def main_reverse_hex(): argv = sys.argv[1:] desc = REVERSE_HEX_DESC + cli.DUMPTYPE_BASE_DESC rootparser = cli.base_argparser(program_name=os.path.basename(sys.argv[0]), description=desc) rootparser.add_argument('dump_folder_name', type=argparse_utils.readable, help='Use this memory dump folder') reverse_hex_argparser(rootparser) opts = rootparser.parse_args(argv) opts.dumptype = cli.DUMPTYPE_BASE # apply verbosity cli.set_logging_level(opts) # execute function opts.func(opts) return def minidump_reverse_hex(): argv = sys.argv[1:] desc = REVERSE_HEX_DESC + cli.DUMPTYPE_MINIDUMP_DESC rootparser = cli.base_argparser(program_name=os.path.basename(sys.argv[0]), description=desc) rootparser.add_argument('dump_filename', type=argparse_utils.readable, help='Use this memory dump file') reverse_hex_argparser(rootparser) opts = rootparser.parse_args(argv) opts.dumptype = cli.DUMPTYPE_MINIDUMP # apply verbosity cli.set_logging_level(opts) # execute function opts.func(opts) return

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6

be1ce108645ef5ab424f6c5a625d728fcf2fffe8

28

py

Python

plotly/graph_objs/pointcloud/marker/__init__.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/graph_objs/pointcloud/marker/__init__.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/graph_objs/pointcloud/marker/__init__.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

from ._border import Border

14

27

0.821429

4

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5.5

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null

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6

07804282e55f19c38513d6468fa5eabd543a015d

195

py

Python

moto/acm/__init__.py

jonnangle/moto-1

40b4e299abb732aad7f56cc0f680c0a272a46594

[ "Apache-2.0" ]

3

2020-08-04T20:29:41.000Z

2020-11-09T09:28:19.000Z

moto/acm/__init__.py

jonnangle/moto-1

40b4e299abb732aad7f56cc0f680c0a272a46594

[ "Apache-2.0" ]

17

2020-08-28T12:53:56.000Z

2020-11-10T01:04:46.000Z

moto/acm/__init__.py

jonnangle/moto-1

40b4e299abb732aad7f56cc0f680c0a272a46594

[ "Apache-2.0" ]

2

2021-11-24T08:05:43.000Z

2021-11-25T16:18:48.000Z

from __future__ import unicode_literals from .models import acm_backends from ..core.models import base_decorator acm_backend = acm_backends["us-east-1"] mock_acm = base_decorator(acm_backends)

27.857143

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null

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1

0

1

0

6

07a20a822260bc272c73446a0812ab8a35cab98c

13,544

py

Python

newsapi/newsapi_client.py

jborchma/newsapi-python

f59f9d67bb218156becbe07740bb7d33a9a19c99

[ "MIT" ]

1

2019-02-22T03:45:39.000Z

newsapi/newsapi_client.py

jborchma/newsapi-python

f59f9d67bb218156becbe07740bb7d33a9a19c99

[ "MIT" ]

null

newsapi/newsapi_client.py

jborchma/newsapi-python

f59f9d67bb218156becbe07740bb7d33a9a19c99

[ "MIT" ]

null

import requests from newsapi.newsapi_auth import NewsApiAuth from newsapi import const from newsapi.newsapi_exception import NewsAPIException class NewsApiClient(object): def __init__(self, api_key): self.auth = NewsApiAuth(api_key=api_key) def get_top_headlines(self, q=None, sources=None, language=None, country=None, category=None, page_size=None, page=None): """ Returns live top and breaking headlines for a country, specific category in a country, single source, or multiple sources.. Optional parameters: (str) q - return headlines w/ specific keyword or phrase. For example: 'bitcoin', 'trump', 'tesla', 'ethereum', etc. (str) sources - return headlines of news sources! some Valid values are: 'bbc-news', 'the-verge', 'abc-news', 'crypto coins news', 'ary news','associated press','wired','aftenposten','australian financial review','axios', 'bbc news','bild','blasting news','bloomberg','business insider','engadget','google news', 'hacker news','info money,'recode','techcrunch','techradar','the next web','the verge' etc. (str) language - The 2-letter ISO-639-1 code of the language you want to get headlines for. Valid values are: 'ar','de','en','es','fr','he','it','nl','no','pt','ru','se','ud','zh' (str) country - The 2-letter ISO 3166-1 code of the country you want to get headlines! Valid values are: 'ae','ar','at','au','be','bg','br','ca','ch','cn','co','cu','cz','de','eg','fr','gb','gr', 'hk','hu','id','ie','il','in','it','jp','kr','lt','lv','ma','mx','my','ng','nl','no','nz', 'ph','pl','pt','ro','rs','ru','sa','se','sg','si','sk','th','tr','tw','ua','us' (str) category - The category you want to get headlines for! Valid values are: 'business','entertainment','general','health','science','sports','technology' (int) page_size - The number of results to return per page (request). 20 is the default, 100 is the maximum. (int) page - Use this to page through the results if the total results found is greater than the page size. """ # Define Payload payload = {} # Keyword/Phrase if q is not None: if type(q) == str: payload['q'] = q else: raise TypeError('keyword/phrase q param should be a of type str') # Sources if (sources is not None) and ((country is not None) or (category is not None)): raise ValueError('cannot mix country/category param with sources param.') # Sources if sources is not None: if type(sources) == str: payload['sources'] = sources else: raise TypeError('sources param should be of type str') # Language if language is not None: if type(language) == str: if language in const.languages: payload['language'] = language else: raise ValueError('invalid language') else: raise TypeError('language param should be of type str') # Country if country is not None: if type(country) == str: if country in const.countries: payload['country'] = country else: raise ValueError('invalid country') else: raise TypeError('country param should be of type str') # Category if category is not None: if type(category) == str: if category in const.categories: payload['category'] = category else: raise ValueError('invalid category') else: raise TypeError('category param should be of type str') # Page Size if page_size is not None: if type(page_size) == int: if 0 <= page_size <= 100: payload['pageSize'] = page_size else: raise ValueError('page_size param should be an int between 1 and 100') else: raise TypeError('page_size param should be an int') # Page if page is not None: if type(page) == int: if page > 0: payload['page'] = page else: raise ValueError('page param should be an int greater than 0') else: raise TypeError('page param should be an int') # Send Request r = requests.get(const.TOP_HEADLINES_URL, auth=self.auth, timeout=30, params=payload) # Check Status of Request if r.status_code != requests.codes.ok: raise NewsAPIException(r.json()) return r.json() def get_everything(self, q=None, sources=None, domains=None, from_param=None, to=None, language=None, sort_by=None, page=None, page_size=None): """ Search through millions of articles from over 5,000 large and small news sources and blogs. Optional parameters: (str) q - return headlines w/ specified coin! Valid values are: 'bitcoin', 'trump', 'tesla', 'ethereum', etc (str) sources - return headlines of news sources! some Valid values are: 'bbc-news', 'the-verge', 'abc-news', 'crypto coins news', 'ary news','associated press','wired','aftenposten','australian financial review','axios', 'bbc news','bild','blasting news','bloomberg','business insider','engadget','google news', 'hacker news','info money,'recode','techcrunch','techradar','the next web','the verge' etc. (str) domains - A comma-seperated string of domains (eg bbc.co.uk, techcrunch.com, engadget.com) to restrict the search to. (str) from_parameter - A date and optional time for the oldest article allowed. (e.g. 2018-03-05 or 2018-03-05T03:46:15) (str) to - A date and optional time for the newest article allowed. (str) language - The 2-letter ISO-639-1 code of the language you want to get headlines for. Valid values are: 'ar','de','en','es','fr','he','it','nl','no','pt','ru','se','ud','zh' (str) sort_by - The order to sort the articles in. Valid values are: 'relevancy','popularity','publishedAt' (int) page_size - The number of results to return per page (request). 20 is the default, 100 is the maximum. (int) page - Use this to page through the results if the total results found is greater than the page size. """ # Define Payload payload = {} # Keyword/Phrase if q is not None: if type(q) == str: payload['q'] = q else: raise TypeError('keyword/phrase q param should be of type str') # Sources if sources is not None: if type(sources) == str: payload['sources'] = sources else: raise TypeError('sources param should be of type str') # Domains To Search if domains is not None: if type(domains) == str: payload['domains'] = domains else: raise TypeError('domains param should be of type str') # Search From This Date ... if from_param is not None: if type(from_param) == str: if (len(from_param)) >= 10: for i in range(len(from_param)): if (i == 4 and from_param[i] != '-') or (i == 7 and from_param[i] != '-'): raise ValueError('from_param should be in the format of YYYY-MM-DD') else: payload['from'] = from_param else: raise ValueError('from_param should be in the format of YYYY-MM-DD') else: raise TypeError('from_param should be of type str') # ... To This Date if to is not None: if type(to) == str: if (len(to)) >= 10: for i in range(len(to)): if (i == 4 and to[i] != '-') or (i == 7 and to[i] != '-'): raise ValueError('to should be in the format of YYYY-MM-DD') else: payload['to'] = to else: raise ValueError('to param should be in the format of YYYY-MM-DD') else: raise TypeError('to param should be of type str') # Language if language is not None: if type(language) == str: if language not in const.languages: raise ValueError('invalid language') else: payload['language'] = language else: raise TypeError('language param should be of type str') # Sort Method if sort_by is not None: if type(sort_by) == str: if sort_by in const.sort_method: payload['sortBy'] = sort_by else: raise ValueError('invalid sort') else: raise TypeError('sort_by param should be of type str') # Page Size if page_size is not None: if type(page_size) == int: if 0 <= page_size <= 100: payload['pageSize'] = page_size else: raise ValueError('page_size param should be an int between 1 and 100') else: raise TypeError('page_size param should be an int') # Page if page is not None: if type(page) == int: if page > 0: payload['page'] = page else: raise ValueError('page param should be an int greater than 0') else: raise TypeError('page param should be an int') # Send Request r = requests.get(const.EVERYTHING_URL, auth=self.auth, timeout=30, params=payload) # Check Status of Request if r.status_code != requests.codes.ok: raise NewsAPIException(r.json()) return r.json() def get_sources(self, category=None, language=None, country=None): """ Returns the subset of news publishers that top headlines... Optional parameters: (str) category - The category you want to get headlines for! Valid values are: 'business','entertainment','general','health','science','sports','technology' (str) language - The 2-letter ISO-639-1 code of the language you want to get headlines for. Valid values are: 'ar','de','en','es','fr','he','it','nl','no','pt','ru','se','ud','zh' (str) country - The 2-letter ISO 3166-1 code of the country you want to get headlines! Valid values are: 'ae','ar','at','au','be','bg','br','ca','ch','cn','co','cu','cz','de','eg','fr','gb','gr', 'hk','hu','id','ie','il','in','it','jp','kr','lt','lv','ma','mx','my','ng','nl','no','nz', 'ph','pl','pt','ro','rs','ru','sa','se','sg','si','sk','th','tr','tw','ua','us' (str) category - The category you want to get headlines for! Valid values are: 'business','entertainment','general','health','science','sports','technology' """ # Define Payload payload = {} # Language if language is not None: if type(language) == str: if language in const.languages: payload['language'] = language else: raise ValueError('invalid language') else: raise TypeError('language param should be of type str') # Country if country is not None: if type(country) == str: if country in const.countries: payload['country'] = country else: raise ValueError('invalid country') else: raise TypeError('country param should be of type str') # Category if category is not None: if type(category) == str: if category in const.categories: payload['category'] = category else: raise ValueError('invalid category') else: raise TypeError('category param should be of type str') # Send Request r = requests.get(const.SOURCES_URL, auth=self.auth, timeout=30, params=payload) # Check Status of Request if r.status_code != requests.codes.ok: raise NewsAPIException(r.json()) return r.json()

42.591195

135

0.5158

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13,544

4.350282

0.166981

0.041558

0.048773

0.030159

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0.741703

0.723232

0.717027

0

0.011385

0.370939

13,544

317

136

42.725552

0.801995

0.434584

0

0.735632

0

0.161976

0

1

0.022989

false

0

0.022989

0

0.068966

0

null

0

1

0

1

0

null

0

6

07e6341724c87920a79484ebaa8329f4136d116a

13,884

py

Python

Tests/Test_rwlock.py

brucewxh/IntraArchiveDeduplicator

7b0c07cc9fffa75e1b7be285f42b0a8fad42dcfb

[ "BSD-3-Clause" ]

86

2015-01-13T15:02:08.000Z

2021-12-24T02:13:03.000Z

Tests/Test_rwlock.py

brucewxh/IntraArchiveDeduplicator

7b0c07cc9fffa75e1b7be285f42b0a8fad42dcfb

[ "BSD-3-Clause" ]

4

2016-11-18T20:08:50.000Z

2018-03-08T23:05:37.000Z

Tests/Test_rwlock.py

brucewxh/IntraArchiveDeduplicator

7b0c07cc9fffa75e1b7be285f42b0a8fad42dcfb

[ "BSD-3-Clause" ]

12

2015-05-03T07:56:50.000Z

2021-03-11T12:38:56.000Z

## ## Unit testing code ## ================= ## import unittest import threading import time import copy import pyximport pyximport.install() import deduplicator.cyHamDb as hamDb class Writer(threading.Thread): def __init__(self, buffer_, rw_lock, init_sleep_time, sleep_time, to_write): """ @param buffer_: common buffer_ shared by the readers and writers @type buffer_: list @type rw_lock: L{RWLock} @param init_sleep_time: sleep time before doing any action @type init_sleep_time: C{float} @param sleep_time: sleep time while in critical section @type sleep_time: C{float} @param to_write: data that will be appended to the buffer """ threading.Thread.__init__(self) self.__buffer = buffer_ self.__rw_lock = rw_lock self.__init_sleep_time = init_sleep_time self.__sleep_time = sleep_time self.__to_write = to_write self.entry_time = None """Time of entry to the critical section""" self.exit_time = None """Time of exit from the critical section""" def run(self): time.sleep(self.__init_sleep_time) self.__rw_lock.get_write_lock() self.entry_time = time.time() # print("Writer sleeping", self.__sleep_time) time.sleep(self.__sleep_time) # print("Freeing write lock", self.__sleep_time) self.__buffer.append(self.__to_write) self.exit_time = time.time() self.__rw_lock.free_write_lock() class Reader(threading.Thread): def __init__(self, buffer_, rw_lock, init_sleep_time, sleep_time): """ @param buffer_: common buffer shared by the readers and writers @type buffer_: list @type rw_lock: L{RWLock} @param init_sleep_time: sleep time before doing any action @type init_sleep_time: C{float} @param sleep_time: sleep time while in critical section @type sleep_time: C{float} """ threading.Thread.__init__(self) self.__buffer = buffer_ self.__rw_lock = rw_lock self.__init_sleep_time = init_sleep_time self.__sleep_time = sleep_time self.buffer_read = None """a copy of a the buffer read while in critical section""" self.entry_time = None """Time of entry to the critical section""" self.exit_time = None """Time of exit from the critical section""" def run(self): time.sleep(self.__init_sleep_time) self.__rw_lock.get_read_lock() self.entry_time = time.time() # print("Reader sleeping", self.__sleep_time) time.sleep(self.__sleep_time) # print("Freeing read lock", self.__sleep_time) self.buffer_read = copy.deepcopy(self.__buffer) self.exit_time = time.time() self.__rw_lock.free_read_lock() class WriterContext(threading.Thread): def __init__(self, buffer_, rw_lock, init_sleep_time, sleep_time, to_write): """ @param buffer_: common buffer_ shared by the readers and writers @type buffer_: list @type rw_lock: L{RWLock} @param init_sleep_time: sleep time before doing any action @type init_sleep_time: C{float} @param sleep_time: sleep time while in critical section @type sleep_time: C{float} @param to_write: data that will be appended to the buffer """ threading.Thread.__init__(self) self.__buffer = buffer_ self.__rw_lock = rw_lock self.__init_sleep_time = init_sleep_time self.__sleep_time = sleep_time self.__to_write = to_write self.entry_time = None """Time of entry to the critical section""" self.exit_time = None """Time of exit from the critical section""" def run(self): time.sleep(self.__init_sleep_time) with self.__rw_lock.writer_context(): self.entry_time = time.time() # print("Writer sleeping", self.__sleep_time) time.sleep(self.__sleep_time) # print("Freeing write lock", self.__sleep_time) self.__buffer.append(self.__to_write) self.exit_time = time.time() class ReaderContext(threading.Thread): def __init__(self, buffer_, rw_lock, init_sleep_time, sleep_time): """ @param buffer_: common buffer shared by the readers and writers @type buffer_: list @type rw_lock: L{RWLock} @param init_sleep_time: sleep time before doing any action @type init_sleep_time: C{float} @param sleep_time: sleep time while in critical section @type sleep_time: C{float} """ threading.Thread.__init__(self) self.__buffer = buffer_ self.__rw_lock = rw_lock self.__init_sleep_time = init_sleep_time self.__sleep_time = sleep_time self.buffer_read = None """a copy of a the buffer read while in critical section""" self.entry_time = None """Time of entry to the critical section""" self.exit_time = None """Time of exit from the critical section""" def run(self): time.sleep(self.__init_sleep_time) with self.__rw_lock.reader_context(): self.entry_time = time.time() # print("Reader sleeping", self.__sleep_time) time.sleep(self.__sleep_time) # print("Freeing read lock", self.__sleep_time) self.buffer_read = copy.deepcopy(self.__buffer) self.exit_time = time.time() class RWLockTestCase(unittest.TestCase): # So overreleasing results in SIGILL # because libpthread is retarded def test_overrelease_read(self): # print("Test: test_overrelease_read") testLock = hamDb.BkHammingTree() testLock.get_read_lock() testLock.free_read_lock() self.assertRaises(RuntimeError, testLock.free_read_lock) def test_overrelease_write(self): # print("Test: test_overrelease_write") testLock = hamDb.BkHammingTree() testLock.get_write_lock() testLock.free_write_lock() self.assertRaises(RuntimeError, testLock.free_write_lock) ############################################################################################### ############################################################################################### ############################################################################################### def test_reentrant_read(self): # print("Test: test_reentrant_read") testLock = hamDb.BkHammingTree() testLock.get_read_lock() testLock.get_read_lock() testLock.free_read_lock() testLock.free_read_lock() def test_non_reentrant_write(self): # print("Test: test_non_reentrant_write") testLock = hamDb.BkHammingTree() testLock.get_write_lock() self.assertRaises(RuntimeError, testLock.get_write_lock, blocking=False) testLock.free_write_lock() def test_readers_nonexclusive_access(self): # print("Test: test_readers_nonexclusive_access") (buffer_, rw_lock, threads) = self.__init_variables() threads.append(Reader(buffer_, rw_lock, 0, 0)) threads.append(Writer(buffer_, rw_lock, 0.2, 0.4, 1)) threads.append(Reader(buffer_, rw_lock, 0.3, 0.3)) threads.append(Reader(buffer_, rw_lock, 0.5, 0)) self.__start_and_join_threads(threads) ## The third reader should enter after the second one but it should ## exit before the second one exits ## (i.e. the readers should be in the critical section ## at the same time) self.assertEqual([], threads[0].buffer_read) self.assertEqual([1], threads[2].buffer_read) self.assertEqual([1], threads[3].buffer_read) self.assert_(threads[1].exit_time <= threads[2].entry_time) self.assert_(threads[2].entry_time <= threads[3].entry_time) self.assert_(threads[3].exit_time < threads[2].exit_time) def test_writers_exclusive_access(self): # print("Test: test_writers_exclusive_access") (buffer_, rw_lock, threads) = self.__init_variables() threads.append(Writer(buffer_, rw_lock, 0, 0.4, 1)) threads.append(Writer(buffer_, rw_lock, 0.1, 0, 2)) threads.append(Reader(buffer_, rw_lock, 0.2, 0)) self.__start_and_join_threads(threads) ## The second writer should wait for the first one to exit self.assertEqual([1, 2], threads[2].buffer_read) self.assert_(threads[0].exit_time <= threads[1].entry_time) self.assert_(threads[1].exit_time <= threads[2].exit_time) def test_writer_priority(self): # print("Test: test_writer_priority") (buffer_, rw_lock, threads) = self.__init_variables() threads.append(Writer(buffer_, rw_lock, 0, 0, 1)) threads.append(Reader(buffer_, rw_lock, 0.1, 0.4)) threads.append(Writer(buffer_, rw_lock, 0.2, 0, 2)) threads.append(Reader(buffer_, rw_lock, 0.3, 0)) threads.append(Reader(buffer_, rw_lock, 0.3, 0)) self.__start_and_join_threads(threads) ## The second writer should go before the second and the third reader self.assertEqual([1], threads[1].buffer_read) self.assertEqual([1, 2], threads[3].buffer_read) self.assertEqual([1, 2], threads[4].buffer_read) self.assert_(threads[0].exit_time < threads[1].entry_time) self.assert_(threads[1].exit_time <= threads[2].entry_time) self.assert_(threads[2].exit_time <= threads[3].entry_time) self.assert_(threads[2].exit_time <= threads[4].entry_time) def test_many_writers_priority(self): # print("Test: test_many_writers_priority") (buffer_, rw_lock, threads) = self.__init_variables() threads.append(Writer(buffer_, rw_lock, 0, 0, 1)) threads.append(Reader(buffer_, rw_lock, 0.1, 0.6)) threads.append(Writer(buffer_, rw_lock, 0.2, 0.1, 2)) threads.append(Reader(buffer_, rw_lock, 0.3, 0)) threads.append(Reader(buffer_, rw_lock, 0.4, 0)) threads.append(Writer(buffer_, rw_lock, 0.5, 0.1, 3)) self.__start_and_join_threads(threads) ## The two last writers should go first -- after the first reader and ## before the second and the third reader self.assertEqual([1], threads[1].buffer_read) self.assertEqual([1, 2, 3], threads[3].buffer_read) self.assertEqual([1, 2, 3], threads[4].buffer_read) self.assert_(threads[0].exit_time < threads[1].entry_time) self.assert_(threads[1].exit_time <= threads[2].entry_time) self.assert_(threads[1].exit_time <= threads[5].entry_time) self.assert_(threads[2].exit_time <= threads[3].entry_time) self.assert_(threads[2].exit_time <= threads[4].entry_time) self.assert_(threads[5].exit_time <= threads[3].entry_time) self.assert_(threads[5].exit_time <= threads[4].entry_time) def test_context_readers_nonexclusive_access(self): # print("Test: test_context_readers_nonexclusive_access") (buffer_, rw_lock, threads) = self.__init_variables() threads.append(ReaderContext(buffer_, rw_lock, 0, 0)) threads.append(WriterContext(buffer_, rw_lock, 0.2, 0.4, 1)) threads.append(ReaderContext(buffer_, rw_lock, 0.3, 0.3)) threads.append(ReaderContext(buffer_, rw_lock, 0.5, 0)) self.__start_and_join_threads(threads) ## The third reader should enter after the second one but it should ## exit before the second one exits ## (i.e. the readers should be in the critical section ## at the same time) self.assertEqual([], threads[0].buffer_read) self.assertEqual([1], threads[2].buffer_read) self.assertEqual([1], threads[3].buffer_read) self.assert_(threads[1].exit_time <= threads[2].entry_time) self.assert_(threads[2].entry_time <= threads[3].entry_time) self.assert_(threads[3].exit_time < threads[2].exit_time) def test_context_writers_exclusive_access(self): # print("Test: test_context_writers_exclusive_access") (buffer_, rw_lock, threads) = self.__init_variables() threads.append(WriterContext(buffer_, rw_lock, 0, 0.4, 1)) threads.append(WriterContext(buffer_, rw_lock, 0.1, 0, 2)) threads.append(ReaderContext(buffer_, rw_lock, 0.2, 0)) self.__start_and_join_threads(threads) ## The second writer should wait for the first one to exit self.assertEqual([1, 2], threads[2].buffer_read) self.assert_(threads[0].exit_time <= threads[1].entry_time) self.assert_(threads[1].exit_time <= threads[2].exit_time) def test_context_writer_priority(self): # print("Test: test_context_writer_priority") (buffer_, rw_lock, threads) = self.__init_variables() threads.append(WriterContext(buffer_, rw_lock, 0, 0, 1)) threads.append(ReaderContext(buffer_, rw_lock, 0.1, 0.4)) threads.append(WriterContext(buffer_, rw_lock, 0.2, 0, 2)) threads.append(ReaderContext(buffer_, rw_lock, 0.3, 0)) threads.append(ReaderContext(buffer_, rw_lock, 0.3, 0)) self.__start_and_join_threads(threads) ## The second writer should go before the second and the third reader self.assertEqual([1], threads[1].buffer_read) self.assertEqual([1, 2], threads[3].buffer_read) self.assertEqual([1, 2], threads[4].buffer_read) self.assert_(threads[0].exit_time < threads[1].entry_time) self.assert_(threads[1].exit_time <= threads[2].entry_time) self.assert_(threads[2].exit_time <= threads[3].entry_time) self.assert_(threads[2].exit_time <= threads[4].entry_time) def test_context_many_writers_priority(self): # print("Test: test_context_many_writers_priority") (buffer_, rw_lock, threads) = self.__init_variables() threads.append(WriterContext(buffer_, rw_lock, 0, 0, 1)) threads.append(ReaderContext(buffer_, rw_lock, 0.1, 0.6)) threads.append(WriterContext(buffer_, rw_lock, 0.2, 0.1, 2)) threads.append(ReaderContext(buffer_, rw_lock, 0.3, 0)) threads.append(ReaderContext(buffer_, rw_lock, 0.4, 0)) threads.append(WriterContext(buffer_, rw_lock, 0.5, 0.1, 3)) self.__start_and_join_threads(threads) ## The two last writers should go first -- after the first reader and ## before the second and the third reader self.assertEqual([1], threads[1].buffer_read) self.assertEqual([1, 2, 3], threads[3].buffer_read) self.assertEqual([1, 2, 3], threads[4].buffer_read) self.assert_(threads[0].exit_time < threads[1].entry_time) self.assert_(threads[1].exit_time <= threads[2].entry_time) self.assert_(threads[1].exit_time <= threads[5].entry_time) self.assert_(threads[2].exit_time <= threads[3].entry_time) self.assert_(threads[2].exit_time <= threads[4].entry_time) self.assert_(threads[5].exit_time <= threads[3].entry_time) self.assert_(threads[5].exit_time <= threads[4].entry_time) @staticmethod def __init_variables(): buffer_ = [] rw_lock = hamDb.BkHammingTree() threads = [] return (buffer_, rw_lock, threads) @staticmethod def __start_and_join_threads(threads): for t in threads: t.start() for t in threads: t.join()

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0

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0

1

0

null

0

6

07f0e110fe7a2a70c7f2992e8d5b001d124947eb

5,338

py

Python

gprm/utils/wrapping_tools.py

siwill22/GPlatesClassStruggle

713a87ff4f054d3a493ec09e5f310aa3036d3bc5

[ "MIT" ]

7

2020-05-04T03:05:09.000Z

2022-01-28T13:52:53.000Z

gprm/utils/wrapping_tools.py

siwill22/GPlatesClassStruggle

713a87ff4f054d3a493ec09e5f310aa3036d3bc5

[ "MIT" ]

null

gprm/utils/wrapping_tools.py

siwill22/GPlatesClassStruggle

713a87ff4f054d3a493ec09e5f310aa3036d3bc5

[ "MIT" ]

3

2021-05-23T01:53:52.000Z

2021-09-14T12:21:53.000Z

# # Functions for wrapping geometries to dateline before returning request geojson # import pygplates def wrap_polylines(polylines,lon0=0,tesselate_degrees=1): data = {"type": "FeatureCollection"} data["features"] = [] for polyline in polylines: if lon0 is not None: wrapper = pygplates.DateLineWrapper(lon0) geometries = wrapper.wrap(polyline.get_geometry(),tesselate_degrees) else: geometries = polyline.get_geometries() for geometry in geometries: feature = {"type": "Feature"} feature["geometry"] = {} feature["geometry"]["type"] = "MultiLineString" point_list = [] for point in geometry.get_points(): point_list.append((point.to_lat_lon()[1],point.to_lat_lon()[0])) feature["geometry"]["coordinates"] = [point_list] data["features"].append(feature) return data def wrap_polygons(polygons,lon0=0,tesselate_degrees=1): data = {"type": "FeatureCollection"} data["features"] = [] for polygon in polygons: if lon0 is not None: wrapper = pygplates.DateLineWrapper(lon0) geometries = wrapper.wrap(polygon.get_geometry(),tesselate_degrees) for geometry in geometries: feature = {"type": "Feature"} feature["geometry"] = {} feature["geometry"]["type"] = "Polygon" point_list = [] for point in geometry.get_exterior_points(): point_list.append((point.to_lat_lon()[1],point.to_lat_lon()[0])) feature["geometry"]["coordinates"] = [point_list] data["features"].append(feature) else: for geometry in polygon.get_geometries(): feature = {"type": "Feature"} feature["geometry"] = {} feature["geometry"]["type"] = "Polygon" point_list = [] for point in geometry.get_points(): point_list.append((point.to_lat_lon()[1],point.to_lat_lon()[0])) if geometry.get_orientation() == pygplates.PolygonOnSphere.Orientation.counter_clockwise: point_list.reverse() feature["geometry"]["coordinates"] = [point_list] data["features"].append(feature) return data def wrap_reconstructed_polygons(reconstructed_polygons,lon0=0,tesselate_degrees=1): data = {"type": "FeatureCollection"} data["features"] = [] for reconstructed_polygon in reconstructed_polygons: rev=False if reconstructed_polygon.get_reconstructed_geometry().get_orientation() == pygplates.PolygonOnSphere.Orientation.counter_clockwise: rev = True if lon0 is not None: wrapper = pygplates.DateLineWrapper(lon0) geometries = wrapper.wrap(reconstructed_polygon.get_reconstructed_geometry(),tesselate_degrees) for geometry in geometries: feature = {"type": "Feature"} feature["geometry"] = {} feature["geometry"]["type"] = "Polygon" point_list = [] for point in geometry.get_exterior_points(): point_list.append((point.to_lat_lon()[1],point.to_lat_lon()[0])) if rev: point_list.reverse() feature["geometry"]["coordinates"] = [point_list] data["features"].append(feature) else: geometry = reconstructed_polygon.get_reconstructed_geometry() feature = {"type": "Feature"} feature["geometry"] = {} feature["geometry"]["type"] = "Polygon" point_list = [] for point in geometry.get_points(): point_list.append((point.to_lat_lon()[1],point.to_lat_lon()[0])) if rev: point_list.reverse() feature["geometry"]["coordinates"] = [point_list] data["features"].append(feature) return data def wrap_plate_boundaries(shared_boundary_sections,lon0=0,tesselate_degrees=1): data = {"type": "FeatureCollection"} data["features"] = [] for shared_boundary_section in shared_boundary_sections: for shared_sub_segment in shared_boundary_section.get_shared_sub_segments(): if lon0 is not None: wrapper = pygplates.DateLineWrapper(lon0) geometries = wrapper.wrap(shared_sub_segment.get_geometry(),tesselate_degrees) else: geometries = shared_sub_segment.get_geometries() for geometry in geometries: feature = {"type": "Feature"} feature["geometry"] = {} feature["geometry"]["type"] = "MultiLineString" point_list = [] for point in geometry.get_points(): point_list.append((point.to_lat_lon()[1],point.to_lat_lon()[0])) feature["geometry"]["coordinates"] = [point_list] feature["feature_type"] = str(shared_sub_segment.get_feature().get_feature_type()) feature["Length"] = float(shared_sub_segment.get_geometry().get_arc_length()) data["features"].append(feature) return data

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ed19ccb72a8f0fd72b29f329be72f2b260ce60e6

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py

Python

ple/games/__init__.py

GokulNC/Helicopter-Game-Reinforcement-Learning

c42eea71294bfc0dd2507d33c319e3b6d6e898e0

[ "MIT" ]

null

ple/games/__init__.py

GokulNC/Helicopter-Game-Reinforcement-Learning

c42eea71294bfc0dd2507d33c319e3b6d6e898e0

[ "MIT" ]

null

ple/games/__init__.py

GokulNC/Helicopter-Game-Reinforcement-Learning

c42eea71294bfc0dd2507d33c319e3b6d6e898e0

[ "MIT" ]

2

2019-10-04T05:39:09.000Z

2019-12-14T12:08:53.000Z

from ple.games.pixelcopter import Pixelcopter

23

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6

ed4d4efe066b0f4fdca9a1931dc1356f60e3815d

17,698

py

Python

tests/components/recorder/test_filters_with_entityfilter.py

mib1185/core

b17d4ac65cde9a27ff6032d70b148792e5eba8df

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/recorder/test_filters_with_entityfilter.py

mib1185/core

b17d4ac65cde9a27ff6032d70b148792e5eba8df

[ "Apache-2.0" ]

24,710

2016-04-13T08:27:26.000Z

2020-03-02T12:59:13.000Z

tests/components/recorder/test_filters_with_entityfilter.py

mib1185/core

b17d4ac65cde9a27ff6032d70b148792e5eba8df

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""The tests for the recorder filter matching the EntityFilter component.""" import json from sqlalchemy import select from sqlalchemy.engine.row import Row from homeassistant.components.recorder import get_instance from homeassistant.components.recorder.db_schema import EventData, States from homeassistant.components.recorder.filters import ( Filters, extract_include_exclude_filter_conf, sqlalchemy_filter_from_include_exclude_conf, ) from homeassistant.components.recorder.util import session_scope from homeassistant.const import ATTR_ENTITY_ID, STATE_ON from homeassistant.core import HomeAssistant from homeassistant.helpers.entityfilter import ( CONF_DOMAINS, CONF_ENTITIES, CONF_ENTITY_GLOBS, CONF_EXCLUDE, CONF_INCLUDE, convert_include_exclude_filter, ) from .common import async_wait_recording_done async def _async_get_states_and_events_with_filter( hass: HomeAssistant, sqlalchemy_filter: Filters, entity_ids: set[str] ) -> tuple[list[Row], list[Row]]: """Get states from the database based on a filter.""" for entity_id in entity_ids: hass.states.async_set(entity_id, STATE_ON) hass.bus.async_fire("any", {ATTR_ENTITY_ID: entity_id}) await async_wait_recording_done(hass) def _get_states_with_session(): with session_scope(hass=hass) as session: return session.execute( select(States.entity_id).filter( sqlalchemy_filter.states_entity_filter() ) ).all() filtered_states_entity_ids = { row[0] for row in await get_instance(hass).async_add_executor_job( _get_states_with_session ) } def _get_events_with_session(): with session_scope(hass=hass) as session: return session.execute( select(EventData.shared_data).filter( sqlalchemy_filter.events_entity_filter() ) ).all() filtered_events_entity_ids = set() for row in await get_instance(hass).async_add_executor_job( _get_events_with_session ): event_data = json.loads(row[0]) if ATTR_ENTITY_ID not in event_data: continue filtered_events_entity_ids.add(json.loads(row[0])[ATTR_ENTITY_ID]) return filtered_states_entity_ids, filtered_events_entity_ids async def test_included_and_excluded_simple_case_no_domains(hass, recorder_mock): """Test filters with included and excluded without domains.""" filter_accept = {"sensor.kitchen4", "switch.kitchen"} filter_reject = { "light.any", "switch.other", "cover.any", "sensor.weather5", "light.kitchen", } conf = { CONF_INCLUDE: { CONF_ENTITY_GLOBS: ["sensor.kitchen*"], CONF_ENTITIES: ["switch.kitchen"], }, CONF_EXCLUDE: { CONF_ENTITY_GLOBS: ["sensor.weather*"], CONF_ENTITIES: ["light.kitchen"], }, } extracted_filter = extract_include_exclude_filter_conf(conf) entity_filter = convert_include_exclude_filter(extracted_filter) sqlalchemy_filter = sqlalchemy_filter_from_include_exclude_conf(extracted_filter) assert sqlalchemy_filter is not None for entity_id in filter_accept: assert entity_filter(entity_id) is True for entity_id in filter_reject: assert entity_filter(entity_id) is False assert not entity_filter.explicitly_included("light.any") assert not entity_filter.explicitly_included("switch.other") assert entity_filter.explicitly_included("sensor.kitchen4") assert entity_filter.explicitly_included("switch.kitchen") assert not entity_filter.explicitly_excluded("light.any") assert not entity_filter.explicitly_excluded("switch.other") assert entity_filter.explicitly_excluded("sensor.weather5") assert entity_filter.explicitly_excluded("light.kitchen") ( filtered_states_entity_ids, filtered_events_entity_ids, ) = await _async_get_states_and_events_with_filter( hass, sqlalchemy_filter, filter_accept | filter_reject ) assert filtered_states_entity_ids == filter_accept assert not filtered_states_entity_ids.intersection(filter_reject) assert filtered_events_entity_ids == filter_accept assert not filtered_events_entity_ids.intersection(filter_reject) async def test_included_and_excluded_simple_case_no_globs(hass, recorder_mock): """Test filters with included and excluded without globs.""" filter_accept = {"switch.bla", "sensor.blu", "sensor.keep"} filter_reject = {"sensor.bli"} conf = { CONF_INCLUDE: { CONF_DOMAINS: ["sensor", "homeassistant"], CONF_ENTITIES: ["switch.bla"], }, CONF_EXCLUDE: { CONF_DOMAINS: ["switch"], CONF_ENTITIES: ["sensor.bli"], }, } extracted_filter = extract_include_exclude_filter_conf(conf) entity_filter = convert_include_exclude_filter(extracted_filter) sqlalchemy_filter = sqlalchemy_filter_from_include_exclude_conf(extracted_filter) assert sqlalchemy_filter is not None for entity_id in filter_accept: assert entity_filter(entity_id) is True for entity_id in filter_reject: assert entity_filter(entity_id) is False ( filtered_states_entity_ids, filtered_events_entity_ids, ) = await _async_get_states_and_events_with_filter( hass, sqlalchemy_filter, filter_accept | filter_reject ) assert filtered_states_entity_ids == filter_accept assert not filtered_states_entity_ids.intersection(filter_reject) assert filtered_events_entity_ids == filter_accept assert not filtered_events_entity_ids.intersection(filter_reject) async def test_included_and_excluded_simple_case_without_underscores( hass, recorder_mock ): """Test filters with included and excluded without underscores.""" filter_accept = {"light.any", "sensor.kitchen4", "switch.kitchen"} filter_reject = {"switch.other", "cover.any", "sensor.weather5", "light.kitchen"} conf = { CONF_INCLUDE: { CONF_DOMAINS: ["light"], CONF_ENTITY_GLOBS: ["sensor.kitchen*"], CONF_ENTITIES: ["switch.kitchen"], }, CONF_EXCLUDE: { CONF_DOMAINS: ["cover"], CONF_ENTITY_GLOBS: ["sensor.weather*"], CONF_ENTITIES: ["light.kitchen"], }, } extracted_filter = extract_include_exclude_filter_conf(conf) entity_filter = convert_include_exclude_filter(extracted_filter) sqlalchemy_filter = sqlalchemy_filter_from_include_exclude_conf(extracted_filter) assert sqlalchemy_filter is not None for entity_id in filter_accept: assert entity_filter(entity_id) is True for entity_id in filter_reject: assert entity_filter(entity_id) is False assert not entity_filter.explicitly_included("light.any") assert not entity_filter.explicitly_included("switch.other") assert entity_filter.explicitly_included("sensor.kitchen4") assert entity_filter.explicitly_included("switch.kitchen") assert not entity_filter.explicitly_excluded("light.any") assert not entity_filter.explicitly_excluded("switch.other") assert entity_filter.explicitly_excluded("sensor.weather5") assert entity_filter.explicitly_excluded("light.kitchen") ( filtered_states_entity_ids, filtered_events_entity_ids, ) = await _async_get_states_and_events_with_filter( hass, sqlalchemy_filter, filter_accept | filter_reject ) assert filtered_states_entity_ids == filter_accept assert not filtered_states_entity_ids.intersection(filter_reject) assert filtered_events_entity_ids == filter_accept assert not filtered_events_entity_ids.intersection(filter_reject) async def test_included_and_excluded_simple_case_with_underscores(hass, recorder_mock): """Test filters with included and excluded with underscores.""" filter_accept = {"light.any", "sensor.kitchen_4", "switch.kitchen"} filter_reject = {"switch.other", "cover.any", "sensor.weather_5", "light.kitchen"} conf = { CONF_INCLUDE: { CONF_DOMAINS: ["light"], CONF_ENTITY_GLOBS: ["sensor.kitchen_*"], CONF_ENTITIES: ["switch.kitchen"], }, CONF_EXCLUDE: { CONF_DOMAINS: ["cover"], CONF_ENTITY_GLOBS: ["sensor.weather_*"], CONF_ENTITIES: ["light.kitchen"], }, } extracted_filter = extract_include_exclude_filter_conf(conf) entity_filter = convert_include_exclude_filter(extracted_filter) sqlalchemy_filter = sqlalchemy_filter_from_include_exclude_conf(extracted_filter) assert sqlalchemy_filter is not None for entity_id in filter_accept: assert entity_filter(entity_id) is True for entity_id in filter_reject: assert entity_filter(entity_id) is False assert not entity_filter.explicitly_included("light.any") assert not entity_filter.explicitly_included("switch.other") assert entity_filter.explicitly_included("sensor.kitchen_4") assert entity_filter.explicitly_included("switch.kitchen") assert not entity_filter.explicitly_excluded("light.any") assert not entity_filter.explicitly_excluded("switch.other") assert entity_filter.explicitly_excluded("sensor.weather_5") assert entity_filter.explicitly_excluded("light.kitchen") ( filtered_states_entity_ids, filtered_events_entity_ids, ) = await _async_get_states_and_events_with_filter( hass, sqlalchemy_filter, filter_accept | filter_reject ) assert filtered_states_entity_ids == filter_accept assert not filtered_states_entity_ids.intersection(filter_reject) assert filtered_events_entity_ids == filter_accept assert not filtered_events_entity_ids.intersection(filter_reject) async def test_included_and_excluded_complex_case(hass, recorder_mock): """Test filters with included and excluded with a complex filter.""" filter_accept = {"light.any", "sensor.kitchen_4", "switch.kitchen"} filter_reject = { "camera.one", "notify.any", "automation.update_readme", "automation.update_utilities_cost", "binary_sensor.iss", } conf = { CONF_INCLUDE: { CONF_ENTITIES: ["group.trackers"], }, CONF_EXCLUDE: { CONF_ENTITIES: [ "automation.update_readme", "automation.update_utilities_cost", "binary_sensor.iss", ], CONF_DOMAINS: [ "camera", "group", "media_player", "notify", "scene", "sun", "zone", ], }, } extracted_filter = extract_include_exclude_filter_conf(conf) entity_filter = convert_include_exclude_filter(extracted_filter) sqlalchemy_filter = sqlalchemy_filter_from_include_exclude_conf(extracted_filter) assert sqlalchemy_filter is not None for entity_id in filter_accept: assert entity_filter(entity_id) is True for entity_id in filter_reject: assert entity_filter(entity_id) is False ( filtered_states_entity_ids, filtered_events_entity_ids, ) = await _async_get_states_and_events_with_filter( hass, sqlalchemy_filter, filter_accept | filter_reject ) assert filtered_states_entity_ids == filter_accept assert not filtered_states_entity_ids.intersection(filter_reject) assert filtered_events_entity_ids == filter_accept assert not filtered_events_entity_ids.intersection(filter_reject) async def test_included_entities_and_excluded_domain(hass, recorder_mock): """Test filters with included entities and excluded domain.""" filter_accept = { "media_player.test", "media_player.test3", "thermostat.test", "zone.home", "script.can_cancel_this_one", } filter_reject = { "thermostat.test2", } conf = { CONF_INCLUDE: { CONF_ENTITIES: ["media_player.test", "thermostat.test"], }, CONF_EXCLUDE: { CONF_DOMAINS: ["thermostat"], }, } extracted_filter = extract_include_exclude_filter_conf(conf) entity_filter = convert_include_exclude_filter(extracted_filter) sqlalchemy_filter = sqlalchemy_filter_from_include_exclude_conf(extracted_filter) assert sqlalchemy_filter is not None for entity_id in filter_accept: assert entity_filter(entity_id) is True for entity_id in filter_reject: assert entity_filter(entity_id) is False ( filtered_states_entity_ids, filtered_events_entity_ids, ) = await _async_get_states_and_events_with_filter( hass, sqlalchemy_filter, filter_accept | filter_reject ) assert filtered_states_entity_ids == filter_accept assert not filtered_states_entity_ids.intersection(filter_reject) assert filtered_events_entity_ids == filter_accept assert not filtered_events_entity_ids.intersection(filter_reject) async def test_same_domain_included_excluded(hass, recorder_mock): """Test filters with the same domain included and excluded.""" filter_accept = { "media_player.test", "media_player.test3", } filter_reject = { "thermostat.test2", "thermostat.test", "zone.home", "script.can_cancel_this_one", } conf = { CONF_INCLUDE: { CONF_DOMAINS: ["media_player"], }, CONF_EXCLUDE: { CONF_DOMAINS: ["media_player"], }, } extracted_filter = extract_include_exclude_filter_conf(conf) entity_filter = convert_include_exclude_filter(extracted_filter) sqlalchemy_filter = sqlalchemy_filter_from_include_exclude_conf(extracted_filter) assert sqlalchemy_filter is not None for entity_id in filter_accept: assert entity_filter(entity_id) is True for entity_id in filter_reject: assert entity_filter(entity_id) is False ( filtered_states_entity_ids, filtered_events_entity_ids, ) = await _async_get_states_and_events_with_filter( hass, sqlalchemy_filter, filter_accept | filter_reject ) assert filtered_states_entity_ids == filter_accept assert not filtered_states_entity_ids.intersection(filter_reject) assert filtered_events_entity_ids == filter_accept assert not filtered_events_entity_ids.intersection(filter_reject) async def test_same_entity_included_excluded(hass, recorder_mock): """Test filters with the same entity included and excluded.""" filter_accept = { "media_player.test", } filter_reject = { "media_player.test3", "thermostat.test2", "thermostat.test", "zone.home", "script.can_cancel_this_one", } conf = { CONF_INCLUDE: { CONF_ENTITIES: ["media_player.test"], }, CONF_EXCLUDE: { CONF_ENTITIES: ["media_player.test"], }, } extracted_filter = extract_include_exclude_filter_conf(conf) entity_filter = convert_include_exclude_filter(extracted_filter) sqlalchemy_filter = sqlalchemy_filter_from_include_exclude_conf(extracted_filter) assert sqlalchemy_filter is not None for entity_id in filter_accept: assert entity_filter(entity_id) is True for entity_id in filter_reject: assert entity_filter(entity_id) is False ( filtered_states_entity_ids, filtered_events_entity_ids, ) = await _async_get_states_and_events_with_filter( hass, sqlalchemy_filter, filter_accept | filter_reject ) assert filtered_states_entity_ids == filter_accept assert not filtered_states_entity_ids.intersection(filter_reject) assert filtered_events_entity_ids == filter_accept assert not filtered_events_entity_ids.intersection(filter_reject) async def test_same_entity_included_excluded_include_domain_wins(hass, recorder_mock): """Test filters with domain and entities and the include domain wins.""" filter_accept = { "media_player.test2", "media_player.test3", "thermostat.test", } filter_reject = { "thermostat.test2", "zone.home", "script.can_cancel_this_one", } conf = { CONF_INCLUDE: { CONF_DOMAINS: ["media_player"], CONF_ENTITIES: ["thermostat.test"], }, CONF_EXCLUDE: { CONF_DOMAINS: ["thermostat"], CONF_ENTITIES: ["media_player.test"], }, } extracted_filter = extract_include_exclude_filter_conf(conf) entity_filter = convert_include_exclude_filter(extracted_filter) sqlalchemy_filter = sqlalchemy_filter_from_include_exclude_conf(extracted_filter) assert sqlalchemy_filter is not None for entity_id in filter_accept: assert entity_filter(entity_id) is True for entity_id in filter_reject: assert entity_filter(entity_id) is False ( filtered_states_entity_ids, filtered_events_entity_ids, ) = await _async_get_states_and_events_with_filter( hass, sqlalchemy_filter, filter_accept | filter_reject ) assert filtered_states_entity_ids == filter_accept assert not filtered_states_entity_ids.intersection(filter_reject) assert filtered_events_entity_ids == filter_accept assert not filtered_events_entity_ids.intersection(filter_reject)

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ed5bdd9e0b7bf3ba36ce4a3d2569c659e98ee5a8

6,043

py

Python

tensorflow/c01/t4.py

tomsnail/opencv_tf_py

cf9aa7fa250546564cff56aa33b5a39991b0d8f1

[ "Apache-2.0" ]

null

tensorflow/c01/t4.py

tomsnail/opencv_tf_py

cf9aa7fa250546564cff56aa33b5a39991b0d8f1

[ "Apache-2.0" ]

null

tensorflow/c01/t4.py

tomsnail/opencv_tf_py

cf9aa7fa250546564cff56aa33b5a39991b0d8f1

[ "Apache-2.0" ]

1

2020-05-22T09:19:56.000Z

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import numpy as np import matplotlib.pyplot as plt import os os.environ['KMP_DUPLICATE_LIB_OK']='True' #修改代价函数为交叉熵 def mnist_op1(): #载入数据 mnist = input_data.read_data_sets("./../../datas/mnist/",one_hot=True) #每个批次的大小 batch_size = 100 #计算一共有多少个批次 n_batch = mnist.train.num_examples // batch_size #定义两个 x = tf.placeholder(tf.float32,[None,784]) y = tf.placeholder(tf.float32,[None,10]) #创建神经网络 W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) prediction = tf.nn.softmax(tf.matmul(x,W)+b) # 二次代价函数 # loss = tf.reduce_mean(tf.square(y - prediction)) #交叉熵 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) # 梯度下降训练函数 train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) # 初始化变量 init = tf.global_variables_initializer() #定义准确率 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大值所在的位置 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # 开始训练 with tf.Session() as sess: sess.run(init) for epoch in range(100): for batch in range(n_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys}) acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}) print(epoch,acc) pass #增加网络层次和节点，并使用dropout防止过拟合 def mnist_op2(): #载入数据 mnist = input_data.read_data_sets("./../../datas/mnist/",one_hot=True) #每个批次的大小 batch_size = 100 #计算一共有多少个批次 n_batch = mnist.train.num_examples // batch_size print(n_batch) #定义两个 x = tf.placeholder(tf.float32,[None,784]) y = tf.placeholder(tf.float32,[None,10]) #定义神经元dropout参数 keep_prob = tf.placeholder(tf.float32) #创建神经网络 W1 = tf.Variable(tf.truncated_normal([784,2000],stddev=0.1)) b1 = tf.Variable(tf.zeros([2000])+0.1) L1 = tf.nn.tanh(tf.matmul(x,W1)+b1) L1_drop = tf.nn.dropout(L1,keep_prob) W2 = tf.Variable(tf.truncated_normal([2000, 2000], stddev=0.1)) b2 = tf.Variable(tf.zeros([2000]) + 0.1) L2 = tf.nn.tanh(tf.matmul(L1_drop, W2) + b2) L2_drop = tf.nn.dropout(L2, keep_prob) W3 = tf.Variable(tf.truncated_normal([2000, 1000], stddev=0.1)) b3 = tf.Variable(tf.zeros([1000]) + 0.1) L3 = tf.nn.tanh(tf.matmul(L2_drop, W3) + b3) L3_drop = tf.nn.dropout(L3, keep_prob) W4 = tf.Variable(tf.truncated_normal([1000, 10], stddev=0.1)) b4 = tf.Variable(tf.zeros([10]) + 0.1) prediction = tf.nn.softmax(tf.matmul(L3_drop,W4)+b4) # 二次代价函数 # loss = tf.reduce_mean(tf.square(y - prediction)) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,logits=prediction)) # 梯度下降训练函数 train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) # 初始化变量 init = tf.global_variables_initializer() #定义准确率 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大值所在的位置 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) saver = tf.train.Saver() # 定义saver # 开始训练 with tf.Session() as sess: sess.run(init) for epoch in range(51): for batch in range(n_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.7}) test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0}) # train_acc = sess.run(accuracy, feed_dict={x: mnist.train.images, y: mnist.train.labels, keep_prob: 1.0}) print(epoch,test_acc) saver.save(sess, './../../datas/model/t4/mnist_model.ckpt') # 模型储存位置 pass #修改优化器 def mnist_op3(): #载入数据 mnist = input_data.read_data_sets("./../../datas/mnist/",one_hot=True) #每个批次的大小 batch_size = 100 #计算一共有多少个批次 n_batch = mnist.train.num_examples // batch_size print(n_batch) #定义两个 x = tf.placeholder(tf.float32,[None,784]) y = tf.placeholder(tf.float32,[None,10]) #定义神经元dropout参数 keep_prob = tf.placeholder(tf.float32) lr = tf.Variable(0.001,dtype=tf.float32) #创建神经网络 W1 = tf.Variable(tf.truncated_normal([784,500],stddev=0.1)) b1 = tf.Variable(tf.zeros([500])+0.1) L1 = tf.nn.tanh(tf.matmul(x,W1)+b1) L1_drop = tf.nn.dropout(L1,keep_prob) W2 = tf.Variable(tf.truncated_normal([500, 300], stddev=0.1)) b2 = tf.Variable(tf.zeros([300]) + 0.1) L2 = tf.nn.tanh(tf.matmul(L1_drop, W2) + b2) L2_drop = tf.nn.dropout(L2, keep_prob) W4 = tf.Variable(tf.truncated_normal([300, 10], stddev=0.1)) b4 = tf.Variable(tf.zeros([10]) + 0.1) prediction = tf.nn.softmax(tf.matmul(L2_drop,W4)+b4) # 交叉熵 # loss = tf.reduce_mean(tf.square(y - prediction)) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,logits=prediction)) # 梯度下降训练函数 # train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) train_step = tf.train.AdamOptimizer(lr).minimize(loss) # 初始化变量 init = tf.global_variables_initializer() #定义准确率 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大值所在的位置 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # 开始训练 with tf.Session() as sess: sess.run(init) for epoch in range(51): sess.run(tf.assign(lr,0.001 * ( 0.95 ** epoch))) for batch in range(n_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0}) test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0}) print(epoch,test_acc) pass if __name__ == '__main__': mnist_op3()

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null

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6

ed5d0c5572937dcc728bfc989d3fc2bbbfcb441b

29

py

Python

examples/performance/__init__.py

reguly/devito

543b7be41ddbf1faa90224cca3824767756c9390

[ "MIT" ]

204

2020-01-09T11:27:58.000Z

2022-03-20T22:53:37.000Z

examples/performance/__init__.py

reguly/devito

543b7be41ddbf1faa90224cca3824767756c9390

[ "MIT" ]

949

2016-04-25T11:41:34.000Z

2019-12-27T10:43:40.000Z

tests/integration/bot/__init__.py

RaenonX/Jelly-Bot-API

c7da1e91783dce3a2b71b955b3a22b68db9056cf

[ "MIT" ]

131

2020-01-08T17:43:13.000Z

2022-03-27T11:36:47.000Z

from .utils import * # noqa

14.5

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null

0

1

0

1

0

1

0

6

ed608d9077bda5ff4cba553de4aa147c887d57a6

208

py

Python

Day5/adventofcode5.py

oomoepoo/Adventofcode2k19

8cb4837b021bbffb7a8800b6d810eb1144ce8867

[ "Unlicense" ]

null

Day5/adventofcode5.py

oomoepoo/Adventofcode2k19

8cb4837b021bbffb7a8800b6d810eb1144ce8867

[ "Unlicense" ]

null

Day5/adventofcode5.py

oomoepoo/Adventofcode2k19

8cb4837b021bbffb7a8800b6d810eb1144ce8867

[ "Unlicense" ]

null

def operate(opcode, arg1, arg2): if (opcode==1): return arg1+arg2 elif (opcode==2): return arg1*arg2 elif (opcode==3): return arg1 elif (opcode==4): return arg1

23.111111

32

0.548077

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false

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null

1

0

1

0

null

0

1

0

6

9c60a1182852ba1c524f7185a2786c9a8943315f

4,843

py

Python

python/paddle/fluid/tests/unittests/test_scatter_op.py

liym27/Paddle

50582071dce846a973a054c40fe194069657960a

[ "Apache-2.0" ]

1

2019-10-10T03:47:33.000Z

python/paddle/fluid/tests/unittests/test_scatter_op.py

liym27/Paddle

50582071dce846a973a054c40fe194069657960a

[ "Apache-2.0" ]

1

2019-07-30T05:22:32.000Z

python/paddle/fluid/tests/unittests/test_scatter_op.py

liym27/Paddle

50582071dce846a973a054c40fe194069657960a

[ "Apache-2.0" ]

1

2020-02-21T07:40:27.000Z

# Copyright (c) 2018 PaddlePaddle 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. from __future__ import print_function import unittest import numpy as np from op_test import OpTest import paddle.fluid.core as core class TestScatterOp(OpTest): def setUp(self): self.op_type = "scatter" ref_np = np.ones((3, 3)).astype("float32") index_np = np.array([1, 2]).astype("int32") updates_np = np.random.random((2, 3)).astype("float32") output_np = np.copy(ref_np) output_np[index_np] = updates_np self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np} self.outputs = {'Out': output_np} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['Updates'], 'Out', in_place=True) class TestScatterOp0(OpTest): def setUp(self): self.op_type = "scatter" ref_np = np.ones((3, 3)).astype("float32") index_np = np.array([1, 2]).astype("int32") updates_np = np.random.random((2, 3)).astype("float32") output_np = np.copy(ref_np) output_np[index_np] = updates_np self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np} self.attrs = {'overwrite': True} self.outputs = {'Out': output_np} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['Updates'], 'Out', in_place=True) class TestScatterOp1(OpTest): def setUp(self): self.op_type = "scatter" ref_np = np.ones((3, 3)).astype("float32") zeros_np = np.zeros([2, 3]).astype('float32') index_np = np.array([1, 1]).astype("int32") updates_np = np.random.random((2, 3)).astype("float32") output_np = np.copy(ref_np) output_np[index_np] = zeros_np for i in range(0, len(index_np)): output_np[index_np[i]] += updates_np[i] self.attrs = {'overwrite': False} self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np} self.outputs = {'Out': output_np} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['Updates'], 'Out', in_place=True) @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestScatterOp2(OpTest): def setUp(self): self.op_type = "scatter" ref_np = np.ones((3, 3)).astype("float32") index_np = np.array([1, 2]).astype("int32") updates_np = np.random.random((2, 3)).astype("float32") output_np = np.copy(ref_np) output_np[index_np] = updates_np self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np} self.outputs = {'Out': output_np} def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) self.check_output_with_place(place, atol=1e-3) def test_check_grad(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) self.check_grad_with_place(place, ['Updates'], 'Out', in_place=True) @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestScatterOp3(OpTest): def setUp(self): self.op_type = "scatter" ref_np = np.ones((3, 3)).astype("float32") zeros_np = np.zeros([2, 3]).astype('float32') index_np = np.array([1, 1]).astype("int32") updates_np = np.random.random((2, 3)).astype("float32") output_np = np.copy(ref_np) output_np[index_np] = zeros_np for i in range(0, len(index_np)): output_np[index_np[i]] += updates_np[i] self.attrs = {'overwrite': False} self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np} self.outputs = {'Out': output_np} def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) self.check_output_with_place(place, atol=1e-3) def test_check_grad(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) self.check_grad_with_place(place, ['Updates'], 'Out', in_place=True) if __name__ == "__main__": unittest.main()

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9c610da191eb72ad34c7b9c1b2e825f23f17257d

37

py

Python

pyro/planning/__init__.py

SherbyRobotics/PyRobotics

86eb1189258f6f41642a149c813dd2fd6853bcc1

[ "MIT" ]

14

2019-05-03T15:22:38.000Z

2022-03-14T15:31:54.000Z

pyro/planning/__init__.py

SherbyRobotics/PyRobotics

86eb1189258f6f41642a149c813dd2fd6853bcc1

[ "MIT" ]

9

2019-08-01T14:22:13.000Z

2021-06-12T01:44:50.000Z

pyro/planning/__init__.py

SherbyRobotics/PyRobotics

86eb1189258f6f41642a149c813dd2fd6853bcc1

[ "MIT" ]

9

2019-05-21T12:38:36.000Z

2022-03-29T16:28:45.000Z

from .plan import OpenLoopController

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9c7dd97a072e77413324988ee7f723748f96b90e

157

py

Python

tests/test_core.py

VincentRouvreau/dockerhub_automated_build

6d85b208c5e7f1f43a29f42df7febf5faa3b9530

[ "MIT" ]

null

tests/test_core.py

VincentRouvreau/dockerhub_automated_build

6d85b208c5e7f1f43a29f42df7febf5faa3b9530

[ "MIT" ]

null

tests/test_core.py

VincentRouvreau/dockerhub_automated_build

6d85b208c5e7f1f43a29f42df7febf5faa3b9530

[ "MIT" ]

null

import pytest from sample import determinant def test_matrice_determinant(): assert determinant([[6,1,1], [4, -2, 5], [2,8,7]]) == pytest.approx(-306.0)

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92c13d74b9efaafc78241e7b3abd20ba1aa80044

138

py

Python

lefi/exts/commands/core/__init__.py

Moros0741/Lefi

707dfcee45c386c4e9a70c776ac8ed1d0417bc14

[ "MIT" ]

null

lefi/exts/commands/core/__init__.py

Moros0741/Lefi

707dfcee45c386c4e9a70c776ac8ed1d0417bc14

[ "MIT" ]

null

lefi/exts/commands/core/__init__.py

Moros0741/Lefi

707dfcee45c386c4e9a70c776ac8ed1d0417bc14

[ "MIT" ]

null

from .command import * from .context import * from .cooldowns import * from .handler import * from .parser import * from .plugin import *

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92d308697281c033358d86647594e3ed10099bb4

12,874

py

Python

src/envs/utils.py

tlan95/captionRL-env

72fa485b3e486f85fc58063a8f68bffcfcce4bd9

[ "MIT" ]

null

src/envs/utils.py

tlan95/captionRL-env

72fa485b3e486f85fc58063a8f68bffcfcce4bd9

[ "MIT" ]

null

src/envs/utils.py

tlan95/captionRL-env

72fa485b3e486f85fc58063a8f68bffcfcce4bd9

[ "MIT" ]

1

2021-12-07T16:40:51.000Z

from object2urdf import ObjectUrdfBuilder import numpy as np from matplotlib import pyplot as plt import pybullet as p import os from src.envs.envList import * from src.envs.reward_function import * from src.envs.env_params import get_env_params from src.envs.descriptions import generate_all_descriptions def generate_urdfs(object_folder="./src/envs/ShapeNet/VEHICLE/"): # Build entire libraries of URDFs builder = ObjectUrdfBuilder(object_folder) builder.build_library(force_overwrite=True, decompose_concave=True, force_decompose=False, center = 'top') # # Generate urdf files for ShapeNet objects # import os # path = os.path.dirname(__file__) # generate_urdfs(path + "/ShapeNet/VEHICLE/" ) # # Reconstruct the initial and final image of one episode. Finally we want the rgb_matrix for every state image of each episode. # pixels = 600 # viewMatrix = p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=[-0.15, 0.14, 0.15], distance=1.3, yaw=-30, pitch=-30, roll=0, upAxisIndex=2) # projectionMatrix = p.computeProjectionMatrixFOV(fov=50, aspect=1, nearVal=0.01, farVal=10) # with np.load('./src/envs/collected_data/UR5/Tianwei/obs_act_etc/84/data.npz', allow_pickle=True) as data: # obj_stuff_data = data['obj_stuff'] # obs_init = data['obs'][0] # obs_final = data['obs'][-1] # env_stuff_data_init = [] # for element in obj_stuff_data: # env_stuff_data_init.append(element) # env_stuff_data_init.append(obs_init) # env_stuff_data_final = [] # for element in obj_stuff_data: # env_stuff_data_final.append(element) # env_stuff_data_final.append(obs_final) # joint_poses_init = data['joint_poses'][0] # joint_poses_final = data['joint_poses'][-1] # print(env_stuff_data_init) # print(env_stuff_data_final) # # print(joint_poses_init) # # print(joint_poses_final) # env = UR5PlayAbsRPY1Obj() # # save all descriptions from initial state to final state # params = get_env_params() # train_des, test_des = sample_descriptions_from_state(obs_init, obs_final, obj_stuff_data, params) # print("train descriptions: ", train_des) # print("test descriptions: ", test_des) # # save initial image of an episode # env.reset(o=env_stuff_data_init[2], description=None, info_reset=env_stuff_data_init[:2], joint_poses = joint_poses_init) # img_arr_init = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, # renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb # plot1 = plt.figure(1) # plt.imshow(img_arr_init) # # save final image of an episode # env.reset(o=env_stuff_data_final[2], description=None, info_reset=env_stuff_data_final[:2], joint_poses = joint_poses_final) # img_arr_final = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, # renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb # plot2 = plt.figure(2) # plt.imshow(img_arr_final) # plt.show() # # See the goal of each episode, and check if all rewards are True # goals = {} # r = {} # nb_true_reward = 0 # for i in range(101): # with np.load('./src/envs/collected_data/UR5/Tianwei6/obs_act_etc/' + str(i) + '/data.npz', allow_pickle=True) as data: # g = data['goal_str'] # goals[i] = g # obj_stuff_data = data['obj_stuff'] # obs_init = data['obs'][0] # obs_final = data['obs'][-1] # params = get_env_params() # reward = False # for gl in g: # reward = get_reward_from_state(obs_init, obs_final, obj_stuff_data, gl, params) # r[i] = reward # if reward == True: # nb_true_reward = nb_true_reward + 1 # print(goals) # print(r) # print("Nb true reward: ", nb_true_reward) # # Reconstruct the initial and final image for each episode. We would like to save the initial and final image in JPG for each recorded episode (train and test separately). # pixels = 600 # viewMatrix = p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=[-0.15, 0.14, 0.15], distance=1.3, yaw=-30, pitch=-30, roll=0, upAxisIndex=2) # projectionMatrix = p.computeProjectionMatrixFOV(fov=50, aspect=1, nearVal=0.01, farVal=10) # for i in range(101): # if os.path.exists('./src/envs/collected_data/UR5/Tianwei6/obs_act_etc/' + str(i) + '/'): # with np.load('./src/envs/collected_data/UR5/Tianwei6/obs_act_etc/' + str(i) + '/data.npz', allow_pickle=True) as data: # goal = data['goal_str'] # obj_stuff_data = data['obj_stuff'] # obs_init = data['obs'][0] # obs_final = data['obs'][-1] # env_stuff_data_init = [] # for element in obj_stuff_data: # env_stuff_data_init.append(element) # env_stuff_data_init.append(obs_init) # env_stuff_data_final = [] # for element in obj_stuff_data: # env_stuff_data_final.append(element) # env_stuff_data_final.append(obs_final) # joint_poses_init = data['joint_poses'][0] # joint_poses_final = data['joint_poses'][-1] # env = UR5PlayAbsRPY1Obj() # params = get_env_params() # train_descriptions, test_descriptions, all_descriptions = generate_all_descriptions(params) # train_des, test_des = sample_descriptions_from_state(obs_init, obs_final, obj_stuff_data, params) # if not os.path.exists('./src/envs/dataset_images/'): # os.makedirs('./src/envs/dataset_images/') # if not os.path.exists('./src/envs/dataset_images/train/'): # os.makedirs('./src/envs/dataset_images/train/') # if not os.path.exists('./src/envs/dataset_images/test/'): # os.makedirs('./src/envs/dataset_images/test/') # if train_des: # count_train = len(list(os.listdir('./src/envs/dataset_images/train/'))) # os.makedirs('./src/envs/dataset_images/train/' + str(count_train) + '/') # # save initial image of an episode # env.reset(o=env_stuff_data_init[2], description=None, info_reset=env_stuff_data_init[:2], joint_poses = joint_poses_init) # img_arr_init = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, # renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb # plt.imsave('./src/envs/dataset_images/train/' + str(count_train) + '/initial.png', img_arr_init) # # save final image of an episode # env.reset(o=env_stuff_data_final[2], description=None, info_reset=env_stuff_data_final[:2], joint_poses = joint_poses_final) # img_arr_final = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, # renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb # plt.imsave('./src/envs/dataset_images/train/' + str(count_train) + '/final.png', img_arr_final) # if test_des: # count_test = len(list(os.listdir('./src/envs/dataset_images/test/'))) # os.makedirs('./src/envs/dataset_images/test/' + str(count_test) + '/') # # save initial image of an episode # env.reset(o=env_stuff_data_init[2], description=None, info_reset=env_stuff_data_init[:2], joint_poses = joint_poses_init) # img_arr_init = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, # renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb # plt.imsave('./src/envs/dataset_images/test/' + str(count_test) + '/initial.png', img_arr_init) # # save final image of an episode # env.reset(o=env_stuff_data_final[2], description=None, info_reset=env_stuff_data_final[:2], joint_poses = joint_poses_final) # img_arr_final = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, # renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb # plt.imsave('./src/envs/dataset_images/test/' + str(count_test) + '/final.png', img_arr_final) # Create the train and the test dataset. pixels = 400 viewMatrix = p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=[-0.15, 0.14, 0.15], distance=1.3, yaw=-30, pitch=-30, roll=0, upAxisIndex=2) projectionMatrix = p.computeProjectionMatrixFOV(fov=50, aspect=1, nearVal=0.01, farVal=10) for i in range(101): if os.path.exists('./src/envs/collected_data/UR5/Tianwei6/obs_act_etc/' + str(i) + '/'): with np.load('./src/envs/collected_data/UR5/Tianwei6/obs_act_etc/' + str(i) + '/data.npz', allow_pickle=True) as data: goal = data['goal_str'] obj_stuff_data = data['obj_stuff'] obs_init = data['obs'][0] obs_final = data['obs'][-1] env_stuff_data_init = [] for element in obj_stuff_data: env_stuff_data_init.append(element) env_stuff_data_init.append(obs_init) env_stuff_data_final = [] for element in obj_stuff_data: env_stuff_data_final.append(element) env_stuff_data_final.append(obs_final) joint_poses_init = data['joint_poses'][0] joint_poses_final = data['joint_poses'][-1] env = UR5PlayAbsRPY1Obj() params = get_env_params() train_descriptions, test_descriptions, all_descriptions = generate_all_descriptions(params) train_des, test_des = sample_descriptions_from_state(obs_init, obs_final, obj_stuff_data, params) if not os.path.exists('./src/envs/dataset/'): os.makedirs('./src/envs/dataset/') if not os.path.exists('./src/envs/dataset/train/'): os.makedirs('./src/envs/dataset/train/') if not os.path.exists('./src/envs/dataset/test/'): os.makedirs('./src/envs/dataset/test/') if train_des: count_train = len(list(os.listdir('./src/envs/dataset/train/'))) os.makedirs('./src/envs/dataset/train/' + str(count_train) + '/') # save initial image of an episode env.reset(o=env_stuff_data_init[2], description=None, info_reset=env_stuff_data_init[:2], joint_poses = joint_poses_init) img_arr_init = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb # save final image of an episode env.reset(o=env_stuff_data_final[2], description=None, info_reset=env_stuff_data_final[:2], joint_poses = joint_poses_final) img_arr_final = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb np.savez('./src/envs/dataset/train/' + str(count_train) + '/data', obs_init=obs_init, obs_final=obs_final, obj_stuff_data=obj_stuff_data, joint_poses_init=joint_poses_init, joint_poses_final=joint_poses_final, img_arr_init=img_arr_init, img_arr_final=img_arr_final, goal=goal, descriptions=train_des) if test_des: count_test = len(list(os.listdir('./src/envs/dataset/test/'))) os.makedirs('./src/envs/dataset/test/' + str(count_test) + '/') # save initial image of an episode env.reset(o=env_stuff_data_init[2], description=None, info_reset=env_stuff_data_init[:2], joint_poses = joint_poses_init) img_arr_init = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb # save final image of an episode env.reset(o=env_stuff_data_final[2], description=None, info_reset=env_stuff_data_final[:2], joint_poses = joint_poses_final) img_arr_final = p.getCameraImage(pixels, pixels, viewMatrix, projectionMatrix, flags=p.ER_NO_SEGMENTATION_MASK, shadow=0, renderer=p.ER_BULLET_HARDWARE_OPENGL)[2][:, :, :3] # just the rgb np.savez('./src/envs/dataset/test/' + str(count_test) + '/data', obs_init=obs_init, obs_final=obs_final, obj_stuff_data=obj_stuff_data, joint_poses_init=joint_poses_init, joint_poses_final=joint_poses_final, img_arr_init=img_arr_init, img_arr_final=img_arr_final, goal=goal, descriptions=test_des)

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92dfe74ec6d7e33ed734c171ec3144ba9a5d84a5

95

py

Python

nnet/layer/__init__.py

zhaoyan1117/NeuralNet

a0343dd469e981bf9b4f18db0209ca9bfaf58c4f

[ "BSD-2-Clause" ]

null

nnet/layer/__init__.py

zhaoyan1117/NeuralNet

a0343dd469e981bf9b4f18db0209ca9bfaf58c4f

[ "BSD-2-Clause" ]

null

nnet/layer/__init__.py

zhaoyan1117/NeuralNet

a0343dd469e981bf9b4f18db0209ca9bfaf58c4f

[ "BSD-2-Clause" ]

null

from ._fully_connected_layer import FullyConnectedLayer from ._output_layer import OutputLayer

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136daa61dca4b491331f6d486c35f27985fc62fe

34,305

py

Python

segregation/aspatial/multigroup_aspatial_indexes.py

weikang9009/segregation

403cc63772545f688308692d446c289ed2e7f99a

[ "BSD-3-Clause" ]

null

segregation/aspatial/multigroup_aspatial_indexes.py

weikang9009/segregation

403cc63772545f688308692d446c289ed2e7f99a

[ "BSD-3-Clause" ]

null

segregation/aspatial/multigroup_aspatial_indexes.py

weikang9009/segregation

403cc63772545f688308692d446c289ed2e7f99a

[ "BSD-3-Clause" ]

null

""" Multigroup Aspatial based Segregation Metrics """ __author__ = "Renan X. Cortes <renanc@ucr.edu>, Sergio J. Rey <sergio.rey@ucr.edu> and Elijah Knaap <elijah.knaap@ucr.edu>" import numpy as np from sklearn.metrics.pairwise import manhattan_distances from segregation.util.util import _dep_message, DeprecationHelper, _nan_handle # Including old and new api in __all__ so users can use both __all__ = [ 'Multi_Dissim', 'MultiDissim', 'Multi_Gini_Seg', 'MultiGiniSeg', 'Multi_Normalized_Exposure', 'MultiNormalizedExposure', 'Multi_Information_Theory', 'MultiInformationTheory', 'Multi_Relative_Diversity', 'MultiRelativeDiversity', 'Multi_Squared_Coefficient_of_Variation', 'MultiSquaredCoefficientVariation', 'Multi_Diversity', 'MultiDiversity', 'Simpsons_Concentration', 'SimpsonsConcentration', 'Simpsons_Interaction', 'SimpsonsInteraction', 'Multi_Divergence', 'MultiDivergence' ] # The Deprecation calls of the classes are located in the end of this script # # suppress numpy divide by zero warnings because it occurs a lot during the # calculation of many indices np.seterr(divide='ignore', invalid='ignore') def _multi_dissim(data, groups): """ Calculation of Multigroup Dissimilarity index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Multigroup Dissimilarity Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Notes ----- Based on Sakoda, James M. "A generalized index of dissimilarity." Demography 18.2 (1981): 245-250. Reference: :cite:`sakoda1981generalized`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) n = df.shape[0] K = df.shape[1] T = df.sum() ti = df.sum(axis=1) pik = df / ti[:, None] Pk = df.sum(axis=0) / df.sum() Is = (Pk * (1 - Pk)).sum() multi_D = 1 / (2 * T * Is) * np.multiply( abs(pik - Pk), np.repeat(ti, K, axis=0).reshape(n, K)).sum() return multi_D, core_data, groups class MultiDissim: """ Calculation of Multigroup Dissimilarity index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Multigroup Dissimilarity Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import MultiDissim Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = MultiDissim(input_df, groups_list) >>> index.statistic 0.41340872573177806 Notes ----- Based on Sakoda, James M. "A generalized index of dissimilarity." Demography 18.2 (1981): 245-250. Reference: :cite:`sakoda1981generalized`. """ def __init__(self, data, groups): aux = _multi_dissim(data, groups) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _multi_dissim def _multi_gini_seg(data, groups): """ Calculation of Multigroup Gini Segregation index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Multigroup Gini Segregation Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Reference: :cite:`reardon2002measures`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) K = df.shape[1] T = df.sum() ti = df.sum(axis=1) pik = df / ti[:, None] Pk = df.sum(axis=0) / df.sum() Is = (Pk * (1 - Pk)).sum() elements_sum = np.empty(K) for k in range(K): aux = np.multiply(np.outer(ti, ti), manhattan_distances(pik[:, k].reshape(-1, 1))).sum() elements_sum[k] = aux multi_Gini_Seg = elements_sum.sum() / (2 * (T**2) * Is) return multi_Gini_Seg, core_data, groups class MultiGiniSeg: """ Calculation of Multigroup Gini Segregation index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Multigroup Gini Segregation Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import MultiGiniSeg Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = MultiGiniSeg(input_df, groups_list) >>> index.statistic 0.5456349992598081 Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Reference: :cite:`reardon2002measures`. """ def __init__(self, data, groups): aux = _multi_gini_seg(data, groups) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _multi_gini_seg def _multi_normalized_exposure(data, groups): """ Calculation of Multigroup Normalized Exposure index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Multigroup Normalized Exposure Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Reference: :cite:`reardon2002measures`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) T = df.sum() ti = df.sum(axis=1) pik = df / ti[:, None] Pk = df.sum(axis=0) / df.sum() MNE = ((ti[:, None] * (pik - Pk)**2) / (1 - Pk)).sum() / T return MNE, core_data, groups class MultiNormalizedExposure: """ Calculation of Multigroup Normalized Exposure index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Multigroup Normalized Exposure Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import MultiNormalizedExposure Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = MultiNormalizedExposure(input_df, groups_list) >>> index.statistic 0.18821879029994157 Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Reference: :cite:`reardon2002measures`. """ def __init__(self, data, groups): aux = _multi_normalized_exposure(data, groups) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _multi_normalized_exposure def _multi_information_theory(data, groups): """ Calculation of Multigroup Information Theory index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Multigroup Information Theory Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Reference: :cite:`reardon2002measures`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) T = df.sum() ti = df.sum(axis=1) pik = df / ti[:, None] Pk = df.sum(axis=0) / df.sum() # The natural logarithm is used, but this could be used with any base following Footnote 3 of pg. 37 # of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. E = (Pk * np.log(1 / Pk)).sum() MIT = np.nansum(ti[:, None] * pik * np.log(pik / Pk)) / (T * E) return MIT, core_data, groups class MultiInformationTheory: """ Calculation of Multigroup Information Theory index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Multigroup Information Theory Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import MultiInformationTheory Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = MultiInformationTheory(input_df, groups_list) >>> index.statistic 0.1710160297858887 Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Reference: :cite:`reardon2002measures`. """ def __init__(self, data, groups): aux = _multi_information_theory(data, groups) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _multi_information_theory def _multi_relative_diversity(data, groups): """ Calculation of Multigroup Relative Diversity index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Multigroup Relative Diversity Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Notes ----- Based on Reardon, Sean F. "Measures of racial diversity and segregation in multigroup and hierarchically structured populations." annual meeting of the Eastern Sociological Society, Philadelphia, PA. 1998. High diversity means less segregation. Reference: :cite:`reardon1998measures`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) T = df.sum() ti = df.sum(axis=1) pik = df / ti[:, None] Pk = df.sum(axis=0) / df.sum() Is = (Pk * (1 - Pk)).sum() MRD = (ti[:, None] * (pik - Pk)**2).sum() / (T * Is) return MRD, core_data, groups class MultiRelativeDiversity: """ Calculation of Multigroup Relative Diversity index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Multigroup Relative Diversity Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import MultiRelativeDiversity Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = MultiRelativeDiversity(input_df, groups_list) >>> index.statistic 0.15820019878220337 Notes ----- Based on Reardon, Sean F. "Measures of racial diversity and segregation in multigroup and hierarchically structured populations." annual meeting of the Eastern Sociological Society, Philadelphia, PA. 1998. High diversity means less segregation. Reference: :cite:`reardon1998measures`. """ def __init__(self, data, groups): aux = _multi_relative_diversity(data, groups) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _multi_relative_diversity def _multi_squared_coefficient_of_variation(data, groups): """ Calculation of Multigroup Squared Coefficient of Variation index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Multigroup Squared Coefficient of Variation Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Reference: :cite:`reardon2002measures`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) K = df.shape[1] T = df.sum() ti = df.sum(axis=1) pik = df / ti[:, None] Pk = df.sum(axis=0) / df.sum() C = ((ti[:, None] * (pik - Pk)**2) / (T * (K - 1) * Pk)).sum() return C, core_data, groups class MultiSquaredCoefficientVariation: """ Calculation of Multigroup Squared Coefficient of Variation index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Multigroup Squared Coefficient of Variation Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import MultiSquaredCoefficientVariation Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = MultiSquaredCoefficientVariation(input_df, groups_list) >>> index.statistic 0.11875484641127525 Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Reference: :cite:`reardon2002measures`. """ def __init__(self, data, groups): aux = _multi_squared_coefficient_of_variation(data, groups) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _multi_squared_coefficient_of_variation def _multi_diversity(data, groups, normalized=False): """ Calculation of Multigroup Diversity index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Multigroup Diversity Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. normalized : bool. Default is False. Wheter the resulting index will be divided by its maximum (natural log of the number of groups) Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67 and Theil, Henry. "Statistical decomposition analysis; with applications in the social and administrative sciences". No. 04; HA33, T4.. 1972. This is also know as Theil's Entropy Index (Equation 2 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67) High diversity means less segregation. Reference: :cite:`reardon2002measures`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) Pk = df.sum(axis=0) / df.sum() E = -(Pk * np.log(Pk)).sum() if normalized: K = df.shape[1] E = E / np.log(K) return E, core_data, groups class MultiDiversity: """ Calculation of Multigroup Diversity index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Multigroup Diversity Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import MultiDiversity Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = MultiDiversity(input_df, groups_list) >>> index.statistic 0.9733112243997906 You can also fit the normalized version of the multigroup diversity index. >>> normalized_index = Multi_Diversity(input_df, groups_list, normalized = True) >>> normalized_index.statistic 0.7020956383415715 Notes ----- Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67 and Theil, Henry. "Statistical decomposition analysis; with applications in the social and administrative sciences". No. 04; HA33, T4.. 1972. This is also know as Theil's Entropy Index (Equation 2 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67) High diversity means less segregation. Reference: :cite:`reardon2002measures`. """ def __init__(self, data, groups, normalized=False): aux = _multi_diversity(data, groups, normalized) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _multi_diversity def _simpsons_concentration(data, groups): """ Calculation of Simpson's Concentration index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Simpson's Concentration Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Notes ----- Based on Simpson, Edward H. "Measurement of diversity." nature 163.4148 (1949): 688. Simpson's concentration index (Lambda) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to belong to the same group. Higher values means higher segregation. Simpson's Concentration + Simpson's Interaction = 1 Reference: :cite:`simpson1949measurement`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) Pk = df.sum(axis=0) / df.sum() Lambda = (Pk * Pk).sum() return Lambda, core_data, groups class SimpsonsConcentration: """ Calculation of Simpson's Concentration index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Simpson's Concentration Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import SimpsonsConcentration Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = SimpsonsConcentration(input_df, groups_list) >>> index.statistic 0.49182413151957904 Notes ----- Based on Simpson, Edward H. "Measurement of diversity." nature 163.4148 (1949): 688. Simpson's concentration index (Lambda) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to belong to the same group. Higher values means higher segregation. Simpson's Concentration + Simpson's Interaction = 1 Reference: :cite:`simpson1949measurement`. """ def __init__(self, data, groups): aux = _simpsons_concentration(data, groups) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _simpsons_concentration def _simpsons_interaction(data, groups): """ Calculation of Simpson's Interaction index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Simpson's Interaction Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Notes ----- Based on Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Simpson's interaction index (I) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to not belong to the same group. Higher values means lesser segregation. Simpson's Concentration + Simpson's Interaction = 1 Reference: :cite:`reardon2002measures`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) Pk = df.sum(axis=0) / df.sum() I = (Pk * (1 - Pk)).sum() return I, core_data, groups class SimpsonsInteraction: """ Calculation of Simpson's Interaction index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Simpson's Interaction Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import SimpsonsInteraction Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = SimpsonsInteraction(input_df, groups_list) >>> index.statistic 0.508175868480421 Notes ----- Based on Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67. Simpson's interaction index (I) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to not belong to the same group. Higher values means lesser segregation. Simpson's Concentration + Simpson's Interaction = 1 Reference: :cite:`reardon2002measures`. """ def __init__(self, data, groups): aux = _simpsons_interaction(data, groups) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _simpsons_interaction def _multi_divergence(data, groups): """ Calculation of Multigroup Divergence index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Returns ------- statistic : float Multigroup Divergence Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Notes ----- Based on Roberto, Elizabeth. "The Divergence Index: A Decomposable Measure of Segregation and Inequality." arXiv preprint arXiv:1508.01167 (2015). Reference: :cite:`roberto2015divergence`. """ core_data = data[groups] data = _nan_handle(core_data) df = np.array(core_data) T = df.sum() ti = df.sum(axis=1) pik = df / ti[:, None] Pk = df.sum(axis=0) / df.sum() Di = np.nansum(pik * np.log(pik / Pk), axis=1) Divergence_Index = ((ti / T) * Di).sum() return Divergence_Index, core_data, groups class MultiDivergence: """ Calculation of Multigroup Divergence index Parameters ---------- data : a pandas DataFrame groups : list of strings. The variables names in data of the groups of interest of the analysis. Attributes ---------- statistic : float Multigroup Divergence Index core_data : a pandas DataFrame A pandas DataFrame that contains the columns used to perform the estimate. Examples -------- In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population. Firstly, we need to perform some import the modules and the respective function. >>> import libpysal >>> import geopandas as gpd >>> from segregation.multigroup_aspatial import MultiDivergence Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index. >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp")) >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_'] The value is estimated below. >>> index = MultiDivergence(input_df, groups_list) >>> index.statistic 0.16645182134289443 Notes ----- Based on Roberto, Elizabeth. "The Divergence Index: A Decomposable Measure of Segregation and Inequality." arXiv preprint arXiv:1508.01167 (2015). Reference: :cite:`roberto2015divergence`. """ def __init__(self, data, groups): aux = _multi_divergence(data, groups) self.statistic = aux[0] self.core_data = aux[1] self._groups = aux[2] self._function = _multi_divergence # Deprecation Calls msg = _dep_message("Multi_Dissim", "MultiDissim") Multi_Dissim = DeprecationHelper(MultiDissim, message=msg) msg = _dep_message("Multi_Gini_Seg", "MultiGiniSeg") Multi_Gini_Seg = DeprecationHelper(MultiGiniSeg, message=msg) msg = _dep_message("Multi_Normalized_Exposure", "MultiNormalizedExposure") Multi_Normalized_Exposure = DeprecationHelper(MultiNormalizedExposure, message=msg) msg = _dep_message("Multi_Information_Theory", "MultiInformationTheory") Multi_Information_Theory = DeprecationHelper(MultiInformationTheory, message=msg) msg = _dep_message("Multi_Relative_Diversity", "MultiRelativeDiversity") Multi_Relative_Diversity = DeprecationHelper(MultiRelativeDiversity, message=msg) msg = _dep_message("Multi_Squared_Coefficient_of_Variation", "MultiSquaredCoefficientVariation") Multi_Squared_Coefficient_of_Variation = DeprecationHelper(MultiSquaredCoefficientVariation, message=msg) msg = _dep_message("Multi_Diversity", "MultiDiversity") Multi_Diversity = DeprecationHelper(MultiDiversity, message=msg) msg = _dep_message("Simpsons_Concentration", "SimpsonsConcentration") Simpsons_Concentration = DeprecationHelper(SimpsonsConcentration, message=msg) msg = _dep_message("Simpsons_Interaction", "SimpsonsInteraction") Simpsons_Interaction = DeprecationHelper(SimpsonsInteraction, message=msg) msg = _dep_message("Multi_Divergence", "MultiDivergence") Multi_Divergence = DeprecationHelper(MultiDivergence, message=msg)

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Python

minesweeper/__init__.py

macTracyHuang/cs50wProject2

7321cef4b95b00b5efa7d22d865fb5a2e90edaf1

[ "MIT" ]

1

2020-06-21T07:00:13.000Z

minesweeper/__init__.py

macTracyHuang/cs50wProject2

7321cef4b95b00b5efa7d22d865fb5a2e90edaf1

[ "MIT" ]

null

minesweeper/__init__.py

macTracyHuang/cs50wProject2

7321cef4b95b00b5efa7d22d865fb5a2e90edaf1

[ "MIT" ]

null

from .minesweeper import bp

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138286d82f03f0e9f67b2e0b7a6473da84ccdb04

41

py

Python

LoRaRF/__init__.py

chandrawi/LoRaRF-Python

6ca07b16b838788da061bb430d73192c4b53e3ee

[ "MIT" ]

6

2021-04-12T20:15:23.000Z

2022-02-01T15:18:18.000Z

LoRaRF/__init__.py

chandrawi/LoRaRF-Python

6ca07b16b838788da061bb430d73192c4b53e3ee

[ "MIT" ]

1

2021-04-09T10:30:49.000Z

2022-01-20T04:22:42.000Z

LoRaRF/__init__.py

chandrawi/LoRaRF-Python

6ca07b16b838788da061bb430d73192c4b53e3ee

[ "MIT" ]

4

2021-07-16T08:29:36.000Z

2022-03-28T10:13:17.000Z

# __init__.py from .SX126x import SX126x

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1396526624099d0e11a5ba0c8a3350fbfb81eae0

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py

Python

tensorflow_federated/python/core/framework/__init__.py

truthiswill/federated

d25eeac036dfc2a485120a195fd904223cfc823a

[ "Apache-2.0" ]

1

2022-02-08T01:11:14.000Z

tensorflow_federated/python/core/framework/__init__.py

truthiswill/federated

d25eeac036dfc2a485120a195fd904223cfc823a

[ "Apache-2.0" ]

null

tensorflow_federated/python/core/framework/__init__.py

truthiswill/federated

d25eeac036dfc2a485120a195fd904223cfc823a

[ "Apache-2.0" ]

null

# Copyright 2018, The TensorFlow Federated Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Libraries for extending the TensorFlow Federated core library.""" from tensorflow_federated.python.core.impl.compiler.building_blocks import ComputationBuildingBlock from tensorflow_federated.python.core.impl.compiler.intrinsic_reductions import replace_intrinsics_with_bodies from tensorflow_federated.python.core.impl.computation.computation_impl import ConcreteComputation from tensorflow_federated.python.core.impl.computation.computation_serialization import deserialize_computation from tensorflow_federated.python.core.impl.computation.computation_serialization import serialize_computation from tensorflow_federated.python.core.impl.context_stack.context_base import Context from tensorflow_federated.python.core.impl.context_stack.context_stack_base import ContextStack from tensorflow_federated.python.core.impl.context_stack.get_context_stack import get_context_stack from tensorflow_federated.python.core.impl.context_stack.set_default_context import set_default_context from tensorflow_federated.python.core.impl.execution_contexts.sync_execution_context import ExecutionContext from tensorflow_federated.python.core.impl.executors.cardinality_carrying_base import CardinalityCarrying from tensorflow_federated.python.core.impl.executors.data_backend_base import DataBackend from tensorflow_federated.python.core.impl.executors.data_descriptor import DataDescriptor from tensorflow_federated.python.core.impl.executors.data_executor import DataExecutor from tensorflow_federated.python.core.impl.executors.eager_tf_executor import EagerTFExecutor from tensorflow_federated.python.core.impl.executors.executor_base import Executor from tensorflow_federated.python.core.impl.executors.executor_factory import ExecutorFactory from tensorflow_federated.python.core.impl.executors.executor_serialization import deserialize_value from tensorflow_federated.python.core.impl.executors.executor_serialization import serialize_value from tensorflow_federated.python.core.impl.executors.executor_service import ExecutorService from tensorflow_federated.python.core.impl.executors.executor_stacks import local_executor_factory from tensorflow_federated.python.core.impl.executors.executor_stacks import remote_executor_factory from tensorflow_federated.python.core.impl.executors.executor_stacks import ResourceManagingExecutorFactory from tensorflow_federated.python.core.impl.executors.executor_stacks import SizeInfo from tensorflow_federated.python.core.impl.executors.executor_stacks import sizing_executor_factory from tensorflow_federated.python.core.impl.executors.executor_stacks import SizingExecutorFactory from tensorflow_federated.python.core.impl.executors.executor_stacks import thread_debugging_executor_factory from tensorflow_federated.python.core.impl.executors.executor_value_base import ExecutorValue from tensorflow_federated.python.core.impl.executors.federated_composing_strategy import FederatedComposingStrategy from tensorflow_federated.python.core.impl.executors.federated_resolving_strategy import FederatedResolvingStrategy from tensorflow_federated.python.core.impl.executors.federating_executor import FederatingExecutor from tensorflow_federated.python.core.impl.executors.federating_executor import FederatingStrategy from tensorflow_federated.python.core.impl.executors.ingestable_base import Ingestable from tensorflow_federated.python.core.impl.executors.reference_resolving_executor import ReferenceResolvingExecutor from tensorflow_federated.python.core.impl.executors.remote_executor import RemoteExecutor from tensorflow_federated.python.core.impl.executors.thread_delegating_executor import ThreadDelegatingExecutor from tensorflow_federated.python.core.impl.executors.transforming_executor import TransformingExecutor from tensorflow_federated.python.core.impl.types.type_analysis import contains as type_contains from tensorflow_federated.python.core.impl.types.type_conversions import type_from_tensors from tensorflow_federated.python.core.impl.types.type_conversions import type_to_tf_tensor_specs from tensorflow_federated.python.core.impl.types.type_serialization import deserialize_type from tensorflow_federated.python.core.impl.types.type_serialization import serialize_type from tensorflow_federated.python.core.impl.wrappers.computation_wrapper_instances import building_block_to_computation

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6

13aed3041ad5321f820556c496aadd9d5ae27d74

185

py

Python

jarvis_cli/interactive/__init__.py

clb6/jarvis-cli

44dfe0a94243e444eaddc72496efd677be9272e7

[ "Apache-2.0" ]

null

jarvis_cli/interactive/__init__.py

clb6/jarvis-cli

44dfe0a94243e444eaddc72496efd677be9272e7

[ "Apache-2.0" ]

3

2016-09-08T03:20:33.000Z

2016-12-08T05:19:57.000Z

jarvis_cli/interactive/__init__.py

clb6/jarvis-cli

44dfe0a94243e444eaddc72496efd677be9272e7

[ "Apache-2.0" ]

null

from .for_events import prompt_event_occurred, prompt_event_category, \ prompt_event_weight, edit_event_description, prompt_event_artifacts from .for_init import prompt_init_config

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6

b944f6fa0cbe518c4c16eb1177af1e2181b5539f

75

py

Python

fb2parser/fbparser.py

schokoro/Kataev

bcbde8ded4bbf472e7d644b38d87f625fdc53264

[ "Unlicense" ]

null

fb2parser/fbparser.py

schokoro/Kataev

bcbde8ded4bbf472e7d644b38d87f625fdc53264

[ "Unlicense" ]

null

fb2parser/fbparser.py

schokoro/Kataev

bcbde8ded4bbf472e7d644b38d87f625fdc53264

[ "Unlicense" ]

null

from bs4 import BeautifulSoup class Fbparser(): """ """ pass

9.375

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null

0

1

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1

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6

b94c86fb60a8d4ba816638cd4f58905d040df276

25

py

Python

course_api/common/test.py

alperen21/Sabanci-University-Course-RESTful-Api

3a8f772b65276d04cc4dd22d2df74253be0ea08d

[ "MIT" ]

null

course_api/common/test.py

alperen21/Sabanci-University-Course-RESTful-Api

3a8f772b65276d04cc4dd22d2df74253be0ea08d

[ "MIT" ]

null

course_api/common/test.py

alperen21/Sabanci-University-Course-RESTful-Api

3a8f772b65276d04cc4dd22d2df74253be0ea08d

[ "MIT" ]

null

from .. import course_api

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6

b9dffbe909d0d0c0888590012ab742406babfe6d

15,780

py

Python

auto_derby/single_mode/context_test.py

DoctrineAlanK/auto-derby

781e860b06b9686e56feab115d2212251cd99d10

[ "MIT" ]

235

2021-05-24T12:09:18.000Z

2022-03-31T03:44:08.000Z

auto_derby/single_mode/context_test.py

DoctrineAlanK/auto-derby

781e860b06b9686e56feab115d2212251cd99d10

[ "MIT" ]

193

2021-05-27T16:49:14.000Z

2022-03-31T16:38:08.000Z

auto_derby/single_mode/context_test.py

DoctrineAlanK/auto-derby

781e860b06b9686e56feab115d2212251cd99d10

[ "MIT" ]

89

2021-05-30T17:07:24.000Z

2022-03-27T15:41:04.000Z

from typing import Text import pytest from .. import _test from .context import Context @pytest.mark.parametrize( "name", tuple( i.stem for i in ((_test.DATA_PATH / "single_mode").glob("command_scene_*.png")) ), ) def test_recognize_command_scene(name: Text): img, _ = _test.use_screenshot(f"single_mode/{name}.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match( ctx, name=name, ) def test_update_by_command_scene_1(): img, _ = _test.use_screenshot("single_mode/command_scene_1.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (1, 12, 2), ctx.date assert round(ctx.vitality, 2) == 0.92, ctx.vitality assert ctx.speed == 281, ctx.speed assert ctx.stamina == 217, ctx.stamina assert ctx.power == 210, ctx.power assert ctx.guts == 187, ctx.guts assert ctx.wisdom == 266, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_command_scene_2(): img, _ = _test.use_screenshot("single_mode/command_scene_2.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (2, 1, 1), ctx.date assert round(ctx.vitality, 2) == 0.92, ctx.vitality assert ctx.speed == 281, ctx.speed assert ctx.stamina == 217, ctx.stamina assert ctx.power == 210, ctx.power assert ctx.guts == 198, ctx.guts assert ctx.wisdom == 266, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_command_scene_3(): img, _ = _test.use_screenshot("single_mode/command_scene_3.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (3, 1, 1), ctx.date assert round(ctx.vitality, 2) == 1, ctx.vitality assert ctx.speed == 589, ctx.speed assert ctx.stamina == 375, ctx.stamina assert ctx.power == 461, ctx.power assert ctx.guts == 263, ctx.guts assert ctx.wisdom == 386, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_command_scene_4(): img, _ = _test.use_screenshot("single_mode/command_scene_4.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (2, 4, 1), ctx.date assert round(ctx.vitality, 2) == 0.95, ctx.vitality assert ctx.speed == 357, ctx.speed assert ctx.stamina == 279, ctx.stamina assert ctx.power == 275, ctx.power assert ctx.guts == 216, ctx.guts assert ctx.wisdom == 250, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_command_scene_5(): img, _ = _test.use_screenshot("single_mode/command_scene_5.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (3, 3, 1), ctx.date assert round(ctx.vitality, 2) == 0.74, ctx.vitality assert ctx.speed == 568, ctx.speed assert ctx.stamina == 368, ctx.stamina assert ctx.power == 341, ctx.power assert ctx.guts == 307, ctx.guts assert ctx.wisdom == 329, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_command_scene_6(): img, _ = _test.use_screenshot("single_mode/command_scene_6.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (2, 10, 1), ctx.date assert round(ctx.vitality, 2) == 0.46, ctx.vitality assert ctx.speed == 510, ctx.speed assert ctx.stamina == 317, ctx.stamina assert ctx.power == 351, ctx.power assert ctx.guts == 298, ctx.guts assert ctx.wisdom == 314, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_command_scene_7(): img, _ = _test.use_screenshot("single_mode/command_scene_7.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (2, 12, 2), ctx.date assert round(ctx.vitality, 2) == 0.33, ctx.vitality assert ctx.speed == 615, ctx.speed assert ctx.stamina == 316, ctx.stamina assert ctx.power == 459, ctx.power assert ctx.guts == 251, ctx.guts assert ctx.wisdom == 382, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_command_scene_issue7(): img, _ = _test.use_screenshot("single_mode/command_scene_issue7.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (1, 0, 0) assert round(ctx.vitality, 2) == 1 assert ctx.speed == 158 assert ctx.stamina == 190 assert ctx.power == 67 assert ctx.guts == 95 assert ctx.wisdom == 90 assert ctx.mood == ctx.MOOD_NORMAL def test_update_by_command_scene_issue12(): img, _ = _test.use_screenshot("single_mode/command_scene_issue12.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (1, 12, 2), ctx.date assert round(ctx.vitality, 2) == 0.80, ctx.vitality assert ctx.speed == 266, ctx.speed assert ctx.stamina == 228, ctx.stamina assert ctx.power == 196, ctx.power assert ctx.guts == 200, ctx.guts assert ctx.wisdom == 176, ctx.wisdom assert ctx.mood == ctx.MOOD_BAD, ctx.mood def test_update_by_command_scene_issue12_2(): img, _ = _test.use_screenshot("single_mode/command_scene_issue12_2.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (1, 10, 1), ctx.date assert round(ctx.vitality, 2) == 0.79, ctx.vitality assert ctx.speed == 241, ctx.speed assert ctx.stamina == 237, ctx.stamina assert ctx.power == 144, ctx.power assert ctx.guts == 187, ctx.guts assert ctx.wisdom == 184, ctx.wisdom assert ctx.mood == ctx.MOOD_GOOD, ctx.mood def test_update_by_command_scene_issue17(): img, _ = _test.use_screenshot("single_mode/command_scene_issue17.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (1, 0, 0), ctx.date assert round(ctx.vitality, 2) == 0.53, ctx.vitality assert ctx.speed == 195, ctx.speed assert ctx.stamina == 150, ctx.stamina assert ctx.power == 119, ctx.power assert ctx.guts == 115, ctx.guts assert ctx.wisdom == 91, ctx.wisdom assert ctx.mood == ctx.MOOD_GOOD, ctx.mood def test_update_by_command_scene_issue17_2(): img, _ = _test.use_screenshot("single_mode/command_scene_issue17_2.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (1, 11, 2), ctx.date assert round(ctx.vitality, 2) == 1, ctx.vitality assert ctx.speed == 262, ctx.speed assert ctx.stamina == 266, ctx.stamina assert ctx.power == 142, ctx.power assert ctx.guts == 156, ctx.guts assert ctx.wisdom == 233, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_command_scene_issue41(): img, _ = _test.use_screenshot("single_mode/command_scene_issue41.png") ctx = Context.new() ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (3, 11, 1), ctx.date assert round(ctx.vitality, 2) == 0.84, ctx.vitality assert ctx.speed == 1200, ctx.speed assert ctx.stamina == 753, ctx.stamina assert ctx.power == 616, ctx.power assert ctx.guts == 364, ctx.guts assert ctx.wisdom == 326, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_command_scene_issue113(): ctx = Context.new() img, _ = _test.use_screenshot("single_mode/command_scene_issue113.png") ctx.update_by_command_scene(img) _test.snapshot_match(ctx) assert ctx.date == (4, 0, 0), ctx.date assert round(ctx.vitality, 2) == 0.72, ctx.vitality assert ctx.speed == 1144, ctx.speed assert ctx.stamina == 482, ctx.stamina assert ctx.power == 459, ctx.power assert ctx.guts == 343, ctx.guts assert ctx.wisdom == 437, ctx.wisdom assert ctx.mood == ctx.MOOD_VERY_GOOD, ctx.mood def test_update_by_class_detail(): img, _ = _test.use_screenshot("single_mode/class_detail.png") ctx = Context.new() ctx.update_by_class_detail(img) _test.snapshot_match(ctx) assert ctx.fan_count == 1, ctx.fan_count assert ctx.is_after_winning == False, ctx.is_after_winning def test_update_by_class_detail_2(): img, _ = _test.use_screenshot("single_mode/class_detail_2.png") ctx = Context.new() ctx.update_by_class_detail(img) _test.snapshot_match(ctx) assert ctx.fan_count == 1225, ctx.fan_count assert ctx.is_after_winning == True, ctx.is_after_winning def test_update_by_class_detail_3(): img, _ = _test.use_screenshot("single_mode/class_detail_3.png") ctx = Context.new() ctx.update_by_class_detail(img) _test.snapshot_match(ctx) assert ctx.fan_count == 11950, ctx.fan_count assert ctx.is_after_winning == True, ctx.is_after_winning def test_update_by_class_detail_4(): img, _ = _test.use_screenshot("single_mode/class_detail_4.png") ctx = Context.new() ctx.update_by_class_detail(img) _test.snapshot_match(ctx) assert ctx.fan_count == 148805, ctx.fan_count assert ctx.is_after_winning == True, ctx.is_after_winning def test_update_by_class_detail_5(): img, _ = _test.use_screenshot("single_mode/class_detail_5.png") ctx = Context.new() ctx.update_by_class_detail(img) _test.snapshot_match(ctx) assert ctx.fan_count == 127591, ctx.fan_count assert ctx.is_after_winning == True, ctx.is_after_winning def test_update_by_class_detail_6(): img, _ = _test.use_screenshot("single_mode/class_detail_6.png") ctx = Context.new() ctx.update_by_class_detail(img) _test.snapshot_match(ctx) assert ctx.fan_count == 121794, ctx.fan_count assert ctx.is_after_winning == True, ctx.is_after_winning def test_update_by_class_detail_issue35(): img, _ = _test.use_screenshot("single_mode/class_detail_issue35.png") ctx = Context.new() ctx.update_by_class_detail(img) _test.snapshot_match(ctx) assert ctx.fan_count == 1129, ctx.fan_count assert ctx.is_after_winning == True, ctx.is_after_winning def test_update_by_class_detail_issue35_2(): img, _ = _test.use_screenshot("single_mode/class_detail_issue35_2.png") ctx = Context.new() ctx.update_by_class_detail(img) _test.snapshot_match(ctx) assert ctx.fan_count == 4119, ctx.fan_count assert ctx.is_after_winning == True, ctx.is_after_winning def test_update_by_class_detail_issue86(): img, _ = _test.use_screenshot("single_mode/class_detail_issue86.png") ctx = Context.new() ctx.update_by_class_detail(img) _test.snapshot_match(ctx) assert ctx.fan_count == 88556, ctx.fan_count assert ctx.is_after_winning == True, ctx.is_after_winning def test_update_by_character_detail(): img, _ = _test.use_screenshot("single_mode/character_detail.png") ctx = Context.new() ctx.update_by_character_detail(img) assert ctx.turf == ctx.STATUS_A, ctx.turf assert ctx.dart == ctx.STATUS_G, ctx.dart assert ctx.sprint == ctx.STATUS_C, ctx.sprint assert ctx.mile == ctx.STATUS_B, ctx.mile assert ctx.intermediate == ctx.STATUS_A, ctx.intermediate assert ctx.long == ctx.STATUS_C, ctx.long assert ctx.lead == ctx.STATUS_D, ctx.lead assert ctx.head == ctx.STATUS_A, ctx.head assert ctx.middle == ctx.STATUS_A, ctx.middle assert ctx.last == ctx.STATUS_G, ctx.last def test_update_by_character_detail_2(): img, _ = _test.use_screenshot("single_mode/character_detail_2.png") ctx = Context.new() ctx.update_by_character_detail(img) assert ctx.turf == ctx.STATUS_A, ctx.turf assert ctx.dart == ctx.STATUS_E, ctx.dart assert ctx.sprint == ctx.STATUS_D, ctx.sprint assert ctx.mile == ctx.STATUS_D, ctx.mile assert ctx.intermediate == ctx.STATUS_A, ctx.intermediate assert ctx.long == ctx.STATUS_A, ctx.long assert ctx.lead == ctx.STATUS_A, ctx.lead assert ctx.head == ctx.STATUS_A, ctx.head assert ctx.middle == ctx.STATUS_B, ctx.middle assert ctx.last == ctx.STATUS_B, ctx.last def test_update_by_character_detail_3(): img, _ = _test.use_screenshot("single_mode/character_detail_3.png") ctx = Context.new() ctx.update_by_character_detail(img) assert ctx.turf == ctx.STATUS_S, ctx.turf assert ctx.dart == ctx.STATUS_G, ctx.dart assert ctx.sprint == ctx.STATUS_C, ctx.sprint assert ctx.mile == ctx.STATUS_B, ctx.mile assert ctx.intermediate == ctx.STATUS_A, ctx.intermediate assert ctx.long == ctx.STATUS_C, ctx.long assert ctx.lead == ctx.STATUS_D, ctx.lead assert ctx.head == ctx.STATUS_A, ctx.head assert ctx.middle == ctx.STATUS_A, ctx.middle assert ctx.last == ctx.STATUS_G, ctx.last def test_update_by_character_detail_4(): img, _ = _test.use_screenshot("single_mode/character_detail_4.png") ctx = Context.new() ctx.update_by_character_detail(img) assert ctx.turf == ctx.STATUS_A, ctx.turf assert ctx.dart == ctx.STATUS_G, ctx.dart assert ctx.sprint == ctx.STATUS_C, ctx.sprint assert ctx.mile == ctx.STATUS_B, ctx.mile assert ctx.intermediate == ctx.STATUS_S, ctx.intermediate assert ctx.long == ctx.STATUS_C, ctx.long assert ctx.lead == ctx.STATUS_D, ctx.lead assert ctx.head == ctx.STATUS_A, ctx.head assert ctx.middle == ctx.STATUS_A, ctx.middle assert ctx.last == ctx.STATUS_G, ctx.last def test_update_by_character_detail_5(): img, _ = _test.use_screenshot("single_mode/character_detail_5.png") ctx = Context.new() ctx.update_by_character_detail(img) assert ctx.turf == ctx.STATUS_A, ctx.turf assert ctx.dart == ctx.STATUS_G, ctx.dart assert ctx.sprint == ctx.STATUS_C, ctx.sprint assert ctx.mile == ctx.STATUS_B, ctx.mile assert ctx.intermediate == ctx.STATUS_A, ctx.intermediate assert ctx.long == ctx.STATUS_A, ctx.long assert ctx.lead == ctx.STATUS_A, ctx.lead assert ctx.head == ctx.STATUS_D, ctx.head assert ctx.middle == ctx.STATUS_F, ctx.middle assert ctx.last == ctx.STATUS_G, ctx.last assert ctx.conditions == set((ctx.CONDITION_HEADACHE,)), ctx.conditions def test_update_by_character_detail_6(): img, _ = _test.use_screenshot("single_mode/character_detail_6.png") ctx = Context.new() ctx.update_by_character_detail(img) assert ctx.turf == ctx.STATUS_A, ctx.turf assert ctx.dart == ctx.STATUS_E, ctx.dart assert ctx.sprint == ctx.STATUS_G, ctx.sprint assert ctx.mile == ctx.STATUS_E, ctx.mile assert ctx.intermediate == ctx.STATUS_A, ctx.intermediate assert ctx.long == ctx.STATUS_A, ctx.long assert ctx.lead == ctx.STATUS_C, ctx.lead assert ctx.head == ctx.STATUS_A, ctx.head assert ctx.middle == ctx.STATUS_A, ctx.middle assert ctx.last == ctx.STATUS_G, ctx.last assert ctx.conditions == set((ctx.CONDITION_OVERWEIGHT,)), ctx.conditions def test_update_by_character_detail_issue39(): img, _ = _test.use_screenshot("single_mode/character_detail_issue39.png") ctx = Context.new() ctx.update_by_character_detail(img) assert ctx.turf == ctx.STATUS_A, ctx.turf assert ctx.dart == ctx.STATUS_F, ctx.dart assert ctx.sprint == ctx.STATUS_F, ctx.sprint assert ctx.mile == ctx.STATUS_C, ctx.mile assert ctx.intermediate == ctx.STATUS_A, ctx.intermediate assert ctx.long == ctx.STATUS_A, ctx.long assert ctx.lead == ctx.STATUS_G, ctx.lead assert ctx.head == ctx.STATUS_A, ctx.head assert ctx.middle == ctx.STATUS_A, ctx.middle assert ctx.last == ctx.STATUS_F, ctx.last

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py

Python

lcd/models/__init__.py

pfe-everis/lcd

25f3fe7dc7e0c8ba02fb380dbcbe7752747b3fb5

[ "BSD-3-Clause" ]

76

2019-11-22T04:28:37.000Z

2022-03-31T12:48:04.000Z

lcd/models/__init__.py

pfe-everis/lcd

25f3fe7dc7e0c8ba02fb380dbcbe7752747b3fb5

[ "BSD-3-Clause" ]

10

2019-12-23T02:28:24.000Z

2022-03-18T08:08:16.000Z

lcd/models/__init__.py

pfe-everis/lcd

25f3fe7dc7e0c8ba02fb380dbcbe7752747b3fb5

[ "BSD-3-Clause" ]

7

2019-11-23T08:21:52.000Z

2021-12-29T14:40:57.000Z

from .pointnet import * from .patchnet import *

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py

Python

typings/bpy/ops/material.py

Argmaster/PyR3

6786bcb6a101fe4bd4cc50fe43767b8178504b15

[ "MIT" ]

2

2021-12-12T18:51:52.000Z

2022-02-23T09:49:16.000Z

src/blender/blender_autocomplete-master/2.92/bpy/ops/material.py

JonasWard/ClayAdventures

a716445ac690e4792e70658319aa1d5299f9c9e9

[ "MIT" ]

2

2021-11-08T12:09:02.000Z

2021-12-12T23:01:12.000Z

src/blender/blender_autocomplete-master/2.92/bpy/ops/material.py

JonasWard/ClayAdventures

a716445ac690e4792e70658319aa1d5299f9c9e9

[ "MIT" ]

null

import sys import typing def copy(): ''' Copy the material settings and nodes ''' pass def new(): ''' Add a new material ''' pass def paste(): ''' Paste the material settings and nodes ''' pass

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6a46deffc0f9f4a54eac36b0579978ff6c8127be

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py

Python

exabel_data_sdk/stubs/exabel/api/math/all_pb2.py

burk/python-sdk

83fb81d09e0d6a407c8907a75bebb895decc7edc

[ "MIT" ]

null

exabel_data_sdk/stubs/exabel/api/math/all_pb2.py

burk/python-sdk

83fb81d09e0d6a407c8907a75bebb895decc7edc

[ "MIT" ]

null

exabel_data_sdk/stubs/exabel/api/math/all_pb2.py

burk/python-sdk

83fb81d09e0d6a407c8907a75bebb895decc7edc

[ "MIT" ]

null

# Generated by generate_protobuf.sh. # Contains all messages in *_pb2.py in a single module. from .aggregation_pb2 import *

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py

Python

run_test.py

neso613/english_asr_pip_wheel

d7db59f97d0317028f610097fda0e35c059ee610

[ "Apache-2.0" ]

null

run_test.py

neso613/english_asr_pip_wheel

d7db59f97d0317028f610097fda0e35c059ee610

[ "Apache-2.0" ]

null

run_test.py

neso613/english_asr_pip_wheel

d7db59f97d0317028f610097fda0e35c059ee610

[ "Apache-2.0" ]

null

from english_asr.conformer import get_text_from_speech text = get_text_from_speech('1.flac') print('English ASR output : ',text)

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py

Python

opentamp/util_classes/ik_controller.py

Algorithmic-Alignment-Lab/openTAMP-legacy

3b7c3be164cc968ad77a928286d6460cd70a670e

[ "MIT" ]

2

2022-03-09T19:48:20.000Z

2022-03-26T17:31:07.000Z

opentamp/util_classes/ik_controller.py

Algorithmic-Alignment-Lab/OpenTAMP

eecb950bd273da8cbed4394487630e8453f2c242

[ "MIT" ]

null

opentamp/util_classes/ik_controller.py

Algorithmic-Alignment-Lab/OpenTAMP

eecb950bd273da8cbed4394487630e8453f2c242

[ "MIT" ]

null

""" Adapted from: https://github.com/StanfordVL/robosuite/blob/master/robosuite/controllers/baxter_ik_controller.py @inproceedings{corl2018surreal, title={SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark}, author={Fan, Linxi and Zhu, Yuke and Zhu, Jiren and Liu, Zihua and Zeng, Orien and Gupta, Anchit and Creus-Costa, Joan and Savarese, Silvio and Fei-Fei, Li}, booktitle={Conference on Robot Learning}, year={2018} } """ import os import numpy as np try: import pybullet as p except ImportError: raise Exception( "Please make sure pybullet is installed. Run `pip install pybullet==1.9.5`" ) import opentamp from opentamp.util_classes import transform_utils as T class BaxterIKController(object): """ Inverse kinematics for the Baxter robot, using Pybullet and the urdf description files. """ def __init__(self, robot_jpos_getter, use_rot_mat=False): """ Args: bullet_data_path (str): base path to bullet data. robot_jpos_getter (function): function that returns the joint positions of the robot to be controlled as a numpy array. """ # Set up inverse kinematics self.robot_jpos_getter = robot_jpos_getter self.use_rot_mat = use_rot_mat path = os.getcwd() + '/opentamp' + "/robot_info/baxter/baxter_description/urdf/baxter_mod.urdf" self.setup_inverse_kinematics(path) self.commanded_joint_positions = robot_jpos_getter() self.sync_state() self._name2id = {} self._id2name = {} for i in range(p.getNumJoints(self.ik_robot)): jnt_info = p.getJointInfo(self.ik_robot, i) link_name = jnt_info[12] self._name2id[link_name] = i self._id2name[i] = link_name def id2name(self, ind): return self._id2name[ind] def name2id(self, name): return self._name2id[name] def get_control(self, right, left): """ Returns joint velocities to control the robot after the target end effector positions and orientations are updated from arguments @left and @right. Args: left (dict): A dictionary to control the left end effector with these keys. dpos (numpy array): a 3 dimensional array corresponding to the desired change in x, y, and z left end effector position. rotation (numpy array): a rotation matrix of shape (3, 3) corresponding to the desired orientation of the left end effector. right (dict): A dictionary to control the left end effector with these keys. dpos (numpy array): a 3 dimensional array corresponding to the desired change in x, y, and z right end effector position. rotation (numpy array): a rotation matrix of shape (3, 3) corresponding to the desired orientation of the right end effector. Returns: velocities (numpy array): a flat array of joint velocity commands to apply to try and achieve the desired input control. """ # Sync joint positions for IK. self.sync_ik_robot(self.robot_jpos_getter()) # Compute target joint positions self.commanded_joint_positions = self.joint_positions_for_eef_command( right, left ) # P controller from joint positions (from IK) to velocities velocities = np.zeros(14) deltas = self._get_current_error( self.robot_jpos_getter(), self.commanded_joint_positions ) for i, delta in enumerate(deltas): velocities[i] = -2 * delta velocities = self.clip_joint_velocities(velocities) self.commanded_joint_velocities = velocities return velocities # For debugging purposes: set joint positions directly # robot.set_joint_positions(self.commanded_joint_positions) def sync_state(self): """ Syncs the internal Pybullet robot state to the joint positions of the robot being controlled. """ # sync IK robot state to the current robot joint positions self.sync_ik_robot(self.robot_jpos_getter()) # make sure target pose is up to date pos_r, orn_r, pos_l, orn_l = self.ik_robot_eef_joint_cartesian_pose() self.ik_robot_target_pos_right = pos_r self.ik_robot_target_orn_right = orn_r self.ik_robot_target_pos_left = pos_l self.ik_robot_target_orn_left = orn_l def setup_inverse_kinematics(self, urdf_path): """ This function is responsible for doing any setup for inverse kinematics. Inverse Kinematics maps end effector (EEF) poses to joint angles that are necessary to achieve those poses. """ # These indices come from the urdf file we're using self.effector_right = 27 self.effector_left = 45 # Use PyBullet to handle inverse kinematics. # Set up a connection to the PyBullet simulator. p.connect(p.DIRECT) p.resetSimulation() self.ik_robot = p.loadURDF(urdf_path, (0, 0, 0), (0, 0, 0, 1), useFixedBase=1, flags=p.URDF_USE_SELF_COLLISION | \ p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS) # Relevant joints we care about. Many of the joints are fixed and don't count, so # we need this second map to use the right ones. self.actual = [13, 14, 15, 16, 17, 19, 20, 31, 32, 33, 34, 35, 37, 38] self.num_joints = p.getNumJoints(self.ik_robot) n = p.getNumJoints(self.ik_robot) self.rest = [] self.lower = [] self.upper = [] self.ranges = [] for i in range(n): info = p.getJointInfo(self.ik_robot, i) # Retrieve lower and upper ranges for each relevant joint if info[3] > -1: self.rest.append(p.getJointState(self.ik_robot, i)[0]) self.lower.append(info[8]) self.upper.append(info[9]) self.ranges.append(info[9] - info[8]) # Simulation will update as fast as it can in real time, instead of waiting for # step commands like in the non-realtime case. p.setRealTimeSimulation(1) def sync_ik_robot(self, joint_positions, simulate=False, sync_last=True): """ Force the internal robot model to match the provided joint angles. Args: joint_positions (list): a list or flat numpy array of joint positions. simulate (bool): If True, actually use physics simulation, else write to physics state directly. sync_last (bool): If False, don't sync the last joint angle. This is useful for directly controlling the roll at the end effector. """ num_joints = len(joint_positions) if not sync_last: num_joints -= 1 for i in range(num_joints): if simulate: p.setJointMotorControl2( self.ik_robot, self.actual[i], p.POSITION_CONTROL, targetVelocity=0, targetPosition=joint_positions[i], force=500, positionGain=0.5, velocityGain=1., ) else: # Note that we use self.actual[i], and not i p.resetJointState(self.ik_robot, self.actual[i], joint_positions[i]) def sync_ik_from_attrs(self, attr_vals): if 'rArmPose' in attr_vals: for i in range(7): p.resetJointState(self.ik_robot, self.actual[i], attr_vals['rArmPose'][i]) if 'lArmPose' in attr_vals: for i in range(7, 14): p.resetJointState(self.ik_robot, self.actual[i], attr_vals['lArmPose'][i-7]) if 'rGripper' in attr_vals: p.resetJointState(self.ik_robot, self.gripper_inds[0], attr_vals['Gripper'][0]) if 'lGripper' in attr_vals: p.resetJointState(self.ik_robot, self.gripper_inds[1], attr_vals['lGripper'][0]) def ik_robot_eef_joint_cartesian_pose(self): """ Returns the current cartesian pose of the last joint of the ik robot with respect to the base frame as a (pos, orn) tuple where orn is a x-y-z-w quaternion. """ out = [] for eff in [self.effector_right, self.effector_left]: eef_pos_in_world = np.array(p.getLinkState(self.ik_robot, eff)[0]) eef_orn_in_world = np.array(p.getLinkState(self.ik_robot, eff)[1]) eef_pose_in_world = T.pose2mat((eef_pos_in_world, eef_orn_in_world)) base_pos_in_world = np.array( p.getBasePositionAndOrientation(self.ik_robot)[0] ) base_orn_in_world = np.array( p.getBasePositionAndOrientation(self.ik_robot)[1] ) base_pose_in_world = T.pose2mat((base_pos_in_world, base_orn_in_world)) world_pose_in_base = T.pose_inv(base_pose_in_world) eef_pose_in_base = T.pose_in_A_to_pose_in_B( pose_A=eef_pose_in_world, pose_A_in_B=world_pose_in_base ) out.extend(T.mat2pose(eef_pose_in_base)) return out def get_manip_trans(self, right=True): eff = self.effector_right if right else self.effector_left eef_pos_in_world = np.array(p.getLinkState(self.ik_robot, eff)[0]) eef_orn_in_world = np.array(p.getLinkState(self.ik_robot, eff)[1]) eef_pose_in_world = T.pose2mat((eef_pos_in_world, eef_orn_in_world)) pos, quat = p.getBasePositionAndOrientation(self.ik_robot) base_pos_in_world = np.array(pos) base_orn_in_world = np.array(quat) base_pose_in_world = T.pose2mat((base_pos_in_world, base_orn_in_world)) world_pose_in_base = T.pose_inv(base_pose_in_world) eef_pose_in_base = T.pose_in_A_to_pose_in_B( pose_A=eef_pose_in_world, pose_A_in_B=world_pose_in_base ) return eef_pose_in_base def get_jnt_angles(self, right=True): jnts = self.actual[:7] if right else self.actual[7:] jnt_info = p.getJointStates(jnts) pos = jnt_info[0] return pos def get_jnt_bounds(self, right=True): return (self.lower[:7], self.upper[:7]) if right else (self.lower[7:], self.upper[7:]) def inverse_kinematics( self, target_position, target_orientation, use_right, rest_poses, ): """ Helper function to do inverse kinematics for a given target position and orientation in the PyBullet world frame. Args: target_position_{right, left}: A tuple, list, or numpy array of size 3 for position. target_orientation_{right, left}: A tuple, list, or numpy array of size 4 for a orientation quaternion. rest_poses: A list of size @num_joints to favor ik solutions close by. Returns: A list of size @num_joints corresponding to the joint angle solution. """ ndof = 48 if use_right: ik_solution = list( p.calculateInverseKinematics( self.ik_robot, self.effector_right, target_position, targetOrientation=target_orientation, restPoses=rest_poses[:7], lowerLimits=self.lower, upperLimits=self.upper, jointRanges=self.ranges, jointDamping=[0.1] * ndof, ) ) return ik_solution[1:8] else: ik_solution = list( p.calculateInverseKinematics( self.ik_robot, self.effector_left, target_position, targetOrientation=target_orientation, restPoses=rest_poses[7:], lowerLimits=self.lower, upperLimits=self.upper, jointRanges=self.ranges, jointDamping=[0.1] * ndof, ) ) return ik_solution[8:15] def bullet_base_pose_to_world_pose(self, pose_in_base): """ Convert a pose in the base frame to a pose in the world frame. Args: pose_in_base: a (pos, orn) tuple. Returns: pose_in world: a (pos, orn) tuple. """ pose_in_base = T.pose2mat(pose_in_base) base_pos_in_world = np.array(p.getBasePositionAndOrientation(self.ik_robot)[0]) base_orn_in_world = np.array(p.getBasePositionAndOrientation(self.ik_robot)[1]) base_pose_in_world = T.pose2mat((base_pos_in_world, base_orn_in_world)) pose_in_world = T.pose_in_A_to_pose_in_B( pose_A=pose_in_base, pose_A_in_B=base_pose_in_world ) return T.mat2pose(pose_in_world) def joint_positions_for_eef_command(self, cmd, use_right): """ This function runs inverse kinematics to back out target joint positions from the provided end effector command. Same arguments as @get_control. Returns: A list of size @num_joints corresponding to the target joint angles. """ dpos = cmd["dpos"] rotation = cmd["rotation"] if use_right: self.target_pos_right = self.ik_robot_target_pos_right #+ np.array([0, 0, 0.913]) self.ik_robot_target_pos_right = dpos # += dpos self.ik_robot_target_orn_right = rotation world_targets = self.bullet_base_pose_to_world_pose( (self.ik_robot_target_pos_right, self.ik_robot_target_orn_right) ) else: self.target_pos_left = self.ik_robot_target_pos_left #+ np.array([0, 0, 0.913]) self.ik_robot_target_pos_left = dpos # += dpos self.ik_robot_target_orn_left = rotation world_targets = self.bullet_base_pose_to_world_pose( (self.ik_robot_target_pos_left, self.ik_robot_target_orn_left) ) # convert from target pose in base frame to target pose in bullet world frame # Empirically, more iterations aren't needed, and it's faster for _ in range(1): rest_poses = self.robot_jpos_getter() arm_joint_pos = self.inverse_kinematics( world_targets[0], world_targets[1], use_right, rest_poses=rest_poses, ) if use_right: both_arm_joint_pos = np.r_[arm_joint_pos, rest_poses[7:]] else: both_arm_joint_pos = np.r_[rest_poses[:7], arm_joint_pos] self.sync_ik_robot(both_arm_joint_pos, sync_last=True) return arm_joint_pos def _get_current_error(self, current, set_point): """ Returns an array of differences between the desired joint positions and current joint positions. Useful for PID control. Args: current: the current joint positions. set_point: the joint positions that are desired as a numpy array. Returns: the current error in the joint positions. """ error = current - set_point return error def clip_joint_velocities(self, velocities): """ Clips joint velocities into a valid range. """ for i in range(len(velocities)): if velocities[i] >= 1.0: velocities[i] = 1.0 elif velocities[i] <= -1.0: velocities[i] = -1.0 return velocities class HSRIKController(object): """ Inverse kinematics for the Baxter robot, using Pybullet and the urdf description files. """ def __init__(self, robot_jpos_getter, use_rot_mat=False): """ Args: bullet_data_path (str): base path to bullet data. robot_jpos_getter (function): function that returns the joint positions of the robot to be controlled as a numpy array. """ # Set up inverse kinematics self.robot_jpos_getter = robot_jpos_getter self.use_rot_mat = use_rot_mat path = os.getcwd() + '/opentamp' + "/robot_info/hsr/simple_hsrb4s.urdf" self.setup_inverse_kinematics(path) self.commanded_joint_positions = robot_jpos_getter() self.sync_state() self._name2id = {} self._id2_name = {} for i in range(p.getNumJoints(self.ik_robot)): jnt_info = p.getJointInfo(i) link_name = jnt_info[12] self._name2id[link_name] = i self._id2name[i] = link_name def id2name(self, ind): return self._id2name[ind] def name2id(self, name): return self._name2id[name] def get_control(self, arm): """ Returns joint velocities to control the robot after the target end effector positions and orientations are updated from arguments @left and @right. Args: aem (dict): A dictionary to control the end effector with these keys. dpos (numpy array): a 3 dimensional array corresponding to the desired change in x, y, and z end effector position. rotation (numpy array): a rotation matrix of shape (3, 3) corresponding to the desired orientation of the left end effector. Returns: velocities (numpy array): a flat array of joint velocity commands to apply to try and achieve the desired input control. """ # Sync joint positions for IK. self.sync_ik_robot(self.robot_jpos_getter()) # Compute target joint positions self.commanded_joint_positions = self.joint_positions_for_eef_command( arm ) # P controller from joint positions (from IK) to velocities velocities = np.zeros(14) deltas = self._get_current_error( self.robot_jpos_getter(), self.commanded_joint_positions ) for i, delta in enumerate(deltas): velocities[i] = -2 * delta velocities = self.clip_joint_velocities(velocities) self.commanded_joint_velocities = velocities return velocities # For debugging purposes: set joint positions directly # robot.set_joint_positions(self.commanded_joint_positions) def sync_state(self): """ Syncs the internal Pybullet robot state to the joint positions of the robot being controlled. """ # sync IK robot state to the current robot joint positions self.sync_ik_robot(self.robot_jpos_getter()) # make sure target pose is up to date pos, orn = self.ik_robot_eef_joint_cartesian_pose() self.ik_robot_target_pos = pos self.ik_robot_target_orn = orn def setup_inverse_kinematics(self, urdf_path): """ This function is responsible for doing any setup for inverse kinematics. Inverse Kinematics maps end effector (EEF) poses to joint angles that are necessary to achieve those poses. """ # These indices come from the urdf file we're using self.effector = 31 # Use PyBullet to handle inverse kinematics. # Set up a connection to the PyBullet simulator. p.connect(p.DIRECT) p.resetSimulation() self.ik_robot = p.loadURDF(urdf_path, (0, 0, 0), (0, 0, 0, 1), useFixedBase=1) # Relevant joints we care about. Many of the joints are fixed and don't count, so # we need this second map to use the right ones. self.actual = [23, 24, 25, 26, 27] self.num_joints = p.getNumJoints(self.ik_robot) n = p.getNumJoints(self.ik_robot) self.rest = [] self.lower = [] self.upper = [] self.ranges = [] for i in range(n): info = p.getJointInfo(self.ik_robot, i) # Retrieve lower and upper ranges for each relevant joint if info[3] > -1: self.rest.append(p.getJointState(self.ik_robot, i)[0]) self.lower.append(info[8]) self.upper.append(info[9]) self.ranges.append(info[9] - info[8]) # Simulation will update as fast as it can in real time, instead of waiting for # step commands like in the non-realtime case. p.setRealTimeSimulation(1) def sync_ik_robot(self, joint_positions, simulate=False, sync_last=True): """ Force the internal robot model to match the provided joint angles. Args: joint_positions (list): a list or flat numpy array of joint positions. simulate (bool): If True, actually use physics simulation, else write to physics state directly. sync_last (bool): If False, don't sync the last joint angle. This is useful for directly controlling the roll at the end effector. """ num_joints = len(joint_positions) if not sync_last: num_joints -= 1 for i in range(num_joints): if simulate: p.setJointMotorControl2( self.ik_robot, self.actual[i], p.POSITION_CONTROL, targetVelocity=0, targetPosition=joint_positions[i], force=500, positionGain=0.5, velocityGain=1., ) else: # Note that we use self.actual[i], and not i p.resetJointState(self.ik_robot, self.actual[i], joint_positions[i]) def ik_robot_eef_joint_cartesian_pose(self): """ Returns the current cartesian pose of the last joint of the ik robot with respect to the base frame as a (pos, orn) tuple where orn is a x-y-z-w quaternion. """ out = [] for eff in [self.effector]: eef_pos_in_world = np.array(p.getLinkState(self.ik_robot, eff)[0]) eef_orn_in_world = np.array(p.getLinkState(self.ik_robot, eff)[1]) eef_pose_in_world = T.pose2mat((eef_pos_in_world, eef_orn_in_world)) base_pos_in_world = np.array( p.getBasePositionAndOrientation(self.ik_robot)[0] ) base_orn_in_world = np.array( p.getBasePositionAndOrientation(self.ik_robot)[1] ) base_pose_in_world = T.pose2mat((base_pos_in_world, base_orn_in_world)) world_pose_in_base = T.pose_inv(base_pose_in_world) eef_pose_in_base = T.pose_in_A_to_pose_in_B( pose_A=eef_pose_in_world, pose_A_in_B=world_pose_in_base ) out.extend(T.mat2pose(eef_pose_in_base)) return out def inverse_kinematics( self, target_position, target_orientation, rest_poses, ): """ Helper function to do inverse kinematics for a given target position and orientation in the PyBullet world frame. Args: target_position_{right, left}: A tuple, list, or numpy array of size 3 for position. target_orientation_{right, left}: A tuple, list, or numpy array of size 4 for a orientation quaternion. rest_poses: A list of size @num_joints to favor ik solutions close by. Returns: A list of size @num_joints corresponding to the joint angle solution. """ ik_solution = list( p.calculateInverseKinematics( self.ik_robot, self.effector, target_position, targetOrientation=target_orientation, restPoses=rest_poses[:7], lowerLimits=self.lower, upperLimits=self.upper, jointRanges=self.ranges, ) ) return ik_solution def bullet_base_pose_to_world_pose(self, pose_in_base): """ Convert a pose in the base frame to a pose in the world frame. Args: pose_in_base: a (pos, orn) tuple. Returns: pose_in world: a (pos, orn) tuple. """ pose_in_base = T.pose2mat(pose_in_base) base_pos_in_world = np.array(p.getBasePositionAndOrientation(self.ik_robot)[0]) base_orn_in_world = np.array(p.getBasePositionAndOrientation(self.ik_robot)[1]) base_pose_in_world = T.pose2mat((base_pos_in_world, base_orn_in_world)) pose_in_world = T.pose_in_A_to_pose_in_B( pose_A=pose_in_base, pose_A_in_B=base_pose_in_world ) return T.mat2pose(pose_in_world) def joint_positions_for_eef_command(self, cmd): """ This function runs inverse kinematics to back out target joint positions from the provided end effector command. Same arguments as @get_control. Returns: A list of size @num_joints corresponding to the target joint angles. """ dpos = cmd["dpos"] rotation = cmd["rotation"] self.target_pos = self.ik_robot_target_pos #+ np.array([0, 0, 0.913]) self.ik_robot_target_pos = dpos # += dpos self.ik_robot_target_orn = rotation world_targets = self.bullet_base_pose_to_world_pose( (self.ik_robot_target_pos, self.ik_robot_target_orn) ) # convert from target pose in base frame to target pose in bullet world frame # New pybullet iterates to convergence, so no need for multiple ik calls for _ in range(1): rest_poses = self.robot_jpos_getter() arm_joint_pos = self.inverse_kinematics( world_targets[0], world_targets[1], rest_poses=rest_poses, ) self.sync_ik_robot(arm_joint_pos, sync_last=True) return arm_joint_pos def _get_current_error(self, current, set_point): """ Returns an array of differences between the desired joint positions and current joint positions. Useful for PID control. Args: current: the current joint positions. set_point: the joint positions that are desired as a numpy array. Returns: the current error in the joint positions. """ error = current - set_point return error def clip_joint_velocities(self, velocities): """ Clips joint velocities into a valid range. """ for i in range(len(velocities)): if velocities[i] >= 1.0: velocities[i] = 1.0 elif velocities[i] <= -1.0: velocities[i] = -1.0 return velocities

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py

Python

vnpy/gateway/tqsdk/__init__.py

oysx/vnpy

626225ab3d4e80e8503bf908b751368732e6de0e

[ "MIT" ]

null

vnpy/gateway/tqsdk/__init__.py

oysx/vnpy

626225ab3d4e80e8503bf908b751368732e6de0e

[ "MIT" ]

null

vnpy/gateway/tqsdk/__init__.py

oysx/vnpy

626225ab3d4e80e8503bf908b751368732e6de0e

[ "MIT" ]

null

from .tqsdk_gateway import TqsdkGateway

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py

Python

lib/__init__.py

foozzi/Loki

3a91382831a714a1a3f3159753fb1982c94cea7c

[ "MIT" ]

null

lib/__init__.py

foozzi/Loki

3a91382831a714a1a3f3159753fb1982c94cea7c

[ "MIT" ]

null

lib/__init__.py

foozzi/Loki

3a91382831a714a1a3f3159753fb1982c94cea7c

[ "MIT" ]

1

2020-01-23T06:21:23.000Z

# Date: 07/03/2018 # Author: Pure-L0G1C

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py

Python

opsgenie_swagger/api/__init__.py

Logicworks/opsgenie-python-sdk

244c4c40ddcc25e70df5ba4425ab8d7c8da59c18

[ "Apache-2.0" ]

null

opsgenie_swagger/api/__init__.py

Logicworks/opsgenie-python-sdk

244c4c40ddcc25e70df5ba4425ab8d7c8da59c18

[ "Apache-2.0" ]

null

opsgenie_swagger/api/__init__.py

Logicworks/opsgenie-python-sdk

244c4c40ddcc25e70df5ba4425ab8d7c8da59c18

[ "Apache-2.0" ]

1

2020-11-07T11:27:13.000Z

from __future__ import absolute_import # flake8: noqa # import apis into api package from opsgenie_swagger.api.account_api import AccountApi from opsgenie_swagger.api.alert_api import AlertApi from opsgenie_swagger.api.contact_api import ContactApi from opsgenie_swagger.api.escalation_api import EscalationApi from opsgenie_swagger.api.forwarding_rule_api import ForwardingRuleApi from opsgenie_swagger.api.heartbeat_api import HeartbeatApi from opsgenie_swagger.api.integration_api import IntegrationApi from opsgenie_swagger.api.integration_action_api import IntegrationActionApi from opsgenie_swagger.api.maintenance_api import MaintenanceApi from opsgenie_swagger.api.notification_rule_api import NotificationRuleApi from opsgenie_swagger.api.notification_rule_step_api import NotificationRuleStepApi from opsgenie_swagger.api.policy_api import PolicyApi from opsgenie_swagger.api.schedule_api import ScheduleApi from opsgenie_swagger.api.schedule_override_api import ScheduleOverrideApi from opsgenie_swagger.api.schedule_rotation_api import ScheduleRotationApi from opsgenie_swagger.api.team_api import TeamApi from opsgenie_swagger.api.team_member_api import TeamMemberApi from opsgenie_swagger.api.team_routing_rule_api import TeamRoutingRuleApi from opsgenie_swagger.api.user_api import UserApi from opsgenie_swagger.api.who_is_on_call_api import WhoIsOnCallApi

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py

Python

silverware/text/__init__.py

idin/silverware

2c47931937f4b1d34e97a1dfa3e58255e57e3545

[ "MIT" ]

1

2021-08-30T01:12:59.000Z

silverware/text/__init__.py

idin/silverware

2c47931937f4b1d34e97a1dfa3e58255e57e3545

[ "MIT" ]

null

silverware/text/__init__.py

idin/silverware

2c47931937f4b1d34e97a1dfa3e58255e57e3545

[ "MIT" ]

null

from .get_html_text import get_html_text from .get_text_and_depth import get_children, has_no_children, has_children

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16004a0c401e36b028bf36d506b9adadd05dbe73

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py

Python

Chapter04/deque_rotate.py

PacktPublishing/Secret-Recipes-of-the-Python-Ninja

805d00c7a54927ba94c9077e9a580508ee3c5e56

[ "MIT" ]

13

2018-06-21T01:44:49.000Z

2021-12-01T10:49:53.000Z

Chapter04/deque_rotate.py

PacktPublishing/Secret-Recipes-of-the-Python-Ninja

805d00c7a54927ba94c9077e9a580508ee3c5e56

[ "MIT" ]

null

Chapter04/deque_rotate.py

PacktPublishing/Secret-Recipes-of-the-Python-Ninja

805d00c7a54927ba94c9077e9a580508ee3c5e56

[ "MIT" ]

6

2018-10-05T08:29:24.000Z

2022-01-11T14:49:50.000Z

>>> d.rotate(1) # right rotation >>> d deque(['l', 'g', 'h', 'i', 'j', 'k']) >>> d.rotate(-1) # left rotation >>> d deque(['g', 'h', 'i', 'j', 'k', 'l'])

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16219d58647292a6ecd12e9fc05117fa8e099780

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py

Python

zipline/pipeline/classifier.py

colin1alexander/zipline

ba42e6d8b972dcce9271526562ceff0cddd3fa30

[ "Apache-2.0" ]

1

2019-03-29T01:46:35.000Z

zipline/pipeline/classifier.py

colin1alexander/zipline

ba42e6d8b972dcce9271526562ceff0cddd3fa30

[ "Apache-2.0" ]

1

2021-08-09T20:43:08.000Z

zipline/pipeline/classifier.py

colin1alexander/zipline

ba42e6d8b972dcce9271526562ceff0cddd3fa30

[ "Apache-2.0" ]

3

2017-08-31T12:34:13.000Z

2021-09-29T22:28:48.000Z

""" classifier.py """ from zipline.pipeline.term import CompositeTerm class Classifier(CompositeTerm): pass

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1673411cd95e62ec0b00051c68840c7dcaadaaac

49

py

Python

hello2.py

kartikchorasiya/Samaaj-Seva-For-HF

9b8c0cb263ab06f08493d2a3aa7fe6099ed128d6

[ "MIT" ]

1

2018-10-16T17:48:27.000Z

hello2.py

kartikchorasiya/Samaaj-Seva-For-HF

9b8c0cb263ab06f08493d2a3aa7fe6099ed128d6

[ "MIT" ]

1

2018-10-16T16:11:42.000Z

hello2.py

kartikchorasiya/Samaaj-Seva-For-HF

9b8c0cb263ab06f08493d2a3aa7fe6099ed128d6

[ "MIT" ]

4

2018-10-16T16:14:23.000Z

2018-10-16T17:41:29.000Z

def hello(): print("Say hello to uncle")

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6

1682481e3b64e0e46ee7a291660f25b4fb6ff559

192

py

Python

vlasisku/components/__init__.py

Pendrokar/vlasisku

e1db74b6cbab51e84cc84cb1d046dbc9622bdf07

[ "Unlicense" ]

null

vlasisku/components/__init__.py

Pendrokar/vlasisku

e1db74b6cbab51e84cc84cb1d046dbc9622bdf07

[ "Unlicense" ]

null

vlasisku/components/__init__.py

Pendrokar/vlasisku

e1db74b6cbab51e84cc84cb1d046dbc9622bdf07

[ "Unlicense" ]

null

from vlasisku.components.app import app from vlasisku.components.general import general from vlasisku.components.opensearch import os as opensearch from vlasisku.components.pages import pages

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py

Python

google/ads/google_ads/v6/proto/services/ad_group_ad_service_pb2_grpc.py

arammaliachi/google-ads-python

a4fe89567bd43eb784410523a6306b5d1dd9ee67

[ "Apache-2.0" ]

null

google/ads/google_ads/v6/proto/services/ad_group_ad_service_pb2_grpc.py

arammaliachi/google-ads-python

a4fe89567bd43eb784410523a6306b5d1dd9ee67

[ "Apache-2.0" ]

null

google/ads/google_ads/v6/proto/services/ad_group_ad_service_pb2_grpc.py

arammaliachi/google-ads-python

a4fe89567bd43eb784410523a6306b5d1dd9ee67

[ "Apache-2.0" ]

null

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! """Client and server classes corresponding to protobuf-defined services.""" import grpc from google.ads.google_ads.v6.proto.resources import ad_group_ad_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_resources_dot_ad__group__ad__pb2 from google.ads.google_ads.v6.proto.services import ad_group_ad_service_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2 class AdGroupAdServiceStub(object): """Proto file describing the Ad Group Ad service. Service to manage ads in an ad group. """ def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.GetAdGroupAd = channel.unary_unary( '/google.ads.googleads.v6.services.AdGroupAdService/GetAdGroupAd', request_serializer=google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2.GetAdGroupAdRequest.SerializeToString, response_deserializer=google_dot_ads_dot_googleads_dot_v6_dot_resources_dot_ad__group__ad__pb2.AdGroupAd.FromString, ) self.MutateAdGroupAds = channel.unary_unary( '/google.ads.googleads.v6.services.AdGroupAdService/MutateAdGroupAds', request_serializer=google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2.MutateAdGroupAdsRequest.SerializeToString, response_deserializer=google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2.MutateAdGroupAdsResponse.FromString, ) class AdGroupAdServiceServicer(object): """Proto file describing the Ad Group Ad service. Service to manage ads in an ad group. """ def GetAdGroupAd(self, request, context): """Returns the requested ad in full detail. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def MutateAdGroupAds(self, request, context): """Creates, updates, or removes ads. Operation statuses are returned. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_AdGroupAdServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'GetAdGroupAd': grpc.unary_unary_rpc_method_handler( servicer.GetAdGroupAd, request_deserializer=google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2.GetAdGroupAdRequest.FromString, response_serializer=google_dot_ads_dot_googleads_dot_v6_dot_resources_dot_ad__group__ad__pb2.AdGroupAd.SerializeToString, ), 'MutateAdGroupAds': grpc.unary_unary_rpc_method_handler( servicer.MutateAdGroupAds, request_deserializer=google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2.MutateAdGroupAdsRequest.FromString, response_serializer=google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2.MutateAdGroupAdsResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'google.ads.googleads.v6.services.AdGroupAdService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) # This class is part of an EXPERIMENTAL API. class AdGroupAdService(object): """Proto file describing the Ad Group Ad service. Service to manage ads in an ad group. """ @staticmethod def GetAdGroupAd(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/google.ads.googleads.v6.services.AdGroupAdService/GetAdGroupAd', google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2.GetAdGroupAdRequest.SerializeToString, google_dot_ads_dot_googleads_dot_v6_dot_resources_dot_ad__group__ad__pb2.AdGroupAd.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def MutateAdGroupAds(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/google.ads.googleads.v6.services.AdGroupAdService/MutateAdGroupAds', google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2.MutateAdGroupAdsRequest.SerializeToString, google_dot_ads_dot_googleads_dot_v6_dot_services_dot_ad__group__ad__service__pb2.MutateAdGroupAdsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata)

48.160714

164

0.731554

607

5,394

5.98682

0.186161

0.042378

0.047056

0.057788

0.772702

0.768299

0.756192

0.703908

0.670336

0

0.008635

0.205599

5,394

111

165

48.594595

0.83944

0.11383

0

0.438356

1

0

0.091452

0.065871

0

1

0.082192

false

0

0.041096

0.027397

0.191781

0

null

0

1

0

1

0

null

0

6

16e2b9ff320947c585dda85353b569e14a0c3552

19,150

py

Python

tests/test_buffering.py

guaacoelho/devito

7e0b873114675752c4a49ed9076ee5d52997833c

[ "MIT" ]

null

tests/test_buffering.py

guaacoelho/devito

7e0b873114675752c4a49ed9076ee5d52997833c

[ "MIT" ]

null

tests/test_buffering.py

guaacoelho/devito

7e0b873114675752c4a49ed9076ee5d52997833c

[ "MIT" ]

null

import pytest import numpy as np from devito import (Constant, Grid, TimeFunction, SparseTimeFunction, Operator, Eq, ConditionalDimension, SubDimension, SubDomain, configuration) from devito.ir import FindSymbols, retrieve_iteration_tree from devito.exceptions import InvalidOperator def test_read_write(): nt = 10 grid = Grid(shape=(4, 4)) u = TimeFunction(name='u', grid=grid, save=nt) u1 = TimeFunction(name='u', grid=grid, save=nt) eqn = Eq(u.forward, u + 1) op0 = Operator(eqn, opt='noop') op1 = Operator(eqn, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 2 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 assert buffers.pop().symbolic_shape[0] == 2 op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, u=u1) assert np.all(u.data == u1.data) def test_write_only(): nt = 10 grid = Grid(shape=(4, 4)) time = grid.time_dim u = TimeFunction(name='u', grid=grid, save=nt) u1 = TimeFunction(name='u', grid=grid, save=nt) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) eqns = [Eq(v.forward, v + 1, implicit_dims=time), Eq(u, v)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 1 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, u=u1, v=v1) assert np.all(u.data == u1.data) assert np.all(v.data == v1.data) def test_read_only(): nt = 10 grid = Grid(shape=(2, 2)) u = TimeFunction(name='u', grid=grid, save=nt) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) for i in range(nt): u.data[i, :] = i eqns = [Eq(v.forward, v + u.backward + u + u.forward + 1.)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 2 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, v=v1) assert np.all(v.data == v1.data) def test_read_only_w_offset(): nt = 10 grid = Grid(shape=(2, 2)) u = TimeFunction(name='u', grid=grid, save=nt) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) for i in range(nt): u.data[i, :] = i eqns = [Eq(v.forward, v + u.backward + u + u.forward + 1.)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') op0.apply(time_M=nt-2, time_m=4) op1.apply(time_M=nt-2, time_m=4, v=v1) assert np.all(v.data == v1.data) def test_read_only_backwards(): nt = 10 grid = Grid(shape=(2, 2)) u = TimeFunction(name='u', grid=grid, save=nt) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) for i in range(nt): u.data[i, :] = i eqns = [Eq(v.backward, v + u.backward + u + u.forward + 1.)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 2 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 op0.apply(time_m=1) op1.apply(time_m=1, v=v1) assert np.all(v.data == v1.data) def test_read_only_backwards_unstructured(): """ Instead of the class `time-1`, `time`, and `time+1`, here we access the buffered Function via `time-2`, `time-1` and `time+2`. """ nt = 10 grid = Grid(shape=(2, 2)) u = TimeFunction(name='u', grid=grid, save=nt) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) for i in range(nt): u.data[i, :] = i eqns = [Eq(v.backward, v + u.backward.backward + u.backward + u.forward.forward + 1.)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 2 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 op0.apply(time_m=2) op1.apply(time_m=2, v=v1) assert np.all(v.data == v1.data) @pytest.mark.parametrize('async_degree', [2, 4]) def test_async_degree(async_degree): nt = 10 grid = Grid(shape=(4, 4)) u = TimeFunction(name='u', grid=grid, save=nt) u1 = TimeFunction(name='u', grid=grid, save=nt) eqn = Eq(u.forward, u + 1) op0 = Operator(eqn, opt='noop') op1 = Operator(eqn, opt=('buffering', {'buf-async-degree': async_degree})) # Check generated code assert len(retrieve_iteration_tree(op1)) == 2 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 assert buffers.pop().symbolic_shape[0] == async_degree op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, u=u1) assert np.all(u.data == u1.data) def test_two_homogeneous_buffers(): nt = 10 grid = Grid(shape=(4, 4)) u = TimeFunction(name='u', grid=grid, save=nt) u1 = TimeFunction(name='u', grid=grid, save=nt) v = TimeFunction(name='v', grid=grid, save=nt) v1 = TimeFunction(name='v', grid=grid, save=nt) eqns = [Eq(u.forward, u + v + u.backward + v.backward + 1.), Eq(v.forward, u + v + u.backward + v.backward + 1.)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') op2 = Operator(eqns, opt=('buffering', 'fuse')) # Check generated code assert len(retrieve_iteration_tree(op1)) == 2 assert len(retrieve_iteration_tree(op2)) == 2 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 2 op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, u=u1, v=v1) assert np.all(u.data == u1.data) assert np.all(v.data == v1.data) def test_two_heterogeneous_buffers(): nt = 10 grid = Grid(shape=(4, 4)) u = TimeFunction(name='u', grid=grid, save=nt) u1 = TimeFunction(name='u', grid=grid, save=nt) v = TimeFunction(name='v', grid=grid, save=nt) v1 = TimeFunction(name='v', grid=grid, save=nt) for i in range(nt): u.data[i, :] = i u1.data[i, :] = i eqns = [Eq(u.forward, u + v + 1), Eq(v.forward, u + v + v.backward)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 3 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 2 op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, u=u1, v=v1) assert np.all(u.data == u1.data) assert np.all(v.data == v1.data) def test_over_injection(): nt = 10 grid = Grid(shape=(4, 4)) src = SparseTimeFunction(name='src', grid=grid, npoint=1, nt=nt) rec = SparseTimeFunction(name='rec', grid=grid, npoint=1, nt=nt) u = TimeFunction(name="u", grid=grid, time_order=2, space_order=2, save=nt) u1 = TimeFunction(name="u", grid=grid, time_order=2, space_order=2, save=nt) src.data[:] = 1. eqns = ([Eq(u.forward, u + 1)] + src.inject(field=u.forward, expr=src) + rec.interpolate(expr=u.forward)) op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) ==\ 5 + bool(configuration['language'] != 'C') buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, u=u1) assert np.all(u.data == u1.data) def test_over_one_subdomain(): class sd0(SubDomain): name = 'd0' def define(self, dimensions): x, y = dimensions return {x: ('middle', 3, 3), y: ('middle', 3, 3)} s_d0 = sd0() nt = 10 grid = Grid(shape=(10, 10), subdomains=(s_d0,)) u = TimeFunction(name="u", grid=grid, save=nt) u1 = TimeFunction(name="u", grid=grid, save=nt) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) eqns = [Eq(v.forward, v + 1, subdomain=s_d0), Eq(u, v, subdomain=s_d0)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, u=u1, v=v1) assert np.all(u.data == u1.data) assert np.all(v.data == v1.data) def test_over_one_subdomain_read_only(): class sd0(SubDomain): name = 'd0' def define(self, dimensions): x, y = dimensions return {x: ('middle', 3, 3), y: ('middle', 3, 3)} s_d0 = sd0() nt = 10 grid = Grid(shape=(10, 10), subdomains=(s_d0,)) u = TimeFunction(name="u", grid=grid, save=nt) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) for i in range(nt): u.data[i, :] = i eqns = [Eq(v.forward, v + u + u.forward + 2., subdomain=s_d0)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, v=v1) assert np.all(v.data == v1.data) def test_over_two_subdomains_illegal(): """ Cannot use buffering when: * an Eq writes to `f` using one set of SubDimensions * another Eq reads from `f` through a different set of SubDimensions as the second Eq may want to read unwritten memory (i.e., zero-valued) in the buffered Function, while with buffering it might end up reading values written in a previous iteration, thus breaking a storage-related RAW dependence. """ class sd0(SubDomain): name = 'd0' def define(self, dimensions): x, y = dimensions return {x: ('middle', 3, 3), y: ('middle', 3, 3)} class sd1(SubDomain): name = 'd0' def define(self, dimensions): x, y = dimensions return {x: ('middle', 2, 2), y: ('middle', 2, 2)} s_d0 = sd0() s_d1 = sd1() nt = 10 grid = Grid(shape=(10, 10), subdomains=(s_d0, s_d1)) u = TimeFunction(name="u", grid=grid, save=nt) eqns = [Eq(u.forward, u + 1, subdomain=s_d0), Eq(u.forward, u.forward + 1, subdomain=s_d1)] try: Operator(eqns, opt='buffering') except InvalidOperator: assert True except: assert False @pytest.mark.xfail(reason="Cannot deal with non-overlapping SubDimensions yet") def test_over_two_subdomains(): class sd0(SubDomain): name = 'd0' def define(self, dimensions): x, y = dimensions return {x: ('left', 2), y: ('left', 2)} class sd1(SubDomain): name = 'd0' def define(self, dimensions): x, y = dimensions return {x: ('middle', 2, 2), y: ('middle', 2, 2)} s_d0 = sd0() s_d1 = sd1() nt = 10 grid = Grid(shape=(10, 10), subdomains=(s_d0, s_d1)) u = TimeFunction(name="u", grid=grid, save=nt) u1 = TimeFunction(name="u", grid=grid, save=nt) eqns = [Eq(u.forward, u + 1, subdomain=s_d0), Eq(u.forward, u.forward + u + 1, subdomain=s_d1)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, u=u1) assert np.all(u.data == u1.data) def test_subdims(): nt = 10 grid = Grid(shape=(10, 10, 10)) x, y, z = grid.dimensions xi = SubDimension.middle(name='xi', parent=x, thickness_left=2, thickness_right=2) yi = SubDimension.middle(name='yi', parent=y, thickness_left=2, thickness_right=2) zi = SubDimension.middle(name='zi', parent=z, thickness_left=2, thickness_right=2) u = TimeFunction(name='u', grid=grid, save=nt) u1 = TimeFunction(name='u', grid=grid, save=nt) eqn = Eq(u.forward, u + 1).xreplace({x: xi, y: yi, z: zi}) op0 = Operator(eqn, opt='noop') op1 = Operator(eqn, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 2 assert len([i for i in FindSymbols().visit(op1) if i.is_Array]) == 1 op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, u=u1) assert np.all(u.data == u1.data) def test_conddim_backwards(): nt = 10 grid = Grid(shape=(4, 4)) time_dim = grid.time_dim x, y = grid.dimensions factor = Constant(name='factor', value=2, dtype=np.int32) time_sub = ConditionalDimension(name="time_sub", parent=time_dim, factor=factor) u = TimeFunction(name='u', grid=grid, time_order=0, save=nt, time_dim=time_sub) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) for i in range(u.save): u.data[i, :] = i eqns = [Eq(v.backward, v.backward + v + u + 1.)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 3 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 op0.apply(time_m=1, time_M=9) op1.apply(time_m=1, time_M=9, v=v1) assert np.all(v.data == v1.data) def test_conddim_backwards_unstructured(): nt = 10 grid = Grid(shape=(4, 4)) time_dim = grid.time_dim x, y = grid.dimensions factor = Constant(name='factor', value=2, dtype=np.int32) time_sub = ConditionalDimension(name="time_sub", parent=time_dim, factor=factor) u = TimeFunction(name='u', grid=grid, time_order=0, save=nt, time_dim=time_sub) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) for i in range(u.save): u.data[i, :] = i ub = u[time_sub - 1, x, y] ubb = u[time_sub - 2, x, y] uff = u[time_sub + 2, x, y] eqns = [Eq(v.backward, v.backward + v + ubb + ub + uff + 1.)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 3 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 # Note 1: cannot use time_m<4 or time_M>14 or there would be OOB accesses # due to `ubb` and `uff`, which read two steps away from the current point, # while `u` has in total `nt=10` entries (so last one has index 9). In # particular, at `time_M=14` we will read from `uff = u[time/factor + 2] = # u[14/2+2] = u[9]`, which is the last available entry in `u`. Likewise, # at `time_m=4` we will read from `ubb = u[time/factor - 2`] = u[4/2 - 2] = # u[0]`, which is clearly the last accessible entry in `u` while iterating # in the backward direction # Note 2: Given `factor=2`, we always write to `v` when `time % 2 == 0`, which # means that we always write to `v[t1] = v[(time+1)%2] = v[1]`, while `v[0]` # remains zero-valued. So the fact that the Eq is also reading from `v` is # only relevant to induce the backward iteration direction op0.apply(time_m=4, time_M=14) op1.apply(time_m=4, time_M=14, v=v1) assert np.all(v.data == v1.data) def test_conddim_w_shifting(): nt = 50 grid = Grid(shape=(5, 5)) time = grid.time_dim factor = Constant(name='factor', value=5, dtype=np.int32) t_sub = ConditionalDimension('t_sub', parent=time, factor=factor) save_shift = Constant(name='save_shift', dtype=np.int32) u = TimeFunction(name='u', grid=grid, time_order=0) u1 = TimeFunction(name='u', grid=grid, time_order=0) usave = TimeFunction(name='usave', grid=grid, time_order=0, save=(int(nt//factor.data)), time_dim=t_sub) for i in range(usave.save): usave.data[i, :] = i eqns = Eq(u.forward, u + usave.subs(t_sub, t_sub - save_shift)) op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 3 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 # From time_m=15 to time_M=35 with a factor=5 -- it means that, thanks # to t_sub, we enter the Eq exactly (35-15)/5 + 1 = 5 times. We set # save_shift=1 so instead of accessing the range usave[15/5:35/5+1], # we rather access the range usave[15/5-1:35:5], which means accessing # the usave values 2, 3, 4, 5, 6. op0.apply(time_m=15, time_M=35, save_shift=1) op1.apply(time_m=15, time_M=35, save_shift=1, u=u1) assert np.allclose(u.data, 20) assert np.all(u.data == u1.data) # Again, but with a different shift op1.apply(time_m=15, time_M=35, save_shift=-2, u=u1) assert np.allclose(u1.data, 20 + 35) def test_multi_access(): nt = 10 grid = Grid(shape=(2, 2)) u = TimeFunction(name='u', grid=grid, save=nt) v = TimeFunction(name='v', grid=grid) v1 = TimeFunction(name='v', grid=grid) w = TimeFunction(name='w', grid=grid) w1 = TimeFunction(name='w', grid=grid) for i in range(nt): u.data[i, :] = i eqns = [Eq(v.forward, v + u.forward + 1.), Eq(w.forward, w + u + 1.)] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len(retrieve_iteration_tree(op1)) == 2 buffers = [i for i in FindSymbols().visit(op1) if i.is_Array] assert len(buffers) == 1 op0.apply(time_M=nt-2) op1.apply(time_M=nt-2, v=v1, w=w1) assert np.all(v.data == v1.data) assert np.all(w.data == w1.data) def test_everything(): nt = 50 grid = Grid(shape=(6, 6)) x, y = grid.dimensions time = grid.time_dim xi = SubDimension.middle(name='xi', parent=x, thickness_left=2, thickness_right=2) yi = SubDimension.middle(name='yi', parent=y, thickness_left=2, thickness_right=2) factor = Constant(name='factor', value=5, dtype=np.int32) t_sub = ConditionalDimension('t_sub', parent=time, factor=factor) save_shift = Constant(name='save_shift', dtype=np.int32) u = TimeFunction(name='u', grid=grid, time_order=0) u1 = TimeFunction(name='u', grid=grid, time_order=0) va = TimeFunction(name='va', grid=grid, time_order=0, save=(int(nt//factor.data)), time_dim=t_sub) vb = TimeFunction(name='vb', grid=grid, time_order=0, save=(int(nt//factor.data)), time_dim=t_sub) for i in range(va.save): va.data[i, :] = i vb.data[i, :] = i*2 - 1 vas = va.subs(t_sub, t_sub - save_shift) vasb = va.subs(t_sub, t_sub - 1 - save_shift) vasf = va.subs(t_sub, t_sub + 1 - save_shift) eqns = [Eq(u.forward, u + (vasb + vas + vasf)*2. + vb)] eqns = [e.xreplace({x: xi, y: yi}) for e in eqns] op0 = Operator(eqns, opt='noop') op1 = Operator(eqns, opt='buffering') # Check generated code assert len([i for i in FindSymbols().visit(op1) if i.is_Array]) == 2 op0.apply(time_m=15, time_M=35, save_shift=0) op1.apply(time_m=15, time_M=35, save_shift=0, u=u1) assert np.all(u.data == u1.data)

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Python

src/clikit/io/output_stream/__init__.py

finswimmer/clikit

206198eb60d53c5daefa715f8a93e1fe85ffcf7e

[ "MIT" ]

78

2018-12-06T16:18:08.000Z

2022-03-18T01:44:06.000Z

src/clikit/io/output_stream/__init__.py

finswimmer/clikit

206198eb60d53c5daefa715f8a93e1fe85ffcf7e

[ "MIT" ]

24

2019-05-11T16:40:06.000Z

2022-02-27T01:11:04.000Z

src/clikit/io/output_stream/__init__.py

finswimmer/clikit

206198eb60d53c5daefa715f8a93e1fe85ffcf7e

[ "MIT" ]

17

2019-01-14T18:18:01.000Z

2022-03-07T23:05:46.000Z

from .buffered_output_stream import BufferedOutputStream from .error_output_stream import ErrorOutputStream from .null_output_stream import NullOutputStream from .standard_output_stream import StandardOutputStream from .stream_output_stream import StreamOutputStream

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Python

lightflow/queue/__init__.py

portrain/Lightflow

31f44a55f27b0c61da2631454ca592b641ab8145

[ "BSD-3-Clause" ]

null

lightflow/queue/__init__.py

portrain/Lightflow

31f44a55f27b0c61da2631454ca592b641ab8145

[ "BSD-3-Clause" ]

null

lightflow/queue/__init__.py

portrain/Lightflow

31f44a55f27b0c61da2631454ca592b641ab8145

[ "BSD-3-Clause" ]

null

from .const import JobExecPath, JobStatus, JobType, JobEventName __all__ = ['JobExecPath', 'JobStatus', 'JobType', 'JobEventName']

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py

Python

src/googleanalytics/exception.py

loggly/python-googleanalytics

f0e594b95c3058a5a91131e9281eff875a4ce8c8

[ "BSD-3-Clause" ]

68

2015-01-14T19:29:03.000Z

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src/googleanalytics/exception.py

loggly/python-googleanalytics

f0e594b95c3058a5a91131e9281eff875a4ce8c8

[ "BSD-3-Clause" ]

1

2019-02-27T21:08:34.000Z

src/googleanalytics/exception.py

loggly/python-googleanalytics

f0e594b95c3058a5a91131e9281eff875a4ce8c8

[ "BSD-3-Clause" ]

28

2015-02-17T20:00:49.000Z

2021-07-21T10:42:36.000Z

class GoogleAnalyticsClientError(Exception): """ General Google Analytics error (error accessing GA) """ def __init__(self, reason): self.reason = reason def __repr__(self): return 'GAError: %s' % self.reason def __str__(self): return 'GAError: %s' % self.reason

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py

Python

proxyless_nas_tensorflow/__init__.py

RogerChern/ProxylessNAS

9d19b74041eb749d540208da4e7e51f9053bbcf9

[ "Apache-2.0" ]

558

2019-04-08T02:28:31.000Z

2020-02-12T08:18:57.000Z

proxyless_nas_tensorflow/__init__.py

RogerChern/ProxylessNAS

9d19b74041eb749d540208da4e7e51f9053bbcf9

[ "Apache-2.0" ]

3

2018-12-06T11:54:55.000Z

2018-12-19T19:21:30.000Z

proxyless_nas_tensorflow/__init__.py

RogerChern/ProxylessNAS

9d19b74041eb749d540208da4e7e51f9053bbcf9

[ "Apache-2.0" ]

130

2019-04-08T01:58:17.000Z

2020-02-07T10:23:34.000Z

from .tf_model_zoo import *

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py

Python

tests/components/test_custom_action.py

LL0814/mindmeld

a616bdcec9c68aaf17c12727118c4436254f6628

[ "Apache-2.0" ]

1

2020-08-06T03:00:45.000Z

tests/components/test_custom_action.py

LL0814/mindmeld

a616bdcec9c68aaf17c12727118c4436254f6628

[ "Apache-2.0" ]

null

tests/components/test_custom_action.py

LL0814/mindmeld

a616bdcec9c68aaf17c12727118c4436254f6628

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ test_custom_action ---------------------------------- Tests for custom actions. """ import pytest from unittest.mock import Mock, patch from mindmeld import Application from mindmeld.components import ( CustomAction, invoke_custom_action, invoke_custom_action_async, ) from mindmeld.components.dialogue import DialogueResponder from mindmeld.components.request import Request def test_custom_action_config(kwik_e_mart_app): """Test to get custom action config from app""" assert kwik_e_mart_app.custom_action_config is not None assert "url" in kwik_e_mart_app.custom_action_config assert kwik_e_mart_app.custom_action_config["url"] == "http://0.0.0.0:8080/" def test_custom_action(): """Test CustomAction.invoke to ensure that the parameters of the JSON body are correct""" action_config = {"url": "http://localhost:8080/v2/action"} action = CustomAction(name="action_call_people", config=action_config) with patch("requests.post") as mock_object: mock_object.return_value = Mock() mock_object.return_value.status_code = 200 mock_object.return_value.json.return_value = {} request = Request( text="sing a song", domain="some domain", intent="some intent" ) responder = DialogueResponder() assert action.invoke(request, responder) assert mock_object.call_args[1]["url"] == action_config["url"] assert "request" in mock_object.call_args[1]["json"] assert "responder" in mock_object.call_args[1]["json"] assert mock_object.call_args[1]["json"]["action"] == "action_call_people" def test_custom_action_merge(): "Test `merge=True` for custom actions" action_config = {"url": "http://localhost:8080/v2/action"} action = CustomAction(name="action_call_people", config=action_config) with patch("requests.post") as mock_object: mock_object.return_value = Mock() mock_object.return_value.status_code = 200 mock_object.return_value.json.return_value = { "directives": ["directive3", "directive4"], "frame": {"k2": "v2"}, "slots": {"s2": "v2"}, "params": { "allowed_intents": ["intent3", "intent4"], "dynamic_resource": {"r2": "v2"}, "language": "some-language", "locale": "some-locale", "time_zone": "some-time-zone", "target_dialogue_state": "some-state", "timestamp": "some-timestamp", }, } request = Request( text="sing a song", domain="some domain", intent="some intent" ) responder = DialogueResponder() responder.directives = ["directive1", "directive2"] responder.frame = {"k1": "v1"} responder.slots = {"s1": "v1"} responder.params.allowed_intents = ("intent1", "intent2") responder.params.dynamic_resource = {"r1": "v1"} assert action.invoke(request, responder) assert responder.directives == [ "directive1", "directive2", "directive3", "directive4", ] assert responder.frame == {"k1": "v1", "k2": "v2"} assert responder.slots == {"s1": "v1", "s2": "v2"} assert responder.params.allowed_intents == ( "intent1", "intent2", "intent3", "intent4", ) assert responder.params.dynamic_resource == {"r1": "v1", "r2": "v2"} assert responder.params.target_dialogue_state == "some-state" assert responder.params.language == "some-language" assert responder.params.locale == "some-locale" assert responder.params.time_zone == "some-time-zone" assert responder.params.timestamp == "some-timestamp" def test_custom_action_no_merge(): "Test `merge=False` for custom actions" action_config = {"url": "http://localhost:8080/v2/action"} action = CustomAction(name="action_call_people", config=action_config, merge=False) with patch("requests.post") as mock_object: mock_object.return_value = Mock() mock_object.return_value.status_code = 200 mock_object.return_value.json.return_value = { "directives": ["directive3", "directive4"], "frame": {"k2": "v2"}, "slots": {"s2": "v2"}, "params": { "allowed_intents": ["intent3", "intent4"], "dynamic_resource": {"r2": "v2"}, "language": "some-language", "locale": "some-locale", "time_zone": "some-time-zone", "target_dialogue_state": "some-state", "timestamp": "some-timestamp", }, } request = Request( text="sing a song", domain="some domain", intent="some intent" ) responder = DialogueResponder() responder.directives = ["directive1", "directive2"] responder.frame = {"k1": "v1"} responder.slots = {"s1": "v1"} responder.params.allowed_intents = ("intent1", "intent2") responder.params.dynamic_resource = {"r1": "v1"} assert action.invoke(request, responder) assert responder.directives == [ "directive3", "directive4", ] assert responder.frame == {"k2": "v2"} assert responder.slots == {"s2": "v2"} assert tuple(responder.params.allowed_intents) == ("intent3", "intent4",) assert responder.params.dynamic_resource == {"r2": "v2"} assert responder.params.target_dialogue_state == "some-state" assert responder.params.language == "some-language" assert responder.params.locale == "some-locale" assert responder.params.time_zone == "some-time-zone" assert responder.params.timestamp == "some-timestamp" def test_invoke_custom_action(): """Test invoke_custom_action to ensure that the parameters of the JSON body are correct""" action_config = {"url": "http://localhost:8080/v2/action"} with patch("requests.post") as mock_object: mock_object.return_value = Mock() mock_object.return_value.status_code = 200 mock_object.return_value.json.return_value = {} request = Request( text="sing a song", domain="some domain", intent="some intent" ) responder = DialogueResponder() assert invoke_custom_action( "action_call_people", action_config, request, responder ) assert mock_object.call_args[1]["url"] == action_config["url"] assert "request" in mock_object.call_args[1]["json"] assert "responder" in mock_object.call_args[1]["json"] assert mock_object.call_args[1]["json"]["action"] == "action_call_people" @pytest.mark.asyncio async def test_custom_action_async(): """Test CustomAction.invoke_async to ensure that the parameters of the JSON body are correct""" action_config = {"url": "http://localhost:8080/v2/action"} action = CustomAction(name="action_call_people", config=action_config) with patch("mindmeld.components.CustomAction.post_async") as mock_object: async def mock_coroutine(): return 200, {} mock_object.return_value = mock_coroutine() request = Request( text="sing a song", domain="some domain", intent="some intent" ) responder = DialogueResponder() assert await action.invoke_async(request, responder) call_args = mock_object.call_args_list[0][0][0] assert "request" in call_args assert "responder" in call_args assert call_args["action"] == "action_call_people" @pytest.mark.asyncio async def test_invoke_custom_action_async(): """Test invoke_custom_action_async to ensure that the parameters of the JSON body are correct""" action_config = {"url": "http://localhost:8080/v2/action"} with patch("mindmeld.components.CustomAction.post_async") as mock_object: async def mock_coroutine(): return 200, {} mock_object.return_value = mock_coroutine() request = Request( text="sing a song", domain="some domain", intent="some intent" ) responder = DialogueResponder() assert await invoke_custom_action_async( "action_call_people", action_config, request, responder ) call_args = mock_object.call_args_list[0][0][0] assert "request" in call_args assert "responder" in call_args assert call_args["action"] == "action_call_people" def test_custom_action_handler(home_assistant_nlp): """Test Application.custom_action handle""" app = Application("home_assistant") app.lazy_init(home_assistant_nlp) app.custom_action_config = {"url": "some-url"} app.custom_action(intent="set_thermostat", action="set-thermostat") app.custom_action(default=True, action="times-and-dates") with patch("requests.post") as mock_object: mock_object.return_value = Mock() mock_object.return_value.status_code = 200 mock_object.return_value.json.return_value = { "directives": ["set-thermostat-action"] } # invoke set thermostat intent res = app.app_manager.parse("turn it to 70 degrees") assert res.directives == ["set-thermostat-action"] assert mock_object.call_args[1]["url"] == "some-url" assert mock_object.call_args[1]["json"]["action"] == "set-thermostat" mock_object.return_value.json.return_value = { "directives": ["time-and-dates-action"] } # invoke time & dates intent res = app.app_manager.parse("change my alarm to 9") assert res.directives == ["time-and-dates-action"] assert mock_object.call_args[1]["url"] == "some-url" assert mock_object.call_args[1]["json"]["action"] == "times-and-dates" def test_custom_action_sequence(home_assistant_nlp): """Test Application.custom_action handle for a sequence of actions""" app = Application("home_assistant") app.lazy_init(home_assistant_nlp) app.custom_action_config = {"url": "some-url"} app.custom_action( intent="set_thermostat", actions=["set-thermostat", "clear-thermostat"] ) with patch("requests.post") as mock_object: mock_object.return_value = Mock() mock_object.return_value.status_code = 200 mock_object.return_value.json.return_value = {"directives": ["some-directive"]} # invoke set thermostat intent and we should expect two directives res = app.app_manager.parse("turn it to 70 degrees") assert res.directives == ["some-directive", "some-directive"] assert mock_object.call_args[1]["url"] == "some-url" @pytest.mark.asyncio async def test_custom_action_handler_async(home_assistant_nlp): """Test Application.custom_action handle with async mode""" app = Application("home_assistant", async_mode=True) app.lazy_init(home_assistant_nlp) app.custom_action_config = {"url": "some-url"} app.custom_action(intent="set_thermostat", action="set-thermostat", async_mode=True) app.custom_action(default=True, action="times-and-dates", async_mode=True) with patch("mindmeld.components.CustomAction.post_async") as mock_object: async def mock_coroutine(): return 200, {"directives": ["set-thermostat-action"]} mock_object.return_value = mock_coroutine() # invoke set thermostat intent res = await app.app_manager.parse("turn it to 70 degrees") assert res.directives == ["set-thermostat-action"]

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bc6ea7c0ac7dfa4310b281540aae1ed5cae23ade

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py

Python

terrascript/kubernetes/__init__.py

vutsalsinghal/python-terrascript

3b9fb5ad77453d330fb0cd03524154a342c5d5dc

[ "BSD-2-Clause" ]

null

terrascript/kubernetes/__init__.py

vutsalsinghal/python-terrascript

3b9fb5ad77453d330fb0cd03524154a342c5d5dc

[ "BSD-2-Clause" ]

null

terrascript/kubernetes/__init__.py

vutsalsinghal/python-terrascript

3b9fb5ad77453d330fb0cd03524154a342c5d5dc

[ "BSD-2-Clause" ]

null

# terrascript/kubernetes/__init__.py import terrascript class kubernetes(terrascript.Provider): pass

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py

Python

scripts/util_funs.py

Lucklyric/Expedia-Recommendation

d0496fec5305b02d4e17785e6ea5d635e51e92c1

[ "Apache-2.0" ]

null

scripts/util_funs.py

Lucklyric/Expedia-Recommendation

d0496fec5305b02d4e17785e6ea5d635e51e92c1

[ "Apache-2.0" ]

null

scripts/util_funs.py

Lucklyric/Expedia-Recommendation

d0496fec5305b02d4e17785e6ea5d635e51e92c1

[ "Apache-2.0" ]

null

import tensorflow as tf import tensorflow.contrib as tc

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py

Python

lvls/blocks.py

Voolshara/Lyceum2

6ef3581ccc4f24afdc7bd843c608f90a109c5a6f

[ "MIT" ]

null

lvls/blocks.py

Voolshara/Lyceum2

6ef3581ccc4f24afdc7bd843c608f90a109c5a6f

[ "MIT" ]

null

lvls/blocks.py

Voolshara/Lyceum2

6ef3581ccc4f24afdc7bd843c608f90a109c5a6f

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import sys import pygame from pygame import * PLATFORM_WIDTH = 20 PLATFORM_HEIGHT = 20 PLATFORM_COLOR = "#FF6262" def load_image(name, directory, colorkey=None): fullname = os.path.join(directory, name) koef = 20 # если файл не существует, то выходим if not os.path.isfile(fullname): print(f"Файл с изображением '{fullname}' не найден") sys.exit() image = pygame.image.load(fullname) image = pygame.transform.scale(image, (koef, koef)) if colorkey is not None: image = image.convert() if colorkey == -1: colorkey = image.get_at((0, 0)) image.set_colorkey(colorkey) else: image = image.convert_alpha() return image class Platform(sprite.Sprite): def __init__(self, x, y): sprite.Sprite.__init__(self) self.image = Surface((PLATFORM_WIDTH, PLATFORM_HEIGHT)) self.image.fill(Color(PLATFORM_COLOR)) self.image = load_image("wall.png", "data/assets", colorkey=True) self.rect = Rect(x, y, PLATFORM_WIDTH, PLATFORM_HEIGHT) class Empty(sprite.Sprite): def __init__(self, x, y): sprite.Sprite.__init__(self) self.image = Surface((PLATFORM_WIDTH, PLATFORM_HEIGHT)) self.image.fill(Color(PLATFORM_COLOR)) self.image = load_image("empty.png", "data/assets", colorkey=True) self.rect = Rect(x, y, PLATFORM_WIDTH, PLATFORM_HEIGHT) class FloorLvl1(sprite.Sprite): def __init__(self, x, y): sprite.Sprite.__init__(self) self.image = Surface((PLATFORM_WIDTH, PLATFORM_HEIGHT)) self.image.fill(Color(PLATFORM_COLOR)) self.image = load_image("floor.png", "data/assets/tiles_1lvl", colorkey=True) self.rect = Rect(x, y, PLATFORM_WIDTH, PLATFORM_HEIGHT) class EarthLvl1(sprite.Sprite): def __init__(self, x, y): sprite.Sprite.__init__(self) self.image = Surface((PLATFORM_WIDTH, PLATFORM_HEIGHT)) self.image.fill(Color(PLATFORM_COLOR)) self.image = load_image("earth.png", "data/assets/tiles_1lvl", colorkey=True) self.rect = Rect(x, y, PLATFORM_WIDTH, PLATFORM_HEIGHT) class DescentLeftLvl1(sprite.Sprite): def __init__(self, x, y): sprite.Sprite.__init__(self) self.image = Surface((PLATFORM_WIDTH, PLATFORM_HEIGHT)) self.image.fill(Color(PLATFORM_COLOR)) self.image = load_image("descent_left.png", "data/assets/tiles_1lvl", colorkey=True) self.rect = Rect(x, y, PLATFORM_WIDTH, PLATFORM_HEIGHT) class DescentRightLvl1(sprite.Sprite): def __init__(self, x, y): sprite.Sprite.__init__(self) self.image = Surface((PLATFORM_WIDTH, PLATFORM_HEIGHT)) self.image.fill(Color(PLATFORM_COLOR)) self.image = load_image("descent_right.png", "data/assets/tiles_1lvl", colorkey=True) self.rect = Rect(x, y, PLATFORM_WIDTH, PLATFORM_HEIGHT) class PlatformLvl1(sprite.Sprite): def __init__(self, x, y): sprite.Sprite.__init__(self) self.image = Surface((PLATFORM_WIDTH, PLATFORM_HEIGHT)) self.image.fill(Color(PLATFORM_COLOR)) self.image = load_image("platform.png", "data/assets/tiles_1lvl", colorkey=True) self.rect = Rect(x, y, PLATFORM_WIDTH, PLATFORM_HEIGHT) class ExtraEarthLvl1(sprite.Sprite): def __init__(self, x, y): sprite.Sprite.__init__(self) self.image = Surface((PLATFORM_WIDTH, PLATFORM_HEIGHT)) self.image.fill(Color(PLATFORM_COLOR)) self.image = load_image("extra_earth.png", "data/assets/tiles_1lvl", colorkey=True) self.rect = Rect(x, y, PLATFORM_WIDTH, PLATFORM_HEIGHT)

35.757282

93

0.673907

477

3,683

4.93501

0.178197

0.091759

0.142736

0.183517

0.737893

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0.200109

3,683

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0.790224

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null

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1

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null

0

6

bce5a6211dfc9c86ea436016b8bbf66e8e2552b7

22,328

py

Python

test_autogalaxy/unit/profiles/mass_profiles/test_mass_profiles.py

jonathanfrawley/PyAutoGalaxy_copy

1cedbfdcf65020538128163f7d8a7f8e169646e0

[ "MIT" ]

null

test_autogalaxy/unit/profiles/mass_profiles/test_mass_profiles.py

jonathanfrawley/PyAutoGalaxy_copy

1cedbfdcf65020538128163f7d8a7f8e169646e0

[ "MIT" ]

null

test_autogalaxy/unit/profiles/mass_profiles/test_mass_profiles.py

jonathanfrawley/PyAutoGalaxy_copy

1cedbfdcf65020538128163f7d8a7f8e169646e0

[ "MIT" ]

null

import math import autogalaxy as ag from autogalaxy import exc import numpy as np import pytest def mass_within_radius_of_profile_from_grid_calculation(radius, profile): mass_total = 0.0 xs = np.linspace(-radius * 1.5, radius * 1.5, 40) ys = np.linspace(-radius * 1.5, radius * 1.5, 40) edge = xs[1] - xs[0] area = edge ** 2 for x in xs: for y in ys: eta = profile.grid_to_elliptical_radii(grid=np.array([[x, y]])) if eta < radius: mass_total += profile.convergence_func(eta) * area return mass_total class TestMassWithin: def test__compare_to_analytic_and_grid_calculations(self): sis = ag.mp.SphericalIsothermal(einstein_radius=2.0) mass = sis.mass_angular_within_circle(radius=2.0) assert math.pi * sis.einstein_radius * 2.0 == pytest.approx(mass, 1e-3) sis = ag.mp.SphericalIsothermal(einstein_radius=4.0) mass = sis.mass_angular_within_circle(radius=4.0) assert math.pi * sis.einstein_radius * 4.0 == pytest.approx(mass, 1e-3) sis = ag.mp.SphericalIsothermal(einstein_radius=2.0) mass_grid = mass_within_radius_of_profile_from_grid_calculation( radius=1.0, profile=sis ) mass = sis.mass_angular_within_circle(radius=1.0) assert mass_grid == pytest.approx(mass, 0.02) class TestRadiusAverageConvergenceOne: def test__radius_of_average_convergence(self): sis = ag.mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=2.0) assert sis.average_convergence_of_1_radius == pytest.approx(2.0, 1e-4) sie = ag.mp.EllipticalIsothermal( centre=(0.0, 0.0), einstein_radius=1.0, elliptical_comps=(0.0, 0.111111) ) assert sie.average_convergence_of_1_radius == pytest.approx(1.0, 1e-4) sie = ag.mp.EllipticalIsothermal( centre=(0.0, 0.0), einstein_radius=3.0, elliptical_comps=(0.0, 0.333333) ) assert sie.average_convergence_of_1_radius == pytest.approx(3.0, 1e-4) sie = ag.mp.EllipticalIsothermal( centre=(0.0, 0.0), einstein_radius=8.0, elliptical_comps=(0.0, 0.666666) ) assert sie.average_convergence_of_1_radius == pytest.approx(8.0, 1e-4) class TestDensityBetweenAnnuli: def test__circular_annuli__sis__analyic_density_agrees(self): einstein_radius = 1.0 sis = ag.mp.SphericalIsothermal( centre=(0.0, 0.0), einstein_radius=einstein_radius ) inner_annuli_radius = 2.0 outer_annuli_radius = 3.0 inner_mass = math.pi * einstein_radius * inner_annuli_radius outer_mass = math.pi * einstein_radius * outer_annuli_radius density_between_annuli = sis.density_between_circular_annuli( inner_annuli_radius=inner_annuli_radius, outer_annuli_radius=outer_annuli_radius, ) annuli_area = (np.pi * outer_annuli_radius ** 2.0) - ( np.pi * inner_annuli_radius ** 2.0 ) assert (outer_mass - inner_mass) / annuli_area == pytest.approx( density_between_annuli, 1e-4 ) def test__circular_annuli__nfw_profile__compare_to_manual_mass(self): nfw = ag.mp.EllipticalNFW( centre=(0.0, 0.0), elliptical_comps=(0.111111, 0.0), kappa_s=1.0 ) inner_mass = nfw.mass_angular_within_circle(radius=1.0) outer_mass = nfw.mass_angular_within_circle(radius=2.0) density_between_annuli = nfw.density_between_circular_annuli( inner_annuli_radius=1.0, outer_annuli_radius=2.0 ) annuli_area = (np.pi * 2.0 ** 2.0) - (np.pi * 1.0 ** 2.0) assert (outer_mass - inner_mass) / annuli_area == pytest.approx( density_between_annuli, 1e-4 ) class TestNormalizationEinstienRadius: def test__mass_angular_from_normalization_and_radius(self): sis = ag.mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=2.0) mass_angular_from_normalization = sis.mass_angular_from_normalization_and_radius( normalization=1.0, radius=2.0 ) assert mass_angular_from_normalization == pytest.approx(2.0 * np.pi, 1.0e-2) mass_angular_from_normalization = sis.mass_angular_from_normalization_and_radius( normalization=1.0, radius=4.0 ) assert mass_angular_from_normalization == pytest.approx(4.0 * np.pi, 1.0e-2) nfw = ag.mp.SphericalNFW(centre=(0.0, 0.0), kappa_s=1.0, scale_radius=1.0) mass_angular_from_normalization = nfw.mass_angular_from_normalization_and_radius( normalization=2.0, radius=2.0 ) assert mass_angular_from_normalization == pytest.approx(15.19525, 1.0e-4) sersic = ag.mp.SphericalSersic( centre=(0.0, 0.0), intensity=1.0, effective_radius=1.0, sersic_index=3.0, mass_to_light_ratio=1.0, ) mass_angular_from_normalization = sersic.mass_angular_from_normalization_and_radius( normalization=2.0, radius=2.0 ) sersic = ag.mp.SphericalSersic( centre=(0.0, 0.0), intensity=1.0, effective_radius=1.0, sersic_index=3.0, mass_to_light_ratio=2.0, ) assert mass_angular_from_normalization == pytest.approx(28.32431, 1.0e-4) mass_angular_from_normalization = sersic.mass_angular_from_normalization_and_radius( normalization=0.1, radius=2.0 ) assert mass_angular_from_normalization == pytest.approx(1.416215, 1.0e-2) def test__normalization_from_mass_angular_and_radius(self): sersic = ag.mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=2.0) normalization = sersic.normalization_from_mass_angular_and_radius( mass_angular=5.0, radius=2.0, normalization_min=0.5, normalization_max=3.0, bins=5, ) assert normalization == pytest.approx(0.79577, 1.0e-2) nfw = ag.mp.SphericalNFW(centre=(0.0, 0.0), kappa_s=3.0, scale_radius=1.0) normalization = nfw.normalization_from_mass_angular_and_radius( mass_angular=6.35829, radius=2.0, normalization_min=0.5, normalization_max=3.0, bins=5, ) assert normalization == pytest.approx(0.83687, 1.0e-2) sersic = ag.mp.SphericalSersic( centre=(0.0, 0.0), intensity=1.0, effective_radius=1.0, sersic_index=3.0, mass_to_light_ratio=1.0, ) normalization = sersic.normalization_from_mass_angular_and_radius( mass_angular=2.15403, radius=2.0, normalization_min=0.01, normalization_max=30.0, bins=5, ) sersic = sersic.with_new_normalization(normalization=normalization) assert normalization == pytest.approx(0.152097, 1.0e-2) with pytest.raises(exc.ProfileException): sersic.normalization_from_mass_angular_and_radius( mass_angular=1.0, radius=2.0, normalization_min=1e-4, normalization_max=1e-3, bins=2, ) def test__einstein_radius_from_normalization(self): sis = ag.mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=2.0) einstein_radius_from_normalization = sis.einstein_radius_from_normalization( normalization=1.0 ) assert einstein_radius_from_normalization == pytest.approx(1.0, 1.0e-2) nfw = ag.mp.SphericalNFW(centre=(0.0, 0.0), kappa_s=1.0, scale_radius=1.0) einstein_radius_from_normalization = nfw.einstein_radius_from_normalization( normalization=2.0 ) assert einstein_radius_from_normalization == pytest.approx(2.35829, 1.0e-4) sersic = ag.mp.SphericalSersic( centre=(0.0, 0.0), intensity=1.0, effective_radius=1.0, sersic_index=3.0, mass_to_light_ratio=1.0, ) einstein_radius_from_normalization = sersic.einstein_radius_from_normalization( normalization=1.0 ) einstein_radius_from_profile = sersic.average_convergence_of_1_radius assert einstein_radius_from_normalization == pytest.approx( einstein_radius_from_profile, 1.0e-4 ) einstein_radius_from_normalization = sersic.einstein_radius_from_normalization( normalization=0.1 ) assert einstein_radius_from_normalization == pytest.approx(0.381544, 1.0e-2) einstein_radius_from_normalization = sersic.einstein_radius_from_normalization( normalization=1e-4 ) assert einstein_radius_from_normalization == None einstein_radius_from_normalization = sersic.einstein_radius_from_normalization( normalization=1e9 ) assert einstein_radius_from_normalization == None def test__normalization_from_einstein_radius(self): sersic = ag.mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=2.0) normalization = sersic.normalization_from_einstein_radius( einstein_radius=1.0, normalization_min=0.5, normalization_max=3.0, bins=5 ) assert normalization == pytest.approx(1.0, 1.0e-2) nfw = ag.mp.SphericalNFW(centre=(0.0, 0.0), kappa_s=3.0, scale_radius=1.0) normalization = nfw.normalization_from_einstein_radius( einstein_radius=2.35829, normalization_min=0.5, normalization_max=3.0, bins=5, ) assert normalization == pytest.approx(2.0, 1.0e-2) sersic = ag.mp.SphericalSersic( centre=(0.0, 0.0), intensity=1.0, effective_radius=1.0, sersic_index=3.0, mass_to_light_ratio=1.0, ) normalization = sersic.normalization_from_einstein_radius( einstein_radius=2.15403, normalization_min=0.01, normalization_max=30.0, bins=5, ) assert normalization == pytest.approx(1.0, 1.0e-2) with pytest.raises(exc.ProfileException): sersic.normalization_from_einstein_radius( einstein_radius=1.0, normalization_min=1e-4, normalization_max=1e-3, bins=2, ) class TestExtractObject: def test__extract_works(self): sis = ag.mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=2.0) einstein_radii = sis.extract_attribute( cls=ag.mp.MassProfile, name="einstein_radius" ) assert einstein_radii.in_list[0] == 2.0 centres = sis.extract_attribute(cls=ag.mp.MassProfile, name="centre") assert centres.in_list[0] == (0.0, 0.0) assert ( sis.extract_attribute(cls=ag.mp.MassProfile, name="einstein_radiu") == None ) sis.extract_attribute(cls=ag.lp.LightProfile, name="einstein_radius") class TestRegression: def test__centre_of_profile_in_right_place(self): grid = ag.Grid2D.uniform(shape_native=(7, 7), pixel_scales=1.0) mass_profile = ag.mp.EllipticalIsothermal( centre=(2.0, 1.0), einstein_radius=1.0 ) convergence = mass_profile.convergence_from_grid(grid=grid) max_indexes = np.unravel_index( convergence.native.argmax(), convergence.shape_native ) assert max_indexes == (1, 4) potential = mass_profile.potential_from_grid(grid=grid) max_indexes = np.unravel_index( potential.native.argmin(), potential.shape_native ) assert max_indexes == (1, 4) deflections = mass_profile.deflections_from_grid(grid=grid) assert deflections.native[1, 4, 0] > 0 assert deflections.native[2, 4, 0] < 0 assert deflections.native[1, 4, 1] > 0 assert deflections.native[1, 3, 1] < 0 mass_profile = ag.mp.SphericalIsothermal(centre=(2.0, 1.0), einstein_radius=1.0) convergence = mass_profile.convergence_from_grid(grid=grid) max_indexes = np.unravel_index( convergence.native.argmax(), convergence.shape_native ) assert max_indexes == (1, 4) mass_profile = ag.mp.SphericalIsothermal(centre=(2.0, 1.0), einstein_radius=1.0) potential = mass_profile.potential_from_grid(grid=grid) max_indexes = np.unravel_index( potential.native.argmin(), potential.shape_native ) assert max_indexes == (1, 4) deflections = mass_profile.deflections_from_grid(grid=grid) assert deflections.native[1, 4, 0] > 0 assert deflections.native[2, 4, 0] < 0 assert deflections.native[1, 4, 1] > 0 assert deflections.native[1, 3, 1] < 0 grid = ag.Grid2DIterate.uniform( shape_native=(7, 7), pixel_scales=1.0, fractional_accuracy=0.99, sub_steps=[2, 4], ) mass_profile = ag.mp.EllipticalIsothermal( centre=(2.0, 1.0), einstein_radius=1.0 ) convergence = mass_profile.convergence_from_grid(grid=grid) max_indexes = np.unravel_index( convergence.native.argmax(), convergence.shape_native ) assert max_indexes == (1, 4) potential = mass_profile.potential_from_grid(grid=grid) max_indexes = np.unravel_index( potential.native.argmin(), potential.shape_native ) assert max_indexes == (1, 4) deflections = mass_profile.deflections_from_grid(grid=grid) assert deflections.native[1, 4, 0] >= 0 assert deflections.native[2, 4, 0] <= 0 assert deflections.native[1, 4, 1] >= 0 assert deflections.native[1, 3, 1] <= 0 mass_profile = ag.mp.SphericalIsothermal(centre=(2.0, 1.0), einstein_radius=1.0) convergence = mass_profile.convergence_from_grid(grid=grid) max_indexes = np.unravel_index( convergence.native.argmax(), convergence.shape_native ) assert max_indexes == (1, 4) potential = mass_profile.potential_from_grid(grid=grid) max_indexes = np.unravel_index( potential.native.argmin(), potential.shape_native ) assert max_indexes == (1, 4) deflections = mass_profile.deflections_from_grid(grid=grid) assert deflections.native[1, 4, 0] >= 0 assert deflections.native[2, 4, 0] <= 0 assert deflections.native[1, 4, 1] >= 0 assert deflections.native[1, 3, 1] <= 0 class TestDecorators: def test__grid_iterate_in__iterates_grid_result_correctly(self, gal_x1_mp): mask = ag.Mask2D.manual( mask=[ [True, True, True, True, True], [True, False, False, False, True], [True, False, False, False, True], [True, False, False, False, True], [True, True, True, True, True], ], pixel_scales=(1.0, 1.0), ) grid = ag.Grid2DIterate.from_mask( mask=mask, fractional_accuracy=1.0, sub_steps=[2] ) mass_profile = ag.mp.EllipticalIsothermal( centre=(0.08, 0.08), einstein_radius=1.0 ) deflections = mass_profile.deflections_from_grid(grid=grid) mask_sub_2 = mask.mask_new_sub_size_from_mask(mask=mask, sub_size=2) grid_sub_2 = ag.Grid2D.from_mask(mask=mask_sub_2) deflections_sub_2 = mass_profile.deflections_from_grid( grid=grid_sub_2 ).slim_binned assert deflections == pytest.approx(deflections_sub_2, 1.0e-6) grid = ag.Grid2DIterate.from_mask( mask=mask, fractional_accuracy=0.99, sub_steps=[2, 4, 8] ) mass_profile = ag.mp.EllipticalIsothermal( centre=(0.08, 0.08), einstein_radius=1.0 ) deflections = mass_profile.deflections_from_grid(grid=grid) mask_sub_4 = mask.mask_new_sub_size_from_mask(mask=mask, sub_size=4) grid_sub_4 = ag.Grid2D.from_mask(mask=mask_sub_4) deflections_sub_4 = mass_profile.deflections_from_grid( grid=grid_sub_4 ).slim_binned assert deflections[0, 0] == deflections_sub_4[0, 0] mask_sub_8 = mask.mask_new_sub_size_from_mask(mask=mask, sub_size=8) grid_sub_8 = ag.Grid2D.from_mask(mask=mask_sub_8) deflections_sub_8 = mass_profile.deflections_from_grid( grid=grid_sub_8 ).slim_binned assert deflections[4, 0] == deflections_sub_8[4, 0] def test__grid_interpolate_in__convergence__interpolates_based_on_intepolate_config( self, ): # `False` in interpolate.ini mask = ag.Mask2D.manual( mask=[ [True, True, True, True, True], [True, False, False, False, True], [True, False, False, False, True], [True, False, False, False, True], [True, True, True, True, True], ], pixel_scales=(1.0, 1.0), ) grid = ag.Grid2D.from_mask(mask=mask) grid_interpolate = ag.Grid2DInterpolate.from_mask( mask=mask, pixel_scales_interp=0.1 ) mass_profile = ag.mp.EllipticalIsothermal(einstein_radius=1.0) convergence = mass_profile.convergence_from_grid(grid=grid) convergence_no_interpolate = mass_profile.convergence_from_grid( grid=grid_interpolate ) assert (convergence == convergence_no_interpolate).all() # `False` in interpolate.ini mass_profile = ag.mp.SphericalIsothermal(einstein_radius=1.0) convergence = mass_profile.convergence_from_grid(grid=grid) convergence_interpolate = mass_profile.convergence_from_grid( grid=grid_interpolate ) assert (convergence != convergence_interpolate).all() array_interp = mass_profile.convergence_from_grid( grid=grid_interpolate.grid_interp ) interpolated_array = grid_interpolate.interpolated_array_from_array_interp( array_interp=array_interp ) assert (convergence_interpolate == interpolated_array).all() def test__grid_interpolate_in__potential__interpolates_based_on_intepolate_config( self, ): # `False` in interpolate.ini mask = ag.Mask2D.manual( mask=[ [True, True, True, True, True], [True, False, False, False, True], [True, False, False, False, True], [True, False, False, False, True], [True, True, True, True, True], ], pixel_scales=(1.0, 1.0), ) grid = ag.Grid2D.from_mask(mask=mask) grid_interpolate = ag.Grid2DInterpolate.from_mask( mask=mask, pixel_scales_interp=0.1 ) mass_profile = ag.mp.EllipticalIsothermal(einstein_radius=1.0) potential = mass_profile.potential_from_grid(grid=grid) potential_no_interpolate = mass_profile.potential_from_grid( grid=grid_interpolate ) assert (potential == potential_no_interpolate).all() # `False` in interpolate.ini mass_profile = ag.mp.SphericalIsothermal(einstein_radius=1.0) potential = mass_profile.potential_from_grid(grid=grid) potential_interpolate = mass_profile.potential_from_grid(grid=grid_interpolate) assert (potential != potential_interpolate).all() array_interp = mass_profile.potential_from_grid( grid=grid_interpolate.grid_interp ) interpolated_array = grid_interpolate.interpolated_array_from_array_interp( array_interp=array_interp ) assert (potential_interpolate == interpolated_array).all() def test__grid_interpolate_in__deflections__interpolates_based_on_intepolate_config( self, ): # `False` in interpolate.ini mask = ag.Mask2D.manual( mask=[ [True, True, True, True, True], [True, False, False, False, True], [True, False, False, False, True], [True, False, False, False, True], [True, True, True, True, True], ], pixel_scales=(1.0, 1.0), ) grid = ag.Grid2D.from_mask(mask=mask) grid_interpolate = ag.Grid2DInterpolate.from_mask( mask=mask, pixel_scales_interp=0.1 ) mass_profile = ag.mp.EllipticalIsothermal(einstein_radius=1.0) deflections = mass_profile.deflections_from_grid(grid=grid) deflections_no_interpolate = mass_profile.deflections_from_grid( grid=grid_interpolate ) assert (deflections == deflections_no_interpolate).all() # `False` in interpolate.ini mass_profile = ag.mp.SphericalIsothermal(einstein_radius=1.0) deflections_interpolate = mass_profile.deflections_from_grid( grid=grid_interpolate ) grid_interp = mass_profile.deflections_from_grid( grid=grid_interpolate.grid_interp ) interpolated_grid = grid_interpolate.interpolated_grid_from_grid_interp( grid_interp=grid_interp ) assert (deflections_interpolate == interpolated_grid).all()

34.036585

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0.03845

0.854031

0.81845

0.79031

0.746899

0.70938

0.639302

0

0.047583

0.29313

22,328

655

94

34.08855

0.769752

0.007211

0

0.506356

0

0.002325

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0.135593

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0.03178

false

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0.059322

0

null

0

1

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null

0

6

4c0e5be3c8a2a2321ebade2962320bd53e890a42

11,615

py

Python

HLTriggerOffline/SUSYBSM/python/SUSYBSM_inclusiveHT_cff.py

pasmuss/cmssw

566f40c323beef46134485a45ea53349f59ae534

[ "Apache-2.0" ]

null

HLTriggerOffline/SUSYBSM/python/SUSYBSM_inclusiveHT_cff.py

pasmuss/cmssw

566f40c323beef46134485a45ea53349f59ae534

[ "Apache-2.0" ]

null

HLTriggerOffline/SUSYBSM/python/SUSYBSM_inclusiveHT_cff.py

pasmuss/cmssw

566f40c323beef46134485a45ea53349f59ae534

[ "Apache-2.0" ]

null

import FWCore.ParameterSet.Config as cms from DQMServices.Core.DQMEDHarvester import DQMEDHarvester SUSY_HLT_InclusiveHT_800 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT800_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT800Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHTo800oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT800_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT_900 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT900_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT900Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHTo900oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT900_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT_aux125 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT125_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT125Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHToAux125oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT125_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT_aux200 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT200_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT200Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHToAux200oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT200_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT_aux250 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT250_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT250Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHToAux250oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT250_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT_aux300 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT300_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT300Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHToAux300oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT300_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT_aux350 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT350_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT350Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHToAux350oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT350_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT_aux400 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT400_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT400Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHToAux400oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT400_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT_aux475 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT475_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT475Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHToAux475oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT475_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT_aux600 = cms.EDAnalyzer("SUSY_HLT_InclusiveHT", trigSummary = cms.InputTag("hltTriggerSummaryAOD"), pfMETCollection = cms.InputTag("pfMet"), pfJetCollection = cms.InputTag("ak4PFJetsCHS"), caloJetCollection = cms.InputTag("ak4CaloJets"), TriggerResults = cms.InputTag('TriggerResults','','HLT'), TriggerPath = cms.string('HLT_PFHT600_v'), TriggerPathAuxiliaryForHadronic = cms.string('HLT_IsoMu24_eta2p1_IterTrk02_v'), TriggerFilter = cms.InputTag('hltPFHT600Jet30', '', 'HLT'), #the last filter in the path PtThrJet = cms.untracked.double(30.0), EtaThrJet = cms.untracked.double(3.0) ) SUSYoHLToInclusiveHToAux600oPOSTPROCESSING = DQMEDHarvester("DQMGenericClient", subDirs = cms.untracked.vstring("HLT/SUSYBSM/HLT_PFHT600_v"), efficiency = cms.vstring( "pfMetTurnOn_eff 'Efficiency vs PFMET' pfMetTurnOn_num pfMetTurnOn_den", "pfHTTurnOn_eff 'Efficiency vs PFHT' pfHTTurnOn_num pfHTTurnOn_den" ), resolution = cms.vstring("") ) SUSY_HLT_InclusiveHT = cms.Sequence(SUSY_HLT_InclusiveHT_aux125 + SUSY_HLT_InclusiveHT_aux200 + SUSY_HLT_InclusiveHT_aux250 + SUSY_HLT_InclusiveHT_aux300 + SUSY_HLT_InclusiveHT_aux350 + SUSY_HLT_InclusiveHT_aux400 + SUSY_HLT_InclusiveHT_aux475 + SUSY_HLT_InclusiveHT_aux600 + SUSY_HLT_InclusiveHT_800 + SUSY_HLT_InclusiveHT_900 ) SUSY_HLT_InclusiveHT_POSTPROCESSING = cms.Sequence(SUSYoHLToInclusiveHToAux125oPOSTPROCESSING + SUSYoHLToInclusiveHToAux200oPOSTPROCESSING + SUSYoHLToInclusiveHToAux250oPOSTPROCESSING + SUSYoHLToInclusiveHToAux300oPOSTPROCESSING + SUSYoHLToInclusiveHToAux350oPOSTPROCESSING + SUSYoHLToInclusiveHToAux400oPOSTPROCESSING + SUSYoHLToInclusiveHToAux475oPOSTPROCESSING + SUSYoHLToInclusiveHToAux600oPOSTPROCESSING + SUSYoHLToInclusiveHTo800oPOSTPROCESSING + SUSYoHLToInclusiveHTo900oPOSTPROCESSING )

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0.165648

11,615

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0.023246

0

0.580357

0

0.283194

0.048522

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0

null

0

1

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null

0

6

4c57b7c3ac565795fedc00ddadef2d00d871e1a0

2,759

py

Python

PA/coursework_2/test_simulator.py

easyCZ/UoE-Projects

7651c8caf329c4f7b4562eba441bfc24124cfcfd

[ "BSD-2-Clause" ]

null

PA/coursework_2/test_simulator.py

easyCZ/UoE-Projects

7651c8caf329c4f7b4562eba441bfc24124cfcfd

[ "BSD-2-Clause" ]

1

2022-02-23T07:34:53.000Z

PA/coursework_2/test_simulator.py

easyCZ/UoE-Projects

7651c8caf329c4f7b4562eba441bfc24124cfcfd

[ "BSD-2-Clause" ]

null

import unittest from protocols import MSI, MESI from models import State, Action class ProtocolTest(unittest.TestCase): def assert_remote(self, current_state, action, new_state): state = self.sut.remote(current_state, action) return self.assertEqual(new_state, state) class MSITest(ProtocolTest): def setUp(self): self.sut = MSI() def assert_local(self, current_state, action, new_state): state = self.sut.local(current_state, action) return self.assertEqual(new_state, state) def test_local_from_invalid_on_read_miss(self): self.assert_local(State.invalid, Action.read_miss, State.shared) def test_local_from_shared_on_read_hit(self): self.assert_local(State.shared, Action.read_hit, State.shared) def test_local_from_shared_on_write_hit(self): self.assert_local(State.shared, Action.write_hit, State.modified) def test_local_from_shared_on_write_miss(self): self.assert_local(State.shared, Action.write_miss, State.modified) def test_local_modified_on_read_hit(self): self.assert_local(State.modified, Action.read_hit, State.modified) def test_local_modified_on_write_hit(self): self.assert_local(State.modified, Action.write_hit, State.modified) def test_local_invalid_on_write_miss(self): self.assert_local(State.invalid, Action.write_miss, State.modified) # Other CPUs def test_remote_shared_on_write_miss(self): self.assert_remote(State.shared, Action.write_miss, State.invalid) def test_remote_shared_on_read_miss(self): self.assert_remote(State.shared, Action.read_miss, State.shared) def test_remote_modified_on_read_miss(self): self.assert_remote(State.modified, Action.read_miss, State.shared) def test_remote_modified_on_write_miss(self): self.assert_remote(State.modified, Action.write_miss, State.invalid) class MESITest(ProtocolTest): def setUp(self): self.sut = MESI() def test_remote_modified_on_write_miss(self): self.assert_remote(State.modified, Action.write_miss, State.invalid) def test_remote_modified_on_read_miss(self): self.assert_remote(State.modified, Action.read_miss, State.shared) def test_remote_exclusive_on_write_miss(self): self.assert_remote(State.exclusive, Action.write_miss, State.invalid) def test_remote_exclusive_on_read_miss(self): self.assert_remote(State.exclusive, Action.read_miss, State.shared) def test_remote_shared_on_read_miss(self): self.assert_remote(State.shared, Action.read_miss, State.shared) def test_remote_shared_on_write_miss(self): self.assert_remote(State.shared, Action.write_miss, State.invalid)

35.831169

77

0.752447

390

2,759

4.982051

0.094872

0.07823

0.122491

0.120432

0.885744

0.865157

0.826557

0.783325

0.4807

0.383942

0

0.158391

2,759

76

78

36.302632

0.836779

0.003625

0

0.4

0

0.42

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0.42

false

0

0.06

0

0.58

0

null

0

1

0

null

0

1

0

1

0

6

4c68d622a07b622aee4f9ce94a4d1a6e69180030

139

py

Python

tictactoe/repositories/match.py

pitzer42/nano_tcg

c984b253b8a53a707460aac21c10f140d16d902e

[ "MIT" ]

1

2020-09-30T21:03:37.000Z

tictactoe/repositories/match.py

pitzer42/nano_tcg

c984b253b8a53a707460aac21c10f140d16d902e

[ "MIT" ]

null

tictactoe/repositories/match.py

pitzer42/nano_tcg

c984b253b8a53a707460aac21c10f140d16d902e

[ "MIT" ]

null

from abc import ABC from gloop.repositories.match import MatchRepository class TicTacToeMatchRepository(MatchRepository, ABC): pass

17.375

53

0.820144

15

139

7.6

0.666667

0

0.136691

139

7

54

19.857143

0.95

0

1

0

true

0.25

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0

0.75

0

1

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null

0

1

0

null

0

1

0

1

0

6

d5cb1e7c2368a83e0da890df8817d478bf726a1c

5,419

py

Python

resrc/list/tests/integration_tests/views_test.py

ignatandrei/resrc

5b88e3cfbc638e5f98cf7bfe6f4a5757840a2565

[ "MIT" ]

1

2015-11-05T19:50:19.000Z

resrc/list/tests/integration_tests/views_test.py

sergiolimajr/resrc

a0714b3ae989821ebe4c5a7b5a2235a85bfa16a9

[ "MIT" ]

2

2020-08-04T18:08:04.000Z

2021-02-02T22:57:59.000Z

resrc/list/tests/integration_tests/views_test.py

sergiolimajr/resrc

a0714b3ae989821ebe4c5a7b5a2235a85bfa16a9

[ "MIT" ]

null

# -*- coding: utf-8 -*-: from django.test import TestCase from django.core.urlresolvers import reverse from django.contrib.auth import authenticate from django.http import Http404 from resrc.link.tests.factories import LinkFactory from resrc.list.tests.factories import ListFactory from resrc.tests.factories import UserFactory class ListViewTestCase(TestCase): def test_single_view(self): alist = ListFactory() alist.save() resp = self.client.get(reverse('list-single', kwargs={'list_pk': alist.pk})) self.assertEqual(resp.status_code, 302) resp = self.client.get(reverse('list-single', kwargs={'list_pk': 77})) self.assertEqual(resp.status_code, 404) def test_single_slug_view(self): alist = ListFactory() alist.save() resp = self.client.get(reverse('list-single-slug', kwargs={'list_pk': alist.pk, 'list_slug': alist.slug})) self.assertEqual(resp.status_code, 200) user = UserFactory() user.save() self.client.login(username=user.username, password='test123') resp = self.client.get(reverse('list-single-slug', kwargs={'list_pk': alist.pk, 'list_slug': alist.slug})) self.assertEqual(resp.status_code, 200) resp = self.client.get(reverse('list-single-slug', kwargs={'list_pk': alist.pk, 'list_slug': alist.slug + "x"})) self.assertEqual(resp.status_code, 404) def test_new_list_view(self): resp = self.client.get(reverse('new-list')) self.assertEqual(resp.status_code, 302) user = UserFactory() user.save() self.client.login(username=user.username, password='test123') resp = self.client.get(reverse('new-list')) self.assertEqual(resp.status_code, 200) def test_ajax_add_to_list_or_create(self): import simplejson resp = self.client.get(reverse('ajax-add-to-list-or-create')) # not authenticated self.assertEqual(resp.content, simplejson.dumps({'result': 'fail'})) user = UserFactory() user.save() self.client.login(username=user.username, password='test123') # authenticated but no post data resp = self.client.get(reverse('ajax-add-to-list-or-create')) self.assertEqual(resp.content, simplejson.dumps({'result': 'fail'})) link = LinkFactory() link.save() link2 = LinkFactory() link2.title = 'new link' link2.save() # authenticated and posting fake content resp = self.client.post(reverse('ajax-add-to-list-or-create'), { 'lk': link.pk, 't': 'haxx0r', }) self.assertEqual(resp.status_code, 404) # authenticated and adding link to default nonexistant bookmark list resp = self.client.post(reverse('ajax-add-to-list-or-create'), { 'lk': link.pk, 't': 'bookmark', }) self.assertEqual(resp.content, simplejson.dumps({'result': 'added'})) # authenticated and adding link to default existing bookmark list resp = self.client.post(reverse('ajax-add-to-list-or-create'), { 'lk': link2.pk, 't': 'bookmark', }) self.assertEqual(resp.content, simplejson.dumps({'result': 'added'})) # authenticated and adding link to default nonexistant reading list resp = self.client.post(reverse('ajax-add-to-list-or-create'), { 'lk': link.pk, 't': 'toread', }) self.assertEqual(resp.content, simplejson.dumps({'result': 'added'})) # authenticated and adding link to default existing reading list resp = self.client.post(reverse('ajax-add-to-list-or-create'), { 'lk': link2.pk, 't': 'toread', }) self.assertEqual(resp.content, simplejson.dumps({'result': 'added'})) # authenticated and removing link from default existing reading list resp = self.client.post(reverse('ajax-add-to-list-or-create'), { 'lk': link2.pk, 't': 'toread', }) self.assertEqual(resp.content, simplejson.dumps({'result': 'removed'})) # authenticated and adding/remove link to un/existing own list list resp = self.client.post(reverse('ajax-add-to-list-or-create'), { 'lk': link.pk, 'ls': 43212234 }) self.assertEqual(resp.status_code, 404) alist = ListFactory() alist.save() resp = self.client.post(reverse('ajax-add-to-list-or-create'), { 'lk': link.pk, 'ls': alist.pk }) self.assertEqual(resp.content, simplejson.dumps({'result': 'added'})) resp = self.client.post(reverse('ajax-add-to-list-or-create'), { 'lk': link.pk, 'ls': alist.pk }) self.assertEqual(resp.content, simplejson.dumps({'result': 'removed'})) def test_ajax_own_lists(self): link = LinkFactory() link.save() link_pk = link.pk resp = self.client.get(reverse('ajax-own-lists', kwargs={'link_pk': link.pk})) self.assertEqual(resp.status_code, 404) user = UserFactory() user.save() self.client.login(username=user.username, password='test123') resp = self.client.get(reverse('ajax-own-lists', kwargs={'link_pk': link.pk})) self.assertEqual(resp.status_code, 200)

38.161972

120

0.613951

651

5,419

5.050691

0.141321

0.072993

0.085158

0.047445

0.804136

0.802616

0.769161

0.736618

0.676095

0

0.016031

0.240266

5,419

141

121

38.432624

0.782609

0.092822

0

0.785047

0

0.13945

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0

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1

0.046729

false

0.037383

0.074766

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0

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0

1

0

1

0

null

0

6

d5dfa72ecf282130d6dc8633656ec5c32a1812db

5,760

py

Python

tests/unit/adapters/netbox_api/models/test_netbox_vlan.py

anirudhkamath/network-importer

13a32270ac838745433bd2b859697e657f95c013

[ "Apache-2.0" ]

88

2020-12-03T16:07:47.000Z

2022-03-21T16:14:24.000Z

tests/unit/adapters/netbox_api/models/test_netbox_vlan.py

anirudhkamath/network-importer

13a32270ac838745433bd2b859697e657f95c013

[ "Apache-2.0" ]

62

2020-12-03T03:49:13.000Z

2022-03-21T22:02:15.000Z

tests/unit/adapters/netbox_api/models/test_netbox_vlan.py

anirudhkamath/network-importer

13a32270ac838745433bd2b859697e657f95c013

[ "Apache-2.0" ]

23

2020-12-03T03:37:53.000Z

2022-03-30T16:28:35.000Z

"""test for NetboxVlan model.""" import os import yaml import pytest import pynetbox from diffsync.exceptions import ObjectNotFound from network_importer.adapters.netbox_api.models import NetboxVlan, NetboxDevice ROOT = os.path.abspath(os.path.dirname(__file__)) FIXTURE_28 = "../fixtures/netbox_28" FIXTURE_29 = "../fixtures/netbox_29" def test_vlan_create_from_pynetbox(netbox_api_base): api = pynetbox.api(url="http://mock", token="1234567890") data = yaml.safe_load(open(f"{ROOT}/{FIXTURE_29}/vlan_101_no_tag.json")) pnb = pynetbox.core.response.Record(values=data, api=api, endpoint=1) item = NetboxVlan.create_from_pynetbox(diffsync=netbox_api_base, obj=pnb, site_name="nyc") assert isinstance(item, NetboxVlan) is True assert item.remote_id == 1 assert item.vid == 101 assert item.associated_devices == [] def test_vlan_create_from_pynetbox_with_tags(netbox_api_base): api = pynetbox.api(url="http://mock", token="1234567890") data = yaml.safe_load(open(f"{ROOT}/{FIXTURE_29}/vlan_101_tags_01.json")) pnb = pynetbox.core.response.Record(values=data, api=api, endpoint=1) netbox_api_base.add(NetboxDevice(name="devA", site_name="nyc", remote_id=30)) item = NetboxVlan.create_from_pynetbox(diffsync=netbox_api_base, obj=pnb, site_name="nyc") assert isinstance(item, NetboxVlan) is True assert item.remote_id == 1 assert item.vid == 101 assert item.associated_devices == ["devA"] # Try again with one additional device in the inventory netbox_api_base.add(NetboxDevice(name="devB", site_name="nyc", remote_id=31)) item = NetboxVlan.create_from_pynetbox(diffsync=netbox_api_base, obj=pnb, site_name="nyc") assert isinstance(item, NetboxVlan) is True assert item.remote_id == 1 assert item.vid == 101 assert item.associated_devices == ["devA", "devB"] def test_translate_attrs_for_netbox_no_attrs(netbox_api_base): vlan = NetboxVlan(vid=100, site_name="HQ", remote_id=30) netbox_api_base.add(vlan) params = vlan.translate_attrs_for_netbox({}) assert "name" in params assert params["name"] == "vlan-100" assert "site" in params assert params["site"] == 10 assert "tags" not in params def test_translate_attrs_for_netbox_with_partial_attrs(netbox_api_base): vlan = NetboxVlan(vid=100, name="MYVLAN", site_name="HQ", remote_id=30) netbox_api_base.add(vlan) netbox_api_base.add(NetboxDevice(name="dev1", site_name="HQ", remote_id=32, device_tag_id=12)) netbox_api_base.add(NetboxDevice(name="dev2", site_name="HQ", remote_id=33, device_tag_id=13)) params = vlan.translate_attrs_for_netbox({"associated_devices": ["dev1", "dev2"]}) assert "name" not in params assert "site" in params assert params["site"] == 10 assert "tags" in params assert sorted(params["tags"]) == [12, 13] def test_translate_attrs_for_netbox_with_attrs(netbox_api_base): vlan = NetboxVlan(vid=100, site_name="HQ", remote_id=30) netbox_api_base.add(vlan) netbox_api_base.add(NetboxDevice(name="dev1", site_name="HQ", remote_id=32, device_tag_id=12)) netbox_api_base.add(NetboxDevice(name="dev2", site_name="HQ", remote_id=33, device_tag_id=13)) params = vlan.translate_attrs_for_netbox({"name": "VOICE", "associated_devices": ["dev1", "dev2"]}) assert "name" in params assert params["name"] == "VOICE" assert "site" in params assert params["site"] == 10 assert "tags" in params assert sorted(params["tags"]) == [12, 13] def test_translate_attrs_for_netbox_with_missing_devices(netbox_api_base): vlan = NetboxVlan(vid=100, site_name="HQ", remote_id=30) netbox_api_base.add(vlan) netbox_api_base.add(NetboxDevice(name="dev1", site_name="HQ", remote_id=32, device_tag_id=12)) params = vlan.translate_attrs_for_netbox({"name": "VOICE", "associated_devices": ["dev1", "dev2"]}) assert "name" in params assert params["name"] == "VOICE" assert "site" in params assert params["site"] == 10 assert "tags" in params assert sorted(params["tags"]) == [12] def test_translate_attrs_for_netbox_missing_site(netbox_api_base): vlan = NetboxVlan(vid=100, site_name="NOTPRESENT", remote_id=30) netbox_api_base.add(vlan) with pytest.raises(ObjectNotFound): vlan.translate_attrs_for_netbox({}) assert True def test_update_clean_tags_no_incoming_tags(netbox_api_base): vlan = NetboxVlan(vid=100, site_name="HQ", remote_id=30) netbox_api_base.add(vlan) api = pynetbox.api(url="http://mock", token="1234567890") data = yaml.safe_load(open(f"{ROOT}/{FIXTURE_29}/vlan_101_tags_01.json")) pnb = pynetbox.core.response.Record(values=data, api=api, endpoint=1) params = vlan.translate_attrs_for_netbox({"name": "VOICE"}) clean_params = vlan.update_clean_tags(nb_params=params, obj=pnb) assert "tags" not in clean_params def test_update_clean_tags_with_incoming_tags(netbox_api_base): vlan = NetboxVlan(vid=100, site_name="HQ", remote_id=30) netbox_api_base.add(vlan) netbox_api_base.add(NetboxDevice(name="dev1", site_name="HQ", remote_id=32, device_tag_id=12)) netbox_api_base.add(NetboxDevice(name="dev2", site_name="HQ", remote_id=33, device_tag_id=13)) api = pynetbox.api(url="http://mock", token="1234567890") data = yaml.safe_load(open(f"{ROOT}/{FIXTURE_29}/vlan_101_tags_01.json")) pnb = pynetbox.core.response.Record(values=data, api=api, endpoint=1) params = vlan.translate_attrs_for_netbox({"name": "VOICE", "associated_devices": ["dev1", "dev2"]}) clean_params = vlan.update_clean_tags(nb_params=params, obj=pnb) assert "tags" in clean_params assert sorted(clean_params["tags"]) == [1, 2, 3, 12, 13]

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null

0

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0

null

0

6

d5e33fbae2604e8fef3fc177cf71cf157376183e

66

py

Python

media_preprocessor/__init__.py

harshh-mahajan/media_preprocessor

5943ab6d6cde27200850398e40717d7b55646ec8

[ "MIT" ]

null

media_preprocessor/__init__.py

harshh-mahajan/media_preprocessor

5943ab6d6cde27200850398e40717d7b55646ec8

[ "MIT" ]

null

media_preprocessor/__init__.py

harshh-mahajan/media_preprocessor

5943ab6d6cde27200850398e40717d7b55646ec8

[ "MIT" ]

1

2020-11-22T16:41:09.000Z

from media_preprocessor.media_preprocessor import preprocess_tool

33

65

0.924242

8

66

7.25

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66

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null

0

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0

1

0

1

0

6

d5ec10cabbd7f27a5cb76bd1dca368f944c99be3

1,349

py

Python

test/processors/test_google.py

mgor/pydoc-markdown

b15127e3c643976e71a10c7fa4d03297ee616542

[ "MIT" ]

null

test/processors/test_google.py

mgor/pydoc-markdown

b15127e3c643976e71a10c7fa4d03297ee616542

[ "MIT" ]

null

test/processors/test_google.py

mgor/pydoc-markdown

b15127e3c643976e71a10c7fa4d03297ee616542

[ "MIT" ]

null

from pydoc_markdown.contrib.processors.google import GoogleProcessor from . import assert_processor_result def test_google_processor(processor=None): assert_processor_result( processor or GoogleProcessor(), """ Args: s (str): A string. b (int): An int. Returns: any: Something funny. """, """ **Arguments**: - `s` _str_ - A string. - `b` _int_ - An int. **Returns**: - `any` - Something funny. """, ) assert_processor_result( processor or GoogleProcessor(), """ Args: s (str): A string. And the description takes multiple lines. b (int): An int. Returns: any: Something funny. """, """ **Arguments**: - `s` _str_ - A string. And the description takes multiple lines. - `b` _int_ - An int. **Returns**: - `any` - Something funny. """, ) assert_processor_result( processor or GoogleProcessor(), """ Example: ```py scanner = ListScanner(lst) for value in scanner.safe_iter(): if some_condition(value): value = scanner.advance() ``` """, """ **Example**: ```py scanner = ListScanner(lst) for value in scanner.safe_iter(): if some_condition(value): value = scanner.advance() ``` """, )

17.075949

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0.30467

1,349

78

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1

0.055556

false

0

0.111111

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0.166667

0

null

0

1

0

null

0

6

9112c14093d4c5fcc9f2c37dc7258c632d54886d

32

py

Python

codes.py

Lauracruces/Python

a485546f7c7f1206a9cdf2932c92b91c2bc73ee1

[ "MIT" ]

null

codes.py

Lauracruces/Python

a485546f7c7f1206a9cdf2932c92b91c2bc73ee1

[ "MIT" ]

null

codes.py

Lauracruces/Python

a485546f7c7f1206a9cdf2932c92b91c2bc73ee1

[ "MIT" ]

null

#pandas import pandas as pandas

10.666667

23

0.8125

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32

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1

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1

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null

0

1

0

1

0

1

0

6

911637d58a61f6403ddb0dc18bda8db8c37b17b1

22,260

py

Python

test/test_onnxrt_operators.py

intelkevinputnam/lpot-docs

1ff32b4d89074a6bd133ba531f7c0cea3b73152f

[ "Apache-2.0" ]

null

test/test_onnxrt_operators.py

intelkevinputnam/lpot-docs

1ff32b4d89074a6bd133ba531f7c0cea3b73152f

[ "Apache-2.0" ]

null

test/test_onnxrt_operators.py

intelkevinputnam/lpot-docs

1ff32b4d89074a6bd133ba531f7c0cea3b73152f

[ "Apache-2.0" ]

null

import os import shutil import unittest import copy import onnx import numpy as np from onnx import helper, TensorProto, numpy_helper, onnx_pb from onnxruntime.quantization.quant_utils import QuantizationMode from lpot.adaptor.ox_utils.onnx_quantizer import ONNXQuantizer import onnxruntime as ort class TestAdaptorONNXRT(unittest.TestCase): qlinear_backend = QuantizationMode.QLinearOps integer_backend = QuantizationMode.IntegerOps q_config = {"weight":{'dtype': 3, 'algorithm': 'minmax', 'scheme':'sym', 'granularity': 'per_tensor'}, 'activation':{'dtype': 2, 'algorithm': 'minmax', 'scheme':'asym', 'granularity':'per_tensor'} } @classmethod def setUpClass(cls): os.makedirs('./onnxrt_test') @classmethod def tearDownClass(cls): shutil.rmtree("./onnxrt_test", ignore_errors=True) def static_test(self, model, q_config, quantize_params, quantizable_op_types): quantizer = ONNXQuantizer(copy.deepcopy(model), q_config, self.qlinear_backend, True, quantize_params, quantizable_op_types) quantizer.quantize_model() assert quantizer.model.model def dynamic_test(self, model, q_config, quantize_params, quantizable_op_types): quantizer = ONNXQuantizer(copy.deepcopy(model), q_config, self.integer_backend, False, quantize_params, quantizable_op_types) quantizer.quantize_model() assert quantizer.model.model def test_conv(self): for op in ['Conv', 'FusedConv']: A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 1, 5, 5]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 1, 3, 3]) C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [1, 1, 5, 1]) D = helper.make_tensor_value_info('D', TensorProto.FLOAT, [1, 1, 5, 1]) conv_node = onnx.helper.make_node(op, ['A', 'B', 'C'], ['D'], name=op, kernel_shape=[3, 3], pads=[1, 1, 1, 1]) graph = helper.make_graph([conv_node], 'test_graph_1', [A, B, C], [D]) model = helper.make_model(graph) q_config = {op: self.q_config}, quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)], "D": [np.float32(10.), np.uint8(0)]} quantizable_op_types = [op] self.static_test(model, q_config, quantize_params, quantizable_op_types) def test_matmul(self): A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 1, 5, 5]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 1, 5, 1]) C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [1, 1, 5, 1]) matmul_node = onnx.helper.make_node('MatMul', ['A', 'B'], ['C'], name='Matmul') graph = helper.make_graph([matmul_node], 'test_graph_1', [A, B], [C]) model = helper.make_model(graph) q_config = {"Matmul": self.q_config} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Matmul"] self.static_test(model, q_config, quantize_params, quantizable_op_types) self.dynamic_test(model, q_config, quantize_params, quantizable_op_types) q_config = {"Matmul": {"weight":{'dtype': 3, 'algorithm': 'minmax', 'scheme':'sym', 'granularity': 'per_tensor'}, 'activation':{'dtype': 3, 'algorithm': 'minmax', 'scheme':'asym', 'granularity':'per_tensor'}}} quantize_params = {} self.dynamic_test(model, q_config, quantize_params, quantizable_op_types) def test_attention(self): A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 1, 5, 5]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 1, 5, 5]) C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [1, 1, 5, 5]) D = helper.make_tensor_value_info('D', TensorProto.FLOAT, [1, 1, 5, 5]) node = onnx.helper.make_node('Attention', ['A', 'B', 'C'], ['D'], name='Attention') graph = helper.make_graph([node], 'test_graph_1', [A, B, C], [D]) model = helper.make_model(graph) q_config = {"Attention": self.q_config} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)], "D": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Attention"] self.static_test(model, q_config, quantize_params, quantizable_op_types) self.dynamic_test(model, q_config, quantize_params, quantizable_op_types) def test_gather(self): a_value = np.random.randn(100, 4).astype(np.float32) A_init = helper.make_tensor('A', TensorProto.FLOAT, [100, 4], a_value.reshape(400).tolist()) b_value = np.random.randint(2, size=(1, 10)).astype(np.int32) B_init = helper.make_tensor('B', TensorProto.INT32, [1, 10], b_value.reshape(10).tolist()) A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [100, 4]) B = helper.make_tensor_value_info('B', TensorProto.INT32, [1, 10]) C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [10, 4]) node = onnx.helper.make_node('Gather', ['A', 'B'], ['C'], name='Gather') graph = helper.make_graph([node], 'test_graph_1', [A, B], [C], [A_init, B_init]) model = helper.make_model(graph) q_config = {'Gather': {"weight":{'dtype': 3, 'algorithm': 'minmax', 'scheme':'sym', 'granularity': 'per_tensor'}, 'activation':{'dtype': 2, 'algorithm': 'minmax', 'scheme':'asym', 'granularity':'per_tensor'} }} quantize_params = {"A": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Gather"] self.static_test(model, q_config, quantize_params, quantizable_op_types) self.dynamic_test(model, q_config, quantize_params, quantizable_op_types) graph = helper.make_graph([node], 'test_graph_1', [A, B], [C]) model = helper.make_model(graph) q_config = {'Gather': {"weight":{'dtype': 3, 'algorithm': 'minmax', 'scheme':'sym', 'granularity': 'per_tensor'}, 'activation':{'dtype': 2, 'algorithm': 'minmax', 'scheme':'asym', 'granularity':'per_tensor'} }} quantize_params = {} self.dynamic_test(model, q_config, quantize_params, quantizable_op_types) def test_split(self): a_value = np.random.randn(100, 4).astype(np.float32) A_init = helper.make_tensor('A', TensorProto.FLOAT, [100, 4], a_value.reshape(400).tolist()) A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [100, 4]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [50, 4]) C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [50, 4]) node = onnx.helper.make_node('Split', ['A'], ['B', 'C'], name='Split') graph = helper.make_graph([node], 'test_graph_1', [A], [B, C], [A_init]) model = helper.make_model(graph) q_config = {'Split': {"weight":{'dtype': 3, 'algorithm': 'minmax', 'scheme':'sym', 'granularity': 'per_tensor'}, 'activation':{'dtype': 2, 'algorithm': 'minmax', 'scheme':'asym', 'granularity':'per_tensor'} }} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Split"] self.static_test(model, q_config, quantize_params, quantizable_op_types) def test_pad(self): b_value = np.array([0, 1, 1, 0, 1, 1]).astype(np.int64) B_init = helper.make_tensor('B', TensorProto.INT64, [6], b_value.reshape(6).tolist()) B = helper.make_tensor_value_info('B', TensorProto.INT64, [6]) C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [1, 7, 7]) d_value = np.random.randn(1).astype(np.float32) D_init = helper.make_tensor('D', TensorProto.FLOAT, [1], d_value.reshape(1).tolist()) D = helper.make_tensor_value_info('D', TensorProto.FLOAT, [1]) e_value = np.random.randn(1, 5, 5).astype(np.float32) E_init = helper.make_tensor('E', TensorProto.FLOAT, [1, 5, 5], e_value.reshape(25).tolist()) E = helper.make_tensor_value_info('E', TensorProto.FLOAT, [1, 1, 5, 5]) f_value = np.random.randn(1, 3, 3).astype(np.float32) F_init = helper.make_tensor('F', TensorProto.FLOAT, [1, 3, 3], f_value.reshape(9).tolist()) F = helper.make_tensor_value_info('F', TensorProto.FLOAT, [1, 1, 3, 3]) for mode in ["constant", "edge", "reflect", "constant_value", "constant_value_wo_init"]: conv_node = onnx.helper.make_node('Conv', ['E', 'F'], ['A'], name='Conv', kernel=[3, 3], padding=[1, 1, 1, 1]) if mode == "constant_value": node = onnx.helper.make_node('Pad', ['A', 'B', 'D'], ['C'], name='Pad', mode="constant") graph = helper.make_graph([conv_node, node], 'test_graph_1', [E, F, B, D], [C], [E_init, F_init, B_init, D_init]) elif mode == "constant_value_wo_init": node = onnx.helper.make_node('Pad', ['A', 'B', 'D'], ['C'], name='Pad', mode="constant") graph = helper.make_graph([conv_node, node], 'test_graph_1', [E, F, B, D], [C], [E_init, F_init, B_init]) else: node = onnx.helper.make_node('Pad', ['A', 'B'], ['C'], name='Pad', mode=mode) graph = helper.make_graph([conv_node, node], 'test_graph_1', [E, F, B], [C], [E_init, F_init, B_init]) model = helper.make_model(graph) q_config = {'Conv': self.q_config, 'Pad': {'activation':{'dtype': 2, 'algorithm': 'minmax', 'scheme':'asym', 'granularity':'per_tensor'} }} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)], "D": [np.float32(10.), np.uint8(0)], "E": [np.float32(10.), np.uint8(0)], "F": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Conv", "Pad"] self.static_test(model, q_config, quantize_params, quantizable_op_types) node = onnx.helper.make_node('Pad', ['E', 'B', 'D'], ['C'], name='Pad', mode="constant") graph = helper.make_graph([node], 'test_graph_1', [E, B, D], [C], [E_init, B_init, D_init]) model = helper.make_model(graph) q_config = {'Pad': {'activation':{'dtype': 2, 'algorithm': 'minmax', 'scheme':'asym', 'granularity':'per_tensor'} }} quantize_params = {"C": [np.float32(10.), np.uint8(0)], "E": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Pad"] self.static_test(model, q_config, quantize_params, quantizable_op_types) def test_binary(self): for op in ['Mul', 'Add']: A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 10]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1]) C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [1, 10]) node = onnx.helper.make_node(op, ['A', 'B'], ['C'], name=op) graph = helper.make_graph([node], 'test_graph_1', [A, B], [C]) model = helper.make_model(graph) q_config = {op: self.q_config} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)]} quantizable_op_types = [op] self.static_test(model, q_config, quantize_params, quantizable_op_types) def test_relu(self): A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 1, 5, 5]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 1, 3, 3]) D = helper.make_tensor_value_info('D', TensorProto.FLOAT, [1, 1, 5, 5]) conv_node = onnx.helper.make_node('Conv', ['A', 'B'], ['C'], name='Conv', kernel_shape=[3, 3], pads=[1, 1, 1, 1]) relu_node = onnx.helper.make_node('Relu', ['C'], ['D'], name='Relu') graph = helper.make_graph([conv_node, relu_node], 'test_graph_1', [A, B], [D]) model = helper.make_model(graph, **{'opset_imports': [helper.make_opsetid('', 13)]}) sess_options = ort.SessionOptions() sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED sess_options.optimized_model_filepath = "./onnxrt_test/optimized_model.onnx" session = ort.InferenceSession(model.SerializeToString(), sess_options) model = onnx.load(sess_options.optimized_model_filepath) q_config = {"Conv": self.q_config, "Relu": self.q_config} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)], "D": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Conv", "Relu"] self.static_test(model, q_config, quantize_params, quantizable_op_types) def test_clip(self): A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 1, 5, 5]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 1, 3, 3]) D = helper.make_tensor_value_info('D', TensorProto.FLOAT, [1, 1, 5, 5]) conv_node = onnx.helper.make_node('Conv', ['A', 'B'], ['C'], name='Conv', kernel_shape=[3, 3], pads=[1, 1, 1, 1]) clip_node = onnx.helper.make_node('Clip', ['C'], ['D'], name='Clip') graph = helper.make_graph([conv_node, clip_node], 'test_graph_1', [A, B], [D]) model = helper.make_model(graph, **{'opset_imports': [helper.make_opsetid('', 13)]}) sess_options = ort.SessionOptions() sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED sess_options.optimized_model_filepath = "./onnxrt_test/optimized_model.onnx" session = ort.InferenceSession(model.SerializeToString(), sess_options) model = onnx.load(sess_options.optimized_model_filepath) q_config = {"Conv": self.q_config, "Clip": self.q_config} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)], "D": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Conv", "Clip"] self.static_test(model, q_config, quantize_params, quantizable_op_types) def test_activation(self): for op in ["Relu", "LeakyRelu", "Sigmoid"]: B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 10]) A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 10]) node = onnx.helper.make_node(op, ['A'], ['B'], name=op) graph = helper.make_graph([node], 'test_graph_1', [A], [B]) model = helper.make_model(graph) q_config = {op: self.q_config} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)]} quantizable_op_types = [op] self.static_test(model, q_config, quantize_params, quantizable_op_types) a_value = np.random.randn(1, 10).astype(np.float32) A_init = helper.make_tensor('A', TensorProto.FLOAT, [1, 10], a_value.reshape(10).tolist()) graph = helper.make_graph([node], 'test_graph_1', [A], [B], [A_init]) model = helper.make_model(graph) self.static_test(model, q_config, quantize_params, quantizable_op_types) def test_pooling(self): for op in ["MaxPool", "GlobalAveragePool"]: B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 1, 5, 5]) A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 1, 5, 5]) a_value = np.random.randn(1, 1, 5, 5).astype(np.float32) A_init = helper.make_tensor('A', TensorProto.FLOAT, [1, 1, 5, 5], a_value.reshape(25).tolist()) node = onnx.helper.make_node(op, ['A'], ['B'], name=op, kernel_shape=[3, 3], pads=[1, 1, 1, 1]) graph = helper.make_graph([node], 'test_graph_1', [A], [B], [A_init]) q_config = {op: self.q_config} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)]} quantizable_op_types = [op] for opset_version in [12, 13]: opset = onnx.OperatorSetIdProto() opset.version = opset_version model = helper.make_model(graph, opset_imports=[opset]) self.static_test(model, q_config, quantize_params, quantizable_op_types) A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 1, 5, 5]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 1, 3, 3]) D = helper.make_tensor_value_info('D', TensorProto.FLOAT, [1, 1, 5, 5]) conv_node = onnx.helper.make_node('Conv', ['A', 'B'], ['C'], name='Conv', kernel_shape=[3, 3], pads=[1, 1, 1, 1]) pool_node = onnx.helper.make_node(op, ['C'], ['D'], name=op) graph = helper.make_graph([conv_node, pool_node], 'test_graph_1', [A, B], [D]) model = helper.make_model(graph) q_config = {"Conv": self.q_config, op: self.q_config} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)], "D": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Conv", op] self.static_test(model, q_config, quantize_params, quantizable_op_types) def test_exclude_node(self): A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 1, 5, 5]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 1, 3, 3]) D = helper.make_tensor_value_info('D', TensorProto.FLOAT, [1, 1, 5, 5]) conv_node = onnx.helper.make_node('Conv', ['A', 'B'], ['C'], name='Conv', kernel_shape=[3, 3], pads=[1, 1, 1, 1]) pool_node = onnx.helper.make_node("MaxPool", ['C'], ['D'], name="MaxPool") graph = helper.make_graph([conv_node, pool_node], 'test_graph_1', [A, B], [D]) model = helper.make_model(graph) q_config = {"Conv": self.q_config, "MaxPool": "fp32"} quantize_params = {"A": [np.float32(10.), np.uint8(0)], "B": [np.float32(10.), np.uint8(0)], "C": [np.float32(10.), np.uint8(0)], "D": [np.float32(10.), np.uint8(0)]} quantizable_op_types = ["Conv", "MaxPool"] self.static_test(model, q_config, quantize_params, quantizable_op_types) if __name__ == "__main__": unittest.main()

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9122a65a266db6e9bcaf3d0fd8235ca1aa3cfe5c

48

py

Python

travelling_salesperson/__init__.py

btallin/travelling_salesperson_solver

f19d0e1cf335a647bf694057a43b4a2c4b24e226

[ "MIT" ]

1

2020-04-26T22:33:21.000Z

travelling_salesperson/__init__.py

btallin/alley_cat_solver

f19d0e1cf335a647bf694057a43b4a2c4b24e226

[ "MIT" ]

null

travelling_salesperson/__init__.py

btallin/alley_cat_solver

f19d0e1cf335a647bf694057a43b4a2c4b24e226

[ "MIT" ]

null

from travelling_salesperson.solver import solve

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914af87388c83d5306b8d84a8f55f176600f1a5d

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py

Python

nfdapi/nfdcore/migrations/0018_auto_20180212_1635.py

kappu72/clevmetro-nfd

584c638190eaa077d010a24fe7f209b1cbb3725d

[ "BSD-2-Clause" ]

3

2018-02-11T21:18:11.000Z

2019-01-19T06:58:58.000Z

nfdapi/nfdcore/migrations/0018_auto_20180212_1635.py

kappu72/clevmetro-nfd

584c638190eaa077d010a24fe7f209b1cbb3725d

[ "BSD-2-Clause" ]

108

2018-02-02T15:42:39.000Z

2019-01-21T13:22:55.000Z

nfdapi/nfdcore/migrations/0018_auto_20180212_1635.py

kappu72/clevmetro-nfd

584c638190eaa077d010a24fe7f209b1cbb3725d

[ "BSD-2-Clause" ]

5

2018-02-02T11:52:48.000Z

2022-03-01T16:09:09.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.4 on 2018-02-12 16:35 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('nfdcore', '0017_auto_20180212_1550'), ] operations = [ migrations.RemoveField( model_name='landanimaldetails', name='gender', ), migrations.RemoveField( model_name='landanimaldetails', name='marks', ), migrations.RemoveField( model_name='pondlakeanimaldetails', name='gender', ), migrations.RemoveField( model_name='pondlakeanimaldetails', name='marks', ), migrations.RemoveField( model_name='streamanimaldetails', name='gender', ), migrations.RemoveField( model_name='streamanimaldetails', name='marks', ), migrations.RemoveField( model_name='wetlandanimaldetails', name='gender', ), migrations.RemoveField( model_name='wetlandanimaldetails', name='marks', ), ]

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null

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6

e676ecb24711167cebd10487d2762eab453b30a3

178

py

Python

retratodefases/utils/__init__.py

Loracio/retrato-de-fases

a2d870a69b911af3b78288708cb569c957506940

[ "MIT" ]

3

2021-03-22T00:07:28.000Z

2021-03-22T12:11:18.000Z

retratodefases/utils/__init__.py

Loracio/retrato-de-fases

a2d870a69b911af3b78288708cb569c957506940

[ "MIT" ]

null

retratodefases/utils/__init__.py

Loracio/retrato-de-fases

a2d870a69b911af3b78288708cb569c957506940

[ "MIT" ]

2

2021-03-20T19:00:53.000Z

2021-03-22T12:19:52.000Z

try: __PHASE_UTILS_IMPORTED__ except NameError: __PHASE_UTILS_IMPORTED__= False if not __PHASE_UTILS_IMPORTED__: from . import utils __PHASE_UTILS_IMPORTED__ = True

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6

e6958cde1cd1e7b762c55d68c7be888f33503a8f

2,270

py

Python

test/tests/loop.py

jonco3/dynamic

76d10b012a7860595c7d9abbdf542c7d8f2a4d53

[ "MIT" ]

1

2020-11-26T23:37:19.000Z

test/tests/loop.py

jonco3/dynamic

76d10b012a7860595c7d9abbdf542c7d8f2a4d53

[ "MIT" ]

null

test/tests/loop.py

jonco3/dynamic

76d10b012a7860595c7d9abbdf542c7d8f2a4d53

[ "MIT" ]

null

# output: ok count = 0 total = 0 last = 0 for i in (1, 2, 3): count += 1 total += i last = i assert count == 3 assert total == 6 assert last == 3 count = 0 total = 0 last = 0 i = 1 while i <= 3: count += 1 total += i last = i i += 1 assert count == 3 assert total == 6 assert last == 3 count = 0 total = 0 last = 0 for i in (1, 2, 3): count += 1 total += i last = i if i == 2: break assert count == 2 assert total == 3 assert last == 2 count = 0 total = 0 last = 0 i = 1 while i <= 3: count += 1 total += i last = i if i == 2: break i += 1 assert count == 2 assert total == 3 assert last == 2 count = 0 total = 0 last = 0 for i in (1, 2, 3): if i == 2: continue count += 1 total += i last = i assert count == 2 assert total == 4 assert last == 3 count = 0 total = 0 last = 0 i = 1 while i <= 3: if i == 2: i += 1 continue count += 1 total += i last = i i += 1 assert count == 2 assert total == 4 assert last == 3 f = 0 for i in (1, 2, 3): try: if i == 2: break finally: f = i assert f == 2 f = 0 for i in (1, 2, 3): try: try: try: if i == 1: break finally: f += 1 except Exception: pass finally: f += 1 assert f == 2 f = 0 for i in (1, 2, 3): try: if i == 2: continue finally: f += 1 assert f == 3 # else clause didElse = False for i in []: pass else: didElse = True assert(didElse) didElse = False for i in (1, 2, 3): pass else: didElse = True assert(didElse) didElse = False for i in (1, 2, 3): if i == 3: continue else: didElse = True assert(didElse) didElse = False for i in (1, 2, 3): if i == 3: break else: didElse = True assert(not didElse) class OwnSequence: def __init__(self, wrapped): self.wrapped = wrapped def __getitem__(self, index): return self.wrapped[index] count = 0 total = 0 last = 0 for i in OwnSequence([1, 2, 3]): count += 1 total += i last = i assert count == 3 assert total == 6 assert last == 3 print('ok')

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